{
 "cells": [
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   "cell_type": "code",
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     "text": [
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8016703128814697 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1e-10} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8038928508758545 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1e-09} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8428130149841309 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1e-08} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8060216903686523 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1e-07} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8030860424041748 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1e-06} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8137733936309814 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1e-05} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7620131969451904 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 0.0001} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8332343101501465 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 0.001} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7803995609283447 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 0.01} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7787373065948486 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 0.1} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7911996841430664 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.778780221939087 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 10.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8241212368011475 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 100.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7918579578399658 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8034930229187012 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 10000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.83528733253479 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 100000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8089466094970703 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8145177364349365 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 10000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7873256206512451 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 100000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7759017944335938 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 1000000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.804816484451294 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 1.0, 'lmda': 10000000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.711700677871704 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1e-10} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.743802785873413 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1e-09} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.8793838024139404 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1e-08} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.8132786750793457 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1e-07} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.9745256900787354 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1e-06} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.7534687519073486 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1e-05} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.7800660133361816 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 0.0001} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.9411191940307617 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 0.001} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.7407543659210205 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 0.01} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.7607641220092773 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 0.1} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.9025001525878906 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.862701654434204 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 10.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.8413772583007812 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 100.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.809281826019287 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.8215384483337402 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 10000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.7482171058654785 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 100000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.6674375534057617 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.7299928665161133 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 10000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.73073673248291 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 100000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.7332351207733154 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 1000000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 3.7009294033050537 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 1.0, 'lmda': 10000000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.795102834701538 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1e-10} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8175323009490967 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1e-09} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7915070056915283 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1e-08} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.789095401763916 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1e-07} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.781489372253418 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1e-06} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8251757621765137 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1e-05} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7852787971496582 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 0.0001} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.816478967666626 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 0.001} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.821610927581787 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 0.01} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7589621543884277 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 0.1} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7933294773101807 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.7772436141967773 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 10.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8266870975494385 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 100.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 2.0591745376586914 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.90696382522583 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 10000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8194646835327148 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 100000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8314118385314941 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8367934226989746 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 10000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8164896965026855 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 100000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.827906847000122 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 1000000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 1.8394818305969238 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 1.0, 'lmda': 10000000000.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.65642762184143 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1e-10} is: \n",
      "[[ 13.          14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.         ...  29.          29.\n",
      "   29.        ]\n",
      " ...\n",
      " [ 20.          20.          29.         ... 365.00000001 365.00000001\n",
      "  365.00000001]\n",
      " [ 20.          20.          29.         ... 365.00000001 365.00000001\n",
      "  365.00000001]\n",
      " [ 20.          20.          29.         ... 365.00000001 365.00000001\n",
      "  365.00000002]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.41731262207031 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1e-09} is: \n",
      "[[ 13.          14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.00000001   4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.         ...  29.          29.\n",
      "   29.        ]\n",
      " ...\n",
      " [ 20.          20.          29.         ... 365.00000015 365.00000015\n",
      "  365.00000015]\n",
      " [ 20.          20.          29.         ... 365.00000015 365.00000015\n",
      "  365.00000015]\n",
      " [ 20.          20.          29.         ... 365.00000015 365.00000015\n",
      "  365.0000002 ]]\n"
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 35.13930344581604 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1e-08} is: \n",
      "[[ 13.00000004  14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.00000008   4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.00000004 ...  29.00000004  29.00000004\n",
      "   29.00000004]\n",
      " ...\n",
      " [ 20.          20.          29.00000004 ... 365.00000148 365.00000148\n",
      "  365.00000148]\n",
      " [ 20.          20.          29.00000004 ... 365.00000148 365.00000148\n",
      "  365.00000148]\n",
      " [ 20.          20.          29.00000004 ... 365.00000148 365.00000148\n",
      "  365.00000202]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.420260429382324 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1e-07} is: \n",
      "[[ 13.0000004  14.          4.        ...  20.         20.\n",
      "   20.       ]\n",
      " [ 14.         20.0000008   4.        ...  20.         20.\n",
      "   20.       ]\n",
      " [  4.          4.         13.0000004 ...  29.0000004  29.0000004\n",
      "   29.0000004]\n",
      " ...\n",
      " [ 20.         20.         29.0000004 ... 365.0000148 365.0000148\n",
      "  365.0000148]\n",
      " [ 20.         20.         29.0000004 ... 365.0000148 365.0000148\n",
      "  365.0000148]\n",
      " [ 20.         20.         29.0000004 ... 365.0000148 365.0000148\n",
      "  365.0000202]]\n"
     ]
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.514784812927246 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1e-06} is: \n",
      "[[ 13.000004  14.         4.       ...  20.        20.        20.      ]\n",
      " [ 14.        20.000008   4.       ...  20.        20.        20.      ]\n",
      " [  4.         4.        13.000004 ...  29.000004  29.000004  29.000004]\n",
      " ...\n",
      " [ 20.        20.        29.000004 ... 365.000148 365.000148 365.000148]\n",
      " [ 20.        20.        29.000004 ... 365.000148 365.000148 365.000148]\n",
      " [ 20.        20.        29.000004 ... 365.000148 365.000148 365.000202]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.89564752578735 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1e-05} is: \n",
      "[[ 13.00004     14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.00008      4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.00004    ...  29.00004     29.00004\n",
      "   29.00004   ]\n",
      " ...\n",
      " [ 20.          20.          29.00004    ... 365.00148001 365.00148001\n",
      "  365.00148   ]\n",
      " [ 20.          20.          29.00004    ... 365.00148001 365.00148001\n",
      "  365.00148   ]\n",
      " [ 20.          20.          29.00004    ... 365.00148    365.00148\n",
      "  365.00202   ]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.520052671432495 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 0.0001} is: \n",
      "[[ 13.0004      14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.0008       4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.0004     ...  29.0004      29.0004\n",
      "   29.0004    ]\n",
      " ...\n",
      " [ 20.          20.          29.0004     ... 365.01480072 365.01480072\n",
      "  365.0148    ]\n",
      " [ 20.          20.          29.0004     ... 365.01480072 365.01480072\n",
      "  365.0148    ]\n",
      " [ 20.          20.          29.0004     ... 365.0148     365.0148\n",
      "  365.0202    ]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.7362539768219 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 0.001} is: \n",
      "[[ 13.004     14.         4.       ...  20.        20.        20.      ]\n",
      " [ 14.        20.008      4.       ...  20.        20.        20.      ]\n",
      " [  4.         4.        13.004    ...  29.004     29.004     29.004   ]\n",
      " ...\n",
      " [ 20.        20.        29.004    ... 365.148072 365.148072 365.148   ]\n",
      " [ 20.        20.        29.004    ... 365.148072 365.148072 365.148   ]\n",
      " [ 20.        20.        29.004    ... 365.148    365.148    365.202   ]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.73691654205322 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 0.01} is: \n",
      "[[ 13.04    14.       4.     ...  20.      20.      20.    ]\n",
      " [ 14.      20.08     4.     ...  20.      20.      20.    ]\n",
      " [  4.       4.      13.04   ...  29.04    29.04    29.04  ]\n",
      " ...\n",
      " [ 20.      20.      29.04   ... 366.4872 366.4872 366.48  ]\n",
      " [ 20.      20.      29.04   ... 366.4872 366.4872 366.48  ]\n",
      " [ 20.      20.      29.04   ... 366.48   366.48   367.02  ]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.62600350379944 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 0.1} is: \n",
      "[[ 13.4   14.     4.   ...  20.    20.    20.  ]\n",
      " [ 14.    20.8    4.   ...  20.    20.    20.  ]\n",
      " [  4.     4.    13.4  ...  29.4   29.4   29.4 ]\n",
      " ...\n",
      " [ 20.    20.    29.4  ... 380.52 380.52 379.8 ]\n",
      " [ 20.    20.    29.4  ... 380.52 380.52 379.8 ]\n",
      " [ 20.    20.    29.4  ... 379.8  379.8  385.2 ]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.593005418777466 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1.0} is: \n",
      "[[ 17.  14.   4. ...  20.  20.  20.]\n",
      " [ 14.  28.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  17. ...  33.  33.  33.]\n",
      " ...\n",
      " [ 20.  20.  33. ... 585. 585. 513.]\n",
      " [ 20.  20.  33. ... 585. 585. 513.]\n",
      " [ 20.  20.  33. ... 513. 513. 567.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.402854681015015 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 10.0} is: \n",
      "[[5.300e+01 1.400e+01 4.000e+00 ... 2.000e+01 2.000e+01 2.000e+01]\n",
      " [1.400e+01 1.000e+02 4.000e+00 ... 2.000e+01 2.000e+01 2.000e+01]\n",
      " [4.000e+00 4.000e+00 5.300e+01 ... 6.900e+01 6.900e+01 6.900e+01]\n",
      " ...\n",
      " [2.000e+01 2.000e+01 6.900e+01 ... 9.045e+03 9.045e+03 1.845e+03]\n",
      " [2.000e+01 2.000e+01 6.900e+01 ... 9.045e+03 9.045e+03 1.845e+03]\n",
      " [2.000e+01 2.000e+01 6.900e+01 ... 1.845e+03 1.845e+03 2.385e+03]]\n"
     ]
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.69613790512085 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 100.0} is: \n",
      "[[4.13000e+02 1.40000e+01 4.00000e+00 ... 2.00000e+01 2.00000e+01\n",
      "  2.00000e+01]\n",
      " [1.40000e+01 8.20000e+02 4.00000e+00 ... 2.00000e+01 2.00000e+01\n",
      "  2.00000e+01]\n",
      " [4.00000e+00 4.00000e+00 4.13000e+02 ... 4.29000e+02 4.29000e+02\n",
      "  4.29000e+02]\n",
      " ...\n",
      " [2.00000e+01 2.00000e+01 4.29000e+02 ... 7.35165e+05 7.35165e+05\n",
      "  1.51650e+04]\n",
      " [2.00000e+01 2.00000e+01 4.29000e+02 ... 7.35165e+05 7.35165e+05\n",
      "  1.51650e+04]\n",
      " [2.00000e+01 2.00000e+01 4.29000e+02 ... 1.51650e+04 1.51650e+04\n",
      "  2.05650e+04]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 35.21447205543518 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1000.0} is: \n",
      "[[4.0130000e+03 1.4000000e+01 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [1.4000000e+01 8.0200000e+03 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [4.0000000e+00 4.0000000e+00 4.0130000e+03 ... 4.0290000e+03\n",
      "  4.0290000e+03 4.0290000e+03]\n",
      " ...\n",
      " [2.0000000e+01 2.0000000e+01 4.0290000e+03 ... 7.2148365e+07\n",
      "  7.2148365e+07 1.4836500e+05]\n",
      " [2.0000000e+01 2.0000000e+01 4.0290000e+03 ... 7.2148365e+07\n",
      "  7.2148365e+07 1.4836500e+05]\n",
      " [2.0000000e+01 2.0000000e+01 4.0290000e+03 ... 1.4836500e+05\n",
      "  1.4836500e+05 2.0236500e+05]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.99727129936218 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 10000.0} is: \n",
      "[[4.00130000e+04 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00200000e+04 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00130000e+04 ... 4.00290000e+04\n",
      "  4.00290000e+04 4.00290000e+04]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00290000e+04 ... 7.20148036e+09\n",
      "  7.20148036e+09 1.48036500e+06]\n",
      " [2.00000000e+01 2.00000000e+01 4.00290000e+04 ... 7.20148036e+09\n",
      "  7.20148036e+09 1.48036500e+06]\n",
      " [2.00000000e+01 2.00000000e+01 4.00290000e+04 ... 1.48036500e+06\n",
      "  1.48036500e+06 2.02036500e+06]]\n"
     ]
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.714210987091064 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 100000.0} is: \n",
      "[[4.0001300e+05 1.4000000e+01 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [1.4000000e+01 8.0002000e+05 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [4.0000000e+00 4.0000000e+00 4.0001300e+05 ... 4.0002900e+05\n",
      "  4.0002900e+05 4.0002900e+05]\n",
      " ...\n",
      " [2.0000000e+01 2.0000000e+01 4.0002900e+05 ... 7.2001480e+11\n",
      "  7.2001480e+11 1.4800365e+07]\n",
      " [2.0000000e+01 2.0000000e+01 4.0002900e+05 ... 7.2001480e+11\n",
      "  7.2001480e+11 1.4800365e+07]\n",
      " [2.0000000e+01 2.0000000e+01 4.0002900e+05 ... 1.4800365e+07\n",
      "  1.4800365e+07 2.0200365e+07]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.71897006034851 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1000000.0} is: \n",
      "[[4.00001300e+06 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00002000e+06 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00001300e+06 ... 4.00002900e+06\n",
      "  4.00002900e+06 4.00002900e+06]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00002900e+06 ... 7.20001480e+13\n",
      "  7.20001480e+13 1.48000365e+08]\n",
      " [2.00000000e+01 2.00000000e+01 4.00002900e+06 ... 7.20001480e+13\n",
      "  7.20001480e+13 1.48000365e+08]\n",
      " [2.00000000e+01 2.00000000e+01 4.00002900e+06 ... 1.48000365e+08\n",
      "  1.48000365e+08 2.02000365e+08]]\n"
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     "name": "stdout",
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.76906633377075 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 10000000.0} is: \n",
      "[[4.00000130e+07 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00000200e+07 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00000130e+07 ... 4.00000290e+07\n",
      "  4.00000290e+07 4.00000290e+07]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00000290e+07 ... 7.20000148e+15\n",
      "  7.20000148e+15 1.48000036e+09]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000290e+07 ... 7.20000148e+15\n",
      "  7.20000148e+15 1.48000036e+09]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000290e+07 ... 1.48000036e+09\n",
      "  1.48000036e+09 2.02000036e+09]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 35.22616124153137 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 100000000.0} is: \n",
      "[[4.00000013e+08 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00000020e+08 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00000013e+08 ... 4.00000029e+08\n",
      "  4.00000029e+08 4.00000029e+08]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00000029e+08 ... 7.20000015e+17\n",
      "  7.20000015e+17 1.48000004e+10]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000029e+08 ... 7.20000015e+17\n",
      "  7.20000015e+17 1.48000004e+10]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000029e+08 ... 1.48000004e+10\n",
      "  1.48000004e+10 2.02000004e+10]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.935295820236206 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 1000000000.0} is: \n",
      "[[4.00000001e+09 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00000002e+09 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00000001e+09 ... 4.00000003e+09\n",
      "  4.00000003e+09 4.00000003e+09]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00000003e+09 ... 7.20000001e+19\n",
      "  7.20000001e+19 1.48000000e+11]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000003e+09 ... 7.20000001e+19\n",
      "  7.20000001e+19 1.48000000e+11]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000003e+09 ... 1.48000000e+11\n",
      "  1.48000000e+11 2.02000000e+11]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.634655714035034 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 2.0, 'lmda': 10000000000.0} is: \n",
      "[[4.00e+10 1.40e+01 4.00e+00 ... 2.00e+01 2.00e+01 2.00e+01]\n",
      " [1.40e+01 8.00e+10 4.00e+00 ... 2.00e+01 2.00e+01 2.00e+01]\n",
      " [4.00e+00 4.00e+00 4.00e+10 ... 4.00e+10 4.00e+10 4.00e+10]\n",
      " ...\n",
      " [2.00e+01 2.00e+01 4.00e+10 ... 7.20e+21 7.20e+21 1.48e+12]\n",
      " [2.00e+01 2.00e+01 4.00e+10 ... 7.20e+21 7.20e+21 1.48e+12]\n",
      " [2.00e+01 2.00e+01 4.00e+10 ... 1.48e+12 1.48e+12 2.02e+12]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.149678707122803 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1e-10} is: \n",
      "[[8.00e-10 8.00e-10 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e-10 1.20e-09 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 8.00e-10 ... 8.00e-10 8.00e-10 8.00e-10]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 8.00e-10 ... 2.64e-08 2.64e-08 2.64e-08]\n",
      " [0.00e+00 0.00e+00 8.00e-10 ... 2.64e-08 2.64e-08 2.64e-08]\n",
      " [0.00e+00 0.00e+00 8.00e-10 ... 2.64e-08 2.64e-08 2.64e-08]]\n"
     ]
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.24059534072876 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1e-09} is: \n",
      "[[8.00e-09 8.00e-09 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e-09 1.20e-08 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 8.00e-09 ... 8.00e-09 8.00e-09 8.00e-09]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 8.00e-09 ... 2.64e-07 2.64e-07 2.64e-07]\n",
      " [0.00e+00 0.00e+00 8.00e-09 ... 2.64e-07 2.64e-07 2.64e-07]\n",
      " [0.00e+00 0.00e+00 8.00e-09 ... 2.64e-07 2.64e-07 2.64e-07]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.805413007736206 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1e-08} is: \n",
      "[[8.00e-08 8.00e-08 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e-08 1.20e-07 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 8.00e-08 ... 8.00e-08 8.00e-08 8.00e-08]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 8.00e-08 ... 2.64e-06 2.64e-06 2.64e-06]\n",
      " [0.00e+00 0.00e+00 8.00e-08 ... 2.64e-06 2.64e-06 2.64e-06]\n",
      " [0.00e+00 0.00e+00 8.00e-08 ... 2.64e-06 2.64e-06 2.64e-06]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.43400478363037 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1e-07} is: \n",
      "[[8.00e-07 8.00e-07 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e-07 1.20e-06 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 8.00e-07 ... 8.00e-07 8.00e-07 8.00e-07]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 8.00e-07 ... 2.64e-05 2.64e-05 2.64e-05]\n",
      " [0.00e+00 0.00e+00 8.00e-07 ... 2.64e-05 2.64e-05 2.64e-05]\n",
      " [0.00e+00 0.00e+00 8.00e-07 ... 2.64e-05 2.64e-05 2.64e-05]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.632941246032715 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1e-06} is: \n",
      "[[8.00e-06 8.00e-06 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e-06 1.20e-05 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 8.00e-06 ... 8.00e-06 8.00e-06 8.00e-06]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 8.00e-06 ... 2.64e-04 2.64e-04 2.64e-04]\n",
      " [0.00e+00 0.00e+00 8.00e-06 ... 2.64e-04 2.64e-04 2.64e-04]\n",
      " [0.00e+00 0.00e+00 8.00e-06 ... 2.64e-04 2.64e-04 2.64e-04]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.6206476688385 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1e-05} is: \n",
      "[[8.00e-05 8.00e-05 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e-05 1.20e-04 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 8.00e-05 ... 8.00e-05 8.00e-05 8.00e-05]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 8.00e-05 ... 2.64e-03 2.64e-03 2.64e-03]\n",
      " [0.00e+00 0.00e+00 8.00e-05 ... 2.64e-03 2.64e-03 2.64e-03]\n",
      " [0.00e+00 0.00e+00 8.00e-05 ... 2.64e-03 2.64e-03 2.64e-03]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.250969409942627 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 0.0001} is: \n",
      "[[0.0008 0.0008 0.     ... 0.     0.     0.    ]\n",
      " [0.0008 0.0012 0.     ... 0.     0.     0.    ]\n",
      " [0.     0.     0.0008 ... 0.0008 0.0008 0.0008]\n",
      " ...\n",
      " [0.     0.     0.0008 ... 0.0264 0.0264 0.0264]\n",
      " [0.     0.     0.0008 ... 0.0264 0.0264 0.0264]\n",
      " [0.     0.     0.0008 ... 0.0264 0.0264 0.0264]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.387526512145996 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 0.001} is: \n",
      "[[0.008      0.008      0.         ... 0.         0.         0.        ]\n",
      " [0.008      0.01200001 0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.008      ... 0.008      0.008      0.008     ]\n",
      " ...\n",
      " [0.         0.         0.008      ... 0.26400015 0.26400015 0.26400015]\n",
      " [0.         0.         0.008      ... 0.26400015 0.26400015 0.26400015]\n",
      " [0.         0.         0.008      ... 0.26400015 0.26400015 0.2640002 ]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.552900314331055 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 0.01} is: \n",
      "[[0.080004   0.08       0.         ... 0.         0.         0.        ]\n",
      " [0.08       0.120008   0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.080004   ... 0.080004   0.080004   0.080004  ]\n",
      " ...\n",
      " [0.         0.         0.080004   ... 2.64014801 2.64014801 2.640148  ]\n",
      " [0.         0.         0.080004   ... 2.64014801 2.64014801 2.640148  ]\n",
      " [0.         0.         0.080004   ... 2.640148   2.640148   2.640202  ]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.285762071609497 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 0.1} is: \n",
      "[[ 0.804    0.8      0.      ...  0.       0.       0.     ]\n",
      " [ 0.8      1.208    0.      ...  0.       0.       0.     ]\n",
      " [ 0.       0.       0.804   ...  0.804    0.804    0.804  ]\n",
      " ...\n",
      " [ 0.       0.       0.804   ... 26.54872 26.54872 26.548  ]\n",
      " [ 0.       0.       0.804   ... 26.54872 26.54872 26.548  ]\n",
      " [ 0.       0.       0.804   ... 26.548   26.548   26.602  ]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.075997591018677 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1.0} is: \n",
      "[[ 12.   8.   0. ...   0.   0.   0.]\n",
      " [  8.  20.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  12. ...  12.  12.  12.]\n",
      " ...\n",
      " [  0.   0.  12. ... 484. 484. 412.]\n",
      " [  0.   0.  12. ... 484. 484. 412.]\n",
      " [  0.   0.  12. ... 412. 412. 466.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.010215520858765 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 10.0} is: \n",
      "[[4.08000e+03 8.00000e+01 0.00000e+00 ... 0.00000e+00 0.00000e+00\n",
      "  0.00000e+00]\n",
      " [8.00000e+01 8.12000e+03 0.00000e+00 ... 0.00000e+00 0.00000e+00\n",
      "  0.00000e+00]\n",
      " [0.00000e+00 0.00000e+00 4.08000e+03 ... 4.08000e+03 4.08000e+03\n",
      "  4.08000e+03]\n",
      " ...\n",
      " [0.00000e+00 0.00000e+00 4.08000e+03 ... 7.35064e+06 7.35064e+06\n",
      "  1.50640e+05]\n",
      " [0.00000e+00 0.00000e+00 4.08000e+03 ... 7.35064e+06 7.35064e+06\n",
      "  1.50640e+05]\n",
      " [0.00000e+00 0.00000e+00 4.08000e+03 ... 1.50640e+05 1.50640e+05\n",
      "  2.04640e+05]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.062121629714966 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 100.0} is: \n",
      "[[4.00080000e+06 8.00000000e+02 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.00000000e+02 8.00120000e+06 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00080000e+06 ... 4.00080000e+06\n",
      "  4.00080000e+06 4.00080000e+06]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00080000e+06 ... 7.20148026e+11\n",
      "  7.20148026e+11 1.48026400e+08]\n",
      " [0.00000000e+00 0.00000000e+00 4.00080000e+06 ... 7.20148026e+11\n",
      "  7.20148026e+11 1.48026400e+08]\n",
      " [0.00000000e+00 0.00000000e+00 4.00080000e+06 ... 1.48026400e+08\n",
      "  1.48026400e+08 2.02026400e+08]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.070118188858032 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1000.0} is: \n",
      "[[4.00000800e+09 8.00000000e+03 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.00000000e+03 8.00001200e+09 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000800e+09 ... 4.00000800e+09\n",
      "  4.00000800e+09 4.00000800e+09]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00000800e+09 ... 7.20001480e+16\n",
      "  7.20001480e+16 1.48000264e+11]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000800e+09 ... 7.20001480e+16\n",
      "  7.20001480e+16 1.48000264e+11]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000800e+09 ... 1.48000264e+11\n",
      "  1.48000264e+11 2.02000264e+11]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.060213327407837 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 10000.0} is: \n",
      "[[4.00000008e+12 8.00000000e+04 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.00000000e+04 8.00000012e+12 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000008e+12 ... 4.00000008e+12\n",
      "  4.00000008e+12 4.00000008e+12]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00000008e+12 ... 7.20000015e+21\n",
      "  7.20000015e+21 1.48000003e+14]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000008e+12 ... 7.20000015e+21\n",
      "  7.20000015e+21 1.48000003e+14]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000008e+12 ... 1.48000003e+14\n",
      "  1.48000003e+14 2.02000003e+14]]\n"
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.099493503570557 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 100000.0} is: \n",
      "[[4.00e+15 8.00e+05 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e+05 8.00e+15 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 4.00e+15 ... 4.00e+15 4.00e+15 4.00e+15]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 4.00e+15 ... 7.20e+26 7.20e+26 1.48e+17]\n",
      " [0.00e+00 0.00e+00 4.00e+15 ... 7.20e+26 7.20e+26 1.48e+17]\n",
      " [0.00e+00 0.00e+00 4.00e+15 ... 1.48e+17 1.48e+17 2.02e+17]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.103667974472046 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1000000.0} is: \n",
      "[[4.00e+18 8.00e+06 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e+06 8.00e+18 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 4.00e+18 ... 4.00e+18 4.00e+18 4.00e+18]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 4.00e+18 ... 7.20e+31 7.20e+31 1.48e+20]\n",
      " [0.00e+00 0.00e+00 4.00e+18 ... 7.20e+31 7.20e+31 1.48e+20]\n",
      " [0.00e+00 0.00e+00 4.00e+18 ... 1.48e+20 1.48e+20 2.02e+20]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.023816108703613 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 10000000.0} is: \n",
      "[[4.00e+21 8.00e+07 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e+07 8.00e+21 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 4.00e+21 ... 4.00e+21 4.00e+21 4.00e+21]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 4.00e+21 ... 7.20e+36 7.20e+36 1.48e+23]\n",
      " [0.00e+00 0.00e+00 4.00e+21 ... 7.20e+36 7.20e+36 1.48e+23]\n",
      " [0.00e+00 0.00e+00 4.00e+21 ... 1.48e+23 1.48e+23 2.02e+23]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.056624174118042 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 100000000.0} is: \n",
      "[[4.00e+24 8.00e+08 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e+08 8.00e+24 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 4.00e+24 ... 4.00e+24 4.00e+24 4.00e+24]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 4.00e+24 ... 7.20e+41 7.20e+41 1.48e+26]\n",
      " [0.00e+00 0.00e+00 4.00e+24 ... 7.20e+41 7.20e+41 1.48e+26]\n",
      " [0.00e+00 0.00e+00 4.00e+24 ... 1.48e+26 1.48e+26 2.02e+26]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.005533933639526 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 1000000000.0} is: \n",
      "[[4.00e+27 8.00e+09 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e+09 8.00e+27 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 4.00e+27 ... 4.00e+27 4.00e+27 4.00e+27]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 4.00e+27 ... 7.20e+46 7.20e+46 1.48e+29]\n",
      " [0.00e+00 0.00e+00 4.00e+27 ... 7.20e+46 7.20e+46 1.48e+29]\n",
      " [0.00e+00 0.00e+00 4.00e+27 ... 1.48e+29 1.48e+29 2.02e+29]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 29.060596466064453 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 2.0, 'lmda': 10000000000.0} is: \n",
      "[[4.00e+30 8.00e+10 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e+10 8.00e+30 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 4.00e+30 ... 4.00e+30 4.00e+30 4.00e+30]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 4.00e+30 ... 7.20e+51 7.20e+51 1.48e+32]\n",
      " [0.00e+00 0.00e+00 4.00e+30 ... 7.20e+51 7.20e+51 1.48e+32]\n",
      " [0.00e+00 0.00e+00 4.00e+30 ... 1.48e+32 1.48e+32 2.02e+32]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.390531063079834 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1e-10} is: \n",
      "[[  8.           8.           0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  8.          12.           0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  0.           0.           8.         ...   8.           8.\n",
      "    8.        ]\n",
      " ...\n",
      " [  0.           0.           8.         ... 264.00000001 264.00000001\n",
      "  264.00000001]\n",
      " [  0.           0.           8.         ... 264.00000001 264.00000001\n",
      "  264.00000001]\n",
      " [  0.           0.           8.         ... 264.00000001 264.00000001\n",
      "  264.00000002]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.39858675003052 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1e-09} is: \n",
      "[[  8.           8.           0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  8.          12.00000001   0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  0.           0.           8.         ...   8.           8.\n",
      "    8.        ]\n",
      " ...\n",
      " [  0.           0.           8.         ... 264.00000015 264.00000015\n",
      "  264.00000015]\n",
      " [  0.           0.           8.         ... 264.00000015 264.00000015\n",
      "  264.00000015]\n",
      " [  0.           0.           8.         ... 264.00000015 264.00000015\n",
      "  264.0000002 ]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.318459033966064 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1e-08} is: \n",
      "[[  8.00000004   8.           0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  8.          12.00000008   0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  0.           0.           8.00000004 ...   8.00000004   8.00000004\n",
      "    8.00000004]\n",
      " ...\n",
      " [  0.           0.           8.00000004 ... 264.00000148 264.00000148\n",
      "  264.00000148]\n",
      " [  0.           0.           8.00000004 ... 264.00000148 264.00000148\n",
      "  264.00000148]\n",
      " [  0.           0.           8.00000004 ... 264.00000148 264.00000148\n",
      "  264.00000202]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.18604373931885 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1e-07} is: \n",
      "[[  8.0000004   8.          0.        ...   0.          0.\n",
      "    0.       ]\n",
      " [  8.         12.0000008   0.        ...   0.          0.\n",
      "    0.       ]\n",
      " [  0.          0.          8.0000004 ...   8.0000004   8.0000004\n",
      "    8.0000004]\n",
      " ...\n",
      " [  0.          0.          8.0000004 ... 264.0000148 264.0000148\n",
      "  264.0000148]\n",
      " [  0.          0.          8.0000004 ... 264.0000148 264.0000148\n",
      "  264.0000148]\n",
      " [  0.          0.          8.0000004 ... 264.0000148 264.0000148\n",
      "  264.0000202]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.15291094779968 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1e-06} is: \n",
      "[[  8.000004   8.         0.       ...   0.         0.         0.      ]\n",
      " [  8.        12.000008   0.       ...   0.         0.         0.      ]\n",
      " [  0.         0.         8.000004 ...   8.000004   8.000004   8.000004]\n",
      " ...\n",
      " [  0.         0.         8.000004 ... 264.000148 264.000148 264.000148]\n",
      " [  0.         0.         8.000004 ... 264.000148 264.000148 264.000148]\n",
      " [  0.         0.         8.000004 ... 264.000148 264.000148 264.000202]]\n"
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     "name": "stdout",
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.9378080368042 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1e-05} is: \n",
      "[[  8.00004      8.           0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  8.          12.00008      0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  0.           0.           8.00004    ...   8.00004      8.00004\n",
      "    8.00004   ]\n",
      " ...\n",
      " [  0.           0.           8.00004    ... 264.00148001 264.00148001\n",
      "  264.00148   ]\n",
      " [  0.           0.           8.00004    ... 264.00148001 264.00148001\n",
      "  264.00148   ]\n",
      " [  0.           0.           8.00004    ... 264.00148    264.00148\n",
      "  264.00202   ]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.21824765205383 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 0.0001} is: \n",
      "[[  8.0004       8.           0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  8.          12.0008       0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  0.           0.           8.0004     ...   8.0004       8.0004\n",
      "    8.0004    ]\n",
      " ...\n",
      " [  0.           0.           8.0004     ... 264.01480072 264.01480072\n",
      "  264.0148    ]\n",
      " [  0.           0.           8.0004     ... 264.01480072 264.01480072\n",
      "  264.0148    ]\n",
      " [  0.           0.           8.0004     ... 264.0148     264.0148\n",
      "  264.0202    ]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.12432861328125 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 0.001} is: \n",
      "[[  8.004      8.         0.       ...   0.         0.         0.      ]\n",
      " [  8.        12.008      0.       ...   0.         0.         0.      ]\n",
      " [  0.         0.         8.004    ...   8.004      8.004      8.004   ]\n",
      " ...\n",
      " [  0.         0.         8.004    ... 264.148072 264.148072 264.148   ]\n",
      " [  0.         0.         8.004    ... 264.148072 264.148072 264.148   ]\n",
      " [  0.         0.         8.004    ... 264.148    264.148    264.202   ]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.32614278793335 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 0.01} is: \n",
      "[[  8.04     8.       0.     ...   0.       0.       0.    ]\n",
      " [  8.      12.08     0.     ...   0.       0.       0.    ]\n",
      " [  0.       0.       8.04   ...   8.04     8.04     8.04  ]\n",
      " ...\n",
      " [  0.       0.       8.04   ... 265.4872 265.4872 265.48  ]\n",
      " [  0.       0.       8.04   ... 265.4872 265.4872 265.48  ]\n",
      " [  0.       0.       8.04   ... 265.48   265.48   266.02  ]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.2137987613678 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 0.1} is: \n",
      "[[  8.4    8.     0.   ...   0.     0.     0.  ]\n",
      " [  8.    12.8    0.   ...   0.     0.     0.  ]\n",
      " [  0.     0.     8.4  ...   8.4    8.4    8.4 ]\n",
      " ...\n",
      " [  0.     0.     8.4  ... 279.52 279.52 278.8 ]\n",
      " [  0.     0.     8.4  ... 279.52 279.52 278.8 ]\n",
      " [  0.     0.     8.4  ... 278.8  278.8  284.2 ]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.11435890197754 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1.0} is: \n",
      "[[ 12.   8.   0. ...   0.   0.   0.]\n",
      " [  8.  20.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  12. ...  12.  12.  12.]\n",
      " ...\n",
      " [  0.   0.  12. ... 484. 484. 412.]\n",
      " [  0.   0.  12. ... 484. 484. 412.]\n",
      " [  0.   0.  12. ... 412. 412. 466.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.42665195465088 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 10.0} is: \n",
      "[[4.800e+01 8.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [8.000e+00 9.200e+01 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 4.800e+01 ... 4.800e+01 4.800e+01 4.800e+01]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 4.800e+01 ... 8.944e+03 8.944e+03 1.744e+03]\n",
      " [0.000e+00 0.000e+00 4.800e+01 ... 8.944e+03 8.944e+03 1.744e+03]\n",
      " [0.000e+00 0.000e+00 4.800e+01 ... 1.744e+03 1.744e+03 2.284e+03]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.08107781410217 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 100.0} is: \n",
      "[[4.08000e+02 8.00000e+00 0.00000e+00 ... 0.00000e+00 0.00000e+00\n",
      "  0.00000e+00]\n",
      " [8.00000e+00 8.12000e+02 0.00000e+00 ... 0.00000e+00 0.00000e+00\n",
      "  0.00000e+00]\n",
      " [0.00000e+00 0.00000e+00 4.08000e+02 ... 4.08000e+02 4.08000e+02\n",
      "  4.08000e+02]\n",
      " ...\n",
      " [0.00000e+00 0.00000e+00 4.08000e+02 ... 7.35064e+05 7.35064e+05\n",
      "  1.50640e+04]\n",
      " [0.00000e+00 0.00000e+00 4.08000e+02 ... 7.35064e+05 7.35064e+05\n",
      "  1.50640e+04]\n",
      " [0.00000e+00 0.00000e+00 4.08000e+02 ... 1.50640e+04 1.50640e+04\n",
      "  2.04640e+04]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.154428482055664 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1000.0} is: \n",
      "[[4.0080000e+03 8.0000000e+00 0.0000000e+00 ... 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00]\n",
      " [8.0000000e+00 8.0120000e+03 0.0000000e+00 ... 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00]\n",
      " [0.0000000e+00 0.0000000e+00 4.0080000e+03 ... 4.0080000e+03\n",
      "  4.0080000e+03 4.0080000e+03]\n",
      " ...\n",
      " [0.0000000e+00 0.0000000e+00 4.0080000e+03 ... 7.2148264e+07\n",
      "  7.2148264e+07 1.4826400e+05]\n",
      " [0.0000000e+00 0.0000000e+00 4.0080000e+03 ... 7.2148264e+07\n",
      "  7.2148264e+07 1.4826400e+05]\n",
      " [0.0000000e+00 0.0000000e+00 4.0080000e+03 ... 1.4826400e+05\n",
      "  1.4826400e+05 2.0226400e+05]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.17109155654907 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 10000.0} is: \n",
      "[[4.00080000e+04 8.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.00000000e+00 8.00120000e+04 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00080000e+04 ... 4.00080000e+04\n",
      "  4.00080000e+04 4.00080000e+04]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00080000e+04 ... 7.20148026e+09\n",
      "  7.20148026e+09 1.48026400e+06]\n",
      " [0.00000000e+00 0.00000000e+00 4.00080000e+04 ... 7.20148026e+09\n",
      "  7.20148026e+09 1.48026400e+06]\n",
      " [0.00000000e+00 0.00000000e+00 4.00080000e+04 ... 1.48026400e+06\n",
      "  1.48026400e+06 2.02026400e+06]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.225682973861694 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 100000.0} is: \n",
      "[[4.0000800e+05 8.0000000e+00 0.0000000e+00 ... 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00]\n",
      " [8.0000000e+00 8.0001200e+05 0.0000000e+00 ... 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00]\n",
      " [0.0000000e+00 0.0000000e+00 4.0000800e+05 ... 4.0000800e+05\n",
      "  4.0000800e+05 4.0000800e+05]\n",
      " ...\n",
      " [0.0000000e+00 0.0000000e+00 4.0000800e+05 ... 7.2001480e+11\n",
      "  7.2001480e+11 1.4800264e+07]\n",
      " [0.0000000e+00 0.0000000e+00 4.0000800e+05 ... 7.2001480e+11\n",
      "  7.2001480e+11 1.4800264e+07]\n",
      " [0.0000000e+00 0.0000000e+00 4.0000800e+05 ... 1.4800264e+07\n",
      "  1.4800264e+07 2.0200264e+07]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.07739186286926 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1000000.0} is: \n",
      "[[4.00000800e+06 8.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.00000000e+00 8.00001200e+06 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000800e+06 ... 4.00000800e+06\n",
      "  4.00000800e+06 4.00000800e+06]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00000800e+06 ... 7.20001480e+13\n",
      "  7.20001480e+13 1.48000264e+08]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000800e+06 ... 7.20001480e+13\n",
      "  7.20001480e+13 1.48000264e+08]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000800e+06 ... 1.48000264e+08\n",
      "  1.48000264e+08 2.02000264e+08]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.102808237075806 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 10000000.0} is: \n",
      "[[4.00000080e+07 8.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.00000000e+00 8.00000120e+07 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000080e+07 ... 4.00000080e+07\n",
      "  4.00000080e+07 4.00000080e+07]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00000080e+07 ... 7.20000148e+15\n",
      "  7.20000148e+15 1.48000026e+09]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000080e+07 ... 7.20000148e+15\n",
      "  7.20000148e+15 1.48000026e+09]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000080e+07 ... 1.48000026e+09\n",
      "  1.48000026e+09 2.02000026e+09]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.14411973953247 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 100000000.0} is: \n",
      "[[4.00000008e+08 8.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.00000000e+00 8.00000012e+08 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000008e+08 ... 4.00000008e+08\n",
      "  4.00000008e+08 4.00000008e+08]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00000008e+08 ... 7.20000015e+17\n",
      "  7.20000015e+17 1.48000003e+10]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000008e+08 ... 7.20000015e+17\n",
      "  7.20000015e+17 1.48000003e+10]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000008e+08 ... 1.48000003e+10\n",
      "  1.48000003e+10 2.02000003e+10]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.30718159675598 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 1000000000.0} is: \n",
      "[[4.00000001e+09 8.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.00000000e+00 8.00000001e+09 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000001e+09 ... 4.00000001e+09\n",
      "  4.00000001e+09 4.00000001e+09]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00000001e+09 ... 7.20000001e+19\n",
      "  7.20000001e+19 1.48000000e+11]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000001e+09 ... 7.20000001e+19\n",
      "  7.20000001e+19 1.48000000e+11]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000001e+09 ... 1.48000000e+11\n",
      "  1.48000000e+11 2.02000000e+11]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 34.202890396118164 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 2.0, 'lmda': 10000000000.0} is: \n",
      "[[4.00e+10 8.00e+00 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [8.00e+00 8.00e+10 0.00e+00 ... 0.00e+00 0.00e+00 0.00e+00]\n",
      " [0.00e+00 0.00e+00 4.00e+10 ... 4.00e+10 4.00e+10 4.00e+10]\n",
      " ...\n",
      " [0.00e+00 0.00e+00 4.00e+10 ... 7.20e+21 7.20e+21 1.48e+12]\n",
      " [0.00e+00 0.00e+00 4.00e+10 ... 7.20e+21 7.20e+21 1.48e+12]\n",
      " [0.00e+00 0.00e+00 4.00e+10 ... 1.48e+12 1.48e+12 2.02e+12]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.765599966049194 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1e-10} is: \n",
      "[[  33.           26.            4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [  26.           40.00000001    4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [   4.            4.           33.         ...   49.\n",
      "    49.           49.        ]\n",
      " ...\n",
      " [  20.           20.           49.         ... 1417.00000026\n",
      "  1417.00000026 1349.00000028]\n",
      " [  20.           20.           49.         ... 1417.00000026\n",
      "  1417.00000027 1349.00000029]\n",
      " [  20.           20.           49.         ... 1349.00000028\n",
      "  1349.00000029 1287.00000047]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.46338510513306 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1e-09} is: \n",
      "[[  33.00000003   26.            4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [  26.           40.00000006    4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [   4.            4.           33.00000003 ...   49.00000003\n",
      "    49.00000003   49.00000003]\n",
      " ...\n",
      " [  20.           20.           49.00000003 ... 1417.00000264\n",
      "  1417.00000262 1349.00000278]\n",
      " [  20.           20.           49.00000003 ... 1417.00000262\n",
      "  1417.00000267 1349.00000291]\n",
      " [  20.           20.           49.00000003 ... 1349.00000278\n",
      "  1349.00000291 1287.00000474]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.3702609539032 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1e-08} is: \n",
      "[[  33.00000032   26.            4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [  26.           40.00000064    4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [   4.            4.           33.00000032 ...   49.00000032\n",
      "    49.00000032   49.00000032]\n",
      " ...\n",
      " [  20.           20.           49.00000032 ... 1417.00002644\n",
      "  1417.0000262  1349.00002784]\n",
      " [  20.           20.           49.00000032 ... 1417.0000262\n",
      "  1417.00002666 1349.00002907]\n",
      " [  20.           20.           49.00000032 ... 1349.00002784\n",
      "  1349.00002907 1287.00004736]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.134641885757446 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1e-07} is: \n",
      "[[  33.0000032   26.           4.        ...   20.          20.\n",
      "    20.       ]\n",
      " [  26.          40.0000064    4.        ...   20.          20.\n",
      "    20.       ]\n",
      " [   4.           4.          33.0000032 ...   49.0000032   49.0000032\n",
      "    49.0000032]\n",
      " ...\n",
      " [  20.          20.          49.0000032 ... 1417.0002644 1417.000262\n",
      "  1349.0002784]\n",
      " [  20.          20.          49.0000032 ... 1417.000262  1417.0002666\n",
      "  1349.0002907]\n",
      " [  20.          20.          49.0000032 ... 1349.0002784 1349.0002907\n",
      "  1287.0004736]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.77328538894653 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1e-06} is: \n",
      "[[  33.000032     26.            4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [  26.           40.000064      4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [   4.            4.           33.000032   ...   49.000032\n",
      "    49.000032     49.000032  ]\n",
      " ...\n",
      " [  20.           20.           49.000032   ... 1417.002644\n",
      "  1417.00262    1349.002784  ]\n",
      " [  20.           20.           49.000032   ... 1417.00262\n",
      "  1417.002666   1349.002907  ]\n",
      " [  20.           20.           49.000032   ... 1349.002784\n",
      "  1349.002907   1287.00473601]]\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.08638668060303 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1e-05} is: \n",
      "[[  33.00032      26.            4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [  26.           40.00064       4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [   4.            4.           33.00032    ...   49.00032\n",
      "    49.00032      49.00032   ]\n",
      " ...\n",
      " [  20.           20.           49.00032    ... 1417.02644033\n",
      "  1417.02620029 1349.02784013]\n",
      " [  20.           20.           49.00032    ... 1417.02620029\n",
      "  1417.0266603  1349.0290702 ]\n",
      " [  20.           20.           49.00032    ... 1349.02784013\n",
      "  1349.0290702  1287.04736051]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.63325810432434 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 0.0001} is: \n",
      "[[  33.0032       26.            4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [  26.           40.00640032    4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [   4.            4.           33.0032     ...   49.0032\n",
      "    49.0032       49.0032    ]\n",
      " ...\n",
      " [  20.           20.           49.0032     ... 1417.26443272\n",
      "  1417.26202868 1349.27841344]\n",
      " [  20.           20.           49.0032     ... 1417.26202868\n",
      "  1417.26663004 1349.2907195 ]\n",
      " [  20.           20.           49.0032     ... 1349.27841344\n",
      "  1349.2907195  1287.4736514 ]]\n"
     ]
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    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.54682493209839 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 0.001} is: \n",
      "[[  33.032        26.            4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [  26.           40.064032      4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [   4.            4.           33.032      ...   49.032\n",
      "    49.032        49.032     ]\n",
      " ...\n",
      " [  20.           20.           49.032      ... 1419.6472735\n",
      "  1419.62286876 1351.78534411]\n",
      " [  20.           20.           49.032      ... 1419.62286876\n",
      "  1419.66900512 1351.90895058]\n",
      " [  20.           20.           49.032      ... 1351.78534411\n",
      "  1351.90895058 1291.74114165]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.84195923805237 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 0.01} is: \n",
      "[[  33.32         26.            4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [  26.           40.6432        4.         ...   20.\n",
      "    20.           20.        ]\n",
      " [   4.            4.           33.32       ...   49.32\n",
      "    49.32         49.32      ]\n",
      " ...\n",
      " [  20.           20.           49.32       ... 1443.76870688\n",
      "  1443.48756    1376.974512  ]\n",
      " [  20.           20.           49.32       ... 1443.48756\n",
      "  1443.96152288 1378.265576  ]\n",
      " [  20.           20.           49.32       ... 1376.974512\n",
      "  1378.265576   1334.875648  ]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.495763063430786 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 0.1} is: \n",
      "[[  36.2      26.        4.     ...   20.       20.       20.    ]\n",
      " [  26.       46.72      4.     ...   20.       20.       20.    ]\n",
      " [   4.        4.       36.2    ...   52.2      52.2      52.2   ]\n",
      " ...\n",
      " [  20.       20.       52.2    ... 1715.6528 1708.44   1640.952 ]\n",
      " [  20.       20.       52.2    ... 1708.44   1714.7888 1659.776 ]\n",
      " [  20.       20.       52.2    ... 1640.952  1659.776  1813.648 ]]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.454060554504395 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1.0} is: \n",
      "[[6.5000e+01 2.6000e+01 4.0000e+00 ... 2.0000e+01 2.0000e+01 2.0000e+01]\n",
      " [2.6000e+01 1.3600e+02 4.0000e+00 ... 2.0000e+01 2.0000e+01 2.0000e+01]\n",
      " [4.0000e+00 4.0000e+00 6.5000e+01 ... 8.1000e+01 8.1000e+01 8.1000e+01]\n",
      " ...\n",
      " [2.0000e+01 2.0000e+01 8.1000e+01 ... 9.1250e+03 7.6650e+03 5.5890e+03]\n",
      " [2.0000e+01 2.0000e+01 8.1000e+01 ... 7.6650e+03 8.4950e+03 6.7820e+03]\n",
      " [2.0000e+01 2.0000e+01 8.1000e+01 ... 5.5890e+03 6.7820e+03 1.2811e+04]]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.75584816932678 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 10.0} is: \n",
      "[[3.530000e+02 2.600000e+01 4.000000e+00 ... 2.000000e+01 2.000000e+01\n",
      "  2.000000e+01]\n",
      " [2.600000e+01 3.880000e+03 4.000000e+00 ... 2.000000e+01 2.000000e+01\n",
      "  2.000000e+01]\n",
      " [4.000000e+00 4.000000e+00 3.530000e+02 ... 3.690000e+02 3.690000e+02\n",
      "  3.690000e+02]\n",
      " ...\n",
      " [2.000000e+01 2.000000e+01 3.690000e+02 ... 4.739057e+06 1.074417e+06\n",
      "  2.755890e+05]\n",
      " [2.000000e+01 2.000000e+01 3.690000e+02 ... 1.074417e+06 4.328477e+06\n",
      "  8.014190e+05]\n",
      " [2.000000e+01 2.000000e+01 3.690000e+02 ... 2.755890e+05 8.014190e+05\n",
      "  2.210647e+06]]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.70102143287659 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 100.0} is: \n",
      "[[3.23300000e+03 2.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [2.60000000e+01 3.26440000e+05 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 3.23300000e+03 ... 3.24900000e+03\n",
      "  3.24900000e+03 3.24900000e+03]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 3.24900000e+03 ... 3.03369858e+10\n",
      "  7.88943417e+08 1.25719749e+08]\n",
      " [2.00000000e+01 2.00000000e+01 3.24900000e+03 ... 7.88943417e+08\n",
      "  2.99503080e+10 5.95792049e+08]\n",
      " [2.00000000e+01 2.00000000e+01 3.24900000e+03 ... 1.25719749e+08\n",
      "  5.95792049e+08 1.69987489e+09]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb24fe26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.31402778625488 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1000.0} is: \n",
      "[[3.20330000e+04 2.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [2.60000000e+01 3.20640400e+07 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 3.20330000e+04 ... 3.20490000e+04\n",
      "  3.20490000e+04 3.20490000e+04]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 3.20490000e+04 ... 2.89507275e+14\n",
      "  7.62870621e+11 1.13346785e+11]\n",
      " [2.00000000e+01 2.00000000e+01 3.20490000e+04 ... 7.62870621e+11\n",
      "  2.89123007e+14 5.77952908e+11]\n",
      " [2.00000000e+01 2.00000000e+01 3.20490000e+04 ... 1.13346785e+11\n",
      "  5.77952908e+11 1.65314474e+12]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688f81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.52541899681091 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 10000.0} is: \n",
      "[[3.20033000e+05 2.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [2.60000000e+01 3.20064004e+09 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 3.20033000e+05 ... 3.20049000e+05\n",
      "  3.20049000e+05 3.20049000e+05]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 3.20049000e+05 ... 2.88150433e+18\n",
      "  7.60286826e+14 1.12134428e+14]\n",
      " [2.00000000e+01 2.00000000e+01 3.20049000e+05 ... 7.60286826e+14\n",
      "  2.88112030e+18 5.76195029e+14]\n",
      " [2.00000000e+01 2.00000000e+01 3.20049000e+05 ... 1.12134428e+14\n",
      "  5.76195029e+14 1.64851405e+15]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2466cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 45.6383421421051 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 100000.0} is: \n",
      "[[3.2000330e+06 2.6000000e+01 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [2.6000000e+01 3.2000640e+11 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [4.0000000e+00 4.0000000e+00 3.2000330e+06 ... 3.2000490e+06\n",
      "  3.2000490e+06 3.2000490e+06]\n",
      " ...\n",
      " [2.0000000e+01 2.0000000e+01 3.2000490e+06 ... 2.8801504e+22\n",
      "  7.6002868e+17 1.1201344e+17]\n",
      " [2.0000000e+01 2.0000000e+01 3.2000490e+06 ... 7.6002868e+17\n",
      "  2.8801120e+22 5.7601950e+17]\n",
      " [2.0000000e+01 2.0000000e+01 3.2000490e+06 ... 1.1201344e+17\n",
      "  5.7601950e+17 1.6480514e+18]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb246377f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.595426082611084 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1000000.0} is: \n",
      "[[3.20000330e+07 2.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [2.60000000e+01 3.20000640e+13 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 3.20000330e+07 ... 3.20000490e+07\n",
      "  3.20000490e+07 3.20000490e+07]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 3.20000490e+07 ... 2.88001504e+26\n",
      "  7.60002868e+20 1.12001344e+20]\n",
      " [2.00000000e+01 2.00000000e+01 3.20000490e+07 ... 7.60002868e+20\n",
      "  2.88001120e+26 5.76001950e+20]\n",
      " [2.00000000e+01 2.00000000e+01 3.20000490e+07 ... 1.12001344e+20\n",
      "  5.76001950e+20 1.64800514e+21]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACnZJREFUeJzt3U+I33edx/Hna9saUSw2dHfotmUtkks9OJShuktZIgX755J6KfVikEI8tIc91lM9ehGLsBYihMaD7eZSmkOwllx66KqNELqpbDFoShPTxN0uKspGG997mG/ojziz8+785vf7zszv+YDh9/t95/ubeX9mkiff729+v5lUFZK0kb8ZewBJO4OxkNRiLCS1GAtJLcZCUouxkNQyeiySPJjkrSRnkzw19jyzkuRckv9IcjrJqWHb3iSvJPnFcHnL2HNOI8mRJJeTnJnYtuYas+o7w/f9jST3jDf55q2z5m8kuTB8r08neXjifV8f1vxWkgfGmXpzRo1FkhuAfwUeAu4Gvpzk7jFnmrEvVNVyVa0Mt58CTlbVPuDkcHsnew548Lpt663xIWDf8HYIeHZOM2615/jrNQN8e/heL1fVCYDh3/ZjwGeG+3x3+D+wI4x9ZHEvcLaqfllVfwJeAA6MPNM8HQCODtePAo+MOMvUqupV4L3rNq+3xgPA92vVj4FPJrltPpNunXXWvJ4DwAtVdaWqfgWcZfX/wI4wdixuB96ZuH1+2LYbFfCjJD9LcmjYtlRVF4fr7wJL44w2U+utcbd/758cTq+OTJxe7ug1jx2LRXJfVd3D6uH3E0n+efKdtfq8+1393PtFWOPgWeDTwDJwEfjWuONsjbFjcQG4c+L2HcO2XaeqLgyXl4EXWT38vHTt0Hu4vDzehDOz3hp37fe+qi5V1dWq+gvwPT441djRax47Fq8D+5LcleQjrD74c3zkmbZcko8n+cS168AXgTOsrvXgsNtB4KVxJpyp9dZ4HPjK8FORzwO/nThd2dGue+zlS6x+r2F1zY8l2ZPkLlYf3P3pvOfbrBvH/ORV9X6SJ4GXgRuAI1X15pgzzcgS8GISWP2a/6CqfpjkdeBYkseBt4FHR5xxakmeB/YDtyY5DzwNfJO113gCeJjVB/n+CHx17gNvgXXWvD/JMqunXOeArwFU1ZtJjgE/B94Hnqiqq2PMvRnxJeqSOsY+DZG0QxgLSS3GQlKLsZDUYiwktcwsFh/21aQTT4FeGK55MeyWNc8kFpt8Nemu+IJ+SK55MeyKNc/qyGLRX00q7TqzegbnWq+u+9x6O38ke+qjfIybs3ehniHmmhfDdl7z//IH/lRX0tl3tKd7D+dxh2D1i3nfB79MSNKc/KROtved1WnIhq+uq6rDVbVSVSs3sWdGY0jaKrOKxUK8mlRaJDM5DVmgV5NKC2Nmj1kMv6T0xKw+vqT58hmcklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIarlxmjsnOQf8HrgKvF9VK0n2Av8GfAo4BzxaVf8z3ZiSxrYVRxZfqKrlqloZbj8FnKyqfcDJ4bakHW4WpyEHgKPD9aPAIzP4HJLmbNpYFPCjJD9LcmjYtlRVF4fr7wJLU34OSdvAVI9ZAPdV1YUkfwe8kuQ/J99ZVZWk1rrjEJdDAB/lY1OOIWnWpjqyqKoLw+Vl4EXgXuBSktsAhsvL69z3cFWtVNXKTeyZZgxJc7DpWCT5eJJPXLsOfBE4AxwHDg67HQRemnZISeOb5jRkCXgxybWP84Oq+mGS14FjSR4H3gYenX5MSWPbdCyq6pfAZ9fY/t/A/dMMJWn78RmcklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIatkwFkmOJLmc5MzEtr1JXknyi+HylmF7knwnydkkbyS5Z5bDS5qfzpHFc8CD1217CjhZVfuAk8NtgIeAfcPbIeDZrRlT0tg2jEVVvQq8d93mA8DR4fpR4JGJ7d+vVT8GPpnktq0aVtJ4NvuYxVJVXRyuvwssDddvB96Z2O/8sE3SDjf1A5xVVUB92PslOZTkVJJTf+bKtGNImrHNxuLStdOL4fLysP0CcOfEfncM2/5KVR2uqpWqWrmJPZscQ9K8bDYWx4GDw/WDwEsT278y/FTk88BvJ05XJO1gN260Q5Lngf3ArUnOA08D3wSOJXkceBt4dNj9BPAwcBb4I/DVGcwsaQQbxqKqvrzOu+5fY98Cnph2KEnbj8/glNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUoux0K70zLnXeObca2OPsasYC0ktG/4pAGkn+pdP/dPYI+w6HllIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEW0hp24+/CmHZNqaotGmXzbs7e+lzuH3sMaeH8pE7yu3ovnX09spDUYiwktRgLSS3GQlKLsZDUYiwktRgLSS3GQlKLsZDUYiwktRgLSS3GQlKLsZDUYiwktWwYiyRHklxOcmZi2zeSXEhyenh7eOJ9X09yNslbSR6Y1eCS5qtzZPEc8OAa279dVcvD2wmAJHcDjwGfGe7z3SQ3bNWwksazYSyq6lXgvebHOwC8UFVXqupXwFng3inmk7RNTPOYxZNJ3hhOU24Ztt0OvDOxz/lhm6QdbrOxeBb4NLAMXAS+9WE/QJJDSU4lOfVnrmxyDEnzsqlYVNWlqrpaVX8BvscHpxoXgDsndr1j2LbWxzhcVStVtXITezYzhqQ52lQsktw2cfNLwLWflBwHHkuyJ8ldwD7gp9ONKGk7uHGjHZI8D+wHbk1yHnga2J9kGSjgHPA1gKp6M8kx4OfA+8ATVXV1NqNLmif/FIC0wPxTAJK2nLGQ1GIsJLUYC2kbePnXp3n516fHHuP/ZSwktWz4o1NJs/fA3y+PPcKGPLKQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkLawZb+/ea5fS5jIe1gl/7xd3P7XMZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNbwzLnXxh5hy027Jv+KurTA/CvqkracsZDUYiwktRgLSS3b4gHOJL8B/gD819izzNmtuOZFsJ3X/A9V9bedHbdFLACSnKqqlbHnmCfXvBh2y5o9DZHUYiwktWynWBwee4ARuObFsCvWvG0es5C0vW2nIwtJ25ixkNRiLCS1GAtJLcZCUsv/ASXmitVDJa/KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2461a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.45240020751953 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 10000000.0} is: \n",
      "[[3.20000033e+08 2.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [2.60000000e+01 3.20000064e+15 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 3.20000033e+08 ... 3.20000049e+08\n",
      "  3.20000049e+08 3.20000049e+08]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 3.20000049e+08 ... 2.88000150e+30\n",
      "  7.60000287e+23 1.12000134e+23]\n",
      " [2.00000000e+01 2.00000000e+01 3.20000049e+08 ... 7.60000287e+23\n",
      "  2.88000112e+30 5.76000195e+23]\n",
      " [2.00000000e+01 2.00000000e+01 3.20000049e+08 ... 1.12000134e+23\n",
      "  5.76000195e+23 1.64800051e+24]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb68902ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.201547384262085 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 100000000.0} is: \n",
      "[[3.20000003e+09 2.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [2.60000000e+01 3.20000006e+17 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 3.20000003e+09 ... 3.20000005e+09\n",
      "  3.20000005e+09 3.20000005e+09]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 3.20000005e+09 ... 2.88000015e+34\n",
      "  7.60000029e+26 1.12000013e+26]\n",
      " [2.00000000e+01 2.00000000e+01 3.20000005e+09 ... 7.60000029e+26\n",
      "  2.88000011e+34 5.76000020e+26]\n",
      " [2.00000000e+01 2.00000000e+01 3.20000005e+09 ... 1.12000013e+26\n",
      "  5.76000020e+26 1.64800005e+27]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.103229999542236 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 1000000000.0} is: \n",
      "[[3.20000000e+10 2.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [2.60000000e+01 3.20000001e+19 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 3.20000000e+10 ... 3.20000000e+10\n",
      "  3.20000000e+10 3.20000000e+10]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 3.20000000e+10 ... 2.88000002e+38\n",
      "  7.60000003e+29 1.12000001e+29]\n",
      " [2.00000000e+01 2.00000000e+01 3.20000000e+10 ... 7.60000003e+29\n",
      "  2.88000001e+38 5.76000002e+29]\n",
      " [2.00000000e+01 2.00000000e+01 3.20000000e+10 ... 1.12000001e+29\n",
      "  5.76000002e+29 1.64800001e+30]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688e5208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.929738998413086 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 3.0, 'lmda': 10000000000.0} is: \n",
      "[[3.200e+11 2.600e+01 4.000e+00 ... 2.000e+01 2.000e+01 2.000e+01]\n",
      " [2.600e+01 3.200e+21 4.000e+00 ... 2.000e+01 2.000e+01 2.000e+01]\n",
      " [4.000e+00 4.000e+00 3.200e+11 ... 3.200e+11 3.200e+11 3.200e+11]\n",
      " ...\n",
      " [2.000e+01 2.000e+01 3.200e+11 ... 2.880e+42 7.600e+32 1.120e+32]\n",
      " [2.000e+01 2.000e+01 3.200e+11 ... 7.600e+32 2.880e+42 5.760e+32]\n",
      " [2.000e+01 2.000e+01 3.200e+11 ... 1.120e+32 5.760e+32 1.648e+33]]\n"
     ]
    },
    {
     "data": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 37.003591537475586 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1e-10} is: \n",
      "[[2.000e-19 1.200e-19 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e-19 2.000e-19 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 2.000e-19 ... 2.000e-19 2.000e-19 2.000e-19]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 2.000e-19 ... 1.052e-17 1.052e-17 9.840e-18]\n",
      " [0.000e+00 0.000e+00 2.000e-19 ... 1.052e-17 1.052e-17 9.840e-18]\n",
      " [0.000e+00 0.000e+00 2.000e-19 ... 9.840e-18 9.840e-18 9.220e-18]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 39.74063420295715 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1e-09} is: \n",
      "[[2.000e-17 1.200e-17 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e-17 2.000e-17 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 2.000e-17 ... 2.000e-17 2.000e-17 2.000e-17]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 2.000e-17 ... 1.052e-15 1.052e-15 9.840e-16]\n",
      " [0.000e+00 0.000e+00 2.000e-17 ... 1.052e-15 1.052e-15 9.840e-16]\n",
      " [0.000e+00 0.000e+00 2.000e-17 ... 9.840e-16 9.840e-16 9.220e-16]]\n"
     ]
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 38.531572341918945 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1e-08} is: \n",
      "[[2.000e-15 1.200e-15 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e-15 2.000e-15 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 2.000e-15 ... 2.000e-15 2.000e-15 2.000e-15]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 2.000e-15 ... 1.052e-13 1.052e-13 9.840e-14]\n",
      " [0.000e+00 0.000e+00 2.000e-15 ... 1.052e-13 1.052e-13 9.840e-14]\n",
      " [0.000e+00 0.000e+00 2.000e-15 ... 9.840e-14 9.840e-14 9.220e-14]]\n"
     ]
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 37.05235052108765 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1e-07} is: \n",
      "[[2.000e-13 1.200e-13 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e-13 2.000e-13 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 2.000e-13 ... 2.000e-13 2.000e-13 2.000e-13]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 2.000e-13 ... 1.052e-11 1.052e-11 9.840e-12]\n",
      " [0.000e+00 0.000e+00 2.000e-13 ... 1.052e-11 1.052e-11 9.840e-12]\n",
      " [0.000e+00 0.000e+00 2.000e-13 ... 9.840e-12 9.840e-12 9.220e-12]]\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 36.662347078323364 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1e-06} is: \n",
      "[[2.000e-11 1.200e-11 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e-11 2.000e-11 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 2.000e-11 ... 2.000e-11 2.000e-11 2.000e-11]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 2.000e-11 ... 1.052e-09 1.052e-09 9.840e-10]\n",
      " [0.000e+00 0.000e+00 2.000e-11 ... 1.052e-09 1.052e-09 9.840e-10]\n",
      " [0.000e+00 0.000e+00 2.000e-11 ... 9.840e-10 9.840e-10 9.220e-10]]\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 38.916892766952515 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1e-05} is: \n",
      "[[2.000e-09 1.200e-09 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e-09 2.000e-09 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 2.000e-09 ... 2.000e-09 2.000e-09 2.000e-09]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 2.000e-09 ... 1.052e-07 1.052e-07 9.840e-08]\n",
      " [0.000e+00 0.000e+00 2.000e-09 ... 1.052e-07 1.052e-07 9.840e-08]\n",
      " [0.000e+00 0.000e+00 2.000e-09 ... 9.840e-08 9.840e-08 9.220e-08]]\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 39.015607833862305 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 0.0001} is: \n",
      "[[2.00000002e-07 1.20000000e-07 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e-07 2.00000002e-07 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000002e-07 ... 2.00000002e-07\n",
      "  2.00000002e-07 2.00000002e-07]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.00000002e-07 ... 1.05200001e-05\n",
      "  1.05200001e-05 9.84000008e-06]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000002e-07 ... 1.05200001e-05\n",
      "  1.05200001e-05 9.84000008e-06]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000002e-07 ... 9.84000008e-06\n",
      "  9.84000008e-06 9.22000008e-06]]\n"
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 39.7978618144989 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 0.001} is: \n",
      "[[2.00000160e-05 1.20000000e-05 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e-05 2.00000160e-05 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000160e-05 ... 2.00000160e-05\n",
      "  2.00000160e-05 2.00000160e-05]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.00000160e-05 ... 1.05200103e-03\n",
      "  1.05200103e-03 9.84000752e-04]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000160e-05 ... 1.05200103e-03\n",
      "  1.05200103e-03 9.84000752e-04]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000160e-05 ... 9.84000752e-04\n",
      "  9.84000752e-04 9.22000804e-04]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 37.74085330963135 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 0.01} is: \n",
      "[[0.00200016 0.0012     0.         ... 0.         0.         0.        ]\n",
      " [0.0012     0.00200016 0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.00200016 ... 0.00200016 0.00200016 0.00200016]\n",
      " ...\n",
      " [0.         0.         0.00200016 ... 0.10521032 0.10521032 0.09840752]\n",
      " [0.         0.         0.00200016 ... 0.10521032 0.10521032 0.09840752]\n",
      " [0.         0.         0.00200016 ... 0.09840752 0.09840752 0.09220804]]\n"
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 38.34471583366394 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 0.1} is: \n",
      "[[ 0.201604    0.12        0.         ...  0.          0.\n",
      "   0.        ]\n",
      " [ 0.12        0.20161616  0.         ...  0.          0.\n",
      "   0.        ]\n",
      " [ 0.          0.          0.201604   ...  0.201604    0.201604\n",
      "   0.201604  ]\n",
      " ...\n",
      " [ 0.          0.          0.201604   ... 10.62461674 10.62462998\n",
      "   9.91626513]\n",
      " [ 0.          0.          0.201604   ... 10.62462998 10.62470302\n",
      "   9.91641722]\n",
      " [ 0.          0.          0.201604   ...  9.91626513  9.91641722\n",
      "   9.3027898 ]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 38.08292031288147 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1.0} is: \n",
      "[[  40.   12.    0. ...    0.    0.    0.]\n",
      " [  12.   68.    0. ...    0.    0.    0.]\n",
      " [   0.    0.   40. ...   40.   40.   40.]\n",
      " ...\n",
      " [   0.    0.   40. ... 5812. 4962. 3816.]\n",
      " [   0.    0.   40. ... 4962. 5996. 5030.]\n",
      " [   0.    0.   40. ... 3816. 5030. 9688.]]\n"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688e59b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 38.2300386428833 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 10.0} is: \n",
      "[[4.16200000e+06 1.20000000e+03 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+03 1.61616200e+09 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.16200000e+06 ... 4.16200000e+06\n",
      "  4.16200000e+06 4.16200000e+06]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.16200000e+06 ... 4.58167818e+14\n",
      "  8.59206324e+13 1.21226362e+12]\n",
      " [0.00000000e+00 0.00000000e+00 4.16200000e+06 ... 8.59206324e+13\n",
      "  3.57910702e+14 5.91280962e+12]\n",
      " [0.00000000e+00 0.00000000e+00 4.16200000e+06 ... 1.21226362e+12\n",
      "  5.91280962e+12 1.68787581e+13]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688e59b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 38.85551071166992 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 100.0} is: \n",
      "[[4.00160020e+12 1.20000000e+05 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+05 1.60016002e+17 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00160020e+12 ... 4.00160020e+12\n",
      "  4.00160020e+12 4.00160020e+12]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00160020e+12 ... 4.48100809e+26\n",
      "  8.00584079e+25 1.12091211e+22]\n",
      " [0.00000000e+00 0.00000000e+00 4.00160020e+12 ... 8.00584079e+25\n",
      "  3.48098011e+26 5.76151612e+22]\n",
      " [0.00000000e+00 0.00000000e+00 4.00160020e+12 ... 1.12091211e+22\n",
      "  5.76151612e+22 1.64839642e+23]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 38.63768792152405 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1000.0} is: \n",
      "[[4.00001600e+18 1.20000000e+07 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+07 1.60000160e+25 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00001600e+18 ... 4.00001600e+18\n",
      "  4.00001600e+18 4.00001600e+18]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00001600e+18 ... 4.48001008e+38\n",
      "  8.00005840e+37 1.12000912e+32]\n",
      " [0.00000000e+00 0.00000000e+00 4.00001600e+18 ... 8.00005840e+37\n",
      "  3.48000980e+38 5.76001516e+32]\n",
      " [0.00000000e+00 0.00000000e+00 4.00001600e+18 ... 1.12000912e+32\n",
      "  5.76001516e+32 1.64800396e+33]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 39.028568506240845 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 10000.0} is: \n",
      "[[4.00000016e+24 1.20000000e+09 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+09 1.60000002e+33 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000016e+24 ... 4.00000016e+24\n",
      "  4.00000016e+24 4.00000016e+24]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00000016e+24 ... 4.48000010e+50\n",
      "  8.00000058e+49 1.12000009e+42]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000016e+24 ... 8.00000058e+49\n",
      "  3.48000010e+50 5.76000015e+42]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000016e+24 ... 1.12000009e+42\n",
      "  5.76000015e+42 1.64800004e+43]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACjpJREFUeJzt3M+P3HUdx/HnS8AajUYatEEgSkwPwsEN2aAHYmpI+HUpXghebAxJPcAfACc8cjEkJEpSk4Z6EOyF0EMjkF44qdSkYlGJjZbQWloVgwZikfL2sN+GSe26b3Z25rs/no9kMzOf/c7u+9Ntn5nv7ExTVUjSSj429gCSNgZjIanFWEhqMRaSWoyFpBZjIall9FgkuSvJa0lOJHl47HlmJcnJJL9NcizJ0WFte5IXk/xxuLx67DmnkWR/knNJjk+sXXaPWfLE8HN/Jckt402+esvs+ftJTg8/62NJ7pn43CPDnl9Lcuc4U6/OqLFIcgXwQ+Bu4Cbg20luGnOmGftmVS1U1eJw+2HgSFXtBI4Mtzeyp4C7Lllbbo93AzuHj73Ak3Oaca09xf/uGeDx4We9UFWHAYa/2/cDNw/3+dHwb2BDGPuRxa3Aiar6U1W9BzwD7B55pnnaDRwYrh8A7h1xlqlV1UvAW5csL7fH3cBPaskvgM8muXY+k66dZfa8nN3AM1V1vqr+DJxg6d/AhjB2LK4D3pi4fWpY24wKeCHJr5PsHdZ2VNWZ4fqbwI5xRpup5fa42X/2Dw2nV/snTi839J7HjsVWcltV3cLSw+8Hk3xj8pO19Lr7Tf3a+62wx8GTwJeBBeAM8INxx1kbY8fiNHDDxO3rh7VNp6pOD5fngGdZevh59uJD7+Hy3HgTzsxye9y0P/uqOltVF6rqA+DHfHiqsaH3PHYsXgZ2JrkxycdZevLn0Mgzrbkkn0ry6YvXgTuA4yztdc9w2B7guXEmnKnl9ngI+M7wW5GvA29PnK5saJc89/Itln7WsLTn+5NsS3IjS0/u/mre863WlWN+86p6P8lDwPPAFcD+qnp1zJlmZAfwbBJY+jP/aVX9PMnLwMEkDwCvA/eNOOPUkjwN7AKuSXIKeBR4jMvv8TBwD0tP8r0LfHfuA6+BZfa8K8kCS6dcJ4HvAVTVq0kOAr8D3gcerKoLY8y9GvEt6pI6xj4NkbRBGAtJLcZCUouxkNRiLCS1zCwWH/XdpBMvgd4y3PPWsFn2PJNYrPLdpJviD/Qjcs9bw6bY86weWWz1d5NKm86sXsF5uXfXfW25gz+ebfUJPslnsn1LvULMPW8N63nP/+Yd3qvz6Rw72su9h/O4vbD0h3nbh/+ZkKQ5+WUdaR87q9OQFd9dV1X7qmqxqhavYtuMxpC0VmYViy3xblJpK5nJacgWejeptGXM7DmL4T8pPTyrry9pvnwFp6QWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqunObOSU4C/wIuAO9X1WKS7cDPgC8BJ4H7quof040paWxr8cjim1W1UFWLw+2HgSNVtRM4MtyWtMHN4jRkN3BguH4AuHcG30PSnE0biwJeSPLrJHuHtR1VdWa4/iawY8rvIWkdmOo5C+C2qjqd5PPAi0n+MPnJqqokdbk7DnHZC/AJPjnlGJJmbapHFlV1erg8BzwL3AqcTXItwHB5bpn77quqxapavIpt04whaQ5WHYskn0ry6YvXgTuA48AhYM9w2B7guWmHlDS+aU5DdgDPJrn4dX5aVT9P8jJwMMkDwOvAfdOPKWlsq45FVf0J+Opl1v8O3D7NUJLWH1/BKanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpJYVY5Fkf5JzSY5PrG1P8mKSPw6XVw/rSfJEkhNJXklyyyyHlzQ/nUcWTwF3XbL2MHCkqnYCR4bbAHcDO4ePvcCTazOmpLGtGIuqegl465Ll3cCB4foB4N6J9Z/Ukl8An01y7VoNK2k8q33OYkdVnRmuvwnsGK5fB7wxcdypYU3SBjf1E5xVVUB91Psl2ZvkaJKj/+H8tGNImrHVxuLsxdOL4fLcsH4auGHiuOuHtf9RVfuqarGqFq9i2yrHkDQvq43FIWDPcH0P8NzE+neG34p8HXh74nRF0gZ25UoHJHka2AVck+QU8CjwGHAwyQPA68B9w+GHgXuAE8C7wHdnMLOkEawYi6r69jKfuv0yxxbw4LRDSVp/fAWnpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRbSlD721a+MPcJcGAtpSh/85vdjjzAXxkJSi7GQ1GIsJLWsGIsk+5OcS3J8Yu37SU4nOTZ83DPxuUeSnEjyWpI7ZzW4pPnqPLJ4CrjrMuuPV9XC8HEYIMlNwP3AzcN9fpTkirUaVtJ4VoxFVb0EvNX8eruBZ6rqfFX9GTgB3DrFfJLWiWmes3goySvDacrVw9p1wBsTx5wa1iRtcKuNxZPAl4EF4Azwg4/6BZLsTXI0ydH/cH6VY0ial1XFoqrOVtWFqvoA+DEfnmqcBm6YOPT6Ye1yX2NfVS1W1eJVbFvNGJLmaFWxSHLtxM1vARd/U3IIuD/JtiQ3AjuBX003oqT14MqVDkjyNLALuCbJKeBRYFeSBaCAk8D3AKrq1SQHgd8B7wMPVtWF2YwuaZ5SVWPPwGeyvb6W28ceQ9pyfllH+Ge9lc6xvoJTUouxkNRiLCS1GAtpHXj+L8d4/i/Hxh7j/zIWklpW/NWppNm78wsLY4+wIh9ZSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqSVVNfYMJPkr8A7wt7FnmbNrcM9bwXre8xer6nOdA9dFLACSHK2qxbHnmCf3vDVslj17GiKpxVhIallPsdg39gAjcM9bw6bY87p5zkLS+raeHllIWseMhaQWYyGpxVhIajEWklr+C1snaM2NmQcwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 38.40130877494812 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 100000.0} is: \n",
      "[[4.00000000e+30 1.20000000e+11 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+11 1.60000000e+41 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000000e+30 ... 4.00000000e+30\n",
      "  4.00000000e+30 4.00000000e+30]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 4.00000000e+30 ... 4.48000000e+62\n",
      "  8.00000001e+61 1.12000000e+52]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000000e+30 ... 8.00000001e+61\n",
      "  3.48000000e+62 5.76000000e+52]\n",
      " [0.00000000e+00 0.00000000e+00 4.00000000e+30 ... 1.12000000e+52\n",
      "  5.76000000e+52 1.64800000e+53]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2466cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 37.19001770019531 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1000000.0} is: \n",
      "[[4.000e+36 1.200e+13 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e+13 1.600e+49 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 4.000e+36 ... 4.000e+36 4.000e+36 4.000e+36]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 4.000e+36 ... 4.480e+74 8.000e+73 1.120e+62]\n",
      " [0.000e+00 0.000e+00 4.000e+36 ... 8.000e+73 3.480e+74 5.760e+62]\n",
      " [0.000e+00 0.000e+00 4.000e+36 ... 1.120e+62 5.760e+62 1.648e+63]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACjpJREFUeJzt3M+P3HUdx/HnS8AajUYatEEgSkwPwsEN2aAHYmpI+HUpXghebAxJPcAfACc8cjEkJEpSk4Z6EOyF0EMjkF44qdSkYlGJjZbQWloVgwZikfL2sN+GSe26b3Z25rs/no9kMzOf/c7u+9Ntn5nv7ExTVUjSSj429gCSNgZjIanFWEhqMRaSWoyFpBZjIall9FgkuSvJa0lOJHl47HlmJcnJJL9NcizJ0WFte5IXk/xxuLx67DmnkWR/knNJjk+sXXaPWfLE8HN/Jckt402+esvs+ftJTg8/62NJ7pn43CPDnl9Lcuc4U6/OqLFIcgXwQ+Bu4Cbg20luGnOmGftmVS1U1eJw+2HgSFXtBI4Mtzeyp4C7Lllbbo93AzuHj73Ak3Oaca09xf/uGeDx4We9UFWHAYa/2/cDNw/3+dHwb2BDGPuRxa3Aiar6U1W9BzwD7B55pnnaDRwYrh8A7h1xlqlV1UvAW5csL7fH3cBPaskvgM8muXY+k66dZfa8nN3AM1V1vqr+DJxg6d/AhjB2LK4D3pi4fWpY24wKeCHJr5PsHdZ2VNWZ4fqbwI5xRpup5fa42X/2Dw2nV/snTi839J7HjsVWcltV3cLSw+8Hk3xj8pO19Lr7Tf3a+62wx8GTwJeBBeAM8INxx1kbY8fiNHDDxO3rh7VNp6pOD5fngGdZevh59uJD7+Hy3HgTzsxye9y0P/uqOltVF6rqA+DHfHiqsaH3PHYsXgZ2JrkxycdZevLn0Mgzrbkkn0ry6YvXgTuA4yztdc9w2B7guXEmnKnl9ngI+M7wW5GvA29PnK5saJc89/Itln7WsLTn+5NsS3IjS0/u/mre863WlWN+86p6P8lDwPPAFcD+qnp1zJlmZAfwbBJY+jP/aVX9PMnLwMEkDwCvA/eNOOPUkjwN7AKuSXIKeBR4jMvv8TBwD0tP8r0LfHfuA6+BZfa8K8kCS6dcJ4HvAVTVq0kOAr8D3gcerKoLY8y9GvEt6pI6xj4NkbRBGAtJLcZCUouxkNRiLCS1zCwWH/XdpBMvgd4y3PPWsFn2PJNYrPLdpJviD/Qjcs9bw6bY86weWWz1d5NKm86sXsF5uXfXfW25gz+ebfUJPslnsn1LvULMPW8N63nP/+Yd3qvz6Rw72su9h/O4vbD0h3nbh/+ZkKQ5+WUdaR87q9OQFd9dV1X7qmqxqhavYtuMxpC0VmYViy3xblJpK5nJacgWejeptGXM7DmL4T8pPTyrry9pvnwFp6QWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqunObOSU4C/wIuAO9X1WKS7cDPgC8BJ4H7quof040paWxr8cjim1W1UFWLw+2HgSNVtRM4MtyWtMHN4jRkN3BguH4AuHcG30PSnE0biwJeSPLrJHuHtR1VdWa4/iawY8rvIWkdmOo5C+C2qjqd5PPAi0n+MPnJqqokdbk7DnHZC/AJPjnlGJJmbapHFlV1erg8BzwL3AqcTXItwHB5bpn77quqxapavIpt04whaQ5WHYskn0ry6YvXgTuA48AhYM9w2B7guWmHlDS+aU5DdgDPJrn4dX5aVT9P8jJwMMkDwOvAfdOPKWlsq45FVf0J+Opl1v8O3D7NUJLWH1/BKanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpJYVY5Fkf5JzSY5PrG1P8mKSPw6XVw/rSfJEkhNJXklyyyyHlzQ/nUcWTwF3XbL2MHCkqnYCR4bbAHcDO4ePvcCTazOmpLGtGIuqegl465Ll3cCB4foB4N6J9Z/Ukl8An01y7VoNK2k8q33OYkdVnRmuvwnsGK5fB7wxcdypYU3SBjf1E5xVVUB91Psl2ZvkaJKj/+H8tGNImrHVxuLsxdOL4fLcsH4auGHiuOuHtf9RVfuqarGqFq9i2yrHkDQvq43FIWDPcH0P8NzE+neG34p8HXh74nRF0gZ25UoHJHka2AVck+QU8CjwGHAwyQPA68B9w+GHgXuAE8C7wHdnMLOkEawYi6r69jKfuv0yxxbw4LRDSVp/fAWnpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRbSlD721a+MPcJcGAtpSh/85vdjjzAXxkJSi7GQ1GIsJLWsGIsk+5OcS3J8Yu37SU4nOTZ83DPxuUeSnEjyWpI7ZzW4pPnqPLJ4CrjrMuuPV9XC8HEYIMlNwP3AzcN9fpTkirUaVtJ4VoxFVb0EvNX8eruBZ6rqfFX9GTgB3DrFfJLWiWmes3goySvDacrVw9p1wBsTx5wa1iRtcKuNxZPAl4EF4Azwg4/6BZLsTXI0ydH/cH6VY0ial1XFoqrOVtWFqvoA+DEfnmqcBm6YOPT6Ye1yX2NfVS1W1eJVbFvNGJLmaFWxSHLtxM1vARd/U3IIuD/JtiQ3AjuBX003oqT14MqVDkjyNLALuCbJKeBRYFeSBaCAk8D3AKrq1SQHgd8B7wMPVtWF2YwuaZ5SVWPPwGeyvb6W28ceQ9pyfllH+Ge9lc6xvoJTUouxkNRiLCS1GAtpHXj+L8d4/i/Hxh7j/zIWklpW/NWppNm78wsLY4+wIh9ZSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqSVVNfYMJPkr8A7wt7FnmbNrcM9bwXre8xer6nOdA9dFLACSHK2qxbHnmCf3vDVslj17GiKpxVhIallPsdg39gAjcM9bw6bY87p5zkLS+raeHllIWseMhaQWYyGpxVhIajEWklr+C1snaM2NmQcwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb6c1443c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 36.65269470214844 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 10000000.0} is: \n",
      "[[4.000e+42 1.200e+15 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e+15 1.600e+57 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 4.000e+42 ... 4.000e+42 4.000e+42 4.000e+42]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 4.000e+42 ... 4.480e+86 8.000e+85 1.120e+72]\n",
      " [0.000e+00 0.000e+00 4.000e+42 ... 8.000e+85 3.480e+86 5.760e+72]\n",
      " [0.000e+00 0.000e+00 4.000e+42 ... 1.120e+72 5.760e+72 1.648e+73]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2466cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 36.98673963546753 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 100000000.0} is: \n",
      "[[4.000e+48 1.200e+17 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e+17 1.600e+65 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 4.000e+48 ... 4.000e+48 4.000e+48 4.000e+48]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 4.000e+48 ... 4.480e+98 8.000e+97 1.120e+82]\n",
      " [0.000e+00 0.000e+00 4.000e+48 ... 8.000e+97 3.480e+98 5.760e+82]\n",
      " [0.000e+00 0.000e+00 4.000e+48 ... 1.120e+82 5.760e+82 1.648e+83]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2468c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 36.94209051132202 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 1000000000.0} is: \n",
      "[[4.000e+054 1.200e+019 0.000e+000 ... 0.000e+000 0.000e+000 0.000e+000]\n",
      " [1.200e+019 1.600e+073 0.000e+000 ... 0.000e+000 0.000e+000 0.000e+000]\n",
      " [0.000e+000 0.000e+000 4.000e+054 ... 4.000e+054 4.000e+054 4.000e+054]\n",
      " ...\n",
      " [0.000e+000 0.000e+000 4.000e+054 ... 4.480e+110 8.000e+109 1.120e+092]\n",
      " [0.000e+000 0.000e+000 4.000e+054 ... 8.000e+109 3.480e+110 5.760e+092]\n",
      " [0.000e+000 0.000e+000 4.000e+054 ... 1.120e+092 5.760e+092 1.648e+093]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb256e1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 36.80334544181824 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 3.0, 'lmda': 10000000000.0} is: \n",
      "[[4.000e+060 1.200e+021 0.000e+000 ... 0.000e+000 0.000e+000 0.000e+000]\n",
      " [1.200e+021 1.600e+081 0.000e+000 ... 0.000e+000 0.000e+000 0.000e+000]\n",
      " [0.000e+000 0.000e+000 4.000e+060 ... 4.000e+060 4.000e+060 4.000e+060]\n",
      " ...\n",
      " [0.000e+000 0.000e+000 4.000e+060 ... 4.480e+122 8.000e+121 1.120e+102]\n",
      " [0.000e+000 0.000e+000 4.000e+060 ... 8.000e+121 3.480e+122 5.760e+102]\n",
      " [0.000e+000 0.000e+000 4.000e+060 ... 1.120e+102 5.760e+102 1.648e+103]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.22227382659912 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1e-10} is: \n",
      "[[  20.           12.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  12.           20.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           20.         ...   20.\n",
      "    20.           20.        ]\n",
      " ...\n",
      " [   0.            0.           20.         ... 1052.00000018\n",
      "  1052.00000018  984.00000018]\n",
      " [   0.            0.           20.         ... 1052.00000018\n",
      "  1052.00000019  984.0000002 ]\n",
      " [   0.            0.           20.         ...  984.00000018\n",
      "   984.0000002   922.00000032]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 55352 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2461a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 41.80288052558899 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1e-09} is: \n",
      "[[  20.00000002   12.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  12.           20.00000003    0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           20.00000002 ...   20.00000002\n",
      "    20.00000002   20.00000002]\n",
      " ...\n",
      " [   0.            0.           20.00000002 ... 1052.00000179\n",
      "  1052.00000181  984.00000181]\n",
      " [   0.            0.           20.00000002 ... 1052.00000181\n",
      "  1052.00000188  984.00000195]\n",
      " [   0.            0.           20.00000002 ...  984.00000181\n",
      "   984.00000195  922.00000315]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 55352 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb689026d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.09841823577881 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1e-08} is: \n",
      "[[  20.0000002    12.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  12.           20.00000032    0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           20.0000002  ...   20.0000002\n",
      "    20.0000002    20.0000002 ]\n",
      " ...\n",
      " [   0.            0.           20.0000002  ... 1052.00001792\n",
      "  1052.00001806  984.00001808]\n",
      " [   0.            0.           20.0000002  ... 1052.00001806\n",
      "  1052.00001876  984.00001954]\n",
      " [   0.            0.           20.0000002  ...  984.00001808\n",
      "   984.00001954  922.00003154]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 55352 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb256f21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.24132227897644 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1e-07} is: \n",
      "[[  20.000002    12.           0.        ...    0.           0.\n",
      "     0.       ]\n",
      " [  12.          20.0000032    0.        ...    0.           0.\n",
      "     0.       ]\n",
      " [   0.           0.          20.000002  ...   20.000002    20.000002\n",
      "    20.000002 ]\n",
      " ...\n",
      " [   0.           0.          20.000002  ... 1052.0001792 1052.0001806\n",
      "   984.0001808]\n",
      " [   0.           0.          20.000002  ... 1052.0001806 1052.0001876\n",
      "   984.0001954]\n",
      " [   0.           0.          20.000002  ...  984.0001808  984.0001954\n",
      "   922.0003154]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 55352 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb256e1f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.24935579299927 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1e-06} is: \n",
      "[[  20.00002    12.          0.       ...    0.          0.\n",
      "     0.      ]\n",
      " [  12.         20.000032    0.       ...    0.          0.\n",
      "     0.      ]\n",
      " [   0.          0.         20.00002  ...   20.00002    20.00002\n",
      "    20.00002 ]\n",
      " ...\n",
      " [   0.          0.         20.00002  ... 1052.001792 1052.001806\n",
      "   984.001808]\n",
      " [   0.          0.         20.00002  ... 1052.001806 1052.001876\n",
      "   984.001954]\n",
      " [   0.          0.         20.00002  ...  984.001808  984.001954\n",
      "   922.003154]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 41.932133436203 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1e-05} is: \n",
      "[[  20.0002       12.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  12.           20.00032       0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           20.0002     ...   20.0002\n",
      "    20.0002       20.0002    ]\n",
      " ...\n",
      " [   0.            0.           20.0002     ... 1052.01792018\n",
      "  1052.01806017  984.01808009]\n",
      " [   0.            0.           20.0002     ... 1052.01806017\n",
      "  1052.01876019  984.01954015]\n",
      " [   0.            0.           20.0002     ...  984.01808009\n",
      "   984.01954015  922.0315404 ]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.69877338409424 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 0.0001} is: \n",
      "[[  20.002        12.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  12.           20.00320016    0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           20.002      ...   20.002\n",
      "    20.002        20.002     ]\n",
      " ...\n",
      " [   0.            0.           20.002      ... 1052.179218\n",
      "  1052.18061656  984.18080912]\n",
      " [   0.            0.           20.002      ... 1052.18061656\n",
      "  1052.18761912  984.19541516]\n",
      " [   0.            0.           20.002      ...  984.18080912\n",
      "   984.19541516  922.31543964]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.39898371696472 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 0.001} is: \n",
      "[[  20.02         12.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  12.           20.032016      0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           20.02       ...   20.02\n",
      "    20.02         20.02      ]\n",
      " ...\n",
      " [   0.            0.           20.02       ... 1053.79380088\n",
      "  1053.80765645  985.80891211]\n",
      " [   0.            0.           20.02       ... 1053.80765645\n",
      "  1053.87791287  985.95551658]\n",
      " [   0.            0.           20.02       ...  985.80891211\n",
      "   985.95551658  925.15796565]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.85403847694397 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 0.01} is: \n",
      "[[  20.2          12.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  12.           20.3216        0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           20.2        ...   20.2\n",
      "    20.2          20.2       ]\n",
      " ...\n",
      " [   0.            0.           20.2        ... 1070.10088288\n",
      "  1070.226048   1002.171312  ]\n",
      " [   0.            0.           20.2        ... 1070.226048\n",
      "  1070.95207088 1003.692176  ]\n",
      " [   0.            0.           20.2        ... 1002.171312\n",
      "  1003.692176    953.938048  ]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.31627321243286 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 0.1} is: \n",
      "[[  22.       12.        0.     ...    0.        0.        0.    ]\n",
      " [  12.       23.36      0.     ...    0.        0.        0.    ]\n",
      " [   0.        0.       22.     ...   22.       22.       22.    ]\n",
      " ...\n",
      " [   0.        0.       22.     ... 1250.1088 1249.608  1174.032 ]\n",
      " [   0.        0.       22.     ... 1249.608  1259.6168 1195.136 ]\n",
      " [   0.        0.       22.     ... 1174.032  1195.136  1278.688 ]]\n"
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     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.66733121871948 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1.0} is: \n",
      "[[  40.   12.    0. ...    0.    0.    0.]\n",
      " [  12.   68.    0. ...    0.    0.    0.]\n",
      " [   0.    0.   40. ...   40.   40.   40.]\n",
      " ...\n",
      " [   0.    0.   40. ... 5812. 4962. 3816.]\n",
      " [   0.    0.   40. ... 4962. 5996. 5030.]\n",
      " [   0.    0.   40. ... 3816. 5030. 9688.]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.950223207473755 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 10.0} is: \n",
      "[[2.200000e+02 1.200000e+01 0.000000e+00 ... 0.000000e+00 0.000000e+00\n",
      "  0.000000e+00]\n",
      " [1.200000e+01 1.940000e+03 0.000000e+00 ... 0.000000e+00 0.000000e+00\n",
      "  0.000000e+00]\n",
      " [0.000000e+00 0.000000e+00 2.200000e+02 ... 2.200000e+02 2.200000e+02\n",
      "  2.200000e+02]\n",
      " ...\n",
      " [0.000000e+00 0.000000e+00 2.200000e+02 ... 3.958972e+06 6.327120e+05\n",
      "  2.222640e+05]\n",
      " [0.000000e+00 0.000000e+00 2.200000e+02 ... 6.327120e+05 3.959012e+06\n",
      "  7.481240e+05]\n",
      " [0.000000e+00 0.000000e+00 2.200000e+02 ... 2.222640e+05 7.481240e+05\n",
      "  2.076862e+06]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.9415488243103 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 100.0} is: \n",
      "[[2.02000000e+03 1.20000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+01 1.63220000e+05 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.02000000e+03 ... 2.02000000e+03\n",
      "  2.02000000e+03 2.02000000e+03]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.02000000e+03 ... 2.96981803e+10\n",
      "  4.64741652e+08 1.21301784e+08]\n",
      " [0.00000000e+00 0.00000000e+00 2.02000000e+03 ... 4.64741652e+08\n",
      "  2.96873087e+10 5.91356384e+08]\n",
      " [0.00000000e+00 0.00000000e+00 2.02000000e+03 ... 1.21301784e+08\n",
      "  5.91356384e+08 1.68795632e+09]]\n"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACnNJREFUeJzt3U+InXe9x/H3x7ZGFMXm1ht626JFsqmLO5ShFm65RAr2zyZ1U+rGIIW4aLcX6qouhSKCoIUIoXFhe7MpzSJYSzaFC9FGCDUVi0FTmpgm91rxXpQbbfy6mCf0EGecb+fMOc/MnPcLhnPOM8+Z+f5mkjfPc+acmVQVkrSeD409gKTtwVhIajEWklqMhaQWYyGpxVhIahk9FkkeTPJmkrNJnhp7nllJci7Jz5OcTnJq2LY7yStJfjVc3jz2nNNIcjjJ5SRnJratusas+M7wfX89yd3jTb5xa6z5G0kuDN/r00kennjf14c1v5nkgXGm3phRY5HkBuC7wEPAXcCXk9w15kwz9oWqWqqq5eH2U8CJqtoLnBhub2fPAQ9et22tNT4E7B3eDgLPzmnGzfYcf79mgG8P3+ulqjoOMPzbfgz43HCf7w3/B7aFsY8s7gHOVtWvq+rPwAvA/pFnmqf9wJHh+hHgkRFnmVpVvQq8e93mtda4H/hBrTgJfDLJrfOZdPOssea17AdeqKorVfUb4Cwr/we2hbFjcRvw9sTt88O2naiAHyf5WZKDw7Y9VXVxuP4OsGec0WZqrTXu9O/9k8Pp1eGJ08ttveaxY7FI7ququ1k5/H4iyb9PvrNWnne/o597vwhrHDwLfBZYAi4C3xp3nM0xdiwuAHdM3L592LbjVNWF4fIy8CIrh5+Xrh16D5eXx5twZtZa44793lfVpaq6WlV/Bb7P+6ca23rNY8fiNWBvkjuTfJiVB3+OjTzTpkvysSQfv3Yd+CJwhpW1Hhh2OwC8NM6EM7XWGo8BXxl+KnIv8IeJ05Vt7brHXr7EyvcaVtb8WJJdSe5k5cHdn857vo26ccxPXlXvJXkSeBm4AThcVW+MOdOM7AFeTAIrX/MfVtWPkrwGHE3yOPAW8OiIM04tyfPAPuCWJOeBp4FvsvoajwMPs/Ig35+Ar8594E2wxpr3JVli5ZTrHPA1gKp6I8lR4BfAe8ATVXV1jLk3Ir5EXVLH2KchkrYJYyGpxVhIajEWklqMhaSWmcXig76adOIp0AvDNS+GnbLmmcRig68m3RFf0A/INS+GHbHmWR1ZLPqrSaUdZ1bP4Fzt1XWfX2vnD2dXfYSP8onsXqhniLnmxbCV1/z//JE/15V09h3t6d7DedxBWPli3vf+LxOSNCc/qRPtfWd1GrLuq+uq6lBVLVfV8k3smtEYkjbLrGKxEK8mlRbJTE5DFujVpNLCmNljFsMvKT0+q48vab58BqekFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJabpzmzknOAf8HXAXeq6rlJLuB/wQ+A5wDHq2q3083pqSxbcaRxReqaqmqlofbTwEnqmovcGK4LWmbm8VpyH7gyHD9CPDIDD6HpDmbNhYF/DjJz5IcHLbtqaqLw/V3gD1Tfg5JW8BUj1kA91XVhST/DLyS5JeT76yqSlKr3XGIy0GAj/DRKceQNGtTHVlU1YXh8jLwInAPcCnJrQDD5eU17nuoqparavkmdk0zhqQ52HAsknwsycevXQe+CJwBjgEHht0OAC9NO6Sk8U1zGrIHeDHJtY/zw6r6UZLXgKNJHgfeAh6dfkxJY9twLKrq18C/rrL9d8D90wwlaevxGZySWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqWTcWSQ4nuZzkzMS23UleSfKr4fLmYXuSfCfJ2SSvJ7l7lsNLmp/OkcVzwIPXbXsKOFFVe4ETw22Ah4C9w9tB4NnNGVPS2NaNRVW9Crx73eb9wJHh+hHgkYntP6gVJ4FPJrl1s4aVNJ6NPmaxp6ouDtffAfYM128D3p7Y7/ywTdI2N/UDnFVVQH3Q+yU5mORUklN/4cq0Y0iasY3G4tK104vh8vKw/QJwx8R+tw/b/k5VHaqq5apavoldGxxD0rxsNBbHgAPD9QPASxPbvzL8VORe4A8TpyuStrEb19shyfPAPuCWJOeBp4FvAkeTPA68BTw67H4ceBg4C/wJ+OoMZpY0gnVjUVVfXuNd96+ybwFPTDuUpK3HZ3BKajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhoR3rm3EmeOXdy7DF2FGMhqWXdPwUgbUf/8Zl7xx5hx/HIQlKLsZDUYiwktRgLSS3GQlKLsZDUYiwktRgLSS3GQlKLsZDUYiwktRgLSS3GQlKLsZBWsRN/F8a0a0pVbdIoG/eJ7K7P5/6xx5AWzk/qBP9b76azr0cWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaSWdWOR5HCSy0nOTGz7RpILSU4Pbw9PvO/rSc4meTPJA7MaXNJ8dY4sngMeXGX7t6tqaXg7DpDkLuAx4HPDfb6X5IbNGlbSeNaNRVW9Crzb/Hj7gReq6kpV/QY4C9wzxXyStohpHrN4Msnrw2nKzcO224C3J/Y5P2yTtM1tNBbPAp8FloCLwLc+6AdIcjDJqSSn/sKVDY4haV42FIuqulRVV6vqr8D3ef9U4wJwx8Sutw/bVvsYh6pquaqWb2LXRsaQNEcbikWSWydufgm49pOSY8BjSXYluRPYC/x0uhElbQU3rrdDkueBfcAtSc4DTwP7kiwBBZwDvgZQVW8kOQr8AngPeKKqrs5mdEnz5J8CkBaYfwpA0qYzFpJajIWkFmMhbQEv//Y0L//29Nhj/EPGQlLLuj86lTR7D/zL0tgjrMsjC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIspG3sn/7r5vV32iTGQtrGfvdvv5/b5zIWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhbSKZ86dHHuETTftmvwr6tIC86+oS9p0xkJSi7GQ1GIsJLVsiQc4k/w38Efgf8aeZc5uwTUvgq285k9X1ac6O26JWAAkOVVVy2PPMU+ueTHslDV7GiKpxVhIatlKsTg09gAjcM2LYUesecs8ZiFpa9tKRxaStjBjIanFWEhqMRaSWoyFpJa/AXkZisawviqIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb68902ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.234039068222046 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1000.0} is: \n",
      "[[2.00200000e+04 1.20000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+01 1.60320200e+07 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.00200000e+04 ... 2.00200000e+04\n",
      "  2.00200000e+04 2.00200000e+04]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.00200000e+04 ... 2.88881802e+14\n",
      "  4.49657807e+11 1.12913809e+11]\n",
      " [0.00000000e+00 0.00000000e+00 2.00200000e+04 ... 4.49657807e+11\n",
      "  2.88869914e+14 5.77517955e+11]\n",
      " [0.00000000e+00 0.00000000e+00 2.00200000e+04 ... 1.12913809e+11\n",
      "  5.77517955e+11 1.65196715e+12]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.92475128173828 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 10000.0} is: \n",
      "[[2.00020000e+05 1.20000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+01 1.60032002e+09 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.00020000e+05 ... 2.00020000e+05\n",
      "  2.00020000e+05 2.00020000e+05]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.00020000e+05 ... 2.88088018e+18\n",
      "  4.48165618e+14 1.12091218e+14]\n",
      " [0.00000000e+00 0.00000000e+00 2.00020000e+05 ... 4.48165618e+14\n",
      "  2.88086819e+18 5.76151620e+14]\n",
      " [0.00000000e+00 0.00000000e+00 2.00020000e+05 ... 1.12091218e+14\n",
      "  5.76151620e+14 1.64839643e+15]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACnZJREFUeJzt3U+I33edx/Hna9saUSw2dHfotmUtkks9OJShuktZIgX755J6KfVikEI8tIc91lM9ehGLsBYihMaD7eZSmkOwllx66KqNELqpbDFoShPTxN0uKspGG997mG/ojziz8+785vf7zszv+YDh9/t95/ubeX9mkiff729+v5lUFZK0kb8ZewBJO4OxkNRiLCS1GAtJLcZCUouxkNQyeiySPJjkrSRnkzw19jyzkuRckv9IcjrJqWHb3iSvJPnFcHnL2HNOI8mRJJeTnJnYtuYas+o7w/f9jST3jDf55q2z5m8kuTB8r08neXjifV8f1vxWkgfGmXpzRo1FkhuAfwUeAu4Gvpzk7jFnmrEvVNVyVa0Mt58CTlbVPuDkcHsnew548Lpt663xIWDf8HYIeHZOM2615/jrNQN8e/heL1fVCYDh3/ZjwGeG+3x3+D+wI4x9ZHEvcLaqfllVfwJeAA6MPNM8HQCODtePAo+MOMvUqupV4L3rNq+3xgPA92vVj4FPJrltPpNunXXWvJ4DwAtVdaWqfgWcZfX/wI4wdixuB96ZuH1+2LYbFfCjJD9LcmjYtlRVF4fr7wJL44w2U+utcbd/758cTq+OTJxe7ug1jx2LRXJfVd3D6uH3E0n+efKdtfq8+1393PtFWOPgWeDTwDJwEfjWuONsjbFjcQG4c+L2HcO2XaeqLgyXl4EXWT38vHTt0Hu4vDzehDOz3hp37fe+qi5V1dWq+gvwPT441djRax47Fq8D+5LcleQjrD74c3zkmbZcko8n+cS168AXgTOsrvXgsNtB4KVxJpyp9dZ4HPjK8FORzwO/nThd2dGue+zlS6x+r2F1zY8l2ZPkLlYf3P3pvOfbrBvH/ORV9X6SJ4GXgRuAI1X15pgzzcgS8GISWP2a/6CqfpjkdeBYkseBt4FHR5xxakmeB/YDtyY5DzwNfJO113gCeJjVB/n+CHx17gNvgXXWvD/JMqunXOeArwFU1ZtJjgE/B94Hnqiqq2PMvRnxJeqSOsY+DZG0QxgLSS3GQlKLsZDUYiwktcwsFh/21aQTT4FeGK55MeyWNc8kFpt8Nemu+IJ+SK55MeyKNc/qyGLRX00q7TqzegbnWq+u+9x6O38ke+qjfIybs3ehniHmmhfDdl7z//IH/lRX0tl3tKd7D+dxh2D1i3nfB79MSNKc/KROtved1WnIhq+uq6rDVbVSVSs3sWdGY0jaKrOKxUK8mlRaJDM5DVmgV5NKC2Nmj1kMv6T0xKw+vqT58hmcklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIarlxmjsnOQf8HrgKvF9VK0n2Av8GfAo4BzxaVf8z3ZiSxrYVRxZfqKrlqloZbj8FnKyqfcDJ4bakHW4WpyEHgKPD9aPAIzP4HJLmbNpYFPCjJD9LcmjYtlRVF4fr7wJLU34OSdvAVI9ZAPdV1YUkfwe8kuQ/J99ZVZWk1rrjEJdDAB/lY1OOIWnWpjqyqKoLw+Vl4EXgXuBSktsAhsvL69z3cFWtVNXKTeyZZgxJc7DpWCT5eJJPXLsOfBE4AxwHDg67HQRemnZISeOb5jRkCXgxybWP84Oq+mGS14FjSR4H3gYenX5MSWPbdCyq6pfAZ9fY/t/A/dMMJWn78RmcklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIatkwFkmOJLmc5MzEtr1JXknyi+HylmF7knwnydkkbyS5Z5bDS5qfzpHFc8CD1217CjhZVfuAk8NtgIeAfcPbIeDZrRlT0tg2jEVVvQq8d93mA8DR4fpR4JGJ7d+vVT8GPpnktq0aVtJ4NvuYxVJVXRyuvwssDddvB96Z2O/8sE3SDjf1A5xVVUB92PslOZTkVJJTf+bKtGNImrHNxuLStdOL4fLysP0CcOfEfncM2/5KVR2uqpWqWrmJPZscQ9K8bDYWx4GDw/WDwEsT278y/FTk88BvJ05XJO1gN260Q5Lngf3ArUnOA08D3wSOJXkceBt4dNj9BPAwcBb4I/DVGcwsaQQbxqKqvrzOu+5fY98Cnph2KEnbj8/glNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUoux0K70zLnXeObca2OPsasYC0ktG/4pAGkn+pdP/dPYI+w6HllIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEW0hp24+/CmHZNqaotGmXzbs7e+lzuH3sMaeH8pE7yu3ovnX09spDUYiwktRgLSS3GQlKLsZDUYiwktRgLSS3GQlKLsZDUYiwktRgLSS3GQlKLsZDUYiwktWwYiyRHklxOcmZi2zeSXEhyenh7eOJ9X09yNslbSR6Y1eCS5qtzZPEc8OAa279dVcvD2wmAJHcDjwGfGe7z3SQ3bNWwksazYSyq6lXgvebHOwC8UFVXqupXwFng3inmk7RNTPOYxZNJ3hhOU24Ztt0OvDOxz/lhm6QdbrOxeBb4NLAMXAS+9WE/QJJDSU4lOfVnrmxyDEnzsqlYVNWlqrpaVX8BvscHpxoXgDsndr1j2LbWxzhcVStVtXITezYzhqQ52lQsktw2cfNLwLWflBwHHkuyJ8ldwD7gp9ONKGk7uHGjHZI8D+wHbk1yHnga2J9kGSjgHPA1gKp6M8kx4OfA+8ATVXV1NqNLmif/FIC0wPxTAJK2nLGQ1GIsJLUYC2kbePnXp3n516fHHuP/ZSwktWz4o1NJs/fA3y+PPcKGPLKQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkLawZb+/ea5fS5jIe1gl/7xd3P7XMZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNbwzLnXxh5hy027Jv+KurTA/CvqkracsZDUYiwktRgLSS3b4gHOJL8B/gD819izzNmtuOZFsJ3X/A9V9bedHbdFLACSnKqqlbHnmCfXvBh2y5o9DZHUYiwktWynWBwee4ARuObFsCvWvG0es5C0vW2nIwtJ25ixkNRiLCS1GAtJLcZCUsv/ASXmitVDJa/KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.67373847961426 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 100000.0} is: \n",
      "[[2.00002000e+06 1.20000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+01 1.60003200e+11 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.00002000e+06 ... 2.00002000e+06\n",
      "  2.00002000e+06 2.00002000e+06]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.00002000e+06 ... 2.88008800e+22\n",
      "  4.48016560e+17 1.12009120e+17]\n",
      " [0.00000000e+00 0.00000000e+00 2.00002000e+06 ... 4.48016560e+17\n",
      "  2.88008680e+22 5.76015160e+17]\n",
      " [0.00000000e+00 0.00000000e+00 2.00002000e+06 ... 1.12009120e+17\n",
      "  5.76015160e+17 1.64803964e+18]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2468cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.39040184020996 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1000000.0} is: \n",
      "[[2.00000200e+07 1.20000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+01 1.60000320e+13 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000200e+07 ... 2.00000200e+07\n",
      "  2.00000200e+07 2.00000200e+07]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.00000200e+07 ... 2.88000880e+26\n",
      "  4.48001656e+20 1.12000912e+20]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000200e+07 ... 4.48001656e+20\n",
      "  2.88000868e+26 5.76001516e+20]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000200e+07 ... 1.12000912e+20\n",
      "  5.76001516e+20 1.64800396e+21]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688f81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.562543630599976 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 10000000.0} is: \n",
      "[[2.00000020e+08 1.20000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+01 1.60000032e+15 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000020e+08 ... 2.00000020e+08\n",
      "  2.00000020e+08 2.00000020e+08]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.00000020e+08 ... 2.88000088e+30\n",
      "  4.48000166e+23 1.12000091e+23]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000020e+08 ... 4.48000166e+23\n",
      "  2.88000087e+30 5.76000152e+23]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000020e+08 ... 1.12000091e+23\n",
      "  5.76000152e+23 1.64800040e+24]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb24fe26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.47181940078735 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 100000000.0} is: \n",
      "[[2.00000002e+09 1.20000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+01 1.60000003e+17 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000002e+09 ... 2.00000002e+09\n",
      "  2.00000002e+09 2.00000002e+09]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.00000002e+09 ... 2.88000009e+34\n",
      "  4.48000017e+26 1.12000009e+26]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000002e+09 ... 4.48000017e+26\n",
      "  2.88000009e+34 5.76000015e+26]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000002e+09 ... 1.12000009e+26\n",
      "  5.76000015e+26 1.64800004e+27]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACnZJREFUeJzt3U+I33edx/Hna9saUSw2dHfotmUtkks9OJShuktZIgX755J6KfVikEI8tIc91lM9ehGLsBYihMaD7eZSmkOwllx66KqNELqpbDFoShPTxN0uKspGG997mG/ojziz8+785vf7zszv+YDh9/t95/ubeX9mkiff729+v5lUFZK0kb8ZewBJO4OxkNRiLCS1GAtJLcZCUouxkNQyeiySPJjkrSRnkzw19jyzkuRckv9IcjrJqWHb3iSvJPnFcHnL2HNOI8mRJJeTnJnYtuYas+o7w/f9jST3jDf55q2z5m8kuTB8r08neXjifV8f1vxWkgfGmXpzRo1FkhuAfwUeAu4Gvpzk7jFnmrEvVNVyVa0Mt58CTlbVPuDkcHsnew548Lpt663xIWDf8HYIeHZOM2615/jrNQN8e/heL1fVCYDh3/ZjwGeG+3x3+D+wI4x9ZHEvcLaqfllVfwJeAA6MPNM8HQCODtePAo+MOMvUqupV4L3rNq+3xgPA92vVj4FPJrltPpNunXXWvJ4DwAtVdaWqfgWcZfX/wI4wdixuB96ZuH1+2LYbFfCjJD9LcmjYtlRVF4fr7wJL44w2U+utcbd/758cTq+OTJxe7ug1jx2LRXJfVd3D6uH3E0n+efKdtfq8+1393PtFWOPgWeDTwDJwEfjWuONsjbFjcQG4c+L2HcO2XaeqLgyXl4EXWT38vHTt0Hu4vDzehDOz3hp37fe+qi5V1dWq+gvwPT441djRax47Fq8D+5LcleQjrD74c3zkmbZcko8n+cS168AXgTOsrvXgsNtB4KVxJpyp9dZ4HPjK8FORzwO/nThd2dGue+zlS6x+r2F1zY8l2ZPkLlYf3P3pvOfbrBvH/ORV9X6SJ4GXgRuAI1X15pgzzcgS8GISWP2a/6CqfpjkdeBYkseBt4FHR5xxakmeB/YDtyY5DzwNfJO113gCeJjVB/n+CHx17gNvgXXWvD/JMqunXOeArwFU1ZtJjgE/B94Hnqiqq2PMvRnxJeqSOsY+DZG0QxgLSS3GQlKLsZDUYiwktcwsFh/21aQTT4FeGK55MeyWNc8kFpt8Nemu+IJ+SK55MeyKNc/qyGLRX00q7TqzegbnWq+u+9x6O38ke+qjfIybs3ehniHmmhfDdl7z//IH/lRX0tl3tKd7D+dxh2D1i3nfB79MSNKc/KROtved1WnIhq+uq6rDVbVSVSs3sWdGY0jaKrOKxUK8mlRaJDM5DVmgV5NKC2Nmj1kMv6T0xKw+vqT58hmcklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIarlxmjsnOQf8HrgKvF9VK0n2Av8GfAo4BzxaVf8z3ZiSxrYVRxZfqKrlqloZbj8FnKyqfcDJ4bakHW4WpyEHgKPD9aPAIzP4HJLmbNpYFPCjJD9LcmjYtlRVF4fr7wJLU34OSdvAVI9ZAPdV1YUkfwe8kuQ/J99ZVZWk1rrjEJdDAB/lY1OOIWnWpjqyqKoLw+Vl4EXgXuBSktsAhsvL69z3cFWtVNXKTeyZZgxJc7DpWCT5eJJPXLsOfBE4AxwHDg67HQRemnZISeOb5jRkCXgxybWP84Oq+mGS14FjSR4H3gYenX5MSWPbdCyq6pfAZ9fY/t/A/dMMJWn78RmcklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIatkwFkmOJLmc5MzEtr1JXknyi+HylmF7knwnydkkbyS5Z5bDS5qfzpHFc8CD1217CjhZVfuAk8NtgIeAfcPbIeDZrRlT0tg2jEVVvQq8d93mA8DR4fpR4JGJ7d+vVT8GPpnktq0aVtJ4NvuYxVJVXRyuvwssDddvB96Z2O/8sE3SDjf1A5xVVUB92PslOZTkVJJTf+bKtGNImrHNxuLStdOL4fLysP0CcOfEfncM2/5KVR2uqpWqWrmJPZscQ9K8bDYWx4GDw/WDwEsT278y/FTk88BvJ05XJO1gN260Q5Lngf3ArUnOA08D3wSOJXkceBt4dNj9BPAwcBb4I/DVGcwsaQQbxqKqvrzOu+5fY98Cnph2KEnbj8/glNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUoux0K70zLnXeObca2OPsasYC0ktG/4pAGkn+pdP/dPYI+w6HllIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEW0hp24+/CmHZNqaotGmXzbs7e+lzuH3sMaeH8pE7yu3ovnX09spDUYiwktRgLSS3GQlKLsZDUYiwktRgLSS3GQlKLsZDUYiwktRgLSS3GQlKLsZDUYiwktWwYiyRHklxOcmZi2zeSXEhyenh7eOJ9X09yNslbSR6Y1eCS5qtzZPEc8OAa279dVcvD2wmAJHcDjwGfGe7z3SQ3bNWwksazYSyq6lXgvebHOwC8UFVXqupXwFng3inmk7RNTPOYxZNJ3hhOU24Ztt0OvDOxz/lhm6QdbrOxeBb4NLAMXAS+9WE/QJJDSU4lOfVnrmxyDEnzsqlYVNWlqrpaVX8BvscHpxoXgDsndr1j2LbWxzhcVStVtXITezYzhqQ52lQsktw2cfNLwLWflBwHHkuyJ8ldwD7gp9ONKGk7uHGjHZI8D+wHbk1yHnga2J9kGSjgHPA1gKp6M8kx4OfA+8ATVXV1NqNLmif/FIC0wPxTAJK2nLGQ1GIsJLUYC2kbePnXp3n516fHHuP/ZSwktWz4o1NJs/fA3y+PPcKGPLKQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkLawZb+/ea5fS5jIe1gl/7xd3P7XMZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNbwzLnXxh5hy027Jv+KurTA/CvqkracsZDUYiwktRgLSS3b4gHOJL8B/gD819izzNmtuOZFsJ3X/A9V9bedHbdFLACSnKqqlbHnmCfXvBh2y5o9DZHUYiwktWynWBwee4ARuObFsCvWvG0es5C0vW2nIwtJ25ixkNRiLCS1GAtJLcZCUsv/ASXmitVDJa/KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.51755976676941 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 1000000000.0} is: \n",
      "[[2.00000000e+10 1.20000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.20000000e+01 1.60000000e+19 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000000e+10 ... 2.00000000e+10\n",
      "  2.00000000e+10 2.00000000e+10]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 2.00000000e+10 ... 2.88000001e+38\n",
      "  4.48000002e+29 1.12000001e+29]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000000e+10 ... 4.48000002e+29\n",
      "  2.88000001e+38 5.76000002e+29]\n",
      " [0.00000000e+00 0.00000000e+00 2.00000000e+10 ... 1.12000001e+29\n",
      "  5.76000002e+29 1.64800000e+30]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb6c144390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 42.55279183387756 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 3.0, 'lmda': 10000000000.0} is: \n",
      "[[2.000e+11 1.200e+01 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.200e+01 1.600e+21 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 2.000e+11 ... 2.000e+11 2.000e+11 2.000e+11]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 2.000e+11 ... 2.880e+42 4.480e+32 1.120e+32]\n",
      " [0.000e+00 0.000e+00 2.000e+11 ... 4.480e+32 2.880e+42 5.760e+32]\n",
      " [0.000e+00 0.000e+00 2.000e+11 ... 1.120e+32 5.760e+32 1.648e+33]]\n"
     ]
    },
    {
     "data": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.88088607788086 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1e-10} is: \n",
      "[[6.50000000e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 7.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.50000000e+01 ... 8.10000000e+01\n",
      "  8.10000000e+01 8.10000000e+01]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.10000000e+01 ... 4.64900000e+03\n",
      "  4.54500000e+03 4.54900000e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10000000e+01 ... 4.54500000e+03\n",
      "  4.44700000e+03 4.44100000e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10000000e+01 ... 4.54900000e+03\n",
      "  4.44100000e+03 4.47100001e+03]]\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.048593521118164 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1e-09} is: \n",
      "[[6.50000002e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 7.20000003e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.50000002e+01 ... 8.10000002e+01\n",
      "  8.10000002e+01 8.10000002e+01]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.10000002e+01 ... 4.64900004e+03\n",
      "  4.54500004e+03 4.54900004e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10000002e+01 ... 4.54500004e+03\n",
      "  4.44700004e+03 4.44100005e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10000002e+01 ... 4.54900004e+03\n",
      "  4.44100005e+03 4.47100008e+03]]\n"
     ]
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    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.604299545288086 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1e-08} is: \n",
      "[[6.50000022e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 7.20000031e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.50000022e+01 ... 8.10000022e+01\n",
      "  8.10000022e+01 8.10000022e+01]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.10000022e+01 ... 4.64900039e+03\n",
      "  4.54500039e+03 4.54900042e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10000022e+01 ... 4.54500039e+03\n",
      "  4.44700040e+03 4.44100045e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10000022e+01 ... 4.54900042e+03\n",
      "  4.44100045e+03 4.47100076e+03]]\n"
     ]
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    {
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.84423804283142 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1e-07} is: \n",
      "[[6.50000224e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 7.20000312e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.50000224e+01 ... 8.10000224e+01\n",
      "  8.10000224e+01 8.10000224e+01]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.10000224e+01 ... 4.64900392e+03\n",
      "  4.54500389e+03 4.54900422e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10000224e+01 ... 4.54500389e+03\n",
      "  4.44700403e+03 4.44100454e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10000224e+01 ... 4.54900422e+03\n",
      "  4.44100454e+03 4.47100759e+03]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.50824785232544 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1e-06} is: \n",
      "[[6.50002240e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 7.20003120e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.50002240e+01 ... 8.10002240e+01\n",
      "  8.10002240e+01 8.10002240e+01]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.10002240e+01 ... 4.64903916e+03\n",
      "  4.54503895e+03 4.54904218e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10002240e+01 ... 4.54503895e+03\n",
      "  4.44704026e+03 4.44104542e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10002240e+01 ... 4.54904218e+03\n",
      "  4.44104542e+03 4.47107588e+03]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.558002948760986 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1e-05} is: \n",
      "[[6.50022400e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 7.20031201e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.50022400e+01 ... 8.10022400e+01\n",
      "  8.10022400e+01 8.10022400e+01]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.10022400e+01 ... 4.64939159e+03\n",
      "  4.54538951e+03 4.54942183e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10022400e+01 ... 4.54538951e+03\n",
      "  4.44740265e+03 4.44145419e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10022400e+01 ... 4.54942183e+03\n",
      "  4.44145419e+03 4.47175889e+03]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.628111362457275 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 0.0001} is: \n",
      "[[6.50224019e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 7.20312061e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.50224019e+01 ... 8.10224019e+01\n",
      "  8.10224019e+01 8.10224019e+01]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.10224019e+01 ... 4.65291887e+03\n",
      "  4.54889780e+03 4.55321930e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10224019e+01 ... 4.54889780e+03\n",
      "  4.45102939e+03 4.44554332e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.10224019e+01 ... 4.55321930e+03\n",
      "  4.44554332e+03 4.47859354e+03]]\n"
     ]
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    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.301332235336304 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 0.001} is: \n",
      "[[6.52241921e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 7.23126085e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.52241921e+01 ... 8.12241921e+01\n",
      "  8.12241921e+01 8.12241921e+01]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.12241921e+01 ... 4.68848458e+03\n",
      "  4.58424911e+03 4.59129182e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.12241921e+01 ... 4.58424911e+03\n",
      "  4.48758275e+03 4.48657940e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.12241921e+01 ... 4.59129182e+03\n",
      "  4.48657940e+03 4.54739909e+03]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.817808389663696 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 0.01} is: \n",
      "[[6.72592640e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 7.51812813e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.72592640e+01 ... 8.32592640e+01\n",
      "  8.32592640e+01 8.32592640e+01]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.32592640e+01 ... 5.07487390e+03\n",
      "  4.96576790e+03 4.98191149e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.32592640e+01 ... 4.96576790e+03\n",
      "  4.88307472e+03 4.91167067e+03]\n",
      " [2.00000000e+01 2.00000000e+01 8.32592640e+01 ... 4.98191149e+03\n",
      "  4.91167067e+03 5.28282860e+03]]\n"
     ]
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    {
     "data": {
      "image/png": "[encoded data, 66072 characters]\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.42012095451355 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 0.1} is: \n",
      "[[8.93840000e+01 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.09772800e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 8.93840000e+01 ... 1.05384000e+02\n",
      "  1.05384000e+02 1.05384000e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.05384000e+02 ... 1.39665408e+04\n",
      "  1.31096732e+04 9.99189165e+03]\n",
      " [2.00000000e+01 2.00000000e+01 1.05384000e+02 ... 1.31096732e+04\n",
      "  1.36994003e+04 1.09159036e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.05384000e+02 ... 9.99189165e+03\n",
      "  1.09159036e+04 1.90906862e+04]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 18976 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb256e1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.30138349533081 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1.0} is: \n",
      "[[5.4500000e+02 4.2000000e+01 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [4.2000000e+01 1.6000000e+03 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [4.0000000e+00 4.0000000e+00 5.4500000e+02 ... 5.6100000e+02\n",
      "  5.6100000e+02 5.6100000e+02]\n",
      " ...\n",
      " [2.0000000e+01 2.0000000e+01 5.6100000e+02 ... 5.5201405e+07\n",
      "  2.7070565e+07 3.7657900e+05]\n",
      " [2.0000000e+01 2.0000000e+01 5.6100000e+02 ... 2.7070565e+07\n",
      "  5.6227287e+07 8.7973800e+05]\n",
      " [2.0000000e+01 2.0000000e+01 5.6100000e+02 ... 3.7657900e+05\n",
      "  8.7973800e+05 8.0506750e+06]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb256f2198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.570582151412964 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 10.0} is: \n",
      "[[8.55050000e+04 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.82399200e+06 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 8.55050000e+04 ... 8.55210000e+04\n",
      "  8.55210000e+04 8.55210000e+04]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 8.55210000e+04 ... 1.45800903e+16\n",
      "  1.04720467e+15 7.94346077e+09]\n",
      " [2.00000000e+01 2.00000000e+01 8.55210000e+04 ... 1.04720467e+15\n",
      "  1.46179765e+16 1.05982134e+11]\n",
      " [2.00000000e+01 2.00000000e+01 8.55210000e+04 ... 7.94346077e+09\n",
      "  1.05982134e+11 2.70394568e+12]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACkZJREFUeJzt3M+L3Hcdx/Hny6ZGlIoN1VDbokVyqQeXslQPRSIF++OSein1YpBCPLR/QD3VoxcRBC1ECI0HW3MpzSFYSy49qY0QaioWg6Y0MU3UihbFaNK3h/2GDjHrvruzM9/dnecDlpn57Hd2359s8mS+szNJVSFJa/nA2ANI2hqMhaQWYyGpxVhIajEWklqMhaSW0WOR5IEkryc5neTJseeZlSRnkvw6yckkJ4a1XUleSvK74fLmseecRpJDSS4mOTWxdt09ZsX3hp/7q0nuHm/y9Vtlz99Kcm74WZ9M8tDE57457Pn1JPePM/X6jBqLJDcA3wceBO4CvprkrjFnmrEvVdVSVS0Pt58EjlfVHuD4cHsrewZ44Jq11fb4ILBn+DgAPD2nGTfaM/zvngG+O/ysl6rqGMDwd/tR4LPDfX4w/BvYEsZ+ZHEPcLqqfl9V/waeA/aNPNM87QMOD9cPAw+POMvUqupl4O1rllfb4z7gR7Xi58DHktw6n0k3zip7Xs0+4LmqulRVfwBOs/JvYEsYOxa3AW9O3D47rG1HBfwsya+SHBjWdlfV+eH6W8DucUabqdX2uN1/9k8Mp1eHJk4vt/Sex47FIrm3qu5m5eH340m+OPnJWnnd/bZ+7f0i7HHwNPAZYAk4D3xn3HE2xtixOAfcMXH79mFt26mqc8PlReB5Vh5+Xrj60Hu4vDjehDOz2h637c++qi5U1ZWqehf4Ie+damzpPY8di1eAPUnuTPJBVp78OTryTBsuyUeS3HT1OvBl4BQre90/HLYfeGGcCWdqtT0eBb42/FbkC8DfJk5XtrRrnnv5Cis/a1jZ86NJdia5k5Und3857/nWa8eY37yqLid5AngRuAE4VFWvjTnTjOwGnk8CK3/mP66qnyZ5BTiS5DHgDeCREWecWpJngb3ALUnOAk8B3+b6ezwGPMTKk3z/BL4+94E3wCp73ptkiZVTrjPANwCq6rUkR4DfAJeBx6vqyhhzr0d8i7qkjrFPQyRtEcZCUouxkNRiLCS1GAtJLTOLxft9N+nES6AXhnteDNtlzzOJxTrfTbot/kDfJ/e8GLbFnmf1yGLR300qbTuzegXn9d5d9/nVDv5gdtaH+DAfza6FeoWYe14Mm3nP/+If/LsupXPsaC/3Hs7jDsDKH+a97/1nQpLm5Bd1vH3srE5D1nx3XVUdrKrlqlq+kZ0zGkPSRplVLBbi3aTSIpnJacgCvZtUWhgze85i+E9Kj83q60uaL1/BKanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpJYd09w5yRngHeAKcLmqlpPsAn4CfBo4AzxSVX+dbkxJY9uIRxZfqqqlqloebj8JHK+qPcDx4bakLW4WpyH7gMPD9cPAwzP4HpLmbNpYFPCzJL9KcmBY211V54frbwG7p/wekjaBqZ6zAO6tqnNJPgG8lOS3k5+sqkpS17vjEJcDAB/iw1OOIWnWpnpkUVXnhsuLwPPAPcCFJLcCDJcXV7nvwaparqrlG9k5zRiS5mDdsUjykSQ3Xb0OfBk4BRwF9g+H7QdemHZISeOb5jRkN/B8kqtf58dV9dMkrwBHkjwGvAE8Mv2Yksa27lhU1e+Bz11n/S/AfdMMJWnz8RWcklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIalkzFkkOJbmY5NTE2q4kLyX53XB587CeJN9LcjrJq0nunuXwkuan88jiGeCBa9aeBI5X1R7g+HAb4EFgz/BxAHh6Y8aUNLY1Y1FVLwNvX7O8Dzg8XD8MPDyx/qNa8XPgY0lu3ahhJY1nvc9Z7K6q88P1t4Ddw/XbgDcnjjs7rEna4qZ+grOqCqj3e78kB5KcSHLiP1yadgxJM7beWFy4enoxXF4c1s8Bd0wcd/uw9j+q6mBVLVfV8o3sXOcYkuZlvbE4Cuwfru8HXphY/9rwW5EvAH+bOF2RtIXtWOuAJM8Ce4FbkpwFngK+DRxJ8hjwBvDIcPgx4CHgNPBP4OszmFnSCNaMRVV9dZVP3XedYwt4fNqhJG0+voJTUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLWvGIsmhJBeTnJpY+1aSc0lODh8PTXzum0lOJ3k9yf2zGlzSfHUeWTwDPHCd9e9W1dLwcQwgyV3Ao8Bnh/v8IMkNGzWspPGsGYuqehl4u/n19gHPVdWlqvoDcBq4Z4r5JG0S0zxn8USSV4fTlJuHtduANyeOOTusSdri1huLp4HPAEvAeeA77/cLJDmQ5ESSE//h0jrHkDQv64pFVV2oqitV9S7wQ9471TgH3DFx6O3D2vW+xsGqWq6q5RvZuZ4xJM3RumKR5NaJm18Brv6m5CjwaJKdSe4E9gC/nG5ESZvBjrUOSPIssBe4JclZ4Clgb5IloIAzwDcAquq1JEeA3wCXgcer6spsRpc0T6mqsWfgo9lVn899Y48hLZxf1HH+Xm+nc6yv4JTUYiwktRgLSS3GQtoEXvzjSV7848mxx/i/jIWkljV/dSpp9u7/5NLYI6zJRxaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhb2I47bp/b9zIW0hZ2+c2zc/texkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQFsQHbrppuvtv0BySNrl333lnqvsbC0ktxkJSi7GQ1GIsJLWkqsaegSR/Av4B/HnsWebsFtzzItjMe/5UVX28c+CmiAVAkhNVtTz2HPPknhfDdtmzpyGSWoyFpJbNFIuDYw8wAve8GLbFnjfNcxaSNrfN9MhC0iZmLCS1GAtJLcZCUouxkNTyXyQza83YF5EKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb24fe26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.20673108100891 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 100.0} is: \n",
      "[[6.59424650e+07 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.32861113e+10 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.59424650e+07 ... 6.59424810e+07\n",
      "  6.59424810e+07 6.59424810e+07]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 6.59424810e+07 ... 9.76031932e+25\n",
      "  7.28718337e+23 4.86537429e+15]\n",
      " [2.00000000e+01 2.00000000e+01 6.59424810e+07 ... 7.28718337e+23\n",
      "  9.76064507e+25 6.46947761e+17]\n",
      " [2.00000000e+01 2.00000000e+01 6.59424810e+07 ... 4.86537429e+15\n",
      "  6.46947761e+17 1.63044342e+19]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb256e1c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.444000005722046 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1000.0} is: \n",
      "[[6.41922241e+10 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.28480608e+14 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.41922241e+10 ... 6.41922241e+10\n",
      "  6.41922241e+10 6.41922241e+10]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 6.41922241e+10 ... 9.37328139e+35\n",
      "  7.02676137e+32 4.63309425e+21]\n",
      " [2.00000000e+01 2.00000000e+01 6.41922241e+10 ... 7.02676137e+32\n",
      "  9.37328460e+35 6.17559179e+24]\n",
      " [2.00000000e+01 2.00000000e+01 6.41922241e+10 ... 4.63309425e+21\n",
      "  6.17559179e+24 1.54524551e+26]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.26343107223511 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 10000.0} is: \n",
      "[[6.40192022e+13 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.28048006e+18 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.40192022e+13 ... 6.40192022e+13\n",
      "  6.40192022e+13 6.40192022e+13]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 6.40192022e+13 ... 9.33539995e+45\n",
      "  7.00123103e+41 4.61050310e+27]\n",
      " [2.00000000e+01 2.00000000e+01 6.40192022e+13 ... 7.00123103e+41\n",
      "  9.33539998e+45 6.14714984e+31]\n",
      " [2.00000000e+01 2.00000000e+01 6.40192022e+13 ... 4.61050310e+27\n",
      "  6.14714984e+31 1.53692259e+33]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2468cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.19285559654236 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 100000.0} is: \n",
      "[[6.40019200e+16 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.28004800e+22 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.40019200e+16 ... 6.40019200e+16\n",
      "  6.40019200e+16 6.40019200e+16]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 6.40019200e+16 ... 9.33161991e+55\n",
      "  6.99868305e+50 4.60825025e+33]\n",
      " [2.00000000e+01 2.00000000e+01 6.40019200e+16 ... 6.99868305e+50\n",
      "  9.33161991e+55 6.14431489e+38]\n",
      " [2.00000000e+01 2.00000000e+01 6.40019200e+16 ... 4.60825025e+33\n",
      "  6.14431489e+38 1.53609224e+40]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb24637da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.172040700912476 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1000000.0} is: \n",
      "[[6.40001920e+19 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.28000480e+26 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.40001920e+19 ... 6.40001920e+19\n",
      "  6.40001920e+19 6.40001920e+19]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 6.40001920e+19 ... 9.33124199e+65\n",
      "  6.99842830e+59 4.60802502e+39]\n",
      " [2.00000000e+01 2.00000000e+01 6.40001920e+19 ... 6.99842830e+59\n",
      "  9.33124199e+65 6.14403149e+45]\n",
      " [2.00000000e+01 2.00000000e+01 6.40001920e+19 ... 4.60802502e+39\n",
      "  6.14403149e+45 1.53600922e+47]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688f8208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.8695502281189 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 10000000.0} is: \n",
      "[[6.40000192e+22 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.28000048e+30 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.40000192e+22 ... 6.40000192e+22\n",
      "  6.40000192e+22 6.40000192e+22]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 6.40000192e+22 ... 9.33120420e+75\n",
      "  6.99840283e+68 4.60800250e+45]\n",
      " [2.00000000e+01 2.00000000e+01 6.40000192e+22 ... 6.99840283e+68\n",
      "  9.33120420e+75 6.14400315e+52]\n",
      " [2.00000000e+01 2.00000000e+01 6.40000192e+22 ... 4.60800250e+45\n",
      "  6.14400315e+52 1.53600092e+54]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACjhJREFUeJzt3M+P3HUdx/HnS4o1Gow0aIOASkwvcHBDNuiBmBoSfl0KF4IXG0NSD/AHwAmPXoyJiZLUpKEeBHsh9NAIpBdOIDVpsBiJjRZpLa2KQaOx2vr2sN+GSe26b3Z25rs/no9kMzOf/c7u+9Ntn5nv7ExTVUjSSj4y9gCSNgZjIanFWEhqMRaSWoyFpBZjIall9FgkuS/JW0lOJnli7HlmJcmpJL9McjzJsWFtR5KXk/xmuLx+7DmnkeRAkvNJTkysXXWPWfL94ef+RpI7xpt89ZbZ87eTnBl+1seTPDDxuSeHPb+V5N5xpl6dUWOR5BrgB8D9wG3A15PcNuZMM/a1qlqoqsXh9hPA0araBRwdbm9kzwD3XbG23B7vB3YNH/uAp+c041p7hv/dM8D3hp/1QlUdARj+bj8C3D7c54fDv4ENYexHFncCJ6vqt1X1L+A5YM/IM83THuDgcP0g8OCIs0ytql4B3rtiebk97gF+XEteBT6V5Mb5TLp2ltnzcvYAz1XVhar6HXCSpX8DG8LYsbgJeGfi9ulhbTMq4KUkv0iyb1jbWVVnh+vvAjvHGW2mltvjZv/ZPz6cXh2YOL3c0HseOxZbyV1VdQdLD78fS/LVyU/W0uvuN/Vr77fCHgdPA18EFoCzwHfHHWdtjB2LM8AtE7dvHtY2nao6M1yeB55n6eHnucsPvYfL8+NNODPL7XHT/uyr6lxVXaqq/wA/4oNTjQ2957Fj8TqwK8mtST7K0pM/h0eeac0l+USS6y5fB+4BTrC0173DYXuBF8aZcKaW2+Nh4BvDb0W+Arw/cbqyoV3x3MtDLP2sYWnPjyTZnuRWlp7c/fm851utbWN+86q6mORx4EXgGuBAVb055kwzshN4Pgks/Zn/pKp+luR14FCSR4G3gYdHnHFqSZ4FdgM3JDkNPAV8h6vv8QjwAEtP8v0D+ObcB14Dy+x5d5IFlk65TgHfAqiqN5McAn4FXAQeq6pLY8y9GvEt6pI6xj4NkbRBGAtJLcZCUouxkNRiLCS1zCwWH/bdpBMvgd4y3PPWsFn2PJNYrPLdpJviD/RDcs9bw6bY86weWWz1d5NKm86sXsF5tXfXfXm5gz+a7fUxPs4ns2NLvULMPW8N63nP/+Tv/KsupHPsaC/3Hs7j9sHSH+ZdH/xnQpLm5LU62j52VqchK767rqr2V9ViVS1ey/YZjSFprcwqFlvi3aTSVjKT05At9G5SacuY2XMWw39SemRWX1/SfPkKTkktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLVsm+bOSU4BfwMuARerajHJDuCnwBeAU8DDVfWX6caUNLa1eGTxtapaqKrF4fYTwNGq2gUcHW5L2uBmcRqyBzg4XD8IPDiD7yFpzqaNRQEvJflFkn3D2s6qOjtcfxfYOeX3kLQOTPWcBXBXVZ1J8hng5SS/nvxkVVWSutodh7jsA/gYH59yDEmzNtUji6o6M1yeB54H7gTOJbkRYLg8v8x991fVYlUtXsv2acaQNAerjkWSTyS57vJ14B7gBHAY2Dscthd4YdohJY1vmtOQncDzSS5/nZ9U1c+SvA4cSvIo8Dbw8PRjShrbqmNRVb8FvnSV9T8Dd08zlKT1x1dwSmoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqWXFWCQ5kOR8khMTazuSvJzkN8Pl9cN6knw/yckkbyS5Y5bDS5qfziOLZ4D7rlh7AjhaVbuAo8NtgPuBXcPHPuDptRlT0thWjEVVvQK8d8XyHuDgcP0g8ODE+o9ryavAp5LcuFbDShrPap+z2FlVZ4fr7wI7h+s3Ae9MHHd6WJO0wU39BGdVFVAf9n5J9iU5luTYv7kw7RiSZmy1sTh3+fRiuDw/rJ8Bbpk47uZh7X9U1f6qWqyqxWvZvsoxJM3LamNxGNg7XN8LvDCx/o3htyJfAd6fOF2RtIFtW+mAJM8Cu4EbkpwGngK+AxxK8ijwNvDwcPgR4AHgJPAP4JszmFnSCFaMRVV9fZlP3X2VYwt4bNqhJK0/voJTUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLSvGIsmBJOeTnJhY+3aSM0mODx8PTHzuySQnk7yV5N5ZDS5pvjqPLJ4B7rvK+veqamH4OAKQ5DbgEeD24T4/THLNWg0raTwrxqKqXgHea369PcBzVXWhqn4HnATunGI+SevENM9ZPJ7kjeE05fph7SbgnYljTg9rkja41cbiaeCLwAJwFvjuh/0CSfYlOZbk2L+5sMoxJM3LqmJRVeeq6lJV/Qf4ER+capwBbpk49OZh7WpfY39VLVbV4rVsX80YkuZoVbFIcuPEzYeAy78pOQw8kmR7kluBXcDPpxtR0nqwbaUDkjwL7AZuSHIaeArYnWQBKOAU8C2AqnozySHgV8BF4LGqujSb0SXNU6pq7Bn4ZHbUl3P32GNIW85rdZS/1nvpHOsrOCW1GAtJLcZCUouxkNaBF/9wnBf/cHzsMf4vYyGpZcVfnUqavXs/uzD2CCvykYWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFtIGtu0Ln5vb9zIW0gZ28dTv5/a9jIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGpJVY09A0n+CPwd+NPYs8zZDbjnrWA97/nzVfXpzoHrIhYASY5V1eLYc8yTe94aNsuePQ2R1GIsJLWsp1jsH3uAEbjnrWFT7HndPGchaX1bT48sJK1jxkJSi7GQ1GIsJLUYC0kt/wUTFWjNk6oV2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.321784019470215 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 100000000.0} is: \n",
      "[[6.40000019e+25 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.28000005e+34 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.40000019e+25 ... 6.40000019e+25\n",
      "  6.40000019e+25 6.40000019e+25]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 6.40000019e+25 ... 9.33120042e+85\n",
      "  6.99840028e+77 4.60800025e+51]\n",
      " [2.00000000e+01 2.00000000e+01 6.40000019e+25 ... 6.99840028e+77\n",
      "  9.33120042e+85 6.14400031e+59]\n",
      " [2.00000000e+01 2.00000000e+01 6.40000019e+25 ... 4.60800025e+51\n",
      "  6.14400031e+59 1.53600009e+61]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACjhJREFUeJzt3M+P3HUdx/HnS4o1Gow0aIOASkwvcHBDNuiBmBoSfl0KF4IXG0NSD/AHwAmPXoyJiZLUpKEeBHsh9NAIpBdOIDVpsBiJjRZpLa2KQaOx2vr2sN+GSe26b3Z25rs/no9kMzOf/c7u+9Ntn5nv7ExTVUjSSj4y9gCSNgZjIanFWEhqMRaSWoyFpBZjIall9FgkuS/JW0lOJnli7HlmJcmpJL9McjzJsWFtR5KXk/xmuLx+7DmnkeRAkvNJTkysXXWPWfL94ef+RpI7xpt89ZbZ87eTnBl+1seTPDDxuSeHPb+V5N5xpl6dUWOR5BrgB8D9wG3A15PcNuZMM/a1qlqoqsXh9hPA0araBRwdbm9kzwD3XbG23B7vB3YNH/uAp+c041p7hv/dM8D3hp/1QlUdARj+bj8C3D7c54fDv4ENYexHFncCJ6vqt1X1L+A5YM/IM83THuDgcP0g8OCIs0ytql4B3rtiebk97gF+XEteBT6V5Mb5TLp2ltnzcvYAz1XVhar6HXCSpX8DG8LYsbgJeGfi9ulhbTMq4KUkv0iyb1jbWVVnh+vvAjvHGW2mltvjZv/ZPz6cXh2YOL3c0HseOxZbyV1VdQdLD78fS/LVyU/W0uvuN/Vr77fCHgdPA18EFoCzwHfHHWdtjB2LM8AtE7dvHtY2nao6M1yeB55n6eHnucsPvYfL8+NNODPL7XHT/uyr6lxVXaqq/wA/4oNTjQ2957Fj8TqwK8mtST7K0pM/h0eeac0l+USS6y5fB+4BTrC0173DYXuBF8aZcKaW2+Nh4BvDb0W+Arw/cbqyoV3x3MtDLP2sYWnPjyTZnuRWlp7c/fm851utbWN+86q6mORx4EXgGuBAVb055kwzshN4Pgks/Zn/pKp+luR14FCSR4G3gYdHnHFqSZ4FdgM3JDkNPAV8h6vv8QjwAEtP8v0D+ObcB14Dy+x5d5IFlk65TgHfAqiqN5McAn4FXAQeq6pLY8y9GvEt6pI6xj4NkbRBGAtJLcZCUouxkNRiLCS1zCwWH/bdpBMvgd4y3PPWsFn2PJNYrPLdpJviD/RDcs9bw6bY86weWWz1d5NKm86sXsF5tXfXfXm5gz+a7fUxPs4ns2NLvULMPW8N63nP/+Tv/KsupHPsaC/3Hs7j9sHSH+ZdH/xnQpLm5LU62j52VqchK767rqr2V9ViVS1ey/YZjSFprcwqFlvi3aTSVjKT05At9G5SacuY2XMWw39SemRWX1/SfPkKTkktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLVsm+bOSU4BfwMuARerajHJDuCnwBeAU8DDVfWX6caUNLa1eGTxtapaqKrF4fYTwNGq2gUcHW5L2uBmcRqyBzg4XD8IPDiD7yFpzqaNRQEvJflFkn3D2s6qOjtcfxfYOeX3kLQOTPWcBXBXVZ1J8hng5SS/nvxkVVWSutodh7jsA/gYH59yDEmzNtUji6o6M1yeB54H7gTOJbkRYLg8v8x991fVYlUtXsv2acaQNAerjkWSTyS57vJ14B7gBHAY2Dscthd4YdohJY1vmtOQncDzSS5/nZ9U1c+SvA4cSvIo8Dbw8PRjShrbqmNRVb8FvnSV9T8Dd08zlKT1x1dwSmoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqWXFWCQ5kOR8khMTazuSvJzkN8Pl9cN6knw/yckkbyS5Y5bDS5qfziOLZ4D7rlh7AjhaVbuAo8NtgPuBXcPHPuDptRlT0thWjEVVvQK8d8XyHuDgcP0g8ODE+o9ryavAp5LcuFbDShrPap+z2FlVZ4fr7wI7h+s3Ae9MHHd6WJO0wU39BGdVFVAf9n5J9iU5luTYv7kw7RiSZmy1sTh3+fRiuDw/rJ8Bbpk47uZh7X9U1f6qWqyqxWvZvsoxJM3LamNxGNg7XN8LvDCx/o3htyJfAd6fOF2RtIFtW+mAJM8Cu4EbkpwGngK+AxxK8ijwNvDwcPgR4AHgJPAP4JszmFnSCFaMRVV9fZlP3X2VYwt4bNqhJK0/voJTUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLSvGIsmBJOeTnJhY+3aSM0mODx8PTHzuySQnk7yV5N5ZDS5pvjqPLJ4B7rvK+veqamH4OAKQ5DbgEeD24T4/THLNWg0raTwrxqKqXgHea369PcBzVXWhqn4HnATunGI+SevENM9ZPJ7kjeE05fph7SbgnYljTg9rkja41cbiaeCLwAJwFvjuh/0CSfYlOZbk2L+5sMoxJM3LqmJRVeeq6lJV/Qf4ER+capwBbpk49OZh7WpfY39VLVbV4rVsX80YkuZoVbFIcuPEzYeAy78pOQw8kmR7kluBXcDPpxtR0nqwbaUDkjwL7AZuSHIaeArYnWQBKOAU8C2AqnozySHgV8BF4LGqujSb0SXNU6pq7Bn4ZHbUl3P32GNIW85rdZS/1nvpHOsrOCW1GAtJLcZCUouxkNaBF/9wnBf/cHzsMf4vYyGpZcVfnUqavXs/uzD2CCvykYWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFtIGtu0Ln5vb9zIW0gZ28dTv5/a9jIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGoxFpJajIWkFmMhqcVYSGpJVY09A0n+CPwd+NPYs8zZDbjnrWA97/nzVfXpzoHrIhYASY5V1eLYc8yTe94aNsuePQ2R1GIsJLWsp1jsH3uAEbjnrWFT7HndPGchaX1bT48sJK1jxkJSi7GQ1GIsJLUYC0kt/wUTFWjNk6oV2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.805800676345825 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 1000000000.0} is: \n",
      "[[6.40000002e+28 4.20000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.20000000e+01 1.28000000e+38 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 6.40000002e+28 ... 6.40000002e+28\n",
      "  6.40000002e+28 6.40000002e+28]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 6.40000002e+28 ... 9.33120004e+95\n",
      "  6.99840003e+86 4.60800003e+57]\n",
      " [2.00000000e+01 2.00000000e+01 6.40000002e+28 ... 6.99840003e+86\n",
      "  9.33120004e+95 6.14400003e+66]\n",
      " [2.00000000e+01 2.00000000e+01 6.40000002e+28 ... 4.60800003e+57\n",
      "  6.14400003e+66 1.53600001e+68]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 52.11367106437683 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 4.0, 'lmda': 10000000000.0} is: \n",
      "[[6.4000e+031 4.2000e+001 4.0000e+000 ... 2.0000e+001 2.0000e+001\n",
      "  2.0000e+001]\n",
      " [4.2000e+001 1.2800e+042 4.0000e+000 ... 2.0000e+001 2.0000e+001\n",
      "  2.0000e+001]\n",
      " [4.0000e+000 4.0000e+000 6.4000e+031 ... 6.4000e+031 6.4000e+031\n",
      "  6.4000e+031]\n",
      " ...\n",
      " [2.0000e+001 2.0000e+001 6.4000e+031 ... 9.3312e+105 6.9984e+095\n",
      "  4.6080e+063]\n",
      " [2.0000e+001 2.0000e+001 6.4000e+031 ... 6.9984e+095 9.3312e+105\n",
      "  6.1440e+073]\n",
      " [2.0000e+001 2.0000e+001 6.4000e+031 ... 4.6080e+063 6.1440e+073\n",
      "  1.5360e+075]]\n"
     ]
    },
    {
     "data": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 45.042237520217896 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1e-10} is: \n",
      "[[3.200e-29 1.600e-29 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.600e-29 3.200e-29 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 3.200e-29 ... 3.200e-29 3.200e-29 3.200e-29]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 3.200e-29 ... 3.232e-27 3.128e-27 3.200e-27]\n",
      " [0.000e+00 0.000e+00 3.200e-29 ... 3.128e-27 3.030e-27 3.092e-27]\n",
      " [0.000e+00 0.000e+00 3.200e-29 ... 3.200e-27 3.092e-27 3.184e-27]]\n"
     ]
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.124547243118286 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1e-09} is: \n",
      "[[3.200e-26 1.600e-26 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.600e-26 3.200e-26 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 3.200e-26 ... 3.200e-26 3.200e-26 3.200e-26]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 3.200e-26 ... 3.232e-24 3.128e-24 3.200e-24]\n",
      " [0.000e+00 0.000e+00 3.200e-26 ... 3.128e-24 3.030e-24 3.092e-24]\n",
      " [0.000e+00 0.000e+00 3.200e-26 ... 3.200e-24 3.092e-24 3.184e-24]]\n"
     ]
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.02215909957886 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1e-08} is: \n",
      "[[3.200e-23 1.600e-23 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.600e-23 3.200e-23 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 3.200e-23 ... 3.200e-23 3.200e-23 3.200e-23]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 3.200e-23 ... 3.232e-21 3.128e-21 3.200e-21]\n",
      " [0.000e+00 0.000e+00 3.200e-23 ... 3.128e-21 3.030e-21 3.092e-21]\n",
      " [0.000e+00 0.000e+00 3.200e-23 ... 3.200e-21 3.092e-21 3.184e-21]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.968430280685425 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1e-07} is: \n",
      "[[3.200e-20 1.600e-20 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.600e-20 3.200e-20 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 3.200e-20 ... 3.200e-20 3.200e-20 3.200e-20]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 3.200e-20 ... 3.232e-18 3.128e-18 3.200e-18]\n",
      " [0.000e+00 0.000e+00 3.200e-20 ... 3.128e-18 3.030e-18 3.092e-18]\n",
      " [0.000e+00 0.000e+00 3.200e-20 ... 3.200e-18 3.092e-18 3.184e-18]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.13302397727966 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1e-06} is: \n",
      "[[3.200e-17 1.600e-17 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.600e-17 3.200e-17 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 3.200e-17 ... 3.200e-17 3.200e-17 3.200e-17]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 3.200e-17 ... 3.232e-15 3.128e-15 3.200e-15]\n",
      " [0.000e+00 0.000e+00 3.200e-17 ... 3.128e-15 3.030e-15 3.092e-15]\n",
      " [0.000e+00 0.000e+00 3.200e-17 ... 3.200e-15 3.092e-15 3.184e-15]]\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.93401503562927 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1e-05} is: \n",
      "[[3.200e-14 1.600e-14 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [1.600e-14 3.200e-14 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 3.200e-14 ... 3.200e-14 3.200e-14 3.200e-14]\n",
      " ...\n",
      " [0.000e+00 0.000e+00 3.200e-14 ... 3.232e-12 3.128e-12 3.200e-12]\n",
      " [0.000e+00 0.000e+00 3.200e-14 ... 3.128e-12 3.030e-12 3.092e-12]\n",
      " [0.000e+00 0.000e+00 3.200e-14 ... 3.200e-12 3.092e-12 3.184e-12]]\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.997790575027466 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 0.0001} is: \n",
      "[[3.20000002e-11 1.60000000e-11 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e-11 3.20000002e-11 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 3.20000002e-11 ... 3.20000002e-11\n",
      "  3.20000002e-11 3.20000002e-11]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 3.20000002e-11 ... 3.23200002e-09\n",
      "  3.12800002e-09 3.20000002e-09]\n",
      " [0.00000000e+00 0.00000000e+00 3.20000002e-11 ... 3.12800002e-09\n",
      "  3.03000002e-09 3.09200002e-09]\n",
      " [0.00000000e+00 0.00000000e+00 3.20000002e-11 ... 3.20000002e-09\n",
      "  3.09200002e-09 3.18400003e-09]]\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.6544508934021 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 0.001} is: \n",
      "[[3.20000160e-08 1.60000000e-08 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e-08 3.20000240e-08 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 3.20000160e-08 ... 3.20000160e-08\n",
      "  3.20000160e-08 3.20000160e-08]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 3.20000160e-08 ... 3.23200229e-06\n",
      "  3.12800211e-06 3.20000214e-06]\n",
      " [0.00000000e+00 0.00000000e+00 3.20000160e-08 ... 3.12800211e-06\n",
      "  3.03000196e-06 3.09200199e-06]\n",
      " [0.00000000e+00 0.00000000e+00 3.20000160e-08 ... 3.20000214e-06\n",
      "  3.09200199e-06 3.18400274e-06]]\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.26693940162659 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 0.01} is: \n",
      "[[3.20016002e-05 1.60000000e-05 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e-05 3.20024003e-05 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 3.20016002e-05 ... 3.20016002e-05\n",
      "  3.20016002e-05 3.20016002e-05]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 3.20016002e-05 ... 3.23222885e-03\n",
      "  3.12821085e-03 3.20021445e-03]\n",
      " [0.00000000e+00 0.00000000e+00 3.20016002e-05 ... 3.12821085e-03\n",
      "  3.03019585e-03 3.09219945e-03]\n",
      " [0.00000000e+00 0.00000000e+00 3.20016002e-05 ... 3.20021445e-03\n",
      "  3.09219945e-03 3.18427449e-03]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.429144620895386 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 0.1} is: \n",
      "[[0.03216167 0.016      0.         ... 0.         0.         0.        ]\n",
      " [0.016      0.03224328 0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.03216167 ... 0.03216167 0.03216167 0.03216167]\n",
      " ...\n",
      " [0.         0.         0.03216167 ... 3.2554357  3.14960373 3.22194735]\n",
      " [0.         0.         0.03216167 ... 3.14960373 3.05010307 3.11249761]\n",
      " [0.         0.         0.03216167 ... 3.22194735 3.11249761 3.21242132]]\n"
     ]
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.200169801712036 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1.0} is: \n",
      "[[3.2000000e+02 1.6000000e+01 0.0000000e+00 ... 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00]\n",
      " [1.6000000e+01 5.3600000e+02 0.0000000e+00 ... 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00]\n",
      " [0.0000000e+00 0.0000000e+00 3.2000000e+02 ... 3.2000000e+02\n",
      "  3.2000000e+02 3.2000000e+02]\n",
      " ...\n",
      " [0.0000000e+00 0.0000000e+00 3.2000000e+02 ... 3.9599184e+07\n",
      "  1.8790076e+07 1.9904800e+05]\n",
      " [0.0000000e+00 0.0000000e+00 3.2000000e+02 ... 1.8790076e+07\n",
      "  4.5132236e+07 5.0194600e+05]\n",
      " [0.0000000e+00 0.0000000e+00 3.2000000e+02 ... 1.9904800e+05\n",
      "  5.0194600e+05 4.7488000e+06]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2468c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.947789669036865 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 10.0} is: \n",
      "[[6.52864162e+14 1.60000000e+04 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+04 6.52896808e+18 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.52864162e+14 ... 6.52864162e+14\n",
      "  6.52864162e+14 6.52864162e+14]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.52864162e+14 ... 9.71153321e+38\n",
      "  4.83215703e+36 2.92253118e+26]\n",
      " [0.00000000e+00 0.00000000e+00 6.52864162e+14 ... 4.83215703e+36\n",
      "  9.73580847e+38 6.40218246e+28]\n",
      " [0.00000000e+00 0.00000000e+00 6.52864162e+14 ... 2.92253118e+26\n",
      "  6.40218246e+28 1.60883971e+30]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb6c144390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.95116639137268 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 100.0} is: \n",
      "[[6.40128006e+27 1.60000000e+07 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+07 6.40128010e+35 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.40128006e+27 ... 6.40128006e+27\n",
      "  6.40128006e+27 6.40128006e+27]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.40128006e+27 ... 9.33493318e+71\n",
      "  4.66723328e+67 2.81704975e+49]\n",
      " [0.00000000e+00 0.00000000e+00 6.40128006e+27 ... 4.66723328e+67\n",
      "  9.33516655e+71 6.14653487e+53]\n",
      " [0.00000000e+00 0.00000000e+00 6.40128006e+27 ... 2.81704975e+49\n",
      "  6.14653487e+53 1.53671603e+55]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.93626260757446 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1000.0} is: \n",
      "[[6.40001280e+040 1.60000000e+010 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [1.60000000e+010 6.40001280e+052 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [0.00000000e+000 0.00000000e+000 6.40001280e+040 ... 6.40001280e+040\n",
      "  6.40001280e+040 6.40001280e+040]\n",
      " ...\n",
      " [0.00000000e+000 0.00000000e+000 6.40001280e+040 ... 9.33123732e+104\n",
      "  4.66561633e+098 2.81601050e+072]\n",
      " [0.00000000e+000 0.00000000e+000 6.40001280e+040 ... 4.66561633e+098\n",
      "  9.33123966e+104 6.14402534e+078]\n",
      " [0.00000000e+000 0.00000000e+000 6.40001280e+040 ... 2.81601050e+072\n",
      "  6.14402534e+078 1.53600716e+080]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.99435877799988 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 10000.0} is: \n",
      "[[6.40000013e+053 1.60000000e+013 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [1.60000000e+013 6.40000013e+069 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000013e+053 ... 6.40000013e+053\n",
      "  6.40000013e+053 6.40000013e+053]\n",
      " ...\n",
      " [0.00000000e+000 0.00000000e+000 6.40000013e+053 ... 9.33120037e+137\n",
      "  4.66560016e+129 2.81600010e+095]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000013e+053 ... 4.66560016e+129\n",
      "  9.33120040e+137 6.14400025e+103]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000013e+053 ... 2.81600010e+095\n",
      "  6.14400025e+103 1.53600007e+105]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb256e1fd0>"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../pygraph/kernels/treePatternKernel.py:169: RuntimeWarning: overflow encountered in double_scalars\n",
      "  kh += kh_tmp\n",
      "../pygraph/kernels/treePatternKernel.py:168: RuntimeWarning: overflow encountered in double_scalars\n",
      "  kh_tmp *= lmda * all_kh[str(pair[0]) + '.' + str(pair[1])]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.15428304672241 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 100000.0} is: \n",
      "[[6.4000e+066 1.6000e+016 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [1.6000e+016 6.4000e+086 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+066 ... 6.4000e+066 6.4000e+066\n",
      "  6.4000e+066]\n",
      " ...\n",
      " [0.0000e+000 0.0000e+000 6.4000e+066 ... 9.3312e+170 4.6656e+160\n",
      "  2.8160e+118]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+066 ... 4.6656e+160 9.3312e+170\n",
      "  6.1440e+128]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+066 ... 2.8160e+118 6.1440e+128\n",
      "  1.5360e+130]]\n"
     ]
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    {
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../pygraph/kernels/treePatternKernel.py:170: RuntimeWarning: overflow encountered in double_scalars\n",
      "  kh *= lmda * (G1.node[n1][node_label] == G2.node[n2][node_label])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.97559952735901 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1000000.0} is: \n",
      "[[6.4000e+079 1.6000e+019 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [1.6000e+019 6.4000e+103 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+079 ... 6.4000e+079 6.4000e+079\n",
      "  6.4000e+079]\n",
      " ...\n",
      " [0.0000e+000 0.0000e+000 6.4000e+079 ... 9.3312e+203 4.6656e+191\n",
      "  2.8160e+141]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+079 ... 4.6656e+191 9.3312e+203\n",
      "  6.1440e+153]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+079 ... 2.8160e+141 6.1440e+153\n",
      "  1.5360e+155]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 43.95881152153015 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 10000000.0} is: \n",
      "[[6.4000e+092 1.6000e+022 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [1.6000e+022 6.4000e+120 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+092 ... 6.4000e+092 6.4000e+092\n",
      "  6.4000e+092]\n",
      " ...\n",
      " [0.0000e+000 0.0000e+000 6.4000e+092 ... 9.3312e+236 4.6656e+222\n",
      "  2.8160e+164]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+092 ... 4.6656e+222 9.3312e+236\n",
      "  6.1440e+178]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+092 ... 2.8160e+164 6.1440e+178\n",
      "  1.5360e+180]]\n"
     ]
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    {
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../pygraph/kernels/treePatternKernel.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  kh *= lmda * (G1.node[n1][node_label] == G2.node[n2][node_label])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.18199348449707 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 100000000.0} is: \n",
      "[[6.4000e+105 1.6000e+025 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [1.6000e+025 6.4000e+137 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+105 ... 6.4000e+105 6.4000e+105\n",
      "  6.4000e+105]\n",
      " ...\n",
      " [0.0000e+000 0.0000e+000 6.4000e+105 ... 9.3312e+269 4.6656e+253\n",
      "  2.8160e+187]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+105 ... 4.6656e+253 9.3312e+269\n",
      "  6.1440e+203]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+105 ... 2.8160e+187 6.1440e+203\n",
      "  1.5360e+205]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.00055909156799 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 1000000000.0} is: \n",
      "[[6.400e+118 1.600e+028 0.000e+000 ... 0.000e+000 0.000e+000 0.000e+000]\n",
      " [1.600e+028 6.400e+154 0.000e+000 ... 0.000e+000 0.000e+000 0.000e+000]\n",
      " [0.000e+000 0.000e+000 6.400e+118 ... 6.400e+118 6.400e+118 6.400e+118]\n",
      " ...\n",
      " [0.000e+000 0.000e+000 6.400e+118 ...        inf        inf 2.816e+210]\n",
      " [0.000e+000 0.000e+000 6.400e+118 ...        inf        inf 6.144e+228]\n",
      " [0.000e+000 0.000e+000 6.400e+118 ... 2.816e+210 6.144e+228 1.536e+230]]\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 44.096922874450684 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 4.0, 'lmda': 10000000000.0} is: \n",
      "[[6.400e+131 1.600e+031 0.000e+000 ... 0.000e+000 0.000e+000 0.000e+000]\n",
      " [1.600e+031 6.400e+171 0.000e+000 ... 0.000e+000 0.000e+000 0.000e+000]\n",
      " [0.000e+000 0.000e+000 6.400e+131 ... 6.400e+131 6.400e+131 6.400e+131]\n",
      " ...\n",
      " [0.000e+000 0.000e+000 6.400e+131 ...        inf        inf 2.816e+233]\n",
      " [0.000e+000 0.000e+000 6.400e+131 ...        inf        inf 6.144e+253]\n",
      " [0.000e+000 0.000e+000 6.400e+131 ... 2.816e+233 6.144e+253 1.536e+255]]\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.5947208404541 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1e-10} is: \n",
      "[[  32.00000001   16.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  16.           32.00000001    0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           32.00000001 ...   32.00000001\n",
      "    32.00000001   32.00000001]\n",
      " ...\n",
      " [   0.            0.           32.00000001 ... 3232.00000222\n",
      "  3128.00000225 3200.00000239]\n",
      " [   0.            0.           32.00000001 ... 3128.00000225\n",
      "  3030.00000237 3092.00000262]\n",
      " [   0.            0.           32.00000001 ... 3200.00000239\n",
      "  3092.00000262 3184.00000434]]\n"
     ]
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    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.63289785385132 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1e-09} is: \n",
      "[[  32.0000001    16.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  16.           32.0000001     0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           32.0000001  ...   32.0000001\n",
      "    32.0000001    32.0000001 ]\n",
      " ...\n",
      " [   0.            0.           32.0000001  ... 3232.00002222\n",
      "  3128.00002248 3200.00002392]\n",
      " [   0.            0.           32.0000001  ... 3128.00002248\n",
      "  3030.00002369 3092.00002625]\n",
      " [   0.            0.           32.0000001  ... 3200.00002392\n",
      "  3092.00002625 3184.00004341]]\n"
     ]
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    {
     "data": {
      "image/png": "[encoded data, 66980 characters]\n",
      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.70455551147461 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1e-08} is: \n",
      "[[  32.00000096   16.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  16.           32.00000104    0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           32.00000096 ...   32.00000096\n",
      "    32.00000096   32.00000096]\n",
      " ...\n",
      " [   0.            0.           32.00000096 ... 3232.00022216\n",
      "  3128.00022484 3200.0002392 ]\n",
      " [   0.            0.           32.00000096 ... 3128.00022484\n",
      "  3030.00023686 3092.00026246]\n",
      " [   0.            0.           32.00000096 ... 3200.0002392\n",
      "  3092.00026246 3184.00043412]]\n"
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    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.85604548454285 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1e-07} is: \n",
      "[[  32.0000096   16.           0.        ...    0.           0.\n",
      "     0.       ]\n",
      " [  16.          32.0000104    0.        ...    0.           0.\n",
      "     0.       ]\n",
      " [   0.           0.          32.0000096 ...   32.0000096   32.0000096\n",
      "    32.0000096]\n",
      " ...\n",
      " [   0.           0.          32.0000096 ... 3232.0022216 3128.0022484\n",
      "  3200.002392 ]\n",
      " [   0.           0.          32.0000096 ... 3128.0022484 3030.0023686\n",
      "  3092.0026246]\n",
      " [   0.           0.          32.0000096 ... 3200.002392  3092.0026246\n",
      "  3184.0043412]]\n"
     ]
    },
    {
     "data": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.93108248710632 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1e-06} is: \n",
      "[[  32.000096     16.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  16.           32.000104      0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           32.000096   ...   32.000096\n",
      "    32.000096     32.000096  ]\n",
      " ...\n",
      " [   0.            0.           32.000096   ... 3232.02221617\n",
      "  3128.02248416 3200.02392006]\n",
      " [   0.            0.           32.000096   ... 3128.02248416\n",
      "  3030.02368618 3092.02624609]\n",
      " [   0.            0.           32.000096   ... 3200.02392006\n",
      "  3092.02624609 3184.04341226]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.449198961257935 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1e-05} is: \n",
      "[[  32.00096001   16.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  16.           32.00104002    0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           32.00096001 ...   32.00096001\n",
      "    32.00096001   32.00096001]\n",
      " ...\n",
      " [   0.            0.           32.00096001 ... 3232.22217717\n",
      "  3128.2248565  3200.23920574]\n",
      " [   0.            0.           32.00096001 ... 3128.2248565\n",
      "  3030.23687824 3092.26246855]\n",
      " [   0.            0.           32.00096001 ... 3200.23920574\n",
      "  3092.26246855 3184.43414602]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.37942147254944 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 0.0001} is: \n",
      "[[  32.00960128   16.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  16.           32.01040176    0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           32.00960128 ...   32.00960128\n",
      "    32.00960128   32.00960128]\n",
      " ...\n",
      " [   0.            0.           32.00960128 ... 3234.22331772\n",
      "  3130.25005023 3202.3925739 ]\n",
      " [   0.            0.           32.00960128 ... 3130.25005023\n",
      "  3032.37042487 3094.62545487]\n",
      " [   0.            0.           32.00960128 ... 3202.3925739\n",
      "  3094.62545487 3188.34380269]]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.683305740356445 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 0.001} is: \n",
      "[[  32.09612806   16.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  16.           32.10417616    0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           32.09612806 ...   32.09612806\n",
      "    32.09612806   32.09612806]\n",
      " ...\n",
      " [   0.            0.           32.09612806 ... 3254.38853506\n",
      "  3150.64970169 3223.97744677]\n",
      " [   0.            0.           32.09612806 ... 3150.64970169\n",
      "  3053.86931114 3118.33161895]\n",
      " [   0.            0.           32.09612806 ... 3223.97744677\n",
      "  3118.33161895 3227.67299453]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.749770164489746 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 0.01} is: \n",
      "[[  32.972864     16.            0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [  16.           33.05776064    0.         ...    0.\n",
      "     0.            0.        ]\n",
      " [   0.            0.           32.972864   ...   32.972864\n",
      "    32.972864     32.972864  ]\n",
      " ...\n",
      " [   0.            0.           32.972864   ... 3472.20027986\n",
      "  3370.10823335 3445.0014973 ]\n",
      " [   0.            0.           32.972864   ... 3370.10823335\n",
      "  3286.04208239 3363.15539345]\n",
      " [   0.            0.           32.972864   ... 3445.0014973\n",
      "  3363.15539345 3644.95802609]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 52.6299684047699 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 0.1} is: \n",
      "[[   42.944         16.             0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [   16.            44.3264         0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [    0.             0.            42.944      ...    42.944\n",
      "     42.944         42.944     ]\n",
      " ...\n",
      " [    0.             0.            42.944      ...  8346.13431859\n",
      "   8037.8729408   6232.308736  ]\n",
      " [    0.             0.            42.944      ...  8037.8729408\n",
      "   8507.94753363  6732.5824384 ]\n",
      " [    0.             0.            42.944      ...  6232.308736\n",
      "   6732.5824384  11086.190464  ]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 52.260963439941406 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1.0} is: \n",
      "[[3.2000000e+02 1.6000000e+01 0.0000000e+00 ... 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00]\n",
      " [1.6000000e+01 5.3600000e+02 0.0000000e+00 ... 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00]\n",
      " [0.0000000e+00 0.0000000e+00 3.2000000e+02 ... 3.2000000e+02\n",
      "  3.2000000e+02 3.2000000e+02]\n",
      " ...\n",
      " [0.0000000e+00 0.0000000e+00 3.2000000e+02 ... 3.9599184e+07\n",
      "  1.8790076e+07 1.9904800e+05]\n",
      " [0.0000000e+00 0.0000000e+00 3.2000000e+02 ... 1.8790076e+07\n",
      "  4.5132236e+07 5.0194600e+05]\n",
      " [0.0000000e+00 0.0000000e+00 3.2000000e+02 ... 1.9904800e+05\n",
      "  5.0194600e+05 4.7488000e+06]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.67429780960083 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 10.0} is: \n",
      "[[7.77920000e+04 1.60000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+01 8.18672000e+05 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 7.77920000e+04 ... 7.77920000e+04\n",
      "  7.77920000e+04 7.77920000e+04]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 7.77920000e+04 ... 1.39109795e+16\n",
      "  7.18668723e+14 4.54391680e+09]\n",
      " [0.00000000e+00 0.00000000e+00 7.77920000e+04 ... 7.18668723e+14\n",
      "  1.42558312e+16 9.54906688e+10]\n",
      " [0.00000000e+00 0.00000000e+00 7.77920000e+04 ... 4.54391680e+09\n",
      "  9.54906688e+10 2.40390375e+12]]\n"
     ]
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    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688e59b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.85410976409912 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 100.0} is: \n",
      "[[6.52896320e+07 1.60000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+01 6.56177043e+09 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.52896320e+07 ... 6.52896320e+07\n",
      "  6.52896320e+07 6.52896320e+07]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.52896320e+07 ... 9.71197172e+25\n",
      "  4.87507385e+23 2.94958331e+15]\n",
      " [0.00000000e+00 0.00000000e+00 6.52896320e+07 ... 4.87507385e+23\n",
      "  9.73625794e+25 6.40475930e+17]\n",
      " [0.00000000e+00 0.00000000e+00 6.52896320e+07 ... 2.94958331e+15\n",
      "  6.40475930e+17 1.60897659e+19]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb246377f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.49191212654114 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1000.0} is: \n",
      "[[6.41280960e+10 1.60000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+01 6.41601761e+13 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.41280960e+10 ... 6.41280960e+10\n",
      "  6.41280960e+10 6.41280960e+10]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.41280960e+10 ... 9.36859917e+35\n",
      "  4.68617006e+32 2.82902806e+21]\n",
      " [0.00000000e+00 0.00000000e+00 6.41280960e+10 ... 4.68617006e+32\n",
      "  9.37094141e+35 6.16941595e+24]\n",
      " [0.00000000e+00 0.00000000e+00 6.41280960e+10 ... 2.82902806e+21\n",
      "  6.16941595e+24 1.54317278e+26]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.46095323562622 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 10000.0} is: \n",
      "[[6.40128010e+13 1.60000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+01 6.40160018e+17 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.40128010e+13 ... 6.40128010e+13\n",
      "  6.40128010e+13 6.40128010e+13]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.40128010e+13 ... 9.33493322e+45\n",
      "  4.66765328e+41 2.81729956e+27]\n",
      " [0.00000000e+00 0.00000000e+00 6.40128010e+13 ... 4.66765328e+41\n",
      "  9.33516660e+45 6.14653512e+31]\n",
      " [0.00000000e+00 0.00000000e+00 6.40128010e+13 ... 2.81729956e+27\n",
      "  6.14653512e+31 1.53671604e+33]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688f81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.47657823562622 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 100000.0} is: \n",
      "[[6.40012800e+16 1.60000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+01 6.40016000e+21 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.40012800e+16 ... 6.40012800e+16\n",
      "  6.40012800e+16 6.40012800e+16]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.40012800e+16 ... 9.33157326e+55\n",
      "  4.66580529e+50 2.81612992e+33]\n",
      " [0.00000000e+00 0.00000000e+00 6.40012800e+16 ... 4.66580529e+50\n",
      "  9.33159658e+55 6.14425345e+38]\n",
      " [0.00000000e+00 0.00000000e+00 6.40012800e+16 ... 2.81612992e+33\n",
      "  6.14425345e+38 1.53607159e+40]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2468cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.4469039440155 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1000000.0} is: \n",
      "[[6.40001280e+19 1.60000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+01 6.40001600e+25 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.40001280e+19 ... 6.40001280e+19\n",
      "  6.40001280e+19 6.40001280e+19]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.40001280e+19 ... 9.33123732e+65\n",
      "  4.66562053e+59 2.81601299e+39]\n",
      " [0.00000000e+00 0.00000000e+00 6.40001280e+19 ... 4.66562053e+59\n",
      "  9.33123966e+65 6.14402534e+45]\n",
      " [0.00000000e+00 0.00000000e+00 6.40001280e+19 ... 2.81601299e+39\n",
      "  6.14402534e+45 1.53600716e+47]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2461a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.54963159561157 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 10000000.0} is: \n",
      "[[6.40000128e+22 1.60000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+01 6.40000160e+29 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.40000128e+22 ... 6.40000128e+22\n",
      "  6.40000128e+22 6.40000128e+22]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.40000128e+22 ... 9.33120373e+75\n",
      "  4.66560205e+68 2.81600130e+45]\n",
      " [0.00000000e+00 0.00000000e+00 6.40000128e+22 ... 4.66560205e+68\n",
      "  9.33120397e+75 6.14400253e+52]\n",
      " [0.00000000e+00 0.00000000e+00 6.40000128e+22 ... 2.81600130e+45\n",
      "  6.14400253e+52 1.53600072e+54]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2466cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 49.59786868095398 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 100000000.0} is: \n",
      "[[6.40000013e+25 1.60000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+01 6.40000016e+33 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.40000013e+25 ... 6.40000013e+25\n",
      "  6.40000013e+25 6.40000013e+25]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.40000013e+25 ... 9.33120037e+85\n",
      "  4.66560021e+77 2.81600013e+51]\n",
      " [0.00000000e+00 0.00000000e+00 6.40000013e+25 ... 4.66560021e+77\n",
      "  9.33120040e+85 6.14400025e+59]\n",
      " [0.00000000e+00 0.00000000e+00 6.40000013e+25 ... 2.81600013e+51\n",
      "  6.14400025e+59 1.53600007e+61]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb24fe26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 50.56888294219971 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 1000000000.0} is: \n",
      "[[6.40000001e+28 1.60000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.60000000e+01 6.40000002e+37 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.40000001e+28 ... 6.40000001e+28\n",
      "  6.40000001e+28 6.40000001e+28]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.40000001e+28 ... 9.33120004e+95\n",
      "  4.66560002e+86 2.81600001e+57]\n",
      " [0.00000000e+00 0.00000000e+00 6.40000001e+28 ... 4.66560002e+86\n",
      "  9.33120004e+95 6.14400003e+66]\n",
      " [0.00000000e+00 0.00000000e+00 6.40000001e+28 ... 2.81600001e+57\n",
      "  6.14400003e+66 1.53600001e+68]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 52.24024176597595 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 4.0, 'lmda': 10000000000.0} is: \n",
      "[[6.4000e+031 1.6000e+001 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [1.6000e+001 6.4000e+041 0.0000e+000 ... 0.0000e+000 0.0000e+000\n",
      "  0.0000e+000]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+031 ... 6.4000e+031 6.4000e+031\n",
      "  6.4000e+031]\n",
      " ...\n",
      " [0.0000e+000 0.0000e+000 6.4000e+031 ... 9.3312e+105 4.6656e+095\n",
      "  2.8160e+063]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+031 ... 4.6656e+095 9.3312e+105\n",
      "  6.1440e+073]\n",
      " [0.0000e+000 0.0000e+000 6.4000e+031 ... 2.8160e+063 6.1440e+073\n",
      "  1.5360e+075]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 82.40116333961487 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1e-10} is: \n",
      "[[1.45000000e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 1.24000000e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.45000000e+02 ... 1.61000000e+02\n",
      "  1.61000000e+02 1.61000000e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.61000000e+02 ... 1.67010000e+04\n",
      "  1.65970000e+04 1.62110001e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61000000e+02 ... 1.65970000e+04\n",
      "  1.65150000e+04 1.61070001e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61000000e+02 ... 1.62110001e+04\n",
      "  1.61070001e+04 1.58470001e+04]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 65600 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb24637da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 60.26471400260925 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1e-09} is: \n",
      "[[1.45000001e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 1.24000001e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.45000001e+02 ... 1.61000001e+02\n",
      "  1.61000001e+02 1.61000001e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.61000001e+02 ... 1.67010005e+04\n",
      "  1.65970005e+04 1.62110005e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61000001e+02 ... 1.65970005e+04\n",
      "  1.65150005e+04 1.61070006e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61000001e+02 ... 1.62110005e+04\n",
      "  1.61070006e+04 1.58470011e+04]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 65600 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2461aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 60.560508012771606 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1e-08} is: \n",
      "[[1.45000011e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 1.24000012e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.45000011e+02 ... 1.61000011e+02\n",
      "  1.61000011e+02 1.61000011e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.61000011e+02 ... 1.67010048e+04\n",
      "  1.65970047e+04 1.62110055e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61000011e+02 ... 1.65970047e+04\n",
      "  1.65150049e+04 1.61070058e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61000011e+02 ... 1.62110055e+04\n",
      "  1.61070058e+04 1.58470105e+04]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 65600 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 61.513394832611084 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1e-07} is: \n",
      "[[1.45000110e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 1.24000118e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.45000110e+02 ... 1.61000110e+02\n",
      "  1.61000110e+02 1.61000110e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.61000110e+02 ... 1.67010478e+04\n",
      "  1.65970472e+04 1.62110547e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61000110e+02 ... 1.65970472e+04\n",
      "  1.65150491e+04 1.61070579e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61000110e+02 ... 1.62110547e+04\n",
      "  1.61070579e+04 1.58471053e+04]]\n"
     ]
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    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2468cfd0>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 59.259434938430786 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1e-06} is: \n",
      "[[1.45001104e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 1.24001184e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.45001104e+02 ... 1.61001104e+02\n",
      "  1.61001104e+02 1.61001104e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.61001104e+02 ... 1.67014776e+04\n",
      "  1.65974719e+04 1.62115469e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61001104e+02 ... 1.65974719e+04\n",
      "  1.65154913e+04 1.61075786e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61001104e+02 ... 1.62115469e+04\n",
      "  1.61075786e+04 1.58480531e+04]]\n"
     ]
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 59.096699714660645 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1e-05} is: \n",
      "[[1.45011040e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 1.24011841e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.45011040e+02 ... 1.61011040e+02\n",
      "  1.61011040e+02 1.61011040e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.61011040e+02 ... 1.67057768e+04\n",
      "  1.66017196e+04 1.62164699e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61011040e+02 ... 1.66017196e+04\n",
      "  1.65199140e+04 1.61127872e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61011040e+02 ... 1.62164699e+04\n",
      "  1.61127872e+04 1.58575342e+04]]\n"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 58.009583950042725 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 0.0001} is: \n",
      "[[1.45110420e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 1.24118460e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.45110420e+02 ... 1.61110420e+02\n",
      "  1.61110420e+02 1.61110420e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.61110420e+02 ... 1.67488609e+04\n",
      "  1.66442755e+04 1.62657545e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61110420e+02 ... 1.66442755e+04\n",
      "  1.65642284e+04 1.61649561e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.61110420e+02 ... 1.62657545e+04\n",
      "  1.61649561e+04 1.59526266e+04]]\n"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 58.62494492530823 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 0.001} is: \n",
      "[[1.46106049e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 1.25190017e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.46106049e+02 ... 1.62106049e+02\n",
      "  1.62106049e+02 1.62106049e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.62106049e+02 ... 1.71890463e+04\n",
      "  1.70778052e+04 1.67641098e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.62106049e+02 ... 1.70778052e+04\n",
      "  1.70162400e+04 1.66950729e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.62106049e+02 ... 1.67641098e+04\n",
      "  1.66950729e+04 1.69322250e+04]]\n"
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 57.8918981552124 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 0.01} is: \n",
      "[[1.56246080e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 1.36457170e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.56246080e+02 ... 1.72246080e+02\n",
      "  1.72246080e+02 1.72246080e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.72246080e+02 ... 2.26768195e+04\n",
      "  2.23045489e+04 2.23285833e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.72246080e+02 ... 2.23045489e+04\n",
      "  2.25500437e+04 2.29275891e+04]\n",
      " [2.00000000e+01 2.00000000e+01 1.72246080e+02 ... 2.23285833e+04\n",
      "  2.29275891e+04 3.01003948e+04]]\n"
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 58.05191445350647 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 0.1} is: \n",
      "[[2.77160000e+02 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 3.22271949e+02 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 2.77160000e+02 ... 2.93160000e+02\n",
      "  2.93160000e+02 2.93160000e+02]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 2.93160000e+02 ... 6.95075231e+05\n",
      "  3.75446134e+05 1.77422769e+05]\n",
      " [2.00000000e+01 2.00000000e+01 2.93160000e+02 ... 3.75446134e+05\n",
      "  6.06927235e+05 3.37486233e+05]\n",
      " [2.00000000e+01 2.00000000e+01 2.93160000e+02 ... 1.77422769e+05\n",
      "  3.37486233e+05 1.90218793e+06]]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 58.13757634162903 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1.0} is: \n",
      "[[4.57700000e+03 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 8.91640000e+04 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.57700000e+03 ... 4.59300000e+03\n",
      "  4.59300000e+03 4.59300000e+03]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.59300000e+03 ... 2.01159621e+13\n",
      "  3.10057363e+11 2.42784376e+09]\n",
      " [2.00000000e+01 2.00000000e+01 4.59300000e+03 ... 3.10057363e+11\n",
      "  4.70005886e+13 3.01441455e+10]\n",
      " [2.00000000e+01 2.00000000e+01 4.59300000e+03 ... 2.42784376e+09\n",
      "  3.01441455e+10 4.04190794e+12]]\n"
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      "text/plain": [
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 59.23965072631836 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 10.0} is: \n",
      "[[1.49598500e+06 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 3.44783080e+10 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.49598500e+06 ... 1.49600100e+06\n",
      "  1.49600100e+06 1.49600100e+06]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.49600100e+06 ... 1.13768199e+29\n",
      "  3.31210879e+22 9.96117761e+18]\n",
      " [2.00000000e+01 2.00000000e+01 1.49600100e+06 ... 3.31210879e+22\n",
      "  4.45961208e+29 8.59201597e+20]\n",
      " [2.00000000e+01 2.00000000e+01 1.49600100e+06 ... 9.96117761e+18\n",
      "  8.59201597e+20 4.16855423e+24]]\n"
     ]
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    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2461a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 58.99919033050537 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 100.0} is: \n",
      "[[1.30059054e+09 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 2.16311217e+17 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.30059054e+09 ... 1.30059056e+09\n",
      "  1.30059056e+09 1.30059056e+09]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.30059056e+09 ... 4.75063841e+47\n",
      "  6.90580493e+35 4.43539095e+30]\n",
      " [2.00000000e+01 2.00000000e+01 1.30059056e+09 ... 6.90580493e+35\n",
      "  1.89563663e+48 4.03114835e+33]\n",
      " [2.00000000e+01 2.00000000e+01 1.30059056e+09 ... 4.43539095e+30\n",
      "  4.03114835e+33 1.44634079e+39]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb256e1c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 57.70133352279663 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1000.0} is: \n",
      "[[1.28204910e+12 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 2.05928848e+24 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.28204910e+12 ... 1.28204910e+12\n",
      "  1.28204910e+12 1.28204910e+12]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.28204910e+12 ... 4.34301799e+66\n",
      "  5.56520941e+49 4.06947164e+42]\n",
      " [2.00000000e+01 2.00000000e+01 1.28204910e+12 ... 5.56520941e+49\n",
      "  1.73677414e+67 3.72852519e+46]\n",
      " [2.00000000e+01 2.00000000e+01 1.28204910e+12 ... 4.06947164e+42\n",
      "  3.72852519e+46 1.29135037e+54]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 58.04664945602417 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 10000.0} is: \n",
      "[[1.28020481e+15 6.60000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [6.60000000e+01 2.04912664e+31 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 1.28020481e+15 ... 1.28020481e+15\n",
      "  1.28020481e+15 1.28020481e+15]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 1.28020481e+15 ... 4.30411880e+85\n",
      "  5.44017562e+63 4.03435025e+54]\n",
      " [2.00000000e+01 2.00000000e+01 1.28020481e+15 ... 5.44017562e+63\n",
      "  1.72160449e+86 3.69944820e+59]\n",
      " [2.00000000e+01 2.00000000e+01 1.28020481e+15 ... 4.03435025e+54\n",
      "  3.69944820e+59 1.27668843e+69]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2461a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 57.89458084106445 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 100000.0} is: \n",
      "[[1.28002048e+018 6.60000000e+001 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [6.60000000e+001 2.04811264e+038 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [4.00000000e+000 4.00000000e+000 1.28002048e+018 ... 1.28002048e+018\n",
      "  1.28002048e+018 1.28002048e+018]\n",
      " ...\n",
      " [2.00000000e+001 2.00000000e+001 1.28002048e+018 ... 4.30024696e+104\n",
      "  5.42775940e+077 4.03085248e+066]\n",
      " [2.00000000e+001 2.00000000e+001 1.28002048e+018 ... 5.42775940e+077\n",
      "  1.72009448e+105 3.69655206e+072]\n",
      " [2.00000000e+001 2.00000000e+001 1.28002048e+018 ... 4.03085248e+066\n",
      "  3.69655206e+072 1.27523034e+084]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2461a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 58.90051460266113 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1000000.0} is: \n",
      "[[1.28000205e+021 6.60000000e+001 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [6.60000000e+001 2.04801126e+045 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [4.00000000e+000 4.00000000e+000 1.28000205e+021 ... 1.28000205e+021\n",
      "  1.28000205e+021 1.28000205e+021]\n",
      " ...\n",
      " [2.00000000e+001 2.00000000e+001 1.28000205e+021 ... 4.29985996e+123\n",
      "  5.42651865e+091 4.03050285e+078]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000205e+021 ... 5.42651865e+091\n",
      "  1.71994355e+124 3.69626257e+085]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000205e+021 ... 4.03050285e+078\n",
      "  3.69626257e+085 1.27508461e+099]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2461aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../pygraph/kernels/treePatternKernel.py:150: RuntimeWarning: overflow encountered in double_scalars\n",
      "  kh_tmp *= lmda * all_kh[str(pair[0]) + '.' + str(pair[1])]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 57.89000582695007 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 10000000.0} is: \n",
      "[[1.28000020e+024 6.60000000e+001 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [6.60000000e+001 2.04800113e+052 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [4.00000000e+000 4.00000000e+000 1.28000020e+024 ... 1.28000020e+024\n",
      "  1.28000020e+024 1.28000020e+024]\n",
      " ...\n",
      " [2.00000000e+001 2.00000000e+001 1.28000020e+024 ... 4.29982126e+142\n",
      "  5.42639459e+105 4.03046788e+090]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000020e+024 ... 5.42639459e+105\n",
      "  1.71992846e+143 3.69623362e+098]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000020e+024 ... 4.03046788e+090\n",
      "  3.69623362e+098 1.27507004e+114]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAECCAYAAADpWvKaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACnBJREFUeJzt3c+P3HUdx/HnS8AaDQYatEEgSkwveHBDNuiBmBqi/LgUDxK82BCSeoA/AE949GJMTJSkJg31IFgPhB4asemFk0JNGixGYqMltJRWxajRWKW8Pey3YVJ33Tc7O/PdH89HspmZ735n9/3Zoc/Md/Y7S6oKSVrNB8YeQNLmYCwktRgLSS3GQlKLsZDUYiwktYweiyT3JXktyekkT4w9z6wkOZPk10lOJjkxbNuZ5FiS3w2XN4495zSSHExyMcmpiW3LrjFLvjc87q8kuXO8ydduhTV/K8m54bE+meSBic99c1jza0nuHWfqtRk1FkmuAb4P3A/cAXwtyR1jzjRjX6yqhapaHG4/ARyvqt3A8eH2ZvY0cN9V21Za4/3A7uFjP/DUnGZcb0/zv2sG+O7wWC9U1VGA4b/th4HPDPf5wfBvYFMY+5nFXcDpqvp9Vf0beBbYO/JM87QXODRcPwQ8OOIsU6uqF4G3r9q80hr3Aj+qJb8Abkhy83wmXT8rrHkle4Fnq+pSVf0BOM3Sv4FNYexY3AK8MXH77LBtKyrg50l+lWT/sG1XVZ0frr8F7BpntJlaaY1b/bF/fDi8OjhxeLmp1zx2LLaTu6vqTpaefj+W5AuTn6yl8+639Ln322GNg6eATwMLwHngO+OOsz7GjsU54LaJ27cO27acqjo3XF4EnmPp6eeFK0+9h8uL4004Myutccs+9lV1oaouV9W7wA9571BjU6957Fi8DOxOcnuSD7L04s+RkWdad0k+kuT6K9eBLwOnWFrrvmG3fcDz40w4Uyut8Qjw9eG3Ip8H/jpxuLKpXfXay1dYeqxhac0PJ9mR5HaWXtx9ad7zrdW1Y37zqnonyePAC8A1wMGqenXMmWZkF/BcElj6mf+4qn6W5GXgcJJHgdeBh0accWpJngH2ADclOQs8CXyb5dd4FHiApRf5/gk8MveB18EKa96TZIGlQ64zwDcAqurVJIeB3wDvAI9V1eUx5l6L+BZ1SR1jH4ZI2iSMhaQWYyGpxVhIajEWklpmFov3+27SiVOgtw3XvD1slTXPJBZrfDfplviBvk+ueXvYEmue1TOL7f5uUmnLmdUZnMu9u+5zK+38weyoD/FhPpqd2+oMMde8PWzkNf+Lf/DvupTOvqOd7j0cx+2HpR/m3e/9MSFJc/LLOt7ed1aHIau+u66qDlTVYlUtXseOGY0hab3MKhbb4t2k0nYyk8OQbfRuUmnbmNlrFsMfKT06q68vab48g1NSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0ktxkJSi7GQ1GIsJLUYC0kt105z5yRngL8Dl4F3qmoxyU7gJ8CngDPAQ1X1l+nGlDS29Xhm8cWqWqiqxeH2E8DxqtoNHB9uS9rkZnEYshc4NFw/BDw4g+8hac6mjUUBP0/yqyT7h227qur8cP0tYNeU30PSBjDVaxbA3VV1LsnHgWNJfjv5yaqqJLXcHYe47Af4EB+ecgxJszbVM4uqOjdcXgSeA+4CLiS5GWC4vLjCfQ9U1WJVLV7HjmnGkDQHa45Fko8kuf7KdeDLwCngCLBv2G0f8Py0Q0oa3zSHIbuA55Jc+To/rqqfJXkZOJzkUeB14KHpx5Q0tjXHoqp+D3x2me1/Bu6ZZihJG49ncEpqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIanFWEhqMRaSWoyFpBZjIall1VgkOZjkYpJTE9t2JjmW5HfD5Y3D9iT5XpLTSV5Jcucsh5c0P51nFk8D91217QngeFXtBo4PtwHuB3YPH/uBp9ZnTEljWzUWVfUi8PZVm/cCh4brh4AHJ7b/qJb8Arghyc3rNayk8az1NYtdVXV+uP4WsGu4fgvwxsR+Z4dtkja5qV/grKoC6v3eL8n+JCeSnPgPl6YdQ9KMrTUWF64cXgyXF4ft54DbJva7ddj2P6rqQFUtVtXidexY4xiS5mWtsTgC7Buu7wOen9j+9eG3Ip8H/jpxuCJpE7t2tR2SPAPsAW5KchZ4Evg2cDjJo8DrwEPD7keBB4DTwD+BR2Yws6QRrBqLqvraCp+6Z5l9C3hs2qEkbTyewSmpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWY6Et6YU3T/LCmyfHHmNLMRaSWlb9XwFIm9G9n1gYe4Qtx2cWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhbSMrfi3MKZdk7GQlrEV3+I+7ZqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpxVhIajEWklqMhaQWYyGpZdVYJDmY5GKSUxPbvpXkXJKTw8cDE5/7ZpLTSV5Lcu+sBpc0X51nFk8D9y2z/btVtTB8HAVIcgfwMPCZ4T4/SHLNeg0raTyrxqKqXgTebn69vcCzVXWpqv4AnAbummI+SRvENK9ZPJ7kleEw5cZh2y3AGxP7nB22Sdrk1hqLp4BPAwvAeeA77/cLJNmf5ESSE//h0hrHkDQva4pFVV2oqstV9S7wQ9471DgH3Dax663DtuW+xoGqWqyqxevYsZYxJM3RmmKR5OaJm18Brvym5AjwcJIdSW4HdgMvTTeipI3g2tV2SPIMsAe4KclZ4ElgT5IFoIAzwDcAqurVJIeB3wDvAI9V1eXZjC5pnlJVY8/AR7OzPpd7xh5D2nZ+Wcf5W72dzr6ewSmpxVhIajEWklqMhbQBHHv3pxx796djj/F/GQtJLav+6lTS7H3pA18de4RV+cxCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtpE5vnO1WNhbSJzfMNaMZCUouxkNRiLCS1GAtJLcZCUouxkNRiLCS1GAtJLcZCUouxkJbxwpsnxx5h3U17arixkJZx7ycWxh5h3U17arixkNRiLCS1GAtJLcZCUkuqauwZSPJH4B/An8aeZc5uwjVvBxt5zZ+sqo91dtwQsQBIcqKqFseeY55c8/awVdbsYYikFmMhqWUjxeLA2AOMwDVvD1tizRvmNQtJG9tGemYhaQMzFpJajIWkFmMhqcVYSGr5L9+hejP16+E2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb6c144390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 61.51721000671387 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 100000000.0} is: \n",
      "[[1.28000002e+027 6.60000000e+001 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [6.60000000e+001 2.04800011e+059 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [4.00000000e+000 4.00000000e+000 1.28000002e+027 ... 1.28000002e+027\n",
      "  1.28000002e+027 1.28000002e+027]\n",
      " ...\n",
      " [2.00000000e+001 2.00000000e+001 1.28000002e+027 ... 4.29981739e+161\n",
      "  5.42638218e+119 4.03046439e+102]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000002e+027 ... 5.42638218e+119\n",
      "  1.71992695e+162 3.69623072e+111]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000002e+027 ... 4.03046439e+102\n",
      "  3.69623072e+111 1.27506858e+129]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb65cd1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 60.46738958358765 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 1000000000.0} is: \n",
      "[[1.28000000e+030 6.60000000e+001 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [6.60000000e+001 2.04800001e+066 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [4.00000000e+000 4.00000000e+000 1.28000000e+030 ... 1.28000000e+030\n",
      "  1.28000000e+030 1.28000000e+030]\n",
      " ...\n",
      " [2.00000000e+001 2.00000000e+001 1.28000000e+030 ... 4.29981700e+180\n",
      "  5.42638094e+133 4.03046404e+114]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000000e+030 ... 5.42638094e+133\n",
      "  1.71992680e+181 3.69623043e+124]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000000e+030 ... 4.03046404e+114\n",
      "  3.69623043e+124 1.27506843e+144]]\n"
     ]
    },
    {
     "data": {
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 58.43429613113403 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'untiln', 'h': 5.0, 'lmda': 10000000000.0} is: \n",
      "[[1.28000000e+033 6.60000000e+001 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [6.60000000e+001 2.04800000e+073 4.00000000e+000 ... 2.00000000e+001\n",
      "  2.00000000e+001 2.00000000e+001]\n",
      " [4.00000000e+000 4.00000000e+000 1.28000000e+033 ... 1.28000000e+033\n",
      "  1.28000000e+033 1.28000000e+033]\n",
      " ...\n",
      " [2.00000000e+001 2.00000000e+001 1.28000000e+033 ... 4.29981696e+199\n",
      "  5.42638081e+147 4.03046400e+126]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000000e+033 ... 5.42638081e+147\n",
      "  1.71992679e+200 3.69623040e+137]\n",
      " [2.00000000e+001 2.00000000e+001 1.28000000e+033 ... 4.03046400e+126\n",
      "  3.69623040e+137 1.27506842e+159]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 53.51155614852905 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1e-10} is: \n",
      "[[8.0000e-39 2.4000e-39 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [2.4000e-39 5.2000e-39 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-39 ... 8.0000e-39 8.0000e-39 8.0000e-39]\n",
      " ...\n",
      " [0.0000e+00 0.0000e+00 8.0000e-39 ... 1.2052e-36 1.2052e-36 1.1662e-36]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-39 ... 1.2052e-36 1.2068e-36 1.1666e-36]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-39 ... 1.1662e-36 1.1666e-36 1.1376e-36]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 53.128520250320435 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1e-09} is: \n",
      "[[8.0000e-35 2.4000e-35 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [2.4000e-35 5.2000e-35 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-35 ... 8.0000e-35 8.0000e-35 8.0000e-35]\n",
      " ...\n",
      " [0.0000e+00 0.0000e+00 8.0000e-35 ... 1.2052e-32 1.2052e-32 1.1662e-32]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-35 ... 1.2052e-32 1.2068e-32 1.1666e-32]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-35 ... 1.1662e-32 1.1666e-32 1.1376e-32]]\n"
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     "name": "stdout",
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.308215856552124 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1e-08} is: \n",
      "[[8.0000e-31 2.4000e-31 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [2.4000e-31 5.2000e-31 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-31 ... 8.0000e-31 8.0000e-31 8.0000e-31]\n",
      " ...\n",
      " [0.0000e+00 0.0000e+00 8.0000e-31 ... 1.2052e-28 1.2052e-28 1.1662e-28]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-31 ... 1.2052e-28 1.2068e-28 1.1666e-28]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-31 ... 1.1662e-28 1.1666e-28 1.1376e-28]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.26672148704529 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1e-07} is: \n",
      "[[8.0000e-27 2.4000e-27 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [2.4000e-27 5.2000e-27 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-27 ... 8.0000e-27 8.0000e-27 8.0000e-27]\n",
      " ...\n",
      " [0.0000e+00 0.0000e+00 8.0000e-27 ... 1.2052e-24 1.2052e-24 1.1662e-24]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-27 ... 1.2052e-24 1.2068e-24 1.1666e-24]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-27 ... 1.1662e-24 1.1666e-24 1.1376e-24]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.09047985076904 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1e-06} is: \n",
      "[[8.0000e-23 2.4000e-23 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [2.4000e-23 5.2000e-23 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-23 ... 8.0000e-23 8.0000e-23 8.0000e-23]\n",
      " ...\n",
      " [0.0000e+00 0.0000e+00 8.0000e-23 ... 1.2052e-20 1.2052e-20 1.1662e-20]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-23 ... 1.2052e-20 1.2068e-20 1.1666e-20]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-23 ... 1.1662e-20 1.1666e-20 1.1376e-20]]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.712746143341064 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1e-05} is: \n",
      "[[8.0000e-19 2.4000e-19 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [2.4000e-19 5.2000e-19 0.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-19 ... 8.0000e-19 8.0000e-19 8.0000e-19]\n",
      " ...\n",
      " [0.0000e+00 0.0000e+00 8.0000e-19 ... 1.2052e-16 1.2052e-16 1.1662e-16]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-19 ... 1.2052e-16 1.2068e-16 1.1666e-16]\n",
      " [0.0000e+00 0.0000e+00 8.0000e-19 ... 1.1662e-16 1.1666e-16 1.1376e-16]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 53.57017707824707 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 0.0001} is: \n",
      "[[8.00000006e-15 2.40000000e-15 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [2.40000000e-15 5.20000004e-15 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 8.00000006e-15 ... 8.00000006e-15\n",
      "  8.00000006e-15 8.00000006e-15]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 8.00000006e-15 ... 1.20520001e-12\n",
      "  1.20520001e-12 1.16620001e-12]\n",
      " [0.00000000e+00 0.00000000e+00 8.00000006e-15 ... 1.20520001e-12\n",
      "  1.20680001e-12 1.16660001e-12]\n",
      " [0.00000000e+00 0.00000000e+00 8.00000006e-15 ... 1.16620001e-12\n",
      "  1.16660001e-12 1.13760001e-12]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 53.36650323867798 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 0.001} is: \n",
      "[[8.00000640e-11 2.40000000e-11 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [2.40000000e-11 5.20000400e-11 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 8.00000640e-11 ... 8.00000640e-11\n",
      "  8.00000640e-11 8.00000640e-11]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 8.00000640e-11 ... 1.20520111e-08\n",
      "  1.20520114e-08 1.16620086e-08]\n",
      " [0.00000000e+00 0.00000000e+00 8.00000640e-11 ... 1.20520114e-08\n",
      "  1.20680118e-08 1.16660090e-08]\n",
      " [0.00000000e+00 0.00000000e+00 8.00000640e-11 ... 1.16620086e-08\n",
      "  1.16660090e-08 1.13760101e-08]]\n"
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     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 53.98498606681824 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 0.01} is: \n",
      "[[8.00064002e-07 2.40000000e-07 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [2.40000000e-07 5.20040005e-07 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 8.00064002e-07 ... 8.00064002e-07\n",
      "  8.00064002e-07 8.00064002e-07]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 8.00064002e-07 ... 1.20531122e-04\n",
      "  1.20531398e-04 1.16628618e-04]\n",
      " [0.00000000e+00 0.00000000e+00 8.00064002e-07 ... 1.20531398e-04\n",
      "  1.20691794e-04 1.16669014e-04]\n",
      " [0.00000000e+00 0.00000000e+00 8.00064002e-07 ... 1.16628618e-04\n",
      "  1.16669014e-04 1.13770133e-04]]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 53.3620331287384 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 0.1} is: \n",
      "[[0.00806419 0.0024     0.         ... 0.         0.         0.        ]\n",
      " [0.0024     0.00524049 0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.00806419 ... 0.00806419 0.00806419 0.00806419]\n",
      " ...\n",
      " [0.         0.         0.00806419 ... 1.21651795 1.21679544 1.17498286]\n",
      " [0.         0.         0.00806419 ... 1.21679544 1.21880968 1.17581221]\n",
      " [0.         0.         0.00806419 ... 1.17498286 1.17581221 1.14809156]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 64656 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5d7e25c0>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 53.35458517074585 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1.0} is: \n",
      "[[1.44000000e+03 2.40000000e+01 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [2.40000000e+01 1.36040000e+04 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 1.44000000e+03 ... 1.44000000e+03\n",
      "  1.44000000e+03 1.44000000e+03]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 1.44000000e+03 ... 5.60478297e+12\n",
      "  3.30569724e+10 6.82240838e+08]\n",
      " [0.00000000e+00 0.00000000e+00 1.44000000e+03 ... 3.30569724e+10\n",
      "  2.54319069e+13 1.39916459e+10]\n",
      " [0.00000000e+00 0.00000000e+00 1.44000000e+03 ... 6.82240838e+08\n",
      "  1.39916459e+10 1.40365885e+12]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688f8320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 54.388861894607544 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 10.0} is: \n",
      "[[6.40130618e+21 2.40000000e+05 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [2.40000000e+05 5.27567127e+34 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 6.40130618e+21 ... 6.40130618e+21\n",
      "  6.40130618e+21 6.40130618e+21]\n",
      " ...\n",
      " [0.00000000e+00 0.00000000e+00 6.40130618e+21 ... 4.65877907e+75\n",
      "  1.09712635e+65 2.72264980e+54]\n",
      " [0.00000000e+00 0.00000000e+00 6.40130618e+21 ... 1.09712635e+65\n",
      "  1.87682260e+76 6.70356268e+57]\n",
      " [0.00000000e+00 0.00000000e+00 6.40130618e+21 ... 2.72264980e+54\n",
      "  6.70356268e+57 1.40492776e+65]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688f8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 52.921815633773804 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 100.0} is: \n",
      "[[6.40000013e+041 2.40000000e+009 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [2.40000000e+009 5.12153620e+066 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000013e+041 ... 6.40000013e+041\n",
      "  6.40000013e+041 6.40000013e+041]\n",
      " ...\n",
      " [0.00000000e+000 0.00000000e+000 6.40000013e+041 ... 4.30327450e+141\n",
      "  1.02470680e+123 2.54129819e+102]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000013e+041 ... 1.02470680e+123\n",
      "  1.72143257e+142 2.65960796e+109]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000013e+041 ... 2.54129819e+102\n",
      "  2.65960796e+109 1.27631386e+121]]\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.73200535774231 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1000.0} is: \n",
      "[[6.40000000e+061 2.40000000e+013 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [2.40000000e+013 5.12001536e+098 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+061 ... 6.40000000e+061\n",
      "  6.40000000e+061 6.40000000e+061]\n",
      " ...\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+061 ... 4.29985152e+207\n",
      "  1.02400707e+181 2.53953778e+150]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+061 ... 1.02400707e+181\n",
      "  1.71994184e+208 2.62182142e+161]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+061 ... 2.53953778e+150\n",
      "  2.62182142e+161 1.27508087e+177]]\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../pygraph/kernels/treePatternKernel.py:201: RuntimeWarning: overflow encountered in double_scalars\n",
      "  kernel = sum(all_kh.values())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.616614818573 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 10000.0} is: \n",
      "[[6.40000000e+081 2.40000000e+017 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [2.40000000e+017 5.12000015e+130 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+081 ... 6.40000000e+081\n",
      "  6.40000000e+081 6.40000000e+081]\n",
      " ...\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+081 ... 4.29981731e+273\n",
      "  1.02400007e+239 2.53952018e+198]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+081 ... 1.02400007e+239\n",
      "  1.71992693e+274 2.62144381e+213]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+081 ... 2.53952018e+198\n",
      "  2.62144381e+213 1.27506854e+233]]\n"
     ]
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    {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../pygraph/kernels/treePatternKernel.py:168: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  kh_tmp *= lmda * all_kh[str(pair[0]) + '.' + str(pair[1])]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 53.70710635185242 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 100000.0} is: \n",
      "[[6.40000000e+101 2.40000000e+021 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [2.40000000e+021 5.12000000e+162 0.00000000e+000 ... 0.00000000e+000\n",
      "  0.00000000e+000 0.00000000e+000]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+101 ... 6.40000000e+101\n",
      "  6.40000000e+101 6.40000000e+101]\n",
      " ...\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+101 ...             inf\n",
      "              inf 2.53952000e+246]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+101 ...             inf\n",
      "              inf 2.62144004e+265]\n",
      " [0.00000000e+000 0.00000000e+000 6.40000000e+101 ... 2.53952000e+246\n",
      "  2.62144004e+265             inf]]\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 51.67221283912659 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1000000.0} is: \n",
      "[[6.40e+121 2.40e+025 0.00e+000 ... 0.00e+000 0.00e+000 0.00e+000]\n",
      " [2.40e+025 5.12e+194 0.00e+000 ... 0.00e+000 0.00e+000 0.00e+000]\n",
      " [0.00e+000 0.00e+000 6.40e+121 ... 6.40e+121 6.40e+121 6.40e+121]\n",
      " ...\n",
      " [0.00e+000 0.00e+000 6.40e+121 ...       inf       inf       inf]\n",
      " [0.00e+000 0.00e+000 6.40e+121 ...       inf       inf       inf]\n",
      " [0.00e+000 0.00e+000 6.40e+121 ...       inf       inf       inf]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 53.50304460525513 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 10000000.0} is: \n",
      "[[6.40e+141 2.40e+029 0.00e+000 ... 0.00e+000 0.00e+000 0.00e+000]\n",
      " [2.40e+029 5.12e+226 0.00e+000 ... 0.00e+000 0.00e+000 0.00e+000]\n",
      " [0.00e+000 0.00e+000 6.40e+141 ... 6.40e+141 6.40e+141 6.40e+141]\n",
      " ...\n",
      " [0.00e+000 0.00e+000 6.40e+141 ...       nan       nan       nan]\n",
      " [0.00e+000 0.00e+000 6.40e+141 ...       nan       nan       inf]\n",
      " [0.00e+000 0.00e+000 6.40e+141 ...       nan       inf       inf]]\n"
     ]
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    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 54.456220388412476 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 100000000.0} is: \n",
      "[[6.40e+161 2.40e+033 0.00e+000 ... 0.00e+000 0.00e+000 0.00e+000]\n",
      " [2.40e+033 5.12e+258 0.00e+000 ... 0.00e+000 0.00e+000 0.00e+000]\n",
      " [0.00e+000 0.00e+000 6.40e+161 ... 6.40e+161 6.40e+161 6.40e+161]\n",
      " ...\n",
      " [0.00e+000 0.00e+000 6.40e+161 ...       nan       nan       nan]\n",
      " [0.00e+000 0.00e+000 6.40e+161 ...       nan       nan       nan]\n",
      " [0.00e+000 0.00e+000 6.40e+161 ...       nan       nan       inf]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 54.954628467559814 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 1000000000.0} is: \n",
      "[[6.4e+181 2.4e+037 0.0e+000 ... 0.0e+000 0.0e+000 0.0e+000]\n",
      " [2.4e+037      inf 0.0e+000 ... 0.0e+000 0.0e+000 0.0e+000]\n",
      " [0.0e+000 0.0e+000 6.4e+181 ... 6.4e+181 6.4e+181 6.4e+181]\n",
      " ...\n",
      " [0.0e+000 0.0e+000 6.4e+181 ...      nan      nan      nan]\n",
      " [0.0e+000 0.0e+000 6.4e+181 ...      nan      nan      nan]\n",
      " [0.0e+000 0.0e+000 6.4e+181 ...      nan      nan      inf]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 55.056718587875366 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'size', 'h': 5.0, 'lmda': 10000000000.0} is: \n",
      "[[6.4e+201 2.4e+041 0.0e+000 ... 0.0e+000 0.0e+000 0.0e+000]\n",
      " [2.4e+041      inf 0.0e+000 ... 0.0e+000 0.0e+000 0.0e+000]\n",
      " [0.0e+000 0.0e+000 6.4e+201 ... 6.4e+201 6.4e+201 6.4e+201]\n",
      " ...\n",
      " [0.0e+000 0.0e+000 6.4e+201 ...      nan      nan      nan]\n",
      " [0.0e+000 0.0e+000 6.4e+201 ...      nan      nan      nan]\n",
      " [0.0e+000 0.0e+000 6.4e+201 ...      nan      nan      inf]]\n"
     ]
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    {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 59.41082525253296 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 5.0, 'lmda': 1e-10} is: \n",
      "[[   80.00000004    24.             0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [   24.            52.00000003     0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [    0.             0.            80.00000004 ...    80.00000004\n",
      "     80.00000004    80.00000004]\n",
      " ...\n",
      " [    0.             0.            80.00000004 ... 12052.00002528\n",
      "  12052.00002538 11662.00002905]\n",
      " [    0.             0.            80.00000004 ... 12052.00002538\n",
      "  12068.00002698 11666.00003099]\n",
      " [    0.             0.            80.00000004 ... 11662.00002905\n",
      "  11666.00003099 11376.00005673]]\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 58.783164262771606 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 5.0, 'lmda': 1e-09} is: \n",
      "[[   80.0000004     24.             0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [   24.            52.0000003      0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [    0.             0.            80.0000004  ...    80.0000004\n",
      "     80.0000004     80.0000004 ]\n",
      " ...\n",
      " [    0.             0.            80.0000004  ... 12052.00025277\n",
      "  12052.00025382 11662.00029054]\n",
      " [    0.             0.            80.0000004  ... 12052.00025382\n",
      "  12068.00026978 11666.00030994]\n",
      " [    0.             0.            80.0000004  ... 11662.00029054\n",
      "  11666.00030994 11376.00056734]]\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 60.10993981361389 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 5.0, 'lmda': 1e-08} is: \n",
      "[[   80.000004      24.             0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [   24.            52.00000304     0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [    0.             0.            80.000004   ...    80.000004\n",
      "     80.000004      80.000004  ]\n",
      " ...\n",
      " [    0.             0.            80.000004   ... 12052.00252772\n",
      "  12052.00253822 11662.00290536]\n",
      " [    0.             0.            80.000004   ... 12052.00253822\n",
      "  12068.00269784 11666.00309938]\n",
      " [    0.             0.            80.000004   ... 11662.00290536\n",
      "  11666.00309938 11376.00567344]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 64656 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 183 built in 60.08261752128601 seconds ---\n",
      "\n",
      "gram matrix with parameters {'kernel_type': 'branching', 'h': 5.0, 'lmda': 1e-07} is: \n",
      "[[   80.00004       24.             0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [   24.            52.0000304      0.         ...     0.\n",
      "      0.             0.        ]\n",
      " [    0.             0.            80.00004    ...    80.00004\n",
      "     80.00004       80.00004   ]\n",
      " ...\n",
      " [    0.             0.            80.00004    ... 12052.02527724\n",
      "  12052.02538223 11662.02905363]\n",
      " [    0.             0.            80.00004    ... 12052.02538223\n",
      "  12068.02697844 11666.03099384]\n",
      " [    0.             0.            80.00004    ... 11662.02905363\n",
      "  11666.03099384 11376.05673453]]\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 64656 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb688ddf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
    "from pygraph.kernels.treePatternKernel import treepatternkernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "estimator = treepatternkernel\n",
    "param_grid_precomputed = {'h': np.linspace(1, 10, 10), \n",
    "                          'lmda': np.logspace(-10, 10, num = 21, base = 10),\n",
    "                          'kernel_type': ['untiln', 'size', 'branching']}\n",
    "param_grid = {'alpha': np.logspace(-10, 10, num = 41, base = 10)}\n",
    "\n",
    "model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, \n",
    "                                       'regression', NUM_TRIALS=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1e-10 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.36956548690796 seconds ---\n",
      "[[ 13.          14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.         ...  29.          29.\n",
      "   29.        ]\n",
      " ...\n",
      " [ 20.          20.          29.         ... 365.00000001 365.00000001\n",
      "  365.00000001]\n",
      " [ 20.          20.          29.         ... 365.00000001 365.00000001\n",
      "  365.00000001]\n",
      " [ 20.          20.          29.         ... 365.00000001 365.00000001\n",
      "  365.00000002]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  91%|█████████▏| 914/1000 [00:01<00:00, 751.28it/s]\n",
      " Mean performance on train set: 5.993535\n",
      "With standard deviation: 0.356922\n",
      "\n",
      " Mean performance on test set: 7.464904\n",
      "With standard deviation: 1.718585\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 795.88it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1e-09 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.47467517852783 seconds ---\n",
      "[[ 13.          14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.00000001   4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.         ...  29.          29.\n",
      "   29.        ]\n",
      " ...\n",
      " [ 20.          20.          29.         ... 365.00000015 365.00000015\n",
      "  365.00000015]\n",
      " [ 20.          20.          29.         ... 365.00000015 365.00000015\n",
      "  365.00000015]\n",
      " [ 20.          20.          29.         ... 365.00000015 365.00000015\n",
      "  365.0000002 ]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 975/1000 [00:01<00:00, 654.33it/s]\n",
      " Mean performance on train set: 5.963041\n",
      "With standard deviation: 0.374107\n",
      "\n",
      " Mean performance on test set: 7.375105\n",
      "With standard deviation: 1.769252\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 711.24it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1e-08 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.32968211174011 seconds ---\n",
      "[[ 13.00000004  14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.00000008   4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.00000004 ...  29.00000004  29.00000004\n",
      "   29.00000004]\n",
      " ...\n",
      " [ 20.          20.          29.00000004 ... 365.00000148 365.00000148\n",
      "  365.00000148]\n",
      " [ 20.          20.          29.00000004 ... 365.00000148 365.00000148\n",
      "  365.00000148]\n",
      " [ 20.          20.          29.00000004 ... 365.00000148 365.00000148\n",
      "  365.00000202]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  97%|█████████▋| 967/1000 [00:01<00:00, 809.48it/s]\n",
      " Mean performance on train set: 5.965110\n",
      "With standard deviation: 0.378249\n",
      "\n",
      " Mean performance on test set: 7.350689\n",
      "With standard deviation: 1.780556\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 786.78it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1e-07 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.74151062965393 seconds ---\n",
      "[[ 13.0000004  14.          4.        ...  20.         20.\n",
      "   20.       ]\n",
      " [ 14.         20.0000008   4.        ...  20.         20.\n",
      "   20.       ]\n",
      " [  4.          4.         13.0000004 ...  29.0000004  29.0000004\n",
      "   29.0000004]\n",
      " ...\n",
      " [ 20.         20.         29.0000004 ... 365.0000148 365.0000148\n",
      "  365.0000148]\n",
      " [ 20.         20.         29.0000004 ... 365.0000148 365.0000148\n",
      "  365.0000148]\n",
      " [ 20.         20.         29.0000004 ... 365.0000148 365.0000148\n",
      "  365.0000202]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  95%|█████████▌| 954/1000 [00:01<00:00, 735.76it/s]\n",
      " Mean performance on train set: 5.966982\n",
      "With standard deviation: 0.382093\n",
      "\n",
      " Mean performance on test set: 7.350999\n",
      "With standard deviation: 1.781470\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 804.24it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1e-06 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.52131748199463 seconds ---\n",
      "[[ 13.000004  14.         4.       ...  20.        20.        20.      ]\n",
      " [ 14.        20.000008   4.       ...  20.        20.        20.      ]\n",
      " [  4.         4.        13.000004 ...  29.000004  29.000004  29.000004]\n",
      " ...\n",
      " [ 20.        20.        29.000004 ... 365.000148 365.000148 365.000148]\n",
      " [ 20.        20.        29.000004 ... 365.000148 365.000148 365.000148]\n",
      " [ 20.        20.        29.000004 ... 365.000148 365.000148 365.000202]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  97%|█████████▋| 970/1000 [00:01<00:00, 759.32it/s]\n",
      " Mean performance on train set: 5.969758\n",
      "With standard deviation: 0.386318\n",
      "\n",
      " Mean performance on test set: 7.351225\n",
      "With standard deviation: 1.780522\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 783.42it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1e-05 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.67099857330322 seconds ---\n",
      "[[ 13.00004     14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.00008      4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.00004    ...  29.00004     29.00004\n",
      "   29.00004   ]\n",
      " ...\n",
      " [ 20.          20.          29.00004    ... 365.00148001 365.00148001\n",
      "  365.00148   ]\n",
      " [ 20.          20.          29.00004    ... 365.00148001 365.00148001\n",
      "  365.00148   ]\n",
      " [ 20.          20.          29.00004    ... 365.00148    365.00148\n",
      "  365.00202   ]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 801.70it/s]\n",
      " Mean performance on train set: 5.970557\n",
      "With standard deviation: 0.390719\n",
      "\n",
      " Mean performance on test set: 7.348129\n",
      "With standard deviation: 1.780293\n",
      "\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 0.0001 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 36.80127692222595 seconds ---\n",
      "[[ 13.0004      14.           4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [ 14.          20.0008       4.         ...  20.          20.\n",
      "   20.        ]\n",
      " [  4.           4.          13.0004     ...  29.0004      29.0004\n",
      "   29.0004    ]\n",
      " ...\n",
      " [ 20.          20.          29.0004     ... 365.01480072 365.01480072\n",
      "  365.0148    ]\n",
      " [ 20.          20.          29.0004     ... 365.01480072 365.01480072\n",
      "  365.0148    ]\n",
      " [ 20.          20.          29.0004     ... 365.0148     365.0148\n",
      "  365.0202    ]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 980/1000 [00:01<00:00, 889.41it/s]\n",
      " Mean performance on train set: 5.942495\n",
      "With standard deviation: 0.331983\n",
      "\n",
      " Mean performance on test set: 7.349836\n",
      "With standard deviation: 1.781100\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 883.76it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 0.001 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 35.8681423664093 seconds ---\n",
      "[[ 13.004     14.         4.       ...  20.        20.        20.      ]\n",
      " [ 14.        20.008      4.       ...  20.        20.        20.      ]\n",
      " [  4.         4.        13.004    ...  29.004     29.004     29.004   ]\n",
      " ...\n",
      " [ 20.        20.        29.004    ... 365.148072 365.148072 365.148   ]\n",
      " [ 20.        20.        29.004    ... 365.148072 365.148072 365.148   ]\n",
      " [ 20.        20.        29.004    ... 365.148    365.148    365.202   ]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  99%|█████████▉| 988/1000 [00:01<00:00, 886.54it/s]\n",
      " Mean performance on train set: 5.933395\n",
      "With standard deviation: 0.324965\n",
      "\n",
      " Mean performance on test set: 7.357745\n",
      "With standard deviation: 1.780977\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 888.00it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 0.01 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 36.001843214035034 seconds ---\n",
      "[[ 13.04    14.       4.     ...  20.      20.      20.    ]\n",
      " [ 14.      20.08     4.     ...  20.      20.      20.    ]\n",
      " [  4.       4.      13.04   ...  29.04    29.04    29.04  ]\n",
      " ...\n",
      " [ 20.      20.      29.04   ... 366.4872 366.4872 366.48  ]\n",
      " [ 20.      20.      29.04   ... 366.4872 366.4872 366.48  ]\n",
      " [ 20.      20.      29.04   ... 366.48   366.48   367.02  ]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 978/1000 [00:01<00:00, 863.94it/s]\n",
      " Mean performance on train set: 5.940695\n",
      "With standard deviation: 0.347431\n",
      "\n",
      " Mean performance on test set: 7.374269\n",
      "With standard deviation: 1.791145\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 878.96it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 0.1 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 36.37146854400635 seconds ---\n",
      "[[ 13.4   14.     4.   ...  20.    20.    20.  ]\n",
      " [ 14.    20.8    4.   ...  20.    20.    20.  ]\n",
      " [  4.     4.    13.4  ...  29.4   29.4   29.4 ]\n",
      " ...\n",
      " [ 20.    20.    29.4  ... 380.52 380.52 379.8 ]\n",
      " [ 20.    20.    29.4  ... 380.52 380.52 379.8 ]\n",
      " [ 20.    20.    29.4  ... 379.8  379.8  385.2 ]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  99%|█████████▉| 993/1000 [00:01<00:00, 860.40it/s]\n",
      " Mean performance on train set: 6.427114\n",
      "With standard deviation: 1.293674\n",
      "\n",
      " Mean performance on test set: 7.329299\n",
      "With standard deviation: 1.913634\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 883.01it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.83972358703613 seconds ---\n",
      "[[ 17.  14.   4. ...  20.  20.  20.]\n",
      " [ 14.  28.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  17. ...  33.  33.  33.]\n",
      " ...\n",
      " [ 20.  20.  33. ... 585. 585. 513.]\n",
      " [ 20.  20.  33. ... 585. 585. 513.]\n",
      " [ 20.  20.  33. ... 513. 513. 567.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 979/1000 [00:01<00:00, 616.77it/s]\n",
      " Mean performance on train set: 6.624254\n",
      "With standard deviation: 1.224196\n",
      "\n",
      " Mean performance on test set: 7.271336\n",
      "With standard deviation: 2.207735\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 630.82it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 10.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.50818395614624 seconds ---\n",
      "[[5.300e+01 1.400e+01 4.000e+00 ... 2.000e+01 2.000e+01 2.000e+01]\n",
      " [1.400e+01 1.000e+02 4.000e+00 ... 2.000e+01 2.000e+01 2.000e+01]\n",
      " [4.000e+00 4.000e+00 5.300e+01 ... 6.900e+01 6.900e+01 6.900e+01]\n",
      " ...\n",
      " [2.000e+01 2.000e+01 6.900e+01 ... 9.045e+03 9.045e+03 1.845e+03]\n",
      " [2.000e+01 2.000e+01 6.900e+01 ... 9.045e+03 9.045e+03 1.845e+03]\n",
      " [2.000e+01 2.000e+01 6.900e+01 ... 1.845e+03 1.845e+03 2.385e+03]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:   8%|▊         | 77/1000 [00:00<00:01, 764.71it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.069543502626658e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.1303298666315776e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.70249458866672e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.63992169055093e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.438093960487116e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0002169262936346e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.5920339281975188e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.5874866272574162e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.0599424240471626e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.468773818521402e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.958334441043603e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  27%|██▋       | 267/1000 [00:00<00:01, 654.82it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.055618175730539e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.7159074038024934e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.895455126720251e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.400306511546424e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.206478316049589e-19 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.8083631222444177e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.49051280863482e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.339852738992424e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.277544863160196e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.458523723353626e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  48%|████▊     | 477/1000 [00:00<00:00, 685.70it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6380760737666547e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.3843421259537676e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.890544546973404e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.054758730954765e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.9172765626494813e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.4455093698440067e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.2914256710839066e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.65667341282596e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.852926745577629e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.285092924342139e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  69%|██████▉   | 694/1000 [00:01<00:00, 712.57it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.700250453064005e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6205193931367065e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.4925504318417794e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.0111387119813346e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.343123723749221e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0143662852277667e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.206690575125046e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.049999246995425e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.4232350203422674e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.350008400303505e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.251763015291957e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  90%|█████████ | 902/1000 [00:01<00:00, 666.85it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.924869742342744e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.4010401637647583e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.5117924740400373e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.626753798403599e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.911227588173856e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.0660043401009468e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.3099139652029694e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.2680602391853274e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.396574210735164e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  97%|█████████▋| 969/1000 [00:01<00:00, 600.11it/s]\n",
      " Mean performance on train set: 6.816974\n",
      "With standard deviation: 1.501822\n",
      "\n",
      " Mean performance on test set: 7.497870\n",
      "With standard deviation: 2.368148\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 648.87it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 100.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.09455919265747 seconds ---\n",
      "[[4.13000e+02 1.40000e+01 4.00000e+00 ... 2.00000e+01 2.00000e+01\n",
      "  2.00000e+01]\n",
      " [1.40000e+01 8.20000e+02 4.00000e+00 ... 2.00000e+01 2.00000e+01\n",
      "  2.00000e+01]\n",
      " [4.00000e+00 4.00000e+00 4.13000e+02 ... 4.29000e+02 4.29000e+02\n",
      "  4.29000e+02]\n",
      " ...\n",
      " [2.00000e+01 2.00000e+01 4.29000e+02 ... 7.35165e+05 7.35165e+05\n",
      "  1.51650e+04]\n",
      " [2.00000e+01 2.00000e+01 4.29000e+02 ... 7.35165e+05 7.35165e+05\n",
      "  1.51650e+04]\n",
      " [2.00000e+01 2.00000e+01 4.29000e+02 ... 1.51650e+04 1.51650e+04\n",
      "  2.05650e+04]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  10%|▉         | 97/1000 [00:00<00:02, 436.93it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.249229588791739e-20 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6092761314568358e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6033357497241564e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.0333531111165975e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.073851980749357e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.497880470461594e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0349745182117167e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  17%|█▋        | 169/1000 [00:00<00:02, 372.64it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.227908145504113e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.1894008132724887e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.9131474526752795e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.0421770253846576e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.864916618602575e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.663676730244888e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  31%|███▏      | 314/1000 [00:00<00:01, 429.29it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.6989200751598342e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.9988176582222278e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.173259131422707e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.069621878854856e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.087601566853754e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.1827598831940232e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.428244298929586e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.1120965359644164e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.8329496119281176e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.5128147762765525e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.173155329882729e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  50%|█████     | 501/1000 [00:01<00:00, 516.57it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.874621241781873e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.9047959204426696e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.03000007539236e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.8198639503150797e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.679200342495213e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6211471280327221e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.74280825574767e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.685372827008377e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.9723233156997277e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.142362330339379e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.0042133764798303e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.987833375253946e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  71%|███████   | 706/1000 [00:01<00:00, 471.97it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.63949546549065e-19 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.76170805410039e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.3721058293845662e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.178277242767302e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.4883373934010664e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.526360275338589e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.827383891217367e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  81%|████████  | 807/1000 [00:01<00:00, 457.30it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.052622499085628e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.70793549450487e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.8190124240850417e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.349104192126423e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.132340452050677e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  91%|█████████ | 909/1000 [00:01<00:00, 451.90it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.78695142234395e-20 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.8765535280551442e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.9917255115528226e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.781650263544808e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.581768670551366e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.0493867289518776e-20 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.9787087068181396e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.756012232435961e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.800283208793992e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.468606690086715e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.119459703249427e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  96%|█████████▌| 956/1000 [00:02<00:00, 445.96it/s]\n",
      " Mean performance on train set: 6.687664\n",
      "With standard deviation: 1.348089\n",
      "\n",
      " Mean performance on test set: 7.428867\n",
      "With standard deviation: 2.647892\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:02<00:00, 467.65it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1000.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.517051219940186 seconds ---\n",
      "[[4.0130000e+03 1.4000000e+01 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [1.4000000e+01 8.0200000e+03 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [4.0000000e+00 4.0000000e+00 4.0130000e+03 ... 4.0290000e+03\n",
      "  4.0290000e+03 4.0290000e+03]\n",
      " ...\n",
      " [2.0000000e+01 2.0000000e+01 4.0290000e+03 ... 7.2148365e+07\n",
      "  7.2148365e+07 1.4836500e+05]\n",
      " [2.0000000e+01 2.0000000e+01 4.0290000e+03 ... 7.2148365e+07\n",
      "  7.2148365e+07 1.4836500e+05]\n",
      " [2.0000000e+01 2.0000000e+01 4.0290000e+03 ... 1.4836500e+05\n",
      "  1.4836500e+05 2.0236500e+05]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  10%|█         | 102/1000 [00:00<00:02, 330.46it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.374017095746491e-19 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.687507275679712e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.7645414168071277e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.8090152927008474e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.474194561968185e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.130856876335615e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  19%|█▉        | 188/1000 [00:00<00:02, 348.40it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.7794644716713837e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.3130943734340723e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.7505566440337117e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.3279670378456666e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.318466984022222e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.418445519765442e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  28%|██▊       | 285/1000 [00:00<00:01, 380.47it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.1356199672921913e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6464620684950592e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.179430869121561e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.473887201835687e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.570987725305032e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.854640029504099e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  38%|███▊      | 385/1000 [00:00<00:01, 389.24it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.3781828327775562e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.955507306233033e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.109947315270106e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.9378280303294975e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.866942525478256e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  49%|████▉     | 494/1000 [00:01<00:01, 398.83it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.636014964778956e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.6552163232757833e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.312233993243073e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.2794244316598437e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.406028628818668e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  60%|██████    | 604/1000 [00:01<00:00, 407.30it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.184686103929999e-20 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.1281587780183657e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.367013528660628e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.155116554595105e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.443029464120917e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.4923079446940085e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.750703061909557e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  71%|███████   | 707/1000 [00:01<00:00, 402.63it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.4970615125032324e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.39494402062226e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.818999641865095e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.051275910233908e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.688197813410084e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  81%|████████  | 806/1000 [00:01<00:00, 404.11it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.43659662072146e-20 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.4284104102664825e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.900304923444742e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.1112688931900636e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.941184656304436e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.746476431972804e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  91%|█████████ | 906/1000 [00:02<00:00, 407.57it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.327409345420052e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.0589341144557062e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.29154188313992e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.225020130252359e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.327720201864263e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  95%|█████████▌| 951/1000 [00:02<00:00, 373.87it/s]\n",
      " Mean performance on train set: 6.819058\n",
      "With standard deviation: 1.410085\n",
      "\n",
      " Mean performance on test set: 7.249143\n",
      "With standard deviation: 2.655536\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:02<00:00, 414.03it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.193651783291256e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.19984276961351e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.0689487149937185e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.8956869823870564e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.790887064559792e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 10000.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.01269268989563 seconds ---\n",
      "[[4.00130000e+04 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00200000e+04 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00130000e+04 ... 4.00290000e+04\n",
      "  4.00290000e+04 4.00290000e+04]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00290000e+04 ... 7.20148036e+09\n",
      "  7.20148036e+09 1.48036500e+06]\n",
      " [2.00000000e+01 2.00000000e+01 4.00290000e+04 ... 7.20148036e+09\n",
      "  7.20148036e+09 1.48036500e+06]\n",
      " [2.00000000e+01 2.00000000e+01 4.00290000e+04 ... 1.48036500e+06\n",
      "  1.48036500e+06 2.02036500e+06]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  11%|█▏        | 114/1000 [00:00<00:02, 303.84it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6978549111114387e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.1828172674052679e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.547017114313022e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.990786470945978e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.8300974250606965e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.727582936838558e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  21%|██        | 208/1000 [00:00<00:02, 342.60it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.569354559683504e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.885865920757663e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.006883677471783e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.2491482969368813e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.596699463334369e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.74024827114128e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0002891123411383e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  31%|███       | 311/1000 [00:00<00:01, 371.07it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.478112292477647e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.3626069490970097e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.751107853461428e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.007160521891646e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.592912372477283e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  41%|████▏     | 414/1000 [00:01<00:01, 394.40it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.286966904954778e-19 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.330065717080935e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.0639972759819077e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.3250852692883386e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.32507703774876e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.514317701906229e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  51%|█████▏    | 514/1000 [00:01<00:01, 411.17it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.5186106207732215e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.2309907116861648e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.582391203608073e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.638503426307468e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0580872068659216e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  62%|██████▏   | 615/1000 [00:01<00:00, 424.96it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.633316922861427e-19 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.253166879599146e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.4688155478710103e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.3252803222906435e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.713726552669558e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.934042177466841e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.468592067289146e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  72%|███████▏  | 716/1000 [00:01<00:00, 433.84it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.66227030386163e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.5342528466878185e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.620745355332433e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.5797394734563764e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.109079490079661e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.548426603146641e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0484899604694826e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  82%|████████▏ | 816/1000 [00:01<00:00, 439.46it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.080827240476694e-20 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.8279322282295696e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.0410721959495632e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.343375151645726e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.351177509861134e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.370583478449445e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  92%|█████████▏| 916/1000 [00:02<00:00, 437.07it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.3677978521118296e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0227997187914302e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.557088603475233e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.657645350184021e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.990628121216557e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  96%|█████████▌| 962/1000 [00:02<00:00, 393.51it/s]\n",
      " Mean performance on train set: 6.934306\n",
      "With standard deviation: 1.384412\n",
      "\n",
      " Mean performance on test set: 7.081832\n",
      "With standard deviation: 2.624800\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:02<00:00, 420.16it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.2964047050969517e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.089708488833387e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.3234351109582e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.293818265362604e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.44555950404844e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 100000.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 35.835275411605835 seconds ---\n",
      "[[4.0001300e+05 1.4000000e+01 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [1.4000000e+01 8.0002000e+05 4.0000000e+00 ... 2.0000000e+01\n",
      "  2.0000000e+01 2.0000000e+01]\n",
      " [4.0000000e+00 4.0000000e+00 4.0001300e+05 ... 4.0002900e+05\n",
      "  4.0002900e+05 4.0002900e+05]\n",
      " ...\n",
      " [2.0000000e+01 2.0000000e+01 4.0002900e+05 ... 7.2001480e+11\n",
      "  7.2001480e+11 1.4800365e+07]\n",
      " [2.0000000e+01 2.0000000e+01 4.0002900e+05 ... 7.2001480e+11\n",
      "  7.2001480e+11 1.4800365e+07]\n",
      " [2.0000000e+01 2.0000000e+01 4.0002900e+05 ... 1.4800365e+07\n",
      "  1.4800365e+07 2.0200365e+07]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  13%|█▎        | 126/1000 [00:00<00:03, 261.95it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.0191112815027622e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.232307074954237e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.097885039345644e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.727869526025791e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  21%|██        | 208/1000 [00:00<00:03, 262.44it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.2411064955279154e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.149899322677468e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.642478117522784e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  30%|███       | 305/1000 [00:01<00:02, 265.15it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.785067039039337e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.225934530879337e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.562790184640486e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  41%|████▏     | 414/1000 [00:01<00:01, 297.05it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.135758789917749e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.564182826681079e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.542436590136228e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  50%|█████     | 500/1000 [00:01<00:01, 319.27it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.195315905739342e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.379873880613873e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.630975349505465e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.518071272961898e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  61%|██████▏   | 613/1000 [00:02<00:01, 303.76it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.9460047081015216e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.839254463570563e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.318583704180543e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  73%|███████▎  | 728/1000 [00:02<00:00, 300.34it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.3984244112232524e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.3291156014339405e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.707621088224988e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0690013288979288e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  81%|████████▏ | 813/1000 [00:02<00:00, 321.97it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.315773170165585e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.138544398203078e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.386508127676171e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  89%|████████▉ | 891/1000 [00:02<00:00, 317.18it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.6842499515474312e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.528434456947986e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.856433851414765e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  96%|█████████▌| 957/1000 [00:03<00:00, 259.36it/s]\n",
      " Mean performance on train set: 9.394995\n",
      "With standard deviation: 1.047066\n",
      "\n",
      " Mean performance on test set: 8.237631\n",
      "With standard deviation: 3.665300\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:03<00:00, 299.25it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1000000.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.056791553686018e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.644703618966645e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.15242050721053e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.77732253074646 seconds ---\n",
      "[[4.00001300e+06 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00002000e+06 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00001300e+06 ... 4.00002900e+06\n",
      "  4.00002900e+06 4.00002900e+06]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00002900e+06 ... 7.20001480e+13\n",
      "  7.20001480e+13 1.48000365e+08]\n",
      " [2.00000000e+01 2.00000000e+01 4.00002900e+06 ... 7.20001480e+13\n",
      "  7.20001480e+13 1.48000365e+08]\n",
      " [2.00000000e+01 2.00000000e+01 4.00002900e+06 ... 1.48000365e+08\n",
      "  1.48000365e+08 2.02000365e+08]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  14%|█▎        | 135/1000 [00:00<00:03, 253.06it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.741195162637844e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.5965964498458038e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.983361443347492e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.7922291165206923e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.685526054240851e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  23%|██▎       | 230/1000 [00:00<00:02, 265.75it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6848871984797616e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.8811603375005575e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.7514073450053307e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.5976314128410034e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.790988115471154e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.477178586927344e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.035215871851048e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  33%|███▎      | 332/1000 [00:01<00:02, 277.35it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.287317576627726e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.886325173924881e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.8372812242318245e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.523017625167697e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.309735186090854e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  43%|████▎     | 431/1000 [00:01<00:02, 258.66it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.2092887442597021e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.444285941342485e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6650957319224102e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.778343353100153e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.067496740668901e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.832366528737191e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  53%|█████▎    | 534/1000 [00:01<00:01, 252.20it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.2368084459111367e-19 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.625050966790768e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.786992563738048e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.5350081600164477e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.2823286559994256e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.880362578796432e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.1100444346816681e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  64%|██████▎   | 637/1000 [00:02<00:01, 260.77it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.1335408080135885e-20 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.4688002952482946e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.6678722631357644e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.288251804550535e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.895017980474164e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  74%|███████▍  | 741/1000 [00:02<00:00, 276.66it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.209157188853246e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.4519382674684447e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.5748326244710203e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.852436562697074e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.536506069365062e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.969036612017947e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  81%|████████▏ | 813/1000 [00:02<00:00, 297.62it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.309383987394578e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.565067260805818e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.516194197490843e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.041064934861363e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.499601220689098e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.051151158798192e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  92%|█████████▏| 915/1000 [00:03<00:00, 298.37it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.7472428919435347e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.703770718809819e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.2818242346374262e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.102554831016506e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.599188283622467e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0647003359802031e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  98%|█████████▊| 977/1000 [00:03<00:00, 278.57it/s]\n",
      " Mean performance on train set: 9.635245\n",
      "With standard deviation: 0.687560\n",
      "\n",
      " Mean performance on test set: 8.529828\n",
      "With standard deviation: 3.580591\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:03<00:00, 284.18it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 10000000.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.502409510736216e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0439081147173944e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.4602149061556115e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.9479003259500843e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.34621741763787e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.0245542507930726e-16 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.46095633506775 seconds ---\n",
      "[[4.00000130e+07 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00000200e+07 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00000130e+07 ... 4.00000290e+07\n",
      "  4.00000290e+07 4.00000290e+07]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00000290e+07 ... 7.20000148e+15\n",
      "  7.20000148e+15 1.48000036e+09]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000290e+07 ... 7.20000148e+15\n",
      "  7.20000148e+15 1.48000036e+09]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000290e+07 ... 1.48000036e+09\n",
      "  1.48000036e+09 2.02000036e+09]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  12%|█▏        | 122/1000 [00:00<00:03, 231.16it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.200122265640733e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.627297027618617e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.893611596005168e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.1052758523976415e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.158312302718829e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.675650470006637e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  23%|██▎       | 227/1000 [00:00<00:03, 253.57it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.2230286797750079e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.0790289882373515e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.6170735381557016e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.90989130271085e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.4735492461286675e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.544548737558878e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  34%|███▍      | 344/1000 [00:01<00:02, 274.36it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.0569387542185164e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.8929851773550792e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.762490810329375e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.031994076021703e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.380795983197089e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  44%|████▎     | 437/1000 [00:01<00:02, 281.37it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.159845331824398e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.592173545119868e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.0031182975801337e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.315102488294594e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.427690586128571e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.36815306216013e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  52%|█████▎    | 525/1000 [00:01<00:01, 277.96it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.096509347533013e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.2037051514926243e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.641239932076709e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.911654384898199e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.195245830759744e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  61%|██████    | 606/1000 [00:02<00:01, 236.45it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.179739003035368e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.121167134816686e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 2.201750997217992e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.6143396276266097e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.876412427833992e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 8.158416120381036e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  73%|███████▎  | 727/1000 [00:02<00:01, 211.65it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.355737066205781e-20 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 6.778261189640049e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.992328403436096e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.166526583123927e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.566565138343654e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.272056103177315e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  82%|████████▏ | 822/1000 [00:03<00:00, 223.94it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.2435735437353417e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.471379508084743e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.686971341479104e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.827108937015577e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  93%|█████████▎| 931/1000 [00:03<00:00, 255.06it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.2357280081107672e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.559072238694825e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.45488311322705e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.6616151717441874e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.807701664283496e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  98%|█████████▊| 983/1000 [00:04<00:00, 220.81it/s]\n",
      " Mean performance on train set: 11.059074\n",
      "With standard deviation: 1.323635\n",
      "\n",
      " Mean performance on test set: 10.964175\n",
      "With standard deviation: 3.358726\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:04<00:00, 242.35it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 100000000.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 9.749823872976888e-19 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 5.347208969568296e-18 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 1.9981807042259307e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 3.298232968104139e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 4.391381083354749e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n",
      "/home/ljia/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number/precision: 7.328796748008544e-17 / 1.1102230246251565e-16\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.91001057624817 seconds ---\n",
      "[[4.00000013e+08 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00000020e+08 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00000013e+08 ... 4.00000029e+08\n",
      "  4.00000029e+08 4.00000029e+08]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00000029e+08 ... 7.20000015e+17\n",
      "  7.20000015e+17 1.48000004e+10]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000029e+08 ... 7.20000015e+17\n",
      "  7.20000015e+17 1.48000004e+10]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000029e+08 ... 1.48000004e+10\n",
      "  1.48000004e+10 2.02000004e+10]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  99%|█████████▉| 992/1000 [00:03<00:00, 267.91it/s]\n",
      " Mean performance on train set: 66.147687\n",
      "With standard deviation: 11.979989\n",
      "\n",
      " Mean performance on test set: 63.221208\n",
      "With standard deviation: 13.381090\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:03<00:00, 252.05it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 1000000000.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 38.43676400184631 seconds ---\n",
      "[[4.00000001e+09 1.40000000e+01 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [1.40000000e+01 8.00000002e+09 4.00000000e+00 ... 2.00000000e+01\n",
      "  2.00000000e+01 2.00000000e+01]\n",
      " [4.00000000e+00 4.00000000e+00 4.00000001e+09 ... 4.00000003e+09\n",
      "  4.00000003e+09 4.00000003e+09]\n",
      " ...\n",
      " [2.00000000e+01 2.00000000e+01 4.00000003e+09 ... 7.20000001e+19\n",
      "  7.20000001e+19 1.48000000e+11]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000003e+09 ... 7.20000001e+19\n",
      "  7.20000001e+19 1.48000000e+11]\n",
      " [2.00000000e+01 2.00000000e+01 4.00000003e+09 ... 1.48000000e+11\n",
      "  1.48000000e+11 2.02000000e+11]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 976/1000 [00:04<00:00, 268.70it/s]\n",
      " Mean performance on train set: 96.664827\n",
      "With standard deviation: 1.871320\n",
      "\n",
      " Mean performance on test set: 100.134704\n",
      "With standard deviation: 13.845906\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:04<00:00, 236.90it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when lmda = 10000000000.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 185 built in 37.32151246070862 seconds ---\n",
      "[[4.00e+10 1.40e+01 4.00e+00 ... 2.00e+01 2.00e+01 2.00e+01]\n",
      " [1.40e+01 8.00e+10 4.00e+00 ... 2.00e+01 2.00e+01 2.00e+01]\n",
      " [4.00e+00 4.00e+00 4.00e+10 ... 4.00e+10 4.00e+10 4.00e+10]\n",
      " ...\n",
      " [2.00e+01 2.00e+01 4.00e+10 ... 7.20e+21 7.20e+21 1.48e+12]\n",
      " [2.00e+01 2.00e+01 4.00e+10 ... 7.20e+21 7.20e+21 1.48e+12]\n",
      " [2.00e+01 2.00e+01 4.00e+10 ... 1.48e+12 1.48e+12 2.02e+12]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance: 100%|█████████▉| 997/1000 [00:03<00:00, 268.42it/s]\n",
      " Mean performance on train set: 98.175092\n",
      "With standard deviation: 4.720613\n",
      "\n",
      " Mean performance on test set: 100.144883\n",
      "With standard deviation: 13.958659\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:03<00:00, 260.49it/s]\n",
      "\n",
      "\n",
      "       lmda    rmse_test    std_test    rmse_train    std_train    k_time\n",
      "-----------  -----------  ----------  ------------  -----------  --------\n",
      "     1e-10       7.4649      1.71858       5.99354     0.356922   37.3696\n",
      "     1e-09       7.37511     1.76925       5.96304     0.374107   37.4747\n",
      "     1e-08       7.35069     1.78056       5.96511     0.378249   37.3297\n",
      "     1e-07       7.351       1.78147       5.96698     0.382093   37.7415\n",
      "     1e-06       7.35123     1.78052       5.96976     0.386318   37.5213\n",
      "     1e-05       7.34813     1.78029       5.97056     0.390719   37.671\n",
      "     0.0001      7.34984     1.7811        5.9425      0.331983   36.8013\n",
      "     0.001       7.35775     1.78098       5.9334      0.324965   35.8681\n",
      "     0.01        7.37427     1.79115       5.94069     0.347431   36.0018\n",
      "     0.1         7.3293      1.91363       6.42711     1.29367    36.3715\n",
      "     1           7.27134     2.20774       6.62425     1.2242     37.8397\n",
      "    10           7.49787     2.36815       6.81697     1.50182    37.5082\n",
      "   100           7.42887     2.64789       6.68766     1.34809    37.0946\n",
      "  1000           7.24914     2.65554       6.81906     1.41008    37.5171\n",
      " 10000           7.08183     2.6248        6.93431     1.38441    37.0127\n",
      "100000           8.23763     3.6653        9.395       1.04707    35.8353\n",
      "     1e+06       8.52983     3.58059       9.63525     0.68756    37.7773\n",
      "     1e+07      10.9642      3.35873      11.0591      1.32363    37.461\n",
      "     1e+08      63.2212     13.3811       66.1477     11.98       37.91\n",
      "     1e+09     100.135      13.8459       96.6648      1.87132    38.4368\n",
      "     1e+10     100.145      13.9587       98.1751      4.72061    37.3215\n"
     ]
    }
   ],
   "source": [
    "# tree pattern kernel, dataset acyclic.\n",
    "%load_ext line_profiler\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.treePatternKernel import treepatternkernel\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
    "\n",
    "\n",
    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', labeled = True, \\\n",
    "    kernel_type = 'untiln', h = 2)\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, treepatternkernel, kernel_para, \\\n",
    "    hyper_name = 'lmda', hyper_range = np.logspace(-10, 10, num = 21, base = 10), \\\n",
    "    normalize = False, model_type = 'regression')\n",
    "\n",
    "# kernel_para['depth'] = 10\n",
    "# %lprun -f untildpathkernel \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results\n",
    "\n",
    "# untiln kernel when h = 2\n",
    "       lmda    rmse_test    std_test    rmse_train    std_train    k_time\n",
    "-----------  -----------  ----------  ------------  -----------  --------\n",
    "     1e-10       7.46524     1.71862       5.99486     0.356634   38.1447\n",
    "     1e-09       7.37326     1.77195       5.96155     0.374395   37.4921\n",
    "     1e-08       7.35105     1.78349       5.96481     0.378047   37.9971\n",
    "     1e-07       7.35213     1.77903       5.96728     0.382251   38.3182\n",
    "     1e-06       7.3524      1.77992       5.9696      0.3863     39.6428\n",
    "     1e-05       7.34958     1.78141       5.97114     0.39017    37.3711\n",
    "     0.0001      7.3513      1.78136       5.94251     0.331843   37.3967\n",
    "     0.001       7.35822     1.78119       5.9326      0.32534    36.7357\n",
    "     0.01        7.37552     1.79037       5.94089     0.34763    36.8864\n",
    "     0.1         7.32951     1.91346       6.42634     1.29405    36.8382\n",
    "     1           7.27134     2.20774       6.62425     1.2242     37.2425\n",
    "    10           7.49787     2.36815       6.81697     1.50182    37.8286\n",
    "   100           7.42887     2.64789       6.68766     1.34809    36.3701\n",
    "  1000           7.24914     2.65554       6.81906     1.41008    36.1695\n",
    " 10000           7.08183     2.6248        6.93431     1.38441    37.5723\n",
    "100000           8.021       3.43694       8.69813     0.909839   37.8158\n",
    "     1e+06       8.49625     3.6332        9.59333     0.96626    38.4688\n",
    "     1e+07      10.9067      3.17593      11.5642      2.07792    36.9926\n",
    "     1e+08      61.1524     10.4355       65.3527     13.9538     37.1321\n",
    "     1e+09      99.943      13.6994       98.8848      5.27014    36.7443\n",
    "     1e+10     100.083      13.8503       97.9168      3.22768    37.096\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 14408 characters]\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6be3d4a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, {'atom': 'C', 'label': 'C'}), (1, {'atom': 'C', 'label': 'C'}), (2, {'atom': 'C', 'label': 'C'}), (3, {'atom': 'C', 'label': 'C'}), (4, {'atom': 'S', 'label': 'S'})]\n",
      "\n",
      " --- kernel matrix of tree pattern kernel of size 39 built in 3.5270774364471436 seconds ---\n",
      "(array([[1.99007809e+036, 4.00000000e+000, 4.00000000e+000, ...,\n",
      "        1.00000000e+001, 1.00000000e+001, 1.00000000e+001],\n",
      "       [4.00000000e+000, 6.37886713e+019, 4.34000000e+002, ...,\n",
      "        6.37886713e+019, 6.37886713e+019, 6.37886713e+019],\n",
      "       [4.00000000e+000, 4.34000000e+002, 1.99007809e+036, ...,\n",
      "        4.40000000e+002, 4.40000000e+002, 4.40000000e+002],\n",
      "       ...,\n",
      "       [1.00000000e+001, 6.37886713e+019, 4.40000000e+002, ...,\n",
      "        2.94561201e+119, 1.16903692e+080, 4.42354433e+082],\n",
      "       [1.00000000e+001, 6.37886713e+019, 4.40000000e+002, ...,\n",
      "        1.16903692e+080, 4.21212139e+264, 1.66634383e+080],\n",
      "       [1.00000000e+001, 6.37886713e+019, 4.40000000e+002, ...,\n",
      "        4.42354433e+082, 1.66634383e+080, 5.17763068e+117]]), 3.5270774364471436)\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.graphfiles import loadDataset\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "\n",
    "from pygraph.kernels.treePatternKernel import treepatternkernel, _treepatternkernel_do\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "\n",
    "dataset, y = loadDataset(datafile)\n",
    "G1 = dataset[100]\n",
    "G2 = dataset[20]\n",
    "data = [G1, G2]\n",
    "# nx.draw_networkx(G1)\n",
    "# plt.show()\n",
    "# print(G1.nodes(data=True)20\n",
    "nx.draw_networkx(G2)\n",
    "plt.show()\n",
    "print(G2.nodes(data=True))\n",
    "\n",
    "\n",
    "%lprun -f _treepatternkernel_do \\\n",
    "    kernel = treepatternkernel(dataset[1:40], node_label = 'atom', edge_label = 'bond_type', labeled = True, \\\n",
    "    kernel_type = 'untiln', lmda = 1, h = 10)\n",
    "\n",
    "print(kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- This is a classification problem ---\n",
      "\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "retrieve patterns: 100%|██████████| 185/185 [00:00<00:00, 2064.69it/s]\n",
      "calculate kernels: 100%|██████████| 185/185 [00:00<00:00, 11170.00it/s]\n",
      "\n",
      " --- kernel matrix of cyclic pattern kernel of size 185 built in 0.10836505889892578 seconds ---\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:24<00:00, 36.41it/s]\n",
      " Mean performance on train set: 0.018072\n",
      "With standard deviation: 0.000000\n",
      "\n",
      " Mean performance on test set: 0.000000\n",
      "With standard deviation: 0.000000\n",
      "\n",
      "\n",
      "  accur_test    std_test    accur_train    std_train    k_time\n",
      "------------  ----------  -------------  -----------  --------\n",
      "           0           0      0.0180723            0  0.108365\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.cyclicPatternKernel import cyclicpatternkernel\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
    "\n",
    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', labeled = True, cycle_bound = 200)\n",
    "\n",
    "# kernel_train_test(datafile, kernel_file_path, treeletkernel, kernel_para, normalize = False)\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, cyclicpatternkernel, kernel_para, \\\n",
    "    normalize = False , model_type = 'classification')\n",
    "\n",
    "# kernel_para['k_func'] = 'minmax'\n",
    "# kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "#     hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = True)\n",
    "# kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "#     hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = False)\n",
    "# # kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)\n",
    "\n",
    "# kernel_para['depth'] = 10\n",
    "# %lprun -f untildpathkernel \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)"
   ]
  }
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