diff --git a/abl/reasoning/reasoner.py b/abl/reasoning/reasoner.py
index 4df047e..8c3faa2 100644
--- a/abl/reasoning/reasoner.py
+++ b/abl/reasoning/reasoner.py
@@ -2,7 +2,7 @@ import inspect
 from typing import Callable, Any, List, Optional, Union
 
 import numpy as np
-from zoopt import Dimension, Objective, Opt, Parameter
+from zoopt import Dimension, Objective, Opt, Parameter, Solution
 
 from ..reasoning import KBBase
 from ..structures import ListData
@@ -175,9 +175,9 @@ class Reasoner:
         symbol_num: int,
         data_example: ListData,
         max_revision_num: int,
-    ) -> List[bool]:
+    ) -> Solution:
         """
-        Get the optimal solution using ZOOpt library. The solution is a list of
+        Get the optimal solution using ZOOpt library. From the solution, we can get a list of
         boolean values, where '1' (True) indicates the indices chosen to be revised.
 
         Parameters
@@ -191,7 +191,7 @@ class Reasoner:
 
         Returns
         -------
-        List[bool]
+        Solution
             The solution for ZOOpt library.
         """
         dimension = Dimension(size=symbol_num, regs=[[0, 1]] * symbol_num, tys=[False] * symbol_num)
@@ -201,14 +201,14 @@ class Reasoner:
             constraint=lambda sol: self._constrain_revision_num(sol, max_revision_num),
         )
         parameter = Parameter(budget=100, intermediate_result=False, autoset=True)
-        solution = Opt.min(objective, parameter).get_x()
+        solution = Opt.min(objective, parameter)
         return solution
 
     def zoopt_revision_score(
         self,
         symbol_num: int,
         data_example: ListData,
-        sol: List[bool],
+        sol: Solution,
     ) -> int:
         """
         Get the revision score for a solution. A lower score suggests that ZOOpt library
@@ -220,7 +220,7 @@ class Reasoner:
             Number of total symbols.
         data_example : ListData
             Data example.
-        sol: List[bool]
+        sol: Solution
             The solution for ZOOpt library.
 
         Returns
@@ -237,7 +237,7 @@ class Reasoner:
         else:
             return symbol_num
 
-    def _constrain_revision_num(self, solution: List[bool], max_revision_num: int) -> int:
+    def _constrain_revision_num(self, solution: Solution, max_revision_num: int) -> int:
         """
         Constrain that the total number of revisions chosen by the solution does not exceed
         maximum number of revisions allowed.
@@ -287,7 +287,7 @@ class Reasoner:
 
         if self.use_zoopt:
             solution = self._zoopt_get_solution(symbol_num, data_example, max_revision_num)
-            revision_idx = np.where(solution != 0)[0]
+            revision_idx = np.where(solution.get_x() != 0)[0]
             candidates, reasoning_results = self.kb.revise_at_idx(
                 pseudo_label=data_example.pred_pseudo_label, 
                 y=data_example.Y, 
diff --git a/examples/hed/README.md b/examples/hed/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/examples/hed/hed_bridge.py b/examples/hed/bridge.py
similarity index 99%
rename from examples/hed/hed_bridge.py
rename to examples/hed/bridge.py
index aafc5a1..3d6d9c9 100644
--- a/examples/hed/hed_bridge.py
+++ b/examples/hed/bridge.py
@@ -10,7 +10,7 @@ from abl.learning import ABLModel, BasicNN
 from abl.reasoning import Reasoner
 from abl.structures import ListData
 from abl.utils import print_log
-from examples.hed.datasets.get_hed import get_pretrain_data
+from examples.hed.datasets.get_dataset import get_pretrain_data
 from examples.hed.utils import InfiniteSampler, gen_mappings
 from examples.models.nn import SymbolNetAutoencoder
 
diff --git a/examples/hed/datasets/__init__.py b/examples/hed/datasets/__init__.py
new file mode 100644
index 0000000..3423195
--- /dev/null
+++ b/examples/hed/datasets/__init__.py
@@ -0,0 +1,4 @@
+from .get_dataset import get_dataset, split_equation
+
+
+__all__ = ["get_dataset", "split_equation"]
\ No newline at end of file
diff --git a/examples/hed/datasets/equation_generator.py b/examples/hed/datasets/equation_generator.py
deleted file mode 100644
index 0fb2a2f..0000000
--- a/examples/hed/datasets/equation_generator.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import os
-import itertools
-import random
-import numpy as np
-from PIL import Image
-import pickle
-
-def get_sign_path_list(data_dir, sign_names):
-    sign_num = len(sign_names)
-    index_dict = dict(zip(sign_names, list(range(sign_num))))
-    ret = [[] for _ in range(sign_num)]
-    for path in os.listdir(data_dir):
-        if (path in sign_names):
-            index = index_dict[path]
-            sign_path = os.path.join(data_dir, path)
-            for p in os.listdir(sign_path):
-                ret[index].append(os.path.join(sign_path, p))
-    return ret
-
-def split_pool_by_rate(pools, rate, seed = None):
-    if seed is not None:
-        random.seed(seed)
-    ret1 = []
-    ret2 = []
-    for pool in pools:
-        random.shuffle(pool)
-        num = int(len(pool) * rate)
-        ret1.append(pool[:num])
-        ret2.append(pool[num:])
-    return ret1, ret2
-
-def int_to_system_form(num, system_num):
-    if num == 0:
-        return "0"
-    ret = ""
-    while (num > 0):
-        ret += str(num % system_num)
-        num //= system_num
-    return ret[::-1]
-
-def generator_equations(left_opt_len, right_opt_len, res_opt_len, system_num, label, generate_type):
-    expr_len = left_opt_len + right_opt_len
-    num_list = "".join([str(i) for i in range(system_num)])
-    ret = []
-    if generate_type == "all":
-        candidates = itertools.product(num_list, repeat = expr_len)
-    else:
-        candidates = [''.join(random.sample(['0', '1'] * expr_len, expr_len))]
-        random.shuffle(candidates)
-    for nums in candidates:
-        left_num = "".join(nums[:left_opt_len])
-        right_num = "".join(nums[left_opt_len:])
-        left_value = int(left_num, system_num)
-        right_value = int(right_num, system_num)
-        result_value = left_value + right_value
-        if (label == 'negative'):
-            result_value += random.randint(-result_value, result_value)
-            if (left_value + right_value == result_value):
-                continue
-        result_num = int_to_system_form(result_value, system_num)
-        #leading zeros
-        if (res_opt_len != len(result_num)):
-            continue
-        if ((left_opt_len > 1 and left_num[0] == '0') or (right_opt_len > 1 and right_num[0] == '0')):
-            continue
-
-        #add leading zeros
-        if (res_opt_len < len(result_num)):
-            continue
-        while (len(result_num) < res_opt_len):
-            result_num = '0' + result_num
-            #continue
-        ret.append(left_num + '+' + right_num + '=' + result_num) # current only consider '+' and '='
-        #print(ret[-1])
-    return ret
-
-def generator_equation_by_len(equation_len, system_num = 2, label = 0, require_num = 1):
-    generate_type = "one"
-    ret = []
-    equation_sign_num = 2 # '+' and '='
-    while len(ret) < require_num:
-        left_opt_len = random.randint(1, equation_len - 1 - equation_sign_num)
-        right_opt_len = random.randint(1, equation_len - left_opt_len - equation_sign_num)
-        res_opt_len = equation_len - left_opt_len - right_opt_len - equation_sign_num
-        ret.extend(generator_equations(left_opt_len, right_opt_len, res_opt_len, system_num, label, generate_type))
-    return ret
-
-def generator_equations_by_len(equation_len, system_num = 2, label = 0, repeat_times = 1, keep = 1, generate_type = "all"):
-    ret = []
-    equation_sign_num = 2 # '+' and '='
-    for left_opt_len in range(1, equation_len - (2 + equation_sign_num) + 1):
-        for right_opt_len in range(1, equation_len - left_opt_len - (1 + equation_sign_num) + 1):
-            res_opt_len = equation_len - left_opt_len - right_opt_len - equation_sign_num
-            for i in range(repeat_times): #generate more equations
-                if random.random() > keep ** (equation_len):
-                    continue
-                ret.extend(generator_equations(left_opt_len, right_opt_len, res_opt_len, system_num, label, generate_type))
-    return ret
-
-def generator_equations_by_max_len(max_equation_len, system_num = 2, label = 0, repeat_times = 1, keep = 1, generate_type = "all", num_per_len = None):
-    ret = []
-    equation_sign_num = 2 # '+' and '='
-    for equation_len in range(3 + equation_sign_num, max_equation_len + 1):
-        if (num_per_len is None):
-            ret.extend(generator_equations_by_len(equation_len, system_num, label, repeat_times, keep, generate_type))
-        else:
-            ret.extend(generator_equation_by_len(equation_len, system_num, label, require_num = num_per_len))
-    return ret
-
-def generator_equation_images(image_pools, equations, signs, shape, seed, is_color):
-    if (seed is not None):
-        random.seed(seed)
-    ret = []
-    sign_num = len(signs)
-    sign_index_dict = dict(zip(signs, list(range(sign_num))))
-    for equation in equations:
-        data = []
-        for sign in equation:
-            index = sign_index_dict[sign]
-            pick = random.randint(0, len(image_pools[index]) - 1)
-            if is_color:
-                image = Image.open(image_pools[index][pick]).convert('RGB').resize(shape)
-            else:
-                image = Image.open(image_pools[index][pick]).convert('I').resize(shape)
-            image_array = np.array(image)
-            image_array = (image_array-127)*(1./128)
-            data.append(image_array)
-        ret.append(np.array(data))
-    return ret
-
-def get_equation_std_data(data_dir, sign_dir_lists, sign_output_lists, shape = (28, 28), train_max_equation_len = 10, test_max_equation_len = 10, system_num = 2, tmp_file_prev =
-None, seed = None, train_num_per_len = 10, test_num_per_len = 10, is_color = False):
-    tmp_file = ""
-    if (tmp_file_prev is not None):
-        tmp_file = "%s_train_len_%d_test_len_%d_sys_%d_.pk" % (tmp_file_prev, train_max_equation_len, test_max_equation_len, system_num)
-    if (os.path.exists(tmp_file)):
-        return pickle.load(open(tmp_file, "rb"))
-
-    image_pools = get_sign_path_list(data_dir, sign_dir_lists)
-    train_pool, test_pool = split_pool_by_rate(image_pools, 0.8, seed)
-
-    ret = {}
-    for label in ["positive", "negative"]:
-        print("Generating equations.")
-        train_equations = generator_equations_by_max_len(train_max_equation_len, system_num, label, num_per_len = train_num_per_len)
-        test_equations = generator_equations_by_max_len(test_max_equation_len, system_num, label, num_per_len = test_num_per_len)
-        print(train_equations)
-        print(test_equations)
-        print("Generated equations.")
-        print("Generating equation image data.")
-        ret["train:%s" % (label)] = generator_equation_images(train_pool, train_equations, sign_output_lists, shape, seed, is_color)
-        ret["test:%s" % (label)] = generator_equation_images(test_pool, test_equations, sign_output_lists, shape, seed, is_color)
-        print("Generated equation image data.")
-
-    if (tmp_file_prev is not None):
-        pickle.dump(ret, open(tmp_file, "wb"))
-    return ret
-
-if __name__ == "__main__":
-    data_dirs = ["./dataset/hed/mnist_images", "./dataset/hed/random_images"] #, "../dataset/cifar10_images"]
-    tmp_file_prevs = ["mnist_equation_data", "random_equation_data"] #, "cifar10_equation_data"]
-    for data_dir, tmp_file_prev in zip(data_dirs, tmp_file_prevs):
-        data = get_equation_std_data(data_dir = data_dir,\
-                                     sign_dir_lists = ['0', '1', '10', '11'],\
-                                     sign_output_lists = ['0', '1', '+', '='],\
-                                     shape = (28, 28),\
-                                     train_max_equation_len = 26, \
-                                     test_max_equation_len = 26, \
-                                     system_num = 2, \
-                                     tmp_file_prev = tmp_file_prev, \
-                                     train_num_per_len = 300, \
-                                     test_num_per_len = 300, \
-                                     is_color = False)
diff --git a/examples/hed/datasets/get_hed.py b/examples/hed/datasets/get_dataset.py
similarity index 98%
rename from examples/hed/datasets/get_hed.py
rename to examples/hed/datasets/get_dataset.py
index 18821f1..39e1934 100644
--- a/examples/hed/datasets/get_hed.py
+++ b/examples/hed/datasets/get_dataset.py
@@ -81,7 +81,7 @@ def split_equation(equations_by_len, prop_train, prop_val):
     return train_equations_by_len, val_equations_by_len
 
 
-def get_hed(dataset="mnist", train=True):
+def get_dataset(dataset="mnist", train=True):
     if dataset == "mnist":
         file = osp.join(CURRENT_DIR, "mnist_equation_data_train_len_26_test_len_26_sys_2_.pk")
     elif dataset == "random":
diff --git a/examples/hed/hed.ipynb b/examples/hed/hed.ipynb
new file mode 100644
index 0000000..4ade93d
--- /dev/null
+++ b/examples/hed/hed.ipynb
@@ -0,0 +1,912 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Handwritten Equation Decipherment (HED)\n",
+    "\n",
+    "This notebook shows an implementation of [Handwritten Equation Decipherment](https://proceedings.neurips.cc/paper_files/paper/2019/file/9c19a2aa1d84e04b0bd4bc888792bd1e-Paper.pdf). As shown below, the handwritten equations consist of sequential pictures of characters. The equations are generated with unknown operation rules from images of symbols ('0', '1', '+' and '='), and each equation is associated with a label indicating whether the equation is correct (i.e., positive) or not (i.e., negative). An agent is required to learn from a training set of such equations and then to predict labels of unseen equations. Note that the operation rules governing the label assignment of labels, \"xnor\" in this example, are unknown, and the sizes of equations can be different."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries and modules\n",
+    "import os.path as osp\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from examples.hed.datasets import get_dataset, split_equation\n",
+    "from examples.models.nn import SymbolNet\n",
+    "from abl.learning import ABLModel, BasicNN\n",
+    "from examples.hed.reasoning import HedKB, HedReasoner\n",
+    "from abl.evaluation import ReasoningMetric, SymbolMetric\n",
+    "from abl.utils import ABLLogger, print_log\n",
+    "from examples.hed.bridge import HEDBridge"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_train_data = get_dataset(train=True)\n",
+    "train_data, val_data = split_equation(total_train_data, 3, 1)\n",
+    "test_data = get_dataset(train=False)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building the Learning Part"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build necessary components for BasicNN\n",
+    "cls = SymbolNet(num_classes=4)\n",
+    "loss_fn = nn.CrossEntropyLoss()\n",
+    "optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-4)\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build BasicNN\n",
+    "# The function of BasicNN is to wrap NN models into the form of an sklearn estimator\n",
+    "base_model = BasicNN(\n",
+    "    cls,\n",
+    "    loss_fn,\n",
+    "    optimizer,\n",
+    "    device,\n",
+    "    batch_size=32,\n",
+    "    num_epochs=1,\n",
+    "    stop_loss=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ABLModel(base_model)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building the Reasoning Part"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kb = HedKB()\n",
+    "reasoner = HedReasoner(kb, dist_func=\"hamming\", use_zoopt=True, max_revision=10)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building Evaluation Metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up metrics\n",
+    "metric_list = [SymbolMetric(prefix=\"hed\"), ReasoningMetric(kb=kb, prefix=\"hed\")]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bridging Learning and Logic Reasoning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bridge = HEDBridge(model, reasoner, metric_list)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform traing and testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12/21 11:23:55 - abl - INFO - Abductive Learning on the HED example.\n",
+      "12/21 11:23:55 - abl - INFO - Loads checkpoint by local backend from path: ./weights/pretrain_weights.pth\n",
+      "12/21 11:23:55 - abl - INFO - ============== equation_len: 5-6 ================\n",
+      "12/21 11:23:55 - abl - INFO - Equation Len(train) [5] Segment Index [1]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [-1.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [-1.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [-1.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [-1.0, 8.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [-1.0, 7.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [-1.0, 8.0]\n",
+      "[zoopt] x: array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 1., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [-4.0, 6.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0.])\n",
+      "[zoopt] value: [-4.0, 8.0]\n",
+      "12/21 11:24:12 - abl - INFO - model loss: 0.53343\n",
+      "12/21 11:24:12 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:24:12 - abl - INFO - mean loss: 0.055, accuray: 0.952\n",
+      "12/21 11:24:12 - abl - INFO - Revisible ratio is 0.400, Character accuracy is 0.952\n",
+      "12/21 11:24:12 - abl - INFO - Equation Len(train) [5] Segment Index [2]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-1.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
+      "       0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-2.0, 8.0]\n",
+      "[zoopt] x: array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-9.0, 5.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-3.0, 8.0]\n",
+      "12/21 11:24:29 - abl - INFO - model loss: 0.33173\n",
+      "12/21 11:24:29 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:24:29 - abl - INFO - mean loss: 0.027, accuray: 1.000\n",
+      "12/21 11:24:29 - abl - INFO - Revisible ratio is 0.900, Character accuracy is 1.000\n",
+      "12/21 11:24:29 - abl - INFO - Equation Len(train) [5] Segment Index [3]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-1.0, 8.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-1.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-1.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-2.0, 7.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
+      "       1., 0.])\n",
+      "[zoopt] value: [-1.0, 3.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-10.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-3.0, 5.0]\n",
+      "12/21 11:24:45 - abl - INFO - model loss: 0.06279\n",
+      "12/21 11:24:45 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:24:45 - abl - INFO - mean loss: 0.022, accuray: 0.981\n",
+      "12/21 11:24:45 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 0.981\n",
+      "12/21 11:24:45 - abl - INFO - Equation Len(train) [5] Segment Index [4]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [-1.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [-2.0, 7.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [-10.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "12/21 11:25:00 - abl - INFO - model loss: 0.00694\n",
+      "12/21 11:25:00 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:00 - abl - INFO - mean loss: 0.001, accuray: 1.000\n",
+      "12/21 11:25:00 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:00 - abl - INFO - Equation Len(train) [5] Segment Index [5]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
+      "       0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
+      "       1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-2.0, 4.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-1.0, 9.0]\n",
+      "[zoopt] x: array([0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-1.0, 8.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "12/21 11:25:19 - abl - INFO - model loss: 0.00063\n",
+      "12/21 11:25:19 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:19 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:19 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:19 - abl - INFO - Equation Len(train) [5] Segment Index [6]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "12/21 11:25:36 - abl - INFO - model loss: 0.00105\n",
+      "12/21 11:25:36 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:36 - abl - INFO - mean loss: 0.001, accuray: 1.000\n",
+      "12/21 11:25:36 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:36 - abl - INFO - Equation Len(train) [5] Segment Index [7]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,\n",
+      "       0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [-2.0, 5.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [-1.0, 8.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0.])\n",
+      "[zoopt] value: [0.0, 10.0]\n",
+      "12/21 11:25:51 - abl - INFO - model loss: 0.00027\n",
+      "12/21 11:25:51 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:51 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:51 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:51 - abl - INFO - Now checking if we can go to next course\n",
+      "12/21 11:25:51 - abl - INFO - Learned rules from data: ['my_op([1], [1], [1, 0])', 'my_op([0], [1], [1])', 'my_op([1], [0], [1])', 'my_op([0], [0], [0])']\n",
+      "12/21 11:25:51 - abl - INFO - True consistent ratio is 1.000, False inconsistent ratio is 1.000\n",
+      "12/21 11:25:51 - abl - INFO - Checkpoints will be saved to ./weights/eq_len_5.pth\n",
+      "12/21 11:25:51 - abl - INFO - ============== equation_len: 6-7 ================\n",
+      "12/21 11:25:51 - abl - INFO - Equation Len(train) [6] Segment Index [1]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:51 - abl - INFO - model loss: 0.00029\n",
+      "12/21 11:25:51 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:51 - abl - INFO - mean loss: 0.001, accuray: 1.000\n",
+      "12/21 11:25:51 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:51 - abl - INFO - Equation Len(train) [6] Segment Index [2]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:52 - abl - INFO - model loss: 0.00022\n",
+      "12/21 11:25:52 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:52 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:52 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:52 - abl - INFO - Equation Len(train) [6] Segment Index [3]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:52 - abl - INFO - model loss: 0.00026\n",
+      "12/21 11:25:52 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:52 - abl - INFO - mean loss: 0.001, accuray: 1.000\n",
+      "12/21 11:25:52 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:52 - abl - INFO - Equation Len(train) [6] Segment Index [4]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:53 - abl - INFO - model loss: 0.00016\n",
+      "12/21 11:25:53 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:53 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:53 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:53 - abl - INFO - Equation Len(train) [6] Segment Index [5]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:53 - abl - INFO - model loss: 0.00188\n",
+      "12/21 11:25:53 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:53 - abl - INFO - mean loss: 0.001, accuray: 1.000\n",
+      "12/21 11:25:53 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:53 - abl - INFO - Now checking if we can go to next course\n",
+      "12/21 11:25:53 - abl - INFO - Learned rules from data: ['my_op([1], [1], [1, 0])', 'my_op([1], [0], [1])', 'my_op([0], [1], [1])', 'my_op([0], [0], [0])']\n",
+      "12/21 11:25:53 - abl - INFO - True consistent ratio is 0.913, False inconsistent ratio is 1.000\n",
+      "12/21 11:25:53 - abl - INFO - Loads checkpoint by local backend from path: ./weights/eq_len_5.pth\n",
+      "12/21 11:25:53 - abl - INFO - Reload Model and retrain\n",
+      "12/21 11:25:53 - abl - INFO - Equation Len(train) [6] Segment Index [6]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:54 - abl - INFO - model loss: 0.00037\n",
+      "12/21 11:25:54 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:54 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:54 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:54 - abl - INFO - Equation Len(train) [6] Segment Index [7]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:54 - abl - INFO - model loss: 0.00026\n",
+      "12/21 11:25:54 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:54 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:54 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:54 - abl - INFO - Equation Len(train) [6] Segment Index [8]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:54 - abl - INFO - model loss: 0.00017\n",
+      "12/21 11:25:54 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:54 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:54 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:54 - abl - INFO - Equation Len(train) [6] Segment Index [9]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:55 - abl - INFO - model loss: 0.00019\n",
+      "12/21 11:25:55 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:55 - abl - INFO - mean loss: 0.127, accuray: 0.969\n",
+      "12/21 11:25:55 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 0.969\n",
+      "12/21 11:25:55 - abl - INFO - Equation Len(train) [6] Segment Index [10]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.])\n",
+      "[zoopt] value: [-8.0, 8.0]\n",
+      "12/21 11:25:55 - abl - INFO - model loss: 0.00018\n",
+      "12/21 11:25:55 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:55 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:55 - abl - INFO - Revisible ratio is 0.800, Character accuracy is 1.000\n",
+      "12/21 11:25:55 - abl - INFO - Equation Len(train) [6] Segment Index [11]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:56 - abl - INFO - model loss: 0.00123\n",
+      "12/21 11:25:56 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:56 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:56 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:56 - abl - INFO - Equation Len(train) [6] Segment Index [12]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:56 - abl - INFO - model loss: 0.00015\n",
+      "12/21 11:25:56 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:56 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:56 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:56 - abl - INFO - Equation Len(train) [6] Segment Index [13]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:56 - abl - INFO - model loss: 0.00013\n",
+      "12/21 11:25:56 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:56 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:56 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:56 - abl - INFO - Equation Len(train) [6] Segment Index [14]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:56 - abl - INFO - model loss: 0.00031\n",
+      "12/21 11:25:56 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:56 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:56 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:56 - abl - INFO - Equation Len(train) [6] Segment Index [15]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:57 - abl - INFO - model loss: 0.00012\n",
+      "12/21 11:25:57 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:57 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:57 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:57 - abl - INFO - Now checking if we can go to next course\n",
+      "12/21 11:25:57 - abl - INFO - Learned rules from data: ['my_op([0], [1], [1])', 'my_op([1], [1], [1, 0])', 'my_op([0], [0], [0])', 'my_op([1], [0], [1])']\n",
+      "12/21 11:25:57 - abl - INFO - True consistent ratio is 1.000, False inconsistent ratio is 1.000\n",
+      "12/21 11:25:57 - abl - INFO - Checkpoints will be saved to ./weights/eq_len_6.pth\n",
+      "12/21 11:25:57 - abl - INFO - ============== equation_len: 7-8 ================\n",
+      "12/21 11:25:57 - abl - INFO - Equation Len(train) [7] Segment Index [1]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:57 - abl - INFO - model loss: 0.00037\n",
+      "12/21 11:25:57 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:57 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:57 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:57 - abl - INFO - Equation Len(train) [7] Segment Index [2]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:58 - abl - INFO - model loss: 0.00004\n",
+      "12/21 11:25:58 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:58 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:58 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:58 - abl - INFO - Equation Len(train) [7] Segment Index [3]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:58 - abl - INFO - model loss: 0.00006\n",
+      "12/21 11:25:58 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:58 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:58 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:58 - abl - INFO - Equation Len(train) [7] Segment Index [4]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:58 - abl - INFO - model loss: 0.00004\n",
+      "12/21 11:25:58 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:58 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:58 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:58 - abl - INFO - Equation Len(train) [7] Segment Index [5]\n",
+      "[zoopt] x: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0.])\n",
+      "[zoopt] value: [-10.0, 10.0]\n",
+      "12/21 11:25:58 - abl - INFO - model loss: 0.00216\n",
+      "12/21 11:25:58 - abl - INFO - Start machine learning model validation\n",
+      "12/21 11:25:58 - abl - INFO - mean loss: 0.000, accuray: 1.000\n",
+      "12/21 11:25:58 - abl - INFO - Revisible ratio is 1.000, Character accuracy is 1.000\n",
+      "12/21 11:25:58 - abl - INFO - Now checking if we can go to next course\n",
+      "12/21 11:25:59 - abl - INFO - Learned rules from data: ['my_op([0], [1], [1])', 'my_op([1], [1], [1, 0])', 'my_op([0], [0], [0])', 'my_op([1], [0], [1])']\n",
+      "12/21 11:25:59 - abl - INFO - True consistent ratio is 1.000, False inconsistent ratio is 0.993\n",
+      "12/21 11:25:59 - abl - INFO - Checkpoints will be saved to ./weights/eq_len_7.pth\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build logger\n",
+    "print_log(\"Abductive Learning on the HED example.\", logger=\"current\")\n",
+    "\n",
+    "# Retrieve the directory of the Log file and define the directory for saving the model weights.\n",
+    "log_dir = ABLLogger.get_current_instance().log_dir\n",
+    "weights_dir = osp.join(log_dir, \"weights\")\n",
+    "\n",
+    "bridge.pretrain(\"./weights\")\n",
+    "bridge.train(train_data, val_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ABL",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "fb6f4ceeabb9a733f366948eb80109f83aedf798cc984df1e68fb411adb27d58"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/hed/hed_example.ipynb b/examples/hed/hed_example.ipynb
deleted file mode 100644
index ea97612..0000000
--- a/examples/hed/hed_example.ipynb
+++ /dev/null
@@ -1,307 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os.path as osp\n",
-    "\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "from zoopt import Dimension, Objective, Opt, Parameter\n",
-    "\n",
-    "from abl.evaluation import ReasoningMetric, SymbolMetric\n",
-    "from abl.learning import ABLModel, BasicNN\n",
-    "from abl.reasoning import PrologKB, Reasoner\n",
-    "from abl.utils import ABLLogger, print_log, reform_list\n",
-    "from examples.hed.datasets.get_hed import get_hed, split_equation\n",
-    "from examples.hed.hed_bridge import HEDBridge\n",
-    "from examples.models.nn import SymbolNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build logger\n",
-    "print_log(\"Abductive Learning on the HED example.\", logger=\"current\")\n",
-    "\n",
-    "# Retrieve the directory of the Log file and define the directory for saving the model weights.\n",
-    "log_dir = ABLLogger.get_current_instance().log_dir\n",
-    "weights_dir = osp.join(log_dir, \"weights\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Logic Part"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize knowledge base and abducer\n",
-    "class HedKB(PrologKB):\n",
-    "    def __init__(self, pseudo_label_list, pl_file):\n",
-    "        super().__init__(pseudo_label_list, pl_file)\n",
-    "\n",
-    "    def consist_rule(self, exs, rules):\n",
-    "        rules = str(rules).replace(\"'\", \"\")\n",
-    "        return len(list(self.prolog.query(\"eval_inst_feature(%s, %s).\" % (exs, rules)))) != 0\n",
-    "\n",
-    "    def abduce_rules(self, pred_res):\n",
-    "        prolog_result = list(self.prolog.query(\"consistent_inst_feature(%s, X).\" % pred_res))\n",
-    "        if len(prolog_result) == 0:\n",
-    "            return None\n",
-    "        prolog_rules = prolog_result[0][\"X\"]\n",
-    "        rules = [rule.value for rule in prolog_rules]\n",
-    "        return rules\n",
-    "\n",
-    "\n",
-    "class HedReasoner(Reasoner):\n",
-    "    def revise_at_idx(self, data_example):\n",
-    "        revision_idx = np.where(np.array(data_example.flatten(\"revision_flag\")) != 0)[0]\n",
-    "        candidate = self.kb.revise_at_idx(\n",
-    "            data_example.pred_pseudo_label, data_example.Y, data_example.X, revision_idx\n",
-    "        )\n",
-    "        return candidate\n",
-    "\n",
-    "    def zoopt_revision_score(self, symbol_num, data_example, sol):\n",
-    "        revision_flag = reform_list(\n",
-    "            list(sol.get_x().astype(np.int32)), data_example.pred_pseudo_label\n",
-    "        )\n",
-    "        data_example.revision_flag = revision_flag\n",
-    "\n",
-    "        lefted_idxs = [i for i in range(len(data_example.pred_idx))]\n",
-    "        candidate_size = []\n",
-    "        max_consistent_idxs = []\n",
-    "        while lefted_idxs:\n",
-    "            idxs = []\n",
-    "            idxs.append(lefted_idxs.pop(0))\n",
-    "            max_candidate_idxs = []\n",
-    "            found = False\n",
-    "            for idx in range(-1, len(data_example.pred_idx)):\n",
-    "                if (not idx in idxs) and (idx >= 0):\n",
-    "                    idxs.append(idx)\n",
-    "                candidates, _ = self.revise_at_idx(data_example[idxs])\n",
-    "                if len(candidates) == 0:\n",
-    "                    if len(idxs) > 1:\n",
-    "                        idxs.pop()\n",
-    "                else:\n",
-    "                    if len(idxs) > len(max_candidate_idxs):\n",
-    "                        found = True\n",
-    "                        max_candidate_idxs = idxs.copy()\n",
-    "            removed = [i for i in lefted_idxs if i in max_candidate_idxs]\n",
-    "            if found:\n",
-    "                removed.insert(0, idxs[0])\n",
-    "                candidate_size.append(len(removed))\n",
-    "                max_consistent_idxs = max_candidate_idxs.copy()\n",
-    "                lefted_idxs = [i for i in lefted_idxs if i not in max_candidate_idxs]\n",
-    "        candidate_size.sort()\n",
-    "        score = 0\n",
-    "        import math\n",
-    "\n",
-    "        for i in range(0, len(candidate_size)):\n",
-    "            score -= math.exp(-i) * candidate_size[i]\n",
-    "        return score, max_consistent_idxs\n",
-    "    \n",
-    "    def _zoopt_get_solution(self, symbol_num, data_example, max_revision_num):\n",
-    "        dimension = Dimension(size=symbol_num, regs=[[0, 1]] * symbol_num, tys=[False] * symbol_num)\n",
-    "        objective = Objective(\n",
-    "            lambda sol: self.zoopt_revision_score(symbol_num, data_example, sol)[0],\n",
-    "            dim=dimension,\n",
-    "            constraint=lambda sol: self._constrain_revision_num(sol, max_revision_num),\n",
-    "        )\n",
-    "        parameter = Parameter(budget=200, intermediate_result=False, autoset=True)\n",
-    "        solution = Opt.min(objective, parameter)\n",
-    "        return solution\n",
-    "\n",
-    "    def abduce(self, data_example):\n",
-    "        symbol_num = data_example.elements_num(\"pred_pseudo_label\")\n",
-    "        max_revision_num = self._get_max_revision_num(self.max_revision, symbol_num)\n",
-    "\n",
-    "        solution = self._zoopt_get_solution(symbol_num, data_example, max_revision_num)\n",
-    "        _, max_candidate_idxs = self.zoopt_revision_score(symbol_num, data_example, solution)\n",
-    "\n",
-    "        abduced_pseudo_label = [[] for _ in range(len(data_example))]\n",
-    "\n",
-    "        if len(max_candidate_idxs) > 0:\n",
-    "            candidates, _ = self.revise_at_idx(data_example[max_candidate_idxs])\n",
-    "            for i, idx in enumerate(max_candidate_idxs):\n",
-    "                abduced_pseudo_label[idx] = candidates[0][i]\n",
-    "        data_example.abduced_pseudo_label = abduced_pseudo_label\n",
-    "        return abduced_pseudo_label\n",
-    "\n",
-    "    def abduce_rules(self, pred_res):\n",
-    "        return self.kb.abduce_rules(pred_res)\n",
-    "\n",
-    "\n",
-    "kb = HedKB(pseudo_label_list=[1, 0, \"+\", \"=\"], pl_file=\"./datasets/learn_add.pl\")\n",
-    "reasoner = HedReasoner(kb, dist_func=\"hamming\", use_zoopt=True, max_revision=10)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Machine Learning Part"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build necessary components for BasicNN\n",
-    "cls = SymbolNet(num_classes=4)\n",
-    "loss_fn = nn.CrossEntropyLoss()\n",
-    "optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, weight_decay=1e-4)\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build BasicNN\n",
-    "# The function of BasicNN is to wrap NN models into the form of an sklearn estimator\n",
-    "base_model = BasicNN(\n",
-    "    cls,\n",
-    "    loss_fn,\n",
-    "    optimizer,\n",
-    "    device,\n",
-    "    batch_size=32,\n",
-    "    num_epochs=1,\n",
-    "    save_interval=1,\n",
-    "    stop_loss=None,\n",
-    "    save_dir=weights_dir,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build ABLModel\n",
-    "# The main function of the ABL model is to serialize data and\n",
-    "# provide a unified interface for different machine learning models\n",
-    "model = ABLModel(base_model)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Metric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up metrics\n",
-    "metric_list = [SymbolMetric(prefix=\"hed\"), ReasoningMetric(kb=kb, prefix=\"hed\")]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bridge Machine Learning and Logic Reasoning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bridge = HEDBridge(model, reasoner, metric_list)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total_train_data = get_hed(train=True)\n",
-    "train_data, val_data = split_equation(total_train_data, 3, 1)\n",
-    "test_data = get_hed(train=False)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Train and Test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bridge.pretrain(\"./weights\")\n",
-    "bridge.train(train_data, val_data)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ABL",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.18"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "fb6f4ceeabb9a733f366948eb80109f83aedf798cc984df1e68fb411adb27d58"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/hed/main.py b/examples/hed/main.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/hed/datasets/BK.pl b/examples/hed/reasoning/BK.pl
similarity index 100%
rename from examples/hed/datasets/BK.pl
rename to examples/hed/reasoning/BK.pl
diff --git a/examples/hed/reasoning/__init__.py b/examples/hed/reasoning/__init__.py
new file mode 100644
index 0000000..52b7e39
--- /dev/null
+++ b/examples/hed/reasoning/__init__.py
@@ -0,0 +1,3 @@
+from .reasoning import HedKB, HedReasoner
+
+__all__ = ["HedKB", "HedReasoner"]
\ No newline at end of file
diff --git a/examples/hed/datasets/learn_add.pl b/examples/hed/reasoning/learn_add.pl
similarity index 100%
rename from examples/hed/datasets/learn_add.pl
rename to examples/hed/reasoning/learn_add.pl
diff --git a/examples/hed/reasoning/reasoning.py b/examples/hed/reasoning/reasoning.py
new file mode 100644
index 0000000..f85b967
--- /dev/null
+++ b/examples/hed/reasoning/reasoning.py
@@ -0,0 +1,93 @@
+import os
+import numpy as np
+import math
+from zoopt import Dimension, Objective, Opt, Parameter
+from abl.reasoning import PrologKB, Reasoner
+from abl.utils import reform_list
+
+CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
+
+class HedKB(PrologKB):
+    def __init__(self, pseudo_label_list=[1, 0, "+", "="], pl_file=os.path.join(CURRENT_DIR, "learn_add.pl")):
+        super().__init__(pseudo_label_list, pl_file)
+
+    def consist_rule(self, exs, rules):
+        rules = str(rules).replace("'", "")
+        return len(list(self.prolog.query("eval_inst_feature(%s, %s)." % (exs, rules)))) != 0
+
+    def abduce_rules(self, pred_res):
+        prolog_result = list(self.prolog.query("consistent_inst_feature(%s, X)." % pred_res))
+        if len(prolog_result) == 0:
+            return None
+        prolog_rules = prolog_result[0]["X"]
+        rules = [rule.value for rule in prolog_rules]
+        return rules
+
+
+class HedReasoner(Reasoner):
+    def revise_at_idx(self, data_example):
+        revision_idx = np.where(np.array(data_example.flatten("revision_flag")) != 0)[0]
+        candidate = self.kb.revise_at_idx(
+            data_example.pred_pseudo_label, data_example.Y, data_example.X, revision_idx
+        )
+        return candidate
+
+    def zoopt_revision_score(self, symbol_num, data_example, sol, get_score=True):
+        revision_flag = reform_list(
+            list(sol.get_x().astype(np.int32)), data_example.pred_pseudo_label
+        )
+        data_example.revision_flag = revision_flag
+
+        lefted_idxs = [i for i in range(len(data_example.pred_idx))]
+        candidate_size = []
+        max_consistent_idxs = []
+        while lefted_idxs:
+            idxs = []
+            idxs.append(lefted_idxs.pop(0))
+            max_candidate_idxs = []
+            found = False
+            for idx in range(-1, len(data_example.pred_idx)):
+                if (not idx in idxs) and (idx >= 0):
+                    idxs.append(idx)
+                candidates, _ = self.revise_at_idx(data_example[idxs])
+                if len(candidates) == 0:
+                    if len(idxs) > 1:
+                        idxs.pop()
+                else:
+                    if len(idxs) > len(max_candidate_idxs):
+                        found = True
+                        max_candidate_idxs = idxs.copy()
+            removed = [i for i in lefted_idxs if i in max_candidate_idxs]
+            if found:
+                removed.insert(0, idxs[0])
+                candidate_size.append(len(removed))
+                max_consistent_idxs = max_candidate_idxs.copy()
+                lefted_idxs = [i for i in lefted_idxs if i not in max_candidate_idxs]
+        candidate_size.sort()
+        score = 0
+
+        for i in range(0, len(candidate_size)):
+            score -= math.exp(-i) * candidate_size[i]
+        if get_score:
+            return score
+        else:
+            return max_consistent_idxs
+
+    def abduce(self, data_example):
+        symbol_num = data_example.elements_num("pred_pseudo_label")
+        max_revision_num = self._get_max_revision_num(self.max_revision, symbol_num)
+
+        solution = self._zoopt_get_solution(symbol_num, data_example, max_revision_num)
+        max_candidate_idxs = self.zoopt_revision_score(symbol_num, data_example, solution, get_score=False)
+
+        abduced_pseudo_label = [[] for _ in range(len(data_example))]
+
+        if len(max_candidate_idxs) > 0:
+            candidates, _ = self.revise_at_idx(data_example[max_candidate_idxs])
+            for i, idx in enumerate(max_candidate_idxs):
+                abduced_pseudo_label[idx] = candidates[0][i]
+        data_example.abduced_pseudo_label = abduced_pseudo_label
+        return abduced_pseudo_label
+
+    def abduce_rules(self, pred_res):
+        return self.kb.abduce_rules(pred_res)
\ No newline at end of file
diff --git a/examples/hed/requirements.txt b/examples/hed/requirements.txt
new file mode 100644
index 0000000..1710e0d
--- /dev/null
+++ b/examples/hed/requirements.txt
@@ -0,0 +1 @@
+abl
\ No newline at end of file