From 709ca71d09ccfd2b0ebbad1d6ec132a2bdbf7da1 Mon Sep 17 00:00:00 2001 From: Shuhui Bu Date: Thu, 13 Sep 2018 15:14:46 +0800 Subject: [PATCH] Reorginze the program structure --- References_notes.md | 14 ++ exercise/{exercise.ipynb => 1_python.ipynb} | 66 ++------- exercise/1_python.py | 74 ++++++++++ exercise/2_numpy.ipynb | 82 +++++++++++ exercise/2_numpy.py | 70 +++++++++ exercise/3_matplotlib.ipynb | 37 +++++ exercise/3_matplotlib.py | 26 ++++ knn/digital classification.ipynb | 82 ----------- knn/knn_classification.ipynb | 139 ++++++++++++++++++ knn/knn_classification.py | 73 +++++++++ matplotlib/stinkbug.webp | Bin 55656 -> 0 bytes .../example.png | Bin .../matplotlib_ani1.ipynb | 0 .../matplotlib_ani1.py | 0 .../matplotlib_ani2.ipynb | 0 .../matplotlib_ani2.py | 0 .../matplotlib_full.ipynb | 0 .../matplotlib_simple_tutorial.ipynb | 0 .../numpy.ipynb | 0 .../scipy.ipynb | 0 .../stockholm_td_adj.dat | 0 .../sympy.ipynb | 0 python/tips/README.md | 16 ++ python/tips/pip.md | 66 +++++++++ python/tips/virtualenv.md | 59 ++++++++ python/tips/virtualenv_wrapper.md | 59 ++++++++ 26 files changed, 724 insertions(+), 139 deletions(-) create mode 100644 References_notes.md rename exercise/{exercise.ipynb => 1_python.ipynb} (66%) create mode 100644 exercise/1_python.py create mode 100644 exercise/2_numpy.ipynb create mode 100644 exercise/2_numpy.py create mode 100644 exercise/3_matplotlib.ipynb create mode 100644 exercise/3_matplotlib.py delete mode 100644 knn/digital classification.ipynb create mode 100644 knn/knn_classification.ipynb create mode 100644 knn/knn_classification.py delete mode 100644 matplotlib/stinkbug.webp rename {matplotlib => numpy_matplotlib_scipy_sympy}/example.png (100%) rename {matplotlib => numpy_matplotlib_scipy_sympy}/matplotlib_ani1.ipynb (100%) rename {matplotlib => numpy_matplotlib_scipy_sympy}/matplotlib_ani1.py (100%) rename {matplotlib => numpy_matplotlib_scipy_sympy}/matplotlib_ani2.ipynb (100%) rename {matplotlib => numpy_matplotlib_scipy_sympy}/matplotlib_ani2.py (100%) rename matplotlib/Lecture-4-Matplotlib.ipynb => numpy_matplotlib_scipy_sympy/matplotlib_full.ipynb (100%) rename matplotlib/tutorial matplotlib.ipynb => numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb (100%) rename numpy_scipy_sympy/Numpy.ipynb => numpy_matplotlib_scipy_sympy/numpy.ipynb (100%) rename numpy_scipy_sympy/Scipy.ipynb => numpy_matplotlib_scipy_sympy/scipy.ipynb (100%) rename {numpy_scipy_sympy => numpy_matplotlib_scipy_sympy}/stockholm_td_adj.dat (100%) rename numpy_scipy_sympy/Sympy.ipynb => numpy_matplotlib_scipy_sympy/sympy.ipynb (100%) create mode 100644 python/tips/README.md create mode 100644 python/tips/pip.md create mode 100644 python/tips/virtualenv.md create mode 100644 python/tips/virtualenv_wrapper.md diff --git a/References_notes.md b/References_notes.md new file mode 100644 index 0000000..50eb9d0 --- /dev/null +++ b/References_notes.md @@ -0,0 +1,14 @@ +## Notebooks: + +machineLearning/10_digits_classification.ipynb + +MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb + +MachineLearningNotebooks/08.%20Practical_NeuralNets.ipynb + + +## Exercise +http://sofasofa.io/competitions.php?type=practice +https://www.kaggle.com/competitions + +https://github.com/wmpscc/DataMiningNotesAndPractice/blob/master/2.KMeans%E7%AE%97%E6%B3%95%E4%B8%8E%E4%BA%A4%E9%80%9A%E4%BA%8B%E6%95%85%E7%90%86%E8%B5%94%E5%AE%A1%E6%A0%B8%E9%A2%84%E6%B5%8B.md \ No newline at end of file diff --git a/exercise/exercise.ipynb b/exercise/1_python.ipynb similarity index 66% rename from exercise/exercise.ipynb rename to exercise/1_python.ipynb index 79151af..baa1375 100644 --- a/exercise/exercise.ipynb +++ b/exercise/1_python.ipynb @@ -34,10 +34,15 @@ "* 高于 100 万元时， 超过 100 万元的部分按 1%提成， \n", "从键盘输入当月利润 I，求应发放奖金总数？\n", "\n", + "\n", "### （4）循环\n", "输出9x9的乘法口诀表\n", "\n", - "### （5）算法\n", + "\n", + "### （5）使用while循环实现输出2-3+4-5+6.....+100的和\n", + "\n", + "\n", + "### （6）算法\n", "给一个数字列表，将其按照由大到小的顺序排列\n", "\n", "例如\n", @@ -45,72 +50,19 @@ "1, 10, 4, 2, 9, 2, 34, 5, 9, 8, 5, 0\n", "```\n", "\n", - "### （6）应用1\n", + "### （7）应用1\n", "做为 Apple Store App 独立开发者，你要搞限时促销，为你的应用生成激活码（或者优惠券），使用 Python 如何生成 200 个激活码（或者优惠券）？\n", "\n", "需要考虑什么是激活码？有什么特性？例如`KR603guyVvR`是一个激活码\n", "\n", - "### （7）应用2\n", + "### （8）应用2\n", "需要把某个目录下面所有的某种类型的文件找到。\n", "例如把`c:`下面所有的`.dll`文件找到\n", "\n", - "### （8）应用3\n", + "### （9）应用3\n", "你有个目录，里面是程序（假如是C或者是Python），统计一下你写过多少行代码。包括空行和注释，但是要分别列出来。\n", "\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 数值计算\n", - "\n", - "\n", - "### （1）对于一个存在在数组，如何添加一个用0填充的边界?\n", - "例如对一个二维矩阵\n", - "```\n", - "10, 34, 54, 23\n", - "31, 87, 53, 68\n", - "98, 49, 25, 11\n", - "84, 32, 67, 88\n", - "```\n", - "\n", - "变换成\n", - "```\n", - " 0, 0, 0, 0, 0, 0\n", - " 0, 10, 34, 54, 23, 0\n", - " 0, 31, 87, 53, 68, 0\n", - " 0, 98, 49, 25, 11, 0\n", - " 0, 84, 32, 67, 88, 0\n", - " 0, 0, 0, 0, 0, 0\n", - "```\n", - "\n", - "### （2） 创建一个 5x5的矩阵，并设置值1,2,3,4落在其对角线下方位置\n", - "\n", - "\n", - "### （3） 创建一个8x8 的矩阵，并且设置成棋盘样式\n", - "\n", - "\n", - "### （4）求解线性方程组\n", - "\n", - "给定一个方程组，如何求出其的方程解。有多种方法，分析各种方法的优缺点（最简单的方式是消元方）。\n", - "\n", - "例如\n", - "```\n", - "3x + 4y + 2z = 10\n", - "5x + 3y + 4z = 14\n", - "8x + 2y + 7z = 20\n", - "```\n", - "\n", - "编程写出求解的程序\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/exercise/1_python.py b/exercise/1_python.py new file mode 100644 index 0000000..393c30b --- /dev/null +++ b/exercise/1_python.py @@ -0,0 +1,74 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext_format_version: '1.2' +# kernelspec: +# display_name: Python 3 +# 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: 3.5.2 +# --- + +# # Python & Machine Learning Exercises + +# ## Python +# +# ### （1）字符串 +# 给定一个文章，找出每个单词的出现次数 +# +# ``` +# One is always on a strange road, watching strange scenery and listening to strange music. Then one day, you will find that the things you try hard to forget are already gone. +# ``` +# +# ### （2）组合 +# 有 1、2、3、4 个数字，能组成多少个互不相同且无重复数字的三位数？都是多少？ +# +# +# ### （3） 判断 +# 企业发放的奖金根据利润提成。利润(I)： +# * 低于或等于 10 万元时，奖金可提 10%； +# * 高于 10 万元，低于 20 万元时，低于 10 万元的部分按 10%提成，高于 10 万元的部分，可提成 7.5%； +# * 20 万到 40 万之间时，高于 20 万元的部分，可提成 5%； +# * 40 万到 60 万之间时，高于 40 万元的部分，可提成 3%； +# * 60 万到 100 万之间时，高于 60 万元的部分，可提成 1.5%， +# * 高于 100 万元时， 超过 100 万元的部分按 1%提成， +# 从键盘输入当月利润 I，求应发放奖金总数？ +# +# +# ### （4）循环 +# 输出9x9的乘法口诀表 +# +# +# ### （5）使用while循环实现输出2-3+4-5+6.....+100的和 +# +# +# ### （6）算法 +# 给一个数字列表，将其按照由大到小的顺序排列 +# +# 例如 +# ``` +# 1, 10, 4, 2, 9, 2, 34, 5, 9, 8, 5, 0 +# ``` +# +# ### （7）应用1 +# 做为 Apple Store App 独立开发者，你要搞限时促销，为你的应用生成激活码（或者优惠券），使用 Python 如何生成 200 个激活码（或者优惠券）？ +# +# 需要考虑什么是激活码？有什么特性？例如`KR603guyVvR`是一个激活码 +# +# ### （8）应用2 +# 需要把某个目录下面所有的某种类型的文件找到。 +# 例如把`c:`下面所有的`.dll`文件找到 +# +# ### （9）应用3 +# 你有个目录，里面是程序（假如是C或者是Python），统计一下你写过多少行代码。包括空行和注释，但是要分别列出来。 +# +# diff --git a/exercise/2_numpy.ipynb b/exercise/2_numpy.ipynb new file mode 100644 index 0000000..2a7a643 --- /dev/null +++ b/exercise/2_numpy.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数值计算\n", + "\n", + "\n", + "### （1）对于一个存在在数组，如何添加一个用0填充的边界?\n", + "例如对一个二维矩阵\n", + "```\n", + "10, 34, 54, 23\n", + "31, 87, 53, 68\n", + "98, 49, 25, 11\n", + "84, 32, 67, 88\n", + "```\n", + "\n", + "变换成\n", + "```\n", + " 0, 0, 0, 0, 0, 0\n", + " 0, 10, 34, 54, 23, 0\n", + " 0, 31, 87, 53, 68, 0\n", + " 0, 98, 49, 25, 11, 0\n", + " 0, 84, 32, 67, 88, 0\n", + " 0, 0, 0, 0, 0, 0\n", + "```\n", + "\n", + "### （2） 创建一个 5x5的矩阵，并设置值1,2,3,4落在其对角线下方位置\n", + "\n", + "\n", + "### （3） 创建一个8x8 的矩阵，并且设置成国际象棋棋盘样式（黑可以用0, 白可以用1）\n", + "\n", + "\n", + "### （4）求解线性方程组\n", + "\n", + "给定一个方程组，如何求出其的方程解。有多种方法，分析各种方法的优缺点（最简单的方式是消元方）。\n", + "\n", + "例如\n", + "```\n", + "3x + 4y + 2z = 10\n", + "5x + 3y + 4z = 14\n", + "8x + 2y + 7z = 20\n", + "```\n", + "\n", + "编程写出求解的程序\n", + "\n", + "\n", + "### （5） 翻转一个数组（第一个元素变成最后一个）\n", + "\n", + "\n", + "### （6） 产生一个十乘十随机数组，并且找出最大和最小值\n", + "\n", + "\n", + "## Reference\n", + "* [100 numpy exercises](https://github.com/rougier/numpy-100)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.5.2" + }, + "main_language": "python" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercise/2_numpy.py b/exercise/2_numpy.py new file mode 100644 index 0000000..0b53cd7 --- /dev/null +++ b/exercise/2_numpy.py @@ -0,0 +1,70 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext_format_version: '1.2' +# kernelspec: +# display_name: Python 3 +# 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: 3.5.2 +# --- + +# ## 数值计算 +# +# +# ### （1）对于一个存在在数组，如何添加一个用0填充的边界? +# 例如对一个二维矩阵 +# ``` +# 10, 34, 54, 23 +# 31, 87, 53, 68 +# 98, 49, 25, 11 +# 84, 32, 67, 88 +# ``` +# +# 变换成 +# ``` +# 0, 0, 0, 0, 0, 0 +# 0, 10, 34, 54, 23, 0 +# 0, 31, 87, 53, 68, 0 +# 0, 98, 49, 25, 11, 0 +# 0, 84, 32, 67, 88, 0 +# 0, 0, 0, 0, 0, 0 +# ``` +# +# ### （2） 创建一个 5x5的矩阵，并设置值1,2,3,4落在其对角线下方位置 +# +# +# ### （3） 创建一个8x8 的矩阵，并且设置成国际象棋棋盘样式（黑可以用0, 白可以用1） +# +# +# ### （4）求解线性方程组 +# +# 给定一个方程组，如何求出其的方程解。有多种方法，分析各种方法的优缺点（最简单的方式是消元方）。 +# +# 例如 +# ``` +# 3x + 4y + 2z = 10 +# 5x + 3y + 4z = 14 +# 8x + 2y + 7z = 20 +# ``` +# +# 编程写出求解的程序 +# +# +# ### （5） 翻转一个数组（第一个元素变成最后一个） +# +# +# ### （6） 产生一个十乘十随机数组，并且找出最大和最小值 +# +# +# ## Reference +# * [100 numpy exercises](https://github.com/rougier/numpy-100) diff --git a/exercise/3_matplotlib.ipynb b/exercise/3_matplotlib.ipynb new file mode 100644 index 0000000..71b36e5 --- /dev/null +++ b/exercise/3_matplotlib.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matplotlib\n", + "\n", + "\n", + "## (1) 画出一个二次函数，同时画出梯形法求积分时的各个梯形\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.5.2" + }, + "main_language": "python" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercise/3_matplotlib.py b/exercise/3_matplotlib.py new file mode 100644 index 0000000..9c0d808 --- /dev/null +++ b/exercise/3_matplotlib.py @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext_format_version: '1.2' +# kernelspec: +# display_name: Python 3 +# 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: 3.5.2 +# --- + +# # Matplotlib +# +# +# ## (1) 画出一个二次函数，同时画出梯形法求积分时的各个梯形 +# +# diff --git a/knn/digital classification.ipynb b/knn/digital classification.ipynb deleted file mode 100644 index 720ea6b..0000000 --- a/knn/digital classification.ipynb +++ /dev/null @@ -1,82 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Digitial Classification\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Automatically created module for IPython interactive environment\n", - "KNN score: 0.953661\n", - "LogisticRegression score: 0.908248\n" - ] - } - ], - "source": [ - "print(__doc__)\n", - "\n", - "from sklearn import datasets, neighbors, linear_model\n", - "\n", - "digits = datasets.load_digits()\n", - "X_digits = digits.data\n", - "y_digits = digits.target\n", - "\n", - "n_samples = len(X_digits)\n", - "n_train = int(0.4 * n_samples)\n", - "\n", - "X_train = X_digits[:n_train]\n", - "y_train = y_digits[:n_train]\n", - "X_test = X_digits[n_train:]\n", - "y_test = y_digits[n_train:]\n", - "\n", - "knn = neighbors.KNeighborsClassifier()\n", - "logistic = linear_model.LogisticRegression()\n", - "\n", - "print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))\n", - "print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "* [Supervised learning: predicting an output variable from high-dimensional observations](http://scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html)\n", - "* [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/knn/knn_classification.ipynb b/knn/knn_classification.ipynb new file mode 100644 index 0000000..be5c04e --- /dev/null +++ b/knn/knn_classification.ipynb @@ -0,0 +1,139 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# KNN Classification\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature dimensions: (1797, 64)\n", + "Label dimensions: (1797,)\n" + ] + } + ], + "source": [ + "% matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import datasets, neighbors, linear_model\n", + "\n", + "# load data\n", + "digits = datasets.load_digits()\n", + "X_digits = digits.data\n", + "y_digits = digits.target\n", + "\n", + "print(\"Feature dimensions: \", X_digits.shape)\n", + "print(\"Label dimensions: \", y_digits.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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K8eTWtHBa6TQKcnonPbU6nuAU6VI+3cGWoLnKoJuuIbdvEeXfqEiq3v85cLKTD9pxb73I/R9nT3ftpUUjCzjplJkUFBR1yXUwaH8tOM8tK9/mu0ROmj3DKeu/OLX9NLg8RXD1THBXSCwc4QYwuPxRt+2XnpzPpGO+QUFen6Tt3fEr/L0y+cpYunypW+/593/byY+8w48uFd+uncVMJYZhGFlGZ++4FXhRRBS4T1UXxG8gItOB6QCFJF6MJSlE2LZwAYiQr0cyTEa3s0kadAAreA0UhjLadAjsuvO3IEIfLQtNhwhU7P0zgjBEh4fYHsKuXy4MvT0Q4b2VixCEoTq4x/dTASo2/iHaP4aE2h7J0tmB+7OqulVEjgReEpE1qvpqcIPoYL4AoJ8M6DgCcRcZ+q1Z5JWU0FJTzZZb5lGkfekvg5xt0qFjImdTKL1p0gbe5bUer6Psxpnk9S+h9WANW753Z2g6Jg24kMLcYhpb66jYvTi89pj9Lb89vn1XaDpOOekaevUqoamphvfeuLfH99NJo6+gML8fjS21VKxZFJqOVNDZZV23Rv/uEpHFeAGEX+34Vz79Kn1L9s3DnnbKrpju+3zmT9tNRxz7cz8+cQ5DOMhe+jOog190D4Xi2bcKpJBBevg6gtN2b53o2m2DU6vfvnW+U3b25W4w5fpHhsRinAxatC6p9ghGwwEY8rI/xTnohw/w4Dg3iPG0/TOAJvJKChjUheMStB9fu/gzTlnQvnnPg3c7ZUEfb4BhVatppY486JKOIPGRZ4J2+DHfTxgnG4CRy9qiKZWwPUkdD/3JfYEWtGPHT8v/Usm7Tn7rJW3zBXox6I3kdKyNW15gbfPrsXTpc+57q/j5BcmeL8Gp5vHvQIJLRjSPdZfinf2oa8deOKNtmeT+DLouteNH8B1CfFu9cO6dTj7o597VZSsOaeMWkSIR6duWBj4H/L1LtSVBU10LLerF8mrVFvaykyLSvxB6pLkxI3S01jfT2uxNimhtbgxNR11dhEi9ty50pKEpvOPS0JQRx6W2LuIfl5bw2qOuLkKkwdMRaQyvf7RqZpy3tRnSHqmiM3fcZcBiEWnb/g+q+nzHP0k9tXsbqeAtUFCUwQynVAanWwYttTVUsCR0HU37aql8xrsDVY0wlKNC0bG3KsK2Ob/1dLRGGBGSjpb9tRlxXHbubmXVq95MSo1EGBJSe+zZHWH7r+/xMpHwjksjDaxkeUYcl0xoj1TRmdBl6yEazqOLBKdTXzL/Bqfsphv8SCt3rHMfC9+ZEJxa3I9TpYu+M+0QH5nk7NW+GeKV8W7EipbP+qaeHPI4dVFyOoKPVR25FbXctNctC+oaD6Nu992MRiXpdpa/353Gfe0tjyXYEqa94bp/nbk5ENUo8SJyXSK/qi6Wjl+Vb8DDxYFcMaNT2D92n+lGao6fXh9k/HI3As7pBwNT85Nsj1HzP3TzI/zp1PGP4N9c664eeUa5HwA7Z2dyKwleM9GdTv7Vm31X1fbcVNvoI8WcSnLHJXiuxv+Pr6zwz4l4M0r8aprnRPwlI5J1F403hwSDGE/u47bVf1wxy8n3WXr40XfiMXdAwzCMLMMGbsMwjCzDBm7DMIwsQ1RT76ooIruBWqBLU+TjKO3Efo5W1Y/59ZiOjNbxUSf3YTpMxz+Djs5oaVdHu6hqt3yAikzYj+nITB22D9tHT9pHKvejqmYqMQzDyDZs4DYMw8gyunPg/thCVCHtx3Sk9vep3I/tw/bRU/aRyv10z8tJwzAMo/swU4lhGEaWYQO3YRhGltEtA7eITBGRShH5UER+kMR+NorIKhF5T0QOezEO02E6TIfpyHYd7ZIqv8KAr2IusA4YDRQAfwPGdXFfG4FS02E6TIfp6Ik6En264457EvChqq5X1SbgMWDqIX7THZgO02E6TEe262iX7hi4hwKbA/kt0e+6Qlusy79GY8GZDtNhOkxHT9LRLp2NORkWh4x1aTpMh+kwHT1NR3fccW8Fhgfyw6LfHTYaiHUJtMW6NB2mw3SYjp6iI+FOU/rBu4tfD4zCN+qP78J+ioC+gfQbwBTTYTpMh+noKToSfVJuKlHVFhGZBbyA92b2flVdfYiftUdSsS5Nh+kwHaYj23Ukwqa8G4ZhZBk2c9IwDCPLsIHbMAwjy7CB2zAMI8uwgdswDCPLsIHbMAwjy7CB2zAMI8uwgdswDCPL+H+2ihC0591JagAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot sample images\n", + "nplot = 10\n", + "fig, axes = plt.subplots(nrows=1, ncols=nplot)\n", + "\n", + "for i in range(nplot):\n", + " img = X_digits[i].reshape(8, 8)\n", + " axes[i].imshow(img)\n", + " axes[i].set_title(y_digits[i])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# split train / test data\n", + "n_samples = len(X_digits)\n", + "n_train = int(0.4 * n_samples)\n", + "\n", + "X_train = X_digits[:n_train]\n", + "y_train = y_digits[:n_train]\n", + "X_test = X_digits[n_train:]\n", + "y_test = y_digits[n_train:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNN score: 0.953661\n", + "LogisticRegression score: 0.908248\n" + ] + } + ], + "source": [ + "# do KNN classification\n", + "knn = neighbors.KNeighborsClassifier()\n", + "logistic = linear_model.LogisticRegression()\n", + "\n", + "print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))\n", + "print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/knn/knn_classification.py b/knn/knn_classification.py new file mode 100644 index 0000000..b0a1732 --- /dev/null +++ b/knn/knn_classification.py @@ -0,0 +1,73 @@ +# --- +# jupyter: +# jupytext_format_version: '1.2' +# kernelspec: +# display_name: Python 3 +# 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: 3.5.2 +# --- + +# # KNN Classification +# +# +# + +# + +% matplotlib inline + +import matplotlib.pyplot as plt +from sklearn import datasets, neighbors, linear_model + +# load data +digits = datasets.load_digits() +X_digits = digits.data +y_digits = digits.target + +print("Feature dimensions: ", X_digits.shape) +print("Label dimensions: ", y_digits.shape) + + +# + +# plot sample images +nplot = 10 +fig, axes = plt.subplots(nrows=1, ncols=nplot) + +for i in range(nplot): + img = X_digits[i].reshape(8, 8) + axes[i].imshow(img) + axes[i].set_title(y_digits[i]) + + +# + +# split train / test data +n_samples = len(X_digits) +n_train = int(0.4 * n_samples) + +X_train = X_digits[:n_train] +y_train = y_digits[:n_train] +X_test = X_digits[n_train:] +y_test = y_digits[n_train:] + + +# + +# do KNN classification +knn = neighbors.KNeighborsClassifier() +logistic = linear_model.LogisticRegression() + +print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test)) +print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test)) +# - + +# ## References +# * [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html) +# diff --git a/matplotlib/stinkbug.webp b/matplotlib/stinkbug.webp deleted file mode 100644 index 08f02e08fe6655c419314f6c5349699b81fc066f..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 55656 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