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LinXueyuanStdio committed Mar 13, 2018
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1,638 changes: 819 additions & 819 deletions 《Python数据挖掘入门与实践》/0.数据挖掘流程简单示例10min.ipynb → 0.数据挖掘流程简单示例10min.ipynb
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714 changes: 357 additions & 357 deletions 《Python数据挖掘入门与实践》/1.近邻算法分类.ipynb → 1.近邻算法分类.ipynb
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2,016 changes: 1,008 additions & 1,008 deletions 《Python数据挖掘入门与实践》/10.用深度学习进行图象分类.ipynb → 10.用深度学习进行图象分类.ipynb
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3,600 changes: 1,800 additions & 1,800 deletions 《Python数据挖掘入门与实践》/11.大数据处理.ipynb → 11.大数据处理.ipynb
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4,664 changes: 2,332 additions & 2,332 deletions 《Python数据挖掘入门与实践》/2.决策树预测获胜球队.ipynb → 2.决策树预测获胜球队.ipynb
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2,940 changes: 1,470 additions & 1,470 deletions 《Python数据挖掘入门与实践》/3.亲和性分析推荐电影.ipynb → 3.亲和性分析推荐电影.ipynb
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1,464 changes: 732 additions & 732 deletions 《Python数据挖掘入门与实践》/4.用转换器抽取特征.ipynb → 4.用转换器抽取特征.ipynb
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1,450 changes: 725 additions & 725 deletions 《Python数据挖掘入门与实践》/5.朴素贝叶斯进行社会媒体挖掘.ipynb → 5.朴素贝叶斯进行社会媒体挖掘.ipynb
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2,222 changes: 1,111 additions & 1,111 deletions 《Python数据挖掘入门与实践》/6.用图挖掘找到感兴趣的人.ipynb → 6.用图挖掘找到感兴趣的人.ipynb
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1,682 changes: 841 additions & 841 deletions 《Python数据挖掘入门与实践》/7.用神经网络破解验证码.ipynb → 7.用神经网络破解验证码.ipynb
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1,148 changes: 574 additions & 574 deletions 《Python数据挖掘入门与实践》/8.作者归属问题.ipynb → 8.作者归属问题.ipynb
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2,292 changes: 1,146 additions & 1,146 deletions 《Python数据挖掘入门与实践》/9.新闻语料分类.ipynb → 9.新闻语料分类.ipynb
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1,030 changes: 515 additions & 515 deletions ...checkpoints/ch1_affinity-checkpoint.ipynb → ...checkpoints/ch1_affinity-checkpoint.ipynb
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310 changes: 155 additions & 155 deletions ...ints/ch1_affinity_create-checkpoint.ipynb → ...ints/ch1_affinity_create-checkpoint.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"X = np.zeros((100, 5), dtype='bool')\n",
"features = [\"bread\", \"milk\", \"cheese\", \"apples\", \"bananas\"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"for i in range(X.shape[0]):\n",
" if np.random.random() < 0.3:\n",
" # A bread winner\n",
" X[i][0] = 1\n",
" if np.random.random() < 0.5:\n",
" # Who likes milk\n",
" X[i][1] = 1\n",
" if np.random.random() < 0.2:\n",
" # Who likes cheese\n",
" X[i][2] = 1\n",
" if np.random.random() < 0.25:\n",
" # Who likes apples\n",
" X[i][3] = 1\n",
" if np.random.random() < 0.5:\n",
" # Who likes bananas\n",
" X[i][4] = 1\n",
" else:\n",
" # Not a bread winner\n",
" if np.random.random() < 0.5:\n",
" # Who likes milk\n",
" X[i][1] = 1\n",
" if np.random.random() < 0.2:\n",
" # Who likes cheese\n",
" X[i][2] = 1\n",
" if np.random.random() < 0.25:\n",
" # Who likes apples\n",
" X[i][3] = 1\n",
" if np.random.random() < 0.5:\n",
" # Who likes bananas\n",
" X[i][4] = 1\n",
" else:\n",
" if np.random.random() < 0.8:\n",
" # Who likes cheese\n",
" X[i][2] = 1\n",
" if np.random.random() < 0.6:\n",
" # Who likes apples\n",
" X[i][3] = 1\n",
" if np.random.random() < 0.7:\n",
" # Who likes bananas\n",
" X[i][4] = 1\n",
" if X[i].sum() == 0:\n",
" X[i][4] = 1 # Must buy something, so gets bananas\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[False False True True True]\n",
" [ True True False True False]\n",
" [ True False True True False]\n",
" [False False True True True]\n",
" [False True False False True]]\n"
]
}
],
"source": [
"print(X[:5])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt(\"affinity_dataset.txt\", X, fmt='%d')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"navigate_num": "#000000",
"navigate_text": "#333333",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "12px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"X = np.zeros((100, 5), dtype='bool')\n",
"features = [\"bread\", \"milk\", \"cheese\", \"apples\", \"bananas\"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"for i in range(X.shape[0]):\n",
" if np.random.random() < 0.3:\n",
" # A bread winner\n",
" X[i][0] = 1\n",
" if np.random.random() < 0.5:\n",
" # Who likes milk\n",
" X[i][1] = 1\n",
" if np.random.random() < 0.2:\n",
" # Who likes cheese\n",
" X[i][2] = 1\n",
" if np.random.random() < 0.25:\n",
" # Who likes apples\n",
" X[i][3] = 1\n",
" if np.random.random() < 0.5:\n",
" # Who likes bananas\n",
" X[i][4] = 1\n",
" else:\n",
" # Not a bread winner\n",
" if np.random.random() < 0.5:\n",
" # Who likes milk\n",
" X[i][1] = 1\n",
" if np.random.random() < 0.2:\n",
" # Who likes cheese\n",
" X[i][2] = 1\n",
" if np.random.random() < 0.25:\n",
" # Who likes apples\n",
" X[i][3] = 1\n",
" if np.random.random() < 0.5:\n",
" # Who likes bananas\n",
" X[i][4] = 1\n",
" else:\n",
" if np.random.random() < 0.8:\n",
" # Who likes cheese\n",
" X[i][2] = 1\n",
" if np.random.random() < 0.6:\n",
" # Who likes apples\n",
" X[i][3] = 1\n",
" if np.random.random() < 0.7:\n",
" # Who likes bananas\n",
" X[i][4] = 1\n",
" if X[i].sum() == 0:\n",
" X[i][4] = 1 # Must buy something, so gets bananas\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[False False True True True]\n",
" [ True True False True False]\n",
" [ True False True True False]\n",
" [False False True True True]\n",
" [False True False False True]]\n"
]
}
],
"source": [
"print(X[:5])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt(\"affinity_dataset.txt\", X, fmt='%d')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"navigate_num": "#000000",
"navigate_text": "#333333",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "12px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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