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Title: GitHub - hbishandb/MachineLearning_Python: 机器学习算法python实现 · GitHub

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Description: 机器学习算法python实现. Contribute to hbishandb/MachineLearning_Python development by creating an account on GitHub.

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https://github.com/hbishandb/MachineLearning_Python#机器学习算法python实现
线性回归https://github.com/hbishandb/MachineLearning_Python/blob/master/LinearRegression
https://github.com/hbishandb/MachineLearning_Python#一线性回归
全部代码https://github.com/hbishandb/MachineLearning_Python/blob/master/LinearRegression/LinearRegression.py
https://github.com/hbishandb/MachineLearning_Python#1代价函数
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https://github.com/hbishandb/MachineLearning_Python#2梯度下降算法
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https://github.com/hbishandb/MachineLearning_Python#3均值归一化
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https://github.com/hbishandb/MachineLearning_Python#4最终运行结果
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LinearRegression_01.png
使用scikit-learn库中的线性模型实现https://github.com/hbishandb/MachineLearning_Python/blob/master/LinearRegression/LinearRegression_scikit-learn.py
https://github.com/hbishandb/MachineLearning_Python#5使用scikit-learn库中的线性模型实现
逻辑回归https://github.com/hbishandb/MachineLearning_Python/blob/master/LogisticRegression
https://github.com/hbishandb/MachineLearning_Python#二逻辑回归
全部代码https://github.com/hbishandb/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression.py
https://github.com/hbishandb/MachineLearning_Python#1代价函数-1
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https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_01.png
https://camo.githubusercontent.com/553e837c6635683f520d7e824445d1daae62ad56e3cf8a832253d92a0522fba7/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742253742685f253543746865746125323025374425323878253239253744
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https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_02.png
https://github.com/hbishandb/MachineLearning_Python#2梯度
https://camo.githubusercontent.com/b4c704104dbc22b23f487f159ef20f29a99de6214668e0f428609a7901a4af9c/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253543667261632537422537422535437061727469616c2532304a25323825354374686574612532302532392537442537442537422537422535437061727469616c25323025374225354374686574612532305f6a25374425374425374425323025334425323025354366726163253742312537442537426d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d253230253742253542253238253742685f253543746865746125323025374425323825374278253545253742253238692532392537442537442532392532302d2532302537427925354525374225323869253239253744253744253239785f6a25354525374225323869253239253744253544253744253230
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_03.jpg
https://github.com/hbishandb/MachineLearning_Python#3正则化
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https://camo.githubusercontent.com/553e837c6635683f520d7e824445d1daae62ad56e3cf8a832253d92a0522fba7/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742253742685f253543746865746125323025374425323878253239253744
https://github.com/hbishandb/MachineLearning_Python#4s型函数即
https://github.com/hbishandb/MachineLearning_Python#5映射为多项式
https://camo.githubusercontent.com/c13817ab54a19a19c1d5ab73af10fd7e059ddf150f07af5d90bd8c6f66bfb656/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d31253230253242253230253742785f31253744253230253242253230253742785f32253744253230253242253230785f3125354532253230253242253230253742785f31253744253742785f32253744253230253242253230785f3225354532
https://github.com/hbishandb/MachineLearning_Python#6使用scipy的优化方法
https://github.com/hbishandb/MachineLearning_Python#7运行结果
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_04.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_05.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_06.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_07.png
使用scikit-learn库中的逻辑回归模型实现https://github.com/hbishandb/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression_scikit-learn.py
https://github.com/hbishandb/MachineLearning_Python#8使用scikit-learn库中的逻辑回归模型实现
逻辑回归_手写数字识别_OneVsAllhttps://github.com/hbishandb/MachineLearning_Python/blob/master/LogisticRegression
https://github.com/hbishandb/MachineLearning_Python#逻辑回归_手写数字识别_onevsall
全部代码https://github.com/hbishandb/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression_OneVsAll.py
https://github.com/hbishandb/MachineLearning_Python#1随机显示100个数字
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_08.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_09.png
https://github.com/hbishandb/MachineLearning_Python#2onevsall
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_11.png
https://github.com/hbishandb/MachineLearning_Python#3手写数字识别
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_10.png
https://github.com/hbishandb/MachineLearning_Python#4预测
https://github.com/hbishandb/MachineLearning_Python#5运行结果
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_12.png
使用scikit-learn库中的逻辑回归模型实现https://github.com/hbishandb/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression_OneVsAll_scikit-learn.py
https://github.com/hbishandb/MachineLearning_Python#6使用scikit-learn库中的逻辑回归模型实现
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/LogisticRegression_13.png
https://github.com/hbishandb/MachineLearning_Python#三bp神经网络
全部代码https://github.com/hbishandb/MachineLearning_Python/blob/master/NeuralNetwok/NeuralNetwork.py
https://github.com/hbishandb/MachineLearning_Python#1神经网络model
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https://github.com/hbishandb/MachineLearning_Python/blob/master/images/NeuralNetwork_01.png
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https://github.com/hbishandb/MachineLearning_Python#2代价函数
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https://github.com/hbishandb/MachineLearning_Python#3正则化-1
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https://github.com/hbishandb/MachineLearning_Python#4反向传播bp
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https://camo.githubusercontent.com/5ed51b9db0d20b277a16da42c6955986d2bbff74048bd16d9758a9b4ce8c63c3/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253543667261632537422537422535437061727469616c2532304a25323825354354686574612532302532392537442537442537422537422535437061727469616c25323025354354686574612532305f253742696a2537442535452537422532386c253239253744253744253744253230253344253230445f253742696a2537442535452537422532386c253239253744
https://github.com/hbishandb/MachineLearning_Python#5bp可以求梯度的原因
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/NeuralNetwork_03.jpg
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/NeuralNetwork_04.png
https://github.com/hbishandb/MachineLearning_Python#6梯度检查
https://camo.githubusercontent.com/dd93629946f8969ee1398fe71b925e5a6e2ebdc38ea6542baf07e2f3b150f8e6/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d25354366726163253742253742644a2532382535437468657461253230253239253744253744253742253742642535437468657461253230253744253744253230253543617070726f78253230253543667261632537422537424a2532382535437468657461253230253230253242253230253543766172657073696c6f6e2532302532392532302d2532304a25323825354374686574612532302532302d253230253543766172657073696c6f6e25323025323925374425374425374225374232253543766172657073696c6f6e253230253744253744
https://github.com/hbishandb/MachineLearning_Python#7权重的随机初始化
https://github.com/hbishandb/MachineLearning_Python#8预测
https://github.com/hbishandb/MachineLearning_Python#9输出结果
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/NeuralNetwork_05.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/NeuralNetwork_06.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/NeuralNetwork_07.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/NeuralNetwork_08.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/NeuralNetwork_09.png
https://github.com/hbishandb/MachineLearning_Python#四svm支持向量机
https://github.com/hbishandb/MachineLearning_Python#1代价函数-2
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https://github.com/hbishandb/MachineLearning_Python/blob/master/images/SVM_07.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/SVM_08.png
https://camo.githubusercontent.com/e174a384cf1c56e89b5de8fcdbacc6c07ff4eee8a0f0a7185c6ba8f18365e683/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d2537426c253545253742253238312532392537442537442537426c253545253742253238322532392537442537442537426c253545253742253238332532392537442537442e2e2e
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https://github.com/hbishandb/MachineLearning_Python#4使用scikit-learn中的svm模型代码
全部代码https://github.com/hbishandb/MachineLearning_Python/blob/master/SVM/SVM_scikit-learn.py
https://github.com/hbishandb/MachineLearning_Python#5运行结果-1
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/SVM_09.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/SVM_10.png
https://github.com/hbishandb/MachineLearning_Python#五k-means聚类算法
全部代码https://github.com/hbishandb/MachineLearning_Python/blob/master/K-Means/K-Menas.py
https://github.com/hbishandb/MachineLearning_Python#1聚类过程
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/K-Means_01.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/K-Means_02.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/K-Means_03.png
https://github.com/hbishandb/MachineLearning_Python#2目标函数
https://camo.githubusercontent.com/953c5f0a06ce3f5a7bd592a4dfbf3a3759343f0299c88b7bdead6d88481bab32/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d4a253238253742632535452537422532383125323925374425374425324325323025354363646f7473253230253243253742632535452537422532386d253239253744253744253243253742755f3125374425324325323025354363646f7473253230253243253742755f6b25374425323925323025334425323025354366726163253742312537442537426d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d25323025374225374325374325374278253545253742253238692532392537442537442532302d253230253742755f253742253742632535452537422532386925323925374425374425374425374425374325374225374325354532253744253744253230
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/K-Means_07.png
https://camo.githubusercontent.com/adfd8af7566c70530283de7ce0eae9564fbfb7aea85797ea53925bd44d41692d/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d2537426325354525374225323869253239253744253744
https://camo.githubusercontent.com/bab62cd41e2de99f132796d387b7275c4d95df955e57f1435b43d6a110dd0a51/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742755f69253744
https://github.com/hbishandb/MachineLearning_Python#3聚类中心的选择
https://github.com/hbishandb/MachineLearning_Python#4聚类个数k的选择
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/K-Means_04.png
https://github.com/hbishandb/MachineLearning_Python#5应用图片压缩
使用scikit-learn库中的线性模型实现聚类https://github.com/hbishandb/MachineLearning_Python/blob/master/K-Means/K-Means_scikit-learn.py
https://github.com/hbishandb/MachineLearning_Python#6使用scikit-learn库中的线性模型实现聚类
https://github.com/hbishandb/MachineLearning_Python#7运行结果-1
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/K-Means_05.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/K-Means_06.png
https://github.com/hbishandb/MachineLearning_Python#六pca主成分分析降维
全部代码https://github.com/hbishandb/MachineLearning_Python/blob/master/PCA/PCA.py
https://github.com/hbishandb/MachineLearning_Python#1用处
https://github.com/hbishandb/MachineLearning_Python#22d--1dnd--kd
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/PCA_01.png
https://camo.githubusercontent.com/9d7fb819f5a864db20e89fb0591b20fef330019f8b4ad1f37651a1afb2ed049d/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f253234253234253742752535452537422532383125323925374425374425324325374275253545253742253238322532392537442537442532302535436c646f7473253230253742752535452537422532386b253239253744253744253234253234
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https://github.com/hbishandb/MachineLearning_Python#3主成分分析pca与线性回归的区别
https://github.com/hbishandb/MachineLearning_Python#4pca降维过程
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https://github.com/hbishandb/MachineLearning_Python/blob/master/images/PCA_02.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/PCA_03.png
https://github.com/hbishandb/MachineLearning_Python#5数据恢复
https://camo.githubusercontent.com/133403f742334ae1c860164abe971cd06e0f5c1a341e0ecd31de6057f795520b/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f253543666e5f636d2532302532342532342537425a25354525374225323869253239253744253744253230253344253230555f253742726564756365253744253545542a2537425825354525374225323869253239253744253744253234253234
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https://github.com/hbishandb/MachineLearning_Python#6主成分个数的选择即要降的维度
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https://github.com/hbishandb/MachineLearning_Python#7使用建议
https://github.com/hbishandb/MachineLearning_Python#8运行结果
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/PCA_04.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/PCA_05.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/PCA_06.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/PCA_07.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/PCA_08.png
使用scikit-learn库中的PCA实现降维https://github.com/hbishandb/MachineLearning_Python/blob/master/PCA/PCA.py_scikit-learn.py
https://github.com/hbishandb/MachineLearning_Python#9使用scikit-learn库中的pca实现降维
https://github.com/hbishandb/MachineLearning_Python#七异常检测-anomaly-detection
全部代码https://github.com/hbishandb/MachineLearning_Python/blob/master/AnomalyDetection/AnomalyDetection.py
https://github.com/hbishandb/MachineLearning_Python#1高斯分布正态分布gaussian-distribution
https://camo.githubusercontent.com/271e086af38ca74716a26e382ab30b1587640c68d13c10aeec4dd2c3e3993b7d/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d2532302532342532347025323878253239253230253344253230253742312532302535436f766572253230253742253543737172742532302537423225354370692532302537442532302535437369676d61253230253744253744253742652535452537422532302d253230253742253742253742253742253238782532302d25323075253239253744253545322537442537442532302535436f766572253230253742322537422535437369676d6125323025354532253744253744253744253744253744253234253234
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https://camo.githubusercontent.com/0047712ecdfaaf1ffe1e4b67dbd83f994b40a4601af598fce1fe640f6e2c5828/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d2532302532342532342537422535437369676d6125323025354532253744253230253344253230253742312532302535436f7665722532306d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d25323025374225374225374225323825374278253545253742253238692532392537442537442532302d2532307525323925374425354532253744253744253230253234253234
https://github.com/hbishandb/MachineLearning_Python#2异常检测算法
https://camo.githubusercontent.com/49c23c2fe3ead190757ced32050f7ca219c3f027a730bff8defe7ad3fee5f59e/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d2532302532342532342535432537422532302537427825354525374225323831253239253744253744253243253742782535452537422532383225323925374425374425324325323025354363646f7473253230253742782535452537422532386d253239253744253744253543253744253230253234253234
https://camo.githubusercontent.com/e4eeece966ac8ebce46a67a856e75feb53e7dec247f14a4aec8b8295d04533c4/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d25323025323425323478253230253543696e253230253742522535456e253744253234253234
https://camo.githubusercontent.com/8f8c43ac13e091f152bf13139adfab4e4008fcf04b3668b5c008a837089c39ae/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d253230253234253234253742785f31253744253243253742785f3225374425323025354363646f7473253230253742785f6e253744253234253234
https://camo.githubusercontent.com/8b3d57f10b0c853a6753eb5c56bf22cf470483846abff97212ecd9ee1061427c/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d253230253234253234702532387825323925323025334425323070253238253742785f31253744253342253742755f312537442532432535437369676d612532305f312535453225323970253238253742785f32253744253342253742755f322537442532432535437369676d612532305f322535453225323925323025354363646f747325323070253238253742785f6e253744253342253742755f6e2537442532432535437369676d612532305f6e2535453225323925323025334425323025354370726f642535436c696d6974735f2537426a253230253344253230312537442535456e25323025374270253238253742785f6a253744253342253742755f6a2537442532432535437369676d612532305f6a25354532253239253744253230253234253234
https://camo.githubusercontent.com/2cec5f033c5a3481217adca97c278bb8795ad60398de9677b794e26b0ab01290/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d253230253234253234253742755f31253744253243253742755f3225374425324325323025354363646f7473253230253243253742755f6e2537442533422535437369676d612532305f31253545322532432535437369676d612532305f322535453225323025354363646f74732532302532432535437369676d612532305f6e25354532253234253234
https://github.com/hbishandb/MachineLearning_Python#3评价px的好坏以及ε的选取
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_01.png
https://camo.githubusercontent.com/2149513ea048b3bd97b94c76c98c28a6df59106ac81f79f612a1c85f9ee026bb/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d253230253234253234253543507225323065636973696f6e25323025334425323025374225374254502537442532302535436f76657225323025374254502532302b2532304650253744253744253234253234
https://camo.githubusercontent.com/58688c42615d2f84f56c20fbbf422d631695c4033864a0f713e149df31ece1cf/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d2532302532342532342537422535436d6174686f70253742253543726d25323052652537442535436e6f6c696d697473253744253230253742253543726d25374263616c6c25374425374425323025334425323025374225374254502537442532302535436f76657225323025374254502532302b253230464e253744253744253234253234
https://camo.githubusercontent.com/8e0e05646b743c15f076b4811991ad6f35c9d43fc70c39805ed129bd17a7c828/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d253230253234253234253742465f3125374453636f72652532302533442532303225374225374250522537442532302535436f766572253230253742502532302b25323052253744253744253234253234
https://github.com/hbishandb/MachineLearning_Python#4选择使用什么样的feature单元高斯分布
https://github.com/hbishandb/MachineLearning_Python#5多元高斯分布
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_04.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_02.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_03.png
https://camo.githubusercontent.com/e4eeece966ac8ebce46a67a856e75feb53e7dec247f14a4aec8b8295d04533c4/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d25323025323425323478253230253543696e253230253742522535456e253744253234253234
https://camo.githubusercontent.com/4017ab68057aef8fd9eb638aa6a3b84323630ecbf1e9d1984eb0c16915fed413/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d2532302532342532342535436d75253230253543696e253230253742522535456e2537442532432535435369676d61253230253543696e253230253742522535452537426e25323025354374696d6573253230253742253543726d2537426e253744253744253744253744253234253234
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https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_05.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_07.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_06.png
https://github.com/hbishandb/MachineLearning_Python#6单元和多元高斯分布特点
https://camo.githubusercontent.com/fca7e1fc92681f5ea2a61d34cb9e4abbd39e9f0874817d8d3bd8d31069f81143/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d2532302532342532342535435369676d61253230253543696e253230253742522535452537426e25323025354374696d6573253230253742253543726d2537426e253744253744253744253744253234253234
https://github.com/hbishandb/MachineLearning_Python#7程序运行结果
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_08.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_09.png
https://github.com/hbishandb/MachineLearning_Python/blob/master/images/AnomalyDetection_10.png
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