更新链接

src/python/1.MLFoundation/NumPy.py

@@ -1,6 +1,11 @@
 #!/usr/bin/python
 # coding:utf8
-
+'''
+Created on 2017-05-18
+Update  on 2017-05-18
+@author: Peter Harrington/山上有课树
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
+'''
 from numpy import random, mat, eye
 
 '''

src/python/10.kmeans/kMeans.py

@@ -1,6 +1,12 @@
 #!/usr/bin/python
 # coding:utf8
-
+'''
+Created on Feb 16, 2011
+Update on 2017-05-18
+k Means Clustering for Ch10 of Machine Learning in Action
+@author: Peter Harrington/那伊抹微笑
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
+'''
 from numpy import *
 
 

src/python/11.Apriori/apriori.py

@@ -3,9 +3,10 @@
 
 '''
 Created on Mar 24, 2011
-Update on 2017-03-16
+Update  on 2017-05-18
 Ch 11 code
 @author: Peter/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 print(__doc__)
 from numpy import *

src/python/12.FrequentPattemTree/fpGrowth.py

@@ -3,12 +3,14 @@
 
 '''
 Created on Jun 14, 2011
+Update  on 2017-05-18
 FP-Growth FP means frequent pattern
 the FP-Growth algorithm needs:
 1. FP-tree (class treeNode)
 2. header table (use dict)
 This finds frequent itemsets similar to apriori but does not find association rules.
 @author: Peter/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 print(__doc__)
 

src/python/13.PCA/pca.py

@@ -3,8 +3,9 @@
 
 '''
 Created on Jun 1, 2011
-Update on 2017-04-06
+Update  on 2017-05-18
 @author: Peter Harrington/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 print(__doc__)
 from numpy import *

src/python/14.SVD/svdRec.py

@@ -1,7 +1,11 @@
 #!/usr/bin/python
-# -*- coding: utf-8 -*-  
-# encoding: utf-8
-
+# coding: utf-8
+'''
+Created on Mar 8, 2011
+Update  on 2017-05-18
+@author: Peter Harrington/山上有课树
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
+'''
 from numpy import linalg as la
 from numpy import *
 

src/python/2.KNN/kNN.py

@@ -1,9 +1,13 @@
 #!/usr/bin/env python
 # encoding: utf-8
 '''
-导入科学计算包numpy和运算符模块operator
+Created on Sep 16, 2010
+Update  on 2017-05-18
+@author: Peter Harrington/羊山
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 from numpy import *
+# 导入科学计算包numpy和运算符模块operator
 import operator
 from os import listdir
 

src/python/3.DecisionTree/DTSklearn.py

@@ -1,6 +1,7 @@
 #!/usr/bin/python
 # coding: utf8
 # 原始链接： http://blog.csdn.net/lsldd/article/details/41223147
+# 《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 import numpy as np
 from sklearn import tree
 from sklearn.metrics import precision_recall_curve

src/python/3.DecisionTree/DecisionTree.py

@@ -1,11 +1,11 @@
 #!/usr/bin/python
 # coding:utf8
-
 '''
 Created on Oct 12, 2010
-Update on 2017-02-27
+Update on 2017-05-18
 Decision Tree Source Code for Machine Learning in Action Ch. 3
 @author: Peter Harrington/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 print(__doc__)
 import operator

src/python/4.NaiveBayes/bayes.py

@@ -1,5 +1,11 @@
 #!/usr/bin/env python
 # -*- coding:utf-8 -*-
+'''
+Created on Oct 19, 2010
+Update  on 2017-05-18
+@author: Peter Harrington/羊山
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
+'''
 from numpy import *
 """
 p(xy)=p(x|y)p(y)=p(y|x)p(x)

src/python/5.Logistic/logistic.py

@@ -3,8 +3,10 @@
 
 '''
 Created on Oct 27, 2010
+Update  on 2017-05-18
 Logistic Regression Working Module
-@author: Peter
+@author: Peter Harrington/羊山
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 from numpy import *
 import matplotlib.pyplot as plt

src/python/6.SVM/svm-complete.py

@@ -3,9 +3,10 @@
 
 """
 Created on Nov 4, 2010
-Update on 2017-03-21
+Update on 2017-05-18
 Chapter 5 source file for Machine Learing in Action
 @author: Peter/geekidentity/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 """
 from numpy import *
 import matplotlib.pyplot as plt

src/python/6.SVM/svm-complete_Non-Kernel.py

@@ -3,9 +3,10 @@
 
 """
 Created on Nov 4, 2010
-Update on 2017-03-21
+Update on 2017-05-18
 Chapter 5 source file for Machine Learing in Action
 @author: Peter/geekidentity/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 """
 from numpy import *
 import matplotlib.pyplot as plt

src/python/6.SVM/svm-simple.py

@@ -3,9 +3,10 @@
 
 """
 Created on Nov 4, 2010
-Update on 2017-03-21
+Update on 2017-05-18
 Chapter 5 source file for Machine Learing in Action
 @author: Peter/geekidentity/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 """
 from numpy import *
 import matplotlib.pyplot as plt

src/python/7.AdaBoost/adaboost.py

@@ -3,8 +3,10 @@
 
 '''
 Created on Nov 28, 2010
+Update  on 2017-05-18
 Adaboost is short for Adaptive Boosting
-@author: Peter/jiangzhonglian
+@author: Peter/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 from numpy import *
 

src/python/7.RandomForest/randomForest.py

@@ -3,8 +3,10 @@
 
 '''
 Created 2017-04-25
+Update  on 2017-05-18
 Random Forest Algorithm on Sonar Dataset
 @author: Flying_sfeng/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 ---
 源代码网址：http://www.tuicool.com/articles/iiUfeim
 Flying_sfeng博客地址：http://blog.csdn.net/flying_sfeng/article/details/64133822

src/python/8.Predictive numerical data regression/regression.py

@@ -2,8 +2,10 @@
 # coding:utf8
 
 '''
-Create by ApacheCN-小瑶
-Date from 2017-02-27
+Created on Jan 8, 2011
+Update  on 2017-05-18
+@author: Peter Harrington/ApacheCN-小瑶
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 
 

src/python/9.RegTrees/regTrees.py

@@ -1,11 +1,11 @@
 #!/usr/bin/python
 # coding:utf8
-
 '''
 Created on Feb 4, 2011
-Update on 2017-03-02
+Update on 2017-05-18
 Tree-Based Regression Methods Source Code for Machine Learning in Action Ch. 9
 @author: Peter Harrington/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 print(__doc__)
 from numpy import *

src/python/9.RegTrees/treeExplore.py

@@ -3,9 +3,10 @@
 
 '''
 Created on 2017-03-08
-Update on 2017-03-08
+Update  on 2017-05-18
 Tree-Based Regression Methods Source Code for Machine Learning in Action Ch. 9
 @author: Peter/片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 import regTrees
 from Tkinter import *