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Binary files /dev/null and b/output/3.DecisionTree/tree.pdf differ
diff --git a/src/python/01.MLFoundation/01.NumPy.py b/src/python/1.MLFoundation/NumPy.py
similarity index 100%
rename from src/python/01.MLFoundation/01.NumPy.py
rename to src/python/1.MLFoundation/NumPy.py
diff --git a/src/python/02.kNN/kNN.py b/src/python/2.KNN/kNN.py
similarity index 93%
rename from src/python/02.kNN/kNN.py
rename to src/python/2.KNN/kNN.py
index f10e2021..6b4de337 100644
--- a/src/python/02.kNN/kNN.py
+++ b/src/python/2.KNN/kNN.py
@@ -151,7 +151,7 @@ def datingClassTest():
     # 设置测试数据的的一个比例（训练数据集比例=1-hoRatio）
     hoRatio = 0.1  # 测试范围,一部分测试一部分作为样本
     # 从文件中加载数据
-    datingDataMat, datingLabels = file2matrix('input/02.KNN/datingTestSet2.txt')  # load data setfrom file
+    datingDataMat, datingLabels = file2matrix('input/2.KNN/datingTestSet2.txt')  # load data setfrom file
     # 归一化数据
     normMat, ranges, minVals = autoNorm(datingDataMat)
     m = normMat.shape[0]
@@ -186,7 +186,7 @@ def img2vector(filename):
 def handwritingClassTest():
     # 1. 导入数据
     hwLabels = []
-    trainingFileList = listdir('input/02.KNN/trainingDigits')  # load the training set
+    trainingFileList = listdir('input/2.KNN/trainingDigits')  # load the training set
     m = len(trainingFileList)
     trainingMat = zeros((m, 1024))
     # hwLabels存储0～9对应的index位置， trainingMat存放的每个位置对应的图片向量
@@ -195,17 +195,17 @@ def handwritingClassTest():
         fileStr = fileNameStr.split('.')[0]  # take off .txt
         classNumStr = int(fileStr.split('_')[0])
         hwLabels.append(classNumStr)
-        trainingMat[i, :] = img2vector('input/02.KNN/trainingDigits/%s' % fileNameStr)
+        trainingMat[i, :] = img2vector('input/2.KNN/trainingDigits/%s' % fileNameStr)
 
     # 2. 导入测试数据
-    testFileList = listdir('input/02.KNN/testDigits')  # iterate through the test set
+    testFileList = listdir('input/2.KNN/testDigits')  # iterate through the test set
     errorCount = 0.0
     mTest = len(testFileList)
     for i in range(mTest):
         fileNameStr = testFileList[i]
         fileStr = fileNameStr.split('.')[0]  # take off .txt
         classNumStr = int(fileStr.split('_')[0])
-        vectorUnderTest = img2vector('input/02.KNN/testDigits/%s' % fileNameStr)
+        vectorUnderTest = img2vector('input/2.KNN/testDigits/%s' % fileNameStr)
         classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
         print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)
         if (classifierResult != classNumStr): errorCount += 1.0
diff --git a/src/python/03.DecisionTree/DTSklearn.py b/src/python/3.DecisionTree/DTSklearn.py
similarity index 97%
rename from src/python/03.DecisionTree/DTSklearn.py
rename to src/python/3.DecisionTree/DTSklearn.py
index f1c7d143..dd7e506f 100644
--- a/src/python/03.DecisionTree/DTSklearn.py
+++ b/src/python/3.DecisionTree/DTSklearn.py
@@ -12,7 +12,7 @@ def createDataSet():
     ''' 数据读入 '''
     data = []
     labels = []
-    with open("input/03.DecisionTree/data.txt") as ifile:
+    with open("input/3.DecisionTree/data.txt") as ifile:
         for line in ifile:
             # 特征： 身高 体重   label： 胖瘦
             tokens = line.strip().split(' ')
@@ -93,7 +93,7 @@ def show_pdf(clf):
     dot_data = StringIO()
     tree.export_graphviz(clf, out_file=dot_data)
     graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
-    graph.write_pdf("output/03.DecisionTree/tree.pdf")
+    graph.write_pdf("output/3.DecisionTree/tree.pdf")
 
     # from IPython.display import Image
     # Image(graph.create_png())
diff --git a/src/python/03.DecisionTree/DecisionTree.py b/src/python/3.DecisionTree/DecisionTree.py
similarity index 100%
rename from src/python/03.DecisionTree/DecisionTree.py
rename to src/python/3.DecisionTree/DecisionTree.py
diff --git a/src/python/03.DecisionTree/DecisionTreePlot.py b/src/python/3.DecisionTree/decisionTreePlot.py
similarity index 100%
rename from src/python/03.DecisionTree/DecisionTreePlot.py
rename to src/python/3.DecisionTree/decisionTreePlot.py
diff --git a/src/python/3.DecisionTree/decisionTreePlot.pyc b/src/python/3.DecisionTree/decisionTreePlot.pyc
new file mode 100644
index 00000000..826e38d7
Binary files /dev/null and b/src/python/3.DecisionTree/decisionTreePlot.pyc differ
diff --git a/src/python/04.NaiveBayes/bayes.py b/src/python/4.NaiveBayes/bayes.py
similarity index 100%
rename from src/python/04.NaiveBayes/bayes.py
rename to src/python/4.NaiveBayes/bayes.py
diff --git a/src/python/05.Logistic/logistic.py b/src/python/5.Logistic/logistic.py
similarity index 98%
rename from src/python/05.Logistic/logistic.py
rename to src/python/5.Logistic/logistic.py
index 3c1b9b8a..94501394 100644
--- a/src/python/05.Logistic/logistic.py
+++ b/src/python/5.Logistic/logistic.py
@@ -129,7 +129,7 @@ def plotBestFit(dataArr, labelMat, weights):
 
 def main():
     # 1.收集并准备数据
-    dataMat, labelMat = loadDataSet("input/05.Logistic/TestSet.txt")
+    dataMat, labelMat = loadDataSet("input/5.Logistic/TestSet.txt")
 
     # print dataMat, '---\n', labelMat
     # 2.训练模型，  f(x)=a1*x1+b2*x2+..+nn*xn中 (a1,b2, .., nn).T的矩阵值
diff --git a/src/python/06.SVM/svmMLiA.py b/src/python/6.SVM/svmMLiA.py
similarity index 100%
rename from src/python/06.SVM/svmMLiA.py
rename to src/python/6.SVM/svmMLiA.py
diff --git a/src/python/07.AdaBoost/adaboost.py b/src/python/7.AdaBoost/adaboost.py
similarity index 98%
rename from src/python/07.AdaBoost/adaboost.py
rename to src/python/7.AdaBoost/adaboost.py
index 427559d4..115eee4b 100644
--- a/src/python/07.AdaBoost/adaboost.py
+++ b/src/python/7.AdaBoost/adaboost.py
@@ -290,13 +290,13 @@ if __name__ == "__main__":
 
     # # 马疝病数据集
     # # 训练集合
-    # dataArr, labelArr = loadDataSet("input/07.AdaBoost/horseColicTraining2.txt")
+    # dataArr, labelArr = loadDataSet("input/7.AdaBoost/horseColicTraining2.txt")
     # weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 40)
     # print weakClassArr, '\n-----\n', aggClassEst.T
     # # 计算ROC下面的AUC的面积大小
     # plotROC(aggClassEst.T, labelArr)
     # # 测试集合
-    # dataArrTest, labelArrTest = loadDataSet("input/07.AdaBoost/horseColicTest2.txt")
+    # dataArrTest, labelArrTest = loadDataSet("input/7.AdaBoost/horseColicTest2.txt")
     # m = shape(dataArrTest)[0]
     # predicting10 = adaClassify(dataArrTest, weakClassArr)
     # errArr = mat(ones((m, 1)))
diff --git a/src/python/08.Predictive numerical data regression/regression.py b/src/python/8.Predictive numerical data regression/regression.py
similarity index 97%
rename from src/python/08.Predictive numerical data regression/regression.py
rename to src/python/8.Predictive numerical data regression/regression.py
index 480cffba..41139a7c 100644
--- a/src/python/08.Predictive numerical data regression/regression.py	
+++ b/src/python/8.Predictive numerical data regression/regression.py	
@@ -233,7 +233,7 @@ def crossValidation(xArr,yArr,numVal=10):
 
     #test for standRegression
 def regression1():
-    xArr, yArr = loadDataSet("testData/Regression_data.txt")
+    xArr, yArr = loadDataSet("input/8.Regression/data.txt")
     xMat = mat(xArr)
     yMat = mat(yArr)
     ws = standRegres(xArr, yArr)
@@ -251,7 +251,7 @@ def regression1():
 
     #test for LWLR
 def regression2():
-    xArr, yArr = loadDataSet("input/08.Regression/data.txt")
+    xArr, yArr = loadDataSet("input/8.Regression/data.txt")
     yHat = lwlrTest(xArr, xArr, yArr, 0.003)
     xMat = mat(xArr)
     srtInd = xMat[:,1].argsort(0)           #argsort()函数是将x中的元素从小到大排列，提取其对应的index(索引)，然后输出
@@ -265,7 +265,7 @@ def regression2():
 
 #test for ridgeRegression
 def regression3():
-    abX,abY = loadDataSet("input/08.Regression/abalone.txt")
+    abX,abY = loadDataSet("input/8.Regression/abalone.txt")
     ridgeWeights = ridgeTest(abX, abY)
     fig = plt.figure()
     ax = fig.add_subplot(111)
@@ -275,7 +275,7 @@ def regression3():
 
 #test for stageWise
 def regression4():
-    xArr,yArr=loadDataSet("input/08.Regression/abalone.txt")
+    xArr,yArr=loadDataSet("input/8.Regression/abalone.txt")
     stageWise(xArr,yArr,0.01,200)
     xMat = mat(xArr)
     yMat = mat(yArr).T
diff --git a/src/python/09.RegTrees/RTSklearn.py b/src/python/9.RegTrees/RTSklearn.py
similarity index 100%
rename from src/python/09.RegTrees/RTSklearn.py
rename to src/python/9.RegTrees/RTSklearn.py
diff --git a/src/python/09.RegTrees/regTrees.py b/src/python/9.RegTrees/regTrees.py
similarity index 96%
rename from src/python/09.RegTrees/regTrees.py
rename to src/python/9.RegTrees/regTrees.py
index fec88808..7f210dfd 100644
--- a/src/python/09.RegTrees/regTrees.py
+++ b/src/python/9.RegTrees/regTrees.py
@@ -290,8 +290,8 @@ if __name__ == "__main__":
     # print mat0, '\n-----------\n', mat1
 
     # # 回归树
-    # myDat = loadDataSet('input/09.RegTrees/data1.txt')
-    # # myDat = loadDataSet('input/09.RegTrees/data2.txt')
+    # myDat = loadDataSet('input/9.RegTrees/data1.txt')
+    # # myDat = loadDataSet('input/9.RegTrees/data2.txt')
     # # print 'myDat=', myDat
     # myMat = mat(myDat)
     # # print 'myMat=',  myMat
@@ -299,13 +299,13 @@ if __name__ == "__main__":
     # print myTree
 
     # # 1. 预剪枝就是：提起设置最大误差数和最少元素数
-    # myDat = loadDataSet('input/09.RegTrees/data3.txt')
+    # myDat = loadDataSet('input/9.RegTrees/data3.txt')
     # myMat = mat(myDat)
     # myTree = createTree(myMat, ops=(0, 1))
     # print myTree
 
     # # 2. 后剪枝就是：通过测试数据，对预测模型进行合并判断
-    # myDatTest = loadDataSet('input/09.RegTrees/data3test.txt')
+    # myDatTest = loadDataSet('input/9.RegTrees/data3test.txt')
     # myMat2Test = mat(myDatTest)
     # myFinalTree = prune(myTree, myMat2Test)
     # print '\n\n\n-------------------'
@@ -313,14 +313,14 @@ if __name__ == "__main__":
 
     # # --------
     # # 模型树求解
-    # myDat = loadDataSet('input/09.RegTrees/data4.txt')
+    # myDat = loadDataSet('input/9.RegTrees/data4.txt')
     # myMat = mat(myDat)
     # myTree = createTree(myMat, modelLeaf, modelErr)
     # print myTree
 
     # 回归树 VS 模型树 VS 线性回归
-    trainMat = mat(loadDataSet('input/09.RegTrees/bikeSpeedVsIq_train.txt'))
-    testMat = mat(loadDataSet('input/09.RegTrees/bikeSpeedVsIq_test.txt'))
+    trainMat = mat(loadDataSet('input/9.RegTrees/bikeSpeedVsIq_train.txt'))
+    testMat = mat(loadDataSet('input/9.RegTrees/bikeSpeedVsIq_test.txt'))
     # 回归树
     myTree1 = createTree(trainMat, ops=(1, 20))
     print myTree1
diff --git a/src/python/09.RegTrees/treeExplore.py b/src/python/9.RegTrees/treeExplore.py
similarity index 100%
rename from src/python/09.RegTrees/treeExplore.py
rename to src/python/9.RegTrees/treeExplore.py