更新构建树的Coding

src/python/01.NumPy.py

@@ -20,10 +20,15 @@ randArray = random.rand(4, 4)
 
 # 转化关系， 数组转化为矩阵
 randMat = mat(randArray)
-# .I表示对矩阵求逆
+# .I表示对矩阵求逆(可以利用矩阵的初等变换
+#    # 意义：逆矩阵是一个判断相似性的工具。逆矩阵A与列向量p相乘后，将得到列向量q，q的第i个分量表示p与A的第i个列向量的相似度。
+#    # 参考案例链接：
+#    # https://www.zhihu.com/question/33258489
+#    # http://blog.csdn.net/vernice/article/details/48506027
+# .T表示对矩阵转置(行列颠倒)
 invRandMat = randMat.I
 # 输出结果
-print randArray, '\n', randMat, '\n', invRandMat
+print randArray, '\n---\n', randMat, '\n+++\n', invRandMat
 # 矩阵和逆矩阵 进行求积 (单位矩阵，对角线都为1嘛，理论上4*4的矩阵其他的都为0)
 myEye = randMat*invRandMat
 # 误差

src/python/03.DecisionTree/DTSklearn.py

@@ -98,6 +98,7 @@ def show_pdf(clf):
     # from IPython.display import Image
     # Image(graph.create_png())
 
+
 if __name__ == '__main__':
     x, y = createDataSet()
 

src/python/03.DecisionTree/DecisionTreePlot.py

@@ -77,9 +77,9 @@ def plotTree(myTree, parentPt, nodeTxt):
     plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD
     for key in secondDict.keys():
         # 判断该节点是否是Node节点
-        if type(secondDict[key]).__name__=='dict':
+        if type(secondDict[key]).__name__ == 'dict':
             # 如果是就递归调用[recursion]
-            plotTree(secondDict[key],cntrPt,str(key))
+            plotTree(secondDict[key], cntrPt, str(key))
         else:
             # 如果不是，就在原来节点一半的地方找到节点的坐标
             plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW
@@ -121,7 +121,7 @@ def createPlot(inTree):
 
 # 测试数据集
 def retrieveTree(i):
-    listOfTrees =[
+    listOfTrees = [
         {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}},
         {'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}}
     ]

src/python/09.RegTrees/TreeNode.py

@@ -0,0 +1,16 @@
+#!/usr/bin/python
+# coding:utf8
+
+'''
+Created on 2017-03-06
+Update on 2017-03-06
+@author: jiangzhonglian
+'''
+
+
+class treeNode():
+    def __init__(self, feat, val, right, left):
+        self.featureToSplitOn = feat
+        self.valueOfSplit = val
+        self.rightBranch = right
+        self.leftBranch = left

src/python/09.RegTrees/regTrees.py

@@ -9,25 +9,136 @@ Tree-Based Regression Methods Source Code for Machine Learning in Action Ch. 9
 '''
 from numpy import *
 
-def loadDataSet(fileName):      #general function to parse tab -delimited floats
-    dataMat = []                #assume last column is target value
+
+# 默认解析的数据是用tab分隔，并且是数值类型
+# general function to parse tab -delimited floats
+def loadDataSet(fileName):
+    """loadDataSet(解析每一行，并转化为float类型)
+
+    Args:
+        fileName 文件名
+    Returns:
+        dataMat 每一行的数据集array类型
+    Raises:
+    """
+    # 假定最后一列是结果值
+    # assume last column is target value
+    dataMat = []
     fr = open(fileName)
     for line in fr.readlines():
         curLine = line.strip().split('\t')
-        fltLine = map(float,curLine) #map all elements to float()
+        # 将所有的元素转化为float类型
+        # map all elements to float()
+        fltLine = map(float, curLine)
         dataMat.append(fltLine)
     return dataMat
 
+
 def binSplitDataSet(dataSet, feature, value):
-    mat0 = dataSet[nonzero(dataSet[:,feature] > value)[0],:][0]
-    mat1 = dataSet[nonzero(dataSet[:,feature] <= value)[0],:][0]
-    return mat0,mat1
+    """binSplitDataSet(将数据集，按照feature列的value进行 二元切分)
 
-def regLeaf(dataSet):#returns the value used for each leaf
-    return mean(dataSet[:,-1])
+    Args:
+        fileName 文件名
+    Returns:
+        dataMat 每一行的数据集array类型
+    Raises:
+    """
+    # # 测试案例
+    # print 'dataSet[:, feature]=', dataSet[:, feature]
+    # print 'nonzero(dataSet[:, feature] > value)[0]=', nonzero(dataSet[:, feature] > value)[0]
+    # print 'nonzero(dataSet[:, feature] <= value)[0]=', nonzero(dataSet[:, feature] <= value)[0]
 
+    # dataSet[:, feature] 取去每一行中，第1列的值(从0开始算)
+    # nonzero(dataSet[:, feature] > value)  返回结果为true行的index下标
+    mat0 = dataSet[nonzero(dataSet[:, feature] > value)[0], :]
+    mat1 = dataSet[nonzero(dataSet[:, feature] <= value)[0], :]
+    return mat0, mat1
+
+
+# 返回每一个叶子结点的均值
+# returns the value used for each leaf
+def regLeaf(dataSet):
+    return mean(dataSet[:, -1])
+
+
+# 计算总方差=方差*样本数
 def regErr(dataSet):
-    return var(dataSet[:,-1]) * shape(dataSet)[0]
+    # shape(dataSet)[0] 表示行数
+    return var(dataSet[:, -1]) * shape(dataSet)[0]
+
+
+# 1.用最佳方式切分数据集
+# 2.生成相应的叶节点
+def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1, 4)):
+    """chooseBestSplit(用最佳方式切分数据集 和 生成相应的叶节点)
+
+    Args:
+        dataSet 数据集
+        leafType 计算叶子节点的函数
+        errType 求总方差
+        ops [容许误差下降值，切分的最少样本数]
+    Returns:
+        bestIndex feature的index坐标
+        bestValue 切分的最优值
+    Raises:
+    """
+    tolS = ops[0]
+    tolN = ops[1]
+    # 如果结果集(最后一列为1个变量)，就返回推出
+    # .T 对数据集进行转置
+    # .tolist()[0] 转化为数组并取第0列
+    if len(set(dataSet[:, -1].T.tolist()[0])) == 1:
+        #  exit cond 1
+        return None, leafType(dataSet)
+    # 计算行列值
+    m, n = shape(dataSet)
+    print m, n
+    # 无分类误差的总方差和
+    # the choice of the best feature is driven by Reduction in RSS error from mean
+    S = errType(dataSet)
+    # inf 正无穷大
+    bestS, bestIndex, bestValue = inf, 0, 0
+    # 循环处理每一列对应的feature值
+    for featIndex in range(n-1):
+        for splitVal in set(dataSet[:, featIndex].T.tolist()[0]):
+            # 对该列进行分组，然后组内的成员的val值进行 二元切分
+            mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal)
+            # 判断二元切分的方式的元素数量是否符合预期
+            if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN):
+                continue
+            newS = errType(mat0) + errType(mat1)
+            # 如果二元切分，算出来的误差在可接受范围内，那么就记录切分点，并记录最小误差
+            if newS < bestS:
+                bestIndex = featIndex
+                bestValue = splitVal
+                bestS = newS
+    # 判断二元切分的方式的元素误差是否符合预期
+    # if the decrease (S-bestS) is less than a threshold don't do the split
+    if (S - bestS) < tolS:
+        return None, leafType(dataSet)
+    mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue)
+    # 对整体的成员进行判断，是否符合预期
+    if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN):
+        return None, leafType(dataSet)
+    return bestIndex, bestValue
+
+
+# assume dataSet is NumPy Mat so we can array filtering
+def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(1, 4)):
+    # 选择最好的切分方式： feature索引值，最优切分值
+    # choose the best split
+    feat, val = chooseBestSplit(dataSet, leafType, errType, ops)
+    # if the splitting hit a stop condition return val
+    if feat is None:
+        return val
+    retTree = {}
+    retTree['spInd'] = feat
+    retTree['spVal'] = val
+    lSet, rSet = binSplitDataSet(dataSet, feat, val)
+    retTree['right'] = createTree(lSet, leafType, errType, ops)
+    retTree['left'] = createTree(rSet, leafType, errType, ops)
+    return retTree
+
 
 def linearSolve(dataSet):   #helper function used in two places
     m,n = shape(dataSet)
@@ -49,43 +160,7 @@ def modelErr(dataSet):
     yHat = X * ws
     return sum(power(Y - yHat,2))
 
-def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):
-    tolS = ops[0]; tolN = ops[1]
-    #if all the target variables are the same value: quit and return value
-    if len(set(dataSet[:,-1].T.tolist()[0])) == 1: #exit cond 1
-        return None, leafType(dataSet)
-    m,n = shape(dataSet)
-    #the choice of the best feature is driven by Reduction in RSS error from mean
-    S = errType(dataSet)
-    bestS = inf; bestIndex = 0; bestValue = 0
-    for featIndex in range(n-1):
-        for splitVal in set(dataSet[:,featIndex]):
-            mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal)
-            if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): continue
-            newS = errType(mat0) + errType(mat1)
-            if newS < bestS: 
-                bestIndex = featIndex
-                bestValue = splitVal
-                bestS = newS
-    #if the decrease (S-bestS) is less than a threshold don't do the split
-    if (S - bestS) < tolS: 
-        return None, leafType(dataSet) #exit cond 2
-    mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue)
-    if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN):  #exit cond 3
-        return None, leafType(dataSet)
-    return bestIndex,bestValue#returns the best feature to split on
-                              #and the value used for that split
 
-def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):#assume dataSet is NumPy Mat so we can array filtering
-    feat, val = chooseBestSplit(dataSet, leafType, errType, ops)#choose the best split
-    if feat == None: return val #if the splitting hit a stop condition return val
-    retTree = {}
-    retTree['spInd'] = feat
-    retTree['spVal'] = val
-    lSet, rSet = binSplitDataSet(dataSet, feat, val)
-    retTree['left'] = createTree(lSet, leafType, errType, ops)
-    retTree['right'] = createTree(rSet, leafType, errType, ops)
-    return retTree  
 
 def isTree(obj):
     return (type(obj).__name__=='dict')
@@ -137,4 +212,21 @@ def createForeCast(tree, testData, modelEval=regTreeEval):
     yHat = mat(zeros((m,1)))
     for i in range(m):
         yHat[i,0] = treeForeCast(tree, mat(testData[i]), modelEval)
-    return yHat
+    return yHat
+
+if __name__ == "__main__":
+
+
+    # # 测试数据集
+    # testMat = mat(eye(4))
+    # print testMat
+    # print type(testMat)
+    # mat0, mat1 = binSplitDataSet(testMat, 1, 0.5)
+    # print mat0, '\n-----------\n', mat1
+
+    # 获取数据集
+    # myDat = loadDataSet('testData/RT_data1.txt')
+    myDat = loadDataSet('testData/RT_data2.txt')
+    myMat = mat(myDat)
+    myTree = createTree(myMat)
+    print myTree