《机器学习实战》读后备忘

最近在看《机器学习实战》第9章，CART算法用于回归，在python2.7版本下跑，发现代码报错，所以勘误了下。下面只是回归树的部分勘误代码：

class treeNode():    def __init__(self, feat, val, right, left):        featureToSplitOn = feat#切分的特征        valueOfSplit = val#切分的值        rightBranch = right#左子树        leftBranch = left#右子树from numpy import *def loadDataSet(fileName):#加载数据    dataMat=[]    fr = open(fileName)    for line in fr.readlines():        curLine = line.strip().split('\t')        fltLine = map(float, curLine)        dataMat.append(fltLine)    return dataMat    def binSplitDataSet(dataSet, feature, value):    #会不会发生没分割的情况？特征值恰巧处于边界？    mat0 = dataSet[nonzero(dataSet[:,feature]>value)[0],:]    mat1 = dataSet[nonzero(dataSet[:,feature]<=value)[0],:]    return mat0, mat1#testMat = mat(eye(4))#print testMat#mat0,mat1 = binSplitDataSet(testMat, 1,0.5)#print 'first: ', mat0#print 'second',mat1def regLeaf(dataSet):    return mean(dataSet[:,-1])#计算数据集标签值得均值    def regErr(dataSet):    return var(dataSet[:,-1])*shape(dataSet)[0]#方差*个数，衡量了一个数据集的混乱程度(个人理解：或者说是聚集程度)    def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):    tolS = ops[0]#数据集的足够有序不用分割程度    tolN = ops[1]#数据集的足够小不用分割程度    if len(set(dataSet[:,-1].T.tolist()[0]))==1:#数据集的标签值一样，没有必要分割        return None, leafType(dataSet)#分割下标为空，返回叶子节点    m,n =shape(dataSet)#数据集大小    S = errType(dataSet)#数据集混乱度    bestS = inf#最佳混乱度    bestIndex = 0#最佳分割特征    bestValue = 0#最佳分割值，分割值是有限个的，后面代码将看到。这些值是从数据集来的    for featIndex in range(n-1):#将去一是为了排除标签，获取特征个数        uniqeFeatVals = set(dataSet[:,featIndex].T.tolist()[0])        minFeatVal = min(uniqeFeatVals)        maxFeatVal = max(uniqeFeatVals)        for splitVal in uniqeFeatVals:#特征确定了，遍历它可能的所有值，如何对处于边界的特征值进行分割？            #如果是边界特征值，不让他去分割！            if (splitVal != minFeatVal) and (splitVal != maxFeatVal):                mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal)                if (shape(mat0)[0]<tolN) or (shape(mat1)[0]<tolN):#数据集太小，不割了                    continue                newS = errType(mat0)+errType(mat1)#计算分割后两个数据集混乱都总和                if newS < bestS:#混乱都越小越好                    bestIndex = featIndex                    bestValue = splitVal                    bestS = newS    if (S-bestS)<tolS:#混乱度足够小，创建叶子        return None, leafType(dataSet)    mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue)    if (shape(mat0)[0]<tolN) or (shape(mat1)[0]<tolN):        return None, leafType(dataSet)#数据集足够小，创建叶子    return bestIndex, bestValue#创建一颗树，递归调用自己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  from numpy import *myDat = loadDataSet('ex00.txt')myMat = mat(myDat)tree = createTree(myMat)tree