吴裕雄 python 机器学习-DMT（1），,import num

吴裕雄 python 机器学习-DMT（1），,import num

import numpy as npimport operator as opfrom math import logdef createDataSet():    dataSet = [[1, 1, ‘yes‘],               [1, 1, ‘yes‘],               [1, 0, ‘no‘],               [0, 1, ‘no‘],               [0, 1, ‘no‘]]    labels = [‘no surfacing‘,‘flippers‘]    return dataSet, labelsdataSet,labels = createDataSet()print(dataSet)print(labels)def calcShannonEnt(dataSet):    labelCounts = {}    for featVec in dataSet:         currentLabel = featVec[-1]        if(currentLabel not in labelCounts.keys()):             labelCounts[currentLabel] = 0        labelCounts[currentLabel] += 1    shannonEnt = 0.0    rowNum = len(dataSet)    for key in labelCounts:        prob = float(labelCounts[key])/rowNum        shannonEnt -= prob * log(prob,2)    return shannonEntshannonEnt = calcShannonEnt(dataSet)print(shannonEnt)def splitDataSet(dataSet, axis, value):    retDataSet = []    for featVec in dataSet:        if(featVec[axis] == value):            reducedFeatVec = featVec[:axis]                reducedFeatVec.extend(featVec[axis+1:])            retDataSet.append(reducedFeatVec)    return retDataSetretDataSet = splitDataSet(dataSet,1,1)print(np.array(retDataSet))retDataSet = splitDataSet(dataSet,1,0)print(retDataSet)def chooseBestFeatureToSplit(dataSet):    numFeatures = np.shape(dataSet)[1]-1          baseEntropy = calcShannonEnt(dataSet)    bestInfoGain = 0.0    bestFeature = -1    for i in range(numFeatures):                featList = [example[i] for example in dataSet]        uniqueVals = set(featList)               newEntropy = 0.0        for value in uniqueVals:            subDataSet = splitDataSet(dataSet, i, value)            prob = len(subDataSet)/float(len(dataSet))            newEntropy += prob * calcShannonEnt(subDataSet)             infoGain = baseEntropy - newEntropy             if (infoGain > bestInfoGain):                   bestInfoGain = infoGain                    bestFeature = i    return bestFeature bestFeature = chooseBestFeatureToSplit(dataSet)print(bestFeature)def majorityCnt(classList):    classCount={}    for vote in classList:        if(vote not in classCount.keys()):             classCount[vote] = 0        classCount[vote] += 1    sortedClassCount = sorted(classCount.items(), key=op.itemgetter(1), reverse=True)    return sortedClassCount[0][0]def createTree(dataSet,labels):    classList = [example[-1] for example in dataSet]    if(classList.count(classList[0]) == len(classList)):         return classList[0]    if len(dataSet[0]) == 1:         return majorityCnt(classList)    bestFeat = chooseBestFeatureToSplit(dataSet)    bestFeatLabel = labels[bestFeat]    myTree = {bestFeatLabel:{}}    del(labels[bestFeat])    featValues = [example[bestFeat] for example in dataSet]    uniqueVals = set(featValues)    for value in uniqueVals:        subLabels = labels[:]           myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)    return myTreemyTree = createTree(dataSet,labels)print(myTree)def classify(inputTree,featLabels,testVec):    for i in inputTree.keys():        firstStr = i        break    secondDict = inputTree[firstStr]    featIndex = featLabels.index(firstStr)    key = testVec[featIndex]    valueOfFeat = secondDict[key]    if isinstance(valueOfFeat, dict):         classLabel = classify(valueOfFeat, featLabels, testVec)    else:        classLabel = valueOfFeat    return classLabelfeatLabels = [‘no surfacing‘, ‘flippers‘]classLabel = classify(myTree,featLabels,[1,1])print(classLabel)import pickledef storeTree(inputTree,filename):    fw = open(filename,‘wb‘)    pickle.dump(inputTree,fw)    fw.close()    def grabTree(filename):    fr = open(filename,‘rb‘)    return pickle.load(fr)filename = "D:\\mytree.txt"storeTree(myTree,filename)mySecTree = grabTree(filename)print(mySecTree)featLabels = [‘no surfacing‘, ‘flippers‘]classLabel = classify(mySecTree,featLabels,[0,0])print(classLabel)

吴裕雄 python 机器学习-DMT（1）