科学经得起实践检验-python3.6通过决策树实战精准准确预测今日大盘走势(含代码)

科学经得起实践检验-python3.6通过决策树实战精准准确预测今日大盘走势(含代码)

春有百花秋有月，夏有凉风冬有雪；

若无闲事挂心头，便是人间好时节。

　　

　　--宋．无门慧开

不废话了，以下训练模型数据，采用本人发明的极致800实时指数近期的一些实际数据，

预测采用今日的真实数据

#coding=utf-8__author__ = 'huangzhi'import mathimport operatordef calcShannonEnt(dataset):    numEntries = len(dataset)    labelCounts = {}    for featVec in dataset:        currentLabel = featVec[-1]        if currentLabel not in labelCounts.keys():            labelCounts[currentLabel] = 0        labelCounts[currentLabel] += 1    shannonEnt = 0.0    for key in labelCounts:        prob = float(labelCounts[key]) / numEntries        shannonEnt -= prob * math.log(prob, 2)    return shannonEntdef CreateDataSet():    # dataset = [[1, 1, 'yes'],    #            [1, 1, 'yes'],    #            [1, 0, 'no'],    #            [0, 1, 'no'],    #            [0, 1, 'no']]    dataset = [[3, 4, 100, 85, 4, 6, 110, 120, 4, 6, 151, 122, 8, 12, 110, 185, '跌'],               [5, 7, 88, 85, 6, 8, 100, 130, 6, 9, 131, 132, 8, 14, 100, 195, '跌'],               [6, 2, 60, 20, 9, 3, 80, 22, 16, 4, 131, 32, 33, 5, 160, 45, '涨'],               [3, 4, 100, 105, 4, 6, 110, 120, 4, 6, 151, 122, 8, 12, 110, 185, '跌'],               [5, 3, 50, 30, 8, 4, 70, 28, 12, 6, 101, 42, 28, 7, 120, 35, '涨'],               [2, 6, 60, 95, 4, 8, 90, 130, 6, 11, 101, 142, 9, 15, 99, 145, '跌'],               [5, 3, 70, 30, 8, 4, 90, 32, 22, 6, 141, 42, 43, 8, 150, 65, '涨'],               [2, 8, 30, 60, 9, 8, 80, 90, 9, 20, 140, 160, 12, 32, 101, 205, '跌']]    labels = ['l1', 'l2', 'l3', 'l4', 'l5', 'l6', 'l7', 'l8', 'l9', 'l11', 'l12', 'l13', 'l14', 'l15', 'l16', 'l17']    return dataset, labelsdef 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 retDataSetdef chooseBestFeatureToSplit(dataSet):    numberFeatures = len(dataSet[0]) - 1    baseEntropy = calcShannonEnt(dataSet)    bestInfoGain = 0.0;    bestFeature = -1;    for i in range(numberFeatures):        featList = [example[i] for example in dataSet]        # print(featList)        uniqueVals = set(featList)        # print(uniqueVals)        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 bestFeaturedef majorityCnt(classList):    classCount = {}    for vote in classList:        if vote not in classCount.keys():            classCount[vote] = 0        classCount[vote] = 1    sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)    return sortedClassCount[0][0]def createTree(dataSet, inputlabels):    labels = inputlabels[:]    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 myTreedef classify(inputTree, featLabels, testVec):    firstStr = list(inputTree.keys())[0]    secondDict = inputTree[firstStr]    featIndex = featLabels.index(firstStr)    for key in secondDict.keys():        if testVec[featIndex] == key:            if type(secondDict[key]).__name__ == 'dict':                classLabel = classify(secondDict[key], featLabels, testVec)            else:                classLabel = secondDict[key]    return classLabelmyDat, labels = CreateDataSet()# print(calcShannonEnt(myDat))# print(splitDataSet(myDat, 1, 1))# print(chooseBestFeatureToSplit(myDat))myTree = createTree(myDat, labels)#通过早上9:41分的实际数据进行预测print(classify(myTree, labels, [1, 6, 156, 169, 1, 6, 156, 169, 1, 6, 156, 169, 1, 6, 156, 169]))#通过早上10:41分的实际数据进行预测print(classify(myTree, labels, [1, 6, 156, 169, 4, 9, 129, 263, 4, 9, 129, 263, 4, 9, 129, 263]))#通过下午13:41分的实际数据进行预测print(classify(myTree, labels, [1, 6, 156, 169, 4, 9, 129, 263, 5, 12, 123, 306, 5, 12, 123, 306]))#通过下午14:41分的实际数据进行预测print(classify(myTree, labels, [1, 6, 156, 169, 4, 9, 129, 263, 5, 12, 123, 306, 6, 13, 99, 397]))

运行结果如下：

D:\Programs\Python\Python36-64\python.exe D:/pyfenlei/决策树/jcs4.py跌跌跌跌