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 math
import operator


def 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 shannonEnt


def 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, labels


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 retDataSet


def 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 bestFeature


def 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 myTree


def 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 classLabel


myDat, 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:ProgramsPythonPython36-64python.exe D:/pyfenlei/决策树/jcs4.py
跌
跌
跌
跌