79 lines
2.0 KiB
Python
79 lines
2.0 KiB
Python
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#!/usr/bin/env python
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# coding: utf-8
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from math import log
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# 计算香农熵
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def calcShannonEnt(dataSet):
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numEntries = len(dataSet)
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labelCounts = {}
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for featVec in dataSet:
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currentLabel = featVec[-1]
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if currentLabel not in labelCounts:
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labelCounts[currentLabel] = 0
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labelCounts[currentLabel] += 1
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shannonEnt = 0.0
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for key in labelCounts:
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prob = float(labelCounts[key]) / numEntries
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shannonEnt -= prob * log(prob, 2)
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return shannonEnt
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# 初始值
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def createDataSet():
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dataSet = [[1, 1, 'yes'],
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[1, 0, 'yes'],
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[1, 0, 'no'],
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[0, 1, 'no'],
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[0, 1, 'no']]
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label = ['no surfaceing', 'flippers']
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return dataSet, label
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# 按照给定特征划分数据集
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def splitDataSet(dataSet, axis, value):
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retDataSet = []
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for featVec in dataSet:
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if featVec[axis] == value:
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reducedFeatVec = featVec[:axis]
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reducedFeatVec.extend(featVec[axis + 1:])
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retDataSet.append(reducedFeatVec)
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return retDataSet
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# 选择最好数据集的划分方式
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def choooseBestFeatureToSplit(dataSet):
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numFeatures = len(dataSet)
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baseEntropy = calcShannonEnt(dataSet)
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bestInfoGain = 0.0
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bestFeature = -1
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for i in range(numFeatures):
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featList = [example[i] for example in dataSet]
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uniqyeVals = set(featList)
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newEntropy = 0.0
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for value in uniqyeVals:
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subDataSet = splitDataSet(dataSet, i, value)
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prob = len(subDataSet)/float(len(dataSet))
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newEntropy += prob * calcShannonEnt(subDataSet)
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infoGain = baseEntropy - newEntropy
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if (infoGain > bestInfoGain):
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bestInfoGain = infoGain
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bestFeature = i
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return bestFeature
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"""
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myData, labels = createDataSet()
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print(myData)
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calcShannonEnt(myData)
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myData[0][-1] = 'maybe'
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print(myData)
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shannonEnt = calcShannonEnt(myData)
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print(shannonEnt)
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myData[0][-1] = 'yes'
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data = splitDataSet(myData, 0, 1)
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print(data)
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data = splitDataSet(myData, 0, 0)
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print(data)
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"""
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