from math import log def calcShannonEnt(dataSet): numEntries = len(dataSet) print("样本总数：" + str(numEntries)) labelCounts = {} #记录每一类标签的数量 #定义特征向量featVec for featVec in dataSet: currentLabel = featVec[-1] #最后一列是类别标签 if currentLabel not in labelCounts.keys(): labelCounts[currentLabel] = 0; labelCounts[currentLabel] += 1 #标签currentLabel出现的次数 print("当前labelCounts状态：" + str(labelCounts)) shannonEnt = 0.0 for key in labelCounts: prob = float(labelCounts[key]) / numEntries #每一个类别标签出现的概率 print(str(key) + "类别的概率：" + str(prob)) print(prob * log(prob, 2) ) shannonEnt -= prob * log(prob, 2) print("熵值：" + str(shannonEnt)) return shannonEnt def createDataSet(): dataSet = [ # [1, 1, 'yes'], # [1, 0, 'yes'], # [1, 1, 'no'], # [0, 1, 'no'], # [0, 1, 'no'], # #以下随意添加，用于测试熵的变化，越混乱越冲突，熵越大 # [1, 1, 'no'], # [1, 1, 'no'], # [1, 1, 'no'], # [1, 1, 'no'], # [1, 1, 'maybe'], # [1, 1, 'maybe1'] # 用下面的8个比较极端的例子看得会更清楚。如果按照这个规则继续增加下去，熵会继续增大。 # [1,1,'1'], # [1,1,'2'], # [1,1,'3'], # [1,1,'4'], # [1,1,'5'], # [1,1,'6'], # [1,1,'7'], # [1,1,'8'], # 这是另一个极端的例子，所有样本的类别是一样的，有序，不混乱，此时熵为0 [1,1,'1'], [1,1,'1'], [1,1,'1'], [1,1,'1'], [1,1,'1'], [1,1,'1'], [1,1,'1'], [1,1,'1'], ] labels = ['no surfacing', 'flippers'] return dataSet, labels def testCalcShannonEnt(): myDat, labels = createDataSet() print(calcShannonEnt(myDat)) if __name__ == '__main__': testCalcShannonEnt() print(log(0.000002, 2))
以下输出结果是每个样本的类别都不同时的输出结果：
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样本总数：8 |
from math import log
def calcShannonEnt(dataSet):numEntries = len(dataSet)print("样本总数：" + str(numEntries))
labelCounts = {} #记录每一类标签的数量
#定义特征向量featVecfor featVec in dataSet:currentLabel = featVec[-1] #最后一列是类别标签
if currentLabel not in labelCounts.keys():labelCounts[currentLabel] = 0;
labelCounts[currentLabel] += 1 #标签currentLabel出现的次数print("当前labelCounts状态：" + str(labelCounts))
shannonEnt = 0.0
for key in labelCounts:prob = float(labelCounts[key]) / numEntries #每一个类别标签出现的概率
print(str(key) + "类别的概率：" + str(prob))print(prob * log(prob, 2) )shannonEnt -= prob * log(prob, 2) print("熵值：" + str(shannonEnt))
return shannonEnt
def createDataSet():dataSet = [# [1, 1, 'yes'],# [1, 0, 'yes'],# [1, 1, 'no'],# [0, 1, 'no'],# [0, 1, 'no'],# #以下随意添加，用于测试熵的变化，越混乱越冲突，熵越大# [1, 1, 'no'],# [1, 1, 'no'],# [1, 1, 'no'],# [1, 1, 'no'],# [1, 1, 'maybe'],# [1, 1, 'maybe1']# 用下面的8个比较极端的例子看得会更清楚。如果按照这个规则继续增加下去，熵会继续增大。# [1,1,'1'],# [1,1,'2'],# [1,1,'3'],# [1,1,'4'],# [1,1,'5'],# [1,1,'6'],# [1,1,'7'],# [1,1,'8'],
# 这是另一个极端的例子，所有样本的类别是一样的，有序，不混乱，此时熵为0[1,1,'1'],[1,1,'1'],[1,1,'1'],[1,1,'1'],[1,1,'1'],[1,1,'1'],[1,1,'1'],[1,1,'1'],]
labels = ['no surfacing', 'flippers']
return dataSet, labels
def testCalcShannonEnt():
myDat, labels = createDataSet()print(calcShannonEnt(myDat))
if __name__ == '__main__':testCalcShannonEnt()print(log(0.000002, 2))