計算信息熵的公式：n是類別數，p(xi)是第i類的概率

假設數據集有m行，即m個樣本，每一行最后一列為該樣本的標簽，計算數據集信息熵的代碼如下：

from math import log def calcShannonEnt(dataSet): numEntries = len(dataSet) # 樣本數 labelCounts = {} # 該數據集每個類別的頻數 for featVec in dataSet: # 對每一行樣本 currentLabel = featVec[-1] # 該樣本的標簽 if currentLabel not in labelCounts.keys(): labelCounts[currentLabel] = 0 labelCounts[currentLabel] += 1 shannonEnt = 0.0 for key in labelCounts: prob = float(labelCounts[key])/numEntries # 計算p(xi) shannonEnt -= prob * log(prob, 2) # log base 2 return shannonEnt

補充知識：python 實現信息熵、條件熵、信息增益、基尼系數

我就廢話不多說了，大家還是直接看代碼吧~

import pandas as pdimport numpy as npimport math## 計算信息熵def getEntropy(s): # 找到各個不同取值出現的次數 if not isinstance(s, pd.core.series.Series): s = pd.Series(s) prt_ary = pd.groupby(s , by = s).count().values / float(len(s)) return -(np.log2(prt_ary) * prt_ary).sum()## 計算條件熵: 條件s1下s2的條件熵def getCondEntropy(s1 , s2): d = dict() for i in list(range(len(s1))): d[s1[i]] = d.get(s1[i] , []) + [s2[i]] return sum([getEntropy(d[k]) * len(d[k]) / float(len(s1)) for k in d])## 計算信息增益def getEntropyGain(s1, s2): return getEntropy(s2) - getCondEntropy(s1, s2)## 計算增益率def getEntropyGainRadio(s1, s2): return getEntropyGain(s1, s2) / getEntropy(s2)## 衡量離散值的相關性import mathdef getDiscreteCorr(s1, s2): return getEntropyGain(s1,s2) / math.sqrt(getEntropy(s1) * getEntropy(s2))# ######## 計算概率平方和def getProbSS(s): if not isinstance(s, pd.core.series.Series): s = pd.Series(s) prt_ary = pd.groupby(s, by = s).count().values / float(len(s)) return sum(prt_ary ** 2)######## 計算基尼系數def getGini(s1, s2): d = dict() for i in list(range(len(s1))): d[s1[i]] = d.get(s1[i] , []) + [s2[i]] return 1-sum([getProbSS(d[k]) * len(d[k]) / float(len(s1)) for k in d])## 對離散型變量計算相關系數，并畫出熱力圖, 返回相關性矩陣def DiscreteCorr(C_data): ## 對離散型變量(C_data)進行相關系數的計算 C_data_column_names = C_data.columns.tolist() ## 存儲C_data相關系數的矩陣 import numpy as np dp_corr_mat = np.zeros([len(C_data_column_names) , len(C_data_column_names)]) for i in range(len(C_data_column_names)): for j in range(len(C_data_column_names)): # 計算兩個屬性之間的相關系數 temp_corr = getDiscreteCorr(C_data.iloc[:,i] , C_data.iloc[:,j]) dp_corr_mat[i][j] = temp_corr # 畫出相關系數圖 fig = plt.figure() fig.add_subplot(2,2,1) sns.heatmap(dp_corr_mat ,vmin= - 1, vmax= 1, cmap= sns.color_palette(’RdBu’ , n_colors= 128) , xticklabels= C_data_column_names , yticklabels= C_data_column_names) return pd.DataFrame(dp_corr_mat)if __name__ == '__main__': s1 = pd.Series([’X1’ , ’X1’ , ’X2’ , ’X2’ , ’X2’ , ’X2’]) s2 = pd.Series([’Y1’ , ’Y1’ , ’Y1’ , ’Y2’ , ’Y2’ , ’Y2’]) print(’CondEntropy:’,getCondEntropy(s1, s2)) print(’EntropyGain:’ , getEntropyGain(s1, s2)) print(’EntropyGainRadio’ , getEntropyGainRadio(s1 , s2)) print(’DiscreteCorr:’ , getDiscreteCorr(s1, s1)) print(’Gini’ , getGini(s1, s2))

以上這篇Python計算信息熵實例就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持好吧啦網。