使用python語言，實現求特征選擇的信息增益，可以同時滿足特征中有連續型和二值離散型屬性的情況。

師兄讓我做一個特征選擇的代碼，我在網上找了一下，大部分都是用來求離散型屬性的信息益益，但是我的數據是同時包含二值離散型和連續型屬性的，所以這里實現了一下。

代碼塊

import numpy as npimport mathclass IG():  def __init__(self,X,y):    X = np.array(X)    n_feature = np.shape(X)[1]    n_y = len(y)    orig_H = 0    for i in set(y):      orig_H += -(y.count(i)/n_y)*math.log(y.count(i)/n_y)    condi_H_list = []    for i in range(n_feature):      feature = X[:,i]      sourted_feature = sorted(feature)      threshold = [(sourted_feature[inde-1]+sourted_feature[inde])/2 for inde in range(len(feature)) if inde != 0 ]      thre_set = set(threshold)      if float(max(feature)) in thre_set:        thre_set.remove(float(max(feature)))      if min(feature) in thre_set:        thre_set.remove(min(feature))      pre_H = 0      for thre in thre_set:        lower = [y[s] for s in range(len(feature)) if feature[s] < thre]        highter = [y[s] for s in range(len(feature)) if feature[s] > thre]        H_l = 0        for l in set(lower):          H_l += -(lower.count(l) / len(lower))*math.log(lower.count(l) / len(lower))        H_h = 0        for h in set(highter):          H_h += -(highter.count(h) / len(highter))*math.log(highter.count(h) / len(highter))        temp_condi_H = len(lower)/n_y *H_l+ len(highter)/n_y * H_h        condi_H = orig_H - temp_condi_H        pre_H = max(pre_H,condi_H)      condi_H_list.append(pre_H)    self.IG = condi_H_list  def getIG(self):    return self.IGif __name__ == "__main__":  X = [[1, 0, 0, 1],     [0, 1, 1, 1],     [0, 0, 1, 0]]  y = [0, 0, 1]  print(IG(X,y).getIG())

輸出結果為：

[0.17441604792151594, 0.17441604792151594, 0.17441604792151594, 0.6365141682948128]

以上就是本文的全部內容，希望對大家的學習有所幫助，也希望大家多多支持武林站長站。