用Python写一段代码实现ReliefF特征选择算法
时间: 2023-03-07 20:44:35 浏览: 161
from numpy import *# 计算每对特征间的距离
def calculate_distance(X, feature_pair):
distance = 0
for i in range(len(feature_pair)):
a, b = feature_pair[i]
distance += (X[a] - X[b]) ** 2
return sqrt(distance)# 计算特征相关系数
def calculate_correlation(X, feature_pair, Y):
a, b = feature_pair
# 计算出a,b特征与Y的相关系数
corr_ab = corrcoef(X[:, a], X[:, b])[0][1]
corr_aY = corrcoef(X[:, a], Y)[0][1]
corr_bY = corrcoef(X[:, b], Y)[0][1]
# 计算出特征之间的相关系数
corr_feature = corr_ab - corr_aY * corr_bY
return corr_feature# 计算每个特征的ReliefF权重
def calculate_weight(X, feature_pairs, Y):
weights = zeros(X.shape[1])
for i in range(len(feature_pairs)):
a, b = feature_pairs[i]
distance = calculate_distance(X[:, a], X[:, b])
correlation = calculate_correlation(X, feature_pairs[i], Y)
weights[a] += correlation / distance
weights[b] += correlation / distance
return weights