def pearson_coefficient(sample_x, sample_y):
"""
皮尔森相关性系数
:param sample_x: x样本数据变量集合
:param sample_y: y样本数据变量集合
:return:x和y的相关系数
"""
ana = AnalysisMath() # 数学分析模块
numerator = cov(sample_x, sample_y) # 分子是协方差
denominator = pow(ana.variance(sample_x) * ana.variance(sample_y), 0.5) # 分母是两个方差的积开平方
return numerator / denominator
def mahalanobis_distance(matrix, coordinate):
"""
求解马氏距离
:param matrix: 矩阵列表
:param coordinate: 坐标
:return result: 两个点的马氏距离
"""
# 马氏距离要求样本数要大于维数，否则无法求协方差矩阵
# 此处进行转置，表示10个样本，每个样本2维
s = Matrix(matrix).t
ana = AnalysisMath()
mean, c, distance = [], [], []
for index, i in enumerate(s):
c.append([cov(i, j) for j in s])
s_i = Matrix(c).i # 协方差矩阵的逆矩阵
for data in s:
mean.append(ana.expect(data))
distance.append([i - j for i, j in zip(coordinate, mean)])
ma = Matrix(Matrix(distance) * s_i) * Matrix(distance).t
return pow(ma[0][0], 0.5)