import random
random.seed(42)
import numpy as np'''计算均值'''
def mean(x):return sum(x) / len(x)'''计算偏差'''
def derta(x):x_bar = mean(x)result = [x_i - x_bar for x_i in x]return result'''计算协方差'''
def covariance(a, b):dot_list = [a_i * b_i for a_i, b_i in zip(derta(a), derta(b))]dot_result = mean(dot_list)return dot_result'''计算标准差'''
def std(x):return np.sqrt(np.mean(([derta_x ** 2 for derta_x in derta(x)])))'''计算相关系数'''
def pearson(x, y):result = covariance(x, y) / (std(x) * std(y))return result'''计算相关系数矩阵'''
def calculate_correlation_matrix(variables):num_variables = len(variables)correlation_matrix = np.zeros((num_variables, num_variables))for i in range(num_variables):for j in range(num_variables):correlation_matrix[i][j] = pearson(variables[i], variables[j])return correlation_matrix'''随机生成变量X和Y'''
X = [random.randint(-20, 100) for _ in range(25)]
Y = [random.randint(-20, 100) for _ in range(25)]
Z = [random.randint(-20, 100) for _ in range(25)]print(X)
print(Y)
print("复现函数皮尔逊相关系数矩阵：\n", calculate_correlation_matrix([X, Y, Z]))
print("调用内置函数皮尔逊相关系数：\n", np.corrcoef([X, Y, Z]))