R平方、两个变量之间的关联程度，关联系数、正相关 负相关 样例代码（python3）

代码：

# -*- coding:utf-8 -*-

import numpy as np
import math


# 相关度
def computeCorrelation(X, Y):
    xBar = np.mean(X)
    yBar = np.mean(Y)
    SSR = 0
    varX = 0
    varY = 0
    for i in range(0, len(X)):
        diffXXbar = X[i] - xBar
        diffYYbar = Y[i] - yBar
        SSR += (diffXXbar * diffYYbar)
        varX += diffXXbar ** 2
        varY += diffYYbar ** 2
    SST = math.sqrt(varX * varY)
    return SSR / SST




# print("相关度r:", computeCorrelation(testX, testY))
# 相关度r: 0.940310076545

# R平方
# 简单线性回归：
# print("r^2:", str(computeCorrelation(testX, testY)**2))
# r^2: 0.884183040052

# 多个x自变量时：
def polyfit(x, y, degree):  # degree自变量x次数
    result = {}
    coeffs = np.polyfit(x, y, degree)
    result['polynomial'] = coeffs.tolist()

    p = np.poly1d(coeffs)
    yhat = p(x)
    ybar = np.sum(y) / len(y)
    ssreg = np.sum((yhat - ybar) ** 2)
    sstot = np.sum((y - ybar) ** 2)
    result['determination'] = ssreg / sstot

    return result

# 测试
testX = [1, 3, 8, 7, 9]
testY = [1,1,1,1,1]

inf=float("inf")
ninf=float("-inf")
nan=float("nan")
result=polyfit(testY, testX, 1)["determination"]

print(inf)
if result == inf:
    print("PPPP")
# 测试
print(result)
# r^2:0.884183040052