计算相关性

def mean(x):
    return sum(x) / len(x)
# 计算每一项数据与均值的差
def de_mean(x):
    x_bar = mean(x)
    return [x_i - x_bar for x_i in x]
# 辅助计算函数 dot product 、sum_of_squares
def dot(v, w):
    return sum(v_i * w_i for v_i, w_i in zip(v, w))
def sum_of_squares(v):
    return dot(v, v)
# 方差
def variance(x):
    n = len(x)
    deviations = de_mean(x)
    return sum_of_squares(deviations) / (n - 1)
# 标准差
import math
def standard_deviation(x):
    return math.sqrt(variance(x))
# 协方差
def covariance(x, y):
    n = len(x)
    return dot(de_mean(x), de_mean(y)) / (n -1)
# 相关系数
def correlation(x, y):
    stdev_x = standard_deviation(x)
    stdev_y = standard_deviation(y)
    if stdev_x > 0 and stdev_y > 0:
        return covariance(x, y) / stdev_x / stdev_y
    else:
        return 0
correlation(acc_list, pvalue_list)

参考：

https://www.jianshu.com/p/c83dd487df09