Exemplo n.º 1
0
def pearson_r(x, y):
    assert len(x) == len(y)
    x_bar = average(x)
    y_bar = average(y)
    s_x = sqrt(s_2(x))
    s_y = sqrt(s_2(y))
    tmp = 0.0
    for i in range(0, len(x)):
        tmp += ((x[i] - x_bar) * (y[i] - y_bar) / s_x / s_y)
    return tmp / (len(x) - 1)
Exemplo n.º 2
0
def infer_by_chi(l1, l2, alpha=0.01):
    ss1 = s_2(l1)
    ss2 = s_2(l2)
    fv = ss1 / ss2
    from scipy.stats import f

    res = f.ppf(1 - alpha, len(l1) - 1, len(l2) - 1)
    print('res {} fv {}'.format(res, fv))
    if res > fv:
        print('sigma1 <= sigma2')
    else:
        print('sigma1 > sigma2')
Exemplo n.º 3
0
def infer_by_chi(l1, l2, alpha=0.01):
    ss1 = s_2(l1)
    ss2 = s_2(l2)
    fv = ss1 / ss2
    from scipy.stats import f

    res = f.ppf(1 - alpha, len(l1) - 1, len(l2) - 1)
    print('res {} fv {}'.format(res, fv))
    if res > fv:
        print('sigma1 <= sigma2')
    else:
        print('sigma1 > sigma2')
Exemplo n.º 4
0
def infer_by_t(l1, l2, alpha=0.01, sigma=0):
    n1 = len(l1)
    n2 = len(l2)
    x_aver = average(l1)
    y_aver = average(l2)
    s_1sq = s_2(l1)
    s_2sq = s_2(l2)
    s_w2 = ((n1 - 1) * s_1sq + (n2 - 1) * s_2sq) / (n1 + n2 - 2)

    tv = ((x_aver - y_aver) - sigma) / sqrt(s_w2 * (1 / n1 + 1 / n2))
    t_a = t.ppf(alpha, n1 + n2 - 2)
    print('tv {} t_a {}'.format(tv, t_a))
    if tv < t_a:
        print('mu1 < mu2')
    else:
        print('mu1 >= mu2')
Exemplo n.º 5
0
def infer_by_t(l1, l2, alpha=0.01, sigma=0):
    n1 = len(l1)
    n2 = len(l2)
    x_aver = average(l1)
    y_aver = average(l2)
    s_1sq = s_2(l1)
    s_2sq = s_2(l2)
    s_w2 = ((n1 - 1) * s_1sq + (n2 - 1) * s_2sq) / (n1 + n2 - 2)

    tv = ((x_aver - y_aver) - sigma) / sqrt(s_w2 * (1 / n1 + 1 / n2))
    t_a = t.ppf(alpha, n1 + n2 - 2)
    print('tv {} t_a {}'.format(tv, t_a))
    if tv < t_a:
        print('mu1 < mu2')
    else:
        print('mu1 >= mu2')
Exemplo n.º 6
0
def t_check(l, mu, alpha=0.05):
    aver = average(l)
    n = len(l)
    ss = s_2(l)
    tt = (aver - mu) * sqrt(n) / sqrt(ss)
    l, r = t.interval(1 - alpha, n - 1)
    if tt > r or tt < l:
        print('reject')
    else:
        print('accept')
Exemplo n.º 7
0
import numpy as np
from numpy import var, std
from scipy.stats import stats
from statistic.check.util import s_2, sigma_2

__author__ = 'zzt'

if __name__ == '__main__':
    l = [420, 500, 920, 1380, 1510, 1650, 1760, 2100, 2300, 2350]
    print(stats.kstest(l, 'expon', [1500.0]))
    x = np.linspace(-15, 15, 9)
    print(std(x)**2)
    print(var(x))
    print(s_2(x))
    print(sigma_2(x))
    print(stats.kstest(x, 'norm', [0, 9]))