scikitlearn-如何获得高斯混合模型的cdf？

Question

现在我做这样的事情，如果有更好的方法，我想知道。

import numpy as np
from scipy import integrate
from sklearn.mixture import GaussianMixture as GMM

model = GMM(n, covariance_type = "full").fit(X)

def cdf(x):
 return integrate.quad(lambda t: np.exp(model.score(t)), -inf, x)[0]

Answer 1

混合高斯分布的CDF的CDF为F_1，F_2，F_3 ...，权重ω_1，ω_2，ω_3...等于F_mixed =ω_1* F_1 +ω_2* F_2 +ω_3* F_3 +。 ..因此，答案是：

from scipy.stats import norm

weights = [0.163, 0.131, 0.486, 0.112, 0.107]
means = [45.279, 55.969, 49.315, 53.846, 61.953]
covars = [0.047, 1.189, 3.632, 0.040, 0.198]


def mix_norm_cdf(x, weights, means, covars):
    mcdf = 0.0
    for i in range(len(weights)):
        mcdf += weights[i] * norm.cdf(x, loc=means[i], scale=covars[i])
    return mcdf


print(mix_norm_cdf(50, weights, means, covars))

输出

0.442351546658755