test_mvkde.py 文件源码

def test_univariate_categorical():
    # This test generates univariate data from a nominal variable with 6 levels
    # and probability vector p_theory, and performs a chi-square test on
    # posterior samples from MvKde.

    rng = gu.gen_rng(2)
    N_SAMPLES = 1000
    p_theory = [.3, .1, .2, .15, .15, .1]
    samples_test = rng.choice(range(6), p=p_theory, size=N_SAMPLES)
    kde = MultivariateKde(
        [7], None, distargs={O: {ST: [C], SA:[{'k': 6}]}}, rng=rng)
    # Incorporate observations.
    for rowid, x in enumerate(samples_test):
        kde.incorporate(rowid, {7: x})
    kde.transition()
    # Posterior samples.
    samples_gen = kde.simulate(-1, [7], N=N_SAMPLES)
    f_obs = np.bincount([s[7] for s in samples_gen])
    f_exp = np.bincount(samples_test)
    _, pval = chisquare(f_obs, f_exp)
    assert 0.05 < pval
    # Get some coverage on logpdf_score.
    assert kde.logpdf_score() < 0