test_psi.py 文件源码

def test_cgauss_likelihood():
    mu = np.array([0], dtype='float')
    sigma = np.array([2], dtype='float')
    x = np.linspace(-1, 2, 2)
    lapse = np.array([0], dtype='float')
    parameters = cartesian((mu, sigma, lapse, x))
    proportionMethod = PsiMarginal.pf(parameters, psyfun='cGauss')
    samples = np.random.normal(mu, sigma, (200000, 1))
    proportionSamples = np.empty([2, ])
    proportionSamples[0] = np.mean(samples <= x[0])  # cdf is p(X<=x), compute this through sampling to check likelihood
    proportionSamples[1] = np.mean(samples <= x[1])
    np.testing.assert_almost_equal(proportionSamples, proportionMethod, decimal=2) == 1