def _test_gm(self, means, variances, props, n):
samples = gm_sample(means, variances, props, n)
# assert_allclose(samples.mean(), gm_mean(means, variances, props),
# rtol=1e-2, atol=5e-3)
assert_almost_equal(samples.mean(), gm_mean(means, variances, props),
decimal=2)
def test_ppm_a_mcmc(self):
nsamples = 500000
mixt_map_samples = np.zeros((nsamples, self.n_pos))
for i, mixt in enumerate(self.mixt_stack):
mixt_map_samples[:, i] = gm_sample(n=nsamples, **mixt)
alpha = .05
ppm = cpt_ppm_a_mcmc(mixt_map_samples, alpha)
assert_almost_equal(ppm[0], [1.645], decimal=2)
def test_ppm_a_mcmc(self):
nsamples = 500000
mixt_map_samples = np.zeros((nsamples, self.n_pos))
for i, mixt in enumerate(self.mixt_stack):
mixt_map_samples[:, i] = gm_sample(n=nsamples, **mixt)
alpha = .05
ppm = cpt_ppm_a_mcmc(mixt_map_samples, alpha)
assert_almost_equal(ppm[0], [1.645], decimal=2)
def test_ppm_g_mcmc(self):
nsamples = 500000
mixt_map_samples = np.zeros((nsamples, self.n_pos))
for i, mixt in enumerate(self.mixt_stack):
mixt_map_samples[:, i] = gm_sample(n=nsamples, **mixt)
gamma = 0.
ppm = cpt_ppm_g_mcmc(mixt_map_samples, gamma)
assert_almost_equal(ppm, [0.5, 1., 0.5], decimal=2)
def test_ppm_g_mcmc(self):
nsamples = 500000
mixt_map_samples = np.zeros((nsamples, self.n_pos))
for i, mixt in enumerate(self.mixt_stack):
mixt_map_samples[:, i] = gm_sample(n=nsamples, **mixt)
gamma = 0.
ppm = cpt_ppm_g_mcmc(mixt_map_samples, gamma)
assert_almost_equal(ppm, [0.5, 1., 0.5], decimal=2)
def _test_gm(self, means, variances, props, n):
samples = gm_sample(means, variances, props, n)
assert_almost_equal(samples.mean(),
gm_mean(means, variances, props),
decimal=2)
def _test_gm(self, means, variances, props, n):
samples = gm_sample(means, variances, props, n)
assert_almost_equal(samples.mean(), gm_mean(means, variances, props),
decimal=2)
