from numpy.testing import assert_allclose

import jax.numpy as np
import jax.random as random
from jax import jit, lax
from jax.scipy.special import expit

import numpyro.distributions as dist
from numpyro.distributions.util import validation_disabled
from numpyro.mcmc import hmc_kernel
from numpyro.util import scan


def test_unnormalized_normal():
    true_mean, true_std = 1., 2.
    warmup_steps, num_samples = 1000, 8000

    def potential_fn(z):
        return 0.5 * np.sum(((z[0] - true_mean) / true_std) ** 2)

    def kinetic_fn(r, m_inv):
        return 0.5 * np.sum(m_inv * r[0] ** 2)

    init_kernel, sample_kernel = hmc_kernel(potential_fn, kinetic_fn)
    init_samples = [np.array([0.])]
    hmc_state = init_kernel(init_samples,
                            num_warmup_steps=warmup_steps)
    sample_kernel = jit(sample_kernel)
    hmc_states = lax.scan(sample_kernel, hmc_state, np.arange(num_samples))
    zs = hmc_states.z[0]
    assert_allclose(np.mean(zs), true_mean, rtol=0.05)
    assert_allclose(np.std(zs), true_std, rtol=0.05)


def test_logistic_regression():
    N, dim = 3000, 3
    warmup_steps, num_samples = 1000, 8000
    data = random.normal(random.PRNGKey(0), (N, dim))
    true_coefs = np.arange(1., dim + 1.)
    probs = expit(np.sum(true_coefs * data, axis=-1))
    labels = dist.bernoulli(probs).rvs()

    with validation_disabled():
        def potential_fn(beta):
            coefs_mean = np.zeros(dim)
            coefs_lpdf = dist.norm(coefs_mean, np.ones(dim)).logpdf(beta)
            probs = np.clip(expit(np.sum(beta * data, axis=-1)),
                            a_min=np.finfo(np.float32).tiny,
                            a_max=(1 - np.finfo(np.float32).eps))
            y_lpdf = dist.bernoulli(probs).logpmf(labels)
            return - (np.sum(coefs_lpdf) + np.sum(y_lpdf))

        def kinetic_fn(beta, m_inv):
            return 0.5 * np.dot(m_inv, beta ** 2)

        init_kernel, sample_kernel = hmc_kernel(potential_fn, kinetic_fn)
        init_samples = np.zeros(dim)
        hmc_state = init_kernel(init_samples,
                                step_size=0.1,
                                num_steps=15,
                                num_warmup_steps=warmup_steps)
        sample_kernel = jit(sample_kernel)
        hmc_states = scan(sample_kernel, hmc_state, np.arange(num_samples))
        assert_allclose(np.mean(hmc_states.z, 0), true_coefs, rtol=0.08)

@@ -2409,15 +2409,14 @@
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update threshold