code stringlengths 3 6.57k |
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np.abs(sigma_hat - sigma) |
pytest.mark.timeout(10) |
test_multivariate_gaussian() |
np.arange(0, dim) |
float(dim) |
np.ones((dim,) |
np.ones((dim, dim) |
np.tri(dim, k=-1) |
np.diag(L_diag) |
L.dot(L.transpose() |
MultivariateGaussian(mu=mx.nd.array(mu) |
mx.nd.array(L) |
distr.sample(num_samples) |
MultivariateGaussianOutput(dim=dim) |
PositiveFloat(0.01) |
PositiveInt(10) |
mx.nd.array([mu_hat]) |
mx.nd.array([L_hat]) |
asnumpy() |
pytest.mark.timeout(10) |
test_lowrank_multivariate_gaussian() |
np.arange(0, dim) |
float(dim) |
np.eye(dim) |
np.arange(dim) |
np.sqrt(np.ones((dim, rank) |
W.dot(W.transpose() |
mx.nd.array([mu]) |
mx.nd.array([np.diag(D) |
mx.nd.array([W]) |
asnumpy() |
distr.sample(num_samples) |
squeeze() |
asnumpy() |
LowrankMultivariateGaussianOutput(dim=dim, rank=rank) |
PositiveFloat(0.01) |
PositiveInt(10) |
mx.nd.array([mu_hat]) |
mx.nd.array([D_hat]) |
mx.nd.array([W_hat]) |
distr.variance.asnumpy() |
pytest.mark.parametrize("mu", [6.0]) |
pytest.mark.parametrize("hybridize", [True, False]) |
test_deterministic_l2(mu: float, hybridize: bool) |
mx.nd.zeros(NUM_SAMPLES) |
Gaussian(mu=mus, sigma=0.1 * mx.nd.ones_like(mus) |
deterministic_distr.sample() |
GaussianFixedVarianceOutput(GaussianOutput) |
domain_map(cls, F, mu, sigma) |
F.ones_like(sigma) |
mu.squeeze(axis=-1) |
sigma.squeeze(axis=-1) |
GaussianFixedVarianceOutput() |
PositiveInt(1) |
np.abs(mu_hat - mu) |
pytest.mark.parametrize("mu", [1.0]) |
pytest.mark.parametrize("hybridize", [True, False]) |
test_deterministic_l1(mu: float, hybridize: bool) |
mx.nd.zeros(NUM_SAMPLES) |
LaplaceFixedVarianceOutput(LaplaceOutput) |
domain_map(cls, F, mu, b) |
F.ones_like(b) |
mu.squeeze(axis=-1) |
b.squeeze(axis=-1) |
Laplace(mu=mus, b=0.1 * mx.nd.ones_like(mus) |
deterministic_distr.sample() |
LaplaceFixedVarianceOutput() |
PositiveFloat(1e-3) |
np.abs(mu_hat - mu) |
pytest.mark.parametrize("mu_alpha", [(2.5, 0.7) |
pytest.mark.parametrize("hybridize", [True, False]) |
test_neg_binomial(mu_alpha: Tuple[float, float], hybridize: bool) |
mx.nd.zeros((NUM_SAMPLES,) |
mx.nd.zeros((NUM_SAMPLES,) |
NegativeBinomial(mu=mus, alpha=alphas) |
neg_bin_distr.sample() |
inv_softplus(mu - START_TOL_MULTIPLE * TOL * mu) |
inv_softplus(alpha + START_TOL_MULTIPLE * TOL * alpha) |
NegativeBinomialOutput() |
PositiveInt(15) |
np.abs(mu_hat - mu) |
np.abs(alpha_hat - alpha) |
pytest.mark.timeout(10) |
pytest.mark.parametrize("mu_b", [(3.3, 0.7) |
pytest.mark.parametrize("hybridize", [True, False]) |
test_laplace(mu_b: Tuple[float, float], hybridize: bool) |
mx.nd.zeros((NUM_SAMPLES,) |
mx.nd.zeros((NUM_SAMPLES,) |
Laplace(mu=mus, b=bs) |
laplace_distr.sample() |
inv_softplus(b + START_TOL_MULTIPLE * TOL * b) |
LaplaceOutput() |
np.abs(mu_hat - mu) |
np.abs(b_hat - b) |
np.array([3, 1, 3, 4]) |
np.array([0.3, 0.2, 0.35, 0.15]) |
pytest.mark.parametrize("hybridize", [True, False]) |
mx.nd.zeros((num_samples,) |
mx.nd.zeros((num_samples, len(slopes) |
mx.nd.array(slopes) |
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