def test_basics(self):
dim_in = 10
dim_out = 3
num_components = 7
num_scales = 5
num_features = 50
num_samples = 100
# create model
mcgsm = MCGSM(dim_in, dim_out, num_components, num_scales,
num_features)
# generate output
input = randn(dim_in, num_samples)
output = mcgsm.sample(input)
loglik = mcgsm.loglikelihood(input, output)
post = mcgsm.posterior(input, output)
samples = mcgsm.sample_posterior(input, output)
# check hyperparameters
self.assertEqual(mcgsm.dim_in, dim_in)
self.assertEqual(mcgsm.dim_out, dim_out)
self.assertEqual(mcgsm.num_components, num_components)
self.assertEqual(mcgsm.num_scales, num_scales)
self.assertEqual(mcgsm.num_features, num_features)
# check parameters
self.assertEqual(mcgsm.priors.shape[0], num_components)
self.assertEqual(mcgsm.priors.shape[1], num_scales)
self.assertEqual(mcgsm.scales.shape[0], num_components)
self.assertEqual(mcgsm.scales.shape[1], num_scales)
self.assertEqual(mcgsm.weights.shape[0], num_components)
self.assertEqual(mcgsm.weights.shape[1], num_features)
self.assertEqual(mcgsm.features.shape[0], dim_in)
self.assertEqual(mcgsm.features.shape[1], num_features)
self.assertEqual(len(mcgsm.cholesky_factors), num_components)
self.assertEqual(len(mcgsm.predictors), num_components)
self.assertEqual(mcgsm.cholesky_factors[0].shape[0], dim_out)
self.assertEqual(mcgsm.cholesky_factors[0].shape[1], dim_out)
self.assertEqual(mcgsm.predictors[0].shape[0], dim_out)
self.assertEqual(mcgsm.predictors[0].shape[1], dim_in)
self.assertEqual(mcgsm.linear_features.shape[0], num_components)
self.assertEqual(mcgsm.linear_features.shape[1], dim_in)
self.assertEqual(mcgsm.means.shape[0], dim_out)
self.assertEqual(mcgsm.means.shape[1], num_components)
# check dimensionality of output
self.assertEqual(output.shape[0], dim_out)
self.assertEqual(output.shape[1], num_samples)
self.assertEqual(loglik.shape[0], 1)
self.assertEqual(loglik.shape[1], num_samples)
self.assertEqual(post.shape[0], num_components)
self.assertEqual(post.shape[1], num_samples)
self.assertLess(max(samples), mcgsm.num_components)
self.assertGreaterEqual(min(samples), 0)
self.assertEqual(samples.shape[0], 1)
self.assertEqual(samples.shape[1], num_samples)
def test_basics(self):
dim_in = 10
dim_out = 3
num_components = 7
num_scales = 5
num_features = 50
num_samples = 100
# create model
mcgsm = MCGSM(dim_in, dim_out, num_components, num_scales, num_features)
# generate output
input = randn(dim_in, num_samples)
output = mcgsm.sample(input)
loglik = mcgsm.loglikelihood(input, output)
post = mcgsm.posterior(input, output)
samples = mcgsm.sample_posterior(input, output)
# check hyperparameters
self.assertEqual(mcgsm.dim_in, dim_in)
self.assertEqual(mcgsm.dim_out, dim_out)
self.assertEqual(mcgsm.num_components, num_components)
self.assertEqual(mcgsm.num_scales, num_scales)
self.assertEqual(mcgsm.num_features, num_features)
# check parameters
self.assertEqual(mcgsm.priors.shape[0], num_components)
self.assertEqual(mcgsm.priors.shape[1], num_scales)
self.assertEqual(mcgsm.scales.shape[0], num_components)
self.assertEqual(mcgsm.scales.shape[1], num_scales)
self.assertEqual(mcgsm.weights.shape[0], num_components)
self.assertEqual(mcgsm.weights.shape[1], num_features)
self.assertEqual(mcgsm.features.shape[0], dim_in)
self.assertEqual(mcgsm.features.shape[1], num_features)
self.assertEqual(len(mcgsm.cholesky_factors), num_components)
self.assertEqual(len(mcgsm.predictors), num_components)
self.assertEqual(mcgsm.cholesky_factors[0].shape[0], dim_out)
self.assertEqual(mcgsm.cholesky_factors[0].shape[1], dim_out)
self.assertEqual(mcgsm.predictors[0].shape[0], dim_out)
self.assertEqual(mcgsm.predictors[0].shape[1], dim_in)
self.assertEqual(mcgsm.linear_features.shape[0], num_components)
self.assertEqual(mcgsm.linear_features.shape[1], dim_in)
self.assertEqual(mcgsm.means.shape[0], dim_out)
self.assertEqual(mcgsm.means.shape[1], num_components)
# check dimensionality of output
self.assertEqual(output.shape[0], dim_out)
self.assertEqual(output.shape[1], num_samples)
self.assertEqual(loglik.shape[0], 1)
self.assertEqual(loglik.shape[1], num_samples)
self.assertEqual(post.shape[0], num_components)
self.assertEqual(post.shape[1], num_samples)
self.assertLess(max(samples), mcgsm.num_components)
self.assertGreaterEqual(min(samples), 0)
self.assertEqual(samples.shape[0], 1)
self.assertEqual(samples.shape[1], num_samples)
