def test_sample(self):
mcgsm = MCGSM(1, 1, 1, 1, 1)
mcgsm.scales = [[0.]]
mcgsm.predictors = [[0.]]
samples = mcgsm.sample(zeros([1, 10000])).flatten()
p = kstest(samples, lambda x: norm.cdf(x, scale=1.))[1]
# make sure Gaussian random number generation works
self.assertTrue(p > 0.0001)
def test_sample(self):
mcgsm = MCGSM(1, 1, 1, 1, 1)
mcgsm.scales = [[0.0]]
mcgsm.predictors = [[0.0]]
samples = mcgsm.sample(zeros([1, 10000])).flatten()
p = kstest(samples, lambda x: norm.cdf(x, scale=1.0))[1]
# make sure Gaussian random number generation works
self.assertTrue(p > 0.0001)
def test_sample_conditionally(self):
mcgsm = MCGSM(3, 2, 2, 2, 4)
# make sure there are differences between components
mcgsm.weights = -log(rand(*mcgsm.weights.shape)) * 10.
mcgsm.scales = square(mcgsm.scales * 3.)
inputs = randn(mcgsm.dim_in, 100000)
# sample directly
outputs0 = mcgsm.sample(inputs)
# sample indirectly
labels = mcgsm.sample_prior(inputs)
outputs1 = mcgsm.sample(inputs, labels)
p = ks_2samp(outputs0.ravel(), outputs1.ravel())[1]
self.assertGreater(p, 1e-5)
def test_sample_conditionally(self):
mcgsm = MCGSM(3, 2, 2, 2, 4)
# make sure there are differences between components
mcgsm.weights = -log(rand(*mcgsm.weights.shape)) * 10.0
mcgsm.scales = square(mcgsm.scales * 3.0)
inputs = randn(mcgsm.dim_in, 100000)
# sample directly
outputs0 = mcgsm.sample(inputs)
# sample indirectly
labels = mcgsm.sample_prior(inputs)
outputs1 = mcgsm.sample(inputs, labels)
p = ks_2samp(outputs0.ravel(), outputs1.ravel())[1]
self.assertGreater(p, 1e-5)
