def testWithLKJSamples(self, dimension, concentration):
bijector = tfb.CorrelationCholesky()
lkj_dist = lkj.LKJ(dimension=dimension,
concentration=np.float64(concentration),
input_output_cholesky=True)
batch_size = 10
y = self.evaluate(lkj_dist.sample([batch_size]))
x = self.evaluate(bijector.inverse(y))
bijector_test_util.assert_bijective_and_finite(bijector,
x,
y,
eval_func=self.evaluate,
event_ndims=1,
inverse_event_ndims=2,
rtol=1e-5)
fldj = bijector.forward_log_det_jacobian(x, event_ndims=1)
fldj_theoretical = bijector_test_util.get_fldj_theoretical(
bijector,
x,
event_ndims=1,
inverse_event_ndims=2,
output_to_unconstrained=tfb.Invert(tfb.FillTriangular()))
self.assertAllClose(self.evaluate(fldj_theoretical),
self.evaluate(fldj),
atol=1e-5,
rtol=1e-5)
def testBijectiveWithLKJSamples(self, dimension, concentration):
bijector = tfb.CorrelationCholesky()
lkj_dist = lkj.LKJ(dimension=dimension,
concentration=np.float64(concentration),
input_output_cholesky=True)
batch_size = 10
y = self.evaluate(lkj_dist.sample([batch_size]))
x = self.evaluate(bijector.inverse(y))
bijector_test_util.assert_bijective_and_finite(bijector,
x,
y,
eval_func=self.evaluate,
event_ndims=1,
inverse_event_ndims=2,
rtol=1e-5)
def testJacobianWithLKJSamples(self, dimension, concentration):
bijector = tfb.CorrelationCholesky()
lkj_dist = lkj.LKJ(dimension=dimension,
concentration=np.float64(concentration),
input_output_cholesky=True)
batch_size = 10
y = self.evaluate(
lkj_dist.sample([batch_size], seed=test_util.test_seed()))
x = self.evaluate(bijector.inverse(y))
fldj = bijector.forward_log_det_jacobian(x, event_ndims=1)
fldj_theoretical = bijector_test_util.get_fldj_theoretical(
bijector,
x,
event_ndims=1,
inverse_event_ndims=2,
output_to_unconstrained=OutputToUnconstrained())
self.assertAllClose(self.evaluate(fldj_theoretical),
self.evaluate(fldj),
atol=1e-5,
rtol=1e-5)
