def testScalarCongruency(self):
bijector_test_util.assert_scalar_congruency(tfb.GompertzCDF(
concentration=0.2, rate=0.2),
lower_x=1.,
upper_x=10.,
eval_func=self.evaluate,
rtol=0.05)
def testVariableRate(self):
x = tf.Variable(1.)
b = tfb.GompertzCDF(concentration=1., rate=x, validate_args=True)
self.evaluate(x.initializer)
self.assertIs(x, b.rate)
self.assertEqual((), self.evaluate(b.forward(1.)).shape)
with self.assertRaisesOpError("Argument `rate` must be positive."):
with tf.control_dependencies([x.assign(-1.)]):
self.evaluate(b.forward(1.))
def testBijectiveAndFinite(self):
bijector = tfb.GompertzCDF(concentration=1.,
rate=0.01,
validate_args=True)
x = np.logspace(-10, 2, num=10).astype(np.float32)
y = np.linspace(0.01, 0.99, num=10).astype(np.float32)
bijector_test_util.assert_bijective_and_finite(bijector,
x,
y,
eval_func=self.evaluate,
event_ndims=0,
rtol=1e-3)
def testBijector(self):
concentration = 0.3
rate = 1.
bijector = tfb.GompertzCDF(concentration=concentration,
rate=rate,
validate_args=True)
self.assertStartsWith(bijector.name, "gompertz")
x = np.array([[[0.1], [0.5], [1.4], [3.]]], dtype=np.float32)
# Gompertz distribution
gompertz_dist = stats.gompertz(c=concentration, scale=1. / rate)
y = gompertz_dist.cdf(x).astype(np.float32)
self.assertAllClose(y, self.evaluate(bijector.forward(x)))
self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
self.assertAllClose(
np.squeeze(gompertz_dist.logpdf(x), axis=-1),
self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=1)))
self.assertAllClose(
self.evaluate(
-bijector.inverse_log_det_jacobian(y, event_ndims=1)),
self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=1)),
rtol=1e-4,
atol=0.)