def testVariableAssertsScale(self):
scale = tf.Variable(1.)
b = tfb.RayleighCDF(scale=scale, validate_args=True)
self.evaluate([scale.initializer])
with self.assertRaisesOpError('Argument `scale` must be positive.'):
with tf.control_dependencies([scale.assign(-1.)]):
self.evaluate(b.forward(3.))
def testBijectiveAndFinite(self):
bijector = tfb.RayleighCDF(scale=20., validate_args=True)
x = np.linspace(1., 8., num=10).astype(np.float32)
y = np.linspace(-np.expm1(-1 / 400.), -np.expm1(-16),
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):
scale = 50.
bijector = tfb.RayleighCDF(scale=scale, validate_args=True)
self.assertStartsWith(bijector.name, 'rayleigh')
test_cdf_func = scipy_stats.rayleigh.cdf
x = np.array([[[.1], [1.], [14.], [20.], [100.]]], dtype=np.float32)
y = test_cdf_func(x, scale=scale).astype(np.float32)
self.assertAllClose(y, self.evaluate(bijector.forward(x)))
self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
self.assertAllClose(
scipy_stats.rayleigh.logpdf(x, scale=scale),
self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=0)))
self.assertAllClose(
self.evaluate(
-bijector.inverse_log_det_jacobian(y, event_ndims=0)),
self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=0)),
rtol=1e-4,
atol=0.)
def testAsserts(self):
with self.assertRaisesOpError('Argument `scale` must be positive.'):
b = tfb.RayleighCDF(scale=-1., validate_args=True)
self.evaluate(b.forward(3.))
def testScalarCongruency(self):
bijector_test_util.assert_scalar_congruency(tfb.RayleighCDF(scale=50.),
lower_x=1.,
upper_x=100.,
eval_func=self.evaluate,
rtol=0.05)
def testBijectorLogDetJacobianZeroAtZero(self):
scale = np.logspace(0.1, 10., num=20).astype(np.float32)
bijector = tfb.RayleighCDF(scale)
fldj = self.evaluate(
bijector.forward_log_det_jacobian(0., event_ndims=0))
self.assertAllNegativeInf(fldj)
