def testScalarCongruency(self): bijector_test_util.assert_scalar_congruency(tfb.GumbelCDF(loc=0.3, scale=20.), lower_x=1., upper_x=100., eval_func=self.evaluate, rtol=0.05)
def testVariableScale(self): x = tf.Variable(1.) b = tfb.GumbelCDF(loc=0., scale=x, validate_args=True) self.evaluate(x.initializer) self.assertIs(x, b.scale) self.assertEqual((), self.evaluate(b.forward(-3.)).shape) with self.assertRaisesOpError("Argument `scale` must be positive."): with tf.control_dependencies([x.assign(-1.)]): self.evaluate(b.forward(-3.))
def testBijectiveAndFinite(self): bijector = tfb.GumbelCDF(loc=0., scale=3.0, validate_args=True) x = np.linspace(-10., 10., 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): loc = 0.3 scale = 5. bijector = tfb.GumbelCDF(loc=loc, scale=scale, validate_args=True) self.assertStartsWith(bijector.name, "gumbel") x = np.array([[[-3.], [0.], [0.5], [4.2], [12.]]], dtype=np.float32) # Gumbel distribution gumbel_dist = stats.gumbel_r(loc=loc, scale=scale) y = gumbel_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(gumbel_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.)