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  /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/
poisson_test.py 116 log_cdf = poisson.log_cdf(x)
117 self.assertEqual(log_cdf.get_shape(), (6,))
118 self.assertAllClose(log_cdf.eval(), stats.poisson.logcdf(x, lam_v))
132 log_cdf = poisson.log_cdf(x)
133 self.assertEqual(log_cdf.get_shape(), (6, 3))
134 self.assertAllClose(log_cdf.eval(), stats.poisson.logcdf(x, lam_v))
negative_binomial_test.py 103 log_cdf = negbinom.log_cdf(x)
104 self.assertEqual([6], log_cdf.get_shape())
105 self.assertAllClose(expected_log_cdf, log_cdf.eval())
119 negbinom.log_cdf(-1.).eval()
geometric_test.py 236 log_cdf = geom.log_cdf(x)
237 self.assertEqual([7,], log_cdf.get_shape())
238 self.assertAllClose(expected_log_cdf, log_cdf.eval())
mixture_same_family_test.py 120 actual_log_cdf = gm.log_cdf(x)
123 gm.components_distribution.log_cdf(x[..., array_ops.newaxis])),
half_normal_test.py 137 log_cdf = halfnorm.log_cdf(x)
138 self._testBatchShapes(halfnorm, log_cdf)
143 self.assertAllClose(expected_logcdf, log_cdf.eval(), atol=0)
188 dist.cdf, dist.log_cdf, dist.survival_function,
transformed_distribution_test.py 74 [log_normal.log_cdf, sp_dist.logcdf],
329 for unsupported_fn in (fake_mvn_static.log_cdf,
logistic_test.py 83 logcdf = dist.log_cdf(x)
cauchy_test.py 192 cdf = cauchy.log_cdf(x)
212 dist.cdf, dist.log_cdf, dist.survival_function,
kumaraswamy_test.py 378 b).log_cdf(x)).eval()
quantized_distribution_test.py 271 self.assertAllClose(sp_normal.logcdf(x), qdist.log_cdf(x).eval())
mixture_test.py 731 # Carry out the test for both d.cdf and exp(d.log_cdf).
733 x_log_cdf_tf = mixture_tf.log_cdf(x_tensor)
779 x_log_cdf_tf = mixture_tf.log_cdf(x_tensor)
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  /external/tensorflow/tensorflow/contrib/distributions/python/ops/
conditional_distribution.py 54 def log_cdf(self, value, name="log_cdf", **condition_kwargs): member in class:ConditionalDistribution
quantized_distribution.py 71 The base distribution's `log_cdf` method must be defined on `y - 1`. If the
99 The base distribution's `log_cdf` method must be defined on `y - 1`.
124 The base distribution's `log_cdf` method must be defined on `y - 1`.
300 "'log_cdf'")
308 return _logsum_expbig_minus_expsmall(self.log_cdf(y), self.log_cdf(y - 1))
319 logcdf_y = self.log_cdf(y)
320 logcdf_y_minus_1 = self.log_cdf(y - 1)
379 result_so_far = self.distribution.log_cdf(j)
conditional_transformed_distribution.py 151 raise NotImplementedError("log_cdf is not implemented when overriding "
154 raise NotImplementedError("log_cdf is not implemented when "
159 return self.distribution.log_cdf(x, **distribution_kwargs)
mixture_same_family.py 268 log_cdf_x = self.components_distribution.log_cdf(x) # [S, B, k]
mixture.py 294 distribution_log_cdfs = [d.log_cdf(x) for d in self.components]
  /external/tensorflow/tensorflow/python/kernel_tests/distributions/
uniform_test.py 126 log_cdf = uniform.log_cdf(x)
127 self.assertAllClose(np.log(_expected_cdf()), log_cdf.eval())
student_t_test.py 118 log_cdf = student.log_cdf(t)
119 self.assertEquals(log_cdf.get_shape(), (6,))
120 log_cdf_values = log_cdf.eval()
categorical_test.py 265 "cat_log_cdf": cat.log_cdf(disc_event_tf),
269 "norm_log_cdf": norm.log_cdf(real_event_tf),
normal_test.py 205 cdf = normal.log_cdf(x)
225 dist.cdf, dist.log_cdf, dist.survival_function,
beta_test.py 342 actual = math_ops.exp(beta_lib.Beta(a, b).log_cdf(x)).eval()
laplace_test.py 152 cdf = laplace.log_cdf(x)
  /external/tensorflow/tensorflow/python/ops/distributions/
transformed_distribution.py 160 * `log_cdf`
162 Programmatically: `distribution.log_cdf(bijector.inverse(x))`
468 raise NotImplementedError("log_cdf is not implemented when overriding "
471 raise NotImplementedError("log_cdf is not implemented when "
474 return self.distribution.log_cdf(x)
distribution.py 57 "log_cdf",
290 The shape of arguments to `__init__`, `cdf`, `log_cdf`, `prob`, and
325 # same holds for `log_cdf` and the likelihood functions.
746 raise NotImplementedError("log_cdf is not implemented")
759 def log_cdf(self, value, name="log_cdf"): member in class:Distribution
765 log_cdf(x) := Log[ P[X <= x] ]
768 Often, a numerical approximation can be used for `log_cdf(x)` that yields
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