Lines Matching refs:gamma
29 from tensorflow.python.ops.distributions import gamma as gamma_lib
54 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
56 self.assertEqual(gamma.batch_shape_tensor().eval(), (5,))
57 self.assertEqual(gamma.batch_shape, tensor_shape.TensorShape([5]))
58 self.assertAllEqual(gamma.event_shape_tensor().eval(), [])
59 self.assertEqual(gamma.event_shape, tensor_shape.TensorShape([]))
69 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
70 log_pdf = gamma.log_prob(x)
72 pdf = gamma.prob(x)
76 expected_log_pdf = stats.gamma.logpdf(x, alpha_v, scale=1 / beta_v)
88 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
89 log_pdf = gamma.log_prob(x)
92 pdf = gamma.prob(x)
97 expected_log_pdf = stats.gamma.logpdf(x, alpha_v, scale=1 / beta_v)
109 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
110 log_pdf = gamma.log_prob(x)
113 pdf = gamma.prob(x)
119 expected_log_pdf = stats.gamma.logpdf(x, alpha_v, scale=1 / beta_v)
132 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
133 cdf = gamma.cdf(x)
137 expected_cdf = stats.gamma.cdf(x, alpha_v, scale=1 / beta_v)
144 gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v)
145 self.assertEqual(gamma.mean().get_shape(), (3,))
148 expected_means = stats.gamma.mean(alpha_v, scale=1 / beta_v)
149 self.assertAllClose(gamma.mean().eval(), expected_means)
155 gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v)
157 self.assertEqual(gamma.mode().get_shape(), (3,))
158 self.assertAllClose(gamma.mode().eval(), expected_modes)
165 gamma = gamma_lib.Gamma(concentration=alpha_v,
169 gamma.mode().eval()
176 gamma = gamma_lib.Gamma(concentration=alpha_v,
181 self.assertEqual(gamma.mode().get_shape(), (3,))
182 self.assertAllClose(gamma.mode().eval(), expected_modes)
188 gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v)
189 self.assertEqual(gamma.variance().get_shape(), (3,))
192 expected_variances = stats.gamma.var(alpha_v, scale=1 / beta_v)
193 self.assertAllClose(gamma.variance().eval(), expected_variances)
199 gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v)
200 self.assertEqual(gamma.stddev().get_shape(), (3,))
203 expected_stddev = stats.gamma.std(alpha_v, scale=1. / beta_v)
204 self.assertAllClose(gamma.stddev().eval(), expected_stddev)
210 gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v)
211 self.assertEqual(gamma.entropy().get_shape(), (3,))
214 expected_entropy = stats.gamma.entropy(alpha_v, scale=1 / beta_v)
215 self.assertAllClose(gamma.entropy().eval(), expected_entropy)
224 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
225 samples = gamma.sample(n, seed=137)
234 stats.gamma.mean(
239 stats.gamma.var(alpha_v, scale=1 / beta_v),
249 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
250 samples = gamma.sample(n, seed=137)
259 stats.gamma.mean(
264 stats.gamma.var(alpha_v, scale=1 / beta_v),
271 gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v)
273 samples = gamma.sample(n, seed=137)
284 stats.gamma.mean(
289 stats.gamma.var(alpha_bc, scale=1 / beta_bc),
304 ks, _ = stats.kstest(samples, stats.gamma(alpha, scale=1 / beta).cdf)
310 gamma = gamma_lib.Gamma(concentration=[7., 11.], rate=[[5.], [6.]])
312 samples = gamma.sample(num, seed=137)
313 pdfs = gamma.prob(samples)
324 stats.gamma.mean(
329 stats.gamma.var([[7., 11.], [7., 11.]],
348 gamma = gamma_lib.Gamma(concentration=alpha_v,
352 gamma.mean().eval()
355 gamma = gamma_lib.Gamma(concentration=alpha_v,
359 gamma.mean().eval()
365 gamma = gamma_lib.GammaWithSoftplusConcentrationRate(
368 gamma.concentration.eval())
370 gamma.rate.eval())
381 g0 = gamma_lib.Gamma(concentration=alpha0, rate=beta0)
382 g1 = gamma_lib.Gamma(concentration=alpha1, rate=beta1)
