Lines Matching refs:scale
46 """Compute Wishart variance for numpy scale matrix."""
56 scale = make_pd(1., 2)
58 w = distributions.WishartCholesky(df, chol(scale))
59 # sp.stats.wishart(df=4, scale=make_pd(1., 2)).entropy()
62 w = distributions.WishartCholesky(df=1, scale=[[1.]])
63 # sp.stats.wishart(df=1,scale=1).entropy()
76 scale=chol(make_pd(1., 2)))
79 w = distributions.WishartCholesky(df=5, scale=[[1.]])
84 scale = make_pd(1., 2)
86 w = distributions.WishartCholesky(df, chol(scale))
87 self.assertAllEqual(df * scale, w.mean().eval())
91 scale = make_pd(1., 2)
93 w = distributions.WishartCholesky(df, chol(scale))
94 self.assertAllEqual((df - 2. - 1.) * scale, w.mode().eval())
98 scale = make_pd(1., 2)
100 w = distributions.WishartCholesky(df, chol(scale))
101 self.assertAllEqual(chol(wishart_var(df, scale)), w.stddev().eval())
105 scale = make_pd(1., 2)
107 w = distributions.WishartCholesky(df, chol(scale))
108 self.assertAllEqual(wishart_var(df, scale), w.variance().eval())
112 scale = make_pd(1., 2)
116 df, chol(scale), cholesky_input_output_matrices=False)
122 df, scale, cholesky_input_output_matrices=False)
126 df, chol(scale), cholesky_input_output_matrices=True)
134 df, scale, cholesky_input_output_matrices=True)
145 scale=chol(make_pd(1., 3)),
171 scale=chol(make_pd(1., 3)),
179 scale=chol(make_pd(1., 3)),
198 # math.log(stats.wishart.pdf(x[0], df=2+0, scale=x[0]))
200 # math.log(stats.wishart.pdf(x[1], df=2+1, scale=x[1]))
202 # math.log(stats.wishart.pdf(x[2], df=2+2, scale=x[2]))
204 # math.log(stats.wishart.pdf(x[3], df=2+3, scale=x[3]))
211 scale=chol_x,
219 # math.log(stats.wishart.pdf(x[0], df=4, scale=x[0]))
221 # math.log(stats.wishart.pdf(x[1], df=4, scale=x[0]))
223 # math.log(stats.wishart.pdf(x[2], df=4, scale=x[0]))
225 # math.log(stats.wishart.pdf(x[3], df=4, scale=x[0]))
232 scale=chol_x[0],
236 scale=x[0],
254 scale=chol_x[0],
258 scale=x[0],
275 scale = make_pd(1., 2)
276 chol_scale = chol(scale)
278 w = distributions.WishartCholesky(df=4, scale=chol_scale)
283 df=[4., 4], scale=np.array([chol_scale, chol_scale]))
288 w = distributions.WishartCholesky(df=4, scale=scale_deferred)
299 scale = make_pd(1., 2)
300 chol_scale = chol(scale)
302 w = distributions.WishartCholesky(df=4, scale=chol_scale)
307 df=[4., 4], scale=np.array([chol_scale, chol_scale]))
312 w = distributions.WishartCholesky(df=4, scale=scale_deferred)
334 scale=chol_scale_deferred,
344 df=df_deferred, scale=chol_scale_deferred)
354 with self.assertRaisesOpError("scale must be square"):
357 scale=np.array([[2., 3., 4.], [1., 2., 3.]], dtype=np.float32),
359 sess.run(chol_w.scale().eval())
364 scale=chol_scale_deferred,
385 df=2, scale=chol_scale, validate_args=False)
389 scale=np.asarray(