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How to use scale method in ATX

Best Python code snippet using ATX

affine.py

Source:affine.py

...266                array_ops.zeros([], identity_multiplier.dtype),267                ["identity_multiplier should be non-zero."])],268            identity_multiplier)269      return identity_multiplier270    scale = distribution_util.make_tril_scale(271        loc=shift,272        scale_tril=tril,273        scale_diag=diag,274        scale_identity_multiplier=identity_multiplier,275        validate_args=validate_args,276        assert_positive=False,277        shape_hint=shape_hint)278    if perturb_factor is not None:279      return linalg.LinearOperatorLowRankUpdate(280          scale,281          u=perturb_factor,282          diag_update=perturb_diag,283          is_diag_update_positive=perturb_diag is None,284          is_non_singular=True,  # Implied by is_positive_definite=True.285          is_self_adjoint=True,286          is_positive_definite=True,287          is_square=True)288    return scale289  @property290  def shift(self):291    """The `shift` `Tensor` in `Y = scale @ X + shift`."""292    return self._shift293  @property294  def scale(self):295    """The `scale` `LinearOperator` in `Y = scale @ X + shift`."""296    return self._scale297  def _forward(self, x):298    y = x299    if self._is_only_identity_multiplier:300      y *= self._scale301      if self.shift is not None:302        return y + self.shift303      return y304    y, sample_shape = self._shaper.make_batch_of_event_sample_matrices(305        y, expand_batch_dim=False)306    with ops.control_dependencies(self._maybe_check_scale() if307                                  self.validate_args else []):308      y = self.scale.matmul(y)309    y = self._shaper.undo_make_batch_of_event_sample_matrices(310        y, sample_shape, expand_batch_dim=False)311    if self.shift is not None:312      y += self.shift313    return y314  def _inverse(self, y):315    x = y316    if self.shift is not None:317      x -= self.shift318    if self._is_only_identity_multiplier:319      return x / self._scale320    x, sample_shape = self._shaper.make_batch_of_event_sample_matrices(321        x, expand_batch_dim=False)322    # Solve fails if the op is singular so we may safely skip this assertion.323    x = self.scale.solve(x)324    x = self._shaper.undo_make_batch_of_event_sample_matrices(325        x, sample_shape, expand_batch_dim=False)326    return x327  def _forward_log_det_jacobian(self, x):328    # is_constant_jacobian = True for this bijector, hence the329    # `log_det_jacobian` need only be specified for a single input, as this will330    # be tiled to match `event_ndims`.331    if self._is_only_identity_multiplier:332      # We don't pad in this case and instead let the fldj be applied333      # via broadcast.334      event_size = array_ops.shape(x)[-1]335      event_size = math_ops.cast(event_size, dtype=self._scale.dtype)336      return math_ops.log(math_ops.abs(self._scale)) * event_size337    return self.scale.log_abs_determinant()338  def _maybe_check_scale(self):339    try:340      return [self.scale.assert_non_singular()]341    except NotImplementedError:342      pass...

laplace_test.py

Source:laplace_test.py

1# Copyright 2016 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15from __future__ import absolute_import16from __future__ import division17from __future__ import print_function18import numpy as np19from scipy import stats20import tensorflow as tf21class LaplaceTest(tf.test.TestCase):22  def testLaplaceShape(self):23    with self.test_session():24      loc = tf.constant([3.0] * 5)25      scale = tf.constant(11.0)26      laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)27      self.assertEqual(laplace.batch_shape().eval(), (5,))28      self.assertEqual(laplace.get_batch_shape(), tf.TensorShape([5]))29      self.assertAllEqual(laplace.event_shape().eval(), [])30      self.assertEqual(laplace.get_event_shape(), tf.TensorShape([]))31  def testLaplaceLogPDF(self):32    with self.test_session():33      batch_size = 634      loc = tf.constant([2.0] * batch_size)35      scale = tf.constant([3.0] * batch_size)36      loc_v = 2.037      scale_v = 3.038      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)39      laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)40      expected_log_pdf = stats.laplace.logpdf(x, loc_v, scale=scale_v)41      log_pdf = laplace.log_pdf(x)42      self.assertEqual(log_pdf.get_shape(), (6,))43      self.assertAllClose(log_pdf.eval(), expected_log_pdf)44      pdf = laplace.pdf(x)45      self.assertEqual(pdf.get_shape(), (6,))46      self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf))47  def testLaplaceLogPDFMultidimensional(self):48    with self.test_session():49      batch_size = 650      loc = tf.constant([[2.0, 4.0]] * batch_size)51      scale = tf.constant([[3.0, 4.0]] * batch_size)52      loc_v = np.array([2.0, 4.0])53      scale_v = np.array([3.0, 4.0])54      x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T55      laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)56      expected_log_pdf = stats.laplace.logpdf(x, loc_v, scale=scale_v)57      log_pdf = laplace.log_pdf(x)58      log_pdf_values = log_pdf.eval()59      self.assertEqual(log_pdf.get_shape(), (6, 2))60      self.assertAllClose(log_pdf_values, expected_log_pdf)61      pdf = laplace.pdf(x)62      pdf_values = pdf.eval()63      self.assertEqual(pdf.get_shape(), (6, 2))64      self.assertAllClose(pdf_values, np.exp(expected_log_pdf))65  def testLaplaceLogPDFMultidimensionalBroadcasting(self):66    with self.test_session():67      batch_size = 668      loc = tf.constant([[2.0, 4.0]] * batch_size)69      scale = tf.constant(3.0)70      loc_v = np.array([2.0, 4.0])71      scale_v = 3.072      x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T73      laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)74      expected_log_pdf = stats.laplace.logpdf(x, loc_v, scale=scale_v)75      log_pdf = laplace.log_pdf(x)76      log_pdf_values = log_pdf.eval()77      self.assertEqual(log_pdf.get_shape(), (6, 2))78      self.assertAllClose(log_pdf_values, expected_log_pdf)79      pdf = laplace.pdf(x)80      pdf_values = pdf.eval()81      self.assertEqual(pdf.get_shape(), (6, 2))82      self.assertAllClose(pdf_values, np.exp(expected_log_pdf))83  def testLaplaceCDF(self):84    with self.test_session():85      batch_size = 686      loc = tf.constant([2.0] * batch_size)87      scale = tf.constant([3.0] * batch_size)88      loc_v = 2.089      scale_v = 3.090      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)91      laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)92      expected_cdf = stats.laplace.cdf(x, loc_v, scale=scale_v)93      cdf = laplace.cdf(x)94      self.assertEqual(cdf.get_shape(), (6,))95      self.assertAllClose(cdf.eval(), expected_cdf)96  def testLaplaceMean(self):97    with self.test_session():98      loc_v = np.array([1.0, 3.0, 2.5])99      scale_v = np.array([1.0, 4.0, 5.0])100      laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)101      expected_means = stats.laplace.mean(loc_v, scale=scale_v)102      self.assertEqual(laplace.mean().get_shape(), (3,))103      self.assertAllClose(laplace.mean().eval(), expected_means)104  def testLaplaceMode(self):105    with self.test_session():106      loc_v = np.array([0.5, 3.0, 2.5])107      scale_v = np.array([1.0, 4.0, 5.0])108      laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)109      self.assertEqual(laplace.mode().get_shape(), (3,))110      self.assertAllClose(laplace.mode().eval(), loc_v)111  def testLaplaceVariance(self):112    with self.test_session():113      loc_v = np.array([1.0, 3.0, 2.5])114      scale_v = np.array([1.0, 4.0, 5.0])115      laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)116      expected_variances = stats.laplace.var(loc_v, scale=scale_v)117      self.assertEqual(laplace.variance().get_shape(), (3,))118      self.assertAllClose(laplace.variance().eval(), expected_variances)119  def testLaplaceStd(self):120    with self.test_session():121      loc_v = np.array([1.0, 3.0, 2.5])122      scale_v = np.array([1.0, 4.0, 5.0])123      laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)124      expected_std = stats.laplace.std(loc_v, scale=scale_v)125      self.assertEqual(laplace.std().get_shape(), (3,))126      self.assertAllClose(laplace.std().eval(), expected_std)127  def testLaplaceEntropy(self):128    with self.test_session():129      loc_v = np.array([1.0, 3.0, 2.5])130      scale_v = np.array([1.0, 4.0, 5.0])131      expected_entropy = stats.laplace.entropy(loc_v, scale=scale_v)132      laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)133      self.assertEqual(laplace.entropy().get_shape(), (3,))134      self.assertAllClose(laplace.entropy().eval(), expected_entropy)135  def testLaplaceSample(self):136    with tf.Session():137      loc_v = 4.0138      scale_v = 3.0139      loc = tf.constant(loc_v)140      scale = tf.constant(scale_v)141      n = 100000142      laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)143      samples = laplace.sample(n, seed=137)144      sample_values = samples.eval()145      self.assertEqual(samples.get_shape(), (n,))146      self.assertEqual(sample_values.shape, (n,))147      self.assertAllClose(sample_values.mean(),148                          stats.laplace.mean(loc_v, scale=scale_v),149                          rtol=0.05, atol=0.)150      self.assertAllClose(sample_values.var(),151                          stats.laplace.var(loc_v, scale=scale_v),152                          rtol=0.05, atol=0.)153      self.assertTrue(self._kstest(loc_v, scale_v, sample_values))154  def testLaplaceSampleMultiDimensional(self):155    with tf.Session():156      loc_v = np.array([np.arange(1, 101, dtype=np.float32)])  # 1 x 100157      scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T  # 10 x 1158      laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)159      n = 10000160      samples = laplace.sample(n, seed=137)161      sample_values = samples.eval()162      self.assertEqual(samples.get_shape(), (n, 10, 100))163      self.assertEqual(sample_values.shape, (n, 10, 100))164      zeros = np.zeros_like(loc_v + scale_v)  # 10 x 100165      loc_bc = loc_v + zeros166      scale_bc = scale_v + zeros167      self.assertAllClose(168          sample_values.mean(axis=0),169          stats.laplace.mean(loc_bc, scale=scale_bc),170          rtol=0.35, atol=0.)171      self.assertAllClose(172          sample_values.var(axis=0),173          stats.laplace.var(loc_bc, scale=scale_bc),174          rtol=0.10, atol=0.)175      fails = 0176      trials = 0177      for ai, a in enumerate(np.reshape(loc_v, [-1])):178        for bi, b in enumerate(np.reshape(scale_v, [-1])):179          s = sample_values[:, bi, ai]180          trials += 1181          fails += 0 if self._kstest(a, b, s) else 1182      self.assertLess(fails, trials * 0.03)183  def _kstest(self, loc, scale, samples):184    # Uses the Kolmogorov-Smirnov test for goodness of fit.185    ks, _ = stats.kstest(samples, stats.laplace(loc, scale=scale).cdf)186    # Return True when the test passes.187    return ks < 0.02188  def testLaplacePdfOfSampleMultiDims(self):189    with tf.Session() as sess:190      laplace = tf.contrib.distributions.Laplace(191          loc=[7., 11.], scale=[[5.], [6.]])192      num = 50000193      samples = laplace.sample(num, seed=137)194      pdfs = laplace.pdf(samples)195      sample_vals, pdf_vals = sess.run([samples, pdfs])196      self.assertEqual(samples.get_shape(), (num, 2, 2))197      self.assertEqual(pdfs.get_shape(), (num, 2, 2))198      self.assertAllClose(199          stats.laplace.mean([[7., 11.], [7., 11.]],200                             scale=np.array([[5., 5.], [6., 6.]])),201          sample_vals.mean(axis=0),202          rtol=0.05, atol=0.)203      self.assertAllClose(204          stats.laplace.var([[7., 11.], [7., 11.]],205                            scale=np.array([[5., 5.], [6., 6.]])),206          sample_vals.var(axis=0),207          rtol=0.05, atol=0.)208      self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02)209      self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02)210      self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02)211      self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02)212  def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3):213    s_p = zip(sample_vals, pdf_vals)214    prev = (0, 0)215    total = 0216    for k in sorted(s_p, key=lambda x: x[0]):217      pair_pdf = (k[1] + prev[1]) / 2218      total += (k[0] - prev[0]) * pair_pdf219      prev = k220    self.assertNear(1., total, err=err)221  def testLaplaceNonPositiveInitializationParamsRaises(self):222    with self.test_session():223      loc_v = tf.constant(0.0, name="loc")224      scale_v = tf.constant(-1.0, name="scale")225      laplace = tf.contrib.distributions.Laplace(226          loc=loc_v, scale=scale_v, validate_args=True)227      with self.assertRaisesOpError("scale"):228        laplace.mean().eval()229      loc_v = tf.constant(1.0, name="loc")230      scale_v = tf.constant(0.0, name="scale")231      laplace = tf.contrib.distributions.Laplace(232          loc=loc_v, scale=scale_v, validate_args=True)233      with self.assertRaisesOpError("scale"):234        laplace.mean().eval()235  def testLaplaceWithSoftplusScale(self):236    with self.test_session():237      loc_v = tf.constant([0.0, 1.0], name="loc")238      scale_v = tf.constant([-1.0, 2.0], name="scale")239      laplace = tf.contrib.distributions.LaplaceWithSoftplusScale(240          loc=loc_v, scale=scale_v)241      self.assertAllClose(tf.nn.softplus(scale_v).eval(), laplace.scale.eval())242      self.assertAllClose(loc_v.eval(), laplace.loc.eval())243if __name__ == "__main__":...

mvn_diag_plus_low_rank.py

Source:mvn_diag_plus_low_rank.py

...192          loc, scale_diag, scale_identity_multiplier, scale_perturb_factor,193          scale_perturb_diag]):194        has_low_rank = (scale_perturb_factor is not None or195                        scale_perturb_diag is not None)196        scale = distribution_util.make_diag_scale(197            loc=loc,198            scale_diag=scale_diag,199            scale_identity_multiplier=scale_identity_multiplier,200            validate_args=validate_args,201            assert_positive=has_low_rank)202        scale_perturb_factor = _convert_to_tensor(203            scale_perturb_factor,204            name="scale_perturb_factor")205        scale_perturb_diag = _convert_to_tensor(206            scale_perturb_diag,207            name="scale_perturb_diag")208        if has_low_rank:209          scale = linalg.LinearOperatorLowRankUpdate(210              scale,...

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