text: # Copyright 2018 The TensorFlow Probability Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  """Tests for RationalQuadratic kernels."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl.testing import parameterized
  
  import numpy as np
  import tensorflow.compat.v2 as tf
  
  import tensorflow_probability as tfp
  
  from tensorflow_probability.python.internal import test_util
  
  
  @test_util.test_all_tf_execution_regimes
  class RationalQuadraticTest(test_util.TestCase):
  
    def _rational_quadratic(
        self, amplitude, length_scale, scale_mixture_rate, x, y):
      return (amplitude ** 2) * (1. + np.sum((x - y) ** 2) / (
          2 * scale_mixture_rate * length_scale ** 2)) ** (-scale_mixture_rate)
  
    def testMismatchedFloatTypesAreBad(self):
      with self.assertRaises(TypeError):
        tfp.math.psd_kernels.RationalQuadratic(np.float32(1.), np.float64(1.))
  
    def testBatchShape(self):
      amplitude = np.random.uniform(2, 3., size=[3, 1, 2]).astype(np.float32)
      length_scale = np.random.uniform(2, 3., size=[1, 3, 1]).astype(np.float32)
      scale_mixture_rate = np.random.uniform(
          2, 3., size=[3, 1, 1]).astype(np.float32)
      k = tfp.math.psd_kernels.RationalQuadratic(amplitude, length_scale,
                                                 scale_mixture_rate)
      self.assertAllEqual(tf.TensorShape([3, 3, 2]), k.batch_shape)
      self.assertAllEqual([3, 3, 2], self.evaluate(k.batch_shape_tensor()))
  
    @parameterized.parameters(
        {'feature_ndims': 1, 'dtype': np.float32, 'dims': 3},
        {'feature_ndims': 1, 'dtype': np.float32, 'dims': 4},
        {'feature_ndims': 2, 'dtype': np.float32, 'dims': 2},
        {'feature_ndims': 2, 'dtype': np.float64, 'dims': 3},
        {'feature_ndims': 3, 'dtype': np.float64, 'dims': 2},
        {'feature_ndims': 3, 'dtype': np.float64, 'dims': 3})
    def testValuesAreCorrect(self, feature_ndims, dtype, dims):
      amplitude = np.array(5., dtype=dtype)
      length_scale = np.array(.2, dtype=dtype)
      scale_mixture_rate = np.array(3., dtype=dtype)
  
      np.random.seed(42)
      k = tfp.math.psd_kernels.RationalQuadratic(amplitude, length_scale,
                                                 scale_mixture_rate,
                                                 feature_ndims)
      shape = [dims] * feature_ndims
      for _ in range(5):
        x = np.random.uniform(-1, 1, size=shape).astype(dtype)
        y = np.random.uniform(-1, 1, size=shape).astype(dtype)
        self.assertAllClose(
            self._rational_quadratic(
                amplitude, length_scale, scale_mixture_rate, x, y),
            self.evaluate(k.apply(x, y)))
  
    def testNoneScaleMixture(self):
      amplitude = 5.
      length_scale = .2
  
      np.random.seed(42)
  
      k = tfp.math.psd_kernels.RationalQuadratic(
          amplitude=amplitude, length_scale=length_scale, scale_mixture_rate=None)
      x = np.random.uniform(-1, 1, size=[5]).astype(np.float32)
      y = np.random.uniform(-1, 1, size=[5]).astype(np.float32)
      self.assertAllClose(
          # Ensure that a None value for scale_mixture_rate has the same semantics
          # as scale_mixture_rate=1.
          self._rational_quadratic(
              amplitude=amplitude,
              length_scale=length_scale, scale_mixture_rate=1., x=x, y=y),
          self.evaluate(k.apply(x, y)))
  
    def testShapesAreCorrect(self):
      k = tfp.math.psd_kernels.RationalQuadratic(amplitude=1., length_scale=1.)
  
      x = np.ones([4, 3], np.float32)
      y = np.ones([5, 3], np.float32)
  
      self.assertAllEqual([4, 5], k.matrix(x, y).shape)
      self.assertAllEqual(
          [2, 4, 5],
          k.matrix(tf.stack([x]*2), tf.stack([y]*2)).shape)
  
      k = tfp.math.psd_kernels.RationalQuadratic(
          amplitude=np.ones([2, 1, 1], np.float32),
          length_scale=np.ones([1, 3, 1], np.float32),
          scale_mixture_rate=np.ones([2, 1, 1, 1], np.float32))
  
      self.assertAllEqual(
          [2, 2, 3, 2, 4, 5],
          #`-----'  |  `--'
          #  |      |    `- matrix shape
          #  |      `- from input batch shapes
          #  `- from broadcasting kernel params
          k.matrix(
              tf.stack([x]*2),  # shape [2, 4, 3]
              tf.stack([y]*2)   # shape [2, 5, 3]
          ).shape)
  
    def testValidateArgs(self):
      with self.assertRaisesOpError('amplitude must be positive'):
        k = tfp.math.psd_kernels.RationalQuadratic(
            amplitude=-1.,
            length_scale=1.,
            scale_mixture_rate=1.,
            validate_args=True)
        self.evaluate(k.apply([1.], [1.]))
  
      with self.assertRaisesOpError('length_scale must be positive'):
        k = tfp.math.psd_kernels.RationalQuadratic(
            amplitude=1.,
            length_scale=-1.,
            scale_mixture_rate=1.,
            validate_args=True)
        self.evaluate(k.apply([1.], [1.]))
  
      with self.assertRaisesOpError('scale_mixture_rate must be positive'):
        k = tfp.math.psd_kernels.RationalQuadratic(
            amplitude=1.,
            length_scale=1.,
            scale_mixture_rate=-1.,
            validate_args=True)
        self.evaluate(k.apply([1.], [1.]))
  
      # But `None`'s are ok
      k = tfp.math.psd_kernels.RationalQuadratic(
          amplitude=None,
          length_scale=None,
          scale_mixture_rate=None,
          validate_args=True)
      self.evaluate(k.apply([1.], [1.]))
  
  if __name__ == '__main__':
    tf.test.main()
  