text: # Copyright 2018 Google LLC
  #
  # 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
  #
  #     https://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 nngp.py."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import tensorflow as tf
  
  import nngp
  
  
  class NNGPTest(tf.test.TestCase):
  
    def ExactQabArcCos(self, var_aa, corr_ab):
      """Exact integration result from Cho & Saul (2009).
  
      Specifically:
        qaa = 0.5*qaa
        qab = (qaa/2*pi)*(sin angle + (pi-angle)*cos angle),
  
        where cos angle = corr_ab.
  
      Args:
        var_aa: 1d tensor of variance grid points.
        corr_ab: 1d tensor of correlation grid points.
      Returns:
        qab_exact: tensor, exact covariance matrix.
      """
      angle = tf.acos(corr_ab)
      jtheta = tf.sin(angle) + (np.pi - angle) * tf.cos(angle)
  
      term1 = tf.tile(tf.expand_dims(var_aa, 1), [1, corr_ab.shape[0]])
      term2 = tf.tile(tf.expand_dims(jtheta, 0), [var_aa.shape[0], 1])
      qab_exact = (1 / (2 * np.pi)) * term1 * term2
  
      return qab_exact
  
    def testComputeQmapGridRelu(self):
      """Test checks the compute_qmap_grid function.
  
      (i) Checks sizes are appropriate and (ii) checks
      accuracy of the numerical values generated by the
      grid by comparing against the analytically known
      form for Relu (Cho and Saul, '09).
      """
      n_gauss, n_var, n_corr = 301, 33, 31
      kernel = nngp.NNGPKernel(
          nonlin_fn=tf.nn.relu, n_gauss=n_gauss, n_var=n_var, n_corr=n_corr)
  
      var_aa_grid = kernel.var_aa_grid
      corr_ab_grid = kernel.corr_ab_grid
      qaa_grid = kernel.qaa_grid
      qab_grid = kernel.qab_grid
  
      qaa_exact = 0.5 * var_aa_grid
      qab_exact = self.ExactQabArcCos(var_aa_grid, corr_ab_grid)
  
      with self.test_session() as sess:
        self.assertEqual(var_aa_grid.shape.as_list(), [n_var])
        self.assertEqual(corr_ab_grid.shape.as_list(), [n_corr])
        self.assertAllClose(sess.run(qaa_exact), sess.run(qaa_grid), rtol=1e-6)
        self.assertAllClose(
            sess.run(qab_exact), sess.run(qab_grid), rtol=1e-6, atol=2e-2)
  
    def testComputeQmapGridReluLogSpacing(self):
      n_gauss, n_var, n_corr = 301, 33, 31
      kernel = nngp.NNGPKernel(
          nonlin_fn=tf.nn.relu, n_gauss=n_gauss, n_var=n_var, n_corr=n_corr)
  
      var_aa_grid = kernel.var_aa_grid
      corr_ab_grid = kernel.corr_ab_grid
      qaa_grid = kernel.qaa_grid
      qab_grid = kernel.qab_grid
  
      qaa_exact = 0.5 * var_aa_grid
      qab_exact = self.ExactQabArcCos(var_aa_grid, corr_ab_grid)
  
      with self.test_session() as sess:
        self.assertEqual(var_aa_grid.shape.as_list(), [n_var])
        self.assertEqual(corr_ab_grid.shape.as_list(), [n_corr])
        self.assertAllClose(
            sess.run(qaa_exact), sess.run(qaa_grid), rtol=1e-6, atol=2e-2)
        self.assertAllClose(
            sess.run(qab_exact), sess.run(qab_grid), rtol=1e-6, atol=2e-2)
  
    def testComputeQmapGridEvenNGauss(self):
      n_gauss, n_var, n_corr = 102, 33, 31
  
      with self.assertRaises(ValueError):
        nngp.NNGPKernel(
            nonlin_fn=tf.nn.relu, n_gauss=n_gauss, n_var=n_var, n_corr=n_corr)
  
    def testGetVarFixedPoint(self):
      n_gauss, n_var, n_corr = 101, 33, 31
      weight_var, bias_var = 1.9, 0.2
      analytic_fixed_point = bias_var / (1. - weight_var/2)
  
      kernel = nngp.NNGPKernel(
          nonlin_fn=tf.nn.relu, weight_var=weight_var, bias_var=bias_var,
          n_gauss=n_gauss, n_var=n_var, n_corr=n_corr)
      fixed_point, _ = kernel.get_var_fixed_point()
  
      self.assertAllClose(analytic_fixed_point, fixed_point[0], atol=1e-4)
  
  
  if __name__ == "__main__":
    tf.test.main()
  