text: # Copyright 2017 the GPflow 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.from __future__ import print_function
  
  import tensorflow as tf
  
  import numpy as np
  from numpy.testing import assert_almost_equal, assert_allclose
  
  import gpflow
  from gpflow.test_util import GPflowTestCase
  from gpflow.core import AutoFlow
  
  
  class DumbModel(gpflow.models.Model):
      def __init__(self):
          gpflow.models.Model.__init__(self)
          self.a = gpflow.Param(3.)
  
      @gpflow.params_as_tensors
      def _build_likelihood(self):
          return -tf.square(self.a)
  
  
  class NoArgsModel(DumbModel):
      @gpflow.autoflow()
      @gpflow.params_as_tensors
      def function1(self):
          return self.a
  
      @gpflow.autoflow()
      @gpflow.params_as_tensors
      def function2(self):
          return self.a + 1.0
  
  class TestNoArgs(GPflowTestCase):
      def test_autoflow_functioning(self):
          with self.test_context():
              m = NoArgsModel()
              m.compile()
  
              def get_keys():
                  return [k for k in m.__dict__ if k.startswith(AutoFlow.__autoflow_prefix__)]
  
              names = [m.function1.__name__, m.function2.__name__]
              names = [AutoFlow.__autoflow_prefix__ + name for name in names]
              first_key = names[0]
              second_key = names[1]
  
              assert_allclose(m.function1(), 3.)
              assert_allclose(m.function2(), 4.)
              self.assertEqual(len(get_keys()), 2)
  
              AutoFlow.clear_autoflow(m, name=first_key)
              self.assertEqual(len(get_keys()), 1)
              assert_allclose(m.function1(), 3.)
              self.assertEqual(len(get_keys()), 2)
  
              AutoFlow.clear_autoflow(m, name=second_key)
              self.assertEqual(len(get_keys()), 1)
              assert_allclose(m.function2(), 4.)
              self.assertEqual(len(get_keys()), 2)
  
              AutoFlow.clear_autoflow(m, name=first_key)
              AutoFlow.clear_autoflow(m, name=second_key)
              self.assertEqual(len(get_keys()), 0)
              assert_allclose(m.function1(), 3.)
              assert_allclose(m.function2(), 4.)
              self.assertEqual(len(get_keys()), 2)
  
              AutoFlow.clear_autoflow(m)
              self.assertEqual(len(get_keys()), 0)
  
  
  class AddModel(DumbModel):
      @gpflow.autoflow((tf.float64,), (tf.float64,))
      @gpflow.params_as_tensors
      def add(self, x, y):
          return tf.add(x, y)
  
  
  class TestShareArgs(GPflowTestCase):
      """
      This is designed to replicate bug #85, where having two models caused
      autoflow functions to fail because the tf_args were shared over the
      instances.
      """
      def setUp(self):
          with self.test_context():
              self.m1 = AddModel()
              self.m2 = AddModel()
  
      def test_share_args(self):
          with self.test_context():
              rng = np.random.RandomState(0)
              x = rng.randn(10, 20)
              y = rng.randn(10, 20)
              ans = x + y
              self.m1.add(x, y)
              self.m2.add(x, y)
              assert_almost_equal(self.m1.add(x, y), ans)
              assert_almost_equal(self.m2.add(x, y), ans)
              assert_almost_equal(self.m1.add(y, x), ans)
  
  
  class IncrementModel(DumbModel):
      def __init__(self):
          DumbModel.__init__(self)
          self.b = gpflow.DataHolder(np.array([3.]))
  
      @gpflow.autoflow((tf.float64,))
      @gpflow.params_as_tensors
      def inc(self, x):
          return x + self.b
  
  
  class TestDataHolder(GPflowTestCase):
      def test_add(self):
          with self.test_context():
              m = IncrementModel()
              x = np.random.randn(10, 20)
              m.compile()
              assert_almost_equal(x + m.a.read_value(), m.inc(x))
  
  
  class TestGPmodel(GPflowTestCase):
      def prepare(self):
          rng = np.random.RandomState(0)
          X, Y = rng.randn(2, 10, 1)
          m = gpflow.models.SVGP(X, Y,
                                 kern=gpflow.kernels.Matern32(1),
                                 likelihood=gpflow.likelihoods.StudentT(),
                                 Z=X[::2].copy())
          m.compile()
          xnew = np.random.randn(100, 1)
          ynew = np.random.randn(100, 1)
          return m, xnew, ynew
  
      def test_predict_f(self):
          with self.test_context():
              m, x, _y = self.prepare()
              _mu, _var = m.predict_f(x)
  
      def test_predict_y(self):
          with self.test_context():
              m, x, _y = self.prepare()
              _mu, _var = m.predict_y(x)
  
      def test_predict_density(self):
          with self.test_context():
              m, x, y = self.prepare()
              m.predict_density(x, y)
  
      def test_multiple_AFs(self):
          with self.test_context():
              m, _x, _y = self.prepare()
              m.compute_log_likelihood()
              m.compute_log_prior()
              m.compute_log_likelihood()
  
  
  class TestFixAndPredict(GPflowTestCase):
      """
      Bug #54 says that if a model parameter is fixed  between calls to predict
      (an autoflow fn) then the second call fails. This test ensures replicates
      that and ensures that the bugfix remains in furure.
      """
  
      def prepare(self):
          rng = np.random.RandomState(0)
          X, Y = rng.randn(2, 10, 1)
          m = gpflow.models.SVGP(X, Y, kern=gpflow.kernels.Matern32(1),
                                    likelihood=gpflow.likelihoods.StudentT(),
                                    Z=X[::2].copy())
          xtest = np.random.randn(100, 1)
          ytest = np.random.randn(100, 1)
          return m, xtest, ytest
  
      def test(self):
          with self.test_context():
              m, x, y = self.prepare()
              m.compile()
              m.kern.variance.trainable = False
              _, _ = m.predict_f(m.X.read_value())
  
  
  class TestSVGP(GPflowTestCase):
      """
      This replicates Alex's code from bug #99
      """
      def test(self):
          rng = np.random.RandomState(1)
          X = rng.randn(10, 1)
          Y = rng.randn(10, 1)
          Z = rng.randn(3, 1)
          model = gpflow.models.SVGP(
              X=X, Y=Y, kern=gpflow.kernels.RBF(1),
              likelihood=gpflow.likelihoods.Gaussian(), Z=Z)
          model.compile()
          model.compute_log_likelihood()
  
  
  if __name__ == '__main__':
      tf.test.main()
  