Revision 48270681afc13081094f7f398a1e194c6b07ba9b authored by vdutor on 03 January 2018, 17:44:53 UTC, committed by Mark van der Wilk on 03 January 2018, 17:44:53 UTC
* Outline of new expectations code. * Quadrature code now uses TensorFlow shape inference. * General expectations work. * Expectations RBF kern, not tested * Add Identity mean function * General unittests for Expectations * Add multipledispatch package to travis * Update tests_expectations * Expectations of mean functions * Mean function uncertain conditional * Uncertain conditional with mean_function. Tested. * Support for Add and Prod kernels and quadrature fallback decorator * Refactor expectations unittests * Psi stats Linear kernel * Split expectations in different files * Expectation Linear kernel and Linear mean function * Remove None's from expectations api * Removed old ekernels framework * Add multipledispatch to setup file * Work on PR feedback, not finished * Addressed PR feedback * Support for pairwise xKxz * Enable expectations unittests * Renamed `TimeseriesGaussian` to `MarkovGaussian` and added tests. * Rename some variable, plus note for later test of <x Kxz>_q. * Update conditionals.py Add comment * Change order of inputs to (feat, kern) * Stef/expectations (#601) * adding gaussmarkov quad * don't override the markvogaussian in the quadrature * can't test * adding external test * quadrature code done and works for MarkovGauss * MarkovGaussian with quad implemented. All tests pass * Shape comments. * Removed superfluous autoflow functions for kernel expectations * Update kernels.py * Update quadrature.py
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test_triang.py
# 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_array_almost_equal
from gpflow import misc
from gpflow.test_util import GPflowTestCase
class TestVecToTri(GPflowTestCase):
def reference_inverse(self, matrices):
# This is the inverse operation of the vec_to_tri op being tested.
D, N, _ = matrices.shape
M = (N * (N + 1)) // 2
tril_indices = np.tril_indices(N)
output = np.zeros((D, M))
for vector_index in range(D):
matrix = matrices[vector_index, :]
output[vector_index, :] = matrix[tril_indices]
return output
def get_example_matrices(self, D, N ):
rng = np.random.RandomState(1)
random_matrices = rng.randn(D, N, N)
for matrix_index in range(D):
for row_index in range(N):
for col_index in range(N):
if col_index > row_index:
random_matrices[matrix_index, row_index, col_index] = 0.
return random_matrices
def test_basic_functionality(self):
with self.test_context() as sess:
N = 3
D = 3
reference_matrices = self.get_example_matrices(D, N)
input_vector_tensor = tf.constant(self.reference_inverse(reference_matrices))
test_matrices_tensor = misc.vec_to_tri(input_vector_tensor, N)
test_matrices = sess.run(test_matrices_tensor)
assert_array_almost_equal(reference_matrices, test_matrices)
def test_differentiable(self):
with self.test_context() as sess:
N = 3
D = 3
reference_matrices = self.get_example_matrices(D, N)
input_vector_array = self.reference_inverse(reference_matrices)
input_vector_tensor = tf.constant(input_vector_array)
test_matrices_tensor = misc.vec_to_tri(input_vector_tensor, N)
reduced_sum = tf.reduce_sum(test_matrices_tensor)
gradient = tf.gradients(reduced_sum, input_vector_tensor)[0]
reference_gradient = np.ones_like(input_vector_array)
test_gradient = sess.run(gradient)
assert_array_almost_equal(reference_gradient, test_gradient)
if __name__ == "__main__":
tf.test.main()
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