https://github.com/GPflow/GPflow
Revision 399e7320faf73df58efc868ab8010bb2428585b8 authored by Artem Artemev on 01 December 2017, 00:45:12 UTC, committed by Artem Artemev on 01 December 2017, 00:45:12 UTC
1 parent cf629d5
Tip revision: 399e7320faf73df58efc868ab8010bb2428585b8 authored by Artem Artemev on 01 December 2017, 00:45:12 UTC
Normalize tf floats and ints.
Normalize tf floats and ints.
Tip revision: 399e732
test_priors.py
# Copyright 2016 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_allclose
import gpflow
from gpflow import settings
from gpflow.test_util import GPflowTestCase
class FlatModel(gpflow.models.Model):
def _build_likelihood(self):
return np.array(0., dtype=settings.np_float)
class TestPriorMode(GPflowTestCase):
"""
these tests optimize the prior to find the mode numerically. Make sure the
mode is the same as the known mode.
"""
def prepare(self, autobuild=False):
return FlatModel(autobuild=autobuild)
def testGaussianMode(self):
with self.test_context():
m = self.prepare()
m.x = gpflow.Param(1., autobuild=False)
m.x.prior = gpflow.priors.Gaussian(3., 1.)
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
_ = [assert_allclose(v, 3) for v in m.read_trainables().values()]
def testGaussianModeMatrix(self):
with self.test_context():
m = self.prepare()
m.x = gpflow.Param(np.random.randn(4, 4), prior=gpflow.priors.Gaussian(-1., 10.))
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
_ = [assert_allclose(v, -1.) for v in m.read_trainables().values()]
def testGammaMode(self):
with self.test_context():
m = self.prepare()
m.x = gpflow.Param(1.0, autobuild=False)
shape, scale = 4., 5.
m.x.prior = gpflow.priors.Gamma(shape, scale)
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
true_mode = (shape - 1.) * scale
assert_allclose(m.x.read_value(), true_mode, 1e-3)
def testLaplaceMode(self):
with self.test_context():
m = self.prepare()
m.x = gpflow.Param(1.0, prior=gpflow.priors.Laplace(3., 10.))
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
_ = [assert_allclose(v, 3) for v in m.read_trainables().values()]
def testLogNormalMode(self):
with self.test_context():
m = self.prepare()
transform = gpflow.transforms.Exp()
prior = gpflow.priors.LogNormal(3., 10.)
m.x = gpflow.Param(1.0, prior=prior, transform=transform)
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
xmax = [transform.backward(x) for x in m.read_trainables().values()]
assert_allclose(xmax, 3, rtol=1e4)
def testBetaMode(self):
with self.test_context():
m = self.prepare()
transform = gpflow.transforms.Logistic()
m.x = gpflow.Param(0.1, prior=gpflow.priors.Beta(3., 3.), transform=transform)
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
xmax = [transform.backward(x) for x in m.read_trainables().values()]
assert_allclose(0.0, xmax, atol=1.e-5)
def testUniform(self):
with self.test_context():
m = self.prepare()
m.x = gpflow.Param(
1.0, prior=gpflow.priors.Uniform(-2., 3.),
transform=gpflow.transforms.Logistic(-2., 3.))
m.compile()
m.x = np.random.randn(1)[0]
p1 = m.compute_log_prior()
m.x = np.random.randn(1)[0]
p2 = m.compute_log_prior()
# prior should no be the same because a transformation has been applied.
self.assertTrue(p1 != p2)
if __name__ == "__main__":
tf.test.main()
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