Revision

**138cd0ccaea183c3b4c17c6bbeafbdf148521170**authored by James Hensman on**08 June 2017, 09:58:17 UTC**, committed by James Hensman on**08 June 2017, 09:58:17 UTC****1 parent**0b3ce9b

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 unittest
import GPflow
import numpy as np
import tensorflow as tf
class PriorModeTests(unittest.TestCase):
"""
these tests optimize the prior to find the mode numerically. Make sure the
mode is the same as the known mode.
"""
def setUp(self):
tf.reset_default_graph()
class FlatModel(GPflow.model.Model):
def build_likelihood(self):
return 0
self.m = FlatModel()
def testGaussianMode(self):
self.m.x = GPflow.param.Param(1.0)
self.m.x.prior = GPflow.priors.Gaussian(3, 1)
self.m.optimize(disp=0)
xmax = self.m.get_free_state()
self.assertTrue(np.allclose(xmax, 3))
def testGaussianModeMatrix(self):
self.m.x = GPflow.param.Param(np.random.randn(4, 4))
self.m.x.prior = GPflow.priors.Gaussian(-1, 10)
self.m.optimize(disp=0)
xmax = self.m.get_free_state()
self.assertTrue(np.allclose(xmax, -1))
def testGammaMode(self):
self.m.x = GPflow.param.Param(1.0)
shape, scale = 4., 5.
self.m.x.prior = GPflow.priors.Gamma(shape, scale)
self.m.optimize(disp=0)
true_mode = (shape - 1.) * scale
self.assertTrue(np.allclose(self.m.x.value, true_mode, 1e-3))
def testLaplaceMode(self):
self.m.x = GPflow.param.Param(1.0)
self.m.x.prior = GPflow.priors.Laplace(3, 10)
self.m.optimize(disp=0)
xmax = self.m.get_free_state()
self.assertTrue(np.allclose(xmax, 3))
def testLogNormalMode(self):
self.m.x = GPflow.param.Param(1.0)
self.m.x.prior = GPflow.priors.LogNormal(3, 10)
self.m.x.transform = GPflow.transforms.Exp()
self.m.optimize(disp=0)
xmax = self.m.get_free_state()
self.assertTrue(np.allclose(xmax, 3))
def testUniform(self):
self.m.x = GPflow.param.Param(1.0)
self.m.x.prior = GPflow.priors.Uniform(-2, 3)
self.m.x.transform = GPflow.transforms.Logistic(-2, 3)
self.m.set_state(np.random.randn(1))
p1 = self.m.compute_log_prior()
self.m.set_state(np.random.randn(1))
p2 = self.m.compute_log_prior()
self.assertFalse(p1 == p2) # prior should no be the same because a transfomration has been applied.
if __name__ == "__main__":
unittest.main()
```

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