https://github.com/GPflow/GPflow
Tip revision: af90c6e97f09f0b9a77d2fcc796f8a031ad097e8 authored by alexggmatthews on 06 June 2016, 17:06:36 UTC
Building up cone.
Building up cone.
Tip revision: af90c6e
test_priors.py
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(display=0)
xmax = self.m.get_free_state()
self.failUnless(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(display=0)
xmax = self.m.get_free_state()
self.failUnless(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(display=0)
print(self.m.x._array, (shape - 1) * scale)
true_mode = (shape - 1.) * scale
self.failUnless(np.allclose(self.m.x._array, true_mode, 1e-3))
if __name__ == "__main__":
unittest.main()