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_hmc.py
import GPflow
import numpy as np
import unittest
import tensorflow as tf
class SampleGaussianTest(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
self.f = lambda x : (0.5*np.sum(np.square(x)), x)
self.x0 = np.zeros(3)
def test_mean_cov(self):
samples = GPflow.hmc.sample_HMC(self.f, num_samples=1000, Lmax=20, epsilon=0.05,
x0=self.x0, verbose=False, thin=10, burn=0)
mean = samples.mean(0)
cov = np.cov(samples.T)
self.failUnless(np.allclose(mean, np.zeros(3), 1e-1, 1e-1))
self.failUnless(np.allclose(cov, np.eye(3), 1e-1, 1e-1))
def test_rng(self):
"""
Make sure all randomness can be atributed to the rng
"""
samples1 = GPflow.hmc.sample_HMC(self.f, num_samples=1000, Lmax=20, epsilon=0.05,
x0=self.x0, verbose=False, thin=10, burn=0, RNG=np.random.RandomState(10))
samples2 = GPflow.hmc.sample_HMC(self.f, num_samples=1000, Lmax=20, epsilon=0.05,
x0=self.x0, verbose=False, thin=10, burn=0, RNG=np.random.RandomState(10))
samples3 = GPflow.hmc.sample_HMC(self.f, num_samples=1000, Lmax=20, epsilon=0.05,
x0=self.x0, verbose=False, thin=10, burn=0, RNG=np.random.RandomState(11))
self.failUnless(np.all(samples1==samples2))
self.failIf(np.all(samples1==samples3))
def test_burn(self):
samples = GPflow.hmc.sample_HMC(self.f, num_samples=100, Lmax=20, epsilon=0.05,
x0=self.x0, verbose=False, thin=1, burn=10, RNG=np.random.RandomState(11))
self.failUnless(samples.shape == (100,3))
self.failIf(np.all(samples[0] == self.x0))
class SampleModelTest(unittest.TestCase):
"""
Create a very simple model and make sure samples form is make sense.
"""
def setUp(self):
tf.reset_default_graph()
rng = np.random.RandomState(0)
class Quadratic(GPflow.model.Model):
def __init__(self):
GPflow.model.Model.__init__(self)
self.x = GPflow.param.Param(rng.randn(2))
def build_likelihood(self):
return -tf.reduce_sum(tf.square(self.x))
self.m = Quadratic()
def test_mean(self):
samples = self.m.sample(num_samples=200, Lmax=20, epsilon=0.05)
self.failUnless(samples.shape == (200,2))
self.failUnless(np.allclose(samples.mean(0), np.zeros(2), 1e-1, 1e-1))
if __name__ == "__main__":
unittest.main()