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Max Zwiessele 2013-07-29 15:29:00 +01:00
parent 92c6e652d9
commit 3051c2a7e5
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GPy/testing/psi_stat_expectation_tests.py Normal file
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'''
Created on 26 Apr 2013
@author: maxz
'''
import unittest
import GPy
import numpy as np
from GPy import testing
__test__ = False
np.random.seed(0)
def ard(p):
try:
if p.ARD:
return "ARD"
except:
pass
return ""
@testing.deepTest(__test__)
class Test(unittest.TestCase):
input_dim = 9
num_inducing = 4
N = 3
Nsamples = 6e6
def setUp(self):
self.kerns = (
# (GPy.kern.rbf(self.input_dim, ARD=True) +
# GPy.kern.linear(self.input_dim, ARD=True) +
# GPy.kern.bias(self.input_dim) +
# GPy.kern.white(self.input_dim)),
(GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
GPy.kern.rbf(self.input_dim, np.random.rand(), np.random.rand(self.input_dim), ARD=True) +
GPy.kern.linear(self.input_dim, np.random.rand(self.input_dim), ARD=True) +
GPy.kern.bias(self.input_dim) +
GPy.kern.white(self.input_dim)),
# GPy.kern.rbf(self.input_dim), GPy.kern.rbf(self.input_dim, ARD=True),
# GPy.kern.linear(self.input_dim, ARD=False), GPy.kern.linear(self.input_dim, ARD=True),
# GPy.kern.linear(self.input_dim) + GPy.kern.bias(self.input_dim),
# GPy.kern.rbf(self.input_dim) + GPy.kern.bias(self.input_dim),
# GPy.kern.linear(self.input_dim) + GPy.kern.bias(self.input_dim) + GPy.kern.white(self.input_dim),
# GPy.kern.rbf(self.input_dim) + GPy.kern.bias(self.input_dim) + GPy.kern.white(self.input_dim),
# GPy.kern.bias(self.input_dim), GPy.kern.white(self.input_dim),
)
self.q_x_mean = np.random.randn(self.input_dim)
self.q_x_variance = np.exp(np.random.randn(self.input_dim))
self.q_x_samples = np.random.randn(self.Nsamples, self.input_dim) * np.sqrt(self.q_x_variance) + self.q_x_mean
self.Z = np.random.randn(self.num_inducing, self.input_dim)
self.q_x_mean.shape = (1, self.input_dim)
self.q_x_variance.shape = (1, self.input_dim)
def test_psi0(self):
for kern in self.kerns:
psi0 = kern.psi0(self.Z, self.q_x_mean, self.q_x_variance)
Kdiag = kern.Kdiag(self.q_x_samples)
self.assertAlmostEqual(psi0, np.mean(Kdiag), 1)
# print kern.parts[0].name, np.allclose(psi0, np.mean(Kdiag))
def test_psi1(self):
for kern in self.kerns:
Nsamples = 100
psi1 = kern.psi1(self.Z, self.q_x_mean, self.q_x_variance)
K_ = np.zeros((Nsamples, self.num_inducing))
diffs = []
for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
K = kern.K(q_x_sample_stripe, self.Z)
K_ += K
diffs.append(((psi1 - (K_ / (i + 1)))).mean())
K_ /= self.Nsamples / Nsamples
msg = "psi1: " + "+".join([p.name + ard(p) for p in kern.parts])
try:
import pylab
pylab.figure(msg)
pylab.plot(diffs)
self.assertTrue(np.allclose(psi1.squeeze(), K_,
rtol=1e-1, atol=.1),
msg=msg + ": not matching")
# sys.stdout.write(".")
except:
# import ipdb;ipdb.set_trace()
# kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
# sys.stdout.write("E") # msg + ": not matching"
pass
def test_psi2(self):
for kern in self.kerns:
Nsamples = 100
psi2 = kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
K_ = np.zeros((self.num_inducing, self.num_inducing))
diffs = []
for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
K = kern.K(q_x_sample_stripe, self.Z)
K = (K[:, :, None] * K[:, None, :]).mean(0)
K_ += K
diffs.append(((psi2 - (K_ / (i + 1)))).mean())
K_ /= self.Nsamples / Nsamples
msg = "psi2: {}".format("+".join([p.name + ard(p) for p in kern.parts]))
try:
import pylab
pylab.figure(msg)
pylab.plot(diffs)
self.assertTrue(np.allclose(psi2.squeeze(), K_,
rtol=1e-1, atol=.1),
msg=msg + ": not matching")
# sys.stdout.write(".")
except:
# import ipdb;ipdb.set_trace()
# kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
# sys.stdout.write("E")
print msg + ": not matching"
pass
if __name__ == "__main__":
import sys
__test__ = 'deep' in sys.argv
sys.argv = ['',
'Test.test_psi0',
'Test.test_psi1',
'Test.test_psi2',
]
unittest.main()