mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-12 21:42:39 +02:00
124 lines
4.8 KiB
Python
124 lines
4.8 KiB
Python
'''
|
|
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):
|
|
D = 9
|
|
M = 4
|
|
N = 3
|
|
Nsamples = 6e6
|
|
|
|
def setUp(self):
|
|
self.kerns = (
|
|
# (GPy.kern.rbf(self.D, ARD=True) +
|
|
# GPy.kern.linear(self.D, ARD=True) +
|
|
# GPy.kern.bias(self.D) +
|
|
# GPy.kern.white(self.D)),
|
|
(GPy.kern.rbf(self.D, np.random.rand(), np.random.rand(self.D), ARD=True) +
|
|
GPy.kern.rbf(self.D, np.random.rand(), np.random.rand(self.D), ARD=True) +
|
|
GPy.kern.linear(self.D, np.random.rand(self.D), ARD=True) +
|
|
GPy.kern.bias(self.D) +
|
|
GPy.kern.white(self.D)),
|
|
# GPy.kern.rbf(self.D), GPy.kern.rbf(self.D, ARD=True),
|
|
# GPy.kern.linear(self.D, ARD=False), GPy.kern.linear(self.D, ARD=True),
|
|
# GPy.kern.linear(self.D) + GPy.kern.bias(self.D),
|
|
# GPy.kern.rbf(self.D) + GPy.kern.bias(self.D),
|
|
# GPy.kern.linear(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
|
|
# GPy.kern.rbf(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
|
|
# GPy.kern.bias(self.D), GPy.kern.white(self.D),
|
|
)
|
|
self.q_x_mean = np.random.randn(self.D)
|
|
self.q_x_variance = np.exp(np.random.randn(self.D))
|
|
self.q_x_samples = np.random.randn(self.Nsamples, self.D) * np.sqrt(self.q_x_variance) + self.q_x_mean
|
|
self.Z = np.random.randn(self.M, self.D)
|
|
self.q_x_mean.shape = (1, self.D)
|
|
self.q_x_variance.shape = (1, self.D)
|
|
|
|
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.M))
|
|
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.M, self.M))
|
|
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()
|