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tests ignored my nosetests (__test__ = False)

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Max Zwiessele 2013-04-26 17:17:36 +01:00
parent 5abe3dee4c
commit 0332fa14f8
1 changed files with 48 additions and 43 deletions
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91
GPy/testing/kern_psi_stat_tests.py
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@ -8,6 +8,8 @@ import GPy
import numpy as np
import pylab
__test__ = False
class Test(unittest.TestCase):
D = 9
M = 5
@ -30,49 +32,52 @@ class Test(unittest.TestCase):
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((self.N, 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))) ** 2).mean())
# K_ /= self.Nsamples / Nsamples
# # pylab.figure("+".join([p.name for p in kern.parts]) + "psi1")
# # pylab.plot(diffs)
# self.assertTrue(np.allclose(psi1.flatten() , K.mean(0), rtol=1e-1))
#
# 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))) ** 2).mean())
# K_ /= self.Nsamples / Nsamples
# try:
# # pylab.figure("+".join([p.name for p in kern.parts]) + "psi2")
# # pylab.plot(diffs)
# self.assertTrue(np.allclose(psi2.squeeze(), K_,
# rtol=1e-1, atol=.1),
# msg="{}: not matching".format("+".join([p.name for p in kern.parts])))
# except:
# print "{}: not matching".format(kern.parts[0].name)
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((self.N, 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))) ** 2).mean())
K_ /= self.Nsamples / Nsamples
# pylab.figure("+".join([p.name for p in kern.parts]) + "psi1")
# pylab.plot(diffs)
self.assertTrue(np.allclose(psi1.flatten() , K.mean(0), rtol=1e-1))
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))) ** 2).mean())
K_ /= self.Nsamples / Nsamples
try:
# pylab.figure("+".join([p.name for p in kern.parts]) + "psi2")
# pylab.plot(diffs)
self.assertTrue(np.allclose(psi2.squeeze(), K_,
rtol=1e-1, atol=.1),
msg="{}: not matching".format("+".join([p.name for p in kern.parts])))
except:
print "{}: not matching".format(kern.parts[0].name)
if __name__ == "__main__":
import sys;sys.argv = ['', 'Test.test_psi2']
import sys;sys.argv = ['',
'Test.test_psi0',
'Test.test_psi1',
'Test.test_psi2']
unittest.main()