def testTwoSamples_low_covariance(seed):
np_rng = npr.RandomState(seed)
obs_inputs = np.array([1.3, -2.0, 0.0])
obs_outputs = np.array([5.0, 2.3, 8.0])
in_lo_cov = np.array([1.4, -20.0])
sigma = 2.1
l = 1.8
observations = OrderedDict(zip(obs_inputs, obs_outputs))
mean = gp.mean_const(0.)
covariance = cov.scale(sigma**2, cov.se(l**2))
# Fix n = 200, not higher even if we are doing a long-running
# inference quality test, because we're trying to show that a
# Pearson r^2 test of independence will fail to reject the null
# hypothesis that the GP at two points with low covariance are
# simply independent. If we raised the number of samples
# significantly higher, the test is likely to reject the null
# hypothesis.
n = 200
lo_cov_x = []
lo_cov_y = []
for i in range(n):
x, y = gp._gp_sample(mean, covariance, observations, in_lo_cov, np_rng)
lo_cov_x.append(x)
lo_cov_y.append(y)
return reportPearsonIndependence(lo_cov_x, lo_cov_y)
def testOneSample(seed):
np_rng = npr.RandomState(seed)
obs_inputs = np.array([1.3, -2.0, 0.0])
obs_outputs = np.array([5.0, 2.3, 8.0])
test_input = 1.4
expect_mu = 4.6307
expect_sig = 0.0027
sigma = 2.1
l = 1.8
observations = OrderedDict(zip(obs_inputs, obs_outputs))
mean = gp.mean_const(0.)
covariance = cov.scale(sigma**2, cov.se(l**2))
# _gp_sample(..., test_input) should be normally distributed with
# mean expect_mu.
n = default_num_samples(4)
def sample():
s = gp._gp_sample(mean, covariance, observations, [test_input], np_rng)
return s[0]
samples = np.array([sample() for _ in xrange(n)])
assert samples.shape == (n, )
return reportKnownGaussian(expect_mu, np.sqrt(expect_sig), samples)
def testNormalParameters():
obs_inputs = np.array([1.3, -2.0, 0.0])
obs_outputs = np.array([5.0, 2.3, 8.0])
test_inputs = np.array([1.4, -3.2])
expect_mu = np.array([4.6307, -0.9046])
expect_sig = np.array([[0.0027, -0.0231], [-0.0231, 1.1090]])
sigma = 2.1
l = 1.8
observations = OrderedDict(zip(obs_inputs, obs_outputs))
mean = gp.mean_const(0.)
covariance = cov.scale(sigma**2, cov.se(l**2))
actual_mu, actual_sig = gp._gp_mvnormal(mean, covariance, observations,
test_inputs)
np.testing.assert_almost_equal(actual_mu, expect_mu, decimal=4)
np.testing.assert_almost_equal(actual_sig, expect_sig, decimal=4)
def interpret_mean_kernel(ast):
if ast[0]['value'] == 'gp_mean_const':
c = ast[1]['value']
return gp.mean_const(c)
else:
assert False, ''