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psi_stat_expectaions now working with new parameterized

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Max Zwiessele 2014-03-11 16:23:51 +00:00
parent 74999a89ad
commit e078bb47e1
1 changed files with 13 additions and 44 deletions
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57
GPy/testing/psi_stat_expectation_tests.py
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@ -12,6 +12,7 @@ import numpy
from GPy.kern import RBF
from GPy.kern import Linear
from copy import deepcopy
from GPy.core.parameterization.variational import NormalPosterior
__test__ = lambda: 'deep' in sys.argv
# np.random.seed(0)
@ -28,53 +29,20 @@ def ard(p):
class Test(unittest.TestCase):
input_dim = 9
num_inducing = 13
N = 300
N = 1000
Nsamples = 1e6
def setUp(self):
i_s_dim_list = [2,4,3]
indices = numpy.cumsum(i_s_dim_list).tolist()
input_slices = [slice(a,b) for a,b in zip([None]+indices, indices)]
#input_slices[2] = deepcopy(input_slices[1])
input_slice_kern = GPy.kern.kern(9,
[
RBF(i_s_dim_list[0], np.random.rand(), np.random.rand(i_s_dim_list[0]), ARD=True),
RBF(i_s_dim_list[1], np.random.rand(), np.random.rand(i_s_dim_list[1]), ARD=True),
Linear(i_s_dim_list[2], np.random.rand(i_s_dim_list[2]), ARD=True)
],
input_slices = input_slices
)
self.kerns = (
# input_slice_kern,
# (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.Linear(self.input_dim, 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.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.bias(self.input_dim, np.random.rand())),
# (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.bias(self.input_dim, np.random.rand())
# #+GPy.kern.white(self.input_dim, np.random.rand())),
# ),
# GPy.kern.white(self.input_dim, np.random.rand())),
# 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),
GPy.kern.RBF(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.Bias(self.input_dim)+GPy.kern.White(self.input_dim),
GPy.kern.Linear(self.input_dim) + GPy.kern.Bias(self.input_dim) + GPy.kern.White(self.input_dim),
GPy.kern.Linear(self.input_dim, ARD=True) + 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_mean = np.random.randn(self.input_dim)[None]
self.q_x_variance = np.exp(.5*np.random.randn(self.input_dim))[None]
self.q_x_samples = np.random.randn(self.Nsamples, self.input_dim) * np.sqrt(self.q_x_variance) + self.q_x_mean
self.q_x = NormalPosterior(self.q_x_mean, self.q_x_variance)
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)
@ -114,8 +82,9 @@ class Test(unittest.TestCase):
def test_psi2(self):
for kern in self.kerns:
kern.randomize()
Nsamples = int(np.floor(self.Nsamples/self.N))
psi2 = kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
psi2 = kern.psi2(self.Z, self.q_x)
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)):
@ -130,8 +99,8 @@ class Test(unittest.TestCase):
pylab.figure(msg)
pylab.plot(diffs, marker='x', mew=.2)
# print msg, np.allclose(psi2.squeeze(), K_, rtol=1e-1, atol=.1)
self.assertTrue(np.allclose(psi2.squeeze(), K_),
#rtol=1e-1, atol=.1),
self.assertTrue(np.allclose(psi2.squeeze(), K_,
atol=.1, rtol=1),
msg=msg + ": not matching")
# sys.stdout.write(".")
except: