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correcting linearCF, mu to go

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Max Zwiessele 2013-05-02 16:37:47 +01:00
parent 42474f0044
commit 5051a2fc89
3 changed files with 59 additions and 37 deletions
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5
GPy/testing/kern_psi_stat_tests.py
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@ -21,7 +21,8 @@ def ard(p):
class Test(unittest.TestCase):
D = 9
M = 3
M = 4
N = 3
Nsamples = 6e6
def setUp(self):
@ -73,7 +74,7 @@ class Test(unittest.TestCase):
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())
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:

16
GPy/testing/psi_stat_tests.py
View file

@ -52,16 +52,16 @@ class Test(unittest.TestCase):
Q = 5
N = 50
M = 10
D = 10
D = 20
X = numpy.random.randn(N, Q)
X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
Z = numpy.random.permutation(X)[:M]
Y = X.dot(numpy.random.randn(Q, D))
kernels = [GPy.kern.linear(Q), GPy.kern.rbf(Q), GPy.kern.bias(Q)]
kernels = [GPy.kern.linear(Q, ARD=True, variances=numpy.random.rand(Q)), GPy.kern.rbf(Q, ARD=True), GPy.kern.bias(Q)]
kernels = [GPy.kern.linear(Q), GPy.kern.rbf(Q), GPy.kern.bias(Q),
GPy.kern.linear(Q) + GPy.kern.bias(Q),
GPy.kern.rbf(Q) + GPy.kern.bias(Q)]
# kernels = [GPy.kern.linear(Q), GPy.kern.rbf(Q), GPy.kern.bias(Q),
# GPy.kern.linear(Q) + GPy.kern.bias(Q),
# GPy.kern.rbf(Q) + GPy.kern.bias(Q)]
def testPsi0(self):
for k in self.kernels:
@ -121,9 +121,9 @@ if __name__ == "__main__":
Q = 5
N = 50
M = 10
D = 10
D = 15
X = numpy.random.randn(N, Q)
X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
X_var = .5 * numpy.ones_like(X) + .1 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
Z = numpy.random.permutation(X)[:M]
Y = X.dot(numpy.random.randn(Q, D))
# kernel = GPy.kern.bias(Q)
@ -146,7 +146,7 @@ if __name__ == "__main__":
# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.rbf(Q))
m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
M=M, kernel=GPy.kern.linear(Q))
M=M, kernel=GPy.kern.linear(Q, ARD=True, variances=numpy.random.rand(Q)))
m3.ensure_default_constraints()
# + GPy.kern.bias(Q))
# m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,