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Merge branch 'devel' into mrd

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Max Zwiessele 2013-04-23 14:11:12 +01:00
commit d4e0194cf7
1 changed files with 16 additions and 16 deletions
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32
GPy/testing/psi_stat_tests.py
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@ -39,10 +39,6 @@ class PsiStatModel(model):
self.Z = x[start: end].reshape(self.M, self.Q) self.Z = x[start: end].reshape(self.M, self.Q)
self.kern._set_params(x[end:]) self.kern._set_params(x[end:])
def log_likelihood(self): def log_likelihood(self):
# if '2' in self.which:
# norm = self.N ** 2
# else: # '0', '1' in self.which:
# norm = self.N
return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum() return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
def _log_likelihood_gradients(self): def _log_likelihood_gradients(self):
psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance) psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance)
@ -64,23 +60,27 @@ class Test(unittest.TestCase):
Z = numpy.random.permutation(X)[:M] Z = numpy.random.permutation(X)[:M]
Y = X.dot(numpy.random.randn(Q, D)) Y = X.dot(numpy.random.randn(Q, D))
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): def testPsi0(self):
kernel = GPy.kern.linear(Q) for k in self.kernels:
m = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z, m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,
M=M, kernel=kernel, mu_or_S=0, dL=numpy.ones((1))) M=self.M, kernel=k)
assert m.checkgrad(), "linear x psi0" assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi1(self): def testPsi1(self):
kernel = GPy.kern.linear(Q) for k in self.kernels:
m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z, m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,
M=M, kernel=kernel, mu_or_S=0, dL=numpy.ones((1, 1))) M=self.M, kernel=k)
assert(m.checkgrad()) assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2(self): def testPsi2(self):
kernel = GPy.kern.linear(Q) for k in self.kernels:
m = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z, m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,
M=M, kernel=kernel, mu_or_S=0, dL=numpy.ones((1, 1, 1))) M=self.M, kernel=k)
assert(m.checkgrad()) assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
if __name__ == "__main__": if __name__ == "__main__":