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bugfix: cross term psi1 bias + linear

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Max Zwiessele 2013-04-23 16:21:41 +01:00
parent 743112c448
commit 389a04d2b5
4 changed files with 28 additions and 11 deletions
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11
GPy/kern/kern.py
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@ -455,10 +455,15 @@ class kern(parameterised):
p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1._psi1 * 2., Z, mu, S, target[ps2]) p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1._psi1 * 2., Z, mu, S, target[ps2])
# linear X bias # linear X bias
elif p1.name == 'bias' and p2.name == 'linear': elif p1.name == 'bias' and p2.name == 'linear':
p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1.variance * 2., Z, mu, S, target) p2.dpsi1_dtheta(dL_dpsi2.sum(1) * p1.variance * 2., Z, mu, S, target[ps2]) # [ps1])
psi1 = np.zeros((mu.shape[0], Z.shape[0]))
p2.psi1(Z, mu, S, psi1)
p1.dpsi1_dtheta(dL_dpsi2.sum(1) * psi1 * 2., Z, mu, S, target[ps1])
elif p2.name == 'bias' and p1.name == 'linear': elif p2.name == 'bias' and p1.name == 'linear':
p1.dpsi1_dtheta(dL_dpsi2.sum(1) * p2.variance * 2., Z, mu, S, target) p1.dpsi1_dtheta(dL_dpsi2.sum(1) * p2.variance * 2., Z, mu, S, target[ps1])
pass psi1 = np.zeros((mu.shape[0], Z.shape[0]))
p1.psi1(Z, mu, S, psi1)
p2.dpsi1_dtheta(dL_dpsi2.sum(1) * psi1 * 2., Z, mu, S, target[ps2])
# rbf X linear # rbf X linear
elif p1.name == 'linear' and p2.name == 'rbf': elif p1.name == 'linear' and p2.name == 'rbf':
raise NotImplementedError # TODO raise NotImplementedError # TODO

2
GPy/kern/linear.py
View file

@ -114,7 +114,7 @@ class linear(kernpart):
def psi1(self,Z,mu,S,target): def psi1(self,Z,mu,S,target):
"""the variance, it does nothing""" """the variance, it does nothing"""
self.K(mu,Z,target) self._psi1 = self.K(mu, Z, target)
def dpsi1_dtheta(self,dL_dpsi1,Z,mu,S,target): def dpsi1_dtheta(self,dL_dpsi1,Z,mu,S,target):
"""the variance, it does nothing""" """the variance, it does nothing"""

2
GPy/testing/bgplvm_tests.py
View file

@ -60,7 +60,7 @@ class BGPLVMTests(unittest.TestCase):
#@unittest.skip('psi2 cross terms are NotImplemented for this combination') #@unittest.skip('psi2 cross terms are NotImplemented for this combination')
def test_linear_bias_kern(self): def test_linear_bias_kern(self):
N, M, Q, D = 10, 3, 2, 4 N, M, Q, D = 30, 5, 4, 30
X = np.random.rand(N, Q) X = np.random.rand(N, Q)
k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001) k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X) K = k.K(X)

24
GPy/testing/psi_stat_tests.py
View file

@ -105,6 +105,18 @@ if __name__ == "__main__":
import sys import sys
interactive = 'i' in sys.argv interactive = 'i' in sys.argv
if interactive: if interactive:
N, M, Q, D = 30, 5, 4, 30
X = numpy.random.rand(N, Q)
k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = numpy.random.multivariate_normal(numpy.zeros(N), K, D).T
Y -= Y.mean(axis=0)
k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
m.ensure_default_constraints()
m.randomize()
# self.assertTrue(m.checkgrad())
Q = 5 Q = 5
N = 50 N = 50
M = 10 M = 10
@ -119,17 +131,17 @@ if __name__ == "__main__":
GPy.kern.linear(Q) + GPy.kern.bias(Q), GPy.kern.linear(Q) + GPy.kern.bias(Q),
GPy.kern.rbf(Q) + GPy.kern.bias(Q)] GPy.kern.rbf(Q) + GPy.kern.bias(Q)]
for k in kernels: # for k in kernels:
m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z, # m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
M=M, kernel=k) # M=M, kernel=k)
assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts))) # assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
# #
# m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z, # m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.linear(Q)) # M=M, kernel=GPy.kern.linear(Q))
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z, # m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=kernel) # M=M, kernel=kernel)
m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z, # m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
M=M, kernel=kernel) # M=M, kernel=kernel)
m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z, m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
M=M, kernel=GPy.kern.rbf(Q)) M=M, kernel=GPy.kern.rbf(Q))
m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z, m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,