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working on psi cross terms

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This commit is contained in:
Nicolo Fusi 2013-03-11 14:03:23 +00:00
parent 84119a19b3
commit 05ca5cfe6d
3 changed files with 30 additions and 6 deletions
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8
GPy/kern/kern.py
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@ -444,9 +444,9 @@ class kern(parameterised):
pass pass
#rbf X bias #rbf X bias
elif p1.name=='bias' and p2.name=='rbf': elif p1.name=='bias' and p2.name=='rbf':
target += p2.dpsi1_dX(dL_dpsi2.sum(1)*p1.variance,Z,mu,S,target) p2.dpsi1_dX(dL_dpsi2.sum(1)*p1.variance,Z,mu,S,target)
elif p2.name=='bias' and p1.name=='rbf': elif p2.name=='bias' and p1.name=='rbf':
target += p1.dpsi1_dZ(dL_dpsi2.sum(2)*p2.variance,Z,mu,S,target) p1.dpsi1_dZ(dL_dpsi2.sum(2)*p2.variance,Z,mu,S,target)
#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
@ -471,9 +471,9 @@ class kern(parameterised):
pass pass
#rbf X bias #rbf X bias
elif p1.name=='bias' and p2.name=='rbf': elif p1.name=='bias' and p2.name=='rbf':
target += p2.dpsi1_dmuS(partial.sum(1)*p1.variance,Z,mu,S,target_mu,target_S) p2.dpsi1_dmuS(partial.sum(1)*p1.variance,Z,mu,S,target_mu,target_S)
elif p2.name=='bias' and p1.name=='rbf': elif p2.name=='bias' and p1.name=='rbf':
target += p1.dpsi1_dmuS(partial.sum(2)*p2.variance,Z,mu,S,target_mu,target_S) p1.dpsi1_dmuS(partial.sum(2)*p2.variance,Z,mu,S,target_mu,target_S)
#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

26
GPy/testing/bgplvm_tests.py
View file

@ -45,6 +45,32 @@ class BGPLVMTests(unittest.TestCase):
m.randomize() m.randomize()
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())
def test_rbf_bias_kern(self):
N, M, Q, D = 10, 3, 2, 4
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
Y -= Y.mean(axis=0)
k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
m.constrain_positive('(rbf|bias|noise|white|S)')
m.randomize()
self.assertTrue(m.checkgrad())
def test_linear_bias_kern(self):
N, M, Q, D = 10, 3, 2, 4
X = np.random.rand(N, Q)
k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.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.constrain_positive('(linear|bias|noise|white|S)')
m.randomize()
self.assertTrue(m.checkgrad())
if __name__ == "__main__": if __name__ == "__main__":
print "Running unit tests, please be (very) patient..." print "Running unit tests, please be (very) patient..."

2
setup.py
View file

@ -32,7 +32,5 @@ setup(name = 'GPy',
#setup_requires=['sphinx'], #setup_requires=['sphinx'],
#cmdclass = {'build_sphinx': BuildDoc}, #cmdclass = {'build_sphinx': BuildDoc},
classifiers=[ classifiers=[
"Development Status :: 1 - Alpha",
"Topic :: Machine Learning",
"License :: OSI Approved :: BSD License"], "License :: OSI Approved :: BSD License"],
) )