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ensure_default_constraints() now also works with the BGPLVM

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Nicolo Fusi 2013-03-12 15:36:29 +00:00
parent f1af69e7b7
commit 21c3988a21
2 changed files with 7 additions and 7 deletions
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2
GPy/models/Bayesian_GPLVM.py
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@ -41,7 +41,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
def _get_param_names(self): def _get_param_names(self):
X_names = sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[]) X_names = sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
S_names = sum([['S_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[]) S_names = sum([['X_variance_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
return (X_names + S_names + sparse_GP._get_param_names(self)) return (X_names + S_names + sparse_GP._get_param_names(self))
def _get_params(self): def _get_params(self):

12
GPy/testing/bgplvm_tests.py
View file

@ -15,7 +15,7 @@ class BGPLVMTests(unittest.TestCase):
Y -= Y.mean(axis=0) Y -= Y.mean(axis=0)
k = GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001) k = GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M) m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
m.constrain_positive('(rbf|bias|noise|white|S)') m.ensure_default_constraints()
m.randomize() m.randomize()
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())
@ -28,7 +28,7 @@ class BGPLVMTests(unittest.TestCase):
Y -= Y.mean(axis=0) Y -= Y.mean(axis=0)
k = GPy.kern.linear(Q) + GPy.kern.white(Q, 0.00001) k = GPy.kern.linear(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M) m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
m.constrain_positive('(linear|bias|noise|white|S)') m.ensure_default_constraints()
m.randomize() m.randomize()
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())
@ -41,7 +41,7 @@ class BGPLVMTests(unittest.TestCase):
Y -= Y.mean(axis=0) Y -= Y.mean(axis=0)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001) k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M) m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
m.constrain_positive('(rbf|bias|noise|white|S)') m.ensure_default_constraints()
m.randomize() m.randomize()
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())
@ -54,7 +54,7 @@ class BGPLVMTests(unittest.TestCase):
Y -= Y.mean(axis=0) Y -= Y.mean(axis=0)
k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001) 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 = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
m.constrain_positive('(rbf|bias|noise|white|S)') m.ensure_default_constraints()
m.randomize() m.randomize()
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())
@ -68,9 +68,9 @@ class BGPLVMTests(unittest.TestCase):
Y -= Y.mean(axis=0) Y -= Y.mean(axis=0)
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)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M) m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
m.constrain_positive('(linear|bias|noise|white|S)') m.ensure_default_constraints()
m.randomize() m.randomize()
self.assertTrue(m.checkgrad()) self.assertTrue(m.checkgrad())
if __name__ == "__main__": if __name__ == "__main__":