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slicing finished with independent outputs

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This commit is contained in:
Max Zwiessele 2014-03-17 16:22:16 +00:00
parent 62d594d977
commit 19dc7cecf4
3 changed files with 115 additions and 54 deletions
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80
GPy/testing/kernel_tests.py
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@ -94,7 +94,7 @@ class Kern_check_dKdiag_dX(Kern_check_dK_dX):
def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verbose=False):
def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verbose=False, fixed_X_dims=None):
"""
This function runs on kernels to check the correctness of their
implementation. It checks that the covariance function is positive definite
@ -109,11 +109,11 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
"""
pass_checks = True
if X==None:
if X is None:
X = np.random.randn(10, kern.input_dim)
if output_ind is not None:
X[:, output_ind] = np.random.randint(kern.output_dim, X.shape[0])
if X2==None:
if X2 is None:
X2 = np.random.randn(20, kern.input_dim)
if output_ind is not None:
X2[:, output_ind] = np.random.randint(kern.output_dim, X2.shape[0])
@ -164,7 +164,10 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
if verbose:
print("Checking gradients of K(X, X) wrt X.")
try:
result = Kern_check_dK_dX(kern, X=X, X2=None).checkgrad(verbose=verbose)
testmodel = Kern_check_dK_dX(kern, X=X, X2=None)
if fixed_X_dims is not None:
testmodel.X[:,fixed_X_dims].fix()
result = testmodel.checkgrad(verbose=verbose)
except NotImplementedError:
result=True
if verbose:
@ -173,14 +176,17 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
print("Check passed.")
if not result:
print("Gradient of K(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
Kern_check_dK_dX(kern, X=X, X2=None).checkgrad(verbose=True)
testmodel.checkgrad(verbose=True)
pass_checks = False
return False
if verbose:
print("Checking gradients of K(X, X2) wrt X.")
try:
result = Kern_check_dK_dX(kern, X=X, X2=X2).checkgrad(verbose=verbose)
testmodel = Kern_check_dK_dX(kern, X=X, X2=X2)
if fixed_X_dims is not None:
testmodel.X[:,fixed_X_dims].fix()
result = testmodel.checkgrad(verbose=verbose)
except NotImplementedError:
result=True
if verbose:
@ -188,8 +194,8 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
if result and verbose:
print("Check passed.")
if not result:
print("Gradient of K(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
Kern_check_dK_dX(kern, X=X, X2=X2).checkgrad(verbose=True)
print("Gradient of K(X, X2) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
testmodel.checkgrad(verbose=True)
pass_checks = False
return False
@ -300,24 +306,50 @@ class KernelTestsMiscellaneous(unittest.TestCase):
class KernelTestsNonContinuous(unittest.TestCase):
def setUp(self):
N = 100
N1 = 110
self.D = 2
D = self.D
self.X = np.random.randn(N,D)
self.X2 = np.random.randn(N1,D)
self.X_block = np.zeros((N+N1, D+D+1))
self.X_block[0:N, 0:D] = self.X
self.X_block[N:N+N1, D:D+D] = self.X2
self.X_block[0:N, -1] = 1
self.X_block[N:N+1, -1] = 2
self.X_block = self.X_block[self.X_block.argsort(0)[:, -1], :]
N0 = 3
N1 = 9
N2 = 4
N = N0+N1+N2
self.D = 3
self.X = np.random.randn(N, self.D+1)
indices = np.random.random_integers(0, 2, size=N)
self.X[indices==0, -1] = 0
self.X[indices==1, -1] = 1
self.X[indices==2, -1] = 2
#self.X = self.X[self.X[:, -1].argsort(), :]
self.X2 = np.random.randn((N0+N1)*2, self.D+1)
self.X2[:(N0*2), -1] = 0
self.X2[(N0*2):, -1] = 1
def test_IndependentOutputs(self):
k = GPy.kern.RBF(self.D)
kern = GPy.kern.IndependentOutputs(k, -1)
self.assertTrue(check_kernel_gradient_functions(kern, X=self.X_block, X2=self.X_block, verbose=verbose))
kern = GPy.kern.IndependentOutputs(k, -1, 'ind_single')
self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
k = [GPy.kern.RBF(1, active_dims=[1], name='rbf1'), GPy.kern.RBF(self.D, name='rbf012'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf02')]
kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')
self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
if __name__ == "__main__":
print "Running unit tests, please be (very) patient..."
unittest.main()
#unittest.main()
np.random.seed(0)
N0 = 3
N1 = 9
N2 = 4
N = N0+N1+N2
D = 3
X = np.random.randn(N, D+1)
indices = np.random.random_integers(0, 2, size=N)
X[indices==0, -1] = 0
X[indices==1, -1] = 1
X[indices==2, -1] = 2
#X = X[X[:, -1].argsort(), :]
X2 = np.random.randn((N0+N1)*2, D+1)
X2[:(N0*2), -1] = 0
X2[(N0*2):, -1] = 1
k = [GPy.kern.RBF(1, active_dims=[1], name='rbf1'), GPy.kern.RBF(D, name='rbf012'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf02')]
kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')
assert(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
k = GPy.kern.RBF(D)
kern = GPy.kern.IndependentOutputs(k, -1, 'ind_single')
assert(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))