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testing imports update and expected failure for crossterms

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Max Zwiessele 2013-11-20 12:47:55 +00:00
parent f114b9fff5
commit d4dff8360b
4 changed files with 28 additions and 12 deletions
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2
GPy/testing/bgplvm_tests.py
View file

@ -4,7 +4,7 @@
import unittest
import numpy as np
import GPy
from GPy.models.bayesian_gplvm import BayesianGPLVM
from ..models import BayesianGPLVM
class BGPLVMTests(unittest.TestCase):
def test_bias_kern(self):

34
GPy/testing/psi_stat_gradient_tests.py
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@ -63,40 +63,54 @@ class DPsiStatTest(unittest.TestCase):
def testPsi0(self):
for k in self.kernels:
m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,
m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,\
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
# def testPsi1(self):
# for k in self.kernels:
# m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
# num_inducing=self.num_inducing, kernel=k)
# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi1(self):
for k in self.kernels:
m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_lin(self):
k = self.kernels[0]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_lin_bia(self):
k = self.kernels[3]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_rbf(self):
k = self.kernels[1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_rbf_bia(self):
k = self.kernels[-1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_bia(self):
k = self.kernels[2]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
@ -134,8 +148,8 @@ if __name__ == "__main__":
# num_inducing=num_inducing, kernel=k)
# 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,
# num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim))
m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim)+GPy.kern.bias(input_dim))
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing, kernel=kernel)
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,

2
GPy/testing/sparse_gplvm_tests.py
View file

@ -4,7 +4,7 @@
import unittest
import numpy as np
import GPy
from GPy.models.sparse_gplvm import SparseGPLVM
from ..models import SparseGPLVM
class sparse_GPLVMTests(unittest.TestCase):
def test_bias_kern(self):

2
GPy/testing/unit_tests.py
View file

@ -163,11 +163,13 @@ class GradientTests(unittest.TestCase):
rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2)
@unittest.expectedFailure
def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs'''
rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1)
@unittest.expectedFailure
def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs'''
rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)