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parameter_testing

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Max Zwiessele 2013-10-07 07:41:51 +01:00
parent f7f62ec605
commit 31c82a74ce
1 changed files with 60 additions and 23 deletions
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83
GPy/testing/parameter_testing.py
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@ -8,7 +8,8 @@ from GPy.kern.constructors import rbf, linear, white
from GPy.models.gp_regression import GPRegression
import numpy
from GPy.models.bayesian_gplvm import BayesianGPLVM
from GPy.core.parameter import Parameter, Parameters
from GPy.core.parameter import Param, Parameterized
from GPy.likelihoods.gaussian import Gaussian
class Test(unittest.TestCase):
@ -17,40 +18,44 @@ class Test(unittest.TestCase):
self.rbf_variance = numpy.random.rand()
self.rbf_lengthscale = numpy.random.rand(self.Q)
self.linear_variance = numpy.random.rand(self.Q)
self.noise_variance = numpy.random.rand(1)
self.kern = (rbf(self.Q, self.rbf_variance, self.rbf_lengthscale, ARD=True)
+ linear(self.Q, self.linear_variance, ARD=True))
+ linear(self.Q, self.linear_variance, ARD=True)
+ white(self.Q, self.noise_variance))
self.X = numpy.random.rand(self.N, self.Q) + 10
self.X_variance = numpy.random.rand(self.N, self.Q) * .2
K = self.kern.K(self.X)
self.Y = numpy.random.multivariate_normal(numpy.zeros(self.N), K + numpy.eye(self.N) * .2, self.D).T
self.bgplvm = BayesianGPLVM(self.Y, self.Q, self.X, self.X_variance, kernel=self.kern)
self.bgplvm = BayesianGPLVM(Gaussian(self.Y, variance=self.noise_variance), self.Q, self.X, self.X_variance, kernel=self.kern)
self.bgplvm.ensure_default_constraints()
self.bgplvm.tie_params("noise_variance|white_variance")
self.parameter = Parameters([
Parameters([
Parameter('X', self.X),
Parameter('X_variance', self.X_variance),
],
prefix='X'),
Parameter('iip', self.bgplvm.Z),
Parameters([
Parameter('rbf_variance', self.rbf_variance),
Parameter('rbf_lengthscale', self.rbf_lengthscale)
],
'rbf'
),
Parameter('linear_variance', self.linear_variance),
Parameter('noise_variance', self.linear_variance),
self.parameter = Parameterized([
Parameterized([
Param('X', self.X),
Param('X_variance', self.X_variance),
]),
Param('iip', self.bgplvm.Z),
Parameterized([
Param('rbf_variance', self.rbf_variance),
Param('rbf_lengthscale', self.rbf_lengthscale)
]),
Param('linear_variance', self.linear_variance),
Param('white_variance', self.noise_variance),
Param('noise_variance', self.noise_variance),
])
self.parameter['.*variance'].constrain_positive()
self.parameter['.*length'].constrain_positive()
self.parameter.white.tie_to(self.parameter.noise)
def tearDown(self):
pass
def testGrepParamNames(self):
def testGrepParamNamesTest(self):
assert(self.bgplvm.grep_param_names('X_\d') == self.parameter.grep_param_names('X_\d'))
assert(self.bgplvm.grep_param_names('X_\d+_1') == self.parameter.grep_param_names('X_\d+_1'))
assert(self.bgplvm.grep_param_names('X_\d_1') == self.parameter.grep_param_names('X_\d_1'))
@ -63,10 +68,42 @@ class Test(unittest.TestCase):
assert(self.bgplvm.grep_param_names('dont_match') == self.parameter.grep_param_names('dont_match'))
assert(self.bgplvm.grep_param_names('.*') == self.parameter.grep_param_names('.*'))
def testConstraints(self):
assert(self.bgplvm.constraints)
def testGetParams(self):
assert(numpy.allclose(self.bgplvm._get_params(), self.parameter._get_params()))
assert(numpy.allclose(self.bgplvm._get_params_transformed(), self.parameter._get_params_transformed()))
def testSetParams(self):
self.bgplvm.randomize()
self.parameter._set_params(self.bgplvm._get_params())
assert(numpy.allclose(self.bgplvm._get_params(), self.parameter._get_params()))
assert(numpy.allclose(self.bgplvm._get_params_transformed(), self.parameter._get_params_transformed()))
self.bgplvm.randomize()
self.parameter._set_params_transformed(self.bgplvm._get_params_transformed())
assert(numpy.allclose(self.bgplvm._get_params(), self.parameter._get_params()))
assert(numpy.allclose(self.bgplvm._get_params_transformed(), self.parameter._get_params_transformed()))
def testSlicing(self):
assert(numpy.alltrue(self.parameter.X[:,1] == self.X[:,1]))
assert(numpy.alltrue(self.parameter.X[:,1] == self.X[:,1]))
assert(numpy.alltrue(self.parameter.X_variance[1,1] == self.X_variance[1,1]))
assert(numpy.alltrue(self.parameter.X_variance[:] == self.X_variance[:]))
assert(numpy.alltrue(self.parameter.X[:,:][:,0:2][:,1] == self.X[:,1]))
assert(numpy.alltrue(self.parameter.X[:,1] == self.X[:,1]))
assert(numpy.alltrue(self.parameter.X_variance[1,1] == self.X_variance[1,1]))
assert(numpy.alltrue(self.parameter.X_variance[:] == self.X_variance[:]))
def testNdarrayFunc(self):
assert(numpy.alltrue(self.parameter.X * self.parameter.X == self.X * self.X))
assert(numpy.alltrue(self.parameter.X * self.parameter.X == self.X * self.X))
if __name__ == "__main__":
# import sys;sys.argv = ['', 'Test.testName']
import sys;sys.argv = ['',
'Test.testSlicing',
'Test.testGetParams',
'Test.testNdarrayFunc',
'Test.testSetParams',
]
unittest.main()