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psi stat testing improvements, gradients not working yet

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Max Zwiessele 2014-03-12 09:51:46 +00:00
parent 5f2b383510
commit 02bce95c41
2 changed files with 8 additions and 6 deletions
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2
GPy/testing/psi_stat_expectation_tests.py
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@ -34,7 +34,7 @@ class Test(unittest.TestCase):
def setUp(self):
self.kerns = (
GPy.kern.RBF(self.input_dim, ARD=True)+GPy.kern.Bias(self.input_dim)+GPy.kern.White(self.input_dim),
GPy.kern.RBF([0,1,2], ARD=True)+GPy.kern.Bias(self.input_dim)+GPy.kern.White(self.input_dim),
GPy.kern.RBF(self.input_dim)+GPy.kern.Bias(self.input_dim)+GPy.kern.White(self.input_dim),
GPy.kern.Linear(self.input_dim) + GPy.kern.Bias(self.input_dim) + GPy.kern.White(self.input_dim),
GPy.kern.Linear(self.input_dim, ARD=True) + GPy.kern.Bias(self.input_dim) + GPy.kern.White(self.input_dim),

12
GPy/testing/psi_stat_gradient_tests.py
View file

@ -11,6 +11,7 @@ import itertools
from GPy.core import Model
from GPy.core.parameterization.param import Param
from GPy.core.parameterization.transformations import Logexp
from GPy.core.parameterization.variational import NormalPosterior
class PsiStatModel(Model):
def __init__(self, which, X, X_variance, Z, num_inducing, kernel):
@ -18,23 +19,24 @@ class PsiStatModel(Model):
self.which = which
self.X = Param("X", X)
self.X_variance = Param('X_variance', X_variance, Logexp())
self.q = NormalPosterior(self.X, self.X_variance)
self.Z = Param("Z", Z)
self.N, self.input_dim = X.shape
self.num_inducing, input_dim = Z.shape
assert self.input_dim == input_dim, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
self.kern = kernel
self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance)
self.add_parameters(self.X, self.X_variance, self.Z, self.kern)
self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.q)
self.add_parameters(self.q, self.Z, self.kern)
def log_likelihood(self):
return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
def parameters_changed(self):
psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.q)
self.X.gradient = psimu
self.X_variance.gradient = psiS
#psimu, psiS = numpy.ones(self.N * self.input_dim), numpy.ones(self.N * self.input_dim)
try: psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
try: psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.q)
except AttributeError: psiZ = numpy.zeros_like(self.Z)
self.Z.gradient = psiZ
#psiZ = numpy.ones(self.num_inducing * self.input_dim)
@ -176,6 +178,6 @@ if __name__ == "__main__":
+GPy.kern.White(input_dim)
)
)
m2.ensure_default_constraints()
#m2.ensure_default_constraints()
else:
unittest.main()