apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-24 14:15:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

psi stat testing improvements, gradients not working yet

Browse source
This commit is contained in:
Max Zwiessele 2014-03-12 09:51:46 +00:00
parent 5f2b383510
commit 02bce95c41
2 changed files with 8 additions and 6 deletions
Download patch file Download diff file Expand all files Collapse all files

2
GPy/testing/psi_stat_expectation_tests.py
View file

@ -34,7 +34,7 @@ class Test(unittest.TestCase):
def setUp(self): def setUp(self):
self.kerns = ( 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.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) + 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), 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 import Model
from GPy.core.parameterization.param import Param from GPy.core.parameterization.param import Param
from GPy.core.parameterization.transformations import Logexp from GPy.core.parameterization.transformations import Logexp
from GPy.core.parameterization.variational import NormalPosterior
class PsiStatModel(Model): class PsiStatModel(Model):
def __init__(self, which, X, X_variance, Z, num_inducing, kernel): def __init__(self, which, X, X_variance, Z, num_inducing, kernel):
@ -18,23 +19,24 @@ class PsiStatModel(Model):
self.which = which self.which = which
self.X = Param("X", X) self.X = Param("X", X)
self.X_variance = Param('X_variance', X_variance, Logexp()) self.X_variance = Param('X_variance', X_variance, Logexp())
self.q = NormalPosterior(self.X, self.X_variance)
self.Z = Param("Z", Z) self.Z = Param("Z", Z)
self.N, self.input_dim = X.shape self.N, self.input_dim = X.shape
self.num_inducing, input_dim = Z.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) assert self.input_dim == input_dim, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
self.kern = kernel self.kern = kernel
self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance) self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.q)
self.add_parameters(self.X, self.X_variance, self.Z, self.kern) self.add_parameters(self.q, self.Z, self.kern)
def log_likelihood(self): def log_likelihood(self):
return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum() return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
def parameters_changed(self): 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.gradient = psimu
self.X_variance.gradient = psiS self.X_variance.gradient = psiS
#psimu, psiS = numpy.ones(self.N * self.input_dim), numpy.ones(self.N * self.input_dim) #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) except AttributeError: psiZ = numpy.zeros_like(self.Z)
self.Z.gradient = psiZ self.Z.gradient = psiZ
#psiZ = numpy.ones(self.num_inducing * self.input_dim) #psiZ = numpy.ones(self.num_inducing * self.input_dim)
@ -176,6 +178,6 @@ if __name__ == "__main__":
+GPy.kern.White(input_dim) +GPy.kern.White(input_dim)
) )
) )
m2.ensure_default_constraints() #m2.ensure_default_constraints()
else: else:
unittest.main() unittest.main()