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all parameterization stuff now in seperate module -> GPy.core.parameterization

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Max Zwiessele 2013-12-16 13:45:24 +00:00
parent acbda64769
commit 0733886ba0
30 changed files with 344 additions and 354 deletions
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11
GPy/models/gplvm.py
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@ -4,16 +4,12 @@
import numpy as np
import pylab as pb
import sys, pdb
from .. import kern
from ..core import Model
from ..util.linalg import pdinv, PCA
from ..core.priors import Gaussian as Gaussian_prior
from ..util.linalg import PCA
from ..core import GP
from ..likelihoods import Gaussian
from .. import util
from GPy.util import plot_latent
from GPy.core.parameter import Param
from ..core import Param
class GPLVM(GP):
@ -37,7 +33,6 @@ class GPLVM(GP):
GP.__init__(self, X, likelihood, kernel, normalize_X=False, name=name)
self.X = Param('q_mean', self.X)
self.add_parameter(self.X, gradient=self.dK_dX, index=0)
#self.set_prior('.*X', Gaussian_prior(0, 1))
self.ensure_default_constraints()
def initialise_latent(self, init, input_dim, Y):
@ -86,7 +81,7 @@ class GPLVM(GP):
def plot(self):
assert self.likelihood.Y.shape[1] == 2
pb.scatter(self.likelihood.Y[:, 0], self.likelihood.Y[:, 1], 40, self.X[:, 0].copy(), linewidth=0, cmap=pb.cm.jet)
pb.scatter(self.likelihood.Y[:, 0], self.likelihood.Y[:, 1], 40, self.X[:, 0].copy(), linewidth=0, cmap=pb.cm.jet) # @UndefinedVariable
Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
mu, var, upper, lower = self.predict(Xnew)
pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)