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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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17
GPy/models/bayesian_gplvm.py
View file

@ -2,17 +2,16 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import itertools
from matplotlib import pyplot
from gplvm import GPLVM
from .. import kern
from ..core import SparseGP
from ..likelihoods import Gaussian
from .. import kern
import itertools
from matplotlib.colors import colorConverter
from GPy.inference.optimization import SCG
from GPy.util import plot_latent, linalg
from GPy.models.gplvm import GPLVM
from GPy.util.plot_latent import most_significant_input_dimensions
from matplotlib import pyplot
from GPy.core.variational import Normal
from ..inference.optimization import SCG
from ..util import plot_latent, linalg
from ..util.plot_latent import most_significant_input_dimensions
from ..core.parameterization.variational import Normal
class BayesianGPLVM(SparseGP, GPLVM):
"""

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)