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This commit is contained in:
Zhenwen Dai 2014-03-21 22:10:24 +00:00
commit 8a83845937
65 changed files with 1197 additions and 600 deletions
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14
GPy/models/bayesian_gplvm.py
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@ -75,15 +75,19 @@ class BayesianGPLVM(SparseGP):
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X)
def plot_latent(self, plot_inducing=True, *args, **kwargs):
"""
See GPy.plotting.matplot_dep.dim_reduction_plots.plot_latent
"""
def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, plot_inducing=True, legend=True,
plot_limits=None,
aspect='auto', updates=False, **kwargs):
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_latent(self, plot_inducing=plot_inducing, *args, **kwargs)
return dim_reduction_plots.plot_latent(self, labels, which_indices,
resolution, ax, marker, s,
fignum, plot_inducing, legend,
plot_limits, aspect, updates, **kwargs)
def do_test_latents(self, Y):
"""

2
GPy/models/gp_coregionalized_regression.py
View file

@ -36,7 +36,7 @@ class GPCoregionalizedRegression(GP):
#Kernel
if kernel is None:
kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=GPy.kern.rbf(X.shape[1]-1), W_rank=1,name=kernel_name)
kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kern.RBF(X.shape[1]-1), W_rank=1,name=kernel_name)
#Likelihood
likelihood = util.multioutput.build_likelihood(Y_list,self.output_index,likelihoods_list)

4
GPy/models/gp_regression.py
View file

@ -20,14 +20,14 @@ class GPRegression(GP):
"""
def __init__(self, X, Y, kernel=None):
def __init__(self, X, Y, kernel=None, Y_metadata=None):
if kernel is None:
kernel = kern.RBF(X.shape[1])
likelihood = likelihoods.Gaussian()
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression')
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata)
def _getstate(self):
return GP._getstate(self)

16
GPy/models/gplvm.py
View file

@ -67,12 +67,22 @@ class GPLVM(GP):
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) # @UndefinedVariable
Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
mu, var, upper, lower = self.predict(Xnew)
mu, _ = self.predict(Xnew)
pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
def plot_latent(self, *args, **kwargs):
def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, legend=True,
plot_limits=None,
aspect='auto', updates=False, **kwargs):
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_latent(self, *args, **kwargs)
return dim_reduction_plots.plot_latent(self, labels, which_indices,
resolution, ax, marker, s,
fignum, False, legend,
plot_limits, aspect, updates, **kwargs)
def plot_magnification(self, *args, **kwargs):
return util.plot_latent.plot_magnification(self, *args, **kwargs)

4
GPy/models/sparse_gp_coregionalized_regression.py
View file

@ -43,14 +43,14 @@ class SparseGPCoregionalizedRegression(SparseGP):
#Kernel
if kernel is None:
kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=GPy.kern.rbf(X.shape[1]-1), W_rank=1,name=kernel_name)
kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kern.RBF(X.shape[1]-1), W_rank=1,name=kernel_name)
#Likelihood
likelihood = util.multioutput.build_likelihood(Y_list,self.output_index,likelihoods_list)
#Inducing inputs list
if len(Z_list):
assert len(Z_list) == self.output_dim, 'Number of outputs do not match length of inducing inputs list.'
assert len(Z_list) == Ny, 'Number of outputs do not match length of inducing inputs list.'
else:
if isinstance(num_inducing,np.int):
num_inducing = [num_inducing] * Ny