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ENH: fixed up BCGPLVM to work with new framework

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James Hensman 2015-08-17 11:06:45 +01:00
parent 80f554adfd
commit e3fd7885bf
3 changed files with 38 additions and 27 deletions
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44
GPy/models/bcgplvm.py
View file

@ -1,11 +1,11 @@
# Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
# Copyright (c) 2015 James Hensman
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core import GP
from ..models import GPLVM
from ..mappings import *
from . import GPLVM
from .. import mappings
class BCGPLVM(GPLVM):
@ -16,33 +16,31 @@ class BCGPLVM(GPLVM):
:type Y: np.ndarray
:param input_dim: latent dimensionality
:type input_dim: int
:param init: initialisation method for the latent space
:type init: 'PCA'|'random'
:param mapping: mapping for back constraint
:type mapping: GPy.core.Mapping object
"""
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False, mapping=None):
def __init__(self, Y, input_dim, kernel=None, mapping=None):
if mapping is None:
mapping = Kernel(X=Y, output_dim=input_dim)
mapping = mappings.MLP(input_dim=Y.shape[1],
output_dim=input_dim,
hidden_dim=10)
else:
assert mapping.input_dim==Y.shape[1], "mapping input dim does not work for Y dimension"
assert mapping.output_dim==input_dim, "mapping output dim does not work for self.input_dim"
GPLVM.__init__(self, Y, input_dim, X=mapping.f(Y), kernel=kernel, name="bcgplvm")
self.unlink_parameter(self.X)
self.mapping = mapping
GPLVM.__init__(self, Y, input_dim, init, X, kernel, normalize_Y)
self.X = self.mapping.f(self.likelihood.Y)
self.link_parameter(self.mapping)
def _get_param_names(self):
return self.mapping._get_param_names() + GP._get_param_names(self)
self.X = self.mapping.f(self.Y)
def _get_params(self):
return np.hstack((self.mapping._get_params(), GP._get_params(self)))
def parameters_changed(self):
self.X = self.mapping.f(self.Y)
GP.parameters_changed(self)
Xgradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
self.mapping.update_gradients(Xgradient, self.Y)
def _set_params(self, x):
self.mapping._set_params(x[:self.mapping.num_params])
self.X = self.mapping.f(self.likelihood.Y)
GP._set_params(self, x[self.mapping.num_params:])
def _log_likelihood_gradients(self):
dL_df = self.kern.gradients_X(self.dL_dK, self.X)
dL_dtheta = self.mapping.df_dtheta(dL_df, self.likelihood.Y)
return np.hstack((dL_dtheta.flatten(), GP._log_likelihood_gradients(self)))

11
GPy/models/gplvm.py
View file

@ -58,12 +58,15 @@ class GPLVM(GP):
return target
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) # @UndefinedVariable
assert self.Y.shape[1] == 2, "too high dimensional to plot. Try plot_latent"
from matplotlib import pyplot as plt
plt.scatter(self.Y[:, 0],
self.Y[:, 1],
40, self.X[:, 0].copy(),
linewidth=0, cmap=plt.cm.jet)
Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
mu, _ = self.predict(Xnew)
import pylab as pb
pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
plt.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,