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88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
# ## Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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import pylab as pb
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from .. import kern
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from ..util.linalg import PCA
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from ..core import GP, Param
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from ..likelihoods import Gaussian
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from .. import util
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class GPLVM(GP):
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"""
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Gaussian Process Latent Variable Model
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"""
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def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False, name="gplvm"):
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"""
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:param Y: observed data
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:type Y: np.ndarray
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:param input_dim: latent dimensionality
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:type input_dim: int
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:param init: initialisation method for the latent space
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:type init: 'PCA'|'random'
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"""
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if X is None:
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from ..util.initialization import initialize_latent
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X = initialize_latent(init, input_dim, Y)
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if kernel is None:
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kernel = kern.RBF(input_dim, ARD=input_dim > 1) + kern.Bias(input_dim, np.exp(-2))
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likelihood = Gaussian()
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super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
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self.X = Param('latent_mean', X)
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self.add_parameter(self.X, index=0)
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def parameters_changed(self):
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super(GPLVM, self).parameters_changed()
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self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
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def _getstate(self):
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return GP._getstate(self)
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def _setstate(self, state):
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GP._setstate(self, state)
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def jacobian(self,X):
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target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
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for i in range(self.output_dim):
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target[:,:,i]=self.kern.gradients_X(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
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return target
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def magnification(self,X):
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target=np.zeros(X.shape[0])
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#J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
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J = self.jacobian(X)
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for i in range(X.shape[0]):
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target[i]=np.sqrt(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
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return target
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def plot(self):
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assert self.likelihood.Y.shape[1] == 2
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pb.scatter(self.likelihood.Y[:, 0], self.likelihood.Y[:, 1], 40, self.X[:, 0].copy(), linewidth=0, cmap=pb.cm.jet) # @UndefinedVariable
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Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
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mu, _ = self.predict(Xnew)
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pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
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def plot_latent(self, labels=None, which_indices=None,
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resolution=50, ax=None, marker='o', s=40,
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fignum=None, legend=True,
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plot_limits=None,
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aspect='auto', updates=False, **kwargs):
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import sys
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import dim_reduction_plots
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return dim_reduction_plots.plot_latent(self, labels, which_indices,
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resolution, ax, marker, s,
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fignum, False, legend,
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plot_limits, aspect, updates, **kwargs)
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def plot_magnification(self, *args, **kwargs):
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return util.plot_latent.plot_magnification(self, *args, **kwargs)
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