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44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
# Copyright (c) 2012-2014, 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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from .. import kern
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from ..core import GP, Param
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from ..likelihoods import Gaussian
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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, 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, fracs = initialize_latent(init, input_dim, Y)
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else:
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fracs = np.ones(input_dim)
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if kernel is None:
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kernel = kern.RBF(input_dim, lengthscale=fracs, 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.link_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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