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70 lines
2.4 KiB
Python
70 lines
2.4 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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import sys, pdb
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from sparse_gp_regression import SparseGPRegression
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from gplvm import GPLVM
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# from .. import kern
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# from ..core import model
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# from ..util.linalg import pdinv, PCA
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class SparseGPLVM(SparseGPRegression, GPLVM):
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"""
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Sparse Gaussian Process Latent Variable Model
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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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def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10):
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X = self.initialise_latent(init, input_dim, Y)
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SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
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self.ensure_default_constraints()
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def getstate(self):
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return SparseGPRegression.getstate(self)
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def setstate(self, state):
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return SparseGPRegression.setstate(self, state)
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def _get_param_names(self):
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return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
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+ SparseGPRegression._get_param_names(self))
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def _get_params(self):
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return np.hstack((self.X.flatten(), SparseGPRegression._get_params(self)))
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def _set_params(self, x):
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self.X = x[:self.X.size].reshape(self.num_data, self.input_dim).copy()
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SparseGPRegression._set_params(self, x[self.X.size:])
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def log_likelihood(self):
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return SparseGPRegression.log_likelihood(self)
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def dL_dX(self):
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dL_dX = self.kern.dKdiag_dX(self.dL_dpsi0, self.X)
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dL_dX += self.kern.dK_dX(self.dL_dpsi1, self.X, self.Z)
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return dL_dX
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def _log_likelihood_gradients(self):
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return np.hstack((self.dL_dX().flatten(), SparseGPRegression._log_likelihood_gradients(self)))
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def plot(self):
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GPLVM.plot(self)
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# passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
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mu, var, upper, lower = SparseGPRegression.predict(self, self.Z + np.random.randn(*self.Z.shape) * 0.0001)
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pb.plot(mu[:, 0] , mu[:, 1], 'ko')
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def plot_latent(self, *args, **kwargs):
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GPLVM.plot_latent(self, *args, **kwargs)
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#pb.plot(self.Z[:, input_1], self.Z[:, input_2], '^w')
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