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82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
# Copyright (c) 2012-2014 The 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 scipy import stats, special
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import link_functions
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from likelihood import Likelihood
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from gaussian import Gaussian
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from ..core.parameterization import Param
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from ..core.parameterization.transformations import Logexp
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from ..core.parameterization import Parameterized
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import itertools
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class MixedNoise(Likelihood):
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def __init__(self, likelihoods_list, name='mixed_noise'):
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#NOTE at the moment this likelihood only works for using a list of gaussians
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super(Likelihood, self).__init__(name=name)
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self.link_parameters(*likelihoods_list)
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self.likelihoods_list = likelihoods_list
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self.log_concave = False
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def gaussian_variance(self, Y_metadata):
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assert all([isinstance(l, Gaussian) for l in self.likelihoods_list])
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ind = Y_metadata['output_index'].flatten()
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variance = np.zeros(ind.size)
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for lik, j in zip(self.likelihoods_list, range(len(self.likelihoods_list))):
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variance[ind==j] = lik.variance
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return variance
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def betaY(self,Y,Y_metadata):
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#TODO not here.
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return Y/self.gaussian_variance(Y_metadata=Y_metadata)[:,None]
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def update_gradients(self, gradients):
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self.gradient = gradients
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def exact_inference_gradients(self, dL_dKdiag, Y_metadata):
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assert all([isinstance(l, Gaussian) for l in self.likelihoods_list])
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ind = Y_metadata['output_index'].flatten()
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return np.array([dL_dKdiag[ind==i].sum() for i in range(len(self.likelihoods_list))])
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def predictive_values(self, mu, var, full_cov=False, Y_metadata=None):
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ind = Y_metadata['output_index'].flatten()
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_variance = np.array([self.likelihoods_list[j].variance for j in ind ])
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if full_cov:
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var += np.eye(var.shape[0])*_variance
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else:
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var += _variance
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return mu, var
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def predictive_variance(self, mu, sigma, Y_metadata):
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_variance = self.gaussian_variance(Y_metadata)
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return _variance + sigma**2
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def predictive_quantiles(self, mu, var, quantiles, Y_metadata):
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ind = Y_metadata['output_index'].flatten()
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outputs = np.unique(ind)
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Q = np.zeros( (mu.size,len(quantiles)) )
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for j in outputs:
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q = self.likelihoods_list[j].predictive_quantiles(mu[ind==j,:],
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var[ind==j,:],quantiles,Y_metadata=None)
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Q[ind==j,:] = np.hstack(q)
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return [q[:,None] for q in Q.T]
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def samples(self, gp, Y_metadata):
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"""
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Returns a set of samples of observations based on a given value of the latent variable.
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:param gp: latent variable
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"""
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N1, N2 = gp.shape
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Ysim = np.zeros((N1,N2))
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ind = Y_metadata['output_index'].flatten()
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for j in np.unique(ind):
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flt = ind==j
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gp_filtered = gp[flt,:]
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n1 = gp_filtered.shape[0]
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lik = self.likelihoods_list[j]
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_ysim = np.array([np.random.normal(lik.gp_link.transf(gpj), scale=np.sqrt(lik.variance), size=1) for gpj in gp_filtered.flatten()])
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Ysim[flt,:] = _ysim.reshape(n1,N2)
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return Ysim
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