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tidied up gp_base and gp
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James Hensman
parent
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commit
3549a676a8
4 changed files with 244 additions and 419 deletions
134
GPy/core/gp.py
134
GPy/core/gp.py
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@ -1,10 +1,7 @@
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# 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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from ..util.linalg import pdinv, tdot, dpotrs, dtrtrs
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from ..likelihoods import EP
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from gp_base import GPBase
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class GP(GPBase):
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@ -23,104 +20,36 @@ class GP(GPBase):
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"""
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def __init__(self, X, likelihood, kernel, normalize_X=False):
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super(GP, self).__init__(X, likelihood, kernel, normalize_X=normalize_X)
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#self._set_params(self._get_params())
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def getstate(self):
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return GPBase.getstate(self)
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def setstate(self, state):
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GPBase.setstate(self, state)
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#self._set_params(self._get_params())
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self.Posterior = self.inference_method.inference(K, likelihood, self.Y)
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def parameters_changed(self):
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super(GP, self).parameters_changed()
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self.K = self.kern.K(self.X)
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self.K += self.likelihood.covariance_matrix
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self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K)
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# the gradient of the likelihood wrt the covariance matrix
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if self.likelihood.YYT is None:
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# alpha = np.dot(self.Ki, self.likelihood.Y)
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alpha, _ = dpotrs(self.L, self.likelihood.Y, lower=1)
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self.dL_dK = 0.5 * (tdot(alpha) - self.output_dim * self.Ki)
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else:
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# tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)
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tmp, _ = dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)
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tmp, _ = dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)
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self.dL_dK = 0.5 * (tmp - self.output_dim * self.Ki)
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# def _get_params(self):
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# return np.hstack((self.kern._get_params_transformed(), self.likelihood._get_params()))
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# def _get_param_names(self):
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# return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
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def update_likelihood_approximation(self, **kwargs):
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"""
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Approximates a non-gaussian likelihood using Expectation Propagation
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For a Gaussian likelihood, no iteration is required:
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this function does nothing
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"""
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self.likelihood.restart()
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self.likelihood.fit_full(self.kern.K(self.X), **kwargs)
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# self._set_params(self._get_params()) # update the GP
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def _model_fit_term(self):
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"""
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Computes the model fit using YYT if it's available
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"""
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if self.likelihood.YYT is None:
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tmp, _ = dtrtrs(self.L, np.asfortranarray(self.likelihood.Y), lower=1)
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return -0.5 * np.sum(np.square(tmp))
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# return -0.5 * np.sum(np.square(np.dot(self.Li, self.likelihood.Y)))
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else:
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return -0.5 * np.sum(np.multiply(self.Ki, self.likelihood.YYT))
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self.Posterior = self.inference_method.inference(K, likelihood, self.Y)
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def log_likelihood(self):
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"""
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The log marginal likelihood of the GP.
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return self.posterior.log_marginal
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For an EP model, can be written as the log likelihood of a regression
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model for a new variable Y* = v_tilde/tau_tilde, with a covariance
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matrix K* = K + diag(1./tau_tilde) plus a normalization term.
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"""
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return (-0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) -
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0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z)
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# def _log_likelihood_gradients(self):
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# """
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# The gradient of all parameters.
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#
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# Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta
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# """
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# #return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
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#
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# if not isinstance(self.likelihood,EP):
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# tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
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# else:
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# tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
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# return tmp
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def _raw_predict(self, _Xnew, which_parts='all', full_cov=False, stop=False):
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"""
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Internal helper function for making predictions, does not account
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for normalization or likelihood
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full_cov is a boolean which defines whether the full covariance matrix
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of the prediction is computed. If full_cov is False (default), only the
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diagonal of the covariance is returned.
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"""
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Kx = self.kern.K(_Xnew, self.X, which_parts=which_parts).T
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# KiKx = np.dot(self.Ki, Kx)
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KiKx, _ = dpotrs(self.L, np.asfortranarray(Kx), lower=1)
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mu = np.dot(KiKx.T, self.likelihood.Y)
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LiKx, _ = dptrrs(self.posterior._woodbury_chol, np.asfortranarray(Kx), lower=1)
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mu = np.dot(Kx.T, self.posterior._woodbury_vector)
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if full_cov:
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Kxx = self.kern.K(_Xnew, which_parts=which_parts)
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var = Kxx - np.dot(KiKx.T, Kx)
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var = Kxx - tdot(LiKx.T)
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else:
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Kxx = self.kern.Kdiag(_Xnew, which_parts=which_parts)
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var = Kxx - np.sum(np.multiply(KiKx, Kx), 0)
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var = var[:, None]
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if stop:
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debug_this # @UndefinedVariable
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var = Kxx - np.sum(LiKx.T*LiKx, 0)
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var = var.reshape(self.num_data, 1)
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return mu, var
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def predict(self, Xnew, which_parts='all', full_cov=False, **likelihood_args):
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@ -150,41 +79,4 @@ class GP(GPBase):
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mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, **likelihood_args)
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return mean, var, _025pm, _975pm
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def _raw_predict_single_output(self, _Xnew, output, which_parts='all', full_cov=False,stop=False):
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"""
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For a specific output, calls _raw_predict() at the new point(s) _Xnew.
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This functions calls _add_output_index(), so _Xnew should not have an index column specifying the output.
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---------
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:param Xnew: The points at which to make a prediction
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:type Xnew: np.ndarray, Nnew x self.input_dim
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:param output: output to predict
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:type output: integer in {0,..., output_dim-1}
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:param which_parts: specifies which outputs kernel(s) to use in prediction
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:type which_parts: ('all', list of bools)
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:param full_cov: whether to return the full covariance matrix, or just the diagonal
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.. Note:: For multiple non-independent outputs models only.
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"""
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_Xnew = self._add_output_index(_Xnew, output)
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return self._raw_predict(_Xnew, which_parts=which_parts,full_cov=full_cov, stop=stop)
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def predict_single_output(self, Xnew,output=0, which_parts='all', full_cov=False, likelihood_args=dict()):
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"""
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For a specific output, calls predict() at the new point(s) Xnew.
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This functions calls _add_output_index(), so Xnew should not have an index column specifying the output.
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:param Xnew: The points at which to make a prediction
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:type Xnew: np.ndarray, Nnew x self.input_dim
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:param which_parts: specifies which outputs kernel(s) to use in prediction
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:type which_parts: ('all', list of bools)
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:param full_cov: whether to return the full covariance matrix, or just the diagonal
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:type full_cov: bool
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:returns: mean: posterior mean, a Numpy array, Nnew x self.input_dim
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:returns: var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
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:returns: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
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.. Note:: For multiple non-independent outputs models only.
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"""
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Xnew = self._add_output_index(Xnew, output)
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return self.predict(Xnew, which_parts=which_parts, full_cov=full_cov, likelihood_args=likelihood_args)
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