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https://github.com/SheffieldML/GPy.git
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[updates] starting to extract out standalone modules
This commit is contained in:
Max Zwiessele
commit
9b2e49c949
19 changed files with 563 additions and 958 deletions
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@ -8,5 +8,4 @@ from parameterization.observable_array import ObsAr
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from gp import GP
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from gp import GP
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from sparse_gp import SparseGP
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from sparse_gp import SparseGP
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from svigp import SVIGP
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from mapping import *
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from mapping import *
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125
GPy/core/gp.py
125
GPy/core/gp.py
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@ -3,16 +3,12 @@
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import numpy as np
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import numpy as np
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import sys
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import sys
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import warnings
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from .. import kern
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from .. import kern
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from ..util.linalg import dtrtrs
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from model import Model
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from model import Model
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from parameterization import ObsAr
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from parameterization import ObsAr
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from .. import likelihoods
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from .. import likelihoods
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from ..likelihoods.gaussian import Gaussian
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from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation
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from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation, LatentFunctionInference
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from parameterization.variational import VariationalPosterior
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from parameterization.variational import VariationalPosterior
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from scipy.sparse.base import issparse
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import logging
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import logging
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from GPy.util.normalizer import MeanNorm
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from GPy.util.normalizer import MeanNorm
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@ -26,7 +22,7 @@ class GP(Model):
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:param Y: output observations
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:param Y: output observations
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:param kernel: a GPy kernel, defaults to rbf+white
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:param kernel: a GPy kernel, defaults to rbf+white
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:param likelihood: a GPy likelihood
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:param likelihood: a GPy likelihood
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:param :class:`~GPy.inference.latent_function_inference.LatentFunctionInference` inference_method: The inference method to use for this GP
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:param inference_method: The :class:`~GPy.inference.latent_function_inference.LatentFunctionInference` inference method to use for this GP
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:rtype: model object
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:rtype: model object
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:param Norm normalizer:
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:param Norm normalizer:
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normalize the outputs Y.
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normalize the outputs Y.
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@ -95,10 +91,13 @@ class GP(Model):
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def set_XY(self, X=None, Y=None):
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def set_XY(self, X=None, Y=None):
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"""
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"""
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Set the input / output of the model
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Set the input / output data of the model
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This is useful if we wish to change our existing data but maintain the same model
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:param X: input observations
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:param X: input observations
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:type X: np.ndarray
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:param Y: output observations
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:param Y: output observations
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:type Y: np.ndarray
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"""
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"""
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self.update_model(False)
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self.update_model(False)
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if Y is not None:
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if Y is not None:
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@ -112,7 +111,6 @@ class GP(Model):
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if X is not None:
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if X is not None:
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if self.X in self.parameters:
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if self.X in self.parameters:
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# LVM models
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# LVM models
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from ..core.parameterization.variational import VariationalPosterior
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if isinstance(self.X, VariationalPosterior):
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if isinstance(self.X, VariationalPosterior):
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assert isinstance(X, type(self.X)), "The given X must have the same type as the X in the model!"
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assert isinstance(X, type(self.X)), "The given X must have the same type as the X in the model!"
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self.unlink_parameter(self.X)
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self.unlink_parameter(self.X)
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@ -129,22 +127,39 @@ class GP(Model):
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def set_X(self,X):
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def set_X(self,X):
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"""
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"""
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Set the input of the model
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Set the input data of the model
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:param X: input observations
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:type X: np.ndarray
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"""
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"""
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self.set_XY(X=X)
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self.set_XY(X=X)
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def set_Y(self,Y):
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def set_Y(self,Y):
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"""
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"""
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Set the input of the model
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Set the output data of the model
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:param X: output observations
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:type X: np.ndarray
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"""
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"""
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self.set_XY(Y=Y)
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self.set_XY(Y=Y)
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def parameters_changed(self):
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def parameters_changed(self):
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"""
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Method that is called upon any changes to :class:`~GPy.core.parameterization.param.Param` variables within the model.
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In particular in the GP class this method reperforms inference, recalculating the posterior and log marginal likelihood and gradients of the model
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.. warning::
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This method is not designed to be called manually, the framework is set up to automatically call this method upon changes to parameters, if you call
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this method yourself, there may be unexpected consequences.
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"""
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self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y_normalized, self.Y_metadata)
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self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y_normalized, self.Y_metadata)
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self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'])
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self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'])
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self.kern.update_gradients_full(self.grad_dict['dL_dK'], self.X)
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self.kern.update_gradients_full(self.grad_dict['dL_dK'], self.X)
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def log_likelihood(self):
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def log_likelihood(self):
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"""
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The log marginal likelihood of the model, :math:`p(\mathbf{y})`, this is the objective function of the model being optimised
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"""
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return self._log_marginal_likelihood
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return self._log_marginal_likelihood
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def _raw_predict(self, _Xnew, full_cov=False, kern=None):
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def _raw_predict(self, _Xnew, full_cov=False, kern=None):
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@ -155,12 +170,10 @@ class GP(Model):
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of the prediction is computed. If full_cov is False (default), only the
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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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diagonal of the covariance is returned.
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$$
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.. math::
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p(f*|X*, X, Y) = \int^{\inf}_{\inf} p(f*|f,X*)p(f|X,Y) df
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p(f*|X*, X, Y) = \int^{\inf}_{\inf} p(f*|f,X*)p(f|X,Y) df
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= N(f*| K_{x*x}(K_{xx} + \Sigma)^{-1}Y, K_{x*x*} - K_{xx*}(K_{xx} + \Sigma)^{-1}K_{xx*}
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= N(f*| K_{x*x}(K_{xx} + \Sigma)^{-1}Y, K_{x*x*} - K_{xx*}(K_{xx} + \Sigma)^{-1}K_{xx*}
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\Sigma := \texttt{Likelihood.variance / Approximate likelihood covariance}
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\Sigma := \texttt{Likelihood.variance / Approximate likelihood covariance}
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$$
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"""
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"""
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if kern is None:
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if kern is None:
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kern = self.kern
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kern = self.kern
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@ -185,23 +198,20 @@ class GP(Model):
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Predict the function(s) at the new point(s) Xnew.
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Predict the function(s) at the new point(s) Xnew.
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:param Xnew: The points at which to make a prediction
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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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:type Xnew: np.ndarray (Nnew x self.input_dim)
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:param full_cov: whether to return the full covariance matrix, or just
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:param full_cov: whether to return the full covariance matrix, or just
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the diagonal
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the diagonal
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:type full_cov: bool
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:type full_cov: bool
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:param Y_metadata: metadata about the predicting point to pass to the likelihood
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:param Y_metadata: metadata about the predicting point to pass to the likelihood
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:param kern: The kernel to use for prediction (defaults to the model
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:param kern: The kernel to use for prediction (defaults to the model
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kern). this is useful for examining e.g. subprocesses.
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kern). this is useful for examining e.g. subprocesses.
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:returns: mean: posterior mean, a Numpy array, Nnew x self.input_dim
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:returns: (mean, var, lower_upper):
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:returns: var: posterior variance, a Numpy array, Nnew x 1 if
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mean: posterior mean, a Numpy array, Nnew x self.input_dim
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full_cov=False, Nnew x Nnew otherwise
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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,
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lower_upper: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
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Numpy arrays, Nnew x self.input_dim
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If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
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If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
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This is to allow for different normalizations of the output dimensions.
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This is to allow for different normalizations of the output dimensions.
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"""
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"""
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#predict the latent function values
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#predict the latent function values
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mu, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern)
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mu, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern)
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@ -213,6 +223,16 @@ class GP(Model):
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return mean, var
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return mean, var
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def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None):
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def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None):
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"""
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Get the predictive quantiles around the prediction at X
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:param X: The points at which to make a prediction
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:type X: np.ndarray (Xnew x self.input_dim)
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:param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval
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:type quantiles: tuple
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:returns: list of quantiles for each X and predictive quantiles for interval combination
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:rtype: [np.ndarray (Xnew x self.input_dim), np.ndarray (Xnew x self.input_dim)]
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"""
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m, v = self._raw_predict(X, full_cov=False)
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m, v = self._raw_predict(X, full_cov=False)
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if self.normalizer is not None:
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if self.normalizer is not None:
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m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
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m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
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@ -225,7 +245,12 @@ class GP(Model):
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Given a set of points at which to predict X* (size [N*,Q]), compute the
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Given a set of points at which to predict X* (size [N*,Q]), compute the
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derivatives of the mean and variance. Resulting arrays are sized:
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derivatives of the mean and variance. Resulting arrays are sized:
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dmu_dX* -- [N*, Q ,D], where D is the number of output in this GP (usually one).
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dmu_dX* -- [N*, Q ,D], where D is the number of output in this GP (usually one).
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dv_dX* -- [N*, Q], (since all outputs have the same variance)
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dv_dX* -- [N*, Q], (since all outputs have the same variance)
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:param X: The points at which to get the predictive gradients
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:type X: np.ndarray (Xnew x self.input_dim)
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:returns: dmu_dX, dv_dX
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:rtype: [np.ndarray (N*, Q ,D), np.ndarray (N*,Q) ]
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"""
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"""
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dmu_dX = np.empty((Xnew.shape[0],Xnew.shape[1],self.output_dim))
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dmu_dX = np.empty((Xnew.shape[0],Xnew.shape[1],self.output_dim))
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@ -245,12 +270,13 @@ class GP(Model):
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Samples the posterior GP at the points X.
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Samples the posterior GP at the points X.
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:param X: The points at which to take the samples.
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:param X: The points at which to take the samples.
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:type X: np.ndarray, Nnew x self.input_dim.
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:type X: np.ndarray (Nnew x self.input_dim)
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:param size: the number of a posteriori samples.
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:param size: the number of a posteriori samples.
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:type size: int.
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:type size: int.
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:param full_cov: whether to return the full covariance matrix, or just the diagonal.
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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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:type full_cov: bool.
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:returns: Ysim: set of simulations, a Numpy array (N x samples).
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:returns: Ysim: set of simulations
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:rtype: np.ndarray (N x samples)
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"""
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"""
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m, v = self._raw_predict(X, full_cov=full_cov)
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m, v = self._raw_predict(X, full_cov=full_cov)
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if self.normalizer is not None:
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if self.normalizer is not None:
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@ -268,7 +294,7 @@ class GP(Model):
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Samples the posterior GP at the points X.
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Samples the posterior GP at the points X.
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:param X: the points at which to take the samples.
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:param X: the points at which to take the samples.
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:type X: np.ndarray, Nnew x self.input_dim.
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:type X: np.ndarray (Nnew x self.input_dim.)
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:param size: the number of a posteriori samples.
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:param size: the number of a posteriori samples.
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:type size: int.
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:type size: int.
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:param full_cov: whether to return the full covariance matrix, or just the diagonal.
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:param full_cov: whether to return the full covariance matrix, or just the diagonal.
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@ -292,6 +318,37 @@ class GP(Model):
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This is a call to plot with plot_raw=True.
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This is a call to plot with plot_raw=True.
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Data will not be plotted in this, as the GP's view of the world
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Data will not be plotted in this, as the GP's view of the world
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may live in another space, or units then the data.
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may live in another space, or units then the data.
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Can plot only part of the data and part of the posterior functions
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using which_data_rowsm which_data_ycols.
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:type plot_limits: np.array
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:type which_data_ycols: 'all' or a list of integers
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:type resolution: int
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:param levels: number of levels to plot in a contour plot.
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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:type levels: int
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:param samples: the number of a posteriori samples to plot
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:type samples: int
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:param fignum: figure to plot on.
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:type fignum: figure number
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:param ax: axes to plot on.
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:type ax: axes handle
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:param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
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:type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
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:type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param Y_metadata: additional data associated with Y which may be needed
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:type Y_metadata: dict
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:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
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:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
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"""
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"""
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import models_plots
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from ..plotting.matplot_dep import models_plots
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@ -325,12 +382,13 @@ class GP(Model):
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:param which_data_rows: which of the training data to plot (default all)
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
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:type which_data_rows: 'all' or a slice object to slice model.X, model.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:type which_data_rows: 'all' or a list of integers
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:type which_data_ycols: 'all' or a list of integers
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:type fixed_inputs: a list of tuples
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:type resolution: int
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:type resolution: int
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:param levels: number of levels to plot in a contour plot.
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:param levels: number of levels to plot in a contour plot.
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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:type levels: int
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:type levels: int
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:param samples: the number of a posteriori samples to plot
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:param samples: the number of a posteriori samples to plot
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:type samples: int
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:type samples: int
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@ -338,11 +396,14 @@ class GP(Model):
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:type fignum: figure number
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:type fignum: figure number
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:param ax: axes to plot on.
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:param ax: axes to plot on.
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:type ax: axes handle
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:type ax: axes handle
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:type output: integer (first output is 0)
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:param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
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:param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
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:type linecol:
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:type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
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:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
|
:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
|
||||||
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
|
:type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
|
||||||
|
:param Y_metadata: additional data associated with Y which may be needed
|
||||||
|
:type Y_metadata: dict
|
||||||
|
:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
|
||||||
|
:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
|
||||||
"""
|
"""
|
||||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
||||||
from ..plotting.matplot_dep import models_plots
|
from ..plotting.matplot_dep import models_plots
|
||||||
|
|
@ -372,10 +433,8 @@ class GP(Model):
|
||||||
:type max_f_eval: int
|
:type max_f_eval: int
|
||||||
:messages: whether to display during optimisation
|
:messages: whether to display during optimisation
|
||||||
:type messages: bool
|
:type messages: bool
|
||||||
:param optimizer: which optimizer to use (defaults to self.preferred optimizer)
|
:param optimizer: which optimizer to use (defaults to self.preferred optimizer), a range of optimisers can be found in :module:`~GPy.inference.optimization`, they include 'scg', 'lbfgs', 'tnc'.
|
||||||
:type optimizer: string
|
:type optimizer: string
|
||||||
|
|
||||||
TODO: valid args
|
|
||||||
"""
|
"""
|
||||||
self.inference_method.on_optimization_start()
|
self.inference_method.on_optimization_start()
|
||||||
try:
|
try:
|
||||||
|
|
@ -394,7 +453,11 @@ class GP(Model):
|
||||||
:param optimize: whether to optimize the location of new X (True by default)
|
:param optimize: whether to optimize the location of new X (True by default)
|
||||||
:type optimize: boolean
|
:type optimize: boolean
|
||||||
:return: a tuple containing the posterior estimation of X and the model that optimize X
|
:return: a tuple containing the posterior estimation of X and the model that optimize X
|
||||||
|
<<<<<<< HEAD
|
||||||
:rtype: (GPy.core.parameterization.variational.VariationalPosterior or numpy.ndarray, GPy.core.Model)
|
:rtype: (GPy.core.parameterization.variational.VariationalPosterior or numpy.ndarray, GPy.core.Model)
|
||||||
|
=======
|
||||||
|
:rtype: (:class:`~GPy.core.parameterization.variational.VariationalPosterior` or numpy.ndarray, :class:`~GPy.core.model.Model`)
|
||||||
|
>>>>>>> 22d30d9d39c70f806fe5bcb815cce9c8eb0f8dca
|
||||||
"""
|
"""
|
||||||
from ..inference.latent_function_inference.inferenceX import infer_newX
|
from ..inference.latent_function_inference.inferenceX import infer_newX
|
||||||
return infer_newX(self, Y_new, optimize=optimize)
|
return infer_newX(self, Y_new, optimize=optimize)
|
||||||
|
|
|
||||||
|
|
@ -234,9 +234,9 @@ class Model(Parameterized):
|
||||||
|
|
||||||
"""
|
"""
|
||||||
if self.is_fixed:
|
if self.is_fixed:
|
||||||
raise RuntimeError, "Cannot optimize, when everything is fixed"
|
print 'nothing to optimize'
|
||||||
if self.size == 0:
|
if self.size == 0:
|
||||||
raise RuntimeError, "Model without parameters cannot be optimized"
|
print 'nothing to optimize'
|
||||||
|
|
||||||
if start == None:
|
if start == None:
|
||||||
start = self.optimizer_array
|
start = self.optimizer_array
|
||||||
|
|
|
||||||
|
|
@ -60,7 +60,6 @@ class SparseGP_MPI(SparseGP):
|
||||||
mpi_comm.Bcast(self.param_array, root=0)
|
mpi_comm.Bcast(self.param_array, root=0)
|
||||||
self.update_model(True)
|
self.update_model(True)
|
||||||
|
|
||||||
|
|
||||||
def __getstate__(self):
|
def __getstate__(self):
|
||||||
dc = super(SparseGP_MPI, self).__getstate__()
|
dc = super(SparseGP_MPI, self).__getstate__()
|
||||||
dc['mpi_comm'] = None
|
dc['mpi_comm'] = None
|
||||||
|
|
|
||||||
|
|
@ -1,434 +0,0 @@
|
||||||
# Copyright (c) 2012, James Hensman and Nicolo' Fusi
|
|
||||||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
from ..util.linalg import pdinv, mdot, tdot, dpotrs, dtrtrs, jitchol, backsub_both_sides
|
|
||||||
from gp import GP
|
|
||||||
import time
|
|
||||||
import sys
|
|
||||||
|
|
||||||
|
|
||||||
class SVIGP(GP):
|
|
||||||
"""
|
|
||||||
|
|
||||||
Stochastic Variational inference in a Gaussian Process
|
|
||||||
|
|
||||||
:param X: inputs
|
|
||||||
:type X: np.ndarray (N x Q)
|
|
||||||
:param Y: observed data
|
|
||||||
:type Y: np.ndarray of observations (N x D)
|
|
||||||
:param batchsize: the size of a h
|
|
||||||
|
|
||||||
Additional kwargs are used as for a sparse GP. They include:
|
|
||||||
|
|
||||||
:param q_u: canonical parameters of the distribution sqehd into a 1D array
|
|
||||||
:type q_u: np.ndarray
|
|
||||||
:param M: Number of inducing points (optional, default 10. Ignored if Z is not None)
|
|
||||||
:type M: int
|
|
||||||
:param kernel: the kernel/covariance function. See link kernels
|
|
||||||
:type kernel: a GPy kernel
|
|
||||||
:param Z: inducing inputs (optional, see note)
|
|
||||||
:type Z: np.ndarray (M x Q) | None
|
|
||||||
:param X_uncertainty: The uncertainty in the measurements of X (Gaussian variance)
|
|
||||||
:type X_uncertainty: np.ndarray (N x Q) | None
|
|
||||||
:param Zslices: slices for the inducing inputs (see slicing TODO: link)
|
|
||||||
:param M: Number of inducing points (optional, default 10. Ignored if Z is not None)
|
|
||||||
:type M: int
|
|
||||||
:param beta: noise precision. TODO: ignore beta if doing EP
|
|
||||||
:type beta: float
|
|
||||||
:param normalize_(X|Y): whether to normalize the data before computing (predictions will be in original scales)
|
|
||||||
:type normalize_(X|Y): bool
|
|
||||||
|
|
||||||
"""
|
|
||||||
|
|
||||||
|
|
||||||
def __init__(self, X, Y, kernel, Z, q_u=None, batchsize=10):
|
|
||||||
raise NotImplementedError, "This is a work in progress, see github issue "
|
|
||||||
GP.__init__(self, X, Y, kernel)
|
|
||||||
self.batchsize=batchsize
|
|
||||||
self.Z = Z
|
|
||||||
self.num_inducing = Z.shape[0]
|
|
||||||
|
|
||||||
self.batchcounter = 0
|
|
||||||
self.epochs = 0
|
|
||||||
self.iterations = 0
|
|
||||||
|
|
||||||
self.vb_steplength = 0.05
|
|
||||||
self.param_steplength = 1e-5
|
|
||||||
self.momentum = 0.9
|
|
||||||
|
|
||||||
if q_u is None:
|
|
||||||
q_u = np.hstack((np.random.randn(self.num_inducing*self.output_dim),-.5*np.eye(self.num_inducing).flatten()))
|
|
||||||
|
|
||||||
self.set_vb_param(q_u)
|
|
||||||
|
|
||||||
self._permutation = np.random.permutation(self.num_data)
|
|
||||||
self.load_batch()
|
|
||||||
|
|
||||||
self._param_trace = []
|
|
||||||
self._ll_trace = []
|
|
||||||
self._grad_trace = []
|
|
||||||
|
|
||||||
#set the adaptive steplength parameters
|
|
||||||
#self.hbar_t = 0.0
|
|
||||||
#self.tau_t = 100.0
|
|
||||||
#self.gbar_t = 0.0
|
|
||||||
#self.gbar_t1 = 0.0
|
|
||||||
#self.gbar_t2 = 0.0
|
|
||||||
#self.hbar_tp = 0.0
|
|
||||||
#self.tau_tp = 10000.0
|
|
||||||
#self.gbar_tp = 0.0
|
|
||||||
#self.adapt_param_steplength = True
|
|
||||||
#self.adapt_vb_steplength = True
|
|
||||||
#self._param_steplength_trace = []
|
|
||||||
#self._vb_steplength_trace = []
|
|
||||||
|
|
||||||
def _compute_kernel_matrices(self):
|
|
||||||
# kernel computations, using BGPLVM notation
|
|
||||||
self.Kmm = self.kern.K(self.Z)
|
|
||||||
self.psi0 = self.kern.Kdiag(self.X_batch)
|
|
||||||
self.psi1 = self.kern.K(self.X_batch, self.Z)
|
|
||||||
self.psi2 = None
|
|
||||||
|
|
||||||
def dL_dtheta(self):
|
|
||||||
dL_dtheta = self.kern._param_grad_helper(self.dL_dKmm, self.Z)
|
|
||||||
if self.has_uncertain_inputs:
|
|
||||||
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z, self.X_batch, self.X_variance_batch)
|
|
||||||
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1, self.Z, self.X_batch, self.X_variance_batch)
|
|
||||||
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z, self.X_batch, self.X_variance_batch)
|
|
||||||
else:
|
|
||||||
dL_dtheta += self.kern._param_grad_helper(self.dL_dpsi1, self.X_batch, self.Z)
|
|
||||||
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X_batch)
|
|
||||||
return dL_dtheta
|
|
||||||
|
|
||||||
def _set_params(self, p, computations=True):
|
|
||||||
self.kern._set_params_transformed(p[:self.kern.num_params])
|
|
||||||
self.likelihood._set_params(p[self.kern.num_params:])
|
|
||||||
if computations:
|
|
||||||
self._compute_kernel_matrices()
|
|
||||||
self._computations()
|
|
||||||
|
|
||||||
def _get_params(self):
|
|
||||||
return np.hstack((self.kern._get_params_transformed() , self.likelihood._get_params()))
|
|
||||||
|
|
||||||
def _get_param_names(self):
|
|
||||||
return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
|
|
||||||
|
|
||||||
def load_batch(self):
|
|
||||||
"""
|
|
||||||
load a batch of data (set self.X_batch and self.Y_batch from self.X, self.Y)
|
|
||||||
"""
|
|
||||||
|
|
||||||
#if we've seen all the data, start again with them in a new random order
|
|
||||||
if self.batchcounter+self.batchsize > self.num_data:
|
|
||||||
self.batchcounter = 0
|
|
||||||
self.epochs += 1
|
|
||||||
self._permutation = np.random.permutation(self.num_data)
|
|
||||||
|
|
||||||
this_perm = self._permutation[self.batchcounter:self.batchcounter+self.batchsize]
|
|
||||||
|
|
||||||
self.X_batch = self.X[this_perm]
|
|
||||||
self.Y_batch = self.Y[this_perm]
|
|
||||||
|
|
||||||
self.batchcounter += self.batchsize
|
|
||||||
|
|
||||||
self.data_prop = float(self.batchsize)/self.num_data
|
|
||||||
|
|
||||||
self._compute_kernel_matrices()
|
|
||||||
self._computations()
|
|
||||||
|
|
||||||
def _computations(self,do_Kmm=True, do_Kmm_grad=True):
|
|
||||||
"""
|
|
||||||
All of the computations needed. Some are optional, see kwargs.
|
|
||||||
"""
|
|
||||||
|
|
||||||
if do_Kmm:
|
|
||||||
self.Lm = jitchol(self.Kmm)
|
|
||||||
|
|
||||||
# The rather complex computations of self.A
|
|
||||||
if self.has_uncertain_inputs:
|
|
||||||
if self.likelihood.is_heteroscedastic:
|
|
||||||
psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.batchsize, 1, 1))).sum(0)
|
|
||||||
else:
|
|
||||||
psi2_beta = self.psi2.sum(0) * self.likelihood.precision
|
|
||||||
evals, evecs = np.linalg.eigh(psi2_beta)
|
|
||||||
clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable
|
|
||||||
tmp = evecs * np.sqrt(clipped_evals)
|
|
||||||
else:
|
|
||||||
if self.likelihood.is_heteroscedastic:
|
|
||||||
tmp = self.psi1.T * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.batchsize)))
|
|
||||||
else:
|
|
||||||
tmp = self.psi1.T * (np.sqrt(self.likelihood.precision))
|
|
||||||
tmp, _ = dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
|
|
||||||
self.A = tdot(tmp)
|
|
||||||
|
|
||||||
self.V = self.likelihood.precision*self.likelihood.Y
|
|
||||||
self.VmT = np.dot(self.V,self.q_u_expectation[0].T)
|
|
||||||
self.psi1V = np.dot(self.psi1.T, self.V)
|
|
||||||
|
|
||||||
self.B = np.eye(self.num_inducing)*self.data_prop + self.A
|
|
||||||
self.Lambda = backsub_both_sides(self.Lm, self.B.T)
|
|
||||||
self.LQL = backsub_both_sides(self.Lm,self.q_u_expectation[1].T,transpose='right')
|
|
||||||
|
|
||||||
self.trace_K = self.psi0.sum() - np.trace(self.A)/self.likelihood.precision
|
|
||||||
self.Kmmi_m, _ = dpotrs(self.Lm, self.q_u_expectation[0], lower=1)
|
|
||||||
self.projected_mean = np.dot(self.psi1,self.Kmmi_m)
|
|
||||||
|
|
||||||
# Compute dL_dpsi
|
|
||||||
self.dL_dpsi0 = - 0.5 * self.output_dim * self.likelihood.precision * np.ones(self.batchsize)
|
|
||||||
self.dL_dpsi1, _ = dpotrs(self.Lm,np.asfortranarray(self.VmT.T),lower=1)
|
|
||||||
self.dL_dpsi1 = self.dL_dpsi1.T
|
|
||||||
|
|
||||||
dL_dpsi2 = -0.5 * self.likelihood.precision * backsub_both_sides(self.Lm, self.LQL - self.output_dim * np.eye(self.num_inducing))
|
|
||||||
if self.has_uncertain_inputs:
|
|
||||||
self.dL_dpsi2 = np.repeat(dL_dpsi2[None,:,:],self.batchsize,axis=0)
|
|
||||||
else:
|
|
||||||
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2,self.psi1.T).T
|
|
||||||
self.dL_dpsi2 = None
|
|
||||||
|
|
||||||
# Compute dL_dKmm
|
|
||||||
if do_Kmm_grad:
|
|
||||||
tmp = np.dot(self.LQL,self.A) - backsub_both_sides(self.Lm,np.dot(self.q_u_expectation[0],self.psi1V.T),transpose='right')
|
|
||||||
tmp += tmp.T
|
|
||||||
tmp += -self.output_dim*self.B
|
|
||||||
tmp += self.data_prop*self.LQL
|
|
||||||
self.dL_dKmm = 0.5*backsub_both_sides(self.Lm,tmp)
|
|
||||||
|
|
||||||
#Compute the gradient of the log likelihood wrt noise variance
|
|
||||||
self.partial_for_likelihood = -0.5*(self.batchsize*self.output_dim - np.sum(self.A*self.LQL))*self.likelihood.precision
|
|
||||||
self.partial_for_likelihood += (0.5*self.output_dim*self.trace_K + 0.5 * self.likelihood.trYYT - np.sum(self.likelihood.Y*self.projected_mean))*self.likelihood.precision**2
|
|
||||||
|
|
||||||
|
|
||||||
def log_likelihood(self):
|
|
||||||
"""
|
|
||||||
As for uncollapsed sparse GP, but account for the proportion of data we're looking at right now.
|
|
||||||
|
|
||||||
NB. self.batchsize is the size of the batch, not the size of X_all
|
|
||||||
"""
|
|
||||||
assert not self.likelihood.is_heteroscedastic
|
|
||||||
A = -0.5*self.batchsize*self.output_dim*(np.log(2.*np.pi) - np.log(self.likelihood.precision))
|
|
||||||
B = -0.5*self.likelihood.precision*self.output_dim*self.trace_K
|
|
||||||
Kmm_logdet = 2.*np.sum(np.log(np.diag(self.Lm)))
|
|
||||||
C = -0.5*self.output_dim*self.data_prop*(Kmm_logdet-self.q_u_logdet - self.num_inducing)
|
|
||||||
C += -0.5*np.sum(self.LQL * self.B)
|
|
||||||
D = -0.5*self.likelihood.precision*self.likelihood.trYYT
|
|
||||||
E = np.sum(self.V*self.projected_mean)
|
|
||||||
return (A+B+C+D+E)/self.data_prop
|
|
||||||
|
|
||||||
def _log_likelihood_gradients(self):
|
|
||||||
return np.hstack((self.dL_dtheta(), self.likelihood._gradients(partial=self.partial_for_likelihood)))/self.data_prop
|
|
||||||
|
|
||||||
def vb_grad_natgrad(self):
|
|
||||||
"""
|
|
||||||
Compute the gradients of the lower bound wrt the canonical and
|
|
||||||
Expectation parameters of u.
|
|
||||||
|
|
||||||
Note that the natural gradient in either is given by the gradient in
|
|
||||||
the other (See Hensman et al 2012 Fast Variational inference in the
|
|
||||||
conjugate exponential Family)
|
|
||||||
"""
|
|
||||||
|
|
||||||
# Gradient for eta
|
|
||||||
dL_dmmT_S = -0.5*self.Lambda/self.data_prop + 0.5*self.q_u_prec
|
|
||||||
Kmmipsi1V,_ = dpotrs(self.Lm,self.psi1V,lower=1)
|
|
||||||
dL_dm = (Kmmipsi1V - np.dot(self.Lambda,self.q_u_mean))/self.data_prop
|
|
||||||
|
|
||||||
# Gradients for theta
|
|
||||||
S = self.q_u_cov
|
|
||||||
Si = self.q_u_prec
|
|
||||||
m = self.q_u_mean
|
|
||||||
dL_dSi = -mdot(S,dL_dmmT_S, S)
|
|
||||||
|
|
||||||
dL_dmhSi = -2*dL_dSi
|
|
||||||
dL_dSim = np.dot(dL_dSi,m) + np.dot(Si, dL_dm)
|
|
||||||
|
|
||||||
return np.hstack((dL_dm.flatten(),dL_dmmT_S.flatten())) , np.hstack((dL_dSim.flatten(), dL_dmhSi.flatten()))
|
|
||||||
|
|
||||||
|
|
||||||
def optimize(self, iterations, print_interval=10, callback=lambda:None, callback_interval=5):
|
|
||||||
|
|
||||||
param_step = 0.
|
|
||||||
|
|
||||||
#Iterate!
|
|
||||||
for i in range(iterations):
|
|
||||||
|
|
||||||
#store the current configuration for plotting later
|
|
||||||
self._param_trace.append(self._get_params())
|
|
||||||
self._ll_trace.append(self.log_likelihood() + self.log_prior())
|
|
||||||
|
|
||||||
#load a batch
|
|
||||||
self.load_batch()
|
|
||||||
|
|
||||||
#compute the (stochastic) gradient
|
|
||||||
natgrads = self.vb_grad_natgrad()
|
|
||||||
grads = self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
|
|
||||||
self._grad_trace.append(grads)
|
|
||||||
|
|
||||||
#compute the steps in all parameters
|
|
||||||
vb_step = self.vb_steplength*natgrads[0]
|
|
||||||
if (self.epochs>=1):#only move the parameters after the first epoch
|
|
||||||
param_step = self.momentum*param_step + self.param_steplength*grads
|
|
||||||
else:
|
|
||||||
param_step = 0.
|
|
||||||
|
|
||||||
self.set_vb_param(self.get_vb_param() + vb_step)
|
|
||||||
#Note: don't recompute everything here, wait until the next iteration when we have a new batch
|
|
||||||
self._set_params(self._untransform_params(self._get_params_transformed() + param_step), computations=False)
|
|
||||||
|
|
||||||
#print messages if desired
|
|
||||||
if i and (not i%print_interval):
|
|
||||||
print i, np.mean(self._ll_trace[-print_interval:]) #, self.log_likelihood()
|
|
||||||
print np.round(np.mean(self._grad_trace[-print_interval:],0),3)
|
|
||||||
sys.stdout.flush()
|
|
||||||
|
|
||||||
#callback
|
|
||||||
if i and not i%callback_interval:
|
|
||||||
callback(self) # Change this to callback()
|
|
||||||
time.sleep(0.01)
|
|
||||||
|
|
||||||
if self.epochs > 10:
|
|
||||||
self._adapt_steplength()
|
|
||||||
|
|
||||||
self.iterations += 1
|
|
||||||
|
|
||||||
|
|
||||||
def _adapt_steplength(self):
|
|
||||||
if self.adapt_vb_steplength:
|
|
||||||
# self._adaptive_vb_steplength()
|
|
||||||
self._adaptive_vb_steplength_KL()
|
|
||||||
self._vb_steplength_trace.append(self.vb_steplength)
|
|
||||||
assert self.vb_steplength > 0
|
|
||||||
|
|
||||||
if self.adapt_param_steplength:
|
|
||||||
self._adaptive_param_steplength()
|
|
||||||
# self._adaptive_param_steplength_log()
|
|
||||||
# self._adaptive_param_steplength_from_vb()
|
|
||||||
self._param_steplength_trace.append(self.param_steplength)
|
|
||||||
|
|
||||||
def _adaptive_param_steplength(self):
|
|
||||||
decr_factor = 0.02
|
|
||||||
g_tp = self._transform_gradients(self._log_likelihood_gradients())
|
|
||||||
self.gbar_tp = (1-1/self.tau_tp)*self.gbar_tp + 1/self.tau_tp * g_tp
|
|
||||||
self.hbar_tp = (1-1/self.tau_tp)*self.hbar_tp + 1/self.tau_tp * np.dot(g_tp.T, g_tp)
|
|
||||||
new_param_steplength = np.dot(self.gbar_tp.T, self.gbar_tp) / self.hbar_tp
|
|
||||||
#- hack
|
|
||||||
new_param_steplength *= decr_factor
|
|
||||||
self.param_steplength = (self.param_steplength + new_param_steplength)/2
|
|
||||||
#-
|
|
||||||
self.tau_tp = self.tau_tp*(1-self.param_steplength) + 1
|
|
||||||
|
|
||||||
def _adaptive_param_steplength_log(self):
|
|
||||||
stp = np.logspace(np.log(0.0001), np.log(1e-6), base=np.e, num=18000)
|
|
||||||
self.param_steplength = stp[self.iterations]
|
|
||||||
|
|
||||||
def _adaptive_param_steplength_log2(self):
|
|
||||||
self.param_steplength = (self.iterations + 0.001)**-0.5
|
|
||||||
|
|
||||||
def _adaptive_param_steplength_from_vb(self):
|
|
||||||
self.param_steplength = self.vb_steplength * 0.01
|
|
||||||
|
|
||||||
def _adaptive_vb_steplength(self):
|
|
||||||
decr_factor = 0.1
|
|
||||||
g_t = self.vb_grad_natgrad()[0]
|
|
||||||
self.gbar_t = (1-1/self.tau_t)*self.gbar_t + 1/self.tau_t * g_t
|
|
||||||
self.hbar_t = (1-1/self.tau_t)*self.hbar_t + 1/self.tau_t * np.dot(g_t.T, g_t)
|
|
||||||
new_vb_steplength = np.dot(self.gbar_t.T, self.gbar_t) / self.hbar_t
|
|
||||||
#- hack
|
|
||||||
new_vb_steplength *= decr_factor
|
|
||||||
self.vb_steplength = (self.vb_steplength + new_vb_steplength)/2
|
|
||||||
#-
|
|
||||||
self.tau_t = self.tau_t*(1-self.vb_steplength) + 1
|
|
||||||
|
|
||||||
def _adaptive_vb_steplength_KL(self):
|
|
||||||
decr_factor = 0.1
|
|
||||||
natgrad = self.vb_grad_natgrad()
|
|
||||||
g_t1 = natgrad[0]
|
|
||||||
g_t2 = natgrad[1]
|
|
||||||
self.gbar_t1 = (1-1/self.tau_t)*self.gbar_t1 + 1/self.tau_t * g_t1
|
|
||||||
self.gbar_t2 = (1-1/self.tau_t)*self.gbar_t2 + 1/self.tau_t * g_t2
|
|
||||||
self.hbar_t = (1-1/self.tau_t)*self.hbar_t + 1/self.tau_t * np.dot(g_t1.T, g_t2)
|
|
||||||
self.vb_steplength = np.dot(self.gbar_t1.T, self.gbar_t2) / self.hbar_t
|
|
||||||
self.vb_steplength *= decr_factor
|
|
||||||
self.tau_t = self.tau_t*(1-self.vb_steplength) + 1
|
|
||||||
|
|
||||||
def _raw_predict(self, X_new, X_variance_new=None, which_parts='all',full_cov=False):
|
|
||||||
"""Internal helper function for making predictions, does not account for normalization"""
|
|
||||||
|
|
||||||
#TODO: make this more efficient!
|
|
||||||
self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
|
|
||||||
tmp = self.Kmmi- mdot(self.Kmmi,self.q_u_cov,self.Kmmi)
|
|
||||||
|
|
||||||
if X_variance_new is None:
|
|
||||||
Kx = self.kern.K(X_new,self.Z)
|
|
||||||
mu = np.dot(Kx,self.Kmmi_m)
|
|
||||||
if full_cov:
|
|
||||||
Kxx = self.kern.K(X_new)
|
|
||||||
var = Kxx - mdot(Kx,tmp,Kx.T)
|
|
||||||
else:
|
|
||||||
Kxx = self.kern.Kdiag(X_new)
|
|
||||||
var = (Kxx - np.sum(Kx*np.dot(Kx,tmp),1))[:,None]
|
|
||||||
return mu, var
|
|
||||||
else:
|
|
||||||
assert X_variance_new.shape == X_new.shape
|
|
||||||
Kx = self.kern.psi1(self.Z,X_new, X_variance_new)
|
|
||||||
mu = np.dot(Kx,self.Kmmi_m)
|
|
||||||
Kxx = self.kern.psi0(self.Z,X_new,X_variance_new)
|
|
||||||
psi2 = self.kern.psi2(self.Z,X_new,X_variance_new)
|
|
||||||
diag_var = Kxx - np.sum(np.sum(psi2*tmp[None,:,:],1),1)
|
|
||||||
if full_cov:
|
|
||||||
raise NotImplementedError
|
|
||||||
else:
|
|
||||||
return mu, diag_var[:,None]
|
|
||||||
|
|
||||||
def predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
|
|
||||||
# normalize X values
|
|
||||||
Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
|
|
||||||
if X_variance_new is not None:
|
|
||||||
X_variance_new = X_variance_new / self._Xscale ** 2
|
|
||||||
|
|
||||||
# here's the actual prediction by the GP model
|
|
||||||
mu, var = self._raw_predict(Xnew, X_variance_new, full_cov=full_cov, which_parts=which_parts)
|
|
||||||
|
|
||||||
# now push through likelihood
|
|
||||||
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov)
|
|
||||||
|
|
||||||
return mean, var, _025pm, _975pm
|
|
||||||
|
|
||||||
|
|
||||||
def set_vb_param(self,vb_param):
|
|
||||||
"""set the distribution q(u) from the canonical parameters"""
|
|
||||||
self.q_u_canonical_flat = vb_param.copy()
|
|
||||||
self.q_u_canonical = self.q_u_canonical_flat[:self.num_inducing*self.output_dim].reshape(self.num_inducing,self.output_dim),self.q_u_canonical_flat[self.num_inducing*self.output_dim:].reshape(self.num_inducing,self.num_inducing)
|
|
||||||
|
|
||||||
self.q_u_prec = -2.*self.q_u_canonical[1]
|
|
||||||
self.q_u_cov, q_u_Li, q_u_L, tmp = pdinv(self.q_u_prec)
|
|
||||||
self.q_u_Li = q_u_Li
|
|
||||||
self.q_u_logdet = -tmp
|
|
||||||
self.q_u_mean, _ = dpotrs(q_u_Li, np.asfortranarray(self.q_u_canonical[0]),lower=1)
|
|
||||||
|
|
||||||
self.q_u_expectation = (self.q_u_mean, np.dot(self.q_u_mean,self.q_u_mean.T)+self.q_u_cov*self.output_dim)
|
|
||||||
|
|
||||||
|
|
||||||
def get_vb_param(self):
|
|
||||||
"""
|
|
||||||
Return the canonical parameters of the distribution q(u)
|
|
||||||
"""
|
|
||||||
return self.q_u_canonical_flat
|
|
||||||
|
|
||||||
|
|
||||||
def plot(self, *args, **kwargs):
|
|
||||||
"""
|
|
||||||
See GPy.plotting.matplot_dep.svig_plots.plot
|
|
||||||
"""
|
|
||||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
|
||||||
from ..plotting.matplot_dep import svig_plots
|
|
||||||
svig_plots.plot(self,*args,**kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
def plot_traces(self):
|
|
||||||
"""
|
|
||||||
See GPy.plotting.matplot_dep.svig_plots.plot_traces
|
|
||||||
"""
|
|
||||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
|
||||||
from ..plotting.matplot_dep import svig_plots
|
|
||||||
svig_plots.plot_traces(self)
|
|
||||||
|
|
@ -4,6 +4,4 @@
|
||||||
import classification
|
import classification
|
||||||
import regression
|
import regression
|
||||||
import dimensionality_reduction
|
import dimensionality_reduction
|
||||||
import tutorials
|
|
||||||
import stochastic
|
|
||||||
import non_gaussian
|
import non_gaussian
|
||||||
|
|
|
||||||
|
|
@ -28,13 +28,13 @@ def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
|
||||||
m = GPy.models.SparseGPClassification(X, Y, kernel=kernel, num_inducing=num_inducing)
|
m = GPy.models.SparseGPClassification(X, Y, kernel=kernel, num_inducing=num_inducing)
|
||||||
|
|
||||||
# Contrain all parameters to be positive
|
# Contrain all parameters to be positive
|
||||||
m.tie_params('.*len')
|
#m.tie_params('.*len')
|
||||||
m['.*len'] = 10.
|
m['.*len'] = 10.
|
||||||
m.update_likelihood_approximation()
|
|
||||||
|
|
||||||
# Optimize
|
# Optimize
|
||||||
if optimize:
|
if optimize:
|
||||||
m.optimize(max_iters=max_iters)
|
for _ in range(5):
|
||||||
|
m.optimize(max_iters=int(max_iters/5))
|
||||||
print(m)
|
print(m)
|
||||||
|
|
||||||
#Test
|
#Test
|
||||||
|
|
@ -150,7 +150,7 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
|
||||||
print m
|
print m
|
||||||
return m
|
return m
|
||||||
|
|
||||||
def toy_heaviside(seed=default_seed, optimize=True, plot=True):
|
def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
|
||||||
"""
|
"""
|
||||||
Simple 1D classification example using a heavy side gp transformation
|
Simple 1D classification example using a heavy side gp transformation
|
||||||
|
|
||||||
|
|
@ -166,16 +166,18 @@ def toy_heaviside(seed=default_seed, optimize=True, plot=True):
|
||||||
Y[Y.flatten() == -1] = 0
|
Y[Y.flatten() == -1] = 0
|
||||||
|
|
||||||
# Model definition
|
# Model definition
|
||||||
noise_model = GPy.likelihoods.bernoulli(GPy.likelihoods.noise_models.gp_transformations.Heaviside())
|
kernel = GPy.kern.RBF(1)
|
||||||
likelihood = GPy.likelihoods.EP(Y, noise_model)
|
likelihood = GPy.likelihoods.Bernoulli(gp_link=GPy.likelihoods.link_functions.Heaviside())
|
||||||
m = GPy.models.GPClassification(data['X'], likelihood=likelihood)
|
ep = GPy.inference.latent_function_inference.expectation_propagation.EP()
|
||||||
|
m = GPy.core.GP(X=data['X'], Y=Y, kernel=kernel, likelihood=likelihood, inference_method=ep, name='gp_classification_heaviside')
|
||||||
|
#m = GPy.models.GPClassification(data['X'], likelihood=likelihood)
|
||||||
|
|
||||||
# Optimize
|
# Optimize
|
||||||
if optimize:
|
if optimize:
|
||||||
m.update_likelihood_approximation()
|
|
||||||
# Parameters optimization:
|
# Parameters optimization:
|
||||||
m.optimize()
|
for _ in range(5):
|
||||||
#m.pseudo_EM()
|
m.optimize(max_iters=int(max_iters/5))
|
||||||
|
print m
|
||||||
|
|
||||||
# Plot
|
# Plot
|
||||||
if plot:
|
if plot:
|
||||||
|
|
|
||||||
|
|
@ -59,7 +59,7 @@ def student_t_approx(optimize=True, plot=True):
|
||||||
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
|
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
|
||||||
laplace_inf = GPy.inference.latent_function_inference.Laplace()
|
laplace_inf = GPy.inference.latent_function_inference.Laplace()
|
||||||
m3 = GPy.core.GP(X, Y.copy(), kernel3, likelihood=t_distribution, inference_method=laplace_inf)
|
m3 = GPy.core.GP(X, Y.copy(), kernel3, likelihood=t_distribution, inference_method=laplace_inf)
|
||||||
m3['.*t_noise'].constrain_bounded(1e-6, 10.)
|
m3['.*t_scale2'].constrain_bounded(1e-6, 10.)
|
||||||
m3['.*white'].constrain_fixed(1e-5)
|
m3['.*white'].constrain_fixed(1e-5)
|
||||||
m3.randomize()
|
m3.randomize()
|
||||||
|
|
||||||
|
|
@ -67,7 +67,7 @@ def student_t_approx(optimize=True, plot=True):
|
||||||
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
|
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
|
||||||
laplace_inf = GPy.inference.latent_function_inference.Laplace()
|
laplace_inf = GPy.inference.latent_function_inference.Laplace()
|
||||||
m4 = GPy.core.GP(X, Yc.copy(), kernel4, likelihood=t_distribution, inference_method=laplace_inf)
|
m4 = GPy.core.GP(X, Yc.copy(), kernel4, likelihood=t_distribution, inference_method=laplace_inf)
|
||||||
m4['.*t_noise'].constrain_bounded(1e-6, 10.)
|
m4['.*t_scale2'].constrain_bounded(1e-6, 10.)
|
||||||
m4['.*white'].constrain_fixed(1e-5)
|
m4['.*white'].constrain_fixed(1e-5)
|
||||||
m4.randomize()
|
m4.randomize()
|
||||||
print m4
|
print m4
|
||||||
|
|
@ -124,6 +124,7 @@ def student_t_approx(optimize=True, plot=True):
|
||||||
return m1, m2, m3, m4
|
return m1, m2, m3, m4
|
||||||
|
|
||||||
def boston_example(optimize=True, plot=True):
|
def boston_example(optimize=True, plot=True):
|
||||||
|
raise NotImplementedError("Needs updating")
|
||||||
import sklearn
|
import sklearn
|
||||||
from sklearn.cross_validation import KFold
|
from sklearn.cross_validation import KFold
|
||||||
optimizer='bfgs'
|
optimizer='bfgs'
|
||||||
|
|
@ -152,8 +153,8 @@ def boston_example(optimize=True, plot=True):
|
||||||
noise = 1e-1 #np.exp(-2)
|
noise = 1e-1 #np.exp(-2)
|
||||||
rbf_len = 0.5
|
rbf_len = 0.5
|
||||||
data_axis_plot = 4
|
data_axis_plot = 4
|
||||||
kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1]) + GPy.kern.bias(X.shape[1])
|
kernelstu = GPy.kern.RBF(X.shape[1]) + GPy.kern.white(X.shape[1]) + GPy.kern.bias(X.shape[1])
|
||||||
kernelgp = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1]) + GPy.kern.bias(X.shape[1])
|
kernelgp = GPy.kern.RBF(X.shape[1]) + GPy.kern.white(X.shape[1]) + GPy.kern.bias(X.shape[1])
|
||||||
|
|
||||||
#Baseline
|
#Baseline
|
||||||
score_folds[0, n] = rmse(Y_test, np.mean(Y_train))
|
score_folds[0, n] = rmse(Y_test, np.mean(Y_train))
|
||||||
|
|
@ -162,8 +163,8 @@ def boston_example(optimize=True, plot=True):
|
||||||
print "Gauss GP"
|
print "Gauss GP"
|
||||||
mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp.copy())
|
mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp.copy())
|
||||||
mgp.constrain_fixed('.*white', 1e-5)
|
mgp.constrain_fixed('.*white', 1e-5)
|
||||||
mgp['rbf_len'] = rbf_len
|
mgp['.*len'] = rbf_len
|
||||||
mgp['noise'] = noise
|
mgp['.*noise'] = noise
|
||||||
print mgp
|
print mgp
|
||||||
if optimize:
|
if optimize:
|
||||||
mgp.optimize(optimizer=optimizer, messages=messages)
|
mgp.optimize(optimizer=optimizer, messages=messages)
|
||||||
|
|
@ -198,9 +199,9 @@ def boston_example(optimize=True, plot=True):
|
||||||
stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution)
|
stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution)
|
||||||
mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu.copy(), likelihood=stu_t_likelihood)
|
mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu.copy(), likelihood=stu_t_likelihood)
|
||||||
mstu_t.constrain_fixed('.*white', 1e-5)
|
mstu_t.constrain_fixed('.*white', 1e-5)
|
||||||
mstu_t.constrain_bounded('.*t_noise', 0.0001, 1000)
|
mstu_t.constrain_bounded('.*t_scale2', 0.0001, 1000)
|
||||||
mstu_t['rbf_len'] = rbf_len
|
mstu_t['rbf_len'] = rbf_len
|
||||||
mstu_t['.*t_noise'] = noise
|
mstu_t['.*t_scale2'] = noise
|
||||||
print mstu_t
|
print mstu_t
|
||||||
if optimize:
|
if optimize:
|
||||||
mstu_t.optimize(optimizer=optimizer, messages=messages)
|
mstu_t.optimize(optimizer=optimizer, messages=messages)
|
||||||
|
|
|
||||||
|
|
@ -1,40 +0,0 @@
|
||||||
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
|
|
||||||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
||||||
|
|
||||||
try:
|
|
||||||
import pylab as pb
|
|
||||||
except:
|
|
||||||
pass
|
|
||||||
import numpy as np
|
|
||||||
import GPy
|
|
||||||
|
|
||||||
def toy_1d(optimize=True, plot=True):
|
|
||||||
N = 2000
|
|
||||||
M = 20
|
|
||||||
|
|
||||||
#create data
|
|
||||||
X = np.linspace(0,32,N)[:,None]
|
|
||||||
Z = np.linspace(0,32,M)[:,None]
|
|
||||||
Y = np.sin(X) + np.cos(0.3*X) + np.random.randn(*X.shape)/np.sqrt(50.)
|
|
||||||
|
|
||||||
m = GPy.models.SVIGPRegression(X,Y, batchsize=10, Z=Z)
|
|
||||||
m.constrain_bounded('noise_variance',1e-3,1e-1)
|
|
||||||
m.constrain_bounded('white_variance',1e-3,1e-1)
|
|
||||||
|
|
||||||
m.param_steplength = 1e-4
|
|
||||||
|
|
||||||
if plot:
|
|
||||||
fig = pb.figure()
|
|
||||||
ax = fig.add_subplot(111)
|
|
||||||
def cb(foo):
|
|
||||||
ax.cla()
|
|
||||||
m.plot(ax=ax,Z_height=-3)
|
|
||||||
ax.set_ylim(-3,3)
|
|
||||||
fig.canvas.draw()
|
|
||||||
|
|
||||||
if optimize:
|
|
||||||
m.optimize(500, callback=cb, callback_interval=1)
|
|
||||||
|
|
||||||
if plot:
|
|
||||||
m.plot_traces()
|
|
||||||
return m
|
|
||||||
|
|
@ -1,156 +0,0 @@
|
||||||
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
|
|
||||||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
||||||
|
|
||||||
|
|
||||||
"""
|
|
||||||
Code of Tutorials
|
|
||||||
"""
|
|
||||||
|
|
||||||
try:
|
|
||||||
import pylab as pb
|
|
||||||
pb.ion()
|
|
||||||
except:
|
|
||||||
pass
|
|
||||||
import numpy as np
|
|
||||||
import GPy
|
|
||||||
|
|
||||||
def tuto_GP_regression(optimize=True, plot=True):
|
|
||||||
"""The detailed explanations of the commands used in this file can be found in the tutorial section"""
|
|
||||||
|
|
||||||
X = np.random.uniform(-3.,3.,(20,1))
|
|
||||||
Y = np.sin(X) + np.random.randn(20,1)*0.05
|
|
||||||
|
|
||||||
kernel = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)
|
|
||||||
|
|
||||||
m = GPy.models.GPRegression(X, Y, kernel)
|
|
||||||
|
|
||||||
print m
|
|
||||||
if plot:
|
|
||||||
m.plot()
|
|
||||||
|
|
||||||
m.constrain_positive('')
|
|
||||||
|
|
||||||
m.unconstrain('') # may be used to remove the previous constrains
|
|
||||||
m.constrain_positive('.*rbf_variance')
|
|
||||||
m.constrain_bounded('.*lengthscale',1.,10. )
|
|
||||||
m.constrain_fixed('.*noise',0.0025)
|
|
||||||
|
|
||||||
if optimize:
|
|
||||||
m.optimize()
|
|
||||||
m.optimize_restarts(num_restarts = 10)
|
|
||||||
|
|
||||||
#######################################################
|
|
||||||
#######################################################
|
|
||||||
# sample inputs and outputs
|
|
||||||
X = np.random.uniform(-3.,3.,(50,2))
|
|
||||||
Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(50,1)*0.05
|
|
||||||
|
|
||||||
# define kernel
|
|
||||||
ker = GPy.kern.Matern52(2,ARD=True) + GPy.kern.white(2)
|
|
||||||
|
|
||||||
# create simple GP model
|
|
||||||
m = GPy.models.GPRegression(X, Y, ker)
|
|
||||||
|
|
||||||
# contrain all parameters to be positive
|
|
||||||
m.constrain_positive('')
|
|
||||||
|
|
||||||
# optimize and plot
|
|
||||||
if optimize:
|
|
||||||
m.optimize('tnc', max_f_eval = 1000)
|
|
||||||
if plot:
|
|
||||||
m.plot()
|
|
||||||
|
|
||||||
print m
|
|
||||||
return(m)
|
|
||||||
|
|
||||||
def tuto_kernel_overview(optimize=True, plot=True):
|
|
||||||
"""The detailed explanations of the commands used in this file can be found in the tutorial section"""
|
|
||||||
ker1 = GPy.kern.rbf(1) # Equivalent to ker1 = GPy.kern.rbf(input_dim=1, variance=1., lengthscale=1.)
|
|
||||||
ker2 = GPy.kern.rbf(input_dim=1, variance = .75, lengthscale=2.)
|
|
||||||
ker3 = GPy.kern.rbf(1, .5, .5)
|
|
||||||
|
|
||||||
print ker2
|
|
||||||
|
|
||||||
if plot:
|
|
||||||
ker1.plot()
|
|
||||||
ker2.plot()
|
|
||||||
ker3.plot()
|
|
||||||
|
|
||||||
k1 = GPy.kern.rbf(1,1.,2.)
|
|
||||||
k2 = GPy.kern.Matern32(1, 0.5, 0.2)
|
|
||||||
|
|
||||||
# Product of kernels
|
|
||||||
k_prod = k1.prod(k2) # By default, tensor=False
|
|
||||||
k_prodtens = k1.prod(k2,tensor=True)
|
|
||||||
|
|
||||||
# Sum of kernels
|
|
||||||
k_add = k1.add(k2) # By default, tensor=False
|
|
||||||
k_addtens = k1.add(k2,tensor=True)
|
|
||||||
|
|
||||||
k1 = GPy.kern.rbf(1,1.,2)
|
|
||||||
k2 = GPy.kern.periodic_Matern52(1,variance=1e3, lengthscale=1, period = 1.5, lower=-5., upper = 5)
|
|
||||||
|
|
||||||
k = k1 * k2 # equivalent to k = k1.prod(k2)
|
|
||||||
print k
|
|
||||||
|
|
||||||
# Simulate sample paths
|
|
||||||
X = np.linspace(-5,5,501)[:,None]
|
|
||||||
Y = np.random.multivariate_normal(np.zeros(501),k.K(X),1)
|
|
||||||
|
|
||||||
k1 = GPy.kern.rbf(1)
|
|
||||||
k2 = GPy.kern.Matern32(1)
|
|
||||||
k3 = GPy.kern.white(1)
|
|
||||||
|
|
||||||
k = k1 + k2 + k3
|
|
||||||
print k
|
|
||||||
|
|
||||||
k.constrain_positive('.*var')
|
|
||||||
k.constrain_fixed(np.array([1]),1.75)
|
|
||||||
k.tie_params('.*len')
|
|
||||||
k.unconstrain('white')
|
|
||||||
k.constrain_bounded('white',lower=1e-5,upper=.5)
|
|
||||||
print k
|
|
||||||
|
|
||||||
k_cst = GPy.kern.bias(1,variance=1.)
|
|
||||||
k_mat = GPy.kern.Matern52(1,variance=1., lengthscale=3)
|
|
||||||
Kanova = (k_cst + k_mat).prod(k_cst + k_mat,tensor=True)
|
|
||||||
print Kanova
|
|
||||||
|
|
||||||
# sample inputs and outputs
|
|
||||||
X = np.random.uniform(-3.,3.,(40,2))
|
|
||||||
Y = 0.5*X[:,:1] + 0.5*X[:,1:] + 2*np.sin(X[:,:1]) * np.sin(X[:,1:])
|
|
||||||
|
|
||||||
# Create GP regression model
|
|
||||||
m = GPy.models.GPRegression(X, Y, Kanova)
|
|
||||||
|
|
||||||
if plot:
|
|
||||||
fig = pb.figure(figsize=(5,5))
|
|
||||||
ax = fig.add_subplot(111)
|
|
||||||
m.plot(ax=ax)
|
|
||||||
|
|
||||||
pb.figure(figsize=(20,3))
|
|
||||||
pb.subplots_adjust(wspace=0.5)
|
|
||||||
axs = pb.subplot(1,5,1)
|
|
||||||
m.plot(ax=axs)
|
|
||||||
pb.subplot(1,5,2)
|
|
||||||
pb.ylabel("= ",rotation='horizontal',fontsize='30')
|
|
||||||
axs = pb.subplot(1,5,3)
|
|
||||||
m.plot(ax=axs, which_parts=[False,True,False,False])
|
|
||||||
pb.ylabel("cst +",rotation='horizontal',fontsize='30')
|
|
||||||
axs = pb.subplot(1,5,4)
|
|
||||||
m.plot(ax=axs, which_parts=[False,False,True,False])
|
|
||||||
pb.ylabel("+ ",rotation='horizontal',fontsize='30')
|
|
||||||
axs = pb.subplot(1,5,5)
|
|
||||||
pb.ylabel("+ ",rotation='horizontal',fontsize='30')
|
|
||||||
m.plot(ax=axs, which_parts=[False,False,False,True])
|
|
||||||
|
|
||||||
return(m)
|
|
||||||
|
|
||||||
|
|
||||||
def model_interaction(optimize=True, plot=True):
|
|
||||||
X = np.random.randn(20,1)
|
|
||||||
Y = np.sin(X) + np.random.randn(*X.shape)*0.01 + 5.
|
|
||||||
k = GPy.kern.rbf(1) + GPy.kern.bias(1)
|
|
||||||
m = GPy.models.GPRegression(X, Y, kernel=k)
|
|
||||||
return m
|
|
||||||
|
|
||||||
|
|
@ -7,6 +7,205 @@ The package for the psi statistics computation
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
|
try:
|
||||||
|
from scipy import weave
|
||||||
|
|
||||||
|
def _psicomputations(variance, lengthscale, Z, variational_posterior):
|
||||||
|
"""
|
||||||
|
Z - MxQ
|
||||||
|
mu - NxQ
|
||||||
|
S - NxQ
|
||||||
|
gamma - NxQ
|
||||||
|
"""
|
||||||
|
# here are the "statistics" for psi0, psi1 and psi2
|
||||||
|
# Produced intermediate results:
|
||||||
|
# _psi1 NxM
|
||||||
|
mu = variational_posterior.mean
|
||||||
|
S = variational_posterior.variance
|
||||||
|
gamma = variational_posterior.binary_prob
|
||||||
|
|
||||||
|
N,M,Q = mu.shape[0],Z.shape[0],mu.shape[1]
|
||||||
|
l2 = np.square(lengthscale)
|
||||||
|
log_denom1 = np.log(S/l2+1)
|
||||||
|
log_denom2 = np.log(2*S/l2+1)
|
||||||
|
log_gamma = np.log(gamma)
|
||||||
|
log_gamma1 = np.log(1.-gamma)
|
||||||
|
variance = float(variance)
|
||||||
|
psi0 = np.empty(N)
|
||||||
|
psi0[:] = variance
|
||||||
|
psi1 = np.empty((N,M))
|
||||||
|
psi2n = np.empty((N,M,M))
|
||||||
|
|
||||||
|
from ....util.misc import param_to_array
|
||||||
|
S = param_to_array(S)
|
||||||
|
mu = param_to_array(mu)
|
||||||
|
gamma = param_to_array(gamma)
|
||||||
|
Z = param_to_array(Z)
|
||||||
|
|
||||||
|
support_code = """
|
||||||
|
#include <math.h>
|
||||||
|
"""
|
||||||
|
code = """
|
||||||
|
for(int n=0; n<N; n++) {
|
||||||
|
for(int m1=0;m1<M;m1++) {
|
||||||
|
double log_psi1=0;
|
||||||
|
for(int m2=0;m2<=m1;m2++) {
|
||||||
|
double log_psi2_n=0;
|
||||||
|
for(int q=0;q<Q;q++) {
|
||||||
|
double Snq = S(n,q);
|
||||||
|
double lq = l2(q);
|
||||||
|
double Zm1q = Z(m1,q);
|
||||||
|
double Zm2q = Z(m2,q);
|
||||||
|
|
||||||
|
if(m2==0) {
|
||||||
|
// Compute Psi_1
|
||||||
|
double muZ = mu(n,q)-Z(m1,q);
|
||||||
|
|
||||||
|
double psi1_exp1 = log_gamma(n,q) - (muZ*muZ/(Snq+lq) +log_denom1(n,q))/2.;
|
||||||
|
double psi1_exp2 = log_gamma1(n,q) -Zm1q*Zm1q/(2.*lq);
|
||||||
|
log_psi1 += (psi1_exp1>psi1_exp2)?psi1_exp1+log1p(exp(psi1_exp2-psi1_exp1)):psi1_exp2+log1p(exp(psi1_exp1-psi1_exp2));
|
||||||
|
}
|
||||||
|
// Compute Psi_2
|
||||||
|
double muZhat = mu(n,q) - (Zm1q+Zm2q)/2.;
|
||||||
|
double Z2 = Zm1q*Zm1q+ Zm2q*Zm2q;
|
||||||
|
double dZ = Zm1q - Zm2q;
|
||||||
|
|
||||||
|
double psi2_exp1 = dZ*dZ/(-4.*lq)-muZhat*muZhat/(2.*Snq+lq) - log_denom2(n,q)/2. + log_gamma(n,q);
|
||||||
|
double psi2_exp2 = log_gamma1(n,q) - Z2/(2.*lq);
|
||||||
|
log_psi2_n += (psi2_exp1>psi2_exp2)?psi2_exp1+log1p(exp(psi2_exp2-psi2_exp1)):psi2_exp2+log1p(exp(psi2_exp1-psi2_exp2));
|
||||||
|
}
|
||||||
|
double exp_psi2_n = exp(log_psi2_n);
|
||||||
|
psi2n(n,m1,m2) = variance*variance*exp_psi2_n;
|
||||||
|
if(m1!=m2) { psi2n(n,m2,m1) = variance*variance*exp_psi2_n;}
|
||||||
|
}
|
||||||
|
psi1(n,m1) = variance*exp(log_psi1);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
"""
|
||||||
|
weave.inline(code, support_code=support_code, arg_names=['psi1','psi2n','N','M','Q','variance','l2','Z','mu','S','gamma','log_denom1','log_denom2','log_gamma','log_gamma1'], type_converters=weave.converters.blitz)
|
||||||
|
|
||||||
|
psi2 = psi2n.sum(axis=0)
|
||||||
|
return psi0,psi1,psi2,psi2n
|
||||||
|
|
||||||
|
from GPy.util.caching import Cacher
|
||||||
|
psicomputations = Cacher(_psicomputations, limit=1)
|
||||||
|
|
||||||
|
def psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
|
||||||
|
ARD = (len(lengthscale)!=1)
|
||||||
|
|
||||||
|
_,psi1,_,psi2n = psicomputations(variance, lengthscale, Z, variational_posterior)
|
||||||
|
|
||||||
|
mu = variational_posterior.mean
|
||||||
|
S = variational_posterior.variance
|
||||||
|
gamma = variational_posterior.binary_prob
|
||||||
|
N,M,Q = mu.shape[0],Z.shape[0],mu.shape[1]
|
||||||
|
l2 = np.square(lengthscale)
|
||||||
|
log_denom1 = np.log(S/l2+1)
|
||||||
|
log_denom2 = np.log(2*S/l2+1)
|
||||||
|
log_gamma = np.log(gamma)
|
||||||
|
log_gamma1 = np.log(1.-gamma)
|
||||||
|
variance = float(variance)
|
||||||
|
|
||||||
|
dvar = np.zeros(1)
|
||||||
|
dmu = np.zeros((N,Q))
|
||||||
|
dS = np.zeros((N,Q))
|
||||||
|
dgamma = np.zeros((N,Q))
|
||||||
|
dl = np.zeros(Q)
|
||||||
|
dZ = np.zeros((M,Q))
|
||||||
|
dvar += np.sum(dL_dpsi0)
|
||||||
|
|
||||||
|
from ....util.misc import param_to_array
|
||||||
|
S = param_to_array(S)
|
||||||
|
mu = param_to_array(mu)
|
||||||
|
gamma = param_to_array(gamma)
|
||||||
|
Z = param_to_array(Z)
|
||||||
|
|
||||||
|
support_code = """
|
||||||
|
#include <math.h>
|
||||||
|
"""
|
||||||
|
code = """
|
||||||
|
for(int n=0; n<N; n++) {
|
||||||
|
for(int m1=0;m1<M;m1++) {
|
||||||
|
double log_psi1=0;
|
||||||
|
for(int m2=0;m2<M;m2++) {
|
||||||
|
double log_psi2_n=0;
|
||||||
|
for(int q=0;q<Q;q++) {
|
||||||
|
double Snq = S(n,q);
|
||||||
|
double lq = l2(q);
|
||||||
|
double Zm1q = Z(m1,q);
|
||||||
|
double Zm2q = Z(m2,q);
|
||||||
|
double gnq = gamma(n,q);
|
||||||
|
double mu_nq = mu(n,q);
|
||||||
|
|
||||||
|
if(m2==0) {
|
||||||
|
// Compute Psi_1
|
||||||
|
double lpsi1 = psi1(n,m1)*dL_dpsi1(n,m1);
|
||||||
|
if(q==0) {dvar(0) += lpsi1/variance;}
|
||||||
|
|
||||||
|
double Zmu = Zm1q - mu_nq;
|
||||||
|
double denom = Snq+lq;
|
||||||
|
double Zmu2_denom = Zmu*Zmu/denom;
|
||||||
|
|
||||||
|
double exp1 = log_gamma(n,q)-(Zmu*Zmu/(Snq+lq)+log_denom1(n,q))/(2.);
|
||||||
|
double exp2 = log_gamma1(n,q)-Zm1q*Zm1q/(2.*lq);
|
||||||
|
double d_exp1,d_exp2;
|
||||||
|
if(exp1>exp2) {
|
||||||
|
d_exp1 = 1.;
|
||||||
|
d_exp2 = exp(exp2-exp1);
|
||||||
|
} else {
|
||||||
|
d_exp1 = exp(exp1-exp2);
|
||||||
|
d_exp2 = 1.;
|
||||||
|
}
|
||||||
|
double exp_sum = d_exp1+d_exp2;
|
||||||
|
|
||||||
|
dmu(n,q) += lpsi1*Zmu*d_exp1/(denom*exp_sum);
|
||||||
|
dS(n,q) += lpsi1*(Zmu2_denom-1.)*d_exp1/(denom*exp_sum)/2.;
|
||||||
|
dgamma(n,q) += lpsi1*(d_exp1/gnq-d_exp2/(1.-gnq))/exp_sum;
|
||||||
|
dl(q) += lpsi1*((Zmu2_denom+Snq/lq)/denom*d_exp1+Zm1q*Zm1q/(lq*lq)*d_exp2)/(2.*exp_sum);
|
||||||
|
dZ(m1,q) += lpsi1*(-Zmu/denom*d_exp1-Zm1q/lq*d_exp2)/exp_sum;
|
||||||
|
}
|
||||||
|
// Compute Psi_2
|
||||||
|
double lpsi2 = psi2n(n,m1,m2)*dL_dpsi2(m1,m2);
|
||||||
|
if(q==0) {dvar(0) += lpsi2*2/variance;}
|
||||||
|
|
||||||
|
double dZm1m2 = Zm1q - Zm2q;
|
||||||
|
double Z2 = Zm1q*Zm1q+Zm2q*Zm2q;
|
||||||
|
double muZhat = mu_nq - (Zm1q + Zm2q)/2.;
|
||||||
|
double denom = 2.*Snq+lq;
|
||||||
|
double muZhat2_denom = muZhat*muZhat/denom;
|
||||||
|
|
||||||
|
double exp1 = dZm1m2*dZm1m2/(-4.*lq)-muZhat*muZhat/(2.*Snq+lq) - log_denom2(n,q)/2. + log_gamma(n,q);
|
||||||
|
double exp2 = log_gamma1(n,q) - Z2/(2.*lq);
|
||||||
|
double d_exp1,d_exp2;
|
||||||
|
if(exp1>exp2) {
|
||||||
|
d_exp1 = 1.;
|
||||||
|
d_exp2 = exp(exp2-exp1);
|
||||||
|
} else {
|
||||||
|
d_exp1 = exp(exp1-exp2);
|
||||||
|
d_exp2 = 1.;
|
||||||
|
}
|
||||||
|
double exp_sum = d_exp1+d_exp2;
|
||||||
|
|
||||||
|
dmu(n,q) += -2.*lpsi2*muZhat/denom*d_exp1/exp_sum;
|
||||||
|
dS(n,q) += lpsi2*(2.*muZhat2_denom-1.)/denom*d_exp1/exp_sum;
|
||||||
|
dgamma(n,q) += lpsi2*(d_exp1/gnq-d_exp2/(1.-gnq))/exp_sum;
|
||||||
|
dl(q) += lpsi2*(((Snq/lq+muZhat2_denom)/denom+dZm1m2*dZm1m2/(4.*lq*lq))*d_exp1+Z2/(2.*lq*lq)*d_exp2)/exp_sum;
|
||||||
|
dZ(m1,q) += 2.*lpsi2*((muZhat/denom-dZm1m2/(2*lq))*d_exp1-Zm1q/lq*d_exp2)/exp_sum;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
"""
|
||||||
|
weave.inline(code, support_code=support_code, arg_names=['dL_dpsi1','dL_dpsi2','psi1','psi2n','N','M','Q','variance','l2','Z','mu','S','gamma','log_denom1','log_denom2','log_gamma','log_gamma1','dvar','dl','dmu','dS','dgamma','dZ'], type_converters=weave.converters.blitz)
|
||||||
|
|
||||||
|
dl *= 2.*lengthscale
|
||||||
|
if not ARD:
|
||||||
|
dl = dl.sum()
|
||||||
|
|
||||||
|
return dvar, dl, dZ, dmu, dS, dgamma
|
||||||
|
|
||||||
|
except:
|
||||||
|
|
||||||
def psicomputations(variance, lengthscale, Z, variational_posterior):
|
def psicomputations(variance, lengthscale, Z, variational_posterior):
|
||||||
"""
|
"""
|
||||||
Z - MxQ
|
Z - MxQ
|
||||||
|
|
|
||||||
|
|
@ -4,7 +4,6 @@
|
||||||
from gp_regression import GPRegression
|
from gp_regression import GPRegression
|
||||||
from gp_classification import GPClassification
|
from gp_classification import GPClassification
|
||||||
from sparse_gp_regression import SparseGPRegression, SparseGPRegressionUncertainInput
|
from sparse_gp_regression import SparseGPRegression, SparseGPRegressionUncertainInput
|
||||||
from svigp_regression import SVIGPRegression
|
|
||||||
from sparse_gp_classification import SparseGPClassification
|
from sparse_gp_classification import SparseGPClassification
|
||||||
from gplvm import GPLVM
|
from gplvm import GPLVM
|
||||||
from bcgplvm import BCGPLVM
|
from bcgplvm import BCGPLVM
|
||||||
|
|
|
||||||
|
|
@ -25,8 +25,6 @@ class BayesianGPLVM(SparseGP_MPI):
|
||||||
Z=None, kernel=None, inference_method=None, likelihood=None,
|
Z=None, kernel=None, inference_method=None, likelihood=None,
|
||||||
name='bayesian gplvm', mpi_comm=None, normalizer=None,
|
name='bayesian gplvm', mpi_comm=None, normalizer=None,
|
||||||
missing_data=False, stochastic=False, batchsize=1):
|
missing_data=False, stochastic=False, batchsize=1):
|
||||||
self.mpi_comm = mpi_comm
|
|
||||||
self.__IN_OPTIMIZATION__ = False
|
|
||||||
|
|
||||||
self.logger = logging.getLogger(self.__class__.__name__)
|
self.logger = logging.getLogger(self.__class__.__name__)
|
||||||
if X is None:
|
if X is None:
|
||||||
|
|
|
||||||
|
|
@ -9,6 +9,7 @@ from .. import likelihoods
|
||||||
from .. import kern
|
from .. import kern
|
||||||
from ..inference.latent_function_inference import VarDTC
|
from ..inference.latent_function_inference import VarDTC
|
||||||
from ..core.parameterization.variational import NormalPosterior
|
from ..core.parameterization.variational import NormalPosterior
|
||||||
|
from GPy.inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
|
||||||
|
|
||||||
class SparseGPRegression(SparseGP_MPI):
|
class SparseGPRegression(SparseGP_MPI):
|
||||||
"""
|
"""
|
||||||
|
|
@ -30,7 +31,7 @@ class SparseGPRegression(SparseGP_MPI):
|
||||||
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, normalizer=None):
|
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, normalizer=None, mpi_comm=None):
|
||||||
num_data, input_dim = X.shape
|
num_data, input_dim = X.shape
|
||||||
|
|
||||||
# kern defaults to rbf (plus white for stability)
|
# kern defaults to rbf (plus white for stability)
|
||||||
|
|
@ -49,7 +50,13 @@ class SparseGPRegression(SparseGP_MPI):
|
||||||
if not (X_variance is None):
|
if not (X_variance is None):
|
||||||
X = NormalPosterior(X,X_variance)
|
X = NormalPosterior(X,X_variance)
|
||||||
|
|
||||||
SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC(), normalizer=normalizer)
|
if mpi_comm is not None:
|
||||||
|
from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
|
||||||
|
infr = VarDTC_minibatch(mpi_comm=mpi_comm)
|
||||||
|
else:
|
||||||
|
infr = VarDTC()
|
||||||
|
|
||||||
|
SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=infr, normalizer=normalizer, mpi_comm=mpi_comm)
|
||||||
|
|
||||||
def parameters_changed(self):
|
def parameters_changed(self):
|
||||||
from ..inference.latent_function_inference.var_dtc_parallel import update_gradients_sparsegp,VarDTC_minibatch
|
from ..inference.latent_function_inference.var_dtc_parallel import update_gradients_sparsegp,VarDTC_minibatch
|
||||||
|
|
|
||||||
|
|
@ -1,45 +0,0 @@
|
||||||
# Copyright (c) 2012, James Hensman
|
|
||||||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
from ..core import SVIGP
|
|
||||||
from .. import likelihoods
|
|
||||||
from .. import kern
|
|
||||||
|
|
||||||
class SVIGPRegression(SVIGP):
|
|
||||||
"""
|
|
||||||
Gaussian Process model for regression
|
|
||||||
|
|
||||||
This is a thin wrapper around the SVIGP class, with a set of sensible defalts
|
|
||||||
|
|
||||||
:param X: input observations
|
|
||||||
:param Y: observed values
|
|
||||||
:param kernel: a GPy kernel, defaults to rbf+white
|
|
||||||
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
|
|
||||||
:type normalize_X: False|True
|
|
||||||
:param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales)
|
|
||||||
:type normalize_Y: False|True
|
|
||||||
:rtype: model object
|
|
||||||
|
|
||||||
.. Note:: Multiple independent outputs are allowed using columns of Y
|
|
||||||
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, q_u=None, batchsize=10, normalize_Y=False):
|
|
||||||
# kern defaults to rbf (plus white for stability)
|
|
||||||
if kernel is None:
|
|
||||||
kernel = kern.rbf(X.shape[1], variance=1., lengthscale=4.) + kern.white(X.shape[1], 1e-3)
|
|
||||||
|
|
||||||
# Z defaults to a subset of the data
|
|
||||||
if Z is None:
|
|
||||||
i = np.random.permutation(X.shape[0])[:num_inducing]
|
|
||||||
Z = X[i].copy()
|
|
||||||
else:
|
|
||||||
assert Z.shape[1] == X.shape[1]
|
|
||||||
|
|
||||||
# likelihood defaults to Gaussian
|
|
||||||
likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
|
|
||||||
|
|
||||||
SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
|
|
||||||
self.load_batch()
|
|
||||||
|
|
||||||
|
|
@ -6,7 +6,6 @@ import models_plots
|
||||||
import priors_plots
|
import priors_plots
|
||||||
import variational_plots
|
import variational_plots
|
||||||
import kernel_plots
|
import kernel_plots
|
||||||
import svig_plots
|
|
||||||
import dim_reduction_plots
|
import dim_reduction_plots
|
||||||
import mapping_plots
|
import mapping_plots
|
||||||
import Tango
|
import Tango
|
||||||
|
|
|
||||||
|
|
@ -21,6 +21,7 @@ comm = MPI.COMM_WORLD
|
||||||
N = 100
|
N = 100
|
||||||
x = np.linspace(-6., 6., N)
|
x = np.linspace(-6., 6., N)
|
||||||
y = np.sin(x) + np.random.randn(N) * 0.05
|
y = np.sin(x) + np.random.randn(N) * 0.05
|
||||||
|
comm.Bcast(y)
|
||||||
data = np.vstack([x,y])
|
data = np.vstack([x,y])
|
||||||
infr = GPy.inference.latent_function_inference.VarDTC_minibatch(mpi_comm=comm)
|
infr = GPy.inference.latent_function_inference.VarDTC_minibatch(mpi_comm=comm)
|
||||||
m = GPy.models.BayesianGPLVM(data.T,1,mpi_comm=comm)
|
m = GPy.models.BayesianGPLVM(data.T,1,mpi_comm=comm)
|
||||||
|
|
@ -39,10 +40,44 @@ if comm.rank==0:
|
||||||
(stdout, stderr) = p.communicate()
|
(stdout, stderr) = p.communicate()
|
||||||
L1 = float(stdout.splitlines()[-2])
|
L1 = float(stdout.splitlines()[-2])
|
||||||
L2 = float(stdout.splitlines()[-1])
|
L2 = float(stdout.splitlines()[-1])
|
||||||
self.assertAlmostEqual(L1, L2)
|
self.assertTrue(np.allclose(L1,L2))
|
||||||
import os
|
import os
|
||||||
os.remove('mpi_test__.py')
|
os.remove('mpi_test__.py')
|
||||||
|
|
||||||
|
def test_SparseGPRegression_MPI(self):
|
||||||
|
code = """
|
||||||
|
import numpy as np
|
||||||
|
import GPy
|
||||||
|
from mpi4py import MPI
|
||||||
|
np.random.seed(123456)
|
||||||
|
comm = MPI.COMM_WORLD
|
||||||
|
N = 100
|
||||||
|
x = np.linspace(-6., 6., N)
|
||||||
|
y = np.sin(x) + np.random.randn(N) * 0.05
|
||||||
|
comm.Bcast(y)
|
||||||
|
data = np.vstack([x,y])
|
||||||
|
#infr = GPy.inference.latent_function_inference.VarDTC_minibatch(mpi_comm=comm)
|
||||||
|
m = GPy.models.SparseGPRegression(data[:1].T,data[1:2].T,mpi_comm=comm)
|
||||||
|
m.optimize(max_iters=10)
|
||||||
|
if comm.rank==0:
|
||||||
|
print float(m.objective_function())
|
||||||
|
m.inference_method.mpi_comm=None
|
||||||
|
m.mpi_comm=None
|
||||||
|
m._trigger_params_changed()
|
||||||
|
print float(m.objective_function())
|
||||||
|
"""
|
||||||
|
with open('mpi_test__.py','w') as f:
|
||||||
|
f.write(code)
|
||||||
|
f.close()
|
||||||
|
p = subprocess.Popen('mpirun -n 4 python mpi_test__.py',stdout=subprocess.PIPE,shell=True)
|
||||||
|
(stdout, stderr) = p.communicate()
|
||||||
|
L1 = float(stdout.splitlines()[-2])
|
||||||
|
L2 = float(stdout.splitlines()[-1])
|
||||||
|
self.assertTrue(np.allclose(L1,L2))
|
||||||
|
import os
|
||||||
|
os.remove('mpi_test__.py')
|
||||||
|
|
||||||
|
|
||||||
except:
|
except:
|
||||||
pass
|
pass
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -5,23 +5,28 @@
|
||||||
|
|
||||||
Welcome to GPy's documentation!
|
Welcome to GPy's documentation!
|
||||||
===============================
|
===============================
|
||||||
For a quick start, you can have a look at one of the tutorials:
|
|
||||||
|
|
||||||
* `Basic Gaussian process regression <tuto_GP_regression.html>`_
|
`GPy <http://sheffieldml.github.io/GPy/>`_ is a Gaussian Process (GP) framework written in Python, from the Sheffield machine learning group.
|
||||||
* `Interacting with models <tuto_interacting_with_models.html>`_
|
|
||||||
* `A kernel overview <tuto_kernel_overview.html>`_
|
|
||||||
* `Writing new kernels <tuto_creating_new_kernels.html>`_
|
|
||||||
* `Writing new models <tuto_creating_new_models.html>`_
|
|
||||||
* `Parameterization handles <tuto_parameterized.html>`_
|
|
||||||
|
|
||||||
You may also be interested by some examples in the GPy/examples folder.
|
The `GPy homepage <http://sheffieldml.github.io/GPy/>`_ contains tutorials for users and further information on the project, including installation instructions.
|
||||||
|
This documentation is mostly aimed at developers interacting closely with the code-base.
|
||||||
|
|
||||||
|
The code can be found on our `Github project page <https://github.com/SheffieldML/GPy>`_. It is open source and provided under the BSD license.
|
||||||
|
|
||||||
|
.. * `Basic Gaussian process regression <tuto_GP_regression.html>`_
|
||||||
|
.. * `Interacting with models <tuto_interacting_with_models.html>`_
|
||||||
|
.. * `A kernel overview <tuto_kernel_overview.html>`_
|
||||||
|
.. * `Writing new kernels <tuto_creating_new_kernels.html>`_
|
||||||
|
.. * `Writing new models <tuto_creating_new_models.html>`_
|
||||||
|
.. * `Parameterization handles <tuto_parameterized.html>`_
|
||||||
|
|
||||||
|
.. You may also be interested by some examples in the GPy/examples folder.
|
||||||
|
|
||||||
Contents:
|
Contents:
|
||||||
|
|
||||||
.. toctree::
|
.. toctree::
|
||||||
:maxdepth: 2
|
:maxdepth: 2
|
||||||
|
|
||||||
installation
|
|
||||||
GPy
|
GPy
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
30
doc/log.txt
30
doc/log.txt
|
|
@ -1,7 +1,7 @@
|
||||||
/home/alans/Work/GPy/GPy/__init__.py:docstring of GPy.load:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
|
/home/alans/Work/GPy/GPy/__init__.py:docstring of GPy.load:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
|
||||||
/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP:7: WARNING: Field list ends without a blank line; unexpected unindent.
|
/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP.optimize:8: ERROR: Unknown interpreted text role "module".
|
||||||
/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP:10: ERROR: Unexpected indentation.
|
|
||||||
/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:5: ERROR: Unexpected indentation.
|
/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:5: ERROR: Unexpected indentation.
|
||||||
|
/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:8: WARNING: Block quote ends without a blank line; unexpected unindent.
|
||||||
/home/alans/Work/GPy/GPy/core/model.py:docstring of GPy.core.model.Model.optimize_restarts:29: WARNING: Explicit markup ends without a blank line; unexpected unindent.
|
/home/alans/Work/GPy/GPy/core/model.py:docstring of GPy.core.model.Model.optimize_restarts:29: WARNING: Explicit markup ends without a blank line; unexpected unindent.
|
||||||
/home/alans/Work/GPy/doc/GPy.core.rst:65: WARNING: autodoc: failed to import module u'GPy.core.symbolic'; the following exception was raised:
|
/home/alans/Work/GPy/doc/GPy.core.rst:65: WARNING: autodoc: failed to import module u'GPy.core.symbolic'; the following exception was raised:
|
||||||
Traceback (most recent call last):
|
Traceback (most recent call last):
|
||||||
|
|
@ -23,31 +23,6 @@ ImportError: No module named lambdify
|
||||||
/home/alans/Work/GPy/GPy/core/parameterization/ties_and_remappings.py:docstring of GPy.core.parameterization.ties_and_remappings.Tie:18: SEVERE: Unexpected section title or transition.
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/home/alans/Work/GPy/GPy/core/parameterization/ties_and_remappings.py:docstring of GPy.core.parameterization.ties_and_remappings.Tie:18: SEVERE: Unexpected section title or transition.
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================================
|
================================
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/home/alans/Work/GPy/doc/GPy.examples.rst:18: WARNING: autodoc: failed to import module u'GPy.examples.coreg_example'; the following exception was raised:
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Traceback (most recent call last):
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File "/home/alans/anaconda/envs/GPy/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 335, in import_object
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__import__(self.modname)
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File "/home/alans/Work/GPy/GPy/examples/coreg_example.py", line 44, in <module>
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m = GPy.models.SparseGPCoregionalizedRegression(X_list=[X1], Y_list=[Y1], Z_list = [Z1], kernel=kern)
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File "/home/alans/Work/GPy/GPy/core/parameterization/parameterized.py", line 19, in __call__
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self = super(ParametersChangedMeta, self).__call__(*args, **kw)
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File "/home/alans/Work/GPy/GPy/models/sparse_gp_coregionalized_regression.py", line 66, in __init__
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self['.*inducing'][:,-1].fix()
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File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 444, in constrain_fixed
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||||||
self.notify_observers(self, None if trigger_parent else -np.inf)
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||||||
File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 148, in notify_observers
|
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||||||
[callble(self, which=which) for _, _, callble in self.observers]
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File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 1077, in _pass_through_notify_observers
|
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||||||
self.notify_observers(which=which)
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||||||
File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 148, in notify_observers
|
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||||||
[callble(self, which=which) for _, _, callble in self.observers]
|
|
||||||
File "/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py", line 1075, in _parameters_changed_notification
|
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||||||
self.parameters_changed()
|
|
||||||
File "/home/alans/Work/GPy/GPy/core/sparse_gp.py", line 66, in parameters_changed
|
|
||||||
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata)
|
|
||||||
File "/home/alans/Work/GPy/GPy/inference/latent_function_inference/var_dtc.py", line 170, in inference
|
|
||||||
import ipdb; ipdb.set_trace()
|
|
||||||
ImportError: No module named ipdb
|
|
||||||
/home/alans/Work/GPy/GPy/kern/_src/coregionalize.py:docstring of GPy.kern._src.coregionalize.Coregionalize:5: ERROR: Unexpected indentation.
|
/home/alans/Work/GPy/GPy/kern/_src/coregionalize.py:docstring of GPy.kern._src.coregionalize.Coregionalize:5: ERROR: Unexpected indentation.
|
||||||
/home/alans/Work/GPy/doc/GPy.kern._src.rst:73: WARNING: autodoc: failed to import module u'GPy.kern._src.hierarchical'; the following exception was raised:
|
/home/alans/Work/GPy/doc/GPy.kern._src.rst:73: WARNING: autodoc: failed to import module u'GPy.kern._src.hierarchical'; the following exception was raised:
|
||||||
Traceback (most recent call last):
|
Traceback (most recent call last):
|
||||||
|
|
@ -187,6 +162,7 @@ Parameter handles
|
||||||
|
|
||||||
:py:class:`~GPy.core.parameterization.param.Param`
|
:py:class:`~GPy.core.parameterization.param.Param`
|
||||||
===========
|
===========
|
||||||
|
/home/alans/Work/GPy/doc/installation.rst:: WARNING: document isn't included in any toctree
|
||||||
/home/alans/Work/GPy/doc/kernel_implementation.rst:: WARNING: document isn't included in any toctree
|
/home/alans/Work/GPy/doc/kernel_implementation.rst:: WARNING: document isn't included in any toctree
|
||||||
/home/alans/Work/GPy/doc/modules.rst:: WARNING: document isn't included in any toctree
|
/home/alans/Work/GPy/doc/modules.rst:: WARNING: document isn't included in any toctree
|
||||||
/home/alans/Work/GPy/doc/tuto_GP_regression.rst:: WARNING: document isn't included in any toctree
|
/home/alans/Work/GPy/doc/tuto_GP_regression.rst:: WARNING: document isn't included in any toctree
|
||||||
|
|
|
||||||
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