Revision

**b08f3062c96677de266af26767634fd7c6e6611d**authored by Alexander G. de G. Matthews on**09 September 2016, 10:59:46 UTC**, committed by James Hensman on**09 September 2016, 10:59:46 UTC*** Renaming tf_hacks to tf_wraps * Changing tf_hacks to tf_wraps in code. * adding a tf_hacks file that raises deprecationwarnings * release notes * bumpng version on docs * importing tf_hacks, tf_wraps

**1 parent**61b0659

gpr.py

```
# Copyright 2016 James Hensman, Valentine Svensson, alexggmatthews, fujiisoup
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import tensorflow as tf
from .model import GPModel
from .densities import multivariate_normal
from .mean_functions import Zero
from . import likelihoods
from .tf_wraps import eye
from .param import DataHolder
class GPR(GPModel):
"""
Gaussian Process Regression.
This is a vanilla implementation of GP regression with a Gaussian
likelihood. Multiple columns of Y are treated independently.
The log likelihood i this models is sometimes referred to as the 'marginal log likelihood', and is given by
.. math::
\\log p(\\mathbf y \\,|\\, \\mathbf f) = \\mathcal N\\left(\\mathbf y\,|\, 0, \\mathbf K + \\sigma_n \\mathbf I\\right)
"""
def __init__(self, X, Y, kern, mean_function=Zero()):
"""
X is a data matrix, size N x D
Y is a data matrix, size N x R
kern, mean_function are appropriate GPflow objects
"""
likelihood = likelihoods.Gaussian()
X = DataHolder(X, on_shape_change='pass')
Y = DataHolder(Y, on_shape_change='pass')
GPModel.__init__(self, X, Y, kern, likelihood, mean_function)
self.num_latent = Y.shape[1]
def build_likelihood(self):
"""
Construct a tensorflow function to compute the likelihood.
\log p(Y | theta).
"""
K = self.kern.K(self.X) + eye(tf.shape(self.X)[0]) * self.likelihood.variance
L = tf.cholesky(K)
m = self.mean_function(self.X)
return multivariate_normal(self.Y, m, L)
def build_predict(self, Xnew, full_cov=False):
"""
Xnew is a data matrix, point at which we want to predict
This method computes
p(F* | Y )
where F* are points on the GP at Xnew, Y are noisy observations at X.
"""
Kx = self.kern.K(self.X, Xnew)
K = self.kern.K(self.X) + eye(tf.shape(self.X)[0]) * self.likelihood.variance
L = tf.cholesky(K)
A = tf.matrix_triangular_solve(L, Kx, lower=True)
V = tf.matrix_triangular_solve(L, self.Y - self.mean_function(self.X))
fmean = tf.matmul(tf.transpose(A), V) + self.mean_function(Xnew)
if full_cov:
fvar = self.kern.K(Xnew) - tf.matmul(tf.transpose(A), A)
shape = tf.pack([1, 1, tf.shape(self.Y)[1]])
fvar = tf.tile(tf.expand_dims(fvar, 2), shape)
else:
fvar = self.kern.Kdiag(Xnew) - tf.reduce_sum(tf.square(A), 0)
fvar = tf.tile(tf.reshape(fvar, (-1, 1)), [1, tf.shape(self.Y)[1]])
return fmean, fvar
```

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