Revision d088d9138cc2b4320d71b6372d60eeb2b91c9b01 authored by James Hensman on 08 September 2016, 16:21:12 UTC, committed by James Hensman on 08 September 2016, 16:21:12 UTC
1 parent 8a1d6f0
gpmc.py
# Copyright 2016 James Hensman, alexggmatthews
#
# 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.
import numpy as np
import tensorflow as tf
from .model import GPModel
from .param import Param, DataHolder
from .conditionals import conditional
from .priors import Gaussian
from .mean_functions import Zero
from .tf_hacks import eye
class GPMC(GPModel):
def __init__(self, X, Y, kern, likelihood,
mean_function=Zero(), num_latent=None):
"""
X is a data matrix, size N x D
Y is a data matrix, size N x R
kern, likelihood, mean_function are appropriate GPflow objects
This is a vanilla implementation of a GP with a non-Gaussian
likelihood. The latent function values are represented by centered
(whitened) variables, so
v ~ N(0, I)
f = Lv + m(x)
with
L L^T = K
"""
X = DataHolder(X, on_shape_change='recompile')
Y = DataHolder(Y, on_shape_change='recompile')
GPModel.__init__(self, X, Y, kern, likelihood, mean_function)
self.num_data = X.shape[0]
self.num_latent = num_latent or Y.shape[1]
self.V = Param(np.zeros((self.num_data, self.num_latent)))
self.V.prior = Gaussian(0., 1.)
def _compile(self):
"""
Before calling the standard compile function, check to see if the size
of the data has changed and add parameters appropriately.
This is necessary because the shape of the parameters depends on the
shape of the data.
"""
if not self.num_data == self.X.shape[0]:
self.num_data = self.X.shape[0]
self.V = Param(np.zeros((self.num_data, self.num_latent)))
self.V.prior = Gaussian(0., 1.)
super(GPMC, self)._compile()
def build_likelihood(self):
"""
Construct a tf function to compute the likelihood of a general GP
model.
\log p(Y, V | theta).
"""
K = self.kern.K(self.X)
L = tf.cholesky(K) + eye(tf.shape(self.X)[0])*1e-6
F = tf.matmul(L, self.V) + self.mean_function(self.X)
return tf.reduce_sum(self.likelihood.logp(F, self.Y))
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* | (F=LV) )
where F* are points on the GP at Xnew, F=LV are points on the GP at X.
"""
mu, var = conditional(Xnew, self.X, self.kern, self.V,
full_cov=full_cov,
q_sqrt=None, whiten=True)
return mu + self.mean_function(Xnew), var
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