https://github.com/GPflow/GPflow
Tip revision: af90c6e97f09f0b9a77d2fcc796f8a031ad097e8 authored by alexggmatthews on 06 June 2016, 17:06:36 UTC
Building up cone.
Building up cone.
Tip revision: af90c6e
gpmc.py
import numpy as np
import tensorflow as tf
from .model import GPModel
from .param import Param
from .conditionals import conditional
from .priors import Gaussian
from .mean_functions import Zero
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
"""
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 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)
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,
num_columns=self.num_latent, full_cov=full_cov,
q_sqrt=None, whiten=True)
return mu + self.mean_function(Xnew), var