https://github.com/GPflow/GPflow
Tip revision: 986aced42824b12ead047c6d8b93982ffe89e7db authored by ST John on 16 March 2020, 19:11:58 UTC
rescue code from #754
rescue code from #754
Tip revision: 986aced
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 typing import Optional, Tuple
import tensorflow as tf
import gpflow
from .model import GPModel
from ..kernels import Kernel
from ..logdensities import multivariate_normal
from ..mean_functions import MeanFunction
Data = Tuple[tf.Tensor, tf.Tensor]
class GPR(GPModel):
r"""
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 of this model is sometimes referred to as the 'log
marginal likelihood', and is given by
.. math::
\log p(\mathbf y \,|\, \mathbf f) =
\mathcal N(\mathbf{y} \,|\, 0, \mathbf{K} + \sigma_n \mathbf{I})
"""
def __init__(
self,
data: Data,
kernel: Kernel,
mean_function: Optional[MeanFunction] = None,
noise_variance: float = 1.0,
):
likelihood = gpflow.likelihoods.Gaussian(noise_variance)
_, Y_data = data
super().__init__(kernel, likelihood, mean_function, num_latent_gps=Y_data.shape[-1])
self.data = data
def log_likelihood(self):
r"""
Computes the log marginal likelihood.
.. math::
\log p(Y | \theta).
"""
X, Y = self.data
K = self.kernel(X)
num_data = X.shape[0]
k_diag = tf.linalg.diag_part(K)
s_diag = tf.fill([num_data], self.likelihood.variance)
ks = tf.linalg.set_diag(K, k_diag + s_diag)
L = tf.linalg.cholesky(ks)
m = self.mean_function(X)
# [R,] log-likelihoods for each independent dimension of Y
log_prob = multivariate_normal(Y, m, L)
return tf.reduce_sum(log_prob)
def predict_f(self, Xnew: tf.Tensor, full_cov: bool = False, full_output_cov: bool = False):
r"""
This method computes predictions at X \in R^{N \x D} input points
.. math::
p(F* | Y)
where F* are points on the GP at new data points, Y are noisy observations at training data points.
"""
X_data, Y_data = self.data
err = Y_data - self.mean_function(X_data)
kmm = self.kernel(X_data)
knn = self.kernel(Xnew, full_cov=full_cov)
kmn = self.kernel(X_data, Xnew)
num_data = X_data.shape[0]
s = tf.linalg.diag(tf.fill([num_data], self.likelihood.variance))
conditional = gpflow.conditionals.base_conditional
f_mean_zero, f_var = conditional(
kmn, kmm + s, knn, err, full_cov=full_cov, white=False
) # [N, P], [N, P] or [P, N, N]
f_mean = f_mean_zero + self.mean_function(Xnew)
return f_mean, f_var