# SPDX-License-Identifier: MPL-2.0
# Copyright (C) 2020- The University of Tokyo
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
import numpy as np
import scipy
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class prior:
"""prior of gaussian process"""
def __init__(self, mean, cov):
"""
Parameters
----------
mean: numpy.ndarray
mean values of prior
cov: numpy.ndarray
covariance matrix of priors
"""
self.mean = mean
self.cov = cov
self.num_params = self.cov.num_params + self.mean.num_params
self.params = self.cat_params(self.mean.params, self.cov.params)
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def cat_params(self, mean_params, cov_params):
"""
Parameters
----------
mean_params: numpy.ndarray
Mean values of parameters
cov_params: numpy.ndarray
Covariance matrix of parameters
Returns
-------
numpy.ndarray
"""
return np.append(mean_params, cov_params)
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def decomp_params(self, params):
"""
decomposing the parameters to those of mean values and covariance matrix for priors
Parameters
----------
params: numpy.ndarray
parameters
Returns
-------
mean_params: numpy.ndarray
cov_params: numpy.ndarray
"""
if params is None:
params = np.copy(self.params)
mean_params = params[0 : self.mean.num_params]
cov_params = params[self.mean.num_params :]
return mean_params, cov_params
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def get_mean(self, num_data, params=None):
"""
Calculating the mean value of priors
Parameters
----------
num_data: int
Total number of data
params: numpy.ndarray
Parameters
Returns
-------
float
"""
if params is None:
params = np.copy(self.params)
return self.mean.get_mean(num_data, params[0 : self.mean.num_params])
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def get_cov(self, X, Z=None, params=None, diag=False):
"""
Calculating the variance-covariance matrix of priors
Parameters
----------
X: numpy.ndarray
N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
Z: numpy.ndarray
N x d dimensional matrix. Each row of Z denotes the d-dimensional feature vector of tests.
params: numpy.ndarray
Parameters.
diag: bool
If X is the diagonalization matrix, true.
Returns
-------
numpy.ndarray
"""
if params is None:
params = np.copy(self.params)
return self.cov.get_cov(X, Z, params=params[self.mean.num_params :], diag=diag)
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def get_grad_mean(self, num_data, params=None):
"""
Calculating the gradiant of mean values of priors
Parameters
----------
num_data: int
Total number of data
params: numpy.ndarray
Parameters
Returns
-------
numpy.ndarray
"""
if params is None:
params = np.copy(self.params)
mean_params, cov_params = self.decomp_params(params)
return self.mean.get_grad(num_data, params=mean_params)
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def get_grad_cov(self, X, params=None):
"""
Calculating the covariance matrix priors
Parameters
----------
X: numpy.ndarray
N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
params: numpy.ndarray
Parameters.
Returns
-------
numpy.ndarray
"""
if params is None:
params = np.copy(self.params)
mean_params, cov_params = self.decomp_params(params)
return self.cov.get_grad(X, params=cov_params)
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def set_params(self, params):
"""
Setting parameters
Parameters
----------
params: numpy.ndarray
Parameters.
"""
mean_params, cov_params = self.decomp_params(params)
self.set_mean_params(mean_params)
self.set_cov_params(cov_params)
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def set_mean_params(self, params):
"""
Setting parameters for mean values of priors
Parameters
----------
params: numpy.ndarray
Parameters
"""
if self.mean.num_params != 0:
self.params[0 : self.mean.num_params] = params
self.mean.set_params(params)
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def set_cov_params(self, params):
"""
Setting parameters for covariance matrix of priors
Parameters
----------
params: numpy.ndarray
Parameters
"""
self.params[self.mean.num_params :] = params
self.cov.set_params(params)
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def sampling(self, X, N=1):
"""
Sampling from GP prior
Parameters
----------
X: numpy.ndarray
N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
N: int
Returns
-------
float
"""
num_data = X.shape[0]
G = self.get_cov(X) + 1e-8 * np.identity(num_data)
U = scipy.linalg.cholesky(G, check_finite=False)
Z = np.random.randn(N, num_data)
return np.dot(Z, U) + self.get_mean(num_data)