physbo.gp.lik.gauss のソースコード

# 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


[ドキュメント] class gauss: """Gaussian likelihood function""" def __init__(self, std=1, max_params=1e6, min_params=1e-6): """ Parameters ---------- std: numpy.ndarray or float standard deviation. max_params: float The maximum value of the parameter. If the parameter is greater than this value, it will be replaced by this value. min_params: float The minimum value of the parameter. If the parameter is less than this value, it will be replaced by this value. """ self.min_params = np.log(min_params) self.max_params = np.log(max_params) self.num_params = 1 self.std = std self.params = np.log(std) self.set_params(self.params)
[ドキュメント] def supp_params(self, params=None): """ Set maximum (minimum) values for parameters when the parameter is greater(less) than this value. Parameters ---------- params: numpy.ndarray Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables. Returns ------- """ if params is None: params = np.copy(params) if params > self.max_params: params = self.max_params if params < self.min_params: params = self.min_params return params
[ドキュメント] def trans_params(self, params=None): """ Get exp[params]. Parameters ---------- params: numpy.ndarray Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables. Returns ------- std: numpy.ndarray """ if params is None: params = np.copy(self.params) std = np.exp(params) return std
[ドキュメント] def get_params_bound(self): """ Get boundary array. Returns ------- bound: list A num_params-dimensional array with the tuple (min_params, max_params). """ bound = [(self.min_params, self.max_params) for i in range(0, self.num_params)] return bound
[ドキュメント] def get_cov(self, num_data, params=None): """ Get a covariance matrix Parameters ---------- num_data: int params: numpy.ndarray Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables. Returns ------- numpy.ndarray Diagonal element matrix of exp(2.0*params) """ std = self.trans_params(params) var = std**2 return var * np.identity(num_data)
[ドキュメント] def get_grad(self, num_data, params=None): """ Get a gradient matrix Parameters ---------- num_data: int params: numpy.ndarray Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables. Returns ------- numpy.ndarray Diagonal element matrix of 2.0 * exp(2.0*params) """ std = self.trans_params(params) var = std**2 return var * np.identity(num_data) * 2
[ドキュメント] def set_params(self, params): """ Set parameters. Parameters ---------- params: numpy.ndarray Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables. Returns ------- """ self.params = self.supp_params(params) self.std = self.trans_params(params)
[ドキュメント] def get_cand_params(self, t): """ Getting candidate parameters. Parameters ---------- t: numpy.ndarray N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized). Returns ------- numpy.ndarray log[ standard deviation of t] - log 10.0 """ return np.log(np.std(t) / 10)
# [TODO] Check: This function seems not to be used.
[ドキュメント] def sampling(self, fmean): """ Sampling by adding noise Parameters ---------- fmean: numpy.ndarray Returns ------- """ num_data = fmean.shape[0] eps = self.std * np.random.randn(num_data) return fmean + eps