physbo.gp.predictor 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 physbo.predictor


[ドキュメント] class predictor(physbo.predictor.base_predictor): """predictor""" def __init__(self, config, model=None): """ Parameters ---------- config: physbo.misc.set_config configuration model: physbo.gp.core.model """ super(predictor, self).__init__(config, model)
[ドキュメント] def fit(self, training, num_basis=None): """ Fitting model to training dataset Parameters ---------- training: physbo.variable dataset for training num_basis: int the number of basis (default: self.config.predict.num_basis) """ if self.model.prior.cov.num_dim is None: self.model.prior.cov.num_dim = training.X.shape[1] self.model.fit(training.X, training.t, self.config) self.delete_stats()
[ドキュメント] def get_basis(self, *args, **kwds): """ Parameters ---------- args kwds Returns ------- """ pass
[ドキュメント] def get_post_params(self, *args, **kwds): """ Parameters ---------- args kwds Returns ------- """ pass
[ドキュメント] def update(self, training, test): self.prepare(training)
[ドキュメント] def prepare(self, training): """ Initializing model by using training data set Parameters ---------- training: physbo.variable dataset for training """ self.model.prepare(training.X, training.t)
[ドキュメント] def delete_stats(self): self.model.stats = None
[ドキュメント] def get_post_fmean(self, training, test): """ Calculating posterior mean value of model Parameters ---------- training: physbo.variable training dataset. If already trained, the model does not use this. test: physbo.variable inputs Returns ------- numpy.ndarray """ if self.model.stats is None: self.prepare(training) return self.model.get_post_fmean(training.X, test.X)
[ドキュメント] def get_post_fcov(self, training, test, diag=True): """ Calculating posterior variance-covariance matrix of model Parameters ---------- training: physbo.variable training dataset. If already trained, the model does not use this. test: physbo.variable inputs diag: bool Diagonlization flag in physbo.exact.get_post_fcov function. Returns ------- numpy.ndarray """ if self.model.stats is None: self.prepare(training) return self.model.get_post_fcov(training.X, test.X, diag=diag)
[ドキュメント] def get_post_samples(self, training, test, alpha=1): """ Drawing samples of mean values of model Parameters ---------- training: physbo.variable training dataset. If already trained, the model does not use this. test: physbo.variable inputs (not used) alpha: float tuning parameter of the covariance by multiplying alpha**2 for np.random.multivariate_normal. Returns ------- numpy.ndarray """ if self.model.stats is None: self.prepare(training) return self.model.post_sampling(training.X, test.X, alpha=alpha)
[ドキュメント] def get_predict_samples(self, training, test, N=1): """ Drawing samples of values of model Parameters ---------- training: physbo.variable training dataset. If already trained, the model does not use this. test: physbo.variable inputs N: int number of samples (default: 1) Returns ------- numpy.ndarray (N x len(test)) """ if self.model.stats is None: self.prepare(training) return self.model.predict_sampling(training.X, test.X, N=N)