physbo.blm.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 using Baysean linear model Attributes ========== blm: physbo.blm.core.model config: physbo.misc.set_config configuration """ def __init__(self, config, model=None): """ Parameters ========== config: physbo.misc.set_config configuration model: physbo.gp.core.model See also ======== physbo.base_predictor """ super(predictor, self).__init__(config, model) self.blm = None
[ドキュメント] def fit(self, training, num_basis=None): """ fit 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 num_basis is None: num_basis = 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.blm = self.model.export_blm(num_basis) self.delete_stats()
[ドキュメント] def prepare(self, training): """ initializes model by using training data set Parameters ========== training: physbo.variable dataset for training """ self.blm.prepare(training.X, training.t, training.Z)
[ドキュメント] def delete_stats(self): """ resets model """ self.blm.stats = None
[ドキュメント] def get_basis(self, X): """ calculates feature maps Psi(X) Parameters ========== X: numpy.ndarray inputs Returns ======= Psi: numpy.ndarray feature maps """ return self.blm.lik.get_basis(X)
[ドキュメント] def get_post_fmean(self, training, test): """ calculates 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.blm.stats is None: self.prepare(training) return self.blm.get_post_fmean(test.X, test.Z)
[ドキュメント] def get_post_fcov(self, training, test): """ calculates 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 Returns ======= numpy.ndarray """ if self.blm.stats is None: self.prepare(training) return self.blm.get_post_fcov(test.X, test.Z)
[ドキュメント] def get_post_params(self, training, test): """ calculates posterior weights Parameters ========== training: physbo.variable training dataset. If already trained, the model does not use this. test: physbo.variable inputs (not used) Returns ======= numpy.ndarray """ if self.blm.stats is None: self.prepare(training) return self.blm.get_post_params_mean()
[ドキュメント] def get_post_samples(self, training, test, N=1, alpha=1.0): """ draws 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 N: int number of samples (default: 1) alpha: float noise for sampling source (default: 1.0) Returns ======= numpy.ndarray """ if self.blm.stats is None: self.prepare(training) return self.blm.post_sampling(test.X, Psi=test.Z, N=N, alpha=alpha)
[ドキュメント] def get_predict_samples(self, training, test, N=1): """ draws 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) alpha: float noise for sampling source (default: 1.0) Returns ======= numpy.ndarray (N x len(test)) """ if self.blm.stats is None: self.prepare(training) return self.blm.predict_sampling(test.X, Psi=test.Z, N=N).transpose()
[ドキュメント] def update(self, training, test): """ updates the model. If not yet initialized (prepared), the model will be prepared by ``training``. Otherwise, the model will be updated by ``test``. Parameters ========== training: physbo.variable training dataset for initialization (preparation). If already prepared, the model ignore this. test: physbo.variable training data for update. If not prepared, the model ignore this. """ if self.model.stats is None: self.prepare(training) return None if hasattr(test.t, "__len__"): N = len(test.t) else: N = 1 if N == 1: if test.Z is None: if test.X.ndim == 1: self.blm.update_stats(test.X, test.t) else: self.blm.update_stats(test.X[0, :], test.t) else: if test.Z.ndim == 1: self.blm.update_stats(test.X, test.t, psi=test.Z) else: self.blm.update_stats(test.X[0, :], test.t, psi=test.Z[0, :]) else: for n in range(N): if test.Z is None: self.blm.update_stats(test.X[n, :], test.t[n]) else: self.blm.update_stats(test.X[n, :], test.t[n], psi=test.Z[n, :])