一、简介
基于matlab贝叶斯网络改进LSTM预测
二、源代码
%%%%%%%%%% Gaussian Process Regression (GPR) %%%%%%%%%
% Demo: prediction using GPR
% ---------------------------------------------------------------------%
clc
close all
clear all
addpath(genpath(pwd))
% load data
%{
x : training inputs
y : training targets
xt: testing inputs
yt: testing targets
%}
% multiple input-single output
load('./data/data_1.mat')
% Set the mean function, covariance function and likelihood function
% Take meanConst, covRQiso and likGauss as examples
% Initialization of hyperparameters
hyp = struct('mean', 3, 'cov', [0 0 0], 'lik', -1);
% meanfunc = [];
% covfunc = @covSEiso;
% likfunc = @likGauss;
% % Initialization of hyperparameters
% hyp = struct('mean', [], 'cov', [0 0], 'lik', -1);
% Optimization of hyperparameters
hyp2 = minimize(hyp, @gp, -20, @infGaussLik, meanfunc, covfunc, likfunc,x, y);
% Regression using GPR
% yfit is the predicted mean, and ys is the predicted variance
% Visualization of prediction results
plotResult(yt, yfit)
% load data
%{
x : training inputs
y : training targets
xt: testing inputs
yt: testing targets
%}
% multiple input-multiple output
load('./data/data_2.mat')
% Set the mean function, covariance function and likelihood function
% Take meanConst, covRQiso and likGauss as examples
meanfunc = @meanConst;
covfunc = @covRQiso;
likfunc = @likGauss;
% Initialization of hyperparameters
hyp = struct('mean', 3, 'cov', [2 2 2], 'lik', -1);
% meanfunc = [];
% covfunc = @covSEiso;
% likfunc = @likGauss;
%
% hyp = struct('mean', [], 'cov', [0 0], 'lik', -1);
% Optimization of hyperparameters
% Regression using GPR
% yfit is the predicted mean, and ys is the predicted variance
三、运行结果
四、备注
版本:2014a