close all
clear all
clc
load x.txt;
load y.txt;
inputs = x';
targets = y;
% 创建一个模式识别网络（两层BP网络），同时给出中间层神经元的个数，这里使用20
hiddenLayerSize = 20;
net = patternnet(hiddenLayerSize);
% 对数据进行预处理，这里使用了归一化函数（一般不用修改）
% For a list of all processing functions type: help nnprocess
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
% 把训练数据分成三部分，训练网络、验证网络、测试网络
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand'; % Divide data randomly
net.divideMode = 'sample'; % Divide up every sample
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% 训练函数
% For a list of all training functions type: help nntrain
net.trainFcn = 'trainlm'; % Levenberg-Marquardt
% 使用均方误差来评估网络
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse'; % Mean squared error
% 画图函数
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit'};
% 开始训练网络（包含了训练和验证的过程）
[net,tr] = train(net,inputs,targets);
% 测试网络
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
% 获得训练、验证和测试的结果
trainTargets = targets .* tr.trainMask{1};
valTargets = targets .* tr.valMask{1};
testTargets = targets .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,outputs)
valPerformance = perform(net,valTargets,outputs)
testPerformance = perform(net,testTargets,outputs)
% 可以查看网络的各个参数
view(net)
% 根据画图的结果，决定是否满意
% Uncomment these lines to enable various plots.
figure, plotperform(tr)
figure, plottrainstate(tr)
figure, plotconfusion(targets,outputs)
figure, ploterrhist(errors)
% Test the Network
load z.txt;
testinputs= z';
testoutputs = net(testinputs);