load fisheriris;
[m,n]=size(meas);
data=zeros(m,n+1);
data(:,1:n)=meas;
for i=1:m                   　　　　　　　　　 %将字符串类别标签用数值形式表示
    if strcmp(species{i},\'setosa\')　　　　　　%strcmp（\'A\',\'B\'）用于比较字符串，找出特定的字符串；类比find（a==b）用来找出特定数值
        data(i,n+1)=1;
    elseif strcmp(species{i},\'versicolor\')
        data(i,n+1)=2;
    elseif strcmp(species{i},\'virginica\')
        data(i,n+1)=3;
    end
end

%选择训练样本个数
num_train = 60;　　　　　　　　　　　　　　　　　%共150个样本，60个训练集，90个测试集
%构造随机选择序列
choose = randperm(length(data));           %随机种子打乱数据样本的顺序
train_data = data(choose(1:num_train),:);  %随机选取60个样本
label_temp = train_data(:,end);            %提取训练数据的标签  train(:,end)提取最后一列；
label_train = zeros(length(train_data),3); %创建矩阵以储存向量形式的标签；
%把输出分类标签1，2，3 改为工具箱要求的格式 1=[1 0 0],2=[0 1 0],3=[0 0 1]
for i = 1:length(train_data)
    label_train(i,label_temp(i)) = 1; 
end
train_data = train_data(:,1:end-1)\';       %提取数据集特征（剔除标签），并进行转置（转置也可以不必，后续GUI中转化为行形式即可）
label_train = label_train\';                %将向量形式表示的标签进行转置（也而不必，理由同上）

test_data = data(choose(num_train+1:end),:);  %提取测试集数据
label_temp = test_data(:,end);                %提取测试集数据的标签
label_test = zeros(length(test_data),3);      %创建矩阵，准备存放向量形式的测试数据的标签
%把输出分类标签改为工具箱要求的格式
for i = 1:length(test_data)
    label_test(i,label_temp(i)) = 1; 
end
test_data = test_data(:,1:end-1)\';            %提取测试数据的特征，并进行转置
label_test = label_test\';                     %提取测试数据的标签，并进行转置

%有三种方式
%法1.命令窗口输入nnstart,选择pattern recognition app，用matlab自带GUI进行网络设置（最简单）
%法2.完成法1后，自动生成代码，将创建网络的代码用m文件保存，下次要调用该网络可直接调用该m文件
%法3.编写如下代码
% Create a Pattern Recognition Network
hiddenLayerSize = 10;                         %隐藏层神经元个数设置为10
net = patternnet(hiddenLayerSize);            %创建模式识别神经网络patternnet
% 将训练集再按比例70:15:15分为训练集、验证集、测试集
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Train the Network
[net,tr] = train(net,train_data,label_train); %tr为训练过程参数？
% Test the Network
predict = net(test_data);       %得到每个样本属于第一类、第二类、第三类的概率
[~,predict] = max(predict);     %选择概率最大的类别作为某一个测试样本的类别
%% show the result --testings
fig=figure;
gscatter(test_data(1,:),test_data(2,:),predict);
[~,label_test] = max(label_test);
accuracy = length(find(predict==label_test))/length(test_data);
title([\'predict the testing data and the accuracy is :\',num2str(accuracy)]);

% Solve a Pattern Recognition Problem with a Neural Network
% Script generated by Neural Pattern Recognition app
% Created 21-May-2020 20:32:42
%
% This script assumes these variables are defined:
%
%   train_data - input data.
%   label_train - target data.

x = train_data;
t = label_train;

% Choose a Training Function
% For a list of all training functions type: help nntrain
% \'trainlm\' is usually fastest.
% \'trainbr\' takes longer but may be better for challenging problems.
% \'trainscg\' uses less memory. Suitable in low memory situations.
trainFcn = \'trainscg\';  % Scaled conjugate gradient backpropagation.

% Create a Pattern Recognition Network
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize);

% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% Train the Network
[net,tr] = train(net,x,t);

% Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
tind = vec2ind(t);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);

% View the Network
view(net)

% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotconfusion(t,y)
%figure, plotroc(t,y)