matlab plsda demo
时间: 2023-10-20 07:19:58 浏览: 37
Here is a MATLAB implementation of Partial Least Squares Discriminant Analysis (PLS-DA) with a demo dataset:
```matlab
% Load demo dataset
load('fisheriris.mat');
X = meas;
Y = species;
% Split the data into training and testing sets
cv = cvpartition(Y,'HoldOut',0.3);
Xtrain = X(training(cv),:);
Ytrain = Y(training(cv),:);
Xtest = X(test(cv),:);
Ytest = Y(test(cv),:);
% Perform PLS-DA on the training data
[XL,YL,XS,YS,beta,pctVar,mse,stats] = plsregress(Xtrain,Ytrain,2);
% Make predictions on the testing data
Ypred = [ones(size(Xtest,1),1) Xtest] * beta;
[~,Ypred] = max(Ypred,[],2);
Ypred = categorical(Ypred);
accuracy = sum(Ypred == Ytest)/length(Ytest);
% Plot the PLS-DA scores
scatter(YS(:,1),YS(:,2),25,grp2idx(Ytrain),'filled');
title('PLS-DA Scores');
xlabel('PLS-DA Score 1');
ylabel('PLS-DA Score 2');
```
This code loads the Iris dataset, splits it into training and testing sets, performs PLS-DA with two components on the training data, and makes predictions on the testing data. It then calculates the accuracy of the predictions and plots the PLS-DA scores. Note that the `plsregress` function is used for PLS-DA instead of the regular `regress` function since it allows for categorical response variables.