libsvm回归预测matlab代码

Sure, here's an example of MATLAB code for regression prediction using LibSVM:

% Load the LIBSVM library
addpath('path_to_libsvm_folder');

% Load the training data
data = load('training_data.txt');
X = data(:, 1:end-1);
y = data(:, end);

% Load the test data
test_data = load('test_data.txt');
X_test = test_data(:, 1:end-1);

% Set the parameters for training
svm_model = svmtrain(y, X, '-s 3 -t 2 -c 1 -g 0.1');

% Predict the test data
[predicted_label, accuracy, decision_values] = svmpredict(zeros(size(X_test, 1), 1), X_test, svm_model);

% Display the predicted labels
disp(predicted_label);

Make sure to replace `path_to_libsvm_folder` with the actual path to the LibSVM library on your system. Also, replace `training_data.txt` and `test_data.txt` with the paths to your actual training and test data files.

In this example, we use the `-s 3` option to enable regression mode, `-t 2` for using the radial basis function (RBF) kernel, `-c 1` to set the cost parameter, and `-g 0.1` to set the gamma parameter. You can adjust these parameters according to your specific needs.

The predicted labels for the test data will be displayed at the end of the code.