demo_libsvm_kernel.m

% This code just simply run the SVM on the example data set "heart_scale",

% which is scaled properly. The code divides the data into 2 parts

% train: 1 to 200

% test: 201:270

% This code also shows how to specify the kernel type in the libsvm

% Then plot the results vs their true class. In order to visualize the high

% dimensional data, we apply MDS to the 13D data and reduce the dimension

% to 2D

clear

clc

close all

% addpath to the libsvm toolbox

addpath('../libsvm-3.12/matlab');

% addpath to the data

dirData = '../libsvm-3.12';

addpath(dirData);

% read the data set

[heart_scale_label, heart_scale_inst] = libsvmread(fullfile(dirData,'heart_scale'));

[N D] = size(heart_scale_inst);

% Determine the train and test index

trainIndex = zeros(N,1); trainIndex(1:200) = 1;

testIndex = zeros(N,1); testIndex(201:N) = 1;

trainData = heart_scale_inst(trainIndex==1,:);

trainLabel = heart_scale_label(trainIndex==1,:);

testData = heart_scale_inst(testIndex==1,:);

testLabel = heart_scale_label(testIndex==1,:);

% Train the SVM

model = svmtrain(trainLabel, [(1:200)' trainData*trainData'], '-c 1 -g 0.07 -b 1 -t 4');

% Use the SVM model to classify the data

[predict_label, accuracy, prob_values] = svmpredict(testLabel, [(1:70)' testData*trainData'], model, '-b 1'); % test the training data

% ================================

% ===== Showing the results ======

% ================================

% Assign color for each class

colorList = generateColorList(2); % This is my own way to assign the color...don't worry about it

% colorList = prism(100);

% true (ground truth) class

trueClassIndex = zeros(N,1);

trueClassIndex(heart_scale_label==1) = 1;

trueClassIndex(heart_scale_label==-1) = 2;

colorTrueClass = colorList(trueClassIndex,:);

% result Class

resultClassIndex = zeros(length(predict_label),1);

resultClassIndex(predict_label==1) = 1;

resultClassIndex(predict_label==-1) = 2;

colorResultClass = colorList(resultClassIndex,:);

% Reduce the dimension from 13D to 2D

distanceMatrix = pdist(heart_scale_inst,'euclidean');

newCoor = mdscale(distanceMatrix,2);

% Plot the whole data set

x = newCoor(:,1);

y = newCoor(:,2);

patchSize = 30; %max(prob_values,[],2);

colorTrueClassPlot = colorTrueClass;

figure; scatter(x,y,patchSize,colorTrueClassPlot,'filled');

title('whole data set');

% Plot the test data

x = newCoor(testIndex==1,1);

y = newCoor(testIndex==1,2);

patchSize = 80*max(prob_values,[],2);

colorTrueClassPlot = colorTrueClass(testIndex==1,:);

figure; hold on;

scatter(x,y,2*patchSize,colorTrueClassPlot,'o','filled');

scatter(x,y,patchSize,colorResultClass,'o','filled');

% Plot the training set

x = newCoor(trainIndex==1,1);

y = newCoor(trainIndex==1,2);

patchSize = 30;

colorTrueClassPlot = colorTrueClass(trainIndex==1,:);

scatter(x,y,patchSize,colorTrueClassPlot,'o');

title('classification results');