demo_libsvm_ovr_multiclass.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:

% Here we do OVR SVM with cross validation

% 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 = './data';

addpath(dirData);

% read the data set

[dnaTrainLabel, dnaTrainData] = libsvmread(fullfile(dirData,'dna.scale'));

NTrain = size(dnaTrainData,1);

[dnaTrainLabel, permIndex] = sortrows(dnaTrainLabel);

dnaTrainData = dnaTrainData(permIndex,:);

[dnaTestLabel, dnaTestData] = libsvmread(fullfile(dirData,'dna.scale.t'));

NTest = size(dnaTestData,1);

[dnaTestLabel, permIndex] = sortrows(dnaTestLabel);

dnaTestData = dnaTestData(permIndex,:);

% combine the data together just to fit my format

totalData = [dnaTrainData; dnaTestData];

totalLabel = [dnaTrainLabel; dnaTestLabel];

figure;

subplot(1,2,1); imagesc(totalLabel); title('class label');

subplot(1,2,2); imagesc(totalData); title('features');

[N D] = size(totalData);

labelList = unique(totalLabel(:));

NClass = length(labelList);

% #######################

% Determine the train and test index

% #######################

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

testIndex = zeros(N,1); testIndex( (NTrain+1):N) = 1;

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

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

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

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

% #######################

% Parameter selection using 3-fold cross validation

% #######################

bestcv = 0;

for log2c = -1:1:3,

  for log2g = -4:1:2,

    cmd = ['-q -c ', num2str(2^log2c), ' -g ', num2str(2^log2g)];

    cv = get_cv_ac(trainLabel, trainData, cmd, 3);

    if (cv >= bestcv),

      bestcv = cv; bestc = 2^log2c; bestg = 2^log2g;

    end

    fprintf('%g %g %g (best c=%g, g=%g, rate=%g)\n', log2c, log2g, cv, bestc, bestg, bestcv);

  end

end

% #######################

% Train the SVM in one-vs-rest (OVR) mode

% #######################

bestParam = ['-q -c ', num2str(bestc), ' -g ', num2str(bestg)];

model = ovrtrain(trainLabel, trainData, bestParam);

% #######################

% Classify samples using OVR model

% #######################

[predict_label, accuracy, prob_values] = ovrpredict(testLabel, testData, model);

fprintf('Accuracy = %g%%\n', accuracy * 100);

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

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

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

% Assign color for each class

colorList = generateColorList(NClass); % 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);

for i = 1:NClass

    trueClassIndex(totalLabel==labelList(i)) = i;

end

colorTrueClass = colorList(trueClassIndex,:);

% result Class

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

for i = 1:NClass

    resultClassIndex(predict_label==labelList(i)) = i;

end

colorResultClass = colorList(resultClassIndex,:);

% Reduce the dimension from 13D to 2D

distanceMatrix = pdist(totalData,'euclidean');

% newCoor = mdscale(distanceMatrix,2); % take longer time, but more beautiful

newCoor = cmdscale(distanceMatrix); %

% 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 = 30;% 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');