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Classifier_And_PCA.m
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% Mallab version: R2017b
% Soumen Ghosh
% IIIT Sri City, Chittoor, AP, India
% https://sites.google.com/site/soumenca/
clc;
clear all;
close all;
data = csvread('/home/soumen/Desktop/TML_project/FERA-2013/originalFaceFeature_FERA-13.csv');
for x = 50:50:300
fprintf(".......Output.......\n");
reducedData = funPCA(data, x);
funSVM(reducedData); % SVM Call
funKNN(reducedData); % KNN Call
funPNN(reducedData); % PNN Call
[y, mse] = lmsFun(reducedData); % LMS Call
[y1, mse1] = klmsFun(reducedData); % KLMS Call
[y2, mse2] = mccFun(reducedData); % MCC Call
[y3, mse3] = kmcFun(reducedData); % KMC Call
end
%% Classification .............Least Mean Squares (LMS)......
function [yd, mse] = lmsFun(reducedData)
[fr, noFeature] = size(reducedData);
[trainIndex,valIndex,testIndex] = dividerand(fr, .75, 0, .25);
trainData = reducedData(trainIndex, 1:noFeature-1);
testData = reducedData(testIndex, 1:noFeature-1);
trainClass = reducedData(trainIndex, noFeature);
testClass = reducedData(testIndex, noFeature);
%Training..............................
d = trainClass';
u = trainData';
[M, N] = size(u);
mu = 0.0085;
w = zeros(1,M);
for i = 1:N
e(i) = d(i) - w * u(:,i);
w = w + mu * e(i) * (u(:,i)');
mse(i) = abs(e(i))^2;
end
yd=(w * u);
%Testing................................
d = testClass';
u = testData';
[M, N] = size(u);
for i = 1:N
e(i) = d(i) - w * u(:,i);
w = w + mu * e(i) * (u(:,i)');
mse(i) = abs(e(i))^2;
end
yd_test=(w * u);
%Accuracy computation...................
rangeY = min(yd_test):range(yd_test)/6:max(yd_test);
yd_D = discretize(yd_test, rangeY);
results = d == yd_D;
true = sum(results == 1);
accuracy = (true / length(d)) * 100;
fprintf("The Accuracy of prediction using LMS is %0.4f\n", accuracy);
cnfmat = confusionmat(d, yd_D)
end
%% Classification .............Maximum Correntropy Criterion (MCC)......
function [yd, mse] = mccFun(reducedData)
[fr, noFeature] = size(reducedData);
[trainIndex,valIndex,testIndex] = dividerand(fr, .75, 0, .25);
trainData = reducedData(trainIndex, 1:noFeature-1);
testData = reducedData(testIndex, 1:noFeature-1);
trainClass = reducedData(trainIndex, noFeature);
testClass = reducedData(testIndex, noFeature);
%Training..............................
d = trainClass';
u = trainData';
[M, N] = size(u);
mu = 0.001;
sigma = 0.5;
w = zeros(1,M);
for i = 1:N
e(i) = d(i) - w * u(:,i);
w = w + mu * exp(- sigma * sum(e(i)).^2) * e(i) * (u(:,i)');
mse(i) = abs(e(i))^2;
end
yd=(w * u);
%Testing................................
d = testClass';
u = testData';
[M, N] = size(u);
for i = 1:N
e(i) = d(i) - w * u(:,i);
w = w + mu * exp(- sigma * (e(i)).^2) * e(i) * (u(:,i)');
mse(i) = abs(e(i))^2;
end
yd_test=(w * u);
%Accuracy computation...................
rangeY = min(yd_test):range(yd_test)/6:max(yd_test);
yd_D = discretize(yd_test, rangeY);
results = d == yd_D;
true = sum(results == 1);
accuracy = (true / length(d)) * 100;
fprintf("The Accuracy of prediction using MCC is %0.4f\n", accuracy);
cnfmat = confusionmat(d, yd_D)
end
%% Classification .............KLMS......
function [yd_test, mse] = klmsFun(reducedData)
[fr, noFeature] = size(reducedData);
[trainIndex,valIndex,testIndex] = dividerand(fr, .75, 0, .25);
trainData = reducedData(trainIndex, 1:noFeature-1);
testData = reducedData(testIndex, 1:noFeature-1);
trainClass = reducedData(trainIndex, noFeature);
testClass = reducedData(testIndex, noFeature);
%Training..............................
d = trainClass';
u = trainData';
[M, N] = size(u);
mu = 0.1;
sigma = 0.004;
e = d;
y(1) = mu * e(1) * exp(- sigma * e(1) ^2); %exp((-e(1)^2) / (2*sigma^2));
mse(1) = 0;
for n = 2:N
i = 1:(n-1);
y(n) = mu * e(i)*(exp(- sigma * sum((u(:,i)-u(:,n)).^2)))';
e(n) = d(n) - y(n);
mse(n) = abs(e(n))^2;
end
%Testing................................
d = testClass';
u = testData';
[M, N] = size(u);
y1(1) = mu * e(1) * exp(- sigma * e(1) ^2); %exp((-e(1)^2) / (2*sigma^2));
for n = 2:N
i = 1:(n-1);
y1(n) = mu * e(i)*(exp(- sigma * sum((u(:,i)-u(:,n)).^2)))';
e(n) = d(n) - y1(n);
mse(n) = abs(e(n))^2;
end
yd_test = y1;
%Accuracy computation...................
rangeY = min(yd_test):range(yd_test)/6:max(yd_test);
yd_D = discretize(yd_test, rangeY);
results = d == yd_D;
true = sum(results == 1);
accuracy = (true / length(d)) * 100;
fprintf("The Accuracy of prediction using KLMS is %0.4f\n", accuracy);
cnfmat = confusionmat(d, yd_D)
end
%% Classification .............KMC......
function [yd_test, mse] = kmcFun(reducedData)
[fr, noFeature] = size(reducedData);
[trainIndex,valIndex,testIndex] = dividerand(fr, .75, 0, .25);
trainData = reducedData(trainIndex, 1:noFeature-1);
testData = reducedData(testIndex, 1:noFeature-1);
trainClass = reducedData(trainIndex, noFeature);
testClass = reducedData(testIndex, noFeature);
%Training..............................
d = trainClass';
u = trainData';
[M, N] = size(u);
mu = 0.5;
sigma = 0.0004;
e = d;
y(1) = mu * e(1) * exp((-e(1)^2) / (2*sigma^2));
mse(1) = 0;
for n = 2:N
ii = 1:(n-1);
y(n) = mu * e(ii).* exp(- sigma * (e(ii)).^2) * (exp(- sigma * sum((u(:,ii)-u(:,n)).^2)))';
e(n) = d(n) - y(n);
mse(n) = abs(e(n))^2;
end
clear d;
clear u;
%Testing................................
d = testClass';
u = testData';
[M, N] = size(u);
y1(1) = mu * e(1) * exp((-e(1)^2) / (2*sigma^2));
for n = 2:N
ii = 1:(n-1);
y1(n) = mu * e(ii).* exp(- sigma * (e(ii)).^2) * (exp(- sigma * sum((u(:,ii)-u(:,n)).^2)))';
e(n) = d(n) - y1(n);
mse(n) = abs(e(n))^2;
end
yd_test = y1;
%Accuracy computation...................
rangeY = min(yd_test):range(yd_test)/6:max(yd_test);
yd_D = discretize(yd_test, rangeY);
results = d == yd_D;
true = sum(results == 1);
accuracy = (true / length(d)) * 100;
fprintf("The Accuracy of prediction using KMC is %0.4f\n", accuracy);
cnfmat = confusionmat(d, yd_D)
end
%% Classification ................SVM.........
function [] = funSVM(reducedData)
[fr, noFeature] = size(reducedData);
[trainIndex,valIndex,testIndex] = dividerand(fr, .75, 0, .25);
trainData = reducedData(trainIndex, 1:noFeature-1);
testData = reducedData(testIndex, 1:noFeature-1);
valData = reducedData(valIndex, 1:noFeature-1);
trainClass = reducedData(trainIndex, noFeature);
testClass = reducedData(testIndex, noFeature);
valClass = reducedData(valIndex, noFeature);
%t = templateSVM('Standardize',1,'KernelFunction','gaussian');
%mdl = fitcecoc(trainData, trainClass, 'Learners', t);
mdl = fitcecoc(trainData, trainClass);
predictClass = predict(mdl, testData);
results = testClass == predictClass;
true = sum(results == 1);
accuracy = (true / length(testClass)) * 100;
fprintf("The Accuracy of prediction using SVM is %0.4f\n", accuracy);
cnfmat = confusionmat(testClass, predictClass)
end
%% Classification .............KNN.......
function [] = funKNN(reducedData)
[fr, noFeature] = size(reducedData);
[trainIndex,valIndex,testIndex] = dividerand(fr, .75, 0, .25);
trainData = reducedData(trainIndex, 1:noFeature-1);
testData = reducedData(testIndex, 1:noFeature-1);
valData = reducedData(valIndex, 1:noFeature-1);
trainClass = reducedData(trainIndex, noFeature);
testClass = reducedData(testIndex, noFeature);
valClass = reducedData(valIndex, noFeature);
Mdl = fitcknn(trainData, trainClass,'NumNeighbors',7);
predictClass = predict(Mdl, testData);
results = testClass == predictClass;
true = sum(results == 1);
accuracy = (true / length(testClass)) * 100;
fprintf("The Accuracy of prediction using KNN is %0.4f\n", accuracy);
cnfmat = confusionmat(testClass, predictClass)
end
%% Classification .............PNN.......
function [] = funPNN(reducedData)
[fr, noFeature] = size(reducedData);
[trainIndex,valIndex,testIndex] = dividerand(fr, .75, 0, .25);
trainData = reducedData(trainIndex, 1:noFeature-1);
testData = reducedData(testIndex, 1:noFeature-1);
valData = reducedData(valIndex, 1:noFeature-1);
trainClass = reducedData(trainIndex, noFeature);
testClass = reducedData(testIndex, noFeature);
valClass = reducedData(valIndex, noFeature);
T = ind2vec(trainClass');
net = newpnn(trainData',T);
Y = net(testData');
predictClass = vec2ind(Y);
testClass = testClass';
results = testClass == predictClass;
true = sum(results == 1);
accuracy = (true / length(testClass)) * 100;
fprintf("The Accuracy of prediction using PNN is %0.4f\n", accuracy);
cnfmat = confusionmat(testClass, predictClass)
end
%% PCA Implementation .............
function reducedData = funPCA(feature, noFeature)
[~, fc] = size(feature);
feature1 = feature(:,1:(fc-1));
%noFeature = 80;
coeff= pca(feature1);
reducedDimension = coeff(:, 1:noFeature);
reducedData = feature1 * reducedDimension;
reducedData = [reducedData, feature(:,fc)];
fprintf("The feature of the original data is: %d\n", fc-1);
fprintf("The feature of the reduced data is: %d\n", noFeature);
end