LAMPIRAN A KODE PROGRAM · Universitas Kristen Maranatha A-2 %Program utama clc close all clear all...

Universitas Kristen Maranatha A-1

LAMPIRAN A

KODE PROGRAM


%Program utama

clc close all

clear all

load Yale_32x32.mat;

fea_awal=fea

indeks_masukan=input('Masukkan gambar yang mau dikenali (1 sd 165) = ');

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

[baris kolom]=size(fea);

temp=[];

for m=1:baris

if (mod(m,11)==0)

m=m+1;

else

% disp(m);

temp=[temp; fea(m,:)];

end;

if m>baris

break;

end;

end;

fea_baru=temp;

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

% Menampilkan citra dari Yale database

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

faceW = 32;

faceH = 32;

numPerLine = 10;

ShowLine = 4;

Y = reshape(fea(indeks_masukan,:),[faceH,faceW]);

imshow(uint8(Y));colormap(gray);

% title('Gambar masukan');

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

fea=fea_baru;


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

% Memilih citra untuk di training

% trainIdx = (1:size(fea,1));

trainIdx = (1:40);

% Memisahkan feature

fea_Train = fea(trainIdx,:);

gnd_Train = gnd(trainIdx);

%% Pemilihan parameter kernel untuk inisialisasi OLPP

options = [];

options.Metric = 'Cosine';

options.NeighborMode = 'Supervised';

options.WeightMode = 'Cosine';

options.gnd = gnd_Train;

%% tambahan Luhur

% options = [];

% options.Metric = 'Euclidean';

% options.NeighborMode = 'Supervised';

% options.gnd = gnd_Train;

% options.WeightMode = 'HeatKernel';

% options.t = 1;

%-------------------------------------%

% Proses konstruksi kernel

W = constructW(fea_Train,options);

%% Pemilihan parameter tensor OLPP

options.Regu = 1;

options.ReguAlpha = 0.1;

options.ReguType = 'Custom';

load('TensorR_32x32.mat');

options.regularizerR = regularizerR;

% Menampilkan operasi OLPP

disp('Mulai perhitungan dengan OLPP');

[eigvector, eigvalue] = OLPP(W, options, fea_Train);

% Mencari eigen vector dan nilai eigen OLPP

newfea = fea*eigvector;

disp('Perhitungan OLPP telah selesai');


% euclidean distance classifier

cek=fea_awal(indeks_masukan,:)*eigvector;

for i=1:size(newfea,1) % semula 10

D(i) = EuDist2(cek,newfea(i,:));

% D(i) = EuDist2(newfea(indeks_masukan,:),newfea(i,:));

end;

disp(D)

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

min_D=D(1);

indeks=1;

for m=2:length(D)

if D(m)<min_D

min_D=D(m);

indeks=m;

end;

end;

min_D,

indeks

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

figure;

imshow(uint8(reshape(fea(indeks,:),32,32)));

% title('Gambar yang paling mirip dengan masukan');

cek_masukan=ceil(indeks_masukan/11);

disp(['Gambar masukan adalah orang ke-',num2str(cek_masukan)]);

cek_keluaran=ceil(indeks/10);

disp(['Gambar keluaran adalah orang ke-',num2str(cek_keluaran)]);

if (cek_masukan == cek_keluaran)

disp(['Hasil pengenalan BENAR']);

else

disp(['Hasil pengenalan SALAH']);

end;

--------------------------------------------------------------------------------------------------------------

--------------------------------------------------------------------------------------------------------------

% Pembentukan Matriks Bobot

function W = constructW(fea,options)


if (~exist('options','var'))

options = [];

else

if ~strcmpi(class(options),'struct')

error('parameter error!');

end

end

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

if ~isfield(options,'Metric')

options.Metric = 'Euclidean';

end

switch lower(options.Metric)

case {lower('Euclidean')}

;

case {lower('Cosine')}

if ~isfield(options,'bNormalized')

options.bNormalized = 0;

end

otherwise

error('Metric does not exist!');

end

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

if ~isfield(options,'NeighborMode')

options.NeighborMode = 'KNN';

end

switch lower(options.NeighborMode)

case {lower('KNN')}

if ~isfield(options,'k')

options.k = 5;

end

if options.k < 1

options.k = 1;

end

case {lower('epsilonNeighbor')}

if ~isfield(options,'epsilon')

options.epsilon = 0.5;

end

case {lower('Supervised')}

if ~isfield(options,'bLDA')

options.bLDA = 0;

end

if options.bLDA


options.bSelfConnected = 1;

end

if ~isfield(options,'gnd')

error('Label(gnd) should be provided under ''Supervised'' NeighborMode!');

end

if length(options.gnd) ~= size(fea,1)

error('gnd doesn''t match with fea!');

end

otherwise

error('NeighborMode does not exist!');

end

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

if ~isfield(options,'WeightMode')

options.WeightMode = 'Binary';

end

switch lower(options.WeightMode)

case {lower('Binary')}

;

case {lower('HeatKernel')}

if ~strcmpi(options.Metric,'Euclidean')

warning('''HeatKernel'' WeightMode should be used under ''Euclidean'' Metric!');

options.Metric = 'Euclidean';

end

if ~isfield(options,'t')

options.t = 1;

end


if ~strcmpi(options.Metric,'Cosine')

warning('''Cosine'' WeightMode should be used under ''Cosine'' Metric!');

options.Metric = 'Cosine';

end

if ~isfield(options,'bNormalized')

options.bNormalized = 0;

end

otherwise

error('WeightMode does not exist!');

end

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

if ~isfield(options,'bSelfConnected')

options.bSelfConnected = 1;

end


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

[nSmp, nFea] = size(fea);

if strcmpi(options.NeighborMode,'Supervised') & (options.bLDA |

strcmpi(options.WeightMode,'Binary'))

;

else

bDistance = 0;

if strcmpi(options.Metric,'Euclidean')

D = zeros(nSmp);

for i=1:nSmp-1

for j=i+1:nSmp

D(i,j) = norm(fea(i,:) - fea(j,:));

end

end

D = D+D';

bDistance = 1;

else

if options.bNormalized

D = fea * fea';

else

feaNorm = sum(fea.^2,2).^.5;

fea = fea ./ repmat(max(1e-10,feaNorm),1,size(fea,2));

D = fea * fea';

end

end

end

switch lower(options.NeighborMode)

case {lower('KNN')}

if options.k >= nSmp

G = ones(nSmp,nSmp);

else

G = zeros(nSmp,nSmp);

if bDistance

[dump idx] = sort(D, 2); % sort each row

else

[dump idx] = sort(-D, 2); % sort each row

end

for i=1:nSmp

G(i,idx(i,1:options.k+1)) = 1;

end

end


case {lower('epsilonNeighbor')}

if bDistance

[i,j] = find(D < options.epsilon);

else

[i,j] = find(D > options.epsilon);

end

G = sparse(i,j,1);

case {lower('Supervised')}

G = zeros(nSmp,nSmp);

Label = unique(options.gnd);

nLabel = length(Label);

if options.bLDA

for idx=1:nLabel

classIdx = find(options.gnd==Label(idx));

G(classIdx,classIdx) = 1/length(classIdx);

end

W = sparse(G);

return;

else

for idx=1:nLabel

classIdx = find(options.gnd==Label(idx));

G(classIdx,classIdx) = 1;

end

end

if strcmpi(options.WeightMode,'Binary')

if ~options.bSelfConnected

G = G - diag(diag(G));

end

W = sparse(G);

return;

end

otherwise

error('NeighborMode does not exist!');

end

if ~options.bSelfConnected

G = G - diag(diag(G));

end

switch lower(options.WeightMode)

case {lower('Binary')}

W = max(G,G');

W = sparse(W);

case {lower('HeatKernel')}


D = exp(-D.^2/options.t);

W = D.*G;

W = max(W,W');

W = sparse(W);


W = D.*G;

W = max(W,W');

W = sparse(W);

otherwise

error('WeightMode does not exist!');

end

----------------------------------------------------------------------------------------------------------------

% OLPP

function [eigvector, eigvalue, bSuccess, elapse] = OLPP(W, options, data)

bGlobal = 0;

if ~exist('data','var')

bGlobal = 1;

global data;

end


options = [];

end

[nSmp,nFea] = size(data);

if size(W,1) ~= nSmp

error('W and data mismatch!');

end

D = full(sum(W,2));

if isfield(options,'Regu') & options.Regu

options.ReguAlpha = options.ReguAlpha*sum(D)/length(D);

end

D = sparse(1:nSmp,1:nSmp,D,nSmp,nSmp);

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

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


if isfield(options,'keepMean') & options.keepMean

;

else

if issparse(data)

data = full(data);

end

sampleMean = mean(data);

data = (data - repmat(sampleMean,nSmp,1));

end

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

if bGlobal & isfield(options,'keepMean') & options.keepMean

[eigvector, eigvalue, bSuccess, elapse] = OLGE(W, D, options);

else

[eigvector, eigvalue, bSuccess, elapse] = OLGE(W, D, options, data);

end

eigIdx = find(eigvalue < 1e-3);

eigvalue (eigIdx) = [];

eigvector(:,eigIdx) = [];

=================================================================

% OLGE

function [eigvector, eigvalue, bSuccess, elapse] = OLGE(W, D, options, data)

if ~exist('data','var')

global data;

end


options = [];

end

if isfield(options,'ReducedDim')

Dim = options.ReducedDim;

else

Dim = 30;

end

if ~isfield(options,'Regu') | ~options.Regu

bPCA = 1;

if ~isfield(options,'PCARatio')

options.PCARatio = 1;

end

else


bPCA = 0;

if ~isfield(options,'ReguType')

options.ReguType = 'Ridge';

end

if ~isfield(options,'ReguAlpha')

options.ReguAlpha = 0.1;

end

end

if ~isfield(options,'bDisp')

options.bDisp = 1;

end

bD = 1;

if ~exist('D','var') | isempty(D)

bD = 0;

end

[nSmp,nFea] = size(data);

if size(W,1) ~= nSmp

error('W and data mismatch!');

end

if bD & (size(D,1) ~= nSmp)

error('D and data mismatch!');

end

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

% SVD

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

tmp_T = cputime;

if bPCA

if nSmp > nFea

ddata = data'*data;

if issparse(ddata)

ddata = full(ddata);

end

ddata = max(ddata,ddata');

[eigvector_PCA, eigvalue_PCA] = eig(ddata);

eigvalue_PCA = diag(eigvalue_PCA);

clear ddata;

maxEigValue = max(abs(eigvalue_PCA));


eigIdx = find(eigvalue_PCA/maxEigValue < 1e-12);

eigvalue_PCA(eigIdx) = [];

eigvector_PCA(:,eigIdx) = [];

[junk, index] = sort(-eigvalue_PCA);

eigvalue_PCA = eigvalue_PCA(index);

eigvector_PCA = eigvector_PCA(:, index);

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

if options.PCARatio > 1

idx = options.PCARatio;

if idx < length(eigvalue_PCA)

eigvalue_PCA = eigvalue_PCA(1:idx);

eigvector_PCA = eigvector_PCA(:,1:idx);

end

elseif options.PCARatio < 1

sumEig = sum(eigvalue_PCA);

sumEig = sumEig*options.PCARatio;

sumNow = 0;

for idx = 1:length(eigvalue_PCA)

sumNow = sumNow + eigvalue_PCA(idx);

if sumNow >= sumEig

break;

end

end


eigvector_PCA = eigvector_PCA(:,1:idx);

end

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

data = data*eigvector_PCA;

else

ddata = data*data';

if issparse(ddata)

ddata = full(ddata);

end

ddata = max(ddata,ddata');

[eigvector, eigvalue_PCA] = eig(ddata);

eigvalue_PCA = diag(eigvalue_PCA);

clear ddata;

maxEigValue = max(eigvalue_PCA);

eigIdx = find(eigvalue_PCA/maxEigValue < 1e-12);

eigvalue_PCA(eigIdx) = [];

eigvector(:,eigIdx) = [];


[junk, index] = sort(-eigvalue_PCA);

eigvalue_PCA = eigvalue_PCA(index);

eigvector = eigvector(:, index);

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

if options.PCARatio > 1

idx = options.PCARatio;

if idx < length(eigvalue_PCA)


eigvector = eigvector(:,1:idx);

end

elseif options.PCARatio < 1

sumEig = sum(eigvalue_PCA);

sumEig = sumEig*options.PCARatio;

sumNow = 0;

for idx = 1:length(eigvalue_PCA)

sumNow = sumNow + eigvalue_PCA(idx);

if sumNow >= sumEig

break;

end

end


eigvector = eigvector(:,1:idx);

end

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

eigvalue_PCA = eigvalue_PCA.^.5;

eigvalue_PCAMinus = eigvalue_PCA.^-1;

eigvector_PCA = (data'*eigvector).*repmat(eigvalue_PCAMinus',nFea,1);

data = eigvector.*repmat(eigvalue_PCA',nSmp,1);

end

if bD

DPrime = data'*D*data;

DPrime = max(DPrime,DPrime');

else

DPrime = data'*data;


end

else

if bD

DPrime = data'*D*data;

else

DPrime = data'*data;


end

switch lower(options.ReguType)

case {lower('Ridge')}

for i=1:size(DPrime,1)

DPrime(i,i) = DPrime(i,i) + options.ReguAlpha;

end

case {lower('Tensor')}

DPrime = DPrime + options.ReguAlpha*options.regularizerR;

case {lower('Custom')}

DPrime = DPrime + options.ReguAlpha*options.regularizerR;

otherwise

error('ReguType does not exist!');

end


end

WPrime = data'*W*data;

WPrime = max(WPrime,WPrime');

elapse.timePCA = cputime - tmp_T;

tmp_T = cputime;

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

% Generalized Eigen with Orthogonal Constraint

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

dimM = size(WPrime,2);

if Dim > dimM

Dim = dimM;

end

rDPrime = chol(DPrime);

lDPrime = rDPrime';

Q0 = rDPrime\(lDPrime\WPrime); % Q0 = inv(DPrime)*WPrime;

eigvector = [];

eigvalue = [];

tmpD = [];

Q = Q0;


bSuccess = 1;

for i = 1:Dim,

try

option = struct('disp',0);

[eigVec, eigv] = eigs(Q,1,'lr',option);

catch

disp('eigs Error!');

bSuccess = 0;

elapse = 0;

return;

end

if ~isreal(eigVec)

disp('Virtual part!');

bSuccess = 0;

break;

end

if eigv < 1e-3

break;

end

eigvector = [eigvector, eigVec]; % Each col of D is a eigenvector

eigvalue = [eigvalue;eigv];

tmpD = [tmpD, rDPrime\(lDPrime\eigVec)]; % tmpD = inv(DPrime)*D;

DTran = eigvector';

tmptmpD = DTran*tmpD;

tmptmpD = max(tmptmpD,tmptmpD');

rtmptmpD = chol(tmptmpD);

tmptmpD = rtmptmpD\(rtmptmpD'\DTran); % tmptmpD = inv(D'*inv(DPrime)*D)*D'

Q = -tmpD*tmptmpD;

for j=1:dimM

Q(j,j) = Q(j,j) + 1;

end

Q = Q*Q0;

if (mod(i,10) == 0) & options.bDisp

disp([num2str(i),' eigenvector calculated!']);

end

end

if bPCA


if bSuccess

eigvector = eigvector_PCA*eigvector;

elseif size(eigvector,1) == size(eigvector_PCA,2)

eigvector = eigvector_PCA*eigvector;

end

end

elapse.timeMethod = cputime - tmp_T;

elapse.timeAll = elapse.timePCA + elapse.timeMethod;

===================================================================

% Perhitungan jarak eucledian

function D = EuDist2(fea_a,fea_b,bSqrt)

if ~exist('bSqrt','var')

bSqrt = 1;

end

if (~exist('fea_b','var')) | isempty(fea_b)

[nSmp, nFea] = size(fea_a);

aa = sum(fea_a.*fea_a,2);

ab = fea_a*fea_a';

aa = full(aa);

ab = full(ab);

if bSqrt

D = sqrt(repmat(aa, 1, nSmp) + repmat(aa', nSmp, 1) - 2*ab);

D = real(D);

else

D = repmat(aa, 1, nSmp) + repmat(aa', nSmp, 1) - 2*ab;

end

D = max(D,D');

D = D - diag(diag(D));

D = abs(D);

else

[nSmp_a, nFea] = size(fea_a);

[nSmp_b, nFea] = size(fea_b);

aa = sum(fea_a.*fea_a,2);


bb = sum(fea_b.*fea_b,2);

ab = fea_a*fea_b';

aa = full(aa);

bb = full(bb);

ab = full(ab);

if bSqrt

D = sqrt(repmat(aa, 1, nSmp_b) + repmat(bb', nSmp_a, 1) - 2*ab);

D = real(D);

else

D = repmat(aa, 1, nSmp_b) + repmat(bb', nSmp_a, 1) - 2*ab;

end

D = abs(D);

end

Universitas Kristen Maranatha B-1

LAMPIRAN B

DATA PENGAMATAN YALE DATABASE (GAMBAR MASUKAN TERDAPAT PADA TRAINING SET)


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Universitas Kristen Maranatha C-1

LAMPIRAN C

DATA PENGAMATAN ORL DATABASE (GAMBAR MASUKAN TERDAPAT PADA TRAINING SET)


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Universitas Kristen Maranatha D-1

LAMPIRAN D

DATA PENGAMATAN ORL DATABASE (GAMBAR MASUKAN TIDAK TERDAPAT PADA TRAINING SET)


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Universitas Kristen Maranatha E-1

LAMPIRAN E

DATA PENGAMATAN DATABASE SENDIRI (GAMBAR MASUKAN TERDAPAT PADA TRAINING SET)


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LAMPIRAN A KODE PROGRAM · Universitas Kristen Maranatha A-2 %Program utama clc close all clear all...

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Transcript of LAMPIRAN A KODE PROGRAM · Universitas Kristen Maranatha A-2 %Program utama clc close all clear all...