plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | mjkmoynihan/kinectPointCloudICP-master | quaternion.m | .m | kinectPointCloudICP-master/quaternion.m | 99,294 | utf_8 | 54975bc94faef7265c5d2b47cac3f33d | classdef quaternion
% classdef quaternion, implements quaternion mathematics and 3D rotations
%
% Properties (SetAccess = protected):
% e(4,1) components, basis [1; i; j; k]: e(1) + i*e(2) + j*e(3) + k*e(4)
% i*j=k, j*i=-k, j*k=i, k*j=-i, k*i=j, i*k=-j, i*i = j*j = k*k = -1
%
% Constructors:
% q = quater... |
github | mjkmoynihan/kinectPointCloudICP-master | pctransformNonRigid.m | .m | kinectPointCloudICP-master/pctransformNonRigid.m | 3,002 | utf_8 | a64ee3d49fc95f7db86cee7d87bb5306 | function ptCloudOut = pctransformNonRigid(ptCloudIn, tform)
%PCTRANSFORM Rigid transform a 3-D point cloud.
% ptCloudOut = PCTRANSFORM(ptCloudIn, tform) apply forward
% rigid transform to a point cloud. ptCloudIn is a pointCloud object.
% tform is an affine3d object, and it has to be a valid rigid transform
% (... |
github | mjkmoynihan/kinectPointCloudICP-master | ICP_finite.m | .m | kinectPointCloudICP-master/ICP_finite.m | 13,222 | utf_8 | 2053920a9f4cb05296054dec4e1f59ed | function [Points_Moved,M]=ICP_finite(Points_Static, Points_Moving, Options)
% This function ICP_FINITE is an kind of Iterative Closest Point
% registration algorithm for point clouds (vertice data) using finite
% difference methods.
%
% Normal ICP solves translation and rotation with analytical equations.
% By us... |
github | mjkmoynihan/kinectPointCloudICP-master | rigid_transform_3D.m | .m | kinectPointCloudICP-master/rigid_transform_3D.m | 1,302 | utf_8 | caf305bfa6b1ee0940ab99dbd75fa636 | % [1]N. Ho, "Nghia Ho | Where boredom, free time, and curiosity meet together",
% Nghiaho.com, 2016. [Online]. Available: http://nghiaho.com/.
% [Accessed: 30- Aug- 2016].
% This function finds the optimal Rigid/Euclidean transform in 3D space
% It expects as input a Nx3 matrix of 3D points.
% It returns R, t
% Yo... |
github | yuting27/Poddle-Vs.-Fried-Chicken-master | extract_sift_poddleVsChicken.m | .m | Poddle-Vs.-Fried-Chicken-master/lib/extract_sift_poddleVsChicken.m | 6,258 | utf_8 | dfd65c8f6bb526802e8b408b0243ff6c | % Extract SIFT features for poddle vs. fried chicken image set
% Require vlfeat-0.9.20
% Adapted codes from http://www.vlfeat.org/applications/caltech-101-code.html
function extract_sift_poddleVsChicken()
conf.calDir = './' ; % calculating directory
conf.dataDir = './images/' ; % data (image) directory
conf.outDir =... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema1SegundoGrau.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema1SegundoGrau.m | 581 | utf_8 | 32fde491e2f8d927e4ebac5b61821ae9 | function Problema1SegundoGrau
clc; clear all; warning off;
x0 = [2.5 2.5]; % chute inicial
options = optimset('LargeScale','off');
[x,fval] = fminsearch(@Problema1SegundoGrauOtimizacao,x0);
%[x,fval] = fminunc(@Problema1SegundoGrauOtimizacao,x0, options);
disp(x);
%sup... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex2.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema3Ex2.m | 1,091 | utf_8 | b540a33afba7dd2b436e2aec472d89b1 | %format long
function Problema3Ex2
clear all; clc; warning off;
x0 = [0 0];% chute inicial
options = optimset('LargeScale','off');
[x,fval] = fminsearch(@Problema3Ex2Otimizacao,x0)
end
function [fval] = Problema3Ex2Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema2Ex1.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema2Ex1.m | 800 | windows_1250 | 3497ff69027b14227034443eb598af0c | function Problema2Ex1
clc; clear all; warning off;
options = optimset('Algorithm','active-set');
A = [];
b = [];
Aeq = [4, 2*pi];
beq = [10];
lb = [];
lb = zeros(2,1);
ub = [];
x0 = [5 5];
[x, fval, exitflag, output] = fmincon(@AreaTotal, x0, A, b, Aeq, beq, lb, [], [], op... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex7.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema3Ex7.m | 1,340 | utf_8 | 18c9a05b951a8dfb5776a1508f8a5cd9 | %format long
function Problema3Ex7
clear all; clc; warning off;
x0 = [0 -1];% chute inicial
options = optimset('LargeScale','off');
%[x,fval,exitflag,output] = fminsearch(@Problema3Ex7Otimizacao,x0)
[x,fval,exitflag,output] = fminunc(@Problema3Ex7Otimizacao,x0)
fprintf('fval: %d\n', fval);
f... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex4.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema3Ex4.m | 1,137 | utf_8 | ef250128c520b7a89a32ecb978dc91b8 | %format long
function Problema3Ex4
clear all; clc; warning off;
x0 = [10 0];% chute inicial
options = optimset('LargeScale','off');
[x,fval, exitflag, output] = fminsearch(@Problema3Ex4Otimizacao,x0)
end
function [fval] = Problema3Ex4Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema2Ex2.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema2Ex2.m | 600 | windows_1250 | 0c7459dd0a594441ef54510c2521e809 | function Problema2Ex2
clc; clear all; warning off;
options = optimset('Display', 'final-detailed');
A = [];
b = [];
Aeq = [2, 2];
beq = [100];
lb = [];
lb = zeros(2,1);
ub = [];
x0 = [5 5];
[x, fval, exitflag, output] = fmincon(@AreaTotal, x0, A, b, Aeq, beq, lb, [], [], o... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema2Ex4.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema2Ex4.m | 1,595 | utf_8 | 336c5dbff4ed06240e58b768f99a5320 | %format long
function Problema2Ex4
clc; clear all; warning off;
x0 = [0.5; -2.0; -2.0]; % chute inicial
%[x,fval] = fminsearch(@Problema2Ex4Otimizacao,x0)
[x,fval, exitflag, output] = fminunc(@Problema2Ex4Otimizacao,x0)
fprintf('fval: %d\n', fval);
fprintf('saida %d\n', exitflag);
disp('dado... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex6.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema3Ex6.m | 1,072 | utf_8 | 6f52c087d4d765233e5f98fd53900e94 | %format long
function Problema3Ex6
clear all; clc; warning off;
x0 = [-1 0];% chute inicial
options = optimset('LargeScale','off');
[x,fval] = fminsearch(@Problema3Ex6Otimizacao,x0)
end
function [fval] = Problema3Ex6Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema2Ex3.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema2Ex3.m | 1,474 | utf_8 | d0208dea6618b9277f544c75d4bb466a | function Problema2Ex3
clc; clear all; warning off;
x0 = [0.2 0.8]; % chute inicial
[x,fval, exitflag, output] = fminunc(@Problema2Ex3Otimizacao,x0)
%[x,fval, exitflag, output] = fminsearch(@Problema2Ex3Otimizacao,x0)
fprintf('fval: %d\n', fval);
fprintf('saida %d\n', exitflag);
disp('dados s... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema1SegundoGrauOrig.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema1SegundoGrauOrig.m | 453 | utf_8 | 4d74b8ac0dea6d11f7f0e92eab687efc | function Problema1SegundoGrau
clc; clear all; warning off;
x0 = [2.5 2.5]; % chute inicial
options = optimset('LargeScale','off');
[x,fval] = fminsearch(@Problema1SegundoGrauOtimizacaoOrig,x0);
disp(x);
end
function [Faval] = Problema1SegundoGrauOtimizacaoOrig(X)
a = 1;
... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex3.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema3Ex3.m | 1,195 | utf_8 | 8ff0dd0cb6c10433cc4c68c91aa245de | %format long
function Problema3Ex4
clear all; clc; warning off;
x0 = [-0.47 -0.22];% chute inicial
options = optimset('LargeScale','off');
[x,fval] = fminsearch(@Problema3Ex4Otimizacao,x0)
end
function [fval] = Problema3Ex4Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:d... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex1.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema3Ex1.m | 1,095 | utf_8 | 08f52888c428baac18d189b06f8f1669 | %format long
function Problema3Ex1
clear all; clc; warning off;
x0 = [-1 0];% chute inicial
options = optimset('LargeScale','off');
[x,fval] = fminsearch(@Problema3Ex1Otimizacao,x0)
end
function [fval] = Problema3Ex1Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex5.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Problema3Ex5.m | 1,070 | utf_8 | 29b9eb1153583b96abcc628c55b517d1 | %format long
function Problema3Ex5
clear all; clc; warning off;
x0 = [80 0];% chute inicial
options = optimset('LargeScale','off');
[x,fval] = fminsearch(@Problema3Ex5Otimizacao,x0)
end
function [fval] = Problema3Ex5Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
... |
github | braully/msc-dsc-exercices-2015-2025-master | Carne.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Exemplos/Carne.m | 944 | UNKNOWN | 943a00d34aaa93bcd212d595174fa1f8 | function Carne
%Algoritmo Simplex
clc; clear all; warning off;
%problema da carne de porco e de vaca
options = optimset('LargeScale', 'off', 'Simplex', 'on', 'Display', 'off');
f = [14; 8]; %fun��o linear de custo, avalia��o, objetivo etc.
lb = zeros(2,1); %Vetor de limites inferi... |
github | braully/msc-dsc-exercices-2015-2025-master | pplane8.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-deterministico/Exemplos/pplane8.m | 219,141 | utf_8 | 70c4899f7780864ee3832e8f4ca35069 |
function output = pplane8(action,input1,input2,input3)
% pplane8 is an interactive tool for studying planar autonomous systems of
% differential equations. When pplane8 is executed, a pplane8 Setup
% window is opened. The user may enter the differential
% equation and specify a display window using the inter... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex7Otimizacao.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema3Ex7Otimizacao.m | 474 | utf_8 | 41e52015bdfbd228b37f96f9a0d66028 | function [fval] = Problema3Ex7Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
[t,x] = ode45(@exemplo37,tpo,x0);
x1 = x(:,1);
x2 = x(:,2);
%função de avaliação
aval1 = (max(x1) - min(x1))*100;
aval2 = (max(x2) - min(x2))*100;
fval = aval1 + aval2;
end
... |
github | braully/msc-dsc-exercices-2015-2025-master | AlgoritmoGenetico.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/AlgoritmoGenetico.m | 9,300 | UNKNOWN | 2fa1921e3bbe0dcc46ad91487513bd40 | function AlgoritmoGenetico(funcaoObjetivo, numeroVariaveis, opcoes)
% Parametros possiveis de ser passados passados nas opções:
% opcoes.numeroMaximoGeracoes
% opcoes.numeroIndividuosPopulacao
% opcoes.numeroIndividuosPopulacao
% opcoes.limiteSuperior
% opcoes.limiteInferior
% opcoes.... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema1SegundoGrau.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema1SegundoGrau.m | 622 | utf_8 | 1d20f5ebd8efd8e83dfba141573922da | function Problema1SegundoGrau
clc;
clear all;
warning off;
opcoes.numeroMaximoGeracoes=50;
opcoes.numeroIndividuosPopulacao=20;
opcoes.limiteSuperior = 10;
opcoes.limiteInferior = -20;
opcoes.limiteInferiorEstocastico = 0.3;
opcoes.limiteSuperiorEstocastico = 10;
... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex2.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema3Ex2.m | 624 | utf_8 | 1b7500d4bf6d3ff9440630dc327dc4c3 | %format long
function Problema3Ex2
clear all; clc; warning off;
AlgoritmoGenetico(@Problema3Ex2Otimizacao, 2)
end
function [fval] = Problema3Ex2Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
[t,x] = ode45(@exemplo32,tpo,x0);
x1 = x(:,1);
x2 = x(:,2);
%fu... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema2Ex1.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema2Ex1.m | 1,050 | utf_8 | 60c77611b57f22fcc194f45683aa9b8c | function Problema2Ex1
clc; clear all; warning off;
A = [];
b = [];
Aeq = [4, 2*pi];
beq = [10];
lb = zeros(2,1);
%options = gaoptimset('MutationFcn',@mutationadaptfeasible);
%options = gaoptimset(options,'PlotFcns',{@gaplotbestf, @gaplotgenealogy}, 'Display','iter');
options ... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex7.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema3Ex7.m | 362 | utf_8 | 8ed141bf2cca11ee0c6600a521e7a8b1 | %format long
function Problema3Ex7
clear all; clc; warning off;
opcoes.numeroMaximoGeracoes=100;
opcoes.numeroIndividuosPopulacao=20;
opcoes.limiteSuperior = 50;
opcoes.limiteInferior = -50;
opcoes.limiteInferiorEstocastico = 0.8;
opcoes.limiteSuperiorEstocastico = 20;
AlgoritmoGenetico(... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex4.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema3Ex4.m | 620 | utf_8 | 80c02d76c60f9b440e00b45a6d76e3ab | %format long
function Problema3Ex4
clear all; clc; warning off;
AlgoritmoGenetico(@Problema3Ex4Otimizacao, 2)
end
function [fval] = Problema3Ex4Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
[t,x] = ode45(@exemplo34,tpo,x0);
x1 = x(:,1);
x2 = x(:,2);
aval... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema2Ex2.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema2Ex2.m | 576 | windows_1250 | dc25e59e4498ac045c44a7e0f7f77b3a | function Problema2Ex2
clc; clear all; warning off;
A = [];
b = [];
Aeq = [2, 2];
beq = [100];
lb = zeros(2,1);
options = gaoptimset('PlotFcns',{@gaplotbestf}, 'Display','iter');
[x, fval, exitflag, output] = ga(@AreaTotal, 2, A, b, Aeq, beq, lb, [], [], [], options);
fprintf('fv... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema2Ex4.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema2Ex4.m | 732 | utf_8 | 0fe4d37ace4cc27c4e4dc88815b9b9ef | %format long
function Problema2Ex4
clc; clear all; warning off;
AlgoritmoGenetico(@Problema2Ex4Otimizacao, 3)
end
function [fval] = Problema2Ex4Otimizacao(X)
ti = 0; % tempo inicial
tf = 40; % tempo final
dt = 0.1; % derivada de t
tpo= ti:dt:tf; % vetor tempo
x0 = X;
[t,x]=ode45(@exempl... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex6.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema3Ex6.m | 607 | utf_8 | 722c202e0c255ad3198bea5c7ea61e79 | %format long
function Problema3Ex6
clear all; clc; warning off;
AlgoritmoGenetico(@Problema3Ex6Otimizacao, 2)
end
function [fval] = Problema3Ex6Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
[t,x] = ode45(@exemplo36,tpo,x0);
x1 = x(:,1);
x2 = x(:,2);
... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema2Ex3.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema2Ex3.m | 530 | utf_8 | bfa09b6e567a50452c944130f36b534c | function Problema2Ex3
clc; clear all; warning off;
AlgoritmoGenetico(@Problema2Ex3Otimizacao, 2)
end
function [fval] = Problema2Ex3Otimizacao(X)
x1(1) = X(1); % Condições iniciais
x2(1) = X(2); % Condições iniciais
t = 0:1:100; % Número de instantes a considerar
for ia = 1:size(t,2)-1 % Início ... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema1SegundoGrauOrig.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema1SegundoGrauOrig.m | 453 | utf_8 | 4d74b8ac0dea6d11f7f0e92eab687efc | function Problema1SegundoGrau
clc; clear all; warning off;
x0 = [2.5 2.5]; % chute inicial
options = optimset('LargeScale','off');
[x,fval] = fminsearch(@Problema1SegundoGrauOtimizacaoOrig,x0);
disp(x);
end
function [Faval] = Problema1SegundoGrauOtimizacaoOrig(X)
a = 1;
... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex3.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema3Ex3.m | 717 | utf_8 | a03672bac71c359c16c453a4ed7fa9fe | %format long
function Problema3Ex4
clear all; clc; warning off;
AlgoritmoGenetico(@Problema3Ex4Otimizacao, 2)
end
function [fval] = Problema3Ex4Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
[t,x] = ode45(@exemplo34,tpo,x0);
x1 = x(:,1);
x2 = x(:,2);
%fun... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex1.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema3Ex1.m | 596 | utf_8 | ca2d7fcce9ecbd9f86c9ed4ed3bba96b | %format long
function Problema3Ex1
clear all; clc; warning off;
AlgoritmoGenetico(@Problema3Ex1Otimizacao, 2)
end
function [fval] = Problema3Ex1Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
[t,x] = ode45(@exemplo31,tpo,x0);
x1 = x(:,1);
x2 = x(:,2);
aval... |
github | braully/msc-dsc-exercices-2015-2025-master | Problema3Ex5.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Problema3Ex5.m | 604 | utf_8 | f78b27ddc0b23805e22feb193b2deee9 | %format long
function Problema3Ex5
clear all; clc; warning off;
AlgoritmoGenetico(@Problema3Ex5Otimizacao, 2)
end
function [fval] = Problema3Ex5Otimizacao(X)
x0 = X;
t0 = 0;
tf = 30;
dt = 0.01;
tpo = t0:dt:tf;
[t,x] = ode45(@exemplo35,tpo,x0);
x1 = x(:,1);
x2 = x(:,2);
... |
github | braully/msc-dsc-exercices-2015-2025-master | royalRoads.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Exemplos/speedy-ga/royalRoads.m | 297 | utf_8 | c5c668ec83e1ad6483374cf69eb4a87a | % The royal roads function. The chromosome length (i.e. len)
% should be a multiple of 8
function fitness=R1(pop)
[popSize len]=size(pop);
fitness=zeros(popSize,1);
for i=1:8:len
temp=sum(pop(:,i:i+7),2);
temp=double(temp==8);
fitness=fitness+temp*8;
end
fitness=fitness';
|
github | braully/msc-dsc-exercices-2015-2025-master | oneMax.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Exemplos/speedy-ga/oneMax.m | 66 | utf_8 | 31a9c0ac4446868351f87baa83fc5051 | % onemax
function fitness=oneMax(pop)
fitness=sum(pop,2)';
|
github | braully/msc-dsc-exercices-2015-2025-master | royalRoads.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Exemplos/turbo-ga/royalRoads.m | 297 | utf_8 | c5c668ec83e1ad6483374cf69eb4a87a | % The royal roads function. The chromosome length (i.e. len)
% should be a multiple of 8
function fitness=R1(pop)
[popSize len]=size(pop);
fitness=zeros(popSize,1);
for i=1:8:len
temp=sum(pop(:,i:i+7),2);
temp=double(temp==8);
fitness=fitness+temp*8;
end
fitness=fitness';
|
github | braully/msc-dsc-exercices-2015-2025-master | oneMax.m | .m | msc-dsc-exercices-2015-2025-master/otimizacao-aplicada-euristica/Exemplos/turbo-ga/oneMax.m | 66 | utf_8 | 31a9c0ac4446868351f87baa83fc5051 | % onemax
function fitness=oneMax(pop)
fitness=sum(pop,2)';
|
github | Marsan-Ma-zz/checkins-master | fast_tsne.m | .m | checkins-master/lib/bhtsne/fast_tsne.m | 4,820 | utf_8 | ea635b52c1f372c46c31b2b389a87b27 | function mappedX = fast_tsne(X, no_dims, initial_dims, perplexity, theta)
%FAST_TSNE Runs the C++ implementation of Barnes-Hut t-SNE
%
% mappedX = fast_tsne(X, no_dims, initial_dims, perplexity, theta)
%
% Runs the C++ implementation of Barnes-Hut-SNE. The high-dimensional
% datapoints are specified in the NxD... |
github | wangzheallen/vsad-master | vsad_encoding.m | .m | vsad-master/code/vsad_encoding.m | 1,420 | utf_8 | ef9ef3ac11647f89498f5d2d28904b88 | function vsad = vsad_encoding(features,encoders,scores,codebook_selected,Id)
encoder.numWords = codebook_selected;
encoder.priors = encoders.priors(Id);
encoder.means = encoders.means(:,Id);
descrs = features;
vsad = encode_one(descrs,encoder,scores,codebook_selected,Id);
function code = encode_one(descr,encoder,sco... |
github | seismology-RUB/NEXD-2D-master | read_input.m | .m | NEXD-2D-master/simulations/example_poro/tools/read_input.m | 3,071 | utf_8 | c3c6a59afa68bd5f3e19fa716aa089d0 | % version='$Rev: 20 $ ($Date: 2017-06-07 18:22:15 +0200 (Mi, 07 Jun 2017) $, $Author: Janis Heuel, Marc S. Boxberg $)'
%
% Function to read the parameters from input files.
% The following parameters are needed to use this function in the
% main program:
% filename: contains relative path and name of th... |
github | megjhani/Unmixing_MCSU-master | MCUAlgorithm_Adaptive_Param.m | .m | Unmixing_MCSU-master/MCUAlgorithm_Adaptive_Param.m | 11,086 | utf_8 | 89c2949a52407faf42cd3ce0e16e39a7 | function [ S_hat A ] = MCUAlgorithm_Adaptive_Param( Y,r,T,noOfSources,sigma,lamda,M,D_s,A,size_x,size_y )
%MCUALGORITHM Summary of this function goes here
% Observation matrix Y
% r patch size;k dictonary size,n number of sources, lamda regularization
% parameter, M total number of iterations
%% initialization
... |
github | sanworks/ArCOM-master | ArCOM.m | .m | ArCOM-master/MATLAB/ArCOM.m | 14,258 | utf_8 | 872431d59037d6bba6a61ea81e73d8f2 | %{
----------------------------------------------------------------------------
This file is part of the Sanworks ArCOM repository
Copyright (C) 2016 Sanworks LLC, Sound Beach, New York, USA
----------------------------------------------------------------------------
This program is free software: you can redistribu... |
github | tsgouvea/TaskMatching-master | TruncatedExponential.m | .m | TaskMatching-master/TruncatedExponential.m | 832 | utf_8 | 97ea87699e198a8adac799f49537cd5d | % TruncatedExponential draws random numbers from an exponential distribution
% between a specified min and max value. Does not reset RNG!
% input(mandatory): min_value, max_value, alpha
% input (optional): [m,n] m rows and n columns, default m=1,n=1
% output random numbers in m x n matrix
% Torben Ott, July 2016
fun... |
github | tsgouvea/TaskMatching-master | SessionSummary.m | .m | TaskMatching-master/SessionSummary.m | 15,022 | utf_8 | e5d417d8f653e40c5ac3155c08675f19 | function GUIHandles = SessionSummary(Data, GUIHandles, iTrial, nTrialsToShow)
%global nTrialsToShow %this is for convenience
%global BpodSystem
%global TaskParameters
if nargin < 4 %custom number of trials to display
nTrialsToShow = 90; %default
end
if nargin < 2 % plot initialized (either beginning of session or... |
github | CUMCM/2016-A-master | moor.m | .m | 2016-A-master/moor.m | 6,279 | utf_8 | c27b22ef1d00d6b6f1707bd058fb6373 | function [tilt,elev,xsbed,xbuoy,f] = moor(Lc,chain,vw,vs,M,depth,isplot)
% MOOR 2016 CUMCM Problem A - Optimal Design of Mooring System
% Reference: http://canuck.seos.uvic.ca/rkd/mooring/moordyn.php
%
% USAGE: [tiltdrum,elevanch,xsbed,xbuoy,f] = moor(Lc,chain,v,M,isplot)
%
% tilt = tilt angle of the drum
% ... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | solve_l1l2.m | .m | OnlineLRR-ICML2016-master/LRR/solve_l1l2.m | 289 | utf_8 | fa51ba8ec5605cfefa47565ff84111bf | function [E] = solve_l1l2(W,lambda)
n = size(W,2);
E = W;
for i=1:n
E(:,i) = solve_l2(W(:,i),lambda);
end
end
function [x] = solve_l2(w,lambda)
% min lambda |x|_2 + |x-w|_2^2
nw = norm(w);
if nw>lambda
x = (nw-lambda)*w/nw;
else
x = zeros(length(w),1);
end
end |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | solve_proj2.m | .m | OnlineLRR-ICML2016-master/OR-PCA/solve_proj2.m | 1,248 | utf_8 | 534da237841b1ca41c8397d4a9492550 | % solve the problem:
% min_{x,e} 0.5*|z-Dx-e|_2^2 + 0.5*lambda1*|x|_2^2 + lambda2*|e|_1
%
% solve the projection by APG
% input:
% z - data point
% D - basis matrix
% lambda1, lambda2 - tradeoff parameters
% output:
% r - projection coefficient
% e - sparse noise
% copyright Jiashi Feng (jshfeng... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | stoc_rpca.m | .m | OnlineLRR-ICML2016-master/OR-PCA/stoc_rpca.m | 1,225 | utf_8 | 4e9e136bed184585937e227f36220b4d | % Stochastic optimization for the robust PCA
% Input:
% D: [m x n] data matrix, m - ambient dimension, n - samples number
% lambda1, lambda2: trade-off parameters
% nrank: the groundtruth rank of the data
% Output:
% L: [m x r] the basis of the subspace
% R: [r x n] the coefficient ... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | lanbpro.m | .m | OnlineLRR-ICML2016-master/PROPACK/lanbpro.m | 19,514 | utf_8 | 897b157335c2a5c269845380328709c4 | function [U,B_k,V,p,ierr,work] = lanbpro(varargin)
%LANBPRO Lanczos bidiagonalization with partial reorthogonalization.
% LANBPRO computes the Lanczos bidiagonalization of a real
% matrix using the with partial reorthogonalization.
%
% [U_k,B_k,V_k,R,ierr,work] = LANBPRO(A,K,R0,OPTIONS,U_old,B_old,V_old)
% ... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | lanpro.m | .m | OnlineLRR-ICML2016-master/PROPACK/lanpro.m | 14,762 | utf_8 | ff3aa513289e3776117575af43b5ed1b | function [Q_k,T_k,r,anorm,ierr,work] = lanpro(A,nin,kmax,r,options,...
Q_k,T_k,anorm)
%LANPRO Lanczos tridiagonalization with partial reorthogonalization
% LANPRO computes the Lanczos tridiagonalization of a real symmetric
% matrix using the symmetric Lanczos algorithm with partial
% reorthogonalization... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | admmLasso_mat_func.m | .m | OnlineLRR-ICML2016-master/SSC/admmLasso_mat_func.m | 3,427 | utf_8 | ccd45ff24e0430e3e4f808a26300af6e | %--------------------------------------------------------------------------
% This function takes a DxN matrix of N data points in a D-dimensional
% space and returns a NxN coefficient matrix of the sparse representation
% of each data point in terms of the rest of the points
% Y: DxN data matrix
% affine: if true th... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | Misclassification.m | .m | OnlineLRR-ICML2016-master/SSC/Misclassification.m | 949 | utf_8 | 8b4e016b278ffacea542ab73e6ab7b14 | %--------------------------------------------------------------------------
% This function takes the groups resulted from spectral clutsering and the
% ground truth to compute the misclassification rate.
% groups: [grp1,grp2,grp3] for three different forms of Spectral Clustering
% s: ground truth vector
% Missrate: 3x... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | BuildAdjacency.m | .m | OnlineLRR-ICML2016-master/SSC/BuildAdjacency.m | 969 | utf_8 | e6246b92c3306608cee7c6769448d13e | %--------------------------------------------------------------------------
% This function takes a NxN coefficient matrix and returns a NxN adjacency
% matrix by choosing the K strongest connections in the similarity graph
% CMat: NxN coefficient matrix
% K: number of strongest edges to keep; if K=0 use all the exitin... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | Hungarian.m | .m | OnlineLRR-ICML2016-master/SSC/Hungarian.m | 9,328 | utf_8 | 51e60bc9f1f362bfdc0b4f6d67c44e80 | function [Matching,Cost] = Hungarian(Perf)
%
% [MATCHING,COST] = Hungarian_New(WEIGHTS)
%
% A function for finding a minimum edge weight matching given a MxN Edge
% weight matrix WEIGHTS using the Hungarian Algorithm.
%
% An edge weight of Inf indicates that the pair of vertices given by its
% position have no... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | DataProjection.m | .m | OnlineLRR-ICML2016-master/SSC/DataProjection.m | 733 | utf_8 | 608c1dd2735280c008ffa8c973aff3d2 | %--------------------------------------------------------------------------
% This function takes the D x N data matrix with columns indicating
% different data points and project the D dimensional data into a r
% dimensional subspace using PCA.
% X: D x N matrix of N data points
% r: dimension of the PCA projection, i... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | SpectralClustering.m | .m | OnlineLRR-ICML2016-master/SSC/SpectralClustering.m | 1,332 | utf_8 | d24271f54ea09be2383a732f93f4c9a1 | %--------------------------------------------------------------------------
% This function takes an adjacency matrix of a graph and computes the
% clustering of the nodes using the spectral clustering algorithm of
% Ng, Jordan and Weiss.
% CMat: NxN adjacency matrix
% n: number of groups for clustering
% groups: N-d... |
github | jieshen-sjtu/OnlineLRR-ICML2016-master | SSC.m | .m | OnlineLRR-ICML2016-master/SSC/SSC.m | 1,266 | utf_8 | f63b9f13f01382c5eea4c4143462a4cf | %--------------------------------------------------------------------------
% This is the function to call the sparse optimization program, to call the
% spectral clustering algorithm and to compute the clustering error.
% r = projection dimension, if r = 0, then no projection
% affine = use the affine constraint if t... |
github | guevaracodina/oct12-master | oct_doppler_cfg.m | .m | oct12-master/oct_doppler_cfg.m | 5,922 | utf_8 | d2fc80b2905ee9afbb9804df8b2cbe0c | function reconstruct1 = oct_doppler_cfg
% Example script that creates an cfg_exbranch to sum two numbers. The
% inputs are entered as two single numbers, the output is just a single
% number.
%
% This code is part of a batch job configuration system for MATLAB. See
% help matlabbatch
% for a general overview.
%__... |
github | guevaracodina/oct12-master | oct_reconstruct_struct_cfg.m | .m | oct12-master/oct_reconstruct_struct_cfg.m | 4,193 | utf_8 | 79fbd0666bc59b2f43f687d30872665d | function reconstruct1 = oct_reconstruct_struct_cfg
% Example script that creates an cfg_exbranch to sum two numbers. The
% inputs are entered as two single numbers, the output is just a single
% number.
%
% This code is part of a batch job configuration system for MATLAB. See
% help matlabbatch
% for a general ov... |
github | guevaracodina/oct12-master | oct_dispersion_comp_run.m | .m | oct12-master/oct_dispersion_comp_run.m | 4,192 | utf_8 | 27bfaba1a38fd65b709c38c5d6778500 | function out = oct_dispersion_comp_run(job)
% At this point, the folder contains a list of dat and mat files
% respectively containing acquisition information and data. This module
% will dispersion_comp the acquisition info.
rev = '$Rev$'; %#ok
% Reference from previous computation.
OCTmat=job.OCTmat;
% Loop over a... |
github | guevaracodina/oct12-master | oct_convert_bin2mat_cfg.m | .m | oct12-master/oct_convert_bin2mat_cfg.m | 2,492 | utf_8 | bb343e2a02da1c9d1114cdc47ae6bd03 | function convert1 = oct_convert_bin2mat_cfg
% Example script that creates an cfg_exbranch to sum two numbers. The
% inputs are entered as two single numbers, the output is just a single
% number.
%
% This code is part of a batch job configuration system for MATLAB. See
% help matlabbatch
% for a general overview.... |
github | guevaracodina/oct12-master | oct_filtervolume_cfg.m | .m | oct12-master/oct_filtervolume_cfg.m | 2,423 | utf_8 | b0308890da0002818aace100717aa290 | function reconstruct1 = oct_doppler_cfg
% Example script that creates an cfg_exbranch to sum two numbers. The
% inputs are entered as two single numbers, the output is just a single
% number.
%
% This code is part of a batch job configuration system for MATLAB. See
% help matlabbatch
% for a general overview.
%__... |
github | guevaracodina/oct12-master | convnfft.m | .m | oct12-master/convnfft.m | 6,549 | utf_8 | a8564c830f2165a5da2007b0cd9f6ef8 | function A = convnfft(A, B, shape, dims, options)
% CONVNFFT FFT-BASED N-dimensional convolution.
% C = CONVNFFT(A, B) performs the N-dimensional convolution of
% matrices A and B. If nak = size(A,k) and nbk = size(B,k), then
% size(C,k) = max([nak+nbk-1,nak,nbk]);
%
% C = CONVNFFT(A, B, SHAPE) controls... |
github | guevaracodina/oct12-master | oct_ecg_doppler_cfg.m | .m | oct12-master/oct_ecg_doppler_cfg.m | 6,149 | utf_8 | 776a5a2b7756c77ca1d8ec73a01c4152 | function ecg_recons1 = oct_ecg_doppler_cfg
%
% ECG-gated doppler reconstruction.
%_______________________________________________________________________
rev = '$Rev$';
%% Input Items
OCTmat = cfg_files; %Select NIRS.mat for this subject
OCTmat.name = 'OCT.mat'; % The displayed name
OCTmat.tag = 'OC... |
github | guevaracodina/oct12-master | oct_concatenate_cfg.m | .m | oct12-master/oct_concatenate_cfg.m | 3,715 | utf_8 | 01cfcb21869a22c71470fbe6f00678e6 | function concatenate1 = oct_concatenate_cfg
% Example script that creates an cfg_exbranch to sum two numbers. The
% inputs are entered as two single numbers, the output is just a single
% number.
%
% This code is part of a batch job configuration system for MATLAB. See
% help matlabbatch
% for a general overview.... |
github | guevaracodina/oct12-master | oct_create_dicom_cfg.m | .m | oct12-master/oct_create_dicom_cfg.m | 4,141 | utf_8 | 31a3fe72ca00d6ebfbd256ee99cf808e | function reconstruct1 = oct_create_dicom_cfg
% Example script that creates an cfg_exbranch to sum two numbers. The
% inputs are entered as two single numbers, the output is just a single
% number.
%
% This code is part of a batch job configuration system for MATLAB. See
% help matlabbatch
% for a general overview... |
github | guevaracodina/oct12-master | oct_ecg_pulsatility_cfg.m | .m | oct12-master/oct_ecg_pulsatility_cfg.m | 3,059 | utf_8 | 081cb35702add4cb46c5c3dfcab51891 | function ecg_pulse1 = oct_ecg_pulsatility_cfg
%
% ECG-gated doppler reconstruction.
%_______________________________________________________________________
rev = '$Rev$';
%% Input Items
OCTmat = cfg_files; %Select NIRS.mat for this subject
OCTmat.name = 'OCT.mat'; % The displayed name
OCTmat... |
github | guevaracodina/oct12-master | oct_angiogram_cfg.m | .m | oct12-master/oct_angiogram_cfg.m | 5,221 | utf_8 | d987ac2f16b2d8a3970cdf2d1c00d5bb | function reconstruct1 = oct_angiogram_cfg
% Example script that creates an cfg_exbranch to sum two numbers. The
% inputs are entered as two single numbers, the output is just a single
% number.
%
% This code is part of a batch job configuration system for MATLAB. See
% help matlabbatch
% for a general overview.
%... |
github | guevaracodina/oct12-master | oct_define_geometry_cfg.m | .m | oct12-master/oct_define_geometry_cfg.m | 7,239 | utf_8 | d62d2411eaa86edafc5baea2da8551d2 | function param3dhd1 = oct_define_geometry_cfg
% Example script that creates an cfg_exbranch to sum two numbers. The
% inputs are entered as two single numbers, the output is just a single
% number.
%
% This code is part of a batch job configuration system for MATLAB. See
% help matlabbatch
% for a general overvie... |
github | guevaracodina/oct12-master | oct_convert_bin2mat_run.m | .m | oct12-master/oct_convert_bin2mat_run.m | 7,449 | utf_8 | ad327ffb770c24f23d0098dde4af717d | function out = oct_convert_bin2mat_run(job)
% Function that converts all files in the directory path to .dat files
% which are memmap versions of the data. A choice was made to do this in
% place so that no copy of the data is done.
rev = '$Rev$';
% This function will convert all the .bin files in a directory to the ... |
github | guevaracodina/oct12-master | oct_dispersion_comp_cfg.m | .m | oct12-master/oct_dispersion_comp_cfg.m | 5,144 | utf_8 | 31944a8de84d7ce5fc79b93b570af890 | function dispersion1 = oct_dispersion_comp_cfg
% Example script that creates an cfg_exbranch to sum two numbers. The
% inputs are entered as two single numbers, the output is just a single
% number.
%
% This code is part of a batch job configuration system for MATLAB. See
% help matlabbatch
% for a general overvi... |
github | guevaracodina/oct12-master | dicom_folder_info.m | .m | oct12-master/dicom_toolbox/dicom_folder_info.m | 8,439 | utf_8 | 053623c60565c593a20915e2d3b8ae85 | function datasets=dicom_folder_info(link,subfolders)
% Function DICOM_FOLDER_INFO gives information about all Dicom files
% in a certain folder (and subfolders), or of a certain dataset
%
% datasets=dicom_folder_info(link,subfolders)
%
% inputs,
% link : A link to a folder like "C:\temp" or a link to the first... |
github | guevaracodina/oct12-master | dicom_read_volume.m | .m | oct12-master/dicom_toolbox/dicom_read_volume.m | 1,583 | utf_8 | 011d6f1944b18224f71d68ddbdd135af | function voxelvolume = dicom_read_volume(info)
% function for reading volume of Dicom files
%
% volume = dicom_read_volume(file-header)
%
% examples:
% 1: info = dicom_read_header()
% V = dicom_read_volume(info);
% imshow(squeeze(V(:,:,round(end/2))),[]);
%
% 2: V = dicom_read_volume('volume.dcm');
i... |
github | guevaracodina/oct12-master | dicom_write_volume.m | .m | oct12-master/dicom_toolbox/dicom_write_volume.m | 2,471 | utf_8 | f55e8f8c7c8bef713edd32c439195f6e | function dicom_write_volume(Volume,filename,volscale,info)
% This function DICOM_WRITE_VOLUME will write a Matlab 3D volume as
% a stack of 2D slices in separate dicom files.
%
% dicom_write_volume(Volume,Filename,Scales,Info)
%
% inputs,
% Volume: The 3D Matlab volume
% Filename: The name of the dicom file... |
github | guevaracodina/oct12-master | choose_from_list.m | .m | oct12-master/dicom_toolbox/choose_from_list.m | 1,062 | utf_8 | 9a5736ab8c4022c2092521054615df27 | function [id,name] = choose_from_list(varargin)
%
% example :
%
% c{1}='apple'
% c{2}='orange'
% c{3}='berries'
% [id,name]=choose_from_list(c,'Select a Fruit');
%
if(strcmp(varargin{1},'press'))
handles=guihandles;
id=get(handles.listbox1,'Value');
setMyData(id);
uiresume
return
end
% listbox1 Positio... |
github | mohamedadaly/TRex-master | s015_fp_bp.m | .m | TRex-master/samples/matlab/s015_fp_bp.m | 2,214 | utf_8 | ecf99605e9a07458a359f5ce24b61b39 | % -----------------------------------------------------------------------
% This file is part of the ASTRA Toolbox
%
% Copyright: 2010-2015, iMinds-Vision Lab, University of Antwerp
% 2014-2015, CWI, Amsterdam
% License: Open Source under GPLv3
% Contact: astra@uantwerpen.be
% Website: http://sf.net/project... |
github | mohamedadaly/TRex-master | ROIselectfull.m | .m | TRex-master/matlab/tools/ROIselectfull.m | 380 | utf_8 | 25fe7250e4be546bc58acc9a153a4f7e | function V_out = ROIselectfull(input, ROI)
s1 = size(input,1);
s2 = size(input,2);
[x y] = meshgrid(-(s2-1)/2:(s2-1)/2,(s1-1)/2:-1:-(s1-1)/2);
A = Afstand(x,y,0,0);
V_out = zeros(size(input));
for slice = 1:size(input,3);
V = input(:,:,slice);
V(A > ROI/2) = 0;
V_out(:,:,slice) = V;
end
end
function A ... |
github | mohamedadaly/TRex-master | astra_data_gui.m | .m | TRex-master/matlab/tools/astra_data_gui.m | 15,477 | utf_8 | 49ae47083bc7261a66c3ab9b69728086 | function varargout = astra_data_gui(varargin)
% ASTRA_DATA_GUI M-file for ASTRA_DATA_GUI.fig
% ASTRA_DATA_GUI, by itself, creates a new ASTRA_DATA_GUI or raises the existing
% singleton*.
%
% H = ASTRA_DATA_GUI returns the handle to a new ASTRA_DATA_GUI or the handle to
% the existing singleton*.
%
... |
github | mohamedadaly/TRex-master | DARToptimizerBoneStudy.m | .m | TRex-master/matlab/algorithms/DART/tools/DARToptimizerBoneStudy.m | 3,040 | utf_8 | c8faee073fb583daf1e907e23309555b | %--------------------------------------------------------------------------
% This file is part of the ASTRA Toolbox
%
% Copyright: 2010-2014, iMinds-Vision Lab, University of Antwerp
% 2014, CWI, Amsterdam
% License: Open Source under GPLv3
% Contact: astra@uantwerpen.be
% Website: http://sf.net/projec... |
github | mohamedadaly/TRex-master | dart_create_base_phantom.m | .m | TRex-master/matlab/algorithms/DART/tools/dart_create_base_phantom.m | 881 | utf_8 | 77f6412d7fae2ad180c7c997062dde96 | %--------------------------------------------------------------------------
% This file is part of the ASTRA Toolbox
%
% Copyright: 2010-2014, iMinds-Vision Lab, University of Antwerp
% 2014, CWI, Amsterdam
% License: Open Source under GPLv3
% Contact: astra@uantwerpen.be
% Website: http://sf.net/projec... |
github | mohamedadaly/TRex-master | dart_scheduler.m | .m | TRex-master/matlab/algorithms/DART/tools/dart_scheduler.m | 1,118 | utf_8 | 7fdccaf91d22c8e17241b9d1ade99af5 | %--------------------------------------------------------------------------
% This file is part of the ASTRA Toolbox
%
% Copyright: 2010-2014, iMinds-Vision Lab, University of Antwerp
% 2014, CWI, Amsterdam
% License: Open Source under GPLv3
% Contact: astra@uantwerpen.be
% Website: http://sf.net/projec... |
github | superyyzg/L0-SSC-master | update_hatalpha.m | .m | L0-SSC-master/matlab/update_hatalpha.m | 1,715 | utf_8 | 9d4d36bd53844a4508ba15c01bb7c9c1 | function [hatalpha] = update_hatalpha(invAs,alpha,L0,Y1,Y2,beta,adjmat,thr)
mat_alpha = alpha - Y2/beta;
K = (beta*L0 + Y1);
n = size(K,1);
K_diag = diag(K);
K_diagr = repmat(K_diag,1,n);
K_diagc = repmat(K_diag',n,1);
cK = 0.5* (K_diagr + K_diagc - K - K');
max_hatalpha_iter = 50;
hatalpha = alpha;
for iter = 1:m... |
github | superyyzg/L0-SSC-master | updateW.m | .m | L0-SSC-master/matlab/updateW.m | 902 | utf_8 | 502c0e565bad8daae6a063bde36c59ff | function [W,sr,full_diag,Uz] = updateW(alpha,k,initW,Y,gamma,beta,max_W_iter)
W0 = initW;
n = size(W0,1);
%debug info
sr = zeros(max_W_iter,1);
full_diag = zeros(n,max_W_iter);
for iter = 1:max_W_iter,
L = graph_laplacian(W0);
%[U,~,~] = lansvd((L+1e-6*eye(n)),k,'S');
[U,S,~] = svd(L);
Uz = U;
... |
github | superyyzg/L0-SSC-master | l1ls_featuresign.m | .m | L0-SSC-master/matlab/l1ls_featuresign.m | 7,088 | utf_8 | 334d3b5a9c6458e11bb0c8b6204079f0 | function Xout = l1ls_featuresign (A, Y, gamma, Xinit)
% The feature-sign search algorithm
% L1-regularized least squares problem solver
%
% This code solves the following problem:
%
% minimize_s 0.5*||y - A*x||^2 + gamma*||x||_1
%
% The detail of the algorithm is described in the following paper:
% 'Efficient Spar... |
github | superyyzg/L0-SSC-master | proximal_manifold.m | .m | L0-SSC-master/matlab/proximal_manifold.m | 1,760 | utf_8 | ba3512ef66d91422f64e42edc3a54877 | function [alpha,perf,obj] = proximal_manifold(data,k,tlabel,opt,alpha0,lambda,gamma,maxIter,thr)
basic_nargins = 5;
if (nargin < basic_nargins+1)
% default rl1graph regularization parameter
lambda = 0.1;
end
if (nargin < basic_nargins+2)
% default rl1graph regularization parameter
gamma = 0.1;
end
if (... |
github | superyyzg/L0-SSC-master | proximal_sparse_manifold.m | .m | L0-SSC-master/matlab/proximal_sparse_manifold.m | 4,129 | utf_8 | c542d14e0c169735975a9e3a0d99deb8 | function [alpha,perf,obj] = proximal_sparse_manifold(data,k,tlabel,opt,KMax,alpha0,lambda,gamma,maxIter,thr)
basic_nargins = 6;
if (nargin < basic_nargins+1)
% default rl1graph regularization parameter
lambda = 0.1;
end
if (nargin < basic_nargins+2)
% default rl1graph regularization parameter
gamma = 0... |
github | superyyzg/L0-SSC-master | mysmce.m | .m | L0-SSC-master/matlab/mysmce.m | 856 | utf_8 | b374378202d52d07a7a219ccc8083ce1 | %--------------------------------------------------------------------------
% Copyright @ Ehsan Elhamifar, 2012
% Changed for RSMG
%--------------------------------------------------------------------------
function [perf,smce_alpha] = mysmce(data,k,tlabel,lambda,KMax,verbose)
%addpath(fullfile('.','utility','SMCE_v... |
github | superyyzg/L0-SSC-master | proximal_l0graph.m | .m | L0-SSC-master/matlab/proximal_l0graph.m | 3,288 | utf_8 | edafd1d1e324dbc29963c4b674bc2dc8 | function [alpha,perf,effective_lambda] = proximal_l0graph(data,k,tlabel,alpha0,lambda,maxIter,thr)
basic_nargins = 4;
if (nargin < basic_nargins+1)
% default rl1graph regularization parameter
lambda = 0.1;
end
if (nargin < basic_nargins+2)
% default error thresholds to stop ADMM
maxIter = 100;
end
... |
github | superyyzg/L0-SSC-master | proximal_l0rl1graph.m | .m | L0-SSC-master/matlab/proximal_l0rl1graph.m | 5,291 | utf_8 | 071f97dc66a1bed2d98a6061d7cc439f | function [alpha,perf] = proximal_l0rl1graph(data,k,tlabel,alpha0,lambda_l1,lambda_l0,knn,maxSingleIter,maxIter,verbose,thr)
basic_nargins = 4;
if (nargin < basic_nargins+1)
% default rl1graph regularization parameter
lambda_l1 = 0.1;
end
if (nargin < basic_nargins+2)
% default rl1graph regularization param... |
github | superyyzg/L0-SSC-master | litekmeans.m | .m | L0-SSC-master/matlab/utility/litekmeans.m | 16,124 | utf_8 | 8ba49eb699d347d877d8510764b39e49 | function [label, center, bCon, sumD, D] = litekmeans(X, k, varargin)
%LITEKMEANS K-means clustering, accelerated by matlab matrix operations.
%
% label = LITEKMEANS(X, K) partitions the points in the N-by-P data matrix
% X into K clusters. This partition minimizes the sum, over all
% clusters, of the within-clus... |
github | superyyzg/L0-SSC-master | Hungarian.m | .m | L0-SSC-master/matlab/utility/Hungarian.m | 9,049 | utf_8 | bf5c068d26692aa6387bb69df9b6f852 | function [Matching,Cost] = Hungarian(Perf)
%
% [MATCHING,COST] = Hungarian_New(WEIGHTS)
%
% A function for finding a minimum edge weight matching given a MxN Edge
% weight matrix WEIGHTS using the Hungarian Algorithm.
%
% An edge weight of Inf indicates that the pair of vertices given by its
% position have no adjacen... |
github | superyyzg/L0-SSC-master | sc.m | .m | L0-SSC-master/matlab/utility/sc.m | 4,182 | utf_8 | 42ecb7dc45b49e68963c9726cb9c54cb | % Ng, A., Jordan, M., and Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. In T. Dietterich,
% S. Becker, and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14
% (pp. 849 856). MIT Press.
% Asad Ali
% GIK Institute of Engineering Sciences & Technology, Pakistan
% Email: a... |
github | superyyzg/L0-SSC-master | smce_clustering.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/smce_clustering.m | 879 | utf_8 | 32bfa549381900bec346daa914a1eb9b | %--------------------------------------------------------------------------
% Copyright @ Ehsan Elhamifar, 2012
%--------------------------------------------------------------------------
function [Y,grp,missrate] = smce_clustering(W,n,dim,gtruth)
if (n == 1)
gtruth = ones(1,size(W,1));
end
MAXiter = 1000;
REPli... |
github | superyyzg/L0-SSC-master | smce_optimization.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/smce_optimization.m | 2,262 | utf_8 | 6aa64046f58247423a09d6e777b74a82 | %--------------------------------------------------------------------------
% This function solves the optimization function of SMCE for the given
% data points
% X: DxN matrix of N data points in the D-dimensional space
% lambda: regularization parameter of the SMCE optimization program
% KMax = maximum neighborhood s... |
github | superyyzg/L0-SSC-master | admm_vec_func.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/admm_vec_func.m | 2,625 | utf_8 | ee4f55e9c313b48fd1e3bba1fc30089b | %--------------------------------------------------------------------------
% This function takes a DxN matrix of N data points in a D-dimensional
% space and returns a NxN coefficient matrix of the sparse representation
% of each data point in terms of the rest of the points obtained by SMCE
% Y: DxN data matrix of ... |
github | superyyzg/L0-SSC-master | errorCoef.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/errorCoef.m | 539 | utf_8 | db957863a88b8f094bf27c6efe7763a5 | %--------------------------------------------------------------------------
% This function computes the maximum error between elements of two
% coefficient matrices
% C: NxN coefficient matrix
% Z: NxN coefficient matrix
% err: mean absolute error between C and Z
%-----------------------------------------------------... |
github | superyyzg/L0-SSC-master | missclassGroups.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/missclassGroups.m | 1,166 | utf_8 | a6167c9dd26a06b8ed4bfcf5e7d6c0ca | %--------------------------------------------------------------------------
% Copyright @ Ehsan Elhamifar, 2012
%--------------------------------------------------------------------------
function [miss,index] = missclassGroups(Segmentation,RefSegmentation)
% [miss,index] = missclass(Segmentation,RefSegmentation,ngro... |
github | superyyzg/L0-SSC-master | manifoldGen.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/manifoldGen.m | 3,116 | utf_8 | 06b767ee2c3b9b1913ca24602aebb260 | %--------------------------------------------------------------------------
% This function generates manifols 'sphere' or '2trefoils'
% D = dimension of the ambient space
% sigma = variance of the noise added to the data
% N = number of points in each manifold
%---------------------------------------------------------... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.