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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | leonid-pishchulin/humanshape-master | absor.m | .m | humanshape-master/fitting/absor.m | 7,870 | utf_8 | e46999e29e91188fe9d21896c871dbc4 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | fitPose.m | .m | humanshape-master/fitting/fitPose.m | 2,391 | utf_8 | c334e8f4171c4d0b53601951ed9cc0aa | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | NRD.m | .m | humanshape-master/fitting/NRD.m | 7,266 | utf_8 | 69e69794a5573cc49352b8e9278a8af1 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | prepareScan.m | .m | humanshape-master/fitting/prepareScan.m | 2,096 | utf_8 | ec52a2caafeccfcaddb0be468dc02a6a | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | fitModel.m | .m | humanshape-master/fitting/fitModel.m | 2,559 | utf_8 | 4f38775a775415f76ace7be19fd0d142 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | expParams.m | .m | humanshape-master/fitting/expParams.m | 2,304 | utf_8 | 360d68bdf608735d5de035fb7f4eff6b | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | visLandmarks.m | .m | humanshape-master/fitting/visLandmarks.m | 1,570 | utf_8 | c96dd754b89012cf4b6dfab83556e844 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | visFitDir.m | .m | humanshape-master/fitting/visFitDir.m | 1,738 | utf_8 | c4eeab2df979b5ed08775cd5a61ba92d | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | rigidAlignTemplate2Scan.m | .m | humanshape-master/fitting/rigidAlignTemplate2Scan.m | 1,850 | utf_8 | 28403c4f5d0da5161bed61a015805f7b | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | readLandMarksAll.m | .m | humanshape-master/fitting/readLandMarksAll.m | 1,872 | utf_8 | 7cdc6825ab2bd54fa634148889a4a435 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | registerJoint.m | .m | humanshape-master/fitting/registerJoint.m | 2,535 | utf_8 | fbdad97168144efbd34845bf57bb9599 | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | learnPCA.m | .m | humanshape-master/learning/learnPCA.m | 1,843 | utf_8 | 33c13214054e5e858c8889e5ac8d001f | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | readFits.m | .m | humanshape-master/learning/readFits.m | 1,502 | utf_8 | c8909157da012da871bbb1f70de6ae8e | %{
This file is part of the evaluation of the 3D human shape model as described in the paper:
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt and Bernt Schiele
Building Statistical Shape Spaces for 3D Human Modeling
ArXiv, March 2015
Please cite the paper if you are using thi... |
github | leonid-pishchulin/humanshape-master | gammarnd.m | .m | humanshape-master/external/lbfgsb-for-matlab/gammarnd.m | 423 | utf_8 | e69826f386e98d7396cc6a06584c872c | % GAMMARND(A) produces a single random deviate from the Gamma
% distribution with mean A and variance A.
function x = gammarnd (a)
reject = true;
while reject
y0 = log(a)-1/sqrt(a);
c = a - exp(y0);
y = log(rand).*sign(rand-0.5)/c + log(a);
f = a*y-exp(y) - (a*y0 - exp(y0));
... |
github | mriphysics/water_selective_pulses-master | sinc_gauss_subpulse.m | .m | water_selective_pulses-master/lib/sinc_gauss_subpulse.m | 1,259 | utf_8 | 2a932088c9f69186833666615d9a8348 | %%=========================================================================
% 4-2-09: SJM Generate subpulse waveform for binomial sequence
function [pulse,t] = sinc_gauss_subpulse(dur,varargin)
%% set constants and defaults
t0 = dur/2; % time of pulse centre (?)
ncycles = 5; % scanner sets this to 2 if ... |
github | mriphysics/water_selective_pulses-master | gen_binomial_sequence.m | .m | water_selective_pulses-master/lib/gen_binomial_sequence.m | 20,266 | utf_8 | 5abe978bc07a0ac7c6b9244fb3800428 | %%=========================================================================
% 4-2-09: SJM Generate pulse and gradient waveforms for binomial sequence
% 23-3-09: allow sequence with no flyback gradient
function [rfpulse,g,timing] = gen_binomial_sequence(weights,varargin)
%% --- Define Constants and parameters --... |
github | harrydragon/MATLAB-master | A_fhp.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Measurements/A_fhp.m | 576 | utf_8 | 546e3a8b121df921d171522cafec09af | % A_fhp.m
%
% Takes measurements in the upper half-plane of the 2D Fourier transform.
%
% Usage: b = A_fhp(x, OMEGA)
%
% x - N vector
%
% b - K vector = [mean; real part(OMEGA); imag part(OMEGA)]
%
% OMEGA - K/2-1 vector denoting which Fourier coefficients to use
% (the real and imag parts of each freq are kept... |
github | harrydragon/MATLAB-master | At_fhp.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Measurements/At_fhp.m | 613 | utf_8 | e42938636cace8af895181b10ca2fa1c | % At_fhp.m
%
% Adjoint of At_fhp (2D Fourier half plane measurements).
%
% Usage: x = At_fhp(b, OMEGA, n)
%
% b - K vector = [mean; real part(OMEGA); imag part(OMEGA)]
%
% OMEGA - K/2-1 vector denoting which Fourier coefficients to use
% (the real and imag parts of each freq are kept).
%
% n - Image is nxn pixe... |
github | harrydragon/MATLAB-master | A_f.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Measurements/A_f.m | 659 | utf_8 | 21e5a10a1fc2848a7455f0901d893064 | % A_f.m
%
% Takes "scrambled Fourier" measurements.
%
% Usage: b = A_f(x, OMEGA, P)
%
% x - N vector
%
% b - K vector = [real part; imag part]
%
% OMEGA - K/2 vector denoting which Fourier coefficients to use
% (the real and imag parts of each freq are kept).
%
% P - Permutation to apply to the input vector. F... |
github | harrydragon/MATLAB-master | LineMask.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Measurements/LineMask.m | 832 | utf_8 | 42787892f182a5dbbca55315f88daaec | % LineMask.m
%
% Returns the indicator of the domain in 2D fourier space for the
% specified line geometry.
% Usage : [M,Mh,mi,mhi] = LineMask(L,N)
%
% Written by : Justin Romberg
% Created : 1/26/2004
% Revised : 12/2/2004
function [M,Mh,mi,mhi] = LineMask(L,N)
thc = linspace(0, pi-pi/L, L);
%thc = linspace(pi/(2... |
github | harrydragon/MATLAB-master | At_f.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Measurements/At_f.m | 718 | utf_8 | c3639ceb479abeddedfe954c615b1f6d | % At_f.m
%
% Adjoint for "scrambled Fourier" measurements.
%
% Usage: x = At_f(b, N, OMEGA, P)
%
% b - K vector = [real part; imag part]
%
% N - length of output x
%
% OMEGA - K/2 vector denoting which Fourier coefficients to use
% (the real and imag parts of each freq are kept).
%
% P - Permutation to apply to... |
github | harrydragon/MATLAB-master | l1qc_newton.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/l1qc_newton.m | 4,413 | utf_8 | 750b8c23345a6bdd982b28e78c131e5e | % l1qc_newton.m
%
% Newton algorithm for log-barrier subproblems for l1 minimization
% with quadratic constraints.
%
% Usage:
% [xp,up,niter] = l1qc_newton(x0, u0, A, At, b, epsilon, tau,
% newtontol, newtonmaxiter, cgtol, cgmaxiter)
%
% x0,u0 - starting points
%
% A - Either a handle to a... |
github | harrydragon/MATLAB-master | tvqc_newton.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/tvqc_newton.m | 5,549 | utf_8 | 8a5ce49eabb2be396220c5a0ba033f26 | % tvqc_newton.m
%
% Newton algorithm for log-barrier subproblems for TV minimization
% with quadratic constraints.
%
% Usage:
% [xp,tp,niter] = tvqc_newton(x0, t0, A, At, b, epsilon, tau,
% newtontol, newtonmaxiter, cgtol, cgmaxiter)
%
% x0,t0 - starting points
%
% A - Either a handle to a... |
github | harrydragon/MATLAB-master | cgsolve.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/cgsolve.m | 1,693 | utf_8 | f818f81750d26fa1c5a1c11e4f6d73d5 | % cgsolve.m
%
% Solve a symmetric positive definite system Ax = b via conjugate gradients.
%
% Usage: [x, res, iter] = cgsolve(A, b, tol, maxiter, verbose)
%
% A - Either an NxN matrix, or a function handle.
%
% b - N vector
%
% tol - Desired precision. Algorithm terminates when
% norm(Ax-b)/norm(b) < tol .
%
% ma... |
github | harrydragon/MATLAB-master | tvdantzig_newton.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/tvdantzig_newton.m | 5,825 | utf_8 | 7ee8416bac076fa4edfcebce1cc20961 | % tvdantzig_newton.m
%
% Newton iterations for TV Dantzig log-barrier subproblem.
%
% Usage : [xp, tp, niter] = tvdantzig_newton(x0, t0, A, At, b, epsilon, tau,
% newtontol, newtonmaxiter, cgtol, cgmaxiter)
%
% x0,t0 - Nx1 vectors, initial points.
%
% A - Either a handle to a f... |
github | harrydragon/MATLAB-master | l1eq_pd.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/l1eq_pd.m | 6,002 | utf_8 | 519eccf3e3f28108ab3af997823e117a | % l1eq_pd.m
%
% Solve
% min_x ||x||_1 s.t. Ax = b
%
% Recast as linear program
% min_{x,u} sum(u) s.t. -u <= x <= u, Ax=b
% and use primal-dual interior point method
%
% Usage: xp = l1eq_pd(x0, A, At, b, pdtol, pdmaxiter, cgtol, cgmaxiter)
%
% x0 - Nx1 vector, initial point.
%
% A - Either a handle to a function t... |
github | harrydragon/MATLAB-master | l1decode_pd.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/l1decode_pd.m | 5,068 | utf_8 | 5a7f73e754c11d737b01d0fd89e745e7 | % l1decode_pd.m
%
% Decoding via linear programming.
% Solve
% min_x ||b-Ax||_1 .
%
% Recast as the linear program
% min_{x,u} sum(u) s.t. -Ax - u + y <= 0
% Ax - u - y <= 0
% and solve using primal-dual interior point method.
%
% Usage: xp = l1decode_pd(x0, A, At, y, pdtol, pdmaxiter, cgtol... |
github | harrydragon/MATLAB-master | l1dantzig_pd.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/l1dantzig_pd.m | 7,016 | utf_8 | fd7af40253904ac7727a4b14f7a1254f | % l1dantzig_pd.m
%
% Solves
% min_x ||x||_1 subject to ||A'(Ax-b)||_\infty <= epsilon
%
% Recast as linear program
% min_{x,u} sum(u) s.t. x - u <= 0
% -x - u <= 0
% A'(Ax-b) - epsilon <= 0
% -A'(Ax-b) - epsilon <= 0
% and use primal-dual interior point method.
%
% U... |
github | harrydragon/MATLAB-master | tveq_newton.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/tveq_newton.m | 5,524 | utf_8 | d470ee389c618dbc138a667cbea5e8ea | % tveq_newton.m
%
% Newton algorithm for log-barrier subproblems for TV minimization
% with equality constraints.
%
% Usage:
% [xp,tp,niter] = tveq_newton(x0, t0, A, At, b, tau,
% newtontol, newtonmaxiter, slqtol, slqmaxiter)
%
% x0,t0 - starting points
%
% A - Either a handle to a functio... |
github | harrydragon/MATLAB-master | tvqc_logbarrier.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/tvqc_logbarrier.m | 3,654 | utf_8 | ad1aa6471d767fcc63a34dbe98197e59 | % tvqc_logbarrier.m
%
% Solve quadractically constrained TV minimization
% min TV(x) s.t. ||Ax-b||_2 <= epsilon.
%
% Recast as the SOCP
% min sum(t) s.t. ||D_{ij}x||_2 <= t, i,j=1,...,n
% ||Ax - b||_2 <= epsilon
% and use a log barrier algorithm.
%
% Usage: xp = tvqc_logbarrier(x0, A, At, b, epsil... |
github | harrydragon/MATLAB-master | l1qc_logbarrier.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/l1qc_logbarrier.m | 3,536 | utf_8 | 9bffbc3122958794179c8c7417cb8cd5 | % l1qc_logbarrier.m
%
% Solve quadratically constrained l1 minimization:
% min ||x||_1 s.t. ||Ax - b||_2 <= \epsilon
%
% Reformulate as the second-order cone program
% min_{x,u} sum(u) s.t. x - u <= 0,
% -x - u <= 0,
% 1/2(||Ax-b||^2 - \epsilon^2) <= 0
% and use a log barrier al... |
github | harrydragon/MATLAB-master | tvdantzig_logbarrier.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/tvdantzig_logbarrier.m | 3,633 | utf_8 | 0aa874d89c9fb18aae6b73a6ce0359a0 | % tvdantzig_logbarrier.m
%
% Solve the total variation Dantzig program
%
% min_x TV(x) subject to ||A'(Ax-b)||_\infty <= epsilon
%
% Recast as the SOCP
% min sum(t) s.t. ||D_{ij}x||_2 <= t, i,j=1,...,n
% <a_{ij},Ax - b> <= epsilon i,j=1,...,n
% and use a log barrier algorithm.
%
% Usage: xp = tvd... |
github | harrydragon/MATLAB-master | tveq_logbarrier.m | .m | MATLAB-master/MN/LAB2/compressed-sensing-tutorial/l1magic/Optimization/tveq_logbarrier.m | 3,514 | utf_8 | 271088c0c59558548a5362667ae6e32e | % tveq_logbarrier.m
%
% Solve equality constrained TV minimization
% min TV(x) s.t. Ax=b.
%
% Recast as the SOCP
% min sum(t) s.t. ||D_{ij}x||_2 <= t, i,j=1,...,n
% Ax=b
% and use a log barrier algorithm.
%
% Usage: xp = tveq_logbarrier(x0, A, At, b, lbtol, mu, slqtol, slqmaxiter)
%
% x0 - Nx1 vec... |
github | qinhongwei/softmax-vs-svm-master | ncc.m | .m | softmax-vs-svm-master/matlab/ncc.m | 1,126 | utf_8 | 59b545adfead378d92807f3a3ff542bf | %{
Copyright (C) 2013 Yichuan Tang. contact: tang at cs.toronto.edu
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This progr... |
github | qinhongwei/softmax-vs-svm-master | net_layers_deserialize.m | .m | softmax-vs-svm-master/matlab/net_layers_deserialize.m | 3,154 | utf_8 | 269936aa2b2c0f4821fa832a57016a7d | %{
Copyright (C) 2013 Yichuan Tang.
contact: tang at cs.toronto.edu
http://www.cs.toronto.edu/~tang
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your optio... |
github | qinhongwei/softmax-vs-svm-master | default_nn_callback.m | .m | softmax-vs-svm-master/matlab/default_nn_callback.m | 700 | utf_8 | be83e1e0df74e965e2dfe1c1b312094f | % with support for multiple streams
function [X Y ValidX ValidY] = default_nn_callback()
global input;
global D;
global Dy;
global nSamples;
global nBatches;
global nValidBatches;
X = cell(1,numel(input.X));
ValidX = cell(1,numel(input.X));
for i = 1:numel(input.X)
X{i} = single(batchdata_reshape( input.X{i}, [nSa... |
github | qinhongwei/softmax-vs-svm-master | myclassify_conv_nn_softmax.m | .m | softmax-vs-svm-master/matlab/myclassify_conv_nn_softmax.m | 8,435 | utf_8 | 1e4d22431a9cb2b8fc3f5335321033e6 | %{
Copyright (C) 2013 Yichuan Tang.
contact: tang at cs.toronto.edu
http://www.cs.toronto.edu/~tang
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your optio... |
github | qinhongwei/softmax-vs-svm-master | write_grid_images.m | .m | softmax-vs-svm-master/matlab/write_grid_images.m | 1,986 | utf_8 | c8d5dd90264f265f1143d1b592c1d14d | %{
Copyright (C) 2013 Yichuan Tang. contact: tang at cs.toronto.edu
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This progr... |
github | qinhongwei/softmax-vs-svm-master | net_layers_serialize.m | .m | softmax-vs-svm-master/matlab/net_layers_serialize.m | 4,130 | utf_8 | 471d9ad42f4cc8cc075581d08f8349d5 | %{
Copyright (C) 2013 Yichuan Tang.
contact: tang at cs.toronto.edu
http://www.cs.toronto.edu/~tang
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your optio... |
github | qinhongwei/softmax-vs-svm-master | net_layers_init.m | .m | softmax-vs-svm-master/matlab/net_layers_init.m | 3,672 | utf_8 | 884f9286d5809467179316a67a031c52 | %{
Copyright (C) 2013 Yichuan Tang.
contact: tang at cs.toronto.edu
http://www.cs.toronto.edu/~tang
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your optio... |
github | qinhongwei/softmax-vs-svm-master | gprintf.m | .m | softmax-vs-svm-master/matlab/gprintf.m | 246 | utf_8 | 3ca2eb849c7b776acc6dd087c45f95d8 |
%usage:
% instead of fprintf, use gprintf
function [] = gprintf(msg, varargin )
txtmsg = sprintf(msg, varargin{1:end} );
figure(999); clf;
axis off;
set(gcf, 'Color', 'white');
text( -0.1, 0.5, txtmsg, 'EdgeColor', 'blue', 'FontSize', 30); |
github | qinhongwei/softmax-vs-svm-master | sc.m | .m | softmax-vs-svm-master/matlab/sc.m | 146 | utf_8 | eed0b7ba6a5844fb8c71f2e2fc4d471d | % Seralize in Column major fashion: same as (:), but we can use
% e.g. sc( im(1:10,1:10)' )
function [ rowvec ] = sc( image )
rowvec = image(:)'; |
github | qinhongwei/softmax-vs-svm-master | fe_cv_48.m | .m | softmax-vs-svm-master/matlab/fe_cv_48.m | 5,576 | utf_8 | c5d75b465014af16fb49c68f39d06ac2 | %{
Copyright (C) 2013 Yichuan Tang.
contact: tang at cs.toronto.edu
http://www.cs.toronto.edu/~tang
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your optio... |
github | AlfredXiangWu/face_verification_experiment-master | vl_roc.m | .m | face_verification_experiment-master/code/vl_roc.m | 10,161 | utf_8 | 1a2dd324e9332fc31af31dfb41425776 | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC ROC curve.
% [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
% Characteristic (ROC) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative one. SCORES are... |
github | AlfredXiangWu/face_verification_experiment-master | face_db_align.m | .m | face_verification_experiment-master/code/face_db_align.m | 4,509 | utf_8 | 4426c0a166ad354a4842788735db7a86 | function res = face_db_align(face_dir, ffp_dir, ec_mc_y, ec_y, img_size, save_dir)
% center of eyes (ec), center of l&r mouth(mc), rotate and resize
% ec_mc_y: y_mc-y_ec, diff of height of ec & mc, to scale the image.
% ec_y: top of ec, to crop the face.
clck = clock();
log_fn = sprintf('fa2_%4d%02d%02d%02d%02d%02d.l... |
github | AlfredXiangWu/face_verification_experiment-master | vl_pr.m | .m | face_verification_experiment-master/code/vl_pr.m | 9,135 | utf_8 | c5d1b9d67f843d10c0b2c6b48fab3c53 | function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative on... |
github | AlfredXiangWu/face_verification_experiment-master | evaluate.m | .m | face_verification_experiment-master/code/+evaluation/evaluate.m | 794 | utf_8 | 314cb210d3873a7ed213132bfdb21993 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function result = evaluate(config, scores, gt)
scores = reshape(scores, 1, []);
switch config
case 'ap'
[res, extra] = evaluation.ap.ev... |
github | AlfredXiangWu/face_verification_experiment-master | eval_best.m | .m | face_verification_experiment-master/code/+evaluation/+accuracy/eval_best.m | 760 | utf_8 | d6b2827fa8c6d71da9202777323361a7 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function [res, extra] = eval_best(config, scores, gt)
% finds an optimal threshold - the threshold which maximises the accuracy
% threshold scores and get th... |
github | AlfredXiangWu/face_verification_experiment-master | eval.m | .m | face_verification_experiment-master/code/+evaluation/+accuracy/eval.m | 364 | utf_8 | 4c83bb71f43434ef3a659bc96f38cd50 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function [res, extra] = eval(config, scores, gt)
% predicted labels
class = 2 * (scores >= config.threshold) - 1;
% class-n accuracy
res = mean(c... |
github | AlfredXiangWu/face_verification_experiment-master | eval.m | .m | face_verification_experiment-master/code/+evaluation/+ap/eval.m | 288 | utf_8 | 857d1a2eadc52f2dc2c02c62e5272211 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function [res, extra] = eval(config, scores, gt)
[~,~,info] = vl_pr(gt, scores);
res = info.auc * 100;
extra = info;
end
|
github | AlfredXiangWu/face_verification_experiment-master | eval.m | .m | face_verification_experiment-master/code/+evaluation/+roc/eval.m | 415 | utf_8 | 26bc17027058b476501887c08907e512 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function [res, extra] = eval(config, scores, gt)
[~,~,info] = vl_roc(gt, scores);
% the accuracy at the ROC operating point where the error rates are equ... |
github | WirelessTestbedsAcademy/BasicCR-master | prepend_cp.m | .m | BasicCR-master/TUD/prepend_cp.m | 72 | utf_8 | c5a2fd0aec9158610ad64a0d16db9c81 |
function xcp = prepend_cp(x, NCP)
xcp = [x(end-NCP+1:end); x];
end
|
github | WirelessTestbedsAcademy/BasicCR-master | sense.m | .m | BasicCR-master/TUD/sense.m | 3,462 | utf_8 | 74de904be23ae38f6299addf0563dadc | %White space sensing
%Input: frequency and corresponding magnitude (column vectors)
%Output: matrix whitespace - first column: start of white space area -
%second column - end of white space areas
function whitespace = sense(frequency, magnitude, treshold, min_space)
%Input data - data(:,1) = frequency - data(:,2... |
github | RSBradley/TomoTools-master | TTinputdlg.m | .m | TomoTools-master/misc/TTinputdlg.m | 17,065 | utf_8 | 32a9815baeb7296b77485e02bc9ccb28 | function Answer=TTinputdlg(Prompt, Title, NumLines, DefAns, Resize)
%INPUTDLG Input dialog box.
% ANSWER = INPUTDLG(PROMPT) creates a modal dialog box that returns user
% input for multiple prompts in the cell array ANSWER. PROMPT is a cell
% array containing the PROMPT strings.
%
% INPUTDLG uses UIWAIT to suspend ... |
github | RSBradley/TomoTools-master | PhaseRetrieval_addin.m | .m | TomoTools-master/addins/PhaseRetrieval_addin.m | 18,617 | utf_8 | 7342946cb14b3c317f692650577771aa | function mod_hdl = PhaseRetrieval_addin(handles)
% Panel addin for Phase Retrieval
% Written by: Rob S. Bradley (c) 2015
%% LOAD DEFAULTS FOR SIZING COMPONENTS =================================
margin = handles.defaults.margin_sz;
button_sz = handles.defaults.button_sz;
edit_sz = handles.defaults.edit_sz;
info_sz = ... |
github | RSBradley/TomoTools-master | ShortScanAlignment_addin.m | .m | TomoTools-master/addins/ShortScanAlignment_addin.m | 12,493 | utf_8 | 35a9476293554b035e0351d9931d4699 | function mod_hdl = ShortScanAlignment_addin(handles)
% Panel addin for Alignment of projection images using an additional short scan
% Written by: Rob S. Bradley (c) 2015
%% LOAD DEFAULTS FOR SIZING COMPONENTS =================================
margin = handles.defaults.margin_sz;
button_sz = handles.defaults.button... |
github | RSBradley/TomoTools-master | Export_addin.m | .m | TomoTools-master/addins/Export_addin.m | 12,389 | utf_8 | a671a6fe1f72486c2721805b89a8604d | function mod_hdl = Export_addin(handles)
% Panel addin for exporting data
% Robert S. Bradley (c) 2015
%% LOAD DEFAULTS FOR SIZING COMPONENTS =================================
margin = handles.defaults.margin_sz;
button_sz = handles.defaults.button_sz;
edit_sz = handles.defaults.edit_sz;
info_sz = handles.defaults.in... |
github | RSBradley/TomoTools-master | Reconstruction_addin.m | .m | TomoTools-master/addins/Reconstruction_addin.m | 42,638 | utf_8 | 04f583ae213e4be1cd7d7af2eb686c75 | function mod_hdl = Reconstruction_addin(handles)
% Panel addin for reconstruction with the ASTRA TOOLBOX
% Written by: Rob S. Bradley (c) 2015
%
%
% To do:
%1. add check to see if ASTRA toolbox is installed
%% LOAD DEFAULTS FOR SIZING COMPONENTS =================================
margin = handles.defaults.margin_sz;
... |
github | RSBradley/TomoTools-master | filterProjections.m | .m | TomoTools-master/addins/reconstruction/filterProjections.m | 4,649 | utf_8 | a50408c992bfd4db62f57af77f613a68 | function [p,H] = filterProjections(p_in, filter, R12, pixel_size, angles, detector_offsets, CS, d, usegpu)
%assume p_in is cols x angles x rows
%R12 = StoRA + DtoRA distances
%detectors offsets = x offset, y offset in same units as pixel_size
p = p_in;
if nargin<9
usegpu = 0;
end
if nargin<8
d=1;
end
if nar... |
github | RSBradley/TomoTools-master | ssvkernel.m | .m | TomoTools-master/third party/ssvkernel.m | 11,279 | utf_8 | dfbbc615aaad4670546428e79bb9de6c | function [y,t,optw,gs,C,confb95,yb] = ssvkernel(x,tin, WIN)
% [y,t,optw,gs,C,confb95,yb] = ssvkernel(x,t,W)
%
% Function `ssvkernel' returns an optimized kernel density estimate
% using a Gauss kernel function with bandwidths locally adapted to data.
%
% Examples:
% >> x = 0.5-0.5*log(rand(1,1e3)); t = linspace(0,3,50... |
github | RSBradley/TomoTools-master | splash.m | .m | TomoTools-master/third party/splash.m | 7,702 | utf_8 | ece14f227e8663fd9b05e997b7d6024c | function varargout = splash(varargin)
%SPLASH Creates a splash screen.
% SPLASH(FILENAME,FMT,TIME) creates a splash screen using the image from the
% file specified by the string FILENAME, where the string FMT specifies
% the format of the file and TIME is the duration time of the splash
% screen in millisecon... |
github | RSBradley/TomoTools-master | xml_write.m | .m | TomoTools-master/third party/XML read write/xml_write.m | 15,936 | utf_8 | 90d418cb00695e0c8880b36158d7cbec | function DOMnode = xml_write(filename, tree, RootName, Pref)
%XML_WRITE Writes Matlab data structures to XML file
%
% DESCRIPTION
% xml_write( filename, tree) Converts Matlab data structure 'tree' containing
% cells, structs, numbers and strings to Document Object Model (DOM) node
% tree, then saves it to XML file 'fi... |
github | RSBradley/TomoTools-master | xml_read.m | .m | TomoTools-master/third party/XML read write/xml_read.m | 22,164 | utf_8 | baf8a33b1b8bc8c7eb42d93556b11196 | function [tree, RootName, DOMnode] = xml_read(xmlfile, Pref)
%XML_READ reads xml files and converts them into Matlab's struct tree.
%
% DESCRIPTION
% tree = xml_read(xmlfile) reads 'xmlfile' into data structure 'tree'
%
% tree = xml_read(xmlfile, Pref) reads 'xmlfile' into data structure 'tree'
% according to your pref... |
github | RSBradley/TomoTools-master | pptimgdump.m | .m | TomoTools-master/file readers/TXM read-write/freadss/pptimgdump.m | 10,794 | utf_8 | 61b537317ecfff758a6bd82aadd6c013 | function varargout = pptimgdump(pptfname, gunzp)
% PPTIMGDUMP - Dumps images from a PowerPoint presentation
%
% [FNAMES, ERR, ERRMSG] = PPTIMGDUMP(PPTFNAME, GUNZP)
%
% PPTFNAME - PowerPoint filename (char)
% GUNZP - (Optional logical) If true, compressed WMF and EMF files will be
% uncompressed. Default i... |
github | RSBradley/TomoTools-master | h5load.m | .m | TomoTools-master/file readers/NeXus read/h5load.m | 2,729 | utf_8 | bf89c907b03ccd0afc84b3acc7f939be | function data=h5load(filename, path)
%
% data = H5LOAD(filename)
% data = H5LOAD(filename, path_in_file)
%
% Load data in a HDF5 file to a Matlab structure.
%
% Parameters
% ----------
%
% filename
% Name of the file to load data from
% path_in_file : optional
% Path to the part of the HDF5 file to load
%
% Au... |
github | RSBradley/TomoTools-master | tifftagsprocess.m | .m | TomoTools-master/file readers/Tiffstack read-write/tifftagsprocess.m | 18,812 | utf_8 | 45a641808aea37790c37372ac168bebd | function info = tifftagsprocess ( info )
% TIFFTAGSPROCESS Processes raw TIFF tags into human-readable form
%
% INFO = TIFFTAGSPROCESS(TAGS) processes the cell array TAGS into a
% structure with name/value pairs. There will be one structure for
% each image in the image file. If one of the tag elements
% ind... |
github | asbroad/fast-rcnn-master | voc_eval.m | .m | fast-rcnn-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,389 | utf_8 | fd77d0da53b2585aa65e0da5edc5fe33 | function res = voc_eval(path, comp_id, test_set, output_dir, rm_res)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir, rm_res);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n... |
github | asbroad/fast-rcnn-master | fast_rcnn_load_net.m | .m | fast-rcnn-master/matlab/fast_rcnn_load_net.m | 687 | utf_8 | a32914abb31b109189f11729893e76a1 | % --------------------------------------------------------
% Fast R-CNN
% Copyright (c) 2015 Microsoft
% Licensed under The MIT License [see LICENSE for details]
% Written by Ross Girshick
% --------------------------------------------------------
function model = fast_rcnn_load_net(def, net, use_gpu)
% Load a Fast R-... |
github | asbroad/fast-rcnn-master | showboxes.m | .m | fast-rcnn-master/matlab/showboxes.m | 741 | utf_8 | 1429b3b8aebb962f3aefc255f17c204b | % --------------------------------------------------------
% Fast R-CNN
% Copyright (c) 2015 Microsoft
% Licensed under The MIT License [see LICENSE for details]
% Written by Ross Girshick
% --------------------------------------------------------
function showboxes(im, boxes)
image(im);
axis image;
axis off;
set(gcf... |
github | asbroad/fast-rcnn-master | fast_rcnn_demo.m | .m | fast-rcnn-master/matlab/fast_rcnn_demo.m | 1,815 | utf_8 | bf4f15d2215f13cd6cfa12f13d5b6aa8 | % --------------------------------------------------------
% Fast R-CNN
% Copyright (c) 2015 Microsoft
% Licensed under The MIT License [see LICENSE for details]
% Written by Ross Girshick
% --------------------------------------------------------
function fast_rcnn_demo()
% Fast R-CNN demo (in matlab).
[folder, name... |
github | asbroad/fast-rcnn-master | fast_rcnn_im_detect.m | .m | fast-rcnn-master/matlab/fast_rcnn_im_detect.m | 4,211 | utf_8 | 728920133ba2a640b1cb4e52f41c1977 | % --------------------------------------------------------
% Fast R-CNN
% Copyright (c) 2015 Microsoft
% Licensed under The MIT License [see LICENSE for details]
% Written by Ross Girshick
% --------------------------------------------------------
function dets = fast_rcnn_im_detect(model, im, boxes)
% Perform detecti... |
github | yanweifu/embedding_zero-shot-learning-master | Fu_libsvmsvr_multi_label_wrapper_par_for_save_dataWeight.m | .m | embedding_zero-shot-learning-master/internal/Fu_libsvmsvr_multi_label_wrapper_par_for_save_dataWeight.m | 29,448 | utf_8 | e5cf5a8e216119a5051f3a0f4963eec2 | function [pL_Xtr, pL_Xte, acc_te, acc_tr, te_au, te_r2, te_r0, curve, models] = Fu_libsvmsvr_multi_label_wrapper_par_for_save_dataWeight(mat_name, Xtrain,train_attr,Xtest, test_attr,opts)
%
% [pL_Ytrain, pL_Ytest, acc_te, acc_tr, te_au, te_r2, te_r0, curve, models] = Fu_libsvmsvr_multi_label_wrapper(Ytrain,train_attr... |
github | yanweifu/embedding_zero-shot-learning-master | addpath_folder.m | .m | embedding_zero-shot-learning-master/internal/addpath_folder.m | 536 | utf_8 | 1e7305ba182e6849f98d850a21a9daa3 | %% install all
%% addpath_folder(path)
% hierarchically add all inner folders.
% this function is equal to 'genpath'; but I just like the way of I am
% doing it.
function addpath_folder(path)
addpath(path);
folders = dir(path);
% the first two is '.', and '..' .
for i= 1:length(folders)
if (folders(i).isdir)&&~str... |
github | yanweifu/embedding_zero-shot-learning-master | slmetric_pw.m | .m | embedding_zero-shot-learning-master/internal/pwmetric/slmetric_pw.m | 11,906 | utf_8 | e3864f6e2643ac4e2c007a18ef7febc6 | function M = slmetric_pw(X1, X2, mtype, varargin)
%SLMETRIC_PW Compute the metric between column vectors pairwisely
%
% [ Syntax ]
% - M = slmetric_pw(X1, X2, mtype);
% - M = slmetric_pw(X1, X2, mtype, ...);
%
% [ Arguments ]
% - X1, X2: the sample matrices
% - mtype: the string indicating... |
github | yanweifu/embedding_zero-shot-learning-master | myProcessOptions.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/misc/myProcessOptions.m | 674 | utf_8 | b94d252a960faa95a3074129247619e6 | function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_PSSgb.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_PSSgb.m | 5,227 | utf_8 | a9eb66c8e982fbaafe68d8ed69218c35 | function [w] = L1General2_PSSG0(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,corrections,Dtype,quadraticInit] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'correction... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_OWL.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_OWL.m | 5,044 | utf_8 | 3b4c561f079b4d1741041922651ae160 | function [w] = L1General2_OWL(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,corrections,quadraticInit] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'corrections',100,'... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_BBST.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_BBST.m | 4,299 | utf_8 | 151b03a7bf3bac74010da64d507dde36 | function [w] = L1General2_SpaRSA(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,memory] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'memory',10);
if verbose
fp... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_OPG.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_OPG.m | 3,409 | utf_8 | f6601dd4111eaefdae1b3d602de90e6f | function [w] = L1General2_AS(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,L] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'L',[]);
if verbose
fprintf('%6s %6s %12s %12s %12s %6s\n',... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_PSSsp.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_PSSsp.m | 5,053 | utf_8 | 8be1cd58185971670efc67b56c8c9d37 | function [w] = L1General2_OWL(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,corrections,quadraticInit] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'corrections',100,'... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_SPG.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_SPG.m | 4,551 | utf_8 | 8e1b336360d38712a37a4900eb8273d1 | function [w] = L1General2_AS(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,memory] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'memory',10);
if verbose
fprint... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_BBSG.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_BBSG.m | 4,569 | utf_8 | e48e1daae5448713c3a04496920edea0 | function [w] = L1General2_OWL(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,memory] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'memory',10);
if verbose
fprin... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_TMP.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_TMP.m | 4,730 | utf_8 | c9148f721a10663dc2ec18257e9348f4 | function [w] = L1General2_AS(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,corrections] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'corrections',100);
if verbose
... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_AS.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_AS.m | 6,048 | utf_8 | aa13552f321f752ce1161d9ef7e136c4 | function [w] = L1General2_AS(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,corrections] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'corrections',100);
if verbose
... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_PSSas.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_PSSas.m | 6,379 | utf_8 | 29e805f9ea293c2ada8d5dd18b83ea69 | function [w] = L1General2_AS(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,corrections,K] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'corrections',100,'K',[]);
if... |
github | yanweifu/embedding_zero-shot-learning-master | L1General2_DSST.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General2_2010/L1General2_DSST.m | 3,905 | utf_8 | 4de1ace68c73063b3c091b5e9d80dcb6 | function [w] = L1General2_DSST(funObj,w,lambda,options)
%% Process Options
if nargin < 4
options = [];
end
[verbose,optTol,progTol,maxIter,suffDec,quadraticInit] = ...
myProcessOptions(options,'verbose',1,'optTol',1e-5,'progTol',1e-9,...
'maxIter',500,'suffDec',1e-4,'quadraticInit',0);
if verb... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralGrafting.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralGrafting.m | 4,097 | utf_8 | 3fdae178a155569198960ba1e25acd59 | function [w,fEvals] = L1GeneralGrafting(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Grafting
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:})
% w - initial guess
% lambda - scale of L1 penalty on each va... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralOrthantWise.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralOrthantWise.m | 5,626 | utf_8 | 04cc0fb983a64b9cb8009024692af548 | function [w,fEvals] = L1GeneralOrthantWise(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Orthant-Wise Regression
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:})
% w - initial guess
% lambda - scale of L1 ... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralCoordinateDescent.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralCoordinateDescent.m | 8,597 | utf_8 | 4a1dd52c83f35ed83d78967c796a1037 | function [w,fEvals] = L1GeneralCoordinateDescent(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Coordinate Descent
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:})
% w - initial guess
% lambda - scale of L1... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralSubGradient.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralSubGradient.m | 4,155 | utf_8 | 667d775124fb6a0a434c48472315cb87 | function [w,fEvals] = L1GeneralSubGradient(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Sub-Gradient Descent on non-zero and zero but non-optimal variables
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:})
% ... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralProjectedSubGradientBB.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralProjectedSubGradientBB.m | 4,081 | utf_8 | 240a02f224d1ac119c5300b7f2ddb554 | function [w,fEvals] = L1GeneralProjectedSubGradientBB(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Projected sub-gradient descent with Barzilai-Borwein step length
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralSequentialQuadraticProgramming.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralSequentialQuadraticProgramming.m | 5,297 | utf_8 | fa82a694ea14eee0030a84c2085d2694 | function [w,fEvals] = L1GeneralSequentialQuadraticProgramming(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Sequential Quadratic Programming
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:})
% w - initial gues... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralProjection.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralProjection.m | 5,206 | utf_8 | 7987245d4a49e5737089ed6469e9a667 | function [w,fEvals] = L1GeneralProjection(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Two-Metric Projection method w/ non-negative variables
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:})
% w - initial gu... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralProjectedSubGradient.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralProjectedSubGradient.m | 6,077 | utf_8 | 70a1bc715db891a68aeac1332d715f74 | function [w,fEvals] = L1GeneralProjectedSubGradient(gradFunc,w,lambda,params,varargin)
% [w,fEvals] = L1GeneralProjectedSubGradient(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Orthant-Wise Regression
%
% Parameters
% gradFunc - func... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralIteratedRidge.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralIteratedRidge.m | 3,800 | utf_8 | 6ef4f393b86e0cb73d6b08efebc43c47 | function [w,fEvals] = L1GeneralIteratedRidge(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Iterated L2-Penalized Optimization using the approximation
% |w| =~ norm(w,2)/norm(w_old,1)
%
% Parameters
% gradFunc - function of the form gradFunc(... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralPatternSearch.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralPatternSearch.m | 4,525 | utf_8 | cc39ce8e2d6008c2ff382e805ae512a9 | function [w,fEvals] = L1GeneralPatternSearch(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Pattern Search
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:})
% w - initial guess
% lambda - scale of L1 penalty... |
github | yanweifu/embedding_zero-shot-learning-master | L1GeneralCompositeGradientAccelerated.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/L1General_2010/L1GeneralCompositeGradientAccelerated.m | 2,041 | utf_8 | 2aaf2b53e60b30e1dd7c71b9bb2f8ccb | function [w,fEvals] = L1GeneralProjectedSubGradient(gradFunc,w,lambda,params,varargin)
% Process input options
[verbose,maxIter,optTol,L] = ...
myProcessOptions(params,'verbose',1,'maxIter',500,...
'optTol',1e-6,'L',[]);
% Start log
if verbose
fprintf('%10s %10s %15s %15s %15s %8s\n','Iteration',... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Sample_VarMCMC.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/sample/UGM_Sample_VarMCMC.m | 2,621 | utf_8 | d9379d26bd1ac94a7da06ca58c6d39ce | function [samples] = UGM_Sample_VarMCMC(nodePot,edgePot,edgeStruct,burnIn,varProb)
% MCMC sampler that switches between random walk MH and variational MF
% sampling
%
% varProb is the probability of trying the variational move
% (set to 0 for purely variational proposals)
[nNodes,maxStates] = size(nodePot);
nEdges = s... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Sample_Junction.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/sample/UGM_Sample_Junction.m | 11,254 | utf_8 | 4a1d85d76ac905bfff520451b30b06e3 | function [samples] = UGM_Sample_Junction(nodePot,edgePot,edgeStruct,ordering)
debug = 0;
[nNodes,maxState] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
V = edgeStruct.V;
E = edgeStruct.E;
nStates = edgeStruct.nStates;
maxIter = edgeStruct.maxIter;
if nargin < 4
ordering = 1:nNodes;
... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Sample_Gibbs.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/sample/UGM_Sample_Gibbs.m | 1,461 | utf_8 | 7cfa79d5135d1ca84977f1f8c0db0b2b | function [samples] = UGM_Sample_Gibbs(nodePot,edgePot,edgeStruct,burnIn,y)
% [samples] = UGM_Sample_Gibbs(nodePot,edgePot,edgeStruct,burnIn,y)
% Single Site Gibbs Sampling
if nargin < 5
% Initialize
[junk y] = max(nodePot,[],2);
end
if edgeStruct.useMex
samples = UGM_Sample_GibbsC(nodePot,edgePot,edgeStruct.edgeE... |
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