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github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_alldist.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_alldist.m | 2,373 | utf_8 | 9ea1a36c97fe715dfa2b8693876808ff | function results = vl_test_alldist(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equa... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_ihashsum.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_ihashsum.m | 581 | utf_8 | edc283062469af62056b0782b171f5fc | function results = vl_test_ihashsum(varargin)
% VL_TEST_IHASHSUM
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(round(16*rand(2,100))) ;
sel = find(all(s.data==0)) ;
s.data(1,sel)=1 ;
function test_hash(s)
D = size(s.data,1) ;
K = 5 ;
h = zeros(1,K,'uint32') ;
id = zeros(D,K,'uint8');
next = zer... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_grad.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_grad.m | 434 | utf_8 | 4d03eb33a6a4f68659f868da95930ffb | function results = vl_test_grad(varargin)
% VL_TEST_GRAD
vl_test_init ;
function s = setup()
s.I = rand(150,253) ;
s.I_small = rand(2,2) ;
function test_equiv(s)
vl_assert_equal(gradient(s.I), vl_grad(s.I)) ;
function test_equiv_small(s)
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
function test_equiv... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_whistc.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_whistc.m | 1,384 | utf_8 | 81c446d35c82957659840ab2a579ec2c | function results = vl_test_whistc(varargin)
% VL_TEST_WHISTC
vl_test_init ;
function test_acc()
x = ones(1, 10) ;
e = 1 ;
o = 1:10 ;
vl_assert_equal(vl_whistc(x, o, e), 55) ;
function test_basic()
x = 1:10 ;
e = 1:10 ;
o = ones(1, 10) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
x = linspace(-1,11,100) ;
o =... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_roc.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_roc.m | 1,019 | utf_8 | 9b2ae71c9dc3eda0fc54c65d55054d0c | function results = vl_test_roc(varargin)
% VL_TEST_ROC
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[tpr,tnr] = vl_roc(s.labels,s.scores0) ;
vl_assert_almost_equal(tpr, [0 1 2 2 2 2] / 2) ;
vl_assert_almost_equal(tnr,... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_dsift.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_dsift.m | 2,048 | utf_8 | fbbfb16d5a21936c1862d9551f657ccc | function results = vl_test_dsift(varargin)
% VL_TEST_DSIFT
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = rgb2gray(single(I)) ;
function test_fast_slow(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_alldist2.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_alldist2.m | 2,284 | utf_8 | 89a787e3d83516653ae8d99c808b9d67 | function results = vl_test_alldist2(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
% TODO: test integer classes
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist2(zeros(15,12), zeros(15,0), 'kl2'), ...
... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_fisher.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_fisher.m | 1,703 | utf_8 | 41b28dce7f0d0ae5cb6abd942acbef56 | function results = vl_test_fisher(varargin)
% VL_TEST_FISHER
vl_test_init ;
function s = setup()
randn('state',0) ;
dimension = 5 ;
numData = 21 ;
numComponents = 3 ;
s.x = randn(dimension,numData) ;
s.mu = randn(dimension,numComponents) ;
s.sigma2 = ones(dimension,numComponents) ;
s.prior = ones(1,numComponents) ;
s... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_imsmooth.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_imsmooth.m | 1,837 | utf_8 | 718235242cad61c9804ba5e881c22f59 | function results = vl_test_imsmooth(varargin)
% VL_TEST_IMSMOOTH
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
I = max(min(vl_imdown(I),1),0) ;
s.I = single(I) ;
function test_pad_by_continuity(s)
% Convolving a constant signal padded with continuity does not change... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_svmtrain.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_svmtrain.m | 4,277 | utf_8 | 071b7c66191a22e8236fda16752b27aa | function results = vl_test_svmtrain(varargin)
% VL_TEST_SVMTRAIN
vl_test_init ;
end
function s = setup()
randn('state',0) ;
Np = 10 ;
Nn = 10 ;
xp = diag([1 3])*randn(2, Np) ;
xn = diag([1 3])*randn(2, Nn) ;
xp(1,:) = xp(1,:) + 2 + 1 ;
xn(1,:) = xn(1,:) - 2 + 1 ;
s.x = [xp xn] ;
s.y = [ones(1,Np) ... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_phow.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_phow.m | 549 | utf_8 | f761a3bb218af855986263c67b2da411 | function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color',... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_kmeans.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_kmeans.m | 3,632 | utf_8 | 719f7fca81e19eed5cc45c2ca251aad0 | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
function s = setup()
randn('sta... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_hikmeans.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_hikmeans.m | 463 | utf_8 | dc3b493646e66316184e86ff4e6138ab | function results = vl_test_hikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[tree, assign] = vl_hikmeans(s.data,3,100) ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
function test_elka... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_aib.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_aib.m | 1,277 | utf_8 | 78978ae54e7ebe991d136336ba4bf9c6 | function results = vl_test_aib(varargin)
% VL_TEST_AIB
vl_test_init ;
function s = setup()
s = [] ;
function test_basic(s)
Pcx = [.3 .3 0 0
0 0 .2 .2] ;
% This results in the AIB tree
%
% 1 - \
% 5 - \
% 2 - / \
% - 7
% 3 - \ /
% 6 - /
% 4 - /
%
% coded by the map [5 ... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_plotbox.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_plotbox.m | 414 | utf_8 | aa06ce4932a213fb933bbede6072b029 | function results = vl_test_plotbox(varargin)
% VL_TEST_PLOTBOX
vl_test_init ;
function test_basic(s)
figure(1) ; clf ;
vl_plotbox([-1 -1 1 1]') ;
xlim([-2 2]) ;
ylim([-2 2]) ;
close(1) ;
function test_multiple(s)
figure(1) ; clf ;
randn('state', 0) ;
vl_plotbox(randn(4,10)) ;
close(1) ;
function test_style(s)
figure... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_imarray.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_imarray.m | 795 | utf_8 | c5e6a5aa8c2e63e248814f5bd89832a8 | function results = vl_test_imarray(varargin)
% VL_TEST_IMARRAY
vl_test_init ;
function test_movie_rgb(s)
A = rand(23,15,3,4) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_indexed(s)
cmap = get(0,'DefaultFigureColormap') ;
A = uint8(size(cmap,1)*rand(23,15,4)) ;
A = min(A,size(cmap,1)-1) ;
B = vl_imarray(A,'m... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_homkermap.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_homkermap.m | 1,903 | utf_8 | c157052bf4213793a961bde1f73fb307 | function results = vl_test_homkermap(varargin)
% VL_TEST_HOMKERMAP
vl_test_init ;
function check_ker(ker, n, window, period)
args = {n, ker, 'window', window} ;
if nargin > 3
args = {args{:}, 'period', period} ;
end
x = [-1 -.5 0 .5 1] ;
y = linspace(0,2,100) ;
for conv = {@single, @double}
x = feval(conv{1}, x) ;... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_slic.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_slic.m | 200 | utf_8 | 12a6465e3ef5b4bcfd7303cd8a9229d4 | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1) ;
|
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_ikmeans.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_ikmeans.m | 466 | utf_8 | 1ee2f647ac0035ed0d704a0cd615b040 | function results = vl_test_ikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[centers, assign] = vl_ikmeans(s.data,100) ;
assign_ = vl_ikmeanspush(s.data, centers) ;
vl_assert_equal(assign,assign_) ;
function test_elk... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_mser.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_mser.m | 242 | utf_8 | 1ad33563b0c86542a2978ee94e0f4a39 | function results = vl_test_mser(varargin)
% VL_TEST_MSER
vl_test_init ;
function s = setup()
s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ;
function test_mser(s)
[regions,frames] = vl_mser(s.im) ;
mask = vl_erfill(s.im, regions(1)) ;
|
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_inthist.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_inthist.m | 811 | utf_8 | 459027d0c54d8f197563a02ab66ef45d | function results = vl_test_inthist(varargin)
% VL_TEST_INTHIST
vl_test_init ;
function s = setup()
rand('state',0) ;
s.labels = uint32(8*rand(123, 76, 3)) ;
function test_basic(s)
l = 10 ;
hist = vl_inthist(s.labels, 'numlabels', l) ;
hist_ = inthist_slow(s.labels, l) ;
vl_assert_equal(double(hist),hist_) ;
function... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_imdisttf.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_imdisttf.m | 1,885 | utf_8 | ae921197988abeb984cbcdf9eaf80e77 | function results = vl_test_imdisttf(varargin)
% VL_TEST_DISTTF
vl_test_init ;
function test_basic()
for conv = {@single, @double}
conv = conv{1} ;
I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ;
D = vl_imdisttf(I);
assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
I(2,2) = -3 ;
[D,map] = vl_imdisttf(I) ;
asse... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_vlad.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_vlad.m | 1,977 | utf_8 | d3797288d6edb1d445b890db3780c8ce | function results = vl_test_vlad(varargin)
% VL_TEST_VLAD
vl_test_init ;
function s = setup()
randn('state',0) ;
s.x = randn(128,256) ;
s.mu = randn(128,16) ;
assignments = rand(16, 256) ;
s.assignments = bsxfun(@times, assignments, 1 ./ sum(assignments,1)) ;
function test_basic (s)
x = [1, 2, 3] ;
mu = [0, 0, 0] ;
a... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_pr.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_pr.m | 3,763 | utf_8 | 4d1da5ccda1a7df2bec35b8f12fdd620 | function results = vl_test_pr(varargin)
% VL_TEST_PR
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[rc,pr] = vl_pr(s.labels,s.scores0) ;
vl_assert_almost_equal(pr, [1 1/1 2/2 2/3 2/4 2/5]) ;
vl_assert_almost_equal(rc, ... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_hog.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_hog.m | 1,555 | utf_8 | eed7b2a116d142040587dc9c4eb7cd2e | function results = vl_test_hog(varargin)
% VL_TEST_HOG
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
[x,y]= meshgrid(linspace(-1,1,128)) ;
s.round = single(x.^2+y.^2);
s.imSmall = s.im(1:128,1:128,:) ;
s.imSmall = s.im ;
s.imSmallFlipped = s.imSmall(:,end:-1:1,:) ;
function test_basic... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_argparse.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_argparse.m | 795 | utf_8 | e72185b27206d0ee1dfdc19fe77a5be6 | function results = vl_test_argparse(varargin)
% VL_TEST_ARGPARSE
vl_test_init ;
function test_basic()
opts.field1 = 1 ;
opts.field2 = 2 ;
opts.field3 = 3 ;
opts_ = opts ;
opts_.field1 = 3 ;
opts_.field2 = 10 ;
opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ;
assert(isequal(opts, opts_)) ;
opts_.field1 = 9 ;
... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_liop.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_liop.m | 1,023 | utf_8 | a162be369073bed18e61210f44088cf3 | function results = vl_test_liop(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
randn('state',0) ;
s.patch = randn(65,'single') ;
xr = -32:32 ;
[x,y] = meshgrid(xr) ;
s.blob = - single(x.^2+y.^2) ;
function test_basic(s)
d = vl_liop(s.patch) ;
function test_blob(s)
% with a blob, all local intensity ord... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_test_binsearch.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/xtest/vl_test_binsearch.m | 1,339 | utf_8 | 85dc020adce3f228fe7dfb24cf3acc63 | function results = vl_test_binsearch(varargin)
% VL_TEST_BINSEARCH
vl_test_init ;
function test_inf_bins()
x = [-inf -1 0 1 +inf] ;
vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ;
vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ;
vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ;
vl_a... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_roc.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/plotop/vl_roc.m | 8,747 | utf_8 | 6b8b4786c9242d5112ca90a616db507a | 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 | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_click.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/plotop/vl_click.m | 2,661 | utf_8 | 6982e869cf80da57fdf68f5ebcd05a86 | function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any k... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_pr.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/plotop/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 | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_ubcread.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/sift/vl_ubcread.m | 3,015 | utf_8 | e8ddd3ecd87e76b6c738ba153fef050f | function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', '... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_frame2oell.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/sift/vl_frame2oell.m | 2,160 | utf_8 | 457c5f2e8b637108c8c1b2256396de13 | function eframes = vl_frame2oell(frames)
% FRAMES2OELL Convert generic feature frames to oriented ellipses
% EFRAMES = VL_FRAME2OELL(FRAMES) converts the specified FRAMES to
% the oriented ellipses EFRAMES.
%
% A frame is either a point, disc, oriented disc, ellipse, or
% oriented ellipse. These are represene... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | vl_plotsiftdescriptor.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/toolbox/sift/vl_plotsiftdescriptor.m | 4,725 | utf_8 | 395bf4e0d7417674401ddf34cc8a70da | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptors D, stored as
% columns of the matrix D. D has the same format used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warped to
% the SIFT fram... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | phow_caltech101.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/apps/phow_caltech101.m | 11,595 | utf_8 | cdd4c2add2b7bbfe66a43831513f99fc | function phow_caltech101()
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedu... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | sift_mosaic.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/apps/sift_mosaic.m | 4,621 | utf_8 | 8fa3ad91b401b8f2400fb65944c79712 | function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAI... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | encodeImage.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/apps/recognition/encodeImage.m | 5,278 | utf_8 | 5d9dc6161995b8e10366b5649bf4fda4 | function descrs = encodeImage(encoder, im, varargin)
% ENCODEIMAGE Apply an encoder to an image
% DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER
% to image IM, returning a corresponding code vector PSI.
%
% IM can be an image, the path to an image, or a cell array of
% the same, to operate on multiple ... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | experiments.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/apps/recognition/experiments.m | 6,905 | utf_8 | 1e4a4911eed4a451b9488b9e6cc9b39c | function experiments()
% EXPERIMENTS Run image classification experiments
% The experimens download a number of benchmark datasets in the
% 'data/' subfolder. Make sure that there are several GBs of
% space available.
%
% By default, experiments run with a lite option turned on. This
% quickly runs all... |
github | ivpshu/Saliency-Tree-A-Novel-Saliency-Detection-Framework-master | getDenseSIFT.m | .m | Saliency-Tree-A-Novel-Saliency-Detection-Framework-master/ST_release/ST_win/vlfeat-0.9.17/apps/recognition/getDenseSIFT.m | 1,679 | utf_8 | 2059c0a2a4e762226d89121408c6e51c | function features = getDenseSIFT(im, varargin)
% GETDENSESIFT Extract dense SIFT features
% FEATURES = GETDENSESIFT(IM) extract dense SIFT features from
% image IM.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made availab... |
github | Tinkerforge/laser-range-finder-bricklet-master | matlab_example_callback.m | .m | laser-range-finder-bricklet-master/software/examples/matlab/matlab_example_callback.m | 1,185 | utf_8 | 50e2f5c4e40c10eac1343d202ca138b6 | function matlab_example_callback()
import com.tinkerforge.IPConnection;
import com.tinkerforge.BrickletLaserRangeFinder;
HOST = 'localhost';
PORT = 4223;
UID = 'XYZ'; % Change XYZ to the UID of your Laser Range Finder Bricklet
ipcon = IPConnection(); % Create IP connection
lrf = handle(Bri... |
github | Tinkerforge/laser-range-finder-bricklet-master | octave_example_callback.m | .m | laser-range-finder-bricklet-master/software/examples/matlab/octave_example_callback.m | 1,118 | utf_8 | 44f4cbc8d62b26b98468c7e17919c3cb | function octave_example_callback()
more off;
HOST = "localhost";
PORT = 4223;
UID = "XYZ"; % Change XYZ to the UID of your Laser Range Finder Bricklet
ipcon = javaObject("com.tinkerforge.IPConnection"); % Create IP connection
lrf = javaObject("com.tinkerforge.BrickletLaserRangeFinder", UID, ip... |
github | Tinkerforge/laser-range-finder-bricklet-master | octave_example_threshold.m | .m | laser-range-finder-bricklet-master/software/examples/matlab/octave_example_threshold.m | 1,163 | utf_8 | 2dd14858650fd9bb21ba9fe54f480c86 | function octave_example_threshold()
more off;
HOST = "localhost";
PORT = 4223;
UID = "XYZ"; % Change XYZ to the UID of your Laser Range Finder Bricklet
ipcon = javaObject("com.tinkerforge.IPConnection"); % Create IP connection
lrf = javaObject("com.tinkerforge.BrickletLaserRangeFinder", UID, i... |
github | Tinkerforge/laser-range-finder-bricklet-master | matlab_example_threshold.m | .m | laser-range-finder-bricklet-master/software/examples/matlab/matlab_example_threshold.m | 1,230 | utf_8 | 93ef166252d9aa3b4fe5c59818d846c0 | function matlab_example_threshold()
import com.tinkerforge.IPConnection;
import com.tinkerforge.BrickletLaserRangeFinder;
HOST = 'localhost';
PORT = 4223;
UID = 'XYZ'; % Change XYZ to the UID of your Laser Range Finder Bricklet
ipcon = IPConnection(); % Create IP connection
lrf = handle(Br... |
github | akhileshtyagi/PUF-master | FrameVerification.m | .m | PUF-master/Voice_V2/FrameVerification.m | 5,544 | utf_8 | dea6a761ee9dc30c416e05cf788dedfb | function result= FrameVerification()
warning off all
host = '/Users/Guo/Desktop/Nexus_2/Nexus_2_guo_semester/';
target1 = '/Users/Guo/Desktop/Nexus_2/Nexus_2_guo_semester/';
target2 = '/Users/Guo/Desktop/Nexus_3/Nexus_3_guo_semester/';
final = '.wav';
index = 1;
k =16;
num = 20;
train = 50;
%%Get mean code book of all... |
github | akhileshtyagi/PUF-master | Encoding_Average_Data.m | .m | PUF-master/Voice_V2/Encoding_Average_Data.m | 5,638 | utf_8 | e4e6c266360576b1c119586532b665d1 | function result = toTimeDomain()
warning off all
host = 'C:\Users\User\Desktop\Research\Nexus_1\Nexus_1_liang_zero\';
final = '.wav';
index = 1;
k =16;
num = 20;
train = 50;
sample = 50000;
for x = 1:train
[y,Fs,bits] = wavread(strcat(host,int2str(x),final));
book{x} = y;
sample = min(size(y,1... |
github | akhileshtyagi/PUF-master | Pixel_Algorithm.m | .m | PUF-master/Voice_V2/Pixel_Algorithm.m | 4,586 | utf_8 | 9a01d6d0f3694377d7ad72379e6561af | function result= Pixel_Algorithm()
warning off all
host = '/Users/Guo/Desktop/Nexus_3/Nexus_3_guo_semester/';
target1 = '/Users/Guo/Desktop/Nexus_3/Nexus_3_guo_semester/';
target2 = '/Users/Guo/Desktop/Nexus_2/Nexus_2_guo_semester/';
final = '.wav';
index = 1;
k =16;
num = 20;
train = 50;
%%Get mean code book of all s... |
github | akhileshtyagi/PUF-master | getMFCC.m | .m | PUF-master/Voice_V2/Continuous Mode Voice Recognition/getMFCC.m | 1,214 | utf_8 | 494d1eba9d73c7737d92e0dfdb3cbbcf | function result = getMFCC(y,Fs)
r = mfcc(y,Fs);
result = r(2:14,:);
end
%%
function m = melfb(p, n, fs)
f0 = 700 / fs;
fn2 = floor(n/2);
lr = log(1 + 0.5/f0) / (p+1);
% convert to fft bin numbers with 0 for DC term
bl = n * (f0 * (exp([0 1 p p+1] * lr) - 1));
b1 = floor(bl(1)) + 1;
b2 = ceil(bl(2));
b3 = floor(bl(3... |
github | akhileshtyagi/PUF-master | ToStatechain.m | .m | PUF-master/Voice_V2/Continuous Mode Voice Recognition/ToStatechain.m | 1,037 | utf_8 | 2fe22b553ea9018858e5bb4abb29bf39 | %%Convert each frame to a sta
function chain = ToStatechain(segment)
%Calculate number of frames
number_of_frames = size(segment,2);
%create chain
chain = zeros(1,number_of_frames);
%divide each frame into states
for i = 1 : number_of_frames
chain(1,i) = toState(segment(:,i)');
end
end
function r2 = toState(fr... |
github | SBU-BMI/nscale-master | svsNuclei.m | .m | nscale-master/original-matlab/segment/svsNuclei.m | 21,757 | utf_8 | b8e2eb1bc0b5045eee66527ba94aaab7 | function nuclei=svsNuclei(impath,filename,resultpath, folder,tile)
if nargin==0
image = {'astroII.1.ndpi-0000004096-0000004096.tif',...
'2487_71614_48776_2462x1616.tif',...
'2487_69236_37042_2462x1616.tif',...
'2497_31102_51514_2462x1616.tif',...
'2496_58448_6... |
github | SBU-BMI/nscale-master | svsNucleiPerfTest.m | .m | nscale-master/original-matlab/segment/svsNucleiPerfTest.m | 22,557 | utf_8 | 550e0eff16c4a757a85a80b2aba7bda6 | function nuclei=svsNucleiPerfTest(impath,filename,resultpath, folder,tile)
if nargin==0
image = {'astroII.1/astroII.1.ndpi-0000008192-0000008192.tif',...
'gbm2.1/gbm2.1.ndpi-0000004096-0000004096.tif',...
'oligoIII.1/oligoIII.1.ndpi-0000012288-0000028672.tif',...
'oligoas... |
github | SBU-BMI/nscale-master | svsNucleiCoder.m | .m | nscale-master/original-matlab/segment/svsNucleiCoder.m | 26,998 | utf_8 | 4c651b448cb7f907143c9b0e8fb6aff4 | %#codegen
function svsNucleiCoder(impath,filename,resultpath, folder,tile)
% removed return var of "nuclei"
if nargin==0
image = {'astroII.1.ndpi-0000004096-0000004096.tif',...
'2487_71614_48776_2462x1616.tif',...
'2487_69236_37042_2462x1616.tif',...
'2497_31102_... |
github | SBU-BMI/nscale-master | svsNucleiInstrumented.m | .m | nscale-master/original-matlab/segment/svsNucleiInstrumented.m | 32,501 | utf_8 | 716a49d9df186564803fb1a9eab00e42 | function []=svsNucleiInstrumented(impath,filename,fileext, resultpath, validationpath)
%%, folder,tile)
if nargin==0
image = {'astroII.1/astroII.1.ndpi-0000008192-0000008192',...
'gbm2.1/gbm2.1.ndpi-0000004096-0000004096',...
'oligoIII.1/oligoIII.1.ndpi-0000012288-0000028672',...
... |
github | SBU-BMI/nscale-master | addfeat.m | .m | nscale-master/original-matlab/classify/addfeat.m | 680 | utf_8 | 0697af31eaeaf9d7c321babdc4452fb9 | % add one feature to an existing feature set
function [winner, Cost, newFeat] = addfeat(X, Y, selectioncriteria, X_k)
[n,d]=size(X);
N=length(X_k);
cost=zeros(d-N,N+2); % initialize costs table
for i=1:d-N
cost(i,2:N+1)=X_k;
end;
ind=1;
for i=1:d
if (isempty(find(X_k == i)))
cost(ind,N... |
github | SBU-BMI/nscale-master | L1FeatureSelection.m | .m | nscale-master/original-matlab/classify/L1FeatureSelection.m | 4,471 | utf_8 | 8cc3b4bd93c1afaeaec267ef1ab89187 | function w = L1FeatureSelection(X, labels, sigma, tau, lambda)
%inputs:
%X - J x N matrix with J-dimensional feature vectors in columns.
%labels - N-length array with class labels
%sigma - exponential kernel parameter k(d) = exp(-d/sigma).
%tau - stopping criterion for iteration |w_last - w| <= tau.
%lambda - reg... |
github | SBU-BMI/nscale-master | removefeat.m | .m | nscale-master/original-matlab/classify/removefeat.m | 879 | utf_8 | 3b38bb164fed02485d903a527c0ef65d | % remove one feature to an existing feature set
% the one with least effect
function [winner, Cost, newFeat] = removefeat(X, Y, selectioncriteria, X_k)
%[n,d]=size(X);
N=length(X_k);
cost=zeros(N,N); % initialize costs table
for i=1:N
cost(i,2:N)=MysetDiff(X_k,X_k(i));
subset=cost(i,2:end);
cost... |
github | SBU-BMI/nscale-master | SFFS.m | .m | nscale-master/original-matlab/classify/SFFS.m | 4,961 | utf_8 | 211b7e89a24741112964780890ef3739 | % INPUTS
% X: each row is a data point
% Y: label column vector
% new_size: the final number of features to be kept
% start_size:the initial number of features
% OUTPUTS
% winner: the feature indices of the final selected features
% Cost: the value of the objective function with winner
% winners:feature in... |
github | SBU-BMI/nscale-master | regionprops.m | .m | nscale-master/original-matlab/features/regionprops.m | 39,501 | utf_8 | 6862bed5a4d853c0f9074489669ca797 | function outstats = regionprops(varargin)
%REGIONPROPS Measure properties of image regions.
% STATS = REGIONPROPS(BW,PROPERTIES) measures a set of properties for
% each connected component (object) in the binary image BW, which must be
% a logical array; it can have any dimension.
%
% STATS = REGIONPROPS(CC,PR... |
github | SBU-BMI/nscale-master | KMeansModelSelection.m | .m | nscale-master/src/pipeline/validationData/patientAggregate/ConsensusClustering/KMeansModelSelection.m | 4,263 | utf_8 | 107a3911a0bb62108c89e8a191049c74 | function [Labels CophenetCorr Ordered Indices Initializations ks] = KMeansModelSelection(A, d, trials)
%Uses consensus clustering via k-means to perform a model selection,
%determining the number of natural clusters within the data.
%inputs:
%A - matrix of feature vectors in columns.
%d - maximum number of cluste... |
github | SBU-BMI/nscale-master | NNMFModelSelection.m | .m | nscale-master/src/pipeline/validationData/patientAggregate/ConsensusClustering/NNMFModelSelection.m | 3,259 | utf_8 | cc6bcfd4b3c29d0d877e6c0dc543b77a | function [Labels CophenetCorr Ordered ks] = NNMFModelSelection(A, d, trials)
%Uses consensus clustering via non-negative matrix factorization to perform
%a model selection, determining the number of natural clusters within the
%data.
%inputs:
%A - matrix of feature vectors in columns.
%d - maximum number of clust... |
github | SBU-BMI/nscale-master | checkDir.m | .m | nscale-master/src/common/log-analysis/checkDir.m | 2,932 | utf_8 | 1478441d5e845de06930a1774fe5f0ab | function checkDir ( dirname, allEventTypes, allTypeNames, colorMap, errorfid )
%checkDir check to see if we have missing intermediate files
% Detailed explanation goes here
close all;
timeInterval = 200000;
procWidth = 1;
files = dir(fullfile(dirname, '*.csv'));
for i = 1:length(files)
[~, n, ~] = fileparts(fi... |
github | SBU-BMI/nscale-master | plotProcEvents.m | .m | nscale-master/src/common/log-analysis/plotProcEvents.m | 9,213 | utf_8 | 2b9ae72ff691cf06305c6258e315c507 | function [ img norm_events sum_events ] = plotProcEvents( events, fields, pixelWidth, figname_prefix, allEventTypes, colorMap, lineTypes, timeInterval)
%plotTiming draws an image that represents the different activities in MPI
% The function first parses the data and generate a normalized event map
% with dimension... |
github | SBU-BMI/nscale-master | allfitdist.m | .m | nscale-master/src/common/log-analysis/allfitdist/allfitdist.m | 14,556 | utf_8 | 493599fd4f244070854240bb42ac1e5f | function [D PD] = allfitdist(data,sortby,varargin)
%ALLFITDIST Fit all valid parametric probability distributions to data.
% [D PD] = ALLFITDIST(DATA) fits all valid parametric probability
% distributions to the data in vector DATA, and returns a struct D of
% fitted distributions and parameters and a struct ... |
github | SBU-BMI/nscale-master | plotProcEvents_old.m | .m | nscale-master/src/common/log-analysis/old/plotProcEvents_old.m | 9,008 | utf_8 | f7c7f36151c2d390c5922fab952712e9 | function [ img norm_events ] = plotProcEvents_old( proc_events, barWidth, pixelWidth, figname_prefix, allEventTypes, colorMap)
%plotTiming draws an image that represents the different activities in MPI
% The function first parses the data and generate a normalized event map
% with dimension p x ((max t - min t)/pix... |
github | SBU-BMI/nscale-master | summarize.m | .m | nscale-master/src/common/log-analysis/old/summarize.m | 20,303 | utf_8 | c264b26fcc80807c73adabb06a6c6782 | function summarize( proc_events, sample_interval, fid, proc_type, allEventTypes, allTypeNames)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
%% check the parameters.
if (isempty(proc_events))
printf(2, 'ERROR: no events to process\n');
return;
end... |
github | SBU-BMI/nscale-master | ComputeAndIOConcurrency.m | .m | nscale-master/src/common/log-analysis/old/ComputeAndIOConcurrency.m | 5,063 | utf_8 | 3e8ba61b25f8b429bda33a5e69554385 | function [ concurrencyAll loadAll nodes concurrencyPerNode loadPerNode procsPerNode] = ComputeAndIOConcurrency( header, type, startTimes, endTimes, location )
%ComputeAndIOConcurrency Compute the level of concurrency
% header just gives information about start and end (1xN)
% type specifies whether it's computat... |
github | SBU-BMI/nscale-master | summarize_old.m | .m | nscale-master/src/common/log-analysis/old/summarize_old.m | 26,687 | utf_8 | ecd4507562f8ba78b26764d26fda6482 | function [] = summarize_old( proc_events, sample_interval, fid, proc_type, allEventTypes, allTypeNames)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
%% check the parameters.
if (isempty(proc_events))
printf(2, 'ERROR: no events to process\n');
return;... |
github | SBU-BMI/nscale-master | JunFeatureExtraction.m | .m | nscale-master/src/normalization/matlab/Nuclei/JunFeatureExtraction.m | 7,767 | utf_8 | e7b70d46be68e005ce710273fdcd4747 | function [f, Names, CentroidX, CentroidY] = JunFeatureExtraction(L, color_img, K, Fs, delta)
%Calculate features from color image and segmentation label image.
%inputs:
%L - T x T label image.
%color_img - T x T x 3 color image.
%K - number of points for boundary resampling to calculate fourier descriptors (recomm... |
github | AlonLabWIS/ParTI-master | freezeColors.m | .m | ParTI-master/freezeColors.m | 10,084 | utf_8 | c391c17641741effdd19084bb2b1ba06 | function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors... |
github | AlonLabWIS/ParTI-master | rotlorentz.m | .m | ParTI-master/SeDuMi_1_3/rotlorentz.m | 1,734 | utf_8 | 2360f679ed280039c549592a3c993aff | % c = rotlorentz(c,K)
% Rotates vectors from Qcone to Rcone or from Rcone into Qcone.
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also sedumi
function c = rotlorentz(c,K)
%
% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko
% Copyright (C) 2005 McMaster University, Ham... |
github | AlonLabWIS/ParTI-master | PopK.m | .m | ParTI-master/SeDuMi_1_3/PopK.m | 2,058 | utf_8 | f5d78e3b27636285dfc03b5da8fcbc93 | % [y, ddotx, Dx, xTy] = PopK(d,x,K,lpq)
% POPK Implements the quadratic operator for symmetric cones K.
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also sedumi
function [y, ddotx, Dx, xTy] = PopK(d,x,K,lpq)
%
% This file is part of SeDuMi 1.1 by Imre Polik and Olek... |
github | AlonLabWIS/ParTI-master | updtransfo.m | .m | ParTI-master/SeDuMi_1_3/updtransfo.m | 4,731 | utf_8 | c73de722512195da9daad6fa6bf75a0d | % [d,vfrm] = updtransfo(x,z,w, dIN,K)
% UPDTRANSFO Updated the Nesterov-Todd transformation using a
% numerically stable method.
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also sedumi
function [d,vfrm] = updtransfo(x,z,w, dIN,K)
%
% This file is par... |
github | AlonLabWIS/ParTI-master | symbcholden.m | .m | ParTI-master/SeDuMi_1_3/symbcholden.m | 2,555 | utf_8 | ba6fe026b0102d1fd909b6903c62aa1f | % Lden = symbcholden(L,dense,DAt)
% SYMBCHOLDEN Creates Lden.{LAD, perm,dz, sign, first}
%
% ******************** INTERNAL FUNCTION OF SEDUMI ********************
%
% See also sedumi, dpr1fact
function Lden = symbcholden(L,dense,DAt)
%
% This file is part of SeDuMi ... |
github | AlonLabWIS/ParTI-master | psdinvscale.m | .m | ParTI-master/SeDuMi_1_3/psdinvscale.m | 2,422 | utf_8 | 06f82b385e12594de31afb4f00a28806 | % y = psdinvscale(ud,x,K ,transp)
% PSDINVSCALE Computes length lenud (=sum(K.s.^2)) vector y.
% Computes y = D(d^{-1}) x with d in K.
% Y = Ud' \ X / Ud
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also scaleK, factorK.
function y = psdinvscale(... |
github | AlonLabWIS/ParTI-master | eyeK.m | .m | ParTI-master/SeDuMi_1_3/eyeK.m | 1,833 | utf_8 | 3e5751a6b689151b000068b15e4c6e3f | % eyeK Identity w.r.t. symmetric cone.
% x = eyeK(K) produces the identity solution w.r.t. the symmetric cone,
% that is described by the structure K. This is the vector for which
% eigK(x) is the all-1 vector.
%
% See also eigK.
function x = eyeK(K)
% This file is part of SeDuMi 1.1 by Imre Poli... |
github | AlonLabWIS/ParTI-master | sparfwslv.m | .m | ParTI-master/SeDuMi_1_3/sparfwslv.m | 2,276 | utf_8 | b0e876743c52edb4ceb0cee125503411 | % SPARFWSLV Solves block sparse upper-triangular system.
% y = sparfwslv(L,b) yields the same result as
% y = L.L\b(L.perm,:)
% However, SPARFWSLV is faster than the built-in operator "\",
% because it uses dense linear algebra and loop-unrolling on
% supernodes.
%
% For sparse b, one... |
github | AlonLabWIS/ParTI-master | fwdpr1.m | .m | ParTI-master/SeDuMi_1_3/fwdpr1.m | 1,880 | utf_8 | 750b7c554452e985b5efa0cf663e2a6f | % y = fwdpr1(Lden, b)
% FWDPR1 Solves "PROD_k L(pk,betak) * y = b", where
% where L(p,beta) = eye(n) + tril(p*beta',-1).
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also sedumi, dpr1fact, bwdpr1
function y = fwdpr1(Lden, b)
%
% ... |
github | AlonLabWIS/ParTI-master | sortnnz.m | .m | ParTI-master/SeDuMi_1_3/sortnnz.m | 2,004 | utf_8 | 5787292e2973a25b28e10c4231898cd3 | % perm = sortnnz(At,Ajc1,Ajc2)
% SORTNNZ Sorts columns in At
% in increasing order of nnzs; only the nnzs between Ajc1 and Ajc2
% are considered for each column. If Ajc1 or Ajc2 is empty, we use
% the start or end of the columns in At.
%
% *******************... |
github | AlonLabWIS/ParTI-master | loopPcg.m | .m | ParTI-master/SeDuMi_1_3/loopPcg.m | 6,270 | utf_8 | c663b50c5b75f46e8f1ed2b8234e7b12 | % [y,k, DAy] = loopPcg(L,Lden,At,dense,d, DAt,K, b,p,ssqrNew,cgpars, restol)
%
% LOOPPCG Solve y from AP(d)A' * y = b
% using PCG-method and Cholesky L as conditioner.
% If L is sufficiently accurate, then only 1 CG-step is needed.
% It assumes that the previous step was p, with
% ssqrNew = bOld'*inv(L*THETA*L')*... |
github | AlonLabWIS/ParTI-master | finsymbden.m | .m | ParTI-master/SeDuMi_1_3/finsymbden.m | 2,134 | utf_8 | 6655ba7d4743010e2bc35c7b275189e9 | % Lden = finsymbden(LAD,perm,dz,firstq)
% FINSYMBDEN Updates perm and dz by inserting the
% last Lorentz trace columns (last columns of LAD). It creates the fields
% Lden.sign - +1 for "normal" columns, -1 for Lorentz trace columns
% Lden.first - First pivot column that will a... |
github | AlonLabWIS/ParTI-master | minpsdeig.m | .m | ParTI-master/SeDuMi_1_3/minpsdeig.m | 2,402 | utf_8 | 76851a44cd0651e86e2cc15f3f3a1a10 | % mineig = minpsdeig(x,K)
% MINPSDEIG Computes the smallest spectral coefficients of x w.r.t. K
% Uses an iterative method if the matrix is large, takes the minimum of all
% the eigenvalues if the matrix is small.
%
% ********** INTERNAL FUNCTION OF SEDUMI *********... |
github | AlonLabWIS/ParTI-master | getDAtm.m | .m | ParTI-master/SeDuMi_1_3/getDAtm.m | 2,007 | utf_8 | 34c7a3a2c33ccc2675038ad7141d5ac1 | % DAt = getDAtm(A,Ablkjc,dense,DAtdenq,d,K)
% GETDATM Computes d[k]'*Aj[k] for each lorentz block k and constraint j.
%
% ******************** INTERNAL FUNCTION OF SEDUMI ********************
%
% See also sedumi, getada2.
function DAt = getDAtm(A,Ablkjc,dense,DAtdenq,d,K)... |
github | AlonLabWIS/ParTI-master | findblks.m | .m | ParTI-master/SeDuMi_1_3/findblks.m | 2,043 | utf_8 | 3f129b5e60da81b88cd358b1cd2e6c3d | % Ablk = findblks(At,Ablkjc,blk0,blk1,blkstart)
% FINDBLKS Find nonzero blocks
% in A, with subscripts per column bounded bij Ablkjc([blk0,blk1]),
% block partitioned by blkstart.
% If blk0 < 1 (blk1 > size(Ablkjc,2)) then start (stop) searching at column
% start (end) of A.
%
% ... |
github | AlonLabWIS/ParTI-master | invcholfac.m | .m | ParTI-master/SeDuMi_1_3/invcholfac.m | 1,859 | utf_8 | 755e6abd3ff3729ea8ca73c9f551cf9e | % y = invcholfac(u,K, perm)
% INVCHOLFAC Computes y(perm,perm) = u' * u, with u upper triangular.
%
% ******************** INTERNAL FUNCTION OF SEDUMI ********************
%
% See also sedumi, getada3
function y = invcholfac(u,K, perm)
%
% This file is part ... |
github | AlonLabWIS/ParTI-master | qframeit.m | .m | ParTI-master/SeDuMi_1_3/qframeit.m | 1,773 | utf_8 | d09af1c0a97090bae278b9ab98bae212 | % x = qframeit(lab,frmq,K)
%
% *********************** INTERNAL FUNCTION OF SEDUMI *******************
%
% See also sedumi
% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko
% Copyright (C) 2005 McMaster University, Hamilton, CANADA (since 1.1)
%
% Copyright (C) 2001 Jos F. Sturm (up to ... |
github | AlonLabWIS/ParTI-master | incorder.m | .m | ParTI-master/SeDuMi_1_3/incorder.m | 2,194 | utf_8 | 97e815101c79e061951a6a6d797cdd88 | % [perm, dz] = incorder(At [,Ajc1,ifirst])
% INCORDER
% perm sorts the columns of At greedily, by iteratively picking
% the 1st unprocessed column with the least number of nonzero
% subscripts THAT ARE NOT YET COVERED (hence incremental) by
% the previously processed columns.
% dz... |
github | AlonLabWIS/ParTI-master | qreshape.m | .m | ParTI-master/SeDuMi_1_3/qreshape.m | 2,004 | utf_8 | 8858b2ff82c1d786a4839a098e36b22b | % y = qreshape(x,flag, K)
% QRESHAPE Reshuffles entries associated with Lorentz blocks.
% If flag = 0 then y = [x1 for each block; x2 for each block]
% If flag = 1 then y = [x block 1; x block 2; etc], etc
% Thus, x = qreshape(qreshape(x,0,K),1,K).
%
% *********... |
github | AlonLabWIS/ParTI-master | dpr1fact.m | .m | ParTI-master/SeDuMi_1_3/dpr1fact.m | 2,130 | utf_8 | a7d6a597603312764583d96eae64edd0 | % [Lden,L.d] = dpr1fact(x, d, Lsym, smult, maxu)
% DPR1FACT Factor d[iag] p[lus] r[ank] 1:
% [Lden,L.d] = dpr1fact(x, d, Lsym, smult, maxu)
% Computes fi and d such that
% diag(d_IN) + x*diag(smult)*x' =
%(PI_{i=1}^n L(p_OUT^i,beta_i)) * diag(d_OUT) * (PI_{i=1}^n L(p_OUT^... |
github | AlonLabWIS/ParTI-master | iswnbr.m | .m | ParTI-master/SeDuMi_1_3/iswnbr.m | 4,448 | utf_8 | c9e284b91161b0871549bd7f27bafcbf | % [delta,h,alpha] = iswnbr(vSQR,thetaSQR)
% ISWNBR Checks feasibility w.r.t. wide region/neighborhood of Sturm-Zhang.
% vTAR:= (1-alpha)*max(h,v) projection v onto theta-central region
% delta = (sqrt(n)/theta) * norm(vTAR - v) / norm(v)
%
% ********** INTERNAL FUNCTION OF SEDUMI *... |
github | AlonLabWIS/ParTI-master | fwblkslv.m | .m | ParTI-master/SeDuMi_1_3/fwblkslv.m | 2,004 | utf_8 | 29331f9be35273a4679f1718f0f770c1 | % FWBLKSLV Solves block sparse upper-triangular system.
% y = fwblkslv(L,b) yields the same result as
% y = L.L\b(L.perm,:)
% However, FWBLKSLV is faster than the built-in operator "\",
% because it uses dense linear algebra and loop-unrolling on
% supernodes.
%
% Typical use, with X ... |
github | AlonLabWIS/ParTI-master | trydif.m | .m | ParTI-master/SeDuMi_1_3/trydif.m | 2,561 | utf_8 | 188705faedb79d0ad34ec9dcdf8d1f2c | % [t,wr,w] = trydif(t,wrIN,wIN, x,z, pars,K)
% TRYDIF Tries feasibility of differentiated step length w.r.t.
% wide region and its neighborhood.
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also sedumi, stepdif
function [t,wr,w] = trydif(t,wrIN,wIN, x,z, pars,K)
%
% This... |
github | AlonLabWIS/ParTI-master | asmDxq.m | .m | ParTI-master/SeDuMi_1_3/asmDxq.m | 2,803 | utf_8 | 8d99cd2fd94ebd77f97312468887a518 | % y = asmDxq(d, x, K [, ddotx])
% ASMDXQ Assemble y = D(d)x for x in Lorentz part of K.
% [y,t] = AasmDxq(d, x, K [, ddotx]) then y[k]+t(k)*d[k] = D(dk)xk.
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also sedumi
function [y,t] = asmDxq(d, x, K, ddot... |
github | AlonLabWIS/ParTI-master | symfctmex.m | .m | ParTI-master/SeDuMi_1_3/symfctmex.m | 2,036 | utf_8 | f6b84d76ece75f7290517e625e446e0f | % [L,perm,xsuper,split,tmpsiz] = symfctmex(X, perm, cachsz)
% Computes sparse symbolic factor L, updated permutation PERM,
% super-node partition XSUPER, and a splitting of supernodes
% (SPLIT) to optimize use of the computer cache (assuming
% CACHSZ*1024 byte available). TMPSIZ is the amount of floating
... |
github | AlonLabWIS/ParTI-master | symbchol.m | .m | ParTI-master/SeDuMi_1_3/symbchol.m | 3,280 | utf_8 | b1399e65f8a5d81fa2cd34978f28f52d | % L = symbchol(X)
% SYMBCHOL Symbolic block sparse Cholesky factorization.
% L = symbchol(X) returns a structure L that can be used
% by the efficient block sparse Cholesky solver SPARCHOL.
% The fields in L have the following meaning:
%
% L.perm ... |
github | AlonLabWIS/ParTI-master | getsymbada.m | .m | ParTI-master/SeDuMi_1_3/getsymbada.m | 2,353 | utf_8 | 1cde359d723d90aa1e2d454be98d3642 | % SYMBADA = getsymbada(At,Ajc,DAt,psdblkstart)
% GETSYMBADA
% Ajc points to start of PSD-nonzeros per column
% DAt.q has the nz-structure of ddotA.
%
% ******************** INTERNAL FUNCTION OF SEDUMI ********************
%
% See also sedumi, partitA, getada1, getada2.
func... |
github | AlonLabWIS/ParTI-master | statsK.m | .m | ParTI-master/SeDuMi_1_3/statsK.m | 1,824 | utf_8 | 0a46c333f48d0aca7d2fbf79d3a0f760 | % K = statsK(K)
% STATSK Collects statistics (max and sum of dimensions) of cone K
%
% ******************** INTERNAL FUNCTION OF SEDUMI ********************
%
% See also sedumi
function K = statsK(K)
%
% This file is part of SeDuMi 1.1 by Imre Polik and Oleksa... |
github | AlonLabWIS/ParTI-master | qinvjmul.m | .m | ParTI-master/SeDuMi_1_3/qinvjmul.m | 2,473 | utf_8 | f77d40ca42c72f1e46286c5ac4b3d71c | % y = qinvjmul(labx,frmx,b,K)
% QINVJMUL Inverse of Jordan multiply for Lorentz blocks
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also sedumi
function y = qinvjmul(labx,frmx,b,K)
%
% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Roman... |
github | AlonLabWIS/ParTI-master | whichcpx.m | .m | ParTI-master/SeDuMi_1_3/whichcpx.m | 1,811 | utf_8 | d327df172a4d2386a5a003c9b1b1c266 | % cpx = whichcpx(K)
% WHICHCPX yields structure cpx.{f,q,r,x}
%
% ******************** INTERNAL FUNCTION OF SEDUMI ********************
%
% See also sedumi
function cpx = whichcpx(K)
%
% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko
... |
github | AlonLabWIS/ParTI-master | triumtriu.m | .m | ParTI-master/SeDuMi_1_3/triumtriu.m | 2,016 | utf_8 | 64224c5cc11e9543095c85203848fc01 | % y = triumtriu(r,u,K)
% TRIUMTRIU Computes y = r * u
% Both r and u should be upper triangular.
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also sedumi
function y = triumtriu(r,u,K)
%
% This file is part of SeDuMi 1.1 by Imre Polik and Ol... |
github | AlonLabWIS/ParTI-master | getada2.m | .m | ParTI-master/SeDuMi_1_3/getada2.m | 1,955 | utf_8 | f11451d9ac4e7f81983e3db2b9b12e33 | % ADA = getada2(ADA, DAt,Aord, K)
% GETADA2 Compute ADA += DAt.q'*DAt.q
% IMPORTANT: Updated ADA only on triu(ADA(Aord.qperm,Aord.qperm)).
% Remaining entries are not affected.
%
% ******************** INTERNAL FUNCTION OF SEDUMI ********************
%
% See also sedu... |
github | AlonLabWIS/ParTI-master | urotorder.m | .m | ParTI-master/SeDuMi_1_3/urotorder.m | 1,843 | utf_8 | 3f00fac00ed650f384981a17ffb08e89 | % [u,perm,gjc,g] = urotorder(u,K, maxu,permIN)
% UROTORDER Stable reORDERing of triu U-factor by Givens ROTations.
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
%
% See also sedumi
function [u,perm,gjc,g] = urotorder(u,K, maxu,permIN)
%
% This file is part of SeDuMi 1.1 by Im... |
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