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github | sbargoti/matlab-code-master | vl_test_fisher.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_fisher.m | 2,097 | utf_8 | c9afd9ab635bd412cbf8be3c2d235f6b | 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 | sbargoti/matlab-code-master | vl_test_imsmooth.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_svmtrain.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_phow.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_kmeans.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_kmeans.m | 3,632 | utf_8 | 0e1d6f4f8101c8982a0e743e0980c65a | 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 | sbargoti/matlab-code-master | vl_test_hikmeans.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_aib.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_plotbox.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_imarray.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_homkermap.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_slic.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_ikmeans.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_mser.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_inthist.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_imdisttf.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_vlad.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_pr.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_hog.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_argparse.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_liop.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_test_binsearch.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_roc.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/plotop/vl_roc.m | 10,113 | utf_8 | 22fd8ff455ee62a96ffd94b9074eafeb | 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 [1]. LABELS is a row vector of ground
% truth labels, greater than zero for a positive sample and smaller
% than zero for a negative o... |
github | sbargoti/matlab-code-master | vl_click.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_pr.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/plotop/vl_pr.m | 9,138 | utf_8 | c7fe6832d2b6b9917896810c52a05479 | 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 | sbargoti/matlab-code-master | vl_ubcread.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | vl_frame2oell.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/sift/vl_frame2oell.m | 2,806 | utf_8 | c93792632f630743485fa4c2cf12d647 | function eframes = vl_frame2oell(frames)
% VL_FRAMES2OELL Convert a geometric frame to an oriented ellipse
% EFRAME = VL_FRAME2OELL(FRAME) converts the generic FRAME to an
% oriented ellipses EFRAME. FRAME and EFRAME can be matrices, with
% one frame per column.
%
% A frame is either a point, a disc, an orien... |
github | sbargoti/matlab-code-master | vl_plotsiftdescriptor.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/sift/vl_plotsiftdescriptor.m | 5,114 | utf_8 | a4e125a8916653f00143b61cceda2f23 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptor D. If D is a
% matrix, it plots one descriptor per column. D has the same format
% used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warpe... |
github | sbargoti/matlab-code-master | phow_caltech101.m | .m | matlab-code-master/vlfeat-0.9.20/apps/phow_caltech101.m | 11,594 | utf_8 | 7f4890a2e6844ca56debbfe23cca64f3 | 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 | sbargoti/matlab-code-master | sift_mosaic.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | encodeImage.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | experiments.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | getDenseSIFT.m | .m | matlab-code-master/vlfeat-0.9.20/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 | sbargoti/matlab-code-master | myClassifierPerf.m | .m | matlab-code-master/multClassImgSeg/code/usefulFns/myClassifierPerf.m | 1,506 | utf_8 | 712b5911e9e0e715ba18b5bc87a1f5ef | % the point of this function is to ignore the void class that is not in the
% evaluation.
function myClassifierPerf(CPDataset, validClassIdx, classNameList)
confMatrix = CPDataset.CountingMatrix(validClassIdx,validClassIdx);
P = zeros(1,length(validClassIdx));
R = zeros(1,length(validClassIdx));
F = zer... |
github | sbargoti/matlab-code-master | stackedAELinearCost.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/stackedae/stackedAELinearCost.m | 8,836 | utf_8 | 873ad745733e9cbfac82a5612acf10e6 | function [ cost, grad ] = stackedAELinearCost(theta, inputSize, hiddenSize, ...
numClasses, netconfig, ...
lambda, data, locations, labels, nScale, locationFeatFlag)
% stackedAECost:... |
github | sbargoti/matlab-code-master | stackedAELocLinearPredict.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/stackedae/stackedAELocLinearPredict.m | 3,967 | utf_8 | 9224087c3402a19cc5847f1da2557abf | function [pred] = stackedAELocLinearPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data, location, nScale, locationFeatFlag)
% stackedAEPredict: Takes a trained theta and a test data set,
% and returns the predicted labels for each example.
... |
github | sbargoti/matlab-code-master | stackedAELinearImgPredict.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/stackedae/stackedAELinearImgPredict.m | 3,867 | utf_8 | c7cfe2618504499528efc14316b05c36 | function [predIm, imFeatResp, hypothesis] = stackedAELinearImgPredict(theta, patchsize, hiddenSize, numClasses, netconfig, currentImage, ZCAWhite, meanPatch, resizeF, locationFeatFlag)
%% initialisation
[maxVal iMaxResizeF] = max(resizeF);
[maxImDim1 maxImDim2 maxImDim3] = s... |
github | sbargoti/matlab-code-master | stackedAEPredict.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/stackedae/stackedAEPredict.m | 2,117 | utf_8 | 3ed1d16546225dbb451b6c4995d24b88 | function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data)
% stackedAEPredict: Takes a trained theta and a test data set,
% and returns the predicted labels for each example.
% theta: trained weights f... |
github | sbargoti/matlab-code-master | stackedAECost.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/stackedae/stackedAECost.m | 5,283 | utf_8 | b44fd923b5964c7efde5024d38587b60 | function [ cost, grad ] = stackedAECost(theta, inputSize, hiddenSize, ...
numClasses, netconfig, ...
lambda, data, labels)
% stackedAECost: Takes a trained softmaxTheta and a trainin... |
github | sbargoti/matlab-code-master | stackedAELinearPredict.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/stackedae/stackedAELinearPredict.m | 3,503 | utf_8 | 381ca2db3ad3430851026c39a967a63d | function [pred] = stackedAELinearPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data, nScale)
% stackedAEPredict: Takes a trained theta and a test data set,
% and returns the predicted labels for each example.
% theta: tra... |
github | sbargoti/matlab-code-master | sparseAutoencoderCost.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/sparseAutoencoder/sparseAutoencoderCost.m | 5,850 | utf_8 | b79bad633e38613242c185009a5f281a | function [cost,grad] = sparseAutoencoderCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay parameter
% sparsityPar... |
github | sbargoti/matlab-code-master | train_svm.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/kmeans_demo/train_svm.m | 784 | utf_8 | e7a6ba7cce76dd8025df49c63d496207 | function theta = train_svm(trainXC, trainY, C)
numClasses = max(trainY);
%w0 = zeros(size(trainXC,2)*(numClasses-1), 1);
w0 = zeros(size(trainXC,2)*numClasses, 1);
w = minFunc(@my_l2svmloss, w0, struct('MaxIter', 1000, 'MaxFunEvals', 1000), ...
trainXC, trainY, numClasses, C);
theta = reshap... |
github | sbargoti/matlab-code-master | WolfeLineSearch.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/kmeans_demo/minFunc/WolfeLineSearch.m | 11,395 | utf_8 | 3d2acf1139093fe11df95ccdf888aab8 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | sbargoti/matlab-code-master | minFunc_processInputOptions.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/kmeans_demo/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | dc74c67d849970de7f16c873fcf155bc |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | sbargoti/matlab-code-master | softmaxCost.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/softmax/softmaxCost.m | 1,865 | utf_8 | afccc76054e87408dbdfbe90cb4706ba | function [cost, grad] = softmaxCost(theta, numClasses, inputSize, lambda, data, labels)
% numClasses - the number of classes
% inputSize - the size N of the input vector
% lambda - weight decay parameter
% data - the N x M input matrix, where each column data(:, i) corresponds to
% a single test set
% labels -... |
github | sbargoti/matlab-code-master | softmaxPredict.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/softmax/softmaxPredict.m | 1,137 | utf_8 | f2453aa6fbffb0a06651ff1777b3d3c8 | function [pred, h] = softmaxPredict(softmaxModel, data)
% softmaxModel - model trained using softmaxTrain
% data - the N x M input matrix, where each column data(:, i) corresponds to
% a single test set
%
% Your code should produce the prediction matrix
% pred, where pred(i) is argmax_c P(y(c) | x(i)).
% Unr... |
github | sbargoti/matlab-code-master | svmImgPredict.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/svm/svmImgPredict.m | 5,036 | utf_8 | cc63cf2e37b94b0016953cc510038ada | function [predIm, imFeatResp, hypothesis] = svmImgPredict(theta, svmTheta, patchsize, hiddenSize, numClasses, netconfig, currentImage, ZCAWhite, meanPatch, resizeF, locationFeatFlag)
% stackedAEPredict: Takes a trained theta and a test data set,
% and returns the predicted labels for each example.
% theta: trained we... |
github | sbargoti/matlab-code-master | pred_svm.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/svm/pred_svm.m | 108 | utf_8 | a4c4d64cdbf5c22a98976fe5f672cd2a | % from kmeansDemo
function labels = pred_svm(trainXCs, theta)
[val,labels] = max(trainXCs*theta, [], 2); |
github | sbargoti/matlab-code-master | train_svm.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/svm/train_svm.m | 802 | utf_8 | a15f3bccaa351a48b6661ecab2fc5e12 | % from kmeansDemo
function theta = train_svm(trainXC, trainY, C)
numClasses = max(trainY);
%w0 = zeros(size(trainXC,2)*(numClasses-1), 1);
w0 = zeros(size(trainXC,2)*numClasses, 1);
w = minFunc(@my_l2svmloss, w0, struct('MaxIter', 1000, 'MaxFunEvals', 1000), ...
trainXC, trainY, numClasses, C);... |
github | sbargoti/matlab-code-master | removeDC.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/multiScaleUFL/removeDC.m | 291 | utf_8 | ec338a3aec557484408b5da2a9d2bfcc | % Removes DC component from image patches
% Data given as a matrix where each patch is one column vectors
% That is, the patches are vectorized.
function Y=removeDC(X);
% Subtract local mean gray-scale value from each patch in X to give output Y
Y = X-ones(size(X,1),1)*mean(X);
return;
|
github | sbargoti/matlab-code-master | feedForwardAutoencoder.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/stl/feedForwardAutoencoder.m | 1,519 | utf_8 | 9d316ddd66bb77ea9e53c7c626d915c3 | function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data)
% theta: trained weights from the autoencoder
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% data: Our matrix containing the training data as columns. So, data(:,i) i... |
github | sbargoti/matlab-code-master | sampleIMAGESHSV.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/sampleImages/sampleIMAGESHSV.m | 4,311 | utf_8 | 1051976d14d968b983e0e9659027b898 | function patches = sampleIMAGESHSV(resizeF, patchsize, imageChannels, numpatches, imagePath, fileName)
%% Internal Paramters
% imagePath = 'D:\Work\dataSet\VOC2011\JPEGImages'; %(on my comp)
% imagePath = '/nethome/calvin/dataSet/VOC2011/JPEGImages'; % (on archipelago)
% imagePath = VOCopts.imgpath;
% ima... |
github | sbargoti/matlab-code-master | sampleVelodyneIMAGES.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/sampleImages/sampleVelodyneIMAGES.m | 2,866 | utf_8 | 924cc1ca8ef9453d010bde1f09b219ad | % function [patches ] = sampleIMAGES(patchsize)
function [patches invalidPatches]= sampleVelodyneIMAGES(images, invalidmask, nband, patchsize, numPatches)
disp(' Sample RGBD image patches ')
IMAGES = double(images);
numpatches = numPatches;
[dim1 dim2 numImage] = size(IMAGES);
patches = zeros(patchsize*patchsize*nban... |
github | sbargoti/matlab-code-master | sampleColourIMAGES.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/sampleImages/sampleColourIMAGES.m | 2,853 | utf_8 | c85cc22002f2d671cc73b1673aae7702 | % function [patches ] = sampleIMAGES(patchsize)
function patches = sampleColourIMAGES(images, patchsize, numPatches)
% sampleIMAGES
% Returns 10000 patches for training
% load IMAGES; % load images from disk
% images = images.IMAGES;
IMAGES = double(images);
% patchsize = 12; % we'll use 8x8 patches
% numpatc... |
github | sbargoti/matlab-code-master | sampleIMAGESRGBNIR.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/sampleImages/sampleIMAGESRGBNIR.m | 4,284 | utf_8 | 25cfb1461d10d2338ddb78751604d934 | function patches = sampleIMAGESRGBNIR(resizeF, patchsize, imageChannels, numpatches, imagePath, fileName)
%% Internal Paramters
% imagePath = 'D:\Work\dataSet\VOC2011\JPEGImages'; %(on my comp)
% imagePath = '/nethome/calvin/dataSet/VOC2011/JPEGImages'; % (on archipelago)
% imagePath = VOCopts.imgpath;
% ... |
github | sbargoti/matlab-code-master | sampleRGBDIMAGES.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/sampleImages/sampleRGBDIMAGES.m | 2,777 | utf_8 | 400a38c386290fd03de1b48923fd1b32 | % function [patches ] = sampleIMAGES(patchsize)
function patches = sampleRGBDIMAGES(images, invalidmask, nband, patchsize, numPatches)
disp(' Sample RGBD image patches ')
IMAGES = double(images);
numpatches = numPatches;
% dont need this cuz the data dimension is alway 4D
% if nband == 1
% [dim1 dim2 numImage] = s... |
github | sbargoti/matlab-code-master | sampleIMAGES.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/sampleImages/sampleIMAGES.m | 2,833 | utf_8 | c82930707b7caa63a41bb75ef0ea7b84 | % function [patches ] = sampleIMAGES(patchsize)
function patches = sampleIMAGES(images, patchsize, numPatches);
% sampleIMAGES
% Returns 10000 patches for training
% load IMAGES; % load images from disk
% images = images.IMAGES;
IMAGES = double(images);
% patchsize = 12; % we'll use 8x8 patches
% numpatches =... |
github | sbargoti/matlab-code-master | sampleRGBDIMAGESperFrame.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/sampleImages/sampleRGBDIMAGESperFrame.m | 3,471 | utf_8 | 9ad5f9298f7b88d9452c57b4e3de7f67 | % function [patches ] = sampleIMAGES(patchsize)
% this is the same as sampleRGBDIMAGES, but does it per frame for large
% image set
% function patches = sampleRGBDIMAGESperFrame(images, invalidmask, nband, patchsize, numPatches)
function patches = sampleRGBDIMAGESperFrame(imgList, dataPath, resizeF, nband, patchsize, n... |
github | sbargoti/matlab-code-master | sparseAutoencoderLinearCost.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/linear_decoder/sparseAutoencoderLinearCost.m | 5,981 | utf_8 | 03d42402ab21b97ecc077c190fcc5892 | function [cost,grad] = sparseAutoencoderLinearCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay par... |
github | sbargoti/matlab-code-master | sparseAutoencoderLinearMaskCost.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/linear_decoder/sparseAutoencoderLinearMaskCost.m | 5,984 | utf_8 | 54e5d2ef21cb33586bda05c64a39a3a4 | function [cost,grad] = sparseAutoencoderLinearMaskCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay... |
github | sbargoti/matlab-code-master | denoiseAutoencoderLinearCost.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/linear_decoder/denoiseAutoencoderLinearCost.m | 4,962 | utf_8 | f055438aec1b52cc711ae0024957ba75 | function [cost,grad] = denoiseAutoencoderLinearCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data, corruptData)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: we... |
github | sbargoti/matlab-code-master | checkNumericalGradient.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/numericalGradient/checkNumericalGradient.m | 1,982 | utf_8 | 689a352eb2927b0838af5dc508f6374d | function [] = checkNumericalGradient()
% This code can be used to check your numerical gradient implementation
% in computeNumericalGradient.m
% It analytically evaluates the gradient of a very simple function called
% simpleQuadraticFunction (see below) and compares the result with your numerical
% solution. Your num... |
github | sbargoti/matlab-code-master | identityConvolve.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/cnn/identityConvolve.m | 4,227 | utf_8 | fdad599c758c8bc447417ff182a7d4c2 | function convolvedFeatures = identityConvolve(patchDim, numFeatures, images, W, b, ZCAWhite, meanPatch, CNNmethod)
%cnnConvolve Returns the convolution of the features given by W and b with
%the given images
%
% Parameters:
% patchDim - patch (feature) dimension
% numFeatures - number of features
% images - large im... |
github | sbargoti/matlab-code-master | cnnConvolveMod.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/cnn/cnnConvolveMod.m | 3,884 | utf_8 | 094f9c4f5f0665f38ede932492bfe2fe | function convolvedFeatures = cnnConvolve(patchDim, numFeatures, images, W, b, ZCAWhite, meanPatch)
%cnnConvolve Returns the convolution of the features given by W and b with
%the given images
%
% Parameters:
% patchDim - patch (feature) dimension
% numFeatures - number of features
% images - large images to convolve... |
github | sbargoti/matlab-code-master | calcPatchStats.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/cnn/calcPatchStats.m | 4,138 | utf_8 | 16195786446e60b169a96adf8a6f7f76 | % for scene classification, extract image statistics using learnt feature
% and perform pooling. use "sceneClustering" function to perform kmean
% clustering based on the pooled features
function [pooledFeatures] = calcPatchStats(patches, patchSize, nPool)
% dbstop if error
% %%
%
% currentPath = cd;
% ro... |
github | sbargoti/matlab-code-master | cnnConvolveGrey.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/cnn/cnnConvolveGrey.m | 4,197 | utf_8 | 912320221a1d00d1f428d796d6008dda | function convolvedFeatures = cnnConvolveGrey(patchDim, numFeatures, images, W, b, ZCAWhite, meanPatch, CNNmethod)
%cnnConvolve Returns the convolution of the features given by W and b with
%the given images
%
% Parameters:
% patchDim - patch (feature) dimension
% numFeatures - number of features
% images - large ima... |
github | sbargoti/matlab-code-master | cnnConvolve.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/cnn/cnnConvolve.m | 5,229 | utf_8 | 9a63fa0640a547bf9abe780a0ec04dcd | function convolvedFeatures = cnnConvolve(patchDim, numFeatures, images, W, b, ZCAWhite, meanPatch, CNNmethod)
%cnnConvolve Returns the convolution of the features given by W and b with
%the given images
%
% Parameters:
% patchDim - patch (feature) dimension
% numFeatures - number of features
% images - large images ... |
github | sbargoti/matlab-code-master | WolfeLineSearch.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/minFunc/WolfeLineSearch.m | 11,478 | utf_8 | d10187f2fedfa4143ebd6300537b6be4 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | sbargoti/matlab-code-master | minFunc_processInputOptions.m | .m | matlab-code-master/multClassImgSeg/code/UFLDL/code/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | dc74c67d849970de7f16c873fcf155bc |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | sbargoti/matlab-code-master | labelDecompress.m | .m | matlab-code-master/multClassImgSeg/code/Projects/imageClassSeg/labelDecompress.m | 289 | utf_8 | f2d1c925ca1b0e9959806a0b09e8aff4 | % to get rid of none-existing label index so the classifier is not confused
function [fullLabelData] = labelDecompress(cmpLabelData, labelMap)
fullLabelData = zeros(size(cmpLabelData));
for i = 1:length(labelMap)
fullLabelData(cmpLabelData == i) = labelMap(i);
end
|
github | sbargoti/matlab-code-master | labelCompress.m | .m | matlab-code-master/multClassImgSeg/code/Projects/imageClassSeg/labelCompress.m | 988 | utf_8 | bcc7b04d3fc8f9d2654012f4ce4466b4 | % to get rid of none-existing label index so the classifier is not confused
function [labelData, labelMap, classNameList] = labelCompress(oldLabelData, oldClassNameList)
exsitinglabels = unique(oldLabelData);
[dim1 dim2 dim3] = size(oldLabelData);
% label analysis remove class that is too small
nLabel = ma... |
github | sbargoti/matlab-code-master | loadImagesLabelsMod.m | .m | matlab-code-master/multClassImgSeg/code/Projects/imageClassSeg/loadImagesLabelsMod.m | 3,437 | utf_8 | 1850609856568f0b870c3ee28a76ba72 | function [imageData, labelledImageData, labelData] = loadImagesLabelsMod(imageDataPath, labelDataPath, classColour)
%% find all images
% every image in the dataset
imType = {'*.jpg', '*.png', '*.bmp'};
% fullFileList = dir([imageDataPath '*jpg']);
for i = 1:length(imType)
fullFileList = dir([imageDataPath i... |
github | sbargoti/matlab-code-master | plotLabelLegend.m | .m | matlab-code-master/multClassImgSeg/code/Projects/imageClassSeg/plotLabelLegend.m | 573 | utf_8 | 7abe04fea8c846972d97bd2a01ba2214 | % plot label legend
function []=plotLabelLegend(labelMap)
classNameList = ...
{'Void ',...
'leaves ',...
'almonds ',...
'trunk ',...
'ground ',...
'sky '};
classColour = [0 0 0
64 128 64
128 0 0
128 128 0
192 192 128
0 0 255];
figure;hold on
for i = 1: length(labelMap)
c... |
github | sbargoti/matlab-code-master | loadRGBIRLabels.m | .m | matlab-code-master/multClassImgSeg/code/Projects/imageClassSeg/loadRGBIRLabels.m | 3,741 | utf_8 | 11850d6e9c77c8420d2935733e467ad6 | function [RGBimageData IRimageData labelData labelIdx unlabelIdx classNameList] = loadRGBIRLabels(RGBDataPath, IRDataPath, labelDataPath)
% every image in the dataset
fullRGBFileList = dir([RGBDataPath '*jpg']);
% image with existing labels
labelFileList = dir([labelDataPath '*png']); % obtain label list
subFi... |
github | sbargoti/matlab-code-master | loadImagesLabels.m | .m | matlab-code-master/multClassImgSeg/code/Projects/imageClassSeg/loadImagesLabels.m | 3,126 | utf_8 | 956b06e7c6cf4305a860f20920503dee | function [imageData, labelData, labelIdx] = loadImagesLabels(imageDataPath, labelDataPath, classColour)
%% find all images
% every image in the dataset
imType = {'*.jpg', '*.png', '*.bmp'};
% fullFileList = dir([imageDataPath '*jpg']);
for i = 1:length(imType)
fullFileList = dir([imageDataPath imType{i}]);
... |
github | sbargoti/matlab-code-master | collectSeedPoints.m | .m | matlab-code-master/multClassImgSeg/code/Projects/labelAssist/collectSeedPoints.m | 2,136 | utf_8 | f2f5ec788ee0f01323850f53b6eee3e3 | % collect multiple regions for grab cut
% figure(1);imagesc(currentImage);
%
function [initMap] = collectSeedPoints(currentImage, labels)
reply = [];
grabCutRegion = zeros(size(labels));
while isempty(reply)
oldGrabCutRegion = grabCutRegion;
grabCutRegion = roipoly(currentImage);
grabCu... |
github | indigits/sparse-plex-master | ex_givens_rot_1.m | .m | sparse-plex-master/examples/linear_algebra/givens/ex_givens_rot_1.m | 505 | utf_8 | a60d12b9d5e73d42131ba521960cd0a6 | function ex_givens_rot_1()
clc;
close all;
test_ab(3, 4);
test_ab(4, 3);
test_ab(3, -4);
test_ab(4, -3);
test_ab(-3, 4);
test_ab(-4, 3);
test_ab(-3, -4);
test_ab(-4, -3);
end
function test_ab(a, b)
x = [a;b];
[c, s] = spx.la.givens.rotation(a,b);
G1 = [c s; -s c];
theta = rad2deg(spx.la.givens.the... |
github | indigits/sparse-plex-master | timeit.m | .m | sparse-plex-master/library/ext/timeit.m | 61,409 | utf_8 | 56d91534e01a00998d9f83f3f1bca3eb | function [t, measurement_overhead, measurement_details] = timeit(f, num_outputs)
%TIMEIT Measure time required to run function.
% T = TIMEIT(F) measures the time (in seconds) required to run F, which is a
% function handle. TIMEIT calls F with either no output arguments or one
% output argument depending on narg... |
github | indigits/sparse-plex-master | cgsolve.m | .m | sparse-plex-master/library/ext/cgsolve.m | 1,691 | utf_8 | d1e3c459a716b242d537a40c89e6f823 | % 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 | indigits/sparse-plex-master | lmsvd.m | .m | sparse-plex-master/library/ext/lmsvd.m | 9,236 | utf_8 | 0677b5ae718f8bc99eac787b36766a51 | function [U,S,V,Out] = lmsvd(A,r,opts)
%
% LMSVD: Limited Memory Block Krylov Subspace Optimization for
% Computing Principal Singular Value Decompositions
%
% LMSVD is a MATLAB solver for computing dominant singular value
% decompositions of large matrices. The approach is based on a block
% K... |
github | indigits/sparse-plex-master | pdftops.m | .m | sparse-plex-master/library/ext/export_fig/pdftops.m | 3,574 | utf_8 | 92ff676904575e16046dfff010b4e145 | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for future refere... |
github | indigits/sparse-plex-master | crop_borders.m | .m | sparse-plex-master/library/ext/export_fig/crop_borders.m | 3,666 | utf_8 | ebb9c61581b6f0d4a2db2fd1d9e30685 | function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how much padding to ha... |
github | indigits/sparse-plex-master | isolate_axes.m | .m | sparse-plex-master/library/ext/export_fig/isolate_axes.m | 4,721 | utf_8 | 253cd7b7d8fc7cb00d0cc55926f32de5 | function fh = isolate_axes(ah, vis)
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
%... |
github | indigits/sparse-plex-master | im2gif.m | .m | sparse-plex-master/library/ext/export_fig/im2gif.m | 6,048 | utf_8 | 5a7437140f8d013158a195de1e372737 | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function converts a mu... |
github | indigits/sparse-plex-master | read_write_entire_textfile.m | .m | sparse-plex-master/library/ext/export_fig/read_write_entire_textfile.m | 924 | utf_8 | 779e56972f5d9778c40dee98ddbd677e | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fname - Pathn... |
github | indigits/sparse-plex-master | pdf2eps.m | .m | sparse-plex-master/library/ext/export_fig/pdf2eps.m | 1,471 | utf_8 | a1f41f0c7713c73886a2323e53ed982b | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.com/xpdf ... |
github | indigits/sparse-plex-master | print2array.m | .m | sparse-plex-master/library/ext/export_fig/print2array.m | 9,369 | utf_8 | ca18a1e6c5a944b591a0557bd69f1c2c | function [A, bcol] = print2array(fig, res, renderer, gs_options)
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% A = print2array(figure_handle... |
github | indigits/sparse-plex-master | append_pdfs.m | .m | sparse-plex-master/library/ext/export_fig/append_pdfs.m | 2,678 | utf_8 | 949c7c4ec3f5af6ff23099f17b1dfd79 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the output fi... |
github | indigits/sparse-plex-master | using_hg2.m | .m | sparse-plex-master/library/ext/export_fig/using_hg2.m | 1,002 | utf_8 | b1620dd31f4d0b8acea2723e354a3518 | %USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for improved per... |
github | indigits/sparse-plex-master | eps2pdf.m | .m | sparse-plex-master/library/ext/export_fig/eps2pdf.m | 7,661 | utf_8 | ab0c84a2a57942e7e121faef8e0742df | function eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop... |
github | indigits/sparse-plex-master | export_fig.m | .m | sparse-plex-master/library/ext/export_fig/export_fig.m | 55,737 | utf_8 | 13561a4a251d34d72090032c52f4becf | function [imageData, alpha] = export_fig(varargin)
%EXPORT_FIG Exports figures in a publication-quality format
%
% Examples:
% imageData = export_fig
% [imageData, alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -transparent
% ex... |
github | indigits/sparse-plex-master | ghostscript.m | .m | sparse-plex-master/library/ext/export_fig/ghostscript.m | 7,492 | utf_8 | 7a1e094c8bf153e1b239765ff6fd43df | function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path
% is s... |
github | indigits/sparse-plex-master | fix_lines.m | .m | sparse-plex-master/library/ext/export_fig/fix_lines.m | 6,290 | utf_8 | 8437006b104957762090e3d875688cb6 | %FIX_LINES Improves the line style of eps files generated by print
%
% Examples:
% fix_lines fname
% fix_lines fname fname2
% fstrm_out = fixlines(fstrm_in)
%
% This function improves the style of lines in eps files generated by
% MATLAB's print function, making them more similar to those seen on
% screen. Grid ... |
github | indigits/sparse-plex-master | spx_ini2struct.m | .m | sparse-plex-master/library/ext/ini2struct/spx_ini2struct.m | 4,218 | utf_8 | cd7ddb88f53f3893e9278ef92dbf3323 | function Result = spx_ini2struct(FileName)
%==========================================================================
% Author: Andriy Nych ( nych.andriy@gmail.com )
% Version: 733341.4155741782200
%==========================================================================
%
% INI = ini2struct(FileName)
%
% ... |
github | indigits/sparse-plex-master | la.m | .m | sparse-plex-master/library/+spx/la.m | 230 | utf_8 | 3529905c0d7c3e7ec0534cf8904e0843 | classdef la
methods(Static)
function no = nonorthogonality(U)
% Computes the deviation from orthogonality of a basis
[m, k] = size(U);
gap = U' * U - eye(k);
no = norm(gap);
end
end % methods
end % classdef
|
github | indigits/sparse-plex-master | io.m | .m | sparse-plex-master/library/+spx/io.m | 1,417 | utf_8 | 8778772b4fe7eeea3306bd5bdc8ba4b7 | classdef io
methods(Static)
function result = yes_no(flag)
if flag == 0
result = 'No';
else
result = 'Yes';
end
end
function result = true_false(flag)
if flag == 0
result = 'false';
else
result = 'true';
end
end
function result = true_false_short(flag)
if ... |
github | indigits/sparse-plex-master | lang.m | .m | sparse-plex-master/library/+spx/lang.m | 368 | utf_8 | 3f722bee1cc3f99c869fb0c1469389aa | classdef lang
% Useful methods for extending Language core functionality
methods(Static)
function result = is_class(classpath)
% Returns true if classpath points to a MATLAB class.
result = (exist(classpath, 'class') ~= 0);
end
function varargout = noop(varargin)
% Does nothing
for i=1:nargout
... |
github | indigits/sparse-plex-master | rmse.m | .m | sparse-plex-master/library/+spx/rmse.m | 391 | utf_8 | 4275c4106e0ea15a3c453a7991e2b045 | classdef rmse
methods(Static)
function [r, sse, mse] = vec(x, y)
% Computes Root Mean Square Error between two data-vectors
% Identify the non-nan locations
I = ~isnan(x) & ~isnan(y);
% keep only the non-nan part
x = x(I);
y = y(I);
gap = x(:)-y(:);
sse = sum(gap.^2);
mse = sse / ... |
github | indigits/sparse-plex-master | log.m | .m | sparse-plex-master/library/+spx/log.m | 179 | utf_8 | ebf77bc0ffb657e0be04c3fb0fda05d6 | classdef log
methods(Static)
function display(message)
% compute current time string
curtime = datestr(now, 'HH:MM:SS');
disp([curtime, ' ', message]);
end
end
end |
github | indigits/sparse-plex-master | fs.m | .m | sparse-plex-master/library/+spx/fs.m | 670 | utf_8 | 411db87ee54601deb4229105dab524f0 | classdef fs
% file system related utilities
methods(Static)
function result = is_directory(dirpath)
% Returns if dirpath is a directory or not.
result = exist(dirpath, 'dir') ~= 0;
end
function result = is_file(filepath)
% Returns true if filepath is a file
result = (exist(filepath, 'file') ~= 0) & (... |
github | indigits/sparse-plex-master | sparse.m | .m | sparse-plex-master/library/+spx/sparse.m | 533 | utf_8 | 3b6f7db229e11235f4721adcd736023d | classdef sparse
% Helper methods for working with sparse matrices
methods(Static)
function A = join_data_indices(data, omega, n1, n2)
% Constructs a sparse matrix from the data and indices in
m = length(omega);
[i, j] = ind2sub([n1,n2], omega);
A = sparse(i,j,data,n1,n2,m);
end
function [data, indic... |
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