plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | BII-wushuang/FLLIT-master | train_boost_hd.m | .m | FLLIT-master/src/KernelBoost-v0.1/train_boost_hd.m | 6,367 | utf_8 | d698663f6ad6929515c06eb563151f6f | %
% samples_idx(:,1) => sample image no
% samples_idx(:,2) => sample row
% samples_idx(:,3) => sample column
% samples_idx(:,4) => sample label (-1/+1)
function [weak_learners] = train_boost_hd(params,data,hd,samples_idx)
% Train a KernelBoost classifier on the given samples
% the classifier combine the histogram dis... |
github | pirovc/fgap-master | fgap.m | .m | fgap-master/fgap.m | 66,228 | utf_8 | 9568dac0e7f59e09c35645295dfbc255 | % FGAP: an automated gap closing tool
% Vitor C Piro, Helisson Faoro, Vinicius A Weiss, Maria BR Steffens, Fabio O Pedrosa, Emanuel M Souza and Roberto T Raittz
% BMC Research Notes 2014, 7:371 doi:10.1186/1756-0500-7-371
%
% The MIT License (MIT)
%
% Copyright (c) 2014 UFPR - Universidade Federal do Paraná (Vitor C.... |
github | rashwin1989/plicFoam-master | umfpack_report.m | .m | plicFoam-master/UMFPACK/MATLAB/umfpack_report.m | 16,015 | utf_8 | ab2ab9204411376267d5931f57b6b59b | function umfpack_report (Control, Info)
%UMFPACK_REPORT prints optional control settings and statistics
%
% Example:
% umfpack_report (Control, Info) ;
%
% Prints the current Control settings for umfpack2, and the statistical
% information returned by umfpack2 in the Info array. If Control is
% an empty matrix... |
github | rashwin1989/plicFoam-master | umfpack_make.m | .m | plicFoam-master/UMFPACK/MATLAB/umfpack_make.m | 11,580 | utf_8 | 64f05bca5117d3c3bdc652a04a176080 | function umfpack_make (lapack)
%UMFPACK_MAKE to compile umfpack2 for use in MATLAB
%
% Compiles the umfpack2 mexFunction and then runs a simple demo.
%
% Example:
% umfpack_make % use default LAPACK and BLAS
% umfpack_make ('lcc_lib/libmwlapack.lib') % for Windows
% umfpack_make ... |
github | rashwin1989/plicFoam-master | umfpack_btf.m | .m | plicFoam-master/UMFPACK/MATLAB/umfpack_btf.m | 4,651 | utf_8 | 905709090e298d8bfd5e9937181c95b6 | function [x, info] = umfpack_btf (A, b, Control)
%UMFPACK_BTF factorize A using a block triangular form
%
% Example:
% x = umfpack_btf (A, b, Control)
%
% solve Ax=b by first permuting the matrix A to block triangular form via dmperm
% and then using UMFPACK to factorize each diagonal block. Adjacent 1-by-1
% blocks... |
github | pooya-git/DeepNeuralDecoder-master | SteaneTrainingSetd5.m | .m | DeepNeuralDecoder-master/Data/Generator/Steane_CNOT_D5/SteaneTrainingSetd5.m | 113,759 | utf_8 | f0dabe553929f5beb0a63b422cbf428b | % MIT License
%
% Copyright (c) 2018 Chris Chamberland
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, mo... |
github | pooya-git/DeepNeuralDecoder-master | SteaneTrainingSetd3.m | .m | DeepNeuralDecoder-master/Data/Generator/Steane_CNOT_D3/SteaneTrainingSetd3.m | 61,494 | utf_8 | 17049c07b5d6cc375a5d6f39bd9c3c9b | % MIT License
%
% Copyright (c) 2018 Chris Chamberland
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, mo... |
github | pooya-git/DeepNeuralDecoder-master | KnillTrainingSetd5.m | .m | DeepNeuralDecoder-master/Data/Generator/Knill_CNOT_D5/KnillTrainingSetd5.m | 115,585 | utf_8 | 2d49b56c69ce10f5248a535f239fb15b | % MIT License
%
% Copyright (c) 2018 Chris Chamberland
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, mo... |
github | pooya-git/DeepNeuralDecoder-master | KnillTrainingSetd3.m | .m | DeepNeuralDecoder-master/Data/Generator/Knill_CNOT_D3/KnillTrainingSetd3.m | 54,575 | utf_8 | 9fda8f3ecaae3b68dc4a0a6fa3389b07 | % MIT License
%
% Copyright (c) 2018 Chris Chamberland
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, mo... |
github | pooya-git/DeepNeuralDecoder-master | SurfaceCodeTrainingSetd3.m | .m | DeepNeuralDecoder-master/Data/Generator/Surface_1EC_D3/SurfaceCodeTrainingSetd3.m | 32,816 | utf_8 | 15195dd006d9959226e29930688b525b | % MIT License
%
% Copyright (c) 2018 Chris Chamberland
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, mo... |
github | pooya-git/DeepNeuralDecoder-master | SurfaceCodeTrainingSetd5.m | .m | DeepNeuralDecoder-master/Data/Generator/Surface_1EC_D5/SurfaceCodeTrainingSetd5.m | 29,093 | utf_8 | c7189ded75707169c19b5618d5c38518 | % MIT License
%
% Copyright (c) 2018 Chris Chamberland
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, mo... |
github | cellgeometry/heteromotility-master | dist_v_mag.m | .m | heteromotility-master/analysis/dist_v_mag.m | 583 | utf_8 | e60f1112cfd713a52381b1a4c364beac | %% Calculate distance and vector magnitude at each point in a divergence matrix
function [result] = dist_v_mag(div, m_u, m_v, m_s);
% div = N x M divergence matrix
% m_u = N x M matrix of vector field x-component
% m_v = N x M matrix of vector field y-component
% m_s = 2 x 1 vector i, j index for minimum divergence (m... |
github | MohamedAbdelsalam9/SceneRecognition-master | get_image_paths.m | .m | SceneRecognition-master/code/get_image_paths.m | 1,671 | utf_8 | 50df335e4058c9fe2f305b5711efa9a3 | %we used this function from James Hayes Course to load the image paths
%This function returns cell arrays containing the file path for each train
%and test image, as well as cell arrays with the label of each train and
%test image.
function [train_image_paths, test_image_paths, train_labels, test_labels] = ...
... |
github | MohamedAbdelsalam9/SceneRecognition-master | create_results_webpage.m | .m | SceneRecognition-master/code/create_results_webpage.m | 12,092 | utf_8 | cb211d5fedc6451c228c5b7a3f1104cc | % This function creates a webpage (html and images) visualizing the
% classiffication results. This webpage will contain
% (1) A confusion matrix plot
% (2) A table with one row per category, with 3 columns - training
% examples, true positives, false positives, and false negatives.
% false positives are instanc... |
github | MohamedAbdelsalam9/SceneRecognition-master | svm_classify.m | .m | SceneRecognition-master/code/svm_classify.m | 3,037 | utf_8 | 20eadab16b1ff5e3902e2b9641cf7254 | %train a one vs all linear SVM classifier, and apply each classifier of the 15 to each image
%and the category is assigned based on the highest result
%5 fold cross validation is applied to get the best regularization parameter (lambda) and avoid overfitting
function predicted_categories = svm_classify(train_featu... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_compile.m | .m | SceneRecognition-master/code/vlfeat/toolbox/vl_compile.m | 5,060 | utf_8 | 978f5189bb9b2a16db3368891f79aaa6 | function vl_compile(compiler)
% VL_COMPILE Compile VLFeat MEX files
% VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command
% works only under Windows and is used to re-build problematic
% binaries. The preferred method of compiling VLFeat on both UNIX
% and Windows is through the provided Makefile... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_noprefix.m | .m | SceneRecognition-master/code/vlfeat/toolbox/vl_noprefix.m | 1,875 | utf_8 | 97d8755f0ba139ac1304bc423d3d86d3 | function vl_noprefix
% VL_NOPREFIX Create a prefix-less version of VLFeat commands
% VL_NOPREFIX() creats prefix-less stubs for VLFeat functions
% (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs
% are included in the VLFeat binary distribution anyways. Moreover,
% on UNIX platforms, the stub... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_pegasos.m | .m | SceneRecognition-master/code/vlfeat/toolbox/misc/vl_pegasos.m | 2,837 | utf_8 | d5e0915c439ece94eb5597a07090b67d | % VL_PEGASOS [deprecated]
% VL_PEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_pegasos(X,Y,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer supported.\n');
en... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_svmpegasos.m | .m | SceneRecognition-master/code/vlfeat/toolbox/misc/vl_svmpegasos.m | 1,178 | utf_8 | 009c2a2b87a375d529ed1a4dbe3af59f | % VL_SVMPEGASOS [deprecated]
% VL_SVMPEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_svmpegasos(DATA,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer suppor... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_override.m | .m | SceneRecognition-master/code/vlfeat/toolbox/misc/vl_override.m | 4,654 | utf_8 | e233d2ecaeb68f56034a976060c594c5 | function config = vl_override(config,update,varargin)
% VL_OVERRIDE Override structure subset
% CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds
% of the structure UPDATE to the corresponding fields of the
% struture CONFIG.
%
% Usually CONFIG is interpreted as a list of paramters with their
... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_quickvis.m | .m | SceneRecognition-master/code/vlfeat/toolbox/quickshift/vl_quickvis.m | 3,696 | utf_8 | 27f199dad4c5b9c192a5dd3abc59f9da | function [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts)
% VL_QUICKVIS Create an edge image from a Quickshift segmentation.
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. RATIO controls the tradeoff
% bet... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_demo_aib.m | .m | SceneRecognition-master/code/vlfeat/toolbox/demo/vl_demo_aib.m | 2,928 | utf_8 | 590c6db09451ea608d87bfd094662cac | function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_demo_alldist.m | .m | SceneRecognition-master/code/vlfeat/toolbox/demo/vl_demo_alldist.m | 5,460 | utf_8 | 6d008a64d93445b9d7199b55d58db7eb | function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 's... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_demo_ikmeans.m | .m | SceneRecognition-master/code/vlfeat/toolbox/demo/vl_demo_ikmeans.m | 774 | utf_8 | 17ff0bb7259d390fb4f91ea937ba7de0 | function vl_demo_ikmeans()
% VL_DEMO_IKMEANS
numData = 10000 ;
dimension = 2 ;
data = uint8(255*rand(dimension,numData)) ;
numClusters = 3^3 ;
[centers, assignments] = vl_ikmeans(data, numClusters);
figure(1) ; clf ; axis off ;
plotClusters(data, centers, assignments) ;
vl_demo_print('ikmeans_2d',0.6);
[tree, assig... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_demo_svm.m | .m | SceneRecognition-master/code/vlfeat/toolbox/demo/vl_demo_svm.m | 1,235 | utf_8 | 7cf6b3504e4fc2cbd10ff3fec6e331a7 | % VL_DEMO_SVM Demo: SVM: 2D linear learning
function vl_demo_svm
y=[];X=[];
% Load training data X and their labels y
load('vl_demo_svm_data.mat')
Xp = X(:,y==1);
Xn = X(:,y==-1);
figure
plot(Xn(1,:),Xn(2,:),'*r')
hold on
plot(Xp(1,:),Xp(2,:),'*b')
axis equal ;
vl_demo_print('svm_training') ;
% Parameters
lambda =... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_demo_kdtree_sift.m | .m | SceneRecognition-master/code/vlfeat/toolbox/demo/vl_demo_kdtree_sift.m | 6,832 | utf_8 | e676f80ac330a351f0110533c6ebba89 | function vl_demo_kdtree_sift
% VL_DEMO_KDTREE_SIFT
% Demonstrates the use of a kd-tree forest to match SIFT
% features. If FLANN is present, this function runs a comparison
% against it.
% AUTORIGHS
rand('state',0) ;
randn('state',0);
do_median = 0 ;
do_mean = 1 ;
% try to setup flann
if ~exist('flann_search'... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_impattern.m | .m | SceneRecognition-master/code/vlfeat/toolbox/imop/vl_impattern.m | 6,876 | utf_8 | 1716a4d107f0186be3d11c647bc628ce | function im = vl_impattern(varargin)
% VL_IMPATTERN Generate an image from a stock pattern
% IM=VLPATTERN(NAME) returns an instance of the specified
% pattern. These stock patterns are useful for testing algoirthms.
%
% All generated patterns are returned as an image of class
% DOUBLE. Both gray-scale and colou... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_tpsu.m | .m | SceneRecognition-master/code/vlfeat/toolbox/imop/vl_tpsu.m | 1,755 | utf_8 | 09f36e1a707c069b375eb2817d0e5f13 | function [U,dU,delta]=vl_tpsu(X,Y)
% VL_TPSU Compute the U matrix of a thin-plate spline transformation
% U=VL_TPSU(X,Y) returns the matrix
%
% [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ]
% [ ]
% [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ]
%
% where X... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_xyz2lab.m | .m | SceneRecognition-master/code/vlfeat/toolbox/imop/vl_xyz2lab.m | 1,570 | utf_8 | 09f95a6f9ae19c22486ec1157357f0e3 | function J=vl_xyz2lab(I,il)
% VL_XYZ2LAB Convert XYZ color space to LAB
% J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format.
%
% VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55,
% D65, D75, D93. The default illuminatn is E.
%
% See also: VL_XYZ2LUV(), VL_HELP().
% Copyright ... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_gmm.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_gmm.m | 1,332 | utf_8 | 76782cae6c98781c6c38d4cbf5549d94 | function results = vl_test_gmm(varargin)
% VL_TEST_GMM
% 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 ;
end
function s = setup()
randn('st... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_twister.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_twister.m | 1,251 | utf_8 | 2bfb5a30cbd6df6ac80c66b73f8646da | function results = vl_test_twister(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_illegal_args()
vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ;
function te... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_kdtree.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_kdtree.m | 2,449 | utf_8 | 9d7ad2b435a88c22084b38e5eb5f9eb9 | function results = vl_test_kdtree(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = single(randn(10, 1000)) ;
s.Q = single(randn(10, 10)) ;
function test_nearest(s)
for tmethod = {'median', 'mean'}
for type = {@single, @double}
conv = type{1} ;
tmethod = char(tmethod) ;... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_imwbackward.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_imwbackward.m | 514 | utf_8 | 33baa0784c8f6f785a2951d7f1b49199 | function results = vl_test_imwbackward(varargin)
% VL_TEST_IMWBACKWARD
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
function test_identity(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_alphanum.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_alphanum.m | 1,624 | utf_8 | 2da2b768c2d0f86d699b8f31614aa424 | function results = vl_test_alphanum(varargin)
% VL_TEST_ALPHANUM
vl_test_init ;
function s = setup()
s.strings = ...
{'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Al... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_printsize.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_printsize.m | 1,447 | utf_8 | 0f0b6437c648b7a2e1310900262bd765 | function results = vl_test_printsize(varargin)
% VL_TEST_PRINTSIZE
vl_test_init ;
function s = setup()
s.fig = figure(1) ;
s.usletter = [8.5, 11] ; % inches
s.a4 = [8.26772, 11.6929] ;
clf(s.fig) ; plot(1:10) ;
function teardown(s)
close(s.fig) ;
function test_basic(s)
for sigma = [1 0.5 0.2]
vl_printsize(s.fig, s... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_cummax.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_cummax.m | 838 | utf_8 | 5e98ee1681d4823f32ecc4feaa218611 | function results = vl_test_cummax(varargin)
% VL_TEST_CUMMAX
vl_test_init ;
function test_basic()
vl_assert_almost_equal(...
vl_cummax(1), 1) ;
vl_assert_almost_equal(...
vl_cummax([1 2 3 4], 2), [1 2 3 4]) ;
function test_multidim()
a = [1 2 3 4 3 2 1] ;
b = [1 2 3 4 4 4 4] ;
for k=1:6
dims = ones(1,6) ;
dim... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_imintegral.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_imintegral.m | 1,429 | utf_8 | 4750f04ab0ac9fc4f55df2c8583e5498 | function results = vl_test_imintegral(varargin)
% VL_TEST_IMINTEGRAL
vl_test_init ;
function state = setup()
state.I = ones(5,6) ;
state.correct = [ 1 2 3 4 5 6 ;
2 4 6 8 10 12 ;
3 6 9 12 15 18 ;
4 8 12 ... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_sift.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_sift.m | 1,318 | utf_8 | 806c61f9db9f2ebb1d649c9bfcf3dc0a | function results = vl_test_sift(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
s.I = im2single(imread(fullfile(vl_root,'data','box.pgm'))) ;
[s.ubc.f, s.ubc.d] = ...
vl_ubcread(fullfile(vl_root,'data','box.sift')) ;
function test_ubc_descriptor(s)
err = [] ;
[f, d] = vl_sift(s.I,...
... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_binsum.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_binsum.m | 1,377 | utf_8 | f07f0f29ba6afe0111c967ab0b353a9d | function results = vl_test_binsum(varargin)
% VL_TEST_BINSUM
vl_test_init ;
function test_three_args()
vl_assert_almost_equal(...
vl_binsum([0 0], 1, 2), [0 1]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, 1), [0 7]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ;
function te... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_lbp.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_lbp.m | 892 | utf_8 | a79c0ce0c85e25c0b1657f3a0b499538 | function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_unfiorm_lbps(s)
% enumerate the 56 uniform lbps
q = 0 ;
for i=0:7
for j=1:7
I = zeros(3) ;
p = mod(s.pixels - i + 8, 8) + 1 ;
I(p <= j) = 1 ;
f = vl_lbp(single(I), 3) ;
q = q + 1 ;
vl_assert_equal(find(f... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_colsubset.m | .m | SceneRecognition-master/code/vlfeat/toolbox/xtest/vl_test_colsubset.m | 828 | utf_8 | be0c080007445b36333b863326fb0f15 | function results = vl_test_colsubset(varargin)
% VL_TEST_COLSUBSET
vl_test_init ;
function s = setup()
s.x = [5 2 3 6 4 7 1 9 8 0] ;
function test_beginning(s)
vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ;
vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ;
function test_ending(s)
vl_assert_equa... |
github | MohamedAbdelsalam9/SceneRecognition-master | vl_test_alldist.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_ihashsum.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_grad.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_whistc.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_roc.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_dsift.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_alldist2.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_fisher.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_imsmooth.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_svmtrain.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_phow.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_kmeans.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_hikmeans.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_aib.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_plotbox.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_imarray.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_homkermap.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_slic.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_ikmeans.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_mser.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_inthist.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_imdisttf.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_vlad.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_pr.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_hog.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_argparse.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_liop.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_test_binsearch.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_roc.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_click.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_pr.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_ubcread.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_frame2oell.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | vl_plotsiftdescriptor.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | phow_caltech101.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | sift_mosaic.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | encodeImage.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | experiments.m | .m | SceneRecognition-master/code/vlfeat/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 | MohamedAbdelsalam9/SceneRecognition-master | getDenseSIFT.m | .m | SceneRecognition-master/code/vlfeat/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 | ColeLab/ColeAnticevicNetPartition-master | save.m | .m | ColeAnticevicNetPartition-master/code/gifti-1.6/@gifti/save.m | 9,801 | utf_8 | d7999ec374bfbb32da9b38b849ef15ab | function save(this,filename,encoding)
% Save GIfTI object in a GIfTI format file
% FORMAT save(this,filename,encoding)
% this - GIfTI object
% filename - name of GIfTI file to be created [Default: 'untitled.gii']
% encoding - optional argument to specify encoding format, among
% ASCII, Base64Bi... |
github | ColeLab/ColeAnticevicNetPartition-master | gifti.m | .m | ColeAnticevicNetPartition-master/code/gifti-1.6/@gifti/gifti.m | 3,918 | utf_8 | 59128f02da0486e8123d2abf2d645bf1 | function this = gifti(varargin)
% GIfTI Geometry file format class
% Geometry format under the Neuroimaging Informatics Technology Initiative
% (NIfTI):
% http://www.nitrc.org/projects/gifti/
% http://nifti.nimh.nih.gov/
%_______________________________________________________... |
github | ColeLab/ColeAnticevicNetPartition-master | saveas.m | .m | ColeAnticevicNetPartition-master/code/gifti-1.6/@gifti/saveas.m | 13,730 | utf_8 | 93c2b293811510f3a6bd3359f2f5e8d5 | function saveas(this,filename,format)
% Save GIfTI object in external file format
% FORMAT saveas(this,filename,format)
% this - GIfTI object
% filename - name of file to be created [Default: 'untitled.vtk']
% format - optional argument to specify encoding format, among
% VTK (.vtk,.vtp), Col... |
github | ColeLab/ColeAnticevicNetPartition-master | xml_parser.m | .m | ColeAnticevicNetPartition-master/code/gifti-1.6/@gifti/private/xml_parser.m | 16,915 | utf_8 | de0a4766201059ea1860acc2a4a1a019 | function tree = xml_parser(xmlstr)
% XML (eXtensible Markup Language) Processor
% FORMAT tree = xml_parser(xmlstr)
%
% xmlstr - XML string to parse
% tree - tree structure corresponding to the XML file
%__________________________________________________________________________
%
% xml_parser.m is an XML 1.0... |
github | ColeLab/ColeAnticevicNetPartition-master | read_gifti_file_standalone.m | .m | ColeAnticevicNetPartition-master/code/gifti-1.6/@gifti/private/read_gifti_file_standalone.m | 8,244 | utf_8 | 018a4c448f6eeab3ab67b1b95d3086ea | function this = read_gifti_file_standalone(filename, this)
% Low level reader of GIfTI 1.0 files
% FORMAT this = read_gifti_file(filename, this)
% filename - XML GIfTI filename
% this - structure with fields 'metaData', 'label' and 'data'.
%________________________________________________________________... |
github | ColeLab/ColeAnticevicNetPartition-master | mvtk_write.m | .m | ColeAnticevicNetPartition-master/code/gifti-1.6/@gifti/private/mvtk_write.m | 18,882 | utf_8 | 006b1aa29abf46ab17ab3eb11c9cb992 | function mvtk_write(M,filename,format)
% Write geometric data on disk using VTK file format (legacy/XML,ascii/binary)
% FORMAT mvtk_write(M,filename,format)
%
% M - data structure
% filename - output filename [Default: 'untitled']
% format - VTK file format: legacy, legacy-ascii, legacy-binary, xml,
% ... |
github | ColeLab/ColeAnticevicNetPartition-master | isintent.m | .m | ColeAnticevicNetPartition-master/code/gifti-1.6/@gifti/private/isintent.m | 2,803 | utf_8 | 059679d968315674d5e6cccbbd6f128c | function [a, b] = isintent(this,intent)
% Correspondance between fieldnames and NIfTI intent codes
% FORMAT ind = isintent(this,intent)
% this - GIfTI object
% intent - fieldnames
% a - indices of found intent(s)
% b - indices of dataarrays of found intent(s)
%________________________________... |
github | anilbas/3DMMasSTN-master | dagnn_3dmmasstn.m | .m | 3DMMasSTN-master/dagnn_3dmmasstn.m | 3,391 | utf_8 | 1390f606b065f165349ee949d51a3164 | function [net, info] = dagnn_3dmmasstn(imdb,varargin)
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.networkType = 'dagnn' ;
opts.derOutputs = {'objective1',0.8998,'objective2',0.1,'objective3',0.0001,'objective4',0.0001};
% expDir: Output directory for the net-e... |
github | KleinYuan/doppia-master | use_cimgmatlab.m | .m | doppia-master/libs/CImg/examples/use_cimgmatlab.m | 1,242 | utf_8 | c7cefea57d7bf6077ec6a77f3bdda6b6 | /*-----------------------------------------------------------------------
File : use_cimgmatlab.m
Description: Example of use for the CImg plugin 'plugins/cimgmatlab.h'
which allows to use CImg in order to develop matlab external
functions (mex functions).
User should be familiar with Matlab C/C++ mex functi... |
github | M-T3K/UPM-master | Lagrange.m | .m | UPM-master/Algoritmica Numerica I/prac_provincias/Lagrange.m | 351 | utf_8 | e95f109d17853801395ded5014eac57f |
% p(x) = sum(i = 0..n, Li(x)*f(x_i))
function px = Lagrange(x, y, xx)
px = 0;
n = length(x)
for i = 1:n
Li = 1;
% Hacemos Bases de Lagrange
for j = 1:n
if j ~= i
Li = Li .* ( (xx - x(j) )/ (x(i) - x(j) ) ); % Li(x_i)
end
end
p... |
github | M-T3K/UPM-master | cost.m | .m | UPM-master/Algoritmica Numerica I/prac_provincias/cost.m | 536 | utf_8 | 02d733abf7d4d7402613f3ee45fda36b |
% trazo se refiere a la funcion
% x_p se refiere a las coordenadas x de los puntos elegidos
% y_p se refiere a las coorenadas y de los puntos elegidos
function coste = cost(trazo, len, xx, x_p, y_p)
coste = 0;
n = length(x_p);
m = length(trazo);
for j = 1:m
for i = 1:n
if xx(j) ==... |
github | M-T3K/UPM-master | Lagrangio.m | .m | UPM-master/Algoritmica Numerica I/prac_provincias/Lagrangio.m | 379 | utf_8 | b6531bc3e5ca77772970459ea57f1ea3 |
% p(x) = sum(i = 0..n, Li(x)*f(x_i))
function px = Lagrangio(x, y, xx)
px = 0;
n = length(x)
for i = 1:n
Li = 1;
% Hacemos Bases de Lagrange
for j = 1:n
if j ~= i
Li = Li .* ( (xx - x(j) )/ (x(i) - x(j) ) ); % Li(x_i)
end
end
... |
github | fbs2112/adaptive_multifilters-master | formatFig.m | .m | adaptive_multifilters-master/Misc/formatFig.m | 4,039 | utf_8 | 522ecaf1d786e78433b2e93b3ad16ca6 | function formatFig(varargin)
%
% This function was created by Leonardo Nunes (lonnes@lps.ufrj.br).
%
% This script configures the current figure. The folowing figure properties are configured:
% - text (ticks and labels) size
% - text (ticks and labels) font
% - line width
% - figure size
%
% If the variabl... |
github | xylimeng/WARP-master | phantom3d.m | .m | WARP-master/phantom3d.m | 8,295 | utf_8 | dfa7b40691a0d7f82426d690e64afcda | function [p,ellipse]=phantom3d(varargin)
%PHANTOM3D Three-dimensional analogue of MATLAB Shepp-Logan phantom
% P = PHANTOM3D(DEF,N) generates a 3D head phantom that can
% be used to test 3-D reconstruction algorithms.
%
% DEF is a string that specifies the type of head phantom to generate.
% Valid values ar... |
github | xylimeng/WARP-master | mex_this.m | .m | WARP-master/mex_this.m | 644 | utf_8 | 8a2d4ddbd2fe11a7a6155a00fa0a0e2b | % 'path' is the absolute path of the directory containing the header 'armadillo'
% For example, it could be '/usr/local/include' in macOS
function mex_this(path)
if nargin == 0
path = '/usr/local/include'; % default
end
ipath = ['-I' path];
mex('-v', ipath, '-larmadillo', '-lgfortran', 'treeLikelihood.cpp'... |
github | xylimeng/WARP-master | sim_data.m | .m | WARP-master/sim_data.m | 2,141 | utf_8 | 8109f9b7e8ce14b9c0cb272cdd5ab881 | % function to generate the true 3D images
% f1: f1 in Peihua Qiu's paper
% f2: f2 in Peihua Qiu's paper
% f3: 3D phantom
% Input: n - length of each dimensiona;
% sigma_noise - standard deviation of Gaussian noise;
% type - type of simulated data, could be f1, f2 or f3
function [true, obs] ... |
github | dustin-cook/Opensees-master | collect_sensitivity_data.m | .m | Opensees-master/collect_sensitivity_data.m | 4,863 | utf_8 | c3f518cdc4fd91f19e9f82de229a7001 | clear all
close all
clc
% Collect results from model sensitivity study
% Define Model
analysis.model_id = 18;
analysis.proceedure = 'NDP';
analysis.id = 'baseline_beams';
analysis.model = 'ICBS_model_5ew_col_base';
%% Define outputs directories
analysis_name = [analysis.proceedure '_' analysis.id];
outputs_dir = ['o... |
github | dustin-cook/Opensees-master | process_multiple_IDA.m | .m | Opensees-master/process_multiple_IDA.m | 5,504 | utf_8 | df05c881e505cb66a8b6a1aead7363d6 | %% Script to:
% 1. pull sensitivity results from Dropbox
% 2. run frag curve post processors on all of them
% 3. save results in repo folders
% 4. Create cummulative plots
clear all
close all
clc
import ida.*
import plotting_tools.*
%% Define inputs
model_baseline_name = 'NDP_baseline_1';
model.name{1} = 'ICBS_mode... |
github | dustin-cook/Opensees-master | xml2struct.m | .m | Opensees-master/+file_exchange/xml2struct.m | 6,766 | utf_8 | fef7daf056271b3557a6d729d3dd392a | function [ s ] = xml2struct( file )
%Convert xml file into a MATLAB structure
% [ s ] = xml2struct( file )
%
% A file containing:
% <XMLname attrib1="Some value">
% <Element>Some text</Element>
% <DifferentElement attrib2="2">Some more text</Element>
% <DifferentElement attrib3="2" attrib4="1">Even more text</Dif... |
github | dustin-cook/Opensees-master | main_ASCE_41_post_process.m | .m | Opensees-master/+asce_41/main_ASCE_41_post_process.m | 4,838 | utf_8 | b2dd01dfc4dd125661da841739818ef9 | function [ capacity, torsion ] = main_ASCE_41_post_process( analysis, ele_prop_table )
% Description: Main script that post process an ASCE 41 analysis
% Created By: Dustin Cook
% Date Created: 1/3/2019
% Inputs:
% Outputs:
% Assumptions:
%% Initial Setup
% Import Packages
import asce_41.*
% Define Read and Write... |
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