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 | kushagraagrawal/Machine_Learning_Assignments-master | loadjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | kushagraagrawal/Machine_Learning_Assignments-master | loadubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | kushagraagrawal/Machine_Learning_Assignments-master | saveubjson.m | .m | Machine_Learning_Assignments-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | emenems/mGlobe-master | mGlobe_view1D.m | .m | mGlobe-master/mGlobe_view1D.m | 20,422 | utf_8 | b7a9c58ee2b439967fd2998ab1dcb232 | function check_out = mGlobe_view1D(plot_file,plot_option,file_type,time_column,data_column,corr_option,save_option,export_option,print_file,export_file,demean_type,trend_type)
%MGLOBE_VIEW1D Function for the 1D visualization
% Function is used for the results visualization of 1D data, i.e. time
% dependent variatio... |
github | t0903/vcnn_double-bladed-master | denoise_configure.m | .m | vcnn_double-bladed-master/applications/image_denoise/denoise_configure.m | 1,450 | utf_8 | ade88ab407e7fc4188d5726e5aa36f33 | %--------------------------------------------------------------------------------------------------------
% The system is created based on the principles described in the following paper
% Jimmy SJ. Ren and Li Xu, "On Vectorization of Deep Convolutional Neural Networks for Vision Tasks",
% The 29th AAAI Conference on ... |
github | t0903/vcnn_double-bladed-master | mnist_configure.m | .m | vcnn_double-bladed-master/applications/MNIST/mnist_configure.m | 1,495 | utf_8 | a7166f7e3171187fad85927503c9dfd7 | %--------------------------------------------------------------------------------------------------------
% The system is created based on the principles described in the following paper
% Jimmy SJ. Ren and Li Xu, "On Vectorization of Deep Convolutional Neural Networks for Vision Tasks",
% The 29th AAAI Conference on ... |
github | t0903/vcnn_double-bladed-master | compile_kernels.m | .m | vcnn_double-bladed-master/cuda/compile_kernels.m | 230 | utf_8 | 6491408032af3ec30546ba3e7dd70de8 | function compile_kernels()
[dirname, ~, ~] = fileparts(mfilename('fullpath'));
compile_(dirname, 'im2col.cu');
end
function compile_(dirname, cu_fn)
system(sprintf('(cd %s && nvcc --ptx %s)', dirname, cu_fn));
end
|
github | iscoe/cajal3d-i2g-master | relabel_id.m | .m | cajal3d-i2g-master/packages/utilities/relabel_id.m | 2,175 | utf_8 | 257018a50eb0e6f0a86abf87cc6ab077 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% © [2014] The Johns Hopkins University / Applied Physics Laboratory All Rights Reserved. Contact the JHU/APL Office of Technology Transfer for any additional rights. www.jhuapl.edu/ott
%
% Licensed under the Apache License, Version 2.0 (the ... |
github | iscoe/cajal3d-i2g-master | imagesc2.m | .m | cajal3d-i2g-master/packages/utilities/imagesc2.m | 1,354 | utf_8 | 8c80a3a9eba379ba2bb5aad2d3dffdaf | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% © [2014] The Johns Hopkins University / Applied Physics Laboratory All Rights Reserved. Contact the JHU/APL Office of Technology Transfer for any additional rights. www.jhuapl.edu/ott
%
% Licensed under the Apache License, Version 2.0 (the ... |
github | iscoe/cajal3d-i2g-master | segmentWatershed.m | .m | cajal3d-i2g-master/packages/segment_watershed/segmentWatershed.m | 2,248 | utf_8 | 815629c0db9057f94fd8f114d9d48968 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% © [2014] The Johns Hopkins University / Applied Physics Laboratory All Rights Reserved. Contact the JHU/APL Office of Technology Transfer for any additional rights. www.jhuapl.edu/ott
%
% Licensed under the Apache License, Version 2.0 (the ... |
github | iscoe/cajal3d-i2g-master | ellipseMatrix.m | .m | cajal3d-i2g-master/packages/vesicle_detector/ellipseMatrix.m | 2,527 | utf_8 | 0be1cfb719f615e223cdea7fcddf0eda | function ret = ellipseMatrix(y0, x0, a, b, teta, Image, color)
% Set the elements of the Matrix Image which are in the interior of the
% ellipse E with the value 'color'. The ellipse E has center (y0, x0), the
% major axe = a, the minor axe = b, and teta is the angle macked by the
% major axe with the orizontal axe.
% ... |
github | iscoe/cajal3d-i2g-master | SNEMI3D_metrics.m | .m | cajal3d-i2g-master/packages/metrics/SNEMI3D_metrics.m | 1,560 | utf_8 | f7d2049d9d3cbda226c0922a0c9afc04 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SNEMI3D challenge: 3D segmentation of neurites in EM images
%
% Script to calculate the segmentation error between some 3D
% original labels and their corresponding proposed labels.
%
% The evaluation metric is:
% - Rand error: 1 - F-score of adapt... |
github | iscoe/cajal3d-i2g-master | centeredRotate.m | .m | cajal3d-i2g-master/external/tools/centeredRotate.m | 384 | utf_8 | 8a5504633b52137cd854a0bc413527f4 | %a = angle should be in rad
%function imRot = centeredRotate(im, a)
function imRot = centeredRotate(im, a)
a = double(a);
% im = max(max(im)) - im;
Trot = [cos(a) sin(a) 0; -sin(a) cos(a) 0; 0 0 1];
Ttrans = [1 0 0; 0 1 0; size(im,1)/2 size(im,2)/2 100];
imRot = transformImageFast(double(im), double(im), (inv(... |
github | iscoe/cajal3d-i2g-master | HOG.m | .m | cajal3d-i2g-master/external/tools/hog/HOG.m | 2,036 | utf_8 | 6cb21c9fce876f5500f5db116313a152 | %Image descriptor based on Histogram of Orientated Gradients for gray-level images. This code
%was developed for the work: O. Ludwig, D. Delgado, V. Goncalves, and U. Nunes, 'Trainable
%Classifier-Fusion Schemes: An Application To Pedestrian Detection,' In: 12th International IEEE
%Conference On Intelligent Trans... |
github | iscoe/cajal3d-i2g-master | classRF_predict.m | .m | cajal3d-i2g-master/external/tools/Random Forest/classRF_predict.m | 2,166 | utf_8 | 7e026fb9b31f99feae58d36b9cf6c2e0 | %**************************************************************
%* mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
%* Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
%* License: GPLv2
%* Version: 0.02
%
% Calls Classification Random Forest
% A wrapper matlab file that calls... |
github | iscoe/cajal3d-i2g-master | classRF_train.m | .m | cajal3d-i2g-master/external/tools/Random Forest/classRF_train.m | 14,829 | utf_8 | 82a321d0a7c77f33b104acec4394c6ee | %**************************************************************
%* mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
%* Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
%* License: GPLv2
%* Version: 0.02
%
% Calls Classification Random Forest
% A wrapper matlab file that calls... |
github | iscoe/cajal3d-i2g-master | structureTensorImage2.m | .m | cajal3d-i2g-master/external/tools/structure_tensor/structureTensorImage2.m | 2,078 | utf_8 | a9f4eb4f44095b9695bc66b79eca3cbd | %[eig1, eig2, cw] = structureTensorImage2(im, s, sg)
%s: smoothing sigma
%sg: sigma gaussian for summation weights
%window size is adapted
function [eig1, eig2, cw] = structureTensorImage2(im, s, sg)
w = 2*sg;
[gx,gy,mag] = gradientImg(double(im),s);
clear mag;
S_0_x = gx.*gx;
S_0_xy = gx.*gy;
S_0_y = gy... |
github | iscoe/cajal3d-i2g-master | gradientImg.m | .m | cajal3d-i2g-master/external/tools/structure_tensor/gradientImg.m | 413 | utf_8 | caa4df879004f65177163e8185112940 | %function [gx, gy, mag] = gradientImg(im, s)
function [gx, gy, mag] = gradientImg(im, s)
% $$$ fg = fspecial('gaussian',4*s,s);
% $$$ fs = fspecial('sobel');
% $$$
% $$$ fgy = filter2(fs,fg);
% $$$ fgx = filter2(fs',fg);
% $$$
% $$$ fgm = sqrt(fgx.^2 + fgy.^2);
% $$$
% $$$ gy = filter2(fgy,im);
% $$$ gx = filter2(... |
github | iscoe/cajal3d-i2g-master | vl_compile.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_noprefix.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_pegasos.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_svmpegasos.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_override.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_quickvis.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_demo_aib.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_demo_alldist.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_demo_svm.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_demo_kdtree_sift.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_impattern.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_tpsu.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_xyz2lab.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_gmm.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_twister.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_kdtree.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_imwbackward.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_alphanum.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_printsize.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_cummax.m | .m | cajal3d-i2g-master/external/vlfeat/toolbox/xtest/vl_test_cummax.m | 762 | utf_8 | 3dddb5736dfffacdd94b156e67cb9c14 | 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 | iscoe/cajal3d-i2g-master | vl_test_imintegral.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_sift.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_binsum.m | .m | cajal3d-i2g-master/external/vlfeat/toolbox/xtest/vl_test_binsum.m | 1,301 | utf_8 | 5bbd389cbc4d997e413d809fe4efda6d | 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 | iscoe/cajal3d-i2g-master | vl_test_lbp.m | .m | cajal3d-i2g-master/external/vlfeat/toolbox/xtest/vl_test_lbp.m | 1,056 | utf_8 | 3b5cca50109af84014e56a4280a3352a | function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_one_on()
I = {} ;
I{1} = [0 0 0 ; 0 0 1 ; 0 0 0] ;
I{2} = [0 0 0 ; 0 0 0 ; 0 0 1] ;
I{3} = [0 0 0 ; 0 0 0 ; 0 1 0] ;
I{4} = [0 0 0 ; 0 0 0 ; 1 0 0] ;
I{5} = [0 0 0 ; 1 0 0 ; 0 0 0] ;
I{6} = [1 0 0 ; 0 0 0 ; 0 0 0] ;
I{7} = [0 1 0 ;... |
github | iscoe/cajal3d-i2g-master | vl_test_colsubset.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_alldist.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_ihashsum.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_grad.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_whistc.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_roc.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_dsift.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_alldist2.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_fisher.m | .m | cajal3d-i2g-master/external/vlfeat/toolbox/xtest/vl_test_fisher.m | 1,703 | utf_8 | 41b28dce7f0d0ae5cb6abd942acbef56 | function results = vl_test_fisher(varargin)
% VL_TEST_FISHER
vl_test_init ;
function s = setup()
randn('state',0) ;
dimension = 5 ;
numData = 21 ;
numComponents = 3 ;
s.x = randn(dimension,numData) ;
s.mu = randn(dimension,numComponents) ;
s.sigma2 = ones(dimension,numComponents) ;
s.prior = ones(1,numComponents) ;
s... |
github | iscoe/cajal3d-i2g-master | vl_test_imsmooth.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_svmtrain.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_phow.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_kmeans.m | .m | cajal3d-i2g-master/external/vlfeat/toolbox/xtest/vl_test_kmeans.m | 3,632 | utf_8 | 719f7fca81e19eed5cc45c2ca251aad0 | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
function s = setup()
randn('sta... |
github | iscoe/cajal3d-i2g-master | vl_test_hikmeans.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_aib.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_plotbox.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_imarray.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_homkermap.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_slic.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_ikmeans.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_mser.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_inthist.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_imdisttf.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_vlad.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_pr.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_hog.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_argparse.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_liop.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_test_binsearch.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_roc.m | .m | cajal3d-i2g-master/external/vlfeat/toolbox/plotop/vl_roc.m | 8,747 | utf_8 | 6b8b4786c9242d5112ca90a616db507a | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC ROC curve.
% [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
% Characteristic (ROC) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative one. SCORES are... |
github | iscoe/cajal3d-i2g-master | vl_click.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_pr.m | .m | cajal3d-i2g-master/external/vlfeat/toolbox/plotop/vl_pr.m | 9,135 | utf_8 | c5d1b9d67f843d10c0b2c6b48fab3c53 | function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative on... |
github | iscoe/cajal3d-i2g-master | vl_ubcread.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | vl_frame2oell.m | .m | cajal3d-i2g-master/external/vlfeat/toolbox/sift/vl_frame2oell.m | 2,160 | utf_8 | 457c5f2e8b637108c8c1b2256396de13 | function eframes = vl_frame2oell(frames)
% FRAMES2OELL Convert generic feature frames to oriented ellipses
% EFRAMES = VL_FRAME2OELL(FRAMES) converts the specified FRAMES to
% the oriented ellipses EFRAMES.
%
% A frame is either a point, disc, oriented disc, ellipse, or
% oriented ellipse. These are represene... |
github | iscoe/cajal3d-i2g-master | vl_plotsiftdescriptor.m | .m | cajal3d-i2g-master/external/vlfeat/toolbox/sift/vl_plotsiftdescriptor.m | 4,725 | utf_8 | 395bf4e0d7417674401ddf34cc8a70da | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptors D, stored as
% columns of the matrix D. D has the same format used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warped to
% the SIFT fram... |
github | iscoe/cajal3d-i2g-master | phow_caltech101.m | .m | cajal3d-i2g-master/external/vlfeat/apps/phow_caltech101.m | 11,595 | utf_8 | cdd4c2add2b7bbfe66a43831513f99fc | function phow_caltech101()
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedu... |
github | iscoe/cajal3d-i2g-master | sift_mosaic.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | encodeImage.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | experiments.m | .m | cajal3d-i2g-master/external/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 | iscoe/cajal3d-i2g-master | getDenseSIFT.m | .m | cajal3d-i2g-master/external/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 | puluosite/matsim-master | cyclicInterp.m | .m | matsim-master/util/cyclicInterp.m | 707 | utf_8 | 1c48dbdca3cbb51c3899e3a383fc40cd | % --------------------------------------------------------------
% - Returns a point on a periodic waveform at a given time -
% --------------------------------------------------------------
% t : time samples along one period excluding the end point
% x : waveform alond one period excluding the end point ... |
github | puluosite/matsim-master | diode_rlc.m | .m | matsim-master/ckt/diode_rlc.m | 1,047 | utf_8 | 143bc25e932792086e42a22ee5da5790 | % Simple ckt with RLC and Diode
function ckt = diode_rlc()
% - initialize circuit
ckt = initCircuit('diode_rlc'); % \util\initCircuit.m
% - models -
diode = {'diode','Is', 1e-16, 'n', 1, 'vt', 2.57e-2,'cjo',2e-12};
ckt = addDevice(ckt, 'Vin', {'in','0'}, {'vol', 0, 'freq', 100, 'phase', 0, 'amplitude', 10});
% ... |
github | puluosite/matsim-master | ring_4stgdiff_maneatis.m | .m | matsim-master/ckt/ring_4stgdiff_maneatis.m | 4,215 | utf_8 | 8f64da9b9e3c0e5bb5e10a7630e566ce | % - fully differential ring oscilltor with 4 Maneatis delay cells -
function ckt = ring_4stgdiff_maneatis(Vctrl)
% - initialize circuit -
ckt = initCircuit('ring_4stgdiff_maneatis');
% - models -
nmos = {'mosRabaey', 'type', +1, 'vt0', 0.43, 'gamma', 0.4, 'beta', 117.7e-6, 'lambda', 0.06, 'ld', 0.005, 'wd', 0.01... |
github | puluosite/matsim-master | bdf.m | .m | matsim-master/math/bdf.m | 744 | utf_8 | 500601f057a4e8f8f7a9ddf095f5ff4f | % -------------------------------------------------------------------------
% - -
% - Backward Differentiation Formula coefficients alpha and beta - -
% - ... |
github | puluosite/matsim-master | loadCircuit.m | .m | matsim-master/analyses/loadCircuit.m | 4,580 | utf_8 | 83b6a1477fa8160d9b5c5b2680cda759 | function [ckt, data] = loadCircuit(ckt, x, info)
global MODE_DC
global MODE_TRAN
global MODE_SEN
global MODE_SEN2
% - device models are capable of providing -
% i q
% G = di/dv C = dq/dv
% di/dp dq/dp
% dG/dp dC/dp
% dG/dv dC/dv
% - circuit loading options given by info.mode -
% MODE_DC a... |
github | puluosite/matsim-master | analysisTran.m | .m | matsim-master/analyses/analysisTran.m | 8,272 | utf_8 | dd2e4444143104eb46a3c851591360b7 | function ckt = analysisTran(ckt, analysisIndex)
global anIndTran
anIndTran = analysisIndex;
ckt = createToleranceVectors(ckt);
% ckt = initialCondition(ckt);
% - function handles and parameters for nonlinear solver -
solverParms = {@initGuess, @f_dfdx, 0, @checkConv,...
ckt.analyses{anIndTran}.tol.absTol,...
... |
github | puluosite/matsim-master | addAnalysis.m | .m | matsim-master/analyses/addAnalysis.m | 19,470 | utf_8 | 266e9ee82b0ef023119848973b8d63ac | function ckt = addAnalysis(ckt, analysisType, analysisName, analysisParms)
if getAnalysisIndex(ckt, analysisName)~=0
error(['Analysis ''',analysisName,''' already exists']);
end
analysis.type = analysisType;
analysis.name = analysisName;
% - common analysis parameters -
analysis.tol.vAbsTol = getParmValue(analys... |
github | puluosite/matsim-master | analysisDC.m | .m | matsim-master/analyses/analysisDC.m | 9,118 | utf_8 | 6aa1e09ec6743ab42c914779a2d0d706 | function ckt = analysisDC(ckt, analysisIndex)
global dcAnIndex
dcAnIndex = analysisIndex;
ckt = createToleranceVectors(ckt);
% --- initialize iteration counter ---
ckt.analyses{analysisIndex}.iter = 0;
ckt.analyses{analysisIndex}.grad.calcGradStatus = ckt.analyses{analysisIndex}.grad.calcGrad;
% - set device param... |
github | Mericam/exposure-fusion-master | reconstruct_laplacian_pyramid.m | .m | exposure-fusion-master/reconstruct_laplacian_pyramid.m | 2,029 | utf_8 | 781e9eceeb3be093c8a98263a39abb9e | %{
Copyright (c) 2015, Tom Mertens
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the foll... |
github | Mericam/exposure-fusion-master | load_images.m | .m | exposure-fusion-master/load_images.m | 2,607 | utf_8 | fbded8e53dfec23dfcb4b72cc1ea5b0a | %{
Copyright (c) 2015, Tom Mertens
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the foll... |
github | Mericam/exposure-fusion-master | laplacian_pyramid.m | .m | exposure-fusion-master/laplacian_pyramid.m | 2,410 | utf_8 | a7875da4442019b9b9f1e4e230ad45f9 | %{
Copyright (c) 2015, Tom Mertens
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the foll... |
github | Mericam/exposure-fusion-master | gaussian_pyramid.m | .m | exposure-fusion-master/gaussian_pyramid.m | 1,900 | utf_8 | bf54e584b82973ae0433b11b6b093337 | %{
Copyright (c) 2015, Tom Mertens
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the foll... |
github | Mericam/exposure-fusion-master | pyramid_filter.m | .m | exposure-fusion-master/pyramid_filter.m | 1,586 | utf_8 | 36759b6c9ca8a56c3c5f3e514a4808ad | %{
Copyright (c) 2015, Tom Mertens
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the foll... |
github | Mericam/exposure-fusion-master | downsample.m | .m | exposure-fusion-master/downsample.m | 1,984 | utf_8 | 4f7b72f6ee6f7171afe1c6694ba4b058 | %{
Copyright (c) 2015, Tom Mertens
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the foll... |
github | Mericam/exposure-fusion-master | exposure_fusion.m | .m | exposure-fusion-master/exposure_fusion.m | 3,990 | utf_8 | 52bbf7d780cb8ef52f4b89eeb2c86ebd | %{
Copyright (c) 2015, Tom Mertens
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following di... |
github | Mericam/exposure-fusion-master | upsample.m | .m | exposure-fusion-master/upsample.m | 2,204 | utf_8 | 52f65e1657b5e5eab38588f893afd223 | %{
Copyright (c) 2015, Tom Mertens
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the foll... |
github | Mericam/exposure-fusion-master | example.m | .m | exposure-fusion-master/example.m | 1,633 | utf_8 | b317192c0c1eea8ca069df2c81e3a252 | %{
Copyright (c) 2015, Tom Mertens
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the foll... |
github | Mericam/exposure-fusion-master | display_pyramid.m | .m | exposure-fusion-master/display_pyramid.m | 607 | utf_8 | 31e807b97ef4e2fd3ae6de5fa7575f62 | % Display the contents of a pyramid, as returned by functions
% 'laplacian_pyramid' or 'gaussian pyramid'
%
% tom.mertens@gmail.com, August 2007
%
function display_pyramid(pyr)
L = length(pyr);
r = size(pyr{1},1);
c = size(pyr{1},2);
k = size(pyr{1},3);
R = zeros(r,2*c,k);
offset = 1;
for l = 1:L
... |
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