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 |
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github | weifanjiang/UWA_OCT_Image_Processing-master | Snake.m | .m | UWA_OCT_Image_Processing-master/Snake.m | 12,530 | utf_8 | 89478754a3bf6f3b3f51604af4e97df5 | %% <Snake.m>
%
% Weifan Jiang
% This function finds and marks the lymphatic vessels within an OCT image
% with the active contour model (Snake) algorithm.
%=========================================================================
function Snake(img)
%% Function Parameters:
% img: the OCT image needs to b... |
github | weifanjiang/UWA_OCT_Image_Processing-master | Gradient_Segmentation.m | .m | UWA_OCT_Image_Processing-master/Gradient_Segmentation.m | 6,265 | utf_8 | a53c1107b94131770459c208d748d110 | %% <Gradient_Segmentation.m>
%
% Weifan Jiang
% This function finds and marks the lymphatic vessels within an OCT image
% with an algorithm based on gradient calculation.
%=========================================================================
function Gradient_Segmentation(img)
%% Function Parameters:
% im... |
github | weifanjiang/UWA_OCT_Image_Processing-master | Before_Segmentation.m | .m | UWA_OCT_Image_Processing-master/Before_Segmentation.m | 4,269 | utf_8 | e916d2d94e98712f3ffa7138709dc1e3 | %% <Before_Segmentation.m>
%
% Weifan Jiang
% This function pre-processes an OCT image, including denoising,
% smoothing and contrast enhancement.
%=========================================================================
function final = Before_Segmentation(I, lower_bound, lower_value, higher_bound, higher_value)
... |
github | LorisMarini/content_caching_with_reinforcement_learning-master | Configure_The_Network.m | .m | content_caching_with_reinforcement_learning-master/network_configurations/Configure_The_Network.m | 5,652 | utf_8 | a8761718fc1d3605ad9b8219e6207502 | function [ Network_Parameters ] = Configure_The_Network( N_Networks, Diameter, Radius_Protected_Area, Step_Size, H, N, Alpha)
%{
------------------------- AUTHORSHIP -------------------------
Developer: Loris Marini
Affiliation: The University of Sydney
Contact: mrnlrs.tor@gmail.com
Notes:
-------------... |
github | LorisMarini/content_caching_with_reinforcement_learning-master | PLAY.m | .m | content_caching_with_reinforcement_learning-master/Multi_Agent_CIDGPA_Framework/PLAY.m | 11,557 | utf_8 | eef3771b11c2b4b66f71ed303fd16d17 |
function [ Conv_Actions, Game_Iterations, History_Delays, Convergence_Delays, New_Learning] = PLAY( Network_Delays, Popularities, Learning, Reward_Type, Resolution, P_Threshold )
%{
-------------------------- AUTHORSHIP ---------------------------------
Developer: Loris Marini
Affiliation: The University... |
github | LorisMarini/content_caching_with_reinforcement_learning-master | INITIALIZE.m | .m | content_caching_with_reinforcement_learning-master/Multi_Agent_CIDGPA_Framework/INITIALIZE.m | 9,684 | utf_8 | 148284711069c94d8689ab4b2a9b11dd |
function [ Learning, Iterations ] = INITIALIZE( Learning_Setup, Network_Delays, M, Popularities, Reward_Type, Initialization_Number )
%{
-------------------------- AUTHORSHIP ---------------------------------
Developer: Loris Marini
Affiliation: The University of Sydney
Contact: mrnlrs.tor@gmail.com
Notes:... |
github | locatelf/cone-greedy-master | PWNMP.m | .m | cone-greedy-master/NMF/dense_square/PWNMP.m | 2,143 | utf_8 | 5ee02a618bbd1dfeffef955fc3b18dc4 | function [X_r, residual_time,test_error] = PWNMP(Y,X_0,R,nil,LMO_it,X_tst)
%% variables init
convergence_check = zeros(1,R+1);
X_r=X_0;
S = zeros(size(X_0));
alpha = 0;
test_error = zeros(R,1);
residual_time = zeros(1,R);
cnt = 0;
A = zeros(size(Y,1),1);
B = zeros(size(Y,2),1);
for r = 1:R
r
%% call to the ... |
github | locatelf/cone-greedy-master | nnls1_asgivens.m | .m | cone-greedy-master/TensorFactorization/nnls1_asgivens.m | 4,652 | utf_8 | fe4f132c0c6503c13c348aa65cc9e7ef | function [ x,y,success,iter ] = nnls1_asgivens( A,b,overwrite, isInputProd, init )
% Nonnegativity-constrained least squares for single righthand side : minimize |Ax-b|_2
% Jingu Kim (jingu.kim@gmail.com)
%
% Reference:
% Jingu Kim and Haesun Park. Fast Nonnegative Matrix Factorization: An Activeset-like Method an... |
github | locatelf/cone-greedy-master | cast_to_set.m | .m | cone-greedy-master/TensorFactorization/cast_to_set.m | 624 | utf_8 | 0a51464057642c461237748e511a466f | function rez = cast_to_set(rez,normalized,non_negative,sparsity)
if strcmp(non_negative,'true')
rez(rez<0)=0;
end
if ~strcmp(sparsity,'0')
rez = to_sparse(rez,str2double(sparsity));
end
if strcmp(normalized,'true')
rez=rez./norm(rez);
end
end
function in = to_sparse(in... |
github | locatelf/cone-greedy-master | nmf.m | .m | cone-greedy-master/TensorFactorization/nmf.m | 23,853 | utf_8 | 7462a6647d7acb938254b76c028c50d1 | % Nonnegative Matrix Factorization Algorithms Toolbox
%
% Written by Jingu Kim (jingu.kim@gmail.com)
% Work done at
% School of Computational Science and Engineering
% College of Computing, Georgia Institute of Technology
%
% Please send bug reports, comments, or questions to Jin... |
github | locatelf/cone-greedy-master | nnlsm_blockpivot.m | .m | cone-greedy-master/TensorFactorization/nnlsm_blockpivot.m | 4,542 | utf_8 | 376a788b205edbb0344ec40fc5afbf9f | % Nonnegativity Constrained Least Squares with Multiple Righthand Sides
% using Block Principal Pivoting method
%
% This software solves the following problem: given A and B, find X such that
% minimize || AX-B ||_F^2 where X>=0 elementwise.
%
% Reference:
% Jingu Kim and Haesun Park. Fast Nonne... |
github | locatelf/cone-greedy-master | nnlsm_activeset.m | .m | cone-greedy-master/TensorFactorization/nnlsm_activeset.m | 5,185 | utf_8 | 96f73fcf70f7083cd2d2a9bd9f71767a | % Nonnegativity Constrained Least Squares with Multiple Righthand Sides
% using Active Set method
%
% This software solves the following problem: given A and B, find X such that
% minimize || AX-B ||_F^2 where X>=0 elementwise.
%
% Reference:
% Charles L. Lawson and Richard J. Hanson, Solving Leas... |
github | locatelf/cone-greedy-master | ncp.m | .m | cone-greedy-master/TensorFactorization/ncp.m | 16,643 | utf_8 | 688c72175fc36a416b097532b15c08b8 | % Nonnegative Tensor Factorization (Canonical Decomposition / PARAFAC)
%
% Written by Jingu Kim (jingu.kim@gmail.com)
% School of Computational Science and Engineering,
% Georgia Institute of Technology
%
% This software implements nonnegativity-constrained low-rank approximation of tensors in PAR... |
github | locatelf/cone-greedy-master | lsqnonnegMy.m | .m | cone-greedy-master/NNMP/lsqnonnegMy.m | 8,296 | utf_8 | fda5216b3d7550977d2efa475e8ca118 | function [x,allres,resnorm,resid,exitflag,output,lambda] = lsqnonnegMy(C,d,options,varargin)
%LSQNONNEG Linear least squares with nonnegativity constraints.
% X = LSQNONNEG(C,d) returns the vector X that minimizes NORM(d-C*X)
% subject to X >= 0. C and d must be real.
%
% X = LSQNONNEG(C,d,OPTIONS) minimizes with... |
github | locatelf/cone-greedy-master | experiment_EEAs.m | .m | cone-greedy-master/HyperspectralImaging/experiment_EEAs.m | 1,254 | utf_8 | 6824855f221c01fc60b03f94ac1d27fd | % Running the algorithm on data set M
function [Kall, Hall, resultsErr, Hall2, resultsErr2, nEEAs,resultsErr3,resultsErr4,resultsErr5] = experiment_EEAs( M , r )
% 2. Running the EEAs on the full and subsampled data set
maxitNNLS = 10;
nEEAs{1} = 'SPA ';
nEEAs{2} = 'VCA ';
nEEAs{3} = 'XRAY ';
nEEAs{4... |
github | locatelf/cone-greedy-master | fquad.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/fquad.m | 1,774 | utf_8 | 85e40c6d6d0a040702758d817f0a5008 | % Select treshold to split the entries of x into two subsets
%
% See Section 3.2 in
% Gillis, Kuang, Park, `Hierarchical Clustering of Hyperspectral Images
% using Rank-Two Nonnegative Matrix Factorization', arXiv.
function [thres,delta,fobj] = fquad(x,s);
if nargin == 1
s = 0.01; % grid for the val... |
github | locatelf/cone-greedy-master | anls_entry_rank2_precompute_opt.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/anls_entry_rank2_precompute_opt.m | 1,376 | utf_8 | 12fa56b139f94a061bb74c871176ec31 | % Solve min_H ||M - WH'||_2 s.t. H >= 0
%
% where left = W^TW and right = M^TW
%
% See Kuang, Park, `Fast Rank-2 Nonnegative Matrix Factorization
% for Hierarchical Document Clustering', KDD '13.
%
% See also Algorithm 4 in
% Gillis, Kuang, Park, `Hierarchical Clustering of Hyperspectral Images
% using Rank-Two N... |
github | locatelf/cone-greedy-master | hierclust2nmf.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/hierclust2nmf.m | 9,170 | utf_8 | 2ed0455c900bc24ed801586e0ea7ebd8 | % Hierarchical custering based on rank-two nonnegative matrix factorization
%
% Given a data matrix M (m-by-n) representing n data points in an
% m-dimensional space, this algorithm computes a set of clusters obtained
% using the hierarchical rank-two NMF method described in
%
% Gillis, Kuang, Park, `Hierarchic... |
github | locatelf/cone-greedy-master | fastsvds.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/fastsvds.m | 683 | utf_8 | b81c39bffb76f2522e20bd1c42ae60b0 | % "Fast" but less accurate SVD by computing the SVD of MM^T or M^TM
% ***IF*** one of the dimensions of M is much smaller than the other.
% Note. This is numerically less stable, but useful for large hyperspectral
% images.
function [u,s,v] = fastsvds(M,r);
[m,n] = size(M);
rationmn = 10; % Parameter, s... |
github | locatelf/cone-greedy-master | splitclust.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/splitclust.m | 1,644 | utf_8 | 3db01b4e0e55f4537496b1675c54445a | % Given a matrix M, split its columns into two subsets
%
% See Section 3 in
%
% Gillis, Kuang, Park, `Hierarchical Clustering of Hyperspectral Images
% using Rank-Two Nonnegative Matrix Factorization', arXiv.
%
%
% ****** Input ******
% M : m-by-n data matrix (or a H-by-L-by-m tensor)
% algo : alg... |
github | locatelf/cone-greedy-master | vectoind.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/vectoind.m | 220 | utf_8 | 8820e9734a50ee8f7795f604051c3e34 | % From cluster indicator vector to indicator matrix
function V = vectoind(IDX,r)
m = length(IDX);
if nargin == 1
r = max(IDX(:));
end
V = zeros(m,r);
for i = 1 : r
V(find(IDX==i),i) = 1;
end |
github | locatelf/cone-greedy-master | rank2nmf.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/rank2nmf.m | 945 | utf_8 | 22891abe4abe34be96416d412279a329 | % Given a data matrix M (m-by-n), computes a rank-two NMF of M.
%
% See Algorithm 3 in
%
% Gillis, Kuang, Park, `Hierarchical Clustering of Hyperspectral Images
% using Rank-Two Nonnegative Matrix Factorization', arXiv.
%
% ****** Input ******
% M : a nonnegative m-by-n data matrix
%
% ****** Ou... |
github | locatelf/cone-greedy-master | affclust.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/affclust.m | 572 | utf_8 | f25d9a40ba0a120bd94d132688082bb1 | % Display clusters
function [a, Vaff] = affclust(K,H,L,ncol,bw);
n = H*L;
if iscell(K)
K = clu2vec(K,n);
end
% K is an indicator vector of type IDX
A = vectoind(K);
r = size(A,2);
% 'Optimize' display in 16/9
if nargin < 4
ncol = 1; nrow = ceil(r/ncol);
while (r > 1 && L*ncol*9 < H... |
github | locatelf/cone-greedy-master | affichage.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/affichage.m | 1,155 | utf_8 | ebfdbc0a5a5f07a8cbf18f410f322bdc | % Display (=affichage in French) of a NMF solution, for image datasets
%
% a = affichage(V,lig,Li,Co)
%
% Input.
% V : (m x r) matrix whose colums contains vectorized images
% lig : number of images per row in the display
% (Co,Li) : dimensions of images
% bw :... |
github | locatelf/cone-greedy-master | nnlsm_blockpivot.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/nnlsm_blockpivot.m | 4,413 | utf_8 | cb9bf3455d6fd3ae19ea98b4dd754547 | % Nonnegativity Constrained Least Squares with Multiple Righthand Sides
% using Block Principal Pivoting method
%
% This software solves the following problem: given A and B, find X such that
% minimize || AX-B ||_F^2 where X>=0 elementwise.
%
% Reference:
% Jingu Kim and Haesun Park, Tow... |
github | locatelf/cone-greedy-master | FastSepNMF.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/FastSepNMF.m | 3,369 | utf_8 | d1efacdacf79352c067116cbd6ec4a61 | % FastSepNMF - Fast and robust recursive algorithm for separable NMF
%
% *** Description ***
% At each step of the algorithm, the column of M maximizing ||.||_2 is
% extracted, and M is updated by projecting its columns onto the orthogonal
% complement of the extracted column.
%
% See N. Gillis and S.A. Vava... |
github | locatelf/cone-greedy-master | clu2vec.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/clu2vec.m | 302 | utf_8 | 59890ba3279c8d45d1c01fb24000bdc6 | % Transform a cluster cell to a vector
function IDX = clu2vec(K,m,r);
if nargin < 3
r = length(K);
end
if nargin < 2
% Compute max entry in K
m = 0;
for i = 1 : r
m = max(0, max(K{i}));
end
end
IDX = zeros(m,1);
for i = 1 : r
IDX(K{i}) = i;
end |
github | locatelf/cone-greedy-master | reprvec.m | .m | cone-greedy-master/HyperspectralImaging/hierclust2nmf_v2/reprvec.m | 726 | utf_8 | d3e05d1279b90585355adc9d6de3c3a7 | % Extract "most" representative column from a matrix M as follows:
%
% First, it computes the best rank-one approximation u v^T of M.
% Then, it identifies the column of M minimizing the MRSA with the first
% singular vector u of M.
%
% See Section 4.4.1 of
% Gillis, Kuang, Park, `Hierarchical Clustering o... |
github | locatelf/cone-greedy-master | EEAs.m | .m | cone-greedy-master/HyperspectralImaging/Endmember Extraction Algorithms/EEAs.m | 328 | utf_8 | b80a0699acd0816f24d73bf3969910ec | % Different EEA algorithms
function K = EEAs(M,r,algo);
if algo == 1
K = FastSepNMF(M,r);
elseif algo == 2
K = VCA(M,'Endmembers',r,'verbose','off');
elseif algo == 3
K = FastConicalHull(M,r);
elseif algo == 4
[~, ~, K] = hierclust2nmf(M,r,1,[],0);
elseif algo == 5
K = SNPA(M,r... |
github | locatelf/cone-greedy-master | RVCA.m | .m | cone-greedy-master/HyperspectralImaging/Endmember Extraction Algorithms/RVCA.m | 358 | utf_8 | 5f0767712f593be37f43c851734d741d | % Robust VCA
function K = RVCA(M,r,rparam);
maxiter = 10;
if nargin <= 2
rparam = 10;
end
emin = +Inf;
for i = 1 : rparam
[A, K] = VCA(M,'Endmembers',r,'verbose','off');
H = nnlsHALSupdt(M,M(:,K),[],maxiter);
err = norm(M-M(:,K)*H,'fro');
if err < emin
Kf = ... |
github | locatelf/cone-greedy-master | SimplexProj.m | .m | cone-greedy-master/HyperspectralImaging/Endmember Extraction Algorithms/SimplexProj.m | 1,374 | utf_8 | f6f156f333432d0897537cbf10fa3595 | function x = SimplexProj(y)
% Given y, computes its projection x* onto the simplex
%
% Delta = { x | x >= 0 and sum(x) <= 1 },
%
% that is, x* = argmin_x ||x-y||_2 such that x in Delta.
%
%
% See Appendix A.1 in N. Gillis, Successive Nonnegative Projection
% Algorithm for Robust Nonnegative... |
github | krasvas/LandRate-master | fixations_t2.m | .m | LandRate-master/fixations_t2.m | 3,218 | utf_8 | 35b1c3cbde506d34b53f3d7774c7b5f1 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | angle_to_tracker.m | .m | LandRate-master/angle_to_tracker.m | 2,653 | utf_8 | d6c6e7e24857b7c66791375b69c56293 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | min_duration.m | .m | LandRate-master/min_duration.m | 1,455 | utf_8 | 08bbb2e381ecc7d1eaf94b5d1cd77dde | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | points_in_region.m | .m | LandRate-master/points_in_region.m | 1,683 | utf_8 | 81e57f7705f5cae4ec62c23ff24f6996 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | ROI_analysis.m | .m | LandRate-master/ROI_analysis.m | 6,080 | utf_8 | daaa2c110078af2f97a7f3a6c2ec7388 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | heatmap_generator.m | .m | LandRate-master/heatmap_generator.m | 6,654 | utf_8 | 11496414805a2ede9a65bd0da6982178 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | heatmap_generator_EyeMMV_modified.m | .m | LandRate-master/heatmap_generator_EyeMMV_modified.m | 6,927 | utf_8 | 5ca3103a40391da6e0a0aaf6652b4ee8 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | fixations_3s.m | .m | LandRate-master/fixations_3s.m | 2,772 | utf_8 | dd807d2f0ba54a516cf1196248a718ce | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | metrics_analysis.m | .m | LandRate-master/metrics_analysis.m | 10,253 | utf_8 | b519fe7acad925cc6d0c0035d27752b8 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | visualizations.m | .m | LandRate-master/visualizations.m | 4,618 | utf_8 | b86cb5e5590444b6dd3ba5d7f54d2128 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | saccade_analysis_EyeMMV_modified.m | .m | LandRate-master/saccade_analysis_EyeMMV_modified.m | 10,726 | utf_8 | da79190df4a2472a20a0d9e862b846b2 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | find_point_area.m | .m | LandRate-master/find_point_area.m | 2,943 | utf_8 | bf1a31dbfba11e00783fb8c1b8ad7345 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | scan_path_visualization_EyeMMV_modified.m | .m | LandRate-master/scan_path_visualization_EyeMMV_modified.m | 5,064 | utf_8 | b33ae032caf71d2aec57f5b53ebe1667 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | distance2p.m | .m | LandRate-master/distance2p.m | 1,124 | utf_8 | cd76c27ed4b3f0855a9ff19c2bddc429 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | values_normalization.m | .m | LandRate-master/values_normalization.m | 148 | utf_8 | dff1706d74406c28b86fb02ac727bfc4 | %Values normalization function between 0 and 1
function normalized_values=values_normalization(values)
normalized_values=values./max(values);
end |
github | krasvas/LandRate-master | direction_angle.m | .m | LandRate-master/direction_angle.m | 1,372 | utf_8 | 36805f28c8d3616a761d5db8a6e1dfe7 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | krasvas/LandRate-master | fixation_detection_EyeMMV_modified.m | .m | LandRate-master/fixation_detection_EyeMMV_modified.m | 8,663 | utf_8 | c5aff6169465d2a52873e3049320b768 | % This is a modified version of EyeMMV's toolbox fixation detection
% algorithm
% EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can ... |
github | krasvas/LandRate-master | visualizations_stimulus.m | .m | LandRate-master/visualizations_stimulus.m | 4,473 | utf_8 | f29efbcf0b0c5b77cde3750f23f30b48 | % EyeMMV toolbox (Eye Movements Metrics & Visualizations): An eye movement post-analysis tool.
% Copyright (C) 2014 Vassilios Krassanakis (National Technical University of Athens)
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public Lic... |
github | tomluc/Pinax-camera-model-master | RayTrace.m | .m | Pinax-camera-model-master/MATLAB/Optimal_d_0/RayTrace.m | 1,672 | utf_8 | cf6dfb029293b40cc5f65303955b0b07 | %
% Copyright (c) 2017 Jacobs University Robotics Group
% All rights reserved.
%
%
% Unless specified otherwise this code examples are released under
% Creative Commons CC BY-NC-ND 4.0 license (free for non-commercial use).
% Details may be found here: https://creativecommons.org/licenses/by-nc-nd/4.0/
%
%
... |
github | tomluc/Pinax-camera-model-master | optim_d_0.m | .m | Pinax-camera-model-master/MATLAB/Optimal_d_0/optim_d_0.m | 1,848 | utf_8 | f357020570d91223d794d7378da9c884 | %
% Copyright (c) 2017 Jacobs University Robotics Group
% All rights reserved.
%
%
% Unless specified otherwise this code examples are released under
% Creative Commons CC BY-NC-ND 4.0 license (free for non-commercial use).
% Details may be found here: https://creativecommons.org/licenses/by-nc-nd/4.0/
%
%
... |
github | tomluc/Pinax-camera-model-master | SolveForwardProjectionCase3.m | .m | Pinax-camera-model-master/MATLAB/Find_correction_map/SolveForwardProjectionCase3.m | 5,661 | utf_8 | 7d7e2e3c69273bfc11e9f03096a468ec |
% Copyright 2009 Mitsubishi Electric Research Laboratories All Rights Reserved.
%
% Permission to use, copy and modify this software and its documentation without fee for educational, research and non-profit purposes, is hereby granted, provided that the above copyright notice and the following three paragraphs ... |
github | tomluc/Pinax-camera-model-master | RayTrace.m | .m | Pinax-camera-model-master/MATLAB/Find_correction_map/RayTrace.m | 1,681 | utf_8 | 9a53429738aea5dd3b07e1a43324ac47 | %
% Copyright (c) 2017 Jacobs University Robotics Group
% All rights reserved.
%
%
% Unless specified otherwise this code examples are released under
% Creative Commons CC BY-NC-ND 4.0 license (free for non-commercial use).
% Details may be found here: https://creativecommons.org/licenses/by-nc-nd/4.0/
%
%
... |
github | tomluc/Pinax-camera-model-master | RefractedRay.m | .m | Pinax-camera-model-master/MATLAB/Find_correction_map/RefractedRay.m | 614 | utf_8 | 603b41ff3d8a155e43e94de324e462b9 |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (c) MERL 2012
% CVPR 2012 Paper Title: A Theory of Multi-Layer Flat Refractive Geometry
% Author: Amit Agrawal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute refracted ray direction at a refraction boundary
function [vr,a,b,tir] = RefractedRay(vi,n,mu1,mu2)
... |
github | V170SC/ESN-homeokinesis-master | mackeyglass_rk4.m | .m | ESN-homeokinesis-master/ESNConceptorsTest/MackeyGlassGenerator/mackeyglass_rk4.m | 1,157 | utf_8 | aab166beab25f407a8396d0149f4f480 | %% mackeyglass_rk4
% This function computes the numerical solution of the Mackey-Glass
% delayed differential equation using the 4-th order Runge-Kutta method
%%
% $$k_1=\Delta t \cdot mackeyglass\_eq(x(t), x(t-\tau), a, b)$$
%%
% $$k_2=\Delta t \cdot mackeyglass\_eq(x(t+\frac{1}{2}k_1), x(t-\tau), a, b)$$
%%
% $$k_3... |
github | V170SC/ESN-homeokinesis-master | mackeyglass_eq.m | .m | ESN-homeokinesis-master/ESNConceptorsTest/MackeyGlassGenerator/mackeyglass_eq.m | 357 | utf_8 | 7ee7c2d23205ea0883eaa806816697e7 | %% makeyglass_eq
% This function returns dx/dt of Mackey-Glass delayed differential equation
%%
%
% $$\frac{dx(t)}{dt}=\frac{ax(t-\tau)}{1+x(t-\tau)^{10}}-bx(t)$$
%
%%
% *Matlab code:*
function x_dot = mackeyglass_eq(x_t, x_t_minus_tau, a, b, n)
x_dot = -b*x_t + a*x_t_minus_tau/(1 + x_t_minus_tau^n);
end
%%
%... |
github | Regon94/Smooth-Particle-Hydrodynamics-master | Post_multifluid_energy.m | .m | Smooth-Particle-Hydrodynamics-master/Post_multifluid_energy.m | 4,422 | utf_8 | 02c1a46f09d25f338dddf38fd7a2d0c0 | %% Function to calculate the energy of each fluid in a multi-fluid system
function [KE_alpha, KE_beta, PE_alpha, PE_beta] = Post_multifluid_energy()
%% initialisation
clear particles
%close all
clc
time = save_pos_t;
n = length(time);
E_kin_alpha = zeros(n,1);
E_kin_beta = zeros(n,1);
E_pot_alpha... |
github | Regon94/Smooth-Particle-Hydrodynamics-master | Free_Bubble.m | .m | Smooth-Particle-Hydrodynamics-master/Free_Bubble.m | 3,971 | utf_8 | 2017ecdee885e0b5d75c900aa7fa6c45 | %% Bubble Formation Dynamic Test Case
% Roger Gonzalez
% 12/06/17
function [particles, rho_0,gamma,c_0,p_0,Xi,my,alpha, a_wall, int_fluid, int_boundary] = Free_Bubble(kernel, dx, d, v_max, alpha)
% Square domain
origin = [0 0];
% first fluid phase
% specify coordinates of edges for fluid
... |
github | Regon94/Smooth-Particle-Hydrodynamics-master | der_color_field.m | .m | Smooth-Particle-Hydrodynamics-master/der_color_field.m | 1,449 | utf_8 | 5ce0f648cfe22f24743494652a876eb1 | %% Calculating the gradient of the smoothed color field
%part of the Surface area minimization in Akinci Surface tension model
%
% Roger Gonzalez
% 04/07/2017
function [boundary_der_color_field] = der_color_field(particles, a, b, r_c, h, rho_0, p_0, Xi, gamma, eqn_of_state, range)
%
% a - fluid particle wr... |
github | Regon94/Smooth-Particle-Hydrodynamics-master | Meniscus.m | .m | Smooth-Particle-Hydrodynamics-master/Meniscus.m | 3,808 | utf_8 | 78b1db669f26941672c04687dafe35fd | %% Surface Tension Force model
% Roger Gonzalez
% 21/06/17
function [particles, rho_0,gamma,c_0,p_0,Xi,my,alpha, a_wall, int_fluid, int_boundary] = Meniscus(kernel, dx, d, v_max, alpha)
% rectangular domain
origin = [0 0];
% first fluid phase
% specify coordinates of edges for fluid
f_... |
github | Regon94/Smooth-Particle-Hydrodynamics-master | PairwiseForce.m | .m | Smooth-Particle-Hydrodynamics-master/PairwiseForce.m | 5,508 | utf_8 | 79c32e9db906e42ecb4c0644cbf4e43a | %% Surface Tension Force model
% Roger Gonzalez
% 30/05/17
function [PF, Virial_Pressure, adhesion_F, der_color_field_i] = PairwiseForce(ST_model, particles, h, dist, a, b, domain, rho_0, p_0, r_c, rho_b, m_b, Xi, gamma, eqn_of_state, int_boundary,idx_all)
% Have to individually import mass and density for the... |
github | Regon94/Smooth-Particle-Hydrodynamics-master | save_vtu.m | .m | Smooth-Particle-Hydrodynamics-master/save_vtu.m | 4,555 | utf_8 | 20ab9ee84fcea2f326bfb92a45538c04 | % Script to plot data from SPH slosh simulation
function [] = save_vtu(particles,n_save, dirname)
% specify n_save = 0 for boundary particles
%% saving directory
% check for existence of paraviewfiles/vtu directory. this is the directory where
% the .vtu files will be stored. if it does not exist create it
... |
github | Regon94/Smooth-Particle-Hydrodynamics-master | Drop.m | .m | Smooth-Particle-Hydrodynamics-master/Drop.m | 3,806 | utf_8 | e800b4320755500092ff36b0628c2c6c | %% Surface Tension Force model
% Roger Gonzalez
% 03/07/2017
function [particles, rho_0,gamma,c_0,p_0,Xi,my,alpha, a_wall, int_fluid, int_boundary] = Drop(kernel, dx, d, v_max, alpha)
% rectangular domain
origin = [0 0];
% first fluid phase
% specify coordinates of edges for fluid
f_lo... |
github | fan9193/exercise2-master | trandn.m | .m | exercise2-master/trandn.m | 3,549 | utf_8 | 307219e197d890623614a7c90eb2bef8 | function x=trandn(l,u)
%% truncated normal generator
% * efficient generator of a vector of length(l)=length(u)
% from the standard multivariate normal distribution,
% truncated over the region [l,u];
% infinite values for 'u' and 'l' are accepted;
% * Remark:
% If you wish to simulate a random variable
% 'Z' f... |
github | cvjena/caffe_pp2-master | classification_demo.m | .m | caffe_pp2-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | USF-IMARS/wv-land-cover-master | DT_Filter.m | .m | wv-land-cover-master/3d_wetlands/DT_Filter.m | 2,287 | utf_8 | 7c137152cbf1babe3afba4c1bd3106a5 | %% DT_Filter.M
%% Written by Matt McCarthy 8/29/2016
function dt_filt = DT_Filter(file,x,sz2,sz3,dev,FW,FU,UG,WA);
filt = x;
sz_sm(1) = sz2; % Size of unwarped(smaller) file
sz_sm(2) = sz3;
fwfilt = 75
wafilt = 50
sz1 = size(file);
dt_filt =zeros(sz1(1),sz1(2),'uint8');
for a = filt+1:sz_sm(1)-filt-... |
github | USF-IMARS/wv-land-cover-master | wv_classify.m | .m | wv-land-cover-master/3d_wetlands/wv_classify.m | 37,674 | utf_8 | a528dc999ab6d7c2903872339d10beb3 | %% WV2 Processing
% Loads TIFF WorldView-2 image files preprocessed through Polar Geospatial
% Laboratory python code, which orthorectifies and projects .NTF files and outputs as
% TIFF files
% Radiometrically calibrates digital count data
% Atmospherically corrects images by subtracting Rayleigh Path Radiance
% ... |
github | ruihou/caffe-3d-master | classification_demo.m | .m | caffe-3d-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | shainova/EMLN-master | bipartite_modularity_diag_coupling.m | .m | EMLN-master/NEE2017/Modularity/bipartite_modularity_diag_coupling.m | 2,782 | utf_8 | 9feea2fb0a17cc601ebb4f1774696b75 | % NOTE!!! This file accompanies the following publication and can
% only be understood by reading the details in the manuscript and its
% SI. Please cite the original publication if using this code.
%
% Pilosof S, Porter MA, Pascual M, Kefi S.
% The multilayer nature of ecological networks.
% Nature Ecology & Evolutio... |
github | shainova/EMLN-master | modularity_weighted_multilayer_null2.m | .m | EMLN-master/NEE2017/Modularity/modularity_weighted_multilayer_null2.m | 4,423 | utf_8 | a439e7f70710149743c202654e640b2b | % NOTE!!! This file accompanies the following publication and can
% only be understood by reading the details in the manuscript and its
% SI. Please cite the original publication if using this code.
%
% Pilosof S, Porter MA, Pascual M, Kefi S.
% The multilayer nature of ecological networks.
% Nature Ecology & Evolutio... |
github | shainova/EMLN-master | modularity_interlayer_infinity.m | .m | EMLN-master/NEE2017/Modularity/modularity_interlayer_infinity.m | 4,086 | utf_8 | 1fa8825e8c7b6600a5d00d009e204c44 | % NOTE!!! This file accompanies the following publication and can
% only be understood by reading the details in the manuscript and its
% SI. Please cite the original publication if using this code.
%
% Pilosof S, Porter MA, Pascual M, Kefi S.
% The multilayer nature of ecological networks.
% Nature Ecology & Evolutio... |
github | shainova/EMLN-master | modularity_weighted_multilayer_obs.m | .m | EMLN-master/NEE2017/Modularity/modularity_weighted_multilayer_obs.m | 4,156 | utf_8 | 266904c5c388af9b8be6e1a43d0732ab | % NOTE!!! This file accompanies the following publication and can
% only be understood by reading the details in the manuscript and its
% SI. Please cite the original publication if using this code.
%
% Pilosof S, Porter MA, Pascual M, Kefi S.
% The multilayer nature of ecological networks.
% Nature Ecology & Evolutio... |
github | shainova/EMLN-master | modularity_weighted_multilayer_null1.m | .m | EMLN-master/NEE2017/Modularity/modularity_weighted_multilayer_null1.m | 3,654 | utf_8 | a48fbfce9c1c4c393c423628e0ccad8c | % NOTE!!! This file accompanies the following publication and can
% only be understood by reading the details in the manuscript and its
% SI. Please cite the original publication if using this code.
%
% Pilosof S, Porter MA, Pascual M, Kefi S.
% The multilayer nature of ecological networks.
% Nature Ecology & Evolutio... |
github | shainova/EMLN-master | modularity_weighted_multilayer_null3.m | .m | EMLN-master/NEE2017/Modularity/modularity_weighted_multilayer_null3.m | 3,373 | utf_8 | aa4169e5f07c13619ac8715f8a34ae15 | % NOTE!!! This file accompanies the following publication and can
% only be understood by reading the details in the manuscript and its
% SI. Please cite the original publication if using this code.
%
% Pilosof S, Porter MA, Pascual M, Kefi S.
% The multilayer nature of ecological networks.
% Nature Ecology & Evolutio... |
github | shainova/EMLN-master | genlouvain.m | .m | EMLN-master/NEE2017/Modularity/genlouvain.m | 11,697 | utf_8 | 92c2a8f309fb987f9c38da6c4be282ac | function [S,Q] = genlouvain(B,limit,verbose,randord,randmove)
%GENLOUVAIN Louvain-like community detection, specified quality function.
% Version 2.0 (July 2014)
%
% [S,Q] = GENLOUVAIN(B) with matrix B implements a Louvain-like greedy
% community detection method using the modularity/quality matrix B that
... |
github | shainova/EMLN-master | single_layer_bipartite_B_matrix.m | .m | EMLN-master/NEE2017/Modularity/single_layer_bipartite_B_matrix.m | 554 | utf_8 | ce18365cfc84fce465315d920e40b606 | % NOTE!!! This file accompanies the following publication and can
% only be understood by reading the details in the manuscript and its
% SI. Please cite the original publication if using this code.
%
% Pilosof S, Porter MA, Pascual M, Kefi S.
% The multilayer nature of ecological networks.
% Nature Ecology & Evolutio... |
github | shainova/EMLN-master | muxOctaveLib.m | .m | EMLN-master/NEE2017/Reducibility/muxOctaveLib.m | 58,399 | utf_8 | 410cc4713aa4cfb0d67441e66f48983c | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% MuxNetLib: Octave library for Multiplex Network Analysis in muxViz
%
% Version: 0.1
% Last update: Nov 2015
% Authors: Manlio De Domenico
%
% History:
%
% May 2014: First release, including part of muxNet
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | LiHeUA/FastESC-master | demo_EBMM.m | .m | FastESC-master/EBMM_Release/demo_EBMM.m | 4,568 | utf_8 | 5e8cf84542aa9a2863c390e4c0f1bdd7 | function demo_EBMM
% Demot of Extended Basic Matrix Multiplication algorithm.
% Select cT columns (or rows) from A (or B) to form C (or R) so that
% AB\approx CR.
% Also verify Theorem 1 in [1].
%
% Details of this algorithm can be found in Alg. 2 in [1].
%
% [1] Li He, Nilanjan Ray and Hong Zhang, Fast Large-Scale ... |
github | mirtaheri/Grid-visualization-in-Matlab-master | plotCustMark.m | .m | Grid-visualization-in-Matlab-master/funcs/plotCustMark.m | 1,239 | utf_8 | 2a019f8cd68d58ea1aae70790ecba3d0 |
function patchHndl = plotCustMark(xData,yData,markerDataX,markerDataY,markerSize, lineThick, face_color)
% this function uses codes from: https://it.mathworks.com/matlabcentral/fileexchange/39487-custom-marker-plot
xData = reshape(xData,length(xData),1) ;
yData = reshape(yData,length(yData),1) ;
markerDataX =... |
github | leonid-pishchulin/poseval-master | savejson.m | .m | poseval-master/matlab/external/jsonlab/savejson.m | 18,981 | utf_8 | 63859e6bc24eb998f433f53d5880015b | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | leonid-pishchulin/poseval-master | loadjson.m | .m | poseval-master/matlab/external/jsonlab/loadjson.m | 16,145 | ibm852 | 7582071c5bd7f5e5f74806ce191a9078 | 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 | leonid-pishchulin/poseval-master | loadubjson.m | .m | poseval-master/matlab/external/jsonlab/loadubjson.m | 13,300 | utf_8 | b15e959f758c5c2efa2711aa79c443fc | 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
%
% $Id$
%
% input:
% fname: ... |
github | leonid-pishchulin/poseval-master | saveubjson.m | .m | poseval-master/matlab/external/jsonlab/saveubjson.m | 17,723 | utf_8 | 3414421172c05225dfbd4a9c8c76e6b3 | 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 | lhmRyan/dual-purpose-hashing-DPH-master | classification_demo.m | .m | dual-purpose-hashing-DPH-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | yuqingtong1990/webrtc_vs2015-master | apmtest.m | .m | webrtc_vs2015-master/webrtc/modules/audio_processing/test/apmtest.m | 9,470 | utf_8 | ad72111888b4bb4b7c4605d0bf79d572 | function apmtest(task, testname, filepath, casenumber, legacy)
%APMTEST is a tool to process APM file sets and easily display the output.
% APMTEST(TASK, TESTNAME, CASENUMBER) performs one of several TASKs:
% 'test' Processes the files to produce test output.
% 'list' Prints a list of cases in the test set,... |
github | yuqingtong1990/webrtc_vs2015-master | plot_neteq_delay.m | .m | webrtc_vs2015-master/webrtc/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,563 | utf_8 | 8b6a66813477863da513b1e6971dbc97 | function [delay_struct, delayvalues] = plot_neteq_delay(delayfile, varargin)
% InfoStruct = plot_neteq_delay(delayfile)
% InfoStruct = plot_neteq_delay(delayfile, 'skipdelay', skip_seconds)
%
% Henrik Lundin, 2006-11-17
% Henrik Lundin, 2011-05-17
%
try
s = parse_delay_file(delayfile);
catch
error(lasterr);
e... |
github | kalov/ShapePFCN-master | classification_demo.m | .m | ShapePFCN-master/caffe-ours/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | usgs/landslides-mLS-master | mLS.m | .m | landslides-mLS-master/mLS.m | 7,878 | utf_8 | 5fc99d5ed047ae1cd3d70caf7f13cc6c | % This script is provided as a supplementary material of a paper
% published in Earth Surface Processes and Landforms. The details of the
% method followed in the given script is described in the corresponding
% paper. If you publish use this script or a its modified version please
% cite the following paper:
%... |
github | WenbingLv/NPC-radiomics-master | getGLCM_Symmetric.m | .m | NPC-radiomics-master/getGLCM_Symmetric.m | 5,813 | utf_8 | 5c7c1e015f2cfab250ac285142fb04c5 | function coocMat = getGLCM_Symmetric(varargin)
%inputStr = {TumorVolume,'Distance',[],'Direction',[],'numgray',levelsM+1};
%
%ljlubme@gmail.com
%Southern Medical University
%
%Default settings
coocMat= NaN;
distance = [1;2;4;8];
numLevels = 16;
offSet = [1 0 0; 1 1 0; 0 1 0; -1 1 0]; %2D Co-Occurrence direc... |
github | WenbingLv/NPC-radiomics-master | getGLCM_Asymmetric.m | .m | NPC-radiomics-master/getGLCM_Asymmetric.m | 5,891 | utf_8 | d600aef8916ea9dd698c1241d56050d9 | function coocMat = getGLCM_Asymmetric(varargin)
%inputStr = {TumorVolume,'Distance',[],'Direction',[],'numgray',levelsM+1};
%
%ljlubme@gmail.com
%Southern Medical University
%
%Default settings
coocMat= NaN;
distance = [1;2;4;8];
numLevels = 16;
offSet = [1 0 0; 1 1 0; 0 1 0; -1 1 0]; %2D Co-Occurrence dir... |
github | WenbingLv/NPC-radiomics-master | computeBoundingBox.m | .m | NPC-radiomics-master/computeBoundingBox.m | 3,017 | utf_8 | 71c3aad5fb0ffd5ca96a185b0ee529e2 | function [boxBound] = computeBoundingBox(mask)
% -------------------------------------------------------------------------
% function [boxBound] = computeBoundingBox(mask)
% -------------------------------------------------------------------------
% DESCRIPTION:
% This function computes the smallest box containing the... |
github | PerfXLab/caffe_perfdnn-master | classification_demo.m | .m | caffe_perfdnn-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | BrainardLab/TeachingCode-master | initialiseProcedure.m | .m | TeachingCode-master/ArduinoAnomaloscope/xxxContributed/arduinoHFP/initialiseProcedure.m | 1,144 | utf_8 | 86882fe4db83c69d7a985d7d13165b79 |
function [increaseKey, decreaseKey, deltaKey, finishKey, ...
increaseInputs, decreaseInputs, deltaIndex, rDeltas]=initialiseProcedure
increaseKey=KbName('up'); % key code for increasing red intensity
decreaseKey=KbName('down'); % key code for decreaseing red intensity
deltaKey=KbName('spac... |
github | BrainardLab/TeachingCode-master | ArduinoMethodOfAdjustmentHFP.m | .m | TeachingCode-master/ArduinoAnomaloscope/xxxContributed/arduinoHFP/ArduinoMethodOfAdjustmentHFP.m | 3,327 | utf_8 | 2afbe378afee5e39e4b9675765af3179 |
% if the code doesn't work, check that the arduino port (written in
% ConstantsHFP) is the right one (for windows, check Device Manager->ports)
function ArduinoImplementedHFP
% clear everything before starting program
delete(instrfindall)
clear
addpath('C:\Users\mediaworld\Documents\MATLAB\internship\HFP_Co... |
github | BrainardLab/TeachingCode-master | RenderSpectrumOnMonitorTutorial.m | .m | TeachingCode-master/ICVS2020Tutorials/RenderSpectrumOnMonitorTutorial.m | 11,826 | utf_8 | 749776d43b6f5da2a72aa5cc8f8786df | % RenderSpectrumOnMonitorTutorial
%
% Exercise to learn about rendering metamers on a monitor.
%
% This tutorial is available in the github repository
% https://github.com/BrainardLab/TeachingCode
% You can either clone the respository or just download a copy from
% that page (see green "Code" button).
%
% To run thi... |
github | BrainardLab/TeachingCode-master | ColourCamouflageImageTutorial.m | .m | TeachingCode-master/ICVS2020Tutorials/ColourCamouflageImageTutorial.m | 8,062 | utf_8 | f7e8af66320136d261df52d4cfeecef3 | % ColourCamouflageImageTutorial
%
% Example code to colour a 3-colour image with dichromat confusion colours
% for use in camouflage example.
%
% To run this, you will need both the Psychophysics Toolbox (PsychToolbox)
% and the BrainardLabToolbox on your path. You can get the PsychToolbox
% from
% psychtoolbox.org... |
github | BrainardLab/TeachingCode-master | RenderSpectrumOnMonitorForDogTutorial.m | .m | TeachingCode-master/ICVS2020Tutorials/RenderSpectrumOnMonitorForDogTutorial.m | 11,021 | utf_8 | 0e4da36aa7791abdabd1128cfd429255 | % RenderSpectrumOnMonitorForDogTutorial
%
% Exercise to learn about rendering metamers on a monitor. This version is
% for a dichromat. As an example, we'll use the cone spectral
% sensitivities of the dog.
%
% Before working through this tutorial, you should work through the
% tutorial RenderSpectrumOnMonitorTutorial... |
github | BrainardLab/TeachingCode-master | RenderImageOnMonitorForDogTutorial.m | .m | TeachingCode-master/ICVS2020Tutorials/RenderImageOnMonitorForDogTutorial.m | 5,976 | utf_8 | 3d978efe6447db441c9fa1acd1786e2c | % RenderImageOnMonitorForDogTutorial
%
% Render an RGB image as a metamer for a dichromat. This tutorial builds
% on the ideas introduced in RenderSpectrumOnMonitorTutorial and
% RenderSpectrumOnMonitorForDogTutorial.
%
% In this version, you can control the metameric image you produce by
% changing the parameter lamb... |
github | BrainardLab/TeachingCode-master | GLW_CircularApertureStimulus.m | .m | TeachingCode-master/GLWindowExamples/GLW_CircularApertureStimulus.m | 3,239 | utf_8 | 4a08aa08a3f6335936cb7c5f2a94a11e | function GLW_CircularApertureStimulus()
% GLW_CircularApertureStimulus()
%
% Demonstrate how to generate a noise stimulus with a circular aperture using
% GLWindow.
%
% The program terminates when the user presses the'q' key.
%
%
% 12/3/13 npc Wrote it.
% Generate 256x256 noise stimulus
imageSize = 256;
... |
github | BrainardLab/TeachingCode-master | GLW_DriftingGrating.m | .m | TeachingCode-master/GLWindowExamples/GLW_DriftingGrating.m | 5,238 | utf_8 | 29ff1094bb6efc08206567a0e2b1a378 | function GLW_DriftingGrating
% GLW_DriftingGrating Demonstrates how to drift a grating in GLWindow.
%
% Syntax:
% GLW_DriftingGrating
%
% Description:
% The function drifts a grating. Might not be completely done
%
% Press - 'd' to dump image of window into a file
% - 'q' to quit
% 12/5/12 dhb ... |
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