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 | superyyzg/L0-SSC-master | SpectralEmbedding.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/SpectralEmbedding.m | 431 | utf_8 | ef03a11d88d2595fc8034f93c86c49eb | %--------------------------------------------------------------------------
% Copyright @ Ehsan Elhamifar, 2012
%--------------------------------------------------------------------------
function [Y,Eval] = SpectralEmbedding(W,d)
N = size(W,1);
if (d > N-1)
d = N-1;
end
D = diag( 1./sqrt(sum(W,1)+eps) );
L = ey... |
github | superyyzg/L0-SSC-master | medianSort.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/medianSort.m | 598 | utf_8 | 0ab1f6c9979b5a09283f91de50353362 | % This function gets a similarty matrix C and sort the elements of each
% column from largest to smallest in a new matrix W, finally average these
% sorted columns to get a mean vector v which indicates the histogram of
% the magnitude of the columns of C.
%--------------------------------------------------------------... |
github | superyyzg/L0-SSC-master | SpectralClustering.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/SpectralClustering.m | 635 | utf_8 | 2216de206ecb29cbac9609938c120ad2 | %--------------------------------------------------------------------------
% Copyright @ Ehsan Elhamifar, 2012
%--------------------------------------------------------------------------
function [Y,Eval,Grp] = SpectralClustering(W,d,n,MAXiter,REPlic)
N = size(W,1);
D = diag( 1./sqrt(sum(W,1)+eps) );
L = eye(N) - D... |
github | superyyzg/L0-SSC-master | processC.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/processC.m | 693 | utf_8 | c03679af4dd637489433da8ab9edca0c | %--------------------------------------------------------------------------
% Copyright @ Ehsan Elhamifar, 2012
%--------------------------------------------------------------------------
function Cp = processC(C,ro)
if (nargin < 2)
ro = 1;
end
if (ro < 1)
[m,N] = size(C);
Cp = zeros(m,N);
[S,Ind] = ... |
github | superyyzg/L0-SSC-master | msc.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/msc.m | 662 | utf_8 | f9e2c5edb5bf6e3472c7a2344fa32f45 | % This function gets a coefficient matrix and the indices of the clustering
% of the data points and computed the msc vectors for each cluster
% W: coefficient matrix
% indg: indices of the memberships of the points to the clusters
% msc: each cell of msc is the msc vector corresponding to a cluster
%------------------... |
github | superyyzg/L0-SSC-master | smce.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/smce.m | 729 | utf_8 | 1f3e078a4ba2e87185eb1f4d687f3c67 | %--------------------------------------------------------------------------
% Copyright @ Ehsan Elhamifar, 2012
%--------------------------------------------------------------------------
function [Yc,Yj,clusters,missrate,W] = smce(Y,lambda,KMax,dim,n,gtruth,verbose)
if (nargin < 7)
verbose = true;
end
if (nargin... |
github | superyyzg/L0-SSC-master | smce_embedding.m | .m | L0-SSC-master/matlab/utility/SMCE_v1.2/smce_embedding.m | 607 | utf_8 | 8e2037e26ab47de8ebae94709df8d628 | %--------------------------------------------------------------------------
% Copyright @ Ehsan Elhamifar, 2012
%--------------------------------------------------------------------------
function [Yg,indg] = smce_embedding(W,grp,dim)
if (nargin < 2)
grp = ones(1,size(W,1));
end
if (nargin < 3)
dim = 3 * ones... |
github | samarth-robo/py-faster-rcnn-master | voc_eval.m | .m | py-faster-rcnn-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | willpower2727/ExperimentalGUI-master | AdaptationGUI.m | .m | ExperimentalGUI-master/AdaptationGUI.m | 34,838 | utf_8 | 08ce58f9b1fd5089c38c681e3418947c | function varargout = AdaptationGUI(varargin)
% ADAPTATIONGUI MATLAB code for AdaptationGUI.fig
% ADAPTATIONGUI, by itself, creates a new ADAPTATIONGUI or raises the existing
% singleton*.
%
% H = ADAPTATIONGUI returns the handle to a new ADAPTATIONGUI or the handle to
% the existing singleton*.
%
% ... |
github | willpower2727/ExperimentalGUI-master | AdaptationGUI_selfControl.m | .m | ExperimentalGUI-master/AdaptationGUI_selfControl.m | 28,020 | utf_8 | 3f21ed08cdd8985c78a9231b7b5aba80 | function varargout = AdaptationGUI_selfControl(varargin)
% ADAPTATIONGUI_SELFCONTROL MATLAB code for AdaptationGUI_selfControl.fig
% ADAPTATIONGUI_SELFCONTROL, by itself, creates a new ADAPTATIONGUI_SELFCONTROL or raises the existing
% singleton*.
%
% H = ADAPTATIONGUI_SELFCONTROL returns the handle to a... |
github | willpower2727/ExperimentalGUI-master | Dulce_grad_gamma_rev1.m | .m | ExperimentalGUI-master/controllers/Dulce_grad_gamma_rev1.m | 10,389 | utf_8 | 480e6cb3c542724b8d65fb4ad331dab1 |
function [Rgammarecord,Lgammarecord,Rmeangamma,Lmeangamma,Rstdgamma,Lstdgamma] = Dulce_grad_gamma_rev1(velL,velR,FzThreshold)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates the belts alternatively, i.e... |
github | willpower2727/ExperimentalGUI-master | Dulce_grad_betarev2.m | .m | ExperimentalGUI-master/controllers/Dulce_grad_betarev2.m | 12,471 | utf_8 | 1fcfa1dbcbe5260880a02148adc5bc39 |
function [RatioR,RatioL,RatiomeanR,RatiomeanL,Rstd,Lstd,alphaR,alphaL,alphaRmean,alphaLmean,alphaRstd,alphaLstd,betameanR,betameanL,Rsci,Lsci,XmeanR,XmeanL,RatioXmeanR,RatioXmeanL,RsciX,LsciX] = Dulce_grad_betarev2(velL,velR,FzThreshold)
%This function takes two vectors of speeds (one for each treadmill belt)
%and suc... |
github | willpower2727/ExperimentalGUI-master | Dulce_grad_beta.m | .m | ExperimentalGUI-master/controllers/Dulce_grad_beta.m | 9,810 | utf_8 | b8d3013106a2c4e9d96e68879445b488 |
function [Rbetarecord,Lbetarecord,Rmeanbeta,Lmeanbeta,Rstdbeta,Lstdbeta] = Dulce_grad_beta(velL,velR,FzThreshold)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates the belts alternatively, i.e., a single ... |
github | willpower2727/ExperimentalGUI-master | controlSpeedWithSteps_selfSelect_bak.m | .m | ExperimentalGUI-master/controllers/controlSpeedWithSteps_selfSelect_bak.m | 19,334 | utf_8 | e914d71e7b20bfda0ea9dbbc57cbb35b |
function [RTOTime, LTOTime, RHSTime, LHSTime, commSendTime, commSendFrame] = controlSpeedWithSteps_selfSelect(velL,velR,FzThreshold,profilename)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates the belts... |
github | willpower2727/ExperimentalGUI-master | controlSpeedWithSteps_maxAccel.m | .m | ExperimentalGUI-master/controllers/controlSpeedWithSteps_maxAccel.m | 17,212 | utf_8 | bfee22d8dbb5f2367a217a74fe63b31b |
function [RTOTime, LTOTime, RHSTime, LHSTime, commSendTime, commSendFrame] = controlSpeedWithSteps_edit1(velL,velR,FzThreshold,profilename)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates the belts alte... |
github | willpower2727/ExperimentalGUI-master | controlbytime.m | .m | ExperimentalGUI-master/controllers/controlbytime.m | 15,623 | utf_8 | 1075e1606cab2990220fe250dea2af45 |
function [] = controlbytime(velL,velR,FzThreshold,profilename)
%This function takes two inputs, speed profiles for RER test,
%and updates belt speeds based on time elapsed
%The function only updates the belts alternatively, i.e., a single belt
%speed cannot be updated twice without the other being updated
%
%speed up... |
github | willpower2727/ExperimentalGUI-master | Dulce_grad_betarev3.m | .m | ExperimentalGUI-master/controllers/Dulce_grad_betarev3.m | 9,964 | utf_8 | 4448a12d3e1e97c67cd2e47be6ec3d0f |
function [Rbetarecord,Lbetarecord,Rmeanbeta,Lmeanbeta,Rstdbeta,Lstdbeta,alphaR,alphaL,Rmean,Lmean,Rstd,Lstd] = Dulce_grad_betarev3(velL,velR,FzThreshold)
%Dulce's script computes alpha and beta values for each step, then at the
%end computes the average ratio of alpha and beta
%
%Rev 3 is designed to work when the t... |
github | willpower2727/ExperimentalGUI-master | controlSpeedWithSteps_selfSelectUnsigned_bak.m | .m | ExperimentalGUI-master/controllers/controlSpeedWithSteps_selfSelectUnsigned_bak.m | 20,257 | utf_8 | 0d342c43ab7791c48fa0b693bfa4000a |
function [RTOTime, LTOTime, RHSTime, LHSTime, commSendTime, commSendFrame] = controlSpeedWithSteps_selfSelectUnsigned(velL,velR,FzThreshold,profilename)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates t... |
github | willpower2727/ExperimentalGUI-master | SL_BF_findtargets.m | .m | ExperimentalGUI-master/controllers/SL_BF_findtargets.m | 20,139 | utf_8 | 8a80721999fee42f141b6aa6e98a5e5f |
function [] = SL_BF_findtargets(velL,velR,FzThreshold,profilename)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates the belts alternatively, i.e., a single belt
%speed cannot be updated twice without the... |
github | willpower2727/ExperimentalGUI-master | SL_BF_findtargets_OG_old.m | .m | ExperimentalGUI-master/controllers/SL_BF_findtargets_OG_old.m | 19,137 | utf_8 | eaf71b88cea7d603604345d79ba53f66 |
function [] = SL_BF_findtargets_OG(velL,velR,FzThreshold,profilename)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates the belts alternatively, i.e., a single belt
%speed cannot be updated twice without ... |
github | willpower2727/ExperimentalGUI-master | controlSpeedWithSteps_edit1.m | .m | ExperimentalGUI-master/controllers/controlSpeedWithSteps_edit1.m | 21,468 | utf_8 | 0b645f2474526d7715af2a2b4f4a7233 |
function [RTOTime, LTOTime, RHSTime, LHSTime, commSendTime, commSendFrame] = controlSpeedWithSteps_edit1(velL,velR,FzThreshold,profilename)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates the belts alte... |
github | willpower2727/ExperimentalGUI-master | controlSpeedWithSteps_selfSelect_bak_01082016.m | .m | ExperimentalGUI-master/controllers/controlSpeedWithSteps_selfSelect_bak_01082016.m | 37,361 | utf_8 | 93f92e84a60ff00c475d823da2137f77 |
function [RTOTime, LTOTime, RHSTime, LHSTime, commSendTime, commSendFrame] = controlSpeedWithSteps_selfSelect(velL,velR,FzThreshold,profilename,mode,signList,paramComputeFunc,paramCalibFunc)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilatera... |
github | willpower2727/ExperimentalGUI-master | controlSpeedWithSteps_selfSelect_bak_20160801.m | .m | ExperimentalGUI-master/controllers/controlSpeedWithSteps_selfSelect_bak_20160801.m | 36,580 | utf_8 | 7971e4079510c05529590ea898222453 |
function [RTOTime, LTOTime, RHSTime, LHSTime, commSendTime, commSendFrame] = controlSpeedWithSteps_selfSelect(velL,velR,FzThreshold,profilename,mode,signList,paramComputeFunc,paramCalibFunc)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilatera... |
github | willpower2727/ExperimentalGUI-master | controlSpeedWithSteps_edit1_old.m | .m | ExperimentalGUI-master/controllers/controlSpeedWithSteps_edit1_old.m | 9,524 | utf_8 | 51f725472c07d70fecc2b2db0cd0bf77 |
function [RTOTime, LTOTime, RHSTime, LHSTime, commSendTime, commSendFrame] = controlSpeedWithSteps_edit1_old(velL,velR,FzThreshold)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates the belts alternativel... |
github | willpower2727/ExperimentalGUI-master | SL_BF_findtargets_OG.m | .m | ExperimentalGUI-master/controllers/SL_BF_findtargets_OG.m | 17,613 | utf_8 | 97ffa700f9c7b137a6321d2d299b271f |
function [] = SL_BF_findtargets_OG(velL,velR,FzThreshold,profilename)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Toe-Off
%The function only updates the belts alternatively, i.e., a single belt
%speed cannot be updated twice without ... |
github | willpower2727/ExperimentalGUI-master | controlSpeedWithSteps_edit2.m | .m | ExperimentalGUI-master/controllers/controlSpeedWithSteps_edit2.m | 8,889 | utf_8 | 21c3d48052ec0b6fe61048d97205c407 |
function [RTOTime, LTOTime, RHSTime, LHSTime, commSendTime, commSendFrame] = controlSpeedWithSteps_edit2(velL,velR,FzThreshold)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilateral Heel Strike
%The function only updates the belts simultaneous... |
github | willpower2727/ExperimentalGUI-master | controlSpeedWithSteps_selfSelect.m | .m | ExperimentalGUI-master/controllers/controlSpeedWithSteps_selfSelect.m | 38,821 | utf_8 | f9a03476e1f598f86188ae7c63d6f0b7 |
function [RTOTime, LTOTime, RHSTime, LHSTime, commSendTime, commSendFrame] = controlSpeedWithSteps_selfSelect(velL,velR,FzThreshold,profilename,mode,signList,paramComputeFunc,paramCalibFunc)
%This function takes two vectors of speeds (one for each treadmill belt)
%and succesively updates the belt speed upon ipsilatera... |
github | jnhwkim/spectral-lib-master | litekmeans.m | .m | spectral-lib-master/mresgraph/litekmeans.m | 2,811 | utf_8 | d54ed90117e4361e91005164655a2073 | function [outlabel,outm] = litekmeans(X, k, constrained, maxsize)
% Perform k-means clustering.
% X: d x n data matrix
% k: number of seeds
% Written by Michael Chen (sth4nth@gmail.com).
% Modified by Joan Bruna to have constant cluster sizes
n = size(X,2);
last = 0;
minener = 1e+20;
outiters=2;
maxiters=100;
if... |
github | reproducibilitystamp/tmc-master | t_mc_figs.m | .m | tmc-master/matlab/t_mc_figs.m | 35,401 | utf_8 | 3efdd8540b3411c042ed408dc35866c3 |
% 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 disclaimer.
% * Redistributions in bi... |
github | jy9387/BSCP-master | pyramid_pooling_scplus.m | .m | BSCP-master/pyramid_pooling_scplus.m | 2,814 | utf_8 | 158c664b9298dd0d3b6fc45bd64c7ae2 | function pyramid_pooling_scplus(img_dir, fea_dir, pyramid)
database = retr_database_dir(img_dir, '*_codescplus.mat');
for n = 1:length( database.path )
label = database.label(n);
path = database.path{n};
load(path);
%assert(label0==label);
code=code_scplus;
fprintf('pyramid pooling ... |
github | jy9387/BSCP-master | extr_raw_pnts.m | .m | BSCP-master/extr_raw_pnts.m | 1,370 | utf_8 | 54e63e121955fea78f0754617d2e16a7 | function pnts = extr_raw_pnts(c, maxvalue, N, nn)
%-------------------------------------------------------
%[SegmentX, SegmentY,NO]=GenSegmentsNew(a,b,maxvalue,nn)
%This function is used to generate all the segments
%vectors of the input contour
%a and b are the input contour sequence
% maxvalue is the stop co... |
github | jy9387/BSCP-master | shape_context_plus.m | .m | BSCP-master/shape_context_plus.m | 3,994 | utf_8 | 7b8f7610f1d3a61fafe1213fa52106c9 | % th
function [sc_plus,V,dis_mat,ang_mat,dif_mat] = shape_context_plus( cont, n_ref, n_dist, n_theta, n_skdiff, bTangent)
if ~exist('n_ref') n_ref = 5; end
if ~exist('n_dist') n_dist = 5; end
if ~exist('n_theta') n_theta = 12; end
if ~exist('n_skdiff') n_skdiff = 2; end
if ~exist('bTangent') bT... |
github | jy9387/BSCP-master | dist_sk2bd.m | .m | BSCP-master/dist_sk2bd.m | 6,094 | utf_8 | 5d4e410cd8240a66cf926b25fb4e534a |
function dist_sk2bd(img_dir, fmt)
% find min dist from every point on the skeleton to points on the boudary;
database = retr_database_dir(img_dir, fmt);
tic
for n = 1:length(database.path)
fprintf('Distance between skeleton and boundary: %d of %d\n', n, length(database.path));
func_dist_s... |
github | jy9387/BSCP-master | normalize_size.m | .m | BSCP-master/normalize_size.m | 530 | utf_8 | cb47e0fcc69aa31d853d6aa8f2605c19 |
function normalize_size(img_dir, fmt)
database = retr_database_dir(img_dir, fmt);
tic
%matlabpool(4);
for n = 1:length(database.path)
fprintf('Normalize size: %d of %d\n', n, length(database.path));
func_normalize_size(database.path{n} );
end
%matlabpool close;
toc
functio... |
github | jy9387/BSCP-master | extr_cfplus.m | .m | BSCP-master/extr_cfplus.m | 2,927 | utf_8 | 263102cba3b009756c4d6f325813657c | function extr_cfplus(img_dir, fmt, scplus_para)
database = retr_database_dir(img_dir, fmt);
tic
for n = 1 : length(database.path)
fprintf('Extracting SSC features: %d of %d\n', n, length(database.path));
func_extr_cfplus(database.path{n}, scplus_para );
end
toc
function func_extr_cfplus(... |
github | jy9387/BSCP-master | encode_csplus.m | .m | BSCP-master/encode_csplus.m | 1,021 | utf_8 | c4003a10b91db3fae3a0bf18d0974731 |
function encode_csplus(img_dir, codebook_path, para)
database = retr_database_dir(img_dir, '*_csplusfeat.mat');
load(codebook_path);
dict = dict;
parfor n = 1:length(database.path)
fprintf('Encoding ssc: %d of %d\n', n, length(database.path));
func_encode_cfplus(database.pa... |
github | jy9387/BSCP-master | curvature.m | .m | BSCP-master/curvature.m | 941 | utf_8 | 6ebbe13181ccc9deb5bbc617f07372bc | % curvature
% Longin Latecki January 2007
%
% This is a function that computes the curvature of a point P(pIndex)
%
% Parameters:
% 1. P - the X-by-2 matrix of points
% 2. index - the index of the point that is currently being worked on
% 3. nbsize - the number of neighbor points to use on each side of... |
github | jy9387/BSCP-master | Normlize.m | .m | BSCP-master/Normlize.m | 700 | utf_8 | 3f694fa875b0c03b7107d4b2c33abd4d | function Normlize
img_dir = 'data/Animal/';
database = retr_database_dir(img_dir, '*.tif');
for n = 1:length(database.path)
I = imread(database.path{n});
[x, y] = find(I > 0);
pnts = [x, y];
pnts = pnts - repmat(mean(pnts), size(pnts, 1), 1);
pnts_cov = cov(pnts);
pr... |
github | jy9387/BSCP-master | contour_curvature.m | .m | BSCP-master/contour_curvature.m | 274 | utf_8 | 09230a6eadbc3ee576ca6fbb291090b0 |
function cs = contour_curvature( cont )
ispl = 1 : 10 : size(cont, 1);
cs = zeros( length(ispl), 1 );
for i = 1:length( ispl )
cs(i) = curvature( cont, ispl(i), 5 );
if isnan( cs(i) )
disp('sss');
end
end |
github | jy9387/BSCP-master | normalize_shape.m | .m | BSCP-master/normalize_shape.m | 1,202 | utf_8 | f78fbde787f3921d487aedb37ac8f7d6 |
function normalize_shape(img_dir, fmt)
database = retr_database_dir(img_dir, fmt);
tic
%matlabpool(4);
for n = 1:length(database.path)
fprintf('Normalize shape: %d of %d\n', n, length(database.path));
func_normalize_shape(database.path{n} );
end
%matlabpool close;... |
github | jy9387/BSCP-master | LLC_coding_appr.m | .m | BSCP-master/include/llc/LLC_coding_appr.m | 1,623 | utf_8 | 94e896ace12b18b9e1c648dfd46e5ffc | % ========================================================================
% USAGE: [Coeff]=LLC_coding_appr(B,X,knn,lambda)
% Approximated Locality-constraint Linear Coding
%
% Inputs
% B -M x d codebook, M entries in a d-dim space
% X -N x d matrix, N data points in a d-dim space
% ... |
github | jy9387/BSCP-master | vq_coding.m | .m | BSCP-master/include/llc/vq_coding.m | 1,211 | utf_8 | 0d6035b8acd1c82b23de519acfc20691 | % ========================================================================
% USAGE: [Coeff]=LLC_coding_appr(B,X,knn,lambda)
% Approximated Locality-constraint Linear Coding
%
% Inputs
% B -M x d codebook, M entries in a d-dim space
% X -N x d matrix, N data points in a d-dim space
% ... |
github | jy9387/BSCP-master | soft_coding.m | .m | BSCP-master/include/llc/soft_coding.m | 1,283 | utf_8 | 8a32a18c91c8d55d8897b1e618099686 | % ========================================================================
% USAGE: [Coeff]=LLC_coding_appr(B,X,knn,lambda)
% Approximated Locality-constraint Linear Coding
%
% Inputs
% B -M x d codebook, M entries in a d-dim space
% X -N x d matrix, N data points in a d-dim space
% ... |
github | jy9387/BSCP-master | DPMatching.m | .m | BSCP-master/include/idsc_distribute/common_innerdist/DPMatching.m | 2,787 | utf_8 | 676509c26800991c0103908aaa2a6c4e | function [C,T]=DPMatching(A, bCircular, thre)
%
%[C,T]=DPMatching(A, thre)
% A - a square cost matrix.
% thre- use average*thre as the occlusion
% C - the optimal assignment.
% T - the cost of the optimal assignment.
%
% bCircular
if ~exist('thre') thre = .2; end
if ~exist('bCircular') bCircular =... |
github | jy9387/BSCP-master | disp_graph.m | .m | BSCP-master/include/idsc_distribute/common_innerdist/disp_graph.m | 214 | utf_8 | 3cb8731d0b7626215825bc11bc691c88 | % function disp_graph(V,E)
function disp_graph(V,E)
N = size(V,1);
X = V(:,1);
Y = V(:,2);
Xs = [X(E(:,1)),X(E(:,2))]';
Ys = [Y(E(:,1)),Y(E(:,2))]';
plot(Xs,Ys,'r');
plot(X,Y,'b.');
return;
|
github | jy9387/BSCP-master | comp_sc_hist.m | .m | BSCP-master/include/idsc_distribute/common_innerdist/comp_sc_hist.m | 1,710 | utf_8 | fb7f1a6008eba5b81d3ee2e00a65831c | % [sc_hist] = comp_sc_hist(dists,angles);
%
% Compute shape context from input distances and angles
% Inputs:
% dists : distance and orientation matrix, the i-th COLUMN contains
% angles : dist and angles from i-th point to all other points
function [sc_hist] = comp_sc_hist(dists,angles,n_dist,n_theta)... |
github | jy9387/BSCP-master | compu_contour_SC.m | .m | BSCP-master/include/idsc_distribute/common_innerdist/compu_contour_SC.m | 1,378 | utf_8 | 6cc68e5a44edbd95de064e38fd7d94a4 | % th
function [sc,V,dis_mat,ang_mat] = compu_contour_SC( cont, n_dist, n_theta, bTangent)
if ~exist('bTangent') bTangent = 0; end
%------ Parameters ----------------------------------------------
n_pt= size(cont,1);
X = cont(:,1);
Y = cont(:,2);
V = cont;
%-- Orientations and geodesic distanc... |
github | jy9387/BSCP-master | shape_context.m | .m | BSCP-master/include/idsc_distribute/common_innerdist/shape_context.m | 3,180 | utf_8 | d4715372cf1013110e0e2cd972120e9b | % th
function [sc,V,dis_mat,ang_mat] = shape_context( cont, n_ref, n_dist, n_theta, bTangent)
if ~exist('n_ref') n_ref = 5; end
if ~exist('n_dist') n_dist = 5; end
if ~exist('n_theta') n_theta = 12; end
if ~exist('bTangent') bTangent = 1; end
%------ Parameters ---------------... |
github | jy9387/BSCP-master | compu_contour_innerdist_SC.m | .m | BSCP-master/include/idsc_distribute/common_innerdist/compu_contour_innerdist_SC.m | 2,692 | utf_8 | b876ec91537af37a0924cee477c03fc3 | % The algorithm:
%
% 1. Initialize the graph
% for each p1
% for each p2
% if (p1,p2) inside the shape
% add edge e(p1,p2) into E
% set dis_mat(p1,p2)=dis_mat(p2,p1)
% set ang_mat(p1,p2), ang_mat(p2,p1)
% set viewable(p1,p2)=viewable(p2,p1)=1
%
% 2. for each p1,
% 2.1 compute the di... |
github | brian-lau/MatlabR-master | setupMatR.m | .m | MatlabR-master/setupMatR.m | 2,033 | utf_8 | e1b7c939522bfdfe04f27259ff2212f7 | % Not sufficient that these classes are in the dynamic path
function setupMatR()
jpath = javaclasspath('-static');
[~,jars] = cellfun(@(x) fileparts(x),jpath,'uni',0);
if any(strcmp(jars,'REngine')) && any(strcmp(jars,'RserveEngine'))
% Already in user's static javaclasspath
else
path = fileparts(which('MatR'))... |
github | karanchawla/AerialRobotics-Coursera-master | QuadPlot.m | .m | AerialRobotics-Coursera-master/GainTuningQuiz/QuadPlot.m | 5,220 | utf_8 | 1f4b7d11af220cf6f1d5e395f4a180f9 | classdef QuadPlot < handle
%QUADPLOT Visualization class for quad
properties (SetAccess = public)
k = 0;
qn; % quad number
time = 0; % time
state; % state
rot; % rotation matrix body to world
color; % color of quad
... |
github | xyza11808/MATLAB-master | CellRespAna.m | .m | MATLAB-master/CellRespAna.m | 2,292 | utf_8 | ed19b7fd3e4e09dbaafc99ce575ea92b | function RespSummaryStrc = CellRespAna(RespCell)
% this function is used only for analysis detailed cell response type and
% given output result in a structure
% the fileds within a structure contains:
% LeftType: 0 for not response, 1 for sound response, 2 for choice response, 3 for both response
% RightType... |
github | xyza11808/MATLAB-master | reward_train_model.m | .m | MATLAB-master/reward_train_model.m | 10,516 | utf_8 | 68e362387d7f2087a71bca58b459552a | function reward_train_model()
numTrial = 100;
para = struct; % store all parameters which once set won't change
netStr = struct; % store network structure info,
preStimTime = 200; % ms % phase1 pre-stimulus time 200 ms
stimTime = 1000; %ms % stimulus lasts for 1000 ms
ITI = 500;%ms % inter tri... |
github | xyza11808/MATLAB-master | brewermap.m | .m | MATLAB-master/brewermap.m | 18,969 | utf_8 | c56317f08bb46c82f476d53190d66b6b | function [map,num,typ,scheme] = brewermap(N,scheme) %#ok<*ISMAT>
% The complete selection of ColorBrewer colorschemes (RGB colormaps).
%
% (c) 2014-2020 Stephen Cobeldick
%
% Returns any RGB colormap from the ColorBrewer colorschemes, especially
% intended for mapping and plots with attractive, distinguishable colors.
... |
github | xyza11808/MATLAB-master | cum_gaussfit_max1.m | .m | MATLAB-master/cum_gaussfit_max1.m | 1,172 | utf_8 | 9ae6f17cb5983a943f4accb6157f8298 | % originally modified by GY from GCD (cum_gaussfit_max.m)
function [alpha, beta] = cum_gaussfit_max1(data_cum)
% Gaussian fits a accumulative Gaussian function to data using maximum likelihood
% maximization under binomial assumptions. It uses Gaussian_Fun for
% error calculation. Data must be in 3 columns: x, %-corr... |
github | xyza11808/MATLAB-master | SigPeakIsolation.m | .m | MATLAB-master/SigPeakIsolation.m | 869 | utf_8 | 9994c533ae99147e6a41731787f545ca | function SigPeakIsolation(RawData,trialTypes,TrialOutCome,varargin)
% this function is tried to detect significant transcent from raw
% Fluorescence trace, and only significant events will be keeped
%
if ~isequal(size(RawData,1),length(trialTypes),length(TrialOutCome))
warning('Input variable have different trial ... |
github | xyza11808/MATLAB-master | HDspike_sort.m | .m | MATLAB-master/HDspike_sort.m | 1,507 | utf_8 | b90321ac0cff80c90b53d419ad235b8b |
function [FringRate]=HDspike_sort(data,time,fs,cond)
%HDspike_sort function is used for high dimensional data spike sorting(no more than four dimension)
%Tipically, this function works as HDspike_sort(data,time,fs,cond).the first two dimension data will be used as amplitude and
%timeline,the third dimension will be tr... |
github | xyza11808/MATLAB-master | plottest2.m | .m | MATLAB-master/plottest2.m | 6,273 | utf_8 | a8e5c4857aed58f17ffee30564cb693f | function varargout = plottest2(varargin)
% PLOTTEST2 MATLAB code for plottest2.fig
% PLOTTEST2, by itself, creates a new PLOTTEST2 or raises the existing
% singleton*.
%
% H = PLOTTEST2 returns the handle to a new PLOTTEST2 or the handle to
% the existing singleton*.
%
% PLOTTEST2('CALLBACK',hO... |
github | xyza11808/MATLAB-master | ActiveCellGene.m | .m | MATLAB-master/ActiveCellGene.m | 11,929 | utf_8 | 52e8e2e9e53d6ea3d97e9c96fc1c306e | function ActiveCellGene(RawData,BehavResult,TrialOut,FrameRate,TimeWin,varargin)
% Test of significant response of neuron
SmoothRawData = RawData;
[~,UpdateStd] = GPsmoothRaw(RawData(1:20,:,:));
[TrialNum,ROINum,FrameNum] = size(SmoothRawData); % matrix dimension
% TrialSigResult = zeros(TrialNum,ROINum);
if isempty(... |
github | xyza11808/MATLAB-master | fminsearchbnd.m | .m | MATLAB-master/fminsearchbnd.m | 8,113 | utf_8 | bdc1aaa1af316964dd3c3bc3107d18a9 | function [x,fval,exitflag,output] = fminsearchbnd(fun,x0,LB,UB,options,varargin)
% FMINSEARCHBND: FMINSEARCH, but with bound constraints by transformation
% usage: x=FMINSEARCHBND(fun,x0)
% usage: x=FMINSEARCHBND(fun,x0,LB)
% usage: x=FMINSEARCHBND(fun,x0,LB,UB)
% usage: x=FMINSEARCHBND(fun,x0,LB,UB,options)
% usage: x... |
github | xyza11808/MATLAB-master | VisualCellMorphCheck.m | .m | MATLAB-master/VisualCellMorphCheck.m | 18,233 | utf_8 | 5e1eae59bad3d9686884840e39740094 | function varargout = VisualCellMorphCheck(varargin)
% VISUALCELLMORPHCHECK MATLAB code for VisualCellMorphCheck.fig
% VISUALCELLMORPHCHECK, by itself, creates a new VISUALCELLMORPHCHECK or raises the existing
% singleton*.
%
% H = VISUALCELLMORPHCHECK returns the handle to a new VISUALCELLMORPHCHECK or t... |
github | xyza11808/MATLAB-master | uTest_anyEq.m | .m | MATLAB-master/uTest_anyEq.m | 18,239 | utf_8 | 5a7808f3003ce1b21d4d8f79426c5c29 | function uTest_anyEq(doSpeed)
% Unit test: anyEq
% This is a routine for automatic testing. It is not needed for processing and
% can be deleted or moved to a folder, where it does not bother.
%
% uTest_anyEq(doSpeed)
% INPUT:
% doSpeed: If ANY(doSpeed) is TRUE, the speed is measured. Optional.
% OUTPUT:
% On failu... |
github | xyza11808/MATLAB-master | testGUI1.m | .m | MATLAB-master/testGUI1.m | 5,607 | utf_8 | 0e8924138f5a1e80362d2ee35c875511 | function varargout = testGUI1(varargin)
% TESTGUI1 MATLAB code for testGUI1.fig
% TESTGUI1, by itself, creates a new TESTGUI1 or raises the existing
% singleton*.
%
% H = TESTGUI1 returns the handle to a new TESTGUI1 or the handle to
% the existing singleton*.
%
% TESTGUI1('CALLBACK',hObject,ev... |
github | xyza11808/MATLAB-master | SameNeuron_acrossSess_check.m | .m | MATLAB-master/SameNeuron_acrossSess_check.m | 35,359 | utf_8 | 09590480c4588f6987c86d643ad96d9a | function varargout = SameNeuron_acrossSess_check(varargin)
% SAMENEURON_ACROSSSESS_CHECK MATLAB code for SameNeuron_acrossSess_check.fig
% SAMENEURON_ACROSSSESS_CHECK, by itself, creates a new SAMENEURON_ACROSSSESS_CHECK or raises the existing
% singleton*.
%
% H = SAMENEURON_ACROSSSESS_CHECK returns the... |
github | xyza11808/MATLAB-master | behav_cell2struct.m | .m | MATLAB-master/behav_cell2struct.m | 4,220 | utf_8 | 9a4df39bb5ddcdd797d096a58a622c9d | function varargout = behav_cell2struct(varargin)
%this function is used for convert the cell formed behavior analysis adata
%into structure form, to be allowed for further analysis
%June 18th, 2015, by XIN
if nargin < 1
disp('Please select your cell format behavior data position:\n');
[filename,filepath,~]=uige... |
github | xyza11808/MATLAB-master | OverLapPixelData.m | .m | MATLAB-master/OverLapPixelData.m | 6,213 | utf_8 | bc5072b3f9a4e2d716cd4cb38d28656d | function [ROIinfoNew , ROIlabel] = OverLapPixelData(ROIinfoStrc,varargin)
%this function will be used to find overlapped ROIs and return its
%integrated mask for each section of overlapped ROIs
%return variable will contains a inds for each overlap of ROIs, the
%integrated ROI mask for that label. and number of ROIs co... |
github | xyza11808/MATLAB-master | Mannual_events_Adding.m | .m | MATLAB-master/Mannual_events_Adding.m | 19,065 | utf_8 | 4c6dea5db868c5e48a4bc465b3af6d97 | function varargout = Mannual_events_Adding(varargin)
% MANNUAL_EVENTS_ADDING MATLAB code for Mannual_events_Adding.fig
% MANNUAL_EVENTS_ADDING, by itself, creates a new MANNUAL_EVENTS_ADDING or raises the existing
% singleton*.
%
% H = MANNUAL_EVENTS_ADDING returns the handle to a new MANNUAL_EVENTS_ADDI... |
github | xyza11808/MATLAB-master | venn.m | .m | MATLAB-master/venn.m | 34,551 | utf_8 | 6f3bc6581d0f355bcb955c9e8260bb0b | function varargout = venn (varargin)
%VENN Plot 2- or 3- circle area-proportional Venn diagram
%
% venn(A, I)
% venn(Z)
% venn(..., F)
% venn(..., 'ErrMinMode', MODE)
% H = venn(...)
% [H, S] = venn(...)
% [H, S] = venn(..., 'Plot', 'off')
% S = venn(..., 'Plot', 'off')
% [...] = venn(..., P1, V1, P2, V2, ..... |
github | xyza11808/MATLAB-master | ROIremoveNMtest.m | .m | MATLAB-master/ROIremoveNMtest.m | 6,189 | utf_8 | 0b4cc4c071da485b91372b16d3242e3c | function ROIremoveNMtest(InputData,TrialFreq,AlignFrame,FrameRate,varargin)
%this function will be used for remove top selective ROIs from All ROI
%population and see the change of the NeuroMetric curve change
if nargin>4
TimeWin = varargin{1};
end
if ~exist('TimeWin','var') || isempty(TimeWin)
TimeWin = 1.5;
e... |
github | xyza11808/MATLAB-master | TrialByTNMtest.m | .m | MATLAB-master/TrialByTNMtest.m | 8,287 | utf_8 | 7bc3108648eb7040e862aa27693d45f2 | function TrialByTNMtest(RawDataAll,BehavStrc,TrialResult,AlignFrame,FrameRate,varargin)
%this function will calculate pca points trial by trial and then group them
%according to its corresponded trial type, with this group of points we
%will then using a SVM mechine to classified the two trial types and see
%its outcom... |
github | xyza11808/MATLAB-master | FitPsycheCurveWH_nx.m | .m | MATLAB-master/FitPsycheCurveWH_nx.m | 1,510 | utf_8 | d6f10d48b5f28e4f77f7c47ce55f0c98 | % http://matlaboratory.blogspot.co.uk/2015/05/fitting-better-psychometric-curve.html
% NX modified, 2017-8-2
function fit_results = FitPsycheCurveWH_nx(xAxis, yData, varargin)
useLims=0;
% Start points and limits
if nargin > 2
if numel(varargin{:})>1
useLims=1;
UL=varargin{1}(1,:);
SP=vararg... |
github | xyza11808/MATLAB-master | frequency_shift_script.m | .m | MATLAB-master/frequency_shift_script.m | 1,220 | utf_8 | 0443773c80c0022b7512478e9afb0adf | %
% clc;
%
% f_raw=fs;
% fs=5*f_raw;
% t=1/fs:1/fs:1;
% sig_raw=sin(2*pi*f_raw*t);
% N=length(sig_raw);
%
% Y=fft(sig_raw);
% magY=abs(Y(1:1:N/2+1))*2/N;
% f=((1:N/2+1)-1)'*fs/N;
% figure;
% h=stem(f,magY,'fill','--');
% set(h,'MarkerEdgeColor','red','Marker','*')
% grid on
%
% %just shift the value, and set the re... |
github | xyza11808/MATLAB-master | Gao_2pData_ROIDraw_code.m | .m | MATLAB-master/Gao_2pData_ROIDraw_code.m | 29,470 | utf_8 | 19712ea25ae8c7736e067da218bfce95 | function varargout = Gao_2pData_ROIDraw_code(varargin)
% GAO_2PDATA_ROIDRAW_CODE MATLAB code for Gao_2pData_ROIDraw_code.fig
% GAO_2PDATA_ROIDRAW_CODE, by itself, creates a new GAO_2PDATA_ROIDRAW_CODE or raises the existing
% singleton*.
%
% H = GAO_2PDATA_ROIDRAW_CODE returns the handle to a new GAO_2PD... |
github | xyza11808/MATLAB-master | LDA_coef.m | .m | MATLAB-master/LDA_coef.m | 2,282 | utf_8 | 52afccb9018e3bd91294c43d0ea64c70 | % LDA - MATLAB subroutine to perform linear discriminant analysis
% by Will Dwinnell and Deniz Sevis
%
% Use:
% W = LDA(Input,Target,Priors)
%
% W = discovered linear coefficients (first column is the constants)
% Input = predictor data (variables in columns, observations in rows)
% Target = target variable (c... |
github | xyza11808/MATLAB-master | ThresSubData.m | .m | MATLAB-master/ThresSubData.m | 640 | utf_8 | 773fedb024fe2630f7de7a5ec8e61655 | function CellSubData = ThresSubData(CellData)
% clear the cellData values below than the threshold value, which is the
% one time std for each ROI calculated from non-zeros values
Cell2ROIData = cell2mat(CellData');
Cell2ROIDataTemp = Cell2ROIData;
Cell2ROIDataTemp(Cell2ROIDataTemp < 1e-6) = nan;
ROIDataThres = std(C... |
github | xyza11808/MATLAB-master | Test_input_mtx_gao.m | .m | MATLAB-master/Test_input_mtx_gao.m | 12,322 | utf_8 | 008b5d7086ad5133ded1bc75ed941a7c | function varargout = Test_input_mtx_gao(varargin)
% TEST_INPUT_MTX_GAO MATLAB code for Test_input_mtx_gao.fig
% TEST_INPUT_MTX_GAO, by itself, creates a new TEST_INPUT_MTX_GAO or raises the existing
% singleton*.
%
% H = TEST_INPUT_MTX_GAO returns the handle to a new TEST_INPUT_MTX_GAO or the handle to
%... |
github | xyza11808/MATLAB-master | test_axis1.m | .m | MATLAB-master/test_axis1.m | 6,296 | utf_8 | 67b838eb603f4990aa1e4217ee13b403 | function varargout = test_axis1(varargin)
% TEST_AXIS1 MATLAB code for test_axis1.fig
% TEST_AXIS1, by itself, creates a new TEST_AXIS1 or raises the existing
% singleton*.
%
% H = TEST_AXIS1 returns the handle to a new TEST_AXIS1 or the handle to
% the existing singleton*.
%
% TEST_AXIS1('CALL... |
github | xyza11808/MATLAB-master | cov_cus.m | .m | MATLAB-master/cov_cus.m | 6,125 | utf_8 | c0b710814220ef9b4d4668f26fb81b49 | function c = cov_cus(x,xSize,varargin)
%COV Covariance matrix.
% COV(X), if X is a vector, returns the variance. For matrices, where
% each row is an observation, and each column a variable, COV(X) is the
% covariance matrix. DIAG(COV(X)) is a vector of variances for each
% column, and SQRT(DIAG(COV(X))) i... |
github | xyza11808/MATLAB-master | Gao_image_plot.m | .m | MATLAB-master/Gao_image_plot.m | 26,516 | utf_8 | 81e5b921c651ed1f042409e8676c7fd6 | function varargout = Gao_image_plot(varargin)
% GAO_IMAGE_PLOT MATLAB code for Gao_image_plot.fig
% GAO_IMAGE_PLOT, by itself, creates a new GAO_IMAGE_PLOT or raises the existing
% singleton*.
%
% H = GAO_IMAGE_PLOT returns the handle to a new GAO_IMAGE_PLOT or the handle to
% the existing singleton... |
github | xyza11808/MATLAB-master | ROI_inds_selection.m | .m | MATLAB-master/ROI_inds_selection.m | 10,355 | utf_8 | e91fdf299c722872615db98280251140 | function varargout = ROI_inds_selection(varargin)
% ROI_INDS_SELECTION MATLAB code for ROI_inds_selection.fig
% ROI_INDS_SELECTION, by itself, creates a new ROI_INDS_SELECTION or raises the existing
% singleton*.
%
% H = ROI_INDS_SELECTION returns the handle to a new ROI_INDS_SELECTION or the handle to
%... |
github | xyza11808/MATLAB-master | Mannual_events_check.m | .m | MATLAB-master/Mannual_events_check.m | 17,955 | utf_8 | cf2c06d73d94913ab8e867ac8899e640 | function varargout = Mannual_events_check(varargin)
% MANNUAL_EVENTS_CHECK MATLAB code for Mannual_events_check.fig
% MANNUAL_EVENTS_CHECK, by itself, creates a new MANNUAL_EVENTS_CHECK or raises the existing
% singleton*.
%
% H = MANNUAL_EVENTS_CHECK returns the handle to a new MANNUAL_EVENTS_CHECK or t... |
github | xyza11808/MATLAB-master | savemultfigs.m | .m | MATLAB-master/savemultfigs.m | 17,140 | utf_8 | 04b2699884802b93fb89f30be07654d1 | function varargout = savemultfigs(varargin)
% SAVEMULTFIGS is a simple GUI that allows to easily and quickly save
% multiple figures in several formats in just a few clicks!
%
% Author: Nicolas Beuchat, EPFL/HMS
% nicolas.beuchat [at] gmail.com
% Creation date: 2-14-2012
% Last update: 2-17-2012
%
% TO... |
github | xyza11808/MATLAB-master | freq_shift_full.m | .m | MATLAB-master/freq_shift_full.m | 1,850 | utf_8 | 423e8a24a8d882771186befc6ce66828 | %
% clc;
%
% f_raw=fs;
% fs=5*f_raw;
% t=1/fs:1/fs:1;
% sig_raw=sin(2*pi*f_raw*t);
% N=length(sig_raw);
%
% Y=fft(sig_raw);
% magY=abs(Y(1:1:N/2+1))*2/N;
% f=((1:N/2+1)-1)'*fs/N;
% figure;
% h=stem(f,magY,'fill','--');
% set(h,'MarkerEdgeColor','red','Marker','*')
% grid on
%
% %just shift the value, and set the re... |
github | xyza11808/MATLAB-master | ROIType_BranceIndex_selection.m | .m | MATLAB-master/ROIType_BranceIndex_selection.m | 25,059 | utf_8 | 63df660edeaa6642c7b6f3efe4dfb98f | function varargout = ROIType_BranceIndex_selection(varargin)
% ROITYPE_BRANCEINDEX_SELECTION MATLAB code for ROIType_BranceIndex_selection.fig
% ROITYPE_BRANCEINDEX_SELECTION, by itself, creates a new ROITYPE_BRANCEINDEX_SELECTION or raises the existing
% singleton*.
%
% H = ROITYPE_BRANCEINDEX_SELECTION... |
github | xyza11808/MATLAB-master | batch_ROI_analysis.m | .m | MATLAB-master/batch_ROI_analysis.m | 2,953 | utf_8 | 3f2bda941faebb96129faf0d581a6276 | function CaTrials = batch_ROI_analysis(CaTrials_init, trialRange)
% Batch extract time series of mean pixel intensity of all ROIs, and save
% to a structure array.
% - NX 2015.4
ROIinfo = CaTrials_init.ROIinfo;
file_mainName = CaTrials_init.FileName_prefix;
datafile_list = dir([file_mainName, '*.tif']);
if nargin ... |
github | xyza11808/MATLAB-master | MeanFigureShow.m | .m | MATLAB-master/MeanFigureShow.m | 13,205 | utf_8 | 4299cc962f5af8d3eff8b5aaa0d83c54 | function varargout = MeanFigureShow(varargin)
% MEANFIGURESHOW MATLAB code for MeanFigureShow.fig
% MEANFIGURESHOW, by itself, creates a new MEANFIGURESHOW or raises the existing
% singleton*.
%
% H = MEANFIGURESHOW returns the handle to a new MEANFIGURESHOW or the handle to
% the existing singleton... |
github | xyza11808/MATLAB-master | Figure_axes_setGUI.m | .m | MATLAB-master/Figure_axes_setGUI.m | 27,407 | utf_8 | 59de8078dd7c72f70774d07fee15b1bc | function varargout = Figure_axes_setGUI(varargin)
% FIGURE_AXES_SETGUI MATLAB code for Figure_axes_setGUI.fig
% FIGURE_AXES_SETGUI, by itself, creates a new FIGURE_AXES_SETGUI or raises the existing
% singleton*.
%
% H = FIGURE_AXES_SETGUI returns the handle to a new FIGURE_AXES_SETGUI or the handle to
%... |
github | xyza11808/MATLAB-master | ftSel_SVMRFECBR_ori.m | .m | MATLAB-master/ftSel_SVMRFECBR_ori.m | 4,671 | utf_8 | 25c3ded52c87280209b8b0268bae04ab | function [ftRank,ftScore] = ftSel_SVMRFECBR_ori(ft,label,param)
% Feature selection using SVM-recursive feature elimination (SVM-RFE) with
% correlation bias reduction (CBR). LIBSVM is needed.
% This is the original linear version of SVM-RFE in Guyon "Gene selection
% for cancer classification using support vector mac... |
github | xyza11808/MATLAB-master | PupilScaleSelection.m | .m | MATLAB-master/PupilScaleSelection.m | 23,940 | utf_8 | 7b400be45d89b500674e96aead769aaf | function varargout = PupilScaleSelection(varargin)
% PUPILSCALESELECTION MATLAB code for PupilScaleSelection.fig
% PUPILSCALESELECTION, by itself, creates a new PUPILSCALESELECTION or raises the existing
% singleton*.
%
% H = PUPILSCALESELECTION returns the handle to a new PUPILSCALESELECTION or the hand... |
github | xyza11808/MATLAB-master | PossibleMoveArtifactRemoveFun.m | .m | MATLAB-master/PossibleMoveArtifactRemoveFun.m | 6,641 | utf_8 | 08dd4cf11514c2ad121a7c28149e4610 | function [MoveFreeTrace, PeakScaleInds, ResidueSTD] = PossibleMoveArtifactRemoveFun(RawTrace)
% this function finds possible movement artifactfact and remove it from raw
% trace
MoveFreeTrace = RawTrace;
PeakScaleInds = {};
k = 1;
% SMTrace = smooth(RawTrace(:),7,'sgolay',3);
SMTrace = smooth(RawTrace(:),4);
SMResidu... |
github | xyza11808/MATLAB-master | test_gui.m | .m | MATLAB-master/test_gui.m | 10,037 | utf_8 | e123f7a04354ee3a72e86b7395af0766 | function varargout = test_gui(varargin)
% TEST_GUI MATLAB code for test_gui.fig
% TEST_GUI, by itself, creates a new TEST_GUI or raises the existing
% singleton*.
%
% H = TEST_GUI returns the handle to a new TEST_GUI or the handle to
% the existing singleton*.
%
% TEST_GUI('CALLBACK',hObject,ev... |
github | xyza11808/MATLAB-master | frame_browser_lite_01.m | .m | MATLAB-master/frame_browser_lite_01.m | 1,108 | utf_8 | 4b9a917f9bdae9c5b73111250cc0bed6 | function frame_browser_lite_01(im, clim, roiPos)
if nargin < 2
cl = [0 500];
else
cl = clim;
end
if nargin < 3
roiPos = [];
end
hf = figure('Position', [4 237 474 445]);
imagesc(im(:,:,1), cl); colormap(gray);
n = 1;
while ishandle(hf) %n > 0 && n <= size(ims,3)
ch = getkey2(hf);
if ~ishandle... |
github | xyza11808/MATLAB-master | fminsearchcon.m | .m | MATLAB-master/fminsearchcon.m | 11,294 | utf_8 | 4dddbc4f2d4a336f53f14475d4195d6f | function [x,fval,exitflag,output]=fminsearchcon(fun,x0,LB,UB,A,b,nonlcon,options,varargin)
% FMINSEARCHCON: Extension of FMINSEARCHBND with general inequality constraints
% usage: x=FMINSEARCHCON(fun,x0)
% usage: x=FMINSEARCHCON(fun,x0,LB)
% usage: x=FMINSEARCHCON(fun,x0,LB,UB)
% usage: x=FMINSEARCHCON(fun,x0,LB,UB,A,b... |
github | xyza11808/MATLAB-master | WorkingModelExample.m | .m | MATLAB-master/WorkingModelExample.m | 67,429 | utf_8 | 398189a631f6ec9728dd6456062f5bef | function varargout = WorkingModelExample(varargin)
% WORKINGMODELEXAMPLE MATLAB code for WorkingModelExample.fig
% WORKINGMODELEXAMPLE, by itself, creates a new WORKINGMODELEXAMPLE or raises the existing
% singleton*.
%
% H = WORKINGMODELEXAMPLE returns the handle to a new WORKINGMODELEXAMPLE or the hand... |
github | xyza11808/MATLAB-master | multiCClass.m | .m | MATLAB-master/multiCClass.m | 12,920 | utf_8 | 734c813855c9e989a814968fb0245cf8 | function varargout = multiCClass(RawDataAll,BehavStrc,TrialResult,AlignFrame,FrameRate,varargin)
% this function is trying to performing a multiclass classification of
% different sounds and seeing whether there is any pattern exists for
% stimulus belongs to different octave diff and categories
%Time scale selection,... |
github | xyza11808/MATLAB-master | RewardOmitPlot.m | .m | MATLAB-master/RewardOmitPlot.m | 18,548 | utf_8 | cbc65d8fb843e48e153cee276f78e7cf | function varargout = RewardOmitPlot(AllData,OmitInds,TrialResult,TrialType,TimeAnswer,TimeOnset,FrameRate,varargin)
%this function will be used for plots of all reward omit trials compared
%with normal reward trials. only correct trials will be considered here
%XIN Yu, 10, Nov, 2015
DataSize=size(AllData);
TrialNum=Da... |
github | xyza11808/MATLAB-master | spike_sort.m | .m | MATLAB-master/spike_sort.m | 4,960 | utf_8 | 3660436344fdbedbe04177efb0b6037f |
function [n,SpikeTime,SpikeAmp,FireRateBin,STA,bin,data_backup]=spike_sort(data,t,bin)
%%spike sorting
%the same function as spike_sort_im,but wih no image plot
%%default value of input
if nargin<3
bin=0.02*length(data)/t;
if nargin<2
error(message('MATLAB:NotEnoughInputs'));
end
end
%%vector le... |
github | xyza11808/MATLAB-master | RF_GUI_TEST.m | .m | MATLAB-master/RF_GUI_TEST.m | 9,486 | utf_8 | 9bae68a4bd12c35f2c6d1e6adae670a0 | function varargout = RF_GUI_TEST(varargin)
% RF_GUI_TEST MATLAB code for RF_GUI_TEST.fig
% RF_GUI_TEST, by itself, creates a new RF_GUI_TEST or raises the existing
% singleton*.
%
% H = RF_GUI_TEST returns the handle to a new RF_GUI_TEST or the handle to
% the existing singleton*.
%
% RF_GUI_TE... |
github | xyza11808/MATLAB-master | parseAttributes.m | .m | MATLAB-master/parseAttributes.m | 575 | utf_8 | 7217d7bfb1199e1b0799e315d828bbae | % ----- Local function PARSEATTRIBUTES -----
function attributes = parseAttributes(theNode)
% Create attributes structure.
attributes = [];
if theNode.hasAttributes
theAttributes = theNode.getAttributes;
numAttributes = theAttributes.getLength;
allocCell = cell(1, numAttributes);
attributes = struct('Name'... |
github | xyza11808/MATLAB-master | Popu_3d_Plot.m | .m | MATLAB-master/Popu_3d_Plot.m | 1,731 | utf_8 | e93a0c18e3aa3b79608765933804e12e | function hf=Popu_3d_Plot(varargin)
%this functin will be used for 3d plot of give data in a 2D space
%if no data is given, function will end, or give a demo later
if nargin<1
disp('No input data, quit function.\n');
end
if nargin>1
InputData=varargin{1};
InputRank=varargin{2};
if isempty(InputRank) || ~sum(Input... |
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