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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 | Shenc0411/CS445-master | vl_test_phow.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_phow.m | 549 | utf_8 | f761a3bb218af855986263c67b2da411 | function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color',... |
github | Shenc0411/CS445-master | vl_test_kmeans.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_kmeans.m | 3,632 | utf_8 | 0e1d6f4f8101c8982a0e743e0980c65a | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
function s = setup()
randn('sta... |
github | Shenc0411/CS445-master | vl_test_hikmeans.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_hikmeans.m | 463 | utf_8 | dc3b493646e66316184e86ff4e6138ab | function results = vl_test_hikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[tree, assign] = vl_hikmeans(s.data,3,100) ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
function test_elka... |
github | Shenc0411/CS445-master | vl_test_aib.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_aib.m | 1,277 | utf_8 | 78978ae54e7ebe991d136336ba4bf9c6 | function results = vl_test_aib(varargin)
% VL_TEST_AIB
vl_test_init ;
function s = setup()
s = [] ;
function test_basic(s)
Pcx = [.3 .3 0 0
0 0 .2 .2] ;
% This results in the AIB tree
%
% 1 - \
% 5 - \
% 2 - / \
% - 7
% 3 - \ /
% 6 - /
% 4 - /
%
% coded by the map [5 ... |
github | Shenc0411/CS445-master | vl_test_plotbox.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_plotbox.m | 414 | utf_8 | aa06ce4932a213fb933bbede6072b029 | function results = vl_test_plotbox(varargin)
% VL_TEST_PLOTBOX
vl_test_init ;
function test_basic(s)
figure(1) ; clf ;
vl_plotbox([-1 -1 1 1]') ;
xlim([-2 2]) ;
ylim([-2 2]) ;
close(1) ;
function test_multiple(s)
figure(1) ; clf ;
randn('state', 0) ;
vl_plotbox(randn(4,10)) ;
close(1) ;
function test_style(s)
figure... |
github | Shenc0411/CS445-master | vl_test_imarray.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_imarray.m | 795 | utf_8 | c5e6a5aa8c2e63e248814f5bd89832a8 | function results = vl_test_imarray(varargin)
% VL_TEST_IMARRAY
vl_test_init ;
function test_movie_rgb(s)
A = rand(23,15,3,4) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_indexed(s)
cmap = get(0,'DefaultFigureColormap') ;
A = uint8(size(cmap,1)*rand(23,15,4)) ;
A = min(A,size(cmap,1)-1) ;
B = vl_imarray(A,'m... |
github | Shenc0411/CS445-master | vl_test_homkermap.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_homkermap.m | 1,903 | utf_8 | c157052bf4213793a961bde1f73fb307 | function results = vl_test_homkermap(varargin)
% VL_TEST_HOMKERMAP
vl_test_init ;
function check_ker(ker, n, window, period)
args = {n, ker, 'window', window} ;
if nargin > 3
args = {args{:}, 'period', period} ;
end
x = [-1 -.5 0 .5 1] ;
y = linspace(0,2,100) ;
for conv = {@single, @double}
x = feval(conv{1}, x) ;... |
github | Shenc0411/CS445-master | vl_test_slic.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_slic.m | 200 | utf_8 | 12a6465e3ef5b4bcfd7303cd8a9229d4 | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1) ;
|
github | Shenc0411/CS445-master | vl_test_ikmeans.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_ikmeans.m | 466 | utf_8 | 1ee2f647ac0035ed0d704a0cd615b040 | function results = vl_test_ikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[centers, assign] = vl_ikmeans(s.data,100) ;
assign_ = vl_ikmeanspush(s.data, centers) ;
vl_assert_equal(assign,assign_) ;
function test_elk... |
github | Shenc0411/CS445-master | vl_test_mser.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_mser.m | 242 | utf_8 | 1ad33563b0c86542a2978ee94e0f4a39 | function results = vl_test_mser(varargin)
% VL_TEST_MSER
vl_test_init ;
function s = setup()
s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ;
function test_mser(s)
[regions,frames] = vl_mser(s.im) ;
mask = vl_erfill(s.im, regions(1)) ;
|
github | Shenc0411/CS445-master | vl_test_inthist.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_inthist.m | 811 | utf_8 | 459027d0c54d8f197563a02ab66ef45d | function results = vl_test_inthist(varargin)
% VL_TEST_INTHIST
vl_test_init ;
function s = setup()
rand('state',0) ;
s.labels = uint32(8*rand(123, 76, 3)) ;
function test_basic(s)
l = 10 ;
hist = vl_inthist(s.labels, 'numlabels', l) ;
hist_ = inthist_slow(s.labels, l) ;
vl_assert_equal(double(hist),hist_) ;
function... |
github | Shenc0411/CS445-master | vl_test_imdisttf.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_imdisttf.m | 1,885 | utf_8 | ae921197988abeb984cbcdf9eaf80e77 | function results = vl_test_imdisttf(varargin)
% VL_TEST_DISTTF
vl_test_init ;
function test_basic()
for conv = {@single, @double}
conv = conv{1} ;
I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ;
D = vl_imdisttf(I);
assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
I(2,2) = -3 ;
[D,map] = vl_imdisttf(I) ;
asse... |
github | Shenc0411/CS445-master | vl_test_vlad.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_vlad.m | 1,977 | utf_8 | d3797288d6edb1d445b890db3780c8ce | function results = vl_test_vlad(varargin)
% VL_TEST_VLAD
vl_test_init ;
function s = setup()
randn('state',0) ;
s.x = randn(128,256) ;
s.mu = randn(128,16) ;
assignments = rand(16, 256) ;
s.assignments = bsxfun(@times, assignments, 1 ./ sum(assignments,1)) ;
function test_basic (s)
x = [1, 2, 3] ;
mu = [0, 0, 0] ;
a... |
github | Shenc0411/CS445-master | vl_test_pr.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_pr.m | 3,763 | utf_8 | 4d1da5ccda1a7df2bec35b8f12fdd620 | function results = vl_test_pr(varargin)
% VL_TEST_PR
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[rc,pr] = vl_pr(s.labels,s.scores0) ;
vl_assert_almost_equal(pr, [1 1/1 2/2 2/3 2/4 2/5]) ;
vl_assert_almost_equal(rc, ... |
github | Shenc0411/CS445-master | vl_test_hog.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_hog.m | 1,555 | utf_8 | eed7b2a116d142040587dc9c4eb7cd2e | function results = vl_test_hog(varargin)
% VL_TEST_HOG
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
[x,y]= meshgrid(linspace(-1,1,128)) ;
s.round = single(x.^2+y.^2);
s.imSmall = s.im(1:128,1:128,:) ;
s.imSmall = s.im ;
s.imSmallFlipped = s.imSmall(:,end:-1:1,:) ;
function test_basic... |
github | Shenc0411/CS445-master | vl_test_argparse.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_argparse.m | 795 | utf_8 | e72185b27206d0ee1dfdc19fe77a5be6 | function results = vl_test_argparse(varargin)
% VL_TEST_ARGPARSE
vl_test_init ;
function test_basic()
opts.field1 = 1 ;
opts.field2 = 2 ;
opts.field3 = 3 ;
opts_ = opts ;
opts_.field1 = 3 ;
opts_.field2 = 10 ;
opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ;
assert(isequal(opts, opts_)) ;
opts_.field1 = 9 ;
... |
github | Shenc0411/CS445-master | vl_test_liop.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_liop.m | 1,023 | utf_8 | a162be369073bed18e61210f44088cf3 | function results = vl_test_liop(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
randn('state',0) ;
s.patch = randn(65,'single') ;
xr = -32:32 ;
[x,y] = meshgrid(xr) ;
s.blob = - single(x.^2+y.^2) ;
function test_basic(s)
d = vl_liop(s.patch) ;
function test_blob(s)
% with a blob, all local intensity ord... |
github | Shenc0411/CS445-master | vl_test_binsearch.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/xtest/vl_test_binsearch.m | 1,339 | utf_8 | 85dc020adce3f228fe7dfb24cf3acc63 | function results = vl_test_binsearch(varargin)
% VL_TEST_BINSEARCH
vl_test_init ;
function test_inf_bins()
x = [-inf -1 0 1 +inf] ;
vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ;
vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ;
vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ;
vl_a... |
github | Shenc0411/CS445-master | vl_roc.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/plotop/vl_roc.m | 9,777 | utf_8 | 8d45b3dad4c701e12284b8c5a7f91efc | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC ROC curve.
% [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
% Characteristic (ROC) curve [1]. LABELS is a row vector of ground
% truth labels, greater than zero for a positive sample and smaller
% than zero for a negative o... |
github | Shenc0411/CS445-master | vl_click.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/plotop/vl_click.m | 2,661 | utf_8 | 6982e869cf80da57fdf68f5ebcd05a86 | function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any k... |
github | Shenc0411/CS445-master | vl_pr.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/plotop/vl_pr.m | 9,135 | utf_8 | c5d1b9d67f843d10c0b2c6b48fab3c53 | function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative on... |
github | Shenc0411/CS445-master | vl_ubcread.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/sift/vl_ubcread.m | 3,015 | utf_8 | e8ddd3ecd87e76b6c738ba153fef050f | function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', '... |
github | Shenc0411/CS445-master | vl_frame2oell.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/sift/vl_frame2oell.m | 2,806 | utf_8 | c93792632f630743485fa4c2cf12d647 | function eframes = vl_frame2oell(frames)
% VL_FRAMES2OELL Convert a geometric frame to an oriented ellipse
% EFRAME = VL_FRAME2OELL(FRAME) converts the generic FRAME to an
% oriented ellipses EFRAME. FRAME and EFRAME can be matrices, with
% one frame per column.
%
% A frame is either a point, a disc, an orien... |
github | Shenc0411/CS445-master | vl_plotsiftdescriptor.m | .m | CS445-master/mp5/vlfeat-0.9.19/toolbox/sift/vl_plotsiftdescriptor.m | 5,114 | utf_8 | a4e125a8916653f00143b61cceda2f23 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptor D. If D is a
% matrix, it plots one descriptor per column. D has the same format
% used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warpe... |
github | Shenc0411/CS445-master | phow_caltech101.m | .m | CS445-master/mp5/vlfeat-0.9.19/apps/phow_caltech101.m | 11,594 | utf_8 | 7f4890a2e6844ca56debbfe23cca64f3 | function phow_caltech101()
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedu... |
github | Shenc0411/CS445-master | sift_mosaic.m | .m | CS445-master/mp5/vlfeat-0.9.19/apps/sift_mosaic.m | 4,621 | utf_8 | 8fa3ad91b401b8f2400fb65944c79712 | function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAI... |
github | Shenc0411/CS445-master | encodeImage.m | .m | CS445-master/mp5/vlfeat-0.9.19/apps/recognition/encodeImage.m | 5,278 | utf_8 | 5d9dc6161995b8e10366b5649bf4fda4 | function descrs = encodeImage(encoder, im, varargin)
% ENCODEIMAGE Apply an encoder to an image
% DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER
% to image IM, returning a corresponding code vector PSI.
%
% IM can be an image, the path to an image, or a cell array of
% the same, to operate on multiple ... |
github | Shenc0411/CS445-master | experiments.m | .m | CS445-master/mp5/vlfeat-0.9.19/apps/recognition/experiments.m | 6,905 | utf_8 | 1e4a4911eed4a451b9488b9e6cc9b39c | function experiments()
% EXPERIMENTS Run image classification experiments
% The experimens download a number of benchmark datasets in the
% 'data/' subfolder. Make sure that there are several GBs of
% space available.
%
% By default, experiments run with a lite option turned on. This
% quickly runs all... |
github | Shenc0411/CS445-master | getDenseSIFT.m | .m | CS445-master/mp5/vlfeat-0.9.19/apps/recognition/getDenseSIFT.m | 1,679 | utf_8 | 2059c0a2a4e762226d89121408c6e51c | function features = getDenseSIFT(im, varargin)
% GETDENSESIFT Extract dense SIFT features
% FEATURES = GETDENSESIFT(IM) extract dense SIFT features from
% image IM.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made availab... |
github | evanypeng/ICCV2017_RevisitCCIT_code-master | build_mmx.m | .m | ICCV2017_RevisitCCIT_code-master/util/mmx/mmx_package/build_mmx.m | 8,089 | utf_8 | fe6dfb85a5c474af0fc5dbcbfc231f68 | function build_mmx(verbose)
% BUILD_MMX - compiles mmx() for different platforms and provides help
% regarding compilation.
%
% BUILD_MMX will try to compile, in this order, 3 different builds of mmx:
% mmx_mkl_single - linked to Intel's single-threaded MKL library (usually fastest)
% mmx_mkl_mul... |
github | duqbo/varpro2-master | varpro_opts.m | .m | varpro2-master/src/varpro_opts.m | 4,713 | utf_8 | 2c493bac13f3036c9820d527950e4f5c | function opts = varpro_opts(varargin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Create options structure for varpro routines
%
% INPUT:
%
% The input should be pairs of strings and
% values for setting fields of the structure
%
% OUTPUT:
%
% The output will be a structure with the
% given values for the specified... |
github | duqbo/varpro2-master | match_vectors.m | .m | varpro2-master/src/match_vectors.m | 481 | utf_8 | 572dfefc3527774f1c52834bf15354b0 |
function indices = match_vectors(v1,v2)
%MATCH_VECTORS Wrapper for MUNKRES
%
% Sets up a cost function so that the indices
% returned by munkres correspond to the permutation
% which minimizes the 1-norm of the difference
% between v1(indices) and v2( (indices ~= 0) )
%
% Example:
%
% >> indices = match_vectors(v1,... |
github | duqbo/varpro2-master | varpro2expfun.m | .m | varpro2-master/src/varpro2expfun.m | 337 | utf_8 | 4a95a7400aab8cdadcdbb96aaf6748a7 |
function A = varpro2expfun(alpha,t)
%
% matrix of exponentials
%
% Input
%
% alpha - vector of exponent values
% t - vector of times
%
% Output
%
% A(i,j) = exp(alpha_j t_i)
%
m = length(t);
n = length(alpha);
A = zeros(m,n);
ttemp = reshape(t,m,1);
atemp = reshape(alpha,n,1);
temp = ttemp*transpose(atemp);
A =... |
github | duqbo/varpro2-master | varpro2dexpfun.m | .m | varpro2-master/src/varpro2dexpfun.m | 487 | utf_8 | 3224b37a08883b9cc3d7ab6801aa106e |
function A = varpro2dexpfun(alpha,t,i)
%
% Derivatives of the matrix of exponentials
%
% Input
%
% alpha - vector of exponent values
% t - vector of times
% i - the desired derivative
%
% Output
%
% If Phi_i,j = exp(alpha_j t_i)
% then A = d/d(alpha_i) Phi in sparse
% format
%
m = length(t);
n = length(alpha);
if (... |
github | duqbo/varpro2-master | munkres.m | .m | varpro2-master/src/munkres.m | 7,171 | utf_8 | b44ad4f1a20fc5d03db019c44a65bac3 | function [assignment,cost] = munkres(costMat)
% MUNKRES Munkres (Hungarian) Algorithm for Linear Assignment Problem.
%
% [ASSIGN,COST] = munkres(COSTMAT) returns the optimal column indices,
% ASSIGN assigned to each row and the minimum COST based on the assignment
% problem represented by the COSTMAT, where the... |
github | gallunf/SR2-master | SR2.m | .m | SR2-master/Experiment/SR2.m | 41,020 | utf_8 | 489758e109280113949978673f45e0bb | function varargout = SR2(varargin)
% SR2 M-file for SR2.fig
% SR2, by itself, creates a new SR2 or raises the existing
% singleton*.
%
% H = SR2 returns the handle to a new SR2 or the handle to
% the existing singleton*.
%
% SR2('CALLBACK',hObject,eventData,handles,...) calls the local
% f... |
github | gallunf/SR2-master | CRM_resp_gui.m | .m | SR2-master/Experiment/CRM_resp_gui.m | 15,017 | utf_8 | 0416c995ef68731f227f9a03d1c7e6aa | function varargout = CRM_resp_gui(varargin)
% CRM_RESP_GUI M-file for CRM_resp_gui.fig
% CRM_RESP_GUI, by itself, creates a new CRM_RESP_GUI or raises the existing
% singleton*.
%
% H = CRM_RESP_GUI returns the handle to a new CRM_RESP_GUI or the handle to
% the existing singleton*.
%
% CRM_RES... |
github | gallunf/SR2-master | run_SR2.m | .m | SR2-master/Experiment/run_SR2.m | 41,079 | utf_8 | c978b5428101845656e0b9cc059670c8 | function varargout = run_SR2(varargin)
% run_SR2 M-file for run_SR2.fig
% run_SR2, by itself, creates a new run_SR2 or raises the existing
% singleton*.
%
% H = run_SR2 returns the handle to a new run_SR2 or the handle to
% the existing singleton*.
%
% run_SR2('CALLBACK',hObject,eventData,handl... |
github | rfsantacruz/deep-perm-net-master | voc_eval.m | .m | deep-perm-net-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,467 | utf_8 | a251b8cc6ec333e2df948dfdd2f90a17 | function res = voc_eval(path, comp_id, test_set, output_dir, rm_res)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
VOCopts.detrespath=[VOCopts.resdir 'Main/%s_det_' VOCopts.testset '_%s.txt'];
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, outp... |
github | rfsantacruz/deep-perm-net-master | classification_demo.m | .m | deep-perm-net-master/caffe-perm/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 | Liel-Research-Group/Internal_Codes-master | BSSA_14.m | .m | Internal_Codes-master/ZR19 GMPE/BSSA_14.m | 4,633 | utf_8 | c248b3af769f194dc5fdb150bf45392a | %Eric Johnson
%Liel Research Group
%NGA-West2 Equations for Predicting PGA, PGV, and 5% Damped PSA for Shallow Crustal Earthquakes
%David M. Boore, Jonathan P. Stewart, Emel Seyhan, and Gail M. Atkinson
%
%This GMPE is dependent on the following files:
% BSSA_14.mat
%
%Inputs required are:
% siteprop.Rjb, ... |
github | Liel-Research-Group/Internal_Codes-master | HA_15.m | .m | Internal_Codes-master/ZR19 GMPE/HA_15.m | 2,131 | utf_8 | 44df058a83125eb61b8b4e1b7b01427b | %Eric Johnson
%Liel Research Group
%Referenced Empirical Ground-Motion Model for Eastern North America
%Behzad Hassani and Gail M. Atkinson
%
%This GMPE is dependent on the following files:
% HA_15.mat, BSSA_14.m, and BSSA_14.mat
%
%Inputs required are:
% siteprop.Rjb, siteprop.VS30, siteprop.T, faultprop.... |
github | Liel-Research-Group/Internal_Codes-master | ZR_19.m | .m | Internal_Codes-master/ZR19 GMPE/ZR_19.m | 2,771 | utf_8 | 932f24b35c9eec6d63dce3332eae0ae1 | %Eric Johnson
%Liel Research Group
%Ground Motion Model for Small-to-Moderate Earthquakes in Texas, Oklahoma, and Kansas
%Georgios Zalachoris and Ellen M. Rathje
%
%This GMPE is dependent on the following files:
% ZR_19.mat, HA_15.m, HA_15.mat, BSSA_14.m, and BSSA_14.mat
%
%Inputs required are:
% siteprop.Rjb, site... |
github | halfways/caffe-master | classification_demo.m | .m | caffe-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 | lcnhappe/happe-master | wICA.m | .m | happe-master/scripts/wICA.m | 4,565 | utf_8 | b83858321e2ca4c7c711b6e02dcd3af7 | %function [wIC,A,W,IC] = wICA(data, type= 'fastica', plotting= 1, Fs= 250, L=5)
function [wIC,A,W,IC] = wICA(EEG,varargin)
%--------------- function [wIC,A,W] = wICA(data,varargin) -----------------
%
% Performs ICA on data matrix (row vector) and subsequent wavelet
% thresholding to remove low-amplitude activity from ... |
github | lcnhappe/happe-master | h_epoch_interp_spl.m | .m | happe-master/scripts/h_epoch_interp_spl.m | 5,579 | utf_8 | b3f72af0ca562a5a7d2b9cd9eca573ff | % Edit to the EEGLAB interpolation function to interpolate different
% channels within each epoch
% Cleaned up and removed irrelevant sections.
%
% Additions Copyright (C) 2010 Hugh Nolan, Robert Whelan and Richard Reilly, Trinity College Dublin,
% Ireland
%
% Based on:
%
% eeg_interp() - interpolate data channels
%
% ... |
github | lcnhappe/happe-master | eeglab.m | .m | happe-master/Packages/eeglab14_0_0b/eeglab.m | 112,891 | utf_8 | fffefc22649f15632d7eca04b8c03088 | % eeglab() - Matlab graphic user interface environment for
% electrophysiological data analysis incorporating the ICA/EEG toolbox
% (Makeig et al.) developed at CNL / The Salk Institute, 1997-2001.
% Released 11/2002- as EEGLAB (Delorme, Makeig, et al.) at the Swartz Center
% for Computational Neuroscience,... |
github | lcnhappe/happe-master | WriteMatrix2Text.m | .m | happe-master/Packages/eeglab14_0_0b/CSDtoolbox/func/WriteMatrix2Text.m | 1,415 | utf_8 | 40fcf238991341aab27833a32ab9aeb5 | % function WriteMatrix2Text ( X, FileName, FmtStg, CaseCol )
%
% This is a generic routine to write a data matrix to an ASCII file.
%
% Usage: WriteMatrix2Text ( X, FileName, FmtStg, CaseCol );
%
% Input arguments: X data matrix
% FileName file name string
% FmtStg... |
github | lcnhappe/happe-master | display.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/display.m | 1,180 | utf_8 | d3d679b5764eca6d062540923f669f7b | % display() - display an EEG data class underlying structure
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the ... |
github | lcnhappe/happe-master | reshape.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/reshape.m | 1,449 | utf_8 | d59a7e256f6fc43fe77f3be9319fcc46 | % reshape() - reshape of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free ... |
github | lcnhappe/happe-master | end.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/end.m | 901 | utf_8 | 0e38d125a547083cb574fbd3bb455fbd | % end() - last index to memmapdata array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Founda... |
github | lcnhappe/happe-master | subsasgn.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/subsasgn.m | 1,590 | utf_8 | 6b2894eb17dab5aae0637b84992ffb7d | % subsasgn() - define index assignment for eegdata objects
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Fr... |
github | lcnhappe/happe-master | isnumeric.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/isnumeric.m | 877 | utf_8 | 34baf204e1b984ee69cf7f462fe2e524 | % isnumeric() - returns 1
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either v... |
github | lcnhappe/happe-master | length.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/length.m | 913 | utf_8 | f0841237745a123f3215e00164cc4a1a | % length() - length of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free So... |
github | lcnhappe/happe-master | sum.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/sum.m | 1,913 | utf_8 | dbd7353e16ccf6a1a0b69875cd9050cb | % sum() - sum of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software... |
github | lcnhappe/happe-master | size.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/size.m | 1,383 | utf_8 | c7033b3ab1405ded2c8794f2c214beb9 | % size() - size of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Softwa... |
github | lcnhappe/happe-master | subsref.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/subsref.m | 4,622 | utf_8 | 096fd75b6454f11ffee5a172000a64c1 | % subsref() - index eegdata class
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; e... |
github | lcnhappe/happe-master | ndims.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/ndims.m | 1,169 | utf_8 | 8c3ed2dde450e1422a2552d22e9c150b | % ndims() - number of dimension of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
%... |
github | lcnhappe/happe-master | memmapdata.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@memmapdata/memmapdata.m | 1,994 | utf_8 | 5f967fcb7b637954900e09789144dde6 | % memmapdata() - create a memory-mapped data class
%
% Usage:
% >> data_class = memmapdata(data);
%
% Inputs:
% data - input data or data file
%
% Outputs:
% data_class - output dataset class
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
... |
github | lcnhappe/happe-master | display.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/display.m | 1,161 | utf_8 | b43db5e3387d5dcb39fafa9aa03939f9 | % display() - display an EEG data class underlying structure
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the ... |
github | lcnhappe/happe-master | reshape.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/reshape.m | 1,242 | utf_8 | cc03295fbaa6179eb2e8b266daf6b644 | % reshape() - reshape of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free ... |
github | lcnhappe/happe-master | subsasgn_old.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/subsasgn_old.m | 9,802 | utf_8 | 0fc68a1bfa60e3118b3a4b6efd10fa52 | % subsasgn() - define index assignment for eegdata objects
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Fr... |
github | lcnhappe/happe-master | end.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/end.m | 901 | utf_8 | 0e38d125a547083cb574fbd3bb455fbd | % end() - last index to memmapdata array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Founda... |
github | lcnhappe/happe-master | subsasgn.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/subsasgn.m | 10,655 | utf_8 | c4514f5b1f0fec385eeb19fee7a5db56 | % subsasgn() - define index assignment for eegdata objects
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Fr... |
github | lcnhappe/happe-master | isnumeric.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/isnumeric.m | 877 | utf_8 | 34baf204e1b984ee69cf7f462fe2e524 | % isnumeric() - returns 1
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either v... |
github | lcnhappe/happe-master | checkcopies_local.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/checkcopies_local.m | 634 | utf_8 | 8eb8d5fec95c91346e91a09010082b47 | % subfunction checking the number of local copies
% -----------------------------------------------
function ncopies = checkcopies_local(obj, arg);
ncopies = 0;
if isstruct(arg)
for ilen = 1:length(arg)
for index = fieldnames(arg)'
ncopies = ncopies + checkcopies_local(obj, arg(ilen).(index{1}))... |
github | lcnhappe/happe-master | changefile.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/changefile.m | 187 | utf_8 | e75127c90da43ddce182d36cf0abbdee | % this function is called when the file is being saved
function obj = changefile(obj, newfile)
movefile(obj.dataFile, newfile);
obj.dataFile = newfile;
obj.writable = false; |
github | lcnhappe/happe-master | var.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/var.m | 1,416 | utf_8 | 2360192fa42b3c35ebd7743ffe4fe8b6 | % var() - variance of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Sof... |
github | lcnhappe/happe-master | length.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/length.m | 913 | utf_8 | f0841237745a123f3215e00164cc4a1a | % length() - length of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free So... |
github | lcnhappe/happe-master | sum.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/sum.m | 1,212 | utf_8 | 2fbce1d1b6e2a5edf32742897441a732 | % sum() - sum of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software... |
github | lcnhappe/happe-master | size.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/size.m | 1,751 | utf_8 | daf6932de04161ccb2df3df31b45203a | % size() - size of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Softwa... |
github | lcnhappe/happe-master | subsref.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/subsref.m | 2,711 | utf_8 | 1da2db6dbbc53f36d6d2954f095f9064 | % subsref() - index eegdata class
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; e... |
github | lcnhappe/happe-master | ndims.m | .m | happe-master/Packages/eeglab14_0_0b/functions/@mmo/ndims.m | 1,095 | utf_8 | 7ddcbafa2aaf95308a1a4de4a272f0ae | % ndims() - number of dimension of memory mapped underlying array
%
% Author: Arnaud Delorme, SCCN, INC, UCSD, Nov. 2008
% Copyright (C) 2008 Arnaud Delorme, SCCN, INC, UCSD
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
%... |
github | lcnhappe/happe-master | correctfit.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/correctfit.m | 3,222 | utf_8 | 69c8b2f965023329820bdfd3c7e82176 | % correctfit() - correct fit using observed p-values. Use this function
% if for some reason, the distribution of p values is
% not uniform between 0 and 1
%
% Usage:
% >> [p phat pci zerofreq] = correctfit(pval, 'key', 'val');
%
% Inputs:
% pval - input p value
%
% Optional inputs... |
github | lcnhappe/happe-master | timef.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/timef.m | 42,818 | utf_8 | 6663467d76d01722baccf168fec18e1b | % timef() - Returns estimates and plots of mean event-related spectral
% perturbation (ERSP) and inter-trial coherence (ITC) changes
% across event-related trials (epochs) of a single input time series.
% * Uses either fixed-window, zero-padded FFTs (fastest), wavelet
% 0-padded D... |
github | lcnhappe/happe-master | rsadjust.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/rsadjust.m | 2,955 | utf_8 | 45d8f416e2d360293ee83cb15f5e3976 | % rsadjust() - adjust l-values (Ramberg-Schmeiser distribution)
% with respect to signal mean and variance
%
% Usage: p = rsadjust(l3, l4, m, var, skew)
%
% Input:
% l3 - value lambda3 for Ramberg-Schmeiser distribution
% l4 - value lambda4 for Ramberg-Schmeiser distribution
% m - mean of t... |
github | lcnhappe/happe-master | newcrossf.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/newcrossf.m | 63,870 | utf_8 | cb2a530dbabd60d30681d5c293194574 | % newcrossf() - Returns estimates and plots event-related coherence (ERCOH)
% between two input data time series. A lower panel (optionally) shows
% the coherence phase difference between the processes. In this panel:
% In the plot output by > newcrossf(x,y,...);
% 90 degrees (orang... |
github | lcnhappe/happe-master | crossf.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/crossf.m | 59,295 | utf_8 | 395468032fe3d1abba14d59dfe6d1317 | % crossf() - Returns estimates and plots event-related coherence (ERCOH)
% between two input data time series (X,Y). A lower panel (optionally)
% shows the coherence phase difference between the processes.
% In this panel, output by > crossf(X,Y,...);
% 90 degrees (orange) means X l... |
github | lcnhappe/happe-master | dftfilt2.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/dftfilt2.m | 5,665 | utf_8 | fa2b4cd1d3a209b72ce506ee4e6fee46 | % dftfilt2() - discrete complex wavelet filters
%
% Usage:
% >> wavelet = dftfilt2( freqs, cycles, srate, cyclefact)
%
% Inputs:
% freqs - frequency array
% cycles - cycles array. If one value is given, all wavelets have
% the same number of cycles. If two values are given, the
% tw... |
github | lcnhappe/happe-master | newtimef.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/newtimef.m | 97,481 | utf_8 | 9b205e33083e0973b5d9e671eac0abb5 | % newtimef() - Return estimates and plots of mean event-related (log) spectral
% perturbation (ERSP) and inter-trial coherence (ITC) events across
% event-related trials (epochs) of a single input channel time series.
%
% * Also can compute and statistically compare transforms for two time
%... |
github | lcnhappe/happe-master | rspdfsolv.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/rspdfsolv.m | 2,680 | utf_8 | f54da2906c104216b972ff3b6bbf6f3e | % rspdfsolv() - sub-function used by rsfit() to searc for optimal
% parameter for Ramberg-Schmeiser distribution
%
% Usage: res = rspdfsolv(l, l3, l4)
%
% Input:
% l - [lambda3 lamda4] parameters to optimize
% skew - expected skewness
% kurt - expected kurtosis
%
% Output:
% res - residual
%
%... |
github | lcnhappe/happe-master | dftfilt.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/dftfilt.m | 2,686 | utf_8 | b950c4302f749a1e9cffcd4c7f4ebe6a | % dftfilt() - discrete Fourier filter
%
% Usage:
% >> b = dftfilt(n,W,c,k,q)
%
% Inputs:
% n - number of input samples
% W - maximum angular freq. relative to n, 0 < W <= .5
% c - cycles
% k - oversampling
% q - [0;1] 0->fft, 1->c cycles
%
% Authors: Sigurd Enghoff, Arnaud Delorme & Scott Makeig,
% ... |
github | lcnhappe/happe-master | rspfunc.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/rspfunc.m | 1,468 | utf_8 | d6a6d26022ec2e38fa4adca9a28a5dde | % rspfunc() - sub-function used by rsget()
%
% Usage: res = rspfunc(pval, l, rval)
%
% Input:
% pval - p-value to optimize
% l - [l1 l2 l3 l4] l-values for Ramberg-Schmeiser distribution
% rval - expected r-value
%
% Output:
% res - residual
%
% Author: Arnaud Delorme, SCCN, 2003
%
% See also: rsget()
%
% R... |
github | lcnhappe/happe-master | correct_mc.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/correct_mc.m | 5,698 | utf_8 | 92f18a363d2fec001a50fafbcf9e06b5 | % correct_mc() - compute an upper limit for the number of independant
% time-frequency estimate in a given time-frequency image.
% This number can be used to correct for multiple comparisons.
%
% Usage:
% [ncorrect array] = correct_mc( EEG, cycles, maxfreq, timesout);
%
% Inputs:
% ... |
github | lcnhappe/happe-master | pac.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/pac.m | 21,849 | utf_8 | a7d13b249d1c873e24a0f43a7cda2c5c | % pac() - compute phase-amplitude coupling (power of first input
% correlation with phase of second). There is no graphical output
% to this function.
%
% Usage:
% >> pac(x,y,srate);
% >> [coh,timesout,freqsout1,freqsout2,cohboot] ...
% = pac(x,y,srate,'key1', 'val1', 'key2', val... |
github | lcnhappe/happe-master | bootstat.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/bootstat.m | 20,825 | utf_8 | 406a9744b6812cd9f9f1eed16ca63e82 | % bootstat() - accumulate surrogate data to assess significance by permutation of some
% measure of two input variables.
%
% If 'distfit','on', fits the psd with a 4th-order polynomial using the
% data kurtosis, as in Ramberg, J.S., Tadikamalla, P.R., Dudewicz E.J.,
% ... |
github | lcnhappe/happe-master | timefreq.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/timefreq.m | 33,427 | utf_8 | dc70f7b5afd51d05bcf926a934072865 | % timefreq() - compute time/frequency decomposition of data trials. This
% function is a compute-only function called by
% the more complete time/frequency functions newtimef()
% and newcrossf() which also plot timefreq() results.
%
% Usage:
% >> [tf, freqs, times] =... |
github | lcnhappe/happe-master | pac_cont.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/pac_cont.m | 17,447 | utf_8 | 9b94d431be58419e3715e54645687469 | % pac_cont() - compute phase-amplitude coupling (power of first input
% correlation with phase of second). There is no graphical output
% to this function.
%
% Usage:
% >> pac_cont(x,y,srate);
% >> [pac timesout pvals] = pac_cont(x,y,srate,'key1', 'val1', 'key2', val2' ...);
%
% Inputs:
% x ... |
github | lcnhappe/happe-master | dftfilt3.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/dftfilt3.m | 7,390 | utf_8 | 7ef2a5114ee38513d5a55a8f38553cb1 | % dftfilt3() - discrete complex wavelet filters
%
% Usage:
% >> [wavelet,cycles,freqresol,timeresol] = dftfilt3( freqs, cycles, srate, varargin)
%
% Inputs:
% freqs - vector of frequencies of interest.
% cycles - cycles array. If cycles=0, then the Hanning tapered Short-term FFT is used.
% If o... |
github | lcnhappe/happe-master | rsget.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/rsget.m | 1,800 | utf_8 | 47daf6498859ced84787d07802f22789 | % rsget() - get the p-value for a given collection of l-values
% (Ramberg-Schmeiser distribution)
%
% Usage: p = getfit(l, val)
%
% Input:
% l - [l1 l2 l3 l4] l-values for Ramberg-Schmeiser distribution
% val - value in the distribution to get a p-value estimate at
%
% Output:
% p - p-value
%
% Aut... |
github | lcnhappe/happe-master | rsfit.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/rsfit.m | 6,481 | utf_8 | ea9ce35750ccb745a5f9471fc6554a91 | % rsfit() - find p value for a given value in a given distribution
% using Ramberg-Schmeiser distribution
%
% Usage: >> p = rsfit(x, val)
% >> [p c l chi2] = rsfit(x, val, plot)
%
% Input:
% x - [float array] accumulation values
% val - [float] value to test
% plot - [0|1|2] plot fit. Using... |
github | lcnhappe/happe-master | timewarp.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/timewarp.m | 3,452 | utf_8 | 910bc9b6a9fca6b6a50908d815239dc6 | % timewarp() - Given two event marker vectors, computes a matrix
% that can be used to warp a time series so that its
% evlatencies match newlatencies. Values of the warped
% timeserie that falls between two frames in the original
% timeserie will be linear ... |
github | lcnhappe/happe-master | angtimewarp.m | .m | happe-master/Packages/eeglab14_0_0b/functions/timefreqfunc/angtimewarp.m | 4,683 | utf_8 | f46e870ae5d7873544a08ffa9a4ff38b | % angtimewarp() - Given two event marker vectors, computes a
% warping of the input angular time series so that its
% evlatencies match newlatencies. Values of the warped
% timeserie that falls between two frames in the original
% timeserie will be linearl... |
github | lcnhappe/happe-master | shortread.m | .m | happe-master/Packages/eeglab14_0_0b/functions/miscfunc/shortread.m | 2,642 | utf_8 | 7efadd1fb9c9395f1cd3c917e6a93675 | % shortread() - Read matrix from short file.
%
% Usage:
% >> A = shortread(filename,size,'format',offset)
%
% Inputs:
% filename - Read matrix a from specified file while assuming four byte
% short integers.
% size - The vector SIZE determine the number of short elements to be
% read... |
github | lcnhappe/happe-master | scanfold.m | .m | happe-master/Packages/eeglab14_0_0b/functions/miscfunc/scanfold.m | 2,396 | utf_8 | fc03538051488f1358961f46484d958d | % scanfold() - scan folder content
%
% Usage:
% >> [cellres textres] = scanfold(foldname);
% >> [cellres textres] = scanfold(foldname, ignorelist, maxdepth);
%
% Inputs:
% foldname - [string] name of the folder
% ignorelist - [cell] list of folders to ignore
% maxdepth - [integer] maximum folder dep... |
github | lcnhappe/happe-master | datlim.m | .m | happe-master/Packages/eeglab14_0_0b/functions/miscfunc/datlim.m | 641 | utf_8 | f4c0160c492f5049e9dbcc932cafe0e7 | % datlim() - return min and max of a matrix
%
% Usage:
% >> limits_vector = datlim(data);
%
% Input:
% data - numeric array
% Outputs:
% limits_vector = [minval maxval]
%
% Author: Scott Makeig, SCCN/INC/UCSD, May 28, 2005
function [limits_vector] = datlim(data)
if ~isnumeric(data)
erro... |
github | lcnhappe/happe-master | lapplot.m | .m | happe-master/Packages/eeglab14_0_0b/functions/miscfunc/lapplot.m | 4,148 | utf_8 | bbfb726aee519b4b7b446c516439e24f | % lapplot() - Compute the discrete laplacian of EEG scalp distribution(s)
%
% Usage:
% >> laplace = lapplot(map,eloc_file,draw)
%
% Inputs:
% map - Activity levels, size (nelectrodes,nmaps)
% eloc_file - Electrode location filename (.loc file)
% For format, see >> topo... |
github | lcnhappe/happe-master | compmap.m | .m | happe-master/Packages/eeglab14_0_0b/functions/miscfunc/compmap.m | 9,132 | utf_8 | 91717f8d02db6c0193adc7ece08828ca | % compmap() - Plot multiple topoplot() maps of ICA component topographies
% Click on an individual map to view separately.
% Usage:
% >> compmap (winv,'eloc_file',compnos,'title',rowscols,labels,printflag)
%
% Inputs:
% winv - Inverse weight matrix = EEG scalp maps. Each column is a
% ... |
github | lcnhappe/happe-master | topoimage.m | .m | happe-master/Packages/eeglab14_0_0b/functions/miscfunc/topoimage.m | 21,312 | utf_8 | 952b9066313797fa812f1352f2586565 | % topoimage() - plot concatenated multichannel time/frequency images
% in a topographic format
% Uses a channel location file with the same format as topoplot()
% or else plots data on a rectangular grid of axes.
% Click on individual images to examine separatel... |
github | lcnhappe/happe-master | getallmenuseeglab.m | .m | happe-master/Packages/eeglab14_0_0b/functions/miscfunc/getallmenuseeglab.m | 3,609 | utf_8 | 3f20087025ca923857b9ce9ec4553255 | % getallmenuseeglab() - get all submenus of a window or a menu and return
% a tree. The function will also look for callback.
%
% Usage:
% >> [tree nb] = getallmenuseeglab( handler );
%
% Inputs:
% handler - handler of the window or of a menu
%
% Outputs:
% tree - text output
% nb ... |
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