sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c629f79d0ea985f577585d0583cc9b2331c44c57ace15ff02d03133d07317a9d | MATLAB | 1,078 | 36 | % RSKELF_SV Solve using recursive skeletonization factorization.
%
% Typical complexity: same as RSKELF_MV.
%
% Y = RSKELF_SV(F,X) produces the matrix Y by applying the inverse of the
% factored matrix F to the matrix X.
%
% Y = RSKELF_SV(F,X,TRANS) computes Y = F\X if TRANS = 'N' (default),
% Y = F.'\X... |
0abf61885f2ed4d7d5c545f605955692da19be2730bb0320f5b6badb60ddf0bc | MATLAB | 1,080 | 33 | function Y = srskelf_sv_p(F,X)
% Y = SRSKELF_SV_P(F,X) The strong skeletonization factorization stored in
% F is used to solve FY=X, where the "_P" suffix indicates that the
% factorization in F exploits the fact that F is positive-definite. The
% input X is a vector or matrix of appropriate size.
% Initialize
... |
1b7087742e0fd98035dde08831ed49f91d27c37af981352dfd2692513025ffc3 | MATLAB | 1,081 | 33 | function [S] = wtorus(radii,scales,nosc,nu,nv,norder)
npatches = double(2*nu*nv);
npols = int32((norder+1)*(norder+2)/2);
npts = double(npatches*npols);
srcvals = zeros(12,npts);
srccoefs = zeros(9,npts);
wts = zeros(npts,1);
iptype = zeros(npatches,1);
ixyzs = zeros(npatches+1,1);
n... |
6eb4559fab1a377931155870ba1670357c43f450d61248f48b5d488bd2409f60 | MATLAB | 1,081 | 59 | %
disp("This is an example matlab driver");
disp("On output the code prints the pot+grad in example1 and, pot+pottarg in example2");
disp(" ");
disp(" ");
%% source to source, charges only, pot+grad example
%
clear srcinfo ns U eps pg;
disp("Example 1: source to source, charge, pot+grad");
disp(" ");
disp(" ");
ns... |
78056b06d3297abe39ec1f814366f78b768bcf077933a1d3cf991372b7cc1eaf | MATLAB | 1,081 | 15 | clear;clc;close all
ROOTPATH = strcat(NoveltySU.utils.getRootDirPath(mfilename("fullpath"), 4), "Data\Processed Data\");
cd(NoveltySU.utils.getRootDirPath(mfilename("fullpath"), 2))
protStr = "Active duration";
temp = NoveltySU.utils.dirItem(strcat(ROOTPATH, protStr), "spkData.mat");
popRes = cell2mat(cellfun(@(x)... |
bd908a6c27c0e73df0e2fc44eb4acd920d226f8701985abee1669162fbbfcbeb | MATLAB | 1,081 | 33 | function Y = srskelf_mv_p(F,X)
% Y = SRSKELF_MV_P(F,X) The strong skeletonization factorization stored in
% F is used to compute Y=FX, where the "_P" suffix indicates that the
% factorization in F exploits the fact that F is positive-definite. The
% input X is a vector or matrix of appropriate size.
% Initializ... |
761a8dc1e7a9d350a51fa547a479eac6ff9ed836535d2422eef3ad20cc53137f | MATLAB | 1,082 | 47 | function answer = isEndKeywordMissing(fname)
% Return true if the syntactically optional end keyword is missing
% from the end of the function.
% This file is from pmtk3.googlecode.com
if ~isfunction(fname)
answer = false;
return
end
text = removeComments(getText(fname));
text = cellfuncell(@removeStrings... |
0f9553c3d70b38a624fbfe0ff50d9fb00ae1e509a0233704af908e833b9d68f6 | MATLAB | 1,083 | 20 | clear;clc;close all
ROOTPATH = strcat(NoveltySU.utils.getRootDirPath(mfilename("fullpath"), 4), "Data\Processed Data\");
cd(NoveltySU.utils.getRootDirPath(mfilename("fullpath"), 2))
protStr = "Active duration";
temp = NoveltySU.utils.dirItem(strcat(ROOTPATH, protStr), "spkData.mat");
popRes = cell2mat(cellfun(@(x)... |
3da46f7103995cb7cc0f6ca309e5005060444a778d658c92777ddaca2ffeaa5f | MATLAB | 1,083 | 22 | function [rf_fs,gx_fs,gy_fs,gz_fs] = prepare_fat_sat(params, sat_ppm)
lims = params.gen.lims;
sat_freq = sat_ppm*1e-6*lims.B0*lims.gamma;
if params.gen.field_strength == 7 || params.gen.field_strength == 7i
rf_fs = mr.makeGaussPulse(params.gen.fs_angle*pi/180,'system',lims,'Duration',5e-3,...... |
b6ae2da481bfe5ff5aa85d19b04419eea5d01ad41a5509aa09b7a6fb2aae2569 | MATLAB | 1,083 | 33 | function sp_site_effect_removal(fn_img,fn_sm,fn_vp,fn_gs,fn_ds,fn_mask)
[filepath,name,ext] = fileparts(fn_img);
% load the preprocessed image
hdr = spm_vol(fn_img);
img_pre = spm_read_vols(hdr);
% load parametric maps
hdr1 = spm_vol(fn_sm);
img_sm = spm_read_vols(hdr1);
hdr1 = spm_vol(fn_vp);
img_vp = spm... |
0d2555c403a000483f951f1e694a017e40469f19f1ff79b443a06432815860a6 | MATLAB | 1,084 | 32 | % ExtractFlorescenceTimeseries.m
% Sarah West
% 4/5/22
% Extracts mean flourescence timeseries from source masks.
function [parameters] = ExtractFluorescenceTimeseries(parameters)
% Announce what extraction you're on.
MessageToUser('Extracting ', parameters);
% Make sure data & source dimensions agr... |
1925ee57388c19e61e815eee301460aa0241d2edf6d869f603463b9893edeb3a | MATLAB | 1,084 | 31 | function Iout=affine_transform(Iin,M,mode)
% Function affine_transform, is a wrapper of the (mex) functions
% affine_transform_2d_double and affine_transform_3d mex functions
%
% Iout = affine_transform(Iin,M,mode)
%
% inputs,
% Iin : Input image.
% M : Transformation matrix
% mode: If 0: linear interpo... |
2333adc12c4b60996c8ab693c9d1292c0ba5987679be06a4656635208968d4da | MATLAB | 1,084 | 55 | function RasterPlot2(spk, plotRange, varargin)
% spk is a cell array of spike times (each cell is one trial)
% default values for optional arguments
fig = [];
tickColor = 'k';
tickWidth = 1;
tickSpread = 1;
zerolineWidth = 1;
yVal = [];
for i = 1:2:length(varargin)
switch lower(varargin{i})
case 'fig'
... |
7432a70e02367f9be84dfbe5a3c9ab02a3df43f2c4521d3edf58dea48618ec85 | MATLAB | 1,084 | 29 | function x = waverec2_gpu(c,s,varargin)
%WAVEREC2 Multilevel 2-D wavelet reconstruction.
% WAVEREC2 performs a multilevel 2-D wavelet reconstruction
% using either a specific wavelet ('wname', see WFILTERS) or
% specific reconstruction filters (Lo_R and Hi_R).
%
% X = WAVEREC2(C,S,'wname') reconstructs the mat... |
7ea64317bc2da805f3ee322f9021a3ecfceb763fadf2726f7ebef3ada6ff8a23 | MATLAB | 1,084 | 38 | function x = steepestDescent(f, x0, varargin)
% f is the function of the form [fx, gx] = f(x) where gx is a column vector
% outputfn has the form stop = outputfn(x, optimValues, state)
% This file is from pmtk3.googlecode.com
[ftol, maxIter, exactLineSearch, outputFn, stepSize] = process_options(...
varargin, 'f... |
cdb35887e3f5618956ab4853ddd452bd2f65529c2097518402d7646645b678dd | MATLAB | 1,084 | 40 |
%%
if 0
b = firpm(100,[0 0.1 0.11 1],[0 1 1 1]);
h = ftrans2(b);
[H,w] = freqz(b,1,64,'whole');
figure;
set(gcf,'Position',[ 200 500 1600 440]);
colormap(jet(64))
subplot(1,3,1); imagesc(imgPair.moving.input)
subplot(1,3,2);plot(w/pi-1,fftshift(abs(H)))
%subplot(1,3,2); freqz2(h,[3... |
0deb5dd83e12294aa540b9ef6fb94b0bb46e81ad506bb5424707fe5a70a755df | MATLAB | 1,085 | 43 | % _ _
% ___ _ __ __ _| |__ ___ ___ _ __| |_
% / __| '__/ _` | '_ \/ __|/ _ \| '__| __|
% | (__| | | (_| | |_) \__ \ (_) | | | |_
% \___|_| \__,_|_.__/|___/\___/|_| \__|
%
% throws away all data in the current channel
% and resets the state of this channel to 0
functio... |
122684fa15196122f3d7f1e052872e708d40ae895e4562904b08126715d84cf7 | MATLAB | 1,085 | 60 | %{
_ _
___ _ __ __ _| |__ ___ ___ _ __| |_
/ __| '__/ _` | '_ \/ __|/ _ \| '__| __|
| (__| | | (_| | |_) \__ \ (_) | | | |_
\___|_| \__,_|_.__/|___/\___/|_| \__|
# NNmake
**Syntax**
```
crabsort.NNmake(input_size,n_classes... |
77f422390fdf854a4b06f073ccf40e8a6abe1e5bc56b4c8172192202f59231a5 | MATLAB | 1,085 | 42 | % HIFDE_SPDIAG_SV_P Dispatch for HIFDE_SPDIAG with DINV = 1 and F.SYMM = 'P'.
function D = hifde_spdiag_sv_p(F,spinfo)
% initialize
N = F.N;
n = length(spinfo.i);
P = zeros(N,1); % for indexing
D = zeros(N,1); % for output
% loop over all leaf blocks from top to bottom
for i = n:-1:1
% find act... |
1826d221538ea635798134a9bcec74c3489e88026f132753e32aec1a73461e81 | MATLAB | 1,086 | 44 | %% Bayesian updating of a 2D Gaussian
%
%%
% This file is from pmtk3.googlecode.com
function gaussBayesUnequalCov()
Sigmas = {0.01*eye(2), 0.01*eye(2)};
helper(Sigmas, 'Equal')
Sigmas = {0.05*eye(2), 0.01*eye(2)};
helper(Sigmas, 'Two')
%Sigmas = {0.01*[10 1; 1 1], 0.01*[1 1; 1 10]};
Sigmas = {0.01*[1 1; 1 10], 0.0... |
2a38b981a3a8cc8992a44df2b7b5e9f074b1dabb7c5a9afa6e569f9aa87771de | MATLAB | 1,086 | 40 | function filterSteerable( theta )
% Steerable 2D Gaussian derivative filter (for visualization).
%
% This function is a demonstration of steerable filters. The directional
% derivative of G in an arbitrary direction theta can be found by taking a
% linear combination of the directional derivatives dxG and dyG.
%
% USA... |
3e1a207bd106c3e94b29b73eb7ed811a9fd9ebc4ac65604fa0b0460e294bb619 | MATLAB | 1,086 | 47 | function [returnVar,msg] = RemoveDCfromDat_AllDat(varargin)
% USAGE:
% RemoveDCfromDat_AllDat(fbasename)
%
% removes DC offset in all channels from all dat files starting with
% 'fbasename'. Assumes that there is at least one xml file associated to
% one the dat file (must be created before with Neuroscope and/or... |
57d25116e94889348d983e0c0bd6c4b734cbf359cc8c6d10c9d319dc382acadf | MATLAB | 1,086 | 44 | function err = compute_errors(pos_pred, pos_gt)
%COMPUTE_ERRORS Computes error rates given predicted and ground truth positions.
% Usage:
% err = compute_errors(pos_pred, pos_gt)
%
% Args:
% pos_pred: predicted positions (J x 2 x N)
% pos_gt: ground truth predictions (J x 2 x N)
%
% Returns:
% err: struct w... |
d3b19368a9e644b4fc7d58093017ed9e003a108661cafcc7b4d6bdc3ba33faf0 | MATLAB | 1,086 | 37 | function [data_compressed] = append_data_compressed_v9 (data_compressed, total_ROI, Dnum, F_F0, total_volumes, sliceNum, flyback)
data_compressed(Dnum+1:Dnum+total_ROI, :) = zeros; %delta F_F0 of channel 1
A = sliceNum+flyback;
index = linspace(1,A,A);
z_plane_index = repmat(index, 1, total_volumes);
count = 1;
k =... |
e8467a1812eb1b3e98f1180c3ca1c6fe2bc945021b15d205bf14b6ae073b97f2 | MATLAB | 1,087 | 36 | function [ stats_map ] = adjustMapsForView( structural, red_map,...
blue_map, FDR_red_map, FDR_blue_map)
%% set colors
red_rgb = [243, 23, 23];
blue_rgb = [0, 112, 255];
ref_rgb = [0, 0, 0];
%% obtain five 4d (3d*color) maps
map_cell = {structural [1,1,1]
red_map, -... |
436b7d7fc6cb089e24e6a379e31692d1d6e8666dd1a92356253ee6b27673e9ed | MATLAB | 1,088 | 38 | function showRegions(binaryLabelImg)
% generate colors
maximum = max(binaryLabelImg(:));
colorMap = zeros(maximum, 3);
for i = 1:maximum
val = double(i)/double(maximum);
% r channel
colorMap(i,1) = ceil(255*val);
% g channel
if val < 0.5
colorMap(i,2) = ceil(255*val*2);
else
col... |
aacc8de18e076b098dc34077d6472a5e98eb67466f58bbc00d1e889bb090534c | MATLAB | 1,088 | 34 | function result = ReadYaml(filename, nosuchfileaction, makeords, treatasdata, dictionary)
import yaml.*;
if ~exist('nosuchfileaction','var')
nosuchfileaction = 0;
end
if ~ismember(nosuchfileaction,[0,1])
error('nosuchfileexception parameter must be 0,1 or missing.');
end
if ~exist('makeords','var')
... |
1c79ee016fc7404622ca4530b6e3679478a7f955f3c2f4dd9659417bd29fbada | MATLAB | 1,089 | 45 | function y = detransition(y,varargin)
% For fMRI timeseries that contains large 'jump' artifacts due to motion correction
% or other problems.
%
% :Usage:
% ::
%
% y = detransition(y,[doplot])
%
% Removes these large spikes.
% Updated version built into spikecorrect in trimts
doplot = 1;
if length(varargin) > 0, d... |
1f3fbb3a187476440966071108c06d194f7825ce570761fba37cc878594bd59e | MATLAB | 1,089 | 39 | function score=fast_pacman(decoded_event)
%https://blog.usejournal.com/dynamic-programming-finding-optimal-paths-2cf89f3d1437
[score1,maxsum1]=maxsum_algorithm(decoded_event); %start from top and go to bottom right
[score2,maxsum2]=maxsum_algorithm(flipud(decoded_event)); %start from bottom and go to top right
if s... |
742eabeab19879f5c2e470f18d8b6ffc69afbc9bf4d9985d9bdce90f10435ef3 | MATLAB | 1,089 | 51 | % this class stores attributes used to
% detect spikes in a trace
classdef spikeDetectionParameters < Hashable
properties
% these go directly into findpeaks
MinPeakHeight (1,1) double {mustBeFinite} = 10
MinPeakProminence (1,1) double {mustBeFinite} = 1
Threshold (1,1) double {mustBeFinite} = 0
MinPeakDis... |
764e864e5c716f90487ecb22d85ae02aff92db9bb2e044a2e1414055afdfa54c | MATLAB | 1,089 | 34 | function [ F,J] = gausslinefun( params, x)
%DOGFUN evaluates modified gaussian with params at x
% Output:
% F: function value
% J: Jacobian of the parameters
%Written by Dimos.
%==========================================================================
%Unwrap inputs
%====================================... |
d309e36b20464d7ba84dcdf2858b36f7a67a844f383d9a61e76f10838944a9c8 | MATLAB | 1,089 | 31 | %tracks the particle between two subsequent stacks and calculates the
%strain.
fpos = '/media/hdd2/ShearedColloids/sr15e-6/all_tracks/pos/';
savepath = '/media/hdd2/P2-Entropy/final_calculations/Dmin/allTrack_t=0-20/d6/';
coord = 'STS_ASM385_70_2-12_6_T=0-24_';
dia = 1.6;
X = load([fpos coord 'PosX.dat'])/dia;
Y =... |
e0c5145f347726804a057d51279db0aa70b37860694a9f43b6e337446e011cbd | MATLAB | 1,089 | 49 | function out = permtest(a,b,tail,nrand)
% paired or one-sample permutation test
if ~exist('b', 'var'), b = zeros(size(a)); end
if ~exist('nrand', 'var'), nrand = 10000; end
if ~exist('tail', 'var'), tail = 0; end
a = a(:);
b = b(:);
if numel(b) == 1, b = b*ones(size(a)); end % expand b
% if there are nans, i... |
138019fe21c854f9c57120a712fd23e685e867bfdb2f253ca82c76ecc32c9e02 | MATLAB | 1,090 | 34 | tabNames = cell(1,19);
tabNames{1} = 'Name';
tabNames{2} = 'Date';
tabNames{3} = 'Number_of_Pulses';
tabNames{4} = 'Pulse_Width';
tabNames{5} = 'ISI';
tabNames{6} = 'Electrode_Number';
tabNames{7} = 'Power';
tabNames{8} = 'Cluster';
tabNames{9} = 'Max_Z_Score';
tabNames{10} = 'Binned_PSTH';
tabNames{11} = 'a... |
5b202d7e483d92c1a3d3985f06fc9db4687b458339ac6bcd9d62ccf9c0e51b70 | MATLAB | 1,090 | 51 | %% Reproduce fig 1 of "Bayesian lasso regression", Chris Hans, 2009
%
%%
% This file is from pmtk3.googlecode.com
function lassoPostPlot()
bols = 1.96;
xtx = 1;
s2 = 1;
v2 = s2/(xtx);
v = sqrt(v2);
taus= [2.5 2 1 0.5];
figure; hold on
[styles, colors, symbols] = plotColors;
for i=1:length(taus)
tau = taus(i);... |
7368dd133e63410e3beab46acbde2bc74adc43954556163ace034b7fec49f2d0 | MATLAB | 1,090 | 38 | function link = googleCodeLink(fname, displayName, type)
%% Return the html link to fname on a PMTK associated google code repository
%
%%
% This file is from pmtk3.googlecode.com
if nargin < 3
type = 'html';
end
fname = which(fname);
if nargin < 2
displayName = fnameOnly(fname);
end
if startswith(fname, p... |
750a086ba467f692408d7e074a94c3e7de0d85494549a7d66fb909e6075a0be0 | MATLAB | 1,090 | 27 | function downloadPretrainedWeights(varargin)
% aptroot is a native path
aptroot = myparse(varargin,...
'aptroot',APT.Root...
);
urlsAll = DeepTracker.pretrained_weights_urls;
weightfilerelpaths = DeepTracker.pretrained_weights_files_relative_path;
deepnetRootPathNativeAsChar = fullfile(aptroot, 'd... |
aba65b049fff42fb244e369f2a19ed78086e4dc1890bf54e1cb4925e0238b87d | MATLAB | 1,090 | 29 | %_______________________________________________________________________
%
% select = selectTargets(screenInfo, xy, diamTar)
%_______________________________________________________________________
%
% puts the targets into correct coordinates for checking to see if a
% touch is within the boundaries of the targets. Th... |
dfc99404578070cf4a7dadd2817c19065c85bf41248918013ac42d3eeb0bafc7 | MATLAB | 1,091 | 49 | function [muPost, SigmaPost, loglik] = binaryFAinferLatent(model, y, varargin)
% Infer distribution over latent factors given observed data
%
% y(n,t) in {0,1} or {1,2} or NaN
% Optional:
% 'x' x(n,d) in real
%
%
% Output:
% mu(:,n)
% Sigma(:,:,n)
% loglikCases(n)
[computeLoglik, computeSigma, x] = process_options(va... |
90654c3e03603af1ce4d6adafe1fb15ebf0097d7a0064f461e5828f8f4bb138f | MATLAB | 1,092 | 37 | function [posR, triR] = remove_double_vertices(pos, tri)
% REMOVE_DOUBLE_VERTICES removes double vertices from a triangular mesh
% renumbering the vertex-indices for the triangles and removing all
% triangles with one of the specified vertices.
%
% Use as
% [pos, tri] = remove_double_vertices(pos, tri)
pos1 = uniqu... |
f83e64460525020fd540cbe4628e5f5187a6161674b8d61bed13a5589e73d8d4 | MATLAB | 1,092 | 43 | % _ _
% ___ _ __ __ _| |__ ___ ___ _ __| |_
% / __| '__/ _` | '_ \/ __|/ _ \| '__| __|
% | (__| | | (_| | |_) \__ \ (_) | | | |_
% \___|_| \__,_|_.__/|___/\___/|_| \__|
%
% attempt to automatically determine the best prominence
% to detect spikes
function best_prom =... |
2f734a32d968446a2640b0faab4f53db7dc3302862aa596471422fc29c1168a6 | MATLAB | 1,093 | 29 | % SMOOTH_HIST performs 19 point gaussian sigma 3 smoothing
% smoothHist.m
%
% Copyright 2008 Vanderbilt University. All rights reserved.
% John Haitas, Jeremiah Cohen, and Jeff Schall
%
% This file is part of JPSTH Toolbox.
%
% JPSTH Toolbox is free software: you can redistribute it and/or modify
% it under the te... |
7b5f052c2e84ca2d5faa6f72e86224a43d36be276fea8ae869e9f94c5f6961b4 | MATLAB | 1,093 | 38 | function [RgMeans,nRg] = lme_mass_RgMean(Rgs,Data)
% [RgMeans,nRg] = lme_mass_RgMean(Rgs,Data)
%
% Mean of the data for each region.
%
% Input
% Rgs: 1 x nv segmentation vector containing a region number assigned
% to each vertice along the surface.
% Data: Any data (eg. a matrix whose colums are estimators of the co... |
8c4ea422276b2ed22bd8016373c040c65f977578cc6470c08c802123de5b246f | MATLAB | 1,093 | 38 | %% EEG analysis script for:
%% The transformation of sensory to perceptual braille letter representations
%% in the visually deprived brain
% Marleen Haupt and Monika Graumann
% 22.02.2024
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Preprocessed EEG data has to be downloaded to data folder
%%%%%%%... |
d209c9df72288b9388270dcb30088ae11d2094ec0b8ba8e98c6423d70bd37cca | MATLAB | 1,093 | 38 | function D = build_logEuclidean(datadir, kerneldir)
% D = build_logEuclidean(datadir, kerneldir)
%
% build log Euclidean distance matrix
% This is the Frobenius norm of the logarithm map of the time-averaged
% covariance matrices (Jayasumana et al., arXiv 2013)
% The distance matrix will be used in Gaussian kernel fo... |
d4c5e604b1b6ab48d204ec67c5dbdab3a9fc5dc77981a24323b247ac8542db3c | MATLAB | 1,093 | 46 | function [ff, loglik] = hmmFwdBackMaxProduct(initDist, transmat, softev)
%
% We don't keep the traceback pointers.
%
% INPUT:
% initDist(i) = p(S(1) = i)
% transmat(i,j) = p(S(t) = j | S(t-1)=i)
% softev(i,t) = p(y(t)| S(t)=i)
%
% OUTPUT
% ff(t, i, j) = phat(S(t) = i, S(t+1)=j)
% where phat(s(t)) = max_{-s(t)} p(s(t... |
d62921d33deaf7cacc5dc833b85f1cb061662b97c7bf77a10e9ada92e76a3468 | MATLAB | 1,093 | 48 | function [chm skip] = wmodkurt2(ch,kt,W,Wsum,Niter,ep,nsc)
%-----------------------------------------
% chm = wmodkurt2(ch,kt,W,Wsum,Niter,ep,nsc)
%
% modify the weighted kurtosis of an image matrix by taking
% gradient steps (see wkurt2.m)
%
% ch: image
% kt: target kurtosis
% W: weighting functions
% Wsum: sums of w... |
d98b4a3ca73dd3a34cb040181364f38a588ad36a0431003df91af9647e224d83 | MATLAB | 1,093 | 36 | function [RR, RRs, RRe, RRf, RRlg] = induced_TFC(Sig, mRT, power, freq)
if nargin > 3
Ana = filter_hilbert(Sig',LAN.srate,min(freq),max(freq),0)';
Env = abs(Ana);
Fase = angle(Ana);
Log = abs(real(Ana));
[b,a] = butter(2,20/(LAN.srate/2));
Log = filtfilt(b,a,double(Log));
end
RR=[];RRs = [];RR... |
58d7fa50c2a11244a61fec5a12b3cd3d94e7e927b7489c60905ddca00f2471a4 | MATLAB | 1,094 | 37 | function varargout = cfg_TA_get_defaults(defstr)
% Get dafual values of TA
currentPath = fileparts(which(mfilename));
persistent defaults;
if isempty(defaults)
defaults.space.def = 0;
defaults.quantlevel.def = 8;
defaults.neighborsize.def = 1;
defaults.distance.def = 1;
defaults.smoothing.d... |
7517d6b0bb48f7dc1488457cc8851dae2c7956eb06bf0ea8cb820166db92899d | MATLAB | 1,094 | 40 | function opt=varargin2struct(varargin)
%
% opt=varargin2struct('param1',value1,'param2',value2,...)
% or
% opt=varargin2struct(...,optstruct,...)
%
% convert a series of input parameters into a structure
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% 'param', value: the... |
e512955a5e7c42971ea4e04e10e25e8b8b8e85b4e18d535ac03b7ce4062b82b7 | MATLAB | 1,094 | 44 | function y = VBA_spm_detrend(x,p)
% Polynomial detrending over columns
% FORMAT y = spm_detrend(x,p)
% x - data matrix
% p - order of polynomial [default: 0]
%
% y - detrended data matrix
%__________________________________________________________________________
%
% spm_detrend removes linear and nonlinear tren... |
f40f567a7ef9d7af3ea65fe8ab4275d38f0ac3bd04eb03510d660892736ae6ab | MATLAB | 1,094 | 37 | % This script displays a mesh from a *.mat P-t file and reports the
% number of triangles, minimum triangle quality, and minimum edge length
% Copyright SNM 2012-2018
% The Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General
% Hospital & ECE Dept., Worcester Polytechnic Inst.
s = pwd;... |
0d7ec1d6bc3c530790ff8d8ba1ed773cb9c821728c42a8b7efd408bc2c8a57f2 | MATLAB | 1,095 | 41 | function ampl = GetMeanAmpl(data, times, timeWindow)
% function ampl = GetMeanAmpl(data, times, timeWindow)
%
% Return mean amplitude within time windows
%
% Inputs:
% data: matrix with time in 2nd dimension, any other shape allowed
% times: [1 nTimes] vector of times
% timeWindow: [min max] times of window (in... |
2d398b34bb4467bb0e92f70dfb2c3b38f7cd5b55c11de72d2d722e8ec01ef0f4 | MATLAB | 1,095 | 30 | function trialAll = PassiveProcess_reward(epocs)
%% Information extraction
trialOnsetIndex = epocs.Swep.data + 1;
trialOnsetTimeAll = epocs.Swep.onset * 1000; % ms
water = epocs.watr.data; % Hz
n = length(trialOnsetIndex);
temp = cell(n, 1);
trialAll = struct('trialNum', temp, ...... |
39a1cddf32ff4dfa21b7dd57d2a01ae9cb63bcbdcbc4def912b1c7a4e39afe58 | MATLAB | 1,095 | 41 | %% Alternating Poisson Regression for fitting CP to sparse count data
%% Set up a sample problem
% We follow the general procedure outlined by E. C. Chi and T. G. Kolda,
% On Tensors, Sparsity, and Nonnegative Factorizations, arXiv:1112.2414
% [math.NA], December 2011 (http://arxiv.org/abs/1112.2414).
% Pick the siz... |
1d4b318af025a178c8610a6b7c37035f44d0036128944eeb600726bdd7843594 | MATLAB | 1,096 | 42 |
load(fullfile(saveLoc, 'xMediationMatrices'))
% load('E:\Matalb analyses\xMediationMatrices_230522')
% indY = [6 5 4 3 7];
% indX = [7 3 4 5 6];
indX = [6 4 5 7 8];
indY = [8 7 5 4 6];
indX = [7 5 6 8 9];
indY = flip(indX);
figure('Position', [100 100 900 400]); hold on
% ind = 3:length(uni_s)... |
5cb6a5c6f9901db90af52404f27f0d8dbf205d7674456f357dffcfe8a116a27a | MATLAB | 1,096 | 34 | function order = CBIG_ASDf_hunMatch(K, Mean_ref, Mean)
% order = CBIG_ASDf_hunMatch(K, Mean_ref, Mean)
%
% This function performs Hungarian matching between the factor-specific RSFC
% patterns (i.e., E(RSFC patterns|Factor)) of two random initializations.
%
% Input:
% - K:
% An integer indicating the num... |
a2dbd1b54eb7c7ec306e724370462700c85e9f062badefc397a60dbd4f59e188 | MATLAB | 1,096 | 40 | %% Display four data sets each with the same correlation of 0.81.
%
%%
% This file is from pmtk3.googlecode.com
loadData('anscombe'); %stacked columnwise, source: R - builtin dataset
pointSpec = {'o','MarkerSize' , 11,...
'LineWidth' , 1.2,...
'MarkerEdgeColor', 'r',...
... |
15d38577bca58a5df0afb043a6b586a77d92d917c2369e5305791bcd7794e44c | MATLAB | 1,097 | 35 | function [yes_or_no] = isMTL(elec_number, elec_label_list)
%createColormapFromAnnotations - Creates a colormap for Annotation of the
%Surface
% surface - Surface Data object
% returns:
% annotation_remap - remapped annotations from 1 to max
% cmap - colormap associated with annotations
% See also Model3DView, plot3DMo... |
bddd37e2c60af3e2f7779ff748dad293ddf253db401c45086db8056c62ba43c7 | MATLAB | 1,097 | 58 | % h = drawflyo(trk,varargin)
% h = drawflyo(x,y,theta,quartermaj,quartermin,varargin)
% draw triangle corresponding to fly position
% extra arguments will be fed to plot
function varargout = drawflyo(x,y,varargin)
% draw an isosceles triangle with center (x,y)
% with rotation theta
% with height maj*4
% with base min*... |
e4b9e895d2f2faee03c0cf8d055e54c73c8093ca00125c0ddf6b5962c601c765 | MATLAB | 1,097 | 33 | function Y = srskelf_mv_nn(F,X)
% Y = SRSKELF_MV_NN(F,X) The strong skeletonization factorization stored in
% F is used to compute Y=FX, where the "_NN" suffix indicates that the
% factorization in F is not symmetric and we are not taking the conjugate
% transpose. The input X is a vector or matrix of appropriate s... |
ea7441fcc1b3fbbd3702f53b93e55133bcfef38dc1635e19a121312b0468807a | MATLAB | 1,097 | 35 | % StrideDurationHistograms.m
% Sarah West
% 7/21/23
% Plots lengths of mouse paw strides in time ("durations"; calculated from paw
% velocities already segmented into individual strides) as histograms
function [parameters] = StrideDurationHistograms(parameters)
% Inputs
data = parameters.data; % column ... |
ed43fab8b5dcf4a3c2d702d6f4064827f89698a1fb725432975ece753ed01fce | MATLAB | 1,097 | 40 | % stim_demo.m
displayName = 'Carrasco_L1'; % 'meg_lcd','Carrasco_L2','Carrasco_L1','Carrasco_R1'
d = loadDisplayParams(displayName);
load stimuli/taDetectDiscrim99.mat
stimulus.target.contrast = [0 .2 .8 .95];
[bg, m, t] = contrastTarget(d,stimulus.target);
% a = bg{1};
% ma = m{1,1,1}(:,:,2);
% % ami = a.*ma*.5 + a... |
8383680a3be27bf85e50f8d98954432d35ff8aefb89e710dca6af1858f388c3c | MATLAB | 1,098 | 36 | function [avg_corr, sem_corr, z_scores, p, h] = averageCorrelation(correlation_list)
% Function to compute the average of correlation coefficients using Fisher's z transformation
% independently for each row
% Inputs:
% - correlation_lists: A list of correlation coefficients
% Output:
% - avg_corr: Average correlat... |
dc22ee55d17c70f784c0ca0b46e182579ea99d154b7ad93a3530005988943c39 | MATLAB | 1,098 | 36 | % RSKELF_MV Multiply using recursive skeletonization factorization.
%
% Typical complexity: O(N) in 1D and O(N^(2*(1 - 1/D))) in D dimensions.
%
% Y = RSKELF_MV(F,X) produces the matrix Y by applying the factored matrix F
% to the matrix X.
%
% Y = RSKELF_MV(F,X,TRANS) computes Y = F*X if TRANS = 'N' (defa... |
1d2f7ac1d3e424db20d8a8cf3d99b3e0602c09f7627fd60e67fbe8a50075cfef | MATLAB | 1,099 | 26 | function iptstandardhelp(helpmenu)
%iptstandardhelp Add Toolbox, Demos, and About to help menu.
% iptstandardhelp(HELPMENU) adds Image Processing Toolbox Help,
% Demos, and About Image Processing Toolbox to HELPMENU, which is a
% uimenu object.
% Copyright 1993-2011 The MathWorks, Inc.
% $Revision: 1.1.8.3 $... |
4e48abddb94393ea6276af9f52b14b7dfa6d485b4d0e6b76bb71dbf19eeca20c | MATLAB | 1,099 | 28 | function [sg] = g_convSig_approx(x,P,u,in)
% this function evaluates a logistic convolution of the inputs
% function [gx,dgdx,dgdp] = g_convSig(x,P,u,in)
% This function evaluates the following multiple convolution model:
% p(y(t)=1) = sigm(cst + sum_k sum_tau w(k,tau)*u(k,t-tau))
% where u can be of any dimension (k... |
6eeef1b64bff1d6958f5412e262f4c0d39c7cf1b46fa6c4f0d9f389b5159e936 | MATLAB | 1,099 | 33 | function Y = srskelf_mv_nc(F,X)
% Y = SRSKELF_MV_NC(F,X) The strong skeletonization factorization stored in
% F is used to compute Y=FX, where the "_NC" suffix indicates that the
% factorization in F is not symmetric and we are taking the conjugate
% transpose. The input X is a vector or matrix of appropriate size.... |
798c63240f735dc8ec227ef1caae13c5fcaf6f6a2538032021adc4736594bb7d | MATLAB | 1,099 | 54 | %% Colormaps that are pretty awesome: DEMO2
% In this demo we will show how to supereasily use the new colormaps
%
%
% this is an exampe of 4 colormaps that are being considered as the default
% colormap in python's matplotlib lybrary.
%
% All of them look quite good and they dont have any official name, so at
... |
7d761224e2779082e32cb205ae32c7f6234eecf2db0942165edadf64c68da2b8 | MATLAB | 1,099 | 56 | function outIndex = deterministicR(inIndex,q);
% PURPOSE : Performs the resampling stage of the SIR
% in order(number of samples) steps. It uses Kitagawa's
% deterministic resampling algorithm.
% INPUTS : - inIndex = Input particle indices.
% - q = Normalised importance ratios.
% OUTPUTS ... |
08fd54cfcc58a390303fc6fcba98d81b42c473be2c1b9ec7c88f868c68fa042a | MATLAB | 1,100 | 38 | function Z = mvg(MU,SIGMA,n)
%
% Syntax: Z = mvg(MU,SIGMA);
% Z = mvg(MU,SIGMA,n);
%
% Inputs: MU is the desired (d x 1) mean vector
%
% SIGMA is the desired (d x d) covariance matrix
%
% [OPTIONAL] n is the desired numbe... |
7a2c71aeeae6740a92c7fa0921ddd2f7556e0742edd4037be40b0672185772a9 | MATLAB | 1,100 | 44 | clear all; %#ok<CLALL>
%% Setup path to engine
addpath(fullfile('Engine'));
addpath(fullfile('Model'));
addpath(fullfile('data'));
%% options
NC = 4000;
subject='110411';
Cl = load(sprintf('%s_cluster_%d.mat', subject, NC));
Ntri = size(Cl.index_int,1);
indicator_mat = sparse(1:Ntri,Cl.index_int, ones(1,Ntri));
%% ... |
7b6eaa2f04703fa836a13b8536a3b388e8f8a18a4a4ee62f8adcd895e0f9a2b1 | MATLAB | 1,100 | 37 | %% (pxTools) getCovArrFromJointArr;
%// Resampling method to quickly convert p(X1|X0) into P(X1|X1) or P(X0|X0)
% Copyright (C) 2023 Trevor S. Smith
% Drexel University College of Medicine
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public... |
9c57d12668a4eb7c54e8b9a5b85acacab019ce8b2034137bb03c67b92a887c2a | MATLAB | 1,100 | 24 | % This function combines data across trials for each fly
function [FlyData]=Combine_Data_Across_Trials(DarkData)
Flies=unique(DarkData.Fly);
FlyData.FlyForward_w2p=[];
FlyData.FlyRotate_w2p=[];
FlyData.BumpPosition=[];
FlyData.BumpR=[];
FlyData.FlyPosition_w2p_aligned=[];
FlyData.TwoP_Times=[];
FlyData.Two... |
9f3976b300826aae7241251acebb7d5817ed176a7b644d3e06a247010a9f2fbd | MATLAB | 1,100 | 43 | % RSKELF_SPDIAG_SV_P Dispatch for RSKELF_SPDIAG with DINV = 1 and F.SYMM = 'P'.
function D = rskelf_spdiag_sv_p(F,spinfo)
% initialize
N = F.N;
n = length(spinfo.i);
P = zeros(N,1); % for indexing
D = zeros(N,1); % for output
% loop over all leaf blocks from top to bottom
for i = n:-1:1
% find ... |
c2b0bce88c887bfaf73ce8e797df8693b5a904b63a0ea28f405e273690c17ab0 | MATLAB | 1,100 | 33 | function Y = srskelf_sv_nc(F,X)
% Y = SRSKELF_SV_NC(F,X) The strong skeletonization factorization stored in
% F is used to solve FY=X, where the "_NN" suffix indicates that the
% factorization in F is not symmetric and we are taking the conjugate
% transpose. The input X is a vector or matrix of appropriate size.
... |
23a12a3a98621fa441368b5815acfb8c12529486bb06e05547fc278416d90009 | MATLAB | 1,101 | 46 | function [p, V, Q, iter] = policy_iteration(T, R, discount_factor, use_val_iter, oldp)
% POLICY_ITERATION
% [new_policy, V, Q, niters] = policy_iteration(T, R, discount_factor, use_val_iter, old_policy)
%
% If use_val_iter is not specified, we use value determination instead of value iteration.
% If the old_policy is n... |
23ada6e91aa922a6a77ef905751ddaf4ad1b70b7da8c17ca30e6cdc273adef9f | MATLAB | 1,101 | 33 | function [] = int_F_data(data, sbxframes,plane, numplanes, outfile)
load(data);
int_data = [];
vid_start = 1;
new_vid_start = 1;
for j=1:size(sbxframes,2);
old_length = length([plane:numplanes:sbxframes(j)]);
new_length = length([2:3:sbxframes(j)]);
for roi = 1: size(data.F,2)
int_F(new_vid_s... |
30329c64d4377cfc543e67b5be5668edd931b7efdac7b606e07f885084fa783f | MATLAB | 1,101 | 37 | function fcs = gaussMkFullCondSamplers(model, visVars, visVals)
% Returns a cell array of function handles
% containing the full conditionals for an MVN
% for use by e.g. gibbs sampling.
%
% For instance, fcs{i}(xh), returns a single sample for ith hidden
% variable, from the full conditional, (i.e. the distribution c... |
3562af51d7e2b60075f23edd3549bcdad4fc21817b2447f8336c856ed85ccfc3 | MATLAB | 1,101 | 44 |
function OutputMat = CelltoMatUE (inputcell, varargin)
%
% This function is an extension to MATLAB original cell2mat function but it
% works with cells with different dimentions. If the cell size is un-even
% it generate the zero in its place.
%
% ===============================Inputs================================... |
62409e10373da318878a9ab1eecd6c1033bd68919f01ec4ac542aa0255574dbe | MATLAB | 1,101 | 43 | % RSKELF_SPDIAG_MV_P Dispatch for RSKELF_SPDIAG with DINV = 0 and F.SYMM = 'P'.
function D = rskelf_spdiag_mv_p(F,spinfo)
% initialize
N = F.N;
n = length(spinfo.i);
P = zeros(N,1); % for indexing
D = zeros(N,1); % for output
% loop over all leaf blocks from top to bottom
for i = n:-1:1
% find ... |
942c00e3bd23bbeb9d86a8cf5b3a7fed5cfc8ae8a7ae4ce197240ef7f77c3dab | MATLAB | 1,101 | 35 | function [colors, colorMap] = pmtkColors()
% http://www.mathworks.com/access/helpdesk/help/techdoc/ref/colorspec.html
% This file is from pmtk3.googlecode.com
lightblue = [55 155 255] / 255;
orange = [255 128 0 ] / 255;
green = [0 255 64 ] / 255;
magenta = [255 0 128 ] / 255;
... |
afd8fdd3d3de8f14408caa8f60e9960b6cf2d0abae5962583d780ba87d5d0755 | MATLAB | 1,101 | 36 | function [pk1 pk1t pk2 pk2t] = PeakResponse(traj, evt, win, pol)
% pk1: value measured at time of peak mean response
% pk2: value measured at peak of each individual trial
nEvt = length(evt);
ti = traj.times(1);
dt = traj.times(2) - ti;
win = round(win/dt);
r = zeros(nEvt, win(2));
for k = 1:nEvt
p0 = round((evt(... |
b783dfddfb84bcc0db9c8ff4acf23bf4c1a40f91833b7d26d3d84e927663f837 | MATLAB | 1,101 | 39 | function [xtrain, ytrain, xtest, ytestNoisefree, ytestNoisy, sigma2] = polyDataMake(varargin)
%% Sample Data
% This file is from pmtk3.googlecode.com
[sampling, deg, n] = process_options(varargin, ...
'sampling', 'sparse', 'deg', 3, 'n', 21);
setSeed(0);
switch sampling
case 'irregular', xtrain = [-1:0.1:-0.5... |
0b468680ad4a8252ea306db99607b49a9656c7dc06b41e28686adc1e6d1ba1c8 | MATLAB | 1,102 | 38 | function [XData_Clean,YData_Clean,NumRemoved]=RemoveUndefinedDataPairs(XData,YData)
XData_Clean=[];
YData_Clean=[];
CleanCount=0;
NumRemoved=0;
if any(size(XData)~=size(YData))
error('data must be the same size...')
end
if size(XData,2)>size(XData,1)
NumEntries=size(... |
91603b39cae6657c4063d251160c238fdc0cbe57bf8d61ecc5aaffe6956a0c46 | MATLAB | 1,102 | 53 | function h = plotimage(mesh,val)
% PLOTIMAGE Fast prewiev of data within FEM mesh in the NIRFAST format.
% Plots an image of the values on the mesh.
%
% SYNTAX:
% H = PLOTIMAGE(MESH,VAL)
%
% H = PLOTIMAGE(MESH,VAL) MESH is the input mesh in NIRFAST format
% (workspace variable), VAL is n by 1 vector of values to pl... |
a0e9f7aeb289b4980c8344a1d0ac52820f8bceef09ed3cd899b1b242b229e9c7 | MATLAB | 1,102 | 38 | function v=forward_general_memory(dips,fp);
% usage: v=forward_general_memory(dips,fp);
% makes forward for 'arbitrary' methods for dipoles.
%
% same as forward_general except that it calculates dipoles
% (at most) in bunches of thousand to evoid memory overflow
% for very many dipoles
%
% input:
% dips Nx6 matrix... |
b853654971eabd17846007bfec8b451fcfc4f685680bc572b2ef31db31269ded | MATLAB | 1,102 | 30 |
function [dip, p_value, xlow,xup]=hartigansdipsigniftest(xpdf,nboot)
% function [dip,p_value,xlow,xup]=HartigansDipSignifTest(xpdf,nboot)
%
% calculates Hartigan's DIP statistic and its significance for the empirical p.d.f XPDF (vector of sample values)
% This routine calls the matlab routine 'HartigansDipTest' t... |
d47f34e8e2b6327b2e428bdb5fbd232ab103f6db59ae4cc0463840cd464d62b7 | MATLAB | 1,102 | 37 | %% Illustration of how singularities can arrise in the likelihood function of GMMS
%
%%
% This file is from pmtk3.googlecode.com
f1 = @(x) gaussProb(x,0.5,0.12);
%f1 = @(x) gaussProb(x,0.5,0.12);
%f2 = @(x) gaussProb(x,0.88,0.003)/2;
f2 = @(x) gaussProb(x,0.15,0.0003)/2;
f3 = @(x) f1(x) + f2(x);
domain = 0:0.001:1;
... |
32495d848f1dd7088ab107241fe6bd29e213f270aa42ba1639d2c3c5f8ce5203 | MATLAB | 1,103 | 27 | function [fcost,u_error,u_sol,e_sol,F_err] = Spring2D(k0,F,u_output,params,optims,condition)
if isequal(condition,'1')
k = Convert_k(k0,params);
elseif isequal(condition,'0')
k = k0;
end
D = params.C.'*diag(k)*params.C;
F([2*params.ind_fix-1,2*params.ind_fix]) = [];
D(:,[... |
628e3f7724fd5f09a9f49749bc9d2d6046e002f099fe5c498a236eecde12620b | MATLAB | 1,103 | 51 | function F = angauss( I, s, r )
% ANGAUSS Anisotropic gaussian filtering.
% INPUT:
% Is - Input tomogram
% s - Gaussian standard deviation
% r - Aspect ration in Z axis, if 1 isotropic
% OUTPUT:
% S - Filtered output
%
% See also: diff3d
%
% AUTHOR: Antonio Martinez-Sanchez (an.ma... |
55049f7a2af429aa1e9e53d1bced9da83136af8e090811f43ec7c6d012505063 | MATLAB | 1,104 | 59 |
setSeed(1);
priorVar = 0.01;
n=10;
m=3;
Nobs = m;
D = n+1; % numnber of variables
Nhid = D-Nobs;
xs = linspace(0, 1, D);
perm = randperm(D);
obsNdx = perm(1:Nobs);
hidNdx = setdiff(1:D, obsNdx);
% Noisy observations of the x values at obsNdx
xobs = randn(Nobs, 1);
%obsNoiseVar = 1;
%y = xobs + sqrt(obsNoiseVar)*ra... |
5bda6748cccd8858c884a48e0913289d2d39217664b9bdcf1c779559c1d76460 | MATLAB | 1,104 | 41 | %% Plot SL and RL coherence for all sessions combined
dimms = [1 2 5 31 32 55 56]; rows = length(dimms); % 33
sc = 1;
plotFilt = ones(1, 50)/50;
xLimsSL = [0 0.5];
xLimsRL = [-0.5 0];
figure('Position', [600 100 800 900]); hold on; rows = 8; i = 0;
for sess = 1 : 8
for dim = dimms
... |
69e7f1413b724ca044ee355d123d33e25eba172c4145cdb2b9c013b9228cc5bd | MATLAB | 1,104 | 35 | function dyn = fcn_flow_graph(aij,r,t)
%FCN_flow_graph continuous time random walk on network
%
% DYN = FCN_flow_graph(AIJ,R,T) instantiates a continuous time
% random walk on network with adjacency matrix AIJ. Waiting time
% for walkers at each node are distributed as Poisson with rate
% ... |
7bf509745d6c30e6558da19c454e74e883948972277e364280f3b54af203036f | MATLAB | 1,104 | 43 | % demo for Dirichlet Process (CRP) classification schemes
n = 1e2; % number of samples
alpha = 1; % concentration parameter of the DP
p = 6; % dimension of the data
mu0 = zeros(p,1); %1st-order moment of the prior for SS
S0 = 1e1*eye(p); %2nd-order moment of the prior for SS
% a0 = 1e2; % shape parameter
% b0 = a0*1e... |
8830f28bc2f2bbfea208e1cd2fef49b6cde13bb2524247cc54c9db759aae2376 | MATLAB | 1,104 | 44 | function newlist = wlAux_selectEventsByTime( oldlist, timerange, timestamps )
% function newlist = wlAux_selectEventsByTime( oldlist, timerange, timestamps )
%
% This selects events from an event list that overlap a specified time
% range.
%
% "oldlist" is an array of event structures, per EVENTFORMAT.txt.
% "timerang... |
028b3efc8809d053b4c8efb9f220ee6b625ac4469b7a4b4fca73ad92aaac3f75 | MATLAB | 1,105 | 33 | function Y = srskelf_sv_nn(F,X)
% Y = SRSKELF_SV_NN(F,X) The strong skeletonization factorization stored in
% F is used to solve FY=X, where the "_NN" suffix indicates that the
% factorization in F is not symmetric and we are not taking the conjugate
% transpose. The input X is a vector or matrix of appropriate siz... |
270d890e47a0fb1f201fa29e063c512d890641021261a5354c8830fac26b7d25 | MATLAB | 1,105 | 20 | % DETECTOR
% See also
%
% Please see acfReadme for an overview of the detection code.
%
% Aggregate channel features object detector:
% acfDemoCal - Demo for aggregate channel features object detector on Caltech dataset.
% acfDemoInria - Demo for aggregate channel features object detector on Inria dataset.
% ac... |
58de1445a0b5b3a59d0cc4ed1f16a537944507905737b4fe505d5fe809455dbd | MATLAB | 1,105 | 30 | function Draw_Foci(foci,faces)
% This function draw spheres of a specified color and radius at the
% location given by the structure foci. Fields include: location, color,
% radius. It is assumed that coordinate space and voxel-index space are
% exactly aligned. foci.color are rows of RGB values [0,1].
if ~exist('fac... |
74cb8bd0a2cac7484356477269a9832e3317a138ac21757fc24427defcd3f070 | MATLAB | 1,105 | 42 | classdef WaitBarWithCancelContext < handle
properties
message = '' % "Base" message
nobar = false % If true, no graphical fraction bar shown
shownumden = false % If true, show fractional degree of completion eg (3/500) in message
numerator = nan % Numerator if shownumden==true
denominator = nan... |
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