sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
462a2c2c69de344d1dd658079c18cfad778015fcb44b0f271da00725891e0fe2 | MATLAB | 460 | 16 | function [LIBSVMTRAIN, LIBSVMPREDICT] = nk_DefineLIBSVMfun(SVM)
switch SVM.LIBSVM.LIBSVMver
case 2
LIBSVMTRAIN = 'svmtrain289';
LIBSVMPREDICT = 'svmpredict289';
case 0
LIBSVMTRAIN = 'svmtrain312';
LIBSVMPREDICT = 'svmpredict312';
case 1
LIBSVMTRAIN = 'svmtrain291';
... |
ae1994f168eb40c88b197737f0159296a55de75f864d7af11abef799c8cbfe6c | MATLAB | 461 | 16 | function acc = eval_MTL_accuracy (Y, X, W, C)
%calculate accuracy for classification
task_num = length(X);
total_sample = 0;
acc_list = zeros(task_num, 1);
%calculate corrested classified numbers for every dataset
for t = 1: task_num
acc_list(t) = nnz(sign(X{t} * W(:, t) + C(t)) =... |
f5b8ae3f59c0ee2e5c0cefa655ca1deef374fba410f72bf054cc3b7cd59cb85b | MATLAB | 461 | 19 | function P = nk_ConvProbabilities(C, ngroups)
m = size(C,1);
P = nan(m,ngroups);
if iscell(C)
for i=1:m
if isempty(C{i}) || sum(isnan(C{i}))==size(C{i},2), continue, end
for j=1:ngroups
P(i,j) = sum(C{i}==j)/numel(C{i});
end
end
else
indn = ~(sum(isnan(C),2)==size(C,2));... |
0448303f2f621684563c412ac67cb8e64e7f9dcaeb1068e338d91133381e1440 | MATLAB | 463 | 11 | function x_new = NextSolution1(x_curr, itt, n, featureID_order)
% The new voxel is introduced to the solution in the order determined by
% futureID_order.
moduloItt = mod(itt,n); if moduloItt == 0, moduloItt = n; end
x_new = x_curr;
% @#$% user-defined
% ====================================================
% Appproach ... |
386d88c66df435fc05c7a1ddcbb5564b9eef9cf8f3c2f22711332c44df1d082f | MATLAB | 463 | 14 | function [ nexFile ] = nexCreateFileData( timestampFrequency )
% [nexFile] = nexCreateFileData(timestampFrequency) -- creates empty nex file data structure
%
% INPUT:
% timestampFrequency - timestamp frequency in Hertz
%
nexFile.version = 100;
nexFile.comment = '';
nexFile.freq = timestampFrequency;
n... |
d1299de0870253f4d2f2e597f51f4094251660de52d66f26766ba019518f70d8 | MATLAB | 463 | 17 | function [ sY, IN ] = nk_PerfZeroOut(sY, IN)
% Zeros out completely non-finite features
global VERBOSE
mY = size(sY,1);
% Zero-out non-finite data
if IN.zerooutflag == 1
if ~isfield(IN,'indnonfin')
IN.indnonfin = sum(~isfinite(sY)) == mY;
end
if sum(IN.indnonfin)>0
if VERBOSE, fprintf(' zero-... |
5138d70073bac9cafbed0fd1e1d46d6706139d53a9fb99c8688aeddd04ba2475 | MATLAB | 466 | 23 | function [vec,I]=greatsort(x);
%GREATSORT sorts the vector x in descending order.
%
% Required input arguments:
% x : vector to be sorted
%
% Output arguments:
% vec : sorted vector
% I : index => x(I)=vec.
%
% I/O: [vec,I]=greatsort(x);
%
% This function is part of LIBRA: the Matlab Library for Robust Ana... |
821760d68cf032131168fd901ff05f05839e18f274caf79ed326aa21616fc468 | MATLAB | 466 | 25 | function Xts = nk_PLSpredict(brainlv_tr,Ytr,Xtr,Yts)
mx = max(Xtr);
Xd = single(zeros(size(Xtr,1),mx));
dx = Xtr(1); cnt=1;Xd(1)=dx;
for i=2:length(Xtr)
if Xtr(i) > dx,
dx=Xtr(i);
cnt=cnt+1;
end
Xd(i,cnt)=1;
end
%[mtr,ntr]=size(Ytr);
mts=size(Yts,1);
% Mean centering
mYtr = mean(Ytr);
mXtr ... |
e99eaa1af66e88cf026790d989df49adff6d3d1967792efcd1108c04d8bcacce | MATLAB | 468 | 5 | % Compiles the MIToolbox functions
mex -I../include MIToolboxMex.c ../src/MutualInformation.c ../src/Entropy.c ../src/CalculateProbability.c ../src/ArrayOperations.c
mex -I../include RenyiMIToolboxMex.c ../src/RenyiMutualInformation.c ../src/RenyiEntropy.c ../src/CalculateProbability.c ../src/ArrayOperations.c
mex -I.... |
af2fbed1c4e0d98124547cf52fa689e5b867f1e9c34b6902d23ec9a3b421a22a | MATLAB | 470 | 20 | function CutterOption_EvalOverlap(self)
nC = length(self.Clusters);
Overlap = zeros(nC,nC);
Overlap(2:end,1) = 2:nC;
Overlap(1,2:end) = 2:nC;
for iC = 2:nC
Ci = self.Clusters{iC};
Overlap(iC,iC) = length(Ci.GetSpikes);
for jC = (iC+1:nC)
Cj = self.Clusters{jC};
Overlap(iC,jC) = length(int... |
9ad420022c0af571e550307e591b9a4ff8aa07d3b7db83f7529bf7c20f698597 | MATLAB | 472 | 24 | function codes=genCode(W)
% generate codes for columns of logical matrix W
% W: logical, matrix of m x n
% code: column vector of n x 1
% Yifeng Li
% Mar. 18, 2013
% example:
% W=[true,false,false,true,true,true,;
% false,false,true,false,true,true];
% codes=genCode(W)
[m,n]=size(W);
codes=zeros(n,1);
for i=1:n
... |
3c237d8e94d990f39eb9ed8f261e25aa69a6acd7546e46e836551a97afba7cc5 | MATLAB | 473 | 22 | function txt = lfpCursor(empt, event_obj)
% Customizes text of data tips
persistent toggle
if isempty(toggle)
toggle = cputime;
end
pos = get(event_obj,'Position');
tag = get(event_obj.Target,'Tag');
txt = {['X: ',num2str(pos(1))],...
['Y: ',num2str(pos(2))],...
['TAG: ' tag]};
if cputime > toggle+0.5
lw = g... |
b09f294f323402762f1f52c4698e2f408633eb72981dab4b25d28ac211a3a44c | MATLAB | 473 | 24 | function IsoD = CalculateIsolationDistance( self )
% Calculate Isolation Distance
% ADR 2012/12
% construct components
% 1. FD for each element of ClusterSeparationFeatures
MCS = MClust.GetSettings();
[T, F] = MClust.CalculateFeatures(MCS.ClusterSeparationFeatures);
nSpikes = length(T);
nFeat = length(F);
FD = nan... |
8fe5a477d3e3a047618acb7db4ad5ad0c3a0b687b26606b53241bebf0df812b5 | MATLAB | 474 | 23 | function [PREPROC, cmdseqold, cmdseqnew] = nk_NumberCmdStr(PREPROC)
nA = numel(PREPROC.ACTPARAM);
cmdseqold = cell(nA,1);
cmdseqnew = cell(nA,1);
for i=1:nA
cmdseqold{i} = PREPROC.ACTPARAM{i}.cmd;
end
uCmd = unique(cmdseqold);
nuCmd = numel(uCmd);
for i=1:nuCmd
idx = find(strcmp(cmdseqold,uCmd{i}));
for... |
be2703317e5c5e45bb89b9dffbf663231cfd8443c6ffaac9107fe57362efe5da | MATLAB | 474 | 20 | function [Params, Desc] = nk_ReturnParam(Desc, Params_desc, opt)
Params = [];
if iscell(Params_desc)
for p=1:numel(Params_desc)
switch Params_desc{p}
case Desc
if iscell(opt(p))
Params = opt{p};
else
Params = opt(p);
... |
3f67b55e7cce1cb8acf2ee705a66d5fb265bf656be0d50123be6d7f026a64287 | MATLAB | 476 | 22 | function disp(obj)
% Disp a NIFTI-1 object
%__________________________________________________________________________
% Copyright (C) 2005-2017 Wellcome Trust Centre for Neuroimaging
%
% $Id: disp.m 7147 2017-08-03 14:07:01Z spm $
sz = size(obj);
fprintf('NIFTI object: ');
if length(sz)>4
fprintf('%d-D\n',lengt... |
7c45784e35bdf39b53f95e95f700a7e3ea474eb9f9e69d2dcb163ace05ce71e0 | MATLAB | 476 | 20 | function C = AUtoPhysicalUnits(X,B)
% INPUT
% X: data matrix (DxN; D: dimensionality of input data, N: number of samples)
% B: estimated components (DxK; K: number of components)
%
% Note that this function can also be used to calculate coefficients for out of sample subjects
%
% OUTPUT
% C: new matrix (KxN) - values... |
c153c1f4ff5bcad3761dd1139c39e087d044bf0552476c5f1b589942b5b542a0 | MATLAB | 477 | 25 | function clouds(f)
figure
xyz = f.xyz;
state_color = f.state_colors;
cdata = bt_utils.state_cdata;
xyzlim = [0 -1 0 ;...
1 1 1.3];
gridres = 0.025;
for j = 1:size(cdata,1)
pts = state_color == j;
if any(pts)
[Vcount] = utils.simple_bin3d(xyz(pts,:),xyzlim,gridres);
v... |
33f9c94e6d788c6c553b60dfe5ff1ac3cca2797a3e94417a6d33cf2fdda62b2b | MATLAB | 478 | 20 | function [scaled] = Scale(Data, Lower, Upper)
% [scaled] = Scale(Data);
% [scaled] = Scale(Data, Lower, Upper);
%
% scale the elements of all the column vectors in
% the range of [Lower Upper]. (default [-1 1])
if (nargin<3)
Lower = -1;
Upper = 1;
elseif (Lower > Upper)
disp (['Wrong Lower or Upper values!'])... |
e5358af043f6be325d3784fff8755868fdb99ddeb60cd99e17fb05601ddba26c | MATLAB | 479 | 23 | function [ V, Ro ] = add_outlier(rho, F, N, Vo)
dense = rho * F;
Ro = zeros(F,N);
for i = 1 : N
n_before = 0;
for f = 1 : dense
c = randi(F);
Ro(c,i) = randi([30,50]);
n = nnz(Ro(:,i));
if n_before == n
... |
951fe2af12311cf3e0abc8a1af6f36a270e0dc93c48430c8dd4fe77fbcd6f33c | MATLAB | 480 | 18 | function adjust = itSol(sdat,g_hat,d_hat,g_bar,t2,a,b, conv)
g_old = g_hat;
d_old = d_hat;
change = 1;
count = 0;
n = size(sdat,2);
while change>conv
g_new = postmean(g_hat,g_bar,n,d_old,t2);
sum2 = sum(((sdat-g_new'*repmat(1,1,size(sdat,2))).^2)');
d_new = postvar(sum2,n,a,b);
change = m... |
24a46873f89cf1611fb30389df910ee750ade28277060bbdeb4160849ec8b832 | MATLAB | 482 | 15 | function loaded = loadobj(loaded)
% This function is designed to convert older instances
% into newer instances
return
% Error is: "You cannot set the read-only property 'param_symbols' of full."
if isempty(loaded.model.param_symbols)
disp('Upgrading model parameter symbols and units using sam... |
e6a7fed43083ddd933863be0bbe4fc7541df56be04b431d20ddc793bab950934 | MATLAB | 482 | 21 | function [newL, newR] = rowsum_R_one(L, R, varargin)
% normalizes rows in R so that the product LR stays the same
% handle_zeros option: leaves rows that sum up to 0 as they were
coeffL = sum(R,2);
% added by HK
coeffL = max(coeffL, 1e-16);
if not(isempty(varargin)) && varargin{1}==1
... |
1df39d2214edffab559a5258da64f0459f6240b96ba995c3a238dbee83110693 | MATLAB | 483 | 18 | function MutualInformation = nk_MutualInfo(Y, labels)
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
% function MutualInformation = nk_MutualInfo(symY, labels)
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
ix = size(Y,2);
MutualInformation = zeros(ix,1);
try
parfor i=1:ix
sY = Y(:,... |
b387a33eda0c7f2be4faa1a7704a541dda025ae07033555b4dc9a71e8c2b25ea | MATLAB | 483 | 11 | function x_new = NextSolution3(x_curr, itt, n, featureID_order)
% The new voxel is introduced to the solution in the order determined by
% futureID_order.
moduloItt = mod(itt,n); if moduloItt == 0, moduloItt = n; end
x_new = x_curr;
% @#$% user-defined
% ====================================================
% Toggle the... |
f7b41a9c763bcf84b8ba14dbd05ff64961e46f71057fbf35dc4061629d59e405 | MATLAB | 483 | 22 | function [vscaled,minv,maxv]= scale_matrix(v,minv,maxv)
% scale_matrix scales a matrix into [0 1] along the vertical direction,
% this is a simplified version
%
% SYNTAX
%
% Inputs: v: m*n matrix;
% Outputs:
% Wenbin, 08-Mar-2007
vr =size(v,1);
if vr <2
error('The rows of input matrix must be greater than 1');
end
if ... |
d707047d8a56c71269219f7e8b44a79b579450904ce40ed6f275295e4e9a422e | MATLAB | 484 | 14 | function [blocks,block_color,state_numbers] = state_blocks(self)
% For a given state_str
% return a matrix of the states and their corresponding colours
% which can be used as indexes for plotting
state_number = self.state_colors;
trans = find(diff(state_number))+1;
trans = [1; trans(:); self.latest];
blocks =... |
c76a93d77ff23ca5e1b97b8a1a01dab82898e9d4f09fa6a90dc7d931515937a0 | MATLAB | 485 | 13 | function [expectedC,expectedL] = ER_Expected_L_C(k,n)
% ER_EXPECTED_L_C the expected path-length and clustering of an ER random graph
% [C,L] = ER_EXPECTED_L_C(K,N) for a network of N nodes and mena degree K,
% computes the expected shortest path-length L and expected clustering
% coefficient C if that network w... |
464a0123e3af181409532d806940d77224719e07a350034a6120b3caf82f5645 | MATLAB | 488 | 16 | function [ts] = PL2StartStopTs(filename, startOrStop)
% PL2StartStopTs(filename, startOrStop): read recording start or recording stop event timestamps from a .pl2 file
%
% [ts] = PL2StartStopTs(filename, 'start')
% [ts] = PL2StartStopTs(filename, 'stop')
%
% INPUT:
% filename - if empty string, will use File Open dia... |
0c4d8b75e8a7f40aee90cd4432282de45c01a8bd697da4d18d6da1257f759ac1 | MATLAB | 490 | 17 | function modeflag = nk_DefineModeflag(modeflag)
defmode = 1;
if exist('modeflag','var') && ~isempty(modeflag)
modes = {'classification','regression'};
defmode = find(strcmp(modes,modeflag));
if isempty(defmode),defmode = 1; end
end
modeflag = nk_input('Would you like to perform ...',0,'m', ...
... |
e8944c7b28c027f4dcf6a0fd887d666300ccfbf85cfcd7120d0e335ccf7e2959 | MATLAB | 490 | 19 | function t = structn(obj)
% Convert a NIFTI-1 object into a form of struct
%__________________________________________________________________________
% Copyright (C) 2005-2017 Wellcome Trust Centre for Neuroimaging
%
% $Id: structn.m 7147 2017-08-03 14:07:01Z spm $
if numel(obj)~=1
error('Too many elements to c... |
11b0f0897d70a065326897b48af955ac0cdde4ecc38a63e8f0f37eb741829752 | MATLAB | 491 | 18 | function Q = mripy_normalize_weight(Q, exclude_diag)
%MRIPY_NORMALIZE_WEIGHT Normalize positive and negative weights to sum up to ±1
% 2017-08-06: Created by qcc
if nargin < 2
exclude_diag = false;
end
if exclude_diag
Q(eye(length(Q))>0.5) = 0;
end
P = (Q > 0);
N = (Q < 0);
... |
20188e7f58859cc3f614cc5d32aea43cd008edda0b64773a5b09585b7c0d1ef9 | MATLAB | 494 | 18 | function o = niftistruc(fmt)
% Create a data structure describing NIFTI headers
%__________________________________________________________________________
% Copyright (C) 2005-2017 Wellcome Trust Centre for Neuroimaging
%
% $Id: niftistruc.m 7147 2017-08-03 14:07:01Z spm $
if ~nargin, fmt = 'nifti1'; end
switch low... |
a10a6daf0062b68984790878e273b6614499e429d2a345fdd2d0c27bfa87c24e | MATLAB | 494 | 14 | function handles = binarize_regr(handles)
m = str2double(get(handles.txtBinarize,'String'));
if isempty(m)
errordlg('Enter numeric threshold')
elseif m < min(handles.labels) || m > max(handles.labels)
errordlg(sprintf('Threshold out of target range [%g %g]',min(handles.curRegr.labels),max(handles.curRegr.... |
3d64ce993929c7d7ca6fb72c171e21f917a3f5e42cfa24bce65b108e6150b467 | MATLAB | 497 | 24 | nvids = numel(proc.npix);
t0 = 20000;
clf;
np = [0 proc.npix];
np = cumsum(np);
mall=[];
nc = 10;
for ic = 1:nc
for k = 1:nvids
i1 = proc.uMotMask{1}(np(k)+[1:proc.npix(k)], ic);
ib = NaN*zeros(floor(proc.nY{k}/proc.sc), floor(proc.nX{k}/proc.sc));
ib(proc.wpix{k}) = i1;
my_subplo... |
5a3ef9eba2d3976a8202f30e43c45a1d3107c45791ac0102ae5cf5f1f6a9c65e | MATLAB | 497 | 19 | function gCell = sparseCellArray(Y1, Y2, sp) % in case of test kernel, Y1 should be Ytest
if ~isequal(Y1,Y2)
test = 1;
Y = vertcat(Y1,Y2);
else
test = 0;
Y = Y1;
end
gCell= {};
for i = 1:size(Y,1)
% apply sparsity threshold
spArray = apply_sparsity_th... |
e2334e628c1eb306d9770ccd53d1be945ee396f80f62ec4bc504970ab5aa6c01 | MATLAB | 497 | 14 | function DScore = simbamain(Y, labels, cpus, SortInd)
global FEATSEL
gpu = FEATSEL.simba.gpu;
extra_param = FEATSEL.simba.extra_param;
if nargin < 5, if ~isempty(SortInd), Y = resamp(Y, SortInd, cpus); end; end
if FEATSEL.salthreshmode == 1, extra_param.salCI = FEATSEL.salCI; end
if isfield(extra_param,'beta') && st... |
6ae7bb30c4418945d414d048c9f22e8069db2ee21ccd2105c30948ce97e43d4c | MATLAB | 498 | 24 | function OK = WriteWVfiles(self)
% OK = WriteWVfiles(MCD)
MCD = self;
nClust = length(MCD.Clusters);
WV = MCD.LoadNeuralWaveforms();
WVT = WV.range();
WVD = WV.data();
for iC = 1:nClust
tSpikes = MCD.Clusters{iC}.GetSpikes;
if ~isempty(tSpikes)
fnWV = [MCD.TfileBaseName(iC) '-wv.mat'];
[mW... |
8d31634b38c22fe6e10ac2b693d99697902b6330802f28bdf58166574ed1799d | MATLAB | 498 | 22 | function map = coolhot1(siz)
% Create a colormap that is a combination of
% 'cool' and 'hot'.
%
% Usage:
% coolhot(SIZ)
%
% - SIZ = Size of the colorbar. To ensure symmetry,
% an even number is recommended.
%
% _____________________________________
% Anderson M. Winkler
% Yale University / Institute of Living
... |
8e2413fc146b836751d062e255c89e6bb6337e4d1623044435f87400c1d713f4 | MATLAB | 501 | 19 | function nk_ThreshImages
P = spm_select(Inf,'image','Select images');
V = spm_vol(P);
threshval = spm_input('Specify percentile threshold for images ',0,'e');
for i=1:numel(V)
[p,n,e] = fileparts(V(i).fname);
Vx = spm_read_vols(V(i));
ind = Vx(:) > 0;
Thresh = prctile(Vx(ind), threshval);
VxP = ... |
defadcdface416c0cb90ee2758daaa163b6d5848275eff4b2bfb9a42aec0d7e3 | MATLAB | 502 | 16 | function output = joint(X,arities)
%function output = joint(X,arities)
%returns the joint random variable of the matrix X
%assuming the variables are in columns
%
%if passed a vector of the arities then it produces a correct
%joint variable, otherwise it may not include all states
%
%if the joint variable is only compa... |
ed75a65a1eb8f560b246edde3e5533165ed29d1d3a8b5816909408af80f4a150 | MATLAB | 504 | 25 | function tsout = merge(varargin)
% tsout = merge(tsA, tsB, ...)
%
% merges the sequences of tsds
% checks to make sure sizes match
nTSD = length(varargin);
assert(nTSD>=2, 'Cannot concatenate < 2 tsds.');
assert(isa(varargin{1},'tsd'), 'Initial tsd not a tsd.');
T = varargin{1}.range;
D = varargin{1}.data;
for iTS... |
e59115a783d77983078ee61b9769e88d15d2e35854ef41a3ef0cbba493866f7b | MATLAB | 507 | 1 | function CutterOption_00_CheckAllClusters(self)
% CheckClusters(self)
%
%
% INPUTS
%
% OUTPUTS
%
% NONE
% NCST 2002
%
% Status: PROMOTED (Release version)
% See documentation for copyright (owned by original authors) and warranties (none!).
% This code released as part of MClust 3.0.
% Version control M3.0.
% Extensi... |
4d601b00b0a037e78133cfccf63d8548e4d9c8493d27de02b24710b49b0b153a | MATLAB | 509 | 23 | function out = unmask(mask, data, fillVal)
%% UNMASK Adds empty rows to data (which may have been removed previously)
%% Examples
% unmask([1 0 1 0 1 0], (1:3)')
% unmask([1 0 1 0 1 0 1 0]', magic(4), 0)
%
%
%% TODO
% * docs
%
%
%% Authors
% Mehul Gajwani, Monash University, 2024
%
%
assert((numel(mask)==len... |
c36bfbcd91329ed0ccfb17a8ca4f7847ed6faf57a8a5fb50fceaf77d204f9a50 | MATLAB | 509 | 21 | function A = nk_Ambiguity(P,~)
%
% Compute the entropy-based ambiguity according to the formula given in
% Padraig Cunningham TCD-CS-2000-07
m = size(P,1);
Mx = 0;
for i=1:m % Loop through samples x_i
Kx = 0;
pos = P(i,:)~=0;
minx = min(P(i,pos));
maxx = max(P(i,pos));
for j=minx:maxx
... |
afb98024b23e344eb3fe13f11d1848ff2e2c388d70dabac4c9931dbe11051d65 | MATLAB | 510 | 17 | function [verts, faces] = mripy_average_asc(fnames, output)
%MRIPY_AVERAGE_ASC Average the volumetric geometry of surface meshes.
% 2017-08-17: Created by qcc
N = length(fnames);
[verts, faces] = mripy_read_asc(fnames{1});
for k = 2:N
[v, f] = mripy_read_asc(fnames{k});
assert(all(size(v)=... |
ab3650c7d33679ff817319d2256d3d2bd25862b55690dbb2bc83dba71fed9020 | MATLAB | 513 | 24 | function ClusterFunc_AddSpikesByPolygon(self)
% PreCut Clusters - ClusterFunction_AddSpikesByPolygon
%
% Adds ability to add individual spikes
MCC = self.getAssociatedCutter();
MCC.StoreUndo('Add Spikes by Polygon');
[xg,yg] = DrawPolygonOnAxes(MCC, false);
plot(xg,yg,'-', 'color', self.color);
drawnow;
% get axes... |
37eb95786a71ae73aa9e281e28e8212b7c6b4e2e87a2a751376993948f7035ba | MATLAB | 514 | 15 | classdef reduced_alpha_emphasized < bt.model.reduced
% This model is the same as reduced, but with increased alpha weighting
methods
function self = reduced_alpha_emphasized() % Constructor
self = self@bt.model.reduced
self.name = 'reduced_alpha_emphasized';
end
function w = get_weights(self,target_f) %... |
cc5e4911c4710fbe7ab694dfcc8522654877f44de29010eae2b5e23892de1657 | MATLAB | 514 | 28 | function [w, b] = nk_GetPrimalW(model)
global SVM MODEFL
switch SVM.prog
case 'LIBSVM'
w = model.SVs' * model.sv_coef;
b = -model.rho;
if strcmp(MODEFL,'classification')
if model.Label(1) == -1
w = -w;
b = -b;
end
en... |
0f941179abe873c42686be25af1b3c7ddb9b06c91c91ddcd3d15d3efa8dfeffe | MATLAB | 516 | 21 | function A = nk_Diversity(E, L, m, n)
% function A = nk_Diversity(P, vec, m, n)
% Compute entropy-based measure of ensemble diversity
ind0 = L ~= 0;
E = E(ind0,:); L = L(ind0);
if ~exist('m','var'), m = size(E,1); end
if ~exist('n','var'), n = size(E,2); end
E = sign(E);
rL = repmat (L, m, n);
cE = E == rL; ... |
ee57bb02c8ee46161ad5798920914c90e6f2f72201f8dd3f1018d59f239c4d3c | MATLAB | 516 | 19 | % SB1_DIAGNOSTIC Output neat diagnostic info with verbosity control
%
%
% Copyright 2009 :: Michael E. Tipping
%
% This file is part of the SPARSEBAYES baseline implementation (V1.10)
%
% Contact the author: m a i l [at] m i k e t i p p i n g . c o m
%
function SB1_Diagnostic(level, message_, varargin)
Diagnostic = ge... |
6e39d79cdf32a4e6b8a25615fd374758219df29cf30f29f3722a5cfebea700c3 | MATLAB | 518 | 18 | function [out,f] = Mysvmdecision(Xnew,svm_struct)
%SVMDECISION evaluates the SVM decision function
% Copyright 2004-2006 The MathWorks, Inc.
% $Revision: 1.1.12.4 $ $Date: 2006/06/16 20:07:18 $
sv = svm_struct.SupportVectors;
alphaHat = svm_struct.Alpha;
bias = svm_struct.Bias;
kfun = svm_struct.KernelFunction;
... |
d55fae66eff2d6f25f7fe9355e1f5a6f6d541b08789e600ab25c77c116a9c129 | MATLAB | 519 | 26 | function ClusterFunc_AddSpikesByCvxHull(self)
% PreCut Clusters - ClusterFunction_AddSpikesByConvexHull
%
% Adds ability to add individual spikes
MCC = self.getAssociatedCutter();
MCC.StoreUndo('Add Spikes by Cvx Hull');
[xg,yg] = DrawPolygonOnAxes(MCC, true);
plot(xg,yg,'-', 'color', self.color);
drawnow;
% ge... |
4264782b0c4ae551303f34c8151bccaaaffe69af7d425eb2729bf0574b01e4d9 | MATLAB | 521 | 29 | function rr = smoothPupil(parea)
win = 30;
rr = parea(:);
RR = zeros(win, numel(rr));
for k =-win/2:win/2
if k<0
RR(k+win/2+1, 1:end+k) = rr(-k+1:end);
else
RR(k+win/2+1, k+1:end) = rr(1:end-k);
end
end
mrr = nanmedian(RR,1)';
ix = find(isnan(mrr) | isnan(rr));
ix2 = find(~(isnan(mrr) | ... |
c553ff49e1e34ef33a59e5d23bc6b3270b57b38e4d673c4761465c1ece86e26e | MATLAB | 522 | 16 | clear
load('ProcessedData\tables_compiled_bestD1.mat');
%% generate heatmaps for trials by D1 responsive order
% for now, i'm editing colormaps in the GUI as I haven't figured out
% how to to do that right in code as of yet
figure, tiledlayout(1,5)
for i = 1:length(tables_compiled)
D_now = tables_compiled{... |
70bec558fba0d175227dac840bb833567fc167e17e6256486689d85712c6d536 | MATLAB | 525 | 20 | function [C_tri]=transitivity_bu(A)
%TRANSITIVITY_BU Transitivity
%
% T = transitivity_bu(A);
%
% Transitivity is the ratio of 'triangles to triplets' in the network.
% (A classical version of the clustering coefficient).
%
% Input: A binary undirected connection matrix
%
% Output: T t... |
5d245cbb21e1dd2011b5eaf3ed3f0da3c30429650d750fb5fc07d1a56f197424 | MATLAB | 526 | 20 | function cost=IMRelief_cost(alpha, Z, Weight, descent, Targets, lambda)
%Function ComputeAlpha: compute alpha using linear search
Weight = Weight-alpha*descent;
a = ((Weight(:).^2)')*Z; %margin
Result = 1./(1+exp(-a)); %probability of being class 1
index = Result==1;
Result(index) = 1-10^(-10);
index = Result==0;
Re... |
d0df914bab608584ea88d0e1892bc3025a41a975c4b1a118af8ef08639aefc8c | MATLAB | 527 | 22 | %% Master_script_AST
clearvars; close all;clc;
%% load data
load('dn.mat') % calcium signal
load('behEvtTbls.mat')
%% extract peri-event signal array
sigArray = periEventSigArray(dn,behEvtTbls);
%% calculate convergence and visualization
sessionName = {'Rev','EDS'};
distMethod = 'correlation';
%distM... |
e07c25cd9ed211b7e47f55f56b1d98423700bc5e0c1d90c7b165f43d8a26fe1a | MATLAB | 527 | 10 | function I = nk_SignBasedConsistency(E)
% Compute modified version of sign-based consistency criterion using a binary
% classifier ensemble. See paper by Vanessa Gomez-Verdejo et al.,
% Neuroinformatics, 2019, 17:593-609. We additionally downweight the
% consistency vector I by the number of nonfinite values in the ens... |
6a051ac08362e94cf41063d1b6d2b356f19c19951eaa36c6c541a2fcc320e900 | MATLAB | 528 | 25 | function N = nk_FormatNicely(S)
[m,n] = size(S);
I = strfind(cellstr(S),'[');
ind0 = ~cellfun(@isempty,I);
if ~isempty(I)
try
maxI = max(cell2mat(I(ind0)));
catch
maxI = max(cellfun(@(v) v(1), I(ind0)));
end
else
maxI=[];
end
if isempty(maxI), N = S; return; end
N = cell(m,1);
for i=... |
cbd0283d35d742dd60fca2c73b142558c6d096ccf985e5129c2489f604c9dc5b | MATLAB | 528 | 13 | function altlabels = nk_DataLabel_config(n_subjects_all, altlabelnames)
% this function asks the user to define alternative labels for the
% validation data as used in the locked analyses
unique_altlabels = unique(altlabelnames);
c = cell(length(unique_altlabels),1);
altlabels = cell2struct(c, unique_altlabels);
for... |
fbf58fa928a61c03bfcad33e7a73249a564fe14558664d766d5cb5606f9305d9 | MATLAB | 530 | 14 | % =========================================================================
% FORMAT param = MAERR(expected, predicted)
% =========================================================================
% Compute Mean Absolute Error of regression
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% (c... |
2898024ea1ae87abdae39e3fcd906d8a4cb6417730438d043244338ae2d423a7 | MATLAB | 532 | 25 | function ClusterFunc_RemoveSpikesByCvxHull(self)
% PreCut Clusters - ClusterFunc_RemoveSpikesByCvxHull
%
% Adds ability to add individual spikes
MCC = self.getAssociatedCutter();
[xg,yg] = DrawPolygonOnAxes(MCC, true);
plot(xg,yg,'-', 'color', self.color);
drawnow;
MCC.StoreUndo('Add Spikes');
% get axes
xFeat = ... |
4182ae69e231e70e08dff63adb4fdd50733130d5b42fca89b04cddd6d3bc696a | MATLAB | 532 | 25 | function ClusterFunc_RemoveSpikesByPolygon(self)
% PreCut Clusters - ClusterFunc_RemoveSpikesByPolygon
%
% Adds ability to add individual spikes
MCC = self.getAssociatedCutter();
[xg,yg] = DrawPolygonOnAxes(MCC, false);
plot(xg,yg,'-', 'color', self.color);
drawnow;
MCC.StoreUndo('Add Spikes');
% get axes
xFeat = ... |
5f740550ca028cee44b8105c65672339283bdddbd879376c379cbd7385f6a526 | MATLAB | 532 | 14 | % =========================================================================
% FORMAT param = NMRSD(expected, predicted)
% =========================================================================
% Compute Normalized Root of Mean Squared Deviation (NRMSD) of regression
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
addd5750becb9700556e155adc271d17cc6b9effae9504149f6d10b2fca40d01 | MATLAB | 532 | 25 | function Vm = nk_Write2Atlas(Y, fname, Vm)
if ~exist('Vm','var') || ~exist(Vm, 'file')
Vm = spm_select(1,'image','Select atlas image');
end
Vmvol = spm_vol(Vm);
A = spm_read_vols(Vmvol);
Al = unique(A(:));
if Al(1) == 0, Al(1)=[]; end
if numel(Al) ~= numel(Y)
error('Vector Y does not have the same dimensional... |
7c37c754a93aeddd459879ca87966fac216c418d2ec6cc4a63df618a4b19241c | MATLAB | 534 | 20 | function view(tree)
% XMLTREE/VIEW View Method (deprecated)
% FORMAT view(tree)
%
% tree - XMLTree object
%__________________________________________________________________________
%
% Display an XML tree in a graphical interface.
%
% This function is DEPRECATED: use EDITOR instead.
%_______________________________... |
6d429bcc433d5ab2f541547b21828246a8789539ffd108a5df215971f6731183 | MATLAB | 537 | 28 | function ClusterFunc_AddLimit(self)
% Convex Hull clsuter add limit
MCC = self.getAssociatedCutter();
[xg,yg] = DrawPolygonOnAxes(MCC, true);
plot(xg,yg,'-', 'color', self.color);
drawnow;
MCC.StoreUndo('Add Limit');
% get axes
xFeat = MCC.get_xFeature();
yFeat = MCC.get_yFeature();
iL = self.findLimit(xFeat, yF... |
a240fd24fdcecd6f7705da4c38cb94344d26360458785eb228317c42bfb85354 | MATLAB | 537 | 1 | function CutterOption_CloseAllNonEssentialFigs(self)
% CloseAllNonEssentialFigs(self)
%
%
% INPUTS
%
% OUTPUTS
%
% NONE
% NCST 2002
%
% Status: PROMOTED (Release version)
% See documentation for copyright (owned by original authors) and warranties (none!).
% This code released as part of MClust 3.0.
% Version control... |
0e00324d972fbe400ae7db6bac76689fdea371ca8de0e4721d66efb41db2c194 | MATLAB | 538 | 25 | function P=innerProduct(A,B,option)
% compute the inner product of two matrices which containing missing values
% A: matrix
% B: matrix
% P, matrix, the inner product of A and B. That is A'*B;
% Yifeng Li
[ra,ca]=size(A);
[rb,cb]=size(B);
if ra~=rb
P=[];
error('A and B should have the same number of rows');
e... |
3e89a6d4b2773a23277a06b3d1a8bb8a4d6f9029a8c4753e0f08e0da57b5d435 | MATLAB | 538 | 30 | function n = normc(m)
%NORMC Normalize columns of a matrix.
%
% Syntax
%
% normc(M)
%
% Description
%
% NORMC(M) normalizes the columns of M to a length of 1.
%
% Examples
%
% m = [1 2; 3 4]
% n = normc(m)
%
% See also NORMR
% Mark Beale, 1-31-92
% Copyright 1992-2007 The MathWorks, Inc.
% $Revisio... |
6509837b7269f5b53ebc14f90f89f5a264b45d2de6e48a8e6f1130f0765bd533 | MATLAB | 538 | 23 | function G = generateDAG(T)
[mT,nT]=size(T);
G = zeros(mT,mT-1);
G(1,1)=1;
i=2;
oT=T;
while i<mT
cnt = 1;
d = G(:,i-1);
dvec = find(d);
for j=1:numel(dvec)
jT = oT(:,d(dvec(j)));
tT = oT(:,d(dvec(j))+1:nT);
Ip = find( tT( jT == 1, :) == 1 );
In = find( tT (jT == -1, ... |
0c56cbc1f5c5b5867e4b13b160504c47d1ade0a8df8b2ce79fc4fff8a77d0408 | MATLAB | 539 | 25 | function ClusterFunc_LimitSpikesByPolygon(self)
% PreCut Clusters - ClusterFunction_LimitSpikesByConvexHull
%
% Adds ability to limit spikes
MCC = self.getAssociatedCutter();
MCC.StoreUndo('Limit Spikes by Polygon');
[xg,yg] = DrawPolygonOnAxes(MCC, false);
plot(xg,yg,'-', 'color', self.color);
drawnow;
% get axe... |
64fe5a0f709a3891b54556bddcbf1857b81f3ec00f5d9a4773228a846f4e19c0 | MATLAB | 539 | 15 | % UIWAIT makes nk_PrintResults2 wait for user response (see UIRESUME)
% uiwait(handles.figure1);
function load_popupmenu1(handles)
switch handles.modeflag
case 'classification'
for i = 1:handles.nclass
popuplist{i} = sprintf('Classifier %g: %s', i, handles.BinClass{i}.description);
end
... |
bf128a1ff81f2832fa3402caec6e631f0fa9b94f5a7da2367a89ad3190676552 | MATLAB | 539 | 31 | function mED = nk_LobagMulti(E, T, L, C, uC, luC, G)
[dum, hE] = nk_MultiEnsPerf(E, T, L, C, G);
if nargin < 6
uC = unique(C); luC = length(uC);
end
% Biased results
bias = hE ~= L;
% Biased variance
vb = 0;
% Unbiased variance
vu = 0;
for i=1:luC
vu = vu + var(E(~bias,C==uC(i)),1,2);
vb = vb + v... |
37418e5e430724452886d5616cb38312b0004a35a9dea51f784bd129a7ca404d | MATLAB | 540 | 22 | % Extract_varargin
%
% NOT A FUNCTION -- this allows it to access the current workspace
%
% expects varargin to consist of sequences of 'variable', value
% sets variable to value for each pair.
% changes the current workspace!
%
% ADR 1998
% version L4.0
% status: PROMOTED
%
%
warning('MATLAB:depreciated', 'e... |
47288a9a14bef0193682ca2a92445544d77b6c990e1e65d2b77e8732cb9cbd85 | MATLAB | 541 | 13 | % =========================================================================
% FORMAT param = RMSD(expected, predicted)
% =========================================================================
% Compute Root of Mean Squared Deviation (RMSD) of regression
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
24d4faeffdce154d8b7073f232bbc9ac1fd6d6a6b9bc3de02b872304efb47d28 | MATLAB | 542 | 25 | function ClusterFunc_LimitSpikesByCvxHull(self)
% PreCut Clusters - ClusterFunction_LimitSpikesByConvexHull
%
% Adds ability to limit spikes
MCC = self.getAssociatedCutter();
MCC.StoreUndo('Limit Spikes by Convex hull');
[xg,yg] = DrawPolygonOnAxes(MCC, true);
plot(xg,yg,'-', 'color', self.color);
drawnow;
% get ... |
a2bb35adc5ae37b023a50b2d7a98387bd8fe48dc05b551aaadcae5519766c306 | MATLAB | 542 | 17 | function num_closed_loops = count_closed_loops(A)
% Get the size of the adjacency matrix (number of nodes)
[n, m] = size(A);
% Initialize the closed loop counter
num_closed_loops = 0;
% Loop through the upper triangular part of the matrix to avoid double counting
for i = 1:n
fo... |
fdb016e6925e89f5504cd0bae6ff085b5a1d8ac5595a02fe1d54ff012e68471f | MATLAB | 543 | 22 | function hX = h(rX, epsilon)
lenVec=size(rX,1);
numVec=size(rX,2);
% Compute the conjunction of all binary
% rank distance matrices.
Cx=0;
for n=1:lenVec
xBinVec = abs(rX(1:lenVec-n,1)-rX(1+n:lenVec,1))<epsilon;
for i = 2:numVec
xBinVec = xBinVec & (abs(rX(1:lenVec-n,i)-rX(1+n:lenVec,i))<epsilon);
... |
039ec422dc96cc8220691f76950017cde170d345ab165cb3db3c4753b10cc8c6 | MATLAB | 544 | 26 | function t = fieldnames(obj)
% Fieldnames of a NIFTI-1 object
%__________________________________________________________________________
% Copyright (C) 2005-2017 Wellcome Trust Centre for Neuroimaging
%
% $Id: fieldnames.m 7147 2017-08-03 14:07:01Z spm $
if isfield(obj.hdr,'magic')
t = {...
'dat'
... |
fb7c00d467181bf889e633458854fdd7626cbb4ffe44cc1da96ad44cbe409a18 | MATLAB | 544 | 18 | function [ res ] = MCE( labels, predictions )
ordered = sortrows([predictions,labels]);
N = size(ordered,1);
rest = mod(N,10);
for i = 1 : 10
if (i <= rest)
group = ordered((i-1) * ceil(N / 10) + 1 : i * ceil(N / 10),:);
else
group = ordered(rest + (i-1) * fix(... |
156bc48a99fbcd6b2062ed70c4a30a25e503bf0e41b29b215c8af6daeb774db5 | MATLAB | 545 | 24 | function D = nk_AUCFeatRank(Y,L, AUCparam)
global VERBOSE
[~,nY] = size(Y);
D = zeros(nY,1);
if length(unique(L))==2
uL = unique(L);
Ltemp = L;
Ltemp(L == min(uL)) = -1;
Ltemp(L ~= min(uL)) = 1;
else
Ltemp = L;
end
for i=1:nY
D(i) = AUC(Ltemp,Y(:,i));
end
if exist('AUCparam','var')
in... |
a94e3faec324d80fee6e37a3302a726768d2f94f6dd3b049cc77e97ac0720f35 | MATLAB | 547 | 26 | function output = condh(X,Y)
%function output = condh(X,Y)
%X & Y can be matrices which are converted into a joint variable
%before computation
%
%expects variables to be column-wise
%
%returns the conditional entropy of X given Y, H(X|Y)
if nargin == 2
if (size(X,2)>1)
mergedFirst = MIToolboxMex(3,X);
else
... |
d1c517e1c7ba11bac489c3c0142328e6d62ff7fa1b46fe3cba4812f93d1e12e4 | MATLAB | 550 | 19 | function data = mripy_write_geometry(fname, verts, faces)
%MRIPY_WRITE_GEOMETRY Write FS/SUMA surface (vertices and faces) as
% three.js BufferGeometry.
%
% References
% ----------
% https://threejs.org/docs/#api/core/BufferGeometry
%
% 2017-08-17: Created by qcc
data.position = reshape(verts(:,1:3)', []... |
d6987bfc4b871ba461df14abed6d3228b445ce7a20c8d9f0c886616c9209b5ec | MATLAB | 550 | 14 | % =========================================================================
% FORMAT param = MSE(expected, predicted)
% =========================================================================
% Compute Mean Standand Error of regression
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% (c) ... |
2c5094d685844c0a237d987b8316b3d07a4e39dcfb1cf49bb0b802d1c83a68f9 | MATLAB | 551 | 22 | function indperm = nk_VisXPermHelper(act, N, nperms, L)
s = RandStream.create('mt19937ar','seed',sum(100*clock));
RandStream.setGlobalStream(s);
switch act
case 'genpermlabel'
indperm = zeros(N, nperms);
for perms = 1:nperms
indperm(:,perms) = randperm(N);
end
case 'genper... |
cd50b67357284b2abb2ee41f279d6b058b9e55166cde9b8a5396c30ac05eee84 | MATLAB | 551 | 22 | function spectrum(self)
% Plot the spectrum
if self.latest == 0
return
elseif self.latest > 1
fit = self.subrange(self.latest);
fprintf(2,'Multiple fits provided - displaying the last one\n')
else
fit = self;
end
figure
set(gca,'XScale','log','YScale','log')
hold(gca,'on');
box(gca,'on');
set(gca,'X... |
e88de83940292ecbaee4d4e2f435ba9e8bda8696bbdf9d096fe3d394ef83fc02 | MATLAB | 552 | 23 | function NeuroMinerMCCMain(action, paramfile)
nm_pth = which('nm');
pth = fileparts(nm_pth);
hpc_pth = [pth filesep 'hpc'];
addpath(hpc_pth);
switch action
case 'preproc'
nk_Preprocess_batch(paramfile)
case 'train'
nk_MLOptimizer_batch(paramfile)
case 'visualize'
nk_VisModels_batch... |
35935780bb6b651c693ec213b996b123825ae44140fcfc920314429d94737eb6 | MATLAB | 554 | 21 | function resample_image(V, voxsiz)
if ~exist('V','var') || isempty(V)
V = spm_select([1 Inf],'image');
end
if ~exist('voxsiz','var') || isempty(voxsiz)
voxsiz = [3 3 3]; % new voxel size {mm}
end
V = spm_vol(V);
VV = [V(1) V(1)];
for i=1:numel(V)
bb = spm_get_bbox(V(i));
VV(1:2) = V(i);
VV... |
ac3b617c412eda3ce7ca0c3418503d82721f98cdf9babecdb5bdb8d9cb925fa3 | MATLAB | 555 | 17 | clear
load('ProcessedData\tables_compiled_bestD1.mat');
%% generate heatmaps for trials by D1 responsive order
% for now, i'm editing colormaps in GUI as I haven't figured out
% how to to do that right in code as of yet
figure, tiledlayout(1,5)
D1 = tables_compiled{1,1}.Deltas(1:8,:).';
[~,idx] = sort(mean(D1,... |
a6a930840f4db47a5e785f867ed627804720cd232ef872eb0ca303668ef1bbb1 | MATLAB | 556 | 21 | % Objective function for NDM
%
% param(1) = beta
% param(2) = x0_value
function [f] = objfun_NDM(param,seed_location,pathology,time_stamps,C)
%y = param(1)*param(2) - pathology(2,1) + C(1,1) + seed_location(1);
% Define Laplacian matrix L
rowdegree = (sum(C, 2)).';
L = diag(rowdegree) - C;
% Calculate pred... |
647ffeb6c61727633e9cc2d16fe1d232a7a63b714934f0596510303c432d574c | MATLAB | 557 | 31 | function chain_diagnostics_parallel(model,out,chisq_out,accept)
keyboard
n = size(out{1},1);
m = length(out);
for k = 1:size(out{j},2) % For each variable
for j = 1:m
subplot(size(out{1},2),1,k);
h = autocorr(out{j}(:,k),1000);
plot(h)
hold all
end
end
theta = NaN;
for k = 1:size(out{j},2) ... |
793d7cadb5a43287c3b905e6fcb68d100ee51c259c194f256f7b926c8add388e | MATLAB | 557 | 23 | function output = mi(X,Y)
%function output = mi(X,Y)
%X & Y can be matrices which are converted into a joint variable
%before computation
%
%expects variables to be column-wise
%
%returns the mutual information between X and Y, I(X;Y)
if (~isa(X,'double') || ~isa(Y,'double'))
error('Error, inputs must be double vect... |
a8a2bec1b966d8cfb00b5d27e662632950ca397f9ce9095f31fa6b84606ca4b6 | MATLAB | 557 | 20 | function a = reshape(b,varargin)
% Overloaded reshape function for file_array objects
%__________________________________________________________________________
% Copyright (C) 2005-2017 Wellcome Trust Centre for Neuroimaging
%
% $Id: reshape.m 7147 2017-08-03 14:07:01Z spm $
if length(struct(b))~=1, error('Can onl... |
ba45644763d595438e8d03556770b9f87f2e5dae43e6cc8a5085e4509afc52d7 | MATLAB | 557 | 21 | function [DScore, idxF] = gflipmain(Y, labels, cpus, FeatInd, SortInd)
global FEATSEL
ix = size(Y,2);
if nargin < 6, if ~isempty(SortInd), Y = resamp(Y, SortInd, cpus); end; end
if nargin < 4 || isempty(FeatInd), FeatInd = true(ix,1); end
gpu = FEATSEL.gflip.gpu; extra_param = FEATSEL.gflip.extra_param;
idxF = f... |
8d61481d965eb7c0312e2c357d7a76c0e26607cd122c7f8d10268be707da818a | MATLAB | 558 | 23 |
function graph = array_to_graph2(a)
% binarize adjacency matrix
a(a>0) = 1;
am = reshape(a,[sqrt(length(a)),sqrt(length(a))]);
graph.edges = adj2edge(am);
%graph.edges = cellfun(@(x) find(x),num2cell(am,2),'un',0);
nodes = 1:sqrt(length(a));
graph.nodelabels = nodes';
end
function el=adj2edge(adj)
n=length(adj)... |
09d0958ae3b7479b4525c556bb0be45fae69ebc8e48c8e4bca88b9f64e044287 | MATLAB | 560 | 11 | function [pth, fil] = nk_GenerateNMFilePath(RootDir, AnalDesc, DatType, OptDesc, StrOut, DatID, CV2Perm , CV2Fold, CV1Perm, CV1Fold, ext)
if ~exist('ext','var') || isempty(ext)
ext = '.mat';
end
CV2Desc = sprintf('_oCV%g.%g', CV2Perm, CV2Fold);
if exist('CV1Perm','var') && exist('CV1Fold','var') && ~isempty(CV1Perm... |
82b6134bfffe7b9beb9eb82ec31da7cf8fe4f256a2d6480b23f6b7aa9038825c | MATLAB | 560 | 23 | function [V, Ro] = add_outlier_new(rho, F, N, Vo, outlier_min, outlier_max, val_min, val_max)
dense = rho * F;
Ro = zeros(F, N);
for i = 1 : N
n_before = 0;
for f = 1 : dense
c = randi(F);
Ro(c,i) = randi([outlier_min, outlier_max])/10;
... |
ffaf1622d01c3f57ba51aa69cc8320f020ba74f752129845620d83c1437018f6 | MATLAB | 564 | 18 | function S = cv_apply_sparsity_thres(A, thres)
% if mod(sqrt(size(A,2),1)) == 0 % the full connectivity matrix is entered
% nEdges = (size(A,2)*thres)-size(A,2); % number of edges minus diagonal
% else % only upper triangle (without diagonal was entered)
nEdges = size(A,2)*thres; % number of edges
% end
nEdges = ce... |
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