plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | NeuroDataDesign/orange-panda-f16s17-master | rejkurt.m | .m | orange-panda-f16s17-master/notes/bad_chan_detect/rejkurt.m | 4,149 | utf_8 | 6a7be2765a555e3f683f629bd6493a23 | % rejkurt() - calculation of kutosis of a 1D, 2D or 3D array and
% rejection of outliers values of the input data array
% using the discrete kutosis of the values in that dimension.
%
% Usage:
% >> [kurtosis rej] = rejkurt( signal, threshold, kurtosis, normalize);
%
% Inputs:
% signal... |
github | NeuroDataDesign/orange-panda-f16s17-master | pop_rejspec.m | .m | orange-panda-f16s17-master/notes/bad_chan_detect/pop_rejspec.m | 14,657 | utf_8 | bb4bfd5ea24f96a7f9ac51a55585a1df | % pop_rejspec() - rejection of artifact in a dataset using
% thresholding of frequencies in the data.
% Usage:
% >> pop_rejspec(INEEG, typerej); % pop-up interactive window mode
% >> [OUTEEG, Indices] = pop_rejspec( INEEG, typerej, 'key', val, ...);
%
% Pop-up window options:
% "Electrode|Componen... |
github | NeuroDataDesign/orange-panda-f16s17-master | realproba.m | .m | orange-panda-f16s17-master/notes/bad_chan_detect/realproba.m | 2,322 | utf_8 | dbc34c6982eedb377e62e6b1a49afdb7 | % realproba() - compute the effective probability of the value
% in the sample.
%
% Usage:
% >> [probaMap, probaDist ] = realproba( data, discret);
%
% Inputs:
% data - the data onto which compute the probability
% discret - discretisation factor (default: (size of data)/5)
% ... |
github | NeuroDataDesign/orange-panda-f16s17-master | pop_rejkurt.m | .m | orange-panda-f16s17-master/notes/bad_chan_detect/pop_rejkurt.m | 11,390 | utf_8 | 869a31b3c44d2c5f44d64a48a4d0b935 | % pop_rejkurt() - rejection of artifact in a dataset using kurtosis
% of activity (i.e. to detect peaky distribution of
% activity).
%
% Usage:
% >> pop_rejkurt( INEEG, typerej) % pop-up interative window mode
% >> [OUTEEG, locthresh, globthresh, nrej] = ...
% = pop_rejkurt( INEEG,... |
github | NeuroDataDesign/orange-panda-f16s17-master | pop_rejepoch.m | .m | orange-panda-f16s17-master/notes/bad_chan_detect/pop_rejepoch.m | 2,957 | utf_8 | a252e31365db94282bc48a802d83b48b | % pop_rejepoch() - Reject pre-labeled trials in a EEG dataset.
% Ask for confirmation and accept the rejection
%
% Usage:
% >> OUTEEG = pop_rejepoch( INEEG, trialrej, confirm)
%
% Inputs:
% INEEG - Input dataset
% trialrej - Array of 0s and 1s (depicting rejected trials) (size is
... |
github | NeuroDataDesign/orange-panda-f16s17-master | pop_jointprob.m | .m | orange-panda-f16s17-master/notes/bad_chan_detect/pop_jointprob.m | 11,899 | utf_8 | ee029c3b4c51ff027f3c2b69eff95f16 | % pop_jointprob() - reject artifacts in an EEG dataset using joint
% probability of the recorded electrode or component
% activities observed at each time point. e.g., Observing
% large absoluate values at most electrodes or components
% is im... |
github | andossy/SSCP_2018-master | OnsetQRS.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/Tools/OnsetQRS.m | 1,022 | utf_8 | 2a966cefbfebd7f780ade812346ecdef | function [OnsQRS]=OnsetQRS(P);
% Function definition
tCycle = P.General.tCycle;
ModC= @(ti) mod(ti,tCycle);
% Contractilities
CL = sum([GetFt('Patch','C','Lv') GetFt('Patch','C','Sv') GetFt('Patch','C','Rv')],2);
CDotL = sum([GetFt('Patch','CDot','Lv') GetFt('Patch','CDot','Sv') GetFt('Patch','CDot','Rv')]... |
github | andossy/SSCP_2018-master | ValveEvents.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/Tools/ValveEvents.m | 4,256 | utf_8 | 61a724afaf35c1b72d8a79d20aab8f77 | function [nVAvalveOp,nVAvalveCl,nAVvalveOp,nAVvalveCl] = ValveEvents(P,nREF,LR)
t = (1:length(P.t))';
tCycle = t(end);
ModC = @(ti) mod(ti,length(t));
reref = [nREF:length(t) 1:nREF-1];
if LR == 'L'
ind = 1;
elseif LR == 'R'
ind = 2;
end
VAvalve = {'LvSyAr... |
github | andossy/SSCP_2018-master | ArtFlowEvents.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/Tools/ArtFlowEvents.m | 2,548 | utf_8 | d83c833aa7cb1113a50f3db4f0b690d2 | function [tArtOpen, tArtClose, tArtMax, vArtMax, vArtMed]=ArtFlowEvents(P, ValveArt)
% Function definition
tCycle = P.General.tCycle;
ModC= @(ti) mod(ti,tCycle);
Dt= P.General.Dt;
% Arterial valve flow
q = GetFt('Valve','q',ValveArt);
qDot = GetFt('Valve','qDot',ValveArt);
t = (tCycle/length(q))*[0:le... |
github | andossy/SSCP_2018-master | AvFlowEvents.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/Tools/AvFlowEvents.m | 3,767 | utf_8 | cd719c4a4ec0aa020f0c5d4bd93eb51c | function [tAvOpen, tAvClose, tAvDecay, vAvMaxE, vAvMaxA]=AvFlowEvents(P, ValveAv, tArtClose)
% Function definition
tCycle = P.General.tCycle;
ModC= @(ti) mod(ti,tCycle);
Dt= P.General.Dt;
% Atrioventricular valve flow
q = GetFt('Valve','q',ValveAv);
qDot = GetFt('Valve','qDot',ValveAv);
t = (tCycle/le... |
github | andossy/SSCP_2018-master | SplitMerge.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/SplitMerge.m | 3,692 | utf_8 | 3ee3f87b573a232205cb5657fed2e7b4 | function SplitMerge(PatchName,nSM)
% function SplitMerge(PatchName,nSM);
% SplitMerge splits or merges patches within a wall
% PatchName is string
% nSM= number of patches after splitting,
% if nSM<0, - number of merging patches, starting from named patch
% if more merging than possible, than nSM merging is maxim... |
github | andossy/SSCP_2018-master | Adapt0P_ValveRepl.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/Adapt0P_ValveRepl.m | 4,033 | utf_8 | d61e710b4302a91d75c48507e9a12b21 | function Adapt0P_ValveRepl
%function Adapt0P;
global P
save PTemp P; %saves last intermediate solution
% 1st part like Adapt0P controls systemic blood pressure and flow
% by adjustment of circulatory blood volume and peripheral resistance
%
% Assessment of stationarity of flows
FlowVec=mean(P.Valve.q);
% t... |
github | andossy/SSCP_2018-master | ArtVenAdapt.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/ArtVenAdapt.m | 3,745 | utf_8 | 3abf5e7f0a70602016d0f8c657aa00d1 | function ArtVenAdapt(StrAV,AdaptType)
% function ArtVenAdapt(StrAV,AdaptType);
% Adaptation of Diameter and Wall thickness of Art and Ven to
% pressure and flow.
% StrAV= array of ArtVen names, e.g. {'Sy','Pu'}
% AdaptType= {'Diameter', 'WallVolume'} indicates type of adaptation
% Theo Arts, Maastricht University... |
github | andossy/SSCP_2018-master | AdaptExcP.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/AdaptExcP.m | 4,474 | utf_8 | 5a70e2a1fbf89eaa1875b15b03ca79d1 | function AdaptExcP
global P
save PTemp P; %saves last intermediate solution
% Assessment of stationarity of flows
FlowVec=mean(P.Valve.q);
% test on presence of FlowVec with right size
if isfield(P.Adapt, 'FlowVec');
FlowVecPrev= P.Adapt.FlowVec;
if length(FlowVecPrev)~=length(FlowVec);
Flo... |
github | andossy/SSCP_2018-master | TriSegV2p.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/TriSegV2p.m | 4,401 | utf_8 | 90609beca08c3087e95b1a0a59f1caa2 | function TriSegV2p
% function TriSegV2p
% TriSeg is a 3-wall structure (Left,Septal,Right) with 2 cavities (R,L)
% Calculates: cavity volumes V -> dimensions of the 'double bubble',
% myofiber stress Sf, wall tension T and cavity pressures p
% VS and YS repesent septal volume displacement and junction radius.
% S... |
github | andossy/SSCP_2018-master | AdaptRestP.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/AdaptRestP.m | 4,374 | utf_8 | 1ab99905b1fd8f4e7b4a77065810c184 | function AdaptRestP
% function AdaptRestP
% Simulates adaptation of vessels to hemodynamics at rest
% Adaptation of vessel cross-section
% Theo Arts, Maastricht University, Oct 30, 2011
global P
save PTemp P; %saves last intermediate solution
% 1st part like Adapt0P controls systemic blood pressure and flo... |
github | andossy/SSCP_2018-master | Adapt0P.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/Adapt0P.m | 3,666 | utf_8 | 330ca72888521ecc83cbe1391a788445 | function Adapt0P
%function Adapt0P;
global P
save PTemp P; %saves last intermediate solution
% 1st part like Adapt0P controls systemic blood pressure and flow
% by adjustment of circulatory blood volume and peripheral resistance
%
% Assessment of stationarity of flows
FlowVec=mean(P.Valve.q);
% test on pre... |
github | andossy/SSCP_2018-master | CircDisplayP.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/CircDisplayP.m | 1,711 | utf_8 | ccd3ac280c68dc79bd4e0dbde9efcc9e | function CircDisplayP
global P
SVarDot(0,P.SVar',[]);
% Variables as f(t)
% scaling of graphics
q0 = P.General.q0;
p0 = P.General.p0;
tCycle = P.General.tCycle;
%=== scaling reference values
qSc= Rnd(q0);
VSc= Rnd(q0*tCycle/10);
pSc= Rnd(0.1*p0);
t= P.t-P.t(1);
OFFSET= -2... |
github | andossy/SSCP_2018-master | PNew.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/PNew.m | 9,474 | utf_8 | 28f97436c5854eefe13ea308c7c9725c | function PNew
% based on ParRef
% should be made stand-alone
% Theo Arts, Maastricht University, Oct 30, 2011
global P;P=[];
% load ParRef;
P.General.rhob=1050;
P.General.q0=45e-6;
P.General.p0=12000;
P.General.tCycle=0.600;
P.General.FacpControl=1;
P.General.dTauAv=0;
pLv=19000;
pRv=7500;
pLa=2800;... |
github | andossy/SSCP_2018-master | PatchAdapt.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/PatchAdapt.m | 5,130 | utf_8 | ffd8b844eca9ef7862f14b05075e6cb9 | function PatchAdapt(StrPatch,AdaptType)
% function PatchAdapt(StrPatch,AdaptType);
% StrPatch= array of Patch names, e.g. {'Lv1','Sv1'}
% AdaptType= {'WallVolume','WallArea','EcmStress'} indicates type of adaptation
global P
% Determine Patch and AdaptType indices
iPatch= Str2Index(StrPatch ,P.Patch.Name);
i... |
github | andossy/SSCP_2018-master | pNodeVDot.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/pNodeVDot.m | 2,120 | utf_8 | 215f004af0e2b2ffe8b26de4b298dc92 | function pNodeVDot
% function pNodeVDot
% Pressures in cavities p -> pressure in Nodes: p
% -> flows to cavities: VDot
% Theo Arts, Maastricht University, Oct 30, 2011
global P
P.Node.q=0*P.Node.q; P.Node.Y=P.Node.q;% zero initialization
iNodeProx=P.Valve.iNodeProx; % nodes proximal to ... |
github | andossy/SSCP_2018-master | SarcEf2Sf.m | .m | SSCP_2018-master/CircAdapt-MultiPatch2015/CircAdapt/SarcEf2Sf.m | 3,570 | utf_8 | 27868f9977f160eb77aa2ac461d67f01 | function SarcEf2Sf;
%Theo Arts, Maastricht University. July 30, 2006.
global P; % general time
Sarc= P.Patch;
%==== Input variables
t = P.t;
Ef = Sarc.Ef ;
nt = size(Ef,1); % nt: number of times; nr: number of sarcomeres
Col1 = ones(nt,1);
tc = Tc(t,Sarc.ActivationDelay);
Lsi =... |
github | quantiacs-legacy/HenryCarstens-101-Trading-Ideas-master | Carstens_Illustration3.m | .m | HenryCarstens-101-Trading-Ideas-master/Carstens_Illustration3.m | 1,343 | utf_8 | c2da6a1ae4a8e96558376f1bfeccde9b | function [p, settings] = Carstens_Illustration3(DATE, OPEN, HIGH, LOW, CLOSE, exposure, settings)
settings.markets = {'CASH', 'F_CL'};
settings.budget = 1000000;
settings.slippage = 0.0;
settings.samplebegin = 20040101;
settings.sampleend = 20140101;
settings.lookback = 504;
p = zeros(1,numel(setting... |
github | quantiacs-legacy/HenryCarstens-101-Trading-Ideas-master | Carstens_Illustration7.m | .m | HenryCarstens-101-Trading-Ideas-master/Carstens_Illustration7.m | 2,043 | utf_8 | 997f6f2dcd96314c0ccebfac8712795e | function [p, settings] = Carstens_Illustration7(DATE, OPEN, HIGH, LOW, CLOSE, exposure, settings)
settings.markets = {'CASH','F_NG', 'F_CL'};
settings.budget = 1000000;
settings.slippage = 0.0;
settings.samplebegin = 20040101;
settings.sampleend = 20200101;
settings.lookback = 504;
if ~any(strcmp('lon... |
github | oartal/FilamentDetection-master | fdt_filaments.m | .m | FilamentDetection-master/fdt_filaments.m | 3,629 | utf_8 | 44252ef349ad3ffcd835366897d3542c | function [out shape] = fdt_filaments(BW,lon,lat,mdistance,lfgt,mdc,pfig,sfig,index,mm,yy)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FUNCTION [out shape] = fdt_filaments(BW,lon,lat,mdistance,pfig,sfig,index,mm,yy)
%
% The function fdt_filaments choose the segments that corresponded ... |
github | oartal/FilamentDetection-master | mexnc.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/mexnc.m | 24,903 | utf_8 | e32545440ada477478dfeb21c2446428 | function [varargout] = mexnc ( varargin )
% MEXNC is a gateway to the netCDF interface. To use this function, you
% should be familiar with the information about netCDF contained in the
% "User's Guide for netCDF". This documentation may be obtained from
% Unidata at
% <http://my.unidata.ucar.edu/co... |
github | oartal/FilamentDetection-master | mexnc_tmw.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/private/mexnc_tmw.m | 76,084 | utf_8 | cbcabbd3a364a9a2ee2940db62d74d6e | function [varargout] = mexnc_tmw(varargin)
% MEXNC_TMW: this translation layer channels mexnc calls into the
% mathworks netcdf package
varargout = cell(1,nargout);
op = lower(varargin{1});
% If the leading three chars are 'nc_', then strip it.
if (numel(op) > 3) && strcmp(op(1:3),'nc_')
op = op(4:end);
end
v =... |
github | oartal/FilamentDetection-master | test_put_get_var_uchar.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_put_get_var_uchar.m | 12,922 | utf_8 | 801a0edc0ce12597b8f4169901f948b4 | function test_put_get_var_uchar ( ncfile )
% TEST_GET_PUT_VAR_UCHAR
%
% Tests expected to succeed.
% Test 001: write to a singleton value, read them back using [put/get]_var_uchar
% Test 002: write to a singleton value, read them back using [put/get]_var1_uchar
% [PUT,GET]_VAR_UCHAR: Write a 6x4 array of monotoni... |
github | oartal/FilamentDetection-master | test_attname.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_attname.m | 4,061 | utf_8 | b09da14d8b5d9fd3c33800b608ea714f | function test_attname ( ncfile )
if nargin < 1
ncfile = 'foo.nc';
end
create_ncfile(ncfile);
test_existance(ncfile);
test_bad_ncid(ncfile);
test_bad_varid(ncfile);
test_bad_attnum(ncfile);
fprintf('ATTNAME succeeded.\n');
%--------------------------------------------------------------------------
function crea... |
github | oartal/FilamentDetection-master | test_endef.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_endef.m | 1,970 | utf_8 | 321a3b37888519448ffa4ab0efae00e4 | function test_endef ( ncfile )
% TEST_ENDEF
%
% Tests ENDEF by defining a new dimension. Then tests
% REDEF by defining another dimension.
%
% Test 1: Usual ENDEF
% Test 2: File is not in define mode.
% Test 3: Bad ncid.
test_001 ( ncfile );
test_002 ( ncfile );
test_003 ( ncfile );
fprintf ( 1, 'ENDEF succeede... |
github | oartal/FilamentDetection-master | test_put_get_var_double.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_put_get_var_double.m | 15,905 | utf_8 | ae11e09cd69826d23dc5b69830025343 | function test_put_get_var_double ( ncfile )
% TEST_GET_PUT_VAR_DOUBLE
%
% Tests expected to succeed.
% Test 001: write to a singleton value, read them back using [put/get]_var_double
% Test 002: write to a singleton value, read them back using [put/get]_var1_double
% Test 003: write to an entire value, read them bac... |
github | oartal/FilamentDetection-master | test_varput1.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_varput1.m | 12,951 | utf_8 | ccbafbea09e331644652d6e5e45274f6 | function test_varput1 ( ncfile )
% TEST_VARPUT_1
%
% This routine tests VARGET1, VARPUT1
%
% Test 010: test writing a short datum to a double precision variable. Bad test, same reason.
if nargin < 1
ncfile = 'foo.nc';
end
mexnc ( 'setopts', 0 );
create_testfile ( ncfile );
test_varget1_singleton ( ncfile );
test_v... |
github | oartal/FilamentDetection-master | test_redef_def_dim.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_redef_def_dim.m | 7,749 | utf_8 | 297bba2d21eb39665b7ca97b51ba6afb | function test_redef_def_dim ( ncfile )
% TEST_REDEF_DEF_DIM
%
if nargin < 1
ncfile = 'foo.nc';
end
test_definingDimension(ncfile); % create simple dimensions
test_badNcid(ncfile); % invalid ncid
test_emptyNcid(ncfile); % ncid = []
test_nonNumericNcid(ncfile); ... |
github | oartal/FilamentDetection-master | test_attcopy.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_attcopy.m | 7,929 | utf_8 | 7900e5425250248bb27dc91a2686634c | function test_attcopy ( ncfile1, ncfile2 )
% TEST_ATTCOPY
if nargin < 2
ncfile1 = 'foo1.nc';
ncfile2 = 'foo2.nc';
end
mexnc ( 'setopts', 0 );
create_testfile ( ncfile1 );
create_testfile ( ncfile2 );
test_copy_double_att ( ncfile1, ncfile2 );
test_copy_bad_src_ncid ( ncfile1, ncfile2 );
test_bad_src_varid (... |
github | oartal/FilamentDetection-master | test_chunking.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_chunking.m | 7,088 | utf_8 | 6bee2b429053fe55ebf3f8f0a237900a | function test_chunking ( ncfile )
if nargin == 0
ncfile = 'foo.nc';
end
v = mexnc('inq_libvers');
if v(1) ~= '4'
fprintf('chunking tests filtered out when the library version is less than 4.0.\n');
return
end
test_netcdf3(ncfile); clear mex; % #1
test_netcdf3_64bit(ncfile); clear mex; ... |
github | oartal/FilamentDetection-master | test_put_get_var_float.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_put_get_var_float.m | 14,303 | utf_8 | 982a9e59d385ce032c3356b0646bb511 | function test_put_get_var_float ( ncfile )
% TEST_GET_PUT_VAR_FLOAT
%
% Tests expected to succeed.
% Test 001: write to a singleton value, read them back using [put/get]_var_float
% Test 002: write to a singleton value, read them back using [put/get]_var1_float
% Test 003:
% [PUT,GET]_VAR_FLOAT: Write a 6x4 arr... |
github | oartal/FilamentDetection-master | test_attput.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_attput.m | 23,892 | utf_8 | 3d913ce099da8eec230eb1623317d4b6 | function test_attput ( ncfile )
if nargin < 1
ncfile = 'foo.nc';
end
create_testfile ( ncfile );
test_write_double ( ncfile );
test_write_float ( ncfile );
test_write_int32 ( ncfile );
test_write_int16 ( ncfile );
test_write_uchar ( ncfile );
test_write_schar ( ncfile );
test_write_char ( ncfile );
test_read_double ... |
github | oartal/FilamentDetection-master | test_put_get_var_int.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_put_get_var_int.m | 13,063 | utf_8 | 5dbad4c9c8f2cd8b733d16ae15a1d2bf | function test_put_get_var_int ( ncfile )
% TEST_GET_PUT_VAR_INT
%
% Tests expected to succeed.
% Test 001: write to a singleton value, read them back using [put/get]_var_double
% Test 002: write to a singleton value, read them back using [put/get]_var1_double
% [PUT,GET]_VAR_INT: Write a 6x4 array of monotonicall... |
github | oartal/FilamentDetection-master | test_copy_att.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_copy_att.m | 12,121 | utf_8 | 8f930f8580066de568f0f2d7bc4b7e86 | function test_copy_att ( ncfile1, ncfile2 )
if nargin < 1
ncfile1 = 'foo1.nc';
ncfile2 = 'foo2.nc';
end
create_ncfiles(ncfile1,ncfile2);
test_copy(ncfile1,ncfile2);
test_bad_source_ncid(ncfile1,ncfile2);
test_bad_source_varid(ncfile1,ncfile2);
test_bad_destination_ncid(ncfile1,ncfile2);
test_bad_destination_varid(n... |
github | oartal/FilamentDetection-master | test_diminq.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_diminq.m | 3,939 | utf_8 | 7347e62781e3f9c4e3ed344b86eba16e | function test_dim_inq ( ncfile )
% TEST_DIM_INQ
%
% Tests number of dimensions, variables, global attributes, record dimension for
% foo.nc.
%
% Tests bad ncid as well.
%
% Test 1: Normal inquiry
% Test 2: Bad ncid.
% Test 3: Empty set ncid.
% Test 4: Bad dimid.
% Test 5: Empty set dimid.
% Test 6: character di... |
github | oartal/FilamentDetection-master | test_put_get_att.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_put_get_att.m | 13,744 | utf_8 | 17777e041be9584feffd1f4aa8051ab3 | function test_put_get_att ( ncfile )
% TEST_PUT_GET_ATT: tests the PUT_ATT and GET_ATT family of calls
%
if nargin < 1
ncfile = 'foo.nc';
end
create_test_file ( ncfile );
test_writeReadDouble ( ncfile );
test_writeReadFloat ( ncfile );
test_writeReadInt ( ncfile );
test_writeReadShort ( ncfile );
test_writeReadNcB... |
github | oartal/FilamentDetection-master | test_open.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_open.m | 3,390 | utf_8 | 0ae9f30a03566559d5c9ae450dea291d | function test_open ( ncfile )
% Tests run are
%
% Test 1: test write mode
% Test 2: test share mode
% Test 3: bitwise or of write mode and share mode
% Test 4: only two input arguments given
% Test 5: filename argument is bad
% Test 6: filename argument is non character
% Test 7: mode argument is non character a... |
github | oartal/FilamentDetection-master | test_inq_att.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_inq_att.m | 6,377 | utf_8 | 1598c33367d6fa307e806f64525cc139 | function test_inq_att ( ncfile )
% TEST_INQ_ATT:
%
% The matlab API is
%
% [datatype, attlen, status] = mexnc ( 'inq_att', ncid, varid, attname );
%
%
% Test 1: Normal retrieval.
% Test 2: Invalid ncid
% Test 3: Invalid varid
% Test 4: Invalid name.
% Test 5: ncid = []
% Test 6: varid = []
% Test 7:... |
github | oartal/FilamentDetection-master | test_create.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_create.m | 7,420 | utf_8 | 3a00186f9277eb2402fb8518fb2e0452 | function test_create ( ncfile )
% Tests run are open with
% Test 1: nc_clobber_mode
% Test 2: nc_noclobber_mode
% Test 3: clobber and share and 64 bit offset
% Test 4: share mode. Should also clobber it.
% Test 5: share | 64bit_offset
% Test 6: 64 bit offset. Should also clobber it.
% Test 7: noclobber mode... |
github | oartal/FilamentDetection-master | test__open.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test__open.m | 3,259 | utf_8 | 2d6deef5ea44d249b7154cb4d36985dd | function test__open ( ncfile )
% Tests run are
%
% Test 1: test write mode
% Test 2: test share mode
% Test 3: bitwise or of write mode and share mode
% Test 4: only two input arguments given
% Test 5: filename argument is bad
% Test 6: filename argument is non character
% Test 7: mode argument is non character ... |
github | oartal/FilamentDetection-master | test_varid.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_varid.m | 2,826 | utf_8 | 6e43d4ffa4f00da6433989a79e2cc039 | function test_varid ( ncfile )
% TEST_VARID
% test 1: simple check for an existing variable
% test 2: variable does not exist
% test 3: bad ncid
% test 4: illegal variable name
create_testfile ( ncfile );
test_001 ( ncfile );
test_002 ( ncfile );
test_003 ( ncfile );
test_003 ( ncfile );
test_004 ( ncfile );
fpri... |
github | oartal/FilamentDetection-master | test_varputg.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_varputg.m | 15,790 | utf_8 | afbacc33f3a138043dd37217f7a4057c | function test_varputg ( ncfile )
% TEST_VARPUTG
%
% This routine tests VARGETG, VARPUTG
%
% Test 1: test VARPUTG/VARGETG with double precision data
% Test 2: test VARPUTG/VARGETG with float data, should not be accepted
% Test 3:
% Test 4: test 1D
% Test 005: test 2D VARPUTG/VARGETG
% Test 006: test 2D VARPUTG/V... |
github | oartal/FilamentDetection-master | test_attdel.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_attdel.m | 3,831 | utf_8 | 2e6d9fc99bc6095a7b0a184f19ff64fb | function test_attdel ( ncfile )
if nargin < 1
ncfile = 'foo.nc';
end
create_test_file(ncfile);
test_delete_double(ncfile);
test_bad_ncid(ncfile);
test_bad_varid(ncfile);
test_does_not_exist(ncfile);
fprintf('ATTDEL succeeded.\n' );
%--------------------------------------------------------------------------
fu... |
github | oartal/FilamentDetection-master | test_def_dim.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_def_dim.m | 10,073 | utf_8 | 0aa95e0b6ff69b04cb4bf67ab67cd88c | function test_def_dim ( ncfile )
% TEST_DEF_DIM
%
if nargin < 1
ncfile = 'foo.nc';
end
test_define ( ncfile );
test_dimLengthIsUnlimitedCharCase ( ncfile );
test_neg_badNcid ( ncfile );
test_neg_emptyNcid ( ncfile );
test_neg_ncidIsNonNumeric ( ncfile );
test_neg_dimAlreadyExists ( ncfile );
test_neg_dimNameIsEmpty... |
github | oartal/FilamentDetection-master | test__create.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test__create.m | 4,274 | utf_8 | 9276271d2082353058b51fbf5a5f2056 | function test__create ( ncfile )
% TEST__CREATE:
%
% Tests run are open with
% Test 1: nc_clobber_mode. Check the initial file size.
% Test 2: nc_noclobber_mode
% Test 3: clobber and share and 64 bit offset
% Test 4: share mode. Should also clobber it.
% Test 5: share | 64bit_offset
% Test 6: 64 bit offset. ... |
github | oartal/FilamentDetection-master | test_dimid.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_dimid.m | 3,472 | utf_8 | 189ed8f2894db751f854ebd0d1f40847 | function test_dimid ( ncfile )
% TEST_DIMID
%
% Test 1: Retrieve a dimid.
% Test 2: Bad ncid.
% Test 3: Empty set ncid.
% Test 4: Empty string dim name.
% Test 5: Empty set dim name.
% Test 6: Bad dim name.
if nargin < 1
ncfile = 'foo.nc';
end
create_ncfile(ncfile);
test_normal_dimid(ncfile);
test_bad_nci... |
github | oartal/FilamentDetection-master | test_inq.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_inq.m | 10,964 | utf_8 | c7391679eb23c235ded34f1542b729d9 | function test_inq ( ncfile )
% TEST_INQ
%
% Tests number of dimensions, variables, global attributes, record dimension for
% foo.nc. Also tests helper routines, "nc_inq_ndims", "nc_inq_nvars", "nc_inq_ncatts".
%
% Tests bad ncid as well.
%
% Test 1: Normal retrieval
% Test 2: Bad ncid.
% Test 3: Empty set ncid.
% T... |
github | oartal/FilamentDetection-master | test_def_var.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_def_var.m | 12,901 | utf_8 | 770fc59572df0dc90d6365aaddfae6f1 | function test_def_var ( ncfile )
% TEST_DEF_VAR
%
% Test 1: Create a double var
% Test 2: Create a float var
% Test 3: Create an int32 var
% Test 4: Create an int16 var
% Test 5: Create a byte var
% Test 6: Create a char var
% Test 7: Bad ncid.
% Test 8: Empty name.
% Test 9: Bogus datatype.
% Test 10: Bad nu... |
github | oartal/FilamentDetection-master | test_vardef.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_vardef.m | 3,544 | utf_8 | 3e5a189c4332e4c900f8c68ab809e432 | function test_vardef ( ncfile )
% TEST_VARDEF
if nargin < 1
ncfile = 'foo.nc';
end
% Test: Create a singleton dimension using [] as the list of dimids.
test_empty_set(ncfile);
% Test: Create a singleton dimension using 0 as number of dimensions
test_zero_dims(ncfile);
% Test: Test with bad ncid, bad dimensio... |
github | oartal/FilamentDetection-master | test_parameter.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_parameter.m | 3,185 | utf_8 | 4e590da950e6996d0a94e03d9ad26b63 | function test_parameter ( )
%
% This routine tests the TYPELEN operation
parms = { 'max_nc_name', ...
'max_nc_dims', ...
'max_nc_vars', ...
'max_nc_attrs', ...
'nc_byte', ...
'nc_char', ...
'nc_clobber', ...
'nc_double', ...
'nc_fatal', ...
'nc_fill', ...
'nc_float', ...
'n... |
github | oartal/FilamentDetection-master | test_mexnc.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_mexnc.m | 4,431 | utf_8 | 2d725beb642f4353e0dcf613817ad8c8 | function test_mexnc()
% TEST_MEXNC: Wrapper routine that invokes all tests for MEXNC
%
% USAGE: test_mexnc;
p = which ( 'mexnc', '-all' );
if isempty(p)
fprintf ( 1, 'Could not find mexnc on the matlab path. Read the README!!\n' );
fprintf ( 1, 'Bye\n' );
return
end
fprintf('Your path for mexnc is list... |
github | oartal/FilamentDetection-master | test_del_att.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_del_att.m | 7,821 | utf_8 | 81cb8b8f126dc4c16f7ba1a428ab6115 | function test_del_att ( ncfile )
if nargin == 0
ncfile = 'foo.nc';
end
create_ncfile(ncfile);
test_normal_delete(ncfile);
test_bad_ncid(ncfile);
test_bad_varid(ncfile);
test_empty_name(ncfile);
test_bad_name(ncfile);
test_empty_ncid(ncfile);
test_empty_varid(ncfile);
test_empty_attname(ncfile);
test_bad_ncid_dataty... |
github | oartal/FilamentDetection-master | test_attinq.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_attinq.m | 4,380 | utf_8 | 09fbfafad372c8f7f77dbba658290bb4 | function test_attinq ( ncfile )
if nargin < 1
ncfile = 'foo.nc';
end
create_test_file(ncfile);
test_double_precision(ncfile);
test_bad_ncid(ncfile);
test_bad_varid(ncfile);
test_att_does_not_exist(ncfile);
test_non_char_att_name(ncfile);
fprintf ( 1, 'ATTINQ succeeded.\n' );
%-----------------------------------... |
github | oartal/FilamentDetection-master | test_dimdef.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_dimdef.m | 4,612 | utf_8 | 42736459060b6e8d20512e56ab2ed303 | function test_dimdef ( ncfile )
%
% Test: Define a dimension.
% Test: Bad ncid.
% Test: Empty string name.
% Test: Empty set name.
% Test: Negative dimension length
% Test: Empty set length.
if nargin == 0
ncfile = 'foo.nc';
end
mexnc ( 'setopts', 0 );
test_define_dimension(ncfile);
test_define_unlimited_dime... |
github | oartal/FilamentDetection-master | test_put_get_var_short.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_put_get_var_short.m | 13,321 | utf_8 | 7beec260ca8cd9a5ec7a165f3a75b080 | function test_put_get_var_short ( ncfile )
% TEST_GET_PUT_VAR_SHORT
%
% Tests expected to succeed.
% Test 001: write to a singleton value, read them back using [put/get]_var_double
% Test 002: write to a singleton value, read them back using [put/get]_var1_double
% [PUT,GET]_VAR_SHORT: Write a 6x4 array of monoto... |
github | oartal/FilamentDetection-master | test_typelen.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_typelen.m | 2,895 | utf_8 | 1eb6b48f400035fad6c7dcad5dea93e2 | function test_typelen ( )
% TEST_TYPELEN
%
% This routine tests the TYPELEN operation
%
% Test 001: NC_DOUBLE
% Test 002: NC_FLOAT
% Test 003: NC_INT
% Test 004: NC_SHORT
% Test 005: NC_BYTE
% Test 006: NC_CHAR
% Test 007: NC_NAT
% Test 008: invalid input
test_001;
test_002;
test_003;
test_004;
test_005;
test... |
github | oartal/FilamentDetection-master | test_varput.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_varput.m | 25,773 | utf_8 | ee522b485fbe21aed6ceeaaac4828587 | function test_varput ( ncfile )
% TEST_VARPUT
%
if ( nargin < 1 )
ncfile = 'foo.nc';
end
mexnc ( 'setopts', 0 );
create_testfile ( ncfile );
test_read_col_inds ( ncfile );
test_double_precision ( ncfile );
test_scaling ( ncfile );
test_scaling_flag_set_to_zero ( ncfile );
test_... |
github | oartal/FilamentDetection-master | test_deflate.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_deflate.m | 9,378 | utf_8 | fc17a1f3cec509615aa617b55c4c47ad | function test_deflate ( ncfile )
if nargin == 0
ncfile = 'foo.nc';
end
v = mexnc('inq_libvers');
if v(1) ~= '4'
fprintf('deflate tests filtered out when the library version is less than 4.0.\n');
return
end
test_netcdf3_classic(ncfile);
test_netcdf3_64bit(ncfile);
test_netcdf4_1d_shuffle_off_... |
github | oartal/FilamentDetection-master | test_inquire.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_inquire.m | 4,016 | utf_8 | 557d0dadf3cbf7536383053e6c62722c | function test_inquire ( ncfile )
% TEST_INQUIRE
%
% Tests number of dimensions, variables, global attributes, record dimension for
% foo.nc
%
% Test 001: standard test
% Test 002: 1x5 output vector
% Test 003: bad ncid
if ( nargin == 0 )
ncfile = 'foo.nc';
end
create_testfile (ncfile);
test_001 ( ncfile );
test_0... |
github | oartal/FilamentDetection-master | test_put_get_var_schar.m | .m | FilamentDetection-master/netcdf_toolbox/mexnc/tests/test_put_get_var_schar.m | 12,920 | utf_8 | 7478c652da66e48e05b001f31c9c4414 | function test_put_get_var_schar ( ncfile )
% TEST_GET_PUT_VAR_SCHAR
%
% Tests expected to succeed.
% Test 001: write to a singleton value, read them back using [put/get]_var_double
% Test 002: write to a singleton value, read them back using [put/get]_var1_double
% [PUT,GET]_VAR_SCHAR: Write a 6x4 array of monoto... |
github | ustczyshi/EMD-master | extrema.m | .m | EMD-master/extrema.m | 2,179 | utf_8 | 41e10fe23ce2818cdeb742f9e3600de0 | % This is function to find all the extrema
function [spmax, spmin]= extrema(in_data)
%get data length
dsize=length(in_data);
% -----------find all the maximums------------------
spmax(1,1) = 1;
spmax(1,2) = in_data(1);
jj=2;%index for data traversal
kk=2;%to store the index of t... |
github | ustczyshi/EMD-master | eemd.m | .m | EMD-master/eemd.m | 3,080 | utf_8 | 6738c0cbbda8fec5663496252788ce0b | % This function does the EEMD decompositions of a vector
% Y: Inputted data;
% Nstd: ratio of the standard deviation of the added noise with respect to Y;
% NE: Ensemble number being used
% TNM: total number of modes (not including the trend)
%
function allmode=eemd(Y,Nstd,NE,TNM)
% get data length
xs... |
github | limosek/somtoolbox-master | som_probability_gmm.m | .m | somtoolbox-master/som_probability_gmm.m | 2,782 | utf_8 | 1d0b944d5fda0f9051e055d366e40be7 | function [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P)
%SOM_PROBABILITY_GMM Probabilities based on a gaussian mixture model.
%
% [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P)
%
% [K,P] = som_estimate_gmm(sM,D);
% [pd,Pdm,pmd] = som_probability_gmm(D,sM,K,P);
% som_show(sM,'color',pmd(:,1),'color',Pdm(:,1)) ... |
github | limosek/somtoolbox-master | som_clget.m | .m | somtoolbox-master/som_clget.m | 3,421 | utf_8 | 342e1efa120e8a6a043285303abc1698 | function a = som_clget(sC, mode, ind)
%SOM_CLGET Get properties of specified clusters.
%
% a = som_clget(sC, mode, ind)
%
% inds = som_clget(sC,'dinds',20);
% col = som_clget(sC,'depth',[1 2 3 20 54]);
%
% Input and output arguments:
% sC (struct) clustering struct
% mode (string) what kind ... |
github | limosek/somtoolbox-master | lvq3.m | .m | somtoolbox-master/lvq3.m | 5,955 | utf_8 | 25396031026089e698ca5d953130926c | function codebook = lvq3(codebook,data,rlen,alpha,win,epsilon)
%LVQ3 trains codebook with LVQ3 -algorithm
%
% sM = lvq3(sM,D,rlen,alpha,win,epsilon)
%
% sM = lvq3(sM,sD,50*length(sM.codebook),0.05,0.2,0.3);
%
% Input and output arguments:
% sM (struct) map struct, the class information must be
% ... |
github | limosek/somtoolbox-master | som_select.m | .m | somtoolbox-master/som_select.m | 20,306 | utf_8 | 66d1e2f235d1d946c33b5a69592db5e9 | function varargout=som_select(c_vect,plane_h,arg)
%SOM_SELECT Manual selection of map units from a visualization.
%
% som_select(c_vect,[plane_h])
%
% som_select(3)
% som_select(sM.labels(:,1))
%
% Input arguments ([]'s are optional):
% c_vect (scalar) number of classes
% (vector) initial ... |
github | limosek/somtoolbox-master | som_unit_coords.m | .m | somtoolbox-master/som_unit_coords.m | 8,087 | utf_8 | 98f66ff14384523f863cba1090e4438f | function Coords = som_unit_coords(topol,lattice,shape)
%SOM_UNIT_COORDS Locations of units on the SOM grid.
%
% Co = som_unit_coords(topol, [lattice], [shape])
%
% Co = som_unit_coords(sMap);
% Co = som_unit_coords(sMap.topol);
% Co = som_unit_coords(msize, 'hexa', 'cyl');
% Co = som_unit_coords([10 4 4], 'rect'... |
github | limosek/somtoolbox-master | vis_footnote.m | .m | somtoolbox-master/vis_footnote.m | 3,092 | utf_8 | ec924f77ac6dea10972b0183848b48bc | function h=vis_footnote(txt)
% VIS_FOOTNOTE Adds a movable text to the current figure
%
% h = vis_footnote(T)
%
% Input and output arguments ([]'s are optional)
% [T] (string) text to be written
% (scalar) font size to use in all strings
%
% h (vector) handles to axis objects created by this function... |
github | limosek/somtoolbox-master | vis_trajgui.m | .m | somtoolbox-master/vis_trajgui.m | 41,542 | utf_8 | eded3a83cecca44d4bcf525cfa2fc3b4 | function vis_trajgui(trajStruct,arg)
% VIS_TRAJGUI subfuntion for SOM_TRAJECTORY
%
% This function is the actual GUI called by SOM_TRAJECTORY
% function.
%
% See also SOM_TRAJECTORY.
% Contributed code to SOM Toolbox 2.0, February 11th, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas.
% http://www.cis... |
github | limosek/somtoolbox-master | som_order_cplanes.m | .m | somtoolbox-master/som_order_cplanes.m | 8,526 | utf_8 | c1a59c9faaa44012e6029d111a992278 | function P = som_order_cplanes(sM, varargin)
%SOM_ORDER_CPLANES Orders and shows the SOM component planes.
%
% P = som_order_cplanes(sM, [[argID,] value, ...])
%
% som_order_cplanes(sM);
% som_order_cplanes(sM,'comp',1:30,'simil',C,'pca');
% P = som_order_cplanes(sM);
%
% Input and output arguments ([]'s are optio... |
github | limosek/somtoolbox-master | som_batchtrain.m | .m | somtoolbox-master/som_batchtrain.m | 20,587 | utf_8 | e5a5b26aefef122062e0956ad4772007 | function [sMap,sTrain] = som_batchtrain(sMap, D, varargin)
%SOM_BATCHTRAIN Use batch algorithm to train the Self-Organizing Map.
%
% [sM,sT] = som_batchtrain(sM, D, [argID, value, ...])
%
% sM = som_batchtrain(sM,D);
% sM = som_batchtrain(sM,sD,'radius',[10 3 2 1 0.1],'tracking',3);
% [M,sT] = som_batchtr... |
github | limosek/somtoolbox-master | som_stats_report.m | .m | somtoolbox-master/som_stats_report.m | 3,635 | utf_8 | 99b18a3a7688d89cd011ef5eb9be9eed | function som_stats_report(csS,fname,fmt,texonly)
% SOM_STATS_REPORT Make report of the statistics.
%
% som_stats_report(csS, fname, fmt, [standalone])
%
% som_stats_report(csS, 'data_stats', 'ps')
%
% Input and output arguments ([]'s are optional):
% csS (cell array) of statistics structs
% ... |
github | limosek/somtoolbox-master | som_eucdist2.m | .m | somtoolbox-master/som_eucdist2.m | 2,273 | utf_8 | 2e5293f401d49afedb6df3e63f493bd4 | function d=som_eucdist2(Data, Proto)
%SOM_EUCDIST2 Calculates matrix of squared euclidean distances between set of vectors or map, data struct
%
% d=som_eucdist2(D, P)
%
% d=som_eucdist(sMap, sData);
% d=som_eucdist(sData, sMap);
% d=som_eucdist(sMap1, sMap2);
% d=som_eucdist(datamatrix1, datamatrix2);
%
% Input ... |
github | limosek/somtoolbox-master | som_norm_variable.m | .m | somtoolbox-master/som_norm_variable.m | 19,569 | utf_8 | b5c3e9de5462b8068dfe0977ad2bedd8 | function [x,sNorm] = som_norm_variable(x, method, operation)
%SOM_NORM_VARIABLE Normalize or denormalize a scalar variable.
%
% [x,sNorm] = som_norm_variable(x, method, operation)
%
% xnew = som_norm_variable(x,'var','do');
% [dummy,sN] = som_norm_variable(x,'log','init');
% [xnew,sN] = som_norm_variable(x,sN,'... |
github | limosek/somtoolbox-master | cca.m | .m | somtoolbox-master/cca.m | 7,994 | utf_8 | bfa57098d29dbadef26bc1b0c121fc81 | function [P] = cca(D, P, epochs, Mdist, alpha0, lambda0)
%CCA Projects data vectors using Curvilinear Component Analysis.
%
% P = cca(D, P, epochs, [Dist], [alpha0], [lambda0])
%
% P = cca(D,2,10); % projects the given data to a plane
% P = cca(D,pcaproj(D,2),5); % same, but with PCA initialization
% P = ... |
github | limosek/somtoolbox-master | sompak_sammon.m | .m | somtoolbox-master/sompak_sammon.m | 4,324 | utf_8 | b8872265327c912a6d5d659af45bcd0e | function sMap=sompak_sammon(sMap,ft,cout,ct,rlen)
%SOMPAK_SAMMON Call SOM_PAK Sammon's mapping program from Matlab.
%
% P = sompak_sammon(sMap,ft,cout,ct,rlen)
%
% ARGUMENTS ([]'s are optional and can be given as empty: [] or '')
% sMap (struct) map struct
% (string) filename
% [ft] (string) 'pak' or 'b... |
github | limosek/somtoolbox-master | som_show_add.m | .m | somtoolbox-master/som_show_add.m | 48,988 | utf_8 | 3e19f28e1478bdd3f3559e06100d4213 | function h=som_show_add(mode,D,varargin)
%SOM_SHOW_ADD Shows hits, labels and trajectories on SOM_SHOW visualization
%
% h = som_show_add(mode, D, ['argID',value,...])
%
% som_show_add('label',sMap)
% som_show_add('hit',som_hits(sMap,sD))
% som_show_add('traj',som_bmus(sMap,sD))
% som_show_add('comet',som_bmus(sMa... |
github | limosek/somtoolbox-master | som_fuzzycolor.m | .m | somtoolbox-master/som_fuzzycolor.m | 6,310 | utf_8 | 7a81442a0716dffb892a5c5c39b4f915 | function [color,X]=som_fuzzycolor(sM,T,R,mode,initRGB,S)
% SOM_FUZZYCOLOR Heuristic contraction projection/soft cluster color coding for SOM
%
% function [color,X]=som_fuzzycolor(map,[T],[R],[mode],[initRGB],[S])
%
% sM (map struct)
% [T] (scalar) parameter that defines the speed of contraction
% ... |
github | limosek/somtoolbox-master | som_stats.m | .m | somtoolbox-master/som_stats.m | 9,260 | utf_8 | e723e0c5b846c2dc0797de37dd0f3267 | function csS = som_stats(D,varargin)
%SOM_STATS Calculate descriptive statistics for the data.
%
% csS = som_stats(D,[sort]);
%
% csS = som_stats(D);
% csS = som_stats(D,'nosort');
% som_table_print(som_stats_table(csS))
%
% Input and output arguments ([]'s are optional):
% D (matrix) a matrix, ... |
github | limosek/somtoolbox-master | knn_old.m | .m | somtoolbox-master/knn_old.m | 7,202 | utf_8 | 6492a002b782bf1c97b6ad4a322945c9 | function [Class,P]=knn_old(Data, Proto, proto_class, K)
%KNN_OLD A K-nearest neighbor classifier using Euclidean distance
%
% [Class,P]=knn_old(Data, Proto, proto_class, K)
%
% [sM_class,P]=knn_old(sM, sData, [], 3);
% [sD_class,P]=knn_old(sD, sM, class);
% [class,P]=knn_old(data, proto, class);
% [class,P]=knn_o... |
github | limosek/somtoolbox-master | som_trajectory.m | .m | somtoolbox-master/som_trajectory.m | 9,594 | utf_8 | 2d0ff57acb6c0b506cbf17f5b6cce158 | function som_trajectory(bmus,varargin)
%SOM_TRAJECTORY Launch a "comet" trajectory visualization GUI.
%
% som_show(sM,'umat','all')
% bmus = som_bmus(sM,sD);
% som_trajectory(bmus)
% som_trajectory(bmus, 'data1', sD, 'trajsize', [12 6 3 1]')
% som_trajectory(bmus, 'data1', sD.data(:,[1 2 3]), 'name1', {'fii' 'faa... |
github | limosek/somtoolbox-master | som_vs1to2.m | .m | somtoolbox-master/som_vs1to2.m | 7,007 | utf_8 | 312b6d698a99d77d7dd20f978ce93616 | function sS = som_vs1to2(sS)
%SOM_VS1TO2 Convert version 1 structure to version 2.
%
% sSnew = som_vs1to2(sSold)
%
% sMnew = som_vs1to2(sMold);
% sDnew = som_vs1to2(sDold);
%
% Input and output arguments:
% sSold (struct) a SOM Toolbox version 1 structure
% sSnew (struct) a SOM Toolbox version 2 struct... |
github | limosek/somtoolbox-master | rep_utils.m | .m | somtoolbox-master/rep_utils.m | 18,710 | utf_8 | 211ffa96c93e996856ed2695add316f4 | function aout = rep_utils(action,fmt,fid)
%REP_UTILS Utilities for print reports and report elements.
%
% aout = rep_utils(action,fmt,[fid])
%
% Input and output arguments ([]'s are optional):
% action (string) action identifier
% (cell array) {action,par1,par2,...}
% ... |
github | limosek/somtoolbox-master | som_vs2to1.m | .m | somtoolbox-master/som_vs2to1.m | 8,364 | utf_8 | bb3fb3916dd7b294d2e384b5e867bd8b | function sS = som_vs2to1(sS)
%SOM_VS2TO1 Convert version 2 struct to version 1.
%
% sSold = som_vs2to1(sSnew)
%
% sMold = som_vs2to1(sMnew);
% sDold = som_vs2to1(sDnew);
%
% Input and output arguments:
% sSnew (struct) a SOM Toolbox version 2 struct
% sSold (struct) a SOM Toolbox version 1 struct
%
% F... |
github | limosek/somtoolbox-master | som_dendrogram.m | .m | somtoolbox-master/som_dendrogram.m | 9,043 | utf_8 | b50ac89c47d2f2eeec638acc6880840c | function [h,Coord,Color,height] = som_dendrogram(Z,varargin)
%SOM_DENDROGRAM Visualize a dendrogram.
%
% [h,Coord,Color,height] = som_dendrogram(Z, [[argID,] value, ...])
%
% Z = som_linkage(sM);
% som_dendrogram(Z);
% som_dendrogram(Z,sM);
% som_dendrogram(Z,'coord',co);
%
% Input and output arguments ([]'s ... |
github | limosek/somtoolbox-master | som_plotplane.m | .m | somtoolbox-master/som_plotplane.m | 8,886 | utf_8 | 7269a0fb143b91e19d19472fb84550b0 | function h=som_plotplane(varargin)
%SOM_PLOTPLANE Visualize the map prototype vectors as line graphs
%
% h=som_plotplane(lattice, msize, data, [color], [scaling], [pos])
% h=som_plotplane(topol, data, [color], [scaling], [pos])
%
% som_plotplane('hexa',[5 5], rand(25,4), jet(25))
% som_plotplane(sM, sM.codebook)
%... |
github | limosek/somtoolbox-master | som_seqtrain.m | .m | somtoolbox-master/som_seqtrain.m | 20,937 | utf_8 | a015731f9030951dd150427c344324eb | function [sMap, sTrain] = som_seqtrain(sMap, D, varargin)
%SOM_SEQTRAIN Use sequential algorithm to train the Self-Organizing Map.
%
% [sM,sT] = som_seqtrain(sM, D, [[argID,] value, ...])
%
% sM = som_seqtrain(sM,D);
% sM = som_seqtrain(sM,sD,'alpha_type','power','tracking',3);
% [M,sT] = som_seqtrain(M,D... |
github | limosek/somtoolbox-master | som_kmeanscolor2.m | .m | somtoolbox-master/som_kmeanscolor2.m | 5,861 | utf_8 | 28d03bf9bb92b3a39fb21a02e1dc4d60 | function [color,centroids]=som_kmeanscolor2(mode,sM,C,initRGB,contrast,R)
% SOM_KMEANSCOLOR2 Color codes a SOM according to averaged or best K-means clustering
%
% color = som_kmeanscolor2('average',sM, C, [initRGB], [contrast],[R])
%
% color=som_kmeanscolor2('average',sM,[2 4 8 16],som_colorcode(sM,'rgb1'),'enhance... |
github | limosek/somtoolbox-master | som_stats_plot.m | .m | somtoolbox-master/som_stats_plot.m | 4,914 | utf_8 | 88955c8c9ddabf4190811924c7570ac8 | function som_stats_plot(csS,plottype,varargin)
%SOM_STATS_PLOT Plots of data set statistics.
%
% som_stats_plot(csS, plottype, [argID, value, ...])
%
% som_stats_plot(csS,'stats')
% som_stats_plot(csS,'stats','p','vert','color','r')
%
% Input and output arguments ([]'s are optional):
% csS (cell array)... |
github | limosek/somtoolbox-master | sompak_train.m | .m | somtoolbox-master/sompak_train.m | 6,498 | utf_8 | 748ede556cdbdcc88aea633bc58087fc | function sMap=sompak_train(sMap,ft,cout,ct,din,dt,rlen,alpha,radius)
%SOMPAK_TRAIN Call SOM_PAK training program from Matlab.
%
% sMap=sompak_train(sMap,ft,cout,ct,din,dt,rlen,alpha,radius)
%
% ARGUMENTS ([]'s are optional and can be given as empty: [] or '')
% sMap (struct) map struct
% (string) filename
... |
github | limosek/somtoolbox-master | som_kmeanscolor.m | .m | somtoolbox-master/som_kmeanscolor.m | 4,380 | utf_8 | d2315894d2ce4c9257c55a0cac67be4e | function [color,best,kmeans]=som_kmeanscolor(sM,C,initRGB,contrast)
% SOM_KMEANSCOLOR Map unit color code according to K-means clustering
%
% [color, best, kmeans] = som_kmeanscolor(sM, C, [initRGB],[contrast])
%
% color = som_kmeanscolor(sM,15,som_colorcode(sM,'rgb1'),'enhance');
% [color,best] = som_kmeansc... |
github | limosek/somtoolbox-master | vis_valuetype.m | .m | somtoolbox-master/vis_valuetype.m | 7,567 | utf_8 | 118c3bf7fcc1ea6b5993b18610373726 | function flag=vis_valuetype(value, valid, str);
% VIS_VALUETYPE Used for type checks in SOM Toolbox visualization routines
%
% flag = vis_valuetype(value, valid, str)
%
% Input and output arguments:
% value (varies) variable to be checked
% valid (cell array) size 1xN, cells are strings or vectors (see below)
... |
github | limosek/somtoolbox-master | som_neighf.m | .m | somtoolbox-master/som_neighf.m | 3,523 | utf_8 | f6b1364bce6a70274ab7d180a2ba9b04 | function H = som_neighf(sMap,radius,neigh,ntype)
%SOM_NEIGHF Return neighborhood function values.
%
% H = som_neighf(sMap,[radius],[neigh],[ntype]);
%
% Input and output arguments ([]'s are optional):
% sMap (struct) map or topology struct
% [radius] (scalar) neighborhood radius (by default, the last used v... |
github | limosek/somtoolbox-master | som_gui.m | .m | somtoolbox-master/som_gui.m | 99,759 | utf_8 | 5d7577b4d6a8d45f74ccf00b176dccee | function som_gui(varargin)
%SOM_GUI A GUI for initialization and training of SOM.
%
% som_gui([sD])
%
% som_gui
% som_gui(sD)
%
% Input and output arguments ([]'s are optional)
% [sD] (struct) SOM data struct
% (matrix) a data matrix, size dlen x dim
%
% Actually, there are more arguments th... |
github | limosek/somtoolbox-master | som_dmatminima.m | .m | somtoolbox-master/som_dmatminima.m | 2,013 | utf_8 | 6a222b64d869b1d71e699346d573dbe6 | function minima = som_dmatminima(sM,U,Ne)
%SOM_DMATMINIMA Find clusters based on local minima of U-matrix.
%
% minima = som_dmatminima(sM,[U],[Ne])
%
% Input and output arguments ([]'s are optional):
% sM (struct) map struct
% U (matrix) the distance matrix from which minima is
% ... |
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