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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 | martinarielhartmann/mirtooloct-master | evaleach.m | .m | mirtooloct-master/MIRToolbox/@mirdesign/evaleach.m | 33,230 | utf_8 | a6791354f1633edd3bc91578a489bed5 | function [y d2] = evaleach(d,single,name)
% Top-down traversal of the design flowchart, at the beginning of the
% evaluation phase.
% Called by mirfunction, mireval, mirframe and mirsegment.
% This is during that traversal that we check whether a chunk decomposition
% needs to be performed or not, and carry out that ch... |
github | martinarielhartmann/mirtooloct-master | plus.m | .m | mirtooloct-master/MIRToolbox/@mirdesign/plus.m | 159 | utf_8 | 4c83543fc53f3b9d77b3dea756c818b3 | function varargout = plus(a,b)
varargout = mirfunction(@pluscell,{a,b},{},1,struct,@init,@plus);
function [x type] = init(x,option)
type = get(x{1},'Type'); |
github | martinarielhartmann/mirtooloct-master | max.m | .m | mirtooloct-master/MIRToolbox/@mirdesign/max.m | 156 | utf_8 | 152a5a361e15225fc399aaf072410341 | function varargout = max(a,b)
varargout = mirfunction(@maxcell,{a,b},{},1,struct,@init,@max);
function [x type] = init(x,option)
type = get(x{1},'Type'); |
github | martinarielhartmann/mirtooloct-master | mtimes.m | .m | mirtooloct-master/MIRToolbox/@mirdesign/mtimes.m | 169 | utf_8 | 91a0abb2d0c449aeac1669a109b4cc06 | function varargout = mtimes(a,b)
varargout = mirfunction(@mtimescell,{a,b},{},1,struct,@init,@mtimescell);
function [x type] = init(x,option)
type = get(x{1},'Type'); |
github | martinarielhartmann/mirtooloct-master | mirspectrum.m | .m | mirtooloct-master/MIRToolbox/@mirspectrum/mirspectrum.m | 35,371 | utf_8 | fcf94ab6871881278d4ed96df6962384 | function varargout = mirspectrum(orig,varargin)
% s = mirspectrum(x) computes the spectrum of the audio signal x, showing
% the distribution of the energy along the frequencies.
% (x can be the name of an audio file as well.)
% Optional argument:
% mirspectrum(...,'Frame',l,h) computes spectrogram... |
github | martinarielhartmann/mirtooloct-master | mirhisto.m | .m | mirtooloct-master/MIRToolbox/@mirhisto/mirhisto.m | 3,357 | utf_8 | 6f716ac28bcb19a4355603f708ace933 | function varargout = mirhisto(x,varargin)
% h = mirhisto(x) constructs the histogram from x. The elements of x are
% binned into equally spaced containers.
% Optional argument:
% mirhisto(...,'Number',n): specifies the number of containers.
% Default value : n = 10.
% mirhisto(...,'Ampli... |
github | martinarielhartmann/mirtooloct-master | mirclassify.m | .m | mirtooloct-master/MIRToolbox/@mirclassify/mirclassify.m | 8,794 | utf_8 | 2defa2037b7f54ef59ab70750bf85223 | function c = mirclassify(x,varargin)
% Optional argument:
% mirclassify(...,'Nearest') uses the minimum distance strategy.
% (by default)
% mirclassify(...,'Nearest',k) uses the k-nearest-neighbour strategy.
% Default value: k = 1, corresponding to the minimum distance
% ... |
github | martinarielhartmann/mirtooloct-master | mirkeystrength.m | .m | mirtooloct-master/MIRToolbox/@mirkeystrength/mirkeystrength.m | 3,445 | utf_8 | 25ad1c532626e1a1e817061b825a234b | function varargout = mirkeystrength(orig,varargin)
% ks = mirkeystrength(x) computes the key strength, i.e., the probability
% associated with each possible key candidate.
% Optional parameters:
% mirkeystrength(...,'Frame',l,h) orders a frame decomposition of window
% length l (in seconds) and h... |
github | martinarielhartmann/mirtooloct-master | mircepstrum.m | .m | mirtooloct-master/MIRToolbox/@mircepstrum/mircepstrum.m | 4,674 | utf_8 | ce23ede2483821abfd1017cf8cdf0265 | function varargout = mircepstrum(orig,varargin)
% s = mircepstrum(x) computes the cepstrum, which indicates
% periodicities, and is used for instance for pitch detection.
% x can be either a spectrum, an audio signal, or the name of an audio file.
% Optional parameter:
% mircepstrum(...,'Min',min) spe... |
github | martinarielhartmann/mirtooloct-master | mirpartial.m | .m | mirtooloct-master/MIRToolbox/@mirpartial/mirpartial.m | 1,009 | utf_8 | 2a4f8c2bb5ed2f391f6fe7eab0719176 | function varargout = mirpartial(orig,varargin)
max.key = 'Max';
max.type = 'Integer';
max.default = Inf;
option.max = max;
specif.option = option;
varargout = mirfunction(@mirpartial,orig,varargin,nargout,specif,@init,@main);
function [x type] = init(x,option)
type... |
github | martinarielhartmann/mirtooloct-master | mirplay.m | .m | mirtooloct-master/MIRToolbox/@mirmidi/mirplay.m | 523 | utf_8 | 278bd0f7f6429d6298d1a5e2583f7110 | function varargout = mirplay(a,varargin)
% mirplay method for mirmidi objects.
specif.option = struct;
specif.eachchunk = 'Normal';
varargout = mirfunction(@mirplay,a,varargin,nargout,specif,@init,@main);
if nargout == 0
varargout = {};
end
function [x type] = init(x,option)
type = '';
function noargout = ma... |
github | martinarielhartmann/mirtooloct-master | mirmidi.m | .m | mirtooloct-master/MIRToolbox/@mirmidi/mirmidi.m | 3,166 | utf_8 | 0a67555741f7166d9cdf9af228766d2d | function varargout = mirmidi(orig,varargin)
% m = mirmidi(x) converts into a MIDI sequence.
% Option associated to mirpitch function can be specified:
% 'Contrast' with default value c = .3
thr.key = 'Contrast';
thr.type = 'Integer';
thr.default = .3;
option.thr = thr;
mono.key = 'Mono';
... |
github | martinarielhartmann/mirtooloct-master | mirsave.m | .m | mirtooloct-master/MIRToolbox/@mirmidi/mirsave.m | 2,242 | utf_8 | 058b8881d0014a70aa350bbd21fcb9fe | function mirsave(m,f)
ext = 0; % Specified new extension
if nargin == 1
f = '.mir';
elseif length(f)>3 && strcmpi(f(end-3:end),'.mid')
ext = '.mid';
if length(f)==4
f = '.mir';
end
elseif length(f)>2 && strcmpi(f(end-2:end),'.ly')
ext = '.ly';
if length(f)==3
f = '.mir';
... |
github | martinarielhartmann/mirtooloct-master | mirtonalcentroid.m | .m | mirtooloct-master/MIRToolbox/@mirtonalcentroid/mirtonalcentroid.m | 2,827 | utf_8 | ea99af0926744591d9cf85297a10c9d2 | function varargout = mirtonalcentroid(orig,varargin)
% c = mirtonalcentroid(x) calculates the 6-dimensional tonal centroid
% vector from the chromagram.
% It corresponds to a projection of the chords along circles of fifths,
% of minor thirds, and of major thirds.
% [c ch] = mirtonalcentroid(x) also... |
github | martinarielhartmann/mirtooloct-master | mirplay.m | .m | mirtooloct-master/MIRToolbox/@mirsimatrix/mirplay.m | 2,286 | utf_8 | 50e71f123737e04a5b795409d5de3af3 | function mirplay(e,varargin)
% mirplay method for mirsimatrix objects.
specif.option = struct;
specif.eachchunk = 'Normal';
varargout = mirfunction(@mirplay,e,varargin,nargout,specif,@init,@main);
if nargout == 0
varargout = {};
end
function [x type] = init(x,option)
type = '';
function noargout = main(m,op... |
github | martinarielhartmann/mirtooloct-master | mirsimatrix.m | .m | mirtooloct-master/MIRToolbox/@mirsimatrix/mirsimatrix.m | 25,398 | utf_8 | ed638b58e2097bd95838bdda1b1b83c5 | function varargout = mirsimatrix(orig,varargin)
% m = mirsimatrix(x) computes the similarity matrix resulting from the
% mutual comparison between each possible frame analysis in x.
% By default, x is the spectrum of the frame decomposition.
% But it can be any other frame analysis.
% Opt... |
github | martinarielhartmann/mirtooloct-master | mirautocor.m | .m | mirtooloct-master/MIRToolbox/@mirautocor/mirautocor.m | 25,732 | utf_8 | 738d1a46a0baaa7a8a3825ebbe1bda84 | function varargout = mirautocor(orig,varargin)
% a = mirautocor(x) computes the autocorrelation function related to x.
% Optional parameters:
% mirautocor(...,'Min',mi) indicates the lowest delay taken into
% consideration. The unit can be precised:
% mirautocor(...,'Min',mi,'s') (defa... |
github | martinarielhartmann/mirtooloct-master | combine.m | .m | mirtooloct-master/MIRToolbox/@mirdata/combine.m | 3,357 | utf_8 | 501ac7884a5a69e4c2961e5522b546a1 | function c = combine(varargin)
c = varargin{1};
l = length(varargin);
p = cell(1,l);
ch = cell(1,l);
d = cell(1,l);
fp = cell(1,l);
fr = cell(1,l);
sr = cell(1,l);
n = cell(1,l);
la = cell(1,l);
le = cell(1,l);
cl = cell(1,l);
pp = cell(1,l);
pm = cell(1,l);
pv = cell(1,l);
ppp = cell(1,l);
ppv = cell(1,l);
tp = cell(... |
github | martinarielhartmann/mirtooloct-master | miraudio.m | .m | mirtooloct-master/MIRToolbox/@miraudio/miraudio.m | 13,358 | utf_8 | 6e9885c23f30543534bc72a8250a76cb | function varargout = miraudio(orig,varargin)
% a = miraudio('filename') loads the sound file 'filename' (in WAV or AU
% format) into a miraudio object.
% a = miraudio('Folder') loads all the sound files in the CURRENT folder
% into a miraudio object.
% a = miraudio(v,sr), where v is a column vector, t... |
github | martinarielhartmann/mirtooloct-master | miremotion.m | .m | mirtooloct-master/MIRToolbox/@miremotion/miremotion.m | 14,268 | utf_8 | e1e8b3cb2dcd7d9172b5b27dcc2240b7 | function varargout = miremotion(orig,varargin)
% Predicts emotion along three dimensions and five basic concepts.
% Optional parameters:
% miremotion(...,'Dimensions',0) excludes all three dimensions.
% miremotion(...,'Dimensions',3) includes all three dimensions (default).
% miremotion(...,'Activity') includes t... |
github | martinarielhartmann/mirtooloct-master | mirplay.m | .m | mirtooloct-master/MIRToolbox/@mirpattern/mirplay.m | 899 | utf_8 | ea782c3f96091cb9b147f96756704a55 | function varargout = mirplay(p,varargin)
pat.key = 'Pattern';
pat.type = 'Integer';
pat.default = 0;
option.pat = pat;
specif.option = option;
specif.eachchunk = 'Normal';
varargout = mirfunction(@mirplay,p,varargin,nargout,specif,@init,@main);
if nargout == 0
varargout =... |
github | martinarielhartmann/mirtooloct-master | mirpattern.m | .m | mirtooloct-master/MIRToolbox/@mirpattern/mirpattern.m | 1,867 | utf_8 | 1def40f8b242e54a318195d5ecc0c60f | function varargout = mirpattern(orig,varargin)
% p = mirpattern(a)
period.key = 'Period';
period.type = 'Boolean';
period.when = 'After';
period.default = 0;
option.period = period;
specif.option = option;
varargout = mirfunction(@mirpattern,orig,varargin,nargout,sp... |
github | martinarielhartmann/mirtooloct-master | mirpitch.m | .m | mirtooloct-master/MIRToolbox/@mirpitch/mirpitch.m | 34,447 | utf_8 | 60e5f74d92ec86e6551b87495a9a6792 | function varargout = mirpitch(orig,varargin)
% p = mirpitch(x) evaluates the pitch frequencies (in Hz).
% Specification of the method(s) for pitch estimation (these methods can
% be combined):
% mirpitch(...,'Autocor') computes an autocorrelation function
% (Default method)
% mirpitc... |
github | martinarielhartmann/mirtooloct-master | mirplay.m | .m | mirtooloct-master/MIRToolbox/@mirenvelope/mirplay.m | 3,662 | utf_8 | 2e8e380750d648bcb991781e813423fd | function mirplay(e,varargin)
% mirplay method for mirenvelope objects. Help displayed in ../mirplay.m
ch.key = 'Channel';
ch.type = 'Integer';
ch.default = 0;
option.ch = ch;
sg.key = 'Segment';
sg.type = 'Integer';
sg.default = 0;
option.sg = sg;
... |
github | martinarielhartmann/mirtooloct-master | mirenvelope.m | .m | mirtooloct-master/MIRToolbox/@mirenvelope/mirenvelope.m | 26,047 | utf_8 | b2e8c24762ea7e5c9b41c1db89666a70 | function varargout = mirenvelope(orig,varargin)
% e = mirenvelope(x) extracts the envelope of x, showing the global shape
% of the waveform.
% mirenvelope(...,m) specifies envelope extraction method.
% Possible values:
% m = 'Filter' uses a low-pass filtering. (Default strategy)
% m ... |
github | martinarielhartmann/mirtooloct-master | mirquery.m | .m | mirtooloct-master/MIRToolbox/@mirquery/mirquery.m | 2,122 | utf_8 | 69ba1787084c74f96a14a9faf74ff367 | function res = mirquery(varargin)
% r = mirquery(q,b), where
% q is the analysis of one audio file and
% b is the analysis of a folder of audio files,
% according to the same mirtoolbox feature,
% returns the name of the audio files in the database b in an
% increasi... |
github | martinarielhartmann/mirtooloct-master | som_probability_gmm.m | .m | mirtooloct-master/somtoolbox/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 | martinarielhartmann/mirtooloct-master | som_clget.m | .m | mirtooloct-master/somtoolbox/som_clget.m | 3,420 | utf_8 | 34bca7118530f042e1b9d90718cf688a | 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 | martinarielhartmann/mirtooloct-master | lvq3.m | .m | mirtooloct-master/somtoolbox/lvq3.m | 5,951 | utf_8 | 3d1d8a994701991b148ab22ae8bceb1a | 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 | martinarielhartmann/mirtooloct-master | som_select.m | .m | mirtooloct-master/somtoolbox/som_select.m | 20,295 | utf_8 | 8d0b3f1b93252ad6250273831b30b5ad | 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 | martinarielhartmann/mirtooloct-master | som_unit_coords.m | .m | mirtooloct-master/somtoolbox/som_unit_coords.m | 8,082 | utf_8 | 1656dc53e5cdea92d6870107451337dd | 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 | martinarielhartmann/mirtooloct-master | vis_footnote.m | .m | mirtooloct-master/somtoolbox/vis_footnote.m | 3,091 | utf_8 | bdff65a1392daa41414831644ccc8235 | 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 | martinarielhartmann/mirtooloct-master | vis_trajgui.m | .m | mirtooloct-master/somtoolbox/vis_trajgui.m | 41,530 | utf_8 | 7afe711e9155c89b97c444b1d7e39710 | 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 | martinarielhartmann/mirtooloct-master | som_order_cplanes.m | .m | mirtooloct-master/somtoolbox/som_order_cplanes.m | 8,524 | utf_8 | 3b6f8da3cb8f17ae375f464280e0f4df | 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 | martinarielhartmann/mirtooloct-master | som_batchtrain.m | .m | mirtooloct-master/somtoolbox/som_batchtrain.m | 20,584 | utf_8 | 55d753f2fe72fda2647ec34374600a86 | 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 | martinarielhartmann/mirtooloct-master | som_stats_report.m | .m | mirtooloct-master/somtoolbox/som_stats_report.m | 3,633 | utf_8 | eca74b20e5ec9e82e9aeee118cd81303 | 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 | martinarielhartmann/mirtooloct-master | som_eucdist2.m | .m | mirtooloct-master/somtoolbox/som_eucdist2.m | 2,270 | utf_8 | 6f74c5daaf9a1667b8937a1ceb29ffa2 | 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 | martinarielhartmann/mirtooloct-master | som_norm_variable.m | .m | mirtooloct-master/somtoolbox/som_norm_variable.m | 19,542 | utf_8 | 9323ed0f31d148b4f88cacf4454fdb22 | 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 | martinarielhartmann/mirtooloct-master | cca.m | .m | mirtooloct-master/somtoolbox/cca.m | 7,987 | utf_8 | f9446f8801dde781d7e8f400842fb0c2 | 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 | martinarielhartmann/mirtooloct-master | sompak_sammon.m | .m | mirtooloct-master/somtoolbox/sompak_sammon.m | 4,311 | utf_8 | e86c45a58b8ef54fa7ae35f5aa42efc7 | 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 | martinarielhartmann/mirtooloct-master | som_show_add.m | .m | mirtooloct-master/somtoolbox/som_show_add.m | 48,954 | utf_8 | 2f99faff178e5d12162026e4b926a00c | 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 | martinarielhartmann/mirtooloct-master | som_fuzzycolor.m | .m | mirtooloct-master/somtoolbox/som_fuzzycolor.m | 6,305 | utf_8 | ba6ea6d7d1079610c8988bcb30365eb4 | 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 | martinarielhartmann/mirtooloct-master | som_stats.m | .m | mirtooloct-master/somtoolbox/som_stats.m | 9,256 | utf_8 | 913c885c15a80104f02cd88b0bcbd0c8 | 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 | martinarielhartmann/mirtooloct-master | knn_old.m | .m | mirtooloct-master/somtoolbox/knn_old.m | 7,196 | utf_8 | 91ff9ef390bf0c8610ff647a8a3e29bd | 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 | martinarielhartmann/mirtooloct-master | som_trajectory.m | .m | mirtooloct-master/somtoolbox/som_trajectory.m | 9,582 | utf_8 | 943fbebf146286d22761e716b557cf75 | 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 | martinarielhartmann/mirtooloct-master | som_vs1to2.m | .m | mirtooloct-master/somtoolbox/som_vs1to2.m | 7,005 | utf_8 | ff7eede3183ba5dfa54884255dc76c4e | 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 | martinarielhartmann/mirtooloct-master | rep_utils.m | .m | mirtooloct-master/somtoolbox/rep_utils.m | 18,698 | utf_8 | 729db4f7bcce5f1f91eab7a63c3acdc1 | 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 | martinarielhartmann/mirtooloct-master | som_vs2to1.m | .m | mirtooloct-master/somtoolbox/som_vs2to1.m | 8,359 | utf_8 | 7ae2b5258b2e375dd754f63d956a5dcd | 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 | martinarielhartmann/mirtooloct-master | som_dendrogram.m | .m | mirtooloct-master/somtoolbox/som_dendrogram.m | 9,039 | utf_8 | 43f32770e95d19d4a12855bd8bfb91f3 | 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 | martinarielhartmann/mirtooloct-master | som_plotplane.m | .m | mirtooloct-master/somtoolbox/som_plotplane.m | 8,877 | utf_8 | afada5a6c93a0270c32acbd532af0679 | 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 | martinarielhartmann/mirtooloct-master | som_seqtrain.m | .m | mirtooloct-master/somtoolbox/som_seqtrain.m | 20,925 | utf_8 | 10804404d112d52c0bf9538861655c63 | 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 | martinarielhartmann/mirtooloct-master | som_kmeanscolor2.m | .m | mirtooloct-master/somtoolbox/som_kmeanscolor2.m | 5,856 | utf_8 | b167d8ae2e8d9e2099002b0b6c376772 | 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 | martinarielhartmann/mirtooloct-master | som_stats_plot.m | .m | mirtooloct-master/somtoolbox/som_stats_plot.m | 4,911 | utf_8 | 04eb5e46e18587318f497985e2ef3672 | 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 | martinarielhartmann/mirtooloct-master | sompak_train.m | .m | mirtooloct-master/somtoolbox/sompak_train.m | 6,477 | utf_8 | c1f8430b537283c08dd052265ce60c99 | 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 | martinarielhartmann/mirtooloct-master | som_kmeanscolor.m | .m | mirtooloct-master/somtoolbox/som_kmeanscolor.m | 4,375 | utf_8 | b90f1bc73191e2f5c68e2e8afe11269a | 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 | martinarielhartmann/mirtooloct-master | vis_valuetype.m | .m | mirtooloct-master/somtoolbox/vis_valuetype.m | 7,502 | utf_8 | cb7a373fcda9120d69231b2748371740 | 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 | martinarielhartmann/mirtooloct-master | som_neighf.m | .m | mirtooloct-master/somtoolbox/som_neighf.m | 3,518 | utf_8 | d37974390d75ef52b98cff7dc7fa6a53 | 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 | martinarielhartmann/mirtooloct-master | som_gui.m | .m | mirtooloct-master/somtoolbox/som_gui.m | 99,745 | utf_8 | 46047f777569e35ebc2596223c5cc512 | 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 | martinarielhartmann/mirtooloct-master | som_dmatminima.m | .m | mirtooloct-master/somtoolbox/som_dmatminima.m | 2,009 | utf_8 | 9e535d4906164073484193ea7ef10560 | 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
% ... |
github | martinarielhartmann/mirtooloct-master | som_stats_table.m | .m | mirtooloct-master/somtoolbox/som_stats_table.m | 3,684 | utf_8 | 0dec0a499ac0af8b81c9d1eae7c1e819 | function [sTstats,csThist] = som_stats_table(csS,histlabel)
%SOM_STATS_TABLE Statistics table.
%
% [sTstats,csThist] = som_stats_table(csS)
%
% sTstats = som_stats_table(csS);
% som_table_print(sTstats);
%
% Input and output arguments ([]'s are optional):
% csS (cell array) of statistics struct... |
github | martinarielhartmann/mirtooloct-master | som_barplane.m | .m | mirtooloct-master/somtoolbox/som_barplane.m | 13,935 | utf_8 | 75467b21870c4a890f9d50bd7db8c647 | function h = som_barplane(varargin)
%SOM_BARPLANE Visualize the map prototype vectors as bar charts
%
% h = som_barplane(lattice, msize, data, [color], [scaling], [gap], [pos])
% h = som_barplane(topol, data, [color], [scaling], [gap], [pos])
%
% som_barplane('hexa',[5 5], rand(25,4), jet(4))
% som_barplane(sM, sM.... |
github | martinarielhartmann/mirtooloct-master | som_recolorbar.m | .m | mirtooloct-master/somtoolbox/som_recolorbar.m | 12,433 | utf_8 | f6539ba0228a1c13d3b9e317d75ddabf | function h=som_recolorbar(p, ticks, scale, labels)
%SOM_RECOLORBAR Refresh and rescale colorbars in the current SOM_SHOW fig.
%
% h = som_recolorbar([p], [ticks], [scaling], [labels])
%
% colormap(jet); som_recolorbar
%
% Input and output arguments ([]'s are optional)
% [p] (vector) subplot number vector ... |
github | martinarielhartmann/mirtooloct-master | som_show.m | .m | mirtooloct-master/somtoolbox/som_show.m | 28,122 | utf_8 | d4dcabfc93b9206fb6cb14eeffb497f1 | function h=som_show(sMap, varargin)
% SOM_SHOW Basic SOM visualizations: component planes, u-matrix etc.
%
% h = som_show(sMap, ['argID', value, ...])
%
% som_show(sMap);
% som_show(sMap,'bar','none');
% som_show(sMap,'comp',[1:3],'umat','all');
% som_show(sMap,'comp',[1 2],'umat',{[1 2],'1,2 only'},'comp',[3:6])... |
github | martinarielhartmann/mirtooloct-master | som_show_gui.m | .m | mirtooloct-master/somtoolbox/som_show_gui.m | 21,167 | utf_8 | 04711e9a1ccd09acfe6f6d238cf212f5 | function fig = som_show_gui(input,varargin)
%SOM_SHOW_GUI A GUI for using SOM_SHOW and associated functions.
%
% h = som_show_gui(sM);
%
% Input and output arguments:
% sM (struct) a map struct: the SOM to visualize
% h (scalar) a handle to the GUI figure
%
% This is a graphical user interface to make... |
github | martinarielhartmann/mirtooloct-master | som_label.m | .m | mirtooloct-master/somtoolbox/som_label.m | 9,503 | utf_8 | 8288c9b7322af1c25b20c13d01f82aef | function [sTo] = som_label(sTo, mode, inds, labels)
%SOM_LABEL Give/clear labels to/from map or data struct.
%
% sTo = som_label(sTo, mode, inds [, labels])
%
% sD = som_label(sD,'add',20,'a_label');
% sM = som_label(sM,'replace',[2 4],'a_label');
% sM = som_label(sM,'add',som_bmus(sM,x),'BMU');
% sD = som_la... |
github | martinarielhartmann/mirtooloct-master | som_clset.m | .m | mirtooloct-master/somtoolbox/som_clset.m | 10,182 | utf_8 | 38150d23d264f8096f36eb455bd10bdb | function [sC,old2new,newi] = som_clset(sC,action,par1,par2)
% SOM_CLSET Create and/or set values in the som_clustering struct.
%
% first argument
% sC (struct) a som_clustering struct
% Z (matrix) size nb-1 x 3, as given by LINKAGE function
% base (vector) size dlen x 1, a partiti... |
github | martinarielhartmann/mirtooloct-master | sompak_init.m | .m | mirtooloct-master/somtoolbox/sompak_init.m | 6,118 | utf_8 | 326ecf24211ec0f7576c9caa06b50998 | function sMap=sompak_init(sData,ft,init_type,cout,ct,xdim,ydim,topol,neigh)
%SOMPAK_INIT Call SOM_PAK initialization programs from Matlab.
%
% sMap=sompak_init(sData,ft,init_type,cout,ct,xdim,ydim,topol,neigh)
%
% ARGUMENTS ([]'s are optional and can be given as empty: [] or '')
% sData (struct) data struct
% ... |
github | martinarielhartmann/mirtooloct-master | som_distortion3.m | .m | mirtooloct-master/somtoolbox/som_distortion3.m | 5,404 | utf_8 | 13e98906922a0ded1181732268ac671c | function [Err,sPropTotal,sPropMunits,sPropComps] = som_distortion3(sM,D,rad)
%SOM_DISTORTION3 Map distortion measures.
%
% [sE,Err] = som_distortion3(sM,[D],[rad]);
%
% sE = som_distortion3(sM);
%
% Input and output arguments ([]'s are optional):
% sM (struct) map struct
% [D] (matrix) a ma... |
github | martinarielhartmann/mirtooloct-master | som_drmake.m | .m | mirtooloct-master/somtoolbox/som_drmake.m | 5,514 | utf_8 | 89709796bebe24488d9640dffa2a6465 | function [sR,best,sig,Cm] = som_drmake(D,inds1,inds2,sigmea,nanis)
% SOM_DRMAKE Make descriptive rules for given group within the given data.
%
% sR = som_drmake(D,[inds1],[inds2],[sigmea],[nanis])
%
% D (struct) map or data struct
% (matrix) the data, of size [dlen x dim]
% [inds1] (vector) ind... |
github | martinarielhartmann/mirtooloct-master | preprocess.m | .m | mirtooloct-master/somtoolbox/preprocess.m | 176,966 | utf_8 | a8170717a6766685b74076da97fb351e | function preprocess(sData,arg2)
%PREPROCESS A GUI for data preprocessing.
%
% preprocess(sData)
%
% preprocess(sData)
%
% Launches a preprocessing GUI. The optional input argument can be
% either a data struct or a struct array of such. However, primarily
% the processed data sets are loaded to the application us... |
github | martinarielhartmann/mirtooloct-master | som_kmeans.m | .m | mirtooloct-master/somtoolbox/som_kmeans.m | 4,244 | utf_8 | dca5b32dd99e19a186277df2a168dcf2 | function [codes,clusters,err] = som_kmeans(method, D, k, epochs, verbose)
% SOM_KMEANS K-means algorithm.
%
% [codes,clusters,err] = som_kmeans(method, D, k, [epochs], [verbose])
%
% Input and output arguments ([]'s are optional):
% method (string) k-means algorithm type: 'batch' or 'seq'
% D (ma... |
github | martinarielhartmann/mirtooloct-master | sompak_rb_control.m | .m | mirtooloct-master/somtoolbox/sompak_rb_control.m | 7,240 | utf_8 | bcfb4ce0fc8edd340c4a5b73afa57746 | function varargout=sompak_rb_control(str)
%SOMPAK_RB_CONTROL An auxiliary function for SOMPAK_*_GUI functions.
%
% This is an auxiliary function for SOMPAK_GUI, SOMPAK_INIT_GUI,
% SOMPAK_SAMMON_GUI and SOMPAK_TRAIN_GUI functions. It controls the
% radio buttons in the GUIs.
%
% See also SOMPAK_GUI, SOMPAK_INIT_GU... |
github | martinarielhartmann/mirtooloct-master | lvq1.m | .m | mirtooloct-master/somtoolbox/lvq1.m | 5,022 | utf_8 | 86a6a5093c040aededf200f82b599cc0 | function codebook=lvq1(codebook, data, rlen, alpha);
%LVQ1 Trains a codebook with the LVQ1 -algorithm.
%
% sM = lvq1(sM, D, rlen, alpha)
%
% sM = lvq1(sM,sD,30*length(sM.codebook),0.08);
%
% Input and output arguments:
% sM (struct) map struct, the class information must be
% present on the... |
github | martinarielhartmann/mirtooloct-master | som_colorcode.m | .m | mirtooloct-master/somtoolbox/som_colorcode.m | 7,983 | utf_8 | e110e71452756946ea1943fa0b627791 | function colors=som_colorcode(m, colorcode, scaling)
%SOM_COLORCODE Calculates a heuristic color coding for the SOM grid
%
% colors = som_colorcode(m, colorcode, scaling)
%
% Input and output arguments ([]'s are optional):
% m (struct) map or topol struct
% (cell array) of form {str,[m1 m2]... |
github | martinarielhartmann/mirtooloct-master | som_pieplane.m | .m | mirtooloct-master/somtoolbox/som_pieplane.m | 9,892 | utf_8 | 7d00431bbaad7c3344ddb3a827ada08b | function h=som_pieplane(varargin)
%SOM_PIEPLANE Visualize the map prototype vectors as pie charts
%
% h=som_pieplane(lattice, msize, data, [color], [s], [pos])
% h=som_pieplane(topol, data, [color], [s], [pos])
%
% som_pieplane('hexa',[5 5], rand(25,4), jet(4), rand(25,1))
% som_pieplane(sM, sM.codebook);
%
% Input... |
github | martinarielhartmann/mirtooloct-master | metrop.m | .m | mirtooloct-master/netlab/metrop.m | 4,976 | utf_8 | 53e05637fbfd2fcd95efaadd86e97ce9 | function [samples, energies, diagn] = metrop(f, x, options, gradf, varargin)
%METROP Markov Chain Monte Carlo sampling with Metropolis algorithm.
%
% Description
% SAMPLES = METROP(F, X, OPTIONS) uses the Metropolis algorithm to
% sample from the distribution P ~ EXP(-F), where F is the first
% argument to METROP. T... |
github | martinarielhartmann/mirtooloct-master | hmc.m | .m | mirtooloct-master/netlab/hmc.m | 7,683 | utf_8 | 64c15e958297afe69787b8617dc1a56a | function [samples, energies, diagn] = hmc(f, x, options, gradf, varargin)
%HMC Hybrid Monte Carlo sampling.
%
% Description
% SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo
% algorithm to sample from the distribution P ~ EXP(-F), where F is the
% first argument to HMC. The Markov chain starts at the poi... |
github | martinarielhartmann/mirtooloct-master | gtminit.m | .m | mirtooloct-master/netlab/gtminit.m | 5,204 | utf_8 | ab76f6114a7e85375ade5e5889d5f6a7 | function net = gtminit(net, options, data, samp_type, varargin)
%GTMINIT Initialise the weights and latent sample in a GTM.
%
% Description
% NET = GTMINIT(NET, OPTIONS, DATA, SAMPTYPE) takes a GTM NET and
% generates a sample of latent data points and sets the centres (and
% widths if appropriate) of NET.RBFNET.
%
% I... |
github | martinarielhartmann/mirtooloct-master | mlphess.m | .m | mirtooloct-master/netlab/mlphess.m | 1,633 | utf_8 | b91a15ca11b4886de6c1671c33a735d3 | function [h, hdata] = mlphess(net, x, t, hdata)
%MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network.
%
% Description
% H = MLPHESS(NET, X, T) takes an MLP network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding... |
github | martinarielhartmann/mirtooloct-master | glmhess.m | .m | mirtooloct-master/netlab/glmhess.m | 4,024 | utf_8 | 2d706b82d25cb35ff9467fe8837ef26f | function [h, hdata] = glmhess(net, x, t, hdata)
%GLMHESS Evaluate the Hessian matrix for a generalised linear model.
%
% Description
% H = GLMHESS(NET, X, T) takes a GLM network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to t... |
github | martinarielhartmann/mirtooloct-master | rbfhess.m | .m | mirtooloct-master/netlab/rbfhess.m | 3,138 | utf_8 | 0a6ef29c8be32e9991cacfe42bdfa0b3 | function [h, hdata] = rbfhess(net, x, t, hdata)
%RBFHESS Evaluate the Hessian matrix for RBF network.
%
% Description
% H = RBFHESS(NET, X, T) takes an RBF network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to the second deriva... |
github | JoHof/semantic-profiles-master | plotTrainingData.m | .m | semantic-profiles-master/testData/plotTrainingData.m | 1,013 | utf_8 | 64d0eb06ad1195992191e94809702544 | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
function [ ] = plotTrainingData(data, weakLabels, trueLabels )
%% plotting the training data
figure;
subplot(2,2,1);
scatter(data(1,weakLabels(:,1)),data(2,weakLabels(:,1)),'... |
github | JoHof/semantic-profiles-master | semSynthWeakTrainingData.m | .m | semantic-profiles-master/testData/semSynthWeakTrainingData.m | 600 | utf_8 | 6e81b0bda8c665bba8190c8fdb846553 | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
function [ data, weakLabels, trueLabels ] = semSynthWeakTrainingData()
[data, trueLabels] = semSynthTestData();
classes = unique(trueLabels);
numClasses = length(classes);
tC... |
github | JoHof/semantic-profiles-master | preRecall.m | .m | semantic-profiles-master/testData/preRecall.m | 1,559 | utf_8 | d7829c75a484a69707cce9697b83588c | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
function [ mprecision MAP base] = preRecall( trainingVectors,testVectors,trainingLabels,testLabels, queryInDatabase )
M = pdist2(trainingVectors',testVectors');
[~, in... |
github | JoHof/semantic-profiles-master | semSynthTestData.m | .m | semantic-profiles-master/testData/semSynthTestData.m | 998 | utf_8 | c2bcd9636ff4921a96bffa06a904bdb8 | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
function [data labels] = semSynthTestData()
CL1 = 200;
CL2 = 200;
CL3 = 300;
data1 = zeros(CL1,2);
data2 = zeros(CL2,2);
data3 = zeros(CL3,2);
data32 = zeros(CL3,2);
for i=1:... |
github | JoHof/semantic-profiles-master | spgetprofiles.m | .m | semantic-profiles-master/semProf/spgetprofiles.m | 1,782 | utf_8 | 591269a51cfc50e22f35a0fad3ed1754 | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
%% [ semProfiles ] = spgetprofiles(records, model)
%
% calculates the semantic profiles for a novel set of records given trained
% model
%
% Input:
%
% records: a set of vec... |
github | JoHof/semantic-profiles-master | sptrainmodel.m | .m | semantic-profiles-master/semProf/sptrainmodel.m | 4,115 | utf_8 | 2e41e116bceaf8f144b2a80f93ed9830 | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
%% [ r ] = sptrainmodel(records, classLabels, p)
%
% calculates the semantic profiles for a novel set of records given trained
% model
%
% Input:
%
% records: a set of vecor... |
github | JoHof/semantic-profiles-master | defaultParams.m | .m | semantic-profiles-master/semProf/utilFunctions/defaultParams.m | 522 | utf_8 | 388caf785537f6f53256b4f8201f1a20 | % Functional Matlab Library
% (c) 2013 Rene Donner, rene.donner@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
%% function p = defaultParams(p,defaultp)
%
% Compare fields of "p" with "defaultp".
% If field from "defaultp" is not existent in "p", add it.
function p = defaultPara... |
github | JoHof/semantic-profiles-master | d2b.m | .m | semantic-profiles-master/semProf/utilFunctions/d2b.m | 482 | utf_8 | 515c221dc19dfff23e689bc46e828679 | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
function y = d2b(x,nBits)
% Convert a decimanl number into a binary array
%
% Similar to dec2bin but yields a numerical array instead of a string and is found to
% be rather ... |
github | JoHof/semantic-profiles-master | b2d.m | .m | semantic-profiles-master/semProf/utilFunctions/b2d.m | 409 | utf_8 | 4ea979cc601122efc8af700fdf979da7 | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
function y = b2d(x)
% Convert a binary array to a decimal number
%
% Similar to bin2dec but works with arrays instead of strings and is found to be
% rather faster
z = singl... |
github | JoHof/semantic-profiles-master | createFerns.m | .m | semantic-profiles-master/semProf/randomFerns/createFerns.m | 1,959 | utf_8 | 1bf77a9732ac27cc920c3ec0aa7da732 | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
function [ wordVector, ferns ] = createFerns( featureVector, num_ferns, num_nodes, dim )
%CREATE_FERNS Trains ferns out of featureVectore provided
vector_dim = size(feat... |
github | JoHof/semantic-profiles-master | getFernsResponse.m | .m | semantic-profiles-master/semProf/randomFerns/getFernsResponse.m | 1,382 | utf_8 | c064bca01e8de189931c41bf41c5f578 | % (c) 2015 Johannes Hofmanninger, johannes.hofmanninger@meduniwien.ac.at
% For academic research / private use only, commercial use prohibited
function [ leafIndizes ] = getFernsResponse( queryVector, ferns )
% gets the fern response for provided vector and fern
num_ferns = size(ferns.dims,2);
num_nodes = siz... |
github | maeager/Agilent2Dicom-master | call_mci.m | .m | Agilent2Dicom-master/matlab/call_mci.m | 4,352 | utf_8 | 5af977ebde5cecfe6cf1052bc018648f | function call_mci(in1,in2,out,saveRI)
% Calling MCI - max contrast imaging
%
% - (C) 2015 Michael Eager (michael.eager@monash.edu)
% - Monash Biomedical Imaging
[a,b,c] = fileparts(mfilename('fullpath')) ;
[a,b,c] = fileparts(a) ;
root_path=a;
addpath(fullfile(root_path,'matlab'))
addpath(fullfile(root_path,'matlab/NI... |
github | maeager/Agilent2Dicom-master | call_swi.m | .m | Agilent2Dicom-master/matlab/call_swi.m | 5,198 | utf_8 | f4bde8fd7e63390cac2b6b4cfe3c7669 | function call_swi(in1,in2,out,order,preprocess,saveRI,swineg,swipos)
% Calling susceptibility weighted imaging filter
%
% - (C) 2015 Michael Eager (michael.eager@monash.edu)
% - Monash Biomedical Imaging
[a,b,c] = fileparts(mfilename('fullpath')) ;
[a,b,c] = fileparts(a) ;
root_path=a;
addpath(fullfile(root_path,'matl... |
github | maeager/Agilent2Dicom-master | call_mee.m | .m | Agilent2Dicom-master/matlab/call_mee.m | 4,619 | utf_8 | b8b8430d463afad48497ddc86880095b | function call_mee(in1,in2,out,porder,preprocess,saveRI,useswi)
% Calling MEE - multi-echo enhancement
%
% - (C) 2015 Michael Eager (michael.eager@monash.edu)
% - Monash Biomedical Imaging
[a,b,c] = fileparts(mfilename('fullpath')) ;
[a,b,c] = fileparts(a) ;
root_path=a;
addpath(fullfile(root_path,'./matlab'))
addpath(... |
github | maeager/Agilent2Dicom-master | ReadProcpar.m | .m | Agilent2Dicom-master/matlab/ReadProcpar.m | 1,951 | utf_8 | da490bc01ee896267897830ecffd509e | function vals = ReadProcpar( ppName, ppPath )
% Get the values for parameter name in procpar file path
% Usage: vals = getPPV( ppName, ppPath )
% fn = 'I_t.fid/procpar'
ppPath;
fp = fopen( ppPath, 'r');
done = 0;
vals = [];
while( done == 0 )
line = fgetl(fp);
if (line == -1)
done = 1;
el... |
github | michtesar/asymmetry_toolbox-master | eegplugin_faa.m | .m | asymmetry_toolbox-master/faa/eegplugin_faa.m | 445 | utf_8 | 83c4cda851f338a5163d7300dde14946 | % This book/study is a result of the research funded by the project
% Nr. LO1611 with a financial support from the MEYS under the NPU I program.
function eegplugin_faa(fig, try_strings, catch_strings)
% Create menu
toolsmenu = findobj(fig, 'tag', 'tools');
submenu = uimenu( toolsmenu, 'label', 'Compute FAA... |
github | OrangeOwlSolutions/Optimization-master | dbrent.m | .m | Optimization-master/Polak-Ribiere/Matlab/dbrent.m | 7,146 | utf_8 | b79e27e7cc8bb2f0c8c9687e2a3ec230 | % Given a function costfunctional and its derivative function grad_costfunctional, and given a bracketing triplet of abscissas ax,
% bx, cx [such that ax < bx < cx, and f(bx) < f(ax) and f(bx) < f(cx), tipically the output of mnbrak], this routine isolates the
% minimum to a fractional precision of about tol using a m... |
github | OrangeOwlSolutions/Optimization-master | mnbrak.m | .m | Optimization-master/Polak-Ribiere/Matlab/mnbrak.m | 3,496 | utf_8 | 8c9911a490d4309896419e613ecbe22e | % Given a function costfunctional, and given distinct initial points ax and bx, this routine searches in
% the downhill direction (defined by the function as evaluated at the initial points) and returns
% new points ax, bx, cx that bracket a minimum of the function. The points ax, bx and cx are such that
% the minimum... |
github | OrangeOwlSolutions/Optimization-master | linmin.m | .m | Optimization-master/Polak-Ribiere/Matlab/linmin.m | 690 | utf_8 | a6bebec04d1c563720ecad807edcdca0 | % --- Line minimization ... see Numerical Recipes
function [x p] = linmin(x, p, itmax, costfunctional, grad_costfunctional)
% --- p Search direction
% --- x Unknowns (input - output)
% --- itmax Maximum number of iterations
%... |
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