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 | SYSU-Jmiao/CVX-master | cvx_setdual.m | .m | CVX-master/lib/@cvxtuple/cvx_setdual.m | 954 | utf_8 | f7aebcdf404f92b69aeb57a379454249 | function x = setdual( x, y )
x.dual_ = y;
x.value_ = do_setdual( x.value_, y );
function x = do_setdual( x, y )
switch class( x ),
case 'struct',
nx = numel( x );
if nx > 1,
error( 'Dual variables may not be attached to struct arrays.' );
end
f = fieldnames(x);
y... |
github | SYSU-Jmiao/CVX-master | testall.m | .m | CVX-master/lib/@cvxtuple/testall.m | 452 | utf_8 | a576b02def941cd1a47e7117b3e8bf82 | function y = testall( func, x )
y = do_test( func, x.value_ );
function y = do_test( func, x )
switch class( x ),
case 'struct',
y = do_test( func, struct2cell( x ) );
case 'cell',
y = all( cellfun( func, x ) );
otherwise,
y = feval( func, x );
end
% Copyright 2005-2014 CVX Researc... |
github | SYSU-Jmiao/CVX-master | disp.m | .m | CVX-master/lib/@cvxtuple/disp.m | 1,366 | utf_8 | 3a4d5bcb53c9621413fb3d692ab7e328 | function disp( x, prefix )
if nargin < 2,
prefix = '';
end
disp( [ prefix, 'cvx tuple object: ' ] );
prefix = [ prefix, ' ' ];
do_disp( x.value_, {}, prefix, prefix, '' );
if ~isempty( x.dual_ ),
dn = cvx_subs2str( x.dual_ );
disp( [ prefix, 'dual variable: ', dn(2:end) ] );
end
function do_disp( x, f, f... |
github | SYSU-Jmiao/CVX-master | sparsify.m | .m | CVX-master/lib/@cvx/sparsify.m | 4,193 | utf_8 | 1554241e0afa377229282abbd78c22ba | function x = sparsify( x, mode )
global cvx___
error( nargchk( 2, 2, nargin ) );
persistent remap
%
% Check mode argument
%
if ~ischar( mode ) || size( mode, 1 ) ~= 1,
error( 'Second arugment must be a string.' );
end
isobj = strcmp( mode, 'objective' );
pr = cvx___.problems( end );
touch( pr.se... |
github | SYSU-Jmiao/CVX-master | cvx_glpk.m | .m | CVX-master/shims/cvx_glpk.m | 4,344 | utf_8 | 2a1ccba01ab8cc7e099853c4bbfe81b1 | function shim = cvx_glpk( shim )
% CVX_SOLVER_SHIM GLPK interface for CVX.
% This procedure returns a 'shim': a structure containing the necessary
% information CVX needs to use this solver in its modeling framework.
if ~isempty( shim.solve ),
return
end
if isempty( shim.name ),
fname = 'glpk.m';
ps =... |
github | SYSU-Jmiao/CVX-master | cvx_sedumi.m | .m | CVX-master/shims/cvx_sedumi.m | 10,740 | utf_8 | fb45695a2c0dd7b2bd1dd9884504f6fb | function shim = cvx_sedumi( shim )
% CVX_SOLVER_SHIM SeDuMi interface for CVX.
% This procedure returns a 'shim': a structure containing the necessary
% information CVX needs to use this solver in its modeling framework.
global cvx___
if ~isempty( shim.solve ),
return
end
if isempty( shim.name ),
fname = ... |
github | SYSU-Jmiao/CVX-master | cvx_sdpt3.m | .m | CVX-master/shims/cvx_sdpt3.m | 12,657 | utf_8 | 508f2ab7f97c915f8b3d4d479b38aa53 | function shim = cvx_sdpt3( shim )
% CVX_SOLVER_SHIM SDPT3 interface for CVX.
% This procedure returns a 'shim': a structure containing the necessary
% information CVX needs to use this solver in its modeling framework.
global cvx___
if ~isempty( shim.solve ),
return
end
if isempty( shim.name ),
fname = 's... |
github | banrenmengma/learngit-master | submit.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex8/submit.m | 17,520 | utf_8 | fe0a14d9dd1965046f3df3145fbd68e8 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | banrenmengma/learngit-master | submitWeb.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex8/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | banrenmengma/learngit-master | submit.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex6/submit.m | 16,841 | utf_8 | 604768ed33bd41bacfef101961116b7f | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | banrenmengma/learngit-master | porterStemmer.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | banrenmengma/learngit-master | submitWeb.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex6/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | banrenmengma/learngit-master | submit.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex7/submit.m | 16,963 | utf_8 | 8bb0f19135b652bd6edfd2b6d278d5fd | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | banrenmengma/learngit-master | submitWeb.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex7/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | banrenmengma/learngit-master | submit.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex2/submit.m | 17,091 | utf_8 | 3b6638411cb46d732ec244810a990332 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | banrenmengma/learngit-master | submitWeb.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex2/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | banrenmengma/learngit-master | submit.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex4/submit.m | 17,134 | utf_8 | 10fba0d21f2af5e4d04897a341cfcd04 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | banrenmengma/learngit-master | submitWeb.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex4/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | banrenmengma/learngit-master | submit.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex3/submit.m | 17,046 | utf_8 | c7135bfddeae00e31f5f69bc9baab6c5 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | banrenmengma/learngit-master | submitWeb.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex3/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | banrenmengma/learngit-master | submit.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex1/submit.m | 17,322 | utf_8 | 1995f570bcc848877231540b6c11ef51 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | banrenmengma/learngit-master | submitWeb.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex1/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | banrenmengma/learngit-master | submit.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex5/submit.m | 17,216 | utf_8 | b070c19173581637d05a4053abf70108 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | banrenmengma/learngit-master | submitWeb.m | .m | learngit-master/machine learning/机器学习-斯坦福-Andrew NG-2012/作业/答案/ex5/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | shenwei1231/DeepContour-master | edgesTrainDL.m | .m | DeepContour-master/edgesTrainDL.m | 11,747 | utf_8 | e009cddc51ca56a30587ea1c3ded16fe | function model = edgesTrainDL(dlPara, varargin )
% Train structured edge detector with deep contour features.
% The code is modified from Structured Edge Detection Toolbox
% get default parameters
dfs={'imWidth',32, 'gtWidth',16, 'nPos',5e5, 'nNeg',5e5, 'nImgs',inf, ...
'nTrees',10, 'fracFtrs',0.25, 'minCount',1, '... |
github | shenwei1231/DeepContour-master | edgesDLDemo.m | .m | DeepContour-master/edgesDLDemo.m | 1,186 | utf_8 | b34480d1c4eacae106b6e5c219b13841 | % Demo for Deep Countour Detector (please see readme.txt first).
function edgesDLDemo(dlPara)
%% set opts for training (see edgesTrain.m)
opts=edgesTrainDL(); % default options (good settings)
opts.modelDir='models/'; % model will be in models/forest
opts.modelFnm='modelBsds'; % model na... |
github | TsotsosLab/AIM-master | kernest.m | .m | AIM-master/kernest.m | 584 | utf_8 | 1d5e523022c0662c3609219bbc3550c9 | % Nonparametric kernel density estimation in 1D
function distr=kernest(inmap,h,precision)
imsize=prod(size(inmap));
% Transform data to 1-D
%x = inmap(1:imsize);
% Length for normalization
x=inmap;
Nx=length(x);
% x-axis for plotting purposes
ax=[0:precision:1]; % x axis
% Gaussian kernel
%h = 0.2;
y=zeros(size(a... |
github | jkuczm/MathematicaCellsToTeX-master | Utilities.m | .m | MathematicaCellsToTeX-master/CellsToTeX/Tests/Utilities.m | 1,612 | utf_8 | 9ce6e7d5c0ea4476487695660dbada5f | (* ::Package:: *)
BeginPackage["CellsToTeX`Tests`Utilities`"]
Unprotect["`*"]
ClearAll["`*"]
(* ::Section:: *)
(*Usage messages*)
mockFunction::usage =
"\
mockFunction[sym, log, body] \
assigns down value to given symbol sym such that when sym[...] is evaluated
it appends all arguments to given log and returns ... |
github | vistep/voicesSeparating-master | PreProccess.m | .m | voicesSeparating-master/PreProccess.m | 1,226 | utf_8 | 3ca5d2638d722b6e681ff2c46dae137e |
function postProcData = PreProccess(preProcData, FrameSize, FrameShift)
%
% postProcData: outputData. martrix, FrameZize*frameAmount
% newFrameAmount: output parameter. Maybe the size of voiceData will be change in preproccess.
% preProcData: inputData. vector, voiceDataCnt*1
% FrameZize: input parameter.
% Frame... |
github | vistep/voicesSeparating-master | sigma.m | .m | voicesSeparating-master/voicebox/sigma.m | 8,947 | utf_8 | 9172b365d3dfc09737dafb2f695d915d | function [gci goi] = sigma(lx,fs,fmax)
% Singularity in EGG by Multiscale Analysis (SIGMA) Algorithm
%
% [gci goi] = sigma(lx,fs,fmax)
%
% Inputs:
% lx Nx1 vector LX signal
% fs Sampling freq (Hz)
% fmax [Optional] max laryngeal freq
% Outputs:
% gci Vector of gcis as ... |
github | vistep/voicesSeparating-master | fxpefac.m | .m | voicesSeparating-master/voicebox/fxpefac.m | 15,781 | utf_8 | 2a2a5f8d78c6f2ceb9e61ea82346d2c6 | function [fx,tx,pv,fv]=fxpefac(s,fs,tinc,m,pp)
%FXPEFAC PEFAC pitch tracker [FX,TT,PV,FV]=(S,FS,TINC,M,PP)
%
% Input: s(ns) Speech signal
% fs Sample frequency (Hz)
% tinc Time increment between frames (s) [0.01]
% or [start increment end]
% m ... |
github | vistep/voicesSeparating-master | psycestu.m | .m | voicesSeparating-master/voicebox/psycestu.m | 10,646 | utf_8 | be192cd7406b19804284cba252356c33 | function [xx,ii,m,v]=psycestu(iq,x,r,xp)
% psycestu estimate unimodal psychometric function
%
% Usage: [xx,ii,m,v]=psycestu(-n,p,q,xp) % initialize n models
% [xx,ii,m,v]=psycestu(i,x,r) % supply a trial result to psycest
% psycestu(i) % plot pdf of model i
% [p,q]=psy... |
github | vistep/voicesSeparating-master | sphrharm.m | .m | voicesSeparating-master/voicebox/sphrharm.m | 18,380 | utf_8 | 7b0c8fcf3c1988b0154c93c514286293 | function [u,v,w]=sphrharm(m,a,b,c,d)
%SPHRHARM forward and inverse spherical harmonic transform
%
% Usage: (1) y=('f',n,x) % Calculate complex transform of spatial data x up to order n
%
% (2) y=('fr',n,x) % Calculate real transform of spatial data x(ne,na) up to order n
% %... |
github | vistep/voicesSeparating-master | dypsa_206.m | .m | voicesSeparating-master/voicebox/dypsa_206.m | 19,799 | utf_8 | ca33f9b9519ad43fbe50e27ea001b7fc | function [gci,goi] = dypsa(s,fs)
%DYPSA Derive glottal closure instances and openings from speech
% [gci,goi] = dypsa(s,fs) returns vectors gci and goi indicating samples
% when glottal closure and opening instances occur in the speech s
%
% Inputs:
% s is the speech signal
% fs is the sampling freq... |
github | vistep/voicesSeparating-master | fxrapt.m | .m | voicesSeparating-master/voicebox/fxrapt.m | 16,554 | utf_8 | b86263e09937e90ecd5b90595b488fec | function [fx,tt]=fxrapt(s,fs,mode)
%FXRAPT RAPT pitch tracker [FX,VUV]=(S,FS)
%
% Input: s(ns) Speech signal
% fs Sample frequency (Hz)
% mode 'g' will plot a graph [default if no output arguments]
% 'u' will include unvoiced fames (with fx=NaN)
%
% Outputs: fx... |
github | vistep/voicesSeparating-master | estnoisem.m | .m | voicesSeparating-master/voicebox/estnoisem.m | 15,891 | utf_8 | c24d580bba927c7412494f512b156253 | function [x,zo,xs]=estnoisem(yf,tz,pp)
%ESTNOISEM - estimate noise spectrum using minimum statistics
%
% Usage: ninc=round(0.016*fs); % frame increment [fs=sample frequency]
% ovf=2; % overlap factor
% f=rfft(enframe(s,hanning(ovf*ninc,'periodic'),ninc),ovf*ninc,2);
% ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | qfuser_linear.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/fusion/funcs/qfuser_linear.m | 2,337 | utf_8 | 0fe31df563db3c6f4f08ea791e83c340 | function [fusion,w0] = qfuser_linear(w,scores,scrQ,ndx,w_init)
% This function does the actual quality fusion (and is passed to
% the training function when training the quality fusion weights).
% The scores from the linear fusion are added to the combined
% quality measure for each trial to produce the final score.
% ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | AWB_sparse.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/fusion/funcs/AWB_sparse.m | 2,062 | utf_8 | dcb6e85fdcca1dfb1b5cdee3eb6ab112 | function fh = AWB_sparse(qual,ndx,w)
% Produces trial quality measures from segment quality measures
% using the weighting matrix 'w'.
% This is almost an MV2DF, but it does not return derivatives on numeric
% input, w.
%
% Algorithm: Y = A*reshape(w,..)*B
% Inputs:
% qual: A Quality object containing quality measure... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | dcfplot.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/plotting/dcfplot.m | 1,889 | utf_8 | 9fbbba6b08ba70f285386536481e29d5 | function dcfplot(devkeyname,evalkeyname,devscrfilename,evalscrfilename,outfilename,plot_title,xmin,xmax,ymin,ymax,prior)
% Makes a Norm_DCF plot of the dev and eval scores for a system.
% Inputs:
% devkeyname: The name of the file containing the Key for
% the dev scores.
% evalkeyname: The name of the file co... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | fast_actDCF.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/stats/fast_actDCF.m | 3,032 | utf_8 | 5e62c5e1058f0ba3f5a59149249da2a9 | function [dcf,Pmiss,Pfa] = fast_actDCF(tar,non,plo,normalize)
% Computes the actual average cost of making Bayes decisions with scores
% calibrated to act as log-likelihood-ratios. The average cost (DCF) is
% computed for a given range of target priors and for unity cost of error.
% If un-normalized, DCF is just the B... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | fast_minDCF.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/stats/fast_minDCF.m | 2,585 | utf_8 | 6a709a2b121037d7919f57c87d835531 | function [minDCF,Pmiss,Pfa,prbep,eer] = fast_minDCF(tar,non,plo,normalize)
% Inputs:
%
% tar: vector of target scores
% non: vector of non-target scores
% plo: vector of prior-log-odds: plo = logit(Ptar)
% = log(Ptar) - log(1-Ptar)
%
% normalize: if true, return normalized ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | rocch.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/det/rocch.m | 2,725 | utf_8 | 68aaac9f8a1f40d0d5eac901abc533d5 | function [pmiss,pfa] = rocch(tar_scores,nontar_scores)
% ROCCH: ROC Convex Hull.
% Usage: [pmiss,pfa] = rocch(tar_scores,nontar_scores)
% (This function has the same interface as compute_roc.)
%
% Note: pmiss and pfa contain the coordinates of the vertices of the
% ROC Convex Hull.
%
% For a demonstration that pl... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | compute_roc.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/det/compute_roc.m | 1,956 | utf_8 | 16907ef9816ee330ac64b4eeb708366b | function [Pmiss, Pfa] = compute_roc(true_scores, false_scores)
% compute_roc computes the (observed) miss/false_alarm probabilities
% for a set of detection output scores.
%
% true_scores (false_scores) are detection output scores for a set of
% detection trials, given that the target hypothesis is true (false).
% ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | rocchdet.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/det/rocchdet.m | 5,471 | utf_8 | 2452dd1f98aad313c79879d410214cb2 | function [x,y,eer,mindcf] = rocchdet(tar,non,dcfweights,pfa_min,pfa_max,pmiss_min,pmiss_max,dps)
% ROCCHDET: Computes ROC Convex Hull and then maps that to the DET axes.
%
% (For demo, type 'rocchdet' on command line.)
%
% Inputs:
%
% tar: vector of target scores
% non: vector of non-target scores
%
% dcfw... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | map_mod_names.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/manip/map_mod_names.m | 3,127 | utf_8 | 6aa97cdf9b5df6095e803bd14f612e52 | function ndx = map_mod_names(ndx,src_map,dst_map)
% Changes the model names in an index using two maps. The one map
% lists the training segment for each model name and the other map
% lists the new model name for each training segment. Existing
% model names are replaced by new model names that are mapped to
% the s... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | maplookup.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/manip/maplookup.m | 3,084 | utf_8 | 9e8a55e6a2201b6a0e975469dfe9c299 | function [values,is_present] = maplookup(map,keys)
% Does a map lookup, to map mutliple keys to multiple values in one call.
% The parameter 'map' represents a function, where each key maps to a
% unique value. Each value may be mapped to by one or more keys.
%
% Inputs:
% map.keySet: a one-dimensional cell array;
%... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | test_binary_classifier.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/test_binary_classifier.m | 1,332 | utf_8 | 9683ce2757d7eb67c8a8ec37954cbab4 | function obj_val = test_binary_classifier(objective_function,classf, ...
prior,system,input_data)
% Returns the result of the objective function evaluated on the
% scores.
%
% Inputs:
% objective_function: a function handle to the objective function
% to feed the scores into
% classf: le... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | evaluate_objective.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/evaluate_objective.m | 1,417 | utf_8 | 70262971965caac5629612bd125dd0a2 | function obj_val = evaluate_objective(objective_function,scores,classf, ...
prior)
% Returns the result of the objective function evaluated on the
% scores.
%
% Inputs:
% objective_function: a function handle to the objective function
% to feed the scores into
% scores: length T vector o... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | train_binary_classifier.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/train_binary_classifier.m | 3,938 | utf_8 | de96b98d88aa8e3d0c36785a2f9a3a94 | function [w,cxe,w_pen,optimizerState,converged] = ...
train_binary_classifier(classifier,classf,w0,objective_function,prior,...
penalizer,lambda,maxiters,maxCG,optimizerState,...
quiet,cstepHessian)
%
% Supervised training of a regularized fusion.
%
%
% Inp... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | qfuser_v5.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v5.m | 921 | utf_8 | f82cbe0c178dae2a667496466b612770 | function [fusion,w0] = qfuser_v5(w,scores,wfuse)
if nargin==0
test_this();
return;
end
% block 1
f1 = linear_fuser([],scores.scores);
w1 = wfuse;
[whead,wtail] = splitvec_fh(length(w1));
f1 = f1(whead);
% block 2
modelQ = scores.modelQ;
[q,n1] = size(modelQ);
modelQ = [modelQ;ones(1,n1)];
segQ = scores.segQ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | qfuser_v2.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v2.m | 1,166 | utf_8 | e10bf159cbd2dacaf85be8d4a90554f6 | function [fusion,params] = qfuser_v2(w,scores)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% quality_input: K-by-T matrix of quality measures
%
% Output:
% fusion: is numeric if w is numeric, or a handle to an MV2DF, represe... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | linear_fuser.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/linear_fuser.m | 2,654 | utf_8 | 627fab3e121d1d87d9fad2a3234d26f8 | function [fusion,params] = linear_fuser(w,scores)
%
% Does affine fusion of scores: It does a weighted sum of scores and adds
% an offset.
%
% Inputs:
% scores: M-by-N matrix of N scores for each of M input systems.
% w: Optional:
% - when supplied, the output 'fusion' i... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | qfuser_v3.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v3.m | 1,290 | utf_8 | a2245f6284afa9f203096fc932e8cf07 | function [fusion,params] = qfuser_v3(w,scores)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% quality_input: K-by-T matrix of quality measures
%
% Output:
% fusion: is numeric if w is numeric, or a handle to an MV2DF, represe... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | qfuser_v6.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v6.m | 1,013 | utf_8 | 0bcb6e5fbd79494afd1c1c36eff1e95c | function [fusion,w0] = qfuser_v6(w,scores,wfuse)
if nargin==0
test_this();
return;
end
% block 1
f1 = linear_fuser([],scores.scores);
w1 = wfuse;
[whead,wtail] = splitvec_fh(length(w1));
f1 = f1(whead);
% block 2
modelQ = scores.modelQ;
[q,n1] = size(modelQ);
modelQ = [modelQ;ones(1,n1)];
segQ = scores.segQ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | qfuser_v1.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v1.m | 1,137 | utf_8 | 8dcda09e63d0f7e6a3f1fc2298b84d7e | function [fusion,params] = qfuser_v1(w,scores)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% quality_input: K-by-T matrix of quality measures
%
% Output:
% fusion: is numeric if w is numeric, or a handle to an MV2DF, represe... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | qfuser_v7.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v7.m | 1,107 | utf_8 | 8d156ad2d97a7aa1b90d702cb2f0a195 | function [fusion,w0] = qfuser_v7(w,scores,wfuse)
if nargin==0
test_this();
return;
end
% block 1
f1 = linear_fuser([],scores.scores);
w1 = wfuse;
[whead,wtail] = splitvec_fh(length(w1));
f1 = f1(whead);
% block 2
modelQ = scores.modelQ;
[q,n1] = size(modelQ);
modelQ = [modelQ;ones(1,n1)];
segQ = scores.segQ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | qfuser_v4.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v4.m | 1,388 | utf_8 | cd65aea99057c92c142fc7e024dc1d53 | function [fusion,w0] = qfuser_v4(w,scores,wfuse)
% qindx: index set for rows of scores.scores which are per-trial quality
% measures.
%
% sindx: index set for rows of scores.scores which are normal discriminative
% scores.
if nargin==0
test_this();
return;
end
sindx = scores.sindx;
qindx = sco... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | scal_fuser.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scal_fuser.m | 2,918 | utf_8 | 7e49185b74a064be721d9c243a08c07f | function [fusion,params] = scal_fuser(w,scores)
%
% Does scal calibration
%
% Inputs:
% scores: M-by-N matrix of N scores for each of M input systems.
% w: Optional:
% - when supplied, the output 'fusion' is the vector of fused scores.
% - when w=[], the output 'fusion' is a function handle, t... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | scal_fuser_slow.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scal_fuser_slow.m | 2,972 | utf_8 | abc2a78dc2b6cf08cfdd508f4dabdb71 | function [fusion,params] = scal_fuser_slow(w,scores)
%
% Does scal calibration
%
% Inputs:
% scores: M-by-N matrix of N scores for each of M input systems.
% w: Optional:
% - when supplied, the output 'fusion' is the vector of fused scores.
% - when w=[], the output 'fusion' is a function hand... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | logsumexp_special.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/logsumexp_special.m | 1,102 | utf_8 | a15ffa60b181fdc8b0a1e3fb4bcfd403 | function [y,deriv] = logsumexp_special(w)
% This is a MV2DF. See MV2DF_API_DEFINITION.readme.
%
% If w = [x;r], where r is scalar and x vector, then
% y = log(exp(x)+exp(r))
if nargin==0
test_this();
return;
end
if isempty(w)
y = @(w)logsumexp_special(w);
return;
end
if isa(w,'function_handle')
... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | scalibration_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scalibration_fh.m | 1,735 | utf_8 | b9918a8e2a9fa07dfcef33933013931b | function f = scalibration_fh(w)
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the s-calibration function. The whole mapping works like
% this, in MATLAB-style pseudocode:
%
% If y = f([x;r;s]), where x,r,s are column vectors of size m, then y
% is a column vector of ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | scalibration_fragile_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scalibration_fragile_fh.m | 2,389 | utf_8 | 8eec3ccf6bcd5f130a3d399194acd676 | function f = scalibration_fragile_fh(direction,w)
%
% Don't use this function, it is just for reference. It will break for
% large argument values.
%
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the logsumexp function. The whole mapping works like
% this, in MATLAB-styl... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | scal_simple_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scal_simple_fh.m | 1,903 | utf_8 | b6e3992c13b4424d2129302a3c51424c | function f = scal_simple_fh(w)
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the s-calibration function. The whole mapping works like
% this, in MATLAB-style pseudocode:
%
% If y = f([x;r;s]), where r,s are scalar, x is column vector of size m,
% then y is a column ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | quality_fuser_v3.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/aside/quality_fuser_v3.m | 1,930 | utf_8 | e6ebfe6102255c3b1fc656cbc542c607 | function [fusion,params] = quality_fuser_v3(w,scores,train_vecs,test_vecs,train_ndx,test_ndx,ddim)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% train_vecs: K1-by-M matrix, one column-vector for each of M training
% ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | quality_fuser_v1.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/aside/quality_fuser_v1.m | 2,318 | utf_8 | c0a844776f61e7b9ab295d3b9790f44a | function [fusion,params] = quality_fuser_v1(w,scores,train_vecs,test_vecs,train_ndx,test_ndx,ddim)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% train_vecs: K1-by-M matrix, one column-vector for each of M training
% ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | quality_fuser_v2.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/aside/quality_fuser_v2.m | 1,911 | utf_8 | 24b7a76202dff9aee018b2e095a6b3f8 | function [fusion,params] = quality_fuser_v2(w,scores,train_vecs,test_vecs,train_ndx,test_ndx,ddim)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% train_vecs: K1-by-M matrix, one column-vector for each of M training
% ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | quality_fuser_v4.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/aside/quality_fuser_v4.m | 1,284 | utf_8 | 107ce638d9e258b90abb823cad708a79 | function [fusion,params] = quality_fuser_v4(w,scores,quality_inputs)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% quality_input: K-by-T matrix of quality measures
%
% Output:
% fusion: is numeric if w is numeric, ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | sigmoid_logdistance.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/sigmoid_logdistance.m | 1,561 | utf_8 | b124d29ef74e835d894f8dd7de72c760 | function [sld,params] = sigmoid_logdistance(w,input_data,ddim)
%
% Algorithm: sld = sigmoid(
% log(
% sum(bsxfun(@minus,M*input_data,c).^2,1)
% ))
%
%
% Inputs:
% w: is vec([M,c]), where M is ddim-by-D and c is ddim-by-1
% Use w=[] to let output sld be a... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | QtoLLH.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/QtoLLH.m | 612 | utf_8 | e0bc4e7d0bfd4082fc37fb474bc44c8c | function [LLH,w0] = QtoLLH(w,Q,n)
%
if nargin==0
test_this();
return;
end
if ~exist('Q','var') || isempty(Q)
LLH = sprintf(['QtoLLH:',repmat(' %g',1,length(w))],w);
return;
end
[m,k] = size(Q);
wsz = m*n;
if nargout>1, w0 = zeros(wsz,1); end
LLH = linTrans(w,@(w)map_this(w),@(w)transmap_this(w)... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | fused_sigmoid.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/fused_sigmoid.m | 1,293 | utf_8 | 1f35e45a3c945008307dd1222a281bb8 | function [ps,params] = fused_sigmoid(w,input_data)
%
% Algorithm: ps = sigmoid( alpha'*input_data +beta)
%
%
% Inputs:
% w: is [alpha; beta], where alpha is D-by-1 and beta is scalar.
% Use w=[] to let output ps be an MV2DF function handle.
% If w is a function handle to an MV2DF then ps is the function hand... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | sigmoid_log_sumsqdist.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/sigmoid_log_sumsqdist.m | 1,638 | utf_8 | 0b9f81df4bc93fe52ac7dbaa98594160 | function [sig,params] = sigmoid_log_sumsqdist(w,data1,data2,ndx1,ndx2,ddim)
%
% Similar to prod_sigmoid_logdist, but adds square distances from two sides
% before doing sigmoid(log()).
%
if nargin==0
test_this();
return;
end
datadim = size(data1,1);
assert(datadim==size(data2,1),'data1 and data2 must have s... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | prmtrzd_sig_log_dist.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/prmtrzd_sig_log_dist.m | 1,767 | utf_8 | edf7387841000e5933bda732bca5b79b | function [ps,params] = prmtrzd_sig_log_dist(w,input_data,ddim)
%
% Algorithm: ps = sigmoid(
% offs+scal*log(
% sum(bsxfun(@minus,M*input_data,c).^2,1)
% ))
%
%
% Inputs:
% w: is [ vec(M); c; scal; offs], where M is ddim-by-D; c is ddim-by-1;
% and scal and o... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | QQtoLLH.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/QQtoLLH.m | 623 | utf_8 | 73d37a5922aafaa7dd4dd6e5a2cfbe51 | function [LLH,w0] = QQtoLLH(w,qleft,qright,n)
%
if nargin==0
test_this();
return;
end
qleft = [qleft;ones(1,size(qleft,2))];
qright = [qright;ones(1,size(qright,2))];
qdim = size(qleft,1);
qdim2 = size(qright,1);
assert(qdim==qdim2);
q2 = qdim*(qdim+1)/2;
wsz = n*q2;
if nargout>1, w0 = zeros(wsz,1); end... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | QQtoP.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/QQtoP.m | 771 | utf_8 | 0a940f8a8a56510a32ad6a45accddc02 | function [P,params] = QQtoP(w,qleft,qright,n)
%
if nargin==0
test_this();
return;
end
qleft = [qleft;ones(1,size(qleft,2))];
qright = [qright;ones(1,size(qright,2))];
[qdim,nleft] = size(qleft);
[qdim2,nright] = size(qright);
assert(qdim==qdim2);
q2 = qdim*(qdim+1)/2;
wsz = n*q2;
[whead,wtail] = splitvec_... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | prod_sigmoid_logdist.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/prod_sigmoid_logdist.m | 2,506 | utf_8 | f31a256c5434fca4b6c0641d23a2ebc1 | function [sig,params] = prod_sigmoid_logdist(w,data1,data2,ndx1,ndx2,ddim)
%
% Algorithm: sig = distribute(ndx1,sigmoid(
% log(
% sum(bsxfun(@minus,M*data_1,c).^2,1)
% )))
% *
% distribute(ndx2,sigmoid(
% log( ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | outerprod_of_sigmoids.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/outerprod_of_sigmoids.m | 1,033 | utf_8 | 2577209cada6747a9615d0ce3b375b7f | function [Q,params] = outerprod_of_sigmoids(w,qleft,qright)
%
if nargin==0
test_this();
return;
end
[qdim,nleft] = size(qleft);
[qdim2,nright] = size(qright);
assert(qdim==qdim2);
wsz = qdim+1;
[whead,wtail] = splitvec_fh(wsz,w);
params.get_w0 = @(ssat) init_w0(ssat);
params.tail = wtail;
% fleft = sigmoid... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | parallel_cal.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/parallel_cal.m | 983 | utf_8 | 252822b934d5469fc96448f80d2f3e90 | function [calscores,w0] = parallel_cal(w,scores,wfuse)
%
if nargin==0
test_this();
return;
end
if ~exist('scores','var') || isempty(scores)
calscores = sprintf(['parallel calibration:',repmat(' %g',1,length(w))],w);
return;
end
[m,n] = size(scores);
if nargout>1, w0 = init_w0(wfuse); end
cals... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | parallel_cal_augm.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/parallel_cal_augm.m | 1,115 | utf_8 | f5a8bba6d164ab5577c8429ce5835305 | function [calscores,params] = parallel_cal_augm(w,scores)
%
if nargin==0
test_this();
return;
end
if ~exist('scores','var') || isempty(scores)
calscores = sprintf(['parallel calibration:',repmat(' %g',1,length(w))],w);
return;
end
[m,n] = size(scores);
scores = [scores;zeros(1,n)];
wsz = 2*m;
[... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | prod_of_prmtrzd_sigmoids.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/prod_of_prmtrzd_sigmoids.m | 1,537 | utf_8 | 6c18de0879f128ada38d483ace60b57f | function [ps,params] = prod_of_prmtrzd_sigmoids(w,input_data)
%
% Algorithm: ps = prod_i sigmoid( alpha_i*input_data(i,:) + beta_i)
%
%
% Inputs:
% w: is vec([alpha; beta]), where alpha and beta are 1-by-D.
% Use w=[] to let output ps be an MV2DF function handle.
% If w is a function handle to an MV2DF then ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | prmtrzd_sigmoid.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/quality_modules/prmtrzd_sigmoid.m | 1,312 | utf_8 | 616d28e7f84f188fcd7759c98a9c3c66 | function [ps,params] = prmtrzd_sigmoid(w,input_data)
%
% Algorithm: ps = sigmoid( w0+w1'*input_data ), where
% w = [w1;w0]; w0 is scalar; and w1 is vector
%
%
% Inputs:
% w = [w1;w0]; w0 is scalar; and w1 is vector.
% Use w=[] to let output ps be an MV2DF function handle.
% If w is a function handle to a... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | augmentmatrix_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/augmentmatrix_fh.m | 826 | utf_8 | d2182cb06b78c3d43519f09b297ddad2 | function fh = augmentmatrix_fh(m,value,w)
% This is almost an MV2DF, but it does not return derivatives on numeric
% input, w.
%
% Algorithm: y = [reshape(w,m,n);ones(1,n)](:)
if nargin==0
test_this();
return;
end
function y = map_this(w)
n = length(w)/m;
y = [reshape(w,m,n);value*ones... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | bsx_col_plus_row.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/bsx_col_plus_row.m | 912 | utf_8 | 59d5f6f7ea9d75509fbc6bf64b63b465 | function fh = bsx_col_plus_row(m,n,w)
% This is almost an MV2DF, but it does not return derivatives on numeric
% input, w.
%
% Algorithm: col = w(1:m)
% row = w(m+1:end)
% y = bsxfun(@plus,col(:),row(:)'),
%
if nargin==0
test_this();
return;
end
function y = map_this(w)
... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | duplicator_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/duplicator_fh.m | 805 | utf_8 | 890c37f077bde2305a0f1c545b71c36a | function f = duplicator_fh(duplication_indices,xdim,w)
%
% This factory creates a function handle to an MV2DF, which represents the
% function:
%
% y = x(duplication_indices)
%
if nargin==0
test_this();
return;
end
map = @(x) x(duplication_indices);
%xdim = max(duplication_indices);
ydim = length(duplica... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | splitvec_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/splitvec_fh.m | 1,343 | utf_8 | aff993bc1037dc1d6673762983fd5497 | function [head,tail] = splitvec_fh(head_size,w)
%
%
% If head_size <0 then tail_size = - head_size
if nargin==0
test_this();
return;
end
tail_size = - head_size;
function w = transmap_head(y,sz)
w=zeros(sz,1);
w(1:head_size)=y;
end
function w = transmap_tail(y,sz)
w=zeros(sz,1); ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | log_distance_mv2df.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/log_distance_mv2df.m | 1,968 | utf_8 | ab190182251a8ee9a8cce755c6615e99 | function [y,deriv] = log_distance_mv2df(w,input_data,new_dim)
% This is an MV2DF. See MV2DF_API_DEFINITION.readme.
%
% The function projects each column of input_data to a subspace and then
% computes log distance from a centroid. The input_data is fixed, but
% the projection and centroid parameters are variable.
%
%... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | AWB_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/AWB_fh.m | 675 | utf_8 | 3ab5ec4ad82fe2f901f95abf30fb3193 | function fh = AWB_fh(A,B,w)
% This is almost an MV2DF, but it does not return derivatives on numeric
% input, w.
%
% Algorithm: Y = A*reshape(w,..)*B
if nargin==0
test_this();
return;
end
[m,n] = size(A);
[r,s] = size(B);
function y = map_this(w)
w = reshape(w,n,r);
y = A*w*B;
end
... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | xoverxplusalpha.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/xoverxplusalpha.m | 792 | utf_8 | 9fbd612d42a50cee70f2b05dce2bf16c | function [y,deriv] = xoverxplusalpha(w,x)
% This is an MV2DF. See MV2DF_API_DEFINITION.readme.
%
% alpha --> x./(x+alpha)
%
if nargin==0
test_this();
return;
end
if isempty(w)
y = @(w)xoverxplusalpha(w,x);
return;
end
if isa(w,'function_handle')
f = xoverxplusalpha([],x);
y = compose_mv(... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | tril_to_symm_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/tril_to_symm_fh.m | 786 | utf_8 | 9ea53a1f6c15720e67c1c446d7dfad43 | function fh = tril_to_symm_fh(m,w)
% This is almost an MV2DF, but it does not return derivatives on numeric
% input, w.
%
% Algorithm: w is vector of sizem*(m+1)/2
% w -> m-by-m lower triangular matrix Y
% Y -> Y + Y'
if nargin==0
test_this();
return;
end
indx = tril(true(m));
fun... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | square_distance_mv2df.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/square_distance_mv2df.m | 1,835 | utf_8 | a5d544c6956f70a3c3afdec634a2c891 | function [y,deriv] = square_distance_mv2df(w,input_data,new_dim)
% This is an MV2DF. See MV2DF_API_DEFINITION.readme.
%
% The function computes the square distance of the vectors for each trial.
% y.' = sum((W(:,1:end-1).'*input_data + W(:,end)).^2,1)
%
% W is the augmented matrix [M c] where M maps a score vect... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | addtotranspose_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/addtotranspose_fh.m | 493 | utf_8 | c009e482a302e2825fb3f59940bcc79e | function fh = addtotranspose_fh(m,w)
% This is almost an MV2DF, but it does not return derivatives on numeric
% input, w.
if nargin==0
test_this();
return;
end
function y = map_this(w)
w = reshape(w,m,m);
y = w+w.';
end
map = @(y) map_this(y);
transmap = @(y) map_this(y);
fh = l... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | subvec_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/subvec_fh.m | 544 | utf_8 | a8942d310965ca178a123eb3f4a78f21 | function fh = subvec_fh(first,len,w)
% This is almost an MV2DF, but it does not return derivatives on numeric
% input, w.
if nargin==0
test_this();
return;
end
map = @(w) w(first:first+len-1);
function w = transmap_this(y,sz)
w=zeros(sz,1);
w(first:first+len-1)=y;
end
transmap = @(y,sz) ... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | linTrans_adaptive.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/mv2df_function_library/templates/linTrans_adaptive.m | 1,173 | utf_8 | 66276c8cd337da71a4e14efc67112765 | function [y,deriv] = linTrans_adaptive(w,map,transmap)
% This is an MV2DF. See MV2DF_API_DEFINITION.readme.
%
% Applies linear transform y = map(w). It needs the transpose of map,
% transmap for computing the gradient. map and transmap are function
% handles.
if nargin==0
test_this();
return;
end
if isempty(... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | logsumexp_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/MV2DF/function_library/vector/logsumexp_fh.m | 1,287 | utf_8 | 764511ba624a62ac12e572a26a5e7aa2 | function f = logsumexp_fh(m,direction,w)
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the logsumexp function. The whole mapping works like
% this, in MATLAB-style psuedocode:
%
% F: R^(m*n) --> R^n, where y = F(x) is computed thus:
%
% n = length(x)/m
% If directi... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | one_over_one_plus_w_mv2df.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/MV2DF/function_library/vector/one_over_one_plus_w_mv2df.m | 717 | utf_8 | d735233c52193c323d03cdb85d0948f5 | function [y,deriv] = one_over_one_plus_w_mv2df(w)
% This is an MV2DF. See MV2DF_API_DEFINITION.readme.
% y = 1 ./ (1 + w)
if nargin==0
test_this();
return;
end
if isempty(w)
y = @(w)one_over_one_plus_w_mv2df(w);
return;
end
if isa(w,'function_handle')
outer = one_over_one_plus_w_mv2df([]);
y... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | sigmoid_mv2df.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/MV2DF/function_library/vector/sigmoid_mv2df.m | 758 | utf_8 | e0591c88d68032fcf2a300fe7f2e8df0 | function [y,deriv] = sigmoid_mv2df(w)
% This is an MV2DF. See MV2DF_API_DEFINITION.readme.
% y = sigmoid(w) = 1./(1+exp(-w)), vectorized as MATLAB usually does.
if nargin==0
test_this();
return;
end
if isempty(w)
y = @(w)sigmoid_mv2df(w);
return;
end
if isa(w,'function_handle')
outer = sigmoid_m... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | neglogsigmoid_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/MV2DF/function_library/vector/neglogsigmoid_fh.m | 1,075 | utf_8 | dc180d133fc039197aa99a5e4186c6a7 | function f = neglogsigmoid_fh(w)
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the logsigmoid function. The mapping is, in
% MATLAB-style code:
%
% y = log(sigmoid(w)) = log(1./1+exp(-w)) = -log(1+exp(-w))
%
% Inputs:
% m: the number of inputs to each individual lo... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | logsumsquares_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/MV2DF/function_library/vector/logsumsquares_fh.m | 1,275 | utf_8 | c1e543f6680e7257b1f55ff61d967598 | function f = logsumsquares_fh(m,direction,w)
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the logsumsquares function. The whole mapping works like
% this, in MATLAB-style psuedocode:
%
% F: R^(m*n) --> R^n, where y = F(x) is computed thus:
%
% n = length(x)/m
% If... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | expneg_mv2df.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/MV2DF/function_library/vector/expneg_mv2df.m | 719 | utf_8 | 37343094bc02e34877dc38687780fae4 | function [y,deriv] = expneg_mv2df(w)
% This is an MV2DF. See MV2DF_API_DEFINITION.readme.
% y = exp(-w), vectorized as MATLAB usually does.
if nargin==0
test_this();
return;
end
if isempty(w)
y = @(w)expneg_mv2df(w);
return;
end
if isa(w,'function_handle')
outer = expneg_mv2df([]... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | square_mv2df.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/MV2DF/function_library/vector/square_mv2df.m | 634 | utf_8 | f7604570a85ea6be67d98ae414127642 | function [y,deriv] = square_mv2df(w)
% This is an MV2DF. See MV2DF_API_DEFINITION.readme.
% y = w.^2
if nargin==0
test_this();
return;
end
if isempty(w)
y = @(w)square_mv2df(w);
return;
end
if isa(w,'function_handle')
outer = square_mv2df([]);
y = compose_mv(outer,w,[]);
return;
end
w... |
github | StevenLOL/Research_speech_speaker_verification_nist_sre2010-master | logsigmoid_fh.m | .m | Research_speech_speaker_verification_nist_sre2010-master/SRE2010/utils/bosaris_toolkit/utility_funcs/Optimization_Toolkit/MV2DF/function_library/vector/logsigmoid_fh.m | 1,068 | utf_8 | 65bf6e2f03af50449d9492d02f7e3c98 | function f = logsigmoid_fh(w)
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the logsigmoid function. The mapping is, in
% MATLAB-style code:
%
% y = log(sigmoid(w)) = log(1./1+exp(-w)) = -log(1+exp(-w))
%
% Inputs:
% m: the number of inputs to each individual logsu... |
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