From: <sch...@us...> - 2009-03-09 11:25:47
|
Revision: 5590 http://octave.svn.sourceforge.net/octave/?rev=5590&view=rev Author: schloegl Date: 2009-03-09 11:25:34 +0000 (Mon, 09 Mar 2009) Log Message: ----------- mex support improved: tests show a significant advantage in terms of speed Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/sumskipnan.m trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp Modified: trunk/octave-forge/extra/NaN/inst/sumskipnan.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/sumskipnan.m 2009-03-08 15:35:04 UTC (rev 5589) +++ trunk/octave-forge/extra/NaN/inst/sumskipnan.m 2009-03-09 11:25:34 UTC (rev 5590) @@ -47,6 +47,7 @@ % This function is part of the NaN-toolbox % http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ + global FLAG_NANS_OCCURED; FLAG_NANS_OCCURED = logical(0); @@ -79,7 +80,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % non-float data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -if ~isa(x,'float') %%%% || flag_implicit_skip_nan, %%% skip always NaN's +if ~isa(x,'float') %%%% || FLAG_implicit_skip_nan, %%% skip always NaN's x = double(x); o = sum(x,DIM); if nargin>1 @@ -104,21 +105,19 @@ % use Matlab-MEX function when available %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% it seems there is no advantage in using the mex-file, therefore this option is disabled by default. -if 0, -%%try +%%if 0, +try %% using sumskipnan_mex.mex + %% !!! hack: FLAG_NANS_OCCURED is an output argument !!! if (nargout<3), - [o,count] = sumskipnan_mex(x,DIM); - FLAG_NANS_OCCURED = any(count(:)<size(x,DIM)); + [o,count] = sumskipnan_mex(x,DIM,FLAG_NANS_OCCURED); return; elseif (nargout==3), - [o,count,SSQ] = sumskipnan_mex(x,DIM); - FLAG_NANS_OCCURED = any(count(:)<size(x,DIM)); + [o,count,SSQ] = sumskipnan_mex(x,DIM,FLAG_NANS_OCCURED); return; elseif (nargout==4) && isreal(i), - [o,count,SSQ,S4M] = sumskipnan_mex(x,DIM); - FLAG_NANS_OCCURED = any(count(:)<size(x,DIM)); + [o,count,SSQ,S4M] = sumskipnan_mex(x,DIM,FLAG_NANS_OCCURED); return; end; end; @@ -176,7 +175,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% performance tweak: some tests have shown that x*ones(:,1) is faster than sum(x,2) -FLAG = (length(size(x))<3); +FLAG = (length(size(x))<3); if FLAG, FLAG = DIM; end; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Modified: trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-03-08 15:35:04 UTC (rev 5589) +++ trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-03-09 11:25:34 UTC (rev 5590) @@ -33,16 +33,27 @@ // Version: 1.0 // Date: 17 september 2003 // -// modified: -// Alois Schloegl <a.s...@ie...> -// $Revision$ -// $Id$ +// $Id$ +// Copyright (C) 2009 Alois Schloegl <a.s...@ie...> +// This function is part of the NaN-toolbox +// http://hci.tugraz.at/~schloegl/matlab/NaN/ // //------------------------------------------------------------------- //#include <stdlib> +#include <inttypes.h> #include <math.h> #include "mex.h" //------------------------------------------------------------------- + +int __sumskipnan2__(double *data, size_t Ni, size_t stride, double *s, size_t *No, char *flag_anyISNAN); +int __sumskipnan3__(double *data, size_t Ni, size_t stride, double *s, double *s2, size_t *No, char *flag_anyISNAN); +int __sumskipnan4__(double *data, size_t Ni, size_t stride, double *s, double *s2, double *s4, size_t *No, char *flag_anyISNAN); +int __sumskipnan2_single__(float *data, size_t Ni, size_t stride, double *s, size_t *No, char *flag_anyISNAN); +int __sumskipnan3_single__(float *data, size_t Ni, size_t stride, double *s, double *s2, size_t *No, char *flag_anyISNAN); +int __sumskipnan4_single__(float *data, size_t Ni, size_t stride, double *s, double *s2, double *s4, size_t *No, char *flag_anyISNAN); + + + void mexFunction(int POutputCount, mxArray* POutput[], int PInputCount, const mxArray *PInputs[]) { const int *SZ; @@ -63,11 +74,11 @@ unsigned long ix1, ix2; // index to input and output unsigned j, k, l; // running indices int *SZ2; // size of output + char flag_isNaN = 0; - // check for proper number of input and output arguments - if ((PInputCount <= 0) || (PInputCount > 2)) - mexErrMsgTxt("SumSkipNan.MEX requires 1 or 2 arguments."); + if ((PInputCount <= 0) || (PInputCount > 3)) + mexErrMsgTxt("SumSkipNan.MEX requires 1,2 or 3 arguments."); if (POutputCount > 4) mexErrMsgTxt("SumSkipNan.MEX has 1 to 4 output arguments."); @@ -82,7 +93,7 @@ LInputI = mxGetPi(PInputs[0]); // get 2nd argument - if (PInputCount == 2){ + if (PInputCount > 1){ switch (mxGetNumberOfElements(PInputs[1])) { case 0: x = 0.0; // accept empty element break; @@ -96,6 +107,7 @@ DIM = (unsigned)floor(x); } + // get size ND = mxGetNumberOfDimensions(PInputs[0]); // NN = mxGetNumberOfElements(PInputs[0]); @@ -150,7 +162,7 @@ mxFree(SZ2); - if ((POutputCount == 2) && !mxIsComplex(PInputs[0])) + if ((POutputCount <3) && !mxIsComplex(PInputs[0])) { // OUTER LOOP: along dimensions > DIM for (l = 0; l<D3; l++) @@ -161,52 +173,15 @@ // Inner LOOP: along dimensions < DIM for (k = 0; k<D1; k++, ix1++, ix2++) { - LCount = 0; - LSum = 0.0; - - // LOOP along dimension DIM - for (j=0; j<D2; j++) - { - x = LInput[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LCount++; - LSum += x; - } - } - LOutputSum[ix2] = LSum; - LOutputCount[ix2] = (double)LCount; + int flag=0; + size_t count; + __sumskipnan2__(LInput+ix1, D2, D1, LOutputSum+ix2, &count, &flag_isNaN); + if (POutputCount > 1) + LOutputCount[ix2] = double(count); } } - return; } - else if ((POutputCount == 1) && !mxIsComplex(PInputs[0])) - { - // OUTER LOOP: along dimensions > DIM - for (l = 0; l<D3; l++) - { - ix2 = l*D1; // index for output - ix1 = ix2*D2; // index for input - // Inner LOOP: along dimensions < DIM - for (k = 0; k<D1; k++, ix1++, ix2++) - { - LSum = 0.0; - - // LOOP along dimension DIM - for (j=0; j<D2; j++) - { - x = LInput[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LSum += x; - } - } - LOutputSum[ix2] = LSum; - } - } - return; - } else if ((POutputCount == 3) && !mxIsComplex(PInputs[0])) { // OUTER LOOP: along dimensions > DIM @@ -218,25 +193,10 @@ // Inner LOOP: along dimensions < DIM for (k = 0; k<D1; k++, ix1++, ix2++) { - LCount = 0; - LSum = 0.0; - LSum2 = 0.0; - - // LOOP along dimension DIM - for (j=0; j<D2; j++) - { - x = LInput[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LCount++; - LSum += x; - LSum2 += x*x; - } - } - LOutputSum[ix2] = LSum; - LOutputCount[ix2] = (double)LCount; - LOutputSum2[ix2] = LSum2; - } + size_t count; + __sumskipnan3__(LInput+ix1, D2, D1, LOutputSum+ix2, LOutputSum2+ix2, &count, &flag_isNaN); + LOutputCount[ix2]=double(count); + } } } else if ((POutputCount == 4) && !mxIsComplex(PInputs[0])) @@ -250,32 +210,13 @@ // Inner LOOP: along dimensions < DIM for (k = 0; k<D1; k++, ix1++, ix2++) { - LCount = 0; - LSum = 0.0; - LSum2 = 0.0; - LSum4 = 0.0; - - // LOOP along dimension DIM - for (j=0; j<D2; j++) - { - x = LInput[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LCount++; - LSum += x; - x2 = x*x; - LSum2 += x2; - LSum4 += x2*x2; - } - } - LOutputSum[ix2] = LSum; - LOutputCount[ix2] = (double)LCount; - LOutputSum2[ix2] = LSum2; - LOutputSum4[ix2] = LSum4; + size_t count; + __sumskipnan4__(LInput+ix1, D2, D1, LOutputSum+ix2, LOutputSum2+ix2, LOutputSum4+ix2, &count, &flag_isNaN); + LOutputCount[ix2]=double(count); } } } - else if ((POutputCount == 2) && mxIsComplex(PInputs[0])) + else if ((POutputCount < 3) && mxIsComplex(PInputs[0])) { // OUTER LOOP: along dimensions > DIM for (l = 0; l<D3; l++) @@ -286,31 +227,17 @@ // Inner LOOP: along dimensions < DIM for (k = 0; k<D1; k++, ix1++, ix2++) { - LCount = 0; - LSum = 0.0; - - // LOOP along dimension DIM - for (j=0; j<D2; j++) - { - x = LInput[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LCount++; - LSum += x; - } - x = LInputI[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LCountI++; - LSum += x; - } - } - if (LCount != LCountI) + int flag=0; + size_t count,countI; + __sumskipnan2__(LInput+ix1, D2, D1, LOutputSum+ix2, &count, &flag_isNaN); + + __sumskipnan2__(LInputI+ix1, D2, D1, LOutputSumI+ix2, &countI, &flag_isNaN); + + if (count != countI) mexErrMsgTxt("Number of NaNs is different for REAL and IMAG part"); - LOutputSum[ix2] = LSum; - LOutputCount[ix2] = (double)LCount; - + if (POutputCount > 1) + LOutputCount[ix2]=double(count); } } } @@ -325,35 +252,17 @@ // Inner LOOP: along dimensions < DIM for (k = 0; k<D1; k++, ix1++, ix2++) { - LCount = 0; - LSum = 0.0; - LSum2 = 0.0; - - // LOOP along dimension DIM - for (j=0; j<D2; j++) - { - x = LInput[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LCount++; - LSum += x; - LSum2 += x*x; - } - x = LInputI[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LCountI++; - LSum += x; - LSum2 += x*x; - } - } - if (LCount != LCountI) + size_t count,countI; + double ssq1,ssq2; + __sumskipnan3__(LInput+ix1, D2, D1, LOutputSum+ix2, &ssq1, &count, &flag_isNaN); + + __sumskipnan3__(LInputI+ix1, D2, D1, LOutputSumI+ix2, &ssq2, &countI, &flag_isNaN); + + if (count != countI) mexErrMsgTxt("Number of NaNs is different for REAL and IMAG part"); - LOutputSum[ix2] = LSum; - LOutputCount[ix2] = (double)LCount; - LOutputSum2[ix2] = LSum2; - + LOutputCount[ix2]= double(count); + LOutputSum2[ix2] = ssq1+ssq2; } } } @@ -406,6 +315,235 @@ } } } + + if (flag_isNaN && (PInputCount > 2)) { + // set FLAG_NANS_OCCURED + switch (mxGetClassID(PInputs[2])) { + case mxDOUBLE_CLASS: + *(double*)mxGetData(PInputs[2]) = 1.0; + break; + case mxSINGLE_CLASS: + *(float*)mxGetData(PInputs[2]) = 1.0; + break; + case mxLOGICAL_CLASS: + case mxCHAR_CLASS: + case mxINT8_CLASS: + case mxUINT8_CLASS: + *(uint8_t*)mxGetData(PInputs[2]) = 1; + break; + case mxINT16_CLASS: + case mxUINT16_CLASS: + *(uint16_t*)mxGetData(PInputs[2]) = 1; + break; + case mxINT32_CLASS: + case mxUINT32_CLASS: + *(uint32_t*)mxGetData(PInputs[2])= 1; + break; + case mxINT64_CLASS: + case mxUINT64_CLASS: + *(uint64_t*)mxGetData(PInputs[2]) = 1; + break; + case mxFUNCTION_CLASS: + case mxUNKNOWN_CLASS: + case mxCELL_CLASS: + case mxSTRUCT_CLASS: + ; + } + } } +int __sumskipnan2__(double *data, size_t Ni, size_t stride, double *s, size_t *No, char *flag_anyISNAN) +{ + register double sum=0; + register size_t count=0; + register char flag=0; + // LOOP along dimension DIM + + for (size_t j=0; j<Ni; j++, data += stride) + { + register double x = *data; + if (x==x) + { + count++; + sum += x; + } + else + flag = 1; + } + + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; + *s = sum; + *No = count; + +} + +int __sumskipnan3__(double *data, size_t Ni, size_t stride, double *s, double *s2, size_t *No, char *flag_anyISNAN) +{ + register double sum=0; + register double msq=0; + register size_t count=0; + register char flag=0; + // LOOP along dimension DIM + + for (size_t j=0; j<Ni; j++, data += stride) + { + register double x = *data; + if (x==x) + { + count++; + sum += x; + msq += x*x; + } + else + flag = 1; + } + + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; + *s = sum; + *s2 = msq; + *No = count; +} + +int __sumskipnan4__(double *data, size_t Ni, size_t stride, double *s, double *s2, double *s4, size_t *No, char *flag_anyISNAN) +{ + register double _s0=0; + register double _s2=0; + register double _s4=0; + register size_t count=0; + register char flag=0; + // LOOP along dimension DIM + + for (size_t j=0; j<Ni; j++, data += stride) + { + register double x = *data; + if (x==x) + { + count++; + _s0 += x; + x =x*x; + _s2 += x; + _s4 += x*x; + } + else + flag = 1; + } + + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; + *s = _s0; + *s2 = _s2; + *s4 = _s4; + *No = count; +} + +int __sumskipnan2_single__(float *data, size_t Ni, size_t stride, double *s, size_t *No, char *flag_anyISNAN) +{ + register double sum=0; + register size_t count=0; + register char flag=0; + // LOOP along dimension DIM + + for (size_t j=0; j<Ni; j++, data += stride) + { + register double x = *data; + if (x==x) + { + count++; + sum += x; + } + else + flag = 1; + } + + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; + *s = sum; + *No = count; + +} + +int __sumskipnan3_single__(float *data, size_t Ni, size_t stride, double *s, double *s2, size_t *No, char *flag_anyISNAN) +{ + register double sum=0; + register double msq=0; + register size_t count=0; + register char flag=0; + // LOOP along dimension DIM + + for (size_t j=0; j<Ni; j++, data += stride) + { + register double x = *data; + if (x==x) + { + count++; + sum += x; + msq += x*x; + } + else + flag = 1; + } + + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; + *s = sum; + *s2 = msq; + *No = count; +} + +int __sumskipnan4_single__(float *data, size_t Ni, size_t stride, double *s, double *s2, double *s4, size_t *No, char *flag_anyISNAN) +{ + register double _s0=0; + register double _s2=0; + register double _s4=0; + register size_t count=0; + register char flag=0; + // LOOP along dimension DIM + + for (size_t j=0; j<Ni; j++, data += stride) + { + register double x = *data; + if (x==x) + { + count++; + _s0 += x; + x =x*x; + _s2 += x; + _s4 += x*x; + } + else + flag = 1; + } + + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; + *s = _s0; + *s2 = _s2; + *s4 = _s4; + *No = count; +} + + +#ifdef experimental + /* x86 assembler code, currently broken, + the main advantage would be the support of the extended accuracy + */ + __asm__ (" movl $0, %eax;\n" + " fldz;\n" + " movl _D2, %ebx;\n" + " movl _ptr, %edx;\n" + "loop3: \n" + " fld (%%edx);\n" + " fcom (ST0);\n" + " jne end_isnan;" + " fadd (ST0)\n" + " inc %eax\n" + " \n" + "end_isnan: \n" + " fdecstp;\n" + " add %edx,_stride\n" + " loop loop3 %ebx;\n" + " fstp _LSum;\n" + " movl %eax, _LCount;\n" + " nop\n" + ); + // #__asm__ ("fldz " : : : "%eax"); +#endif + + This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-03-10 12:53:22
|
Revision: 5599 http://octave.svn.sourceforge.net/octave/?rev=5599&view=rev Author: schloegl Date: 2009-03-10 12:52:20 +0000 (Tue, 10 Mar 2009) Log Message: ----------- flag_nans_occured is not reset at every sumskipnan but only at the next call to sumskipnan; this enables a flexible control of the granularity on explicit NaN-checks Modified Paths: -------------- trunk/octave-forge/extra/NaN/doc/README.TXT trunk/octave-forge/extra/NaN/inst/covm.m trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m trunk/octave-forge/extra/NaN/inst/sumskipnan.m Modified: trunk/octave-forge/extra/NaN/doc/README.TXT =================================================================== --- trunk/octave-forge/extra/NaN/doc/README.TXT 2009-03-10 11:18:13 UTC (rev 5598) +++ trunk/octave-forge/extra/NaN/doc/README.TXT 2009-03-10 12:52:20 UTC (rev 5599) @@ -85,8 +85,8 @@ What is the difference to previous implementations? =================================================== 1) The default behavior of previous implementations is that NaNs in the input -data results in NaNs in the output data. In many applications is this behavior -quite boring. In this implementation, NaNs are handled as missing values and +data results in NaNs in the output data. In many applications this behavior +is not what you want. In this implementation, NaNs are handled as missing values and are skipped. 2) In previous implementations the workaround was using different functions @@ -104,9 +104,7 @@ (Note, NANSUM from Matlab does not support the DIM-argument, and NANSUM(NaN) gives NaN instead of 0); -4) Defining the estimation mode (biased or unbiased) -This feature is removed. -Unbiased estimates are provided. +4) [obsolete] 5) The DIMENSION argument is implemented in most routines. These should work in all Matlab and Octave versions. A workaround for a bug in @@ -159,7 +157,7 @@ In Stats-tb V2.2(R11) TINV has also the same problem. For these reasons, the NaN-tb is a bug fix. Furthermore, the check of the input -arguments is implemented simpler. Overall, the code is cleaner and leaner. +arguments is implemented simpler. Overall, the code becomes cleaner and leaner. Q: WHY SKIPPING NaN's?: @@ -184,35 +182,56 @@ more readable code and in improved performance, too. -Installing the NaN-tb with Octave: ----------------------------------- -a) -- +Q: What if I need to check for NaN's: +------------------------------------- +A: You can always check whether there were some skipped NaN's in your +data with the command FLAG_NANS_OCCURED(). -b) extract files from NaNnnn.tar.gz and move them into -.../octave/.../m/statistics/base/ +m = mean(x); +if flag_nans_occured() + % do your error handling, e.g. + error('there were NaN's in x, ignore m'); +end; -c) Alternatively, the files can be moved into any other directory; but you -must remove from .../octave/.../m/statistics/base/ -mean.m, meansq.m, median.m, moment.m, skewness.m, kurtosis.m, std.m, var.m -and from .../source_forge/.../statistics/* -zscore.m, mad.m, geomean.m, harmmean.m +Its also easy to control the granularity of the checks -d) (re-)start Octave and run NANINSTTEST. -This checks whether all previous functions have been replaced +flag_nans_occured(); % reset flag + % do any statistical analysis you want +if flag_nans_occured() + % check, whether some NaN's occured. +end; -Installing the NaN-tb for Matlab: ----------------------------------- -Ensure that the NaN-directory is first in your path. This should override any -alternative function definition (except built-in's) with the same name. +Installing the NaN-tb for Octave and Matlab: +-------------------------------------------- +a) Extract files and save them in /your/directory/structure/to/NaN/ -(re-)start Matlab or clear functions and run NANINSTTEST. -This checks whether all previous functions have been replaced +b) Include the path with one of the following commands: + addpath('/your/directory/structure/to/NaN/') + path('/your/directory/structure/to/NaN/',path) + Make sure the functions in the NaN-toolbox are found before the default functions. + +c) run NANINSTTEST +This checks whether the installation was successful. +d) [OPTIONAL]: + To improve speed, you can use the MEX-version of SUMSKIPNAN. + Some precompiled binaries are provided. If your platform is not supported, + compile the C-Mex-function SUMSKIPNAN_MEX.CPP using + mex sumskipnan_mex.cpp + The oct-file sumskipnan_oct.cc is broken, but Octave can also use + the mex-file. + mkoctfile --mex sumskipnan_mex.cpp + Run NANINSTTEST again to check the stability of the compiled SUMSKIPNAN. + +e) HINT: if SUMSKIPNAN_MEX causes problems, you can savely remove it. +Then the (slower) M-file is used. + + $Id$ Copyright (C) 2000-2005,2009 by Alois Schloegl <a.s...@ie...> - WWW: http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ + WWW: http://hci.tugraz.at/~schloegl/matlab/NaN/ LICENSE: @@ -228,3 +247,4 @@ You should have received a copy of the GNU General Public License along with this program; if not, see <http://www.gnu.org/licenses/>. + Modified: trunk/octave-forge/extra/NaN/inst/covm.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/covm.m 2009-03-10 11:18:13 UTC (rev 5598) +++ trunk/octave-forge/extra/NaN/inst/covm.m 2009-03-10 12:52:20 UTC (rev 5599) @@ -32,9 +32,9 @@ % see also: DECOVM, XCOVF % $Id$ -% Copyright (C) 2000-2005 by Alois Schloegl <a.s...@ie...> +% Copyright (C) 2000-2005,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://hci.tugraz.at/~schloegl/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by @@ -51,9 +51,10 @@ global FLAG_NANS_OCCURED; -FLAG_NANS_OCCURED = logical(0); +if isempty(FLAG_NANS_OCCURED), + FLAG_NANS_OCCURED = logical(0); % default value +end; - if nargin<3, if nargin==2, if isnumeric(Y), Modified: trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m 2009-03-10 11:18:13 UTC (rev 5598) +++ trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m 2009-03-10 12:52:20 UTC (rev 5599) @@ -1,12 +1,13 @@ -function FLAG_NANS_OCCURED=flag_nans_occured() -% The use of FLAG_NANS_OCCURED is in experimental state. -% -% FLAG_NANS_OCCURED checkes whether the last call to sumskipnan +function [flag]=flag_nans_occured() +% FLAG_NANS_OCCURED checks whether the last call(s) to sumskipnan or covm % contained any not-a-numbers in the input argument. Because many other % functions like mean, std, etc. are also using sumskipnan, % also these functions can be checked for NaN's in the input data. +% +% A call to FLAG_NANS_OCCURED() resets also the flag whether NaN's occured. +% Only sumskipnan or covm can set the flag again. % -% see also: SUMSKIPNAN +% see also: SUMSKIPNAN, COVM % $Id$ % Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> @@ -29,11 +30,12 @@ global FLAG_NANS_OCCURED; %%% check whether FLAG was already defined -if ~exist('FLAG_NANS_OCCURED','var'), - FLAG_NANS_OCCURED = 0; % default value -end; if isempty(FLAG_NANS_OCCURED), - FLAG_NANS_OCCURED = 0; % default value + FLAG_NANS_OCCURED = logical(0); % default value end; +flag = FLAG_NANS_OCCURED; % return value + +FLAG_NANS_OCCURED = logical(0); % reset flag + return; Modified: trunk/octave-forge/extra/NaN/inst/sumskipnan.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/sumskipnan.m 2009-03-10 11:18:13 UTC (rev 5598) +++ trunk/octave-forge/extra/NaN/inst/sumskipnan.m 2009-03-10 12:52:20 UTC (rev 5599) @@ -49,7 +49,6 @@ global FLAG_NANS_OCCURED; -FLAG_NANS_OCCURED = logical(0); if nargin<2, DIM = []; @@ -80,7 +79,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % non-float data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -if ~isa(x,'float') %%%% || FLAG_implicit_skip_nan, %%% skip always NaN's +if ~isa(x,'float') %%%% || ~flag_implicit_skip_nan(), %%% skip always NaN's x = double(x); o = sum(x,DIM); if nargin>1 @@ -103,12 +102,15 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % use Matlab-MEX function when available %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -%% it seems there is no advantage in using the mex-file, therefore this option is disabled by default. %%if 0, try %% using sumskipnan_mex.mex - %% !!! hack: FLAG_NANS_OCCURED is an output argument !!! + + %% !!! hack: FLAG_NANS_OCCURED is an output argument, reserve memory !!! + if isempty(FLAG_NANS_OCCURED), + FLAG_NANS_OCCURED = logical(0); % default value + end; if (nargout<3), [o,count] = sumskipnan_mex(x,DIM,FLAG_NANS_OCCURED); return; This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-03-11 08:23:46
|
Revision: 5601 http://octave.svn.sourceforge.net/octave/?rev=5601&view=rev Author: schloegl Date: 2009-03-11 08:23:44 +0000 (Wed, 11 Mar 2009) Log Message: ----------- minimize cache misses - increases performance; support for complex data moved to sumskipnan.m Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/sumskipnan.m trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp Modified: trunk/octave-forge/extra/NaN/inst/sumskipnan.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/sumskipnan.m 2009-03-10 12:54:07 UTC (rev 5600) +++ trunk/octave-forge/extra/NaN/inst/sumskipnan.m 2009-03-11 08:23:44 UTC (rev 5601) @@ -1,4 +1,4 @@ -function [o,count,SSQ,S4M] = sumskipnan(x,DIM) +function [o,count,SSQ] = sumskipnan(x,DIM) % SUMSKIPNAN adds all non-NaN values. % % All NaN's are skipped; NaN's are considered as missing values. @@ -111,14 +111,36 @@ if isempty(FLAG_NANS_OCCURED), FLAG_NANS_OCCURED = logical(0); % default value end; - if (nargout<3), - [o,count] = sumskipnan_mex(x,DIM,FLAG_NANS_OCCURED); + + if (nargout==1), + o = sumskipnan_mex5(real(x),DIM,FLAG_NANS_OCCURED); + if (iscomplex(x)) + io = sumskipnan_mex5(imag(x),DIM,FLAG_NANS_OCCURED); + o = o + i*io; + end; return; - elseif (nargout==3), - [o,count,SSQ] = sumskipnan_mex(x,DIM,FLAG_NANS_OCCURED); + elseif (nargout==2), + [o,count] = sumskipnan_mex5(real(x),DIM,FLAG_NANS_OCCURED); + if (iscomplex(x)) + [io,icount] = sumskipnan_mex5(imag(x),DIM,FLAG_NANS_OCCURED); + if any(count(:)-icount(:)) + error('Number of NaNs differ for REAL and IMAG part'); + else + o = o+i*io; + end; + end; return; - elseif (nargout==4) && isreal(i), - [o,count,SSQ,S4M] = sumskipnan_mex(x,DIM,FLAG_NANS_OCCURED); + elseif (nargout>=3), + [o,count,SSQ] = sumskipnan_mex5(real(x),DIM,FLAG_NANS_OCCURED); + if (iscomplex(x)) + [io,icount,iSSQ] = sumskipnan_mex5(imag(x),DIM,FLAG_NANS_OCCURED); + if any(count(:)-icount(:)) + error('Number of NaNs differ for REAL and IMAG part'); + else + o = o+i*io; + SSQ = SSQ+iSSQ; + end; + end; return; end; end; Modified: trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-03-10 12:54:07 UTC (rev 5600) +++ trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-03-11 08:23:44 UTC (rev 5601) @@ -3,9 +3,10 @@ //------------------------------------------------------------------- // C-MEX implementation of SUMSKIPNAN - this function is part of the NaN-toolbox. // +// // This program is free software; you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by -// the Free Software Foundation; either version 2 of the License, or +// the Free Software Foundation; either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, @@ -21,17 +22,13 @@ // // Input: // - array to sum -// - dimension to sum (1=columns; 2=rows; doesn't work for dim>2!!) +// - dimension to sum // // Output: // - sums // - count of valid elements (optional) // - sums of squares (optional) -// - sums of squares of squares (optional) // -// Author: Patrick Houweling (pho...@ya...) -// Version: 1.0 -// Date: 17 september 2003 // // $Id$ // Copyright (C) 2009 Alois Schloegl <a.s...@ie...> @@ -39,59 +36,46 @@ // http://hci.tugraz.at/~schloegl/matlab/NaN/ // //------------------------------------------------------------------- -//#include <stdlib> + #include <inttypes.h> #include <math.h> #include "mex.h" -//------------------------------------------------------------------- -int __sumskipnan2__(double *data, size_t Ni, size_t stride, double *s, size_t *No, char *flag_anyISNAN); -int __sumskipnan3__(double *data, size_t Ni, size_t stride, double *s, double *s2, size_t *No, char *flag_anyISNAN); -int __sumskipnan4__(double *data, size_t Ni, size_t stride, double *s, double *s2, double *s4, size_t *No, char *flag_anyISNAN); -int __sumskipnan2_single__(float *data, size_t Ni, size_t stride, double *s, size_t *No, char *flag_anyISNAN); -int __sumskipnan3_single__(float *data, size_t Ni, size_t stride, double *s, double *s2, size_t *No, char *flag_anyISNAN); -int __sumskipnan4_single__(float *data, size_t Ni, size_t stride, double *s, double *s2, double *s4, size_t *No, char *flag_anyISNAN); +inline int __sumskipnan2__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN); +inline int __sumskipnan3__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN); - -void mexFunction(int POutputCount, mxArray* POutput[], int PInputCount, const mxArray *PInputs[]) +void mexFunction(int POutputCount, mxArray* POutput[], int PInputCount, const mxArray *PInputs[]) { const int *SZ; double* LInput; - double* LInputI; double* LOutputSum; - double* LOutputSumI; double* LOutputCount; double* LOutputSum2; - double* LOutputSum4; - double x, x2, x2i; - unsigned long LCount, LCountI; - double LSum, LSum2, LSum4; + double x; + unsigned long LCount; unsigned DIM = 0; unsigned long D1, D2, D3; // NN; // unsigned ND, ND2; // number of dimensions: input, output unsigned long ix1, ix2; // index to input and output - unsigned j, k, l; // running indices + unsigned j, l; // running indices int *SZ2; // size of output char flag_isNaN = 0; + // check for proper number of input and output arguments if ((PInputCount <= 0) || (PInputCount > 3)) mexErrMsgTxt("SumSkipNan.MEX requires 1,2 or 3 arguments."); if (POutputCount > 4) - mexErrMsgTxt("SumSkipNan.MEX has 1 to 4 output arguments."); + mexErrMsgTxt("SumSkipNan.MEX has 1 to 3 output arguments."); // get 1st argument - if(mxIsDouble(PInputs[0])) + if(mxIsDouble(PInputs[0]) && !mxIsComplex(PInputs[0])) LInput = mxGetPr(PInputs[0]); else - mexErrMsgTxt("First argument must be DOUBLE."); + mexErrMsgTxt("First argument must be REAL/DOUBLE."); - - if(mxIsComplex(PInputs[0])) - LInputI = mxGetPi(PInputs[0]); - // get 2nd argument if (PInputCount > 1){ switch (mxGetNumberOfElements(PInputs[1])) { @@ -107,15 +91,14 @@ DIM = (unsigned)floor(x); } - // get size ND = mxGetNumberOfDimensions(PInputs[0]); // NN = mxGetNumberOfElements(PInputs[0]); SZ = mxGetDimensions(PInputs[0]); // if DIM==0 (undefined), look for first dimension with more than 1 element. - for (k = 0; (DIM < 1) && (k < ND); k++) - if (SZ[k]>1) DIM = k+1; + for (j = 0; (DIM < 1) && (j < ND); j++) + if (SZ[j]>1) DIM = j+1; if (DIM < 1) DIM=1; // in case DIM is still undefined @@ -137,200 +120,121 @@ // create outputs #define TYP mxDOUBLE_CLASS - if(mxIsComplex(PInputs[0])) - { POutput[0] = mxCreateNumericArray(ND2, SZ2, TYP, mxCOMPLEX); - LOutputSum = mxGetPr(POutput[0]); - LOutputSumI= mxGetPi(POutput[0]); - } - else - { POutput[0] = mxCreateNumericArray(ND2, SZ2, TYP, mxREAL); - LOutputSum = mxGetPr(POutput[0]); - } + POutput[0] = mxCreateNumericArray(ND2, SZ2, TYP, mxREAL); + LOutputSum = mxGetPr(POutput[0]); if (POutputCount >= 2){ POutput[1] = mxCreateNumericArray(ND2, SZ2, TYP, mxREAL); - LOutputCount = mxGetPr(POutput[1]); + LOutputCount = mxGetPr(POutput[1]); } if (POutputCount >= 3){ POutput[2] = mxCreateNumericArray(ND2, SZ2, TYP, mxREAL); LOutputSum2 = mxGetPr(POutput[2]); } - if (POutputCount >= 4){ - POutput[3] = mxCreateNumericArray(ND2, SZ2, TYP, mxREAL); - LOutputSum4 = mxGetPr(POutput[3]); - } mxFree(SZ2); - if ((POutputCount <3) && !mxIsComplex(PInputs[0])) - { + if ((POutputCount == 1) && !mxIsComplex(PInputs[0])) { // OUTER LOOP: along dimensions > DIM - for (l = 0; l<D3; l++) - { - ix2 = l*D1; // index for output - ix1 = ix2*D2; // index for input - - // Inner LOOP: along dimensions < DIM - for (k = 0; k<D1; k++, ix1++, ix2++) + for (l = 0; l<D3; l++) { + ix1 = l*D1*D2; // index for input + if (D1==1) { - int flag=0; - size_t count; - __sumskipnan2__(LInput+ix1, D2, D1, LOutputSum+ix2, &count, &flag_isNaN); - if (POutputCount > 1) - LOutputCount[ix2] = double(count); + ix2 = l*D1; // index for output + double count; + __sumskipnan2__(LInput+ix1, D2, LOutputSum+ix2, &count, &flag_isNaN); + } + else for (j=0; j<D2; j++) { + // minimize cache misses + ix2 = l*D1; // index for output + // Inner LOOP: along dimensions < DIM + do { + register double x = *LInput; + if (x==x) { + LOutputSum[ix2] += x; + } + else + flag_isNaN = 1; + LInput++; + ix2++; + } while (ix2 != (l+1)*D1); } } } - else if ((POutputCount == 3) && !mxIsComplex(PInputs[0])) - { + else if ((POutputCount == 2) && !mxIsComplex(PInputs[0])) { // OUTER LOOP: along dimensions > DIM - for (l = 0; l<D3; l++) - { - ix2 = l*D1; // index for output - ix1 = ix2*D2; // index for input - - // Inner LOOP: along dimensions < DIM - for (k = 0; k<D1; k++, ix1++, ix2++) + for (l = 0; l<D3; l++) { + ix1 = l*D1*D2; // index for input + if (D1==1) { - size_t count; - __sumskipnan3__(LInput+ix1, D2, D1, LOutputSum+ix2, LOutputSum2+ix2, &count, &flag_isNaN); - LOutputCount[ix2]=double(count); + ix2 = l*D1; // index for output + __sumskipnan2__(LInput+ix1, D2, LOutputSum+ix2, LOutputCount+ix2, &flag_isNaN); } + else for (j=0; j<D2; j++) { + // minimize cache misses + ix2 = l*D1; // index for output + // Inner LOOP: along dimensions < DIM + do { + register double x = *LInput; + if (x==x) { + LOutputCount[ix2] += 1.0; + LOutputSum[ix2] += x; + } + else + flag_isNaN = 1; + LInput++; + ix2++; + } while (ix2 != (l+1)*D1); + } } } - else if ((POutputCount == 4) && !mxIsComplex(PInputs[0])) - { - // OUTER LOOP: along dimensions > DIM - for (l = 0; l<D3; l++) - { - ix2 = l*D1; // index for output - ix1 = ix2*D2; // index for input - // Inner LOOP: along dimensions < DIM - for (k = 0; k<D1; k++, ix1++, ix2++) + else if ((POutputCount == 3) && !mxIsComplex(PInputs[0])) { + // OUTER LOOP: along dimensions > DIM + for (l = 0; l<D3; l++) { + ix1 = l*D1*D2; // index for input + if (D1==1) { + ix2 = l*D1; // index for output size_t count; - __sumskipnan4__(LInput+ix1, D2, D1, LOutputSum+ix2, LOutputSum2+ix2, LOutputSum4+ix2, &count, &flag_isNaN); - LOutputCount[ix2]=double(count); + __sumskipnan3__(LInput+ix1, D2, LOutputSum+ix2, LOutputSum2+ix2, LOutputCount+ix2, &flag_isNaN); + } + else for (j=0; j<D2; j++) { + // minimize cache misses + ix2 = l*D1; // index for output + // Inner LOOP: along dimensions < DIM + do { + register double x = *LInput; + if (x==x) { + LOutputCount[ix2] += 1.0; + LOutputSum[ix2] += x; + LOutputSum2[ix2] += x*x; + } + else + flag_isNaN = 1; + LInput++; + ix2++; + } while (ix2 != (l+1)*D1); } } } - else if ((POutputCount < 3) && mxIsComplex(PInputs[0])) - { - // OUTER LOOP: along dimensions > DIM - for (l = 0; l<D3; l++) - { - ix2 = l*D1; // index for output - ix1 = ix2*D2; // index for input - // Inner LOOP: along dimensions < DIM - for (k = 0; k<D1; k++, ix1++, ix2++) - { - int flag=0; - size_t count,countI; - __sumskipnan2__(LInput+ix1, D2, D1, LOutputSum+ix2, &count, &flag_isNaN); - - __sumskipnan2__(LInputI+ix1, D2, D1, LOutputSumI+ix2, &countI, &flag_isNaN); - - if (count != countI) - mexErrMsgTxt("Number of NaNs is different for REAL and IMAG part"); - - if (POutputCount > 1) - LOutputCount[ix2]=double(count); - } - } - } - else if ((POutputCount == 3) && mxIsComplex(PInputs[0])) - { - // OUTER LOOP: along dimensions > DIM - for (l = 0; l<D3; l++) - { - ix2 = l*D1; // index for output - ix1 = ix2*D2; // index for input - - // Inner LOOP: along dimensions < DIM - for (k = 0; k<D1; k++, ix1++, ix2++) - { - size_t count,countI; - double ssq1,ssq2; - __sumskipnan3__(LInput+ix1, D2, D1, LOutputSum+ix2, &ssq1, &count, &flag_isNaN); - - __sumskipnan3__(LInputI+ix1, D2, D1, LOutputSumI+ix2, &ssq2, &countI, &flag_isNaN); - - if (count != countI) - mexErrMsgTxt("Number of NaNs is different for REAL and IMAG part"); - - LOutputCount[ix2]= double(count); - LOutputSum2[ix2] = ssq1+ssq2; - } - } - } - else if ((POutputCount == 4) && mxIsComplex(PInputs[0])) - { - // OUTER LOOP: along dimensions > DIM - for (l = 0; l<D3; l++) - { - ix2 = l*D1; // index for output - ix1 = ix2*D2; // index for input - - // Inner LOOP: along dimensions < DIM - for (k = 0; k<D1; k++, ix1++, ix2++) - { - LCount = 0; - LSum = 0.0; - LSum2 = 0.0; - LSum4 = 0.0; - - // LOOP along dimension DIM - for (j=0; j<D2; j++) - { - x = LInput[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LCount++; - LSum += x; - x2 = x*x; - LSum2 += x2; - } - x = LInputI[ix1 + j*D1]; - if (!mxIsNaN(x)) - { - LCountI++; - LSum += x; - x2i = x*x; - LSum2 += x2i; - x2 += x2i; - LSum4 += x2*x2; - } - } - if (LCount != LCountI) - mexErrMsgTxt("Number of NaNs is different for REAL and IMAG part"); - - LOutputSum[ix2] = LSum; - LOutputCount[ix2] = (double)LCount; - LOutputSum2[ix2] = LSum2; - LOutputSum4[ix2] = LSum4; - - } - } - } - - if (flag_isNaN && (PInputCount > 2)) { + if (flag_isNaN && (PInputCount > 2)) { // set FLAG_NANS_OCCURED switch (mxGetClassID(PInputs[2])) { - case mxDOUBLE_CLASS: - *(double*)mxGetData(PInputs[2]) = 1.0; - break; - case mxSINGLE_CLASS: - *(float*)mxGetData(PInputs[2]) = 1.0; - break; case mxLOGICAL_CLASS: case mxCHAR_CLASS: case mxINT8_CLASS: case mxUINT8_CLASS: *(uint8_t*)mxGetData(PInputs[2]) = 1; break; + case mxDOUBLE_CLASS: + *(double*)mxGetData(PInputs[2]) = 1.0; + break; + case mxSINGLE_CLASS: + *(float*)mxGetData(PInputs[2]) = 1.0; + break; case mxINT16_CLASS: case mxUINT16_CLASS: *(uint16_t*)mxGetData(PInputs[2]) = 1; @@ -347,21 +251,22 @@ case mxUNKNOWN_CLASS: case mxCELL_CLASS: case mxSTRUCT_CLASS: - ; + mexPrintf("Type of 3rd input argument not supported."); } } } -int __sumskipnan2__(double *data, size_t Ni, size_t stride, double *s, size_t *No, char *flag_anyISNAN) +#define stride 1 +inline int __sumskipnan2__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN) { register double sum=0; register size_t count=0; register char flag=0; // LOOP along dimension DIM - for (size_t j=0; j<Ni; j++, data += stride) - { + void *end = data + stride*Ni; + do { register double x = *data; if (x==x) { @@ -370,98 +275,18 @@ } else flag = 1; - } - if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; - *s = sum; - *No = count; - -} - -int __sumskipnan3__(double *data, size_t Ni, size_t stride, double *s, double *s2, size_t *No, char *flag_anyISNAN) -{ - register double sum=0; - register double msq=0; - register size_t count=0; - register char flag=0; - // LOOP along dimension DIM - - for (size_t j=0; j<Ni; j++, data += stride) - { - register double x = *data; - if (x==x) - { - count++; - sum += x; - msq += x*x; - } - else - flag = 1; + data +=stride; } - - if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; - *s = sum; - *s2 = msq; - *No = count; -} - -int __sumskipnan4__(double *data, size_t Ni, size_t stride, double *s, double *s2, double *s4, size_t *No, char *flag_anyISNAN) -{ - register double _s0=0; - register double _s2=0; - register double _s4=0; - register size_t count=0; - register char flag=0; - // LOOP along dimension DIM + while (data < end); - for (size_t j=0; j<Ni; j++, data += stride) - { - register double x = *data; - if (x==x) - { - count++; - _s0 += x; - x =x*x; - _s2 += x; - _s4 += x*x; - } - else - flag = 1; - } - if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; - *s = _s0; - *s2 = _s2; - *s4 = _s4; - *No = count; -} - -int __sumskipnan2_single__(float *data, size_t Ni, size_t stride, double *s, size_t *No, char *flag_anyISNAN) -{ - register double sum=0; - register size_t count=0; - register char flag=0; - // LOOP along dimension DIM - - for (size_t j=0; j<Ni; j++, data += stride) - { - register double x = *data; - if (x==x) - { - count++; - sum += x; - } - else - flag = 1; - } - - if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; *s = sum; - *No = count; + *No = (double)count; } -int __sumskipnan3_single__(float *data, size_t Ni, size_t stride, double *s, double *s2, size_t *No, char *flag_anyISNAN) +inline int __sumskipnan3__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN) { register double sum=0; register double msq=0; @@ -469,81 +294,24 @@ register char flag=0; // LOOP along dimension DIM - for (size_t j=0; j<Ni; j++, data += stride) - { + void *end = data + stride*Ni; + do { register double x = *data; - if (x==x) - { + if (x==x) { count++; sum += x; msq += x*x; } else flag = 1; + + data++; // stride=1 } + while (data < end); if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; *s = sum; *s2 = msq; - *No = count; + *No = (double)count; } -int __sumskipnan4_single__(float *data, size_t Ni, size_t stride, double *s, double *s2, double *s4, size_t *No, char *flag_anyISNAN) -{ - register double _s0=0; - register double _s2=0; - register double _s4=0; - register size_t count=0; - register char flag=0; - // LOOP along dimension DIM - - for (size_t j=0; j<Ni; j++, data += stride) - { - register double x = *data; - if (x==x) - { - count++; - _s0 += x; - x =x*x; - _s2 += x; - _s4 += x*x; - } - else - flag = 1; - } - - if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; - *s = _s0; - *s2 = _s2; - *s4 = _s4; - *No = count; -} - - -#ifdef experimental - /* x86 assembler code, currently broken, - the main advantage would be the support of the extended accuracy - */ - __asm__ (" movl $0, %eax;\n" - " fldz;\n" - " movl _D2, %ebx;\n" - " movl _ptr, %edx;\n" - "loop3: \n" - " fld (%%edx);\n" - " fcom (ST0);\n" - " jne end_isnan;" - " fadd (ST0)\n" - " inc %eax\n" - " \n" - "end_isnan: \n" - " fdecstp;\n" - " add %edx,_stride\n" - " loop loop3 %ebx;\n" - " fstp _LSum;\n" - " movl %eax, _LCount;\n" - " nop\n" - ); - // #__asm__ ("fldz " : : : "%eax"); -#endif - - This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-05-06 11:09:11
|
Revision: 5766 http://octave.svn.sourceforge.net/octave/?rev=5766&view=rev Author: schloegl Date: 2009-05-06 11:08:58 +0000 (Wed, 06 May 2009) Log Message: ----------- NaN-toolbox: support weighting of samples Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/covm.m trunk/octave-forge/extra/NaN/inst/sumskipnan.m trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp Modified: trunk/octave-forge/extra/NaN/inst/covm.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/covm.m 2009-05-05 13:45:56 UTC (rev 5765) +++ trunk/octave-forge/extra/NaN/inst/covm.m 2009-05-06 11:08:58 UTC (rev 5766) @@ -1,4 +1,4 @@ -function [CC,NN] = covm(X,Y,Mode); +function [CC,NN] = covm(X,Y,Mode,W); % COVM generates covariance matrix % X and Y can contain missing values encoded with NaN. % NaN's are skipped, NaN do not result in a NaN output. @@ -8,6 +8,8 @@ % calculates the (auto-)correlation matrix of X % COVM(X,Y,Mode); % calculates the crosscorrelation between X and Y +% COVM(...,W); +% weighted crosscorrelation % % Mode = 'M' minimum or standard mode [default] % C = X'*X; or X'*Y correlation matrix @@ -55,6 +57,7 @@ FLAG_NANS_OCCURED = logical(0); % default value end; +W = []; if nargin<3, if nargin==2, if isnumeric(Y), @@ -69,6 +72,16 @@ elseif nargin==0, error('Missing argument(s)'); end; + +elseif (nargin==3) && isnumeric(Mode) && ~isnumeric(Y); + W = Mode; + Mode = Y; + Y = 0; + +elseif (nargin==4) && ~isnumeric(Mode) && isnumeric(Y); + ; %% ok +else + error('invalid input arguments'); end; Mode = upper(Mode); @@ -88,19 +101,35 @@ warning('Covariance is ill-defined, because of too few observations (rows)'); end; +if ~isempty(W) + W = W(:); + if (r1~=numel(W)) + error('Error COVM: size of weight vector does not fit number of rows'); + end; + w = spdiags(W(:),0,numel(W),numel(W)); + nn = sum(W(:)); +else + w = 1; + nn = r1; +end; + if ~isempty(Y), if (~any(Mode=='D') & ~any(Mode=='E')), % if Mode == M - NN = real(X==X)'*real(Y==Y); + NN = (w*real(X==X))'*(w*real(Y==Y)); FLAG_NANS_OCCURED = any(NN(:)<r1); X(X~=X) = 0; % skip NaN's Y(Y~=Y) = 0; % skip NaN's - CC = X'*Y; + CC = (w*X)'*(w*Y); else % if any(Mode=='D') | any(Mode=='E'), - [S1,N1] = sumskipnan(X,1); - [S2,N2] = sumskipnan(Y,1); + %[S1,N1] = sumskipnan(X,1,W); + %[S2,N2] = sumskipnan(Y,1,W); + S1 = sumskipnan(w*X,1); + S2 = sumskipnan(w*Y,1); + N1 = sum(w*(~isnan(X)),1); + N2 = sum(w*(~isnan(Y)),1); - NN = real(X==X)'*real(Y==Y); - FLAG_NANS_OCCURED = any(NN(:)<r1); + NN = (w*real(X==X))'*(w*real(Y==Y)); + FLAG_NANS_OCCURED = any(NN(:)~=nn); if any(Mode=='D'), % detrending mode X = X - ones(r1,1)*(S1./N1); @@ -124,16 +153,19 @@ else if (~any(Mode=='D') & ~any(Mode=='E')), % if Mode == M - tmp = real(X==X); + tmp = w*real(X==X); NN = tmp'*tmp; X(X~=X) = 0; % skip NaN's - CC = X'*X; + tmp = w*X; + CC = tmp'*tmp; FLAG_NANS_OCCURED = any(NN(:)<r1); else % if any(Mode=='D') | any(Mode=='E'), - [S,N] = sumskipnan(X,1); - tmp = real(X==X); + %[S,N] = sumskipnan(X,1,W); + S = sumskipnan(w*X,1); + N = sum(w*real(~isnan(X)),1); + tmp = w*real(X==X); NN = tmp'*tmp; - FLAG_NANS_OCCURED = any(NN(:)<r1); + FLAG_NANS_OCCURED = any(NN(:)~=nn); if any(Mode=='D'), % detrending mode X = X - ones(r1,1)*(S./N); if any(Mode=='1'), % 'D1' @@ -144,7 +176,8 @@ end; X(X~=X) = 0; % skip NaN's - CC = X'*X; + tmp = w*X; + CC = tmp'*tmp; if any(Mode=='E'), % extended mode NN = [r1, N; N', NN]; Modified: trunk/octave-forge/extra/NaN/inst/sumskipnan.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/sumskipnan.m 2009-05-05 13:45:56 UTC (rev 5765) +++ trunk/octave-forge/extra/NaN/inst/sumskipnan.m 2009-05-06 11:08:58 UTC (rev 5766) @@ -1,4 +1,4 @@ -function [o,count,SSQ] = sumskipnan(x,DIM) +function [o,count,SSQ] = sumskipnan(x, DIM, W) % SUMSKIPNAN adds all non-NaN values. % % All NaN's are skipped; NaN's are considered as missing values. @@ -11,8 +11,12 @@ % % Y = sumskipnan(x [,DIM]) % [Y,N,SSQ] = sumskipnan(x [,DIM]) +% [...] = sumskipnan(x, DIM, W) % -% DIM dimension +% x input data +% DIM dimension (default: []) +% empty DIM sets DIM to first non singleton dimension +% W weight vector for weighted sum, numel(W) must fit size(x,DIM) % Y resulting sum % N number of valid (not missing) elements % SSQ sum of squares @@ -23,6 +27,7 @@ % features: % - can deal with NaN's (missing values) % - implements dimension argument. +% - computes weighted sum % - compatible with Matlab and Octave % % see also: FLAG_NANS_OCCURED, SUM, NANSUM, MEAN, STD, VAR, RMS, MEANSQ, @@ -31,7 +36,7 @@ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or +% the Free Software Foundation; either version 3 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, @@ -53,6 +58,9 @@ if nargin<2, DIM = []; end; +if nargin<3, + W = []; +end; % an efficient implementation in C of the following lines % could significantly increase performance @@ -75,11 +83,13 @@ end if (DIM<1) DIM = 1; end; %% Hack, because min([])=0 for FreeMat v3.5 - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % non-float data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -if ~isa(x,'float') || ~flag_implicit_skip_nan(), %%% skip always NaN's +if (isempty(W) && (~(isa(x,'float') || isa(x,'double')))) || ~flag_implicit_skip_nan(), %%% skip always NaN's + if ~isempty(W) + error('SUMSKIPNAN: weighted sum of integers not supported, yet'); + end; x = double(x); o = sum(x,DIM); if nargout>1 @@ -95,6 +105,10 @@ return; end; +if ~isempty(W) && (size(x,DIM)~=numel(W)) + error('SUMSKIPNAN: size of weight vector does not match size(x,DIM)'); +end; + %% mex and oct files expect double x = double(x); @@ -112,16 +126,16 @@ end; if (nargout<2), - o = sumskipnan_mex(real(x),DIM,FLAG_NANS_OCCURED); + o = sumskipnan_mex(real(x),DIM,FLAG_NANS_OCCURED,W); if (~isreal(x)) - io = sumskipnan_mex(imag(x),DIM,FLAG_NANS_OCCURED); + io = sumskipnan_mex(imag(x),DIM,FLAG_NANS_OCCURED,W); o = o + i*io; end; return; elseif (nargout==2), - [o,count] = sumskipnan_mex(real(x),DIM,FLAG_NANS_OCCURED); + [o,count] = sumskipnan_mex(real(x),DIM,FLAG_NANS_OCCURED,W); if (~isreal(x)) - [io,icount] = sumskipnan_mex(imag(x),DIM,FLAG_NANS_OCCURED); + [io,icount] = sumskipnan_mex(imag(x),DIM,FLAG_NANS_OCCURED,W); if any(count(:)-icount(:)) error('Number of NaNs differ for REAL and IMAG part'); else @@ -130,9 +144,9 @@ end; return; elseif (nargout>=3), - [o,count,SSQ] = sumskipnan_mex(real(x),DIM,FLAG_NANS_OCCURED); + [o,count,SSQ] = sumskipnan_mex(real(x),DIM,FLAG_NANS_OCCURED,W); if (~isreal(x)) - [io,icount,iSSQ] = sumskipnan_mex(imag(x),DIM,FLAG_NANS_OCCURED); + [io,icount,iSSQ] = sumskipnan_mex(imag(x),DIM,FLAG_NANS_OCCURED,W); if any(count(:)-icount(:)) error('Number of NaNs differ for REAL and IMAG part'); else @@ -145,6 +159,10 @@ end; +if ~isempty(W) + error('weighted sumskipnan requires sumskipnan_mex'); +end; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % count non-NaN's %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Modified: trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-05-05 13:45:56 UTC (rev 5765) +++ trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-05-06 11:08:58 UTC (rev 5766) @@ -23,6 +23,8 @@ // Input: // - array to sum // - dimension to sum +// - flag (is actually an output argument telling whether some NaN was observed) +// - weight vector to compute weighted sum // // Output: // - sums @@ -44,6 +46,9 @@ inline int __sumskipnan2__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN); inline int __sumskipnan3__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN); +inline int __sumskipnan2w__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); +inline int __sumskipnan3w__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W); + //#define NO_FLAG void mexFunction(int POutputCount, mxArray* POutput[], int PInputCount, const mxArray *PInputs[]) @@ -54,6 +59,7 @@ double* LOutputCount; double* LOutputSum2; double x; + double* W = NULL; // weight vector //unsigned long LCount; mwSize DIM = 0; @@ -65,8 +71,8 @@ char flag_isNaN = 0; // check for proper number of input and output arguments - if ((PInputCount <= 0) || (PInputCount > 3)) - mexErrMsgTxt("SumSkipNan.MEX requires 1,2 or 3 arguments."); + if ((PInputCount <= 0) || (PInputCount > 4)) + mexErrMsgTxt("SumSkipNan.MEX requires between 1 and 4 arguments."); if (POutputCount > 4) mexErrMsgTxt("SumSkipNan.MEX has 1 to 3 output arguments."); @@ -117,6 +123,16 @@ SZ2[DIM-1] = 1; // size of output is same as size of input but SZ(DIM)=1; + // get weight vector for weighted sumskipnan + if (PInputCount > 3) { + if (!mxGetNumberOfElements(PInputs[3])) + ; // empty weight vector - no weighting + else if (mxGetNumberOfElements(PInputs[3])==D2) + W = mxGetPr(PInputs[3]); + else + mexErrMsgTxt("Error SUMSKIPNAN.MEX: length of weight vector does not match size of dimension"); + } + // create outputs #define TYP mxDOUBLE_CLASS @@ -142,15 +158,30 @@ if (D1==1) { double count; - __sumskipnan2__(LInput+ix1, D2, LOutputSum+ix0, &count, &flag_isNaN); + if (W) + __sumskipnan2w__(LInput+ix1, D2, LOutputSum+ix0, &count, &flag_isNaN, W); + else + __sumskipnan2__(LInput+ix1, D2, LOutputSum+ix0, &count, &flag_isNaN); } else for (j=0; j<D2; j++) { // minimize cache misses ix2 = ix0; // index for output // Inner LOOP: along dimensions < DIM - do { + if (W) do { register double x = *LInput; if (!isnan(x)) { + LOutputSum[ix2] += W[j]*x; + } +#ifndef NO_FLAG + else + flag_isNaN = 1; +#endif + LInput++; + ix2++; + } while (ix2 != (l+1)*D1); + else do { + register double x = *LInput; + if (!isnan(x)) { LOutputSum[ix2] += x; } #ifndef NO_FLAG @@ -171,15 +202,31 @@ ix1 = ix0*D2; // index for input if (D1==1) { - __sumskipnan2__(LInput+ix1, D2, LOutputSum+ix0, LOutputCount+ix0, &flag_isNaN); + if (W) + __sumskipnan2w__(LInput+ix1, D2, LOutputSum+ix0, LOutputCount+ix0, &flag_isNaN, W); + else + __sumskipnan2__(LInput+ix1, D2, LOutputSum+ix0, LOutputCount+ix0, &flag_isNaN); } else for (j=0; j<D2; j++) { // minimize cache misses ix2 = ix0; // index for output // Inner LOOP: along dimensions < DIM - do { + if (W) do { register double x = *LInput; if (!isnan(x)) { + LOutputCount[ix2] += W[j]; + LOutputSum[ix2] += W[j]*x; + } +#ifndef NO_FLAG + else + flag_isNaN = 1; +#endif + LInput++; + ix2++; + } while (ix2 != (l+1)*D1); + else do { + register double x = *LInput; + if (!isnan(x)) { LOutputCount[ix2] += 1.0; LOutputSum[ix2] += x; } @@ -202,15 +249,33 @@ if (D1==1) { size_t count; - __sumskipnan3__(LInput+ix1, D2, LOutputSum+ix0, LOutputSum2+ix0, LOutputCount+ix0, &flag_isNaN); + if (W) + __sumskipnan3w__(LInput+ix1, D2, LOutputSum+ix0, LOutputSum2+ix0, LOutputCount+ix0, &flag_isNaN, W); + else + __sumskipnan3__(LInput+ix1, D2, LOutputSum+ix0, LOutputSum2+ix0, LOutputCount+ix0, &flag_isNaN); } else for (j=0; j<D2; j++) { // minimize cache misses ix2 = ix0; // index for output // Inner LOOP: along dimensions < DIM - do { + if (W) do { register double x = *LInput; if (!isnan(x)) { + LOutputCount[ix2] += W[j]; + double t = W[j]*x; + LOutputSum[ix2] += t; + LOutputSum2[ix2] += t*t; + } +#ifndef NO_FLAG + else + flag_isNaN = 1; +#endif + LInput++; + ix2++; + } while (ix2 != (l+1)*D1); + else do { + register double x = *LInput; + if (!isnan(x)) { LOutputCount[ix2] += 1.0; LOutputSum[ix2] += x; LOutputSum2[ix2] += x*x; @@ -331,3 +396,72 @@ *No = (double)count; } +#define stride 1 +inline int __sumskipnan2w__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W) +{ + register double sum=0; + register double count=0; + register char flag=0; + // LOOP along dimension DIM + + void *end = data + stride*Ni; + do { + register double x = *data; + if (!isnan(x)) + { + count += *W; + sum += *W*x; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + + data +=stride; + W++; + } + while (data < end); + +#ifndef NO_FLAG + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; +#endif + *s = sum; + *No = count; + +} + +inline int __sumskipnan3w__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W) +{ + register double sum=0; + register double msq=0; + register double count=0; + register char flag=0; + // LOOP along dimension DIM + + void *end = data + stride*Ni; + do { + register double x = *data; + if (!isnan(x)) { + count += *W; + double t = *W*x; + sum += t; + msq += t*t; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + + data++; // stride=1 + W++; + } + while (data < end); + +#ifndef NO_FLAG + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; +#endif + *s = sum; + *s2 = msq; + *No = count; +} + This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-05-06 16:21:17
|
Revision: 5772 http://octave.svn.sourceforge.net/octave/?rev=5772&view=rev Author: schloegl Date: 2009-05-06 16:21:10 +0000 (Wed, 06 May 2009) Log Message: ----------- NaN-tb: mex-version of covm added Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/covm.m Added Paths: ----------- trunk/octave-forge/extra/NaN/src/covm_mex.cpp Modified: trunk/octave-forge/extra/NaN/inst/covm.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/covm.m 2009-05-06 16:09:52 UTC (rev 5771) +++ trunk/octave-forge/extra/NaN/inst/covm.m 2009-05-06 16:21:10 UTC (rev 5772) @@ -73,7 +73,10 @@ error('Missing argument(s)'); end; -elseif (nargin==3) && isnumeric(Mode) && ~isnumeric(Y); +elseif (nargin==3) && isnumeric(Y) && ~isnumeric(Mode); + W = []; + +elseif (nargin==3) && ~isnumeric(Y) && isnumeric(Mode); W = Mode; Mode = Y; Y = []; @@ -100,87 +103,143 @@ warning('Covariance is ill-defined, because of too few observations (rows)'); end; +mexFLAG2 = exist('covm_mex','file'); +mexFLAG = exist('sumskipnan_mex','file'); if ~isempty(W) W = W(:); if (r1~=numel(W)) error('Error COVM: size of weight vector does not fit number of rows'); end; - w = spdiags(W(:),0,numel(W),numel(W)); - nn = sum(W(:)); + %w = spdiags(W(:),0,numel(W),numel(W)); + %nn = sum(W(:)); + if ~mexFLAG && ~mexFLAG2 + error('Error COVM: weighted COVM requires sumskipnan_mex but it is not available'); + end; + nn = sum(W); else - w = 1; nn = r1; end; + if ~isempty(Y), if (~any(Mode=='D') & ~any(Mode=='E')), % if Mode == M - NN = (w*real(X==X))'*(w*real(Y==Y)); - FLAG_NANS_OCCURED = any(NN(:)<r1); - X(X~=X) = 0; % skip NaN's - Y(Y~=Y) = 0; % skip NaN's - CC = (w*X)'*(w*Y); + if mexFLAG2, + [CC,NN] = covm_mex(X,Y,FLAG_NANS_OCCURED,W); + elseif mexFLAG, + [ix,iy] = meshgrid(1:c2,1:c1); + [CC,NN] = sumskipnan_mex(X(:,iy).*Y(:,ix),1,FLAG_NANS_OCCURED,W); + CC = reshape(CC,c1,c2); + NN = reshape(NN,c1,c2); + else + NN = real(X==X)'*real(Y==Y); + FLAG_NANS_OCCURED = any(NN(:)<nn); + X(X~=X) = 0; % skip NaN's + Y(Y~=Y) = 0; % skip NaN's + CC = X'*Y; + end; + else % if any(Mode=='D') | any(Mode=='E'), - %[S1,N1] = sumskipnan(X,1,W); - %[S2,N2] = sumskipnan(Y,1,W); - S1 = sumskipnan(w*X,1); - S2 = sumskipnan(w*Y,1); - N1 = sum(w*(~isnan(X)),1); - N2 = sum(w*(~isnan(Y)),1); - - NN = (w*real(X==X))'*(w*real(Y==Y)); - FLAG_NANS_OCCURED = any(NN(:)~=nn); - - if any(Mode=='D'), % detrending mode - X = X - ones(r1,1)*(S1./N1); - Y = Y - ones(r1,1)*(S2./N2); + if mexFLAG, + [S1,N1] = sumskipnan_mex(X,1,FLAG_NANS_OCCURED,W); + [S2,N2] = sumskipnan_mex(Y,1,FLAG_NANS_OCCURED,W); + + if any(Mode=='D'), % detrending mode + X = X - ones(r1,1)*(S1./N1); + Y = Y - ones(r1,1)*(S2./N2); + end; + if mexFLAG2, + [CC,NN] = covm_mex(X,Y,FLAG_NANS_OCCURED,W); + else + [ix,iy] = meshgrid(1:c1,1:c1); + [CC,NN] = sumskipnan_mex(X(:,iy).*X(:,ix),1,FLAG_NANS_OCCURED,W); + CC = reshape(CC,c1,c1); + NN = reshape(NN,c1,c1); + end; if any(Mode=='1'), % 'D1' NN = NN; else % 'D0' NN = max(NN-1,0); end; - end; + else + [S1,N1] = sumskipnan(X,1); + [S2,N2] = sumskipnan(Y,1); + NN = real(X==X)'*real(Y==Y); - X(X~=X) = 0; % skip NaN's - Y(Y~=Y) = 0; % skip NaN's - CC = X'*Y; + if any(Mode=='D'), % detrending mode + X = X - ones(r1,1)*(S1./N1); + Y = Y - ones(r1,1)*(S2./N2); + if any(Mode=='1'), % 'D1' + NN = NN; + else % 'D0' + NN = max(NN-1,0); + end; + end; + X(X~=X) = 0; % skip NaN's + Y(Y~=Y) = 0; % skip NaN's + CC = X'*Y; + end; if any(Mode=='E'), % extended mode - NN = [r1, N2; N1', NN]; - CC = [r1, S2; S1', CC]; + NN = [nn, N2; N1', NN]; + CC = [nn, S2; S1', CC]; end; end; else if (~any(Mode=='D') & ~any(Mode=='E')), % if Mode == M - tmp = w*real(X==X); - NN = tmp'*tmp; - X(X~=X) = 0; % skip NaN's - tmp = w*X; - CC = tmp'*tmp; - FLAG_NANS_OCCURED = any(NN(:)<r1); + if mexFLAG2, + [CC,NN] = covm_mex(X,[],1,FLAG_NANS_OCCURED,W); + elseif mexFLAG, + [ix,iy] = meshgrid(1:c1,1:c1); + [CC,NN] = sumskipnan_mex(X(:,iy).*X(:,ix),1,FLAG_NANS_OCCURED,W); + CC = reshape(CC,c1,c1); + NN = reshape(NN,c1,c1); + else + tmp = real(X==X); + NN = tmp'*tmp; + X(X~=X) = 0; % skip NaN's + CC = X'*X; + FLAG_NANS_OCCURED = any(NN(:)<nn); + end; else % if any(Mode=='D') | any(Mode=='E'), - %[S,N] = sumskipnan(X,1,W); - S = sumskipnan(w*X,1); - N = sum(w*real(~isnan(X)),1); - tmp = w*real(X==X); - NN = tmp'*tmp; - FLAG_NANS_OCCURED = any(NN(:)~=nn); - if any(Mode=='D'), % detrending mode - X = X - ones(r1,1)*(S./N); - if any(Mode=='1'), % 'D1' - NN = NN; + if mexFLAG, + [S,N] = sumskipnan_mex(X,1,FLAG_NANS_OCCURED,W); + + if any(Mode=='D'), % detrending mode + X = X - ones(r1,1)*(S./N); + end; + if mexFLAG2 + [CC,NN] = covm_mex(X,[],FLAG_NANS_OCCURED,W); + else + [ix,iy] = meshgrid(1:c1,1:c1); + [CC,NN] = sumskipnan_mex(X(:,iy).*X(:,ix),1,FLAG_NANS_OCCURED,W); + CC = reshape(CC,c1,c1); + NN = reshape(NN,c1,c1); + end; + if any(Mode=='1'), % 'D1' + NN = NN; else % 'D0' - NN = max(NN-1,0); - end; - end; + NN = max(NN-1,0); + end; + else + [S,N] = sumskipnan(X,1); + tmp = real(X==X); + NN = tmp'*tmp; + if any(Mode=='D'), % detrending mode + X = X - ones(r1,1)*(S./N); + if any(Mode=='1'), % 'D1' + NN = NN; + else % 'D0' + NN = max(NN-1,0); + end; + end; - X(X~=X) = 0; % skip NaN's - tmp = w*X; - CC = tmp'*tmp; - + X(X~=X) = 0; % skip NaN's + CC = X'*X; + end; if any(Mode=='E'), % extended mode - NN = [r1, N; N', NN]; - CC = [r1, S; S', CC]; + NN = [nn, N; N', NN]; + CC = [nn, S; S', CC]; end; end end; Added: trunk/octave-forge/extra/NaN/src/covm_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/covm_mex.cpp (rev 0) +++ trunk/octave-forge/extra/NaN/src/covm_mex.cpp 2009-05-06 16:21:10 UTC (rev 5772) @@ -0,0 +1,221 @@ +//------------------------------------------------------------------- +#pragma hdrstop +//------------------------------------------------------------------- +// C-MEX implementation of COVM - this function is part of the NaN-toolbox. +// +// +// This program is free software; you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation; either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program; if not, see <http://www.gnu.org/licenses/>. +// +// +// sumskipnan: sums all non-NaN values +// +// Input: +// - X +// - Y [optional] +// - flag (is actually an output argument telling whether some NaN was observed) +// - weight vector to compute weighted correlation +// +// Output: +// - crosscorrelation +// - count of valid elements (optional) +// +// +// $Id$ +// Copyright (C) 2009 Alois Schloegl <a.s...@ie...> +// This function is part of the NaN-toolbox +// http://hci.tugraz.at/~schloegl/matlab/NaN/ +// +//------------------------------------------------------------------- + +#include <inttypes.h> +#include <math.h> +#include "mex.h" + +//#define NO_FLAG + +void mexFunction(int POutputCount, mxArray* POutput[], int PInputCount, const mxArray *PInputs[]) +{ + const mwSize *SZ; + double *X0,*Y0,*X,*Y,*W=NULL; + double *CC; + double *NN; + double* LOutputSum2; + double x; + //unsigned long LCount; + + mwSize rX,cX,rY,cY,nW=0; + mwSize i,j,k,l; // running indices + char flag_isNaN = 0; + + // check for proper number of input and output arguments + if ((PInputCount <= 0) || (PInputCount > 4)) + mexErrMsgTxt("covm.MEX requires between 1 and 4 arguments."); + if (POutputCount > 2) + mexErrMsgTxt("covm.MEX has 1 to 2 output arguments."); + + // get 1st argument + if(mxIsDouble(PInputs[0]) && !mxIsComplex(PInputs[0])) + X0 = mxGetPr(PInputs[0]); + else + mexErrMsgTxt("First argument must be REAL/DOUBLE."); + rX = mxGetM(PInputs[0]); + cX = mxGetN(PInputs[0]); + + // get 2nd argument + if (PInputCount > 1) { + if (!mxGetNumberOfElements(PInputs[1])) + Y0 = NULL; + + else if (mxIsDouble(PInputs[1]) && !mxIsComplex(PInputs[1])) + Y0 = mxGetPr(PInputs[1]); + + else + mexErrMsgTxt("Second argument must be REAL/DOUBLE."); + } + else + Y0 = NULL; + + + // get weight vector for weighted sumskipnan + if (PInputCount > 3) { + // get 4th argument + nW = mxGetNumberOfElements(PInputs[3]); + if (!nW) + ; + else if (nW == rX) + W = mxGetPr(PInputs[3]); + else + mexErrMsgTxt("number of elements in W must match numbers of rows in X"); + + } + + if (!Y0) { + Y0 = X0; + rY = rX; + cY = cX; + } + else { + rY = mxGetM(PInputs[1]); + cY = mxGetN(PInputs[1]); + } + + // create outputs + + POutput[0] = mxCreateDoubleMatrix(cX, cY, mxREAL); + CC = mxGetPr(POutput[0]); + + if (POutputCount > 1) { + POutput[1] = mxCreateDoubleMatrix(cX, cY, mxREAL); + NN = mxGetPr(POutput[1]); + } + + /* TODO: + this is a very basic algorithm, + it's likely it can be improved for speed by a different memory access sequence + */ + + if (W) + for (i=0; i<cX; i++) + for (j=0; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; + register double cc=0.0; + register double nn=0.0; + for (k=0; k<rX; k++) { + double x = X[k]; + double y = Y[k]; + + if (!isnan(x) && !isnan(y)) + { + cc += x*y*W[k]; + nn += W[k]; + } +#ifndef NO_FLAG + else + flag_isNaN = 1; +#endif + } + CC[i+j*cX] = cc; + if (POutputCount > 1) + NN[i+j*cX] = nn; + } + else for (i=0; i<cX; i++) + for (j=0; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; + register double cc=0.0; + register mwSize nn=0.0; + for (k=0; k<rX; k++) { + double x = X[k]; + double y = Y[k]; + + + if (!isnan(x) && !isnan(y)) + { + cc += x*y; + nn++; + } +#ifndef NO_FLAG + else + flag_isNaN = 1; +#endif + } + CC[i+j*cX] = cc; + if (POutputCount > 1) + NN[i+j*cX] = (double)nn; + } + + +#ifndef NO_FLAG + //mexPrintf("Third argument must be not empty - otherwise status whether a NaN occured or not cannot be returned."); + /* this is a hack, the third input argument is used to return whether a NaN occured or not. + this requires that the input argument is a non-empty variable + */ + if (flag_isNaN && (PInputCount > 2) && mxGetNumberOfElements(PInputs[2])) { + // set FLAG_NANS_OCCURED + switch (mxGetClassID(PInputs[2])) { + case mxLOGICAL_CLASS: + case mxCHAR_CLASS: + case mxINT8_CLASS: + case mxUINT8_CLASS: + *(uint8_t*)mxGetData(PInputs[2]) = 1; + break; + case mxDOUBLE_CLASS: + *(double*)mxGetData(PInputs[2]) = 1.0; + break; + case mxSINGLE_CLASS: + *(float*)mxGetData(PInputs[2]) = 1.0; + break; + case mxINT16_CLASS: + case mxUINT16_CLASS: + *(uint16_t*)mxGetData(PInputs[2]) = 1; + break; + case mxINT32_CLASS: + case mxUINT32_CLASS: + *(uint32_t*)mxGetData(PInputs[2])= 1; + break; + case mxINT64_CLASS: + case mxUINT64_CLASS: + *(uint64_t*)mxGetData(PInputs[2]) = 1; + break; + case mxFUNCTION_CLASS: + case mxUNKNOWN_CLASS: + case mxCELL_CLASS: + case mxSTRUCT_CLASS: + mexPrintf("Type of 3rd input argument not supported."); + } + } +#endif +} + This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-05-07 09:33:49
|
Revision: 5779 http://octave.svn.sourceforge.net/octave/?rev=5779&view=rev Author: schloegl Date: 2009-05-07 09:33:46 +0000 (Thu, 07 May 2009) Log Message: ----------- NaN-tb: performance test of alternative algorithm, and optimization for speed; support complex matrices; bug fix in sumskipnan_mex Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/covm.m trunk/octave-forge/extra/NaN/src/covm_mex.cpp trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp Modified: trunk/octave-forge/extra/NaN/inst/covm.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/covm.m 2009-05-06 20:08:25 UTC (rev 5778) +++ trunk/octave-forge/extra/NaN/inst/covm.m 2009-05-07 09:33:46 UTC (rev 5779) @@ -53,9 +53,6 @@ global FLAG_NANS_OCCURED; -if isempty(FLAG_NANS_OCCURED), - FLAG_NANS_OCCURED = logical(0); % default value -end; W = []; if nargin<3, @@ -104,7 +101,7 @@ end; mexFLAG2 = exist('covm_mex','file'); -mexFLAG = exist('sumskipnan_mex','file'); +mexFLAG = exist('sumskipnan_mex','file'); if ~isempty(W) W = W(:); if (r1~=numel(W)) @@ -112,73 +109,112 @@ end; %w = spdiags(W(:),0,numel(W),numel(W)); %nn = sum(W(:)); - if ~mexFLAG && ~mexFLAG2 - error('Error COVM: weighted COVM requires sumskipnan_mex but it is not available'); - end; nn = sum(W); else nn = r1; end; + +if mexFLAG2 && mexFLAG && ~isempty(W), + %% the mex-functions here are much slower than the m-scripts below + %% however, the mex-functions support weighting of samples. + if isempty(FLAG_NANS_OCCURED), + %% mex-files require that FLAG_NANS_OCCURED is not empty, + %% otherwise, the status of NAN occurence can not be returned. + FLAG_NANS_OCCURED = logical(0); % default value + end; + + if (~any(Mode=='D') & ~any(Mode=='E')), % if Mode == M + [CC,NN] = covm_mex(real(X),real(Y),FLAG_NANS_OCCURED,W); + %% complex matrices + if ~isreal(X) && ~isreal(Y) + [iCC,inn] = covm_mex(imag(X),imag(Y),FLAG_NANS_OCCURED,W); + CC = CC + iCC; + end; + if isempty(Y) Y = X; end; + if ~isreal(X) + [iCC,inn] = covm_mex(imag(X),real(Y),FLAG_NANS_OCCURED,W); + CC = CC - i*iCC; + end; + if ~isreal(Y) + [iCC,inn] = covm_mex(real(X),imag(Y),FLAG_NANS_OCCURED,W); + CC = CC + i*iCC; + end; + + else % if any(Mode=='D') | any(Mode=='E'), + [S1,N1] = sumskipnan(X,1,W); + if ~isempty(Y) + [S2,N2] = sumskipnan(Y,1,W); + else + S2 = S1; N2 = N1; + end; + if any(Mode=='D'), % detrending mode + X = X - ones(r1,1)*(S1./N1); + if ~isempty(Y) + Y = Y - ones(r1,1)*(S2./N2); + end; + end; + [CC,NN] = covm_mex(real(X), real(Y), FLAG_NANS_OCCURED, W); + %% complex matrices + if ~isreal(X) && ~isreal(Y) + [iCC,inn] = covm_mex(imag(X),imag(Y),FLAG_NANS_OCCURED,W); + CC = CC + iCC; + end; + if isempty(Y) Y = X; end; + if ~isreal(X) + [iCC,inn] = covm_mex(imag(X),real(Y),FLAG_NANS_OCCURED,W); + CC = CC - i*iCC; + end; + if ~isreal(Y) + [iCC,inn] = covm_mex(real(X),imag(Y),FLAG_NANS_OCCURED,W); + CC = CC + i*iCC; + end; -if ~isempty(Y), + if any(Mode=='1'), % 'D1' + NN = NN; + else % 'D0' + NN = max(NN-1,0); + end; + if any(Mode=='E'), % extended mode + NN = [nn, N2; N1', NN]; + CC = [nn, S2; S1', CC]; + end; + end; + + +elseif ~isempty(W), + + error('Error COVM: weighted COVM requires sumskipnan_mex and covm_mex but it is not available'); + + %% weighted covm without mex-file support + %% this part is not working. + +elseif ~isempty(Y), if (~any(Mode=='D') & ~any(Mode=='E')), % if Mode == M - if mexFLAG2, - [CC,NN] = covm_mex(X,Y,FLAG_NANS_OCCURED,W); - elseif mexFLAG, - [ix,iy] = meshgrid(1:c2,1:c1); - [CC,NN] = sumskipnan_mex(X(:,iy).*Y(:,ix),1,FLAG_NANS_OCCURED,W); - CC = reshape(CC,c1,c2); - NN = reshape(NN,c1,c2); - else - NN = real(X==X)'*real(Y==Y); - FLAG_NANS_OCCURED = any(NN(:)<nn); - X(X~=X) = 0; % skip NaN's - Y(Y~=Y) = 0; % skip NaN's - CC = X'*Y; - end; + NN = real(X==X)'*real(Y==Y); + FLAG_NANS_OCCURED = any(NN(:)<nn); + X(X~=X) = 0; % skip NaN's + Y(Y~=Y) = 0; % skip NaN's + CC = X'*Y; else % if any(Mode=='D') | any(Mode=='E'), - if mexFLAG, - [S1,N1] = sumskipnan_mex(X,1,FLAG_NANS_OCCURED,W); - [S2,N2] = sumskipnan_mex(Y,1,FLAG_NANS_OCCURED,W); - - if any(Mode=='D'), % detrending mode - X = X - ones(r1,1)*(S1./N1); - Y = Y - ones(r1,1)*(S2./N2); - end; - if mexFLAG2, - [CC,NN] = covm_mex(X,Y,FLAG_NANS_OCCURED,W); - else - [ix,iy] = meshgrid(1:c1,1:c1); - [CC,NN] = sumskipnan_mex(X(:,iy).*X(:,ix),1,FLAG_NANS_OCCURED,W); - CC = reshape(CC,c1,c1); - NN = reshape(NN,c1,c1); - end; - if any(Mode=='1'), % 'D1' - NN = NN; - else % 'D0' - NN = max(NN-1,0); + [S1,N1] = sumskipnan(X,1); + [S2,N2] = sumskipnan(Y,1); + NN = real(X==X)'*real(Y==Y); + + if any(Mode=='D'), % detrending mode + X = X - ones(r1,1)*(S1./N1); + Y = Y - ones(r1,1)*(S2./N2); + if any(Mode=='1'), % 'D1' + NN = NN; + else % 'D0' + NN = max(NN-1,0); end; - else - [S1,N1] = sumskipnan(X,1); - [S2,N2] = sumskipnan(Y,1); - NN = real(X==X)'*real(Y==Y); + end; + X(X~=X) = 0; % skip NaN's + Y(Y~=Y) = 0; % skip NaN's + CC = X'*Y; - if any(Mode=='D'), % detrending mode - X = X - ones(r1,1)*(S1./N1); - Y = Y - ones(r1,1)*(S2./N2); - if any(Mode=='1'), % 'D1' - NN = NN; - else % 'D0' - NN = max(NN-1,0); - end; - end; - X(X~=X) = 0; % skip NaN's - Y(Y~=Y) = 0; % skip NaN's - CC = X'*Y; - end; - if any(Mode=='E'), % extended mode NN = [nn, N2; N1', NN]; CC = [nn, S2; S1', CC]; @@ -187,64 +223,38 @@ else if (~any(Mode=='D') & ~any(Mode=='E')), % if Mode == M - if mexFLAG2, - [CC,NN] = covm_mex(X,[],1,FLAG_NANS_OCCURED,W); - elseif mexFLAG, - [ix,iy] = meshgrid(1:c1,1:c1); - [CC,NN] = sumskipnan_mex(X(:,iy).*X(:,ix),1,FLAG_NANS_OCCURED,W); - CC = reshape(CC,c1,c1); - NN = reshape(NN,c1,c1); - else - tmp = real(X==X); - NN = tmp'*tmp; - X(X~=X) = 0; % skip NaN's - CC = X'*X; - FLAG_NANS_OCCURED = any(NN(:)<nn); - end; + tmp = real(X==X); + NN = tmp'*tmp; + X(X~=X) = 0; % skip NaN's + CC = X'*X; + FLAG_NANS_OCCURED = any(NN(:)<nn); + else % if any(Mode=='D') | any(Mode=='E'), - if mexFLAG, - [S,N] = sumskipnan_mex(X,1,FLAG_NANS_OCCURED,W); - - if any(Mode=='D'), % detrending mode - X = X - ones(r1,1)*(S./N); - end; - if mexFLAG2 - [CC,NN] = covm_mex(X,[],FLAG_NANS_OCCURED,W); - else - [ix,iy] = meshgrid(1:c1,1:c1); - [CC,NN] = sumskipnan_mex(X(:,iy).*X(:,ix),1,FLAG_NANS_OCCURED,W); - CC = reshape(CC,c1,c1); - NN = reshape(NN,c1,c1); - end; + [S,N] = sumskipnan(X,1); + tmp = real(X==X); + NN = tmp'*tmp; + if any(Mode=='D'), % detrending mode + X = X - ones(r1,1)*(S./N); if any(Mode=='1'), % 'D1' NN = NN; else % 'D0' NN = max(NN-1,0); end; - else - [S,N] = sumskipnan(X,1); - tmp = real(X==X); - NN = tmp'*tmp; - if any(Mode=='D'), % detrending mode - X = X - ones(r1,1)*(S./N); - if any(Mode=='1'), % 'D1' - NN = NN; - else % 'D0' - NN = max(NN-1,0); - end; - end; + end; - X(X~=X) = 0; % skip NaN's - CC = X'*X; - end; + X(X~=X) = 0; % skip NaN's + CC = X'*X; if any(Mode=='E'), % extended mode NN = [nn, N; N', NN]; CC = [nn, S; S', CC]; end; end + end; + if nargout<2 CC = CC./NN; % unbiased end; + return; Modified: trunk/octave-forge/extra/NaN/src/covm_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/covm_mex.cpp 2009-05-06 20:08:25 UTC (rev 5778) +++ trunk/octave-forge/extra/NaN/src/covm_mex.cpp 2009-05-07 09:33:46 UTC (rev 5779) @@ -64,6 +64,11 @@ if (POutputCount > 2) mexErrMsgTxt("covm.MEX has 1 to 2 output arguments."); +/* TODO: + support for complex matrices +*/ + + // get 1st argument if(mxIsDouble(PInputs[0]) && !mxIsComplex(PInputs[0])) X0 = mxGetPr(PInputs[0]); @@ -109,6 +114,8 @@ rY = mxGetM(PInputs[1]); cY = mxGetN(PInputs[1]); } + if (rX != rY) + mexErrMsgTxt("number of rows in X and Y do not match"); // create outputs @@ -120,12 +127,40 @@ NN = mxGetPr(POutput[1]); } - /* TODO: - this is a very basic algorithm, - it's likely it can be improved for speed by a different memory access sequence - */ - - if (W) +#if 0 + /* this solution is slower than the alternative solution below + for transposed matrices, this might be faster. + */ + for (k=0; k<rX; k++) { + double w; + if (W) w = W[k]; + else w = 1.0; + for (i=0; i<cX; i++) { + double x = X0[k+i*rX]; + if (isnan(x)) { +#ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + for (j=0; j<cY; j++) { + double y = Y0[k+j*rY]; + if (isnan(y)) { +#ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + CC[i+j*cX] += x*y*w; + if (POutputCount > 1) + NN[i+j*cX] += w; + } + } + } + +#else + // this version is faster than the one above. + if (W) /* weighted version */ for (i=0; i<cX; i++) for (j=0; j<cY; j++) { X = X0+i*rX; @@ -150,7 +185,8 @@ if (POutputCount > 1) NN[i+j*cX] = nn; } - else for (i=0; i<cX; i++) + else /* no weights, all weights are 1 */ + for (i=0; i<cX; i++) for (j=0; j<cY; j++) { X = X0+i*rX; Y = Y0+j*rY; @@ -160,7 +196,6 @@ double x = X[k]; double y = Y[k]; - if (!isnan(x) && !isnan(y)) { cc += x*y; @@ -213,9 +248,10 @@ case mxUNKNOWN_CLASS: case mxCELL_CLASS: case mxSTRUCT_CLASS: - mexPrintf("Type of 3rd input argument not supported."); + mexPrintf("Type of 3rd input argument cannot be used to return status of NaN occurence."); } } #endif +#endif } Modified: trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-05-06 20:08:25 UTC (rev 5778) +++ trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-05-07 09:33:46 UTC (rev 5779) @@ -266,7 +266,7 @@ LOutputCount[ix2] += W[j]; double t = W[j]*x; LOutputSum[ix2] += t; - LOutputSum2[ix2] += t*t; + LOutputSum2[ix2] += x*t; } #ifndef NO_FLAG else @@ -451,7 +451,7 @@ count += *W; double t = *W*x; sum += t; - msq += t*t; + msq += x*t; } #ifndef NO_FLAG else This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-05-12 11:33:16
|
Revision: 5807 http://octave.svn.sourceforge.net/octave/?rev=5807&view=rev Author: schloegl Date: 2009-05-12 11:32:58 +0000 (Tue, 12 May 2009) Log Message: ----------- NaN: weightening of data; fix support for FreeMat v3.6 Modified Paths: -------------- trunk/octave-forge/extra/NaN/doc/README.TXT trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m trunk/octave-forge/extra/NaN/inst/rms.m trunk/octave-forge/extra/NaN/inst/sem.m trunk/octave-forge/extra/NaN/inst/std.m trunk/octave-forge/extra/NaN/inst/var.m Property Changed: ---------------- trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m Modified: trunk/octave-forge/extra/NaN/doc/README.TXT =================================================================== --- trunk/octave-forge/extra/NaN/doc/README.TXT 2009-05-12 10:55:57 UTC (rev 5806) +++ trunk/octave-forge/extra/NaN/doc/README.TXT 2009-05-12 11:32:58 UTC (rev 5807) @@ -5,16 +5,16 @@ FEATURES of the NaN-tb: ----------------------- - - implements statistical tools + - statistical toolbox - NaN's are treated as missing values + - supports DIM argument + - supports unbiased estimation + - supports weightening of data - less but more powerful functions (no nan-FUN needed) - fixes known bugs - compatible to Matlab and Octave - easy to use - - supports DIM argument - - supports unbiased estimation; - - The toolbox was tested with - Matlab 5.2, 5.3, 6.1, 6.5, 7.0, 7.6 and Octave 2.1.x, 2.9.x, 3.0.1, 3.1.51+ + - The toolbox is tested with Octave 3.x and Matlab 7.x, FreeMat v3.6 Currently are implemented: @@ -144,7 +144,7 @@ Permits to implement useful modifications. 12) NORMPDF, NORMCDF, NORMINV -In the Matlab statistics toolbox V 3.0, NORMPDF, NORMCDF and NORMINV give +In the Matlab statistics toolbox V 3.0, NORMPDF, NORMCDF and NORMINV gave incorrect results for SIGMA=0; A Similar problem was observed in Octave with NORMAL_INV, NORMAL_PDF, and NORMALCDF. @@ -237,7 +237,7 @@ LICENSE: This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by - the Free Software Foundation; either version 2 of the License, or + the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, Modified: trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m 2009-05-12 10:55:57 UTC (rev 5806) +++ trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m 2009-05-12 11:32:58 UTC (rev 5807) @@ -41,7 +41,7 @@ % along with this program; if not, write to the Free Software % Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA -% $Id: flag_implicit_skip_nan.m,v 1.8 2003/11/05 10:40:21 schloegl dead $ +% $Id$ % Copyright (C) 2001-2003,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox % http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ @@ -49,6 +49,8 @@ global FLAG_implicit_skip_nan; +if strcmp(version,'3.6'), FLAG_implicit_skip_nan=(1==1); end; %% hack for the use with Freemat3.6 + %%% set DEFAULT value of FLAG if isempty(FLAG_implicit_skip_nan), FLAG_implicit_skip_nan = (1==1); %logical(1); % logical.m not available on 2.0.16 @@ -60,5 +62,5 @@ if (~i) warning('flag_implicit_skipnan(0): You are warned!!! You have turned off skipping NaN in sumskipnan. This is not recommended. Make sure you really know what you do.') end; -end; +end; Property changes on: trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m ___________________________________________________________________ Added: svn:keywords + Id Revision Modified: trunk/octave-forge/extra/NaN/inst/rms.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/rms.m 2009-05-12 10:55:57 UTC (rev 5806) +++ trunk/octave-forge/extra/NaN/inst/rms.m 2009-05-12 11:32:58 UTC (rev 5807) @@ -1,17 +1,23 @@ -function o=rms(x,DIM) +function o=rms(x,DIM,W) % RMS calculates the root mean square % can deal with complex data. % -% y = rms(x,DIM) +% y = rms(x,DIM,W) % % DIM dimension % 1 STD of columns % 2 STD of rows % N STD of N-th dimension % default or []: first DIMENSION, with more than 1 element +% W weights to compute weighted s.d. (default: []) +% if W=[], all weights are 1. +% number of elements in W must match size(x,DIM) % +% y estimated standard deviation +% % features: % - can deal with NaN's (missing values) +% - weighting of data % - dimension argument also in Octave % - compatible to Matlab and Octave % @@ -33,15 +39,17 @@ % $Id$ -% Copyright (C) 2000-2003,2008 by Alois Schloegl <a.s...@ie...> +% Copyright (C) 2000-2003,2008,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox % http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ if nargin<2, [o,N,ssq] = sumskipnan(x); +elseif nargin<3 + [o,N,ssq] = sumskipnan(x,DIM); else - [o,N,ssq] = sumskipnan(x,DIM); + [o,N,ssq] = sumskipnan(x,DIM,W); end; o = sqrt(ssq./N); Modified: trunk/octave-forge/extra/NaN/inst/sem.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/sem.m 2009-05-12 10:55:57 UTC (rev 5806) +++ trunk/octave-forge/extra/NaN/inst/sem.m 2009-05-12 11:32:58 UTC (rev 5807) @@ -1,7 +1,7 @@ -function [SE,M]=sem(x,DIM) +function [SE,M]=sem(x,DIM, W) % SEM calculates the standard error of the mean % -% [SE,M] = SEM(x [, DIM]) +% [SE,M] = SEM(x [, DIM [,W]]) % calculates the standard error (SE) in dimension DIM % the default DIM is the first non-single dimension % M returns the mean. @@ -12,9 +12,13 @@ % 2: SEM of rows % N: SEM of N-th dimension % default or []: first DIMENSION, with more than 1 element +% W weights to compute weighted mean and s.d. (default: []) +% if W=[], all weights are 1. +% number of elements in W must match size(x,DIM) % % features: % - can deal with NaN's (missing values) +% - weighting of data % - dimension argument % - compatible to Matlab and Octave % @@ -22,7 +26,7 @@ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or +% the Free Software Foundation; either version 3 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, @@ -33,12 +37,15 @@ % You should have received a copy of the GNU General Public License % along with this program; If not, see <http://www.gnu.org/licenses/>. -% Copyright (C) 2000-2003 by Alois Schloegl <a.s...@ie...> -% $Revision$ +% Copyright (C) 2000-2003,2008,2009 by Alois Schloegl <a.s...@ie...> % $Id$ +% This function is part of the NaN-toolbox +% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ -if nargin>1, +if nargin>2, + [S,N,SSQ] = sumskipnan(x,DIM,W); +elseif nargin>1, [S,N,SSQ] = sumskipnan(x,DIM); else [S,N,SSQ] = sumskipnan(x); Modified: trunk/octave-forge/extra/NaN/inst/std.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/std.m 2009-05-12 10:55:57 UTC (rev 5806) +++ trunk/octave-forge/extra/NaN/inst/std.m 2009-05-12 11:32:58 UTC (rev 5807) @@ -1,7 +1,7 @@ -function [o,v]=std(x,opt,DIM) +function [o,v]=std(x,opt,DIM,W) % STD calculates the standard deviation. % -% [y,v] = std(x [, opt[, DIM]]) +% [y,v] = std(x [, opt[, DIM [, W]]]) % % opt option % 0: normalizes with N-1 [default] @@ -14,12 +14,16 @@ % DIM dimension % N STD of N-th dimension % default or []: first DIMENSION, with more than 1 element +% W weights to compute weighted s.d. (default: []) +% if W=[], all weights are 1. +% number of elements in W must match size(x,DIM) % % y estimated standard deviation % % features: % - provides an unbiased estimation of the S.D. % - can deal with NaN's (missing values) +% - weighting of data % - dimension argument also in Octave % - compatible to Matlab and Octave % @@ -32,7 +36,7 @@ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or +% the Free Software Foundation; either version 3 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, @@ -44,10 +48,13 @@ % along with this program; If not, see <http://www.gnu.org/licenses/>. % $Id$ -% Copyright (C) 2000-2003, 2006,2009 by Alois Schloegl <a.s...@ie...> +% Copyright (C) 2000-2003,2006,2009 by Alois Schloegl <a.s...@ie...> % This is part of the NaN-toolbox for Octave and Matlab % see also: http://hci.tugraz.at/schloegl/matlab/NaN/ +if nargin<4, + W = []; +end; if nargin<3, DIM = []; end; @@ -57,13 +64,13 @@ end; -[y,n,ssq] = sumskipnan(x,DIM); +[y,n,ssq] = sumskipnan(x,DIM,W); if all(ssq(:).*n(:) > 2*(y(:).^2)) %% rounding error is neglectable y = ssq - y.*y./n; else %% rounding error is not neglectable - [y,n] = sumskipnan(center(x,DIM).^2,DIM); + [y,n] = sumskipnan(center(x,DIM).^2,DIM,W); end; Modified: trunk/octave-forge/extra/NaN/inst/var.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/var.m 2009-05-12 10:55:57 UTC (rev 5806) +++ trunk/octave-forge/extra/NaN/inst/var.m 2009-05-12 11:32:58 UTC (rev 5807) @@ -1,20 +1,31 @@ -function y=var(x,opt,DIM) +function y=var(x,opt,DIM,W) % VAR calculates the variance. % % y = var(x [, opt[, DIM]]) % calculates the variance in dimension DIM % the default DIM is the first non-single dimension % -% opt 0: normalizes with N-1 [default} +% opt 0: normalizes with N-1 [default] % 1: normalizes with N % DIM dimension % 1: VAR of columns % 2: VAR of rows % N: VAR of N-th dimension -% default or []: first DIMENSION, with more than 1 element +% default or []: first DIMENSION, with more than 1 element +% W weights to compute weighted variance (default: []) +% if W=[], all weights are 1. +% number of elements in W must match size(x,DIM) +% +% usage: +% var(x) +% var(x, opt, DIM) +% var(x, [], DIM) +% var(x, W, DIM) +% var(x, opt, DIM, W) % % features: % - can deal with NaN's (missing values) +% - weighting of data % - dimension argument % - compatible to Matlab and Octave % @@ -22,7 +33,7 @@ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or +% the Free Software Foundation; either version 3 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, @@ -37,30 +48,42 @@ % Copyright (C) 2000-2003,2006,2009 by Alois Schloegl <a.s...@ie...> % This is part of the NaN-toolbox for Octave and Matlab % see also: http://hci.tugraz.at/schloegl/matlab/NaN/ - -if nargin>1, - if ~isempty(opt) & opt~=0, - fprintf(2,'Warning STD: OPTION not supported.\n'); - end; -else - opt = 0; -end; - -if nargin<3, - DIM = []; -end; + +if nargin<3, + DIM = []; +end; + +if nargin==1, + W = []; + opt = []; + +elseif any(nargin==[2,3]) + if (numel(opt)<2), + W = []; + else + W = opt; + opt = []; + end; +elseif (nargin==4) && (numel(opt)<2) && (numel(DIM)<2), + ; +else + fprintf(1,'Error VAR: incorrect usage\n'); + help var; + return; +end; + if isempty(DIM), DIM=min(find(size(x)>1)); if isempty(DIM), DIM=1; end; end; -[y,n,ssq] = sumskipnan(x,DIM); +[y,n,ssq] = sumskipnan(x,DIM,W); if all(ssq(:).*n(:) > 2*(y(:).^2)) %% rounding error is neglectable y = ssq - y.*y./n; else %% rounding error is not neglectable - [y,n] = sumskipnan(center(x,DIM).^2,DIM); + [y,n] = sumskipnan(center(x,DIM).^2,DIM,W); end; if (opt~=1) This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-05-12 16:14:00
|
Revision: 5809 http://octave.svn.sourceforge.net/octave/?rev=5809&view=rev Author: schloegl Date: 2009-05-12 16:13:45 +0000 (Tue, 12 May 2009) Log Message: ----------- NaN: weightening of data; fix support for FreeMat v3.6 Modified Paths: -------------- trunk/octave-forge/extra/NaN/doc/INSTALL trunk/octave-forge/extra/NaN/inst/geomean.m trunk/octave-forge/extra/NaN/inst/harmmean.m trunk/octave-forge/extra/NaN/inst/zscore.m Modified: trunk/octave-forge/extra/NaN/doc/INSTALL =================================================================== --- trunk/octave-forge/extra/NaN/doc/INSTALL 2009-05-12 12:51:51 UTC (rev 5808) +++ trunk/octave-forge/extra/NaN/doc/INSTALL 2009-05-12 16:13:45 UTC (rev 5809) @@ -7,26 +7,26 @@ addpath('/your/directory/structure/to/NaN/') path('/your/directory/structure/to/NaN/',path) Make sure the functions in the NaN-toolbox are found before the default functions. - + c) run NANINSTTEST This checks whether the installation was successful. d) [OPTIONAL]: - To improve speed, you can use the MEX-version of SUMSKIPNAN. + For support of weighted statistics, you need the MEX-version of SUMSKIPNAN and COVM. Some precompiled binaries are provided. If your platform is not supported, - compile the C-Mex-function SUMSKIPNAN_MEX.CPP using + compile the C-Mex-functions SUMSKIPNAN_MEX.CPP and COVM_MEX.CPP using mex sumskipnan_mex.cpp - The oct-file sumskipnan_oct.cc is broken, but Octave can also use - the mex-file. + mex covm_mex.cpp + or for Octave use the mex-file. mkoctfile --mex sumskipnan_mex.cpp + mkoctfile --mex covm_mex.cpp Run NANINSTTEST again to check the stability of the compiled SUMSKIPNAN. e) HINT: if SUMSKIPNAN_MEX causes problems, you can savely remove it. -Then the (slower) M-file is used. $Id$ - Copyright (c) 2000-2003,2005,2006,2009 by Alois Schloegl <a.s...@ie...> + Copyright (c) 2000-2003,2005,2006,2009 by Alois Schloegl <a.s...@ie...> WWW: http://hci.tugraz.at/~schloegl/matlab/NaN/ Modified: trunk/octave-forge/extra/NaN/inst/geomean.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/geomean.m 2009-05-12 12:51:51 UTC (rev 5808) +++ trunk/octave-forge/extra/NaN/inst/geomean.m 2009-05-12 16:13:45 UTC (rev 5809) @@ -1,4 +1,4 @@ -function [y] = geomean(x,DIM) +function [y] = geomean(x,DIM,W) % GEOMEAN calculates the geomentric mean of data elements. % % y = geomean(x [,DIM [,W]]) is the same as @@ -22,7 +22,7 @@ % % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or +% the Free Software Foundation; either version 3 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, @@ -43,8 +43,11 @@ if nargin<2 DIM=min(find(size(x)>1)); if isempty(DIM), DIM=1; end; +end +if nargin<3 + W = []; end; -[y, n] = sumskipnan(log(x),DIM); +[y, n] = sumskipnan(log(x),DIM,W); y = exp (y./n); Modified: trunk/octave-forge/extra/NaN/inst/harmmean.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/harmmean.m 2009-05-12 12:51:51 UTC (rev 5808) +++ trunk/octave-forge/extra/NaN/inst/harmmean.m 2009-05-12 16:13:45 UTC (rev 5809) @@ -1,4 +1,4 @@ -function [y] = harmmean(x,DIM) +function [y] = harmmean(x,DIM,W) % HARMMEAN calculates the harmonic mean of data elements. % The harmonic mean is the inverse of the mean of the inverse elements. % @@ -47,7 +47,10 @@ DIM=min(find(size(x)>1)); if isempty(DIM), DIM=1; end; end; +if nargin<3 + W = []; +end; -[y, n] = sumskipnan(1./x,DIM); +[y, n] = sumskipnan(1./x,DIM,W); y = n./y; Modified: trunk/octave-forge/extra/NaN/inst/zscore.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/zscore.m 2009-05-12 12:51:51 UTC (rev 5808) +++ trunk/octave-forge/extra/NaN/inst/zscore.m 2009-05-12 16:13:45 UTC (rev 5809) @@ -35,9 +35,10 @@ % along with this program; If not, see <http://www.gnu.org/licenses/>. -% Copyright (C) 2000-2003 by Alois Schloegl <a.s...@ie...> -% $Revision$ % $Id$ +% Copyright (C) 2000-2003,2009 by Alois Schloegl <a.s...@ie...> +% This is part of the NaN-toolbox for Octave and Matlab +% see also: http://hci.tugraz.at/schloegl/matlab/NaN/ if any(size(i)==0); return; end; @@ -46,7 +47,7 @@ DIM=[]; end if nargin<3 - w = []; + W = []; end if isempty(DIM), DIM=min(find(size(i)>1)); This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-07-02 14:15:39
|
Revision: 5985 http://octave.svn.sourceforge.net/octave/?rev=5985&view=rev Author: schloegl Date: 2009-07-02 12:27:35 +0000 (Thu, 02 Jul 2009) Log Message: ----------- add classification methods, change license to GPL v3 or later, ver2.0 Modified Paths: -------------- trunk/octave-forge/extra/NaN/COPYING trunk/octave-forge/extra/NaN/DESCRIPTION trunk/octave-forge/extra/NaN/doc/README.TXT Added Paths: ----------- trunk/octave-forge/extra/NaN/inst/classify.m trunk/octave-forge/extra/NaN/inst/decovm.m trunk/octave-forge/extra/NaN/inst/kappa.m trunk/octave-forge/extra/NaN/inst/test_sc.m trunk/octave-forge/extra/NaN/inst/train_lda_sparse.m trunk/octave-forge/extra/NaN/inst/train_sc.m trunk/octave-forge/extra/NaN/inst/xval.m Modified: trunk/octave-forge/extra/NaN/COPYING =================================================================== --- trunk/octave-forge/extra/NaN/COPYING 2009-07-01 20:55:24 UTC (rev 5984) +++ trunk/octave-forge/extra/NaN/COPYING 2009-07-02 12:27:35 UTC (rev 5985) @@ -1,283 +1,625 @@ GNU GENERAL PUBLIC LICENSE - Version 2, June 1991 + Version 3, 29 June 2007 - Copyright (C) 1989, 1991 Free Software Foundation, Inc. <http://fsf.org/> + Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble - The licenses for most software are designed to take away your -freedom to share and change it. By contrast, the GNU General Public -License is intended to guarantee your freedom to share and change free -software--to make sure the software is free for all its users. This -General Public License applies to most of the Free Software -Foundation's software and to any other program whose authors commit to -using it. (Some other Free Software Foundation software is covered by -the GNU Library General Public License instead.) You can apply it to + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + END OF TERMS AND CONDITIONS - + How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest @@ -286,15 +628,15 @@ To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively -convey the exclusion of warranty; and each file should have at least +state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. <one line to give the program's name and a brief idea of what it does.> Copyright (C) <year> <name of author> - This program is free software; you can redistribute it and/or modify + This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by - the Free Software Foundation; either version 2 of the License, or + the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, @@ -303,35 +645,30 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program; if not, see <http://www.gnu.org/licenses/>. + along with this program. If not, see <http://www.gnu.org/licenses/>. Also add information on how to contact you by electronic and paper mail. -If the program is interactive, make it output a short notice like this -when it starts in an interactive mode: + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: - Gnomovision version 69, Copyright (C) year name of author - Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + <program> Copyright (C) <year> <name of author> + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate -parts of the General Public License. Of course, the commands you use may -be called something other than `show w' and `show c'; they could even be -mouse-clicks or menu items--whatever suits your program. +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". -You should also get your employer (if you work as a programmer) or your -school, if any, to sign a "copyright disclaimer" for the program, if -necessary. Here is a sample; alter the names: + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +<http://www.gnu.org/licenses/>. - Yoyodyne, Inc., hereby disclaims all copyright interest in the program - `Gnomovision' (which makes passes at compilers) written by James Hacker. - - <signature of Ty Coon>, 1 April 1989 - Ty Coon, President of Vice - -This General Public License does not permit incorporating your program into -proprietary programs. If your program is a subroutine library, you may -consider it more useful to permit linking proprietary applications with the -library. If this is what you want to do, use the GNU Library General -Public License instead of this License. + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +<http://www.gnu.org/philosophy/why-not-lgpl.html>. Modified: trunk/octave-forge/extra/NaN/DESCRIPTION =================================================================== --- trunk/octave-forge/extra/NaN/DESCRIPTION 2009-07-01 20:55:24 UTC (rev 5984) +++ trunk/octave-forge/extra/NaN/DESCRIPTION 2009-07-02 12:27:35 UTC (rev 5985) @@ -1,10 +1,10 @@ Name: NaN -Version: 1.0.9 -Date: 2009-05-03 +Version: 2.0.0 +Date: 2009-06-30 Author: Alois Schloegl <a.s...@ie...> -Maintainer: Matthew W. Roberts +Maintainer: Alois Schloegl Title: NaN statisical toolbox Description: Missing value statistical toolbox -Depends: octave (>= 2.9.7) -License: GPL version 2 or later -Url: http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN +Depends: octave (>= 3.0.1) +License: GPL version 3 or later +Url: http://hci.tu-graz.ac.at/~schloegl/matlab/NaN Modified: trunk/octave-forge/extra/NaN/doc/README.TXT =================================================================== --- trunk/octave-forge/extra/NaN/doc/README.TXT 2009-07-01 20:55:24 UTC (rev 5984) +++ trunk/octave-forge/extra/NaN/doc/README.TXT 2009-07-02 12:27:35 UTC (rev 5985) @@ -6,17 +6,19 @@ FEATURES of the NaN-tb: ----------------------- - statistical toolbox + - classification toolobx - NaN's are treated as missing values - - supports DIM argument - - supports unbiased estimation - - supports weightening of data + - supports DIM argument + - supports weightening of data + - less round-off errors using extended double - less but more powerful functions (no nan-FUN needed) + - supports unbiased estimation - fixes known bugs - - compatible to Matlab and Octave + - compatible with Matlab and Octave - easy to use - - The toolbox is tested with Octave 3.x and Matlab 7.x, FreeMat v3.6 - - + - The toolbox is tested with Octave 3.x and Matlab 7.x + + Currently are implemented: -------------------------- level 1: basic functions (not derived) @@ -25,6 +27,8 @@ SUMSKIPNAN is central, it implements skipping NaN's, the DIM-argument and returns the number of valid elements, too. COVM covariance estimation (several modes) + Round-off errors avoided by using internally extended accuracy + DECOVM decomposes the extended covarianced matrix into mean and cov XCOVF cross-correlation function NANFILTER filter function CONVSKIPNAN convolution @@ -38,34 +42,51 @@ rms, sem, skewness, statistic, std, var -level 2: derived functions +level 2a: derived functions MEAN mean (options: arithmetic, geometric, harmonic) - SEM standard error of the mean (does not depend on distribution) VAR variance STD standard deviation MEDIAN median (currently only for 2-dim matrices) + SEM standard error of the mean (does not depend on distribution) + TRIMMEAN trimmed mean + medAbsDev median absolute deviation + MEANSQ mean square RMS root mean square + STATISTIC estimates various statistics at once MOMENT moment SKEWNESS skewness KURTOSIS excess + +* IQR interquartile range MAD mean absolute deviation +* RANGE range (max-min) + CENTER removes mean - ZSCORE normalizes x with z = (x-mean)/std + ZSCORE normalizes x to zero mean and variance 1 (z = (x-mean)/std) + zScoreMedian non-parametric z-score, normalizes is to zero median and 1/(1.483*median absolute deviation) + HARMMEAN harmonic mean GEOMEAN geometric mean + NANTEST checks whether all functions have been replaced DETREND detrending of data with missing values and non-equidistant sampled data + COR correlation matrix + COV covariance matrix CORRCOEF correlation coefficient, including rank correlation, significance test and confidence intervals SPEARMAN, RANKCORR spearman's rank correlation coefficient. They might be replaced by CORRCOEF. - COV covariance matrix + PARTCORRCOEF partial correlation coefficient RANKS calculates ranks for non-parametric statistics + TIEDRANK similar to RANKS, used for compatibility reasons + + QUANTILE q-th quantile + PRCTILE,PERCENTILE p-th percentile TRIMEAN trimean - QUANTILE q-th quantile - PERCENTILE p-th percentile + + ECDF empirical cumulative distribution function NORMPDF normal probability distribution NORMCDF normal cumulative distribution NORMINV inverse of the normal cumulative distribution @@ -73,9 +94,16 @@ TCDF student cumulative distribution TINV inverse of the student cumulative distribution NANSUM, NANSTD fixes for buggy versions included - - - + +level 2b: classification, cross-validation + TRAIN_SC train classifier + TEST_SC test classifier + CLASSIFY classify data (no cross validation) + XVAL classify data with cross validation + KAPPA performance evaluation + TRAIN_LDA_SPARSE utility function + + REFERENCE(S): ---------------------------------- [1] http://www.itl.nist.gov/ @@ -145,7 +173,7 @@ 12) NORMPDF, NORMCDF, NORMINV In the Matlab statistics toolbox V 3.0, NORMPDF, NORMCDF and NORMINV gave -incorrect results for SIGMA=0; A Similar problem was observed in Octave +incorrect results for SIGMA=0; A similar problem was observed in Octave with NORMAL_INV, NORMAL_PDF, and NORMALCDF. The problem is fixed with this version. Furthermore, the check of the input Added: trunk/octave-forge/extra/NaN/inst/classify.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/classify.m (rev 0) +++ trunk/octave-forge/extra/NaN/inst/classify.m 2009-07-02 12:27:35 UTC (rev 5985) @@ -0,0 +1,77 @@ +function [CLASS,ERR,POSTERIOR,LOGP,COEF]=classify(sample,training,classlabel,TYPE) +% CLASSIFY classifies sample data into categories +% defined by the training data and its group information +% +% CLASS = classify(sample, training, group) +% CLASS = classify(sample, training, group, TYPE) +% [CLASS,ERR,POSTERIOR,LOGP,COEF] = CLASSIFY(...) +% +% CLASS contains the assigned group. +% ERR is the classification error on the training set weighted by the +% prior propability of each group. +% +% The same classifier as in TRAIN_SC are supported. +% +% ATTENTION: no cross-validation is applied, therefore the +% classification error is too optimistic (overfitting). +% Use XVAL instead to obtain cross-validated performance. +% +% see also: TRAIN_SC, TEST_SC, XVAL +% +% References: +% [1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed. +% John Wiley & Sons, 2001. + +% $Id: classify.m 2140 2009-07-02 12:03:55Z schloegl $ +% Copyright (C) 2008,2009 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. + +if nargin<4 + TYPE = 'linear'; +end; + +if strcmp(TYPE,'linear') + TYPE = 'LDA'; +elseif strcmp(TYPE,'quadratic') + TYPE = 'QDA2'; % result is closer to Matlab +elseif strcmp(TYPE,'diagLinear') + TYPE = 'NBC'; +elseif strcmp(TYPE,'diagQuadratic') + TYPE = 'aNBC'; +elseif strcmp(TYPE,'mahalanobis') + TYPE = 'MDA'; +end; + +[group,I,classlabel] = unique(classlabel); + +CC = train_sc(training,classlabel,TYPE); +R = test_sc(CC,sample); +CLASS = group(R.classlabel); + +if nargout>1, + R = test_sc(CC,training,[],classlabel); + ERR = 1-R.ACC; +end; + +if nargout>2, + warning('output arguments POSTERIOR,LOGP and COEF ... [truncated message content] |
From: <sch...@us...> - 2009-07-30 07:30:22
|
Revision: 6066 http://octave.svn.sourceforge.net/octave/?rev=6066&view=rev Author: schloegl Date: 2009-07-30 07:30:14 +0000 (Thu, 30 Jul 2009) Log Message: ----------- COVM_MEX: prevent seg-fault for the case of X and Y have same starting location, but different size; COVM: remove redundant code, fix computation of mean of complex data Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/covm.m trunk/octave-forge/extra/NaN/src/covm_mex.cpp Property Changed: ---------------- trunk/octave-forge/extra/NaN/inst/covm.m Modified: trunk/octave-forge/extra/NaN/inst/covm.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/covm.m 2009-07-29 23:14:03 UTC (rev 6065) +++ trunk/octave-forge/extra/NaN/inst/covm.m 2009-07-30 07:30:14 UTC (rev 6066) @@ -40,7 +40,7 @@ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or +% the Free Software Foundation; either version 3 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, @@ -133,27 +133,10 @@ FLAG_NANS_OCCURED = logical(0); % default value end; - if (~any(Mode=='D') && ~any(Mode=='E')), % if Mode == M - [CC,NN] = covm_mex(real(X),real(Y),FLAG_NANS_OCCURED,W); - %% complex matrices - if ~isreal(X) && ~isreal(Y) - [iCC,inn] = covm_mex(imag(X),imag(Y),FLAG_NANS_OCCURED,W); - CC = CC + iCC; - end; - if isempty(Y) Y = X; end; - if ~isreal(X) - [iCC,inn] = covm_mex(imag(X),real(Y),FLAG_NANS_OCCURED,W); - CC = CC - i*iCC; - end; - if ~isreal(Y) - [iCC,inn] = covm_mex(real(X),imag(Y),FLAG_NANS_OCCURED,W); - CC = CC + i*iCC; - end; - - else % if any(Mode=='D') || any(Mode=='E'), - [S1,N1] = sumskipnan_mex(X,1,W); + if any(Mode=='D') || any(Mode=='E'), + [S1,N1] = sumskipnan(X,1,W); if ~isempty(Y) - [S2,N2] = sumskipnan_mex(Y,1,W); + [S2,N2] = sumskipnan(Y,1,W); else S2 = S1; N2 = N1; end; @@ -163,31 +146,32 @@ Y = Y - ones(r1,1)*(S2./N2); end; end; - [CC,NN] = covm_mex(real(X), real(Y), FLAG_NANS_OCCURED, W); - %% complex matrices - if ~isreal(X) && ~isreal(Y) - [iCC,inn] = covm_mex(imag(X),imag(Y),FLAG_NANS_OCCURED,W); - CC = CC + iCC; - end; - if isempty(Y) Y = X; end; - if ~isreal(X) - [iCC,inn] = covm_mex(imag(X),real(Y),FLAG_NANS_OCCURED,W); - CC = CC - i*iCC; - end; - if ~isreal(Y) - [iCC,inn] = covm_mex(real(X),imag(Y),FLAG_NANS_OCCURED,W); - CC = CC + i*iCC; - end; - - if any(Mode=='D') && ~any(Mode=='1'), % 'D1' - NN = max(NN-1,0); - end; - if any(Mode=='E'), % extended mode - NN = [nn, N2; N1', NN]; - CC = [nn, S2; S1', CC]; - end; + end; + + [CC,NN] = covm_mex(real(X), real(Y), FLAG_NANS_OCCURED, W); + %% complex matrices + if ~isreal(X) && ~isreal(Y) + [iCC,inn] = covm_mex(imag(X), imag(Y), FLAG_NANS_OCCURED, W); + CC = CC + iCC; end; + if isempty(Y) Y = X; end; + if ~isreal(X) + [iCC,inn] = covm_mex(imag(X), real(Y), FLAG_NANS_OCCURED, W); + CC = CC - i*iCC; + end; + if ~isreal(Y) + [iCC,inn] = covm_mex(real(X), imag(Y), FLAG_NANS_OCCURED, W); + CC = CC + i*iCC; + end; + if any(Mode=='D') && ~any(Mode=='1'), % 'D1' + NN = max(NN-1,0); + end; + if any(Mode=='E'), % extended mode + NN = [nn, N2; N1', NN]; + CC = [nn, S2; S1', CC]; + end; + elseif ~isempty(W), Property changes on: trunk/octave-forge/extra/NaN/inst/covm.m ___________________________________________________________________ Added: Id + addstat.m Modified: trunk/octave-forge/extra/NaN/src/covm_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/covm_mex.cpp 2009-07-29 23:14:03 UTC (rev 6065) +++ trunk/octave-forge/extra/NaN/src/covm_mex.cpp 2009-07-30 07:30:14 UTC (rev 6066) @@ -18,7 +18,7 @@ // along with this program; if not, see <http://www.gnu.org/licenses/>. // // -// sumskipnan: sums all non-NaN values +// covm: in-product of matrices, NaN are skipped. // // Input: // - X: @@ -27,8 +27,9 @@ // - W: weight vector to compute weighted correlation // // Output: -// - CC = X' * diag(W) * Y while NaN's are skipped -// - NN = real(~isnan(X))*diag(W)*real(~isnan(Y)) count of valid (non-NaN) elements +// - CC = X' * sparse(diag(W)) * Y while NaN's are skipped +// - NN = real(~isnan(X)')*sparse(diag(W))*real(~isnan(Y)) count of valid (non-NaN) elements +// computed more efficiently // // $Id$ // Copyright (C) 2009 Alois Schloegl <a.s...@ie...> @@ -60,16 +61,16 @@ // check for proper number of input and output arguments if ((PInputCount <= 0) || (PInputCount > 4)) { mexPrintf("usage: [CC,NN] = covm_mex(X [,Y [,flag [,W]]])\n\n"); - mexPrintf("Donot use COVM_MEX directly, use COVM instead. \n"); + mexPrintf("Do not use COVM_MEX directly, use COVM instead. \n"); /* - mexPrintf("COVM_MEX computes the covariance matrix of real matrices and skips NaN's\n"); + mexPrintf("\nCOVM_MEX computes the covariance matrix of real matrices and skips NaN's\n"); mexPrintf("\t[CC,NN] = covm_mex(...)\n\t\t computes CC=X'*Y, NN contains the number of not-NaN elements\n"); - mexPrintf("\t\t CC./NN is the covariance matrix\n"); - mexPrintf("\t... = covm_mex(X,Y,...)\n\t\t computes CC=X'*Y, number of rows of X and Y must match\n"); - mexPrintf("\t... = covm_mex(X,[], ...)\n\t\t computes CC=X'*X\n"); + mexPrintf("\t\t CC./NN is the unbiased covariance matrix\n"); + mexPrintf("\t... = covm_mex(X,Y,...)\n\t\t computes CC=X'*sparse(diag(W))*Y, number of rows of X and Y must match\n"); + mexPrintf("\t... = covm_mex(X,[], ...)\n\t\t computes CC=X'*sparse(diag(W))*X\n"); mexPrintf("\t... = covm_mex(...,flag,...)\n\t\t if flag is not empty, it is set to 1 if some NaN occured in X or Y\n"); mexPrintf("\t... = covm_mex(...,W)\n\t\t W to compute weighted covariance, number of elements must match the number of rows of X\n"); - mexPrintf("\t\t if isempty(W), all rows get an equal weight of 1\n"); + mexPrintf("\t\t if isempty(W), all weights are 1\n"); mexPrintf("\t[CC,NN]=covm_mex(X,Y,flag,W)\n"); */ return; } @@ -194,7 +195,7 @@ /*------ version 2 --------------------- this version seems to be faster than the one above. */ - if (X0 != Y0) + if ( (X0 != Y0) || (cX != cY) ) /******** X!=Y, output is not symetric *******/ if (W) /* weighted version */ for (i=0; i<cX; i++) @@ -204,7 +205,7 @@ long double cc=0.0; long double nn=0.0; for (k=0; k<rX; k++) { - double z = X[k]*Y[k]; + long double z = ((long double)X[k])*Y[k]; if (isnan(z)) { #ifndef NO_FLAG flag_isNaN = 1; @@ -226,7 +227,7 @@ long double cc=0.0; size_t nn=0; for (k=0; k<rX; k++) { - double z = X[k]*Y[k]; + long double z = ((long double)X[k])*Y[k]; if (isnan(z)) { #ifndef NO_FLAG flag_isNaN = 1; @@ -240,7 +241,7 @@ if (NN != NULL) NN[i+j*cX] = (double)nn; } - else // if (X0==Y0) + else // if (X0==Y0) && (cX==cY) /******** X==Y, output is symetric *******/ if (W) /* weighted version */ for (i=0; i<cX; i++) @@ -250,7 +251,7 @@ long double cc=0.0; long double nn=0.0; for (k=0; k<rX; k++) { - double z = X[k]*Y[k]; + long double z = ((long double)X[k])*Y[k]; if (isnan(z)) { #ifndef NO_FLAG flag_isNaN = 1; @@ -275,7 +276,7 @@ long double cc=0.0; size_t nn=0; for (k=0; k<rX; k++) { - double z = X[k]*Y[k]; + long double z = ((long double)X[k])*Y[k]; if (isnan(z)) { #ifndef NO_FLAG flag_isNaN = 1; This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-07-30 18:13:15
|
Revision: 6067 http://octave.svn.sourceforge.net/octave/?rev=6067&view=rev Author: schloegl Date: 2009-07-30 18:12:57 +0000 (Thu, 30 Jul 2009) Log Message: ----------- add liblinear classifier Modified Paths: -------------- trunk/octave-forge/extra/NaN/doc/README.TXT trunk/octave-forge/extra/NaN/inst/train_sc.m Modified: trunk/octave-forge/extra/NaN/doc/README.TXT =================================================================== --- trunk/octave-forge/extra/NaN/doc/README.TXT 2009-07-30 07:30:14 UTC (rev 6066) +++ trunk/octave-forge/extra/NaN/doc/README.TXT 2009-07-30 18:12:57 UTC (rev 6067) @@ -6,14 +6,15 @@ FEATURES of the NaN-tb: ----------------------- - statistical toolbox - - classification toolobx + - machine learning and classification toolbox - NaN's are treated as missing values + - supports weightening of data + - supports DIM argument - - supports weightening of data - less round-off errors using extended double - less but more powerful functions (no nan-FUN needed) - supports unbiased estimation - - fixes known bugs + - fixes known bugs - compatible with Matlab and Octave - easy to use - The toolbox is tested with Octave 3.x and Matlab 7.x @@ -40,8 +41,7 @@ sumskipnan, covm, center, cor, coefficient of variation, corrcoef, geomean, harmmean, kurtosis, mad, mean, meandev, meansq, moment, nanmean, nanstd, nansum, rms, sem, skewness, statistic, std, var - - + level 2a: derived functions MEAN mean (options: arithmetic, geometric, harmonic) VAR variance @@ -50,7 +50,7 @@ SEM standard error of the mean (does not depend on distribution) TRIMMEAN trimmed mean medAbsDev median absolute deviation - + MEANSQ mean square RMS root mean square @@ -59,9 +59,9 @@ SKEWNESS skewness KURTOSIS excess -* IQR interquartile range +* IQR interquartile range MAD mean absolute deviation -* RANGE range (max-min) +* RANGE range (max-min) CENTER removes mean ZSCORE normalizes x to zero mean and variance 1 (z = (x-mean)/std) @@ -242,21 +242,24 @@ c) run NANINSTTEST This checks whether the installation was successful. -d) [OPTIONAL]: - To improve speed, you can use the MEX-version of SUMSKIPNAN. - Some precompiled binaries are provided. If your platform is not supported, +d) Compile mex files: + This is useful to improve speed, and is required if you used weighted samples. + Check if precompiled binaries are provided. If your platform is not supported, compile the C-Mex-function SUMSKIPNAN_MEX.CPP using mex sumskipnan_mex.cpp - The oct-file sumskipnan_oct.cc is broken, but Octave can also use - the mex-file. - mkoctfile --mex sumskipnan_mex.cpp + mex covm_mex.cpp + mex histo_mex.cpp Run NANINSTTEST again to check the stability of the compiled SUMSKIPNAN. -e) HINT: if SUMSKIPNAN_MEX causes problems, you can savely remove it. -Then the (slower) M-file is used. +e) [OPTIONAL] + In case you want to use SVM classifiers, you need to install additional toolboxes: + libSVM: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ + LibLinear: http://www.csie.ntu.edu.tw/~cjlin/liblinear/ + OSU-SVM: https://sourceforge.net/projects/svm/ + simpleSVM: https://sourceforge.net/projects/simplesvm/ + - $Id$ Copyright (C) 2000-2005,2009 by Alois Schloegl <a.s...@ie...> WWW: http://hci.tugraz.at/~schloegl/matlab/NaN/ Modified: trunk/octave-forge/extra/NaN/inst/train_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/train_sc.m 2009-07-30 07:30:14 UTC (rev 6066) +++ trunk/octave-forge/extra/NaN/inst/train_sc.m 2009-07-30 18:12:57 UTC (rev 6067) @@ -18,7 +18,8 @@ % 'QDA' quadratic discriminant analysis [1] % 'LD2' linear discriminant analysis (see LDBC2) [1] % MODE.hyperparameter.gamma: regularization parameter [default 0] -% 'LD3' linear discriminant analysis (see LDBC3) [1] +% 'LD3', 'FDA', 'LDA' +% linear discriminant analysis (see LDBC3) [1] % MODE.hyperparameter.gamma: regularization parameter [default 0] % 'LD4' linear discriminant analysis (see LDBC4) [1] % MODE.hyperparameter.gamma: regularization parameter [default 0] @@ -37,25 +38,42 @@ % '###/GSVD' GSVD and statistical classifier [2,3], % '###/sparse' sparse [5] % '###' must be 'LDA' or any other classifier -% 'SVM','SVM1r' support vector machines, one-vs-rest -% MODE.hyperparameter.c_value = -% 'PSVM' Proximal SVM [8] -% MODE.hyperparameter.nu (default: 1.0) % 'PLS' (linear) partial least squares regression % 'REG' regression analysis; % 'WienerHopf' Wiener-Hopf equation % 'NBC' Naive Bayesian Classifier [6] % 'aNBC' Augmented Naive Bayesian Classifier [6] % 'NBPW' Naive Bayesian Parzen Window [9] +% 'PSVM' Proximal SVM [8] +% MODE.hyperparameter.nu (default: 1.0) +% 'LPM' Linear Programming Machine +% uses and requires train_LPM of the iLog CPLEX optimizer +% MODE.hyperparameter.c_value = +% 'CSP' CommonSpatialPattern is very experimental and just a hack +% uses a smoothing window of 50 samples. +% 'SVM','SVM1r' support vector machines, one-vs-rest +% uses and requires svmtrain.mex from libSVM +% MODE.hyperparameter.c_value = % 'SVM11' support vector machines, one-vs-one + voting +% uses and requires svmtrain.mex from libSVM % MODE.hyperparameter.c_value = % 'RBF' Support Vector Machines with RBF Kernel +% uses and requires svmtrain.mex from libSVM % MODE.hyperparameter.c_value = % MODE.hyperparameter.gamma = -% 'LPM' Linear Programming Machine -% MODE.hyperparameter.c_value = -% 'CSP' CommonSpatialPattern is very experimental and just a hack -% uses a smoothing window of 50 samples. +% 'SVM:LIB' uses and requires svmtrain.mex from libSVM +% 'SVM:bioinfo' uses and requires svmtrain from the bioinfo toolbox +% 'SVM:OSU' uses and requires mexSVMTrain from the OSU-SVM toolbox +% 'SVM:LOO' uses and requires svcm_train from the LOO-SVM toolbox +% 'SVM:Gunn' uses and requires svc-functios from the Gunn-SVM toolbox +% 'SVM:KM' uses and requires svmclass-function from the KM-SVM toolbox +% 'SVM:LINz' LibLinear [10] (requires train.mex from LibLinear somewhere in the path) +% z=0 (default) LibLinear with -- L2-regularized logistic regression +% z=1 LibLinear with -- L2-loss support vector machines (dual) +% z=2 LibLinear with -- L2-loss support vector machines (primal) +% z=3 LibLinear with -- L1-loss support vector machines (dual) +% 'SVM:LIN4' LibLinear with -- multi-class support vector machines by Crammer and Singer + % % {'MDA','MD2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','REG','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse','RDA','GDBC','SVM','RBF'} % @@ -94,8 +112,11 @@ % Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface. % IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). % 1-8 June 2008 Page(s):2390 - 2397 +% [10] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. +% LIBLINEAR: A Library for Large Linear Classification, Journal of Machine Learning Research 9(2008), 1871-1874. +% Software available at http://www.csie.ntu.edu.tw/~cjlin/liblinear - + % $Id: train_sc.m 2140 2009-07-02 12:03:55Z schloegl $ % Copyright (C) 2005,2006,2007,2008,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox @@ -270,33 +291,6 @@ CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; -elseif ~isempty(strfind(MODE.TYPE,'WienerHopf')) - % Q: equivalent to LDA, Regression? - M = length(CC.Labels); - %if M==2, M==1; end; - CC.weights = repmat(NaN,size(D,2)+1,M); - cc = covm(D,'E',W); - for k = 1:M, - w = cc\covm([ones(sz(1),1),D],real(classlabel==CC.Labels(k)),'M',W); - CC.weights(:,k) = w; - end; - CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; - - -elseif ~isempty(strfind(MODE.TYPE,'WienerHopf')) - %% OBSOLETE ??? - % Q: equivalent to LDA, Regression? - M = length(CC.Labels); - %if M==2, M==1; end; - CC.weights = repmat(NaN,size(D,2)+1,M); - for k = 1:M, - ix = ~any(isnan([classlabel,D]),2); - w = covm(D(ix,:),'E')\covm([ones(sum(ix),1),D(ix,:)],(classlabel(ix,:)==CC.Labels(k)),'M'); - CC.weights(:,k) = w; - end; - CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; - - elseif ~isempty(strfind(lower(MODE.TYPE),'/gsvd')) if ~isempty(W) error(sprintf('Error TRAIN_SC: Classifier (%s) does not support weighted samples.',MODE.TYPE)); @@ -461,6 +455,32 @@ CC.datatype = ['classifier:',lower(MODE.TYPE)]; +elseif ~isempty(strfind(lower(MODE.TYPE),'svm:lin4')) + if ~isempty(W) + error(sprintf('Error TRAIN_SC: Classifier (%s) does not support weighted samples.',MODE.TYPE)); + end; + + if ~isfield(MODE.hyperparameter,'c_value') + MODE.hyperparameter.c_value = 1; + end + M = length(CC.Labels); + if M==2, M=1; end; + CC.weights = repmat(NaN, sz(2)+1, M); + + % pre-whitening + [D,r,m]=zscore(D,1); + s = sparse(2:sz(2)+1,1:sz(2),r,sz(2)+1,sz(2),2*sz(2)); + s(1,:) = -m.*r; + + CC.options = sprintf('-s 4 -c %f ', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, + model = train(cl, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, + CC.weights = model.w([end,1:end-1],:)'; + + CC.weights = s * CC.weights(2:end,:) + sparse(1,1:M,CC.weights(1,:),sz(2)+1,M); % include pre-whitening transformation + CC.hyperparameter.c_value = MODE.hyperparameter.c_value; + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + elseif ~isempty(strfind(lower(MODE.TYPE),'svm')) if ~isempty(W) error(sprintf('Error TRAIN_SC: Classifier (%s) does not support weighted samples.',MODE.TYPE)); @@ -471,6 +491,8 @@ end if any(MODE.TYPE==':'), % nothing to be done + elseif exist('train','file')==3, + MODE.TYPE = 'SVM:LIN'; %% liblinear elseif exist('svmtrain','file')==3, MODE.TYPE = 'SVM:LIB'; elseif exist('svmtrain','file')==2, @@ -499,10 +521,23 @@ for k = 1:M, cl = sign((classlabel~=CC.Labels(k))-.5); - if strcmp(MODE.TYPE, 'SVM:LIB'); + if strncmp(MODE.TYPE, 'SVM:LIN',7); if isfield(MODE,'options') CC.options = MODE.options; else + t = 0; + if length(MODE.TYPE>7), t=MODE.TYPE(8)-'0'; end; + if (t<0 || t>4) t=0; end; + CC.options = sprintf('-s %i -c %f ',t, MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, + end; + model = train(cl, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, + w = model.w(1:end-1)'; + Bias = model.w(end); + + elseif strcmp(MODE.TYPE, 'SVM:LIB'); + if isfield(MODE,'options') + CC.options = MODE.options; + else CC.options = sprintf('-s 0 -c %f -t 0 -d 1', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, end; model = svmtrain(cl, D, CC.options); % C-SVC, C=1, linear kernel, degree = 1, @@ -586,7 +621,7 @@ ECM = CC.MD./CC.NN; NC = size(ECM); - if strncmpi(MODE.TYPE,'LD',2); + if strncmpi(MODE.TYPE,'LD',2) || strncmpi(MODE.TYPE,'FDA',3), %if NC(1)==2, NC(1)=1; end; % linear two class problem needs only one discriminant CC.weights = repmat(NaN,NC(2),NC(1)); % memory allocation @@ -609,8 +644,8 @@ cov = COV2; case 6 % LD6 cov = COV1; - otherwise % LD3, LDA - cov = COV0/2; + otherwise % LD3, LDA, FDA + cov = COV0; end if isfield(MODE.hyperparameter,'gamma') cov = cov + mean(diag(cov))*eye(size(cov))*MODE.hyperparameter.gamma; This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-08-05 01:03:05
|
Revision: 6076 http://octave.svn.sourceforge.net/octave/?rev=6076&view=rev Author: schloegl Date: 2009-08-05 01:02:52 +0000 (Wed, 05 Aug 2009) Log Message: ----------- <NEW> FSS: feature subset selection Modified Paths: -------------- trunk/octave-forge/extra/NaN/INDEX trunk/octave-forge/extra/NaN/doc/README.TXT trunk/octave-forge/extra/NaN/inst/partcorrcoef.m Added Paths: ----------- trunk/octave-forge/extra/NaN/inst/fss.m Modified: trunk/octave-forge/extra/NaN/INDEX =================================================================== --- trunk/octave-forge/extra/NaN/INDEX 2009-08-04 12:51:17 UTC (rev 6075) +++ trunk/octave-forge/extra/NaN/INDEX 2009-08-05 01:02:52 UTC (rev 6076) @@ -6,4 +6,4 @@ nansum nanstd normpdf normcdf norminv meandev mod rem percentile quantile rankcorr ranks rms sumskipnan var mean sem spearman trimean tpdf tcdf tinv zscore - conv2nan flag_implicit_significance xcovf + conv2nan flag_implicit_significance xcovf fss Modified: trunk/octave-forge/extra/NaN/doc/README.TXT =================================================================== --- trunk/octave-forge/extra/NaN/doc/README.TXT 2009-08-04 12:51:17 UTC (rev 6075) +++ trunk/octave-forge/extra/NaN/doc/README.TXT 2009-08-05 01:02:52 UTC (rev 6076) @@ -87,6 +87,8 @@ TRIMEAN trimean ECDF empirical cumulative distribution function + CDFPLOT plot empirical cumulative distribution function + GSCATTER scatter plot of grouped data NORMPDF normal probability distribution NORMCDF normal cumulative distribution NORMINV inverse of the normal cumulative distribution @@ -102,6 +104,7 @@ XVAL classify data with cross validation KAPPA performance evaluation TRAIN_LDA_SPARSE utility function + FSS feature subset selection and feature ranking REFERENCE(S): Added: trunk/octave-forge/extra/NaN/inst/fss.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/fss.m (rev 0) +++ trunk/octave-forge/extra/NaN/inst/fss.m 2009-08-05 01:02:52 UTC (rev 6076) @@ -0,0 +1,67 @@ +function [idx,score] = fss(D,cl,N,MODE) +% FSS - feature subset selection +% the method is motivated by the max-relevance-min-redundancy (mRMR) +% approach [1], but instead of mutual information partial correlation +% is used. +% +% [idx,score] = fss(D,cl,MODE) +% +% D data - each column represents a feature +% cl classlabel +% Mode 'Pearson' [default] correlation +% 'rank' correlation +% +% score score of the feature +% idx ranking of the feature +% [tmp,idx]=sort(-score) +% +% REFERENCES: +% [1] Peng, H.C., Long, F., and Ding, C., +% Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy, +% IEEE Transactions on Pattern Analysis and Machine Intelligence, +% Vol. 27, No. 8, pp.1226-1238, 2005. + + +% $Id$ +% Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ + + +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 3 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; If not, see <http://www.gnu.org/licenses/>. + + +if nargin<3, + MODE = []; + N = []; +elseif ischar(N) + MODE = N; + N = []; +elseif nargin<4, + MODE = []; +end; + +if isempty(N) N = size(D,2); end +score = repmat(NaN,1,size(D,2)); + +for k=1:N, + f = isnan(score); + r = partcorrcoef(cl,D(:,f),D(:,~f),MODE); + [s,ix] = max(sumsq(r,1)); + f = find(f); + idx(k) = f(ix); + score(idx(k)) = s; +end; + + Modified: trunk/octave-forge/extra/NaN/inst/partcorrcoef.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/partcorrcoef.m 2009-08-04 12:51:17 UTC (rev 6075) +++ trunk/octave-forge/extra/NaN/inst/partcorrcoef.m 2009-08-05 01:02:52 UTC (rev 6076) @@ -85,21 +85,25 @@ error('Error CORRCOEF: Missing argument(s)\n'); end; -if size(Z,1)~=1, +if size(Z,2)~=1, warning('PARTCORRCOEF: Z has more than 1 dimension'); end; rxy=corrcoef(X,Y,Mode); -rxz=corrcoef(X,Z,Mode); -if isempty(Y), - ryz = rxz; -else - ryz = corrcoef(Y,Z,Mode); +if isempty(Z) + R = rxy; +else + rxz=corrcoef(X,Z,Mode); + if isempty(Y), + ryz = rxz; + else + ryz = corrcoef(Y,Z,Mode); + end; + + %rxy,rxz,ryz + R = (rxy-rxz*ryz')./sqrt((1-rxz.^2)*(1-ryz.^2)'); end; -%rxy,rxz,ryz -R = (rxy-rxz*ryz')./sqrt((1-rxz.^2)*(1-ryz.^2)'); - if nargout<2, return, end; This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-08-06 15:42:18
|
Revision: 6081 http://octave.svn.sourceforge.net/octave/?rev=6081&view=rev Author: schloegl Date: 2009-08-06 15:42:04 +0000 (Thu, 06 Aug 2009) Log Message: ----------- add conversion from categorial to binary data Modified Paths: -------------- trunk/octave-forge/extra/NaN/INDEX Added Paths: ----------- trunk/octave-forge/extra/NaN/inst/cat2bin.m Modified: trunk/octave-forge/extra/NaN/INDEX =================================================================== --- trunk/octave-forge/extra/NaN/INDEX 2009-08-06 13:44:45 UTC (rev 6080) +++ trunk/octave-forge/extra/NaN/INDEX 2009-08-06 15:42:04 UTC (rev 6081) @@ -1,9 +1,11 @@ -nan >> Missing data statistics -Missing data statistics +nan >> A statistics and machine learning toolbox +A statistics and machine learning toolbox for data with and w/o missing values coefficient_of_variation geomean meansq skewness covm cor cov corrcoef harmmean median statistic - detrend kurtosis moment std mad naninsttest nantest + detrend kurtosis moment std mad naninsttest nantest nansum nanstd normpdf normcdf norminv meandev mod rem - percentile quantile rankcorr ranks rms sumskipnan - var mean sem spearman trimean tpdf tcdf tinv zscore - conv2nan flag_implicit_significance xcovf fss + percentile quantile rankcorr ranks rms sumskipnan + var mean sem spearman trimean tpdf tcdf tinv zscore + conv2nan flag_implicit_significance xcovf train_sc test_sc + xval classify train_lda_sparse decovm gscatter mahal + cdfplot hist2res fss cat2bin Added: trunk/octave-forge/extra/NaN/inst/cat2bin.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/cat2bin.m (rev 0) +++ trunk/octave-forge/extra/NaN/inst/cat2bin.m 2009-08-06 15:42:04 UTC (rev 6081) @@ -0,0 +1,81 @@ +function [B,BLab]=cat2bin(D, Label, MODE) +% CAT2BIN converts categorial into binary data +% each category of each column in D is converted into a logical column +% +% B = cat2bin(C); +% [B,BinLabel] = cat2bin(C,Label); +% [B,BinLabel] = cat2bin(C,Label,MODE) +% +% C categorial data +% B binary data +% Label description of each column in C +% BinLabel description of each column in B +% MODE default [], ignores NaN +% 'notIgnoreNAN' includes binary column for NaN +% +% example: +% cat2bin([1;2;5;1;5]) results in +% 1 0 0 +% 0 1 0 +% 0 0 1 +% 1 0 0 +% 0 0 1 + +% $Id$ +% Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. + +if nargin<3, + MODE = []; +end; +if ~strcmpi(MODE,'notIgnoreNAN') + MODE = []; +end; + +% convert data +B = []; + +c = 0; +k1 = 0; +BLab = []; +for m = 1:size(D,2) + h = histo_mex(D(:,m)); + x = h.X(h.H>0); + if isempty(MODE) + x = x(x==x); + end; + for k = 1:size(D,1), + if ~isnan(D(k,m)) + B(k, c + find(D(k,m)==x)) = 1; + end; + end; + + c = c + length(x); + if nargout>1, + for k = 1:length(x), + k1 = k1+1; + if isempty(Label) + BLab{k1} = ['#',int2str(m),':',int2str(x(k))]; + else + BLab{k1} = [Label{m},':',int2str(x(k))]; + end; + end; + end; +end; + + This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-09-30 22:18:34
|
Revision: 6286 http://octave.svn.sourceforge.net/octave/?rev=6286&view=rev Author: schloegl Date: 2009-09-30 22:18:27 +0000 (Wed, 30 Sep 2009) Log Message: ----------- any combination of naive and Kahan summation with double and extended can be controlled through flag_accuracy_level Modified Paths: -------------- trunk/octave-forge/extra/NaN/src/covm_mex.cpp trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp Added Paths: ----------- trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m Added: trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m (rev 0) +++ trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m 2009-09-30 22:18:27 UTC (rev 6286) @@ -0,0 +1,68 @@ +function FLAG = flag_accuracy_level(i) +% FLAG_ACCURACY_LEVEL sets and gets accuracy level +% used in SUMSKIPNAN_MEX and COVM_MEX +% The error margin of the naive summation is N*eps (N is the number of samples), +% the error margin is only 2*eps if Kahan's summation is used [1]. +% +% 0: maximum speed and minimum accuracy (error = N*2^-52) +% of double (64bit float) without Kahan summation +% 1: {default] accuracy of extend double (80bit float) +% without Kahan summation (error = N*2^-64) +% 2: minimum speed and minimum accuracy (error = 2^-64) +% of double with Kahan summation +% 3: accuracy (error = 2^-52) +% of double (64bit float) with Kahan summation +% +% First tests suggest that 1 is a good solution +% +% FLAG = flag_accuracy_level() +% gets current level +% +% flag_accuracy_level(FLAG) +% sets mode +% +% This function is experimental and might disappear without further notice, +% so donot rely on it. +% +% Reference: +% [1] David Goldberg, +% What Every Computer Scientist Should Know About Floating-Point Arithmetic +% ACM Computing Surveys, Vol 23, No 1, March 1991. + + +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 3 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA + +% $Id$ +% Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ + + +persistent FLAG_ACCURACY_LEVEL; + +%% if strcmp(version,'3.6'), FLAG_ACCURACY_LEVEL=1; end; %% hack for the use with Freemat3.6 + +%%% set DEFAULT value of FLAG +if isempty(FLAG_ACCURACY_LEVEL), + FLAG_ACCURACY_LEVEL = 1; +end; + +if nargin>0, + if (i>3) i=3; end; + if (i<0) i=0; end; + FLAG_ACCURACY_LEVEL = double(i); +end; +FLAG = FLAG_ACCURACY_LEVEL; + Modified: trunk/octave-forge/extra/NaN/src/covm_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/covm_mex.cpp 2009-09-30 19:09:46 UTC (rev 6285) +++ trunk/octave-forge/extra/NaN/src/covm_mex.cpp 2009-09-30 22:18:27 UTC (rev 6286) @@ -109,18 +109,18 @@ W = mxGetPr(PInputs[3]); else mexErrMsgTxt("number of elements in W must match numbers of rows in X"); - } - if ((PInputCount > 4) && mxGetChars(PInputs[4])) { - switch (*mxGetChars(PInputs[4])) { - case 'S': - case 's': - flag_speed = 1; - break; - } - } - //mexPrintf("Flag Speed=%i\n",flag_speed); + int ACC_LEVEL = 1; + { + mxArray *LEVEL = NULL; + int s = mexCallMATLAB(1, &LEVEL, 0, NULL, "flag_accuracy_level"); + if (!s) { + ACC_LEVEL = (int) mxGetScalar(LEVEL); + } + mxDestroyArray(LEVEL); + } + mexPrintf("Accuracy Level=%i\n",ACC_LEVEL); if (Y0==NULL) { Y0 = X0; @@ -202,7 +202,7 @@ } #else - if (flag_speed) { + if (ACC_LEVEL == 0) { /*------ version 2 --------------------- this version seems to be faster than the one above. it is also faster but less accurate than the version below @@ -214,6 +214,111 @@ for (j=0; j<cY; j++) { X = X0+i*rX; Y = Y0+j*rY; + double cc=0.0; + double nn=0.0; + for (k=0; k<rX; k++) { + double z = X[k]*Y[k]; + if (isnan(z)) { +#ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + cc += z*W[k]; + nn += W[k]; + } + CC[i+j*cX] = cc; + if (NN != NULL) + NN[i+j*cX] = nn; + } + else /* no weights, all weights are 1 */ + for (i=0; i<cX; i++) + for (j=0; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; + double cc=0.0; + size_t nn=0; + for (k=0; k<rX; k++) { + double z = X[k]*Y[k]; + if (isnan(z)) { +#ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + cc += z; + nn++; + } + CC[i+j*cX] = cc; + if (NN != NULL) + NN[i+j*cX] = (double)nn; + } + else // if (X0==Y0) && (cX==cY) + /******** X==Y, output is symetric *******/ + if (W) /* weighted version */ + for (i=0; i<cX; i++) + for (j=i; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; + double cc=0.0; + double nn=0.0; + for (k=0; k<rX; k++) { + double z = X[k]*Y[k]; + if (isnan(z)) { +#ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + cc += z*W[k]; + nn += W[k]; + } + CC[i+j*cX] = cc; + CC[j+i*cX] = cc; + if (NN != NULL) { + NN[i+j*cX] = nn; + NN[j+i*cX] = nn; + } + } + else /* no weights, all weights are 1 */ + for (i=0; i<cX; i++) + for (j=i; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; + double cc=0.0; + size_t nn=0; + for (k=0; k<rX; k++) { + double z = X[k]*Y[k]; + if (isnan(z)) { +#ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + cc += z; + nn++; + } + CC[i+j*cX] = cc; + CC[j+i*cX] = cc; + if (NN != NULL) { + NN[i+j*cX] = (double)nn; + NN[j+i*cX] = (double)nn; + } + } + + } + else if (ACC_LEVEL == 1) { + /*------ version 2 --------------------- + this version seems to be faster than the one above. + it is also faster but less accurate than the version below + */ + if ( (X0 != Y0) || (cX != cY) ) + /******** X!=Y, output is not symetric *******/ + if (W) /* weighted version */ + for (i=0; i<cX; i++) + for (j=0; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; long double cc=0.0; long double nn=0.0; for (k=0; k<rX; k++) { @@ -307,7 +412,7 @@ } } - else { + else if (ACC_LEVEL == 2) { /*------ version 3 --------------------- using Kahan's summation formula [1] this gives more accurate results while the computational effort within the loop is about 4x as high @@ -454,8 +559,156 @@ } } } + else if (ACC_LEVEL == 3) { + /*------ version 3 --------------------- + using Kahan's summation formula [1] + this gives more accurate results while the computational effort within the loop is about 4x as high + However, first test show an increase in computational time of only about 25 %. + [1] David Goldberg, + What Every Computer Scientist Should Know About Floating-Point Arithmetic + ACM Computing Surveys, Vol 23, No 1, March 1991 + */ + if ( (X0 != Y0) || (cX != cY) ) + /******** X!=Y, output is not symetric *******/ + if (W) /* weighted version */ + for (i=0; i<cX; i++) + for (j=0; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; + double cc=0.0; + double nn=0.0; + double rc=0.0; + double rn=0.0; + for (k=0; k<rX; k++) { + double t,y; + double z = X[k]*Y[k]; + if (isnan(z)) { #ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + // cc += z*W[k]; [1] + y = z*W[k]-rc; + t = cc+y; + rc= (t-cc)-y; + cc= t; + + // nn += W[k]; [1] + y = z*W[k]-rn; + t = nn+y; + rn= (t-nn)-y; + nn= t; + } + CC[i+j*cX] = cc; + if (NN != NULL) + NN[i+j*cX] = nn; + } + else /* no weights, all weights are 1 */ + for (i=0; i<cX; i++) + for (j=0; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; + double cc=0.0; + double rc=0.0; + size_t nn=0; + for (k=0; k<rX; k++) { + double t,y; + double z = X[k]*Y[k]; + if (isnan(z)) { +#ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + // cc += z; [1] + y = z-rc; + t = cc+y; + rc= (t-cc)-y; + cc= t; + + nn++; + } + CC[i+j*cX] = cc; + if (NN != NULL) + NN[i+j*cX] = (double)nn; + } + else // if (X0==Y0) && (cX==cY) + /******** X==Y, output is symetric *******/ + if (W) /* weighted version */ + for (i=0; i<cX; i++) + for (j=i; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; + double cc=0.0; + double nn=0.0; + double rc=0.0; + double rn=0.0; + for (k=0; k<rX; k++) { + double t,y; + double z = X[k]*Y[k]; + if (isnan(z)) { +#ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + // cc += z*W[k]; [1] + y = z*W[k]-rc; + t = cc+y; + rc= (t-cc)-y; + cc= t; + + // nn += W[k]; [1] + y = z*W[k]-rn; + t = nn+y; + rn= (t-nn)-y; + nn= t; + } + CC[i+j*cX] = cc; + CC[j+i*cX] = cc; + if (NN != NULL) { + NN[i+j*cX] = nn; + NN[j+i*cX] = nn; + } + } + else /* no weights, all weights are 1 */ + for (i=0; i<cX; i++) + for (j=i; j<cY; j++) { + X = X0+i*rX; + Y = Y0+j*rY; + double cc=0.0; + double rc=0.0; + size_t nn=0; + for (k=0; k<rX; k++) { + double t,y; + double z = X[k]*Y[k]; + if (isnan(z)) { +#ifndef NO_FLAG + flag_isNaN = 1; +#endif + continue; + } + // cc += z; [1] + y = z-rc; + t = cc+y; + rc= (t-cc)-y; + cc= t; + + nn++; + } + CC[i+j*cX] = cc; + CC[j+i*cX] = cc; + if (NN != NULL) { + NN[i+j*cX] = (double)nn; + NN[j+i*cX] = (double)nn; + } + } + } + + +#ifndef NO_FLAG //mexPrintf("Third argument must be not empty - otherwise status whether a NaN occured or not cannot be returned."); /* this is a hack, the third input argument is used to return whether a NaN occured or not. this requires that the input argument is a non-empty variable Modified: trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-09-30 19:09:46 UTC (rev 6285) +++ trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-09-30 22:18:27 UTC (rev 6286) @@ -53,10 +53,14 @@ #include <math.h> #include "mex.h" +inline int __sumskipnan2wr__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); +inline int __sumskipnan3wr__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W); inline int __sumskipnan2w__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); inline int __sumskipnan3w__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W); inline int __sumskipnan2we__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); inline int __sumskipnan3we__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W); +inline int __sumskipnan2wer__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); +inline int __sumskipnan3wer__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W); //#define NO_FLAG @@ -149,8 +153,21 @@ W = mxGetPr(PInputs[3]); else mexErrMsgTxt("Error SUMSKIPNAN.MEX: length of weight vector does not match size of dimension"); - } + } + int ACC_LEVEL = 1; + { + mxArray *LEVEL = NULL; + int s = mexCallMATLAB(1, &LEVEL, 0, NULL, "flag_accuracy_level"); + if (!s) { + ACC_LEVEL = (int) mxGetScalar(LEVEL); + if ((D1>1) && (ACC_LEVEL>2)) + mexWarnMsgTxt("Warning: Kahan summation not supported with stride > 1 !"); + } + mxDestroyArray(LEVEL); + } + mexPrintf("Accuracy Level=%i\n",ACC_LEVEL); + // create outputs #define TYP mxDOUBLE_CLASS @@ -172,23 +189,101 @@ if (D1*D2*D3<1) // zero size array ; // do nothing - else if ((POutputCount <= 1) && !mxIsComplex(PInputs[0])) { + else if ((D1==1) && (ACC_LEVEL<1)) { + // extended accuray, naive summation, error = N*2^-64 + switch (POutputCount) { + case 1: + for (l = 0; l<D3; l++) { + double count; + __sumskipnan2wr__(LInput+l*D2, D2, LOutputSum+l, &count, &flag_isNaN, W); + } + break; + case 2: + for (l = 0; l<D3; l++) { + __sumskipnan2wr__(LInput+l*D2, D2, LOutputSum+l, LOutputCount+l, &flag_isNaN, W); + } + break; + case 3: + for (l = 0; l<D3; l++) { + __sumskipnan3wr__(LInput+l*D2, D2, LOutputSum+l, LOutputSum2+l, LOutputCount+l, &flag_isNaN, W); + } + break; + } + } + else if ((D1==1) && (ACC_LEVEL==1)) { + // extended accuray, naive summation, error = N*2^-64 + switch (POutputCount) { + case 1: + for (l = 0; l<D3; l++) { + double count; + __sumskipnan2w__(LInput+l*D2, D2, LOutputSum+l, &count, &flag_isNaN, W); + } + break; + case 2: + for (l = 0; l<D3; l++) { + __sumskipnan2w__(LInput+l*D2, D2, LOutputSum+l, LOutputCount+l, &flag_isNaN, W); + } + break; + case 3: + for (l = 0; l<D3; l++) { + __sumskipnan3w__(LInput+l*D2, D2, LOutputSum+l, LOutputSum2+l, LOutputCount+l, &flag_isNaN, W); + } + break; + } + } + else if ((D1==1) && (ACC_LEVEL==2)) { + // ACC_LEVEL==2: extended accuracy and Kahan Summation, error = 2^-64 + switch (POutputCount) { + case 1: + for (l = 0; l<D3; l++) { + double count; + __sumskipnan2we__(LInput+l*D2, D2, LOutputSum+l, &count, &flag_isNaN, W); + } + break; + case 2: + for (l = 0; l<D3; l++) { + __sumskipnan2we__(LInput+l*D2, D2, LOutputSum+l, LOutputCount+l, &flag_isNaN, W); + } + break; + case 3: + for (l = 0; l<D3; l++) { + __sumskipnan3we__(LInput+l*D2, D2, LOutputSum+l, LOutputSum2+l, LOutputCount+l, &flag_isNaN, W); + } + break; + } + } + else if ((D1==1) && (ACC_LEVEL==3)) { + // ACC_LEVEL==2: extended accuracy and Kahan Summation, error = 2^-64 + switch (POutputCount) { + case 1: + for (l = 0; l<D3; l++) { + double count; + __sumskipnan2wer__(LInput+l*D2, D2, LOutputSum+l, &count, &flag_isNaN, W); + } + break; + case 2: + for (l = 0; l<D3; l++) { + __sumskipnan2wer__(LInput+l*D2, D2, LOutputSum+l, LOutputCount+l, &flag_isNaN, W); + } + break; + case 3: + for (l = 0; l<D3; l++) { + __sumskipnan3wer__(LInput+l*D2, D2, LOutputSum+l, LOutputSum2+l, LOutputCount+l, &flag_isNaN, W); + } + break; + } + } + else if (POutputCount <= 1) { // OUTER LOOP: along dimensions > DIM for (l = 0; l<D3; l++) { ix0 = l*D1; // index for output ix1 = ix0*D2; // index for input - if (D1==1) - { - double count; - __sumskipnan2we__(LInput+ix1, D2, LOutputSum+ix0, &count, &flag_isNaN, W); - } - else { - for (j=0; j<D2; j++) { + for (j=0; j<D2; j++) { // minimize cache misses ix2 = ix0; // index for output // Inner LOOP: along dimensions < DIM if (W) do { - register long double x = *LInput; + long double x = *LInput; if (!isnan(x)) { LongOutputSum[ix2] += W[j]*x; } @@ -200,7 +295,7 @@ ix2++; } while (ix2 != (l+1)*D1); else do { - register long double x = *LInput; + long double x = *LInput; if (!isnan(x)) { LongOutputSum[ix2] += x; } @@ -211,32 +306,26 @@ LInput++; ix2++; } while (ix2 != (l+1)*D1); - } // end for (j= + } // end for (j= - /* copy to output */ - for (j=0; j<D1; j++) { - LOutputSum[ix0+j] = LongOutputSum[ix0+j]; - } - } // end else + /* copy to output */ + for (j=0; j<D1; j++) { + LOutputSum[ix0+j] = LongOutputSum[ix0+j]; + } } } - else if ((POutputCount == 2) && !mxIsComplex(PInputs[0])) { + else if (POutputCount == 2) { // OUTER LOOP: along dimensions > DIM for (l = 0; l<D3; l++) { ix0 = l*D1; ix1 = ix0*D2; // index for input - if (D1==1) - { - __sumskipnan2we__(LInput+ix1, D2, LOutputSum+ix0, LOutputCount+ix0, &flag_isNaN, W); - } - else { - for (j=0; j<D2; j++) { + for (j=0; j<D2; j++) { // minimize cache misses ix2 = ix0; // index for output // Inner LOOP: along dimensions < DIM if (W) do { - register long double x = *LInput; + long double x = *LInput; if (!isnan(x)) { LongOutputCount[ix2] += W[j]; LongOutputSum[ix2] += W[j]*x; @@ -249,7 +338,7 @@ ix2++; } while (ix2 != (l+1)*D1); else do { - register long double x = *LInput; + long double x = *LInput; if (!isnan(x)) { LongOutputCount[ix2] += 1.0; LongOutputSum[ix2] += x; @@ -261,33 +350,27 @@ LInput++; ix2++; } while (ix2 != (l+1)*D1); - } // end for (j= + } // end for (j= /* copy to output */ - for (j=0; j<D1; j++) { - LOutputSum[ix0+j] = LongOutputSum[ix0+j]; - LOutputCount[ix0+j] = LongOutputCount[ix0+j]; - } - } // end else + for (j=0; j<D1; j++) { + LOutputSum[ix0+j] = LongOutputSum[ix0+j]; + LOutputCount[ix0+j] = LongOutputCount[ix0+j]; + } // end else } } - else if ((POutputCount == 3) && !mxIsComplex(PInputs[0])) { + else if (POutputCount == 3) { // OUTER LOOP: along dimensions > DIM for (l = 0; l<D3; l++) { ix0 = l*D1; ix1 = ix0*D2; // index for input - if (D1==1) - { - __sumskipnan3we__(LInput+ix1, D2, LOutputSum+ix0, LOutputSum2+ix0, LOutputCount+ix0, &flag_isNaN, W); - } - else { - for (j=0; j<D2; j++) { + for (j=0; j<D2; j++) { // minimize cache misses ix2 = ix0; // index for output // Inner LOOP: along dimensions < DIM if (W) do { - register long double x = *LInput; + long double x = *LInput; if (!isnan(x)) { LongOutputCount[ix2] += W[j]; double t = W[j]*x; @@ -302,7 +385,7 @@ ix2++; } while (ix2 != (l+1)*D1); else do { - register long double x = *LInput; + long double x = *LInput; if (!isnan(x)) { LongOutputCount[ix2] += 1.0; LongOutputSum[ix2] += x; @@ -315,15 +398,14 @@ LInput++; ix2++; } while (ix2 != (l+1)*D1); - } // end for (j= + } // end for (j= - /* copy to output */ - for (j=0; j<D1; j++) { - LOutputSum[ix0+j] = LongOutputSum[ix0+j]; - LOutputCount[ix0+j] = LongOutputCount[ix0+j]; - LOutputSum2[ix0+j] = LongOutputSum2[ix0+j]; - } - } // end else + /* copy to output */ + for (j=0; j<D1; j++) { + LOutputSum[ix0+j] = LongOutputSum[ix0+j]; + LOutputCount[ix0+j] = LongOutputCount[ix0+j]; + LOutputSum2[ix0+j] = LongOutputSum2[ix0+j]; + } } } if (LongOutputSum) mxFree(LongOutputSum); @@ -385,7 +467,7 @@ // with weight vector long double count = 0.0; do { - register double x = *data; + long double x = *data; if (!isnan(x)) { count += *W; @@ -405,7 +487,7 @@ // w/o weight vector size_t countI = 0; do { - double x = *data; + long double x = *data; if (!isnan(x)) { countI++; @@ -441,7 +523,117 @@ // with weight vector long double count = 0.0; do { + long double x = *data; + if (!isnan(x)) { + count += *W; + long double t = *W*x; + sum += t; + msq += x*t; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + data++; // stride=1 + W++; + } + while (data < end); + *No = count; + } else { + // w/o weight vector + size_t countI = 0; + do { + long double x = *data; + if (!isnan(x)) { + countI++; + sum += x; + msq += x*x; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + data++; // stride=1 + } + while (data < end); + *No = (double)countI; + } + +#ifndef NO_FLAG + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; +#endif + *s = sum; + *s2 = msq; +} + +inline int __sumskipnan2wr__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W) +{ + double sum=0; + char flag=0; + // LOOP along dimension DIM + + void *end = data + stride*Ni; + if (W) { + // with weight vector + double count = 0.0; + do { double x = *data; + if (!isnan(x)) + { + count += *W; + sum += *W*x; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + + data++; // stride=1 + W++; + } + while (data < end); + *No = count; + } else { + // w/o weight vector + size_t countI = 0; + do { + double x = *data; + if (!isnan(x)) + { + countI++; + sum += x; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + data++; // stride=1 + } + while (data < end); + *No = (double)countI; + } + +#ifndef NO_FLAG + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; +#endif + *s = sum; + +} + + +inline int __sumskipnan3wr__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W) +{ + double sum=0; + double msq=0; + char flag=0; + // LOOP along dimension DIM + + void *end = data + stride*Ni; + if (W) { + // with weight vector + double count = 0.0; + do { + double x = *data; if (!isnan(x)) { count += *W; double t = *W*x; @@ -486,7 +678,16 @@ +/*************************************** + using Kahan's summation formula [1] + this gives more accurate results while the computational effort within the loop is about 4x as high + First tests show a penalty of about 40% in terms of computational time. + [1] David Goldberg, + What Every Computer Scientist Should Know About Floating-Point Arithmetic + ACM Computing Surveys, Vol 23, No 1, March 1991. + ****************************************/ + inline int __sumskipnan2we__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W) { long double sum=0; @@ -499,7 +700,7 @@ long double count = 0.0; long double rc=0.0, rn=0.0; do { - register double x = *data; + long double x = *data; long double t,y; if (!isnan(x)) { @@ -530,7 +731,7 @@ size_t countI = 0; long double rc=0.0; do { - double x = *data; + long double x = *data; long double t,y; if (!isnan(x)) { @@ -559,17 +760,6 @@ } - -/*************************************** - using Kahan's summation formula [1] - this gives more accurate results while the computational effort within the loop is about 4x as high - First tests show a penalty of about 40% in terms of computational time. - - [1] David Goldberg, - What Every Computer Scientist Should Know About Floating-Point Arithmetic - ACM Computing Surveys, Vol 23, No 1, March 1991. - ****************************************/ - inline int __sumskipnan3we__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W) { long double sum=0; @@ -583,7 +773,7 @@ long double count = 0.0; long double rc=0.0, rn=0.0, rq=0.0; do { - double x = *data; + long double x = *data; long double t,y; if (!isnan(x)) { //count += *W; [1] @@ -619,7 +809,7 @@ size_t countI = 0; long double rc=0.0, rq=0.0; do { - double x = *data; + long double x = *data; long double t,y; if (!isnan(x)) { countI++; @@ -652,3 +842,157 @@ *s2 = msq; } +inline int __sumskipnan2wer__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W) +{ + double sum=0; + char flag=0; + // LOOP along dimension DIM + + void *end = data + stride*Ni; + if (W) { + // with weight vector + double count = 0.0; + double rc=0.0, rn=0.0; + do { + double x = *data; + double t,y; + if (!isnan(x)) + { + //count += *W; [1] + y = *W-rn; + t = count+y; + rn= (t-count)-y; + count= t; + + //sum += *W*x; [1] + y = *W*x-rc; + t = sum+y; + rc= (t-sum)-y; + sum= t; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + + data++; // stride=1 + W++; + } + while (data < end); + *No = count; + } else { + // w/o weight vector + size_t countI = 0; + double rc=0.0; + do { + double x = *data; + double t,y; + if (!isnan(x)) + { + countI++; + // sum += x; [1] + y = x-rc; + t = sum+y; + rc= (t-sum)-y; + sum= t; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + data++; // stride=1 + } + while (data < end); + *No = (double)countI; + } + +#ifndef NO_FLAG + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; +#endif + *s = sum; + +} + + +inline int __sumskipnan3wer__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W) +{ + double sum=0; + double msq=0; + char flag=0; + // LOOP along dimension DIM + + void *end = data + stride*Ni; + if (W) { + // with weight vector + double count = 0.0; + double rc=0.0, rn=0.0, rq=0.0; + do { + double x = *data; + double t,y; + if (!isnan(x)) { + //count += *W; [1] + y = *W-rn; + t = count+y; + rn= (t-count)-y; + count= t; + + double w = *W*x; + //sum += *W*x; [1] + y = *W*x-rc; + t = sum+y; + rc= (t-sum)-y; + sum= t; + + // msq += x*w; + y = w*x-rq; + t = msq+y; + rq= (t-msq)-y; + msq= t; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + data++; // stride=1 + W++; + } + while (data < end); + *No = count; + } else { + // w/o weight vector + size_t countI = 0; + double rc=0.0, rq=0.0; + do { + double x = *data; + double t,y; + if (!isnan(x)) { + countI++; + //sum += x; [1] + y = x-rc; + t = sum+y; + rc= (t-sum)-y; + sum= t; + + // msq += x*x; + y = x*x-rq; + t = msq+y; + rq= (t-msq)-y; + msq= t; + } +#ifndef NO_FLAG + else + flag = 1; +#endif + data++; // stride=1 + } + while (data < end); + *No = (double)countI; + } + +#ifndef NO_FLAG + if (flag && (flag_anyISNAN != NULL)) *flag_anyISNAN = 1; +#endif + *s = sum; + *s2 = msq; +} + This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-10-01 13:40:05
|
Revision: 6287 http://octave.svn.sourceforge.net/octave/?rev=6287&view=rev Author: schloegl Date: 2009-10-01 12:13:51 +0000 (Thu, 01 Oct 2009) Log Message: ----------- default accuracy level set to 0; level 2 and 3 exchanged; added test to determine optimal accuracy level Modified Paths: -------------- trunk/octave-forge/extra/NaN/doc/README.TXT trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m trunk/octave-forge/extra/NaN/src/covm_mex.cpp trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp Added Paths: ----------- trunk/octave-forge/extra/NaN/inst/acctest.m Modified: trunk/octave-forge/extra/NaN/doc/README.TXT =================================================================== --- trunk/octave-forge/extra/NaN/doc/README.TXT 2009-09-30 22:18:27 UTC (rev 6286) +++ trunk/octave-forge/extra/NaN/doc/README.TXT 2009-10-01 12:13:51 UTC (rev 6287) @@ -41,6 +41,11 @@ sumskipnan, covm, center, cor, coefficient of variation, corrcoef, geomean, harmmean, kurtosis, mad, mean, meandev, meansq, moment, nanmean, nanstd, nansum, rms, sem, skewness, statistic, std, var + FLAG_IMPLICIT_SKIP_NAN can be used to turn off and on the NaN-skipping behaviour. This can + be useful for debugging or for compatibility reasons. + FLAG_ACCURACY_LEVEL can be used to increase the accuracy of summations (sumskipnan and covm) + at the cost of speed. + LOAD_FISHERIRIS loads famous fisher iris data set level 2a: derived functions MEAN mean (options: arithmetic, geometric, harmonic) Added: trunk/octave-forge/extra/NaN/inst/acctest.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/acctest.m (rev 0) +++ trunk/octave-forge/extra/NaN/inst/acctest.m 2009-10-01 12:13:51 UTC (rev 6287) @@ -0,0 +1,86 @@ +% ACCTEST is a script to evaluate the accuracy and speed of +% of differnt accuracy levels in SUMSKIPNAN_MEX and COVM_MEX +% +% see also: FLAG_ACCURACY_LEVEL, SUMSKIPNAN_MEX, COVM_MEX +% +% Reference: +% [1] David Goldberg, +% What Every Computer Scientist Should Know About Floating-Point Arithmetic +% ACM Computing Surveys, Vol 23, No 1, March 1991. + +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 3 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA + +% $Id$ +% Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ + +clear +flag=0; +N = 1e7; +x=randn(N,10)+1e6; + +flag_accuracy_level(0); +tic,t=cputime();[cc0,nn0]=covm_mex(x,[],flag);t0=[cputime-t,toc]; + +flag_accuracy_level(1); +tic,t=cputime();[cc1,nn1]=covm_mex(x,[],flag);t1=[cputime-t,toc]; + +flag_accuracy_level(2); +tic,t=cputime();[cc2,nn2]=covm_mex(x,[],flag);t2=[cputime-t,toc]; + +flag_accuracy_level(3); +tic,t=cputime();[cc3,nn3]=covm_mex(x,[],flag);t3=[cputime-t,toc]; + +flag_accuracy_level(0); +tic,t=cputime();[c0,n0]=sumskipnan_mex(x,1,flag);t0s=[cputime-t,toc]; + +flag_accuracy_level(1); +tic,t=cputime();[c1,n1]=sumskipnan_mex(x,1,flag);t1s=[cputime-t,toc]; + +flag_accuracy_level(2); +tic,t=cputime();[c2,n2]=sumskipnan_mex(x,1,flag);t2s=[cputime-t,toc]; + +flag_accuracy_level(3); +tic,t=cputime();[c3,n3]=sumskipnan_mex(x,1,flag);t3s=[cputime-t,toc]; + +cc = {cc0,cc1,cc2,cc3}; +c = {c0,c1,c2,c3}; +tt = [t0;t1;t2;t3]; +t = [t0s;t1s;t2s;t3s]; +fprintf('Sum squared differences between accuracy levels:\n'); +fprintf('Level:\t|(0) naive-dou\t|(1) naive-ext\t|(2) kahan-dou \t| (3) kahan-ext\n') +fprintf('error:\t|N*2^-52\t|N*2^-64\t| 2^-52 \t| 2^-64\n') +fprintf('COVM_MEX:\ntime:\t|%f\t|%f\t| %f \t| %f',tt(:,1)) +for K1=1:4, +fprintf('\n(%i)\t',K1-1); +for K2=1:4, + EE(K1,K2)=sum(sum((cc{K1}-cc{K2}).^2)); + E(K1,K2)=sum(sum((c{K1}-c{K2}).^2)); + fprintf('|%8g\t',EE(K1,K2)/nn1(1)); +end; +end; +fprintf('\nSUMSKIPNAN_MEX:\n') +fprintf('time:\t|%f\t|%f\t| %f \t| %f',t(:,1)) +for K1=1:4, +fprintf('\n(%i)\t',K1-1); +for K2=1:4, + fprintf('|%8g\t',E(K1,K2)/n1(1)); +end; +end; +fprintf('\n'); + + + Modified: trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m 2009-09-30 22:18:27 UTC (rev 6286) +++ trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m 2009-10-01 12:13:51 UTC (rev 6287) @@ -4,26 +4,23 @@ % The error margin of the naive summation is N*eps (N is the number of samples), % the error margin is only 2*eps if Kahan's summation is used [1]. % -% 0: maximum speed and minimum accuracy (error = N*2^-52) -% of double (64bit float) without Kahan summation -% 1: {default] accuracy of extend double (80bit float) -% without Kahan summation (error = N*2^-64) -% 2: minimum speed and minimum accuracy (error = 2^-64) -% of double with Kahan summation -% 3: accuracy (error = 2^-52) -% of double (64bit float) with Kahan summation +% 0: maximum speed [default] +% accuracy of double (64bit) with naive summation (error = N*2^-52) +% 1: accuracy of extended (80bit) with naive summation (error = N*2^-64) +% 2: accuracy of double (64bit) with Kahan summation (error = 2^-52) +% 3: accuracy of extended (80bit) with Kahan summation (error = 2^-64) % -% First tests suggest that 1 is a good solution -% +% Please not, that level 3 might be equally accurate but slower than 1 or 2 or +% on some platforms. In order to determine what is good for you, you might want +% to run ACCTEST. +% % FLAG = flag_accuracy_level() % gets current level -% -% flag_accuracy_level(FLAG) -% sets mode +% flag_accuracy_level(FLAG) +% sets accuracy level % -% This function is experimental and might disappear without further notice, -% so donot rely on it. -% +% see also: ACCTEST +% % Reference: % [1] David Goldberg, % What Every Computer Scientist Should Know About Floating-Point Arithmetic @@ -54,9 +51,15 @@ %% if strcmp(version,'3.6'), FLAG_ACCURACY_LEVEL=1; end; %% hack for the use with Freemat3.6 +%% set the default accuracy level for your platform, ACCTEST might help to determine the optimum for your platform. +DEFAULT_ACCURACY_LEVEL = 0; %% maximum speed, accuracy sufficient for most needs. +%% DEFAULT_ACCURACY_LEVEL = 1; %% slower, but better accuracy for: Intel Atom +%% DEFAULT_ACCURACY_LEVEL = 2; %% slower, but better accuracy for: AMDx64 Opteron, Phenom, Intel Pentium +%% DEFAULT_ACCURACY_LEVEL = 3; %% similar accuracy than 1 or 2 (depending on platform) but even slower. + %%% set DEFAULT value of FLAG if isempty(FLAG_ACCURACY_LEVEL), - FLAG_ACCURACY_LEVEL = 1; + FLAG_ACCURACY_LEVEL = DEFAULT_ACCURACY_LEVEL; end; if nargin>0, Modified: trunk/octave-forge/extra/NaN/src/covm_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/covm_mex.cpp 2009-09-30 22:18:27 UTC (rev 6286) +++ trunk/octave-forge/extra/NaN/src/covm_mex.cpp 2009-10-01 12:13:51 UTC (rev 6287) @@ -19,6 +19,8 @@ // // // covm: in-product of matrices, NaN are skipped. +// usage: +// [cc,nn] = covm_mex(X,Y,flag,W); // // Input: // - X: @@ -120,7 +122,7 @@ } mxDestroyArray(LEVEL); } - mexPrintf("Accuracy Level=%i\n",ACC_LEVEL); + // mexPrintf("Accuracy Level=%i\n",ACC_LEVEL); if (Y0==NULL) { Y0 = X0; @@ -204,8 +206,7 @@ #else if (ACC_LEVEL == 0) { /*------ version 2 --------------------- - this version seems to be faster than the one above. - it is also faster but less accurate than the version below + using naive summation with double accuracy [1] */ if ( (X0 != Y0) || (cX != cY) ) /******** X!=Y, output is not symetric *******/ @@ -309,8 +310,7 @@ } else if (ACC_LEVEL == 1) { /*------ version 2 --------------------- - this version seems to be faster than the one above. - it is also faster but less accurate than the version below + using naive summation with extended accuracy [1] */ if ( (X0 != Y0) || (cX != cY) ) /******** X!=Y, output is not symetric *******/ @@ -412,9 +412,9 @@ } } - else if (ACC_LEVEL == 2) { + else if (ACC_LEVEL == 3) { /*------ version 3 --------------------- - using Kahan's summation formula [1] + using Kahan's summation with extended (long double) accuracy [1] this gives more accurate results while the computational effort within the loop is about 4x as high However, first test show an increase in computational time of only about 25 %. @@ -559,9 +559,9 @@ } } } - else if (ACC_LEVEL == 3) { + else if (ACC_LEVEL == 2) { /*------ version 3 --------------------- - using Kahan's summation formula [1] + using Kahan's summation with double accuracy [1] this gives more accurate results while the computational effort within the loop is about 4x as high However, first test show an increase in computational time of only about 25 %. Modified: trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-09-30 22:18:27 UTC (rev 6286) +++ trunk/octave-forge/extra/NaN/src/sumskipnan_mex.cpp 2009-10-01 12:13:51 UTC (rev 6287) @@ -53,10 +53,10 @@ #include <math.h> #include "mex.h" +inline int __sumskipnan2w__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); +inline int __sumskipnan3w__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W); inline int __sumskipnan2wr__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); inline int __sumskipnan3wr__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W); -inline int __sumskipnan2w__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); -inline int __sumskipnan3w__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W); inline int __sumskipnan2we__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); inline int __sumskipnan3we__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W); inline int __sumskipnan2wer__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W); @@ -166,7 +166,7 @@ } mxDestroyArray(LEVEL); } - mexPrintf("Accuracy Level=%i\n",ACC_LEVEL); + // mexPrintf("Accuracy Level=%i\n",ACC_LEVEL); // create outputs #define TYP mxDOUBLE_CLASS @@ -190,7 +190,7 @@ if (D1*D2*D3<1) // zero size array ; // do nothing else if ((D1==1) && (ACC_LEVEL<1)) { - // extended accuray, naive summation, error = N*2^-64 + // double accuray, naive summation, error = N*2^-52 switch (POutputCount) { case 1: for (l = 0; l<D3; l++) { @@ -231,8 +231,8 @@ break; } } - else if ((D1==1) && (ACC_LEVEL==2)) { - // ACC_LEVEL==2: extended accuracy and Kahan Summation, error = 2^-64 + else if ((D1==1) && (ACC_LEVEL==3)) { + // ACC_LEVEL==3: extended accuracy and Kahan Summation, error = 2^-64 switch (POutputCount) { case 1: for (l = 0; l<D3; l++) { @@ -252,8 +252,8 @@ break; } } - else if ((D1==1) && (ACC_LEVEL==3)) { - // ACC_LEVEL==2: extended accuracy and Kahan Summation, error = 2^-64 + else if ((D1==1) && (ACC_LEVEL==2)) { + // ACC_LEVEL==2: double accuracy and Kahan Summation, error = 2^-52 switch (POutputCount) { case 1: for (l = 0; l<D3; l++) { This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2009-11-02 15:57:20
|
Revision: 6426 http://octave.svn.sourceforge.net/octave/?rev=6426&view=rev Author: schloegl Date: 2009-11-02 15:57:13 +0000 (Mon, 02 Nov 2009) Log Message: ----------- removed in order to avoid warning messages that confuse the use Modified Paths: -------------- trunk/octave-forge/extra/NaN/INDEX trunk/octave-forge/extra/NaN/doc/README.TXT Removed Paths: ------------- trunk/octave-forge/extra/NaN/inst/mod.m trunk/octave-forge/extra/NaN/inst/rem.m Modified: trunk/octave-forge/extra/NaN/INDEX =================================================================== --- trunk/octave-forge/extra/NaN/INDEX 2009-11-01 23:17:33 UTC (rev 6425) +++ trunk/octave-forge/extra/NaN/INDEX 2009-11-02 15:57:13 UTC (rev 6426) @@ -3,7 +3,7 @@ coefficient_of_variation geomean meansq skewness covm cor cov corrcoef harmmean median statistic detrend kurtosis moment std mad naninsttest nantest - nansum nanstd normpdf normcdf norminv meandev mod rem + nansum nanstd normpdf normcdf norminv meandev percentile quantile rankcorr ranks rms sumskipnan var mean sem spearman trimean tpdf tcdf tinv zscore conv2nan flag_implicit_significance xcovf train_sc test_sc Modified: trunk/octave-forge/extra/NaN/doc/README.TXT =================================================================== --- trunk/octave-forge/extra/NaN/doc/README.TXT 2009-11-01 23:17:33 UTC (rev 6425) +++ trunk/octave-forge/extra/NaN/doc/README.TXT 2009-11-02 15:57:13 UTC (rev 6426) @@ -34,8 +34,6 @@ NANFILTER filter function CONVSKIPNAN convolution CONV2SKIPNAN (CONV2NAN) 2-dimensional convolution - MOD modulus - REM remainder FLAG_NANS_OCCURED returns 0 if no NaN's appeared in the input data of the last call to one of the following functions, and 1 otherwise: sumskipnan, covm, center, cor, coefficient of variation, corrcoef, geomean, harmmean, Deleted: trunk/octave-forge/extra/NaN/inst/mod.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/mod.m 2009-11-01 23:17:33 UTC (rev 6425) +++ trunk/octave-forge/extra/NaN/inst/mod.m 2009-11-02 15:57:13 UTC (rev 6426) @@ -1,62 +0,0 @@ -function [z,e] = mod(x,y) -% MOD(x,y) calculates Modules Y from X -% -% z = x - y * floor(x/y); -% e = eps * floor(x/y); -% -% [z,e] = MOD(X,Y) -% z is the modulus of Y for X -% e is the error tolerance, for checking the accuracy -% z(e > abs(y)) is not defined -% -% z has always the same sign than y -% -% see also: REM - - -% This program is free software; you can redistribute it and/or modify -% it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or -% (at your option) any later version. -% -% This program is distributed in the hope that it will be useful, -% but WITHOUT ANY WARRANTY; without even the implied warranty of -% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -% GNU General Public License for more details. -% -% You should have received a copy of the GNU General Public License -% along with this program; If not, see <http://www.gnu.org/licenses/>. - -% $Id$ -% Copyright (C) 2004,2009 by Alois Schloegl <a.s...@ie...> -% This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ - - -s = warning; -warning('off'); - -if ((numel(x)~=1) && (numel(y)~=1) && any(size(x)~=size(y))) - error('Size of input arguments do not fit.'); -end; - -t = floor(x./y); -z = x - y.*t; - -if numel(x)==1, - z(~t) = x; % remainder is x if y = inf -else - z(~t) = x(~t); % remainder is x if y = inf -end; -z(repmat(~y,size(z)./size(y))) = 0; % remainder must be 0 if y==0 - -warning(s); % reset warning status - -if nargout > 1, - e = (abs(t)*eps); % error interval - %z(e > abs(y)) = NaN; % uncertainty of rounding error to large -end; - - - - Deleted: trunk/octave-forge/extra/NaN/inst/rem.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/rem.m 2009-11-01 23:17:33 UTC (rev 6425) +++ trunk/octave-forge/extra/NaN/inst/rem.m 2009-11-02 15:57:13 UTC (rev 6426) @@ -1,60 +0,0 @@ -function [z,e] = rem(x,y) -% REM calculates remainder of X / Y -% -% z = x - y * fix(x/y); -% e = eps * fix(x/y); -% -% [z,e] = REM(X,Y) -% z is the remainder of X / Y -% e is the error tolerance, for checking the accuracy -% z(e > abs(y)) is not defined -% -% z has always the same sign than x -% -% see also: MOD - -% This program is free software; you can redistribute it and/or modify -% it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or -% (at your option) any later version. -% -% This program is distributed in the hope that it will be useful, -% but WITHOUT ANY WARRANTY; without even the implied warranty of -% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -% GNU General Public License for more details. -% -% You should have received a copy of the GNU General Public License -% along with this program; If not, see <http://www.gnu.org/licenses/>. - -% $Id$ -% Copyright (C) 2004,2009 by Alois Schloegl <a.s...@ie...> -% This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ - - -s = warning; -warning('off'); - -if ((numel(x)~=1) && (numel(y)~=1) && any(size(x)~=size(y))) - error('size of input arguments do not fit.'); -end; - -t = fix(x./y); -z = x - y.*t; - -if numel(x)==1, - z(~t) = x; % remainder is x if y = inf -else - z(~t) = x(~t); % remainder is x if y = inf -end; -z(repmat(~y,size(z)./size(y))) = 0; % remainder must be 0 if y==0 - -warning(s); % reset warning status - -if nargout > 1, - e = (abs(t)*eps); % error interval - %z(e > abs(y)) = NaN; % uncertainty of rounding error to large -end; - - - This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2010-01-07 22:19:26
|
Revision: 6716 http://octave.svn.sourceforge.net/octave/?rev=6716&view=rev Author: schloegl Date: 2010-01-07 22:19:19 +0000 (Thu, 07 Jan 2010) Log Message: ----------- add test routines for some functions of NaN-toolbox Added Paths: ----------- trunk/octave-forge/extra/NaN/test/ trunk/octave-forge/extra/NaN/test/test_classify.m trunk/octave-forge/extra/NaN/test/test_fss.m trunk/octave-forge/extra/NaN/test/test_mex_accuracy.m trunk/octave-forge/extra/NaN/test/test_train_sc.m trunk/octave-forge/extra/NaN/test/test_xval.m Property changes on: trunk/octave-forge/extra/NaN/test ___________________________________________________________________ Added: keywords + Id Rev Added: trunk/octave-forge/extra/NaN/test/test_classify.m =================================================================== --- trunk/octave-forge/extra/NaN/test/test_classify.m (rev 0) +++ trunk/octave-forge/extra/NaN/test/test_classify.m 2010-01-07 22:19:19 UTC (rev 6716) @@ -0,0 +1,51 @@ +% TEST_CLASSIFY tests and compares NaN/CLASSIFY.M with the matlab version of CLASSIFY + +% $Id$ +% Copyright (C) 2009,2010 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. + + +clear +load_fisheriris +SL = meas(51:end,1); +SW = meas(51:end,2); +group = species(51:end); +h1 = gscatter(SL,SW,group,'rb','v^',[],'off'); +set(h1,'LineWidth',2) +legend('Fisher versicolor','Fisher virginica','Location','NW') + +[X,Y] = meshgrid(linspace(4.5,8),linspace(2,4)); +X = X(:); Y = Y(:); + +classifiers={'linear','quadratic','diagLinear','diagQuadratic','mahalanobis'}; + +p = which('train_sc.m'); +p = fileparts(p); +rmpath(p); +for k=1:length(classifiers) +[C1,err(1,k),P1,logp1,coeff1] = classify([X Y],[SL SW],group,classifiers{k}); +end; + +addpath(p); +for k=1:length(classifiers) +[C2,err(2,k),P2,logp2,coeff2] = classify([X Y],[SL SW],group,classifiers{k}); +end; + +err, + + \ No newline at end of file Property changes on: trunk/octave-forge/extra/NaN/test/test_classify.m ___________________________________________________________________ Added: keywords + Id Rev Added: trunk/octave-forge/extra/NaN/test/test_fss.m =================================================================== --- trunk/octave-forge/extra/NaN/test/test_fss.m (rev 0) +++ trunk/octave-forge/extra/NaN/test/test_fss.m 2010-01-07 22:19:19 UTC (rev 6716) @@ -0,0 +1,50 @@ +% TEST_FSS test of fss.m + +% $Id$ +% Copyright (C) 2009,2010 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. + +clear +if ~exist('ue6.mat','file') + if strncmp(computer,'PCWIN',5) + fprintf(1,'Download http://hci.tugraz.at/~schloegl/LV/SMBS/UE6/ue6.mat and save in local directory %s\nPress any key to continue ...\n',pwd); + pause; + else + unix('wget http://hci.tugraz.at/~schloegl/LV/SMBS/UE6/ue6.mat'); + end; +end +load ue6; + +N = 50; % select N highest ranked features +[ix,score] = fss(data, C, N); + + +%% compute cross-validated result; +for k=1:N + R{k}=xval(data(:,ix(1:k)),C); + ACC(k)=R{k}.ACC; +end + + +%% display +plot(ACC*100); +set(gca,'YLim',[0,100]) +ylabel('Accuracy [%]') +title('selection of N out of 2540 features') + +ix, Property changes on: trunk/octave-forge/extra/NaN/test/test_fss.m ___________________________________________________________________ Added: keywords + Id Rev Added: trunk/octave-forge/extra/NaN/test/test_mex_accuracy.m =================================================================== --- trunk/octave-forge/extra/NaN/test/test_mex_accuracy.m (rev 0) +++ trunk/octave-forge/extra/NaN/test/test_mex_accuracy.m 2010-01-07 22:19:19 UTC (rev 6716) @@ -0,0 +1,93 @@ +% TEST_MEX_ACCURACY evaluates the accuracy and speed of +% different accuracy levels in SUMSKIPNAN_MEX and COVM_MEX +% +% see also: FLAG_ACCURACY_LEVEL, SUMSKIPNAN_MEX, COVM_MEX +% +% Reference: +% [1] David Goldberg, +% What Every Computer Scientist Should Know About Floating-Point Arithmetic +% ACM Computing Surveys, Vol 23, No 1, March 1991. + +% $Id$ +% Copyright (C) 2009,2010 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 3 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA + +clear +flag=0; +N = 1e7; +x=randn(N,10)+1e6; + +level = flag_accuracy_level; %% backup original level +flag_accuracy_level(0); +tic,t=cputime();[cc0,nn0]=covm_mex(x,[],flag);t0=[cputime-t,toc]; + +flag_accuracy_level(1); +tic,t=cputime();[cc1,nn1]=covm_mex(x,[],flag);t1=[cputime-t,toc]; + +flag_accuracy_level(2); +tic,t=cputime();[cc2,nn2]=covm_mex(x,[],flag);t2=[cputime-t,toc]; + +flag_accuracy_level(3); +tic,t=cputime();[cc3,nn3]=covm_mex(x,[],flag);t3=[cputime-t,toc]; + +tic,t=cputime();cc4=x'*x;nn4=size(x,1);t4=[cputime-t,toc]; + +flag_accuracy_level(0); +tic,t=cputime();[c0,n0]=sumskipnan_mex(x,1,flag);t0s=[cputime-t,toc]; + +flag_accuracy_level(1); +tic,t=cputime();[c1,n1]=sumskipnan_mex(x,1,flag);t1s=[cputime-t,toc]; + +flag_accuracy_level(2); +tic,t=cputime();[c2,n2]=sumskipnan_mex(x,1,flag);t2s=[cputime-t,toc]; + +flag_accuracy_level(3); +tic,t=cputime();[c3,n3]=sumskipnan_mex(x,1,flag);t3s=[cputime-t,toc]; + +tic,t=cputime();c4=sum(x,1);n4=size(x,1);t4s=[cputime-t,toc]; + +flag_accuracy_level(level); %% restore original level + +cc = {cc0,cc1,cc2,cc3}; +c = {c0,c1,c2,c3}; +tt = [t0;t1;t2;t3;t4]; +t = [t0s;t1s;t2s;t3s;t4s]; +fprintf('Sum squared differences between accuracy levels:\n'); +fprintf('Level:\t|(0) naive-dou\t|(1) naive-ext\t|(2) kahan-dou \t| (3) kahan-ext\n') +fprintf('error:\t|N*2^-52\t|N*2^-64\t| 2^-52 \t| 2^-64\n') +fprintf('COVM_MEX:\ntime:\t|%f\t|%f\t| %f \t| %f',tt(:,1)) +for K1=1:4, +fprintf('\n(%i)\t',K1-1); +for K2=1:4, + EE(K1,K2)=sum(sum((cc{K1}-cc{K2}).^2)); + E(K1,K2) =sum(sum((c{K1}-c{K2}).^2)); + fprintf('|%8g\t',EE(K1,K2)/nn1(1)); +end; +end; +fprintf('\nSUMSKIPNAN_MEX:\n') +fprintf('time:\t|%f\t|%f\t| %f \t| %f',t(:,1)) +for K1=1:4, +fprintf('\n(%i)\t',K1-1); +for K2=1:4, + fprintf('|%8g\t',E(K1,K2)/n1(1)); +end; +end; +fprintf('\n'); + + + Property changes on: trunk/octave-forge/extra/NaN/test/test_mex_accuracy.m ___________________________________________________________________ Added: keywords + Id Rev Added: trunk/octave-forge/extra/NaN/test/test_train_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/test/test_train_sc.m (rev 0) +++ trunk/octave-forge/extra/NaN/test/test_train_sc.m 2010-01-07 22:19:19 UTC (rev 6716) @@ -0,0 +1,95 @@ +% Test train_sc and test_sc, weighted samples + + + +% $Id$ +% Copyright (C) 2010 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. + + +clear +classifier= {'REG','REG2','MDA','MD2','QDA','QDA2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','PLA', 'LMS','LDA/DELETION','MDA/DELETION','NBC/DELETION','RDA/DELETION','RDA','GDBC','SVM','RBF'};% 'LDA/GSVD','MDA/GSVD', 'LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse', + +N=1e2; +c=[1:N]'*2>N; + +W3 = [ones(1,N/2)/5,ones(1,N/10)]; +for l=1:length(classifier), + fprintf(1,'%s\n',classifier(l)); +for k=1:10, + +x=randn(N,2); +x=x+[c,c]; + +ix = 1:0.6*N; + +try, +CC = train_sc(x(ix,:),c(ix)+1,classifier{l}); +R1 = test_sc(CC,x,[],c+1); + +CC = train_sc(x,c+1,classifier{l}); +R2 = test_sc(CC,x,[],c+1); + +CC = train_sc(x(ix,:),c(ix)+1,classifier{l},W3); +R3 = test_sc(CC,x,[],c+1); + +acc1(k,l)=[R1.ACC]; +kap1(k,l)=[R1.kappa]; +acc2(k,l)=[R2.ACC]; +kap2(k,l)=[R2.kappa]; +acc3(k,l)=[R3.ACC]; +kap3(k,l)=[R3.kappa]; +end; + +end; +end; + +[se,m]=sem(acc1);m +[se,m]=sem(acc2);m +[se,m]=sem(acc3);m + +%[diff(m),diff(m)/sqrt(sum(se.^2))] +%[se,m]=sem(kap);[diff(m),diff(m)/sqrt(sum(se.^2))] + +%These are tests to compare varios classiers + +return + + +N=1e2; +c=[1:N]'*2>N; + +for k=1:1000,k + +x=randn(N,2); +x=x+[c,c]; + +ix = 1:0.6*N; +[R1,CC]=xval(x(ix,:),c(ix)+1,'REG'); +[R2,CC]=xval(x,c+1,'REG'); +[R3,CC]=xval(x(ix,:),c(ix)+1,'LDA'); +[R4,CC]=xval(x,c+1,'LDA'); + +acc(k,1:4)=[R1.ACC,R2.ACC,R3.ACC,R4.ACC]; +kap(k,1:4)=[R1.kappa,R2.kappa,R3.kappa,R4.kappa]; + +end; + +[se,m]=sem(acc),%[diff(m),diff(m)/sqrt(sum(se.^2))] +%[se,m]=sem(kap);[diff(m),diff(m)/sqrt(sum(se.^2))] + Property changes on: trunk/octave-forge/extra/NaN/test/test_train_sc.m ___________________________________________________________________ Added: keywords + Id Rev Added: trunk/octave-forge/extra/NaN/test/test_xval.m =================================================================== --- trunk/octave-forge/extra/NaN/test/test_xval.m (rev 0) +++ trunk/octave-forge/extra/NaN/test/test_xval.m 2010-01-07 22:19:19 UTC (rev 6716) @@ -0,0 +1,55 @@ +% test_classifier; + +% $Id$ +% Copyright (C) 2010 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. + + +clear +N=100; %% number of samples +M=10; %% number of features +classifier= {'REG','REG2','MDA','MD2','QDA','QDA2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse', 'PLA', 'LMS','LDA/DELETION','MDA/DELETION','NBC/DELETION','RDA/DELETION','REG/DELETION','REG2/DELETION','RDA','GDBC','SVM','RBF'}; + +x = randn(N,M); %% data +c = ([1:N]'>(N/2))+1; %% classlabel +%w = [ones(1,N/2)/5,ones(1,N/10),zeros(1,4*N/10)]; +w = []; %% no weightening + +x = randn(N,M); +x = x+c*ones(1,M); + +x(2:2:N/2,2) = NaN; +x(3,2:2:end) = NaN; + +for k = 1:length(classifier); + try, + R{k} = xval(x, {c,w}, classifier{k}); + catch, + R{k} = []; + end; +end; + + +for k = 1:length(R) + if isempty(R{k}) + fprintf(1,'%8s \t failed\n',classifier{k}); + else + fprintf(1,'%8s\t%i\t%5.2f\t%5.2f+-%5.2f\n',classifier{k},sum(R{k}.data(:)),R{k}.ACC*100,R{k}.kappa,R{k}.kappa_se); + end; +end + Property changes on: trunk/octave-forge/extra/NaN/test/test_xval.m ___________________________________________________________________ Added: keywords + Id Rev This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2010-01-08 11:04:52
|
Revision: 6719 http://octave.svn.sourceforge.net/octave/?rev=6719&view=rev Author: schloegl Date: 2010-01-08 11:04:43 +0000 (Fri, 08 Jan 2010) Log Message: ----------- train_sc: fix nested function for matlab; test_fss: improve report Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/train_sc.m trunk/octave-forge/extra/NaN/test/test_fss.m Modified: trunk/octave-forge/extra/NaN/inst/train_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-07 22:28:15 UTC (rev 6718) +++ trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-08 11:04:43 UTC (rev 6719) @@ -853,7 +853,7 @@ %CC.datatype='LLBC'; end; end; -end; +end function [rix,cix] = row_vs_col_deletion(d,c,w) @@ -884,4 +884,5 @@ rix = 1:size(d,1); % select all rows %fprintf(1,'column-wise deletion (%i,%i,%i)\n',n,nr,nc); end; -end; +end; +end Modified: trunk/octave-forge/extra/NaN/test/test_fss.m =================================================================== --- trunk/octave-forge/extra/NaN/test/test_fss.m 2010-01-07 22:28:15 UTC (rev 6718) +++ trunk/octave-forge/extra/NaN/test/test_fss.m 2010-01-08 11:04:43 UTC (rev 6719) @@ -30,6 +30,7 @@ end load ue6; + N = 50; % select N highest ranked features [ix,score] = fss(data, C, N); @@ -37,14 +38,24 @@ %% compute cross-validated result; for k=1:N R{k}=xval(data(:,ix(1:k)),C); +end; + +fprintf(1,'#\tFeature\tN\tACC [%%]\tKappa+-se\t I [bit]\n'); +for k=1:N + n(k)=sum(R{k}.data(:)); ACC(k)=R{k}.ACC; + KAP(k)=R{k}.kappa; + KAP_Se(k)=R{k}.kappa_se; + MI(k)=R{k}.MI; + + fprintf(1,'%3i:\t%4i\t%i\t%5.2f\t%5.2f+-%5.2f\t%4.2f\n',k,ix(k),n(k),ACC(k),KAP(k),KAP_Se(k),MI(k)); end %% display -plot(ACC*100); +plot(ACC*100,'x'); set(gca,'YLim',[0,100]) ylabel('Accuracy [%]') title('selection of N out of 2540 features') -ix, + This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2010-01-10 00:51:18
|
Revision: 6727 http://octave.svn.sourceforge.net/octave/?rev=6727&view=rev Author: schloegl Date: 2010-01-10 00:13:09 +0000 (Sun, 10 Jan 2010) Log Message: ----------- {-1,+1} encoding of classlabel supported; test of liblinear and libSVM with Octave3.2 Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/test_sc.m trunk/octave-forge/extra/NaN/inst/train_sc.m trunk/octave-forge/extra/NaN/inst/xval.m trunk/octave-forge/extra/NaN/test/test_xval.m Modified: trunk/octave-forge/extra/NaN/inst/test_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/test_sc.m 2010-01-09 21:04:41 UTC (rev 6726) +++ trunk/octave-forge/extra/NaN/inst/test_sc.m 2010-01-10 00:13:09 UTC (rev 6727) @@ -30,7 +30,7 @@ % $Id: test_sc.m 2140 2009-07-02 12:03:55Z schloegl $ % Copyright (C) 2005,2006,2008,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License @@ -119,7 +119,6 @@ else error('QDA: hyperparamters lambda and/or gamma not defined') end; - elseif strcmp(CC.datatype,'classifier:csp') @@ -130,10 +129,10 @@ elseif strcmp(CC.datatype,'classifier:svm:lib:1vs1') || strcmp(CC.datatype,'classifier:svm:lib:rbf'); - [cl] = svmpredict(classlabel, D, CC.model); %Use the classifier + cl = svmpredict(ones(size(D,1),1), D, CC.model); %Use the classifier %Create a pseudo tsd matrix for bci4eval - d = zeros(size(cl,1), CC.model.nr_class); + d = zeros(size(D,1), CC.model.nr_class); for i = 1:size(cl,1) d(i,cl(i)) = 1; end @@ -147,9 +146,6 @@ for k = 1:size(CC.weights,2), d(:,k) = D * CC.weights(2:end,k) + CC.weights(1,k); end; - if size(CC.weights,2)==1, - d = [d, -d]; - end; elseif ~isempty(POS1) % GSVD, sparse & DELETION @@ -285,9 +281,14 @@ return; end; -[tmp,cl] = max(d,[],2); -cl = CC.Labels(cl); -cl(isnan(tmp)) = NaN; +if size(d,2)>1, + [tmp,cl] = max(d,[],2); + cl = CC.Labels(cl); + cl(isnan(tmp)) = NaN; +elseif size(d,2)==1, + cl = (d<0) + 2*(d>0); + cl(isnan(d)) = NaN; +end; R.output = d; R.classlabel = cl; Modified: trunk/octave-forge/extra/NaN/inst/train_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-09 21:04:41 UTC (rev 6726) +++ trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-10 00:13:09 UTC (rev 6727) @@ -3,13 +3,26 @@ % % CC = train_sc(D,classlabel) % CC = train_sc(D,classlabel,MODE) -% CC = train_sc(D,classlabel, 'REG', W) -% weighting D(k,:) with weight W(k) +% CC = train_sc(D,classlabel,MODE, W) +% weighting D(k,:) with weight W(k) (not all classifiers supported weighting) % % CC contains the model parameters of a classifier which can be applied % to test data using test_sc. % R = test_sc(CC,D,...) % +% D training samples (each row is a sample, each column is a feature) +% classlabel labels of each sample, must have the same number of rows as D. +% Two different encodings are supported: +% {-1,1}-encoding (multiple classes with separate columns for each class) or +% 1..M encoding. +% So [1;2;3;1;4] is equivalent to +% [+1,-1,-1,-1; +% [-1,+1,-1,-1; +% [-1,-1,+1,-1; +% [+1,-1,-1,-1] +% [-1,-1,-1,+1] +% Note, samples with classlabel=0 are ignored. +% % The following classifier types are supported MODE.TYPE % 'MDA' mahalanobis distance based classifier [1] % 'MD2' mahalanobis distance based classifier [1] @@ -151,7 +164,9 @@ % along with this program; if not, write to the Free Software % Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. - +if nargin<2, + error('insufficient input arguments\n\tusage: train_sc(D,C,...)\n'); +end; if nargin<3, MODE = 'LDA'; end; if nargin<4, W = []; end; if ischar(MODE) @@ -169,13 +184,10 @@ end; sz = size(D); -if sz(1)~=length(classlabel), +if sz(1)~=size(classlabel,1), error('length of data and classlabel does not fit'); end; -%CC.Labels = unique(classlabel); -CC.Labels = 1:max(classlabel); - % remove all NaN's if 1, % several classifier can deal with NaN's, there is no need to remove them. @@ -203,9 +215,8 @@ MODE.hyperparameter = []; end - -if 0, - +if 0, + ; elseif ~isempty(strfind(lower(MODE.TYPE),'/delet')) % [5] J.D. Tebbens and P.Schlesinger (2006), % Improving Implementation of Linear Discriminant Analysis for the Small Sample Size Problem @@ -214,7 +225,7 @@ POS1 = find(MODE.TYPE=='/'); [rix,cix] = row_vs_col_deletion(D); if ~isempty(W), W=W(rix); end; - CC = train_sc(D(rix,cix),classlabel(rix),MODE.TYPE(1:POS1(1)-1),W); + CC = train_sc(D(rix,cix),classlabel(rix,:),MODE.TYPE(1:POS1(1)-1),W); CC.G = sparse(cix, 1:length(cix), 1, size(D,2), length(cix)); if isfield(CC,'weights') W = [CC.weights(1,:); CC.weights(2:end,:)]; @@ -228,6 +239,7 @@ elseif ~isempty(strfind(lower(MODE.TYPE),'nbpw')) error('NBPW not implemented yet') %%%% Naive Bayesian Parzen Window Classifier. + [classlabel,CC.Labels] = CL1M(classlabel); for k = 1:length(CC.Labels), [d,CC.MEAN(k,:)] = center(D(classlabel==CC.Labels(k),:),1); [CC.VAR(k,:),CC.N(k,:)] = sumskipnan(d.^2,1); @@ -245,6 +257,7 @@ else CC.V = eye(size(D,2)); end; + [classlabel,CC.Labels] = CL1M(classlabel); for k = 1:length(CC.Labels), ix = classlabel==CC.Labels(k); %% [d,CC.MEAN(k,:)] = center(D(ix,:),1); @@ -274,6 +287,7 @@ if ~isfield(MODE.hyperparameter,'c_value') MODE.hyperparameter.c_value = 1; end + [classlabel,CC.Labels] = CL1M(classlabel); M = length(CC.Labels); if M==2, M=1; end; % For a 2-class problem, only 1 Discriminant is needed @@ -286,12 +300,12 @@ CC.datatype = ['classifier:',lower(MODE.TYPE)]; -elseif ~isempty(strfind(lower(MODE.TYPE),'pla')); +elseif ~isempty(strfind(lower(MODE.TYPE),'pla')), % Perceptron Learning Algorithm - M = length(CC.Labels); - [rix,cix] = row_vs_col_deletion(D); + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); weights = sparse(length(cix)+1,M); %ix = randperm(size(D,1)); %% randomize samples ??? @@ -302,7 +316,8 @@ alpha = 1; end; for k = rix(:)', - e = ((classlabel(k)==(1:M))-.5) - sign([1, D(k,cix)] * weights)/2; + %e = ((classlabel(k)==(1:M))-.5) - sign([1, D(k,cix)] * weights)/2; + e = CL101(k,:) - sign([1, D(k,cix)] * weights); weights = weights + alpha * [1,D(k,cix)]' * e ; end; @@ -311,7 +326,8 @@ W = W*MODE.hyperparameter.alpha; end; for k = rix(:)', - e = ((classlabel(k)==(1:M))-.5) - sign([1, D(k,cix)] * weights)/2; + %e = ((classlabel(k)==(1:M))-.5) - sign([1, D(k,cix)] * weights)/2; + e = CL101(k,:) - sign([1, D(k,cix)] * weights); weights = weights + W(k) * [1,D(k,cix)]' * e ; end; end @@ -323,9 +339,9 @@ elseif ~isempty(strfind(lower(MODE.TYPE),'adaline')) || ~isempty(strfind(lower(MODE.TYPE),'lms')), % adaptive linear elemente, least mean squares, delta rule, Widrow-Hoff, - M = length(CC.Labels); - [rix,cix] = row_vs_col_deletion(D); + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); weights = sparse(length(cix)+1,M); %ix = randperm(size(D,1)); %% randomize samples ??? @@ -336,7 +352,8 @@ alpha = 1; end; for k = rix(:)', - e = (classlabel(k)==(1:M)) - [1, D(k,cix)] * weights; + %e = (classlabel(k)==(1:M)) - [1, D(k,cix)] * weights; + e = CL101(k,:) - sign([1, D(k,cix)] * weights); weights = weights + alpha * [1,D(k,cix)]' * e ; end; @@ -345,7 +362,8 @@ W = W*MODE.hyperparameter.alpha; end; for k = rix(:)', - e = (classlabel(k)==(1:M)) - [1, D(k,cix)] * weights; + %e = (classlabel(k)==(1:M)) - [1, D(k,cix)] * weights; + e = CL101(k,:) - sign([1, D(k,cix)] * weights); weights = weights + W(k) * [1,D(k,cix)]' * e ; end; end @@ -360,16 +378,19 @@ error('Classifier (%s) does not support weighted samples.',MODE.TYPE); end; - M = length(CC.Labels); - CC.weights = ones(size(D,2),M); + [rix,cix] = row_vs_col_deletion(D); + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); + weights = ones(length(cix),M); theta = size(D,2)/2; - for k = 1:size(D,1), - e = (1 + sign(D(k,:) * CC.weights - theta))/2 - (classlabel(k)==CC.Labels(k)); - CC.weights = CC.weights.* 2^(D(k,:)' * e); + for k = rix(:)', + e = CL101(k,:) - sign(D(k,cix) * weights - theta); + weights = weights.* 2.^(D(k,cix)' * e); end; - - CC.weights = [zeros(1,M), CC.weights]; + + CC.weights = sparse(size(D,2)+1,M); + CC.weights(cix+1,:) = weights; CC.datatype = ['classifier:',lower(MODE.TYPE)]; @@ -377,46 +398,51 @@ % 4th version: support for weighted samples - work well with unequally distributed data: % regression analysis, can handle sparse data, too. - M = length(CC.Labels); - if nargin<4, W = []; end; - wD = [ones(size(D,1),1),D]; + [rix, cix] = row_vs_col_deletion(D); + wD = [ones(length(rix),1),D(rix,cix)]; - if isempty(W) - W = 1; - else + if ~isempty(W) %% wD = diag(W)*wD W = W(:); for k=1:size(wD,2) - wD(:,k) = W.*wD(:,k); + wD(:,k) = W(rix).*wD(:,k); end; end; + [CL101, CC.Labels] = cl101(classlabel(rix,:)); + M = size(CL101,2); CC.weights = sparse(sz(2)+1,M); - [rix, cix] = row_vs_col_deletion(wD); - [q,r] = qr(wD(rix,cix),0); - for k = 1:M, - ix = 2*(classlabel==CC.Labels(k)) - 1; - CC.weights(cix,k) = r\(q'*(W.*ix)); - end; + %[rix, cix] = row_vs_col_deletion(wD); + [q,r] = qr(wD,0); + + if isempty(W) + CC.weights([1,cix+1],:) = r\(q'*CL101); + else + CC.weights([1,cix+1],:) = r\(q'*(W(rix,ones(1,M)).*CL101)); + end; + %for k = 1:M, + % CC.weights(cix,k) = r\(q'*(W.*CL101(rix,k))); + %end; CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; elseif ~isempty(strfind(MODE.TYPE,'WienerHopf')) % Q: equivalent to LDA % equivalent to Regression, except regression can not deal with NaN's - M = length(CC.Labels); - %if M==2, M==1; end; - CC.weights = repmat(NaN,size(D,2)+1,M); + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); + CC.weights = sparse(size(D,2)+1,M); cc = covm(D,'E',W); - c1 = classlabel(~isnan(classlabel)); - c2 = ones(sum(~isnan(classlabel)),M); - for k = 1:M, - c2(:,k) = c1==CC.Labels(k); - end; - CC.weights = cc\covm([ones(size(c2,1),1),D(~isnan(classlabel),:)],2*real(c2)-1,'M',W); + %c1 = classlabel(~isnan(classlabel)); + %c2 = ones(sum(~isnan(classlabel)),M); + %for k = 1:M, + % c2(:,k) = c1==CC.Labels(k); + %end; + %CC.weights = cc\covm([ones(size(c2,1),1),D(~isnan(classlabel),:)],2*real(c2)-1,'M',W); + CC.weights = cc\covm([ones(size(D,1),1),D],CL101,'M',W); CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; @@ -430,6 +456,7 @@ % dx.doi.org/10.1109/TPAMI.2004.46 % [3] http://www-static.cc.gatech.edu/~kihwan23/face_recog_gsvd.htm + [classlabel,CC.Labels] = CL1M(classlabel); [rix,cix] = row_vs_col_deletion(D); Hw = zeros(length(rix)+length(CC.Labels), length(cix)); @@ -456,18 +483,17 @@ R = R(1:t,1:t); %P = P(1:size(D,1),1:t); %Q = Q(1:t,:); - [U,E,W] = svd(P(1:size(D,1),1:t),0); + [U,E,W] = svd(P(1:length(rix),1:t),0); %[size(U);size(E);size(W)] clear U E P; %[size(Q);size(R);size(W)] - + %G = Q(1:t,:)'*[R\W']; G = Q(:,1:t)*(R\W'); % this works as well and needs only 'econ'-SVD %G = G(:,1:t); % not needed % do not use this, gives very bad results for Medline database %G = G(:,1:K); this seems to be a typo in [2] and [3]. - CC = train_sc(D(:,cix)*G,classlabel,MODE.TYPE(1:find(MODE.TYPE=='/')-1)); CC.G = sparse(size(D,2),size(G,2)); CC.G(cix,:) = G; @@ -487,6 +513,7 @@ % Improving Implementation of Linear Discriminant Analysis for the Small Sample Size Problem % http://www.cs.cas.cz/mweb/download/publi/JdtSchl2006.pdf + [classlabel,CC.Labels] = CL1M(classlabel); [rix,cix] = row_vs_col_deletion(D); warning('sparse LDA is sensitive to linear transformations') @@ -539,6 +566,7 @@ CC.prewhite = sparse(2:sz(2)+1,1:sz(2),r,sz(2)+1,sz(2),2*sz(2)); CC.prewhite(1,:) = -m.*r; + [classlabel,CC.Labels] = CL1M(classlabel); CC.model = svmtrain(classlabel, D, CC.options); % Call the training mex File CC.datatype = ['classifier:',lower(MODE.TYPE)]; @@ -551,7 +579,6 @@ if ~isfield(MODE.hyperparameter,'c_value') MODE.hyperparameter.c_value = 1; end - %CC = train_svm11(D,classlabel,MODE.hyperparameter.c_value); CC.options=sprintf('-c %g -t 0',MODE.hyperparameter.c_value); %use linear kernel, set C CC.hyperparameter.c_value = MODE.hyperparameter.c_value; @@ -561,6 +588,7 @@ CC.prewhite = sparse(2:sz(2)+1,1:sz(2),r,sz(2)+1,sz(2),2*sz(2)); CC.prewhite(1,:) = -m.*r; + [classlabel,CC.Labels] = CL1M(classlabel); CC.model = svmtrain(classlabel, D, CC.options); % Call the training mex File FUN = 'SVM:LIB:1vs1'; @@ -580,9 +608,11 @@ nu = 1; end; [m,n] = size(D); - CC.weights = repmat(NaN,n+1,length(CC.Labels)); - for k = 1:length(CC.Labels), - d = sparse(1:m,1:m,(classlabel==CC.Labels(k))*2-1); + [CL101,CC.Labels] = cl101(classlabel); + CC.weights = sparse(n+1,size(CL101,2)); + M = size(CL101,2); + for k = 1:M, + d = sparse(1:m,1:m,CL101(:,k)); H = d * [-ones(m,1),D]; %%% r = sum(H,1)'; r = sumskipnan(H,1,W)'; @@ -591,7 +621,8 @@ r = (speye(n+1)/nu + HTH)\r; %solve (I/nu+H’*H)r=H’*e u = nu*(1-(H*r)); %%% CC.weights(:,k) = u'*H; - [CC.weights(:,k),nn] = covm(u,H,'M',W); + [c,nn] = covm(u,H,'M',W); + CC.weights(:,k) = c'; end; CC.hyperparameter.nu = nu; CC.datatype = ['classifier:',lower(MODE.TYPE)]; @@ -605,20 +636,25 @@ if ~isfield(MODE.hyperparameter,'c_value') MODE.hyperparameter.c_value = 1; end - M = length(CC.Labels); - if M==2, M=1; end; - CC.weights = repmat(NaN, sz(2)+1, M); + [classlabel,CC.Labels] = CL1M(classlabel); + M = length(CC.Labels); + CC.weights = sparse(size(D,2)+1,M); + + [rix,cix] = row_vs_col_deletion(D); + % pre-whitening - [D,r,m]=zscore(D,1); - s = sparse(2:sz(2)+1,1:sz(2),r,sz(2)+1,sz(2),2*sz(2)); + [D,r,m]=zscore(D(rix,cix),1); + sz2 = length(cix); + s = sparse(2:sz2+1,1:sz2,r,sz2+1,sz2,2*sz2); s(1,:) = -m.*r; CC.options = sprintf('-s 4 -c %f ', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, model = train(classlabel, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, - CC.weights = model.w([end,1:end-1],:)'; + weights = model.w([end,1:end-1],:)'; - CC.weights = s * CC.weights(2:end,:) + sparse(1,1:M,CC.weights(1,:),sz(2)+1,M); % include pre-whitening transformation + CC.weights([1,cix+1],:) = s * weights(2:end,:) + sparse(1,1:M,weights(1,:),sz2+1,M); % include pre-whitening transformation + CC.weights([1,cix+1],:) = s * CC.weights(cix+1,:) + sparse(1,1:M,CC.weights(1,:),sz2+1,M); % include pre-whitening transformation CC.hyperparameter.c_value = MODE.hyperparameter.c_value; CC.datatype = ['classifier:',lower(MODE.TYPE)]; @@ -652,17 +688,19 @@ end; %%CC = train_svm(D,classlabel,MODE); - M = length(CC.Labels); - if M==2, M=1; end; - CC.weights = repmat(NaN, sz(2)+1, M); + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); + [rix,cix] = row_vs_col_deletion(D); + CC.weights = sparse(sz(2)+1, M); % pre-whitening - [D,r,m]=zscore(D,1); - s = sparse(2:sz(2)+1,1:sz(2),r,sz(2)+1,sz(2),2*sz(2)); + [D,r,m]=zscore(D(rix,cix),1); + sz2 = length(cix); + s = sparse(2:sz2+1,1:sz2,r,sz2+1,sz2,2*sz2); s(1,:) = -m.*r; for k = 1:M, - cl = sign((classlabel~=CC.Labels(k))-.5); + cl = CL101(:,k); if strncmp(MODE.TYPE, 'SVM:LIN',7); if isfield(MODE,'options') CC.options = MODE.options; @@ -673,19 +711,19 @@ CC.options = sprintf('-s %i -c %f ',t, MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, end; model = train(cl, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, - w = model.w(1:end-1)'; - Bias = model.w(end); + w = -model.w(1:end-1)'; + Bias = -model.w(end); - elseif strcmp(MODE.TYPE, 'SVM:LIB'); + elseif strcmp(MODE.TYPE, 'SVM:LIB'); %% tested with libsvm-mat-2.9-1 if isfield(MODE,'options') CC.options = MODE.options; else CC.options = sprintf('-s 0 -c %f -t 0 -d 1', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, end; model = svmtrain(cl, D, CC.options); % C-SVC, C=1, linear kernel, degree = 1, - w = -cl(1) * model.SVs' * model.sv_coef; %Calculate decision hyperplane weight vector + w = cl(1) * model.SVs' * model.sv_coef; %Calculate decision hyperplane weight vector % ensure correct sign of weight vector and Bias according to class label - Bias = -model.rho * cl(1); + Bias = model.rho * cl(1); elseif strcmp(MODE.TYPE, 'SVM:bioinfo'); CC.SVMstruct = svmtrain(D, cl,'AUTOSCALE', 0); % @@ -717,15 +755,16 @@ end CC.weights(1,k) = -Bias; - CC.weights(2:end,k) = w; + CC.weights(cix+1,k) = w; end; - CC.weights = s * CC.weights(2:end,:) + sparse(1,1:M,CC.weights(1,:),sz(2)+1,M); % include pre-whitening transformation + CC.weights([1,cix+1],:) = s * CC.weights(cix+1,:) + sparse(1,1:M,CC.weights(1,:),sz2+1,M); % include pre-whitening transformation CC.hyperparameter.c_value = MODE.hyperparameter.c_value; CC.datatype = ['classifier:',lower(MODE.TYPE)]; elseif ~isempty(strfind(lower(MODE.TYPE),'csp')) CC.datatype = ['classifier:',lower(MODE.TYPE)]; + [classlabel,CC.Labels] = CL1M(classlabel); CC.MD = repmat(NaN,[sz(2)+[1,1],length(CC.Labels)]); CC.NN = CC.MD; for k = 1:length(CC.Labels), @@ -749,6 +788,7 @@ else % Linear and Quadratic statistical classifiers CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + [classlabel,CC.Labels] = CL1M(classlabel); CC.MD = repmat(NaN,[sz(2)+[1,1],length(CC.Labels)]); CC.NN = CC.MD; for k = 1:length(CC.Labels), @@ -857,8 +897,10 @@ end function [rix,cix] = row_vs_col_deletion(d,c,w) + % decides whether row-wise or column-wise deletion removes less data. + % rix and cix are the resulting index vectors + % either row-wise or column-wise deletion, but not a combination of both, is used. - % returns number of values for row-wise and column-wise deletion if nargin > 2, if isempty(w) || all(w==w(1)), ix = ~isnan(c); @@ -886,4 +928,60 @@ %fprintf(1,'column-wise deletion (%i,%i,%i)\n',n,nr,nc); end; end + +function [CL101,Labels] = cl101(classlabel) + %% convert classlabels to {-1,1} encoding + + if (all(classlabel>=0) && all(classlabel==fix(classlabel)) && (size(classlabel,2)==1)) + M = max(classlabel); + if M==2, + CL101 = (classlabel==2)-(classlabel==1); + else + CL101 = zeros(size(classlabel,1),M); + for k=1:M, + %% One-versus-Rest scheme + CL101(:,k) = 2*real(classlabel==k) - 1; + end; + end; + CL101(isnan(classlabel),:) = NaN; %% or zero ??? + + elseif all((classlabel==1) | (classlabel==-1) | (classlabel==0) ) + CL101 = classlabel; + M = size(CL101,2); + else + classlabel, + error('format of classlabel unsupported'); + end; + Labels = 1:M; + return; +end; + + +function [cl1m, Labels] = CL1M(classlabel) + %% convert classlabels to 1..M encoding + if (all(classlabel>=0) && all(classlabel==fix(classlabel)) && (size(classlabel,2)==1)) + cl1m = classlabel; + + elseif all((classlabel==1) | (classlabel==-1) | (classlabel==0) ) + CL101 = classlabel; + M = size(classlabel,2); + if any(sum(classlabel==1,2)>1) + warning('invalid format of classlabel - at most one category may have +1'); + end; + [tmp, cl1m] = max(classlabel,[],2); + if (M==1), + cl1m = (classlabel==-1) + 2*(classlabel==+1); + end; + if any(tmp ~= 1) + warning('some class might not be properly represented - you might what to add another column to classlabel = [max(classlabel,[],2)<1,classlabel]'); + end; + cl1m(tmp<1)= 0; %% or NaN ??? + else + classlabel + error('format of classlabel unsupported'); + end; + Labels = 1:max(cl1m); + return; +end; + end Modified: trunk/octave-forge/extra/NaN/inst/xval.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/xval.m 2010-01-09 21:04:41 UTC (rev 6726) +++ trunk/octave-forge/extra/NaN/inst/xval.m 2010-01-10 00:13:09 UTC (rev 6727) @@ -15,12 +15,28 @@ % % Input: % D: data features (one feature per column, one sample per row) -% classlabel classlabel (same number of rows) +% classlabel labels of each sample, must have the same number of rows as D. +% Two different encodings are supported: +% {-1,1}-encoding (multiple classes with separate columns for each class) or +% 1..M encoding. +% So [1;2;3;1;4] is equivalent to +% [+1,-1,-1,-1; +% [-1,+1,-1,-1; +% [-1,-1,+1,-1; +% [+1,-1,-1,-1] +% [-1,-1,-1,+1] +% Note, samples with classlabel=0 are ignored. +% % CLASSIFIER can be any classifier supported by train_sc (default='LDA') -% {'MDA','MD2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','REG','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse','RDA','GDBC','SVM','RBF', PLA} +% {'REG','MDA','MD2','QDA','QDA2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf', 'RDA','GDBC', +% 'SVM','RBF','PSVM','SVM11','SVM:LIN4','SVM:LIN0','SVM:LIN1','SVM:LIN2','SVM:LIN3','WINNOW'} +% these can be modified by ###/GSVD, ###/sparse and ###/DELETION. +% /DELETION removes in case of NaN's either the rows or the columns (which removes less data values) with any NaN +% /sparse and /GSVD preprocess the data an reduce it to some lower-dimensional space. % Hyperparameters (like alpha for PLA, gamma/lambda for RDA, c_value for SVM, etc) can be defined as % CLASSIFIER.hyperparameter.alpha, etc. and -% CLASSIFIER.TYPE = 'PLA' (as listed above). See train_sc for details. +% CLASSIFIER.TYPE = 'PLA' (as listed above). +% See train_sc for details. % W: weights for each sample (row) in D. % default: [] (i.e. all weights are 1) % number of elements in W must match the number of rows of D @@ -54,7 +70,7 @@ % $Id: xval.m 2124 2009-06-10 20:34:02Z schloegl $ % Copyright (C) 2008,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License @@ -70,19 +86,19 @@ % along with this program; if not, write to the Free Software % Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. -if (nargin<3) || isempty(MODE), - MODE = 'LDA'; +if (nargin<3) || isempty(MODE), + MODE = 'LDA'; end; -if ischar(MODE) - tmp = MODE; - clear MODE; +if ischar(MODE) + tmp = MODE; + clear MODE; MODE.TYPE = tmp; elseif ~isfield(MODE,'TYPE') - MODE.TYPE=''; -end; + MODE.TYPE=''; +end; sz = size(D); -NG = []; +NG = []; W = []; if iscell(classlabel) @@ -102,7 +118,7 @@ else [Label,tmp1,NG] = unique(classlabel(:,3)); end; -else +else C = classlabel; end; if all(W==1), W = []; end; @@ -124,11 +140,11 @@ NG = ceil((1:length(C))'*arg4/length(C)); elseif length(arg4)==2, NG = ceil((1:length(C))'*arg4(1)/length(C)); - end; - -end; -end; + end; +end; +end; + sz = size(D); if sz(1)~=length(C), error('length of data and classlabel does not fit'); @@ -140,7 +156,7 @@ cl = repmat(NaN,size(classlabel,1),1); for k = 1:max(NG), ix = ix0(NG(ix0)~=k); - if isempty(W) + if isempty(W), CC = train_sc(D(ix,:), C(ix), MODE); else CC = train_sc(D(ix,:), C(ix), MODE, W(ix)); @@ -157,7 +173,7 @@ R.ERR = 1-R.ACC; if isnumeric(R.Label) R.Label = cellstr(int2str(R.Label)); -end; +end; if nargout>1, % final classifier Modified: trunk/octave-forge/extra/NaN/test/test_xval.m =================================================================== --- trunk/octave-forge/extra/NaN/test/test_xval.m 2010-01-09 21:04:41 UTC (rev 6726) +++ trunk/octave-forge/extra/NaN/test/test_xval.m 2010-01-10 00:13:09 UTC (rev 6727) @@ -20,10 +20,11 @@ % Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. +if 1, clear N=100; %% number of samples M=10; %% number of features -classifier= {'REG','REG2','MDA','MD2','QDA','QDA2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse', 'PLA', 'LMS','LDA/DELETION','MDA/DELETION','NBC/DELETION','RDA/DELETION','REG/DELETION','REG2/DELETION','RDA','GDBC','SVM','RBF'}; +classifier= {'REG','MDA','MD2','QDA','QDA2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse', 'PLA', 'LMS','LDA/DELETION','MDA/DELETION','NBC/DELETION','RDA/DELETION','REG/DELETION','RDA','GDBC','SVM','RBF','PSVM','SVM11','SVM:LIN4','SVM:LIN0','SVM:LIN1','SVM:LIN2','SVM:LIN3','WINNOW'}; x = randn(N,M); %% data c = ([1:N]'>(N/2))+1; %% classlabel @@ -33,18 +34,19 @@ x = randn(N,M); x = x+c*ones(1,M); -x(2:2:N/2,2) = NaN; +%x(2:2:N/2,2) = NaN; +x(2:2:N,2) = NaN; x(3,2:2:end) = NaN; +end; -for k = 1:length(classifier); - try, - R{k} = xval(x, {c,w}, classifier{k}); - catch, - R{k} = []; - end; +for k = 35:length(classifier); +% try, + [R{k},CC{k}] = xval(x, {c,w}, classifier{k}); +% catch, +% R{k} = []; +% end; end; - for k = 1:length(R) if isempty(R{k}) fprintf(1,'%8s \t failed\n',classifier{k}); This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2010-01-10 21:39:06
|
Revision: 6734 http://octave.svn.sourceforge.net/octave/?rev=6734&view=rev Author: schloegl Date: 2010-01-10 21:39:00 +0000 (Sun, 10 Jan 2010) Log Message: ----------- Support Vector Machine (SVM) added Modified Paths: -------------- trunk/octave-forge/extra/NaN/doc/README.TXT trunk/octave-forge/extra/NaN/inst/train_sc.m Modified: trunk/octave-forge/extra/NaN/doc/README.TXT =================================================================== --- trunk/octave-forge/extra/NaN/doc/README.TXT 2010-01-10 21:22:21 UTC (rev 6733) +++ trunk/octave-forge/extra/NaN/doc/README.TXT 2010-01-10 21:39:00 UTC (rev 6734) @@ -109,8 +109,10 @@ TRAIN_LDA_SPARSE utility function FSS feature subset selection and feature ranking CAT2BIN converts categorial to binary data + SVMTRAIN_MEX libSVM-training algorithm + REFERENCE(S): ---------------------------------- [1] http://www.itl.nist.gov/ Modified: trunk/octave-forge/extra/NaN/inst/train_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-10 21:22:21 UTC (rev 6733) +++ trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-10 21:39:00 UTC (rev 6734) @@ -76,16 +76,13 @@ % 'CSP' CommonSpatialPattern is very experimental and just a hack % uses a smoothing window of 50 samples. % 'SVM','SVM1r' support vector machines, one-vs-rest -% uses and requires svmtrain.mex from libSVM % MODE.hyperparameter.c_value = % 'SVM11' support vector machines, one-vs-one + voting -% uses and requires svmtrain.mex from libSVM % MODE.hyperparameter.c_value = % 'RBF' Support Vector Machines with RBF Kernel -% uses and requires svmtrain.mex from libSVM % MODE.hyperparameter.c_value = % MODE.hyperparameter.gamma = -% 'SVM:LIB' uses and requires svmtrain.mex from libSVM +% 'SVM:LIB' libSVM [default SVM algorithm) % 'SVM:bioinfo' uses and requires svmtrain from the bioinfo toolbox % 'SVM:OSU' uses and requires mexSVMTrain from the OSU-SVM toolbox % 'SVM:LOO' uses and requires svcm_train from the LOO-SVM toolbox @@ -97,10 +94,9 @@ % z=2 LibLinear with -- L2-loss support vector machines (primal) % z=3 LibLinear with -- L1-loss support vector machines (dual) % 'SVM:LIN4' LibLinear with -- multi-class support vector machines by Crammer and Singer - % -% {'MDA','MD2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','REG','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse','RDA','GDBC','SVM','RBF'} -% +% {'REG','MDA','MD2','QDA','QDA2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse', 'PLA', 'LMS','LDA/DELETION','MDA/DELETION','NBC/DELETION','RDA/DELETION','REG/DELETION','RDA','GDBC','SVM','RBF','PSVM','SVM11','SVM:LIN4','SVM:LIN0','SVM:LIN1','SVM:LIN2','SVM:LIN3','WINNOW'}; +% % CC contains the model parameters of a classifier. Some time ago, % CC was a statistical classifier containing the mean % and the covariance of the data of each class (encoded in the @@ -145,8 +141,8 @@ % Machine Learning 285-318(2) % http://en.wikipedia.org/wiki/Winnow_(algorithm) -% $Id: train_sc.m 2140 2009-07-02 12:03:55Z schloegl $ -% Copyright (C) 2005,2006,2007,2008,2009 by Alois Schloegl <a.s...@ie...> +% $Id$ +% Copyright (C) 2005,2006,2007,2008,2009,2010 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox % http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ @@ -567,7 +563,7 @@ CC.prewhite(1,:) = -m.*r; [classlabel,CC.Labels] = CL1M(classlabel); - CC.model = svmtrain(classlabel, D, CC.options); % Call the training mex File + CC.model = svmtrain_mex(classlabel, D, CC.options); % Call the training mex File CC.datatype = ['classifier:',lower(MODE.TYPE)]; @@ -589,7 +585,7 @@ CC.prewhite(1,:) = -m.*r; [classlabel,CC.Labels] = CL1M(classlabel); - CC.model = svmtrain(classlabel, D, CC.options); % Call the training mex File + CC.model = svmtrain_mex(classlabel, D, CC.options); % Call the training mex File FUN = 'SVM:LIB:1vs1'; CC.datatype = ['classifier:',lower(FUN)]; @@ -671,8 +667,11 @@ % nothing to be done elseif exist('train','file')==3, MODE.TYPE = 'SVM:LIN'; %% liblinear - elseif exist('svmtrain','file')==3, + elseif exist('svmtrain_mex','file')==3, MODE.TYPE = 'SVM:LIB'; + elseif (exist('svmtrain','file')==3), + MODE.TYPE = 'SVM:LIB'; + fprintf(1,'You need to rename %s to svmtrain_mex.mex !! \n Press any key to continue !!!\n',which('svmtrain.mex')); elseif exist('svmtrain','file')==2, MODE.TYPE = 'SVM:bioinfo'; elseif exist('mexSVMTrain','file')==3, @@ -720,15 +719,15 @@ else CC.options = sprintf('-s 0 -c %f -t 0 -d 1', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, end; - model = svmtrain(cl, D, CC.options); % C-SVC, C=1, linear kernel, degree = 1, + model = svmtrain_mex(cl, D, CC.options); % C-SVC, C=1, linear kernel, degree = 1, w = cl(1) * model.SVs' * model.sv_coef; %Calculate decision hyperplane weight vector % ensure correct sign of weight vector and Bias according to class label Bias = model.rho * cl(1); elseif strcmp(MODE.TYPE, 'SVM:bioinfo'); CC.SVMstruct = svmtrain(D, cl,'AUTOSCALE', 0); % - Bias = CC.SVMstruct.Bias; - w = CC.SVMstruct.Alpha'*CC.SVMstruct.SupportVectors; + Bias = -CC.SVMstruct.Bias; + w = -CC.SVMstruct.Alpha'*CC.SVMstruct.SupportVectors; elseif strcmp(MODE.TYPE, 'SVM:OSU'); [AlphaY, SVs, Bias] = mexSVMTrain(D', cl', [0 1 1 1 MODE.hyperparameter.c_value]); % Linear Kernel, C=1; degree=1, c-SVM This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2010-01-10 23:26:06
|
Revision: 6739 http://octave.svn.sourceforge.net/octave/?rev=6739&view=rev Author: schloegl Date: 2010-01-10 23:25:59 +0000 (Sun, 10 Jan 2010) Log Message: ----------- add support for non-linear SVM and 1-1 scheme Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/test_sc.m trunk/octave-forge/extra/NaN/inst/train_sc.m Added Paths: ----------- trunk/octave-forge/extra/NaN/src/svmpredict_mex.cpp Modified: trunk/octave-forge/extra/NaN/inst/test_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/test_sc.m 2010-01-10 22:48:26 UTC (rev 6738) +++ trunk/octave-forge/extra/NaN/inst/test_sc.m 2010-01-10 23:25:59 UTC (rev 6739) @@ -27,8 +27,8 @@ % [1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed. % John Wiley & Sons, 2001. -% $Id: test_sc.m 2140 2009-07-02 12:03:55Z schloegl $ -% Copyright (C) 2005,2006,2008,2009 by Alois Schloegl <a.s...@ie...> +% $Id$ +% Copyright (C) 2005,2006,2008,2009,2010 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox % http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ @@ -129,7 +129,7 @@ elseif strcmp(CC.datatype,'classifier:svm:lib:1vs1') || strcmp(CC.datatype,'classifier:svm:lib:rbf'); - cl = svmpredict(ones(size(D,1),1), D, CC.model); %Use the classifier + cl = svmpredict_mex(ones(size(D,1),1), D, CC.model); %Use the classifier %Create a pseudo tsd matrix for bci4eval d = zeros(size(D,1), CC.model.nr_class); Modified: trunk/octave-forge/extra/NaN/inst/train_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-10 22:48:26 UTC (rev 6738) +++ trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-10 23:25:59 UTC (rev 6739) @@ -542,7 +542,7 @@ end; % Martin Hieden's RBF-SVM - if exist('svmpredict','file')==3, + if exist('svmpredict_mex','file')==3, MODE.TYPE = 'SVM:LIB:RBF'; else error('No SVM training algorithm available. Install LibSVM for Matlab.\n'); Added: trunk/octave-forge/extra/NaN/src/svmpredict_mex.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/svmpredict_mex.cpp (rev 0) +++ trunk/octave-forge/extra/NaN/src/svmpredict_mex.cpp 2010-01-10 23:25:59 UTC (rev 6739) @@ -0,0 +1,374 @@ +/* + +$Id$ +Copyright (c) 2000-2009 Chih-Chung Chang and Chih-Jen Lin +Copyright (c) 2010 Alois Schloegl <a.s...@ie...> +This function is part of the NaN-toolbox +http://hci.tugraz.at/~schloegl/matlab/NaN/ + +This code was extracted from libsvm-mat-2.9-1 in Jan 2010 and +modified for the use with Octave + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 3 of the License, or +(at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program; if not, see <http://www.gnu.org/licenses/>. + +*/ + + +#include <stdio.h> +#include <stdlib.h> +#include <string.h> +#include "svm.h" + +#include "mex.h" +#include "svm_model_matlab.h" + +#ifdef MX_API_VER +#if MX_API_VER < 0x07030000 +typedef int mwIndex; +#endif +#endif + +#define CMD_LEN 2048 + +void read_sparse_instance(const mxArray *prhs, int index, struct svm_node *x) +{ + int i, j, low, high; + mwIndex *ir, *jc; + double *samples; + + ir = mxGetIr(prhs); + jc = mxGetJc(prhs); + samples = mxGetPr(prhs); + + // each column is one instance + j = 0; + low = (int)jc[index], high = (int)jc[index+1]; + for(i=low;i<high;i++) + { + x[j].index = (int)ir[i] + 1; + x[j].value = samples[i]; + j++; + } + x[j].index = -1; +} + +static void fake_answer(mxArray *plhs[]) +{ + plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL); + plhs[1] = mxCreateDoubleMatrix(0, 0, mxREAL); + plhs[2] = mxCreateDoubleMatrix(0, 0, mxREAL); +} + +void predict(mxArray *plhs[], const mxArray *prhs[], struct svm_model *model, const int predict_probability) +{ + int label_vector_row_num, label_vector_col_num; + int feature_number, testing_instance_number; + int instance_index; + double *ptr_instance, *ptr_label, *ptr_predict_label; + double *ptr_prob_estimates, *ptr_dec_values, *ptr; + struct svm_node *x; + mxArray *pplhs[1]; // transposed instance sparse matrix + + int correct = 0; + int total = 0; + double error = 0; + double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0; + + int svm_type=svm_get_svm_type(model); + int nr_class=svm_get_nr_class(model); + double *prob_estimates=NULL; + + // prhs[1] = testing instance matrix + feature_number = (int)mxGetN(prhs[1]); + testing_instance_number = (int)mxGetM(prhs[1]); + label_vector_row_num = (int)mxGetM(prhs[0]); + label_vector_col_num = (int)mxGetN(prhs[0]); + + if(label_vector_row_num!=testing_instance_number) + { + mexPrintf("Length of label vector does not match # of instances.\n"); + fake_answer(plhs); + return; + } + if(label_vector_col_num!=1) + { + mexPrintf("label (1st argument) should be a vector (# of column is 1).\n"); + fake_answer(plhs); + return; + } + + ptr_instance = mxGetPr(prhs[1]); + ptr_label = mxGetPr(prhs[0]); + + // transpose instance matrix + if(mxIsSparse(prhs[1])) + { + if(model->param.kernel_type == PRECOMPUTED) + { + // precomputed kernel requires dense matrix, so we make one + mxArray *rhs[1], *lhs[1]; + rhs[0] = mxDuplicateArray(prhs[1]); + if(mexCallMATLAB(1, lhs, 1, rhs, "full")) + { + mexPrintf("Error: cannot full testing instance matrix\n"); + fake_answer(plhs); + return; + } + ptr_instance = mxGetPr(lhs[0]); + mxDestroyArray(rhs[0]); + } + else + { + mxArray *pprhs[1]; + pprhs[0] = mxDuplicateArray(prhs[1]); + if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose")) + { + mexPrintf("Error: cannot transpose testing instance matrix\n"); + fake_answer(plhs); + return; + } + } + } + + if(predict_probability) + { + if(svm_type==NU_SVR || svm_type==EPSILON_SVR) + mexPrintf("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(model)); + else + prob_estimates = (double *) malloc(nr_class*sizeof(double)); + } + + plhs[0] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL); + if(predict_probability) + { + // prob estimates are in plhs[2] + if(svm_type==C_SVC || svm_type==NU_SVC) + plhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class, mxREAL); + else + plhs[2] = mxCreateDoubleMatrix(0, 0, mxREAL); + } + else + { + // decision values are in plhs[2] + if(svm_type == ONE_CLASS || + svm_type == EPSILON_SVR || + svm_type == NU_SVR) + plhs[2] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL); + else + plhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class*(nr_class-1)/2, mxREAL); + } + + ptr_predict_label = mxGetPr(plhs[0]); + ptr_prob_estimates = mxGetPr(plhs[2]); + ptr_dec_values = mxGetPr(plhs[2]); + x = (struct svm_node*)malloc((feature_number+1)*sizeof(struct svm_node) ); + for(instance_index=0;instance_index<testing_instance_number;instance_index++) + { + int i; + double target_label, predict_label; + + target_label = ptr_label[instance_index]; + + if(mxIsSparse(prhs[1]) && model->param.kernel_type != PRECOMPUTED) // prhs[1]^T is still sparse + read_sparse_instance(pplhs[0], instance_index, x); + else + { + for(i=0;i<feature_number;i++) + { + x[i].index = i+1; + x[i].value = ptr_instance[testing_instance_number*i+instance_index]; + } + x[feature_number].index = -1; + } + + if(predict_probability) + { + if(svm_type==C_SVC || svm_type==NU_SVC) + { + predict_label = svm_predict_probability(model, x, prob_estimates); + ptr_predict_label[instance_index] = predict_label; + for(i=0;i<nr_class;i++) + ptr_prob_estimates[instance_index + i * testing_instance_number] = prob_estimates[i]; + } else { + predict_label = svm_predict(model,x); + ptr_predict_label[instance_index] = predict_label; + } + } + else + { + predict_label = svm_predict(model,x); + ptr_predict_label[instance_index] = predict_label; + + if(svm_type == ONE_CLASS || + svm_type == EPSILON_SVR || + svm_type == NU_SVR) + { + double res; + svm_predict_values(model, x, &res); + ptr_dec_values[instance_index] = res; + } + else + { + double *dec_values = (double *) malloc(sizeof(double) * nr_class*(nr_class-1)/2); + svm_predict_values(model, x, dec_values); + for(i=0;i<(nr_class*(nr_class-1))/2;i++) + ptr_dec_values[instance_index + i * testing_instance_number] = dec_values[i]; + free(dec_values); + } + } + + if(predict_label == target_label) + ++correct; + error += (predict_label-target_label)*(predict_label-target_label); + sump += predict_label; + sumt += target_label; + sumpp += predict_label*predict_label; + sumtt += target_label*target_label; + sumpt += predict_label*target_label; + ++total; + } + if(svm_type==NU_SVR || svm_type==EPSILON_SVR) + { + mexPrintf("Mean squared error = %g (regression)\n",error/total); + mexPrintf("Squared correlation coefficient = %g (regression)\n", + ((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/ + ((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt)) + ); + } + else + mexPrintf("Accuracy = %g%% (%d/%d) (classification)\n", + (double)correct/total*100,correct,total); + + // return accuracy, mean squared error, squared correlation coefficient + plhs[1] = mxCreateDoubleMatrix(3, 1, mxREAL); + ptr = mxGetPr(plhs[1]); + ptr[0] = (double)correct/total*100; + ptr[1] = error/total; + ptr[2] = ((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/ + ((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt)); + + free(x); + if(prob_estimates != NULL) + free(prob_estimates); +} + +void exit_with_help() +{ + mexPrintf( + "Usage: [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')\n" + "Parameters:\n" + " model: SVM model structure from svmtrain.\n" + " libsvm_options:\n" + " -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n" + "Returns:\n" + " predicted_label: SVM prediction output vector.\n" + " accuracy: a vector with accuracy, mean squared error, squared correlation coefficient.\n" + " prob_estimates: If selected, probability estimate vector.\n" + ); +} + +void mexFunction( int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] ) +{ + int prob_estimate_flag = 0; + struct svm_model *model; + + if(nrhs > 4 || nrhs < 3) + { + exit_with_help(); + fake_answer(plhs); + return; + } + + if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1])) { + mexPrintf("Error: label vector and instance matrix must be double\n"); + fake_answer(plhs); + return; + } + + if(mxIsStruct(prhs[2])) + { + const char *error_msg; + + // parse options + if(nrhs==4) + { + int i, argc = 1; + char cmd[CMD_LEN], *argv[CMD_LEN/2]; + + // put options in argv[] + mxGetString(prhs[3], cmd, mxGetN(prhs[3]) + 1); + if((argv[argc] = strtok(cmd, " ")) != NULL) + while((argv[++argc] = strtok(NULL, " ")) != NULL) + ; + + for(i=1;i<argc;i++) + { + if(argv[i][0] != '-') break; + if(++i>=argc) + { + exit_with_help(); + fake_answer(plhs); + return; + } + switch(argv[i-1][1]) + { + case 'b': + prob_estimate_flag = atoi(argv[i]); + break; + default: + mexPrintf("Unknown option: -%c\n", argv[i-1][1]); + exit_with_help(); + fake_answer(plhs); + return; + } + } + } + + model = matlab_matrix_to_model(prhs[2], &error_msg); + if (model == NULL) + { + mexPrintf("Error: can't read model: %s\n", error_msg); + fake_answer(plhs); + return; + } + + if(prob_estimate_flag) + { + if(svm_check_probability_model(model)==0) + { + mexPrintf("Model does not support probabiliy estimates\n"); + fake_answer(plhs); + svm_destroy_model(model); + return; + } + } + else + { + if(svm_check_probability_model(model)!=0) + printf("Model supports probability estimates, but disabled in predicton.\n"); + } + + predict(plhs, prhs, model, prob_estimate_flag); + // destroy model + svm_destroy_model(model); + } + else + { + mexPrintf("model file should be a struct array\n"); + fake_answer(plhs); + } + + return; +} This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2010-01-11 10:17:26
|
Revision: 6742 http://octave.svn.sourceforge.net/octave/?rev=6742&view=rev Author: schloegl Date: 2010-01-11 10:17:20 +0000 (Mon, 11 Jan 2010) Log Message: ----------- PSVM improved/fixed; compilation of libsvm for matlab improved; silence libsvm Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/train_sc.m trunk/octave-forge/extra/NaN/src/Makefile Property Changed: ---------------- trunk/octave-forge/extra/NaN/inst/train_sc.m Modified: trunk/octave-forge/extra/NaN/inst/train_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-10 23:54:57 UTC (rev 6741) +++ trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-11 10:17:20 UTC (rev 6742) @@ -553,7 +553,7 @@ if ~isfield(MODE.hyperparameter,'c_value') MODE.hyperparameter.c_value = 1; end - CC.options = sprintf('-c %g -t 2 -g %g', MODE.hyperparameter.c_value, MODE.hyperparameter.gamma); %use RBF kernel, set C, set gamma + CC.options = sprintf('-c %g -t 2 -g %g -q', MODE.hyperparameter.c_value, MODE.hyperparameter.gamma); %use RBF kernel, set C, set gamma CC.hyperparameter.c_value = MODE.hyperparameter.c_value; CC.hyperparameter.gamma = MODE.hyperparameter.gamma; @@ -576,7 +576,7 @@ MODE.hyperparameter.c_value = 1; end - CC.options=sprintf('-c %g -t 0',MODE.hyperparameter.c_value); %use linear kernel, set C + CC.options=sprintf('-c %g -t 0 -q',MODE.hyperparameter.c_value); %use linear kernel, set C CC.hyperparameter.c_value = MODE.hyperparameter.c_value; % pre-whitening @@ -609,7 +609,7 @@ M = size(CL101,2); for k = 1:M, d = sparse(1:m,1:m,CL101(:,k)); - H = d * [-ones(m,1),D]; + H = d * [ones(m,1),D]; %%% r = sum(H,1)'; r = sumskipnan(H,1,W)'; %%% r = (speye(n+1)/nu + H' * H)\r; %solve (I/nu+H’*H)r=H’*e @@ -697,7 +697,7 @@ sz2 = length(cix); s = sparse(2:sz2+1,1:sz2,r,sz2+1,sz2,2*sz2); s(1,:) = -m.*r; - + for k = 1:M, cl = CL101(:,k); if strncmp(MODE.TYPE, 'SVM:LIN',7); @@ -717,7 +717,7 @@ if isfield(MODE,'options') CC.options = MODE.options; else - CC.options = sprintf('-s 0 -c %f -t 0 -d 1', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, + CC.options = sprintf('-s 0 -c %f -t 0 -d 1 -q', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, end; model = svmtrain_mex(cl, D, CC.options); % C-SVC, C=1, linear kernel, degree = 1, w = cl(1) * model.SVs' * model.sv_coef; %Calculate decision hyperplane weight vector Property changes on: trunk/octave-forge/extra/NaN/inst/train_sc.m ___________________________________________________________________ Added: svn:keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/src/Makefile =================================================================== --- trunk/octave-forge/extra/NaN/src/Makefile 2010-01-10 23:54:57 UTC (rev 6741) +++ trunk/octave-forge/extra/NaN/src/Makefile 2010-01-11 10:17:20 UTC (rev 6742) @@ -5,6 +5,7 @@ OCTMEX = mkoctfile --mex MATLABDIR = /usr/local/matlab +MEX_OPTION = CC\#$(CXX) CXX\#$(CXX) CFLAGS\#"$(CFLAGS)" CXXFLAGS\#"$(CFLAGS)" # comment the following line if you use MATLAB on 32-bit computer MEX_OPTION += -largeArrayDims MATMEX = $(MATLABDIR)/bin/mex $(MEX_OPTION) This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2010-01-11 20:48:04
|
Revision: 6748 http://octave.svn.sourceforge.net/octave/?rev=6748&view=rev Author: schloegl Date: 2010-01-11 20:47:51 +0000 (Mon, 11 Jan 2010) Log Message: ----------- weighted libLinear supported Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/train_sc.m trunk/octave-forge/extra/NaN/src/Makefile trunk/octave-forge/extra/NaN/src/linear.cpp trunk/octave-forge/extra/NaN/src/linear.h trunk/octave-forge/extra/NaN/src/svm_model_matlab.h trunk/octave-forge/extra/NaN/src/train.c Modified: trunk/octave-forge/extra/NaN/inst/train_sc.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-11 17:12:37 UTC (rev 6747) +++ trunk/octave-forge/extra/NaN/inst/train_sc.m 2010-01-11 20:47:51 UTC (rev 6748) @@ -547,15 +547,13 @@ else error('No SVM training algorithm available. Install LibSVM for Matlab.\n'); end; - if ~isfield(MODE.hyperparameter,'gamma') - MODE.hyperparameter.gamma = 1; + CC.options = '-t 2 -q'; %use RBF kernel, set C, set gamma + if isfield(MODE.hyperparameter,'gamma') + CC.options = sprintf('%s -c %g', CC.options, MODE.hyperparameter.c_value); % set C end - if ~isfield(MODE.hyperparameter,'c_value') - MODE.hyperparameter.c_value = 1; + if isfield(MODE.hyperparameter,'c_value') + CC.options = sprintf('%s -g %g', CC.options, MODE.hyperparameter.gamma); % set C end - CC.options = sprintf('-c %g -t 2 -g %g -q', MODE.hyperparameter.c_value, MODE.hyperparameter.gamma); %use RBF kernel, set C, set gamma - CC.hyperparameter.c_value = MODE.hyperparameter.c_value; - CC.hyperparameter.gamma = MODE.hyperparameter.gamma; % pre-whitening [D,r,m]=zscore(D,1); @@ -626,7 +624,7 @@ elseif ~isempty(strfind(lower(MODE.TYPE),'svm:lin4')) if ~isempty(W) - error('Classifier (%s) does not support weighted samples.',MODE.TYPE); +% error('Classifier (%s) does not support weighted samples.',MODE.TYPE); end; if ~isfield(MODE.hyperparameter,'c_value') @@ -645,8 +643,8 @@ s = sparse(2:sz2+1,1:sz2,r,sz2+1,sz2,2*sz2); s(1,:) = -m.*r; - CC.options = sprintf('-s 4 -c %f ', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, - model = train(classlabel, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, + CC.options = sprintf('-s 4 -B 1 -c %f -q', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, + model = train(W, classlabel, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, weights = model.w([end,1:end-1],:)'; CC.weights([1,cix+1],:) = s * weights(2:end,:) + sparse(1,1:M,weights(1,:),sz2+1,M); % include pre-whitening transformation @@ -656,9 +654,6 @@ elseif ~isempty(strfind(lower(MODE.TYPE),'svm')) - if ~isempty(W) - error('Classifier (%s) does not support weighted samples.',MODE.TYPE); - end; if ~isfield(MODE.hyperparameter,'c_value') MODE.hyperparameter.c_value = 1; @@ -686,6 +681,10 @@ error('No SVM training algorithm available. Install OSV-SVM, or LOO-SVM, or libSVM for Matlab.\n'); end; + if ~strncmp(MODE.TYPE, 'SVM:LIN',7) && ~isempty(W) + error('Classifier (%s) does not support weighted samples.',MODE.TYPE); + end; + %%CC = train_svm(D,classlabel,MODE); [CL101,CC.Labels] = cl101(classlabel); M = size(CL101,2); @@ -707,11 +706,13 @@ t = 0; if length(MODE.TYPE)>7, t=MODE.TYPE(8)-'0'; end; if (t<0 || t>4) t=0; end; - CC.options = sprintf('-s %i -c %f ',t, MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, + CC.options = sprintf('-s %i -B 1 -c %f -q',t, MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, end; - model = train(cl, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, - w = -model.w(1:end-1)'; - Bias = -model.w(end); + model = train(W, cl, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, + w = -model.w'; + Bias = -model.bias; + w = -model.w(:,1:end-1)'; + Bias = -model.w(:,end)'; elseif strcmp(MODE.TYPE, 'SVM:LIB'); %% tested with libsvm-mat-2.9-1 if isfield(MODE,'options') Modified: trunk/octave-forge/extra/NaN/src/Makefile =================================================================== --- trunk/octave-forge/extra/NaN/src/Makefile 2010-01-11 17:12:37 UTC (rev 6747) +++ trunk/octave-forge/extra/NaN/src/Makefile 2010-01-11 20:47:51 UTC (rev 6748) @@ -29,15 +29,15 @@ %.$(MEX_EXT): %.cpp $(MATMEX) $< ## Matlab -svmtrain_mex.mex svmpredict_mex.mex: svmtrain_mex.cpp svm.h +svmtrain_mex.mex svmpredict_mex.mex: svmtrain_mex.cpp svm.h svm.cpp svm_model_matlab.c $(CXX) $(CFLAGS) -I /usr/include/octave -c svm.cpp - $(CC) $(CFLAGS) -I /usr/include/octave -c svm_model_matlab.c + $(CXX) $(CFLAGS) -I /usr/include/octave -c svm_model_matlab.c env CC=$(CXX) $(OCTMEX) svmtrain_mex.cpp svm.o svm_model_matlab.o env CC=$(CXX) $(OCTMEX) svmpredict_mex.cpp svm.o svm_model_matlab.o -svmtrain_mex.$(MEX_EXT) svmpredict_mex.$(MEX_EXT): svmtrain_mex.cpp svm.h +svmtrain_mex.$(MEX_EXT) svmpredict_mex.$(MEX_EXT): svmtrain_mex.cpp svm.h svm.cpp svm_model_matlab.c $(CXX) $(CFLAGS) -I $(MATLABDIR)/extern/include -c svm.cpp - $(CC) $(CFLAGS) -I $(MATLABDIR)/extern/include -c svm_model_matlab.c + $(CXX) $(CFLAGS) -I $(MATLABDIR)/extern/include -c svm_model_matlab.c $(MATMEX) svmtrain_mex.cpp svm.o svm_model_matlab.o $(MATMEX) svmpredict_mex.cpp svm.o svm_model_matlab.o Modified: trunk/octave-forge/extra/NaN/src/linear.cpp =================================================================== --- trunk/octave-forge/extra/NaN/src/linear.cpp 2010-01-11 17:12:37 UTC (rev 6747) +++ trunk/octave-forge/extra/NaN/src/linear.cpp 2010-01-11 20:47:51 UTC (rev 6748) @@ -106,9 +106,9 @@ for (i=0; i<l; i++) { if (y[i] == 1) - C[i] = Cp; + C[i] = prob->W[i] * Cp; else - C[i] = Cn; + C[i] = prob->W[i] * Cn; } } @@ -265,9 +265,9 @@ for (i=0; i<l; i++) { if (y[i] == 1) - C[i] = Cp; + C[i] = prob->W[i] * Cp; else - C[i] = Cn; + C[i] = prob->W[i] * Cn; } } @@ -418,7 +418,7 @@ // // solution will be put in w -#define GETI(i) (prob->y[i]) +#define GETI(i) (i) // To support weights for instances, use GETI(i) (i) class Solver_MCSVM_CS @@ -446,15 +446,18 @@ this->eps = eps; this->max_iter = max_iter; this->prob = prob; - this->C = weighted_C; this->B = new double[nr_class]; this->G = new double[nr_class]; + this->C = new double[prob->l]; + for(int i = 0; i < prob->l; i++) + this->C[i] = prob->W[i] * weighted_C[prob->y[i]]; } Solver_MCSVM_CS::~Solver_MCSVM_CS() { delete[] B; delete[] G; + delete[] C; } int compare_double(const void *a, const void *b) @@ -727,7 +730,7 @@ // solution will be put in w #undef GETI -#define GETI(i) (y[i]+1) +#define GETI(i) (i) // To support weights for instances, use GETI(i) (i) static void solve_l2r_l1l2_svc( @@ -752,14 +755,25 @@ double PGmax_new, PGmin_new; // default solver_type: L2R_L2LOSS_SVC_DUAL - double diag[3] = {0.5/Cn, 0, 0.5/Cp}; - double upper_bound[3] = {INF, 0, INF}; + double *diag = new double[l]; + double *upper_bound = new double[l]; + double *C_ = new double[l]; + for(i=0; i<l; i++) + { + if(prob->y[i]>0) + C_[i] = prob->W[i] * Cp; + else + C_[i] = prob->W[i] * Cn; + diag[i] = 0.5/C_[i]; + upper_bound[i] = INF; + } if(solver_type == L2R_L1LOSS_SVC_DUAL) { - diag[0] = 0; - diag[2] = 0; - upper_bound[0] = Cn; - upper_bound[2] = Cp; + for(i=0; i<l; i++) + { + diag[i] = 0; + upper_bound[i] = C_[i]; + } } for(i=0; i<w_size; i++) @@ -903,6 +917,9 @@ info("Objective value = %lf\n",v/2); info("nSV = %d\n",nSV); + delete [] upper_bound; + delete [] diag; + delete [] C_; delete [] QD; delete [] alpha; delete [] y; @@ -921,7 +938,7 @@ // solution will be put in w #undef GETI -#define GETI(i) (y[i]+1) +#define GETI(i) (i) // To support weights for instances, use GETI(i) (i) static void solve_l1r_l2_svc( @@ -950,15 +967,21 @@ double *xj_sq = new double[w_size]; feature_node *x; - double C[3] = {Cn,0,Cp}; + double *C = new double[l]; for(j=0; j<l; j++) { b[j] = 1; if(prob_col->y[j] > 0) + { y[j] = 1; + C[j] = prob_col->W[j] * Cp; + } else + { y[j] = -1; + C[j] = prob_col->W[j] * Cn; + } } for(j=0; j<w_size; j++) { @@ -1182,6 +1205,7 @@ info("Objective value = %lf\n", v); info("#nonzeros/#features = %d/%d\n", nnz, w_size); + delete [] C; delete [] index; delete [] y; delete [] b; @@ -1234,15 +1258,21 @@ double *xjpos_sum = new double[w_size]; feature_node *x; - double C[3] = {Cn,0,Cp}; + double *C = new double[l]; for(j=0; j<l; j++) { exp_wTx[j] = 1; if(prob_col->y[j] > 0) + { y[j] = 1; + C[j] = prob_col->W[j] * Cp; + } else + { y[j] = -1; + C[j] = prob_col->W[j] * Cn; + } } for(j=0; j<w_size; j++) { @@ -1463,6 +1493,7 @@ info("Objective value = %lf\n", v); info("#nonzeros/#features = %d/%d\n", nnz, w_size); + delete [] C; delete [] index; delete [] y; delete [] exp_wTx; @@ -1486,9 +1517,13 @@ prob_col->n = n; prob_col->y = new int[l]; prob_col->x = new feature_node*[n]; + prob_col->W = new double[l]; for(i=0; i<l; i++) + { prob_col->y[i] = prob->y[i]; + prob_col->W[i] = prob->W[i]; + } for(i=0; i<n+1; i++) col_ptr[i] = 0; @@ -1633,6 +1668,7 @@ solve_l1r_l2_svc(&prob_col, w, eps*min(pos,neg)/prob->l, Cp, Cn); delete [] prob_col.y; delete [] prob_col.x; + delete [] prob_col.W; delete [] x_space; break; } @@ -1644,6 +1680,7 @@ solve_l1r_lr(&prob_col, w, eps*min(pos,neg)/prob->l, Cp, Cn); delete [] prob_col.y; delete [] prob_col.x; + delete [] prob_col.W; delete [] x_space; break; } @@ -1654,10 +1691,40 @@ } // +// Remove zero weighed data as libsvm and some liblinear solvers require C > 0. +// +static void remove_zero_weight(problem *newprob, const problem *prob) +{ + int i; + int l = 0; + for(i=0;i<prob->l;i++) + if(prob->W[i] > 0) l++; + *newprob = *prob; + newprob->l = l; + newprob->x = Malloc(feature_node*,l); + newprob->y = Malloc(int,l); + newprob->W = Malloc(double,l); + + int j = 0; + for(i=0;i<prob->l;i++) + if(prob->W[i] > 0) + { + newprob->x[j] = prob->x[i]; + newprob->y[j] = prob->y[i]; + newprob->W[j] = prob->W[i]; + j++; + } +} + +// // Interface functions // model* train(const problem *prob, const parameter *param) { + problem newprob; + remove_zero_weight(&newprob, prob); + prob = &newprob; + int i,j; int l = prob->l; int n = prob->n; @@ -1702,8 +1769,12 @@ // constructing the subproblem feature_node **x = Malloc(feature_node *,l); + double *W = Malloc(double,l); for(i=0;i<l;i++) + { x[i] = prob->x[perm[i]]; + W[i] = prob->W[perm[i]]; + } int k; problem sub_prob; @@ -1711,9 +1782,13 @@ sub_prob.n = n; sub_prob.x = Malloc(feature_node *,sub_prob.l); sub_prob.y = Malloc(int,sub_prob.l); + sub_prob.W = Malloc(double,sub_prob.l); for(k=0; k<sub_prob.l; k++) + { sub_prob.x[k] = x[k]; + sub_prob.W[k] = W[k]; + } // multi-class svm by Crammer and Singer if(param->solver_type == MCSVM_CS) @@ -1768,13 +1843,18 @@ } free(x); + free(W); free(label); free(start); free(count); free(perm); free(sub_prob.x); free(sub_prob.y); + free(sub_prob.W); free(weighted_C); + free(newprob.x); + free(newprob.y); + free(newprob.W); return model_; } @@ -2075,18 +2155,21 @@ subprob.l = l-(end-begin); subprob.x = Malloc(struct feature_node*,subprob.l); subprob.y = Malloc(int,subprob.l); + subprob.W = Malloc(double,subprob.l); k=0; for(j=0;j<begin;j++) { subprob.x[k] = prob->x[perm[j]]; subprob.y[k] = prob->y[perm[j]]; + subprob.W[k] = prob->W[perm[j]]; ++k; } for(j=end;j<l;j++) { subprob.x[k] = prob->x[perm[j]]; subprob.y[k] = prob->y[perm[j]]; + subprob.W[k] = prob->W[perm[j]]; ++k; } struct model *submodel = train(&subprob,param); @@ -2095,6 +2178,7 @@ destroy_model(submodel); free(subprob.x); free(subprob.y); + free(subprob.W); } free(fold_start); free(perm); Modified: trunk/octave-forge/extra/NaN/src/linear.h =================================================================== --- trunk/octave-forge/extra/NaN/src/linear.h 2010-01-11 17:12:37 UTC (rev 6747) +++ trunk/octave-forge/extra/NaN/src/linear.h 2010-01-11 20:47:51 UTC (rev 6748) @@ -43,6 +43,7 @@ int *y; struct feature_node **x; double bias; /* < 0 if no bias term */ + double *W; /* instance weight */ }; enum { L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR }; /* solver_type */ Modified: trunk/octave-forge/extra/NaN/src/svm_model_matlab.h =================================================================== --- trunk/octave-forge/extra/NaN/src/svm_model_matlab.h 2010-01-11 17:12:37 UTC (rev 6747) +++ trunk/octave-forge/extra/NaN/src/svm_model_matlab.h 2010-01-11 20:47:51 UTC (rev 6748) @@ -39,16 +39,8 @@ */ -#ifdef __cplusplus -extern "C" { -#endif - const char *model_to_matlab_structure(mxArray *plhs[], int num_of_feature, struct svm_model *model); struct svm_model *matlab_matrix_to_model(const mxArray *matlab_struct, const char **error_message); -#ifdef __cplusplus -} -#endif - Modified: trunk/octave-forge/extra/NaN/src/train.c =================================================================== --- trunk/octave-forge/extra/NaN/src/train.c 2010-01-11 17:12:37 UTC (rev 6747) +++ trunk/octave-forge/extra/NaN/src/train.c 2010-01-11 20:47:51 UTC (rev 6748) @@ -51,7 +51,7 @@ void exit_with_help() { mexPrintf( - "Usage: model = train(training_label_vector, training_instance_matrix, 'liblinear_options', 'col');\n" + "Usage: model = train(weight_vector, training_label_vector, training_instance_matrix, 'liblinear_options', 'col');\n" "liblinear_options:\n" "-s type : set type of solver (default 1)\n" " 0 -- L2-regularized logistic regression\n" @@ -109,7 +109,7 @@ return retval; } -// nrhs should be 3 +// nrhs should be 4 int parse_command_line(int nrhs, const mxArray *prhs[], char *model_file_name) { int i, argc = 1; @@ -133,20 +133,20 @@ else liblinear_print_string = liblinear_default_print_string; - if(nrhs <= 1) + if(nrhs <= 2) return 1; - if(nrhs == 4) + if(nrhs == 5) { - mxGetString(prhs[3], cmd, mxGetN(prhs[3])+1); + mxGetString(prhs[4], cmd, mxGetN(prhs[4])+1); if(strcmp(cmd, "col") == 0) col_format_flag = 1; } // put options in argv[] - if(nrhs > 2) + if(nrhs > 3) { - mxGetString(prhs[2], cmd, mxGetN(prhs[2]) + 1); + mxGetString(prhs[3], cmd, mxGetN(prhs[3]) + 1); if((argv[argc] = strtok(cmd, " ")) != NULL) while((argv[++argc] = strtok(NULL, " ")) != NULL) ; @@ -216,16 +216,17 @@ plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL); } -int read_problem_sparse(const mxArray *label_vec, const mxArray *instance_mat) +int read_problem_sparse(const mxArray *weight_vec, const mxArray *label_vec, const mxArray *instance_mat) { int i, j, k, low, high; mwIndex *ir, *jc; - int elements, max_index, num_samples, label_vector_row_num; - double *samples, *labels; + int elements, max_index, num_samples, label_vector_row_num, weight_vector_row_num; + double *samples, *labels, *weights; mxArray *instance_mat_col; // instance sparse matrix in column format prob.x = NULL; prob.y = NULL; + prob.W = NULL; x_space = NULL; if(col_format_flag) @@ -246,8 +247,16 @@ // the number of instance prob.l = (int) mxGetN(instance_mat_col); + weight_vector_row_num = (int) mxGetM(weight_vec); label_vector_row_num = (int) mxGetM(label_vec); + if(weight_vector_row_num == 0) + ;//mexPrintf("Warning: treat each instance with weight 1.0\n"); + else if(weight_vector_row_num!=prob.l) + { + mexPrintf("Length of weight vector does not match # of instances.\n"); + return -1; + } if(label_vector_row_num!=prob.l) { mexPrintf("Length of label vector does not match # of instances.\n"); @@ -255,6 +264,7 @@ } // each column is one instance + weights = mxGetPr(weight_vec); labels = mxGetPr(label_vec); samples = mxGetPr(instance_mat_col); ir = mxGetIr(instance_mat_col); @@ -266,6 +276,7 @@ max_index = (int) mxGetM(instance_mat_col); prob.y = Malloc(int, prob.l); + prob.W = Malloc(double,prob.l); prob.x = Malloc(struct feature_node*, prob.l); x_space = Malloc(struct feature_node, elements); @@ -276,6 +287,9 @@ { prob.x[i] = &x_space[j]; prob.y[i] = (int) labels[i]; + prob.W[i] = 1; + if(weight_vector_row_num > 0) + prob.W[i] *= (double) weights[i]; low = (int) jc[i], high = (int) jc[i+1]; for(k=low;k<high;k++) { @@ -311,12 +325,12 @@ srand(1); // Transform the input Matrix to libsvm format - if(nrhs > 0 && nrhs < 5) + if(nrhs > 2 && nrhs < 6) { int err=0; - if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1])) { - mexPrintf("Error: label vector and instance matrix must be double\n"); + if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1]) || !mxIsDouble(prhs[2])) { + mexPrintf("Error: weight vector, label vector and instance matrix must be double\n"); fake_answer(plhs); return; } @@ -329,8 +343,8 @@ return; } - if(mxIsSparse(prhs[1])) - err = read_problem_sparse(prhs[0], prhs[1]); + if(mxIsSparse(prhs[2])) + err = read_problem_sparse(prhs[0], prhs[1], prhs[2]); else { mexPrintf("Training_instance_matrix must be sparse\n"); @@ -374,6 +388,7 @@ destroy_param(¶m); free(prob.y); free(prob.x); + free(prob.W); free(x_space); } else This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2010-01-13 22:45:08
|
Revision: 6768 http://octave.svn.sourceforge.net/octave/?rev=6768&view=rev Author: schloegl Date: 2010-01-13 22:44:58 +0000 (Wed, 13 Jan 2010) Log Message: ----------- add make file for ml* for win Modified Paths: -------------- trunk/octave-forge/extra/NaN/doc/INSTALL Added Paths: ----------- trunk/octave-forge/extra/NaN/src/make.m Modified: trunk/octave-forge/extra/NaN/doc/INSTALL =================================================================== --- trunk/octave-forge/extra/NaN/doc/INSTALL 2010-01-13 11:50:42 UTC (rev 6767) +++ trunk/octave-forge/extra/NaN/doc/INSTALL 2010-01-13 22:44:58 UTC (rev 6768) @@ -5,28 +5,29 @@ b) Include the path with one of the following commands: addpath('/your/directory/structure/to/NaN/') - path('/your/directory/structure/to/NaN/',path) - Make sure the functions in the NaN-toolbox are found before the default functions. - + addpath('/your/directory/structure/to/NaN/inst') + addpath('/your/directory/structure/to/NaN/src') + + The NaN-toolbox redefines some standard functions like mean, var, std, cor, cov, corrcoef, etc. + You can avoid this by including the directories at the end of the path + + addpath('/your/directory/structure/to/NaN/','-end') + addpath('/your/directory/structure/to/NaN/inst','-end') + addpath('/your/directory/structure/to/NaN/src','-end') + + Make sure the functions in the NaN-toolbox are found before the default functions. + c) run NANINSTTEST -This checks whether the installation was successful. +This checks whether the installation was successful. -d) [OPTIONAL]: - For support of weighted statistics, you need the MEX-version of SUMSKIPNAN and COVM. - Some precompiled binaries are provided. If your platform is not supported, - compile the C-Mex-functions SUMSKIPNAN_MEX.CPP and COVM_MEX.CPP using - mex sumskipnan_mex.cpp - mex covm_mex.cpp - mex histo_mex.cpp - or for Octave use the mex-file. - mkoctfile --mex sumskipnan_mex.cpp - mkoctfile --mex covm_mex.cpp - mkoctfile --mex histo_mex.cpp +d) [OPTIONAL]: + Several mex-files are strongly recommened. + Run MAKE from the directory .../NaN/src/ - Run NANINSTTEST again to check the stability of the compiled SUMSKIPNAN. + Run NANINSTTEST again to check the stability of the compiled SUMSKIPNAN. + $Id$ + Copyright (c) 2000-2003,2005,2006,2009,2010 by Alois Schloegl <a.s...@ie...> + This is part of the NaN-toolbox + http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ - $Id$ - Copyright (c) 2000-2003,2005,2006,2009 by Alois Schloegl <a.s...@ie...> - WWW: http://hci.tugraz.at/~schloegl/matlab/NaN/ - Added: trunk/octave-forge/extra/NaN/src/make.m =================================================================== --- trunk/octave-forge/extra/NaN/src/make.m (rev 0) +++ trunk/octave-forge/extra/NaN/src/make.m 2010-01-13 22:44:58 UTC (rev 6768) @@ -0,0 +1,26 @@ +% This make.m is used for Matlab under Windows + +% $Id$ +% Copyright (C) 2010 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ + +% add -largeArrayDims on 64-bit machines + +mex covm_mex.cpp +mex histo_mex.cpp +mex sumskipnan_mex.cpp +mex -c svm.cpp +mex -c svm_model_matlab.c +mex -c tron.cpp +mex -c linear.cpp +mex -c linear_model_matlab.c +if strcmp(computer,'PCWIN') + mex svmtrain.cpp svm.obj svm_model_matlab.obj + mex svmpredict_mex.cpp svm.obj svm_model_matlab.obj + mex train.c tron.obj linear.obj linear_model_matlab.obj +else + mex svmtrain.cpp svm.o svm_model_matlab.o + mex svmpredict_mex.cpp svm.o svm_model_matlab.o + mex train.c tron.o linear.o linear_model_matlab.o +end; \ No newline at end of file Property changes on: trunk/octave-forge/extra/NaN/src/make.m ___________________________________________________________________ Added: svn:executable + * This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |
From: <sch...@us...> - 2010-02-28 20:19:22
|
Revision: 6973 http://octave.svn.sourceforge.net/octave/?rev=6973&view=rev Author: schloegl Date: 2010-02-28 20:19:12 +0000 (Sun, 28 Feb 2010) Log Message: ----------- update website, svn keywords, prepare for next release Modified Paths: -------------- trunk/octave-forge/extra/NaN/inst/cat2bin.m trunk/octave-forge/extra/NaN/inst/cdfplot.m trunk/octave-forge/extra/NaN/inst/center.m trunk/octave-forge/extra/NaN/inst/classify.m trunk/octave-forge/extra/NaN/inst/coefficient_of_variation.m trunk/octave-forge/extra/NaN/inst/conv2nan.m trunk/octave-forge/extra/NaN/inst/cor.m trunk/octave-forge/extra/NaN/inst/corrcoef.m trunk/octave-forge/extra/NaN/inst/cov.m trunk/octave-forge/extra/NaN/inst/covm.m trunk/octave-forge/extra/NaN/inst/decovm.m trunk/octave-forge/extra/NaN/inst/detrend.m trunk/octave-forge/extra/NaN/inst/ecdf.m trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m trunk/octave-forge/extra/NaN/inst/flag_implicit_significance.m trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m trunk/octave-forge/extra/NaN/inst/fss.m trunk/octave-forge/extra/NaN/inst/geomean.m trunk/octave-forge/extra/NaN/inst/gscatter.m trunk/octave-forge/extra/NaN/inst/harmmean.m trunk/octave-forge/extra/NaN/inst/hist2res.m trunk/octave-forge/extra/NaN/inst/iqr.m trunk/octave-forge/extra/NaN/inst/kappa.m trunk/octave-forge/extra/NaN/inst/kurtosis.m trunk/octave-forge/extra/NaN/inst/load_fisheriris.m trunk/octave-forge/extra/NaN/inst/mad.m trunk/octave-forge/extra/NaN/inst/mahal.m trunk/octave-forge/extra/NaN/inst/mean.m trunk/octave-forge/extra/NaN/inst/meandev.m trunk/octave-forge/extra/NaN/inst/meansq.m trunk/octave-forge/extra/NaN/inst/medAbsDev.m trunk/octave-forge/extra/NaN/inst/median.m trunk/octave-forge/extra/NaN/inst/mod.m trunk/octave-forge/extra/NaN/inst/moment.m trunk/octave-forge/extra/NaN/inst/naninsttest.m trunk/octave-forge/extra/NaN/inst/nanmean.m trunk/octave-forge/extra/NaN/inst/nanstd.m trunk/octave-forge/extra/NaN/inst/nansum.m trunk/octave-forge/extra/NaN/inst/nantest.m trunk/octave-forge/extra/NaN/inst/normcdf.m trunk/octave-forge/extra/NaN/inst/norminv.m trunk/octave-forge/extra/NaN/inst/normpdf.m trunk/octave-forge/extra/NaN/inst/partcorrcoef.m trunk/octave-forge/extra/NaN/inst/percentile.m trunk/octave-forge/extra/NaN/inst/prctile.m trunk/octave-forge/extra/NaN/inst/quantile.m trunk/octave-forge/extra/NaN/inst/rankcorr.m trunk/octave-forge/extra/NaN/inst/ranks.m trunk/octave-forge/extra/NaN/inst/rem.m trunk/octave-forge/extra/NaN/inst/rms.m trunk/octave-forge/extra/NaN/inst/sem.m trunk/octave-forge/extra/NaN/inst/skewness.m trunk/octave-forge/extra/NaN/inst/spearman.m trunk/octave-forge/extra/NaN/inst/statistic.m trunk/octave-forge/extra/NaN/inst/std.m trunk/octave-forge/extra/NaN/inst/sumskipnan.m trunk/octave-forge/extra/NaN/inst/sumsq.m trunk/octave-forge/extra/NaN/inst/tcdf.m trunk/octave-forge/extra/NaN/inst/test_sc.m trunk/octave-forge/extra/NaN/inst/tiedrank.m trunk/octave-forge/extra/NaN/inst/tinv.m trunk/octave-forge/extra/NaN/inst/tpdf.m trunk/octave-forge/extra/NaN/inst/train_lda_sparse.m trunk/octave-forge/extra/NaN/inst/train_sc.m trunk/octave-forge/extra/NaN/inst/trimean.m trunk/octave-forge/extra/NaN/inst/trimmean.m trunk/octave-forge/extra/NaN/inst/var.m trunk/octave-forge/extra/NaN/inst/xcovf.m trunk/octave-forge/extra/NaN/inst/xval.m trunk/octave-forge/extra/NaN/inst/zScoreMedian.m trunk/octave-forge/extra/NaN/inst/zscore.m trunk/octave-forge/extra/NaN/test/test_fss.m trunk/octave-forge/extra/NaN/test/test_xval.m Property Changed: ---------------- trunk/octave-forge/extra/NaN/inst/cat2bin.m trunk/octave-forge/extra/NaN/inst/cdfplot.m trunk/octave-forge/extra/NaN/inst/center.m trunk/octave-forge/extra/NaN/inst/conv2nan.m trunk/octave-forge/extra/NaN/inst/cor.m trunk/octave-forge/extra/NaN/inst/corrcoef.m trunk/octave-forge/extra/NaN/inst/cov.m trunk/octave-forge/extra/NaN/inst/covm.m trunk/octave-forge/extra/NaN/inst/decovm.m trunk/octave-forge/extra/NaN/inst/detrend.m trunk/octave-forge/extra/NaN/inst/ecdf.m trunk/octave-forge/extra/NaN/inst/flag_implicit_significance.m trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m trunk/octave-forge/extra/NaN/inst/fss.m trunk/octave-forge/extra/NaN/inst/geomean.m trunk/octave-forge/extra/NaN/inst/gscatter.m trunk/octave-forge/extra/NaN/inst/harmmean.m trunk/octave-forge/extra/NaN/inst/hist2res.m trunk/octave-forge/extra/NaN/inst/iqr.m trunk/octave-forge/extra/NaN/inst/kurtosis.m trunk/octave-forge/extra/NaN/inst/mahal.m trunk/octave-forge/extra/NaN/inst/mean.m trunk/octave-forge/extra/NaN/inst/meansq.m trunk/octave-forge/extra/NaN/inst/medAbsDev.m trunk/octave-forge/extra/NaN/inst/median.m trunk/octave-forge/extra/NaN/inst/mod.m trunk/octave-forge/extra/NaN/inst/moment.m trunk/octave-forge/extra/NaN/inst/naninsttest.m trunk/octave-forge/extra/NaN/inst/nanmean.m trunk/octave-forge/extra/NaN/inst/nanstd.m trunk/octave-forge/extra/NaN/inst/nansum.m trunk/octave-forge/extra/NaN/inst/nantest.m trunk/octave-forge/extra/NaN/inst/normcdf.m trunk/octave-forge/extra/NaN/inst/norminv.m trunk/octave-forge/extra/NaN/inst/normpdf.m trunk/octave-forge/extra/NaN/inst/percentile.m trunk/octave-forge/extra/NaN/inst/prctile.m trunk/octave-forge/extra/NaN/inst/quantile.m trunk/octave-forge/extra/NaN/inst/rankcorr.m trunk/octave-forge/extra/NaN/inst/ranks.m trunk/octave-forge/extra/NaN/inst/rem.m trunk/octave-forge/extra/NaN/inst/rms.m trunk/octave-forge/extra/NaN/inst/sem.m trunk/octave-forge/extra/NaN/inst/spearman.m trunk/octave-forge/extra/NaN/inst/statistic.m trunk/octave-forge/extra/NaN/inst/sumskipnan.m trunk/octave-forge/extra/NaN/inst/tcdf.m trunk/octave-forge/extra/NaN/inst/tiedrank.m trunk/octave-forge/extra/NaN/inst/tinv.m trunk/octave-forge/extra/NaN/inst/tpdf.m trunk/octave-forge/extra/NaN/inst/train_lda_sparse.m trunk/octave-forge/extra/NaN/inst/trimean.m trunk/octave-forge/extra/NaN/inst/trimmean.m trunk/octave-forge/extra/NaN/inst/xcovf.m trunk/octave-forge/extra/NaN/inst/zscore.m Modified: trunk/octave-forge/extra/NaN/inst/cat2bin.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/cat2bin.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/cat2bin.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -26,7 +26,7 @@ % $Id$ % Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License @@ -53,14 +53,14 @@ k1 = 0; BLab = []; for m = 1:size(D,2) - h = histo_mex(D(:,m)); + h = histo_mex(D(:,m)); x = h.X(h.H>0); if strcmpi(MODE,'notIgnoreNaN') ; elseif strcmpi(MODE,'IgnoreZeros') - x = x(x~=0); + x = x(x~=0); elseif strcmpi(MODE,'IgnoreZeros+NaN') - x = x((x~=0) & (x==x)); + x = x((x~=0) & (x==x)); else x = x(x==x); end; @@ -69,20 +69,20 @@ B(k, c + find(D(k,m)==x)) = 1; elseif isnan(x(end)), B(k, c + length(x)) = 1; - end; + end; end; - c = c + length(x); + c = c + length(x); if nargout>1, for k = 1:length(x), k1 = k1+1; if isempty(Label) BLab{k1} = ['#',int2str(m),':',int2str(x(k))]; - else + else BLab{k1} = [Label{m},':',int2str(x(k))]; - end; - end; - end; -end; + end; + end; + end; +end; Property changes on: trunk/octave-forge/extra/NaN/inst/cat2bin.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/cdfplot.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/cdfplot.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/cdfplot.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -7,16 +7,16 @@ % % h is the handle to the cdf curve % stats is a struct containing various summary statistics -% like mean, std, median, min, max, etc. +% like mean, std, median, min, max, etc. % % see also: ecdf, median, statistics, hist2res % % References: -% $Id$ -% Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> +% $Id$ +% Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License @@ -37,11 +37,11 @@ hh = plot(his.X,his.H/sum(his.H)); if nargout>0, - h = hh; + h = hh; end; if nargout>1, - stats = hist2res(his); - stats.median = quantile(his,.5); + stats = hist2res(his); + stats.median = quantile(his,.5); end; - + Property changes on: trunk/octave-forge/extra/NaN/inst/cdfplot.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/center.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/center.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/center.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -36,27 +36,27 @@ % along with this program; If not, see <http://www.gnu.org/licenses/>. -% $Id$ -% Copyright (C) 2000-2003,2005,2009 by Alois Schloegl <a.s...@ie...> -% This is part of the NaN-toolbox. For more details see -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ - +% $Id$ +% Copyright (C) 2000-2003,2005,2009 by Alois Schloegl <a.s...@ie...> +% This is part of the NaN-toolbox. For more details see +% http://biosig-consulting.com/matlab/NaN/ + if any(size(i)==0); return; end; - + if nargin<3, W = []; end; if nargin>1, - [S,N] = sumskipnan(i,DIM,W); -else - [S,N] = sumskipnan(i,[],W); + [S,N] = sumskipnan(i,DIM,W); +else + [S,N] = sumskipnan(i,[],W); end; S = S./N; szi = size(i); szs = size(S); if length(szs)<length(szi); - szs(length(szs)+1:length(szi)) = 1; + szs(length(szs)+1:length(szi)) = 1; end; i = i - repmat(S,szi./szs); % remove mean Property changes on: trunk/octave-forge/extra/NaN/inst/center.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/classify.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/classify.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/classify.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -25,7 +25,7 @@ % $Id$ % Copyright (C) 2008,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License Modified: trunk/octave-forge/extra/NaN/inst/coefficient_of_variation.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/coefficient_of_variation.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/coefficient_of_variation.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -12,7 +12,7 @@ % $Id$ % Copyright (C) 1997-2003 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Modified: trunk/octave-forge/extra/NaN/inst/conv2nan.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/conv2nan.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/conv2nan.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -10,11 +10,11 @@ % This function is part of the NaN-toolbox % http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ -% -% $Revision$ % $Id$ -% Copyright (C) 2000-2005 by Alois Schloegl <a.s...@ie...> +% Copyright (C) 2000-2005,2010 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/conv2nan.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/cor.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/cor.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/cor.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -30,7 +30,7 @@ % $Id$ % Copyright (C) 2000-2004,2010 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify Property changes on: trunk/octave-forge/extra/NaN/inst/cor.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/corrcoef.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/corrcoef.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/corrcoef.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -90,7 +90,7 @@ % $Id$ % Copyright (C) 2000-2004,2008,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/corrcoef.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/cov.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/cov.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/cov.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -28,7 +28,7 @@ % $Id$ % Copyright (C) 2000-2003,2005,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/cov.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/covm.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/covm.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/covm.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -36,7 +36,7 @@ % $Id$ % Copyright (C) 2000-2005,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tugraz.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/covm.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/decovm.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/decovm.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/decovm.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -13,7 +13,7 @@ % $Id: decovm.m 2140 2009-07-02 12:03:55Z schloegl $ % Copyright (c) 1999-2002,2009 by Alois Schloegl % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License Property changes on: trunk/octave-forge/extra/NaN/inst/decovm.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/detrend.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/detrend.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/detrend.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -49,7 +49,7 @@ % $Id$ % Copyright (C) 2001,2007 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ if (nargin == 1) Property changes on: trunk/octave-forge/extra/NaN/inst/detrend.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/ecdf.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/ecdf.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/ecdf.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -17,7 +17,7 @@ % $Id$ % Copyright (C) 2009,2010 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/ecdf.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/flag_accuracy_level.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -43,8 +43,8 @@ % $Id$ % Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> -% This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% This function is part of the NaN-toolbox +% http://biosig-consulting.com/matlab/NaN/ persistent FLAG_ACCURACY_LEVEL; Modified: trunk/octave-forge/extra/NaN/inst/flag_implicit_significance.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/flag_implicit_significance.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/flag_implicit_significance.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -31,7 +31,7 @@ % $Id$ % Copyright (C) 2000-2002,2009,2010 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/flag_implicit_significance.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -44,7 +44,7 @@ % $Id$ % Copyright (C) 2001-2003,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ persistent FLAG_implicit_skip_nan; Property changes on: trunk/octave-forge/extra/NaN/inst/flag_implicit_skip_nan.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -12,7 +12,7 @@ % $Id$ % Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/flag_nans_occured.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/fss.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/fss.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/fss.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -36,14 +36,11 @@ % Volume 86, Issue 1, 15 March 2007, Pages 68-81 % http://dx.doi.org/10.1016/j.chemolab.2006.08.007 - - % $Id$ % Copyright (C) 2009,2010 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ - % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation; either version 3 of the License, or Property changes on: trunk/octave-forge/extra/NaN/inst/fss.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/geomean.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/geomean.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/geomean.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -37,7 +37,7 @@ % $Id$ % Copyright (C) 2000-2002,2009 by Alois Schloegl <a.s...@ie...> % This is part of the NaN-toolbox. For more details see -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ if nargin<2 Property changes on: trunk/octave-forge/extra/NaN/inst/geomean.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/gscatter.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/gscatter.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/gscatter.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -22,7 +22,7 @@ % $Id$ % Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License Property changes on: trunk/octave-forge/extra/NaN/inst/gscatter.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/harmmean.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/harmmean.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/harmmean.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -39,7 +39,7 @@ % $Id$ % Copyright (C) 2000-2002,2009 by Alois Schloegl <a.s...@ie...> % This is part of the NaN-toolbox. For more details see -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ Property changes on: trunk/octave-forge/extra/NaN/inst/harmmean.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/hist2res.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/hist2res.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/hist2res.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -41,7 +41,8 @@ % $Id: hist2res.m,v 1.4 2007/02/06 09:21:09 schloegl Exp $ % Copyright (c) 1996-2002,2006 by Alois Schloegl <a.s...@ie...> -% This is part of the BIOSIG-toolbox http://biosig.sf.net/ +% This function is part of the NaN-toolbox +% http://biosig-consulting.com/matlab/NaN/ if strcmp(H.datatype,'HISTOGRAM') Property changes on: trunk/octave-forge/extra/NaN/inst/hist2res.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/iqr.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/iqr.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/iqr.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -16,7 +16,7 @@ % $Id$ % Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/iqr.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/kappa.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/kappa.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/kappa.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -29,13 +29,11 @@ % [4] Kraemer, H. C. (1982). Kappa coefficient. In S. Kotz and N. L. Johnson (Eds.), % Encyclopedia of Statistical Sciences. New York: John Wiley & Sons. % [5] http://ourworld.compuserve.com/homepages/jsuebersax/kappa.htm -% -% % $Id: kappa.m 2141 2009-07-02 12:05:29Z schloegl $ % Copyright (c) 1997-2006,2008,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % % BioSig is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by @@ -169,6 +167,7 @@ X.kappa = kap; X.kappa_se = se; X.data = H; +X.H = X.data; X.z = z; X.ACC = p0; X.sACC = SA; Modified: trunk/octave-forge/extra/NaN/inst/kurtosis.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/kurtosis.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/kurtosis.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -33,9 +33,10 @@ % You should have received a copy of the GNU General Public License % along with this program; If not, see <http://www.gnu.org/licenses/>. +% $Id$ % Copyright (C) 2000-2003 by Alois Schloegl <a.s...@ie...> -% $Revision$ -% $Id$ +% This function is part of the NaN-toolbox for Octave and Matlab +% http://biosig-consulting.com/matlab/NaN/ if nargin==1, Property changes on: trunk/octave-forge/extra/NaN/inst/kurtosis.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/load_fisheriris.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/load_fisheriris.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/load_fisheriris.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -10,7 +10,7 @@ % $Id$ % Copyright (C) 2009,2010 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License Modified: trunk/octave-forge/extra/NaN/inst/mad.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/mad.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/mad.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -28,7 +28,7 @@ % $Id$ % Copyright (C) 2000-2002,2010 by Alois Schloegl <a.s...@ie...> % This is part of the NaN-toolbox. For more details see -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Modified: trunk/octave-forge/extra/NaN/inst/mahal.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/mahal.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/mahal.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -17,7 +17,7 @@ % $Id: train_sc.m 2140 2009-07-02 12:03:55Z schloegl $ % Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License Property changes on: trunk/octave-forge/extra/NaN/inst/mahal.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/mean.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/mean.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/mean.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -39,7 +39,7 @@ % $Id$ % Copyright (C) 2000-2004,2008,2009 by Alois Schloegl <a.s...@ie...> % This is part of the NaN-toolbox. For more details see -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/mean.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/meandev.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/meandev.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/meandev.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -40,7 +40,7 @@ % $Id$ % Copyright (C) 2000-2002,2010 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox for Octave and Matlab -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ if nargin==1, DIM = find(size(i)>1,1); Modified: trunk/octave-forge/extra/NaN/inst/meansq.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/meansq.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/meansq.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -33,11 +33,10 @@ % You should have received a copy of the GNU General Public License % along with this program; If not, see <http://www.gnu.org/licenses/>. - % Copyright (C) 2000-2003,2009 by Alois Schloegl <a.s...@ie...> % $Id$ -% This is part of the NaN-toolbox for Octave and Matlab -% see also: http://hci.tugraz.at/schloegl/matlab/NaN/ +% This function is part of the NaN-toolbox for Octave and Matlab +% http://biosig-consulting.com/matlab/NaN/ if nargin<3, Property changes on: trunk/octave-forge/extra/NaN/inst/meansq.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/medAbsDev.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/medAbsDev.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/medAbsDev.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -14,7 +14,7 @@ % Copyright (C) 2009 Alois Schloegl % $Id$ % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/medAbsDev.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/median.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/median.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/median.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -31,7 +31,7 @@ % $Id$ % Copyright (C) 2000-2003,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ global FLAG_NANS_OCCURED; Property changes on: trunk/octave-forge/extra/NaN/inst/median.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/mod.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/mod.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/mod.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -30,7 +30,7 @@ % $Id$ % Copyright (C) 2004,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ s = warning; Property changes on: trunk/octave-forge/extra/NaN/inst/mod.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/moment.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/moment.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/moment.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -26,8 +26,8 @@ % $Id$ % Copyright (C) 2000-2002,2010 by Alois Schloegl <a.s...@ie...> -% This script is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% This functions is part of the NaN-toolbox +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/moment.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/naninsttest.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/naninsttest.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/naninsttest.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -3,9 +3,14 @@ % % see also: NANTEST +% $Id$ +% Copyright (C) 2000-2003 by Alois Schloegl <a.s...@ie...> +% This script is part of the NaN-toolbox +% http://biosig-consulting.com/matlab/NaN/ + % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or +% the Free Software Foundation; either version 3 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, @@ -16,11 +21,7 @@ % You should have received a copy of the GNU General Public License % along with this program; If not, see <http://www.gnu.org/licenses/>. -% $Revision$ -% $Id$ -% Copyright (C) 2000-2003 by Alois Schloegl <a.s...@ie...> - r = zeros(25,2); x = [5,NaN,0,1,nan]; Property changes on: trunk/octave-forge/extra/NaN/inst/naninsttest.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/nanmean.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/nanmean.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/nanmean.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -17,7 +17,7 @@ % $Id$ % Copyright (C) 2009 by Alois Schloegl <a.s...@ie...> % This is part of the NaN-toolbox. For more details see -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/nanmean.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/nanstd.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/nanstd.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/nanstd.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -20,7 +20,7 @@ % $Id$ % Copyright (C) 2000-2003,2006,2008,2009,2010 by Alois Schloegl <a.s...@ie...> % This is part of the NaN-toolbox. For more details see -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/nanstd.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/nansum.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/nansum.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/nansum.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -17,7 +17,7 @@ % $Id$ % Copyright (C) 2000-2003,2008 by Alois Schloegl <a.s...@ie...> % This is part of the NaN-toolbox. For more details see -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/nansum.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/nantest.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/nantest.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/nantest.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -27,7 +27,7 @@ % $Id$ % Copyright (C) 2000-2004,2009 by Alois Schloegl <a.s...@ie...> % This script is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ %FLAG_WARNING = warning; %warning('off'); Property changes on: trunk/octave-forge/extra/NaN/inst/nantest.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/normcdf.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/normcdf.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/normcdf.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -14,8 +14,8 @@ % $Id$ % Copyright (C) 2000-2003,2010 by Alois Schloegl <a.s...@ie...> -% This script is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% This function is part of the NaN-toolbox +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/normcdf.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/norminv.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/norminv.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/norminv.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -14,8 +14,8 @@ % $Id$ % Copyright (C) 2000-2003,2010 by Alois Schloegl <a.s...@ie...> -% This script is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% This function is part of the NaN-toolbox +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/norminv.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/normpdf.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/normpdf.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/normpdf.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -14,8 +14,8 @@ % $Id$ % Copyright (C) 2000-2003,2010 by Alois Schloegl <a.s...@ie...> -% This script is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% This function is part of the NaN-toolbox +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/normpdf.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/partcorrcoef.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/partcorrcoef.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/partcorrcoef.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -52,7 +52,7 @@ % $Id$ % Copyright (C) 2000-2002,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://hci.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Modified: trunk/octave-forge/extra/NaN/inst/percentile.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/percentile.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/percentile.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -16,7 +16,7 @@ % $Id$ % Copyright (C) 1996-2003,2005,2006,2007 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/percentile.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/prctile.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/prctile.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/prctile.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -14,10 +14,10 @@ % % see also: HISTO2, HISTO3, QUANTILE -% $Id: percentile.m 4585 2008-02-04 13:47:45Z adb014 $ +% $Id$ % Copyright (C) 1996-2003,2005,2006,2007,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/prctile.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/quantile.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/quantile.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/quantile.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -17,7 +17,7 @@ % $Id$ % Copyright (C) 1996-2003,2005,2006,2007,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by @@ -71,22 +71,22 @@ if (q(k2)<0) || (q(k2)>1) Q(k2,k1) = NaN; elseif q(k2)==0, - Q(k2,k1) = t2(1); + Q(k2,k1) = t2(1); elseif q(k2)==1, - Q(k2,k1) = t2(end); - else + Q(k2,k1) = t2(end); + else n=1; - while (q(k2)*N > x(n)), + while (q(k2)*N > x(n)), n=n+1; - end; + end; if q(k2)*N==x(n) % mean of upper and lower bound Q(k2,k1) = (t2(n)+t2(n+1))/2; else Q(k2,k1) = t2(n); - end; - end; + end; + end; end end; Property changes on: trunk/octave-forge/extra/NaN/inst/quantile.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/rankcorr.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/rankcorr.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/rankcorr.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -17,9 +17,10 @@ % [1] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html % [2] http://mathworld.wolfram.com/CorrelationCoefficient.html -% $Revision$ % $Id$ % Copyright (C) 2000-2003 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/rankcorr.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/ranks.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/ranks.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/ranks.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -26,7 +26,7 @@ % $Id$ % Copyright (C) 2000-2002,2005,2010 by Alois Schloegl <a.s...@ie...> % This script is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Property changes on: trunk/octave-forge/extra/NaN/inst/ranks.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/rem.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/rem.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/rem.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -29,7 +29,7 @@ % $Id$ % Copyright (C) 2004,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ s = warning; Property changes on: trunk/octave-forge/extra/NaN/inst/rem.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/rms.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/rms.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/rms.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -39,9 +39,9 @@ % $Id$ -% Copyright (C) 2000-2003,2008,2009 by Alois Schloegl <a.s...@ie...> +% Copyright (C) 2000-2003,2008,2009 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ if nargin<2, Property changes on: trunk/octave-forge/extra/NaN/inst/rms.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/sem.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/sem.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/sem.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -40,7 +40,7 @@ % Copyright (C) 2000-2003,2008,2009 by Alois Schloegl <a.s...@ie...> % $Id$ % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ if nargin>2, Property changes on: trunk/octave-forge/extra/NaN/inst/sem.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/skewness.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/skewness.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/skewness.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -22,7 +22,7 @@ % $Id$ % Copyright (C) 2000-2003,2010 by Alois Schloegl <a.s...@ie...> % This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by Modified: trunk/octave-forge/extra/NaN/inst/spearman.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/spearman.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/spearman.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -17,12 +17,14 @@ % [1] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html % [2] http://mathworld.wolfram.com/CorrelationCoefficient.html -% Version 1.27 Date: 12 Aug 2002 -% Copyright (C) 2000-2002 by Alois Schloegl <a.s...@ie...> +% $Id$ +% Copyright (C) 2000-2002 by Alois Schloegl <a.s...@ie...> +% This function is part of the NaN-toolbox +% http://biosig-consulting.com/matlab/NaN/ % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 2 of the License, or +% the Free Software Foundation; either version 3 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, Property changes on: trunk/octave-forge/extra/NaN/inst/spearman.m ___________________________________________________________________ Added: keywords + Id Rev Modified: trunk/octave-forge/extra/NaN/inst/statistic.m =================================================================== --- trunk/octave-forge/extra/NaN/inst/statistic.m 2010-02-28 11:41:24 UTC (rev 6972) +++ trunk/octave-forge/extra/NaN/inst/statistic.m 2010-02-28 20:19:12 UTC (rev 6973) @@ -1,173 +1,173 @@ -function [varargout]=statistic(i,DIM,fun) -% STATISTIC estimates various statistics at once. -% -% R = STATISTIC(x,DIM) -% calculates all statistic (see list of fun) in dimension DIM -% R is a struct with all statistics -% -% y = STATISTIC(x,fun) -% estimate of fun on dimension DIM -% y gives the statistic of fun -% -% DIM dimension -% 1: STATS of columns -% 2: STATS of rows -% N: STATS of N-th dimension -% default or []: first DIMENSION, with more than 1 element -% -% fun 'mean' mean -% 'std' standard deviation -% 'var' variance -% 'sem' standard error of the mean -% 'rms' root mean square -% 'meansq' mean of squares -% 'sum' sum -% 'sumsq' sum of squares -% 'CM#' central moment of order # -% 'skewness' skewness -% 'kurtosis' excess coefficient (Fisher kurtosis) -% 'mad' mean absolute deviation -% -% features: -% - can deal with NaN's (missing values) -% - dimension argument -% - compatible to Matlab and Octave -% -% see also: SUMSKIPNAN -% -% REFERENCE(S): -% [1] http://www.itl.nist.gov/ -% [2] http://mathworld.wolfram.com/ - -% $Id$ -% Copyright (C) 2000-2003,2010 by Alois Schloegl <a.s...@ie...> -% This function is part of the NaN-toolbox -% http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/ - -% This program is free software; you can redistribute it and/or modify -% it under the terms of the GNU General Public License as published by -% the Free Software Foundation; either version 3 of the License, or -% (at your option) any later version. -% -% This program is distributed in the hope that it will be useful, -% but WITHOUT ANY WARRANTY; without even the implied warranty of -% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -% GNU General Public License for more details. -% -% You should have received a copy of the GNU General Public License -% along with this program; If not, see <http://www.gnu.org/licenses/>. - - - -if nargin==1, - DIM=[]; - fun=[]; -elseif nargin==2, - if ~isnumeric(DIM), - fun=DIM; - DIM=[]; - else - fun=[]; - end -end -if isempty(DIM), - DIM = find(size(i)>1,1); - if isempty(DIM), DIM=1; end; -end; - -%R.N = sumskipnan(~isnan(i),DIM); % number of elements -[R.SUM,R.N,R.SSQ] = sumskipnan(i,DIM); % sum -%R.S3P = sumskipnan(i.^3,DIM); % sum of 3rd power -R.S4P = sumskipnan(i.^4,DIM); % sum of 4th power -%R.S5P = sumskipnan(i.^5,DIM); % sum of 5th power - -R.MEAN = R.SUM./R.N; % mean -R.MSQ = R.SSQ./R.N; % mean square -R.RMS = sqrt(R.MSQ); % root mean square -%R.SSQ0 = R.SSQ-R.SUM.*R.MEAN; % sum square of mean removed -R.SSQ0 = R.SSQ - real(R.SUM).*real(R.MEAN) - imag(R.SUM).*imag(R.MEAN); % sum square of mean removed - -%if flag_implicit_unbiased_estim; %% ------- unbiased estimates ----------- - n1 = max(R.N-1,0); % in case of n=0 and n=1, the (biased) variance, STD and SEM are INF -%else -% n1 = R.N; -%end; - -R.VAR = R.SSQ0./n1; % variance (unbiased) -R.STD = sqrt(R.VAR); % standard deviation -R.SEM = sqrt(R.SSQ0./(R.N.*n1)); % standard error of the mean -R.SEV = sqrt(n1.*(n1.*R.S4P./R.N+(R.N.^2-2*R.N+3).*(R.SSQ./R.N).^2)./(R.N.^3)); % standard error of the variance -R.COEFFICIENT_OF_VARIATION = R.STD./R.MEAN; - -q = quantile(i, (1:3)/4, DIM); - -%sz=size(i);sz(DIM)=1; -%Q0500=repmat(nan,sz); -%Q0250=Q0500; -%Q0750=Q0500; -%MODE=Q0500; -%for k=1:size(i,2), -% tmp = sort(i(:,k)); - %ix = find(~~diff([-inf;tmp;inf])) - %ix2=diff(ix) - %MODE(k)= tmp(max(ix2)==ix2) -% Q0500(k) = flix(tmp,R.N(k)/2 + 0.5); -% Q0250(k) = flix(tmp,R.N(k)/4 + 0.5); -% Q0750(k) = flix(tmp,R.N(k)*3/4 + 0.5); -%end; -%R.MEDIAN = Q0500; -%R.Quartiles = [Q0250; Q0750]; - -%R.Skewness.Fisher = (R.CM3)./(R.STD.^3); %%% same as R.SKEWNESS - -%R.Skewness.Pearson_Mode = (R.MEAN-R.MODE)./R.STD; -%R.Skewness.Pearson_coeff1 = (3*R.MEAN-R.MODE)./R.STD; -%R.Skewness.Pearson_coeff2 = (3*R.MEAN-R.MEDIAN)./R.STD; -%R.Skewness.Bowley = (Q0750+Q0250 - 2*Q0500)./(Q0750-Q0250); % quartile skewness coefficient - -R.CM2 = R.SSQ0./n1; -szi = size(i); szm = [size(R.MEAN),1]; -i = i - repmat(R.MEAN,szi./szm(1:length(szi))); -R.CM3 = sumskipnan(i.^3,DIM)./n1; -R.CM4 = sumskipnan(i.^4,DIM)./n1; -%R.CM5 = sumskipnan(i.^5,DIM)./n1; - -R.SKEWNESS = R.CM3./(R.STD.^3); -R.KURTOSIS = R.CM4./(R.VAR.^2)-3; -[R.MAD,N] = sumskipnan(abs(i),DIM); % mean absolute deviation -R.MAD = R.MAD./n1; - -R.datatype = 'STAT Level 3'; - -tmp = version; -if 0, %str2num(tmp(... 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