tsa/ 0000775 0023567 0023567 00000000000 12264575422 012066 5 ustar schloegl schloegl tsa/src/ 0000775 0023567 0023567 00000000000 12264575422 012655 5 ustar schloegl schloegl tsa/src/Makefile 0000664 0023567 0023567 00000011160 12264575422 014314 0 ustar schloegl schloegl ####################################################
# Copyright 2010,2011,2012 Alois Schloegl
# This is part of the TSA-toolbox - a statistics and machine learning toolbox for data with and without missing values.
# http://pub.ist.ac.at/~schloegl/matlab/tsa/
####################################################
### modify directories according to your needs
# Define non-default octave-version
# Octave - global install (e.g. from debian package)
# OCTAVE_VERSION=
# Better alternative: define an OCTAVE_VERSION bash variable (or in .bashrc or .profile)
# OCTAVE_VERSION=-3.6.3
# Matlab configuration
# MATLABDIR = /usr/local/MATLAB/R2010b
# comment the following line if you use MATLAB on 32-bit operating system
MEX_OPTION += -largeArrayDims
# Mingw crosscompiler: available at http://www.nongnu.org/mingw-cross-env/
CROSS = $(HOME)/src/mxe.github.schloegl/usr/bin/i686-pc-mingw32
CROSS64 = $(HOME)/src/mxe.github.schloegl/usr/bin/x86_64-w64-mingw32
# include directory for Win32-Matlab include
W32MAT_INC = -I$(HOME)/bin/win32/Matlab/R2010b/extern/include/
W64MAT_INC = -I$(HOME)/bin/win64/Matlab/R2010b/extern/include/
# path to GNUMEX libraries, available from here http://sourceforge.net/projects/gnumex/
GNUMEX = $(HOME)/bin/win32/gnumex
GNUMEX64 = $(HOME)/bin/win64/gnumex
# building gnumex64 was difficult, these hints were quite useful:
# http://sourceforge.net/mailarchive/forum.php?thread_name=AANLkTinZvxgC9ezp2P3UCX_a7TAUYuVsp2U40MQUV6qr%40mail.gmail.com&forum_name=gnumex-users
# Instead of building "mex shortpath.c" and "mex uigetpath.c", I used empty m-functions within argout=argin;
####################################################
W32MAT_INC += -I$(subst /usr/bin/,/usr/,$(CROSS))/include/
W64MAT_INC += -I$(subst /usr/bin/,/usr/,$(CROSS64))/include/
LDLIBS_W32 = $(subst /usr/bin/,/usr/,$(CROSS))/lib/
LDLIBS_W64 = $(subst /usr/bin/,/usr/,$(CROSS64))/lib/
W32_LIBS = $(LDLIBS_W32)liblapack.a
W64_LIBS = $(LDLIBS_W64)liblapack.a
W32_LIBS += $(LDLIBS_W32)libblas.a
W64_LIBS += $(LDLIBS_W64)libblas.a
CC = gcc
CXX = g++
CFLAGS = -fopenmp -Wall -Wextra -Wconversion -O2 -fPIC
OCTMEX = mkoctfile$(OCTAVE_VERSION) --mex
RM = rm
MEX_OPTION += -lgomp CC\#$(CXX) CXX\#$(CXX) CFLAGS\#"$(CFLAGS)" CXXFLAGS\#"$(CFLAGS)"
MATMEX = $(MATLABDIR)/bin/mex $(MEX_OPTION)
PROGS = covm_mex.mex sumskipnan_mex.mex #
### per default only the mex-files for octave are built
mex4o octave: $(PROGS)
### Matlab configuration - search for a matlab directory if not defined above
ifndef MATLABDIR
ifneq ('',$(shell ls -1 /usr/local/MATLAB))
# use oldest, typically mex-files a compatible with newer Matlab versions
MATLABDIR=$(shell ls -dt1 /usr/local/MATLAB/R* |tail -1)
# alternatively, use latest matlab version
#MATLABDIR=$(shell ls -dt1 /usr/local/MATLAB/R* |head -1)
endif
endif
### if the MATLABDIR has been found or defined
ifneq ('',MATLABDIR)
ifneq ('',$(lshell ls -1 $(MATLABDIR)))
MEX_EXT = $(shell $(MATLABDIR)/bin/mexext)
mex4m matlab: $(patsubst %.mex, %.$(MEX_EXT), $(PROGS))
endif
endif
mexw32 win32: $(patsubst %.mex, %.mexw32, $(PROGS))
mexw64 win64: $(patsubst %.mex, %.mexw64, $(PROGS))
all: octave win32 win64 mex4m
clean:
-$(RM) *.o *.obj *.o64 core octave-core *.oct *~ *.mex*
#########################################################
# Octave, MATLAB on Linux
#########################################################
%.oct: %.cc
mkoctfile$(OCTAVE_VERSION) "$<"
%.mex: %.cpp
$(OCTMEX) "$<" -lgomp -llapack -lblas ## Octave
%.$(MEX_EXT): %.cpp
$(MATMEX) "$<" -lgomp -llapack -lblas ## Matlab
#########################################################
# MATLAB/WIN32
#########################################################
%.obj: %.cpp
$(CROSS)-$(CXX) -fopenmp -c -DMATLAB_MEX_FILE -x c++ -o "$@" $(W32MAT_INC) -O2 -DMX_COMPAT_32 "$<"
%.obj: %.c
$(CROSS)-$(CXX) -fopenmp -c -DMATLAB_MEX_FILE -x c++ -o "$@" $(W32MAT_INC) -O2 -DMX_COMPAT_32 "$<"
%.mexw32: %.obj
$(CROSS)-$(CXX) -shared $(GNUMEX)/mex.def -o "$@" -L$(GNUMEX) -s "$<" -llibmx -llibmex -llibmat -lcholmod -lgomp -lpthread -L$(LDLIBS_W32) -lblas -llapack
#########################################################
# MATLAB/WIN64
#########################################################
## ToDO: fix OpenMP support: currently -fopenmp causes Matlab to crash
%.o64: %.cpp
$(CROSS64)-$(CXX) -c -DMATLAB_MEX_FILE -x c++ -o "$@" $(W64MAT_INC) -O2 "$<"
%.o64: %.c
$(CROSS64)-$(CXX) -c -DMATLAB_MEX_FILE -x c++ -o "$@" $(W64MAT_INC) -O2 "$<"
%.mexw64: %.o64
$(CROSS64)-$(CXX) -shared $(GNUMEX64)/mex.def -o "$@" -L$(GNUMEX64) -s "$<" -llibmx -llibmex -llibmat -lcholmod -lgomp -lpthread -L$(LDLIBS_W64) -lblas -llapack
tsa/src/covm_mex.cpp 0000664 0023567 0023567 00000046125 12264575422 015206 0 ustar schloegl schloegl /*
//-------------------------------------------------------------------
// 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 .
//
//
// covm: in-product of matrices, NaN are skipped.
// usage:
// [cc,nn] = covm_mex(X,Y,flag,W);
//
// Input:
// - X:
// - Y: [optional], if empty, Y=X;
// - flag: if not empty, it is set to 1 if some NaN was observed
// - W: weight vector to compute weighted correlation
//
// Output:
// - 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: covm_mex.cpp 8223 2011-04-20 09:16:06Z schloegl $
// Copyright (C) 2009,2010,2011 Alois Schloegl
// This function is part of the NaN-toolbox
// http://pub.ist.ac.at/~schloegl/matlab/NaN/
//
//-------------------------------------------------------------------
*/
#ifdef __GNUC__
#include
#endif
#include
#include "mex.h"
/*#define NO_FLAG*/
void mexFunction(int POutputCount, mxArray* POutput[], int PInputCount, const mxArray *PInputs[])
{
double *X0=NULL, *Y0=NULL, *W=NULL;
double *CC;
double *NN = NULL;
size_t rX,cX,rY,cY;
size_t i;
char flag_isNaN = 0;
int ACC_LEVEL;
/*********** check input arguments *****************/
// check for proper number of input and output arguments
if ((PInputCount <= 0) || (PInputCount > 5)) {
mexPrintf("usage: [CC,NN] = covm_mex(X [,Y [,flag [,W [,'E']]]])\n\n");
mexPrintf("Do not use COVM_MEX directly, use COVM instead. \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 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 weights are 1\n");
mexPrintf("\t[CC,NN]=covm_mex(X,Y,flag,W)\n");
*/ return;
}
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.");
}
// get weight vector for weighted sumskipnan
if (PInputCount > 3) {
// get 4th argument
size_t 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");
}
#ifdef __GNUC__
ACC_LEVEL = 0;
{
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);
#endif
if (Y0==NULL) {
Y0 = X0;
rY = rX;
cY = cX;
}
else {
rY = mxGetM(PInputs[1]);
cY = mxGetN(PInputs[1]);
}
if (rX != rY)
mexErrMsgTxt("number of rows in X and Y do not match");
/*********** create output arguments *****************/
POutput[0] = mxCreateDoubleMatrix(cX, cY, mxREAL);
CC = mxGetPr(POutput[0]);
if (POutputCount > 1) {
POutput[1] = mxCreateDoubleMatrix(cX, cY, mxREAL);
NN = mxGetPr(POutput[1]);
}
/*********** compute covariance *****************/
#if 0
/*------ version 1 ---------------------
this solution is slower than the alternative solution below
for transposed matrices, this might be faster.
*/
for (k=0; k 2) && mxGetNumberOfElements(PInputs[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:
*(char*)mxGetData(PInputs[2]) = 1;
break;
#ifdef __GNUC__
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:
#endif
default:
mexPrintf("Type of 3rd input argument cannot be used to return status of NaN occurence.");
}
}
#endif
#endif
}
tsa/src/sumskipnan_mex.cpp 0000664 0023567 0023567 00000060452 12264575422 016431 0 ustar schloegl schloegl
//-------------------------------------------------------------------
// 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 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 .
//
//
// sumskipnan: sums all non-NaN values
// usage:
// [o,count,SSQ] = sumskipnan_mex(x,DIM,flag,W);
//
// SUMSKIPNAN uses two techniques to reduce errors:
// 1) long double (80bit) instead of 64-bit double is used internally
// 2) The Kahan Summation formula is used to reduce the error margin from N*eps to 2*eps
// The latter is only implemented in case of stride=1 (column vectors only, summation along 1st dimension).
//
// Input:
// - x data array
// - DIM (optional) dimension to sum
// - flag (optional) is actually an output argument telling whether some NaN was observed
// - W (optional) weight vector to compute weighted sum (default 1)
//
// Output:
// - o (weighted) sum along dimension DIM
// - count of valid elements
// - sums of squares
//
//
// $Id: sumskipnan_mex.cpp 12492 2014-01-10 13:34:15Z schloegl $
// Copyright (C) 2009,2010,2011 Alois Schloegl
// This function is part of the NaN-toolbox
// http://pub.ist.ac.at/~schloegl/matlab/NaN/
//
//-------------------------------------------------------------------
#include
#include
#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 __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
#ifdef tmwtypes_h
#if (MX_API_VER<=0x07020000)
typedef int mwSize;
#endif
#endif
void mexFunction(int POutputCount, mxArray* POutput[], int PInputCount, const mxArray *PInputs[])
{
const mwSize *SZ;
double* LInput;
double* LOutputSum;
double* LOutputCount;
double* LOutputSum2;
long double* LongOutputSum = NULL;
long double* LongOutputCount = NULL;
long double* LongOutputSum2 = NULL;
double x;
double* W = NULL; // weight vector
mwSize DIM = 0;
mwSize D1, D2, D3; // NN; //
mwSize ND, ND2; // number of dimensions: input, output
mwSize ix0, ix1, ix2; // index to input and output
mwSize j, l; // running indices
mwSize *SZ2; // size of output
char flag_isNaN = 0;
// check for proper number of input and output 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.");
// get 1st argument
if(mxIsDouble(PInputs[0]) && !mxIsComplex(PInputs[0]))
LInput = mxGetPr(PInputs[0]);
else
mexErrMsgTxt("First argument must be REAL/DOUBLE.");
// get 2nd argument
if (PInputCount > 1) {
switch (mxGetNumberOfElements(PInputs[1])) {
case 0: x = 0.0; // accept empty element
break;
case 1: x = (mxIsNumeric(PInputs[1]) ? mxGetScalar(PInputs[1]) : -1.0);
break;
default:x = -1.0; // invalid
}
if ((x < 0) || (x > 65535) || (x != floor(x)))
mexErrMsgTxt("Error SUMSKIPNAN.MEX: DIM-argument must be a positive integer scalar");
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 (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
ND2 = (ND>DIM ? ND : DIM); // number of dimensions of output
SZ2 = (mwSize*)mxCalloc(ND2, sizeof(mwSize)); // allocate memory for output size
for (j=0; j ND, add extra elements 1
SZ2[j] = 1;
for (j=0, D1=1; j 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");
}
int ACC_LEVEL = 0;
{
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
POutput[0] = mxCreateNumericArray(ND2, SZ2, TYP, mxREAL);
LOutputSum = mxGetPr(POutput[0]);
if (D1!=1 && D2>0) LongOutputSum = (long double*) mxCalloc(D1*D3,sizeof(long double));
if (POutputCount >= 2) {
POutput[1] = mxCreateNumericArray(ND2, SZ2, TYP, mxREAL);
LOutputCount = mxGetPr(POutput[1]);
if (D1!=1 && D2>0) LongOutputCount = (long double*) mxCalloc(D1*D3,sizeof(long double));
}
if (POutputCount >= 3) {
POutput[2] = mxCreateNumericArray(ND2, SZ2, TYP, mxREAL);
LOutputSum2 = mxGetPr(POutput[2]);
if (D1!=1 && D2>0) LongOutputSum2 = (long double*) mxCalloc(D1*D3,sizeof(long double));
}
mxFree(SZ2);
if (!D1 || !D2 || !D3) // zero size array
; // do nothing
else if (D1==1) {
if (ACC_LEVEL<1) {
// double accuray, naive summation, error = N*2^-52
switch (POutputCount) {
case 0:
case 1:
#pragma omp parallel for schedule(dynamic)
for (l = 0; l DIM
for (l = 0; l DIM
for (l = 0; l DIM
for (l = 0; l 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:
default:
mexPrintf("Type of 3rd input argument not supported.");
}
}
#endif
}
#define stride 1
inline int __sumskipnan2w__(double *data, size_t Ni, double *s, double *No, char *flag_anyISNAN, double *W)
{
long double sum=0;
char flag=0;
// LOOP along dimension DIM
double *end = data + stride*Ni;
if (W) {
// with weight vector
long double count = 0.0;
do {
long 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 {
long 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 __sumskipnan3w__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W)
{
long double sum=0;
long double msq=0;
char flag=0;
// LOOP along dimension DIM
double *end = data + stride*Ni;
if (W) {
// 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
double *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
double *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;
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 {
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;
}
/***************************************
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;
char flag=0;
// LOOP along dimension DIM
double *end = data + stride*Ni;
if (W) {
// with weight vector
long double count = 0.0;
long double rc=0.0, rn=0.0;
do {
long double x = *data;
long 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;
long double rc=0.0;
do {
long double x = *data;
long 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 __sumskipnan3we__(double *data, size_t Ni, double *s, double *s2, double *No, char *flag_anyISNAN, double *W)
{
long double sum=0;
long double msq=0;
char flag=0;
// LOOP along dimension DIM
double *end = data + stride*Ni;
if (W) {
// with weight vector
long double count = 0.0;
long double rc=0.0, rn=0.0, rq=0.0;
do {
long double x = *data;
long double t,y;
if (!isnan(x)) {
//count += *W; [1]
y = *W-rn;
t = count+y;
rn= (t-count)-y;
count= t;
long 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;
long double rc=0.0, rq=0.0;
do {
long double x = *data;
long 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;
}
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
double *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
double *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;
}
tsa/VERSION 0000664 0023567 0023567 00000000137 12264575422 013137 0 ustar schloegl schloegl # TSA-toolbox http://pub.ist.ac.at/~schloegl/matlab/tsa
# Version: 4.2.7
# Date: 2014-01-12
tsa/DESCRIPTION 0000664 0023567 0023567 00000000451 12264575422 013574 0 ustar schloegl schloegl Name: TSA
Version: 4.2.7
Date: 2014-01-12
Author: Alois Schloegl
Maintainer: Alois Schloegl
Title: The TSA-toolbox
Description: A toolbox for Time Series Analysis .
Depends: octave (> 2.9.0)
License: GPL version 3 or later
Url: http://pub.ist.ac.at/~schloegl/matlab/tsa
tsa/INDEX 0000664 0023567 0023567 00000001273 10625373341 012656 0 ustar schloegl schloegl tsa >> Time Series Analysis
Univariate (stationary) analysis
acovf
acorf
biacovf
bispec
durlev
lattice
rmle
pacf
parcor
invest0
invest1
selmo
selmo2
histo
histo2
histo3
hup
ucp
y2res
ar_spa
detrend
flix
Multivariate stationary analysis
mvar
mvfilter
mvfreqz
arfit2
histo4
Adaptive (time-varying) analysis
aar
aarmam
adim
amarma
mvaar
Conversions between forms
ac2poly
ac2rc
ar2rc
rc2ar
poly2ac
poly2ar
poly2rc
rc2ac
rc2poly
ar2poly
Utility functions
arcext
sinvest1
sbispec
flag_implicit_samplerate
Test suites
tsademo
bisdemo
invfdemo
tsa/COPYING 0000664 0023567 0023567 00000104513 11441754133 013117 0 ustar schloegl schloegl GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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.
tsa/doc/ 0000775 0023567 0023567 00000000000 12264575422 012633 5 ustar schloegl schloegl tsa/doc/README.TXT 0000664 0023567 0023567 00000003753 12263773327 014204 0 ustar schloegl schloegl TSA (Time Series Analysis) Toolbox 4.2
=======================================
The TSA toolbox is useful for analysing Time Series.
The methods are based on stochastic concepts and
maximum entropy methods. The programs should be compatible
to Matlab as well as Octave.
It includes
- Stochastic Signal processing
- Autoregressive Model Identification
- adaptive autoregressive modelling using Kalman filtering
- multivariate autoregressive modelling
- maximum entropy spectral estimation
- matched (inverse) filter design
- Histogram analysis
- Calcution of the entropy of a time series
- Non-linear analysis (3rd order statistics)
- Test for UnitCircle- and Hurwitz- Polynomials
- multiple signal processing
- Several criteria (AIC, BIC, FPE, MDL, SBC, CAT, PHI) for
model order selection an autoregressive model are included.
- Fast algorithms are used
- missing values (encoded as NaN's) are considered
Furthermore includes the toolbox a fast version of the Yule-Walker method
for estimating Autoregressive parameters, Autocorrelation Function (ACF),
Partial autocorrelation (PARCOR), reflection coefficients and some
other useful staff. Demo programs can be started with "demo" or "demotsa".
% This library is free software; you can redistribute it and/or
% modify it under the terms of the GNU Library General Public
% License as published by the Free Software Foundation; either
% Version 3 of the License, or (at your option) any later version.
%
% This library 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
% Library General Public License for more details.
%
% You should have received a copy of the GNU Library General Public
% License along with this library; if not, see
% .
Copyright (C) 1996-2003,2010,2012,2014 by Alois Schloegl
E-Mail: alois.schloegl@ist.ac.at
WWW: http://pub.ist.ac.at/~schloegl/matlab/tsa/
tsa/inst/ 0000775 0023567 0023567 00000000000 12264575422 013043 5 ustar schloegl schloegl tsa/inst/mvfreqz.m 0000664 0023567 0023567 00000023417 12244747160 014720 0 ustar schloegl schloegl function [S,h,PDC,COH,DTF,DC,pCOH,dDTF,ffDTF, pCOH2, PDCF, coh,GGC,Af,GPDC,GGC2, DCOH]=mvfreqz(B,A,C,N,Fs)
% MVFREQZ multivariate frequency response
% [S,h,PDC,COH,DTF,DC,pCOH,dDTF,ffDTF,pCOH2,PDCF,coh,GGC,Af,GPDC,GGC2,DCOH] = mvfreqz(B,A,C,f,Fs)
% [...] = mvfreqz(B,A,C,N,Fs)
%
% INPUT:
% =======
% A, B multivariate polynomials defining the transfer function
%
% a0*Y(n) = b0*X(n) + b1*X(n-1) + ... + bq*X(n-q)
% - a1*Y(n-1) - ... - ap*Y(:,n-p)
%
% A=[a0,a1,a2,...,ap] and B=[b0,b1,b2,...,bq] must be matrices of
% size Mx((p+1)*M) and Mx((q+1)*M), respectively.
%
% C is the covariance of the input noise X (i.e. D'*D if D is the mixing matrix)
% N if scalar, N is the number of frequencies
% if N is a vector, N are the designated frequencies.
% Fs sampling rate [default 2*pi]
%
% A,B,C and D can by obtained from a multivariate time series
% through the following commands:
% [AR,RC,PE] = mvar(Y,P);
% M = size(AR,1); % number of channels
% A = [eye(M),-AR];
% B = eye(M);
% C = PE(:,M*P+1:M*(P+1));
%
% Fs sampling rate in [Hz]
% (N number of frequencies for computing the spectrum, this will become OBSOLETE),
% f vector of frequencies (in [Hz])
%
%
% OUTPUT:
% =======
% S power spectrum
% h transfer functions, abs(h.^2) is the non-normalized DTF [11]
% PDC partial directed coherence [2]
% DC directed coupling [13]
% COH coherency (complex coherence) [5]
% DTF directed transfer function [3,13]
% pCOH partial coherence
% dDTF direct Directed Transfer function
% ffDTF full frequency Directed Transfer Function
% pCOH2 partial coherence - alternative method
% GGC a modified version of Geweke's Granger Causality [Geweke 1982]
% !!! it uses a Multivariate AR model, and computes the bivariate GGC as in [Bressler et al 2007].
% This is not the same as using bivariate AR models and GGC as in [Bressler et al 2007]
% Af Frequency transform of A(z), abs(Af.^2) is the non-normalized PDC [11]
% PDCF Partial Directed Coherence Factor [2]
% GPDC Generalized Partial Directed Coherence [9,10]
% DCOH directed coherence or Generalized DTF (GDTF) [12] (equ. 11a)
%
% see also: FREQZ, MVFILTER, MVAR
%
% REFERENCE(S):
% [1] H. Liang et al. Neurocomputing, 32-33, pp.891-896, 2000.
% [2] L.A. Baccala and K. Samashima, Biol. Cybern. 84,463-474, 2001.
% [3] A. Korzeniewska, et al. Journal of Neuroscience Methods, 125, 195-207, 2003.
% [4] Piotr J. Franaszczuk, Ph.D. and Gregory K. Bergey, M.D.
% Fast Algorithm for Computation of Partial Coherences From Vector Autoregressive Model Coefficients
% World Congress 2000, Chicago.
% [5] Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, Hallett M.
% Identifying true brain interaction from EEG data using the imaginary part of coherency.
% Clin Neurophysiol. 2004 Oct;115(10):2292-307.
% [6] Schlogl A., Supp G.
% Analyzing event-related EEG data with multivariate autoregressive parameters.
% (Eds.) C. Neuper and W. Klimesch,
% Progress in Brain Research: Event-related Dynamics of Brain Oscillations.
% Analysis of dynamics of brain oscillations: methodological advances. Elsevier.
% Progress in Brain Research 159, 2006, p. 135 - 147
% [7] Bressler S.L., Richter C.G., Chen Y., Ding M. (2007)
% Cortical fuctional network organization from autoregressive modelling of loal field potential oscillations.
% Statistics in Medicine, doi: 10.1002/sim.2935
% [8] Geweke J., 1982
% J.Am.Stat.Assoc., 77, 304-313.
% [9] L.A. Baccala, D.Y. Takahashi, K. Sameshima. (2006)
% Generalized Partial Directed Coherence.
% Submitted to XVI Congresso Brasileiro de Automatica, Salvador, Bahia.
% [10] L.A. Baccala, D.Y. Takahashi, K. Sameshima.
% Computer Intensive Testing for the Influence Between Time Series,
% Eds. B. Schelter, M. Winterhalder, J. Timmer:
% Handbook of Time Series Analysis - Recent Theoretical Developments and Applications
% Wiley, p.413, 2006.
% [11] M. Eichler
% On the evaluation of informatino flow in multivariate systems by the directed transfer function
% Biol. Cybern. 94: 469-482, 2006.
% [12] L. Faes, S. Erla, and G. Nollo, (2012)
% Measuring Connectivity in Linear Multivariate Processes: Definitions, Interpretation, and Practical Analysis
% Computational and Mathematical Methods in Medicine Volume 2012 (2012), Article ID 140513, 18 pages
% doi:10.1155/2012/140513
% [13] Maciej Kaminski, Mingzhou Ding, Wilson A. Truccolo, Steven L. Bressler
% Evaluating causal relations in neural systems: Granger causality,
% directed transfer function and statistical assessment of significance.
% Biol. Cybern. 85, 145-157 (2001)
%
% $Id: mvfreqz.m 12366 2013-11-25 22:25:52Z schloegl $
% Copyright (C) 1996-2008 by Alois Schloegl
% Copyright (C) 2013 Martin Billinger
% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sourceforge.net/
% http://biosig.sourceforge.net/
%
% 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 .
[K1,K2] = size(A);
p = K2/K1-1;
%a=ones(1,p+1);
[K1,K2] = size(B);
q = K2/K1-1;
%b=ones(1,q+1);
if nargin<3
C = eye(K1,K1);
end;
if nargin<5,
Fs= 1;
end;
if nargin<4,
N = 512;
f = (0:N-1)*(Fs/(2*N));
end;
if all(size(N)==1),
fprintf(1,'Warning MVFREQZ: The forth input argument N is a scalar, this is ambigous.\n');
fprintf(1,' In the past, N was used to indicate the number of spectral lines. This might change.\n');
fprintf(1,' In future versions, it will indicate the spectral line.\n');
f = (0:N-1)*(Fs/(2*N));
else
f = N;
end;
N = length(f);
s = exp(i*2*pi*f/Fs);
z = i*2*pi/Fs;
h=zeros(K1,K1,N);
Af=zeros(K1,K1,N);
g=zeros(K1,K1,N);
S=zeros(K1,K1,N);
S1=zeros(K1,K1,N);
DTF=zeros(K1,K1,N);
COH=zeros(K1,K1,N);
%COH2=zeros(K1,K1,N);
PDC=zeros(K1,K1,N);
%PDC3=zeros(K1,K1,N);
PDCF = zeros(K1,K1,N);
pCOH = zeros(K1,K1,N);
GGC=zeros(K1,K1,N);
GGC2=zeros(K1,K1,N);
DCOH=zeros(K1,K1,N);
invC=inv(C);
tmp1=zeros(1,K1);
tmp2=zeros(1,K1);
M = zeros(K1,K1,N);
detG = zeros(N,1);
%D = sqrtm(C);
%iD= inv(D);
ddc2 = diag(diag(C).^(-1/2));
ddc2i = diag(diag(C).^(1/2));
for n=1:N,
atmp = zeros(K1);
for k = 1:p+1,
atmp = atmp + A(:,k*K1+(1-K1:0))*exp(z*(k-1)*f(n));
end;
% compensation of instantaneous correlation
% atmp = iD*atmp*D;
btmp = zeros(K1);
for k = 1:q+1,
btmp = btmp + B(:,k*K1+(1-K1:0))*exp(z*(k-1)*f(n));
end;
h(:,:,n) = atmp\btmp;
Af(:,:,n) = atmp/btmp;
S(:,:,n) = h(:,:,n)*C*h(:,:,n)'/Fs;
S1(:,:,n) = h(:,:,n)*h(:,:,n)';
ctmp = ddc2*atmp; %% used for GPDC
dtmp = h(:,:,n) * ddc2i; %% used for directed coherence (DCOH)
for k1 = 1:K1,
tmp = squeeze(atmp(:,k1));
tmp1(k1) = sqrt(tmp'*tmp);
tmp2(k1) = sqrt(tmp'*invC*tmp);
%tmp = squeeze(atmp(k1,:)');
%tmp3(k1) = sqrt(tmp'*tmp);
tmp = squeeze(ctmp(:,k1));
tmp3(k1) = sqrt(tmp'*tmp);
tmp = dtmp(k1,:);
tmp4(k1) = sqrt(tmp*tmp');
end;
PDCF(:,:,n) = abs(atmp)./tmp2(ones(1,K1),:);
PDC(:,:,n) = abs(atmp)./tmp1(ones(1,K1),:);
GPDC(:,:,n) = abs(ctmp)./tmp3(ones(1,K1),:);
%PDC3(:,:,n) = abs(atmp)./tmp3(:,ones(1,K1));
DCOH(:,:,n) = abs(dtmp) ./ tmp4(ones(1,K1),:)';
g = atmp/btmp;
G(:,:,n) = g'*invC*g;
detG(n) = det(G(:,:,n));
end;
if nargout<4, return; end;
%%%%% directed transfer function
for k1=1:K1;
DEN=sum(abs(h(k1,:,:)).^2,2);
for k2=1:K2;
%COH2(k1,k2,:) = abs(S(k1,k2,:).^2)./(abs(S(k1,k1,:).*S(k2,k2,:)));
COH(k1,k2,:) = (S(k1,k2,:))./sqrt(abs(S(k1,k1,:).*S(k2,k2,:)));
coh(k1,k2,:) = (S1(k1,k2,:))./sqrt(abs(S1(k1,k1,:).*S1(k2,k2,:)));
%DTF(k1,k2,:) = sqrt(abs(h(k1,k2,:).^2))./DEN;
DTF(k1,k2,:) = abs(h(k1,k2,:))./sqrt(DEN);
ffDTF(k1,k2,:) = abs(h(k1,k2,:))./sqrt(sum(DEN,3));
pCOH2(k1,k2,:) = abs(G(k1,k2,:).^2)./(G(k1,k1,:).*G(k2,k2,:));
%M(k2,k1,:) = ((-1)^(k1+k2))*squeeze(G(k1,k2,:))./detG; % oder ist M = G?
end;
end;
dDTF = pCOH2.*ffDTF;
if nargout<6, return; end;
DC = zeros(K1);
for k = 1:p,
DC = DC + A(:,k*K1+(1:K1)).^2;
end;
if nargout<13, return; end;
for k1=1:K1;
for k2=1:K2;
% Bivariate Granger Causality (similar to Bressler et al. 2007. )
GGC(k1,k2,:) = ((C(k1,k1)*C(k2,k2)-C(k1,k2)^2)/C(k2,k2))*real(h(k1,k2,:).*conj(h(k1,k2,:)))./abs(S(k2,k2,:));
%GGC2(k1,k2,:) = -log(1-((C(k1,k1)*C(k2,k2)-C(k1,k2)^2)/C(k2,k2))*real(h(k1,k2,:).*conj(h(k1,k2,:)))./S(k2,k2,:));
end;
end;
return;
if nargout<7, return; end;
for k1=1:K1;
for k2=1:K2;
M(k2,k1,:) = ((-1)^(k1+k2))*squeeze(G(k1,k2,:))./detG; % oder ist M = G?
end;
end;
for k1=1:K1;
for k2=1:K2;
pCOH(k1,k2,:) = abs(M(k1,k2,:).^2)./(M(k1,k1,:).*M(k2,k2,:));
end;
end;
tsa/inst/invest1.m 0000664 0023567 0023567 00000010746 12115040716 014606 0 ustar schloegl schloegl function [AutoCov,AutoCorr,ARPMX,E,C,s]=invest1(Y,Pmax,D);
% First Investigation of a signal (time series) - interactive
% [AutoCov,AutoCorr,ARPMX,E,CRITERIA,MOPS]=invest1(Y,Pmax,show);
%
% Y time series
% Pmax maximal order (optional)
% show optional; if given the parameters are shown
%
% AutoCov Autocorrelation
% AutoCorr normalized Autocorrelation
% PartACF Partial Autocorrelation
% E Error function E(p)
% CRITERIA curves of the various (see below) criteria,
% MOPS=[optFPE optAIC optBIC optSBC optMDL optCAT optPHI];
% optimal model order according to various criteria
%
% FPE Final Prediction Error (Kay, 1987)
% AIC Akaike Information Criterion (Marple, 1987)
% BIC Bayesian Akaike Information Criterion (Wei, 1994)
% SBC Schwartz's Bayesian Criterion (Wei, 1994)
% MDL Minimal Description length Criterion (Marple, 1987)
% CAT Parzen's CAT Criterion (Wei, 1994)
% PHI Phi criterion (Pukkila et al. 1988)
% minE order where E is minimal
%
% REFERENCES:
% P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% optFPE order where FPE is minimal
% optAIC order where AIC is minimal
% optBIC order where BIC is minimal
% optSBC order where SBC is minimal
% optMDL order where MDL is minimal
% optCAT order where CAT is minimal
% optPHI order where PHI is minimal
% optRC2 max reflection coefficient larger than std-error
% $Id: invest1.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008,2010 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://biosig-consulting.com/matlab/tsa/
%
% 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 .
N=length(Y);
[nr,nc]=size(Y);
if nc==1 Y=transpose(Y); nc=nr; nr=1; end;
if nargin<2 Pmax=min([100 nc/3]); end;
if exist('OCTAVE_VERSION'),
fprintf(2,'Warning INVEST1: DIFF-based optimization not possible\n');
%%% missing DIM-argument in DIFF.M
else
%tmp=Y-mean(Y,2)*ones(1,nc);
RMS(:,1) = mean(Y.^2,2);
Dmax = min(Pmax,5);
for k = 1:Dmax,
RMS(:,k+1) = mean(diff(Y,k,2).^2,2);
end;
[tmp, orderDIFF] = min(RMS,[],2);
% show a nice histogram
h = histo3(orderDIFF-1);
X = 0:Dmax; H = zeros(1,Dmax+1); for k=1:length(h.X), H(find(X==h.X(k)))=h.H(k); end;
%X = 0:Dmax; H = zeros(1,Dmax+1); for k=1:length(x), H(find(X==x(k)))=h(k); end;
bar(X,H);
drawnow;
if nargin>2
oD=0;
else
oD=input('Which order should be used for differentiating [default=0] ?: ');
end;
if oD>0
Y=diff(Y,oD,2);
end;
end;
[AutoCov, AutoCorr, ARPMX, E, NC] = invest0(Y,Pmax);
[FPE,AIC,BIC,SBC,MDL,CATcrit,PHI,optFPE,optAIC,optBIC,optSBC,optMDL,optCAT,optPHI,s,C] = selmo(E,NC);
if 0,
optRC2=zeros(nr+1,1);
for k=0:nr,
if k>0
optRC2(k+1)=max(find(abs(ARPMX(k,(1:Pmax).*(2:Pmax+1)/2))*sqrt(size(Y,2))>1));
else
optRC2(k+1)=max(find(mean(abs(ARPMX(:,(1:Pmax).*(2:Pmax+1)/2))*sqrt(size(Y,2)),2)>1));
end;
end;
%GERSCH=min(find(rc.^2<(0.05/1.05)));
s=[s optRC2];
end;
%CRITERIA=([FPE;AIC;BIC;SBC;MDL;CATcrit;PHI])';
MOPS = s(1:size(s,1),:); %[optFPE optAIC optBIC optSBC optMDL optCAT optPHI];
if nargin==3,
if size(ARPMX,2)==2*Pmax,
%invest1(eeg8s,30,'s');
AR=ARPMX(:,1:Pmax);
RC=ARPMX(:,Pmax+1:2*Pmax);
else
AR=ARPMX(:,Pmax/2*(Pmax-1)+(1:Pmax));
RC=ARPMX(:,(1:Pmax).*(2:Pmax+1)/2);
end;
oo=optBIC;
sinvest1;
end;
tsa/inst/histo.m 0000664 0023567 0023567 00000004223 12115040716 014334 0 ustar schloegl schloegl function [H,X]=histo(Y,Mode)
% HISTO calculates histogram for each column
% [H,X] = HISTO(Y,Mode)
%
% Mode
% 'rows' : frequency of each row
% '1x' : single bin-values
% 'nx' : separate bin-values for each column
% X are the bin-values
% H is the frequency of occurence of value X
%
% HISTO(Y) with no output arguments:
% plots the histogram bar(X,H)
%
% more histogram-based results can be obtained by HIST2RES2
%
% see also: HISTO, HISTO2, HISTO3, HISTO4
%
% REFERENCE(S):
% C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
% $Id: histo.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2002,2008 by Alois Schloegl
% This is part of the TSA-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
%
% 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 .
if nargin<2,
Mode='1x';
end;
Mode=lower(Mode);
if strcmp(Mode,'rows')
R = histo4(Y);
elseif strcmp(Mode,'column')
R = histo4(Y');
R.X = R.X';
elseif strcmp(Mode,'1x')
R = histo3(Y);
elseif strcmp(Mode,'nx')
R = histo2(Y);
end;
H = R.H;
X = R.X;
if nargout == 0,
if any(size(X)==1),
if exist('OCTAVE_VERSION')<5,
bar(R.X,R.H,'stacked');
else
bar(R.X,R.H);
end
else
warning('2-dim X-values not supported\n')
%bar3(R.X,R.H);
end;
end;
tsa/inst/durlev.m 0000664 0023567 0023567 00000010442 12115040716 014507 0 ustar schloegl schloegl function [MX,res,arg3] = durlev(AutoCov);
% function [AR,RC,PE] = durlev(ACF);
% function [MX,PE] = durlev(ACF);
% estimates AR(p) model parameter by solving the
% Yule-Walker with the Durbin-Levinson recursion
% for multiple channels
% INPUT:
% ACF Autocorrelation function from lag=[0:p]
%
% OUTPUT
% AR autoregressive model parameter
% RC reflection coefficients (= -PARCOR coefficients)
% PE remaining error variance
% MX transformation matrix between ARP and RC (Attention: needs O(p^2) memory)
% AR(:,K) = MX(:,K*(K-1)/2+(1:K));
% RC(:,K) = MX(:,(1:K).*(2:K+1)/2);
%
% All input and output parameters are organized in rows, one row
% corresponds to the parameters of one channel
%
% see also ACOVF ACORF AR2RC RC2AR LATTICE
%
% REFERENCES:
% Levinson N. (1947) "The Wiener RMS(root-mean-square) error criterion in filter design and prediction." J. Math. Phys., 25, pp.261-278.
% Durbin J. (1960) "The fitting of time series models." Rev. Int. Stat. Inst. vol 28., pp 233-244.
% P.J. Brockwell and R. A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% S. Haykin "Adaptive Filter Theory" 3rd ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% $Id: durlev.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
% Inititialization
[lr,lc]=size(AutoCov);
res=[AutoCov(:,1), zeros(lr,lc-1)];
d=zeros(lr,1);
if nargout<3 % needs O(p^2) memory
MX=zeros(lr,lc*(lc-1)/2);
idx=0;
idx1=0;
% Durbin-Levinson Algorithm
for K=1:lc-1,
%idx=K*(K-1)/2; %see below
% for L=1:lr, d(L)=arp(L,1:K-1)*transpose(AutoCov(L,K:-1:2));end; % Matlab 4.x, Octave
% d=sum(MX(:,idx+(1:K-1)).*AutoCov(:,K:-1:2),2); % Matlab 5.x
MX(:,idx+K)=(AutoCov(:,K+1)-sum(MX(:,idx1+(1:K-1)).*AutoCov(:,K:-1:2),2))./res(:,K);
%rc(:,K)=arp(:,K);
%if K>1 %for compatibility with OCTAVE 2.0.13
MX(:,idx+(1:K-1))=MX(:,idx1+(1:K-1))-MX(:,(idx+K)*ones(K-1,1)).*MX(:,idx1+(K-1:-1:1));
%end;
% for L=1:lr, d(L)=MX(L,idx+(1:K))*(AutoCov(L,K+1:-1:2).');end; % Matlab 4.x, Octave
% d=sum(MX(:,idx+(1:K)).*AutoCov(:,K+1:-1:2),2); % Matlab 5.x
res(:,K+1) = res(:,K).*(1-abs(MX(:,idx+K)).^2);
idx1=idx;
idx=idx+K;
end;
%arp=MX(:,K*(K-1)/2+(1:K));
%rc =MX(:,(1:K).*(2:K+1)/2);
else % needs O(p) memory
arp=zeros(lr,lc-1);
rc=zeros(lr,lc-1);
% Durbin-Levinson Algorithm
for K=1:lc-1,
% for L=1:lr, d(L)=arp(L,1:K-1)*transpose(AutoCov(L,K:-1:2));end; % Matlab 4.x, Octave
% d=sum(arp(:,1:K-1).*AutoCov(:,K:-1:2),2); % Matlab 5.x
arp(:,K) = (AutoCov(:,K+1)-sum(arp(:,1:K-1).*AutoCov(:,K:-1:2),2))./res(:,K); % Yule-Walker
rc(:,K) = arp(:,K);
%if K>1 %for compatibility with OCTAVE 2.0.13
arp(:,1:K-1)=arp(:,1:K-1)-arp(:,K*ones(K-1,1)).*arp(:,K-1:-1:1);
%end;
%for L=1:lr, d(L)=arp(L,1:K)*(AutoCov(L,K+1:-1:2).');end; % Matlab 4.x, Octave
% d=sum(arp(:,1:K).*AutoCov(:,K+1:-1:2),2); % Matlab 5.x
res(:,K+1) = res(:,K).*(1-abs(arp(:,K)).^2);
end;
% assign output arguments
arg3=res;
res=rc;
MX=arp;
end; %if
tsa/inst/ar2rc.m 0000664 0023567 0023567 00000006062 12115040716 014222 0 ustar schloegl schloegl function [MX,res,arg3] = ar2rc(ar);
% converts autoregressive parameters into reflection coefficients
% with the Durbin-Levinson recursion for multiple channels
% function [AR,RC,PE] = ar2rc(AR);
% function [MX,PE] = ar2rc(AR);
%
% INPUT:
% AR autoregressive model parameter
%
% OUTPUT
% AR autoregressive model parameter
% RC reflection coefficients (= -PARCOR coefficients)
% PE remaining error variance (relative to PE(1)=1)
% MX transformation matrix between ARP and RC (Attention: needs O(p^2) memory)
% AR = MX(:,K*(K-1)/2+(1:K));
% RC = MX(:,(1:K).*(2:K+1)/2);
%
% All input and output parameters are organized in rows, one row
% corresponds to the parameters of one channel
%
% see also ACOVF ACORF DURLEV RC2AR
%
% REFERENCES:
% P.J. Brockwell and R. A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% S. Haykin "Adaptive Filter Theory" 3rd ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% $Id: ar2rc.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
% Inititialization
[lr,lc]=size(ar);
res=[ones(lr,1) zeros(lr,lc)];
if nargout<3 % needs O(p^2) memory
MX=zeros(lr,lc*(lc+1)/2);
MX(:,lc*(lc-1)/2+(1:lc))=ar;
% Durbin-Levinson Algorithm
idx=lc*(lc-1)/2;
for K=lc:-1:2;
%idx=K*(K-1)/2; %see below
MX(:,(K-2)*(K-1)/2+(1:K-1)) = (MX(:,idx+(1:K-1)) + MX(:,(idx+K)*ones(K-1,1)).*MX(:,idx+(K-1:-1:1)))./((ones(lr,1)-abs(MX(:,idx+K)).^2)*ones(1,K-1));
idx=idx-K+1;
end;
for K=1:lc
idx=K*(K-1)/2; %see below
res(:,K+1) = res(:,K).*(1-abs(MX(:,idx+K)).^2);
end;
%arp=MX(:,K*(K-1)/2+(1:K));
%rc =MX(:,(1:K).*(2:K+1)/2);
else % needs O(p) memory
%ar=zeros(lr,lc);
rc=zeros(lr,lc);
rc(:,lc)=ar(:,lc);
MX=ar; % assign output
% Durbin-Levinson Algorithm
for K=lc-1:-1:1,
ar(:,1:K)=(ar(:,1:K)+ar(:,(K+1)*ones(K,1)).*ar(:,K:-1:1))./((ones(lr,1)-abs(ar(:,K+1)).^2)*ones(1,K));
rc(:,K)=ar(:,K);
end;
for K=1:lc,
res(:,K+1) = res(:,K) .* (1-abs(ar(:,K)).^2);
end;
% assign output arguments
arg3=res;
res=rc;
%MX=ar;
end; %if
tsa/inst/rc2ar.m 0000664 0023567 0023567 00000006517 12115040716 014227 0 ustar schloegl schloegl function [MX,res,arg3,acf] = rc2ar(rc);
% converts reflection coefficients into autoregressive parameters
% uses the Durbin-Levinson recursion for multiple channels
% function [AR,RC,PE,ACF] = rc2ar(RC);
% function [MX,PE] = rc2ar(RC);
%
% INPUT:
% RC reflection coefficients
%
% OUTPUT
% AR autoregressive model parameter
% RC reflection coefficients (= -PARCOR coefficients)
% PE remaining error variance (relative to PE(1)=1)
% MX transformation matrix between ARP and RC (Attention: needs O(p^2) memory)
% arp=MX(:,K*(K-1)/2+(1:K));
% rc =MX(:,(1:K).*(2:K+1)/2);
%
% All input and output parameters are organized in rows, one row
% corresponds to the parameters of one channel
%
% see also ACOVF ACORF DURLEV AR2RC
%
% REFERENCES:
% P.J. Brockwell and R. A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% S. Haykin "Adaptive Filter Theory" 3rd ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% $Id: rc2ar.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (c) 1996-2002,2007,2008 by Alois Schloegl
% This function is part of the TSA-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
%
% 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 .
% Inititialization
[lr,lc]=size(rc);
res=[ones(lr,1) zeros(lr,lc)];
if nargout<3 % needs O(p^2) memory
MX=zeros(lr,lc*(lc+1)/2);
idx=0;
% Durbin-Levinson Algorithm
for K=1:lc,
MX(:,idx+K)=rc(:,K);%(AutoCov(:,K+1)-d)./res(:,K);
%rc(:,K)=arp(:,K);
if K>1 %for compatibility with OCTAVE 2.0.13
MX(:,idx+(1:K-1))=MX(:,(K-2)*(K-1)/2+(1:K-1))-MX(:,(idx+K)*ones(K-1,1)).*MX(:,(K-2)*(K-1)/2+(K-1:-1:1));
end;
res(:,K+1) = res(:,K).*(1-abs(MX(:,idx+K)).^2);
idx=idx+K;
end;
%arp=MX(:,K*(K-1)/2+(1:K));
%rc =MX(:,(1:K).*(2:K+1)/2);
ACF=cumprod(ones(lr,lr)-rc.^2,2);
else % needs O(p) memory
ar=zeros(lr,lc);
acf=[ones(lr,1),zeros(lr,lc)];
%rc=RC; %zeros(lr,lc-1);
% Durbin-Levinson Algorithm
for K=1:lc,
acf(:,K) = -sum(acf(:,K:-1:1).*ar(:,1:K),2);
ar(:,K) = rc(:,K);
if K>1, %for compatibility with OCTAVE 2.0.13
ar(:,1:K-1) = ar(:,1:K-1) - ar(:,K*ones(K-1,1)) .* ar(:,K-1:-1:1);
end;
res(:,K+1) = res(:,K) .* (1-abs(ar(:,K)).^2);
end;
ACF=cumprod(ones(lr,lc)-rc.^2,2);
% assign output arguments
arg3=res;
res=rc;
MX=ar;
end; %if
tsa/inst/aar.m 0000664 0023567 0023567 00000026127 12115040716 013760 0 ustar schloegl schloegl function [a,e,REV,TOC,CPUTIME,ESU] = aar(y, Mode, arg3, arg4, arg5, arg6, arg7, arg8, arg9);
% Calculates adaptive autoregressive (AAR) and adaptive autoregressive moving average estimates (AARMA)
% of real-valued data series using Kalman filter algorithm.
% [a,e,REV] = aar(y, mode, MOP, UC, a0, A, W, V);
%
% The AAR process is described as following
% y(k) - a(k,1)*y(t-1) -...- a(k,p)*y(t-p) = e(k);
% The AARMA process is described as following
% y(k) - a(k,1)*y(t-1) -...- a(k,p)*y(t-p) = e(k) + b(k,1)*e(t-1) + ... + b(k,q)*e(t-q);
%
% Input:
% y Signal (AR-Process)
% Mode is a two-element vector [aMode, vMode],
% aMode determines 1 (out of 12) methods for updating the co-variance matrix (see also [1])
% vMode determines 1 (out of 7) methods for estimating the innovation variance (see also [1])
% aMode=1, vmode=2 is the RLS algorithm as used in [2]
% aMode=-1, LMS algorithm (signal normalized)
% aMode=-2, LMS algorithm with adaptive normalization
%
% MOP model order, default [10,0]
% MOP=[p] AAR(p) model. p AR parameters
% MOP=[p,q] AARMA(p,q) model, p AR parameters and q MA coefficients
% UC Update Coefficient, default 0
% a0 Initial AAR parameters [a(0,1), a(0,2), ..., a(0,p),b(0,1),b(0,2), ..., b(0,q)]
% (row vector with p+q elements, default zeros(1,p) )
% A Initial Covariance matrix (positive definite pxp-matrix, default eye(p))
% W system noise (required for aMode==0)
% V observation noise (required for vMode==0)
%
% Output:
% a AAR(MA) estimates [a(k,1), a(k,2), ..., a(k,p),b(k,1),b(k,2), ..., b(k,q]
% e error process (Adaptively filtered process)
% REV relative error variance MSE/MSY
%
%
% Hint:
% The mean square (prediction) error of different variants is useful for determining the free parameters (Mode, MOP, UC)
%
% REFERENCE(S):
% [1] A. Schloegl (2000), The electroencephalogram and the adaptive autoregressive model: theory and applications.
% ISBN 3-8265-7640-3 Shaker Verlag, Aachen, Germany.
%
% More references can be found at
% http://pub.ist.ac.at/~schloegl/publications/
%
% $Id: aar.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2003 by Alois Schloegl
%
% 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 .
[nc,nr]=size(y);
%if nc=2 p=MOP(1); q=MOP(2); MOP=p+q;
end;
if nargin<4 UC=0; else UC= arg4; end;
a0=zeros(1,MOP);
A0=eye(MOP);
if nargin>4,
if all(size(arg5)==([1,1]*(MOP+1))); % extended covariance matrix of AAR parameters
a0 = arg5(1,2:size(arg5,2));
A0 = arg5(2:size(arg5,1),2:size(arg5,2)) - a0'*a0;
else
a0 = arg5;
if nargin>5
A0 = arg6;
end;
end;
end;
if nargin<7, W = []; else W = arg7; end;
if all(size(W)==MOP),
if aMode ~= 0,
fprintf(1,'aMode should be 0, because W is given.\n');
end;
elseif isempty(W),
if aMode == 0,
fprintf(1,'aMode must be non-zero, because W is not given.\n');
end;
elseif any(size(W)~=MOP),
fprintf(1,'size of W does not fit. It must be %i x %i.\n',MOP,MOP);
return;
end;
if nargin<8, V0 = []; else V0 = arg8; end;
if all(size(V0)==nr),
if vMode ~= 0,
fprintf(1,'vMode should be 0, because V is given.\n');
end;
elseif isempty(V0),
if aMode == 0,
fprintf(1,'vMode must be non-zero, because V is not given.\n');
end;
else
fprintf(1,'size of V does not fit. It must be 1x1.\n');
return;
end;
% if nargin<7 TH=3; else TH = arg7; end;
% TH=TH*var(y);
% TH=TH*mean(detrend(y,0).^2);
MSY=mean(detrend(y,0).^2);
e=zeros(nc,1);
Q=zeros(nc,1);
V=zeros(nc,1);
T=zeros(nc,1);
%DET=zeros(nc,1);
SPUR=zeros(nc,1);
ESU=zeros(nc,1);
a=a0(ones(nc,1),:);
%a=zeros(nc,MOP);
%b=zeros(nc,q);
mu=1-UC; % Patomaeki 1995
lambda=(1-UC); % Schloegl 1996
arc=poly((1-UC*2)*[1;1]);b0=sum(arc); % Whale forgettting factor for Mode=258,(Bianci et al. 1997)
dW=UC/MOP*eye(MOP); % Schloegl
%------------------------------------------------
% First Iteration
%------------------------------------------------
Y=zeros(MOP,1);
C=zeros(MOP);
%X=zeros(q,1);
at=a0;
A=A0;
E=y(1);
e(1)=E;
if ~isempty(V0)
V(1) = V0;
else
V(1) = (1-UC) + UC*E*E;
end;
ESU(1) = 1; %Y'*A*Y;
A1=zeros(MOP);A2=A1;
tic;CPUTIME=cputime;
%------------------------------------------------
% Update Equations
%------------------------------------------------
T0=2;
for t=T0:nc,
%Y=[y(t-1); Y(1:p-1); E ; Y(p+1:MOP-1)]
if t<=p Y(1:t-1)=y(t-1:-1:1); % Autoregressive
else Y(1:p)=y(t-1:-1:t-p);
end;
if t<=q Y(p+(1:t-1))=e(t-1:-1:1); % Moving Average
else Y(p+1:MOP)=e(t-1:-1:t-q);
end;
% Prediction Error
E = y(t) - a(t-1,:)*Y;
e(t) = E;
E2=E*E;
AY=A*Y;
esu=Y'*AY;
ESU(t)=esu;
if isnan(E),
a(t,:)=a(t-1,:);
else
V(t) = V(t-1)*(1-UC)+UC*E2;
if aMode == -1, % LMS
% V(t) = V(t-1)*(1-UC)+UC*E2;
a(t,:)=a(t-1,:) + (UC/MSY)*E*Y';
elseif aMode == -2, % LMS with adaptive estimation of the variance
a(t,:)=a(t-1,:) + UC/V(t)*E*Y';
else % Kalman filtering (including RLS)
if vMode==0, %eMode==4
Q(t) = (esu + V0);
elseif vMode==1, %eMode==4
Q(t) = (esu + V(t));
elseif vMode==2, %eMode==2
Q(t) = (esu + 1);
elseif vMode==3, %eMode==3
Q(t) = (esu + lambda);
elseif vMode==4, %eMode==1
Q(t) = (esu + V(t-1));
elseif vMode==5, %eMode==6
if E2>esu
V(t)=(1-UC)*V(t-1)+UC*(E2-esu);
else
V(t)=V(t-1);
end;
Q(t) = (esu + V(t));
elseif vMode==6, %eMode==7
if E2>esu
V(t)=(1-UC)*V(t-1)+UC*(E2-esu);
else
V(t)=V(t-1);
end;
Q(t) = (esu + V(t-1));
elseif vMode==7, %eMode==8
Q(t) = esu;
end;
k = AY / Q(t); % Kalman Gain
a(t,:) = a(t-1,:) + k'*E;
if aMode==0, %AMode=0
A = A - k*AY' + W; % Schloegl et al. 2003
elseif aMode==1, %AMode=1
A = (1+UC)*(A - k*AY'); % Schloegl et al. 1997
elseif aMode==2, %AMode=11
A = A - k*AY';
A = A + sum(diag(A))*dW;
elseif aMode==3, %AMode=5
A = A - k*AY' + sum(diag(A))*dW;
elseif aMode==4, %AMode=6
A = A - k*AY' + UC*eye(MOP); % Schloegl 1998
elseif aMode==5, %AMode=2
A = A - k*AY' + UC*UC*eye(MOP);
elseif aMode==6, %AMode=2
T(t)=(1-UC)*T(t-1)+UC*(E2-Q(t))/(Y'*Y);
A=A*V(t-1)/Q(t);
if T(t)>0 A=A+T(t)*eye(MOP); end;
elseif aMode==7, %AMode=6
T(t)=(1-UC)*T(t-1)+UC*(E2-Q(t))/(Y'*Y);
A=A*V(t)/Q(t);
if T(t)>0 A=A+T(t)*eye(MOP); end;
elseif aMode==8, %AMode=5
Q_wo = (Y'*C*Y + V(t-1));
C=A-k*AY';
T(t)=(1-UC)*T(t-1)+UC*(E2-Q_wo)/(Y'*Y);
if T(t)>0 A=C+T(t)*eye(MOP); else A=C; end;
elseif aMode==9, %AMode=3
A = A - (1+UC)*k*AY';
A = A + sum(diag(A))*dW;
elseif aMode==10, %AMode=7
A = A - (1+UC)*k*AY' + sum(diag(A))*dW;
elseif aMode==11, %AMode=8
A = A - (1+UC)*k*AY' + UC*eye(MOP); % Schloegl 1998
elseif aMode==12, %AMode=4
A = A - (1+UC)*k*AY' + UC*UC*eye(MOP);
elseif aMode==13
A = A - k*AY' + UC*diag(diag(A));
elseif aMode==14
A = A - k*AY' + (UC*UC)*diag(diag(A));
end;
end;
end;
end;
%a=a(end,:);
TOC = toc;
CPUTIME = cputime - CPUTIME;
%REV = (e'*e)/(y'*y);
REV = mean(e.*e)./mean(y.*y);
tsa/inst/ar_spa.m 0000664 0023567 0023567 00000007332 12115040716 014457 0 ustar schloegl schloegl function [w,A,B,R,P,F,ip] = ar_spa(ARP,nhz,E);
% AR_SPA decomposes an AR-spectrum into its compontents
% [w,A,B,R,P,F,ip] = ar_spa(AR,fs,E);
%
% INPUT:
% AR autoregressive parameters
% fs sampling rate, provide w and B in [Hz], if not given the result is in radians
% E noise level (mean square), gives A and F in units of E, if not given as relative amplitude
%
% OUTPUT
% w center frequency
% A Amplitude
% B bandwidth
% - less important output parameters -
% R residual
% P poles
% ip number of complex conjugate poles
% real(F) power, absolute values are obtained by multiplying with noise variance E(p+1)
% imag(F) assymetry, - " -
%
% All input and output parameters are organized in rows, one row
% corresponds to the parameters of one channel
%
% see also ACOVF ACORF DURLEV IDURLEV PARCOR YUWA
%
% REFERENCES:
% [1] Zetterberg L.H. (1969) Estimation of parameter for linear difference equation with application to EEG analysis. Math. Biosci., 5, 227-275.
% [2] Isaksson A. and Wennberg, A. (1975) Visual evaluation and computer analysis of the EEG - A comparison. Electroenceph. clin. Neurophysiol., 38: 79-86.
% [3] G. Florian and G. Pfurtscheller (1994) Autoregressive model based spectral analysis with application to EEG. IIG - Report Series, University of Technolgy Graz, Austria.
% $Id: ar_spa.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2003 by Alois Schloegl
% This is part of the TSA-toolbox see also:
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sf.net/
%
% 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 .
[NTR,pp]=size(ARP);
R=zeros(size(ARP));
P=zeros(size(ARP));
w=zeros(size(ARP));
A=zeros(size(ARP));
B=zeros(size(ARP));
F=zeros(size(ARP));
F1 = F;
for k = 1:NTR, %if ~mod(k,100),k, end;
[r,p,tmp] = residue(1,[1 -ARP(k,:)]);
[tmp,idx] = sort(-abs(r));
R(k,:) = r(idx)'; % Residual,
P(k,:) = p(idx)'; % Poles
%r(k,:)=roots([1 -ARP(k,:)])';
w(k,:) = angle(p(idx)'); % center frequency (in [radians])
A(k,:) = 1./abs(polyval([1 -ARP(k,:)],exp(i*w(k,:)))); % Amplitude
%A(k,:) = freqz(1,[1 -ARP(k,:)],w(k,:)); % Amplitude
%A2(k,:) = abs(r)'./abs(exp(i*w(k,:))-r'); % Amplitude
B(k,:) = -log(abs(p(idx)')); % Bandwidth
if nargout < 6,
elseif 0,
F(k,:) = (1+sign(imag(r(idx)')))./(polyval([-ARP(k,pp-1:-1:1).*(1:pp-1) pp],1./p(idx).').*polyval([-ARP(k,pp:-1:1) 1],p(idx).'));
elseif 1;
a3 = polyval([-ARP(k,pp-1:-1:1).*(1:pp-1), pp],1./p(idx).');
a = polyval([-ARP(k,pp:-1:1) 1],p(idx).');
%F(k,:) = (1+(imag(P(k,:))~=0))./(a.*a3);
F(k,:) = (1+sign(imag(P(k,:))))./(a.*a3);
end;
end;
A = A.*sqrt(E(:,ones(1,pp))/(2*pi*nhz));
if nargin>1,
if size(nhz,1)==1,
nhz = nhz(ones(NTR,1),:);
end;
w = w.*nhz(:,ones(1,pp))/(2*pi);
B = B.*nhz(:,ones(1,pp))/(2*pi);
end;
if nargin>2,
F = F.*E(:,ones(1,pp));
F1 = F1.*E(:,ones(1,pp));
end;
ip = sum(imag(P)~=0,2)/2;
return;
np(:,1) = sum(imag(P')==0)'; % number of real poles
np(:,2) = pp-np(:,1); % number of imaginary poles
tsa/inst/aarmam.m 0000664 0023567 0023567 00000017440 12115040716 014451 0 ustar schloegl schloegl function [z,e,REV,ESU,V,Z,SPUR] = aarmam(y, Mode, MOP, UC, z0, Z0, V0, W);
% Estimating Adaptive AutoRegressive-Moving-Average-and-mean model (includes mean term)
%
% !! This function is obsolete and is replaced by AMARMA
%
% [z,E,REV,ESU,V,Z,SPUR] = aarmam(y, mode, MOP, UC, z0, Z0, V0, W);
% Estimates AAR parameters with Kalman filter algorithm
% y(t) = sum_i(a_i(t)*y(t-i)) + m(t) + e(t) + sum_i(b_i(t)*e(t-i))
%
% State space model
% z(t) = G*z(t-1) + w(t) w(t)=N(0,W)
% y(t) = H*z(t) + v(t) v(t)=N(0,V)
%
% G = I,
% z = [m(t),a_1(t-1),..,a_p(t-p),b_1(t-1),...,b_q(t-q)];
% H = [1,y(t-1),..,y(t-p),e(t-1),...,e(t-q)];
% W = E{(z(t)-G*z(t-1))*(z(t)-G*z(t-1))'}
% V = E{(y(t)-H*z(t-1))*(y(t)-H*z(t-1))'}
%
%
% Input:
% y Signal (AR-Process)
% Mode determines the type of algorithm
%
% MOP Model order [m,p,q], default [0,10,0]
% m=1 includes the mean term, m=0 does not.
% p and q must be positive integers
% it is recommended to set q=0.
% UC Update Coefficient, default 0
% z0 Initial state vector
% Z0 Initial Covariance matrix
%
% Output:
% z AR-Parameter
% E error process (Adaptively filtered process)
% REV relative error variance MSE/MSY
%
% REFERENCE(S):
% [1] A. Schloegl (2000), The electroencephalogram and the adaptive autoregressive model: theory and applications.
% ISBN 3-8265-7640-3 Shaker Verlag, Aachen, Germany.
%
% More references can be found at
% http://pub.ist.ac.at/~schloegl/publications/
% $Id: aarmam.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008,2012 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sourceforge.net/
% http://biosig.sourceforge.net/
%
% 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 .
%#realonly
%#inbounds
warning('AARMAM is obsolete. Use AMARMA instead!')
[nc,nr]=size(y);
if nargin<2 Mode=0;
elseif ischar(Mode) Mode=bin2dec(Mode);
elseif isnan(Mode) return; end;
if nargin<3 MOP=[0,10,0]; end;
if length(MOP)==0, m=0;p=10; q=0; MOP=p;
elseif length(MOP)==1, m=0;p=MOP(1); q=0; MOP=p;
elseif length(MOP)==2, fprintf(1,'Error AMARMA: MOP is ambiguos\n');
elseif length(MOP)>2, m=MOP(1); p=MOP(2); q=MOP(3);MOP=m+p+q;
end;
if prod(size(Mode))>1
aMode=Mode(1);
eMode=Mode(2);
end;
%fprintf(1,['a' int2str(aMode) 'e' int2str(eMode) ' ']);
e=zeros(nc,1);
V=zeros(nc,1);V(1)=V0;
T=zeros(nc,1);
ESU=zeros(nc,1)+nan;
SPUR=zeros(nc,1)+nan;
z=z0(ones(nc,1),:);
arc=poly((1-UC*2)*[1;1]);b0=sum(arc); % Whale forgetting factor for Mode=258,(Bianci et al. 1997)
dW=UC/MOP*eye(MOP); % Schloegl
%------------------------------------------------
% First Iteration
%------------------------------------------------
H = zeros(MOP,1);
if m,
H(1) = 1;%M0;
if m~=1,
fprintf(2,'Warning AARMAM: m must be 0 or 1\n');
return;
end;
end;
if (p<0) || (q<0) || (round(p)~=p) || (round(q)~=q),
fprintf(2,'Error AARMAM: p and q must be positive integers\n');
return;
end;
E = 0;
Z = Z0;
zt= z0;
A1 = zeros(MOP); A2 = A1;
y_1=0;
%------------------------------------------------
% Update Equations
%------------------------------------------------
for t=1:nc,
% make measurement matrix
if 0,
if t>1,
y_1 = y(t-1);
end;
H=[1; y_1; H(m+(1:p-1)'); E(1:min(1,q-1)) ; H(p+m+(1:q-1)')]; % shift y and e
else % this seem to be slightly faster
if t<=p, H(m+(1:t-1)) = y(t-1:-1:1); % Autoregressive
else H(m+(1:p)) = y(t-1:-1:t-p);
end;
if t<=q, H(m+p+(1:t-1)) = e(t-1:-1:1); % Moving Average
else H(m+p+(1:q)) = e(t-1:-1:t-q);
end;
end;
% Prediction Error
E = y(t) - zt*H;
e(t) = E;
if ~isnan(E),
E2 = E*E;
AY = Z*H;
ESU(t) = H'*AY;
if eMode==1
V0 = V(t-1);
V(t) = V0*(1-UC)+UC*E2;
elseif eMode==2
V0 = 1;
V(t) = V0; %V(t-1)*(1-UC)+UC*E2;
elseif eMode==3
V0 = 1-UC;
V(t) = V0; %(t-1)*(1-UC)+UC*E2;
elseif eMode==4
V0 = V0*(1-UC)+UC*E2;
V(t) = V0;
elseif eMode==5
V(t)=V0;
%V0 = V0;
elseif eMode==6
if E2>ESU(t),
V0=(1-UC)*V0+UC*(E2-ESU(t));
end;
V(t)=V0;
elseif eMode==7
V0=V(t);
if E2>ESU(t)
V(t) = (1-UC)*V0+UC*(E2-ESU(t));
else
V(t) = V0;
end;
elseif eMode==8
V0=0;
V(t) = V0; % (t-1)*(1-UC)+UC*E2;
end;
k = AY / (ESU(t) + V0); % Kalman Gain
zt = zt + k'*E;
%z(t,:) = zt;
if aMode==2
T(t)=(1-UC)*T(t-1)+UC*(E2-Q(t))/(H'*H); % Roberts I 1998
Z=Z*V(t-1)/Q(t);
if T(t)>0 W=T(t)*eye(MOP); else W=zeros(MOP);end;
elseif aMode==5
Q_wo = (H'*C*H + V(t-1)); % Roberts II 1998
T(t)=(1-UC)*T(t-1)+UC*(E2-Q_wo)/(H'*H);
if T(t)>0 W=T(t)*eye(MOP); else W=zeros(MOP); end;
elseif aMode==6
T(t)=(1-UC)*T(t-1)+UC*(E2-Q(t))/(H'*H);
Z=Z*V(t)/Q(t);
if T(t)>0 W=T(t)*eye(MOP); else W=zeros(MOP); end;
elseif aMode==11
%Z = Z - k*AY';
W = sum(diag(Z))*dW;
elseif aMode==12
W = UC*UC*eye(MOP);
elseif aMode==13
W = UC*diag(diag(Z));
elseif aMode==14
W = (UC*UC)*diag(diag(Z));
elseif aMode==15
W = sum(diag(Z))*dW;
elseif aMode==16
W = UC*eye(MOP); % Schloegl 1998
%elseif aMode==17
%W=W;
end;
Z = Z - k*AY'; % Schloegl 1998
else
V(t) = V0;
end;
if any(any(isnan(W))), W=UC*Z;end;
z(t,:) = zt;
Z = Z + W; % Schloegl 1998
SPUR(t) = trace(Z);
end;
REV = mean(e.*e)/mean(y.*y);
if any(~isfinite(Z(:))), REV=inf; end;
tsa/inst/lattice.m 0000664 0023567 0023567 00000011747 12115040716 014644 0 ustar schloegl schloegl function [MX,PE,arg3] = lattice(Y,lc,Mode);
% Estimates AR(p) model parameter with lattice algorithm (Burg 1968)
% for multiple channels.
% If you have the NaN-tools, LATTICE.M can handle missing values (NaN),
%
% [...] = lattice(y [,Pmax [,Mode]]);
%
% [AR,RC,PE] = lattice(...);
% [MX,PE] = lattice(...);
%
% INPUT:
% y signal (one per row), can contain missing values (encoded as NaN)
% Pmax max. model order (default size(y,2)-1))
% Mode 'BURG' (default) Burg algorithm
% 'GEOL' geometric lattice
%
% OUTPUT
% AR autoregressive model parameter
% RC reflection coefficients (= -PARCOR coefficients)
% PE remaining error variance
% MX transformation matrix between ARP and RC (Attention: needs O(p^2) memory)
% AR(:,K) = MX(:, K*(K-1)/2+(1:K)); = MX(:,sum(1:K-1)+(1:K));
% RC(:,K) = MX(:,cumsum(1:K)); = MX(:,(1:K).*(2:K+1)/2);
%
% All input and output parameters are organized in rows, one row
% corresponds to the parameters of one channel
%
% see also ACOVF ACORF AR2RC RC2AR DURLEV SUMSKIPNAN
%
% REFERENCE(S):
% J.P. Burg, "Maximum Entropy Spectral Analysis" Proc. 37th Meeting of the Society of Exp. Geophysiscists, Oklahoma City, OK 1967
% J.P. Burg, "Maximum Entropy Spectral Analysis" PhD-thesis, Dept. of Geophysics, Stanford University, Stanford, CA. 1975.
% P.J. Brockwell and R. A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% S. Haykin "Adaptive Filter Theory" 3rd ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% $Id: lattice.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2002,2008,2010 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://biosig-consulting.com/matlab/tsa/
%
% 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 .
if nargin<3, Mode='BURG';
else Mode=upper(Mode(1:4));end;
BURG=~strcmp(Mode,'GEOL');
% Inititialization
[lr,N]=size(Y);
if nargin<2, lc=N-1; end;
F=Y;
B=Y;
[DEN,nn] = sumskipnan((Y.*Y),2);
PE = [DEN./nn,zeros(lr,lc)];
if nargout<3 % needs O(p^2) memory
MX = zeros(lr,lc*(lc+1)/2);
idx= 0;
% Durbin-Levinson Algorithm
for K=1:lc,
[TMP,nn] = sumskipnan(F(:,K+1:N).*B(:,1:N-K),2);
MX(:,idx+K) = TMP./DEN; %Burg
if K>1, %for compatibility with OCTAVE 2.0.13
MX(:,idx+(1:K-1))=MX(:,(K-2)*(K-1)/2+(1:K-1))-MX(:,(idx+K)*ones(K-1,1)).*MX(:,(K-2)*(K-1)/2+(K-1:-1:1));
end;
tmp = F(:,K+1:N) - MX(:,(idx+K)*ones(1,N-K)).*B(:,1:N-K);
B(:,1:N-K) = B(:,1:N-K) - MX(:,(idx+K)*ones(1,N-K)).*F(:,K+1:N);
F(:,K+1:N) = tmp;
[PE(:,K+1),nn] = sumskipnan([F(:,K+1:N).^2,B(:,1:N-K).^2],2);
if ~BURG,
[f,nf] = sumskipnan(F(:,K+1:N).^2,2);
[b,nb] = sumskipnan(B(:,1:N-K).^2,2);
DEN = sqrt(b.*f);
else
DEN = PE(:,K+1);
end;
idx=idx+K;
PE(:,K+1) = PE(:,K+1)./nn; % estimate of covariance
end;
else % needs O(p) memory
arp=zeros(lr,lc-1);
rc=zeros(lr,lc-1);
% Durbin-Levinson Algorithm
for K=1:lc,
[TMP,nn] = sumskipnan(F(:,K+1:N).*B(:,1:N-K),2);
arp(:,K) = TMP./DEN; %Burg
rc(:,K) = arp(:,K);
if K>1, % for compatibility with OCTAVE 2.0.13
arp(:,1:K-1) = arp(:,1:K-1) - arp(:,K*ones(K-1,1)).*arp(:,K-1:-1:1);
end;
tmp = F(:,K+1:N) - rc(:,K*ones(1,N-K)).*B(:,1:N-K);
B(:,1:N-K) = B(:,1:N-K) - rc(:,K*ones(1,N-K)).*F(:,K+1:N);
F(:,K+1:N) = tmp;
[PE(:,K+1),nn] = sumskipnan([F(:,K+1:N).^2,B(:,1:N-K).^2],2);
if ~BURG,
[f,nf] = sumskipnan(F(:,K+1:N).^2,2);
[b,nb] = sumskipnan(B(:,1:N-K).^2,2);
DEN = sqrt(b.*f);
else
DEN = PE(:,K+1);
end;
PE(:,K+1) = PE(:,K+1)./nn; % estimate of covariance
end;
% assign output arguments
arg3=PE;
PE=rc;
MX=arp;
end; %if
tsa/inst/covm.m 0000664 0023567 0023567 00000015600 12264575422 014167 0 ustar schloegl schloegl 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.
% The output gives NaN only if there are insufficient input data
%
% COVM(X,Mode);
% 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
%
% Mode = 'E' extended mode
% C = [1 X]'*[1 X]; % l is a matching column of 1's
% C is additive, i.e. it can be applied to subsequent blocks and summed up afterwards
% the mean (or sum) is stored on the 1st row and column of C
%
% Mode = 'D' or 'D0' detrended mode
% the mean of X (and Y) is removed. If combined with extended mode (Mode='DE'),
% the mean (or sum) is stored in the 1st row and column of C.
% The default scaling is factor (N-1).
% Mode = 'D1' is the same as 'D' but uses N for scaling.
%
% C = covm(...);
% C is the scaled by N in Mode M and by (N-1) in mode D.
% [C,N] = covm(...);
% C is not scaled, provides the scaling factor N
% C./N gives the scaled version.
%
% see also: DECOVM, XCOVF
% $Id: covm.m 9032 2011-11-08 20:25:36Z schloegl $
% Copyright (C) 2000-2005,2009 by Alois Schloegl
% This function is part of the NaN-toolbox
% http://pub.ist.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 .
global FLAG_NANS_OCCURED;
if nargin<3,
W = [];
if nargin==2,
if isnumeric(Y),
Mode='M';
else
Mode=Y;
Y=[];
end;
elseif nargin==1,
Mode = 'M';
Y = [];
elseif nargin==0,
error('Missing argument(s)');
end;
elseif (nargin==3) && isnumeric(Y) && ~isnumeric(Mode);
W = [];
elseif (nargin==3) && ~isnumeric(Y) && isnumeric(Mode);
W = Mode;
Mode = Y;
Y = [];
elseif (nargin==4) && ~isnumeric(Mode) && isnumeric(Y);
; %% ok
else
error('invalid input arguments');
end;
Mode = upper(Mode);
[r1,c1]=size(X);
if ~isempty(Y)
[r2,c2]=size(Y);
if r1~=r2,
error('X and Y must have the same number of observations (rows).');
end;
else
[r2,c2]=size(X);
end;
persistent mexFLAG2;
persistent mexFLAG;
if isempty(mexFLAG2)
mexFLAG2 = exist('covm_mex','file');
end;
if isempty(mexFLAG)
mexFLAG = exist('sumskipnan_mex','file');
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(:));
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'),
[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;
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),
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
NN = real(X==X)'*real(Y==Y);
FLAG_NANS_OCCURED = any(NN(:)
% This is part of the TSA-toolbox. See also
% http://biosig-consulting.com/matlab/tsa/
%
% 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 .
if nargin<3, Mode='biased'; end;
[lr,lc] = size(Z);
MISSES = sum(isnan(Z)')';
if any(MISSES); % missing values
M = real(~isnan(Z));
Z(isnan(Z))=0;
end;
if (nargin == 1)
KMAX = lc-1;
elseif (KMAX >= lc-1)
KMAX = lc-1;
end;
ACF = zeros(lr,KMAX+1);
if nargin>3, % for testing, use arg4 for comparing the methods,
elseif (KMAX*KMAX > lc*log2(lc)) % & isempty(MISSES);
Mode2 = 1;
elseif (10*KMAX > lc);
Mode2 = 3;
else
Mode2 = 4;
end;
%%%%% ESTIMATION of non-normalized ACF %%%%%
% the following algorithms gve equivalent results, however, the computational effort is different,
% depending on lr,lc and KMAX, a different algorithm is most efficient.
if Mode2==1; % KMAX*KMAX > lc*log(lc); % O(n.logn)+O(K²)
tmp = fft(Z',2^nextpow2(size(Z,2))*2);
tmp = ifft(tmp.*conj(tmp));
ACF = tmp(1:KMAX+1,:)';
if ~any(any(imag(Z))), ACF=real(ACF); end; % should not be neccessary, unfortunately it is.
elseif Mode2==3; % (10*KMAX > lc) % O(n*K) % use fast Built-in filter function
for L = 1:lr,
acf = filter(Z(L,lc:-1:1),1,Z(L,:));
ACF(L,:)= acf(lc:-1:lc-KMAX);
end;
else Mode2==4; % O(n*K)
for L = 1:lr,
for K = 0:KMAX,
ACF(L,K+1) = Z(L,1:lc-K) * Z(L,1+K:lc)';
end;
end;
end;
%%%%% GET number of elements used for estimating ACF - is needed for normalizing ACF %%%%%
if any(MISSES),
% the following algorithms gve equivalent results, however, the computational effort is different,
% depending on lr,lc and KMAX, a different algorithm is most efficient.
if Mode2==1; % KMAX*KMAX > lc*log(lc); % O(n.logn)+O(K²)
tmp = fft(M',2^nextpow2(size(M,2))*2);
tmp = ifft(tmp.*conj(tmp));
NN = tmp(1:KMAX+1,:)';
if ~any(any(imag(M))), NN=real(NN); end; % should not be neccessary, unfortunately it is.
elseif Mode2==3; % (10*KMAX > lc) % O(n*K) % use fast Built-in filter function
for L = 1:lr,
acf = filter(M(L,lc:-1:1),1,M(L,:));
NN(L,:)= acf(lc:-1:lc-KMAX);
end;
else Mode2==4; % O(n*K)
for L = 1:lr,
for K = 0:KMAX,
NN(L,K+1) = M(L,1:lc-K) * M(L,1+K:lc)';
end;
end;
end;
else
NN = (ones(lr,1)*(lc:-1:lc-KMAX));
end;
if strcmp(Mode,'biased')
if ~any(MISSES),
ACF=ACF/lc;
else
%ACF=ACF./((lc-MISSES)*ones(1,KMAX+1));
ACF=ACF./max(NN + ones(lr,1)*(0:KMAX),0);
end;
elseif strcmp(Mode,'unbiased')
ACF=ACF./NN;
%if ~any(MISSES),
% ACF=ACF./(ones(lr,1)*(lc:-1:lc-KMAX));
%else
% ACF=ACF./((lc-MISSES)*ones(1,KMAX+1) - ones(lr,1)*(0:KMAX));
%end;
elseif strcmp(Mode,'coeff')
%ACF = ACF ./ ACF(:,ones(1,KMAX+1)) .* ((lc-MISSES)*ones(1,KMAX+1));
ACF = ACF./NN;
ACF = ACF./(ACF(:,1)*ones(1,size(ACF,2)));
else
end;
tsa/inst/parcor.m 0000664 0023567 0023567 00000004037 12115040716 014477 0 ustar schloegl schloegl function [PARCOR,ARP, PE] = parcor(AutoCov);
% estimates partial autocorrelation coefficients
% Multiple channels can be used (one per row).
%
% [PARCOR, AR, PE] = parcor(AutoCov); % calculates Partial autocorrelation, autoregressive coefficients and residual error variance from the Autocorrelation function.
%
% [PARCOR] = parcor(acovf(x,p)); % calculates the Partial Autocorrelation coefficients of the data series x up to order p
%
% INPUT:
% AutoCov Autocorrelation function for lag=0:P
%
% OUTPUT
% AR autoregressive model parameter
% PARCOR partial correlation coefficients (= -reflection coefficients)
% PE remaining error variance
%
% All input and output parameters are organized in rows, one row
% corresponds to the parameters of one channel.
% The PARCOR coefficients are the negative reflection coefficients.
% A significance test is implemented in PACF.
%
% see also: PACF ACOVF ACORF DURLEV AC2RC
%
% REFERENCES:
% P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% $Id: parcor.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1997-2002,2008 by Alois Schloegl
%
% 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 .
[ARP,PARCOR,PE] = durlev(AutoCov);
PARCOR=-PARCOR;
tsa/inst/sbispec.m 0000664 0023567 0023567 00000002250 12115040716 014634 0 ustar schloegl schloegl function sbispec(BISPEC)
% SBISPEC show BISPECTRUM
% $Id: sbispec.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1997, 1998, 2008 by Alois Schloegl
%
% 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 .
[s1,s2]=size(BISPEC);
t1=(0:s1-1)/max(s1);
t2=(0:s2/2-1)/max(s2);
tmp=tril(NaN*ones(s1,s2),-1);
BISPEC=BISPEC+tmp+rot90(tmp);
BISPEC=BISPEC(1:s1/2,:);
subplot(211);
mesh(t1,t2,abs(BISPEC));
title('Bispectrum - mesh plot');
subplot(212);
contour(t1,t2,abs(BISPEC));
%contour(t1,t2,log(abs(BISPEC)));
title('Bispectrum - contour plot');
tsa/inst/ac2rc.m 0000664 0023567 0023567 00000002547 12115040716 014207 0 ustar schloegl schloegl function [RC,efinal] = ac2rc(AC);
% converts the autocorrelation function into reflection coefficients
% [RC,r0] = ac2rc(r)
%
% see also ACOVF ACORF AR2RC RC2AR DURLEV AC2POLY, POLY2RC, RC2POLY, RC2AC, AC2RC, POLY2AC
%
% $Id: ac2rc.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
if all(size(AC))>1,
fprintf(2,'Error AC2RC: "r" must be a vector\n');
return;
end;
mfilename='AC2RC';
if ~exist('durlev','file')
fprintf(2,'Error %s: DURLEV.M not found. \n Download TSA toolbox from http://pub.ist.ac.at/~schloegl/matlab/tsa/\n',mfilename);
return;
end;
[AR,RC,PE] = durlev(AC(:).');
RC=-RC;
efinal=AC(1);
tsa/inst/flix.m 0000664 0023567 0023567 00000003525 12115040716 014154 0 ustar schloegl schloegl function Y=flix(D,x)
% floating point index - interpolates data in case of non-integer indices
%
% Y=flix(D,x)
% FLIX returns Y=D(x) if x is an integer
% otherwise D(x) is interpolated from the neighbors D(ceil(x)) and D(floor(x))
%
% Applications:
% (1) discrete Dataseries can be upsampled to higher sampling rate
% (2) transformation of non-equidistant samples to equidistant samples
% (3) [Q]=flix(sort(D),q*(length(D)+1)) calculates the q-quantile of data series D
%
% FLIX(D,x) is the same as INTERP1(D,X,'linear'); Therefore, FLIX might
% become obsolete in future.
%
% see also: HIST2RES, Y2RES, PLOTCDF, INTERP1
% $Id: flix.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) by 2001-2005,2008 Alois Schloegl
% This is part of the TSA-toolbox see also:
% http://www.dpmi.tu-graz.ac.at/schloegl/matlab/tsa/
% http://octave.sf.net/
%
% 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 .
D = D(:);
Y = x;
k1 = ((x >= 1) & (x <= size(D,1)));
Y(~k1) = NaN;
k = x - floor(x); % distance to next sample
ix = ~k & k1; % find integer indices
Y(ix) = D(x(ix)); % put integer indices
ix = k & k1; % find non-integer indices
Y(ix) = D(floor(x(ix))).*(1-k(ix)) + D(ceil(x(ix))).*k(ix);
tsa/inst/tsademo.m 0000664 0023567 0023567 00000002476 12115040716 014652 0 ustar schloegl schloegl % TSADEMO demonstrates INVEST1 on EEG data
% $Id: tsademo.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
if exist('OCTAVE_VERSION')>5;
load -force eeg8s.mat
elseif 1
load eeg8s.mat
else
[FileName, PathName]=uigetfile('eeg8s.mat','load demo data EEG8S.MAT');
load([PathName FileName],'eeg8s');
end;
s = eeg8s';
Pmax=100;
[AutoCov,AutoCorr,ARPMX,E,CRITERIA,MOPS]=invest1(s,Pmax,'s');
if size(ARPMX,2)==2*Pmax,
%invest1(eeg8s,30,'s');
AR=ARPMX(:,1:Pmax);
RC=ARPMX(:,Pmax+1:2*Pmax);
else
AR=ARPMX(:,Pmax/2*(Pmax-1)+(1:Pmax));
RC=ARPMX(:,(1:Pmax).*(2:Pmax+1)/2);
end;
tsa/inst/bisdemo.m 0000664 0023567 0023567 00000002273 12115040716 014633 0 ustar schloegl schloegl % BISDEMO (script) Shows BISPECTRUM of eeg8s.mat
% $Id: bisdemo.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1997, 1998,2008 by Alois Schloegl
%
% 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 .
load eeg8s.mat;
[BISPEC,BICOV,ACF]=bispec(eeg8s,30);
[s1,s2]=size(BISPEC);
t1=(1:s1)/max(s1);
t2=(1:s2)/max(s2);
subplot(211);
mesh(t1,t2,abs(BISPEC));
title('Bispectrum - mesh plot');
subplot(212);
if exist('OCTAVE_VERSION')>5
contour(abs(BISPEC),10,t1,t2);
else
contour(t1,t2,abs(BISPEC),10);
end;
title('Bispectrum - contour plot');
tsa/inst/content.m 0000664 0023567 0023567 00000011571 12115040716 014664 0 ustar schloegl schloegl % Time Series Analysis (Ver 3.10)
% Schloegl A. (1996-2003,2008) Time Series Analysis - A Toolbox for the use with Matlab.
% WWW: http://pub.ist.ac.at/~schloegl/matlab/tsa/
%
% $Id: content.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2003,2008 by Alois Schloegl
%
% Time Series Analysis - a toolbox for the use with Matlab
% aar adaptive autoregressive estimator
% acovf (*) Autocovariance function
% acorf (acf) (*) autocorrelation function
% pacf (*) partial autocorrelation function, includes signifcance test and confidence interval
% parcor (*) partial autocorrelation function
% biacovf biautocovariance function (3rd order cumulant)
% bispec Bi-spectrum
% durlev (*) solves Yule-Walker equation - converts ACOVF into AR parameters
% lattice (*) calcultes AR parameters with lattice method
% lpc (*) calculates the prediction coefficients form a given time series
% invest0 (*) a prior investigation (used by invest1)
% invest1 (*) investigates signal (useful for 1st evaluation of the data)
% selmo (*) Select Order of Autoregressive model using different criteria
% histo (*) histogram
% hup (*) test Hurwitz polynomials
% ucp (*) test Unit Circle Polynomials
% y2res (*) computes mean, variance, skewness, kurtosis, entropy, etc. from data series
% ar_spa (*) spectral analysis based on the autoregressive model
% detrend (*) removes trend, can handle missing values, non-equidistant sampled data
% flix floating index, interpolates data for non-interger indices
% quantiles calculates quantiles
%
% Multivariate analysis (planned in future)
% mvar multivariate (vector) autoregressive estimation
% mvfilter multivariate filter
% arfit2 provides compatibility to ARFIT [Schneider and Neumaier, 2001]
% Conversions between Autocorrelation (AC), Autoregressive parameters (AR),
% prediction polynom (POLY) and Reflection coefficient (RC)
% ac2poly (*) transforms autocorrelation into prediction polynom
% ac2rc (*) transforms autocorrelation into reflexion coefficients
% ar2rc (*) transforms autoregressive parameters into reflection coefficients
% rc2ar (*) transforms reflection coefficients into autoregressive parameters
% poly2ac (*) transforms polynom to autocorrelation
% poly2ar (*) transforms polynom to AR
% poly2rc (*)
% rc2ac (*)
% rc2poly (*)
% ar2poly (*)
%
% Utility functions
% sinvest1 shows the parameter calculated by INVEST1
%
% Test suites
% tsademo demonstrates INVEST1 on EEG data
% invfdemo demonstration of matched, inverse filtering
% bisdemo demonstrates bispectral estimation
%
% (*) indicates univariate analysis of multiple data series (each in a row) can be processed.
% (-) indicates that these functions will be removed in future
%
% REFERENCES (sources):
% http://www.itl.nist.gov/
% http://mathworld.wolfram.com/
% P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% O. Foellinger "Lineare Abtastsysteme", Oldenburg Verlag, Muenchen, 1986.
% F. Gausch "Systemtechnik", Textbook, University of Technology Graz, 1993.
% M.S. Grewal and A.P. Andrews "Kalman Filtering" Prentice Hall, 1993.
% S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996.
% E.I. Jury "Theory and Application of the z-Transform Method", Robert E. Krieger Publishing Co., 1973.
% M.S. Kay "Modern Spectal Estimation" Prentice Hall, 1988.
% Ch. Langraf and G. Schneider "Elemente der Regeltechnik", Springer Verlag, 1970.
% S.L. Marple "Digital Spetral Analysis with Applications" Prentice Hall, 1987.
% C.L. Nikias and A.P. Petropulu "Higher-Order Spectra Analysis" Prentice Hall, 1993.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% T. Schneider and A. Neumaier "Algorithm 808: ARFIT - a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models"
% ACM Transactions on Mathematical software, 27(Mar), 58-65.
% C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
%
%
% REFERENCES (applications):
% [1] A. Schlögl, B. Kemp, T. Penzel, D. Kunz, S.-L. Himanen,A. Värri, G. Dorffner, G. Pfurtscheller.
% Quality Control of polysomnographic Sleep Data by Histogram and Entropy Analysis.
% Clin. Neurophysiol. 1999, Dec; 110(12): 2165 - 2170.
% [2] Penzel T, Kemp B, Klösch G, Schlögl A, Hasan J, Varri A, Korhonen I.
% Acquisition of biomedical signals databases
% IEEE Engineering in Medicine and Biology Magazine 2001, 20(3): 25-32
%
% Features:
% - Multiple Signal Processing
% - Efficient algorithms
% - Model order selection tools
% - higher (3rd) order analysis
% - Maximum entropy spectral estimation
% - can deal with missing values (NaN's)
tsa/inst/y2res.m 0000664 0023567 0023567 00000007370 12115040716 014260 0 ustar schloegl schloegl function [R]=y2res(Y)
% Y2RES evaluates basic statistics of a data series
%
% R = y2res(y)
% several statistics are estimated from each column of y
%
% OUTPUT:
% R.N number of samples, NaNs are not counted
% R.SUM sum of samples
% R.MEAN mean
% R.STD standard deviation
% R.VAR variance
% R.Max Maximum
% R.Min Minimum
% ... and many more including:
% MEDIAN, Quartiles, Variance, standard error of the mean (SEM),
% Coefficient of Variation, Quantization (QUANT), TRIMEAN, SKEWNESS,
% KURTOSIS, Root-Mean-Square (RMS), ENTROPY
%
% $Id: y2res.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2005,2008 by Alois Schloegl
% This is part of the TSA-toolbox
% http://octave.sourceforge.net/
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
%
% 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 .
[R.SUM, R.N, R.SSQ] = sumskipnan(Y,1);
%R.S3P = sumskipnan(Y.^3,1);
R.S4P = sumskipnan(Y.^4,1);
%R.S5P = sumskipnan(Y.^5,1);
R.MEAN = R.SUM./R.N;
R.MSQ = R.SSQ./R.N;
R.RMS = sqrt(R.MSQ);
R.SSQ0 = R.SSQ-R.SUM.*R.MEAN; % sum square of mean removed
if 1,%flag_implicit_unbiased_estim,
n1 = max(R.N-1,0); % in case of n=0 and n=1, the (biased) variance, STD and STE 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;
R.CM2 = R.SSQ0./n1;
R.Max = max(Y,[],1);
R.Min = min(Y,[],1);
%R.NormEntropy = log2(sqrt(2*pi*exp(1)))+log2(R.STD);
Q0500=repmat(nan,1,size(Y,2));
Q0250=Q0500;
Q0750=Q0500;
%MODE=Q0500;
for k = 1:size(Y,2),
tmp = sort(Y(:,k));
Q0250(k) = flix(tmp,R.N(k)/4 + 0.75);
Q0500(k) = flix(tmp,R.N(k)/2 + 0.50);
Q0750(k) = flix(tmp,R.N(k)*3/4 + 0.25);
tmp = diff(tmp);
pdf = diff([0; find(tmp>0); R.N(k)])/R.N(k); % empirical probability distribution
R.ENTROPY(k) = -sumskipnan(pdf.*log(pdf));
tmp = tmp(find(tmp));
q = min(tmp);
qerror = 0;
if isempty(q),
q = NaN;
else
tmp = tmp/q;
qerror = max(abs(tmp-round(tmp)));
end;
R.QUANT(k) = q;
R.Qerror(k) = qerror;
end;
if any(R.Qerror*1e6>R.QUANT)
warning('(Y2RES) Quantization might not be equidistant')
end;
R.MEDIAN = Q0500;
R.Quartiles = [Q0250; Q0750];
% R.IQR = H_spread = [Q0750 - Q0250];
R.TRIMEAN = [Q0250 + 2*Q0500 + Q0750]/4;
Y = Y - repmat(R.MEAN,size(Y)./size(R.MEAN));
R.CM3 = sumskipnan(Y.^3,1)./n1;
R.CM4 = sumskipnan(Y.^4,1)./n1;
%R.CM5 = sumskipnan(Y.^5,1)./n1;
R.SKEWNESS = R.CM3./(R.STD.^3);
R.KURTOSIS = R.CM4./(R.VAR.^2)-3;
%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.datatype = 'STAT Level 4';
tsa/inst/histo2.m 0000664 0023567 0023567 00000005633 12115040716 014424 0 ustar schloegl schloegl function R = histo2(Y, W)
% HISTO2 calculates histogram for multiple columns with separate bin values
% for each data column.
%
% R = HISTO2(Y)
% R = HISTO2(Y, W)
% Y data
% W weight vector containing weights of each sample,
% number of rows of Y and W must match.
% default W=[] indicates that each sample is weighted with 1.
%
% R = HISTO(...)
% R is a struct with th fields
% R.X the bin-values, bin-values are computed separately for each
% data column, thus R.X is a matrix, each column contains the
% the bin values of for each data column, unused elements are indicated with NaN.
% In order to have common bin values, use HISTO3.
% R.H is the frequency of occurence of value X
% R.N are the number of valid (not NaN) samples (i.e. sum of weights)
%
% more histogram-based results can be obtained by HIST2RES2
%
% see also: HISTO, HISTO2, HISTO3, HISTO4
%
% REFERENCE(S):
% C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
% $Id: histo2.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2002,2008,2011 by Alois Schloegl
% This is part of the TSA-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
%
% 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 .
%%%%% check input arguments %%%%%
[yr,yc] = size(Y);
if nargin < 2,
W = [];
end;
if ~isempty(W) && (yr ~= numel(W)),
error('number of rows of Y does not match number of elements in W');
end;
%%%%% identify all possible X's and generate overall Histogram %%%%%
N = sum(~isnan(Y), 1);
NN = N;
if isempty(W)
sY = sort(Y,1);
else
[sY, idx] = sort(Y,1);
W = cumsum(W(idx)); %% W becomes cumulative sum
end;
[ix,iy] = find( diff(sY, [], 1) > 0);
nn0 = 0;
for k = 1:yc,
tmp = [ix(iy==k); N(k)];
nn1 = length(tmp);
if isempty(W)
H(1:nn1,k) = [tmp(1); diff(tmp)];
else
%%% Note that W is the cumulative sum
H(1:nn1,k) = [W(tmp(1),k); diff(W(tmp,k))];
NN(k) = W(N(k), k);
end;
X(1:nn1, k) = sY(tmp, k);
if k==1;
nn0 = nn1;
elseif nn1 < nn0,
H (1+nn1:nn0, k) = NaN;
X (1+nn1:nn0, k) = NaN;
elseif nn1 > nn0,
H (1+nn0:nn1, 1:k-1) = NaN;
X (1+nn0:nn1, 1:k-1) = NaN;
nn0 = nn1;
end;
end;
R.datatype = 'HISTOGRAM';
R.H = H;
R.X = X;
R.N = NN;
tsa/inst/ucp.m 0000664 0023567 0023567 00000003160 12115040716 013774 0 ustar schloegl schloegl function b=ucp(c)
% UCP(C) tests if the polynomial C is a Unit-Circle-Polynomial.
% It tests if all roots lie inside the unit circle like
% B=ucp(C) does the same as
% B=all(abs(roots(C))<1) but much faster.
% The Algorithm is based on the Jury-Scheme.
% C are the elements of the Polynomial
% C(1)*X^N + ... + C(N)*X + C(N+1).
%
% REFERENCES:
% O. Foellinger "Lineare Abtastsysteme", Oldenburg Verlag, Muenchen, 1986.
% F. Gausch "Systemtechnik", Textbook, University of Technology Graz, 1993.
% $Id: ucp.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-1999,2008 by Alois Schloegl
%
% 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 .
[lr,lc] = size(c);
% JURY-Scheme
b=ones(lr,1);
lambda=zeros(lr,1);
while (lc > 1),
lambda = c(:,lc)./c(:,1);
% disp([lc,size(lambda), sum(b),toc]);
% ratio must be less then 1
b = b & (abs(lambda) < 1);
% and reduced polynomial must be a UCP, too.
c(:,1:lc-1) = c(:,1:lc-1) - lambda(:,ones(1,lc-1)).*c(:,lc:-1:2);
lc = lc-1;
end;
tsa/inst/flag_implicit_samplerate.m 0000664 0023567 0023567 00000004155 12115040716 020232 0 ustar schloegl schloegl function DIM=flag_implicit_samplerate(i)
% The use of FLAG_IMPLICIT_SAMPLERATE is in experimental state.
% FLAG_IMPLICIT_SAMPLERATE might even become obsolete.
% Do not use it.
% FLAG_IMPLICIT_SAMPLERATE sets and gets default mode for handling NaNs
% The default DIM argument is stored in the global variable FLAG_implicit_samplerate
% The idea is that the DIM-argument is not necessary. This might enable
% more readable code.
%
% flag_implicit_samplerate(0)
% calculation along first non-singleton dimension
% flag_implicit_samplerate(1)
% calculation along columns
% flag_implicit_samplerate(2)
% calculation along rows
%
% DIM = flag_implicit_samplerate()
% gets default mode
%
% flag_implicit_samplerate(DIM)
% sets default DIM
%
% DIM = flag_implicit_samplerate(DIM)
% gets and sets DIM
%
% features:
% - compatible to Matlab and Octave
%
% see also: SINVEST1
% $Id: flag_implicit_samplerate.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 2000-2002,2008 by Alois Schloegl
%
% 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 .
global FLAG_implicit_samplerate;
%%% check whether FLAG was already defined
if exist('FLAG_implicit_samplerate')~=1,
FLAG_implicit_samplerate = 1;
end;
if isempty(FLAG_implicit_samplerate),
FLAG_implicit_samplerate = 1;
end;
if nargin>0,
fprintf(2,'Warning: FLAG_IMPLICIT_SAMPLERATE is in an experimental state\n');
fprintf(2,'It might become obsolete.\n');
FLAG_implicit_samplerate = i;
end;
DIM = FLAG_implicit_samplerate;
tsa/inst/poly2ac.m 0000664 0023567 0023567 00000003204 12115040716 014555 0 ustar schloegl schloegl function ACF=poly2ac(a,efinal)
% converts an AR polynomial into an autocorrelation sequence
% [R] = poly2ac(a [,efinal] );
%
% see also ACOVF ACORF AR2RC RC2AR DURLEV AC2POLY, POLY2RC, RC2POLY, RC2AC, AC2RC, POLY2AC
%
% $Id: poly2ac.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
fprintf(2,'ERROR: POLY2AC does not work yet. Sorry\n');
return;
mfilename='POLY2AC';
if ~exist('rc2ar','file')
fprintf(2,'Error %s: RC2AR.M not found. \n Download TSA toolbox from http://pub.ist.ac.at/~schloegl/matlab/tsa/\n',mfilename);
return;
end;
[AR,RC,PE] = ar2rc(poly2ar(a));
%[AR,RC,PE,ACF] = rc2ar(RC);
if nargin<2, efinal=PE(:,size(PE,2)); end;
ACF=zeros(size(a));
ACF(:,1) = 1;
for k=2:size(a,2),
ACF(:,k)=sum(AR(:,1:k-1).*ACF(:,k-(1:k-1)),2);
end;
R0=(sum(AR(:,1:k-1).*ACF(:,2:size(ACF,2)),2)+1).*efinal; %PE(:,size(PE,2));
ACF = ACF.*R0(:,ones(1,size(ACF,2)));
%ACF=ACF*efinal*PE(1)/PE(length(PE));
tsa/inst/selmo2.m 0000664 0023567 0023567 00000004174 12115040716 014414 0 ustar schloegl schloegl function X = selmo2(y,Pmax);
% SELMO2 - model order selection for univariate and multivariate
% autoregressive models
%
% X = selmo(y,Pmax);
%
% y data series
% Pmax maximum model order
% X.A, X.B, X.C parameters of AR model
% X.OPT... various optimization criteria
%
% see also: SELMO, MVAR,
% $Id: selmo2.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 2007 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sourceforge.net/
% http://biosig.sourceforge.net/
%
% 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 .
[M,N]=size(y);
if M>N,
y=y';
end;
[M,N]=size(y);
% Univariate AR
[AutoCov, AutoCorr, ARPMX, E, NC] = invest0(y,Pmax);
[FPE,AIC,BIC,SBC,MDL,CATcrit,PHI,optFPE,optAIC,optBIC,optSBC,optMDL,optCAT,optPHI,s,C] = selmo(E,NC);
% AIC for MVAR model
[AR,RC,PE] = mvar(y',Pmax);
E2 = repmat(NaN,1,Pmax);
for k = 0:Pmax,
S = PE(:,k*M+(1:M));
E2(k+1) = trace(S);
%%%% FIX ME %%%%% this does not seem right because it depends on the scaling of y
AIC_MV(k+1) = 2*log(det(S))+2*k*M*M/N; % Ding et al. 2000 refering to Akaike 1974
end;
X.A = [eye(M),-AR];
X.B = eye(M);
X.C = S;
X.datatype = 'MVAR';
X.MV.AIC = AIC_MV;
X.UV.FPE = FPE;
X.UV.AIC = AIC;
X.UV.BIC = BIC;
X.UV.MDL = MDL;
X.UV.CAT = CATcrit;
X.UV.PHI = PHI;
X.OPT.FPE = optFPE;
X.OPT.AIC = optAIC;
X.OPT.BIC = optBIC;
X.OPT.MDL = optMDL;
X.OPT.CAT = optCAT;
X.OPT.PHI = optPHI;
[tmp,X.OPT.MVAIC] = min(AIC_MV);
tsa/inst/amarma.m 0000664 0023567 0023567 00000017622 12115045313 014451 0 ustar schloegl schloegl function [z,e,REV,ESU,V,Z,SPUR] = amarma(y, Mode, MOP, UC, z0, Z0, V0, W);
% Adaptive Mean-AutoRegressive-Moving-Average model estimation
% [z,e,ESU,REV,V,Z,SPUR] = amarma(y, mode, MOP, UC, z0, Z0, V0, W);
%
% Estimates model parameters (mean and AR) with Kalman filter algorithm
% y(t) = sum_i(a(i,t)*y(t-i)) + mu(t) + e(t)
% or the more general adaptive mean-autoregressive-moving-avarage parameters
% y(t) = sum_i(a(i,t)*y(t-i)) + mu(t) + e(t) + sum_i(b(i,t)*e(t-i))
%
% State space model:
% z(t)=G*z(t-1) + w(t) w(t)=N(0,W)
% y(t)=H*z(t) + v(t) v(t)=N(0,V)
%
% G = I, (identity matrix)
% z = [mu(t)/(1-sum_i(a(i,t))),a_1(t-1),..,a_p(t-p),b_1(t-1),...,b_q(t-q)];
% H = [1,y(t-1),..,y(t-p),e(t-1),...,e(t-q)];
% W = E{(z(t)-G*z(t-1))*(z(t)-G*z(t-1))'}
% V = E{(y(t)-H*z(t-1))*(y(t)-H*z(t-1))'}
% v = e
%
% Input:
% y Signal (AR-Process)
% Mode
% [0,0] uses V0 and W
%
% MOP Model order [m,p,q], default [0,10,0]
% m=1 includes the mean term, m=0 does not.
% p and q must be positive integers
% it is recommended to set q=0 (i.e. no moving average part)
% because the optimization problem for ARMA models is
% non-linear and can have local optima.
% UC Update Coefficient, default 0
% z0 Initial state vector
% Z0 Initial Covariance matrix
%
% Output:
% z mean-autoregressive-moving-average-parameter
% mu(t) = z(t,1:m) adaptive mean
% a(t,:) = z(t,m+[1:p]) adaptive autoregressive parameters
% b(t,:) = z(t,m+p+[1:q]) adaptive moving average parameters
% e error process (Adaptively filtered process)
% REV relative error variance MSE/MSY
%
%
% see also: AAR
%
% REFERENCE(S):
% [1] A. Schlögl (2000), The electroencephalogram and the adaptive autoregressive model: theory and applications.
% ISBN 3-8265-7640-3 Shaker Verlag, Aachen, Germany.
% [2] Schlögl A, Lee FY, Bischof H, Pfurtscheller G
% Characterization of Four-Class Motor Imagery EEG Data for the BCI-Competition 2005.
% Journal of neural engineering 2 (2005) 4, S. L14-L22
% [3] A. Schlögl , J. Fortin, W. Habenbacher, M. Akay.
% Adaptive mean and trend removal of heart rate variability using Kalman filtering
% Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society,
% 25-28 Oct. 2001, Paper #1383, ISBN 0-7803-7213-1.
%
% More references can be found at
% http://pub.ist.ac.at/~schloegl/publications/
% $Id: amarma.m 11694 2013-03-04 07:18:35Z schloegl $
% Copyright (C) 1998-2002,2005,2006,2007,2008 by Alois Schloegl
%
% 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 .
[nc,nr]=size(y);
if nargin<2 Mode=0;
elseif isnan(Mode) return; end;
if nargin<3, MOP=[0,10,0]; end;
if nargin<8, W = nan ; end;
if length(MOP)==0, m=0;p=10; q=0; MOP=p;
elseif length(MOP)==1, m=0;p=MOP(1); q=0; MOP=p;
elseif length(MOP)==2, fprintf(1,'Error AMARMA: MOP is ambiguos\n');
elseif length(MOP)>2, m=MOP(1); p=MOP(2); q=MOP(3);MOP=m+p+q;
end;
if prod(size(Mode))>1
aMode=Mode(1);
eMode=Mode(2);
end;
%fprintf(1,['a' int2str(aMode) 'e' int2str(eMode) ' ']);
e = zeros(nc,1);
V = zeros(nc,1);V(1)=V0;
T = zeros(nc,1);
ESU = zeros(nc,1)+nan;
SPUR = zeros(nc,1)+nan;
z = z0(ones(nc,1),:);
dW = UC/MOP*eye(MOP); % Schloegl
%------------------------------------------------
% First Iteration
%------------------------------------------------
H = zeros(MOP,1);
if m,
%M0 = z0(1)/(1-sum(z0(2:p+1))); %transformierter Mittelwert
H(1) = 1;%M0;
%z0(1)= 1;
end;
Z = Z0;
zt= z0;
A1 = zeros(MOP); A2 = A1;
%------------------------------------------------
% Update Equations
%------------------------------------------------
for t=1:nc,
%H=[y(t-1); H(1:p-1); E ; H(p+1:MOP-1)]
if t<=p, H(m+(1:t-1)) = y(t-1:-1:1); %H(p)=mu0; % Autoregressive
else H(m+(1:p)) = y(t-1:-1:t-p); %mu0];
end;
if t<=q, H(m+p+(1:t-1)) = e(t-1:-1:1); % Moving Average
else H(m+p+(1:q)) = e(t-1:-1:t-q);
end;
% Prediction Error
E = y(t) - zt*H;
e(t) = E;
if ~isnan(E),
E2 = E*E;
AY = Z*H;
% [zt, t, y(t), E,ESU(t),V(t),H,Z],pause,
ESU(t) = H'*AY;
if eMode==0
V(t) = V0;
elseif eMode==1
V0 = V(t-1);
V(t) = V0*(1-UC)+UC*E2;
elseif eMode==2
V0 = 1;
V(t) = V0; %V(t-1)*(1-UC)+UC*E2;
elseif eMode==3
V0 = 1-UC;
V(t) = V0; %(t-1)*(1-UC)+UC*E2;
elseif eMode==4
V0 = V0*(1-UC)+UC*E2;
V(t) = V0;
elseif eMode==5
V(t)=V0;
%V0 = V0;
elseif eMode==6
if E2>ESU(t)
V0=(1-UC)*V0+UC*(E2-ESU(t));
end;
V(t)=V0;
elseif eMode==7
V0=V(t);
if E2>ESU(t)
V(t) = (1-UC)*V0+UC*(E2-ESU(t));
else
V(t) = V0;
end;
elseif eMode==8
V0=0;
V(t) = V0; % (t-1)*(1-UC)+UC*E2;
end;
%[t,size(H),size(Z)]
k = AY / (ESU(t) + V0); % Kalman Gain
zt = zt + k'*E;
%z(t,:) = zt;
if aMode==0
%W = W; %nop % Schloegl et al. 2003
elseif aMode==2
T(t)=(1-UC)*T(t-1)+UC*(E2-Q(t))/(H'*H); % Roberts I 1998
Z=Z*V(t-1)/Q(t);
if T(t)>0 W=T(t)*eye(MOP); else W=zeros(MOP);end;
elseif aMode==5
Q_wo = (H'*C*H + V(t-1)); % Roberts II 1998
T(t)=(1-UC)*T(t-1)+UC*(E2-Q_wo)/(H'*H);
if T(t)>0 W=T(t)*eye(MOP); else W=zeros(MOP); end;
elseif aMode==6
T(t)=(1-UC)*T(t-1)+UC*(E2-Q(t))/(H'*H);
Z=Z*V(t)/Q(t);
if T(t)>0 W=T(t)*eye(MOP); else W=zeros(MOP); end;
elseif aMode==11
%Z = Z - k*AY';
W = sum(diag(Z))*dW;
elseif aMode==12
W = UC*UC*eye(MOP);
elseif aMode==13
W = UC*diag(diag(Z));
elseif aMode==14
W = (UC*UC)*diag(diag(Z));
elseif aMode==15
W = sum(diag(Z))*dW;
elseif aMode==16
W = UC*eye(MOP); % Schloegl 1998
elseif aMode==17
Z = 0.5*(Z+Z');
W = UC*Z;
elseif aMode==18
W = 0.5*UC*(Z+Z');
%W=W;
end;
Z = Z - k*AY'; % Schloegl 1998
else
V(t) = V0;
end;
if any(any(isnan(W))), W=UC*Z; end;
z(t,:) = zt;
Z = Z + W; % Schloegl 1998
SPUR(t)=trace(Z);
end;
if 0,m,
z(:,1)=M0*z(:,1)./(1-sum(z(:,2:p),2));
end;
REV = mean(e.*e)/mean(y.*y);
if any(~isfinite(Z(:))), REV=inf; end;
tsa/inst/rmle.m 0000664 0023567 0023567 00000005550 12115040716 014151 0 ustar schloegl schloegl function [a,VAR,S,a_aux,b_aux,e_aux,MLE,pos] = rmle(arg1,arg2);
% RMLE estimates AR Parameters using the Recursive Maximum Likelihood
% Estimator according to [1]
%
% Use: [a,VAR]=rmle(x,p)
% Input:
% x is a column vector of data
% p is the model order
% Output:
% a is a vector with the AR parameters of the recursive MLE
% VAR is the excitation white noise variance estimate
%
% Reference(s):
% [1] Kay S.M., Modern Spectral Analysis - Theory and Applications.
% Prentice Hall, p. 232-233, 1988.
%
% $Id: rmle.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 2004 by Jose Luis Gutierrez
% Grupo GENESIS - UTN - Argentina
%
% 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 .
x=arg1*1e-6;
p=arg2;
N=length(x);
S=zeros(p+1,p+1);
a_aux=zeros(p+1,p);, a_aux(1,:)=1;
b_aux=ones(p+1,p);
e_aux=zeros(p,1);, p_aux=zeros(p,1);
MLE=zeros(3,1);
pos=1;
for i=0:p
for j=0:p
for n=0:N-1-i-j
S(i+1,j+1)=S(i+1,j+1)+x(n+1+i)*x(n+1+j);
end
end
end
e0=S(1,1);
c1=S(1,2);
d1=S(2,2);
coef3=1;
coef2=((N-2)*c1)/((N-1)*d1);
coef1=-(e0+N*d1)/((N-1)*d1);
ti=-(N*c1)/((N-1)*d1);
raices=roots([coef3 coef2 coef1 ti]);
for o=1:3
if raices(o)>-1 && raices(o)<1
a_aux(2,1)=raices(o);
b_aux(p+1,1)=raices(o);
end
end
e_aux(1,1)=S(1,1)+2*a_aux(2,1)*S(1,2)+(a_aux(2,1)^2)*S(2,2);
p_aux(1,1)=e_aux(1,1)/N;
for k=2:p
Ck=S(1:k,2:k+1);
Dk=S(2:k+1,2:k+1);
ck=a_aux(1:k,k-1)'*Ck*b_aux(p+1:-1:p+2-k,k-1);
dk=b_aux(p+1:-1:p+2-k,k-1)'*Dk*b_aux(p+1:-1:p+2-k,k-1);
coef3re=1;
coef2re=((N-2*k)*ck)/((N-k)*dk);
coef1re=-(k*e_aux(k-1,1)+N*dk)/((N-k)*dk);
tire=-(N*ck)/((N-k)*dk);
raices=roots([coef3re coef2re coef1re tire]);
for o=1:3
if raices(o,1)>-1 && raices(o,1)<1
MLE(o,1)=((1-raices(o)^2)^(k/2))/(((e_aux(k-1)+2*ck*raices(o)+dk*(raices(o)^2))/N)^(N/2));
end
end
[C,I]=max(MLE);
k_max=raices(I);
for i=1:k-1
a_aux(i+1,k)=a_aux(i+1,k-1)+k_max*a_aux(k-i+1,k-1);
end
a_aux(k+1,k)=k_max;
b_aux(p+1-k:p+1,k)=a_aux(1:k+1,k);
e_aux(k,1)=e_aux(k-1,1)+2*ck*k_max+dk*k_max^2;
p_aux(k,1)=e_aux(k,1)/N;
end
a=a_aux(:,p)';
VAR=p_aux(p)*1e12;
tsa/inst/pacf.m 0000664 0023567 0023567 00000004142 12115040716 014117 0 ustar schloegl schloegl function [PARCOR,sig,cil,ciu]= pacf(Z,KMAX);
% Partial Autocorrelation function
% [parcor,sig,cil,ciu] = pacf(Z,N);
%
% Input:
% Z Signal, each row is analysed
% N # of coefficients
% Output:
% parcor autocorrelation function
% sig p-value for significance test
% cil lower confidence interval
% ciu upper confidence interval
%
% see also: DURLEV, LATTICE, AC2RC, AR2RC,
% FLAG_IMPLICIT_SIGNIFICANCE
% $Id: pacf.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1997-2002,2008 by Alois Schloegl
%
% 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 .
[nr,nc] = size(Z);
if nc
%
% 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 .
if all(size(a))>1,
fprintf(2,'Error poly2rc: "a" must be a vector\n');
return;
end;
a=a(:).';
mfilename='POLY2RC';
if ~exist('ar2rc','file')
fprintf(2,'Error %s: AR2RC.M not found. \n Download TSA toolbox from http://pub.ist.ac.at/~schloegl/matlab/tsa/\n',mfilename);
return;
end;
if nargin<2, efinal=1; end;
[AR,RC,PE] = ar2rc(poly2ar(a));
if nargout>1,
r0=efinal.*PE(:,1)./PE(:,size(PE,2));
end;
tsa/inst/flag_implicit_skip_nan.m 0000664 0023567 0023567 00000005063 12264575422 017712 0 ustar schloegl schloegl function FLAG = flag_implicit_skip_nan(i)
% FLAG_IMPLICIT_SKIP_NAN sets and gets default mode for handling NaNs
% 1 skips NaN's (the default mode if no mode is set)
% 0 NaNs are propagated; input NaN's give NaN's at the output
%
% FLAG = flag_implicit_skip_nan()
% gets current mode
%
% flag_implicit_skip_nan(FLAG)
% sets mode
%
% prevFLAG = flag_implicit_skip_nan(nextFLAG)
% gets previous set FLAG and sets FLAG for the future
% flag_implicit_skip_nan(prevFLAG)
% resets FLAG to previous mode
%
% It is used in:
% SUMSKIPNAN, MEDIAN, QUANTILES, TRIMEAN
% and affects many other functions like:
% CENTER, KURTOSIS, MAD, MEAN, MOMENT, RMS, SEM, SKEWNESS,
% STATISTIC, STD, VAR, ZSCORE etc.
%
% The mode is stored in the global variable FLAG_implicit_skip_nan
% It is recommended to use flag_implicit_skip_nan(1) as default and
% flag_implicit_skip_nan(0) should be used for exceptional cases only.
% This feature might disappear without further notice, so you should really not
% rely on it.
% 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
% $Id: flag_implicit_skip_nan.m 8351 2011-06-24 17:35:07Z carandraug $
% Copyright (C) 2001-2003,2009 by Alois Schloegl
% This function is part of the NaN-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/NaN/
persistent 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
end;
FLAG = FLAG_implicit_skip_nan;
if nargin>0,
FLAG_implicit_skip_nan = (i~=0); %logical(i); %logical.m not available in 2.0.16
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;
tsa/inst/rc2ac.m 0000664 0023567 0023567 00000002761 12115040716 014205 0 ustar schloegl schloegl function ACF=rc2ac(RC,R0)
% converts reflection coefficients to autocorrelation sequence
% [R] = rc2ac(RC,R0);
%
% see also ACOVF ACORF AR2RC RC2AR DURLEV AC2POLY, POLY2RC, RC2POLY, RC2AC, AC2RC, POLY2AC
%
% $Id: rc2ac.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
fprintf(2,'ERROR: RC2AC does not work yet. Sorry\n');
return;
if all(size(RC)>1),
fprintf(2,'Error RC2AC: "K" must be a vector\n');
return;
end;
mfilename='RC2AC';
if ~exist('rc2ar','file')
fprintf(2,'Error %s: RC2AR.M not found. \n Download TSA toolbox from http://pub.ist.ac.at/~schloegl/matlab/tsa/\n',mfilename);
return;
end;
[AR,RC,PE] = rc2ar(RC(:).');
ACF=cumprod(ones(size(RC))-RC.^2,2);
ACF = ACF./ACF(:,ones(1,size(ACF,2)));
if nargin>1
ACF=ACF*R0;
end;
tsa/inst/arcext.m 0000664 0023567 0023567 00000004235 12115040716 014477 0 ustar schloegl schloegl function [AR,RC] = arcext(MX,P);
% ARCEXT extracts AR and RC of order P from Matrix MX
% function [AR,RC] = arcext(MX,P);
%
% INPUT:
% MX AR and RC matrix calculated by durlev
% P model order (default maximum possible)
%
% OUTPUT
% AR autoregressive model parameter
% RC reflection coefficients (= -PARCOR coefficients)
%
% All input and output parameters are organized in rows, one row
% corresponds to the parameters of one channel
%
% see also ACOVF ACORF DURLEV
%
% REFERENCES:
% P.J. Brockwell and R. A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% S. Haykin "Adaptive Filter Theory" 3rd ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% $Id: arcext.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2003,2008,2012 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sourceforge.net/
% http://biosig.sourceforge.net/
%
% 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 .
[lr,lc]=size(MX);
if ~mod(sqrt(8*lc+1)-1,2) % full number of elements
K = (sqrt(8*lc+1)-1)/2;
elseif ~mod(lc,2), % compressed form of MX
K = lc/2;
else % invalid number of elements
fprintf(2,'Warning ARCEXT: Number of elements is different than a triangular matrix\n');
end;
if (K~=P) && (lc~=K*(K+1)/2),
[AR,RC,PE]=rc2ar(MX(:,(1:P).*(2:P+1)/2));
else
AR = MX(:,P*(P-1)/2+(1:P));
RC = MX(:,(1:P).*(2:P+1)/2);
end;
tsa/inst/mvar.m 0000664 0023567 0023567 00000116534 12115040716 014164 0 ustar schloegl schloegl function [ARF,RCF,PE,DC,varargout] = mvar(Y, Pmax, Mode);
% MVAR estimates parameters of the Multi-Variate AutoRegressive model
%
% Y(t) = Y(t-1) * A1 + ... + Y(t-p) * Ap + X(t);
% whereas
% Y(t) is a row vecter with M elements Y(t) = y(t,1:M)
%
% Several estimation algorithms are implemented, all estimators
% can handle data with missing values encoded as NaNs.
%
% [AR,RC,PE] = mvar(Y, p);
% [AR,RC,PE] = mvar(Y, p, Mode);
%
% with
% AR = [A1, ..., Ap];
%
% INPUT:
% Y Multivariate data series
% p Model order
% Mode determines estimation algorithm
%
% OUTPUT:
% AR multivariate autoregressive model parameter
% RC reflection coefficients (= -PARCOR coefficients)
% PE remaining error variances for increasing model order
% PE(:,p*M+[1:M]) is the residual variance for model order p
%
% All input and output parameters are organized in columns, one column
% corresponds to the parameters of one channel.
%
% Mode determines estimation algorithm.
% 1: Correlation Function Estimation method using biased correlation function estimation method
% also called the 'multichannel Yule-Walker' [1,2]
% 6: Correlation Function Estimation method using unbiased correlation function estimation method
%
% 2: Partial Correlation Estimation: Vieira-Morf [2] using unbiased covariance estimates.
% In [1] this mode was used and (incorrectly) denominated as Nutall-Strand.
% Its the DEFAULT mode; according to [1] it provides the most accurate estimates.
% 5: Partial Correlation Estimation: Vieira-Morf [2] using biased covariance estimates.
% Yields similar results than Mode=2;
%
% 3: buggy: Partial Correlation Estimation: Nutall-Strand [2] (biased correlation function)
% 9: Partial Correlation Estimation: Nutall-Strand [2] (biased correlation function)
% 7: Partial Correlation Estimation: Nutall-Strand [2] (unbiased correlation function)
% 8: Least Squares w/o nans in Y(t):Y(t-p)
% 10: ARFIT [3]
% 11: BURGV [4]
% 13: modified BURGV -
% 14: modified BURGV [4]
% 15: Least Squares based on correlation matrix
% 22: Modified Partial Correlation Estimation: Vieira-Morf [2,5] using unbiased covariance estimates.
% 25: Modified Partial Correlation Estimation: Vieira-Morf [2,5] using biased covariance estimates.
%
% 90,91,96: Experimental versions
%
% Imputation methods:
% 100+Mode:
% 200+Mode: forward, past missing values are assumed zero
% 300+Mode: backward, past missing values are assumed zero
% 400+Mode: forward+backward, past missing values are assumed zero
% 1200+Mode: forward, past missing values are replaced with predicted value
% 1300+Mode: backward, 'past' missing values are replaced with predicted value
% 1400+Mode: forward+backward, 'past' missing values are replaced with predicted value
% 2200+Mode: forward, past missing values are replaced with predicted value + noise to prevent decaying
% 2300+Mode: backward, past missing values are replaced with predicted value + noise to prevent decaying
% 2400+Mode: forward+backward, past missing values are replaced with predicted value + noise to prevent decaying
%
%
%
% REFERENCES:
% [1] A. Schloegl, Comparison of Multivariate Autoregressive Estimators.
% Signal processing, Elsevier B.V. (in press).
% available at http://dx.doi.org/doi:10.1016/j.sigpro.2005.11.007
% [2] S.L. Marple 'Digital Spectral Analysis with Applications' Prentice Hall, 1987.
% [3] Schneider and Neumaier)
% [4] Stijn de Waele, 2003
% [5] M. Morf, et al. Recursive Multichannel Maximum Entropy Spectral Estimation,
% IEEE trans. GeoSci. Elec., 1978, Vol.GE-16, No.2, pp85-94.
%
%
% A multivariate inverse filter can be realized with
% [AR,RC,PE] = mvar(Y,P);
% e = mvfilter([eye(size(AR,1)),-AR],eye(size(AR,1)),Y);
%
% see also: MVFILTER, MVFREQZ, COVM, SUMSKIPNAN, ARFIT2
% $Id: mvar.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2006,2011,2012 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sourceforge.net/
% http://biosig.sourceforge.net/
%
% 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 .
% Inititialization
[N,M] = size(Y);
if nargin<2,
Pmax=max([N,M])-1;
end;
if iscell(Y)
Pmax = min(max(N ,M ),Pmax);
C = Y;
end;
if nargin<3,
% according to [1], and other internal validations this is in many cases the best algorithm
Mode=2;
end;
[C(:,1:M),n] = covm(Y,'M');
PE(:,1:M) = C(:,1:M)./n;
if 0,
elseif Mode==0; % this method is broken
fprintf('Warning MVAR: Mode=0 is broken.\n')
C(:,1:M) = C(:,1:M)/N;
F = Y;
B = Y;
PEF = C(:,1:M); %?% PEF = PE(:,1:M);
PEB = C(:,1:M);
for K=1:Pmax,
[D,n] = covm(Y(K+1:N,:),Y(1:N-K,:),'M');
D = D/N;
ARF(:,K*M+(1-M:0)) = D/PEB;
ARB(:,K*M+(1-M:0)) = D'/PEF;
tmp = F(K+1:N,:) - B(1:N-K,:)*ARF(:,K*M+(1-M:0))';
B(1:N-K,:) = B(1:N-K,:) - F(K+1:N,:)*ARB(:,K*M+(1-M:0))';
F(K+1:N,:) = tmp;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
PEF = [eye(M) - ARF(:,K*M+(1-M:0))*ARB(:,K*M+(1-M:0))]*PEF;
PEB = [eye(M) - ARB(:,K*M+(1-M:0))*ARF(:,K*M+(1-M:0))]*PEB;
PE(:,K*M+(1:M)) = PEF;
end;
elseif Mode==1, %%%%% Levinson-Wiggens-Robinson (LWR) algorithm using biased correlation function
% ===== In [1,2] this algorithm is denoted 'Multichannel Yule-Walker' ===== %
C(:,1:M) = C(:,1:M)/N;
PEF = C(:,1:M);
PEB = C(:,1:M);
for K=1:Pmax,
[C(:,K*M+(1:M)),n] = covm(Y(K+1:N,:),Y(1:N-K,:),'M');
C(:,K*M+(1:M)) = C(:,K*M+(1:M))/N;
D = C(:,K*M+(1:M));
for L = 1:K-1,
D = D - ARF(:,L*M+(1-M:0))*C(:,(K-L)*M+(1:M));
end;
ARF(:,K*M+(1-M:0)) = D / PEB;
ARB(:,K*M+(1-M:0)) = D'/ PEF;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
PEF = [eye(M) - ARF(:,K*M+(1-M:0))*ARB(:,K*M+(1-M:0))]*PEF;
PEB = [eye(M) - ARB(:,K*M+(1-M:0))*ARF(:,K*M+(1-M:0))]*PEB;
PE(:,K*M+(1:M)) = PEF;
end;
elseif Mode==6, %%%%% Levinson-Wiggens-Robinson (LWR) algorithm using unbiased correlation function
C(:,1:M) = C(:,1:M)/N;
PEF = C(:,1:M); %?% PEF = PE(:,1:M);
PEB = C(:,1:M);
for K = 1:Pmax,
[C(:,K*M+(1:M)),n] = covm(Y(K+1:N,:),Y(1:N-K,:),'M');
C(:,K*M+(1:M)) = C(:,K*M+(1:M))./n;
%C{K+1} = C{K+1}/N;
D = C(:,K*M+(1:M));
for L = 1:K-1,
D = D - ARF(:,L*M+(1-M:0))*C(:,(K-L)*M+(1:M));
end;
ARF(:,K*M+(1-M:0)) = D / PEB;
ARB(:,K*M+(1-M:0)) = D'/ PEF;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
PEF = [eye(M) - ARF(:,K*M+(1-M:0))*ARB(:,K*M+(1-M:0))]*PEF;
PEB = [eye(M) - ARB(:,K*M+(1-M:0))*ARF(:,K*M+(1-M:0))]*PEB;
PE(:,K*M+(1:M)) = PEF;
end;
elseif Mode==2 %%%%% Partial Correlation Estimation: Vieira-Morf Method [2] with unbiased covariance estimation
%===== In [1] this algorithm is denoted 'Nutall-Strand with unbiased covariance' =====%
%C(:,1:M) = C(:,1:M)/N;
F = Y;
B = Y;
%PEF = C(:,1:M);
%PEB = C(:,1:M);
PEF = PE(:,1:M);
PEB = PE(:,1:M);
for K = 1:Pmax,
[D,n] = covm(F(K+1:N,:),B(1:N-K,:),'M');
D = D./n;
ARF(:,K*M+(1-M:0)) = D / PEB;
ARB(:,K*M+(1-M:0)) = D'/ PEF;
tmp = F(K+1:N,:) - B(1:N-K,:)*ARF(:,K*M+(1-M:0)).';
B(1:N-K,:) = B(1:N-K,:) - F(K+1:N,:)*ARB(:,K*M+(1-M:0)).';
F(K+1:N,:) = tmp;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
[PEF,n] = covm(F(K+1:N,:),F(K+1:N,:),'M');
PEF = PEF./n;
[PEB,n] = covm(B(1:N-K,:),B(1:N-K,:),'M');
PEB = PEB./n;
PE(:,K*M+(1:M)) = PEF;
end;
elseif Mode==5 %%%%% Partial Correlation Estimation: Vieira-Morf Method [2] with biased covariance estimation
%===== In [1] this algorithm is denoted 'Nutall-Strand with biased covariance' ===== %
F = Y;
B = Y;
PEF = C(:,1:M);
PEB = C(:,1:M);
for K=1:Pmax,
[D,n] = covm(F(K+1:N,:),B(1:N-K,:),'M');
%D=D/N;
ARF(:,K*M+(1-M:0)) = D / PEB;
ARB(:,K*M+(1-M:0)) = D'/ PEF;
tmp = F(K+1:N,:) - B(1:N-K,:)*ARF(:,K*M+(1-M:0)).';
B(1:N-K,:) = B(1:N-K,:) - F(K+1:N,:)*ARB(:,K*M+(1-M:0)).';
F(K+1:N,:) = tmp;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
[PEB,n] = covm(B(1:N-K,:),B(1:N-K,:),'M');
%PEB = D/N;
[PEF,n] = covm(F(K+1:N,:),F(K+1:N,:),'M');
%PEF = D/N;
%PE(:,K*M+(1:M)) = PEF;
PE(:,K*M+(1:M)) = PEF./n;
end;
elseif 0, Mode==3 %%%%% Partial Correlation Estimation: Nutall-Strand Method [2] with biased covariance estimation
%%% OBSOLETE because its buggy, use Mode=9 instead.
% C(:,1:M) = C(:,1:M)/N;
F = Y;
B = Y;
PEF = C(:,1:M);
PEB = C(:,1:M);
for K=1:Pmax,
[D, n] = covm(F(K+1:N,:),B(1:N-K,:),'M');
D = D./N;
ARF(:,K*M+(1-M:0)) = 2*D / (PEB+PEF);
ARB(:,K*M+(1-M:0)) = 2*D'/ (PEF+PEB);
tmp = F(K+1:N,:) - B(1:N-K,:)*ARF(:,K*M+(1-M:0)).';
B(1:N-K,:) = B(1:N-K,:) - F(K+1:N,:)*ARB(:,K*M+(1-M:0)).';
F(K+1:N,:) = tmp;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
%RCF{K} = ARF{K};
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
[PEF,n] = covm(F(K+1:N,:),F(K+1:N,:),'M');
PEF = PEF./N;
[PEB,n] = covm(B(1:N-K,:),B(1:N-K,:),'M');
PEB = PEB./N;
%PE(:,K*M+(1:M)) = PEF;
PE(:,K*M+(1:M)) = PEF./n;
end;
elseif Mode==7 %%%%% Partial Correlation Estimation: Nutall-Strand Method [2] with unbiased covariance estimation
F = Y;
B = Y;
PEF = PE(:,1:M);
PEB = PE(:,1:M);
for K = 1:Pmax,
[D] = covm(F(K+1:N,:),B(1:N-K,:),'M');
%D = D./n;
ARF(:,K*M+(1-M:0)) = 2*D / (PEB+PEF);
ARB(:,K*M+(1-M:0)) = 2*D'/ (PEF+PEB);
tmp = F(K+1:N,:) - B(1:N-K,:)*ARF(:,K*M+(1-M:0)).';
B(1:N-K,:) = B(1:N-K,:) - F(K+1:N,:)*ARB(:,K*M+(1-M:0)).';
F(K+1:N,:) = tmp;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
%RCF{K} = ARF{K};
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
[PEF] = covm(F(K+1:N,:),F(K+1:N,:),'M');
%PEF = PEF./n;
[PEB] = covm(B(1:N-K,:),B(1:N-K,:),'M');
%PEB = PEB./n;
%PE{K+1} = PEF;
PE(:,K*M+(1:M)) = PEF;
end;
elseif any(Mode==[3,9]) %%%%% Partial Correlation Estimation: Nutall-Strand Method [2] with biased covariance estimation
%% same as 3 but with fixed normalization
F = Y;
B = Y;
PEF = C(:,1:M);
PEB = C(:,1:M);
for K=1:Pmax,
[D, n] = covm(F(K+1:N,:),B(1:N-K,:),'M');
ARF(:,K*M+(1-M:0)) = 2*D / (PEB+PEF);
ARB(:,K*M+(1-M:0)) = 2*D'/ (PEF+PEB);
tmp = F(K+1:N,:) - B(1:N-K,:)*ARF(:,K*M+(1-M:0)).';
B(1:N-K,:) = B(1:N-K,:) - F(K+1:N,:)*ARB(:,K*M+(1-M:0)).';
F(K+1:N,:) = tmp;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
%RCF{K} = ARF{K};
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
[PEF,nf] = covm(F(K+1:N,:),F(K+1:N,:),'M');
[PEB,nb] = covm(B(1:N-K,:),B(1:N-K,:),'M');
%PE(:,K*M+(1:M)) = PEF;
PE(:,K*M+(1:M)) = PEF./nf;
end;
elseif Mode==4, %%%%% Kay, not fixed yet.
fprintf('Warning MVAR: Mode=4 is broken.\n')
C(:,1:M) = C(:,1:M)/N;
K = 1;
[C(:,M+(1:M)),n] = covm(Y(2:N,:),Y(1:N-1,:));
C(:,M+(1:M)) = C(:,M+(1:M))/N; % biased estimate
D = C(:,M+(1:M));
ARF(:,1:M) = C(:,1:M)\D;
ARB(:,1:M) = C(:,1:M)\D';
RCF(:,1:M) = ARF(:,1:M);
RCB(:,1:M) = ARB(:,1:M);
PEF = C(:,1:M)*[eye(M) - ARB(:,1:M)*ARF(:,1:M)];
PEB = C(:,1:M)*[eye(M) - ARF(:,1:M)*ARB(:,1:M)];
for K=2:Pmax,
[C(:,K*M+(1:M)),n] = covm(Y(K+1:N,:),Y(1:N-K,:),'M');
C(:,K*M+(1:M)) = C(:,K*M+(1:M)) / N; % biased estimate
D = C(:,K*M+(1:M));
for L = 1:K-1,
D = D - C(:,(K-L)*M+(1:M))*ARF(:,L*M+(1-M:0));
end;
ARF(:,K*M+(1-M:0)) = PEB \ D;
ARB(:,K*M+(1-M:0)) = PEF \ D';
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0)) ;
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0)) ;
PEF = PEF*[eye(M) - ARB(:,K*M+(1-M:0)) *ARF(:,K*M+(1-M:0)) ];
PEB = PEB*[eye(M) - ARF(:,K*M+(1-M:0)) *ARB(:,K*M+(1-M:0)) ];
PE(:,K*M+(1:M)) = PEF;
end;
elseif Mode==8, %%%%% Least Squares
ix = Pmax+1:N;
y = repmat(NaN,N-Pmax,M*Pmax);
for k = 1:Pmax,
y(:,k*M+[1-M:0]) = Y(ix-k,:);
end;
ix2 = ~any(isnan([Y(ix,:),y]),2);
ARF = Y(ix(ix2),:)'/y(ix2,:)';
PE = covm(Y(ix,:) - y*ARF');
RCF = zeros(M,M*Pmax);
elseif Mode==10, %%%%% ARFIT
try
RCF = [];
[w, ARF, PE] = arfit(Y, Pmax, Pmax, 'sbc', 'zero');
catch
ARF = zeros(M,M*Pmax);
RCF = ARF;
end;
elseif Mode==11, %%%%% de Waele 2003
%%% OBSOLETE
warning('MVAR: mode=11 is obsolete use Mode 13 or 14!');
[pc,R0] = burgv(reshape(Y',[M,1,N]),Pmax);
try,
[ARF,ARB,Pf,Pb,RCF,RCB] = pc2parv(pc,R0);
catch
[RCF,RCB,Pf,Pb] = pc2rcv(pc,R0);
[ARF,ARB,Pf,Pb] = pc2parv(pc,R0);
end;
ARF = -reshape(ARF(:,:,2:end),[M,M*Pmax]);
RCF = -reshape(RCF(:,:,2:end),[M,M*Pmax]);
PE = reshape(Pf,[M,M*(Pmax+1)]);
elseif Mode==12,
%%% OBSOLETE
warning('MVAR: mode=12 is obsolete use Mode 13 or 14!');
% this is equivalent to Mode==11
[pc,R0] = burgv(reshape(Y',[M,1,N]),Pmax);
[rcf,rcb,Pf,Pb] = pc2rcv(pc,R0);
%%%%% Convert reflection coefficients RC to autoregressive parameters
ARF = zeros(M,M*Pmax);
ARB = zeros(M,M*Pmax);
for K = 1:Pmax,
ARF(:,K*M+(1-M:0)) = -rcf(:,:,K+1);
ARB(:,K*M+(1-M:0)) = -rcb(:,:,K+1);
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
end;
RCF = -reshape(rcf(:,:,2:end),[M,M*Pmax]);
PE = reshape(Pf,[M,M*(Pmax+1)]);
elseif Mode==13,
% this is equivalent to Mode==11 but can deal with missing values
%%%%%%%%%%% [pc,R0] = burgv_nan(reshape(Y',[M,1,N]),Pmax,1);
% Copyright S. de Waele, March 2003 - modified Alois Schloegl, 2009
I = eye(M);
sz = [M,M,Pmax+1];
pc= zeros(sz); pc(:,:,1) =I;
K = zeros(sz); K(:,:,1) =I;
Kb= zeros(sz); Kb(:,:,1) =I;
P = zeros(sz);
Pb= zeros(sz);
%[P(:,:,1)]= covm(Y);
[P(:,:,1)]= PE(:,1:M); % normalized
Pb(:,:,1)= P(:,:,1);
f = Y;
b = Y;
%the recursive algorithm
for i = 1:Pmax,
v = f(2:end,:);
w = b(1:end-1,:);
%% normalized, unbiased
Rvv = covm(v); %Pfhat
Rww = covm(w); %Pbhat
Rvw = covm(v,w); %Pfbhat
Rwv = covm(w,v); % = Rvw', written out for symmetry
delta = lyap(Rvv*inv(P(:,:,i)),inv(Pb(:,:,i))*Rww,-2*Rvw);
TsqrtS = chol( P(:,:,i))'; %square root M defined by: M=Tsqrt(M)*Tsqrt(M)'
TsqrtSb= chol(Pb(:,:,i))';
pc(:,:,i+1) = inv(TsqrtS)*delta*inv(TsqrtSb)';
%The forward and backward reflection coefficient
K(:,:,i+1) = -TsqrtS *pc(:,:,i+1) *inv(TsqrtSb);
Kb(:,:,i+1)= -TsqrtSb*pc(:,:,i+1)'*inv(TsqrtS);
%filtering the reflection coefficient out:
f = (v'+ K(:,:,i+1)*w')';
b = (w'+Kb(:,:,i+1)*v')';
%The new R and Rb:
%residual matrices
P(:,:,i+1) = (I-TsqrtS *pc(:,:,i+1) *pc(:,:,i+1)'*inv(TsqrtS ))*P(:,:,i);
Pb(:,:,i+1) = (I-TsqrtSb*pc(:,:,i+1)'*pc(:,:,i+1) *inv(TsqrtSb))*Pb(:,:,i);
end %for i = 1:Pmax,
R0 = PE(:,1:M);
%% [rcf,rcb,Pf,Pb] = pc2rcv(pc,R0);
rcf = zeros(sz); rcf(:,:,1) = I;
Pf = zeros(sz); Pf(:,:,1) = R0;
rcb = zeros(sz); rcb(:,:,1) = I;
Pb = zeros(sz); Pb(:,:,1) = R0;
for p = 1:Pmax,
TsqrtPf = chol( Pf(:,:,p))'; %square root M defined by: M=Tsqrt(M)*Tsqrt(M)'
TsqrtPb= chol(Pb(:,:,p))';
%reflection coefficients
rcf(:,:,p+1) = -TsqrtPf *pc(:,:,p+1) *inv(TsqrtPb);
rcb(:,:,p+1)= -TsqrtPb*pc(:,:,p+1)'*inv(TsqrtPf );
%residual matrices
Pf(:,:,p+1) = (I-TsqrtPf *pc(:,:,p+1) *pc(:,:,p+1)'*inv(TsqrtPf ))*Pf(:,:,p);
Pb(:,:,p+1) = (I-TsqrtPb*pc(:,:,p+1)'*pc(:,:,p+1) *inv(TsqrtPb))*Pb(:,:,p);
end %for p = 2:order,
%%%%%%%%%%%%%% end %%%%%%
%%%%% Convert reflection coefficients RC to autoregressive parameters
ARF = zeros(M,M*Pmax);
ARB = zeros(M,M*Pmax);
for K = 1:Pmax,
ARF(:,K*M+(1-M:0)) = -rcf(:,:,K+1);
ARB(:,K*M+(1-M:0)) = -rcb(:,:,K+1);
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
end;
RCF = -reshape(rcf(:,:,2:end),[M,M*Pmax]);
PE = reshape(Pf,[M,M*(Pmax+1)]);
elseif Mode==14,
% this is equivalent to Mode==11 but can deal with missing values
%%%%%%%%%%%%%% [pc,R0] = burgv_nan(reshape(Y',[M,1,N]),Pmax,2);
% Copyright S. de Waele, March 2003 - modified Alois Schloegl, 2009
I = eye(M);
sz = [M,M,Pmax+1];
pc= zeros(sz); pc(:,:,1) =I;
K = zeros(sz); K(:,:,1) =I;
Kb= zeros(sz); Kb(:,:,1) =I;
P = zeros(sz);
Pb= zeros(sz);
%[P(:,:,1),nn]= covm(Y);
[P(:,:,1)]= C(:,1:M); %% no normalization
Pb(:,:,1)= P(:,:,1);
f = Y;
b = Y;
%the recursive algorithm
for i = 1:Pmax,
v = f(2:end,:);
w = b(1:end-1,:);
%% no normalization
[Rvv,nn] = covm(v); %Pfhat
[Rww,nn] = covm(w); %Pbhat
[Rvw,nn] = covm(v,w); %Pfbhat
[Rwv,nn] = covm(w,v); % = Rvw', written out for symmetry
delta = lyap(Rvv*inv(P(:,:,i)),inv(Pb(:,:,i))*Rww,-2*Rvw);
TsqrtS = chol( P(:,:,i))'; %square root M defined by: M=Tsqrt(M)*Tsqrt(M)'
TsqrtSb= chol(Pb(:,:,i))';
pc(:,:,i+1) = inv(TsqrtS)*delta*inv(TsqrtSb)';
%The forward and backward reflection coefficient
K(:,:,i+1) = -TsqrtS *pc(:,:,i+1) *inv(TsqrtSb);
Kb(:,:,i+1)= -TsqrtSb*pc(:,:,i+1)'*inv(TsqrtS);
%filtering the reflection coefficient out:
f = (v'+ K(:,:,i+1)*w')';
b = (w'+Kb(:,:,i+1)*v')';
%The new R and Rb:
%residual matrices
P(:,:,i+1) = (I-TsqrtS *pc(:,:,i+1) *pc(:,:,i+1)'*inv(TsqrtS ))*P(:,:,i);
Pb(:,:,i+1) = (I-TsqrtSb*pc(:,:,i+1)'*pc(:,:,i+1) *inv(TsqrtSb))*Pb(:,:,i);
end %for i = 1:Pmax,
R0 = PE(:,1:M);
%%%%%%%%%% [rcf,rcb,Pf,Pb] = pc2rcv(pc,R0);
sz = [M,M,Pmax+1];
rcf = zeros(sz); rcf(:,:,1) = I;
Pf = zeros(sz); Pf(:,:,1) = R0;
rcb = zeros(sz); rcb(:,:,1) = I;
Pb = zeros(sz); Pb(:,:,1) = R0;
for p = 1:Pmax,
TsqrtPf = chol( Pf(:,:,p))'; %square root M defined by: M=Tsqrt(M)*Tsqrt(M)'
TsqrtPb = chol(Pb(:,:,p))';
%reflection coefficients
rcf(:,:,p+1)= -TsqrtPf *pc(:,:,p+1) *inv(TsqrtPb);
rcb(:,:,p+1)= -TsqrtPb *pc(:,:,p+1)'*inv(TsqrtPf );
%residual matrices
Pf(:,:,p+1) = (I-TsqrtPf *pc(:,:,p+1) *pc(:,:,p+1)'*inv(TsqrtPf))*Pf(:,:,p);
Pb(:,:,p+1) = (I-TsqrtPb *pc(:,:,p+1)'*pc(:,:,p+1) *inv(TsqrtPb))*Pb(:,:,p);
end %for p = 2:order,
%%%%%%%%%%%%%% end %%%%%%
%%%%% Convert reflection coefficients RC to autoregressive parameters
ARF = zeros(M,M*Pmax);
ARB = zeros(M,M*Pmax);
for K = 1:Pmax,
ARF(:,K*M+(1-M:0)) = -rcf(:,:,K+1);
ARB(:,K*M+(1-M:0)) = -rcb(:,:,K+1);
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
end;
RCF = -reshape(rcf(:,:,2:end),[M,M*Pmax]);
PE = reshape(Pf,[M,M*(Pmax+1)]);
elseif Mode==15, %%%%% Least Squares
%% FIXME
for K=1:Pmax,
[C(:,K*M+(1:M)),n] = covm(Y(K+1:N,:),Y(1:N-K,:),'M');
% C(K*M+(1:M),:) = C(K*M+(1:M),:)/N;
end;
ARF = C(:,1:M)'/C(:,M+1:end)';
PE = covm(C(:,1:M)' - ARF * C(:,M+1:end)');
RCF = zeros(M, M*Pmax);
elseif 0,
%%%%% ARFIT and handling missing values
% compute QR factorization for model of order pmax
[R, scale] = arqr(Y, Pmax, 0);
% select order of model
popt = Pmax; % estimated optimum order
np = M*Pmax; % number of parameter vectors of length m
% decompose R for the optimal model order popt according to
%
% | R11 R12 |
% R=| |
% | 0 R22 |
%
R11 = R(1:np, 1:np);
R12 = R(1:np, Pmax+1:Pmax+M);
R22 = R(M*Pmax+1:Pmax+M, Pmax+1:Pmax+M);
A = (R11\R12)';
% return covariance matrix
dof = N-Pmax-M*Pmax; % number of block degrees of freedom
PE = R22'*R22./dof; % bias-corrected estimate of covariance matrix
try
RCF = [];
[w, ARF, PE] = arfit(Y, Pmax, Pmax, 'sbc', 'zero');
catch
ARF = zeros(M,M*Pmax);
RCF = ARF;
end;
elseif Mode==22 %%%%% Modified Partial Correlation Estimation: Vieira-Morf [2,5] using unbiased covariance estimates.
%--------Initialization----------
F = Y;
B = Y;
[PEF, n] = covm(Y(2:N,:),'M');
PEF = PEF./n;
[PEB, n] = covm(Y(1:N-1,:),'M');
PEB = PEB./n;
%---------------------------------
for K = 1:Pmax,
%---Update the estimated error covariances(15.89) in [2]---
[PEFhat,n] = covm(F(K+1:N,:),'M');
PEFhat = PEFhat./n;
[PEBhat,n] = covm(B(1:N-K,:),'M');
PEBhat = PEBhat./n;
[PEFBhat,n] = covm(F(K+1:N,:),B(1:N-K,:),'M');
PEFBhat = PEFBhat./n;
%---Compute estimated normalized partial correlation matrix(15.88)in [2]---
Rho = inv(chol(PEFhat)')*PEFBhat*inv(chol(PEBhat));
%---Update forward and backward reflection coefficients (15.82) and (15.83) in [2]---
ARF(:,K*M+(1-M:0)) = chol(PEF)'*Rho*inv(chol(PEB)');
ARB(:,K*M+(1-M:0)) = chol(PEB)'*Rho'*inv(chol(PEF)');
%---------------------------------
tmp = F(K+1:N,:) - B(1:N-K,:)*ARF(:,K*M+(1-M:0)).';
B(1:N-K,:) = B(1:N-K,:) - F(K+1:N,:)*ARB(:,K*M+(1-M:0)).';
F(K+1:N,:) = tmp;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
%---Update forward and backward error covariances (15.75) and (15.76) in [2]---
PEF = (eye(M)-ARF(:,K*M+(1-M:0))*ARB(:,K*M+(1-M:0)))*PEF;
PEB = (eye(M)-ARB(:,K*M+(1-M:0))*ARF(:,K*M+(1-M:0)))*PEB;
PE(:,K*M+(1:M)) = PEF;
end
elseif Mode==25 %%%%Modified Partial Correlation Estimation: Vieira-Morf [2,5] using biased covariance estimates.
%--------Initialization----------
F = Y;
B = Y;
[PEF, n] = covm(Y(2:N,:),'M');
PEF = PEF./N;
[PEB, n] = covm(Y(1:N-1,:),'M');
PEB = PEB./N;
%---------------------------------
for K = 1:Pmax,
%---Update the estimated error covariances(15.89) in [2]---
[PEFhat,n] = covm(F(K+1:N,:),'M');
PEFhat = PEFhat./N;
[PEBhat,n] = covm(B(1:N-K,:),'M');
PEBhat = PEBhat./N;
[PEFBhat,n] = covm(F(K+1:N,:),B(1:N-K,:),'M');
PEFBhat = PEFBhat./N;
%---Compute estimated normalized partial correlation matrix(15.88)in [2]---
Rho = inv(chol(PEFhat)')*PEFBhat*inv(chol(PEBhat));
%---Update forward and backward reflection coefficients (15.82) and (15.83) in [2]---
ARF(:,K*M+(1-M:0)) = chol(PEF)'*Rho*inv(chol(PEB)');
ARB(:,K*M+(1-M:0)) = chol(PEB)'*Rho'*inv(chol(PEF)');
%---------------------------------
tmp = F(K+1:N,:) - B(1:N-K,:)*ARF(:,K*M+(1-M:0)).';
B(1:N-K,:) = B(1:N-K,:) - F(K+1:N,:)*ARB(:,K*M+(1-M:0)).';
F(K+1:N,:) = tmp;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
%---Update forward and backward error covariances (15.75) and (15.76) in [2]---
PEF = (eye(M)-ARF(:,K*M+(1-M:0))*ARB(:,K*M+(1-M:0)))*PEF;
PEB = (eye(M)-ARB(:,K*M+(1-M:0))*ARF(:,K*M+(1-M:0)))*PEB;
PE(:,K*M+(1:M)) = PEF;
end
%%%%% EXPERIMENTAL VERSIONS %%%%%
elseif Mode==90;
% similar to MODE=0
%% not recommended
C(:,1:M) = C(:,1:M)/N;
F = Y;
B = Y;
PEF = PE(:,1:M); %CHANGED%
PEB = PE(:,1:M); %CHANGED%
for K=1:Pmax,
[D,n] = covm(Y(K+1:N,:),Y(1:N-K,:),'M');
D = D/N;
ARF(:,K*M+(1-M:0)) = D/PEB;
ARB(:,K*M+(1-M:0)) = D'/PEF;
tmp = F(K+1:N,:) - B(1:N-K,:)*ARF(:,K*M+(1-M:0))';
B(1:N-K,:) = B(1:N-K,:) - F(K+1:N,:)*ARB(:,K*M+(1-M:0))';
F(K+1:N,:) = tmp;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
PEF = [eye(M) - ARF(:,K*M+(1-M:0))*ARB(:,K*M+(1-M:0))]*PEF;
PEB = [eye(M) - ARB(:,K*M+(1-M:0))*ARF(:,K*M+(1-M:0))]*PEB;
PE(:,K*M+(1:M)) = PEF;
end;
elseif Mode==91, %%%%% Levinson-Wiggens-Robinson (LWR) algorithm using biased correlation function
% ===== In [1,2] this algorithm is denoted 'Multichannel Yule-Walker' ===== %
% similar to MODE=1
%% not recommended
C(:,1:M) = C(:,1:M)/N;
PEF = PE(:,1:M); %CHANGED%
PEB = PE(:,1:M); %CHANGED%
for K=1:Pmax,
[C(:,K*M+(1:M)),n] = covm(Y(K+1:N,:),Y(1:N-K,:),'M');
C(:,K*M+(1:M)) = C(:,K*M+(1:M))/N;
D = C(:,K*M+(1:M));
for L = 1:K-1,
D = D - ARF(:,L*M+(1-M:0))*C(:,(K-L)*M+(1:M));
end;
ARF(:,K*M+(1-M:0)) = D / PEB;
ARB(:,K*M+(1-M:0)) = D'/ PEF;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
PEF = [eye(M) - ARF(:,K*M+(1-M:0))*ARB(:,K*M+(1-M:0))]*PEF;
PEB = [eye(M) - ARB(:,K*M+(1-M:0))*ARF(:,K*M+(1-M:0))]*PEB;
PE(:,K*M+(1:M)) = PEF;
end;
elseif Mode==96, %%%%% Levinson-Wiggens-Robinson (LWR) algorithm using unbiased correlation function
% similar to MODE=6
%% not recommended
C(:,1:M) = C(:,1:M)/N;
PEF = PE(:,1:M); %CHANGED%
PEB = PE(:,1:M); %CHANGED%
for K = 1:Pmax,
[C(:,K*M+(1:M)),n] = covm(Y(K+1:N,:),Y(1:N-K,:),'M');
C(:,K*M+(1:M)) = C(:,K*M+(1:M))./n;
D = C(:,K*M+(1:M));
for L = 1:K-1,
D = D - ARF(:,L*M+(1-M:0))*C(:,(K-L)*M+(1:M));
end;
ARF(:,K*M+(1-M:0)) = D / PEB;
ARB(:,K*M+(1-M:0)) = D'/ PEF;
for L = 1:K-1,
tmp = ARF(:,L*M+(1-M:0)) - ARF(:,K*M+(1-M:0))*ARB(:,(K-L)*M+(1-M:0));
ARB(:,(K-L)*M+(1-M:0)) = ARB(:,(K-L)*M+(1-M:0)) - ARB(:,K*M+(1-M:0))*ARF(:,L*M+(1-M:0));
ARF(:,L*M+(1-M:0)) = tmp;
end;
RCF(:,K*M+(1-M:0)) = ARF(:,K*M+(1-M:0));
RCB(:,K*M+(1-M:0)) = ARB(:,K*M+(1-M:0));
PEF = [eye(M) - ARF(:,K*M+(1-M:0))*ARB(:,K*M+(1-M:0))]*PEF;
PEB = [eye(M) - ARB(:,K*M+(1-M:0))*ARF(:,K*M+(1-M:0))]*PEB;
PE(:,K*M+(1:M)) = PEF;
end;
elseif Mode<100,
fprintf('Warning MVAR: Mode=%i not supported\n',Mode);
%%%%% IMPUTATION METHODS %%%%%
else
Mode0 = rem(Mode,100);
if ((Mode0 >= 10) && (Mode0 < 20)),
Mode0 = 1;
end;
if 0,
elseif Mode>=2400, % forward and backward
% assuming that past missing values are already IMPUTED with the prediction value + innovation process
% no decaying
[ARF,RCF,PE2] = mvar(Y, Pmax, Mode0);
c = chol( PE2 (:, M*Pmax+(1:M)));
Y1 = Y;
Y1(1,isnan(Y1(1,:))) = 0;
z = [];
for k = 2:size(Y,1)
[z1,z] = mvfilter(ARF,eye(M),Y1(k-1,:)',z);
ix = isnan(Y1(k,:));
z1 = z1 + (randn(1,M)*c)';
Y1(k,ix) = z1(ix);
end;
Y2 = flipud(Y);
[ARB,RCF,PE] = mvar(Y2, Pmax, Mode0);
Y2(1,isnan(Y2(1,:))) = 0;
z = [];
for k = 2:size(Y2,1)
[z2,z] = mvfilter(ARB,eye(M),Y2(k-1,:)',z);
ix = isnan(Y(size(Y,1)-k+1,:));
z2 = z2 + (randn(1,M)*c)';
Y2(k,ix) = (z2(ix)' + Y2(k,ix))/2;
end;
Y2 = flipud(Y2);
Z = (Y2+Y1)/2;
Y(isnan(Y)) = Z(isnan(Y));
[ARF,RCF,PE] = mvar(Y, Pmax, rem(Mode,100));
elseif Mode>=2300, % backward prediction
% assuming that past missing values are already IMPUTED with the prediction value + innovation process
% no decaying
Y = flipud(Y);
[ARB,RCF,PE] = mvar(Y, Pmax, Mode0);
c = chol(PE(:,M*Pmax+(1:M)));
Y1 = Y;
Y1(1,isnan(Y1(1,:))) = 0;
z = [];
for k = 2:size(Y,1)
[z1,z] = mvfilter(ARB,eye(M),Y1(k-1,:)',z);
ix = isnan(Y1(k,:));
z1 = z1 + (randn(1,M)*c)';
Y1(k,ix) = z1(ix);
end;
Y1 = flipud(Y1);
[ARF,RCF,PE] = mvar(Y1, Pmax, rem(Mode,100));
elseif Mode>=2200, % forward predictions,
% assuming that past missing values are already IMPUTED with the prediction value + innovation process
% no decaying
[ARF,RCF,PE] = mvar(Y, Pmax, Mode0);
c = chol(PE(:,M*Pmax+(1:M)));
Y1 = Y;
Y1(1,isnan(Y1(1,:))) = 0;
z = [];
for k = 2:size(Y,1)
[z1,z] = mvfilter(ARF,eye(M),Y1(k-1,:)',z);
ix = isnan(Y1(k,:));
z1 = z1 + (randn(1,M)*c)';
Y1(k,ix) = z1(ix);
end;
[ARF,RCF,PE] = mvar(Y1, Pmax, rem(Mode,100));
elseif Mode>=1400, % forward and backward
%assuming that past missing values are already IMPUTED with the prediction value
[ARF,RCF,PE] = mvar(Y, Pmax, Mode0);
Y1 = Y;
Y1(1,isnan(Y1(1,:))) = 0;
z = [];
for k = 2:size(Y,1)
[z1,z] = mvfilter(ARF,eye(M),Y1(k-1,:)',z);
ix = isnan(Y1(k,:));
Y1(k,ix) = z1(ix);
end;
Y2 = flipud(Y);
[ARB,RCF,PE] = mvar(Y2, Pmax, Mode0);
Y2(1,isnan(Y2(1,:))) = 0;
z = [];
for k = 2:size(Y2,1)
[z2,z] = mvfilter(ARB,eye(M),Y2(k-1,:)',z);
ix = isnan(Y2(k,:));
Y2(k,ix) = z2(ix)';
end;
Y2 = flipud(Y2);
Z = (Y2+Y1)/2;
Y(isnan(Y)) = Z(isnan(Y));
[ARF,RCF,PE] = mvar(Y, Pmax, rem(Mode,100));
elseif Mode>=1300, % backward prediction
Y = flipud(Y);
[ARB,RCF,PE] = mvar(Y, Pmax, Mode0);
Y2 = Y;
Y2(1,isnan(Y2(1,:))) = 0;
z = [];
for k = 2:size(Y,1)
[z2,z] = mvfilter(ARB,eye(M),Y2(k-1,:)',z);
ix = isnan(Y2(k,:));
Y2(k,ix) = z2(ix);
end;
Y2 = flipud(Y2);
[ARF,RCF,PE] = mvar(Y2, Pmax, rem(Mode,100));
elseif Mode>=1200, % forward predictions,
%assuming that past missing values are already IMPUTED with the prediction value
[ARF,RCF,PE] = mvar(Y, Pmax, Mode0);
Y1 = Y;
Y1(1,isnan(Y1(1,:))) = 0;
z = [];
for k = 2:size(Y,1)
[z1,z] = mvfilter(ARF,eye(M),Y1(k-1,:)',z);
ix = isnan(Y1(k,:));
Y1(k,ix) = z1(ix);
end;
[ARF,RCF,PE] = mvar(Y1, Pmax, rem(Mode,100));
elseif Mode>=400, % forward and backward
% assuming that 'past' missing values are ZERO
[ARF,RCF,PE] = mvar(Y, Pmax, Mode0);
Y1 = Y;
Y1(isnan(Y)) = 0;
Z1 = mvfilter([zeros(M),ARF],eye(M),Y1')';
Y1(isnan(Y)) = Z1(isnan(Y));
Y = flipud(Y);
[ARB,RCF,PE] = mvar(Y, Pmax, Mode0);
Y2 = Y;
Y2(isnan(Y)) = 0;
Z2 = mvfilter([zeros(M),ARB],eye(M),Y2')';
Y2(isnan(Y)) = Z2(isnan(Y));
Y2 = flipud(Y2);
[ARF,RCF,PE] = mvar((Y1+Y2)/2, Pmax, rem(Mode,100));
elseif Mode>=300, % backward prediction
% assuming that 'past' missing values are ZERO
Y = flipud(Y);
[ARB,RCF,PE] = mvar(Y, Pmax, Mode0);
Y2 = Y;
Y2(isnan(Y)) = 0;
Z2 = mvfilter([zeros(M),ARB],eye(M),Y2')';
Y2(isnan(Y)) = Z2(isnan(Y));
Y2 = flipud(Y2);
[ARF,RCF,PE] = mvar(Y2, Pmax, rem(Mode,100));
elseif Mode>=200,
% forward predictions, assuming that past missing values are ZERO
[ARF,RCF,PE] = mvar(Y, Pmax, Mode0);
Y1 = Y;
Y1(isnan(Y)) = 0;
Z1 = mvfilter([zeros(M),ARF],eye(M),Y1')';
Y1(isnan(Y)) = Z1(isnan(Y));
[ARF,RCF,PE] = mvar(Y1, Pmax, rem(Mode,100));
elseif Mode>=100,
[ARF,RCF,PE] = mvar(Y, Pmax, Mode0);
Z1 = mvfilter(ARF,eye(M),Y')';
Z1 = [zeros(1,M); Z1(1:end-1,:)];
Y(isnan(Y)) = Z1(isnan(Y));
[ARF,RCF,PE] = mvar(Y, Pmax, rem(Mode,100));
end;
end;
if any(ARF(:)==inf),
% Test for matrix division bug.
% This bug was observed in LNX86-ML5.3, 6.1 and 6.5, but not in SGI-ML6.5, LNX86-ML6.5, Octave 2.1.35-40; Other platforms unknown.
p = 3;
FLAG_MATRIX_DIVISION_ERROR = ~all(all(isnan(repmat(0,p)/repmat(0,p)))) | ~all(all(isnan(repmat(nan,p)/repmat(nan,p))));
if FLAG_MATRIX_DIVISION_ERROR,
%fprintf(2,'%%% Warning MVAR: Bug in Matrix-Division 0/0 and NaN/NaN yields INF instead of NAN. Workaround is applied.\n');
warning('MVAR: bug in Matrix-Division 0/0 and NaN/NaN yields INF instead of NAN. Workaround is applied.');
%%%%% Workaround
ARF(ARF==inf)=NaN;
RCF(RCF==inf)=NaN;
end;
end;
%MAR = zeros(M,M*Pmax);
DC = zeros(M);
for K = 1:Pmax,
% VAR{K+1} = -ARF(:,K*M+(1-M:0))';
DC = DC + ARF(:,K*M+(1-M:0))'.^2; %DC meausure [3]
end;
tsa/inst/invfdemo.m 0000664 0023567 0023567 00000003164 12115040716 015020 0 ustar schloegl schloegl % invfdemo demonstrates Inverse Filtering
% $Id: invfdemo.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1997-2002,2008 by Alois Schloegl
%
% 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 .
load eeg8s.mat; % load signal
ly=length(eeg8s);
Fs=128;
%a=earpyw(eeg8s',11); % Calculates AR(11) parameters with Yule-Walker method
a=lattice(eeg8s',11); % Calculates AR(11) parameters with Yule-Walker method
% The AR parameters are the weight taps of IIR Filter
isig=filter([1 -a],1,eeg8s); % Inverse filtering
subplot(221);
plot((1:ly)/Fs,eeg8s);
title('Signal');
xlabel('t [sec]')
subplot(223);
plot((1:ly)/Fs,isig);
xlabel('t [sec]')
title('Inverse filtered process');
subplot(222);
H=abs(fft(eeg8s,128)/ly).^2;
plot(1:Fs,H);
%plot([H mean(H)*ones(Fs,1)]);
ylabel('S(f)')
xlabel('f [Hz]')
title('Spectrum of original signal');
subplot(224);
H=abs(fft(isig,128)/ly).^2;
plot(1:Fs,H);
%plot([H mean(H)*ones(Fs,1)]);
ylabel('S(f)')
xlabel('f [Hz]')
title('Spectrum of inverse filtered signal');
tsa/inst/acorf.m 0000664 0023567 0023567 00000005211 12115040716 014276 0 ustar schloegl schloegl function [AUTOCOV,stderr,lpq,qpval] = acorf(Z,N);
% Calculates autocorrelations for multiple data series.
% Missing values in Z (NaN) are considered.
% Also calculates Ljung-Box Q stats and p-values.
%
% [AutoCorr,stderr,lpq,qpval] = acorf(Z,N);
% If mean should be removed use
% [AutoCorr,stderr,lpq,qpval] = acorf(detrend(Z',0)',N);
% If trend should be removed use
% [AutoCorr,stderr,lpq,qpval] = acorf(detrend(Z')',N);
%
% INPUT
% Z is data series for which autocorrelations are required
% each in a row
% N maximum lag
%
% OUTPUT
% AutoCorr nr x N matrix of autocorrelations
% stderr nr x N matrix of (approx) std errors
% lpq nr x M matrix of Ljung-Box Q stats
% qpval nr x N matrix of p-values on Q stats
%
% All input and output parameters are organized in rows, one row
% corresponds to one series
%
% REFERENCES:
% S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% J.S. Bendat and A.G.Persol "Random Data: Analysis and Measurement procedures", Wiley, 1986.
% calculating lpq, stderr, qpval from
% suggested by Philip Gray, University of New South Wales,
% $Id: acorf.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
[nr,nc] = size(Z);
NC = sum(~isnan(Z),2); % missing values
AUTOCOV = acovf(Z,N);
AUTOCOV = AUTOCOV(:,2:N+1) ./ AUTOCOV(:,ones(1,N));
if nargout > 1
stderr = sqrt(1./NC)*ones(1,N);
lpq = zeros(nr,N);
qpval = zeros(nr,N);
cum=zeros(nr,1);
for k=1:N,
cum = cum+AUTOCOV(:,k).*conj(AUTOCOV(:,k))./(NC-k);
lpq(:,k) = NC.*(NC+2).*cum; % Ljung box Q for k lags
%qpval(:,k) = 1 - chi2cdf(lpq(:,k),k); % p-value of Q stat
qpval(:,k) = 1 - gammainc(lpq(:,k)/2,k/2); % replace chi2cdf by gammainc
end;
end;
tsa/inst/detrend.m 0000664 0023567 0023567 00000011054 12115040716 014633 0 ustar schloegl schloegl function [X,T]=detrend(t,X,p)
% DETREND removes the trend from data, NaN's are considered as missing values
%
% DETREND is fully compatible to previous Matlab and Octave DETREND with the following features added:
% - handles NaN's by assuming that these are missing values
% - handles unequally spaced data
% - second output parameter gives the trend of the data
% - compatible to Matlab and Octave
%
% [...]=detrend([t,] X [,p])
% removes trend for unequally spaced data
% t represents the time points
% X(i) is the value at time t(i)
% p must be a scalar
%
% [...]=detrend(X,0)
% [...]=detrend(X,'constant')
% removes the mean
%
% [...]=detrend(X,p)
% removes polynomial of order p (default p=1)
%
% [...]=detrend(X,1) - default
% [...]=detrend(X,'linear')
% removes linear trend
%
% [X,T]=detrend(...)
%
% X is the detrended data
% T is the removed trend
%
% see also: SUMSKIPNAN, ZSCORE
% Copyright (C) 1995, 1996 Kurt Hornik
% $Id: detrend.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 2001,2007 by Alois Schloegl
% This function is part of the TSA-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% Copyright (C) 1997, 1998, 2008 by Alois Schloegl
%
% 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 .
if (nargin == 1)
p = 1;
X = t;
t = [];
elseif (nargin == 2)
if strcmpi(X,'constant'),
p = 0;
X = t;
t = [];
elseif strcmpi(X,'linear'),
p = 1;
X = t;
t = [];
elseif ischar(X)
error('unknown 2nd input argument');
elseif all(size(X)==1),
p = X;
X = t;
t = [];
else
p = 1;
end;
elseif (nargin == 3)
if ischar(X),
warning('input arguments are not supported');
end;
elseif (nargin > 3)
fprintf (1,'usage: detrend (x [, p])\n');
end;
% check data, must be in culomn order
[m, n] = size (X);
if (m == 1)
X = X';
r=n;
else
r=m;
end
% check time scale
if isempty(t),
t = (1:r).'; % make time scale
elseif ~all(size(t)==size(X))
t = t(:);
end;
% check dimension of t and X
if ~all(size(X,1)==size(t,1))
fprintf (2,'detrend: size(t,1) must same as size(x,1) \n');
end;
% check the order of the polynomial
if (~(all(size(p)==1) & (p == round (p)) & (p >= 0)))
fprintf (2,'detrend: p must be a nonnegative integer\n');
end
if (nargout>1) , % needs more memory
T = zeros(size(X))+nan;
%T=repmat(nan,size(X)); % not supported by Octave 2.0.16
if (size(t,2)>1), % for multiple time scales
for k=1:size(X,2),
idx=find(~isnan(X(:,k)));
b = (t(idx,k) * ones (1, p + 1)) .^ (ones (length(idx),1) * (0 : p));
T(idx,k) = b * (b \ X(idx,k));
end;
else % if only one time scale is used
b = (t * ones (1, p + 1)) .^ (ones (length(t),1) * (0 : p));
for k=1:size(X,2),
idx=find(~isnan(X(:,k)));
T(idx,k) = b(idx,:) * (b(idx,:) \ X(idx,k));
%X(idx,k) = X(idx,k) - T(idx,k); % 1st alternative implementation
%X(:,k) = X(:,k) - T(:,k); % 2nd alternative
end;
end;
X = X-T; % 3nd alternative
if (m == 1)
X = X';
T = T';
end
else % needs less memory
if (size(t,2)>1), % for multiple time scales
for k = 1:size(X,2),
idx = find(~isnan(X(:,k)));
b = (t(idx,k) * ones (1, p + 1)) .^ (ones (length(idx),1) * (0 : p));
X(idx,k) = X(idx,k) - b * (b \ X(idx,k));
end;
else % if only one time scale is used
b = (t * ones (1, p + 1)) .^ (ones (length(t),1) * (0 : p));
for k = 1:size(X,2),
idx = find(~isnan(X(:,k)));
X(idx,k) = X(idx,k) - b(idx,:) * (b(idx,:) \ X(idx,k));
end;
end;
if (m == 1)
X = X';
end
end;
tsa/inst/histo4.m 0000664 0023567 0023567 00000006004 12115040716 014417 0 ustar schloegl schloegl function [R, tix] = histo4(Y, W)
% HISTO4 calculates histogram of multidimensional data samples
% and supports data compression
%
% R = HISTO4(Y)
% R = HISTO4(Y, W)
% Y data: on sample per row, each sample has with size(Y,2) elements
% W weights of each sample (default: [])
% W = [] indicates that each sample has equal weight
% R is a struct with these fields:
% R.X are the bin-values
% R.H is the frequency of occurence of value X (weighted with W)
% R.N are the total number of samples (or sum of W)
%
% HISTO4 might be useful for data compression, because
% [R,tix] = histo4(Y)
% is the compression step
% R.X(tix,:)
% is the decompression step
%
% The effort (in memory and speed) for compression is O(n*log(n))
% The effort (in memory and speed) for decompression is only O(n)
%
% see also: HISTO, HISTO2, HISTO3, HISTO4
%
% REFERENCE(S):
% C.E. Shannon and W. Weaver 'The mathematical theory of communication' University of Illinois Press, Urbana 1949 (reprint 1963).
% $Id: histo4.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2005,2008,2009,2011 by Alois Schloegl
% This is part of the TSA-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
%
% 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 .
%%%%% check input arguments %%%%%
[yr, yc] = size(Y);
if nargin<2,
W = [];
end;
if ~isempty(W) && (yr ~= numel(W)),
error('number of rows of Y does not match number of elements in W');
end;
%%%%% identify all possible X's and generate overall Histogram %%%%%
[Y, idx] = sortrows(Y);
d = diff(Y,[],1);
ix = any( (~isnan(d) & (d~=0) ) | diff(isnan(Y),[],1), 2);
tmp = [find(ix); yr];
R.X = Y(tmp,:);
R.datatype = 'HISTOGRAM';
if isempty(W)
R.H = [tmp(1); diff(tmp)];
R.N = yr;
else
W = cumsum(W(idx));
R.H = [W(tmp(1)); diff(W(tmp))];
R.N = W(end);
end;
%%%%% generate inverse index %%%%%
if nargout>1,
tix = cumsum([1;ix]); % rank
cc = 1;
tmp = sum(ix);
if tmp < 2^8;
tix = uint8(tix);
cc = 8/1;
elseif tmp < 2^16;
tix = uint16(tix);
cc = 8/2;
elseif tmp < 2^32;
tix = uint32(tix);
cc = 8/4;
end;
[tmp, idx] = sort(idx); % inverse index
tix = tix(idx); % inverse sort rank
R.compressionratio = (prod(size(R.X)) + yr/cc) / (yr*yc);
R.tix = tix;
end;
tsa/inst/histo3.m 0000664 0023567 0023567 00000011256 12115040716 014423 0 ustar schloegl schloegl function [R, tix] = histo3(Y, W)
% HISTO3 calculates histogram for multiple columns with common bin values
% among all data columns, and can be useful for data compression.
%
% R = HISTO3(Y)
% R = HISTO3(Y, W)
% Y data
% W weight vector containing weights of each sample,
% number of rows of Y and W must match.
% default W=[] indicates that each sample is weighted with 1.
% R struct with these fields
% R.X the bin-values, bin-values are equal for each channel
% thus R.X is a column vector. If bin values should
% be computed separately for each data column, use HISTO2
% R.H is the frequency of occurence of value X
% R.N are the number of valid (not NaN) samples
%
% Data compression can be performed in this way
% [R,tix] = histo3(Y)
% is the compression step
%
% R.tix provides a compressed data representation.
% R.compressionratio estimates the compression ratio
%
% R.X(tix) and R.X(R.tix)
% reconstruct the orginal signal (decompression)
%
% The effort (in memory and speed) for compression is O(n*log(n)).
% The effort (in memory and speed) for decompression is O(n) only.
%
% see also: HISTO, HISTO2, HISTO3, HISTO4
%
% REFERENCE(S):
% C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
% $Id: histo3.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2002,2008,2011 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sourceforge.net/
% http://biosig.sourceforge.net/
%
% 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 .
%%%%% check input arguments %%%%%
[yr,yc] = size(Y);
if nargin < 2,
W = [];
end;
if ~isempty(W) && (yr ~= numel(W)),
error('number of rows of Y does not match number of elements in W');
end;
%%%%% identify all possible X's and generate overall Histogram %%%%%
[sY, idx] = sort(Y(:),1);
[tmp,idx1] = sort(idx); % generate inverse index
ix = diff(sY, [], 1) > 0;
tmp = [find(ix); sum(~isnan(sY))];
R.datatype = 'HISTOGRAM';
R.X = sY(tmp);
R.N = sum(~isnan(Y), 1);
% generate inverse index
if nargout>1,
tix = cumsum([1; ix]); % rank
tix = reshape(tix(idx1), yr, yc); % inverse sort rank
cc = 1;
tmp = sum(ix) + 1;
if exist('OCTAVE_VERSION') >= 5,
; % NOP; no support for integer datatyp
elseif tmp <= 2^8;
tix = uint8(tix);
cc = 8/1;
elseif tmp <= 2^16;
tix = uint16(tix);
cc = 8/2;
elseif tmp <= 2^32;
tix = uint32(tix);
cc = 8/4;
end;
R.compressionratio = (prod(size(R.X)) + (yr*yc)/cc) / (yr*yc);
R.tix = tix;
end;
if yc==1,
if isempty(W)
R.H = [tmp(1); diff(tmp)];
else
C = cumsum(W(idx)); % cumulative weights
R.H = [C(tmp(1)); diff(C(tmp))];
end;
return;
elseif yc>1,
% allocate memory
H = zeros(size(R.X,1),yc);
% scan each channel
for k = 1:yc,
if isempty(W)
sY = sort(Y(:,k));
else
[sY,ix] = sort(Y(:,k));
C = cumsum(W(ix));
end
ix = find(diff(sY,[],1) > 0);
if size(ix,1) > 0,
tmp = [ix; R.N(k)];
else
tmp = R.N(k);
end;
t = 0;
j = 1;
if isempty(W)
for x = tmp',
acc = sY(x);
while R.X(j)~=acc, j=j+1; end;
%j = find(sY(x)==R.X); % identify position on X
H(j,k) = H(j,k) + (x-t); % add diff(tmp)
t = x;
end;
else
for x = tmp',
acc = sY(x);
while R.X(j)~=acc, j=j+1; end;
%j = find(sY(x)==R.X); % identify position on X
H(j,k) = H(j,k) + C(x)-t; % add diff(tmp)
t = C(x);
end;
end;
end;
R.H = H;
end;
tsa/inst/adim.m 0000664 0023567 0023567 00000010126 12115040716 014117 0 ustar schloegl schloegl function [IR, CC, D] = adim(U, UC, IR, CC, arg5);
% ADIM adaptive information matrix. Estimates the inverse
% correlation matrix in an adaptive way.
%
% [IR, CC] = adim(U, UC [, IR0 [, CC0]]);
% U input data
% UC update coefficient 0 < UC << 1
% IR0 initial information matrix
% CC0 initial correlation matrix
% IR information matrix (inverse correlation matrix)
% CC correlation matrix
%
% The algorithm uses the Matrix Inversion Lemma, also known as
% "Woodbury's identity", to obtain a recursive algorithm.
% IR*CC - UC*I should be approx. zero.
%
% Reference(s):
% [1] S. Haykin. Adaptive Filter Theory, Prentice Hall, Upper Saddle River, NJ, USA
% pp. 565-567, Equ. (13.16), 1996.
% $Id: adim.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2003 by Alois Schloegl
%
% 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 .
[ur,p] = size(U);
Mode_E = 1;
%if nargin < 4,
% m = zeros(size(U)+[0,Mode_E]);
%end;
if nargin<2,
fprintf(2,'Error ADIM: missing update coefficient\n');
return;
else
if ~((UC > 0) & (UC <1)),
fprintf(2,'Error ADIM: update coefficient not within range [0,1]\n');
return;
end;
if UC > 1/p,
fprintf(2,'Warning ADIM: update coefficient should be smaller than 1/number_of_dimensions\n');
end;
end;
if nargin<3,
IR = [];
end;
if nargin<4,
CC = [];
end;
if nargin<5,
arg5 = 6;
end;
if isempty(IR),
IR = eye(p+Mode_E);
end;
if isempty(CC),
CC = eye(p+Mode_E);
end;
D = zeros(ur,(p+Mode_E)^2);
%D = zeros(ur,1);
W = eye(p+Mode_E)*UC/(p+Mode_E);
W2 = eye(p+Mode_E)*UC*UC/(p+Mode_E);
for k = 1:ur,
if ~Mode_E,
% w/o mean term
d = U(k,:);
else
% w/ mean term
d = [1,U(k,:)];
end;
if ~any(isnan(d)),
CC = (1-UC)*CC + UC*(d'*d);
%K = (1+UC)*IR*d'/(1+(1+UC)*d*IR*d');
v = IR*d';
%K = v/(1-UC+d*v);
if arg5==0; % original version according to [1], can become unstable
IR = (1+UC)*IR - (1+UC)/(1-UC+d*v)*v*v';
elseif arg5==1; % this provides IR*CC == I, this seems to be stable
IR = IR - (1+UC)/(1-UC+d*v)*v*v' + sum(diag(IR))*W;
elseif arg5==6; % DEFAULT:
IR = ((1+UC)/2)*(IR+IR') - ((1+UC)/(1-UC+d*v))*v*v';
% more variants just for testing, do not use them.
elseif arg5==2;
IR = IR - (1+UC)/(1-UC+d*v)*v*v' + sum(diag(IR))*W2;
elseif arg5==3;
IR = IR - (1+UC)/(1-UC+d*v)*v*v' + eps*eye(p+Mode_E);
elseif arg5==4;
IR = (1+UC)*IR - (1+UC)/(1-UC+d*v)*v*v' + eps*eye(p+Mode_E);
elseif arg5==5;
IR = IR - (1+UC)/(1-UC+d*v)*v*v' + eps*eye(p+Mode_E);
end;
end;
%D(k) = det(IR);
D(k,:) = IR(:)';
end;
IR = IR / UC;
if Mode_E,
IR(1,1) = IR(1,1) - 1;
end;
tsa/inst/hup.m 0000664 0023567 0023567 00000003400 12115040716 013776 0 ustar schloegl schloegl function b=hup(C)
%HUP(C) tests if the polynomial C is a Hurwitz-Polynomial.
% It tests if all roots lie in the left half of the complex
% plane
% B=hup(C) is the same as
% B=all(real(roots(c))<0) but much faster.
% The Algorithm is based on the Routh-Scheme.
% C are the elements of the Polynomial
% C(1)*X^N + ... + C(N)*X + C(N+1).
%
% HUP2 works also for multiple polynomials,
% each row a poly - Yet not tested
%
% REFERENCES:
% F. Gausch "Systemtechnik", Textbook, University of Technology Graz, 1993.
% Ch. Langraf and G. Schneider "Elemente der Regeltechnik", Springer Verlag, 1970.
% $Id: hup.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (c) 1995-1998,2008 by Alois Schloegl
%
% 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 .
[lr,lc] = size(c);
% Strip leading zeros and throw away.
% not considered yet
%d=(c(:,1)==0);
% Trailing zeros mean there are roots at zero
b=(c(:,lc)~=0);
lambda=b;
n=zeros(lc);
if lc>3
n(4:2:lc,2:2:lc-2)=1;
end;
while lc>1
lambda(b)=c(b,1)./c(b,2);
b = b & (lambda>=0) & (lambda
% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sourceforge.net/
% http://biosig.sourceforge.net/
% 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 .
%%%%% checking of the input arguments was done the same way as ARFIT
if (pmin ~= round(pmin) || pmax ~= round(pmax))
error('Order must be integer.');
end
if (pmax < pmin)
error('PMAX must be greater than or equal to PMIN.')
end
% set defaults and check for optional arguments
if (nargin == 3) % no optional arguments => set default values
mcor = 1; % fit intercept vector
selector = 'sbc'; % use SBC as order selection criterion
elseif (nargin == 4) % one optional argument
if strcmp(selector, 'zero')
mcor = 0; % no intercept vector to be fitted
selector = 'sbc'; % default order selection
else
mcor = 1; % fit intercept vector
end
elseif (nargin == 5) % two optional arguments
if strcmp(no_const, 'zero')
mcor = 0; % no intercept vector to be fitted
else
error(['Bad argument. Usage: ', '[w,A,C,SBC,FPE,th]=AR(v,pmin,pmax,SELECTOR,''zero'')'])
end
end
%%%%% Implementation of the MVAR estimation
[N,M]=size(Y);
if mcor,
[m,N] = sumskipnan(Y,1); % calculate mean
m = m./N;
Y = Y - repmat(m,size(Y)./size(m)); % remove mean
end;
[MAR,RCF,PE] = mvar(Y, pmax, 2); % estimate MVAR(pmax) model
N = min(N);
%if 1;nargout>3;
ne = N-mcor-(pmin:pmax);
for p=pmin:pmax,
% Get downdated logarithm of determinant
logdp(p-pmin+1) = log(det(PE(:,p*M+(1:M))*(N-p-mcor)));
end;
% Schwarz's Bayesian Criterion
sbc = logdp/M - log(ne) .* (1-(M*(pmin:pmax)+mcor)./ne);
% logarithm of Akaike's Final Prediction Error
fpe = logdp/M - log(ne.*(ne-M*(pmin:pmax)-mcor)./(ne+M*(pmin:pmax)+mcor));
% Modified Schwarz criterion (MSC):
% msc(i) = logdp(i)/m - (log(ne) - 2.5) * (1 - 2.5*np(i)/(ne-np(i)));
% get index iopt of order that minimizes the order selection
% criterion specified by the variable selector
if strcmpi(selector,'fpe');
[val, iopt] = min(fpe);
else %if strcmpi(selector,'sbc');
[val, iopt] = min(sbc);
end;
% select order of model
popt = pmin + iopt-1; % estimated optimum order
if popt5, th=[]; fprintf(2,'Warning ARFIT2: output TH not defined\n'); end;
tsa/inst/selmo.m 0000664 0023567 0023567 00000013772 12115040716 014336 0 ustar schloegl schloegl function [FPE,AIC,BIC,SBC,MDL,CATcrit,PHI,optFPE,optAIC,optBIC,optSBC,optMDL,optCAT,optPHI,p,C]=selmo(e,NC);
% Model order selection of an autoregrssive model
% [FPE,AIC,BIC,SBC,MDL,CAT,PHI,optFPE,optAIC,optBIC,optSBC,optMDL,optCAT,optPHI]=selmo(E,N);
%
% E Error function E(p)
% N length of the data set, that was used for calculating E(p)
% show optional; if given the parameters are shown
%
% FPE Final Prediction Error (Kay 1987, Wei 1990, Priestley 1981 -> Akaike 1969)
% AIC Akaike Information Criterion (Marple 1987, Wei 1990, Priestley 1981 -> Akaike 1974)
% BIC Bayesian Akaike Information Criterion (Wei 1990, Priestley 1981 -> Akaike 1978,1979)
% CAT Parzen's CAT Criterion (Wei 1994 -> Parzen 1974)
% MDL Minimal Description length Criterion (Marple 1987 -> Rissanen 1978,83)
% SBC Schwartz's Bayesian Criterion (Wei 1994; Schwartz 1978)
% PHI Phi criterion (Pukkila et al. 1988, Hannan 1980 -> Hannan & Quinn, 1979)
% HAR Haring G. (1975)
% JEW Jenkins and Watts (1968)
%
% optFPE order where FPE is minimal
% optAIC order where AIC is minimal
% optBIC order where BIC is minimal
% optSBC order where SBC is minimal
% optMDL order where MDL is minimal
% optCAT order where CAT is minimal
% optPHI order where PHI is minimal
%
% usually is
% AIC > FPE > *MDL* > PHI > SBC > CAT ~ BIC
%
% REFERENCES:
% P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% Jenkins G.M. Watts D.G "Spectral Analysis and its applications", Holden-Day, 1968.
% G. Haring "Über die Wahl der optimalen Modellordnung bei der Darstellung von stationären Zeitreihen mittels Autoregressivmodell als Basis der Analyse von EEG - Biosignalen mit Hilfe eines Digitalrechners", Habilitationschrift - Technische Universität Graz, Austria, 1975.
% (1)"About selecting the optimal model at the representation of stationary time series by means of an autoregressive model as basis of the analysis of EEG - biosignals by means of a digital computer)"
%
% $Id: selmo.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1997-2002,2008,2012 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sourceforge.net/
% http://biosig.sourceforge.net/
%
% 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 .
[lr,lc]=size(e);
if (lr>1) && (lc>1),
p=zeros(lr+1,9)+NaN;
else
p=zeros(1,9)+NaN;
end;
if nargin<2
NC=lc*ones(lr,1);
NC=(lc-sum(isnan(e)')')*(NC=lc); % first part
%end;% Pmax=min([100 N/3]); end;
%if NC=lc); % first part
else
% NC=NC;
end;
M=lc-1;
m=0:M;
e = e./e(:,ones(1,lc));
for k=0:lr,
if k>0, %
E=e(k,:);
N=NC(k);
elseif lr>1
tmp = e;%(NC>0,:);
tmp(isnan(tmp)) = 0;
E = sum(tmp.*(NC*ones(1,lc)))/sum(NC); % weighted average, weigths correspond to number of valid (not missing) values
N = sum(NC)./sum(NC>0); % corresponding number of values,
else
E = e;
N = NC;
end;
FPE = E.*(N+m)./(N-m); %OK
optFPE=find(FPE==min(FPE))-1; %optimal order
if isempty(optFPE), optFPE=NaN; end;
AIC = N*log(E)+2*m; %OK
optAIC=find(AIC==min(AIC))-1; %optimal order
if isempty(optAIC), optAIC=NaN; end;
AIC4=N*log(E)+4*m; %OK
optAIC4=find(AIC4==min(AIC4))-1; %optimal order
if isempty(optAIC4), optAIC4=NaN; end;
m=1:M;
BIC=[ N*log(E(1)) N*log(E(m+1)) - (N-m).*log(1-m/N) + m*log(N) + m.*log(((E(1)./E(m+1))-1)./m)];
%BIC=[ N*log(E(1)) N*log(E(m+1)) - m + m*log(N) + m.*log(((E(1)./E(m+1))-1)./m)];
%m=0:M; BIC=N*log(E)+m*log(N); % Hannan, 1980 -> Akaike, 1977 and Rissanen 1978
optBIC=find(BIC==min(BIC))-1; %optimal order
if isempty(optBIC), optBIC=NaN; end;
HAR(2:lc)=-(N-m).*log((N-m).*E(m+1)./(N-m+1)./E(m));
HAR(1)=HAR(2);
optHAR=min(find(HAR<=(min(HAR)+0.2)))-1; %optimal order
% optHAR=find(HAR==min(HAR))-1; %optimal order
if isempty(optHAR), optHAR=NaN; end;
m=0:M;
SBC = N*log(E)+m*log(N);
optSBC=find(SBC==min(SBC))-1; %optimal order
if isempty(optSBC), optSBC=NaN; end;
MDL = N*log(E)+log(N)*m;
optMDL=find(MDL==min(MDL))-1; %optimal order
if isempty(optMDL), optMDL=NaN; end;
m=0:M;
%CATcrit= (cumsum(1./E(m+1))/N-1./E(m+1));
E1=N*E./(N-m);
CATcrit= (cumsum(1./E1(m+1))/N-1./E1(m+1));
optCAT=find(CATcrit==min(CATcrit))-1; %optimal order
if isempty(optCAT), optCAT=NaN; end;
PHI = N*log(E)+2*log(log(N))*m;
optPHI=find(PHI==min(PHI))-1; %optimal order
if isempty(optPHI), optPHI=NaN; end;
JEW = E.*(N-m)./(N-2*m-1); % Jenkins-Watt
optJEW=find(JEW==min(JEW))-1; %optimal order
if isempty(optJEW), optJEW=NaN; end;
% in case more than 1 minimum is found, the smaller model order is returned;
p(k+1,:) = [optFPE(1), optAIC(1), optBIC(1), optSBC(1), optCAT(1), optMDL(1), optPHI(1), optJEW(1), optHAR(1)];
end;
C=[FPE;AIC;BIC;SBC;MDL;CATcrit;PHI;JEW;HAR(:)']';
tsa/inst/eeg8s.mat 0000664 0023567 0023567 00000020032 10473652132 014550 0 ustar schloegl schloegl eeg8s À¼ÔØÕv¦ ç?Ôù!}ñ? Š S¡±¿ˆCÂÏl¦Õ? FƒV¾Ò㿘ºp´)"ã¿.4(Wý}å¿îÓ Ÿþê¿:€zø“^î?Ài[ =ÑŸ?¾C&ÆnCé¿ ŠB‹L Àö‚,„šÀ ß6¤* Àœà¹û,ó¿p<Ñï6¶?øßÚ}BFÍ?·•°]³ø?K’ù
J @VM§A61ã?â³Ô-dvó?jÇSÀÇäð?ôÕ(zGä¿0—–†!(¼¿Ð¶A)›¼?˜Ä”O
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M@¦oPº]ã¿ ˆ D )å?°@ Nš†¸?Jø#§yçç?7oìC‚;ð¿ÖO„â¾.î¿À\¬òÇHã¿sÿe½å³ñ¿Xï
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% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
%
% 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 .
[yr,yc] = size(Y);
if yr < yc,
fprintf(2,'Warning LCP: data vector Y must be a column not a row vector\n');
end;
if nargin < 2,
P = yr-1;
end;
% you can use any of the following routines.
% the lattice methods are preferable for stochastic time series.
% but can fail for deterministic signals see:
% http://sourceforge.net/mailarchive/message.php?msg_name=20080516115110.GB20642%40localhost
% [AR,RC,PE] = lattice(Y.',P); % Burg method
% [AR,RC,PE] = lattice(Y.',P,'GEOL'); % geometric lattice
[AR,RC,PE] = durlev(acovf(Y.',P)); % Yule-Walker
A = ar2poly(AR);
tsa/inst/ac2poly.m 0000664 0023567 0023567 00000002603 12115040716 014557 0 ustar schloegl schloegl function [A,E] = ac2poly(acf);
% converts the autocorrelation sequence into an AR polynomial
% [A,Efinal] = ac2poly(r)
%
% see also ACOVF ACORF AR2RC RC2AR DURLEV AC2POLY, POLY2RC, RC2POLY, RC2AC, AC2RC, POLY2AC
%
% $Id: ac2poly.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
if all(size(acf))>1,
fprintf(2,'Error AC2POLY: "r" must be a vector\n');
return;
end;
mfilename='AC2POLY';
if ~exist('durlev','file')
fprintf(2,'Error %s: DURLEV.M not found. \n Download TSA toolbox from http://pub.ist.ac.at/~schloegl/matlab/tsa/\n',mfilename);
return;
end;
[AR,RC,PE] = durlev(acf);
A = [ones(size(AR,1),1),-AR];
E = PE(:,size(PE,2));
tsa/inst/invest0.m 0000664 0023567 0023567 00000005206 12115040716 014600 0 ustar schloegl schloegl function [AutoCov,AutoCorr,MX,E,NC]=invest0(Y,Pmax,Mode);
% First Investigation of a signal (time series) - automated part
% [AutoCov,AutoCorr,ARPMX,E,ACFsd,NC]=invest0(Y,Pmax);
%
% [AutoCov,AutoCorr,ARPMX,E,ACFsd,NC]=invest0(AutoCov,Pmax,Mode);
%
%
% Y time series
% Pmax maximal order (optional)
%
% AutoCov Autocorrelation
% AutoCorr normalized Autocorrelation
% PartACF Partial Autocorrelation
% ARPMX Autoregressive Parameter for order Pmax-1
% E Error function E(p)
% NC Number of values (length-missing values)
%
% REFERENCES:
% P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% M.S. Grewal and A.P. Andrews "Kalman Filtering" Prentice Hall, 1993.
% S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% $Id: invest0.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2003,2008,2010 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://biosig-consulting.com/matlab/tsa/
%
% 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 .
if nargin<3
Mode=0;
else
Mode=1;
end;
[nr,nc]=size(Y);
NC = sumskipnan(real(~isnan(Y)),2); % number of valid components (data points)
if Mode==0
if nargin<2, Pmax = min([100 nc/3]); end;
M = min(Pmax,nc-1);
AutoCov = acovf(Y,M);
else
AutoCov=Y;
M = min(Pmax,nc-1);
nc = Pmax;
%M=size(AutoCov,2)-1;
end;
AutoCorr = AutoCov(:,2:M+1)./AutoCov(:,ones(M,1));
if 1,Pmax<100; % this needs change of sinvest1
K=M-1;
[MX,E]=lattice(Y,Pmax);
%[MX,E]=ulsar(Y,Pmax);
%[MX,E]=durlev(AutoCov);
% ARP=MX(:,K*(K-1)/2+(1:K));
% PartACF =MX(:,(1:K).*(2:K+1)/2);
else %if nargout > 2
[ARP,RC,E]=lattice(Y,Pmax);
%[ARP,PartACF,E]=durlev(AutoCov);
%MX=[ARP,RC];
end;
tsa/inst/sinvest1.m 0000664 0023567 0023567 00000034514 12115040716 014770 0 ustar schloegl schloegl %SINVEST1 shows the parameters of a time series calculated by INVEST1
% only called by INVEST1
% $Id: sinvest1.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
Fs=flag_implicit_samplerate;
M=size(AutoCorr,2);
oo=M-1;
while 1,
K=menu('Select','Autocovariance ACOVF','Autocorrelation ACF', ...
'Partial ACF PACF','Coeff. of Determination R²', ...
'Error curve',...
'Autoregressive Parameters',...
'Information Criteria', ...
'Matched Filter', ...
'Log PSD and Phase', ...
'Poles', ...
'Inverse Filtering', ...
'Spectra H(f,p)', ...
'Entropy H=ln(det(R))', ...
'Histogram of MOPS', ...
'end');
subplot(111);
if K==1
plot(0:M,AutoCov);
title('Autocovariance function ACOVF(k)');
xlabel('Lag k');
elseif K==2
if exist('OCTAVE_VERSION')==5 %%%%% Fuer OCTAVE
plot(1:M,AutoCorr);
elseif strcmp(version,'MIDEVA') %%%%% fuer MatCom
plot(1:M,AutoCorr);
else %%%%% fuer Matlab
% size(AutoCorr),size(ACFsd)]
% errorbar(ones(nr,1)*(1:M),AutoCorr,ACFsd,ACFsd);
% errorbar(1:M,AutoCorr,AutoCorr-ACFsd,AutoCorr+ACFsd);
plot(1:M,AutoCorr,'b',[1,M],[-1,1;-1,1]/sqrt(min(NC)),'b:');
if exist('OCTAVE_VERSION')<5
legend({'ACF','1/sqrt(N)'})
end;
end;
title('Autocorrelation function ACF(k)');
xlabel('Lag k');
elseif K==3
%rc=ARPMX(:,(1:M).*(2:M+1)/2);
%plot(1:M,PartACF);
if size(ARPMX,2)==2*Pmax,
RC=ARPMX(:,Pmax+1:2*Pmax);
else
RC=ARPMX(:,(1:M).*(2:M+1)/2);
end;
% according to http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4463.htm
% is the 95% confidence interval 2/sqrt(N)
%plot(1:M,RC,'b',[1,M],[3,3;2,2;1,1;-1,-1;-2,-2;-3,-3]*(1/sqrt(min(NC))),'b:');
%legend({'Part. ACF','1/sqrt(N)','2/sqrt(N) = 95% confidence interval','3/sqrt(N)'})
plot(1:M,RC,'b',[1,M],[2,2;-2,-2]'*(1/sqrt(min(NC))),'b:');
if exist('OCTAVE_VERSION')<5
legend({'Part. ACF','2/sqrt(N) = 95% confidence interval'})
end;
title('Partial Autocorrelation function PACF(k)');
xlabel('Lag k');
elseif K==4
plot(0:M,(E(1)-E)/E(1));
title('Determination of Regression R²=1-var{E}/var{Y}');
xlabel('Model order p');
elseif K==5
plot(0:M,E,'r');
if exist('OCTAVE_VERSION')<5
v=axis; v(3)=min([v(3); 0]); axis(v);
end
title('Mean Square (prediction) Error decrease with increasing model order');
xlabel('Model order p');
elseif K==6
plot(1:oo,ARPMX(:,oo/2*(oo-1)+(1:oo))','o-');
title( ['AutoRegressive Parameters with model order' int2str(oo)])
elseif K==7
while 1
K=menu('Select Criterion',...
'Final Predection Error FPE', ...
'Akaike Information Criterion AIC', ...
'Bayesian Akaike Information Criterion BIC', ...
'Schwartz''s Bayesian Crit. SBC (=MDL)', ...
'Parzen''s CAT Criterion ', ...
'PHI Criterion', ...
'end');
if K==1
tmp=min(2*optFPE+2,M);
oo=optFPE;
plot(0:tmp-1,FPE(1:tmp),'r',optFPE,FPE(optFPE+1),'ro');
if exist('OCTAVE_VERSION')<5
text(optFPE,FPE(optFPE+1),sprintf('%i',optFPE));
v=axis; v(3)=min([v(3); 0]); axis(v);
end;
title('Final Prediction Error FPE criterion');
elseif K==2
tmp=min(2*optAIC+2,M);
oo=optAIC;
plot(0:tmp-1,AIC(1:tmp),'r',optAIC,AIC(optAIC+1),'ro');
if exist('OCTAVE_VERSION')<5
text(optAIC,AIC(optAIC+1),sprintf('%i',optAIC));
v=axis; v(3)=min([v(3); 0]); axis(v);
end;
title('Akaike''s Information Criterion AIC');
elseif K==3
tmp=min(2*optBIC+2,M);
oo=optBIC;
plot(0:tmp-1,BIC(1:tmp),'r',optBIC,BIC(optBIC+1),'ro');
if exist('OCTAVE_VERSION')<5
text(optBIC,BIC(optBIC+1),sprintf('%i',optBIC));
v=axis; v(3)=min([v(3); 0]); axis(v);
end;
title('Bayesian Akaike Information Criterion BIC');
elseif K==4
tmp=min(2*optSBC+2,M);
oo=optSBC;
plot(0:tmp-1,SBC(1:tmp),'r',optSBC,SBC(optSBC+1),'ro');
if exist('OCTAVE_VERSION')<5
text(optSBC,SBC(optSBC+1),sprintf('%i',optSBC));
v=axis; v(3)=min([v(3); 0]); axis(v);
end;
title('Schwartz''s Bayesian Criterion SBC');
%elseif K==5
% tmp=min(2*optMDL,M);
% oo=optMDL;
% plot(0:tmp-1,MDL(1:tmp),'r',optMDL,MDL(optMDL+1),'ro');
% v=axis; v(3)=min([v(3); 0]); axis(v);
% text(optMDL,MDL(optMDL+1),sprintf('%i',optMDL))
% title('Minimal Description length Criterion MDL');
elseif K==5
tmp=min(2*optCAT+2,M);
oo=optCAT;
plot(0:tmp-1,CATcrit(1:tmp),'r',optCAT,CATcrit(optCAT+1),'ro');
if exist('OCTAVE_VERSION')<5
text(optCAT,CATcrit(optCAT+1),sprintf('%i',optCAT));
v=axis; v(3)=min([v(3); 0]); axis(v);
end;
title('Parzen''s CAT Criterion ');
elseif K==6
tmp=min(2*optPHI+2,M);
oo=optPHI;
plot(0:tmp-1,PHI(1:tmp),'r',optPHI,PHI(optPHI+1),'ro');
if exist('OCTAVE_VERSION')<5
text(optPHI,PHI(optPHI+1),sprintf('%i',optPHI));
v=axis; v(3)=min([v(3); 0]); axis(v);
end;
title('Phi criterion ');
elseif K==7
%[ARP,rc,res] =durlev(sum(AutoCov(:,1:(oo+1)),1));
ARP=ARPMX(:,oo/2*(oo-1)+(1:oo));
break;
end; %IF
end; %WHILE
elseif K==8
% if model order p is given then the filter parameters A are
% A=[1; -earpyw(signal,p)]
% inverse filtering is invfiltsignal=filter(A,1,signal);
h=zeros(nr,512)';
w=zeros(nr,512)';
for k=1:nr,
tmp=freqz(sqrt(E(k,oo+1)),[1 -ARPMX(k,oo/2*(oo-1)+(1:oo))],512);
h(:,k)=tmp(:);
end
plot((0:511)/512/2*Fs,abs(h));
%plot((0:511)/512/2,abs(freqz(1,[1 -ARP]')));
title('Matched Filter');
xlabel('Frequency f');
ylabel('|H(f)|');
elseif K==9
h=zeros(nr,512)';
w=zeros(nr,512)';
for k=1:nr,
tmp=freqz(sqrt(E(k,oo+1)),[1 -ARPMX(k,oo/2*(oo-1)+(1:oo))],512);
h(:,k)=tmp(:);
end;
subplot(211);
semilogy((0:511)/512/2*Fs,abs(h'))
%semilogy((0:511)/512/2,abs(freqz(1,[1 -ARP]')))
title('Logarithmic Spectral Density Fct.');
subplot(212);
plot((0:511)/512/2*Fs,angle(h)');
%plot((0:511)/512/2,angle(freqz(1,[1 -ARP]')));
ylabel('rad');
elseif K==10
% clf;
%r = roots([1 -ARP]);
r = roots([1 -ARPMX(:,oo/2*(oo-1)+(1:oo))]);
t = 0:1/70:2*pi;
plot(cos(t), sin(t), 'b:',real(r), imag(r), 'rx');
% zplane([],[1 -ARPmx(oo+1,1:oo)]);
title('Pole Diagram');
xlabel('real(z)');
ylabel('imag(z)');
MATLAB_VERSION = version;
if MATLAB_VERSION(1)=='4'
ax = gca;
tmp = get(ax,'Aspect');
set(ax,'Aspect',[tmp(1),1]);
elseif MATLAB_VERSION(1)=='5'
ax = gca;
tmp = get(ax,'DataAspectRatio');
set(ax,'PlotBoxAspectRatio',tmp);
end;
elseif K==11
plot([Y(:) filter([1 -ARPMX(:,oo/2*(oo-1)+(1:oo))],1,Y(:))-max(Y)+min(Y)]);
elseif K==12
%[tmp,ARPmx,PE]= acf2pacf(AutoCov(2:length(AutoCov))/AutoCov(1),AutoCov(1));
%[arp,rc,PE,ARPMX] = durlev(AutoCov);
%[tmp,ARPmx]=arp2pacf(AR);
N = Pmax-1; %2*oo-1; %2^(ceil(log(oo)/log(2)));
sdf = zeros(512,N); %length(AR));
for k = 1:N; %[k,size(sdf),N],%length(AR);
% [sdf(:,k),F] = freqz(1,[1 -ARPmx(k,1:k)],N);
[tmp,F] = freqz(1,[1 -ARPMX(1,k/2*(k+1)+(1:k))]',512);
sdf(:,k)=tmp(:);
% sdf(:,k)=sqrt(E(k+1))*sdf(:,k);
end;
mesh(F/2/pi*Fs,1:N,log10(abs(sdf)'));
zlabel('log10 |H(f,p)|');ylabel('model order p'); xlabel('frequency f [2*pi rad/s]');
if exist('OCTAVE_VERSION')<5
view(30,45);
end;
elseif K==13
for k=1:M,
xxx=eig(toeplitz(AutoCov(1:k)/E(k)));
H(k) = .5*sum(log(xxx));
H1(k)= H(k)/(k);
%xxx1=eig(toeplitz(AutoCov(1:k)/E(k)*E(1)));
%xxx1=eig(toeplitz(AutoCorr(1:k)));
%H2(k) =.5*sum(log(xxx1));
%H3(k)=.5*sum(log(xxx1))/(k);
%if any(xxx<=0) fprintf(1,'COV positive definite for p up to %i\n',k-1); break; end;
end;
if 0
subplot(211);
plot(0:k-1,H2');
title('Entropy H(k) depending on number of coefficients')
subplot(212);
plot(0:k-1,H3');
title('Entropy rate H(k) depending on number of coefficients')
else
subplot(311);
plot(0:k-1,H');
title('Entropy H(k) depending on number of coefficients')
subplot(312);
xxx=(-diff(log(E(:))))/2; %[size(xxx)size(H) M k]
plot(0:k-1,H1','b',1:k,xxx(1:k),'g');
%plot(0:k-1,H1','b',0:k-1,log(E(1:k))-log(E(1)),'b',1:k,xxx(1:k),'g');
%plot(0:k-1,H1','b',1:k,xxx,'g',1:k-1,cumprod(1+xxx(1:M-1))./H(2:M)','r');
title('Entropy rate H(k) and Entropy difference H(k)-H(k+1)')
subplot(313);
plot(1:k,log(xxx(1:k)),'g');
title('LOG Entropy difference for p->p+1: H(k)-H(k+1)')
end;
elseif K==14,
tmp = histo3(MOPS(1:max(1,size(MOPS,1)-1),:));
if exist('OCTAVE_VERSION')<5,
bar(tmp.X,tmp.H,'stacked');
else
bar(tmp.X,sum(tmp.H,2));
end;
xlabel('model order p')
if exist('OCTAVE_VERSION')<5
%legend({'FPE','AIC','BIC','SBC','MDL','CAT','PHI','JEW','HAR'});
legend('FPE','AIC','BIC','SBC','MDL','CAT','PHI','JEW','HAR');
end;
elseif K==15
break;
end;
end;
tsa/inst/mvaar.m 0000664 0023567 0023567 00000012070 12115040716 014313 0 ustar schloegl schloegl function [x,e,Kalman,Q2] = mvaar(y,p,UC,mode,Kalman)
% Multivariate (Vector) adaptive AR estimation base on a multidimensional
% Kalman filer algorithm. A standard VAR model (A0=I) is implemented. The
% state vector is defined as X=(A1|A2...|Ap) and x=vec(X')
%
% [x,e,Kalman,Q2] = mvaar(y,p,UC,mode,Kalman)
%
% The standard MVAR model is defined as:
%
% y(n)-A1(n)*y(n-1)-...-Ap(n)*y(n-p)=e(n)
%
% The dimension of y(n) equals s
%
% Input Parameters:
%
% y Observed data or signal
% p prescribed maximum model order (default 1)
% UC update coefficient (default 0.001)
% mode update method of the process noise covariance matrix 0...4 ^
% correspond to S0...S4 (default 0)
%
% Output Parameters
%
% e prediction error of dimension s
% x state vector of dimension s*s*p
% Q2 measurement noise covariance matrix of dimension s x s
%
% $Id: mvaar.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 2001-2002 Christian Kasess
% Copyright (C) 2003, 2008 Alois Schloegl
%
% 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 .
if nargin<4,
mode=0;
end;
if nargin<3,
UC=0.001
end;
if nargin<2,
p=1;
end
if nargin<1,
fprintf(2,'No arguments supplied\n');
return
end;
if ~any(mode==(0:4))
fprintf(2,'Invalid mode (0...4)\n');
return
end;
[M,LEN] = size(y'); %number of channels, total signal length
L = M*M*p;
if LEN<(p+1),
fprintf(2,'Not enough observed data supplied for given model order\n');
return
end
ye = zeros(size(y)); %prediction of y
if nargout>1,
x=zeros(L,LEN);
end;
if nargout>3,
Q2=zeros(M,M,LEN);
end
if nargin<5,
%Kalman Filter initialsiation (Kp (K predicted or a-priori) equals K(n+1,n) )
Kalman=struct('F',eye(L),'H',zeros(M,L),'G',zeros(L,M),'x',zeros(L,1),'Kp',eye(L),'Q1',eye(L)*UC,'Q2',eye(M),'ye',zeros(M,1));
end;
upd = eye(L)/L*UC; %diagonal matrix containing UC
if(mode==3)
Block=kron(eye(M),ones(M*p));
elseif(mode==4)
index=[];
Block1=[];
Block0=[];
for i=1:M,
index=[index ((i-1)*M*p+i:M:i*M*p)];
mone=eye(M);
mone(i,i)=0;
mzero=eye(M)-mone;
Block1=Blkdiag(Block1,kron(eye(p),mone));
Block0=Blkdiag(Block0,kron(eye(p),mzero));
end;
end;
for n = 2:LEN,
if(n<=p)
Yr=[y(n-1:-1:1,:)' zeros(M,p-n+1)]; %vector of past observations
Yr=Yr(:)';
else
Yr=y(n-1:-1:n-p,:)'; %vector of past observations
Yr=Yr(:)';
end
%Update of measurement matrix
Kalman.H=kron(eye(M),Yr);
%calculate prediction error
ye(n,:)=(Kalman.H*Kalman.x)';
err=y(n,:)-ye(n,:);
if ~any(isnan(err(:))),
%update of Q2 using the prediction error of the previous step
Kalman.Q2=(1-UC)*Kalman.Q2+UC*err'*err;
KpH=Kalman.Kp*Kalman.H';
HKp=Kalman.H*Kalman.Kp;
%Kalman gain
Kalman.G=KpH*inv(Kalman.H*KpH+Kalman.Q2);
%calculation of the a-posteriori state error covariance matrix
%K=Kalman.Kp-Kalman.G*KpH'; Althouh PK is supposed to be symmetric, this operation makes the filter unstable
K=Kalman.Kp-Kalman.G*HKp;
%mode==0 no update of Q1
%update of Q1 using the predicted state error cov matrix
if(mode==1)
Kalman.Q1=diag(diag(K)).*UC;
elseif(mode==2)
Kalman.Q1=upd*trace(K);
elseif(mode==3)
Kalman.Q1=diag(sum((Block*diag(diag(K)))'))/(p*M)*UC;
elseif(mode==4)
avg=trace(K(index,index))/(p*M)*UC;
Kalman.Q1=Block1*UC+Block0*avg;
end
%a-priori state error covariance matrix for the next time step
Kalman.Kp=K+Kalman.Q1;
%current estimation of state x
Kalman.x=Kalman.x+Kalman.G*(err)';
end; % isnan>(err)
if nargout>1,
x(:,n) = Kalman.x;
end;
if nargout>3,
Q2(:,:,n)=Kalman.Q2;
end;
end;
e = y - ye;
x = x';
tsa/inst/bispec.m 0000664 0023567 0023567 00000003155 12115040716 014456 0 ustar schloegl schloegl function [BISPEC,BIACF,ACF] = bispec(Z,N);
% Calculates Bispectrum
% [BISPEC] = bispec(Z,N);
%
% Input: Z Signal
% N # of coefficients
% Output: BiACF bi-autocorrelation function = 3rd order cumulant
% BISPEC Bi-spectrum
%
% Reference(s):
% C.L. Nikias and A.P. Petropulu "Higher-Order Spectra Analysis" Prentice Hall, 1993.
% M.B. Priestley, "Non-linear and Non-stationary Time series Analysis", Academic Press, London, 1988.
% $Id: bispec.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1997-2003,2008 by Alois Schloegl
%
% 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 .
P=N+1;
ACF=zeros(1,N+1);
BIACF=zeros(2*N+1,2*N+1);
Z=Z(:);
M=size(Z,1);
M1=sum(Z)/M;
Z=Z-M1*ones(size(Z));
for K=0:N,
jc2=Z(1:M-K).*Z(1+K:M);
ACF(K+1)=sum(jc2)/M;
for L = K:N,
jc3 = sum(jc2(1:M-L).*Z(1+L:M))/M;
BIACF(K+P, L+P) =jc3;
BIACF(L+P, K+P) =jc3;
BIACF(L-K+P, -K+P)=jc3;
BIACF(-K+P, L-K+P)=jc3;
BIACF(K-L+P, -L+P)=jc3;
BIACF(-L+P, K-L+P)=jc3;
end;
end;
BISPEC=fft2(BIACF,128,128);
tsa/inst/mvfilter.m 0000664 0023567 0023567 00000006152 12115040716 015041 0 ustar schloegl schloegl function [x,z]=mvfilter(B,A,x,z)
% Multi-variate filter function
%
% Y = MVFILTER(B,A,X)
% [Y,Z] = MVFILTER(B,A,X,Z)
%
% Y = MVFILTER(B,A,X) filters the data in matrix X with the
% filter described by cell arrays A and B to create the filtered
% data Y. The filter is a 'Direct Form II Transposed'
% implementation of the standard difference equation:
%
% a0*Y(n) = b0*X(:,n) + b1*X(:,n-1) + ... + bq*X(:,n-q)
% - a1*Y(:,n-1) - ... - ap*Y(:,n-p)
%
% A=[a0,a1,a2,...,ap] and B=[b0,b1,b2,...,bq] must be matrices of
% size Mx((p+1)*M) and Mx((q+1)*M), respectively.
% a0,a1,...,ap, b0,b1,...,bq are matrices of size MxM
% a0 is usually the identity matrix I or must be invertible
% X should be of size MxN, if X has size NxM a warning
% is raised, and the output Y is also transposed.
%
% A simulated MV-AR process can be generiated with
% Y = mvfilter(eye(M), [eye(M),-AR],randn(M,N));
%
% A multivariate inverse filter can be realized with
% [AR,RC,PE] = mvar(Y,P);
% E = mvfilter([eye(M),-AR],eye(M),Y);
%
% see also: MVAR, FILTER
% $Id: mvfilter.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2003,2010,2012 by Alois Schloegl
%
% 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 .
[ra, ca] = size(A);
[rb, cb] = size(B);
[M, N ] = size(x);
if (ra~=rb),
fprintf(2,'ERROR MVFILTER: number of rows of A and B do not fit\n');
return;
end;
if nargin<4,
z = []; %zeros(M,oo);
end;
if (M~=ra),
if (N==ra),
fprintf(2,'Warning MVFILTER: dimensions fit only to transposed data. X has been transposed.\n');
x = x.';
%[x,z] = mvfilter(B,A,x,z); x = x.'; return;
else
fprintf(2,'ERROR MVFILTER: dimensions do not fit\n');
return;
end;
end;
p = ca/M-1;
q = cb/M-1;
oo = max(p,q);
if isempty(z)
z = zeros(M,oo);
else
if any(size(z)~=[M,oo])
fprintf('Error MVFILTER: size of z does not fit\n');
[size(z),oo,M]
return;
end;
end;
%%%%% normalization to A{1}=I;
for k=1:p,
A(:,k*M+(1:M)) = A(:,1:M) \ A(:,k*M+(1:M));
end;
for k=0:q,
B(:,k*M+(1:M)) = A(:,1:M) \ B(:,k*M+(1:M));
end;
A(:,1:M) = eye(M);
for k = 1:N,
acc = B(:,1:M) * x(:,k) + z(:,1); % / A{1};
z = [z(:,2:oo), zeros(M,1)];
for l = 1:q,
z(:,l) = z(:,l) + B(:,l*M+(1:M)) * x(:,k);
end;
for l = 1:p,
z(:,l) = z(:,l) - A(:,l*M+(1:M)) * acc;
end;
x(:,k) = acc;
end;
tsa/inst/contents.m 0000664 0023567 0023567 00000013551 12115040716 015047 0 ustar schloegl schloegl % Time Series Analysis - A toolbox for the use with Matlab and Octave.
%
% $Id: contents.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1996-2004,2008 by Alois Schloegl
% WWW: http://pub.ist.ac.at/~schloegl/matlab/tsa/
%
% 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 .
%
%
% Time Series Analysis - a toolbox for the use with Matlab
% aar adaptive autoregressive estimator
% acovf (*) Autocovariance function
% acorf (acf) (*) autocorrelation function
% pacf (*) partial autocorrelation function, includes signifcance test and confidence interval
% parcor (*) partial autocorrelation function
% biacovf biautocovariance function (3rd order cumulant)
% bispec Bi-spectrum
% durlev (*) solves Yule-Walker equation - converts ACOVF into AR parameters
% lattice (*) calcultes AR parameters with lattice method
% lpc (*) calculates the prediction coefficients form a given time series
% invest0 (*) a prior investigation (used by invest1)
% invest1 (*) investigates signal (useful for 1st evaluation of the data)
% rmle AR estimation using recursive maximum likelihood function
% selmo (*) Select Order of Autoregressive model using different criteria
% histo (*) histogram
% hup (*) test Hurwitz polynomials
% ucp (*) test Unit Circle Polynomials
% y2res (*) computes mean, variance, skewness, kurtosis, entropy, etc. from data series
% ar_spa (*) spectral analysis based on the autoregressive model
% detrend (*) removes trend, can handle missing values, non-equidistant sampled data
% flix floating index, interpolates data for non-interger indices
%
%
% Multivariate analysis
% adim adaptive information matrix (inverse correlation matrix)
% mvar multivariate (vector) autoregressive estimation
% mvaar multivariate adaptvie autoregressive estimation using Kalman filtering
% mvfilter multivariate filter
% mvfreqz multivariate spectra
% arfit2 provides compatibility to ARFIT [Schneider and Neumaier, 2001]
%
%
% Conversions between Autocorrelation (AC), Autoregressive parameters (AR),
% prediction polynom (POLY) and Reflection coefficient (RC)
% ac2poly (*) transforms autocorrelation into prediction polynom
% ac2rc (*) transforms autocorrelation into reflexion coefficients
% ar2rc (*) transforms autoregressive parameters into reflection coefficients
% rc2ar (*) transforms reflection coefficients into autoregressive parameters
% poly2ac (*) transforms polynom to autocorrelation
% poly2ar (*) transforms polynom to AR
% poly2rc (*)
% rc2ac (*)
% rc2poly (*)
% ar2poly (*)
%
% Utility functions
% sinvest1 shows the parameter calculated by INVEST1
%
% Test suites
% tsademo demonstrates INVEST1 on EEG data
% invfdemo demonstration of matched, inverse filtering
% bisdemo demonstrates bispectral estimation
%
% (*) indicates univariate analysis of multiple data series (each in a row) can be processed.
% (-) indicates that these functions will be removed in future
%
% REFERENCES (sources):
% http://www.itl.nist.gov/
% http://mathworld.wolfram.com/
% P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% O. Foellinger "Lineare Abtastsysteme", Oldenburg Verlag, Muenchen, 1986.
% F. Gausch "Systemtechnik", Textbook, University of Technology Graz, 1993.
% M.S. Grewal and A.P. Andrews "Kalman Filtering" Prentice Hall, 1993.
% S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996.
% E.I. Jury "Theory and Application of the z-Transform Method", Robert E. Krieger Publishing Co., 1973.
% M.S. Kay "Modern Spectal Estimation" Prentice Hall, 1988.
% Ch. Langraf and G. Schneider "Elemente der Regeltechnik", Springer Verlag, 1970.
% S.L. Marple "Digital Spetral Analysis with Applications" Prentice Hall, 1987.
% C.L. Nikias and A.P. Petropulu "Higher-Order Spectra Analysis" Prentice Hall, 1993.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% T. Schneider and A. Neumaier "Algorithm 808: ARFIT - a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models"
% ACM Transactions on Mathematical software, 27(Mar), 58-65.
% C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
%
%
% REFERENCES (applications):
% [1] A. Schlögl, B. Kemp, T. Penzel, D. Kunz, S.-L. Himanen,A. Värri, G. Dorffner, G. Pfurtscheller.
% Quality Control of polysomnographic Sleep Data by Histogram and Entropy Analysis.
% Clin. Neurophysiol. 1999, Dec; 110(12): 2165 - 2170.
% [2] Penzel T, Kemp B, Klösch G, Schlögl A, Hasan J, Varri A, Korhonen I.
% Acquisition of biomedical signals databases
% IEEE Engineering in Medicine and Biology Magazine 2001, 20(3): 25-32
% [3] Alois Schlögl (2000)
% The electroencephalogram and the adaptive autoregressive model: theory and applications
% Shaker Verlag, Aachen, Germany,(ISBN3-8265-7640-3).
%
% Features:
% - Multiple Signal Processing
% - Efficient algorithms
% - Model order selection tools
% - higher (3rd) order analysis
% - Maximum entropy spectral estimation
% - can deal with missing values (NaN's)
tsa/inst/ar2poly.m 0000664 0023567 0023567 00000002757 12115040716 014610 0 ustar schloegl schloegl function [A] = ar2poly(A);
% converts autoregressive parameters into AR polymials
% Multiple polynomials can be converted.
% function [A] = ar2poly(AR);
%
% INPUT:
% AR AR parameters, each row represents one set of AR parameters
%
% OUTPUT
% A denominator polynom
%
%
% see also ACOVF ACORF DURLEV RC2AR FILTER FREQZ ZPLANE
%
% REFERENCES:
% P.J. Brockwell and R. A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
% S. Haykin "Adaptive Filter Theory" 3rd ed. Prentice Hall, 1996.
% M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
% W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
% $Id: ar2poly.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
% Inititialization
[lr,lc]=size(A);
A = [ones(size(A,1),1),-A];
tsa/inst/poly2ar.m 0000664 0023567 0023567 00000002447 12115040716 014604 0 ustar schloegl schloegl function [A] = poly2ar(A);
% Converts AR polymials into autoregressive parameters.
% Multiple polynomials can be converted.
%
% function [AR] = poly2ar(A);
%
% INPUT:
% A AR polynomial, each row represents one polynomial
%
% OUTPUT
% AR autoregressive model parameter
%
% see also ACOVF ACORF DURLEV RC2AR AR2POLY
% $Id: poly2ar.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008 by Alois Schloegl
%
% 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 .
% Inititialization
[lr,lc]=size(A);
if ~all(A(:,1)==1)
fprintf(2,'Warning POLY2AR: input argument might not be an AR-polynom');
end;
A = -A(:,2:size(A,2))./A(:,ones(1,size(A,2)-1));
tsa/inst/rc2poly.m 0000664 0023567 0023567 00000003243 12115040716 014601 0 ustar schloegl schloegl function [a,efinal] = rc2poly(RC,E);
% converts reflection coefficients into an AR-polynomial
% [a,efinal] = rc2poly(K)
%
% see also ACOVF ACORF AR2RC RC2AR DURLEV AC2POLY, POLY2RC, RC2POLY, RC2AC, AC2RC, POLY2AC
%
% $Id: rc2poly.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1998-2002,2008,2012 by Alois Schloegl
% This is part of the TSA-toolbox. See also
% http://pub.ist.ac.at/~schloegl/matlab/tsa/
% http://octave.sourceforge.net/
% http://biosig.sourceforge.net/
%
% 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 .
if nargout>1 && nargin<2
fprintf('Zero-lag autocorrelation, R0 not specified\n')
return;
end;
mfilename='RC2POLY';
if all(size(RC))>1,
fprintf(2,'Error %s: "K" must be a vector\n',mfilename);
return;
end;
if ~exist('rc2ar','file')
fprintf(2,'Error %s: RC2AR.M not found. \n Download TSA toolbox from http://pub.ist.ac.at/~schloegl/matlab/tsa/\n',mfilename);
return;
end;
[AR,RC,PE] = rc2ar(RC(:).');
a=[1,-AR];
efinal=PE(length(PE))/PE(1);
tsa/inst/sumskipnan.m 0000664 0023567 0023567 00000012714 12264575422 015416 0 ustar schloegl schloegl 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.
% SUMSKIPNAN of NaN's only gives O; and the number of valid elements is return.
% SUMSKIPNAN is also the elementary function for calculating
% various statistics (e.g. MEAN, STD, VAR, RMS, MEANSQ, SKEWNESS,
% KURTOSIS, MOMENT, STATISTIC etc.) from data with missing values.
% SUMSKIPNAN implements the DIMENSION-argument for data with missing values.
% Also the second output argument return the number of valid elements (not NaNs)
%
% Y = sumskipnan(x [,DIM])
% [Y,N,SSQ] = sumskipnan(x [,DIM])
% [...] = sumskipnan(x, DIM, W)
%
% 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
%
% the function FLAG_NANS_OCCURED() returns whether any value in x
% is a not-a-number (NaN)
%
% 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,
% SSQ, MOMENT, SKEWNESS, KURTOSIS, SEM
% 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 .
% $Id: sumskipnan.m 9033 2011-11-08 20:58:07Z schloegl $
% Copyright (C) 2000-2005,2009,2011 by Alois Schloegl
% This function is part of the NaN-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/NaN/
global FLAG_NANS_OCCURED;
if nargin<2,
DIM = [];
end;
if nargin<3,
W = [];
end;
% an efficient implementation in C of the following lines
% could significantly increase performance
% only one loop and only one check for isnan is needed
% An MEX-Implementation is available in sumskipnan.cpp
%
% Outline of the algorithm:
% for { k=1,o=0,count=0; k++; k1,1);
if isempty(DIM), DIM = 1; end;
end
if (DIM<1), DIM = 1; end; %% Hack, because min([])=0 for FreeMat v3.5
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% non-float data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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
sz = size(x);
N = sz(DIM);
sz(DIM) = 1;
count = repmat(N,sz);
if nargout>2
x = x.*x;
SSQ = sum(x,DIM);
end;
end;
return;
end;
if (length(size(x))=3),
[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,W);
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;
if ~isempty(W)
error('weighted sumskipnan requires sumskipnan_mex');
end;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% count non-NaN's
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargout>1,
count = sum(x==x,DIM);
FLAG_NANS_OCCURED = any(count(:)2,
x = real(x).^2 + imag(x).^2;
SSQ = sum(x,DIM);
end;
%!assert(sumskipnan([1,2],1),[1,2])
%!assert(sumskipnan([1,NaN],2),1)
%!assert(sumskipnan([1,NaN],2),1)
%!assert(sumskipnan([nan,1,4,5]),10)
%!assert(sumskipnan([nan,1,4,5]',1,[3;2;1;0]),6)
tsa/inst/biacovf.m 0000664 0023567 0023567 00000002503 12115040716 014616 0 ustar schloegl schloegl function [BIACF,ACF,M1] = biacovf(Z,N);
% BiAutoCovariance function
% [BiACF] = biacovf(Z,N);
%
% Input: Z Signal
% N # of coefficients
% Output: BIACF bi-autocorrelation function (joint cumulant 3rd order
% Output: ACF covariance function (joint cumulant 2nd order)
% $Id: biacovf.m 11693 2013-03-04 06:40:14Z schloegl $
% Copyright (C) 1997, 1998, 2008 by Alois Schloegl
%
% 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 .
ACF=zeros(1,N+1);
BIACF=zeros(N+1,N+1);
Z=Z(:);
M=size(Z,1);
M1=sum(Z)/M;
Z=Z-M1*ones(size(Z));
for K=0:N,
tmp=Z(1:M-K).*Z(1+K:M);
ACF(K+1)=sum(tmp)/M;
for L = K:N,
BIACF(K+1,L+1) = sum(tmp(1:M-L).*Z(1+L:M))/M;
end;
end;
BIACF=BIACF+BIACF'-diag(diag(BIACF));