求C语言版的lyapunov指数计算程序！

weitaolu · 发表于 2007-4-23 18:58

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有没有高人有C语言版的lyapunov指数计算程序？
有的话送我一个，我的油箱：twh4108@126.com。多谢了！！！

yanzy128 · 发表于 2007-4-24 14:10

请问你是计算一维还是高维系统的？
我可以给你、

weitaolu · 发表于 2007-4-24 18:43

四维的，不管是几维的，如果你有的话就发给我一个，发到我信箱里吧。
多谢了！！！

gghhjj · 发表于 2007-4-25 02:08

small data method

/*
l1d2.c ... generates scaling data for calculating the largest
Lyapunov exponent and the correlation dimension
Copyright (c) 1999. Michael T. Rosenstein.
This program is free software; you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation; either version 2 of the License, or (at your option) any later
version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
this program; if not, write to the Free Software Foundation, Inc., 59 Temple
Place - Suite 330, Boston, MA 02111-1307, USA.
You may contact the author by e-mail (mtr@cs.umass.edu) or postal mail
(c/o Department of Computer Science, University of Massachusetts, 140 Governors
Drive, Amherst, MA 01003-4610 USA). For updates to this software, please visit
PhysioNet (http://www.physionet.org/).
reference: M.T. Rosenstein, J.J. Collins, C.J. De Luca,
A practical method for calculating largest
Lyapunov exponents from small data sets,
Physica D 65:117-134, 1993.
email contact: mtr@cs.umass.edu
*/
#include <ctype.h>
#include <float.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define IOTA 10e-15
#define MAX_LN_R 12
#define MIN_LN_R -12
#define N_LN_R 600
typedef struct
{
char fileName[16];
long startIndex, stopIndex;
int seriesN, m, J, W, divergeT;
} test;
double **AllocateDMatrix(long nRows, long nCols);
void ComputeSlopes(void);
void FreeDMatrix(double **mat, long nRows);
void GenerateTemplateFile(void);
long GetData(char *fileName, int tsN, long start, long stop);
void LineFit(double *data, double n, double *m, double *b, double *rr);
void PercentDone(int percentDone);
void ProcessTest(int testN);
void ReadTestFile(char *fileName);
void SaveL1Results(char *fileRoot);
void SaveD2Results(char *fileRoot);
int gNTests, gMaxDivergeT;
double *gData, *gNDivergence;
double **gDivergence, **gCSum;
test *gTest;
int main(void)
{
int i;
char str[256];
printf("\n");
printf("*** L1D2: generates scaling information for L1 and D2 ***\n");
printf(" v1.0 Copyright (c) 1999 M.T. Rosenstein\n");
printf(" (mtr@cs.umass.edu)\n\n");
printf(" reference: M.T. Rosenstein, J.J. Collins, C.J. De Luca,\n");
printf(" A practical method for calculating largest\n");
printf(" Lyapunov exponents from small data sets,\n");
printf(" Physica D 65:117-134, 1993.\n\n");
GenerateTemplateFile();
printf("Enter test file name: ");
scanf("%s", str);
ReadTestFile(str);
printf("\nEnter output file root (no extension): ");
scanf("%s", str);
/* allocate the divergence and correlation sum arrays */
for(i=0; i<gNTests; i++)
if(gTest[i].divergeT > gMaxDivergeT)
gMaxDivergeT = 1+gTest[i].divergeT;
gNDivergence = (double *) calloc(gMaxDivergeT, sizeof(double));
gDivergence = AllocateDMatrix(gMaxDivergeT, 2*gNTests);
gCSum = AllocateDMatrix(N_LN_R, 2*gNTests);
for(i=0; i<gNTests; i++)
ProcessTest(i);
ComputeSlopes();
printf("\n");
SaveL1Results(str);
SaveD2Results(str);
printf("\nSuccess!\n");
return(0);
}
double **AllocateDMatrix(long nRows, long nCols)
{
long i;
double **mat;
/* allocate space for the row pointers */
mat = (double **) calloc(nRows, sizeof(double *));
if(mat == NULL)
{
printf("OUT OF MEMORY: AllocateDMatrix(%ld, %ld)\n\n", nRows, nCols);
exit(1);
};
/* allocate space for each row pointer */
for(i=0; i<nRows; i++)
mat[i] = (double *) calloc(nCols, sizeof(double));
if(mat[i-1] == NULL)
{
printf("OUT OF MEMORY: AllocateDMatrix(%ld, %ld)\n\n", nRows, nCols);
exit(1);
};
return(mat);
}
void ComputeSlopes(void)
{
int i, i2, j;
double k, m, b, rr;
double *data;
data = (double *) calloc(N_LN_R>gMaxDivergeT ? N_LN_R : gMaxDivergeT,
sizeof(double));
if(data == NULL)
{
printf("OUT OF MEMORY: ComputeSlopes\n\n");
exit(1);
};
for(i=0; i<gNTests; i++)
{
i2 = i+gNTests;
/*** work on correlation dimension first ***/
k = (double) N_LN_R/(MAX_LN_R-MIN_LN_R);
/* pack the array column into the local data array */
for(j=0; j<N_LN_R; j++)
data[j] = gCSum[j][i];
/* compute the 7-point slopes */
for(j=3; j<N_LN_R-3; j++)
{
LineFit(data+j-3, 7, &m, &b, &rr);
gCSum[j][i2] = k*m;
};
/* handle the edges */
LineFit(data, 5, &m, &b, &rr); gCSum[2][i2] = k*m;
LineFit(data+N_LN_R-5, 5, &m, &b, &rr); gCSum[N_LN_R-3][i2] = k*m;
LineFit(data, 3, &m, &b, &rr); gCSum[1][i2] = k*m;
LineFit(data+N_LN_R-3, 3, &m, &b, &rr); gCSum[N_LN_R-2][i2] = k*m;
gCSum[0][i2] = k*(data[1]-data[0]);
gCSum[N_LN_R-1][i2] = k*(data[N_LN_R-1]-data[N_LN_R-2]);
/*** now work on divergence data ***/
/* pack the array column into the local data array */
for(j=0; j<gMaxDivergeT; j++)
data[j] = gDivergence[j][i];
/* compute the 7-point slopes */
for(j=3; j<gMaxDivergeT-3; j++)
{
LineFit(data+j-3, 7, &m, &b, &rr);
gDivergence[j][i2] = m;
};
/* handle the edges */
LineFit(data, 5, &m, &b, &rr); gDivergence[2][i2] = m;
LineFit(data+gMaxDivergeT-5, 5, &m, &b, &rr);
gDivergence[gMaxDivergeT-3][i2] = m;
LineFit(data, 3, &m, &b, &rr);
gDivergence[1][i2] = m;
LineFit(data+gMaxDivergeT-3, 3, &m, &b, &rr);
gDivergence[gMaxDivergeT-2][i2] = m;
gDivergence[0][i2] = data[1]-data[0];
gDivergence[gMaxDivergeT-1][i2] = data[gMaxDivergeT-1]-
data[gMaxDivergeT-2];
};
}
void FreeDMatrix(double **mat, long nRows)
{
long i;
/* free space for each row pointer */
for(i=nRows-1; i>=0; i--)
free(mat[i]);
/* free space for the row pointers */
free(mat);
}
void GenerateTemplateFile(void)
{
FILE *outFile;
outFile = fopen("sample.l1d2", "w");
fprintf(outFile, "* Header info starts with an asterisk.\n*\n");
fprintf(outFile, "* Each line of the test file contains the name of a data file followed\n");
fprintf(outFile, "* by the parameters for the test:\n");
fprintf(outFile, "* file_name series# startIndex stopIndex m J W divergeT\n*\n");
fprintf(outFile, "* file_name = name of the data file\n");
fprintf(outFile, "* series# = time series number to use for delay reconstruction\n");
fprintf(outFile, "* startIndex = index of first data point to read (usually 1)\n");
fprintf(outFile, "* stopIndex = index of last data point to read\n");
fprintf(outFile, "* (enter 0 for maximum)\n");
fprintf(outFile, "* m = embedding dimension\n");
fprintf(outFile, "* J = delay in samples\n");
fprintf(outFile, "* W = window size for skipping temporally close nearest neighbors\n");
fprintf(outFile, "* divergT = total divergence time in samples\n");
fprintf(outFile, "* example: lorenz.dat 1 1 0 5 7 100 300\n");
fclose(outFile);
}
long GetData(char *fileName, int tsN, long start, long stop)
{
long i, j, len;
long nHead, nCols, nRows, nPts;
char str[1024];
double dummy;
FILE *inFile;
/* try to open the data file */
inFile = fopen(fileName, "r");
if(inFile == NULL)
{
printf("FILE ERROR: GetData(%s)\n\n", fileName);
exit(1);
};
/* skip the header */
nHead = 0;
fgets(str, 1024, inFile);
while(str[0]=='*')
{
nHead++;
fgets(str, 1024, inFile);
};
/* figure out the number of columns */
len = strlen(str);
i = 0;
nCols = 0;
while(i<len)
{
if(!isspace(str[i]))
{
nCols++;
while(!isspace(str[i]) && i<len)
i++;
while(isspace(str[i]) && i<len)
i++;
}
else
i++;
};
/* figure out the number of rows; assume there's at least one */
nRows = 1;
while(fgets(str, 1024, inFile) != NULL)
nRows++;
if(stop<start)
stop = nRows;
else if(stop>nRows)
stop = nRows;
if(start<1 || start>stop-3)
start = 1;
nPts = stop-start+1;
gData = calloc(nPts, sizeof(double));
/* now read the time series data */
rewind(inFile);
for(i=0; i<nHead; i++)
fgets(str, 1024, inFile);
for(i=1; i<start; i++)
for(j=0; j<nCols; j++)
fscanf(inFile, "%lf", &dummy);
for(i=0; i<nPts; i++)
{
for(j=0; j<tsN; j++)
fscanf(inFile, "%lf", &dummy);
gData[i] = dummy;
for(; j<nCols; j++)
fscanf(inFile, "%lf", &dummy);
};
fclose(inFile);
return(nPts);
}
void LineFit(double *data, double n, double *m, double *b, double *rr)
{
int i;
double sx, sy, sxy, sx2, sy2;
double x, y, k, mTemp, bTemp, rrTemp;
sx = sy = sxy = sx2 = sy2 = 0;
for(i=0; i<n; i++)
{
x = i;
y = data[i];
sx += x; sy += y;
sx2 += x*x; sy2 += y*y;
sxy += x*y;
};
k = n*sx2-sx*sx;
mTemp = (n*sxy-sx*sy)/k;
bTemp = (sx2*sy-sx*sxy)/k;
k = sy*sy/n;
if(k==sy2)
rrTemp = 1.0;
else
{
rrTemp = (bTemp*sy+mTemp*sxy-k)/(sy2-k);
rrTemp = 1.0 - (1.0-rrTemp)*(n-1.0)/(n-2.0);
};
*m = mTemp;
*b = bTemp;
*rr = rrTemp;
}
void PercentDone(int percentDone)
{
static last=100;
if(percentDone<last)
{
last = 0;
printf("0");
fflush(stdout);
}
else if(percentDone>last && percentDone%2==0)
{
last = percentDone;
if(percentDone%10==0)
printf("%d", percentDone/10);
else
printf(".");
fflush(stdout);
};
}
void ProcessTest(int testN)
{
long m, J, W, divergeT, neighborIndex, maxIndex;
long i, j, k, T, CSumIndex, percentDone;
long nPts, nCompletedPairs=0, nVectors;
char *isNeighbor;
double distance, d;
double k1, k2, temp, kNorm;
printf("\nProcessing test %d of %d: ", testN+1, gNTests);
fflush(stdout);
m = gTest[testN].m;
J = gTest[testN].J;
W = gTest[testN].W;
divergeT = gTest[testN].divergeT;
nPts = GetData(gTest[testN].fileName, gTest[testN].seriesN,
gTest[testN].startIndex, gTest[testN].stopIndex);
k1 = (double) N_LN_R/(MAX_LN_R-MIN_LN_R);
k1 *= 0.5; /* accounts for the SQUARED distances later on */
k2 = N_LN_R/2;
nVectors = nPts-J*(m-1);
isNeighbor = (char *) calloc(nVectors, sizeof(char));
if(isNeighbor==NULL)
{
printf("\nOUT OF MEMORY: ProcessTest\n\n");
exit(1);
};
/* clear the divergence arrays */
for(i=0; i<gMaxDivergeT; i++)
gNDivergence[i] = gDivergence[i][testN] = 0;
/* loop through vectors */
i = 0;
while(i<nVectors)
{
percentDone = 100.0*nCompletedPairs/nVectors*2+0.5;
percentDone = 100.0*i/nVectors+0.5;
PercentDone(percentDone);
if(!isNeighbor[i])
{
distance = 10e10;
/* find the nearest neighbor for the vector at i */
/* ignore temporally close neighbors using W */
if(i>W)
for(j=0; j<i-W; j++)
{
/* calculate distance squared */
d=0;
for(k=0; k<m; k++)
{
T = k*J;
temp = gData[i+T]-gData[j+T];
temp *= temp;
d += temp;
};
d += IOTA;
/* map the squared distance to array position */
CSumIndex = k1*log(d)+k2;
if(CSumIndex<0)
CSumIndex = 0;
if(CSumIndex>=N_LN_R)
CSumIndex = N_LN_R-1;
/* increment the correlation sum array */
gCSum[CSumIndex][testN]++;
/* now compare to current nearest neighbor */
/* use IOTA above to ignore nbrs that are too close */
if(d<distance)
{
distance=d;
neighborIndex=j;
};
};
if(i<nVectors-W)
for(j=i+W; j<nVectors; j++)
{
d=0;
for(k=0; k<m; k++)
{
T = k*J;
temp = gData[i+T]-gData[j+T];
temp *= temp;
d += temp;
};
d += IOTA;
CSumIndex = k1*log(d)+k2;
if(CSumIndex<0)
CSumIndex = 0;
if(CSumIndex>=N_LN_R)
CSumIndex = N_LN_R-1;
gCSum[CSumIndex][testN]++;
if(d<distance)
{
distance=d;
neighborIndex=j;
};
};
isNeighbor[neighborIndex] = 1;
/* track divergence */
for(j=0; j<=divergeT; j++)
{
maxIndex = nPts-m*J-j-1;
if(i<maxIndex && neighborIndex<maxIndex)
{
d=0;
for(k=0; k<m; k++)
{
T = k*J+j;
temp = gData[i+T]-gData[neighborIndex+T];
temp *= temp;
d += temp;
};
d += IOTA;
gNDivergence[j]++;
temp = 0.5*log(d);
gDivergence[j][testN] += temp;
};
};
nCompletedPairs++;
};
i++;
};
/* integrate the correlation sum array to get the correlation sum curve */
for(i=1; i<N_LN_R; i++)
gCSum[i][testN] += gCSum[i-1][testN];
/* next normalize values */
kNorm = 1.0/gCSum[N_LN_R-1][testN];
for(i=0; i<N_LN_R; i++)
gCSum[i][testN] *= kNorm;
/* now take natural log of the values */
for(i=0; i<N_LN_R; i++)
{
temp = gCSum[i][testN];
if( (temp<0.000045) || (temp>0.990050) )
gCSum[i][testN] = 0;
else
gCSum[i][testN] = log(temp);
};
/* now take care of Lyapunovv average */
for(i=0; i<=divergeT; i++)
if(gNDivergence[i]>0)
gDivergence[i][testN] /= gNDivergence[i];
free(isNeighbor);
free(gData);
}
void ReadTestFile(char *fileName)
{
int i;
int nHead, nRows;
char str[1024];
FILE *inFile;
printf("\nReading Test File...\n");
/* try to open the data file */
inFile = fopen(fileName, "r");
if(inFile == NULL)
{
printf("FILE ERROR: ReadTestFile(%s)\n\n", fileName);
exit(1);
};
/* skip the header */
nHead = 0;
fgets(str, 1024, inFile);
while(str[0]=='*')
{
nHead++;
fgets(str, 1024, inFile);
};
/* figure out the number of rows; assume there's at least one */
nRows = 1;
while(fgets(str, 1024, inFile) != NULL && !isspace(str[0]))
nRows++;
gNTests = nRows;
/* allocate the test array */
gTest = (test *) calloc(gNTests, sizeof(test));
if(gTest == NULL)
{
printf("OUT OF MEMORY: ReadTestFile(%d)\n\n", gNTests);
exit(1);
};
printf("detected %d %s\n", gNTests, gNTests==1 ? "test" : "tests");
/* rewind the file and skip the header */
rewind(inFile);
for(i=0; i<nHead; i++)
fgets(str, 1024, inFile);
for(i=0; i<gNTests; i++)
fscanf(inFile, "%s %d %ld %ld %d %d %d %d\n",
gTest[i].fileName, &gTest[i].seriesN, &gTest[i].startIndex,
&gTest[i].stopIndex, &gTest[i].m, &gTest[i].J, &gTest[i].W,
&gTest[i].divergeT);
fclose(inFile);
}
void SaveD2Results(char *fileRoot)
{
int i, i1, i2, testN, keepGoing;
char str[256];
double k1, k2;
FILE *outFile;
printf("\nSaving data for correlation dimension...\n");
sprintf(str, "%s.d2", fileRoot);
outFile = fopen(str, "w");
k1 = (double) (MAX_LN_R-MIN_LN_R)/N_LN_R;
k2 = MIN_LN_R;
/* don't save rows of just zeros */
keepGoing = 1;
i1 = 0;
while(keepGoing)
{
for(testN=0; testN<gNTests; testN++)
if(gCSum[i1][testN]!=0)
{
keepGoing = 0;
break;
};
i1 += keepGoing;
};
i1--;
if(i1<0 || i1>=N_LN_R)
i1 = 0;
keepGoing = 1;
i2 = N_LN_R-1;
while(keepGoing)
{
for(testN=0; testN<gNTests; testN++)
if(gCSum[i2][testN]!=0)
{
keepGoing = 0;
break;
};
i2 -= keepGoing;
};
i2++;
if(i2<0 || i2>=N_LN_R)
i2 = N_LN_R-1;
/* write the data */
for(i=i1; i<i2+1; i++)
{
fprintf(outFile, "%lf\t", k1*i+k2);
for(testN=0; testN<gNTests; testN++)
fprintf(outFile, "%lf\t", gCSum[i][testN]);
/* write slope data */
fprintf(outFile, "%lf\t", k1*i+k2);
for(; testN<2*gNTests-1; testN++)
fprintf(outFile, "%lf\t", gCSum[i][testN]);
fprintf(outFile, "%lf\n", gCSum[i][testN]);
};
fclose(outFile);
}
void SaveL1Results(char *fileRoot)
{
int i, testN;
char str[256];
FILE *outFile;
printf("\nSaving data for largest Lyapunov exponent...\n");
sprintf(str, "%s.l1", fileRoot);
outFile = fopen(str, "w");
for(i=0; i<gMaxDivergeT; i++)
{
fprintf(outFile, "%d\t", i);
for(testN=0; testN<gNTests; testN++)
if(i<=gTest[testN].divergeT)
fprintf(outFile, "%lf\t", gDivergence[i][testN]);
else
fprintf(outFile, "\t");
/* write slope data */
fprintf(outFile, "%d\t", i);
for(; testN<2*gNTests-1; testN++)
if(i<=gTest[testN-gNTests].divergeT)
fprintf(outFile, "%lf\t", gDivergence[i][testN]);
else
fprintf(outFile, "\t");
if(i<=gTest[testN-gNTests].divergeT)
fprintf(outFile, "%lf\n", gDivergence[i][testN]);
else
fprintf(outFile, "\n");
};
fclose(outFile);
}

复制代码

yanzy128 · 发表于 2007-4-25 18:41

double CHenon::MaxLypunov()
{
double z0[2],z[2],x[2],y[2];//
double d0=0.0001,lnd0;
double d,sum_ln_di=0.0;
for(int k=0;k<2;k++)
{
z[k]=z0[k]=xini[k]+d0;
x[k]=xini[k];
}
d0=dis(x,z);
lnd0=log(d0);
for(int i=0;i<MAX;i++)
{
fun(x,y);
x[0]=y[0];x[1]=y[1];
fun(z,y);
z[0]=y[0];z[1]=y[1];
d=dis(x,z);
sum_ln_di+=log(d);
for(int j=0;j<2;j++)
{
z[j]=x[j]+d0*(z[j]-x[j])/d;
}
}
d=sum_ln_di/MAX;
d=d-lnd0;
return d;
}
二位henon映射的计算最大李指数的程序

gghhjj · 发表于 2007-4-26 00:57

原帖由 yanzy128 于 2007-4-25 18:41 发表

 登录/注册后可看大图

二位henon映射的计算最大李指数的程序

谢谢提供共享

weitaolu · 发表于 2007-4-26 10:05

多谢大家提供的程序！！！
谢谢！！！:@) :@)

yww3973537 · 发表于 2007-5-23 17:01

有人会编wolf方法计算lyapunov指数的C语言版的程序吗 ?多维的!帮忙发到我邮箱里吧[email=!yww537@163.com]!yww537@163.com[/email] 多谢!

gghhjj · 发表于 2007-5-24 07:20

原帖由 yww3973537 于 2007-5-23 17:01 发表

 登录/注册后可看大图

有人会编wolf方法计算lyapunov指数的C语言版的程序吗 ?多维的!帮忙发到我邮箱里吧yww537@163.com]!yww537@163.com 多谢!

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