void Graph::train(int ants, int max_repetitions, double t0, bool reset) {
if (reset)
std::fill(pheromone[0], pheromone[0] + m * n, t0);
double **tmp = new double*[m];
tmp[0] = new double[m * n];
for (int i = 1; i < m; ++i)
tmp[i] = tmp[i - 1] + n;
for (int r = 0; r < max_repetitions; ++r) {
evaporate();
std::copy(pheromone[0], pheromone[0] + m * n, tmp[0]);
for (int a = 0; a < ants; ++a) {
auto solution = buildSolution();
updatePheromone(tmp, solution);
}
std::copy(tmp[0], tmp[0] + m * n, pheromone[0]);
}
delete[] tmp[0];
delete[] tmp;
}
void mexFunction( int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[] )
{
double *x; /* input data, size N*n */
double *y; /* output data, size N*1 */
double C; /* upper bound for Lagrange multipliers */
double e; /* insensitivity zone epsilon */
long n; /* dimension of input space */
long N; /* number of input data */
long k; /* counter */
int d0,d1; /* true number of dimensions of arguments #0 and #1 */
int label1,label2; /* different class labels */
kernel ker; /* kernel function */
options opt; /* optimizer settings */
const mxArray *mlopt; /* mxArray for getOptionsStruct and getOptions */
struct svm_problem prob; /* LIBSVM problem (i.e. data) */
struct svm_parameter param; /* LIBSVM parameters */
struct svm_model *model; /* calculated LIBSVM model */
if (nrhs<4)
mexErrMsgTxt("Invalid number of input arguments.");
/* --- get input and output data --- */
if ( mxIsEmpty(prhs[0]) ||
!mxIsDouble(prhs[0]) ||
mxIsComplex(prhs[0]) )
mexErrMsgTxt("Invalid first argument (input data).");
d0 = mxGetNumberOfDimensions(prhs[0]); /* compute true number of dims #0 */
while ( mxGetDimensions(prhs[0])[d0-1] == 1 )
d0--;
if ( mxIsEmpty(prhs[1]) ) { /* one-class SVM */
n = mxGetDimensions(prhs[0])[d0-1];
N = mxGetNumberOfElements(prhs[0])/n;
}
else if ( !mxIsDouble(prhs[1]) ||
mxIsComplex(prhs[1]) ) {
mexErrMsgTxt("Invalid second argument (output data).");
}
else {
d1 = mxGetNumberOfDimensions(prhs[1]); /* compute true number of dims #1 */
while ( mxGetDimensions(prhs[1])[d1-1] == 1 )
d1--;
if ( d1 == d0 ) /* exatly 1 regressor */
n = 1;
else if ( d1 == d0-1 ) /* more than 1 regressor */
n = mxGetDimensions(prhs[0])[d1];
else
mexErrMsgTxt("Sizes of input and output data do not match.");
N = 1; /* get number of data */
for (k=0; k<d1; k++) {
if ( mxGetDimensions(prhs[1])[k] != mxGetDimensions(prhs[0])[k] )
mexErrMsgTxt("Sizes of first and second argument do not match.");
else
N *= mxGetDimensions(prhs[1])[k];
}
}
if ( mxGetNumberOfElements(prhs[2]) != 1 ||
!mxIsDouble(prhs[2]) ||
mxIsComplex(prhs[2]) ||
mxGetScalar(prhs[2]) <= 0 )
mexErrMsgTxt("Invalid third argument (upper bound).");
x = mxGetPr(prhs[0]); /* get the input arguments */
y = mxGetPr(prhs[1]);
C = mxGetScalar(prhs[2]);
/* set default values */
opt.tol = 1e-3; /* tolerance for KKT check */
opt.shrink = 1; /* enable shrinking */
opt.cache = 40.0; /* cache size in MB */
opt.weight = 1.0; /* weight for class wlabel */
opt.wlabel = -1; /* class to be weighted */
opt.verbose = 0; /* no verbosity */
opt.style = 0; /* C/eps style */
opt.prob = 0; /* no probability information */
/* --- SVM classification --- */
if ( mxGetNumberOfElements(prhs[3]) == 1 &&
mxIsStruct(prhs[3]) ) {
/* check lables for struct output (1 argument) */
if ( !mxIsEmpty(prhs[1]) && nlhs == 1 ) { /* allow different class labels */
label1 = (int)y[0];
for (k=0; k<N; k++) {
label2 = (int)y[k];
if (label1 != label2)
break;
}
for (k=0; k<N; k++)
if ( y[k]!=label1 && y[k]!=label2 )
mexErrMsgTxt("Only two different class labels are allowed.");
}
/* check lables for vector output (2 arguments) */
if ( !mxIsEmpty(prhs[1]) && nlhs>1 ) { /* class labels must be -1 or +1 */
for (k=0; k<N; k++)
if ( y[k]!=1 && y[k]!=-1 )
mexErrMsgTxt("Only class labels +1 and -1 are allowed.");
label1 = +1;
label2 = -1;
}
/* get kernel and options */
if ( getKernel(&ker,prhs[3],n) ||
ker.type == KERNEL_GAUSS )
mexErrMsgTxt("Invalid fourth argument (kernel function).");
if ( nrhs>4 ) {
getOptionsStruct(&mlopt,prhs+4,nrhs-4);
getOptions(&opt,mlopt,N);
}
/* compute probability information only if it can be returned */
if ( nlhs>1 )
opt.prob = 0;
/* check if weighting makes sense */
if ( !mxIsEmpty(prhs[1]) &&
opt.wlabel!=label1 && opt.wlabel!=label2 ) {
opt.weight = 1;
mexWarnMsgTxt("Class to be weighted does not exist.");
}
/* check nu value (C is treated as nu) */
if ( opt.style == 1 && C > 1 )
mexErrMsgTxt("nu must be in the range (0,1]");
/* solve the classification problem */
if ( mxIsEmpty(prhs[1]) ) {
if ( C > 1 )
mexErrMsgTxt("nu must be in the range (0,1]");
convOptions(¶m,C,0,&ker,&opt,2);
}
else {
convOptions(¶m,C,0,&ker,&opt,1);
}
convData(&prob,x,y,n,N);
/* dump information about options and settings into MATLAB window */
e = -1;
if (opt.verbose > 0) {
if ( mxIsEmpty(prhs[1]) )
dumpOptions(&ker,&opt,C,e,2);
else
dumpOptions(&ker,&opt,C,e,1);
}
/* compute the solution */
model = svm_train(&prob,¶m);
if ( mxIsEmpty(prhs[1]) )
buildSolution(plhs,prhs[3],model,&prob,&opt,nlhs,2,N);
else
buildSolution(plhs,prhs[3],model,&prob,&opt,nlhs,1,N);
/* dump information about solution into MATLAB window */
if (opt.verbose > 0) {
if ( mxIsEmpty(prhs[1]) )
dumpSolution(&opt,model,&prob,y,C,2,N,(nlhs>1));
else
dumpSolution(&opt,model,&prob,y,C,1,N,(nlhs>1));
}
/* the model is no longer needed */
svm_destroy_model(model);
}
/* --- SVM regression --- */
else if ( !mxIsEmpty(prhs[1]) && /* one-class SVM not allowed */
mxGetNumberOfElements(prhs[3]) == 1 &&
mxIsDouble(prhs[3]) &&
!mxIsComplex(prhs[3]) &&
mxGetScalar(prhs[3]) >= 0 ) {
if (nrhs<5)
mexErrMsgTxt("Invalid number of input arguments.");
if ( mxGetNumberOfElements(prhs[4]) != 1 ||
!mxIsStruct(prhs[4]) )
mexErrMsgTxt("Invalid fifth argument (kernel function).");
/* get epsilon, kernel and options */
e = mxGetScalar(prhs[3]);
if ( getKernel(&ker,prhs[4],n) ||
ker.type == KERNEL_GAUSS )
mexErrMsgTxt("Invalid fourth argument (kernel function).");
if ( nrhs>5 ) {
getOptionsStruct(&mlopt,prhs+5,nrhs-5);
getOptions(&opt,mlopt,N);
}
/* check nu value (e is treated as nu) */
if ( opt.style == 1 && e > 1 )
mexErrMsgTxt("Invalid fourth argument (nu).");
/* solve the regression problem */
convOptions(¶m,C,e,&ker,&opt,0);
convData(&prob,x,y,n,N);
/* dump information about options and settings into MATLAB window */
if (opt.verbose > 0)
dumpOptions(&ker,&opt,C,e,0);
model = svm_train(&prob,¶m);
buildSolution(plhs,prhs[4],model,&prob,&opt,nlhs,0,N);
/* dump information about solution into MATLAB window */
if (opt.verbose > 0)
dumpSolution(&opt,model,&prob,y,C,0,N,(nlhs>1));
/* the model is no longer needed */
svm_destroy_model(model);
}
else
mexErrMsgTxt("Invalid fourth argument (kernel function).");
}