/*
* The mex function runs a clustering coefficients problem.
*/
void mexFunction(int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
{
int mrows, ncols;
int n,nz;
/* sparse matrix */
mwIndex *ia, *ja;
/* output data */
double *ccfs;
if (nrhs != 1)
{
mexErrMsgTxt("1 inputs required.");
}
/* The first input must be a sparse matrix. */
mrows = mxGetM(prhs[0]);
ncols = mxGetN(prhs[0]);
if (mrows != ncols ||
!mxIsSparse(prhs[0]))
{
mexErrMsgTxt("Input must be a noncomplex square sparse matrix.");
}
n = mrows;
/* Get the sparse matrix */
/* recall that we've transposed the matrix */
ja = mxGetIr(prhs[0]);
ia = mxGetJc(prhs[0]);
nz = ia[n];
plhs[0] = mxCreateDoubleMatrix(n,1,mxREAL);
ccfs = mxGetPr(plhs[0]);
#ifdef _DEBUG
mexPrintf("clustering_coefficients...");
#endif
clustering_coefficients(n, ja, ia,
ccfs);
#ifdef _DEBUG
mexPrintf("done\n");
#endif
#ifdef _DEBUG
mexPrintf("return\n");
#endif
}
/*
* The mex function runs a clustering coefficients problem.
*/
void mexFunction(int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
{
mwIndex mrows, ncols;
mwIndex n,nz;
/* sparse matrix */
mwIndex *ia, *ja;
double *a;
/* output data */
double *ccfs;
/* used to switch between algorithm types */
int weight_type; /* = 0 if unweighted,
= 1 if a vector of weights,
= 2 if given by the matrix */
int undirected;
/*
* The current calling pattern is
* clustering_coefficients_mex(A,undirected,weight)
* where weight = 0 to use the unweighted version
* weight = 'matrix' to use the values in the matrix
* weight = vector to use a vector of weights
*/
const mxArray* arg_matrix;
const mxArray* arg_undirected;
const mxArray* arg_weight;
int required_arguments = 3;
if (nrhs != required_arguments) {
mexErrMsgIdAndTxt("matlab_bgl:invalidMexArgument",
"the function requires %i arguments, not %i\n",
required_arguments, nrhs);
}
arg_matrix = prhs[0];
arg_undirected = prhs[1];
arg_weight = prhs[2];
undirected = (int)load_scalar_arg(arg_undirected, 1);
if (mxGetNumberOfElements(arg_weight) == 1)
{
/* make sure it is valid */
if ((int)mxGetScalar(arg_weight) != 0) {
mexErrMsgIdAndTxt("matlab_bgl:invalidParameter",
"unknown weight option %g\n", mxGetScalar(arg_weight));
}
weight_type = 0;
a = NULL;
}
else if (mxIsChar(arg_weight)) {
weight_type = 2;
a = mxGetPr(arg_matrix);
}
else if (mxIsDouble(arg_weight)) {
weight_type = 1;
a = mxGetPr(arg_weight);
}
else {
mexErrMsgIdAndTxt("matlab_bgl:invalidParameter",
"unrecognized weight option");
return;
}
/* The first input must be a sparse matrix. */
mrows = mxGetM(arg_matrix);
ncols = mxGetN(arg_matrix);
if (mrows != ncols || !mxIsSparse(arg_matrix) ||
((!mxIsDouble(arg_matrix) || mxIsComplex(arg_matrix)) && (weight_type == 2))
)
{
mexErrMsgTxt("Input must be a square sparse matrix.");
}
n = mrows;
/* recall that we've transposed the matrix */
ja = mxGetIr(prhs[0]);
ia = mxGetJc(prhs[0]);
nz = ia[n];
/* check the reweighting array to make sure it is acceptable */
if (weight_type == 1 && mxGetNumberOfElements(arg_weight) < nz) {
mexErrMsgIdAndTxt("matlab_bgl:invalidParameter",
"the weight array must have length >= nnz(A)");
}
plhs[0] = mxCreateDoubleMatrix(n,1,mxREAL);
ccfs = mxGetPr(plhs[0]);
#ifdef _DEBUG
mexPrintf("clustering_coefficients...");
#endif
if (weight_type == 0) {
clustering_coefficients(n, ja, ia, ccfs, !undirected);
} else if (undirected) {
weighted_clustering_coefficients(n, ja, ia, a, ccfs);
} else {
directed_clustering_coefficients(n, ja, ia, a, ccfs);
}
#ifdef _DEBUG
mexPrintf("done\n");
#endif
#ifdef _DEBUG
mexPrintf("return\n");
#endif
}
