/* READ_MTX - read symmetric sparse matrix in MatrixMarket format
*/
void read_MTX_SSS(char *fname, int *n,
double **va, double **da, int **ja, int **ia) {
int m, nz, ret_code, i;
double *v_coo;
int *i_coo, *j_coo;
MM_typecode matcode;
FILE *f;
f = fopen(fname, "r");
assert(f != NULL);
ret_code = mm_read_banner(f, &matcode);
assert(ret_code == 0);
assert(mm_is_real(matcode) && mm_is_matrix(matcode) &&
mm_is_sparse(matcode) && mm_is_symmetric(matcode));
ret_code = mm_read_mtx_crd_size(f, &m, n, &nz);
assert(ret_code == 0);
assert(m == *n);
/* read COO format */
i_coo = (int *)malloc(nz * sizeof(int));
j_coo = (int *)malloc(nz * sizeof(int));
v_coo = (double *)malloc(nz * sizeof(double));
assert(i_coo && j_coo && v_coo);
for (i = 0; i < nz; i ++) {
fscanf(f, "%d %d %lg\n", &i_coo[i], &j_coo[i], &v_coo[i]);
i_coo[i]--; /* adjust from 1-based to 0-based */
j_coo[i]--;
}
fclose(f);
/* convert to SSS format */
convert_COO_SSS(*n, nz, i_coo, j_coo, v_coo, ia, ja, va, da);
free(i_coo); free(j_coo); free(v_coo);
}
/**
compute validation rmse
*/
void validation_rmse3(float (*prediction_func)(const vertex_data & user, const vertex_data & movie, const vertex_data & time, float rating, double & prediction)
,graphchi_context & gcontext,int tokens_per_row = 4) {
int ret_code;
MM_typecode matcode;
FILE *f;
size_t nz;
if ((f = fopen(validation.c_str(), "r")) == NULL) {
std::cout<<std::endl;
return; //missing validaiton data, nothing to compute
}
if (mm_read_banner(f, &matcode) != 0)
logstream(LOG_FATAL) << "Could not process Matrix Market banner. File: " << validation << std::endl;
if (mm_is_complex(matcode) || !mm_is_sparse(matcode))
logstream(LOG_FATAL) << "Sorry, this application does not support complex values and requires a sparse matrix." << std::endl;
/* find out size of sparse matrix .... */
if ((ret_code = mm_read_mtx_crd_size(f, &Me, &Ne, &nz)) !=0) {
logstream(LOG_FATAL) << "Failed reading matrix size: error=" << ret_code << std::endl;
}
if ((M > 0 && N > 0) && (Me != M || Ne != N))
logstream(LOG_FATAL)<<"Input size of validation matrix must be identical to training matrix, namely " << M << "x" << N << std::endl;
Le = nz;
last_validation_rmse = dvalidation_rmse;
dvalidation_rmse = 0;
int I, J;
double val, time = 1.0;
for (size_t i=0; i<nz; i++)
{
int rc;
rc = fscanf(f, "%d %d %lg %lg\n", &I, &J, &time, &val);
if (rc != tokens_per_row)
logstream(LOG_FATAL)<<"Error when reading input file on line: " << i << " . should have" << tokens_per_row << std::endl;
if (val < minval || val > maxval)
logstream(LOG_FATAL)<<"Value is out of range: " << val << " should be: " << minval << " to " << maxval << std::endl;
if ((uint)time > K)
logstream(LOG_FATAL)<<"Third column value time should be smaller than " << K << " while observed " << time << " in line : " << i << std::endl;
I--; /* adjust from 1-based to 0-based */
J--;
double prediction;
(*prediction_func)(latent_factors_inmem[I], latent_factors_inmem[J+M], latent_factors_inmem[M+N+(uint)time], val, prediction);
dvalidation_rmse += pow(prediction - val, 2);
}
fclose(f);
assert(Le > 0);
dvalidation_rmse = sqrt(dvalidation_rmse / (double)Le);
std::cout<<" Validation RMSE: " << std::setw(10) << dvalidation_rmse << std::endl;
if (halt_on_rmse_increase && dvalidation_rmse > last_validation_rmse && gcontext.iteration > 0){
logstream(LOG_WARNING)<<"Stopping engine because of validation RMSE increase" << std::endl;
gcontext.set_last_iteration(gcontext.iteration);
}
}
int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J,
double **val, MM_typecode *matcode)
{
int ret_code;
FILE *f;
if (strcmp(fname, "stdin") == 0) f=stdin;
else
if ((f = fopen(fname, "r")) == NULL)
return MM_COULD_NOT_READ_FILE;
if ((ret_code = mm_read_banner(f, matcode)) != 0)
return ret_code;
if (!(mm_is_valid(*matcode) && mm_is_sparse(*matcode) &&
mm_is_matrix(*matcode)))
return MM_UNSUPPORTED_TYPE;
if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0)
return ret_code;
//*I = (int *) malloc(*nz * sizeof(int));
//*J = (int *) malloc(*nz * sizeof(int));
//*val = NULL;
*I = new int[*nz];
*J = new int[*nz];
*val = 0;
if (mm_is_complex(*matcode))
{
//*val = (double *) malloc(*nz * 2 * sizeof(double));
*val = new double[2*(*nz)];
ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
*matcode);
if (ret_code != 0) return ret_code;
}
else if (mm_is_real(*matcode))
{
//*val = (double *) malloc(*nz * sizeof(double));
*val = new double[*nz];
ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
*matcode);
if (ret_code != 0) return ret_code;
}
else if (mm_is_pattern(*matcode))
{
ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
*matcode);
if (ret_code != 0) return ret_code;
}
if (f != stdin) fclose(f);
return 0;
}
void convert_2_vec(const char * filename, shotgun_data * prob )
{
int ret_code;
MM_typecode matcode;
FILE *f;
int M, N, nz;
int i;
if ((f = fopen(filename, "r")) == NULL) {
printf("Could not file vector input file : %s.\n", filename);
exit(1);
}
if (mm_read_banner(f, &matcode) != 0)
{
printf("Could not process Matrix Market banner in input file %s.\n", filename);
exit(1);
}
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) && mm_is_matrix(matcode) &&
mm_is_sparse(matcode) )
{
printf("Sorry, this application does not support ");
printf("Market Market type: [%s]\n", mm_typecode_to_str(matcode));
exit(1);
}
/* find out size of sparse matrix .... */
if ((ret_code = mm_read_mtx_crd_size(f, &M, &N, &nz)) !=0){
printf("Could not process Matrix Market size in input file: %s.\n", filename);
exit(1);
}
/* NOTE: when reading in doubles, ANSI C requires the use of the "l" */
/* specifier as in "%lg", "%lf", "%le", otherwise errors will occur */
/* (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15) */
prob->y.reserve(nz);
int I,J;
double val;
for (i=0; i<nz; i++)
{
fscanf(f, "%d %d %lg\n", &I, &J, &val);
I--; /* adjust from 1-based to 0-based */
J--;
assert(J==0);
prob->y.push_back(val);
}
if (f !=stdin) fclose(f);
}
int readSymmMatrix(char* filename, int& n, double*& v)
{
int m;
// try opening the files
FILE *fp;
if ((fp = fopen(filename, "r")) == NULL) {
fprintf(stderr, "Error occurs while reading from file %s.\n", filename);
return 1;
}
// try reading the banner
MM_typecode type;
if (mm_read_banner(fp, &type) != 0) {
fprintf(stderr, "Could not process Matrix Market banner.\n");
return 2;
}
// check the type
if (!mm_is_matrix(type) || !mm_is_array(type) || !mm_is_real(type) || !mm_is_symmetric(type)) {
fprintf(stderr, "Sorry, this application does not support Market Market type: [%s]\n",
mm_typecode_to_str(type));
return 3;
}
// read the sizes of the vectors
if (mm_read_mtx_array_size(fp, &m, &n)) {
fprintf(stderr, "Could not read the size of the matrix.\n");
return 4;
}
// check if it is a square matrix
if (n != m) {
fprintf(stderr, "Needs to be square.\n");
return 5;
}
// allocate the memory
printf("reading %s:\n\ta %d x %d matrix...", filename, m, n);
v = new double[m * n];
for (int j = 0; j < n; ++j) {
for (int i = j; i < m; ++i) {
fscanf(fp, "%lf\n", &v[i * n + j]);
if (i != j) {
v[j * n + i] = v[i * n + j];
}
}
}
printf("done\n");
// close the file
fclose(fp);
return 0;
}
void ReadMatrixMarketVector(std::string filename, T* &rhs)
{
/* Open the file */
FILE *f = fopen(filename.c_str(), "r");
if (f == NULL)
{
std::cout<<"failed to open file "<<filename<<std::endl;
}
/* Read the banner */
MM_typecode matcode;
if (mm_read_banner(f, &matcode) != 0)
{
std::cout<<"failed to read banner in file "<<filename<<std::endl;
}
/* Determine the size */
int status, nrows, ncols;
if ((status = mm_read_mtx_array_size(f, &nrows, &ncols)) != 0)
{
std::cout<<"failed to read number of rows and columns in file "<<filename<<std::endl;
}
if (ncols != 1)
{
fclose(f);
std::cout<<"number of columns is not 1 in file "<<filename<<std::endl;
}
/* Read values into vector */
rhs = new T[nrows];
int nval;
double val;
for (unsigned int n = 0; n < (unsigned int)nrows; n++)
{
nval = fscanf(f, "%lg\n", &val);
if (nval < 1)
{
std::cout<<"failed to read dense line from file "<<filename<<std::endl;
}
rhs[n] = (T)val;
}
/* Close file and return */
fclose(f);
return;
}
void test_predictions3(float (*prediction_func)(const vertex_data & user, const vertex_data & movie, const vertex_data & time, float rating, double & prediction)) {
int ret_code;
MM_typecode matcode;
FILE *f;
uint Me, Ne;
size_t nz;
if ((f = fopen(test.c_str(), "r")) == NULL) {
return; //missing validaiton data, nothing to compute
}
FILE * fout = fopen((test + ".predict").c_str(),"w");
if (fout == NULL)
logstream(LOG_FATAL)<<"Failed to open test prediction file for writing"<<std::endl;
if (mm_read_banner(f, &matcode) != 0)
logstream(LOG_FATAL) << "Could not process Matrix Market banner. File: " << test << std::endl;
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) || !mm_is_sparse(matcode))
logstream(LOG_FATAL) << "Sorry, this application does not support complex values and requires a sparse matrix." << std::endl;
/* find out size of sparse matrix .... */
if ((ret_code = mm_read_mtx_crd_size(f, &Me, &Ne, &nz)) !=0) {
logstream(LOG_FATAL) << "Failed reading matrix size: error=" << ret_code << std::endl;
}
if ((M > 0 && N > 0 ) && (Me != M || Ne != N))
logstream(LOG_FATAL)<<"Input size of test matrix must be identical to training matrix, namely " << M << "x" << N << std::endl;
mm_write_banner(fout, matcode);
mm_write_mtx_crd_size(fout ,M,N,nz);
for (uint i=0; i<nz; i++)
{
int I, J;
double val;
int time;
int rc = fscanf(f, "%d %d %d %lg\n", &I, &J, &time, &val);
if (rc != 4)
logstream(LOG_FATAL)<<"Error when reading input file: " << i << std::endl;
I--; /* adjust from 1-based to 0-based */
J--;
double prediction;
(*prediction_func)(latent_factors_inmem[I], latent_factors_inmem[J+M], latent_factors_inmem[time+M+N], 1, prediction);
fprintf(fout, "%d %d %12.8lg\n", I+1, J+1, prediction);
}
fclose(f);
fclose(fout);
logstream(LOG_INFO)<<"Finished writing " << nz << " predictions to file: " << test << ".predict" << std::endl;
}
/** Reads the dimensions of the matrix (n - number of rows, m - number
of columns, nnzs - number of non-zeros) from the given Matrix
Market file. If the given file contains an array rather than a
COO matrix, nnzs will be set to n;
*/
void read_mm_matrix_size(FILE *f, int *n, int *m, int *nnzs, MM_typecode* mcode) {
if (mm_read_banner(f, mcode) != 0) {
printf("Could not process Matrix Market banner.\n");
exit(1);
}
if (mm_is_array(*mcode)) {
mm_read_mtx_array_size(f, n, m);
*nnzs = *n;
} else {
mm_read_mtx_crd_size(f, n, m, nnzs);
}
}
int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J,
double **val, MM_typecode *matcode)
{
int ret_code;
ZOLTAN_FILE* f;
if ((f = ZOLTAN_FILE_open(fname, "r", STANDARD)) == NULL)
return MM_COULD_NOT_READ_FILE;
if ((ret_code = mm_read_banner(f, matcode)) != 0)
return ret_code;
if (!(mm_is_valid(*matcode) && mm_is_sparse(*matcode) &&
mm_is_matrix(*matcode)))
return MM_UNSUPPORTED_TYPE;
if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0)
return ret_code;
*I = (int *) malloc(*nz * sizeof(int));
*J = (int *) malloc(*nz * sizeof(int));
*val = NULL;
if (mm_is_complex(*matcode))
{
*val = (double *) malloc(*nz * 2 * sizeof(double));
ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
*matcode);
if (ret_code != 0) return ret_code;
}
else if (mm_is_real(*matcode))
{
*val = (double *) malloc(*nz * sizeof(double));
ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
*matcode);
if (ret_code != 0) return ret_code;
}
else if (mm_is_pattern(*matcode))
{
ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
*matcode);
if (ret_code != 0) return ret_code;
}
ZOLTAN_FILE_close(f);
return 0;
}
/** load a matrix market file into a matrix */
void load_matrix_market_matrix(const std::string & filename, int offset, int D){
MM_typecode matcode;
uint i,I,J;
double val;
uint rows, cols;
size_t nnz;
FILE * f = open_file(filename.c_str() ,"r");
int rc = mm_read_banner(f, &matcode);
if (rc != 0)
logstream(LOG_FATAL)<<"Failed to load matrix market banner in file: " << filename << std::endl;
if (mm_is_sparse(matcode)){
int rc = mm_read_mtx_crd_size(f, &rows, &cols, &nnz);
if (rc != 0)
logstream(LOG_FATAL)<<"Failed to load matrix market banner in file: " << filename << std::endl;
}
else { //dense matrix
rc = mm_read_mtx_array_size(f, &rows, &cols);
if (rc != 0)
logstream(LOG_FATAL)<<"Failed to load matrix market banner in file: " << filename << std::endl;
nnz = rows * cols;
}
if (D != (int)cols)
logstream(LOG_FATAL)<<"Wrong matrix size detected, command line argument should be --D=" << D << " instead of : " << cols << std::endl;
for (i=0; i<nnz; i++){
if (mm_is_sparse(matcode)){
rc = fscanf(f, "%u %u %lg\n", &I, &J, &val);
if (rc != 3)
logstream(LOG_FATAL)<<"Error reading input line " << i << std::endl;
I--; J--;
assert(I >= 0 && I < rows);
assert(J >= 0 && J < cols);
//set_val(a, I, J, val);
latent_factors_inmem[I+offset].set_val(J,val);
}
else {
rc = fscanf(f, "%lg", &val);
if (rc != 1)
logstream(LOG_FATAL)<<"Error reading nnz " << i << std::endl;
I = i / cols;
J = i % cols;
latent_factors_inmem[I+offset].set_val(J, val);
}
}
logstream(LOG_INFO) << "Factors from file: loaded matrix of size " << rows << " x " << cols << " from file: " << filename << " total of " << nnz << " entries. "<< i << std::endl;
fclose(f);
}
int readBandedMatrix(char* filename, int& n, int& t, int& nz, long long*& AR, long long*& AC, double*& AV)
{
// try opening the files
FILE *fp;
if ((fp = fopen(filename, "r")) == NULL) {
fprintf(stderr, "Error occurs while reading from file %s.\n", filename);
return 1;
}
// try reading the banner
MM_typecode type;
if (mm_read_banner(fp, &type)) {
fprintf(stderr, "Could not process Matrix Market banner.\n");
return 2;
}
// check the type
if (!mm_is_matrix(type) || !mm_is_coordinate(type) || !mm_is_real(type) || !mm_is_general(type)) {
fprintf(stderr, "Sorry, this application does not support Market Market type: [%s]\n",
mm_typecode_to_str(type));
return 3;
}
// read the sizes and nnz of the matrix
int m;
if (mm_read_mtx_crd_size(fp, &n, &m, &nz)) {
fprintf(stderr, "Could not read the size of the matrix.\n");
return 4;
}
printf("reading %s:\n\ta %d x %d banded matrix ", filename, n, m);
// allocate the memory
AR = new long long[nz];
AC = new long long[nz];
AV = new double[nz];
t = 0;
for (int i = 0; i < nz; ++i) {
fscanf(fp, "%d %d %lf\n", AR + i, AC + i, AV + i);
--AR[i]; // 0-indexing
--AC[i]; // 0-indexing
t = std::max(t, (int)(AR[i] - AC[i]));
}
printf("with bandwidth (2m + 1) = %d...done\n", 2 * t + 1);
// close the file
fclose(fp);
return 0;
}
void read_matrix_market_banner_and_size(FILE * f, MM_typecode & matcode, uint & Me, uint & Ne, size_t & nz, const std::string & filename){
if (mm_read_banner(f, &matcode) != 0)
logstream(LOG_FATAL) << "Could not process Matrix Market banner. File: " << filename << std::endl;
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) || !mm_is_sparse(matcode))
logstream(LOG_FATAL) << "Sorry, this application does not support complex values and requires a sparse matrix." << std::endl;
/* find out size of sparse matrix .... */
if (mm_read_mtx_crd_size(f, &Me, &Ne, &nz) != 0) {
logstream(LOG_FATAL) << "Failed reading matrix size: error" << std::endl;
}
}
/*** fread MatrixMarket format file ***/
mm_real *
mm_real_fread (FILE *fp)
{
MM_typecode typecode;
mm_real *x;
if (mm_read_banner (fp, &typecode) != 0) error_and_exit ("mm_real_fread", "failed to read mm_real.", __FILE__, __LINE__);
if (!is_type_supported (typecode)) {
char msg[128];
sprintf (msg, "matrix type does not supported :[%s].", mm_typecode_to_str (typecode));
error_and_exit ("mm_real_fread", msg, __FILE__, __LINE__);
}
x = (mm_is_sparse (typecode)) ? mm_real_fread_sparse (fp, typecode) : mm_real_fread_dense (fp, typecode);
if (!x) error_and_exit ("mm_real_fread", "failed to read mm_real.", __FILE__, __LINE__);
if (mm_real_is_symmetric (x) && x->m != x->n) error_and_exit ("mm_real_fread", "symmetric matrix must be square.", __FILE__, __LINE__);
return x;
}
bool loadMmProperties(int *rowsCount,
int *columnsCount,
int *nonZerosCount,
bool *isStoredSparse,
int* matrixStorage,
int* matrixType,
FILE *file)
{
MM_typecode matcode;
// supports only valid matrices
if ((mm_read_banner(file, &matcode) != 0)
|| (!mm_is_matrix(matcode))
|| (!mm_is_valid(matcode)))
return false;
if ( mm_read_mtx_crd_size(file, rowsCount, columnsCount, nonZerosCount) != 0 )
return false;
// is it stored sparse?
if (mm_is_sparse(matcode))
*isStoredSparse = true;
else
*isStoredSparse = false;
if (mm_is_integer(matcode))
*matrixStorage = MATRIX_STORAGE_INTEGER;
else if (mm_is_real(matcode))
*matrixStorage = MATRIX_STORAGE_REAL;
else if (mm_is_complex(matcode))
*matrixStorage = MATRIX_STORAGE_COMPLEX;
else if (mm_is_pattern(matcode))
*matrixStorage = MATRIX_STORAGE_PATTERN;
if (mm_is_general(matcode))
*matrixType = MATRIX_TYPE_GENERAL;
else if (mm_is_symmetric(matcode))
*matrixType = MATRIX_TYPE_SYMMETRIC;
else if (mm_is_skew(matcode))
*matrixType = MATRIX_TYPE_SKEW;
else if (mm_is_hermitian(matcode))
*matrixType = MATRIX_TYPE_HERMITIAN;
return true;
}
int DenseMatrix_mm_read_strassen(DenseMatrix *m, char *file_name, int *nr_rows, int *nr_cols) {
int res;
MM_typecode matcode;
FILE *file;
file = fopen(file_name, "r");
CHECK_NULL_RETURN(file);
res = mm_read_banner(file, &matcode);
CHECK_ZERO_ERROR_RETURN(res, "Failed to read matrix code");
CHECK_ERROR_RETURN(!mm_is_matrix(matcode), "File is not a matrix", 1);
if (mm_is_sparse(matcode)) {
int nr_sparse_elements;
res = mm_read_mtx_crd_size(file, nr_rows, nr_cols, &nr_sparse_elements);
CHECK_ZERO_ERROR_RETURN(res, "Failed to read sparse mm dimensions");
int dim = pow2dim(*nr_rows, *nr_cols);
// Initialzie matrix to zero to fill in with sparse elements
res = DenseMatrix_init_zero(m, dim, dim);
CHECK_ZERO_ERROR_RETURN(res, "Failed to allocate memory for mm matrix");
res = DenseMatrix_parse_mm_sparse(m, file, nr_sparse_elements);
} else if (mm_is_dense(matcode)) {
res = mm_read_mtx_array_size(file, nr_rows, nr_cols);
CHECK_ZERO_ERROR_RETURN(res, "Failed to read dense mm dimensions");
int dim = pow2dim(*nr_rows, *nr_cols);
res = DenseMatrix_init_zero(m, dim, dim);
CHECK_ZERO_ERROR_RETURN(res, "Failed to allocate memory for mm matrix");
res = DenseMatrix_parse_mm_dense(m, file);
} else {
ERROR("mm matrix code is not supported. Only supports dense and sparse matrices");
}
CHECK_ZERO_ERROR_RETURN(res, "Failed to parse mm file");
return 0;
}
int get_mm_info(const char *file, int *m, int *n, int *nz)
{
FILE *fp;
MM_typecode matcode;
if ((fp = fopen(file, "r")) == NULL)
{
fprintf(stderr,"ERROR: Could not open file: %s\n",file);
exit(1);
}
if (mm_read_banner(fp, &matcode) != 0)
{
fprintf(stderr,"ERROR: Could not process Matrix Market banner.\n");
exit(1);
}
if (!(mm_is_real(matcode) || mm_is_integer(matcode)))
{
fprintf(stderr,"ERROR: Market Market type: [%s] not supported\n",
mm_typecode_to_str(matcode));
exit(1);
}
if (mm_is_sparse(matcode))
{
if (mm_read_mtx_crd_size(fp, m, n, nz) !=0)
{ /* find out size of sparse matrix */
exit(1);
}
}
else
{
if (mm_read_mtx_array_size(fp, m, n) !=0)
{ /* find out size of dense matrix */
exit(1);
}
*nz = -1;
}
fclose(fp);
return 0;
}
/*---------------------------------------------*
* READ COO Matrix Market *
*---------------------------------------------*/
int read_coo_MM(coo_t *coo, options_t *opts) {
char *matfile = opts->fmatname;
MM_typecode matcode;
FILE *p = fopen(matfile,"r");
if (p == NULL) {
printf("Unable to open file %s\n", matfile);
exit(1);
}
/*----------- READ MM banner */
if (mm_read_banner(p, &matcode) != 0) {
printf("Could not process Matrix Market banner.\n");
exit(1);
}
if (!mm_is_valid(matcode)) {
printf("Invalid Matrix Market file.\n");
exit(1);
}
if (!(mm_is_real(matcode) && mm_is_coordinate(matcode)
&& mm_is_sparse(matcode))) {
printf("Only sparse real-valued coordinate \
matrices are supported\n");
exit(1);
}
int MatrixMarketFileToRowMap(const char* filename,
const Epetra_Comm& comm,
Epetra_BlockMap*& rowmap)
{
FILE* infile = fopen(filename, "r");
MM_typecode matcode;
int err = mm_read_banner(infile, &matcode);
if (err != 0) return(err);
if (!mm_is_matrix(matcode) || !mm_is_coordinate(matcode) ||
!mm_is_real(matcode) || !mm_is_general(matcode)) {
return(-1);
}
int numrows, numcols;
err = mm_read_mtx_array_size(infile, &numrows, &numcols);
if (err != 0) return(err);
fclose(infile);
rowmap = new Epetra_BlockMap(numrows, 1, 0, comm);
return(0);
}
bool MatrixMarket::read_matrix(const char *file_name) {
int ret_code;
MM_typecode matcode;
FILE *f;
int i;//, *I(NULL); //, *J;
// double *val;
if ((f = fopen(file_name, "r")) == NULL)
return false;
if (mm_read_banner(f, &matcode) != 0)
{
printf("Could not process Matrix Market banner.\n");
return false;
}
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) && mm_is_matrix(matcode) &&
mm_is_sparse(matcode) )
{
printf("Sorry, this application does not support ");
printf("Market Market type: [%s]\n", mm_typecode_to_str(matcode));
return false;
}
/* find out size of sparse matrix .... */
int _num_rows, _num_cols, _num_nnzs;
if ((ret_code = mm_read_mtx_crd_size(f, &_num_rows, &_num_cols, &_num_nnzs)) !=0)
return false;
bool is_vector = (_num_rows == _num_nnzs) && (_num_cols == 1);
// convert to 64 bit
m_num_rows = _num_rows;
m_num_cols = _num_cols;
m_num_nnzs = _num_nnzs;
/* reseve memory for matrices */
// I = (int *) malloc(nz * sizeof(int));
// J = (int *) malloc(nz * sizeof(int));
// val = (double *) malloc(nz * sizeof(double));
m_rows.resize(m_num_nnzs);
m_cols.resize(m_num_nnzs);
m_coefs.resize(m_num_nnzs);
/* NOTE: when reading in doubles, ANSI C requires the use of the "l" */
/* specifier as in "%lg", "%lf", "%le", otherwise errors will occur */
/* (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15) */
if (is_vector) {
for (i=0; i<m_num_nnzs; i++) {
fscanf(f, "%lg\n", &(m_coefs[i]));
m_rows[i] = i;
m_cols[i] = 0;
}
if (f !=stdin) fclose(f);
return true;
}
std::vector<std::map<int32_t,double> > elements;
elements.resize(m_num_rows);
for (i=0; i<m_num_nnzs; i++)
{
fscanf(f, "%d %d %lg\n", &(m_rows[i]), &(m_cols[i]), &(m_coefs[i]));
m_rows[i]--; /* adjust from 1-based to 0-based */
m_cols[i]--;
elements[m_rows[i]][m_cols[i]] += m_coefs[i];
}
// now sort by rows
std::vector<int> m_rows2;
std::vector<int32_t> m_cols2;
std::vector<double> m_coefs2;
m_rows2.resize(m_num_nnzs);
m_cols2.resize(m_num_nnzs);
m_coefs2.resize(m_num_nnzs);
int index=0;
for (int row=0; row<m_num_rows; row++) {
assert((int)elements[row].size() > 0);
// Make sure diagonal element is first
if (elements[row].find(row) != elements[row].end()) {
m_cols2[index] = row;
m_rows2[index] = row;
m_coefs2[index] = elements[row][row];
assert (m_coefs2[index] != 0.0);
index++;
}
else
assert(0);
for (auto iter = elements[row].begin(); iter != elements[row].end(); iter++) {
int col = iter->first;
if (col != row) {
double C = iter->second;
assert(col < m_num_cols);
m_rows2[index] = row;
m_cols2[index] = col;
m_coefs2[index] = C;
index++;
}
}
}
if (f !=stdin) fclose(f);
m_rows.swap(m_rows2);
m_cols.swap(m_cols2);
m_coefs.swap(m_coefs2);
/* convert coo to csr row ptrs */
coo_to_csr_rows(m_rows, m_num_rows, m_row_ptrs);
return true;
// /************************/
// /* now write out matrix */
// /************************/
// mm_write_banner(stdout, matcode);
// mm_write_mtx_crd_size(stdout, m_num_rows, m_num_cols, m_num_nnzs);
// for (i=0; i<nz; i++)
// fprintf(stdout, "%d %d %20.19g\n", I[i]+1, J[i]+1, val[i]);
// free(I);
}
int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_,
double **val_, int **I_, int **J_)
{
FILE *f;
MM_typecode matcode;
int M, N, nz;
int i;
double *val;
int *I, *J;
if ((f = fopen(fname, "r")) == NULL)
return -1;
if (mm_read_banner(f, &matcode) != 0)
{
printf("mm_read_unsymetric: Could not process Matrix Market banner ");
printf(" in file [%s]\n", fname);
return -1;
}
if ( !(mm_is_real(matcode) && mm_is_matrix(matcode) &&
mm_is_sparse(matcode)))
{
fprintf(stderr, "Sorry, this application does not support ");
fprintf(stderr, "Market Market type: [%s]\n",
mm_typecode_to_str(matcode));
return -1;
}
/* find out size of sparse matrix: M, N, nz .... */
if (mm_read_mtx_crd_size(f, &M, &N, &nz) !=0)
{
fprintf(stderr, "read_unsymmetric_sparse(): could not parse matrix size.\n");
return -1;
}
*M_ = M;
*N_ = N;
*nz_ = nz;
/* reseve memory for matrices */
I = (int *) malloc(nz * sizeof(int));
J = (int *) malloc(nz * sizeof(int));
val = (double *) malloc(nz * sizeof(double));
*val_ = val;
*I_ = I;
*J_ = J;
/* NOTE: when reading in doubles, ANSI C requires the use of the "l" */
/* specifier as in "%lg", "%lf", "%le", otherwise errors will occur */
/* (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15) */
for (i=0; i<nz; i++)
{
fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]);
I[i]--; /* adjust from 1-based to 0-based */
J[i]--;
}
fclose(f);
return 0;
}
int convert_matrixmarket(std::string base_filename, SharderPreprocessor<als_edge_type> * preprocessor = NULL) {
// Note, code based on: http://math.nist.gov/MatrixMarket/mmio/c/example_read.c
int ret_code;
MM_typecode matcode;
FILE *f;
size_t nz;
std::string suffix = "";
if (preprocessor != NULL) {
suffix = preprocessor->getSuffix();
}
/**
* Create sharder object
*/
int nshards;
if ((nshards = find_shards<als_edge_type>(base_filename+ suffix, get_option_string("nshards", "auto")))) {
logstream(LOG_INFO) << "File " << base_filename << " was already preprocessed, won't do it again. " << std::endl;
FILE * inf = fopen((base_filename + ".gm").c_str(), "r");
int rc = fscanf(inf,"%d\n%d\n%ld\n%lg\n%d\n",&M, &N, &L, &globalMean, &K);
if (rc != 5)
logstream(LOG_FATAL)<<"Failed to read global mean from file" << base_filename+ suffix << ".gm" << std::endl;
fclose(inf);
logstream(LOG_INFO) << "Opened matrix size: " <<M << " x " << N << " Global mean is: " << globalMean << " time bins: " << K << " Now creating shards." << std::endl;
return nshards;
}
sharder<als_edge_type> sharderobj(base_filename + suffix);
sharderobj.start_preprocessing();
if ((f = fopen(base_filename.c_str(), "r")) == NULL) {
logstream(LOG_FATAL) << "Could not open file: " << base_filename << ", error: " << strerror(errno) << std::endl;
}
if (mm_read_banner(f, &matcode) != 0)
logstream(LOG_FATAL) << "Could not process Matrix Market banner. File: " << base_filename << std::endl;
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) || !mm_is_sparse(matcode))
logstream(LOG_FATAL) << "Sorry, this application does not support complex values and requires a sparse matrix." << std::endl;
/* find out size of sparse matrix .... */
if ((ret_code = mm_read_mtx_crd_size(f, &M, &N, &nz)) !=0) {
logstream(LOG_FATAL) << "Failed reading matrix size: error=" << ret_code << std::endl;
}
L=nz;
logstream(LOG_INFO) << "Starting to read matrix-market input. Matrix dimensions: "
<< M << " x " << N << ", non-zeros: " << nz << std::endl;
uint I, J;
double val;
if (!sharderobj.preprocessed_file_exists()) {
for (size_t i=0; i<nz; i++)
{
int rc = fscanf(f, "%d %d %lg\n", &I, &J, &val);
if (rc != 3)
logstream(LOG_FATAL)<<"Error when reading input file: " << i << std::endl;
I--; /* adjust from 1-based to 0-based */
J--;
if (I >= M)
logstream(LOG_FATAL)<<"Row index larger than the matrix row size " << I << " > " << M << " in line: " << i << std::endl;
if (J >= N)
logstream(LOG_FATAL)<<"Col index larger than the matrix col size " << J << " > " << N << " in line; " << i << std::endl;
globalMean += val;
sharderobj.preprocessing_add_edge(I, M + J, als_edge_type((float)val));
}
uint toadd = 0;
if (implicitratingtype == IMPLICIT_RATING_RANDOM)
toadd = add_implicit_edges(implicitratingtype, sharderobj);
globalMean += implicitratingvalue * toadd;
L += toadd;
sharderobj.end_preprocessing();
globalMean /= L;
logstream(LOG_INFO) << "Global mean is: " << globalMean << " Now creating shards." << std::endl;
if (preprocessor != NULL) {
preprocessor->reprocess(sharderobj.preprocessed_name(), base_filename);
}
FILE * outf = fopen((base_filename + ".gm").c_str(), "w");
fprintf(outf, "%d\n%d\n%ld\n%lg\n%d\n", M, N, L, globalMean, K);
fclose(outf);
} else {
logstream(LOG_INFO) << "Matrix already preprocessed, just run sharder." << std::endl;
}
fclose(f);
logstream(LOG_INFO) << "Now creating shards." << std::endl;
// Shard with a specified number of shards, or determine automatically if not defined
nshards = sharderobj.execute_sharding(get_option_string("nshards", "auto"));
logstream(LOG_INFO) << "Successfully finished sharding for " << base_filename + suffix << std::endl;
logstream(LOG_INFO) << "Created " << nshards << " shards." << std::endl;
return nshards;
}
vec load_matrix_market_vector(const std::string & filename, bool optional_field, bool allow_zeros)
{
int ret_code;
MM_typecode matcode;
uint M, N;
size_t i,nz;
logstream(LOG_INFO) <<"Going to read matrix market vector from input file: " << filename << std::endl;
FILE * f = open_file(filename.c_str(), "r", optional_field);
//if optional file not found return
if (f== NULL && optional_field){
return zeros(1);
}
if (mm_read_banner(f, &matcode) != 0)
logstream(LOG_FATAL) << "Could not process Matrix Market banner." << std::endl;
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) && mm_is_matrix(matcode) &&
mm_is_sparse(matcode) )
logstream(LOG_FATAL) << "sorry, this application does not support " << std::endl <<
"Market Market type: " << mm_typecode_to_str(matcode) << std::endl;
/* find out size of sparse matrix .... */
if (mm_is_sparse(matcode)){
if ((ret_code = mm_read_mtx_crd_size(f, &M, &N, &nz)) !=0)
logstream(LOG_FATAL) << "failed to read matrix market cardinality size " << std::endl;
}
else {
if ((ret_code = mm_read_mtx_array_size(f, &M, &N))!= 0)
logstream(LOG_FATAL) << "failed to read matrix market vector size " << std::endl;
if (N > M){ //if this is a row vector, transpose
int tmp = N;
N = M;
M = tmp;
}
nz = M*N;
}
vec ret = zeros(M);
uint row,col;
double val;
for (i=0; i<nz; i++)
{
if (mm_is_sparse(matcode)){
int rc = fscanf(f, "%u %u %lg\n", &row, &col, &val);
if (rc != 3){
logstream(LOG_FATAL) << "Failed reading input file: " << filename << "Problm at data row " << i << " (not including header and comment lines)" << std::endl;
}
row--; /* adjust from 1-based to 0-based */
col--;
}
else {
int rc = fscanf(f, "%lg\n", &val);
if (rc != 1){
logstream(LOG_FATAL) << "Failed reading input file: " << filename << "Problm at data row " << i << " (not including header and comment lines)" << std::endl;
}
row = i;
col = 0;
}
//some users have gibrish in text file - better check both I and J are >=0 as well
assert(row >=0 && row< M);
assert(col == 0);
if (val == 0 && !allow_zeros)
logstream(LOG_FATAL)<<"Zero entries are not allowed in a sparse matrix market vector. Use --zero=true to avoid this error"<<std::endl;
//set observation value
ret[row] = val;
}
fclose(f);
logstream(LOG_INFO)<<"Succesfully read a vector of size: " << M << " [ " << nz << "]" << std::endl;
return ret;
}
int convert_matrixmarket4(std::string base_filename, bool add_time_edges = false, bool square = false) {
// Note, code based on: http://math.nist.gov/MatrixMarket/mmio/c/example_read.c
int ret_code;
MM_typecode matcode;
FILE *f;
size_t nz;
/**
* Create sharder object
*/
int nshards;
if ((nshards = find_shards<als_edge_type>(base_filename, get_option_string("nshards", "auto")))) {
logstream(LOG_INFO) << "File " << base_filename << " was already preprocessed, won't do it again. " << std::endl;
FILE * inf = fopen((base_filename + ".gm").c_str(), "r");
int rc = fscanf(inf,"%d\n%d\n%ld\n%lg\n%d\n",&M, &N, &L, &globalMean, &K);
if (rc != 5)
logstream(LOG_FATAL)<<"Failed to read global mean from file" << base_filename << ".gm" << std::endl;
fclose(inf);
if (K <= 0)
logstream(LOG_FATAL)<<"Incorrect number of time bins K in .gm file " << base_filename << ".gm" << std::endl;
logstream(LOG_INFO) << "Read matrix of size " << M << " x " << N << " Global mean is: " << globalMean << " time bins: " << K << " Now creating shards." << std::endl;
return nshards;
}
sharder<als_edge_type> sharderobj(base_filename);
sharderobj.start_preprocessing();
if ((f = fopen(base_filename.c_str(), "r")) == NULL) {
logstream(LOG_FATAL) << "Could not open file: " << base_filename << ", error: " << strerror(errno) << std::endl;
}
if (mm_read_banner(f, &matcode) != 0)
logstream(LOG_FATAL) << "Could not process Matrix Market banner. File: " << base_filename << std::endl;
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) || !mm_is_sparse(matcode))
logstream(LOG_FATAL) << "Sorry, this application does not support complex values and requires a sparse matrix." << std::endl;
/* find out size of sparse matrix .... */
if ((ret_code = mm_read_mtx_crd_size(f, &M, &N, &nz)) !=0) {
logstream(LOG_FATAL) << "Failed reading matrix size: error=" << ret_code << std::endl;
}
logstream(LOG_INFO) << "Starting to read matrix-market input. Matrix dimensions: "
<< M << " x " << N << ", non-zeros: " << nz << std::endl;
uint I, J;
double val, time;
if (!sharderobj.preprocessed_file_exists()) {
for (size_t i=0; i<nz; i++)
{
int rc = fscanf(f, "%d %d %lg %lg\n", &I, &J, &time, &val);
if (rc != 4)
logstream(LOG_FATAL)<<"Error when reading input file: " << i << std::endl;
if (time < 0)
logstream(LOG_FATAL)<<"Time (third columns) should be >= 0 " << std::endl;
I--; /* adjust from 1-based to 0-based */
J--;
if (I >= M)
logstream(LOG_FATAL)<<"Row index larger than the matrix row size " << I << " > " << M << " in line: " << i << std::endl;
if (J >= N)
logstream(LOG_FATAL)<<"Col index larger than the matrix col size " << J << " > " << N << " in line; " << i << std::endl;
K = std::max((int)time, (int)K);
//avoid self edges
if (square && I == J)
continue;
globalMean += val;
L++;
sharderobj.preprocessing_add_edge(I, (square? J : (M + J)), als_edge_type(val, time+M+N));
//in case of a tensor, add besides of the user-> movie edge also
//time -> user and time-> movie edges
if (add_time_edges){
sharderobj.preprocessing_add_edge((uint)time + M + N, I, als_edge_type(val, M+J));
sharderobj.preprocessing_add_edge((uint)time + M + N, M+J , als_edge_type(val, I));
}
}
uint toadd = 0;
if (implicitratingtype == IMPLICIT_RATING_RANDOM)
toadd = add_implicit_edges4(implicitratingtype, sharderobj);
globalMean += implicitratingvalue * toadd;
L += toadd;
sharderobj.end_preprocessing();
globalMean /= L;
logstream(LOG_INFO) << "Global mean is: " << globalMean << " time bins: " << K << " . Now creating shards." << std::endl;
FILE * outf = fopen((base_filename + ".gm").c_str(), "w");
fprintf(outf, "%d\n%d\n%ld\n%lg\n%d\n", M, N, L, globalMean, K);
fclose(outf);
} else {
logstream(LOG_INFO) << "Matrix already preprocessed, just run sharder." << std::endl;
}
fclose(f);
logstream(LOG_INFO) << "Now creating shards." << std::endl;
// Shard with a specified number of shards, or determine automatically if not defined
nshards = sharderobj.execute_sharding(get_option_string("nshards", "auto"));
return nshards;
}
int main(int argc, char *argv[]) {
FILE *input_file, *output_file, *time_file;
if (argc < 4) {
fprintf(stderr, "Invalid input parameters\n");
fprintf(stderr, "Usage: %s inputfile outputfile timefile \n", argv[0]);
exit(1);
}
else {
input_file = fopen(argv[1], "r");
output_file = fopen(argv[2], "w");
time_file = fopen(argv[3], "w");
if (!input_file || !output_file || !time_file)
exit(1);
}
MM_typecode matcode;
if (mm_read_banner(input_file, &matcode) != 0) {
printf("Could not process Matrix Market banner.\n");
exit(1);
}
if (!mm_is_matrix(matcode) || !mm_is_real(matcode) || !mm_is_coordinate(matcode)) {
printf("Sorry, this application does not support ");
printf("Market Market type: [%s]\n", mm_typecode_to_str(matcode));
exit(1);
}
mtxMatrix inputMatrix, fullMatrix, LMatrix, UMatrix, UMatrixTranspose, MMatrix;
ReadMatrix(inputMatrix, input_file);
Timer timer;
getRowIndex(&inputMatrix, inputMatrix.RowIndex);
inputMatrix.RowIndex[inputMatrix.N] = inputMatrix.NZ;
int diagNum = 0;
for (int i = 0; i < inputMatrix.N; i++) {
for (int j = inputMatrix.RowIndex[i]; j < inputMatrix.RowIndex[i + 1]; j++) {
if (i == inputMatrix.Col[j]) diagNum++;
}
}
if (mm_is_symmetric(matcode)) {
InitializeMatrix(inputMatrix.N, 2 * inputMatrix.NZ - diagNum, fullMatrix);
TriangleToFull(&inputMatrix, &fullMatrix);
FreeMatrix(inputMatrix);
}
else {
fullMatrix = inputMatrix;
}
int *diag = new int[fullMatrix.N];
for (int i = 0; i < fullMatrix.N; i++) {
for (int j = fullMatrix.RowIndex[i]; j < fullMatrix.RowIndex[i + 1]; j++) {
if (i == fullMatrix.Col[j]) diag[i] = j;
}
}
// for (int i = 0; i < fullMatrix.N + 1; i++) {
// printf("RowIndex[%i] = %i\n", i, fullMatrix.RowIndex[i]);
// }
//
//
// for (int i = 0; i < fullMatrix.N; i++) {
// printf("input[%i]= %lf\n", i, fullMatrix.Value[inputMatrix.RowIndex[i]]);
// }
//
// for (int i = 0; i < fullMatrix.N; i++) {
// printf("diag[%i]= %d\n", i, diag[i]);
// }
timer.start();
ilu0(fullMatrix, fullMatrix.Value, diag);
LUmatrixSeparation(fullMatrix, diag, LMatrix, UMatrix);
Transpose(UMatrix, UMatrixTranspose);
Multiplicate(LMatrix, UMatrixTranspose, MMatrix);
timer.stop();
std::ofstream timeLog;
timeLog.open(argv[3]);
timeLog << timer.getElapsed();
WriteFullMatrix(MMatrix, output_file, matcode);
FreeMatrix(fullMatrix);
return 0;
}
cs *read_matrix(const char *filename, MM_typecode &matcode)
{
LogInfo("Reading Matrix from " << std::string(filename) << "\n");
FILE *file = fopen(filename, "r");
if (!file)
{
LogError("Error: Cannot read file " << std::string(filename) << "\n");
return NULL;
}
LogInfo("Reading Matrix Market banner...");
if (mm_read_banner(file, &matcode) != 0)
{
LogError("Error: Could not process Matrix Market banner\n");
fclose(file);
return NULL;
}
if (!mm_is_matrix(matcode) || !mm_is_sparse(matcode)
|| mm_is_complex(matcode))
{
LogError(
"Error: Unsupported matrix format - Must be real and sparse\n");
fclose(file);
return NULL;
}
Int M, N, nz;
if ((mm_read_mtx_crd_size(file, &M, &N, &nz)) != 0)
{
LogError("Error: Could not parse matrix dimension and size.\n");
fclose(file);
return NULL;
}
if (M != N)
{
LogError("Error: Matrix must be square.\n");
fclose(file);
return NULL;
}
LogInfo("Reading matrix data...\n");
Int *I = (Int *)SuiteSparse_malloc(static_cast<size_t>(nz), sizeof(Int));
Int *J = (Int *)SuiteSparse_malloc(static_cast<size_t>(nz), sizeof(Int));
double *val
= (double *)SuiteSparse_malloc(static_cast<size_t>(nz), sizeof(double));
if (!I || !J || !val)
{
LogError("Error: Ran out of memory in Mongoose::read_matrix\n");
SuiteSparse_free(I);
SuiteSparse_free(J);
SuiteSparse_free(val);
fclose(file);
return NULL;
}
mm_read_mtx_crd_data(file, M, N, nz, I, J, val, matcode);
fclose(file); // Close the file
for (Int k = 0; k < nz; k++)
{
--I[k];
--J[k];
if (mm_is_pattern(matcode))
val[k] = 1;
}
cs *A = (cs *)SuiteSparse_malloc(1, sizeof(cs));
if (!A)
{
LogError("Error: Ran out of memory in Mongoose::read_matrix\n");
SuiteSparse_free(I);
SuiteSparse_free(J);
SuiteSparse_free(val);
return NULL;
}
A->nzmax = nz;
A->m = M;
A->n = N;
A->p = J;
A->i = I;
A->x = val;
A->nz = nz;
LogInfo("Compressing matrix from triplet to CSC format...\n");
cs *compressed_A = cs_compress(A);
cs_spfree(A);
if (!compressed_A)
{
LogError("Error: Ran out of memory in Mongoose::read_matrix\n");
return NULL;
}
return compressed_A;
}
csr_matrix_t *csr_mm_load(char *filename)
{
int ret_code;
MM_typecode matcode;
FILE *f;
int entry_count;
int i, M, N, nz;
matrix_entry_t *entries;
matrix_entry_t *entry;
int *row_start;
int *col_index;
double *val;
csr_matrix_t *A;
if ((f = fopen(filename, "r")) == NULL)
exit(1);
if (mm_read_banner(f, &matcode) != 0) {
printf("Could not process Matrix Market banner.\n");
exit(1);
}
if (!mm_is_sparse(matcode) || !mm_is_symmetric(matcode) ||
!mm_is_real(matcode)) {
printf("Sorry, this application does not support ");
printf("Market Market type: [%s]\n", mm_typecode_to_str(matcode));
exit(1);
}
/* find out size of sparse matrix .... */
if ((ret_code = mm_read_mtx_crd_size(f, &M, &N, &nz)) != 0)
exit(1);
/* reserve memory for matrices */
row_start = (int *) malloc((M + 1) * sizeof(int));
col_index = (int *) malloc((2 * nz - M) * sizeof(int));
val = (double *) malloc((2 * nz - M) * sizeof(double));
entries =
(matrix_entry_t *) malloc((2 * nz - M) * sizeof(matrix_entry_t));
/* NOTE: when reading in doubles, ANSI C requires the use of the "l" */
/* specifier as in "%lg", "%lf", "%le", otherwise errors will occur */
/* (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15) */
entry = entries;
entry_count = 0;
for (i = 0; i < nz; i++) {
int row, col;
double val;
fscanf(f, "%d %d %lg\n", &row, &col, &val);
--row; /* adjust to 0-based */
--col;
assert(row >= 0 && col >= 0);
assert(entry_count++ < 2 * nz - M);
entry->i = row;
entry->j = col;
entry->val = val;
++entry;
if (row != col) { /* Fill out the other half... */
assert(entry_count++ < 2 * nz - M);
entry->i = col;
entry->j = row;
entry->val = val;
++entry;
}
}
if (f != stdin)
fclose(f);
/**********************************/
/* now make CSR version of matrix */
/**********************************/
nz = 2 * nz - M;
qsort(entries, nz, sizeof(matrix_entry_t), entry_comparison);
entry = entries;
row_start[0] = 0;
for (i = 0; i < nz; ++i) {
row_start[entry->i + 1] = i + 1;
col_index[i] = entry->j;
val[i] = entry->val;
++entry;
}
free(entries);
A = (csr_matrix_t *) malloc(sizeof(csr_matrix_t));
A->m = M;
A->n = N;
A->nz = nz;
A->row_start = row_start;
A->col_idx = col_index;
A->val = val;
return A;
}
int main(int argc,char **args)
{
/*PETSc Mat Object */
Mat pMat;
/* Input matrix market file and output PETSc binary file */
char inputFile[128],outputFile[128],buf[128];
/* number rows, columns, non zeros etc */
int i,j,m,n,nnz,ierr,col,row;
/*We compute no of nozeros per row for PETSc Mat object pre-allocation*/
int *nnzPtr;
/*Maximum nonzero in nay row */
int maxNNZperRow=0;
/*Row number containing max non zero elements */
int maxRowNum = 0;
/*Just no of comments that will be ignore during successive read of file */
int numComments=0;
PetscScalar zero=0;
/* This is variable of type double */
PetscScalar val;
/*File handle for read and write*/
FILE* file;
/*File handle for writing nonzero elements distribution per row */
FILE *fileRowDist;
/*PETSc Viewer is used for writing PETSc Mat object in binary format */
PetscViewer view;
/*Just record time required for conversion */
PetscLogDouble t1,t2,elapsed_time;
/* MatrixMarket struct */
MM_typecode matcode;
/*Initialise PETSc lib */
PetscInitialize(&argc,&args,(char *)0,PETSC_NULL);
/* Just record time */
//ierr = PetscGetTime(&t1); CHKERRQ(ierr);
/*Get name of matrix market file from command line options and Open file*/
ierr = PetscOptionsGetString(PETSC_NULL,"-fin",inputFile,127,PETSC_NULL); CHKERRQ(ierr);
ierr = PetscFOpen(PETSC_COMM_SELF,inputFile,"r",&file); CHKERRQ(ierr);
if (mm_read_banner(file, &matcode)) {
PetscPrintf(PETSC_COMM_SELF, "Could not read Matrix Market banner.\n");
exit(1);
}
/********************* MM_typecode query fucntions ***************************/
/* #define mm_is_matrix(typecode) ((typecode)[0]=='M') */
/* #define mm_is_sparse(typecode) ((typecode)[1]=='C') */
/* #define mm_is_coordinate(typecode)((typecode)[1]=='C') */
/* #define mm_is_dense(typecode) ((typecode)[1]=='A') */
/* #define mm_is_array(typecode) ((typecode)[1]=='A') */
/* #define mm_is_complex(typecode) ((typecode)[2]=='C') */
/* #define mm_is_real(typecode) ((typecode)[2]=='R') */
/* #define mm_is_pattern(typecode) ((typecode)[2]=='P') */
/* #define mm_is_integer(typecode) ((typecode)[2]=='I') */
/* #define mm_is_symmetric(typecode)((typecode)[3]=='S') */
/* #define mm_is_general(typecode) ((typecode)[3]=='G') */
/* #define mm_is_skew(typecode) ((typecode)[3]=='K') */
/* #define mm_is_hermitian(typecode)((typecode)[3]=='H') */
/* int mm_is_valid(MM_typecode matcode); */
/* Do not convert pattern matrices */
if (mm_is_pattern(matcode)) {
ierr = PetscPrintf(PETSC_COMM_SELF, "%s: Pattern matrix -- skipping.\n", inputFile);
exit(0);
}
/* find out size of sparse matrix .... */
/*Reads size of sparse matrix from matrix market file */
int ret_code;
if ((ret_code = mm_read_mtx_crd_size(file, &m, &n, &nnz)) !=0)
exit(1);
ierr = PetscPrintf(PETSC_COMM_SELF, "%s: ROWS = %d, COLUMNS = %d, NO OF NON-ZEROS = %d\n",inputFile,m,n,nnz);
/* Only consider square matrices */
if (m != n) {
ierr = PetscPrintf(PETSC_COMM_SELF, "%s: Nonsquare matrix -- skipping.\n", inputFile);
exit(0);
}
ierr = MatCreate(PETSC_COMM_WORLD,&pMat);CHKERRQ(ierr);
ierr = MatSetFromOptions(pMat);CHKERRQ(ierr);
//ierr = MatSetOption(pMat, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE); CHKERRQ(ierr);
if (mm_is_symmetric(matcode)) {
ierr = MatSetOption(pMat,MAT_SYMMETRIC,PETSC_TRUE); CHKERRQ(ierr);
ierr = MatSetOption(pMat,MAT_SYMMETRY_ETERNAL,PETSC_TRUE); CHKERRQ(ierr);
}
ierr = MatSetSizes(pMat,PETSC_DECIDE,PETSC_DECIDE,m,n);CHKERRQ(ierr);
ierr = MatSetUp(pMat);CHKERRQ(ierr);
//printf("\n MAX NONZERO FOR ANY ROW ARE : %d & ROW NUM IS : %d", maxNNZperRow, maxRowNum );
/* Its important to pre-allocate memory by passing max non zero for any row in the matrix */
//ierr = MatCreateSeqAIJ(PETSC_COMM_WORLD,m,n,maxNNZperRow,PETSC_NULL,&pMat);
/* OR we can also pass row distribution of nozero elements for every row */
/* ierr = MatCreateSeqAIJ(PETSC_COMM_WORLD,m,n,0,nnzPtr,&pMat);*/
/*Now Set matrix elements values form matrix market file */
for (i=0; i < m; i++){
for (j = 0; j < n; j++) {
if (i != j) continue;
ierr = MatSetValues(pMat,1,&i,1,&j,&zero,INSERT_VALUES); CHKERRQ(ierr);
}
}
for (i=0; i<nnz; i++)
{
/*Read matrix element from matrix market file*/
fscanf(file,"%d %d %le\n",&row,&col,&val);
/*In matrix market format, rows and columns starts from 1 */
row = row-1; col = col-1 ;
/* For every non zero element,insert that value at row,col position */
ierr = MatSetValues(pMat,1,&row,1,&col,&val,INSERT_VALUES); CHKERRQ(ierr);
}
fclose(file);
/*Matrix Read Complete */
ierr = PetscPrintf(PETSC_COMM_SELF,"%s MATRIX READ...DONE!\n", inputFile);
/*Now assemeble the matrix */
ierr = MatAssemblyBegin(pMat,MAT_FINAL_ASSEMBLY);
ierr = MatAssemblyEnd(pMat,MAT_FINAL_ASSEMBLY);
/* Now open output file for writing into PETSc Binary FOrmat*/
ierr = PetscOptionsGetString(PETSC_NULL,"-fout",outputFile,127,PETSC_NULL);CHKERRQ(ierr);
/*With the PETSc Viewer write output to File*/
ierr = PetscViewerBinaryOpen(PETSC_COMM_WORLD,outputFile,FILE_MODE_WRITE,&view);CHKERRQ(ierr);
/*Matview will dump the Mat object to binary file */
ierr = MatView(pMat,view);CHKERRQ(ierr);
//ierr = MatView(pMat,PETSC_VIEWER_STDOUT_WORLD);CHKERRQ(ierr);
ierr = PetscPrintf(PETSC_COMM_SELF,"%s PETSC MATRIX STORED\n", outputFile);
/* Destroy the data structure */
ierr = PetscViewerDestroy(&view);CHKERRQ(ierr);
ierr = MatDestroy(&pMat);CHKERRQ(ierr);
/*Just for statistics*/
/*
ierr = PetscGetTime(&t2);CHKERRQ(ierr);
elapsed_time = t2 - t1;
ierr = PetscPrintf(PETSC_COMM_SELF,"ELAPSE TIME: %g\n",elapsed_time);CHKERRQ(ierr);
*/
ierr = PetscFinalize();CHKERRQ(ierr);
return 0;
}
int MatrixMarketFileToBlockMaps(const char* filename,
const Epetra_Comm& comm,
Epetra_BlockMap*& rowmap,
Epetra_BlockMap*& colmap,
Epetra_BlockMap*& rangemap,
Epetra_BlockMap*& domainmap)
{
FILE* infile = fopen(filename, "r");
if (infile == NULL) {
return(-1);
}
MM_typecode matcode;
int err = mm_read_banner(infile, &matcode);
if (err != 0) return(err);
if (!mm_is_matrix(matcode) || !mm_is_coordinate(matcode) ||
!mm_is_real(matcode) || !mm_is_general(matcode)) {
return(-1);
}
int numrows, numcols, nnz;
err = mm_read_mtx_crd_size(infile, &numrows, &numcols, &nnz);
if (err != 0) return(err);
//for this case, we'll assume that the row-map is the same as
//the range-map.
//create row-map and range-map with linear distributions.
rowmap = new Epetra_BlockMap(numrows, 1, 0, comm);
rangemap = new Epetra_BlockMap(numrows, 1, 0, comm);
int I, J;
double val, imag;
int num_map_cols = 0, insertPoint, foundOffset;
int allocLen = numcols;
int* map_cols = new int[allocLen];
//read through all matrix data and construct a list of the column-
//indices that occur in rows that are local to this processor.
for(int i=0; i<nnz; ++i) {
err = mm_read_mtx_crd_entry(infile, &I, &J, &val,
&imag, matcode);
if (err == 0) {
--I;
--J;
if (rowmap->MyGID(I)) {
foundOffset = Epetra_Util_binary_search(J, map_cols, num_map_cols,
insertPoint);
if (foundOffset < 0) {
Epetra_Util_insert(J, insertPoint, map_cols,
num_map_cols, allocLen);
}
}
}
}
//create colmap with the list of columns associated with rows that are
//local to this processor.
colmap = new Epetra_Map(-1, num_map_cols, map_cols, 0, comm);
//create domainmap which has a linear distribution
domainmap = new Epetra_BlockMap(numcols, 1, 0, comm);
delete [] map_cols;
return(0);
}
void HostMatrixCOO<ValueType>::ReadFileMTX(const std::string filename) {
// Follow example_read.c (from Matrix Market web site)
int ret_code;
MM_typecode matcode;
FILE *f;
int M, N;
int fnz, nnz; // file nnz, real nnz
bool sym = false;
LOG_INFO("ReadFileMTX: filename="<< filename << "; reading...");
if ((f = fopen(filename.c_str(), "r")) == NULL) {
LOG_INFO("ReadFileMTX cannot open file " << filename);
FATAL_ERROR(__FILE__, __LINE__);
}
if (mm_read_banner(f, &matcode) != 0)
{
LOG_INFO("ReadFileMTX could not process Matrix Market banner.");
FATAL_ERROR(__FILE__, __LINE__);
}
/* This is how one can screen matrix types if their application */
/* only supports a subset of the Matrix Market data types. */
if (mm_is_complex(matcode) && mm_is_matrix(matcode) &&
mm_is_sparse(matcode) )
{
LOG_INFO("ReadFileMTX does not support Market Market type:" << mm_typecode_to_str(matcode));
FATAL_ERROR(__FILE__, __LINE__);
}
/* find out size of sparse matrix .... */
if ((ret_code = mm_read_mtx_crd_size(f, &M, &N, &fnz)) !=0) {
LOG_INFO("ReadFileMTX matrix size error");
FATAL_ERROR(__FILE__, __LINE__);
}
nnz = fnz ;
/* reseve memory for matrices */
if(mm_is_symmetric(matcode)) {
if (N != M) {
LOG_INFO("ReadFileMTX non-squared symmetric matrices are not supported");
LOG_INFO("What is symmetric and non-squared matrix? e-mail me");
FATAL_ERROR(__FILE__, __LINE__);
}
nnz = 2*(nnz - N) + N;
sym = true ;
}
this->AllocateCOO(nnz,M,N);
int ii=0;
int col, row;
double val;
int ret;
for (int i=0; i<fnz; ++i) {
ret = fscanf(f, "%d %d %lg\n", &row, &col, &val);
if (!ret) FATAL_ERROR(__FILE__, __LINE__);
row--; /* adjust from 1-based to 0-based */
col--;
assert (ret == 3);
// LOG_INFO(row << " " << col << " " << val);
this->mat_.row[ii] = row;
this->mat_.col[ii] = col;
this->mat_.val[ii] = val;
if (sym && (row!=col)) {
++ii;
this->mat_.row[ii] = col;
this->mat_.col[ii] = row;
this->mat_.val[ii] = val;
}
++ii;
}
LOG_INFO("ReadFileMTX: filename="<< filename << "; done");
fclose(f);
}
magma_int_t
magma_d_csr_mtx(
magma_d_matrix *A,
const char *filename,
magma_queue_t queue )
{
char buffer[ 1024 ];
magma_int_t info = 0;
int csr_compressor = 0; // checks for zeros in original file
magma_d_matrix B={Magma_CSR};
magma_index_t *coo_col = NULL;
magma_index_t *coo_row = NULL;
double *coo_val = NULL;
double *new_val = NULL;
magma_index_t* new_row = NULL;
magma_index_t* new_col = NULL;
magma_int_t symmetric = 0;
std::vector< std::pair< magma_index_t, double > > rowval;
FILE *fid = NULL;
MM_typecode matcode;
fid = fopen(filename, "r");
if (fid == NULL) {
printf("%% Unable to open file %s\n", filename);
info = MAGMA_ERR_NOT_FOUND;
goto cleanup;
}
printf("%% Reading sparse matrix from file (%s):", filename);
fflush(stdout);
if (mm_read_banner(fid, &matcode) != 0) {
printf("\n%% Could not process Matrix Market banner: %s.\n", matcode);
info = MAGMA_ERR_NOT_SUPPORTED;
goto cleanup;
}
if (!mm_is_valid(matcode)) {
printf("\n%% Invalid Matrix Market file.\n");
info = MAGMA_ERR_NOT_SUPPORTED;
goto cleanup;
}
if ( ! ( ( mm_is_real(matcode) ||
mm_is_integer(matcode) ||
mm_is_pattern(matcode) ||
mm_is_real(matcode) ) &&
mm_is_coordinate(matcode) &&
mm_is_sparse(matcode) ) )
{
mm_snprintf_typecode( buffer, sizeof(buffer), matcode );
printf("\n%% Sorry, MAGMA-sparse does not support Market Market type: [%s]\n", buffer );
printf("%% Only real-valued or pattern coordinate matrices are supported.\n");
info = MAGMA_ERR_NOT_SUPPORTED;
goto cleanup;
}
magma_index_t num_rows, num_cols, num_nonzeros;
if (mm_read_mtx_crd_size(fid, &num_rows, &num_cols, &num_nonzeros) != 0) {
info = MAGMA_ERR_UNKNOWN;
goto cleanup;
}
A->storage_type = Magma_CSR;
A->memory_location = Magma_CPU;
A->num_rows = num_rows;
A->num_cols = num_cols;
A->nnz = num_nonzeros;
A->fill_mode = MagmaFull;
CHECK( magma_index_malloc_cpu( &coo_col, A->nnz ) );
CHECK( magma_index_malloc_cpu( &coo_row, A->nnz ) );
CHECK( magma_dmalloc_cpu( &coo_val, A->nnz ) );
if (mm_is_real(matcode) || mm_is_integer(matcode)) {
for(magma_int_t i = 0; i < A->nnz; ++i) {
magma_index_t ROW, COL;
double VAL; // always read in a double and convert later if necessary
fscanf(fid, " %d %d %lf \n", &ROW, &COL, &VAL);
if ( VAL == 0 )
csr_compressor = 1;
coo_row[i] = ROW - 1;
coo_col[i] = COL - 1;
coo_val[i] = MAGMA_D_MAKE( VAL, 0.);
}
} else if (mm_is_pattern(matcode) ) {
for(magma_int_t i = 0; i < A->nnz; ++i) {
magma_index_t ROW, COL;
fscanf(fid, " %d %d \n", &ROW, &COL );
coo_row[i] = ROW - 1;
coo_col[i] = COL - 1;
coo_val[i] = MAGMA_D_MAKE( 1.0, 0.);
}
} else if (mm_is_real(matcode) ){
for(magma_int_t i = 0; i < A->nnz; ++i) {
magma_index_t ROW, COL;
double VAL, VALC; // always read in a double and convert later if necessary
fscanf(fid, " %d %d %lf %lf\n", &ROW, &COL, &VAL, &VALC);
coo_row[i] = ROW - 1;
coo_col[i] = COL - 1;
coo_val[i] = MAGMA_D_MAKE( VAL, VALC);
}
// printf(" ...successfully read real matrix... ");
} else {
printf("\n%% Unrecognized data type\n");
info = MAGMA_ERR_NOT_SUPPORTED;
goto cleanup;
}
fclose(fid);
fid = NULL;
printf(" done. Converting to CSR:");
fflush(stdout);
A->sym = Magma_GENERAL;
if( mm_is_symmetric(matcode) ) {
symmetric = 1;
}
if ( mm_is_symmetric(matcode) || mm_is_symmetric(matcode) ) {
// duplicate off diagonal entries
printf("\n%% Detected symmetric case.");
A->sym = Magma_SYMMETRIC;
magma_index_t off_diagonals = 0;
for(magma_int_t i = 0; i < A->nnz; ++i) {
if (coo_row[i] != coo_col[i])
++off_diagonals;
}
magma_index_t true_nonzeros = 2*off_diagonals + (A->nnz - off_diagonals);
//printf("%% total number of nonzeros: %d\n%%", int(A->nnz));
CHECK( magma_index_malloc_cpu( &new_row, true_nonzeros ));
CHECK( magma_index_malloc_cpu( &new_col, true_nonzeros ));
CHECK( magma_dmalloc_cpu( &new_val, true_nonzeros ));
magma_index_t ptr = 0;
for(magma_int_t i = 0; i < A->nnz; ++i) {
if (coo_row[i] != coo_col[i]) {
new_row[ptr] = coo_row[i];
new_col[ptr] = coo_col[i];
new_val[ptr] = coo_val[i];
ptr++;
new_col[ptr] = coo_row[i];
new_row[ptr] = coo_col[i];
new_val[ptr] = (symmetric == 0) ? coo_val[i] : conj(coo_val[i]);
ptr++;
} else {
new_row[ptr] = coo_row[i];
new_col[ptr] = coo_col[i];
new_val[ptr] = coo_val[i];
ptr++;
}
}
magma_free_cpu(coo_row);
magma_free_cpu(coo_col);
magma_free_cpu(coo_val);
coo_row = new_row;
coo_col = new_col;
coo_val = new_val;
A->nnz = true_nonzeros;
//printf("total number of nonzeros: %d\n", A->nnz);
} // end symmetric case
CHECK( magma_dmalloc_cpu( &A->val, A->nnz ));
CHECK( magma_index_malloc_cpu( &A->col, A->nnz ));
CHECK( magma_index_malloc_cpu( &A->row, A->num_rows+1 ));
// original code from Nathan Bell and Michael Garland
for (magma_index_t i = 0; i < num_rows; i++)
(A->row)[i] = 0;
for (magma_index_t i = 0; i < A->nnz; i++)
(A->row)[coo_row[i]]++;
// cumulative sum the nnz per row to get row[]
magma_int_t cumsum;
cumsum = 0;
for(magma_int_t i = 0; i < num_rows; i++) {
magma_index_t temp = (A->row)[i];
(A->row)[i] = cumsum;
cumsum += temp;
}
(A->row)[num_rows] = A->nnz;
// write Aj,Ax into Bj,Bx
for(magma_int_t i = 0; i < A->nnz; i++) {
magma_index_t row_ = coo_row[i];
magma_index_t dest = (A->row)[row_];
(A->col)[dest] = coo_col[i];
(A->val)[dest] = coo_val[i];
(A->row)[row_]++;
}
magma_free_cpu(coo_row);
magma_free_cpu(coo_col);
magma_free_cpu(coo_val);
coo_row = NULL;
coo_col = NULL;
coo_val = NULL;
int last;
last = 0;
for(int i = 0; i <= num_rows; i++) {
int temp = (A->row)[i];
(A->row)[i] = last;
last = temp;
}
(A->row)[A->num_rows] = A->nnz;
// sort column indices within each row
// copy into vector of pairs (column index, value), sort by column index, then copy back
for (magma_index_t k=0; k < A->num_rows; ++k) {
int kk = (A->row)[k];
int len = (A->row)[k+1] - (A->row)[k];
rowval.resize( len );
for( int i=0; i < len; ++i ) {
rowval[i] = std::make_pair( (A->col)[kk+i], (A->val)[kk+i] );
}
std::sort( rowval.begin(), rowval.end(), compare_first );
for( int i=0; i < len; ++i ) {
(A->col)[kk+i] = rowval[i].first;
(A->val)[kk+i] = rowval[i].second;
}
}
if ( csr_compressor > 0) { // run the CSR compressor to remove zeros
//printf("removing zeros: ");
CHECK( magma_dmtransfer( *A, &B, Magma_CPU, Magma_CPU, queue ));
CHECK( magma_d_csr_compressor(
&(A->val), &(A->row), &(A->col),
&B.val, &B.row, &B.col, &B.num_rows, queue ));
B.nnz = B.row[num_rows];
//printf(" remaining nonzeros:%d ", B.nnz);
magma_free_cpu( A->val );
magma_free_cpu( A->row );
magma_free_cpu( A->col );
CHECK( magma_dmtransfer( B, A, Magma_CPU, Magma_CPU, queue ));
//printf("done.\n");
}
A->true_nnz = A->nnz;
printf(" done.\n");
cleanup:
if ( fid != NULL ) {
fclose( fid );
fid = NULL;
}
magma_dmfree( &B, queue );
magma_free_cpu(coo_row);
magma_free_cpu(coo_col);
magma_free_cpu(coo_val);
return info;
}
