/* W = sqrt(inv(cov(X))) */
void ComputeWhitener (double *W, double *X, int n, int T)
{//ok
double threshold_W = RELATIVE_W_THRESHOLD / sqrt((double) T) ;
double *Cov = (double *) calloc(n*n, sizeof(double)) ;
double rescale ;
int i,j ;
if (Cov == NULL) OutOfMemory() ;
EstCovMat (Cov, X, n, T) ;
printf ("covmat\n");
PrintMat (Cov, n, n) ;
printf ("\n");
Diago (Cov, W, n, threshold_W) ;
printf ("diago\n");
PrintMat (Cov, n, n) ;
printf ("\n");
for (i=0; i<n; i++) {
rescale= 1.0 / sqrt (Cov[i+i*n]) ;
for (j=0; j< n ; j++)
W[i*n+j] = rescale * W[i*n+j] ;
}
free(Cov);//done
}
CAMLprim value c_densematrix_print_mat(value va)
{
CAMLparam1(va);
PrintMat(DLSMAT(va));
fflush(stdout);
CAMLreturn (Val_unit);
}
void printNumberByMat(IplImage* getNumberPicture, vector<ContourData> contourData){
CvMat *srcMat, matHeader;
for (int i = 0; i < contourData.size(); i++) {
cvSetImageROI(getNumberPicture, cvRect(contourData[i].getMinX(), contourData[i].getMinY(), contourData[i].getWidth(), contourData[i].getHeight()));
srcMat = cvGetMat(getNumberPicture, &matHeader);
PrintMat(srcMat, "srcMat");
}
}
int main (int argc, char* argv[])
{
int size = 10, bound = 5, rate = 5;
char* OUTPATH = "data_input";
int b_print = 0; //switch for the print
int option;
int temp,i,j;
int ** A;
FILE *op;
while ((option = getopt(argc, argv, "s:b:r:po:")) != -1)
switch(option){
case 's': size = strtol(optarg, NULL, 10); break;
case 'b': bound = strtol(optarg, NULL, 10);break;
case 'r': rate = strtol(optarg, NULL, 10);
case 'p': b_print = 1; break;
case 'o': OUTPATH = optarg; break;
case '?': printf("Unexpected Options. \n"); return -1;
}
A = CreateMat(size);
srand(time(NULL));
for (i = 0; i < size; ++i){
for (j = i; j < size; ++j){
if (rand() % 100 <= rate)
A[i][j] = INF * bound;
else
A[i][j] = rand() % bound + 1;
A[j][i] = A[i][j];
}
}
for (i = 0; i < size; ++i){
A[i][i] = 0;
}
if ((op = fopen(OUTPATH,"w")) == NULL){
printf("Cant open a file!/n");
return -2;
}
fprintf(op,"%d\n\n", size);
for (i = 0; i < size; ++i) {
for (j = 0; j < size; ++j){
fprintf(op,"%d\t", A[i][j]);
}
fprintf(op,"\n");
}
fclose(op);
if (b_print){
printf("The matrix size is %d\n", size);
printf("=====================================\n");
printf("Matrix A is \n");
PrintMat(A, size);
}
DestroyMat(A, size);
return 0;
}
/*
* function calculates a jacobian matrix
*/
static
int nlsDenseJac(long int N, N_Vector vecX, N_Vector vecFX, DlsMat Jac, void *userData, N_Vector tmp1, N_Vector tmp2)
{
NLS_KINSOL_USERDATA *kinsolUserData = (NLS_KINSOL_USERDATA*) userData;
DATA* data = kinsolUserData->data;
threadData_t *threadData = kinsolUserData->threadData;
int sysNumber = kinsolUserData->sysNumber;
NONLINEAR_SYSTEM_DATA *nlsData = &(data->simulationInfo->nonlinearSystemData[sysNumber]);
NLS_KINSOL_DATA* kinsolData = (NLS_KINSOL_DATA*) nlsData->solverData;
/* prepare variables */
double *x = N_VGetArrayPointer(vecX);
double *fx = N_VGetArrayPointer(vecFX);
double *xScaling = NV_DATA_S(kinsolData->xScale);
double *fRes = NV_DATA_S(kinsolData->fRes);
double xsave, xscale, sign;
double delta_hh;
const double delta_h = sqrt(DBL_EPSILON*2e1);
long int i,j;
/* performance measurement */
rt_ext_tp_tick(&nlsData->jacobianTimeClock);
for(i = 0; i < N; i++)
{
xsave = x[i];
delta_hh = delta_h * (fabs(xsave) + 1.0);
if ((xsave + delta_hh >= nlsData->max[i]))
delta_hh *= -1;
x[i] += delta_hh;
/* Calculate difference quotient */
nlsKinsolResiduals(vecX, kinsolData->fRes, userData);
/* Calculate scaled difference quotient */
delta_hh = 1. / delta_hh;
for(j = 0; j < N; j++)
{
DENSE_ELEM(Jac, j, i) = (fRes[j] - fx[j]) * delta_hh;
}
x[i] = xsave;
}
/* debug */
if (ACTIVE_STREAM(LOG_NLS_JAC)){
infoStreamPrint(LOG_NLS_JAC, 0, "##KINSOL## omc dense matrix.");
PrintMat(Jac);
}
/* performance measurement and statistics */
nlsData->jacobianTime += rt_ext_tp_tock(&(nlsData->jacobianTimeClock));
nlsData->numberOfJEval++;
return 0;
}
static int build_nbayes_classifier( char* data_filename, CvNormalBayesClassifier **nbayes){
const int var_count = 51;
CvMat* data = 0;
CvMat train_data;
CvMat* responses;
int ok = read_num_class_data( data_filename, 51, &data, &responses );
int nsamples_all = 0;
int i, j;
double train_hr = 0, test_hr = 0;
CvANN_MLP mlp;
if( !ok ){
printf( "No se pudo leer la información de entrenamiento %s\n", data_filename );
return -1;
}
printf( "La base de datos %s está siendo cargada...\n", data_filename );
nsamples_all = data->rows;
printf( "Entrenando el clasificador...\n");
// 1. unroll the responses
cvGetRows( data, &train_data, 0, nsamples_all);
// 2. train classifier
CvMat* train_resp = cvCreateMat( nsamples_all, 1, CV_32FC1);
for (int i = 0; i < nsamples_all; i++)
train_resp->data.fl[i] = responses->data.fl[i];
*nbayes = new CvNormalBayesClassifier(&train_data, train_resp);
if(DEBUG){
std::cout << "Train_data = "<< std::endl << std::endl;
PrintMat(&train_data);
std::cout << "Train_resp = "<< std::endl << " " << train_resp << std::endl << std::endl;
PrintMat(train_resp);
}
cvReleaseMat( &train_resp );
cvReleaseMat( &data );
cvReleaseMat( &responses );
return 0;
}
void StartAssignment2()
{
cv::Mat TrainingMask = ReadImageFromTXT("training_mask.txt");
cv::Mat TrainingImg = ReadImageFromTXT("mosaic1_train.txt");
std::vector<int> offsets;
//Direction 1
offsets.push_back(1);
offsets.push_back(0);
//Direction 2
offsets.push_back(1);
offsets.push_back(1);
//Training
//Calculate the features, Q1 to Qn.
std::vector<cv::Mat> QFeatImgs = ComputeQFeatureImgs(TrainingImg, offsets);
//Use the test mask to generate a descriptor using the N means of the N features for all M classes. We should have M descriptors now.
std::vector<ClassDescriptor> Descriptors = ComputeClassDescriptors(QFeatImgs, TrainingMask);
//Use the function cv::calcCovarMatrix() to calculate the covariance matrix for each class descriptor.
//Classification
cv::Mat ImgTOClassify = ReadImageFromTXT("mosaic2_test.txt");
//Calculate the new feature imgs.
QFeatImgs = ComputeQFeatureImgs(ImgTOClassify, offsets);
std::vector<cv::Mat> CovarMats;
//Generate feature imgs. Store each pixel into an array called X;
//Compute the QFeature
//Calculate descriptor from the QFeature images.
//Use the function given in the book to calculate the probability of a pixel being class 1...M. Select the one with the highest probability.
//Input is the image, the class descriptors and the covariance matrices.
auto samples = computeSamples(TrainingMask, QFeatImgs);
cv::Mat classifiedImage = MultivariateGaussian(ImgTOClassify, Descriptors, samples, QFeatImgs);
PrintMat(classifiedImage);
cv::imshow("ClassifiedImage", 63 * classifiedImage);
cv::imshow("ClassifiedImageBase", 63 * TrainingMask);
cv::waitKey();
float accuracy = ConfusionMatrix(TrainingMask, classifiedImage);
std::cout << "Accuracy of classifier: " << accuracy;
}
void classify(CvNormalBayesClassifier *nbayes){
/// Probando el clasificador
CvMat sample = cvMat(1, 51, CV_32FC1, relevanceVector);
CvMat *result = cvCreateMat(1, 1, CV_32FC1);
// Predict
float prediccion = 0.0000;
if(nbayes != NULL){
std::cout << "I GOT HERE" << std::endl;
PrintMat(&sample);
//prediccion = nbayes->predict(&sample, result);
//prediccion = nbayes->predict(&sample, 0);
prediccion = (float) nbayes->predict(&sample);
std::cout << "I GOT HERE 1" << std::endl;
}
/* Imprimiendo el Valor */
//printf("Classify = %f\n", result->data.fl[0]);
printf("Classify = %f\n",prediccion);
// Liberando Memoria */
cvReleaseMat(&result);
}
cv::Mat MultivariateGaussian(cv::Mat Img, std::vector<ClassDescriptor>& Descriptors, std::vector<std::vector<std::vector<float>>>& samples, std::vector<cv::Mat>& featureImgs)
{
int numClasses = 4;
cv::Mat ClassificationMask(Img.rows, Img.cols, CV_8U);
std::vector<cv::Mat> ProbabilityForClass(numClasses);
for (size_t i = 0; i < numClasses; i++) {
ProbabilityForClass[i] = cv::Mat::zeros(Img.rows, Img.cols, CV_32F);
}
for (size_t i = 0; i < numClasses; i++) {
float d = Descriptors[0].num_features_t_;
cv::Mat myu = Descriptors[i].Descriptor;
cv::Mat sigma;
std::vector<std::vector<float>> S = samples[i];
//cv::calcCovarMatrix(S, sigma, myu, CV_COVAR_ROWS, CV_32F);
sigma = CalcCovarMatrix(S, myu, d);
for (size_t y_t = 0; y_t < Img.rows; y_t++) {
std::cout << "Calculating probability row " << y_t << std::endl;
for (size_t x_t = 0; x_t < Img.cols; x_t++) {
//Calculate the probability for all the classes.
cv::Mat x = getPixelDescriptor(featureImgs, y_t, x_t);
//PrintMat(sigma);
//std::cout << "\n\n";
float n = 4; //What is this? Num classes it seems.
if (x.rows != myu.rows || x.rows != sigma.rows)
std::cout << "Something bad happened as the vectors x and myu and the matrix sigma does not match.";
float detSigma = cv::determinant(sigma);
float scalar = 1 / (pow(2 * M_PI, n / 2) * pow(cv::determinant(sigma), 0.5f));
cv::Mat difference;
cv::subtract(x, myu, difference, cv::Mat(), CV_32F);
cv::Mat exponent = -0.5f * difference.t() * sigma.inv() * difference;
float Probability = scalar * exp(exponent.at<float>(0, 0));
if (Probability > 90000.0f) {
PrintMat(sigma);
std::cout << "Infinite probability detected." << std::endl;
}
ProbabilityForClass[i].at<float>(y_t, x_t) = Probability;
}
}
cv::imshow("Probability image", 64*ProbabilityForClass[i]);
cv::waitKey();
}
for (size_t y_t = 0; y_t < Img.rows; y_t++) {
std::cout << "Classified row " << y_t << std::endl;
for (size_t x_t = 0; x_t < Img.cols; x_t++) {
int MostProbableClass = 0;
float maxProbability = 0;
for (size_t i = 0; i < ProbabilityForClass.size(); i++) {
float currentClassProbability = ProbabilityForClass[i].at<float>(y_t, x_t);
if (currentClassProbability > maxProbability) {
maxProbability = currentClassProbability;
MostProbableClass = i;
}
}
ClassificationMask.at<uchar>(y_t, x_t) = MostProbableClass;
}
}
return ClassificationMask;
}
int main (int argc, const char * argv[])
{
if ( argc != 2 )
{
fprintf(stderr, "Usage: <image>\n");
exit(1);
}
IplImage* image = cvLoadImage(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
if ( image == NULL )
{
fprintf(stderr, "Couldn't load image %s\n", argv[1]);
exit(1);
}
IplImage* dst = cvCloneImage(image);
//cvSetZero(dst);
CvMat* rotation = cvCreateMat(2, 3, CV_32FC1);
#if 0
// Optimized for a finding 3 pixel wide lines.
float zeroDegreeLineData[] = {
-10, -10, -10, -10, -10,
3, 3, 3, 3, 3,
14, 14, 14, 14, 14,
3, 3, 3, 3, 3,
-10, -10, -10, -10, -10
};
#if 0
float zeroDegreeLineData[] = {
10, 10, 10, 10, 10,
-3, -3, -3, -3, -3,
-14, -14, -14, -14, -14,
-3, -3, -3, -3, -3,
10, 10, 10, 10, 10
};
#endif
CvMat zeroDegreeLine = cvMat(5, 5, CV_32FC1, zeroDegreeLineData);
PrintMat("Zero Degree Line", &zeroDegreeLine);
cv2DRotationMatrix(cvPoint2D32f(2,2), 60.0, 1.0, rotation);
CvMat* kernel = cvCreateMat(5, 5, CV_32FC1);
#else
// Optimized for finding 1 pixel wide lines. The sum of all co-efficients is 0, so this kernel has
// the tendency to send pixels towards zero
#if 0
float zeroDegreeLineData[] = {
10, 10, 10,
-20, -20, -20,
10, 10, 10
};
#elif 0
float zeroDegreeLineData[] = {
-10, -10, -10,
20, 20, 20,
-10, -10, -10
};
#else
// Line detector optimized to find a horizontal line 1 pixel wide that is darker (smaller value) than it’s surrounding pixels. This works because darker (smaller value) horizontal 1 pixel wide lines will have a smaller magnitude negative
// component, which means their convoluted value will be higher than surrounding pixels. See Convolution.numbers
// for a simple example how this works.
float zeroDegreeLineData[] = {
1, 1, 1,
-2, -2, -2,
1, 1, 1
};
#endif
CvMat zeroDegreeLine = cvMat(3, 3, CV_32FC1, zeroDegreeLineData);
PrintMat("Zero Degree Line", &zeroDegreeLine);
// Going to rotate the horizontal line detecting kernel by 60 degrees to that it will detect 60 degree lines.
cv2DRotationMatrix(cvPoint2D32f(1,1), 60.0, 1.0, rotation);
CvMat* kernel = cvCreateMat(3, 3, CV_32FC1);
#endif
PrintMat("Rotation", rotation);
cvWarpAffine(&zeroDegreeLine, kernel, rotation, CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, cvScalarAll(0));
PrintMat("Kernel", kernel);
cvFilter2D( image, dst, kernel, cvPoint(-1,-1));
cvNamedWindow("main", CV_WINDOW_NORMAL);
cvShowImage("main", image);
cvWaitKey(0);
cvShowImage("main", dst);
cvWaitKey(0);
return 0;
}
void Jade (
double *B, /* Output. Separating matrix. nbc*nbc */
double *X, /* Input. Data set nbc x nbs */
int nbc, /* Input. Number of sensors */
int nbs /* Input. Number of samples */
)
{
double threshold_JD = RELATIVE_JD_THRESHOLD / sqrt((double)nbs) ;
int rots = 1 ;
double *Transf = (double *) calloc(nbc*nbc, sizeof(double)) ;
double *CumTens = (double *) calloc(nbc*nbc*nbc*nbc, sizeof(double)) ;
if ( Transf == NULL || CumTens == NULL ) OutOfMemory() ;
/* Init */
Message0(2, "Init...\n") ;
Identity(B, nbc);
MeanRemoval(X, nbc, nbs) ;
printf ("mean\n");
PrintMat (X, nbc, nbs) ;
printf ("\n");
Message0(2, "Whitening...\n") ;
ComputeWhitener(Transf, X, nbc, nbs) ;
printf ("Whitener:\n");
PrintMat (Transf, nbc, nbc) ;
printf ("\n");
Transform (X, Transf, nbc, nbs) ;
printf ("Trans X\n");
PrintMat (X, nbc, nbs) ;
printf ("\n");
Transform (B, Transf, nbc, nbc) ;
Message0(2, "Estimating the cumulant tensor...\n") ;
EstCumTens (CumTens, X, nbc, nbs) ;
printf ("cum tens \n");
PrintMat (CumTens, nbc*nbc, nbc*nbc) ;
printf ("\n");
Message0(2, "Joint diagonalization...\n") ;
rots = JointDiago (CumTens, Transf, nbc, nbc*nbc, threshold_JD) ;
MessageI(3, "Total number of plane rotations: %6i.\n", rots) ;
MessageF(3, "Size of the total rotation: %10.7e\n", NonIdentity(Transf,nbc) ) ;
printf ("Trans mat\n");
PrintMat (Transf, nbc, nbc) ;
printf ("\n");
Message0(2, "Updating...\n") ;
Transform (X, Transf, nbc, nbs ) ;
Transform (B, Transf, nbc, nbc ) ;
printf ("resultant \n");
PrintMat (X, nbc, nbs) ;
printf ("\n");
printf ("estimated mix \n");
PrintMat (B, nbc, nbc) ;
printf ("\n");
free(Transf) ;
free(CumTens) ;
}
void processImagePair(const char *file1, const char *file2, CvVideoWriter *out, struct CvMat *currentOrientation) {
// Load two images and allocate other structures
IplImage* imgA = cvLoadImage(file1, CV_LOAD_IMAGE_GRAYSCALE);
IplImage* imgB = cvLoadImage(file2, CV_LOAD_IMAGE_GRAYSCALE);
IplImage* imgBcolor = cvLoadImage(file2);
CvSize img_sz = cvGetSize( imgA );
int win_size = 15;
// Get the features for tracking
IplImage* eig_image = cvCreateImage( img_sz, IPL_DEPTH_32F, 1 );
IplImage* tmp_image = cvCreateImage( img_sz, IPL_DEPTH_32F, 1 );
int corner_count = MAX_CORNERS;
CvPoint2D32f* cornersA = new CvPoint2D32f[ MAX_CORNERS ];
cvGoodFeaturesToTrack( imgA, eig_image, tmp_image, cornersA, &corner_count,
0.05, 3.0, 0, 3, 0, 0.04 );
fprintf(stderr, "%s: Corner count = %d\n", file1, corner_count);
cvFindCornerSubPix( imgA, cornersA, corner_count, cvSize( win_size, win_size ),
cvSize( -1, -1 ), cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 50, 0.03 ) );
// Call Lucas Kanade algorithm
char features_found[ MAX_CORNERS ];
float feature_errors[ MAX_CORNERS ];
CvSize pyr_sz = cvSize( imgA->width+8, imgB->height/3 );
IplImage* pyrA = cvCreateImage( pyr_sz, IPL_DEPTH_32F, 1 );
IplImage* pyrB = cvCreateImage( pyr_sz, IPL_DEPTH_32F, 1 );
CvPoint2D32f* cornersB = new CvPoint2D32f[ MAX_CORNERS ];
calcNecessaryImageRotation(imgA);
cvCalcOpticalFlowPyrLK( imgA, imgB, pyrA, pyrB, cornersA, cornersB, corner_count,
cvSize( win_size, win_size ), 5, features_found, feature_errors,
cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.3 ), 0 );
CvMat *transform = cvCreateMat(3,3, CV_32FC1);
CvMat *invTransform = cvCreateMat(3,3, CV_32FC1);
// Find a homography based on the gradient
CvMat cornersAMat = cvMat(1, corner_count, CV_32FC2, cornersA);
CvMat cornersBMat = cvMat(1, corner_count, CV_32FC2, cornersB);
cvFindHomography(&cornersAMat, &cornersBMat, transform, CV_RANSAC, 15, NULL);
cvInvert(transform, invTransform);
cvMatMul(currentOrientation, invTransform, currentOrientation);
// save the translated image
IplImage* trans_image = cvCloneImage(imgBcolor);
cvWarpPerspective(imgBcolor, trans_image, currentOrientation, CV_INTER_CUBIC+CV_WARP_FILL_OUTLIERS);
printf("%s:\n", file1);
PrintMat(currentOrientation);
// cvSaveImage(out, trans_image);
cvWriteFrame(out, trans_image);
cvReleaseImage(&eig_image);
cvReleaseImage(&tmp_image);
cvReleaseImage(&trans_image);
cvReleaseImage(&imgA);
cvReleaseImage(&imgB);
cvReleaseImage(&imgBcolor);
cvReleaseImage(&pyrA);
cvReleaseImage(&pyrB);
cvReleaseData(transform);
delete [] cornersA;
delete [] cornersB;
}
int main(int argc, char *argv[]) {
printf("WG size of kernel 1 = %d, WG size of kernel 2= %d X %d\n", BLOCK_SIZE_0, BLOCK_SIZE_1_X, BLOCK_SIZE_1_Y);
float *a=NULL, *b=NULL, *finalVec=NULL;
float *m=NULL;
int size = -1;
FILE *fp;
// args
char filename[200];
int quiet=1,timing=0,platform=-1,device=-1;
// parse command line
if (parseCommandline(argc, argv, filename,
&quiet, &timing, &platform, &device, &size)) {
printUsage();
return 0;
}
context = cl_init_context(platform,device,quiet);
if(size < 1)
{
fp = fopen(filename, "r");
fscanf(fp, "%d", &size);
a = (float *) malloc(size * size * sizeof(float));
InitMat(fp,size, a, size, size);
b = (float *) malloc(size * sizeof(float));
InitAry(fp, b, size);
fclose(fp);
}
else
{
printf("create input internally before create, size = %d \n", size);
a = (float *) malloc(size * size * sizeof(float));
create_matrix(a, size);
b = (float *) malloc(size * sizeof(float));
for (int i =0; i< size; i++)
b[i]=1.0;
}
if (!quiet) {
printf("The input matrix a is:\n");
PrintMat(a, size, size, size);
printf("The input array b is:\n");
PrintAry(b, size);
}
// create the solution matrix
m = (float *) malloc(size * size * sizeof(float));
// create a new vector to hold the final answer
finalVec = (float *) malloc(size * sizeof(float));
InitPerRun(size,m);
//begin timing
// printf("The result of array b is before run: \n");
// PrintAry(b, size);
// run kernels
ForwardSub(context,a,b,m,size,timing);
// printf("The result of array b is after run: \n");
// PrintAry(b, size);
DIVIDEND_CL_WRAP(clFinish)(command_queue);
//end timing
if (!quiet) {
printf("The result of matrix m is: \n");
PrintMat(m, size, size, size);
printf("The result of matrix a is: \n");
PrintMat(a, size, size, size);
printf("The result of array b is: \n");
PrintAry(b, size);
BackSub(a,b,finalVec,size);
printf("The final solution is: \n");
PrintAry(finalVec,size);
}
free(m);
free(a);
free(b);
free(finalVec);
cl_cleanup();
//OpenClGaussianElimination(context,timing);
return 0;
}
int main (int argc, char* argv[]){
int i, j, option;
int size = 100;
int b_print = 0;
int range = 100;
double **A, **T, **S;
double *b;
double temp;
char* OUTPATH = "data_input";
FILE* fp;
srand(time(NULL));
while ((option = getopt(argc, argv, "s:b:po:")) != -1)
switch(option){
case 's': size = strtol(optarg, NULL, 10); break;
case 'b': range = strtol(optarg, NULL, 10);break;
case 'p': b_print = 1; break;
case 'o': OUTPATH = optarg; break;
case '?': printf("Unexpected Options. \n"); return -1;
}
/*Generate the data*/
A = CreateMat(size, size);
T = CreateMat(size, size);
S = CreateMat(size, size);
b = malloc(size * sizeof(double));
for (i = 0; i < size; ++i)
for (j = 0; j < size; ++j){
A[i][j] = 0;
T[i][j] = 0;
}
MatGen(size, T, (double)range);
GenPerm(size, A);
MatMul(size, T, A, S);
for (i = 0; i < size; ++i){
temp = (double)(rand() % (int)(range * DECIMAL)) / DECIMAL;
if (rand() % 2)
temp *= -1;
b[i] = temp;
}
/*Output the data*/
if ((fp = fopen(OUTPATH,"w")) == NULL){
printf("Fail to open a file to save the data. \n");
return -2;
}
fprintf(fp, "%d\n\n", size);
for (i = 0; i < size; ++i){
for (j = 0; j < size; ++j)
fprintf(fp, "%lf\t", S[i][j]);
fprintf(fp, "\n");
}
fprintf(fp, "\n");
for (i = 0; i < size; ++i)
fprintf(fp, "%lf\n", b[i]);
fclose(fp);
/*Print the result if neccesary*/
if (b_print){
printf("The problem size is %d.\n", size);
printf("============\n The A is \n============\n");
PrintMat(S, size, size);
printf("============\n The b is \n============\n");
PrintVec(b, size);
}
DestroyMat(A, size);
DestroyMat(T, size);
DestroyMat(S, size);
free(b);
return 0;
}