/*
Calculates the parameters of the optimization for the patellar tendon, based upon optimization method 1 (as described in the
FreeBody user manual)
*/
void Muscle::mus_opt_pt_2(Segment **segment_data[]) {
int points=this->pnts, segment_prox_point, segment_dist_point, frame=this->frame;
int segment_most_prox=int (this->gblpnts[0][3]), segment_most_dist=int (this->gblpnts[points-1][3]);
int num_segments=this->segments_spanned;
Vec_DP v0(3), v1(3), v2(3), v3(3), v4(3), v5(3), v6(3), v7(3), r(3), Fnorm(3), Fnorm1(3), Fnorm2(3), opt_vec(3);
for (int i=0; i<points-1; i++) { // This routine iterates for each muscle point
segment_prox_point=int (this->gblpnts[i][3]); // Determine the location of each muscle point (what segment)
segment_dist_point=int (this->gblpnts[i+1][3]);
if ((segment_dist_point == segment_most_dist) && (segment_prox_point != segment_most_dist)) {
for (int j=0; j<3; j++) {
v0[j]=this->gblpnts[i][j];
v1[j]=this->gblpnts[i+1][j];
v2[j]=segment_data[frame][segment_dist_point]->rot_centre[j];
r[j]=v1[j]-v2[j];
Fnorm[j]=v0[j]-v1[j];
}
Fnorm=MATHEMATICS::math_vecnorm(Fnorm);
opt_vec=MATHEMATICS::math_crossprd(r,Fnorm);
for (int j=0; j<3; j++) {
this->opt_2[0][j]=Fnorm[j];
this->opt_2[3][j]=opt_vec[j];
}
this->opt_2[0][3]=segment_most_dist;
this->opt_2[3][3]=segment_most_dist;
for (int j=0; j<3; j++) {
this->opt_2[1][j]=-Fnorm[j];
}
this->opt_2[1][3]=segment_most_prox;
this->opt_2[2][3]=-1;
this->opt_2[4][3]=-1;
this->opt_2[5][3]=-1;
}
}
return;
}
int main(int argc, char *argv[])
{
// tests();
// srand(time(10));
srand(10);
if(argc < 4)
{
std::cout << "Input arguments:\n\t1: Filename for A matrix (as in A ~= WH)\n\t2: New desired dimension\n\t3: Max NMF iterations\n\t4: Max NNLS iterations\n\t5 (optional): delimiter (space is default)\n";
}
else
{
std::string filename = argv[1];
int newDimension = atoi(argv[2]);
int max_iter_nmf = atoi(argv[3]);
int max_iter_nnls = atoi(argv[4]);
char delimiter = (argc > 5) ? *argv[5] : ' ';
DenseMatrix* A = readMatrix(filename,delimiter);
A->copyColumnToRow();
printf("Sparsity of A: %f\n",sparsity(A));
DenseMatrix W = DenseMatrix(A->rows,newDimension);
DenseMatrix H = DenseMatrix(newDimension,A->cols);
NMF_Input input = NMF_Input(&W,&H,A,max_iter_nmf,max_iter_nnls);
std::cout << "Starting NMF computation." << std::endl;
std::clock_t start = std::clock();
double duration;
// nmf_cpu(input);
nmf_cpu_profile(input);
duration = ( std::clock() - start ) / (double) CLOCKS_PER_SEC;
std::cout << "NMF computation complete. Time: " << duration << " s." << std::endl;
W.copyColumnToRow();
dtype AF = FrobeniusNorm(A);
dtype WH_AF = Fnorm(W,H,*A);
printf("Objective value: %f\n",WH_AF/AF);
// DenseMatrix z1 = DenseMatrix(A->rows,newDimension);
// DenseMatrix z2 = DenseMatrix(newDimension,A->cols);
// printf("Calculated solution approximate Frobenius norm: %f\n",Fnorm(z1,z2,*A));
// printcolmajor(H.colmajor,H.rows,H.cols);
if(A) delete A;
}
return 0;
}
