int main(int argc, char *argv[]) {
int n, op;
double *numbers;
scanf("%d %d", &op, &n);
numbers = create_jacobsthal_lucas(n);
switch(op) {
case 1:
print_jacobsthal_lucas(n, numbers);
break;
case 2:
print_variance(n, numbers);
break;
case 3:
print_chebyshev(n, numbers);
break;
default:
printf("Unknown Operator\n");
}
free(numbers);
return 0;
}
int
main(int argc, char *argv[])
{
const size_t nmax = 60;
const size_t mmax = GSL_MIN(nmax, 30);
const double R = R_EARTH_KM;
green_workspace *green_p = green_alloc(nmax, mmax, R);
char *knm_file = "data/stage1_knm.dat";
char *sval_file = "data/stage2b_sval.dat";
char *U_file = "data/stage2b_U.dat";
char *variance_file = "variance_time.txt";
char *pc_file = "pc_time.txt";
char *recon_file = "recon_time.txt";
const double var_thresh = 0.99;
gsl_vector *S; /* singular values of SDM */
gsl_matrix *U; /* left singular vectors of SDM */
gsl_matrix *alpha; /* alpha matrix, P-by-nt */
gsl_matrix *knmt; /* knm~ = U*alpha, nnm-by-nt */
gsl_matrix *knm; /* knm(t) matrix */
size_t nnm;
size_t nt; /* number of time stamps */
size_t P; /* number of principal eigenvectors to use (<= T) */
while (1)
{
int c;
int option_index = 0;
static struct option long_options[] =
{
{ 0, 0, 0, 0 }
};
c = getopt_long(argc, argv, "", long_options, &option_index);
if (c == -1)
break;
switch (c)
{
default:
fprintf(stderr, "Usage: %s <-i stage1_matrix_file>\n", argv[0]);
break;
}
}
fprintf(stderr, "main: reading knm matrix from %s...", knm_file);
knm = pca_read_matrix(knm_file);
fprintf(stderr, "done (%zu-by-%zu matrix read)\n", knm->size1, knm->size2);
fprintf(stderr, "main: reading singular values from %s...", sval_file);
S = pca_read_vector(sval_file);
fprintf(stderr, "done (%zu singular values read)\n", S->size);
fprintf(stderr, "main: reading left singular vectors from %s...", U_file);
U = pca_read_matrix(U_file);
fprintf(stderr, "done (%zu-by-%zu matrix read)\n", U->size1, U->size2);
/* plot a variance curve to help decide how many eigenvectors to keep */
fprintf(stderr, "main: writing variance curve to %s...", variance_file);
print_variance(variance_file, S, var_thresh, &P);
fprintf(stderr, "done (%zu singular vectors needed to explain %.1f%% of variance)\n",
P, var_thresh * 100.0);
nnm = U->size1;
nt = knm->size2;
fprintf(stderr, "main: using %zu largest eigenvectors\n", P);
alpha = gsl_matrix_alloc(P, nt);
knmt = gsl_matrix_alloc(nnm, nt);
/* find alpha such that || knm - U*alpha || is
* minimized in a least squares sense */
solve_PCA(P, knm, U, alpha, knmt);
/* plot reconstructed time series using dominant PCs */
{
const size_t n = 3;
const int m = 1;
const size_t cidx = green_nmidx(n, m, green_p);
FILE *fp = fopen(recon_file, "w");
size_t i;
fprintf(stderr, "main: writing reconstructed (%zu,%d) time series to %s...",
n, m, recon_file);
for (i = 0; i < nt; ++i)
{
double t = (double) i;
fprintf(fp, "%f %f %f\n",
t / 24.0,
gsl_matrix_get(knm, cidx, i),
gsl_matrix_get(knmt, cidx, i));
}
fprintf(stderr, "done\n");
fclose(fp);
}
fprintf(stderr, "main: printing principle component maps to %s...",
pc_file);
print_pc_maps(pc_file, U, green_p);
fprintf(stderr, "done\n");
gsl_matrix_free(U);
gsl_matrix_free(alpha);
gsl_matrix_free(knmt);
gsl_matrix_free(knm);
gsl_vector_free(S);
green_free(green_p);
return 0;
}
