/* nag_wav_dim2_coeff_ins (c09ezc) Example Program.
*
* Copyright 2024 Numerical Algorithms Group.
*
* Mark 30.0, 2024.
*/
#include <math.h>
#include <nag.h>
#include <stdio.h>
#define A(I, J) a[(J - 1) * lda + I - 1]
#define AN(I, J) an[(J - 1) * lda + I - 1]
#define B(I, J) b[(J - 1) * ldb + I - 1]
#define D(I, J) d[(J - 1) * ldd + I - 1]
int main(void) {
/* Scalars */
Integer exit_status = 0;
Integer lstate = 1, lseed = 1;
Integer i, j, k, lda, ldb, ldd, lenc, m, n, mn, nf, nwcn, nwct, nwl;
Integer subid, denoised, cindex, ilev;
Nag_BaseRNG genid;
double mse, thresh, var, xmu;
/* Arrays */
char mode[25], wavnam[25];
double *a = 0, *an = 0, *b = 0, *c = 0, *d = 0, *x = 0;
Integer *dwtlvm = 0, *dwtlvn = 0, *state = 0;
Integer icomm[180], seed[1];
/* Nag Types */
Nag_Wavelet wavnamenum;
Nag_WaveletMode modenum;
Nag_MatrixType matrix = Nag_GeneralMatrix;
Nag_OrderType order = Nag_ColMajor;
Nag_DiagType diag = Nag_NonUnitDiag;
NagError fail;
INIT_FAIL(fail);
printf("nag_wav_dim2_coeff_ins (c09ezc) Example Program Results\n\n");
/* Skip heading in data file and read problem parameters. */
scanf("%*[^\n] %" NAG_IFMT "%" NAG_IFMT "%*[^\n] ", &m, &n);
scanf("%24s%24s%*[^\n] ", wavnam, mode);
printf("MLDWT :: Wavelet : %s\n", wavnam);
printf(" End mode : %s\n", mode);
printf(" m : %4" NAG_IFMT "\n", m);
printf(" n : %4" NAG_IFMT "\n\n", n);
fflush(stdout);
/* Allocate arrays to hold the original data, A, original data plus noise,
* AN, reconstruction using denoised coefficients, B, and randomly generated
* noise, X.
*/
lda = m;
ldb = m;
if (!(a = NAG_ALLOC((lda) * (n), double)) ||
!(an = NAG_ALLOC((lda) * (n), double)) ||
!(b = NAG_ALLOC((ldb) * (n), double)) ||
!(x = NAG_ALLOC((m * n), double))) {
printf("Allocation failure\n");
exit_status = 1;
goto END;
}
/* nag_enum_name_to_value (x04nac).
* Converts NAG enum member name to value.
*/
wavnamenum = (Nag_Wavelet)nag_enum_name_to_value(wavnam);
modenum = (Nag_WaveletMode)nag_enum_name_to_value(mode);
/* Read in the original data. */
for (i = 1; i <= m; i++)
for (j = 1; j <= n; j++)
scanf("%lf", &A(i, j));
/* Output the original data. */
nag_file_print_matrix_real_gen_comp(order, matrix, diag, m, n, a, lda,
"%11.4e", "Input data :", Nag_NoLabels, 0,
Nag_NoLabels, 0, 80, 0, 0, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_file_print_matrix_real_gen_comp (x04cbc).\n%s\n",
fail.message);
exit_status = 2;
goto END;
}
printf("\n");
fflush(stdout);
/* Set up call to nag_rand_dist_normal (g05skc) in order to create some
* randomnoise from a normal distribution to add to the original data.
* Initial call to RNG initializer to get size of STATE array.
*/
seed[0] = 642521;
genid = Nag_MersenneTwister;
subid = 0;
if (!(state = NAG_ALLOC((lstate), Integer))) {
printf("Allocation failure\n");
exit_status = 3;
goto END;
}
/* nag_rand_init_repeat (g05kfc).
* Query the size of state.
*/
lstate = 0;
nag_rand_init_repeat(genid, subid, seed, lseed, state, &lstate, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_init_repeat (g05kfc).\n%s\n", fail.message);
exit_status = 4;
goto END;
}
/* Reallocate STATE */
NAG_FREE(state);
if (!(state = NAG_ALLOC((lstate), Integer))) {
printf("Allocation failure\n");
exit_status = 5;
goto END;
}
/* nag_rand_init_repeat (g05kfc).
* Initialize the generator to a repeatable sequence.
*/
nag_rand_init_repeat(genid, subid, seed, lseed, state, &lstate, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_init_repeat (g05kfc).\n%s\n", fail.message);
exit_status = 6;
goto END;
}
/* Set the distribution parameters for the random noise. */
xmu = 0.0;
var = 0.1E-3;
/* Generate the noise variates */
/* nag_rand_dist_normal (g05skc).
* Generates a vector of pseudorandom numbers from a Normal distribution.
*/
mn = n * m;
nag_rand_dist_normal(mn, xmu, var, state, x, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_dist_normal (g05skc).\n%s\n", fail.message);
exit_status = 7;
goto END;
}
/* Add the noise to the original input and save in AN */
k = 0;
for (j = 1; j <= n; j++) {
for (i = 1; i <= m; i++) {
AN(i, j) = A(i, j) + x[k];
k = k + 1;
}
}
/* Output the noisy data */
nag_file_print_matrix_real_gen_comp(
order, matrix, diag, m, n, an, lda, "%11.4e",
"Original data plus noise :", Nag_NoLabels, 0, Nag_NoLabels, 0, 80, 0, 0,
&fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_file_print_matrix_real_gen_comp (x04cbc).\n%s\n",
fail.message);
exit_status = 8;
goto END;
}
printf("\n");
/* nag_wav_dim2_init (c09abc).
* Two-dimensional wavelet filter initialization.
*/
nag_wav_dim2_init(wavnamenum, Nag_MultiLevel, modenum, m, n, &nwl, &nf, &nwct,
&nwcn, icomm, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_wav_dim2_init (c09abc).\n%s\n", fail.message);
exit_status = 9;
goto END;
}
/* Allocate arrays to hold the coefficients, c, and the dimensions
* of the coefficients at each level, dwtlvm, dwtlvn.
*/
lenc = nwct;
if (!(c = NAG_ALLOC((lenc), double)) ||
!(dwtlvm = NAG_ALLOC((nwl), Integer)) ||
!(dwtlvn = NAG_ALLOC((nwl), Integer))) {
printf("Allocation failure\n");
exit_status = 10;
goto END;
}
/* Perform a forwards multi-level transform on the noisy data. */
/* nag_wav_dim2_multi_fwd (c09ecc).
* Two-dimensional multi-level discrete wavelet transform.
*/
nag_wav_dim2_multi_fwd(m, n, an, lda, lenc, c, nwl, dwtlvm, dwtlvn, icomm,
&fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_wav_dim2_multi_fwd (c09ecc).\n%s\n", fail.message);
exit_status = 11;
goto END;
}
/* Reconstruct without thresholding of detail coefficients. */
/* nag_wav_dim2_multi_inv (c09edc).
* Two-dimensional inverse multi-level discrete wavelet transform.
*/
nag_wav_dim2_multi_inv(nwl, lenc, c, m, n, b, ldb, icomm, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_wav_dim2_multi_inv (c09edc).\n%s\n", fail.message);
exit_status = 12;
goto END;
}
/* Calculate the Mean Square Error of the noisy reconstruction. */
mse = 0.0;
for (j = 1; j <= n; j++)
for (i = 1; i <= m; i++)
mse = mse + pow((A(i, j) - B(i, j)), 2);
mse = mse / (double)(m * n);
printf("Without denoising Mean Square Error is %11.4e\n\n", mse);
fflush(stdout);
/* Now perform the denoising by extracting each of the detail
* coefficients at each level and applying hard thresholding
* Allocate a 2D array to hold the detail coefficients
*/
ldd = dwtlvm[nwl - 1];
if (!(d = NAG_ALLOC((ldd) * (dwtlvn[nwl - 1]), double))) {
printf("Allocation failure\n");
exit_status = 13;
goto END;
}
/* Calculate the threshold based on VisuShrink denoising. */
thresh = sqrt(var) * sqrt(2. * log((double)(m * n)));
denoised = 0;
/* For each level */
for (ilev = nwl; ilev >= 1; ilev -= 1) {
/* Select detail coefficients */
for (cindex = 1; cindex <= 3; cindex++) {
/* Extract coefficients into the 2D array d */
/* nag_wav_dim2_coeff_ext (c09eyc).
* Two-dimensional discrete wavelet transform coefficient extraction.
*/
nag_wav_dim2_coeff_ext(ilev, cindex, lenc, c, d, ldd, icomm, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_wav_dim2_coeff_ext (c09eyc).\n%s\n",
fail.message);
exit_status = 14;
goto END;
}
/* Perform the hard thresholding operation */
for (j = 1; j <= dwtlvn[nwl - ilev]; j++)
for (i = 1; i <= dwtlvm[nwl - ilev]; i++)
if (fabs(D(i, j)) < thresh) {
D(i, j) = 0.0;
denoised = denoised + 1;
}
/* Insert the denoised coefficients back into c. */
/* nag_wav_dim2_coeff_ins (c09ezc).
* Two-dimensional discrete wavelet transform coefficient insertion.
*/
nag_wav_dim2_coeff_ins(ilev, cindex, lenc, c, d, ldd, icomm, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_wav_dim2_coeff_ins (c09ezc).\n%s\n",
fail.message);
exit_status = 15;
goto END;
}
}
}
/* Output the number of coefficients that were set to zero */
printf("Number of coefficients denoised is %4" NAG_IFMT " out of %4" NAG_IFMT
"\n\n",
denoised, nwct - dwtlvm[0] * dwtlvn[0]);
fflush(stdout);
/* Reconstruct original data following thresholding of detail coefficients */
/* nag_wav_dim2_multi_inv (c09edc).
* Two-dimensional inverse multi-level discrete wavelet transform.
*/
nag_wav_dim2_multi_inv(nwl, lenc, c, m, n, b, ldb, icomm, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_wav_dim2_multi_inv (c09edc).\n%s\n", fail.message);
exit_status = 16;
goto END;
}
/* Calculate the Mean Square Error of the denoised reconstruction. */
mse = 0.0;
for (j = 1; j <= n; j++)
for (i = 1; i <= m; i++)
mse = mse + pow((A(i, j) - B(i, j)), 2);
mse = mse / (double)(m * n);
printf("With denoising Mean Square Error is %11.4e \n\n", mse);
fflush(stdout);
/* Output the denoised reconstruction. */
nag_file_print_matrix_real_gen_comp(
order, matrix, diag, m, n, b, ldb, "%11.4e",
"Reconstruction of denoised input :", Nag_NoLabels, 0, Nag_NoLabels, 0,
80, 0, 0, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_file_print_matrix_real_gen_comp (x04cbc).\n%s\n",
fail.message);
exit_status = 17;
goto END;
}
END:
NAG_FREE(a);
NAG_FREE(an);
NAG_FREE(b);
NAG_FREE(c);
NAG_FREE(d);
NAG_FREE(x);
NAG_FREE(dwtlvm);
NAG_FREE(dwtlvn);
NAG_FREE(state);
return exit_status;
}