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-rw-r--r--src/wolff_dgm.c247
1 files changed, 0 insertions, 247 deletions
diff --git a/src/wolff_dgm.c b/src/wolff_dgm.c
deleted file mode 100644
index f11b296..0000000
--- a/src/wolff_dgm.c
+++ /dev/null
@@ -1,247 +0,0 @@
-
-#include <getopt.h>
-
-#include <cluster.h>
-
-double identity(h_t x) {
- return -pow(x, 2);
-}
-
-double basic_H(double *H, h_t x) {
- return -H[0] * pow(x, 2);
-}
-
-int main(int argc, char *argv[]) {
-
- L_t L = 128;
- count_t N = (count_t)1e7;
- count_t min_runs = 10;
- count_t n = 3;
- D_t D = 2;
- double T = 2.26918531421;
- double *H = (double *)calloc(MAX_Q, sizeof(double));
- double eps = 0;
- bool silent = false;
- bool record_autocorrelation = false;
- count_t ac_skip = 1;
- count_t W = 10;
-
- int opt;
- q_t H_ind = 0;
-
- while ((opt = getopt(argc, argv, "N:n:D:L:T:H:m:e:saS:W:")) != -1) {
- switch (opt) {
- case 'N':
- N = (count_t)atof(optarg);
- break;
- case 'n':
- n = (count_t)atof(optarg);
- break;
- case 'D':
- D = atoi(optarg);
- break;
- case 'L':
- L = atoi(optarg);
- break;
- case 'T':
- T = atof(optarg);
- break;
- case 'H':
- H[H_ind] = atof(optarg);
- H_ind++;
- break;
- case 'm':
- min_runs = atoi(optarg);
- break;
- case 'e':
- eps = atof(optarg);
- break;
- case 's':
- silent = true;
- break;
- case 'a':
- record_autocorrelation = true;
- break;
- case 'S':
- ac_skip = (count_t)atof(optarg);
- break;
- case 'W':
- W = (count_t)atof(optarg);
- break;
- default:
- exit(EXIT_FAILURE);
- }
- }
-
- gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937);
- gsl_rng_set(r, rand_seed());
-
- dgm_state_t *s = (dgm_state_t *)calloc(1, sizeof(dgm_state_t));
-
- graph_t *h = graph_create_square(D, L);
- s->g = graph_add_ext(h);
-
- s->spins = (h_t *)calloc(h->nv, sizeof(h_t));
-
- s->H_info = H;
- s->T = T;
- s->H = basic_H;
- s->J = identity;
-
- s->R = (dihinf_t *)calloc(1, sizeof(dihinf_t));
-
- s->M = 0;
- s->E = 0;
-
- double diff = 1e31;
- count_t n_runs = 0;
- count_t n_steps = 0;
-
- meas_t *E, *clust, *M, *dM;
-
- M = (meas_t *)calloc(1, sizeof(meas_t ));
- dM = (meas_t *)calloc(1, sizeof(meas_t ));
-
- E = calloc(1, sizeof(meas_t));
- clust = calloc(1, sizeof(meas_t));
-
- autocorr_t *autocorr;
- if (record_autocorrelation) {
- autocorr = (autocorr_t *)calloc(1, sizeof(autocorr_t));
- autocorr->W = 2 * W + 1;
- autocorr->OO = (double *)calloc(2 * W + 1, sizeof(double));
- }
-
- if (!silent) printf("\n");
- while (((diff > eps || diff != diff) && n_runs < N) || n_runs < min_runs) {
- if (!silent) printf("\033[F\033[JWOLFF: sweep %" PRIu64
- ", dH/H = %.4f, dM/M = %.4f, dC/C = %.4f, dX/X = %.4f, cps: %.1f\n",
- n_runs, fabs(meas_dx(E) / E->x), meas_dx(M) / M->x, meas_dc(E) / meas_c(E), meas_dc(M) / meas_c(M), h->nv / clust->x);
-
- count_t n_flips = 0;
-
- while (n_flips / h->nv < n) {
- v_t v0 = gsl_rng_uniform_int(r, h->nv);
- h_t step = round((((double)s->M) / h->nv) + gsl_ran_gaussian(r, 5));
-
- v_t tmp_flips = flip_cluster_dgm(s, v0, step, r);
- n_flips += tmp_flips;
-
- if (n_runs > 0) {
- n_steps++;
- meas_update(clust, tmp_flips);
- }
-
- if (record_autocorrelation && n_runs > 0) {
- if (n_steps % ac_skip == 0) {
- update_autocorr(autocorr, s->E);
- }
- }
- }
-
- meas_update(M, s->M);
- h_t min_h, max_h;
- min_h = MAX_H;
- max_h = MIN_H;
- for (v_t i = 0; i < h->nv; i++) {
- if (s->spins[i] < min_h) {
- min_h = s->spins[i];
- } else if (s->spins[i] > max_h) {
- max_h = s->spins[i];
- }
- }
- meas_update(dM, max_h - min_h);
- meas_update(E, s->E);
-
- diff = fabs(meas_dc(E) / meas_c(E));
-
- n_runs++;
- }
- if (!silent) {
- printf("\033[F\033[J");
- }
- printf("WOLFF: sweep %" PRIu64
- ", dH/H = %.4f, dM/M = %.4f, dC/C = %.4f, dX/X = %.4f, cps: %.1f\n",
- n_runs, fabs(meas_dx(E) / E->x), meas_dx(M) / M->x, meas_dc(E) / meas_c(E), meas_dc(M) / meas_c(M), h->nv / clust->x);
-
- double tau = 0;
- bool tau_failed = false;
-
- if (record_autocorrelation) {
- double *Gammas = (double *)malloc((W + 1) * sizeof(double));
-
- Gammas[0] = 1 + rho(autocorr, 0);
- for (uint64_t i = 0; i < W; i++) {
- Gammas[1 + i] = rho(autocorr, 2 * i + 1) + rho(autocorr, 2 * i + 2);
- }
-
- uint64_t n;
- for (n = 0; n < W + 1; n++) {
- if (Gammas[n] <= 0) {
- break;
- }
- }
-
- if (n == W + 1) {
- printf("WARNING: correlation function never hit the noise floor.\n");
- tau_failed = true;
- }
-
- if (n < 2) {
- printf("WARNING: correlation function only has one nonnegative term.\n");
- tau_failed = true;
- }
-
- double *conv_Gamma = get_convex_minorant(n, Gammas);
-
- double ttau = - 0.5;
-
- for (uint64_t i = 0; i < n + 1; i++) {
- ttau += conv_Gamma[i];
- }
-
- free(Gammas);
- free(autocorr->OO);
- while (autocorr->Op != NULL) {
- stack_pop_d(&(autocorr->Op));
- }
- free(autocorr);
-
- tau = ttau * ac_skip * clust->x / h->nv;
- }
-
- if (tau_failed) {
- tau = 0;
- }
-
- FILE *outfile = fopen("out.m", "a");
-
- fprintf(outfile, "<|D->%" PRID ",L->%" PRIL ",T->%.15f", D, L, T);
- fprintf(outfile, ",E->%.15f,\\[Delta]E->%.15f,C->%.15f,\\[Delta]C->%.15f,M->%.15f", E->x / h->nv, meas_dx(E) / h->nv, meas_c(E) / h->nv, meas_dc(E) / h->nv, M->x / h->nv);
- fprintf(outfile, ",\\[Delta]M->%.15f", meas_dx(M) / h->nv);
- fprintf(outfile, ",\\[Chi]->%.15f", meas_c(M) / h->nv);
- fprintf(outfile, ",\\[Delta]\\[Chi]->%.15f", meas_dc(M) / h->nv);
- fprintf(outfile, ",w->%.15f,\\[Delta]w->%.15f,wc->%.15f,\\[Delta]wc->%.15f,Subscript[n,\"clust\"]->%.15f,Subscript[\\[Delta]n,\"clust\"]->%.15f,Subscript[m,\"clust\"]->%.15f,Subscript[\\[Delta]m,\"clust\"]->%.15f,\\[Tau]->%.15f|>\n", dM->x, meas_dx(dM), meas_c(dM), meas_dc(dM), clust->x / h->nv, meas_dx(clust) / h->nv, meas_c(clust) / h->nv, meas_dc(clust) / h->nv,tau);
-
- fclose(outfile);
-
- FILE *image = fopen("out.dat", "a");
- for (v_t i = 0; i < h->nv; i++) {
- fprintf(image, "%" PRIh " ", s->spins[i]);
- }
- fprintf(image, "\n");
- fclose(image);
-
- free(E);
- free(clust);
- free(H);
- free(s->R);
- free(s->spins);
- graph_free(s->g);
- free(s);
- graph_free(h);
- gsl_rng_free(r);
-
- return 0;
-}
-