void add_smart_cast_stats_all (LPCSTR from, LPCSTR to)
{
#ifdef SMART_CAST_STATS
# ifdef SMART_CAST_STATS_ALL
stats_all().add (from,to);
# endif
#endif
}
void clear_smart_cast_stats ()
{
#ifdef SMART_CAST_STATS
stats().clear ();
# ifdef SMART_CAST_STATS_ALL
stats_all().clear ();
# endif
#else
Msg ("! SMART_CAST_STATS macros is not defined, stats is disabled");
#endif
}
int main_unnested (int argc, char* argv[])
{
cerr << "K version: " << VERSION << endl;
cerr << "Reference: " << REFERENCE << endl;
cerr << "Compiled by: " << CXX_VERSION << endl;
cerr << "Compilation flags: " << CXXFLAGS << endl;
const char* thisfunction = "main_unnested";
KConfig K;
KInt MI = 500;
KInt MJ = 40;
KInt g = 1;
KScalar U = 1.0;
KScalar s = 1.0;
KScalar h = 0.0;
KScalar S = 0.01; /* selfing rate */
Trajectory trajectory;
if (0) {
K = initiate_quick(MI, MJ, g, U, s, h, S);
K->fitness_function = FITNESS_MULTIPLICATIVE;
} else {
K = initiate_KConfig();
initiate_load_classes(K, MI, MJ);
initiate_genotypes(K, g);
K->U = U;
K->fitness_function = FITNESS_MULTIPLICATIVE;
K->fit_s = s;
K->fit_h = h;
K->S[0] = S;
K->epsilon = 0.00000001;
K->savefile = K->loadfile = "savefile.txt";
if (cmdline_args(K, argc, argv)) {
fatal("Usage: K --help");
}
set_repro(K, K->S[0], 0.0, K->A[0], 0.0);
}
// set_debug(DEBUG_LETHALS);
// set_debug(DEBUG_TRACE1);
// set_debug(DEBUG_TRACE2);
// set_debug(DEBUG_GENERATIONS);
set_debug(DEBUG_FOLLOW_EQUILIBRIUM);
// set_debug(DEBUG_EQUILIBRIUM);
// set_debug(DEBUG_NORMALIZATION);
// set_debug(DEBUG_TRUNCATE);
// set_debug(DEBUG_TRUNCATE_DETAIL);
initiate_model_state(K);
compute_adults_initial(K);
// K->option_table = 1;
// K->load = 1;
if (K->load) {
load_loadfile(K, K->x);
} else {
fill_KArray(K, K->x, 0.0);
K->x[10][0][0] = 1.0;
}
// fill_KArray(K, K->x, 0.0);
// K->x[20][0][0] = 1.0;
/* the above method leaves all adults in the population
** in one load class, L(0,0). Now, we'll shift these
** around a little to examine the results of outcrossing */
/* add a few mutations */
/*
for (i=0; i < 10; i++) {
apply_mutation(K, temp, K->x);
copy_KArray(K, K->x, temp);
}
*/
// fprintf(stderr, "K --------------------------------------------------\n");
// fprintf(stderr, "U\ts\th\tS\n%lg\t%lg\t%lg\t%lg\n", K->U, K->fit_s, K->fit_h, K->S[0]);
while (! is_equilibrium(K) || K->generation <= GENERATION_MINIMUM) {
if (K->generation > GENERATION_CUTOFF) {
fprintf(stderr, "exceeded GENERATION_CUTOFF=%d, stopping\n", GENERATION_CUTOFF);
break;
}
if (DEBUG(DEBUG_GENERATIONS) || (K->progress && K->generation % K->progress == 0))
fprintf(stderr, "generation %d\n", K->generation);
IF_DEBUG(DEBUG_TRACE1)
fprintf(stderr, "generation %d\n", K->generation);
if (K->option_truncate)
truncate_KArray(K, K->x, LOADCLASS_TRUNCATE);
compute_mutation(K);
compute_self_progeny(K);
compute_apomixis_progeny(K);
// compute_outcross_progeny(K); // inefficient Kondrashov method
compute_gametes(K);
compute_zygotes(K);
compute_summed_progeny(K);
compute_selection(K);
compute_adults_nextgen(K);
IF_DEBUG(DEBUG_EQUILIBRIUM) {
fprintf(stderr, "checking equilibrium at end of generation %d------------\n",
K->generation - 1);
fprintf(stderr, "dump of K->x[..][0][0]\n");
dump_KArray(K, K->x, K->MI, 0, 1);
fprintf(stderr, "calling is_equilibrium(K) just to check...\n");
(void) is_equilibrium(K);
fprintf(stderr, "end of equilibrium check ---------------\n");
}
normalize_KArray(K, K->x);
/*
if (K->generation - 1 == 0) {
trajectory.start(K, "trajectory.txt", "end_of_gen", 5);
trajectory.debug(true);
} else if (trajectory.active()) {
trajectory.check(K);
}
*/
// dump_KArray(K, K->x, 100, 0, 0);
}
// fprintf(stderr, "\n");
if (!K->option_nolethal && K->is_lethal) {
/*
** We need one more run through everything to get the
** progeny arrays built correctly. We don't do mutation
** here.
*/
K->createlethal = 1;
IF_DEBUG(DEBUG_LETHALS)
fprintf(stderr, "%s: one more run, to get progeny arrays\n", thisfunction);
IF_DEBUG(DEBUG_LETHALS)
fprintf(stderr, "%s: K->createlethal=%d\n", thisfunction, K->createlethal);
compute_self_progeny(K);
compute_apomixis_progeny(K);
compute_gametes(K);
compute_zygotes(K);
compute_summed_progeny(K);
compute_selection(K);
compute_adults_nextgen(K);
K->generation--;
normalize_KArray(K, K->x);
}
stats_all(K);
stats_print(K);
if (trajectory.active() && trajectory.lastgen() != K->generation) {
trajectory.write(K);
trajectory.stop();
}
// dump_KArray(K, K->x, 10, 0, 0);
//K->save_savefile = 1;
if (K->save) {
save_savefile(K, K->x);
}
return 0;
}
