int main(int argc, char **argv)
{
int i;
int errors;
DataSet *train_set;
DataSet *test_set;
Network *adultnn = create_network(14);
double e;
double acc;
Class predicted, desired;
// training
train_set = read_dataset("adult.train");
if (train_set == NULL) {
fprintf(stderr, "Error reading training set\n");
exit(1);
}
add_layer(adultnn, 28); // hidden layer
add_layer(adultnn, 2); // output layer
initialize_weights(adultnn, SEED);
print_network_structure(adultnn);
printf("Training network with %d epochs...\n", EPOCHS);
e = batch_train(adultnn, train_set, LEARNING_RATE, EPOCHS,
sigmoid, dsigmoid);
printf("Training finished, approximate final SSE: %f\n", e);
print_network_structure(adultnn);
// testing
test_set = read_dataset("adult.test");
if (test_set == NULL) {
fprintf(stderr, "Error reading test set\n");
exit(1);
}
errors = 0;
printf("Testing with %d cases...\n", test_set->n_cases);
for (i = 0; i < test_set->n_cases; ++i) {
predicted = predict_class(adultnn, test_set->input[i]);
desired = output_to_class(test_set->output[i]);
if (predicted != desired)
++errors;
printf("Case %d | predicted: %s, desired: %s, outputs: %4.3f %4.3f \n", i,
class_to_string(predicted), class_to_string(desired),
adultnn->output_layer->y[0], adultnn->output_layer->y[1]);
}
acc = 100.0 - (100.0 * errors / test_set->n_cases);
printf("Testing accuracy: %f\n", acc);
printf("Total classificarion errors: %d\n", errors);
destroy_dataset(train_set);
destroy_dataset(test_set);
return 0;
}
int read_network(NETWORK *network, FILE *stream)
{
fill_buffer(stream);
create_network(network);
get_degrees(network);
read_edges(network);
free_buffer();
return 0;
}
int main()
{
int structure[3] = {2, 2, 1};
float input[2] = {1,2};
float output[1];
struct network *net = create_network(3, structure);
feedforward(net, input, output);
vprint_float(1, output);
network_save_to_file(net, "mynet.net");
srand(time(NULL));
network_set_random_weights_biases(net, -1, 1);
network_load_from_file(net, "mynet.net");
feedforward(net, input, output);
vprint_float(1, output);
}
int create_datacenter(provider *pv, int type, int nnodes){
int i;
pv->datacenters = malloc(sizeof(datacenter)*pv->ndc);
for(i=0;i<pv->ndc;i++){
pv->datacenters[i].type = type;
pv->datacenters[i].nnodes = nnodes;
pv->datacenters[i].allocation_policy = allocation_policy;
pv->datacenters[i].optimization_alg = optimization_alg;
pv->datacenters[i].alloc_apps = malloc(sizeof(allocated_applications));
pv->datacenters[i].alloc_apps->num_alloc_apps = 0;
pv->datacenters[i].alloc_apps->first_app = NULL;
pv->datacenters[i].alloc_apps->last_app = NULL;
create_network(&pv->datacenters[i],type,nnodes);
}
return(1);
}
void main(int argc,char **argv)
{
FILE *fp;
printf( "\nNetwork Generator for n-Component Kohonen Networks\n" );
control_file=argv[1];
if(argc==2)
if((fp=fopen(control_file,"r"))!=NULL){
control(fp); /* get information from control file */
if ((IUnits <= 0) || (OUnits < 0) || (HUnits <= 0))
exit( 1 );
create_network(weight_file);
create_patterns(pattern_file,no_of_exemplars); /* if any */
quit();
}
else
printf("\nControl file %s could not be opened!",argv[1]);
else
printf("\nUsage: convert2snns <control file>");
printf("\n");
} /* main */
void init(const char* datafile) {
DBG("INIT");
DBGV(NBTHREADS);
sys("rm -rf data/*");
sys("rm -rf plots/*");
if(datafile[0]=='*') {
generateData(&datafile[1]);
}
else X.load(datafile);
if(LIMIT_NDATA!=-1 && X.height > LIMIT_NDATA) X.height = LIMIT_NDATA;
n = X.height;
D = X.width;
create_network();
int ndo = 0;
for(int i=0; i<N-1; i++) {
node[i].init(X, ndo, n/N);
ndo += n/N;
}
node[N-1].init(X,ndo, n-ndo);
// mat_plotall.create((D+1)*(int)(sqrt(N)+1),(D+1)*(int)(sqrt(N)+1));
DBGV(LIMIT_NDATA);
DBGV(N);
DBGV(D);
DBGV(n);
if(USE_ENERGY) {
DBGV(KEEP_ENERGY);
}
else {
DBGV(q);
}
}
/* Function to be called from R */
void R_epidemics(int *seqLength, double *mutRate, int *npop, int *nHostPerPop, double *beta, int *nStart, int *t1, int *t2, int *Nsample, int *Tsample, int *duration, int *nbnb, int *listnb, double *pdisp){
int i, nstep, counter_sample = 0, tabidx;
/* Initialize random number generator */
int j;
time_t t;
t = time(NULL); // time in seconds, used to change the seed of the random generator
gsl_rng * rng;
const gsl_rng_type *typ;
gsl_rng_env_setup();
typ=gsl_rng_default;
rng=gsl_rng_alloc(typ);
gsl_rng_set(rng,t); // changes the seed of the random generator
/* transfer simulation parameters */
struct param * par;
par = (struct param *) malloc(sizeof(struct param));
par->L = *seqLength;
par->mu = *mutRate;
par->muL = par->mu * par->L;
par->rng = rng;
par->npop = *npop;
par->popsizes = nHostPerPop;
par->beta = *beta;
par->nstart = *nStart;
par->t1 = *t1;
par->t2 = *t2;
par->t_sample = Tsample;
par->n_sample = *Nsample;
par->duration = *duration;
par->cn_nb_nb = nbnb;
par->cn_list_nb = listnb;
par->cn_weights = pdisp;
/* check/print parameters */
check_param(par);
print_param(par);
/* dispersal matrix */
struct network *cn = create_network(par);
/* print_network(cn, TRUE); */
/* group sizes */
struct ts_groupsizes * grpsizes = create_ts_groupsizes(par);
/* initiate population */
struct metapopulation * metapop;
metapop = create_metapopulation(par);
/* get sampling schemes (timestep+effectives) */
translate_dates(par);
struct table_int *tabdates = get_table_int(par->t_sample, par->n_sample);
printf("\n\nsampling at timesteps:");
print_table_int(tabdates);
/* create sample */
struct sample ** samplist = (struct sample **) malloc(tabdates->n * sizeof(struct sample *));
struct sample *samp;
/* MAKE METAPOPULATION EVOLVE */
nstep = 0;
while(get_total_nsus(metapop)>0 && (get_total_ninf(metapop)+get_total_nexp(metapop))>0 && nstep<par->duration){
nstep++;
/* age metapopulation */
age_metapopulation(metapop, par);
/* process infections */
for(j=0;j<get_npop(metapop);j++){
process_infections(get_populations(metapop)[j], metapop, cn, par);
}
/* draw samples */
if((tabidx = int_in_vec(nstep, tabdates->items, tabdates->n)) > -1){ /* TRUE if step must be sampled */
samplist[counter_sample++] = draw_sample(metapop, tabdates->times[tabidx], par);
}
fill_ts_groupsizes(grpsizes, metapop, nstep);
}
/* we stopped after 'nstep' steps */
if(nstep < par->duration){
printf("\nEpidemics ended at time %d, before last sampling time (%d).\n", nstep, par->duration);
} else {
/* printf("\n\n-- FINAL METAPOPULATION --"); */
/* print_metapopulation(metapop, FALSE); */
/* merge samples */
samp = merge_samples(samplist, tabdates->n, par);
/* write sample to file */
printf("\n\nWriting sample to file 'out-sample.txt'\n");
write_sample(samp);
/* free memory */
free_sample(samp);
}
/* write group sizes to file */
printf("\n\nPrinting group sizes to file 'out-popsize.txt'\n");
write_ts_groupsizes(grpsizes);
/* free memory */
free_metapopulation(metapop);
free_param(par);
for(i=0;i<counter_sample;i++) free_sample(samplist[i]);
free(samplist);
free_table_int(tabdates);
free_network(cn);
free_ts_groupsizes(grpsizes);
}
/* all-in-one function testing epidemics growth, summary statistics, etc. */
void test_epidemics(int seqLength, double mutRate, int npop, int *nHostPerPop, double beta, int nStart, int t1, int t2, int Nsample, int *Tsample, int duration, int *nbnb, int *listnb, double *pdisp){
int i, j, nstep=0, tabidx, counter_sample = 0;
/* Initialize random number generator */
time_t t;
t = time(NULL); // time in seconds, used to change the seed of the random generator
gsl_rng * rng;
const gsl_rng_type *typ;
gsl_rng_env_setup();
typ=gsl_rng_default;
rng=gsl_rng_alloc(typ);
gsl_rng_set(rng,t); // changes the seed of the random generator
/* transfer simulation parameters */
struct param * par;
par = (struct param *) malloc(sizeof(struct param));
par->L = seqLength;
par->mu = mutRate;
par->muL = par->mu * par->L;
par->rng = rng;
par->npop = npop;
par->npop = npop;
par->popsizes = nHostPerPop;
par->beta = beta;
par->nstart = nStart;
par->t1 = t1;
par->t2 = t2;
par->t_sample = Tsample;
par->n_sample = Nsample;
par->duration = duration;
par->cn_nb_nb = nbnb;
par->cn_list_nb = listnb;
par->cn_weights = pdisp;
/* check/print parameters */
check_param(par);
print_param(par);
/* dispersal matrix */
struct network *cn = create_network(par);
/* group sizes */
struct ts_groupsizes * grpsizes = create_ts_groupsizes(par);
/* initiate population */
struct metapopulation * metapop;
metapop = create_metapopulation(par);
/* get sampling schemes (timestep+effectives) */
translate_dates(par);
struct table_int *tabdates = get_table_int(par->t_sample, par->n_sample);
printf("\n\nsampling at timesteps:");
print_table_int(tabdates);
/* create sample */
struct sample ** samplist = (struct sample **) malloc(tabdates->n * sizeof(struct sample *));
struct sample *samp;
/* MAKE METAPOPULATION EVOLVE */
nstep = 0;
while(get_total_nsus(metapop)>0 && (get_total_ninf(metapop)+get_total_nexp(metapop))>0 && nstep<par->duration){
nstep++;
/* age metapopulation */
age_metapopulation(metapop, par);
/* process infections */
for(j=0;j<get_npop(metapop);j++){
process_infections(get_populations(metapop)[j], metapop, cn, par);
}
/* draw samples */
if((tabidx = int_in_vec(nstep, tabdates->items, tabdates->n)) > -1){ /* TRUE if step must be sampled */
samplist[counter_sample++] = draw_sample(metapop, tabdates->times[tabidx], par);
}
fill_ts_groupsizes(grpsizes, metapop, nstep);
}
/* we stopped after 'nstep' steps */
if(nstep < par->duration){
printf("\nEpidemics ended at time %d, before last sampling time (%d).\n", nstep, par->duration);
} else {
printf("\n\n-- FINAL METAPOPULATION --");
print_metapopulation(metapop, TRUE);
/* test samples */
for(i=0;i<tabdates->n;i++) {
printf("\nsample %d\n", i);
print_sample(samplist[i], TRUE);
}
samp = merge_samples(samplist, tabdates->n, par) ;
print_sample(samp, TRUE);
/* test allele listing */
struct snplist *snpbilan;
snpbilan = list_snps(samp, par);
print_snplist(snpbilan);
/* test allele frequencies */
struct allfreq *freq;
freq = get_frequencies(samp, par);
print_allfreq(freq);
/* test Hs*/
double Hs = hs(samp,par);
printf("\nHs = %0.3f\n", Hs);
/* test Hs full genome */
Hs = hs_full_genome(samp,par);
printf("\nHs (full genome) = %0.5f\n", Hs);
/* test nb of snps */
int nball = nb_snps(samp,par);
printf("\nnumber of SNPs = %d\n", nball);
/* test mean nb of snps */
double temp = mean_nb_snps(samp);
printf("\nmean number of SNPs = %.2f\n", temp);
/* test var nb of snps */
temp = var_nb_snps(samp);
printf("\nvariance of number of alleles = %.2f\n", temp);
/* test pairwise distances */
struct distmat_int *mat = pairwise_dist(samp, par);
print_distmat_int(mat);
/* test mean pairwise distances */
temp = mean_pairwise_dist(samp,par);
printf("\nmean pairwise distance: %.2f", temp);
/* test variance of pairwise distances */
temp = var_pairwise_dist(samp,par);
printf("\nvar pairwise distance: %.2f", temp);
/* test Fst */
temp = fst(samp,par);
printf("\nfst: %.2f", temp);
printf("\n\n");
/* free memory */
free_sample(samp);
free_snplist(snpbilan);
free_allfreq(freq);
free_distmat_int(mat);
}
/* write group sizes to file */
printf("\n\nPrinting group sizes to file 'out-popsize.txt'");
write_ts_groupsizes(grpsizes);
/* free memory */
free_metapopulation(metapop);
free_param(par);
for(i=0;i<counter_sample;i++) free_sample(samplist[i]);
free(samplist);
free_table_int(tabdates);
free_network(cn);
free_ts_groupsizes(grpsizes);
}
/* Function to be called from R */
void R_monitor_epidemics(int *seqLength, double *mutRate, int *npop, int *nHostPerPop, double *beta, int *nStart, int *t1, int *t2, int *Nsample, int *Tsample, int *duration, int *nbnb, int *listnb, double *pdisp, int *minSize){
int nstep;
/* Initialize random number generator */
int j;
time_t t;
t = time(NULL); // time in seconds, used to change the seed of the random generator
gsl_rng * rng;
const gsl_rng_type *typ;
gsl_rng_env_setup();
typ=gsl_rng_default;
rng=gsl_rng_alloc(typ);
gsl_rng_set(rng,t); // changes the seed of the random generator
/* transfer simulation parameters */
struct param * par;
par = (struct param *) malloc(sizeof(struct param));
par->L = *seqLength;
par->mu = *mutRate;
par->muL = par->mu * par->L;
par->rng = rng;
par->npop = *npop;
par->popsizes = nHostPerPop;
par->beta = *beta;
par->nstart = *nStart;
par->t1 = *t1;
par->t2 = *t2;
par->t_sample = Tsample;
par->n_sample = *Nsample;
par->duration = *duration;
par->cn_nb_nb = nbnb;
par->cn_list_nb = listnb;
par->cn_weights = pdisp;
/* check/print parameters */
check_param(par);
print_param(par);
/* dispersal matrix */
struct network *cn = create_network(par);
/* print_network(cn, TRUE); */
/* group sizes */
struct ts_groupsizes * grpsizes = create_ts_groupsizes(par);
struct ts_sumstat * sumstats = create_ts_sumstat(par);
/* initiate population */
struct metapopulation * metapop;
metapop = create_metapopulation(par);
/* get sampling schemes (timestep+effectives) */
translate_dates(par);
struct table_int *tabdates = get_table_int(par->t_sample, par->n_sample);
printf("\n\nsampling at timesteps:");
print_table_int(tabdates);
/* create sample */
struct sample *samp;
/* MAKE METAPOPULATION EVOLVE */
nstep = 0;
while(get_total_nsus(metapop)>0 && (get_total_ninf(metapop)+get_total_nexp(metapop))>0 && nstep<par->duration){
nstep++;
/* age metapopulation */
age_metapopulation(metapop, par);
/* process infections */
for(j=0;j<get_npop(metapop);j++){
process_infections(get_populations(metapop)[j], metapop, cn, par);
}
/* draw sample */
samp = draw_sample(metapop, par->n_sample, par);
/* compute statistics */
if(get_total_ninf(metapop)> *minSize) fill_ts_sumstat(sumstats, samp, nstep, par);
/* get group sizes */
fill_ts_groupsizes(grpsizes, metapop, nstep);
}
/* write group sizes to file */
printf("\n\nWriting results to file...");
write_ts_groupsizes(grpsizes);
write_ts_sumstat(sumstats);
printf("done.\n\n");
/* free memory */
free_sample(samp);
free_metapopulation(metapop);
free_param(par);
free_network(cn);
free_ts_groupsizes(grpsizes);
free_ts_sumstat(sumstats);
}
NN::NN(vector<int> layers)
{
create_network(layers);
}
int main(int argc, char *argv[])
{
unsigned int moment;
unsigned int node_index;
unsigned int moment_index;
node_t *node;
Display *display;
GC gc;
int screen_number;
int window_x;
int window_y;
unsigned int each_x;
unsigned int each_y;
unsigned int window_border_width;
unsigned int window_height;
unsigned int window_width;
unsigned long gc_value_mask;
unsigned long window_background_color;
unsigned long window_border_color;
Window root_window;
Window window;
XGCValues gc_values;
Visual* default_visual;
Colormap colormap;
XColor system_color;
XColor exact_color;
unsigned int window_display_x_modulus;
unsigned int window_display_y_modulus;
unsigned int window_x_pixel;
unsigned int window_y_pixel;
display = XOpenDisplay(NULL);
screen_number = DefaultScreen(display);
root_window = RootWindow(display, screen_number);
window_x = 0;
window_y = 0;
window_width = XWINDOW_WIDTH;
window_height = XWINDOW_HEIGHT;
window_border_width = 0;
window_border_color = BlackPixel(display, screen_number);
window_background_color = WhitePixel(display, screen_number);
window = XCreateSimpleWindow(display, root_window, window_x, window_y,
window_width, window_height, window_border_width,
window_border_color, window_background_color);
XMapWindow(display, window);
XFlush(display);
gc_value_mask = 0;
gc = XCreateGC(display, window, gc_value_mask, &gc_values);
default_visual = DefaultVisual(display, DefaultScreen(display));
colormap = XCreateColormap(display, window, default_visual, AllocNone);
XSync(display, False);
window_display_x_modulus = NODES / XWINDOW_WIDTH;
window_display_y_modulus = TIME / XWINDOW_HEIGHT;
srandom(SEED);
/* init_action_tables(); */
network = create_network();
window_y_pixel = 0;
for (moment = 0; moment < TIME; moment++) {
if (0 == (moment % window_display_y_modulus)) {
window_x_pixel = 0;
for (node_index = 0; node_index < NODES; node_index++) {
if (0 != (node_index % window_display_x_modulus)) {
continue;
}
node = network->nodes_display_order[node_index];
system_color.red = 0;
if (0 == node->value) {
system_color.green = USHRT_MAX / 4;
system_color.blue = USHRT_MAX;
}
else if (1 == node->value) {
system_color.green = USHRT_MAX;
system_color.blue = USHRT_MAX / 4;
}
else {
exit(100);
}
/*
system_color.blue = 0;
*/
if (2 == node->link_count) {
system_color.green /= 2;
system_color.blue /= 2;
}
/*
system_color.green = 0;
*/
XAllocColor(display, colormap, &system_color);
XSetForeground(display, gc, system_color.pixel);
XDrawPoint(display, window, gc, window_x_pixel,
window_y_pixel);
window_x_pixel++;
}
XFlush(display);
window_y_pixel++;
}
iterate_network(network);
}
while (1) {
usleep(SLEEP);
}
destroy_network(network);
XUnmapWindow(display, window);
XDestroyWindow(display, window);
XCloseDisplay(display);
return 0;
}