////////DECAY FUNCTIONS///////////
/// There is a single decay rate depending on the sign of the running sum
//Decrement running sum based on decay probability eta * filter state (running sum)
//As we approach the threshold the decay increases proportionally
int synapseSingleFilterUnifiedWithDecayReflecting::doStochasticDecay()
{
double p;
unsigned int rDecaySteps;
//g_rng_r = getRandGeneratorInstance();
if (miRFilterValue == 0) return 0;
if (miRFilterValue > 0) //p side decay
{
p = 1.0-exp(-mdDecayRate*miTimeSinceLastInduction);
rDecaySteps = gsl_ran_binomial(mprng,p,miRFilterValue);
miRFilterValue -= rDecaySteps; //Subtract to move to zero
}
else //Increment Sum - q side is decaying back to zero
{
p = 1.0-exp(-mdDecayRate*miTimeSinceLastInduction);
rDecaySteps = gsl_ran_binomial(mprng,p,-miRFilterValue);
miRFilterValue += rDecaySteps; //Add To move to zero
}
return rDecaySteps;
}
int gslalg_rng_binomial_VM ( Word* args, Word& result,
int message, Word& local, Supplier s )
{
result = qp->ResultStorage( s );
CcInt *res = ((CcInt*) result.addr);
long resL = 0;
CcInt *cN = (CcInt*) args[0].addr;
CcReal *cP = (CcReal*) args[1].addr;
if( cN->IsDefined() && cP->IsDefined() )
{
double p = cP->GetRealval();
long n = cN->GetIntval();
if( n >= 0 && p >= 0 && p <= 1.0 )
{
resL = gsl_ran_binomial(the_gsl_randomgenerator.GetGenerator(), p, n);
res->Set(true, resL);
}
else
{
res->Set(false, 0);
}
}
else
res->Set(false, 0);
return (0);
}
int GSLRNG_binomial(stEval *args, stEval *result, void *i) {
gsl_rng *r = STPOINTER(&args[0]);
double p = STDOUBLE(&args[1]);
int n = STINT(&args[2]);
STINT(result) = gsl_ran_binomial(r,p,n);
return EC_OK;
}
static int multinomial_rng(double *out, gsl_rng *r, apop_model* est){
Nullcheck_mp(est, 1);
double * p = est->parameters->vector->data;
//the trick where we turn the params into a p-vector
int N = p[0];
if (est->parameters->vector->size == 2) {
*out = gsl_ran_binomial_knuth(r, 1-gsl_vector_get(est->parameters->vector, 1), N);
out[1] = N-*out;
goto done;
}
//else, multinomial
//cut/pasted/modded from the GSL. Copyright them.
p[0] = 1 - (apop_sum(est->parameters->vector)-N);
double sum_p = 0.0;
int sum_n = 0;
for (int i = 0; i < est->parameters->vector->size; i++) {
out[i] = (p[i] > 0)
? gsl_ran_binomial (r, p[i] / (1 - sum_p), N - sum_n)
: 0;
sum_p += p[i];
sum_n += out[i];
}
done:
p[0] = N;
return 0;
}
void pplfunc_frandombin(ppl_context *c, pplObj *in, int nArgs, int *status, int *errType, char *errText)
{
char *FunctionDescription = "binomial(p,n)";
if (rndgen==NULL) { rndgen = gsl_rng_alloc(gsl_rng_default); gsl_rng_set(rndgen, 0); }
CHECK_NEEDINT(in[1], "n", "function's second argument must be an integer in the range");
OUTPUT.real = gsl_ran_binomial(rndgen, in[0].real, (unsigned int)in[1].real);
CHECK_OUTPUT_OKAY;
}
long
librandom::GSL_BinomialRandomDev::ldev( RngPtr rng ) const
{
GslRandomGen* gsr_rng = dynamic_cast< GslRandomGen* >( &( *rng ) );
if ( !gsr_rng )
throw UnsuitableRNG(
"The gsl_binomial RDV can only be used with GSL RNGs." );
return gsl_ran_binomial( gsr_rng->rng_, p_, n_ );
}
void librdist_binomial(gsl_rng *rng, int argc, void *argv, int bufc, float *buf){
t_atom *av = (t_atom *)argv;
if(argc != librdist_getnargs(ps_binomial)){
return;
}
const double p = librdist_atom_getfloat(av);
const unsigned int n = (unsigned int)librdist_atom_getlong(av + 1);
int i;
for(i = 0; i < bufc; i++)
buf[i] = (float)gsl_ran_binomial(rng, p, n);
}
unsigned int
gsl_ran_poisson (const gsl_rng * r, double mu)
{
double emu;
double prod = 1.0;
unsigned int k = 0;
while (mu > 10)
{
unsigned int m = mu * (7.0 / 8.0);
double X = gsl_ran_gamma_int (r, m);
if (X >= mu)
{
return k + gsl_ran_binomial (r, mu / X, m - 1);
}
else
{
k += m;
mu -= X;
}
}
/* This following method works well when mu is small */
emu = exp (-mu);
do
{
prod *= gsl_rng_uniform (r);
k++;
}
while (prod > emu);
return k - 1;
}
int main(int argc, char **argv) {
if (argc != 3) {
printf("Provide two arguments: a probability of failure per bit and a message.\n");
printf("More than one error can hit the same bit, and a character is made of 1 byte (8 bits).\n");
}
const size_t size = strlen(argv[2]);
char *received = malloc(size + 1);
strcpy(received, argv[2]);
const double f = atof(argv[1]);
gsl_rng *rng = gsl_rng_alloc(gsl_rng_taus2);
gsl_rng_set(rng, time(NULL)); // Seed with time
const int errors = gsl_ran_binomial(rng, f, size * 8);
for (int i = 0; i < errors; ++i) {
unsigned int toflip = (int)(gsl_rng_uniform(rng) * size);
bitflip(received + toflip, rng);
}
printf("Message sent:\n%s\n", argv[2]);
printf("Message received:\n%s\n", received);
printf("Bits flipped: %d\n", errors);
free(received);
return EXIT_SUCCESS;
}
void
gsl_ran_multinomial (const gsl_rng * r, const size_t K,
const unsigned int N, const double p[], unsigned int n[])
{
size_t k;
double norm = 0.0;
double sum_p = 0.0;
unsigned int sum_n = 0;
/* p[k] may contain non-negative weights that do not sum to 1.0.
* Even a probability distribution will not exactly sum to 1.0
* due to rounding errors.
*/
for (k = 0; k < K; k++)
{
norm += p[k];
}
for (k = 0; k < K; k++)
{
if (p[k] > 0.0)
{
n[k] = gsl_ran_binomial (r, p[k] / (norm - sum_p), N - sum_n);
}
else
{
n[k] = 0;
}
sum_p += p[k];
sum_n += n[k];
}
}
void run_simulation(int thread_id, intVector& rank_abundance, intVector& rank_occurrence)
{
gsl_rng* r = gsl_rng_alloc(gsl_rng_mt19937);
gsl_rng_set(r, random_seed());
std::default_random_engine generator(random_seed());
// RT
intVector sample_after_RT(bins);
// PCR
uint64_t no_PCR_molecules;
std::vector<uint64_t> sample_after_PCR(bins);
// Sequencing sample
intVector sampler_after_seq(bins);
for (int i = 1; i <= (N_trials - 1 - thread_id) / number_threads + 1; ++i) {
if ((thread_id == 0) && (i * number_threads % 10 == 0))
std::cout << "Trial no.: " << i* number_threads << '\n';
// 1.) simulate RT
gsl_ran_multinomial(r, bins, N_RT, prob_RT.data(), sample_after_RT.data());
// 2.) simulate PCR (branching process)
no_PCR_molecules = 0;
for (int j = 0; j < bins; ++j) {
if (sample_after_RT[j]) {
sample_after_PCR[j] = sample_after_RT[j];
// stochastic amplification
for (int k = 0; k < cycles; ++k) {
sample_after_PCR[j] += gsl_ran_binomial(r, p, sample_after_PCR[j]);
}
no_PCR_molecules += sample_after_PCR[j];
}
else {
sample_after_PCR[j] = 0;
}
}
if (thread_id == 0) {
average_PCR_molecules += no_PCR_molecules;
}
// 3.) sequencing sample
if (no_PCR_molecules > 100 * N_reads) {
// multinomial approximation is good
std::sort(sample_after_PCR.begin(), sample_after_PCR.end(), std::greater<intType>());
sampler_after_seq = ran_mult_multinomial(sample_after_PCR, N_reads, r, min_coverage);
++used_with_replacement;
}
else {
// multinomial approximation is not good enough!
sampler_after_seq = ran_mult_hypergeometric(sample_after_PCR, N_reads, r, generator, min_coverage);
++used_without_replacement;
}
std::sort(sampler_after_seq.begin(), sampler_after_seq.end(), std::greater<intType>());
// 4.) enter rankings
for (int j = 0; j < rank_cutoff; ++j) {
if (sampler_after_seq[j]) {
rank_abundance[j] += sampler_after_seq[j];
++rank_occurrence[j];
}
}
}
gsl_rng_free(r);
}
long
librandom::GSL_BinomialRandomDev::ldev()
{
return gsl_ran_binomial( rng_, p_, n_ );
}
unsigned int GslRandGen::binomial(const double & p, unsigned int n)
{
return gsl_ran_binomial(r, p, n);
}
double
test_binomial_large (void)
{
return gsl_ran_binomial (r_global, 0.3, 55);
}
void form_offspring(
const gsl_rng *rand,
int k,
int picked_father,
int counter,
int **ptr_matrix_del,
int **ptr_matrix_env,
int **ptr_offspring_matrix_del,
int **ptr_offspring_matrix_env
){
int i;
/*I think that this works both for selfing and for sexual reproduction*/
for(i=0;i<LOCI_ENV;i++){
ptr_offspring_matrix_env[k][i]=0;
if(ptr_matrix_env[k][i]+ptr_matrix_env[picked_father][i]==0){
ptr_offspring_matrix_env[counter][i]=0;
}
if(ptr_matrix_env[k][i]+ptr_matrix_env[picked_father][i]==4){
ptr_offspring_matrix_env[counter][i]=2;
}
if(ptr_matrix_env[k][i]+ptr_matrix_env[picked_father][i]==1){
ptr_offspring_matrix_env[counter][i]=gsl_ran_bernoulli(rand, 0.5);
}
if(ptr_matrix_env[k][i]+ptr_matrix_env[picked_father][i]==3){
ptr_offspring_matrix_env[counter][i]=1+gsl_ran_bernoulli(rand,0.5);
}
if(ptr_matrix_env[k][i]+ptr_matrix_env[picked_father][i]==2){
if(ptr_matrix_env[k][i]==0 || ptr_matrix_env[picked_father][i]==0){
ptr_offspring_matrix_env[counter][i]=1;
}
else{
ptr_offspring_matrix_env[counter][i]=gsl_ran_binomial(rand,0.5,2);
}
}
}
for(i=0;i<LOCI_DEL;i++){
ptr_offspring_matrix_del[k][i]=0;
if(ptr_matrix_del[k][i]+ptr_matrix_del[picked_father][i]==0){
ptr_offspring_matrix_del[counter][i]=0;
}
if(ptr_matrix_del[k][i]+ptr_matrix_del[picked_father][i]==4){
ptr_offspring_matrix_del[counter][i]=2;
}
if(ptr_matrix_del[k][i]+ptr_matrix_del[picked_father][i]==1){
ptr_offspring_matrix_del[counter][i]=gsl_ran_bernoulli(rand, 0.5);
}
if(ptr_matrix_del[k][i]+ptr_matrix_del[picked_father][i]==3){
ptr_offspring_matrix_del[counter][i]=1+gsl_ran_bernoulli(rand,0.5);
}
if(ptr_matrix_del[k][i]+ptr_matrix_del[picked_father][i]==2){
if(ptr_matrix_del[k][i]==0 || ptr_matrix_del[picked_father][i]==0){
ptr_offspring_matrix_del[counter][i]=1;
}
else{
ptr_offspring_matrix_del[counter][i]=gsl_ran_binomial(rand,0.5,2);
}
}
}
}
main(int argc, char **argv )
{
int **genos[2] ;
FILE *fp1, *palpha ;
int popsize, nloci, ngen , gen ;
int paroff, matpat, ind, loc , nfix ;
double ran1() ;
double s, alpha, sum , sumsq, env, initfreq1, *freqinit ;
double *effects, opt, sig , fitmax, mutrate, *freqsp, *freqsoff , *ptemp ;
double var, meanphen, sumpa, suma, delta, pprime ;
if( argc < 7 ) { fprintf( stderr, " qapprox popsize nloci optimum s(strength of selection) ngen mutrate \n"); exit(1); }
popsize = strtol( argv[1],NULL,10);
nloci = strtol( argv[2],NULL,10);
opt = strtod( argv[3],NULL);
s = strtod( argv[4],NULL);
ngen = strtol( argv[5],NULL,10);
mutrate = strtod( argv[6],NULL);
/* alpha = strtod( argv[7],NULL); */
/* nfix = strtol( argv[8],NULL,10); */
/* initfreq1 = strtod( argv[9],NULL); */
gsl_rng *rng = gsl_rng_alloc(gsl_rng_taus2);
gsl_rng_set(rng, 123 );
/* to gen a binomial: nwt = gsl_ran_binomial( rng, epwt,(unsigned int)n) ; */
env = 0. ;
fp1 = fopen("phenout", "w");
/* vectors to store effect size at each locus, phenotypes of individuals, and fitnesses of individuals */
effects = (double *)malloc( (unsigned)nloci*sizeof(double) ) ;
freqsp = (double *)malloc( (unsigned)nloci*sizeof(double) ) ;
freqsoff = (double *)malloc( (unsigned)nloci*sizeof(double) ) ;
freqinit = (double *)malloc( (unsigned)nloci*sizeof(double) ) ;
palpha = fopen( "alphas","r");
for( loc = 0 ; loc<nloci; loc++) fscanf(palpha," %lf", effects+loc) ;
for( loc = 0; loc <nloci ; loc++) fscanf(stdin," %lf", freqinit+loc ) ;
for( loc = 0; loc < nloci; loc++) printf(" %lf", freqinit[loc] );
printf("\n");
for( loc = 0; loc < nloci; loc++) freqsp[loc] = freqinit[loc] ;
suma = 0.0 ;
for( loc=0; loc <nloci; loc++) suma += effects[loc] ;
/* main loop */
for( gen = 0 ; gen < ngen ; gen++){
/* if( gen == 3000 ) opt += 2.0 ; */
sumpa = var = 0. ;
for( loc=0; loc<nloci; loc++) {
sumpa += freqsp[loc]*effects[loc] ;
var += 2.*effects[loc]*effects[loc]*freqsp[loc]*(1.0-freqsp[loc] ) ;
}
meanphen = 2.*sumpa - suma ;
delta = meanphen - opt ;
fprintf(fp1,"%lf\t%lf\n",meanphen, var ) ;
/* pprime = */
for( loc = 0; loc< nloci; loc++){
pprime = freqsp[loc] + 0.5*s*effects[loc]*effects[loc]*freqsp[loc]*(1.0-freqsp[loc] )*( (2.0*freqsp[loc]-1.0) - 2.0*delta/effects[loc] ) - mutrate*(2.0*freqsp[loc] -1.0 ) ;
freqsoff[loc] = gsl_ran_binomial( rng, pprime, (unsigned int)(2*popsize) )/(2.0*popsize) ;
}
for( loc = 0; loc < nloci; loc++) printf(" %lf", freqsoff[loc] );
printf("\n");
/* fprintf(fp1,"%lf\t%lf\n",sum/popsize, sumsq/popsize - (sum/popsize)*(sum/popsize) ) ; */
ptemp = freqsp;
freqsp = freqsoff ;
freqsoff = ptemp ;
}
/* end of main loop */
}
unsigned long librandom::GSL_BinomialRandomDev::uldev(RngPtr rng) const
{
GslRandomGen* gsr_rng = dynamic_cast<GslRandomGen*>(&(*rng));
assert (gsr_rng && "rng needs to be a GSL RNG");
return gsl_ran_binomial(gsr_rng->rng_, p_, n_);
}
void Model::simulate(std::vector<double> & model_params, std::vector<std::string> & param_names, Trajectory * traj, int start_dt, int end_dt, double step_size, int total_dt, gsl_rng * rng) {
if ((traj->get_state(1)+traj->get_state(2)) < 1.0) {
return;
}
double Beta = model_params[0];
double k=model_params[1];
double rateE2I=model_params[2];
double rateI2R=model_params[3];
double R0_reduce=model_params[6];
int change_T=model_params[7]/step_size;
double Rt = Beta/rateI2R*traj->get_state(0);
double total_infectious=0.0;
double new_infections=0.0;
double divisions = 3.0;
double p1 = rateE2I*step_size/divisions;
double p2 = rateI2R*step_size/divisions;
double S2E = 0.0;
double E2I = 0.0;
double I2R = 0.0;
double currS;
double latent;
double infectious;
double Tg = 1.0/rateE2I + 1.0/rateI2R;
// */
for (int t=start_dt; t<end_dt; ++t) {
double sub_t_I2R = 0.0;
for (int sub_t=0; sub_t<(int) divisions; ++sub_t) {
//
// Transitions
//
// Recoveries: I --> R
currS = traj->get_state(0);
latent = traj->get_state(1);
infectious = traj->get_state(2);
if (infectious > 0) {
if (p2 >= 1.0) I2R = infectious;
else if (use_deterministic) {
I2R = infectious*p2;
}
else {
I2R = gsl_ran_binomial(rng, p2, infectious); // Recoveries
}
}
// Becoming infectious: E --> I
if (latent > 0) {
if (p1 >= 1.0) E2I = latent;
else if (use_deterministic) {
E2I = latent * p1;
}
else {
E2I = gsl_ran_binomial(rng, p1, latent); // Becoming infectious
}
}
traj->set_state(infectious-I2R+E2I, 2); // Infectious
if (use_deterministic) {
S2E = Beta * currS * step_size * infectious;
} else {
// Infections: S --> E
if (I2R > 0.0) {
total_infectious += I2R;
sub_t_I2R += I2R;
// traj->set_traj(I2R, t-start_dt); // People are sampled, i.e. appear in the time-series at the time of recovery
if (currS > 0) {
// Current reproductive number
Rt = Beta / rateI2R * currS;
if (change_T>=t) Rt *= R0_reduce;
// Draw from the negative binomial distribution (gamma-poisson mixture) to determine
// number of secondary infections
S2E = gsl_ran_negative_binomial(rng, k/(k+Rt), k*I2R); // New infections
if (S2E > 0) {
S2E = std::min(currS, S2E);
new_infections += S2E;
traj->set_state(currS-S2E, 0); // Susceptible
}
}
}
}
traj->set_state(latent+S2E-E2I, 1); // Latent
S2E=0.0;
E2I=0.0;
I2R=0.0;
}
traj->set_traj(0, sub_t_I2R, t-start_dt);
double N = traj->get_state(1)+traj->get_state(2);
// Record 1/N for coalescent rate calculation
if (N > 0.0) {
traj->set_traj(1, N, t-start_dt);
traj->set_traj(2, Rt/Tg*(1.0+1.0/k), t-start_dt);
}
else {
traj->set_traj(1, 0.0, t-start_dt);
traj->set_traj(2, 0.0, t-start_dt);
break;
}
}
}
double
test_binomial_huge (void)
{
return gsl_ran_binomial (r_global, 0.3, 5500);
}
double
test_binomial1 (void)
{
return gsl_ran_binomial (r_global, 1, 8);
}
unsigned int
gsl_ran_binomial_tpe (const gsl_rng * rng, double p, unsigned int n)
{
return gsl_ran_binomial (rng, p, n);
}
int
main (int argc, char *argv[])
{
size_t i,j;
size_t n = 0;
double mu = 0, nu = 0, nu1 = 0, nu2 = 0, sigma = 0, a = 0, b = 0, c = 0;
double zeta = 0, sigmax = 0, sigmay = 0, rho = 0;
double p = 0;
double x = 0, y =0, z=0 ;
unsigned int N = 0, t = 0, n1 = 0, n2 = 0 ;
unsigned long int seed = 0 ;
const char * name ;
gsl_rng * r ;
if (argc < 4)
{
printf (
"Usage: gsl-randist seed n DIST param1 param2 ...\n"
"Generates n samples from the distribution DIST with parameters param1,\n"
"param2, etc. Valid distributions are,\n"
"\n"
" beta\n"
" binomial\n"
" bivariate-gaussian\n"
" cauchy\n"
" chisq\n"
" dir-2d\n"
" dir-3d\n"
" dir-nd\n"
" erlang\n"
" exponential\n"
" exppow\n"
" fdist\n"
" flat\n"
" gamma\n"
" gaussian-tail\n"
" gaussian\n"
" geometric\n"
" gumbel1\n"
" gumbel2\n"
" hypergeometric\n"
" laplace\n"
" landau\n"
" levy\n"
" levy-skew\n"
" logarithmic\n"
" logistic\n"
" lognormal\n"
" negative-binomial\n"
" pareto\n"
" pascal\n"
" poisson\n"
" rayleigh-tail\n"
" rayleigh\n"
" tdist\n"
" ugaussian-tail\n"
" ugaussian\n"
" weibull\n") ;
exit (0);
}
argv++ ; seed = atol (argv[0]); argc-- ;
argv++ ; n = atol (argv[0]); argc-- ;
argv++ ; name = argv[0] ; argc-- ; argc-- ;
gsl_rng_env_setup() ;
if (gsl_rng_default_seed != 0) {
fprintf(stderr,
"overriding GSL_RNG_SEED with command line value, seed = %ld\n",
seed) ;
}
gsl_rng_default_seed = seed ;
r = gsl_rng_alloc(gsl_rng_default) ;
#define NAME(x) !strcmp(name,(x))
#define OUTPUT(x) for (i = 0; i < n; i++) { printf("%g\n", (x)) ; }
#define OUTPUT1(a,x) for(i = 0; i < n; i++) { a ; printf("%g\n", x) ; }
#define OUTPUT2(a,x,y) for(i = 0; i < n; i++) { a ; printf("%g %g\n", x, y) ; }
#define OUTPUT3(a,x,y,z) for(i = 0; i < n; i++) { a ; printf("%g %g %g\n", x, y, z) ; }
#define INT_OUTPUT(x) for (i = 0; i < n; i++) { printf("%d\n", (x)) ; }
#define ARGS(x,y) if (argc != x) error(y) ;
#define DBL_ARG(x) if (argc) { x=atof((++argv)[0]);argc--;} else {error( #x);};
#define INT_ARG(x) if (argc) { x=atoi((++argv)[0]);argc--;} else {error( #x);};
if (NAME("bernoulli"))
{
ARGS(1, "p = probability of success");
DBL_ARG(p)
INT_OUTPUT(gsl_ran_bernoulli (r, p));
}
else if (NAME("beta"))
{
ARGS(2, "a,b = shape parameters");
DBL_ARG(a)
DBL_ARG(b)
OUTPUT(gsl_ran_beta (r, a, b));
}
else if (NAME("binomial"))
{
ARGS(2, "p = probability, N = number of trials");
DBL_ARG(p)
INT_ARG(N)
INT_OUTPUT(gsl_ran_binomial (r, p, N));
}
else if (NAME("cauchy"))
{
ARGS(1, "a = scale parameter");
DBL_ARG(a)
OUTPUT(gsl_ran_cauchy (r, a));
}
else if (NAME("chisq"))
{
ARGS(1, "nu = degrees of freedom");
DBL_ARG(nu)
OUTPUT(gsl_ran_chisq (r, nu));
}
else if (NAME("erlang"))
{
ARGS(2, "a = scale parameter, b = order");
DBL_ARG(a)
DBL_ARG(b)
OUTPUT(gsl_ran_erlang (r, a, b));
}
else if (NAME("exponential"))
{
ARGS(1, "mu = mean value");
DBL_ARG(mu) ;
OUTPUT(gsl_ran_exponential (r, mu));
}
else if (NAME("exppow"))
{
ARGS(2, "a = scale parameter, b = power (1=exponential, 2=gaussian)");
DBL_ARG(a) ;
DBL_ARG(b) ;
OUTPUT(gsl_ran_exppow (r, a, b));
}
else if (NAME("fdist"))
{
ARGS(2, "nu1, nu2 = degrees of freedom parameters");
DBL_ARG(nu1) ;
DBL_ARG(nu2) ;
OUTPUT(gsl_ran_fdist (r, nu1, nu2));
}
else if (NAME("flat"))
{
ARGS(2, "a = lower limit, b = upper limit");
DBL_ARG(a) ;
DBL_ARG(b) ;
OUTPUT(gsl_ran_flat (r, a, b));
}
else if (NAME("gamma"))
{
ARGS(2, "a = order, b = scale");
DBL_ARG(a) ;
DBL_ARG(b) ;
OUTPUT(gsl_ran_gamma (r, a, b));
}
else if (NAME("gaussian"))
{
ARGS(1, "sigma = standard deviation");
DBL_ARG(sigma) ;
OUTPUT(gsl_ran_gaussian (r, sigma));
}
else if (NAME("gaussian-tail"))
{
ARGS(2, "a = lower limit, sigma = standard deviation");
DBL_ARG(a) ;
DBL_ARG(sigma) ;
OUTPUT(gsl_ran_gaussian_tail (r, a, sigma));
}
else if (NAME("ugaussian"))
{
ARGS(0, "unit gaussian, no parameters required");
OUTPUT(gsl_ran_ugaussian (r));
}
else if (NAME("ugaussian-tail"))
{
ARGS(1, "a = lower limit");
DBL_ARG(a) ;
OUTPUT(gsl_ran_ugaussian_tail (r, a));
}
else if (NAME("bivariate-gaussian"))
{
ARGS(3, "sigmax = x std.dev., sigmay = y std.dev., rho = correlation");
DBL_ARG(sigmax) ;
DBL_ARG(sigmay) ;
DBL_ARG(rho) ;
OUTPUT2(gsl_ran_bivariate_gaussian (r, sigmax, sigmay, rho, &x, &y),
x, y);
}
else if (NAME("dir-2d"))
{
OUTPUT2(gsl_ran_dir_2d (r, &x, &y), x, y);
}
else if (NAME("dir-3d"))
{
OUTPUT3(gsl_ran_dir_3d (r, &x, &y, &z), x, y, z);
}
else if (NAME("dir-nd"))
{
double *xarr;
ARGS(1, "n1 = number of dimensions of hypersphere");
INT_ARG(n1) ;
xarr = (double *)malloc(n1*sizeof(double));
for(i = 0; i < n; i++) {
gsl_ran_dir_nd (r, n1, xarr) ;
for (j = 0; j < n1; j++) {
if (j) putchar(' ');
printf("%g", xarr[j]) ;
}
putchar('\n');
} ;
free(xarr);
}
else if (NAME("geometric"))
{
ARGS(1, "p = bernoulli trial probability of success");
DBL_ARG(p) ;
INT_OUTPUT(gsl_ran_geometric (r, p));
}
else if (NAME("gumbel1"))
{
ARGS(2, "a = order, b = scale parameter");
DBL_ARG(a) ;
DBL_ARG(b) ;
OUTPUT(gsl_ran_gumbel1 (r, a, b));
}
else if (NAME("gumbel2"))
{
ARGS(2, "a = order, b = scale parameter");
DBL_ARG(a) ;
DBL_ARG(b) ;
OUTPUT(gsl_ran_gumbel2 (r, a, b));
}
else if (NAME("hypergeometric"))
{
ARGS(3, "n1 = tagged population, n2 = untagged population, t = number of trials");
INT_ARG(n1) ;
INT_ARG(n2) ;
INT_ARG(t) ;
INT_OUTPUT(gsl_ran_hypergeometric (r, n1, n2, t));
}
else if (NAME("laplace"))
{
ARGS(1, "a = scale parameter");
DBL_ARG(a) ;
OUTPUT(gsl_ran_laplace (r, a));
}
else if (NAME("landau"))
{
ARGS(0, "no arguments required");
OUTPUT(gsl_ran_landau (r));
}
else if (NAME("levy"))
{
ARGS(2, "c = scale, a = power (1=cauchy, 2=gaussian)");
DBL_ARG(c) ;
DBL_ARG(a) ;
OUTPUT(gsl_ran_levy (r, c, a));
}
else if (NAME("levy-skew"))
{
ARGS(3, "c = scale, a = power (1=cauchy, 2=gaussian), b = skew");
DBL_ARG(c) ;
DBL_ARG(a) ;
DBL_ARG(b) ;
OUTPUT(gsl_ran_levy_skew (r, c, a, b));
}
else if (NAME("logarithmic"))
{
ARGS(1, "p = probability");
DBL_ARG(p) ;
INT_OUTPUT(gsl_ran_logarithmic (r, p));
}
else if (NAME("logistic"))
{
ARGS(1, "a = scale parameter");
DBL_ARG(a) ;
OUTPUT(gsl_ran_logistic (r, a));
}
else if (NAME("lognormal"))
{
ARGS(2, "zeta = location parameter, sigma = scale parameter");
DBL_ARG(zeta) ;
DBL_ARG(sigma) ;
OUTPUT(gsl_ran_lognormal (r, zeta, sigma));
}
else if (NAME("negative-binomial"))
{
ARGS(2, "p = probability, a = order");
DBL_ARG(p) ;
DBL_ARG(a) ;
INT_OUTPUT(gsl_ran_negative_binomial (r, p, a));
}
else if (NAME("pareto"))
{
ARGS(2, "a = power, b = scale parameter");
DBL_ARG(a) ;
DBL_ARG(b) ;
OUTPUT(gsl_ran_pareto (r, a, b));
}
else if (NAME("pascal"))
{
ARGS(2, "p = probability, n = order (integer)");
DBL_ARG(p) ;
INT_ARG(N) ;
INT_OUTPUT(gsl_ran_pascal (r, p, N));
}
else if (NAME("poisson"))
{
ARGS(1, "mu = scale parameter");
DBL_ARG(mu) ;
INT_OUTPUT(gsl_ran_poisson (r, mu));
}
else if (NAME("rayleigh"))
{
ARGS(1, "sigma = scale parameter");
DBL_ARG(sigma) ;
OUTPUT(gsl_ran_rayleigh (r, sigma));
}
else if (NAME("rayleigh-tail"))
{
ARGS(2, "a = lower limit, sigma = scale parameter");
DBL_ARG(a) ;
DBL_ARG(sigma) ;
OUTPUT(gsl_ran_rayleigh_tail (r, a, sigma));
}
else if (NAME("tdist"))
{
ARGS(1, "nu = degrees of freedom");
DBL_ARG(nu) ;
OUTPUT(gsl_ran_tdist (r, nu));
}
else if (NAME("weibull"))
{
ARGS(2, "a = scale parameter, b = exponent");
DBL_ARG(a) ;
DBL_ARG(b) ;
OUTPUT(gsl_ran_weibull (r, a, b));
}
else
{
fprintf(stderr,"Error: unrecognized distribution: %s\n", name) ;
}
return 0 ;
}
