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use std::rc::Rc;
use crate::simulation::error::*;
use crate::simulation::parameter::*;
use crate::simulation::parameter::random::*;
use crate::simulation::event::*;
use crate::simulation::process::*;
use crate::simulation::stream::*;
use dvcompute_utils::simulation::arrival::*;
pub fn random_stream<T, F, M>(f: F) -> Stream<Rc<Arrival<T>>>
where F: Fn() -> M + 'static,
M: Parameter<Item = (f64, T)> + 'static,
T: 'static
{
random_stream_loop(f, None)
}
fn random_stream_loop<T, F, M>(f: F, t0: Option<f64>) -> Stream<Rc<Arrival<T>>>
where F: Fn() -> M + 'static,
M: Parameter<Item = (f64, T)> + 'static,
T: 'static
{
let comp = {
time_event()
.flat_map(move |t1| {
cons_event(move |_p| {
match t0 {
None => Result::Ok(()),
Some(t0) if t1 == t0 => Result::Ok(()),
Some(_) => {
let msg =
"The time of requesting for a new random event is different from \
the time when the previous event has arrived. Probably, your model \
contains a logical error. The random events should be requested permanently. \
At least, they can be lost, for example, when trying to enqueue them, but \
the random stream itself must always work.";
let msg = String::from(msg);
let err = Error::retry(msg);
Result::Err(err)
}
}
})
})
.into_process()
.flat_map(move |()| {
let p = f();
p.into_process()
.flat_map(move |(delay, a)| {
let delay = delay.max(0.0);
hold_process(delay)
.flat_map(move |()| {
time_event()
.into_process()
.flat_map(move |t2| {
let arrival = Arrival {
val: a,
time: t2,
delay: {
match t0 {
None => None,
Some(_) => Some(delay)
}
}
};
let arrival = Rc::new(arrival);
return_process((arrival, random_stream_loop(f, Some(t2))))
})
})
})
})
.into_boxed()
};
Stream::Cons(comp)
}
#[inline]
pub fn random_uniform_stream(min: f64, max: f64) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_uniform_parameter(min, max)
.map(|x| (x, x))
})
}
#[inline]
pub fn random_int_uniform_stream(min: isize, max: isize) -> Stream<Rc<Arrival<isize>>> {
random_stream(move || {
random_int_uniform_parameter(min, max)
.map(|x| (x as f64, x))
})
}
#[inline]
pub fn random_triangular_stream(min: f64, median: f64, max: f64) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_triangular_parameter(min, median, max)
.map(|x| (x, x))
})
}
#[inline]
pub fn random_normal_stream(mu: f64, nu: f64) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_normal_parameter(mu, nu)
.map(|x| {
let x = x.max(0.0);
(x, x)
})
})
}
#[inline]
pub fn random_log_normal_stream(mu: f64, nu: f64) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_log_normal_parameter(mu, nu)
.map(|x| (x, x))
})
}
#[inline]
pub fn random_exponential_stream(mu: f64) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_exponential_parameter(mu)
.map(|x| (x, x))
})
}
#[inline]
pub fn random_erlang_stream(scale: f64, shape: isize) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_erlang_parameter(scale, shape)
.map(|x| (x, x))
})
}
#[inline]
pub fn random_poisson_stream(mu: f64) -> Stream<Rc<Arrival<isize>>> {
random_stream(move || {
random_poisson_parameter(mu)
.map(|x| (x as f64, x))
})
}
#[inline]
pub fn random_binomial_stream(prob: f64, trials: isize) -> Stream<Rc<Arrival<isize>>> {
random_stream(move || {
random_binomial_parameter(prob, trials)
.map(|x| (x as f64, x))
})
}
#[inline]
pub fn random_gamma_stream(kappa: f64, theta: f64) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_gamma_parameter(kappa, theta)
.map(|x| (x, x))
})
}
#[inline]
pub fn random_beta_stream(alpha: f64, beta: f64) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_beta_parameter(alpha, beta)
.map(|x| (x, x))
})
}
#[inline]
pub fn random_weibull_stream(alpha: f64, beta: f64) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_weibull_parameter(alpha, beta)
.map(|x| (x, x))
})
}
#[inline]
pub fn random_discrete_stream(dpdf: Rc<Vec<(f64, f64)>>) -> Stream<Rc<Arrival<f64>>> {
random_stream(move || {
random_discrete_parameter(dpdf.clone())
.map(|x| (x, x))
})
}