Function exponential_random_floats 

pub fn exponential_random_floats(
    seed: Seed,
    prec: u64,
    rm: RoundingMode,
) -> ExponentialRandomFloats<RandomPrimitiveInts<u64>> 

Generates random Floats sampled, with rounding, from the exponential distribution with mean 1.

The result is a correctly-rounded sample: each output is a precision-prec Float, and the probability of any output equals the probability that an exponentially-distributed real number rounds to it under rm. The sampler is von Neumann’s rejection algorithm as used by mpfr_erandom, which draws no transcendental function evaluations; the number of random bits consumed is finite with probability 1 but not bounded. Every output is positive: a zero would require underflow, whose probability is on the order of $2^{-2^{30}}$. The result is never exact, so Exact is not a valid rounding mode.

The output length is infinite.

Expected complexity per iteration

$T(n) = O(n)$

$M(n) = O(n)$

where $T$ is time, $M$ is additional memory, and $n$ is prec.

Panics

Panics if prec is zero or if rm is Exact.

Examples

use itertools::Itertools;
use malachite_base::random::EXAMPLE_SEED;
use malachite_base::rounding_modes::RoundingMode::*;
use malachite_float::float::random::exponential_random_floats;
use malachite_float::ComparableFloat;

// The number after the '#' is the precision.
assert_eq!(
    exponential_random_floats(EXAMPLE_SEED, 10, Nearest)
        .take(20)
        .map(|f| ComparableFloat(f).to_string())
        .collect_vec()
        .as_slice(),
    &[
        "0.63184#10",
        "1.4648#10",
        "0.96582#10",
        "2.6836#10",
        "3.6719#10",
        "2.5703#10",
        "0.097046#10",
        "1.6602#10",
        "0.69629#10",
        "0.052429#10",
        "0.58398#10",
        "0.23486#10",
        "0.88965#10",
        "2.1992#10",
        "1.7480#10",
        "0.16748#10",
        "0.35693#10",
        "1.0996#10",
        "0.44238#10",
        "0.51172#10"
    ]
);