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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements.  See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership.  The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License.  You may obtain a copy of the License at
//
//   http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied.  See the License for the
// specific language governing permissions and limitations
// under the License..

//! The exponential distribution.

use crate::distributions::{ziggurat, ziggurat_tables, IndependentSample, Sample};
use crate::{Rand, Rng};

/// A wrapper around an `f64` to generate Exp(1) random numbers.
///
/// See `Exp` for the general exponential distribution.
///
/// Implemented via the ZIGNOR variant\[1\] of the Ziggurat method. The
/// exact description in the paper was adjusted to use tables for the
/// exponential distribution rather than normal.
///
/// \[1\]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to
/// Generate Normal Random
/// Samples*](http://www.doornik.com/research/ziggurat.pdf). Nuffield
/// College, Oxford
///
/// # Example
///
/// ```rust
/// use sgx_rand::distributions::exponential::Exp1;
///
/// let Exp1(x) = sgx_rand::random();
/// println!("{}", x);
/// ```
#[derive(Clone, Copy, Debug)]
pub struct Exp1(pub f64);

// This could be done via `-rng.gen::<f64>().ln()` but that is slower.
impl Rand for Exp1 {
    #[inline]
    fn rand<R: Rng>(rng: &mut R) -> Exp1 {
        #[inline]
        fn pdf(x: f64) -> f64 {
            (-x).exp()
        }
        #[inline]
        fn zero_case<R: Rng>(rng: &mut R, _u: f64) -> f64 {
            ziggurat_tables::ZIG_EXP_R - rng.gen::<f64>().ln()
        }

        Exp1(ziggurat(
            rng,
            false,
            &ziggurat_tables::ZIG_EXP_X,
            &ziggurat_tables::ZIG_EXP_F,
            pdf,
            zero_case,
        ))
    }
}

/// The exponential distribution `Exp(lambda)`.
///
/// This distribution has density function: `f(x) = lambda *
/// exp(-lambda * x)` for `x > 0`.
///
/// # Example
///
/// ```rust
/// use sgx_rand::distributions::{Exp, IndependentSample};
///
/// let exp = Exp::new(2.0);
/// let v = exp.ind_sample(&mut sgx_rand::thread_rng());
/// println!("{} is from a Exp(2) distribution", v);
/// ```
#[derive(Clone, Copy, Debug)]
pub struct Exp {
    /// `lambda` stored as `1/lambda`, since this is what we scale by.
    lambda_inverse: f64,
}

impl Exp {
    /// Construct a new `Exp` with the given shape parameter
    /// `lambda`. Panics if `lambda <= 0`.
    #[inline]
    pub fn new(lambda: f64) -> Exp {
        assert!(lambda > 0.0, "Exp::new called with `lambda` <= 0");
        Exp {
            lambda_inverse: 1.0 / lambda,
        }
    }
}

impl Sample<f64> for Exp {
    fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 {
        self.ind_sample(rng)
    }
}
impl IndependentSample<f64> for Exp {
    fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
        let Exp1(n) = rng.gen::<Exp1>();
        n * self.lambda_inverse
    }
}