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