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| </pre><pre class="rust"><code><span class="comment">// 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. |
| |
| </span><span class="kw">use </span><span class="kw">crate</span>::distributions::{ziggurat, ziggurat_tables, IndependentSample, Sample}; |
| <span class="doccomment">/// The normal and derived distributions. |
| </span><span class="kw">use crate</span>::{Open01, Rand, Rng}; |
| |
| <span class="doccomment">/// A wrapper around an `f64` to generate N(0, 1) random numbers |
| /// (a.k.a. a standard normal, or Gaussian). |
| /// |
| /// See `Normal` for the general normal distribution. |
| /// |
| /// Implemented via the ZIGNOR variant\[1\] of the Ziggurat method. |
| /// |
| /// \[1\]: 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::normal::StandardNormal; |
| /// |
| /// let StandardNormal(x) = sgx_rand::random(); |
| /// println!("{}", x); |
| /// ``` |
| </span><span class="attribute">#[derive(Clone, Copy, Debug)] |
| </span><span class="kw">pub struct </span>StandardNormal(<span class="kw">pub </span>f64); |
| |
| <span class="kw">impl </span>Rand <span class="kw">for </span>StandardNormal { |
| <span class="kw">fn </span>rand<R: Rng>(rng: <span class="kw-2">&mut </span>R) -> StandardNormal { |
| <span class="attribute">#[inline] |
| </span><span class="kw">fn </span>pdf(x: f64) -> f64 { |
| (-x * x / <span class="number">2.0</span>).exp() |
| } |
| <span class="attribute">#[inline] |
| </span><span class="kw">fn </span>zero_case<R: Rng>(rng: <span class="kw-2">&mut </span>R, u: f64) -> f64 { |
| <span class="comment">// compute a random number in the tail by hand |
| |
| // strange initial conditions, because the loop is not |
| // do-while, so the condition should be true on the first |
| // run, they get overwritten anyway (0 < 1, so these are |
| // good). |
| </span><span class="kw">let </span><span class="kw-2">mut </span>x = <span class="number">1.0f64</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>y = <span class="number">0.0f64</span>; |
| |
| <span class="kw">while </span>-<span class="number">2.0 </span>* y < x * x { |
| <span class="kw">let </span>Open01(x_) = rng.gen::<Open01<f64>>(); |
| <span class="kw">let </span>Open01(y_) = rng.gen::<Open01<f64>>(); |
| |
| x = x_.ln() / ziggurat_tables::ZIG_NORM_R; |
| y = y_.ln(); |
| } |
| |
| <span class="kw">if </span>u < <span class="number">0.0 </span>{ |
| x - ziggurat_tables::ZIG_NORM_R |
| } <span class="kw">else </span>{ |
| ziggurat_tables::ZIG_NORM_R - x |
| } |
| } |
| |
| StandardNormal(ziggurat( |
| rng, |
| <span class="bool-val">true</span>, <span class="comment">// this is symmetric |
| </span><span class="kw-2">&</span>ziggurat_tables::ZIG_NORM_X, |
| <span class="kw-2">&</span>ziggurat_tables::ZIG_NORM_F, |
| pdf, |
| zero_case, |
| )) |
| } |
| } |
| |
| <span class="doccomment">/// The normal distribution `N(mean, std_dev**2)`. |
| /// |
| /// This uses the ZIGNOR variant of the Ziggurat method, see |
| /// `StandardNormal` for more details. |
| /// |
| /// # Example |
| /// |
| /// ```rust |
| /// use sgx_rand::distributions::{Normal, IndependentSample}; |
| /// |
| /// // mean 2, standard deviation 3 |
| /// let normal = Normal::new(2.0, 3.0); |
| /// let v = normal.ind_sample(&mut sgx_rand::thread_rng()); |
| /// println!("{} is from a N(2, 9) distribution", v) |
| /// ``` |
| </span><span class="attribute">#[derive(Clone, Copy, Debug)] |
| </span><span class="kw">pub struct </span>Normal { |
| mean: f64, |
| std_dev: f64, |
| } |
| |
| <span class="kw">impl </span>Normal { |
| <span class="doccomment">/// Construct a new `Normal` distribution with the given mean and |
| /// standard deviation. |
| /// |
| /// # Panics |
| /// |
| /// Panics if `std_dev < 0`. |
| </span><span class="attribute">#[inline] |
| </span><span class="kw">pub fn </span>new(mean: f64, std_dev: f64) -> Normal { |
| <span class="macro">assert!</span>(std_dev >= <span class="number">0.0</span>, <span class="string">"Normal::new called with `std_dev` < 0"</span>); |
| Normal { mean, std_dev } |
| } |
| } |
| <span class="kw">impl </span>Sample<f64> <span class="kw">for </span>Normal { |
| <span class="kw">fn </span>sample<R: Rng>(<span class="kw-2">&mut </span><span class="self">self</span>, rng: <span class="kw-2">&mut </span>R) -> f64 { |
| <span class="self">self</span>.ind_sample(rng) |
| } |
| } |
| <span class="kw">impl </span>IndependentSample<f64> <span class="kw">for </span>Normal { |
| <span class="kw">fn </span>ind_sample<R: Rng>(<span class="kw-2">&</span><span class="self">self</span>, rng: <span class="kw-2">&mut </span>R) -> f64 { |
| <span class="kw">let </span>StandardNormal(n) = rng.gen::<StandardNormal>(); |
| <span class="self">self</span>.mean + <span class="self">self</span>.std_dev * n |
| } |
| } |
| |
| <span class="doccomment">/// The log-normal distribution `ln N(mean, std_dev**2)`. |
| /// |
| /// If `X` is log-normal distributed, then `ln(X)` is `N(mean, |
| /// std_dev**2)` distributed. |
| /// |
| /// # Example |
| /// |
| /// ```rust |
| /// use sgx_rand::distributions::{LogNormal, IndependentSample}; |
| /// |
| /// // mean 2, standard deviation 3 |
| /// let log_normal = LogNormal::new(2.0, 3.0); |
| /// let v = log_normal.ind_sample(&mut sgx_rand::thread_rng()); |
| /// println!("{} is from an ln N(2, 9) distribution", v) |
| /// ``` |
| </span><span class="attribute">#[derive(Clone, Copy, Debug)] |
| </span><span class="kw">pub struct </span>LogNormal { |
| norm: Normal, |
| } |
| |
| <span class="kw">impl </span>LogNormal { |
| <span class="doccomment">/// Construct a new `LogNormal` distribution with the given mean |
| /// and standard deviation. |
| /// |
| /// # Panics |
| /// |
| /// Panics if `std_dev < 0`. |
| </span><span class="attribute">#[inline] |
| </span><span class="kw">pub fn </span>new(mean: f64, std_dev: f64) -> LogNormal { |
| <span class="macro">assert!</span>(std_dev >= <span class="number">0.0</span>, <span class="string">"LogNormal::new called with `std_dev` < 0"</span>); |
| LogNormal { |
| norm: Normal::new(mean, std_dev), |
| } |
| } |
| } |
| <span class="kw">impl </span>Sample<f64> <span class="kw">for </span>LogNormal { |
| <span class="kw">fn </span>sample<R: Rng>(<span class="kw-2">&mut </span><span class="self">self</span>, rng: <span class="kw-2">&mut </span>R) -> f64 { |
| <span class="self">self</span>.ind_sample(rng) |
| } |
| } |
| <span class="kw">impl </span>IndependentSample<f64> <span class="kw">for </span>LogNormal { |
| <span class="kw">fn </span>ind_sample<R: Rng>(<span class="kw-2">&</span><span class="self">self</span>, rng: <span class="kw-2">&mut </span>R) -> f64 { |
| <span class="self">self</span>.norm.ind_sample(rng).exp() |
| } |
| } |
| </code></pre></div> |
| </section></div></main><div id="rustdoc-vars" data-root-path="../../../" data-current-crate="sgx_rand" data-themes="ayu,dark,light" data-resource-suffix="" data-rustdoc-version="1.66.0-nightly (5c8bff74b 2022-10-21)" ></div></body></html> |
