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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
// &quot;License&quot;); 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
// &quot;AS IS&quot; 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="doccomment">//! The exponential distribution.
</span><span class="kw">use </span><span class="kw">crate</span>::distributions::{ziggurat, ziggurat_tables, IndependentSample, Sample};
<span class="kw">use crate</span>::{Rand, Rng};
<span class="doccomment">/// 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!(&quot;{}&quot;, x);
/// ```
</span><span class="attribute">#[derive(Clone, Copy, Debug)]
</span><span class="kw">pub struct </span>Exp1(<span class="kw">pub </span>f64);
<span class="comment">// This could be done via `-rng.gen::&lt;f64&gt;().ln()` but that is slower.
</span><span class="kw">impl </span>Rand <span class="kw">for </span>Exp1 {
<span class="attribute">#[inline]
</span><span class="kw">fn </span>rand&lt;R: Rng&gt;(rng: <span class="kw-2">&amp;mut </span>R) -&gt; Exp1 {
<span class="attribute">#[inline]
</span><span class="kw">fn </span>pdf(x: f64) -&gt; f64 {
(-x).exp()
}
<span class="attribute">#[inline]
</span><span class="kw">fn </span>zero_case&lt;R: Rng&gt;(rng: <span class="kw-2">&amp;mut </span>R, _u: f64) -&gt; f64 {
ziggurat_tables::ZIG_EXP_R - rng.gen::&lt;f64&gt;().ln()
}
Exp1(ziggurat(
rng,
<span class="bool-val">false</span>,
<span class="kw-2">&amp;</span>ziggurat_tables::ZIG_EXP_X,
<span class="kw-2">&amp;</span>ziggurat_tables::ZIG_EXP_F,
pdf,
zero_case,
))
}
}
<span class="doccomment">/// The exponential distribution `Exp(lambda)`.
///
/// This distribution has density function: `f(x) = lambda *
/// exp(-lambda * x)` for `x &gt; 0`.
///
/// # Example
///
/// ```rust
/// use sgx_rand::distributions::{Exp, IndependentSample};
///
/// let exp = Exp::new(2.0);
/// let v = exp.ind_sample(&amp;mut sgx_rand::thread_rng());
/// println!(&quot;{} is from a Exp(2) distribution&quot;, v);
/// ```
</span><span class="attribute">#[derive(Clone, Copy, Debug)]
</span><span class="kw">pub struct </span>Exp {
<span class="doccomment">/// `lambda` stored as `1/lambda`, since this is what we scale by.
</span>lambda_inverse: f64,
}
<span class="kw">impl </span>Exp {
<span class="doccomment">/// Construct a new `Exp` with the given shape parameter
/// `lambda`. Panics if `lambda &lt;= 0`.
</span><span class="attribute">#[inline]
</span><span class="kw">pub fn </span>new(lambda: f64) -&gt; Exp {
<span class="macro">assert!</span>(lambda &gt; <span class="number">0.0</span>, <span class="string">&quot;Exp::new called with `lambda` &lt;= 0&quot;</span>);
Exp {
lambda_inverse: <span class="number">1.0 </span>/ lambda,
}
}
}
<span class="kw">impl </span>Sample&lt;f64&gt; <span class="kw">for </span>Exp {
<span class="kw">fn </span>sample&lt;R: Rng&gt;(<span class="kw-2">&amp;mut </span><span class="self">self</span>, rng: <span class="kw-2">&amp;mut </span>R) -&gt; f64 {
<span class="self">self</span>.ind_sample(rng)
}
}
<span class="kw">impl </span>IndependentSample&lt;f64&gt; <span class="kw">for </span>Exp {
<span class="kw">fn </span>ind_sample&lt;R: Rng&gt;(<span class="kw-2">&amp;</span><span class="self">self</span>, rng: <span class="kw-2">&amp;mut </span>R) -&gt; f64 {
<span class="kw">let </span>Exp1(n) = rng.gen::&lt;Exp1&gt;();
n * <span class="self">self</span>.lambda_inverse
}
}
</code></pre></div>
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