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| </pre><pre class="rust"><code><span class="kw">use crate</span>::{Distribution, InverseGaussian, Standard, StandardNormal}; |
| <span class="kw">use </span>num_traits::Float; |
| <span class="kw">use </span>rand::Rng; |
| <span class="kw">use </span>core::fmt; |
| |
| <span class="doccomment">/// Error type returned from `NormalInverseGaussian::new` |
| </span><span class="attribute">#[derive(Debug, Clone, Copy, PartialEq, Eq)] |
| </span><span class="kw">pub enum </span>Error { |
| <span class="doccomment">/// `alpha <= 0` or `nan`. |
| </span>AlphaNegativeOrNull, |
| <span class="doccomment">/// `|beta| >= alpha` or `nan`. |
| </span>AbsoluteBetaNotLessThanAlpha, |
| } |
| |
| <span class="kw">impl </span>fmt::Display <span class="kw">for </span>Error { |
| <span class="kw">fn </span>fmt(<span class="kw-2">&</span><span class="self">self</span>, f: <span class="kw-2">&mut </span>fmt::Formatter<<span class="lifetime">'_</span>>) -> fmt::Result { |
| f.write_str(<span class="kw">match </span><span class="self">self </span>{ |
| Error::AlphaNegativeOrNull => <span class="string">"alpha <= 0 or is NaN in normal inverse Gaussian distribution"</span>, |
| Error::AbsoluteBetaNotLessThanAlpha => <span class="string">"|beta| >= alpha or is NaN in normal inverse Gaussian distribution"</span>, |
| }) |
| } |
| } |
| |
| <span class="attribute">#[cfg(feature = <span class="string">"std"</span>)] |
| #[cfg_attr(doc_cfg, doc(cfg(feature = <span class="string">"std"</span>)))] |
| </span><span class="kw">impl </span>std::error::Error <span class="kw">for </span>Error {} |
| |
| <span class="doccomment">/// The [normal-inverse Gaussian distribution](https://en.wikipedia.org/wiki/Normal-inverse_Gaussian_distribution) |
| </span><span class="attribute">#[derive(Debug, Clone, Copy)] |
| #[cfg_attr(feature = <span class="string">"serde1"</span>, derive(serde::Serialize, serde::Deserialize))] |
| </span><span class="kw">pub struct </span>NormalInverseGaussian<F> |
| <span class="kw">where |
| </span>F: Float, |
| StandardNormal: Distribution<F>, |
| Standard: Distribution<F>, |
| { |
| alpha: F, |
| beta: F, |
| inverse_gaussian: InverseGaussian<F>, |
| } |
| |
| <span class="kw">impl</span><F> NormalInverseGaussian<F> |
| <span class="kw">where |
| </span>F: Float, |
| StandardNormal: Distribution<F>, |
| Standard: Distribution<F>, |
| { |
| <span class="doccomment">/// Construct a new `NormalInverseGaussian` distribution with the given alpha (tail heaviness) and |
| /// beta (asymmetry) parameters. |
| </span><span class="kw">pub fn </span>new(alpha: F, beta: F) -> <span class="prelude-ty">Result</span><NormalInverseGaussian<F>, Error> { |
| <span class="kw">if </span>!(alpha > F::zero()) { |
| <span class="kw">return </span><span class="prelude-val">Err</span>(Error::AlphaNegativeOrNull); |
| } |
| |
| <span class="kw">if </span>!(beta.abs() < alpha) { |
| <span class="kw">return </span><span class="prelude-val">Err</span>(Error::AbsoluteBetaNotLessThanAlpha); |
| } |
| |
| <span class="kw">let </span>gamma = (alpha * alpha - beta * beta).sqrt(); |
| |
| <span class="kw">let </span>mu = F::one() / gamma; |
| |
| <span class="kw">let </span>inverse_gaussian = InverseGaussian::new(mu, F::one()).unwrap(); |
| |
| <span class="prelude-val">Ok</span>(<span class="self">Self </span>{ |
| alpha, |
| beta, |
| inverse_gaussian, |
| }) |
| } |
| } |
| |
| <span class="kw">impl</span><F> Distribution<F> <span class="kw">for </span>NormalInverseGaussian<F> |
| <span class="kw">where |
| </span>F: Float, |
| StandardNormal: Distribution<F>, |
| Standard: Distribution<F>, |
| { |
| <span class="kw">fn </span>sample<R>(<span class="kw-2">&</span><span class="self">self</span>, rng: <span class="kw-2">&mut </span>R) -> F |
| <span class="kw">where </span>R: Rng + <span class="question-mark">?</span>Sized { |
| <span class="kw">let </span>inv_gauss = rng.sample(<span class="kw-2">&</span><span class="self">self</span>.inverse_gaussian); |
| |
| <span class="self">self</span>.beta * inv_gauss + inv_gauss.sqrt() * rng.sample(StandardNormal) |
| } |
| } |
| |
| <span class="attribute">#[cfg(test)] |
| </span><span class="kw">mod </span>tests { |
| <span class="kw">use super</span>::<span class="kw-2">*</span>; |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_normal_inverse_gaussian() { |
| <span class="kw">let </span>norm_inv_gauss = NormalInverseGaussian::new(<span class="number">2.0</span>, <span class="number">1.0</span>).unwrap(); |
| <span class="kw">let </span><span class="kw-2">mut </span>rng = <span class="kw">crate</span>::test::rng(<span class="number">210</span>); |
| <span class="kw">for _ in </span><span class="number">0</span>..<span class="number">1000 </span>{ |
| norm_inv_gauss.sample(<span class="kw-2">&mut </span>rng); |
| } |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_normal_inverse_gaussian_invalid_param() { |
| <span class="macro">assert!</span>(NormalInverseGaussian::new(-<span class="number">1.0</span>, <span class="number">1.0</span>).is_err()); |
| <span class="macro">assert!</span>(NormalInverseGaussian::new(-<span class="number">1.0</span>, -<span class="number">1.0</span>).is_err()); |
| <span class="macro">assert!</span>(NormalInverseGaussian::new(<span class="number">1.0</span>, <span class="number">2.0</span>).is_err()); |
| <span class="macro">assert!</span>(NormalInverseGaussian::new(<span class="number">2.0</span>, <span class="number">1.0</span>).is_ok()); |
| } |
| } |
| </code></pre></div> |
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