<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Source of the Rust file `/Users/erlendbasso/.cargo/registry/src/github.com-1ecc6299db9ec823/nalgebra-0.32.1/src/linalg/convolution.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>convolution.rs - source</title><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../static.files/SourceSerif4-Regular-1f7d512b176f0f72.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../static.files/FiraSans-Regular-018c141bf0843ffd.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../static.files/FiraSans-Medium-8f9a781e4970d388.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../static.files/SourceCodePro-Regular-562dcc5011b6de7d.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../static.files/SourceSerif4-Bold-124a1ca42af929b6.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../static.files/SourceCodePro-Semibold-d899c5a5c4aeb14a.ttf.woff2"><link rel="stylesheet" href="../../../static.files/normalize-76eba96aa4d2e634.css"><link rel="stylesheet" href="../../../static.files/rustdoc-6827029ac823cab7.css" id="mainThemeStyle"><link rel="stylesheet" id="themeStyle" href="../../../static.files/light-ebce58d0a40c3431.css"><link rel="stylesheet" disabled href="../../../static.files/dark-f23faae4a2daf9a6.css"><link rel="stylesheet" disabled href="../../../static.files/ayu-8af5e100b21cd173.css"><script id="default-settings" ></script><script src="../../../static.files/storage-d43fa987303ecbbb.js"></script><script defer src="../../../static.files/source-script-5cf2e01a42cc9858.js"></script><script defer src="../../../source-files.js"></script><script defer src="../../../static.files/main-c55e1eb52e1886b4.js"></script><noscript><link rel="stylesheet" href="../../../static.files/noscript-13285aec31fa243e.css"></noscript><link rel="icon" href="https://nalgebra.org/img/favicon.ico"></head><body class="rustdoc source"><!--[if lte IE 11]><div class="warning">This old browser is unsupported and will most likely display funky things.</div><![endif]--><nav class="sidebar"></nav><main><div class="width-limiter"><nav class="sub"><a class="sub-logo-container" href="../../../nalgebra/index.html"><img class="rust-logo" src="../../../static.files/rust-logo-151179464ae7ed46.svg" alt="logo"></a><form class="search-form"><span></span><input class="search-input" name="search" aria-label="Run search in the documentation" autocomplete="off" spellcheck="false" placeholder="Click or press ‘S’ to search, ‘?’ for more options…" type="search"><div id="help-button" title="help" tabindex="-1"><a href="../../../help.html">?</a></div><div id="settings-menu" tabindex="-1"><a href="../../../settings.html" title="settings"><img width="22" height="22" alt="Change settings" src="../../../static.files/wheel-5ec35bf9ca753509.svg"></a></div></form></nav><section id="main-content" class="content"><div class="example-wrap"><pre class="src-line-numbers"><a href="#1" id="1">1</a>
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</pre><pre class="rust"><code><span class="kw">use </span>std::cmp;
<span class="kw">use </span><span class="kw">crate</span>::base::allocator::Allocator;
<span class="kw">use </span><span class="kw">crate</span>::base::default_allocator::DefaultAllocator;
<span class="kw">use </span><span class="kw">crate</span>::base::dimension::{Const, Dim, DimAdd, DimDiff, DimSub, DimSum};
<span class="kw">use </span><span class="kw">crate</span>::storage::Storage;
<span class="kw">use crate</span>::{zero, OVector, RealField, Vector, U1};
<span class="kw">impl</span><T: RealField, D1: Dim, S1: Storage<T, D1>> Vector<T, D1, S1> {
<span class="doccomment">/// Returns the convolution of the target vector and a kernel.
///
/// # Arguments
///
/// * `kernel` - A Vector with size > 0
///
/// # Errors
/// Inputs must satisfy `vector.len() >= kernel.len() > 0`.
///
</span><span class="kw">pub fn </span>convolve_full<D2, S2>(
<span class="kw-2">&</span><span class="self">self</span>,
kernel: Vector<T, D2, S2>,
) -> OVector<T, DimDiff<DimSum<D1, D2>, U1>>
<span class="kw">where
</span>D1: DimAdd<D2>,
D2: DimAdd<D1, Output = DimSum<D1, D2>>,
DimSum<D1, D2>: DimSub<U1>,
S2: Storage<T, D2>,
DefaultAllocator: Allocator<T, DimDiff<DimSum<D1, D2>, U1>>,
{
<span class="kw">let </span>vec = <span class="self">self</span>.len();
<span class="kw">let </span>ker = kernel.len();
<span class="kw">if </span>ker == <span class="number">0 </span>|| ker > vec {
<span class="macro">panic!</span>(<span class="string">"convolve_full expects `self.len() >= kernel.len() > 0`, received {} and {} respectively."</span>, vec, ker);
}
<span class="kw">let </span>result_len = <span class="self">self
</span>.data
.shape()
.<span class="number">0
</span>.add(kernel.shape_generic().<span class="number">0</span>)
.sub(Const::<<span class="number">1</span>>);
<span class="kw">let </span><span class="kw-2">mut </span>conv = OVector::zeros_generic(result_len, Const::<<span class="number">1</span>>);
<span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..(vec + ker - <span class="number">1</span>) {
<span class="kw">let </span>u_i = <span class="kw">if </span>i > vec { i - ker } <span class="kw">else </span>{ <span class="number">0 </span>};
<span class="kw">let </span>u_f = cmp::min(i, vec - <span class="number">1</span>);
<span class="kw">if </span>u_i == u_f {
conv[i] += <span class="self">self</span>[u_i].clone() * kernel[(i - u_i)].clone();
} <span class="kw">else </span>{
<span class="kw">for </span>u <span class="kw">in </span>u_i..(u_f + <span class="number">1</span>) {
<span class="kw">if </span>i - u < ker {
conv[i] += <span class="self">self</span>[u].clone() * kernel[(i - u)].clone();
}
}
}
}
conv
}
<span class="doccomment">/// Returns the convolution of the target vector and a kernel.
///
/// The output convolution consists only of those elements that do not rely on the zero-padding.
/// # Arguments
///
/// * `kernel` - A Vector with size > 0
///
///
/// # Errors
/// Inputs must satisfy `self.len() >= kernel.len() > 0`.
///
</span><span class="kw">pub fn </span>convolve_valid<D2, S2>(
<span class="kw-2">&</span><span class="self">self</span>,
kernel: Vector<T, D2, S2>,
) -> OVector<T, DimDiff<DimSum<D1, U1>, D2>>
<span class="kw">where
</span>D1: DimAdd<U1>,
D2: Dim,
DimSum<D1, U1>: DimSub<D2>,
S2: Storage<T, D2>,
DefaultAllocator: Allocator<T, DimDiff<DimSum<D1, U1>, D2>>,
{
<span class="kw">let </span>vec = <span class="self">self</span>.len();
<span class="kw">let </span>ker = kernel.len();
<span class="kw">if </span>ker == <span class="number">0 </span>|| ker > vec {
<span class="macro">panic!</span>(<span class="string">"convolve_valid expects `self.len() >= kernel.len() > 0`, received {} and {} respectively."</span>,vec,ker);
}
<span class="kw">let </span>result_len = <span class="self">self
</span>.data
.shape()
.<span class="number">0
</span>.add(Const::<<span class="number">1</span>>)
.sub(kernel.shape_generic().<span class="number">0</span>);
<span class="kw">let </span><span class="kw-2">mut </span>conv = OVector::zeros_generic(result_len, Const::<<span class="number">1</span>>);
<span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..(vec - ker + <span class="number">1</span>) {
<span class="kw">for </span>j <span class="kw">in </span><span class="number">0</span>..ker {
conv[i] += <span class="self">self</span>[i + j].clone() * kernel[ker - j - <span class="number">1</span>].clone();
}
}
conv
}
<span class="doccomment">/// Returns the convolution of the target vector and a kernel.
///
/// The output convolution is the same size as vector, centered with respect to the ‘full’ output.
/// # Arguments
///
/// * `kernel` - A Vector with size > 0
///
/// # Errors
/// Inputs must satisfy `self.len() >= kernel.len() > 0`.
</span><span class="attr">#[must_use]
</span><span class="kw">pub fn </span>convolve_same<D2, S2>(<span class="kw-2">&</span><span class="self">self</span>, kernel: Vector<T, D2, S2>) -> OVector<T, D1>
<span class="kw">where
</span>D2: Dim,
S2: Storage<T, D2>,
DefaultAllocator: Allocator<T, D1>,
{
<span class="kw">let </span>vec = <span class="self">self</span>.len();
<span class="kw">let </span>ker = kernel.len();
<span class="kw">if </span>ker == <span class="number">0 </span>|| ker > vec {
<span class="macro">panic!</span>(<span class="string">"convolve_same expects `self.len() >= kernel.len() > 0`, received {} and {} respectively."</span>,vec,ker);
}
<span class="kw">let </span><span class="kw-2">mut </span>conv = OVector::zeros_generic(<span class="self">self</span>.shape_generic().<span class="number">0</span>, Const::<<span class="number">1</span>>);
<span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..vec {
<span class="kw">for </span>j <span class="kw">in </span><span class="number">0</span>..ker {
<span class="kw">let </span>val = <span class="kw">if </span>i + j < <span class="number">1 </span>|| i + j >= vec + <span class="number">1 </span>{
zero::<T>()
} <span class="kw">else </span>{
<span class="self">self</span>[i + j - <span class="number">1</span>].clone()
};
conv[i] += val * kernel[ker - j - <span class="number">1</span>].clone();
}
}
conv
}
}
</code></pre></div>
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