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<ol class="chapter"><li class="chapter-item expanded affix "><a href="../../about.html">Rusty Book</a></li><li class="chapter-item expanded affix "><li class="part-title">Awesome</li><li class="spacer"></li><li class="chapter-item expanded "><a href="../../daily-dev.html">日常开发常用库</a></li><li class="chapter-item expanded "><a href="../../superstar.html">Rust 明星项目</a></li><li class="chapter-item expanded "><a href="../../empowering-js.html">使用 Rust 增强 JS</a></li><li class="chapter-item expanded "><a href="../../games.html">Rust开发的游戏</a></li><li class="chapter-item expanded "><a href="../../gamedev.html">游戏引擎</a></li><li class="chapter-item expanded affix "><li class="part-title">Awesome + Cookbook</li><li class="spacer"></li><li class="chapter-item expanded "><a href="../../algos/awesome.html">实用算法</a><a class="toggle"><div>❱</div></a></li><li><ol class="section"><li class="chapter-item expanded "><a href="../../algos/randomness.html">生成随机值</a></li><li class="chapter-item expanded "><a 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<main>
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<h1 id="线性代数"><a class="header" href="#线性代数">线性代数</a></h1>
|
|
|
<h3 id="矩阵相加"><a class="header" href="#矩阵相加">矩阵相加</a></h3>
|
|
|
<p>使用 <a href="https://docs.rs/ndarray/*/ndarray/fn.arr2.html">ndarray::arr2</a> 可以创建二阶矩阵,并计算它们的和。</p>
|
|
|
<pre><pre class="playground"><code class="language-rust editable edition2021">use ndarray::arr2;
|
|
|
|
|
|
fn main() {
|
|
|
let a = arr2(&[[1, 2, 3],
|
|
|
[4, 5, 6]]);
|
|
|
|
|
|
let b = arr2(&[[6, 5, 4],
|
|
|
[3, 2, 1]]);
|
|
|
|
|
|
// 借用 a 和 b,求和后生成新的矩阵 sum
|
|
|
let sum = &a + &b;
|
|
|
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|
println!("{}", a);
|
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|
println!("+");
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|
|
println!("{}", b);
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|
|
println!("=");
|
|
|
println!("{}", sum);
|
|
|
}
|
|
|
</code></pre></pre>
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|
<h3 id="矩阵相乘"><a class="header" href="#矩阵相乘">矩阵相乘</a></h3>
|
|
|
<p><a href="https://docs.rs/ndarray/0.15.4/ndarray/struct.ArrayBase.html#method.dot-1">ndarray::ArrayBase::dot</a> 可以用于计算矩阵乘法。</p>
|
|
|
<pre><pre class="playground"><code class="language-rust editable edition2021">use ndarray::arr2;
|
|
|
|
|
|
fn main() {
|
|
|
let a = arr2(&[[1, 2, 3],
|
|
|
[4, 5, 6]]);
|
|
|
|
|
|
let b = arr2(&[[6, 3],
|
|
|
[5, 2],
|
|
|
[4, 1]]);
|
|
|
|
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|
println!("{}", a.dot(&b));
|
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|
}
|
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|
</code></pre></pre>
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<h3 id="标量向量矩阵相乘"><a class="header" href="#标量向量矩阵相乘">标量、向量、矩阵相乘</a></h3>
|
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|
<p>在 <code>ndarry</code>中,1 阶数组根据上下文既可以作为行向量也可以作为列向量。如果对你来说,这个行或列的方向很重要,可以考虑使用一行或一列的 2 阶数组来表示。</p>
|
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<p>在下面例子中,由于 1 阶数组处于乘号的右边位置,因此 <code>dot</code> 会把它当成列向量来处理。</p>
|
|
|
<pre><pre class="playground"><code class="language-rust editable edition2021">use ndarray::{arr1, arr2, Array1};
|
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|
|
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|
fn main() {
|
|
|
let scalar = 4;
|
|
|
|
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|
let vector = arr1(&[1, 2, 3]);
|
|
|
|
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|
let matrix = arr2(&[[4, 5, 6],
|
|
|
[7, 8, 9]]);
|
|
|
|
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|
let new_vector: Array1<_> = scalar * vector;
|
|
|
println!("{}", new_vector);
|
|
|
|
|
|
let new_matrix = matrix.dot(&new_vector);
|
|
|
println!("{}", new_matrix);
|
|
|
}
|
|
|
</code></pre></pre>
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|
<h3 id="向量比较"><a class="header" href="#向量比较">向量比较</a></h3>
|
|
|
<p>浮点数通常是不精确的,因此比较浮点数不是一件简单的事。<a href="https://docs.rs/approx/*/approx/index.html">approx</a> 提供的 <a href="https://docs.rs/approx/0.5.1/approx/macro.assert_abs_diff_eq.html">assert_abs_diff_eq!</a> 宏提供了方便的按元素比较的方式。为了使用 <code>approx</code> ,你需要在 <code>ndarray</code> 的依赖中开启相应的 feature:例如,在 <code>Cargo.toml</code> 中修改 <code>ndarray</code> 的依赖引入为 <code>ndarray = { version = "0.13", features = ["approx"] }</code>。</p>
|
|
|
<pre><pre class="playground"><code class="language-rust editable edition2021">use approx::assert_abs_diff_eq;
|
|
|
use ndarray::Array;
|
|
|
|
|
|
fn main() {
|
|
|
let a = Array::from(vec![1., 2., 3., 4., 5.]);
|
|
|
let b = Array::from(vec![5., 4., 3., 2., 1.]);
|
|
|
let mut c = Array::from(vec![1., 2., 3., 4., 5.]);
|
|
|
let mut d = Array::from(vec![5., 4., 3., 2., 1.]);
|
|
|
|
|
|
// 消耗 a 和 b 的所有权
|
|
|
let z = a + b;
|
|
|
// 借用 c 和 d
|
|
|
let w = &c + &d;
|
|
|
|
|
|
assert_abs_diff_eq!(z, Array::from(vec![6., 6., 6., 6., 6.]));
|
|
|
|
|
|
println!("c = {}", c);
|
|
|
c[0] = 10.;
|
|
|
d[1] = 10.;
|
|
|
|
|
|
assert_abs_diff_eq!(w, Array::from(vec![6., 6., 6., 6., 6.]));
|
|
|
|
|
|
}
|
|
|
</code></pre></pre>
|
|
|
<h3 id="向量范数-norm-"><a class="header" href="#向量范数-norm-">向量范数( norm )</a></h3>
|
|
|
<p>需要注意的是 <code>Array</code> 和 <code>ArrayView</code> 都是 <code>ArrayBase</code> 的别名。因此一个更通用的参数应该是 <code>&ArrayBase<S, Ix1> where S: Data</code>,特别是在你提供一个公共 API 给其它用户时,但由于咱们是内部使用,因此更精准的 <code>ArrayView1<f64></code> 会更适合。</p>
|
|
|
<pre><pre class="playground"><code class="language-rust editable edition2021">use ndarray::{array, Array1, ArrayView1};
|
|
|
|
|
|
fn l1_norm(x: ArrayView1<f64>) -> f64 {
|
|
|
x.fold(0., |acc, elem| acc + elem.abs())
|
|
|
}
|
|
|
|
|
|
fn l2_norm(x: ArrayView1<f64>) -> f64 {
|
|
|
x.dot(&x).sqrt()
|
|
|
}
|
|
|
|
|
|
fn normalize(mut x: Array1<f64>) -> Array1<f64> {
|
|
|
let norm = l2_norm(x.view());
|
|
|
x.mapv_inplace(|e| e/norm);
|
|
|
x
|
|
|
}
|
|
|
|
|
|
fn main() {
|
|
|
let x = array![1., 2., 3., 4., 5.];
|
|
|
println!("||x||_2 = {}", l2_norm(x.view()));
|
|
|
println!("||x||_1 = {}", l1_norm(x.view()));
|
|
|
println!("Normalizing x yields {:?}", normalize(x));
|
|
|
}
|
|
|
</code></pre></pre>
|
|
|
<h3 id="矩阵的逆变换"><a class="header" href="#矩阵的逆变换">矩阵的逆变换</a></h3>
|
|
|
<p>例子中使用 <a href="https://docs.rs/nalgebra/*/nalgebra/base/type.Matrix3.html">nalgebra::Matrix3</a> 创建一个 3x3 的矩阵,然后尝试对其进行逆变换,获取一个逆矩阵。</p>
|
|
|
<pre><pre class="playground"><code class="language-rust editable edition2021">use nalgebra::Matrix3;
|
|
|
|
|
|
fn main() {
|
|
|
let m1 = Matrix3::new(2.0, 1.0, 1.0, 3.0, 2.0, 1.0, 2.0, 1.0, 2.0);
|
|
|
println!("m1 = {}", m1);
|
|
|
match m1.try_inverse() {
|
|
|
Some(inv) => {
|
|
|
println!("The inverse of m1 is: {}", inv);
|
|
|
}
|
|
|
None => {
|
|
|
println!("m1 is not invertible!");
|
|
|
}
|
|
|
}
|
|
|
}
|
|
|
</code></pre></pre>
|
|
|
<h3 id="序列反序列化一个矩阵"><a class="header" href="#序列反序列化一个矩阵">序列/反序列化一个矩阵</a></h3>
|
|
|
<p>下面将展示如何将矩阵序列化为 JSON ,然后再反序列化为原矩阵。</p>
|
|
|
<pre><pre class="playground"><code class="language-rust editable edition2021">extern crate nalgebra;
|
|
|
extern crate serde_json;
|
|
|
|
|
|
use nalgebra::DMatrix;
|
|
|
|
|
|
fn main() -> Result<(), std::io::Error> {
|
|
|
let row_slice: Vec<i32> = (1..5001).collect();
|
|
|
let matrix = DMatrix::from_row_slice(50, 100, &row_slice);
|
|
|
|
|
|
// 序列化矩阵
|
|
|
let serialized_matrix = serde_json::to_string(&matrix)?;
|
|
|
|
|
|
// 反序列化
|
|
|
let deserialized_matrix: DMatrix<i32> = serde_json::from_str(&serialized_matrix)?;
|
|
|
|
|
|
// 验证反序列化后的矩阵跟原始矩阵相等
|
|
|
assert!(deserialized_matrix == matrix);
|
|
|
|
|
|
Ok(())
|
|
|
}
|
|
|
</code></pre></pre>
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