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feat: refactoring, adds Result to most public API

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This commit is contained in:
Volodymyr Orlov
2020-09-18 15:20:32 -07:00
parent 4921ae76f5
commit a9db970195
24 changed files with 389 additions and 298 deletions
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src/linalg/evd.rs
+8 -7
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@@ -21,7 +21,7 @@
//! &[0.7000, 0.3000, 0.8000],
//! ]);
//!
//! let evd = A.evd(true);
//! let evd = A.evd(true).unwrap();
//! let eigenvectors: DenseMatrix<f64> = evd.V;
//! let eigenvalues: Vec<f64> = evd.d;
//! ```
@@ -34,6 +34,7 @@
//! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
#![allow(non_snake_case)]
use crate::error::Failed;
use crate::linalg::BaseMatrix;
use crate::math::num::RealNumber;
use num::complex::Complex;
@@ -54,14 +55,14 @@ pub struct EVD<T: RealNumber, M: BaseMatrix<T>> {
pub trait EVDDecomposableMatrix<T: RealNumber>: BaseMatrix<T> {
/// Compute the eigen decomposition of a square matrix.
/// * `symmetric` - whether the matrix is symmetric
fn evd(&self, symmetric: bool) -> EVD<T, Self> {
fn evd(&self, symmetric: bool) -> Result<EVD<T, Self>, Failed> {
self.clone().evd_mut(symmetric)
}
/// Compute the eigen decomposition of a square matrix. The input matrix
/// will be used for factorization.
/// * `symmetric` - whether the matrix is symmetric
fn evd_mut(mut self, symmetric: bool) -> EVD<T, Self> {
fn evd_mut(mut self, symmetric: bool) -> Result<EVD<T, Self>, Failed> {
let (nrows, ncols) = self.shape();
if ncols != nrows {
panic!("Matrix is not square: {} x {}", nrows, ncols);
@@ -92,7 +93,7 @@ pub trait EVDDecomposableMatrix<T: RealNumber>: BaseMatrix<T> {
sort(&mut d, &mut e, &mut V);
}
EVD { V: V, d: d, e: e }
Ok(EVD { V: V, d: d, e: e })
}
}
@@ -845,7 +846,7 @@ mod tests {
&[0.6240573, -0.44947578, -0.6391588],
]);
let evd = A.evd(true);
let evd = A.evd(true).unwrap();
assert!(eigen_vectors.abs().approximate_eq(&evd.V.abs(), 1e-4));
for i in 0..eigen_values.len() {
@@ -872,7 +873,7 @@ mod tests {
&[0.6952105, 0.43984484, -0.7036135],
]);
let evd = A.evd(false);
let evd = A.evd(false).unwrap();
assert!(eigen_vectors.abs().approximate_eq(&evd.V.abs(), 1e-4));
for i in 0..eigen_values.len() {
@@ -902,7 +903,7 @@ mod tests {
&[0.6707, 0.1059, 0.901, -0.6289],
]);
let evd = A.evd(false);
let evd = A.evd(false).unwrap();
assert!(eigen_vectors.abs().approximate_eq(&evd.V.abs(), 1e-4));
for i in 0..eigen_values_d.len() {