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fix: formatting

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This commit is contained in:
Volodymyr Orlov
2020-11-03 15:49:04 -08:00
parent 6473a6c4ae
commit b8fea67fd2
5 changed files with 50 additions and 50 deletions
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src/linalg/cholesky.rs
+45 -45
View File
@@ -1,9 +1,9 @@
//! # Cholesky Decomposition
//!
//! every positive definite matrix \\(A \in R^{n \times n}\\) can be factored as
//! every positive definite matrix \\(A \in R^{n \times n}\\) can be factored as
//!
//! \\[A = R^TR\\]
//!
//!
//! where \\(R\\) is upper triangular matrix with positive diagonal elements
//!
//! Example:
@@ -12,8 +12,8 @@
//! use crate::smartcore::linalg::cholesky::*;
//!
//! let A = DenseMatrix::from_2d_array(&[
//! &[25., 15., -5.],
//! &[15., 18., 0.],
//! &[25., 15., -5.],
//! &[15., 18., 0.],
//! &[-5., 0., 11.]
//! ]);
//!
@@ -41,14 +41,14 @@ use crate::math::num::RealNumber;
/// Results of Cholesky decomposition.
pub struct Cholesky<T: RealNumber, M: BaseMatrix<T>> {
R: M,
t: PhantomData<T>
t: PhantomData<T>,
}
impl<T: RealNumber, M: BaseMatrix<T>> Cholesky<T, M> {
pub(crate) fn new(R: M) -> Cholesky<T, M> {
Cholesky {
R: R,
t: PhantomData
t: PhantomData,
}
}
@@ -65,10 +65,10 @@ impl<T: RealNumber, M: BaseMatrix<T>> Cholesky<T, M> {
}
}
R
}
}
/// Get upper triangular matrix.
pub fn U(&self) -> M {
pub fn U(&self) -> M {
let (n, _) = self.R.shape();
let mut R = M::zeros(n, n);
@@ -80,20 +80,20 @@ impl<T: RealNumber, M: BaseMatrix<T>> Cholesky<T, M> {
}
}
R
}
}
/// Solves Ax = b
pub(crate) fn solve(&self, mut b: M) -> Result<M, Failed> {
pub(crate) fn solve(&self, mut b: M) -> Result<M, Failed> {
let (bn, m) = b.shape();
let (rn, _) = self.R.shape();
if bn != rn {
return Err(Failed::because(FailedError::SolutionFailed, &format!(
"Can't solve Ax = b for x. Number of rows in b != number of rows in R."
)));
return Err(Failed::because(
FailedError::SolutionFailed,
&format!("Can't solve Ax = b for x. Number of rows in b != number of rows in R."),
));
}
for k in 0..bn {
for j in 0..m {
for i in 0..k {
@@ -102,7 +102,7 @@ impl<T: RealNumber, M: BaseMatrix<T>> Cholesky<T, M> {
b.div_element_mut(k, j, self.R.get(k, k));
}
}
for k in (0..bn).rev() {
for j in 0..m {
for i in k + 1..bn {
@@ -128,11 +128,12 @@ pub trait CholeskyDecomposableMatrix<T: RealNumber>: BaseMatrix<T> {
let (m, n) = self.shape();
if m != n {
return Err(Failed::because(FailedError::DecompositionFailed, &format!(
"Can't do Cholesky decomposition on a non-square matrix"
)));
return Err(Failed::because(
FailedError::DecompositionFailed,
&format!("Can't do Cholesky decomposition on a non-square matrix"),
));
}
for j in 0..n {
let mut d = T::zero();
for k in 0..j {
@@ -147,9 +148,10 @@ pub trait CholeskyDecomposableMatrix<T: RealNumber>: BaseMatrix<T> {
d = self.get(j, j) - d;
if d < T::zero() {
return Err(Failed::because(FailedError::DecompositionFailed, &format!(
"The matrix is not positive definite."
)));
return Err(Failed::because(
FailedError::DecompositionFailed,
&format!("The matrix is not positive definite."),
));
}
self.set(j, j, d.sqrt());
@@ -172,35 +174,33 @@ mod tests {
#[test]
fn cholesky_decompose() {
let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]]);
let l = DenseMatrix::from_2d_array(&[
&[5.0, 0.0, 0.0],
&[3.0, 3.0, 0.0],
&[-1.0, 1.0, 3.0],
]);
let u = DenseMatrix::from_2d_array(&[
&[5.0, 3.0, -1.0],
&[0.0, 3.0, 1.0],
&[0.0, 0.0, 3.0],
]);
let l =
DenseMatrix::from_2d_array(&[&[5.0, 0.0, 0.0], &[3.0, 3.0, 0.0], &[-1.0, 1.0, 3.0]]);
let u =
DenseMatrix::from_2d_array(&[&[5.0, 3.0, -1.0], &[0.0, 3.0, 1.0], &[0.0, 0.0, 3.0]]);
let cholesky = a.cholesky().unwrap();
assert!(cholesky.L().abs().approximate_eq(&l.abs(), 1e-4));
assert!(cholesky.U().abs().approximate_eq(&u.abs(), 1e-4));
assert!(cholesky.L().matmul(&cholesky.U()).abs().approximate_eq(&a.abs(), 1e-4));
assert!(cholesky.L().abs().approximate_eq(&l.abs(), 1e-4));
assert!(cholesky.U().abs().approximate_eq(&u.abs(), 1e-4));
assert!(cholesky
.L()
.matmul(&cholesky.U())
.abs()
.approximate_eq(&a.abs(), 1e-4));
}
#[test]
fn cholesky_solve_mut() {
let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]]);
let b = DenseMatrix::from_2d_array(&[&[40., 51., 28.]]);
let expected = DenseMatrix::from_2d_array(&[
&[1.0, 2.0, 3.0]
]);
let expected = DenseMatrix::from_2d_array(&[&[1.0, 2.0, 3.0]]);
let cholesky = a.cholesky().unwrap();
assert!(cholesky.solve(b.transpose()).unwrap().transpose().approximate_eq(&expected, 1e-4));
assert!(cholesky
.solve(b.transpose())
.unwrap()
.transpose()
.approximate_eq(&expected, 1e-4));
}
}
src/linalg/mod.rs
+2 -2
View File
@@ -33,6 +33,7 @@
//! let u: DenseMatrix<f64> = svd.U;
//! ```
pub mod cholesky;
/// The matrix is represented in terms of its eigenvalues and eigenvectors.
pub mod evd;
/// Factors a matrix as the product of a lower triangular matrix and an upper triangular matrix.
@@ -49,18 +50,17 @@ pub mod ndarray_bindings;
pub mod qr;
/// Singular value decomposition.
pub mod svd;
pub mod cholesky;
use std::fmt::{Debug, Display};
use std::marker::PhantomData;
use std::ops::Range;
use crate::math::num::RealNumber;
use cholesky::CholeskyDecomposableMatrix;
use evd::EVDDecomposableMatrix;
use lu::LUDecomposableMatrix;
use qr::QRDecomposableMatrix;
use svd::SVDDecomposableMatrix;
use cholesky::CholeskyDecomposableMatrix;
/// Column or row vector
pub trait BaseVector<T: RealNumber>: Clone + Debug {
src/linalg/naive/dense_matrix.rs
+1 -1
View File
@@ -8,11 +8,11 @@ use serde::de::{Deserializer, MapAccess, SeqAccess, Visitor};
use serde::ser::{SerializeStruct, Serializer};
use serde::{Deserialize, Serialize};
use crate::linalg::cholesky::CholeskyDecomposableMatrix;
use crate::linalg::evd::EVDDecomposableMatrix;
use crate::linalg::lu::LUDecomposableMatrix;
use crate::linalg::qr::QRDecomposableMatrix;
use crate::linalg::svd::SVDDecomposableMatrix;
use crate::linalg::cholesky::CholeskyDecomposableMatrix;
use crate::linalg::Matrix;
pub use crate::linalg::{BaseMatrix, BaseVector};
use crate::math::num::RealNumber;
src/linalg/nalgebra_bindings.rs
+1 -1
View File
@@ -42,11 +42,11 @@ use std::ops::{AddAssign, DivAssign, MulAssign, Range, SubAssign};
use nalgebra::{DMatrix, Dynamic, Matrix, MatrixMN, RowDVector, Scalar, VecStorage, U1};
use crate::linalg::cholesky::CholeskyDecomposableMatrix;
use crate::linalg::evd::EVDDecomposableMatrix;
use crate::linalg::lu::LUDecomposableMatrix;
use crate::linalg::qr::QRDecomposableMatrix;
use crate::linalg::svd::SVDDecomposableMatrix;
use crate::linalg::cholesky::CholeskyDecomposableMatrix;
use crate::linalg::Matrix as SmartCoreMatrix;
use crate::linalg::{BaseMatrix, BaseVector};
use crate::math::num::RealNumber;
src/linalg/ndarray_bindings.rs
+1 -1
View File
@@ -49,11 +49,11 @@ use std::ops::SubAssign;
use ndarray::ScalarOperand;
use ndarray::{s, stack, Array, ArrayBase, Axis, Ix1, Ix2, OwnedRepr};
use crate::linalg::cholesky::CholeskyDecomposableMatrix;
use crate::linalg::evd::EVDDecomposableMatrix;
use crate::linalg::lu::LUDecomposableMatrix;
use crate::linalg::qr::QRDecomposableMatrix;
use crate::linalg::svd::SVDDecomposableMatrix;
use crate::linalg::cholesky::CholeskyDecomposableMatrix;
use crate::linalg::Matrix;
use crate::linalg::{BaseMatrix, BaseVector};
use crate::math::num::RealNumber;