work/smartcore
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Patch to version 0.4.0 (#257)

Browse Source 
* uncomment test

* Add random test for logistic regression

* linting

* Bump version

* Add test for logistic regression

* linting

* initial commit

* final

* final-clean

* Bump to 0.4.0

* Fix linter

* cleanup

* Update CHANDELOG with breaking changes

* Update CHANDELOG date

* Add functional methods to DenseMatrix implementation

* linting

* add type declaration in test

* Fix Wasm tests failing

* linting

* fix tests

* linting

* Add type annotations on BBDTree constructor

* fix clippy

* fix clippy

* fix tests

* bump version

* run fmt. fix changelog

---------

Co-authored-by: Edmund Cape <edmund@Edmunds-MacBook-Pro.local>
This commit is contained in:
Lorenzo
2024-03-04 13:51:27 +00:00
committed by GitHub
parent 80a93c1a0e
commit 239c00428f
45 changed files with 759 additions and 406 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/linalg/traits/cholesky.rs
+11 -7
View File
@@ -15,7 +15,7 @@
//! &[25., 15., -5.],
//! &[15., 18., 0.],
//! &[-5., 0., 11.]
//! ]);
//! ]).unwrap();
//!
//! let cholesky = A.cholesky().unwrap();
//! let lower_triangular: DenseMatrix<f64> = cholesky.L();
@@ -175,11 +175,14 @@ mod tests {
)]
#[test]
fn cholesky_decompose() {
let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]]);
let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]])
.unwrap();
let l =
DenseMatrix::from_2d_array(&[&[5.0, 0.0, 0.0], &[3.0, 3.0, 0.0], &[-1.0, 1.0, 3.0]]);
DenseMatrix::from_2d_array(&[&[5.0, 0.0, 0.0], &[3.0, 3.0, 0.0], &[-1.0, 1.0, 3.0]])
.unwrap();
let u =
DenseMatrix::from_2d_array(&[&[5.0, 3.0, -1.0], &[0.0, 3.0, 1.0], &[0.0, 0.0, 3.0]]);
DenseMatrix::from_2d_array(&[&[5.0, 3.0, -1.0], &[0.0, 3.0, 1.0], &[0.0, 0.0, 3.0]])
.unwrap();
let cholesky = a.cholesky().unwrap();
assert!(relative_eq!(cholesky.L().abs(), l.abs(), epsilon = 1e-4));
@@ -197,9 +200,10 @@ mod tests {
)]
#[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 a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]])
.unwrap();
let b = DenseMatrix::from_2d_array(&[&[40., 51., 28.]]).unwrap();
let expected = DenseMatrix::from_2d_array(&[&[1.0, 2.0, 3.0]]).unwrap();
let cholesky = a.cholesky().unwrap();
src/linalg/traits/evd.rs
+13 -7
View File
@@ -19,7 +19,7 @@
//! &[0.9000, 0.4000, 0.7000],
//! &[0.4000, 0.5000, 0.3000],
//! &[0.7000, 0.3000, 0.8000],
//! ]);
//! ]).unwrap();
//!
//! let evd = A.evd(true).unwrap();
//! let eigenvectors: DenseMatrix<f64> = evd.V;
@@ -820,7 +820,8 @@ mod tests {
&[0.9000, 0.4000, 0.7000],
&[0.4000, 0.5000, 0.3000],
&[0.7000, 0.3000, 0.8000],
]);
])
.unwrap();
let eigen_values: Vec<f64> = vec![1.7498382, 0.3165784, 0.1335834];
@@ -828,7 +829,8 @@ mod tests {
&[0.6881997, -0.07121225, 0.7220180],
&[0.3700456, 0.89044952, -0.2648886],
&[0.6240573, -0.44947578, -0.6391588],
]);
])
.unwrap();
let evd = A.evd(true).unwrap();
@@ -852,7 +854,8 @@ mod tests {
&[0.9000, 0.4000, 0.7000],
&[0.4000, 0.5000, 0.3000],
&[0.8000, 0.3000, 0.8000],
]);
])
.unwrap();
let eigen_values: Vec<f64> = vec![1.79171122, 0.31908143, 0.08920735];
@@ -860,7 +863,8 @@ mod tests {
&[0.7178958, 0.05322098, 0.6812010],
&[0.3837711, -0.84702111, -0.1494582],
&[0.6952105, 0.43984484, -0.7036135],
]);
])
.unwrap();
let evd = A.evd(false).unwrap();
@@ -885,7 +889,8 @@ mod tests {
&[4.0, -1.0, 1.0, 1.0],
&[1.0, 1.0, 3.0, -2.0],
&[1.0, 1.0, 4.0, -1.0],
]);
])
.unwrap();
let eigen_values_d: Vec<f64> = vec![0.0, 2.0, 2.0, 0.0];
let eigen_values_e: Vec<f64> = vec![2.2361, 0.9999, -0.9999, -2.2361];
@@ -895,7 +900,8 @@ mod tests {
&[-0.6707, 0.1059, 0.901, 0.6289],
&[0.9159, -0.1378, 0.3816, 0.0806],
&[0.6707, 0.1059, 0.901, -0.6289],
]);
])
.unwrap();
let evd = A.evd(false).unwrap();
src/linalg/traits/high_order.rs
+3 -3
View File
@@ -12,9 +12,9 @@ pub trait HighOrderOperations<T: Number>: Array2<T> {
/// use smartcore::linalg::traits::high_order::HighOrderOperations;
/// use smartcore::linalg::basic::arrays::Array2;
///
/// let a = DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.]]);
/// let b = DenseMatrix::from_2d_array(&[&[5., 6.], &[7., 8.], &[9., 10.]]);
/// let expected = DenseMatrix::from_2d_array(&[&[71., 80.], &[92., 104.]]);
/// let a = DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.]]).unwrap();
/// let b = DenseMatrix::from_2d_array(&[&[5., 6.], &[7., 8.], &[9., 10.]]).unwrap();
/// let expected = DenseMatrix::from_2d_array(&[&[71., 80.], &[92., 104.]]).unwrap();
///
/// assert_eq!(a.ab(true, &b, false), expected);
/// ```
src/linalg/traits/lu.rs
+8 -7
View File
@@ -18,7 +18,7 @@
//! &[1., 2., 3.],
//! &[0., 1., 5.],
//! &[5., 6., 0.]
//! ]);
//! ]).unwrap();
//!
//! let lu = A.lu().unwrap();
//! let lower: DenseMatrix<f64> = lu.L();
@@ -263,13 +263,13 @@ mod tests {
)]
#[test]
fn decompose() {
let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]).unwrap();
let expected_L =
DenseMatrix::from_2d_array(&[&[1., 0., 0.], &[0., 1., 0.], &[0.2, 0.8, 1.]]);
DenseMatrix::from_2d_array(&[&[1., 0., 0.], &[0., 1., 0.], &[0.2, 0.8, 1.]]).unwrap();
let expected_U =
DenseMatrix::from_2d_array(&[&[5., 6., 0.], &[0., 1., 5.], &[0., 0., -1.]]);
DenseMatrix::from_2d_array(&[&[5., 6., 0.], &[0., 1., 5.], &[0., 0., -1.]]).unwrap();
let expected_pivot =
DenseMatrix::from_2d_array(&[&[0., 0., 1.], &[0., 1., 0.], &[1., 0., 0.]]);
DenseMatrix::from_2d_array(&[&[0., 0., 1.], &[0., 1., 0.], &[1., 0., 0.]]).unwrap();
let lu = a.lu().unwrap();
assert!(relative_eq!(lu.L(), expected_L, epsilon = 1e-4));
assert!(relative_eq!(lu.U(), expected_U, epsilon = 1e-4));
@@ -281,9 +281,10 @@ mod tests {
)]
#[test]
fn inverse() {
let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]);
let a = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[0., 1., 5.], &[5., 6., 0.]]).unwrap();
let expected =
DenseMatrix::from_2d_array(&[&[-6.0, 3.6, 1.4], &[5.0, -3.0, -1.0], &[-1.0, 0.8, 0.2]]);
DenseMatrix::from_2d_array(&[&[-6.0, 3.6, 1.4], &[5.0, -3.0, -1.0], &[-1.0, 0.8, 0.2]])
.unwrap();
let a_inv = a.lu().and_then(|lu| lu.inverse()).unwrap();
assert!(relative_eq!(a_inv, expected, epsilon = 1e-4));
}
src/linalg/traits/qr.rs
+12 -7
View File
@@ -13,7 +13,7 @@
//! &[0.9, 0.4, 0.7],
//! &[0.4, 0.5, 0.3],
//! &[0.7, 0.3, 0.8]
//! ]);
//! ]).unwrap();
//!
//! let qr = A.qr().unwrap();
//! let orthogonal: DenseMatrix<f64> = qr.Q();
@@ -201,17 +201,20 @@ mod tests {
)]
#[test]
fn decompose() {
let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]])
.unwrap();
let q = DenseMatrix::from_2d_array(&[
&[-0.7448, 0.2436, 0.6212],
&[-0.331, -0.9432, -0.027],
&[-0.5793, 0.2257, -0.7832],
]);
])
.unwrap();
let r = DenseMatrix::from_2d_array(&[
&[-1.2083, -0.6373, -1.0842],
&[0.0, -0.3064, 0.0682],
&[0.0, 0.0, -0.1999],
]);
])
.unwrap();
let qr = a.qr().unwrap();
assert!(relative_eq!(qr.Q().abs(), q.abs(), epsilon = 1e-4));
assert!(relative_eq!(qr.R().abs(), r.abs(), epsilon = 1e-4));
@@ -223,13 +226,15 @@ mod tests {
)]
#[test]
fn qr_solve_mut() {
let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
let b = DenseMatrix::from_2d_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]);
let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]])
.unwrap();
let b = DenseMatrix::from_2d_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]).unwrap();
let expected_w = DenseMatrix::from_2d_array(&[
&[-0.2027027, -1.2837838],
&[0.8783784, 2.2297297],
&[0.4729730, 0.6621622],
]);
])
.unwrap();
let w = a.qr_solve_mut(b).unwrap();
assert!(relative_eq!(w, expected_w, epsilon = 1e-2));
}
src/linalg/traits/stats.rs
+15 -11
View File
@@ -136,8 +136,8 @@ pub trait MatrixPreprocessing<T: RealNumber>: MutArrayView2<T> + Clone {
/// ```rust
/// use smartcore::linalg::basic::matrix::DenseMatrix;
/// use smartcore::linalg::traits::stats::MatrixPreprocessing;
/// let mut a = DenseMatrix::from_2d_array(&[&[0., 2., 3.], &[-5., -6., -7.]]);
/// let expected = DenseMatrix::from_2d_array(&[&[0., 1., 1.],&[0., 0., 0.]]);
/// let mut a = DenseMatrix::from_2d_array(&[&[0., 2., 3.], &[-5., -6., -7.]]).unwrap();
/// let expected = DenseMatrix::from_2d_array(&[&[0., 1., 1.],&[0., 0., 0.]]).unwrap();
/// a.binarize_mut(0.);
///
/// assert_eq!(a, expected);
@@ -159,8 +159,8 @@ pub trait MatrixPreprocessing<T: RealNumber>: MutArrayView2<T> + Clone {
/// ```rust
/// use smartcore::linalg::basic::matrix::DenseMatrix;
/// use smartcore::linalg::traits::stats::MatrixPreprocessing;
/// let a = DenseMatrix::from_2d_array(&[&[0., 2., 3.], &[-5., -6., -7.]]);
/// let expected = DenseMatrix::from_2d_array(&[&[0., 1., 1.],&[0., 0., 0.]]);
/// let a = DenseMatrix::from_2d_array(&[&[0., 2., 3.], &[-5., -6., -7.]]).unwrap();
/// let expected = DenseMatrix::from_2d_array(&[&[0., 1., 1.],&[0., 0., 0.]]).unwrap();
///
/// assert_eq!(a.binarize(0.), expected);
/// ```
@@ -186,7 +186,8 @@ mod tests {
&[1., 2., 3., 1., 2.],
&[4., 5., 6., 3., 4.],
&[7., 8., 9., 5., 6.],
]);
])
.unwrap();
let expected_0 = vec![4., 5., 6., 3., 4.];
let expected_1 = vec![1.8, 4.4, 7.];
@@ -196,7 +197,7 @@ mod tests {
#[test]
fn test_var() {
let m = DenseMatrix::from_2d_array(&[&[1., 2., 3., 4.], &[5., 6., 7., 8.]]);
let m = DenseMatrix::from_2d_array(&[&[1., 2., 3., 4.], &[5., 6., 7., 8.]]).unwrap();
let expected_0 = vec![4., 4., 4., 4.];
let expected_1 = vec![1.25, 1.25];
@@ -211,7 +212,8 @@ mod tests {
let m = DenseMatrix::from_2d_array(&[
&[0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25],
&[0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25],
]);
])
.unwrap();
let expected_0 = vec![0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0];
let expected_1 = vec![1.25, 1.25];
@@ -230,7 +232,8 @@ mod tests {
&[1., 2., 3., 1., 2.],
&[4., 5., 6., 3., 4.],
&[7., 8., 9., 5., 6.],
]);
])
.unwrap();
let expected_0 = vec![
2.449489742783178,
2.449489742783178,
@@ -251,10 +254,10 @@ mod tests {
#[test]
fn test_scale() {
let m: DenseMatrix<f64> =
DenseMatrix::from_2d_array(&[&[1., 2., 3., 4.], &[5., 6., 7., 8.]]);
DenseMatrix::from_2d_array(&[&[1., 2., 3., 4.], &[5., 6., 7., 8.]]).unwrap();
let expected_0: DenseMatrix<f64> =
DenseMatrix::from_2d_array(&[&[-1., -1., -1., -1.], &[1., 1., 1., 1.]]);
DenseMatrix::from_2d_array(&[&[-1., -1., -1., -1.], &[1., 1., 1., 1.]]).unwrap();
let expected_1: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
&[
-1.3416407864998738,
@@ -268,7 +271,8 @@ mod tests {
0.4472135954999579,
1.3416407864998738,
],
]);
])
.unwrap();
assert_eq!(m.mean(0), vec![3.0, 4.0, 5.0, 6.0]);
assert_eq!(m.mean(1), vec![2.5, 6.5]);
src/linalg/traits/svd.rs
+19 -11
View File
@@ -17,7 +17,7 @@
//! &[0.9, 0.4, 0.7],
//! &[0.4, 0.5, 0.3],
//! &[0.7, 0.3, 0.8]
//! ]);
//! ]).unwrap();
//!
//! let svd = A.svd().unwrap();
//! let u: DenseMatrix<f64> = svd.U;
@@ -489,7 +489,8 @@ mod tests {
&[0.9000, 0.4000, 0.7000],
&[0.4000, 0.5000, 0.3000],
&[0.7000, 0.3000, 0.8000],
]);
])
.unwrap();
let s: Vec<f64> = vec![1.7498382, 0.3165784, 0.1335834];
@@ -497,13 +498,15 @@ mod tests {
&[0.6881997, -0.07121225, 0.7220180],
&[0.3700456, 0.89044952, -0.2648886],
&[0.6240573, -0.44947578, -0.639158],
]);
])
.unwrap();
let V = DenseMatrix::from_2d_array(&[
&[0.6881997, -0.07121225, 0.7220180],
&[0.3700456, 0.89044952, -0.2648886],
&[0.6240573, -0.44947578, -0.6391588],
]);
])
.unwrap();
let svd = A.svd().unwrap();
@@ -577,7 +580,8 @@ mod tests {
-0.2158704,
-0.27529472,
],
]);
])
.unwrap();
let s: Vec<f64> = vec![
3.8589375, 3.4396766, 2.6487176, 2.2317399, 1.5165054, 0.8109055, 0.2706515,
@@ -647,7 +651,8 @@ mod tests {
0.73034065,
-0.43965505,
],
]);
])
.unwrap();
let V = DenseMatrix::from_2d_array(&[
&[
@@ -707,7 +712,8 @@ mod tests {
0.1654796,
-0.32346758,
],
]);
])
.unwrap();
let svd = A.svd().unwrap();
@@ -723,10 +729,11 @@ mod tests {
)]
#[test]
fn solve() {
let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
let b = DenseMatrix::from_2d_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]);
let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]])
.unwrap();
let b = DenseMatrix::from_2d_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]).unwrap();
let expected_w =
DenseMatrix::from_2d_array(&[&[-0.20, -1.28], &[0.87, 2.22], &[0.47, 0.66]]);
DenseMatrix::from_2d_array(&[&[-0.20, -1.28], &[0.87, 2.22], &[0.47, 0.66]]).unwrap();
let w = a.svd_solve_mut(b).unwrap();
assert!(relative_eq!(w, expected_w, epsilon = 1e-2));
}
@@ -737,7 +744,8 @@ mod tests {
)]
#[test]
fn decompose_restore() {
let a = DenseMatrix::from_2d_array(&[&[1.0, 2.0, 3.0, 4.0], &[5.0, 6.0, 7.0, 8.0]]);
let a =
DenseMatrix::from_2d_array(&[&[1.0, 2.0, 3.0, 4.0], &[5.0, 6.0, 7.0, 8.0]]).unwrap();
let svd = a.svd().unwrap();
let u: &DenseMatrix<f32> = &svd.U; //U
let v: &DenseMatrix<f32> = &svd.V; // V