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Patch to version 0.4.0 (#257)

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* 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/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();