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/naive_bayes/bernoulli.rs
+9 -6
View File
@@ -19,14 +19,14 @@
//! &[0, 1, 0, 0, 1, 0],
//! &[0, 1, 0, 1, 0, 0],
//! &[0, 1, 1, 0, 0, 1],
//! ]);
//! ]).unwrap();
//! let y: Vec<u32> = vec![0, 0, 0, 1];
//!
//! let nb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
//!
//! // Testing data point is:
//! // Chinese Chinese Chinese Tokyo Japan
//! let x_test = DenseMatrix::from_2d_array(&[&[0, 1, 1, 0, 0, 1]]);
//! let x_test = DenseMatrix::from_2d_array(&[&[0, 1, 1, 0, 0, 1]]).unwrap();
//! let y_hat = nb.predict(&x_test).unwrap();
//! ```
//!
@@ -527,7 +527,8 @@ mod tests {
&[0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
&[0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
&[0.0, 1.0, 1.0, 0.0, 0.0, 1.0],
]);
])
.unwrap();
let y: Vec<u32> = vec![0, 0, 0, 1];
let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
@@ -558,7 +559,7 @@ mod tests {
// Testing data point is:
// Chinese Chinese Chinese Tokyo Japan
let x_test = DenseMatrix::from_2d_array(&[&[0.0, 1.0, 1.0, 0.0, 0.0, 1.0]]);
let x_test = DenseMatrix::from_2d_array(&[&[0.0, 1.0, 1.0, 0.0, 0.0, 1.0]]).unwrap();
let y_hat = bnb.predict(&x_test).unwrap();
assert_eq!(y_hat, &[1]);
@@ -586,7 +587,8 @@ mod tests {
&[2, 0, 3, 3, 1, 2, 0, 2, 4, 1],
&[2, 4, 0, 4, 2, 4, 1, 3, 1, 4],
&[0, 2, 2, 3, 4, 0, 4, 4, 4, 4],
]);
])
.unwrap();
let y: Vec<u32> = vec![2, 2, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 2, 0, 2];
let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
@@ -643,7 +645,8 @@ mod tests {
&[0, 1, 0, 0, 1, 0],
&[0, 1, 0, 1, 0, 0],
&[0, 1, 1, 0, 0, 1],
]);
])
.unwrap();
let y: Vec<u32> = vec![0, 0, 0, 1];
let bnb = BernoulliNB::fit(&x, &y, Default::default()).unwrap();
src/naive_bayes/categorical.rs
+8 -5
View File
@@ -24,7 +24,7 @@
//! &[3, 4, 2, 4],
//! &[0, 3, 1, 2],
//! &[0, 4, 1, 2],
//! ]);
//! ]).unwrap();
//! let y: Vec<u32> = vec![0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0];
//!
//! let nb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
@@ -455,7 +455,8 @@ mod tests {
&[1, 1, 1, 1],
&[1, 2, 0, 0],
&[2, 1, 1, 1],
]);
])
.unwrap();
let y: Vec<u32> = vec![0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0];
let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
@@ -513,7 +514,7 @@ mod tests {
]
);
let x_test = DenseMatrix::from_2d_array(&[&[0, 2, 1, 0], &[2, 2, 0, 0]]);
let x_test = DenseMatrix::from_2d_array(&[&[0, 2, 1, 0], &[2, 2, 0, 0]]).unwrap();
let y_hat = cnb.predict(&x_test).unwrap();
assert_eq!(y_hat, vec![0, 1]);
}
@@ -539,7 +540,8 @@ mod tests {
&[3, 4, 2, 4],
&[0, 3, 1, 2],
&[0, 4, 1, 2],
]);
])
.unwrap();
let y: Vec<u32> = vec![0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0];
let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
@@ -571,7 +573,8 @@ mod tests {
&[3, 4, 2, 4],
&[0, 3, 1, 2],
&[0, 4, 1, 2],
]);
])
.unwrap();
let y: Vec<u32> = vec![0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0];
let cnb = CategoricalNB::fit(&x, &y, Default::default()).unwrap();
src/naive_bayes/gaussian.rs
+7 -4
View File
@@ -16,7 +16,7 @@
//! &[ 1., 1.],
//! &[ 2., 1.],
//! &[ 3., 2.],
//! ]);
//! ]).unwrap();
//! let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
//!
//! let nb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
@@ -395,7 +395,8 @@ mod tests {
&[1., 1.],
&[2., 1.],
&[3., 2.],
]);
])
.unwrap();
let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
let gnb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
@@ -435,7 +436,8 @@ mod tests {
&[1., 1.],
&[2., 1.],
&[3., 2.],
]);
])
.unwrap();
let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
let priors = vec![0.3, 0.7];
@@ -462,7 +464,8 @@ mod tests {
&[1., 1.],
&[2., 1.],
&[3., 2.],
]);
])
.unwrap();
let y: Vec<u32> = vec![1, 1, 1, 2, 2, 2];
let gnb = GaussianNB::fit(&x, &y, Default::default()).unwrap();
src/naive_bayes/mod.rs
+1 -1
View File
@@ -169,7 +169,7 @@ mod tests {
#[test]
fn test_predict() {
let matrix = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9]]);
let matrix = DenseMatrix::from_2d_array(&[&[1, 2, 3], &[4, 5, 6], &[7, 8, 9]]).unwrap();
let val = vec![];
match Model::fit(TestDistribution(&val)).unwrap().predict(&matrix) {
src/naive_bayes/multinomial.rs
+9 -6
View File
@@ -20,13 +20,13 @@
//! &[0, 2, 0, 0, 1, 0],
//! &[0, 1, 0, 1, 0, 0],
//! &[0, 1, 1, 0, 0, 1],
//! ]);
//! ]).unwrap();
//! let y: Vec<u32> = vec![0, 0, 0, 1];
//! let nb = MultinomialNB::fit(&x, &y, Default::default()).unwrap();
//!
//! // Testing data point is:
//! // Chinese Chinese Chinese Tokyo Japan
//! let x_test = DenseMatrix::from_2d_array(&[&[0, 3, 1, 0, 0, 1]]);
//! let x_test = DenseMatrix::from_2d_array(&[&[0, 3, 1, 0, 0, 1]]).unwrap();
//! let y_hat = nb.predict(&x_test).unwrap();
//! ```
//!
@@ -433,7 +433,8 @@ mod tests {
&[0, 2, 0, 0, 1, 0],
&[0, 1, 0, 1, 0, 0],
&[0, 1, 1, 0, 0, 1],
]);
])
.unwrap();
let y: Vec<u32> = vec![0, 0, 0, 1];
let mnb = MultinomialNB::fit(&x, &y, Default::default()).unwrap();
@@ -467,7 +468,7 @@ mod tests {
// Testing data point is:
// Chinese Chinese Chinese Tokyo Japan
let x_test = DenseMatrix::<u32>::from_2d_array(&[&[0, 3, 1, 0, 0, 1]]);
let x_test = DenseMatrix::<u32>::from_2d_array(&[&[0, 3, 1, 0, 0, 1]]).unwrap();
let y_hat = mnb.predict(&x_test).unwrap();
assert_eq!(y_hat, &[0]);
@@ -495,7 +496,8 @@ mod tests {
&[2, 0, 3, 3, 1, 2, 0, 2, 4, 1],
&[2, 4, 0, 4, 2, 4, 1, 3, 1, 4],
&[0, 2, 2, 3, 4, 0, 4, 4, 4, 4],
]);
])
.unwrap();
let y: Vec<u32> = vec![2, 2, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 2, 0, 2];
let nb = MultinomialNB::fit(&x, &y, Default::default()).unwrap();
@@ -554,7 +556,8 @@ mod tests {
&[0, 1, 0, 0, 1, 0],
&[0, 1, 0, 1, 0, 0],
&[0, 1, 1, 0, 0, 1],
]);
])
.unwrap();
let y = vec![0, 0, 0, 1];
let mnb = MultinomialNB::fit(&x, &y, Default::default()).unwrap();