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feat: documents matrix methods

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This commit is contained in:
Volodymyr Orlov
2020-09-06 18:27:11 -07:00
parent 1e3ed4c924
commit bbe810d164
25 changed files with 587 additions and 245 deletions
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src/neighbors/knn_classifier.rs
+7 -5
View File
@@ -17,7 +17,7 @@
//! use smartcore::math::distance::*;
//!
//! //your explanatory variables. Each row is a training sample with 2 numerical features
//! let x = DenseMatrix::from_array(&[
//! let x = DenseMatrix::from_2d_array(&[
//! &[1., 2.],
//! &[3., 4.],
//! &[5., 6.],
@@ -188,7 +188,8 @@ mod tests {
#[test]
fn knn_fit_predict() {
let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let x =
DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y = vec![2., 2., 2., 3., 3.];
let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default());
let y_hat = knn.predict(&x);
@@ -198,7 +199,7 @@ mod tests {
#[test]
fn knn_fit_predict_weighted() {
let x = DenseMatrix::from_array(&[&[1.], &[2.], &[3.], &[4.], &[5.]]);
let x = DenseMatrix::from_2d_array(&[&[1.], &[2.], &[3.], &[4.], &[5.]]);
let y = vec![2., 2., 2., 3., 3.];
let knn = KNNClassifier::fit(
&x,
@@ -210,13 +211,14 @@ mod tests {
weight: KNNWeightFunction::Distance,
},
);
let y_hat = knn.predict(&DenseMatrix::from_array(&[&[4.1]]));
let y_hat = knn.predict(&DenseMatrix::from_2d_array(&[&[4.1]]));
assert_eq!(vec![3.0], y_hat);
}
#[test]
fn serde() {
let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let x =
DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y = vec![2., 2., 2., 3., 3.];
let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default());
src/neighbors/knn_regressor.rs
+7 -4
View File
@@ -19,7 +19,7 @@
//! use smartcore::math::distance::*;
//!
//! //your explanatory variables. Each row is a training sample with 2 numerical features
//! let x = DenseMatrix::from_array(&[
//! let x = DenseMatrix::from_2d_array(&[
//! &[1., 1.],
//! &[2., 2.],
//! &[3., 3.],
@@ -166,7 +166,8 @@ mod tests {
#[test]
fn knn_fit_predict_weighted() {
let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let x =
DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y: Vec<f64> = vec![1., 2., 3., 4., 5.];
let y_exp = vec![1., 2., 3., 4., 5.];
let knn = KNNRegressor::fit(
@@ -188,7 +189,8 @@ mod tests {
#[test]
fn knn_fit_predict_uniform() {
let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let x =
DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y: Vec<f64> = vec![1., 2., 3., 4., 5.];
let y_exp = vec![2., 2., 3., 4., 4.];
let knn = KNNRegressor::fit(&x, &y, Distances::euclidian(), Default::default());
@@ -201,7 +203,8 @@ mod tests {
#[test]
fn serde() {
let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let x =
DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
let y = vec![1., 2., 3., 4., 5.];
let knn = KNNRegressor::fit(&x, &y, Distances::euclidian(), Default::default());