fix: formatting

src/lib.rs

@@ -2,9 +2,9 @@
 #![warn(missing_doc_code_examples)]
 
 //! # SmartCore
-//! 
+//!
-//! Welcome to SmartCore library, the most complete machine learning library for Rust! 
+//! Welcome to SmartCore library, the most complete machine learning library for Rust!
-//! 
+//!
 //! In SmartCore you will find implementation of these ML algorithms:
 //! * Regression: Linear Regression (OLS), Decision Tree Regressor, Random Forest Regressor
 //! * Classification: Logistic Regressor, Decision Tree Classifier, Random Forest Classifier, Unsupervised Nearest Neighbors (KNN)
@@ -12,33 +12,33 @@
 //! * Matrix decomposition: PCA, LU, QR, SVD, EVD
 //! * Distance Metrics: Euclidian, Minkowski, Manhattan, Hamming, Mahalanobis
 //! * Evaluation Metrics: Accuracy, AUC, Recall, Precision, F1, Mean Absolute Error, Mean Squared Error, R2
-//! 
+//!
 //! Most of algorithms implemented in SmartCore operate on n-dimentional arrays. While you can use Rust vectors with all functions defined in this library
 //! we do recommend to go with one of the popular linear algebra libraries available in Rust. At this moment we support these packages:
 //! * [ndarray](https://docs.rs/ndarray)
 //! * [nalgebra](https://docs.rs/nalgebra/)
-//! 
+//!
 //! ## Getting Started
-//! 
+//!
 //! To start using SmartCore simply add the following to your Cargo.toml file:
 //! ```ignore
 //! [dependencies]
 //! smartcore = "0.1.0"
 //! ```
-//! 
+//!
 //! All ML algorithms in SmartCore are grouped into these generic categories:
-//! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data. 
+//! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
-//! * [Martix Decomposition](decomposition/index.html), various methods for matrix decomposition. 
+//! * [Martix Decomposition](decomposition/index.html), various methods for matrix decomposition.
 //! * [Linear Models](linear/index.html), regression and classification methods where output is assumed to have linear relation to explanatory variables
 //! * [Ensemble Models](ensemble/index.html), variety of regression and classification ensemble models
 //! * [Tree-based Models](tree/index.html), classification and regression trees
 //! * [Nearest Neighbors](neighbors/index.html), K Nearest Neighbors for classification and regression
-//! 
+//!
-//! Each category is assigned to a separate module. 
+//! Each category is assigned to a separate module.
-//! 
+//!
 //! For example, KNN classifier is defined in [smartcore::neighbors::knn](neighbors/knn/index.html). To train and run it using standard Rust vectors you will
 //! run this code:
-//! 
+//!
 //! ```
 //! // DenseMatrix defenition
 //! use smartcore::linalg::naive::dense_matrix::*;
@@ -46,20 +46,20 @@
 //! use smartcore::neighbors::knn::*;
 //! // Various distance metrics
 //! use smartcore::math::distance::*;
-//! 
+//!
 //! // Turn Rust vectors with samples into a matrix
 //! let x = DenseMatrix::from_array(&[
-//!    &[1., 2.], 
+//!    &[1., 2.],
-//!    &[3., 4.], 
+//!    &[3., 4.],
-//!    &[5., 6.], 
+//!    &[5., 6.],
-//!    &[7., 8.], 
+//!    &[7., 8.],
 //!    &[9., 10.]]);
 //! // Our classes are defined as a Vector
 //! let y = vec![2., 2., 2., 3., 3.];
-//! 
+//!
 //! // Train classifier
 //! let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default());
-//! 
+//!
 //! // Predict classes
 //! let y_hat = knn.predict(&x);
 //! ```

src/linear/linear_regression.rs

@@ -12,7 +12,7 @@ pub enum LinearRegressionSolverName {
 }
 
 #[derive(Serialize, Deserialize, Debug)]
-pub struct LinearRegressionParameters {    
+pub struct LinearRegressionParameters {
     solver: LinearRegressionSolverName,
 }
 
@@ -25,8 +25,8 @@ pub struct LinearRegression<T: FloatExt, M: Matrix<T>> {
 
 impl Default for LinearRegressionParameters {
     fn default() -> Self {
-        LinearRegressionParameters {            
+        LinearRegressionParameters {
-            solver: LinearRegressionSolverName::SVD
+            solver: LinearRegressionSolverName::SVD,
         }
     }
 }
@@ -39,7 +39,11 @@ impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
 }
 
 impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
-    pub fn fit(x: &M, y: &M::RowVector, parameters: LinearRegressionParameters) -> LinearRegression<T, M> {
+    pub fn fit(
+        x: &M,
+        y: &M::RowVector,
+        parameters: LinearRegressionParameters,
+    ) -> LinearRegression<T, M> {
         let y_m = M::from_row_vector(y.clone());
         let b = y_m.transpose();
         let (x_nrows, num_attributes) = x.shape();
@@ -103,7 +107,14 @@ mod tests {
             114.2, 115.7, 116.9,
         ]);
 
-        let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionParameters{solver: LinearRegressionSolverName::QR}).predict(&x);
+        let y_hat_qr = LinearRegression::fit(
+            &x,
+            &y,
+            LinearRegressionParameters {
+                solver: LinearRegressionSolverName::QR,
+            },
+        )
+        .predict(&x);
 
         let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
 
@@ -143,7 +154,14 @@ mod tests {
             114.2, 115.7, 116.9,
         ];
 
-        let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionParameters{solver: LinearRegressionSolverName::QR}).predict(&x);
+        let y_hat_qr = LinearRegression::fit(
+            &x,
+            &y,
+            LinearRegressionParameters {
+                solver: LinearRegressionSolverName::QR,
+            },
+        )
+        .predict(&x);
 
         let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
 

src/neighbors/knn.rs

@@ -13,9 +13,9 @@ pub enum KNNAlgorithmName {
 }
 
 #[derive(Serialize, Deserialize, Debug)]
-pub struct KNNClassifierParameters {    
+pub struct KNNClassifierParameters {
     pub algorithm: KNNAlgorithmName,
-    pub k: usize
+    pub k: usize,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
@@ -34,9 +34,9 @@ enum KNNAlgorithm<T: FloatExt, D: Distance<Vec<T>, T>> {
 
 impl Default for KNNClassifierParameters {
     fn default() -> Self {
-        KNNClassifierParameters {            
+        KNNClassifierParameters {
             algorithm: KNNAlgorithmName::CoverTree,
-            k: 3
+            k: 3,
         }
     }
 }
@@ -93,7 +93,7 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
         x: &M,
         y: &M::RowVector,
         distance: D,
-        parameters: KNNClassifierParameters
+        parameters: KNNClassifierParameters,
     ) -> KNNClassifier<T, D> {
         let y_m = M::from_row_vector(y.clone());
 
@@ -118,7 +118,10 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
             )
         );
 
-        assert!(parameters.k > 1, format!("k should be > 1, k=[{}]", parameters.k));
+        assert!(
+            parameters.k > 1,
+            format!("k should be > 1, k=[{}]", parameters.k)
+        );
 
         KNNClassifier {
             classes: classes,
@@ -169,7 +172,10 @@ mod tests {
             &x,
             &y,
             Distances::euclidian(),
-            KNNClassifierParameters{k: 3, algorithm: KNNAlgorithmName::LinearSearch}            
+            KNNClassifierParameters {
+                k: 3,
+                algorithm: KNNAlgorithmName::LinearSearch,
+            },
         );
         let r = knn.predict(&x);
         assert_eq!(5, Vec::len(&r));
@@ -181,12 +187,7 @@ mod tests {
         let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
         let y = vec![2., 2., 2., 3., 3.];
 
-        let knn = KNNClassifier::fit(
+        let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default());
-            &x,
-            &y,            
-            Distances::euclidian(),
-            Default::default()
-        );
 
         let deserialized_knn = bincode::deserialize(&bincode::serialize(&knn).unwrap()).unwrap();
 

src/optimization/mod.rs

@@ -5,7 +5,7 @@ pub type F<'a, T, X> = dyn for<'b> Fn(&'b X) -> T + 'a;
 pub type DF<'a, X> = dyn for<'b> Fn(&'b mut X, &'b X) + 'a;
 
 #[derive(Debug, PartialEq)]
-pub enum FunctionOrder {    
+pub enum FunctionOrder {
     SECOND,
     THIRD,
 }