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. 
-//! * [Martix Decomposition](decomposition/index.html), various methods for matrix decomposition. 
+//! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
+//! * [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.], 
-//!    &[3., 4.], 
-//!    &[5., 6.], 
-//!    &[7., 8.], 
+//!    &[1., 2.],
+//!    &[3., 4.],
+//!    &[5., 6.],
+//!    &[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);
 //! ```