Disambiguate distances. Implement Fastpair. (#220)

src/lib.rs

@@ -10,34 +10,30 @@
 
 //! # SmartCore
 //!
-//! Welcome to SmartCore, the most advanced machine learning library in Rust!
+//! Welcome to SmartCore, machine learning in Rust!
 //!
 //! SmartCore features various classification, regression and clustering algorithms including support vector machines, random forests, k-means and DBSCAN,
 //! as well as tools for model selection and model evaluation.
 //!
-//! SmartCore is well integrated with a with wide variaty of libraries that provide support for large, multi-dimensional arrays and matrices. At this moment,
-//! all Smartcore's algorithms work with ordinary Rust vectors, as well as matrices and vectors defined in these packages:
-//! * [ndarray](https://docs.rs/ndarray)
+//! SmartCore provides its own traits system that extends Rust standard library, to deal with linear algebra and common
+//! computational models. Its API is designed using well recognizable patterns. Extra features (like support for [ndarray](https://docs.rs/ndarray)
+//! structures) is available via optional features.
 //!
 //! ## Getting Started
 //!
 //! To start using SmartCore simply add the following to your Cargo.toml file:
 //! ```ignore
 //! [dependencies]
-//! smartcore = { git = "https://github.com/smartcorelib/smartcore", branch = "v0.5-wip" }
+//! smartcore = { git = "https://github.com/smartcorelib/smartcore", branch = "development" }
 //! ```
 //!
-//! All machine learning algorithms in SmartCore are grouped into these broad categories:
-//! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
-//! * [Matrix 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
-//! * [Naive Bayes](naive_bayes/index.html), statistical classification technique based on Bayes Theorem
-//! * [SVM](svm/index.html), support vector machines
+//! ## Using Jupyter
+//! For quick introduction, Jupyter Notebooks are available [here](https://github.com/smartcorelib/smartcore-jupyter/tree/main/notebooks).
+//! You can set up a local environment to run Rust notebooks using [EVCXR](https://github.com/google/evcxr)
+//! following [these instructions](https://depth-first.com/articles/2020/09/21/interactive-rust-in-a-repl-and-jupyter-notebook-with-evcxr/).
 //!
 //!
+//! ## First Example
 //! For example, you can use this code to fit a [K Nearest Neighbors classifier](neighbors/knn_classifier/index.html) to a dataset that is defined as standard Rust vector:
 //!
 //! ```
@@ -48,14 +44,14 @@
 //! // Various distance metrics
 //! use smartcore::metrics::distance::*;
 //!
-//! // Turn Rust vectors with samples into a matrix
+//! // Turn Rust vector-slices with samples into a matrix
 //! let x = DenseMatrix::from_2d_array(&[
 //!    &[1., 2.],
 //!    &[3., 4.],
 //!    &[5., 6.],
 //!    &[7., 8.],
 //!    &[9., 10.]]);
-//! // Our classes are defined as a Vector
+//! // Our classes are defined as a vector
 //! let y = vec![2, 2, 2, 3, 3];
 //!
 //! // Train classifier
@@ -64,6 +60,17 @@
 //! // Predict classes
 //! let y_hat = knn.predict(&x).unwrap();
 //! ```
+//!
+//! ## Overview
+//! All machine learning algorithms in SmartCore are grouped into these broad categories:
+//! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
+//! * [Matrix 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
+//! * [Naive Bayes](naive_bayes/index.html), statistical classification technique based on Bayes Theorem
+//! * [SVM](svm/index.html), support vector machines
 
 /// Foundamental numbers traits
 pub mod numbers;