Merge pull request #45 from smartcorelib/api_doc

feat: version change + api documentation updated

Author: VolodymyrOrlov

Cargo.toml

@@ -2,7 +2,7 @@
 name = "smartcore"
 description = "The most advanced machine learning library in rust."
 homepage = "https://smartcorelib.org"
-version = "0.1.0"
+version = "0.2.0"
 authors = ["SmartCore Developers"]
 edition = "2018"
 license = "Apache-2.0"

src/cluster/dbscan.rs

@@ -1,6 +1,20 @@
 //! # DBSCAN Clustering
 //!
-//! DBSCAN - Density-Based Spatial Clustering of Applications with Noise.
+//! DBSCAN stands for density-based spatial clustering of applications with noise. This algorithms is good for arbitrary shaped clusters and clusters with noise.
+//! The main idea behind DBSCAN is that a point belongs to a cluster if it is close to many points from that cluster. There are two key parameters of DBSCAN:
+//!
+//! * `eps`, the maximum distance that specifies a neighborhood. Two points are considered to be neighbors if the distance between them are less than or equal to `eps`.
+//! * `min_samples`, minimum number of data points that defines a cluster.
+//!
+//! Based on these two parameters, points are classified as core point, border point, or outlier:
+//!
+//! * A point is a core point if there are at least `min_samples` number of points, including the point itself in its vicinity.
+//! * A point is a border point if it is reachable from a core point and there are less than `min_samples` number of points within its surrounding area.
+//! * All points not reachable from any other point are outliers or noise points.
+//!
+//! The algorithm starts from picking up an arbitrarily point in the dataset.
+//! If there are at least `min_samples` points within a radius of `eps` to the point then we consider all these points to be part of the same cluster.
+//! The clusters are then expanded by recursively repeating the neighborhood calculation for each neighboring point.
 //!
 //! Example:
 //!

src/lib.rs

@@ -10,16 +10,11 @@
 //!
 //! Welcome to SmartCore, the most advanced machine learning library in Rust!
 //!
-//! In SmartCore you will find implementation of these ML algorithms:
-//! * __Regression__: Linear Regression (OLS), Decision Tree Regressor, Random Forest Regressor, K Nearest Neighbors
-//! * __Classification__: Logistic Regressor, Decision Tree Classifier, Random Forest Classifier, Supervised Nearest Neighbors (KNN)
-//! * __Clustering__: K-Means
-//! * __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
+//! 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.
 //!
-//! 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:
+//! 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)
 //! * [nalgebra](https://docs.rs/nalgebra/)
 //!
@@ -28,21 +23,21 @@
 //! To start using SmartCore simply add the following to your Cargo.toml file:
 //! ```ignore
 //! [dependencies]
-//! smartcore = "0.1.0"
+//! smartcore = "0.2.0"
 //! ```
 //!
-//! All ML algorithms in SmartCore are grouped into these generic categories:
+//! All machine learning algorithms in SmartCore are grouped into these broad categories:
 //! * [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
+//! * [Naive Bayes](naive_bayes/index.html), statistical classification technique based on Bayes Theorem
+//! * [SVM](svm/index.html), support vector machines
 //!
-//! Each category is assigned to a separate module.
 //!
-//! For example, KNN classifier is defined in [smartcore::neighbors::knn_classifier](neighbors/knn_classifier/index.html). To train and run it using standard Rust vectors you will
-//! run this code:
+//! 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:
 //!
 //! ```
 //! // DenseMatrix defenition

src/model_selection/mod.rs

@@ -1,13 +1,106 @@
 //! # Model Selection methods
 //!
-//! In statistics and machine learning we usually split our data into multiple subsets: training data and testing data (and sometimes to validate),
-//! and fit our model on the train data, in order to make predictions on the test data. We do that to avoid overfitting or underfitting model to our data.
+//! In statistics and machine learning we usually split our data into two sets: one for training and the other one for testing.
+//! We fit our model to the training data, in order to make predictions on the test data. We do that to avoid overfitting or underfitting model to our data.
 //! Overfitting is bad because the model we trained fits trained data too well and can’t make any inferences on new data.
 //! Underfitted is bad because the model is undetrained and does not fit the training data well.
-//! Splitting data into multiple subsets helps to find the right combination of hyperparameters, estimate model performance and choose the right model for
-//! your data.
+//! Splitting data into multiple subsets helps us to find the right combination of hyperparameters, estimate model performance and choose the right model for
+//! the data.
 //!
-//! In SmartCore you can split your data into training and test datasets using `train_test_split` function.
+//! In SmartCore a random split into training and test sets can be quickly computed with the [train_test_split](./fn.train_test_split.html) helper function.
+//!
+//! ```
+//! use crate::smartcore::linalg::BaseMatrix;
+//! use smartcore::linalg::naive::dense_matrix::DenseMatrix;
+//! use smartcore::model_selection::train_test_split;
+//!
+//! //Iris data
+//! let x = DenseMatrix::from_2d_array(&[
+//!           &[5.1, 3.5, 1.4, 0.2],
+//!           &[4.9, 3.0, 1.4, 0.2],
+//!           &[4.7, 3.2, 1.3, 0.2],
+//!           &[4.6, 3.1, 1.5, 0.2],
+//!           &[5.0, 3.6, 1.4, 0.2],
+//!           &[5.4, 3.9, 1.7, 0.4],
+//!           &[4.6, 3.4, 1.4, 0.3],
+//!           &[5.0, 3.4, 1.5, 0.2],
+//!           &[4.4, 2.9, 1.4, 0.2],
+//!           &[4.9, 3.1, 1.5, 0.1],
+//!           &[7.0, 3.2, 4.7, 1.4],
+//!           &[6.4, 3.2, 4.5, 1.5],
+//!           &[6.9, 3.1, 4.9, 1.5],
+//!           &[5.5, 2.3, 4.0, 1.3],
+//!           &[6.5, 2.8, 4.6, 1.5],
+//!           &[5.7, 2.8, 4.5, 1.3],
+//!           &[6.3, 3.3, 4.7, 1.6],
+//!           &[4.9, 2.4, 3.3, 1.0],
+//!           &[6.6, 2.9, 4.6, 1.3],
+//!           &[5.2, 2.7, 3.9, 1.4],
+//!           ]);
+//! let y: Vec<f64> = vec![
+//!           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+//! ];
+//!
+//! let (x_train, x_test, y_train, y_test) = train_test_split(&x, &y, 0.2, true);
+//!
+//! println!("X train: {:?}, y train: {}, X test: {:?}, y test: {}",
+//!             x_train.shape(), y_train.len(), x_test.shape(), y_test.len());
+//! ```
+//!
+//! When we partition the available data into two disjoint sets, we drastically reduce the number of samples that can be used for training.
+//!
+//! One way to solve this problem is to use k-fold cross-validation. With k-fold validation, the dataset is split into k disjoint sets.
+//! A model is trained using k - 1 of the folds, and the resulting model is validated on the remaining portion of the data.
+//!
+//! The simplest way to run cross-validation is to use the [cross_val_score](./fn.cross_validate.html) helper function on your estimator and the dataset.
+//!
+//! ```
+//! use smartcore::linalg::naive::dense_matrix::DenseMatrix;
+//! use smartcore::model_selection::{KFold, cross_validate};
+//! use smartcore::metrics::accuracy;
+//! use smartcore::linear::logistic_regression::LogisticRegression;
+//!
+//! //Iris data
+//! let x = DenseMatrix::from_2d_array(&[
+//!           &[5.1, 3.5, 1.4, 0.2],
+//!           &[4.9, 3.0, 1.4, 0.2],
+//!           &[4.7, 3.2, 1.3, 0.2],
+//!           &[4.6, 3.1, 1.5, 0.2],
+//!           &[5.0, 3.6, 1.4, 0.2],
+//!           &[5.4, 3.9, 1.7, 0.4],
+//!           &[4.6, 3.4, 1.4, 0.3],
+//!           &[5.0, 3.4, 1.5, 0.2],
+//!           &[4.4, 2.9, 1.4, 0.2],
+//!           &[4.9, 3.1, 1.5, 0.1],
+//!           &[7.0, 3.2, 4.7, 1.4],
+//!           &[6.4, 3.2, 4.5, 1.5],
+//!           &[6.9, 3.1, 4.9, 1.5],
+//!           &[5.5, 2.3, 4.0, 1.3],
+//!           &[6.5, 2.8, 4.6, 1.5],
+//!           &[5.7, 2.8, 4.5, 1.3],
+//!           &[6.3, 3.3, 4.7, 1.6],
+//!           &[4.9, 2.4, 3.3, 1.0],
+//!           &[6.6, 2.9, 4.6, 1.3],
+//!           &[5.2, 2.7, 3.9, 1.4],
+//!           ]);
+//! let y: Vec<f64> = vec![
+//!           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+//! ];
+//!
+//! let cv = KFold::default().with_n_splits(3);
+//!
+//! let results = cross_validate(LogisticRegression::fit,   //estimator
+//!                                 &x, &y,                 //data
+//!                                 Default::default(),     //hyperparameters
+//!                                 cv,                     //cross validation split
+//!                                 &accuracy).unwrap();    //metric
+//!
+//! println!("Training accuracy: {}, test accuracy: {}",
+//!     results.mean_test_score(), results.mean_train_score());
+//! ```
+//!
+//! The function [cross_val_predict](./fn.cross_val_predict.html) has a similar interface to `cross_val_score`,
+//! but instead of test error it calculates predictions for all samples in the test set.
 
 use crate::api::Predictor;
 use crate::error::Failed;