minor fixes

Author: Mec-iS

src/cluster/kmeans.rs

@@ -11,7 +11,7 @@
 //! these re-calculated centroids becoming the new centers of their respective clusters. Next all instances of the training set are re-assigned to their closest cluster again.
 //! This iterative process continues until convergence is achieved and the clusters are considered settled.
 //!
-//! Initial choice of K data points is very important and has big effect on performance of the algorithm. smartcore uses k-means++ algorithm to initialize cluster centers.
+//! Initial choice of K data points is very important and has big effect on performance of the algorithm. `smartcore` uses k-means++ algorithm to initialize cluster centers.
 //!
 //! Example:
 //!

src/dataset/mod.rs

@@ -1,6 +1,6 @@
 //! Datasets
 //!
-//! In this module you will find small datasets that are used in smartcore mostly for demonstration purposes.
+//! In this module you will find small datasets that are used in `smartcore` mostly for demonstration purposes.
 pub mod boston;
 pub mod breast_cancer;
 pub mod diabetes;

src/lib.rs

@@ -75,7 +75,7 @@
 //! ```
 //!
 //! ## Overview
-//! 
+//!
 //! ### Supported algorithms
 //! All machine learning algorithms are grouped into these broad categories:
 //! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
@@ -86,7 +86,7 @@
 //! * [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
-//! 
+//!
 //! ### Linear Algebra traits system
 //! For an introduction to `smartcore`'s traits system see [this notebook](https://github.com/smartcorelib/smartcore-jupyter/blob/5523993c53c6ec1fd72eea130ef4e7883121c1ea/notebooks/01-A-little-bit-about-numbers.ipynb)
 

src/numbers/realnum.rs

@@ -1,5 +1,5 @@
 //! # Real Number
-//! Most algorithms in smartcore rely on basic linear algebra operations like dot product, matrix decomposition and other subroutines that are defined for a set of real numbers, ℝ.
+//! Most algorithms in `smartcore` rely on basic linear algebra operations like dot product, matrix decomposition and other subroutines that are defined for a set of real numbers, ℝ.
 //! This module defines real number and some useful functions that are used in [Linear Algebra](../../linalg/index.html) module.
 
 use num_traits::Float;

src/tree/mod.rs

@@ -9,7 +9,7 @@
 //! Decision trees suffer from high variance and often does not deliver best prediction accuracy when compared to other supervised learning approaches, such as linear and logistic regression.
 //! Hence some techniques such as [Random Forests](../ensemble/index.html) use more than one decision tree to improve performance of the algorithm.
 //!
-//! smartcore uses [CART](https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees_.28CART.29) learning technique to build both classification and regression trees.
+//! `smartcore` uses [CART](https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees_.28CART.29) learning technique to build both classification and regression trees.
 //!
 //! ## References:
 //!