|0.2.0||Jan 3, 2021|
|0.1.0||Sep 26, 2020|
#78 in Machine learning
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The Most Advanced Machine Learning Library In Rust.
Welcome to SmartCore, the most advanced machine learning library 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:
To start using SmartCore simply add the following to your Cargo.toml file:
[dependencies] smartcore = "0.2.0"
All machine learning algorithms in SmartCore are grouped into these broad categories:
- Clustering, unsupervised clustering of unlabeled data.
- Martix Decomposition, various methods for matrix decomposition.
- Linear Models, regression and classification methods where output is assumed to have linear relation to explanatory variables
- Ensemble Models, variety of regression and classification ensemble models
- Tree-based Models, classification and regression trees
- Nearest Neighbors, K Nearest Neighbors for classification and regression
- Naive Bayes, statistical classification technique based on Bayes Theorem
- SVM, support vector machines
For example, you can use this code to fit a K Nearest Neighbors classifier to a dataset that is defined as standard Rust vector:
// DenseMatrix defenition use smartcore::linalg::naive::dense_matrix::*; // KNNClassifier use smartcore::neighbors::knn_classifier::*; // Various distance metrics use smartcore::math::distance::*; // Turn Rust vectors 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 let y = vec![2., 2., 2., 3., 3.]; // Train classifier let knn = KNNClassifier::fit(&x, &y, Default::default()).unwrap(); // Predict classes let y_hat = knn.predict(&x).unwrap();