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* First draft of the new n-dimensional arrays + NB use case * Improves default implementation of multiple Array methods * Refactors tree methods * Adds matrix decomposition routines * Adds matrix decomposition methods to ndarray and nalgebra bindings * Refactoring + linear regression now uses array2 * Ridge & Linear regression * LBFGS optimizer & logistic regression * LBFGS optimizer & logistic regression * Changes linear methods, metrics and model selection methods to new n-dimensional arrays * Switches KNN and clustering algorithms to new n-d array layer * Refactors distance metrics * Optimizes knn and clustering methods * Refactors metrics module * Switches decomposition methods to n-dimensional arrays * Linalg refactoring - cleanup rng merge (#172) * Remove legacy DenseMatrix and BaseMatrix implementation. Port the new Number, FloatNumber and Array implementation into module structure. * Exclude AUC metrics. Needs reimplementation * Improve developers walkthrough New traits system in place at `src/numbers` and `src/linalg` Co-authored-by: Lorenzo <tunedconsulting@gmail.com> * Provide SupervisedEstimator with a constructor to avoid explicit dynamical box allocation in 'cross_validate' and 'cross_validate_predict' as required by the use of 'dyn' as per Rust 2021 * Implement getters to use as_ref() in src/neighbors * Implement getters to use as_ref() in src/naive_bayes * Implement getters to use as_ref() in src/linear * Add Clone to src/naive_bayes * Change signature for cross_validate and other model_selection functions to abide to use of dyn in Rust 2021 * Implement ndarray-bindings. Remove FloatNumber from implementations * Drop nalgebra-bindings support (as decided in conf-call to go for ndarray) * Remove benches. Benches will have their own repo at smartcore-benches * Implement SVC * Implement SVC serialization. Move search parameters in dedicated module * Implement SVR. Definitely too slow * Fix compilation issues for wasm (#202) Co-authored-by: Luis Moreno <morenol@users.noreply.github.com> * Fix tests (#203) * Port linalg/traits/stats.rs * Improve methods naming * Improve Display for DenseMatrix Co-authored-by: Montana Low <montanalow@users.noreply.github.com> Co-authored-by: VolodymyrOrlov <volodymyr.orlov@gmail.com>
76 lines
2.0 KiB
Markdown
76 lines
2.0 KiB
Markdown
# Changelog
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [Unreleased]
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## Added
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- Seeds to multiple algorithims that depend on random number generation.
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- Added feature `js` to use WASM in browser
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- Drop `nalgebra-bindings` feature
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- Complete refactoring with *extensive API changes* that includes:
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* moving to a new traits system, less structs more traits
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* adapting all the modules to the new traits system
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* moving towards Rust 2021, in particular the use of `dyn` and `as_ref`
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* reorganization of the code base, trying to eliminate duplicates
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## BREAKING CHANGE
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- Added a new parameter to `train_test_split` to define the seed.
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## [0.2.1] - 2022-05-10
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## Added
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- L2 regularization penalty to the Logistic Regression
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- Getters for the naive bayes structs
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- One hot encoder
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- Make moons data generator
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- Support for WASM.
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## Changed
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- Make serde optional
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## [0.2.0] - 2021-01-03
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### Added
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- DBSCAN
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- Epsilon-SVR, SVC
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- Ridge, Lasso, ElasticNet
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- Bernoulli, Gaussian, Categorical and Multinomial Naive Bayes
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- K-fold Cross Validation
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- Singular value decomposition
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- New api module
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- Integration with Clippy
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- Cholesky decomposition
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### Changed
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- ndarray upgraded to 0.14
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- smartcore::error:FailedError is now non-exhaustive
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- K-Means
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- PCA
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- Random Forest
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- Linear and Logistic Regression
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- KNN
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- Decision Tree
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## [0.1.0] - 2020-09-25
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### Added
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- First release of smartcore.
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- KNN + distance metrics (Euclidian, Minkowski, Manhattan, Hamming, Mahalanobis)
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- Linear Regression (OLS)
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- Logistic Regression
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- Random Forest Classifier
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- Decision Tree Classifier
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- PCA
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- K-Means
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- Integrated with ndarray
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- Abstract linear algebra methods
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- RandomForest Regressor
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- Decision Tree Regressor
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- Serde integration
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- Integrated with nalgebra
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- LU, QR, SVD, EVD
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- Evaluation Metrics
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