a37b552a7d
* Provide better output in flaky tests * feat: add seed parameter to multiple algorithms * Update changelog Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
70 lines
1.6 KiB
Markdown
70 lines
1.6 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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## 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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