All Versions
26
Latest Version
Avg Release Cycle
16 days
Latest Release
2660 days ago

Changelog History
Page 1

  • v0.5.4 Changes

    January 06, 2017

    🆕 New Contributors

    💥 Breaking Changes

    • None

    🔋 Features

    • ➕ Add a new datasets module behind a datasets feature flag.
    • ➕ Add new classification scores: precision, recall, and f1.
    • ➕ Add a new Transformer::fit function to allow prefitting of a Transformer before use.

    🐛 Bug Fixes

    • None

    Minor Changes

    • LinRegressor now uses solve instead of inverse for improved accuracy and stability.

  • v0.5.3 Changes

    December 20, 2016

    💥 Breaking Changes

    • None

    🔋 Features

    • ➕ Adding a new confusion_matrix module.

    🐛 Bug Fixes

    • None

    Minor Changes

    • ⚡️ Updated rulinalg dependency to 0.3.7.

  • v0.5.2 Changes

    November 17, 2016

    🆕 New Contributors

    💥 Breaking Changes

    • None

    🔋 Features

    • None

    🐛 Bug Fixes

    • Regularization constant for GMM is now only added to diagonal.

    Minor Changes

    • ➕ Added some better Result handling to GMM.

  • v0.5.1 Changes

    October 03, 2016

    This version includes no changes but is a bump due to a crates bug.

    👀 See the notes for 0.5.0 below.

  • v0.5.0 Changes

    🚀 This is another fairly large release. Thank you to everyone who contributed!

    🆕 New Contributors

    💥 Breaking Changes

    🔋 Features

    • ➕ Adding RMSProp gradient descent algorithm. #121
    • ➕ Adding cross validation. #125
    • ➕ Adding a new Shuffler transformer. #135

    🐛 Bug Fixes

    • None

    Minor Changes

    • ➕ Adding benchmarks
    • Initiate GMM with sample covariance of data (instead of identity matrix).

  • v0.4.4 Changes

    August 12, 2016

    💥 Breaking Changes

    • None

    🔋 Features

    • ➕ Adding new Transformer trait for data preprocessing.
    • ➕ Adding a MinMax transformer.
    • ➕ Adding a Standardizer transformer.

    Minor Changes

    • None

  • v0.4.3 Changes

    July 28, 2016

    🆕 New Contributors

    • 🚀 tafia who is responsible for all changes in this release.

    💥 Breaking Changes

    • None

    🔋 Features

    • None

    Minor Changes

    • 👯 Made neural nets more efficient by reducing clones and some restructuring.
    • 🐎 Removing unneeded copying in favour of slicing for performance.
    • Using iter_rows in favour of manually row iterating by chunks.

  • v0.4.2 Changes

    July 24, 2016

    💥 Breaking Changes

    • None

    🔋 Features

    • None

    Minor Changes

    • 🛠 Fixed a significant bug in the K-Means algorithm. Centroids ⚡️ were not updating correctly during M-step.

  • v0.4.1 Changes

    July 21, 2016

    💥 Breaking Changes

    • None

    🔋 Features

    • ➕ Added experimental implementation of DBSCAN clustering.

    Minor Changes

    • ➕ Added new example for K-Means clustering in repo.

  • v0.4.0 Changes

    July 12, 2016

    🚀 This is the biggest release so far. Primarily because the linalg module has been pulled out into its own crate: rulinalg.

    In addition to this there have been a number of improvements to the linalg 🚀 and learning moduled in this release.

    💥 Breaking Changes

    • The linalg module pulled out and replaced by reexports of rulinalg. All structs are now imported at the linalg level, i.e. linalg::matrix::Matrix -> linalg::Matrix.
    • Decomposition methods now return Result instead of panicking on fail.
    • K-Means now has a trait for Initializer - which allows generic initialization algorithms.

    🔋 Features

    • 🆕 New error handling in both the linalg (now rulinalg) and learning modules.
    • 🐛 Bug fixed in eigendecomposition: it can now be used!
    • K-means can now take a generic initialization algorithm.

    Minor Changes

    • Optimization and code cleanup in the decomposition methods.
    • Some optimization in the K-Means model.