DynaML was born out of the need to have a performant, extensible and easy to use Machine Learning research environment. Scala was a natural choice for these requirements due to its sprawling data science ecosystem (i.e. Apache Spark), its functional object-oriented duality and its interoperability with the Java Virtual Machine.
DynaML leverages a number of open source projects and builds on their useful features.
- Breeze for linear algebra operations with vectors, matrices etc.
- Spire for creating algebraic entities like Fields, Groups etc.
- Ammonite for the shell environment.
- DynaML uses the newly minted Wisp plotting library to generate aesthetic charts of common model validation metrics. In version 1.4 there is also integration of plotly which can now be imported and used directly in the shell environment.
The core api consists of :
- Model implementations
- Optimisation solvers
- Probability distributions/random variables
- Kernel functions for Non parametric models
Data Pipes :-
The pipes module aims to separate model pre-processing tasks such as cleaning data files, replacing missing or corrupt records, applying transformations on data etc:
- Ability to create arbitrary workflows from scala functions and join them
- Feature transformations such as wavelet transform, gaussian scaling, auto-encoders etc
- Changes to previous version:
Initial Announcement on mloss.org.
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