About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications.

• Move "flow"-related classes into package "de.claas.mosis.flow" (e.g. Node and Link).
• Refined and improved "flow"-related tests (e.g. Iterator and Node tests).
• Refactored tests for data formats (e.g. PlainText and JSON tests).
• Added visitor design pattern for graph-based functions (e.g. initialization and processing).
• Documented parameters of Processor implementations.

About: MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

What's new in version 3.3?

• support sklearn versions up to 0.12
• cleanly support reload
• fail gracefully if pp server does not start
• several bug-fixes and improvements

About: Intended for .NET developers wanting to implement algorithms directly in a common .NET language (recommended: C#). Support for n-dim generic arrays, LAPACK, FFT, cells, logicals, 2D&3D plotting [...]

Initial Announcement on mloss.org.