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A toolkit with common assertions and mocks that plays nicely with the standard library

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

GenFu is a library you can use to generate realistic test data. It is composed of several property fillers that can populate commonly named properties through reflection using an internal database of values or randomly created data. You can override any of the fillers, give GenFu hints on how to fill them.

Try is an extensible Common Lisp test anti-framework with equal support for interactive and non-interactive workflows, as well as Emacs integration.

Mocking functions. Please get in touch to discuss features! Only regular functions available for now.