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ProSper is a python library containing probabilistic algorithms to learndictionaries. Given a set of data points, the implemented algorithms seek tolearn the elementary components that have generated the data. The librarywidens the scope of dictionary learning approaches beyond implementations ofstandard approaches such as ICA, NMF or standard L1 sparse coding. Theimplemented algorithms are especially well-suited in cases when data consist ofcomponents that combine non-linearly and/or for data requiring flexible priordistributions. Furthermore, the implemented algorithms go beyond standardapproaches by inferring prior and noise parameters of the data, and theyprovide rich a-posteriori approximations for inference. The library is designedto be extendable and it currently includes: Binary Sparse Coding (BSC), TernarySparse Coding (TSC), Discrete Sparse Coding (DSC), Maximal Causes Analysis(MCA), Maximum Magnitude Causes Analysis (MMCA), and Gaussian Sparse Coding(GSC, a recent spike-and-slab sparse coding approach). The algorithms arescalable due to a combination of variational approximations andparallelization. Implementations of all algorithms allow for parallel executionon multiple CPUs and multiple machines for medium to large-scale applications.Typical large-scale runs of the algorithms can use hundreds of CPUs to learnhundreds of dictionary elements from data with tens of millions offloating-point numbers such that models with several hundred thousandparameters can be optimized. The library is designed to have minimaldependencies and to be easy to use. It targets users of dictionary learningalgorithms and Machine Learning researchers.

ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions