Metadata-Version: 1.1 Name: scikit-splearn Version: 1.0.1 Summary: Python module for spectral learning of weighted automata Home-page: https://gitlab.lif.univ-mrs.fr/dominique.benielli/scikit-splearn.git Author: François Denis and Rémi Eyraud and Denis Arrivault and Dominique Benielli Author-email: francois.denis@lif.univ-mrs.fr remi.eyraud[A]lif.uni-mrs.fr.antispamdenis.arrivault@lif.univ-mrs.fr dominique.benielli@univ-amu.fr License: new BSD Description: The **SCIKIT-SPLEARN** package is a Python tool of the `Scikit-Spectral Learning (scikit-splearn) <http://splearning.sourceforge.net/>`_, scikit-splearn is a toolbox in python for spectral learning algorithms. scikit-splearn is a sckit compatible SP2Learning toolbox. scikit-splearn is a toolbox in python for spectral learning algorithms. These algorithms aim at learning Weighted Automata (WA) using what is named a Hankel matrix. The toolbow thus provides also a class for WA (with a bunch of usefull methods), another one for Hankel matrix, and a class for loading data and/or automata, and a class Sample that allows the storage of the data in a usefull dictionnary form. As WA are a generalisation of classical Probabilistic Automaton, everything works for these simpler models. The core of the learning algorithms is to compute a singular values decomposition of the Hankel matrix and then to construct the weighted automata from the elements of the decomposition. This is done in the class Learning. In its classic version, the rows of the Hankel matrix are prefixes while its columns are suffixes. Each cell contains then the probability of the sequence starting with the corresponding prefix and ending with the corresponding suffix. In the case of learning, the cells contain observed frequencies. scikit-splearn provides other versions, where each cell contains the probability that the corresponding sequence is prefix, a suffix, or a factor. Formally, the Hankel matrix is bi-infinite. Hence, in case of learning, one has to concentrate on a finite portion. The parameters lrows and lcolumn allows to specified which subsequences are taken into account as rows and columns of the finite matrix. If, instead of a list, an integer is provided, the finite matrix will have all rows and columns that correspond to subsequences up to these given lengths. The learning method requires also the information about the rank of the matrix. This rank corresponds to the number of states of a minimal WA computing the matrix (in case of learning, this is the estimated number of states of the target automaton). There is no easy way to evaluate the rank, a cross-validation approach is usually used to find the best possible value. Finally, splearn provides 2 ways to store the Hankel matrix: a classical one as an array, and a sparse version using scipy.sparse. The original scikit-splearn Toolbox is developed in Python at `LabEx Archimède <http://labex-archimede.univ-amu.fr/>`_, as a `LIF <http://www.lif.univ-mrs.fr/>`_ and I2M project, Aix-Marseille Université. This package, as well as the scikit-splearn toolbox, is Free software, released under BSD License. The latest version of **scikit-splearn** can be downloaded from the following `PyPI page `pythonhosted site <http://pythonhosted.org/scikit-splearn/>`_. The documentation is available as a `pythonhosted site <http://pythonhosted.org/scikit-splearn/>`_. `gitlab project <https://gitlab.lif.univ-mrs.fr/dominique.benielli/scikit-splearn>`_, which provides the git repository managing the source code and where issues can be reported. .. :changelog: History ======= 1.0.0 (2016-06-30) ------------------ First version 1.0.1 (2016-10-07) ------------------ Bug setup correction .. -*- mode: rst -*- History ------- People ------ .. hlist:: * François Denis * Rémi Eyraud * Denis Arrivault * Dominique Benielli Platform: UNKNOWN Classifier: Development Status :: 5 - Production/Stable Classifier: Intended Audience :: Science/Research Classifier: Intended Audience :: Developers Classifier: Natural Language :: English Classifier: License :: OSI Approved :: BSD License Classifier: Operating System :: MacOS :: MacOS X Classifier: Operating System :: POSIX :: Linux Classifier: Programming Language :: Python :: 3.4 Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Scientific/Engineering :: Mathematics