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python3-wcwidth-0.1.7-1.lbn19.noarch: .. image:: https://img.shields.io/travis/jquast/wcwidth.svg :target: https://travisci.org/jquast/wcwidth :alt: Travis Continous Integration.. image:: https://img.shields.io/coveralls/jquast/wcwidth.svg :target: https://coveralls.io/r/jquast/wcwidth :alt: Coveralls Code Coverage.. image:: https://img.shields.io/pypi/v/wcwidth.svg :target: https://pypi.python.org/pypi/wcwidth/ ...

python3-traitlets-4.2.2-1.lbn19.noarch: A configuration system for Python applications.

python3-tornado-4.3-2.lbn19.x86_64: Tornado is an open source version of the scalable, non-blocking web server and tools. The framework is distinct from most mainstream web server frameworks (and certainly most Python frameworks) because it is non-blocking and reasonably fast. Because it is non-blocking and uses epoll, it can handle thousands of simultaneous standing connections, which means it is ideal for real-time web services.

python3-tkinter-3.4.2-6.lbn19.x86_64: The Tkinter (Tk interface) program is an graphical user interface for the Python scripting language.

python3-thinc-5.0.8-1.lbn19.x86_64: .. image:: https://travisci.org/spacyio/thinc.svg?branchmaster :target: https://travisci.org/spacyio/thinc thinc: Learn supersparse multiclass models ===============thinc is a Cython library for learning models with millions of parameters and dozens of classes. It drives https://spacy.io , a pipeline of very efficient NLP components. I've only used thinc from Cython; no real Python API is ...

python3-terminado-0.6-2.lbn19.noarch: This is a Tornado <http://tornadoweb.org/>_ websocket backend for the term.js <https://github.com/chjj/term.js>_ Javascript terminal emulator It evolved out of pyxterm <https://github.com/mitotic/pyxterm>_, which was part of GraphTerm <https://github.com/mitotic/graphterm>_ (as lineterm.py), v0.57.0 (20140718), and ultimately derived from the publicdomain ...

python3-sputnik-0.9.3-1.lbn19.noarch: .. image:: https://travisci.org/spacyio/sputnik.svg?branchmaster :target: https://travisci.org/spacyio/sputnik Sputnik: a data package manager library ===Sputnik is a library for managing data packages for another library, e.g., models for a machine learning library.It also comes with a command-line interface, run ``sputnik --help`` or ``python -m sputnik --help`` for assistance.Sputnik is a ...

python3-spark-sklearn-0.1.2-1.lbn19.noarch: Sparksklearn Spark integration with scikitlearn and numpy.This package contains some tools to integrate the [Spark computing framework](http://spark.apache.org/) with the popular [scikitlearn machine library](http://scikitlearn.org/stable/). Among other tools: train and evaluate multiple scikitlearn models in parallel. It is a distributed analog to the ...

python3-spacy-0.101.0-1.lbn19.x86_64: .. image:: https://travisci.org/spacyio/spaCy.svg?branchmaster :target: https://travisci.org/spacyio/spaCy spaCy: Industrialstrength NLP spaCy is a library for advanced natural language processing in Python and Cython.Documentation and details: https://spacy.io/spaCy is built on the very latest research, but it isn't researchware. It was designed from day 1 to be used in real products. It's ...

python3-snakebite-2.11.0-1.lbn19.noarch: Snakebite is a python library that provides a pure python HDFS client and a wrapper around Hadoops minicluster. The client uses protobuf for communicating with the NameNode and comes in the form of a library and a command line interface. Currently, the snakebite client supports most actions that involve the Namenode and reading data from DataNodes.

python3-sklearn-pandas-1.1.0-1.lbn19.noarch: Sklearn-pandas ============== This module provides a bridge between `Scikit- Learn <http://scikit-learn.org/stable/>`__'s machine learning methods and `pandas <http://pandas.pydata.org/>`__-style Data Frames. In particular, it provides: 1. A way to map ``DataFrame`` columns to transformations, which are later recombined into features. 2. A way to cross-validate a pipeline that takes a ...

python3-sklearn-contrib-lightning-0.3.0-1.lbn19.x86_64: .. * mode: rst *.. image:: https://travisci.org/scikitlearncontrib/lightning.svg?branchmaster :target: https://travisci.org/scikitlearncontrib/lightning.. image:: https://ci.appveyor.com/api/projects/status/mmm0llccmvn5iooq?svgtrue :target: https://ci.appveyor.com/project/fabianp/lightningbpc6r/branch /masterlightning lightning is a library for largescale linear classification, regression and ...

python3-sklearn-compiledtrees-1.2-1.lbn19.x86_64: In some use cases, predicting given a model is in the hot-path, so speeding up decision tree evaluation is very useful. An effective way of speeding up evaluation of decision trees can be to generate code representing the evaluation of the tree, compile that to optimized object code, and dynamically load that file via dlopen/dlsym or equivalent. See https://courses.cs.washington.edu/courses/cse501/10au/compile-machlearn.pdf for a detailed discussion, and http://tullo.ch/articles/decision-tree-evaluation/ for a more pedagogical explanation and more benchmarks in C++. This package implements compiled decision tree evaluation for the simple case of a single-output regression tree or ensemble.

python3-scipy-0.16.1-6.lbn19.x86_64: Scipy is open-source software for mathematics, science, and engineering. The core library is NumPy which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world's leading scientists and engineers.

python3-scikit-video-0.1.2-1.lbn19.noarch: Video Processing SciKitVideo processing algorithms for SciPy, including IO, quality metrics, filtering, etc.

python3-scikit-sparse-0.3-1.lbn19.x86_64: Sparse matrix tools.This is a home for sparse matrix code in Python that plays well with scipy.sparse, but that is somehow unsuitable for inclusion in scipy proper. Usually this will be because it is released under the GPL.So far we have a wrapper for the CHOLMOD library for sparse Cholesky decomposition. Further contributions are welcome!

python3-scikit-neuralnetwork-0.7-1.lbn19.noarch: :orphan: scikit-neuralnetwork ==================== Deep neural network implementation without the learning cliff! This library implements multi-layer perceptrons as a wrapper for the powerful ``Lasagne`` library that's compatible with ``scikit-learn`` for a more user-friendly and Pythonic interface. **NOTE**: This project is possible thanks to the `nucl.ai Conference <http://nucl.ai/>`_ on ...

python3-scikit-nano-0.3.21-1.lbn19.noarch: scikitnano *scikitnano* is a python toolkit for generating and analyzing nanostructure data.*scikitnano* can generate structure data (i.e., atomic coordinates) for the following classes of nanostructures: * Fullerenes * Graphene * *N*layer graphene * Bilayer graphene with more fine control over relative layer orientation, including relative rotation ...


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