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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 ...
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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!
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python3-scikit-video-0.1.2-1.lbn19.noarch
Video Processing SciKitVideo processing algorithms for SciPy,
including IO,
quality metrics, filtering, etc.
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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.
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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.
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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 ...
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python3-sklearn-deap-0.1.3-1.lbn19.noarch
UNKNOWN
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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 ...
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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.
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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 ...
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