Scikit-criteria is a collection of Multiple-criteria decision analysis ( MCDA. Methods integrated into scientific python stack. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. Provided by Read the Docs. On Read the Docs. Free document hosting provided by Read the Docs.
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Indices and tables — Scikit-Criteria 0.0.1 documentation | scikit-criteria.org Reviews
https://scikit-criteria.org
Scikit-criteria is a collection of Multiple-criteria decision analysis ( MCDA. Methods integrated into scientific python stack. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. Provided by Read the Docs. On Read the Docs. Free document hosting provided by Read the Docs.
scikit-criteria.org
Python Module Index — Scikit-Criteria 0.0.1 documentation
http://www.scikit-criteria.org/en/latest/py-modindex.html
Provided by Read the Docs. On Read the Docs. Free document hosting provided by Read the Docs.
API — Scikit-Criteria 0.0.1 documentation
http://www.scikit-criteria.org/en/latest/api/modules.html
Skcriteria.common.util module. Skcriteria.common.norm module. Skcriteria.common.rank module. Provided by Read the Docs. On Read the Docs. Free document hosting provided by Read the Docs.
Index — Scikit-Criteria 0.0.1 documentation
http://www.scikit-criteria.org/en/latest/genindex.html
Dominance() (in module skcriteria.common.rank). Eps() (in module skcriteria.common.norm). Max() (in module skcriteria.common.norm). Push negatives() (in module skcriteria.common.norm). Rankdata() (in module skcriteria.common.rank). Ratio() (in module skcriteria.moora). Skcriteria.common.norm (module). Skcriteria.common.rank (module). Skcriteria.common.util (module). Sum() (in module skcriteria.common.norm). Vector() (in module skcriteria.common.norm). Wprod() (in module skcriteria.wprod). On Read the Docs.
skcriteria package — Scikit-Criteria 0.0.1 documentation
http://www.scikit-criteria.org/en/latest/api/skcriteria.html
Skcriteria.common.util module. Skcriteria.common.norm module. Skcriteria.common.rank module. Provided by Read the Docs. On Read the Docs. Free document hosting provided by Read the Docs.
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ComputerBlog
Programma 101: Il libro. Informativa estesa sull’uso dei Cookie. Attenti a chi avete tra gli amici su Facebook. Mar 08, 2017. Oggi vi parlerò di un metodo di indagine molto semplice ma potente, perché non sfrutta l’insicurezza del profilo della persona su cui state indagando, ma quella dei suoi amici. Vi spiego la situazione ideale: Avete una persona di cui non sapete. Telegram Desktop introduce i temi. Gen 13, 2017. Falla in Telegram sulle foto. Gen 03, 2017. Dic 03, 2016. Nov 26, 2016. Nov 18, 2016.
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Aiki-Jujitsu, Kick-Boxing and self defence in the Kingston-Upon-Thames area, for the new and the experienced Martial Artist. Find us in our new home, Chessington Sports Centre, for all your Martial Arts needs. Welcome to the website for Spirit Combat Kingston! We also coach Kick-Boxing, Self-Defence (a Ladies' Self Defence syllabus is available) and Weapons Combat. If you're looking for a local martial arts club, that offers cross training mixed into a Traditional Martial Art - look no further! June Stud...
scikit-bio
Scikit-bio is an open-source, BSD-licensed, python package providing data structures, algorithms, and educational resources for bioinformatics. Scikit-bio is currently in beta. We are very actively developing it, and backward-incompatible interface changes can and will arise. For more details, including what we mean by. To install the latest release of scikit-bio:. Equivalently, you can use the. Package manager available in Anaconda. You can verify your installation by running the scikit-bio unit tests:.
Indices and tables — Scikit-Criteria 0.0.1 documentation
Scikit-criteria is a collection of Multiple-criteria decision analysis ( MCDA. Methods integrated into scientific python stack. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. Provided by Read the Docs. On Read the Docs. Free document hosting provided by Read the Docs.
scikit-image: Image processing in Python — scikit-image
Image processing in Python. Is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. Filtering an image with. For more examples, please visit our gallery. Or any NumPy array! If you find this project useful, please cite:. PeerJ 2:e453 (2014) http:/ dx.doi.org/10.7717/peerj.453. Version 0.11.0 04/03/2015. Version 0.10.0 27/05/2014. Belgium, August 2012.
scikit-learn: machine learning in Python — scikit-learn 0.16.1 documentation
Scikit-learn 0.16 (Stable). Machine Learning in Python. Simple and efficient tools for data mining and data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. An introduction to machine learning with scikit-learn. Machine learning: the problem setting. Loading an example dataset. A tutorial on statistical-learning for scientific data processing. Nearest neighbor and the curse of dimensionality. Evalua...
scikit-multilearn | Multi-label classification package for python
Multi-label classification package for python. Download 0.0.1. A native Python implementation of a variety of multi-label classification algorithms. The list includes:. Label cooccurence-based partitioning clasifiers. Hierarchy of Multi-label Classifiers [in-progress]. Classifier chains and others [in-progress]. For reference purposes and integration needs a Meka wrapper class is implemented. Thus providing access to all methods available in meka, mulan and weka - the reference standard of the field.
scikit-nano
The scikit-nano Project is an open-source Python toolkit for nanoscience. Current Version: 0.3.21. You can install the latest version of scikit-nano using pip. Or by downloading the source code and installing it manually - see the Source. Tab above for more details. You can install the latest version of scikit-nano using pip. Or by downloading the source code and installing it manually - see the Source. Tab above for more details. You can install the latest version of scikit-nano using pip. Check out the...
skopt API documentation
Store and load results. Is a simple and efficient library for sequential model-based optimization, accessible to everybody and reusable in various contexts. The library is built on top of NumPy, SciPy and Scikit-Learn. Find the minimum of the noisy function. For more read our introduction to bayesian optimization. And the other examples. The library is still experimental and under heavy development. The development version can be installed through:. Run the tests by executing. In the top level directory.
