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scikit-nano: Python Toolkit for Nanoscience.

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scikit-nano documentation — scikit-nano v0.3.21 Reference Guide

November 10, 2015. Is a python toolkit for generating and analyzing. Core package code (. Structure data I/O (. Last updated on Nov 10, 2015.

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scikit-nano

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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...

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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:.

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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.

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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.

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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...

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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.

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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...

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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.

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Technorazzi: Updates on Science and Technology

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