Search Results for "ofn-extract-objects.py"

tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python...

This is a commandline-terminal sokoban solver written in Ada. It is "generic" in the sense that it contains no domain specific strategies. It also provides a demonstration of the incredible power of the Hungarian Algorithm. The proper command to extract the archive and maintain the directory structure is "7z x filename".

AlgART Java libraries for processing arrays and matrices are open-source product, distributed under MIT license. So, anyone can use them for free without any restrictions. Main features: 63-bit addressing of array elements (64-bit long int indexes), memory model concept (allowing storing data in different schemes from RAM to mapped disk files), wide usage of lazy evaluations, built-in multithreading optimization for multi-core processors, wide set of image processing algorithms over...

LSGTL means LLX’s Static Graph Template Library which is a light-weighted header-only template library developed mainly for static graph analysis. LSGTL is expected to be used in laboratories for research purposes mostly.

A java tool for anytime and interactive sequence mining. Aims at providing users with a way of analyzing her activity traces and extract activity schemes from them.

Extract a Term Concept Map from Wikipedia. It takes an export of the Wikipedia and imports it into a database. The extracted term concept map can then serve as a basis for semantically enhancing applications, with the depth and bredth of the Wikipedia.

Generic engine to filter information. We wish to show that the power of expression of a filter makes it possible to appreciably reduce the size of the code necessary to extract information and that it is possible in Python.