Search Results for "python feature selection"

...Split mesh based on surface connectivity. Extract the largest connected area. Calculate areas, volumes, center of mass, average sizes etc. Calculate vertex and face normals, curvatures, feature edges. Fill mesh holes.

Yet Another Audio Feature Extractor is a toolbox for audio analysis. Easy to use and efficient at extracting a large number of audio features simultaneously. WAV and MP3 files supported, or embedding in C++, Python or Matlab applications.

...The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

This is an automatic spike detection program which takes account of probe geometry and produces a .mask file to be used with the new masked version of KlustaKwik. We recommend you use Python 2.6 or 2.7, e.g. a free academic version can be obtained from Entthought Python. The input files for SpiKeDeteKt are: .dat (raw data file) .probe (probe file, described below - user constructed) parameters.py (optional - otherwise it uses defaultparameters.py) SpiKeDeteKt outputs the following files: .fet.n (feature file) .mask.n (needed for using the new (masked) KlustaKwik) .clu.n (a trivial clue file where everything is put into a single cluster) .fmask.n (trial - float masks instead of binary, we are using this for testing masked KlustaKwik) .spk.n (spike file) .upsk.n (unfiltered spike waveform) .res.n (list of spike times) .xml (an xml file with all the parameters that can subsequently be used by neuroscope or klusters) .fil (highpass filtered data) .h5 (

Surface Defects Analyzer is the project of control system implementation which is used for detecting and measurement of geometric parameters of surface defects based on stereo images processing.

A Python function library to extract EEG feature from EEG time series in standard Python and numpy data structure. Features include classical spectral analysis, entropies, fractal dimensions, DFA, inter-channel synchrony and order, etc.

DimReduction project provide an open-source multiplatform (Java) graphical environment for bioinformatics problems that supports many feature selection algorithms, pattern recognition techniques, criterion functions and graphic visualization tools.

Java GUI to analyze one (or more, in batch mode) datasets with several feature selection methods at the same time and also performing an "ensemble" analysis. It can be easily extended to include any feature selection algorithm.

This is a Python package designed to process Penn Treebank Release II-style combined trees (.mrg files) into useful objects for tree traversal, feature extraction, and statistical analysis. For more information, go to http://mrgutils.sourceforge.net

Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.