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diff --git a/FEAST/FSToolbox/FSAlgorithms.h b/FEAST/FSToolbox/FSAlgorithms.h deleted file mode 100644 index e6ddba8..0000000 --- a/FEAST/FSToolbox/FSAlgorithms.h +++ /dev/null @@ -1,138 +0,0 @@ -/******************************************************************************* -** -** FSAlgorithms.h -** Provides the function definitions for the list of algorithms implemented -** in the FSToolbox. -** -** Author: Adam Pocock -** Created: 27/06/2011 -** -** Copyright 2010/2011 Adam Pocock, The University Of Manchester -** www.cs.manchester.ac.uk -** -** Part of the FEAture Selection Toolbox (FEAST), please reference -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual -** Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -** -** -** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. -** -** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester -** All rights reserved. -** -** Redistribution and use in source and binary forms, with or without modification, -** are permitted provided that the following conditions are met: -** -** - Redistributions of source code must retain the above copyright notice, this -** list of conditions and the following disclaimer. -** - Redistributions in binary form must reproduce the above copyright notice, -** this list of conditions and the following disclaimer in the documentation -** and/or other materials provided with the distribution. -** - Neither the name of The University of Manchester nor the names of its -** contributors may be used to endorse or promote products derived from this -** software without specific prior written permission. -** -** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -** -*******************************************************************************/ - -/******************************************************************************* - * All algorithms take an integer k which determines how many features to - * select, the number of samples and the number of features. Additionally each - * algorithm takes pointers to the data matrix, and the label vector, and - * a pointer to the output vector. The output vector should be pre-allocated - * with sizeof(double)*k bytes. - * - * Some algorithms take additional parameters, which given at the end of the - * standard parameter list. - * - * Each algorithm uses a forward search, and selects the feature which has - * the maxmimum MI with the labels first. - * - * All the algorithms except CMIM use an optimised variant which caches the - * previously calculated MI values. This trades space for time, but can - * allocate large amounts of memory. CMIM uses the optimised implementation - * given in Fleuret (2004). - *****************************************************************************/ - -#ifndef __FSAlgorithms_H -#define __FSAlgorithms_H - -/******************************************************************************* -** mRMR_D() implements the minimum Relevance Maximum Redundancy criterion -** using the difference variant, from -** -** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" -** H. Peng et al. IEEE Pattern Analysis and Machine Intelligence (PAMI) (2005) -*******************************************************************************/ -double* mRMR_D(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** CMIM() implements a discrete version of the -** Conditional Mutual Information Maximisation criterion, using the fast -** exact implementation from -** -** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation" -** F. Fleuret, JMLR (2004) -*******************************************************************************/ -double* CMIM(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** JMI() implements the JMI criterion from -** -** "Data Visualization and Feature Selection: New Algorithms for Nongaussian Data" -** H. Yang and J. Moody, NIPS (1999) -*******************************************************************************/ -double* JMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** DISR() implements the Double Input Symmetrical Relevance criterion -** from -** -** "On the Use of Variable Complementarity for Feature Selection in Cancer Classification" -** P. Meyer and G. Bontempi, (2006) -*******************************************************************************/ -double* DISR(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** ICAP() implements the Interaction Capping criterion from -** -** "Machine Learning Based on Attribute Interactions" -** A. Jakulin, PhD Thesis (2005) -*******************************************************************************/ -double* ICAP(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** CondMI() implements the CMI criterion using a greedy forward search -*******************************************************************************/ -double* CondMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); - -/******************************************************************************* -** betaGamma() implements the Beta-Gamma space from Brown (2009). -** This incoporates MIFS, CIFE, and CondRed. -** -** MIFS - "Using mutual information for selecting features in supervised neural net learning" -** R. Battiti, IEEE Transactions on Neural Networks, 1994 -** -** CIFE - "Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion" -** D. Lin and X. Tang, European Conference on Computer Vision (2006) -** -** The Beta Gamma space is explained in our paper -** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection" -** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan -** Journal of Machine Learning Research (JMLR), 2011 -*******************************************************************************/ -double* BetaGamma(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma); - -#endif |