moved FEAST Libraries

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+/*******************************************************************************
+**
+**  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