Model.tc

/*
 * Open source implementation of the ML3 classifier.
 *
 * If you find this software useful, please cite:
 *
 * "Multiclass Latent Locally Linear Support Vector Machines"
 * Marco Fornoni, Barbara Caputo and Francesco Orabona
 * JMLR Workshop and Conference Proceedings Volume 29 (ACML 2013 Proceedings)
 *
 * Copyright (c) 2013 Idiap Research Institute, http://www.idiap.ch/
 * Written by Marco Fornoni <marco.fornoni@idiap.ch>
 *
 * This file is part of the ML3 Software.
 *
 * ML3 is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License version 3 as
 * published by the Free Software Foundation.
 *
 * ML3 is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with ML3. If not, see <http://www.gnu.org/licenses/>.
 *
 * Model.tc
 * 
 * Class representing a ML3 model
 *
 *  Created on: Apr 20, 2013
 *      Author: Marco Fornoni
 */

//Default destructor
template<typename T>
Model<T>::~Model(){
}

//Default constructor
template<typename T>
Model<T>::Model(){
	tau=1;
	averaging=true;
	initStep=1;
	lambda=1;
	m=10;
	p=2;
	s=0;
	verbose=1;
	nFeats=0;
	nSamp=0;
	nCla=0;
	iter=0;
	maxCCCPIter=30;
	trTime=0;
	returnLocalBeta=false;
}


//Main setter method
template<typename T>
void Model<T>::setParams(std::vector<MatrixXT> _W, std::vector<MatrixXT> _W2, MatrixXT _localBetaClass,  T _lambda, T _p, T _tau, uint _maxCCCPIter, T _m, bool _averaging, uint _initStep, uint _s0, uint _verbose, bool _returnLocalBeta){
	m=_m;
	nCla=_W.size();
	nFeats=_W[0].cols();

	W=_W;
	W2=_W2;
	localBetaClass=_localBetaClass;
	lambda=_lambda;
	p=_p;
	tau= _tau;
	maxCCCPIter=_maxCCCPIter;

	averaging=_averaging;
	initStep=_initStep;
	s=_s0;
	verbose=_verbose;

	uint maxIter=maxCCCPIter+initStep;

	totLoss=VectorXT::Zero(maxIter);
	ael=VectorXT::Zero(maxIter);
	loss=VectorXT::Zero(maxIter);
	obj=VectorXT::Zero(maxIter);
	teAcc=VectorXT::Zero(maxIter);

	returnLocalBeta=_returnLocalBeta;
}

//Model field names
template<typename T>
const char *Model<T>::fnames[]=
{
		"lambda",
		"maxCCCPIter",
		"verbose",
		"nCla",
		"nSamp",
		"nFeats",
		"m",
		"p",
		"tau",
		"s",
		"averaging",
		"initStep",
		"totLoss",
		"ael",
		"loss",
		"obj",
		"teAcc",

		"iter",
		"W",
		"W2",
		"localBetaClass",
		"trTime",

		"returnLocalBeta"
};


//Model output streamer
template<typename T>
std::ostream& operator<<(std::ostream& o, const Model<T> & m)
{
	const uint maxEl=3;
	uint mcol=std::min((const uint)m.W[0].cols(),maxEl);
	uint mrep=std::min((const uint)m.localBetaClass.cols(),maxEl);
	
	o <<"Model:"<< std::endl;
	o << Model<T>::fnames[0]<<": "<<m.lambda<<std::endl;
	o << Model<T>::fnames[1]<<": "<<m.maxCCCPIter<<std::endl;
	o << Model<T>::fnames[2]<<": "<<m.verbose<<std::endl;
	o << Model<T>::fnames[3]<<": "<<m.nCla<<std::endl;
	o << Model<T>::fnames[4]<<": "<<m.nSamp<<std::endl;
	o << Model<T>::fnames[5]<<": "<<m.nFeats<<std::endl;
	o << Model<T>::fnames[6]<<": "<<m.m<<std::endl;
	o << Model<T>::fnames[7]<<": "<<m.p<<std::endl;
	o << Model<T>::fnames[8]<<": "<<m.tau<<std::endl;
	o << Model<T>::fnames[9]<<": "<<m.s<<std::endl;
	o << Model<T>::fnames[10]<<": "<<m.averaging<<std::endl;
	o << Model<T>::fnames[11]<<": "<<m.initStep<<std::endl;
	o << Model<T>::fnames[18]<<": "<<m.W[0].topLeftCorner(1,mcol)<<"..."<<std::endl;
	o << Model<T>::fnames[19]<<": "<<m.W2[0].topLeftCorner(1,mcol)<<"..."<<std::endl;
	if(m.returnLocalBeta)
		o << Model<T>::fnames[20]<<": "<<m.localBetaClass.topLeftCorner(mrep,1)<<"..."<<std::endl;

	return o;
}