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/*********************************************************************
MLDemos: A User-Friendly visualization toolkit for machine learning
Copyright (C) 2010 Basilio Noris
Contact: mldemos@b4silio.com
Mixture of Logisitics Regression
Copyright (C) 2011 Stephane Magnenat
Contact: stephane at magnenat dot net
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public License,
version 3 as published by the Free Software Foundation.
This library 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
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free
Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*********************************************************************/
#ifndef _MLR_MIXTURE_LOGISTIC_REGRESSION_H
#define _MLR_MIXTURE_LOGISTIC_REGRESSION_H
#include <Eigen/Core>
#include <Eigen/Eigen>
#include <vector>
namespace MLR
{
using namespace Eigen;
using namespace std;
double uniformRand(double min, double max);
double gaussianRand(double mean, double sigm);
double sigm(double v);
double sgn(double v);
struct Classifier
{
MatrixXd w;
VectorXd b;
VectorXd v;
double v_b;
double beta;
Classifier(unsigned cutCount, unsigned dataSize, double beta);
unsigned getSize() const;
unsigned bIdx() const;
unsigned vIdx() const;
unsigned vbIdx() const;
vector<double> lowerBounds() const;
vector<double> upperBounds() const;
vector<double> toRawVector() const;
static Classifier fromRawVector(const double *data, int count, int dim, double beta);
void setRandom(double dataAVrStd);
double evalCut(const VectorXd& x, int i) const;
double eval(const VectorXd& x) const;
double sumSquareError(const VectorXd& y, const MatrixXd& x) const;
friend std::ostream& operator<< (std::ostream& stream, const Classifier& that);
};
struct Data
{
VectorXd y;
MatrixXd x;
double beta;
int cutCount;
Data(int count, int dim, double beta, int cutCount):
y(count),
x(count, dim),
beta(beta),
cutCount(cutCount)
{}
};
// objective function
double f(unsigned n, const double* t, double* grad, void* f_data);
struct Norm2ConstraintData
{
unsigned start;
unsigned len;
Norm2ConstraintData(unsigned start, unsigned len):
start(start),
len(len)
{}
};
// constraint function
double norm2_constraint(unsigned n, const double *t, double *grad, void* c_data);
} // MLR
#endif // _MIXTURE_LOGISTIC_REGRESSION_H
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