ridge_regression.h

/* MLPACK 0.2
 *
 * Copyright (c) 2008, 2009 Alexander Gray,
 *                          Garry Boyer,
 *                          Ryan Riegel,
 *                          Nikolaos Vasiloglou,
 *                          Dongryeol Lee,
 *                          Chip Mappus, 
 *                          Nishant Mehta,
 *                          Hua Ouyang,
 *                          Parikshit Ram,
 *                          Long Tran,
 *                          Wee Chin Wong
 *
 * Copyright (c) 2008, 2009 Georgia Institute of Technology
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License as
 * published by the Free Software Foundation; either version 2 of the
 * License, or (at your option) any later version.
 *
 * This program 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 this program; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
 * 02110-1301, USA.
 */
#ifndef RIDGE_REGRESSION_H_
#define RIDGE_REGRESSION_H_

#include "fastlib/fastlib.h"
#include "mlpack/quicsvd/quicsvd.h"
#include "ridge_regression_util.h"

class RidgeRegression {
 public:

  RidgeRegression() {
  }

  void Init(fx_module *module, const Matrix &predictors, 
            const Matrix &predictions, 
            bool use_normal_equation_method = true);

  void Init(fx_module *module, const Matrix &input_data, index_t selector,
            bool use_normal_equation_method = true);

  void Init(fx_module *module, 
            const Matrix &input_data, 
            const GenVector<index_t> &predictor_indices,
            index_t &prediction_index, bool use_normal_equation_method = true);

  void Init(fx_module *module, 
            const Matrix &input_data, 
            const GenVector<index_t> &predictor_indices,
            const Matrix &prediction, bool use_normal_equation_method = true);

  void ReInitTargetValues(const Matrix &input_data, 
                          index_t target_value_index);

  void ReInitTargetValues(const Matrix &target_values_in);

  void Destruct();

  void SVDRegress(double lambda,
                  const GenVector<index_t> *predictor_indices = NULL);

  void CrossValidatedRegression(double lambda_min, double lambda_max,
                                index_t num);

  void FeatureSelectedRegression
  (const GenVector<index_t> &predictor_indices, 
   const GenVector<index_t> &prune_predictor_indices, 
   const Matrix &original_target_training_values,
   GenVector<index_t> *output_predictor_indices);

  double ComputeSquareError();

  void Predict(const Matrix &dataset, Vector *new_predictions);

  void Predict(const Matrix &dataset, 
               const GenVector<index_t> &predictor_indices,
               Vector *new_predictions);

  void factors(Matrix *factors);

 private:

  fx_module *module_;

  Matrix predictors_;

  Matrix predictions_;

  Matrix covariance_;

  Matrix factors_;

  void ComputeLinearModel_(double lambda_sq, const Vector &singular_values, 
                           const Matrix &u, const Matrix &v_t,
                           int num_features);
  
  void BuildDesignMatrixFromIndexSet_
  (const Matrix &input_data, const double *predictions,
   const GenVector<index_t> *predictor_indices);
  
  void BuildCovariance_(const Matrix &input_data, 
                        const GenVector<index_t> *predictor_indices,
                        const double *predictions_in);

  void ExtractCovarianceSubset_
  (const Matrix &precomputed_covariance,
   const GenVector<index_t> *loo_current_predictor_indices,
   Matrix *precomputed_covariance_subset);

  void ExtractSubspace_(Matrix *u, Vector *singular_values, Matrix *v_t,
                        const GenVector<index_t> *predictor_indices);
};

#include "ridge_regression_impl.h"
#endif