nbc_main.cc

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/* 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.
 */
#include "simple_nbc.h"

const fx_entry_doc parm_nbc_main_entries[] = {
  {"train", FX_REQUIRED, FX_STR, NULL,
   " A file containing the training set\n"},
  {"test", FX_REQUIRED, FX_STR, NULL,
   " A file containing the test set\n"},
  {"output", FX_PARAM, FX_STR, NULL,
   " The file in which the output of the test would be "
   "written (defaults to 'output.csv')\n"},
  FX_ENTRY_DOC_DONE
};

const fx_submodule_doc parm_nbc_main_submodules[] = {
  {"nbc", &parm_nbc_doc,
   " Trains on a given set and number of classes and "
   "tests them on a given set\n"},
  FX_SUBMODULE_DOC_DONE
};

const fx_module_doc parm_nbc_main_doc = {
  parm_nbc_main_entries, parm_nbc_main_submodules,
  "This program test drives the Parametric Naive Bayes \n"
  "Classifier assuming that the features are sampled \n"
  "from a Gaussian distribution.\n"
};

int main(int argc, char* argv[]) {

  fx_module *root = fx_init(argc, argv, &parm_nbc_main_doc);


  const char *training_data_filename = fx_param_str_req(root, "train");
  Matrix training_data;
  data::Load(training_data_filename, &training_data);

  const char *testing_data_filename = fx_param_str_req(root, "test");
  Matrix testing_data;
  data::Load(testing_data_filename, &testing_data);


  SimpleNaiveBayesClassifier nbc;

  struct datanode* nbc_module = fx_submodule(root, "nbc");
  
  fx_timer_start(nbc_module, "training");

  nbc.InitTrain(training_data, nbc_module);

  fx_timer_stop(nbc_module, "training");

  Vector results;

  fx_timer_start(nbc_module, "testing");

  nbc.Classify(testing_data, &results);

  fx_timer_stop(nbc_module, "testing");


  const char *output_filename = fx_param_str(root, "output", "output.csv");

  FILE *output_file = fopen(output_filename, "w");

  ot::Print(results, output_file);

  fclose(output_file);

  fx_done(root);

  return 1;
}