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/*
 * 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 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2012 yoo, thereisnoknife@gmail.com
 * Written (W) 2012 Heiko Strathmann
 */

#include <shogun/features/StreamingDenseFeatures.h>
#include <shogun/io/StreamingAsciiFile.h>
#include <shogun/labels/MulticlassLabels.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/kernel/LinearKernel.h>
#include <shogun/kernel/PolyKernel.h>
#include <shogun/kernel/CombinedKernel.h>
#include <shogun/classifier/mkl/MKLMulticlass.h>
#include <shogun/evaluation/StratifiedCrossValidationSplitting.h>
#include <shogun/evaluation/CrossValidation.h>
#include <shogun/evaluation/MulticlassAccuracy.h>

using namespace shogun;

void test_multiclass_mkl_cv()
{
   /* stream data from a file */
   int32_t num_vectors=50;
   int32_t num_feats=2;

   /* file data */
   char fname_feats[]="/home/eric/buildsys/shogun.data/toy/fm_train_real.dat";
   char fname_labels[]="/home/eric/buildsys/shogun.data/toy/label_train_multiclass.dat";
   CStreamingAsciiFile* ffeats_train=new CStreamingAsciiFile(fname_feats);
   CStreamingAsciiFile* flabels_train=new CStreamingAsciiFile(fname_labels);
   SG_REF(ffeats_train);
   SG_REF(flabels_train);

   /* streaming data */
   CStreamingDenseFeatures<float64_t>* stream_features=
         new CStreamingDenseFeatures<float64_t>(ffeats_train, false, 1024);
   CStreamingDenseFeatures<float64_t>* stream_labels=
         new CStreamingDenseFeatures<float64_t>(flabels_train, true, 1024);
   SG_REF(stream_features);
   SG_REF(stream_labels);

   /* matrix data */
   SGMatrix<float64_t> mat=SGMatrix<float64_t>(num_feats, num_vectors);
   SGVector<float64_t> vec;
   stream_features->start_parser();

   index_t count=0;
   while (stream_features->get_next_example() && count<num_vectors)
   {
      vec=stream_features->get_vector();
      for (int32_t i=0; i<num_feats; ++i)
         mat(i,count)=vec[i];

      stream_features->release_example();
      count++;
   }
   stream_features->end_parser();
   mat.num_cols=num_vectors;

   /* dense features from streamed matrix */
   CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(mat);
   CMulticlassLabels* labels=new CMulticlassLabels(num_vectors);
   SG_REF(features);
   SG_REF(labels);

   /* read labels from file */
   int32_t idx=0;
   stream_labels->start_parser();
   while (stream_labels->get_next_example())
   {
      labels->set_int_label(idx++, (int32_t)stream_labels->get_label());
      stream_labels->release_example();
   }
   stream_labels->end_parser();

   /* combined features and kernel */
   CCombinedFeatures *cfeats=new CCombinedFeatures();
   CCombinedKernel *cker=new CCombinedKernel();
   SG_REF(cfeats);
   SG_REF(cker);

   /** 1st kernel: gaussian */
   cfeats->append_feature_obj(features);
   cker->append_kernel(new CGaussianKernel(features, features, 1.2, 10));

   /** 2nd kernel: linear */
   cfeats->append_feature_obj(features);
   cker->append_kernel(new CLinearKernel(features, features));

   /** 3rd kernel: poly */
   cfeats->append_feature_obj(features);
   cker->append_kernel(new CPolyKernel(features, features, 2, true, 10));

   cker->init(cfeats, cfeats);

   /* create mkl instance */
   CMKLMulticlass* mkl=new CMKLMulticlass(1.2, cker, labels);
   SG_REF(mkl);
   mkl->set_epsilon(0.00001);
   mkl->parallel->set_num_threads(1);
   mkl->set_mkl_epsilon(0.001);
   mkl->set_mkl_norm(1.5);

   /* train to see weights */
   mkl->train();
   cker->get_subkernel_weights().display_vector("weights");

   /* cross-validation instances */
   index_t n_folds=3;
   index_t n_runs=5;
   CMulticlassAccuracy* eval_crit=new CMulticlassAccuracy();
   CStratifiedCrossValidationSplitting* splitting=
         new CStratifiedCrossValidationSplitting(labels, n_folds);
   CCrossValidation *cross=new CCrossValidation(mkl, cfeats, labels, splitting,
         eval_crit);
   cross->set_autolock(false);
   cross->set_num_runs(n_runs);
   cross->set_conf_int_alpha(0.05);

   /* perform x-val and print result */
   CModelSelectionOutput *ms_output = new CModelSelectionOutput();
   CrossValidationResult* result=(CrossValidationResult*)cross->evaluate(ms_output);
   SG_SPRINT("mean of %d %d-fold x-val runs: %f\n", n_runs, n_folds,
         result->mean);

   /* assert high accuracy */
   ASSERT(result->mean>0.9);

   /* clean up */
   SG_UNREF(ffeats_train);
   SG_UNREF(flabels_train);
   SG_UNREF(stream_features);
   SG_UNREF(stream_labels);
   SG_UNREF(features);
   SG_UNREF(labels);
   SG_UNREF(cfeats);
   SG_UNREF(cker);
   SG_UNREF(mkl);
   SG_UNREF(cross);
   SG_UNREF(result);
}

int main(int argc, char** argv){
   shogun::init_shogun_with_defaults();

//  sg_io->set_loglevel(MSG_DEBUG);

   /* performs cross-validation on a multi-class mkl machine */
   test_multiclass_mkl_cv();

   exit_shogun();
}