/** 
* @file GPHIKRegressionMex.cpp
* @author Alexander Freytag
* @date 17-01-2014 (dd-mm-yyyy)
* @brief Matlab-Interface of our GPHIKRegression, allowing for training, regression, optimization, variance prediction, incremental learning, and  storing/re-storing.
*/

// STL includes
#include <math.h>
#include <matrix.h>
#include <mex.h>

// NICE-core includes
#include <core/basics/Config.h>
#include <core/basics/Timer.h>
#include <core/vector/MatrixT.h>
#include <core/vector/VectorT.h>

// CodebookRandomForest stuff
#include "vislearning/features/simplefeatures/CodebookRandomForest.h"

#include "vislearning/features/fpfeatures/VectorFeature.h"

// Interface for conversion between Matlab and C objects
#include "gp-hik-core/matlab/classHandleMtoC.h"
#include "gp-hik-core/matlab/ConverterMatlabToNICE.h"
#include "gp-hik-core/matlab/ConverterNICEToMatlab.h"

#include "HelperDataConversionMex.h"

using namespace std; //C basics
using namespace NICE;  // nice-core


NICE::Config parseParametersERC(const mxArray *prhs[], int nrhs)
{
  NICE::Config conf;
  
  // if first argument is the filename of an existing config file,
  // read the config accordingly
  
  int i_start ( 0 );
  std::string variable = MatlabConversion::convertMatlabToString(prhs[i_start]);
  if(variable == "conf")
  {
      conf = NICE::Config ( MatlabConversion::convertMatlabToString( prhs[i_start+1] )  );
      i_start = i_start+2;
  }
  
  // now run over all given parameter specifications
  // and add them to the config
  for( int i=i_start; i < nrhs; i+=2 )
  {
    std::string variable = MatlabConversion::convertMatlabToString(prhs[i]);
    
    /////////////
    //CodebookRandomForest( int maxDepth

//    number_of_trees = conf->gI(section, "number_of_trees", 20 );
//    features_per_tree = conf->gD(section, "features_per_tree", 1.0 );
//    samples_per_tree  = conf->gD(section, "samples_per_tree", 0.2 );
//    use_simple_balancing = conf->gB(section, "use_simple_balancing", false);
//    weight_examples = conf->gB(section, "weight_examples", false);
//    memory_efficient = conf->gB(section, "memory_efficient", false);

    //std::string builder_section = conf->gS(section, "builder_section", "DTBRandom");


    if( variable == "number_of_trees")
    {
        if ( mxIsInt32( prhs[i+1] ) )
        {
            int value = MatlabConversion::convertMatlabToInt32(prhs[i+1]);
            conf.sI("FPCRandomForests", variable, value);
        }
        else
        {
            std::string errorMsg = "Unexpected parameter value for \'" +  variable + "\'. Int32 expected.";
            mexErrMsgIdAndTxt( "mexnice:error", errorMsg.c_str() );
        }

    }

  }

  return conf;
}

// MAIN MATLAB FUNCTION
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{    
    // get the command string specifying what to do
    if (nrhs < 1)
        mexErrMsgTxt("No commands and options passed... Aborting!");        
    
    if( !mxIsChar( prhs[0] ) )
        mexErrMsgTxt("First argument needs to be the command, ie.e, the class method to call... Aborting!");        
    
    std::string cmd = MatlabConversion::convertMatlabToString( prhs[0] );
      
        
    // create object
    if ( !strcmp("new", cmd.c_str() ) )
    {
        // check output variable
        if (nlhs != 1)
            mexErrMsgTxt("New: One output expected.");
        
        // read config settings
        //NICE::Config conf = parseParametersGPHIKRegression(prhs+1,nrhs-1);

        int nMaxDepth = 10;
        // create class instance
        OBJREC::CodebookRandomForest *pRandomForest = new OBJREC::CodebookRandomForest(nMaxDepth);
         
        // handle to the C++ instance
        plhs[0] = MatlabConversion::convertPtr2Mat<OBJREC::CodebookRandomForest>( pRandomForest );
        return;
    }
    
    // in all other cases, there should be a second input,
    // which the be the class instance handle
    if (nrhs < 2)
      mexErrMsgTxt("Second input should be a class instance handle.");
    
    // delete object
    if ( !strcmp("delete", cmd.c_str() ) )
    {
        // Destroy the C++ object
        MatlabConversion::destroyObject<OBJREC::CodebookRandomForest>(prhs[1]);
        return;
    }
    
    // get the class instance pointer from the second input
    // every following function needs the object
    OBJREC::CodebookRandomForest *pCodebookClusterer = MatlabConversion::convertMat2Ptr<OBJREC::CodebookRandomForest>(prhs[1]);
    
    
    ////////////////////////////////////////
    //  Check which class method to call  //
    ////////////////////////////////////////
    
    
    // standard train - assumes initialized object
    if (!strcmp("train", cmd.c_str() ))
    {
        // Check parameters
        if (nlhs < 0 || nrhs < 4)
        {
            mexErrMsgTxt("Train: Unexpected arguments.");
        }
        
        //------------- read the data --------------
        if (nrhs != 4)
        {
            mexErrMsgTxt("needs 2 matrix inputs, first the training features, second the sample labels");
            return;
        }

        const mxArray *t_pArrTrainData   = prhs[2];
        const mxArray *t_pArrTrainLabels = prhs[3];

        //std::vector< const NICE::SparseVector *> examplesTrain;

        int iNumFeatureDimension = mxGetM( t_pArrTrainData ); // feature dimensions

        OBJREC::Examples examplesTrain;

        bool bRet = MatlabConversion::convertDoubleRawPointersToExamples( t_pArrTrainData, t_pArrTrainLabels, examplesTrain);
        if( ~bRet )
        {
            mexErrMsgTxt("Train: Error creating Examples from raw feature matrix and labels.");
        }

        //----------------- train our random Forest -------------
        // read config settings
        OBJREC::FeaturePool fp;
        OBJREC::VectorFeature *pVecFeature = new OBJREC::VectorFeature(iNumFeatureDimension);
        pVecFeature->explode(fp);

        NICE::Config conf = parseParametersERC(prhs+1,nrhs-1);

        OBJREC::FPCRandomForests *pRandForest = new OBJREC::FPCRandomForests(&conf,"FPCRandomForests");

        pRandForest->train(fp, examplesTrain);

        pCodebookClusterer->setClusterForest( pRandForest );


        //----------------- clean up -------------

        delete pVecFeature;
        pVecFeature = NULL;
        // delete all "exploded" features, they are internally cloned in the random trees anyway
        fp.destroy();
        //

        for(int i=0;i<examplesTrain.size(); i++)
        {
            if ( examplesTrain[i].second.vec != NULL )
            {
                delete examplesTrain[i].second.vec;
                examplesTrain[i].second.vec = NULL;
            }
        }
        
        return;
    }
/*
    
    // perform regression    
    if ( !strcmp("estimate", cmd.c_str() ) )
    {
        // Check parameters
        if ( (nlhs < 0) || (nrhs < 2) )
        {
            mexErrMsgTxt("Test: Unexpected arguments.");
        }
        
        //------------- read the data --------------

        double result;
        double uncertainty;        

        if ( mxIsSparse( prhs[2] ) )
        {
            NICE::SparseVector * example;
            example = new NICE::SparseVector ( converterMtoNICE.convertSparseVectorToNice( prhs[2] ) );
            regressor->estimate ( example,  result, uncertainty );
            
            //----------------- clean up -------------
            delete example;
        }
        else
        {
            NICE::Vector * example;
            example = new NICE::Vector ( converterMtoNICE.convertDoubleVectorToNice(prhs[2]) ); 
            regressor->estimate ( example,  result, uncertainty );
            
            //----------------- clean up -------------
            delete example;            
        }
          
          

          // output
          plhs[0] = mxCreateDoubleScalar( result ); 
          
          
          if(nlhs >= 2)
          {
            plhs[1] = mxCreateDoubleScalar( uncertainty );          
          }
          return;
    }
    
    // Uncertainty prediction    
    if ( !strcmp("uncertainty", cmd.c_str() ) )
    {
        // Check parameters
        if ( (nlhs < 0) || (nrhs < 2) )
        {
            mexErrMsgTxt("Test: Unexpected arguments.");
        }
        
        double uncertainty;        
        
        //------------- read the data --------------

        if ( mxIsSparse( prhs[2] ) )
        {
            NICE::SparseVector * example;
            example = new NICE::SparseVector ( converterMtoNICE.convertSparseVectorToNice( prhs[2] ) );
            regressor->predictUncertainty( example, uncertainty );
            
            //----------------- clean up -------------
            delete example;            
        }
        else
        {
            NICE::Vector * example;
            example = new NICE::Vector ( converterMtoNICE.convertDoubleVectorToNice(prhs[2]) ); 
            regressor->predictUncertainty( example, uncertainty );
            
            //----------------- clean up -------------
            delete example;            
        }
        
       

          // output
          plhs[0] = mxCreateDoubleScalar( uncertainty );                    
          return;
    }    
    
    
    // Test - evaluate regressor on whole test set  
    if ( !strcmp("testL2loss", cmd.c_str() ) )
    {        
        // Check parameters
        if (nlhs < 0 || nrhs < 3)
            mexErrMsgTxt("Test: Unexpected arguments.");
        //------------- read the data --------------
        
        
        bool dataIsSparse ( mxIsSparse( prhs[2] ) );
        
        std::vector< const NICE::SparseVector *> dataTest_sparse;
        NICE::Matrix dataTest_dense;

        if ( dataIsSparse )
        {
            dataTest_sparse = converterMtoNICE.convertSparseMatrixToNice( prhs[2] );
        }
        else
        {    
            dataTest_dense = converterMtoNICE.convertDoubleMatrixToNice(prhs[2]);          
        }        

        NICE::Vector yValuesTest;
        yValuesTest = converterMtoNICE.convertDoubleVectorToNice(prhs[3]);
	
        int i_numTestSamples ( yValuesTest.size() );
        
	double l2loss ( 0.0 );
	
	NICE::Vector scores;
	NICE::Vector::iterator itScores;
	if ( nlhs >= 2 )
	{
	  scores.resize( i_numTestSamples );
	  itScores = scores.begin();
	}
          
          

        // ------------------------------------------
        // ------------- REGRESSION --------------
        // ------------------------------------------          
        
        NICE::Timer t;
        double testTime (0.0);
        


        for (int i = 0; i < i_numTestSamples; i++)
        {
            //----------------- convert data to sparse data structures ---------
          

            double result;

            if ( dataIsSparse )
            {                
              // and perform regression
              t.start();
              regressor->estimate( dataTest_sparse[ i ], result);
              t.stop();
              testTime += t.getLast();
            }
            else
            {
                NICE::Vector example ( dataTest_dense.getRow(i) );
              // and perform regression
              t.start();
              regressor->estimate( &example, result );
              t.stop();
              testTime += t.getLast();                
            }

            l2loss += pow ( yValuesTest[i] - result, 2); 
	    
	    if ( nlhs >= 2 )
	    {
	      *itScores = result;
	      itScores++;
	    }	    
        }
        
        std::cerr << "Time for testing: " << testTime << std::endl;          
        
        // clean up
        if ( dataIsSparse )
        {
            for ( std::vector<const NICE::SparseVector *>::iterator it = dataTest_sparse.begin(); it != dataTest_sparse.end(); it++) 
                delete *it;
        }
        


        plhs[0] = mxCreateDoubleScalar( l2loss );

        if(nlhs >= 2)
          plhs[1] = converterNICEtoM.convertVectorFromNice(scores);          
          
          
        return;
    }
    


    ///////////////////// INTERFACE PERSISTENT /////////////////////
    // interface specific methods for store and restore
    ///////////////////// INTERFACE PERSISTENT /////////////////////    
    
  
    
    // store the regressor  to an external file
    if ( !strcmp("store", cmd.c_str() ) || !strcmp("save", cmd.c_str() ) )
    {
        // Check parameters
        if ( nrhs < 3 )
            mexErrMsgTxt("store: no destination given.");        
               
        std::string s_destination = converterMtoNICE.convertMatlabToString( prhs[2] );
          
        std::filebuf fb;
        fb.open ( s_destination.c_str(), ios::out );
        std::ostream os(&fb);
        //
        regressor->store( os );
        //   
        fb.close();        
            
        return;
    }
    
    // load regressor from external file    
    if ( !strcmp("restore", cmd.c_str() ) || !strcmp("load", cmd.c_str() ) )
    {
        // Check parameters
        if ( nrhs < 3 )
            mexErrMsgTxt("restore: no destination given.");        
               
        std::string s_destination = converterMtoNICE.convertMatlabToString( prhs[2] );
        
        std::cerr << " aim at restoring the regressor from " << s_destination << std::endl;
          
        std::filebuf fbIn;
        fbIn.open ( s_destination.c_str(), ios::in );
        std::istream is (&fbIn);
        //
        regressor->restore( is );
        //   
        fbIn.close();        
            
        return;
    }    

*/
    
    // Got here, so command not recognized
    
    std::string errorMsg (cmd.c_str() );
    errorMsg += " -- command not recognized.";
    mexErrMsgTxt( errorMsg.c_str() );

}