/*
 * Copyright (C) 2007-2009 Dynare Team
 *
 * This file is part of Dynare.
 *
 * Dynare 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.
 *
 * Dynare 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 Dynare.  If not, see <http://www.gnu.org/licenses/>.
 */


#include <iostream>
#include <sstream>
#include <fstream>
#include <ctime>
#include <cstdlib>
#include <cstring>
#include <cmath>
using namespace std;
//------------------------------------------------------------------------------
#include "BlockTriangular.hh"
//------------------------------------------------------------------------------

BlockTriangular::BlockTriangular(const SymbolTable &symbol_table_arg) :
  symbol_table(symbol_table_arg),
  normalization(symbol_table_arg),
  incidencematrix(symbol_table_arg)
{
  bt_verbose = 0;
  ModelBlock = NULL;
  periods = 0;
}



//------------------------------------------------------------------------------
// Find the prologue and the epilogue of the model
void
BlockTriangular::Prologue_Epilogue(bool* IM, int* prologue, int* epilogue, int n, simple* Index_Var_IM, simple* Index_Equ_IM, bool* IM0)
{
  bool modifie = 1;
  int i, j, k, l = 0;
  /*Looking for a prologue */
  *prologue = 0;
  while (modifie)
    {
      modifie = 0;
      for (i = *prologue;i < n;i++)
        {
          k = 0;
          for (j = *prologue;j < n;j++)
            {
              if (IM[i*n + j])
                {
                  k++;
                  l = j;
                }
            }
          if ((k == 1) && IM0[Index_Equ_IM[i].index*n + Index_Var_IM[l].index])
            {
              modifie = 1;
              incidencematrix.swap_IM_c(IM, *prologue, i, l, Index_Var_IM, Index_Equ_IM, n);
              (*prologue)++;
            }
        }
    }
  *epilogue = 0;
  modifie = 1;
  while (modifie)
    {
      modifie = 0;
      for (i = *prologue;i < n - *epilogue;i++)
        {
          k = 0;
          for (j = *prologue;j < n - *epilogue;j++)
            {
              if (IM[j*n + i])
                {
                  k++;
                  l = j;
                }
            }
          if ((k == 1) && IM0[Index_Equ_IM[l].index*n + Index_Var_IM[i].index])
            {
              modifie = 1;
              incidencematrix.swap_IM_c(IM, n - (1 + *epilogue), l, i, Index_Var_IM, Index_Equ_IM, n);
              (*epilogue)++;
            }
        }
    }
}


void
BlockTriangular::Allocate_Block(int size, int *count_Equ, int count_Block, BlockType type, BlockSimulationType SimType, Model_Block * ModelBlock)
{
  int i, j, k, l, ls, m, i_1, Lead, Lag, first_count_equ, i1, li;
  int *tmp_size, *tmp_size_other_endo, *tmp_size_exo, *tmp_var, *tmp_endo, *tmp_other_endo, *tmp_exo, tmp_nb_other_endo, tmp_nb_exo, nb_lead_lag_endo;
  bool *tmp_variable_evaluated;
  bool *Cur_IM;
  bool *IM, OK;
  int Lag_Endo, Lead_Endo, Lag_Exo, Lead_Exo, Lag_Other_Endo, Lead_Other_Endo;

  ModelBlock->Periods = periods;
  ModelBlock->Block_List[count_Block].is_linear=true;
  ModelBlock->Block_List[count_Block].Size = size;
  ModelBlock->Block_List[count_Block].Type = type;
  ModelBlock->Block_List[count_Block].Temporary_InUse=new temporary_terms_inuse_type ();
  ModelBlock->Block_List[count_Block].Temporary_InUse->clear();
  ModelBlock->Block_List[count_Block].Simulation_Type = SimType;
  ModelBlock->Block_List[count_Block].Equation = (int*)malloc(ModelBlock->Block_List[count_Block].Size * sizeof(int));
  ModelBlock->Block_List[count_Block].Variable = (int*)malloc(ModelBlock->Block_List[count_Block].Size * sizeof(int));
  ModelBlock->Block_List[count_Block].Temporary_Terms_in_Equation = (temporary_terms_type**)malloc(ModelBlock->Block_List[count_Block].Size * sizeof(temporary_terms_type));
  ModelBlock->Block_List[count_Block].Own_Derivative = (int*)malloc(ModelBlock->Block_List[count_Block].Size * sizeof(int));
  Lead = Lag = 0;
  first_count_equ = *count_Equ;
  tmp_var = (int*)malloc(size * sizeof(int));
  tmp_endo = (int*)malloc((incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1) * sizeof(int));
  tmp_other_endo = (int*)malloc(symbol_table.endo_nbr() * sizeof(int));
  tmp_size = (int*)malloc((incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1) * sizeof(int));
  //cout << "tmp_size = (int*)malloc((incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1= " << incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1 << ") * sizeof(int))\n";
  tmp_size_other_endo = (int*)malloc((incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1) * sizeof(int));
  tmp_size_exo = (int*)malloc((incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1) * sizeof(int));
  memset(tmp_size_exo, 0, (incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1)*sizeof(int));
  memset(tmp_size_other_endo, 0, (incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1)*sizeof(int));
  memset(tmp_size, 0, (incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1)*sizeof(int));
  memset(tmp_endo, 0, (incidencematrix.Model_Max_Lead + incidencematrix.Model_Max_Lag + 1)*sizeof(int));
  memset(tmp_other_endo, 0, symbol_table.endo_nbr()*sizeof(int));
  nb_lead_lag_endo = 0;
  Lag_Endo = Lead_Endo = Lag_Other_Endo = Lead_Other_Endo = Lag_Exo = Lead_Exo = 0;

  //Variable by variable looking for all leads and lags its occurence in each equation of the block
  tmp_variable_evaluated = (bool*)malloc(symbol_table.endo_nbr()*sizeof(bool));
  memset(tmp_variable_evaluated, 0, symbol_table.endo_nbr()*sizeof(bool));
  for (i = 0;i < size;i++)
    {
      ModelBlock->Block_List[count_Block].Temporary_Terms_in_Equation[i]=new temporary_terms_type ();
      ModelBlock->Block_List[count_Block].Temporary_Terms_in_Equation[i]->clear();
      ModelBlock->Block_List[count_Block].Equation[i] = Index_Equ_IM[*count_Equ].index;
      ModelBlock->Block_List[count_Block].Variable[i] = Index_Var_IM[*count_Equ].index;
      i_1 = Index_Var_IM[*count_Equ].index;
      for (k = -incidencematrix.Model_Max_Lag_Endo; k<=incidencematrix.Model_Max_Lead_Endo; k++)
        {
          Cur_IM = incidencematrix.Get_IM(k, eEndogenous);
          if (Cur_IM)
            {
              OK = false;
              if (k >= 0)
                {
                  for (j = 0;j < size;j++)
                    {
                      if (Cur_IM[i_1 + Index_Equ_IM[first_count_equ + j].index*symbol_table.endo_nbr()])
                        {
                          tmp_variable_evaluated[i_1] = true;
                          tmp_size[incidencematrix.Model_Max_Lag_Endo + k]++;
                          if (!OK)
                            {
                              tmp_endo[incidencematrix.Model_Max_Lag + k]++;
                              nb_lead_lag_endo++;
                              OK = true;
                            }
                          if (k > Lead)
                            Lead = k;
                        }
                    }
                }
              else
                {
                  for (j = 0;j < size;j++)
                    {
                      if (Cur_IM[i_1 + Index_Equ_IM[first_count_equ + j].index*symbol_table.endo_nbr()])
                        {
                          tmp_variable_evaluated[i_1] = true;
                          tmp_size[incidencematrix.Model_Max_Lag_Endo + k]++;
                          if (!OK)
                            {
                              tmp_variable_evaluated[i_1] = true;
                              tmp_endo[incidencematrix.Model_Max_Lag + k]++;
                              nb_lead_lag_endo++;
                              OK = true;
                            }
                          if (-k > Lag)
                            Lag = -k;
                        }
                    }
                }
            }
        }
      (*count_Equ)++;
    }
  Lag_Endo = Lag;
  Lead_Endo = Lead;

  tmp_nb_other_endo = 0;
  for (i = 0;i < size;i++)
    {
      for (k = -incidencematrix.Model_Max_Lag_Endo; k<=incidencematrix.Model_Max_Lead_Endo; k++)
        {
          Cur_IM = incidencematrix.Get_IM(k, eEndogenous);
          if (Cur_IM)
            {
              i_1 = Index_Equ_IM[first_count_equ+i].index * symbol_table.endo_nbr();
              for (j = 0;j < symbol_table.endo_nbr();j++)
                if (Cur_IM[i_1 + j])
                  {
                    if (!tmp_variable_evaluated[j])
                      {
                        tmp_other_endo[j] = 1;
                        tmp_nb_other_endo++;
                      }
                    if (k>0 && k>Lead_Other_Endo)
                      Lead_Other_Endo = k;
                    else if (k<0 && (-k)>Lag_Other_Endo)
                      Lag_Other_Endo = -k;
                    if (k>0 && k>Lead)
                      Lead = k;
                    else if (k<0 && (-k)>Lag)
                      Lag = -k;
                    tmp_size_other_endo[k+incidencematrix.Model_Max_Lag_Endo]++;
                  }
            }
        }
    }
  ModelBlock->Block_List[count_Block].nb_other_endo = tmp_nb_other_endo;
  ModelBlock->Block_List[count_Block].Other_Endogenous = (int*)malloc(tmp_nb_other_endo * sizeof(int));


  tmp_exo = (int*)malloc(symbol_table.exo_nbr() * sizeof(int));
  memset(tmp_exo, 0, symbol_table.exo_nbr() *     sizeof(int));
  tmp_nb_exo = 0;
  for (i = 0;i < size;i++)
    {
      for (k = -incidencematrix.Model_Max_Lag_Exo; k<=incidencematrix.Model_Max_Lead_Exo; k++)
        {
          Cur_IM = incidencematrix.Get_IM(k, eExogenous);
          if (Cur_IM)
            {
              i_1 = Index_Equ_IM[first_count_equ+i].index * symbol_table.exo_nbr();
              for (j=0;j<symbol_table.exo_nbr();j++)
                if (Cur_IM[i_1 + j])
                  {
                    if (!tmp_exo[j])
                      {
                        tmp_exo[j] = 1;
                        tmp_nb_exo++;
                      }
                    if (k>0 && k>Lead_Exo)
                      Lead_Exo = k;
                    else if (k<0 && (-k)>Lag_Exo)
                      Lag_Exo = -k;
                    if (k>0 && k>Lead)
                      Lead = k;
                    else if (k<0 && (-k)>Lag)
                      Lag = -k;
                    tmp_size_exo[k+incidencematrix.Model_Max_Lag_Exo]++;
                  }
            }
        }
    }


  ModelBlock->Block_List[count_Block].nb_exo = tmp_nb_exo;
  ModelBlock->Block_List[count_Block].Exogenous = (int*)malloc(tmp_nb_exo * sizeof(int));
  k = 0;
  for (j=0;j<symbol_table.exo_nbr();j++)
    if (tmp_exo[j])
      {
        ModelBlock->Block_List[count_Block].Exogenous[k] = j;
        k++;
      }

  ModelBlock->Block_List[count_Block].nb_exo_det = 0;

  ModelBlock->Block_List[count_Block].Max_Lag = Lag;
  ModelBlock->Block_List[count_Block].Max_Lead = Lead;
  ModelBlock->Block_List[count_Block].Max_Lag_Endo = Lag_Endo;
  ModelBlock->Block_List[count_Block].Max_Lead_Endo = Lead_Endo;
  ModelBlock->Block_List[count_Block].Max_Lag_Other_Endo = Lag_Other_Endo;
  ModelBlock->Block_List[count_Block].Max_Lead_Other_Endo = Lead_Other_Endo;
  ModelBlock->Block_List[count_Block].Max_Lag_Exo = Lag_Exo;
  ModelBlock->Block_List[count_Block].Max_Lead_Exo = Lead_Exo;
  ModelBlock->Block_List[count_Block].IM_lead_lag = (IM_compact*)malloc((Lead + Lag + 1) * sizeof(IM_compact));
  ls = l = li = size;
  i1 = 0;
  ModelBlock->Block_List[count_Block].Nb_Lead_Lag_Endo = nb_lead_lag_endo;
  for (i = 0;i < Lead + Lag + 1;i++)
    {
      if (incidencematrix.Model_Max_Lag_Endo-Lag+i>=0)
        {
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].size = tmp_size[incidencematrix.Model_Max_Lag_Endo - Lag + i];
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].nb_endo = tmp_endo[incidencematrix.Model_Max_Lag_Endo - Lag + i];
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].u = (int*)malloc(tmp_size[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].us = (int*)malloc(tmp_size[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Var = (int*)malloc(tmp_size[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ = (int*)malloc(tmp_size[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Var_Index = (int*)malloc(tmp_size[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_Index = (int*)malloc(tmp_size[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].size_other_endo = tmp_size_other_endo[incidencematrix.Model_Max_Lag_Endo - Lag + i];
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].nb_other_endo = tmp_other_endo[incidencematrix.Model_Max_Lag_Endo - Lag + i];
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].u_other_endo = (int*)malloc(tmp_size_other_endo[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Var_other_endo = (int*)malloc(tmp_size_other_endo[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_other_endo = (int*)malloc(tmp_size_other_endo[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Var_Index_other_endo = (int*)malloc(tmp_size_other_endo[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_Index_other_endo = (int*)malloc(tmp_size_other_endo[incidencematrix.Model_Max_Lag_Endo - Lag + i] * sizeof(int));
        }
      else
        ModelBlock->Block_List[count_Block].IM_lead_lag[i].size = 0;
      if (incidencematrix.Model_Max_Lag_Exo-Lag+i>=0)
        {
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].size_exo = tmp_size_exo[incidencematrix.Model_Max_Lag_Exo - Lag + i];
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Exogenous = (int*)malloc(tmp_size_exo[incidencematrix.Model_Max_Lag_Exo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Exogenous_Index = (int*)malloc(tmp_size_exo[incidencematrix.Model_Max_Lag_Exo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_X = (int*)malloc(tmp_size_exo[incidencematrix.Model_Max_Lag_Exo - Lag + i] * sizeof(int));
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_X_Index = (int*)malloc(tmp_size_exo[incidencematrix.Model_Max_Lag_Exo - Lag + i] * sizeof(int));
        }
      else
        ModelBlock->Block_List[count_Block].IM_lead_lag[i].size_exo = 0;
      ModelBlock->Block_List[count_Block].IM_lead_lag[i].u_init = l;
      memset(tmp_variable_evaluated, 0, symbol_table.endo_nbr()*sizeof(bool));
      IM = incidencematrix.Get_IM(i - Lag, eEndogenous);
      if (IM)
        {
          for (j = first_count_equ;j < size + first_count_equ;j++)
            {
              i_1 = Index_Var_IM[j].index;
              m = 0;
              for (k = first_count_equ;k < size + first_count_equ;k++)
                if (IM[i_1 + Index_Equ_IM[k].index*symbol_table.endo_nbr()])
                  m++;
              if (m > 0)
                {
                  tmp_var[j - first_count_equ] = i1;
                  i1++;
                }
            }
          m = 0;
          for (j = first_count_equ;j < size + first_count_equ;j++)
            {
              i_1 = Index_Equ_IM[j].index * symbol_table.endo_nbr();
              for (k = first_count_equ;k < size + first_count_equ;k++)
                if (IM[Index_Var_IM[k].index + i_1])
                  {
                    if (i == Lag)
                      {
                        ModelBlock->Block_List[count_Block].IM_lead_lag[i].us[m] = ls;
                        ls++;
                      }
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].u[m] = li;
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ[m] = j - first_count_equ;
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].Var[m] = k - first_count_equ;
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_Index[m] = Index_Equ_IM[j].index;
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].Var_Index[m] = Index_Var_IM[k].index;
                    tmp_variable_evaluated[Index_Var_IM[k].index] = true;
                    l++;
                    m++;
                    li++;
                  }
            }
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].u_finish = li - 1;
          m = 0;
          for (j = first_count_equ;j < size + first_count_equ;j++)
            {
              i_1 = Index_Equ_IM[j].index * symbol_table.endo_nbr();
              for (k = 0;k < symbol_table.endo_nbr();k++)
                if ((!tmp_variable_evaluated[Index_Var_IM[k].index]) && IM[Index_Var_IM[k].index + i_1])
                  {
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].u_other_endo[m] = l;
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_other_endo[m] = j - first_count_equ;
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].Var_other_endo[m] = k;
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_Index_other_endo[m] = Index_Equ_IM[j].index;
                    ModelBlock->Block_List[count_Block].IM_lead_lag[i].Var_Index_other_endo[m] = Index_Var_IM[k].index;
                    l++;
                    m++;
                  }
            }
          ModelBlock->Block_List[count_Block].IM_lead_lag[i].size_other_endo = m;
        }
      IM = incidencematrix.Get_IM(i - Lag, eExogenous);
      if (IM)
        {
          m = 0;
          for (j = first_count_equ;j < size + first_count_equ;j++)
            {
              i_1 = Index_Equ_IM[j].index * symbol_table.exo_nbr();
              for (k = 0; k<tmp_nb_exo; k++)
                {
                  if (IM[ModelBlock->Block_List[count_Block].Exogenous[k]+i_1])
                    {
                      ModelBlock->Block_List[count_Block].IM_lead_lag[i].Exogenous[m] = k;
                      ModelBlock->Block_List[count_Block].IM_lead_lag[i].Exogenous_Index[m] = ModelBlock->Block_List[count_Block].Exogenous[k];
                      ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_X[m] = j - first_count_equ;
                      ModelBlock->Block_List[count_Block].IM_lead_lag[i].Equ_X_Index[m] = Index_Equ_IM[j].index;
                      m++;
                    }
                }
            }
        }
    }
  free(tmp_size);
  free(tmp_size_other_endo);
  free(tmp_size_exo);
  free(tmp_endo);
  free(tmp_other_endo);
  free(tmp_exo);
  free(tmp_var);
  free(tmp_variable_evaluated);
}


void
BlockTriangular::Free_Block(Model_Block* ModelBlock) const
{
  int blk, i;
  for (blk = 0;blk < ModelBlock->Size;blk++)
    {


      free(ModelBlock->Block_List[blk].Equation);
      free(ModelBlock->Block_List[blk].Variable);
      free(ModelBlock->Block_List[blk].Exogenous);
      free(ModelBlock->Block_List[blk].Own_Derivative);
      free(ModelBlock->Block_List[blk].Other_Endogenous);
      for (i = 0;i < ModelBlock->Block_List[blk].Max_Lag + ModelBlock->Block_List[blk].Max_Lead + 1;i++)
        {
          if (incidencematrix.Model_Max_Lag_Endo-ModelBlock->Block_List[blk].Max_Lag+i>=0 /*&& ModelBlock->Block_List[blk].IM_lead_lag[i].size*/)
            {
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].u);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].us);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ_Index);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var_Index);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].u_other_endo);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var_other_endo);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ_other_endo);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var_Index_other_endo);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ_Index_other_endo);
            }
          if (incidencematrix.Model_Max_Lag_Exo-ModelBlock->Block_List[blk].Max_Lag+i>=0 /*&& ModelBlock->Block_List[blk].IM_lead_lag[i].size_exo*/)
            {
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Exogenous);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Exogenous_Index);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ_X_Index);
              free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ_X);
            }
        }
      free(ModelBlock->Block_List[blk].IM_lead_lag);
      for(i=0; i<ModelBlock->Block_List[blk].Size; i++)
        delete ModelBlock->Block_List[blk].Temporary_Terms_in_Equation[i];
      free(ModelBlock->Block_List[blk].Temporary_Terms_in_Equation);
      delete(ModelBlock->Block_List[blk].Temporary_InUse);
    }
  free(ModelBlock->Block_List);
  free(ModelBlock);
  free(Index_Equ_IM);
  free(Index_Var_IM);
}


t_type
BlockTriangular::Reduce_Blocks_and_type_determination(int prologue, int epilogue, block_result_t* res, vector<BinaryOpNode *> equations )
{
  int i=0;
  NodeID lhs, rhs;
  ostringstream tmp_output;
  BinaryOpNode *eq_node;
  ostringstream tmp_s;
  temporary_terms_type temporary_terms;
  int count_equ = 0, blck_count_simult =0;
  int Blck_Size;
  int Lead, Lag;
  t_type Type;
  bool *Cur_IM;
  BlockSimulationType Simulation_Type , prev_Type=UNKNOWN;
  for ( i=0; i<prologue+res->n_sets+epilogue; i++)
    {
      int first_count_equ = count_equ;
      if (i < prologue)
        Blck_Size = 1;
      else if (i < prologue+res->n_sets)
        {
          Blck_Size = res->sets_f[res->ordered[blck_count_simult]] - res->sets_s[res->ordered[blck_count_simult]] + 1;
          blck_count_simult++;
        }
      else if (i < prologue+res->n_sets+epilogue)
        Blck_Size = 1;

      Lag = Lead = 0;
      for (count_equ = first_count_equ; count_equ < Blck_Size+first_count_equ; count_equ++)
        {
          int i_1 = Index_Var_IM[count_equ].index;
          for (int k = -incidencematrix.Model_Max_Lag_Endo; k<=incidencematrix.Model_Max_Lead_Endo; k++)
            {
              Cur_IM = incidencematrix.Get_IM(k, eEndogenous);
              if (Cur_IM)
                {
                  for (int j = 0;j < Blck_Size;j++)
                    {
                      if (Cur_IM[i_1 + Index_Equ_IM[first_count_equ + j].index*symbol_table.endo_nbr()])
                        {
                          if (k > Lead)
                            Lead = k;
                          else if (-k > Lag)
                            Lag = -k;
                        }
                    }
                }
            }
        }
      if ((Lag > 0) && (Lead > 0))
        {
          if (Blck_Size == 1)
            Simulation_Type = SOLVE_TWO_BOUNDARIES_SIMPLE;
          else
            Simulation_Type = SOLVE_TWO_BOUNDARIES_COMPLETE;
        }
      else if (Blck_Size > 1)
        {
          if (Lead > 0)
            Simulation_Type = SOLVE_BACKWARD_COMPLETE;
          else
            Simulation_Type = SOLVE_FORWARD_COMPLETE;
        }
      else
        {
          if (Lead > 0)
            Simulation_Type = SOLVE_BACKWARD_SIMPLE;
          else
            Simulation_Type = SOLVE_FORWARD_SIMPLE;
        }
      if (Blck_Size == 1)
        {
          temporary_terms.clear();
          eq_node = equations[Index_Equ_IM[first_count_equ].index];
          lhs = eq_node->get_arg1();
          rhs = eq_node->get_arg2();
          tmp_s.str("");
          tmp_output.str("");
          lhs->writeOutput(tmp_output, oMatlabDynamicModelSparse, temporary_terms);
          tmp_s << "y(it_, " << Index_Var_IM[first_count_equ].index+1 << ")";
          //Determine whether the equation could be evaluated rather than to be solved
          if (tmp_output.str()==tmp_s.str())
            {
              if (Simulation_Type==SOLVE_BACKWARD_SIMPLE)
                Simulation_Type=EVALUATE_BACKWARD;
              else if (Simulation_Type==SOLVE_FORWARD_SIMPLE)
                Simulation_Type=EVALUATE_FORWARD;
            }
          else
            {
              tmp_output.str("");
              rhs->writeOutput(tmp_output, oCDynamicModelSparseDLL, temporary_terms);
              if (tmp_output.str()==tmp_s.str())
                {
                  if (Simulation_Type==SOLVE_BACKWARD_SIMPLE)
                    Simulation_Type=EVALUATE_BACKWARD_R;
                  else if (Simulation_Type==SOLVE_FORWARD_SIMPLE)
                    Simulation_Type=EVALUATE_FORWARD_R;
                }
            }
          if (i > 0)
            {
              if ( ((prev_Type ==  EVALUATE_FORWARD_R || prev_Type ==  EVALUATE_FORWARD) && (Simulation_Type == EVALUATE_FORWARD_R || Simulation_Type == EVALUATE_FORWARD))
                   || ((prev_Type ==  EVALUATE_BACKWARD_R || prev_Type ==  EVALUATE_BACKWARD) && (Simulation_Type == EVALUATE_BACKWARD_R || Simulation_Type == EVALUATE_BACKWARD))
                   )
                {
                  BlockSimulationType c_Type = (Type[Type.size()-1]).first;
                  int c_Size = (Type[Type.size()-1]).second;
                  Type[Type.size()-1]=make_pair(c_Type, ++c_Size);
                }
              else
                Type.push_back(make_pair(Simulation_Type, Blck_Size));
            }
          else
            Type.push_back(make_pair(Simulation_Type, Blck_Size));
        }
      else
        {
          Type.push_back(make_pair(Simulation_Type, Blck_Size));
        }
      prev_Type = Simulation_Type;
    }
  return(Type);
}


//------------------------------------------------------------------------------
// Normalize each equation of the model (endgenous_i = f_i(endogenous_1, ..., endogenous_n) - in order to apply strong connex components search algorithm -
// and find the optimal blocks triangular decomposition
bool
BlockTriangular::Normalize_and_BlockDecompose(bool* IM, Model_Block* ModelBlock, int n, int* prologue, int* epilogue, simple* Index_Var_IM, simple* Index_Equ_IM, bool Do_Normalization, bool mixing, bool* IM_0, jacob_map j_m, vector<BinaryOpNode *> equations )
{
  int i, j, Nb_TotalBlocks, Nb_RecursBlocks, Nb_SimulBlocks;
  BlockType Btype;
  int count_Block, count_Equ;
  block_result_t* res;
  Equation_set* Equation_gr = (Equation_set*) malloc(sizeof(Equation_set));
  bool* SIM0, *SIM00;
  SIM0 = (bool*)malloc(n * n * sizeof(bool));
  memcpy(SIM0,IM_0,n*n*sizeof(bool));
  Prologue_Epilogue(IM, prologue, epilogue, n, Index_Var_IM, Index_Equ_IM, SIM0);
  free(SIM0);
  if (bt_verbose)
    {
      cout << "prologue : " << *prologue << " epilogue : " << *epilogue << "\n";
      cout << "IM_0\n";
      incidencematrix.Print_SIM(IM_0, eEndogenous);
      cout << "IM\n";
      incidencematrix.Print_SIM(IM, eEndogenous);
      for (i = 0;i < n;i++)
        cout << "Index_Var_IM[" << i << "]=" << Index_Var_IM[i].index << " Index_Equ_IM[" << i << "]=" << Index_Equ_IM[i].index << "\n";
    }
  int counted=0;
  if (*prologue+*epilogue<n)
    {
      if (Do_Normalization)
        {
          cout << "Normalizing the model ...\n";
          if (mixing)
            {
              double* max_val=(double*)malloc(n*sizeof(double));
              memset(max_val,0,n*sizeof(double));
              for ( map< pair< int, int >, double >::iterator iter = j_m.begin(); iter != j_m.end(); iter++ )
                {
                  if (fabs(iter->second)>max_val[iter->first.first])
                    max_val[iter->first.first]=fabs(iter->second);
                }
              for ( map< pair< int, int >, double >::iterator iter = j_m.begin(); iter != j_m.end(); iter++ )
                iter->second/=max_val[iter->first.first];
              free(max_val);
              bool OK=false;
              double bi=0.99999999;
              int suppressed=0;
              while (!OK && bi>1e-14)
                {
                  int suppress=0;
                  SIM0 = (bool*)malloc(n * n * sizeof(bool));
                  memset(SIM0,0,n*n*sizeof(bool));
                  SIM00 = (bool*)malloc(n * n * sizeof(bool));
                  memset(SIM00,0,n*n*sizeof(bool));
                  for ( map< pair< int, int >, double >::iterator iter = j_m.begin(); iter != j_m.end(); iter++ )
                    {
                      if (fabs(iter->second)>bi)
                        {
                          SIM0[iter->first.first*n+iter->first.second]=1;
                          if (!IM_0[iter->first.first*n+iter->first.second])
                            {
                              cout << "Error nothing at IM_0[" << iter->first.first << ", " << iter->first.second << "]=" << IM_0[iter->first.first*n+iter->first.second] << "  " << iter->second << "\n";
                            }
                        }
                      else
                        suppress++;
                    }
                  for (i = 0;i < n;i++)
                    for (j = 0;j < n;j++)
                      {
                        SIM00[i*n + j] = SIM0[Index_Equ_IM[i].index * n + Index_Var_IM[j].index];
                      }
                  free(SIM0);
                  if (suppress!=suppressed)
                    {
                      OK=normalization.Normalize(n, *prologue, *epilogue, SIM00, Index_Equ_IM, Equation_gr, 1, IM);
                      if (!OK)
                        normalization.Free_Equation(n-*prologue-*epilogue,Equation_gr);
                    }
                  suppressed=suppress;
                  if (!OK)
                    //bi/=1.07;
                    bi/=3;
                  counted++;
                  if (bi>1e-14)
                    free(SIM00);
                }
              if (!OK)
                {
                  normalization.Set_fp_verbose(true);
                  OK=normalization.Normalize(n, *prologue, *epilogue, SIM00, Index_Equ_IM, Equation_gr, 1, IM);
                  cout << "Error\n";
                  exit(EXIT_FAILURE);
                }
            }
          else
            normalization.Normalize(n, *prologue, *epilogue, IM, Index_Equ_IM, Equation_gr, 0, 0);
        }
      else
        normalization.Gr_to_IM_basic(n, *prologue, *epilogue, IM, Equation_gr, false);
    }
  //cout << "Finding the optimal block decomposition of the model ..." << counted << "\n";
  cout << "Finding the optimal block decomposition of the model ...\n";
  if (*prologue+*epilogue<n)
    {
      if (bt_verbose)
        blocks.Print_Equation_gr(Equation_gr);
      res = blocks.sc(Equation_gr);
      normalization.Free_Equation(n-*prologue-*epilogue,Equation_gr);
      if (bt_verbose)
        blocks.block_result_print(res);
    }
  else
    {
      res = (block_result_t*)malloc(sizeof(*res));
      res->n_sets=0;
    }
  free(Equation_gr);


  blocks.block_result_to_IM(res, IM, *prologue, symbol_table.endo_nbr(), Index_Equ_IM, Index_Var_IM);


  t_type  Type = Reduce_Blocks_and_type_determination(*prologue, *epilogue, res, equations);


  i = 0;
  j = 0;
  Nb_SimulBlocks = 0;
  for (t_type::const_iterator it = Type.begin(); it!=Type.end(); it++)
    {
      if (it->first==SOLVE_FORWARD_COMPLETE || it->first==SOLVE_BACKWARD_COMPLETE || it->first==SOLVE_TWO_BOUNDARIES_COMPLETE)
        {
          Nb_SimulBlocks++;
          if (it->second>j)
            j=it->second;
        }
    }

  Nb_TotalBlocks = Type.size();
  Nb_RecursBlocks = Nb_TotalBlocks - Nb_SimulBlocks;
  cout << Nb_TotalBlocks << " block(s) found:\n";
  cout << "  " << Nb_RecursBlocks << " recursive block(s) and " << res->n_sets << " simultaneous block(s). \n";
  cout << "  the largest simultaneous block has " << j << " equation(s). \n";

  ModelBlock->Size = Nb_TotalBlocks;
  ModelBlock->Periods = periods;
  ModelBlock->Block_List = (Block*)malloc(sizeof(ModelBlock->Block_List[0]) * Nb_TotalBlocks);

  count_Equ = count_Block = 0;
  for (t_type::const_iterator it = Type.begin(); it!=Type.end(); it++)
    {
      if (count_Equ<*prologue)
        Btype = PROLOGUE;
      else if (count_Equ<n-*epilogue)
        if (it->second==1)
          Btype = PROLOGUE;
        else
          Btype = SIMULTANS;
      else
        Btype = EPILOGUE;
      Allocate_Block(it->second, &count_Equ, count_Block++, Btype, it->first, ModelBlock);
    }
  //exit(-1);

  if (res->n_sets)
    blocks.block_result_free(res);
  else
    free(res);


  return 0;
}

//------------------------------------------------------------------------------
// normalize each equation of the dynamic model
// and find the optimal block triangular decomposition of the static model
void
BlockTriangular::Normalize_and_BlockDecompose_Static_0_Model(const jacob_map &j_m, vector<BinaryOpNode *> equations)
{
  bool* SIM, *SIM_0;
  bool* Cur_IM;
  int i, k, size;
  //First create a static model incidence matrix
  size = symbol_table.endo_nbr() * symbol_table.endo_nbr() * sizeof(*SIM);
  SIM = (bool*)malloc(size);
  for (i = 0; i< symbol_table.endo_nbr() * symbol_table.endo_nbr(); i++) SIM[i] = 0;
  for (k = -incidencematrix.Model_Max_Lag_Endo; k<=incidencematrix.Model_Max_Lead_Endo; k++)
    {
      Cur_IM = incidencematrix.Get_IM(k, eEndogenous);
      if (Cur_IM)
        {
          for (i = 0;i < symbol_table.endo_nbr()*symbol_table.endo_nbr();i++)
            {
              SIM[i] = (SIM[i]) || (Cur_IM[i]);
            }
        }
    }
  if (bt_verbose)
    {
      cout << "incidence matrix for the static model (unsorted) \n";
      incidencematrix.Print_SIM(SIM, eEndogenous);
    }
  Index_Equ_IM = (simple*)malloc(symbol_table.endo_nbr() * sizeof(*Index_Equ_IM));
  for (i = 0;i < symbol_table.endo_nbr();i++)
    {
      Index_Equ_IM[i].index = i;
    }
  Index_Var_IM = (simple*)malloc(symbol_table.endo_nbr() * sizeof(*Index_Var_IM));
  for (i = 0;i < symbol_table.endo_nbr();i++)
    {
      Index_Var_IM[i].index = i;
    }
  if (ModelBlock != NULL)
    Free_Block(ModelBlock);
  ModelBlock = (Model_Block*)malloc(sizeof(*ModelBlock));
  Cur_IM = incidencematrix.Get_IM(0, eEndogenous);
  SIM_0 = (bool*)malloc(symbol_table.endo_nbr() * symbol_table.endo_nbr() * sizeof(*SIM_0));
  for (i = 0;i < symbol_table.endo_nbr()*symbol_table.endo_nbr();i++)
    SIM_0[i] = Cur_IM[i];
  Normalize_and_BlockDecompose(SIM, ModelBlock, symbol_table.endo_nbr(), &prologue, &epilogue, Index_Var_IM, Index_Equ_IM, 1, 1, SIM_0, j_m, equations);
  free(SIM_0);
  free(SIM);
}