/**************************************************************************/
/*                            File: hypothesize.cc                    */
/**************************************************************************/
/* Project: Continuous Speech Recognition Search Algorithms               */
/* Author: Neeraj Deshmukh, Aravind Ganapathiraju                         */
/* Class: EE 8993 Spring 1996                        Date: April 2, 1996 */
/**************************************************************************/

/*========================================================================
  This file gives the scores off all possible state sequences through a model
  given a start and end state. 
  ========================================================================*/
/*------------------------------------------------------------------------
  system and ISIP include files
  ------------------------------------------------------------------------*/
#include "test.h"

void hypothesize_cc ( FILE *fp_in,HMM_Model *model,Score_Hyp *hypothesis,
		      Score_Hyp *back_ptr,  float_4 score ,int_2 start_time ,
		      int_2 word_index ) 
{
  // define local variables
  //
  HMM_State *state_ptr;
  HMM_State *model_states_ptr;
  HMM_State *temp = new HMM_State;
  HMM_Trans_State *trans_ptr = new HMM_Trans_State ();
  String state_symbol;

  Score_Hyp *ref_score_hyp;
  int_2 frm_ind = start_time;
  float_4 store = 0;
  int_2 count = 0;
  float_4 prob = 0;
 
  if ( frm_ind == 0 )
    {
      state_symbol = (model->get_start_state_cc ())->get_out_symbol_cc ();
    }
  else
    {
      state_symbol = ((model->get_start_state_cc ())->get_prev_state_cc ())->
	get_out_symbol_cc ();
    } 
  
  model_states_ptr = model->get_states_cc ();
  
  // initialize the list of score_hyps to the one with state as start state
  // of the model
  state_ptr = model_states_ptr->find_state_cc(state_symbol);
  hypothesis->add_score_hyp_cc (state_ptr,back_ptr,0.0,start_time,word_index );
  
  HMM_Matrix **temp_mat = state_ptr->get_mean_cc ();
  
  int_2 test_vec_len = (temp_mat[0]->get_num_columns_cc ());
  HMM_Matrix *test_vector = new HMM_Matrix (1,test_vec_len,0.0);
  
  // go to the point in file specified the start time
  //
  rewind (fp_in);
  fseek(fp_in, test_vec_len*start_time*(sizeof(float_4)), SEEK_SET);
  
  // create score_hyps for all input frame durations
  //
  while (fscanf (fp_in, "%f", &store) != EOF && (count <= 7))
    { 
      frm_ind++;
      test_vector->set_element_cc (0,0,store);
      
      // get the test vector from the input file
      //
      for (int_2 i=1; i<temp_mat[0]->get_num_columns_cc (); i++)
	{
	  fscanf ( fp_in,"%f", &store );
	  test_vector->set_element_cc (0,i,store);
	}
      count++;
      
      HMM_State *temp_state;

      // find all score hyps representing the same state in the HMM
      //
      for ( temp_state= model_states_ptr->get_next_state_cc ();
	    temp_state != NULL;
	    temp_state = temp_state->get_next_state_cc () )
	{
	  ref_score_hyp = hypothesis->find_score_hyp_cc ( temp_state, frm_ind - 1);
	  
	  if (ref_score_hyp != NULL)
	    {
	      // go through the score_hyps  generated during the previous frame
	      //
	      Score_Hyp *temp_hyp;
	      for ( temp_hyp = ref_score_hyp->get_next_hyp_cc ();
		    temp_hyp != NULL; 
		    temp_hyp = temp_hyp->get_next_hyp_cc ())  
		{
		  if ( temp_hyp->get_timestamp_cc () == frm_ind - 1 &&
		      temp_hyp->get_word_index_cc () == word_index )
		    {  
		      
		      if ( ((HMM_State *)temp_hyp->get_element_cc ()) == 
			   temp_state)
			{
			  if ( temp_hyp->get_path_score_cc () >=  
			       ref_score_hyp->get_path_score_cc () )
			    {
			      ref_score_hyp->remove_score_hyp_cc ();
			      ref_score_hyp = temp_hyp;
			    }
			  else 
			    { 
			      temp_hyp->remove_score_hyp_cc ();
			    }
			}
		    }
		}
	    }
	}

      Score_Hyp *temp_hyp;
      for ( temp_hyp = hypothesis->get_next_hyp_cc ();
	    temp_hyp != NULL;
	    temp_hyp = temp_hyp->get_next_hyp_cc ())  
	{
	  state_ptr = (HMM_State *)(temp_hyp->get_element_cc ());
	  
	  // create score_hyps for all transitions from the state
	  // represented by the present score_hyp
	  //
	  for ( trans_ptr = state_ptr->get_transition_cc ();
		trans_ptr != NULL; 
		trans_ptr = trans_ptr->get_next_transition_cc ())
	    {

	      if (temp_hyp->get_timestamp_cc () == frm_ind -1)
		{
		  state_symbol = trans_ptr->get_state_name_cc ();
		  temp = model_states_ptr->find_state_cc (state_symbol);
		  
		  prob = fabs(log10(score)) + 
		    fabs(log10(temp->state_score_cc (test_vector))) + 
		    fabs(log10(state_ptr->get_trans_prob_cc (state_symbol)));
		  hypothesis->add_score_hyp_cc (temp,temp_hyp,prob,frm_ind,
						word_index);
		}
	    }
	}
      
      // this part is supposed to find all hypotheses which end in stop state
      // and then back trace
      for ( temp_hyp = hypothesis->get_next_hyp_cc ();
	    temp_hyp != NULL;
	    temp_hyp = temp_hyp->get_next_hyp_cc ())  
	{
	  temp = (HMM_State *)temp_hyp->get_element_cc ();
	  state_symbol = temp->get_out_symbol_cc ();
	  if ( temp_hyp->get_timestamp_cc () == (frm_ind)
	       && strcmp (state_symbol, (model->get_stop_state_cc())->
			  get_out_symbol_cc ()) == 0) 
	    { 
	      temp_hyp->hyp_backtrace_cc ();
	      fprintf ( stdout, "path score is %f \n",
			temp_hyp->get_path_score_cc ()); 
	    }
	}
    }
}