/**************************************************************************/
/*                            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
  //
  int_2 count   = 0;
  float_4 prob  = 0;
  float_4 store = 0;

  Score_Hyp *ref_score_hyp     = NULL;
  Score_Hyp *temp_hyp          = NULL;
  
  // the very first state
  //
  if ( start_time == 0 ) {
    start_time ++;
  }
  int_2 frm_ind = start_time;
  
  HMM_State *temp_state  = model->get_start_state_cc ();
  String state_symbol    = (temp_state->get_prev_state_cc ())->get_out_symbol_cc ();
  
  // states of the model involved
  //
  HMM_State *model_states_ptr = model->get_states_cc ();
  
  // initialize the list of score_hyps to the one having state element
  // as the start state of the model
  //
  HMM_State *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);
  
  // determine size of input vector
  //
  int_2 test_vec_len = (state_ptr->get_mean_cc ())[0]->get_num_columns_cc ();
  
  // read the inputs
  //
  HMM_Matrix *test_vector = new HMM_Matrix (1, test_vec_len, 1.0);
  
  // go to the point in file specified by the start time
  //
  fseek (fp_in, test_vec_len * start_time * (sizeof (float_4)), SEEK_SET);
  
  // create score_hyps for all allowed input frame durations
  //
  while ((fscanf (fp_in, "%f", &store) != EOF) && ((count < MAX_DUR))) { 
    
    test_vector->set_element_cc (0, 0, store);
    
    // get the test_vector from the input file
    //
    for (int_2 i = 1; i < test_vec_len; i++) {
      fscanf ( fp_in, "%f", &store );
      test_vector->set_element_cc (0, i, store);
    }
    count++;
    
    
    // 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);
      
      if (ref_score_hyp != NULL) {

	// go through the score_hyps generated during this frame
	//
	for (temp_hyp = ref_score_hyp->get_next_hyp_cc ();
	     temp_hyp->get_timestamp_cc () != 0 ;
	     temp_hyp = temp_hyp->get_next_hyp_cc ()) {

	  if (((HMM_State *)temp_hyp->get_element_cc () == temp_state)
	      && (temp_hyp->get_timestamp_cc () == frm_ind)
	      && (temp_hyp->get_word_index_cc () == word_index)) {
	    
	    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 ();
	    }
	  }
	}
      }
    }
    
    // increment frame count
    //
    frm_ind ++;
    
    // dummy index variable
    //
    HMM_Trans_State *trans_ptr = NULL;
    
    // expand the trellis for all states in this model
    //
    for (temp_hyp = hypothesis->get_next_hyp_cc ();
	 temp_hyp->get_timestamp_cc () != 0; 
	 temp_hyp = temp_hyp->get_next_hyp_cc ()) {

      // create score_hyps for all transitions from the state
      // represented by the present score_hyp
      //
      if ((temp_hyp->get_word_index_cc () == word_index)
	  && (temp_hyp->get_timestamp_cc () ==  frm_ind - 1)) {
	state_ptr = (HMM_State *)(temp_hyp->get_element_cc ());
	
	for (trans_ptr = state_ptr->get_transition_cc (); trans_ptr != NULL;
	     trans_ptr = trans_ptr->get_next_transition_cc ()) {

	    prob = 0.0;
	    int_2 time = frm_ind;
	    state_symbol = trans_ptr->get_state_name_cc ();
	    
	    temp_state = model_states_ptr->find_state_cc (state_symbol);

	    if (temp_state != model->get_stop_state_cc ()) {
	      prob -= log10 (score) + 
		log10 (temp_state->state_score_cc (test_vector)) +
		log10 (state_ptr->get_trans_prob_cc (state_symbol));
	    }
	    
	    else {	
	      prob -= log10 (score) + 
		log10 (state_ptr->get_trans_prob_cc (state_symbol));
	      time --;
	    }
	    
	    fprintf (stdout,"%s\n", temp_state->get_out_symbol_cc ());
	    hypothesis->add_score_hyp_cc (temp_state, temp_hyp, prob, 
					  time, word_index);
	}
      }
    }
  }
}