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📄 cmmf_write_1.cc

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// file: cmmf_write_1.cc// // isip include files//#include "convert_mmf.h"#include "convert_mmf_constants.h" // method: write_states_ascii_cc//// arguments://  int_4 num_st : (input) number of states//  int_4 num_feat : (input) number of features in data vector//  char_1* st_file : (input) data file name//  State** state_list: (input) list of states//// return: a State** pointing to a list of States//// this method writes the State file --- each mixture// component in the States is assumed to have a diagonal covariance// matrix//logical_1 write_states_ascii_cc(int_4 num_st_a, int_4 num_feat_a,				char_1* st_file_a, Train_State** state_list_a) {    // open file for writing  //  FILE* fp = fopen((char*)st_file_a, "w");  if (fp == (FILE*)NULL) {    fprintf(stdout, "cannot open file %s\n", st_file_a);    exit(ISIP_PROTO_ERROR);	  }	    // variables to write data  //  int_4 num_mix = (int_4)0;  // output the feature size and number of states  //  fprintf(fp, "feature_size = %ld\n", num_feat_a);  fprintf(fp, "num_states = %ld\n\n", num_st_a);  // print the dummy 0'th state  //  fprintf(fp, "State: %ld\n", (int_4)0);  fprintf(fp, "nummixes: %ld\n\n", (int_4)0);      // for each state print the contents  //  for (int_4 i = 1; i < num_st_a; i++) {        // get the state information    //    float_4* st_mix_wt = state_list_a[i]->get_weights_cc();    float_4** st_mix_mean = state_list_a[i]->get_mean_cc();    float_4** st_mix_covar = state_list_a[i]->get_covar_cc();    float_4* st_mix_scale = state_list_a[i]->get_scale_cc();        // print number of mixtures in the state    //    fprintf(fp, "State: %ld\n", i);    num_mix = state_list_a[i]->get_num_mixtures_cc();    fprintf(fp, "nummixes: %ld\n\n", num_mix);        // loop over all mixtures and print the mixture weight, mean and    // the variance    //    for (int_4 j = 0; j < num_mix; j++) {            // print the mixture index      //      fprintf(fp, "mixture: %ld\n", j+1);	        fprintf(fp, "mix_weight: %1.6e\n", st_mix_wt[j]);      // print the mean vector      //      fprintf(fp, "mean: ");      for (int_4 k = 0; k < num_feat_a; k++) {	fprintf(fp, "%1.6e ", st_mix_mean[j][k]);      }      fprintf(fp, "\n");            // print the variance vector      //      fprintf(fp, "covar: ");      for (int_4 k = 0; k < num_feat_a; k++) {	fprintf(fp, "%1.6e ", st_mix_covar[j][k]);      }      fprintf(fp, "\n");            // print the scale factor      //      fprintf(fp, "scale: %1.6e\n\n", st_mix_scale[j]);    }  }    // close output file  //  fclose(fp);    // exit gracefully  //  return ISIP_TRUE;}

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