smodel.h

General Hidden Markov Model Library 一个通用的隐马尔科夫模型的C代码库

    inside the function, when a final state in the model is reach (a state
    with no output). If the model has no final state, the sequences will
    have length MAX_SEQ_LEN.
    @return             pointer to an array of sequences
    @param smo:         model
    @param seed:        initial parameter for the random value generator
                        (an integer). If seed == 0, then the random value
			generator is not initialized.
    @param global_len:  length of sequences (=0: automatically via final states)
    @param seq_number:  number of sequences
    @param label:       label tag
    @param Tmax:        maximal sequence length, set to MAX_SEQ_LEN if -1 
*/

  ghmm_cseq *ghmm_cmodel_generate_sequences (ghmm_cmodel * smo, int seed,
                                           int global_len, long seq_number,
                                           long label, int Tmax);

/** 
    Computes sum over all sequence of
    seq_w * log( P ( O|lambda ) ). If a sequence can't be generated by smo
    error cost of seq_w * PRENALTY_LOGP are imposed.
   @return       n: number of evaluated sequences, -1: error
   @param smo   ghmm_cmodel
   @param sqd    sequence struct
   @param log\_p  evaluated log likelihood
*/
  int ghmm_cmodel_likelihood (ghmm_cmodel * smo, ghmm_cseq * sqd, double *log_p);

/** 
    Computes log likelihood for all sequence of
    seq_w * log( P ( O|lambda ) ). If a sequence can't be generated by smo
    error cost of seq_w * PRENALTY_LOGP are imposed.
   @return       n: number of evaluated sequences, -1: error
   @param smo   ghmm_cmodel
   @param sqd    sequence struct
   @param log\_p array of evaluated likelihoods
*/
  int ghmm_cmodel_individual_likelihoods (ghmm_cmodel * smo, ghmm_cseq * sqd,
                                     double *log_ps);


/** Reads an XML file with specifications for one or more smodels.
    All parameters in matrix or vector form.
   @return vector of read smodels
   @param filename   input xml file
   @param smo_number  number of smodels to read*/
  ghmm_cmodel **ghmm_cmodel_xml_read (const char *filename, int *smo_number);

/** Writes an XML file with specifications for one or more smodels.
   @return 0:sucess, -1:error
   @param filename   output xml file
   @param smo ghmm_cmodel(s)
   @param smo_number  number of smodels to write*/
  int ghmm_cmodel_xml_write(ghmm_cmodel** smo, const char *file, int smo_number);

/**
   Prints one ghmm_cmodel in matrix form.
   @param file     output file
   @param smo   ghmm_cmodel
*/
  void ghmm_cmodel_print (FILE * file, ghmm_cmodel * smo);

/**
   Prints one ghmm_cmodel with only one transition Matrix A (=Ak\_0).
   @param file     output file
   @param smo   ghmm_cmodel
*/
  void ghmm_cmodel_print_oneA (FILE * file, ghmm_cmodel * smo);

/**
   Prints transition matrix of specified class.
   @param file       output file
   @param smo     ghmm_cmodel
   @param k          transition class
   @param tab      format: leading tab
   @param separator  format: seperator
   @param ending     format: end of data in line
*/
  void ghmm_cmodel_Ak_print (FILE * file, ghmm_cmodel * smo, int k, char *tab,
                        char *separator, char *ending);

/**
   Prints weight matrix of output functions of an smodel.
   @param file       output file
   @param smo     ghmm_cmodel
   @param tab      format: leading tab
   @param separator  format: seperator
   @param ending     format: end of data in line
*/
  void ghmm_cmodel_C_print (FILE * file, ghmm_cmodel * smo, char *tab, char *separator,
                       char *ending);

/**
   Prints mean matrix of output functions of an smodel.
   @param file       output file
   @param smo     ghmm_cmodel
   @param tab      format: leading tab
   @param separator  format: seperator
   @param ending     format: end of data in line
*/
  void ghmm_cmodel_Mue_print (FILE * file, ghmm_cmodel * smo, char *tab,
                         char *separator, char *ending);
/**
   Prints variance matrix of output functions of an smodel.
   @param file       output file
   @param smo     ghmm_cmodel
   @param tab      format: leading tab
   @param separator  format: seperator
   @param ending     format: end of data in line
*/
  void ghmm_cmodel_U_print (FILE * file, ghmm_cmodel * smo, char *tab, char *separator,
                       char *ending);
/**
   Prints initial prob vector of an smodel.
   @param file       output file
   @param smo     ghmm_cmodel
   @param tab      format: leading tab
   @param separator  format: seperator
   @param ending     format: end of data in line
*/
  void ghmm_cmodel_Pi_print (FILE * file, ghmm_cmodel * smo, char *tab, char *separator,
                        char *ending);
/**
   Prints vector of fix\_states.
   @param file       output file
   @param smo     ghmm_cmodel
   @param tab      format: leading tab
   @param separator  format: seperator
   @param ending     format: end of data in line
*/
  void ghmm_cmodel_fix_print (FILE * file, ghmm_cmodel * smo, char *tab,
                         char *separator, char *ending);

/** Computes the density of one symbol (omega) in a given state and a 
    given output component
    @return calculated density
    @param smo ghmm_cmodel
    @param state state 
    @param m output component
    @param omega given symbol
*/
  double ghmm_cmodel_calc_cmbm (ghmm_cmodel * smo, int state, int m, double omega);

/** Computes the density of one symbol (omega) in a given state (sums over
    all output components
    @return calculated density
    @param smo ghmm_cmodel
    @param state state 
    @param omega given symbol
*/
  double ghmm_cmodel_calc_b (ghmm_cmodel * smo, int state, double omega);

/** Computes probabilistic distance of two models
    @return the distance
    @param cm0  ghmm_cmodel used for generating random output
    @param cm   ghmm_cmodel to compare with
    @param maxT  maximum output length (for HMMs with absorbing states multiple
                 sequences with a toal length of at least maxT will be 
		 generated)
    @param symmetric  flag, whether to symmetrize distance (not implemented yet)
    @param verbose  flag, whether to monitor distance in 40 steps. 
                    Prints to stdout (yuk!)
*/
  double ghmm_cmodel_prob_distance (ghmm_cmodel * cm0, ghmm_cmodel * cm, int maxT,
                               int symmetric, int verbose);

/** 
    Computes value of distribution function for a given symbol omega, a given
    ghmm_dstate and a given output component.
    @return   value of distribution function
    @param smo   ghmm_cmodel
    @param state  state
    @param m      component
    @param omega symbol
*/
  double ghmm_cmodel_calc_cmBm (ghmm_cmodel * smo, int state, int m, double omega);

/** 
    Computes value of distribution function for a given symbol omega and
    a given  state. Sums over all components.
    @return   value of distribution function
    @param smo   ghmm_cmodel
    @param state  state
    @param omega symbol
*/
  double ghmm_cmodel_calc_B (ghmm_cmodel * smo, int state, double omega);

/** Computes the number of free parameters in an array of
   smodels. E.g. if the number of parameter from pi is N - 1.
   Counts only those parameters, that can be changed during  
   training. If pi[i] = 0 it is not counted, since it can't be changed.
   @return number of free parameters
   @param smo ghmm_cmodel
   @param smo\_number number of smodels
*/
  int ghmm_cmodel_count_free_parameter (ghmm_cmodel ** smo, int smo_number);


/*============================================================================*/

/* keep the following functions for first distribution???
   --> BK ? 
*/


/** Generates interval(a,b) with  B(a) < 0.01, B(b) > 0.99
    @param smo    continous HMM
    @param state  given state
    @param a      return-value: left side
    @param b      return-value: right side
*/
  void ghmm_cmodel_get_interval_B (ghmm_cmodel * smo, int state, double *a, double *b);

/**
    Get transition probabality from state 'i' to state 'j' to value 'prob'.
    NOTE: No internal checks
    @return  transition probability from state i to state j
    @param mo model
    @param i  state index (source)
    @param j  state index (target)
    @param c  transition class
*/
  double ghmm_cmodel_get_transition(ghmm_cmodel* mo, int i, int j, int c);

/**
    Set transition from state 'i' to state 'j' to value 'prob'.
    NOTE: No internal checks - model might get broken if used without care.
    @param mo model
    @param i  state index (source)
    @param j  state index (target)
    @param prob probabilitys
    @param c  transition class
*/
  void ghmm_cmodel_set_transition (ghmm_cmodel* mo, int i, int j, int c, double prob);

#ifdef __cplusplus
}
#endif
#endif
/*@} (Doc++-Group: smodel) */