这是一款很好用的工具包

				#!/usr/local/bin/gawk -f				#				# compute-best-rover-mix --				#	Compute the best mixture weight for combining multiple sausages				#				# usage: compute-best-rover-mix [lambda="l1 l2 ..."] [precision=p] nbest-rover-ref-posteriors-output				#j				# where the input is the output of nbest-rover -write-ref-posteriors .				# li are initial guesses at the mixture weights, and p is the				# precision with which the best lambda vector is to be found.				#				# $Header: /home/srilm/devel/utils/src/RCS/compute-best-rover-mix.gawk,v 1.2 2004/11/02 02:00:35 stolcke Exp $				#				BEGIN {					verbose = 0;									lambda = "0.5";					precision = 0.001;					M_LN10 = 2.30258509299404568402;	# from 									logINF = -320;									zero_probs = 0;				}				function abs(x) {					return (x < 0) ? -x : x;				}				function log10(x) {					return log(x) / M_LN10;				}				function exp10(x) {					if (x < logINF) {						return 0;					} else {						return exp(x * M_LN10);					}				}				function addlogs(x,y) {				    if (x					temp = x; x = y; y = temp;				    }				    return x + log10(1 + exp10(y - x));				}								function print_vector(x, n) {					result = "(" x[1];					for (k = 2; k 						result = result " " x[k];					}					return result ")"				}								{					nfiles = NF - 4;									if ($4 == 0) {						zero_probs ++;					} else {						sample_no ++;										for (i = 1; i 							samples[i " " sample_no] = $(i + 4);						}					}				}									END {					last_prior = 0.0;									# initialize priors from lambdas					nlambdas = split(lambda, lambdas);					lambda_sum = 0.0;					for (i = 1; i 						priors[i] = lambdas[i];						lambda_sum += lambdas[i];					}					# fill in the missing lambdas					for (i = nlambdas + 1; i 						priors[i] = (1 - lambda_sum)/(nfiles - nlambdas);					}									iter = 0;					have_converged = 0;					while (!have_converged) {					    iter ++;									    num_words = 0;					    delete post_totals;					    log_like = 0;									    for (j = 1; j 										all_inf = 1;						for (i = 1; i 							sample = log10(samples[i " " j]);							logpost[i] = log10(priors[i]) + sample;							all_inf = all_inf && (sample == logINF);							if (i == 1) {								logsum = logpost[i];							} else {								logsum = addlogs(logsum, logpost[i]);							}						}										# skip OOV words						if (all_inf) {							continue;						}										num_words ++;						log_like += logsum;										for (i = 1; i 							post_totals[i] += exp10(logpost[i] - logsum);						}					    }					    printf "iteration %d, lambda = %s, ppl = %g\n", \						    iter, print_vector(priors, nfiles), \						    exp10(-log_like/num_words) >> "/dev/stderr";					    fflush();														    have_converged = 1;					    for (i = 1; i 						last_prior = priors[i];						priors[i] = post_totals[i]/num_words;										if (abs(last_prior - priors[i]) > precision) {							have_converged = 0;						}					    }					}									printf "%d alignment positions, best lambda %s\n", 							num_words, print_vector(priors, nfiles);				}