Document hmm_train executable entirely, and change some outputs from printf to fastlib standards where necessary
This commit is contained in:
Ryan Curtin
@@ -11,12 +11,13 @@
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* See the usage() function for complete option list
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*/
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#include "fastlib/fastlib.h"
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#include <fastlib/fastlib.h>
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#include "support.h"
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#include "discreteHMM.h"
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#include "gaussianHMM.h"
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#include "mixgaussHMM.h"
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#include "mixtureDST.h"
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#include "hmm_documentation.h"
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using namespace hmm_support;
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@@ -25,67 +26,67 @@ success_t train_viterbi();
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void usage();
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const fx_entry_doc hmm_train_main_entries[] = {
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{"input_file", FX_REQUIRED, FX_STR, NULL,
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"Input file containing data sequences to train on.\n"},
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{"guess_file", FX_PARAM, FX_STR, NULL,
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"File containing guess of HMM model profile (.hmm)\n"},
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{"output_file", FX_PARAM, FX_STR, NULL,
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"Output file containing trained HMM profile (default output.<type>.hmm)\n"},
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{"type", FX_REQUIRED, FX_STR, NULL,
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" HMM type : discrete | gaussian | mixture.\n"},
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"HMM type: discrete | gaussian | mixture\n"},
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{"algorithm", FX_PARAM, FX_STR, NULL,
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" Training algoritm: baumwelch | viterbi.\n"},
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{"seqfile", FX_REQUIRED, FX_STR, NULL,
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" Output file for the data sequences.\n"},
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{"guess", FX_PARAM, FX_STR, NULL,
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" File containing guessing HMM model profile.\n"},
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{"numstate", FX_PARAM, FX_INT, NULL,
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" If no guessing profile specified, at least provide the number of states.\n"},
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{"profile", FX_REQUIRED, FX_STR, NULL,
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" Output file containing trained HMM profile.\n"},
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{"maxiter", FX_PARAM, FX_INT, NULL,
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" Maximum number of iterations, default = 500.\n"},
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"Training algorithm: baumwelch | viterbi (default baumwelch)\n"},
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{"num_states", FX_PARAM, FX_INT, NULL,
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"If no guess profile is specified, at least provide the number of states\n"},
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{"max_iter", FX_PARAM, FX_INT, NULL,
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"Maximum number of iterations (default 500)\n"},
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{"tolerance", FX_PARAM, FX_DOUBLE, NULL,
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" Error tolerance on log-likelihood as a stopping criteria.\n"},
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"Error tolerance on log-likelihood as a stopping criteria (default 0.001)\n"},
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FX_ENTRY_DOC_DONE
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};
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const fx_submodule_doc hmm_train_main_submodules[] = {
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const fx_submodule_doc hmm_train_submod_entries[] = {
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{"formats", &hmm_format_doc,
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"Documentation for file formats used by this program and other MLPACK HMM tools\n"},
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FX_SUBMODULE_DOC_DONE
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};
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const fx_module_doc hmm_train_main_doc = {
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hmm_train_main_entries, hmm_train_main_submodules,
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"This is a program training HMM models from data sequences. \n"
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hmm_train_main_entries, hmm_train_submod_entries,
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"The hmm_train utility is for training HMMs from data sequences. Given an\n"
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"input file of data sequences, and optionally a file containing a guess of\n"
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"the HMM profile (or a parameter specifying the number of states), the program\n"
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"will use the specified training algorithm (Viterbi or Baum-Welch, default\n"
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"Baum-Welch) to train the HMM until the log-likelihood error reaches the given\n"
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"value (default 0.001).\n"
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"\n"
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"It is important to note that if the guess file is not specified, the number\n"
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"of states must be specified. Additionally, a guess file must be specified\n"
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"if the HMM type is 'mixture'.\n"
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"\n"
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"See the documentation in the 'formats' submodule (--help=formats) for more\n"
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"information on the file formats that this program uses.\n"
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};
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void usage() {
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printf("\nUsage:\n"
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" train --type=={discrete|gaussian|mixture} OPTION\n"
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"[OPTIONS]\n"
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" --algorithm={baumwelch|viterbi} : algorithm used for training, default Baum-Welch\n"
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" --seqfile=file : file contains input sequences\n"
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" --guess=file : file contains guess HMM profile\n"
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" --numstate=NUM : if no guess profile is specified, at least specify the number of state\n"
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" --profile=file : output file for estimated HMM profile\n"
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" --maxiter=NUM : maximum number of iteration, default=500\n"
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" --tolerance=NUM : error tolerance on log-likelihood, default=1e-3\n"
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);
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}
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int main(int argc, char* argv[]) {
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fx_init(argc, argv, &hmm_train_main_doc);
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success_t s = SUCCESS_PASS;
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if (fx_param_exists(NULL,"type")) {
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if (fx_param_exists(NULL, "type")) {
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const char* algorithm = fx_param_str(NULL, "algorithm", "baumwelch");
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if (strcmp(algorithm,"baumwelch")==0)
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if (strcmp(algorithm, "baumwelch") == 0)
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s = train_baumwelch();
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else if (strcmp(algorithm,"viterbi")==0)
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else if (strcmp(algorithm, "viterbi") == 0)
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s = train_viterbi();
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else {
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printf("Unrecognized algorithm: must be baumwelch or viterbi !!!\n");
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FATAL("Unrecognized training algorithm: must be 'baumwelch' or 'viterbi'.\n");
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s = SUCCESS_FAIL;
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}
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}
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else {
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printf("Unrecognized type: must be: discrete | gaussian | mixture !!!\n");
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FATAL("Unrecognized HMM type; must be 'discrete', 'gaussian', or 'mixture'.\n");
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s = SUCCESS_FAIL;
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}
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if (!PASSED(s)) usage();
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fx_done(NULL);
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}
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@@ -101,10 +102,8 @@ success_t train_baumwelch() {
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return train_baumwelch_gaussian();
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else if (strcmp(type, "mixture")==0)
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return train_baumwelch_mixture();
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else {
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printf("Unrecognized type: must be: discrete | gaussian | mixture !!!\n");
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return SUCCESS_FAIL;
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}
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return SUCCESS_FAIL;
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}
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success_t train_viterbi_discrete();
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@@ -119,40 +118,34 @@ success_t train_viterbi() {
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return train_viterbi_gaussian();
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else if (strcmp(type, "mixture")==0)
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return train_viterbi_mixture();
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else {
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printf("Unrecognized type: must be: discrete | gaussian | mixture !!!\n");
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return SUCCESS_FAIL;
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}
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return SUCCESS_FAIL;
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}
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success_t train_baumwelch_mixture() {
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if (!fx_param_exists(NULL, "seqfile")) {
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printf("--seqfile must be defined.\n");
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return SUCCESS_FAIL;
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}
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MixtureofGaussianHMM hmm;
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ArrayList<Matrix> seqs;
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const char* seqin = fx_param_str_req(NULL, "seqfile");
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const char* proout = fx_param_str(NULL, "profile", "pro.mix.out");
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const char* seqin = fx_param_str_req(NULL, "input_file");
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const char* proout = fx_param_str(NULL, "output_file", "output.mix.hmm");
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load_matrix_list(seqin, &seqs);
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if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess");
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printf("Load parameters from file %s\n", guess);
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if (fx_param_exists(NULL, "guess_file")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess_file");
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NOTIFY("Loading guess parameters from file %s...", guess);
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hmm.InitFromFile(guess);
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}
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else {
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hmm.Init();
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printf("Automatic initialization not supported !!!");
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FATAL("Automatic initialization not supported for mixture training.");
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return SUCCESS_FAIL;
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}
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int maxiter = fx_param_int(NULL, "maxiter", 500);
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int maxiter = fx_param_int(NULL, "max_iter", 500);
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double tol = fx_param_double(NULL, "tolerance", 1e-3);
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NOTIFY("Training using Baum-Welch algorithm...");
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hmm.TrainBaumWelch(seqs, maxiter, tol);
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hmm.SaveProfile(proout);
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@@ -161,36 +154,30 @@ success_t train_baumwelch_mixture() {
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}
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success_t train_baumwelch_gaussian() {
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if (!fx_param_exists(NULL, "seqfile")) {
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printf("--seqfile must be defined.\n");
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return SUCCESS_FAIL;
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}
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GaussianHMM hmm;
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ArrayList<Matrix> seqs;
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const char* seqin = fx_param_str_req(NULL, "seqfile");
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const char* proout = fx_param_str(NULL, "profile", "pro.gauss.out");
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const char* seqin = fx_param_str_req(NULL, "input_file");
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const char* proout = fx_param_str(NULL, "output_file", "output.gauss.hmm");
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load_matrix_list(seqin, &seqs);
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if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess");
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printf("Load parameters from file %s\n", guess);
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if (fx_param_exists(NULL, "guess_file")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess_file");
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NOTIFY("Loading guess parameters from file %s...", guess);
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hmm.InitFromFile(guess);
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}
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else { // otherwise initialized using information from the data
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int numstate = fx_param_int_req(NULL, "numstate");
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printf("Generate HMM parameters: NUMSTATE = %d\n", numstate);
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int numstate = fx_param_int_req(NULL, "num_states");
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NOTIFY("Generating HMM parameters: %d states...", numstate);
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hmm.InitFromData(seqs, numstate);
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printf("Done.\n");
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}
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int maxiter = fx_param_int(NULL, "maxiter", 500);
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int maxiter = fx_param_int(NULL, "max_iter", 500);
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double tol = fx_param_double(NULL, "tolerance", 1e-3);
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printf("Training ...\n");
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NOTIFY("Training using Baum-Welch algorithm...");
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hmm.TrainBaumWelch(seqs, maxiter, tol);
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printf("Done.\n");
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hmm.SaveProfile(proout);
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@@ -198,33 +185,29 @@ success_t train_baumwelch_gaussian() {
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}
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success_t train_baumwelch_discrete() {
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if (!fx_param_exists(NULL, "seqfile")) {
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printf("--seqfile must be defined.\n");
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return SUCCESS_FAIL;
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}
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const char* seqin = fx_param_str_req(NULL, "seqfile");
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const char* proout = fx_param_str(NULL, "profile", "pro.dis.out");
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const char* seqin = fx_param_str_req(NULL, "input_file");
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const char* proout = fx_param_str(NULL, "output_file", "output.dis.hmm");
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ArrayList<Vector> seqs;
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load_vector_list(seqin, &seqs);
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DiscreteHMM hmm;
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if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess");
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printf("Load HMM parameters from file %s\n", guess);
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if (fx_param_exists(NULL, "guess_file")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess_file");
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NOTIFY("Loading HMM parameters from file %s...", guess);
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hmm.InitFromFile(guess);
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}
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else { // otherwise randomly initialized using information from the data
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int numstate = fx_param_int_req(NULL, "numstate");
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printf("Randomly generate parameters: NUMSTATE = %d\n", numstate);
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int numstate = fx_param_int_req(NULL, "num_states");
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NOTIFY("Randomly generating parameters: %d states...", numstate);
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hmm.InitFromData(seqs, numstate);
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}
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int maxiter = fx_param_int(NULL, "maxiter", 500);
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int maxiter = fx_param_int(NULL, "max_iter", 500);
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double tol = fx_param_double(NULL, "tolerance", 1e-3);
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NOTIFY("Training using Baum-Welch algorithm...");
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hmm.TrainBaumWelch(seqs, maxiter, tol);
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hmm.SaveProfile(proout);
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@@ -233,33 +216,29 @@ success_t train_baumwelch_discrete() {
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}
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success_t train_viterbi_mixture() {
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if (!fx_param_exists(NULL, "seqfile")) {
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printf("--seqfile must be defined.\n");
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return SUCCESS_FAIL;
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}
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MixtureofGaussianHMM hmm;
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ArrayList<Matrix> seqs;
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const char* seqin = fx_param_str_req(NULL, "seqfile");
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const char* proout = fx_param_str(NULL, "profile", "pro.mix.out");
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const char* seqin = fx_param_str_req(NULL, "input_file");
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const char* proout = fx_param_str(NULL, "output_file", "output.mix.hmm");
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load_matrix_list(seqin, &seqs);
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if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess");
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printf("Load parameters from file %s\n", guess);
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if (fx_param_exists(NULL, "guess_file")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess_file");
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NOTIFY("Loading parameters from guess file %s...", guess);
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hmm.InitFromFile(guess);
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}
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else {
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hmm.Init();
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printf("Automatic initialization not supported !!!");
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FATAL("Automatic initialization not supported for mixture training.");
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return SUCCESS_FAIL;
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}
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int maxiter = fx_param_int(NULL, "maxiter", 500);
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int maxiter = fx_param_int(NULL, "max_iter", 500);
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double tol = fx_param_double(NULL, "tolerance", 1e-3);
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NOTIFY("Training using Viterbi algorithm...");
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hmm.TrainViterbi(seqs, maxiter, tol);
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hmm.SaveProfile(proout);
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@@ -268,33 +247,29 @@ success_t train_viterbi_mixture() {
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}
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success_t train_viterbi_gaussian() {
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if (!fx_param_exists(NULL, "seqfile")) {
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printf("--seqfile must be defined.\n");
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return SUCCESS_FAIL;
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}
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GaussianHMM hmm;
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ArrayList<Matrix> seqs;
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const char* seqin = fx_param_str_req(NULL, "seqfile");
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const char* proout = fx_param_str(NULL, "profile", "pro.gauss.viterbi.out");
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const char* seqin = fx_param_str_req(NULL, "input_file");
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const char* proout = fx_param_str(NULL, "output_file", "output.gauss.hmm");
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load_matrix_list(seqin, &seqs);
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if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess");
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printf("Load parameters from file %s\n", guess);
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if (fx_param_exists(NULL, "guess_file")) { // guessed parameters in a file
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const char* guess = fx_param_str_req(NULL, "guess_file");
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NOTIFY("Loading parameters from file %s...", guess);
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hmm.InitFromFile(guess);
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}
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else { // otherwise initialized using information from the data
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int numstate = fx_param_int_req(NULL, "numstate");
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printf("Generate parameters: NUMSTATE = %d\n", numstate);
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int numstate = fx_param_int_req(NULL, "num_states");
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NOTIFY("Generating parameters with %d states\n", numstate);
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hmm.InitFromData(seqs, numstate);
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}
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int maxiter = fx_param_int(NULL, "maxiter", 500);
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int maxiter = fx_param_int(NULL, "max_iter", 500);
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double tol = fx_param_double(NULL, "tolerance", 1e-3);
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NOTIFY("Training using Viterbi algorithm...");
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hmm.TrainViterbi(seqs, maxiter, tol);
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hmm.SaveProfile(proout);
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@@ -303,34 +278,30 @@ success_t train_viterbi_gaussian() {
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}
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success_t train_viterbi_discrete() {
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if (!fx_param_exists(NULL, "seqfile")) {
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printf("--seqfile must be defined.\n");
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return SUCCESS_FAIL;
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}
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DiscreteHMM hmm;
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ArrayList<Vector> seqs;
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const char* seqin = fx_param_str_req(NULL, "seqfile");
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const char* proout = fx_param_str(NULL, "profile", "pro.dis.viterbi.out");
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const char* seqin = fx_param_str_req(NULL, "input_file");
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const char* proout = fx_param_str(NULL, "output_file", "output.dis.hmm");
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load_vector_list(seqin, &seqs);
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if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
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if (fx_param_exists(NULL, "guess_file")) { // guessed parameters in a file
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ArrayList<Matrix> matlst;
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const char* guess = fx_param_str_req(NULL, "guess");
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printf("Load parameters from file %s\n", guess);
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const char* guess = fx_param_str_req(NULL, "guess_file");
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NOTIFY("Loading guess parameters from file %s...", guess);
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hmm.InitFromFile(guess);
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}
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else { // otherwise randomly initialized using information from the data
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int numstate = fx_param_int_req(NULL, "numstate");
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printf("Generate parameters with NUMSTATE = %d\n", numstate);
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int numstate = fx_param_int_req(NULL, "num_states");
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NOTIFY("Generate parameters with %d states\n", numstate);
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hmm.InitFromData(seqs, numstate);
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}
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int maxiter = fx_param_int(NULL, "maxiter", 500);
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int maxiter = fx_param_int(NULL, "max_iter", 500);
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double tol = fx_param_double(NULL, "tolerance", 1e-3);
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NOTIFY("Training using Viterbi algorithm...");
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hmm.TrainViterbi(seqs, maxiter, tol);
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hmm.SaveProfile(proout);
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