void testBaumwelch(int seqlen){
int error;
ghmm_dmodel * mo_gen = NULL;
ghmm_dmodel * mo_time = NULL;
ghmm_dmodel * mo_mem = NULL;
ghmm_dseq * my_output = NULL;
if (!(mo_gen = malloc (sizeof (ghmm_dmodel))))
{printf ("malloc failed in line %d", __LINE__); exit(1);}
if (!(mo_time = malloc (sizeof (ghmm_dmodel))))
{printf ("malloc failed in line %d", __LINE__); exit(1);}
if (!(mo_mem = malloc (sizeof (ghmm_dmodel))))
{printf ("malloc failed in line %d", __LINE__); exit(1);}
/* generate a model with variable number of states*/
generateModel(mo_gen, 7, 92304);
generateModel(mo_time, 5, 1704);
generateModel(mo_mem, 5, 1704);
/*generate a random sequence*/
my_output = ghmm_dmodel_label_generate_sequences(mo_gen, 0, seqlen, NR_SEQUENCES, seqlen);
/* ghmm_dmodel_add_noise(mo_time, .499, 0); */
/* randomize the second */
/* ghmm_dmodel_add_noise(mo_mem, .499, 0); */
ghmm_dmodel_print(stdout, mo_time);
ghmm_dmodel_print(stdout, mo_mem);
printf("Distance between the two models: %g\n\n", ghmm_dmodel_distance(mo_time, mo_mem));
/* shifting both models in diffrent directions */
/* train the first */
/*ghmm_dmodel_label_baum_welch(mo_time, my_output);*/
error = ghmm_dmodel_baum_welch(mo_time, my_output);
/* train the second and hope they are equal */
error = ghmm_dmodel_baum_welch(mo_mem, my_output);
ghmm_dmodel_print(stdout, mo_time);
ghmm_dmodel_print(stdout, mo_mem);
printf("Distance between the two trained models: %g\n", ghmm_dmodel_distance(mo_time, mo_mem));
printf("Log-Likelyhood generating: %g\n", ghmm_dmodel_likelihood (mo_gen, my_output));
printf("Log-Likelyhood fb-Baum-Welch: %g\n", ghmm_dmodel_likelihood (mo_time, my_output));
printf("Log-Likelyhood me-Baum-Welch: %g\n", ghmm_dmodel_likelihood (mo_mem, my_output));
/* freeing memory */
ghmm_dmodel_free(&mo_gen);
ghmm_dmodel_free(&mo_time);
ghmm_dmodel_free(&mo_mem);
ghmm_dseq_free(&my_output);
}
int my_model()
{
/* model structure, that contains states */
ghmm_dmodel my_model;
/* array of states */
ghmm_dstate model_states[2];
/* first state */
/* probability of emmission of 0,1 or 2 */
double symbols_0_state[3]={0.5,0.5,0.0};
/* transition to which state is given in the following arrays */
int trans_id_0_state[2]={0,1};
/* transition probability from here to 0-state (self) and 1-state */
double trans_prob_0_state[2]={0.9,0.1};
/* transition probability from 0-state (self) and 1-state to this state */
double trans_prob_0_state_rev[2]={0.9,0.1};
/* second state , comments see above */
double symbols_1_state[3]={0.0,0.0,1.0};
int trans_id_1_state[2]={0,1};
double trans_prob_1_state[2]={0.1,0.9};
double trans_prob_1_state_rev[2]={0.1,0.9};
ghmm_dseq* my_output;
int pow_look[2] = {1,3};
/* flags indicating whether a state is silent */
/*int silent_array[2] = {0,0};*/
/* initialise state 0 */
/* start probability for this state */
model_states[0].pi = 0.5;
/* array with emission probabilities */
model_states[0].b=symbols_0_state;
/* number of fields in out_a and out_id */
model_states[0].out_states=2;
/* transition probability from this state */
model_states[0].out_a=trans_prob_0_state;
/* state ids belonging to the probability */
model_states[0].out_id=trans_id_0_state;
/* transition probability to this state */
/* in_states,in_id and in_a have the same function as above*/
model_states[0].in_states=2;
model_states[0].in_id=trans_id_0_state;
model_states[0].in_a=trans_prob_0_state_rev;
/* should emission probabilities be changed during reestimation? 1: no, else: yes*/
model_states[0].fix=0;
/* initialise state 1 */
/* same meaning as above */
model_states[1].pi = 0.5;
model_states[1].b=symbols_1_state;
model_states[1].out_states=2;
model_states[1].out_a=trans_prob_1_state;
model_states[1].out_id=trans_id_1_state;
model_states[1].in_states=2;
model_states[1].in_id=trans_id_0_state;
model_states[1].in_a=trans_prob_1_state_rev;
model_states[1].fix=0;
/* initialise model */
my_model.N=2; /* number of states, dimension of model.s */
my_model.M=3; /* number of symbols, dimension of states.b */
my_model.s=model_states; /* array of states */
my_model.prior=-1; /* probability of this model, used in a model array */
/*my_model.silent = silent_array;*/
my_model.pow_lookup = pow_look;
my_model.maxorder = 0;
my_model.model_type =0;
/* consistency check */
fprintf(stdout,"checking model:\n");
if (ghmm_dmodel_check(&my_model))
{
fprintf(stderr,"ghmm_dmodel_check failed!\n");
return 1;
}
fprintf(stdout,"model is ok\n");
/* print model parameters */
fprintf(stdout,"two_states_three_symbols model:\n");
ghmm_dmodel_print(stdout,&my_model);
/* generate sequences */
fprintf(stdout,"generating sequences:...");
my_output=ghmm_dmodel_generate_sequences(&my_model, /* model */
0, /* random seed */
100, /* length of each sequence */
100, /* no of sequences */
100); /* maxT */
fprintf(stdout,"Done\n");
/*ghmm_dseq_print(stdout,my_output);*/
/* slight change of emission probabilities in state 0 */
symbols_0_state[0] = 0.6;
symbols_0_state[1] = 0.4;
symbols_0_state[2] = 0.0;
/* reestimation */
fprintf(stdout,"reestimating with Baum-Welch-algorithm...");
ghmm_dmodel_baum_welch(&my_model,my_output);
/* print the result */
fprintf(stdout,"Done\nthe result is:\n");
ghmm_dmodel_print(stdout,&my_model);
ghmm_dseq_free(&my_output);
return 0;
}
