void Testing()
{
TSymbol p1[] =
{
0,1,2,3,0,1,2,
0,1,1,3,0,1,2,
0,1,2,1,0,1,2,
0,1,2,3,1,1,2
};
auto obs = makeSamples(p1, 7, 7*4);
auto model = HiddenMarkovModel();
auto top = ForwardTopology(6, 6, false);
InitializeHiddenMarkovModelWithTopology(model, top, 4);
auto learning = BaumWelchLearning(model, 5e-4, 0);
auto rtol = learning.Run(obs);
TSymbol test[] =
{
0,1,2,3,0,1,2,
0,1,1,3,0,1,2,
3,3,3,3,1,1,1,
2,2,2,2,1,1,1,
};
auto test2 = makeSamples(test, 7, 7*4);
auto r1 = EvaluateModel(model, test2[0]);
auto r2 = EvaluateModel(model, test2[1]);
auto r3 = EvaluateModel(model, test2[2]);
auto r4 = EvaluateModel(model, test2[3]);
const auto TOL = 1e-8;
// valores de referencia con Accord.NET
assert(fabs(r1-(-3.9364056688035625)) < TOL);
assert(fabs(r2-(-5.0350078739166229)) < TOL);
assert(fabs(r3-(-32602.524455560128)) < TOL);
assert(fabs(r4-(-19547.858747514234)) < TOL);
std::cout << "TESTING FINALIZADO CON EXITO" << std::endl;
}
void TrainModel() {
long a;
pthread_t *pt = (pthread_t *)malloc(num_threads * sizeof(pthread_t));
if (model_file[0] == 0) return;
int iter = 0;
FILE *t1 = fopen(model_file, "rb");
FILE *t2 = fopen(model_file_nnet, "rb");
if(t1 != NULL && t2 != NULL) {
fclose(t1);
fclose(t2);
fprintf(stderr, "Restoring nnet from existing files %s, %s\n", model_file, model_file_nnet);
LoadNnet();
} else {
LearnVocabFromTrainFile();
if(maxent_hash_size) {
maxent_hash_size *= 1000000;
maxent_hash_size -= maxent_hash_size % vocab_size;
}
InitNet();
SaveNnet();
}
if(test_file[0] != 0) {
counter = 0;
real sumlogprob = EvaluateModel(test_file, 1);
fprintf(stderr, "Test entropy %f\n", sumlogprob/log10(2)/(real)counter);
return;
}
if(gen > 0) {
Sample(gen, 0);
return;
} else if(gen < 0) {
while(1) {
Sample(-gen, 1);
}
return;
}
fprintf(stderr, "Starting training using file %s\n", train_file);
FILE *fi = fopen(valid_file, "rb");
valid_words = 0;
while (1) {
ReadWordIndex(fi);
++valid_words;
if (feof(fi)) break;
}
valid_file_size = ftell(fi);
fclose(fi);
real old_entropy = 1e99;
real entropy;
real diff = 1e99;
int retry = 0;
int decay = 0;
while(retry < max_retry) {
if(iter != 0) {
if(decay) {
alpha /= 2.0;
maxent_alpha /= 2.0;
}
word_count_actual = 0;
counter = 0;
start = clock();
for (a = 0; a < num_threads; a++) pthread_create(&pt[a], NULL, TrainModelThread, (void *)a);
for (a = 0; a < num_threads; a++) pthread_join(pt[a], NULL);
}
fprintf(stderr, "Iteration %d\t", iter);
sumlogprob_valid = 0;
counter = 0;
sumlogprob_valid = EvaluateModel(valid_file, 0);
entropy = sumlogprob_valid/log10(2)/(real)counter;
fprintf(stderr, "Valid Entropy %f", entropy);
++iter;
diff = old_entropy/entropy;
if (isnan(entropy) || isinf(entropy) || diff < stop) {
if (decay == 1) {
++retry;
fprintf(stderr, "\tRetry %d/%d", retry, max_retry);
} else {
decay = 1;
fprintf(stderr, "\tDecay started");
}
if(isnan(entropy) || isinf(entropy) || diff < reject_threshold) {
fprintf(stderr, "\tNnet rejected");
FreeNnet();
int debug_ = debug_mode;
debug_mode = 0;
LoadNnet();
debug_mode = debug_;
}
}
fprintf(stderr, "\n");
if(diff > 1.0) {
SaveNnet();
old_entropy = entropy;
}
}
}
