/**
* @name improve_by_chopping
*
* Repeatedly chops the worst blob, classifying the new blobs fixing up all
* the data, and incrementally runs the segmentation search until a good word
* is found, or no more chops can be found.
*/
void Wordrec::improve_by_chopping(float rating_cert_scale,
WERD_RES* word,
BestChoiceBundle* best_choice_bundle,
BlamerBundle* blamer_bundle,
LMPainPoints* pain_points,
GenericVector<SegSearchPending>* pending) {
int blob_number;
do { // improvement loop.
// Make a simple vector of BLOB_CHOICEs to make it easy to pick which
// one to chop.
GenericVector<BLOB_CHOICE*> blob_choices;
int num_blobs = word->ratings->dimension();
for (int i = 0; i < num_blobs; ++i) {
BLOB_CHOICE_LIST* choices = word->ratings->get(i, i);
if (choices == NULL || choices->empty()) {
blob_choices.push_back(NULL);
} else {
BLOB_CHOICE_IT bc_it(choices);
blob_choices.push_back(bc_it.data());
}
}
SEAM* seam = improve_one_blob(blob_choices, &best_choice_bundle->fixpt,
false, false, word, &blob_number);
if (seam == NULL) break;
// A chop has been made. We have to correct all the data structures to
// take into account the extra bottom-level blob.
// Put the seam into the seam_array and correct everything else on the
// word: ratings matrix (including matrix location in the BLOB_CHOICES),
// states in WERD_CHOICEs, and blob widths.
word->InsertSeam(blob_number, seam);
// Insert a new entry in the beam array.
best_choice_bundle->beam.insert(new LanguageModelState, blob_number);
// Fixpts are outdated, but will get recalculated.
best_choice_bundle->fixpt.clear();
// Remap existing pain points.
pain_points->RemapForSplit(blob_number);
// Insert a new pending at the chop point.
pending->insert(SegSearchPending(), blob_number);
// Classify the two newly created blobs using ProcessSegSearchPainPoint,
// as that updates the pending correctly and adds new pain points.
MATRIX_COORD pain_point(blob_number, blob_number);
ProcessSegSearchPainPoint(0.0f, pain_point, "Chop1", pending, word,
pain_points, blamer_bundle);
pain_point.col = blob_number + 1;
pain_point.row = blob_number + 1;
ProcessSegSearchPainPoint(0.0f, pain_point, "Chop2", pending, word,
pain_points, blamer_bundle);
if (language_model_->language_model_ngram_on) {
// N-gram evaluation depends on the number of blobs in a chunk, so we
// have to re-evaluate everything in the word.
ResetNGramSearch(word, best_choice_bundle, pending);
blob_number = 0;
}
// Run language model incrementally. (Except with the n-gram model on.)
UpdateSegSearchNodes(rating_cert_scale, blob_number, pending,
word, pain_points, best_choice_bundle, blamer_bundle);
} while (!language_model_->AcceptableChoiceFound() &&
word->ratings->dimension() < kMaxNumChunks);
// If after running only the chopper best_choice is incorrect and no blame
// has been yet set, blame the classifier if best_choice is classifier's
// top choice and is a dictionary word (i.e. language model could not have
// helped). Otherwise blame the tradeoff between the classifier and
// the old language model (permuters).
if (word->blamer_bundle != NULL &&
word->blamer_bundle->incorrect_result_reason() == IRR_CORRECT &&
!word->blamer_bundle->ChoiceIsCorrect(word->best_choice)) {
bool valid_permuter = word->best_choice != NULL &&
Dict::valid_word_permuter(word->best_choice->permuter(), false);
word->blamer_bundle->BlameClassifierOrLangModel(word,
getDict().getUnicharset(),
valid_permuter,
wordrec_debug_blamer);
}
}
void Wordrec::SegSearch(WERD_RES* word_res,
BestChoiceBundle* best_choice_bundle,
BlamerBundle* blamer_bundle) {
LMPainPoints pain_points(segsearch_max_pain_points,
segsearch_max_char_wh_ratio,
assume_fixed_pitch_char_segment,
&getDict(), segsearch_debug_level);
// Compute scaling factor that will help us recover blob outline length
// from classifier rating and certainty for the blob.
float rating_cert_scale = -1.0 * getDict().certainty_scale / rating_scale;
GenericVector<SegSearchPending> pending;
InitialSegSearch(word_res, &pain_points, &pending, best_choice_bundle,
blamer_bundle);
if (!SegSearchDone(0)) { // find a better choice
if (chop_enable && word_res->chopped_word != NULL) {
improve_by_chopping(rating_cert_scale, word_res, best_choice_bundle,
blamer_bundle, &pain_points, &pending);
}
if (chop_debug) SEAM::PrintSeams("Final seam list:", word_res->seam_array);
if (blamer_bundle != NULL &&
!blamer_bundle->ChoiceIsCorrect(word_res->best_choice)) {
blamer_bundle->SetChopperBlame(word_res, wordrec_debug_blamer);
}
}
// Keep trying to find a better path by fixing the "pain points".
MATRIX_COORD pain_point;
float pain_point_priority;
int num_futile_classifications = 0;
STRING blamer_debug;
while (wordrec_enable_assoc &&
(!SegSearchDone(num_futile_classifications) ||
(blamer_bundle != NULL &&
blamer_bundle->GuidedSegsearchStillGoing()))) {
// Get the next valid "pain point".
bool found_nothing = true;
LMPainPointsType pp_type;
while ((pp_type = pain_points.Deque(&pain_point, &pain_point_priority)) !=
LM_PPTYPE_NUM) {
if (!pain_point.Valid(*word_res->ratings)) {
word_res->ratings->IncreaseBandSize(
pain_point.row - pain_point.col + 1);
}
if (pain_point.Valid(*word_res->ratings) &&
!word_res->ratings->Classified(pain_point.col, pain_point.row,
getDict().WildcardID())) {
found_nothing = false;
break;
}
}
if (found_nothing) {
if (segsearch_debug_level > 0) tprintf("Pain points queue is empty\n");
break;
}
ProcessSegSearchPainPoint(pain_point_priority, pain_point,
LMPainPoints::PainPointDescription(pp_type),
&pending, word_res, &pain_points, blamer_bundle);
UpdateSegSearchNodes(rating_cert_scale, pain_point.col, &pending,
word_res, &pain_points, best_choice_bundle,
blamer_bundle);
if (!best_choice_bundle->updated) ++num_futile_classifications;
if (segsearch_debug_level > 0) {
tprintf("num_futile_classifications %d\n", num_futile_classifications);
}
best_choice_bundle->updated = false; // reset updated
// See if it's time to terminate SegSearch or time for starting a guided
// search for the true path to find the blame for the incorrect best_choice.
if (SegSearchDone(num_futile_classifications) &&
blamer_bundle != NULL &&
blamer_bundle->GuidedSegsearchNeeded(word_res->best_choice)) {
InitBlamerForSegSearch(word_res, &pain_points, blamer_bundle,
&blamer_debug);
}
} // end while loop exploring alternative paths
if (blamer_bundle != NULL) {
blamer_bundle->FinishSegSearch(word_res->best_choice,
wordrec_debug_blamer, &blamer_debug);
}
if (segsearch_debug_level > 0) {
tprintf("Done with SegSearch (AcceptableChoiceFound: %d)\n",
language_model_->AcceptableChoiceFound());
}
}
