// Returns a bigger MATRIX with a new column and row in the matrix in order
// to split the blob at the given (ind,ind) diagonal location.
// Entries are relocated to the new MATRIX using the transformation defined
// by MATRIX_COORD::MapForSplit.
// Transfers the pointer data to the new MATRIX and deletes *this.
MATRIX* MATRIX::ConsumeAndMakeBigger(int ind) {
int dim = dimension();
int band_width = bandwidth();
// Check to see if bandwidth needs expanding.
for (int col = ind; col >= 0 && col > ind - band_width; --col) {
if (array_[col * band_width + band_width - 1] != empty_) {
++band_width;
break;
}
}
MATRIX* result = new MATRIX(dim + 1, band_width);
for (int col = 0; col < dim; ++col) {
for (int row = col; row < dim && row < col + bandwidth(); ++row) {
MATRIX_COORD coord(col, row);
coord.MapForSplit(ind);
BLOB_CHOICE_LIST* choices = get(col, row);
if (choices != NULL) {
// Correct matrix location on each choice.
BLOB_CHOICE_IT bc_it(choices);
for (bc_it.mark_cycle_pt(); !bc_it.cycled_list(); bc_it.forward()) {
BLOB_CHOICE* choice = bc_it.data();
choice->set_matrix_cell(coord.col, coord.row);
}
ASSERT_HOST(coord.Valid(*result));
result->put(coord.col, coord.row, choices);
}
}
}
delete this;
return result;
}
/**
* @name chop_word_main
*
* Classify the blobs in this word and permute the results. Find the
* worst blob in the word and chop it up. Continue this process until
* a good answer has been found or all the blobs have been chopped up
* enough. The results are returned in the WERD_RES.
*/
void Wordrec::chop_word_main(WERD_RES *word) {
int num_blobs = word->chopped_word->NumBlobs();
if (word->ratings == NULL) {
word->ratings = new MATRIX(num_blobs, wordrec_max_join_chunks);
}
if (word->ratings->get(0, 0) == NULL) {
// Run initial classification.
for (int b = 0; b < num_blobs; ++b) {
BLOB_CHOICE_LIST* choices = classify_piece(word->seam_array, b, b,
"Initial:", word->chopped_word,
word->blamer_bundle);
word->ratings->put(b, b, choices);
}
} else {
// Blobs have been pre-classified. Set matrix cell for all blob choices
for (int col = 0; col < word->ratings->dimension(); ++col) {
for (int row = col; row < word->ratings->dimension() &&
row < col + word->ratings->bandwidth(); ++row) {
BLOB_CHOICE_LIST* choices = word->ratings->get(col, row);
if (choices != NULL) {
BLOB_CHOICE_IT bc_it(choices);
for (bc_it.mark_cycle_pt(); !bc_it.cycled_list(); bc_it.forward()) {
bc_it.data()->set_matrix_cell(col, row);
}
}
}
}
}
// Run Segmentation Search.
BestChoiceBundle best_choice_bundle(word->ratings->dimension());
SegSearch(word, &best_choice_bundle, word->blamer_bundle);
if (word->best_choice == NULL) {
// SegSearch found no valid paths, so just use the leading diagonal.
word->FakeWordFromRatings();
}
word->RebuildBestState();
// If we finished without a hyphen at the end of the word, let the next word
// be found in the dictionary.
if (word->word->flag(W_EOL) &&
!getDict().has_hyphen_end(*word->best_choice)) {
getDict().reset_hyphen_vars(true);
}
if (word->blamer_bundle != NULL && this->fill_lattice_ != NULL) {
CallFillLattice(*word->ratings, word->best_choices,
*word->uch_set, word->blamer_bundle);
}
if (wordrec_debug_level > 0) {
tprintf("Final Ratings Matrix:\n");
word->ratings->print(getDict().getUnicharset());
}
word->FilterWordChoices(getDict().stopper_debug_level);
}
BLOB_CHOICE_LIST *Wordrec::classify_piece(const GenericVector<SEAM*>& seams,
inT16 start,
inT16 end,
const char* description,
TWERD *word,
BlamerBundle *blamer_bundle) {
if (end > start) join_pieces(seams, start, end, word);
BLOB_CHOICE_LIST *choices = classify_blob(word->blobs[start], description,
White, blamer_bundle);
// Set the matrix_cell_ entries in all the BLOB_CHOICES.
BLOB_CHOICE_IT bc_it(choices);
for (bc_it.mark_cycle_pt(); !bc_it.cycled_list(); bc_it.forward()) {
bc_it.data()->set_matrix_cell(start, end);
}
if (end > start) break_pieces(seams, start, end, word);
return (choices);
}
// Helper recursively prints all paths through the ratings matrix, starting
// at column col.
static void PrintMatrixPaths(int col, int dim,
const MATRIX& ratings,
int length, const BLOB_CHOICE** blob_choices,
const UNICHARSET& unicharset,
const char *label, FILE *output_file) {
for (int row = col; row < dim && row - col < ratings.bandwidth(); ++row) {
if (ratings.get(col, row) != NOT_CLASSIFIED) {
BLOB_CHOICE_IT bc_it(ratings.get(col, row));
for (bc_it.mark_cycle_pt(); !bc_it.cycled_list(); bc_it.forward()) {
blob_choices[length] = bc_it.data();
if (row + 1 < dim) {
PrintMatrixPaths(row + 1, dim, ratings, length + 1, blob_choices,
unicharset, label, output_file);
} else {
PrintPath(length + 1, blob_choices, unicharset, label, output_file);
}
}
}
}
}
// Moved from speckle.cpp
// Adds a noise classification result that is a bit worse than the worst
// current result, or the worst possible result if no current results.
void Classify::AddLargeSpeckleTo(int blob_length, BLOB_CHOICE_LIST *choices) {
BLOB_CHOICE_IT bc_it(choices);
// If there is no classifier result, we will use the worst possible certainty
// and corresponding rating.
float certainty = -getDict().certainty_scale;
float rating = rating_scale * blob_length;
if (!choices->empty() && blob_length > 0) {
bc_it.move_to_last();
BLOB_CHOICE* worst_choice = bc_it.data();
// Add speckle_rating_penalty to worst rating, matching old value.
rating = worst_choice->rating() + speckle_rating_penalty;
// Compute the rating to correspond to the certainty. (Used to be kept
// the same, but that messes up the language model search.)
certainty = -rating * getDict().certainty_scale /
(rating_scale * blob_length);
}
BLOB_CHOICE* blob_choice = new BLOB_CHOICE(UNICHAR_SPACE, rating, certainty,
-1, -1, 0, 0, MAX_FLOAT32, 0,
BCC_SPECKLE_CLASSIFIER);
bc_it.add_to_end(blob_choice);
}
/**
* @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);
}
}
bool Dict::NoDangerousAmbig(WERD_CHOICE *best_choice,
DANGERR *fixpt,
bool fix_replaceable,
MATRIX *ratings) {
if (stopper_debug_level > 2) {
tprintf("\nRunning NoDangerousAmbig() for %s\n",
best_choice->debug_string().string());
}
// Construct BLOB_CHOICE_LIST_VECTOR with ambiguities
// for each unichar id in BestChoice.
BLOB_CHOICE_LIST_VECTOR ambig_blob_choices;
int i;
bool ambigs_found = false;
// For each position in best_choice:
// -- choose AMBIG_SPEC_LIST that corresponds to unichar_id at best_choice[i]
// -- initialize wrong_ngram with a single unichar_id at best_choice[i]
// -- look for ambiguities corresponding to wrong_ngram in the list while
// adding the following unichar_ids from best_choice to wrong_ngram
//
// Repeat the above procedure twice: first time look through
// ambigs to be replaced and replace all the ambiguities found;
// second time look through dangerous ambiguities and construct
// ambig_blob_choices with fake a blob choice for each ambiguity
// and pass them to dawg_permute_and_select() to search for
// ambiguous words in the dictionaries.
//
// Note that during the execution of the for loop (on the first pass)
// if replacements are made the length of best_choice might change.
for (int pass = 0; pass < (fix_replaceable ? 2 : 1); ++pass) {
bool replace = (fix_replaceable && pass == 0);
const UnicharAmbigsVector &table = replace ?
getUnicharAmbigs().replace_ambigs() : getUnicharAmbigs().dang_ambigs();
if (!replace) {
// Initialize ambig_blob_choices with lists containing a single
// unichar id for the correspoding position in best_choice.
// best_choice consisting from only the original letters will
// have a rating of 0.0.
for (i = 0; i < best_choice->length(); ++i) {
BLOB_CHOICE_LIST *lst = new BLOB_CHOICE_LIST();
BLOB_CHOICE_IT lst_it(lst);
// TODO(rays/antonova) Put real xheights and y shifts here.
lst_it.add_to_end(new BLOB_CHOICE(best_choice->unichar_id(i),
0.0, 0.0, -1, 0, 1, 0, BCC_AMBIG));
ambig_blob_choices.push_back(lst);
}
}
UNICHAR_ID wrong_ngram[MAX_AMBIG_SIZE + 1];
int wrong_ngram_index;
int next_index;
int blob_index = 0;
for (i = 0; i < best_choice->length(); blob_index += best_choice->state(i),
++i) {
UNICHAR_ID curr_unichar_id = best_choice->unichar_id(i);
if (stopper_debug_level > 2) {
tprintf("Looking for %s ngrams starting with %s:\n",
replace ? "replaceable" : "ambiguous",
getUnicharset().debug_str(curr_unichar_id).string());
}
int num_wrong_blobs = best_choice->state(i);
wrong_ngram_index = 0;
wrong_ngram[wrong_ngram_index] = curr_unichar_id;
if (curr_unichar_id == INVALID_UNICHAR_ID ||
curr_unichar_id >= table.size() ||
table[curr_unichar_id] == nullptr) {
continue; // there is no ambig spec for this unichar id
}
AmbigSpec_IT spec_it(table[curr_unichar_id]);
for (spec_it.mark_cycle_pt(); !spec_it.cycled_list();) {
const AmbigSpec *ambig_spec = spec_it.data();
wrong_ngram[wrong_ngram_index+1] = INVALID_UNICHAR_ID;
int compare = UnicharIdArrayUtils::compare(wrong_ngram,
ambig_spec->wrong_ngram);
if (stopper_debug_level > 2) {
tprintf("candidate ngram: ");
UnicharIdArrayUtils::print(wrong_ngram, getUnicharset());
tprintf("current ngram from spec: ");
UnicharIdArrayUtils::print(ambig_spec->wrong_ngram, getUnicharset());
tprintf("comparison result: %d\n", compare);
}
if (compare == 0) {
// Record the place where we found an ambiguity.
if (fixpt != nullptr) {
UNICHAR_ID leftmost_id = ambig_spec->correct_fragments[0];
fixpt->push_back(DANGERR_INFO(
blob_index, blob_index + num_wrong_blobs, replace,
getUnicharset().get_isngram(ambig_spec->correct_ngram_id),
leftmost_id));
if (stopper_debug_level > 1) {
tprintf("fixpt+=(%d %d %d %d %s)\n", blob_index,
blob_index + num_wrong_blobs, false,
getUnicharset().get_isngram(
ambig_spec->correct_ngram_id),
getUnicharset().id_to_unichar(leftmost_id));
}
}
if (replace) {
if (stopper_debug_level > 2) {
tprintf("replace ambiguity with %s : ",
getUnicharset().id_to_unichar(
ambig_spec->correct_ngram_id));
UnicharIdArrayUtils::print(
ambig_spec->correct_fragments, getUnicharset());
}
ReplaceAmbig(i, ambig_spec->wrong_ngram_size,
ambig_spec->correct_ngram_id,
best_choice, ratings);
} else if (i > 0 || ambig_spec->type != CASE_AMBIG) {
// We found dang ambig - update ambig_blob_choices.
if (stopper_debug_level > 2) {
tprintf("found ambiguity: ");
UnicharIdArrayUtils::print(
ambig_spec->correct_fragments, getUnicharset());
}
ambigs_found = true;
for (int tmp_index = 0; tmp_index <= wrong_ngram_index;
++tmp_index) {
// Add a blob choice for the corresponding fragment of the
// ambiguity. These fake blob choices are initialized with
// negative ratings (which are not possible for real blob
// choices), so that dawg_permute_and_select() considers any
// word not consisting of only the original letters a better
// choice and stops searching for alternatives once such a
// choice is found.
BLOB_CHOICE_IT bc_it(ambig_blob_choices[i+tmp_index]);
bc_it.add_to_end(new BLOB_CHOICE(
ambig_spec->correct_fragments[tmp_index], -1.0, 0.0,
-1, 0, 1, 0, BCC_AMBIG));
}
}
spec_it.forward();
} else if (compare == -1) {
if (wrong_ngram_index+1 < ambig_spec->wrong_ngram_size &&
((next_index = wrong_ngram_index+1+i) < best_choice->length())) {
// Add the next unichar id to wrong_ngram and keep looking for
// more ambigs starting with curr_unichar_id in AMBIG_SPEC_LIST.
wrong_ngram[++wrong_ngram_index] =
best_choice->unichar_id(next_index);
num_wrong_blobs += best_choice->state(next_index);
} else {
break; // no more matching ambigs in this AMBIG_SPEC_LIST
}
} else {
spec_it.forward();
}
} // end searching AmbigSpec_LIST
} // end searching best_choice
} // end searching replace and dangerous ambigs
// If any ambiguities were found permute the constructed ambig_blob_choices
// to see if an alternative dictionary word can be found.
if (ambigs_found) {
if (stopper_debug_level > 2) {
tprintf("\nResulting ambig_blob_choices:\n");
for (i = 0; i < ambig_blob_choices.length(); ++i) {
print_ratings_list("", ambig_blob_choices.get(i), getUnicharset());
tprintf("\n");
}
}
WERD_CHOICE *alt_word = dawg_permute_and_select(ambig_blob_choices, 0.0);
ambigs_found = (alt_word->rating() < 0.0);
if (ambigs_found) {
if (stopper_debug_level >= 1) {
tprintf ("Stopper: Possible ambiguous word = %s\n",
alt_word->debug_string().string());
}
if (fixpt != nullptr) {
// Note: Currently character choices combined from fragments can only
// be generated by NoDangrousAmbigs(). This code should be updated if
// the capability to produce classifications combined from character
// fragments is added to other functions.
int orig_i = 0;
for (i = 0; i < alt_word->length(); ++i) {
const UNICHARSET &uchset = getUnicharset();
bool replacement_is_ngram =
uchset.get_isngram(alt_word->unichar_id(i));
UNICHAR_ID leftmost_id = alt_word->unichar_id(i);
if (replacement_is_ngram) {
// we have to extract the leftmost unichar from the ngram.
const char *str = uchset.id_to_unichar(leftmost_id);
int step = uchset.step(str);
if (step) leftmost_id = uchset.unichar_to_id(str, step);
}
int end_i = orig_i + alt_word->state(i);
if (alt_word->state(i) > 1 ||
(orig_i + 1 == end_i && replacement_is_ngram)) {
// Compute proper blob indices.
int blob_start = 0;
for (int j = 0; j < orig_i; ++j)
blob_start += best_choice->state(j);
int blob_end = blob_start;
for (int j = orig_i; j < end_i; ++j)
blob_end += best_choice->state(j);
fixpt->push_back(DANGERR_INFO(blob_start, blob_end, true,
replacement_is_ngram, leftmost_id));
if (stopper_debug_level > 1) {
tprintf("fixpt->dangerous+=(%d %d %d %d %s)\n", orig_i, end_i,
true, replacement_is_ngram,
uchset.id_to_unichar(leftmost_id));
}
}
orig_i += alt_word->state(i);
}
}
}
delete alt_word;
}
if (output_ambig_words_file_ != nullptr) {
fprintf(output_ambig_words_file_, "\n");
}
ambig_blob_choices.delete_data_pointers();
return !ambigs_found;
}
