def find_best(self, look_for, start=0, end=-1):
end = len(self.text) if end < 0 else end
if end - start < 2 * len(look_for):
return self.sw_align(look_for, start, end)
# What happens in this case?
window_size = len(look_for)
windows = {}
for i, ngram in enumerate(ngrams(" " + look_for + " ", 3)):
if ngram in self.ngrams:
ngram_bucket = self.ngrams[ngram]
for occurrence in ngram_bucket:
if occurrence < start or occurrence > end:
continue
window = occurrence // window_size
windows[window] = (windows[window] +
1) if window in windows else 1
candidate_windows = sorted(windows.keys(),
key=lambda w: windows[w],
reverse=True)
best = (-1, -1, 0, None)
last_window_grams = 0.1
for window in candidate_windows[:self.max_candidates]:
ngram_factor = windows[window] / last_window_grams
if ngram_factor < self.candidate_threshold:
break
last_window_grams = windows[window]
interval_start = max(start, int((window - 1) * window_size))
interval_end = min(end, int((window + 2) * window_size))
search_result = self.sw_align(look_for, interval_start,
interval_end)
if search_result[2] > best[2]:
best = search_result
return best
def __init__(
self,
text,
max_candidates=10,
candidate_threshold=0.92,
match_score=100,
mismatch_score=-100,
gap_score=-100,
char_similarities=None,
):
self.text = text
self.max_candidates = max_candidates
self.candidate_threshold = candidate_threshold
self.match_score = match_score
self.mismatch_score = mismatch_score
self.gap_score = gap_score
self.char_similarities = char_similarities
assert (self.char_similarities is None
), "Custom character similarities not supported at this time"
self.ngrams = {}
# build inverted index of ngram to where it occurs
# character ngrams. Good.
for i, ngram in enumerate(ngrams(" " + text + " ", 3)):
if ngram in self.ngrams:
ngram_bucket = self.ngrams[ngram]
else:
ngram_bucket = self.ngrams[ngram] = []
ngram_bucket.append(i)
def __init__(self,
text,
max_candidates=10,
candidate_threshold=0.92,
match_score=100,
mismatch_score=-100,
gap_score=-100,
char_similarities=None):
self.text = text
self.max_candidates = max_candidates
self.candidate_threshold = candidate_threshold
self.match_score = match_score
self.mismatch_score = mismatch_score
self.gap_score = gap_score
self.char_similarities = char_similarities
self.ngrams = {}
for i, ngram in enumerate(ngrams(' ' + text + ' ', 3)):
if ngram in self.ngrams:
ngram_bucket = self.ngrams[ngram]
else:
ngram_bucket = self.ngrams[ngram] = []
ngram_bucket.append(i)