def select_anagrams(token, structures):
"""Select possible anagrams for a given token
Parameters:
token (:func:`str`): Cleaned token
structures (:func:`dict`): Datastructures from file
Returns:
:func:`dict` - Possible anagrams (keys) along with their score (values)
"""
anagrams = {}
focus_alphabet = generate_alphabet_from_word(token[1])
token_hash = anagram_hash(token)
hash_list = []
for c in structures["alphabet"]:
for f in focus_alphabet:
hash_list.append(token_hash + c - f)
hash_counter = Counter(hash_list) # Counting retrieval occurence
for h in set(hash_counter.keys()).intersection(set(structures["anagrams"].keys())):
count = hash_counter[h]
anag_list = [anag for anag in structures["anagrams"][h] if edit_distance(anag, token) <= 3]
for anag in anag_list:
anag_score = rate_anagram(structures["occurence_map"], token, anag, count)
if anag_score > 0:
anagrams[anag] = anag_score
return anagrams
def build_candidates_list(token, anagrams_list, ocr_sims_list, structures):
"""Merge anagram and OCRkey list into one list.
Parameters:
token (:func:`str`): Cleaned token
anagrams_list (:func:`dict`): Result of `select_anagrams`
ocr_sims_list (:func:`dict`): Result of `select_ocrsims`
structures (:func:`dict`): Datastructures from file
Returns:
:func:`dict` - Correction tokens (keys) along with their score (values)
"""
final_list = anagrams_list
ocr_list = truncate_ocr_sim_list(token, ocr_sims_list)
strong_ocr_list = ocr_list
weak_ocr_list = {}
if len(ocr_list) > 5:
(strong_ocr_list, weak_ocr_list) = split_ocr_list(token, ocr_list)
for ocr_word, ocr_score in strong_ocr_list.items():
if ocr_word in final_list.keys():
final_list[ocr_word] *= ocr_score
del strong_ocr_list[ocr_word]
strong_ocr_list.update(weak_ocr_list)
for ocr_word, ocr_score in strong_ocr_list.items():
if ocr_word not in final_list.keys():
final_list[ocr_word] = rate_anagram(structures["occurence_map"], token, ocr_word, 1) \
* rate_ocr_key(structures["occurence_map"], token, ocr_word, 0)
return final_list
def build_candidates_list(token, anagrams_list, ocr_sims_list, structures):
"""Merge anagram and OCRkey list into one list.
Parameters:
token (:func:`str`): Cleaned token
anagrams_list (:func:`dict`): Result of `select_anagrams`
ocr_sims_list (:func:`dict`): Result of `select_ocrsims`
structures (:func:`dict`): Datastructures from file
Returns:
:func:`dict` - Correction tokens (keys) along with their score (values)
"""
final_list = anagrams_list
ocr_list = truncate_ocr_sim_list(token, ocr_sims_list)
strong_ocr_list = ocr_list
weak_ocr_list = {}
if len(ocr_list) > 5:
(strong_ocr_list, weak_ocr_list) = split_ocr_list(token, ocr_list)
for ocr_word, ocr_score in strong_ocr_list.items():
if ocr_word in final_list.keys():
final_list[ocr_word] *= ocr_score
del strong_ocr_list[ocr_word]
strong_ocr_list.update(weak_ocr_list)
for ocr_word, ocr_score in strong_ocr_list.items():
if ocr_word not in final_list.keys():
final_list[ocr_word] = rate_anagram(structures["occurence_map"], token, ocr_word, 1) \
* rate_ocr_key(structures["occurence_map"], token, ocr_word, 0)
return final_list
def select_anagrams(token, structures):
"""Select possible anagrams for a given token
Parameters:
token (:func:`str`): Cleaned token
structures (:func:`dict`): Datastructures from file
Returns:
:func:`dict` - Possible anagrams (keys) along with their score (values)
"""
anagrams = {}
focus_alphabet = generate_alphabet_from_word(token[1])
token_hash = anagram_hash(token)
hash_list = []
for c in structures["alphabet"]:
for f in focus_alphabet:
hash_list.append(token_hash + c - f)
hash_counter = Counter(hash_list) # Counting retrieval occurence
for h in set(hash_counter.keys()).intersection(
set(structures["anagrams"].keys())):
count = hash_counter[h]
anag_list = [
anag for anag in structures["anagrams"][h]
if edit_distance(anag, token) <= 3
]
for anag in anag_list:
anag_score = rate_anagram(structures["occurence_map"], token, anag,
count)
if anag_score > 0:
anagrams[anag] = anag_score
return anagrams
