def get_words_from_random_n_roots(number_of_roots: int = 1000):
nepali_dictionary = read_csv(helper.get_root_pos_rule_csv_path())
nepali_dictionary = random.sample(nepali_dictionary, number_of_roots)
for d in nepali_dictionary:
word = d.get('word').strip()
rules = filter(None, d.get('rules').strip().split(','))
if not rules:
continue
get_words(list(rules), [(word, list(rules))])
def _make_dict_trie():
trie = Trie()
csv_path = get_root_pos_rule_csv_path()
root_data = read_csv(csv_path)
for d in root_data:
word = d.get('word').strip()
rules = list(filter(None, d.get('rules').strip().split(',')))
trie.insert(word, rules)
return trie
def make_dict_trie() -> TrieNode:
root = TrieNode('*')
csv_path = helper.get_root_pos_rule_csv_path()
root_data = read_csv(csv_path)
for d in root_data:
word = d.get('word').strip()
pos = d.get('pos').strip() or None
rules = list(filter(None, d.get('rules').strip().split(',')))
add(root, word, rules, pos)
return root
def get_rules_for_words_starting_with(
starting: str, full_word: str) -> Tuple[Set, List[Tuple[str, List]]]:
"""
get all words starting with a letter along with their rules for word formation
:param starting: staring letter of word to be lemmatized, स
:param full_word: word to be lemmatized; सम्बन्धमा
:return: set of rules for all words founds, list of (word, rules)
"""
rules_only = set()
words = list()
csv_path = helper.get_root_pos_rule_csv_path()
root_data = read_csv(csv_path)
for d in root_data:
word = d.get('word').strip()
rules = list(filter(None, d.get('rules').strip().split(',')))
if rules and word.startswith(starting) and len(word) <= len(full_word):
rules_only = rules_only.union(set(rules))
words.append((word, rules))
return rules_only, words
def insert(self, word):
node = self
for letter in word:
if letter not in node.children:
node.children[letter] = TrieNode()
node = node.children[letter]
node.word = word
# read dictionary file into a trie
trie = TrieNode()
for d in read_csv(get_root_pos_rule_csv_path()):
WordCount += 1
trie.insert(d.get('word').strip())
print("Read %d words into %d nodes" % (WordCount, NodeCount))
# The search function returns a list of all words that are less than the given
# maximum distance from the target word
def search(word, max_cost):
# build first row
current_row = range(len(word) + 1)
print(current_row)
results = []
# recursively search each branch of the trie
def __init__(self):
csv_path = helper.get_root_pos_rule_csv_path()
self.data = read_csv(csv_path)