def main():
"""
Simple demo of the Levenshtein class.
"""
print("-----------------------------")
print("| codedrome.com |")
print("| Levenshtein Word Distance |")
print("-----------------------------\n")
source_words = ["banama", "banama", "levinstein"]
target_words = ["banana", "elephant", "levenshtein"]
lp = levenshtein.Levenshtein()
for i in range(0, len(source_words)):
lp.source_word = source_words[i]
lp.target_word = target_words[i]
lp.calculate()
lp.print_grid()
lp.print_cost()
print("")
def __init__(self, fp):
with open(fp) as f:
self.codon_data = json.load(f)
self.search_space = set()
self.inverse_codon_data = {}
for amino_acid in self.codon_data:
self.search_space.add(amino_acid.lower())
for acid_info in self.codon_data[amino_acid]:
data_entry = self.codon_data[amino_acid][acid_info]
if type(data_entry) == list:
for info in data_entry:
self.search_space.add(info.lower())
if info in self.inverse_codon_data:
self.inverse_codon_data[info.lower()].append(amino_acid)
else:
self.inverse_codon_data[info.lower()] = [amino_acid]
else:
self.search_space.add(data_entry.lower())
key = data_entry.lower()
if key in self.inverse_codon_data:
self.inverse_codon_data[key].append(amino_acid)
else:
self.inverse_codon_data[key] = [amino_acid]
self.search_space = list(self.search_space)
#print(len(self.search_space), str(self.search_space))
self.levenshtein_engine = levenshtein.Levenshtein(u=(7, 1, 4), t=(7, 1, 4))
def test_Levenshtein_class(alphabet, symbol, weight_dict, language,
expected_result):
levenshtein_class = levenshtein.Levenshtein(alphabet, symbol, weight_dict,
language)
assert levenshtein_class.weight_dict[
'a'] == expected_result and levenshtein_class.weight_dict['?'] == (1,
1,
1)
def test_weighted_distance(source_input, target_input, weight_dict,
expected_result):
metric = levenshtein.Levenshtein('ab', '.', weight_dict)
assert metric.distance(source_input, target_input) == expected_result
def test_iterative_matrix(source_input, target_input, expected_result):
metric = levenshtein.Levenshtein('a', '.')
assert metric.iterative_matrix(source_input,
target_input) == expected_result
def test_Levenshtein_class_mixed_params(letter, a_symbol):
with pytest.raises(Exception) as e_info:
levenshtein_class = levenshtein.Levenshtein(alphabet=a_symbol,
symbol=letter)
def test_Levenshtein_class_only_symbol(a_symbol):
with pytest.raises(Exception) as e_info:
levenshtein_class = levenshtein.Levenshtein(symbol=a_symbol)
def test_Levenshtein_class_only_alphabet(letter):
with pytest.raises(Exception) as e_info:
levenshtein_class = levenshtein.Levenshtein(alphabet=letter)
def test_Levenshtein_class_no_parameter():
with pytest.raises(Exception) as e_info:
levenshtein_class = levenshtein.Levenshtein()
def test_weighted_similarity(source_input, target_input, expected_result):
metric = levenshtein.Levenshtein('ab', '.')
assert metric.similarity(source_input,
target_input) == pytest.approx(expected_result,
abs=5e-3)
