class CRFEditDistance(object) :
def __init__(self) :
classes = ['match', 'non-match']
self.model = Hacrf(l2_regularization=100.0,
state_machine=DefaultStateMachine(classes))
self.model.parameters = np.array(
[[-0.22937526, 0.51326066],
[ 0.01038001, -0.13348901],
[-0.03062821, 0.13769178],
[ 0.02024813, -0.01835538],
[ 0.09208272, 0.15466022],
[-0.08170265, -0.02484392],
[-0.01762858, 0.17504624],
[ 0.02800866, -0.04442708]],
order='F')
self.parameters = self.model.parameters.T
self.model.classes = ['match', 'non-match']
self.feature_extractor = StringPairFeatureExtractor(match=True,
numeric=False)
def fast_pair(self, x):
x_dot_parameters = np.matmul(x, self.parameters)
probs = forward_predict(x_dot_parameters, 2)
return probs
def train(self, examples, labels) :
examples = [(string_2, string_1)
if len(string_1) > len(string_2)
else (string_1, string_2)
for string_1, string_2
in examples]
print(examples)
extracted_examples = self.feature_extractor.fit_transform(examples)
self.model.fit(extracted_examples, labels, verbosity=1)
def __call__(self, string_1, string_2) :
if len(string_1) > len(string_2) :
string_1, string_2 = string_2, string_1
array1 = np.array(tuple(string_1)).reshape(-1, 1)
array2 = np.array(tuple(string_2)).reshape(1, -1)
features = self.feature_extractor._extract_features(array1, array2)
return self.fast_pair(features)[1]
class CRFEditDistance(object):
def __init__(self):
classes = ['match', 'non-match']
self.model = Hacrf(l2_regularization=100.0,
state_machine=DefaultStateMachine(classes))
self.model.parameters = np.array(
[[-0.22937526, 0.51326066], [0.01038001, -0.13348901],
[-0.03062821, 0.13769178], [0.02024813, -0.01835538],
[0.09208272, 0.15466022], [-0.08170265, -0.02484392],
[-0.01762858, 0.17504624], [0.02800866, -0.04442708]],
order='F')
self.parameters = self.model.parameters.T
self.model.classes = ['match', 'non-match']
self.feature_extractor = StringPairFeatureExtractor(match=True,
numeric=False)
def fast_pair(self, x):
x_dot_parameters = np.matmul(x, self.parameters)
probs = forward_predict(x_dot_parameters, 2)
return probs
def train(self, examples, labels):
examples = [(string_2, string_1) if len(string_1) > len(string_2) else
(string_1, string_2) for string_1, string_2 in examples]
print(examples)
extracted_examples = self.feature_extractor.fit_transform(examples)
self.model.fit(extracted_examples, labels, verbosity=1)
def __call__(self, string_1, string_2):
if len(string_1) > len(string_2):
string_1, string_2 = string_2, string_1
array1 = np.array(tuple(string_1)).reshape(-1, 1)
array2 = np.array(tuple(string_2)).reshape(1, -1)
features = self.feature_extractor._extract_features(array1, array2)
return self.fast_pair(features)[1]
