def addTrainingData(labeled_pairs, data_model, training_data=[]):
fields = data_model['fields']
field_dtype = training_data.dtype[1]
distances = numpy.zeros(1, dtype=field_dtype)
num_training_pairs = len(labeled_pairs[0]) + len(labeled_pairs[1])
new_training_data = numpy.zeros(num_training_pairs,
dtype=training_data.dtype)
i = 0
for (label, examples) in labeled_pairs.items():
for pair in examples:
c_distances = core.calculateDistance(pair[0],
pair[1],
fields,
distances)
example = (label, c_distances)
new_training_data[i] = example
i += 1
training_data = numpy.append(training_data, new_training_data)
return training_data
def addTrainingData(labeled_pairs, training_data, data_model) :
fields = data_model['fields']
field_dtype = [('names', 'a10', (len(fields)),),
('values', 'f4', (len(fields)),)
]
distances = numpy.zeros(1, dtype=field_dtype)
for label, examples in labeled_pairs.items() :
for pair in examples :
c_distances = core.calculateDistance(pair[0],
pair[1],
fields,
distances)
c_distances = dict(zip(fields.keys(), c_distances[0]['values']))
training_data.append((label, c_distances))
return training_data
def trainingDistances(training_pairs, data_model):
fields = data_model['fields']
field_dtype = [('names', 'a10', len(fields)), ('values', 'f4',
len(fields))]
distances = numpy.zeros(1, dtype=field_dtype)
training_data = []
for (label, examples) in training_pairs.items():
for (i, pair) in enumerate(examples):
c_distances = core.calculateDistance(pair[0],
pair[1],
fields,
distances)
c_distances = dict(zip(fields.keys(),
c_distances[0]['values']))
training_data.append((label, c_distances))
return training_data
