def test_load(self):
		examples = train_example.load()

		print("X length: ", len(examples[0].x))
		print("Y length: ", len(examples[0].y))

		self.assertEqual(len(examples), 1)
Example #2
0
def train():
	examples = train_example.load()

	# Split examples in train and validation using 5-fold
	print("examples size:", len(examples))

	wLength = len(examples[0].x[0].coref.feat)
	w = np.array([0]*wLength)

	w = perceptron.structured(w, examples, test=None, epochs=38, argmax=quotation.argmax, phi=quotation.phi)

	return w
Example #3
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def calibration():
	examples = train_example.load()

	# Split examples in train and validation using 5-fold
	print("examples size:", len(examples))
	kf = KFold(len(examples), n_folds=5)

	wLength = len(examples[0].x[0].coref.feat)

	for trainIndex, testIndex in kf:
		train = [ examples[e] for e in trainIndex ]
		print("\n\ntrain size:", len(train))
		validation = [ examples[e] for e in testIndex ]
		print("validation size:", len(validation))

		w = np.array([0]*wLength)

		w = perceptron.structured(w, train, test=validation, epochs=65, argmax=quotation.argmax, phi=quotation.phi)