def test_euclidean_distance(self):
attributes = [
attr.Attribute('A1', ['a', 'b'], 0),
attr.Attribute('A2', ['continuous'], 1),
attr.Attribute('A3', ['continuous'], 2),
attr.Attribute('A4', ['g', 'h'], 3)
]
instance1 = ins.TrainingInstance(['a', 5, 3.4, 'g'], 'y')
instance2 = ins.TestInstance(['a', 5, 3.4, 'g'])
self.assertEqual(
0,
distancemetric.euclidean_distance(instance1, instance2,
attributes))
instance2 = ins.TestInstance(['b', 5, 3.4, 'g'])
self.assertEqual(
1,
distancemetric.euclidean_distance(instance1, instance2,
attributes))
instance2 = ins.TestInstance(['b', 4, 3.4, 'h'])
self.assertEqual(
math.sqrt(3),
distancemetric.euclidean_distance(instance1, instance2,
attributes))
instance2 = ins.TestInstance(['b', 4, 1.4, 'h'])
self.assertEqual(
math.sqrt(7),
distancemetric.euclidean_distance(instance1, instance2,
attributes))
def test_euclidean_distance(self):
attributes = [attr.Attribute('A1', ['a','b'], 0), attr.Attribute('A2', ['continuous'], 1),
attr.Attribute('A3', ['continuous'], 2), attr.Attribute('A4', ['g','h'], 3)]
instance1 = ins.TrainingInstance(['a', 5, 3.4, 'g'], 'y')
instance2 = ins.TestInstance(['a', 5, 3.4, 'g'])
self.assertEqual(0, distancemetric.euclidean_distance(instance1, instance2, attributes))
instance2 = ins.TestInstance(['b', 5, 3.4, 'g'])
self.assertEqual(1, distancemetric.euclidean_distance(instance1, instance2, attributes))
instance2 = ins.TestInstance(['b', 4, 3.4, 'h'])
self.assertEqual(math.sqrt(3), distancemetric.euclidean_distance(instance1, instance2, attributes))
instance2 = ins.TestInstance(['b', 4, 1.4, 'h'])
self.assertEqual(math.sqrt(7), distancemetric.euclidean_distance(instance1, instance2, attributes))
def classify(self, instances):
for each_test in instances:
id = InstanceDistances()
for each_training in self.training:
dist = dm.euclidean_distance(each_test, each_training, self.attributes)
id.distance(dist, each_training)
each_test.classified_klass = id.klass(majority_klass_vote)
