def test_get_next(self):
data = [1,2,3,4,5,6,7,8,9,10]
classes = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
counter = 0
kfold = KFold(2, data, classes)
while kfold.has_next():
train_d1, train_c1, test_d1, test_c1 = kfold.get_next()
self.assertEquals(8, len(train_d1))
self.assertEquals(8, len(train_c1))
self.assertEquals(2, len(test_d1))
self.assertEquals(2, len(test_c1))
counter += 1
self.assertEquals(5, counter)
kfold = KFold(2, data, classes)
train_d1, train_c1, test_d1, test_c1 = kfold.get_next()
self.assertEquals(True, lists_are_equal(train_d1, [3,4,5,6,7,8,9,10]))
self.assertEquals(True, lists_are_equal(train_c1, ['c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']))
self.assertEquals(True, lists_are_equal(test_d1, [1,2]))
self.assertEquals(True, lists_are_equal(test_c1, ['a', 'b']))
train_d1, train_c1, test_d1, test_c1 = kfold.get_next()
self.assertEquals(True, lists_are_equal(train_d1, [1,2,5,6,7,8,9,10]))
self.assertEquals(True, lists_are_equal(train_c1, ['a', 'b', 'e', 'f', 'g', 'h', 'i', 'j']))
self.assertEquals(True, lists_are_equal(test_d1, [3,4]))
self.assertEquals(True, lists_are_equal(test_c1, ['c', 'd']))
train_d1, train_c1, test_d1, test_c1 = kfold.get_next()
train_d1, train_c1, test_d1, test_c1 = kfold.get_next()
train_d1, train_c1, test_d1, test_c1 = kfold.get_next()
self.assertEquals(True, lists_are_equal(train_d1, [1,2,3,4,5,6,7,8]))
self.assertEquals(True, lists_are_equal(train_c1, ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']))
self.assertEquals(True, lists_are_equal(test_d1, [9,10]))
self.assertEquals(True, lists_are_equal(test_c1, ['i', 'j']))
def test_get_next(self):
data = [1,2,3,4,5,6]
classes = ['a', 'b', 'c', 'd', 'e', 'f']
kfold = KFold(3, data, classes)
d1, c1 = kfold.get_next()
d2, c2 = kfold.get_next()
self.assertEquals(1, d1[0])
self.assertEquals(2, d1[1])
self.assertEquals(3, d1[2])
self.assertEquals('a', c1[0])
self.assertEquals('b', c1[1])
self.assertEquals('c', c1[2])
self.assertEquals(4, d2[0])
self.assertEquals(5, d2[1])
self.assertEquals(6, d2[2])
self.assertEquals('d', c2[0])
self.assertEquals('e', c2[1])
self.assertEquals('f', c2[2])
new_data.append(data[index])
new_classes.append(classes[index])
# setup the network
for node in nodes:
BN.setup_node(node, nodes)
# initial confusion matrix
confusion = {'0':{'0':0, '1':0}, '1':{'0':0, '1':0}}
# do k-fold validation
total_count = 0
kfold = KFold(100, new_data, new_classes)
while kfold.has_next():
dat, cls = kfold.get_next()
correct_count = 0
for i in range(0, len(dat)):
row = dat[i]
guess = BNClassifier.classify(row, nodes, dat, cls, ['0', '1'])
if guess == cls[i]:
correct_count += 1
confusion[cls[i]][guess] += 1
total_count += correct_count
for node in nodes:
