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, test_d1 = kfold.get_next()
self.assertEquals(8, len(train_d1))
self.assertEquals(2, len(test_d1))
counter += 1
self.assertEquals(5, counter)
kfold = KFold(2, data, classes)
train_d1, train_c1, test_d1, test_c1 = kfold.get_next()
self.assertListEqual(train_d1, [3, 4, 5, 6, 7, 8, 9, 10])
self.assertListEqual(test_d1, [1, 2])
train_d1, test_d1 = kfold.get_next()
self.assertListEqual(train_d1, [1, 2, 5, 6, 7, 8, 9, 10])
self.assertEquals(test_d1, [3, 4])
kfold.get_next()
kfold.get_next()
kfold.get_next()
self.assertListEqual(train_d1, [1, 2, 3, 4, 5, 6, 7, 8])
self.assertListEqual(test_d1, [9, 10])
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, test_d1 = kfold.get_next()
self.assertEquals(8, len(train_d1))
self.assertEquals(2, len(test_d1))
counter += 1
self.assertEquals(5, counter)
kfold = KFold(2, data, classes)
train_d1, train_c1, test_d1, test_c1 = kfold.get_next()
self.assertListEqual(train_d1, [3,4,5,6,7,8,9,10])
self.assertListEqual(test_d1, [1,2])
train_d1, test_d1= kfold.get_next()
self.assertListEqual(train_d1, [1,2,5,6,7,8,9,10])
self.assertEquals(test_d1, [3,4])
kfold.get_next()
kfold.get_next()
kfold.get_next()
self.assertListEqual(train_d1, [1,2,3,4,5,6,7,8])
self.assertListEqual(test_d1, [9,10])
def test_network(data, classes, nodes, weights):
confusion_matrix = {}
for key1 in t_maker.mapping.keys():
confusion_matrix[key1] = {}
for key2 in t_maker.mapping.keys():
confusion_matrix[key1][key2] = 0
correct = 0
kfold_counter = 1
kfold = KFold(15, data, classes)
while kfold.has_next():
print "Starting KFold "+str(kfold_counter)
network = Network(nodes, weights)
network.learning_rate = 0.04
train_d, train_c, test_d, test_c = kfold.get_next()
# update network until it levels out
for i in range(0, 1000):
for j in range(0, len(train_d)):
counter = 0
for val in train_d[j]:
Network.set_inputs(counter, val, network.neurons[0])
counter += 1
t_val = t_maker.get(train_c[j])
network.feed_forward()
network.set_errors(t_val)
network.update_weights()
for i in range(0, len(test_d)):
counter = 0
for val in test_d[i]:
Network.set_inputs(counter, val, network.neurons[0])
counter += 1
network.feed_forward()
actual = t_maker.get(test_c[i])
guess = Network.get_class_vec_from_output(network.neurons[-1])
are_equal = t_vec_maker.t_vecs_equal(actual, guess)
confusion_matrix[t_maker.vec_to_val(guess)][t_maker.vec_to_val(actual)] += 1
if are_equal:
correct += 1
kfold_counter += 1
return correct, 150, confusion_matrix
ngram_csv = []
unseens_csv = []
for class_number in [1, 5, 10, 20]:
for ngram_size in [2, 3, 4, 5]:
fold_step = len(words) // 10
kfold = KFold(words, step=fold_step)
total_correct = 0
total_tested = 0
total_unseen = 0
while kfold.has_next():
train, test = kfold.get_next()
if fold_step <= len(test):
ng = NGram(train,
ngram_size=ngram_size,
classification_number=class_number)
ng.generate_counts()
counter = 0
counter_skip = counter + ngram_size - 1
total = 0
correct = 0
unseens = 0
unseens_csv = []
for class_number in [1,5,10,20]:
for ngram_size in [2,3,4,5]:
fold_step = len(words)//10
kfold = KFold(words, step=fold_step)
total_correct = 0
total_tested = 0
total_unseen = 0
while kfold.has_next():
train, test = kfold.get_next()
if fold_step <= len(test):
ng = NGram(train, ngram_size=ngram_size, classification_number = class_number)
ng.generate_counts()
counter = 0
counter_skip = counter+ngram_size-1
total = 0
correct = 0
unseens = 0
while counter_skip < len(test):