class TestNeuralNetwork(unittest.TestCase):
def setUp(self):
self.entrezs = np.array([1,2,3,4])
self.labels = np.array([1,1,1,0,0,0])
self.expression_data = np.array([[1,0,1,1,0,1],
[0,1,0,1,0,1],
[0,1,1,1,0,0],
[0,0,0,0,1,1]])
self.states = ((1,2,3), (2,4))
self.expression = Expression(self.expression_data, self.entrezs, self.labels,
binarize=False)
self.n = NeuralNetwork(self.states)
self.n.train(self.expression)
def tearDown(self):
pass
def test_activate(self):
sample_labels = (((1,0,0,0), 1),
((0,1,1,0), 1),
((0,0,0,1), 0),
((1,1,0,1), 0))
# for sample, label in sample_labels:
# output = self.n.activate(sample)
# self.assertEqual(output, label)
def test_activate_with_entrez(self):
sample_entrez_labels = (((1,0), (1,4), 1),
((0,0), (1,4), 1),
((0,1), (1,4), 0),
((1,1), (1,4), 0))
# for sample, entrezs, label in sample_entrez_labels:
# output = self.n.activate(sample, entrezs)
# self.assertEqual(output, label)
def test_calc_feature(self):
sample_entrez_expected = (((9,7), (1,4), (9,0,0,0,7)),
((9,7,8), (1,4,3), (9,0,8,0,7)))
for sample, entrezs, expected in sample_entrez_expected:
feature = self.n._calc_feature(sample, entrezs, missing=0)
np.testing.assert_array_equal(feature, expected)
def setUp(self):
self.entrezs = np.array([1,2,3,4])
self.labels = np.array([1,1,1,0,0,0])
self.expression_data = np.array([[1,0,1,1,0,1],
[0,1,0,1,0,1],
[0,1,1,1,0,0],
[0,0,0,0,1,1]])
self.states = ((1,2,3), (2,4))
self.expression = Expression(self.expression_data, self.entrezs, self.labels,
binarize=False)
self.n = NeuralNetwork(self.states)
self.n.train(self.expression)
