def test_ClassificationStrings_CreateTargets(self):
y = ['cat', 'cat', 'dog', 'mouse']
cdict = c_dictT(y)
y1 = np.vstack(batchT(y, c_dict=cdict)[0])
self.assertTrue(y1.shape[0] == 4)
self.assertTrue(y1.shape[1] == 3)
self.assertTrue(np.all(y1.sum(1) == 1))
def test_ClassificationStrings_CreateTargets(self):
y = ['cat', 'cat', 'dog', 'mouse']
cdict = c_dictT(y)
y1 = np.vstack(batchT(y, c_dict=cdict)[0])
self.assertTrue(y1.shape[0] == 4)
self.assertTrue(y1.shape[1] == 3)
self.assertTrue(np.all(y1.sum(1) == 1))
def test_ClassificationY_CreateTargets(self):
y = np.array([1, 1, 2, 3])
cdict = c_dictT(y)
y1 = np.vstack(batchT(y, c_dict=cdict)[0])
self.assertTrue(y1.shape[0] == 4)
self.assertTrue(y1.shape[1] == 3)
self.assertTrue(np.all(y1.sum(1) == 1))
def test_ClassificationY_CreateTargets(self):
y = np.array([1, 1, 2, 3])
cdict = c_dictT(y)
y1 = np.vstack(batchT(y, c_dict=cdict)[0])
self.assertTrue(y1.shape[0] == 4)
self.assertTrue(y1.shape[1] == 3)
self.assertTrue(np.all(y1.sum(1) == 1))
def test_OneDimensionalY_ReshapeY(self):
y1 = np.array([4, 5, 6])
y2 = np.array([[4], [5], [6]])
y1p = np.vstack(batchT(y1)[0])
self.assertEqual(y2.shape, y1p.shape)
self.assertTrue(np.allclose(y2, y1p))
def test_batchT_NumberOfClassificationTargets(self):
y = ['cat', 'cat', 'dog', 'mouse']
cdict = c_dictT(y)
_, ctargets = batchT(y, c_dict=cdict)
self.assertEqual(3, ctargets)
def test_batchT_GetNumberOfTargets(self):
y = [[1, 2], [3, 4], [5, 6]]
_, targets = batchT(y)
self.assertEqual(2, targets)
def test_OneDimensionalY_ReshapeY(self):
y1 = np.array([4, 5, 6])
y2 = np.array([[4], [5], [6]])
y1p = np.vstack(batchT(y1)[0])
self.assertEqual(y2.shape, y1p.shape)
self.assertTrue(np.allclose(y2, y1p))
def test_batchT_NumberOfClassificationTargets(self):
y = ['cat', 'cat', 'dog', 'mouse']
cdict = c_dictT(y)
_, ctargets = batchT(y, c_dict=cdict)
self.assertEqual(3, ctargets)
def test_batchT_GetNumberOfTargets(self):
y = [[1, 2], [3, 4], [5, 6]]
_, targets = batchT(y)
self.assertEqual(2, targets)