class TestIrregularFunctionalData1D(unittest.TestCase):
"""Test class for the class IrregularFunctionalData in one dimension."""
def setUp(self):
argvals = {'input_dim_0': {0: np.array([1, 2, 3, 4]),
1: np.array([2, 4]),
2: np.array([0, 2, 3])}}
values = {0: np.array([1, 2, 3, 4]),
1: np.array([5, 6]),
2: np.array([8, 9, 7],)}
self.irregu_fd = IrregularFunctionalData(argvals, values)
def test_argvals_stand(self):
is_equal = [np.allclose(self.irregu_fd.argvals_stand['input_dim_0'][0],
np.array([0.25, 0.5, 0.75, 1.])),
np.allclose(self.irregu_fd.argvals_stand['input_dim_0'][1],
np.array([0.5, 1.])),
np.allclose(self.irregu_fd.argvals_stand['input_dim_0'][2],
np.array([0., 0.5, 0.75]))]
self.assertTrue(np.all(is_equal))
def test_n_obs(self):
self.assertEqual(self.irregu_fd.n_obs, 3)
def test_n_dim(self):
self.assertEqual(self.irregu_fd.n_dim, 1)
def test_range_obs(self):
self.assertEqual(self.irregu_fd.range_obs, (1, 9))
def test_range_dim(self):
self.assertEqual(self.irregu_fd.range_dim, {'input_dim_0': (0, 4)})
def test_shape(self):
self.assertEqual(self.irregu_fd.shape, {'input_dim_0': 5})
def test_subset(self):
new_irregu_fd = self.irregu_fd[2]
self.assertIsInstance(new_irregu_fd, IrregularFunctionalData)
self.assertEqual(new_irregu_fd.n_obs, 1)
new_irregu_fd = self.irregu_fd[:2]
self.assertIsInstance(new_irregu_fd, IrregularFunctionalData)
self.assertEqual(new_irregu_fd.n_obs, 2)
def test_as_dense(self):
dense_fd = self.irregu_fd.as_dense()
self.assertIsInstance(dense_fd, DenseFunctionalData)
self.assertEqual(dense_fd.n_obs, 3)
def test_is_compatible(self):
self.assertTrue(self.irregu_fd.is_compatible(self.irregu_fd))
def test_mean(self):
mean_fd = self.irregu_fd.mean()
is_equal = np.allclose(mean_fd.values,
np.array([[8., 1., 5.33333333, 5., 5.]]))
self.assertTrue(is_equal)
class TestIrregularFunctionalData2D(unittest.TestCase):
"""Test class for the class IrregularFunctionalData in two dimension."""
def setUp(self):
argvals = {
'input_dim_0': {
0: np.array([1, 2, 3, 4]),
1: np.array([2, 4]),
2: np.array([4, 5, 6])
},
'input_dim_1': {
0: np.array([5, 6, 7]),
1: np.array([1, 2, 3]),
2: np.array([8, 9])
}
}
values = {
0: np.array([[1, 2, 3], [4, 1, 2], [3, 4, 1], [2, 3, 4]]),
1: np.array([[1, 2, 3], [1, 2, 3]]),
2: np.array([[8, 9], [8, 9], [8, 9]])
}
self.irregu_fd = IrregularFunctionalData(argvals, values)
def test_argvals_stand(self):
is_equal = [
np.allclose(self.irregu_fd.argvals_stand['input_dim_0'][0],
np.array([0., 0.2, 0.4, 0.6])),
np.allclose(self.irregu_fd.argvals_stand['input_dim_0'][1],
np.array([0.2, 0.6])),
np.allclose(self.irregu_fd.argvals_stand['input_dim_0'][2],
np.array([0.6, 0.8, 1.])),
np.allclose(self.irregu_fd.argvals_stand['input_dim_1'][0],
np.array([0.5, 0.625, 0.75])),
np.allclose(self.irregu_fd.argvals_stand['input_dim_1'][1],
np.array([0., 0.125, 0.25])),
np.allclose(self.irregu_fd.argvals_stand['input_dim_1'][2],
np.array([0.875, 1.]))
]
self.assertTrue(np.all(is_equal))
def test_n_obs(self):
self.assertEqual(self.irregu_fd.n_obs, 3)
def test_n_dim(self):
self.assertEqual(self.irregu_fd.n_dim, 2)
def test_range_obs(self):
self.assertEqual(self.irregu_fd.range_obs, (1, 9))
def test_range_dim(self):
self.assertEqual(self.irregu_fd.range_dim, {
'input_dim_0': (1, 6),
'input_dim_1': (1, 9)
})
def test_shape(self):
self.assertEqual(self.irregu_fd.shape, {
'input_dim_0': 6,
'input_dim_1': 8
})
def test_subset(self):
new_irregu_fd = self.irregu_fd[2]
self.assertIsInstance(new_irregu_fd, IrregularFunctionalData)
self.assertEqual(new_irregu_fd.n_obs, 1)
new_irregu_fd = self.irregu_fd[:2]
self.assertIsInstance(new_irregu_fd, IrregularFunctionalData)
self.assertEqual(new_irregu_fd.n_obs, 2)
def test_as_dense(self):
dense_fd = self.irregu_fd.as_dense()
self.assertIsInstance(dense_fd, DenseFunctionalData)
self.assertEqual(dense_fd.n_obs, 3)
def test_is_compatible(self):
self.assertTrue(self.irregu_fd.is_compatible(self.irregu_fd))
def test_mean(self):
N = np.nan
mean_fd = self.irregu_fd.mean()
is_equal = np.allclose(mean_fd.values,
np.array([[[N, N, N, 1., 2., 3., N, N],
[1., 2., 3., 4., 1., 2., N, N],
[N, N, N, 3., 4., 1., N, N],
[1., 2., 3., 2., 3., 4., 8., 9.],
[N, N, N, N, N, N, 8., 9.],
[N, N, N, N, N, N, 8., 9.]]]),
equal_nan=True)
self.assertTrue(is_equal)