def test_sparse_matrix_cols_scaling(self):
origin_matrix_rows = np.array([0, 0, 1, 1, 2, 2])
origin_matrix_cols = np.array([0, 1, 2, 3, 0, 1])
origin_matrix_vals = np.array([1, 2, 7, 8, 9, 10], dtype=sp.float64)
origin_matrix = sp.sparse.coo_matrix(
(origin_matrix_vals, (origin_matrix_rows, origin_matrix_cols)),
shape=(3, 4))
origin_matrix_std = std(origin_matrix, 'cols')
expected_matrix_std = [[
4.35889894, 4.76095229, 5.71547607, 6.53197265
]]
self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std))
scaled_matrix = scale(origin_matrix, 'cols', origin_matrix_std)
expected_matrix_rows = np.array([0, 0, 1, 1, 2, 2])
expected_matrix_cols = np.array([0, 1, 2, 3, 0, 1])
expected_matrix_vals = [
0.22941573387056174, 0.42008402520840293, 1.22474487139158920,
1.22474487139158900, 2.06474160483505550, 2.10042012604201500
]
expected_matrix = sp.sparse.coo_matrix(
(expected_matrix_vals,
(expected_matrix_rows, expected_matrix_cols)),
shape=(3, 4))
self.assertTrue(
np.allclose(scaled_matrix.todense(), expected_matrix.todense()))
def test_sparse_matrix_rows_scaling(self):
origin_matrix_rows = np.array([0, 0, 1, 1, 2, 2])
origin_matrix_cols = np.array([0, 1, 0, 1, 0, 1])
origin_matrix_vals = np.array([1, 2, 5, 6, 9, 10], dtype=sp.float64)
origin_matrix = sp.sparse.coo_matrix(
(origin_matrix_vals, (origin_matrix_rows, origin_matrix_cols)),
shape=(3, 4))
origin_matrix_std = std(origin_matrix, 'rows')
expected_matrix_std = [[1.29099445, 4.50924975, 7.76745347]]
self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std))
scaled_matrix = scale(origin_matrix, 'rows', origin_matrix_std)
expected_matrix_rows = np.array([0, 1, 2, 0, 1, 2])
expected_matrix_cols = np.array([0, 0, 0, 1, 1, 1])
expected_matrix_vals = [
0.7745966692414834, 1.1088319064318592, 1.1586809036417614,
1.5491933384829668, 1.3305982877182312, 1.2874232262686236
]
expected_matrix = sp.sparse.coo_matrix(
(expected_matrix_vals,
(expected_matrix_rows, expected_matrix_cols)),
shape=(3, 4))
self.assertTrue(
np.allclose(scaled_matrix.todense(), expected_matrix.todense()))
def test_sparse_matrix_global_scaling(self):
origin_matrix_rows = np.array([0, 0, 1, 1, 2, 2])
origin_matrix_cols = np.array([0, 1, 2, 3, 0, 1])
origin_matrix_vals = np.array([1, 2, 7, 8, 9, 10], dtype=sp.float64)
origin_matrix = sp.sparse.coo_matrix(
(origin_matrix_vals, (origin_matrix_rows, origin_matrix_cols)),
shape=(3, 4))
origin_matrix_std = std(origin_matrix, 'global')
expected_matrix_std = 3.43592135468
self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std))
scaled_matrix = scale(origin_matrix, 'global', origin_matrix_std)
expected_matrix_rows = np.array([0, 0, 1, 1, 2, 2])
expected_matrix_cols = np.array([0, 1, 2, 3, 0, 1])
expected_matrix_vals = [
0.2910427500435996, 0.5820855000871992, 2.0372992503051970,
2.3283420003487967, 2.6193847503923960, 2.9104275004359956
]
expected_matrix = sp.sparse.coo_matrix(
(expected_matrix_vals,
(expected_matrix_rows, expected_matrix_cols)),
shape=(3, 4))
self.assertTrue(
np.allclose(scaled_matrix.todense(), expected_matrix.todense()))
def test_dense_matrix_rows_scaling(self):
origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
origin_matrix_std = std(origin_matrix, 'rows')
expected_matrix_std = [[1.11803399, 1.11803399, 1.11803399]]
self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std))
scaled_matrix = scale(origin_matrix, 'rows', origin_matrix_std)
expected_matrix = [[0.89442719, 1.78885438, 2.68328157, 3.57770876],
[4.47213595, 5.36656315, 6.26099034, 7.15541753],
[8.04984472, 8.94427191, 9.83869910, 10.73312629]]
self.assertTrue(np.allclose(scaled_matrix, expected_matrix))
def test_sparse_matrix_none_scaling(self):
origin_matrix_rows = np.array([0, 0, 1, 1, 2, 2])
origin_matrix_cols = np.array([0, 1, 2, 3, 0, 1])
origin_matrix_vals = np.array([1, 2, 7, 8, 9, 10], dtype=sp.float64)
origin_matrix = sp.sparse.coo_matrix(
(origin_matrix_vals, (origin_matrix_rows, origin_matrix_cols)),
shape=(3, 4))
origin_matrix_std = std(origin_matrix, 'none')
scaled_matrix = scale(origin_matrix, 'none', origin_matrix_std)
self.assertTrue(
np.allclose(scaled_matrix.todense(), origin_matrix.todense()))
def test_dense_matrix_global_scaling(self):
origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
origin_matrix_std = std(origin_matrix, 'global')
expected_matrix_std = 3.45205252953
self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std))
scaled_matrix = scale(origin_matrix, 'global', origin_matrix_std)
expected_matrix = [[0.28968273, 0.57936546, 0.86904819, 1.15873092],
[1.44841365, 1.73809638, 2.02777911, 2.31746184],
[2.60714457, 2.89682730, 3.18651003, 3.47619276]]
self.assertTrue(np.allclose(scaled_matrix, expected_matrix))
def test_dense_matrix_cols_scaling(self):
origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
origin_matrix_std = std(origin_matrix, 'cols')
expected_matrix_std = [[
3.26598632, 3.26598632, 3.26598632, 3.26598632
]]
self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std))
scaled_matrix = scale(origin_matrix, 'cols', origin_matrix_std)
expected_matrix = [[0.30618622, 0.61237244, 0.91855865, 1.22474487],
[1.53093109, 1.83711731, 2.14330352, 2.44948974],
[2.75567596, 3.06186218, 3.36804840, 3.67423461]]
self.assertTrue(np.allclose(scaled_matrix, expected_matrix))
def test_dense_matrix_none_scaling(self):
origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
origin_matrix_std = std(origin_matrix, 'none')
scaled_matrix = scale(origin_matrix, 'none', origin_matrix_std)
self.assertTrue(np.allclose(scaled_matrix, origin_matrix))