def test_kron_matrix():
# 2018-04-29: moved here from core.tests.test_shape_base.
a = np.ones([2, 2])
m = np.asmatrix(a)
assert_equal(type(np.kron(a, a)), np.ndarray)
assert_equal(type(np.kron(m, m)), np.matrix)
assert_equal(type(np.kron(a, m)), np.matrix)
assert_equal(type(np.kron(m, a)), np.matrix)
def test_fancy_indexing():
# The matrix class messes with the shape. While this is always
# weird (getitem is not used, it does not have setitem nor knows
# about fancy indexing), this tests gh-3110
# 2018-04-29: moved here from core.tests.test_index.
m = np.matrix([[1, 2], [3, 4]])
assert_(isinstance(m[[0, 1, 0], :], np.matrix))
# gh-3110. Note the transpose currently because matrices do *not*
# support dimension fixing for fancy indexing correctly.
x = np.asmatrix(np.arange(50).reshape(5, 10))
assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
def test_list_indexing(self):
A = np.arange(6)
A.shape = (3, 2)
x = asmatrix(A)
assert_array_equal(x[:, [1, 0]], x[:, ::-1])
assert_array_equal(x[[2, 1, 0], :], x[::-1, :])
def test_matrix_std_argmax(self):
# Ticket #83
x = np.asmatrix(np.random.uniform(0, 1, (3, 3)))
assert_equal(x.std().shape, ())
assert_equal(x.argmax().shape, ())
def test_boolean_indexing(self):
A = np.arange(6)
A.shape = (3, 2)
x = asmatrix(A)
assert_array_equal(x[:, np.array([True, False])], x[:, 0])
assert_array_equal(x[np.array([True, False, False]), :], x[0, :])
def test_row_column_indexing(self):
x = asmatrix(np.eye(2))
assert_array_equal(x[0, :], [[1, 0]])
assert_array_equal(x[1, :], [[0, 1]])
assert_array_equal(x[:, 0], [[1], [0]])
assert_array_equal(x[:, 1], [[0], [1]])
def test_scalar_indexing(self):
x = asmatrix(np.zeros((3, 2), float))
assert_equal(x[0, 0], x[0][0])
def test_basic(self):
x = asmatrix(np.zeros((3, 2), float))
y = np.zeros((3, 1), float)
y[:, 0] = [0.8, 0.2, 0.3]
x[:, 1] = y > 0.5
assert_equal(x, [[0, 1], [0, 0], [0, 0]])
def test_asmatrix(self):
A = np.arange(100).reshape(10, 10)
mA = asmatrix(A)
A[0, 0] = -10
assert_(A[0, 0] == mA[0, 0])