def test_multiply():
x = sparse.brandom((4, 2, 6), (2, 1, 2), 0.5, format='bcoo')
x_d = x.todense()
y = sparse.brandom((4, 2, 6), (2, 1, 2), 0.5, format='bcoo')
y_d = y.todense()
z = x * y
z_d = x_d * y_d
assert_eq(z, z_d)
data = np.arange(1, 4).repeat(4).reshape(3, 2, 2)
coords = np.array([[1, 1, 0], [0, 1, 1]])
x = BCOO(coords, data=data, shape=(4, 4), block_shape=(2, 2))
x_d = x.todense()
y = x * x
y_d = x_d * x_d
assert_eq(y, y_d)
data_x = np.arange(1, 4).repeat(4).reshape(3, 2, 2)
coords_x = np.array([[1, 1, 0], [0, 1, 1]])
x = BCOO(coords_x, data=data_x, shape=(4, 4), block_shape=(2, 2))
x_d = x.todense()
data_y = np.array([[[-2.0, -2.5], [-2, -2]], [[-2, -2], [-2, -2]]])
coords_y = np.array([[0, 1], [0, 1]])
y = BCOO(coords_y, data=data_y, shape=(4, 4), block_shape=(2, 2))
y_d = y.todense()
z = x * y
z_d = x_d * y_d
assert_eq(z, z_d)
def test_add_mismatch_block():
data_x = np.arange(1, 4).repeat(4).reshape(3, 2, 2)
coords_x = np.array([[1, 1, 0], [0, 1, 1]])
x = BCOO(coords_x, data=data_x, shape=(4, 4), block_shape=(2, 2))
x_d = x.todense()
data_y = np.array([[[-2.0, -2.5], [-2, -2]], [[-2, -2], [-2, -2]]])
coords_y = np.array([[0, 1], [0, 1]])
y = BCOO(coords_y, data=data_y, shape=(4, 4), block_shape=(2, 2))
y_d = y.todense()
z = x + y
z_d = x_d + y_d
assert_eq(z, z_d)
def test_tobsr():
data = np.arange(1, 7).repeat(4).reshape((-1, 2, 2))
coords = np.array([[0, 0, 0, 2, 1, 2], [0, 1, 1, 0, 2, 2]])
block_shape = (2, 2)
shape = (8, 6)
print coords
print data
#x = BCOO(coords, data=data, shape=shape, block_shape=block_shape, sorted = True, has_duplicates = False)
x = BCOO(coords,
data=data,
shape=shape,
block_shape=block_shape,
sorted=True,
has_duplicates=False)
y = x.todense()
#print y
#print x.coords
z = x.tobsr()
#print z.data
#print z.has_canonical_format
#print z.has_sorted_indices
x2 = BCOO(coords.copy(),
data=data.copy(),
shape=shape,
block_shape=block_shape,
sorted=False,
has_duplicates=True)
y2 = x2.todense()
print y
print y2
print x.coords
print x2.coords
#print y
#print x.coords
z2 = x2.tobsr()
#print z2.data
print z.has_canonical_format
print z.has_sorted_indices
print z2.has_canonical_format
print z2.has_sorted_indices
#print "z"
print z.toarray()
#print "z2"
print z2.toarray()
#assert_eq(z,z2)
exit()
assert_eq(z, y)
def test_add_simple():
data = np.arange(1, 4).repeat(4).reshape(3, 2, 2)
coords = np.array([[1, 1, 0], [0, 1, 1]])
x = BCOO(coords, data=data, shape=(4, 4), block_shape=(2, 2))
x_d = x.todense()
y = x + x
y_d = x_d + x_d
assert_eq(y, y_d)
def test_tobsr():
data = np.arange(1, 7).repeat(4).reshape((-1, 2, 2))
coords = np.array([[0, 0, 0, 2, 1, 2], [0, 1, 1, 0, 2, 2]])
block_shape = (2, 2)
shape = (8, 6)
x = BCOO(coords, data=data, shape=shape, block_shape=block_shape)
y = x.todense()
z = x.tobsr()
assert_eq(z, y)
def test_add_zero():
# the result is zero
data_x = np.arange(3, 6).repeat(4).reshape(3, 2, 2).astype(np.double)
coords_x = np.array([[1, 1, 0], [0, 1, 1]])
x = BCOO(coords_x, data=data_x, shape=(4, 4), block_shape=(2, 2))
x_d = x.todense()
y = -x
y_d = y.todense()
z = x + y
z_d = x_d + y_d
assert_eq(z, z_d)
# all add numbers are zero
a = np.zeros((6, 5, 4, 1), dtype=np.complex)
x = BCOO.from_numpy(a, block_shape=(2, 5, 2, 1))
x_d = x.todense()
y = BCOO.from_numpy(a, block_shape=(2, 5, 2, 1))
y_d = y.todense()
z = x + y
z_d = x_d + y_d
assert_eq(z, z_d)
def test_scaling():
data_x = np.arange(3, 6).repeat(4).reshape(3, 2, 2).astype(np.double)
coords_x = np.array([[1, 1, 0], [0, 1, 1]])
x = BCOO(coords_x, data=data_x, shape=(4, 4), block_shape=(2, 2))
x_d = x.todense()
z = x * -3.1
z_d = x_d * -3.1
assert_eq(z, z_d)
x = sparse.brandom((4, 2, 6), (2, 1, 2), 0.5, format='bcoo')
x_d = x.todense()
scaling_factor = np.random.random()
z = x * scaling_factor
z_d = x_d * scaling_factor
assert_eq(z, z_d)
x = sparse.brandom((4, 2, 6), (2, 1, 2), 0.5, format='bcoo')
x_d = x.todense()
scaling_factor = 0
z = x * scaling_factor
z_d = x_d * scaling_factor
assert_eq(z, z_d)
def test_subtraction():
x = sparse.brandom((4, 2, 6), (2, 1, 2), 0.5, format='bcoo')
x_d = x.todense()
y = sparse.brandom((4, 2, 6), (2, 1, 2), 0.5, format='bcoo')
y_d = y.todense()
z = x - y
z_d = x_d - y_d
assert_eq(z, z_d)
data = np.arange(1, 4).repeat(4).reshape(3, 2, 2)
coords = np.array([[1, 1, 0], [0, 1, 1]])
x = BCOO(coords, data=data, shape=(4, 4), block_shape=(2, 2))
x_d = x.todense()
y = x - x
y_d = x_d - x_d
assert_eq(y, y_d)
# else:
# if beta == 0:
# c[:] = 0
# else:
# c *= beta
# c += ab
# return c
if __name__ == '__main__':
data_x = np.arange(1, 7).repeat(4).reshape((-1, 2, 2))
coords_x = np.array([[0, 0, 0, 2, 1, 2], [0, 1, 1, 0, 2, 2]])
shape_x = (8, 6)
block_shape_x = (2, 2)
x = BCOO(coords_x, data=data_x, shape=shape_x, block_shape=block_shape_x)
x_d = x.todense()
shape_y = (8, 4, 6)
block_shape_y = (2, 2, 2)
y = sparse.brandom(shape_y, block_shape_y, 0.5, format='bcoo')
y_d = y.todense()
c = einsum("ij,ikj->k", x, y, DEBUG=True)
elemC = np.einsum("ij,ikj->k", x_d, y_d)
# another test
'''
shape_x = (8,4,9)
block_shape_x = (1,2,3)
x = sparse.brandom(shape_x, block_shape_x, 0.2, format='bcoo')
x_d = x.todense()