def test_einsum_with_transpose():
shape_x = (8, 6)
block_shape_x = (2, 2)
x = sparse.brandom(shape_x, block_shape_x, 0.3, format='bcoo').T
x_d = x.todense()
shape_y = (8, 4, 6)
block_shape_y = (2, 2, 2)
y = sparse.brandom(shape_y, block_shape_y, 0.3, format='bcoo').transpose(
(2, 0, 1))
y_d = y.todense()
c = bcalc.einsum("ij,ijk->k", x, y, DEBUG=False)
elemC = np.einsum("ij,ijk->k", x_d, y_d)
assert_eq(elemC, c)
shape_x = (8, 4, 9)
block_shape_x = (1, 2, 3)
x = sparse.brandom(shape_x, block_shape_x, 0.2, format='bcoo').transpose(
(1, 0, 2))
x_d = x.todense()
shape_y = (9, 4, 6)
block_shape_y = (3, 2, 2)
y = sparse.brandom(shape_y, block_shape_y, 0.5, format='bcoo').T
y_d = y.todense()
c = bcalc.einsum("jik,nmk->minj", x, y, DEBUG=False)
elemC = np.einsum("jik,nmk->minj", x_d, y_d)
assert_eq(elemC, c)
c = bcalc.einsum("jik,nmk->ijmn", x, y, DEBUG=False)
elemC = np.einsum("jik,nmk->ijmn", x_d, y_d)
assert_eq(elemC, c)
def test_einsum_as_transpose():
# swap axes
shape_y = (8, 4, 6)
block_shape_y = (2, 2, 2)
y = sparse.brandom(shape_y, block_shape_y, 0.3, format='bcoo').transpose(
(2, 0, 1))
y_d = y.todense()
c = bcalc.einsum("ijk->kji", y)
elemC = np.einsum("ijk->kji", y_d)
assert_eq(elemC, c)
c = bcalc.einsum("ijk->ijk", y)
elemC = np.einsum("ijk->ijk", y_d)
assert_eq(elemC, c)
def test_normal_einsum(shape_x, block_shape_x, shape_y, block_shape_y, descr):
x = sparse.brandom(shape_x, block_shape_x, 0.3, format='bcoo')
x_d = x.todense()
y = sparse.brandom(shape_y, block_shape_y, 0.3, format='bcoo')
y_d = y.todense()
c = bcalc.einsum(descr, x, y, DEBUG=False)
elemC = np.einsum(descr, x_d, y_d)
assert_eq(elemC, c)
def test_inner_contraction():
shape_x = (8, 6)
block_shape_x = (2, 2)
x = sparse.brandom(shape_x, block_shape_x, 0.3, format='bcoo')
x_d = x.todense()
shape_y = (8, 6, 6)
block_shape_y = (2, 2, 2)
y = sparse.brandom(shape_y, block_shape_y, 0.3, format='bcoo')
y_d = y.todense()
elemC = np.einsum("in,ijj->n", x_d, y_d)
with pytest.raises(NotImplementedError):
c = bcalc.einsum("in,ijj->n", x, y, DEBUG=False)
assert_eq(elemC, c)
elemC = np.einsum("ijj->i", y_d)
with pytest.raises(NotImplementedError):
c = bcalc.einsum("ijj->i", y, DEBUG=False)
assert_eq(elemC, c)
def test_einsum_mix_types():
shape_x = (5, 1, 4, 2, 3)
block_shape_x = (1, 1, 2, 2, 3)
x = sparse.brandom(shape_x, block_shape_x, 0.5)
x.data = x.data + 1j
x_d = x.todense()
shape_y = (5, 1, 11)
block_shape_y = (1, 1, 1)
y = sparse.brandom(shape_y, block_shape_y, 0.5)
y.data = y.data.astype(np.float32)
y_d = y.todense()
c = bcalc.einsum('ijklm,ijn->lnmk', x, y)
elemC = np.einsum('ijklm,ijn->lnmk', x_d, y_d)
assert_eq(elemC, c)
c = bcalc.einsum('ijklm,ijn->lnmk', x_d, y)
elemC = np.einsum('ijklm,ijn->lnmk', x_d, y_d)
assert_eq(elemC, c)
def test_einsum_shape_error():
# test for shape error while outer_shape is ok
shape_x = (4, 2, 2)
block_shape_x = (2, 2, 2)
x = sparse.brandom(shape_x, block_shape_x, 0.5, format='bcoo')
x_d = x.todense()
shape_y = (1, 1)
block_shape_y = (1, 1)
y = sparse.brandom(shape_y, block_shape_y, 0.5, format='bcoo')
y_d = y.todense()
shape_z = (2, 1)
block_shape_z = (2, 1)
z = sparse.brandom(shape_z, block_shape_z, 0.5, format='bcoo')
z_d = z.todense()
#FIXME:
with pytest.raises(RuntimeError):
c = bcalc.einsum('kxy,yz,zx->k', x, y, z)
# test for block shape error
shape_x = (4, 2, 2)
block_shape_x = (2, 2, 2)
x = sparse.brandom(shape_x, block_shape_x, 0.5, format='bcoo')
x_d = x.todense()
shape_y = (2, 2)
block_shape_y = (1, 1)
y = sparse.brandom(shape_y, block_shape_y, 0.5, format='bcoo')
y_d = y.todense()
shape_z = (2, 2)
block_shape_z = (2, 1)
z = sparse.brandom(shape_z, block_shape_z, 0.5, format='bcoo')
z_d = z.todense()
#FIXME:
with pytest.raises(ValueError):
c = bcalc.einsum('kxy,yz,zx->k', x, y, z)
def test_einsum_broadcast():
shape_x = (8, 6)
block_shape_x = (2, 2)
x = sparse.brandom(shape_x, block_shape_x, 0.3, format='bcoo')
x_d = x.todense()
shape_y = (8, 6, 6)
block_shape_y = (2, 2, 2)
y = sparse.brandom(shape_y, block_shape_y, 0.3, format='bcoo')
y_d = y.todense()
elemC = np.einsum("ij,klm->ijklm", x_d, y_d)
c = bcalc.einsum("ij,klm->ijklm", x, y, DEBUG=False)
assert_eq(elemC, c)
def test_einsum_zeros():
shape_x = (5, 1, 4, 2, 3)
block_shape_x = (1, 1, 2, 2, 3)
x = sparse.bcoo.zeros(shape_x, np.complex, block_shape_x)
x_d = x.todense()
shape_y = (5, 1, 11)
block_shape_y = (1, 1, 1)
y = sparse.bcoo.zeros(shape_y, np.float32, block_shape_y)
y_d = y.todense()
c = bcalc.einsum('ijklm,ijn->lnmk', x, y)
elemC = np.einsum('ijklm,ijn->lnmk', x_d, y_d)
assert_eq(elemC, c)
def test_einsum_misc():
shape_x = (4, 2, 2)
block_shape_x = (2, 2, 2)
x = sparse.brandom(shape_x, block_shape_x, 0.5, format='bcoo')
x_d = x.todense()
shape_y = (2, 2)
block_shape_y = (2, 1)
y = sparse.brandom(shape_y, block_shape_y, 0.5, format='bcoo')
y_d = y.todense()
shape_z = (2, 2)
block_shape_z = (1, 2)
z = sparse.brandom(shape_z, block_shape_z, 0.5, format='bcoo')
z_d = z.todense()
c = bcalc.einsum('kxy,yz->kxz', x, y)
elemC = np.einsum('kxy,yz->kxz', x_d, y_d)
assert_eq(elemC, c)
c = bcalc.einsum('kxy,yz,zx->k', x, y, z)
elemC = np.einsum('kxy,yz,zx->k', x_d, y_d, z_d)
assert_eq(elemC, c)
def test_einsum_multi_tensor():
shape_x = (12, 8, 9)
block_shape_x = (2, 4, 3)
x = sparse.brandom(shape_x, block_shape_x, 0.5, format='bcoo')
x_d = x.todense()
shape_y = (9, 5)
block_shape_y = (3, 1)
y = sparse.brandom(shape_y, block_shape_y, 0.5, format='bcoo')
y_d = y.todense()
shape_z = (5, 8)
block_shape_z = (1, 4)
z = sparse.brandom(shape_z, block_shape_z, 0.5, format='bcoo')
z_d = z.todense()
c = bcalc.einsum('kxy,yz,zx->k', x, y, z)
elemC = np.einsum('kxy,yz,zx->k', x_d, y_d, z_d)
assert_eq(elemC, c)
def test_block_svd():
#np.set_printoptions(3, linewidth = 1000, suppress = True)
np.set_printoptions(2, linewidth=1000, suppress=False)
x = sparse.brandom((16, 8), (2, 2), 0.3, format='bcoo')
'''
a = np.zeros((8, 4))
a[0:2, 2:4] = np.arange(1, 5).reshape((2, 2))
a[4:8, 0:2] = np.arange(1, 9).reshape((4, 2))
x = BCOO.from_numpy(a, block_shape = (2, 2))
'''
y = x.todense()
u, sigma, vt = bcore.block_svd(x)
xnew = bcalc.einsum("ij,jk,kl->il", u, sigma, vt)
assert np.allclose(x.todense(), xnew.todense())
u_d = u.todense()
vt_d = vt.todense()
u_np, sigma_np, vt_np = np.linalg.svd(y, full_matrices=True)
print "u sp"
print u_d
print "vt sp"
print vt_d
print sigma_np
print np.sqrt(y.T.dot(y).dot(vt_np.T) / (vt_np.T))
print "u np"
print u_np
print "vt np"
print vt_np
print "uT A v"
print u_d.T.dot(y.dot(vt_d.T))
print sigma.todense()
print sigma.coords
def dot1(shape_x, block_shape_x, shape_y, block_shape_y):
x = sparse.brandom(shape_x, block_shape_x, .3, format='bcoo')
y = sparse.brandom(shape_y, block_shape_y, .3, format='bcoo')
c = bcalc.einsum("ij,pjk->pik", x, y, DEBUG=False)