def test_block_svd(shape, density, sort, full_matrices, dim_keep, return_dwt):
if dim_keep >= np.min(shape):
dim_keep = None
#np.random.seed(2)
np.set_printoptions(3, linewidth = 1000, suppress = True)
x = sparse.random(shape, density, format='coo')
y = x.todense()
if return_dwt:
u, s, vt, dwt = core.block_svd(x, sort = sort, full_matrices = full_matrices, dim_keep = dim_keep, return_dwt =
return_dwt)
else:
u, s, vt = core.block_svd(x, sort = sort, full_matrices = full_matrices, dim_keep = dim_keep)
xnew = u.dot(s).dot(vt)
u_np, s_np, vt_np = np.linalg.svd(y, full_matrices = False)
if (dim_keep is not None) and (sort == True) and (full_matrices == False):
x = COO(u_np[:, :dim_keep].dot(np.diag(s_np[:dim_keep])).dot(vt_np[:dim_keep, :]))
assert np.allclose(x.todense(), xnew.todense())
if not sort:
s_sort = -np.sort(-s.data)
else:
s_sort = s.data
assert np.allclose(s_sort, s_np[:len(s_sort)])
if return_dwt:
assert(np.allclose(dwt, s_np[len(s_sort):].sum()))
def test_prune_coo():
coords = np.array([[0, 1, 2, 3]])
data = np.array([1, 0, 1, 2])
s1 = COO(coords, data)
s2 = COO(coords, data, prune=True)
assert s2.nnz == 3
# Densify s1 because it isn't canonical
assert_eq(s1.todense(), s2, check_nnz=False)
def test_large_concat_stack():
data = np.array([1], dtype=np.uint8)
coords = np.array([[255]], dtype=np.uint8)
xs = COO(coords, data, shape=(256, ), has_duplicates=False, sorted=True)
x = xs.todense()
assert_eq(np.stack([x, x]), sparse.stack([xs, xs]))
assert_eq(np.concatenate((x, x)), sparse.concatenate((xs, xs)))
def _array_select(clr, i0, i1, j0, j1, field, sparse_array):
is_upper = clr._is_symm_upper
with clr.open("r") as h5:
dtype = h5['pixels'][field].dtype
reader = CSRReader(h5, field, max_chunk=500000000)
if is_upper:
i, j, v = query_rect(reader.query, i0, i1, j0, j1, duplex=True)
else:
i, j, v = reader.query(i0, i1, j0, j1)
if not len(v):
v = v.astype(dtype)
arr = COO((i - i0, j - j0), v, shape=(i1 - i0, j1 - j0))
if not sparse_array:
arr = arr.todense()
return arr
