def naive_scrump(T_A, m, T_B, percentage, exclusion_zone, pre_scrump, s):
distance_matrix = naive.distance_matrix(T_A, T_B, m)
n_A = T_A.shape[0]
n_B = T_B.shape[0]
l = n_A - m + 1
if exclusion_zone is not None:
diags = np.random.permutation(range(exclusion_zone + 1, n_A - m + 1))
else:
diags = np.random.permutation(range(-(n_A - m + 1) + 1, n_B - m + 1))
n_chunks = int(np.ceil(1.0 / percentage))
ndist_counts = core._count_diagonal_ndist(diags, m, n_A, n_B)
diags_ranges = core._get_array_ranges(ndist_counts, n_chunks)
diags_ranges_start = diags_ranges[0, 0]
diags_ranges_stop = diags_ranges[0, 1]
out = np.full((l, 4), np.inf, dtype=object)
out[:, 1:] = -1
left_P = np.full(l, np.inf, dtype=np.float64)
right_P = np.full(l, np.inf, dtype=np.float64)
for diag_idx in range(diags_ranges_start, diags_ranges_stop):
k = diags[diag_idx]
for i in range(n_A - m + 1):
for j in range(n_B - m + 1):
if j - i == k:
if distance_matrix[i, j] < out[i, 0]:
out[i, 0] = distance_matrix[i, j]
out[i, 1] = i + k
if (
exclusion_zone is not None
and distance_matrix[i, j] < out[i + k, 0]
):
out[i + k, 0] = distance_matrix[i, j]
out[i + k, 1] = i
# left matrix profile and left matrix profile indices
if (
exclusion_zone is not None
and i < i + k
and distance_matrix[i, j] < left_P[i + k]
):
left_P[i + k] = distance_matrix[i, j]
out[i + k, 2] = i
# right matrix profile and right matrix profile indices
if (
exclusion_zone is not None
and i + k > i
and distance_matrix[i, j] < right_P[i]
):
right_P[i] = distance_matrix[i, j]
out[i, 3] = i + k
return out
def test_count_diagonal_ndist():
for n_A in range(10, 15):
for n_B in range(10, 15):
for m in range(3, 6):
diags = np.random.permutation(range(-(n_A - m + 1) + 1, n_B - m + 1))
ones_matrix = np.ones((n_A - m + 1, n_B - m + 1), dtype=np.int64)
ref_ndist_counts = np.empty(len(diags))
for i, diag in enumerate(diags):
ref_ndist_counts[i] = ones_matrix.diagonal(offset=diag).sum()
comp_ndist_counts = core._count_diagonal_ndist(diags, m, n_A, n_B)
npt.assert_almost_equal(ref_ndist_counts, comp_ndist_counts)
