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_mass_distance_matrix(T_A, T_B):
m = 3
ref_distance_matrix = naive.distance_matrix(T_A, T_B, m)
k = T_A.shape[0] - m + 1
l = T_B.shape[0] - m + 1
comp_distance_matrix = np.full((k, l), np.inf)
core._mass_distance_matrix(T_A, T_B, m, comp_distance_matrix)
npt.assert_almost_equal(ref_distance_matrix, comp_distance_matrix)
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_B - m + 1
if exclusion_zone is not None:
orders = np.random.permutation(range(exclusion_zone + 1, n_B - m + 1))
else:
orders = np.random.permutation(range(-(n_B - m + 1) + 1, n_A - m + 1))
orders_ranges = naive_get_orders_ranges(1, m, n_A, n_B, orders, 0,
percentage)
orders_ranges_start = orders_ranges[0][0]
orders_ranges_stop = orders_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 order_idx in range(orders_ranges_start, orders_ranges_stop):
k = orders[order_idx]
for i in range(n_B - m + 1):
for j in range(n_A - 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 naive_prescrump(T_A, m, T_B, s, exclusion_zone=None):
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
P = np.empty(l)
I = np.empty(l, dtype=np.int64)
P[:] = np.inf
I[:] = -1
for i in np.random.permutation(range(0, l, s)):
distance_profile = distance_matrix[i]
if exclusion_zone is not None:
naive.apply_exclusion_zone(distance_profile, i, exclusion_zone)
I[i] = np.argmin(distance_profile)
P[i] = distance_profile[I[i]]
if P[i] == np.inf:
I[i] = -1
j = I[i]
for k in range(1, min(s, l - max(i, j))):
d = distance_matrix[i + k, j + k]
if d < P[i + k]:
P[i + k] = d
I[i + k] = j + k
if d < P[j + k]:
P[j + k] = d
I[j + k] = i + k
for k in range(1, min(s, i + 1, j + 1)):
d = distance_matrix[i - k, j - k]
if d < P[i - k]:
P[i - k] = d
I[i - k] = j - k
if d < P[j - k]:
P[j - k] = d
I[j - k] = i - k
return P, I
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_B - m + 1
if exclusion_zone is not None:
orders = np.random.permutation(range(exclusion_zone + 1, n_B - m + 1))
else:
orders = np.random.permutation(range(-(n_B - m + 1) + 1, n_A - m + 1))
orders_ranges = naive_get_orders_ranges(1, m, n_A, n_B, orders, 0, percentage)
orders_ranges_start = orders_ranges[0][0]
orders_ranges_stop = orders_ranges[0][1]
out = np.full((l, 2), np.inf, dtype=object)
out[:, 1] = -1
for order_idx in range(orders_ranges_start, orders_ranges_stop):
k = orders[order_idx]
for i in range(n_B - m + 1):
for j in range(n_A - 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
return out