def test_euclidean():
"""Test euclidean distance."""
assert pytest.approx(np.sqrt(2), 0.0000001) == distance.euclidean(
np.array([1., 1.]), np.array([2., 2.]))
a = np.array([0.5, 0.8, 0.9])
b = np.array([-0.15, 0.38, 0.92])
assert pytest.approx(0.7741447, 0.0000001) == distance.euclidean(a, b)
def find_best_discord_hotsax(series, win_size, a_size, paa_size,
znorm_threshold, globalRegistry): # noqa: C901
"""Find the best discord with hotsax."""
"""[1.0] get the sax data first"""
sax_none = sax_via_window(series, win_size, a_size, paa_size, "none", 0.01)
"""[2.0] build the 'magic' array"""
magic_array = list()
for k, v in sax_none.items():
magic_array.append((k, len(v)))
"""[2.1] sort it desc by the key"""
m_arr = sorted(magic_array, key=lambda tup: tup[1])
"""[3.0] define the key vars"""
bestSoFarPosition = -1
bestSoFarDistance = 0.
distanceCalls = 0
visit_array = np.zeros(len(series), dtype=np.int)
"""[4.0] and we are off iterating over the magic array entries"""
for entry in m_arr:
"""[5.0] some moar of teh vars"""
curr_word = entry[0]
occurrences = sax_none[curr_word]
"""[6.0] jumping around by the same word occurrences makes it easier to
nail down the possibly small distance value -- so we can be efficient
and all that..."""
for curr_pos in occurrences:
if curr_pos in globalRegistry:
continue
"""[7.0] we don't want an overlapping subsequence"""
mark_start = curr_pos - win_size
mark_end = curr_pos + win_size
visit_set = set(range(mark_start, mark_end))
"""[8.0] here is our subsequence in question"""
cur_seq = znorm(series[curr_pos:(curr_pos + win_size)],
znorm_threshold)
"""[9.0] let's see what is NN distance"""
nn_dist = np.inf
do_random_search = 1
"""[10.0] ordered by occurrences search first"""
for next_pos in occurrences:
"""[11.0] skip bad pos"""
if next_pos in visit_set:
continue
else:
visit_set.add(next_pos)
"""[12.0] distance we compute"""
dist = euclidean(
cur_seq,
znorm(series[next_pos:(next_pos + win_size)],
znorm_threshold))
distanceCalls += 1
"""[13.0] keep the books up-to-date"""
if dist < nn_dist:
nn_dist = dist
if dist < bestSoFarDistance:
do_random_search = 0
break
"""[13.0] if not broken above,
we shall proceed with random search"""
if do_random_search:
"""[14.0] build that random visit order array"""
curr_idx = 0
for i in range(0, (len(series) - win_size)):
if not (i in visit_set):
visit_array[curr_idx] = i
curr_idx += 1
it_order = np.random.permutation(visit_array[0:curr_idx])
curr_idx -= 1
"""[15.0] and go random"""
while curr_idx >= 0:
rand_pos = it_order[curr_idx]
curr_idx -= 1
dist = euclidean(
cur_seq,
znorm(series[rand_pos:(rand_pos + win_size)],
znorm_threshold))
distanceCalls += 1
"""[16.0] keep the books up-to-date again"""
if dist < nn_dist:
nn_dist = dist
if dist < bestSoFarDistance:
nn_dist = dist
break
"""[17.0] and BIGGER books"""
if (nn_dist > bestSoFarDistance) and (nn_dist < np.inf):
bestSoFarDistance = nn_dist
bestSoFarPosition = curr_pos
return (bestSoFarPosition, bestSoFarDistance)
def find_best_discord_hotsax(series, win_size, a_size, paa_size,
znorm_threshold, globalRegistry): # noqa: C901
"""Find the best discord with hotsax."""
"""
[1.0] get the sax data first
将一个 time series 转化为 SAX字典 (key: 字符串, value: 窗口索引组成的列表)
"""
sax_none = sax_via_window(series, win_size, a_size, paa_size, "none", 0.01)
"""
[2.0] build the 'magic' array
magic_array: a list of tuples
(字符串, 窗口索引个数)
"""
magic_array = list()
for k, v in sax_none.items():
magic_array.append((k, len(v)))
"""
[2.1] sort it desc by the key
按照 窗口索引个数降序 对 tuple 排序
"""
m_arr = sorted(magic_array, key=lambda tup: tup[1])
"""
[3.0] define the key vars
bestSoFarPosition
bestSoFarDistance对应的窗口开始索引
这个窗口是该时间序列的异常子序列
bestSoFarDistance
max(min(distance))
对于每一个窗口, 我们求出它与其他窗口的最小距离
对所有的最小距离取一个最大值
"""
bestSoFarPosition = -1
bestSoFarDistance = 0.
distanceCalls = 0
visit_array = np.zeros(len(series), dtype=np.int)
"""[4.0] and we are off iterating over the magic array entries"""
for entry in m_arr:
"""[5.0] some moar of teh vars"""
curr_word = entry[0]
# occurrences 当前word 的窗口索引列表
occurrences = sax_none[curr_word]
"""[6.0] jumping around by the same word occurrences makes it easier to
nail down the possibly small distance value 通过在相同的单词之间 跳转, 使得更容易确定可能的小距离值
-- so we can be efficient and all that..."""
# curr_pos 当前窗口索引 开始索引
for curr_pos in occurrences:
# 若 已经在 globalRegistry 跳出本次循环
if curr_pos in globalRegistry:
continue
"""[7.0] we don't want an overlapping subsequence"""
# 避免 重复的子序列
mark_start = curr_pos - win_size
mark_end = curr_pos + win_size
# 我们要找到 与 当前窗口 相似性最大的(距离最小的)窗口, 而 visit_set 定义我们已经看过的窗口的开始索引
visit_set = set(range(mark_start, mark_end))
"""[8.0] here is our subsequence in question"""
# cur_seq 标准化的子序列
cur_seq = znorm(series[curr_pos:(curr_pos + win_size)],
znorm_threshold)
"""[9.0] let's see what is NN distance"""
# 定义 nn_dist 为: 当前窗口 与 其他窗口 的最小距离 (两窗口 不能有重复部分 且 不能相邻?)
nn_dist = np.inf
# 定义 bool 是否进行随机搜索
do_random_search = 1
"""[10.0] ordered by occurrences search first"""
# 通过在相同的单词之间 跳转, 使得更容易确定可能的小距离值
for next_pos in occurrences:
"""[11.0] skip bad pos"""
# 避免 重复子序列
if next_pos in visit_set:
continue
else:
visit_set.add(next_pos)
"""[12.0] distance we compute"""
dist = euclidean(
cur_seq,
znorm(series[next_pos:(next_pos + win_size)],
znorm_threshold))
distanceCalls += 1
"""[13.0] keep the books up-to-date"""
# 更新 nn_dist
if dist < nn_dist:
nn_dist = dist
if dist < bestSoFarDistance:
do_random_search = 0
break
"""[13.0] if not broken above,
we shall proceed with random search"""
# 上面循环正常结束 并没有提前跳出 那我们就要进行随机搜索
if do_random_search:
"""[14.0] build that random visit order array"""
curr_idx = 0
for i in range(0,
(len(series) -
win_size)): # 为什么不是 len(series) - win_size + 1
# 当前窗口开始索引 在上面没有查看过
if not (i in visit_set):
# 将其添加到 visit_array 中
visit_array[curr_idx] = i
curr_idx += 1
# 此时 curr_idx 为 在上面没查看过的窗口开始索引的个数
# 打乱顺序
it_order = np.random.permutation(visit_array[0:curr_idx])
curr_idx -= 1
"""[15.0] and go random"""
while curr_idx >= 0:
# 随机选择 窗口开始索引 it_order[curr_idx]
rand_pos = it_order[curr_idx]
curr_idx -= 1
dist = euclidean(
cur_seq,
znorm(series[rand_pos:(rand_pos + win_size)],
znorm_threshold))
distanceCalls += 1
"""[16.0] keep the books up-to-date again"""
# 更新 nn_dist
if dist < nn_dist:
nn_dist = dist
if dist < bestSoFarDistance:
nn_dist = dist
break
"""[17.0] and BIGGER books"""
# 更新 bestSoFarDistance 和 bestSoFarPosition
if (nn_dist > bestSoFarDistance) and (nn_dist < np.inf):
bestSoFarDistance = nn_dist
bestSoFarPosition = curr_pos
return (bestSoFarPosition, bestSoFarDistance)
def find_best_discord_hotsax(series, win_size, global_registry, sax_data,
magic_array, znorms):
"""Find the best discord with hotsax."""
"""[3.0] define the key vars"""
best_so_far_position = -1
best_so_far_distance = 0.
distance_calls = 0
visit_array = np.zeros(len(series), dtype=np.int)
"""[4.0] and we are off iterating over the magic array entries"""
for entry in magic_array:
"""[5.0] current SAX words and the number of other sequences mapping to the same SAX word."""
curr_word = entry[0]
occurrences = sax_data[curr_word]
"""[6.0] jumping around by the same word occurrences makes it easier to
nail down the possibly small distance value -- so we can be efficient
and all that..."""
for curr_pos in occurrences:
if curr_pos in global_registry:
continue
"""[7.0] we don't want an overlapping subsequence"""
mark_start = curr_pos - win_size + 1
mark_end = curr_pos + win_size
visit_set = set(range(mark_start, mark_end))
"""[8.0] here is our subsequence in question"""
cur_seq = znorms[curr_pos]
"""[9.0] let's see what is NN distance"""
nn_dist = np.inf
do_random_search = True
"""[10.0] ordered by occurrences search first"""
for next_pos in occurrences:
"""[11.0] skip bad pos"""
if next_pos in visit_set:
continue
else:
visit_set.add(next_pos)
"""[12.0] distance we compute"""
dist = euclidean(cur_seq, znorms[next_pos])
distance_calls += 1
"""[13.0] keep the books up-to-date"""
if dist < nn_dist:
nn_dist = dist
if dist < best_so_far_distance:
do_random_search = False
break
"""[13.0] if not broken above,
we shall proceed with random search"""
if do_random_search:
"""[14.0] build that random visit order array"""
curr_idx = 0
for i in range(0, (len(series) - win_size + 1)):
if not (i in visit_set):
visit_array[curr_idx] = i
curr_idx += 1
it_order = np.random.permutation(visit_array[0:curr_idx])
curr_idx -= 1
"""[15.0] and go random"""
while curr_idx >= 0:
rand_pos = it_order[curr_idx]
curr_idx -= 1
dist = euclidean(cur_seq, znorms[rand_pos])
distance_calls += 1
"""[16.0] keep the books up-to-date again"""
if dist < nn_dist:
nn_dist = dist
if dist < best_so_far_distance:
nn_dist = dist
break
"""[17.0] and BIGGER books"""
if (nn_dist > best_so_far_distance) and (nn_dist < np.inf):
best_so_far_distance = nn_dist
best_so_far_position = curr_pos
return best_so_far_position, best_so_far_distance