def use_rangemedian(request):
if request.param == "rangemedian":
assert isinstance(step_detect.get_mu_dist([0], [1]), _rangemedian.RangeMedian)
return True
else:
step_detect._rangemedian = None
def restore():
if HAVE_RANGEMEDIAN:
step_detect._rangemedian = _rangemedian
request.addfinalizer(restore)
assert isinstance(step_detect.get_mu_dist([0], [1]), L1Dist)
return False
def use_rangemedian(request):
if request.param == "rangemedian":
assert isinstance(step_detect.get_mu_dist([0], [1]), _rangemedian.RangeMedian)
return True
else:
step_detect._rangemedian = None
def restore():
if HAVE_RANGEMEDIAN:
step_detect._rangemedian = _rangemedian
request.addfinalizer(restore)
assert isinstance(step_detect.get_mu_dist([0], [1]), L1Dist)
return False
def test_l1dist(use_rangemedian):
random.seed(1)
datasets = [([1, 1, 10, 3, 5, 1, -16, -3, 4, 9], [1] * 10),
([random.gauss(0, 1) for j in range(50)], [1] * 50),
([1, 2, 3, 4], [1, 2, 1, 2])]
def median_iter(y, w):
if all(ww == w[0] for ww in w):
for m, d in rolling_median_dev(y):
yield m, d
else:
for j in range(1, len(y) + 1):
m = step_detect.weighted_median(y[:j], w[:j])
d = sum(ww * abs(yy - m) for yy, ww in zip(y[:j], w[:j]))
yield m, d
for y, w in datasets:
dist = step_detect.get_mu_dist(y, w)
for i in range(len(y)):
for p, (m2, d2) in enumerate(median_iter(y[i:], w[i:])):
j = i + p
m = dist.mu(i, j)
d = dist.dist(i, j)
assert m == m2, (i, j)
assert abs(d - d2) < 1e-10, (i, j)
def test_l1dist(use_rangemedian):
random.seed(1)
datasets = [
([1, 1, 10, 3, 5, 1, -16, -3, 4, 9], [1]*10),
([random.gauss(0, 1) for j in range(50)], [1]*50),
([1, 2, 3, 4], [1, 2, 1, 2])
]
def median_iter(y, w):
if all(ww == w[0] for ww in w):
for m, d in rolling_median_dev(y):
yield m, d
else:
for j in range(1, len(y) + 1):
m = step_detect.weighted_median(y[:j], w[:j])
d = sum(ww*abs(yy - m) for yy, ww in zip(y[:j], w[:j]))
yield m, d
for y, w in datasets:
dist = step_detect.get_mu_dist(y, w)
for i in range(len(y)):
for p, (m2, d2) in enumerate(median_iter(y[i:], w[i:])):
j = i + p
m = dist.mu(i, j)
d = dist.dist(i, j)
assert m == m2, (i, j)
assert abs(d - d2) < 1e-10, (i, j)
def test_l1dist(use_rangemedian):
random.seed(1)
datasets = [[1, 1, 10, 3, 5, 1, -16, -3, 4, 9],
[random.gauss(0, 1) for j in range(50)]]
for dataset in datasets:
dist = step_detect.get_mu_dist(dataset, p=1)
dist.precompute(len(dataset) // 2, 0, len(dataset) // 2)
for i in range(len(dataset)):
for p, (m2, d2) in enumerate(rolling_median_dev(dataset[i:])):
j = i + p
m = dist.mu(i, j)
d = dist.dist(i, j)
assert m == m2, (i, j)
assert abs(d - d2) < 1e-10, (i, j)
def test_l1dist(use_rangemedian):
random.seed(1)
datasets = [
[1, 1, 10, 3, 5, 1, -16, -3, 4, 9],
[random.gauss(0, 1) for j in range(50)]
]
for dataset in datasets:
dist = step_detect.get_mu_dist(dataset, p=1)
dist.precompute(len(dataset)//2, 0, len(dataset)//2)
for i in range(len(dataset)):
for p, (m2, d2) in enumerate(rolling_median_dev(dataset[i:])):
j = i + p
m = dist.mu(i, j)
d = dist.dist(i, j)
assert m == m2, (i, j)
assert abs(d - d2) < 1e-10, (i, j)
