def ergodic_chain(args):
(burnin, run_length, alpha_count_slow, alpha_count_fast,
alpha_mu_slow, alpha_mu_fast, buckets_slow, buckets_fast) = args
np.random.seed((os.getpid() << 16) | (int(time.time()) & 0xFFFF))
rh_slow = create_rolling_histogram_class(
Bucket=create_bucket_class(
alpha_mu=alpha_mu_slow, alpha_count=alpha_count_slow),
target_buckets=buckets_slow)()
rh_fast = create_rolling_histogram_class(
Bucket=create_bucket_class(
alpha_mu=alpha_mu_fast, alpha_count=alpha_count_fast),
target_buckets=buckets_fast)()
jagged = [stats.uniform(x, x + 1) for x in range(200)]
#for val in gen_value(jagged, burnin):
#for val in gen_value([stats.uniform(0, 1)], burnin):
for val in gen_value([stats.norm(0, 1)], burnin):
#for val in gen_value([stats.cauchy(0)], burnin):
rh_slow.update(val)
rh_fast.update(val)
data = [[] for _ in range(len(FUNC_LIST))]
#for val in gen_value(jagged, run_length):
#for val in gen_value([stats.uniform(0, 1)], run_length):
for val in gen_value([stats.norm(0, 1)], run_length):
#for val in gen_value([stats.cauchy(0)], run_length):
rh_slow.update(val)
rh_fast.update(val)
cdf_long = rh_slow.get_CDF()
cdf_short = rh_fast.get_CDF()
for i, func in enumerate(FUNC_LIST):
data[i].append(func(cdf_short, cdf_long))
return data
def get_detection_windows(args):
(burnin, run_length, alpha_count_slow, alpha_count_fast,
alpha_mu_slow, alpha_mu_fast, buckets_slow, buckets_fast,
threshold) = args
np.random.seed((os.getpid() << 16) | (int(time.time()) & 0xFFFF))
rh_slow = create_rolling_histogram_class(
Bucket=create_bucket_class(
alpha_mu=alpha_mu_slow, alpha_count=alpha_count_slow),
target_buckets=buckets_slow)()
rh_fast = create_rolling_histogram_class(
Bucket=create_bucket_class(
alpha_mu=alpha_mu_fast, alpha_count=alpha_count_fast),
target_buckets=buckets_fast)()
#for val in gen_value([dist0], burnin):
# rh_slow.update(val)
# rh_fast.update(val)
data = []
#for i, val in enumerate(gen_value([dist1], run_length)):
for i, val in enumerate(datasets.dataset1()):
rh_slow.update(val)
rh_fast.update(val)
cdf_slow = rh_slow.get_CDF()
cdf_fast = rh_fast.get_CDF()
KS = get_KS(cdf_slow, cdf_fast)
if KS > threshold:
data.append(i)
return data
