# Add intervals to monitor to A-distance object
ad = ADistance()
data = []
for i in range(100):
data.append(random.uniform(0.0, 1.0))
ad.addProportional(data, 0.1, 0.5)
print ad
# ad.add(Interval(-2, -1))
# ad.add(Interval(-1, 0))
# ad.add(Interval(0, 1))
# ad.add(Interval(1, 2))
# Create a change finder and add a single window pair
cf = ChangeFinder(ad)
cf.addWindowPair(WindowPair(100, 100, 0.5))
# Initialize the distance object
ad.init(cf)
cf.computeAlphas(ad.uniformSample(10000), 0.05, 50)
# Process 1000 samples
i = 0
for item in ad.uniformSample(1000):
cf.addData(item)
i = i + 1
if cf.detectChange():
print 'Change detected at sample ' + str(i)
import random
#
# Simple code for testing the OnlineDist module with the ChangeFinder
#
# Create the online dist object and add alphas for online normals
od = OnlineDist()
od.addAlpha(0.95)
od.addAlpha(0.99)
# Create a change finder and add a single window pair
cf = ChangeFinder(od)
cf.addWindowPair(WindowPair(200, 200, 0.99))
# Process 1000 samples
i = 0;
for k in range(1000):
item = random.gauss(10, 1)
cf.addData(item)
i = i + 1
if cf.detectChange():
print 'Change detected at sample ' + str(i)
# Process another 1000 samples from a slightly different distribution
for k in range(1000):
item = random.gauss(13, 1)
# Add intervals to monitor to A-distance object
ad = ADistance()
data = []
for i in range(100):
data.append(random.uniform(0.0, 1.0));
ad.addProportional(data, 0.1, 0.5)
print ad
# ad.add(Interval(-2, -1))
# ad.add(Interval(-1, 0))
# ad.add(Interval(0, 1))
# ad.add(Interval(1, 2))
# Create a change finder and add a single window pair
cf = ChangeFinder(ad)
cf.addWindowPair(WindowPair(100, 100, 0.5))
# Initialize the distance object
ad.init(cf)
cf.computeAlphas(ad.uniformSample(10000), 0.05, 50)
# Process 1000 samples
i = 0;
for item in ad.uniformSample(1000):
cf.addData(item)
i = i + 1
