def testEmptyBins(self):
x = Histogram(10, 2)
self.assertEqual(x.size(), 10)
self.assertEqual(x.min(), float('inf'))
self.assertEqual(x.max(), 1.)
self.assertEqual(x._upperBound(), 10.)
self.assertEqual(len(x.histograms), 2)
p = x.getProbabilities()
self.assertTrue((p == numpy.zeros(10, numpy.float64)).all())
def testConstantValueBins(self):
# Basic tests with constant-value bins.
size = 8
values = range(size)
weights = [1.] * size
mean, variance = _computeMeanAndVariance(values, weights)
x = Histogram(size, 2)
x.setCurrentToMinimum()
for i in range(8):
x.accumulate(i, 1.)
self.assertAlmostEqual(x.getMean(), mean)
self.assertAlmostEqual(x.getUnbiasedVariance(), variance)
self.assertEqual(x.size(), 8)
self.assertEqual(x.min(), 0.)
self.assertEqual(x.max(), 8.)
self.assertEqual(x._upperBound(), 8.)
p = x.getProbabilities()
self.assertTrue((p == 1./8.).all())
def testPoisson1(self):
# Poisson with mean 4. 20 bins. PMF = e^-lambda lambda^n / n!
# Store in the first array of bins.
lam = 4.
size = 20
poisson = [math.exp(-lam)]
for n in range(1, size):
poisson.append(poisson[-1] * lam / n)
cardinality = size
sumOfWeights = sum(poisson)
mean = 0.
for i in range(size):
mean += poisson[i] * i
mean /= sumOfWeights
summedSecondCenteredMoment = 0.
for i in range(size):
summedSecondCenteredMoment += poisson[i] * (i - mean)**2
stream = StringIO(repr(cardinality) + '\n' +
repr(sumOfWeights) + '\n' +
repr(mean) + '\n' +
repr(summedSecondCenteredMoment) + '\n' +
'0\n1\n' +
''.join([repr(_x) + ' ' for _x in poisson]) + '\n' +
'0 ' * len(poisson) + '\n')
x = Histogram()
x.read(stream, 2)
assert x.cardinality == cardinality
assert x.sumOfWeights == sumOfWeights
assert x.mean == mean
assert x.summedSecondCenteredMoment == summedSecondCenteredMoment
assert x.size() == len(poisson)
self.assertAlmostEqual(sum(x.getProbabilities()), 1)
stream = StringIO()
writer = XmlWriter(stream)
writer.beginDocument()
x.writeXml(writer, 0, 0)
writer.endDocument()
