def testVariance(self):
# np.var is population variance
mm = Variance(dependency='close', isPopulation=True)
mm2 = Variance(dependency='close', )
for i, value in enumerate(self.samplesClose):
mm.push(dict(close=value))
mm2.push(dict(close=value))
expected = np.var(self.samplesClose[:i + 1])
calculated = mm.result()
if i == 0:
with self.assertRaises(ArithmeticError):
_ = mm2.result()
self.assertAlmostEqual(expected, calculated, 10, "at index {0:d}\n"
"expected var: {1:f}\n"
"calculated var: {2:f}".format(i, expected, calculated))
if i >= 1:
calculated = mm2.result() * i / (i + 1) # transform sample variance to population variance
self.assertAlmostEqual(expected, calculated, 10, "at index {0:d}\n"
"expected var: {1:f}\n"
"calculated var: {2:f}".format(i, expected,
calculated))
def testVariance(self):
# np.var is population variance
mm = Variance(dependency='close', isPopulation=True)
mm2 = Variance(dependency='close', )
for i, value in enumerate(self.samplesClose):
mm.push(dict(close=value))
mm2.push(dict(close=value))
expected = np.var(self.samplesClose[:i + 1])
calculated = mm.result()
if i == 0:
self.assertTrue(np.isnan(mm2.result()))
self.assertAlmostEqual(
expected, calculated, 10, "at index {0:d}\n"
"expected var: {1:f}\n"
"calculated var: {2:f}".format(i, expected, calculated))
if i >= 1:
calculated = mm2.result() * i / (
i + 1) # transform sample variance to population variance
self.assertAlmostEqual(
expected, calculated, 10, "at index {0:d}\n"
"expected var: {1:f}\n"
"calculated var: {2:f}".format(i, expected, calculated))