def testBiasedRandomWalk(self, timescale):
stdev = 1.
variation = distributions.BiasedRandomWalk(stdev=stdev, timescale=timescale)
sequence = [variation(random_state=self._variation_random_state)
for _ in range(int(max(timescale, 1)*NUM_ITERATIONS*1000))]
self.assertAlmostEqual(np.mean(sequence), 0., delta=0.01)
self.assertAlmostEqual(np.std(sequence), stdev, delta=0.01)
def testBiasedRandomWalkExceptions(self, arg_name, template):
bad_value = -1.
with self.assertRaisesWithLiteralMatch(ValueError,
template.format(bad_value)):
_ = distributions.BiasedRandomWalk(**{arg_name: bad_value})