def test_random_from_uniform_dist(self):
# Test is probabilistic, which is not great, but is necessary.
simulation_pmf = PMF()
for n in range(10000):
x = self.pmf.random()
hit_count = simulation_pmf.get(x, 0)
simulation_pmf[x] = hit_count + 1
simulation_pmf.normalize()
for x in "abcde":
self.assertTrue(0.190 < simulation_pmf[x] < 0.210)
def test_random_from_uniform_dist(self):
# Test is probabilistic, which is not great, but is necessary.
simulation_pmf = PMF()
for n in range(10000):
x = self.pmf.random()
hit_count = simulation_pmf.get(x, 0)
simulation_pmf[x] = hit_count + 1
simulation_pmf.normalize()
for x in 'abcde':
self.assertTrue(0.190 < simulation_pmf[x] < 0.210)
def test_random_from_power_dist(self):
# Test is probabilistic, which is not great, but is necessary.
self.pmf.power_law_dist(xrange(1, 4))
simulation_pmf = PMF()
for n in range(10000):
x = self.pmf.random()
hit_count = simulation_pmf.get(x, 0)
simulation_pmf[x] = hit_count + 1
simulation_pmf.normalize()
self.assertTrue(0.540 < simulation_pmf[1] < 0.550)
self.assertTrue(0.262 < simulation_pmf[2] < 0.283)
self.assertTrue(0.166 < simulation_pmf[3] < 0.197)
def test_random_from_power_dist(self):
# Test is probabilistic, which is not great, but is necessary.
self.pmf.power_law_dist(xrange(1, 4))
simulation_pmf = PMF()
for n in range(10000):
x = self.pmf.random()
hit_count = simulation_pmf.get(x, 0)
simulation_pmf[x] = hit_count + 1
simulation_pmf.normalize()
self.assertTrue(0.540 < simulation_pmf[1] < 0.550)
self.assertTrue(0.262 < simulation_pmf[2] < 0.283)
self.assertTrue(0.166 < simulation_pmf[3] < 0.197)
