def test_reservoir_buffer_max_capacity(self):
reservoir_buffer = nfsp.ReservoirBuffer(reservoir_buffer_capacity=2)
reservoir_buffer.add("entry1")
reservoir_buffer.add("entry2")
reservoir_buffer.add("entry3")
self.assertEqual(len(reservoir_buffer), 2)
def test_reservoir_buffer_add(self):
reservoir_buffer = nfsp.ReservoirBuffer(reservoir_buffer_capacity=10)
self.assertEqual(len(reservoir_buffer), 0)
reservoir_buffer.add("entry1")
self.assertEqual(len(reservoir_buffer), 1)
reservoir_buffer.add("entry2")
self.assertEqual(len(reservoir_buffer), 2)
self.assertIn("entry1", reservoir_buffer)
self.assertIn("entry2", reservoir_buffer)
def test_reservoir_buffer_sample(self):
replay_buffer = nfsp.ReservoirBuffer(reservoir_buffer_capacity=3)
replay_buffer.add("entry1")
replay_buffer.add("entry2")
replay_buffer.add("entry3")
samples = replay_buffer.sample(3)
self.assertIn("entry1", samples)
self.assertIn("entry2", samples)
self.assertIn("entry3", samples)
def test_reservoir_uniform(self):
size = 10
max_value = 100
num_trials = 1000
expected_count = 1. / max_value * size * num_trials
reservoir_buffer = nfsp.ReservoirBuffer(reservoir_buffer_capacity=size)
counter = collections.Counter()
for _ in range(num_trials):
reservoir_buffer.clear()
for idx in range(max_value):
reservoir_buffer.add(idx)
data = reservoir_buffer.sample(size)
counter.update(data)
# Tests the null hypothesis (H0) that data has the given frequencies.
# We reject the null hypothesis if we get a p-value below our threshold.
pvalue = stats.chisquare(list(counter.values()), expected_count).pvalue
self.assertGreater(pvalue, 0.05) # We cannot reject H0.