def test_returns_nonzero_weight_indices_with_nonzero_probability(self):
tries_left = int(10*float(max(self.test_population))/min(self.test_population[numpy.nonzero(self.test_population)])*len(self.test_population))
returned = numpy.zeros_like(self.test_population)
num_returned = 0
# Filter out the zeros. Pretend as if all zero-weight indices have been returned for the purpose of checking later. If they do get returned, fail() is called at that time.
for i, n in enumerate(self.test_population):
if n == 0:
returned[i] = 1
num_returned += 1
while num_returned < len(self.test_population) and tries_left > 0:
i = weighted_choice(self.test_population)
if(self.test_population[i] == 0):
self.fail("Zero weight index returned: " + str(i))
if returned[i] == 0:
returned[i] = 1
num_returned += 1
tries_left -= 1
if num_returned != len(self.test_population):
# Build error message.
not_returned = []
for i, r in enumerate(returned):
if r == 0:
not_returned.append(i)
self.fail("All indices had not been returned after too many tries. Indices " + str(not_returned) + " were not returned.")
def test_returns_indices_within_expected_scope(self):
min_index = 0
max_index = len(self.test_population) # Not inclusive
tries_left = len(self.test_population)*100
while tries_left > 0:
i = weighted_choice(self.test_population)
self.assertTrue(i >= 0)
self.assertTrue(i < max_index)
tries_left -= 1
def test_returns_indices_with_expected_probability(self):
permitted_relative_deviation = 0.05 # 0.01 = 1%
num_samples = len(self.test_population)*10000
return_frequencies = numpy.zeros(len(self.test_population))
expected_frequencies = self.test_population / float(sum(self.test_population)) * num_samples
for loop in xrange(num_samples):
return_frequencies[weighted_choice(self.test_population)] += 1
for i, freq in enumerate(return_frequencies):
if expected_frequencies[i] == 0:
if freq == 0:
continue
else:
self.fail("Zero-weight index was returned: " + i)
relative_deviation = abs(float(freq)/expected_frequencies[i] - 1)
print i, relative_deviation
self.assertTrue(relative_deviation <= permitted_relative_deviation, "Frequency deviates too much: " + str(relative_deviation*100) + "%")
import numpy
import matplotlib.pylab as plot
from wmath import weighted_choice
weights = numpy.arange(25)
num_samples = 100000
frequencies = numpy.zeros_like(weights)
for i in xrange(num_samples):
frequencies[weighted_choice(weights)] += 1
plot.plot(weights, weights/float(sum(weights)))
plot.plot(weights, frequencies/float(num_samples), "b*")
plot.show()
