bpo-30561: Sync-up expovariate() and gammavariate code (GH-1934)

This commit is contained in:
leodema 2018-12-24 07:54:25 +01:00 committed by Raymond Hettinger
parent b7105c9c96
commit 63d152232e
3 changed files with 38 additions and 17 deletions

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@ -582,10 +582,7 @@ class Random(_random.Random):
elif alpha == 1.0:
# expovariate(1/beta)
u = random()
while u <= 1e-7:
u = random()
return -_log(u) * beta
return -_log(1.0 - random()) * beta
else: # alpha is between 0 and 1 (exclusive)

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@ -853,28 +853,48 @@ class TestDistributions(unittest.TestCase):
self.assertRaises(ValueError, random.gammavariate, 2, 0)
self.assertRaises(ValueError, random.gammavariate, 1, -3)
# There are three different possibilities in the current implementation
# of random.gammavariate(), depending on the value of 'alpha'. What we
# are going to do here is to fix the values returned by random() to
# generate test cases that provide 100% line coverage of the method.
@unittest.mock.patch('random.Random.random')
def test_gammavariate_full_code_coverage(self, random_mock):
# There are three different possibilities in the current implementation
# of random.gammavariate(), depending on the value of 'alpha'. What we
# are going to do here is to fix the values returned by random() to
# generate test cases that provide 100% line coverage of the method.
def test_gammavariate_alpha_greater_one(self, random_mock):
# #1: alpha > 1.0: we want the first random number to be outside the
# #1: alpha > 1.0.
# We want the first random number to be outside the
# [1e-7, .9999999] range, so that the continue statement executes
# once. The values of u1 and u2 will be 0.5 and 0.3, respectively.
random_mock.side_effect = [1e-8, 0.5, 0.3]
returned_value = random.gammavariate(1.1, 2.3)
self.assertAlmostEqual(returned_value, 2.53)
# #2: alpha == 1: first random number less than 1e-7 to that the body
# of the while loop executes once. Then random.random() returns 0.45,
# which causes while to stop looping and the algorithm to terminate.
random_mock.side_effect = [1e-8, 0.45]
returned_value = random.gammavariate(1.0, 3.14)
self.assertAlmostEqual(returned_value, 2.507314166123803)
@unittest.mock.patch('random.Random.random')
def test_gammavariate_alpha_equal_one(self, random_mock):
# #3: 0 < alpha < 1. This is the most complex region of code to cover,
# #2.a: alpha == 1.
# The execution body of the while loop executes once.
# Then random.random() returns 0.45,
# which causes while to stop looping and the algorithm to terminate.
random_mock.side_effect = [0.45]
returned_value = random.gammavariate(1.0, 3.14)
self.assertAlmostEqual(returned_value, 1.877208182372648)
@unittest.mock.patch('random.Random.random')
def test_gammavariate_alpha_equal_one_equals_expovariate(self, random_mock):
# #2.b: alpha == 1.
# It must be equivalent of calling expovariate(1.0 / beta).
beta = 3.14
random_mock.side_effect = [1e-8, 1e-8]
gammavariate_returned_value = random.gammavariate(1.0, beta)
expovariate_returned_value = random.expovariate(1.0 / beta)
self.assertAlmostEqual(gammavariate_returned_value, expovariate_returned_value)
@unittest.mock.patch('random.Random.random')
def test_gammavariate_alpha_between_zero_and_one(self, random_mock):
# #3: 0 < alpha < 1.
# This is the most complex region of code to cover,
# as there are multiple if-else statements. Let's take a look at the
# source code, and determine the values that we need accordingly:
#

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@ -0,0 +1,4 @@
random.gammavariate(1.0, beta) now computes the same result as
random.expovariate(1.0 / beta). This synchonizes the two algorithms and
eliminates some idiosyncrasies in the old implementation. It does however
produce a difference stream of random variables than it used to.