| # Copyright 2014 The Chromium Authors. All rights reserved. |
| # Use of this source code is governed by a BSD-style license that can be |
| # found in the LICENSE file. |
| |
| import math |
| import unittest |
| |
| import math_utils |
| |
| |
| class MathUtilsTest(unittest.TestCase): |
| """Tests for mathematical utility functions.""" |
| |
| def testTruncatedMean_EmptyList(self): |
| # TruncatedMean raises an error when passed an empty list. |
| self.assertRaises(TypeError, math_utils.TruncatedMean, [], 0) |
| |
| def testTruncatedMean_TruncateTooMuch(self): |
| # An exception is raised if 50% or more is truncated from both sides. |
| self.assertRaises(TypeError, math_utils.TruncatedMean, [1, 2, 3], 1.0) |
| self.assertRaises( |
| ZeroDivisionError, math_utils.TruncatedMean, [1, 2, 3], 0.5) |
| |
| def testTruncatedMean_AlwaysKeepsAtLeastTwoValues(self): |
| # If the length of the input is 1 or 2, nothing is truncated and |
| # the average is returned. |
| self.assertEqual(5.0, math_utils.TruncatedMean([5.0], 0.0)) |
| self.assertEqual(5.0, math_utils.TruncatedMean([5.0], 0.25)) |
| self.assertEqual(5.0, math_utils.TruncatedMean([5.0], 0.5)) |
| self.assertEqual(5.5, math_utils.TruncatedMean([5.0, 6.0], 0.0)) |
| self.assertEqual(5.5, math_utils.TruncatedMean([5.0, 6.0], 0.25)) |
| self.assertEqual(5.5, math_utils.TruncatedMean([5.0, 6.0], 0.5)) |
| |
| def testTruncatedMean_Interquartile_NumValuesDivisibleByFour(self): |
| self.assertEqual(5.0, math_utils.TruncatedMean([1, 4, 6, 100], 0.25)) |
| self.assertEqual( |
| 6.5, math_utils.TruncatedMean([1, 2, 5, 6, 7, 8, 40, 50], 0.25)) |
| |
| def testTruncatedMean_Weighting(self): |
| # In the list [0, 1, 4, 5, 20, 100], when 25% of the list at the start |
| # and end are discarded, the part that's left is [1, 4, 5, 20], but |
| # first and last values are weighted so that they only count for half |
| # as much. So the truncated mean is (1/2 + 4 + 5 + 20/2) / 5.0. |
| self.assertEqual(6.5, (0.5 + 4 + 5 + 10) / 3.0) |
| self.assertEqual(6.5, math_utils.TruncatedMean([0, 1, 4, 5, 20, 100], 0.25)) |
| |
| def testMean_OneValue(self): |
| self.assertEqual(3.0, math_utils.Mean([3])) |
| |
| def testMean_ShortList(self): |
| self.assertEqual(0.5, math_utils.Mean([-3, 0, 1, 4])) |
| |
| def testMean_CompareAlternateImplementation(self): |
| """Tests Mean by comparing against an alternate implementation.""" |
| def AlternateMean(values): |
| return sum(values) / float(len(values)) |
| test_value_lists = [ |
| [1], |
| [5, 6.5, 1.2, 3], |
| [-3, 0, 1, 4], |
| [-3, -1, 0.12, 0.752, 3.33, 8, 16, 32, 439], |
| ] |
| for value_list in test_value_lists: |
| self.assertEqual(AlternateMean(value_list), math_utils.Mean(value_list)) |
| |
| def testRelativeChange_NonZero(self): |
| # The change is relative to the first value, regardless of which is bigger. |
| self.assertEqual(0.5, math_utils.RelativeChange(1.0, 1.5)) |
| self.assertEqual(0.5, math_utils.RelativeChange(2.0, 1.0)) |
| |
| def testRelativeChange_FromZero(self): |
| # If the first number is zero, then the result is not a number. |
| self.assertEqual(0, math_utils.RelativeChange(0, 0)) |
| self.assertTrue(math.isnan(math_utils.RelativeChange(0, 1))) |
| self.assertTrue(math.isnan(math_utils.RelativeChange(0, -1))) |
| |
| def testRelativeChange_Negative(self): |
| # Note that the return value of RelativeChange is always positive. |
| self.assertEqual(3.0, math_utils.RelativeChange(-1, 2)) |
| self.assertEqual(3.0, math_utils.RelativeChange(1, -2)) |
| self.assertEqual(1.0, math_utils.RelativeChange(-1, -2)) |
| |
| def testVariance_EmptyList(self): |
| self.assertRaises(TypeError, math_utils.Variance, []) |
| |
| def testVariance_OneValue(self): |
| self.assertEqual(0, math_utils.Variance([0])) |
| self.assertEqual(0, math_utils.Variance([4.3])) |
| |
| def testVariance_ShortList(self): |
| # Population variance is the average of squared deviations from the mean. |
| # The deviations from the mean in this example are [3.5, 0.5, -0.5, -3.5], |
| # and the squared deviations are [12.25, 0.25, 0.25, 12.25]. |
| # With sample variance, however, 1 is subtracted from the sample size. |
| # So the sample variance is sum([12.25, 0.25, 0.25, 12.25]) / 3.0. |
| self.assertAlmostEqual(8.333333334, sum([12.25, 0.25, 0.25, 12.25]) / 3.0) |
| self.assertAlmostEqual(8.333333334, math_utils.Variance([-3, 0, 1, 4])) |
| |
| def testStandardDeviation(self): |
| # Standard deviation is the square root of variance. |
| self.assertRaises(TypeError, math_utils.StandardDeviation, []) |
| self.assertEqual(0.0, math_utils.StandardDeviation([4.3])) |
| self.assertAlmostEqual(2.88675135, math.sqrt(8.33333333333333)) |
| self.assertAlmostEqual(2.88675135, |
| math_utils.StandardDeviation([-3, 0, 1, 4])) |
| |
| def testStandardError(self): |
| # Standard error is std. dev. divided by square root of sample size. |
| self.assertEqual(0.0, math_utils.StandardError([])) |
| self.assertEqual(0.0, math_utils.StandardError([4.3])) |
| self.assertAlmostEqual(1.44337567, 2.88675135 / math.sqrt(4)) |
| self.assertAlmostEqual(1.44337567, math_utils.StandardError([-3, 0, 1, 4])) |
| |
| if __name__ == '__main__': |
| unittest.main() |