def test_negation_of_estimate(self) -> None:
a = StatisticalEstimate(1, 2)
actual = -a
expected = StatisticalEstimate(-1, 2)
self.assertEqual(actual, expected)
def test_division_by_scalar(self) -> None:
a = StatisticalEstimate(1, 2)
r_actual = a / 2
expected = StatisticalEstimate(1 / 2, 1)
self.failUnlessRaises(Exception, lambda: 2 / a)
self.assertEqual(r_actual, expected)
def test_subtraction_of_estimate(self) -> None:
a = StatisticalEstimate(1, 2)
b = StatisticalEstimate(1, 3)
actual = a - b
expected = StatisticalEstimate(0, sqrt(4 + 9))
self.assertEqual(actual, expected)
def test_addition_of_estimate(self) -> None:
a = StatisticalEstimate(1, 2)
b = StatisticalEstimate(1, 3)
actual = a + b
expected = StatisticalEstimate(2, sqrt(4 + 9))
self.assertEqual(actual, expected)
def test_addition_of_estimate_nan(self) -> None:
a = StatisticalEstimate(1, 2)
b = StatisticalEstimate(1, float('nan'))
actual = a + b
expected = StatisticalEstimate(2, float('nan'))
self.assertEqual(actual, expected)
def test_multiplication_by_scalar(self) -> None:
a = StatisticalEstimate(1, 2)
l_actual = 2 * a
r_actual = a * 2
expected = StatisticalEstimate(2, 4)
self.assertEqual(l_actual, expected)
self.assertEqual(r_actual, expected)
def test_addition_of_scalar(self) -> None:
a = StatisticalEstimate(1, 2)
l_actual = 2.2 + a
r_actual = a + 2.2
expected = StatisticalEstimate(3.2, 2)
self.assertEqual(l_actual, expected)
self.assertEqual(r_actual, expected)
def test_subtraction_of_scalar(self) -> None:
a = StatisticalEstimate(1, 2)
l_actual = 2.2 - a
r_actual = a - 2.2
l_expected = StatisticalEstimate(+1.2, 2)
r_expected = StatisticalEstimate(-1.2, 2)
self.assertEqual(l_actual, l_expected)
self.assertEqual(r_actual, r_expected)
def test_sum(self) -> None:
a1 = StatisticalEstimate(1, 2)
a2 = StatisticalEstimate(2, 3)
a3 = StatisticalEstimate(3, 4)
a4 = StatisticalEstimate(4, 5)
actual = a1 + a2 + a3 + a4
self.assertEqual(actual.estimate, sum([1, 2, 3, 4]))
self.assertEqual(actual.standard_error, sqrt(sum([4, 9, 16, 25])))
def test_statistics_mean_2(self) -> None:
a = StatisticalEstimate(1.1, 2.1)
actual = mean([a, a, a, a]) #type: ignore
self.assertEqual(actual.estimate, 1.1)
self.assertEqual(actual.standard_error, 2.1 / sqrt(4))
def test_direct_mean(self) -> None:
a = StatisticalEstimate(1, 2)
actual = (a + a + a + a) / 4
self.assertEqual(actual.estimate, 1)
self.assertEqual(actual.standard_error, 2 / sqrt(4))
def test_weighted_mean(self) -> None:
a1 = StatisticalEstimate(1, 2)
a2 = StatisticalEstimate(2, 3)
a3 = StatisticalEstimate(3, 4)
a4 = StatisticalEstimate(4, 5)
w1 = 1
w2 = 2
w3 = 3
w4 = 4
actual = (w1 * a1 + w2 * a2 + w3 * a3 + w4 * a4) / (w1 + w2 + w3 + w4)
expected_estimate = (1 + 4 + 9 + 16) / 10
expected_standard_error = sqrt(
(1 * 4 + 4 * 9 + 9 * 16 + 16 * 25) / 100)
self.assertEqual(actual.estimate, expected_estimate)
self.assertEqual(actual.standard_error, expected_standard_error)
def test_pandas_mean(self) -> None:
check_pandas_support("coba.tests.test_statistics.test_pandas_mean")
import pandas as pd #type: ignore #ignored so that mypy won't complain
a = StatisticalEstimate(1, 2)
df = pd.DataFrame({'a': [a, a, a, a]})
actual = df['a'].mean()
self.assertEqual(actual, 1)
def test_fraction_1(self) -> None:
e = StatisticalEstimate(1.1, 1.1)
f = Fraction(e)
self.assertIsInstance(f.numerator, Rational)
self.assertIsInstance(f.denominator, Rational)
def test_from_statistical_estimate(self):
a = StatisticalEstimate(1, 1)
b = BatchMeanEstimator(a)
self.assertEqual(b.estimate, 1)
self.assertEqual(b.standard_error, 1)