Method test_pdf

Lib/test/test_statistics.py:2964–3001

(self)

        self.assertNotEqual(data1, data2)

    def test_pdf(self):
        NormalDist = self.module.NormalDist
        X = NormalDist(100, 15)
        # Verify peak around center
        self.assertLess(X.pdf(99), X.pdf(100))
        self.assertLess(X.pdf(101), X.pdf(100))
        # Test symmetry
        for i in range(50):
            self.assertAlmostEqual(X.pdf(100 - i), X.pdf(100 + i))
        # Test vs CDF
        dx = 2.0 ** -10
        for x in range(90, 111):
            est_pdf = (X.cdf(x + dx) - X.cdf(x)) / dx
            self.assertAlmostEqual(X.pdf(x), est_pdf, places=4)
        # Test vs table of known values -- CRC 26th Edition
        Z = NormalDist()
        for x, px in enumerate([
            0.3989, 0.3989, 0.3989, 0.3988, 0.3986,
            0.3984, 0.3982, 0.3980, 0.3977, 0.3973,
            0.3970, 0.3965, 0.3961, 0.3956, 0.3951,
            0.3945, 0.3939, 0.3932, 0.3925, 0.3918,
            0.3910, 0.3902, 0.3894, 0.3885, 0.3876,
            0.3867, 0.3857, 0.3847, 0.3836, 0.3825,
            0.3814, 0.3802, 0.3790, 0.3778, 0.3765,
            0.3752, 0.3739, 0.3725, 0.3712, 0.3697,
            0.3683, 0.3668, 0.3653, 0.3637, 0.3621,
            0.3605, 0.3589, 0.3572, 0.3555, 0.3538,
        ]):
            self.assertAlmostEqual(Z.pdf(x / 100.0), px, places=4)
            self.assertAlmostEqual(Z.pdf(-x / 100.0), px, places=4)
        # Error case: variance is zero
        Y = NormalDist(100, 0)
        with self.assertRaises(self.module.StatisticsError):
            Y.pdf(90)
        # Special values
        self.assertEqual(X.pdf(float('-Inf')), 0.0)
        self.assertEqual(X.pdf(float('Inf')), 0.0)
        self.assertTrue(math.isnan(X.pdf(float('NaN'))))

    def test_cdf(self):
        NormalDist = self.module.NormalDist