Method test_random

Lib/test/test_math.py:2783–2830

(self)

        self.assertEqual(math.fma(a - 1.0, a + 1.0, 1.0), a*a)

    def test_random(self):
        # A collection of randomly generated inputs for which the naive FMA
        # (with two rounds) gives a different result from a singly-rounded FMA.

        # tuples (a, b, c, expected)
        test_values = [
            ('0x1.694adde428b44p-1', '0x1.371b0d64caed7p-1',
             '0x1.f347e7b8deab8p-4', '0x1.19f10da56c8adp-1'),
            ('0x1.605401ccc6ad6p-2', '0x1.ce3a40bf56640p-2',
             '0x1.96e3bf7bf2e20p-2', '0x1.1af6d8aa83101p-1'),
            ('0x1.e5abd653a67d4p-2', '0x1.a2e400209b3e6p-1',
             '0x1.a90051422ce13p-1', '0x1.37d68cc8c0fbbp+0'),
            ('0x1.f94e8efd54700p-2', '0x1.123065c812cebp-1',
             '0x1.458f86fb6ccd0p-1', '0x1.ccdcee26a3ff3p-1'),
            ('0x1.bd926f1eedc96p-1', '0x1.eee9ca68c5740p-1',
             '0x1.960c703eb3298p-2', '0x1.3cdcfb4fdb007p+0'),
            ('0x1.27348350fbccdp-1', '0x1.3b073914a53f1p-1',
             '0x1.e300da5c2b4cbp-1', '0x1.4c51e9a3c4e29p+0'),
            ('0x1.2774f00b3497bp-1', '0x1.7038ec336bff0p-2',
             '0x1.2f6f2ccc3576bp-1', '0x1.99ad9f9c2688bp-1'),
            ('0x1.51d5a99300e5cp-1', '0x1.5cd74abd445a1p-1',
             '0x1.8880ab0bbe530p-1', '0x1.3756f96b91129p+0'),
            ('0x1.73cb965b821b8p-2', '0x1.218fd3d8d5371p-1',
             '0x1.d1ea966a1f758p-2', '0x1.5217b8fd90119p-1'),
            ('0x1.4aa98e890b046p-1', '0x1.954d85dff1041p-1',
             '0x1.122b59317ebdfp-1', '0x1.0bf644b340cc5p+0'),
            ('0x1.e28f29e44750fp-1', '0x1.4bcc4fdcd18fep-1',
             '0x1.fd47f81298259p-1', '0x1.9b000afbc9995p+0'),
            ('0x1.d2e850717fe78p-3', '0x1.1dd7531c303afp-1',
             '0x1.e0869746a2fc2p-2', '0x1.316df6eb26439p-1'),
            ('0x1.cf89c75ee6fbap-2', '0x1.b23decdc66825p-1',
             '0x1.3d1fe76ac6168p-1', '0x1.00d8ea4c12abbp+0'),
            ('0x1.3265ae6f05572p-2', '0x1.16d7ec285f7a2p-1',
             '0x1.0b8405b3827fbp-1', '0x1.5ef33c118a001p-1'),
            ('0x1.c4d1bf55ec1a5p-1', '0x1.bc59618459e12p-2',
             '0x1.ce5b73dc1773dp-1', '0x1.496cf6164f99bp+0'),
            ('0x1.d350026ac3946p-1', '0x1.9a234e149a68cp-2',
             '0x1.f5467b1911fd6p-2', '0x1.b5cee3225caa5p-1'),
        ]
        for a_hex, b_hex, c_hex, expected_hex in test_values:
            with self.subTest(a_hex=a_hex, b_hex=b_hex, c_hex=c_hex,
                              expected_hex=expected_hex):
                a = float.fromhex(a_hex)
                b = float.fromhex(b_hex)
                c = float.fromhex(c_hex)
                expected = float.fromhex(expected_hex)
                self.assertEqual(math.fma(a, b, c), expected)
                self.assertEqual(math.fma(b, a, c), expected)

    # Custom assertions.
    def assertIsNaN(self, value):