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251 lines
9.4 KiB
Python
251 lines
9.4 KiB
Python
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# Copyright (c) 2010 Python Software Foundation. All Rights Reserved.
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# Adapted from Python's Lib/test/test_strtod.py (by Mark Dickinson)
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# More test cases for deccheck.py.
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import random
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TEST_SIZE = 2
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def test_short_halfway_cases():
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# exact halfway cases with a small number of significant digits
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for k in 0, 5, 10, 15, 20:
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# upper = smallest integer >= 2**54/5**k
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upper = -(-2**54//5**k)
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# lower = smallest odd number >= 2**53/5**k
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lower = -(-2**53//5**k)
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if lower % 2 == 0:
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lower += 1
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for i in range(10 * TEST_SIZE):
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# Select a random odd n in [2**53/5**k,
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# 2**54/5**k). Then n * 10**k gives a halfway case
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# with small number of significant digits.
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n, e = random.randrange(lower, upper, 2), k
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# Remove any additional powers of 5.
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while n % 5 == 0:
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n, e = n // 5, e + 1
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assert n % 10 in (1, 3, 7, 9)
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# Try numbers of the form n * 2**p2 * 10**e, p2 >= 0,
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# until n * 2**p2 has more than 20 significant digits.
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digits, exponent = n, e
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while digits < 10**20:
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s = '{}e{}'.format(digits, exponent)
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yield s
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# Same again, but with extra trailing zeros.
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s = '{}e{}'.format(digits * 10**40, exponent - 40)
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yield s
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digits *= 2
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# Try numbers of the form n * 5**p2 * 10**(e - p5), p5
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# >= 0, with n * 5**p5 < 10**20.
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digits, exponent = n, e
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while digits < 10**20:
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s = '{}e{}'.format(digits, exponent)
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yield s
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# Same again, but with extra trailing zeros.
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s = '{}e{}'.format(digits * 10**40, exponent - 40)
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yield s
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digits *= 5
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exponent -= 1
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def test_halfway_cases():
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# test halfway cases for the round-half-to-even rule
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for i in range(1000):
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for j in range(TEST_SIZE):
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# bit pattern for a random finite positive (or +0.0) float
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bits = random.randrange(2047*2**52)
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# convert bit pattern to a number of the form m * 2**e
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e, m = divmod(bits, 2**52)
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if e:
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m, e = m + 2**52, e - 1
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e -= 1074
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# add 0.5 ulps
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m, e = 2*m + 1, e - 1
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# convert to a decimal string
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if e >= 0:
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digits = m << e
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exponent = 0
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else:
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# m * 2**e = (m * 5**-e) * 10**e
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digits = m * 5**-e
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exponent = e
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s = '{}e{}'.format(digits, exponent)
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yield s
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def test_boundaries():
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# boundaries expressed as triples (n, e, u), where
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# n*10**e is an approximation to the boundary value and
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# u*10**e is 1ulp
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boundaries = [
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(10000000000000000000, -19, 1110), # a power of 2 boundary (1.0)
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(17976931348623159077, 289, 1995), # overflow boundary (2.**1024)
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(22250738585072013831, -327, 4941), # normal/subnormal (2.**-1022)
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(0, -327, 4941), # zero
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]
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for n, e, u in boundaries:
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for j in range(1000):
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for i in range(TEST_SIZE):
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digits = n + random.randrange(-3*u, 3*u)
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exponent = e
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s = '{}e{}'.format(digits, exponent)
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yield s
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n *= 10
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u *= 10
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e -= 1
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def test_underflow_boundary():
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# test values close to 2**-1075, the underflow boundary; similar
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# to boundary_tests, except that the random error doesn't scale
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# with n
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for exponent in range(-400, -320):
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base = 10**-exponent // 2**1075
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for j in range(TEST_SIZE):
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digits = base + random.randrange(-1000, 1000)
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s = '{}e{}'.format(digits, exponent)
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yield s
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def test_bigcomp():
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for ndigs in 5, 10, 14, 15, 16, 17, 18, 19, 20, 40, 41, 50:
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dig10 = 10**ndigs
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for i in range(100 * TEST_SIZE):
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digits = random.randrange(dig10)
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exponent = random.randrange(-400, 400)
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s = '{}e{}'.format(digits, exponent)
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yield s
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def test_parsing():
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# make '0' more likely to be chosen than other digits
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digits = '000000123456789'
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signs = ('+', '-', '')
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# put together random short valid strings
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# \d*[.\d*]?e
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for i in range(1000):
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for j in range(TEST_SIZE):
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s = random.choice(signs)
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intpart_len = random.randrange(5)
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s += ''.join(random.choice(digits) for _ in range(intpart_len))
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if random.choice([True, False]):
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s += '.'
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fracpart_len = random.randrange(5)
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s += ''.join(random.choice(digits)
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for _ in range(fracpart_len))
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else:
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fracpart_len = 0
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if random.choice([True, False]):
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s += random.choice(['e', 'E'])
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s += random.choice(signs)
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exponent_len = random.randrange(1, 4)
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s += ''.join(random.choice(digits)
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for _ in range(exponent_len))
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if intpart_len + fracpart_len:
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yield s
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test_particular = [
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# squares
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'1.00000000100000000025',
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'1.0000000000000000000000000100000000000000000000000' #...
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'00025',
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'1.0000000000000000000000000000000000000000000010000' #...
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'0000000000000000000000000000000000000000025',
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'1.0000000000000000000000000000000000000000000000000' #...
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'000001000000000000000000000000000000000000000000000' #...
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'000000000025',
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'0.99999999900000000025',
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'0.9999999999999999999999999999999999999999999999999' #...
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'999000000000000000000000000000000000000000000000000' #...
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'000025',
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'0.9999999999999999999999999999999999999999999999999' #...
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'999999999999999999999999999999999999999999999999999' #...
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'999999999999999999999999999999999999999990000000000' #...
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'000000000000000000000000000000000000000000000000000' #...
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'000000000000000000000000000000000000000000000000000' #...
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'0000000000000000000000000000025',
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'1.0000000000000000000000000000000000000000000000000' #...
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'000000000000000000000000000000000000000000000000000' #...
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'100000000000000000000000000000000000000000000000000' #...
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'000000000000000000000000000000000000000000000000001',
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'1.0000000000000000000000000000000000000000000000000' #...
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'000000000000000000000000000000000000000000000000000' #...
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'500000000000000000000000000000000000000000000000000' #...
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'000000000000000000000000000000000000000000000000005',
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'1.0000000000000000000000000000000000000000000000000' #...
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'000000000100000000000000000000000000000000000000000' #...
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'000000000000000000250000000000000002000000000000000' #...
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'000000000000000000000000000000000000000000010000000' #...
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'000000000000000000000000000000000000000000000000000' #...
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'0000000000000000001',
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'1.0000000000000000000000000000000000000000000000000' #...
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'000000000100000000000000000000000000000000000000000' #...
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'000000000000000000249999999999999999999999999999999' #...
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'999999999999979999999999999999999999999999999999999' #...
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'999999999999999999999900000000000000000000000000000' #...
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'000000000000000000000000000000000000000000000000000' #...
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'00000000000000000000000001',
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'0.9999999999999999999999999999999999999999999999999' #...
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'999999999900000000000000000000000000000000000000000' #...
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'000000000000000000249999999999999998000000000000000' #...
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'000000000000000000000000000000000000000000010000000' #...
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'000000000000000000000000000000000000000000000000000' #...
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'0000000000000000001',
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'0.9999999999999999999999999999999999999999999999999' #...
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'999999999900000000000000000000000000000000000000000' #...
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'000000000000000000250000001999999999999999999999999' #...
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'999999999999999999999999999999999990000000000000000' #...
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'000000000000000000000000000000000000000000000000000' #...
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'1',
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# tough cases for ln etc.
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'1.000000000000000000000000000000000000000000000000' #...
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'00000000000000000000000000000000000000000000000000' #...
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'00100000000000000000000000000000000000000000000000' #...
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'00000000000000000000000000000000000000000000000000' #...
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'0001',
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'0.999999999999999999999999999999999999999999999999' #...
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'99999999999999999999999999999999999999999999999999' #...
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'99899999999999999999999999999999999999999999999999' #...
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'99999999999999999999999999999999999999999999999999' #...
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'99999999999999999999999999999999999999999999999999' #...
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'9999'
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]
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TESTCASES = [
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[x for x in test_short_halfway_cases()],
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[x for x in test_halfway_cases()],
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[x for x in test_boundaries()],
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[x for x in test_underflow_boundary()],
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[x for x in test_bigcomp()],
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[x for x in test_parsing()],
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test_particular
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]
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def un_randfloat():
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for i in range(1000):
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l = random.choice(TESTCASES[:6])
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yield random.choice(l)
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for v in test_particular:
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yield v
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def bin_randfloat():
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for i in range(1000):
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l1 = random.choice(TESTCASES)
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l2 = random.choice(TESTCASES)
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yield random.choice(l1), random.choice(l2)
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def tern_randfloat():
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for i in range(1000):
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l1 = random.choice(TESTCASES)
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l2 = random.choice(TESTCASES)
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l3 = random.choice(TESTCASES)
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yield random.choice(l1), random.choice(l2), random.choice(l3)
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