mirror of https://github.com/python/cpython
Rename testSum to testFsum and move it to proper place in test_math.py
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Mark Dickinson
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cda5ce24ed
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@ -364,6 +364,102 @@ class MathTests(unittest.TestCase):
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self.assertEquals(math.frexp(NINF)[0], NINF)
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self.assert_(math.isnan(math.frexp(NAN)[0]))
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def testFsum(self):
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# math.fsum relies on exact rounding for correct operation.
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# There's a known problem with IA32 floating-point that causes
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# inexact rounding in some situations, and will cause the
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# math.fsum tests below to fail; see issue #2937. On non IEEE
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# 754 platforms, and on IEEE 754 platforms that exhibit the
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# problem described in issue #2937, we simply skip the whole
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# test.
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if not float.__getformat__("double").startswith("IEEE"):
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return
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# on IEEE 754 compliant machines, both of the expressions
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# below should round to 10000000000000002.0.
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if 1e16+2.0 != 1e16+2.9999:
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return
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# Python version of math.fsum, for comparison. Uses a
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# different algorithm based on frexp, ldexp and integer
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# arithmetic.
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from sys import float_info
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mant_dig = float_info.mant_dig
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etiny = float_info.min_exp - mant_dig
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def msum(iterable):
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"""Full precision summation. Compute sum(iterable) without any
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intermediate accumulation of error. Based on the 'lsum' function
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at http://code.activestate.com/recipes/393090/
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"""
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tmant, texp = 0, 0
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for x in iterable:
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mant, exp = math.frexp(x)
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mant, exp = int(math.ldexp(mant, mant_dig)), exp - mant_dig
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if texp > exp:
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tmant <<= texp-exp
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texp = exp
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else:
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mant <<= exp-texp
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tmant += mant
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# Round tmant * 2**texp to a float. The original recipe
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# used float(str(tmant)) * 2.0**texp for this, but that's
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# a little unsafe because str -> float conversion can't be
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# relied upon to do correct rounding on all platforms.
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tail = max(len(bin(abs(tmant)))-2 - mant_dig, etiny - texp)
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if tail > 0:
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h = 1 << (tail-1)
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tmant = tmant // (2*h) + bool(tmant & h and tmant & 3*h-1)
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texp += tail
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return math.ldexp(tmant, texp)
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test_values = [
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([], 0.0),
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([0.0], 0.0),
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([1e100, 1.0, -1e100, 1e-100, 1e50, -1.0, -1e50], 1e-100),
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([2.0**53, -0.5, -2.0**-54], 2.0**53-1.0),
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([2.0**53, 1.0, 2.0**-100], 2.0**53+2.0),
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([2.0**53+10.0, 1.0, 2.0**-100], 2.0**53+12.0),
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([2.0**53-4.0, 0.5, 2.0**-54], 2.0**53-3.0),
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([1./n for n in range(1, 1001)],
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float.fromhex('0x1.df11f45f4e61ap+2')),
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([(-1.)**n/n for n in range(1, 1001)],
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float.fromhex('-0x1.62a2af1bd3624p-1')),
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([1.7**(i+1)-1.7**i for i in range(1000)] + [-1.7**1000], -1.0),
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([1e16, 1., 1e-16], 10000000000000002.0),
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([1e16-2., 1.-2.**-53, -(1e16-2.), -(1.-2.**-53)], 0.0),
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# exercise code for resizing partials array
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([2.**n - 2.**(n+50) + 2.**(n+52) for n in range(-1074, 972, 2)] +
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[-2.**1022],
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float.fromhex('0x1.5555555555555p+970')),
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]
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for i, (vals, expected) in enumerate(test_values):
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try:
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actual = math.fsum(vals)
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except OverflowError:
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self.fail("test %d failed: got OverflowError, expected %r "
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"for math.fsum(%.100r)" % (i, expected, vals))
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except ValueError:
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self.fail("test %d failed: got ValueError, expected %r "
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"for math.fsum(%.100r)" % (i, expected, vals))
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self.assertEqual(actual, expected)
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from random import random, gauss, shuffle
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for j in xrange(1000):
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vals = [7, 1e100, -7, -1e100, -9e-20, 8e-20] * 10
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s = 0
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for i in xrange(200):
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v = gauss(0, random()) ** 7 - s
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s += v
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vals.append(v)
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shuffle(vals)
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s = msum(vals)
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self.assertEqual(msum(vals), math.fsum(vals))
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def testHypot(self):
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self.assertRaises(TypeError, math.hypot)
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self.ftest('hypot(0,0)', math.hypot(0,0), 0)
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@ -645,103 +741,6 @@ class MathTests(unittest.TestCase):
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self.assertRaises(ValueError, math.sqrt, NINF)
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self.assert_(math.isnan(math.sqrt(NAN)))
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def testSum(self):
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# math.fsum relies on exact rounding for correct operation.
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# There's a known problem with IA32 floating-point that causes
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# inexact rounding in some situations, and will cause the
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# math.fsum tests below to fail; see issue #2937. On non IEEE
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# 754 platforms, and on IEEE 754 platforms that exhibit the
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# problem described in issue #2937, we simply skip the whole
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# test.
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if not float.__getformat__("double").startswith("IEEE"):
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return
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# on IEEE 754 compliant machines, both of the expressions
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# below should round to 10000000000000002.0.
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if 1e16+2.0 != 1e16+2.9999:
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return
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# Python version of math.fsum, for comparison. Uses a
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# different algorithm based on frexp, ldexp and integer
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# arithmetic.
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from sys import float_info
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mant_dig = float_info.mant_dig
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etiny = float_info.min_exp - mant_dig
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def msum(iterable):
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"""Full precision summation. Compute sum(iterable) without any
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intermediate accumulation of error. Based on the 'lsum' function
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at http://code.activestate.com/recipes/393090/
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"""
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tmant, texp = 0, 0
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for x in iterable:
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mant, exp = math.frexp(x)
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mant, exp = int(math.ldexp(mant, mant_dig)), exp - mant_dig
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if texp > exp:
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tmant <<= texp-exp
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texp = exp
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else:
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mant <<= exp-texp
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tmant += mant
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# Round tmant * 2**texp to a float. The original recipe
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# used float(str(tmant)) * 2.0**texp for this, but that's
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# a little unsafe because str -> float conversion can't be
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# relied upon to do correct rounding on all platforms.
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tail = max(len(bin(abs(tmant)))-2 - mant_dig, etiny - texp)
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if tail > 0:
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h = 1 << (tail-1)
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tmant = tmant // (2*h) + bool(tmant & h and tmant & 3*h-1)
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texp += tail
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return math.ldexp(tmant, texp)
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test_values = [
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([], 0.0),
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([0.0], 0.0),
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([1e100, 1.0, -1e100, 1e-100, 1e50, -1.0, -1e50], 1e-100),
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([2.0**53, -0.5, -2.0**-54], 2.0**53-1.0),
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([2.0**53, 1.0, 2.0**-100], 2.0**53+2.0),
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([2.0**53+10.0, 1.0, 2.0**-100], 2.0**53+12.0),
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([2.0**53-4.0, 0.5, 2.0**-54], 2.0**53-3.0),
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([1./n for n in range(1, 1001)],
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float.fromhex('0x1.df11f45f4e61ap+2')),
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([(-1.)**n/n for n in range(1, 1001)],
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float.fromhex('-0x1.62a2af1bd3624p-1')),
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([1.7**(i+1)-1.7**i for i in range(1000)] + [-1.7**1000], -1.0),
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([1e16, 1., 1e-16], 10000000000000002.0),
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([1e16-2., 1.-2.**-53, -(1e16-2.), -(1.-2.**-53)], 0.0),
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# exercise code for resizing partials array
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([2.**n - 2.**(n+50) + 2.**(n+52) for n in range(-1074, 972, 2)] +
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[-2.**1022],
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float.fromhex('0x1.5555555555555p+970')),
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]
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for i, (vals, expected) in enumerate(test_values):
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try:
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actual = math.fsum(vals)
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except OverflowError:
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self.fail("test %d failed: got OverflowError, expected %r "
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"for math.fsum(%.100r)" % (i, expected, vals))
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except ValueError:
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self.fail("test %d failed: got ValueError, expected %r "
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"for math.fsum(%.100r)" % (i, expected, vals))
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self.assertEqual(actual, expected)
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from random import random, gauss, shuffle
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for j in xrange(1000):
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vals = [7, 1e100, -7, -1e100, -9e-20, 8e-20] * 10
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s = 0
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for i in xrange(200):
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v = gauss(0, random()) ** 7 - s
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s += v
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vals.append(v)
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shuffle(vals)
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s = msum(vals)
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self.assertEqual(msum(vals), math.fsum(vals))
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def testTan(self):
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self.assertRaises(TypeError, math.tan)
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self.ftest('tan(0)', math.tan(0), 0)
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