62 lines
1.3 KiB
Python
62 lines
1.3 KiB
Python
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''' Parameters used in test and benchmark methods '''
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from __future__ import division, print_function, absolute_import
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from random import random
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from scipy.optimize import zeros as cc
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def f1(x):
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return x*(x-1.)
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def f2(x):
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return x**2 - 1
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def f3(x):
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return x*(x-1.)*(x-2.)*(x-3.)
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def f4(x):
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if x > 1:
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return 1.0 + .1*x
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if x < 1:
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return -1.0 + .1*x
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return 0
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def f5(x):
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if x != 1:
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return 1.0/(1. - x)
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return 0
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def f6(x):
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if x > 1:
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return random()
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elif x < 1:
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return -random()
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else:
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return 0
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description = """
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f2 is a symmetric parabola, x**2 - 1
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f3 is a quartic polynomial with large hump in interval
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f4 is step function with a discontinuity at 1
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f5 is a hyperbola with vertical asymptote at 1
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f6 has random values positive to left of 1, negative to right
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of course these are not real problems. They just test how the
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'good' solvers behave in bad circumstances where bisection is
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really the best. A good solver should not be much worse than
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bisection in such circumstance, while being faster for smooth
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monotone sorts of functions.
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"""
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methods = [cc.bisect,cc.ridder,cc.brenth,cc.brentq]
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mstrings = ['cc.bisect','cc.ridder','cc.brenth','cc.brentq']
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functions = [f2,f3,f4,f5,f6]
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fstrings = ['f2','f3','f4','f5','f6']
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