kopia lustrzana https://github.com/micropython/micropython
75 wiersze
1.6 KiB
Python
75 wiersze
1.6 KiB
Python
# Source: https://github.com/python/pyperformance
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# License: MIT
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# Artificial, floating point-heavy benchmark originally used by Factor.
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from math import sin, cos, sqrt
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class Point(object):
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__slots__ = ("x", "y", "z")
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def __init__(self, i):
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self.x = x = sin(i)
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self.y = cos(i) * 3
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self.z = (x * x) / 2
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def __repr__(self):
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return "<Point: x=%s, y=%s, z=%s>" % (self.x, self.y, self.z)
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def normalize(self):
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x = self.x
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y = self.y
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z = self.z
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norm = sqrt(x * x + y * y + z * z)
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self.x /= norm
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self.y /= norm
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self.z /= norm
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def maximize(self, other):
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self.x = self.x if self.x > other.x else other.x
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self.y = self.y if self.y > other.y else other.y
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self.z = self.z if self.z > other.z else other.z
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return self
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def maximize(points):
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next = points[0]
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for p in points[1:]:
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next = next.maximize(p)
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return next
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def benchmark(n):
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points = [None] * n
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for i in range(n):
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points[i] = Point(i)
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for p in points:
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p.normalize()
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return maximize(points)
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###########################################################################
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# Benchmark interface
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bm_params = {
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(50, 25): (1, 150),
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(100, 100): (1, 250),
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(1000, 1000): (10, 1500),
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(5000, 1000): (20, 3000),
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}
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def bm_setup(params):
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state = None
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def run():
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nonlocal state
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for _ in range(params[0]):
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state = benchmark(params[1])
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def result():
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return params[0] * params[1], "Point(%.4f, %.4f, %.4f)" % (state.x, state.y, state.z)
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return run, result
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