115 lines
2.6 KiB
Python
115 lines
2.6 KiB
Python
import numpy
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import random
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# Various pre-crafted datasets/variables for testing
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# !!! Must not be changed -- only appended !!!
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# while testing numpy we better not rely on numpy to produce random
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# sequences
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random.seed(1)
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# but will seed it nevertheless
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numpy.random.seed(1)
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nx, ny = 1000, 1000
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# reduced squares based on indexes_rand, primarily for testing more
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# time-consuming functions (ufunc, linalg, etc)
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nxs, nys = 100, 100
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# a set of interesting types to test
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TYPES1 = [
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'int16', 'float16',
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'int32', 'float32',
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'int64', 'float64', 'complex64',
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'longfloat', 'complex128',
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]
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if 'complex256' in numpy.typeDict:
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TYPES1.append('complex256')
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def memoize(func):
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result = []
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def wrapper():
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if not result:
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result.append(func())
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return result[0]
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return wrapper
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# values which will be used to construct our sample data matrices
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# replicate 10 times to speed up initial imports of this helper
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# and generate some redundancy
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@memoize
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def get_values():
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rnd = numpy.random.RandomState(1)
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values = numpy.tile(rnd.uniform(0, 100, size=nx*ny//10), 10)
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return values
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@memoize
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def get_squares():
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values = get_values()
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squares = {t: numpy.array(values,
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dtype=getattr(numpy, t)).reshape((nx, ny))
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for t in TYPES1}
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# adjust complex ones to have non-degenerated imagery part -- use
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# original data transposed for that
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for t, v in squares.items():
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if t.startswith('complex'):
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v += v.T*1j
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return squares
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@memoize
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def get_squares_():
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# smaller squares
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squares_ = {t: s[:nxs, :nys] for t, s in get_squares().items()}
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return squares_
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@memoize
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def get_vectors():
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# vectors
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vectors = {t: s[0] for t, s in get_squares().items()}
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return vectors
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@memoize
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def get_indexes():
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indexes = list(range(nx))
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# so we do not have all items
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indexes.pop(5)
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indexes.pop(95)
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indexes = numpy.array(indexes)
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return indexes
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@memoize
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def get_indexes_rand():
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rnd = random.Random(1)
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indexes_rand = get_indexes().tolist() # copy
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rnd.shuffle(indexes_rand) # in-place shuffle
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indexes_rand = numpy.array(indexes_rand)
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return indexes_rand
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@memoize
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def get_indexes_():
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# smaller versions
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indexes = get_indexes()
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indexes_ = indexes[indexes < nxs]
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return indexes_
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@memoize
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def get_indexes_rand_():
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indexes_rand = get_indexes_rand()
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indexes_rand_ = indexes_rand[indexes_rand < nxs]
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return indexes_rand_
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class Benchmark:
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goal_time = 0.25
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