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from collections import OrderedDict
from timeit import repeat
import numpy as np
import pandas as pd
from randomgen import MT19937, DSFMT, ThreeFry, PCG64, Xoroshiro128, \
Xorshift1024, Philox, Xoshiro256StarStar, Xoshiro512StarStar
PRNGS = [DSFMT, MT19937, Philox, PCG64, ThreeFry, Xoroshiro128, Xorshift1024,
Xoshiro256StarStar, Xoshiro512StarStar]
funcs = {'32-bit Unsigned Ints': 'random_uintegers(size=1000000,bits=32)',
'64-bit Unsigned Ints': 'random_uintegers(size=1000000,bits=32)',
'Uniforms': 'random_sample(size=1000000)',
'Complex Normals': 'complex_normal(size=1000000)',
'Normals': 'standard_normal(size=1000000)',
'Exponentials': 'standard_exponential(size=1000000)',
'Gammas': 'standard_gamma(3.0,size=1000000)',
'Binomials': 'binomial(9, .1, size=1000000)',
'Laplaces': 'laplace(size=1000000)',
'Poissons': 'poisson(3.0, size=1000000)', }
setup = """
from randomgen import {prng}
rg = {prng}().generator
"""
test = "rg.{func}"
table = OrderedDict()
for prng in PRNGS:
print(prng)
col = OrderedDict()
for key in funcs:
t = repeat(test.format(func=funcs[key]),
setup.format(prng=prng().__class__.__name__),
number=1, repeat=3)
col[key] = 1000 * min(t)
col = pd.Series(col)
table[prng().__class__.__name__] = col
npfuncs = OrderedDict()
npfuncs.update(funcs)
npfuncs['32-bit Unsigned Ints'] = 'randint(2**32,dtype="uint32",size=1000000)'
npfuncs['64-bit Unsigned Ints'] = 'tomaxint(size=1000000)'
del npfuncs['Complex Normals']
setup = """
from numpy.random import RandomState
rg = RandomState()
"""
col = {}
for key in npfuncs:
t = repeat(test.format(func=npfuncs[key]),
setup.format(prng=prng().__class__.__name__),
number=1, repeat=3)
col[key] = 1000 * min(t)
table['NumPy'] = pd.Series(col)
table = pd.DataFrame(table)
table = table.reindex(table.mean(1).sort_values().index)
order = np.log(table).mean().sort_values().index
table = table.T
table = table.reindex(order)
table = table.T
print(table.to_csv(float_format='%0.1f'))
rel = table.loc[:, ['NumPy']].values @ np.ones((1, table.shape[1])) / table
rel.pop(rel.columns[0])
rel = rel.T
rel['Overall'] = np.exp(np.log(rel).mean(1))
rel *= 100
rel = np.round(rel)
rel = rel.T
print(rel.to_csv(float_format='%0d'))
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