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stats.py
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"""
Measures the computation time and approximation error of different functions and parameters.
Requires NumPy.
"""
import inspect
import sys
import time
import random
from pyds import MassFunction
import numpy
iterations = 100
def stats(results):
array = numpy.empty((iterations, 1))
for i, t in enumerate(results):
array[i] = t
return array.mean(), array.std()
def measure_time(f, *args, **kwargs):
def f_measured(i):
t = time.clock()
f(*args, **kwargs)
return time.clock() - t
return stats(map(f_measured, range(iterations)))
def measure_error(actual, f, *args, **kwargs):
return stats(map(lambda _: actual.norm(f(*args, **kwargs)), range(iterations)))
def random_likelihoods(singleton_count):
return [(i, random.random()) for i in range(singleton_count)]
def time_bel_h():
return measure_time(MassFunction.gbt(random_likelihoods(12)).bel, hypothesis=frozenset(range(10)))
def time_bel():
return measure_time(MassFunction({(s,):1.0 for s in range(12)}).normalize().bel)
def time_from_bel():
bel = MassFunction({(s,):1.0 for s in range(10)}).normalize().bel()
return measure_time(MassFunction.from_bel, bel)
def time_pl_h():
return measure_time(MassFunction.gbt(random_likelihoods(12)).pl, frozenset(range(10)))
def time_pl():
return measure_time(MassFunction({(s,):1.0 for s in range(12)}).normalize().pl)
def time_from_pl():
pl = MassFunction({(s,):1.0 for s in range(10)}).normalize().pl()
return measure_time(MassFunction.from_pl, pl)
def time_q_h():
return measure_time(MassFunction.gbt(random_likelihoods(12)).q, frozenset(range(10)))
def time_q():
return measure_time(MassFunction({(s,):1.0 for s in range(12)}).normalize().q)
def time_from_q():
q = MassFunction({(s,):1.0 for s in range(10)}).normalize().q()
return measure_time(MassFunction.from_q, q)
def time_gbt():
return measure_time(MassFunction.gbt, random_likelihoods(12))
def time_gbt_100():
return measure_time(MassFunction.gbt, random_likelihoods(12), sample_count=100)
def time_gbt_1000():
return measure_time(MassFunction.gbt, random_likelihoods(12), sample_count=1000)
def time_combine_conjunctive():
m1 = MassFunction.gbt(random_likelihoods(6))
m2 = MassFunction.gbt(random_likelihoods(6))
return measure_time(m1.combine_conjunctive, m2)
def time_combine_conjunctive_1000():
m1 = MassFunction.gbt(random_likelihoods(6))
m2 = MassFunction.gbt(random_likelihoods(6))
return measure_time(m1.combine_conjunctive, m2, sample_count=1000, importance_sampling=False)
def time_combine_conjunctive_1000_imp():
m1 = MassFunction.gbt(random_likelihoods(6))
m2 = MassFunction.gbt(random_likelihoods(6))
return measure_time(m1.combine_conjunctive, m2, sample_count=1000, importance_sampling=True)
def time_combine_disjunctive():
m1 = MassFunction.gbt(random_likelihoods(6))
m2 = MassFunction.gbt(random_likelihoods(6))
return measure_time(m1.combine_disjunctive, m2)
def time_combine_disjunctive_1000():
m1 = MassFunction.gbt(random_likelihoods(6))
m2 = MassFunction.gbt(random_likelihoods(6))
return measure_time(m1.combine_disjunctive, m2, sample_count=1000)
def time_combine_gbt():
return measure_time(MassFunction.gbt(random_likelihoods(6)).combine_gbt, random_likelihoods(6))
def time_combine_gbt_1000():
return measure_time(MassFunction.gbt(random_likelihoods(6)).combine_gbt, random_likelihoods(6), sample_count=1000)
def time_combine_gbt_1000_imp():
return measure_time(MassFunction.gbt(random_likelihoods(6)).combine_gbt, random_likelihoods(6), sample_count=1000, importance_sampling=True)
def time_pignistic():
return measure_time(MassFunction.gbt(random_likelihoods(12)).pignistic)
def time_markov():
m = MassFunction.gbt(random_likelihoods(4))
return measure_time(MassFunction.gbt(random_likelihoods(4)).markov, lambda s: m)
def time_markov_1000():
samples = MassFunction.gbt(random_likelihoods(4)).sample(1000)
return measure_time(MassFunction.gbt(random_likelihoods(4)).markov, lambda s, n: samples[:n], sample_count=1000)
def error_gbt_100():
lh = random_likelihoods(10)
return measure_error(MassFunction.gbt(lh), MassFunction.gbt, lh, sample_count=100)
def error_gbt_1000():
lh = random_likelihoods(10)
return measure_error(MassFunction.gbt(lh), MassFunction.gbt, lh, sample_count=1000)
def error_combine_conjunctive_100_dir():
m1 = MassFunction.gbt(random_likelihoods(6))
m2 = MassFunction.gbt(random_likelihoods(6))
return measure_error(m1 & m2, m1.combine_conjunctive, m2, sample_count=100, importance_sampling=False)
def error_combine_conjunctive_100_imp():
m1 = MassFunction.gbt(random_likelihoods(6))
m2 = MassFunction.gbt(random_likelihoods(6))
return measure_error(m1 & m2, m1.combine_conjunctive, m2, sample_count=100, importance_sampling=True)
def error_combine_conjunctive_1000_dir():
m1 = MassFunction.gbt(random_likelihoods(6))
m2 = MassFunction.gbt(random_likelihoods(6))
return measure_error(m1 & m2, m1.combine_conjunctive, m2, sample_count=1000, importance_sampling=False)
def error_combine_conjunctive_1000_imp():
m1 = MassFunction.gbt(random_likelihoods(6))
m2 = MassFunction.gbt(random_likelihoods(6))
return measure_error(m1 & m2, m1.combine_conjunctive, m2, sample_count=1000, importance_sampling=True)
def error_combine_gbt_1000_dir():
m = MassFunction.gbt(random_likelihoods(6))
lh = random_likelihoods(10)
return measure_error(m.combine_gbt(lh), m.combine_gbt, lh, sample_count=1000, importance_sampling=False)
def error_combine_gbt_1000_imp():
m = MassFunction.gbt(random_likelihoods(6))
lh = random_likelihoods(10)
return measure_error(m.combine_gbt(lh), m.combine_gbt, lh, sample_count=1000, importance_sampling=True)
def run_measures(prefix):
mod = sys.modules[__name__]
filt = lambda x: inspect.isfunction(x) and inspect.getmodule(x) == mod and x.__name__.startswith(prefix + '_')
print('%-32s%-6s (%4s)' % ('function', 'mean', 'stddev'))
print('-' * 50)
for f in sorted(filter(filt, globals().copy().values()), key=str):
random.seed(0)
print('%-32s%.4f (+-%.4f)' % ((f.__name__[len(prefix) + 1:],) + f()))
if __name__ == '__main__':
print('computation time (seconds):')
run_measures('time')
print('\n')
print('approximation error (Euclidean distance):')
run_measures('error')