def test_fuzzy_histogram_std_behaviour(self):
"""Test the standard behaviour of fuzzy histogram."""
values = scipy.random.randint(0, 10, 100)
_, b = fuzzy_histogram(values, bins=12)
self.assertEqual(len(b), 13, 'violation of requested histogram size.')
self.assertEqual(b[0], values.min(), 'invalid lower histogram border.')
self.assertEqual(b[-1], values.max(), 'invalid upper histogram border.')
h, _ = fuzzy_histogram(values, normed=True)
self.assertAlmostEqual(sum(h), 1.0, msg='histogram not normed.')
_, b = fuzzy_histogram(values, bins=12, guarantee=True)
self.assertEqual(len(b), 13, 'violation of requested histogram size with guarantee set to True.')
_, b = fuzzy_histogram(values, range=(-5, 5))
self.assertEqual(b[0], -5.0, 'violation of requested ranges lower bound.')
self.assertEqual(b[-1], 5.0, 'violation of requested ranges lower bound.')
def test_fuzzy_histogram_contribution(self):
"""Test if all values contribute with nearly one to the created histograms."""
values = scipy.random.randint(0, 100, 1000)
# test triangular
h, _ = fuzzy_histogram(values, membership='triangular', normed=False, guarantee=True)
self.assertAlmostEqual(sum(h), values.size, msg='Triangular contribution does not equal out. {} != {}.'.format(sum(h), values.size))
# test trapezoid
h, _ = fuzzy_histogram(values, membership='trapezoid', normed=False, guarantee=True)
self.assertAlmostEqual(sum(h), values.size, msg='Trapezoid contribution does not equal out. {} != {}.'.format(sum(h), values.size))
# test gaussian
h, _ = fuzzy_histogram(values, membership='gaussian', normed=False, guarantee=True)
self.assertAlmostEqual(sum(h), values.size, msg='Gaussian contribution does not equal out. {} != {}.'.format(sum(h), values.size), delta=values.size * 0.001) # gaussian maximal error eps
# test sigmoid
h, _ = fuzzy_histogram(values, membership='sigmoid', normed=False, guarantee=True)
self.assertAlmostEqual(sum(h), values.size, msg='Sigmoid contribution does not equal out. {} != {}.'.format(sum(h), values.size), delta=values.size * 0.001) # sigmoidal maximal error eps
worst_tp, worst_fp, worst_tn, worst_fn)
print '{:<30}'.format('worst_mix_against_bnd_term'),
print '{:>+15.4f}{:>+15.4f}'.format(worst_mix_accuracy - bdr_accuracy,
worst_mix_fscore - bdr_fscore)
logger.info('Successfully terminated.')
###
# Histogram creations methods
###
__HISTOGRAMS = {
'standard':
numpy.histogram,
'triangular':
lambda *args, **kwargs: fuzzy_histogram(
*args, membership='triangular', **kwargs),
'trapezoid':
lambda *args, **kwargs: fuzzy_histogram(
*args, membership='trapezoid', **kwargs),
'gaussian':
lambda *args, **kwargs: fuzzy_histogram(
*args, membership='gaussian', **kwargs),
'sigmoid':
lambda *args, **kwargs: fuzzy_histogram(
*args, membership='sigmoid', **kwargs)
}
###
# Histogram distance measures
###
# available distance measures and their mapping to the respective functions
def test_fuzzy_histogram_parameters(self):
values = scipy.random.randint(0, 10, 100)
# membership functions
fuzzy_histogram(values, membership='triangular')
fuzzy_histogram(values, membership='trapezoid')
fuzzy_histogram(values, membership='gaussian')
fuzzy_histogram(values, membership='sigmoid')
# int/float
fuzzy_histogram(values, range=(0,10)) # int in range
fuzzy_histogram(values, range=(0.,10.)) # float in range
fuzzy_histogram(values, bins=10) # int in bins
fuzzy_histogram(values, membership='sigmoid', smoothness=1) # int in smoothness
fuzzy_histogram(values, membership='sigmoid', smoothness=1.) # float in smoothness
medium_mix_fscore, medium_mix_accuracy = __calculate_rating(medium_tp, medium_fp, medium_tn, medium_fn)
print '{:<30}'.format('medium_mix_against_bnd_term'),
print '{:>+15.4f}{:>+15.4f}'.format(medium_mix_accuracy - bdr_accuracy, medium_mix_fscore - bdr_fscore)
worst_mix_fscore, worst_mix_accuracy = __calculate_rating(worst_tp, worst_fp, worst_tn, worst_fn)
print '{:<30}'.format('worst_mix_against_bnd_term'),
print '{:>+15.4f}{:>+15.4f}'.format(worst_mix_accuracy - bdr_accuracy, worst_mix_fscore - bdr_fscore)
logger.info('Successfully terminated.')
###
# Histogram creations methods
###
__HISTOGRAMS = {'standard': numpy.histogram,
'triangular': lambda *args, **kwargs: fuzzy_histogram(*args, membership='triangular', **kwargs),
'trapezoid': lambda *args, **kwargs: fuzzy_histogram(*args, membership='trapezoid', **kwargs),
'gaussian': lambda *args, **kwargs: fuzzy_histogram(*args, membership='gaussian', **kwargs),
'sigmoid': lambda *args, **kwargs: fuzzy_histogram(*args, membership='sigmoid', **kwargs)}
###
# Histogram distance measures
###
# available distance measures and their mapping to the respective functions
__DISTANCES = {'chebyshev': chebyshev,
'chebyshev_neg': chebyshev_neg,
'chi_square': chi_square,
'correlate': correlate_1,
'cosine_1': cosine_1,
'cosine_2': cosine_2,
'cosine_alt': cosine_alt,
