def check(t, alpha, eps, delta, beta, samples_size=0, use_exponential=True):
"""
run flat_concave on random data using the given parameters
:param t:
:param alpha:
:param eps:
:param delta:
:param beta:
:param samples_size:
:param use_exponential:
:return:
"""
range_end = 2**t
if samples_size == 0:
samples_size = int(
src.bounds.step6_n2_bound(range_end, eps, alpha, beta))
data_center = np.random.uniform(range_end / 3, range_end / 3 * 2)
# data = src.examples.get_random_data(samples_size, distribution_type='splitted', pivot=data_center)
# data = src.examples.get_random_data(samples_size, 'bimodal')
data = src.examples.get_random_data(samples_size, pivot=data_center)
data = sorted(filter(lambda x: 0 <= x <= range_end, data))
maximum_quality = min_max_maximum_quality(data, 0, range_end)
quality_result_lower_bound = maximum_quality * (1 - alpha)
try:
result = src.flat_concave.evaluate(data, range_end, quality_minmax,
maximum_quality, alpha, eps, delta,
min_max_intervals_bounding,
min_max_maximum_quality,
use_exponential)
result_quality = quality_minmax(data, result)
except ValueError:
# result = -1
result_quality = -1
return result_quality != -1, result_quality >= quality_result_lower_bound, result_quality / float(
maximum_quality)
def check(t, alpha, eps, delta, beta, samples_size=0, use_exponential=True):
"""
run flat_concave on random data using the given parameters
:param t:
:param alpha:
:param eps:
:param delta:
:param beta:
:param samples_size:
:param use_exponential:
:return:
"""
range_end = 2**t
if samples_size == 0:
samples_size = int(src.bounds.step6_n2_bound(range_end, eps, alpha, beta))
data_center = np.random.uniform(range_end/3, range_end/3*2)
# data = src.examples.get_random_data(samples_size, distribution_type='splitted', pivot=data_center)
# data = src.examples.get_random_data(samples_size, 'bimodal')
data = src.examples.get_random_data(samples_size, pivot=data_center)
data = sorted(filter(lambda x: 0 <= x <= range_end, data))
maximum_quality = min_max_maximum_quality(data, 0, range_end)
quality_result_lower_bound = maximum_quality * (1-alpha)
try:
result = src.flat_concave.evaluate(data, range_end, quality_minmax, maximum_quality, alpha, eps, delta,
min_max_intervals_bounding, min_max_maximum_quality,
use_exponential)
result_quality = quality_minmax(data, result)
except ValueError:
# result = -1
result_quality = -1
return result_quality != -1, result_quality >= quality_result_lower_bound, result_quality/float(maximum_quality)
def compare_maximum_in_interval_versions():
range_end = 2**40
samples_size = uniform(1000,2000)
print "range size: %d" % range_end
print "sample size: %d" % samples_size
data_center = np.random.uniform(range_end/3, range_end/3*2)
data = xp.get_random_data(samples_size, pivot=data_center)
data = sorted(filter(lambda x: 0 <= x <= range_end, data))
maximum_quality1 = ql.min_max_maximum_quality(data, 0, range_end)
maximum_quality2 = ql.__old_min_max_maximum_quality__(data, (0, range_end))
print maximum_quality1 == maximum_quality2
def compare_maximum_in_interval_versions():
range_end = 2**40
samples_size = uniform(1000, 2000)
print "range size: %d" % range_end
print "sample size: %d" % samples_size
data_center = np.random.uniform(range_end / 3, range_end / 3 * 2)
data = xp.get_random_data(samples_size, pivot=data_center)
data = sorted(filter(lambda x: 0 <= x <= range_end, data))
maximum_quality1 = ql.min_max_maximum_quality(data, 0, range_end)
maximum_quality2 = ql.__old_min_max_maximum_quality__(data, (0, range_end))
print maximum_quality1 == maximum_quality2
