def __show_boundings_for_scenario(self, scenario):
print("\nUniform Distribution Percentages")
print(
Distribution.percentage_distribution(UniformDistribution,
len(scenario.dataList)))
self.__show_bounding_for_uniform_bounded_scenario(scenario)
self.__show_bounding_for_uniform_extremity_scenario(scenario)
print("\nBinomial Distribution Percentages")
print(
Distribution.percentage_distribution(BinomialDistribution,
len(scenario.dataList)))
self.__show_bounding_for_binomial_bounded_scenario(scenario)
self.__show_bounding_for_binomial_extremity_scenario(scenario)
def test_binomial_expansion_distribution_percent(self):
instance = BinomialDistribution
distribution = Distribution.percentage_distribution(instance, 1)
self.assertEqual(distribution[0], 1.0 / 2)
self.assertEqual(distribution[1], 1.0 / 2)
distribution = Distribution.percentage_distribution(instance, 2)
self.assertEqual(distribution[0], 1.0 / 4)
self.assertEqual(distribution[1], 2.0 / 4)
self.assertEqual(distribution[2], 1.0 / 4)
distribution = Distribution.percentage_distribution(instance, 3)
self.assertEqual(distribution[0], 1.0 / 8)
self.assertEqual(distribution[1], 3.0 / 8)
self.assertEqual(distribution[2], 3.0 / 8)
self.assertEqual(distribution[3], 1.0 / 8)
def __count_in(cls, scenario):
dist = Distribution.percentage_distribution(
scenario.distribution_profile, len(scenario.data_list()))
count_in = distribution.count_ins(scenario.distribution_bounding, dist,
scenario.confidence_interval)
if count_in is None:
return 0
else:
return count_in
def data_lines(cls, scenario):
data_string = ""
data_items = len(scenario.dataList)
dist = Distribution()
uniform_distribution = dist.percentage_distribution(
UniformDistribution(), data_items)
binomial_distribution = dist.percentage_distribution(
BinomialDistribution(), data_items)
data_string += SaveAnalysis.leading_line(binomial_distribution,
scenario,
uniform_distribution)
data_string += SaveAnalysis.measurement_lines(binomial_distribution,
scenario,
uniform_distribution)
data_string += SaveAnalysis.trailing_line(scenario)
return data_string
def test_binomial_expansion_bounded_count_in_values_for_given_confidence(
self):
distribution = Distribution.percentage_distribution(
BinomialDistribution, 1)
bounded_instance = Bounded
confidence_interval = .50
count_in_values = BinomialDistribution.count_ins(
bounded_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, None)
distribution = BinomialDistribution.percentage_distribution(2)
confidence_interval = .50
count_in_values = BinomialDistribution.count_ins(
bounded_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 1)
distribution = BinomialDistribution.percentage_distribution(3)
confidence_interval = .50
count_in_values = BinomialDistribution.count_ins(
bounded_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 1)
distribution = BinomialDistribution.percentage_distribution(5)
confidence_interval = .9
count_in_values = BinomialDistribution.count_ins(
bounded_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 1)
distribution = BinomialDistribution.percentage_distribution(8)
confidence_interval = .9
count_in_values = BinomialDistribution.count_ins(
bounded_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 2)
distribution = BinomialDistribution.percentage_distribution(11)
confidence_interval = .9
count_in_values = BinomialDistribution.count_ins(
bounded_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 3)
distribution = BinomialDistribution.percentage_distribution(13)
confidence_interval = .9
count_in_values = BinomialDistribution.count_ins(
bounded_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 4)
distribution = BinomialDistribution.percentage_distribution(5)
confidence_interval = .9
count_in_values = BinomialDistribution.count_ins(
bounded_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 1)
def test_binary_expansion_extremity_count_in_values(self):
extremity_instance = Extremity
distribution = Distribution.percentage_distribution(
BinomialDistribution, 1)
confidence_interval = .51
count_in_values = BinomialDistribution.count_ins(
extremity_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, None)
distribution = Distribution.percentage_distribution(
BinomialDistribution, 1)
confidence_interval = .5
count_in_values = BinomialDistribution.count_ins(
extremity_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 1)
distribution = Distribution.percentage_distribution(
BinomialDistribution, 2)
confidence_interval = .5
count_in_values = BinomialDistribution.count_ins(
extremity_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 1)
distribution = Distribution.percentage_distribution(
BinomialDistribution, 3)
confidence_interval = .51
count_in_values = BinomialDistribution.count_ins(
extremity_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 1)
distribution = Distribution.percentage_distribution(
BinomialDistribution, 3)
confidence_interval = .5
count_in_values = BinomialDistribution.count_ins(
extremity_instance, distribution, confidence_interval)
self.assertEqual(count_in_values, 2)
distribution = Distribution.percentage_distribution(
BinomialDistribution, 4)
confidence_interval = .9375
count_in_values = Distribution.count_ins(extremity_instance,
distribution,
confidence_interval)
self.assertEqual(count_in_values, 1)
