def normal_hypothesis(distribution: dist.Distribution, size: int):
sample = distribution.create_sample(size)
characteristics = Characteristics(sample)
hypothesis = Hypothesis()
hyp_distribution = dist.NormalDistribution(characteristics.mean(),
characteristics.variance())
hypothesis.check_hypothesis(sample, hyp_distribution)
def uniform_hypothesis(distribution: dist.Distribution, size: int):
sample = distribution.create_sample(size)
characteristics = Characteristics(sample)
hypothesis = Hypothesis()
hyp_distribution = dist.UniformDistribution(characteristics.min(),
characteristics.max())
hypothesis.check_hypothesis(sample, hyp_distribution)
def laplace_hypothesis(distribution: dist.Distribution, size: int):
sample = distribution.create_sample(size)
characteristics = Characteristics(sample)
hypothesis = Hypothesis()
hyp_distribution = dist.LaplaceDistribution(
characteristics.mean(),
characteristics.variance() / (2**0.5))
hypothesis.check_hypothesis(sample, hyp_distribution)
def chekc_hypoth(an: Analyzer):
size = len(an.get_data()[0])
for i in range(0, size):
hypothesis = Hypothesis()
print(an.get_axis_name(i))
hypothesis.check_hypothesis(an.get_axis_data(i))
