def test_results(self):
the_error = distributions.IncorrectDistributionInicializationError
with self.assertRaises(the_error):
distributions.t_Distribution(-1)
t = distributions.t_Distribution(1)
t = distributions.t_Distribution(2)
t = distributions.t_Distribution(3)
t = distributions.t_Distribution(4)
t = distributions.t_Distribution(5)
result_1 = t.pdf(1)
result_2 = t.pdf(-2)
result_3 = t.cdf(1)
result_4 = t.cdf(-2)
self.assertTrue(0.21 < result_1 and result_1 < 0.22)
self.assertTrue(0.06 < result_2 and result_2 < 0.07)
self.assertTrue(0.81 < result_3 and result_3 < 0.82)
self.assertTrue(0.05 < result_4 and result_4 < 0.06)
with self.assertRaises(distributions.IncorrectInputError):
distributions.t_Distribution.t_cdf(3, 0)
with self.assertRaises(distributions.IncorrectInputError):
distributions.t_Distribution.t_pdf(3, 0)
def test_results(self):
normal_dist = distributions.NormalDistribution(0, 1)
t_dist = distributions.t_Distribution(7)
result_norm = distributions.find_percentile(0.95, normal_dist.cdf)
with self.assertRaises(distributions.PercentileError):
distributions.find_percentile(1.2, normal_dist.cdf)
cdf = distributions.NormalDistribution.normal_cdf
result_with_args = distributions.find_percentile(0.8, cdf, (2, 5))
self.assertTrue(1.644 < result_norm and result_norm < 1.645)
def test_performance(self):
data_list = [2, 5, 2, 7, 4, 5, 2, 3, 9, 3, 2, 4, 2]
bad_data = "as"
big_data = []
for i in range(60):
big_data.append(i)
dist_1 = distributions.NormalDistribution(2, 3)
dist_2 = distributions.FisherDistribution(2, 3)
dist_3 = distributions.t_Distribution(7)
dist_4 = distributions.ChiSquaredDistribution(8)
distributions.compare_cdf_with_theoretical(data_list, [dist_1, dist_2, dist_3, dist_4])
distributions.compare_cdf_with_theoretical(data_list, dist_1)
distributions.compare_cdf_with_theoretical(data_list, dist_2)
distributions.compare_cdf_with_theoretical(data_list, dist_3)
distributions.compare_cdf_with_theoretical(data_list, dist_4)
with self.assertRaises(distributions.NotAListError):
distributions.compare_cdf_with_theoretical(bad_data, dist_1)
distributions.compare_cdf_with_theoretical(big_data, dist_1)
