def test_two_variables(self):
"""
检查正态分布的假设检验结果
"""
array_1 = DistributionSampling(seed=2).generate_normal_samplings(
mu=3, sigma=3, size=1000
)
array_2 = DistributionSampling(seed=3).generate_normal_samplings(
mu=3, sigma=4, size=1000
)
array_3 = DistributionSampling(seed=4).generate_normal_samplings(
mu=4, sigma=3, size=1000
)
array_4 = DistributionSampling(seed=5).generate_normal_samplings(
mu=4, sigma=3, size=1000
)
array_5 = DistributionSampling(seed=6).generate_normal_samplings(
mu=5, sigma=3, size=1000
)
# 检查双侧结果，方差一致
self._check_alternative(array_3, array_4, TTestAlternative.TWO_SIDED)
# 检查双侧结果，方差不一致
self._check_alternative(array_1, array_2, TTestAlternative.TWO_SIDED)
# 检查LESS
self._check_alternative(array_3, array_1, TTestAlternative.LESS)
# 检查more
self._check_alternative(array_4, array_5, TTestAlternative.GREATER)
def test_chi_square(self):
array_1 = DistributionSampling(seed=1).generate_bernoulli_samplings(
0.5, 1000)
array_2 = DistributionSampling(seed=2).generate_bernoulli_samplings(
0.6, 1000)
array_3 = DistributionSampling(seed=3).generate_bernoulli_samplings(
0.5, 1000)
test_result = ChiSquareIndependentTwoVariables(array_1, array_2).test()
self.assertTrue(test_result.rejected)
test_result = ChiSquareIndependentTwoVariables(array_1, array_3).test()
self.assertTrue(not test_result.rejected)
def test_normal_result(self):
"""
检查正态分布的假设检验结果
"""
array = DistributionSampling.generate_normal_samplings(mu=5,
sigma=3,
size=2000)
# 检查双侧结果
student_t_test_one_sample = StudentTTestOneVariable(
array, 6, 0.05, TTestAlternative.TWO_SIDED)
two_side_result = student_t_test_one_sample.test()
self.assertTrue(two_side_result.condition_satisfied)
self.assertTrue(two_side_result.rejected)
# 检查小于的结果
student_t_test_one_sample = StudentTTestOneVariable(
array, 4, 0.05, TTestAlternative.LESS)
less_result = student_t_test_one_sample.test()
self.assertTrue(less_result.condition_satisfied)
self.assertTrue(not less_result.rejected)
# 检查大于的结果
student_t_test_one_sample = StudentTTestOneVariable(
array, 6, 0.05, TTestAlternative.GREATER)
greater_result = student_t_test_one_sample.test()
self.assertTrue(greater_result.condition_satisfied)
self.assertTrue(not greater_result.rejected)
def test_one_way(self):
array_1 = DistributionSampling(seed=1).generate_bernoulli_samplings(
0.5, 1000)
array_2 = DistributionSampling(seed=22).generate_bernoulli_samplings(
0.6, 20000)
array_3 = DistributionSampling(seed=3).generate_normal_samplings(
0.6, 0.2, 2000)
array_4 = DistributionSampling(seed=4).generate_normal_samplings(
0.7, 0.1, 1000)
array_5 = DistributionSampling(seed=22).generate_bernoulli_samplings(
0.6, 20000)
result = OneWayAnalysis(array_1, array_2).test()
self.assertTrue(result.rejected)
result = OneWayAnalysis(array_3, array_4).test()
self.assertTrue(result.rejected)
result = OneWayAnalysis(array_2, array_5).test()
self.assertTrue(not result.rejected)
def test_not_normal(self):
"""
检验对于数据是否满足正态分布的判断是否有效
"""
array = DistributionSampling.generate_exponential_samplings(size=1000)
student_t_test_one_sample = StudentTTestOneVariable(
array, 0, 0.05, TTestAlternative.TWO_SIDED)
result = student_t_test_one_sample.test()
self.assertTrue(not result.condition_satisfied)
def test_two_variables(self):
"""
检查正态分布的假设检验结果，这里暂时直接将随机数排序当做配对样本。
"""
array_1 = DistributionSampling.generate_normal_samplings(mu=3,
sigma=3,
size=1000)
array_2 = array_1 + DistributionSampling.generate_normal_samplings(
mu=0, sigma=1, size=1000)
array_3 = array_1 + DistributionSampling.generate_normal_samplings(
mu=0.5, sigma=3, size=1000)
# 检查双侧结果
self.assertTrue(
StudentTTestRelatedTwoVariables(
array_1, array_2, 0.05,
TTestAlternative.TWO_SIDED).test().condition_satisfied)
self.assertTrue(
StudentTTestRelatedTwoVariables(
array_1, array_3, 0.05,
TTestAlternative.TWO_SIDED).test().condition_satisfied)
def test_normal_distribution_test(self):
# 检查数据少于50正态分布是否为正态分布
self.assertTrue(
NormalDistributionTest.test_normal_distribution(
DistributionSampling(seed=1).generate_normal_samplings(
3, 5, 30)))
# 检查数据50-300正态分布是否为正态分布
self.assertTrue(
NormalDistributionTest.test_normal_distribution(
DistributionSampling(seed=2).generate_normal_samplings(
3, 5, 200)))
# 检查数据大于300正态分布是否为正态分布
self.assertTrue(
NormalDistributionTest.test_normal_distribution(
DistributionSampling(seed=3).generate_normal_samplings(
3, 5, 1000)))
# 检查数据小于50指数分布是否不是正态分布
self.assertTrue(not NormalDistributionTest.test_normal_distribution(
DistributionSampling(seed=4).generate_exponential_samplings(
size=30)))
# 检查数据50-300指数分布是否不是正态分布
self.assertTrue(not NormalDistributionTest.test_normal_distribution(
DistributionSampling(seed=5).generate_exponential_samplings(
size=200)))
# 检查数据大于300指数分布是否不是正态分布
self.assertTrue(not NormalDistributionTest.test_normal_distribution(
DistributionSampling(seed=6).generate_exponential_samplings(
size=1000)))