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import unittest |
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from numpy.random.mtrand import normal |
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from pysie.dsl.variable_independence_testing import Anova, ContingencyTable |
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from pysie.stats.samples import Sample |
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class AnovaUnitTest(unittest.TestCase): |
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def test_anova(self): |
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sample = Sample() |
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mu1 = 1.0 |
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sigma1 = 1.0 |
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mu2 = 1.1 |
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sigma2 = 1.0 |
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mu3 = 1.09 |
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sigma3 = 1.0 |
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for i in range(100): |
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sample.add_numeric(normal(mu1, sigma1), 'group1') |
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sample.add_numeric(normal(mu2, sigma2), 'group2') |
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sample.add_numeric(normal(mu3, sigma3), 'group3') |
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testing = Anova(sample=sample) |
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print('p-value: ' + str(testing.p_value)) |
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reject = testing.will_reject(0.01) |
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print('will reject [same mean for all groups] ? ' + str(reject)) |
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self.assertFalse(reject) |
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class ContingencyTableUnitTest(unittest.TestCase): |
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def test_table(self): |
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table = ContingencyTable() |
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table.set_cell('eventA', 'eventB', 10) |
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table.set_cell('eventC', 'eventB', 20) |
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table.set_cell('eventA', 'eventD', 15) |
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table.set_cell('eventC', 'eventD', 10) |
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print(table.get_column_total('eventB')) |
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self.assertEqual(table.get_column_total('eventB'), 30) |
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print(table.get_column_total('eventD')) |
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self.assertEqual(table.get_column_total('eventD'), 25) |
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print(table.get_row_total('eventA')) |
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self.assertEqual(table.get_row_total('eventA'), 25) |
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print(table.get_row_total('eventC')) |
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self.assertEqual(table.get_row_total('eventC'), 30) |
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self.assertEqual(table.get_total(), 55) |
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if __name__ == '__main__': |
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unittest.main() |
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chen0040 /
pysie
