def test_is_none(self):
self.assertEqual(chi_square_p_value(((1, 1), (1, -1))), (None, None),
"Negative numbers should not be allowed")
self.assertEqual(chi_square_p_value(((0, 0), (0, 0))), (None, None),
"Zero sample size should not be allowed")
self.assertIsNone(chi_square_p_value(((1, ), (1, 2))),
"Unequal matrices should not be allowed")
self.assertIsNone(chi_square_p_value(((1, 2, 3), (1, 2, 3), (1, 2))),
"Unequal matrices should not be allowed")
self.assertIsNone(
chi_square_p_value(((100, 10), (200, 20), (300, 30), (400, 40))),
"Matrices have to be square")
def chi_squared_confidence(a_count, a_conversion, b_count, b_conversion):
contingency_table = [[a_count - a_conversion, a_conversion],
[b_count - b_conversion, b_conversion]]
chi_square, p_value = chi_square_p_value(contingency_table)
if p_value is not None:
return (1 - p_value) * 100
else:
return None
def chi_squared_confidence(a_count, a_conversion, b_count, b_conversion):
contingency_table = [[a_count - a_conversion, a_conversion],
[b_count - b_conversion, b_conversion]]
chi_square, p_value = chi_square_p_value(contingency_table)
if p_value is not None:
return (1 - p_value) * 100
else:
return None
def test_stress(self):
# Generate a large matrix
matrix = []
for col in xrange(0, 100):
matrix.append([])
for row in xrange(0, 100):
matrix[col].append(random.randint(0, 10))
self.assertIsNotNone(chi_square_p_value(matrix))
def test_stress(self):
# Generate a large matrix
matrix = []
for col in xrange(0, 100):
matrix.append([])
for row in xrange(0, 100):
matrix[col].append(random.randint(0, 10))
self.assertIsNotNone(chi_square_p_value(matrix))
def assertChiSquareCorrect(self,
matrix,
observed_test_statistic,
p_value,
accuracy=7):
observed_test_statistic_result, p_value_result = chi_square_p_value(
matrix)
self.assertAlmostEqual(observed_test_statistic_result,
observed_test_statistic, accuracy,
'Wrong observed result')
self.assertAlmostEqual(p_value_result, p_value, accuracy,
'Wrong P Value')
def test_error(self):
self.assertRaises(TypeError, chi_square_p_value((1, )))
self.assertRaises(TypeError, chi_square_p_value(((1, 2, 3))))
def assertChiSquareCorrect(self, matrix, observed_test_statistic, p_value, accuracy=7):
observed_test_statistic_result, p_value_result = chi_square_p_value(matrix)
self.assertAlmostEqual(observed_test_statistic_result, observed_test_statistic, accuracy, 'Wrong observed result')
self.assertAlmostEqual(p_value_result, p_value, accuracy, 'Wrong P Value')
def test_is_none(self):
self.assertEqual(chi_square_p_value(((1, 1), (1, -1))), (None, None), "Negative numbers should not be allowed")
self.assertEqual(chi_square_p_value(((0, 0), (0, 0))), (None, None), "Zero sample size should not be allowed")
self.assertIsNone(chi_square_p_value(((1,), (1, 2))), "Unequal matrices should not be allowed")
self.assertIsNone(chi_square_p_value(((1, 2, 3), (1, 2, 3), (1, 2))), "Unequal matrices should not be allowed")
self.assertIsNone(chi_square_p_value(((100, 10), (200, 20), (300, 30), (400, 40))), "Matrices have to be square")
def test_error(self):
self.assertRaises(TypeError, chi_square_p_value((1,)))
self.assertRaises(TypeError, chi_square_p_value(((1,2,3))))
