def test_chi_square(self, exp):
"""Test chi-square distribution"""
# test equal distribution
tbl = np.array([[5, 5], [5, 5]])
chi, p = chisquare(tbl, exp=exp)
self.assertTrue(chi == 0.0)
self.assertTrue(p == 1.0)
# test perfect "generalization"
tbl = np.array([[4, 0], [0, 4]])
chi, p = chisquare(tbl, exp=exp)
self.assertTrue(chi == 8.0)
self.assertTrue(p < 0.05)
def test_chi_square(self, exp):
"""Test chi-square distribution"""
# test equal distribution
tbl = np.array([[5, 5], [5, 5]])
chi, p = chisquare(tbl, exp=exp)
self.assertTrue( chi == 0.0 )
self.assertTrue( p == 1.0 )
# test perfect "generalization"
tbl = np.array([[4, 0], [0, 4]])
chi, p = chisquare(tbl, exp=exp)
self.assertTrue(chi == 8.0)
self.assertTrue(p < 0.05)
def test_chi_square_disbalanced(self):
# test perfect "generalization"
tbl = np.array([[1, 100], [1, 100]])
chi, p = chisquare(tbl, exp='indep_rows')
self.assertTrue(chi == 0)
self.assertTrue(p == 1)
chi, p = chisquare(tbl, exp='uniform')
self.assertTrue(chi > 194)
self.assertTrue(p < 1e-10)
# by default lets do uniform
chi_, p_ = chisquare(tbl)
self.assertTrue(chi == chi_)
self.assertTrue(p == p_)
def test_chi_square_disbalanced(self):
# test perfect "generalization"
tbl = np.array([[1, 100], [1, 100]])
chi, p = chisquare(tbl, exp='indep_rows')
self.assertTrue(chi == 0)
self.assertTrue(p == 1)
chi, p = chisquare(tbl, exp='uniform')
self.assertTrue(chi > 194)
self.assertTrue(p < 1e-10)
# by default lets do uniform
chi_, p_ = chisquare(tbl)
self.assertTrue(chi == chi_)
self.assertTrue(p == p_)
def getconfusion(data):
cv(data)
return chisquare(cv.ca.stats.matrix)[0]
def getconfusion(data):
cv(data)
return chisquare(cv.ca.stats.matrix)[0]