def train(self): X = np.column_stack((self.data['state'], self.data['factors'])) Y = self.data['output'] self.labelCodes = np.unique(Y) self.logreg.fit(X, Y, maxiter=self.maxiter) out = self.logreg.predict(X) depCoef = DependenceCoef(np.ma.array(out), np.ma.array(Y), expand=True) self.Kappa = depCoef.kappa(mode=None) self.pseudoR = depCoef.correctness(percent=False)
def train(self): X = np.column_stack( (self.data['state'], self.data['factors']) ) Y = self.data['output'] self.labelCodes = np.unique(Y) self.logreg.fit(X, Y, maxiter=self.maxiter) out = self.logreg.predict(X) depCoef = DependenceCoef(np.ma.array(out), np.ma.array(Y), expand=True) self.Kappa = depCoef.kappa(mode=None) self.pseudoR = depCoef.correctness(percent = False)
def test_correctness(self): dc = DependenceCoef(self.X, self.X) self.assertEqual(dc.correctness(), 100.0, 'correctness % failed')
def test_correctness(self): dc = DependenceCoef(self.X, self.X) self.assertEqual(dc.correctness(), 100.0, 'correctness % failed')