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')
