def update(self,theta):
     if(self.count % self.update_gap is 0):
         self.test_errors.append(avg_error(theta, self.test))
         self.train_errors.append(self.scaler.scale(avg_error(theta, self.train)))
         self.costs.append(total_cost(theta, self.train))
         self.weight_mags.append(np.dot(theta,theta))
     self.count+=1
 def test_total_cost(self):
     net = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1)
     net[:,5] = np.array([2.]*5)
     flow = np.array([0., 1., 2., 3., 4.])
     self.assertTrue(np.linalg.norm(total_cost(flow, net) - 220.) < 1e-8)
Ejemplo n.º 3
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 def test_total_cost(self):
     net = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1)
     net[:, 5] = np.array([2.] * 5)
     flow = np.array([0., 1., 2., 3., 4.])
     self.assertTrue(np.linalg.norm(total_cost(flow, net) - 220.) < 1e-8)