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)
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)