eta0 = 1
gamma = 0.1
if sys.argv[1] == 'cs':
data = DataLoader('cs')
training_size = 15
eta0 = 0.05
gamma = 5e-1
except:
print 'usage: python %s [mnist|cs]' % sys.argv[0]
sys.exit(1)
if data.type == 'classification':
evaluate = evaluate_accuracy
if data.type == 'regression':
evaluate = evaluate_squared_error
learners = [OnlineLearner(data.P, gamma, eta0=eta0, type=data.type) for _ in xrange(data.K)]
Y_pred = np.zeros((training_size, data.K))
Y_true = np.zeros((training_size, data.K))
for i in xrange(training_size):
x, y = data.next()
for k, learner in enumerate(learners):
Y_pred[i,k] = learner.predict(x)
Y_true[i,k] = y[k]
learner.update(x, y[k])
print evaluate(Y_pred, Y_true)
