def test_mlr_train(self):
mlr = MLR(3, 3)
N = 1000
inputs = zeros([3, N])
inputs[randint(3, size=N), range(N)] = 1.
self.assertLess(mlr._check_gradient(inputs, inputs, 1e-4), 1e-6)
mlr.train(inputs, inputs)
# prediction should be perfect (almost always)
self.assertLess(sum(mlr.sample(inputs) - inputs), 2)
def test_mlr_train(self): mlr = MLR(3, 3) N = 1000 inputs = zeros([3, N]) inputs[randint(3, size=N), range(N)] = 1. self.assertLess(mlr._check_gradient(inputs, inputs, 1e-4), 1e-6) mlr.train(inputs, inputs) # prediction should be perfect (almost always) self.assertLess(sum(mlr.sample(inputs) - inputs), 2)
