def test_evaluate_assert_it_returns_prob_array(self):
arch = MagicMock()
arch.evaluate.return_value = [0.1, 0.8, 0.1]
model = Model(arch)
result = model.evaluate(None)
self.assertEqual(result, [0.1, 0.8, 0.1])
evaluations = self.evaluate(dataset)
error = labels - evaluations
self._weights += np.sum(self._learning_rate * error *
np.transpose(dataset),
axis=1)
return (np.average(error), evaluations)
def evaluate(self, dataset):
sum_ = np.sum(dataset * self._weights, axis=1)
return (sum_ > self._threshold).astype(int)
model = Model(Perceptron(2))
epochs, costs, accuracies, *_ = model.train(
np.array([[0, 0], [0, 1], [1, 0], [1, 1]]),
np.array([0, 0, 0, 1]),
None,
None,
SimpleTrainer(),
PairwiseMeasurer(),
None,
epochs=10,
)
print('Epochs: ', epochs)
print('Costs: ', costs)
print('Accuracies: ', accuracies)
print('Predictions:', model.evaluate(np.array([[0, 0], [0, 1], [1, 0], [1,
1]])))