99ab85ce23fc9ad7c7b04e2ca047f641120539e0,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,LearningNodePerceptron,update_weights,#LearningNodePerceptron#Any#Any#Any#Any#,150

Before Change

            // TODO: Verify perceptron update
            self.perceptron_weight += learning_ratio * \
                np.matmul((normalized_target_value - normalized_pred)
                          .reshape((n_targets, 1)),
                          normalized_sample.reshape((1, n_features + 1)))

        // Predicts new income instances as a multiplication of the neurons
        // weights with the inputs augmented with a bias value
        def predict(self, X):

After Change

                normalized_sample.reshape((1, n_features + 1))

            // Normalize perceptron weights
            for i in range(n_targets):
                sum_w = np.sum(np.absolute(self.perceptron_weight[i, :]))
                self.perceptron_weight[i, :] /= sum_w

        // Predicts new income instances as a multiplication of the neurons
        // weights with the inputs augmented with a bias value
        def predict(self, X):
            return self.perceptron_weight @ X

        def get_weight_seen(self):

Instances