abd0f7b5564e51a412bb7f85e040985d14eb0a4e,src/skmultiflow/meta/adaptive_random_forests.py,AdaptiveRandomForest,predict,#AdaptiveRandomForest#Any#,255

Before Change

        predictions = []
        for i in range(r):
            votes = self.get_votes_for_instance(X[i])
            if votes == {}:
                // Ensemble is empty, all classes equal, default to zero
                predictions.append(0)
            else:
                predictions.append(max(votes, key=votes.get))
        return np.asarray(predictions)

    def predict_proba(self, X):
         Estimates the probability of each sample in X belonging to each of the class-labels.

After Change

        A numpy.ndarray with all the predictions for the samples in X.

        
        y_proba = self.predict_proba(X)
        n_rows = y_proba.shape[0]
        y_pred = np.zeros(n_rows, dtype=int)
        for i in range(n_rows):
            index = np.argmax(y_proba[i])
            y_pred[i] = index
        return y_pred

Instances