163eb7df13667e21b0e02a2706e18d1f53eee610,skrebate/iterrelief.py,IterRelief,fit,#IterRelief#Any#Any#,69

Before Change

        // Determine total number of features
        total_num_features = X.shape[1]

        print("num featuers")
        print(total_num_features)
        // Initialize weights
        distance_weights = np.ones(total_num_features)

After Change

                    if diff >= 0.0001:
                        no_diff = False
                        break;
            if no_diff:
                break;

            mx = max(feature_weights)

            weight_history.append(feature_weights)

            //normalize and update scores
            //negative numbers -> 0, positive numbers become normalized so maximum is 1.
            for i in range(0, len(feature_weights)):
                if feature_weights[i] <= 0:
                    feature_weights[i] = 0
                else:
                    feature_weights[i] = feature_weights[i] / mx

            distance_weights += feature_weights

            iteration += 1

Instances