Test Case: java-instruct-1004

  1import java.util.*;  2import java.util.function.Function;  3​  4public class WeightedRandomSelector {  5​  6    /**  7     * Selects items from a list based on their normalized weights using a probabilistic method.  8     *  9     * @param <T> The type of the items. 10     * @param items The list of items to select from. 11     * @param weightFunction A function that computes the weight for each item. 12     * @param normalizer A function that normalizes the weights. 13     * @return A map of items to their normalized weights. 14     * @throws IllegalArgumentException if the list of items is null or empty, or if the weight function is null. 15     */ 16    public static <T> Map<T, Double> computeNormalizedWeights(List<T> items, Function<T, Double> weightFunction, Function<List<Double>, List<Double>> normalizer) { 17        if (items == null || items.isEmpty()) { 18            throw new IllegalArgumentException("List of items cannot be null or empty."); 19        } 20        if (weightFunction == null) { 21            throw new IllegalArgumentException("Weight function cannot be null."); 22        } 23​ 24        List<Double> weights = new ArrayList<>(); 25        for (T item : items) { 26            weights.add(weightFunction.apply(item)); 27        } 28​ 29        List<Double> normalizedWeights = normalizer.apply(weights); 30​ 31        Map<T, Double> itemToWeight = new HashMap<>(); 32        for (int i = 0; i < items.size(); i++) { 33            itemToWeight.put(items.get(i), normalizedWeights.get(i)); 34        } 35​ 36        return itemToWeight; 37    } 38​ 39    public static <T> T selectItem(List<T> items, Map<T, Double> normalizedWeights) { 40        if (items == null || items.isEmpty()) { 41            throw new IllegalArgumentException("List of items cannot be null or empty."); 42        } 43        if (normalizedWeights == null || normalizedWeights.isEmpty()) { 44            throw new IllegalArgumentException("Normalized weights cannot be null or empty."); 45        } 46​ 47        double randomNumber = new Random().nextDouble(); 48        double cumulativeWeight = 0.0; 49​ 50        for (T item : items) { 51            cumulativeWeight += normalizedWeights.get(item); 52            if (randomNumber < cumulativeWeight) { 53                return item; 54            } 55        } 56​ 57        // Should not happen if weights are properly normalized, but return the last item as a fallback 58        return items.get(items.size() - 1); 59    } 60​ 61    public static void main(String[] args) { 62        List<String> items = Arrays.asList("A", "B", "C"); 63        Function<String, Double> weightFunction = item -> { 64            switch (item) { 65                case "A": 66                    return 1.0; 67                case "B": 68                    return 2.0; 69                case "C": 70                    return 3.0; 71                default: 72                    return 0.0; 73            } 74        }; 75​ 76        Function<List<Double>, List<Double>> normalizer = weights -> { 77            double sum = weights.stream().mapToDouble(Double::doubleValue).sum(); 78            List<Double> normalized = new ArrayList<>(); 79            for (Double weight : weights) { 80                normalized.add(weight / sum); 81            } 82            return normalized; 83        }; 84​ 85        Map<String, Double> normalizedWeights = computeNormalizedWeights(items, weightFunction, normalizer); 86        System.out.println("Normalized Weights: " + normalizedWeights); 87​ 88        // Example of selecting an item based on the normalized weights 89        String selectedItem = selectItem(items, normalizedWeights); 90        System.out.println("Selected Item: " + selectedItem); 91    } 92}

1. WARNINGLine 47

   Depending on the context, generating weak random numbers may expose cryptographic functions which rely on these numbers, to be exploitable. When generating numbers for sensitive values such as tokens, nonces, and cryptographic keys, it is recommended that the `DRBG` instance of `SecureRandom` be used.