Java函数式接口在机器学习和人工智能中的用途

import java.util.Arrays;
import java.util.List;
import java.util.function.Function;
import java.util.function.Predicate;

public class DataPreprocessing {

    public static void main(String[] args) {
        List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);

        // 映射：将每个数字平方
        List<Integer> squaredNumbers = numbers.stream()
                .map(n -> n * n)
                .toList();

        // 过滤：选择偶数
        List<Integer> evenNumbers = numbers.stream()
                .filter(n -> n % 2 == 0)
                .toList();

        // 排序：按降序排列
        List<Integer> sortedNumbers = numbers.stream()
                .sorted((a, b) -> b - a)
                .toList();
    }
}

import java.util.function.BiFunction;

public class ModelTraining {

    public static void main(String[] args) {
        BiFunction<Double[], Double[], Double> lossFunction = (predictions, actuals) -> {
            double sum = 0;
            for (int i = 0; i < predictions.length; i++) {
                sum += Math.pow(predictions[i] - actuals[i], 2);
            }
            return sum / predictions.length;
        };

        // 梯度计算（实现略）

        // 模型训练（实现略）
    }
}

import java.util.function.ToDoubleFunction;

public class ModelEvaluation {

    public static void main(String[] args) {
        ToDoubleFunction<Double[]> accuracyMetric = predictions -> {
            int correct = 0;
            for (double prediction : predictions) {
                if (prediction >= 0.5) {
                    correct++;
                }
            }
            return (double) correct / predictions.length;
        };

        // 交叉验证（实现略）
    }
}