Welchen Mehrwert bietet die Anwendung von Java-Funktionen im Bereich der künstlichen Intelligenz?

import java.util.function.Function;

public class LambdaExample {
  public static void main(String[] args) {
    // 定义一个 lambda 表达式来实现加法
    Function<Integer, Integer> add = (a) -> a + 1;

    // 将 lambda 表达式传递给一个方法
    int result = add.apply(5);
    System.out.println(result); // 输出：6
  }
}

import java.util.Arrays;
import java.util.List;

public class StreamExample {
  public static void main(String[] args) {
    List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5);

    // 使用流式处理过滤出偶数
    List<Integer> evenNumbers = numbers.stream()
        .filter(n -> n % 2 == 0)
        .toList();

    // 打印结果
    for (Integer evenNumber : evenNumbers) {
      System.out.println(evenNumber); // 输出：2, 4
    }
  }
}

import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.core.converters.CSVLoader;

public class MachineLearningExample {
  public static void main(String[] args) throws Exception {
    // 加载训练数据
    CSVLoader loader = new CSVLoader();
    loader.setSource(new File("data.csv"));
    Instances data = loader.getDataSet();

    // 创建决策树分类器
    J48 classifier = new J48();
    classifier.buildClassifier(data);

    // 预测新数据
    double[] newValues = { 1.0, 2.0 };
    int prediction = classifier.classifyInstance(newValues);

    // 打印预测结果
    System.out.println("预测结果：" + prediction); // 例如：0
  }
}

import java.awt.image.BufferedImage;
import java.io.File;
import javax.imageio.ImageIO;

public class ImageRecognitionExample {
  public static void main(String[] args) throws Exception {
    // 加载图像
    BufferedImage image = ImageIO.read(new File("image.jpg"));

    // 转换图像为灰度
    BufferedImage grayImage = new BufferedImage(image.getWidth(), image.getHeight(), BufferedImage.TYPE_BYTE_GRAY);
    for (int i = 0; i < image.getWidth(); i++) {
      for (int j = 0; j < image.getHeight(); j++) {
        int rgb = image.getRGB(i, j);
        int gray = (rgb >> 16) & 0xFF;
        grayImage.setRGB(i, j, (gray << 16) | (gray << 8) | gray);
      }
    }

    // 使用 Sobel 滤波器进行边缘检测
    int[][] sobelX = { {-1, 0, 1}, {-2, 0, 2}, {-1, 0, 1} };
    int[][] sobelY = { {-1, -2, -1}, {0, 0, 0}, {1, 2, 1} };
    BufferedImage edgeImage = new BufferedImage(grayImage.getWidth(), grayImage.getHeight(), BufferedImage.TYPE_BYTE_GRAY);
    for (int i = 1; i < grayImage.getWidth() - 1; i++) {
      for (int j = 1; j < grayImage.getHeight() - 1; j++) {
        int gx = 0;
        int gy = 0;
        for (int k = -1; k <= 1; k++) {
          for (int l = -1; l <= 1; l++) {
            int pixel = grayImage.getRGB(i + k, j + l);
            gx += pixel * sobelX[k + 1][l + 1];
            gy += pixel * sobelY[k + 1][l + 1];
          }
        }
        int edge = Math.abs(gx) + Math.abs(gy);
        edgeImage.setRGB(i, j, (edge << 16) | (edge << 8) | edge);
      }
    }

    // 保存边缘检测后的图像
    ImageIO.write(edgeImage, "jpg", new File("edge_image.jpg"));
  }
}