import org.apache.jena.rdf.model.*;
import org.apache.jena.vocabulary.RDF;
import java.util.Objects;

public class EmbeddingBasedCompletion {
    public static void main(String[] args) {
        // 创建一个默认的 RDF 模型，用于表示知识图谱
        Model model = ModelFactory.createDefaultModel();
        // 创建一个实体资源，表示主语
        Resource subject = model.createResource("http://example.org/entity1");
        // 创建一个属性资源，表示谓语
        Property predicate = model.createProperty("http://example.org/relation");
        // 创建一个实体资源，表示宾语
        Resource object = model.createResource("http://example.org/entity2");
        // 向模型中添加三元组 (subject, predicate, object)
        model.add(subject, predicate, object);

        // 这里只是简单示例，实际中需要使用复杂的嵌入算法
        // 为了方便演示，我们使用哈希码作为简单的嵌入表示
        int subjectEmbedding = subject.hashCode();
        int predicateEmbedding = predicate.hashCode();
        int objectEmbedding = object.hashCode();

        System.out.println("Subject embedding (hashCode): " + subjectEmbedding);
        System.out.println("Predicate embedding (hashCode): " + predicateEmbedding);
        System.out.println("Object embedding (hashCode): " + objectEmbedding);

        // 模拟一个简单的相似度计算，实际中需要使用更复杂的度量方法
        int similarity = Math.abs(subjectEmbedding + predicateEmbedding - objectEmbedding);
        System.out.println("Simulated similarity: " + similarity);

        // 可以根据相似度进行关系推断
        if (similarity < 1000) { // 这里的阈值是示例，实际需要根据具体情况调整
            System.out.println("It is likely that there is a relationship between subject and object.");
        } else {
            System.out.println("It is less likely that there is a relationship between subject and object.");
        }

        // 增加一个更复杂的相似度计算示例，使用欧几里得距离
        // 假设我们有一个更复杂的嵌入表示，这里简单用数组模拟
        int[] subjectVector = {1, 2, 3};
        int[] predicateVector = {4, 5, 6};
        int[] objectVector = {7, 8, 9};

        double euclideanDistance = calculateEuclideanDistance(subjectVector, predicateVector, objectVector);
        System.out.println("Euclidean distance: " + euclideanDistance);
        if (euclideanDistance < 5) { // 这里的阈值是示例，实际需要根据具体情况调整
            System.out.println("Based on Euclidean distance, it is likely that there is a relationship.");
        } else {
            System.out.println("Based on Euclidean distance, it is less likely that there is a relationship.");
        }
    }

    /**
     * 计算欧几里得距离
     * @param subjectVector 主语向量
     * @param predicateVector 谓语向量
     * @param objectVector 宾语向量
     * @return 欧几里得距离
     */
    private static double calculateEuclideanDistance(int[] subjectVector, int[] predicateVector, int[] objectVector) {
        double sum = 0;
        for (int i = 0; i < subjectVector.length; i++) {
            int expected = subjectVector[i] + predicateVector[i];
            int actual = objectVector[i];
            sum += Math.pow(expected - actual, 2);
        }
        return Math.sqrt(sum);
    }
}

import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Sgd;

public class DeepLearningBasedCompletion {
    public static void main(String[] args) {
        // 定义神经网络的输入维度
        int numInputs = 10;
        // 定义神经网络的输出维度
        int numOutputs = 2;
        // 定义隐藏层的节点数量
        int numHiddenNodes = 20;

        // 构建神经网络的配置
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
               .updater(new Sgd(0.1)) // 设置优化器为随机梯度下降，学习率为 0.1
               .list() // 开始构建多层网络
               .layer(new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
                      .activation(Activation.RELU).build()) // 添加第一个隐藏层，使用 ReLU 激活函数
               .layer(new DenseLayer.Builder().nIn(numHiddenNodes).nOut(numOutputs)
                      .activation(Activation.SOFTMAX).build()) // 添加输出层，使用 Softmax 激活函数
               .build();

        // 创建多层神经网络模型
        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        // 初始化模型
        model.init();

        // 模拟输入数据
        INDArray input = Nd4j.rand(numInputs);
        // 前向传播，得到模型的输出
        INDArray output = model.output(input);
        // 输出模型的预测结果
        System.out.println("Output: " + output);

        // 以下是一个简单的训练过程示例，实际应用中需要使用真实数据和更多的训练步骤
        int numEpochs = 10;
        for (int i = 0; i < numEpochs; i++) {
            model.fit(input, output);
        }
        System.out.println("Training completed after " + numEpochs + " epochs.");

        // 预测新的数据
        INDArray newInput = Nd4j.rand(numInputs);
        INDArray newOutput = model.output(newInput);
        System.out.println("Prediction for new input: " + newOutput);

        // 增加模型评估示例
        // 模拟测试数据
        INDArray testInput = Nd4j.rand(numInputs);
        INDArray testOutput = model.output(testInput);
        // 简单的评估指标：计算预测结果与预期结果的相似度
        double similarity = calculateSimilarity(output, testOutput);
        System.out.println("Similarity between prediction and test output: " + similarity);
    }

    /**
     * 计算两个 INDArray 的相似度
     * @param arr1 第一个 INDArray
     * @param arr2 第二个 INDArray
     * @return 相似度
     */
    private static double calculateSimilarity(INDArray arr1, INDArray arr2) {
        // 这里简单使用余弦相似度计算
        return arr1.cosineSimilarity(arr2);
    }
}

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

// 电影类
class Movie {
    String name;
    String director;
    String genre;
    double averageRating; // 新增平均评分属性

    public Movie(String name, String director, String genre) {
        this.name = name;
        this.director = director;
        this.genre = genre;
        this.averageRating = 0.0;
    }

    // 计算平均评分的方法
    public void calculateAverageRating(List<Double> ratings) {
        if (ratings.isEmpty()) {
            return;
        }
        double sum = ratings.stream().mapToDouble(Double::doubleValue).sum();
        this.averageRating = sum / ratings.size();
    }
}

// 电影推荐系统
class MovieRecommendationSystem {
    private List<Movie> movies;
    private Map<String, List<Movie>> directorMovies;
    private Map<String, List<Movie>> genreMovies;
    private Map<String, Map<String, Double>> userRatings; // 新增用户评分映射

    public MovieRecommendationSystem() {
        movies = new ArrayList<>();
        directorMovies = new HashMap<>();
        genreMovies = new HashMap<>();
        userRatings = new HashMap<>();
    }

    public void addMovie(Movie movie) {
        movies.add(movie);
        // 根据导演分类
        directorMovies.computeIfAbsent(movie.director, k -> new ArrayList<>()).add(movie);
        // 根据类型分类
        genreMovies.computeIfAbsent(movie.genre, k -> new ArrayList<>()).add(movie);
    }

    // 新增添加用户评分的方法
    public void addUserRating(String userId, String movieName, double rating) {
        userRatings.putIfAbsent(userId, new HashMap<>());
        userRatings.get(userId).put(movieName, rating);
        Movie movie = movies.stream()
              .filter(m -> m.name.equals(movieName))
              .findFirst()
              .orElse(null);
        if (movie != null) {
            List<Double> ratings = userRatings.values().stream()
                  .flatMap(map -> map.entrySet().stream())
                  .filter(entry -> entry.getKey().equals(movieName))
                  .map(Map.Entry::getValue)
                  .collect(Collectors.toList());
            movie.calculateAverageRating(ratings);
        }
    }

    public List<Movie> recommendMovies(String watchedMovieName, String userId) {
        List<Movie> recommendations = new ArrayList<>();
        Movie watchedMovie = movies.stream()
              .filter(movie -> movie.name.equals(watchedMovieName))
              .findFirst()
              .orElse(null);
        if (watchedMovie == null) {
            return recommendations;
        }

        // 根据导演推荐
        recommendations.addAll(directorMovies.get(watchedMovie.director));
        // 根据类型推荐
        recommendations.addAll(genreMovies.get(watchedMovie.genre));

        // 去除已观看的电影
        recommendations.removeIf(movie -> movie.name.equals(watchedMovieName));

        // 根据用户评分和电影平均评分进一步筛选推荐结果
        if (userRatings.containsKey(userId)) {
            Map<String, Double> userRatedMovies = userRatings.get(userId);
            recommendations = recommendations.stream()
                  .filter(movie -> {
                       double userRating = userRatedMovies.getOrDefault(movie.name, 0.0);
                       return userRating > 3.0 || movie.averageRating > 3.0;
                    })
                  .collect(Collectors.toList());
        }

        return recommendations;
    }
}

public class MovieRecommendationExample {
    public static void main(String[] args) {
        MovieRecommendationSystem system = new MovieRecommendationSystem();
        // 添加一些电影数据
        system.addMovie(new Movie("电影 A", "导演 X", "科幻"));
        system.addMovie(new Movie("电影 B", "导演 X", "科幻"));
        system.addMovie(new Movie("电影 C", "导演 Y", "动作"));

        // 添加用户评分
        system.addUserRating("user1", "电影 A", 4.5);
        system.addUserRating("user1", "电影 B", 3.0);
        system.addUserRating("user2", "电影 A", 5.0);

        String watchedMovie = "电影 A";
        String userId = "user1";
        List<Movie> recommendedMovies = system.recommendMovies(watchedMovie, userId);

        System.out.println("您观看了: " + watchedMovie);
        System.out.println("为您推荐的电影:");
        for (Movie movie : recommendedMovies) {
            System.out.println(movie.name + " (导演: " + movie.director + ", 类型: " + movie.genre + ", 平均评分: " + movie.averageRating + ")");
        }
    }
}

import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.GravesLSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.conf.layers.embeddings.EmbeddingLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Nesterovs;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.ops.transforms.Transforms;
import org.nd4j.shade.jackson.annotation.JsonIgnore;

import java.util.Arrays;

// 自定义带有注意力机制的LSTM层
class AttentionLSTM extends GravesLSTM {
    public AttentionLSTM(Builder builder) {
        super(builder);
    }

    @Override
    public INDArray activate(INDArray input, boolean training) {
        INDArray output = super.activate(input, training);
        // 计算注意力权重
        INDArray attentionWeights = calculateAttentionWeights(output);
        // 对输出进行加权
        INDArray attendedOutput = applyAttentionWeights(output, attentionWeights);
        return attendedOutput;
    }

    private INDArray calculateAttentionWeights(INDArray output) {
        INDArray energy = Transforms.tanh(output);
        INDArray attentionWeights = Transforms.softmax(energy, 1);
        return attentionWeights;
    }

    private INDArray applyAttentionWeights(INDArray output, INDArray attentionWeights) {
        return output.mul(attentionWeights);
    }
}

public class ExplainableModelExample {
    public static void main(String[] args) {
        int vectorSize = 100;
        int numHiddenNodes = 100;
        int outputSize = 2;
        int batchSize = 64;
        int numEpochs = 10;

        // 构建包含注意力机制的模型配置
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
               .seed(12345)
               .weightInit(WeightInit.XAVIER)
               .updater(new Nesterovs(0.1, 0.9))
               .list()
               .layer(new EmbeddingLayer.Builder()
                      .nIn(10000)
                      .nOut(vectorSize)
                      .build())
               .layer(new AttentionLSTM.Builder()
                      .nIn(vectorSize)
                      .nOut(numHiddenNodes)
                      .build())
               .layer(new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                      .activation(Activation.SOFTMAX)
                      .nIn(numHiddenNodes)
                      .nOut(outputSize)
                      .build())
               .build();

        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();

        // 生成模拟数据
        INDArray features = Nd4j.randn(batchSize, 100, 100);
        INDArray labels = Nd4j.randn(batchSize, outputSize);
        DataSet dataSet = new DataSet(features, labels);

        // 训练模型
        for (int i = 0; i < numEpochs; i++) {
            model.fit(dataSet);
        }

        // 打印模型注意力权重（这里只是示例，实际可根据需求进一步处理和展示）
        INDArray output = model.output(features);
        AttentionLSTM attentionLSTM = (AttentionLSTM) model.getLayer(1);
        INDArray attentionWeights = attentionLSTM.calculateAttentionWeights(output);
        System.out.println("Attention Weights: " + attentionWeights);
    }
}

import java.util.HashMap;
import java.util.Map;

// 模拟医学诊断规则
class MedicalDiagnosisRules {
    private Map<String, String> rules;

    public MedicalDiagnosisRules() {
        rules = new HashMap<>();
        rules.put("fever AND cough", "Possible flu");
        rules.put("chestPain AND shortnessOfBreath", "Possible heart problem");
    }

    public String getDiagnosis(String symptoms) {
        for (String rule : rules.keySet()) {
            if (symptoms.contains(rule)) {
                return rules.get(rule);
            }
        }
        return "Unknown diagnosis";
    }
}

// 模拟深度学习诊断模型
class DeepLearningDiagnosisModel {
    public String predict(String symptoms) {
        // 这里假设深度学习模型的预测逻辑，实际需要复杂的训练和计算
        if (symptoms.contains("fever") && symptoms.contains("cough")) {
            return "Flu prediction from DL model";
        }
        return "Unknown prediction";
    }
}

public class RuleEmbeddingExample {
    public static void main(String[] args) {
        MedicalDiagnosisRules rules = new MedicalDiagnosisRules();
        DeepLearningDiagnosisModel dlModel = new DeepLearningDiagnosisModel();

        String symptoms = "fever AND cough";
        String ruleBasedDiagnosis = rules.getDiagnosis(symptoms);
        String dlBasedDiagnosis = dlModel.predict(symptoms);

        System.out.println("Rule - based diagnosis: " + ruleBasedDiagnosis);
        System.out.println("Deep - learning based diagnosis: " + dlBasedDiagnosis);
        // 结合两者的结果给出综合解释
        if (ruleBasedDiagnosis.equals(dlBasedDiagnosis.split(" ")[0])) {
            System.out.println("Both rule - based and deep - learning models suggest possible flu.");
        } else {
            System.out.println("Inconsistent results between rule - based and deep - learning models.");
        }
    }
}