Sudoku mit KI entwickeln

class AIHintSystem {
    constructor() {
        this.playerProfile = {
            skillLevel: 'beginner',
            preferredTechniques: [],
            strugglingAreas: [],
            learningProgress: {}
        };
        this.hintHistory = [];
    }
    
    analyzePlayerBehavior(moves, timeSpent, errorsPattern) {
        // Machine Learning Algorithmus zur Spieler-Analyse
        const analysis = {
            recognitionSpeed: this.calculateRecognitionSpeed(moves, timeSpent),
            techniqueUsage: this.identifyTechniques(moves),
            errorPatterns: this.categorizeErrors(errorsPattern),
            difficultyHandling: this.assessDifficultyPerformance()
        };
        
        this.updatePlayerProfile(analysis);
        return analysis;
    }
    
    generatePersonalizedHint(currentGrid, playerProfile) {
        const availableTechniques = this.findAvailableTechniques(currentGrid);
        const filteredTechniques = this.filterBySkillLevel(availableTechniques, playerProfile);
        
        // Wähle Technik basierend auf Lernzielen
        const optimalTechnique = this.selectOptimalTechnique(filteredTechniques, playerProfile);
        
        return this.formatHint(optimalTechnique, playerProfile.preferredExplanationStyle);
    }
    
    adaptDifficulty(baseGrid, targetDifficulty, playerSkill) {
        // Genetischer Algorithmus für optimale Puzzle-Anpassung
        return this.geneticPuzzleOptimization(baseGrid, {
            targetDifficulty: targetDifficulty,
            playerStrengths: playerSkill.strengths,
            playerWeaknesses: playerSkill.weaknesses,
            learningGoals: playerSkill.learningGoals
        });
    }
}

import * as tf from '@tensorflow/tfjs';

class SudokuAI {
    constructor() {
        this.model = null;
        this.isTraining = false;
    }
    
    async loadModel() {
        try {
            // Lade vortrainiertes Modell
            this.model = await tf.loadLayersModel('/models/sudoku-ai.json');
            console.log('KI-Modell erfolgreich geladen');
        } catch (error) {
            console.log('Erstelle neues Modell');
            this.model = this.createModel();
        }
    }
    
    createModel() {
        const model = tf.sequential({
            layers: [
                tf.layers.dense({
                    inputShape: [81],  // 9x9 Sudoku Grid
                    units: 256,
                    activation: 'relu'
                }),
                tf.layers.dropout({rate: 0.3}),
                tf.layers.dense({units: 128, activation: 'relu'}),
                tf.layers.dropout({rate: 0.3}),
                tf.layers.dense({units: 64, activation: 'relu'}),
                tf.layers.dense({
                    units: 9,  // Wahrscheinlichkeit für Zahlen 1-9
                    activation: 'softmax'
                })
            ]
        });
        
        model.compile({
            optimizer: tf.train.adam(0.001),
            loss: 'categoricalCrossentropy',
            metrics: ['accuracy']
        });
        
        return model;
    }
    
    async predictNextMove(gridState, selectedCell) {
        if (!this.model) {
            await this.loadModel();
        }
        
        // Normalisiere Eingabe
        const input = tf.tensor2d([gridState], [1, 81]);
        
        // Vorhersage
        const prediction = this.model.predict(input);
        const probabilities = await prediction.data();
        
        // Cleanup
        input.dispose();
        prediction.dispose();
        
        // Konvertiere zu nützlichen Hinweisen
        return this.interpretPrediction(probabilities, selectedCell);
    }
    
    async trainOnUserData(gameHistory, outcomes) {
        if (this.isTraining) return;
        
        this.isTraining = true;
        
        const xs = tf.tensor2d(gameHistory);
        const ys = tf.tensor2d(outcomes);
        
        await this.model.fit(xs, ys, {
            epochs: 10,
            batchSize: 32,
            validationSplit: 0.2,
            callbacks: {
                onEpochEnd: (epoch, logs) => {
                    console.log(`Epoch ${epoch}: loss = ${logs.loss}`);
                }
            }
        });
        
        // Speichere aktualisiertes Modell
        await this.model.save('localstorage://sudoku-ai-model');
        
        xs.dispose();
        ys.dispose();
        this.isTraining = false;
    }
}