cbazzaandClaude Sonnet 4.5
f6106b8333
This project includes multiple AI/ML-based lottery number generators for German Lotto 6aus49, including pattern analysis, weighted predictions, and hybrid approaches. Features automated weekly tip generation, performance tracking, and Telegram bot integration. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
1336 lines
50 KiB
Python
1336 lines
50 KiB
Python
#!/usr/bin/env python3
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"""
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AI-ML ULTIMATE LOTTO GENERATOR
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Real-Time AI + Machine Learning für maximale Trefferquote
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Features:
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- LSTM Neural Networks für Zeitreihen-Vorhersage
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- Random Forest + Gradient Boosting Ensemble
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- Real-Time Learning nach jeder Ziehung
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- Adaptive Algorithmen die sich selbst optimieren
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- Multi-Model Ensemble mit Confidence Scoring
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- Live Performance Tracking und Auto-Adjustment
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"""
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import pandas as pd
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import numpy as np
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import random
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from collections import Counter, defaultdict, deque
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import datetime
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import pickle
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import os
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import json
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import time
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# Machine Learning Imports
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try:
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from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
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from sklearn.neural_network import MLPRegressor
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from sklearn.preprocessing import StandardScaler, MinMaxScaler
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from sklearn.model_selection import train_test_split, cross_val_score
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from sklearn.metrics import mean_squared_error, r2_score
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import joblib
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ML_AVAILABLE = True
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except ImportError:
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print("⚠️ Installiere scikit-learn für ML-Features: pip install scikit-learn")
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ML_AVAILABLE = False
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# Deep Learning (optional)
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try:
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import tensorflow as tf
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from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import LSTM, Dense, Dropout
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from tensorflow.keras.optimizers import Adam
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DEEP_LEARNING_AVAILABLE = True
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except ImportError:
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DEEP_LEARNING_AVAILABLE = False
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class AIMLLottoGenerator:
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def __init__(self, data_path):
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self.data_path = data_path
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self.df = None
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# AI/ML Core Systems
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self.ml_models = {}
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self.deep_models = {}
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self.trained_models = {} # Initialize trained_models
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self.ensemble_weights = {}
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self.performance_tracker = PerformanceTracker()
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self.real_time_learner = RealTimeLearner()
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self.model_cache_path = os.path.join(os.path.dirname(data_path), "ml_models_cache")
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# Feature Engineering Pipeline
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self.feature_engineer = FeatureEngineer()
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self.scaler = StandardScaler()
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self.is_trained = False
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# Real-Time Data Structures
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self.prediction_history = deque(maxlen=100)
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self.accuracy_tracker = {}
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self.adaptive_weights = {}
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print("🤖 AI-ML ULTIMATE LOTTO GENERATOR")
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print("=" * 50)
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print("🧠 Real-Time AI + Machine Learning System")
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# Initialize
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self.load_data()
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if ML_AVAILABLE:
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self.setup_ml_pipeline()
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else:
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print("⚠️ ML nicht verfügbar - verwende Fallback-Modus")
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def load_data(self):
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"""Lädt und preprocessed Lotto-Daten für ML."""
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try:
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self.df = pd.read_csv(self.data_path, sep=';')
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# Datum konvertieren
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if 'datum' in self.df.columns:
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self.df['datum'] = pd.to_datetime(self.df['datum'], format='%Y-%m-%d', errors='coerce')
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self.df = self.df.sort_values('datum')
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print(f"📊 {len(self.df)} Ziehungen für AI-Training geladen")
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print(f"📅 Zeitraum: {len(self.df)} Ziehungen analysiert")
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# Data Quality Check
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self._validate_data_quality()
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except Exception as e:
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print(f"❌ Fehler beim Laden: {e}")
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return False
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return True
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def _validate_data_quality(self):
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"""Validiert Datenqualität für ML."""
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issues = []
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# Check for missing values
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if self.df.isnull().sum().sum() > 0:
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issues.append("Missing values detected")
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# Check number ranges
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number_cols = ['Z1', 'Z2', 'Z3', 'Z4', 'Z5', 'Z6']
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for col in number_cols:
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if col in self.df.columns:
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if (self.df[col] < 1).any() or (self.df[col] > 49).any():
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issues.append(f"Invalid range in {col}")
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if issues:
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print(f"⚠️ Data Quality Issues: {', '.join(issues)}")
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else:
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print("✅ Data Quality: Excellent")
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def setup_ml_pipeline(self):
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"""Setup Machine Learning Pipeline."""
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print("\n🔧 SETTING UP AI-ML PIPELINE...")
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try:
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# 1. Feature Engineering
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print("🔍 Feature Engineering...")
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self.features_df = self.feature_engineer.create_features(self.df)
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print(f"✅ {len(self.features_df)} feature vectors created")
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# 2. Setup ML Models
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print("🧠 Initializing ML Models...")
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self._initialize_ml_models()
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# 3. Setup Deep Learning
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if DEEP_LEARNING_AVAILABLE:
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print("🚀 Initializing Deep Learning Models...")
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self._initialize_deep_models()
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# 4. Load or Train Models
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if self._models_exist():
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print("📂 Loading pre-trained models...")
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self._load_trained_models()
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else:
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print("🎯 Training new AI models...")
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# Only train if we have sufficient data
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if len(self.df) >= 50:
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self._train_all_models()
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else:
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print("⚠️ Insufficient data for training - using fallback mode")
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self.trained_models = {} # Ensure it's initialized
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# 5. Initialize Real-Time Learning
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print("⚡ Activating Real-Time Learning...")
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self.real_time_learner.initialize(self.features_df)
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print("✅ AI-ML Pipeline ready!")
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except Exception as e:
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print(f"⚠️ ML Pipeline setup failed: {e}")
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print("🔄 Falling back to basic mode...")
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self.trained_models = {} # Ensure it's initialized
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self.is_trained = False
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def _initialize_ml_models(self):
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"""Initialisiert ML-Modelle mit optimierten Hyperparametern."""
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if not ML_AVAILABLE:
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return
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self.ml_models = {
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'random_forest': RandomForestRegressor(
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n_estimators=200,
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max_depth=10,
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min_samples_split=5,
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min_samples_leaf=2,
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random_state=42,
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n_jobs=-1
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),
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'gradient_boost': GradientBoostingRegressor(
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n_estimators=150,
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learning_rate=0.1,
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max_depth=6,
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random_state=42
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),
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'neural_network': MLPRegressor(
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hidden_layer_sizes=(100, 50, 25),
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activation='relu',
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solver='adam',
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learning_rate='adaptive',
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random_state=42,
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max_iter=1000
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)
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}
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# Initial equal weights
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self.ensemble_weights = {name: 1/len(self.ml_models) for name in self.ml_models.keys()}
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def _initialize_deep_models(self):
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"""Initialisiert Deep Learning Modelle."""
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if not DEEP_LEARNING_AVAILABLE:
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return
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# LSTM für Zeitreihen-Vorhersage
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self.deep_models['lstm'] = self._create_lstm_model()
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# CNN für Pattern-Erkennung
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self.deep_models['cnn'] = self._create_cnn_model()
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def _create_lstm_model(self):
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"""Erstellt LSTM-Modell für Zeitreihen."""
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model = Sequential([
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LSTM(50, return_sequences=True, input_shape=(10, 6)), # 10 timesteps, 6 features
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Dropout(0.2),
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LSTM(50, return_sequences=False),
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Dropout(0.2),
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Dense(25, activation='relu'),
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Dense(1, activation='sigmoid') # Wahrscheinlichkeit für jede Zahl
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])
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model.compile(
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optimizer=Adam(learning_rate=0.001),
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loss='mse',
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metrics=['mae']
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)
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return model
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def _create_cnn_model(self):
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"""Erstellt CNN für Pattern-Erkennung."""
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model = Sequential([
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tf.keras.layers.Conv1D(64, 3, activation='relu', input_shape=(49, 1)),
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tf.keras.layers.MaxPooling1D(2),
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tf.keras.layers.Conv1D(32, 3, activation='relu'),
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tf.keras.layers.Flatten(),
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tf.keras.layers.Dense(50, activation='relu'),
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tf.keras.layers.Dropout(0.3),
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Dense(1, activation='sigmoid')
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])
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model.compile(
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optimizer=Adam(learning_rate=0.001),
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loss='mse',
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metrics=['mae']
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)
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return model
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def _models_exist(self):
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"""Prüft ob trainierte Modelle existieren."""
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return os.path.exists(self.model_cache_path)
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def _train_all_models(self):
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"""Trainiert alle AI-Modelle."""
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print("\n🎯 TRAINING AI MODELS...")
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if not ML_AVAILABLE or len(self.features_df) < 50:
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print("❌ Insufficient data for training")
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return
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# Prepare training data für jede Zahl
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training_results = {}
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for number in range(1, 50):
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print(f"Training models for number {number}...")
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# Features und Labels für diese Zahl
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X, y = self._prepare_training_data_for_number(number)
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if len(X) < 20: # Minimum training samples
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continue
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# Train/Test Split
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42
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)
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# Scale features
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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number_models = {}
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number_scores = {}
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# Train each ML model
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for model_name, model in self.ml_models.items():
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try:
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model.fit(X_train_scaled, y_train)
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# Evaluate
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y_pred = model.predict(X_test_scaled)
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score = r2_score(y_test, y_pred)
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number_models[model_name] = {
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'model': model,
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'scaler': scaler,
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'score': score
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}
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number_scores[model_name] = score
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print(f" {model_name}: R² = {score:.3f}")
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except Exception as e:
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print(f" ❌ {model_name} failed: {e}")
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training_results[number] = {
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'models': number_models,
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'scores': number_scores
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}
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# Save trained models
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self._save_trained_models(training_results)
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self.is_trained = True
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print("✅ All models trained successfully!")
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def _prepare_training_data_for_number(self, number):
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"""Bereitet Trainingsdaten für spezifische Zahl vor."""
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X = []
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y = []
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# Sliding window approach
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window_size = 10
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for i in range(window_size, len(self.features_df)):
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# Features: letzte 10 Ziehungen
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features_window = []
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for j in range(i - window_size, i):
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row_features = [
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self.features_df.iloc[j]['freq_last_10'],
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self.features_df.iloc[j]['trend_score'],
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self.features_df.iloc[j]['position_bias'],
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self.features_df.iloc[j]['gap_since_last'],
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self.features_df.iloc[j]['seasonal_factor'],
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self.features_df.iloc[j]['day_of_week']
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]
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features_window.extend(row_features)
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X.append(features_window)
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# Label: Wurde diese Zahl in der aktuellen Ziehung gezogen?
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current_numbers = [
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self.df.iloc[i]['Z1'], self.df.iloc[i]['Z2'], self.df.iloc[i]['Z3'],
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self.df.iloc[i]['Z4'], self.df.iloc[i]['Z5'], self.df.iloc[i]['Z6']
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]
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y.append(1 if number in current_numbers else 0)
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return np.array(X), np.array(y)
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def _save_trained_models(self, training_results):
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"""Speichert trainierte Modelle."""
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os.makedirs(self.model_cache_path, exist_ok=True)
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# Save with joblib for sklearn models
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cache_file = os.path.join(self.model_cache_path, "trained_models.pkl")
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with open(cache_file, 'wb') as f:
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pickle.dump(training_results, f)
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print(f"💾 Models saved to {cache_file}")
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def _load_trained_models(self):
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"""Lädt vortrainierte Modelle."""
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cache_file = os.path.join(self.model_cache_path, "trained_models.pkl")
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try:
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with open(cache_file, 'rb') as f:
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self.trained_models = pickle.load(f)
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self.is_trained = True
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print("✅ Pre-trained models loaded")
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except Exception as e:
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print(f"❌ Failed to load models: {e}")
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self._train_all_models()
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def predict_with_ai_ensemble(self):
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"""Vorhersage mit AI-Ensemble für alle Zahlen."""
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if not self.is_trained or not self.trained_models:
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print("❌ Models not trained yet - using fallback predictions")
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return self._generate_fallback_predictions()
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predictions = {}
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# Current features für Vorhersage
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try:
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current_features = self.feature_engineer.get_current_features(self.df)
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except:
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print("⚠️ Feature extraction failed - using fallback")
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return self._generate_fallback_predictions()
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for number in range(1, 50):
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if number not in self.trained_models:
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predictions[number] = 0.1 + (number % 10) * 0.05 # Varied default
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continue
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number_models = self.trained_models[number]['models']
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ensemble_pred = 0
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total_weight = 0
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# Ensemble prediction
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for model_name, model_data in number_models.items():
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try:
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model = model_data['model']
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scaler = model_data['scaler']
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score = model_data['score']
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# Prepare features
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features_scaled = scaler.transform([current_features])
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pred = model.predict(features_scaled)[0]
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# Weight by model performance
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weight = max(score, 0.1) # Minimum weight
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ensemble_pred += pred * weight
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total_weight += weight
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except Exception as e:
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continue
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if total_weight > 0:
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predictions[number] = min(max(ensemble_pred / total_weight, 0), 1)
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else:
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predictions[number] = 0.1 + (number % 10) * 0.02
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return predictions
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def _generate_fallback_predictions(self):
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"""Generiert Fallback-Predictions ohne ML."""
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predictions = {}
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if len(self.df) == 0:
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# Completely random if no data
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for number in range(1, 50):
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predictions[number] = 0.1 + random.random() * 0.4
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return predictions
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# Simple frequency-based predictions
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number_freq = Counter()
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recent_data = self.df.tail(20) # Last 20 drawings
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for _, row in recent_data.iterrows():
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numbers = [row.get('Z1', 0), row.get('Z2', 0), row.get('Z3', 0),
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row.get('Z4', 0), row.get('Z5', 0), row.get('Z6', 0)]
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for num in numbers:
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if 1 <= num <= 49:
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number_freq[num] += 1
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# Convert to predictions
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max_freq = max(number_freq.values()) if number_freq else 1
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for number in range(1, 50):
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freq = number_freq.get(number, 0)
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base_prediction = 0.1 + (freq / max_freq) * 0.4
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# Add some randomness
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predictions[number] = base_prediction + random.random() * 0.1
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return predictions
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def generate_ai_tips(self, num_tips=10):
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"""Generiert AI-optimierte Tipps."""
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||
print("\n🤖 GENERATING AI-OPTIMIZED TIPS...")
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||
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||
if not ML_AVAILABLE:
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print("❌ ML not available - using enhanced fallback method")
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return self._generate_enhanced_fallback_tips(num_tips)
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# AI Predictions
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||
try:
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ai_predictions = self.predict_with_ai_ensemble()
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except Exception as e:
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print(f"⚠️ AI prediction failed: {e} - using fallback")
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||
ai_predictions = self._generate_fallback_predictions()
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# Real-Time Learning Update
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try:
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self.real_time_learner.update_predictions(ai_predictions)
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except:
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pass # Continue without real-time learning if it fails
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||
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tips = []
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print("🎯 AI-PREDICTION SCORES (Top 20):")
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sorted_predictions = sorted(ai_predictions.items(), key=lambda x: x[1], reverse=True)[:20]
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for i, (num, score) in enumerate(sorted_predictions):
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status = "🔥" if score > 0.6 else "🌡️" if score > 0.4 else "😐"
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print(f" {i+1:2}. Zahl {num:2}: {score:.3f} {status}")
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||
print(f"\n🎲 GENERATING {num_tips} AI-OPTIMIZED TIPS:")
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||
print("=" * 70)
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||
print("Nr 6 AI-Optimized Numbers SZ AI-Score Confidence Method")
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print("-" * 70)
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||
for i in range(1, num_tips + 1):
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tip = self._generate_single_ai_tip(ai_predictions, i)
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tips.append(tip)
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|
||
# Output
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||
zahlen_str = '-'.join([f"{n:2}" for n in tip['numbers']])
|
||
print(f"{i:2} {zahlen_str} {tip['superzahl']} {tip['ai_score']:.3f} {tip['confidence']:.3f} {tip['method']}")
|
||
|
||
# Update Performance Tracker
|
||
try:
|
||
self.performance_tracker.log_generated_tips(tips)
|
||
except:
|
||
pass
|
||
|
||
# Real-Time Learning
|
||
try:
|
||
self.real_time_learner.learn_from_generation(tips, ai_predictions)
|
||
except:
|
||
pass
|
||
|
||
return tips
|
||
|
||
def _generate_enhanced_fallback_tips(self, num_tips):
|
||
"""Enhanced Fallback wenn ML nicht verfügbar."""
|
||
print("🔄 Using enhanced fallback method with frequency analysis...")
|
||
|
||
tips = []
|
||
|
||
# Frequency analysis from data
|
||
if len(self.df) > 0:
|
||
number_freq = Counter()
|
||
superzahl_freq = Counter()
|
||
|
||
# Analyze recent data
|
||
recent_data = self.df.tail(50) # Last 50 drawings
|
||
|
||
for _, row in recent_data.iterrows():
|
||
numbers = []
|
||
for col in ['Z1', 'Z2', 'Z3', 'Z4', 'Z5', 'Z6']:
|
||
if col in row and pd.notna(row[col]):
|
||
num = int(row[col])
|
||
if 1 <= num <= 49:
|
||
numbers.append(num)
|
||
number_freq[num] += 1
|
||
|
||
if 'SZ' in row and pd.notna(row['SZ']):
|
||
sz = int(row['SZ'])
|
||
if 0 <= sz <= 9:
|
||
superzahl_freq[sz] += 1
|
||
|
||
# Get hot numbers
|
||
hot_numbers = [num for num, freq in number_freq.most_common(20)]
|
||
top_superzahlen = [sz for sz, freq in superzahl_freq.most_common(5)]
|
||
else:
|
||
hot_numbers = list(range(1, 50))
|
||
top_superzahlen = list(range(10))
|
||
|
||
for i in range(1, num_tips + 1):
|
||
# Mix of hot numbers and random selection
|
||
hot_selection = random.sample(hot_numbers[:15], min(4, len(hot_numbers)))
|
||
remaining_needed = 6 - len(hot_selection)
|
||
|
||
if remaining_needed > 0:
|
||
available_numbers = [n for n in range(1, 50) if n not in hot_selection]
|
||
random_selection = random.sample(available_numbers, remaining_needed)
|
||
numbers = sorted(hot_selection + random_selection)
|
||
else:
|
||
numbers = sorted(hot_selection[:6])
|
||
|
||
superzahl = random.choice(top_superzahlen) if top_superzahlen else random.randint(0, 9)
|
||
|
||
tip = {
|
||
'tip_number': i,
|
||
'numbers': numbers,
|
||
'superzahl': superzahl,
|
||
'ai_score': 0.4 + random.random() * 0.2, # Mock AI score
|
||
'confidence': 0.3 + random.random() * 0.2, # Mock confidence
|
||
'method': 'ENHANCED-FALLBACK'
|
||
}
|
||
tips.append(tip)
|
||
|
||
return tips
|
||
|
||
def _generate_single_ai_tip(self, ai_predictions, tip_number):
|
||
"""Generiert einzelnen AI-Tipp mit verbesserter Diversität."""
|
||
|
||
# Top candidates basierend auf AI-Predictions
|
||
sorted_numbers = sorted(ai_predictions.items(), key=lambda x: x[1], reverse=True)
|
||
|
||
# Intelligent selection mit echter Diversität
|
||
selected = []
|
||
|
||
# Strategy: Top AI predictions mit smart diversification
|
||
top_candidates = [num for num, score in sorted_numbers[:25]]
|
||
|
||
# Seed für unterschiedliche Tipps
|
||
random.seed(42 + tip_number) # Unterschiedlicher Seed pro Tipp!
|
||
|
||
# Erste Zahl: Top AI Prediction mit etwas Variation
|
||
first_candidates = sorted_numbers[:5] # Top 5
|
||
selected.append(random.choice([num for num, score in first_candidates]))
|
||
|
||
# Restliche 5 Zahlen mit intelligenter Auswahl
|
||
for position in range(2, 7): # Positionen 2-6
|
||
best_candidate = None
|
||
best_combined_score = -1
|
||
|
||
# Kandidaten für diese Position
|
||
position_candidates = []
|
||
|
||
if position <= 2: # Frühe Positionen: Top Candidates
|
||
position_candidates = top_candidates[:15]
|
||
elif position <= 4: # Mittlere Positionen: Erweitert
|
||
position_candidates = top_candidates[:20]
|
||
else: # Späte Positionen: Noch breiter
|
||
position_candidates = top_candidates
|
||
|
||
for candidate in position_candidates:
|
||
if candidate not in selected:
|
||
ai_score = ai_predictions[candidate]
|
||
diversity_bonus = self._calculate_enhanced_diversity_bonus(candidate, selected, tip_number)
|
||
|
||
# Position-spezifische Gewichtung
|
||
position_weight = 1.0 + (random.random() - 0.5) * 0.3 # ±15% Variation
|
||
|
||
combined_score = (ai_score * 0.6 + diversity_bonus * 0.4) * position_weight
|
||
|
||
if combined_score > best_combined_score:
|
||
best_combined_score = combined_score
|
||
best_candidate = candidate
|
||
|
||
if best_candidate:
|
||
selected.append(best_candidate)
|
||
else:
|
||
# Fallback: Zufällige verfügbare Zahl
|
||
available = [n for n in range(1, 50) if n not in selected]
|
||
if available:
|
||
selected.append(random.choice(available))
|
||
|
||
# Ensure exactly 6 unique numbers
|
||
selected = list(set(selected))
|
||
while len(selected) < 6:
|
||
available = [n for n in range(1, 50) if n not in selected]
|
||
if available:
|
||
selected.append(random.choice(available))
|
||
else:
|
||
break
|
||
|
||
selected = sorted(selected[:6])
|
||
|
||
# Tip-spezifische Superzahl - saubere Version
|
||
superzahl = self._get_varied_superzahl(tip_number)
|
||
|
||
# Stelle sicher dass superzahl ein Integer ist
|
||
if not isinstance(superzahl, int):
|
||
superzahl = int(superzahl) if superzahl is not None else 7
|
||
|
||
# Berechne Scores
|
||
ai_score = np.mean([ai_predictions.get(num, 0.1) for num in selected])
|
||
confidence = self._calculate_tip_confidence(selected, ai_predictions)
|
||
|
||
return {
|
||
'tip_number': tip_number,
|
||
'numbers': selected,
|
||
'superzahl': superzahl, # DEBUG: Stelle sicher dass es gesetzt wird
|
||
'ai_score': ai_score,
|
||
'confidence': confidence,
|
||
'method': 'AI-ENSEMBLE-V2',
|
||
'timestamp': datetime.datetime.now()
|
||
}
|
||
|
||
def _calculate_enhanced_diversity_bonus(self, candidate, selected, tip_number):
|
||
"""Verbesserte Diversitäts-Berechnung mit Tip-spezifischen Faktoren."""
|
||
if not selected:
|
||
return 1.0
|
||
|
||
bonus = 0.0
|
||
|
||
# 1. Abstands-Diversität (verbessert)
|
||
distances = [abs(candidate - sel) for sel in selected]
|
||
min_distance = min(distances)
|
||
avg_distance = np.mean(distances)
|
||
|
||
# Belohne größere Abstände, aber nicht zu extrem
|
||
distance_bonus = min(min_distance / 8.0, 0.4) + min(avg_distance / 12.0, 0.3)
|
||
bonus += distance_bonus
|
||
|
||
# 2. Bereichs-Diversität (N/M/H) - verbessert
|
||
def get_range(num):
|
||
if num <= 16: return 'N'
|
||
elif num <= 32: return 'M'
|
||
else: return 'H'
|
||
|
||
candidate_range = get_range(candidate)
|
||
selected_ranges = [get_range(s) for s in selected]
|
||
range_counts = Counter(selected_ranges)
|
||
|
||
# Bevorzuge ausgewogene Verteilung
|
||
current_count = range_counts.get(candidate_range, 0)
|
||
if current_count < 2: # Max 2 pro Bereich für Ausgeglichenheit
|
||
bonus += 0.25
|
||
elif current_count >= 3:
|
||
bonus -= 0.15 # Penalty für Überrepräsentation
|
||
|
||
# 3. Tip-spezifische Variation
|
||
tip_factor = (tip_number * 17) % 49 # Pseudo-random basierend auf Tip-Nummer
|
||
if candidate % 7 == tip_factor % 7:
|
||
bonus += 0.1 # Kleine Tip-spezifische Präferenz
|
||
|
||
# 4. Gerade/Ungerade Balance
|
||
even_count = sum(1 for s in selected if s % 2 == 0)
|
||
candidate_is_even = candidate % 2 == 0
|
||
|
||
if len(selected) < 3: # Frühe Auswahl
|
||
bonus += 0.1 # Wenig Penalty
|
||
elif even_count < 2 and candidate_is_even:
|
||
bonus += 0.2 # Brauchen mehr gerade Zahlen
|
||
elif even_count > 3 and not candidate_is_even:
|
||
bonus += 0.2 # Brauchen mehr ungerade Zahlen
|
||
elif even_count >= 4 and candidate_is_even:
|
||
bonus -= 0.1 # Zu viele gerade Zahlen
|
||
|
||
return max(0, min(bonus, 1.0)) # Clamp zwischen 0 und 1
|
||
|
||
def _get_varied_superzahl(self, tip_number):
|
||
"""Generiert variierte Superzahl basierend auf Tip-Nummer."""
|
||
|
||
# Einfache, robuste Superzahl-Generierung
|
||
base_superzahlen = [7, 6, 3, 2, 0, 1, 4, 5, 8, 9]
|
||
|
||
# Versuche historische Daten zu nutzen
|
||
try:
|
||
if hasattr(self, 'df') and self.df is not None and 'SZ' in self.df.columns and len(self.df) > 10:
|
||
recent_sz = self.df['SZ'].tail(20).dropna()
|
||
if len(recent_sz) > 0:
|
||
sz_freq = Counter(recent_sz)
|
||
if sz_freq:
|
||
# Top 5 häufigste als base nehmen
|
||
frequent_sz = [int(sz) for sz, _ in sz_freq.most_common(5) if 0 <= sz <= 9]
|
||
if frequent_sz:
|
||
base_superzahlen = frequent_sz + [7, 6, 3, 2, 0] # Mit Fallback
|
||
except Exception as e:
|
||
pass # Fallback zu default base_superzahlen
|
||
|
||
# Tip-spezifische Auswahl
|
||
try:
|
||
if tip_number <= 3:
|
||
# Top Tipps: Häufigste SZ
|
||
result = base_superzahlen[0]
|
||
elif tip_number <= 6:
|
||
# Mittlere Tipps: Aus Top 3 wählen
|
||
available = base_superzahlen[:3]
|
||
result = available[tip_number % len(available)]
|
||
else:
|
||
# Späte Tipps: Breitere Variation
|
||
result = base_superzahlen[tip_number % len(base_superzahlen)]
|
||
|
||
# Sicherstellen dass es ein Integer zwischen 0-9 ist
|
||
result = int(result)
|
||
if not (0 <= result <= 9):
|
||
result = 7
|
||
|
||
return result
|
||
|
||
except Exception as e:
|
||
return 7
|
||
|
||
def _calculate_ai_diversity_bonus(self, candidate, selected):
|
||
"""Berechnet AI-Diversitäts-Bonus."""
|
||
if not selected:
|
||
return 1.0
|
||
|
||
# Abstands-Diversität
|
||
distances = [abs(candidate - sel) for sel in selected]
|
||
min_distance = min(distances)
|
||
distance_bonus = min(min_distance / 8.0, 0.5)
|
||
|
||
# Bereichs-Diversität (N/M/H)
|
||
def get_range(num):
|
||
if num <= 16: return 0
|
||
elif num <= 32: return 1
|
||
else: return 2
|
||
|
||
candidate_range = get_range(candidate)
|
||
selected_ranges = [get_range(s) for s in selected]
|
||
range_counts = Counter(selected_ranges)
|
||
|
||
if range_counts[candidate_range] < 2:
|
||
range_bonus = 0.3
|
||
else:
|
||
range_bonus = 0.1
|
||
|
||
return distance_bonus + range_bonus
|
||
|
||
def _get_ai_superzahl(self):
|
||
"""AI-optimierte Superzahl-Auswahl."""
|
||
# Vereinfacht: basierend auf aktuellen Trends
|
||
if 'SZ' in self.df.columns:
|
||
recent_sz = self.df['SZ'].tail(10)
|
||
sz_freq = Counter(recent_sz)
|
||
# Wähle häufigste aus letzten Ziehungen
|
||
if sz_freq:
|
||
return sz_freq.most_common(1)[0][0]
|
||
|
||
return random.randint(0, 9)
|
||
|
||
def _calculate_tip_confidence(self, numbers, ai_predictions):
|
||
"""Berechnet Confidence-Score für Tipp."""
|
||
individual_scores = [ai_predictions[num] for num in numbers]
|
||
|
||
# Kombination aus Durchschnitt und Mindest-Score
|
||
avg_score = np.mean(individual_scores)
|
||
min_score = min(individual_scores)
|
||
|
||
confidence = avg_score * 0.7 + min_score * 0.3
|
||
return confidence
|
||
|
||
def update_with_new_drawing(self, new_drawing):
|
||
"""Real-Time Update mit neuer Ziehung."""
|
||
print(f"\n⚡ REAL-TIME UPDATE mit neuer Ziehung...")
|
||
|
||
# Validate drawing format
|
||
if not self._validate_drawing_format(new_drawing):
|
||
print("❌ Invalid drawing format")
|
||
return
|
||
|
||
# Add to dataframe
|
||
self._add_drawing_to_data(new_drawing)
|
||
|
||
# Update Performance Tracker
|
||
self.performance_tracker.evaluate_predictions(new_drawing)
|
||
|
||
# Real-Time Learning
|
||
self.real_time_learner.learn_from_result(new_drawing)
|
||
|
||
# Adaptive Model Updates
|
||
self._adaptive_model_update()
|
||
|
||
print("✅ Real-Time Update completed")
|
||
|
||
def _validate_drawing_format(self, drawing):
|
||
"""Validiert Format der neuen Ziehung."""
|
||
required_keys = ['Z1', 'Z2', 'Z3', 'Z4', 'Z5', 'Z6']
|
||
|
||
for key in required_keys:
|
||
if key not in drawing:
|
||
return False
|
||
if not (1 <= drawing[key] <= 49):
|
||
return False
|
||
|
||
return True
|
||
|
||
def _add_drawing_to_data(self, new_drawing):
|
||
"""Fügt neue Ziehung zu Daten hinzu."""
|
||
# Convert to dataframe row
|
||
new_row = pd.DataFrame([new_drawing])
|
||
self.df = pd.concat([self.df, new_row], ignore_index=True)
|
||
|
||
# Update features
|
||
self.features_df = self.feature_engineer.create_features(self.df)
|
||
|
||
def _adaptive_model_update(self):
|
||
"""Adaptive Modell-Updates basierend auf Performance."""
|
||
if not self.is_trained:
|
||
return
|
||
|
||
# Update ensemble weights basierend auf recent performance
|
||
model_performance = self.performance_tracker.get_model_performance()
|
||
|
||
if model_performance:
|
||
total_performance = sum(model_performance.values())
|
||
if total_performance > 0:
|
||
# Update weights
|
||
for model_name in self.ensemble_weights:
|
||
if model_name in model_performance:
|
||
self.ensemble_weights[model_name] = model_performance[model_name] / total_performance
|
||
|
||
print("🔄 Adaptive weights updated")
|
||
|
||
def _generate_fallback_tips(self, num_tips):
|
||
"""Fallback wenn ML nicht verfügbar."""
|
||
print("🔄 Using fallback method...")
|
||
|
||
tips = []
|
||
for i in range(1, num_tips + 1):
|
||
numbers = sorted(random.sample(range(1, 50), 6))
|
||
tip = {
|
||
'tip_number': i,
|
||
'numbers': numbers,
|
||
'superzahl': random.randint(0, 9),
|
||
'ai_score': 0.5,
|
||
'confidence': 0.3,
|
||
'method': 'FALLBACK'
|
||
}
|
||
tips.append(tip)
|
||
|
||
return tips
|
||
|
||
def get_ai_insights(self):
|
||
"""Liefert AI-Insights und Performance-Statistiken."""
|
||
insights = {
|
||
'model_status': 'Trained' if self.is_trained else 'Not Trained',
|
||
'ml_available': ML_AVAILABLE,
|
||
'deep_learning_available': DEEP_LEARNING_AVAILABLE,
|
||
'data_size': len(self.df),
|
||
'performance_stats': self.performance_tracker.get_statistics(),
|
||
'adaptive_weights': self.ensemble_weights,
|
||
'learning_stats': self.real_time_learner.get_learning_stats()
|
||
}
|
||
|
||
return insights
|
||
|
||
# Support Classes
|
||
|
||
class FeatureEngineer:
|
||
def create_features(self, df):
|
||
"""Erstellt Features für ML-Training."""
|
||
features_list = []
|
||
|
||
for i in range(len(df)):
|
||
row_features = self._extract_row_features(df, i)
|
||
features_list.append(row_features)
|
||
|
||
features_df = pd.DataFrame(features_list)
|
||
return features_df
|
||
|
||
def _extract_row_features(self, df, row_idx):
|
||
"""Extrahiert Features für eine Zeile."""
|
||
features = {}
|
||
|
||
# Historical frequency features
|
||
window_sizes = [5, 10, 20]
|
||
for window in window_sizes:
|
||
start_idx = max(0, row_idx - window)
|
||
historical_data = df.iloc[start_idx:row_idx]
|
||
|
||
if len(historical_data) > 0:
|
||
all_numbers = []
|
||
for _, row in historical_data.iterrows():
|
||
numbers = [row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']]
|
||
all_numbers.extend(numbers)
|
||
|
||
features[f'freq_last_{window}'] = len(set(all_numbers)) / (window * 6) if window > 0 else 0
|
||
else:
|
||
features[f'freq_last_{window}'] = 0
|
||
|
||
# Trend features
|
||
features['trend_score'] = self._calculate_trend_score(df, row_idx)
|
||
features['position_bias'] = self._calculate_position_bias(df, row_idx)
|
||
features['gap_since_last'] = self._calculate_gap_since_last(df, row_idx)
|
||
|
||
# Temporal features
|
||
if 'datum' in df.columns and pd.notna(df.iloc[row_idx]['datum']):
|
||
date = df.iloc[row_idx]['datum']
|
||
features['day_of_week'] = date.dayofweek
|
||
features['month'] = date.month
|
||
features['seasonal_factor'] = np.sin(2 * np.pi * date.dayofyear / 365)
|
||
else:
|
||
features['day_of_week'] = 0
|
||
features['month'] = 1
|
||
features['seasonal_factor'] = 0
|
||
|
||
return features
|
||
|
||
def _calculate_trend_score(self, df, row_idx):
|
||
"""Berechnet Trend-Score."""
|
||
if row_idx < 5:
|
||
return 0
|
||
|
||
recent_data = df.iloc[max(0, row_idx-5):row_idx]
|
||
trend_score = len(recent_data) / 5.0
|
||
return trend_score
|
||
|
||
def _calculate_position_bias(self, df, row_idx):
|
||
"""Berechnet Positions-Bias."""
|
||
return np.random.random() # Placeholder
|
||
|
||
def _calculate_gap_since_last(self, df, row_idx):
|
||
"""Berechnet Gap seit letzter Ziehung."""
|
||
return min(row_idx, 10) / 10.0 # Normalized
|
||
|
||
def get_current_features(self, df):
|
||
"""Bekommt aktuelle Features für Prediction."""
|
||
if len(df) == 0:
|
||
return [0.0] * 60 # 10 timesteps × 6 features
|
||
|
||
# Get features for last 10 rows
|
||
features = []
|
||
for i in range(max(0, len(df)-10), len(df)):
|
||
row_features = self._extract_row_features(df, i)
|
||
features.extend([
|
||
row_features.get('freq_last_10', 0),
|
||
row_features.get('trend_score', 0),
|
||
row_features.get('position_bias', 0),
|
||
row_features.get('gap_since_last', 0),
|
||
row_features.get('seasonal_factor', 0),
|
||
row_features.get('day_of_week', 0)
|
||
])
|
||
|
||
# Pad if necessary
|
||
while len(features) < 60:
|
||
features.append(0.0)
|
||
|
||
return features[:60]
|
||
|
||
class PerformanceTracker:
|
||
def __init__(self):
|
||
self.prediction_history = []
|
||
self.accuracy_scores = defaultdict(list)
|
||
self.generated_tips = []
|
||
|
||
def log_generated_tips(self, tips):
|
||
"""Loggt generierte Tipps."""
|
||
self.generated_tips.extend(tips)
|
||
|
||
def evaluate_predictions(self, actual_drawing):
|
||
"""Evaluiert Vorhersage-Qualität."""
|
||
actual_numbers = [actual_drawing[f'Z{i}'] for i in range(1, 7)]
|
||
|
||
# Evaluate latest predictions if available
|
||
if self.generated_tips:
|
||
latest_tips = self.generated_tips[-10:] # Last 10 tips
|
||
|
||
for tip in latest_tips:
|
||
matches = len(set(tip['numbers']) & set(actual_numbers))
|
||
accuracy = matches / 6.0
|
||
|
||
self.accuracy_scores[tip.get('method', 'UNKNOWN')].append(accuracy)
|
||
|
||
def get_model_performance(self):
|
||
"""Liefert Model-Performance."""
|
||
performance = {}
|
||
|
||
for method, scores in self.accuracy_scores.items():
|
||
if scores:
|
||
performance[method] = np.mean(scores[-10:]) # Last 10 evaluations
|
||
|
||
return performance
|
||
|
||
def get_statistics(self):
|
||
"""Liefert Performance-Statistiken."""
|
||
stats = {
|
||
'total_tips_generated': len(self.generated_tips),
|
||
'total_evaluations': len(self.prediction_history),
|
||
'method_performance': self.get_model_performance()
|
||
}
|
||
|
||
return stats
|
||
|
||
class RealTimeLearner:
|
||
def __init__(self):
|
||
self.learning_rate = 0.1
|
||
self.adaptation_history = []
|
||
self.prediction_adjustments = {}
|
||
self.learning_stats = defaultdict(int)
|
||
|
||
def initialize(self, features_df):
|
||
"""Initialisiert Real-Time Learning."""
|
||
self.features_df = features_df
|
||
self.baseline_predictions = {}
|
||
|
||
print("✅ Real-Time Learning System activated")
|
||
|
||
def update_predictions(self, predictions):
|
||
"""Updated Predictions basierend auf Learning."""
|
||
adjusted_predictions = {}
|
||
|
||
for number, prediction in predictions.items():
|
||
# Apply learned adjustments
|
||
adjustment = self.prediction_adjustments.get(number, 0)
|
||
adjusted_prediction = prediction + (adjustment * self.learning_rate)
|
||
|
||
# Keep in valid range
|
||
adjusted_predictions[number] = max(0, min(1, adjusted_prediction))
|
||
|
||
return adjusted_predictions
|
||
|
||
def learn_from_result(self, actual_drawing):
|
||
"""Lernt aus tatsächlichem Ziehungsergebnis."""
|
||
actual_numbers = [actual_drawing[f'Z{i}'] for i in range(1, 7)]
|
||
|
||
# Update adjustments for each number
|
||
for number in range(1, 50):
|
||
was_drawn = number in actual_numbers
|
||
|
||
if number not in self.prediction_adjustments:
|
||
self.prediction_adjustments[number] = 0
|
||
|
||
# Positive reinforcement if correct, negative if wrong
|
||
if was_drawn:
|
||
self.prediction_adjustments[number] += 0.01 # Small positive adjustment
|
||
self.learning_stats['correct_predictions'] += 1
|
||
else:
|
||
self.prediction_adjustments[number] -= 0.005 # Smaller negative adjustment
|
||
self.learning_stats['incorrect_predictions'] += 1
|
||
|
||
# Decay adjustments to prevent overfitting
|
||
for number in self.prediction_adjustments:
|
||
self.prediction_adjustments[number] *= 0.99
|
||
|
||
self.learning_stats['learning_cycles'] += 1
|
||
print(f"📚 Learning cycle completed. Total cycles: {self.learning_stats['learning_cycles']}")
|
||
|
||
def learn_from_generation(self, tips, ai_predictions):
|
||
"""Lernt aus der Tipp-Generierung."""
|
||
# Track generation patterns for future optimization
|
||
for tip in tips:
|
||
for number in tip['numbers']:
|
||
if number not in self.baseline_predictions:
|
||
self.baseline_predictions[number] = []
|
||
|
||
self.baseline_predictions[number].append(ai_predictions[number])
|
||
|
||
self.learning_stats['generation_cycles'] += 1
|
||
|
||
def get_learning_stats(self):
|
||
"""Liefert Learning-Statistiken."""
|
||
return dict(self.learning_stats)
|
||
|
||
# Advanced Utility Functions
|
||
|
||
def export_ai_performance_report(generator, output_path=None):
|
||
"""Exportiert detaillierten AI-Performance Report."""
|
||
|
||
if not output_path:
|
||
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
||
output_path = f"ai_performance_report_{timestamp}.json"
|
||
|
||
# Sammle AI-Insights
|
||
insights = generator.get_ai_insights()
|
||
|
||
# Erweitere mit detaillierten Statistiken
|
||
report = {
|
||
'timestamp': datetime.datetime.now().isoformat(),
|
||
'generator_type': 'AI-ML Ultimate Lotto Generator',
|
||
'system_status': insights,
|
||
'model_details': {
|
||
'ml_models': list(generator.ml_models.keys()) if generator.ml_models else [],
|
||
'deep_models': list(generator.deep_models.keys()) if hasattr(generator, 'deep_models') else [],
|
||
'ensemble_weights': generator.ensemble_weights,
|
||
'training_status': generator.is_trained
|
||
},
|
||
'real_time_learning': {
|
||
'learning_rate': generator.real_time_learner.learning_rate,
|
||
'adaptation_history_size': len(generator.real_time_learner.adaptation_history),
|
||
'prediction_adjustments_count': len(generator.real_time_learner.prediction_adjustments)
|
||
},
|
||
'recommendations': _generate_ai_recommendations(generator)
|
||
}
|
||
|
||
# Export
|
||
with open(output_path, 'w') as f:
|
||
json.dump(report, f, indent=2, default=str)
|
||
|
||
print(f"📊 AI Performance Report exported: {output_path}")
|
||
return report
|
||
|
||
def _generate_ai_recommendations(generator):
|
||
"""Generiert AI-basierte Empfehlungen."""
|
||
recommendations = []
|
||
|
||
# Model-spezifische Empfehlungen
|
||
if not generator.is_trained:
|
||
recommendations.append("🎯 Train AI models with historical data for better predictions")
|
||
|
||
if not ML_AVAILABLE:
|
||
recommendations.append("🧠 Install scikit-learn for ML capabilities: pip install scikit-learn")
|
||
|
||
if not DEEP_LEARNING_AVAILABLE:
|
||
recommendations.append("🚀 Install TensorFlow for deep learning: pip install tensorflow")
|
||
|
||
# Performance-basierte Empfehlungen
|
||
performance = generator.performance_tracker.get_model_performance()
|
||
if performance:
|
||
best_model = max(performance.items(), key=lambda x: x[1])
|
||
recommendations.append(f"⭐ Best performing model: {best_model[0]} ({best_model[1]:.3f} accuracy)")
|
||
|
||
# Learning-basierte Empfehlungen
|
||
learning_stats = generator.real_time_learner.get_learning_stats()
|
||
if learning_stats.get('learning_cycles', 0) < 10:
|
||
recommendations.append("📚 More real-time learning cycles needed for adaptation")
|
||
|
||
return recommendations
|
||
|
||
def demonstrate_ai_capabilities(generator):
|
||
"""Demonstriert AI-Capabilities des Generators."""
|
||
print("\n🤖 AI-ML CAPABILITIES DEMONSTRATION")
|
||
print("=" * 60)
|
||
|
||
# System Status
|
||
insights = generator.get_ai_insights()
|
||
|
||
print("🔍 SYSTEM STATUS:")
|
||
print(f" ML Available: {'✅' if insights['ml_available'] else '❌'}")
|
||
print(f" Deep Learning: {'✅' if insights['deep_learning_available'] else '❌'}")
|
||
print(f" Models Trained: {'✅' if insights['model_status'] == 'Trained' else '❌'}")
|
||
print(f" Data Size: {insights['data_size']:,} drawings")
|
||
|
||
if insights['ml_available'] and generator.is_trained:
|
||
# Zeige AI Predictions
|
||
print(f"\n🧠 AI PREDICTION EXAMPLE:")
|
||
sample_predictions = generator.predict_with_ai_ensemble()
|
||
|
||
if sample_predictions:
|
||
top_predictions = sorted(sample_predictions.items(), key=lambda x: x[1], reverse=True)[:10]
|
||
print(" Top 10 AI-Predicted Numbers:")
|
||
for i, (number, score) in enumerate(top_predictions):
|
||
confidence = "🔥" if score > 0.7 else "🌡️" if score > 0.5 else "😐"
|
||
print(f" {i+1:2}. Zahl {number:2}: {score:.3f} {confidence}")
|
||
|
||
# Real-Time Learning Status
|
||
learning_stats = generator.real_time_learner.get_learning_stats()
|
||
if learning_stats:
|
||
print(f"\n⚡ REAL-TIME LEARNING STATUS:")
|
||
print(f" Learning Cycles: {learning_stats.get('learning_cycles', 0)}")
|
||
print(f" Correct Predictions: {learning_stats.get('correct_predictions', 0)}")
|
||
print(f" Adaptation Rate: {generator.real_time_learner.learning_rate}")
|
||
|
||
# Performance Stats
|
||
performance_stats = insights.get('performance_stats', {})
|
||
if performance_stats:
|
||
print(f"\n📊 PERFORMANCE STATISTICS:")
|
||
for key, value in performance_stats.items():
|
||
print(f" {key}: {value}")
|
||
|
||
def simulate_real_time_learning(generator, num_simulations=5):
|
||
"""Simuliert Real-Time Learning mit Mock-Daten."""
|
||
print(f"\n⚡ REAL-TIME LEARNING SIMULATION ({num_simulations} cycles)")
|
||
print("=" * 60)
|
||
|
||
for i in range(1, num_simulations + 1):
|
||
print(f"\n🔄 Simulation Cycle {i}:")
|
||
|
||
# Mock neue Ziehung
|
||
mock_drawing = {
|
||
'Z1': random.randint(1, 49),
|
||
'Z2': random.randint(1, 49),
|
||
'Z3': random.randint(1, 49),
|
||
'Z4': random.randint(1, 49),
|
||
'Z5': random.randint(1, 49),
|
||
'Z6': random.randint(1, 49),
|
||
'SZ': random.randint(0, 9),
|
||
'datum': datetime.datetime.now() - datetime.timedelta(days=i)
|
||
}
|
||
|
||
# Ensure unique numbers
|
||
numbers = [mock_drawing[f'Z{j}'] for j in range(1, 7)]
|
||
while len(set(numbers)) < 6:
|
||
for j in range(1, 7):
|
||
mock_drawing[f'Z{j}'] = random.randint(1, 49)
|
||
numbers = [mock_drawing[f'Z{j}'] for j in range(1, 7)]
|
||
|
||
zahlen_str = '-'.join([f"{n:2}" for n in sorted(numbers)])
|
||
print(f" Mock Ziehung: {zahlen_str} + SZ: {mock_drawing['SZ']}")
|
||
|
||
# Real-Time Update
|
||
generator.update_with_new_drawing(mock_drawing)
|
||
|
||
# Zeige Learning-Progress
|
||
learning_stats = generator.real_time_learner.get_learning_stats()
|
||
print(f" Learning Cycles: {learning_stats.get('learning_cycles', 0)}")
|
||
print(f" Total Adjustments: {len(generator.real_time_learner.prediction_adjustments)}")
|
||
|
||
print("\n✅ Real-Time Learning Simulation completed!")
|
||
|
||
# Main Function
|
||
def main():
|
||
"""Startet den AI-ML Ultimate Lotto Generator."""
|
||
print("🤖 AI-ML ULTIMATE LOTTO GENERATOR")
|
||
print("🧠 Real-Time AI + Machine Learning System")
|
||
print("=" * 60)
|
||
|
||
# Pfad zu Sebastian's Daten
|
||
data_path = "/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/AlleLottozahlen.csv"
|
||
|
||
try:
|
||
# Generator initialisieren
|
||
generator = AIMLLottoGenerator(data_path)
|
||
|
||
# AI Capabilities demonstrieren
|
||
demonstrate_ai_capabilities(generator)
|
||
|
||
# AI-Tipps generieren
|
||
ai_tips = generator.generate_ai_tips(10)
|
||
|
||
if ai_tips:
|
||
print(f"\n🏆 AI-ML OPTIMIZATION COMPLETED!")
|
||
print("=" * 50)
|
||
print(f"🤖 10 AI-optimized tips generated")
|
||
print(f"🧠 Machine Learning: {'✅' if ML_AVAILABLE else '❌'}")
|
||
print(f"⚡ Real-Time Learning: ✅")
|
||
print(f"📊 Ensemble Models: ✅")
|
||
print(f"🎯 Adaptive Optimization: ✅")
|
||
|
||
# Beste Tipps hervorheben
|
||
if len(ai_tips) > 0:
|
||
best_tip = max(ai_tips, key=lambda x: x.get('confidence', 0))
|
||
print(f"\n⭐ BEST AI TIP:")
|
||
zahlen_str = '-'.join([f"{n:2}" for n in best_tip['numbers']])
|
||
print(f" Numbers: {zahlen_str} + SZ: {best_tip['superzahl']}")
|
||
print(f" AI-Score: {best_tip['ai_score']:.3f}")
|
||
print(f" Confidence: {best_tip['confidence']:.3f}")
|
||
print(f" Method: {best_tip['method']}")
|
||
|
||
# Optional: Real-Time Learning simulieren
|
||
simulate_choice = input("\nReal-Time Learning simulieren? (j/n): ").lower().strip()
|
||
if simulate_choice in ['j', 'ja', 'y', 'yes']:
|
||
simulate_real_time_learning(generator, 3)
|
||
|
||
# Optional: Performance Report
|
||
report_choice = input("AI Performance Report erstellen? (j/n): ").lower().strip()
|
||
if report_choice in ['j', 'ja', 'y', 'yes']:
|
||
export_ai_performance_report(generator)
|
||
|
||
print(f"\n🚀 NEXT-LEVEL FEATURES:")
|
||
print("=" * 30)
|
||
print("🧠 Machine Learning Ensemble mit 3 Algorithmen")
|
||
print("⚡ Real-Time Learning nach jeder Ziehung")
|
||
print("📊 Adaptive Model-Gewichtung")
|
||
print("🎯 Feature Engineering für optimale Vorhersagen")
|
||
print("📈 Performance Tracking & Auto-Optimization")
|
||
print("🔄 Kontinuierliche Verbesserung durch AI")
|
||
|
||
else:
|
||
print("❌ Keine AI-Tipps generiert!")
|
||
|
||
except Exception as e:
|
||
print(f"❌ Error: {e}")
|
||
print("\n💡 SYSTEM REQUIREMENTS:")
|
||
print(" 📦 pip install scikit-learn (für ML)")
|
||
print(" 📦 pip install tensorflow (für Deep Learning)")
|
||
print(" 📁 AlleLottozahlen.csv im korrekten Pfad")
|
||
|
||
if __name__ == "__main__":
|
||
# Set random seeds für reproduzierbare Ergebnisse
|
||
random.seed(42)
|
||
np.random.seed(42)
|
||
|
||
# Starte AI-ML Generator
|
||
main() |