423 lines
16 KiB
Python
423 lines
16 KiB
Python
 
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#!/usr/bin/env python3
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"""
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AI-GENERATOR MIT MUSTER-GEWICHTUNG
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Erweitert den AI-Generator um explizite Muster-Gewichtung (NNMMHH, etc.)
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Neue Features:
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- Historische Muster-Analyse (NNMMHH, NMMHHH, etc.)
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- Muster-Erfolgsquoten berechnen
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- Muster-basierte Tip-Optimierung
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- Pattern-Scoring für bessere Kombinationen
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"""
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import pandas as pd
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import numpy as np
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from collections import Counter, defaultdict
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import random
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class PatternWeightedAI:
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def __init__(self, df):
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self.df = df
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self.pattern_frequencies = Counter()
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self.pattern_success_rates = {}
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self.optimal_patterns = []
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# Analysiere historische Muster
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self._analyze_historical_patterns()
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def _analyze_historical_patterns(self):
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"""Analysiert alle historischen Muster und deren Erfolgsquoten."""
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print("\n🎨 MUSTER-ANALYSE GESTARTET...")
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if len(self.df) == 0:
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return
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total_drawings = len(self.df)
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for _, row in self.df.iterrows():
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numbers = sorted([row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']])
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pattern = self._get_pattern(numbers)
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self.pattern_frequencies[pattern] += 1
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# Berechne Erfolgsquoten
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for pattern, count in self.pattern_frequencies.items():
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success_rate = count / total_drawings
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self.pattern_success_rates[pattern] = success_rate
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# Identifiziere optimale Muster (Top 10)
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self.optimal_patterns = [
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pattern for pattern, _ in self.pattern_frequencies.most_common(10)
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]
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print("🎯 MUSTER-ERFOLGSQUOTEN:")
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print("Pattern Häufigkeit Erfolgsrate Bewertung")
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print("-" * 50)
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for i, (pattern, count) in enumerate(self.pattern_frequencies.most_common(15)):
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success_rate = self.pattern_success_rates[pattern]
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if success_rate >= 0.08:
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bewertung = "🏆 EXCELLENT"
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elif success_rate >= 0.06:
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bewertung = "🥇 SEHR GUT"
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elif success_rate >= 0.04:
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bewertung = "🥈 GUT"
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elif success_rate >= 0.02:
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bewertung = "🥉 DURCHSCHNITT"
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else:
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bewertung = "❌ SCHWACH"
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print(f"{pattern:<10} {count:>8} {success_rate:>8.3f} {bewertung}")
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def _get_pattern(self, numbers):
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"""Konvertiert Zahlen zu N/M/H Muster."""
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pattern = ""
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for num in numbers:
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if 1 <= num <= 16:
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pattern += "N" # Niedrig
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elif 17 <= num <= 32:
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pattern += "M" # Mittel
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else:
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pattern += "H" # Hoch
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return pattern
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def calculate_pattern_weight(self, numbers):
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"""Berechnet Gewichtung basierend auf Muster-Erfolgsquote."""
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pattern = self._get_pattern(sorted(numbers))
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# Basis-Gewichtung aus historischer Erfolgsquote
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base_weight = self.pattern_success_rates.get(pattern, 0.01)
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# Bonus für Top-Muster
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if pattern in self.optimal_patterns[:5]:
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bonus = 0.3
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elif pattern in self.optimal_patterns[:10]:
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bonus = 0.2
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else:
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bonus = 0.0
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# Penalty für nie aufgetretene Muster
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if pattern not in self.pattern_frequencies:
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penalty = -0.2
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else:
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penalty = 0.0
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final_weight = base_weight + bonus + penalty
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return max(0.01, min(1.0, final_weight)) # Clamp 0.01-1.0
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def get_pattern_recommendations(self):
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"""Liefert Muster-Empfehlungen für Tip-Generierung."""
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recommendations = {}
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# Top 5 erfolgreichste Muster
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recommendations['top_patterns'] = self.optimal_patterns[:5]
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# Muster mit bester Erfolgsquote
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if self.pattern_success_rates:
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best_pattern = max(self.pattern_success_rates.items(), key=lambda x: x[1])
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recommendations['best_pattern'] = best_pattern[0]
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recommendations['best_success_rate'] = best_pattern[1]
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# Muster-Statistiken
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recommendations['total_patterns'] = len(self.pattern_frequencies)
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recommendations['pattern_diversity'] = len([p for p, rate in self.pattern_success_rates.items() if rate >= 0.02])
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return recommendations
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def optimize_combination_for_pattern(self, target_pattern="NNMMHH"):
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"""Optimiert Zahlen-Kombination für spezifisches Muster."""
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# Definiere Bereiche
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ranges = {
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'N': list(range(1, 17)), # Niedrig: 1-16
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'M': list(range(17, 33)), # Mittel: 17-32
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'H': list(range(33, 50)) # Hoch: 33-49
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}
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# Parse target pattern
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pattern_counts = Counter(target_pattern)
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needed_n = pattern_counts.get('N', 0)
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needed_m = pattern_counts.get('M', 0)
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needed_h = pattern_counts.get('H', 0)
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selected = []
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# Wähle Zahlen für Muster
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if needed_n > 0:
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n_numbers = random.sample(ranges['N'], min(needed_n, len(ranges['N'])))
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selected.extend(n_numbers)
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if needed_m > 0:
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m_numbers = random.sample(ranges['M'], min(needed_m, len(ranges['M'])))
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selected.extend(m_numbers)
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if needed_h > 0:
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h_numbers = random.sample(ranges['H'], min(needed_h, len(ranges['H'])))
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selected.extend(h_numbers)
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# Auffüllen falls nötig
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while len(selected) < 6:
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all_ranges = ranges['N'] + ranges['M'] + ranges['H']
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available = [n for n in all_ranges if n not in selected]
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if available:
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selected.append(random.choice(available))
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else:
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break
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return sorted(selected[:6])
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class EnhancedAIGenerator:
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"""Erweitert den ursprünglichen AI-Generator um Muster-Gewichtung."""
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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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self.pattern_ai = None
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# Load data
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self._load_data()
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# Initialize Pattern AI
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if self.df is not None and len(self.df) > 0:
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self.pattern_ai = PatternWeightedAI(self.df)
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def _load_data(self):
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"""Lädt Daten."""
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try:
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self.df = pd.read_csv(self.data_path, sep=';')
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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 geladen")
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except Exception as e:
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print(f"❌ Fehler beim Laden: {e}")
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self.df = pd.DataFrame()
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def generate_pattern_optimized_tips(self, num_tips=10):
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"""Generiert Tipps mit expliziter Muster-Gewichtung."""
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if not self.pattern_ai:
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print("❌ Pattern AI nicht verfügbar")
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return []
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print("\n🎨 PATTERN-OPTIMIERTE TIPP-GENERIERUNG")
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print("=" * 60)
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# Muster-Empfehlungen abrufen
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recommendations = self.pattern_ai.get_pattern_recommendations()
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print("🎯 MUSTER-EMPFEHLUNGEN:")
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print(f" Bestes Muster: {recommendations.get('best_pattern', 'N/A')} ({recommendations.get('best_success_rate', 0)*100:.1f}%)")
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print(f" Top 5 Muster: {', '.join(recommendations.get('top_patterns', [])[:5])}")
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print(f" Pattern-Diversität: {recommendations.get('pattern_diversity', 0)} erfolgreiche Muster")
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tips = []
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print(f"\n🎲 GENERIERE {num_tips} PATTERN-OPTIMIERTE TIPPS:")
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print("=" * 80)
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print("Nr 6 Pattern-Numbers Pattern Weight Confidence Success-Rate")
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print("-" * 80)
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# Verschiedene Strategien für verschiedene Tipps
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strategies = [
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('best', "Bestes Muster"),
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('top5', "Top 5 Rotation"),
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('balanced', "Ausgewogene Muster"),
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('diverse', "Diversifizierte Muster")
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]
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for i in range(1, num_tips + 1):
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strategy = strategies[(i-1) % len(strategies)]
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tip = self._generate_pattern_tip(i, strategy[0], recommendations)
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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']])
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success_rate = self.pattern_ai.pattern_success_rates.get(tip['pattern'], 0)
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print(f"{i:2} {zahlen_str} {tip['pattern']:<8} {tip['pattern_weight']:.3f} {tip['confidence']:.3f} {success_rate:.3f}")
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# Zusammenfassung
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self._print_pattern_summary(tips)
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return tips
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def _generate_pattern_tip(self, tip_number, strategy, recommendations):
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"""Generiert einzelnen pattern-optimierten Tipp."""
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# Seed für Konsistenz
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random.seed(42 + tip_number)
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if strategy == 'best':
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# Nutze bestes Muster
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target_pattern = recommendations.get('best_pattern', 'NNMMHH')
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elif strategy == 'top5':
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# Rotiere durch Top 5
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top_patterns = recommendations.get('top_patterns', ['NNMMHH'])
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target_pattern = top_patterns[(tip_number - 1) % len(top_patterns)]
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elif strategy == 'balanced':
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# Ausgewogene beliebte Muster
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balanced_patterns = ['NNMMHH', 'NMMHHH', 'NMMMHH', 'NNMHHH']
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target_pattern = balanced_patterns[(tip_number - 1) % len(balanced_patterns)]
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else: # diverse
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# Diversifizierte Muster für Abdeckung
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diverse_patterns = ['NNMMHH', 'MMHHHH', 'NNNNMM', 'NMHHHH', 'NNNMMH']
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target_pattern = diverse_patterns[(tip_number - 1) % len(diverse_patterns)]
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# Generiere Kombination für Ziel-Muster
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numbers = self.pattern_ai.optimize_combination_for_pattern(target_pattern)
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# Validiere und korrigiere falls nötig
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actual_pattern = self.pattern_ai._get_pattern(numbers)
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# Pattern Weight berechnen
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pattern_weight = self.pattern_ai.calculate_pattern_weight(numbers)
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# Confidence basierend auf Pattern Success Rate
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success_rate = self.pattern_ai.pattern_success_rates.get(actual_pattern, 0.01)
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confidence = pattern_weight * 0.6 + success_rate * 0.4
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# Superzahl
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superzahl = self._get_pattern_superzahl(tip_number)
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return {
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'tip_number': tip_number,
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'numbers': numbers,
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'pattern': actual_pattern,
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'target_pattern': target_pattern,
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'pattern_weight': pattern_weight,
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'confidence': confidence,
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'success_rate': success_rate,
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'superzahl': superzahl,
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'strategy': strategy
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}
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def _get_pattern_superzahl(self, tip_number):
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"""Pattern-optimierte Superzahl."""
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# Basis häufigste Superzahlen
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frequent_sz = [7, 6, 3, 2, 0, 1, 4, 5, 8, 9]
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# Tip-spezifische Auswahl
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return frequent_sz[tip_number % len(frequent_sz)]
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def _print_pattern_summary(self, tips):
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"""Druckt Pattern-Zusammenfassung."""
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print(f"\n🏆 PATTERN-OPTIMIERUNG ZUSAMMENFASSUNG:")
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print("=" * 50)
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# Pattern-Verteilung
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pattern_dist = Counter([tip['pattern'] for tip in tips])
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print("📊 PATTERN-VERTEILUNG:")
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for pattern, count in pattern_dist.most_common():
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avg_success = np.mean([self.pattern_ai.pattern_success_rates.get(pattern, 0)] * count)
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print(f" {pattern}: {count}x (Ø Success: {avg_success:.3f})")
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# Durchschnittliche Metriken
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avg_weight = np.mean([tip['pattern_weight'] for tip in tips])
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avg_confidence = np.mean([tip['confidence'] for tip in tips])
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avg_success = np.mean([tip['success_rate'] for tip in tips])
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print(f"\n📈 DURCHSCHNITTLICHE METRIKEN:")
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print(f" Pattern-Weight: {avg_weight:.3f}")
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print(f" Confidence: {avg_confidence:.3f}")
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print(f" Success-Rate: {avg_success:.3f}")
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# Beste Tipps
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best_tip = max(tips, key=lambda x: x['confidence'])
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print(f"\n⭐ BESTER PATTERN-TIPP:")
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zahlen_str = '-'.join([f"{n:2}" for n in best_tip['numbers']])
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print(f" Tipp {best_tip['tip_number']}: {zahlen_str}")
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print(f" Pattern: {best_tip['pattern']} (Weight: {best_tip['pattern_weight']:.3f})")
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print(f" Success-Rate: {best_tip['success_rate']:.3f}")
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def demonstrate_pattern_weighting():
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"""Demonstriert Pattern-Gewichtung mit Beispiel-Daten."""
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print("🎨 PATTERN-GEWICHTUNG DEMONSTRATION")
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print("=" * 50)
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# Beispiel-Daten erstellen
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sample_data = []
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patterns_to_simulate = ['NNMMHH', 'NMMHHH', 'NMMMHH', 'NNMHHH', 'MMHHHH']
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for i in range(100):
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# Simuliere Ziehungen mit verschiedenen Mustern
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pattern = random.choice(patterns_to_simulate)
|
||
|
|
numbers = []
|
||
|
|
|
||
|
|
for char in pattern:
|
||
|
|
if char == 'N':
|
||
|
|
numbers.append(random.randint(1, 16))
|
||
|
|
elif char == 'M':
|
||
|
|
numbers.append(random.randint(17, 32))
|
||
|
|
else: # 'H'
|
||
|
|
numbers.append(random.randint(33, 49))
|
||
|
|
|
||
|
|
# Sicherstellen dass alle Zahlen einzigartig sind
|
||
|
|
numbers = sorted(list(set(numbers)))
|
||
|
|
while len(numbers) < 6:
|
||
|
|
missing_range = random.choice(['N', 'M', 'H'])
|
||
|
|
if missing_range == 'N':
|
||
|
|
new_num = random.randint(1, 16)
|
||
|
|
elif missing_range == 'M':
|
||
|
|
new_num = random.randint(17, 32)
|
||
|
|
else:
|
||
|
|
new_num = random.randint(33, 49)
|
||
|
|
|
||
|
|
if new_num not in numbers:
|
||
|
|
numbers.append(new_num)
|
||
|
|
numbers.sort()
|
||
|
|
|
||
|
|
numbers = numbers[:6]
|
||
|
|
|
||
|
|
sample_data.append({
|
||
|
|
'Z1': numbers[0], 'Z2': numbers[1], 'Z3': numbers[2],
|
||
|
|
'Z4': numbers[3], 'Z5': numbers[4], 'Z6': numbers[5],
|
||
|
|
'SZ': random.randint(0, 9)
|
||
|
|
})
|
||
|
|
|
||
|
|
# DataFrame erstellen
|
||
|
|
df_sample = pd.DataFrame(sample_data)
|
||
|
|
|
||
|
|
# Enhanced AI Generator mit Pattern-Gewichtung
|
||
|
|
print("\n🚀 STARTE PATTERN-GEWICHTETEN GENERATOR...")
|
||
|
|
|
||
|
|
# Simuliere Generator
|
||
|
|
generator = EnhancedAIGenerator.__new__(EnhancedAIGenerator)
|
||
|
|
generator.df = df_sample
|
||
|
|
generator.pattern_ai = PatternWeightedAI(df_sample)
|
||
|
|
|
||
|
|
# Generiere pattern-optimierte Tipps
|
||
|
|
pattern_tips = generator.generate_pattern_optimized_tips(8)
|
||
|
|
|
||
|
|
print(f"\n💡 PATTERN-GEWICHTUNG ERKLÄRT:")
|
||
|
|
print("=" * 40)
|
||
|
|
print("🎯 Jede Kombination wird bewertet basierend auf:")
|
||
|
|
print(" 1. Historischer Erfolgsquote des Musters")
|
||
|
|
print(" 2. Bonus für Top-5 erfolgreichste Muster")
|
||
|
|
print(" 3. Penalty für nie aufgetretene Muster")
|
||
|
|
print(" 4. Kombinierte Pattern-Weight für finalen Score")
|
||
|
|
|
||
|
|
return pattern_tips
|
||
|
|
|
||
|
|
def main():
|
||
|
|
"""Hauptfunktion für Pattern-gewichteten Generator."""
|
||
|
|
data_path = "/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/AlleLottozahlen.csv"
|
||
|
|
|
||
|
|
try:
|
||
|
|
# Versuche mit echten Daten
|
||
|
|
generator = EnhancedAIGenerator(data_path)
|
||
|
|
|
||
|
|
if generator.pattern_ai and len(generator.df) > 0:
|
||
|
|
pattern_tips = generator.generate_pattern_optimized_tips(10)
|
||
|
|
else:
|
||
|
|
print("🔄 Echte Daten nicht verfügbar - verwende Demo...")
|
||
|
|
pattern_tips = demonstrate_pattern_weighting()
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
print(f"⚠️ Fallback zu Demo-Modus: {e}")
|
||
|
|
pattern_tips = demonstrate_pattern_weighting()
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|