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6d7e6ec8a4
| Author | SHA1 | Date | |
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  cbazzaandClaude Sonnet 5
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6d7e6ec8a4 | ||
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cbazzaandClaude Sonnet 5
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cbazzaandClaude Sonnet 5
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a45092b162 | ||
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cbazzaandClaude Sonnet 5
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79c4c0d4b3 | ||
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cbazzaandClaude Sonnet 5
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6630c588a7 | ||
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cbazzaandClaude Sonnet 5
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3fcc1fc210 | ||
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cbazzaandClaude Sonnet 5
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e05f1f12ba | ||
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cbazzaandClaude Sonnet 5
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8c2089d734 | ||
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cbazzaandClaude Sonnet 5
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6c1139c909 |
@@ -60,3 +60,6 @@ data/*.backup*
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data/generated_tips/weekly_lotto_tips_*.csv
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data/performance_reports/
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data/data/
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# graphify Code-Graph (regenerierbar via /graphify --update)
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graphify-out/
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File diff suppressed because it is too large
Load Diff
@@ -1,46 +0,0 @@
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{
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"timestamp": "2025-09-26T08:10:41.418372",
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"generator_type": "AI-ML Ultimate Lotto Generator",
|
||||
"system_status": {
|
||||
"model_status": "Trained",
|
||||
"ml_available": true,
|
||||
"deep_learning_available": false,
|
||||
"data_size": 4945,
|
||||
"performance_stats": {
|
||||
"total_tips_generated": 10,
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||||
"total_evaluations": 0,
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||||
"method_performance": {}
|
||||
},
|
||||
"adaptive_weights": {
|
||||
"random_forest": 0.3333333333333333,
|
||||
"gradient_boost": 0.3333333333333333,
|
||||
"neural_network": 0.3333333333333333
|
||||
},
|
||||
"learning_stats": {
|
||||
"generation_cycles": 1
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}
|
||||
},
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"model_details": {
|
||||
"ml_models": [
|
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"random_forest",
|
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"gradient_boost",
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"neural_network"
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||||
],
|
||||
"deep_models": [],
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||||
"ensemble_weights": {
|
||||
"random_forest": 0.3333333333333333,
|
||||
"gradient_boost": 0.3333333333333333,
|
||||
"neural_network": 0.3333333333333333
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||||
},
|
||||
"training_status": true
|
||||
},
|
||||
"real_time_learning": {
|
||||
"learning_rate": 0.1,
|
||||
"adaptation_history_size": 0,
|
||||
"prediction_adjustments_count": 0
|
||||
},
|
||||
"recommendations": [
|
||||
"\ud83d\ude80 Install TensorFlow for deep learning: pip install tensorflow",
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"\ud83d\udcda More real-time learning cycles needed for adaptation"
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]
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}
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@@ -1,52 +0,0 @@
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{
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"timestamp": "2025-09-26T08:13:58.454714",
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"generator_type": "AI-ML Ultimate Lotto Generator",
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"system_status": {
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"model_status": "Trained",
|
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"ml_available": true,
|
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"deep_learning_available": false,
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"data_size": 4948,
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"performance_stats": {
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"total_tips_generated": 10,
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"total_evaluations": 0,
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"method_performance": {
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"AI-ENSEMBLE": 0.16666666666666669
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||||
}
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||||
},
|
||||
"adaptive_weights": {
|
||||
"random_forest": 0.3333333333333333,
|
||||
"gradient_boost": 0.3333333333333333,
|
||||
"neural_network": 0.3333333333333333
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||||
},
|
||||
"learning_stats": {
|
||||
"generation_cycles": 1,
|
||||
"incorrect_predictions": 129,
|
||||
"correct_predictions": 18,
|
||||
"learning_cycles": 3
|
||||
}
|
||||
},
|
||||
"model_details": {
|
||||
"ml_models": [
|
||||
"random_forest",
|
||||
"gradient_boost",
|
||||
"neural_network"
|
||||
],
|
||||
"deep_models": [],
|
||||
"ensemble_weights": {
|
||||
"random_forest": 0.3333333333333333,
|
||||
"gradient_boost": 0.3333333333333333,
|
||||
"neural_network": 0.3333333333333333
|
||||
},
|
||||
"training_status": true
|
||||
},
|
||||
"real_time_learning": {
|
||||
"learning_rate": 0.1,
|
||||
"adaptation_history_size": 0,
|
||||
"prediction_adjustments_count": 49
|
||||
},
|
||||
"recommendations": [
|
||||
"\ud83d\ude80 Install TensorFlow for deep learning: pip install tensorflow",
|
||||
"\u2b50 Best performing model: AI-ENSEMBLE (0.167 accuracy)",
|
||||
"\ud83d\udcda More real-time learning cycles needed for adaptation"
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||||
]
|
||||
}
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@@ -1,52 +0,0 @@
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{
|
||||
"timestamp": "2025-09-26T08:24:39.210488",
|
||||
"generator_type": "AI-ML Ultimate Lotto Generator",
|
||||
"system_status": {
|
||||
"model_status": "Trained",
|
||||
"ml_available": true,
|
||||
"deep_learning_available": false,
|
||||
"data_size": 4948,
|
||||
"performance_stats": {
|
||||
"total_tips_generated": 10,
|
||||
"total_evaluations": 0,
|
||||
"method_performance": {
|
||||
"AI-ENSEMBLE-V2": 0.11666666666666665
|
||||
}
|
||||
},
|
||||
"adaptive_weights": {
|
||||
"random_forest": 0.3333333333333333,
|
||||
"gradient_boost": 0.3333333333333333,
|
||||
"neural_network": 0.3333333333333333
|
||||
},
|
||||
"learning_stats": {
|
||||
"generation_cycles": 1,
|
||||
"incorrect_predictions": 129,
|
||||
"correct_predictions": 18,
|
||||
"learning_cycles": 3
|
||||
}
|
||||
},
|
||||
"model_details": {
|
||||
"ml_models": [
|
||||
"random_forest",
|
||||
"gradient_boost",
|
||||
"neural_network"
|
||||
],
|
||||
"deep_models": [],
|
||||
"ensemble_weights": {
|
||||
"random_forest": 0.3333333333333333,
|
||||
"gradient_boost": 0.3333333333333333,
|
||||
"neural_network": 0.3333333333333333
|
||||
},
|
||||
"training_status": true
|
||||
},
|
||||
"real_time_learning": {
|
||||
"learning_rate": 0.1,
|
||||
"adaptation_history_size": 0,
|
||||
"prediction_adjustments_count": 49
|
||||
},
|
||||
"recommendations": [
|
||||
"\ud83d\ude80 Install TensorFlow for deep learning: pip install tensorflow",
|
||||
"\u2b50 Best performing model: AI-ENSEMBLE-V2 (0.117 accuracy)",
|
||||
"\ud83d\udcda More real-time learning cycles needed for adaptation"
|
||||
]
|
||||
}
|
||||
@@ -1,52 +0,0 @@
|
||||
{
|
||||
"timestamp": "2025-09-26T15:28:08.871286",
|
||||
"generator_type": "AI-ML Ultimate Lotto Generator",
|
||||
"system_status": {
|
||||
"model_status": "Trained",
|
||||
"ml_available": true,
|
||||
"deep_learning_available": false,
|
||||
"data_size": 4948,
|
||||
"performance_stats": {
|
||||
"total_tips_generated": 10,
|
||||
"total_evaluations": 0,
|
||||
"method_performance": {
|
||||
"AI-ENSEMBLE-V2": 0.15
|
||||
}
|
||||
},
|
||||
"adaptive_weights": {
|
||||
"random_forest": 0.3333333333333333,
|
||||
"gradient_boost": 0.3333333333333333,
|
||||
"neural_network": 0.3333333333333333
|
||||
},
|
||||
"learning_stats": {
|
||||
"generation_cycles": 1,
|
||||
"incorrect_predictions": 129,
|
||||
"correct_predictions": 18,
|
||||
"learning_cycles": 3
|
||||
}
|
||||
},
|
||||
"model_details": {
|
||||
"ml_models": [
|
||||
"random_forest",
|
||||
"gradient_boost",
|
||||
"neural_network"
|
||||
],
|
||||
"deep_models": [],
|
||||
"ensemble_weights": {
|
||||
"random_forest": 0.3333333333333333,
|
||||
"gradient_boost": 0.3333333333333333,
|
||||
"neural_network": 0.3333333333333333
|
||||
},
|
||||
"training_status": true
|
||||
},
|
||||
"real_time_learning": {
|
||||
"learning_rate": 0.1,
|
||||
"adaptation_history_size": 0,
|
||||
"prediction_adjustments_count": 49
|
||||
},
|
||||
"recommendations": [
|
||||
"\ud83d\ude80 Install TensorFlow for deep learning: pip install tensorflow",
|
||||
"\u2b50 Best performing model: AI-ENSEMBLE-V2 (0.150 accuracy)",
|
||||
"\ud83d\udcda More real-time learning cycles needed for adaptation"
|
||||
]
|
||||
}
|
||||
@@ -3136,3 +3136,16 @@ datum;Z1;Z2;Z3;Z4;Z5;Z6;SZ
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||||
2026-06-17;3;4;20;26;27;39;9
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||||
2026-06-20;2;8;13;15;27;35;3
|
||||
2026-06-24;25;36;40;43;44;49;3
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||||
2026-06-27;26;32;35;39;47;49;8
|
||||
2026-07-01;2;4;5;13;41;48;4
|
||||
2026-07-04;7;10;28;31;36;37;5
|
||||
2026-07-08;15;28;30;31;37;45;5
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||||
2026-07-11;1;4;6;20;41;48;3
|
||||
2026-07-15;7;11;35;37;39;42;1
|
||||
2026-07-18;5;17;33;35;39;47;6
|
||||
2026-07-22;3;5;10;14;25;49;3
|
||||
2026-07-25;18;21;31;36;42;44;3
|
||||
2026-07-29;3;16;22;43;48;49;8
|
||||
2026-08-01;19;20;22;25;31;42;8
|
||||
2026-08-05;15;23;24;36;44;49;0
|
||||
2026-08-08;2;8;22;30;38;43;4
|
||||
|
||||
|
@@ -510,6 +510,83 @@
|
||||
"file": "weekly_lotto_tips_20260704_165051.csv",
|
||||
"avg_confidence": 0.4386488588333005,
|
||||
"avg_quality": 0.544752806661678
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-07T21:00:21.461059",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260707_210021.csv",
|
||||
"avg_confidence": 0.4405040572832767,
|
||||
"avg_quality": 0.5324123722429387
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-10T21:00:21.723322",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260710_210021.csv",
|
||||
"avg_confidence": 0.42372549130958015,
|
||||
"avg_quality": 0.5212700855283287
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-11T18:58:43.810972",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260711_185843.csv",
|
||||
"avg_confidence": 0.5946226596089659,
|
||||
"avg_quality": 0.57911050120868
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||||
},
|
||||
{
|
||||
"timestamp": "2026-07-14T21:01:01.178782",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260714_210101.csv",
|
||||
"avg_confidence": 0.587763724588359,
|
||||
"avg_quality": 0.5722808767000556
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-17T21:00:28.149191",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260717_210028.csv",
|
||||
"avg_confidence": 0.4392081930355287,
|
||||
"avg_quality": 0.5408983355595894
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-21T21:00:18.226971",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260721_210018.csv",
|
||||
"avg_confidence": 0.42365005901301317,
|
||||
"avg_quality": 0.5233790166576258
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-24T21:00:19.858896",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260724_210019.csv",
|
||||
"avg_confidence": 0.46327566159952005,
|
||||
"avg_quality": 0.5649913485262748
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-28T21:00:20.506506",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260728_210020.csv",
|
||||
"avg_confidence": 0.45335184838187415,
|
||||
"avg_quality": 0.5498233984937678
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-31T21:00:18.028990",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260731_210018.csv",
|
||||
"avg_confidence": 0.46070277567210083,
|
||||
"avg_quality": 0.5545559923014279
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-08-04T18:07:57.975459",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260804_180757.csv",
|
||||
"avg_confidence": 0.4399957403666754,
|
||||
"avg_quality": 0.5718251526795421
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-08-07T11:02:40.552126",
|
||||
"num_tips": 10,
|
||||
"file": "weekly_lotto_tips_20260807_110240.csv",
|
||||
"avg_confidence": 0.45460136598458617,
|
||||
"avg_quality": 0.5817841547618449
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -727,6 +727,162 @@
|
||||
"main": 0.5,
|
||||
"sz_rate": 0.1
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-01T20:00:49.647993",
|
||||
"draw_date": "2026-06-27",
|
||||
"evaluation": {
|
||||
"main": 2,
|
||||
"sz": false,
|
||||
"tip": 10
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.5,
|
||||
"sz_rate": 0.0
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-04T20:11:43.733891",
|
||||
"draw_date": "2026-07-01",
|
||||
"evaluation": {
|
||||
"main": 1,
|
||||
"sz": false,
|
||||
"tip": 1
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.5,
|
||||
"sz_rate": 0.0
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-08T20:00:49.471290",
|
||||
"draw_date": "2026-07-04",
|
||||
"evaluation": {
|
||||
"main": 1,
|
||||
"sz": false,
|
||||
"tip": 3
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.2,
|
||||
"sz_rate": 0.0
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-11T18:58:15.238449",
|
||||
"draw_date": "2026-07-08",
|
||||
"evaluation": {
|
||||
"main": 3,
|
||||
"sz": false,
|
||||
"tip": 5
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 1.1,
|
||||
"sz_rate": 0.0
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-15T20:24:57.275663",
|
||||
"draw_date": "2026-07-11",
|
||||
"evaluation": {
|
||||
"main": 2,
|
||||
"sz": false,
|
||||
"tip": 3
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.7,
|
||||
"sz_rate": 0.1
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-18T20:17:07.482284",
|
||||
"draw_date": "2026-07-15",
|
||||
"evaluation": {
|
||||
"main": 2,
|
||||
"sz": false,
|
||||
"tip": 6
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.6,
|
||||
"sz_rate": 0.0
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-22T20:07:14.787102",
|
||||
"draw_date": "2026-07-18",
|
||||
"evaluation": {
|
||||
"main": 1,
|
||||
"sz": true,
|
||||
"tip": 8
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.6,
|
||||
"sz_rate": 0.1
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-25T20:16:15.815983",
|
||||
"draw_date": "2026-07-22",
|
||||
"evaluation": {
|
||||
"main": 2,
|
||||
"sz": false,
|
||||
"tip": 1
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.9,
|
||||
"sz_rate": 0.2
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-07-29T20:00:50.348674",
|
||||
"draw_date": "2026-07-25",
|
||||
"evaluation": {
|
||||
"main": 1,
|
||||
"sz": true,
|
||||
"tip": 5
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.3,
|
||||
"sz_rate": 0.2
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-08-04T17:19:36.246227",
|
||||
"draw_date": "2026-08-01",
|
||||
"evaluation": {
|
||||
"main": 1,
|
||||
"sz": false,
|
||||
"tip": 3
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.1,
|
||||
"sz_rate": 0.0
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-08-07T10:50:43.433684",
|
||||
"draw_date": "2026-08-05",
|
||||
"evaluation": {
|
||||
"main": 1,
|
||||
"sz": true,
|
||||
"tip": 10
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 0.6,
|
||||
"sz_rate": 0.1
|
||||
}
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-08-08T20:01:01.666002",
|
||||
"draw_date": "2026-08-08",
|
||||
"evaluation": {
|
||||
"main": 3,
|
||||
"sz": true,
|
||||
"tip": 3
|
||||
},
|
||||
"avg_matches": {
|
||||
"main": 1.7,
|
||||
"sz_rate": 0.1
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,9 +1,9 @@
|
||||
{
|
||||
"trained_at": "2026-06-26T14:10:06.690748",
|
||||
"num_samples": 3117,
|
||||
"trained_at": "2026-08-08T20:01:01.661343",
|
||||
"num_samples": 3130,
|
||||
"num_features": 20,
|
||||
"final_loss": 0.3737889459499946,
|
||||
"final_val_loss": 0.3734370008111,
|
||||
"best_val_loss": 0.37323174327611924,
|
||||
"epochs_trained": 21
|
||||
"final_loss": 0.3730404165726674,
|
||||
"final_val_loss": 0.37338354587554934,
|
||||
"best_val_loss": 0.37333065271377563,
|
||||
"epochs_trained": 30
|
||||
}
|
||||
Binary file not shown.
@@ -1,61 +1,61 @@
|
||||
{
|
||||
"adjustments": {
|
||||
"1": -0.14326784276963783,
|
||||
"2": -0.1525534748665565,
|
||||
"3": -0.1393848401818052,
|
||||
"4": -0.15268751532539623,
|
||||
"5": -0.15506552975096333,
|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
"13": -0.12532209209903564,
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||||
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||||
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|
||||
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|
||||
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||||
"18": -0.15501024547420914,
|
||||
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|
||||
"20": -0.03322127697562629,
|
||||
"21": -0.16975758227090082,
|
||||
"22": -0.182973260374265,
|
||||
"23": -0.12780934048230247,
|
||||
"24": -0.1402631357111807,
|
||||
"25": -0.11733705227250311,
|
||||
"26": -0.12434717982485279,
|
||||
"27": -0.11233845524788302,
|
||||
"28": -0.11612535987778047,
|
||||
"29": -0.1430943915483658,
|
||||
"30": -0.15160170827723393,
|
||||
"31": -0.20645466361298564,
|
||||
"32": -0.1029764704365987,
|
||||
"33": -0.1694565595438057,
|
||||
"34": -0.18020704673063262,
|
||||
"35": -0.1400930247830341,
|
||||
"36": -0.1639711617652944,
|
||||
"37": -0.0941141248773726,
|
||||
"38": -0.18196200314587896,
|
||||
"39": -0.13995951255010242,
|
||||
"40": -0.12879042332946317,
|
||||
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|
||||
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|
||||
"43": -0.1103318569550083,
|
||||
"44": -0.11278440678909801,
|
||||
"45": -0.19489524700365307,
|
||||
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|
||||
"47": -0.1523431425271016,
|
||||
"48": -0.12731457913035776,
|
||||
"49": -0.12915634085886468
|
||||
"1": -0.17447656252852098,
|
||||
"2": -0.16855522073505835,
|
||||
"3": -0.17058243243886537,
|
||||
"4": -0.16912426083501933,
|
||||
"5": -0.1565265415734563,
|
||||
"6": -0.19628647888692194,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"11": -0.16157092178319896,
|
||||
"12": -0.189676855297913,
|
||||
"13": -0.15770967693836213,
|
||||
"14": -0.20841555764666236,
|
||||
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|
||||
"16": -0.15932412950943126,
|
||||
"17": -0.18379637840568513,
|
||||
"18": -0.18529572568314806,
|
||||
"19": -0.17369115610408078,
|
||||
"20": -0.05548311599661672,
|
||||
"21": -0.19925193540074856,
|
||||
"22": -0.19675940102420744,
|
||||
"23": -0.17433014779398687,
|
||||
"24": -0.18611585394489455,
|
||||
"25": -0.13486703237614187,
|
||||
"26": -0.15685839750255778,
|
||||
"27": -0.1596892046986258,
|
||||
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|
||||
"29": -0.18879522587303826,
|
||||
"30": -0.16751076836769452,
|
||||
"31": -0.19038127206562197,
|
||||
"32": -0.13663412879912137,
|
||||
"33": -0.199114452992506,
|
||||
"34": -0.2239169570738135,
|
||||
"35": -0.14257523105459294,
|
||||
"36": -0.17943758617917807,
|
||||
"37": -0.09913819921912113,
|
||||
"38": -0.21065276811475495,
|
||||
"39": -0.14244888114149687,
|
||||
"40": -0.17525860003957378,
|
||||
"41": -0.18030894861191304,
|
||||
"42": -0.15471345040511847,
|
||||
"43": -0.14286525121431798,
|
||||
"44": -0.1453351098570295,
|
||||
"45": -0.22340680701777196,
|
||||
"46": -0.19115312169289994,
|
||||
"47": -0.16872377372528913,
|
||||
"48": -0.1451124664635304,
|
||||
"49": -0.14670732656278024
|
||||
},
|
||||
"stats": {
|
||||
"incorrect": 2150,
|
||||
"correct": 300,
|
||||
"cycles": 50,
|
||||
"last_update": "2026-06-26T14:10:06.693936"
|
||||
"incorrect": 2623,
|
||||
"correct": 366,
|
||||
"cycles": 61,
|
||||
"last_update": "2026-08-08T20:01:01.664655"
|
||||
},
|
||||
"learning_rate": 0.1,
|
||||
"last_saved": "2026-06-26T14:10:06.693991"
|
||||
"last_saved": "2026-08-08T20:01:01.664706"
|
||||
}
|
||||
Binary file not shown.
@@ -0,0 +1,151 @@
|
||||
# Architektur
|
||||
|
||||
Dieses Dokument beschreibt den aktiven Datenfluss, die Kernkomponenten und die
|
||||
wichtigsten Design-Entscheidungen des Lotto-6aus49-Systems. Es wurde mit Hilfe
|
||||
einer Code-Graph-Analyse (`graphify-out/`, siehe unten) erstellt und sollte bei
|
||||
größeren strukturellen Änderungen aktualisiert werden.
|
||||
|
||||
## 1. Aktiver Datenfluss
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
subgraph Ingestion["1. Daten-Ingestion"]
|
||||
A[GitHub Lotto Archive] -->|LottoAPIUpdater| B[AlleLottozahlen.csv]
|
||||
end
|
||||
|
||||
subgraph Training["2. Training"]
|
||||
B --> C[FeatureEngineer<br/>20 Features]
|
||||
C --> D[AIMLEngine<br/>RandomForest, 40 Zahlen]
|
||||
C --> E[DeepLearningEngine<br/>LSTM, PyTorch]
|
||||
D --> F[Hybrid Predictor<br/>RF 40% + LSTM 60%]
|
||||
E --> F
|
||||
end
|
||||
|
||||
subgraph Generation["3. Tipp-Generierung"]
|
||||
F --> G[UltimateAIMLHybridGenerator]
|
||||
G --> H1[HYBRID-OPT]
|
||||
G --> H2[BALANCED-SPREAD]
|
||||
G --> H3[HIGH-EV]
|
||||
G --> H4[SOFT-CONTRARIAN]
|
||||
H1 & H2 & H3 & H4 --> I[Quality/Popularity Score]
|
||||
I --> J[10 Tipps als CSV]
|
||||
end
|
||||
|
||||
subgraph Learning["4. Learning-Loop"]
|
||||
B -->|neue Ziehung| K[AutoUpdateAndLearn]
|
||||
K -->|evaluiert| J
|
||||
K -->|retrained| D
|
||||
K -->|retrained| E
|
||||
K --> L[learning_log.json /<br/>strategy_weights]
|
||||
end
|
||||
|
||||
J --> M[LottoNotifier<br/>Telegram]
|
||||
K --> M
|
||||
|
||||
L -.->|beeinflusst nächsten Lauf| G
|
||||
```
|
||||
|
||||
**Zwei unabhängige Cron-Zyklen** (siehe Abschnitt 4):
|
||||
|
||||
| Schritt | Entrypoint | Ausführt |
|
||||
| --- | --- | --- |
|
||||
| Ingestion + Training + Learning | `run_update_and_learn.sh` → `scripts/automation/auto_update_and_learn.py` | Mi + Sa, 20:00 Uhr |
|
||||
| Tipp-Generierung | `run_tip_generator.sh` → `scripts/automation/weekly_tip_generator.py` | Di + Fr, 21:00 Uhr |
|
||||
|
||||
Wichtig: Die Tipp-Generierung läuft **vor** dem nächsten Update-Lauf. Das
|
||||
System hinkt daher strukturell immer bis zu einem Zyklus hinter neu
|
||||
verfügbaren Ziehungen hinterher, falls die externe Datenquelle verspätet
|
||||
aktualisiert (siehe Abschnitt 5).
|
||||
|
||||
## 2. Kernkomponenten (aktive Pipeline)
|
||||
|
||||
Alle Pfade relativ zum Projekt-Root.
|
||||
|
||||
| Komponente | Datei | Rolle |
|
||||
| --- | --- | --- |
|
||||
| `LottoAPIUpdater` | `scripts/utils/update_from_api.py` | Holt neue Ziehungen über `fetch_from_all_apis()`: Lottoland zuerst (liefert die neueste Ziehung meist noch am Ziehungstag), GitHub-Archiv für die Historie/als Fallback (kann tagelang hinterherhinken). `auto_update_and_learn.py` nutzt seit August 2026 `api_name='all'`, davor fest `api_name='github'` — daher wiederholt verspätete Ziehungen in der Vergangenheit. |
|
||||
| `AutoUpdateAndLearn` | `scripts/automation/auto_update_and_learn.py` | Orchestriert den 4-Schritte-Workflow: Daten aktualisieren → neue Ziehung prüfen → letzte Tipps evaluieren → Learning-Update (Retraining). |
|
||||
| `FeatureEngineer` | `scripts/generators/ultimate_ai_ml_hybrid_generator.py` | Baut 20 Features (Frequenzen, Gaps, Momentum, Trends, Beziehungen, Zyklen) aus den Rohdaten. |
|
||||
| `AIMLEngine` | `scripts/generators/ultimate_ai_ml_hybrid_generator.py` | RandomForest-Modelle, ein Modell pro Zahl (1–49). Cached unter `data/ultimate_ml_models/`. |
|
||||
| `DeepLearningEngine` | `scripts/utils/deep_learning_engine_pytorch.py` | LSTM (PyTorch) über Sequenzen der letzten 20 Ziehungen. Wird mit RF zu 40/60 kombiniert (`Hybrid Predictor`). |
|
||||
| `UltimateAIMLHybridGenerator` | `scripts/generators/ultimate_ai_ml_hybrid_generator.py` | **God Node der Pipeline** (35 Kanten im Code-Graph) — verbindet Training, die 4 Tipp-Strategien und den Quality/Popularity-Score. Zentraler Einstiegspunkt für `generate_ultimate_tips()`. |
|
||||
| `PatternEngine` | `scripts/generators/ultimate_ai_ml_hybrid_generator.py` | Historische Verteilungsmuster (N/M/H-Zonen etc.), fließt als `pattern_weight` in die Tipp-Bewertung ein. |
|
||||
| `HybridOptimizer` | `scripts/generators/ultimate_ai_ml_hybrid_generator.py` | Kandidaten-Generierung für die HYBRID-OPT-Strategie. |
|
||||
| `RealTimeLearner` | `scripts/generators/ultimate_ai_ml_hybrid_generator.py` | Persistiert Learning-State (`learning_state.json`), passt `strategy_weights` nach jedem Zyklus an (`_update_strategy_weights`). |
|
||||
| `PerformanceTracker` | `scripts/generators/ultimate_ai_ml_hybrid_generator.py` | Loggt Trefferauswertungen nach `data/learning_log.json`. |
|
||||
| `WeeklyTipGenerator` | `scripts/automation/weekly_tip_generator.py` | Ruft `generate_ultimate_tips()`, exportiert CSV nach `data/generated_tips/`, aktualisiert `generation_history.json`. |
|
||||
| `LottoNotifier` | `scripts/utils/notifier.py` | Telegram-Benachrichtigungen für neue Tipps und Ziehungsergebnisse. |
|
||||
|
||||
### Die 4 Tipp-Strategien
|
||||
|
||||
Pro Lauf werden 10 Tipps über 4 Strategien verteilt (Gewichtung passt sich
|
||||
über `RealTimeLearner` dynamisch an, Startwerte: HYBRID-OPT 40%,
|
||||
BALANCED-SPREAD 30%, HIGH-EV 20%, SOFT-CONTRARIAN 10%):
|
||||
|
||||
- **HYBRID-OPT** — AI-Score + Pattern-Gewicht kombiniert (`HybridOptimizer`)
|
||||
- **BALANCED-SPREAD** — erzwingt Verteilung über N/M/H-Zonen, Summenbereich 127–171
|
||||
- **HIGH-EV** — 2–3 Zahlen >31, meidet empirisch belegte populäre Einzelzahlen (siehe Abschnitt 3)
|
||||
- **SOFT-CONTRARIAN** — bevorzugt in den letzten 30 Ziehungen unterrepräsentierte Zahlen
|
||||
|
||||
## 3. Design-Entscheidung: Quality-Score = EV-Optimierung, nicht Trefferprognose
|
||||
|
||||
Lotto-6aus49-Ziehungen sind mechanisch geprüfte, unabhängige Zufallsereignisse
|
||||
(i.i.d.). Kein Modell — auch kein LSTM — kann daraus einen Vorteil gegenüber
|
||||
reinem Zufall bei der **Trefferwahrscheinlichkeit** ableiten. Das bestätigen
|
||||
auch die eigenen `learning_log.json`-Daten: die durchschnittlichen
|
||||
Haupttreffer schwanken um den Erwartungswert von Zufallstipps (~0.73 Treffer
|
||||
pro 6er-Tipp), ohne erkennbaren Aufwärtstrend trotz kontinuierlichem Retraining.
|
||||
|
||||
Der `_calculate_quality_score()` in `ultimate_ai_ml_hybrid_generator.py`
|
||||
optimiert deshalb bewusst nicht auf Trefferwahrscheinlichkeit, sondern auf
|
||||
**Expected Value im Gewinnfall**: unpopuläre Zahlenkombinationen haben bei
|
||||
einem Treffer weniger Mitgewinner und damit eine höhere Auszahlung.
|
||||
`_calculate_popularity_score()` bewertet dafür (Gewicht 50% der Quality-Formel):
|
||||
|
||||
1. Geburtstags-Range (>31 bevorzugt)
|
||||
2. Empirisch belegte populäre Einzelzahlen (`POPULAR_PLAYER_PICKS`)
|
||||
3. Zahlenfolgen/arithmetische Muster (nicht nur direkte Nachbarn)
|
||||
4. Odd/Even-Split-Extremität (Menschen bevorzugen "ausgeglichen aussehende" 3-3-Splits)
|
||||
|
||||
Die Superzahl-Auswahl (`_get_smart_superzahl`) folgt derselben Logik: eine
|
||||
feste EV-Rangfolge unpopulärer Ziffern statt (bedeutungsloser) historischer
|
||||
Ziehungshäufigkeit, da die Superzahl pro Ziehung unabhängig gleichverteilt ist.
|
||||
|
||||
AI-Score, Pattern-Gewicht und Recency fließen weiterhin mit reduziertem
|
||||
Gewicht in die Quality-Formel ein — nicht weil sie die Trefferchance erhöhen,
|
||||
sondern weil eine gewisse Portfolio-Diversität über die 10 Wochentipps
|
||||
gewünscht ist.
|
||||
|
||||
## 4. Automatisierung
|
||||
|
||||
Die Automatisierung läuft über **launchd** (`~/Library/LaunchAgents/`), nicht
|
||||
über `crontab` — die Kommentare in `crontab -l` sind veraltete Doku-Reste und
|
||||
spiegeln nicht den tatsächlichen Zeitplan wider:
|
||||
|
||||
| launchd Job | Plist | Zeitplan (tatsächlich) |
|
||||
| --- | --- | --- |
|
||||
| `com.lotto.update` | `scripts/automation/com.lotto.update.plist` | Mi + Sa, 20:00 Uhr |
|
||||
| `com.lotto.weekly` | `scripts/automation/com.lotto.weekly.plist` | Di + Fr, 21:00 Uhr |
|
||||
|
||||
Logs: `logs/update_stdout.log` (Update+Learning), `logs/stdout.log`
|
||||
(Tipp-Generierung).
|
||||
|
||||
## 5. Bekannte Schwachstellen / offene Punkte
|
||||
|
||||
- **`update_from_web.py`** (lotto.de-Scraper) ist als dritter Fallback
|
||||
implementiert, aber nirgends in der automatisierten Pipeline eingebunden.
|
||||
- **Utility-Skripte** in `scripts/utils/` (`health_check.py`, `validate_csv.py`,
|
||||
`verify_draws.py`, `model_evaluator.py`) existieren, sind aber nicht in die
|
||||
Cron-Automatisierung eingebunden; manuelle Ausführung bei Bedarf.
|
||||
|
||||
## 6. Code-Graph
|
||||
|
||||
Eine navigierbare Graph-Ansicht aller Module, Klassen und ihrer Beziehungen
|
||||
liegt unter `graphify-out/`:
|
||||
|
||||
- `graphify-out/graph.html` — interaktive Visualisierung (im Browser öffnen)
|
||||
- `graphify-out/GRAPH_REPORT.md` — God Nodes, Communities, auffällige Verbindungen
|
||||
- `graphify-out/graph.json` — Rohdaten (GraphRAG-fähig)
|
||||
|
||||
Bei größeren strukturellen Änderungen: `/graphify --update` zum
|
||||
inkrementellen Neuaufbau.
|
||||
@@ -1,422 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
AI-GENERATOR MIT MUSTER-GEWICHTUNG
|
||||
Erweitert den AI-Generator um explizite Muster-Gewichtung (NNMMHH, etc.)
|
||||
|
||||
Neue Features:
|
||||
- Historische Muster-Analyse (NNMMHH, NMMHHH, etc.)
|
||||
- Muster-Erfolgsquoten berechnen
|
||||
- Muster-basierte Tip-Optimierung
|
||||
- Pattern-Scoring für bessere Kombinationen
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from collections import Counter, defaultdict
|
||||
import random
|
||||
|
||||
class PatternWeightedAI:
|
||||
def __init__(self, df):
|
||||
self.df = df
|
||||
self.pattern_frequencies = Counter()
|
||||
self.pattern_success_rates = {}
|
||||
self.optimal_patterns = []
|
||||
|
||||
# Analysiere historische Muster
|
||||
self._analyze_historical_patterns()
|
||||
|
||||
def _analyze_historical_patterns(self):
|
||||
"""Analysiert alle historischen Muster und deren Erfolgsquoten."""
|
||||
print("\n🎨 MUSTER-ANALYSE GESTARTET...")
|
||||
|
||||
if len(self.df) == 0:
|
||||
return
|
||||
|
||||
total_drawings = len(self.df)
|
||||
|
||||
for _, row in self.df.iterrows():
|
||||
numbers = sorted([row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']])
|
||||
pattern = self._get_pattern(numbers)
|
||||
self.pattern_frequencies[pattern] += 1
|
||||
|
||||
# Berechne Erfolgsquoten
|
||||
for pattern, count in self.pattern_frequencies.items():
|
||||
success_rate = count / total_drawings
|
||||
self.pattern_success_rates[pattern] = success_rate
|
||||
|
||||
# Identifiziere optimale Muster (Top 10)
|
||||
self.optimal_patterns = [
|
||||
pattern for pattern, _ in self.pattern_frequencies.most_common(10)
|
||||
]
|
||||
|
||||
print("🎯 MUSTER-ERFOLGSQUOTEN:")
|
||||
print("Pattern Häufigkeit Erfolgsrate Bewertung")
|
||||
print("-" * 50)
|
||||
|
||||
for i, (pattern, count) in enumerate(self.pattern_frequencies.most_common(15)):
|
||||
success_rate = self.pattern_success_rates[pattern]
|
||||
|
||||
if success_rate >= 0.08:
|
||||
bewertung = "🏆 EXCELLENT"
|
||||
elif success_rate >= 0.06:
|
||||
bewertung = "🥇 SEHR GUT"
|
||||
elif success_rate >= 0.04:
|
||||
bewertung = "🥈 GUT"
|
||||
elif success_rate >= 0.02:
|
||||
bewertung = "🥉 DURCHSCHNITT"
|
||||
else:
|
||||
bewertung = "❌ SCHWACH"
|
||||
|
||||
print(f"{pattern:<10} {count:>8} {success_rate:>8.3f} {bewertung}")
|
||||
|
||||
def _get_pattern(self, numbers):
|
||||
"""Konvertiert Zahlen zu N/M/H Muster."""
|
||||
pattern = ""
|
||||
for num in numbers:
|
||||
if 1 <= num <= 16:
|
||||
pattern += "N" # Niedrig
|
||||
elif 17 <= num <= 32:
|
||||
pattern += "M" # Mittel
|
||||
else:
|
||||
pattern += "H" # Hoch
|
||||
return pattern
|
||||
|
||||
def calculate_pattern_weight(self, numbers):
|
||||
"""Berechnet Gewichtung basierend auf Muster-Erfolgsquote."""
|
||||
pattern = self._get_pattern(sorted(numbers))
|
||||
|
||||
# Basis-Gewichtung aus historischer Erfolgsquote
|
||||
base_weight = self.pattern_success_rates.get(pattern, 0.01)
|
||||
|
||||
# Bonus für Top-Muster
|
||||
if pattern in self.optimal_patterns[:5]:
|
||||
bonus = 0.3
|
||||
elif pattern in self.optimal_patterns[:10]:
|
||||
bonus = 0.2
|
||||
else:
|
||||
bonus = 0.0
|
||||
|
||||
# Penalty für nie aufgetretene Muster
|
||||
if pattern not in self.pattern_frequencies:
|
||||
penalty = -0.2
|
||||
else:
|
||||
penalty = 0.0
|
||||
|
||||
final_weight = base_weight + bonus + penalty
|
||||
return max(0.01, min(1.0, final_weight)) # Clamp 0.01-1.0
|
||||
|
||||
def get_pattern_recommendations(self):
|
||||
"""Liefert Muster-Empfehlungen für Tip-Generierung."""
|
||||
recommendations = {}
|
||||
|
||||
# Top 5 erfolgreichste Muster
|
||||
recommendations['top_patterns'] = self.optimal_patterns[:5]
|
||||
|
||||
# Muster mit bester Erfolgsquote
|
||||
if self.pattern_success_rates:
|
||||
best_pattern = max(self.pattern_success_rates.items(), key=lambda x: x[1])
|
||||
recommendations['best_pattern'] = best_pattern[0]
|
||||
recommendations['best_success_rate'] = best_pattern[1]
|
||||
|
||||
# Muster-Statistiken
|
||||
recommendations['total_patterns'] = len(self.pattern_frequencies)
|
||||
recommendations['pattern_diversity'] = len([p for p, rate in self.pattern_success_rates.items() if rate >= 0.02])
|
||||
|
||||
return recommendations
|
||||
|
||||
def optimize_combination_for_pattern(self, target_pattern="NNMMHH"):
|
||||
"""Optimiert Zahlen-Kombination für spezifisches Muster."""
|
||||
|
||||
# Definiere Bereiche
|
||||
ranges = {
|
||||
'N': list(range(1, 17)), # Niedrig: 1-16
|
||||
'M': list(range(17, 33)), # Mittel: 17-32
|
||||
'H': list(range(33, 50)) # Hoch: 33-49
|
||||
}
|
||||
|
||||
# Parse target pattern
|
||||
pattern_counts = Counter(target_pattern)
|
||||
needed_n = pattern_counts.get('N', 0)
|
||||
needed_m = pattern_counts.get('M', 0)
|
||||
needed_h = pattern_counts.get('H', 0)
|
||||
|
||||
selected = []
|
||||
|
||||
# Wähle Zahlen für Muster
|
||||
if needed_n > 0:
|
||||
n_numbers = random.sample(ranges['N'], min(needed_n, len(ranges['N'])))
|
||||
selected.extend(n_numbers)
|
||||
|
||||
if needed_m > 0:
|
||||
m_numbers = random.sample(ranges['M'], min(needed_m, len(ranges['M'])))
|
||||
selected.extend(m_numbers)
|
||||
|
||||
if needed_h > 0:
|
||||
h_numbers = random.sample(ranges['H'], min(needed_h, len(ranges['H'])))
|
||||
selected.extend(h_numbers)
|
||||
|
||||
# Auffüllen falls nötig
|
||||
while len(selected) < 6:
|
||||
all_ranges = ranges['N'] + ranges['M'] + ranges['H']
|
||||
available = [n for n in all_ranges if n not in selected]
|
||||
if available:
|
||||
selected.append(random.choice(available))
|
||||
else:
|
||||
break
|
||||
|
||||
return sorted(selected[:6])
|
||||
|
||||
class EnhancedAIGenerator:
|
||||
"""Erweitert den ursprünglichen AI-Generator um Muster-Gewichtung."""
|
||||
|
||||
def __init__(self, data_path):
|
||||
self.data_path = data_path
|
||||
self.df = None
|
||||
self.pattern_ai = None
|
||||
|
||||
# Load data
|
||||
self._load_data()
|
||||
|
||||
# Initialize Pattern AI
|
||||
if self.df is not None and len(self.df) > 0:
|
||||
self.pattern_ai = PatternWeightedAI(self.df)
|
||||
|
||||
def _load_data(self):
|
||||
"""Lädt Daten."""
|
||||
try:
|
||||
self.df = pd.read_csv(self.data_path, sep=';')
|
||||
if 'datum' in self.df.columns:
|
||||
self.df['datum'] = pd.to_datetime(self.df['datum'], format='%Y-%m-%d', errors='coerce')
|
||||
self.df = self.df.sort_values('datum')
|
||||
print(f"✅ {len(self.df)} Ziehungen geladen")
|
||||
except Exception as e:
|
||||
print(f"❌ Fehler beim Laden: {e}")
|
||||
self.df = pd.DataFrame()
|
||||
|
||||
def generate_pattern_optimized_tips(self, num_tips=10):
|
||||
"""Generiert Tipps mit expliziter Muster-Gewichtung."""
|
||||
|
||||
if not self.pattern_ai:
|
||||
print("❌ Pattern AI nicht verfügbar")
|
||||
return []
|
||||
|
||||
print("\n🎨 PATTERN-OPTIMIERTE TIPP-GENERIERUNG")
|
||||
print("=" * 60)
|
||||
|
||||
# Muster-Empfehlungen abrufen
|
||||
recommendations = self.pattern_ai.get_pattern_recommendations()
|
||||
|
||||
print("🎯 MUSTER-EMPFEHLUNGEN:")
|
||||
print(f" Bestes Muster: {recommendations.get('best_pattern', 'N/A')} ({recommendations.get('best_success_rate', 0)*100:.1f}%)")
|
||||
print(f" Top 5 Muster: {', '.join(recommendations.get('top_patterns', [])[:5])}")
|
||||
print(f" Pattern-Diversität: {recommendations.get('pattern_diversity', 0)} erfolgreiche Muster")
|
||||
|
||||
tips = []
|
||||
|
||||
print(f"\n🎲 GENERIERE {num_tips} PATTERN-OPTIMIERTE TIPPS:")
|
||||
print("=" * 80)
|
||||
print("Nr 6 Pattern-Numbers Pattern Weight Confidence Success-Rate")
|
||||
print("-" * 80)
|
||||
|
||||
# Verschiedene Strategien für verschiedene Tipps
|
||||
strategies = [
|
||||
('best', "Bestes Muster"),
|
||||
('top5', "Top 5 Rotation"),
|
||||
('balanced', "Ausgewogene Muster"),
|
||||
('diverse', "Diversifizierte Muster")
|
||||
]
|
||||
|
||||
for i in range(1, num_tips + 1):
|
||||
strategy = strategies[(i-1) % len(strategies)]
|
||||
tip = self._generate_pattern_tip(i, strategy[0], recommendations)
|
||||
tips.append(tip)
|
||||
|
||||
# Output
|
||||
zahlen_str = '-'.join([f"{n:2}" for n in tip['numbers']])
|
||||
success_rate = self.pattern_ai.pattern_success_rates.get(tip['pattern'], 0)
|
||||
|
||||
print(f"{i:2} {zahlen_str} {tip['pattern']:<8} {tip['pattern_weight']:.3f} {tip['confidence']:.3f} {success_rate:.3f}")
|
||||
|
||||
# Zusammenfassung
|
||||
self._print_pattern_summary(tips)
|
||||
|
||||
return tips
|
||||
|
||||
def _generate_pattern_tip(self, tip_number, strategy, recommendations):
|
||||
"""Generiert einzelnen pattern-optimierten Tipp."""
|
||||
|
||||
# Seed für Konsistenz
|
||||
random.seed(42 + tip_number)
|
||||
|
||||
if strategy == 'best':
|
||||
# Nutze bestes Muster
|
||||
target_pattern = recommendations.get('best_pattern', 'NNMMHH')
|
||||
elif strategy == 'top5':
|
||||
# Rotiere durch Top 5
|
||||
top_patterns = recommendations.get('top_patterns', ['NNMMHH'])
|
||||
target_pattern = top_patterns[(tip_number - 1) % len(top_patterns)]
|
||||
elif strategy == 'balanced':
|
||||
# Ausgewogene beliebte Muster
|
||||
balanced_patterns = ['NNMMHH', 'NMMHHH', 'NMMMHH', 'NNMHHH']
|
||||
target_pattern = balanced_patterns[(tip_number - 1) % len(balanced_patterns)]
|
||||
else: # diverse
|
||||
# Diversifizierte Muster für Abdeckung
|
||||
diverse_patterns = ['NNMMHH', 'MMHHHH', 'NNNNMM', 'NMHHHH', 'NNNMMH']
|
||||
target_pattern = diverse_patterns[(tip_number - 1) % len(diverse_patterns)]
|
||||
|
||||
# Generiere Kombination für Ziel-Muster
|
||||
numbers = self.pattern_ai.optimize_combination_for_pattern(target_pattern)
|
||||
|
||||
# Validiere und korrigiere falls nötig
|
||||
actual_pattern = self.pattern_ai._get_pattern(numbers)
|
||||
|
||||
# Pattern Weight berechnen
|
||||
pattern_weight = self.pattern_ai.calculate_pattern_weight(numbers)
|
||||
|
||||
# Confidence basierend auf Pattern Success Rate
|
||||
success_rate = self.pattern_ai.pattern_success_rates.get(actual_pattern, 0.01)
|
||||
confidence = pattern_weight * 0.6 + success_rate * 0.4
|
||||
|
||||
# Superzahl
|
||||
superzahl = self._get_pattern_superzahl(tip_number)
|
||||
|
||||
return {
|
||||
'tip_number': tip_number,
|
||||
'numbers': numbers,
|
||||
'pattern': actual_pattern,
|
||||
'target_pattern': target_pattern,
|
||||
'pattern_weight': pattern_weight,
|
||||
'confidence': confidence,
|
||||
'success_rate': success_rate,
|
||||
'superzahl': superzahl,
|
||||
'strategy': strategy
|
||||
}
|
||||
|
||||
def _get_pattern_superzahl(self, tip_number):
|
||||
"""Pattern-optimierte Superzahl."""
|
||||
# Basis häufigste Superzahlen
|
||||
frequent_sz = [7, 6, 3, 2, 0, 1, 4, 5, 8, 9]
|
||||
|
||||
# Tip-spezifische Auswahl
|
||||
return frequent_sz[tip_number % len(frequent_sz)]
|
||||
|
||||
def _print_pattern_summary(self, tips):
|
||||
"""Druckt Pattern-Zusammenfassung."""
|
||||
print(f"\n🏆 PATTERN-OPTIMIERUNG ZUSAMMENFASSUNG:")
|
||||
print("=" * 50)
|
||||
|
||||
# Pattern-Verteilung
|
||||
pattern_dist = Counter([tip['pattern'] for tip in tips])
|
||||
print("📊 PATTERN-VERTEILUNG:")
|
||||
for pattern, count in pattern_dist.most_common():
|
||||
avg_success = np.mean([self.pattern_ai.pattern_success_rates.get(pattern, 0)] * count)
|
||||
print(f" {pattern}: {count}x (Ø Success: {avg_success:.3f})")
|
||||
|
||||
# Durchschnittliche Metriken
|
||||
avg_weight = np.mean([tip['pattern_weight'] for tip in tips])
|
||||
avg_confidence = np.mean([tip['confidence'] for tip in tips])
|
||||
avg_success = np.mean([tip['success_rate'] for tip in tips])
|
||||
|
||||
print(f"\n📈 DURCHSCHNITTLICHE METRIKEN:")
|
||||
print(f" Pattern-Weight: {avg_weight:.3f}")
|
||||
print(f" Confidence: {avg_confidence:.3f}")
|
||||
print(f" Success-Rate: {avg_success:.3f}")
|
||||
|
||||
# Beste Tipps
|
||||
best_tip = max(tips, key=lambda x: x['confidence'])
|
||||
print(f"\n⭐ BESTER PATTERN-TIPP:")
|
||||
zahlen_str = '-'.join([f"{n:2}" for n in best_tip['numbers']])
|
||||
print(f" Tipp {best_tip['tip_number']}: {zahlen_str}")
|
||||
print(f" Pattern: {best_tip['pattern']} (Weight: {best_tip['pattern_weight']:.3f})")
|
||||
print(f" Success-Rate: {best_tip['success_rate']:.3f}")
|
||||
|
||||
def demonstrate_pattern_weighting():
|
||||
"""Demonstriert Pattern-Gewichtung mit Beispiel-Daten."""
|
||||
|
||||
print("🎨 PATTERN-GEWICHTUNG DEMONSTRATION")
|
||||
print("=" * 50)
|
||||
|
||||
# Beispiel-Daten erstellen
|
||||
sample_data = []
|
||||
patterns_to_simulate = ['NNMMHH', 'NMMHHH', 'NMMMHH', 'NNMHHH', 'MMHHHH']
|
||||
|
||||
for i in range(100):
|
||||
# Simuliere Ziehungen mit verschiedenen Mustern
|
||||
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()
|
||||
@@ -125,7 +125,10 @@ class AutoUpdateAndLearn:
|
||||
|
||||
try:
|
||||
updater = LottoAPIUpdater(self.data_file)
|
||||
success = updater.update(api_name='github', create_backup=True)
|
||||
# 'all': Lottoland zuerst (liefert die neueste Ziehung meist noch am
|
||||
# Ziehungstag), GitHub-Archiv als Quelle für die Historie/Fallback -
|
||||
# das GitHub-Archiv allein hinkt wiederholt tagelang hinterher.
|
||||
success = updater.update(api_name='all', create_backup=True)
|
||||
|
||||
if success:
|
||||
print(" ✅ Daten erfolgreich aktualisiert")
|
||||
|
||||
@@ -75,6 +75,11 @@ class UltimateAIMLHybridGenerator:
|
||||
Ultimate Generator - OPTIMIZED VERSION
|
||||
"""
|
||||
|
||||
# Empirisch belegte "griffige" Einzelzahlen, die Spieler überproportional oft frei
|
||||
# wählen (Quelle: Analysen realer Tippscheine, z.B. frz. 6aus49 über 25 Jahre:
|
||||
# populärste Zahlen 5,7,9,11,12,13). Unabhängig vom Geburtstags-Bias (<=31).
|
||||
POPULAR_PLAYER_PICKS = {5, 7, 9, 11, 12, 13}
|
||||
|
||||
def __init__(self, data_path, fast_mode=True):
|
||||
self.data_path = data_path
|
||||
self.df = None
|
||||
@@ -406,7 +411,7 @@ class UltimateAIMLHybridGenerator:
|
||||
"""High-EV: 2–3 Zahlen >31, max. 1 Lucky Number, soft Consecutive-Vermeidung."""
|
||||
random.seed(42 + tip_number * 41)
|
||||
|
||||
lucky_numbers = {3, 7, 9, 11, 13, 17, 19, 21, 23}
|
||||
lucky_numbers = UltimateAIMLHybridGenerator.POPULAR_PLAYER_PICKS
|
||||
above_31_target = 2 + (tip_number % 2) # wechselt zwischen 2 und 3
|
||||
|
||||
selected = []
|
||||
@@ -628,49 +633,70 @@ class UltimateAIMLHybridGenerator:
|
||||
return (distance_score * 0.4 + range_score * 0.4 + balance_score * 0.2)
|
||||
|
||||
def _get_smart_superzahl(self, tip_number):
|
||||
"""Intelligente Superzahl."""
|
||||
base_sz = [7, 6, 3, 2, 0, 1, 4, 5, 8, 9]
|
||||
|
||||
try:
|
||||
if 'SZ' in self.df.columns and len(self.df) > 10:
|
||||
recent_sz = self.df['SZ'].tail(30).dropna()
|
||||
if len(recent_sz) > 0:
|
||||
sz_freq = Counter(recent_sz)
|
||||
top_sz = [int(sz) for sz, _ in sz_freq.most_common(7) if 0 <= sz <= 9]
|
||||
if top_sz:
|
||||
base_sz = top_sz + [7, 6, 3]
|
||||
except:
|
||||
pass
|
||||
|
||||
if tip_number <= 3:
|
||||
sz = base_sz[0]
|
||||
else:
|
||||
sz = base_sz[tip_number % len(base_sz)]
|
||||
|
||||
return int(sz) if 0 <= int(sz) <= 9 else 7
|
||||
"""
|
||||
EV-optimierte Superzahl: bevorzugt Ziffern, die Spieler erfahrungsgemäß
|
||||
seltener frei wählen (0 wirkt nicht wie eine "eigene" Zahl; 7/3 gelten als
|
||||
klassische Glückszahlen und werden überproportional gewählt). Historische
|
||||
Ziehungshäufigkeit fließt bewusst NICHT mehr ein - die SZ wird pro Ziehung
|
||||
unabhängig und gleichverteilt (0-9) gezogen, "häufig gezogen in letzten 30
|
||||
Ziehungen" ist reines Rauschen ohne Vorhersagewert.
|
||||
"""
|
||||
ev_order = [0, 9, 8, 4, 2, 6, 1, 5, 3, 7] # unpopulärste zuerst
|
||||
return ev_order[tip_number % len(ev_order)]
|
||||
|
||||
@staticmethod
|
||||
def _calculate_popularity_score(numbers):
|
||||
"""
|
||||
Schätzt den Erwartungswert-Vorteil durch Vermeidung populärer Zahlenkombinationen.
|
||||
Höher = unpopulärer = höherer Gewinnanteil bei einem Treffer.
|
||||
Basis: Spieler bevorzugen Geburtstagszahlen (1-31), Glückszahlen und Zahlenfolgen.
|
||||
Höher = unpopulärer bei Mitspielern = höherer Gewinnanteil bei einem Treffer
|
||||
(Hit-Wahrscheinlichkeit selbst ist bei i.i.d. Ziehungen nicht beeinflussbar).
|
||||
|
||||
Basis: dokumentierte Spielerverhalten-Biases (Henze/Riedwyl-artige Analysen
|
||||
realer Tippscheine): Geburtstagszahlen (<=31), einzelne "griffige" Zahlen,
|
||||
Zahlenfolgen/arithmetische Muster (z.B. 5-10-15-20-25-30) und "zufällig
|
||||
aussehende" ausgeglichene Odd/Even-Splits werden von Menschen
|
||||
überproportional oft gewählt - jede einzelne Kombination ist aber exakt
|
||||
gleich wahrscheinlich, unabhängig von diesen Eigenschaften.
|
||||
|
||||
Hinweis: eine "Summe nahe am Mittel"-Komponente wurde bewusst NICHT
|
||||
aufgenommen - sie widerspricht sich mit der Muster-Erkennung, da gerade
|
||||
die meistgespielten sequenziellen Kombinationen (z.B. 1-2-3-4-5-6) durch
|
||||
ihre enge Zahlen-Clusterung zugleich eine extreme Summe erzeugen. Ohne
|
||||
Trennung von "Summe extrem durch Streuung" vs. "Summe extrem durch engen
|
||||
Cluster" hätte dieses Merkmal genau die falschesten Kombinationen
|
||||
aufgewertet.
|
||||
"""
|
||||
n = len(numbers)
|
||||
sorted_nums = sorted(numbers)
|
||||
|
||||
# Anteil Zahlen > 31 (Geburtstags-Range vermeiden)
|
||||
# 1) Geburtstags-Range (1-31) meiden
|
||||
above_31_ratio = sum(1 for x in numbers if x > 31) / n
|
||||
|
||||
# Aufeinanderfolgende Zahlen vermeiden (visuelle Muster)
|
||||
consecutive_pairs = sum(1 for i in range(n - 1) if sorted_nums[i + 1] - sorted_nums[i] == 1)
|
||||
consecutive_ratio = consecutive_pairs / (n - 1) if n > 1 else 0
|
||||
# 2) Empirisch belegte populäre Einzelzahlen meiden
|
||||
popular_ratio = sum(
|
||||
1 for x in numbers
|
||||
if x in UltimateAIMLHybridGenerator.POPULAR_PLAYER_PICKS
|
||||
) / n
|
||||
|
||||
# Häufig gespielte "Glückszahlen" vermeiden
|
||||
lucky_numbers = {3, 7, 9, 11, 13, 17, 19, 21, 23}
|
||||
lucky_ratio = sum(1 for x in numbers if x in lucky_numbers) / n
|
||||
# 3) Muster/Zahlenfolgen meiden: direkte Nachbarn UND allgemeine
|
||||
# arithmetische Folgen (konstante Schrittweite, z.B. 5-10-15-20-25-30
|
||||
# zählt zu den meistgespielten Kombinationen überhaupt)
|
||||
diffs = [sorted_nums[i + 1] - sorted_nums[i] for i in range(n - 1)]
|
||||
small_step_ratio = sum(1 for d in diffs if d <= 5) / (n - 1) if diffs else 0
|
||||
is_perfect_progression = len(diffs) > 1 and len(set(diffs)) == 1
|
||||
pattern_penalty = small_step_ratio * (0.6 if not is_perfect_progression else 1.0)
|
||||
|
||||
score = above_31_ratio * 0.5 + (1 - consecutive_ratio) * 0.3 + (1 - lucky_ratio) * 0.2
|
||||
# 4) Odd/Even-Split: Menschen bevorzugen "ausgeglichen aussehende" 3-3
|
||||
# Splits, obwohl jede einzelne Kombination gleich wahrscheinlich ist
|
||||
even_count = sum(1 for x in numbers if x % 2 == 0)
|
||||
split_extremity = abs(even_count - 3) / 3 # 0.0 bei 3-3, 1.0 bei 6-0/0-6
|
||||
|
||||
score = (
|
||||
above_31_ratio * 0.30
|
||||
+ (1 - popular_ratio) * 0.20
|
||||
+ (1 - pattern_penalty) * 0.30
|
||||
+ split_extremity * 0.20
|
||||
)
|
||||
return min(max(score, 0.0), 1.0)
|
||||
|
||||
def _calculate_quality_score(self, numbers, ai_predictions, pattern_weight):
|
||||
@@ -702,12 +728,16 @@ class UltimateAIMLHybridGenerator:
|
||||
else:
|
||||
recency_quality = 0.5 # neutral wenn noch nicht berechnet
|
||||
|
||||
# Popularity/EV dominiert bewusst: AI-Score, Pattern und Recency sagen nichts
|
||||
# über die (unbeeinflussbare) Trefferwahrscheinlichkeit aus - Recency/Pattern
|
||||
# folgen zudem Heuristiken, die auch andere Systemspieler nutzen und damit die
|
||||
# Popularity eher untergraben statt sie zu unterstützen.
|
||||
quality = (
|
||||
ai_quality * 0.30
|
||||
+ pattern_quality * 0.20
|
||||
ai_quality * 0.15
|
||||
+ pattern_quality * 0.10
|
||||
+ diversity_quality * 0.15
|
||||
+ popularity_quality * 0.20
|
||||
+ recency_quality * 0.15
|
||||
+ popularity_quality * 0.50
|
||||
+ recency_quality * 0.10
|
||||
)
|
||||
return min(quality, 1.0)
|
||||
|
||||
|
||||
@@ -1,842 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
SUPER-LOTTO 6AUS49 GENERATOR
|
||||
Mit vollständigen historischen Daten und nie gezogenen Kombinationen
|
||||
|
||||
Nutzt Sebastian's komplette Datenbasis:
|
||||
- AlleLottozahlen.csv: Alle historischen Ziehungen mit Multi-Trend-Analyse
|
||||
- Fehlende_Lotto_Kombinationen.csv: Alle nie gezogenen Kombinationen
|
||||
- Maximale Optimierung durch vollständige Datenbasis
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import random
|
||||
from collections import Counter, defaultdict
|
||||
import datetime
|
||||
import pickle
|
||||
import os
|
||||
|
||||
class SuperLotto6aus49Generator:
|
||||
def __init__(self):
|
||||
# Pfade zu Sebastian's Daten
|
||||
self.base_path = "/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks"
|
||||
self.historical_data_path = f"{self.base_path}/AlleLottozahlen.csv"
|
||||
self.unused_combinations_path = f"{self.base_path}/Fehlende_Lotto_Kombinationen.csv"
|
||||
|
||||
# Daten-Container
|
||||
self.df_historical = None
|
||||
self.df_unused = None
|
||||
self.drawn_combinations = set()
|
||||
|
||||
# Basis-Analysen
|
||||
self.number_frequencies = Counter()
|
||||
self.position_frequencies = defaultdict(Counter)
|
||||
self.pattern_frequencies = Counter()
|
||||
self.supernumber_frequencies = Counter()
|
||||
self.weekday_frequencies = Counter()
|
||||
|
||||
# Multi-Trend-Analysen
|
||||
self.number_sequences = defaultdict(list)
|
||||
self.momentum_scores = {}
|
||||
self.trend_predictions = {}
|
||||
self.sequential_dependencies = defaultdict(lambda: defaultdict(int))
|
||||
self.hot_numbers = []
|
||||
self.warm_numbers = []
|
||||
self.cold_numbers = []
|
||||
|
||||
# Unused Combinations Intelligence
|
||||
self.unused_combinations_sample = []
|
||||
self.unused_patterns = Counter()
|
||||
self.unused_by_ranges = {'N': [], 'M': [], 'H': []}
|
||||
|
||||
# Cache für Performance
|
||||
self.cache_file = f"{self.base_path}/super_lotto_cache.pkl"
|
||||
|
||||
print("🚀 SUPER-LOTTO 6AUS49 GENERATOR")
|
||||
print("=" * 50)
|
||||
print("📊 Lade vollständige Sebastian's Datenbasis...")
|
||||
|
||||
# Lade und analysiere alle Daten
|
||||
self.load_all_data()
|
||||
|
||||
def load_all_data(self):
|
||||
"""Lädt alle verfügbaren Daten und führt komplette Analyse durch."""
|
||||
|
||||
# 1. Historische Ziehungen laden
|
||||
print("📈 Lade historische Ziehungen...")
|
||||
self._load_historical_data()
|
||||
|
||||
# 2. Nie gezogene Kombinationen laden
|
||||
print("🎯 Lade nie gezogene Kombinationen...")
|
||||
self._load_unused_combinations()
|
||||
|
||||
# 3. Basis-Analysen
|
||||
print("🔍 Führe Basis-Analysen durch...")
|
||||
self._perform_basic_analysis()
|
||||
|
||||
# 4. Multi-Trend-Analysen
|
||||
print("📊 Multi-Trend-Analyse...")
|
||||
self._perform_momentum_analysis()
|
||||
self._perform_sequential_analysis()
|
||||
|
||||
# 5. Unused Combinations Intelligence
|
||||
print("🎲 Analysiere nie gezogene Kombinationen...")
|
||||
self._analyze_unused_combinations()
|
||||
|
||||
print("✅ Komplette Super-Analyse abgeschlossen!")
|
||||
self._print_super_analysis_summary()
|
||||
|
||||
def _load_historical_data(self):
|
||||
"""Lädt historische Lotto-Daten."""
|
||||
try:
|
||||
# Sebastian's Format: tag;datum;Z1;Z2;Z3;Z4;Z5;Z6;SZ
|
||||
self.df_historical = pd.read_csv(self.historical_data_path, sep=';')
|
||||
|
||||
# Datum konvertieren (verschiedene Formate unterstützen)
|
||||
date_formats = ['%Y-%m-%d', '%d.%m.%Y', '%d/%m/%Y']
|
||||
for date_format in date_formats:
|
||||
try:
|
||||
self.df_historical['datum'] = pd.to_datetime(self.df_historical['datum'], format=date_format)
|
||||
break
|
||||
except:
|
||||
continue
|
||||
|
||||
# Sortiere chronologisch (älteste zuerst für Trend-Analyse)
|
||||
self.df_historical = self.df_historical.sort_values('datum')
|
||||
|
||||
print(f"✅ {len(self.df_historical)} historische Ziehungen geladen")
|
||||
print(f"📅 Zeitraum: {self.df_historical['datum'].min()} bis {self.df_historical['datum'].max()}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Fehler beim Laden historischer Daten: {e}")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _load_unused_combinations(self):
|
||||
"""Lädt alle nie gezogenen Kombinationen."""
|
||||
try:
|
||||
# Große Datei in Chunks laden für bessere Performance
|
||||
chunk_size = 100000
|
||||
chunks = []
|
||||
|
||||
print("⏳ Lade nie gezogene Kombinationen (große Datei)...")
|
||||
|
||||
for chunk in pd.read_csv(self.unused_combinations_path, sep=';', chunksize=chunk_size):
|
||||
chunks.append(chunk)
|
||||
if len(chunks) % 50 == 0:
|
||||
print(f" 📊 {len(chunks) * chunk_size:,} Kombinationen geladen...")
|
||||
|
||||
self.df_unused = pd.concat(chunks, ignore_index=True)
|
||||
|
||||
print(f"✅ {len(self.df_unused):,} nie gezogene Kombinationen verfügbar!")
|
||||
print(f"💡 Das sind {len(self.df_unused)/13983816*100:.1f}% aller möglichen Kombinationen")
|
||||
|
||||
# Sample für Performance (arbeiten mit repräsentativem Subset)
|
||||
sample_size = min(500000, len(self.df_unused)) # Max 500k für Performance
|
||||
self.unused_combinations_sample = self.df_unused.sample(n=sample_size, random_state=42)
|
||||
|
||||
print(f"🎯 Arbeite mit {len(self.unused_combinations_sample):,} Sample-Kombinationen")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Fehler beim Laden nie gezogener Kombinationen: {e}")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _perform_basic_analysis(self):
|
||||
"""Basis-Analyse der historischen Daten."""
|
||||
|
||||
for _, row in self.df_historical.iterrows():
|
||||
# Gezogene Kombinationen
|
||||
numbers = [row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']]
|
||||
combo = tuple(sorted(numbers))
|
||||
self.drawn_combinations.add(combo)
|
||||
|
||||
# Zahlenfrequenzen
|
||||
for num in numbers:
|
||||
self.number_frequencies[num] += 1
|
||||
|
||||
# Positionsfrequenzen
|
||||
sorted_numbers = sorted(numbers)
|
||||
for i, num in enumerate(sorted_numbers):
|
||||
self.position_frequencies[f'pos_{i+1}'][num] += 1
|
||||
|
||||
# Muster-Analyse
|
||||
pattern = self._get_pattern(sorted_numbers)
|
||||
self.pattern_frequencies[pattern] += 1
|
||||
|
||||
# Superzahl
|
||||
if 'SZ' in row and pd.notna(row['SZ']):
|
||||
self.supernumber_frequencies[int(row['SZ'])] += 1
|
||||
|
||||
# Wochentag-Analyse
|
||||
if 'tag' in row:
|
||||
weekday = row['tag'].replace('.', '').replace(';', '')
|
||||
self.weekday_frequencies[weekday] += 1
|
||||
|
||||
def _perform_momentum_analysis(self, window_size=20):
|
||||
"""Erweiterte Momentum-Analyse mit größerem Fenster."""
|
||||
print(f"🔥 Super-Momentum-Analyse (Fenster: {window_size})")
|
||||
|
||||
# Zahlensequenzen aufbauen
|
||||
for number in range(1, 50):
|
||||
sequence = []
|
||||
for _, row in self.df_historical.iterrows():
|
||||
drawn_numbers = [row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']]
|
||||
sequence.append(1 if number in drawn_numbers else 0)
|
||||
self.number_sequences[number] = sequence
|
||||
|
||||
# Super-Momentum-Scores
|
||||
for number in range(1, 50):
|
||||
recent_sequence = self.number_sequences[number][-window_size:]
|
||||
|
||||
hit_rate = sum(recent_sequence) / len(recent_sequence)
|
||||
trend_score = self._calculate_trend_score(recent_sequence)
|
||||
recency_score = self._calculate_recency_score(recent_sequence)
|
||||
acceleration_score = self._calculate_acceleration_score(recent_sequence)
|
||||
|
||||
# Super-Momentum mit Beschleunigung
|
||||
momentum_score = (hit_rate * 0.35) + (trend_score * 0.3) + \
|
||||
(recency_score * 0.2) + (acceleration_score * 0.15)
|
||||
|
||||
self.momentum_scores[number] = {
|
||||
'hit_rate': hit_rate,
|
||||
'trend_score': trend_score,
|
||||
'recency_score': recency_score,
|
||||
'acceleration_score': acceleration_score,
|
||||
'momentum_score': momentum_score,
|
||||
'status': self._get_momentum_status(momentum_score)
|
||||
}
|
||||
|
||||
# Kategorisierung
|
||||
sorted_momentum = sorted(self.momentum_scores.items(),
|
||||
key=lambda x: x[1]['momentum_score'], reverse=True)
|
||||
|
||||
self.hot_numbers = [num for num, data in sorted_momentum[:15]
|
||||
if data['momentum_score'] > 0.3]
|
||||
self.warm_numbers = [num for num, data in sorted_momentum[15:30]
|
||||
if 0.2 <= data['momentum_score'] <= 0.3]
|
||||
self.cold_numbers = [num for num, data in sorted_momentum[30:]
|
||||
if data['momentum_score'] < 0.2]
|
||||
|
||||
print(f"🔥 {len(self.hot_numbers)} super-heiße Zahlen")
|
||||
print(f"🌡️ {len(self.warm_numbers)} warme Zahlen")
|
||||
print(f"🧊 {len(self.cold_numbers)} kalte Zahlen")
|
||||
|
||||
def _calculate_acceleration_score(self, sequence):
|
||||
"""Berechnet Beschleunigung der Treffer (NEU!)."""
|
||||
if len(sequence) < 4:
|
||||
return 0
|
||||
|
||||
# Teile Sequenz in zwei Hälften
|
||||
mid = len(sequence) // 2
|
||||
first_half_rate = sum(sequence[:mid]) / mid
|
||||
second_half_rate = sum(sequence[mid:]) / (len(sequence) - mid)
|
||||
|
||||
# Beschleunigung = Verbesserung in zweiter Hälfte
|
||||
acceleration = second_half_rate - first_half_rate
|
||||
return max(0, acceleration) # Nur positive Beschleunigung
|
||||
|
||||
def _perform_sequential_analysis(self):
|
||||
"""Sequenzielle Abhängigkeiten zwischen Ziehungen."""
|
||||
print("🔗 Super-Sequential-Analyse")
|
||||
|
||||
for i in range(3, len(self.df_historical)):
|
||||
current_numbers = set([self.df_historical.iloc[i]['Z1'], self.df_historical.iloc[i]['Z2'],
|
||||
self.df_historical.iloc[i]['Z3'], self.df_historical.iloc[i]['Z4'],
|
||||
self.df_historical.iloc[i]['Z5'], self.df_historical.iloc[i]['Z6']])
|
||||
|
||||
for j in range(1, 4): # 3 Ziehungen zurück
|
||||
prev_numbers = set([self.df_historical.iloc[i-j]['Z1'], self.df_historical.iloc[i-j]['Z2'],
|
||||
self.df_historical.iloc[i-j]['Z3'], self.df_historical.iloc[i-j]['Z4'],
|
||||
self.df_historical.iloc[i-j]['Z5'], self.df_historical.iloc[i-j]['Z6']])
|
||||
|
||||
for prev_num in prev_numbers:
|
||||
for curr_num in current_numbers:
|
||||
self.sequential_dependencies[f"lag_{j}"][f"{prev_num}_{curr_num}"] += 1
|
||||
|
||||
def _analyze_unused_combinations(self):
|
||||
"""Analysiert nie gezogene Kombinationen für Intelligence."""
|
||||
print("🎯 Super-Intelligence für nie gezogene Kombinationen")
|
||||
|
||||
# Muster der nie gezogenen Kombinationen
|
||||
for _, row in self.unused_combinations_sample.iterrows():
|
||||
numbers = [row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']]
|
||||
pattern = self._get_pattern(numbers)
|
||||
self.unused_patterns[pattern] += 1
|
||||
|
||||
# Verteilung nach N/M/H-Bereichen
|
||||
for num in numbers:
|
||||
if 1 <= num <= 16:
|
||||
self.unused_by_ranges['N'].append(num)
|
||||
elif 17 <= num <= 32:
|
||||
self.unused_by_ranges['M'].append(num)
|
||||
else:
|
||||
self.unused_by_ranges['H'].append(num)
|
||||
|
||||
print(f"📊 Nie gezogene Muster analysiert:")
|
||||
for pattern, count in self.unused_patterns.most_common(5):
|
||||
percentage = (count / len(self.unused_combinations_sample)) * 100
|
||||
print(f" {pattern}: {percentage:.1f}%")
|
||||
|
||||
def generate_super_combination(self):
|
||||
"""Generiert Super-Kombination mit kompletter Intelligence."""
|
||||
max_attempts = 2000
|
||||
|
||||
for attempt in range(max_attempts):
|
||||
numbers = []
|
||||
|
||||
# Super-Strategie:
|
||||
# 40% aus nie gezogenen hot trends
|
||||
# 30% aus momentum analysis
|
||||
# 20% aus sequential dependencies
|
||||
# 10% random balance
|
||||
|
||||
# 2-3 Zahlen aus hot numbers mit unused combination bias
|
||||
hot_unused_candidates = []
|
||||
for combo_idx in range(min(10000, len(self.unused_combinations_sample))):
|
||||
combo = self.unused_combinations_sample.iloc[combo_idx]
|
||||
combo_numbers = [combo['Z1'], combo['Z2'], combo['Z3'], combo['Z4'], combo['Z5'], combo['Z6']]
|
||||
hot_in_combo = [n for n in combo_numbers if n in self.hot_numbers[:10]]
|
||||
if len(hot_in_combo) >= 2:
|
||||
hot_unused_candidates.extend(hot_in_combo)
|
||||
|
||||
if hot_unused_candidates:
|
||||
hot_picks = random.sample(list(set(hot_unused_candidates)), min(3, len(set(hot_unused_candidates))))
|
||||
numbers.extend(hot_picks)
|
||||
|
||||
# 2 Zahlen aus Trend-Predictions
|
||||
trend_candidates = [num for num, data in sorted(self.momentum_scores.items(),
|
||||
key=lambda x: x[1]['momentum_score'], reverse=True)[:12]]
|
||||
remaining_trend = [n for n in trend_candidates if n not in numbers]
|
||||
if len(remaining_trend) >= 2:
|
||||
trend_picks = random.sample(remaining_trend, 2)
|
||||
numbers.extend(trend_picks)
|
||||
|
||||
# 1 Zahl für Balance
|
||||
remaining_slots = 6 - len(numbers)
|
||||
if remaining_slots > 0:
|
||||
balance_candidates = self.warm_numbers + self.cold_numbers[:8]
|
||||
remaining_balance = [n for n in balance_candidates if n not in numbers]
|
||||
if remaining_balance:
|
||||
balance_picks = random.sample(remaining_balance, min(remaining_slots, len(remaining_balance)))
|
||||
numbers.extend(balance_picks)
|
||||
|
||||
# Auffüllen falls nötig
|
||||
while len(numbers) < 6:
|
||||
available = [n for n in range(1, 50) if n not in numbers]
|
||||
additional = random.choice(available)
|
||||
numbers.append(additional)
|
||||
|
||||
numbers = sorted(numbers[:6])
|
||||
|
||||
# Super-Validierung
|
||||
if self._validate_super_combination(numbers):
|
||||
return numbers
|
||||
|
||||
# Fallback
|
||||
return self._generate_super_fallback()
|
||||
|
||||
def _validate_super_combination(self, numbers):
|
||||
"""Super-Validierung mit unused combinations check."""
|
||||
combo_tuple = tuple(sorted(numbers))
|
||||
|
||||
# Prüfe ob in historischen Daten (sollte nicht sein)
|
||||
if combo_tuple in self.drawn_combinations:
|
||||
return False
|
||||
|
||||
# Prüfe ob in unused combinations (sollte sein!)
|
||||
unused_check = False
|
||||
sample_size = min(50000, len(self.unused_combinations_sample))
|
||||
for i in range(sample_size):
|
||||
row = self.unused_combinations_sample.iloc[i]
|
||||
unused_combo = tuple(sorted([row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']]))
|
||||
if combo_tuple == unused_combo:
|
||||
unused_check = True
|
||||
break
|
||||
|
||||
# Basis-Validierungen
|
||||
if len(set(numbers)) != 6:
|
||||
return False
|
||||
|
||||
distances = [numbers[i+1] - numbers[i] for i in range(5)]
|
||||
if min(distances) < 1 or max(distances) > 18:
|
||||
return False
|
||||
|
||||
even_count = sum(1 for n in numbers if n % 2 == 0)
|
||||
if even_count == 0 or even_count == 6:
|
||||
return False
|
||||
|
||||
total = sum(numbers)
|
||||
if total < 90 or total > 200:
|
||||
return False
|
||||
|
||||
# Super-Check: Mindestens 1 hot number
|
||||
hot_count = sum(1 for n in numbers if n in self.hot_numbers)
|
||||
if hot_count == 0:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _generate_super_fallback(self):
|
||||
"""Super-Fallback mit unused combinations."""
|
||||
# Wähle zufällig aus unused combinations
|
||||
random_idx = random.randint(0, len(self.unused_combinations_sample) - 1)
|
||||
row = self.unused_combinations_sample.iloc[random_idx]
|
||||
return sorted([row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']])
|
||||
|
||||
def get_super_supernumber(self):
|
||||
"""Super-optimierte Superzahl."""
|
||||
if not self.supernumber_frequencies:
|
||||
return random.randint(0, 9)
|
||||
|
||||
# Erweiterte Trend-Analyse für Superzahl
|
||||
recent_data = self.df_historical.tail(15)
|
||||
trend_scores = {}
|
||||
|
||||
for sz in range(0, 10):
|
||||
recent_count = (recent_data['SZ'] == sz).sum() if 'SZ' in recent_data.columns else 0
|
||||
total_count = self.supernumber_frequencies[sz]
|
||||
|
||||
# Multi-Faktor Score
|
||||
trend_score = (recent_count / len(recent_data)) * 0.5 + \
|
||||
(total_count / len(self.df_historical)) * 0.3 + \
|
||||
(sz % 2) * 0.1 + \
|
||||
(1 if sz in [0, 3, 7] else 0) * 0.1 # Beliebte Zahlen-Bonus
|
||||
|
||||
trend_scores[sz] = trend_score
|
||||
|
||||
# Gewichtete Auswahl
|
||||
candidates = list(trend_scores.keys())
|
||||
weights = list(trend_scores.values())
|
||||
|
||||
return random.choices(candidates, weights=weights)[0]
|
||||
|
||||
def generate_super_tips(self, num_tips=10):
|
||||
"""Generiert Super-Tipps mit kompletter Intelligence."""
|
||||
print(f"\n🚀 SUPER-TIPP-GENERIERUNG")
|
||||
print("=" * 50)
|
||||
print(f"🎯 Nutzt KOMPLETTE Sebastian's Datenbasis:")
|
||||
print(f" 📈 {len(self.df_historical)} historische Ziehungen")
|
||||
print(f" 🎲 {len(self.df_unused):,} nie gezogene Kombinationen")
|
||||
print(f" 🔥 Super-Momentum-Analyse")
|
||||
print(f" 🧠 Unused-Combinations-Intelligence")
|
||||
|
||||
generated_tips = []
|
||||
strategy_stats = {
|
||||
'unused_combo_hits': 0,
|
||||
'hot_number_avg': 0,
|
||||
'momentum_scores': []
|
||||
}
|
||||
|
||||
print(f"\n🎲 GENERIERE {num_tips} SUPER-TIPPS:")
|
||||
print("=" * 70)
|
||||
print(f"{'Nr':<3} {'6 Super-Zahlen':<25} {'SZ':<3} {'🔥':<3} {'🎯':<3} {'Status'}")
|
||||
print("-" * 70)
|
||||
|
||||
attempts = 0
|
||||
max_attempts = num_tips * 100
|
||||
|
||||
while len(generated_tips) < num_tips and attempts < max_attempts:
|
||||
attempts += 1
|
||||
|
||||
combination = self.generate_super_combination()
|
||||
|
||||
if combination and tuple(combination) not in [tuple(tip['zahlen']) for tip in generated_tips]:
|
||||
# Analyse der Kombination
|
||||
hot_count = sum(1 for n in combination if n in self.hot_numbers)
|
||||
momentum_avg = np.mean([self.momentum_scores[n]['momentum_score'] for n in combination])
|
||||
|
||||
# Check ob in unused combinations
|
||||
combo_tuple = tuple(sorted(combination))
|
||||
unused_hit = False
|
||||
for i in range(min(10000, len(self.unused_combinations_sample))):
|
||||
row = self.unused_combinations_sample.iloc[i]
|
||||
if combo_tuple == tuple(sorted([row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']])):
|
||||
unused_hit = True
|
||||
strategy_stats['unused_combo_hits'] += 1
|
||||
break
|
||||
|
||||
superzahl = self.get_super_supernumber()
|
||||
pattern = self._get_pattern(combination)
|
||||
|
||||
tip = {
|
||||
'tipp_nr': len(generated_tips) + 1,
|
||||
'zahlen': combination,
|
||||
'z1': combination[0], 'z2': combination[1], 'z3': combination[2],
|
||||
'z4': combination[3], 'z5': combination[4], 'z6': combination[5],
|
||||
'superzahl': superzahl,
|
||||
'hot_count': hot_count,
|
||||
'momentum_avg': momentum_avg,
|
||||
'unused_hit': unused_hit,
|
||||
'pattern': pattern,
|
||||
'super_score': hot_count * 0.4 + momentum_avg * 0.6
|
||||
}
|
||||
|
||||
generated_tips.append(tip)
|
||||
strategy_stats['hot_number_avg'] += hot_count
|
||||
strategy_stats['momentum_scores'].append(momentum_avg)
|
||||
|
||||
# Status
|
||||
status = "🎯 UNUSED!" if unused_hit else "📊 TREND"
|
||||
zahlen_str = f"{combination[0]:2}-{combination[1]:2}-{combination[2]:2}-{combination[3]:2}-{combination[4]:2}-{combination[5]:2}"
|
||||
print(f"{len(generated_tips):2}. {zahlen_str:<25} {superzahl:<3} {hot_count:<3} {momentum_avg:.2f} {status}")
|
||||
|
||||
# Super-Zusammenfassung
|
||||
self._print_super_summary(generated_tips, strategy_stats, attempts)
|
||||
|
||||
# Export
|
||||
self._export_super_tips(generated_tips)
|
||||
|
||||
return generated_tips
|
||||
|
||||
def _print_super_summary(self, tips, stats, attempts):
|
||||
"""Super-Zusammenfassung."""
|
||||
print(f"\n🏆 SUPER-LOTTO ZUSAMMENFASSUNG:")
|
||||
print("=" * 45)
|
||||
print(f"✅ {len(tips)} Super-Tipps generiert")
|
||||
print(f"🎯 {stats['unused_combo_hits']}/{len(tips)} aus nie gezogenen Kombinationen")
|
||||
print(f"🔥 Ø {stats['hot_number_avg']/len(tips):.1f} heiße Zahlen pro Tipp")
|
||||
print(f"📊 Ø Momentum-Score: {np.mean(stats['momentum_scores']):.3f}")
|
||||
print(f"⚡ Erfolgsrate: {len(tips)/attempts*100:.1f}%")
|
||||
|
||||
# Super-Intelligence Insights
|
||||
print(f"\n💡 SUPER-INTELLIGENCE INSIGHTS:")
|
||||
print("=" * 40)
|
||||
|
||||
# Top Momentum-Zahlen
|
||||
top_momentum = sorted(self.momentum_scores.items(),
|
||||
key=lambda x: x[1]['momentum_score'], reverse=True)[:8]
|
||||
print(f"🔥 TOP MOMENTUM-ZAHLEN:")
|
||||
for i, (num, data) in enumerate(top_momentum):
|
||||
print(f" {i+1}. Zahl {num:2}: {data['momentum_score']:.3f} {data['status']}")
|
||||
|
||||
# Pattern-Verteilung nie gezogener Kombinationen
|
||||
print(f"\n🎨 NIE GEZOGENE MUSTER (häufigste):")
|
||||
for pattern, count in self.unused_patterns.most_common(3):
|
||||
percentage = (count / len(self.unused_combinations_sample)) * 100
|
||||
print(f" {pattern}: {percentage:.1f}% nie gezogen")
|
||||
|
||||
# Super-Empfehlungen
|
||||
print(f"\n🚀 SUPER-EMPFEHLUNGEN:")
|
||||
print(f" 🎯 {stats['unused_combo_hits']} Tipps stammen aus nie gezogenen Kombinationen")
|
||||
print(f" 🔥 Fokus auf Top-{len(self.hot_numbers)} Momentum-Zahlen")
|
||||
print(f" 📊 Nutzt {len(self.df_historical)} historische Ziehungen für Trends")
|
||||
print(f" 💎 Maximale Optimierung durch {len(self.df_unused):,} nie gezogene Kombinationen!")
|
||||
|
||||
def _export_super_tips(self, tips):
|
||||
"""Exportiert Super-Tipps."""
|
||||
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
output_file = f"{self.base_path}/super_lotto_tipps_{timestamp}.csv"
|
||||
|
||||
# Erweiterte Export-Daten
|
||||
export_data = []
|
||||
for tip in tips:
|
||||
tip_data = tip.copy()
|
||||
tip_data['momentum_scores'] = [self.momentum_scores[n]['momentum_score'] for n in tip['zahlen']]
|
||||
tip_data['individual_status'] = [self.momentum_scores[n]['status'] for n in tip['zahlen']]
|
||||
export_data.append(tip_data)
|
||||
|
||||
df_export = pd.DataFrame(export_data)
|
||||
df_export.to_csv(output_file, sep=';', index=False)
|
||||
|
||||
print(f"\n💾 SUPER-EXPORT:")
|
||||
print("=" * 25)
|
||||
print(f"✅ Super-Tipps gespeichert: super_lotto_tipps_{timestamp}.csv")
|
||||
print(f"🚀 Basiert auf kompletter Sebastian's Datenbasis")
|
||||
print(f"📊 Mit nie gezogenen Kombinationen optimiert")
|
||||
|
||||
def _print_super_analysis_summary(self):
|
||||
"""Super-Analyse Zusammenfassung."""
|
||||
print(f"\n📈 SUPER-ANALYSE ZUSAMMENFASSUNG:")
|
||||
print("=" * 50)
|
||||
|
||||
# Datenbasis-Info
|
||||
print(f"📊 DATENBASIS:")
|
||||
print(f" 📈 Historische Ziehungen: {len(self.df_historical):,}")
|
||||
print(f" 🎲 Nie gezogene Kombinationen: {len(self.df_unused):,}")
|
||||
print(f" 📅 Zeitraum: {len(self.df_historical)} Ziehungen")
|
||||
|
||||
# Top Zahlen mit Super-Intelligence
|
||||
print(f"\n🔥 SUPER-HOT ZAHLEN:")
|
||||
for i, num in enumerate(self.hot_numbers[:8]):
|
||||
momentum_data = self.momentum_scores[num]
|
||||
freq = self.number_frequencies[num]
|
||||
print(f" {i+1}. Zahl {num:2}: Score {momentum_data['momentum_score']:.3f} "
|
||||
f"({freq}x gezogen) {momentum_data['status']}")
|
||||
|
||||
# Nie gezogene Muster-Intelligence
|
||||
print(f"\n🎯 NIE GEZOGENE MUSTER-INTELLIGENCE:")
|
||||
for pattern, count in self.unused_patterns.most_common(5):
|
||||
historical_count = self.pattern_frequencies.get(pattern, 0)
|
||||
unused_percentage = (count / len(self.unused_combinations_sample)) * 100
|
||||
print(f" {pattern}: {unused_percentage:.1f}% nie gezogen "
|
||||
f"(historisch: {historical_count}x)")
|
||||
|
||||
# Sequential Dependencies Insights
|
||||
print(f"\n🔗 SEQUENTIAL INSIGHTS:")
|
||||
if self.sequential_dependencies:
|
||||
top_sequence = None
|
||||
max_count = 0
|
||||
for lag, transitions in self.sequential_dependencies.items():
|
||||
for transition, count in transitions.items():
|
||||
if count > max_count:
|
||||
max_count = count
|
||||
top_sequence = (lag, transition, count)
|
||||
|
||||
if top_sequence:
|
||||
lag, transition, count = top_sequence
|
||||
prev_num, curr_num = transition.split('_')
|
||||
print(f" Stärkste Abhängigkeit: Nach Zahl {prev_num} kommt oft Zahl {curr_num} ({count}x)")
|
||||
|
||||
# Hilfsfunktionen
|
||||
def _get_pattern(self, numbers):
|
||||
"""N/M/H-Muster für 6aus49."""
|
||||
pattern = []
|
||||
for num in numbers:
|
||||
if 1 <= num <= 16:
|
||||
pattern.append('N')
|
||||
elif 17 <= num <= 32:
|
||||
pattern.append('M')
|
||||
else:
|
||||
pattern.append('H')
|
||||
return ''.join(pattern)
|
||||
|
||||
def _calculate_trend_score(self, sequence):
|
||||
"""Trend-Score Berechnung."""
|
||||
if len(sequence) < 2:
|
||||
return 0
|
||||
x = np.arange(len(sequence))
|
||||
y = np.array(sequence)
|
||||
weights = np.exp(x / len(x))
|
||||
try:
|
||||
coeffs = np.polyfit(x, y, 1, w=weights)
|
||||
return coeffs[0]
|
||||
except:
|
||||
return 0
|
||||
|
||||
def _calculate_recency_score(self, sequence):
|
||||
"""Recency-Score Berechnung."""
|
||||
try:
|
||||
last_hit_index = len(sequence) - 1 - sequence[::-1].index(1)
|
||||
recency = 1 - (len(sequence) - 1 - last_hit_index) / len(sequence)
|
||||
return recency
|
||||
except ValueError:
|
||||
return 0
|
||||
|
||||
def _get_momentum_status(self, score):
|
||||
"""Momentum-Status."""
|
||||
if score > 0.5:
|
||||
return "🔥 ULTRA-HEISS"
|
||||
elif score > 0.35:
|
||||
return "🌡️ SEHR HEISS"
|
||||
elif score > 0.25:
|
||||
return "😐 HEISS"
|
||||
elif score > 0.15:
|
||||
return "🧊 WARM"
|
||||
else:
|
||||
return "❄️ KALT"
|
||||
|
||||
# Zusätzliche Super-Funktionen für erweiterte Analyse
|
||||
|
||||
def analyze_winning_probability(generator, tip_numbers):
|
||||
"""Analysiert Gewinnwahrscheinlichkeit basierend auf Super-Intelligence."""
|
||||
base_prob = 1 / 13983816
|
||||
|
||||
# Super-Faktoren
|
||||
factors = {
|
||||
'unused_combination': 1.0,
|
||||
'momentum_boost': 1.0,
|
||||
'pattern_boost': 1.0,
|
||||
'sequential_boost': 1.0
|
||||
}
|
||||
|
||||
# Check ob nie gezogene Kombination
|
||||
combo_tuple = tuple(sorted(tip_numbers))
|
||||
for i in range(min(50000, len(generator.unused_combinations_sample))):
|
||||
row = generator.unused_combinations_sample.iloc[i]
|
||||
if combo_tuple == tuple(sorted([row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']])):
|
||||
factors['unused_combination'] = 1.5 # 50% Boost für nie gezogene Kombination
|
||||
break
|
||||
|
||||
# Momentum-Boost
|
||||
hot_count = sum(1 for n in tip_numbers if n in generator.hot_numbers)
|
||||
momentum_avg = np.mean([generator.momentum_scores[n]['momentum_score'] for n in tip_numbers])
|
||||
factors['momentum_boost'] = 1 + (hot_count * 0.1) + (momentum_avg * 0.3)
|
||||
|
||||
# Pattern-Boost
|
||||
pattern = generator._get_pattern(sorted(tip_numbers))
|
||||
if pattern in generator.unused_patterns:
|
||||
unused_pattern_freq = generator.unused_patterns[pattern] / len(generator.unused_combinations_sample)
|
||||
factors['pattern_boost'] = 1 + (unused_pattern_freq * 0.2)
|
||||
|
||||
# Sequential-Boost (vereinfacht)
|
||||
sequential_score = 0
|
||||
for i in range(len(tip_numbers)-1):
|
||||
transition_key = f"{tip_numbers[i]}_{tip_numbers[i+1]}"
|
||||
for lag_data in generator.sequential_dependencies.values():
|
||||
if transition_key in lag_data:
|
||||
sequential_score += lag_data[transition_key]
|
||||
|
||||
if sequential_score > 0:
|
||||
factors['sequential_boost'] = 1 + (sequential_score / 1000) # Normalisiert
|
||||
|
||||
# Gesamt-Multiplikator
|
||||
total_multiplier = 1
|
||||
for factor_value in factors.values():
|
||||
total_multiplier *= factor_value
|
||||
|
||||
estimated_prob = base_prob * total_multiplier
|
||||
|
||||
return {
|
||||
'base_probability': base_prob,
|
||||
'factors': factors,
|
||||
'total_multiplier': total_multiplier,
|
||||
'estimated_probability': estimated_prob,
|
||||
'improvement_factor': total_multiplier
|
||||
}
|
||||
|
||||
def generate_super_analysis_report(generator, tips):
|
||||
"""Generiert detaillierten Super-Analyse-Report."""
|
||||
report = []
|
||||
|
||||
report.append("🚀 SUPER-LOTTO 6AUS49 ANALYSE-REPORT")
|
||||
report.append("=" * 50)
|
||||
report.append(f"📊 Basierend auf Sebastian's kompletter Datenbasis")
|
||||
report.append(f"📈 {len(generator.df_historical):,} historische Ziehungen")
|
||||
report.append(f"🎲 {len(generator.df_unused):,} nie gezogene Kombinationen")
|
||||
report.append("")
|
||||
|
||||
# Tip-by-Tip Analyse
|
||||
report.append("📋 DETAILLIERTE TIPP-ANALYSE:")
|
||||
report.append("-" * 40)
|
||||
|
||||
for tip in tips:
|
||||
report.append(f"\n🎯 TIPP {tip['tipp_nr']}:")
|
||||
zahlen_str = f"{tip['z1']:2}-{tip['z2']:2}-{tip['z3']:2}-{tip['z4']:2}-{tip['z5']:2}-{tip['z6']:2}"
|
||||
report.append(f" Zahlen: {zahlen_str} + SZ: {tip['superzahl']}")
|
||||
report.append(f" 🔥 Heiße Zahlen: {tip['hot_count']}/6")
|
||||
report.append(f" 📊 Momentum-Score: {tip['momentum_avg']:.3f}")
|
||||
report.append(f" 🎯 Nie gezogen: {'✅ JA' if tip['unused_hit'] else '❌ NEIN'}")
|
||||
report.append(f" 🎨 Muster: {tip['pattern']}")
|
||||
|
||||
# Wahrscheinlichkeits-Analyse
|
||||
prob_analysis = analyze_winning_probability(generator, tip['zahlen'])
|
||||
report.append(f" 📈 Verbesserungs-Faktor: {prob_analysis['improvement_factor']:.2f}x")
|
||||
|
||||
# Individuelle Zahlen-Analyse
|
||||
report.append(" 🔍 Zahlen-Details:")
|
||||
for num in tip['zahlen']:
|
||||
momentum_data = generator.momentum_scores[num]
|
||||
freq = generator.number_frequencies[num]
|
||||
report.append(f" Zahl {num:2}: {momentum_data['status']} "
|
||||
f"(Score: {momentum_data['momentum_score']:.3f}, {freq}x gezogen)")
|
||||
|
||||
# Super-Intelligence Zusammenfassung
|
||||
report.append(f"\n🧠 SUPER-INTELLIGENCE ZUSAMMENFASSUNG:")
|
||||
report.append("=" * 45)
|
||||
|
||||
# Nie gezogene Kombinationen Statistik
|
||||
unused_hits = sum(1 for tip in tips if tip['unused_hit'])
|
||||
report.append(f"🎯 {unused_hits}/{len(tips)} Tipps aus nie gezogenen Kombinationen")
|
||||
|
||||
# Momentum-Statistiken
|
||||
avg_hot_numbers = sum(tip['hot_count'] for tip in tips) / len(tips)
|
||||
avg_momentum = sum(tip['momentum_avg'] for tip in tips) / len(tips)
|
||||
report.append(f"🔥 Ø {avg_hot_numbers:.1f} heiße Zahlen pro Tipp")
|
||||
report.append(f"📊 Ø Momentum-Score: {avg_momentum:.3f}")
|
||||
|
||||
# Top Empfehlungen
|
||||
report.append(f"\n💡 TOP EMPFEHLUNGEN:")
|
||||
report.append(f"✅ Verwenden Sie die Tipps mit nie gezogenen Kombinationen")
|
||||
report.append(f"🔥 Fokussieren Sie sich auf die {len(generator.hot_numbers)} heißesten Zahlen")
|
||||
report.append(f"📈 Super-Momentum-Analyse zeigt beste Trends")
|
||||
report.append(f"🎲 {len(generator.df_unused):,} nie gezogene Kombinationen = riesiger Vorteil!")
|
||||
|
||||
return "\n".join(report)
|
||||
|
||||
def export_comprehensive_analysis(generator, tips):
|
||||
"""Exportiert umfassende Analyse in Text-Datei."""
|
||||
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
report_file = f"{generator.base_path}/super_lotto_analysis_{timestamp}.txt"
|
||||
|
||||
report = generate_super_analysis_report(generator, tips)
|
||||
|
||||
with open(report_file, 'w', encoding='utf-8') as f:
|
||||
f.write(report)
|
||||
|
||||
print(f"📄 Umfassende Analyse gespeichert: super_lotto_analysis_{timestamp}.txt")
|
||||
|
||||
def main():
|
||||
"""Hauptfunktion für Super-Lotto Generator."""
|
||||
print("🎲 SUPER-LOTTO 6AUS49 GENERATOR")
|
||||
print("🚀 Mit Sebastian's kompletter Datenbasis")
|
||||
print("=" * 50)
|
||||
|
||||
try:
|
||||
# Generator mit Sebastian's Daten initialisieren
|
||||
generator = SuperLotto6aus49Generator()
|
||||
|
||||
# Super-Tipps generieren
|
||||
tips = generator.generate_super_tips(10)
|
||||
|
||||
if tips:
|
||||
print(f"\n🏆 SUPER-OPTIMIERUNG ABGESCHLOSSEN!")
|
||||
print("=" * 45)
|
||||
print(f"🎲 10 Super-Tipps mit maximaler Intelligence generiert")
|
||||
print(f"📊 Nutzt {len(generator.df_historical):,} historische Ziehungen")
|
||||
print(f"🎯 Optimiert mit {len(generator.df_unused):,} nie gezogenen Kombinationen")
|
||||
print(f"🔥 Multi-Momentum-Analyse mit Beschleunigung")
|
||||
print(f"🧠 Sequential Dependencies Intelligence")
|
||||
print(f"🍀 Maximale Gewinnchancen durch Super-Intelligence!")
|
||||
|
||||
# Erweiterte Analyse anbieten
|
||||
print(f"\n📊 ERWEITERTE ANALYSE:")
|
||||
print("=" * 30)
|
||||
|
||||
# Beispiel Super-Analyse
|
||||
if len(tips) > 0:
|
||||
sample_tip = tips[0]
|
||||
prob_analysis = analyze_winning_probability(generator, sample_tip['zahlen'])
|
||||
|
||||
print(f"\n🔍 SUPER-ANALYSE für Tipp 1:")
|
||||
zahlen_str = f"{sample_tip['z1']:2}-{sample_tip['z2']:2}-{sample_tip['z3']:2}-{sample_tip['z4']:2}-{sample_tip['z5']:2}-{sample_tip['z6']:2}"
|
||||
print(f" 🎲 Super-Kombination: {zahlen_str} + SZ: {sample_tip['superzahl']}")
|
||||
print(f" 🔥 Heiße Zahlen: {sample_tip['hot_count']}/6")
|
||||
print(f" 📊 Momentum-Score: {sample_tip['momentum_avg']:.3f}")
|
||||
print(f" 🎯 Nie gezogen: {'✅ JA' if sample_tip['unused_hit'] else '❌ NEIN'}")
|
||||
print(f" 📈 Verbesserungs-Faktor: {prob_analysis['improvement_factor']:.2f}x")
|
||||
print(f" 💎 Super-Score: {sample_tip['super_score']:.3f}")
|
||||
|
||||
# Angebot für vollständigen Report
|
||||
create_report = input("\nVollständigen Analyse-Report erstellen? (j/n): ").lower().strip()
|
||||
if create_report == 'j' or create_report == 'ja':
|
||||
export_comprehensive_analysis(generator, tips)
|
||||
print("✅ Vollständiger Report erstellt!")
|
||||
|
||||
print(f"\n🎯 SUPER-EMPFEHLUNGEN:")
|
||||
print("=" * 30)
|
||||
unused_count = sum(1 for tip in tips if tip['unused_hit'])
|
||||
print(f"🎲 {unused_count} Tipps stammen aus nie gezogenen Kombinationen")
|
||||
print(f"🔥 Alle Tipps nutzen Super-Momentum-Analyse")
|
||||
print(f"📊 Basiert auf kompletter historischer Datenbasis")
|
||||
print(f"💡 Maximale Optimierung durch Sebastian's Daten!")
|
||||
|
||||
else:
|
||||
print("❌ Keine Super-Tipps generiert!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Fehler: {e}")
|
||||
print("💡 Stellen Sie sicher, dass Sebastian's CSV-Dateien verfügbar sind:")
|
||||
print(" 📁 AlleLottozahlen.csv")
|
||||
print(" 📁 Fehlende_Lotto_Kombinationen.csv")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Reproduzierbarer Seed
|
||||
random.seed(42)
|
||||
np.random.seed(42)
|
||||
|
||||
# Super-Generator starten
|
||||
main()
|
||||
@@ -1,594 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
ULTIMATE HYBRID LOTTO GENERATOR
|
||||
Kombiniert AI-ML Generator + Pattern-Weighted Generator
|
||||
|
||||
Features:
|
||||
- AI-ML Ensemble (Random Forest + Gradient Boosting + Neural Networks)
|
||||
- Pattern-Gewichtung (NNMMHH, NMMHHH, etc.)
|
||||
- Real-Time Learning
|
||||
- Multi-Strategy Tip Generation
|
||||
- Performance Comparison zwischen beiden Ansätzen
|
||||
- Adaptive Strategy Selection
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import random
|
||||
from collections import Counter, defaultdict, deque
|
||||
import datetime
|
||||
import os
|
||||
|
||||
# ML Imports (optional)
|
||||
try:
|
||||
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
|
||||
from sklearn.neural_network import MLPRegressor
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
ML_AVAILABLE = True
|
||||
except ImportError:
|
||||
ML_AVAILABLE = False
|
||||
|
||||
class UltimateHybridLottoGenerator:
|
||||
def __init__(self, data_path):
|
||||
self.data_path = data_path
|
||||
self.df = None
|
||||
|
||||
# Beide Subsysteme
|
||||
self.ai_ml_system = AIMLSubsystem()
|
||||
self.pattern_system = PatternSubsystem()
|
||||
self.hybrid_optimizer = HybridOptimizer()
|
||||
|
||||
# Performance Tracking
|
||||
self.strategy_performance = {
|
||||
'ai_ml': {'tips': [], 'confidence': [], 'success_rate': 0.0},
|
||||
'pattern': {'tips': [], 'confidence': [], 'success_rate': 0.0},
|
||||
'hybrid': {'tips': [], 'confidence': [], 'success_rate': 0.0}
|
||||
}
|
||||
|
||||
# Adaptive Weights
|
||||
self.adaptive_weights = {
|
||||
'ai_ml': 0.4,
|
||||
'pattern': 0.3,
|
||||
'hybrid': 0.3
|
||||
}
|
||||
|
||||
print("🚀 ULTIMATE HYBRID LOTTO GENERATOR")
|
||||
print("=" * 60)
|
||||
print("🤖 AI-ML System + 🎨 Pattern System + ⚡ Hybrid Optimizer")
|
||||
|
||||
# Initialize
|
||||
self.load_and_initialize()
|
||||
|
||||
def load_and_initialize(self):
|
||||
"""Lädt Daten und initialisiert alle Subsysteme."""
|
||||
try:
|
||||
self.df = pd.read_csv(self.data_path, sep=';')
|
||||
|
||||
if 'datum' in self.df.columns:
|
||||
self.df['datum'] = pd.to_datetime(self.df['datum'], format='%Y-%m-%d', errors='coerce')
|
||||
self.df = self.df.sort_values('datum')
|
||||
|
||||
print(f"📊 {len(self.df)} Ziehungen geladen")
|
||||
|
||||
# Initialize subsystems
|
||||
print("🔧 Initialisiere AI-ML System...")
|
||||
self.ai_ml_system.initialize(self.df)
|
||||
|
||||
print("🎨 Initialisiere Pattern System...")
|
||||
self.pattern_system.initialize(self.df)
|
||||
|
||||
print("⚡ Initialisiere Hybrid Optimizer...")
|
||||
self.hybrid_optimizer.initialize(self.df, self.ai_ml_system, self.pattern_system)
|
||||
|
||||
print("✅ Alle Systeme bereit!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Initialization error: {e}")
|
||||
self.df = pd.DataFrame()
|
||||
|
||||
def generate_ultimate_tips(self, num_tips=10):
|
||||
"""Generiert Ultimate Tipps mit allen drei Strategien."""
|
||||
print(f"\n🎯 ULTIMATE TIP GENERATION")
|
||||
print("=" * 60)
|
||||
|
||||
if len(self.df) == 0:
|
||||
print("❌ Keine Daten verfügbar")
|
||||
return []
|
||||
|
||||
# Strategy Distribution basierend auf Performance
|
||||
strategies = self._determine_strategy_distribution(num_tips)
|
||||
|
||||
print(f"📊 STRATEGY DISTRIBUTION:")
|
||||
for strategy, count in strategies.items():
|
||||
weight = self.adaptive_weights[strategy]
|
||||
print(f" {strategy.upper()}: {count} tips (Weight: {weight:.2f})")
|
||||
|
||||
all_tips = []
|
||||
|
||||
print(f"\n🎲 GENERATING {num_tips} ULTIMATE TIPS:")
|
||||
print("=" * 85)
|
||||
print("Nr 6 Ultimate Numbers SZ Strategy AI-Score Pattern-W Confidence")
|
||||
print("-" * 85)
|
||||
|
||||
tip_counter = 1
|
||||
|
||||
# AI-ML Tips
|
||||
if strategies['ai_ml'] > 0:
|
||||
ai_tips = self._generate_ai_ml_tips(strategies['ai_ml'], tip_counter)
|
||||
all_tips.extend(ai_tips)
|
||||
tip_counter += len(ai_tips)
|
||||
|
||||
# Pattern Tips
|
||||
if strategies['pattern'] > 0:
|
||||
pattern_tips = self._generate_pattern_tips(strategies['pattern'], tip_counter)
|
||||
all_tips.extend(pattern_tips)
|
||||
tip_counter += len(pattern_tips)
|
||||
|
||||
# Hybrid Tips
|
||||
if strategies['hybrid'] > 0:
|
||||
hybrid_tips = self._generate_hybrid_tips(strategies['hybrid'], tip_counter)
|
||||
all_tips.extend(hybrid_tips)
|
||||
|
||||
# Output all tips
|
||||
for tip in all_tips:
|
||||
self._print_tip_line(tip)
|
||||
|
||||
# Performance Analysis
|
||||
self._analyze_tip_portfolio(all_tips)
|
||||
|
||||
# Update adaptive weights
|
||||
self._update_adaptive_weights(all_tips)
|
||||
|
||||
return all_tips
|
||||
|
||||
def _determine_strategy_distribution(self, num_tips):
|
||||
"""Bestimmt Strategy-Verteilung basierend auf Performance."""
|
||||
strategies = {}
|
||||
|
||||
# Basis-Verteilung basierend auf Adaptive Weights
|
||||
ai_count = max(1, int(num_tips * self.adaptive_weights['ai_ml']))
|
||||
pattern_count = max(1, int(num_tips * self.adaptive_weights['pattern']))
|
||||
hybrid_count = num_tips - ai_count - pattern_count
|
||||
|
||||
# Sicherstellen dass hybrid_count >= 0
|
||||
if hybrid_count < 0:
|
||||
if ai_count > pattern_count:
|
||||
ai_count += hybrid_count
|
||||
else:
|
||||
pattern_count += hybrid_count
|
||||
hybrid_count = 0
|
||||
|
||||
strategies['ai_ml'] = ai_count
|
||||
strategies['pattern'] = pattern_count
|
||||
strategies['hybrid'] = hybrid_count
|
||||
|
||||
return strategies
|
||||
|
||||
def _generate_ai_ml_tips(self, count, start_number):
|
||||
"""Generiert AI-ML basierte Tipps."""
|
||||
tips = []
|
||||
|
||||
if not ML_AVAILABLE:
|
||||
# Fallback zu frequency-based
|
||||
for i in range(count):
|
||||
tip = self._generate_frequency_tip(start_number + i, 'AI-ML-FALLBACK')
|
||||
tips.append(tip)
|
||||
return tips
|
||||
|
||||
# AI Predictions
|
||||
ai_predictions = self.ai_ml_system.get_predictions()
|
||||
|
||||
for i in range(count):
|
||||
tip_number = start_number + i
|
||||
|
||||
# AI-optimierte Kombination
|
||||
numbers = self._select_ai_optimized_numbers(ai_predictions, tip_number)
|
||||
superzahl = self._get_smart_superzahl(tip_number)
|
||||
|
||||
# Scores
|
||||
ai_score = np.mean([ai_predictions.get(n, 0.1) for n in numbers])
|
||||
pattern_weight = self.pattern_system.calculate_pattern_weight(numbers)
|
||||
confidence = ai_score * 0.7 + pattern_weight * 0.3
|
||||
|
||||
tip = {
|
||||
'tip_number': tip_number,
|
||||
'numbers': numbers,
|
||||
'superzahl': superzahl,
|
||||
'strategy': 'AI-ML',
|
||||
'ai_score': ai_score,
|
||||
'pattern_weight': pattern_weight,
|
||||
'confidence': confidence
|
||||
}
|
||||
|
||||
tips.append(tip)
|
||||
|
||||
return tips
|
||||
|
||||
def _generate_pattern_tips(self, count, start_number):
|
||||
"""Generiert Pattern-basierte Tipps."""
|
||||
tips = []
|
||||
|
||||
# Top Patterns aus historischen Daten
|
||||
top_patterns = self.pattern_system.get_top_patterns(count)
|
||||
|
||||
for i in range(count):
|
||||
tip_number = start_number + i
|
||||
|
||||
# Wähle Pattern
|
||||
target_pattern = top_patterns[i % len(top_patterns)] if top_patterns else 'NNMMHH'
|
||||
|
||||
# Pattern-optimierte Kombination
|
||||
numbers = self.pattern_system.optimize_for_pattern(target_pattern, tip_number)
|
||||
superzahl = self._get_smart_superzahl(tip_number)
|
||||
|
||||
# Scores
|
||||
pattern_weight = self.pattern_system.calculate_pattern_weight(numbers)
|
||||
ai_score = 0.3 + random.random() * 0.2 # Mock AI score für Pattern-Tips
|
||||
confidence = pattern_weight * 0.7 + ai_score * 0.3
|
||||
|
||||
tip = {
|
||||
'tip_number': tip_number,
|
||||
'numbers': numbers,
|
||||
'superzahl': superzahl,
|
||||
'strategy': 'PATTERN',
|
||||
'ai_score': ai_score,
|
||||
'pattern_weight': pattern_weight,
|
||||
'confidence': confidence,
|
||||
'target_pattern': target_pattern
|
||||
}
|
||||
|
||||
tips.append(tip)
|
||||
|
||||
return tips
|
||||
|
||||
def _generate_hybrid_tips(self, count, start_number):
|
||||
"""Generiert Hybrid-optimierte Tipps."""
|
||||
tips = []
|
||||
|
||||
for i in range(count):
|
||||
tip_number = start_number + i
|
||||
|
||||
# Hybrid optimization
|
||||
hybrid_result = self.hybrid_optimizer.optimize_combination(tip_number)
|
||||
|
||||
numbers = hybrid_result['numbers']
|
||||
superzahl = self._get_smart_superzahl(tip_number)
|
||||
|
||||
tip = {
|
||||
'tip_number': tip_number,
|
||||
'numbers': numbers,
|
||||
'superzahl': superzahl,
|
||||
'strategy': 'HYBRID',
|
||||
'ai_score': hybrid_result['ai_score'],
|
||||
'pattern_weight': hybrid_result['pattern_weight'],
|
||||
'confidence': hybrid_result['confidence']
|
||||
}
|
||||
|
||||
tips.append(tip)
|
||||
|
||||
return tips
|
||||
|
||||
def _select_ai_optimized_numbers(self, ai_predictions, tip_number):
|
||||
"""Wählt AI-optimierte Zahlen aus."""
|
||||
if not ai_predictions:
|
||||
return sorted(random.sample(range(1, 50), 6))
|
||||
|
||||
# Top AI candidates
|
||||
sorted_predictions = sorted(ai_predictions.items(), key=lambda x: x[1], reverse=True)
|
||||
|
||||
selected = []
|
||||
random.seed(42 + tip_number) # Konsistenz mit Variation
|
||||
|
||||
# Strategy: Top AI + Diversität
|
||||
for i in range(6):
|
||||
candidates = [num for num, score in sorted_predictions[:25] if num not in selected]
|
||||
|
||||
if not candidates:
|
||||
candidates = [n for n in range(1, 50) if n not in selected]
|
||||
|
||||
if candidates:
|
||||
# Gewichtete Auswahl mit etwas Zufall
|
||||
weights = [ai_predictions.get(c, 0.1) + random.random() * 0.1 for c in candidates]
|
||||
selected.append(random.choices(candidates, weights=weights)[0])
|
||||
|
||||
return sorted(selected)
|
||||
|
||||
def _get_smart_superzahl(self, tip_number):
|
||||
"""Intelligente Superzahl-Auswahl."""
|
||||
base_sz = [7, 6, 3, 2, 0, 1, 4, 5, 8, 9]
|
||||
|
||||
# Aus historischen Daten
|
||||
if '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)
|
||||
frequent_sz = [int(sz) for sz, _ in sz_freq.most_common(5) if 0 <= sz <= 9]
|
||||
if frequent_sz:
|
||||
base_sz = frequent_sz
|
||||
|
||||
return base_sz[tip_number % len(base_sz)]
|
||||
|
||||
def _generate_frequency_tip(self, tip_number, strategy):
|
||||
"""Fallback frequency-based tip."""
|
||||
if len(self.df) == 0:
|
||||
numbers = sorted(random.sample(range(1, 50), 6))
|
||||
else:
|
||||
# Frequency analysis
|
||||
number_freq = Counter()
|
||||
for _, row in self.df.tail(30).iterrows():
|
||||
for col in ['Z1', 'Z2', 'Z3', 'Z4', 'Z5', 'Z6']:
|
||||
if col in row and pd.notna(row[col]):
|
||||
number_freq[int(row[col])] += 1
|
||||
|
||||
# Mix frequent + random
|
||||
frequent = [num for num, _ in number_freq.most_common(20)]
|
||||
numbers = random.sample(frequent[:15], 4) + random.sample(range(1, 50), 2)
|
||||
numbers = sorted(list(set(numbers))[:6])
|
||||
|
||||
while len(numbers) < 6:
|
||||
candidates = [n for n in range(1, 50) if n not in numbers]
|
||||
numbers.append(random.choice(candidates))
|
||||
numbers = sorted(numbers)
|
||||
|
||||
return {
|
||||
'tip_number': tip_number,
|
||||
'numbers': numbers,
|
||||
'superzahl': self._get_smart_superzahl(tip_number),
|
||||
'strategy': strategy,
|
||||
'ai_score': 0.3,
|
||||
'pattern_weight': 0.3,
|
||||
'confidence': 0.3
|
||||
}
|
||||
|
||||
def _print_tip_line(self, tip):
|
||||
"""Druckt eine Tipp-Zeile."""
|
||||
zahlen_str = '-'.join([f"{n:2d}" for n in tip['numbers']])
|
||||
|
||||
print(f"{tip['tip_number']:2d} {zahlen_str} {tip['superzahl']:2d} "
|
||||
f"{tip['strategy']:<9} {tip['ai_score']:.3f} {tip['pattern_weight']:.3f} {tip['confidence']:.3f}")
|
||||
|
||||
def _analyze_tip_portfolio(self, tips):
|
||||
"""Analysiert das Tipp-Portfolio."""
|
||||
print(f"\n📊 PORTFOLIO ANALYSIS:")
|
||||
print("=" * 50)
|
||||
|
||||
# Strategy-wise stats
|
||||
strategy_stats = defaultdict(list)
|
||||
for tip in tips:
|
||||
strategy_stats[tip['strategy']].append(tip)
|
||||
|
||||
for strategy, strategy_tips in strategy_stats.items():
|
||||
avg_confidence = np.mean([t['confidence'] for t in strategy_tips])
|
||||
avg_ai = np.mean([t['ai_score'] for t in strategy_tips])
|
||||
avg_pattern = np.mean([t['pattern_weight'] for t in strategy_tips])
|
||||
|
||||
print(f"{strategy}:")
|
||||
print(f" Tips: {len(strategy_tips)}, Avg Confidence: {avg_confidence:.3f}")
|
||||
print(f" Avg AI-Score: {avg_ai:.3f}, Avg Pattern-Weight: {avg_pattern:.3f}")
|
||||
|
||||
# Best tip
|
||||
best_tip = max(tips, key=lambda x: x['confidence'])
|
||||
print(f"\n⭐ BEST TIP:")
|
||||
zahlen_str = '-'.join([f"{n:2d}" for n in best_tip['numbers']])
|
||||
print(f" #{best_tip['tip_number']}: {zahlen_str} + SZ {best_tip['superzahl']}")
|
||||
print(f" Strategy: {best_tip['strategy']}, Confidence: {best_tip['confidence']:.3f}")
|
||||
|
||||
def _update_adaptive_weights(self, tips):
|
||||
"""Updated adaptive weights basierend auf tip quality."""
|
||||
strategy_confidence = defaultdict(list)
|
||||
|
||||
for tip in tips:
|
||||
strategy_confidence[tip['strategy']].append(tip['confidence'])
|
||||
|
||||
# Update weights basierend auf average confidence
|
||||
total_confidence = 0
|
||||
strategy_avg = {}
|
||||
|
||||
for strategy, confidences in strategy_confidence.items():
|
||||
avg_conf = np.mean(confidences)
|
||||
strategy_avg[strategy] = avg_conf
|
||||
total_confidence += avg_conf
|
||||
|
||||
# Normalize to weights
|
||||
if total_confidence > 0:
|
||||
for strategy in ['ai_ml', 'pattern', 'hybrid']:
|
||||
strategy_key = strategy.upper().replace('_', '-')
|
||||
if strategy_key in strategy_avg:
|
||||
self.adaptive_weights[strategy] = strategy_avg[strategy_key] / total_confidence
|
||||
|
||||
print(f"\n🔄 UPDATED ADAPTIVE WEIGHTS:")
|
||||
for strategy, weight in self.adaptive_weights.items():
|
||||
print(f" {strategy.upper()}: {weight:.3f}")
|
||||
|
||||
# Subsystem Classes
|
||||
|
||||
class AIMLSubsystem:
|
||||
def __init__(self):
|
||||
self.predictions = {}
|
||||
self.is_trained = False
|
||||
|
||||
def initialize(self, df):
|
||||
if ML_AVAILABLE and len(df) > 50:
|
||||
self._train_simple_model(df)
|
||||
else:
|
||||
self._create_fallback_predictions(df)
|
||||
|
||||
def _train_simple_model(self, df):
|
||||
# Simplified ML training
|
||||
number_freq = Counter()
|
||||
for _, row in df.iterrows():
|
||||
for col in ['Z1', 'Z2', 'Z3', 'Z4', 'Z5', 'Z6']:
|
||||
if col in row and pd.notna(row[col]):
|
||||
number_freq[int(row[col])] += 1
|
||||
|
||||
max_freq = max(number_freq.values()) if number_freq else 1
|
||||
|
||||
for num in range(1, 50):
|
||||
freq = number_freq.get(num, 0)
|
||||
base_pred = freq / max_freq
|
||||
# Add ML-like variation
|
||||
ml_variation = np.random.normal(0, 0.1)
|
||||
self.predictions[num] = max(0.1, min(0.9, base_pred + ml_variation))
|
||||
|
||||
self.is_trained = True
|
||||
|
||||
def _create_fallback_predictions(self, df):
|
||||
# Simple frequency-based predictions
|
||||
for num in range(1, 50):
|
||||
self.predictions[num] = 0.1 + random.random() * 0.4
|
||||
|
||||
def get_predictions(self):
|
||||
return self.predictions
|
||||
|
||||
class PatternSubsystem:
|
||||
def __init__(self):
|
||||
self.pattern_frequencies = Counter()
|
||||
self.pattern_weights = {}
|
||||
|
||||
def initialize(self, df):
|
||||
self._analyze_patterns(df)
|
||||
|
||||
def _analyze_patterns(self, df):
|
||||
total = len(df)
|
||||
|
||||
for _, row in df.iterrows():
|
||||
numbers = sorted([row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']])
|
||||
pattern = self._get_pattern(numbers)
|
||||
self.pattern_frequencies[pattern] += 1
|
||||
|
||||
# Calculate weights
|
||||
for pattern, count in self.pattern_frequencies.items():
|
||||
self.pattern_weights[pattern] = count / total
|
||||
|
||||
def _get_pattern(self, numbers):
|
||||
pattern = ""
|
||||
for num in numbers:
|
||||
if 1 <= num <= 16:
|
||||
pattern += "N"
|
||||
elif 17 <= num <= 32:
|
||||
pattern += "M"
|
||||
else:
|
||||
pattern += "H"
|
||||
return pattern
|
||||
|
||||
def calculate_pattern_weight(self, numbers):
|
||||
pattern = self._get_pattern(sorted(numbers))
|
||||
return self.pattern_weights.get(pattern, 0.01)
|
||||
|
||||
def get_top_patterns(self, count):
|
||||
return [pattern for pattern, _ in self.pattern_frequencies.most_common(count)]
|
||||
|
||||
def optimize_for_pattern(self, target_pattern, seed):
|
||||
random.seed(42 + seed)
|
||||
|
||||
ranges = {
|
||||
'N': list(range(1, 17)),
|
||||
'M': list(range(17, 33)),
|
||||
'H': list(range(33, 50))
|
||||
}
|
||||
|
||||
pattern_counts = Counter(target_pattern)
|
||||
selected = []
|
||||
|
||||
for char, count in pattern_counts.items():
|
||||
if char in ranges and count > 0:
|
||||
available = [n for n in ranges[char] if n not in selected]
|
||||
if len(available) >= count:
|
||||
selected.extend(random.sample(available, count))
|
||||
|
||||
while len(selected) < 6:
|
||||
all_available = [n for n in range(1, 50) if n not in selected]
|
||||
if all_available:
|
||||
selected.append(random.choice(all_available))
|
||||
|
||||
return sorted(selected[:6])
|
||||
|
||||
class HybridOptimizer:
|
||||
def __init__(self):
|
||||
self.ai_system = None
|
||||
self.pattern_system = None
|
||||
|
||||
def initialize(self, df, ai_system, pattern_system):
|
||||
self.ai_system = ai_system
|
||||
self.pattern_system = pattern_system
|
||||
|
||||
def optimize_combination(self, seed):
|
||||
random.seed(42 + seed)
|
||||
|
||||
# Get AI predictions
|
||||
ai_preds = self.ai_system.get_predictions()
|
||||
|
||||
# Multi-objective optimization
|
||||
best_score = -1
|
||||
best_combination = None
|
||||
|
||||
for attempt in range(100): # Limited search
|
||||
# Generate candidate
|
||||
candidate = self._generate_candidate(ai_preds, attempt)
|
||||
|
||||
# Score combination
|
||||
ai_score = np.mean([ai_preds.get(n, 0.1) for n in candidate])
|
||||
pattern_weight = self.pattern_system.calculate_pattern_weight(candidate)
|
||||
|
||||
# Multi-objective score
|
||||
combined_score = ai_score * 0.6 + pattern_weight * 0.4
|
||||
|
||||
if combined_score > best_score:
|
||||
best_score = combined_score
|
||||
best_combination = candidate
|
||||
|
||||
return {
|
||||
'numbers': best_combination or sorted(random.sample(range(1, 50), 6)),
|
||||
'ai_score': np.mean([ai_preds.get(n, 0.1) for n in best_combination]) if best_combination else 0.3,
|
||||
'pattern_weight': self.pattern_system.calculate_pattern_weight(best_combination) if best_combination else 0.3,
|
||||
'confidence': best_score if best_score > 0 else 0.3
|
||||
}
|
||||
|
||||
def _generate_candidate(self, ai_preds, attempt):
|
||||
# Verschiedene Generierungsstrategien
|
||||
if attempt < 30:
|
||||
# AI-focused
|
||||
candidates = sorted(ai_preds.items(), key=lambda x: x[1], reverse=True)[:20]
|
||||
return sorted(random.sample([num for num, _ in candidates], 6))
|
||||
elif attempt < 60:
|
||||
# Pattern-focused
|
||||
target_patterns = ['NNMMHH', 'NMMHHH', 'NMMMHH']
|
||||
pattern = random.choice(target_patterns)
|
||||
return self.pattern_system.optimize_for_pattern(pattern, attempt)
|
||||
else:
|
||||
# Random with bias
|
||||
return sorted(random.sample(range(1, 50), 6))
|
||||
|
||||
def main():
|
||||
"""Startet den Ultimate Hybrid Generator."""
|
||||
data_path = "/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/AlleLottozahlen.csv"
|
||||
|
||||
try:
|
||||
# Initialize Ultimate Generator
|
||||
generator = UltimateHybridLottoGenerator(data_path)
|
||||
|
||||
# Generate ultimate tips
|
||||
ultimate_tips = generator.generate_ultimate_tips(10)
|
||||
|
||||
print(f"\n🏆 ULTIMATE GENERATION COMPLETED!")
|
||||
print("=" * 50)
|
||||
print(f"🚀 {len(ultimate_tips)} Ultimate Tips generiert")
|
||||
print(f"🤖 AI-ML System: {'✅' if ML_AVAILABLE else '⚠️ Fallback'}")
|
||||
print(f"🎨 Pattern System: ✅")
|
||||
print(f"⚡ Hybrid Optimizer: ✅")
|
||||
print(f"📊 Adaptive Strategy Selection: ✅")
|
||||
|
||||
print(f"\n💡 SYSTEM ADVANTAGES:")
|
||||
print(f" 🔬 Wissenschaftlich: Multi-System Validation")
|
||||
print(f" 🎯 Adaptiv: Performance-basierte Gewichtung")
|
||||
print(f" ⚖️ Ausgewogen: AI + Pattern + Hybrid Balance")
|
||||
print(f" 📈 Lernend: Kontinuierliche Verbesserung")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
random.seed(42)
|
||||
np.random.seed(42)
|
||||
|
||||
main()
|
||||
@@ -1,840 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Ultimate Lotto 6aus49 Generator mit Multi-Ziehungs-Trend-Analyse
|
||||
|
||||
Speziell optimiert für deutsches Lotto 6 aus 49:
|
||||
- 6 Zahlen aus 49 (statt 5 aus 50)
|
||||
- 1 Superzahl 0-9 (statt 2 Eurozahlen)
|
||||
- Angepasste N/M/H-Bereiche für 49er-System
|
||||
- Multi-Ziehungs-Trend-Analyse
|
||||
- Momentum-Tracking über mehrere Ziehungen
|
||||
- Sequenzielle Abhängigkeiten
|
||||
- Zyklische Muster-Erkennung
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import random
|
||||
import numpy as np
|
||||
from itertools import combinations
|
||||
from collections import Counter, defaultdict, deque
|
||||
import datetime
|
||||
|
||||
class UltimateLotto6aus49Generator:
|
||||
def __init__(self, data_path=None):
|
||||
# Pfad zur Lotto-Daten CSV-Datei
|
||||
self.data_path = data_path or input("Pfad zur Lotto 6aus49 CSV-Datei: ").strip()
|
||||
self.df = None
|
||||
self.drawn_combinations = set()
|
||||
|
||||
# Basis-Analyse
|
||||
self.number_frequencies = Counter()
|
||||
self.position_frequencies = defaultdict(Counter)
|
||||
self.pattern_frequencies = Counter()
|
||||
self.supernumber_frequencies = Counter() # Nur 1 Superzahl beim Lotto
|
||||
self.number_distances = []
|
||||
|
||||
# Multi-Ziehungs-Trend-Analyse
|
||||
self.number_sequences = defaultdict(list)
|
||||
self.momentum_scores = {}
|
||||
self.trend_predictions = {}
|
||||
self.sequential_dependencies = defaultdict(lambda: defaultdict(int))
|
||||
self.cycle_patterns = {}
|
||||
self.hot_numbers = []
|
||||
self.warm_numbers = []
|
||||
self.cold_numbers = []
|
||||
|
||||
# Lotto 6aus49 spezifische Bereiche (angepasst für 1-49)
|
||||
self.lotto_ranges = {
|
||||
'N': list(range(1, 17)), # Niedrig: 1-16 (etwa 1/3)
|
||||
'M': list(range(17, 33)), # Mittel: 17-32 (etwa 1/3)
|
||||
'H': list(range(33, 50)) # Hoch: 33-49 (etwa 1/3)
|
||||
}
|
||||
|
||||
# Initialisierung
|
||||
if self._file_exists():
|
||||
self.load_and_analyze_all_data()
|
||||
|
||||
def _file_exists(self):
|
||||
"""Prüft ob Datei existiert."""
|
||||
try:
|
||||
with open(self.data_path, 'r'):
|
||||
return True
|
||||
except FileNotFoundError:
|
||||
print(f"❌ Datei nicht gefunden: {self.data_path}")
|
||||
print("💡 Bitte stellen Sie sicher, dass die Lotto-Daten im korrekten Format vorliegen:")
|
||||
print(" Spalten: Datum, Z1, Z2, Z3, Z4, Z5, Z6, SZ (Superzahl)")
|
||||
return False
|
||||
|
||||
def load_and_analyze_all_data(self):
|
||||
"""Lädt Lotto-Daten und führt alle Analysen durch."""
|
||||
try:
|
||||
# CSV laden mit flexibler Spaltenerkennung
|
||||
self.df = pd.read_csv(self.data_path, sep=';')
|
||||
|
||||
# Spalten-Mapping für verschiedene CSV-Formate
|
||||
column_mapping = {
|
||||
'Ziehungsdatum': 'Datum',
|
||||
'Gewinnzahl1': 'Z1', 'Gewinnzahl2': 'Z2', 'Gewinnzahl3': 'Z3',
|
||||
'Gewinnzahl4': 'Z4', 'Gewinnzahl5': 'Z5', 'Gewinnzahl6': 'Z6',
|
||||
'Superzahl': 'SZ', 'SuperZahl': 'SZ'
|
||||
}
|
||||
|
||||
# Spalten umbenennen falls nötig
|
||||
for old_name, new_name in column_mapping.items():
|
||||
if old_name in self.df.columns and new_name not in self.df.columns:
|
||||
self.df.rename(columns={old_name: new_name}, inplace=True)
|
||||
|
||||
# Benötigte Spalten prüfen
|
||||
required_columns = ['Z1', 'Z2', 'Z3', 'Z4', 'Z5', 'Z6']
|
||||
missing_columns = [col for col in required_columns if col not in self.df.columns]
|
||||
|
||||
if missing_columns:
|
||||
print(f"❌ Fehlende Spalten: {missing_columns}")
|
||||
print(f"🔍 Verfügbare Spalten: {list(self.df.columns)}")
|
||||
return False
|
||||
|
||||
# Chronologische Sortierung
|
||||
if 'Datum' in self.df.columns:
|
||||
# Verschiedene Datumsformate versuchen
|
||||
date_formats = ['%d.%m.%Y', '%Y-%m-%d', '%d/%m/%Y']
|
||||
for date_format in date_formats:
|
||||
try:
|
||||
self.df['Datum'] = pd.to_datetime(self.df['Datum'], format=date_format)
|
||||
break
|
||||
except:
|
||||
continue
|
||||
|
||||
if pd.api.types.is_datetime64_any_dtype(self.df['Datum']):
|
||||
self.df = self.df.sort_values('Datum')
|
||||
|
||||
print(f"🎲 ULTIMATE LOTTO 6AUS49 GENERATOR")
|
||||
print("=" * 60)
|
||||
print(f"📊 Analysiere {len(self.df)} Lotto-Ziehungen...")
|
||||
print(f"🎯 System: 6 aus 49 + Superzahl (0-9)")
|
||||
|
||||
# Alle Analysen durchführen
|
||||
self._perform_lotto_basic_analysis()
|
||||
self._perform_lotto_momentum_analysis()
|
||||
self._perform_lotto_sequential_analysis()
|
||||
self._perform_lotto_cycle_analysis()
|
||||
self._generate_lotto_trend_predictions()
|
||||
|
||||
print(f"✅ Komplette Lotto-Analyse abgeschlossen!")
|
||||
self._print_lotto_analysis_summary()
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Fehler beim Laden der Lotto-Daten: {e}")
|
||||
print("💡 Stellen Sie sicher, dass die CSV-Datei das korrekte Format hat.")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _perform_lotto_basic_analysis(self):
|
||||
"""Führt Basis-Analysen für Lotto 6aus49 durch."""
|
||||
for _, row in self.df.iterrows():
|
||||
# 6 Gewinnzahlen
|
||||
numbers = [row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']]
|
||||
combo = tuple(sorted(numbers))
|
||||
self.drawn_combinations.add(combo)
|
||||
|
||||
# Zahlenfrequenzen (1-49)
|
||||
for num in numbers:
|
||||
if 1 <= num <= 49: # Validierung für Lotto-Bereich
|
||||
self.number_frequencies[num] += 1
|
||||
|
||||
# Positionsfrequenzen
|
||||
sorted_numbers = sorted(numbers)
|
||||
for i, num in enumerate(sorted_numbers):
|
||||
self.position_frequencies[f'pos_{i+1}'][num] += 1
|
||||
|
||||
# Lotto-Muster analysieren (angepasste Bereiche)
|
||||
pattern = self._get_lotto_pattern(sorted_numbers)
|
||||
self.pattern_frequencies[pattern] += 1
|
||||
|
||||
# Superzahl (0-9)
|
||||
if 'SZ' in row and pd.notna(row['SZ']):
|
||||
superzahl = int(row['SZ'])
|
||||
if 0 <= superzahl <= 9:
|
||||
self.supernumber_frequencies[superzahl] += 1
|
||||
|
||||
# Zahlenabstände (für 6 Zahlen)
|
||||
distances = [sorted_numbers[i+1] - sorted_numbers[i] for i in range(5)]
|
||||
self.number_distances.extend(distances)
|
||||
|
||||
def _get_lotto_pattern(self, numbers):
|
||||
"""Bestimmt N/M/H-Muster für Lotto 6aus49."""
|
||||
pattern = []
|
||||
for num in numbers:
|
||||
if 1 <= num <= 16:
|
||||
pattern.append('N') # Niedrig
|
||||
elif 17 <= num <= 32:
|
||||
pattern.append('M') # Mittel
|
||||
else:
|
||||
pattern.append('H') # Hoch (33-49)
|
||||
return ''.join(pattern)
|
||||
|
||||
def _perform_lotto_momentum_analysis(self, window_size=12):
|
||||
"""Momentum-Analyse für Lotto 6aus49."""
|
||||
print(f"\n🔥 LOTTO MOMENTUM-ANALYSE (Fenster: {window_size})")
|
||||
|
||||
# Zahlensequenzen für 1-49
|
||||
for number in range(1, 50):
|
||||
sequence = []
|
||||
for _, row in self.df.iterrows():
|
||||
drawn_numbers = [row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']]
|
||||
sequence.append(1 if number in drawn_numbers else 0)
|
||||
self.number_sequences[number] = sequence
|
||||
|
||||
# Momentum-Scores
|
||||
momentum_results = {}
|
||||
for number in range(1, 50):
|
||||
recent_sequence = self.number_sequences[number][-window_size:]
|
||||
|
||||
hit_rate = sum(recent_sequence) / len(recent_sequence)
|
||||
trend_score = self._calculate_trend_score(recent_sequence)
|
||||
recency_score = self._calculate_recency_score(recent_sequence)
|
||||
|
||||
# Lotto-angepasste Gewichtung (6 aus 49 vs 5 aus 50)
|
||||
momentum_score = (hit_rate * 0.45) + (trend_score * 0.35) + (recency_score * 0.2)
|
||||
|
||||
momentum_results[number] = {
|
||||
'hit_rate': hit_rate,
|
||||
'trend_score': trend_score,
|
||||
'recency_score': recency_score,
|
||||
'momentum_score': momentum_score,
|
||||
'status': self._get_momentum_status(momentum_score)
|
||||
}
|
||||
|
||||
self.momentum_scores = momentum_results
|
||||
|
||||
# Kategorisierung für Lotto
|
||||
sorted_momentum = sorted(momentum_results.items(),
|
||||
key=lambda x: x[1]['momentum_score'], reverse=True)
|
||||
|
||||
self.hot_numbers = [num for num, data in sorted_momentum[:18]
|
||||
if data['momentum_score'] > 0.25] # Angepasst für 6aus49
|
||||
self.warm_numbers = [num for num, data in sorted_momentum[18:30]
|
||||
if 0.15 <= data['momentum_score'] <= 0.25]
|
||||
self.cold_numbers = [num for num, data in sorted_momentum[30:]
|
||||
if data['momentum_score'] < 0.15][:20]
|
||||
|
||||
print(f"🔥 {len(self.hot_numbers)} heiße Lotto-Zahlen identifiziert")
|
||||
print(f"🌡️ {len(self.warm_numbers)} warme Lotto-Zahlen identifiziert")
|
||||
print(f"🧊 {len(self.cold_numbers)} kalte Lotto-Zahlen identifiziert")
|
||||
|
||||
def _perform_lotto_sequential_analysis(self, look_back=3):
|
||||
"""Sequenzielle Abhängigkeiten für Lotto."""
|
||||
print(f"\n🔗 LOTTO SEQUENZIELLE ABHÄNGIGKEITEN")
|
||||
|
||||
for i in range(look_back, len(self.df)):
|
||||
current_numbers = set([self.df.iloc[i]['Z1'], self.df.iloc[i]['Z2'],
|
||||
self.df.iloc[i]['Z3'], self.df.iloc[i]['Z4'],
|
||||
self.df.iloc[i]['Z5'], self.df.iloc[i]['Z6']])
|
||||
|
||||
for j in range(1, look_back + 1):
|
||||
prev_numbers = set([self.df.iloc[i-j]['Z1'], self.df.iloc[i-j]['Z2'],
|
||||
self.df.iloc[i-j]['Z3'], self.df.iloc[i-j]['Z4'],
|
||||
self.df.iloc[i-j]['Z5'], self.df.iloc[i-j]['Z6']])
|
||||
|
||||
for prev_num in prev_numbers:
|
||||
for curr_num in current_numbers:
|
||||
self.sequential_dependencies[f"lag_{j}"][f"{prev_num}_{curr_num}"] += 1
|
||||
|
||||
def _perform_lotto_cycle_analysis(self, max_cycle_length=15):
|
||||
"""Zyklische Muster-Analyse für Lotto."""
|
||||
print(f"\n🔄 LOTTO ZYKLUS-ANALYSE")
|
||||
|
||||
pattern_sequence = []
|
||||
for _, row in self.df.iterrows():
|
||||
numbers = sorted([row['Z1'], row['Z2'], row['Z3'], row['Z4'], row['Z5'], row['Z6']])
|
||||
pattern = self._get_lotto_pattern(numbers)
|
||||
pattern_sequence.append(pattern)
|
||||
|
||||
self.cycle_patterns = {}
|
||||
for cycle_length in range(3, max_cycle_length + 1):
|
||||
cycles = self._find_pattern_cycles(pattern_sequence, cycle_length)
|
||||
if cycles:
|
||||
self.cycle_patterns[cycle_length] = cycles
|
||||
|
||||
cycle_count = sum(len(cycles) for cycles in self.cycle_patterns.values())
|
||||
print(f"🔄 {cycle_count} Lotto-Zyklen erkannt")
|
||||
|
||||
def _generate_lotto_trend_predictions(self):
|
||||
"""Trend-Vorhersagen für Lotto 6aus49."""
|
||||
print(f"\n🎯 LOTTO TREND-VORHERSAGEN")
|
||||
|
||||
for number in range(1, 50):
|
||||
if number in self.momentum_scores:
|
||||
momentum_data = self.momentum_scores[number]
|
||||
|
||||
# Lotto-spezifische Gewichtung
|
||||
momentum_weight = momentum_data['momentum_score'] * 0.4
|
||||
frequency_weight = (self.number_frequencies[number] / (len(self.df) * 6)) * 0.35 # 6 Zahlen pro Ziehung
|
||||
trend_weight = max(0, momentum_data['trend_score']) * 0.25
|
||||
|
||||
prediction_score = momentum_weight + frequency_weight + trend_weight
|
||||
|
||||
self.trend_predictions[number] = {
|
||||
'prediction_score': prediction_score,
|
||||
'recommendation': self._get_prediction_recommendation(prediction_score),
|
||||
'confidence': self._get_confidence_level(prediction_score)
|
||||
}
|
||||
|
||||
def generate_lotto_ultimate_combination(self):
|
||||
"""Generiert ultimative Lotto 6aus49 Kombination."""
|
||||
max_attempts = 1000
|
||||
|
||||
for attempt in range(max_attempts):
|
||||
numbers = []
|
||||
|
||||
# Lotto-Strategie: 6 Zahlen aus 49
|
||||
# 50% Top-Trend, 30% Heiß, 20% Balance
|
||||
|
||||
# 3 Zahlen aus Top-Trends
|
||||
top_trend_numbers = [num for num, data in sorted(self.trend_predictions.items(),
|
||||
key=lambda x: x[1]['prediction_score'], reverse=True)[:20]
|
||||
if data['recommendation'] in ['SEHR EMPFOHLEN', 'EMPFOHLEN']]
|
||||
|
||||
if len(top_trend_numbers) >= 3:
|
||||
trend_picks = random.sample(top_trend_numbers[:12], 3)
|
||||
numbers.extend(trend_picks)
|
||||
|
||||
# 2 heiße Zahlen
|
||||
if len(self.hot_numbers) >= 2:
|
||||
remaining_hot = [n for n in self.hot_numbers if n not in numbers]
|
||||
if len(remaining_hot) >= 2:
|
||||
hot_picks = random.sample(remaining_hot[:10], min(2, len(remaining_hot)))
|
||||
numbers.extend(hot_picks)
|
||||
|
||||
# 1 warme/kalte Zahl für Balance
|
||||
remaining_slots = 6 - len(numbers)
|
||||
if remaining_slots > 0:
|
||||
balance_pool = self.warm_numbers + self.cold_numbers[:5]
|
||||
remaining_balance = [n for n in balance_pool if n not in numbers]
|
||||
if remaining_balance:
|
||||
balance_picks = random.sample(remaining_balance, min(remaining_slots, len(remaining_balance)))
|
||||
numbers.extend(balance_picks)
|
||||
|
||||
# Auffüllen bis 6 Zahlen
|
||||
while len(numbers) < 6:
|
||||
available_numbers = [n for n in range(1, 50) if n not in numbers]
|
||||
weights = [self.trend_predictions[n]['prediction_score'] for n in available_numbers]
|
||||
|
||||
if sum(weights) > 0:
|
||||
additional_number = random.choices(available_numbers, weights=weights)[0]
|
||||
else:
|
||||
additional_number = random.choice(available_numbers)
|
||||
|
||||
numbers.append(additional_number)
|
||||
|
||||
# Sortieren und validieren
|
||||
numbers = sorted(numbers[:6])
|
||||
|
||||
if self._validate_lotto_combination(numbers):
|
||||
return numbers
|
||||
|
||||
# Fallback
|
||||
return self._generate_lotto_fallback()
|
||||
|
||||
def _validate_lotto_combination(self, numbers):
|
||||
"""Validierung für Lotto 6aus49."""
|
||||
if tuple(numbers) in self.drawn_combinations:
|
||||
return False
|
||||
|
||||
if len(set(numbers)) != 6:
|
||||
return False
|
||||
|
||||
# Lotto-spezifische Validierungen
|
||||
hot_count = sum(1 for n in numbers if n in self.hot_numbers)
|
||||
trend_count = sum(1 for n in numbers
|
||||
if self.trend_predictions[n]['recommendation'] == 'SEHR EMPFOHLEN')
|
||||
|
||||
# Mindestens 1 heiße oder sehr empfohlene Zahl
|
||||
if hot_count == 0 and trend_count == 0:
|
||||
return False
|
||||
|
||||
# Abstände prüfen (für 6 Zahlen)
|
||||
distances = [numbers[i+1] - numbers[i] for i in range(5)]
|
||||
if min(distances) < 1 or max(distances) > 15:
|
||||
return False
|
||||
|
||||
# Gerade/Ungerade Balance
|
||||
even_count = sum(1 for n in numbers if n % 2 == 0)
|
||||
if even_count == 0 or even_count == 6:
|
||||
return False
|
||||
|
||||
# Summen-Validierung für 6aus49
|
||||
total = sum(numbers)
|
||||
if total < 90 or total > 200:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _generate_lotto_fallback(self):
|
||||
"""Fallback für Lotto 6aus49."""
|
||||
numbers = []
|
||||
|
||||
# Erweiterte Verteilung für 6 Zahlen: 2N + 2M + 2H
|
||||
numbers.extend(random.sample(self.lotto_ranges['N'], 2))
|
||||
numbers.extend(random.sample(self.lotto_ranges['M'], 2))
|
||||
numbers.extend(random.sample(self.lotto_ranges['H'], 2))
|
||||
|
||||
return sorted(numbers)
|
||||
|
||||
def get_optimized_supernumber(self):
|
||||
"""Optimierte Superzahl-Auswahl (0-9)."""
|
||||
if not self.supernumber_frequencies:
|
||||
return random.randint(0, 9)
|
||||
|
||||
# Trend-gewichtete Superzahl-Auswahl
|
||||
recent_df = self.df.tail(8) if len(self.df) >= 8 else self.df
|
||||
supernumber_trends = {}
|
||||
|
||||
for sz in range(0, 10):
|
||||
recent_count = (recent_df['SZ'] == sz).sum() if 'SZ' in recent_df.columns else 0
|
||||
total_count = self.supernumber_frequencies[sz]
|
||||
trend_score = (recent_count / len(recent_df)) * 0.6 + (total_count / len(self.df)) * 0.4
|
||||
supernumber_trends[sz] = trend_score
|
||||
|
||||
# Gewichtete Auswahl
|
||||
candidates = list(supernumber_trends.keys())
|
||||
weights = list(supernumber_trends.values())
|
||||
|
||||
if sum(weights) > 0:
|
||||
return random.choices(candidates, weights=weights)[0]
|
||||
else:
|
||||
return random.randint(0, 9)
|
||||
|
||||
def generate_lotto_ultimate_tips(self, num_tips=10):
|
||||
"""Generiert ultimate Lotto 6aus49 Tipps."""
|
||||
print(f"\n🚀 ULTIMATE LOTTO 6AUS49 TIPP-GENERIERUNG")
|
||||
print("=" * 55)
|
||||
print(f"🎯 System: 6 Zahlen aus 49 + 1 Superzahl (0-9)")
|
||||
print(f"🔬 Multi-Trend-Analyse für maximale Trefferquote")
|
||||
|
||||
generated_tips = []
|
||||
strategy_distribution = Counter()
|
||||
|
||||
print(f"\n🎲 GENERIERE {num_tips} ULTIMATE LOTTO-TIPPS:")
|
||||
print("=" * 65)
|
||||
print(f"{'Nr':<3} {'6 Zahlen aus 49':<25} {'SZ':<3} {'Muster':<8} {'🔥':<3} {'🎯':<3} {'Strategie'}")
|
||||
print("-" * 65)
|
||||
|
||||
attempts = 0
|
||||
max_attempts = num_tips * 50
|
||||
|
||||
while len(generated_tips) < num_tips and attempts < max_attempts:
|
||||
attempts += 1
|
||||
|
||||
combination = self.generate_lotto_ultimate_combination()
|
||||
|
||||
if combination and tuple(combination) not in [tuple(tip['zahlen']) for tip in generated_tips]:
|
||||
pattern = self._get_lotto_pattern(combination)
|
||||
|
||||
# Lotto-Trend-Analyse
|
||||
hot_count = sum(1 for n in combination if n in self.hot_numbers)
|
||||
trend_count = sum(1 for n in combination
|
||||
if self.trend_predictions[n]['recommendation'] in ['SEHR EMPFOHLEN', 'EMPFOHLEN'])
|
||||
|
||||
# Superzahl
|
||||
superzahl = self.get_optimized_supernumber()
|
||||
|
||||
# Strategie-Klassifikation
|
||||
if hot_count >= 4:
|
||||
strategy = "🔥 MOMENTUM"
|
||||
elif trend_count >= 4:
|
||||
strategy = "🎯 TREND"
|
||||
elif pattern in ['NNMMHH', 'NMMHHH', 'NNNMMM']:
|
||||
strategy = "🎨 MUSTER"
|
||||
else:
|
||||
strategy = "⚖️ BALANCE"
|
||||
|
||||
strategy_distribution[strategy] += 1
|
||||
|
||||
tip = {
|
||||
'tipp_nr': len(generated_tips) + 1,
|
||||
'zahlen': combination,
|
||||
'z1': combination[0], 'z2': combination[1], 'z3': combination[2],
|
||||
'z4': combination[3], 'z5': combination[4], 'z6': combination[5],
|
||||
'superzahl': superzahl,
|
||||
'muster': pattern,
|
||||
'summe': sum(combination),
|
||||
'hot_count': hot_count,
|
||||
'trend_count': trend_count,
|
||||
'strategy': strategy
|
||||
}
|
||||
|
||||
generated_tips.append(tip)
|
||||
|
||||
# Output
|
||||
zahlen_str = f"{combination[0]:2}-{combination[1]:2}-{combination[2]:2}-{combination[3]:2}-{combination[4]:2}-{combination[5]:2}"
|
||||
print(f"{len(generated_tips):2}. {zahlen_str:<25} {superzahl:<3} {pattern:<8} {hot_count:<3} {trend_count:<3} {strategy}")
|
||||
|
||||
# Lotto-Zusammenfassung
|
||||
self._print_lotto_summary(generated_tips, attempts, strategy_distribution)
|
||||
|
||||
# Export
|
||||
self._export_lotto_tips(generated_tips)
|
||||
|
||||
return generated_tips
|
||||
|
||||
def _print_lotto_summary(self, tips, attempts, strategy_distribution):
|
||||
"""Druckt Lotto-spezifische Zusammenfassung."""
|
||||
print(f"\n🏆 ULTIMATE LOTTO 6AUS49 ZUSAMMENFASSUNG:")
|
||||
print("=" * 50)
|
||||
print(f"✅ {len(tips)} Ultimate Lotto-Tipps generiert")
|
||||
print(f"🎯 Erfolgsrate: {(len(tips)/attempts)*100:.1f}%")
|
||||
print(f"🔥 Durchschnitt {sum(tip['hot_count'] for tip in tips)/len(tips):.1f} heiße Zahlen pro Tipp")
|
||||
print(f"📈 Durchschnitt {sum(tip['trend_count'] for tip in tips)/len(tips):.1f} Trend-Zahlen pro Tipp")
|
||||
|
||||
# Strategie-Verteilung
|
||||
print(f"\n📊 STRATEGIE-VERTEILUNG:")
|
||||
for strategy, count in strategy_distribution.most_common():
|
||||
print(f" {strategy}: {count} Tipps")
|
||||
|
||||
# Lotto-spezifische Insights
|
||||
print(f"\n💡 LOTTO 6AUS49 INSIGHTS:")
|
||||
|
||||
# Top Trend-Zahlen
|
||||
top_trend = sorted(self.trend_predictions.items(),
|
||||
key=lambda x: x[1]['prediction_score'], reverse=True)[:6]
|
||||
print(f"🎯 TOP 6 TREND-ZAHLEN:")
|
||||
for i, (number, data) in enumerate(top_trend):
|
||||
status = self.momentum_scores[number]['status']
|
||||
print(f" {i+1}. Zahl {number:2}: {data['recommendation']} {status}")
|
||||
|
||||
# Häufigste Superzahlen
|
||||
if self.supernumber_frequencies:
|
||||
top_sz = self.supernumber_frequencies.most_common(3)
|
||||
print(f"\n🎲 TOP 3 SUPERZAHLEN:")
|
||||
for sz, count in top_sz:
|
||||
percentage = (count / len(self.df)) * 100
|
||||
print(f" Superzahl {sz}: {count}x ({percentage:.1f}%)")
|
||||
|
||||
# Empfohlene Muster
|
||||
top_patterns = self.pattern_frequencies.most_common(3)
|
||||
print(f"\n🎨 TOP 3 LOTTO-MUSTER:")
|
||||
for pattern, count in top_patterns:
|
||||
percentage = (count / len(self.drawn_combinations)) * 100
|
||||
print(f" {pattern}: {count}x ({percentage:.1f}%)")
|
||||
|
||||
def _export_lotto_tips(self, tips):
|
||||
"""Exportiert Lotto-Tipps."""
|
||||
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
output_file = f"ultimate_lotto_6aus49_tipps_{timestamp}.csv"
|
||||
|
||||
# Export-Daten erweitern
|
||||
export_data = []
|
||||
for tip in tips:
|
||||
tip_data = tip.copy()
|
||||
tip_data['trend_scores'] = [self.trend_predictions[n]['prediction_score']
|
||||
for n in tip['zahlen']]
|
||||
tip_data['avg_trend_score'] = np.mean(tip_data['trend_scores'])
|
||||
export_data.append(tip_data)
|
||||
|
||||
tips_df = pd.DataFrame(export_data)
|
||||
tips_df.to_csv(output_file, sep=';', index=False)
|
||||
|
||||
print(f"\n💾 LOTTO-EXPORT:")
|
||||
print("=" * 25)
|
||||
print(f"✅ Lotto-Tipps gespeichert: {output_file}")
|
||||
print(f"🎲 Format: 6 Zahlen aus 49 + Superzahl")
|
||||
print(f"🚀 Ultimate Multi-Trend-Optimierung")
|
||||
|
||||
def _print_lotto_analysis_summary(self):
|
||||
"""Druckt Lotto-Analyse-Zusammenfassung."""
|
||||
print(f"\n📈 LOTTO 6AUS49 ANALYSE-ZUSAMMENFASSUNG:")
|
||||
print("=" * 55)
|
||||
|
||||
# Top Zahlen
|
||||
print(f"\n🔢 HÄUFIGSTE LOTTO-ZAHLEN:")
|
||||
for i, (number, count) in enumerate(self.number_frequencies.most_common(10)):
|
||||
percentage = (count / (len(self.df) * 6)) * 100
|
||||
print(f"{i+1:2}. Zahl {number:2}: {count:3}x ({percentage:.2f}%)")
|
||||
|
||||
# Top Muster
|
||||
print(f"\n🎨 ERFOLGREICHSTE LOTTO-MUSTER:")
|
||||
for pattern, count in self.pattern_frequencies.most_common(5):
|
||||
percentage = (count / len(self.drawn_combinations)) * 100
|
||||
print(f" {pattern}: {count}x ({percentage:.1f}%)")
|
||||
|
||||
# Hilfsfunktionen (gleich wie Eurojackpot)
|
||||
def _calculate_trend_score(self, sequence):
|
||||
if len(sequence) < 2:
|
||||
return 0
|
||||
x = np.arange(len(sequence))
|
||||
y = np.array(sequence)
|
||||
weights = np.exp(x / len(x))
|
||||
try:
|
||||
coeffs = np.polyfit(x, y, 1, w=weights)
|
||||
return coeffs[0]
|
||||
except:
|
||||
return 0
|
||||
|
||||
def _calculate_recency_score(self, sequence):
|
||||
try:
|
||||
last_hit_index = len(sequence) - 1 - sequence[::-1].index(1)
|
||||
recency = 1 - (len(sequence) - 1 - last_hit_index) / len(sequence)
|
||||
return recency
|
||||
except ValueError:
|
||||
return 0
|
||||
|
||||
def _get_momentum_status(self, score):
|
||||
if score > 0.4:
|
||||
return "🔥 SEHR HEISS"
|
||||
elif score > 0.25:
|
||||
return "🌡️ HEISS"
|
||||
elif score > 0.15:
|
||||
return "😐 WARM"
|
||||
elif score > 0.08:
|
||||
return "🧊 KÜHL"
|
||||
else:
|
||||
return "❄️ EISKALT"
|
||||
|
||||
def _get_prediction_recommendation(self, score):
|
||||
if score > 0.3:
|
||||
return "SEHR EMPFOHLEN"
|
||||
elif score > 0.2:
|
||||
return "EMPFOHLEN"
|
||||
elif score > 0.12:
|
||||
return "NEUTRAL"
|
||||
else:
|
||||
return "VERMEIDEN"
|
||||
|
||||
def _get_confidence_level(self, score):
|
||||
if score > 0.3:
|
||||
return "HOCH"
|
||||
elif score > 0.2:
|
||||
return "MITTEL"
|
||||
else:
|
||||
return "NIEDRIG"
|
||||
|
||||
def _find_pattern_cycles(self, sequence, cycle_length):
|
||||
cycle_patterns = defaultdict(list)
|
||||
for i in range(len(sequence) - cycle_length):
|
||||
pattern = ''.join(sequence[i:i+cycle_length])
|
||||
cycle_patterns[pattern].append(i)
|
||||
return {pattern: positions for pattern, positions in cycle_patterns.items()
|
||||
if len(positions) >= 2}
|
||||
|
||||
# Zusätzliche Lotto-spezifische Analysefunktionen
|
||||
|
||||
def analyze_lotto_tip_quality(generator, tip_numbers):
|
||||
"""Analysiert Qualität eines Lotto 6aus49 Tipps."""
|
||||
quality_score = 0
|
||||
analysis = {}
|
||||
|
||||
# Momentum-Analyse
|
||||
hot_count = sum(1 for n in tip_numbers if n in generator.hot_numbers)
|
||||
analysis['hot_numbers'] = hot_count
|
||||
quality_score += hot_count * 0.15 # Angepasst für 6 Zahlen
|
||||
|
||||
# Trend-Analyse
|
||||
trend_scores = [generator.trend_predictions[n]['prediction_score'] for n in tip_numbers]
|
||||
avg_trend = np.mean(trend_scores)
|
||||
analysis['avg_trend_score'] = avg_trend
|
||||
quality_score += avg_trend * 0.35
|
||||
|
||||
# Positions-Analyse (6 Positionen)
|
||||
position_quality = 0
|
||||
for i, num in enumerate(sorted(tip_numbers)):
|
||||
pos_freq = generator.position_frequencies[f'pos_{i+1}'][num]
|
||||
if pos_freq > 0:
|
||||
position_quality += pos_freq
|
||||
analysis['position_quality'] = position_quality
|
||||
quality_score += (position_quality / len(generator.df)) * 0.25
|
||||
|
||||
# Muster-Analyse
|
||||
pattern = generator._get_lotto_pattern(sorted(tip_numbers))
|
||||
pattern_freq = generator.pattern_frequencies[pattern]
|
||||
pattern_score = pattern_freq / len(generator.df)
|
||||
analysis['pattern'] = pattern
|
||||
analysis['pattern_score'] = pattern_score
|
||||
quality_score += pattern_score * 0.25
|
||||
|
||||
analysis['total_quality_score'] = quality_score
|
||||
analysis['quality_rating'] = get_lotto_quality_rating(quality_score)
|
||||
|
||||
return analysis
|
||||
|
||||
def get_lotto_quality_rating(score):
|
||||
"""Lotto-spezifische Quality-Ratings."""
|
||||
if score > 0.7:
|
||||
return "🏆 LOTTO PREMIUM"
|
||||
elif score > 0.5:
|
||||
return "🥇 SEHR GUT"
|
||||
elif score > 0.35:
|
||||
return "🥈 GUT"
|
||||
elif score > 0.2:
|
||||
return "🥉 DURCHSCHNITT"
|
||||
else:
|
||||
return "⚠️ SCHWACH"
|
||||
|
||||
def predict_lotto_jackpot_probability(generator, tip_numbers):
|
||||
"""Schätzt Lotto-Jackpot-Wahrscheinlichkeit."""
|
||||
base_probability = 1 / 13983816 # Lotto 6aus49 Grundwahrscheinlichkeit
|
||||
|
||||
trend_multiplier = 1.0
|
||||
for number in tip_numbers:
|
||||
momentum_score = generator.momentum_scores[number]['momentum_score']
|
||||
trend_score = generator.trend_predictions[number]['prediction_score']
|
||||
|
||||
# Lotto-angepasste Gewichtung
|
||||
number_multiplier = 1 + (momentum_score * 0.08) + (trend_score * 0.12)
|
||||
trend_multiplier *= number_multiplier
|
||||
|
||||
# Pattern-Bonus für Lotto
|
||||
pattern = generator._get_lotto_pattern(sorted(tip_numbers))
|
||||
pattern_frequency = generator.pattern_frequencies[pattern] / len(generator.df)
|
||||
pattern_multiplier = 1 + (pattern_frequency * 0.15)
|
||||
|
||||
estimated_probability = base_probability * trend_multiplier * pattern_multiplier
|
||||
|
||||
return {
|
||||
'base_probability': base_probability,
|
||||
'trend_multiplier': trend_multiplier,
|
||||
'pattern_multiplier': pattern_multiplier,
|
||||
'estimated_probability': estimated_probability,
|
||||
'improvement_factor': (estimated_probability / base_probability)
|
||||
}
|
||||
|
||||
def create_lotto_sample_data():
|
||||
"""Erstellt Beispiel-Daten für Lotto 6aus49 (für Tests)."""
|
||||
print("📋 BEISPIEL LOTTO-DATEN ERSTELLEN")
|
||||
print("=" * 35)
|
||||
|
||||
sample_data = []
|
||||
base_date = datetime.datetime(2020, 1, 4) # Erster Samstag 2020
|
||||
|
||||
for i in range(100): # 100 Beispiel-Ziehungen
|
||||
# Datum (jeden Samstag)
|
||||
date = base_date + datetime.timedelta(weeks=i)
|
||||
|
||||
# 6 zufällige Zahlen aus 1-49
|
||||
numbers = sorted(random.sample(range(1, 50), 6))
|
||||
|
||||
# Superzahl 0-9
|
||||
superzahl = random.randint(0, 9)
|
||||
|
||||
sample_data.append({
|
||||
'Datum': date.strftime('%d.%m.%Y'),
|
||||
'Z1': numbers[0], 'Z2': numbers[1], 'Z3': numbers[2],
|
||||
'Z4': numbers[3], 'Z5': numbers[4], 'Z6': numbers[5],
|
||||
'SZ': superzahl
|
||||
})
|
||||
|
||||
# CSV speichern
|
||||
df_sample = pd.DataFrame(sample_data)
|
||||
sample_file = "lotto_sample_data.csv"
|
||||
df_sample.to_csv(sample_file, sep=';', index=False)
|
||||
|
||||
print(f"✅ Beispiel-Daten erstellt: {sample_file}")
|
||||
print(f"📊 {len(sample_data)} Lotto-Ziehungen")
|
||||
print(f"💡 Verwenden Sie diese Datei zum Testen des Generators!")
|
||||
|
||||
return sample_file
|
||||
|
||||
def main():
|
||||
"""Hauptfunktion für Ultimate Lotto 6aus49 Generator."""
|
||||
print("🎲 ULTIMATE LOTTO 6AUS49 GENERATOR")
|
||||
print("🚀 Mit Multi-Ziehungs-Trend-Analyse")
|
||||
print("=" * 50)
|
||||
|
||||
# Datei-Pfad abfragen
|
||||
print("📁 LOTTO-DATEN LADEN:")
|
||||
print("Geben Sie den Pfad zur Lotto 6aus49 CSV-Datei ein.")
|
||||
print("(Oder drücken Sie Enter für Beispiel-Daten)")
|
||||
|
||||
data_path = input("CSV-Pfad: ").strip()
|
||||
|
||||
# Beispiel-Daten erstellen falls kein Pfad angegeben
|
||||
if not data_path:
|
||||
print("\n🔧 Erstelle Beispiel-Daten für Demonstration...")
|
||||
data_path = create_lotto_sample_data()
|
||||
print(f"📂 Verwende Beispiel-Datei: {data_path}")
|
||||
|
||||
try:
|
||||
# Generator initialisieren
|
||||
generator = UltimateLotto6aus49Generator(data_path)
|
||||
|
||||
if not hasattr(generator, 'df') or generator.df is None:
|
||||
print("❌ Generator konnte nicht initialisiert werden!")
|
||||
return
|
||||
|
||||
# Ultimate Tipps generieren
|
||||
tips = generator.generate_lotto_ultimate_tips(10)
|
||||
|
||||
if tips:
|
||||
print(f"\n🏆 ULTIMATE LOTTO 6AUS49 OPTIMIERUNG ABGESCHLOSSEN!")
|
||||
print("=" * 55)
|
||||
print(f"🎲 10 Ultimate Lotto-Tipps generiert")
|
||||
print(f"📈 Maximale Trefferwahrscheinlichkeit durch:")
|
||||
print(f" • Multi-Ziehungs-Momentum-Analyse")
|
||||
print(f" • Sequenzielle Abhängigkeiten")
|
||||
print(f" • Zyklische Muster-Erkennung")
|
||||
print(f" • Lotto-spezifische Optimierungen")
|
||||
print(f"🍀 Viel Erfolg bei der nächsten Lotto-Ziehung!")
|
||||
|
||||
# Erweiterte Analyse (optional)
|
||||
print(f"\n📊 ERWEITERTE LOTTO-ANALYSE:")
|
||||
print("=" * 35)
|
||||
|
||||
# Beispiel-Analyse für ersten Tipp
|
||||
if len(tips) > 0:
|
||||
sample_tip = tips[0]['zahlen']
|
||||
quality_analysis = analyze_lotto_tip_quality(generator, sample_tip)
|
||||
probability_analysis = predict_lotto_jackpot_probability(generator, sample_tip)
|
||||
|
||||
print(f"\n🔍 BEISPIEL-ANALYSE für Lotto-Tipp 1:")
|
||||
tip_str = '-'.join([f"{n:2}" for n in sample_tip])
|
||||
print(f" 🎲 Zahlen: {tip_str} + SZ: {tips[0]['superzahl']}")
|
||||
print(f" 🏆 Quality: {quality_analysis['quality_rating']}")
|
||||
print(f" 📈 Score: {quality_analysis['total_quality_score']:.3f}")
|
||||
print(f" 🔥 Heiße Zahlen: {quality_analysis['hot_numbers']}/6")
|
||||
print(f" 🎯 Trend-Score: {quality_analysis['avg_trend_score']:.3f}")
|
||||
print(f" 🎨 Muster: {quality_analysis['pattern']}")
|
||||
print(f" 📊 Verbesserungs-Faktor: {probability_analysis['improvement_factor']:.2f}x")
|
||||
|
||||
# Strategische Empfehlungen
|
||||
print(f"\n💡 STRATEGISCHE LOTTO-EMPFEHLUNGEN:")
|
||||
print("=" * 40)
|
||||
|
||||
# Top Trend-Zahlen
|
||||
top_trend = sorted(generator.trend_predictions.items(),
|
||||
key=lambda x: x[1]['prediction_score'], reverse=True)[:8]
|
||||
print(f"🎯 TOP 8 TREND-ZAHLEN für kommende Ziehungen:")
|
||||
for i, (number, data) in enumerate(top_trend):
|
||||
status = generator.momentum_scores[number]['status']
|
||||
print(f" {i+1}. Zahl {number:2}: {data['recommendation']} {status}")
|
||||
|
||||
# Momentum-Verteilung
|
||||
very_hot_lotto = [n for n in generator.hot_numbers
|
||||
if generator.momentum_scores[n]['momentum_score'] > 0.3]
|
||||
if very_hot_lotto:
|
||||
print(f"\n🔥 MOMENTUM-ALERT für Lotto:")
|
||||
print(f" Sehr heiße Zahlen: {very_hot_lotto}")
|
||||
print(f" → Verwenden Sie 2-3 dieser Zahlen in Ihren Tipps!")
|
||||
|
||||
# Superzahl-Empfehlung
|
||||
if generator.supernumber_frequencies:
|
||||
top_superzahl = generator.supernumber_frequencies.most_common(3)
|
||||
print(f"\n🎲 TOP SUPERZAHL-EMPFEHLUNGEN:")
|
||||
for sz, count in top_superzahl:
|
||||
percentage = (count / len(generator.df)) * 100
|
||||
print(f" Superzahl {sz}: {count}x ({percentage:.1f}%)")
|
||||
|
||||
else:
|
||||
print("❌ Keine Tipps generiert!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Fehler: {e}")
|
||||
print("💡 Stellen Sie sicher, dass die CSV-Datei korrekt formatiert ist:")
|
||||
print(" Spalten: Datum, Z1, Z2, Z3, Z4, Z5, Z6, SZ")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Reproduzierbarer Zufallsseed
|
||||
random.seed(42)
|
||||
np.random.seed(42)
|
||||
|
||||
# Ultimate Lotto Generator starten
|
||||
main()
|
||||
Reference in New Issue
Block a user