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proyecto_tinka_ml/proyecto_tinka_ml/README.md

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+# Sistema de Análisis y Generación de Combinaciones — Tinka (con ML)
+
+**Aviso:** La lotería es aleatoria. Este proyecto es **educativo**; no garantiza aciertos.
+
+## 1) Requisitos
+- Python 3.9+
+- Instala dependencias:
+```bash
+pip install -r requirements.txt
+```
+
+## 2) Configurar BD
+Edita `config.py` con tus credenciales (XAMPP por defecto). La tabla `resultados` debe tener:
+`id_sorteo`, `fecha_sorteo`, `numeros`, `boliyapa`, `jackpot`, `created_at` (opcional).
+
+## 3) Ejecutar
+```bash
+python main.py
+```
+
+## 4) Menú
+1. Dashboard de estadísticas (exporta `data/reporte.html`)
+2. Análisis por número
+3. Generar combinaciones (elige estrategia y **cantidad**)
+4. Evaluar tu combinación
+5. Mejores históricas por score heurístico
+6. Exportar análisis a HTML
+7. Refrescar datos desde la BD (actualiza caché)
+8. **Recomendación automática (ML)**: calcula probabilidades bayesianas (globales + recientes), genera un pool (estrategias clásicas + **Thompson Sampling**) y rankea con un score ML (log-prob + heurísticas). Guarda:
+   - `data/probabilidades.json`
+   - `data/combinaciones_generadas.json`
+
+## 5) Notas
+- Cambios robustos en caché (JSON atómico).
+- SQL con SQLAlchemy para evitar warnings.
+- Visualización simple con matplotlib (PNG embebido).

proyecto_tinka_ml/proyecto_tinka_ml/analizador.py

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+"""Lógica de análisis estadístico para Tinka/Boliyapa."""
+from __future__ import annotations
+from typing import Dict, List, Tuple
+import numpy as np
+import pandas as pd
+from itertools import combinations
+from collections import Counter
+from config import TOTAL_NUMBERS, COMBINATION_SIZE, LAST_N_WINDOWS, SUM_RANGE
+from utils import parse_numbers, is_prime
+
+def _explode_numeros(df: pd.DataFrame) -> pd.DataFrame:
+    rows = []
+    for _, r in df.iterrows():
+        nums = parse_numbers(r['numeros'])
+        for n in nums:
+            rows.append({'id_sorteo': r['id_sorteo'], 'fecha_sorteo': r['fecha_sorteo'], 'numero': n})
+    return pd.DataFrame(rows, columns=['id_sorteo','fecha_sorteo','numero'])
+
+def _coocurrencias(df: pd.DataFrame, k: int = 2):
+    cnt = Counter()
+    for _, r in df.iterrows():
+        arr = sorted(parse_numbers(r['numeros']))
+        for combo in combinations(arr, k):
+            cnt[combo] += 1
+    return cnt
+
+def _ultimos(df: pd.DataFrame, n: int):
+    return df.tail(n) if len(df) >= n else df.copy()
+
+def analisis_frecuencias(df: pd.DataFrame) -> Dict:
+    exploded = _explode_numeros(df)
+    freq_abs = Counter(exploded['numero'].tolist())
+    for k in range(1, TOTAL_NUMBERS+1):
+        freq_abs.setdefault(k, 0)
+    total_bolas = len(exploded)
+    freq_rel = {k: (v/total_bolas if total_bolas else 0.0) for k, v in freq_abs.items()}
+    top = sorted(freq_abs.items(), key=lambda x: (-x[1], x[0]))
+    hot = [k for k,_ in top[:15]]
+    cold = [k for k,_ in sorted(freq_abs.items(), key=lambda x: (x[1], x[0]))[:15]]
+    ult10 = set(_explode_numeros(_ultimos(df, 10))['numero'].tolist())
+    # dormidos: no aparecen hace >20 sorteos
+    last_seen_idx = {}
+    for idx, r in enumerate(df.itertuples(index=False), start=1):
+        for n in parse_numbers(r.numeros):
+            last_seen_idx[n] = idx
+    dormidos = []
+    threshold = max(1, len(df) - 20)
+    for n in range(1, TOTAL_NUMBERS+1):
+        if last_seen_idx.get(n, 0) < threshold:
+            dormidos.append(n)
+    return {'freq_abs': dict(sorted(freq_abs.items())),
+            'freq_rel': dict(sorted(freq_rel.items())),
+            'hot_15': hot,
+            'cold_15': cold,
+            'en_racha_ult10': sorted(ult10),
+            'dormidos_mas20': sorted(dormidos)}
+
+def analisis_temporal(df: pd.DataFrame) -> Dict:
+    exploded = _explode_numeros(df)
+    tmp = exploded.copy()
+    tmp['fecha_sorteo'] = pd.to_datetime(tmp['fecha_sorteo'])
+    tmp['anio'] = tmp['fecha_sorteo'].dt.year
+    tmp['mes'] = tmp['fecha_sorteo'].dt.month
+    by_mes = tmp.groupby(['anio','mes','numero']).size().reset_index(name='freq')
+    gaps = {}
+    for n in range(1, TOTAL_NUMBERS+1):
+        apar = tmp.loc[tmp['numero']==n, 'fecha_sorteo'].sort_values().tolist()
+        if len(apar) >= 2:
+            difs = [(apar[i]-apar[i-1]).days for i in range(1, len(apar))]
+            gaps[n] = float(np.mean(difs))
+        else:
+            gaps[n] = None
+    ventanas = {}
+    for win in LAST_N_WINDOWS:
+        sub = _ultimos(df, win)
+        ventanas[str(win)] = analisis_frecuencias(sub)
+    return {'por_mes': by_mes.to_dict(orient='records'), 'gaps_prom_dias': gaps, 'ventanas': ventanas}
+
+def analisis_patrones(df: pd.DataFrame) -> Dict:
+    even, odd = 0, 0
+    ranges = {'1-9':0,'10-18':0,'19-27':0,'28-36':0,'37-45':0}
+    suma_vals = []
+    consecutivos = 0
+    distancias = []
+    primos_cnt = 0
+    total_combinaciones = 0
+    for _, r in df.iterrows():
+        arr = sorted(parse_numbers(r['numeros']))
+        total_combinaciones += 1
+        suma_vals.append(sum(arr))
+        for x in arr:
+            if x % 2 == 0: even += 1
+            else: odd += 1
+            if 1 <= x <= 9: ranges['1-9'] += 1
+            elif 10 <= x <= 18: ranges['10-18'] += 1
+            elif 19 <= x <= 27: ranges['19-27'] += 1
+            elif 28 <= x <= 36: ranges['28-36'] += 1
+            elif 37 <= x <= 45: ranges['37-45'] += 1
+            if is_prime(x): primos_cnt += 1
+        consecutivos += sum(1 for a,b in zip(arr, arr[1:]) if b==a+1)
+        distancias += [abs(b-a) for a,b in zip(arr, arr[1:])]
+    return {'pares': even, 'impares': odd, 'rangos': ranges,
+            'suma_min': int(min(suma_vals)) if suma_vals else None,
+            'suma_max': int(max(suma_vals)) if suma_vals else None,
+            'suma_prom': float(np.mean(suma_vals)) if suma_vals else None,
+            'consecutivos_total': int(consecutivos),
+            'dist_prom': float(np.mean(distancias)) if distancias else None,
+            'primos_total': int(primos_cnt),
+            'total_combinaciones': total_combinaciones}
+
+def analisis_coocurrencias(df: pd.DataFrame) -> Dict:
+    pairs = _coocurrencias(df, k=2)
+    trios = _coocurrencias(df, k=3)
+    top_pairs = pairs.most_common(20)
+    top_trios = trios.most_common(20)
+    return {'pairs_top20': [{'pair': list(k), 'freq': v} for k,v in top_pairs],
+            'trios_top20': [{'trio': list(k), 'freq': v} for k,v in top_trios]}
+
+def analisis_boliyapa(df: pd.DataFrame) -> Dict:
+    bol = df['boliyapa'].dropna().astype(int).tolist()
+    cnt = Counter(bol)
+    total = len(bol)
+    freq_rel = {k: (v/total if total else 0.0) for k,v in cnt.items()}
+    return {'freq_abs': dict(sorted(cnt.items())),
+            'freq_rel': dict(sorted(freq_rel.items()))}
+
+def analisis_chicuadrado(df: pd.DataFrame) -> Dict:
+    exploded = []
+    for _, r in df.iterrows():
+        exploded += parse_numbers(r['numeros'])
+    from collections import Counter
+    cnt = Counter(exploded)
+    import numpy as np
+    obs = np.array([cnt.get(i, 0) for i in range(1, TOTAL_NUMBERS+1)], dtype=float)
+    n = obs.sum()
+    if n == 0:
+        return {'chi2': None, 'p_value': None, 'expected': None}
+    expected = np.ones_like(obs) * (n / TOTAL_NUMBERS)
+    chi2 = ((obs - expected)**2 / expected).sum()
+    p_value = None
+    try:
+        from scipy.stats import chi2 as chi2_dist
+        dfree = TOTAL_NUMBERS - 1
+        p_value = float(1 - chi2_dist.cdf(chi2, dfree))
+    except Exception:
+        p_value = None
+    std_dev = float(np.std(obs))
+    return {'chi2': float(chi2), 'p_value': p_value, 'std_freq': std_dev, 'expected_each': float(n / TOTAL_NUMBERS)}
+
+def analisis_completo(df: pd.DataFrame) -> Dict:
+    return {'frecuencias': analisis_frecuencias(df),
+            'temporal': analisis_temporal(df),
+            'patrones': analisis_patrones(df),
+            'coocurrencias': analisis_coocurrencias(df),
+            'boliyapa': analisis_boliyapa(df),
+            'chi_cuadrado': analisis_chicuadrado(df)}

proyecto_tinka_ml/proyecto_tinka_ml/config.py

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+"""Configuración del sistema Tinka/Boliyapa."""
+from pathlib import Path
+
+# === BD (XAMPP por defecto) ===
+DB_CONFIG = {
+    "host": "localhost",
+    "port": 3306,
+    "database": "tinka_db",
+    "user": "root",
+    "password": ""
+}
+
+# Tabla con los resultados
+TABLE_NAME = "resultados"
+
+# === Carpetas ===
+BASE_DIR = Path(__file__).resolve().parent
+DATA_DIR = BASE_DIR / "data"
+LOGS_DIR = BASE_DIR / "logs"
+
+# Archivos de datos
+CACHE_FILE = DATA_DIR / "cache_sorteos.json"
+COMBOS_FILE = DATA_DIR / "combinaciones_generadas.json"
+
+# Parámetros generales
+TOTAL_NUMBERS = 45
+COMBINATION_SIZE = 6
+
+# Parámetros heurísticos
+SUM_RANGE = (90, 180)
+LAST_N_WINDOWS = [50, 100, 200]

proyecto_tinka_ml/proyecto_tinka_ml/data/cache_sorteos.json

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+[
+  {
+    "id_sorteo": 1206,
+    "fecha_sorteo": "2025-06-25",
+    "numeros": "38 30 08 27 48 14",
+    "boliyapa": 17,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:07"
+  },
+  {
+    "id_sorteo": 1207,
+    "fecha_sorteo": "2025-06-29",
+    "numeros": "47 37 32 22 24 23",
+    "boliyapa": 4,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:08"
+  },
+  {
+    "id_sorteo": 1208,
+    "fecha_sorteo": "2025-07-02",
+    "numeros": "14 28 32 07 10 29",
+    "boliyapa": 36,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:55:10"
+  },
+  {
+    "id_sorteo": 1209,
+    "fecha_sorteo": "2025-07-06",
+    "numeros": "29 20 14 11 03 15",
+    "boliyapa": 35,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:55:10"
+  },
+  {
+    "id_sorteo": 1210,
+    "fecha_sorteo": "2025-07-09",
+    "numeros": "35 28 09 42 31 16",
+    "boliyapa": 45,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:55:11"
+  },
+  {
+    "id_sorteo": 1211,
+    "fecha_sorteo": "2025-07-13",
+    "numeros": "09 32 43 49 38 24",
+    "boliyapa": 5,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:10"
+  },
+  {
+    "id_sorteo": 1212,
+    "fecha_sorteo": "2025-07-16",
+    "numeros": "36 22 37 11 07 01",
+    "boliyapa": 24,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:11"
+  },
+  {
+    "id_sorteo": 1213,
+    "fecha_sorteo": "2025-07-20",
+    "numeros": "42 45 28 06 19 05",
+    "boliyapa": 10,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:12"
+  },
+  {
+    "id_sorteo": 1214,
+    "fecha_sorteo": "2025-07-23",
+    "numeros": "22 13 24 43 02 01",
+    "boliyapa": 21,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:13"
+  },
+  {
+    "id_sorteo": 1215,
+    "fecha_sorteo": "2025-07-27",
+    "numeros": "36 24 04 21 34 09",
+    "boliyapa": 17,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:15"
+  },
+  {
+    "id_sorteo": 1216,
+    "fecha_sorteo": "2025-07-30",
+    "numeros": "43 28 03 09 01 25",
+    "boliyapa": 23,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:16"
+  },
+  {
+    "id_sorteo": 1217,
+    "fecha_sorteo": "2025-08-03",
+    "numeros": "45 41 02 14 23 46",
+    "boliyapa": 22,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:55:12"
+  },
+  {
+    "id_sorteo": 1218,
+    "fecha_sorteo": "2025-08-06",
+    "numeros": "38 23 06 21 33 36",
+    "boliyapa": 16,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:55:13"
+  },
+  {
+    "id_sorteo": 1219,
+    "fecha_sorteo": "2025-08-10",
+    "numeros": "24 46 29 19 25 47",
+    "boliyapa": 26,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:17"
+  },
+  {
+    "id_sorteo": 1220,
+    "fecha_sorteo": "2025-08-13",
+    "numeros": "36 07 43 32 20 33",
+    "boliyapa": 17,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:18"
+  },
+  {
+    "id_sorteo": 1221,
+    "fecha_sorteo": "2025-08-17",
+    "numeros": "34 11 39 07 26 27",
+    "boliyapa": 37,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:19"
+  },
+  {
+    "id_sorteo": 1222,
+    "fecha_sorteo": "2025-08-20",
+    "numeros": "28 37 09 29 06 49",
+    "boliyapa": 48,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:21"
+  },
+  {
+    "id_sorteo": 1223,
+    "fecha_sorteo": "2025-08-24",
+    "numeros": "12 37 16 08 22 17",
+    "boliyapa": 7,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:22"
+  },
+  {
+    "id_sorteo": 1224,
+    "fecha_sorteo": "2025-08-27",
+    "numeros": "40 19 13 46 30 36",
+    "boliyapa": 3,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:23"
+  },
+  {
+    "id_sorteo": 1225,
+    "fecha_sorteo": "2025-08-31",
+    "numeros": "30 35 32 12 38 04",
+    "boliyapa": 29,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:24"
+  },
+  {
+    "id_sorteo": 1226,
+    "fecha_sorteo": "2025-09-03",
+    "numeros": "23 21 15 14 48 18",
+    "boliyapa": 10,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:55:14"
+  },
+  {
+    "id_sorteo": 1227,
+    "fecha_sorteo": "2025-09-07",
+    "numeros": "29 15 07 33 17 23",
+    "boliyapa": 8,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:55:15"
+  },
+  {
+    "id_sorteo": 1228,
+    "fecha_sorteo": "2025-09-10",
+    "numeros": "01 16 06 45 33 38",
+    "boliyapa": 27,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:26"
+  },
+  {
+    "id_sorteo": 1229,
+    "fecha_sorteo": "2025-09-14",
+    "numeros": "35 06 12 33 38 02",
+    "boliyapa": 14,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:27"
+  },
+  {
+    "id_sorteo": 1230,
+    "fecha_sorteo": "2025-09-17",
+    "numeros": "39 44 19 34 24 14",
+    "boliyapa": 47,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:28"
+  },
+  {
+    "id_sorteo": 1231,
+    "fecha_sorteo": "2025-09-21",
+    "numeros": "46 44 02 28 20 32",
+    "boliyapa": 39,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:29"
+  },
+  {
+    "id_sorteo": 1232,
+    "fecha_sorteo": "2025-09-24",
+    "numeros": "21 39 43 46 31 24",
+    "boliyapa": 41,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:31"
+  },
+  {
+    "id_sorteo": 1233,
+    "fecha_sorteo": "2025-09-28",
+    "numeros": "24 17 31 09 35 44",
+    "boliyapa": 33,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:38:32"
+  },
+  {
+    "id_sorteo": 1234,
+    "fecha_sorteo": "2025-10-01",
+    "numeros": "13 36 37 41 14 10",
+    "boliyapa": 1,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:55:16"
+  },
+  {
+    "id_sorteo": 1235,
+    "fecha_sorteo": "2025-10-05",
+    "numeros": "02 31 06 14 45 23",
+    "boliyapa": 5,
+    "jackpot": 0.0,
+    "created_at": "2025-10-08 14:55:17"
+  }
+]

proyecto_tinka_ml/proyecto_tinka_ml/data/combinaciones_generadas.json

@@ -0,0 +1,19 @@
+[
+  {
+    "estrategia": "auto_ml",
+    "n": 1,
+    "ranked": [
+      {
+        "combo": [
+          2,
+          14,
+          23,
+          24,
+          33,
+          37
+        ],
+        "score": -107.88746739789008
+      }
+    ]
+  }
+]

proyecto_tinka_ml/proyecto_tinka_ml/data/probabilidades.json

@@ -0,0 +1,503 @@
+{
+  "global": {
+    "1": {
+      "alpha": 8.0,
+      "beta": 52.0,
+      "p": 0.13333333333333333
+    },
+    "2": {
+      "alpha": 9.0,
+      "beta": 51.0,
+      "p": 0.15
+    },
+    "3": {
+      "alpha": 6.0,
+      "beta": 54.0,
+      "p": 0.1
+    },
+    "4": {
+      "alpha": 6.0,
+      "beta": 54.0,
+      "p": 0.1
+    },
+    "5": {
+      "alpha": 5.0,
+      "beta": 55.0,
+      "p": 0.08333333333333333
+    },
+    "6": {
+      "alpha": 10.0,
+      "beta": 50.0,
+      "p": 0.16666666666666666
+    },
+    "7": {
+      "alpha": 9.0,
+      "beta": 51.0,
+      "p": 0.15
+    },
+    "8": {
+      "alpha": 6.0,
+      "beta": 54.0,
+      "p": 0.1
+    },
+    "9": {
+      "alpha": 10.0,
+      "beta": 50.0,
+      "p": 0.16666666666666666
+    },
+    "10": {
+      "alpha": 6.0,
+      "beta": 54.0,
+      "p": 0.1
+    },
+    "11": {
+      "alpha": 7.0,
+      "beta": 53.0,
+      "p": 0.11666666666666667
+    },
+    "12": {
+      "alpha": 7.0,
+      "beta": 53.0,
+      "p": 0.11666666666666667
+    },
+    "13": {
+      "alpha": 7.0,
+      "beta": 53.0,
+      "p": 0.11666666666666667
+    },
+    "14": {
+      "alpha": 12.0,
+      "beta": 48.0,
+      "p": 0.2
+    },
+    "15": {
+      "alpha": 7.0,
+      "beta": 53.0,
+      "p": 0.11666666666666667
+    },
+    "16": {
+      "alpha": 7.0,
+      "beta": 53.0,
+      "p": 0.11666666666666667
+    },
+    "17": {
+      "alpha": 7.0,
+      "beta": 53.0,
+      "p": 0.11666666666666667
+    },
+    "18": {
+      "alpha": 5.0,
+      "beta": 55.0,
+      "p": 0.08333333333333333
+    },
+    "19": {
+      "alpha": 8.0,
+      "beta": 52.0,
+      "p": 0.13333333333333333
+    },
+    "20": {
+      "alpha": 7.0,
+      "beta": 53.0,
+      "p": 0.11666666666666667
+    },
+    "21": {
+      "alpha": 8.0,
+      "beta": 52.0,
+      "p": 0.13333333333333333
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proyecto_tinka_ml/proyecto_tinka_ml/data/reporte.html

@@ -0,0 +1,76 @@
+<!DOCTYPE html>
+<html lang="es">
+<head>
+<meta charset="utf-8"/>
+<title>Reporte Tinka</title>
+<style>
+body { font-family: Arial, Helvetica, sans-serif; margin: 20px; }
+h1, h2 { margin: 0.4em 0; }
+pre { background: #f6f8fa; padding: 12px; overflow: auto; }
+.small { color:#555; font-size: 0.9em; }
+</style>
+</head>
+<body>
+<h1>Reporte de Análisis — Tinka</h1>
+<p class="small">Este reporte se genera a partir de los resultados históricos presentes en tu base de datos.</p>
+
+<h2>Frecuencias</h2>
+<img 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" alt="Frecuencias"/>
+<pre>01: ██████���██████████················· 4
+02: █████████████████████············· 5
+03: ████████·························· 2
+04: ████████·························· 2
+05: ████······························ 1
+06: █████████████████████████········· 6
+07: █████████████████████············· 5
+08: ████████·························· 2
+09: █████████████████████████········· 6
+10: ████████·························· 2
+11: ████████████······················ 3
+12: ████████████······················ 3
+13: ████████████······················ 3
+14: ██████████████████████████████████ 8
+15: ████████████······················ 3
+16: ████████████······················ 3
+17: ████████████······················ 3
+18: ████······························ 1
+19: █████████████████················· 4
+20: ████████████······················ 3
+21: █████████████████················· 4
+22: █████████████████················· 4
+23: █████████████████████████········· 6
+24: ██████████████████████████████████ 8
+25: ████████·························· 2
+26: ████······························ 1
+27: ████████·························· 2
+28: █████████████████████████········· 6
+29: █████████████████████············· 5
+30: ████████████······················ 3
+31: █████████████████················· 4
+32: █████████████████████████········· 6
+33: █████████████████████············· 5
+34: ████████████······················ 3
+35: █████████████████················· 4
+36: █████████████████████████········· 6
+37: █████████████████████············· 5
+38: █████████████████████████········· 6
+39: ████████████······················ 3
+40: ████······························ 1
+41: ████████·························· 2
+42: ████████·························· 2
+43: █████████████████████············· 5
+44: ████████████······················ 3
+45: █████████████████················· 4
+46: █████████████████████············· 5
+47: ████████·························· 2
+48: ████████·························· 2
+49: ████████·························· 2</pre>
+
+<h2>Chi-cuadrado</h2>
+<p>Chi2 = 37.89349112426035 &nbsp;&nbsp; p-value = 0.7296561046478667</p>
+<p class="small">Si p-value es muy pequeño (&lt; 0.05), indica desviación respecto a uniformidad; recuerda que los sorteos son procesos aleatorios.</p>
+
+<h2>Disclaimer</h2>
+<p class="small">Este sistema es informativo y educativo. La lotería es aleatoria. Juega con responsabilidad.</p>
+</body>
+</html>

proyecto_tinka_ml/proyecto_tinka_ml/db_connector.py

@@ -0,0 +1,69 @@
+"""Conexión y consultas a MySQL (XAMPP) con caché local robusto."""
+from __future__ import annotations
+from pathlib import Path
+from urllib.parse import quote_plus
+import pandas as pd
+from sqlalchemy import create_engine
+
+from config import DB_CONFIG, TABLE_NAME, CACHE_FILE
+from utils import load_json, save_json
+
+SQL_BASE = f"""SELECT id_sorteo, fecha_sorteo, numeros, boliyapa, jackpot,
+       COALESCE(created_at, NOW()) AS created_at
+FROM {TABLE_NAME}
+WHERE numeros IS NOT NULL AND numeros <> ''
+ORDER BY fecha_sorteo ASC, id_sorteo ASC
+"""
+
+def get_engine():
+    user = DB_CONFIG.get("user", "")
+    pwd = quote_plus(DB_CONFIG.get("password", ""))
+    host = DB_CONFIG.get("host", "localhost")
+    port = DB_CONFIG.get("port", 3306)
+    db   = DB_CONFIG.get("database", "")
+    url = f"mysql+mysqlconnector://{user}:{pwd}@{host}:{port}/{db}"
+    return create_engine(url, pool_pre_ping=True, pool_recycle=1800)
+
+def fetch_from_db() -> pd.DataFrame:
+    eng = get_engine()
+    try:
+        df = pd.read_sql_query(SQL_BASE, eng)
+        return df
+    finally:
+        eng.dispose()
+
+def load_cached():
+    data = load_json(CACHE_FILE, default=None)
+    if not data: return None
+    try:
+        df = pd.DataFrame(data)
+        if "fecha_sorteo" in df.columns:
+            df["fecha_sorteo"] = pd.to_datetime(df["fecha_sorteo"]).dt.date
+        return df
+    except Exception:
+        return None
+
+def save_cache(df: pd.DataFrame) -> None:
+    df = df.copy()
+    for col in ("fecha_sorteo", "created_at"):
+        if col in df.columns:
+            df[col] = df[col].astype(str)
+    save_json(CACHE_FILE, df.to_dict(orient="records"))
+
+def get_data(use_cache: bool = True):
+    if use_cache:
+        cached = load_cached()
+        if cached is not None and len(cached) > 0:
+            return cached.copy()
+    df = fetch_from_db()
+    if "fecha_sorteo" in df.columns:
+        df["fecha_sorteo"] = pd.to_datetime(df["fecha_sorteo"]).dt.date
+    save_cache(df)
+    return df
+
+def refresh_cache():
+    df = fetch_from_db()
+    if "fecha_sorteo" in df.columns:
+        df["fecha_sorteo"] = pd.to_datetime(df["fecha_sorteo"]).dt.date
+    save_cache(df)
+    return df

proyecto_tinka_ml/proyecto_tinka_ml/generador.py

@@ -0,0 +1,165 @@
+"""Generación y evaluación de combinaciones a partir de las estadísticas."""
+from __future__ import annotations
+from typing import List, Dict, Tuple
+import random
+import numpy as np
+
+from config import TOTAL_NUMBERS, COMBINATION_SIZE, SUM_RANGE
+
+def _validate_combo(combo: List[int]) -> bool:
+    if len(combo) != COMBINATION_SIZE: return False
+    if len(set(combo)) != COMBINATION_SIZE: return False
+    if not all(1 <= x <= TOTAL_NUMBERS for x in combo): return False
+    return True
+
+def _meets_heuristics(combo: List[int]) -> bool:
+    s = sum(combo)
+    if not (SUM_RANGE[0] <= s <= SUM_RANGE[1]):
+        return False
+    ev = sum(1 for x in combo if x % 2 == 0)
+    od = COMBINATION_SIZE - ev
+    if abs(ev - od) > 2:
+        return False
+    return True
+
+def _weighted_choice(population: List[int], weights: List[float], k: int) -> List[int]:
+    arr = np.array(population)
+    w = np.array(weights, dtype=float)
+    if w.sum() <= 0:
+        w = np.ones_like(w) / len(w)
+    else:
+        w = w / w.sum()
+    picks = set()
+    for _ in range(2000):
+        x = np.random.choice(arr, p=w)
+        picks.add(int(x))
+        if len(picks) == k:
+            break
+    if len(picks) < k:
+        rest = [p for p in population if p not in picks]
+        random.shuffle(rest)
+        picks.update(rest[:(k-len(picks))])
+    return sorted(picks)
+
+def _rango_bucket(n: int) -> int:
+    if   1 <= n <= 9:   return 0
+    elif 10 <= n <= 18: return 1
+    elif 19 <= n <= 27: return 2
+    elif 28 <= n <= 36: return 3
+    elif 37 <= n <= 45: return 4
+    return 4
+
+def _consecutivos(combo: List[int]) -> int:
+    arr = sorted(combo)
+    return sum(1 for a, b in zip(arr, arr[1:]) if b == a + 1)
+
+# Estrategias clásicas
+def estrategia_frecuencia_pura(freq_abs: Dict[int,int], n_combos: int = 10) -> List[List[int]]:
+    top = sorted(freq_abs.items(), key=lambda x: (-x[1], x[0]))
+    top_pool = [k for k,_ in top[:min(25, len(top))]]
+    combos, tries = [], 0
+    while len(combos) < n_combos and tries < 8000:
+        c = sorted(random.sample(top_pool, COMBINATION_SIZE))
+        if _validate_combo(c) and _meets_heuristics(c) and c not in combos:
+            combos.append(c)
+        tries += 1
+    return combos
+
+def estrategia_equilibrio_hot_cold(freq_abs: Dict[int,int], hot_15: List[int], cold_15: List[int], n_combos: int = 10) -> List[List[int]]:
+    combos, tries = [], 0
+    pool_cold = cold_15[:]
+    pool_hot  = hot_15[:]
+    if len(pool_hot) < 3 or len(pool_cold) < 3:
+        return estrategia_frecuencia_pura(freq_abs, n_combos)
+    while len(combos) < n_combos and tries < 8000:
+        c = sorted(random.sample(pool_hot, 3) + random.sample(pool_cold, 3))
+        if _validate_combo(c) and _meets_heuristics(c) and c not in combos:
+            combos.append(c)
+        tries += 1
+    return combos
+
+def estrategia_temporal_inteligente(freq_last: Dict[int,int], hot_cycle: List[int], n_combos: int = 10) -> List[List[int]]:
+    keys = list(range(1, TOTAL_NUMBERS+1))
+    weights = [freq_last.get(k, 0) + (1.5 if k in hot_cycle else 0.0) + 0.01 for k in keys]
+    combos, tries = [], 0
+    while len(combos) < n_combos and tries < 10000:
+        c = _weighted_choice(keys, weights, COMBINATION_SIZE)
+        c = sorted(c)
+        if _validate_combo(c) and _meets_heuristics(c) and c not in combos:
+            combos.append(c)
+        tries += 1
+    return combos
+
+def estrategia_patrones_detectados(pairs_top: List[Dict], trios_top: List[Dict], n_combos: int = 10) -> List[List[int]]:
+    combos, tries = [], 0
+    pairs = [tuple(p['pair']) for p in pairs_top]
+    trios = [tuple(t['trio']) for t in trios_top]
+    while len(combos) < n_combos and tries < 12000:
+        base = set()
+        if trios and random.random() < 0.6:
+            base.update(random.choice(trios))
+        guard = 0
+        while len(base) < 4 and pairs and guard < 10:
+            cand = random.choice(pairs)
+            if not (set(cand) & base):
+                base.update(cand)
+            guard += 1
+        rest = [x for x in range(1, TOTAL_NUMBERS+1) if x not in base]
+        random.shuffle(rest)
+        while len(base) < 6 and rest:
+            base.add(rest.pop())
+        c = sorted(list(base))[:6]
+        if _validate_combo(c) and _meets_heuristics(c) and c not in combos:
+            combos.append(c)
+        tries += 1
+    return combos
+
+def estrategia_random_ponderado(freq_abs: Dict[int,int], n_combos: int = 10) -> List[List[int]]:
+    keys = list(range(1, TOTAL_NUMBERS+1))
+    raw = [freq_abs.get(k, 0) + 0.01 for k in keys]
+    combos, tries = [], 0
+    while len(combos) < n_combos and tries < 8000:
+        c = _weighted_choice(keys, raw, COMBINATION_SIZE)
+        c = sorted(c)
+        if _validate_combo(c) and _meets_heuristics(c) and c not in combos:
+            combos.append(c)
+        tries += 1
+    return combos
+
+# Scoring ML
+def score_combo_ml(combo: List[int], probs: Dict[int, float]) -> float:
+    eps = 1e-9
+    ll = sum(np.log(max(probs.get(x, eps), eps)) for x in combo) * 10.0
+    suma = sum(combo)
+    penalty_sum = abs(135 - suma) * 1.0
+    ev = sum(1 for x in combo if x % 2 == 0)
+    od = COMBINATION_SIZE - ev
+    penalty_parity = abs(ev - od) * 2.0
+    consec = _consecutivos(combo)
+    penalty_consec = max(0, consec - 1) * 3.0
+    buckets = [0,0,0,0,0]
+    for x in combo:
+        if   1 <= x <= 9:   buckets[0]+=1
+        elif 10 <= x <= 18: buckets[1]+=1
+        elif 19 <= x <= 27: buckets[2]+=1
+        elif 28 <= x <= 36: buckets[3]+=1
+        elif 37 <= x <= 45: buckets[4]+=1
+    cobertura = sum(1 for b in buckets if b > 0)
+    penalty_bucket = 0.0
+    if max(buckets) > 3:
+        penalty_bucket += (max(buckets) - 3) * 2.0
+    if cobertura < 3:
+        penalty_bucket += (3 - cobertura) * 2.0
+    return float(ll - penalty_sum - penalty_parity - penalty_consec - penalty_bucket)
+
+def rankear_combos_ml(combos: List[List[int]], probs: Dict[int, float]):
+    scored = []
+    seen = set()
+    for c in combos:
+        t = tuple(sorted(c))
+        if t in seen: 
+            continue
+        seen.add(t)
+        scored.append((list(t), score_combo_ml(list(t), probs)))
+    scored.sort(key=lambda x: x[1], reverse=True)
+    return scored

proyecto_tinka_ml/proyecto_tinka_ml/main.py

@@ -0,0 +1,250 @@
+"""Interfaz de consola (menú) para el sistema Tinka con opción ML."""
+from __future__ import annotations
+from pathlib import Path
+import pandas as pd
+
+from config import DATA_DIR, COMBOS_FILE
+from db_connector import get_data, refresh_cache
+from analizador import analisis_completo, analisis_frecuencias, analisis_temporal
+from generador import (
+    estrategia_frecuencia_pura,
+    estrategia_equilibrio_hot_cold,
+    estrategia_temporal_inteligente,
+    estrategia_patrones_detectados,
+    estrategia_random_ponderado,
+    rankear_combos_ml,
+)
+from utils import parse_numbers, save_json, load_json
+from visualizador import render_ascii_hist, html_report
+from ml import (
+    beta_binomial_posteriors,
+    beta_binomial_posteriors_ewma,
+    blend_probabilities,
+    save_probabilities,
+    thompson_sampling_pool,
+)
+
+def pause():
+    input("\nPresiona ENTER para continuar...")
+
+def _int_input_default(prompt: str, default: int) -> int:
+    s = input(prompt).strip()
+    if not s:
+        return default
+    try:
+        v = int(s)
+        if v <= 0: return default
+        return v
+    except Exception:
+        return default
+
+def show_dashboard(df: pd.DataFrame):
+    stats = analisis_completo(df)
+    print("\n=== DASHBOARD RÁPIDO ===\n")
+    frec = stats['frecuencias']
+    print("Top 15 calientes:", frec['hot_15'])
+    print("Top 15 fríos   :", frec['cold_15'])
+    print("En racha (ult10):", frec['en_racha_ult10'])
+    print("Dormidos (>20)  :", frec['dormidos_mas20'])
+    print("\nHistograma (ASCII):\n")
+    print(render_ascii_hist(frec['freq_abs']))
+    print("\nChi-cuadrado:", stats['chi_cuadrado'])
+    out_html = DATA_DIR / "reporte.html"
+    out_html.write_text(html_report(stats), encoding="utf-8")
+    print(f"\nReporte HTML exportado en: {out_html}")
+
+def analisis_por_numero(df: pd.DataFrame):
+    try:
+        n = int(input("Número (1-45): ").strip())
+    except Exception:
+        print("Entrada inválida"); return
+    if not (1 <= n <= 45):
+        print("Fuera de rango"); return
+    frec = analisis_frecuencias(df)
+    fa = frec['freq_abs'].get(n, 0)
+    fr = frec['freq_rel'].get(n, 0.0)
+    ult10 = n in frec['en_racha_ult10']
+    dorm = n in frec['dormidos_mas20']
+    print(f"\nNúmero {n}: freq_abs={fa}, freq_rel={fr:.4f}, en_racha_ult10={ult10}, dormido>20={dorm}\n")
+
+def _imprimir_combos(combos):
+    for i, c in enumerate(combos, 1):
+        print(f"{i:02d})", " ".join(f"{x:02d}" for x in c))
+
+def generar_combinaciones(df: pd.DataFrame):
+    stats = analisis_completo(df)
+    frec = stats['frecuencias']
+    cooc = stats['coocurrencias']
+    temp = analisis_temporal(df)
+    last50 = temp['ventanas']['50']
+
+    print("\nEstrategias:")
+    print(" 1) Frecuencia pura")
+    print(" 2) Equilibrio caliente-frío")
+    print(" 3) Temporal inteligente (últimos 50)")
+    print(" 4) Patrones detectados (pares/tríos)")
+    print(" 5) Random ponderado")
+    op = input("Elige 1-5: ").strip()
+
+    n = _int_input_default("¿Cuántas combinaciones quieres generar? [10 por defecto]: ", 10)
+
+    combos = []
+    if op == "1":
+        combos = estrategia_frecuencia_pura(frec['freq_abs'], n_combos=n)
+        etiqueta = "frecuencia_pura"
+    elif op == "2":
+        combos = estrategia_equilibrio_hot_cold(frec['freq_abs'], frec['hot_15'], frec['cold_15'], n_combos=n)
+        etiqueta = "equilibrio_hot_cold"
+    elif op == "3":
+        combos = estrategia_temporal_inteligente(last50['freq_abs'], last50['hot_15'], n_combos=n)
+        etiqueta = "temporal_inteligente_50"
+    elif op == "4":
+        combos = estrategia_patrones_detectados(cooc['pairs_top20'], cooc['trios_top20'], n_combos=n)
+        etiqueta = "patrones_detectados"
+    elif op == "5":
+        combos = estrategia_random_ponderado(frec['freq_abs'], n_combos=n)
+        etiqueta = "random_ponderado"
+    else:
+        print("Opción no válida"); return
+
+    if not combos:
+        print("\nNo se pudieron generar combinaciones."); return
+
+    print("\n=== Combinaciones sugeridas ===\n")
+    _imprimir_combos(combos)
+
+    record = load_json(COMBOS_FILE, default=[])
+    record.append({"estrategia": etiqueta, "n": n, "combos": combos})
+    save_json(COMBOS_FILE, record)
+    print(f"\nGuardado en {COMBOS_FILE}")
+
+def comparar_mi_combinacion(df: pd.DataFrame):
+    s = input("Ingresa tus 6 números separados por espacio: ").strip()
+    arr = sorted(parse_numbers(s))
+    if len(arr) != 6 or min(arr) < 1 or max(arr) > 45 or len(set(arr)) != 6:
+        print("Entrada inválida"); return
+    stats = analisis_completo(df)
+    frec = stats['frecuencias']
+    hot = set(frec['hot_15'])
+    cold = set(frec['cold_15'])
+    score = sum(frec['freq_abs'].get(x,0) for x in arr)
+    suma = sum(arr)
+    ev = sum(1 for x in arr if x % 2 == 0)
+    od = 6 - ev
+    in_hot = [x for x in arr if x in hot]
+    in_cold = [x for x in arr if x in cold]
+    print("\n=== Evaluación ===")
+    print("Tu combinación :", " ".join(f"{x:02d}" for x in arr))
+    print("Suma           :", suma, "(recomendado 90-180)")
+    print("Pares/Impares  :", ev, "/", od)
+    print("En HOT         :", in_hot)
+    print("En COLD        :", in_cold)
+    print("Puntuación (sum freq_abs):", score)
+
+def ver_mejores_historicas(df: pd.DataFrame):
+    frec = analisis_frecuencias(df)['freq_abs']
+    rows = []
+    for _, r in df.iterrows():
+        arr = sorted(parse_numbers(r['numeros']))
+        s = sum(arr)
+        score = sum(frec.get(x,0) for x in arr)
+        penal = abs(135 - s)
+        rows.append({"id_sorteo": int(r['id_sorteo']), "fecha": str(r['fecha_sorteo']),
+                     "combo": " ".join(f"{x:02d}" for x in arr),
+                     "suma": s, "score": score - penal})
+    top = sorted(rows, key=lambda x: x['score'], reverse=True)[:10]
+    print("\n=== Top 10 históricas por score heurístico ===\n")
+    for i, row in enumerate(top, 1):
+        print(f"{i:02d}) {row['fecha']}  id={row['id_sorteo']}  {row['combo']}  score={row['score']:.2f}")
+
+def exportar_analisis(df: pd.DataFrame):
+    html = html_report(analisis_completo(df))
+    out = DATA_DIR / "reporte.html"
+    out.write_text(html, encoding="utf-8")
+    print(f"Reporte exportado en {out}")
+
+def actualizar_cache():
+    df = refresh_cache()
+    print(f"Cache actualizado. Registros: {len(df)}")
+
+# -- Opción 8: Recomendación automática (ML)
+def recomendacion_ml_menu(df: pd.DataFrame):
+    print("\nCalculando probabilidades bayesianas (global + reciente)...")
+    posts_global = beta_binomial_posteriors(df, prior_strength=30.0, p0=(6.0/45.0))
+    posts_recent = beta_binomial_posteriors_ewma(df, halflife_draws=50, prior_strength=15.0, p0=(6.0/45.0))
+    probs_blend = blend_probabilities(posts_global, posts_recent, w_recent=0.30)
+    save_probabilities(posts_global, posts_recent, probs_blend)
+
+    n = _int_input_default("¿Cuántas recomendaciones quieres? [1 por defecto]: ", 1)
+
+    stats = analisis_completo(df)
+    frec = stats['frecuencias']
+    cooc = stats['coocurrencias']
+    temp = analisis_temporal(df)
+    last50 = temp['ventanas']['50']
+
+    pool = []
+    N = max(10, n * 12)
+    pool += estrategia_equilibrio_hot_cold(frec['freq_abs'], frec['hot_15'], frec['cold_15'], n_combos=N)
+    pool += estrategia_temporal_inteligente(last50['freq_abs'], last50['hot_15'], n_combos=N)
+    pool += estrategia_random_ponderado(frec['freq_abs'], n_combos=N // 2)
+    pool += estrategia_patrones_detectados(cooc['pairs_top20'], cooc['trios_top20'], n_combos=max(5, n*6))
+
+    # Thompson Sampling extra (con posts recientes para mayor reactividad)
+    from ml import thompson_sampling_pool
+    pool += thompson_sampling_pool(posts_recent, n_combos=max(10, n*10), k=6)
+
+    if not pool:
+        print("No se pudieron generar candidatas. Actualiza la base y reintenta."); return
+
+    ranked = rankear_combos_ml(pool, probs_blend)
+    topn = ranked[:n]
+
+    print("\n=== Recomendación automática (ML) ===\n")
+    for i, (combo, score) in enumerate(topn, 1):
+        print(f"{i:02d})", " ".join(f"{x:02d}" for x in combo), f"   score={score:.2f}")
+
+    record = load_json(COMBOS_FILE, default=[])
+    record.append({"estrategia": "auto_ml", "n": n,
+                   "ranked": [{"combo": c, "score": float(s)} for (c, s) in topn]})
+    save_json(COMBOS_FILE, record)
+    print(f"\nGuardado en {COMBOS_FILE}")
+
+def main():
+    print("""====================================================
+   SISTEMA DE ANÁLISIS Y PREDICCIÓN — TINKA
+   (Educativo/Estadístico) — by tu asesor
+====================================================
+AVISO: La lotería es aleatoria. Este sistema NO garantiza aciertos.
+Usa la información con responsabilidad.
+""" )
+    df = get_data(use_cache=True)
+    if len(df)==0:
+        print("No hay datos en la base. Ejecuta tu scraper primero.")
+        return
+    while True:
+        print("""Menú:
+ 1) Ver dashboard completo de estadísticas
+ 2) Análisis específico por número
+ 3) Generar combinaciones (elegir estrategia y cantidad)
+ 4) Comparar mi combinación vs estadísticas
+ 5) Ver mejores combinaciones históricas
+ 6) Exportar análisis a HTML
+ 7) Actualizar datos desde BD (refresh cache)
+ 8) Recomendación automática (ML)
+ 0) Salir
+""" )
+        op = input("Elige opción: ").strip()
+        if op == "1": show_dashboard(df); pause()
+        elif op == "2": analisis_por_numero(df); pause()
+        elif op == "3": generar_combinaciones(df); pause()
+        elif op == "4": comparar_mi_combinacion(df); pause()
+        elif op == "5": ver_mejores_historicas(df); pause()
+        elif op == "6": exportar_analisis(df); pause()
+        elif op == "7": df = refresh_cache(); print("Datos recargados."); pause()
+        elif op == "8": recomendacion_ml_menu(df); pause()
+        elif op == "0": print("¡Hasta luego!"); break
+        else: print("Opción inválida")
+
+if __name__ == "__main__":
+    main()

proyecto_tinka_ml/proyecto_tinka_ml/ml.py

@@ -0,0 +1,100 @@
+"""ML ligero: Beta-Binomial, EWMA y Thompson Sampling."""
+from __future__ import annotations
+from typing import Dict, List, Tuple
+from collections import defaultdict
+import numpy as np
+import pandas as pd
+from config import DATA_DIR
+from utils import parse_numbers, save_json, load_json
+
+PROBS_FILE = DATA_DIR / "probabilidades.json"
+
+def _counts_from_df(df: pd.DataFrame) -> Dict[int, int]:
+    cnt = defaultdict(int)
+    for _, r in df.iterrows():
+        for n in parse_numbers(r['numeros']):
+            cnt[int(n)] += 1
+    return dict(cnt)
+
+def _counts_ewma(df: pd.DataFrame, halflife_draws: int = 50) -> Dict[int, float]:
+    if len(df) == 0:
+        return {i: 0.0 for i in range(1, 46)}
+    gamma = 0.5 ** (1.0 / max(1, halflife_draws))
+    w = 1.0
+    cnt = {i: 0.0 for i in range(1, 46)}
+    total_weight = 0.0
+    for _, r in df.iloc[::-1].iterrows():
+        nums = set(parse_numbers(r['numeros']))
+        for i in range(1, 46):
+            if i in nums:
+                cnt[i] += w
+        total_weight += w
+        w *= gamma
+    cnt['__total_weight__'] = total_weight
+    return cnt
+
+def beta_binomial_posteriors(df: pd.DataFrame, prior_strength: float = 30.0, p0: float = 6.0/45.0):
+    s = _counts_from_df(df)
+    D = float(len(df))
+    a0 = prior_strength * p0
+    b0 = prior_strength * (1.0 - p0)
+    posts = {}
+    for i in range(1, 46):
+        successes = float(s.get(i, 0))
+        failures = max(0.0, D - successes)
+        alpha = a0 + successes
+        beta  = b0 + failures
+        posts[i] = {"alpha": alpha, "beta": beta, "p": alpha/(alpha+beta)}
+    return posts
+
+def beta_binomial_posteriors_ewma(df: pd.DataFrame, halflife_draws: int = 50, prior_strength: float = 15.0, p0: float = 6.0/45.0):
+    ew = _counts_ewma(df, halflife_draws=halflife_draws)
+    total_w = float(ew.get('__total_weight__', 0.0))
+    a0 = prior_strength * p0
+    b0 = prior_strength * (1.0 - p0)
+    posts = {}
+    for i in range(1, 46):
+        successes = float(ew.get(i, 0.0))
+        failures = max(0.0, total_w - successes)
+        alpha = a0 + successes
+        beta  = b0 + failures
+        posts[i] = {"alpha": alpha, "beta": beta, "p": alpha/(alpha+beta)}
+    return posts
+
+def blend_probabilities(global_posts, recent_posts, w_recent: float = 0.30):
+    w_recent = max(0.0, min(1.0, w_recent))
+    out = {}
+    for i in range(1, 46):
+        pg = float(global_posts[i]["p"])
+        pr = float(recent_posts[i]["p"])
+        out[i] = (1.0 - w_recent) * pg + w_recent * pr
+    return out
+
+def save_probabilities(probs_global, probs_recent, probs_blend):
+    data = {"global": probs_global, "recent": probs_recent, "blend": probs_blend}
+    save_json(PROBS_FILE, data)
+
+def load_probabilities():
+    return load_json(PROBS_FILE, default=None)
+
+def thompson_sampling_combination(posts, k: int = 6):
+    draws = []
+    for i in range(1, 46):
+        a = float(posts[i]['alpha']); b = float(posts[i]['beta'])
+        a = max(a, 1e-3); b = max(b, 1e-3)
+        theta = np.random.beta(a, b)
+        draws.append((i, float(theta)))
+    draws.sort(key=lambda x: x[1], reverse=True)
+    return sorted([i for (i, t) in draws[:k]])
+
+def thompson_sampling_pool(posts, n_combos: int = 10, k: int = 6):
+    combos = []
+    seen = set()
+    tries = 0
+    while len(combos) < n_combos and tries < n_combos * 200:
+        c = tuple(thompson_sampling_combination(posts, k=k))
+        if c not in seen:
+            combos.append(list(c))
+            seen.add(c)
+        tries += 1
+    return combos

proyecto_tinka_ml/proyecto_tinka_ml/requirements.txt

@@ -0,0 +1,6 @@
+pandas
+numpy
+mysql-connector-python
+matplotlib
+scipy
+SQLAlchemy

proyecto_tinka_ml/proyecto_tinka_ml/utils.py

@@ -0,0 +1,54 @@
+"""Funciones auxiliares y utilidades comunes."""
+from __future__ import annotations
+from typing import List
+from pathlib import Path
+from math import sqrt
+import json, os
+
+def ensure_dirs(path: Path) -> None:
+    Path(path).parent.mkdir(parents=True, exist_ok=True)
+
+def parse_numbers(s: str) -> List[int]:
+    nums = []
+    for token in s.replace(',', ' ').split():
+        token = token.strip()
+        if token.isdigit():
+            nums.append(int(token))
+    return nums
+
+def is_prime(n: int) -> bool:
+    if n < 2: return False
+    if n in (2,3): return True
+    if n % 2 == 0: return False
+    r = int(sqrt(n)); f = 3
+    while f <= r:
+        if n % f == 0: return False
+        f += 2
+    return True
+
+def ascii_bar(label: str, value: int, max_value: int, width: int = 40) -> str:
+    if max_value <= 0:
+        bar = ""
+    else:
+        fill = int((value / max_value) * width)
+        bar = "█" * fill + "·" * (width - fill)
+    return f"{label:>2}: {bar} {value}"
+
+def save_json(path: Path, data) -> None:
+    ensure_dirs(path)
+    tmp = str(path) + ".tmp"
+    with open(tmp, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2, default=str)
+    os.replace(tmp, path)
+
+def load_json(path: Path, default=None):
+    p = Path(path)
+    if not p.exists():
+        return default
+    try:
+        txt = p.read_text(encoding="utf-8").strip()
+        if not txt:
+            return default
+        return json.loads(txt)
+    except Exception:
+        return default

proyecto_tinka_ml/proyecto_tinka_ml/visualizador.py

@@ -0,0 +1,63 @@
+"""Visualizaciones básicas: ASCII y PNG/HTML."""
+from __future__ import annotations
+from typing import Dict
+import base64
+from io import BytesIO
+import matplotlib.pyplot as plt
+from utils import ascii_bar
+
+def render_ascii_hist(freq_abs: Dict[int,int]) -> str:
+    maxv = max(freq_abs.values()) if freq_abs else 0
+    lines = []
+    for n, v in sorted(freq_abs.items()):
+        lines.append(ascii_bar(f"{n:02d}", v, maxv, width=34))
+    return "\n".join(lines)
+
+def plot_freq_png(freq_abs: Dict[int,int]) -> bytes:
+    xs = list(sorted(freq_abs.keys()))
+    ys = [freq_abs[k] for k in xs]
+    fig, ax = plt.subplots(figsize=(10,4))
+    ax.bar(xs, ys)
+    ax.set_title("Frecuencia absoluta de números (1-45)")
+    ax.set_xlabel("Número")
+    ax.set_ylabel("Frecuencia")
+    buf = BytesIO()
+    fig.tight_layout()
+    fig.savefig(buf, format="png", dpi=150)
+    plt.close(fig)
+    return buf.getvalue()
+
+def html_report(stats: Dict) -> str:
+    png = plot_freq_png(stats['frecuencias']['freq_abs'])
+    b64 = base64.b64encode(png).decode("ascii")
+    chi2 = stats['chi_cuadrado']
+    html = f"""<!DOCTYPE html>
+<html lang="es">
+<head>
+<meta charset="utf-8"/>
+<title>Reporte Tinka</title>
+<style>
+body {{ font-family: Arial, Helvetica, sans-serif; margin: 20px; }}
+h1, h2 {{ margin: 0.4em 0; }}
+pre {{ background: #f6f8fa; padding: 12px; overflow: auto; }}
+.small {{ color:#555; font-size: 0.9em; }}
+</style>
+</head>
+<body>
+<h1>Reporte de Análisis — Tinka</h1>
+<p class="small">Este reporte se genera a partir de los resultados históricos presentes en tu base de datos.</p>
+
+<h2>Frecuencias</h2>
+<img src="data:image/png;base64,{b64}" alt="Frecuencias"/>
+<pre>{render_ascii_hist(stats['frecuencias']['freq_abs'])}</pre>
+
+<h2>Chi-cuadrado</h2>
+<p>Chi2 = {chi2.get('chi2')} &nbsp;&nbsp; p-value = {chi2.get('p_value')}</p>
+<p class="small">Si p-value es muy pequeño (&lt; 0.05), indica desviación respecto a uniformidad; recuerda que los sorteos son procesos aleatorios.</p>
+
+<h2>Disclaimer</h2>
+<p class="small">Este sistema es informativo y educativo. La lotería es aleatoria. Juega con responsabilidad.</p>
+</body>
+</html>
+"""
+    return html