chispazo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CONCURSO  R1  R2  R3  R4  R5       FECHA\n",
      "0         9668   5  10  13  15  20  02/03/2023\n",
      "1         9667   6   7  19  23  24  02/03/2023\n",
      "2         9666   7  19  20  25  28  01/03/2023\n",
      "3         9665   1   6  16  18  23  01/03/2023\n",
      "4         9664  11  12  13  17  26  28/02/2023\n",
      "...        ...  ..  ..  ..  ..  ..         ...\n",
      "9658        10   1   3   8  11  17  05/02/1999\n",
      "9659         9   4  17  18  21  22  02/02/1999\n",
      "9660         8   4   8   9  20  22  29/01/1999\n",
      "9661         7   2   8  11  15  22  26/01/1999\n",
      "9662         6   1   9  10  16  17  22/01/1999\n",
      "\n",
      "[9663 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import os\n",
    "import requests\n",
    "from array import array\n",
    "import concurrent.futures\n",
    "\n",
    "\n",
    "class Chispazo:\n",
    "    def __init__(self) -> None:\n",
    "        self.ranges_per_position = ((1, 24), (2, 25), (3, 26), (4, 27), (5, 28))\n",
    "        self.data = pd.read_csv(\"games_csv/Chispazo.csv\")\n",
    "        self.frequencies_per_position = [\n",
    "            self.data[f\"R{i}\"].value_counts().sort_index() for i in range(1, 6)\n",
    "        ]\n",
    "        self.most_frequent_numbers = []\n",
    "        self.less_frequent_numbers = []\n",
    "        for i in range(5):\n",
    "            s = self.frequencies_per_position[i]\n",
    "            q = s.quantile(0.5)\n",
    "            self.most_frequent_numbers.append(list(s[s >= q].index))\n",
    "            self.less_frequent_numbers.append(list(s[s < q].index))\n",
    "\n",
    "        self.num_frequencies = self.data.iloc[:, 1:6].stack().value_counts()\n",
    "        # self.number_of_frequencies_per_position={1:10,2:12,3:16,4:12,5:10}\n",
    "        self.number_of_frequencies_per_position = {1: 9, 2: 9, 3: 9, 4: 9, 5: 9}\n",
    "        self.top_frequencies_per_position = [\n",
    "            self.data[f\"R{i}\"]\n",
    "            .value_counts()\n",
    "            .nlargest(self.number_of_frequencies_per_position[i])\n",
    "            for i in range(1, 6)\n",
    "        ]\n",
    "        self.top_nums = [\n",
    "            list(freq_pos.index) for freq_pos in self.top_frequencies_per_position\n",
    "        ]\n",
    "        self.num_positions = 5\n",
    "\n",
    "        self.most_frequent_total_combinations = 1\n",
    "        self.less_frequent_total_combinations = 1\n",
    "        for i in range(5):\n",
    "            most_freq_count = len(self.most_frequent_numbers[i])\n",
    "            less_freq_count = len(self.less_frequent_numbers[i])\n",
    "            self.most_frequent_total_combinations *= most_freq_count\n",
    "            self.less_frequent_total_combinations *= less_freq_count\n",
    "\n",
    "    # def download_csv(game_name: str = \"Chispazo\"):\n",
    "    #     \"\"\"\n",
    "    #     download csv file from the records page\n",
    "    #     \"\"\"\n",
    "    #     url = f\"https://www.pronosticos.gob.mx/Documentos/Historicos/{game_name}.csv\"\n",
    "    #     response = requests.get(url, verify=False)\n",
    "    #     with open(f\"games_csv/{game_name}.csv\", \"wb\") as file:\n",
    "    #         file.write(response.content)\n",
    "\n",
    "    def generate_random_combination(self) -> list:\n",
    "        \"\"\"\n",
    "        generate random combination of numbers beetween 1,28 using the rango of posiitons for each value\n",
    "\n",
    "        \"\"\"\n",
    "        combination = array(\"b\")\n",
    "        latest_sum = 0\n",
    "        for rango in self.ranges_per_position:\n",
    "            random_value = np.random.randint(latest_sum + 1, rango[1] + 1)\n",
    "            combination.append(random_value)\n",
    "            latest_sum = random_value\n",
    "        return combination\n",
    "\n",
    "    def generate_combination_top_frequencies(self) -> list:\n",
    "        \"\"\"\n",
    "        generan una cierta cantidad de combinaciones junto con sus respectivas probabilidades, ordenadas de mayor a menor.\n",
    "        \"\"\"\n",
    "        nums = []\n",
    "        for i in range(self.num_positions):\n",
    "            top_nums_i = self.top_nums[i]\n",
    "            nums += top_nums_i\n",
    "        nums = list(set(nums))  # eliminar elementos repetidos\n",
    "        values = []\n",
    "        for i in range(self.num_positions):\n",
    "            num = random.choice(self.top_nums[i])\n",
    "            values.append(num)\n",
    "        return values\n",
    "\n",
    "    def calculate_probability_v1(self, combinacion: list):\n",
    "        \"\"\"\n",
    "        La primera función utiliza la frecuencia de cada número en cada posición de la combinación\n",
    "        y el número total de frecuencias de cada número para calcular la\n",
    "        probabilidad de que la combinación sea ganadora.\n",
    "        \"\"\"\n",
    "        probabilidad = 1\n",
    "        for i, num in enumerate(combinacion):\n",
    "            probabilidad *= (\n",
    "                self.frequencies_per_position[i].get(num, 0) / self.num_frequencies[num]\n",
    "            )\n",
    "        return probabilidad\n",
    "\n",
    "    def calculate_probability_v2(self, row: pd.core.series.Series):\n",
    "        \"\"\"\n",
    "        La segunda función utiliza dos listas de números, los más frecuentes y los menos frecuentes,\n",
    "        y asigna un peso menor a los números menos frecuentes. Luego, cuenta la cantidad de números en\n",
    "        la combinación que se encuentran en la lista de números más frecuentes y los que se encuentran en la lista de números menos\n",
    "        frecuentes con un peso menor y divide la cantidad total por 5 para obtener la probabilidad de que la combinación sea ganadora.\n",
    "        \"\"\"\n",
    "        # total_combinaciones = 10068347520 # número total de combinaciones posibles\n",
    "        count = 0\n",
    "        for r in range(1, 6):\n",
    "            if row[f\"R{r}\"] in self.most_frequent_numbers[r - 1]:\n",
    "                count += 1\n",
    "            elif row[f\"R{r}\"] in self.less_frequent_numbers[r - 1]:\n",
    "                count += 0.5  # agregamos un peso menor para los números poco frecuentes\n",
    "        probabilidad = count / 5\n",
    "        return probabilidad\n",
    "\n",
    "    def calculate_probability_v3(self, row: pd.core.series.Series):\n",
    "        \"\"\"\n",
    "        La tercera función utiliza dos listas de números, los más frecuentes y los menos frecuentes,\n",
    "        y asigna un peso proporcional a la posición de los números en las listas.\n",
    "        Luego, cuenta la cantidad de números en la combinación que se encuentran en la lista de números más frecuentes\n",
    "        y los que se encuentran en la lista de números menos frecuentes con un peso proporcional y divide la cantidad total por 5\n",
    "        para obtener la probabilidad de que la combinación sea ganadora.\n",
    "        \"\"\"\n",
    "\n",
    "        count = 0\n",
    "        for r in range(1, 6):\n",
    "            number = row[f\"R{r}\"]\n",
    "            try:\n",
    "                position = self.frequencies_per_position[r - 1].index.get_loc(number)\n",
    "            except KeyError as e:\n",
    "                return 0.0001\n",
    "            frequency = self.frequencies_per_position[r - 1][number]\n",
    "            weight = (\n",
    "                1\n",
    "                + (self.frequencies_per_position[r - 1].size - position)\n",
    "                / self.frequencies_per_position[r - 1].size\n",
    "            )\n",
    "            if number in self.most_frequent_numbers[r - 1]:\n",
    "                count += weight * frequency / self.most_frequent_total_combinations\n",
    "            elif number in self.less_frequent_numbers[r - 1]:\n",
    "                count += (\n",
    "                    0.5 * weight * frequency / self.less_frequent_total_combinations\n",
    "                )\n",
    "        probabilidad = count / 5\n",
    "        return probabilidad\n",
    "\n",
    "    def calculate_standard_deviation(self, combination):\n",
    "        \"\"\"\n",
    "        Calcula la desviación estándar de una combinación de números utilizando los resultados históricos de Chispazo.\n",
    "        \"\"\"\n",
    "        df_comb = self.data[\n",
    "            (self.data[\"R1\"].isin(combination))\n",
    "            & (self.data[\"R2\"].isin(combination))\n",
    "            & (self.data[\"R3\"].isin(combination))\n",
    "            & (self.data[\"R4\"].isin(combination))\n",
    "            & (self.data[\"R5\"].isin(combination))\n",
    "        ]\n",
    "        if len(df_comb) == 0:\n",
    "            # La combinación no ha sido ganadora en el pasado\n",
    "            return np.nan\n",
    "        else:\n",
    "            return df_comb[\"Premio\"].std()\n",
    "\n",
    "    def generate_combinations(self, n=9):\n",
    "        \"\"\"\n",
    "        genera n numero de combinaciones con su respectiva probabilidad cada una, ordenada de mayor a menor\n",
    "        \"\"\"\n",
    "        # combinaciones = [self.generate_random_combination() for i in range(n)]\n",
    "        # probabilidades = [self.calculate_probability_v1(c) for c in combinaciones]\n",
    "        # combinaciones_probables = sorted(\n",
    "        #     list(zip(combinaciones, probabilidades)), key=lambda x: x[1], reverse=True\n",
    "        # )\n",
    "        # return combinaciones_probables\n",
    "    \n",
    "        combinaciones_probables = []\n",
    "    \n",
    "        with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "            futures = [executor.submit(self.generate_random_combination) for _ in range(n)]\n",
    "            \n",
    "            for future in concurrent.futures.as_completed(futures):\n",
    "                combinacion = future.result()\n",
    "                probabilidad = self.calculate_probability_v1(combinacion)\n",
    "                combinaciones_probables.append((combinacion, probabilidad))\n",
    "        \n",
    "        combinaciones_probables = sorted(combinaciones_probables, key=lambda x: x[1], reverse=True)\n",
    "        return combinaciones_probables\n",
    "    \n",
    "\n",
    "    def generate_combinations_top_frequencies(self, n=9):\n",
    "        \"\"\"\n",
    "        genera n numero de combinaciones con su respectiva probabilidad cada una,\n",
    "        ordenada de mayor a menor usando solos los numeros mas frecuentes de cada posición\n",
    "        \"\"\"\n",
    "        combinaciones = [self.generate_combination_top_frequencies() for i in range(n)]\n",
    "        probabilidades = [self.calculate_probability_v1(c) for c in combinaciones]\n",
    "        combinaciones_probables = sorted(\n",
    "            list(zip(combinaciones, probabilidades)), key=lambda x: x[1], reverse=True\n",
    "        )\n",
    "        return combinaciones_probables\n",
    "\n",
    "    def generar_probabilidad_de_resultados_historicos(\n",
    "        self,\n",
    "        file_name: str = \"games_csv/ChispazoProbabilidad.csv\",\n",
    "    ):\n",
    "        \"\"\"\n",
    "        agragar una columna de probabilidad a la data historica con sus respectivos porcentajes de probabilidad\n",
    "        \"\"\"\n",
    "        self.data[\"Probabilidad\"] = self.data[[\"R1\", \"R2\", \"R3\", \"R4\", \"R5\"]].apply(\n",
    "            lambda row: round(self.calculate_probability_v3(row), 4), axis=1\n",
    "        )\n",
    "        # self.data.to_csv(file_name, index=False, mode='a', header=not os.path.exists(file_name))\n",
    "        self.data.to_csv(file_name, index=False, header=not os.path.exists(file_name))\n",
    "\n",
    "    def guardar_combinaciones_probables(\n",
    "        self, file_name: str, threshold: int, iterations: int = 100000\n",
    "    ):\n",
    "        combinaciones_probables = self.generar_combinaciones_probables(iterations)\n",
    "        df_combinaciones_probables = pd.DataFrame(\n",
    "            columns=[f\"R{i}\" for i in range(1, 6)]\n",
    "        )\n",
    "        probabilidad_column = []\n",
    "        for combinacion, probabilidad in combinaciones_probables:\n",
    "            if probabilidad > threshold:\n",
    "                df_combinaciones_probables = df_combinaciones_probables.append(\n",
    "                    pd.Series(combinacion, index=df_combinaciones_probables.columns),\n",
    "                    ignore_index=True,\n",
    "                )\n",
    "                probabilidad_column.append(probabilidad)\n",
    "\n",
    "        df_combinaciones_probables.insert(\n",
    "            loc=len(df_combinaciones_probables.columns),\n",
    "            column=\"probabilidad\",\n",
    "            value=probabilidad_column,\n",
    "        )\n",
    "        try:\n",
    "            df_existing = pd.read_csv(file_name)\n",
    "            df_existing = df_existing.append(\n",
    "                df_combinaciones_probables, ignore_index=True\n",
    "            )\n",
    "            df_existing.to_csv(\n",
    "                file_name, index=False, mode=\"a\", header=not os.path.isfile(file_name)\n",
    "            )\n",
    "        except FileNotFoundError:\n",
    "            df_combinaciones_probables.to_csv(file_name, index=False)\n",
    "\n",
    "\n",
    "ch = Chispazo()\n",
    "print(ch.data.head(-5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.004265617117189303\n"
     ]
    }
   ],
   "source": [
    "result_today=[2,18,21,25,27]\n",
    "print(ch.calculate_probability_v1(result_today))\n",
    "# print(ch.calculate_probability_v2(result_today))\n",
    "# print(ch.calculate_probability_v3(result_today))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch.generar_probabilidad_de_resultados_historicos()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. array('b', [1, 6, 15, 23, 28]) - probabilidad: 7.20%\n",
      "2. array('b', [1, 8, 14, 22, 27]) - probabilidad: 6.99%\n",
      "3. array('b', [1, 6, 15, 18, 28]) - probabilidad: 6.77%\n",
      "4. array('b', [1, 7, 13, 16, 28]) - probabilidad: 6.33%\n",
      "5. array('b', [1, 11, 12, 23, 28]) - probabilidad: 6.28%\n",
      "6. array('b', [2, 6, 16, 19, 28]) - probabilidad: 6.23%\n",
      "7. array('b', [1, 10, 18, 20, 28]) - probabilidad: 6.09%\n",
      "8. array('b', [2, 8, 15, 20, 27]) - probabilidad: 5.77%\n",
      "9. array('b', [1, 5, 16, 23, 27]) - probabilidad: 5.56%\n",
      "10. array('b', [2, 10, 14, 18, 28]) - probabilidad: 5.55%\n",
      "11. array('b', [2, 8, 19, 22, 28]) - probabilidad: 5.39%\n",
      "12. array('b', [1, 7, 10, 22, 27]) - probabilidad: 5.37%\n",
      "13. array('b', [1, 7, 19, 23, 27]) - probabilidad: 5.32%\n",
      "14. array('b', [1, 7, 10, 19, 27]) - probabilidad: 5.20%\n",
      "15. array('b', [1, 9, 14, 23, 25]) - probabilidad: 5.15%\n",
      "16. array('b', [1, 7, 13, 24, 26]) - probabilidad: 5.13%\n",
      "17. array('b', [2, 4, 15, 20, 28]) - probabilidad: 5.01%\n",
      "18. array('b', [3, 8, 16, 17, 28]) - probabilidad: 5.00%\n",
      "19. array('b', [2, 7, 13, 21, 26]) - probabilidad: 4.99%\n",
      "20. array('b', [2, 8, 17, 19, 27]) - probabilidad: 4.99%\n",
      "21. array('b', [1, 9, 11, 20, 26]) - probabilidad: 4.96%\n",
      "22. array('b', [1, 6, 17, 24, 27]) - probabilidad: 4.95%\n",
      "23. array('b', [1, 10, 18, 23, 27]) - probabilidad: 4.93%\n",
      "24. array('b', [2, 6, 13, 24, 27]) - probabilidad: 4.89%\n",
      "25. array('b', [1, 10, 16, 21, 25]) - probabilidad: 4.89%\n",
      "26. array('b', [1, 10, 16, 25, 28]) - probabilidad: 4.79%\n",
      "27. array('b', [2, 8, 16, 23, 26]) - probabilidad: 4.78%\n",
      "28. array('b', [1, 5, 16, 22, 26]) - probabilidad: 4.75%\n",
      "29. array('b', [1, 10, 12, 20, 25]) - probabilidad: 4.73%\n",
      "30. array('b', [1, 9, 11, 23, 26]) - probabilidad: 4.70%\n",
      "31. array('b', [2, 7, 15, 24, 27]) - probabilidad: 4.70%\n",
      "32. array('b', [4, 7, 12, 21, 28]) - probabilidad: 4.60%\n",
      "33. array('b', [2, 9, 18, 21, 27]) - probabilidad: 4.50%\n",
      "34. array('b', [1, 10, 12, 25, 28]) - probabilidad: 4.46%\n",
      "35. array('b', [2, 7, 16, 25, 28]) - probabilidad: 4.46%\n",
      "36. array('b', [1, 7, 10, 23, 26]) - probabilidad: 4.39%\n",
      "37. array('b', [1, 6, 12, 24, 26]) - probabilidad: 4.39%\n",
      "38. array('b', [1, 10, 13, 24, 25]) - probabilidad: 4.37%\n",
      "39. array('b', [2, 7, 15, 21, 25]) - probabilidad: 4.35%\n",
      "40. array('b', [1, 7, 20, 23, 27]) - probabilidad: 4.31%\n",
      "41. array('b', [2, 9, 16, 25, 28]) - probabilidad: 4.29%\n",
      "42. array('b', [4, 8, 14, 21, 27]) - probabilidad: 4.28%\n",
      "43. array('b', [2, 10, 13, 21, 25]) - probabilidad: 4.25%\n",
      "44. array('b', [3, 5, 13, 21, 27]) - probabilidad: 4.25%\n",
      "45. array('b', [2, 12, 16, 17, 28]) - probabilidad: 4.24%\n",
      "46. array('b', [1, 11, 12, 19, 25]) - probabilidad: 4.22%\n",
      "47. array('b', [1, 14, 17, 21, 28]) - probabilidad: 4.20%\n",
      "48. array('b', [1, 9, 15, 25, 27]) - probabilidad: 4.20%\n",
      "49. array('b', [1, 9, 17, 24, 26]) - probabilidad: 4.17%\n",
      "50. array('b', [1, 5, 17, 21, 26]) - probabilidad: 4.16%\n",
      "51. array('b', [2, 9, 20, 22, 28]) - probabilidad: 4.14%\n",
      "52. array('b', [1, 4, 14, 21, 25]) - probabilidad: 4.11%\n",
      "53. array('b', [3, 6, 16, 20, 26]) - probabilidad: 4.09%\n",
      "54. array('b', [3, 7, 15, 24, 27]) - probabilidad: 4.06%\n",
      "55. array('b', [3, 9, 16, 22, 26]) - probabilidad: 3.99%\n",
      "56. array('b', [4, 5, 14, 23, 28]) - probabilidad: 3.99%\n",
      "57. array('b', [2, 7, 13, 25, 27]) - probabilidad: 3.97%\n",
      "58. array('b', [2, 8, 14, 24, 25]) - probabilidad: 3.96%\n",
      "59. array('b', [3, 6, 14, 22, 26]) - probabilidad: 3.96%\n",
      "60. array('b', [1, 9, 18, 26, 28]) - probabilidad: 3.95%\n",
      "61. array('b', [2, 7, 19, 20, 26]) - probabilidad: 3.92%\n",
      "62. array('b', [2, 7, 12, 26, 28]) - probabilidad: 3.92%\n",
      "63. array('b', [3, 9, 14, 23, 26]) - probabilidad: 3.91%\n",
      "64. array('b', [1, 14, 19, 20, 28]) - probabilidad: 3.90%\n",
      "65. array('b', [3, 9, 16, 19, 26]) - probabilidad: 3.87%\n",
      "66. array('b', [1, 3, 17, 23, 28]) - probabilidad: 3.83%\n",
      "67. array('b', [2, 10, 15, 25, 28]) - probabilidad: 3.82%\n",
      "68. array('b', [4, 5, 15, 23, 28]) - probabilidad: 3.82%\n",
      "69. array('b', [2, 11, 12, 21, 26]) - probabilidad: 3.82%\n",
      "70. array('b', [1, 7, 14, 25, 26]) - probabilidad: 3.81%\n",
      "71. array('b', [1, 3, 17, 19, 28]) - probabilidad: 3.79%\n",
      "72. array('b', [1, 14, 19, 22, 28]) - probabilidad: 3.77%\n",
      "73. array('b', [1, 8, 16, 22, 23]) - probabilidad: 3.77%\n",
      "74. array('b', [4, 11, 13, 24, 28]) - probabilidad: 3.76%\n",
      "75. array('b', [2, 10, 11, 21, 26]) - probabilidad: 3.73%\n",
      "76. array('b', [5, 8, 14, 24, 28]) - probabilidad: 3.72%\n",
      "77. array('b', [3, 9, 14, 23, 25]) - probabilidad: 3.71%\n",
      "78. array('b', [1, 10, 15, 22, 24]) - probabilidad: 3.70%\n",
      "79. array('b', [2, 10, 16, 24, 26]) - probabilidad: 3.69%\n",
      "80. array('b', [3, 12, 16, 22, 27]) - probabilidad: 3.69%\n",
      "81. array('b', [1, 3, 16, 19, 27]) - probabilidad: 3.69%\n",
      "82. array('b', [3, 8, 16, 26, 28]) - probabilidad: 3.68%\n",
      "83. array('b', [3, 6, 16, 25, 28]) - probabilidad: 3.67%\n",
      "84. array('b', [2, 5, 19, 23, 27]) - probabilidad: 3.66%\n",
      "85. array('b', [1, 5, 15, 17, 25]) - probabilidad: 3.65%\n",
      "86. array('b', [1, 7, 10, 24, 25]) - probabilidad: 3.64%\n",
      "87. array('b', [2, 7, 12, 24, 25]) - probabilidad: 3.64%\n",
      "88. array('b', [4, 9, 10, 23, 28]) - probabilidad: 3.64%\n",
      "89. array('b', [3, 7, 16, 26, 28]) - probabilidad: 3.62%\n",
      "90. array('b', [3, 11, 16, 24, 27]) - probabilidad: 3.62%\n",
      "91. array('b', [4, 10, 19, 20, 28]) - probabilidad: 3.62%\n",
      "92. array('b', [4, 11, 16, 24, 28]) - probabilidad: 3.61%\n",
      "93. array('b', [3, 13, 15, 20, 28]) - probabilidad: 3.59%\n",
      "94. array('b', [3, 10, 15, 22, 26]) - probabilidad: 3.55%\n",
      "95. array('b', [5, 8, 15, 22, 27]) - probabilidad: 3.55%\n",
      "96. array('b', [2, 14, 16, 20, 27]) - probabilidad: 3.53%\n",
      "97. array('b', [3, 10, 16, 21, 25]) - probabilidad: 3.52%\n",
      "98. array('b', [3, 6, 15, 25, 28]) - probabilidad: 3.51%\n",
      "99. array('b', [5, 11, 13, 18, 28]) - probabilidad: 3.50%\n",
      "100. array('b', [4, 7, 15, 20, 26]) - probabilidad: 3.49%\n",
      "101. array('b', [2, 9, 17, 24, 26]) - probabilidad: 3.48%\n",
      "102. array('b', [1, 10, 19, 24, 26]) - probabilidad: 3.48%\n",
      "103. array('b', [1, 6, 10, 24, 25]) - probabilidad: 3.48%\n",
      "104. array('b', [3, 5, 11, 23, 27]) - probabilidad: 3.47%\n",
      "105. array('b', [4, 5, 11, 23, 28]) - probabilidad: 3.46%\n",
      "106. array('b', [3, 10, 19, 23, 27]) - probabilidad: 3.44%\n",
      "107. array('b', [1, 4, 12, 17, 26]) - probabilidad: 3.43%\n",
      "108. array('b', [3, 6, 11, 22, 26]) - probabilidad: 3.43%\n",
      "109. array('b', [4, 9, 16, 21, 26]) - probabilidad: 3.40%\n",
      "110. array('b', [1, 11, 17, 20, 24]) - probabilidad: 3.36%\n",
      "111. array('b', [1, 9, 21, 23, 27]) - probabilidad: 3.35%\n",
      "112. array('b', [1, 14, 15, 24, 27]) - probabilidad: 3.34%\n",
      "113. array('b', [4, 11, 12, 17, 28]) - probabilidad: 3.34%\n",
      "114. array('b', [5, 6, 15, 21, 27]) - probabilidad: 3.33%\n",
      "115. array('b', [3, 12, 19, 23, 28]) - probabilidad: 3.33%\n",
      "116. array('b', [4, 7, 19, 21, 27]) - probabilidad: 3.33%\n",
      "117. array('b', [1, 11, 19, 24, 26]) - probabilidad: 3.31%\n",
      "118. array('b', [1, 3, 11, 24, 28]) - probabilidad: 3.29%\n",
      "119. array('b', [2, 11, 18, 20, 25]) - probabilidad: 3.28%\n",
      "120. array('b', [3, 6, 9, 24, 28]) - probabilidad: 3.28%\n",
      "121. array('b', [3, 6, 13, 25, 27]) - probabilidad: 3.27%\n",
      "122. array('b', [3, 9, 12, 18, 25]) - probabilidad: 3.26%\n",
      "123. array('b', [4, 8, 17, 24, 27]) - probabilidad: 3.23%\n",
      "124. array('b', [1, 15, 18, 23, 28]) - probabilidad: 3.23%\n",
      "125. array('b', [2, 9, 17, 25, 27]) - probabilidad: 3.23%\n",
      "126. array('b', [1, 3, 13, 19, 26]) - probabilidad: 3.21%\n",
      "127. array('b', [2, 8, 17, 26, 27]) - probabilidad: 3.21%\n",
      "128. array('b', [1, 8, 20, 24, 26]) - probabilidad: 3.19%\n",
      "129. array('b', [2, 12, 20, 22, 28]) - probabilidad: 3.19%\n",
      "130. array('b', [1, 15, 19, 21, 28]) - probabilidad: 3.18%\n",
      "131. array('b', [1, 11, 16, 22, 23]) - probabilidad: 3.16%\n",
      "132. array('b', [2, 12, 17, 22, 26]) - probabilidad: 3.14%\n",
      "133. array('b', [1, 15, 19, 23, 28]) - probabilidad: 3.13%\n",
      "134. array('b', [6, 7, 15, 21, 28]) - probabilidad: 3.10%\n",
      "135. array('b', [1, 7, 14, 20, 22]) - probabilidad: 3.10%\n",
      "136. array('b', [1, 10, 12, 21, 23]) - probabilidad: 3.09%\n",
      "137. array('b', [4, 10, 18, 21, 27]) - probabilidad: 3.09%\n",
      "138. array('b', [2, 14, 19, 23, 28]) - probabilidad: 3.08%\n",
      "139. array('b', [2, 7, 9, 25, 28]) - probabilidad: 3.08%\n",
      "140. array('b', [4, 11, 16, 17, 27]) - probabilidad: 3.07%\n",
      "141. array('b', [2, 12, 16, 24, 26]) - probabilidad: 3.06%\n",
      "142. array('b', [3, 4, 15, 24, 27]) - probabilidad: 3.06%\n",
      "143. array('b', [4, 6, 12, 19, 26]) - probabilidad: 3.05%\n",
      "144. array('b', [4, 11, 15, 16, 28]) - probabilidad: 3.04%\n",
      "145. array('b', [1, 5, 9, 25, 28]) - probabilidad: 3.04%\n",
      "146. array('b', [5, 6, 13, 21, 26]) - probabilidad: 3.04%\n",
      "147. array('b', [2, 9, 20, 24, 27]) - probabilidad: 3.03%\n",
      "148. array('b', [2, 9, 11, 16, 26]) - probabilidad: 3.03%\n",
      "149. array('b', [3, 12, 14, 20, 25]) - probabilidad: 3.02%\n",
      "150. array('b', [1, 4, 15, 19, 24]) - probabilidad: 3.02%\n",
      "151. array('b', [1, 8, 10, 25, 26]) - probabilidad: 3.02%\n",
      "152. array('b', [6, 10, 16, 20, 28]) - probabilidad: 3.01%\n",
      "153. array('b', [2, 7, 19, 25, 27]) - probabilidad: 3.00%\n",
      "154. array('b', [3, 5, 12, 15, 28]) - probabilidad: 3.00%\n",
      "155. array('b', [3, 12, 18, 21, 27]) - probabilidad: 3.00%\n",
      "156. array('b', [1, 10, 19, 23, 24]) - probabilidad: 2.99%\n",
      "157. array('b', [3, 5, 16, 26, 28]) - probabilidad: 2.98%\n",
      "158. array('b', [4, 5, 17, 19, 27]) - probabilidad: 2.98%\n",
      "159. array('b', [2, 11, 12, 20, 24]) - probabilidad: 2.97%\n",
      "160. array('b', [4, 8, 9, 21, 27]) - probabilidad: 2.96%\n",
      "161. array('b', [3, 12, 13, 18, 26]) - probabilidad: 2.96%\n",
      "162. array('b', [3, 5, 11, 21, 26]) - probabilidad: 2.95%\n",
      "163. array('b', [4, 7, 18, 24, 27]) - probabilidad: 2.95%\n",
      "164. array('b', [2, 11, 15, 22, 24]) - probabilidad: 2.94%\n",
      "165. array('b', [1, 3, 14, 25, 28]) - probabilidad: 2.94%\n",
      "166. array('b', [5, 8, 19, 24, 28]) - probabilidad: 2.93%\n",
      "167. array('b', [4, 9, 11, 23, 26]) - probabilidad: 2.89%\n",
      "168. array('b', [3, 4, 19, 23, 27]) - probabilidad: 2.89%\n",
      "169. array('b', [4, 11, 16, 22, 25]) - probabilidad: 2.87%\n",
      "170. array('b', [3, 7, 13, 25, 26]) - probabilidad: 2.86%\n",
      "171. array('b', [3, 12, 16, 25, 28]) - probabilidad: 2.86%\n",
      "172. array('b', [5, 9, 13, 23, 25]) - probabilidad: 2.85%\n",
      "173. array('b', [2, 12, 18, 23, 26]) - probabilidad: 2.85%\n",
      "174. array('b', [2, 5, 15, 22, 24]) - probabilidad: 2.84%\n",
      "175. array('b', [1, 12, 16, 25, 26]) - probabilidad: 2.83%\n",
      "176. array('b', [2, 4, 11, 16, 27]) - probabilidad: 2.83%\n",
      "177. array('b', [4, 8, 19, 22, 26]) - probabilidad: 2.83%\n",
      "178. array('b', [2, 10, 20, 24, 27]) - probabilidad: 2.82%\n",
      "179. array('b', [2, 9, 18, 26, 27]) - probabilidad: 2.82%\n",
      "180. array('b', [1, 12, 17, 26, 27]) - probabilidad: 2.81%\n",
      "181. array('b', [3, 5, 11, 21, 25]) - probabilidad: 2.80%\n",
      "182. array('b', [4, 10, 12, 21, 25]) - probabilidad: 2.80%\n",
      "183. array('b', [4, 11, 16, 25, 28]) - probabilidad: 2.80%\n",
      "184. array('b', [1, 11, 13, 17, 23]) - probabilidad: 2.80%\n",
      "185. array('b', [4, 8, 13, 26, 27]) - probabilidad: 2.79%\n",
      "186. array('b', [3, 5, 12, 26, 28]) - probabilidad: 2.78%\n",
      "187. array('b', [1, 13, 15, 17, 25]) - probabilidad: 2.78%\n",
      "188. array('b', [2, 6, 20, 21, 25]) - probabilidad: 2.78%\n",
      "189. array('b', [4, 10, 16, 26, 28]) - probabilidad: 2.77%\n",
      "190. array('b', [2, 4, 20, 24, 28]) - probabilidad: 2.77%\n",
      "191. array('b', [5, 7, 12, 19, 26]) - probabilidad: 2.77%\n",
      "192. array('b', [6, 8, 14, 23, 27]) - probabilidad: 2.76%\n",
      "193. array('b', [2, 5, 12, 26, 27]) - probabilidad: 2.76%\n",
      "194. array('b', [3, 14, 15, 23, 27]) - probabilidad: 2.76%\n",
      "195. array('b', [6, 9, 17, 22, 28]) - probabilidad: 2.75%\n",
      "196. array('b', [4, 7, 15, 24, 25]) - probabilidad: 2.75%\n",
      "197. array('b', [3, 5, 11, 25, 28]) - probabilidad: 2.74%\n",
      "198. array('b', [4, 9, 14, 15, 27]) - probabilidad: 2.74%\n",
      "199. array('b', [3, 6, 11, 25, 27]) - probabilidad: 2.72%\n",
      "200. array('b', [2, 6, 8, 24, 27]) - probabilidad: 2.71%\n",
      "201. array('b', [3, 8, 14, 21, 23]) - probabilidad: 2.70%\n",
      "202. array('b', [5, 10, 14, 20, 25]) - probabilidad: 2.69%\n",
      "203. array('b', [1, 11, 20, 24, 26]) - probabilidad: 2.68%\n",
      "204. array('b', [3, 11, 15, 25, 27]) - probabilidad: 2.68%\n",
      "205. array('b', [4, 11, 15, 25, 28]) - probabilidad: 2.67%\n",
      "206. array('b', [4, 7, 18, 25, 28]) - probabilidad: 2.67%\n",
      "207. array('b', [2, 10, 12, 25, 26]) - probabilidad: 2.66%\n",
      "208. array('b', [3, 7, 9, 25, 28]) - probabilidad: 2.66%\n",
      "209. array('b', [2, 10, 15, 22, 23]) - probabilidad: 2.65%\n",
      "210. array('b', [2, 3, 16, 21, 26]) - probabilidad: 2.65%\n",
      "211. array('b', [5, 6, 15, 21, 25]) - probabilidad: 2.64%\n",
      "212. array('b', [1, 9, 20, 26, 27]) - probabilidad: 2.64%\n",
      "213. array('b', [1, 15, 17, 20, 26]) - probabilidad: 2.63%\n",
      "214. array('b', [2, 14, 16, 19, 25]) - probabilidad: 2.63%\n",
      "215. array('b', [1, 7, 13, 19, 21]) - probabilidad: 2.61%\n",
      "216. array('b', [3, 5, 20, 24, 28]) - probabilidad: 2.61%\n",
      "217. array('b', [5, 11, 16, 22, 26]) - probabilidad: 2.61%\n",
      "218. array('b', [3, 4, 8, 20, 28]) - probabilidad: 2.61%\n",
      "219. array('b', [1, 12, 18, 26, 27]) - probabilidad: 2.60%\n",
      "220. array('b', [3, 8, 21, 24, 28]) - probabilidad: 2.60%\n",
      "221. array('b', [2, 3, 15, 24, 27]) - probabilidad: 2.59%\n",
      "222. array('b', [6, 7, 12, 24, 28]) - probabilidad: 2.59%\n",
      "223. array('b', [2, 10, 12, 22, 23]) - probabilidad: 2.59%\n",
      "224. array('b', [3, 6, 11, 14, 28]) - probabilidad: 2.58%\n",
      "225. array('b', [5, 9, 16, 26, 28]) - probabilidad: 2.57%\n",
      "226. array('b', [4, 8, 14, 16, 25]) - probabilidad: 2.57%\n",
      "227. array('b', [1, 7, 15, 27, 28]) - probabilidad: 2.57%\n",
      "228. array('b', [4, 8, 17, 24, 25]) - probabilidad: 2.57%\n",
      "229. array('b', [7, 9, 16, 22, 28]) - probabilidad: 2.57%\n",
      "230. array('b', [1, 6, 14, 27, 28]) - probabilidad: 2.57%\n",
      "231. array('b', [1, 16, 18, 20, 28]) - probabilidad: 2.56%\n",
      "232. array('b', [5, 6, 14, 26, 28]) - probabilidad: 2.55%\n",
      "233. array('b', [3, 4, 17, 25, 28]) - probabilidad: 2.55%\n",
      "234. array('b', [1, 12, 19, 22, 24]) - probabilidad: 2.53%\n",
      "235. array('b', [1, 11, 21, 22, 26]) - probabilidad: 2.53%\n",
      "236. array('b', [6, 7, 10, 21, 28]) - probabilidad: 2.53%\n",
      "237. array('b', [2, 13, 17, 22, 25]) - probabilidad: 2.52%\n",
      "238. array('b', [1, 12, 19, 26, 27]) - probabilidad: 2.52%\n",
      "239. array('b', [1, 13, 17, 25, 27]) - probabilidad: 2.52%\n",
      "240. array('b', [6, 8, 13, 24, 27]) - probabilidad: 2.51%\n",
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      "242. array('b', [4, 10, 16, 25, 27]) - probabilidad: 2.51%\n",
      "243. array('b', [3, 11, 12, 16, 26]) - probabilidad: 2.49%\n",
      "244. array('b', [5, 10, 17, 24, 27]) - probabilidad: 2.47%\n",
      "245. array('b', [5, 10, 20, 22, 28]) - probabilidad: 2.45%\n",
      "246. array('b', [5, 6, 18, 24, 27]) - probabilidad: 2.44%\n",
      "247. array('b', [4, 9, 17, 24, 25]) - probabilidad: 2.43%\n",
      "248. array('b', [4, 7, 19, 26, 28]) - probabilidad: 2.43%\n",
      "249. array('b', [3, 5, 18, 26, 28]) - probabilidad: 2.43%\n",
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      "251. array('b', [4, 8, 19, 24, 26]) - probabilidad: 2.42%\n",
      "252. array('b', [3, 14, 16, 20, 25]) - probabilidad: 2.42%\n",
      "253. array('b', [4, 8, 13, 14, 27]) - probabilidad: 2.41%\n",
      "254. array('b', [6, 7, 14, 20, 26]) - probabilidad: 2.40%\n",
      "255. array('b', [3, 12, 13, 26, 27]) - probabilidad: 2.40%\n",
      "256. array('b', [3, 11, 19, 24, 26]) - probabilidad: 2.39%\n",
      "257. array('b', [4, 14, 15, 17, 28]) - probabilidad: 2.38%\n",
      "258. array('b', [1, 4, 8, 22, 25]) - probabilidad: 2.38%\n",
      "259. array('b', [6, 11, 16, 24, 28]) - probabilidad: 2.37%\n",
      "260. array('b', [6, 11, 16, 24, 28]) - probabilidad: 2.37%\n",
      "261. array('b', [3, 15, 18, 21, 28]) - probabilidad: 2.37%\n",
      "262. array('b', [1, 4, 8, 21, 25]) - probabilidad: 2.37%\n",
      "263. array('b', [5, 8, 20, 21, 27]) - probabilidad: 2.37%\n",
      "264. array('b', [3, 9, 14, 18, 23]) - probabilidad: 2.37%\n",
      "265. array('b', [2, 15, 16, 23, 26]) - probabilidad: 2.37%\n",
      "266. array('b', [3, 12, 20, 24, 28]) - probabilidad: 2.36%\n",
      "267. array('b', [4, 9, 16, 18, 24]) - probabilidad: 2.36%\n",
      "268. array('b', [1, 3, 10, 22, 25]) - probabilidad: 2.35%\n",
      "269. array('b', [1, 9, 20, 25, 26]) - probabilidad: 2.35%\n",
      "270. array('b', [2, 5, 19, 22, 24]) - probabilidad: 2.34%\n",
      "271. array('b', [4, 9, 19, 26, 28]) - probabilidad: 2.34%\n",
      "272. array('b', [7, 10, 16, 23, 28]) - probabilidad: 2.34%\n",
      "273. array('b', [1, 3, 14, 23, 24]) - probabilidad: 2.33%\n",
      "274. array('b', [2, 13, 18, 22, 25]) - probabilidad: 2.33%\n",
      "275. array('b', [2, 14, 18, 22, 26]) - probabilidad: 2.33%\n",
      "276. array('b', [1, 6, 11, 16, 23]) - probabilidad: 2.31%\n",
      "277. array('b', [4, 12, 15, 18, 26]) - probabilidad: 2.31%\n",
      "278. array('b', [3, 6, 11, 15, 26]) - probabilidad: 2.31%\n",
      "279. array('b', [1, 15, 18, 19, 26]) - probabilidad: 2.29%\n",
      "280. array('b', [1, 13, 18, 20, 24]) - probabilidad: 2.28%\n",
      "281. array('b', [6, 7, 16, 20, 25]) - probabilidad: 2.28%\n",
      "282. array('b', [3, 8, 15, 16, 24]) - probabilidad: 2.28%\n",
      "283. array('b', [1, 9, 17, 27, 28]) - probabilidad: 2.28%\n",
      "284. array('b', [1, 4, 18, 22, 23]) - probabilidad: 2.28%\n",
      "285. array('b', [2, 10, 21, 24, 27]) - probabilidad: 2.27%\n",
      "286. array('b', [1, 3, 9, 20, 26]) - probabilidad: 2.27%\n",
      "287. array('b', [6, 9, 11, 23, 27]) - probabilidad: 2.27%\n",
      "288. array('b', [7, 8, 16, 23, 27]) - probabilidad: 2.27%\n",
      "289. array('b', [1, 3, 18, 26, 28]) - probabilidad: 2.26%\n",
      "290. array('b', [6, 11, 19, 20, 28]) - probabilidad: 2.26%\n",
      "291. array('b', [5, 9, 13, 23, 24]) - probabilidad: 2.26%\n",
      "292. array('b', [3, 12, 21, 22, 28]) - probabilidad: 2.22%\n",
      "293. array('b', [5, 6, 18, 25, 28]) - probabilidad: 2.21%\n",
      "294. array('b', [2, 5, 11, 21, 23]) - probabilidad: 2.20%\n",
      "295. array('b', [6, 10, 17, 24, 28]) - probabilidad: 2.19%\n",
      "296. array('b', [5, 13, 15, 24, 28]) - probabilidad: 2.19%\n",
      "297. array('b', [2, 10, 19, 21, 23]) - probabilidad: 2.18%\n",
      "298. array('b', [3, 11, 21, 24, 28]) - probabilidad: 2.18%\n",
      "299. array('b', [7, 10, 17, 20, 28]) - probabilidad: 2.17%\n",
      "300. array('b', [6, 10, 13, 22, 26]) - probabilidad: 2.17%\n",
      "301. array('b', [2, 8, 17, 19, 22]) - probabilidad: 2.17%\n",
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      "303. array('b', [1, 6, 22, 25, 28]) - probabilidad: 2.16%\n",
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      "306. array('b', [5, 9, 16, 19, 24]) - probabilidad: 2.15%\n",
      "307. array('b', [1, 5, 7, 24, 27]) - probabilidad: 2.14%\n",
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      "310. array('b', [1, 5, 21, 26, 28]) - probabilidad: 2.14%\n",
      "311. array('b', [1, 9, 21, 26, 27]) - probabilidad: 2.13%\n",
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      "315. array('b', [5, 12, 15, 23, 26]) - probabilidad: 2.13%\n",
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      "317. array('b', [3, 7, 22, 24, 28]) - probabilidad: 2.10%\n",
      "318. array('b', [4, 13, 16, 21, 25]) - probabilidad: 2.10%\n",
      "319. array('b', [4, 7, 19, 26, 27]) - probabilidad: 2.08%\n",
      "320. array('b', [2, 4, 10, 23, 24]) - probabilidad: 2.07%\n",
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      "328. array('b', [2, 14, 15, 26, 27]) - probabilidad: 2.04%\n",
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      "330. array('b', [4, 5, 12, 26, 27]) - probabilidad: 2.03%\n",
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      "333. array('b', [3, 12, 20, 24, 27]) - probabilidad: 2.01%\n",
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      "337. array('b', [6, 10, 18, 23, 27]) - probabilidad: 1.99%\n",
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      "340. array('b', [5, 7, 18, 25, 27]) - probabilidad: 1.98%\n",
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      "1889. array('b', [7, 8, 24, 27, 28]) - probabilidad: 0.20%\n",
      "1890. array('b', [10, 15, 18, 27, 28]) - probabilidad: 0.20%\n",
      "1891. array('b', [11, 15, 18, 22, 23]) - probabilidad: 0.19%\n",
      "1892. array('b', [13, 15, 17, 25, 28]) - probabilidad: 0.19%\n",
      "1893. array('b', [5, 20, 21, 26, 28]) - probabilidad: 0.19%\n",
      "1894. array('b', [7, 14, 24, 25, 27]) - probabilidad: 0.19%\n",
      "1895. array('b', [10, 17, 18, 23, 24]) - probabilidad: 0.19%\n",
      "1896. array('b', [9, 13, 21, 27, 28]) - probabilidad: 0.19%\n",
      "1897. array('b', [8, 17, 20, 21, 23]) - probabilidad: 0.19%\n",
      "1898. array('b', [7, 16, 23, 24, 27]) - probabilidad: 0.19%\n",
      "1899. array('b', [10, 14, 22, 26, 27]) - probabilidad: 0.19%\n",
      "1900. array('b', [11, 17, 18, 22, 26]) - probabilidad: 0.19%\n",
      "1901. array('b', [7, 17, 22, 26, 27]) - probabilidad: 0.19%\n",
      "1902. array('b', [10, 15, 22, 26, 28]) - probabilidad: 0.19%\n",
      "1903. array('b', [9, 13, 16, 18, 19]) - probabilidad: 0.19%\n",
      "1904. array('b', [8, 12, 24, 25, 27]) - probabilidad: 0.19%\n",
      "1905. array('b', [6, 15, 24, 26, 27]) - probabilidad: 0.19%\n",
      "1906. array('b', [11, 15, 22, 24, 28]) - probabilidad: 0.19%\n",
      "1907. array('b', [7, 17, 23, 24, 28]) - probabilidad: 0.19%\n",
      "1908. array('b', [2, 21, 22, 24, 28]) - probabilidad: 0.19%\n",
      "1909. array('b', [5, 20, 22, 24, 27]) - probabilidad: 0.19%\n",
      "1910. array('b', [9, 16, 21, 26, 27]) - probabilidad: 0.19%\n",
      "1911. array('b', [7, 18, 21, 26, 28]) - probabilidad: 0.19%\n",
      "1912. array('b', [8, 13, 24, 25, 28]) - probabilidad: 0.19%\n",
      "1913. array('b', [6, 18, 19, 27, 28]) - probabilidad: 0.19%\n",
      "1914. array('b', [8, 16, 21, 22, 23]) - probabilidad: 0.19%\n",
      "1915. array('b', [1, 20, 23, 26, 28]) - probabilidad: 0.19%\n",
      "1916. array('b', [1, 20, 23, 26, 28]) - probabilidad: 0.19%\n",
      "1917. array('b', [3, 20, 22, 26, 27]) - probabilidad: 0.19%\n",
      "1918. array('b', [2, 17, 23, 27, 28]) - probabilidad: 0.19%\n",
      "1919. array('b', [1, 19, 23, 24, 25]) - probabilidad: 0.19%\n",
      "1920. array('b', [6, 18, 19, 20, 21]) - probabilidad: 0.18%\n",
      "1921. array('b', [10, 16, 20, 21, 24]) - probabilidad: 0.18%\n",
      "1922. array('b', [4, 19, 22, 24, 25]) - probabilidad: 0.18%\n",
      "1923. array('b', [9, 16, 18, 27, 28]) - probabilidad: 0.18%\n",
      "1924. array('b', [10, 16, 20, 26, 27]) - probabilidad: 0.18%\n",
      "1925. array('b', [1, 17, 24, 27, 28]) - probabilidad: 0.18%\n",
      "1926. array('b', [3, 4, 25, 26, 27]) - probabilidad: 0.18%\n",
      "1927. array('b', [8, 17, 22, 25, 28]) - probabilidad: 0.18%\n",
      "1928. array('b', [13, 15, 17, 26, 28]) - probabilidad: 0.18%\n",
      "1929. array('b', [8, 13, 22, 27, 28]) - probabilidad: 0.18%\n",
      "1930. array('b', [5, 17, 24, 26, 28]) - probabilidad: 0.18%\n",
      "1931. array('b', [7, 14, 24, 26, 27]) - probabilidad: 0.18%\n",
      "1932. array('b', [2, 3, 25, 26, 28]) - probabilidad: 0.18%\n",
      "1933. array('b', [5, 19, 22, 25, 28]) - probabilidad: 0.18%\n",
      "1934. array('b', [3, 12, 25, 26, 27]) - probabilidad: 0.18%\n",
      "1935. array('b', [10, 14, 20, 27, 28]) - probabilidad: 0.18%\n",
      "1936. array('b', [7, 15, 24, 26, 28]) - probabilidad: 0.18%\n",
      "1937. array('b', [8, 13, 23, 26, 27]) - probabilidad: 0.18%\n",
      "1938. array('b', [4, 17, 24, 26, 27]) - probabilidad: 0.18%\n",
      "1939. array('b', [4, 19, 22, 25, 27]) - probabilidad: 0.18%\n",
      "1940. array('b', [4, 19, 22, 25, 27]) - probabilidad: 0.18%\n",
      "1941. array('b', [5, 11, 25, 26, 28]) - probabilidad: 0.18%\n",
      "1942. array('b', [4, 15, 24, 27, 28]) - probabilidad: 0.18%\n",
      "1943. array('b', [7, 17, 20, 27, 28]) - probabilidad: 0.18%\n",
      "1944. array('b', [13, 15, 16, 22, 24]) - probabilidad: 0.18%\n",
      "1945. array('b', [11, 14, 21, 25, 27]) - probabilidad: 0.18%\n",
      "1946. array('b', [2, 18, 24, 25, 27]) - probabilidad: 0.18%\n",
      "1947. array('b', [12, 15, 16, 19, 23]) - probabilidad: 0.18%\n",
      "1948. array('b', [6, 19, 22, 24, 28]) - probabilidad: 0.18%\n",
      "1949. array('b', [3, 18, 22, 27, 28]) - probabilidad: 0.18%\n",
      "1950. array('b', [6, 13, 23, 27, 28]) - probabilidad: 0.18%\n",
      "1951. array('b', [10, 17, 20, 24, 26]) - probabilidad: 0.18%\n",
      "1952. array('b', [8, 13, 24, 26, 28]) - probabilidad: 0.18%\n",
      "1953. array('b', [1, 21, 22, 23, 25]) - probabilidad: 0.17%\n",
      "1954. array('b', [1, 21, 22, 25, 28]) - probabilidad: 0.17%\n",
      "1955. array('b', [12, 15, 20, 22, 25]) - probabilidad: 0.17%\n",
      "1956. array('b', [8, 14, 22, 27, 28]) - probabilidad: 0.17%\n",
      "1957. array('b', [10, 16, 21, 26, 28]) - probabilidad: 0.17%\n",
      "1958. array('b', [6, 16, 22, 27, 28]) - probabilidad: 0.17%\n",
      "1959. array('b', [6, 16, 22, 27, 28]) - probabilidad: 0.17%\n",
      "1960. array('b', [11, 15, 16, 27, 28]) - probabilidad: 0.17%\n",
      "1961. array('b', [11, 18, 19, 23, 28]) - probabilidad: 0.17%\n",
      "1962. array('b', [8, 17, 22, 26, 28]) - probabilidad: 0.17%\n",
      "1963. array('b', [11, 12, 13, 15, 21]) - probabilidad: 0.17%\n",
      "1964. array('b', [14, 15, 16, 23, 27]) - probabilidad: 0.17%\n",
      "1965. array('b', [6, 19, 21, 22, 25]) - probabilidad: 0.17%\n",
      "1966. array('b', [10, 11, 22, 27, 28]) - probabilidad: 0.17%\n",
      "1967. array('b', [9, 18, 21, 24, 28]) - probabilidad: 0.17%\n",
      "1968. array('b', [12, 15, 18, 23, 24]) - probabilidad: 0.17%\n",
      "1969. array('b', [9, 17, 20, 25, 26]) - probabilidad: 0.17%\n",
      "1970. array('b', [10, 18, 19, 23, 26]) - probabilidad: 0.17%\n",
      "1971. array('b', [14, 15, 16, 19, 27]) - probabilidad: 0.17%\n",
      "1972. array('b', [7, 13, 24, 25, 26]) - probabilidad: 0.17%\n",
      "1973. array('b', [12, 14, 21, 23, 26]) - probabilidad: 0.17%\n",
      "1974. array('b', [4, 20, 21, 25, 26]) - probabilidad: 0.17%\n",
      "1975. array('b', [1, 15, 25, 26, 27]) - probabilidad: 0.17%\n",
      "1976. array('b', [1, 19, 23, 26, 27]) - probabilidad: 0.17%\n",
      "1977. array('b', [3, 14, 25, 26, 28]) - probabilidad: 0.17%\n",
      "1978. array('b', [10, 16, 18, 20, 22]) - probabilidad: 0.17%\n",
      "1979. array('b', [2, 6, 25, 27, 28]) - probabilidad: 0.17%\n",
      "1980. array('b', [9, 18, 20, 22, 25]) - probabilidad: 0.17%\n",
      "1981. array('b', [2, 18, 24, 26, 27]) - probabilidad: 0.17%\n",
      "1982. array('b', [9, 18, 20, 21, 25]) - probabilidad: 0.17%\n",
      "1983. array('b', [2, 5, 6, 16, 17]) - probabilidad: 0.17%\n",
      "1984. array('b', [2, 14, 25, 26, 27]) - probabilidad: 0.17%\n",
      "1985. array('b', [6, 14, 23, 27, 28]) - probabilidad: 0.17%\n",
      "1986. array('b', [6, 17, 23, 26, 28]) - probabilidad: 0.17%\n",
      "1987. array('b', [5, 17, 23, 25, 26]) - probabilidad: 0.17%\n",
      "1988. array('b', [9, 17, 20, 22, 23]) - probabilidad: 0.17%\n",
      "1989. array('b', [2, 15, 25, 26, 28]) - probabilidad: 0.17%\n",
      "1990. array('b', [5, 17, 24, 25, 27]) - probabilidad: 0.17%\n",
      "1991. array('b', [1, 19, 24, 26, 28]) - probabilidad: 0.17%\n",
      "1992. array('b', [3, 5, 6, 14, 19]) - probabilidad: 0.17%\n",
      "1993. array('b', [2, 20, 23, 25, 28]) - probabilidad: 0.16%\n",
      "1994. array('b', [7, 15, 24, 25, 27]) - probabilidad: 0.16%\n",
      "1995. array('b', [11, 15, 21, 24, 26]) - probabilidad: 0.16%\n",
      "1996. array('b', [11, 14, 19, 27, 28]) - probabilidad: 0.16%\n",
      "1997. array('b', [8, 17, 22, 24, 25]) - probabilidad: 0.16%\n",
      "1998. array('b', [1, 20, 22, 27, 28]) - probabilidad: 0.16%\n",
      "1999. array('b', [6, 20, 21, 22, 25]) - probabilidad: 0.16%\n",
      "2000. array('b', [11, 14, 19, 20, 21]) - probabilidad: 0.16%\n",
      "2001. array('b', [5, 18, 21, 27, 28]) - probabilidad: 0.16%\n",
      "2002. array('b', [3, 17, 23, 27, 28]) - probabilidad: 0.16%\n",
      "2003. array('b', [1, 4, 25, 27, 28]) - probabilidad: 0.16%\n",
      "2004. array('b', [13, 14, 17, 21, 23]) - probabilidad: 0.16%\n",
      "2005. array('b', [8, 19, 21, 23, 28]) - probabilidad: 0.16%\n",
      "2006. array('b', [4, 20, 22, 26, 27]) - probabilidad: 0.16%\n",
      "2007. array('b', [1, 12, 25, 27, 28]) - probabilidad: 0.16%\n",
      "2008. array('b', [9, 14, 17, 18, 19]) - probabilidad: 0.16%\n",
      "2009. array('b', [13, 14, 20, 26, 28]) - probabilidad: 0.16%\n",
      "2010. array('b', [11, 15, 20, 25, 26]) - probabilidad: 0.16%\n",
      "2011. array('b', [13, 15, 17, 26, 27]) - probabilidad: 0.16%\n",
      "2012. array('b', [13, 15, 17, 26, 27]) - probabilidad: 0.16%\n",
      "2013. array('b', [13, 15, 17, 26, 27]) - probabilidad: 0.16%\n",
      "2014. array('b', [8, 9, 11, 13, 20]) - probabilidad: 0.16%\n",
      "2015. array('b', [5, 12, 25, 26, 28]) - probabilidad: 0.16%\n",
      "2016. array('b', [11, 13, 22, 25, 27]) - probabilidad: 0.16%\n",
      "2017. array('b', [5, 15, 24, 27, 28]) - probabilidad: 0.16%\n",
      "2018. array('b', [3, 8, 25, 27, 28]) - probabilidad: 0.16%\n",
      "2019. array('b', [3, 8, 25, 27, 28]) - probabilidad: 0.16%\n",
      "2020. array('b', [9, 15, 21, 27, 28]) - probabilidad: 0.15%\n",
      "2021. array('b', [10, 17, 20, 26, 27]) - probabilidad: 0.15%\n",
      "2022. array('b', [10, 11, 13, 16, 18]) - probabilidad: 0.15%\n",
      "2023. array('b', [9, 12, 24, 26, 27]) - probabilidad: 0.15%\n",
      "2024. array('b', [9, 12, 24, 26, 27]) - probabilidad: 0.15%\n",
      "2025. array('b', [2, 17, 24, 27, 28]) - probabilidad: 0.15%\n",
      "2026. array('b', [12, 16, 18, 26, 28]) - probabilidad: 0.15%\n",
      "2027. array('b', [7, 18, 22, 26, 28]) - probabilidad: 0.15%\n",
      "2028. array('b', [9, 14, 24, 25, 28]) - probabilidad: 0.15%\n",
      "2029. array('b', [3, 19, 21, 27, 28]) - probabilidad: 0.15%\n",
      "2030. array('b', [12, 14, 15, 27, 28]) - probabilidad: 0.15%\n",
      "2031. array('b', [1, 19, 23, 25, 26]) - probabilidad: 0.15%\n",
      "2032. array('b', [9, 16, 22, 23, 24]) - probabilidad: 0.15%\n",
      "2033. array('b', [6, 16, 24, 25, 27]) - probabilidad: 0.15%\n",
      "2034. array('b', [12, 16, 18, 24, 26]) - probabilidad: 0.15%\n",
      "2035. array('b', [10, 12, 22, 27, 28]) - probabilidad: 0.15%\n",
      "2036. array('b', [8, 13, 24, 26, 27]) - probabilidad: 0.15%\n",
      "2037. array('b', [9, 17, 22, 26, 28]) - probabilidad: 0.15%\n",
      "2038. array('b', [11, 14, 21, 25, 26]) - probabilidad: 0.15%\n",
      "2039. array('b', [12, 15, 19, 25, 26]) - probabilidad: 0.15%\n",
      "2040. array('b', [10, 11, 24, 25, 27]) - probabilidad: 0.15%\n",
      "2041. array('b', [8, 15, 24, 25, 28]) - probabilidad: 0.15%\n",
      "2042. array('b', [1, 4, 8, 12, 17]) - probabilidad: 0.15%\n",
      "2043. array('b', [9, 19, 20, 21, 27]) - probabilidad: 0.15%\n",
      "2044. array('b', [12, 16, 19, 26, 28]) - probabilidad: 0.15%\n",
      "2045. array('b', [13, 14, 19, 20, 23]) - probabilidad: 0.15%\n",
      "2046. array('b', [9, 13, 15, 16, 19]) - probabilidad: 0.15%\n",
      "2047. array('b', [6, 20, 21, 26, 28]) - probabilidad: 0.15%\n",
      "2048. array('b', [6, 20, 21, 26, 28]) - probabilidad: 0.15%\n",
      "2049. array('b', [6, 19, 21, 24, 25]) - probabilidad: 0.15%\n",
      "2050. array('b', [8, 15, 22, 27, 28]) - probabilidad: 0.15%\n",
      "2051. array('b', [6, 13, 24, 27, 28]) - probabilidad: 0.15%\n",
      "2052. array('b', [6, 13, 24, 27, 28]) - probabilidad: 0.15%\n",
      "2053. array('b', [9, 15, 23, 26, 28]) - probabilidad: 0.15%\n",
      "2054. array('b', [5, 19, 22, 26, 27]) - probabilidad: 0.15%\n",
      "2055. array('b', [12, 17, 18, 21, 26]) - probabilidad: 0.15%\n",
      "2056. array('b', [10, 14, 21, 27, 28]) - probabilidad: 0.15%\n",
      "2057. array('b', [2, 5, 25, 27, 28]) - probabilidad: 0.15%\n",
      "2058. array('b', [2, 21, 22, 23, 25]) - probabilidad: 0.15%\n",
      "2059. array('b', [4, 18, 24, 26, 28]) - probabilidad: 0.15%\n",
      "2060. array('b', [5, 20, 22, 25, 27]) - probabilidad: 0.15%\n",
      "2061. array('b', [3, 14, 25, 26, 27]) - probabilidad: 0.14%\n",
      "2062. array('b', [3, 14, 25, 26, 27]) - probabilidad: 0.14%\n",
      "2063. array('b', [8, 18, 21, 26, 28]) - probabilidad: 0.14%\n",
      "2064. array('b', [8, 18, 21, 26, 28]) - probabilidad: 0.14%\n",
      "2065. array('b', [4, 14, 25, 26, 28]) - probabilidad: 0.14%\n",
      "2066. array('b', [11, 17, 21, 24, 28]) - probabilidad: 0.14%\n",
      "2067. array('b', [2, 20, 23, 24, 25]) - probabilidad: 0.14%\n",
      "2068. array('b', [12, 17, 18, 23, 26]) - probabilidad: 0.14%\n",
      "2069. array('b', [9, 14, 24, 26, 28]) - probabilidad: 0.14%\n",
      "2070. array('b', [3, 15, 25, 26, 28]) - probabilidad: 0.14%\n",
      "2071. array('b', [10, 12, 24, 26, 28]) - probabilidad: 0.14%\n",
      "2072. array('b', [4, 19, 23, 24, 27]) - probabilidad: 0.14%\n",
      "2073. array('b', [7, 12, 24, 27, 28]) - probabilidad: 0.14%\n",
      "2074. array('b', [13, 14, 20, 25, 27]) - probabilidad: 0.14%\n",
      "2075. array('b', [6, 15, 23, 27, 28]) - probabilidad: 0.14%\n",
      "2076. array('b', [7, 16, 22, 27, 28]) - probabilidad: 0.14%\n",
      "2077. array('b', [13, 17, 19, 24, 28]) - probabilidad: 0.14%\n",
      "2078. array('b', [6, 18, 22, 25, 26]) - probabilidad: 0.14%\n",
      "2079. array('b', [7, 18, 21, 25, 26]) - probabilidad: 0.14%\n",
      "2080. array('b', [12, 18, 19, 20, 28]) - probabilidad: 0.14%\n",
      "2081. array('b', [1, 19, 24, 26, 27]) - probabilidad: 0.14%\n",
      "2082. array('b', [3, 20, 21, 27, 28]) - probabilidad: 0.14%\n",
      "2083. array('b', [12, 13, 22, 23, 25]) - probabilidad: 0.14%\n",
      "2084. array('b', [8, 15, 24, 26, 28]) - probabilidad: 0.14%\n",
      "2085. array('b', [13, 15, 19, 26, 27]) - probabilidad: 0.14%\n",
      "2086. array('b', [8, 19, 20, 23, 26]) - probabilidad: 0.14%\n",
      "2087. array('b', [8, 19, 20, 23, 26]) - probabilidad: 0.14%\n",
      "2088. array('b', [9, 10, 23, 27, 28]) - probabilidad: 0.14%\n",
      "2089. array('b', [9, 10, 23, 27, 28]) - probabilidad: 0.14%\n",
      "2090. array('b', [9, 10, 23, 27, 28]) - probabilidad: 0.14%\n",
      "2091. array('b', [8, 18, 22, 24, 27]) - probabilidad: 0.14%\n",
      "2092. array('b', [6, 14, 24, 27, 28]) - probabilidad: 0.14%\n",
      "2093. array('b', [12, 14, 20, 21, 23]) - probabilidad: 0.14%\n",
      "2094. array('b', [7, 18, 22, 25, 27]) - probabilidad: 0.14%\n",
      "2095. array('b', [11, 14, 22, 26, 27]) - probabilidad: 0.14%\n",
      "2096. array('b', [13, 16, 18, 21, 25]) - probabilidad: 0.14%\n",
      "2097. array('b', [1, 3, 4, 27, 28]) - probabilidad: 0.14%\n",
      "2098. array('b', [10, 14, 23, 26, 28]) - probabilidad: 0.14%\n",
      "2099. array('b', [13, 14, 21, 22, 25]) - probabilidad: 0.14%\n",
      "2100. array('b', [4, 21, 22, 24, 28]) - probabilidad: 0.14%\n",
      "2101. array('b', [9, 13, 24, 25, 27]) - probabilidad: 0.14%\n",
      "2102. array('b', [10, 18, 21, 24, 28]) - probabilidad: 0.14%\n",
      "2103. array('b', [10, 14, 15, 16, 20]) - probabilidad: 0.14%\n",
      "2104. array('b', [8, 18, 20, 25, 26]) - probabilidad: 0.14%\n",
      "2105. array('b', [8, 16, 23, 25, 28]) - probabilidad: 0.14%\n",
      "2106. array('b', [13, 14, 16, 18, 22]) - probabilidad: 0.14%\n",
      "2107. array('b', [5, 20, 23, 24, 28]) - probabilidad: 0.14%\n",
      "2108. array('b', [5, 20, 23, 24, 28]) - probabilidad: 0.14%\n",
      "2109. array('b', [8, 16, 21, 27, 28]) - probabilidad: 0.13%\n",
      "2110. array('b', [12, 15, 20, 23, 24]) - probabilidad: 0.13%\n",
      "2111. array('b', [11, 12, 13, 18, 19]) - probabilidad: 0.13%\n",
      "2112. array('b', [8, 10, 24, 27, 28]) - probabilidad: 0.13%\n",
      "2113. array('b', [3, 20, 23, 26, 28]) - probabilidad: 0.13%\n",
      "2114. array('b', [3, 19, 23, 24, 25]) - probabilidad: 0.13%\n",
      "2115. array('b', [11, 14, 16, 19, 20]) - probabilidad: 0.13%\n",
      "2116. array('b', [9, 15, 23, 25, 27]) - probabilidad: 0.13%\n",
      "2117. array('b', [7, 8, 25, 26, 28]) - probabilidad: 0.13%\n",
      "2118. array('b', [1, 13, 25, 27, 28]) - probabilidad: 0.13%\n",
      "2119. array('b', [10, 13, 17, 18, 19]) - probabilidad: 0.13%\n",
      "2120. array('b', [4, 8, 11, 13, 16]) - probabilidad: 0.13%\n",
      "2121. array('b', [2, 20, 23, 26, 27]) - probabilidad: 0.13%\n",
      "2122. array('b', [4, 8, 25, 27, 28]) - probabilidad: 0.13%\n",
      "2123. array('b', [4, 18, 24, 25, 27]) - probabilidad: 0.13%\n",
      "2124. array('b', [6, 9, 25, 26, 27]) - probabilidad: 0.13%\n",
      "2125. array('b', [1, 20, 24, 26, 27]) - probabilidad: 0.13%\n",
      "2126. array('b', [12, 13, 19, 27, 28]) - probabilidad: 0.13%\n",
      "2127. array('b', [8, 18, 21, 25, 27]) - probabilidad: 0.13%\n",
      "2128. array('b', [8, 18, 21, 25, 27]) - probabilidad: 0.13%\n",
      "2129. array('b', [13, 16, 19, 25, 28]) - probabilidad: 0.13%\n",
      "2130. array('b', [9, 14, 24, 25, 27]) - probabilidad: 0.13%\n",
      "2131. array('b', [4, 13, 25, 26, 27]) - probabilidad: 0.13%\n",
      "2132. array('b', [4, 13, 25, 26, 27]) - probabilidad: 0.13%\n",
      "2133. array('b', [6, 19, 22, 26, 28]) - probabilidad: 0.13%\n",
      "2134. array('b', [12, 14, 21, 26, 27]) - probabilidad: 0.13%\n",
      "2135. array('b', [10, 12, 24, 25, 27]) - probabilidad: 0.13%\n",
      "2136. array('b', [5, 19, 22, 25, 26]) - probabilidad: 0.13%\n",
      "2137. array('b', [9, 16, 17, 19, 20]) - probabilidad: 0.13%\n",
      "2138. array('b', [12, 14, 18, 27, 28]) - probabilidad: 0.13%\n",
      "2139. array('b', [10, 18, 22, 23, 28]) - probabilidad: 0.13%\n",
      "2140. array('b', [10, 14, 23, 24, 25]) - probabilidad: 0.13%\n",
      "2141. array('b', [12, 17, 18, 26, 28]) - probabilidad: 0.13%\n",
      "2142. array('b', [9, 20, 21, 23, 28]) - probabilidad: 0.13%\n",
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      "8975. array('b', [22, 24, 25, 26, 27]) - probabilidad: 0.00%\n",
      "8976. array('b', [23, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8977. array('b', [24, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8978. array('b', [24, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8979. array('b', [23, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8980. array('b', [22, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8981. array('b', [23, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8982. array('b', [24, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8983. array('b', [22, 23, 24, 26, 27]) - probabilidad: 0.00%\n",
      "8984. array('b', [23, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8985. array('b', [22, 24, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8986. array('b', [23, 24, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8987. array('b', [24, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8988. array('b', [23, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8989. array('b', [22, 23, 24, 25, 28]) - probabilidad: 0.00%\n",
      "8990. array('b', [23, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8991. array('b', [23, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8992. array('b', [23, 24, 25, 27, 28]) - probabilidad: 0.00%\n",
      "8993. array('b', [22, 24, 25, 27, 28]) - probabilidad: 0.00%\n",
      "8994. array('b', [22, 23, 25, 26, 27]) - probabilidad: 0.00%\n",
      "8995. array('b', [22, 24, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8996. array('b', [22, 24, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8997. array('b', [23, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "8998. array('b', [23, 24, 25, 26, 27]) - probabilidad: 0.00%\n",
      "8999. array('b', [23, 25, 26, 27, 28]) - probabilidad: 0.00%\n",
      "9000. array('b', [22, 23, 26, 27, 28]) - probabilidad: 0.00%\n"
     ]
    }
   ],
   "source": [
    "combinaciones_probables = ch.generate_combinations(90)\n",
    "for i, c in enumerate(combinaciones_probables):\n",
    "    print(f'{i+1}. {c[0]} - probabilidad: {c[1]*100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. [2, 7, 17, 24, 28] - probabilidad: 5.06%\n",
      "2. [2, 7, 17, 19, 26] - probabilidad: 4.10%\n",
      "3. [5, 6, 11, 19, 27] - probabilidad: 2.94%\n",
      "4. [3, 12, 17, 24, 27] - probabilidad: 2.77%\n",
      "5. [3, 6, 18, 16, 26] - probabilidad: 2.43%\n",
      "6. [7, 5, 15, 24, 26] - probabilidad: 1.28%\n",
      "7. [8, 5, 16, 19, 26] - probabilidad: 1.18%\n",
      "8. [6, 4, 10, 20, 22] - probabilidad: 0.73%\n",
      "9. [8, 7, 16, 24, 22] - probabilidad: 0.66%\n"
     ]
    }
   ],
   "source": [
    "combinaciones_probables_top_10 = ch.generate_combinations_top_frequencies(9)\n",
    "for i, c in enumerate(combinaciones_probables_top_10):\n",
    "    print(f'{i+1}. {c[0]} - probabilidad: {c[1]*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch.guardar_combinaciones_probables(\"resultados/probables.csv\",0.08,iterations=1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generar_combinacion_probabilidad(dummy_arg):\n",
    "    combinacion = ch.generate_random_combination()\n",
    "    probabilidad = ch.calculate_probability_v3(pd.Series(combinacion,index=['R1','R2','R3','R4','R5']))\n",
    "    return (*combinacion, probabilidad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(7244503, 1, 7, 13, 20, 28, 0.007562025435006263), (7494503, 1, 7, 13, 20, 28, 0.007562025435006263), (7744503, 1, 7, 13, 20, 28, 0.007562025435006263), (7994503, 1, 7, 13, 20, 28, 0.007562025435006263), (8244503, 1, 7, 13, 20, 28, 0.007562025435006263), (8494503, 1, 7, 13, 20, 28, 0.007562025435006263), (8744503, 1, 7, 13, 20, 28, 0.007562025435006263), (8994503, 1, 7, 13, 20, 28, 0.007562025435006263), (9244503, 1, 7, 13, 20, 28, 0.007562025435006263), (9494503, 1, 7, 13, 20, 28, 0.007562025435006263)]\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import sqlite3\n",
    "from multiprocess import Pool\n",
    "\n",
    "# Conexión a la base de datos\n",
    "conn = sqlite3.connect('chispazoon.db')\n",
    "c = conn.cursor()\n",
    "\n",
    "# Creación de la tabla para almacenar los resultados\n",
    "c.execute('''CREATE TABLE IF NOT EXISTS resultados\n",
    "             (id INTEGER PRIMARY KEY AUTOINCREMENT, r1 INTEGER, r2 INTEGER, r3 INTEGER, r4 INTEGER, r5 INTEGER, probabilidad REAL)''')\n",
    "conn.commit()\n",
    "\n",
    "# Función para generar combinaciones y calcular probabilidad en paralelo\n",
    "n = 10000\n",
    "resultados = []\n",
    "pool = Pool()  # Inicialización del pool de procesos\n",
    "resultados = pool.map(generar_combinacion_probabilidad, range(n))  # Ejecución en paralelo de la función\n",
    "pool.close()  # Cierre del pool de procesos\n",
    "pool.join()  # Espera a que todos los procesos finalicen\n",
    "\n",
    "# Almacenamiento de resultados en la base de datos\n",
    "c.executemany('INSERT INTO resultados (R1, R2, R3, R4, R5, probabilidad) VALUES (?, ?, ?, ?, ?, ?)', resultados)\n",
    "conn.commit()\n",
    "\n",
    "# Selección de los resultados con la mayor probabilidad\n",
    "c.execute('SELECT * FROM resultados ORDER BY probabilidad DESC LIMIT 10')\n",
    "top_resultados = c.fetchall()\n",
    "print(top_resultados)\n",
    "\n",
    "# Cierre de la conexión a la base de datos\n",
    "conn.close()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FREQUENCIA DE LOS NUMEROS EN COMBINACIONES GANADORAS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# num_frequencies = ch.data.iloc[:, 1:6].stack().value_counts()\n",
    "import matplotlib\n",
    "\n",
    "# matplotlib.rcParams.update({'font.size': 14})\n",
    "matplotlib.rcParams['axes.facecolor'] = 'lightgray'\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 4))\n",
    "ax.bar(ch.num_frequencies.index, ch.num_frequencies.values, color='orange')\n",
    "ax.set_xlabel('Número ganador')\n",
    "ax.set_ylabel('Frecuencia')\n",
    "plt.xticks(range(1,29),rotation=90)\n",
    "ax.set_title('Frecuencia de números ganadores en sorteos ganadores')\n",
    "fig.set_facecolor('lightblue')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## desviación estándar de la combinación de números más frecuentes según su posición"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   CONCURSO  R1  R2  R3  R4  R5       FECHA\n",
      "0      9668   5  10  13  15  20  02/03/2023\n",
      "1      9667   6   7  19  23  24  02/03/2023\n",
      "2      9666   7  19  20  25  28  01/03/2023\n",
      "3      9665   1   6  16  18  23  01/03/2023\n",
      "4      9664  11  12  13  17  26  28/02/2023\n"
     ]
    }
   ],
   "source": [
    "from itertools import product\n",
    "from math import sqrt\n",
    "combinations = list(product(ch.freq_pos[0].sort_values(ascending=False).head(2).index.tolist(), \n",
    "                             ch.freq_pos[1].sort_values(ascending=False).head(2).index.tolist(),\n",
    "                             ch.freq_pos[2].sort_values(ascending=False).head(2).index.tolist(), \n",
    "                             ch.freq_pos[3].sort_values(ascending=False).head(2).index.tolist(), \n",
    "                             ch.freq_pos[4].sort_values(ascending=False).head(2).index.tolist(), ))\n",
    "\n",
    "top_nums = []\n",
    "for i in range(1, 6):\n",
    "    # Ordenar los valores de freq_pos[i-1] de mayor a menor frecuencia\n",
    "    sorted_values = ch.freq_pos[i-1].sort_values(ascending=False)\n",
    "    # Obtener los 10 valores más frecuentes y convertirlos a una lista\n",
    "    top = sorted_values.head(10).index.tolist()\n",
    "    top_nums.append(top)\n",
    "\n",
    "# print(len(combinations),combinations[:90])\n",
    "print(ch.data.head())\n",
    "# frecuencias = ch.data['combinacion'].apply(lambda x: sum(combinations.apply(lambda y: y == x)))\n",
    "frequencies = []\n",
    "for combination in combinations:\n",
    "    freq = ch.data.apply(lambda x: all(elem in x[1:6] for elem in combination), axis=1).sum()\n",
    "    frequencies.append(freq)\n",
    "\n",
    "# # Calcular la media y la desviación estándar de las frecuencias\n",
    "mean = sum(frequencies) / len(frequencies)\n",
    "std_dev = sqrt(sum([(x - mean)**2 for x in frequencies]) / len(frequencies))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 6000x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crear una tabla de frecuencias para cada posición\n",
    "\n",
    "# Crear la figura y los subplots\n",
    "fig, axs = plt.subplots(nrows=1, ncols=5, figsize=(60, 6))\n",
    "fig.set_facecolor('lightblue')\n",
    "\n",
    "# Crear cada tabla de frecuencias como un gráfico de barras y ajustar los límites del eje x\n",
    "for i in range(5):\n",
    "    axs[i].bar(ch.frequencies_per_position[i].index, ch.frequencies_per_position[i].values,color=\"orange\")\n",
    "    axs[i].set_title(f'Posición {i+1}')\n",
    "    axs[i].set_xlim(ch.ranges_per_position[i])\n",
    "    axs[i].set_xticks(range(*ch.ranges_per_position[i]))\n",
    "    axs[i].set_xticklabels(range(*ch.ranges_per_position[i]))\n",
    "# Ajustar la posición de los subplots y mostrar la figura\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "relaciones = pd.DataFrame(columns=['posicion', 'num_actual', 'num_siguiente', 'relacion'])\n",
    "\n",
    "# Calcular las relaciones entre los números más frecuentes en cada posición\n",
    "for i in range(1, 5):\n",
    "    # Obtener la lista de los 10 números más frecuentes en la posición actual\n",
    "    top_actual = freq_pos[i-1].sort_values(ascending=False).head(10)\n",
    "    \n",
    "    # Obtener la lista de los 10 números más frecuentes en la posición siguiente\n",
    "    top_siguiente = freq_pos[i].sort_values(ascending=False).head(10)\n",
    "    \n",
    "    # Calcular la relación entre cada par de números más frecuentes\n",
    "    for num_actual, freq_actual in top_actual.items():\n",
    "        for num_siguiente, freq_siguiente in top_siguiente.items():\n",
    "            relacion = freq_actual / freq_siguiente\n",
    "            nueva_relacion ={'posicion': i, 'num_actual': num_actual, 'num_siguiente': num_siguiente, 'relacion': relacion}\n",
    "            relaciones = pd.concat([relaciones, pd.DataFrame.from_records([{'posicion': i, 'num_actual': num_actual, 'num_siguiente': num_siguiente, 'relacion': relacion}])])\n",
    "\n",
    "\n",
    "grupos = relaciones.groupby('posicion')\n",
    "\n",
    "# Crear una figura con subplots para cada posición\n",
    "fig, axs = plt.subplots(len(grupos), figsize=(30, 3 * len(grupos)))\n",
    "fig.set_facecolor('lightblue')\n",
    "\n",
    "\n",
    "# Iterar sobre cada grupo y crear una gráfica para cada uno\n",
    "for i, (posicion, datos) in enumerate(grupos):\n",
    "    axs[i].bar(datos['num_siguiente'], datos['relacion'], color='orange')\n",
    "    axs[i].set_xlabel('Número siguiente')\n",
    "    axs[i].set_ylabel('Relación')\n",
    "    axs[i].set_title(f'Relación entre los 10 números más frecuentes en la posición {posicion}')\n",
    "    axs[i].set_ylim(0, 5) # Ajustar el límite vertical para ver mejor las relaciones\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}