GeneticAlgorithms.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPLSWpu/jb+bLKw4lIIcqHI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Freddy-94/GeneticAlgorithms/blob/main/GeneticAlgorithms.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Alfredo Bernal Luna\n",
        "### Practice No. 3. Genetic Algorithms."
      ],
      "metadata": {
        "id": "_hwm50Tn7hf2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modules to be used in this program:"
      ],
      "metadata": {
        "id": "OFwH-eoRACP1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from numpy import sqrt, cos, e, pi, exp # for Ackley's function plot\n",
        "import math\n",
        "import random\n",
        "import itertools\n",
        "from decimal import Decimal\n",
        "import matplotlib.pyplot as plt  # to plot"
      ],
      "metadata": {
        "id": "EU4LoTx1AGEe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Definition of cost functions:\n",
        "\n",
        "1. Parabola.\n",
        "\n",
        "   Let $f : {[-5.12, 5.12]} ^n \\to \\mathbb{R}$ given by:\n",
        "\n",
        "  \\begin{align}\n",
        "          f(\\mathbf{x}) = \\sum_{i=1}^n x_{i}^{2} \n",
        "  \\end{align}\n",
        "\n",
        "2. Rosenbrock's function.\n",
        "\n",
        "   Let $f : {[-2.048, 2.048]} ^n \\to \\mathbb{R}$ given by:\n",
        "\n",
        "  \\begin{align}\n",
        "          f(\\mathbf{x}) = \\sum_{i=1}^n 100 (x_{i}^{2} - x_{i+1})^{2} + (1 - x_{i})^{2}\n",
        "  \\end{align}\n",
        "\n",
        "3. Ackley's function.\n",
        "\n",
        "   Let $f : {[-32.768, 32.768]} ^n \\to \\mathbb{R}$ given by:\n",
        "\n",
        "  \\begin{align}\n",
        "          f(\\mathbf{x}) = -20\\mathrm{e}^{-0.2\\sqrt{1/n\\sum_{i=1}^n x_{i}^{2}}} - \\mathrm{e}^{1/n\\sum_{i=1}^n \\cos {2\\pi x_{i}}} + 20 + \\mathrm{e}\n",
        "  \\end{align}"
      ],
      "metadata": {
        "id": "G_ke2uHD8hVL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def parabola(n, x):\n",
        "    parabola_value = 0\n",
        "    for i in range(0, n):\n",
        "        parabola_value += x[i]**2\n",
        "    return parabola_value\n",
        "\n",
        "def rosenbrock(n, x):\n",
        "    sum = 0\n",
        "    for i in range(0, n-1):\n",
        "        sum += ((100 * (x[i]**2 - x[i+1])**2) + (1 - x[i])**2)\n",
        "    rosenbrock_value = sum\n",
        "    return rosenbrock_value\n",
        "\n",
        "def ackley(n, x): \n",
        "    gen_cos_avg = 0\n",
        "    root_mean_square = 0\n",
        "    for i in range(0, n):\n",
        "        gen_cos_avg += cos(2*pi*x[i])\n",
        "        root_mean_square += x[i]**2\n",
        "    root_mean_square = sqrt((1/n)*(root_mean_square))\n",
        "    gen_cos_avg /= n          \n",
        "    ackley = -20*exp((-0.2)*(root_mean_square)) - exp(gen_cos_avg) +  20 + e\n",
        "    return ackley"
      ],
      "metadata": {
        "id": "RGx-XnG-_3Ga"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test of the cost functions (dimension = 10):"
      ],
      "metadata": {
        "id": "LYIfAvP4-qGv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.   Parabola:**"
      ],
      "metadata": {
        "id": "QRpvt2gb_Bo6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "parabola(10, [3.1315, 2.6548, 1.265, 0.264, 0.00156, 2.165, 3.125, 2.568, 1.698, 1.145])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0E6HvXv8AYbQ",
        "outputId": "7ee8dc1c-f84f-45d7-f20a-1ddc4690833d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43.765881723599996"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Rosenbrock:**"
      ],
      "metadata": {
        "id": "CxVpe1aOFloP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rosenbrock(10, [1.0261, -1.6548, -1.265, 0.264, 0.00156, -2.0165, -0.1235, 2.0326, 1.698, 1.145])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Vq7LrVDFvno",
        "outputId": "4cc47bac-a6c8-46f8-f837-676b753ed3d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6003.8176985519785"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Ackley:**"
      ],
      "metadata": {
        "id": "FA5uuDgmI6ZH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ackley(10, [31.0261, -31.6548, -21.265, 20.264, 10.00156, -20.0165, -30.1235, 20.0326, 10.698, 10.145])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EG0cn1wiGq_t",
        "outputId": "143b9785-6baf-4b5a-ed7a-a26bf5dad30c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-45.61924996821731"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Graphs of cost functions, in 3D:"
      ],
      "metadata": {
        "id": "smLUBDKS_pkE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_cost_function_3d(vars_domain, num_points_to_plot, cost_fun):\n",
        "    \"\"\"\n",
        "    Function to plot the graphs of the cost functions, in 3 dimensions. This function might be \n",
        "    useful to gain more intuition on the problem at hand\n",
        "    \"\"\"\n",
        "    x1 = np.linspace(vars_domain[0], vars_domain[1], num_points_to_plot)\n",
        "    x2 = x1\n",
        "    coord_array_x1, coord_array_x2 = np.meshgrid(x1, x2)\n",
        "    cost = cost_fun(2, [coord_array_x1, coord_array_x2]) \n",
        "    fig = plt.figure()\n",
        "    ax = plt.axes(projection = '3d')\n",
        "    ax.plot_surface(coord_array_x1, coord_array_x2, cost, rstride = 1, cstride = 1,\n",
        "                    cmap = 'viridis', edgecolor = 'none')\n",
        "    ax.set_xlabel('x')\n",
        "    ax.set_ylabel('y')\n",
        "    ax.set_zlabel('z')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "xpG1DfPyHN2l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.   Parabola:** "
      ],
      "metadata": {
        "id": "vWO0FEUTFPSs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_cost_function_3d([-5.12, 5.12], 100, parabola)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "xHA9vaJ4HXWc",
        "outputId": "07b6426f-bb29-4f01-9c85-371dca359628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Rosenbrock:** "
      ],
      "metadata": {
        "id": "CVu83KiwHqcn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_cost_function_3d([-2.048, 2.048], 100, rosenbrock)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "YFedTCvu_vOL",
        "outputId": "bbf3dcfc-e795-46f7-d661-0de416a986cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Ackley:**"
      ],
      "metadata": {
        "id": "MZTixTdVIxUu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_cost_function_3d([-32.768, 32.768], 100, ackley)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "4cLiwXg2JO5Z",
        "outputId": "56c309bf-19c9-4e52-d85e-19427dd7ecd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Binary encoding (decoding) of variables of cost functions:"
      ],
      "metadata": {
        "id": "afS85aXaJyhQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def bit_size_binary(lim_inf, lim_sup, precision):\n",
        "    \"\"\"\n",
        "    Function to obtain the number of bits needed in order to convert from decimal representation\n",
        "    to binary.\n",
        "    \"\"\"\n",
        "    bit_size = math.log((lim_sup) * (10**precision) - (lim_inf) * (10**precision), 2)\n",
        "    return math.ceil(bit_size)\n",
        "\n",
        "def int_to_bin(num, bit_size = 10):\n",
        "    \"\"\"\n",
        "    Function to convert an integer from decimal representation, to a binary representation, given\n",
        "    the size of the representation we want to show; i.e., given the number of bits we are given.\n",
        "    \"\"\"\n",
        "    bin = \"\"\n",
        "    power = 1\n",
        "    while power <= num//2:\n",
        "        power *= 2\n",
        "    while power > 0:\n",
        "        if num < power:\n",
        "            bin += \"0\"\n",
        "        else:\n",
        "            bin += \"1\"\n",
        "            num -= power\n",
        "        power //= 2\n",
        "    return bin.zfill(bit_size)\n",
        "    \n",
        "def binary_coding_dict(bit_size, lim_inf, lim_sup, precision):\n",
        "    \"\"\"\n",
        "    This function generates a dictionary of a decimal representation of a value that a \n",
        "    variable in the interval might take, along with its corresponding binary representation; i.e.,\n",
        "    the set of keys of the dictionary consists of the decimal representations of a value that a variable\n",
        "    in the interval might take, with some given precision, and its values are the corresponding binary\n",
        "    representations of the decimal representation.\n",
        "\n",
        "    bit_size is the size of the binary representation that we'll be using to represent each one of its coordinates.\n",
        "    lim_inf and lim_sup, are the corresponding bounds of the interval that all of our variables can take. This means that \n",
        "    our program currently supports only representations for variables having the same domain; i.e., vectors in a domain \n",
        "    of the form: [a,b]x[a,b]x...x[a,b].\n",
        "    Hence, this also means that our program currently works for functions f of the form:\n",
        "    \n",
        "    f : [a,b]x[a,b]x...x[a,b] -> R\n",
        "    \"\"\"\n",
        "    dict_binary_coding = {}\n",
        "    prec = 1 * (10**(-precision))\n",
        "    count = 0\n",
        "    \"\"\"\n",
        "    while loop to obtain all possible values that a variable can take\n",
        "    \"\"\"    \n",
        "    while  round(lim_inf, precision) <= round(lim_sup, precision):\n",
        "        dict_binary_coding[lim_inf] = int_to_bin(count, bit_size)\n",
        "        count += 1\n",
        "        lim_inf = round(lim_inf + prec, precision)\n",
        "    return dict_binary_coding        \n",
        "            \n",
        "def binary_encoding(x, prec, bin_coding_dict):\n",
        "    \"\"\"\n",
        "    This function recover the binary representation for all of the variables that the \n",
        "    input of the cost function might take, and returns it's corresponding vector in a \n",
        "    string binary representation.\n",
        "    x is the input vector in decimal representation, and bin_coding_dict\n",
        "    is the dictionary obtained in the function binary_coding_dict above.    \n",
        "    \"\"\"\n",
        "    bin_vector = []\n",
        "    # print(bin_coding_dict)\n",
        "    for i in range(0, len(x)):\n",
        "        try:\n",
        "            bin_vector.append(bin_coding_dict[round(x[i], prec)])\n",
        "        except:\n",
        "            print(f\"\\nThe function is not defined for the passed number: {x[i]}\\n\")\n",
        "            print(f\"Please provide an accurate input for coordinate: {i+1}\\n\")\n",
        "    return bin_vector\n",
        "\n",
        "def binary_decoding(bin_vector, bin_coding_dict):\n",
        "    \"\"\"\n",
        "    This function recover the \"original\" vector in its decimal representation, given we pass its\n",
        "    binary representation, by querying the dictionary having the decimal and associated binary \n",
        "    representations, defined in the function above. Notice this implementation might not be very\n",
        "    efficient, so that's why we program the below function (so there's no need to build a dict)\n",
        "    each time we need to decode a binary vector. However, we think it was helpful to see that\n",
        "    below formula actually works, by taking a peak at this dict\n",
        "    \"\"\"\n",
        "    decoded_vector = []\n",
        "    for coordinate in bin_vector:\n",
        "        decimals = [dec for dec, bin in bin_coding_dict.items() if bin == coordinate]\n",
        "        decoded_vector += decimals   \n",
        "    return decoded_vector\n",
        "\n",
        "def bin_decoding_no_dict(bin_vector, lim_sup, precision):\n",
        "    \"\"\"\n",
        "    Binary decoding function for a given bin_vector, and a given precision.\n",
        "    As per above implementations, we are considering vectors that share the same\n",
        "    domain, for each one of its variables; i.e., the input vector can take values\n",
        "    in a domain of the form:  [a, b] x [a, b] x ... x [a, b]\n",
        "    This is the same as the functions that our current program can handle.\n",
        "    \"\"\"\n",
        "    dec_vect = []\n",
        "    for bin_repr in bin_vector:\n",
        "        bin_repr = bin_repr[::-1] # reverse current binary representation\n",
        "        dec_repr = 0\n",
        "        for i in range(len(bin_repr)):\n",
        "            if bin_repr[i] == '1':\n",
        "                dec_repr += 2**i\n",
        "        dec_repr = round((dec_repr/(10**precision))-lim_sup, precision+1)\n",
        "        dec_vect.append(dec_repr)\n",
        "    return dec_vect\n",
        "       "
      ],
      "metadata": {
        "id": "Xyfu-yxv7XJE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test of binary decoding function with no dict, for a vector in $\\mathbb{R}^{3}$. Notice we need to provide the liminf of each one of the variables. In our particular solution, we are assuming that all of the variables are in the same interval \\[a, b]."
      ],
      "metadata": {
        "id": "tKglKNjP7MOE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bin_decoding_no_dict(['00100110101001110', '10100001010110010', '10010000010111011'], 5.12,  4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dmhe2R5K3lQG",
        "outputId": "9308baf9-b5ba-48fe-f860-ec7be594bcc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-3.141, 3.141, 2.2715]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test of binary coding for vectors in $\\mathbb{R}^{3}$, for cost functions defined in its corresponding domain, with a precision of 4 decimal digits. Note that we can give to the function any dimension of $\\mathbb{R}^{n}$, along with any precision we wish."
      ],
      "metadata": {
        "id": "NK0y64VjLHOx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.   Parabola's domain:** "
      ],
      "metadata": {
        "id": "sTV3e0BpMUqT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numer_bits_available = bit_size_binary(-5.12, 5.12, 4) # Number of bits that the variables in the parabola function can take, given its definition\n",
        "bin_dict = binary_coding_dict(numer_bits_available, -5.12, 5.12, 4) # Dictionary storing the variables representation in decimal, with a precision of 4 decimal values\n",
        "vector_to_encode = [-3.1410, 3.1410, 2.2715]\n",
        "vector_in_binary = binary_encoding(vector_to_encode, 4, bin_dict) # Input vector in decimal representation, converted to binary vector  \n",
        "print(vector_in_binary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82NvKM1uKdga",
        "outputId": "124d9412-bc7e-4058-dde4-dd3cb7aebde6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['00100110101001110', '10100001010110010', '10010000010111011']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decimal-Binary dict to verify that the encoding was done effectively:"
      ],
      "metadata": {
        "id": "NyB7E0hmRWJQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print([bin_dict[-3.1410], bin_dict[3.1410], bin_dict[2.2715]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpdR2Cd7RVTq",
        "outputId": "9cbec913-fac2-465d-d71b-bb631b2f05f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['00100110101001110', '10100001010110010', '10010000010111011']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Rosenbrock's domain:** "
      ],
      "metadata": {
        "id": "--aINyzwMVpy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numer_bits_available = bit_size_binary(-2.048, 2.048, 4) # Number of bits that the variables in the parabola function can take, given its definition\n",
        "bin_dict = binary_coding_dict(numer_bits_available, -2.048, 2.048, 4) # Dictionary storing the variables representation in decimal, with a precision of 4 decimal values\n",
        "vector_to_encode = [-1.1410, 0.1410, 2.0215]\n",
        "vector_in_binary = binary_encoding(vector_to_encode, 4, bin_dict) # Input vector in decimal representation, converted to binary vector  \n",
        "print(vector_in_binary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gzaG41vUMYda",
        "outputId": "b94e7cdc-4912-4e7f-bd29-63467c4f0df8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['0010001101101110', '0101010110000010', '1001111011110111']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decimal-Binary dict to verify that the encoding was done effectively:"
      ],
      "metadata": {
        "id": "3XAIYX6FWpRI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print([bin_dict[-1.1410], bin_dict[0.1410], bin_dict[2.0215]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qPYuty7LWsM5",
        "outputId": "2cde4558-8ecd-4019-9003-04546ad242c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['0010001101101110', '0101010110000010', '1001111011110111']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Ackley's domain:**"
      ],
      "metadata": {
        "id": "aVTCn51uMZF8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numer_bits_available = bit_size_binary(-32.768, 32.768, 4) # Number of bits that the variables in the parabola function can take, given its definition\n",
        "bin_dict = binary_coding_dict(numer_bits_available, -32.768, 32.768, 4) # Dictionary storing the variables representation in decimal, with a precision of 4 decimal values\n",
        "vector_to_encode = [-30.1410, 20.1410, 15.0215]\n",
        "vector_in_binary = binary_encoding(vector_to_encode, 4, bin_dict) # Input vector in decimal representation, converted to binary vector  \n",
        "print(vector_in_binary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvqS9XmeMcJB",
        "outputId": "c8a7feae-59c6-4891-8ce1-bb1cf54cea7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['00000110011010011110', '10000001001011000010', '01110100101011000111']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decimal-Binary dict to verify that the encoding was done effectively:"
      ],
      "metadata": {
        "id": "CZKhEuQOXIye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print([bin_dict[-30.1410], bin_dict[20.1410], bin_dict[15.0215]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVSfIgw-XF6m",
        "outputId": "688a4047-7ac5-4a4f-ea53-59e724f25f15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['00000110011010011110', '10000001001011000010', '01110100101011000111']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cross-over Classes to implement different types of cross-over operators. Particularly, in this program we're considering only two. Namely, two cross-over and uniform cross-over operators:"
      ],
      "metadata": {
        "id": "5vAJThgDPW3F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class TwoCrossover:\n",
        "    def __init__(self, parent1, parent2):\n",
        "        self.parent1 = parent1\n",
        "        self.parent2 = parent2\n",
        "\n",
        "    def crossover(self, cut_point1, cut_point2):\n",
        "        \"\"\"\n",
        "        Implementation of the two crossover operator\n",
        "        \"\"\"\n",
        "        children = []\n",
        "        child1 = self.parent1[:cut_point1] + self.parent2[cut_point1:cut_point2] + self.parent1[cut_point2:]\n",
        "        children.append(child1)\n",
        "        child2 = self.parent2[:cut_point1] + self.parent1[cut_point1:cut_point2] + self.parent2[cut_point2:]\n",
        "        children.append(child2)\n",
        "        return children    \n",
        "\n",
        "class UniformCrossover:\n",
        "    def __init__(self, parent1, parent2):\n",
        "        self.parent1 = parent1\n",
        "        self.parent2 = parent2\n",
        "\n",
        "    def crossover(self, prob_vect, cross_perc):\n",
        "        \"\"\"\n",
        "        Implementation of the uniform crossover operator\n",
        "        prob_vect is a probability vector generated by np.random.rand(n),\n",
        "        where n is the length of the parents\n",
        "        \"\"\"\n",
        "        for i in range(len(prob_vect)):\n",
        "            # print(prob_vect[i]) to validate that the unifor crossover operation is implemented correctly\n",
        "            if prob_vect[i] < cross_perc:\n",
        "                self.parent1[i], self.parent2[i] = self.parent2[i], self.parent1[i]\n",
        "        return [self.parent1, self.parent2]"
      ],
      "metadata": {
        "id": "OaUYpaCPPZ8T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mutation operator:"
      ],
      "metadata": {
        "id": "UHzR9dVJveFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def mutation(chromosome, delta, mut_perc):\n",
        "    \"\"\"\n",
        "    Implementation of the mutation operator \n",
        "    \"\"\"\n",
        "    new_chrom = []\n",
        "    for gen in chromosome:\n",
        "        if random.uniform(0,1) <= mut_perc:\n",
        "            if random.uniform(0,1) <= 0.5:\n",
        "                gen += delta\n",
        "            else:\n",
        "                gen -= delta\n",
        "        new_chrom.append(gen)\n",
        "    return new_chrom"
      ],
      "metadata": {
        "id": "_fjdojf1vknC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Genetic algorithm implementation."
      ],
      "metadata": {
        "id": "Tg-4FEcZYNYg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random initial population:"
      ],
      "metadata": {
        "id": "WPB5yhP4tSfs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def initial_population(population_size, vars_domain, dim, precision): \n",
        "    \"\"\"\n",
        "    This function creates the initial random population to start the algorithm.\n",
        "    population_size must be an even number, in order to create the tournament. See below \n",
        "    bin_tournament function.\n",
        "    \"\"\"    \n",
        "    population = []\n",
        "    for i in range(0, population_size):\n",
        "        individual = []    \n",
        "        for i in range(0, dim):\n",
        "            individual.append(round(random.uniform(vars_domain[0], vars_domain[1]), precision))\n",
        "        population.append(individual)\n",
        "    return population"
      ],
      "metadata": {
        "id": "H839AbOStdLC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test random initial population (20 individuals for parabola cost function, in dim = 10, with precision of two decimal digits):"
      ],
      "metadata": {
        "id": "SWfAqjMStisw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "population = initial_population(20, [-5.12, 5.12], 10, 2)\n",
        "population"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gaH8FKztk8Z",
        "outputId": "faa21d5b-df7d-4a60-8ac0-870933724260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2.79, -1.68, -5.01, 3.11, -5.07, 0.96, -4.69, 0.06, 4.47, -2.38],\n",
              " [4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22],\n",
              " [4.34, 2.09, -1.24, 3.01, -4.11, -2.75, -4.73, -1.2, 0.55, 4.33],\n",
              " [3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16],\n",
              " [-0.41, 1.38, 3.09, -1.18, -1.51, 2.25, 5.02, 1.49, 3.93, -0.52],\n",
              " [3.3, 3.74, 0.25, -0.9, 3.84, 4.23, -3.42, -1.86, -2.81, -0.29],\n",
              " [-3.07, -4.99, -3.53, 0.38, -0.95, 2.09, -3.87, 3.35, 0.92, -2.94],\n",
              " [-2.01, -2.88, 0.74, 0.04, -4.61, 3.86, -3.47, 4.73, -2.95, -0.17],\n",
              " [-4.88, 3.89, 0.2, 2.06, 1.28, 2.33, -2.67, 0.17, 0.96, -2.59],\n",
              " [-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              " [-5.12, 1.4, -1.26, -3.55, 4.64, -2.01, 0.31, -3.72, 1.26, 2.56],\n",
              " [2.96, -1.97, -3.24, 3.51, 3.01, 2.61, 2.14, 4.64, -1.61, -1.2],\n",
              " [4.58, 2.24, 4.19, -1.48, -0.38, -1.72, -1.21, -2.67, -0.34, 2.6],\n",
              " [1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41],\n",
              " [0.35, -1.53, 1.78, -3.55, 2.2, 2.58, 2.42, 2.59, 3.36, 0.17],\n",
              " [4.3, -3.09, 2.09, -4.78, 0.32, 0.46, -3.18, 3.9, 3.78, -2.41],\n",
              " [3.69, 0.72, -1.18, -2.76, -0.0, -4.58, 2.09, -1.93, -4.32, -2.24],\n",
              " [0.08, 0.61, 1.69, 2.36, 1.13, -4.36, -2.01, 2.69, 2.21, -5.07],\n",
              " [0.42, 1.74, -4.99, 4.59, -3.8, 1.03, -3.21, 1.76, 3.79, -2.53],\n",
              " [5.09, 0.92, 0.46, 2.3, 4.53, 4.63, 0.02, 3.51, -1.13, -3.72]]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Table of individuals, with their corresponding apptitudes (evaluation of cost function). "
      ],
      "metadata": {
        "id": "A5otBtRmuHe9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def table_pop_fitnesss(population, cost_fun):\n",
        "    \"\"\"\n",
        "    This function provides a list of lists, showing the initial population, along its \n",
        "    corresponding aptitudes (their corresponding values that are obtained after evaluating\n",
        "    the given cost function)\n",
        "    \"\"\"\n",
        "    table = []\n",
        "    for individual in population:\n",
        "        table += [[individual, cost_fun(len(individual), individual)]]\n",
        "    return table"
      ],
      "metadata": {
        "id": "pStTFWVkuUnY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test for the table of individuals, with their corresponding apptitudes (parabola cost function). Notice we are testing with the above random population obtained. "
      ],
      "metadata": {
        "id": "QmnYt-4IuYwv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fitness_table = table_pop_fitnesss(population, parabola)\n",
        "fitness_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZfdWnUtukUW",
        "outputId": "856b8ff1-b76d-46bc-d1c2-76111732575d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[[2.79, -1.68, -5.01, 3.11, -5.07, 0.96, -4.69, 0.06, 4.47, -2.38],\n",
              "  119.65019999999998],\n",
              " [[4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22], 101.1764],\n",
              " [[4.34, 2.09, -1.24, 3.01, -4.11, -2.75, -4.73, -1.2, 0.55, 4.33],\n",
              "  101.12029999999999],\n",
              " [[3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16], 81.8524],\n",
              " [[-0.41, 1.38, 3.09, -1.18, -1.51, 2.25, 5.02, 1.49, 3.93, -0.52],\n",
              "  63.491400000000006],\n",
              " [[3.3, 3.74, 0.25, -0.9, 3.84, 4.23, -3.42, -1.86, -2.81, -0.29],\n",
              "  81.52480000000001],\n",
              " [[-3.07, -4.99, -3.53, 0.38, -0.95, 2.09, -3.87, 3.35, 0.92, -2.94], 87.8903],\n",
              " [[-2.01, -2.88, 0.74, 0.04, -4.61, 3.86, -3.47, 4.73, -2.95, -0.17], 92.1806],\n",
              " [[-4.88, 3.89, 0.2, 2.06, 1.28, 2.33, -2.67, 0.17, 0.96, -2.59],\n",
              "  65.08489999999999],\n",
              " [[-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              "  140.87180000000004],\n",
              " [[-5.12, 1.4, -1.26, -3.55, 4.64, -2.01, 0.31, -3.72, 1.26, 2.56], 90.0099],\n",
              " [[2.96, -1.97, -3.24, 3.51, 3.01, 2.61, 2.14, 4.64, -1.61, -1.2], 81.4737],\n",
              " [[4.58, 2.24, 4.19, -1.48, -0.38, -1.72, -1.21, -2.67, -0.34, 2.6],\n",
              "  64.31190000000001],\n",
              " [[1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41], 101.6466],\n",
              " [[0.35, -1.53, 1.78, -3.55, 2.2, 2.58, 2.42, 2.59, 3.36, 0.17], 53.6137],\n",
              " [[4.3, -3.09, 2.09, -4.78, 0.32, 0.46, -3.18, 3.9, 3.78, -2.41],\n",
              "  100.98749999999998],\n",
              " [[3.69, 0.72, -1.18, -2.76, -0.0, -4.58, 2.09, -1.93, -4.32, -2.24], 75.8939],\n",
              " [[0.08, 0.61, 1.69, 2.36, 1.13, -4.36, -2.01, 2.69, 2.21, -5.07],\n",
              "  70.95589999999999],\n",
              " [[0.42, 1.74, -4.99, 4.59, -3.8, 1.03, -3.21, 1.76, 3.79, -2.53],\n",
              "  98.83980000000001],\n",
              " [[5.09, 0.92, 0.46, 2.3, 4.53, 4.63, 0.02, 3.51, -1.13, -3.72], 101.6497]]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stochastic reminder parents selection without replacement."
      ],
      "metadata": {
        "id": "RochJMh2vPNG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def stoch_rem_par_sel(table_pop_fitness):\n",
        "    \"\"\"\n",
        "    Stochastic reminder parents selection without replacement implementation\n",
        "    Notice this function has already the individuals (in decimal representation),\n",
        "    along with its corresponding aptitudes (values output by the cost function)\n",
        "    \"\"\"\n",
        "    total_fitness = 0\n",
        "    exp_val_vector = [] # expected value vector that will have the expected values for each of the individuals of the population\n",
        "    for i in range(len(table_pop_fitness)):\n",
        "        total_fitness += table_pop_fitness[i][1]\n",
        "        exp_val_vector.append(table_pop_fitness[i][1])\n",
        "    avg_fitness = total_fitness/len(table_pop_fitness)\n",
        "    exp_val_vector = np.array(exp_val_vector) # converted to a numpy array to perform division easier\n",
        "    exp_val_vector = exp_val_vector/avg_fitness\n",
        "    exp_val_vector = exp_val_vector.tolist()  # convert again to a list\n",
        "    dec_exp_val_vector = [] # Vector that will contain the decimal part of the expected values\n",
        "    parents = [] # Vector that will have the selected parents to reproduce\n",
        "    for exp_val in exp_val_vector:\n",
        "        exp_val_dec = round(exp_val % 1, 2) # decimal part of the expected values, with a precision of 2 decimal values\n",
        "        dec_exp_val_vector.append(exp_val_dec)\n",
        "        exp_val_int = int(exp_val) # integer part of each expected value\n",
        "        if exp_val_int > 0:\n",
        "            parents += [table_pop_fitness[exp_val_vector.index(exp_val)]] * exp_val_int   # posicion (index) in table pop fitness is the same as the expected values vector \n",
        "    for dec_exp_val in dec_exp_val_vector:\n",
        "        flip = random.uniform(0,1)\n",
        "        if flip < dec_exp_val: # and table_pop_fitness[dec_exp_val_vector.index(dec_exp_val)] not in parents: Notice we are allowing to repeat parents also by this flip\n",
        "            parents.append(table_pop_fitness[dec_exp_val_vector.index(dec_exp_val)])     # posicion (index) in table pop fitness is the same as the dec_exp_val_vector values vector        \n",
        "    return parents"
      ],
      "metadata": {
        "id": "gHb0hInAvUm9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test of the parents selection strategy by stochastic reminder without replacement. Notice we are using our above fitness table."
      ],
      "metadata": {
        "id": "ifzOwBsY1jH_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "parents_selected = stoch_rem_par_sel(fitness_table)\n",
        "print(f\"The number of parents selected by the stochastic reminder strategy is of: {len(parents_selected)}\\n\")\n",
        "parents_selected"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pMHAXYGm1640",
        "outputId": "1f7b0c7d-b365-4675-9374-19608fcea9ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of parents selected by the stochastic reminder strategy is of: 23\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[[2.79, -1.68, -5.01, 3.11, -5.07, 0.96, -4.69, 0.06, 4.47, -2.38],\n",
              "  119.65019999999998],\n",
              " [[4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22], 101.1764],\n",
              " [[4.34, 2.09, -1.24, 3.01, -4.11, -2.75, -4.73, -1.2, 0.55, 4.33],\n",
              "  101.12029999999999],\n",
              " [[-2.01, -2.88, 0.74, 0.04, -4.61, 3.86, -3.47, 4.73, -2.95, -0.17], 92.1806],\n",
              " [[-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              "  140.87180000000004],\n",
              " [[-5.12, 1.4, -1.26, -3.55, 4.64, -2.01, 0.31, -3.72, 1.26, 2.56], 90.0099],\n",
              " [[1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41], 101.6466],\n",
              " [[4.3, -3.09, 2.09, -4.78, 0.32, 0.46, -3.18, 3.9, 3.78, -2.41],\n",
              "  100.98749999999998],\n",
              " [[0.42, 1.74, -4.99, 4.59, -3.8, 1.03, -3.21, 1.76, 3.79, -2.53],\n",
              "  98.83980000000001],\n",
              " [[5.09, 0.92, 0.46, 2.3, 4.53, 4.63, 0.02, 3.51, -1.13, -3.72], 101.6497],\n",
              " [[3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16], 81.8524],\n",
              " [[-0.41, 1.38, 3.09, -1.18, -1.51, 2.25, 5.02, 1.49, 3.93, -0.52],\n",
              "  63.491400000000006],\n",
              " [[3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16], 81.8524],\n",
              " [[-3.07, -4.99, -3.53, 0.38, -0.95, 2.09, -3.87, 3.35, 0.92, -2.94], 87.8903],\n",
              " [[-4.88, 3.89, 0.2, 2.06, 1.28, 2.33, -2.67, 0.17, 0.96, -2.59],\n",
              "  65.08489999999999],\n",
              " [[-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              "  140.87180000000004],\n",
              " [[3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16], 81.8524],\n",
              " [[-0.41, 1.38, 3.09, -1.18, -1.51, 2.25, 5.02, 1.49, 3.93, -0.52],\n",
              "  63.491400000000006],\n",
              " [[0.35, -1.53, 1.78, -3.55, 2.2, 2.58, 2.42, 2.59, 3.36, 0.17], 53.6137],\n",
              " [[4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22], 101.1764],\n",
              " [[3.69, 0.72, -1.18, -2.76, -0.0, -4.58, 2.09, -1.93, -4.32, -2.24], 75.8939],\n",
              " [[0.08, 0.61, 1.69, 2.36, 1.13, -4.36, -2.01, 2.69, 2.21, -5.07],\n",
              "  70.95589999999999],\n",
              " [[1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41], 101.6466]]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Binary tournament for survivors selections. With this function we'll select the \"best\" individuals that will compose the population of the next generation."
      ],
      "metadata": {
        "id": "OUBF_BCD4rMN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def bin_tournament_survivors(table_pop_fitness):\n",
        "    \"\"\"\n",
        "    This function starts the binary tournament between individuals of a population,\n",
        "    and returns the selected survivors. Notice the first condition is to guarantee an even size of population\n",
        "    Future work: Generalize this function for allowing k-matches; i.e., matches of k individuals, where 1<=k<=len(population) \n",
        "    \"\"\"\n",
        "    if len(table_pop_fitness) % 2 == 1: # Validation just for binary tournament\n",
        "        table_pop_fitness = sorted(table_pop_fitness, key = lambda x : float(x[1])) # order table in terms of values of the cost_fun passed        \n",
        "        del table_pop_fitness[-1]  # This new population removes the least suitable individual; i.e., the individual \"least\" such                                                                           \n",
        "                                   # that cost_fun(least) > cost_fun(individual), for all individual in ordered_population (our problems involve a minimum optimal)\n",
        "    survivors = []                                                             \n",
        "    for i in range(0, 2):                                                         \n",
        "        np.random.shuffle(table_pop_fitness)\n",
        "        # print(table_pop_fitness)       \n",
        "        for j in range(0, len(table_pop_fitness), 2):\n",
        "            if table_pop_fitness[j][1] > table_pop_fitness[j+1][1]: #and table_pop_fitness[j][1] not in survivors: -Notice again we are allowing repetition of survivors            \n",
        "                survivors.append(table_pop_fitness[j]) # append survivor with its corresponding aptitude\n",
        "            elif table_pop_fitness[j+1][1] > table_pop_fitness[j][1]: #and table_pop_fitness[j+1][1] not in survivors:  -Notice again we are allowing repetition of survivors        \n",
        "                survivors.append(table_pop_fitness[j+1]) # similarly, append survivor with its corresponding aptitude\n",
        "    return survivors"
      ],
      "metadata": {
        "id": "63LPVelz4_JN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test of the binary selection tournament, between individuals of a population. Notice we are using again our selected parents to cross from above."
      ],
      "metadata": {
        "id": "NaU7tjO07OiV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "survivors = bin_tournament_survivors(parents_selected)\n",
        "survivors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDOXuHOk7N4C",
        "outputId": "bb047d7a-4466-486b-85af-30bc19f35cfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[[0.08, 0.61, 1.69, 2.36, 1.13, -4.36, -2.01, 2.69, 2.21, -5.07],\n",
              "  70.95589999999999],\n",
              " [[4.34, 2.09, -1.24, 3.01, -4.11, -2.75, -4.73, -1.2, 0.55, 4.33],\n",
              "  101.12029999999999],\n",
              " [[-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              "  140.87180000000004],\n",
              " [[-3.07, -4.99, -3.53, 0.38, -0.95, 2.09, -3.87, 3.35, 0.92, -2.94], 87.8903],\n",
              " [[4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22], 101.1764],\n",
              " [[1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41], 101.6466],\n",
              " [[2.79, -1.68, -5.01, 3.11, -5.07, 0.96, -4.69, 0.06, 4.47, -2.38],\n",
              "  119.65019999999998],\n",
              " [[0.42, 1.74, -4.99, 4.59, -3.8, 1.03, -3.21, 1.76, 3.79, -2.53],\n",
              "  98.83980000000001],\n",
              " [[5.09, 0.92, 0.46, 2.3, 4.53, 4.63, 0.02, 3.51, -1.13, -3.72], 101.6497],\n",
              " [[4.3, -3.09, 2.09, -4.78, 0.32, 0.46, -3.18, 3.9, 3.78, -2.41],\n",
              "  100.98749999999998],\n",
              " [[-5.12, 1.4, -1.26, -3.55, 4.64, -2.01, 0.31, -3.72, 1.26, 2.56], 90.0099],\n",
              " [[4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22], 101.1764],\n",
              " [[4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22], 101.1764],\n",
              " [[-2.01, -2.88, 0.74, 0.04, -4.61, 3.86, -3.47, 4.73, -2.95, -0.17], 92.1806],\n",
              " [[2.79, -1.68, -5.01, 3.11, -5.07, 0.96, -4.69, 0.06, 4.47, -2.38],\n",
              "  119.65019999999998],\n",
              " [[4.3, -3.09, 2.09, -4.78, 0.32, 0.46, -3.18, 3.9, 3.78, -2.41],\n",
              "  100.98749999999998],\n",
              " [[-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              "  140.87180000000004],\n",
              " [[3.69, 0.72, -1.18, -2.76, -0.0, -4.58, 2.09, -1.93, -4.32, -2.24], 75.8939],\n",
              " [[0.08, 0.61, 1.69, 2.36, 1.13, -4.36, -2.01, 2.69, 2.21, -5.07],\n",
              "  70.95589999999999],\n",
              " [[-3.07, -4.99, -3.53, 0.38, -0.95, 2.09, -3.87, 3.35, 0.92, -2.94], 87.8903],\n",
              " [[4.34, 2.09, -1.24, 3.01, -4.11, -2.75, -4.73, -1.2, 0.55, 4.33],\n",
              "  101.12029999999999],\n",
              " [[1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41], 101.6466]]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Parents reproduction function. Observe that the parameters passed to this function are the class name of the cross operator, along with its particular parameters to perform the operator."
      ],
      "metadata": {
        "id": "5mzI6d0KjoLa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def parents_reproduction(parents, cross_over_class, cross_class_param1, cross_class_param2):\n",
        "    pars = [prnt[0] for prnt in parents] # parents only (without their apptitudes obtained by the cost function) in order to reproduce\n",
        "    #print(pars)\n",
        "    children_dec_vector = []\n",
        "    for parent1, parent2 in itertools.combinations(pars, 2): # Reproduction of selected parents by Stochastic reminder without replacement                               \n",
        "        co = cross_over_class(parent1, parent2) # Instantiation of crossover object           \n",
        "        children_dec_vectors = co.crossover(cross_class_param1, cross_class_param2) # Two children vectors in binary representation\n",
        "        children_dec_vector += children_dec_vectors\n",
        "    return children_dec_vector"
      ],
      "metadata": {
        "id": "SQLOO5XxkUuC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test of parents reproduction function. We'll be using the parents obtained above along with the two cross over operators. Notice the first line of the function above. We'll be dealing only with the parents and not their corresponding aptitudes. "
      ],
      "metadata": {
        "id": "uBv_OgMnltDK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Two Cross - over operator:"
      ],
      "metadata": {
        "id": "IGTV7_Xemuf2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "children = parents_reproduction(parents_selected, TwoCrossover, 3, 7)\n",
        "print(len(children))\n",
        "children"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1KW0-vWvsUXy",
        "outputId": "3c2c71e4-4269-4d9e-8b2b-812589aa273a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "506\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2.79, -1.68, -5.01, 2.6, -2.07, 1.92, 4.03, 0.06, 4.47, -2.38],\n",
              " [4.06, -4.78, 0.47, 3.11, -5.07, 0.96, -4.69, 3.5, 0.77, -4.22],\n",
              " [2.79, -1.68, -5.01, 3.01, -4.11, -2.75, -4.73, 0.06, 4.47, -2.38],\n",
              " [4.34, 2.09, -1.24, 3.11, -5.07, 0.96, -4.69, -1.2, 0.55, 4.33],\n",
              " [2.79, -1.68, -5.01, 0.04, -4.61, 3.86, -3.47, 0.06, 4.47, -2.38],\n",
              " [-2.01, -2.88, 0.74, 3.11, -5.07, 0.96, -4.69, 4.73, -2.95, -0.17],\n",
              " [2.79, -1.68, -5.01, -4.9, 5.11, 5.07, 0.33, 0.06, 4.47, -2.38],\n",
              " [-0.23, 0.37, -4.25, 3.11, -5.07, 0.96, -4.69, 4.36, -3.44, -3.98],\n",
              " [2.79, -1.68, -5.01, -3.55, 4.64, -2.01, 0.31, 0.06, 4.47, -2.38],\n",
              " [-5.12, 1.4, -1.26, 3.11, -5.07, 0.96, -4.69, -3.72, 1.26, 2.56],\n",
              " [2.79, -1.68, -5.01, -0.16, -2.7, 4.38, 2.42, 0.06, 4.47, -2.38],\n",
              " [1.62, 4.48, -2.38, 3.11, -5.07, 0.96, -4.69, 4.3, -1.73, -4.41],\n",
              " [2.79, -1.68, -5.01, -4.78, 0.32, 0.46, -3.18, 0.06, 4.47, -2.38],\n",
              " [4.3, -3.09, 2.09, 3.11, -5.07, 0.96, -4.69, 3.9, 3.78, -2.41],\n",
              " [2.79, -1.68, -5.01, 4.59, -3.8, 1.03, -3.21, 0.06, 4.47, -2.38],\n",
              " [0.42, 1.74, -4.99, 3.11, -5.07, 0.96, -4.69, 1.76, 3.79, -2.53],\n",
              " [2.79, -1.68, -5.01, 2.3, 4.53, 4.63, 0.02, 0.06, 4.47, -2.38],\n",
              " [5.09, 0.92, 0.46, 3.11, -5.07, 0.96, -4.69, 3.51, -1.13, -3.72],\n",
              " [2.79, -1.68, -5.01, 1.3, 4.43, 0.18, -4.04, 0.06, 4.47, -2.38],\n",
              " [3.83, 0.85, -1.32, 3.11, -5.07, 0.96, -4.69, -2.2, 4.71, -0.16],\n",
              " [2.79, -1.68, -5.01, -1.18, -1.51, 2.25, 5.02, 0.06, 4.47, -2.38],\n",
              " [-0.41, 1.38, 3.09, 3.11, -5.07, 0.96, -4.69, 1.49, 3.93, -0.52],\n",
              " [2.79, -1.68, -5.01, 1.3, 4.43, 0.18, -4.04, 0.06, 4.47, -2.38],\n",
              " [3.83, 0.85, -1.32, 3.11, -5.07, 0.96, -4.69, -2.2, 4.71, -0.16],\n",
              " [2.79, -1.68, -5.01, 0.38, -0.95, 2.09, -3.87, 0.06, 4.47, -2.38],\n",
              " [-3.07, -4.99, -3.53, 3.11, -5.07, 0.96, -4.69, 3.35, 0.92, -2.94],\n",
              " [2.79, -1.68, -5.01, 2.06, 1.28, 2.33, -2.67, 0.06, 4.47, -2.38],\n",
              " [-4.88, 3.89, 0.2, 3.11, -5.07, 0.96, -4.69, 0.17, 0.96, -2.59],\n",
              " [2.79, -1.68, -5.01, -4.9, 5.11, 5.07, 0.33, 0.06, 4.47, -2.38],\n",
              " [-0.23, 0.37, -4.25, 3.11, -5.07, 0.96, -4.69, 4.36, -3.44, -3.98],\n",
              " [2.79, -1.68, -5.01, 1.3, 4.43, 0.18, -4.04, 0.06, 4.47, -2.38],\n",
              " [3.83, 0.85, -1.32, 3.11, -5.07, 0.96, -4.69, -2.2, 4.71, -0.16],\n",
              " [2.79, -1.68, -5.01, -1.18, -1.51, 2.25, 5.02, 0.06, 4.47, -2.38],\n",
              " [-0.41, 1.38, 3.09, 3.11, -5.07, 0.96, -4.69, 1.49, 3.93, -0.52],\n",
              " [2.79, -1.68, -5.01, -3.55, 2.2, 2.58, 2.42, 0.06, 4.47, -2.38],\n",
              " [0.35, -1.53, 1.78, 3.11, -5.07, 0.96, -4.69, 2.59, 3.36, 0.17],\n",
              " [2.79, -1.68, -5.01, 2.6, -2.07, 1.92, 4.03, 0.06, 4.47, -2.38],\n",
              " [4.06, -4.78, 0.47, 3.11, -5.07, 0.96, -4.69, 3.5, 0.77, -4.22],\n",
              " [2.79, -1.68, -5.01, -2.76, -0.0, -4.58, 2.09, 0.06, 4.47, -2.38],\n",
              " [3.69, 0.72, -1.18, 3.11, -5.07, 0.96, -4.69, -1.93, -4.32, -2.24],\n",
              " [2.79, -1.68, -5.01, 2.36, 1.13, -4.36, -2.01, 0.06, 4.47, -2.38],\n",
              " [0.08, 0.61, 1.69, 3.11, -5.07, 0.96, -4.69, 2.69, 2.21, -5.07],\n",
              " [2.79, -1.68, -5.01, -0.16, -2.7, 4.38, 2.42, 0.06, 4.47, -2.38],\n",
              " [1.62, 4.48, -2.38, 3.11, -5.07, 0.96, -4.69, 4.3, -1.73, -4.41],\n",
              " [4.06, -4.78, 0.47, 3.01, -4.11, -2.75, -4.73, 3.5, 0.77, -4.22],\n",
              " [4.34, 2.09, -1.24, 2.6, -2.07, 1.92, 4.03, -1.2, 0.55, 4.33],\n",
              " [4.06, -4.78, 0.47, 0.04, -4.61, 3.86, -3.47, 3.5, 0.77, -4.22],\n",
              " [-2.01, -2.88, 0.74, 2.6, -2.07, 1.92, 4.03, 4.73, -2.95, -0.17],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, -3.55, 4.64, -2.01, 0.31, 3.5, 0.77, -4.22],\n",
              " [-5.12, 1.4, -1.26, 2.6, -2.07, 1.92, 4.03, -3.72, 1.26, 2.56],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [4.06, -4.78, 0.47, -4.78, 0.32, 0.46, -3.18, 3.5, 0.77, -4.22],\n",
              " [4.3, -3.09, 2.09, 2.6, -2.07, 1.92, 4.03, 3.9, 3.78, -2.41],\n",
              " [4.06, -4.78, 0.47, 4.59, -3.8, 1.03, -3.21, 3.5, 0.77, -4.22],\n",
              " [0.42, 1.74, -4.99, 2.6, -2.07, 1.92, 4.03, 1.76, 3.79, -2.53],\n",
              " [4.06, -4.78, 0.47, 2.3, 4.53, 4.63, 0.02, 3.5, 0.77, -4.22],\n",
              " [5.09, 0.92, 0.46, 2.6, -2.07, 1.92, 4.03, 3.51, -1.13, -3.72],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, -1.18, -1.51, 2.25, 5.02, 3.5, 0.77, -4.22],\n",
              " [-0.41, 1.38, 3.09, 2.6, -2.07, 1.92, 4.03, 1.49, 3.93, -0.52],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, 0.38, -0.95, 2.09, -3.87, 3.5, 0.77, -4.22],\n",
              " [-3.07, -4.99, -3.53, 2.6, -2.07, 1.92, 4.03, 3.35, 0.92, -2.94],\n",
              " [4.06, -4.78, 0.47, 2.06, 1.28, 2.33, -2.67, 3.5, 0.77, -4.22],\n",
              " [-4.88, 3.89, 0.2, 2.6, -2.07, 1.92, 4.03, 0.17, 0.96, -2.59],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, -1.18, -1.51, 2.25, 5.02, 3.5, 0.77, -4.22],\n",
              " [-0.41, 1.38, 3.09, 2.6, -2.07, 1.92, 4.03, 1.49, 3.93, -0.52],\n",
              " [4.06, -4.78, 0.47, -3.55, 2.2, 2.58, 2.42, 3.5, 0.77, -4.22],\n",
              " [0.35, -1.53, 1.78, 2.6, -2.07, 1.92, 4.03, 2.59, 3.36, 0.17],\n",
              " [4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22],\n",
              " [4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22],\n",
              " [4.06, -4.78, 0.47, -2.76, -0.0, -4.58, 2.09, 3.5, 0.77, -4.22],\n",
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              " [4.34, 2.09, -1.24, -4.9, 5.11, 5.07, 0.33, -1.2, 0.55, 4.33],\n",
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              " [4.34, 2.09, -1.24, -3.55, 4.64, -2.01, 0.31, -1.2, 0.55, 4.33],\n",
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              " [4.34, 2.09, -1.24, -4.78, 0.32, 0.46, -3.18, -1.2, 0.55, 4.33],\n",
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              " [4.34, 2.09, -1.24, 4.59, -3.8, 1.03, -3.21, -1.2, 0.55, 4.33],\n",
              " [0.42, 1.74, -4.99, 3.01, -4.11, -2.75, -4.73, 1.76, 3.79, -2.53],\n",
              " [4.34, 2.09, -1.24, 2.3, 4.53, 4.63, 0.02, -1.2, 0.55, 4.33],\n",
              " [5.09, 0.92, 0.46, 3.01, -4.11, -2.75, -4.73, 3.51, -1.13, -3.72],\n",
              " [4.34, 2.09, -1.24, 1.3, 4.43, 0.18, -4.04, -1.2, 0.55, 4.33],\n",
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              " [4.34, 2.09, -1.24, -1.18, -1.51, 2.25, 5.02, -1.2, 0.55, 4.33],\n",
              " [-0.41, 1.38, 3.09, 3.01, -4.11, -2.75, -4.73, 1.49, 3.93, -0.52],\n",
              " [4.34, 2.09, -1.24, 1.3, 4.43, 0.18, -4.04, -1.2, 0.55, 4.33],\n",
              " [3.83, 0.85, -1.32, 3.01, -4.11, -2.75, -4.73, -2.2, 4.71, -0.16],\n",
              " [4.34, 2.09, -1.24, 0.38, -0.95, 2.09, -3.87, -1.2, 0.55, 4.33],\n",
              " [-3.07, -4.99, -3.53, 3.01, -4.11, -2.75, -4.73, 3.35, 0.92, -2.94],\n",
              " [4.34, 2.09, -1.24, 2.06, 1.28, 2.33, -2.67, -1.2, 0.55, 4.33],\n",
              " [-4.88, 3.89, 0.2, 3.01, -4.11, -2.75, -4.73, 0.17, 0.96, -2.59],\n",
              " [4.34, 2.09, -1.24, -4.9, 5.11, 5.07, 0.33, -1.2, 0.55, 4.33],\n",
              " [-0.23, 0.37, -4.25, 3.01, -4.11, -2.75, -4.73, 4.36, -3.44, -3.98],\n",
              " [4.34, 2.09, -1.24, 1.3, 4.43, 0.18, -4.04, -1.2, 0.55, 4.33],\n",
              " [3.83, 0.85, -1.32, 3.01, -4.11, -2.75, -4.73, -2.2, 4.71, -0.16],\n",
              " [4.34, 2.09, -1.24, -1.18, -1.51, 2.25, 5.02, -1.2, 0.55, 4.33],\n",
              " [-0.41, 1.38, 3.09, 3.01, -4.11, -2.75, -4.73, 1.49, 3.93, -0.52],\n",
              " [4.34, 2.09, -1.24, -3.55, 2.2, 2.58, 2.42, -1.2, 0.55, 4.33],\n",
              " [0.35, -1.53, 1.78, 3.01, -4.11, -2.75, -4.73, 2.59, 3.36, 0.17],\n",
              " [4.34, 2.09, -1.24, 2.6, -2.07, 1.92, 4.03, -1.2, 0.55, 4.33],\n",
              " [4.06, -4.78, 0.47, 3.01, -4.11, -2.75, -4.73, 3.5, 0.77, -4.22],\n",
              " [4.34, 2.09, -1.24, -2.76, -0.0, -4.58, 2.09, -1.2, 0.55, 4.33],\n",
              " [3.69, 0.72, -1.18, 3.01, -4.11, -2.75, -4.73, -1.93, -4.32, -2.24],\n",
              " [4.34, 2.09, -1.24, 2.36, 1.13, -4.36, -2.01, -1.2, 0.55, 4.33],\n",
              " [0.08, 0.61, 1.69, 3.01, -4.11, -2.75, -4.73, 2.69, 2.21, -5.07],\n",
              " [4.34, 2.09, -1.24, -0.16, -2.7, 4.38, 2.42, -1.2, 0.55, 4.33],\n",
              " [1.62, 4.48, -2.38, 3.01, -4.11, -2.75, -4.73, 4.3, -1.73, -4.41],\n",
              " [-2.01, -2.88, 0.74, -4.9, 5.11, 5.07, 0.33, 4.73, -2.95, -0.17],\n",
              " [-0.23, 0.37, -4.25, 0.04, -4.61, 3.86, -3.47, 4.36, -3.44, -3.98],\n",
              " [-2.01, -2.88, 0.74, -3.55, 4.64, -2.01, 0.31, 4.73, -2.95, -0.17],\n",
              " [-5.12, 1.4, -1.26, 0.04, -4.61, 3.86, -3.47, -3.72, 1.26, 2.56],\n",
              " [-2.01, -2.88, 0.74, -0.16, -2.7, 4.38, 2.42, 4.73, -2.95, -0.17],\n",
              " [1.62, 4.48, -2.38, 0.04, -4.61, 3.86, -3.47, 4.3, -1.73, -4.41],\n",
              " [-2.01, -2.88, 0.74, -4.78, 0.32, 0.46, -3.18, 4.73, -2.95, -0.17],\n",
              " [4.3, -3.09, 2.09, 0.04, -4.61, 3.86, -3.47, 3.9, 3.78, -2.41],\n",
              " [-2.01, -2.88, 0.74, 4.59, -3.8, 1.03, -3.21, 4.73, -2.95, -0.17],\n",
              " [0.42, 1.74, -4.99, 0.04, -4.61, 3.86, -3.47, 1.76, 3.79, -2.53],\n",
              " [-2.01, -2.88, 0.74, 2.3, 4.53, 4.63, 0.02, 4.73, -2.95, -0.17],\n",
              " [5.09, 0.92, 0.46, 0.04, -4.61, 3.86, -3.47, 3.51, -1.13, -3.72],\n",
              " [-2.01, -2.88, 0.74, 1.3, 4.43, 0.18, -4.04, 4.73, -2.95, -0.17],\n",
              " [3.83, 0.85, -1.32, 0.04, -4.61, 3.86, -3.47, -2.2, 4.71, -0.16],\n",
              " [-2.01, -2.88, 0.74, -1.18, -1.51, 2.25, 5.02, 4.73, -2.95, -0.17],\n",
              " [-0.41, 1.38, 3.09, 0.04, -4.61, 3.86, -3.47, 1.49, 3.93, -0.52],\n",
              " [-2.01, -2.88, 0.74, 1.3, 4.43, 0.18, -4.04, 4.73, -2.95, -0.17],\n",
              " [3.83, 0.85, -1.32, 0.04, -4.61, 3.86, -3.47, -2.2, 4.71, -0.16],\n",
              " [-2.01, -2.88, 0.74, 0.38, -0.95, 2.09, -3.87, 4.73, -2.95, -0.17],\n",
              " [-3.07, -4.99, -3.53, 0.04, -4.61, 3.86, -3.47, 3.35, 0.92, -2.94],\n",
              " [-2.01, -2.88, 0.74, 2.06, 1.28, 2.33, -2.67, 4.73, -2.95, -0.17],\n",
              " [-4.88, 3.89, 0.2, 0.04, -4.61, 3.86, -3.47, 0.17, 0.96, -2.59],\n",
              " [-2.01, -2.88, 0.74, -4.9, 5.11, 5.07, 0.33, 4.73, -2.95, -0.17],\n",
              " [-0.23, 0.37, -4.25, 0.04, -4.61, 3.86, -3.47, 4.36, -3.44, -3.98],\n",
              " [-2.01, -2.88, 0.74, 1.3, 4.43, 0.18, -4.04, 4.73, -2.95, -0.17],\n",
              " [3.83, 0.85, -1.32, 0.04, -4.61, 3.86, -3.47, -2.2, 4.71, -0.16],\n",
              " [-2.01, -2.88, 0.74, -1.18, -1.51, 2.25, 5.02, 4.73, -2.95, -0.17],\n",
              " [-0.41, 1.38, 3.09, 0.04, -4.61, 3.86, -3.47, 1.49, 3.93, -0.52],\n",
              " [-2.01, -2.88, 0.74, -3.55, 2.2, 2.58, 2.42, 4.73, -2.95, -0.17],\n",
              " [0.35, -1.53, 1.78, 0.04, -4.61, 3.86, -3.47, 2.59, 3.36, 0.17],\n",
              " [-2.01, -2.88, 0.74, 2.6, -2.07, 1.92, 4.03, 4.73, -2.95, -0.17],\n",
              " [4.06, -4.78, 0.47, 0.04, -4.61, 3.86, -3.47, 3.5, 0.77, -4.22],\n",
              " [-2.01, -2.88, 0.74, -2.76, -0.0, -4.58, 2.09, 4.73, -2.95, -0.17],\n",
              " [3.69, 0.72, -1.18, 0.04, -4.61, 3.86, -3.47, -1.93, -4.32, -2.24],\n",
              " [-2.01, -2.88, 0.74, 2.36, 1.13, -4.36, -2.01, 4.73, -2.95, -0.17],\n",
              " [0.08, 0.61, 1.69, 0.04, -4.61, 3.86, -3.47, 2.69, 2.21, -5.07],\n",
              " [-2.01, -2.88, 0.74, -0.16, -2.7, 4.38, 2.42, 4.73, -2.95, -0.17],\n",
              " [1.62, 4.48, -2.38, 0.04, -4.61, 3.86, -3.47, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, -3.55, 4.64, -2.01, 0.31, 4.36, -3.44, -3.98],\n",
              " [-5.12, 1.4, -1.26, -4.9, 5.11, 5.07, 0.33, -3.72, 1.26, 2.56],\n",
              " [-0.23, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [1.62, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, -4.78, 0.32, 0.46, -3.18, 4.36, -3.44, -3.98],\n",
              " [4.3, -3.09, 2.09, -4.9, 5.11, 5.07, 0.33, 3.9, 3.78, -2.41],\n",
              " [-0.23, 0.37, -4.25, 4.59, -3.8, 1.03, -3.21, 4.36, -3.44, -3.98],\n",
              " [0.42, 1.74, -4.99, -4.9, 5.11, 5.07, 0.33, 1.76, 3.79, -2.53],\n",
              " [-0.23, 0.37, -4.25, 2.3, 4.53, 4.63, 0.02, 4.36, -3.44, -3.98],\n",
              " [5.09, 0.92, 0.46, -4.9, 5.11, 5.07, 0.33, 3.51, -1.13, -3.72],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, -1.18, -1.51, 2.25, 5.02, 4.36, -3.44, -3.98],\n",
              " [-0.41, 1.38, 3.09, -4.9, 5.11, 5.07, 0.33, 1.49, 3.93, -0.52],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, 0.38, -0.95, 2.09, -3.87, 4.36, -3.44, -3.98],\n",
              " [-3.07, -4.99, -3.53, -4.9, 5.11, 5.07, 0.33, 3.35, 0.92, -2.94],\n",
              " [-0.23, 0.37, -4.25, 2.06, 1.28, 2.33, -2.67, 4.36, -3.44, -3.98],\n",
              " [-4.88, 3.89, 0.2, -4.9, 5.11, 5.07, 0.33, 0.17, 0.96, -2.59],\n",
              " [-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              " [-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, -1.18, -1.51, 2.25, 5.02, 4.36, -3.44, -3.98],\n",
              " [-0.41, 1.38, 3.09, -4.9, 5.11, 5.07, 0.33, 1.49, 3.93, -0.52],\n",
              " [-0.23, 0.37, -4.25, -3.55, 2.2, 2.58, 2.42, 4.36, -3.44, -3.98],\n",
              " [0.35, -1.53, 1.78, -4.9, 5.11, 5.07, 0.33, 2.59, 3.36, 0.17],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, -2.76, -0.0, -4.58, 2.09, 4.36, -3.44, -3.98],\n",
              " [3.69, 0.72, -1.18, -4.9, 5.11, 5.07, 0.33, -1.93, -4.32, -2.24],\n",
              " [-0.23, 0.37, -4.25, 2.36, 1.13, -4.36, -2.01, 4.36, -3.44, -3.98],\n",
              " [0.08, 0.61, 1.69, -4.9, 5.11, 5.07, 0.33, 2.69, 2.21, -5.07],\n",
              " [-0.23, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [1.62, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [-5.12, 1.4, -1.26, -0.16, -2.7, 4.38, 2.42, -3.72, 1.26, 2.56],\n",
              " [1.62, 4.48, -2.38, -3.55, 4.64, -2.01, 0.31, 4.3, -1.73, -4.41],\n",
              " [-5.12, 1.4, -1.26, -4.78, 0.32, 0.46, -3.18, -3.72, 1.26, 2.56],\n",
              " [4.3, -3.09, 2.09, -3.55, 4.64, -2.01, 0.31, 3.9, 3.78, -2.41],\n",
              " [-5.12, 1.4, -1.26, 4.59, -3.8, 1.03, -3.21, -3.72, 1.26, 2.56],\n",
              " [0.42, 1.74, -4.99, -3.55, 4.64, -2.01, 0.31, 1.76, 3.79, -2.53],\n",
              " [-5.12, 1.4, -1.26, 2.3, 4.53, 4.63, 0.02, -3.72, 1.26, 2.56],\n",
              " [5.09, 0.92, 0.46, -3.55, 4.64, -2.01, 0.31, 3.51, -1.13, -3.72],\n",
              " [-5.12, 1.4, -1.26, 1.3, 4.43, 0.18, -4.04, -3.72, 1.26, 2.56],\n",
              " [3.83, 0.85, -1.32, -3.55, 4.64, -2.01, 0.31, -2.2, 4.71, -0.16],\n",
              " [-5.12, 1.4, -1.26, -1.18, -1.51, 2.25, 5.02, -3.72, 1.26, 2.56],\n",
              " [-0.41, 1.38, 3.09, -3.55, 4.64, -2.01, 0.31, 1.49, 3.93, -0.52],\n",
              " [-5.12, 1.4, -1.26, 1.3, 4.43, 0.18, -4.04, -3.72, 1.26, 2.56],\n",
              " [3.83, 0.85, -1.32, -3.55, 4.64, -2.01, 0.31, -2.2, 4.71, -0.16],\n",
              " [-5.12, 1.4, -1.26, 0.38, -0.95, 2.09, -3.87, -3.72, 1.26, 2.56],\n",
              " [-3.07, -4.99, -3.53, -3.55, 4.64, -2.01, 0.31, 3.35, 0.92, -2.94],\n",
              " [-5.12, 1.4, -1.26, 2.06, 1.28, 2.33, -2.67, -3.72, 1.26, 2.56],\n",
              " [-4.88, 3.89, 0.2, -3.55, 4.64, -2.01, 0.31, 0.17, 0.96, -2.59],\n",
              " [-5.12, 1.4, -1.26, -4.9, 5.11, 5.07, 0.33, -3.72, 1.26, 2.56],\n",
              " [-0.23, 0.37, -4.25, -3.55, 4.64, -2.01, 0.31, 4.36, -3.44, -3.98],\n",
              " [-5.12, 1.4, -1.26, 1.3, 4.43, 0.18, -4.04, -3.72, 1.26, 2.56],\n",
              " [3.83, 0.85, -1.32, -3.55, 4.64, -2.01, 0.31, -2.2, 4.71, -0.16],\n",
              " [-5.12, 1.4, -1.26, -1.18, -1.51, 2.25, 5.02, -3.72, 1.26, 2.56],\n",
              " [-0.41, 1.38, 3.09, -3.55, 4.64, -2.01, 0.31, 1.49, 3.93, -0.52],\n",
              " [-5.12, 1.4, -1.26, -3.55, 2.2, 2.58, 2.42, -3.72, 1.26, 2.56],\n",
              " [0.35, -1.53, 1.78, -3.55, 4.64, -2.01, 0.31, 2.59, 3.36, 0.17],\n",
              " [-5.12, 1.4, -1.26, 2.6, -2.07, 1.92, 4.03, -3.72, 1.26, 2.56],\n",
              " [4.06, -4.78, 0.47, -3.55, 4.64, -2.01, 0.31, 3.5, 0.77, -4.22],\n",
              " [-5.12, 1.4, -1.26, -2.76, -0.0, -4.58, 2.09, -3.72, 1.26, 2.56],\n",
              " [3.69, 0.72, -1.18, -3.55, 4.64, -2.01, 0.31, -1.93, -4.32, -2.24],\n",
              " [-5.12, 1.4, -1.26, 2.36, 1.13, -4.36, -2.01, -3.72, 1.26, 2.56],\n",
              " [0.08, 0.61, 1.69, -3.55, 4.64, -2.01, 0.31, 2.69, 2.21, -5.07],\n",
              " [-5.12, 1.4, -1.26, -0.16, -2.7, 4.38, 2.42, -3.72, 1.26, 2.56],\n",
              " [1.62, 4.48, -2.38, -3.55, 4.64, -2.01, 0.31, 4.3, -1.73, -4.41],\n",
              " [1.62, 4.48, -2.38, -4.78, 0.32, 0.46, -3.18, 4.3, -1.73, -4.41],\n",
              " [4.3, -3.09, 2.09, -0.16, -2.7, 4.38, 2.42, 3.9, 3.78, -2.41],\n",
              " [1.62, 4.48, -2.38, 4.59, -3.8, 1.03, -3.21, 4.3, -1.73, -4.41],\n",
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              " [1.62, 4.48, -2.38, 2.3, 4.53, 4.63, 0.02, 4.3, -1.73, -4.41],\n",
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              " [3.83, 0.85, -1.32, 0.38, -0.95, 2.09, -3.87, -2.2, 4.71, -0.16],\n",
              " [-3.07, -4.99, -3.53, 1.3, 4.43, 0.18, -4.04, 3.35, 0.92, -2.94],\n",
              " [3.83, 0.85, -1.32, 2.06, 1.28, 2.33, -2.67, -2.2, 4.71, -0.16],\n",
              " [-4.88, 3.89, 0.2, 1.3, 4.43, 0.18, -4.04, 0.17, 0.96, -2.59],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16],\n",
              " [3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16],\n",
              " [3.83, 0.85, -1.32, -1.18, -1.51, 2.25, 5.02, -2.2, 4.71, -0.16],\n",
              " [-0.41, 1.38, 3.09, 1.3, 4.43, 0.18, -4.04, 1.49, 3.93, -0.52],\n",
              " [3.83, 0.85, -1.32, -3.55, 2.2, 2.58, 2.42, -2.2, 4.71, -0.16],\n",
              " [0.35, -1.53, 1.78, 1.3, 4.43, 0.18, -4.04, 2.59, 3.36, 0.17],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, -2.76, -0.0, -4.58, 2.09, -2.2, 4.71, -0.16],\n",
              " [3.69, 0.72, -1.18, 1.3, 4.43, 0.18, -4.04, -1.93, -4.32, -2.24],\n",
              " [3.83, 0.85, -1.32, 2.36, 1.13, -4.36, -2.01, -2.2, 4.71, -0.16],\n",
              " [0.08, 0.61, 1.69, 1.3, 4.43, 0.18, -4.04, 2.69, 2.21, -5.07],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [-3.07, -4.99, -3.53, 2.06, 1.28, 2.33, -2.67, 3.35, 0.92, -2.94],\n",
              " [-4.88, 3.89, 0.2, 0.38, -0.95, 2.09, -3.87, 0.17, 0.96, -2.59],\n",
              " [-3.07, -4.99, -3.53, -4.9, 5.11, 5.07, 0.33, 3.35, 0.92, -2.94],\n",
              " [-0.23, 0.37, -4.25, 0.38, -0.95, 2.09, -3.87, 4.36, -3.44, -3.98],\n",
              " [-3.07, -4.99, -3.53, 1.3, 4.43, 0.18, -4.04, 3.35, 0.92, -2.94],\n",
              " [3.83, 0.85, -1.32, 0.38, -0.95, 2.09, -3.87, -2.2, 4.71, -0.16],\n",
              " [-3.07, -4.99, -3.53, -1.18, -1.51, 2.25, 5.02, 3.35, 0.92, -2.94],\n",
              " [-0.41, 1.38, 3.09, 0.38, -0.95, 2.09, -3.87, 1.49, 3.93, -0.52],\n",
              " [-3.07, -4.99, -3.53, -3.55, 2.2, 2.58, 2.42, 3.35, 0.92, -2.94],\n",
              " [0.35, -1.53, 1.78, 0.38, -0.95, 2.09, -3.87, 2.59, 3.36, 0.17],\n",
              " [-3.07, -4.99, -3.53, 2.6, -2.07, 1.92, 4.03, 3.35, 0.92, -2.94],\n",
              " [4.06, -4.78, 0.47, 0.38, -0.95, 2.09, -3.87, 3.5, 0.77, -4.22],\n",
              " [-3.07, -4.99, -3.53, -2.76, -0.0, -4.58, 2.09, 3.35, 0.92, -2.94],\n",
              " [3.69, 0.72, -1.18, 0.38, -0.95, 2.09, -3.87, -1.93, -4.32, -2.24],\n",
              " [-3.07, -4.99, -3.53, 2.36, 1.13, -4.36, -2.01, 3.35, 0.92, -2.94],\n",
              " [0.08, 0.61, 1.69, 0.38, -0.95, 2.09, -3.87, 2.69, 2.21, -5.07],\n",
              " [-3.07, -4.99, -3.53, -0.16, -2.7, 4.38, 2.42, 3.35, 0.92, -2.94],\n",
              " [1.62, 4.48, -2.38, 0.38, -0.95, 2.09, -3.87, 4.3, -1.73, -4.41],\n",
              " [-4.88, 3.89, 0.2, -4.9, 5.11, 5.07, 0.33, 0.17, 0.96, -2.59],\n",
              " [-0.23, 0.37, -4.25, 2.06, 1.28, 2.33, -2.67, 4.36, -3.44, -3.98],\n",
              " [-4.88, 3.89, 0.2, 1.3, 4.43, 0.18, -4.04, 0.17, 0.96, -2.59],\n",
              " [3.83, 0.85, -1.32, 2.06, 1.28, 2.33, -2.67, -2.2, 4.71, -0.16],\n",
              " [-4.88, 3.89, 0.2, -1.18, -1.51, 2.25, 5.02, 0.17, 0.96, -2.59],\n",
              " [-0.41, 1.38, 3.09, 2.06, 1.28, 2.33, -2.67, 1.49, 3.93, -0.52],\n",
              " [-4.88, 3.89, 0.2, -3.55, 2.2, 2.58, 2.42, 0.17, 0.96, -2.59],\n",
              " [0.35, -1.53, 1.78, 2.06, 1.28, 2.33, -2.67, 2.59, 3.36, 0.17],\n",
              " [-4.88, 3.89, 0.2, 2.6, -2.07, 1.92, 4.03, 0.17, 0.96, -2.59],\n",
              " [4.06, -4.78, 0.47, 2.06, 1.28, 2.33, -2.67, 3.5, 0.77, -4.22],\n",
              " [-4.88, 3.89, 0.2, -2.76, -0.0, -4.58, 2.09, 0.17, 0.96, -2.59],\n",
              " [3.69, 0.72, -1.18, 2.06, 1.28, 2.33, -2.67, -1.93, -4.32, -2.24],\n",
              " [-4.88, 3.89, 0.2, 2.36, 1.13, -4.36, -2.01, 0.17, 0.96, -2.59],\n",
              " [0.08, 0.61, 1.69, 2.06, 1.28, 2.33, -2.67, 2.69, 2.21, -5.07],\n",
              " [-4.88, 3.89, 0.2, -0.16, -2.7, 4.38, 2.42, 0.17, 0.96, -2.59],\n",
              " [1.62, 4.48, -2.38, 2.06, 1.28, 2.33, -2.67, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, -1.18, -1.51, 2.25, 5.02, 4.36, -3.44, -3.98],\n",
              " [-0.41, 1.38, 3.09, -4.9, 5.11, 5.07, 0.33, 1.49, 3.93, -0.52],\n",
              " [-0.23, 0.37, -4.25, -3.55, 2.2, 2.58, 2.42, 4.36, -3.44, -3.98],\n",
              " [0.35, -1.53, 1.78, -4.9, 5.11, 5.07, 0.33, 2.59, 3.36, 0.17],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, -2.76, -0.0, -4.58, 2.09, 4.36, -3.44, -3.98],\n",
              " [3.69, 0.72, -1.18, -4.9, 5.11, 5.07, 0.33, -1.93, -4.32, -2.24],\n",
              " [-0.23, 0.37, -4.25, 2.36, 1.13, -4.36, -2.01, 4.36, -3.44, -3.98],\n",
              " [0.08, 0.61, 1.69, -4.9, 5.11, 5.07, 0.33, 2.69, 2.21, -5.07],\n",
              " [-0.23, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [1.62, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, -1.18, -1.51, 2.25, 5.02, -2.2, 4.71, -0.16],\n",
              " [-0.41, 1.38, 3.09, 1.3, 4.43, 0.18, -4.04, 1.49, 3.93, -0.52],\n",
              " [3.83, 0.85, -1.32, -3.55, 2.2, 2.58, 2.42, -2.2, 4.71, -0.16],\n",
              " [0.35, -1.53, 1.78, 1.3, 4.43, 0.18, -4.04, 2.59, 3.36, 0.17],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, -2.76, -0.0, -4.58, 2.09, -2.2, 4.71, -0.16],\n",
              " [3.69, 0.72, -1.18, 1.3, 4.43, 0.18, -4.04, -1.93, -4.32, -2.24],\n",
              " [3.83, 0.85, -1.32, 2.36, 1.13, -4.36, -2.01, -2.2, 4.71, -0.16],\n",
              " [0.08, 0.61, 1.69, 1.3, 4.43, 0.18, -4.04, 2.69, 2.21, -5.07],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [-0.41, 1.38, 3.09, -3.55, 2.2, 2.58, 2.42, 1.49, 3.93, -0.52],\n",
              " [0.35, -1.53, 1.78, -1.18, -1.51, 2.25, 5.02, 2.59, 3.36, 0.17],\n",
              " [-0.41, 1.38, 3.09, 2.6, -2.07, 1.92, 4.03, 1.49, 3.93, -0.52],\n",
              " [4.06, -4.78, 0.47, -1.18, -1.51, 2.25, 5.02, 3.5, 0.77, -4.22],\n",
              " [-0.41, 1.38, 3.09, -2.76, -0.0, -4.58, 2.09, 1.49, 3.93, -0.52],\n",
              " [3.69, 0.72, -1.18, -1.18, -1.51, 2.25, 5.02, -1.93, -4.32, -2.24],\n",
              " [-0.41, 1.38, 3.09, 2.36, 1.13, -4.36, -2.01, 1.49, 3.93, -0.52],\n",
              " [0.08, 0.61, 1.69, -1.18, -1.51, 2.25, 5.02, 2.69, 2.21, -5.07],\n",
              " [-0.41, 1.38, 3.09, -0.16, -2.7, 4.38, 2.42, 1.49, 3.93, -0.52],\n",
              " [1.62, 4.48, -2.38, -1.18, -1.51, 2.25, 5.02, 4.3, -1.73, -4.41],\n",
              " [0.35, -1.53, 1.78, 2.6, -2.07, 1.92, 4.03, 2.59, 3.36, 0.17],\n",
              " [4.06, -4.78, 0.47, -3.55, 2.2, 2.58, 2.42, 3.5, 0.77, -4.22],\n",
              " [0.35, -1.53, 1.78, -2.76, -0.0, -4.58, 2.09, 2.59, 3.36, 0.17],\n",
              " [3.69, 0.72, -1.18, -3.55, 2.2, 2.58, 2.42, -1.93, -4.32, -2.24],\n",
              " [0.35, -1.53, 1.78, 2.36, 1.13, -4.36, -2.01, 2.59, 3.36, 0.17],\n",
              " [0.08, 0.61, 1.69, -3.55, 2.2, 2.58, 2.42, 2.69, 2.21, -5.07],\n",
              " [0.35, -1.53, 1.78, -0.16, -2.7, 4.38, 2.42, 2.59, 3.36, 0.17],\n",
              " [1.62, 4.48, -2.38, -3.55, 2.2, 2.58, 2.42, 4.3, -1.73, -4.41],\n",
              " [4.06, -4.78, 0.47, -2.76, -0.0, -4.58, 2.09, 3.5, 0.77, -4.22],\n",
              " [3.69, 0.72, -1.18, 2.6, -2.07, 1.92, 4.03, -1.93, -4.32, -2.24],\n",
              " [4.06, -4.78, 0.47, 2.36, 1.13, -4.36, -2.01, 3.5, 0.77, -4.22],\n",
              " [0.08, 0.61, 1.69, 2.6, -2.07, 1.92, 4.03, 2.69, 2.21, -5.07],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [3.69, 0.72, -1.18, 2.36, 1.13, -4.36, -2.01, -1.93, -4.32, -2.24],\n",
              " [0.08, 0.61, 1.69, -2.76, -0.0, -4.58, 2.09, 2.69, 2.21, -5.07],\n",
              " [3.69, 0.72, -1.18, -0.16, -2.7, 4.38, 2.42, -1.93, -4.32, -2.24],\n",
              " [1.62, 4.48, -2.38, -2.76, -0.0, -4.58, 2.09, 4.3, -1.73, -4.41],\n",
              " [0.08, 0.61, 1.69, -0.16, -2.7, 4.38, 2.42, 2.69, 2.21, -5.07],\n",
              " [1.62, 4.48, -2.38, 2.36, 1.13, -4.36, -2.01, 4.3, -1.73, -4.41]]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mutation of children function. Notice that this operation is split in two functions:\n",
        "\n",
        "1.   Mutation of the individual\n",
        "2.   Fix of the individual (in case a gen of the individual get out of the inverval allowed by the cost function)"
      ],
      "metadata": {
        "id": "tkqUZuknyWTS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Mutation of the individual"
      ],
      "metadata": {
        "id": "Riw_LeMizBgW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def mutate(children, delta_mut, mut_avg):\n",
        "    mutated_children = [] # vector that will contain the children mutated\n",
        "    for child in children:\n",
        "        mutated_children.append(mutation(child, delta_mut, mut_avg))\n",
        "    return mutated_children"
      ],
      "metadata": {
        "id": "BpAon1rGzPPG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test of mutation operator. We'll be using the children obtained after the reproduction of parents above."
      ],
      "metadata": {
        "id": "3MyTdyrlz0Hz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mutate_children = mutate(children, 2.23, 0.01)\n",
        "mutate_children"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wxg312-1z8gg",
        "outputId": "8584977c-cdcf-475d-aa50-dd085b1fd3d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2.79, -1.68, -5.01, 2.6, -2.07, 1.92, 4.03, 0.06, 4.47, -2.38],\n",
              " [4.06, -4.78, 0.47, 3.11, -5.07, 0.96, -4.69, 3.5, 0.77, -4.22],\n",
              " [2.79, -1.68, -5.01, 3.01, -4.11, -2.75, -4.73, 0.06, 4.47, -2.38],\n",
              " [4.34, 2.09, -1.24, 3.11, -5.07, 0.96, -4.69, -1.2, 0.55, 4.33],\n",
              " [2.79, -1.68, -5.01, 0.04, -4.61, 3.86, -3.47, 0.06, 4.47, -2.38],\n",
              " [-2.01, -2.88, 0.74, 3.11, -5.07, 0.96, -4.69, 4.73, -2.95, -0.17],\n",
              " [2.79, -1.68, -5.01, -4.9, 5.11, 5.07, 0.33, 0.06, 4.47, -2.38],\n",
              " [-0.23, 0.37, -4.25, 3.11, -5.07, 0.96, -4.69, 4.36, -3.44, -3.98],\n",
              " [2.79,\n",
              "  -1.68,\n",
              "  -5.01,\n",
              "  -5.779999999999999,\n",
              "  4.64,\n",
              "  -2.01,\n",
              "  0.31,\n",
              "  0.06,\n",
              "  4.47,\n",
              "  -2.38],\n",
              " [-5.12, 1.4, -1.26, 3.11, -5.07, 0.96, -4.69, -3.72, 1.26, 2.56],\n",
              " [2.79, -1.68, -5.01, -0.16, -2.7, 4.38, 2.42, 0.06, 4.47, -2.38],\n",
              " [1.62, 4.48, -2.38, 3.11, -5.07, 0.96, -4.69, 4.3, -1.73, -4.41],\n",
              " [2.79, -1.68, -5.01, -4.78, 0.32, 0.46, -3.18, 0.06, 4.47, -2.38],\n",
              " [4.3, -3.09, 2.09, 3.11, -5.07, 0.96, -4.69, 3.9, 3.78, -2.41],\n",
              " [2.79, -1.68, -5.01, 4.59, -3.8, 1.03, -3.21, 0.06, 4.47, -2.38],\n",
              " [0.42, 1.74, -4.99, 3.11, -5.07, 0.96, -4.69, 1.76, 3.79, -2.53],\n",
              " [2.79, -1.68, -5.01, 2.3, 4.53, 4.63, 0.02, 0.06, 4.47, -2.38],\n",
              " [2.86, 0.92, 0.46, 3.11, -5.07, 0.96, -4.69, 3.51, -1.13, -3.72],\n",
              " [2.79, -1.68, -7.24, 1.3, 4.43, 0.18, -4.04, 0.06, 4.47, -2.38],\n",
              " [3.83, 0.85, -1.32, 3.11, -5.07, 0.96, -4.69, -2.2, 4.71, -0.16],\n",
              " [2.79, -1.68, -5.01, 1.05, -1.51, 2.25, 5.02, 0.06, 4.47, -2.38],\n",
              " [-0.41, 1.38, 3.09, 3.11, -5.07, 0.96, -4.69, 1.49, 3.93, -0.52],\n",
              " [2.79, -1.68, -5.01, 1.3, 4.43, 0.18, -4.04, 0.06, 4.47, -2.38],\n",
              " [3.83, 0.85, -1.32, 3.11, -5.07, 0.96, -4.69, -2.2, 4.71, -0.16],\n",
              " [2.79,\n",
              "  -1.68,\n",
              "  -5.01,\n",
              "  0.38,\n",
              "  -0.95,\n",
              "  2.09,\n",
              "  -3.87,\n",
              "  0.06,\n",
              "  6.699999999999999,\n",
              "  -2.38],\n",
              " [-3.07, -4.99, -3.53, 3.11, -5.07, 0.96, -4.69, 3.35, 0.92, -2.94],\n",
              " [2.79, -1.68, -5.01, 2.06, 1.28, 2.33, -2.67, 0.06, 4.47, -2.38],\n",
              " [-4.88, 3.89, 0.2, 3.11, -5.07, 0.96, -4.69, 0.17, 0.96, -2.59],\n",
              " [2.79, -1.68, -5.01, -4.9, 5.11, 5.07, 0.33, 0.06, 4.47, -2.38],\n",
              " [-0.23, 0.37, -4.25, 3.11, -5.07, 0.96, -4.69, 4.36, -3.44, -3.98],\n",
              " [2.79, -1.68, -5.01, 1.3, 4.43, 0.18, -4.04, 0.06, 4.47, -2.38],\n",
              " [3.83, 0.85, -1.32, 3.11, -5.07, 0.96, -4.69, -2.2, 4.71, -0.16],\n",
              " [2.79,\n",
              "  -1.68,\n",
              "  -5.01,\n",
              "  -1.18,\n",
              "  -1.51,\n",
              "  0.020000000000000018,\n",
              "  5.02,\n",
              "  0.06,\n",
              "  4.47,\n",
              "  -2.38],\n",
              " [-0.41, 1.38, 3.09, 3.11, -5.07, 0.96, -4.69, 1.49, 3.93, -0.52],\n",
              " [2.79, -1.68, -5.01, -3.55, 2.2, 2.58, 2.42, 0.06, 4.47, -2.38],\n",
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              " [4.06, -4.78, 0.47, -3.55, 4.64, -2.01, 0.31, 3.5, 0.77, -4.22],\n",
              " [-5.12, 1.4, -1.26, 2.6, -2.07, 1.92, 4.03, -3.72, 1.26, 2.56],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [4.06, -4.78, 0.47, -4.78, 0.32, 0.46, -3.18, 3.5, 0.77, -4.22],\n",
              " [4.3, -3.09, 2.09, 2.6, -2.07, 1.92, 4.03, 3.9, 3.78, -2.41],\n",
              " [4.06, -4.78, 0.47, 4.59, -3.8, 1.03, -3.21, 3.5, 0.77, -4.22],\n",
              " [0.42, 1.74, -4.99, 2.6, -2.07, 1.92, 4.03, 1.76, 3.79, -2.53],\n",
              " [4.06, -4.78, 0.47, 2.3, 4.53, 4.63, 0.02, 3.5, 0.77, -4.22],\n",
              " [5.09, 0.92, 0.46, 2.6, -2.07, 1.92, 4.03, 3.51, -1.13, -3.72],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, -1.18, -1.51, 2.25, 5.02, 3.5, 0.77, -4.22],\n",
              " [-0.41, 1.38, 3.09, 2.6, -2.07, 1.92, 4.03, 1.49, 3.93, -0.52],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, 0.38, -0.95, 2.09, -3.87, 3.5, 0.77, -4.22],\n",
              " [-3.07, -4.99, -3.53, 2.6, -2.07, 1.92, 4.03, 3.35, 0.92, -2.94],\n",
              " [4.06, -4.78, 0.47, 2.06, 1.28, 2.33, -2.67, 3.5, 0.77, -4.22],\n",
              " [-4.88, 3.89, 0.2, 2.6, -2.07, 1.92, 4.03, 0.17, 0.96, -2.59],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [1.6, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, -1.18, -1.51, 2.25, 5.02, 3.5, 0.77, -4.22],\n",
              " [-0.41, 1.38, 3.09, 2.6, -2.07, 1.92, 4.03, 1.49, 3.93, -0.52],\n",
              " [4.06, -4.78, 0.47, -3.55, 2.2, 2.58, 2.42, 3.5, 0.77, -4.22],\n",
              " [0.35, -1.53, 1.78, 2.6, -2.07, 1.92, 4.03, 2.59, 3.36, 0.17],\n",
              " [4.06, -4.78, 0.47, 2.6, -2.07, -0.31000000000000005, 4.03, 3.5, 0.77, -4.22],\n",
              " [4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22],\n",
              " [4.06, -4.78, 0.47, -2.76, -0.0, -4.58, 2.09, 3.5, 0.77, -4.22],\n",
              " [3.69, 0.72, -1.18, 2.6, -2.07, 1.92, 4.03, -1.93, -4.32, -2.24],\n",
              " [4.06, -4.78, 0.47, 2.36, 1.13, -4.36, -2.01, 3.5, 0.77, -4.22],\n",
              " [0.08, 0.61, 1.69, 2.6, -2.07, 1.92, 4.03, 2.69, 2.21, -5.07],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [4.34,\n",
              "  2.09,\n",
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              "  3.86,\n",
              "  -3.47,\n",
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              "  4.33],\n",
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              " [4.34, 2.09, -1.24, -4.9, 5.11, 5.07, 0.33, -1.2, 0.55, 4.33],\n",
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              " [4.34, 2.09, -1.24, -3.55, 4.64, -2.01, 0.31, -1.2, 0.55, 4.33],\n",
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              " [1.62, 4.48, -2.38, 3.01, -4.11, -2.75, -4.73, 4.3, -1.73, -4.41],\n",
              " [4.34, 2.09, -1.24, -4.78, 0.32, 0.46, -3.18, -1.2, 0.55, 4.33],\n",
              " [4.3, -3.09, 2.09, 3.01, -4.11, -2.75, -4.73, 3.9, 3.78, -2.41],\n",
              " [4.34, 2.09, -1.24, 4.59, -3.8, 1.03, -3.21, -1.2, 0.55, 4.33],\n",
              " [0.42, 1.74, -4.99, 3.01, -4.11, -2.75, -4.73, 1.76, 3.79, -2.53],\n",
              " [4.34, 2.09, -1.24, 2.3, 4.53, 4.63, 0.02, -1.2, 0.55, 4.33],\n",
              " [5.09,\n",
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              "  -2.75,\n",
              "  -4.73,\n",
              "  1.2799999999999998,\n",
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              " [4.34, 2.09, -1.24, -1.18, -1.51, 2.25, 5.02, -1.2, 0.55, 4.33],\n",
              " [-0.41, 1.38, 3.09, 3.01, -4.11, -2.75, -4.73, 1.49, 3.93, -0.52],\n",
              " [4.34, 2.09, -1.24, 1.3, 4.43, 0.18, -4.04, -1.2, 0.55, 4.33],\n",
              " [3.83, 0.85, -1.32, 3.01, -4.11, -2.75, -4.73, -2.2, 4.71, -0.16],\n",
              " [4.34, 2.09, -1.24, 0.38, -0.95, 2.09, -1.6400000000000001, -1.2, 0.55, 4.33],\n",
              " [-3.07, -4.99, -3.53, 3.01, -4.11, -2.75, -4.73, 3.35, 0.92, -2.94],\n",
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              " [4.34, 2.09, -1.24, -4.9, 7.34, 5.07, 0.33, -1.2, 0.55, 4.33],\n",
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              " [4.34, 2.09, -1.24, 1.3, 4.43, 0.18, -4.04, -1.2, 0.55, 4.33],\n",
              " [3.83, 0.85, -1.32, 3.01, -4.11, -2.75, -4.73, -2.2, 4.71, -0.16],\n",
              " [4.34, 2.09, -1.24, -1.18, -1.51, 2.25, 5.02, -1.2, 0.55, 4.33],\n",
              " [-0.41, 1.38, 3.09, 3.01, -4.11, -2.75, -4.73, 1.49, 3.93, -0.52],\n",
              " [4.34, 2.09, -1.24, -3.55, 2.2, 2.58, 2.42, -1.2, 0.55, 4.33],\n",
              " [0.35, -1.53, 1.78, 3.01, -4.11, -2.75, -4.73, 2.59, 3.36, -2.06],\n",
              " [4.34, 2.09, -1.24, 2.6, -2.07, 1.92, 4.03, -1.2, 0.55, 4.33],\n",
              " [4.06, -4.78, 0.47, 3.01, -4.11, -2.75, -4.73, 3.5, 0.77, -4.22],\n",
              " [6.57, 2.09, -1.24, -2.76, -0.0, -4.58, 2.09, -1.2, 0.55, 4.33],\n",
              " [3.69, 0.72, -1.18, 3.01, -4.11, -2.75, -4.73, -1.93, -4.32, -2.24],\n",
              " [4.34, 2.09, -1.24, 2.36, 1.13, -4.36, -2.01, -1.2, 0.55, 4.33],\n",
              " [0.08, 0.61, 1.69, 3.01, -4.11, -2.75, -4.73, 2.69, 2.21, -5.07],\n",
              " [4.34, 2.09, -1.24, -0.16, -2.7, 2.15, 2.42, -1.2, 0.55, 4.33],\n",
              " [1.62, 4.48, -2.38, 3.01, -4.11, -2.75, -4.73, 4.3, -1.73, -4.41],\n",
              " [-2.01, -2.88, 0.74, -4.9, 5.11, 5.07, 0.33, 4.73, -2.95, -0.17],\n",
              " [-0.23,\n",
              "  -1.8599999999999999,\n",
              "  -4.25,\n",
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              "  -3.47,\n",
              "  4.36,\n",
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              "  -3.98],\n",
              " [-2.01, -2.88, 0.74, -3.55, 4.64, -2.01, 0.31, 4.73, -2.95, -0.17],\n",
              " [-5.12, 1.4, -1.26, 0.04, -4.61, 3.86, -3.47, -3.72, 1.26, 2.56],\n",
              " [-2.01, -2.88, 0.74, -0.16, -2.7, 4.38, 2.42, 4.73, -2.95, -0.17],\n",
              " [1.62, 4.48, -2.38, 0.04, -4.61, 3.86, -3.47, 4.3, 0.5, -4.41],\n",
              " [-2.01, -2.88, 0.74, -4.78, 0.32, 0.46, -3.18, 4.73, -2.95, -0.17],\n",
              " [4.3,\n",
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              "  -4.61,\n",
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              " [0.42, 1.74, -4.99, 0.04, -4.61, 3.86, -3.47, 1.76, 3.79, -2.53],\n",
              " [-2.01, -2.88, 0.74, 2.3, 4.53, 4.63, 0.02, 4.73, -2.95, -0.17],\n",
              " [5.09, 0.92, 0.46, 0.04, -4.61, 3.86, -3.47, 3.51, -1.13, -3.72],\n",
              " [-2.01, -2.88, 0.74, 1.3, 4.43, 0.18, -4.04, 4.73, -2.95, -0.17],\n",
              " [3.83, 0.85, -1.32, 0.04, -4.61, 3.86, -3.47, -2.2, 4.71, -0.16],\n",
              " [-2.01, -2.88, 0.74, -1.18, -1.51, 2.25, 5.02, 4.73, -2.95, -0.17],\n",
              " [-0.41, 1.38, 3.09, 0.04, -4.61, 3.86, -3.47, 1.49, 3.93, -0.52],\n",
              " [-2.01, -2.88, 0.74, 1.3, 4.43, 0.18, -4.04, 4.73, -2.95, -0.17],\n",
              " [3.83, 0.85, -1.32, 0.04, -4.61, 3.86, -3.47, -2.2, 4.71, -0.16],\n",
              " [-2.01, -2.88, 0.74, 0.38, -0.95, 2.09, -3.87, 4.73, -2.95, -0.17],\n",
              " [-3.07, -4.99, -3.53, 0.04, -4.61, 3.86, -3.47, 3.35, 0.92, -2.94],\n",
              " [-2.01,\n",
              "  -0.6499999999999999,\n",
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              "  2.33,\n",
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              "  4.73,\n",
              "  -2.95,\n",
              "  -0.17],\n",
              " [-4.88, 3.89, 0.2, 0.04, -4.61, 3.86, -3.47, 0.17, 0.96, -2.59],\n",
              " [-2.01, -2.88, 0.74, -4.9, 5.11, 5.07, 0.33, 4.73, -2.95, -0.17],\n",
              " [-0.23, 0.37, -4.25, 0.04, -4.61, 3.86, -3.47, 4.36, -3.44, -3.98],\n",
              " [-2.01, -2.88, 0.74, 1.3, 4.43, -2.05, -4.04, 4.73, -2.95, -0.17],\n",
              " [3.83, 0.85, -1.32, 0.04, -4.61, 3.86, -3.47, -2.2, 4.71, -0.16],\n",
              " [-2.01, -2.88, 0.74, -1.18, -1.51, 2.25, 5.02, 4.73, -2.95, -0.17],\n",
              " [-0.41, 1.38, 3.09, 0.04, -4.61, 3.86, -3.47, 1.49, 3.93, -0.52],\n",
              " [-2.01, -2.88, 0.74, -3.55, 2.2, 2.58, 2.42, 4.73, -2.95, -0.17],\n",
              " [0.35, -1.53, 1.78, 0.04, -4.61, 3.86, -3.47, 2.59, 3.36, 0.17],\n",
              " [-2.01, -2.88, 0.74, 2.6, -2.07, 1.92, 4.03, 4.73, -2.95, -0.17],\n",
              " [4.06, -4.78, 0.47, 0.04, -4.61, 3.86, -3.47, 3.5, 0.77, -4.22],\n",
              " [-2.01, -2.88, 0.74, -2.76, -0.0, -4.58, 2.09, 4.73, -2.95, -0.17],\n",
              " [3.69, 0.72, -1.18, 0.04, -4.61, 3.86, -3.47, -1.93, -4.32, -2.24],\n",
              " [-2.01, -2.88, 0.74, 2.36, 1.13, -4.36, -2.01, 4.73, -2.95, -0.17],\n",
              " [0.08, 0.61, 1.69, 0.04, -4.61, 3.86, -3.47, 2.69, 2.21, -5.07],\n",
              " [-2.01, -2.88, 0.74, -0.16, -2.7, 4.38, 2.42, 4.73, -2.95, -0.17],\n",
              " [1.62, 4.48, -2.38, 0.04, -4.61, 3.86, -3.47, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, -3.55, 4.64, -2.01, 0.31, 4.36, -3.44, -3.98],\n",
              " [-5.12, 1.4, -1.26, -4.9, 5.11, 5.07, 0.33, -3.72, 1.26, 2.56],\n",
              " [-0.23, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [1.62, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, -4.78, 0.32, 0.46, -3.18, 4.36, -3.44, -3.98],\n",
              " [4.3, -3.09, 2.09, -4.9, 5.11, 5.07, 0.33, 3.9, 3.78, -2.41],\n",
              " [-0.23, 0.37, -4.25, 4.59, -3.8, 1.03, -3.21, 4.36, -3.44, -3.98],\n",
              " [0.42, 1.74, -4.99, -4.9, 5.11, 5.07, 0.33, 1.76, 3.79, -2.53],\n",
              " [-0.23, 0.37, -4.25, 2.3, 4.53, 4.63, 0.02, 4.36, -3.44, -3.98],\n",
              " [5.09, 0.92, 0.46, -4.9, 5.11, 5.07, 0.33, 3.51, -1.13, -3.72],\n",
              " [-2.46, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, -1.18, -1.51, 2.25, 5.02, 4.36, -3.44, -3.98],\n",
              " [-0.41, 1.38, 3.09, -4.9, 5.11, 5.07, 0.33, 1.49, 3.93, -0.52],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, 0.38, -0.95, 2.09, -3.87, 4.36, -3.44, -3.98],\n",
              " [-3.07, -4.99, -3.53, -4.9, 5.11, 5.07, 0.33, 3.35, 0.92, -2.94],\n",
              " [-0.23, 0.37, -4.25, 2.06, 1.28, 2.33, -2.67, 4.36, -3.44, -3.98],\n",
              " [-4.88, 3.89, 0.2, -4.9, 5.11, 5.07, 0.33, 0.17, 0.96, -2.59],\n",
              " [-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              " [-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, -1.18, -1.51, 2.25, 5.02, 4.36, -3.44, -3.98],\n",
              " [-0.41, 1.38, 3.09, -4.9, 5.11, 5.07, 0.33, 1.49, 3.93, -0.52],\n",
              " [-0.23, 0.37, -4.25, -3.55, 2.2, 2.58, 2.42, 4.36, -3.44, -3.98],\n",
              " [0.35, -1.53, 1.78, -4.9, 5.11, 5.07, 0.33, 2.59, 3.36, 0.17],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, -2.76, -0.0, -4.58, 2.09, 4.36, -3.44, -3.98],\n",
              " [3.69, 0.72, -1.18, -4.9, 5.11, 5.07, 0.33, -1.93, -4.32, -2.24],\n",
              " [-0.23, 0.37, -4.25, 2.36, 1.13, -4.36, -2.01, 4.36, -3.44, -3.98],\n",
              " [0.08, 0.61, 1.69, -4.9, 5.11, 5.07, 0.33, 2.69, 2.21, -5.07],\n",
              " [-2.46, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [-0.6099999999999999, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [-5.12, 1.4, -1.26, -0.16, -2.7, 4.38, 2.42, -3.72, 1.26, 2.56],\n",
              " [1.62, 4.48, -2.38, -3.55, 4.64, -2.01, 0.31, 4.3, -1.73, -4.41],\n",
              " [-5.12, 1.4, -1.26, -4.78, 0.32, 0.46, -3.18, -3.72, 1.26, 2.56],\n",
              " [4.3, -3.09, 2.09, -3.55, 4.64, -2.01, 0.31, 3.9, 3.78, -2.41],\n",
              " [-5.12, 1.4, -1.26, 4.59, -3.8, 1.03, -3.21, -3.72, 1.26, 2.56],\n",
              " [0.42, 1.74, -4.99, -3.55, 4.64, -2.01, 0.31, 1.76, 3.79, -2.53],\n",
              " [-5.12, 1.4, -1.26, 2.3, 4.53, 4.63, 0.02, -3.72, 1.26, 2.56],\n",
              " [5.09, 0.92, 0.46, -3.55, 4.64, -2.01, 0.31, 3.51, -1.13, -3.72],\n",
              " [-5.12, 1.4, -1.26, 1.3, 4.43, 0.18, -4.04, -3.72, 1.26, 2.56],\n",
              " [3.83, 0.85, -1.32, -3.55, 4.64, -2.01, 0.31, -2.2, 4.71, -0.16],\n",
              " [-2.89, 1.4, -1.26, -1.18, -1.51, 2.25, 7.25, -3.72, 1.26, 2.56],\n",
              " [-0.41, 1.38, 3.09, -3.55, 4.64, -2.01, 0.31, 1.49, 3.93, -0.52],\n",
              " [-5.12, 1.4, -1.26, 1.3, 4.43, 0.18, -4.04, -3.72, 3.49, 2.56],\n",
              " [3.83, 0.85, -1.32, -3.55, 4.64, -2.01, 0.31, -2.2, 4.71, -0.16],\n",
              " [-5.12, 1.4, -1.26, 0.38, -0.95, 2.09, -3.87, -3.72, 1.26, 2.56],\n",
              " [-3.07, -4.99, -3.53, -3.55, 4.64, -2.01, 0.31, 3.35, 0.92, -2.94],\n",
              " [-5.12, 1.4, -1.26, 2.06, -0.95, 2.33, -2.67, -3.72, 1.26, 2.56],\n",
              " [-4.88, 3.89, 0.2, -3.55, 4.64, -2.01, 0.31, 0.17, 0.96, -2.59],\n",
              " [-5.12, 1.4, -1.26, -4.9, 5.11, 5.07, 0.33, -3.72, 1.26, 2.56],\n",
              " [-0.23, 0.37, -4.25, -3.55, 4.64, -2.01, 0.31, 4.36, -3.44, -3.98],\n",
              " [-5.12, 1.4, -1.26, 1.3, 4.43, 0.18, -4.04, -3.72, 1.26, 2.56],\n",
              " [3.83, 0.85, -1.32, -3.55, 4.64, -2.01, 0.31, -2.2, 4.71, -0.16],\n",
              " [-5.12, 1.4, -1.26, -1.18, -1.51, 2.25, 5.02, -3.72, 1.26, 2.56],\n",
              " [-0.41, 1.38, 3.09, -3.55, 4.64, -2.01, 0.31, 1.49, 3.93, -0.52],\n",
              " [-5.12, 1.4, -1.26, -3.55, 2.2, 2.58, 2.42, -3.72, 1.26, 2.56],\n",
              " [0.35, -1.53, 1.78, -3.55, 4.64, -2.01, -1.92, 2.59, 3.36, 0.17],\n",
              " [-5.12, 1.4, -1.26, 2.6, -2.07, 1.92, 4.03, -3.72, 1.26, 2.56],\n",
              " [4.06, -4.78, 0.47, -3.55, 4.64, -4.24, 0.31, 3.5, 0.77, -4.22],\n",
              " [-5.12, 1.4, -1.26, -2.76, -0.0, -4.58, 2.09, -3.72, 1.26, 2.56],\n",
              " [3.69, 0.72, -1.18, -3.55, 4.64, -4.24, -1.92, -1.93, -4.32, -2.24],\n",
              " [-5.12, 1.4, -1.26, 2.36, 1.13, -4.36, -2.01, -3.72, 1.26, 2.56],\n",
              " [0.08, 0.61, 1.69, -3.55, 4.64, -2.01, 0.31, 2.69, 2.21, -5.07],\n",
              " [-5.12, 1.4, -1.26, -0.16, -2.7, 4.38, 2.42, -3.72, 1.26, 2.56],\n",
              " [1.62, 4.48, -2.38, -3.55, 4.64, -2.01, 0.31, 4.3, -1.73, -4.41],\n",
              " [1.62, 4.48, -2.38, -4.78, 0.32, 0.46, -3.18, 4.3, -1.73, -4.41],\n",
              " [4.3, -3.09, 2.09, -0.16, -2.7, 4.38, 2.42, 3.9, 3.78, -2.41],\n",
              " [1.62, 4.48, -2.38, 4.59, -3.8, 1.03, -3.21, 4.3, -1.73, -4.41],\n",
              " [0.42, 1.74, -4.99, -0.16, -2.7, 4.38, 2.42, 1.76, 3.79, -2.53],\n",
              " [1.62, 4.48, -2.38, 2.3, 4.53, 4.63, 0.02, 4.3, -1.73, -4.41],\n",
              " [5.09, 0.92, 0.46, -0.16, -2.7, 4.38, 2.42, 3.51, -1.13, -3.72],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, -1.18, -1.51, 2.25, 5.02, 4.3, -1.73, -4.41],\n",
              " [-0.41, 1.38, 3.09, -0.16, -2.7, 4.38, 2.42, 1.49, 3.93, -0.52],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, 0.38, -0.95, 2.09, -3.87, 4.3, -1.73, -4.41],\n",
              " [-3.07, -4.99, -3.53, -0.16, -2.7, 4.38, 2.42, 3.35, 0.92, -2.94],\n",
              " [1.62, 4.48, -2.38, 2.06, 1.28, 2.33, -2.67, 4.3, -1.73, -4.41],\n",
              " [-4.88, 3.89, 0.2, -0.16, -2.7, 4.38, 2.42, 0.17, 0.96, -2.59],\n",
              " [1.62, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, -1.18, -1.51, 2.25, 5.02, 4.3, -1.73, -4.41],\n",
              " [-0.41, 1.38, 3.09, -0.16, -2.7, 4.38, 2.42, 1.49, 3.93, -0.52],\n",
              " [1.62, 4.48, -2.38, -3.55, 2.2, 2.58, 2.42, 4.3, -1.73, -4.41],\n",
              " [0.35, -1.53, 1.78, -0.16, -2.7, 4.38, 2.42, 2.59, 3.36, 0.17],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, -2.76, -0.0, -4.58, 2.09, 4.3, -1.73, -4.41],\n",
              " [3.69, 0.72, -1.18, -0.16, -2.7, 4.38, 2.42, -1.93, -4.32, -2.24],\n",
              " [1.62, 4.48, -2.38, 2.36, 1.13, -4.36, -2.01, 4.3, -1.73, -4.41],\n",
              " [0.08, 0.61, 1.69, -0.16, -2.7, 4.38, 2.42, 2.69, 2.21, -7.300000000000001],\n",
              " [1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41],\n",
              " [1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41],\n",
              " [4.3, -3.09, 2.09, 4.59, -3.8, 1.03, -3.21, 3.9, 3.78, -2.41],\n",
              " [2.65, 1.74, -4.99, -4.78, 0.32, 0.46, -3.18, 1.76, 3.79, -2.53],\n",
              " [4.3, -3.09, 2.09, 2.3, 4.53, 4.63, 0.02, 3.9, 3.78, -2.41],\n",
              " [5.09, 0.92, 0.46, -4.78, 0.32, 0.46, -3.18, 3.51, -1.13, -3.72],\n",
              " [4.3, -3.09, 2.09, 1.3, 4.43, 0.18, -4.04, 3.9, 3.78, -2.41],\n",
              " [3.83, 0.85, -1.32, -4.78, 0.32, 0.46, -3.18, -2.2, 4.71, -0.16],\n",
              " [4.3, -3.09, 2.09, -1.18, -1.51, 2.25, 5.02, 3.9, 3.78, -2.41],\n",
              " [-0.41, 1.38, 3.09, -4.78, 0.32, 0.46, -3.18, 1.49, 3.93, -0.52],\n",
              " [4.3, -3.09, 2.09, 1.3, 4.43, 0.18, -4.04, 3.9, 3.78, -2.41],\n",
              " [3.83, 0.85, -1.32, -4.78, 0.32, 0.46, -3.18, -2.2, 4.71, -0.16],\n",
              " [4.3, -3.09, 2.09, 0.38, -0.95, 2.09, -3.87, 3.9, 3.78, -2.41],\n",
              " [-3.07, -4.99, -3.53, -4.78, 0.32, 0.46, -3.18, 3.35, 0.92, -2.94],\n",
              " [4.3, -3.09, 2.09, 2.06, 1.28, 2.33, -2.67, 3.9, 3.78, -2.41],\n",
              " [-4.88, 3.89, 0.2, -4.78, 0.32, 0.46, -3.18, 0.17, 0.96, -2.59],\n",
              " [4.3, -3.09, 2.09, -4.9, 5.11, 5.07, 0.33, 3.9, 3.78, -2.41],\n",
              " [-0.23, 0.37, -4.25, -4.78, 0.32, 0.46, -3.18, 4.36, -3.44, -3.98],\n",
              " [4.3, -3.09, 2.09, 1.3, 4.43, 0.18, -4.04, 3.9, 3.78, -2.41],\n",
              " [3.83, 0.85, -1.32, -4.78, 0.32, 0.46, -3.18, -2.2, 4.71, -0.16],\n",
              " [4.3, -3.09, 2.09, -1.18, -1.51, 2.25, 5.02, 3.9, 3.78, -2.41],\n",
              " [-0.41, 1.38, 5.32, -4.78, 0.32, 0.46, -3.18, 1.49, 3.93, -0.52],\n",
              " [4.3, -3.09, 2.09, -3.55, 2.2, 2.58, 2.42, 3.9, 3.78, -2.41],\n",
              " [0.35, -1.53, 1.78, -4.78, 0.32, 0.46, -3.18, 2.59, 3.36, 0.17],\n",
              " [4.3, -3.09, 2.09, 2.6, -2.07, 1.92, 4.03, 3.9, 3.78, -2.41],\n",
              " [4.06, -4.78, 0.47, -4.78, 0.32, 0.46, -3.18, 3.5, 0.77, -4.22],\n",
              " [4.3, -3.09, 2.09, -2.76, -0.0, -4.58, 2.09, 3.9, 3.78, -2.41],\n",
              " [3.69, 0.72, -1.18, -4.78, 0.32, 0.46, -3.18, -1.93, -4.32, -2.24],\n",
              " [4.3, -3.09, 2.09, 2.36, 1.13, -4.36, -2.01, 3.9, 3.78, -2.41],\n",
              " [0.08, 0.61, 1.69, -4.78, 0.32, 0.46, -3.18, 2.69, 2.21, -5.07],\n",
              " [4.3, -3.09, 2.09, -0.16, -2.7, 4.38, 2.42, 3.9, 3.78, -2.41],\n",
              " [3.85, 4.48, -2.38, -4.78, 0.32, 0.46, -3.18, 4.3, -1.73, -4.41],\n",
              " [0.42, 1.74, -4.99, 2.3, 4.53, 4.63, 0.02, 1.76, 3.79, -2.53],\n",
              " [5.09, 0.92, 0.46, 4.59, -3.8, 1.03, -3.21, 3.51, -1.13, -3.72],\n",
              " [0.42, 1.74, -4.99, 1.3, 4.43, 0.18, -4.04, 1.76, 3.79, -2.53],\n",
              " [3.83, 0.85, -1.32, 4.59, -3.8, 1.03, -3.21, -2.2, 4.71, -0.16],\n",
              " [0.42, 1.74, -4.99, -3.41, -1.51, 2.25, 5.02, 1.76, 3.79, -2.53],\n",
              " [-0.41, 1.38, 3.09, 4.59, -3.8, 1.03, -3.21, 1.49, 3.93, -0.52],\n",
              " [0.42, 1.74, -4.99, 1.3, 4.43, 0.18, -4.04, 1.76, 3.79, -2.53],\n",
              " [3.83, 0.85, -1.32, 4.59, -3.8, 1.03, -3.21, -2.2, 4.71, -0.16],\n",
              " [0.42, 1.74, -4.99, 0.38, -0.95, 2.09, -3.87, 1.76, 3.79, -2.53],\n",
              " [-3.07, -4.99, -3.53, 4.59, -3.8, 1.03, -3.21, 3.35, 0.92, -2.94],\n",
              " [0.42, 1.74, -4.99, 2.06, 1.28, 2.33, -2.67, 1.76, 3.79, -2.53],\n",
              " [-4.88, 3.89, 0.2, 4.59, -3.8, 1.03, -3.21, 0.17, 0.96, -2.59],\n",
              " [0.42, 1.74, -4.99, -4.9, 5.11, 5.07, 0.33, 1.76, 3.79, -2.53],\n",
              " [-0.23, 0.37, -4.25, 4.59, -3.8, 1.03, -3.21, 4.36, -3.44, -3.98],\n",
              " [0.42, 1.74, -4.99, 1.3, 4.43, 0.18, -4.04, 1.76, 3.79, -2.53],\n",
              " [3.83, 0.85, -1.32, 4.59, -3.8, 1.03, -3.21, -2.2, 4.71, -0.16],\n",
              " [0.42, 1.74, -4.99, -1.18, -1.51, 2.25, 5.02, 1.76, 3.79, -2.53],\n",
              " [-0.41, 1.38, 3.09, 4.59, -3.8, 1.03, -3.21, 1.49, 1.7000000000000002, -0.52],\n",
              " [0.42, 1.74, -4.99, -3.55, 2.2, 2.58, 2.42, 1.76, 3.79, -2.53],\n",
              " [0.35, -1.53, 1.78, 4.59, -3.8, 1.03, -3.21, 2.59, 3.36, 0.17],\n",
              " [0.42, 1.74, -4.99, 2.6, -2.07, 1.92, 4.03, 1.76, 3.79, -2.53],\n",
              " [4.06, -4.78, 0.47, 4.59, -3.8, 1.03, -3.21, 3.5, 0.77, -4.22],\n",
              " [0.42, 1.74, -4.99, -2.76, -0.0, -4.58, 2.09, 1.76, 3.79, -2.53],\n",
              " [3.69, 0.72, -1.18, 4.59, -3.8, 1.03, -3.21, -1.93, -4.32, -2.24],\n",
              " [0.42, 1.74, -4.99, 2.36, 1.13, -4.36, -2.01, 1.76, 3.79, -2.53],\n",
              " [0.08, 0.61, 1.69, 4.59, -3.8, 1.03, -3.21, 2.69, 2.21, -5.07],\n",
              " [0.42, 1.74, -4.99, -0.16, -2.7, 4.38, 2.42, 1.76, 3.79, -2.53],\n",
              " [1.62, 4.48, -2.38, 4.59, -3.8, 1.03, -3.21, 4.3, -1.73, -4.41],\n",
              " [5.09, 0.92, 0.46, 1.3, 4.43, 0.18, -4.04, 3.51, -1.13, -3.72],\n",
              " [3.83, 0.85, -1.32, 2.3, 6.76, 4.63, 0.02, -2.2, 4.71, -0.16],\n",
              " [5.09, 0.92, 0.46, -1.18, -1.51, 2.25, 5.02, 3.51, -1.13, -3.72],\n",
              " [-0.41, 1.38, 3.09, 2.3, 4.53, 4.63, 0.02, 1.49, 3.93, -0.52],\n",
              " [5.09, 0.92, 0.46, 1.3, 4.43, 0.18, -4.04, 3.51, -1.13, -3.72],\n",
              " [3.83, 0.85, -1.32, 2.3, 4.53, 4.63, 0.02, -2.2, 4.71, -0.16],\n",
              " [5.09, 0.92, 0.46, 0.38, -0.95, 2.09, -3.87, 3.51, -1.13, -3.72],\n",
              " [-3.07, -4.99, -3.53, 2.3, 4.53, 4.63, 0.02, 3.35, 0.92, -2.94],\n",
              " [5.09, 0.92, 0.46, 2.06, 1.28, 2.33, -2.67, 3.51, -1.13, -3.72],\n",
              " [-4.88, 3.89, 0.2, 2.3, 4.53, 4.63, 0.02, 0.17, 0.96, -2.59],\n",
              " [5.09, 0.92, 0.46, -4.9, 5.11, 5.07, 0.33, 3.51, -1.13, -3.72],\n",
              " [-0.23, 0.37, -4.25, 2.3, 4.53, 4.63, 0.02, 4.36, -3.44, -3.98],\n",
              " [5.09, 0.92, 0.46, 1.3, 4.43, 0.18, -4.04, 3.51, -1.13, -3.72],\n",
              " [3.83, 0.85, -1.32, 2.3, 4.53, 4.63, 0.02, -2.2, 4.71, -0.16],\n",
              " [5.09, -1.31, 0.46, -1.18, -1.51, 2.25, 5.02, 3.51, -1.13, -3.72],\n",
              " [-0.41, 1.38, 3.09, 2.3, 4.53, 4.63, 0.02, 1.49, 3.93, -0.52],\n",
              " [5.09, 0.92, 0.46, -3.55, 2.2, 2.58, 2.42, 3.51, -1.13, -3.72],\n",
              " [0.35, -1.53, 1.78, 2.3, 4.53, 4.63, 0.02, 2.59, 3.36, 0.17],\n",
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              " [4.06, -4.78, 0.47, 2.3, 4.53, 4.63, 0.02, 3.5, 0.77, -4.22],\n",
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              " [-3.07, -4.99, -3.53, 1.3, 4.43, 0.18, -4.04, 3.35, 0.92, -2.94],\n",
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              " [0.08, 0.61, 1.69, 2.6, -2.07, 1.92, 4.03, 2.69, 2.21, -5.07],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [3.69, 0.72, -1.18, 2.36, 1.13, -6.59, -2.01, -1.93, -4.32, -2.24],\n",
              " [0.08, 0.61, 1.69, -2.76, -0.0, -4.58, 2.09, 2.69, 2.21, -5.07],\n",
              " [3.69, 0.72, -1.18, -0.16, -2.7, 4.38, 2.42, -4.16, -4.32, -2.24],\n",
              " [1.62, 4.48, -2.38, -2.76, -0.0, -4.58, 2.09, 4.3, -1.73, -4.41],\n",
              " [0.08, 0.61, 1.69, -0.16, -2.7, 4.38, 2.42, 2.69, 2.21, -5.07],\n",
              " [1.62, 4.48, -2.38, 2.36, 1.13, -4.36, -2.01, 4.3, -1.73, -4.41]]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Fix of the individual (in case a gen of the individual get out of the inverval allowed by the cost function)"
      ],
      "metadata": {
        "id": "BhslJ7Ab3IpT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fix_mutated_children(mutated_children, vars_domain):    \n",
        "    for mutated_child in mutated_children:\n",
        "        for i in range(len(mutated_child)):\n",
        "            if mutated_child[i] < vars_domain[0]:\n",
        "                mutated_child[i] = vars_domain[0]\n",
        "            elif mutated_child[i] > vars_domain[1]:\n",
        "                mutated_child[i] = vars_domain[1]\n",
        "    mutated_children_fixed = mutated_children           \n",
        "    return mutated_children_fixed"
      ],
      "metadata": {
        "id": "PFwTBcui3Wj6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test of fix of mutated children. We'll be using the mutated children obtained after the above operator. Notice that the vars_domain in this case is the one defined for the parabola cost function [-5.12, 5.12]"
      ],
      "metadata": {
        "id": "m0iQHF6G4H8b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fixed_mut_child = fix_mutated_children(mutate_children, [-5.12, 5.12])\n",
        "fixed_mut_child"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e7jjiTa4bON",
        "outputId": "8605de23-27d0-4492-d73c-ed0374301511"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2.79, -1.68, -5.01, 2.6, -2.07, 1.92, 4.03, 0.06, 4.47, -2.38],\n",
              " [4.06, -4.78, 0.47, 3.11, -5.07, 0.96, -4.69, 3.5, 0.77, -4.22],\n",
              " [2.79, -1.68, -5.01, 3.01, -4.11, -2.75, -4.73, 0.06, 4.47, -2.38],\n",
              " [4.34, 2.09, -1.24, 3.11, -5.07, 0.96, -4.69, -1.2, 0.55, 4.33],\n",
              " [2.79, -1.68, -5.01, 0.04, -4.61, 3.86, -3.47, 0.06, 4.47, -2.38],\n",
              " [-2.01, -2.88, 0.74, 3.11, -5.07, 0.96, -4.69, 4.73, -2.95, -0.17],\n",
              " [2.79, -1.68, -5.01, -4.9, 5.11, 5.07, 0.33, 0.06, 4.47, -2.38],\n",
              " [-0.23, 0.37, -4.25, 3.11, -5.07, 0.96, -4.69, 4.36, -3.44, -3.98],\n",
              " [2.79, -1.68, -5.01, -5.12, 4.64, -2.01, 0.31, 0.06, 4.47, -2.38],\n",
              " [-5.12, 1.4, -1.26, 3.11, -5.07, 0.96, -4.69, -3.72, 1.26, 2.56],\n",
              " [2.79, -1.68, -5.01, -0.16, -2.7, 4.38, 2.42, 0.06, 4.47, -2.38],\n",
              " [1.62, 4.48, -2.38, 3.11, -5.07, 0.96, -4.69, 4.3, -1.73, -4.41],\n",
              " [2.79, -1.68, -5.01, -4.78, 0.32, 0.46, -3.18, 0.06, 4.47, -2.38],\n",
              " [4.3, -3.09, 2.09, 3.11, -5.07, 0.96, -4.69, 3.9, 3.78, -2.41],\n",
              " [2.79, -1.68, -5.01, 4.59, -3.8, 1.03, -3.21, 0.06, 4.47, -2.38],\n",
              " [0.42, 1.74, -4.99, 3.11, -5.07, 0.96, -4.69, 1.76, 3.79, -2.53],\n",
              " [2.79, -1.68, -5.01, 2.3, 4.53, 4.63, 0.02, 0.06, 4.47, -2.38],\n",
              " [2.86, 0.92, 0.46, 3.11, -5.07, 0.96, -4.69, 3.51, -1.13, -3.72],\n",
              " [2.79, -1.68, -5.12, 1.3, 4.43, 0.18, -4.04, 0.06, 4.47, -2.38],\n",
              " [3.83, 0.85, -1.32, 3.11, -5.07, 0.96, -4.69, -2.2, 4.71, -0.16],\n",
              " [2.79, -1.68, -5.01, 1.05, -1.51, 2.25, 5.02, 0.06, 4.47, -2.38],\n",
              " [-0.41, 1.38, 3.09, 3.11, -5.07, 0.96, -4.69, 1.49, 3.93, -0.52],\n",
              " [2.79, -1.68, -5.01, 1.3, 4.43, 0.18, -4.04, 0.06, 4.47, -2.38],\n",
              " [3.83, 0.85, -1.32, 3.11, -5.07, 0.96, -4.69, -2.2, 4.71, -0.16],\n",
              " [2.79, -1.68, -5.01, 0.38, -0.95, 2.09, -3.87, 0.06, 5.12, -2.38],\n",
              " [-3.07, -4.99, -3.53, 3.11, -5.07, 0.96, -4.69, 3.35, 0.92, -2.94],\n",
              " [2.79, -1.68, -5.01, 2.06, 1.28, 2.33, -2.67, 0.06, 4.47, -2.38],\n",
              " [-4.88, 3.89, 0.2, 3.11, -5.07, 0.96, -4.69, 0.17, 0.96, -2.59],\n",
              " [2.79, -1.68, -5.01, -4.9, 5.11, 5.07, 0.33, 0.06, 4.47, -2.38],\n",
              " [-0.23, 0.37, -4.25, 3.11, -5.07, 0.96, -4.69, 4.36, -3.44, -3.98],\n",
              " [2.79, -1.68, -5.01, 1.3, 4.43, 0.18, -4.04, 0.06, 4.47, -2.38],\n",
              " [3.83, 0.85, -1.32, 3.11, -5.07, 0.96, -4.69, -2.2, 4.71, -0.16],\n",
              " [2.79,\n",
              "  -1.68,\n",
              "  -5.01,\n",
              "  -1.18,\n",
              "  -1.51,\n",
              "  0.020000000000000018,\n",
              "  5.02,\n",
              "  0.06,\n",
              "  4.47,\n",
              "  -2.38],\n",
              " [-0.41, 1.38, 3.09, 3.11, -5.07, 0.96, -4.69, 1.49, 3.93, -0.52],\n",
              " [2.79, -1.68, -5.01, -3.55, 2.2, 2.58, 2.42, 0.06, 4.47, -2.38],\n",
              " [0.35, -1.53, 1.78, 3.11, -5.07, 0.96, -4.69, 2.59, 3.36, 0.17],\n",
              " [2.79, -1.68, -5.01, 2.6, -2.07, 1.92, 4.03, 0.06, 4.47, -2.38],\n",
              " [4.06, -4.78, 0.47, 3.11, -5.07, 0.96, -4.69, 3.5, 0.77, -4.22],\n",
              " [2.79, -1.68, -5.01, -2.76, -0.0, -4.58, 2.09, 0.06, 4.47, -2.38],\n",
              " [1.46, 0.72, -1.18, 3.11, -5.07, 0.96, -4.69, -1.93, -4.32, -2.24],\n",
              " [2.79, -1.68, -5.01, 2.36, 1.13, -4.36, -2.01, 0.06, 4.47, -2.38],\n",
              " [0.08, 0.61, 1.69, 3.11, -5.07, 0.96, -4.69, 2.69, 2.21, -5.07],\n",
              " [2.79, -1.68, -5.01, -0.16, -2.7, 4.38, 2.42, 0.06, 4.47, -2.38],\n",
              " [1.62, 4.48, -2.38, 3.11, -5.07, 0.96, -4.69, 4.3, -1.73, -4.41],\n",
              " [4.06, -4.78, 0.47, 3.01, -4.11, -2.75, -4.73, 3.5, 0.77, -4.22],\n",
              " [4.34, 2.09, -1.24, 2.6, -2.07, 1.92, 4.03, -1.2, 0.55, 4.33],\n",
              " [1.8299999999999996, -4.78, 0.47, 0.04, -4.61, 3.86, -3.47, 3.5, 0.77, -4.22],\n",
              " [-2.01, -2.88, 0.74, 2.6, -2.07, 1.92, 4.03, 4.73, -2.95, -0.17],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, -3.55, 4.64, -2.01, 0.31, 3.5, 0.77, -4.22],\n",
              " [-5.12, 1.4, -1.26, 2.6, -2.07, 1.92, 4.03, -3.72, 1.26, 2.56],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [4.06, -4.78, 0.47, -4.78, 0.32, 0.46, -3.18, 3.5, 0.77, -4.22],\n",
              " [4.3, -3.09, 2.09, 2.6, -2.07, 1.92, 4.03, 3.9, 3.78, -2.41],\n",
              " [4.06, -4.78, 0.47, 4.59, -3.8, 1.03, -3.21, 3.5, 0.77, -4.22],\n",
              " [0.42, 1.74, -4.99, 2.6, -2.07, 1.92, 4.03, 1.76, 3.79, -2.53],\n",
              " [4.06, -4.78, 0.47, 2.3, 4.53, 4.63, 0.02, 3.5, 0.77, -4.22],\n",
              " [5.09, 0.92, 0.46, 2.6, -2.07, 1.92, 4.03, 3.51, -1.13, -3.72],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, -1.18, -1.51, 2.25, 5.02, 3.5, 0.77, -4.22],\n",
              " [-0.41, 1.38, 3.09, 2.6, -2.07, 1.92, 4.03, 1.49, 3.93, -0.52],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, 0.38, -0.95, 2.09, -3.87, 3.5, 0.77, -4.22],\n",
              " [-3.07, -4.99, -3.53, 2.6, -2.07, 1.92, 4.03, 3.35, 0.92, -2.94],\n",
              " [4.06, -4.78, 0.47, 2.06, 1.28, 2.33, -2.67, 3.5, 0.77, -4.22],\n",
              " [-4.88, 3.89, 0.2, 2.6, -2.07, 1.92, 4.03, 0.17, 0.96, -2.59],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [1.6, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, -1.18, -1.51, 2.25, 5.02, 3.5, 0.77, -4.22],\n",
              " [-0.41, 1.38, 3.09, 2.6, -2.07, 1.92, 4.03, 1.49, 3.93, -0.52],\n",
              " [4.06, -4.78, 0.47, -3.55, 2.2, 2.58, 2.42, 3.5, 0.77, -4.22],\n",
              " [0.35, -1.53, 1.78, 2.6, -2.07, 1.92, 4.03, 2.59, 3.36, 0.17],\n",
              " [4.06, -4.78, 0.47, 2.6, -2.07, -0.31000000000000005, 4.03, 3.5, 0.77, -4.22],\n",
              " [4.06, -4.78, 0.47, 2.6, -2.07, 1.92, 4.03, 3.5, 0.77, -4.22],\n",
              " [4.06, -4.78, 0.47, -2.76, -0.0, -4.58, 2.09, 3.5, 0.77, -4.22],\n",
              " [3.69, 0.72, -1.18, 2.6, -2.07, 1.92, 4.03, -1.93, -4.32, -2.24],\n",
              " [4.06, -4.78, 0.47, 2.36, 1.13, -4.36, -2.01, 3.5, 0.77, -4.22],\n",
              " [0.08, 0.61, 1.69, 2.6, -2.07, 1.92, 4.03, 2.69, 2.21, -5.07],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [4.34,\n",
              "  2.09,\n",
              "  -1.24,\n",
              "  0.04,\n",
              "  -4.61,\n",
              "  3.86,\n",
              "  -3.47,\n",
              "  -3.4299999999999997,\n",
              "  0.55,\n",
              "  4.33],\n",
              " [-2.01, -2.88, 0.74, 3.01, -4.11, -2.75, -4.73, 4.73, -2.95, -0.17],\n",
              " [4.34, 2.09, -1.24, -4.9, 5.11, 5.07, 0.33, -1.2, 0.55, 4.33],\n",
              " [-0.23, 0.37, -4.25, 3.01, -4.11, -2.75, -4.73, 4.36, -3.44, -3.98],\n",
              " [4.34, 2.09, -1.24, -3.55, 4.64, -2.01, 0.31, -1.2, 0.55, 4.33],\n",
              " [-5.12, 1.4, -1.26, 3.01, -4.11, -2.75, -4.73, -3.72, 1.26, 2.56],\n",
              " [4.34, 2.09, -1.24, -0.16, -2.7, 4.38, 2.42, -1.2, 0.55, 4.33],\n",
              " [1.62, 4.48, -2.38, 3.01, -4.11, -2.75, -4.73, 4.3, -1.73, -4.41],\n",
              " [4.34, 2.09, -1.24, -4.78, 0.32, 0.46, -3.18, -1.2, 0.55, 4.33],\n",
              " [4.3, -3.09, 2.09, 3.01, -4.11, -2.75, -4.73, 3.9, 3.78, -2.41],\n",
              " [4.34, 2.09, -1.24, 4.59, -3.8, 1.03, -3.21, -1.2, 0.55, 4.33],\n",
              " [0.42, 1.74, -4.99, 3.01, -4.11, -2.75, -4.73, 1.76, 3.79, -2.53],\n",
              " [4.34, 2.09, -1.24, 2.3, 4.53, 4.63, 0.02, -1.2, 0.55, 4.33],\n",
              " [5.09,\n",
              "  0.92,\n",
              "  0.46,\n",
              "  3.01,\n",
              "  -4.11,\n",
              "  -2.75,\n",
              "  -4.73,\n",
              "  1.2799999999999998,\n",
              "  -1.13,\n",
              "  -3.72],\n",
              " [4.34, 2.09, -1.24, 1.3, 4.43, 0.18, -4.04, -1.2, 0.55, 4.33],\n",
              " [3.83, 0.85, -1.32, 3.01, -4.11, -2.75, -4.73, -2.2, 4.71, -0.16],\n",
              " [4.34, 2.09, -1.24, -1.18, -1.51, 2.25, 5.02, -1.2, 0.55, 4.33],\n",
              " [-0.41, 1.38, 3.09, 3.01, -4.11, -2.75, -4.73, 1.49, 3.93, -0.52],\n",
              " [4.34, 2.09, -1.24, 1.3, 4.43, 0.18, -4.04, -1.2, 0.55, 4.33],\n",
              " [3.83, 0.85, -1.32, 3.01, -4.11, -2.75, -4.73, -2.2, 4.71, -0.16],\n",
              " [4.34, 2.09, -1.24, 0.38, -0.95, 2.09, -1.6400000000000001, -1.2, 0.55, 4.33],\n",
              " [-3.07, -4.99, -3.53, 3.01, -4.11, -2.75, -4.73, 3.35, 0.92, -2.94],\n",
              " [4.34, 2.09, -1.24, 2.06, 1.28, 2.33, -2.67, -1.2, 0.55, 4.33],\n",
              " [-4.88, 3.89, 0.2, 3.01, -4.11, -2.75, -4.73, 0.17, 0.96, -2.59],\n",
              " [4.34, 2.09, -1.24, -4.9, 5.12, 5.07, 0.33, -1.2, 0.55, 4.33],\n",
              " [-0.23, 0.37, -4.25, 3.01, -4.11, -2.75, -4.73, 4.36, -3.44, -3.98],\n",
              " [4.34, 2.09, -1.24, 1.3, 4.43, 0.18, -4.04, -1.2, 0.55, 4.33],\n",
              " [3.83, 0.85, -1.32, 3.01, -4.11, -2.75, -4.73, -2.2, 4.71, -0.16],\n",
              " [4.34, 2.09, -1.24, -1.18, -1.51, 2.25, 5.02, -1.2, 0.55, 4.33],\n",
              " [-0.41, 1.38, 3.09, 3.01, -4.11, -2.75, -4.73, 1.49, 3.93, -0.52],\n",
              " [4.34, 2.09, -1.24, -3.55, 2.2, 2.58, 2.42, -1.2, 0.55, 4.33],\n",
              " [0.35, -1.53, 1.78, 3.01, -4.11, -2.75, -4.73, 2.59, 3.36, -2.06],\n",
              " [4.34, 2.09, -1.24, 2.6, -2.07, 1.92, 4.03, -1.2, 0.55, 4.33],\n",
              " [4.06, -4.78, 0.47, 3.01, -4.11, -2.75, -4.73, 3.5, 0.77, -4.22],\n",
              " [5.12, 2.09, -1.24, -2.76, -0.0, -4.58, 2.09, -1.2, 0.55, 4.33],\n",
              " [3.69, 0.72, -1.18, 3.01, -4.11, -2.75, -4.73, -1.93, -4.32, -2.24],\n",
              " [4.34, 2.09, -1.24, 2.36, 1.13, -4.36, -2.01, -1.2, 0.55, 4.33],\n",
              " [0.08, 0.61, 1.69, 3.01, -4.11, -2.75, -4.73, 2.69, 2.21, -5.07],\n",
              " [4.34, 2.09, -1.24, -0.16, -2.7, 2.15, 2.42, -1.2, 0.55, 4.33],\n",
              " [1.62, 4.48, -2.38, 3.01, -4.11, -2.75, -4.73, 4.3, -1.73, -4.41],\n",
              " [-2.01, -2.88, 0.74, -4.9, 5.11, 5.07, 0.33, 4.73, -2.95, -0.17],\n",
              " [-0.23,\n",
              "  -1.8599999999999999,\n",
              "  -4.25,\n",
              "  0.04,\n",
              "  -4.61,\n",
              "  3.86,\n",
              "  -3.47,\n",
              "  4.36,\n",
              "  -3.44,\n",
              "  -3.98],\n",
              " [-2.01, -2.88, 0.74, -3.55, 4.64, -2.01, 0.31, 4.73, -2.95, -0.17],\n",
              " [-5.12, 1.4, -1.26, 0.04, -4.61, 3.86, -3.47, -3.72, 1.26, 2.56],\n",
              " [-2.01, -2.88, 0.74, -0.16, -2.7, 4.38, 2.42, 4.73, -2.95, -0.17],\n",
              " [1.62, 4.48, -2.38, 0.04, -4.61, 3.86, -3.47, 4.3, 0.5, -4.41],\n",
              " [-2.01, -2.88, 0.74, -4.78, 0.32, 0.46, -3.18, 4.73, -2.95, -0.17],\n",
              " [4.3,\n",
              "  -3.09,\n",
              "  -0.14000000000000012,\n",
              "  0.04,\n",
              "  -4.61,\n",
              "  3.86,\n",
              "  -3.47,\n",
              "  3.9,\n",
              "  3.78,\n",
              "  -2.41],\n",
              " [-2.01, -2.88, 0.74, 4.59, -3.8, 1.03, -3.21, 4.73, -5.12, -0.17],\n",
              " [0.42, 1.74, -4.99, 0.04, -4.61, 3.86, -3.47, 1.76, 3.79, -2.53],\n",
              " [-2.01, -2.88, 0.74, 2.3, 4.53, 4.63, 0.02, 4.73, -2.95, -0.17],\n",
              " [5.09, 0.92, 0.46, 0.04, -4.61, 3.86, -3.47, 3.51, -1.13, -3.72],\n",
              " [-2.01, -2.88, 0.74, 1.3, 4.43, 0.18, -4.04, 4.73, -2.95, -0.17],\n",
              " [3.83, 0.85, -1.32, 0.04, -4.61, 3.86, -3.47, -2.2, 4.71, -0.16],\n",
              " [-2.01, -2.88, 0.74, -1.18, -1.51, 2.25, 5.02, 4.73, -2.95, -0.17],\n",
              " [-0.41, 1.38, 3.09, 0.04, -4.61, 3.86, -3.47, 1.49, 3.93, -0.52],\n",
              " [-2.01, -2.88, 0.74, 1.3, 4.43, 0.18, -4.04, 4.73, -2.95, -0.17],\n",
              " [3.83, 0.85, -1.32, 0.04, -4.61, 3.86, -3.47, -2.2, 4.71, -0.16],\n",
              " [-2.01, -2.88, 0.74, 0.38, -0.95, 2.09, -3.87, 4.73, -2.95, -0.17],\n",
              " [-3.07, -4.99, -3.53, 0.04, -4.61, 3.86, -3.47, 3.35, 0.92, -2.94],\n",
              " [-2.01,\n",
              "  -0.6499999999999999,\n",
              "  0.74,\n",
              "  2.06,\n",
              "  1.28,\n",
              "  2.33,\n",
              "  -2.67,\n",
              "  4.73,\n",
              "  -2.95,\n",
              "  -0.17],\n",
              " [-4.88, 3.89, 0.2, 0.04, -4.61, 3.86, -3.47, 0.17, 0.96, -2.59],\n",
              " [-2.01, -2.88, 0.74, -4.9, 5.11, 5.07, 0.33, 4.73, -2.95, -0.17],\n",
              " [-0.23, 0.37, -4.25, 0.04, -4.61, 3.86, -3.47, 4.36, -3.44, -3.98],\n",
              " [-2.01, -2.88, 0.74, 1.3, 4.43, -2.05, -4.04, 4.73, -2.95, -0.17],\n",
              " [3.83, 0.85, -1.32, 0.04, -4.61, 3.86, -3.47, -2.2, 4.71, -0.16],\n",
              " [-2.01, -2.88, 0.74, -1.18, -1.51, 2.25, 5.02, 4.73, -2.95, -0.17],\n",
              " [-0.41, 1.38, 3.09, 0.04, -4.61, 3.86, -3.47, 1.49, 3.93, -0.52],\n",
              " [-2.01, -2.88, 0.74, -3.55, 2.2, 2.58, 2.42, 4.73, -2.95, -0.17],\n",
              " [0.35, -1.53, 1.78, 0.04, -4.61, 3.86, -3.47, 2.59, 3.36, 0.17],\n",
              " [-2.01, -2.88, 0.74, 2.6, -2.07, 1.92, 4.03, 4.73, -2.95, -0.17],\n",
              " [4.06, -4.78, 0.47, 0.04, -4.61, 3.86, -3.47, 3.5, 0.77, -4.22],\n",
              " [-2.01, -2.88, 0.74, -2.76, -0.0, -4.58, 2.09, 4.73, -2.95, -0.17],\n",
              " [3.69, 0.72, -1.18, 0.04, -4.61, 3.86, -3.47, -1.93, -4.32, -2.24],\n",
              " [-2.01, -2.88, 0.74, 2.36, 1.13, -4.36, -2.01, 4.73, -2.95, -0.17],\n",
              " [0.08, 0.61, 1.69, 0.04, -4.61, 3.86, -3.47, 2.69, 2.21, -5.07],\n",
              " [-2.01, -2.88, 0.74, -0.16, -2.7, 4.38, 2.42, 4.73, -2.95, -0.17],\n",
              " [1.62, 4.48, -2.38, 0.04, -4.61, 3.86, -3.47, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, -3.55, 4.64, -2.01, 0.31, 4.36, -3.44, -3.98],\n",
              " [-5.12, 1.4, -1.26, -4.9, 5.11, 5.07, 0.33, -3.72, 1.26, 2.56],\n",
              " [-0.23, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [1.62, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, -4.78, 0.32, 0.46, -3.18, 4.36, -3.44, -3.98],\n",
              " [4.3, -3.09, 2.09, -4.9, 5.11, 5.07, 0.33, 3.9, 3.78, -2.41],\n",
              " [-0.23, 0.37, -4.25, 4.59, -3.8, 1.03, -3.21, 4.36, -3.44, -3.98],\n",
              " [0.42, 1.74, -4.99, -4.9, 5.11, 5.07, 0.33, 1.76, 3.79, -2.53],\n",
              " [-0.23, 0.37, -4.25, 2.3, 4.53, 4.63, 0.02, 4.36, -3.44, -3.98],\n",
              " [5.09, 0.92, 0.46, -4.9, 5.11, 5.07, 0.33, 3.51, -1.13, -3.72],\n",
              " [-2.46, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, -1.18, -1.51, 2.25, 5.02, 4.36, -3.44, -3.98],\n",
              " [-0.41, 1.38, 3.09, -4.9, 5.11, 5.07, 0.33, 1.49, 3.93, -0.52],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, 0.38, -0.95, 2.09, -3.87, 4.36, -3.44, -3.98],\n",
              " [-3.07, -4.99, -3.53, -4.9, 5.11, 5.07, 0.33, 3.35, 0.92, -2.94],\n",
              " [-0.23, 0.37, -4.25, 2.06, 1.28, 2.33, -2.67, 4.36, -3.44, -3.98],\n",
              " [-4.88, 3.89, 0.2, -4.9, 5.11, 5.07, 0.33, 0.17, 0.96, -2.59],\n",
              " [-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              " [-0.23, 0.37, -4.25, -4.9, 5.11, 5.07, 0.33, 4.36, -3.44, -3.98],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, -1.18, -1.51, 2.25, 5.02, 4.36, -3.44, -3.98],\n",
              " [-0.41, 1.38, 3.09, -4.9, 5.11, 5.07, 0.33, 1.49, 3.93, -0.52],\n",
              " [-0.23, 0.37, -4.25, -3.55, 2.2, 2.58, 2.42, 4.36, -3.44, -3.98],\n",
              " [0.35, -1.53, 1.78, -4.9, 5.11, 5.07, 0.33, 2.59, 3.36, 0.17],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, -2.76, -0.0, -4.58, 2.09, 4.36, -3.44, -3.98],\n",
              " [3.69, 0.72, -1.18, -4.9, 5.11, 5.07, 0.33, -1.93, -4.32, -2.24],\n",
              " [-0.23, 0.37, -4.25, 2.36, 1.13, -4.36, -2.01, 4.36, -3.44, -3.98],\n",
              " [0.08, 0.61, 1.69, -4.9, 5.11, 5.07, 0.33, 2.69, 2.21, -5.07],\n",
              " [-2.46, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [-0.6099999999999999, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [-5.12, 1.4, -1.26, -0.16, -2.7, 4.38, 2.42, -3.72, 1.26, 2.56],\n",
              " [1.62, 4.48, -2.38, -3.55, 4.64, -2.01, 0.31, 4.3, -1.73, -4.41],\n",
              " [-5.12, 1.4, -1.26, -4.78, 0.32, 0.46, -3.18, -3.72, 1.26, 2.56],\n",
              " [4.3, -3.09, 2.09, -3.55, 4.64, -2.01, 0.31, 3.9, 3.78, -2.41],\n",
              " [-5.12, 1.4, -1.26, 4.59, -3.8, 1.03, -3.21, -3.72, 1.26, 2.56],\n",
              " [0.42, 1.74, -4.99, -3.55, 4.64, -2.01, 0.31, 1.76, 3.79, -2.53],\n",
              " [-5.12, 1.4, -1.26, 2.3, 4.53, 4.63, 0.02, -3.72, 1.26, 2.56],\n",
              " [5.09, 0.92, 0.46, -3.55, 4.64, -2.01, 0.31, 3.51, -1.13, -3.72],\n",
              " [-5.12, 1.4, -1.26, 1.3, 4.43, 0.18, -4.04, -3.72, 1.26, 2.56],\n",
              " [3.83, 0.85, -1.32, -3.55, 4.64, -2.01, 0.31, -2.2, 4.71, -0.16],\n",
              " [-2.89, 1.4, -1.26, -1.18, -1.51, 2.25, 5.12, -3.72, 1.26, 2.56],\n",
              " [-0.41, 1.38, 3.09, -3.55, 4.64, -2.01, 0.31, 1.49, 3.93, -0.52],\n",
              " [-5.12, 1.4, -1.26, 1.3, 4.43, 0.18, -4.04, -3.72, 3.49, 2.56],\n",
              " [3.83, 0.85, -1.32, -3.55, 4.64, -2.01, 0.31, -2.2, 4.71, -0.16],\n",
              " [-5.12, 1.4, -1.26, 0.38, -0.95, 2.09, -3.87, -3.72, 1.26, 2.56],\n",
              " [-3.07, -4.99, -3.53, -3.55, 4.64, -2.01, 0.31, 3.35, 0.92, -2.94],\n",
              " [-5.12, 1.4, -1.26, 2.06, -0.95, 2.33, -2.67, -3.72, 1.26, 2.56],\n",
              " [-4.88, 3.89, 0.2, -3.55, 4.64, -2.01, 0.31, 0.17, 0.96, -2.59],\n",
              " [-5.12, 1.4, -1.26, -4.9, 5.11, 5.07, 0.33, -3.72, 1.26, 2.56],\n",
              " [-0.23, 0.37, -4.25, -3.55, 4.64, -2.01, 0.31, 4.36, -3.44, -3.98],\n",
              " [-5.12, 1.4, -1.26, 1.3, 4.43, 0.18, -4.04, -3.72, 1.26, 2.56],\n",
              " [3.83, 0.85, -1.32, -3.55, 4.64, -2.01, 0.31, -2.2, 4.71, -0.16],\n",
              " [-5.12, 1.4, -1.26, -1.18, -1.51, 2.25, 5.02, -3.72, 1.26, 2.56],\n",
              " [-0.41, 1.38, 3.09, -3.55, 4.64, -2.01, 0.31, 1.49, 3.93, -0.52],\n",
              " [-5.12, 1.4, -1.26, -3.55, 2.2, 2.58, 2.42, -3.72, 1.26, 2.56],\n",
              " [0.35, -1.53, 1.78, -3.55, 4.64, -2.01, -1.92, 2.59, 3.36, 0.17],\n",
              " [-5.12, 1.4, -1.26, 2.6, -2.07, 1.92, 4.03, -3.72, 1.26, 2.56],\n",
              " [4.06, -4.78, 0.47, -3.55, 4.64, -4.24, 0.31, 3.5, 0.77, -4.22],\n",
              " [-5.12, 1.4, -1.26, -2.76, -0.0, -4.58, 2.09, -3.72, 1.26, 2.56],\n",
              " [3.69, 0.72, -1.18, -3.55, 4.64, -4.24, -1.92, -1.93, -4.32, -2.24],\n",
              " [-5.12, 1.4, -1.26, 2.36, 1.13, -4.36, -2.01, -3.72, 1.26, 2.56],\n",
              " [0.08, 0.61, 1.69, -3.55, 4.64, -2.01, 0.31, 2.69, 2.21, -5.07],\n",
              " [-5.12, 1.4, -1.26, -0.16, -2.7, 4.38, 2.42, -3.72, 1.26, 2.56],\n",
              " [1.62, 4.48, -2.38, -3.55, 4.64, -2.01, 0.31, 4.3, -1.73, -4.41],\n",
              " [1.62, 4.48, -2.38, -4.78, 0.32, 0.46, -3.18, 4.3, -1.73, -4.41],\n",
              " [4.3, -3.09, 2.09, -0.16, -2.7, 4.38, 2.42, 3.9, 3.78, -2.41],\n",
              " [1.62, 4.48, -2.38, 4.59, -3.8, 1.03, -3.21, 4.3, -1.73, -4.41],\n",
              " [0.42, 1.74, -4.99, -0.16, -2.7, 4.38, 2.42, 1.76, 3.79, -2.53],\n",
              " [1.62, 4.48, -2.38, 2.3, 4.53, 4.63, 0.02, 4.3, -1.73, -4.41],\n",
              " [5.09, 0.92, 0.46, -0.16, -2.7, 4.38, 2.42, 3.51, -1.13, -3.72],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, -1.18, -1.51, 2.25, 5.02, 4.3, -1.73, -4.41],\n",
              " [-0.41, 1.38, 3.09, -0.16, -2.7, 4.38, 2.42, 1.49, 3.93, -0.52],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, 0.38, -0.95, 2.09, -3.87, 4.3, -1.73, -4.41],\n",
              " [-3.07, -4.99, -3.53, -0.16, -2.7, 4.38, 2.42, 3.35, 0.92, -2.94],\n",
              " [1.62, 4.48, -2.38, 2.06, 1.28, 2.33, -2.67, 4.3, -1.73, -4.41],\n",
              " [-4.88, 3.89, 0.2, -0.16, -2.7, 4.38, 2.42, 0.17, 0.96, -2.59],\n",
              " [1.62, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, -1.18, -1.51, 2.25, 5.02, 4.3, -1.73, -4.41],\n",
              " [-0.41, 1.38, 3.09, -0.16, -2.7, 4.38, 2.42, 1.49, 3.93, -0.52],\n",
              " [1.62, 4.48, -2.38, -3.55, 2.2, 2.58, 2.42, 4.3, -1.73, -4.41],\n",
              " [0.35, -1.53, 1.78, -0.16, -2.7, 4.38, 2.42, 2.59, 3.36, 0.17],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, -2.76, -0.0, -4.58, 2.09, 4.3, -1.73, -4.41],\n",
              " [3.69, 0.72, -1.18, -0.16, -2.7, 4.38, 2.42, -1.93, -4.32, -2.24],\n",
              " [1.62, 4.48, -2.38, 2.36, 1.13, -4.36, -2.01, 4.3, -1.73, -4.41],\n",
              " [0.08, 0.61, 1.69, -0.16, -2.7, 4.38, 2.42, 2.69, 2.21, -5.12],\n",
              " [1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41],\n",
              " [1.62, 4.48, -2.38, -0.16, -2.7, 4.38, 2.42, 4.3, -1.73, -4.41],\n",
              " [4.3, -3.09, 2.09, 4.59, -3.8, 1.03, -3.21, 3.9, 3.78, -2.41],\n",
              " [2.65, 1.74, -4.99, -4.78, 0.32, 0.46, -3.18, 1.76, 3.79, -2.53],\n",
              " [4.3, -3.09, 2.09, 2.3, 4.53, 4.63, 0.02, 3.9, 3.78, -2.41],\n",
              " [5.09, 0.92, 0.46, -4.78, 0.32, 0.46, -3.18, 3.51, -1.13, -3.72],\n",
              " [4.3, -3.09, 2.09, 1.3, 4.43, 0.18, -4.04, 3.9, 3.78, -2.41],\n",
              " [3.83, 0.85, -1.32, -4.78, 0.32, 0.46, -3.18, -2.2, 4.71, -0.16],\n",
              " [4.3, -3.09, 2.09, -1.18, -1.51, 2.25, 5.02, 3.9, 3.78, -2.41],\n",
              " [-0.41, 1.38, 3.09, -4.78, 0.32, 0.46, -3.18, 1.49, 3.93, -0.52],\n",
              " [4.3, -3.09, 2.09, 1.3, 4.43, 0.18, -4.04, 3.9, 3.78, -2.41],\n",
              " [3.83, 0.85, -1.32, -4.78, 0.32, 0.46, -3.18, -2.2, 4.71, -0.16],\n",
              " [4.3, -3.09, 2.09, 0.38, -0.95, 2.09, -3.87, 3.9, 3.78, -2.41],\n",
              " [-3.07, -4.99, -3.53, -4.78, 0.32, 0.46, -3.18, 3.35, 0.92, -2.94],\n",
              " [4.3, -3.09, 2.09, 2.06, 1.28, 2.33, -2.67, 3.9, 3.78, -2.41],\n",
              " [-4.88, 3.89, 0.2, -4.78, 0.32, 0.46, -3.18, 0.17, 0.96, -2.59],\n",
              " [4.3, -3.09, 2.09, -4.9, 5.11, 5.07, 0.33, 3.9, 3.78, -2.41],\n",
              " [-0.23, 0.37, -4.25, -4.78, 0.32, 0.46, -3.18, 4.36, -3.44, -3.98],\n",
              " [4.3, -3.09, 2.09, 1.3, 4.43, 0.18, -4.04, 3.9, 3.78, -2.41],\n",
              " [3.83, 0.85, -1.32, -4.78, 0.32, 0.46, -3.18, -2.2, 4.71, -0.16],\n",
              " [4.3, -3.09, 2.09, -1.18, -1.51, 2.25, 5.02, 3.9, 3.78, -2.41],\n",
              " [-0.41, 1.38, 5.12, -4.78, 0.32, 0.46, -3.18, 1.49, 3.93, -0.52],\n",
              " [4.3, -3.09, 2.09, -3.55, 2.2, 2.58, 2.42, 3.9, 3.78, -2.41],\n",
              " [0.35, -1.53, 1.78, -4.78, 0.32, 0.46, -3.18, 2.59, 3.36, 0.17],\n",
              " [4.3, -3.09, 2.09, 2.6, -2.07, 1.92, 4.03, 3.9, 3.78, -2.41],\n",
              " [4.06, -4.78, 0.47, -4.78, 0.32, 0.46, -3.18, 3.5, 0.77, -4.22],\n",
              " [4.3, -3.09, 2.09, -2.76, -0.0, -4.58, 2.09, 3.9, 3.78, -2.41],\n",
              " [3.69, 0.72, -1.18, -4.78, 0.32, 0.46, -3.18, -1.93, -4.32, -2.24],\n",
              " [4.3, -3.09, 2.09, 2.36, 1.13, -4.36, -2.01, 3.9, 3.78, -2.41],\n",
              " [0.08, 0.61, 1.69, -4.78, 0.32, 0.46, -3.18, 2.69, 2.21, -5.07],\n",
              " [4.3, -3.09, 2.09, -0.16, -2.7, 4.38, 2.42, 3.9, 3.78, -2.41],\n",
              " [3.85, 4.48, -2.38, -4.78, 0.32, 0.46, -3.18, 4.3, -1.73, -4.41],\n",
              " [0.42, 1.74, -4.99, 2.3, 4.53, 4.63, 0.02, 1.76, 3.79, -2.53],\n",
              " [5.09, 0.92, 0.46, 4.59, -3.8, 1.03, -3.21, 3.51, -1.13, -3.72],\n",
              " [0.42, 1.74, -4.99, 1.3, 4.43, 0.18, -4.04, 1.76, 3.79, -2.53],\n",
              " [3.83, 0.85, -1.32, 4.59, -3.8, 1.03, -3.21, -2.2, 4.71, -0.16],\n",
              " [0.42, 1.74, -4.99, -3.41, -1.51, 2.25, 5.02, 1.76, 3.79, -2.53],\n",
              " [-0.41, 1.38, 3.09, 4.59, -3.8, 1.03, -3.21, 1.49, 3.93, -0.52],\n",
              " [0.42, 1.74, -4.99, 1.3, 4.43, 0.18, -4.04, 1.76, 3.79, -2.53],\n",
              " [3.83, 0.85, -1.32, 4.59, -3.8, 1.03, -3.21, -2.2, 4.71, -0.16],\n",
              " [0.42, 1.74, -4.99, 0.38, -0.95, 2.09, -3.87, 1.76, 3.79, -2.53],\n",
              " [-3.07, -4.99, -3.53, 4.59, -3.8, 1.03, -3.21, 3.35, 0.92, -2.94],\n",
              " [0.42, 1.74, -4.99, 2.06, 1.28, 2.33, -2.67, 1.76, 3.79, -2.53],\n",
              " [-4.88, 3.89, 0.2, 4.59, -3.8, 1.03, -3.21, 0.17, 0.96, -2.59],\n",
              " [0.42, 1.74, -4.99, -4.9, 5.11, 5.07, 0.33, 1.76, 3.79, -2.53],\n",
              " [-0.23, 0.37, -4.25, 4.59, -3.8, 1.03, -3.21, 4.36, -3.44, -3.98],\n",
              " [0.42, 1.74, -4.99, 1.3, 4.43, 0.18, -4.04, 1.76, 3.79, -2.53],\n",
              " [3.83, 0.85, -1.32, 4.59, -3.8, 1.03, -3.21, -2.2, 4.71, -0.16],\n",
              " [0.42, 1.74, -4.99, -1.18, -1.51, 2.25, 5.02, 1.76, 3.79, -2.53],\n",
              " [-0.41, 1.38, 3.09, 4.59, -3.8, 1.03, -3.21, 1.49, 1.7000000000000002, -0.52],\n",
              " [0.42, 1.74, -4.99, -3.55, 2.2, 2.58, 2.42, 1.76, 3.79, -2.53],\n",
              " [0.35, -1.53, 1.78, 4.59, -3.8, 1.03, -3.21, 2.59, 3.36, 0.17],\n",
              " [0.42, 1.74, -4.99, 2.6, -2.07, 1.92, 4.03, 1.76, 3.79, -2.53],\n",
              " [4.06, -4.78, 0.47, 4.59, -3.8, 1.03, -3.21, 3.5, 0.77, -4.22],\n",
              " [0.42, 1.74, -4.99, -2.76, -0.0, -4.58, 2.09, 1.76, 3.79, -2.53],\n",
              " [3.69, 0.72, -1.18, 4.59, -3.8, 1.03, -3.21, -1.93, -4.32, -2.24],\n",
              " [0.42, 1.74, -4.99, 2.36, 1.13, -4.36, -2.01, 1.76, 3.79, -2.53],\n",
              " [0.08, 0.61, 1.69, 4.59, -3.8, 1.03, -3.21, 2.69, 2.21, -5.07],\n",
              " [0.42, 1.74, -4.99, -0.16, -2.7, 4.38, 2.42, 1.76, 3.79, -2.53],\n",
              " [1.62, 4.48, -2.38, 4.59, -3.8, 1.03, -3.21, 4.3, -1.73, -4.41],\n",
              " [5.09, 0.92, 0.46, 1.3, 4.43, 0.18, -4.04, 3.51, -1.13, -3.72],\n",
              " [3.83, 0.85, -1.32, 2.3, 5.12, 4.63, 0.02, -2.2, 4.71, -0.16],\n",
              " [5.09, 0.92, 0.46, -1.18, -1.51, 2.25, 5.02, 3.51, -1.13, -3.72],\n",
              " [-0.41, 1.38, 3.09, 2.3, 4.53, 4.63, 0.02, 1.49, 3.93, -0.52],\n",
              " [5.09, 0.92, 0.46, 1.3, 4.43, 0.18, -4.04, 3.51, -1.13, -3.72],\n",
              " [3.83, 0.85, -1.32, 2.3, 4.53, 4.63, 0.02, -2.2, 4.71, -0.16],\n",
              " [5.09, 0.92, 0.46, 0.38, -0.95, 2.09, -3.87, 3.51, -1.13, -3.72],\n",
              " [-3.07, -4.99, -3.53, 2.3, 4.53, 4.63, 0.02, 3.35, 0.92, -2.94],\n",
              " [5.09, 0.92, 0.46, 2.06, 1.28, 2.33, -2.67, 3.51, -1.13, -3.72],\n",
              " [-4.88, 3.89, 0.2, 2.3, 4.53, 4.63, 0.02, 0.17, 0.96, -2.59],\n",
              " [5.09, 0.92, 0.46, -4.9, 5.11, 5.07, 0.33, 3.51, -1.13, -3.72],\n",
              " [-0.23, 0.37, -4.25, 2.3, 4.53, 4.63, 0.02, 4.36, -3.44, -3.98],\n",
              " [5.09, 0.92, 0.46, 1.3, 4.43, 0.18, -4.04, 3.51, -1.13, -3.72],\n",
              " [3.83, 0.85, -1.32, 2.3, 4.53, 4.63, 0.02, -2.2, 4.71, -0.16],\n",
              " [5.09, -1.31, 0.46, -1.18, -1.51, 2.25, 5.02, 3.51, -1.13, -3.72],\n",
              " [-0.41, 1.38, 3.09, 2.3, 4.53, 4.63, 0.02, 1.49, 3.93, -0.52],\n",
              " [5.09, 0.92, 0.46, -3.55, 2.2, 2.58, 2.42, 3.51, -1.13, -3.72],\n",
              " [0.35, -1.53, 1.78, 2.3, 4.53, 4.63, 0.02, 2.59, 3.36, 0.17],\n",
              " [5.09, 0.92, 0.46, 2.6, -2.07, 1.92, 4.03, 3.51, -1.13, -3.72],\n",
              " [4.06, -4.78, 0.47, 2.3, 4.53, 4.63, 0.02, 3.5, 0.77, -4.22],\n",
              " [5.12, 0.92, 0.46, -2.76, -0.0, -4.58, 2.09, 3.51, -1.13, -3.72],\n",
              " [3.69, 0.72, -1.18, 2.3, 4.53, 4.63, 0.02, -1.93, -4.32, -2.24],\n",
              " [5.09, 0.92, 0.46, 2.36, 1.13, -4.36, -2.01, 3.51, -1.13, -3.72],\n",
              " [0.08, 0.61, 1.69, 2.3, 4.53, 4.63, 0.02, 2.69, 2.21, -5.07],\n",
              " [5.09, 0.92, 0.46, -0.16, -2.7, 4.38, 2.42, 3.51, -1.13, -3.72],\n",
              " [1.62, 4.48, -2.38, 2.3, 4.53, 4.63, 0.02, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, -1.18, -1.51, 2.25, 5.02, -2.2, 4.71, -0.16],\n",
              " [-0.41, 1.38, 3.09, 1.3, 4.43, 0.18, -4.04, 1.49, 3.93, -0.52],\n",
              " [3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16],\n",
              " [3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16],\n",
              " [3.83, 0.85, -1.32, 0.38, -0.95, 2.09, -3.87, -2.2, 4.71, -0.16],\n",
              " [-3.07, -4.99, -3.53, 1.3, 4.43, 0.18, -4.04, 3.35, 0.92, -2.94],\n",
              " [3.83, 0.85, -1.32, 2.06, 1.28, 2.33, -2.67, -2.2, 4.71, -0.16],\n",
              " [-4.88, 3.89, 0.2, 1.3, 4.43, 0.18, -4.04, 0.17, 0.96, -2.59],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16],\n",
              " [3.83, 0.85, 0.9099999999999999, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16],\n",
              " [3.83, 0.85, -1.32, -1.18, -1.51, 2.25, 5.02, -2.2, 4.71, -0.16],\n",
              " [-0.41, 1.38, 3.09, 1.3, 4.43, 0.18, -4.04, 1.49, 3.93, -0.52],\n",
              " [3.83, 0.85, -1.32, -3.55, 2.2, 2.58, 2.42, -2.2, 4.71, -0.16],\n",
              " [0.35, -1.53, 1.78, 1.3, 4.43, 0.18, -4.04, 2.59, 3.36, 0.17],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, -2.76, -0.0, -4.58, 2.09, -2.2, 4.71, -0.16],\n",
              " [3.69, 0.72, -1.18, 1.3, 4.43, 0.18, -4.04, -1.93, -4.32, -2.24],\n",
              " [3.83, 0.85, -1.32, 2.36, 1.13, -4.36, -2.01, -2.2, 4.71, -0.16],\n",
              " [0.08, 0.61, 1.69, 1.3, 4.43, 0.18, -4.04, 2.69, 2.21, -5.07],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [-0.41, 1.38, 3.09, 1.3, 4.43, 0.18, -4.04, 1.49, 3.93, -0.52],\n",
              " [3.83, -1.38, -1.32, -1.18, -1.51, 2.25, 5.02, -2.2, 4.71, -0.16],\n",
              " [-0.41, 1.38, 3.09, 0.38, -0.95, 2.09, -3.87, 1.49, 3.93, -0.52],\n",
              " [-3.07, -4.99, -3.53, -1.18, -1.51, 2.25, 5.02, 3.35, 0.92, -2.94],\n",
              " [-0.41, 1.38, 3.09, 2.06, 1.28, 2.33, -2.67, 1.49, 3.93, -0.52],\n",
              " [-4.88, 3.89, 0.2, -1.18, -1.51, 2.25, 5.02, 0.17, 0.96, -2.59],\n",
              " [-0.41, 1.38, 3.09, -4.9, 5.11, 5.07, 0.33, 1.49, 3.93, -0.52],\n",
              " [-0.23, 0.37, -4.25, -1.18, -1.51, 2.25, 5.02, 4.36, -3.44, -3.98],\n",
              " [-0.41, 1.38, 3.09, 1.3, 4.43, 0.18, -4.04, 1.49, 3.93, -0.52],\n",
              " [3.83, 0.85, -1.32, -1.18, -3.74, 2.25, 5.02, -2.2, 4.71, -0.16],\n",
              " [-0.41,\n",
              "  1.38,\n",
              "  3.09,\n",
              "  -1.18,\n",
              "  -1.51,\n",
              "  2.25,\n",
              "  5.02,\n",
              "  -0.74,\n",
              "  1.7000000000000002,\n",
              "  -0.52],\n",
              " [-0.41, 1.38, 3.09, -1.18, -1.51, 2.25, 5.02, 1.49, 3.93, -0.52],\n",
              " [-0.41, 1.38, 3.09, -3.55, 2.2, 2.58, 2.42, 1.49, 3.93, -0.52],\n",
              " [0.35, -1.53, 1.78, -1.18, -1.51, 2.25, 5.02, 2.59, 3.36, 0.17],\n",
              " [-0.41, 1.38, 3.09, 2.6, -2.07, 1.92, 4.03, 1.49, 3.93, -0.52],\n",
              " [4.06, -4.78, 0.47, -1.18, -1.51, 2.25, 5.12, 3.5, 0.77, -4.22],\n",
              " [-0.41, 1.38, 3.09, -2.76, -0.0, -4.58, 2.09, 1.49, 3.93, -0.52],\n",
              " [3.69, 0.72, -1.18, -1.18, -1.51, 2.25, 5.02, -1.93, -4.32, -2.24],\n",
              " [-0.41, 1.38, 3.09, 2.36, 1.13, -4.36, -2.01, 1.49, 3.93, -0.52],\n",
              " [0.08, 0.61, 1.69, -1.18, -1.51, 2.25, 5.02, 2.69, 2.21, -5.07],\n",
              " [-0.41, 1.38, 3.09, -0.16, -2.7, 4.38, 2.42, 1.49, 3.93, -2.75],\n",
              " [1.62, 4.48, -4.609999999999999, -1.18, -1.51, 2.25, 5.02, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, 0.38, -0.95, 2.09, -3.87, -2.2, 4.71, -0.16],\n",
              " [-3.07, -4.99, -3.53, 1.3, 4.43, 0.18, -4.04, 3.35, 0.92, -2.94],\n",
              " [3.83, 0.85, -1.32, 2.06, 1.28, 2.33, -2.67, -2.2, 4.71, -0.16],\n",
              " [-4.88, 3.89, 0.2, 1.3, 4.43, 0.18, -4.04, 0.17, 0.96, -2.59],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -5.12, -3.98],\n",
              " [3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16],\n",
              " [3.83, 0.85, -1.32, 1.3, 4.43, 0.18, -4.04, -2.2, 4.71, -0.16],\n",
              " [3.83, 0.85, -1.32, -1.18, -1.51, 2.25, 5.02, -2.2, 4.71, -0.16],\n",
              " [-0.41, 1.38, 3.09, 1.3, 4.43, 0.18, -4.04, 1.49, 3.93, -0.52],\n",
              " [3.83, 0.85, -1.32, -3.55, 2.2, 2.58, 2.42, -2.2, 4.71, -0.16],\n",
              " [0.35, -1.53, 1.78, 1.3, 4.43, 0.18, -4.04, 2.59, 3.36, 0.17],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, -2.76, -0.0, -4.58, 2.09, -2.2, 4.71, -0.16],\n",
              " [3.69, 0.72, -1.18, 1.3, 4.43, 0.18, -4.04, -1.93, -4.32, -2.24],\n",
              " [3.83, 0.85, -1.32, 2.36, 1.13, -4.36, -2.01, -2.2, 4.71, -0.16],\n",
              " [0.08, 0.61, 1.69, 1.3, 4.43, 0.18, -4.04, 2.69, 2.21, -5.07],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [1.62, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [-3.07, -4.99, -3.53, 2.06, 1.28, 2.33, -2.67, 3.35, 0.92, -2.94],\n",
              " [-4.88, 3.89, 0.2, 0.38, -0.95, 2.09, -3.87, 0.17, 0.96, -4.82],\n",
              " [-3.07, -4.99, -3.53, -4.9, 5.11, 5.07, 0.33, 3.35, 0.92, -2.94],\n",
              " [-0.23, 0.37, -4.25, 0.38, -0.95, 2.09, -3.87, 4.36, -3.44, -3.98],\n",
              " [-3.07, -4.99, -3.53, 1.3, 4.43, 0.18, -4.04, 3.35, 0.92, -2.94],\n",
              " [3.83, 0.85, -1.32, 0.38, -0.95, 2.09, -3.87, -2.2, 4.71, -0.16],\n",
              " [-3.07, -4.99, -3.53, -1.18, -1.51, 2.25, 5.02, 3.35, 0.92, -0.71],\n",
              " [-0.41, 1.38, 3.09, 0.38, -0.95, 2.09, -3.87, 1.49, 3.93, -0.52],\n",
              " [-3.07, -4.99, -3.53, -3.55, 2.2, 2.58, 2.42, 3.35, 0.92, -2.94],\n",
              " [0.35, -1.53, 1.78, 0.38, -0.95, 2.09, -3.87, 4.82, 3.36, 0.17],\n",
              " [-3.07, -4.99, -3.53, 2.6, -2.07, 1.92, 4.03, 3.35, 0.92, -2.94],\n",
              " [4.06, -4.78, 0.47, 0.38, -0.95, 2.09, -3.87, 3.5, 0.77, -4.22],\n",
              " [-3.07, -4.99, -3.53, -2.76, -0.0, -4.58, 2.09, 3.35, 0.92, -2.94],\n",
              " [3.69, 0.72, -1.18, 0.38, -0.95, 2.09, -3.87, -1.93, -4.32, -2.24],\n",
              " [-3.07, -4.99, -3.53, 2.36, 1.13, -4.36, -2.01, 3.35, 0.92, -2.94],\n",
              " [0.08, 0.61, 1.69, 0.38, -0.95, 2.09, -3.87, 2.69, 2.21, -5.07],\n",
              " [-3.07, -4.99, -3.53, -0.16, -2.7, 4.38, 2.42, 3.35, 0.92, -2.94],\n",
              " [1.62, 4.48, -2.38, 0.38, -0.95, 2.09, -3.87, 4.3, -1.73, -4.41],\n",
              " [-4.88, 3.89, 0.2, -4.9, 5.11, 5.07, 0.33, 2.4, 0.96, -2.59],\n",
              " [-0.23, 0.37, -4.25, 2.06, 1.28, 2.33, -2.67, 4.36, -3.44, -3.98],\n",
              " [-4.88, 3.89, 0.2, 1.3, 4.43, 0.18, -4.04, 0.17, 0.96, -2.59],\n",
              " [3.83, 0.85, -1.32, 2.06, 1.28, 2.33, -2.67, -2.2, 4.71, -0.16],\n",
              " [-4.88, 3.89, 0.2, -1.18, -1.51, 2.25, 5.02, 0.17, 0.96, -2.59],\n",
              " [-0.41, 1.38, 3.09, 2.06, 1.28, 2.33, -2.67, 1.49, 3.93, -0.52],\n",
              " [-4.88, 3.89, 0.2, -3.55, 2.2, 2.58, 2.42, 0.17, 0.96, -2.59],\n",
              " [0.35, -1.53, 1.78, 2.06, 1.28, 2.33, -4.9, 2.59, 3.36, 0.17],\n",
              " [-4.88, 3.89, 0.2, 2.6, -2.07, 1.92, 4.03, 0.17, 0.96, -2.59],\n",
              " [4.06, -4.78, 0.47, 2.06, 1.28, 2.33, -2.67, 3.5, 0.77, -4.22],\n",
              " [-4.88, 3.89, 0.2, -2.76, -0.0, -2.35, 2.09, 0.17, 0.96, -2.59],\n",
              " [3.69, 0.72, -1.18, 2.06, 1.28, 2.33, -2.67, -1.93, -4.32, -2.24],\n",
              " [-4.88, 3.89, 0.2, 2.36, 1.13, -4.36, -2.01, 0.17, 0.96, -2.59],\n",
              " [0.08, 0.61, 1.69, 2.06, 1.28, 2.33, -2.67, 2.69, 2.21, -5.07],\n",
              " [-4.88, 3.89, 0.2, -0.16, -2.7, 4.38, 2.42, 0.17, 0.96, -2.59],\n",
              " [1.62, 4.48, -2.38, 2.06, 1.28, 2.33, -2.67, 4.3, -1.73, -4.41],\n",
              " [-0.23, 0.37, -4.25, 1.3, 4.43, 0.18, -4.04, 4.36, -3.44, -3.98],\n",
              " [3.83, 0.85, -1.32, -4.9, 5.11, 5.07, 0.33, -2.2, 4.71, -0.16],\n",
              " [-0.23, 0.37, -4.25, -1.18, -1.51, 2.25, 5.02, 4.36, -3.44, -3.98],\n",
              " [-0.41, 1.38, 3.09, -4.9, 5.11, 5.07, 0.33, 1.49, 3.93, -0.52],\n",
              " [-0.23, 0.37, -4.25, -3.55, 2.2, 2.58, 2.42, 4.36, -3.44, -3.98],\n",
              " [0.35, -1.53, 1.78, -4.9, 5.11, 5.07, 0.33, 2.59, 3.36, 0.17],\n",
              " [-0.23, 0.37, -4.25, 2.6, -2.07, 1.92, 4.03, 4.36, -3.44, -3.98],\n",
              " [4.06, -4.78, 0.47, -4.9, 5.11, 5.07, 0.33, 3.5, 0.77, -4.22],\n",
              " [-0.23, 0.37, -4.25, -2.76, -0.0, -4.58, 2.09, 4.36, -3.44, -3.98],\n",
              " [3.69, 0.72, -3.41, -4.9, 5.11, 5.07, 0.33, -1.93, -4.32, -2.24],\n",
              " [-0.23, 0.37, -4.25, 2.36, 1.13, -4.36, -2.01, 4.36, -3.44, -3.98],\n",
              " [0.08,\n",
              "  0.61,\n",
              "  1.69,\n",
              "  -4.9,\n",
              "  5.11,\n",
              "  5.07,\n",
              "  0.33,\n",
              "  2.69,\n",
              "  -0.020000000000000018,\n",
              "  -5.07],\n",
              " [-0.23, 0.37, -4.25, -0.16, -2.7, 4.38, 2.42, 4.36, -3.44, -3.98],\n",
              " [1.62, 4.48, -2.38, -4.9, 5.11, 5.07, 0.33, 4.3, -1.73, -4.41],\n",
              " [3.83, 0.85, -1.32, -1.18, -1.51, 2.25, 5.02, -2.2, 4.71, -0.16],\n",
              " [-0.41, 1.38, 0.8599999999999999, 1.3, 4.43, 0.18, -4.04, 1.49, 3.93, -0.52],\n",
              " [3.83, 0.85, -1.32, -3.55, 2.2, 2.58, 2.42, -2.2, 4.71, -0.16],\n",
              " [0.35, -1.53, 1.78, 1.3, 4.43, 0.18, -4.04, 2.59, 3.36, 0.17],\n",
              " [3.83, 0.85, -1.32, 2.6, -2.07, 1.92, 4.03, -2.2, 4.71, -0.16],\n",
              " [4.06, -4.78, 0.47, 1.3, 4.43, 0.18, -4.04, 3.5, 0.77, -4.22],\n",
              " [3.83, 0.85, -1.32, -2.76, -0.0, -4.58, 2.09, -2.2, 4.71, -0.16],\n",
              " [3.69, 0.72, -1.18, 1.3, 4.43, 0.18, -4.04, -1.93, -4.32, -2.24],\n",
              " [3.83, 0.85, -1.32, 2.36, 1.13, -4.36, -2.01, -2.2, 4.71, -0.16],\n",
              " [0.08,\n",
              "  0.61,\n",
              "  1.69,\n",
              "  1.3,\n",
              "  4.43,\n",
              "  0.18,\n",
              "  -4.04,\n",
              "  2.69,\n",
              "  -0.020000000000000018,\n",
              "  -5.07],\n",
              " [3.83, 0.85, -1.32, -0.16, -2.7, 4.38, 2.42, -2.2, 4.71, -0.16],\n",
              " [3.85, 4.48, -2.38, 1.3, 4.43, 0.18, -4.04, 4.3, -1.73, -4.41],\n",
              " [-0.41, 1.38, 3.09, -3.55, 2.2, 2.58, 2.42, 1.49, 3.93, -0.52],\n",
              " [0.35, -1.53, 1.78, -1.18, -1.51, 2.25, 5.02, 2.59, 3.36, 0.17],\n",
              " [-0.41, 1.38, 3.09, 2.6, -2.07, 1.92, 4.03, 1.49, 3.93, -0.52],\n",
              " [4.06, -4.78, 0.47, -1.18, -1.51, 2.25, 5.02, 3.5, 0.77, -4.22],\n",
              " [-0.41, 1.38, 3.09, -2.76, -0.0, -4.58, 2.09, 1.49, 3.93, -0.52],\n",
              " [3.69, 0.72, -1.18, -1.18, -1.51, 2.25, 5.02, -1.93, -4.32, -2.24],\n",
              " [-0.41, 1.38, 3.09, 2.36, 1.13, -4.36, -2.01, 1.49, 3.93, -0.52],\n",
              " [0.08, 0.61, 1.69, -1.18, -1.51, 2.25, 5.02, 2.69, 2.21, -5.07],\n",
              " [-0.41, 1.38, 3.09, -0.16, -2.7, 4.38, 2.42, 1.49, 3.93, -0.52],\n",
              " [1.62, 4.48, -2.38, -1.18, -1.51, 2.25, 5.02, 4.3, -1.73, -4.41],\n",
              " [0.35, -1.53, 1.78, 2.6, -2.07, 1.92, 4.03, 2.59, 3.36, 0.17],\n",
              " [4.06, -4.78, 0.47, -3.55, 2.2, 2.58, 2.42, 3.5, 0.77, -4.22],\n",
              " [0.35, -1.53, 1.78, -2.76, -0.0, -4.58, 2.09, 2.59, 3.36, 0.17],\n",
              " [3.69, 0.72, -1.18, -3.55, 2.2, 2.58, 2.42, -1.93, -4.32, -2.24],\n",
              " [0.35, -1.53, 1.78, 2.36, 1.13, -4.36, -2.01, 2.59, 3.36, 0.17],\n",
              " [0.08, 0.61, 3.92, -3.55, 2.2, 2.58, 2.42, 2.69, 2.21, -5.07],\n",
              " [0.35, -1.53, 1.78, -0.16, -2.7, 4.38, 2.42, 2.59, 3.36, 0.17],\n",
              " [1.62, 4.48, -2.38, -3.55, 2.2, 2.58, 2.42, 4.3, -1.73, -4.41],\n",
              " [4.06, -4.78, 0.47, -2.76, -0.0, -4.58, 2.09, 3.5, 0.77, -4.22],\n",
              " [3.69, 0.72, -1.18, 2.6, -2.07, 1.92, 4.03, -1.93, -4.32, -2.24],\n",
              " [4.06, -4.78, 0.47, 0.1299999999999999, 1.13, -4.36, -2.01, 3.5, 0.77, -4.22],\n",
              " [0.08, 0.61, 1.69, 2.6, -2.07, 1.92, 4.03, 2.69, 2.21, -5.07],\n",
              " [4.06, -4.78, 0.47, -0.16, -2.7, 4.38, 2.42, 3.5, 0.77, -4.22],\n",
              " [1.62, 4.48, -2.38, 2.6, -2.07, 1.92, 4.03, 4.3, -1.73, -4.41],\n",
              " [3.69, 0.72, -1.18, 2.36, 1.13, -5.12, -2.01, -1.93, -4.32, -2.24],\n",
              " [0.08, 0.61, 1.69, -2.76, -0.0, -4.58, 2.09, 2.69, 2.21, -5.07],\n",
              " [3.69, 0.72, -1.18, -0.16, -2.7, 4.38, 2.42, -4.16, -4.32, -2.24],\n",
              " [1.62, 4.48, -2.38, -2.76, -0.0, -4.58, 2.09, 4.3, -1.73, -4.41],\n",
              " [0.08, 0.61, 1.69, -0.16, -2.7, 4.38, 2.42, 2.69, 2.21, -5.07],\n",
              " [1.62, 4.48, -2.38, 2.36, 1.13, -4.36, -2.01, 4.3, -1.73, -4.41]]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot of bests, worsts, and average apptitudes of individuals."
      ],
      "metadata": {
        "id": "wiAzwiwc2VXQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def conv_plot(best_aptitudes, worst_aptitudes, avg_aptitudes):\n",
        "    pop_size = list(range(1, len(best_aptitudes) + 1))\n",
        "    plt.scatter(pop_size, best_aptitudes, color='green', label='best')\n",
        "    plt.plot(pop_size, best_aptitudes, color='green')\n",
        "    plt.scatter(pop_size, worst_aptitudes, color='red', label='worst')\n",
        "    plt.plot(pop_size, worst_aptitudes, color='red')\n",
        "    plt.scatter(pop_size, avg_aptitudes, color='blue', label='average')\n",
        "    plt.plot(pop_size, avg_aptitudes, color='blue')\n",
        "    plt.legend()\n",
        "    plt.xlabel('Number of individuals')\n",
        "    plt.ylabel('Aptitud')\n",
        "    plt.title(\"Convergence plot\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "pZPfZK8B2pKF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Integration of the above functions, in order to run the genetic algorithm as a whole."
      ],
      "metadata": {
        "id": "gB56LvD2DPY-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def genetic_algo(population_size, precision, vars_domain, dim, cost_fun, cross_over_class, cross_class_param1, cross_class_param2, delta_mut, mut_avg, error):\n",
        "    \"\"\"\n",
        "    Future work: Implement cross operator of more than two cross operators \n",
        "    \"\"\"\n",
        "    bit_size = bit_size_binary(vars_domain[0], vars_domain[1], precision) \n",
        "    bin_coding_dict = binary_coding_dict(bit_size, vars_domain[0], vars_domain[1], precision)  # binary representation dict for values with precision 4 defined in the cost function \n",
        "    init_pop = initial_population(population_size, vars_domain, dim, precision) # random generation of starting population\n",
        "    evaluation = np.array(table_pop_fitnesss(init_pop, cost_fun), dtype=object) # evaluation of aptitudes via the cost_fun (individuals and aptitues are in decimal repr)\n",
        "    #print(evaluation)\n",
        "    evaluation = sorted(evaluation, key = lambda x : float(x[1])) # order table in terms of values of cost_fun to find the best and worst values found. Notice we don't care about the precision of the aptitudes\n",
        "    #print(evaluation)\n",
        "    best = [evaluation[-1][0], evaluation[-1][1]] # best value obtained for an individual of the starting population\n",
        "    #print(best)\n",
        "    worst = [evaluation[1][0], evaluation[1][1]]  # worst value obtained for an individual of the starting population\n",
        "    #print(worst)\n",
        "    bests = []\n",
        "    bests.append(best[1])\n",
        "    worsts = []\n",
        "    worsts.append(worst[1])\n",
        "    len_pop = len(evaluation)\n",
        "    avgs = []\n",
        "    avg = 0\n",
        "    for i in range(len(evaluation)):\n",
        "        avg += evaluation[i][1]\n",
        "    avgs.append(avg/len(evaluation))  \n",
        "    iteration = 0  # count the number of iterations needed in order to approximal the global optimum\n",
        "    while abs(best[1] - worst[1]) > error: # Stop criteria - The best and the worst values obtained by all of the individuals are very similar\n",
        "        parents = stoch_rem_par_sel(evaluation) # parents in decimal representation\n",
        "        children = parents_reproduction(parents, cross_over_class, cross_class_param1, cross_class_param2) # children obtained after parents reproduction\n",
        "        mutated_children = mutate(children, delta_mut, mut_avg) # mutated children\n",
        "        mutated_children_fixed = fix_mutated_children(mutated_children, vars_domain)\n",
        "        pars = [prnt[0] for prnt in parents] # only parents\n",
        "        parents_and_children = pars + mutated_children_fixed # parents and children fixed in a same bag\n",
        "        table_parents_children = np.array(table_pop_fitnesss(parents_and_children, cost_fun), dtype=object) # Aptitude table for the parents and the fixed children\n",
        "        #print(table_parents_children[0])\n",
        "        survivors = bin_tournament_survivors(table_parents_children) # Survivors of the best individuals\n",
        "        survivors = sorted(survivors, key = lambda x : float(x[1]))[0:population_size]     # Just take best individuals   \n",
        "        best = [survivors[-1][0], survivors[-1][1]] # best value obtained for an individual of the population after the binary torunament \n",
        "        #print(best)\n",
        "        bests.append(best[1])\n",
        "        worst = [survivors[1][0], survivors[1][1]] # worst value obtained for an individual of the population after the binary torunament \n",
        "        #print(worst)\n",
        "        worsts.append(worst[1]) \n",
        "        avg = 0\n",
        "        for j in range(len(survivors)):\n",
        "            avg += survivors[i][1]\n",
        "        avgs.append(avg/len(survivors))  \n",
        "        evaluation = survivors\n",
        "        iteration += 1 # increment the number of iterations to see how many it takes for the algorithm to find a reasonable approximate \n",
        "    print(f\"Numer of iterations needed: {iteration}\\n\")\n",
        "    print(f\"Global optimum approximation (best individual) is: {best}\")\n",
        "    return best, bests, worsts, avgs "
      ],
      "metadata": {
        "id": "nq6FfMo-XRlE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test of the genetic algorithm.\n",
        "\n",
        "### For each cost function, we show the results obtained with both Cross- over operators:\n",
        "\n",
        "###1. Two - Crossover \n",
        "###2. Uniform - Crossover \n",
        "\n",
        "### Populations of 20, 50, and 100, \n",
        "### Cross percentage of 0.5, 0.7, and 0.9 (only Uniform cross-over)\n",
        "### and $\\epsilon$ (error) = 0.001"
      ],
      "metadata": {
        "id": "-TVjE17dGXuV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Parabola.\n",
        "#### Dimension = 10\n",
        "#### Population = 20\n",
        "#### Cross - Over operator = Two Cross over\n",
        "#### Cut1 in index 3\n",
        "#### Cut2 in index 7\n",
        "#### Mutation percentage = 0.01\n",
        "#### Delta = 0.23\n",
        "#### $\\epsilon$ = 0.001 "
      ],
      "metadata": {
        "id": "I5z3GuX8K-ZE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best, bests, worsts, avgs = genetic_algo(20, 2, [-5.12, 5.12], 10, parabola, TwoCrossover, 3, 7, 0.23, 0.01, 0.001)\n",
        "conv_plot(bests, worsts, avgs)"
      ],
      "metadata": {
        "id": "qjImt1s9khfn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "outputId": "dfaf52ff-74b4-4aa2-e090-b8e7fb2570c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numer of iterations needed: 29\n",
            "\n",
            "Global optimum approximation (best individual) is: [[1.6400000000000001, 0.17, 0.41000000000000003, 0.38, -1.8200000000000003, 1.49, -0.8700000000000001, 1.54, 0.02, 0.99], 12.672500000000001]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Same as above, although population = 50"
      ],
      "metadata": {
        "id": "XhsBzDoGNqEX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best, bests, worsts, avgs = genetic_algo(50, 2, [-5.12, 5.12], 10, parabola, TwoCrossover, 3, 7, 0.23, 0.01, 0.001)\n",
        "conv_plot(bests, worsts, avgs)"
      ],
      "metadata": {
        "id": "KLkkiBnGfq-K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "outputId": "07c179c0-9a49-450e-bf7a-9ab31ccec4b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numer of iterations needed: 54\n",
            "\n",
            "Global optimum approximation (best individual) is: [[-0.03000000000000022, 0.11999999999999997, -0.03999999999999998, -0.03000000000000022, 0.06, -5.551115123125783e-17, 0.0699999999999999, 0.04000000000000001, 0.01999999999999999, -0.010000000000000009], 0.0284]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Same as above, although population = 100"
      ],
      "metadata": {
        "id": "XmUjHVDTN5jG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best, bests, worsts, avgs = genetic_algo(100, 2, [-5.12, 5.12], 10, parabola, TwoCrossover, 3, 7, 0.23, 0.01, 0.001)\n",
        "conv_plot(bests, worsts, avgs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "iCy2RevQNzKj",
        "outputId": "1641c535-2d39-4121-fce5-320eea18db48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numer of iterations needed: 58\n",
            "\n",
            "Global optimum approximation (best individual) is: [[-0.12000000000000008, 0.0999999999999997, 0.08000000000000004, 0.019999999999999962, -0.11000000000000001, -0.07000000000000003, 0.060000000000000026, -0.07999999999999999, 0.08000000000000004, 0.03], 0.06549999999999997]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rosenbrock test"
      ],
      "metadata": {
        "id": "6-rQcklxOo2s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best, bests, worsts, avgs = genetic_algo(100, 2, [-2.048, 2.048], 10, rosenbrock, TwoCrossover, 3, 7, 0.23, 0.01, 0.001)\n",
        "conv_plot(bests, worsts, avgs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "QwhhGD6fN9xw",
        "outputId": "06c46640-57e0-4d31-e76a-c433e21b6a00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numer of iterations needed: 19\n",
            "\n",
            "Global optimum approximation (best individual) is: [[0.98, 0.8500000000000001, 0.8600000000000001, 0.78, 0.75, 0.53, 0.28, 0.01, 0.16, 0.08000000000000002], 11.282411999999992]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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q5K0IGIgqmVqjxqgtoyAgiqwrWDZ6y2ioNRWbHEZERNZBrVEj+mo0Vp9ejeir0fzvu5ViIKpke+L3FOkZKkxA4HradeyJ31OJrSIiInOIOh+FoAVBeHbFs3gj6g08u+JZBC0IMvtIQH5+PkaMGAGVSgVPT09MmDABQmj/pzsnJweffPIJateuDRcXF7Rr1w7R0dG6ba9du4aXX34ZHh4ecHFxQZMmTbB582ZcvXoVzz77LADAw8MDkiRh8ODBZj2OysQ5RJUsIT3BpOWIiMg6FUyPeHxEoGB6xPr+6802Z3TFihUYOnQoDh8+jKNHj2L48OGoU6cO3nnnHYwYMQLnzp3DmjVr4Ofnh40bN6J79+44ffo06tevj8jISOTm5mL37t1wcXHBuXPn4OrqioCAAGzYsAF9+/ZFbGwslEolnJyczNJ+S2AgqmS+br4mLUdERNanrOkREiSM3jIavUJ6meXq4oCAAHz55ZeQJAkhISE4ffo0vvzyS3Tr1g3Lli1DfHw8/Pz8AACffPIJtmzZgmXLlmH69OmIj49H37590bRpUwBA3bp1dfXWqFEDAODl5QV3d3eTt9uSOGRWyTrW6Qh/pT8kFH/fBAkSApQB6FinYyW3jIiITMXS0yOeeuopvfvzhIWF4dKlSzh9+jTUajUaNGgAV1dX3WvXrl24cuUKAGDkyJH4v//7P7Rv3x6TJk3CqVOnzNJGa8NAVMnkMjkWdF8AAEVCUcHn+d3n835EREQ2zFqnR2RkZEAul+PYsWOIiYnRvc6fP48FC7TfTcOGDcM///yDt956C6dPn0abNm2waNGiSm2nJTAQWUCfRn2wvv961FbW1lvur/Q365gyERFVDktPjzh06JDe54MHD6J+/fpo2bIl1Go1kpOT8cQTT+i9fHx8dOUDAgLw3nvvISoqCmPGjMF3330HwHSPybBGDEQW0qdRH1wddRU96/cEAAxuPhhxo+IYhoiIqgBLT4+Ij4/Hxx9/jNjYWKxevRqLFi3CqFGj0KBBA4SHhyMiIgJRUVGIi4vD4cOHMWPGDPzxxx8AgNGjR2Pr1q2Ii4vD8ePHsXPnTjRq1AgAEBgYCEmS8Pvvv+P27dvIyMgwS/stgYHIguQyOVr6tAQAOCmcOExGRFRFWHp6REREBLKysvDkk08iMjISo0aNwvDhwwEAy5YtQ0REBMaMGYOQkBD07t0bR44cQZ06dQBoe38iIyPRqFEjdO/eHQ0aNMA333wDAKhduzamTJmCTz/9FN7e3hgxYoRZ2m8Jkii4MQGVKC0tDSqVCqmpqVAqlSat+6vDX+HDPz9En0Z9sKH/BpPWTURE5ZOdnY24uDgEBwfD0dGx3PUU95imAGUA5nefzxEBEyntXBnz/c3L7i3M28UbAJCUkWThlhARkan1adQHvUJ68UHeNoCByMK8XR8GokwGIiKiqkguk6NzUGdLN4PKwDlEFsYeIiIiIstjILKwgh6i9Nx0ZOVlWbg1RERE1RMDkYWpHFSwl2vv68BhMyIiIstgILIwSZI4bEZERGRhDERWgBOriYiILIuByAqwh4iIiMiyGIisgC4QsYeIiIjIIhiIrIBuyIw9RERERBbBQGQF2ENERERVhSRJ+OWXXyzdDKPxTtVWgJOqiYiqMLUa2LMHSEgAfH2Bjh0BuW0+uiM3Nxf29vaWboZZsIfICnBSNRFRFRUVBQQFAc8+C7zxhvZnUJB2uRn8/vvvcHd3h1qtBgDExMRAkiR8+umnujLDhg3Dm2++CQDYsGEDmjRpAgcHBwQFBWHu3Ll69QUFBWHatGmIiIiAUqnE8OHDkZubixEjRsDX1xeOjo4IDAzEjBkzdOUB4NVXX4UkSbrPtoCByAqwh4iIqAqKigL69QNu3NBffvOmdrkZQlHHjh2Rnp6OEydOAAB27doFT09PREdH68rs2rULnTt3xrFjx9C/f38MHDgQp0+fxuTJkzFhwgQsX75cr84vvvgCzZs3x4kTJzBhwgQsXLgQmzZtws8//4zY2FisXLlSF3yOHDkCAFi2bBkSEhJ0n20Bh8ysQEEPUUp2CnLyc+Bg52DhFhERUYWo1cCoUYAQRdcJAUgSMHo00KuXSYfPVCoVWrRogejoaLRp0wbR0dH46KOPMGXKFGRkZCA1NRWXL19Gp06dMHnyZDz//POYMGECAKBBgwY4d+4c5syZg8GDB+vqfO655zBmzBjd5/j4eNSvXx8dOnSAJEkIDAzUratVqxYAwN3dHT4+PiY7rsrAHiIr4OHkATuZNpsmZyZbuDVERFRhe/YU7RkqTAjg+nVtORPr1KkToqOjIYTAnj170KdPHzRq1Ah79+7Frl274Ofnh/r16+P8+fNo37693rbt27fHpUuXdENuANCmTRu9MoMHD0ZMTAxCQkIwcuRI/PXXXyY/BktgILICMkkGLxcvABw2IyKqEhISTFvOCJ07d8bevXtx8uRJKBQKNGzYEJ07d0Z0dDR27dqFTp06GVWfi4uL3udWrVohLi4O06ZNQ1ZWFvr3749+/fqZ8hAsgoHISnBiNRFRFeLra9pyRiiYR/Tll1/qwk9BIIqOjkbnzp0BAI0aNcK+ffv0tt23bx8aNGgAeRnDeEqlEgMGDMB3332HtWvXYsOGDbh37x4AQKFQ6PUw2QoGIivBidVERFVIx46Av792rlBxJAkICNCWMzEPDw80a9YMK1eu1IWfZ555BsePH8fFixd1IWnMmDHYvn07pk2bhosXL2LFihX46quv8Mknn5Ra/7x587B69WpcuHABFy9exLp16+Dj4wN3d3cA2ivNtm/fjsTERNy/f9/kx2cuDERWgj1ERERViFwOLFigff94KCr4PH++2e5H1KlTJ6jVal0gqlGjBho3bgwfHx+EhIQA0A59/fzzz1izZg1CQ0MxceJETJ06VW9CdXHc3Nwwe/ZstGnTBm3btsXVq1exefNmyGTaSDF37lxs27YNAQEBaNmypVmOzxwkIYqbAk+FpaWlQaVSITU1FUql0iz7GL9tPGbvn41R7UZhfvf5ZtkHEREZJjs7G3FxcQgODoajo2P5K4qK0l5tVniCdUCANgz16VPhdlLp58qY729edm8lOGRGRFQF9emjvbS+itypuipjILISHDIjIqqi5HLg4dAVWS/OIbIS7CEiIiKyHAYiK8EeIiIiIsthILISBT1Ed7PuIk+dZ+HWEBERVS8MRFaiplNNyCTt6bj94LaFW0NERFS9WDQQqdVqTJgwAcHBwXByckK9evUwbdo0FL4TgBACEydOhK+vL5ycnNClSxdcunRJr5579+4hPDwcSqUS7u7uGDp0KDIyMvTKnDp1Ch07doSjoyMCAgIwe/bsSjlGQ8llcng6ewLgsBkREVFls2ggmjVrFhYvXoyvvvoK58+fx6xZszB79mwsWrRIV2b27NlYuHAhlixZgkOHDsHFxQXdunVDdna2rkx4eDjOnj2Lbdu24ffff8fu3bsxfPhw3fq0tDR07doVgYGBOHbsGObMmYPJkydj6dKllXq8ZdHNI+LEaiIiokpl0cvu9+/fj169eqFnz54AtLf7Xr16NQ4fPgxA2zs0f/58fP755+jVqxcA4Mcff4S3tzd++eUXDBw4EOfPn8eWLVtw5MgR3RN5Fy1ahB49euCLL76An58fVq5cidzcXPzwww+wt7dHkyZNEBMTg3nz5ukFJ0vzdvXG6eTT7CEiIiKqZBbtIXr66aexfft2XLx4EQBw8uRJ7N27Fy+++CIAIC4uDomJiejSpYtuG5VKhXbt2uHAgQMAgAMHDsDd3V0XhgCgS5cukMlkOHTokK7MM888A3t7e12Zbt26ITY2ttjnrOTk5CAtLU3vVRnYQ0RERGQZFu0h+vTTT5GWloaGDRtCLpdDrVbjP//5D8LDwwEAiYmJAABvb2+97by9vXXrEhMT4eXlpbfezs4ONWrU0CsTHBxcpI6CdR4eHnrrZsyYgSlTppjoKA3HS++JiIgsw6I9RD///DNWrlyJVatW4fjx41ixYgW++OILrFixwpLNwmeffYbU1FTd6/r165WyX96ckYio6lGrgehoYPVq7U+12tItMj21Wg2NRmPpZlSIRQPR2LFj8emnn2LgwIFo2rQp3nrrLXz00UeYMWMGAMDHxwcAkJSkHxCSkpJ063x8fJCcnKy3Pj8/H/fu3dMrU1wdhfdRmIODA5RKpd6rMhT0ECVnJpdRkoiIbEFUFBAUBDz7LPDGG9qfQUHa5ea0ZcsWdOjQAe7u7qhZsyZeeuklXLlyBYB2usr48eP1yt++fRsKhQK7d+8GoJ068sknn6B27dpwcXFBu3btEB0drSu/fPlyuLu7Y9OmTWjcuDEcHBwQHx+PI0eO4IUXXoCnpydUKhU6deqE48eP6+3rwoUL6NChAxwdHdG4cWP8/fffkCQJv/zyi67M9evX0b9/f7i7u6NGjRro1asXrl69apbfVQGLBqIHDx5AJtNvglwu16XM4OBg+Pj4YPv27br1aWlpOHToEMLCwgAAYWFhSElJwbFjx3RlduzYAY1Gg3bt2unK7N69G3l5j254uG3bNoSEhBQZLrMk9hAREVUdUVFAv376D7oHgJs3tcvNGYoyMzPx8ccf4+jRo9i+fTtkMhleffVVaDQahIeHY82aNXq3uFm7di38/PzQsWNHAMCIESNw4MABrFmzBqdOncJrr72G7t2769325sGDB5g1axb++9//4uzZs/Dy8kJ6ejoGDRqEvXv34uDBg6hfvz569OiB9PR0ANqepN69e8PZ2RmHDh3C0qVL8e9//1uv7Xl5eejWrRvc3NywZ88e7Nu3D66urujevTtyc3PN90sTFjRo0CBRu3Zt8fvvv4u4uDgRFRUlPD09xbhx43RlZs6cKdzd3cWvv/4qTp06JXr16iWCg4NFVlaWrkz37t1Fy5YtxaFDh8TevXtF/fr1xeuvv65bn5KSIry9vcVbb70lzpw5I9asWSOcnZ3Ft99+a1A7U1NTBQCRmppquoMvxvFbxwUmQ3jP8TbrfoiIqHRZWVni3Llzet81xsjPF8LfXwig+JckCREQoC1XGW7fvi0AiNOnT4vk5GRhZ2cndu/erVsfFhYmxo8fL4QQ4tq1a0Iul4ubN2/q1fH888+Lzz77TAghxLJlywQAERMTU+p+1Wq1cHNzE7/99psQQog///xT2NnZiYSEBF2Zbdu2CQBi48aNQgghfvrpJxESEiI0Go2uTE5OjnBychJbt24tso/SzpUx398WnVS9aNEiTJgwAR988AGSk5Ph5+eHd999FxMnTtSVGTduHDIzMzF8+HCkpKSgQ4cO2LJlCxwdHXVlVq5ciREjRuD555+HTCZD3759sXDhQt16lUqFv/76C5GRkWjdujU8PT0xceJEq7rkHnjUQ3T7wW2oNWrIZXILt4iIiMpjz56iPUOFCQFcv64t17mz6fd/6dIlTJw4EYcOHcKdO3d0Iy/x8fEIDQ1F165dsXLlSnTs2BFxcXE4cOAAvv32WwDA6dOnoVar0aBBA706c3JyULNmTd1ne3t7NGvWTK9MUlISPv/8c0RHRyM5ORlqtRoPHjxAfHw8ACA2NhYBAQF601WefPJJvTpOnjyJy5cvw83NTW95dna2btjPHCwaiNzc3DB//nzMnz+/xDKSJGHq1KmYOnVqiWVq1KiBVatWlbqvZs2aYc+ePeVtaqWo5VwLAKARGtzNugsvF68ytiAiImuUkGDacsZ6+eWXERgYiO+++w5+fn7QaDQIDQ3VDTmFh4dj5MiRWLRoEVatWoWmTZuiadOmAICMjAzI5XIcO3YMcrn+/5i7urrq3js5OUGSJL31gwYNwt27d7FgwQIEBgbCwcEBYWFhRg11ZWRkoHXr1li5cmWRdbVq1TK4HmNZNBCRPoVcgZpONXE36y6SMpIYiIiIbJSvr2nLGePu3buIjY3Fd999p5sTtHfvXr0yvXr1wvDhw7FlyxasWrUKERERunUtW7aEWq1GcnKybntD7du3D9988w169OgBQDs5+s6dO7r1ISEhuH79OpKSknS3vzly5IheHa1atcLatWvh5eVVaRc1AXy4q9XhxGoiItvXsSPg7w881oGiI0lAQIC2nKl5eHigZs2aWLp0KS5fvowdO3bg448/1ivj4uKC3r17Y8KECTh//jxef/113boGDRogPDwcERERiIqKQlxcHA4fPowZM2bgjz/+KHXf9evXx08//YTz58/j0KFDCA8Ph5OTk279Cy+8gHr16mHQoEE4deoU9u3bh88//xwAdL1N4eHh8PT0RK9evbBnzx7ExcUhOjoaI0eOxI3SxiEriIHIyvDmjEREtk8uBxYs0L5/PBQVfJ4/X1vO1GQyGdasWYNjx44hNDQUH330EebMmVOkXHh4OE6ePImOHTuiTp06euuWLVuGiIgIjBkzBiEhIejduzeOHDlSpNzjvv/+e9y/fx+tWrXCW2+9hZEjR+rdPFkul+OXX35BRkYG2rZti2HDhumuMiuYG+zs7Izdu3ejTp066NOnDxo1aoShQ4ciOzvbrD1GkhCFrrujYqWlpUGlUiE1NdXs3Xevb3gda86swdyuc/Fx2Mdlb0BERCaXnZ2NuLg4BAcH613EY6yoKGDUKP0J1gEB2jDUp0/F21kV7Nu3Dx06dMDly5dRr149o7cv7VwZ8/3NOURWhj1ERERVR58+QK9e2qvJEhK0c4Y6djRPz5Ct2LhxI1xdXVG/fn1cvnwZo0aNQvv27csVhkyJgcjK8AGvRERVi1xunkvrbVV6ejrGjx+P+Ph4eHp6okuXLpg7d66lm8VAZG04qZqIiKqyiIgIvavarAUnVVsZDpkRERFVPgYiK8MeIiIi68Hrjqyfqc4RA5GVKfzEe43QWLg1RETVk0KhAKB9gClZt4K7YD9+V21jcQ6RlSm4O3W+Jh/3s+6jpnPNMrYgIiJTk8vlcHd3R3JyMgDtvXEef0wFWZ5Go8Ht27fh7OwMO7uKRRoGIivjYOcAd0d3pGSnICkziYGIiMhCCh5AWhCKyDrJZDLUqVOnwoGVgcgKebt4awNRRhIa12ps6eYQEVVLkiTB19cXXl5eyMvLs3RzqAT29vaQySo+A4iByAp5u3oj9m4sJ1YTEVkBuVxe4fkpZP04qdoK8dJ7IiKiysVAZIV4t2oiIqLKxUBkhXT3ImIPERERUaVgILJC7CEiIiKqXAxEVoh3qyYiIqpcDERWiJOqiYiIKhcDkRUq3EPE5+gQERGZHwORFSroIcpV5yI1J9XCrSEiIqr6GIiskJPCCW72bgA4bEZERFQZGIisFCdWExERVR4GIivFidVERESVh4HISrGHiIiIqPIwEFkp9hARERFVHgYiK8W7VRMREVUeBiIrxSEzIiKiysNAZKU4ZEZERFR5GIisFHuIiIiIKg8DkZUq3EPEx3cQERGZFwORlSroIcrKz0JGboaFW0NERFS1MRBZKVd7VzgrnAFw2IyIiMjcGIisGCdWExERVQ4GIivGidVERESVg4HIirGHiIiIqHIwEFkx3q2aiIiocjAQWTHdkBl7iIiIiMyKgciKsYeIiIiocjAQWTFOqiYiIqocDERWjJOqiYiIKgcDkRVjDxEREVHlYCCyYgU9RBm5GXiQ98DCrSEiIqq6GIismNJBCQe5AwAOmxEREZkTA5EVkySJw2ZERESVgIHIynFiNRERkfkxEFk59hARERGZHwORlWMPERERkfkxEFk53q2aiIjI/BiIrFzBkFlyZrKFW0JERFR1MRBZOfYQERERmR8DkZXjE++JiIjMj4HIyrGHiIiIyPwYiKxcQQ9RSnYKcvJzLNwaIiKiqomByMp5OHrATmYHgBOriYiIzIWByMpJkgQvFy8AHDYjIiIyFwYiG8CbMxIREZkXA5EN4OM7iIiIzIuByAawh4iIiMi8GIhsAC+9JyIiMi8GIhvAITMiIiLzYiCyARwyIyIiMi+LB6KbN2/izTffRM2aNeHk5ISmTZvi6NGjuvVCCEycOBG+vr5wcnJCly5dcOnSJb067t27h/DwcCiVSri7u2Po0KHIyMjQK3Pq1Cl07NgRjo6OCAgIwOzZsyvl+EyBPURERETmZdFAdP/+fbRv3x4KhQJ//vknzp07h7lz58LDw0NXZvbs2Vi4cCGWLFmCQ4cOwcXFBd26dUN2drauTHh4OM6ePYtt27bh999/x+7duzF8+HDd+rS0NHTt2hWBgYE4duwY5syZg8mTJ2Pp0qWVerzlxR4iIiIiMxMWNH78eNGhQ4cS12s0GuHj4yPmzJmjW5aSkiIcHBzE6tWrhRBCnDt3TgAQR44c0ZX5888/hSRJ4ubNm0IIIb755hvh4eEhcnJy9PYdEhJiUDtTU1MFAJGammrU8ZlKUkaSwGQITIbIzc+1SBuIiIhsjTHf3xbtIdq0aRPatGmD1157DV5eXmjZsiW+++473fq4uDgkJiaiS5cuumUqlQrt2rXDgQMHAAAHDhyAu7s72rRpoyvTpUsXyGQyHDp0SFfmmWeegb29va5Mt27dEBsbi/v375v7MCusplNNyCTtqbr94LaFW0NERFT1WDQQ/fPPP1i8eDHq16+PrVu34v3338fIkSOxYsUKAEBiYiIAwNvbW287b29v3brExER4eXnprbezs0ONGjX0yhRXR+F9FJaTk4O0tDS9lyXJZXLUcq4FgMNmRERE5mDRQKTRaNCqVStMnz4dLVu2xPDhw/HOO+9gyZIllmwWZsyYAZVKpXsFBARYtD0AJ1YTERGZk0UDka+vLxo3bqy3rFGjRoiPjwcA+Pj4AACSkvRDQFJSkm6dj48PkpP1nwKfn5+Pe/fu6ZUpro7C+yjss88+Q2pqqu51/fr18h6iyXBiNRERkflYNBC1b98esbGxessuXryIwMBAAEBwcDB8fHywfft23fq0tDQcOnQIYWFhAICwsDCkpKTg2LFjujI7duyARqNBu3btdGV2796NvLw8XZlt27YhJCRE74q2Ag4ODlAqlXovS2MPERERkflYNBB99NFHOHjwIKZPn47Lly9j1apVWLp0KSIjIwEAkiRh9OjR+L//+z9s2rQJp0+fRkREBPz8/NC7d28A2h6l7t2745133sHhw4exb98+jBgxAgMHDoSfnx8A4I033oC9vT2GDh2Ks2fPYu3atViwYAE+/vhjSx260dhDREREZD52ltx527ZtsXHjRnz22WeYOnUqgoODMX/+fISHh+vKjBs3DpmZmRg+fDhSUlLQoUMHbNmyBY6OjroyK1euxIgRI/D8889DJpOhb9++WLhwoW69SqXCX3/9hcjISLRu3Rqenp6YOHGi3r2KrB2fZ0ZERGQ+khBCWLoR1i4tLQ0qlQqpqakWGz778eSPGPTLIHSp2wXb3tpmkTYQERHZEmO+vy3+6A4yDIfMiIiIzIeByEZwUjUREZH5MBDZiIIeojsP7kCtUVu4NURERFULA5GNqOVSCxIkaIQGdx7csXRziIiIqhQGIhthJ7NDTeeaADhsRkREZGoMRDaEE6uJiIjMg4HIhnBiNRERkXkwENkQ9hARERGZBwORDeHdqomIiMyDgciGcMiMiIjIPBiIbAiHzIiIiMyDgciGsIeIiIjIPBiIbAh7iIiIiMyDgciGFPQQJWcmQyM0Fm4NERFR1cFAZEO8XLwAAGqhxr2sexZuDRERUdXBQGRD7OX28HD0AMBhMyIiIlNiILIxnFhNRERkegxENoYTq4mIiEyPgcjGsIeIiIjI9BiIbAx7iIiIiEyPgcjG8HlmREREpsdAZGM4ZEZERGR6DEQ2hkNmREREpsdAZGPYQ0RERGR6DEQ2pqCHKDkzGUIIC7eGiIioamAgsjEFPUS56lykZKdYtjFERERVhJ0hhT7++GODK5w3b165G0Nlc7RzhNJBibScNCRlJsHDycPSTSIiIrJ5BgWiEydO6H0+fvw48vPzERISAgC4ePEi5HI5WrdubfoWUhHeLt7aQJSRhIaeDS3dHCIiIptnUCDauXOn7v28efPg5uaGFStWwMND2ztx//59vP322+jYsaN5Wkl6vF29ceneJU6sJiIiMhGj5xDNnTsXM2bM0IUhAPDw8MD//d//Ye7cuSZtHBWPl94TERGZltGBKC0tDbdv3y6y/Pbt20hPTzdJo6h0vFs1ERGRaRkdiF599VW8/fbbiIqKwo0bN3Djxg1s2LABQ4cORZ8+fczRRnqM7l5E7CEiIiIyCYPmEBW2ZMkSfPLJJ3jjjTeQl5enrcTODkOHDsWcOXNM3kAqSncvogfJFm4JERFR1WB0IHJ2dsY333yDOXPm4MqVKwCAevXqwcXFxeSNo+Kxh4iIiMi0jA5EBVxcXNCsWTNTtoUMxDlEREREpmV0IHr22WchSVKJ63fs2FGhBlHZCvcQCSFKPR9ERERUNqMDUYsWLfQ+5+XlISYmBmfOnMGgQYNM1S4qRUEPUVZ+FjJyM+Dm4GbhFhEREdk2owPRl19+WezyyZMnIyMjo8INorK52LvAReGCzLxMJGUmMRARERFVkMke7vrmm2/ihx9+MFV1VAZOrCYiIjIdkwWiAwcOwNHR0VTVURk4sZqIiMh0jB4ye/zmi0IIJCQk4OjRo5gwYYLJGkalYw8RERGR6RgdiJRKpd5VTTKZDCEhIZg6dSq6du1q0sZRydhDREREZDpGB6Lly5eboRlkLD7glYiIyHSMnkNUt25d3L17t8jylJQU1K1b1ySNorLphszYQ0RERFRhRgeiq1evQq1WF1mek5ODmzdvmqRRVDYOmREREZmOwUNmmzZt0r3funUrVCqV7rNarcb27dsRFBRk0sZRyTipmoiIyHQMDkS9e/cGAEiSVOSO1AqFAkFBQZg7d65JG0clYw8RERGR6RgciDQaDQAgODgYR44cgaenp9kaRWUr6CHKyM3Ag7wHcFY4W7hFREREtsvoOURxcXEMQ1bAzd4NjnbaG2Fy2IyIiKhiDOohWrhwIYYPHw5HR0csXLiw1LIjR440ScOodJIkwcvFC/Gp8UjKTEKwR7Clm0RERGSzDApEX375JcLDw+Ho6Fjiw10B7Zc0A1Hl8Xbx1gYi9hARERFViEGBKC4urtj3ZFm8FxEREZFpGD2HaOrUqXjw4EGR5VlZWZg6dapJGkWG4d2qiYiITMPoQDRlyhRkZGQUWf7gwQNMmTLFJI0iw/DSeyIiItMwOhAJIfQe7lrg5MmTqFGjhkkaRYbhkBkREZFpGHwfIg8PD0iSBEmS0KBBA71QpFarkZGRgffee88sjaTicciMiIjINAwORPPnz4cQAkOGDMGUKVP0Ht1hb2+PoKAghIWFmaWRVZVaDezZAyQkAL6+QMeOgFxu+PbsISIiIjINgwNRweM6goOD8fTTT0OhUJitUdVBVBQwahRw48ajZf7+wIIFQJ8+htXBHiIiIiLTMDgQFejUqRPUajXWr1+P8+fPAwAaN26MXr16wc7O6OqqpagooF8/QAj95TdvapevX29YKCroIUrNSUV2frbuztVERERkHEmIx7+WS3f27Fm88sorSExMREhICADg4sWLqFWrFn777TeEhoaapaGWlJaWBpVKhdTUVCiVygrVpVYDQUH6PUOFSZK2pyguruzhMyEEHP7PAXmaPFwbfQ11VHUq1DYiIqKqxJjvb6OvMhs2bBiaNGmCGzdu4Pjx4zh+/DiuX7+OZs2aYfjw4eVudHWxZ0/JYQjQ9hpdv64tV5aCx3cAHDYjIiKqCKPHuGJiYnD06FF4eHjolnl4eOA///kP2rZta9LGVUUJCaYt5+3qjZvpNzmxmoiIqAKM7iFq0KABkpKKfvkmJyfjiSeeMEmjqjJfX9OW48RqIiKiijM6EM2YMQMjR47E+vXrcePGDdy4cQPr16/H6NGjMWvWLKSlpeleVFTHjto5QsXc2xKAdnlAgLacIXjpPRERUcUZHYheeuklnDt3Dv3790dgYCACAwPRv39/nDlzBi+//DI8PDzg7u6uN6RmiJkzZ0KSJIwePVq3LDs7G5GRkahZsyZcXV3Rt2/fIr1T8fHx6NmzJ5ydneHl5YWxY8ciPz9fr0x0dDRatWoFBwcHPPHEE1i+fLmxh20ycrn20nqg5FA0f77h9yNiDxEREVHFGT2HaOfOnSWuO3XqFJo1a2Z0I44cOYJvv/22yLYfffQR/vjjD6xbtw4qlQojRoxAnz59sG/fPgDaO2T37NkTPj4+2L9/PxISEhAREQGFQoHp06cDAOLi4tCzZ0+89957WLlyJbZv345hw4bB19cX3bp1M7qtptCnj/bS+sfvQ1SrFrBkieH3IQL4PDMiIiKTEBWUlpYmvv32W9G2bVshk8mM3j49PV3Ur19fbNu2TXTq1EmMGjVKCCFESkqKUCgUYt26dbqy58+fFwDEgQMHhBBCbN68WchkMpGYmKgrs3jxYqFUKkVOTo4QQohx48aJJk2a6O1zwIABolu3bga3MTU1VQAQqampRh9fafLzhdi5U4jOnYUAhBg50vg6Vp5aKTAZovPyziZtGxERka0z5vvb6CGzArt378agQYPg6+uLL774As899xwOHjxodD2RkZHo2bMnunTporf82LFjyMvL01vesGFD1KlTBwcOHAAAHDhwAE2bNoW3t7euTLdu3ZCWloazZ8/qyjxed7du3XR1FCcnJ0dvLpS55kPJ5UDnzsCIEdrPW7caXweHzIiIiCrOqCGzxMRELF++HN9//z3S0tLQv39/5OTk4JdffkHjxo2N3vmaNWtw/PhxHDlypNh92dvbw93dXW+5t7c3EhMTdWUKh6GC9QXrSiuTlpaGrKwsODk5Fdn3jBkzMGXKFKOPp7y6dAHs7IDYWOCff4C6dQ3flpOqiYiIKs7gHqKXX34ZISEhOHXqFObPn49bt25h0aJF5d7x9evXMWrUKKxcuRKOjtb1yInPPvsMqamputf169fNuj+VCnj6ae37P/80btuCHqJ7WfeQp84zccuIiIiqB4MD0Z9//omhQ4diypQp6NmzJ+TGPJa9GMeOHUNycjJatWoFOzs72NnZYdeuXVi4cCHs7Ozg7e2N3NxcpKSk6G2XlJQEHx8fAICPj0+Rq84KPpdVRqlUFts7BAAODg5QKpV6L3N78UXtT2MDUU3nmpBL2nORnJls4lYRERFVDwYHor179yI9PR2tW7dGu3bt8NVXX+HOnTvl3vHzzz+P06dPIyYmRvdq06YNwsPDde8VCgW2b9+u2yY2Nhbx8fEICwsDAISFheH06dNITn4UBLZt2walUqkbwgsLC9Oro6BMQR3WoiAQ7dgBZGcbvp1MkqGWSy0AHDYjIiIqL4MD0VNPPYXvvvsOCQkJePfdd7FmzRr4+flBo9Fg27ZtSE9PN2rHbm5uCA0N1Xu5uLigZs2aCA0NhUqlwtChQ/Hxxx9j586dOHbsGN5++22EhYXhqaeeAgB07doVjRs3xltvvYWTJ09i69at+PzzzxEZGQkHBwcAwHvvvYd//vkH48aNw4ULF/DNN9/g559/xkcffWRUe82tWTPAzw/IygJ27zZuW06sJiIiqhijrzJzcXHBkCFDsHfvXpw+fRpjxozBzJkz4eXlhVdeecWkjfvyyy/x0ksvoW/fvnjmmWfg4+ODqKgo3Xq5XI7ff/8dcrkcYWFhePPNNxEREYGpU6fqygQHB+OPP/7Atm3b0Lx5c8ydOxf//e9/LXYPopJIEtC9u/a90fOIOLGaiIioQiQhhKhoJWq1Gr/99ht++OEHbNq0yRTtsippaWlQqVRITU0163yi9euB114DQkKACxcM3y5iYwR+OvUTZj4/E+M7jDdb+4iIiGyJMd/f5b4PUWFyuRy9e/eukmGoMnXpor03UWwsEBdn+Ha8WzUREVHFmCQQkWm4uwPt22vfGzNsxiEzIiKiimEgsjLlufyek6qJiIgqhoHIypTn8nv2EBEREVUMA5GVKbj8/sEDYM8ew7ZhDxEREVHFMBBZmcKX32/ebNg2BT1Edx7cQb4m30wtIyIiqroYiKyQsfOIPJ09IUGCgMCdB+W/ezgREVF1xUBkhYy9/N5OZgdPZ08AHDYjIiIqDwYiK+TuDjz9tPa9ob1EnFhNRERUfgxEVsrYYTNOrCYiIio/BiIrZezl9+whIiIiKj8GIivVvDng62v45ffsISIiIio/BiIrJUnGDZvxeWZERETlx0BkxYwKRA+HzJIzk83YIiIioqqJgciKFVx+f+ECcPVq6WXZQ0RERFR+DERWzJjL73WTqjmHiIiIyGgMRFbO0GGzgh6i5MxkaITGzK0iIiKqWhiIrFxBINq+vfTL771cvAAAaqHGvax7ldAyIiKiqoOByMoZevm9Qq5ADacaADhsRkREZCwGIisnSUD37tr3hg6bcWI1ERGRcRiIbIDB84g4sZqIiKhcGIhswAsvGHb5PXuIiIiIyoeByAa4uwNhYdr3pfUS8fEdRERE5cNAZCMMGTbjA16JiIjKh4HIRvToof25YweQk1N8GQ6ZERERlQ8DkY0ouPw+M7Pky+85qZqIiKh8GIhshCGX37OHiIiIqHwYiGxIWfOICj/xXghRSa0iIiKyfQxENqTg8vvz54u//L7g8R256lykZKdUatuIiIhsGQORDSnr8ntHO0eoHFQAOGxGRERkDAYiG2PosBknVhMRERmOgcjGFASiki6/58RqIiIi4zEQ2ZgWLQAfn5Ivv2cPERERkfEYiGxMWZffs4eIiIjIeAxENqi0eUR8nhkREZHxGIhsUOHL769d01/H55kREREZj4HIBnl4lHz5PYfMiIiIjMdAZKNKGjbjpGoiIiLjMRDZqIJAtH27/uX3hXuI+PgOIiIiwzAQ2ajCl9/v3ftoecHjO7Lzs5Gem26ZxhEREdkYBiIbVfjy+82bHy13sXeBi8IFAIfNiIiIDMVAZMPKnEfEidVEREQGYSCyYS+8AMhkRS+/572IiIiIjMNAZMNKuvyePURERETGYSCyccUNm7GHiIiIyDgMRDauuMvveXNGIiIi4zAQ2bgWLQBvb/3L7zlkRkREZBwGIhsnkxUdNuOQGRERkXEYiKqAIoGIPURERERGYSCqAgouvz93DoiPZw8RERGRsRiIqoDHL78v6CHKzMtEZm6mBVtGRERkGxiIqoiCYbPNmwE3ezc42jkC4LAZERGRIRiIqojCl9/n5kocNiMiIjICA1EV8fjl95xYTUREZDgGoipCJgO6d9e+//NPTqwmIiIyBgNRFVL48nverZqIiMhwDERVSOHL7x0zGwBgDxEREZEhGIiqkBo1gKee0r6/fbI1APYQERERGYKBqIrp0UP788qREAAMRERERIZgIKpiCuYRnT3kDeQrOGRGRERkAAaiKqbg8vusTDsgvgN7iIiIiAzAQFTFFL78HpdfRFpOGrLzsy3aJiIiImvHQFQFFQybSZe1E4o4bEZERFQ6iwaiGTNmoG3btnBzc4OXlxd69+6N2NhYvTLZ2dmIjIxEzZo14erqir59+yIpSf8LPj4+Hj179oSzszO8vLwwduxY5Ofn65WJjo5Gq1at4ODggCeeeALLly839+FZTMHl9yK5CZASwGEzIiKiMlg0EO3atQuRkZE4ePAgtm3bhry8PHTt2hWZmY+e0P7RRx/ht99+w7p167Br1y7cunULffr00a1Xq9Xo2bMncnNzsX//fqxYsQLLly/HxIkTdWXi4uLQs2dPPPvss4iJicHo0aMxbNgwbN26tVKPt7IUvvwel1/E1BXRmL8mBrl5aou2i4iIyGoJK5KcnCwAiF27dgkhhEhJSREKhUKsW7dOV+b8+fMCgDhw4IAQQojNmzcLmUwmEhMTdWUWL14slEqlyMnJEUIIMW7cONGkSRO9fQ0YMEB069bNoHalpqYKACI1NbVCx1eZOvSIF4AQCNko0Oo7AQghV90UYxccsHTTiIiIKoUx399WNYcoNTUVAFCjRg0AwLFjx5CXl4cuXbroyjRs2BB16tTBgQMHAAAHDhxA06ZN4e3trSvTrVs3pKWl4ezZs7oyhesoKFNQx+NycnKQlpam97Il4xYexN7NtbUf4p4HnG4DANSpPpgz6kmMW3jQgq0jIiKyPlYTiDQaDUaPHo327dsjNDQUAJCYmAh7e3u4u7vrlfX29kZiYqKuTOEwVLC+YF1pZdLS0pCVlVWkLTNmzIBKpdK9AgICTHKMlSE3T415E+toPyjSgVw3IM/14Vrt6Z43KYDDZ0RERIVYTSCKjIzEmTNnsGbNGks3BZ999hlSU1N1r+vXr1u6SQb7ZsNpqFP9AMgA1cN2pwQWKiGDOqU2vtlw2hLNIyIiskp2lm4AAIwYMQK///47du/eDX9/f91yHx8f5ObmIiUlRa+XKCkpCT4+Proyhw8f1quv4Cq0wmUevzItKSkJSqUSTk5ORdrj4OAABwcHkxxbZbty7cGjD/YPJ6ffeAqQZwNqx+LLERERVXMW7SESQmDEiBHYuHEjduzYgeDgYL31rVu3hkKhwPbt23XLYmNjER8fj7CwMABAWFgYTp8+jeTkZF2Zbdu2QalUonHjxroyhesoKFNQR1VSL9D50YeEFoDbTeCBF1D7cMnliIiIqjlJCCEstfMPPvgAq1atwq+//oqQkBDdcpVKpeu5ef/997F582YsX74cSqUSH374IQBg//79ALSX3bdo0QJ+fn6YPXs2EhMT8dZbb2HYsGGYPn06AO1l96GhoYiMjMSQIUOwY8cOjBw5En/88Qe6detWZjvT0tKgUqmQmpoKpVJp6l+DSeXmqeFcKwnqVB8AMiBwF3CtE6C6CqQGAJAgd0/Ag2Qf2CvkFm4tERGR+Rj1/W3uS95KA6DY17Jly3RlsrKyxAcffCA8PDyEs7OzePXVV0VCQoJePVevXhUvvviicHJyEp6enmLMmDEiLy9Pr8zOnTtFixYthL29vahbt67ePspia5fdj11wQABq7UuRLuB4T3sJvv9eAah56T0REVULxnx/W7SHyFbYUg9RgXELD2LexDraCdaB0cC1zkCtsxjzrzR8MbrqDRUSERE9zpjvb6u5yoxMa/bIp/Dgtje+XB2D8LfyAMUD4HYTeNW0zcniRERE5sQeIgPYYg/R45q9vAunf++EGk2O4e6Z1pZuDhERkdmxh4iK+OY/9QBZPu6dbY3/bTlv6eYQERFZFQaiaqJDM38EdTgEAPhsSoplG0NERGRlGIiqkTmTawEAbhx6EtuPXrNwa4iIiKwHA1E10u/ZBqjV4ggg5PhwAgMRERFRAQaiambCZ/YAgPN/P4lTV5LLKE1ERFQ9MBBVM5H9msG17mkg3xHvfn7O0s0hIiKyCgxE1YxMJuHDj7UPdj34SwtcT061cIuIiIgsj4GoGpr6XlvY+1wBst3x3pQTlm4OERGRxTEQVUN2chnC370FANjyv4ZIy8yxcIuIiIgsi4Gomlo4vh1kqgRo0nzw4czDlm4OERGRRTEQVVOuTvZ4KSIWALB6qT9y89QWbhEREZHlMBBVY4sntobkmIK85GD8+2v2EhERUfXFQFSN+Xm64em+MQCAxQuU0Gj4nF8iIqqeGIiquW+nNQHsspB5tQnmr4mxdHOIiIgsgoGommsSXAuh3bTDZdNnch4RERFVTwxEhEXTggFJjbun22D1Xxcs3RwiIqJKx0BE6NyyDgLbHwQAfDr1noVbQ0REVPkYiAgAMGuSJwAgfn87RJ+It3BriIiIKhcDEQEABnQJQc2mRwEhx4jPr1q6OURERJWKgYh0Pv+XHQDg7F9tcTbutoVbQ0REVHkYiEhnZP/mcAk+A+Q74d0JZy3dHCIiokrDQEQ6MpmED0ZnAAD2b2iBW3fSLdwiIiKiysFARHqmRz4Je69/ILLd8e6UY5ZuDhERUaVgICI9dnIZXn/3JgBg808hyMjKtXCLiIiIzI+BiIpYOP5JyJQJ0KT6YuSsQ5ZuDhERkdkxEFVlajUQHQ2sXq39qTbs0RxKFwd0fzMWALBycW3kqzXmayMREZEVYCCypHIGFoNERQFBQcCzzwJvvKH9GRSkXW6Abye3AhxTkZtcFxMWHzFdu4iIiKwQA5GlVDCwlFl3v37AjRv6y2/e1C43YB/+tZQIe/UEAGDRPBdoNKLi7SIiIrJSDESWYILAUkR2NpCUBJw7B7z/PiCKCTAFy0aPNqg3aun/NQHsspEZF4pFP580vk1EREQ2QhKiuG9OKiwtLQ0qlQqpqalQKpUVq0yt1vYEPR6GCqtVC/j6ayA9HUhNLfpKSSm6LNfIq8F27gQ6dy6zWJMeu3Huz2dQq/kRJMe0NW4fREREFmTM97ddJbWJCuzZU3oYAoDbt4H+/ctXv5MTkJVVdrmEBIOqWzQtEM9vUeP2ybZYv/Mi+j3boHztIiIismIMRJXNwCCCBg2AevUAlUr7cnd/9L7wq/ByNzdg927tfKSy+Poa1IznWgci4Kn9uH7gaYydfJuBiIiIqiQGospmYBDBt98aNKRVRMeOgL+/dj5SSaOhtWtryxloxiQPvNkduLq3HfaeuoEOzfyNbxcREZEV46TqylYQWCSp+PWSBAQEGBVY9MjlwIIFj+oqjrMzkJlpcJXh3RqhRugxQGOHD/59pXztIiIismIMRJWttMBS8Hn+fG258urTB1i/XtsTVJi3t3ZY7dIloGdPo0LRp+O0bTu9tS3OX71T/rYRERFZIQYiSygpsPj7a5f36WOafVy9qr2abNUq7c+bN7U3gFSpgL17gVdeMWwCNoAx4S3hHHgOyHPGuxPPVLx9REREVoSX3RvApJfdF6ZWa686S0jQzi3q2LFiPUOGOngQeOEFICMDePFFYONGwMGhzM3GfHkA8z4Og+R0H7duKOBTw9X8bSUiIionY76/GYgMYLZAZEm7dwPdu2t7iF59FVi7FlAoSt0kN08N19rxyLsdjN6jdmHj/E6V1FgiIiLjGfP9zSGz6uqZZ4Bff9X2DG3cCERElHn3anuFHAOHXwcA/LbiCWRkGXkzSCIiIivFQFSdvfCCds6SnR2wZg0wbBigKf3J9gs/fRIytySoU2qjy/ub8eGs/Zi/Jga5eSZ8MC0REVElYyCq7l56SRuG5HJg+XJgxIiS718EwN3VEYEdDgAADm0KxVefPoWPXm8B51pJGLfwYCU1moiIyLQYiAjo2xf48UftZf+LFwNjxpQYisYtPIi47Z0Ah1Tg/hOA31EAgDrVB3NGPclQRERENomBiLTeeAP473+177/8Evj88yJFcvPUmDexDpCrAnxOaBfmOz5cq/2nNG9SAIfPiIjI5jAQ0SNDhgBffaV9P3068J//6K3+ZsNpqFP9AMiA240AeQ6Q3AwIjAac7gKQQZ1SG99sOF3ZLSciIqoQBiLSFxkJfPGF9v3nnwPz5ulWXbn24FG5B96A/8PhsWudgVwXIGAv4HlevxwREZENYCCiosaMAaZOffT+m28AAPUCnfXLxbd/GILOAWpH4HoH4E4jLP3GBZEz9/GyfCIishm8MaMBquSNGcsiBPDvfwMzZmg/f/89ct8aBOdaSVCn+kA/Swug1lnAMRW42RbQ2AMAZK7J6NDnHL78d0O0auBT6YdARETVG2/MSBUnSdo5RKNHaz8PGwb7dWvx8dT4hwUK369IAm43Bq6HYdCk3Xj27WjIlInQZHhh94+d0bpRTdRpvx/fbDgFjYb5m4iIrA8DEZVMkrRziN57T9tjFBGB2bVvYuyCw5CrEvWKyt0TMHbBYSyf2AU7fuiM9KSaGDlnP5T1TwIaBa7vfxqR/ZrBNSgWgyfuwb00wx4qS0REVBk4ZGaAajlkVphGAwwdqr1xo0IBbNyI3OdfwI/TN+HmpQzUru+KiH+9AntH+2I3X/3XBUyecxsXo9sA+U4AAMn5Htr0PIV5nz+BDs38i2yTm6fGNxtO48q1B6gX6IwP+jaFvaISHnxLRERVBh/uamLVPhAB2uecvfmm9q7WCgWgUgF37jxa7+8PLFgA9OlTYhWXrt/D6Jmn8Neaesi/F/BwqQY+rY/go1EKfBLeEjKZhHELD2LexDoPL/HXkqtu4eOp8Zg98ikzHSAREVU1DEQmxkD0UF4e0LEjcOhQ0XWSpP25fn2poQjQ9v5M+e9RLPnaDvfOttYtt/e+gqCnTuLi5ueBPDfoj+hq5yyNXXCYoYiIiAzCQGRiDEQPqdVAYCBw82bx6yVJ21MUF6d9NpoBft93Bf+aeQOn/2oF5LppF9qnAb4ngNQAIKVuodIayN0T8CDZh8NnRERUJl5lRuaxZ0/JYQjQTry+fh3YutXgKl9qXw+nfuuE6zcEmr32K+AeB+QqgWudtGHI6xRQZ7f2bth+x6AWeZi/5kSFDyU3T435a2Lw4az9mL8mho8bISKq5uws3QCyIQkJhpXr2RPw8QHq19e+GjR49LNePcDJqcgm/rWUeKZ1LZxaFwj4HgMkNXCrjfbRII8ZP7g2Pv8kDjX8k+EfnIWGITK0aarEM6180OIJb8hkUqnNezRHqYVu2ScmnqPESeFERLaFgYgM5+treNnERO1rzx795ZIEBAQUDUr166N+bQUAGZDwcF6R23XA/RogywfUDkBWDe0wWr4z8pKDkZQcjKTjwDEAKwvqt0+Hk88N1Aq4j6B6uQhtpEC75h7o3Ko26nirMG7hQcwZ9WSR5qpTfTBnlA+AgxUOReYOXAxbRESmxzlEBuAcoofUaiAoSDtsVtw/m4I5RCdOAP/8A1y6BFy8qP1Z8D41tcTqhVyOOHUALqAhLiIEl1AfV1APN1Ebt+CH+3CHN67j5+PAwTP3cPxMBi7ECty86oz7N72Qf9cfECUHA5lrMjSuNwH7dEDSAHlO2nlLec5AriuQ4wq58m6F5ijpB65HI9ISNBCo+KTwyrgCj4GLiKoKTqo2MQaiQqKigH79tO8L/9Mx5CozIbSX6heEpMd/ZpV+s8ZsOEANOVxC6wKNGwN+fkDt2rqfGR61sOe2wJ4LGTh5LgtXLsmQGK9E+i1faNK9DTs+RSYkxzTYu2RC4ZIFJ9dsOLvlwlWphlKlgbs74OEug2cNO3jVtIePpyP8ajkjwMsNXh7OqOufgswHvihuep4EDZydb+Feim+5AkZJYcuUV+BVSuDKzjX4Hlblqp+BjogeYiAyMQaix0RFAaNGATduPFoWEADMn1/mJfcl0mi0D5H98EOcQAscxpPwQSKCEQc/3IIn7hpel0qlH5b8/JDo5oMZu7Lwe0wD3FY4I0Nyg8j2AHJUQI6bdiK3KcizAYc0wD4DUDwA5DmAPE877CdpAEmD+k3S0KS+Cko3GdxVcngoFajpbo8aKnt4eTihlocTfGq6wMvdRTcfKjdPXcJz5ApU/Ao8c/duAcDMoT9i4bJnkSACdMt8pesY+fZOfPp9RIXqBswf6Bi2iGwLA5GJMRAVQ63Wzg9KSNDOLerY0eBL7UsUHQ08+6y2esiwBx2RAF/4IgEdsRtyPPynOmkS4O4O3LqlHb67devR+8xMg3algYRkeCEDrgCAfMiRAWekSS6QXBXIdvbE/Xw3pGi0r/tqJVI1SqRq3JAqlEjVqJCmUSFVqJAm3JEhlBAmv2hTA9hnQuaYCUnxAGop52HAytUGLJlaG7Lw6E+4xVOpaNmwBtxc5VC62EHpageVmwLubvZQudqjhtIRNZSOqKlygtLZQS9w1XBPNFvvFqANQ//64c2HrdUPXAAwfcj/KhSKzN2DViV6z8xcP5G1YSAyMQaiSmLoHKWS7nMkBJCeXjQkPfypOXkKt65kwRtJUCDfpE3XQEI63JACd71XOtyQAVejf5o+XBXfanv5A9jJsmAvy4KdlA2FlA2FTPtTJuVrr/aTNBAyDYQk4FYjF0qVHSQFIFcIyOwlyO0BmYMEuYMMckcZZA4y2DnKYeckh0Ihg71Cgkymwf++zcIDodTO8xKyhy85oJEDQoKHJgXLojzh5uoIF0cFnBzt4GRvBxcnBZwdFXB2UJQYxszdg1YVes/MXb+thzlbrt+W227u+hmISvD1119jzpw5SExMRPPmzbFo0SI8+WTRK44ex0BUiSoyR6ks0dGIenYhXsPP8MQd+CEBjsjWVl/QS4F/ofNX/YFWrYyuPu/wUfQbHYDbqAUBGWTQwBUZcEcKVEiFB+7DH9fxwcvXIVe6ASkp2knmhX+mp0MAeABng4JTFpzwAM56r+KWFV6eBxvuEZDU2iFIeS4keT4kWT4kuzwIWT404mGAk6kBKf9hubxCyzSoUz8DT9RxgbMz4OYqg9JVBjcXOVRKBdwf9qTVUDnAw80Bnion1PJwhqPCDp41kmy696wy6rflMGfL9dty2yujfgaiYqxduxYRERFYsmQJ2rVrh/nz52PdunWIjY2Fl5dXqdsyEFUyc8xRAnQ9UBtutMVoLMANPPoDDEA8vsRo9A04atSdth+vf3XNIQhPXQYAer08BV88K93fxut3fii5frUaSEsrEpTyN2/B1KU+ACSokAoVUuGKjIf7kSAAOCELvZ6+A1nd4Ic7lYp95WhkSM+VkJZvh4w8O2Tk2+HSuWTE3KqDXCiQK7NDrqSAGjLIBCATgCQkSAJwcciDnb0jNPkS1PlyqNUyaNRyqNVyqDVyaDRy5EOBfNghDwrklfK+8CsX9kXe58LB+HNgBhLUkMsfQC7Pgp08C3J5FuSyHAhJo33JtD8d3PLh5CzT3sxEISDZCUh2gNxOQG4nIJMJyO0AuVxALn+4XA7IZRocPZiLPOEAQNLbM4T2hyMeoO8bTrC3l8POToKdXCrxp8JOpv2p0P6UhAb/GpGONFHzUe+cxg5Q22tvZ6G2g0/+HcSl1YGjs/G/86oQ5my1fltue2XUDzAQFatdu3Zo27YtvvrqKwCARqNBQEAAPvzwQ3z66aelbstAZAHmmKME6Hqg8oUMe9FBN0epA/bCTtJUrAfqYf0b+v6v5MC14c3y1f+wd6sf1gMoPmytRz/02TkS6NzZ6Opzt21Hva4NcBO1ix2uk6CBP27g8l+XYP/C8yVXJIT2mXc5OUB2tu614YtNmLn0GTgiW/dyRQbckA4l0nQ/e7Q4hZD6NYH0dGhS06BOz0Ru6gPkpucgPz0H+Wqp2PD0+PscOCALTsiECzLhggdw1vtZ0vvCy6wlkFUmOykbclkOFPIc2Ol+5kJhlwuFPBf28lwo7PJhb5ene92/nQsBARk0kElqyIUGolCuUyAXjZoqIH/8hqlS0a+ex4toNALnTuYhDwrohcVCX1v2yEVoKzvIZLKH/2tQaBeF3z++ewkQGg1OHs3Xq18UHI2k/Z8NOykXbdrbQ5LLtRvJJYiH/3MhZJK20ZJ2mZBJkAqtVwuBv/7IxQM4QwMJGsj0GwUBJ2ShRx9HyGTGDZNrNBpsjspBFhx1bX/0KxUABJyRhZf7OUJup61b9vCXIgkBqaDphX4fsoe/P0kCoNFg/f+ykAVnSELSVv7wxEoPd+GCDAx6zxkKhR1kMgEJEuQy7X+qJUmCXCZBJgEymXZfcpkEuVx6uL0asz7PQLpQaVsmJGiEAlfTn3m4Dw18pRuIe+BToeEzBqLH5ObmwtnZGevXr0fv3r11ywcNGoSUlBT8+uuveuVzcnKQk5Oj+5yWloaAgAAGoqrCXD1QherPH/kR9t4MfhS4/ONgt+DL8tdvC71bpcjNzkWQcxISRcmBq8z/+AmhDVjp6dpetEI/8//4E58v8YcGcl3QkkOt/ZKGBnKo4YZ0DOyUoO1B02hKf6nVyM0TSM+T48r5Ozib6IdcSYFcyQ65sEc+5JBDQCYAuRCQCQ3s5RrIhUzbewY51JAjH3a6nyW9f3xZHhS67Y15FWxvSLlc2CMbjsiBo9HnkshcvKQEJAv9GwB/N2E9hk3tV+46jQlE1eJO1Xfu3IFarYa3t/69aLy9vXHhwoUi5WfMmIEpU6ZUVvOosvXpA/TqZZ4eqIf12/Xqhc6mrF8uBxYsQN9+/dBLbCq+d2v++vLvQy7H6z/0gn3ffkUClz9uaAPX92+Wu357R3uMfHsn/vXDmw8nIhcNXB++HQ17x1K6xyVJ+9gXJyfgsWFuO1dXPLmk7B402eRJBveg2QOoCcBt23b0NaT3bMvD3jMhgPx8bXjLynrUU1bC+12r92DNplA4IQuOyIYTsmBXzKT/Hu0voMUzDQ1qe2Exuy9g8z797aSHPToS1BAQUMsE2jaPR+1GgXiQKyEzR0JWnoTsPBmydC85cvLlyFLbITvPDtlqO9y7m4eUbCXyoUCesNcGO1E00Crs8mFnryi2fQJSCcuB/Bw18tTyEssXvFfI1ZDbF/46K1Sm0P/yC10vkPZnfp4aeWo7vfWS0K4teEEAdjIN5HJtD5QkACEkbW+UeFhOiIfLCnppHvamaAp6VR7uURR/rJIkgBJ+DyXT7rPo0kfHKPCw50p6bNnjZXR9axI0hdZrhH55DWS6zxrIHpWX5A+XPVonhKQr/6jso88CEjRCprdMJbuDZLV+ILp5KcPI30sFiGrg5s2bAoDYv3+/3vKxY8eKJ598skj57OxskZqaqntdv35dABCpqamV1WSi4m3YIIS/vxDa/85rXwEB2uUmqj+vdh2xE53EKgwUO9FJ5PnXMVn9M4asEL5SvF7z/aRrYsaQFRWrOD9fCH9/sR6vCn/o1x+Aa2I9XtX+nvLzy1X3KlWEkKAWEtR6dRcsW+UeUb66hRA5WTnCV4ovUnfhffhJ10ROVo7V1f/dhHXF1vn467sJ68rVdtZfNdteGfUXSE1NNfj7u1oEopycHCGXy8XGjRv1lkdERIhXXnmlzO2N+YUSmV1+vhA7dwqxapX2Zzm/iC1Vf05WjvhuwjoxeeAy8d2EdeX+oi9iwwYhJEnkQa4f6CAXQpIqFuo2bCg9bFUwMM4YsqLUwFXRwGiu+m05zNl6/bbc9sqovwADUTGefPJJMWLECN1ntVotateuLWbMmFHmtgxERDbCnD1ottp7Zub6bTXMVYX6bbntlVG/EAxExVqzZo1wcHAQy5cvF+fOnRPDhw8X7u7uIjExscxtGYiIbIg5e7hstffMzPXbapirCvXbctsro35jvr+rxVVmBb766ivdjRlbtGiBhQsXol27dmVux8vuiYhKZ8t3M7b1+m257eaun5fdmxgDERERke0x5vu7Mh6YRERERGTVGIiIiIio2mMgIiIiomqPgYiIiIiqPQYiIiIiqvYYiIiIiKjaYyAiIiKiao+BiIiIiKo9BiIiIiKq9uws3QBbUHAz77S0NAu3hIiIiAxV8L1tyEM5GIgMkJ6eDgAICAiwcEuIiIjIWOnp6VCpVKWW4bPMDKDRaHDr1i24ublBkiRLN8es0tLSEBAQgOvXr1f557bxWKuu6nS8PNaqqzodr7mOVQiB9PR0+Pn5QSYrfZYQe4gMIJPJ4O/vb+lmVCqlUlnl/wAL8Firrup0vDzWqqs6Ha85jrWsnqECnFRNRERE1R4DEREREVV7DESkx8HBAZMmTYKDg4Olm2J2PNaqqzodL4+16qpOx2sNx8pJ1URERFTtsYeIiIiIqj0GIiIiIqr2GIiIiIio2mMgIiIiomqPgagamTFjBtq2bQs3Nzd4eXmhd+/eiI2NLXWb5cuXQ5IkvZejo2Mltbj8Jk+eXKTdDRs2LHWbdevWoWHDhnB0dETTpk2xefPmSmptxQQFBRU5VkmSEBkZWWx5Wzunu3fvxssvvww/Pz9IkoRffvlFb70QAhMnToSvry+cnJzQpUsXXLp0qcx6v/76awQFBcHR0RHt2rXD4cOHzXQEhivtWPPy8jB+/Hg0bdoULi4u8PPzQ0REBG7dulVqneX5W6gMZZ3XwYMHF2l39+7dy6zXGs8rUPbxFvc3LEkS5syZU2Kd1npuDfmuyc7ORmRkJGrWrAlXV1f07dsXSUlJpdZb3r91QzEQVSO7du1CZGQkDh48iG3btiEvLw9du3ZFZmZmqdsplUokJCToXteuXaukFldMkyZN9Nq9d+/eEsvu378fr7/+OoYOHYoTJ06gd+/e6N27N86cOVOJLS6fI0eO6B3ntm3bAACvvfZaidvY0jnNzMxE8+bN8fXXXxe7fvbs2Vi4cCGWLFmCQ4cOwcXFBd26dUN2dnaJda5duxYff/wxJk2ahOPHj6N58+bo1q0bkpOTzXUYBintWB88eIDjx49jwoQJOH78OKKiohAbG4tXXnmlzHqN+VuoLGWdVwDo3r27XrtXr15dap3Wel6Bso+38HEmJCTghx9+gCRJ6Nu3b6n1WuO5NeS75qOPPsJvv/2GdevWYdeuXbh16xb69OlTar3l+Vs3iqBqKzk5WQAQu3btKrHMsmXLhEqlqrxGmcikSZNE8+bNDS7fv39/0bNnT71l7dq1E++++66JW2Z+o0aNEvXq1RMajabY9bZ6ToUQAoDYuHGj7rNGoxE+Pj5izpw5umUpKSnCwcFBrF69usR6nnzySREZGan7rFarhZ+fn5gxY4ZZ2l0ejx9rcQ4fPiwAiGvXrpVYxti/BUso7lgHDRokevXqZVQ9tnBehTDs3Pbq1Us899xzpZaxhXMrRNHvmpSUFKFQKMS6det0Zc6fPy8AiAMHDhRbR3n/1o3BHqJqLDU1FQBQo0aNUstlZGQgMDAQAQEB6NWrF86ePVsZzauwS5cuwc/PD3Xr1kV4eDji4+NLLHvgwAF06dJFb1m3bt1w4MABczfTpHJzc/G///0PQ4YMKfVBxLZ6Th8XFxeHxMREvXOnUqnQrl27Es9dbm4ujh07preNTCZDly5dbO58p6amQpIkuLu7l1rOmL8FaxIdHQ0vLy+EhITg/fffx927d0ssW5XOa1JSEv744w8MHTq0zLK2cG4f/645duwY8vLy9M5Vw4YNUadOnRLPVXn+1o3FQFRNaTQajB49Gu3bt0doaGiJ5UJCQvDDDz/g119/xf/+9z9oNBo8/fTTuHHjRiW21njt2rXD8uXLsWXLFixevBhxcXHo2LEj0tPTiy2fmJgIb29vvWXe3t5ITEysjOaazC+//IKUlBQMHjy4xDK2ek6LU3B+jDl3d+7cgVqttvnznZ2djfHjx+P1118v9WGYxv4tWIvu3bvjxx9/xPbt2zFr1izs2rULL774ItRqdbHlq8p5BYAVK1bAzc2tzCEkWzi3xX3XJCYmwt7evkiQL+1cledv3Vh82n01FRkZiTNnzpQ53hwWFoawsDDd56effhqNGjXCt99+i2nTppm7meX24osv6t43a9YM7dq1Q2BgIH7++WeD/q/LVn3//fd48cUX4efnV2IZWz2n9EheXh769+8PIQQWL15callb/VsYOHCg7n3Tpk3RrFkz1KtXD9HR0Xj++ect2DLz++GHHxAeHl7mxQ62cG4N/a6xBuwhqoZGjBiB33//HTt37oS/v79R2yoUCrRs2RKXL182U+vMw93dHQ0aNCix3T4+PkWucEhKSoKPj09lNM8krl27hr///hvDhg0zajtbPacAdOfHmHPn6ekJuVxus+e7IAxdu3YN27ZtK7V3qDhl/S1Yq7p168LT07PEdtv6eS2wZ88exMbGGv13DFjfuS3pu8bHxwe5ublISUnRK1/auSrP37qxGIiqESEERowYgY0bN2LHjh0IDg42ug61Wo3Tp0/D19fXDC00n4yMDFy5cqXEdoeFhWH79u16y7Zt26bXk2Ltli1bBi8vL/Ts2dOo7Wz1nAJAcHAwfHx89M5dWloaDh06VOK5s7e3R+vWrfW20Wg02L59u9Wf74IwdOnSJfz999+oWbOm0XWU9bdgrW7cuIG7d++W2G5bPq+Fff/992jdujWaN29u9LbWcm7L+q5p3bo1FAqF3rmKjY1FfHx8ieeqPH/r5Wk4VRPvv/++UKlUIjo6WiQkJOheDx480JV56623xKeffqr7PGXKFLF161Zx5coVcezYMTFw4EDh6Ogozp49a4lDMNiYMWNEdHS0iIuLE/v27RNdunQRnp6eIjk5WQhR9Dj37dsn7OzsxBdffCHOnz8vJk2aJBQKhTh9+rSlDsEoarVa1KlTR4wfP77IOls/p+np6eLEiRPixIkTAoCYN2+eOHHihO7KqpkzZwp3d3fx66+/ilOnTolevXqJ4OBgkZWVpavjueeeE4sWLdJ9XrNmjXBwcBDLly8X586dE8OHDxfu7u4iMTGx0o+vsNKONTc3V7zyyivC399fxMTE6P0N5+Tk6Op4/FjL+luwlNKONT09XXzyySfiwIEDIi4uTvz999+iVatWon79+iI7O1tXh62cVyHK/ncshBCpqanC2dlZLF68uNg6bOXcGvJd895774k6deqIHTt2iKNHj4qwsDARFhamV09ISIiIiorSfTbkb70iGIiqEQDFvpYtW6Yr06lTJzFo0CDd59GjR4s6deoIe3t74e3tLXr06CGOHz9e+Y030oABA4Svr6+wt7cXtWvXFgMGDBCXL1/WrX/8OIUQ4ueffxYNGjQQ9vb2okmTJuKPP/6o5FaX39atWwUAERsbW2SdrZ/TnTt3FvvvtuCYNBqNmDBhgvD29hYODg7i+eefL/J7CAwMFJMmTdJbtmjRIt3v4cknnxQHDx6spCMqWWnHGhcXV+Lf8M6dO3V1PH6sZf0tWEppx/rgwQPRtWtXUatWLaFQKERgYKB45513igQbWzmvQpT971gIIb799lvh5OQkUlJSiq3DVs6tId81WVlZ4oMPPhAeHh7C2dlZvPrqqyIhIaFIPYW3MeRvvSKkhzslIiIiqrY4h4iIiIiqPQYiIiIiqvYYiIiIiKjaYyAiIiKiao+BiIiIiKo9BiIiIiKq9hiIiIiIqNpjICIis7h69SokSUJMTIylm6Jz4cIFPPXUU3B0dESLFi2KLdO5c2eMHj26Qvt5/Nijo6MhSVKRZzcZun1xjK3TUJIk4ZdffjFpnUS2gIGIqIoaPHgwJEnCzJkz9Zb/8ssvkCTJQq2yrEmTJsHFxQWxsbFFnl1XICoqCtOmTTPpfp9++mkkJCRApVIZVD4gIAAJCQkIDQ01aTuIqGQMRERVmKOjI2bNmoX79+9buikmk5ubW+5tr1y5gg4dOiAwMLDEB6PWqFEDbm5u5d5Hcezt7eHj42NwEJXL5fDx8YGdnZ1J20FEJWMgIqrCunTpAh8fH8yYMaPEMpMnTy4yfDR//nwEBQXpPg8ePBi9e/fG9OnT4e3tDXd3d0ydOhX5+fkYO3YsatSoAX9/fyxbtqxI/RcuXMDTTz8NR0dHhIaGYteuXXrrz5w5gxdffBGurq7w9vbGW2+9hTt37ujWd+7cGSNGjMDo0aPh6emJbt26FXscGo0GU6dOhb+/PxwcHNCiRQts2bJFt16SJBw7dgxTp06FJEmYPHlysfU8PmQWFBSE6dOnY8iQIXBzc0OdOnWwdOlSvW0OHz6Mli1bwtHREW3atMGJEyf01hce3kpLS4OTkxP+/PNPvTIbN26Em5sbHjx4UOyQ2ebNm9GgQQM4OTnh2WefxdWrV/W2N+Q8HjlyBC+88AI8PT2hUqnQqVMnHD9+vNjfA6ANnyNGjICvry8cHR0RGBhY6r8lIlvGQERUhcnlckyfPh2LFi3CjRs3KlTXjh07cOvWLezevRvz5s3DpEmT8NJLL8HDwwOHDh3Ce++9h3fffbfIfsaOHYsxY8bgxIkTCAsLw8svv4y7d+8CAFJSUvDcc8+hZcuWOHr0KLZs2YKkpCT0799fr44VK1bA3t4e+/btw5IlS4pt34IFCzB37lx88cUXOHXqFLp164ZXXnkFly5dAgAkJCSgSZMmGDNmDBISEvDJJ58YfOxz587VBZ0PPvgA77//PmJjYwEAGRkZeOmll9C4cWMcO3YMkydPLrVupVKJl156CatWrdJbvnLlSvTu3RvOzs5Ftrl+/Tr69OmDl19+GTExMRg2bBg+/fRTg9tfID09HYMGDcLevXtx8OBB1K9fHz169EB6enqx5RcuXIhNmzbh559/RmxsLFauXKkXsIiqFJM9JpaIrMqgQYNEr169hBBCPPXUU2LIkCFCCCE2btwoCv/pT5o0STRv3lxv2y+//FIEBgbq1RUYGCjUarVuWUhIiOjYsaPuc35+vnBxcRGrV68WQgjd09lnzpypK5OXlyf8/f3FrFmzhBBCTJs2TXTt2lVv39evXxcAdE+x7tSpk2jZsmWZx+vn5yf+85//6C1r27at+OCDD3SfmzdvXuTp6I/r1KmTGDVqlO5zYGCgePPNN3WfNRqN8PLyEosXLxZCaJ9QXrNmTZGVlaUrs3jxYgFAnDhxQgjx6Enn9+/fF0Joz4Grq6vIzMwUQgiRmpoqHB0dxZ9//imEePS7K9j+s88+E40bN9Zr5/jx4/XqNOQ8Pk6tVgs3Nzfx22+/6ZYBEBs3bhRCCPHhhx+K5557Tmg0mhLrIKoq2ENEVA3MmjULK1aswPnz58tdR5MmTSCTPfpPhre3N5o2bar7LJfLUbNmTSQnJ+ttFxYWpntvZ2eHNm3a6Npx8uRJ7Ny5E66urrpXw4YNAWjn+xRo3bp1qW1LS0vDrVu30L59e73l7du3r9AxF2jWrJnuvSRJ8PHx0R3n+fPn0axZMzg6OurKFD7m4vTo0QMKhQKbNm0CAGzYsAFKpRJdunQptvz58+fRrl07vWVl7aM4SUlJeOedd1C/fn2oVCoolUpkZGQgPj6+2PKDBw9GTEwMQkJCMHLkSPz1119G75PIVjAQEVUDzzzzDLp164bPPvusyDqZTAYhhN6yvLy8IuUUCoXeZ0mSil2m0WgMbldGRoZuGKjw69KlS3jmmWd05VxcXAyu0xwqepyPs7e3R79+/XTDZqtWrcKAAQMqNInakPM4aNAgxMTEYMGCBdi/fz9iYmJQs2bNEieqt2rVCnFxcZg2bRqysrLQv39/9OvXr9xtJLJmDERE1cTMmTPx22+/4cCBA3rLa9WqhcTERL0vU1PeO+jgwYO69/n5+Th27BgaNWoEQPuFe/bsWQQFBeGJJ57QexkTgpRKJfz8/LBv3z695fv27UPjxo1NcyAlaNSoEU6dOoXs7GzdssLHXJLw8HBs2bIFZ8+exY4dOxAeHl7qPg4fPqy37PF9GHIe9+3bh5EjR6JHjx5o0qQJHBwc9CawF0epVGLAgAH47rvvsHbtWmzYsAH37t0r8/iIbA0DEVE10bRpU4SHh2PhwoV6yzt37ozbt29j9uzZuHLlCr7++usiV0BVxNdff42NGzfiwoULiIyMxP379zFkyBAAQGRkJO7du4fXX38dR44cwZUrV7B161a8/fbbUKvVRu1n7NixmDVrFtauXYvY2Fh8+umniImJwahRo0x2LMV54403IEkS3nnnHZw7dw6bN2/GF198UeZ2zzzzDHx8fBAeHo7g4OAiQ2KFvffee7h06RLGjh2L2NhYrFq1CsuXL9crY8h5rF+/Pn766SecP38ehw4dQnh4OJycnErc77x587B69WpcuHABFy9exLp16+Dj4wN3d/cyj4/I1jAQEVUjU6dOLTLU06hRI3zzzTf4+uuv0bx5cxw+fNioK7DKMnPmTMycORPNmzfH3r17sWnTJnh6egKArldHrVaja9euaNq0KUaPHg13d3e9+UqGGDlyJD7++GOMGTMGTZs2xZYtW7Bp0ybUr1/fZMdSHFdXV/z22284ffo0WrZsiX//+9+YNWtWmdtJkoTXX38dJ0+eLLV3CADq1KmDDRs24JdffkHz5s2xZMkSTJ8+Xa+MIefx+++/x/3799GqVSu89dZbGDlyJLy8vErcr5ubG2bPno02bdqgbdu2uHr1KjZv3mz0uSGyBZJ4fNCZiIiIqJphzCciIqJqj4GIiIiIqj0GIiIiIqr2GIiIiIio2mMgIiIiomqPgYiIiIiqPQYiIiIiqvYYiIiIiKjaYyAiIiKiao+BiIiIiKo9BiIiIiKq9hiIiIiIqNr7f87xpKpInPuHAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ackley test"
      ],
      "metadata": {
        "id": "h4dZIm9HOsNC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best, bests, worsts, avgs = genetic_algo(500, 2, [-2.048, 2.048], 10, ackley, TwoCrossover, 3, 7, 0.23, 0.01, 0.001)\n",
        "conv_plot(bests, worsts, avgs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "gqNaUcD8OdFb",
        "outputId": "b4bfdeb6-61ca-4504-968c-d227b5b13cf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numer of iterations needed: 7\n",
            "\n",
            "Global optimum approximation (best individual) is: [[-2.048, 1.03, 0.98, -1.99, -1.01, 1.98, 1.97, 2.02, -0.03, -2.048], -18555.82129753589]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d4Yyq1w-O1wd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}