EDL_10_3_NEAT_Circles.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EDL_10_3_NEAT_Circles.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPgsprBfdqFiyACMgBYKq8n",
      "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/cxbxmxcx/EvolutionaryDeepLearning/blob/main/EDL_10_3_NEAT_Circles.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/CodeReclaimers/neat-python.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Z3Ayt-wXGOD",
        "outputId": "a301498f-aff2-4e25-9acf-fe7dcbdb70c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/CodeReclaimers/neat-python.git\n",
            "  Cloning https://github.com/CodeReclaimers/neat-python.git to /tmp/pip-req-build-6vzewv35\n",
            "  Running command git clone -q https://github.com/CodeReclaimers/neat-python.git /tmp/pip-req-build-6vzewv35\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gEpTXjwrHo-Q"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import sklearn\n",
        "import sklearn.datasets\n",
        "import sklearn.linear_model\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "import time\n",
        "import random\n",
        "\n",
        "import neat"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Dataset Parameters { run: \"auto\", vertical-output: true, display-mode: \"form\" }\n",
        "number_samples = 10 #@param {type:\"slider\", min:10, max:1000, step:5}\n",
        "difficulty = 1 #@param {type:\"slider\", min:1, max:5, step:1}\n",
        "problem = \"circles\" #@param [\"classification\", \"blobs\", \"gaussian quantiles\", \"moons\", \"circles\"]\n",
        "number_features = 2\n",
        "number_classes = 2 \n",
        "middle_layer = 5 #@param {type:\"slider\", min:5, max:25, step:1}\n",
        "\n",
        "def load_data(problem):  \n",
        "  if problem == \"classification\":\n",
        "    clusters = 1 if difficulty < 3 else 2\n",
        "    informs = 1 if difficulty < 4 else 2\n",
        "    data = sklearn.datasets.make_classification(\n",
        "        n_samples = number_samples,\n",
        "        n_features=number_features, \n",
        "        n_redundant=0, \n",
        "        class_sep=1/difficulty,\n",
        "        n_informative=informs, \n",
        "        n_clusters_per_class=clusters)\n",
        "    \n",
        "  if problem == \"blobs\":\n",
        "    data = sklearn.datasets.make_blobs(\n",
        "        n_samples = number_samples,\n",
        "        n_features=number_features, \n",
        "        centers=number_classes,\n",
        "        cluster_std = difficulty)\n",
        "  \n",
        "  if problem == \"gaussian quantiles\":\n",
        "    data = sklearn.datasets.make_gaussian_quantiles(mean=None, \n",
        "                                                  cov=difficulty,\n",
        "                                                  n_samples=number_samples,\n",
        "                                                  n_features=number_features,\n",
        "                                                  n_classes=number_classes,\n",
        "                                                  shuffle=True,\n",
        "                                                  random_state=None)\n",
        "    \n",
        "  if problem == \"moons\":\n",
        "    data = sklearn.datasets.make_moons(\n",
        "        n_samples = number_samples)\n",
        "    \n",
        "  if problem == \"circles\":\n",
        "    data = sklearn.datasets.make_circles(\n",
        "        n_samples = number_samples)\n",
        "    \n",
        "  return data\n",
        "\n",
        "data = load_data(problem)\n",
        "X, Y = data\n",
        "\n",
        "\n",
        "# Input Data\n",
        "plt.figure(\"Input Data\")\n",
        "plt.scatter(X[:, 0], X[:, 1], c=Y, s=40, cmap=plt.cm.Spectral)"
      ],
      "metadata": {
        "id": "6S1csdi_Xdhb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "3fe05fd0-3af6-4f4a-af51-b892e29656f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f84601d63d0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile config\n",
        "[NEAT]\n",
        "fitness_criterion     = max\n",
        "fitness_threshold     = 3.0\n",
        "pop_size              = 250\n",
        "reset_on_extinction   = 1\n",
        "\n",
        "[DefaultGenome]\n",
        "num_inputs              = 2\n",
        "num_hidden              = 10\n",
        "num_outputs             = 1\n",
        "initial_connection      = partial_direct 0.5\n",
        "feed_forward            = True\n",
        "compatibility_disjoint_coefficient    = 1.0\n",
        "compatibility_weight_coefficient      = 0.6\n",
        "conn_add_prob           = 0.2\n",
        "conn_delete_prob        = 0.2\n",
        "node_add_prob           = 0.2\n",
        "node_delete_prob        = 0.2\n",
        "activation_default      = sigmoid\n",
        "activation_options      = sigmoid identity relu\n",
        "activation_mutate_rate  = 0.1\n",
        "aggregation_default     = sum\n",
        "aggregation_options     = sum\n",
        "aggregation_mutate_rate = 0.0\n",
        "bias_init_mean          = 0.0\n",
        "bias_init_stdev         = 1.0\n",
        "bias_replace_rate       = 0.1\n",
        "bias_mutate_rate        = 0.7\n",
        "bias_mutate_power       = 0.5\n",
        "bias_max_value          = 30.0\n",
        "bias_min_value          = -30.0\n",
        "response_init_mean      = 1.0\n",
        "response_init_stdev     = 0.0\n",
        "response_replace_rate   = 0.0\n",
        "response_mutate_rate    = 0.0\n",
        "response_mutate_power   = 0.0\n",
        "response_max_value      = 30.0\n",
        "response_min_value      = -30.0\n",
        "\n",
        "weight_max_value        = 30\n",
        "weight_min_value        = -30\n",
        "weight_init_mean        = 0.0\n",
        "weight_init_stdev       = 1.0\n",
        "weight_mutate_rate      = 0.8\n",
        "weight_replace_rate     = 0.1\n",
        "weight_mutate_power     = 0.5\n",
        "enabled_default         = True\n",
        "enabled_mutate_rate     = 0.01\n",
        "\n",
        "[DefaultSpeciesSet]\n",
        "compatibility_threshold = 3.0\n",
        "\n",
        "[DefaultStagnation]\n",
        "species_fitness_func = max\n",
        "max_stagnation  = 20\n",
        "\n",
        "[DefaultReproduction]\n",
        "elitism            = 2\n",
        "survival_threshold = 0.2"
      ],
      "metadata": {
        "id": "hd9WgGFyX5aZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecb40ab8-1b9b-4c21-b781-6537e9eaa930"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting config\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load configuration.\n",
        "config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\n",
        "                     neat.DefaultSpeciesSet, neat.DefaultStagnation,\n",
        "                     'config')\n",
        "\n",
        "print(config.genome_type, config.genome_config,config.pop_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWw1ctXr5FPM",
        "outputId": "38124778-97bd-42f0-ce19-f522dfa3deba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'neat.genome.DefaultGenome'> <neat.genome.DefaultGenomeConfig object at 0x7f846013e610> 250\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "key = \"fred\"\n",
        "genome = config.genome_type(key)\n",
        "genome.configure_new(config.genome_config)\n",
        "\n",
        "net = neat.nn.FeedForwardNetwork.create(genome, config)"
      ],
      "metadata": {
        "id": "Pr22TQRl7nTc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fitness = 4\n",
        "for x, y in zip(X,Y):  \n",
        "  output = net.activate(x)\n",
        "  fitness -= (output-y)**2  \n",
        "\n",
        "print(fitness)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZshMKDF85fm",
        "outputId": "b0e2e86a-e25d-4de7-bb95-077bbe636483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.19427527]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Draw Network from Genome\n",
        "import graphviz\n",
        "\n",
        "def draw_net(config, genome, view=False, filename=None, node_names=None, show_disabled=True, prune_unused=False,\n",
        "             node_colors=None, fmt='svg'):\n",
        "    \"\"\" Receives a genome and draws a neural network with arbitrary topology. \"\"\"\n",
        "    # Attributes for network nodes.\n",
        "    if graphviz is None:\n",
        "        print(\"This display is not available due to a missing optional dependency (graphviz)\")\n",
        "        return\n",
        "\n",
        "    # If requested, use a copy of the genome which omits all components that won't affect the output.\n",
        "    if prune_unused:\n",
        "        genome = genome.get_pruned_copy(config.genome_config)\n",
        "\n",
        "    if node_names is None:\n",
        "        node_names = {}\n",
        "\n",
        "    assert type(node_names) is dict\n",
        "\n",
        "    if node_colors is None:\n",
        "        node_colors = {}\n",
        "\n",
        "    assert type(node_colors) is dict\n",
        "\n",
        "    node_attrs = {\n",
        "        'shape': 'circle',\n",
        "        'fontsize': '9',\n",
        "        'height': '0.2',\n",
        "        'width': '0.2'}\n",
        "\n",
        "    dot = graphviz.Digraph(format=fmt, node_attr=node_attrs)\n",
        "\n",
        "    inputs = set()\n",
        "    for k in config.genome_config.input_keys:\n",
        "        inputs.add(k)\n",
        "        name = node_names.get(k, str(k))\n",
        "        input_attrs = {'style': 'filled', 'shape': 'box', 'fillcolor': node_colors.get(k, 'lightgray')}\n",
        "        dot.node(name, _attributes=input_attrs)\n",
        "\n",
        "    outputs = set()\n",
        "    for k in config.genome_config.output_keys:\n",
        "        outputs.add(k)\n",
        "        name = node_names.get(k, str(k))\n",
        "        node_attrs = {'style': 'filled', 'fillcolor': node_colors.get(k, 'lightblue')}\n",
        "\n",
        "        dot.node(name, _attributes=node_attrs)\n",
        "\n",
        "    used_nodes = set(genome.nodes.keys())\n",
        "    for n in used_nodes:\n",
        "        if n in inputs or n in outputs:\n",
        "            continue\n",
        "\n",
        "        attrs = {'style': 'filled',\n",
        "                 'fillcolor': node_colors.get(n, 'white')}\n",
        "        dot.node(str(n), _attributes=attrs)\n",
        "\n",
        "    for cg in genome.connections.values():\n",
        "        if cg.enabled or show_disabled:\n",
        "            # if cg.input not in used_nodes or cg.output not in used_nodes:\n",
        "            #    continue\n",
        "            input, output = cg.key\n",
        "            a = node_names.get(input, str(input))\n",
        "            b = node_names.get(output, str(output))\n",
        "            style = 'solid' if cg.enabled else 'dotted'\n",
        "            color = 'green' if cg.weight > 0 else 'red'\n",
        "            width = str(0.1 + abs(cg.weight / 5.0))\n",
        "            dot.edge(a, b, _attributes={'style': style, 'color': color, 'penwidth': width})\n",
        "\n",
        "    dot.render(filename, view=view)\n",
        "    dot.view()\n",
        "\n",
        "    return dot\n",
        "\n",
        "node_names = {-1: 'X1', -2: 'X2', 0: 'Output'}\n",
        "draw_net(config, genome, True, node_names=node_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "cellView": "form",
        "id": "SdbsJ1XobEDx",
        "outputId": "e83aa4a4-b4ae-4549-a65b-0b0c4647f1ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_genomes(genomes, config):\n",
        "  for genome_id, genome in genomes:\n",
        "    genome.fitness = 4.0    \n",
        "    net = neat.nn.FeedForwardNetwork.create(genome, config)\n",
        "    for x, y in zip(X,Y):               \n",
        "      output = net.activate(x)[0]           \n",
        "      genome.fitness -= (output-y)**2      "
      ],
      "metadata": {
        "id": "xP2r1cae_NrH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Helper function to show prediction results\n",
        "def show_predictions(net, X, Y, name=\"\"):\n",
        "    \"\"\" display the labeled data X and a surface of prediction of model \"\"\"\n",
        "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01))\n",
        "    X_temp = np.c_[xx.flatten(), yy.flatten()]\n",
        "    Z = []\n",
        "    print(X_temp.shape)\n",
        "    for x in X_temp:\n",
        "      Z.append(net.activate(x))\n",
        "    Z = np.array(Z)\n",
        "    plt.figure(\"Predictions \" + name)\n",
        "    plt.contourf(xx, yy, Z.reshape(xx.shape), cmap=plt.cm.Spectral)\n",
        "    plt.ylabel('x2')\n",
        "    plt.xlabel('x1')\n",
        "    plt.scatter(X[:, 0], X[:, 1],c=Y, s=40, cmap=plt.cm.Spectral)\n",
        "    plt.show()\n",
        "\n",
        "show_predictions(net, X, Y)"
      ],
      "metadata": {
        "id": "EZdg6UAiDJxb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "bdcbcb10-c7cf-4fa8-da33-51c5f5e1da1e",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(148971, 2)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Reporter Class\n",
        "from neat.math_util import mean, stdev\n",
        "\n",
        "class CustomReporter(neat.reporting.BaseReporter):\n",
        "  \"\"\"Uses `print` to output information about the run; an example reporter class.\"\"\"\n",
        "\n",
        "  def __init__(self, show_species_detail, gen_display=100):\n",
        "      self.show_species_detail = show_species_detail\n",
        "      self.generation = None\n",
        "      self.generation_start_time = None\n",
        "      self.generation_times = []\n",
        "      self.num_extinctions = 0\n",
        "      self.gen_display = gen_display\n",
        "\n",
        "  def start_generation(self, generation):\n",
        "      clear_output()\n",
        "      self.generation = generation\n",
        "      print('\\n ****** Running generation {0} ****** \\n'.format(generation))\n",
        "      self.generation_start_time = time.time()\n",
        "\n",
        "  def end_generation(self, config, population, species_set):\n",
        "      ng = len(population)\n",
        "      ns = len(species_set.species)      \n",
        "      if self.show_species_detail:\n",
        "          print('Population of {0:d} members in {1:d} species:'.format(ng, ns))\n",
        "          print(\"   ID   age  size   fitness   adj fit  stag\")\n",
        "          print(\"  ====  ===  ====  =========  =======  ====\")\n",
        "          for sid in sorted(species_set.species):\n",
        "              s = species_set.species[sid]\n",
        "              a = self.generation - s.created\n",
        "              n = len(s.members)\n",
        "              f = \"--\" if s.fitness is None else f\"{s.fitness:.3f}\"\n",
        "              af = \"--\" if s.adjusted_fitness is None else f\"{s.adjusted_fitness:.3f}\"\n",
        "              st = self.generation - s.last_improved\n",
        "              print(f\"  {sid:>4}  {a:>3}  {n:>4}  {f:>9}  {af:>7}  {st:>4}\")\n",
        "      else:\n",
        "          print('Population of {0:d} members in {1:d} species'.format(ng, ns))\n",
        "\n",
        "      elapsed = time.time() - self.generation_start_time\n",
        "      self.generation_times.append(elapsed)\n",
        "      self.generation_times = self.generation_times[-10:]\n",
        "      average = sum(self.generation_times) / len(self.generation_times)\n",
        "      print('Total extinctions: {0:d}'.format(self.num_extinctions))\n",
        "      if len(self.generation_times) > 1:\n",
        "          print(\"Generation time: {0:.3f} sec ({1:.3f} average)\".format(elapsed, average))\n",
        "      else:\n",
        "          print(\"Generation time: {0:.3f} sec\".format(elapsed))\n",
        "\n",
        "  def post_evaluate(self, config, population, species, best_genome):\n",
        "      # pylint: disable=no-self-use\n",
        "      fitnesses = [c.fitness for c in population.values()]\n",
        "      fit_mean = mean(fitnesses)\n",
        "      fit_std = stdev(fitnesses)\n",
        "      best_species_id = species.get_species_id(best_genome.key)\n",
        "      print('Population\\'s average fitness: {0:3.5f} stdev: {1:3.5f}'.format(fit_mean, fit_std))\n",
        "      print(\n",
        "          'Best fitness: {0:3.5f} - size: {1!r} - species {2} - id {3}'.format(best_genome.fitness,\n",
        "                                                                                best_genome.size(),\n",
        "                                                                                best_species_id,\n",
        "                                                                                best_genome.key))\n",
        "      if (self.generation) % self.gen_display == 0 :        \n",
        "        net = neat.nn.FeedForwardNetwork.create(best_genome, config)      \n",
        "        show_predictions(net, X, Y)     \n",
        "        time.sleep(5) \n",
        "      \n",
        "  def complete_extinction(self):\n",
        "      self.num_extinctions += 1\n",
        "      print('All species extinct.')\n",
        "\n",
        "  def found_solution(self, config, generation, best):\n",
        "      print('\\nBest individual in generation {0} meets fitness threshold - complexity: {1!r}'.format(\n",
        "          self.generation, best.size()))\n",
        "\n",
        "  def species_stagnant(self, sid, species):\n",
        "      if self.show_species_detail:\n",
        "          print(\"\\nSpecies {0} with {1} members is stagnated: removing it\".format(sid, len(species.members)))\n",
        "\n",
        "  def info(self, msg):\n",
        "      print(msg)"
      ],
      "metadata": {
        "id": "03p8sG4bRYbJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Evolving the Network\n",
        "DISPLAY_ON_GENERATION = 100 #@param {type:\"slider\", min:1, max:100, step:1}\n",
        "# Create the population, which is the top-level object for a NEAT run.\n",
        "p = neat.Population(config)\n",
        "\n",
        "# Add a stdout reporter to show progress in the terminal.\n",
        "p.add_reporter(CustomReporter(False, gen_display=DISPLAY_ON_GENERATION))\n",
        "\n",
        "# Run until a solution is found.\n",
        "winner = p.run(eval_genomes)\n",
        "\n",
        "# Display the winning genome.\n",
        "print('\\nBest genome:\\n{!s}'.format(winner))\n",
        "\n",
        "# Show output of the most fit genome against training data.\n",
        "print('\\nOutput:')\n",
        "winner_net = neat.nn.FeedForwardNetwork.create(winner, config)\n",
        "show_predictions(winner_net, X, Y)\n",
        "draw_net(config, winner, True, node_names=node_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "pYYZ4cXVX1Gu",
        "outputId": "356e406a-f169-49b0-d6bc-65c203c97ee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " ****** Running generation 1100 ****** \n",
            "\n",
            "Population's average fitness: -5.40378 stdev: 44.62928\n",
            "Best fitness: 1.77546 - size: (9, 9) - species 487 - id 246332\n",
            "(148971, 2)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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jgfe/j6z4FpLOdV4UUJMaqdW47/YPsuy5vSzdu5eB+fPZddmljNT8ozlpNrAoNDUR7F+zmv1rVpedRFKHud9A6mLRSHoHhr3arkrlFoXUpRYeHGDRgRPNqygm1Puq7F+zkJHaqScWSO3kFoXUhea9NMiiAyea9/BoNO/j0XtyhBW/eQnSK9SosywKqQv1H2je8Gm05uXZG8w5MVxKJs1eFoXUhXqGJzgmkdBT93iFOsuikLpQvXfi4xBDfR6jUGdZFFIXOrxs3ik3fWrQPKA9NMdzUNRZ5+Q77sIVdf7Tv3q+7BjSWXlqS/D391Q5eaw5vu7a5O0fqtM3z/uNa/q98XMTzzsni2Le4CDXPrOj7BjSWbn2fLj1E3D0RJW+3gZ9tYQXyk6l2eicLIrGoRMMfP2XZceQpkWN5m6ngbKDaNY6J4vixJHgke+ck9+aJHWcv02lGWbwZIMjh4YZGYEFC6ssOK9CeA9ytZFFIc0gLx6oc2Df8Ct/nH306Ai9B4LfuqSPSsWyUHt4eqw0Q9TrjVeVBEA2YGgwOXTQv9ZW+1gU0gxx7KXGuNMz4chh/1pb7VNKUUTErRGxPSIaEbG+YLmbI+LJiNgREXd2MqPUbTJhwssBep1AtVFZWxTbgPcBD060QERUgS8A7wKuAm6PiKs6E0/qPgsWVhjvKEQELOx354Dap5R3V2Y+kZlPTrLY9cCOzNyZmUPAPcAt7U8ndafevgqLl1QZfYJTBPTUgiVLvS2t2qebz3paA+waNb4beNNEC0fEBmADwIra3PYmk0qybGUv8xaMcPjQMI0RmL+wwqJFPVSqnvGk9mlbUUTE/cDKcWZ9KjO/Od3ry8yNwEaAK+Yuco+tzlnzF1SZv8AryKpz2lYUmfmOs3yKPcCFo8YvaE2TJHVQNx8BexhYFxGXREQvcBuwqeRMkjTrlHV67HsjYjdwI/DtiLivNX11RGwGyMxh4JPAfcATwP/OzO1l5JWk2ayUg9mZeS9w7zjTnwPePWp8M7C5g9EkSWN0864nSVIXsCgkSYUsCklSIYtCklTIopAkFbIoJEmFLApJUiGLQpJUyKKQJBWyKCRJhSwKSVIhi0KSVMiikCQVsigkSYUsCklSIYtCklTIopAkFbIoJEmFyrpn9q0RsT0iGhGxvmC5ZyPisYh4JCK2dDKjJKmplHtmA9uA9wF/NYVl35qZB9qcR5I0gVKKIjOfAIiIMlYvSToN3X6MIoHvRcQvImJD2WEkaTZq2xZFRNwPrBxn1qcy85tTfJrfycw9EbEc+H5E/CozH5xgfRuADQAranPPKLMk6VRtK4rMfMc0PMee1td9EXEvcD0wblFk5kZgI8AVcxfl2a5bktTUtbueImJ+RCx8eRj4fZoHwSVJHVTW6bHvjYjdwI3AtyPivtb01RGxubXYCuDHEbEV+Dnw7cz8bhl5JWk2K+usp3uBe8eZ/hzw7tbwTuB1HY4mSRqja3c9SZK6g0UhSSpkUUiSClkUkqRCFoUkqZBFIUkqZFFIkgpZFJKkQhaFJKmQRSFJKmRRSJIKWRSSpEIWhSSpkEUhSSpkUUiSClkUkqRCFoUkqZBFIUkqZFFIkgpZFJKkQpGZZWeYdhGxH/h1h1a3FDjQoXVNB/O2l3nby7ztc1FmLhtvxjlZFJ0UEVsyc33ZOabKvO1l3vYybznc9SRJKmRRSJIKWRRnb2PZAU6TedvLvO1l3hJ4jEKSVMgtCklSIYtCklTIojhNEXFrRGyPiEZETHjaW0Q8GxGPRcQjEbGlkxnH5Jhq3psj4smI2BERd3Yy45gcSyLi+xHxVOvr4gmWG2m9to9ExKYScha+XhHRFxFfa83/WURc3OmMY/JMlvcjEbF/1Gv6sTJytrJ8MSL2RcS2CeZHRPxF63t5NCLe0OmMY/JMlvemiDgy6rX9dKcznrXM9HEaD+BK4HLgR8D6guWeBZbOhLxAFXgaWAv0AluBq0rK+2fAna3hO4HPTbDcsRJf00lfL+ATwF+2hm8DvtbleT8CfL6sjGOy/B7wBmDbBPPfDXwHCOAG4Gddnvcm4Ftlv65n83CL4jRl5hOZ+WTZOaZqinmvB3Zk5s7MHALuAW5pf7px3QLc3Rq+G/jDknIUmcrrNfr7+Abw9oiIDmYcrZv+fyeVmQ8CLxYscgvw5Wx6CFgUEas6k+5UU8g741kU7ZPA9yLiFxGxoewwk1gD7Bo1vrs1rQwrMnNva/h5YMUEy82JiC0R8VBEdLpMpvJ6vbJMZg4DR4DzO5LuVFP9/31/a1fONyLiws5EOyPd9H6dqhsjYmtEfCciri47zOnqKTtAN4qI+4GV48z6VGZ+c4pP8zuZuScilgPfj4hftT55TLtpytsxRXlHj2RmRsRE529f1Hp91wI/iIjHMvPp6c46i/wd8NXMHIyIf0Fza+htJWc6V/yS5vv1WES8G/hbYF3JmU6LRTGOzHzHNDzHntbXfRFxL83N/7YUxTTk3QOM/gR5QWtaWxTljYgXImJVZu5t7U7YN8FzvPz67oyIHwHX0twP3wlTeb1eXmZ3RPQA/cDBzsQ7xaR5M3N0trtoHivqVh19v56tzHxp1PDmiPivEbE0M2fKxQLd9dQOETE/Iha+PAz8PjDuGRFd4mFgXURcEhG9NA++dvxMopZNwB2t4TuAU7aIImJxRPS1hpcCbwEe71jCqb1eo7+PDwA/yNaRzRJMmnfMPv73AE90MN/p2gR8uHX20w3AkVG7K7tORKx8+fhURFxP8/duWR8azkzZR9Nn2gN4L819ooPAC8B9remrgc2t4bU0zyzZCmynuQuoa/O2xt8N/D+an8rLzHs+8ADwFHA/sKQ1fT1wV2v4zcBjrdf3MeCjJeQ85fUCPgO8pzU8B/g6sAP4ObC25PftZHn/Q+u9uhX4IXBFiVm/CuwF6q337keBjwMfb80P4Aut7+UxCs4+7JK8nxz12j4EvLnMvGfy8BIekqRC7nqSJBWyKCRJhSwKSVIhi0KSVMiikCQVsiikDoqI70bE4Yj4VtlZpKmyKKTO+o/Ah8oOIZ0Oi0Jqg4i4rnWBvTmtv9TfHhG/nZkPAEfLziedDq/1JLVBZj7cuqHSvwfmAv8zM7v5Mi7ShCwKqX0+Q/M6SyeBPyk5i3TG3PUktc/5wAJgIc1rP0kzkkUhtc9fAf8G+GvgcyVnkc6Yu56kNoiIDwP1zPxfEVEFfhIRbwP+HXAFsCAidtO88u19ZWaVJuPVYyVJhdz1JEkqZFFIkgpZFJKkQhaFJKmQRSFJKmRRSJIKWRSSpEL/HyEQZUT+e+3zAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-ce34c9bb52a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Run until a solution is found.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mwinner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_genomes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Display the winning genome.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/neat/population.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fitness_function, n)\u001b[0m\n\u001b[1;32m     95\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbest\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitness\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mbest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m                     \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreporters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;31m# Track the best genome ever seen.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/neat/reporting.py\u001b[0m in \u001b[0;36mpost_evaluate\u001b[0;34m(self, config, population, species, best_genome)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpost_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspecies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest_genome\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreporters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspecies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest_genome\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpost_reproduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspecies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-12-7ec8df56df79>\u001b[0m in \u001b[0;36mpost_evaluate\u001b[0;34m(self, config, population, species, best_genome)\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mneat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFeedForwardNetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_genome\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mshow_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mcomplete_extinction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "draw_net(config, winner, True, node_names=node_names)"
      ],
      "metadata": {
        "id": "6wKvJfHjoOzf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}