notebooks/aco_pp.ipynb

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    "<a href=\"https://colab.research.google.com/github/ZosoV/aco_random_walk/blob/main/notebooks/aco_pp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "# Python Implementation of ACO for Path Planning\n",
    "\n",
    "This is a python implementation of the Ant Colony Optimization algorithm for the Sorthest Path problem"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/israel.pineda/oscar/aco_random_walk\n"
     ]
    }
   ],
   "source": [
    "COLAB_ENV = False\n",
    "\n",
    "if COLAB_ENV:\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/gdrive')\n",
    "    %cd /content/gdrive/MyDrive/Proyectos/aco_random_walk/\n",
    "else:\n",
    "    %cd /home/israel.pineda/oscar/aco_random_walk/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "environment.yml  model\t    random_walk  stuff\t      test_aco_rw.py  utils\r\n",
      "LICENSE\t\t notebooks  README.md\t test_aco.py  test_rw.py\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
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  {
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    "id": "Byk0Mu6xXV7g"
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   "source": [
    "# import necessary libraries\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "import enum\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import time\n",
    "import pylab as pl\n",
    "from IPython import display\n",
    "\n",
    "RANDOM_SEED = 0\n",
    "np.random.seed(RANDOM_SEED) "
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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      "displayName": "Oscar Guarnizo",
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    "id": "SUmruGudZVzm"
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   "source": [
    "DATA_DIR = \"stuff/data/random_mazes/\"\n",
    "SAVING_DIR = \"stuff/results/aco_pp/\"\n",
    "\n",
    "if not os.path.exists(DATA_DIR):\n",
    "  os.mkdir(DATA_DIR)\n",
    "\n",
    "if not os.path.exists(SAVING_DIR):\n",
    "  os.mkdir(SAVING_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cQoBE3GYLdhZ"
   },
   "source": [
    "# Ant Colony Optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CstsjiPaQlBi"
   },
   "source": [
    "First, we did a little review of the algorithm that we are using. The algorithm is based on **Ant Colony System** with some additional functions, found here\n",
    "[Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique](https://ieeexplore.ieee.org/abstract/document/4129846)"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "K5s5S4eeRTnQ"
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   "source": [
    "## Ant Colony System\n",
    "This is an iterative algorithm. In each iteration, \n",
    "\n",
    "1. A number of ants find differents solutions. To do that, each ant follows a `pseudorandom  proportional rule`. Thus, the probability for an ant to move from city $i$ to city $j$ depends on a random variable $q$ uniformly distributed over $[0,1]$, and a parameter $q_0$. Therefore, if $q \\le q_0$, then $j=\\arg \\max _{c_{i l} \\in N(s p)}\\left\\{\\tau_{i l} \\eta_{i l}^{\\beta}\\right\\}$, otherwise use the next equation\n",
    "\n",
    "$$ p_{i j}^{k}=\\left\\{\\begin{array}{ll}\n",
    "\\frac{\\tau_{i j}^{\\alpha} \\cdot \\eta_{i j}^{\\beta}}{\\sum_{c_{i l} \\in \\mathrm{N}\\left(s^{p}\\right)} \\tau_{i l}^{\\alpha} \\cdot \\eta_{i l}^{\\beta}} & \\text { if } c_{i j} \\in \\mathbf{N}\\left(s^{p}\\right) \\\\\n",
    "0 & \\text { otherwise }\n",
    "\\end{array}\\right. $$\n",
    "\n",
    "where $\\mathrm{N}(s^p)$ is the set of feasible neighbors components; edges $(i,l)$ where $l$ is a feasible neighbor. The parameters $\\alpha$ and $\\beta$ control the relative importance of the pheromone versus the heurisitc information $\\eta_{ij}$\n",
    "\n",
    "\n",
    "2. A `local pheromone evaporation` is performed by all the ants after each construction step. Each ant applies it only to the last edge traversed:\n",
    "\n",
    "$$ \\tau_{i j}=(1-\\varphi) \\cdot \\tau_{i j}+\\varphi \\cdot \\tau_{0} $$\n",
    "\n",
    "where $\\varphi \\in (0,1]$ is the pheromone decay coefficient, and $\\tau_{0}$ the initial value pheromone. \n",
    "\n",
    "Note: the main goal of `local pheromone evaporation` is to diversify  the search performed by subsequent ants during an iteration.\n",
    "\n",
    "3. Then, only the best ant updates the pheromone trails (also known as `offline  pheromone  update`), following\n",
    "$$ \\tau_{i j} \\leftarrow\\left\\{\\begin{array}{ll}\n",
    "(1-\\rho) \\cdot \\tau_{i j}+\\rho \\cdot \\Delta \\tau_{i j} & \\text { if }(i, \\jmath) \\text { belongs to best tour, } \\\\\n",
    "\\tau_{i j} & \\text { otherwise. }\n",
    "\\end{array}\\right. $$\n",
    "\n",
    "where \n",
    "\n",
    "$$ \\Delta \\tau_{i j}^{\\text {best }}=\\left\\{\\begin{array}{ll}\n",
    "1 / L_{\\text {best }} & \\text { if }(i, j) \\text { belongs to the best tour, } \\\\\n",
    "0 & \\text { otherwise }\n",
    "\\end{array}\\right.\n",
    "$$\n",
    "\n",
    "$L_{best}$ is the length of the tour of the best ant. I could be either the best found in the current iteration (*iteration-best*, $L_{ib}$) or the best solution found so far since the start of the algorithm (*best-so-far*, $L_{bs}$).\n",
    "\n",
    "\n",
    " \n",
    "\n",
    "\n"
   ]
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    "id": "IKHBfElBVA0h"
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   "source": [
    "## Base ACO class\n",
    "\n",
    "In this section, we defined an ant colony optimizer class, which will be used as a base to develop the Travelling Salesman Problem and Path Planning (Shortest Path). In this part, we intended to build a general class that could be inheriting and, with few changes, adapt to any problem that we pretend to solve.\n"
   ]
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  {
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      "displayName": "Oscar Guarnizo",
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   "source": [
    "class AntColonyOptimizer():\n",
    "  def __init__(self, graph, ants, alpha, beta, p, \n",
    "               local_p = None, intensity = None, q_0 = None):\n",
    "    \"\"\"\n",
    "    Ant colony optimizer.  Traverse a graph and finds the min weight distance\n",
    "    :param graph: graph environment\n",
    "    :param ants:  number of ants to traverse the graph\n",
    "    :param alpha: weighting of pheromone\n",
    "    :param beta:  weighting of heuristic\n",
    "    :param p:     evaporation rate at which pheromone evaporates\n",
    "    :param local_p: local evaporation rate at which pheromone evaporates (optional)\n",
    "    :param intensity: the amount of pheromones to add per edge (optional)\n",
    "    :param q_0: probability to choose the best construction step (optional)\n",
    "    \"\"\"\n",
    "    self.graph = graph\n",
    "    self.ants = ants\n",
    "    self.alpha = alpha\n",
    "    self.beta = beta\n",
    "    self.p = p\n",
    "    self.local_p = local_p\n",
    "    self.intensity = intensity\n",
    "    self.q_0 = q_0\n",
    "\n",
    "    self.best_path = []\n",
    "    self.reset_environment()\n",
    "\n",
    "\n",
    "  def reset_environment(self):\n",
    "    \"\"\" return to the base state of the environment\n",
    "    \"\"\"\n",
    "    self.current_position = self.start_node\n",
    "    self.visited_nodes = [self.start_node]\n",
    "    self.current_path = []\n",
    "\n",
    "  def is_visited(self, neighbor):\n",
    "    \"\"\"return if the neighbor has alreadey been visited\n",
    "    \"\"\"\n",
    "    return neighbor in self.visited_nodes   \n",
    "  \n",
    "  def get_total_distance(self, path):\n",
    "    \"\"\"return the total distance of the given path\n",
    "    \"\"\"\n",
    "    t_distance = 0\n",
    "    for node1, node2 in path:\n",
    "      t_distance += self.graph[node1][node2]['distance']\n",
    "\n",
    "    # At the beginning, the total distance is define by inf\n",
    "    if len(path) == 0 : t_distance = float('inf')\n",
    "    return t_distance\n",
    "\n",
    "  def update_best_path(self):\n",
    "    if self.get_total_distance(self.current_path) < self.get_total_distance(self.best_path):\n",
    "      self.best_path = self.current_path\n",
    "  \n",
    "  def local_evaporation(self, neighbor):\n",
    "    edge = (self.current_position, neighbor)\n",
    "    self.graph.edges[edge]['pheromone'] = (1 - self.local_p) * self.graph.edges[edge]['pheromone'] + self.local_p * self.graph.tau_0\n",
    "\n",
    "  def offline_pheromone_update(self):\n",
    "    \"\"\"\n",
    "    Here, it is performed two steps at the same time:\n",
    "\n",
    "    1. global evaporation of the pheromone using self.p\n",
    "    2. contribution of pheromones over the best path found so far. The \n",
    "    contribution or reward is defined according to the distance of the \n",
    "    best path found so far. Also, if intensity is defined you could \n",
    "    update using that constant vaue\n",
    "    \"\"\"\n",
    "    cost = self.get_total_distance(self.best_path)\n",
    "    reward = self.intensity if self.intensity is not None else 1 / cost\n",
    "\n",
    "    #print(\"[INFO] Updating current path - cost: {} reward: {}\".format(cost, reward))\n",
    "\n",
    "    for node1, node2, _ in self.graph.edges(data=True):\n",
    "      # 1. evaporation\n",
    "      self.graph[node1][node2]['pheromone'] = (1 - self.p) * self.graph[node1][node2]['pheromone']\n",
    "\n",
    "      # 2. contribution update\n",
    "      if (node1, node2) in self.best_path or (node2, node1) in self.best_path:\n",
    "        self.graph[node1][node2]['pheromone'] += reward"
   ]
  },
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    "id": "lZgzEnt27O2h"
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   "source": [
    "# Define the Environment\n",
    "\n",
    "First, we define the environment where the algorithm will take place. In this case, the environment is a weighted graph $G(N,E)$ with the the following characteristic:\n",
    "\n",
    "1. The nodes are joined by equidistance edges, in a lattice form. \n",
    "2. Each edge have two weights the equal `distance` ($d=1$) and a `pheromone` value.\n",
    "3. The nodes have different states, such as: `free`, `obstacle` or `target.`"
   ]
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    "# creating enumerations for status of a each node\n",
    "# the number associated represent a mark to display\n",
    "# the environment later in a grid mode\n",
    "class State(enum.Enum):\n",
    "    free = 0.9\n",
    "    wall = 0.1\n",
    "    target = 0.5"
   ]
  },
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   "execution_count": 7,
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   "source": [
    "#Class to define the environment\n",
    "class PPGraph(nx.Graph):\n",
    "  def __init__(self, size, tau_0, filename = None):\n",
    "    \"\"\"Define a graph environment given a size\n",
    "    and tau_0 (initial pheromone).\n",
    "    The obstacle are loaded from filename\n",
    "    \"\"\"\n",
    "    self.size = size\n",
    "    self.tau_0 = tau_0\n",
    "    nx.Graph.__init__(self)\n",
    "    \n",
    "    self.__create_graph(filename)\n",
    "    \n",
    "  def __create_graph(self, filename):\n",
    "    \"\"\"Create a square lattice graph with size x size nodes.\n",
    "    \"\"\"\n",
    "    \n",
    "    walls = np.load(filename) if filename is not None else []\n",
    "        \n",
    "    if self.size + 1 in walls: print(\"What happend?\")\n",
    "        \n",
    "    self.clear() # Remove all the nodes and edges of the last graph\n",
    "    current_node = 0\n",
    "    for j in range(self.size - 1, -1, -1):\n",
    "      for i in range(self.size):  \n",
    "\n",
    "          #Adding neighbors to the current node\n",
    "          self.__adding_edges(i,j,current_node, self.tau_0)\n",
    "\n",
    "          #Adding the node to the network\n",
    "          self.add_node(current_node, pos=(i,j), state = State.free, counter = 0)\n",
    "        \n",
    "          current_node += 1\n",
    "            \n",
    "    for wall_id in walls:\n",
    "        self.nodes[wall_id]['state'] = State.wall\n",
    "            \n",
    "            \n",
    "  def __adding_edges(self,i,j,current_node, tau_0):\n",
    "    \"\"\"Defines the neighbors per current node\"\"\"\n",
    "    #There are four possible connection\n",
    "    directions = {\n",
    "        \"up\" : current_node - self.size,\n",
    "        \"down\" : current_node + self.size,\n",
    "        \"left\" : current_node - 1,\n",
    "        \"right\" : current_node + 1\n",
    "    }\n",
    "\n",
    "    #For border cases of the network, we have to pop some connections\n",
    "    if i == 0:\n",
    "      directions.pop(\"left\")\n",
    "    if i == self.size - 1:\n",
    "      directions.pop(\"right\")\n",
    "    if j == 0:\n",
    "      directions.pop(\"down\")\n",
    "    if j == self.size - 1:\n",
    "      directions.pop(\"up\")\n",
    "\n",
    "    for _, neighbor in directions.items():\n",
    "      # adding edge      \n",
    "      self.add_edge(current_node, neighbor, \n",
    "                    pheromone = tau_0, \n",
    "                    distance = 1)\n",
    "\n",
    "\n",
    "  #Function to plot the structure of the graph\n",
    "  def draw_graph(self, node_size=25, with_labels=False):\n",
    "    \"\"\"\n",
    "    Draw the graph.\n",
    "        \n",
    "    node_size: the size of all the nodes\n",
    "    node_color: the color of all the nodes\n",
    "    with_labels: True or False to show the ids of the nodes\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.set_aspect('equal') #set the x and y axes to the same scale\n",
    "    pos = nx.get_node_attributes(self, 'pos')\n",
    "    color_values = [ self.nodes[i]['state'].value for i in self.nodes() ]\n",
    "    nx.draw(self, pos, cmap = plt.get_cmap('viridis'), node_color=color_values\\\n",
    "            , node_size=node_size, with_labels=with_labels, vmin = 0., vmax = 1.0)\n",
    "    \n",
    "  def get_pheromones_matrix(self):\n",
    "    # get the number of pheromones per node\n",
    "    pheromones = []\n",
    "    for i in sorted(self.nodes()):\n",
    "        if self.nodes[i]['state'] != State.wall:\n",
    "            neighbors = self[i]\n",
    "            accu_pheromone = 0\n",
    "            counter = 0\n",
    "            for neighbor_node, edge in neighbors.items():\n",
    "                if self.nodes[i]['state'] != State.wall:\n",
    "                    accu_pheromone += edge['pheromone']\n",
    "                    counter += 1\n",
    "                \n",
    "            pheromones.append(accu_pheromone/counter)\n",
    "        else:\n",
    "            pheromones.append(0)\n",
    "            \n",
    "    return np.array(pheromones).reshape((self.size, self.size))\n",
    "\n",
    "  def get_exploration_matrix(self):\n",
    "    # get the number of times that the ants visits per node.\n",
    "    exploration = [ self.nodes[i]['counter'] for i in sorted(self.nodes()) ]\n",
    "    exploration = np.array(exploration).reshape((self.size, self.size))\n",
    "    \n",
    "    return exploration\n"
   ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = PPGraph(size = 10, tau_0 = 0.1)\n",
    "graph.draw_graph(with_labels= True)\n",
    "graph.get_exploration_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
       "       [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
       "       [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
       "       [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
       "       [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
       "       [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
       "       [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
       "       [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
       "       [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
       "       [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = PPGraph(size = 10, tau_0 = 0.1)\n",
    "graph.draw_graph(with_labels= True)\n",
    "graph.get_pheromones_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "executionInfo": {
     "elapsed": 390,
     "status": "ok",
     "timestamp": 1621635083902,
     "user": {
      "displayName": "Oscar Guarnizo",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GheAnA882D9R56DoQ_3Y3rbrnPGkT1zqcIi2ysoQg=s64",
      "userId": "12551427865753564350"
     },
     "user_tz": 300
    },
    "id": "dMBlvuhb1qhw",
    "outputId": "216ce798-3d64-4962-8f2b-bfb4f11f4126"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Loading mazes from saving files\n",
    "SIZE = 20\n",
    "maze_files = \"stuff/data/random_mazes/maze_size_{}/\".format(SIZE)\n",
    "for maze_file in os.listdir(maze_files)[:2]:\n",
    "    maze_file = os.path.join(maze_files,maze_file)\n",
    "    graph = PPGraph(size = SIZE, tau_0 = 0.1, filename = maze_file)\n",
    "    graph.draw_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FLfUawYrcQxo"
   },
   "source": [
    "### Path Planng ACO (ACOPP)\n",
    "\n",
    "In this version of the algorithm, we did few changes to deal with our problem.\n",
    "\n",
    "1. `penalty`: A great prevalent problem in this algorithm is the capabality to avoid already visited nodes. In the TSP problem, we solve it only excluding these visited nodes. However, in this case, the algorithm tends to get stuck several times when we do so. This is mainly due to each node is not fully connected with all the other nodes. For this reason, we propose a `penalty` parameter, which reduces the pheromones (in a percent) of edges linked with already visited nodes. In this way, we avoid to fall in stuck condition whereas encourage to avoid already visited nodes\n",
    "\n",
    "2. `proximity`: In this problem, use the heuristic $\\eta_{ij}$ as `1 / distance` does not make sense due to all the edges have equal lengths. Thus, we considered a more informative metric, known as `proximity`. The `proximity` represent the **inverse relative distance of a neighbor node to the target node.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZTylO7rTiAg3"
   },
   "source": [
    "### Proximity and Multiplication Step\n",
    "\n",
    "The `proximity` represent the inverse relative distance of a neighbor node to the target node. In this section, we review the distribution of this metric in environments with different sizes. Also, we propose a **proximity 2** to reduce the sparse values. The proximity of current node $i$ respect to the neighbor node $j$ is\n",
    "\n",
    "$$ \\mathrm{proximity1}_{ij} = \\frac{1}{d_{jt}} = \\eta_{ij} $$\n",
    "\n",
    "where $d_{jt}$ is the manhattan or euclidean distance of the neighbor node $j$ to the target node $t$.\n",
    "\n",
    "Additionally, here we propose to multiply the previous inverse distance to $d_{it}$, the distance of the current node $i$ to the target node $t$, as\n",
    "\n",
    "$$ \\mathrm{proximity2}_{ij} = \\frac{d_{it}}{d_{jt}} = \\eta_{ij} $$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 383,
     "status": "ok",
     "timestamp": 1621635089521,
     "user": {
      "displayName": "Oscar Guarnizo",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GheAnA882D9R56DoQ_3Y3rbrnPGkT1zqcIi2ysoQg=s64",
      "userId": "12551427865753564350"
     },
     "user_tz": 300
    },
    "id": "7Wxql3gn3Yph"
   },
   "outputs": [],
   "source": [
    "def get_distance(pos1, pos2, distance):\n",
    "  x1, y1 = pos1\n",
    "  x2, y2 = pos2\n",
    "  \n",
    "  if distance == 'euclidean':\n",
    "    result = np.sqrt((x2-x1)**2 + (y2-y1)**2)\n",
    "  elif distance == 'manhattan':\n",
    "    result = np.abs(x2-x1) + np.abs(y2-y1)\n",
    "\n",
    "  return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 146,
     "status": "ok",
     "timestamp": 1621635093290,
     "user": {
      "displayName": "Oscar Guarnizo",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GheAnA882D9R56DoQ_3Y3rbrnPGkT1zqcIi2ysoQg=s64",
      "userId": "12551427865753564350"
     },
     "user_tz": 300
    },
    "id": "nm6EZC5vmca-"
   },
   "outputs": [],
   "source": [
    "def get_proximity(graph, current_node, neighbor_node, target_node, \n",
    "                  mode = 'proximity_1', distance = 'euclidean'):\n",
    "  \"\"\"\n",
    "  Proximity. Calculate the proximity of the neighbor node.\n",
    "  :param graph:         graph environment\n",
    "  :param current_node:  index of the current node i\n",
    "  :param neighbor_node: index of the neihbor node j\n",
    "  :param target_node:   index of the neihbor node t\n",
    "  :param mode:          perform the proximity 1 or proximity 2\n",
    "  :param distance:      used distance to calculate the proximity\n",
    "  \"\"\"\n",
    "  pos_neighbor = graph.nodes[neighbor_node]['pos']\n",
    "  pos_target = graph.nodes[target_node]['pos']\n",
    "  pos_current = graph.nodes[current_node]['pos']\n",
    "\n",
    "  # distance: current node to target\n",
    "  dis_curr2target = get_distance(pos_current, pos_target, distance)\n",
    "  \n",
    "  # distance: neighbor to target\n",
    "  dis_neig2target = get_distance(pos_neighbor, pos_target, distance)\n",
    "  \n",
    "  if neighbor_node == target_node:\n",
    "    proximity = 1\n",
    "  else:\n",
    "\n",
    "    if mode == 'proximity_1':\n",
    "      proximity = 1/ dis_neig2target\n",
    "    elif mode == 'proximity_2':\n",
    "      proximity = dis_curr2target / dis_neig2target\n",
    "\n",
    "  return proximity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define the ACOPP Algorithm\n",
    "For create an instance of this class, you need to add several parameters. Below you can see the explanations of each parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "XI7CWT8QrygT"
   },
   "outputs": [],
   "source": [
    "class ACOPP(AntColonyOptimizer):\n",
    "  def __init__(self, graph, ants, alpha, beta, p, penalty, local_p = None, intensity = None, q_0 = None):\n",
    "    \"\"\"\n",
    "    Ant colony optimizer for Path Planning.  \n",
    "    Traverses a graph and finds the min weight distance \n",
    "    between a start and target node\n",
    "    \n",
    "    :param graph: graph environment\n",
    "    :param ants:  number of ants to traverse the graph\n",
    "    :param alpha: weighting of pheromone\n",
    "    :param beta:  weighting of heuristic\n",
    "    :param p:     evaporation rate at which pheromone evaporates\n",
    "    :param local_p: local evaporation rate at which pheromone evaporates (optional)\n",
    "    :param penalty: penalization percent of already visited nodes \n",
    "    :param intensity: the amount of pheromones to add per edge (optional)\n",
    "    :param q_0: probability to choose the best construction step (optional)\n",
    "    \"\"\"    \n",
    "    self.start_node = 0\n",
    "    self.penalty = penalty\n",
    "    self.size = graph.size\n",
    "    self.target_node = self.size * self.size - 1\n",
    "    self.list_distances = []\n",
    "\n",
    "    super().__init__(graph, ants, alpha, beta, p, local_p, intensity, q_0)\n",
    "\n",
    "    self.graph.nodes[self.graph.size * self.graph.size - 1]['state'] = State.target\n",
    "\n",
    "  def update_state(self, mode = 'proximity_1', \n",
    "                   normalization = None,\n",
    "                   mean = 0, \n",
    "                   std = 1, \n",
    "                   distance = 'euclidean'):\n",
    "    neighbors = self.graph[self.current_position]\n",
    "\n",
    "    # Process to get the weight of each neighbor\n",
    "    weights = []\n",
    "    aux_weights = []\n",
    "    neighbors_idx = []\n",
    "    for neighbor_node, edge in neighbors.items():\n",
    "      if self.graph.nodes[neighbor_node]['state'] != State.wall:\n",
    "          proximity = get_proximity(self.graph, \n",
    "                                    self.current_position,\n",
    "                                    neighbor_node,\n",
    "                                    self.target_node,\n",
    "                                    mode,\n",
    "                                    distance)\n",
    "          if normalization is not None:\n",
    "            proximity = (proximity - mean) / std      \n",
    "\n",
    "          if not self.is_visited(neighbor_node):\n",
    "            # get the weight per neighbor and append it\n",
    "            weights.append( (edge['pheromone'] ** self.alpha) * \n",
    "                          (proximity ** self.beta))\n",
    "            aux_weights.append( edge['pheromone'] * \n",
    "                          (proximity ** self.beta))\n",
    "            neighbors_idx.append(neighbor_node)\n",
    "          else:\n",
    "            penalty_pheromone = (1 - self.penalty) * edge['pheromone']\n",
    "            weights.append( (penalty_pheromone ** self.alpha) * \n",
    "                  (proximity ** self.beta))\n",
    "            aux_weights.append( penalty_pheromone * \n",
    "                          (proximity ** self.beta))\n",
    "            neighbors_idx.append(neighbor_node)\n",
    "    \n",
    "    acu_weight = sum(weights)\n",
    "    probabilities = np.array(weights)/acu_weight\n",
    "    #print(\"[INFO] probabilities: {}\".format(probabilities))\n",
    "\n",
    "    # with probability q_0 select the best trial\n",
    "    if self.q_0 is not None and np.random.rand() < self.q_0:\n",
    "        new_position = neighbors_idx[np.argmax(aux_weights)]\n",
    "    else:\n",
    "      # choose an option following the wheel selection algorithm  \n",
    "      new_position = np.random.choice(neighbors_idx, p=probabilities)\n",
    "\n",
    "    # perform local evaporation\n",
    "    if self.local_p is not None:\n",
    "      self.local_evaporation(new_position)\n",
    "\n",
    "    self.graph.nodes[self.current_position]['counter'] += 1\n",
    "     \n",
    "    self.current_path.append((self.current_position, new_position))\n",
    "    self.visited_nodes.append(new_position)\n",
    "    self.current_position = new_position\n",
    "\n",
    "  def end_route(self):\n",
    "    return self.current_position == self.target_node\n",
    "\n",
    "  def fit(self, total_iter, steps_die = None, draw_mode = None, iter_show_step = 1, \n",
    "          speed = 0.05, show_last = False, draw_additional = True, save_matrixes = False, verbose = False):\n",
    "    # define 2 draw_mode per_iteration or per_ants\n",
    "    list_distances = []\n",
    "    list_distances_avg = []\n",
    "    list_distances_std = []\n",
    "    list_distances_sem = []\n",
    "    pheromones_matrixes = []\n",
    "    exploration_matrixes = []\n",
    "    \n",
    "    for iter in range(total_iter):\n",
    "\n",
    "      distance_per_ants = []\n",
    "    \n",
    "      if save_matrixes:\n",
    "        pheromones_matrixes.append(self.graph.get_pheromones_matrix())\n",
    "        exploration_matrixes.append(self.graph.get_exploration_matrix())\n",
    "\n",
    "      for ant in range(self.ants):\n",
    "        get_target = False\n",
    "        is_stuck = False\n",
    "        step = 0\n",
    "        while not get_target and not is_stuck:\n",
    "          self.update_state()\n",
    "          if show_last:\n",
    "              if iter == total_iter -1 and ant == self.ants - 1:\n",
    "                draw_progress(self,iter,ant,step, draw_additional = draw_additional)\n",
    "                display.clear_output(wait=True)\n",
    "                display.display(pl.gcf())\n",
    "                time.sleep(speed)\n",
    "                plt.clf()\n",
    "          else:\n",
    "              if draw_mode == \"per_ants\" and iter % iter_show_step == 0: \n",
    "                    draw_progress(self,iter,ant,step, draw_additional = draw_additional)\n",
    "                    display.clear_output(wait=True)\n",
    "                    display.display(pl.gcf())\n",
    "                    time.sleep(speed)\n",
    "                    plt.clf()\n",
    "              if draw_mode == \"per_iteration\" and ant == self.ants -1 and iter % iter_show_step == 0:\n",
    "                    draw_progress(self,iter,ant,step, draw_additional = draw_additional)\n",
    "                    display.clear_output(wait=True)\n",
    "                    display.display(pl.gcf())\n",
    "                    time.sleep(speed)\n",
    "                    plt.clf()            \n",
    "            \n",
    "          step += 1\n",
    "            \n",
    "          if steps_die == step: \n",
    "            is_stuck = True\n",
    "          get_target = self.end_route()\n",
    "          if get_target: self.graph.nodes[self.current_position]['counter'] += 1\n",
    "          \n",
    "        if not is_stuck: \n",
    "            self.update_best_path()\n",
    "            is_stuck = False\n",
    "\n",
    "        current_distance = self.get_total_distance(self.current_path)\n",
    "        #print(\"[INFO] iter: {} ant: {} current: {}\".format(iter, ant, current_distance))\n",
    "        distance_per_ants.append(current_distance)\n",
    "        self.reset_environment()\n",
    "      \n",
    "      self.offline_pheromone_update()\n",
    "\n",
    "      # Track the best distance so far\n",
    "      best_distance = self.get_total_distance(self.best_path)\n",
    "      if iter % step_plot == 0 and verbose:\n",
    "        print(\"[INFO] iter: {} best: {} d_mean: {} d_stdv: {}\".format(iter, \n",
    "                                                              best_distance, \n",
    "                                                              np.mean(distance_per_ants),\n",
    "                                                              np.std(distance_per_ants)))\n",
    "      list_distances.append(best_distance)\n",
    "      list_distances_avg.append(np.mean(distance_per_ants))\n",
    "      list_distances_std.append(np.std(distance_per_ants))\n",
    "      list_distances_sem.append(np.std(distance_per_ants, ddof=1) / np.sqrt(np.size(distance_per_ants)))\n",
    "      self.list_distances.append(best_distance)\n",
    "\n",
    "    history = {\n",
    "        \"distances_best\" : list_distances,\n",
    "        \"distances_avg\"  : list_distances_avg,\n",
    "        \"distances_std\"  : list_distances_std,\n",
    "        \"distances_sem\"  : list_distances_sem,\n",
    "        \"pheromones_matrix\" : pheromones_matrixes,\n",
    "        \"exploration_matrix\" : exploration_matrixes,\n",
    "    }\n",
    "\n",
    "    return history\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xsi3xRAHn-_j"
   },
   "source": [
    "## Plotting the Progress (Maze, Pheromones per Node, Exploration per Node)\n",
    "\n",
    "Now, we have the guidelines to the algorithm, here we define a class with all the process, inheriting the base `AntColonyOptimizer` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Function to plot the graph in a grid prespective, the pheromones and exploration\n",
    "def draw_progress(base_class, iter, ant, step, draw_additional = False):\n",
    "  # Create a canvas to display the visited cells, the ant, and the target\n",
    "  # values: to traget, free cells, and obstacles\n",
    "  canvas = [ base_class.graph.nodes[i]['state'].value for i in sorted(base_class.graph.nodes()) ]\n",
    "  # values: to the visited cells\n",
    "  for node in base_class.visited_nodes: canvas[node] = 0.2\n",
    "  # values: to the ant\n",
    "  canvas[base_class.current_position] = 0.3\n",
    "\n",
    "  canvas = np.array(canvas).reshape((base_class.size, base_class.size))\n",
    "\n",
    "  def get_pheromones_per_node(graph):\n",
    "    pheromones = []\n",
    "    for i in sorted(graph.nodes()):\n",
    "        if graph.nodes[i]['state'] != State.wall:\n",
    "            neighbors = graph[i]\n",
    "            accu_pheromone = 0\n",
    "            counter = 0\n",
    "            for neighbor_node, edge in neighbors.items():\n",
    "                if graph.nodes[i]['state'] != State.wall:\n",
    "                    accu_pheromone += edge['pheromone']\n",
    "                    counter += 1\n",
    "                \n",
    "            pheromones.append(accu_pheromone/counter)\n",
    "        else:\n",
    "            pheromones.append(0)\n",
    "            \n",
    "    return np.array(pheromones).reshape((graph.size, graph.size))\n",
    "\n",
    "  def draw_grid_inner(ax):\n",
    "      ax.set_title('Iteration {} - Ant {} - Step {}'.format(iter,ant,step))\n",
    "      ax.pcolor(canvas, edgecolors='k', cmap='Paired', linewidths=0.2, vmin = 0., vmax = 1.0)\n",
    "      ax.set_aspect('equal') #set the x and y axes to the same scale\n",
    "      plt.xticks(np.arange(0.5, base_class.size, step=1), labels=[i for i in range(base_class.size)]) \n",
    "      plt.yticks(np.arange(base_class.size - 0.5, 0, step=-1), labels=[i for i in range(base_class.size)])\n",
    "      ax.invert_yaxis() #invert the y-axis so the first row of data is at the top\n",
    "  \n",
    "  def pheromones_plot(fig, ax):\n",
    "      pheromones = get_pheromones_per_node(base_class.graph)\n",
    "      im = ax.pcolor(pheromones, edgecolors='k', cmap='viridis', linewidths=0.2)\n",
    "      ax.set_title('Pheromones Distribution')\n",
    "      ax.set_aspect('equal') #set the x and y axes to the same scale\n",
    "      ax.set_xticks([])\n",
    "      ax.set_yticks([])\n",
    "      ax.invert_yaxis() #invert the y-axis so the first row of data is at the top\n",
    "      \n",
    "      cbar=fig.colorbar(im, label='pheromones amount', ax = ax)\n",
    "    \n",
    "  def exploration_plot(fig, ax):\n",
    "      exploration = [ base_class.graph.nodes[i]['counter'] for i in sorted(base_class.graph.nodes()) ]\n",
    "      exploration = np.array(exploration).reshape((base_class.size, base_class.size))\n",
    "      im2 = ax.pcolor(exploration, edgecolors='k', cmap='plasma', linewidths=0.2)\n",
    "      ax.set_title('Exploration')\n",
    "      ax.set_aspect('equal') #set the x and y axes to the same scale\n",
    "      ax.set_xticks([])\n",
    "      ax.set_yticks([])\n",
    "      ax.invert_yaxis() #invert the y-axis so the first row of data is at the top\n",
    "      \n",
    "      cbar=fig.colorbar(im2, label='number of times visited', ax = ax)\n",
    "    \n",
    "  fig, ax = plt.subplots(figsize=(3*base_class.size/4,2*base_class.size/4))\n",
    "\n",
    "  ax1 = plt.subplot(231)\n",
    "  ax2 = plt.subplot(232)\n",
    "  ax3 = plt.subplot(233)\n",
    "\n",
    "  #ax4 = plt.subplot(212)\n",
    "\n",
    "  #Plot the maze\n",
    "  if draw_additional:\n",
    "\n",
    "      draw_grid_inner(ax1)\n",
    "        \n",
    "      pheromones_plot(fig,ax2)\n",
    "    \n",
    "      exploration_plot(fig,ax3)\n",
    "     \n",
    "      #list_iterations = np.arange(0, len(base_class.list_distances))\n",
    "      #ax4.plot(list_iterations, base_class.list_distances, \"b-\") #, label = label)\n",
    "    \n",
    "  else:\n",
    "      draw_grid_inner(ax1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auxiliar Function \n",
    "This function allows us to plot the progress per iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Function to plot the best distance per iteration\n",
    "def plot_distance(list_distances, yerror = None, save = False, add_title = \"\", name2save = \"\"):\n",
    "\n",
    "  list_iterations = np.arange(0, len(list_distances))\n",
    "  #path_to_save = \"stuff/figures/\"\n",
    "  #if not os.path.exists(path_to_save):\n",
    "  #    os.mkdir(path_to_save)\n",
    "\n",
    "  title = 'Average Distance vs Iterations'\n",
    "  title += '\\n {}'.format(add_title)\n",
    "\n",
    "  plt.title(title)\n",
    "  if yerror is not None:\n",
    "        \n",
    "    markers, caps, bars = plt.errorbar(list_iterations, list_distances, yerr=yerror, color = \"b\")\n",
    "    # loop through bars and caps and set the alpha value\n",
    "    [bar.set_alpha(0.5) for bar in bars]\n",
    "  else:\n",
    "    plt.plot(list_iterations, list_distances, \"b-\") #, label = label)\n",
    "  #plt.legend()\n",
    "  #if save: plt.savefig(os.path.join(path_to_save,name2save))\n",
    "\n",
    "  plt.show()\n",
    "  plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M2-DcxukcEWA"
   },
   "source": [
    "# Free Environment Experiment\n",
    "Now execute the algorithm given some parameters in a **free environment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "JdcCz7Ld7Nuq"
   },
   "outputs": [],
   "source": [
    "# Define the parameters of our model\n",
    "ants = 10\n",
    "tau_0 = 1\n",
    "alpha = 3. \n",
    "beta = 2.\n",
    "p = 0.3\n",
    "intensity = None\n",
    "local_p = 0\n",
    "q_0 = 0.3\n",
    "\n",
    "# Additional attributes\n",
    "penalty = 0.7 # Nota el grado de penalización provoca que haya mas o menos ruido\n",
    "\n",
    "total_iter = 100\n",
    "step_plot = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
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       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
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       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
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       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
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       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
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       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
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       "<Figure size 1080x720 with 0 Axes>"
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       "<Figure size 1080x720 with 0 Axes>"
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       "<Figure size 1080x720 with 0 Axes>"
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       "<Figure size 1080x720 with 0 Axes>"
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       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
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       "<Figure size 1080x720 with 0 Axes>"
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       "<Figure size 1080x720 with 0 Axes>"
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       "<Figure size 1080x720 with 0 Axes>"
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       "<Figure size 1080x720 with 0 Axes>"
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       "<Figure size 1080x720 with 0 Axes>"
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       "<Figure size 1080x720 with 0 Axes>"
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     "data": {
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       "<Figure size 1080x720 with 0 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the graph\n",
    "graph = PPGraph(size = 20, tau_0 = tau_0)\n",
    "\n",
    "# Create the optimizer using the current graph\n",
    "optimizer = ACOPP(graph, ants, alpha, beta, p, penalty, local_p, intensity, q_0)\n",
    "\n",
    "# Execute the optimizer\n",
    "history = optimizer.fit(total_iter, draw_mode = \"per_iteration\", show_last = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_bound = 90\n",
    "text_title = r\"ants: {} $\\alpha$: {} $\\beta$: {} $\\rho$: {} $q_0$: {} $\\lambda$: {}\".format(ants,alpha,beta,p,q_0, penalty)\n",
    "plot_distance(history['distances_avg'][:max_bound], history['distances_sem'][:max_bound], add_title = text_title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obstacle Environment Experiment\n",
    "Execute the algorithm given some parameters in a **obstacle environment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIZE = 20\n",
    "folder_dir = \"stuff/data/random_mazes/maze_size_{}/\".format(SIZE)\n",
    "list_files = os.listdir(folder_dir)\n",
    "\n",
    "maze_file = os.path.join(folder_dir,list_files[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the parameters of our model\n",
    "ants = 10\n",
    "tau_0 = 1\n",
    "alpha = 3. \n",
    "beta = 2.\n",
    "p = 0.3\n",
    "intensity = None\n",
    "local_p = 0\n",
    "q_0 = 0.3\n",
    "\n",
    "# Additional attributes\n",
    "penalty = 0.5 # Nota el grado de penalización provoca que haya mas o menos ruido\n",
    "\n",
    "total_iter = 100\n",
    "step_plot = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
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    {
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    {
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   ],
   "source": [
    "# Create the graph\n",
    "graph = PPGraph(size = 20, tau_0 = tau_0, filename = maze_file)\n",
    "# Create the envioronment\n",
    "optimizer = ACOPP(graph, ants, alpha, beta, p, penalty, local_p, intensity, q_0)\n",
    "\n",
    "# Execute optimizer\n",
    "history = optimizer.fit(total_iter, steps_die = 100, draw_mode = \"per_iteration\", show_last = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "text_title = r\"ants: {} $\\alpha$: {} $\\beta$: {} $\\rho$: {} $q_0$: {} $\\lambda$: {}\".format(ants,alpha,beta,p,q_0, penalty)\n",
    "plot_distance(history['distances_avg'], history['distances_std'], add_title = text_title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Pheromones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "cellView": "form",
    "id": "y-J_ekQCR51j"
   },
   "outputs": [],
   "source": [
    "#@title Function to display the histogram of pheromones\n",
    "def display_pheromones(env, bins):\n",
    "  #A = nx.attr_matrix(self.graph, edge_attr=\"pheromone\", rc_order= np.arange(0,self.size * self.size))\n",
    "\n",
    "  #print(A)\n",
    "  edge_attributes = nx.get_edge_attributes(env.graph,'pheromone')\n",
    "\n",
    "  pheromones = []\n",
    "  phero_dist = []\n",
    "\n",
    "  count = 0\n",
    "  for _, _, attrib in env.graph.edges(data=True):\n",
    "    pheromones.append(attrib['pheromone'])\n",
    "    if attrib['pheromone'] == 0.1: count+=0\n",
    "\n",
    "    phero_dist_val = (attrib['pheromone'] ** env.alpha) * (attrib['distance'] ** env.beta)\n",
    "    phero_dist.append(phero_dist_val)\n",
    "\n",
    "  fig, ax = plt.subplots(1,2, figsize = (10,5))\n",
    "  ax[0].hist(pheromones, bins=bins)\n",
    "  ax[0].set_title('Pheromones Distribution')\n",
    "\n",
    "  ax[1].hist(phero_dist, bins=bins)\n",
    "  ax[1].set_title('Pheromones-Distance Distribution')\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "id": "VYed3av-Nbvz",
    "outputId": "63ba18d0-d902-4c7d-e0db-6ce19c8830da",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_pheromones(optimizer,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Different Pheromone and Exploration Matrix\n",
    "\n",
    "Here, we saved the pheromone and exploration matrixes in the convergence point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAVING_DIR = \"stuff/distribution\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Function to plot the graph in a grid prespective, the pheromones and exploration\n",
    "def plot_matrixes(base_class = None, pheromones = None, exploration = None):\n",
    "\n",
    "  def maze_plot(ax):\n",
    "      canvas = [ base_class.graph.nodes[i]['state'].value for i in sorted(base_class.graph.nodes()) ]\n",
    "      # values: to the visited cells\n",
    "      for node in base_class.visited_nodes: canvas[node] = 0.2\n",
    "      # values: to the ant\n",
    "      canvas[base_class.current_position] = 0.3\n",
    "\n",
    "      canvas = np.array(canvas).reshape((base_class.size, base_class.size))\n",
    "\n",
    "      ax.pcolor(canvas, edgecolors='k', cmap='Paired', linewidths=0.2, vmin = 0., vmax = 1.0)\n",
    "      ax.set_aspect('equal') #set the x and y axes to the same scale\n",
    "      plt.xticks(np.arange(0.5, base_class.size, step=1), labels=[i for i in range(base_class.size)]) \n",
    "      plt.yticks(np.arange(base_class.size - 0.5, 0, step=-1), labels=[i for i in range(base_class.size)])\n",
    "      ax.invert_yaxis() #invert the y-axis so the first row of data is at the top\n",
    "  \n",
    "  def pheromones_plot(fig, ax):\n",
    "      im = ax.pcolor(pheromones, edgecolors='k', cmap='viridis', linewidths=0.2)\n",
    "      ax.set_title('Pheromones Distribution')\n",
    "      ax.set_aspect('equal') #set the x and y axes to the same scale\n",
    "      ax.set_xticks([])\n",
    "      ax.set_yticks([])\n",
    "      ax.invert_yaxis() #invert the y-axis so the first row of data is at the top\n",
    "      \n",
    "      cbar=fig.colorbar(im, label='pheromones amount', ax = ax)\n",
    "    \n",
    "  def exploration_plot(fig, ax):\n",
    "      im2 = ax.pcolor(exploration, edgecolors='k', cmap='plasma', linewidths=0.2)\n",
    "      ax.set_title('Exploration')\n",
    "      ax.set_aspect('equal') #set the x and y axes to the same scale\n",
    "      ax.set_xticks([])\n",
    "      ax.set_yticks([])\n",
    "      ax.invert_yaxis() #invert the y-axis so the first row of data is at the top\n",
    "      \n",
    "      cbar=fig.colorbar(im2, label='number of times visited', ax = ax)\n",
    "    \n",
    "  fig, ax = plt.subplots(figsize=(3*base_class.size/2,2*base_class.size/2))\n",
    "\n",
    "  ax1 = plt.subplot(231)\n",
    "  ax2 = plt.subplot(232)\n",
    "  ax3 = plt.subplot(233)\n",
    "\n",
    "  #ax4 = plt.subplot(212)\n",
    "\n",
    "  #Plot the maze\n",
    "  \n",
    "  if base_class is not None:\n",
    "      maze_plot(ax1)\n",
    "  \n",
    "  if pheromones is not None:\n",
    "      pheromones_plot(fig,ax2)\n",
    "    \n",
    "  if exploration is not None:\n",
    "      exploration_plot(fig,ax3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the parameters of our model\n",
    "ants = 10\n",
    "tau_0 = 1\n",
    "alpha = 3. \n",
    "beta = 2.\n",
    "p = 0.3\n",
    "intensity = None\n",
    "local_p = 0\n",
    "q_0 = 0.7\n",
    "\n",
    "# Additional attributes\n",
    "penalty = 0.8 # Nota el grado de penalización provoca que haya mas o menos ruido\n",
    "\n",
    "total_iter = 25\n",
    "step_plot = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the graph\n",
    "graph = PPGraph(size = 10, tau_0 = tau_0)\n",
    "\n",
    "# Create the optimizer using the current graph\n",
    "optimizer = ACOPP(graph, ants, alpha, beta, p, penalty, local_p, intensity, q_0)\n",
    "\n",
    "# Execute the optimizer\n",
    "history = optimizer.fit(total_iter, save_matrixes = True) #, draw_mode = \"per_iteration\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_bound = 25\n",
    "text_title = r\"ants: {} $\\alpha$: {} $\\beta$: {} $\\rho$: {} $q_0$: {} $\\lambda$: {}\".format(ants,alpha,beta,p,q_0, penalty)\n",
    "plot_distance(history['distances_avg'][:max_bound], history['distances_sem'][:max_bound], add_title = text_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- 0:00:00.414374 --- \n",
      " --- 0:00:00.814994 --- \n",
      " --- 0:00:01.236321 --- \n",
      " --- 0:00:01.671741 --- \n",
      " --- 0:00:02.076520 --- \n",
      " --- 0:00:02.495329 --- \n",
      " --- 0:00:02.892107 --- \n",
      " --- 0:00:03.283447 --- \n",
      " --- 0:00:03.679166 --- \n",
      " --- 0:00:04.080988 --- \n",
      " --- 0:00:04.480238 --- \n",
      " --- 0:00:04.887357 --- \n",
      " --- 0:00:05.290101 --- \n",
      " --- 0:00:05.702666 --- \n",
      " --- 0:00:06.101063 --- \n",
      " --- 0:00:06.509402 --- \n",
      " --- 0:00:06.916249 --- \n",
      " --- 0:00:07.309748 --- \n",
      " --- 0:00:07.703508 --- \n",
      " --- 0:00:08.099013 --- \n",
      " --- 0:00:08.525641 --- \n",
      " --- 0:00:08.953733 --- \n",
      " --- 0:00:09.377114 --- \n",
      " --- 0:00:09.778767 --- \n",
      " --- 0:00:10.176261 --- \n",
      " --- 0:00:10.573814 --- \n",
      " --- 0:00:10.961269 --- \n",
      " --- 0:00:11.359168 --- \n",
      " --- 0:00:11.758191 --- \n",
      " --- 0:00:12.151032 --- \n",
      "End Sucessfully\n",
      "--- 0:00:12.151642 seconds ---\n"
     ]
    }
   ],
   "source": [
    "acu_pheromones = []\n",
    "acu_exploration = []\n",
    "EXECUTIONS = 30\n",
    "CONVERGENCE_POINT = 10\n",
    "SIZE = 10\n",
    "\n",
    "start_time = time.time()\n",
    "for i in range(EXECUTIONS):\n",
    "    # print(f\"Execution {i}\")\n",
    "    # Create the graph\n",
    "    graph = PPGraph(size = SIZE, tau_0 = tau_0)\n",
    "\n",
    "    # Create the optimizer using the current graph\n",
    "    optimizer = ACOPP(graph, ants, alpha, beta, p, penalty, local_p, intensity, q_0)\n",
    "\n",
    "    # Execute the optimizer\n",
    "    history = optimizer.fit(total_iter, save_matrixes = True) #, draw_mode = \"per_iteration\")\n",
    "    \n",
    "    #max_bound = 20\n",
    "    #text_title = r\"ants: {} $\\alpha$: {} $\\beta$: {} $\\rho$: {} $q_0$: {} $\\lambda$: {}\".format(ants,alpha,beta,p,q_0, penalty)\n",
    "    #plot_distance(history['distances_avg'][:max_bound], history['distances_sem'][:max_bound], add_title = text_title)\n",
    "    \n",
    "    acu_pheromones.append(history['pheromones_matrix'][CONVERGENCE_POINT])\n",
    "    acu_exploration.append(history['exploration_matrix'][CONVERGENCE_POINT])\n",
    "    print(\" --- %s --- \" % (str(datetime.timedelta(seconds=time.time() - start_time))))\n",
    "print(\"End Sucessfully\")\n",
    "total_seconds = time.time() - start_time\n",
    "print(\"--- %s seconds ---\" % (str(datetime.timedelta(seconds=total_seconds))))\n",
    "\n",
    "acu_pheromones = np.array(acu_pheromones)\n",
    "acu_exploration = np.array(acu_exploration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 10, 10)\n",
      "(30, 10, 10)\n"
     ]
    }
   ],
   "source": [
    "# Saving files\n",
    "BEST_CHOSEN = 10\n",
    "\n",
    "print(acu_pheromones.shape)\n",
    "print(acu_exploration.shape)\n",
    "\n",
    "\n",
    "np.save(os.path.join(SAVING_DIR,f\"env_{graph.size}_pheromones_avg.npy\"),acu_pheromones.mean(axis=0))\n",
    "np.save(os.path.join(SAVING_DIR,f\"env_{graph.size}_exploration_avg.npy\"),acu_exploration.mean(axis=0))\n",
    "np.save(os.path.join(SAVING_DIR,f\"env_{graph.size}_pheromones_acu.npy\"),acu_pheromones)\n",
    "np.save(os.path.join(SAVING_DIR,f\"env_{graph.size}_exploration_acu.npy\"),acu_exploration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env_100_exploration_acu.npy  env_50_exploration_acu.npy\r\n",
      "env_100_exploration_avg.npy  env_50_exploration_avg.npy\r\n",
      "env_100_pheromones_acu.npy   env_50_pheromones_acu.npy\r\n",
      "env_100_pheromones_avg.npy   env_50_pheromones_avg.npy\r\n",
      "env_10_exploration_acu.npy   env_60_exploration_acu.npy\r\n",
      "env_10_exploration_avg.npy   env_60_exploration_avg.npy\r\n",
      "env_10_pheromones_acu.npy    env_60_pheromones_acu.npy\r\n",
      "env_10_pheromones_avg.npy    env_60_pheromones_avg.npy\r\n",
      "env_20_exploration_acu.npy   env_70_exploration_acu.npy\r\n",
      "env_20_exploration_avg.npy   env_70_exploration_avg.npy\r\n",
      "env_20_pheromones_acu.npy    env_70_pheromones_acu.npy\r\n",
      "env_20_pheromones_avg.npy    env_70_pheromones_avg.npy\r\n",
      "env_30_exploration_acu.npy   env_80_exploration_acu.npy\r\n",
      "env_30_exploration_avg.npy   env_80_exploration_avg.npy\r\n",
      "env_30_pheromones_acu.npy    env_80_pheromones_acu.npy\r\n",
      "env_30_pheromones_avg.npy    env_80_pheromones_avg.npy\r\n",
      "env_40_exploration_acu.npy   env_90_exploration_acu.npy\r\n",
      "env_40_exploration_avg.npy   env_90_exploration_avg.npy\r\n",
      "env_40_pheromones_acu.npy    env_90_pheromones_acu.npy\r\n",
      "env_40_pheromones_avg.npy    env_90_pheromones_avg.npy\r\n"
     ]
    }
   ],
   "source": [
    "!ls $SAVING_DIR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01495616, 0.00696104, 0.00402472, ..., 0.00162841, 0.00162841,\n",
       "        0.00162841],\n",
       "       [0.01468394, 0.01014573, 0.00649773, ..., 0.00162841, 0.00162841,\n",
       "        0.00162841],\n",
       "       [0.00632792, 0.00964196, 0.01093963, ..., 0.00162841, 0.00162841,\n",
       "        0.00162841],\n",
       "       ...,\n",
       "       [0.00162841, 0.00162841, 0.00162841, ..., 0.01359101, 0.00472591,\n",
       "        0.00223704],\n",
       "       [0.00162841, 0.00162841, 0.00162841, ..., 0.01142615, 0.01361967,\n",
       "        0.00690434],\n",
       "       [0.00162841, 0.00162841, 0.00162841, ..., 0.00280177, 0.01412282,\n",
       "        0.01495616]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load(os.path.join(SAVING_DIR,f\"env_{graph.size}_pheromones_avg.npy\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 60, 60)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load(os.path.join(SAVING_DIR,f\"env_{graph.size}_pheromones_acu.npy\")).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5400x3600 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_matrixes(optimizer, acu_pheromones.mean(axis=0), acu_exploration.mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_matrixes(optimizer, acu_pheromones[BEST_CHOSEN], acu_exploration[BEST_CHOSEN])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12920975, 0.15882847, 0.15882847, 0.04035361, 0.04035361,\n",
       "        0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.04035361],\n",
       "       [0.04035361, 0.04035361, 0.12920975, 0.04035361, 0.04035361,\n",
       "        0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.04035361],\n",
       "       [0.04035361, 0.04035361, 0.12920975, 0.04035361, 0.04035361,\n",
       "        0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.04035361],\n",
       "       [0.04035361, 0.04035361, 0.12920975, 0.12920975, 0.12920975,\n",
       "        0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.04035361],\n",
       "       [0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.12920975,\n",
       "        0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.04035361],\n",
       "       [0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.12920975,\n",
       "        0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.04035361],\n",
       "       [0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.12920975,\n",
       "        0.12920975, 0.12920975, 0.04035361, 0.04035361, 0.04035361],\n",
       "       [0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.04035361,\n",
       "        0.04035361, 0.12920975, 0.04035361, 0.04035361, 0.04035361],\n",
       "       [0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.04035361,\n",
       "        0.04035361, 0.12920975, 0.12920975, 0.12920975, 0.04035361],\n",
       "       [0.04035361, 0.04035361, 0.04035361, 0.04035361, 0.04035361,\n",
       "        0.04035361, 0.04035361, 0.04035361, 0.15882847, 0.12920975]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pheromones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11821442, 0.05004656, 0.02824752, 0.02824752, 0.02824752,\n",
       "        0.02824752, 0.02824752, 0.02824752, 0.02824752, 0.02824752],\n",
       "       [0.12640434, 0.11821442, 0.05278673, 0.03642726, 0.02824752,\n",
       "        0.02824752, 0.02824752, 0.02824752, 0.02824752, 0.02824752],\n",
       "       [0.02836981, 0.09367521, 0.10176323, 0.05268483, 0.02824752,\n",
       "        0.02824752, 0.02824752, 0.02824752, 0.02824752, 0.02824752],\n",
       "       [0.02836981, 0.03642726, 0.0936854 , 0.10176323, 0.02824752,\n",
       "        0.02824752, 0.02824752, 0.02824752, 0.02824752, 0.02824752],\n",
       "       [0.02836981, 0.02833924, 0.04469871, 0.1181227 , 0.10994297,\n",
       "        0.02824752, 0.02824752, 0.02824752, 0.02824752, 0.02824752],\n",
       "       [0.02824752, 0.02824752, 0.02833924, 0.03651897, 0.11821442,\n",
       "        0.11003468, 0.03642726, 0.02824752, 0.02824752, 0.02824752],\n",
       "       [0.02824752, 0.02824752, 0.02824752, 0.02824752, 0.03642726,\n",
       "        0.11003468, 0.11003468, 0.03641707, 0.02824752, 0.02824752],\n",
       "       [0.02824752, 0.02824752, 0.02824752, 0.02824752, 0.02824752,\n",
       "        0.03642726, 0.11004487, 0.11821442, 0.03642726, 0.02824752],\n",
       "       [0.02824752, 0.02824752, 0.02824752, 0.02824752, 0.02824752,\n",
       "        0.02824752, 0.02824752, 0.11003468, 0.10185495, 0.02824752],\n",
       "       [0.02824752, 0.02824752, 0.02824752, 0.02824752, 0.02824752,\n",
       "        0.02824752, 0.02824752, 0.05006015, 0.14820338, 0.11821442]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "aco_pp.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}