Modelo_3.ipynb

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    "# Desenvolvimento de ACs para a simulação da propagação de notícias, considerando NOVAS, VELHAS e SEM notícia\n",
    "\n",
    "# Importar as bibliotecas utilizadas no decorrer do programa:\n",
    "from tomato.classes import cell\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Determinação das regras de transição:\n",
    "class NewsPropagation(cell.CellTemplate):\n",
    "    \n",
    "    def update(self, state_matrix):\n",
    "        \n",
    "        self.state_matrix = state_matrix\n",
    "        \n",
    "        # p_1: probabilidade que a informação seja propagada\n",
    "        p_1 = np.random.normal(0, 0.65)\n",
    "        \n",
    "        # R: threshold\n",
    "        R = 1.05\n",
    "        \n",
    "        if self.value == -1:\n",
    "            \n",
    "            if self.information_accepted_new < 3:\n",
    "                    p_1 = p_1*1.5\n",
    "            \n",
    "            # Para que a célula adquira informação, m*p_1 >= R\n",
    "            if (self.information_accepted_new*p_1) >= R:\n",
    "                    self.value = 1\n",
    "                    \n",
    "            else:\n",
    "                self.value = -1\n",
    "                \n",
    "        if self.value == 1:\n",
    "            # Se não há células SEM informação na vizinhança, torna-se informação ANTIGA\n",
    "            if self.information_not_accepted == 0:\n",
    "                self.value = 0\n",
    "                \n",
    "        if self.value == 0:\n",
    "            # Se não há células SEM informação na vizinhança, torna-se SEM informação\n",
    "            if self.information_not_accepted == 0:\n",
    "                self.value = -1                \n",
    "        \n",
    "            \n",
    "    \n",
    "    @property\n",
    "    def neighbors(self):\n",
    "        return self.moore_neighborhood\n",
    "        # Considera-se a vizinhança de MOORE\n",
    "\n",
    "    @property\n",
    "    def information_accepted_new(self):\n",
    "        return self.neighbors.count(1)\n",
    "        # Conta o n° de vizinhos com informação NOVA\n",
    "        \n",
    "    @property\n",
    "    def information_accepted_old(self):\n",
    "        return self.neighbors.count(0)\n",
    "        # Conta o n° de vizinhos com informação VELHA\n",
    "\n",
    "    @property\n",
    "    def information_not_accepted(self):\n",
    "        return self.neighbors.count(-1)\n",
    "        # Conta o n° de vizinhos SEM informação\n",
    "\n",
    "    @staticmethod\n",
    "    def display(value):\n",
    "        if value == -1:\n",
    "            return (0, 0, 0)\n",
    "        # Células SEM informação\n",
    "\n",
    "        elif value == 0:\n",
    "            return (128, 128, 128)\n",
    "        # Células com informações ANTIGAS\n",
    "        \n",
    "        elif value == 1:\n",
    "            return (255, 255, 255)\n",
    "        # Células com informações NOVAS\n",
    "\n",
    "    @staticmethod\n",
    "    def from_display(value):\n",
    "        if (value == (0, 0, 0)).all():\n",
    "            return -1\n",
    "        \n",
    "        elif (value == (128, 128, 128)).all():\n",
    "            return 0\n",
    "        \n",
    "        else:\n",
    "            return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d451cb51",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tomato as tt\n",
    "from tomato.functions import utils\n",
    "\n",
    "rule = NewsPropagation\n",
    "\n",
    "CELL_SIZE = 40\n",
    "dimensions = (100, 100)\n",
    "\n",
    "state_matrix = np.random.choice(a=[-1, 0, 1], size = dimensions, p=[0.50, 0.25, 0.25])\n",
    "\n",
    "\n",
    "initial_state_matrix = state_matrix\n",
    "\n",
    "board = tt.Board(rule, cell_size=CELL_SIZE)\n",
    "board.load_state(initial_state_matrix)\n",
    "\n",
    "# Criando vetores numpy para armazenar as populações de cada espécie de célula\n",
    "news_new_pop = np.zeros(100)\n",
    "news_old_pop = np.zeros(100)\n",
    "no_news_pop = np.zeros(100)\n",
    "\n",
    "while board.generation < 100:\n",
    "    # Matriz com os estados de cada célula:\n",
    "    state_matrix = board.state_matrix\n",
    "\n",
    "    news_new_pop[board.generation] = np.count_nonzero(state_matrix == 1)\n",
    "    news_old_pop[board.generation] = np.count_nonzero(state_matrix == 0)\n",
    "    no_news_pop[board.generation] = np.count_nonzero(state_matrix == -1)\n",
    "\n",
    "    # Iterar a simulação depois de terminada a nossa coleta de dados da geração\n",
    "    board.update()\n",
    "\n",
    "# Avisando que a simulação acabou e fazendo umas estatísticas básicas\n",
    "print(\"Prontinho!\")\n",
    "print(f\"news_new_pop: initial {news_new_pop[0]} | final {news_new_pop[-1]} | avg {np.mean(news_new_pop)}\")\n",
    "print(f\"news_old_pop: initial {news_old_pop[0]} | final {news_old_pop[-1]} | avg {np.mean(news_old_pop)}\")\n",
    "print(f\"no_news_pop: initial {no_news_pop[0]} | final {no_news_pop[-1]} | avg {np.mean(no_news_pop)}\")\n",
    "\n",
    "initial_news_new = news_new_pop[0]/(news_new_pop[0] + no_news_pop[0] + news_old_pop[0])\n",
    "initial_news_old = news_old_pop[0]/(news_new_pop[0] + no_news_pop[0] + news_old_pop[0])\n",
    "initial_no_news = no_news_pop[0]/(news_new_pop[0] + no_news_pop[0] + news_old_pop[0])\n",
    "\n",
    "final_news_new = news_new_pop[-1]/(news_new_pop[-1] + no_news_pop[-1] + news_old_pop[-1])\n",
    "final_news_old = news_old_pop[-1]/(news_new_pop[-1] + no_news_pop[-1] + news_old_pop[-1])\n",
    "final_no_news = no_news_pop[-1]/(news_new_pop[-1] + no_news_pop[-1] + news_old_pop[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccfdbb3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(news_new_pop, color=\"red\", label=\"New News\")\n",
    "ax.plot(news_old_pop, color=\"green\", label=\"Old News\")\n",
    "ax.plot(no_news_pop, color=\"blue\", label=\"No news\")\n",
    "\n",
    "ax.set_title(\"Evolução das populações\")\n",
    "ax.set_xlabel(\"Geração\")\n",
    "ax.set_ylabel(\"População\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f019a6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "mycolors = [\"red\", \"green\", \"blue\"]\n",
    "mylabels = [\"New news\", \"Old news\", \"No news\"]\n",
    "myexplode = [0.05, 0.05, 0.05]\n",
    "\n",
    "# Populações iniciais:\n",
    "y = np.array([initial_news_new, initial_news_old, initial_no_news])\n",
    "plt.title(\"Populações iniciais\")\n",
    "plt.pie(y, labels = mylabels, startangle = 90, explode = myexplode, shadow = True, autopct='%1.1f%%', colors = mycolors)\n",
    "plt.show()\n",
    "\n",
    "# Populações finais:\n",
    "y = np.array([final_news_new, final_news_old, final_no_news])\n",
    "plt.title(\"Populações finais\")\n",
    "plt.pie(y, labels = mylabels, startangle = 90, explode = myexplode, shadow = True, autopct='%1.1f%%', colors = mycolors)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ba3e207",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tomato as tt\n",
    "from tomato.functions import utils\n",
    "\n",
    "rule = NewsPropagation\n",
    "\n",
    "CELL_SIZE = 1\n",
    "dimensions = (100, 100)\n",
    "\n",
    "state_matrix = np.random.choice(a=[-1, 0, 1], size = dimensions, p=[0.80, 0.05, 0.15])\n",
    "\n",
    "initial_state_matrix = state_matrix\n",
    "\n",
    "board = tt.Board(rule, cell_size=CELL_SIZE)\n",
    "board.load_state(initial_state_matrix)\n",
    "\n",
    "board.start(\n",
    "    state_matrix, \n",
    "    inline = True, \n",
    "    generations = 100, \n",
    "    generate_figures = True, \n",
    "    generate_figures_dir = \"Trabalho_Final\", # Especificar o nome da pasta\n",
    "    generate_gif = True # Criar um gif com as imagens ao final da execução\n",
    ")\n",
    "\n",
    "board.save_png(\"Modelo_3_GIF.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e896972a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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