main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problema da Mochila Binária\n",
    "\n",
    "O problema da mochila (em inglês, Knapsack problem) é um problema de **otimização combinatória**. O nome dá-se devido ao modelo de uma situação em que é necessário preencher uma mochila com objetos de diferentes _pesos_ e _valores_. O objetivo é que se preencha a mochila com o maior valor possível, não ultrapassando o peso máximo suportado pela mochila.\n",
    "\n",
    "<img src='imagens/knapsack.png' width='300'>\n",
    "\n",
    "O problema da mochila é um dos 21 problemas NP-completos de Richard Karp, exposto em 1972. A formulação do problema é extremamente simples, porém sua solução é mais complexa. Este problema é a base do primeiro algoritmo de chave pública (chaves assimétricas).\n",
    "\n",
    "Normalmente este problema é resolvido com programação dinâmica, obtendo então a resolução exata do problema, mas também sendo possível usar PSE (procedimento de separação e evolução). Existem também outras técnicas, como usar algoritmo guloso, meta-heurística (**algoritmos genéticos**) para soluções aproximadas. \n",
    "\n",
    "Aqui, utilizaremos um algoritmo genético simples para tentar resolver o problema."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Importação das funções\n",
    "\n",
    "Todas as funções necessárias para o funcionamento do algoritmo são importadas abaixo. Suas implementações se encontram no repositório."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Source developed in Python 3.7\n",
    "Zaqueu Cavalcante (zaqueudovale@gmail.com) \n",
    "Date: 14/03/2020\n",
    "\"\"\"\n",
    "\n",
    "from gera_populacao_inicial import gera_populacao_inicial\n",
    "from calcula_valores_populacao import calcula_valores_populacao\n",
    "from calcula_pesos_populacao import calcula_pesos_populacao\n",
    "from plot_espaco_busca import plot_espaco_busca\n",
    "from funcao_objetivo import funcao_objetivo\n",
    "from plot_funcao_objetivo import plot_funcao_objetivo\n",
    "from gera_ranking import gera_ranking\n",
    "from seleciona_pais import seleciona_pais\n",
    "from cruzamento import cruzamento\n",
    "from seleciona_mutantes import seleciona_mutantes\n",
    "from realiza_mutacao import realiza_mutacao\n",
    "from decodifica_mochila import decodifica_mochila\n",
    "from print_lista import print_lista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 - Representação de cada indivíduo/mochila\n",
    "\n",
    "Primeiramente, definimos uma lista de objetos que podem ser adicionados à mochila, cada qual com determinado valor e peso.\n",
    "\n",
    "Cada lista [nome, valor, peso] representa um objeto. O valor está em R$ e o peso em kg.\n",
    "\n",
    "Você pode adiconar mais objetos ou alterar os já existentes, para ver como o programa se comporta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "objetos = [['Caneta', 1.5, 0.1], \n",
    "           ['Mouse', 50, 0.2], \n",
    "           ['Caderno', 60, 1.0], \n",
    "           ['Carregador', 100, 0.5], \n",
    "           ['Post-it', 5, 0.1], \n",
    "           ['Livro', 120, 0.6], \n",
    "           ['Notebook', 110, 3.0], \n",
    "           ['Smartphone', 90, 0.4], \n",
    "           ['Garrafa', 30, 1.5], \n",
    "           ['Pen-drive', 80, 0.05], \n",
    "           ['Calculadora', 120, 0.5],\n",
    "           ['Pasta', 25, 0.8]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A seguir, montamos três vetores, contendo os nomes, valores e pesos dos objetos definidos.\n",
    "\n",
    "Além disso, calculamos o valor e peso máximo que uma mochila que contivesse todos os objetos teria. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_objetos = len(objetos)\n",
    "nomes, valores, pesos  = [], [], []\n",
    "\n",
    "for i in range(0, n_objetos):\n",
    "    nomes.append(objetos[i][0])   \n",
    "    valores.append(objetos[i][1]) \n",
    "    pesos.append(objetos[i][2])  \n",
    "\n",
    "valor_max = sum(valores)    # Valor máximo (mochila com todos os objetos).\n",
    "peso_max = sum(pesos)    # Peso máximo (mochila com todos os objetos)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim, definimos que cada indivíduo será representado através de uma lista, na qual:\n",
    "- 0 indica que o objeto não está presente na mochila.\n",
    "- 1 indica que o objeto está presente na mochila.\n",
    "\n",
    "Exemplo de mochila, na qual apenas o primeiro e o penúltimo objetos estão presentes: \n",
    "\n",
    "    m = [1,0,0,0,0,0,0,0,1,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 - Geração da população inicial\n",
    "\n",
    "Aqui, iniciaremos a população de forma aleatória. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tamanho_populacao = 300\n",
    "peso_limite = 6.5    # Peso limite de uma mochila válida.\n",
    "\n",
    "populacao = gera_populacao_inicial(tamanho_populacao, n_objetos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 - Ciclo evolutivo\n",
    "\n",
    "Nessa etapa, todos os indivíduos da população serão avaliados em relação à sua **aptidão** de resolver o problema, se **reproduzirão** e sofrerão eventuais **mutações** para formar uma prole que **herde** as características das mochilas mais aptadas.\n",
    "\n",
    "Desta forma, se pretende que as populações apresentem mochilas cada vez melhores ao passar das gerações, convergindo para as melhores soluções ao final."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_geracoes = 10    # Número máximo de gerações (critério de parada).\n",
    "\n",
    "prob_mutacao = 10    # Probabilidade de ocorrer mutação, em %.\n",
    "taxa_mutacao = 5    # Quantidade de genes que sofrerão mutação, em %."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
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       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
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       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
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       "            pass_mouse_events = false;\n",
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       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
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       "        stop: function(event, ui) {\n",
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       "\n",
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       "    var fig = this;\n",
       "\n",
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       "    nav_element.attr('style', 'width: 100%');\n",
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       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
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       "\n",
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       "        icon_img.addClass('ui-corner-all');\n",
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       "        var tooltip_span = $('<span/>');\n",
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       "        tooltip_span.html(tooltip);\n",
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       "\n",
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       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
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       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
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       "\n",
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       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
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       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
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       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"949.6\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "plt.rcParams['animation.html'] = 'html5'\n",
    "\n",
    "fig = plt.figure(figsize=(9.5, 5))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax2 = fig.add_subplot(122, projection='3d')\n",
    "\n",
    "for geracao in range(1, n_geracoes+1):\n",
    "    \n",
    "    # 4 - AVALIAÇÃO DA APTIDÃO DA POPULAÇÃO ATUAL\n",
    "    \"\"\"\"\n",
    "    t = 'Espaço de Busca - Geração ' + str(geracao)\n",
    "    valores_mochilas = calcula_valores_populacao(populacao, valores)\n",
    "    pesos_mochilas = calcula_pesos_populacao(populacao, pesos)\n",
    "    plot_espaco_busca(valores_mochilas, valor_max, pesos_mochilas, peso_limite, peso_max, t)\n",
    "    \n",
    "    t = 'Função Objetivo - Geração ' + str(geracao)\n",
    "    aptidoes = funcao_objetivo(valores_mochilas, pesos_mochilas, valor_max, peso_limite)\n",
    "    plot_funcao_objetivo(valores_mochilas, pesos_mochilas, aptidoes, peso_max, t)\n",
    "    \"\"\"\n",
    "    \n",
    "    ax1.cla()\n",
    "    ax2.cla()\n",
    "    \n",
    "    t = 'Espaço de Busca - Geração ' + str(geracao)\n",
    "    valores_mochilas = calcula_valores_populacao(populacao, valores)\n",
    "    pesos_mochilas = calcula_pesos_populacao(populacao, pesos)\n",
    "    \n",
    "    for i in range(0, len(pesos_mochilas)):\n",
    "        if (pesos_mochilas[i] <= peso_limite):\n",
    "            ax1.scatter(pesos_mochilas[i], valores_mochilas[i], color='g')\n",
    "        else:\n",
    "            ax1.scatter(pesos_mochilas[i], valores_mochilas[i], color='r')\n",
    "    \n",
    "    ax1.grid()\n",
    "    ax1.set_xlabel('Pesos (kg)')\n",
    "    ax1.set_ylabel('Valores (R$)')\n",
    "    ax1.set_title(t, fontweight=\"bold\")\n",
    "    ax1.set_xlim(0, peso_max)\n",
    "    ax1.set_ylim(0, valor_max)\n",
    "    ax1.axvline(x=peso_limite, color='y')\n",
    "    ax1.text(peso_limite, 150, ' Peso \\n limite')\n",
    "    \n",
    "    t = 'Função Objetivo - Geração ' + str(geracao)\n",
    "    aptidoes = funcao_objetivo(valores_mochilas, pesos_mochilas, valor_max, peso_limite)\n",
    "    \n",
    "    for i in range(0, len(pesos_mochilas)):\n",
    "        if (pesos_mochilas[i] <= peso_max):\n",
    "            ax2.scatter(pesos_mochilas[i], valores_mochilas[i], aptidoes[i], color='b')\n",
    "        else:\n",
    "            ax2.scatter(pesos_mochilas[i], valores_mochilas[i], aptidoes[i], color='b')\n",
    "\n",
    "    ax2.grid()\n",
    "    ax2.set_xlabel('Pesos (kg)')\n",
    "    ax2.set_ylabel('Valores (R$)')\n",
    "    ax2.set_zlabel('Aptidão')\n",
    "    ax2.set_title(t, fontweight=\"bold\")\n",
    "    ax2.set_xlim(0, peso_max)\n",
    "    ax2.set_ylim(0, valor_max)\n",
    "    \n",
    "    fig.canvas.draw()\n",
    "    \n",
    "    # 5 - RANKING DE APTIDÃO\n",
    "    \n",
    "    # Geração de uma lista das mochilas que compõe a geração atual, por ordem de aptidão (da maior para a menor).\n",
    "\n",
    "    ranking = gera_ranking(aptidoes[:])\n",
    "                \n",
    "    # 6 - SELEÇÃO DOS PAIS\n",
    "\n",
    "    pais = seleciona_pais(ranking)\n",
    "    \n",
    "    # 7 - CRUZAMENTO\n",
    "    \n",
    "    # O método utilizado será o de 'recombinação com um ponto de corte'.\n",
    "\n",
    "    populacao = cruzamento(populacao, pais)\n",
    "    \n",
    "    # 8 - MUTAÇÃO\n",
    "    \n",
    "    # Consiste em mudar, aleatoriamente, alguma característica do indivíduo.\n",
    "    # No caso, algumas mochilas (dentro da nova população) serão selecionadas. \n",
    "    # Cada uma delas terá UM objeto retirado ou adicionado.\n",
    "    \n",
    "    mutantes = seleciona_mutantes(populacao, prob_mutacao)    \n",
    "    populacao = realiza_mutacao(populacao, mutantes, taxa_mutacao)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9 - Melhor solução "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$ Melhor mochila $\n",
      "-> Itens: \n",
      "Caneta\n",
      "Mouse\n",
      "Caderno\n",
      "Carregador\n",
      "Post-it\n",
      "Livro\n",
      "Notebook\n",
      "Smartphone\n",
      "Pen-drive\n",
      "Calculadora\n",
      "-------------------\n",
      "Valor: R$ 736.5\n",
      "Peso: 6.45 kg\n"
     ]
    }
   ],
   "source": [
    "melhor_mochila = populacao[ranking[0]]\n",
    "mochila_decodificada = decodifica_mochila(melhor_mochila, nomes, valores, pesos)\n",
    "print('$ Melhor mochila $')\n",
    "print('-> Itens: ')\n",
    "print_lista(mochila_decodificada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Referências\n",
    "\n",
    "[Wikipédia - Problema da Mochila](https://pt.wikipedia.org/wiki/Problema_da_mochila)"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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