From 2a9407b96abaef395f41531536f92f204a348aed Mon Sep 17 00:00:00 2001 From: reginaldo Date: Thu, 23 Oct 2025 06:35:57 -0300 Subject: [PATCH 1/4] initial --- .gitignore | 1 + agents.ipynb | 523 +++++++++++++-- exemplo1.ipynb | 179 ++++++ search.ipynb | 1676 +++++++++++++++++++++++++++++++----------------- 4 files changed, 1743 insertions(+), 636 deletions(-) create mode 100644 exemplo1.ipynb diff --git a/.gitignore b/.gitignore index 58e83214e..9413288d5 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,7 @@ __pycache__/ # Distribution / packaging .Python +venv/ env/ build/ develop-eggs/ diff --git a/agents.ipynb b/agents.ipynb index 636df75e3..ff9e4f049 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -43,9 +43,152 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "

\n", + "\n", + "
class Agent(Thing):\n",
+       "    """An Agent is a subclass of Thing with one required instance attribute \n",
+       "    (aka slot), .program, which should hold a function that takes one argument,\n",
+       "    the percept, and returns an action. (What counts as a percept or action \n",
+       "    will depend on the specific environment in which the agent exists.)\n",
+       "    Note that 'program' is a slot, not a method. If it were a method, then the\n",
+       "    program could 'cheat' and look at aspects of the agent. It's not supposed\n",
+       "    to do that: the program can only look at the percepts. An agent program\n",
+       "    that needs a model of the world (and of the agent itself) will have to\n",
+       "    build and maintain its own model. There is an optional slot, .performance,\n",
+       "    which is a number giving the performance measure of the agent in its\n",
+       "    environment."""\n",
+       "\n",
+       "    def __init__(self, program=None):\n",
+       "        self.alive = True\n",
+       "        self.bump = False\n",
+       "        self.holding = []\n",
+       "        self.performance = 0\n",
+       "        if program is None or not isinstance(program, collections.abc.Callable):\n",
+       "            print("Can't find a valid program for {}, falling back to default.".format(self.__class__.__name__))\n",
+       "\n",
+       "            def program(percept):\n",
+       "                return eval(input('Percept={}; action? '.format(percept)))\n",
+       "\n",
+       "        self.program = program\n",
+       "\n",
+       "    def can_grab(self, thing):\n",
+       "        """Return True if this agent can grab this thing.\n",
+       "        Override for appropriate subclasses of Agent and Thing."""\n",
+       "        return False\n",
+       "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "psource(Agent)" ] @@ -75,9 +218,222 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "

\n", + "\n", + "
class Environment:\n",
+       "    """Abstract class representing an Environment. 'Real' Environment classes\n",
+       "    inherit from this. Your Environment will typically need to implement:\n",
+       "        percept:           Define the percept that an agent sees.\n",
+       "        execute_action:    Define the effects of executing an action.\n",
+       "                           Also update the agent.performance slot.\n",
+       "    The environment keeps a list of .things and .agents (which is a subset\n",
+       "    of .things). Each agent has a .performance slot, initialized to 0.\n",
+       "    Each thing has a .location slot, even though some environments may not\n",
+       "    need this."""\n",
+       "\n",
+       "    def __init__(self):\n",
+       "        self.things = []\n",
+       "        self.agents = []\n",
+       "\n",
+       "    def thing_classes(self):\n",
+       "        return []  # List of classes that can go into environment\n",
+       "\n",
+       "    def percept(self, agent):\n",
+       "        """Return the percept that the agent sees at this point. (Implement this.)"""\n",
+       "        raise NotImplementedError\n",
+       "\n",
+       "    def execute_action(self, agent, action):\n",
+       "        """Change the world to reflect this action. (Implement this.)"""\n",
+       "        raise NotImplementedError\n",
+       "\n",
+       "    def default_location(self, thing):\n",
+       "        """Default location to place a new thing with unspecified location."""\n",
+       "        return None\n",
+       "\n",
+       "    def exogenous_change(self):\n",
+       "        """If there is spontaneous change in the world, override this."""\n",
+       "        pass\n",
+       "\n",
+       "    def is_done(self):\n",
+       "        """By default, we're done when we can't find a live agent."""\n",
+       "        return not any(agent.is_alive() for agent in self.agents)\n",
+       "\n",
+       "    def step(self):\n",
+       "        """Run the environment for one time step. If the\n",
+       "        actions and exogenous changes are independent, this method will\n",
+       "        do. If there are interactions between them, you'll need to\n",
+       "        override this method."""\n",
+       "        if not self.is_done():\n",
+       "            actions = []\n",
+       "            for agent in self.agents:\n",
+       "                if agent.alive:\n",
+       "                    actions.append(agent.program(self.percept(agent)))\n",
+       "                else:\n",
+       "                    actions.append("")\n",
+       "            for (agent, action) in zip(self.agents, actions):\n",
+       "                self.execute_action(agent, action)\n",
+       "            self.exogenous_change()\n",
+       "\n",
+       "    def run(self, steps=1000):\n",
+       "        """Run the Environment for given number of time steps."""\n",
+       "        for step in range(steps):\n",
+       "            if self.is_done():\n",
+       "                return\n",
+       "            self.step()\n",
+       "\n",
+       "    def list_things_at(self, location, tclass=Thing):\n",
+       "        """Return all things exactly at a given location."""\n",
+       "        if isinstance(location, numbers.Number):\n",
+       "            return [thing for thing in self.things\n",
+       "                    if thing.location == location and isinstance(thing, tclass)]\n",
+       "        return [thing for thing in self.things\n",
+       "                if all(x == y for x, y in zip(thing.location, location)) and isinstance(thing, tclass)]\n",
+       "\n",
+       "    def some_things_at(self, location, tclass=Thing):\n",
+       "        """Return true if at least one of the things at location\n",
+       "        is an instance of class tclass (or a subclass)."""\n",
+       "        return self.list_things_at(location, tclass) != []\n",
+       "\n",
+       "    def add_thing(self, thing, location=None):\n",
+       "        """Add a thing to the environment, setting its location. For\n",
+       "        convenience, if thing is an agent program we make a new agent\n",
+       "        for it. (Shouldn't need to override this.)"""\n",
+       "        if not isinstance(thing, Thing):\n",
+       "            thing = Agent(thing)\n",
+       "        if thing in self.things:\n",
+       "            print("Can't add the same thing twice")\n",
+       "        else:\n",
+       "            thing.location = location if location is not None else self.default_location(thing)\n",
+       "            self.things.append(thing)\n",
+       "            if isinstance(thing, Agent):\n",
+       "                thing.performance = 0\n",
+       "                self.agents.append(thing)\n",
+       "\n",
+       "    def delete_thing(self, thing):\n",
+       "        """Remove a thing from the environment."""\n",
+       "        try:\n",
+       "            self.things.remove(thing)\n",
+       "        except ValueError as e:\n",
+       "            print(e)\n",
+       "            print("  in Environment delete_thing")\n",
+       "            print("  Thing to be removed: {} at {}".format(thing, thing.location))\n",
+       "            print("  from list: {}".format([(thing, thing.location) for thing in self.things]))\n",
+       "        if thing in self.agents:\n",
+       "            self.agents.remove(thing)\n",
+       "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "psource(Environment)" ] @@ -114,9 +470,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Can't find a valid program for BlindDog, falling back to default.\n" + ] + } + ], "source": [ "class BlindDog(Agent):\n", " def eat(self, thing):\n", @@ -137,9 +501,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], "source": [ "print(dog.alive)" ] @@ -163,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -217,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -287,9 +659,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BlindDog decided to move down at location: 1\n", + "BlindDog decided to move down at location: 2\n", + "BlindDog decided to move down at location: 3\n", + "BlindDog decided to move down at location: 4\n", + "BlindDog ate Food at location: 5\n" + ] + } + ], "source": [ "park = Park()\n", "dog = BlindDog(program)\n", @@ -313,9 +697,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BlindDog decided to move down at location: 5\n", + "BlindDog decided to move down at location: 6\n", + "BlindDog drank Water at location: 7\n" + ] + } + ], "source": [ "park.run(5)" ] @@ -329,9 +723,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BlindDog decided to move down at location: 7\n", + "BlindDog decided to move down at location: 8\n", + "BlindDog decided to move down at location: 9\n", + "BlindDog decided to move down at location: 10\n", + "BlindDog decided to move down at location: 11\n", + "BlindDog decided to move down at location: 12\n", + "BlindDog decided to move down at location: 13\n", + "BlindDog decided to move down at location: 14\n", + "BlindDog drank Water at location: 15\n" + ] + } + ], "source": [ "park.add_thing(water, 15)\n", "park.run(10)" @@ -357,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -423,9 +833,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "park = Park2D(5,20, color={'BlindDog': (200,0,0), 'Water': (0, 200, 200), 'Food': (230, 115, 40)}) # park width is set to 5, and height to 20\n", "dog = BlindDog(program)\n", @@ -482,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -553,7 +976,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -618,9 +1041,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "park = Park2D(5,5, color={'EnergeticBlindDog': (200,0,0), 'Water': (0, 200, 200), 'Food': (230, 115, 40)})\n", "dog = EnergeticBlindDog(program)\n", @@ -654,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -696,24 +1132,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[], [], [], [], [, None]]\n" + ] + } + ], "source": [ "step()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -727,7 +1176,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/exemplo1.ipynb b/exemplo1.ipynb new file mode 100644 index 000000000..cdbc84c55 --- /dev/null +++ b/exemplo1.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8909c6b1", + "metadata": {}, + "outputs": [], + "source": [ + "from collections import deque\n", + "\n", + "# 1. Definição do Grafo (Estados e Transições)\n", + "# Representamos o mapa como um dicionário: Cidade: [Vizinhos]\n", + "MAPA_ROMENIA = {\n", + " 'Arad': ['Zerind', 'Timisoara', 'Sibiu'],\n", + " 'Zerind': ['Arad', 'Oradea'],\n", + " 'Timisoara': ['Arad', 'Lugoj'],\n", + " 'Sibiu': ['Arad', 'Oradea', 'Fagaras', 'Rimnicu Vilcea'],\n", + " 'Oradea': ['Zerind', 'Sibiu'],\n", + " 'Lugoj': ['Timisoara', 'Mehadia'],\n", + " 'Mehadia': ['Lugoj', 'Drobeta'],\n", + " 'Drobeta': ['Mehadia', 'Craiova'],\n", + " 'Craiova': ['Drobeta', 'Rimnicu Vilcea', 'Pitesti'],\n", + " 'Rimnicu Vilcea': ['Sibiu', 'Craiova', 'Pitesti'],\n", + " 'Fagaras': ['Sibiu', 'Bucharest'],\n", + " 'Pitesti': ['Rimnicu Vilcea', 'Craiova', 'Bucharest'],\n", + " 'Hirsova': ['Urziceni', 'Eforie'],\n", + " 'Urziceni': ['Bucharest', 'Vaslui', 'Hirsova'],\n", + " 'Iasi': ['Neamt', 'Vaslui'],\n", + " 'Vaslui': ['Iasi', 'Urziceni'],\n", + " 'Neamt': ['Iasi'],\n", + " 'Giurgiu': ['Bucharest'],\n", + " 'Eforie': ['Hirsova'],\n", + " 'Bucharest': ['Fagaras', 'Pitesti', 'Giurgiu', 'Urziceni'] # O Goal Test é aqui!\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f834938b", + "metadata": {}, + "outputs": [], + "source": [ + "def search(initial_state, goal_test_city, graph):\n", + " \"\"\"\n", + " Implementa a Busca em Largura (BFS) para encontrar um caminho\n", + " do estado inicial ao objetivo (Goal).\n", + " \n", + " A Solução é uma sequência de ações (cidades a visitar).\n", + " \"\"\"\n", + " \n", + " # 1. Definição do Problema:\n", + " # O estado é o nome da cidade.\n", + " # A Goal Test é verificar se o estado atual é igual à cidade de destino.\n", + " \n", + " # Se o estado inicial já for o objetivo, a sequência é vazia.\n", + " if initial_state == goal_test_city:\n", + " return [initial_state]\n", + "\n", + " # Fila (Queue) para o BFS: Armazena caminhos potenciais\n", + " # Cada elemento da fila é um [caminho] do início até a cidade atual.\n", + " queue = deque([[initial_state]])\n", + " \n", + " # Conjunto de cidades já visitadas (para evitar ciclos e repetições)\n", + " visited = {initial_state}\n", + " \n", + " # 2. Processo de Busca (Search):\n", + " while queue:\n", + " # Pega o caminho mais antigo na fila (FIFO - First-In, First-Out)\n", + " path = queue.popleft()\n", + " current_city = path[-1] # Última cidade no caminho (estado atual)\n", + "\n", + " # 3. Modelo de Transição e Ações:\n", + " # Itera sobre os vizinhos (estados sucessores) da cidade atual\n", + " for next_city in graph.get(current_city, []):\n", + " \n", + " # 4. Teste de Objetivo (Goal Test):\n", + " if next_city == goal_test_city:\n", + " # Solução encontrada! Retorna a sequência completa de ações (caminho).\n", + " return path + [next_city]\n", + "\n", + " # Se ainda não visitado, adicione o novo estado à fila para exploração futura\n", + " if next_city not in visited:\n", + " visited.add(next_city)\n", + " new_path = path + [next_city]\n", + " queue.append(new_path)\n", + " \n", + " # Se a fila esvaziar e o objetivo não for encontrado, não há solução.\n", + " return \"failure\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0a7bae5a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estado Inicial: Arad\n", + "Estado Objetivo: Bucharest\n", + "------------------------------\n", + "Solução (Sequência de Cidades): ['Arad', 'Sibiu', 'Fagaras', 'Bucharest']\n", + "Número de Ações (paradas): 3\n", + "\n", + "Execução das Ações:\n", + " Ação 1: Dirigir de Arad para Sibiu\n", + " Ação 2: Dirigir de Sibiu para Fagaras\n", + " Ação 3: Dirigir de Fagaras para Bucharest\n" + ] + } + ], + "source": [ + "# 1. Formulação do Problema (Goal e Estado Inicial)\n", + "ESTADO_INICIAL = 'Arad'\n", + "ESTADO_OBJETIVO = 'Bucharest'\n", + "\n", + "# 2. Busca (Search)\n", + "solucao_path = search(ESTADO_INICIAL, ESTADO_OBJETIVO, MAPA_ROMENIA)\n", + "\n", + "# 3. Execução (Execution) - Sequência de Ações\n", + "print(f\"Estado Inicial: {ESTADO_INICIAL}\")\n", + "print(f\"Estado Objetivo: {ESTADO_OBJETIVO}\")\n", + "print(\"-\" * 30)\n", + "\n", + "if solucao_path == \"failure\":\n", + " print(\"Busca falhou: Não foi encontrado um caminho para Bucareste.\")\n", + "else:\n", + " print(f\"Solução (Sequência de Cidades): {solucao_path}\")\n", + " print(f\"Número de Ações (paradas): {len(solucao_path) - 1}\")\n", + " \n", + " # Simulação da execução das ações:\n", + " print(\"\\nExecução das Ações:\")\n", + " for i in range(len(solucao_path) - 1):\n", + " cidade_origem = solucao_path[i]\n", + " cidade_destino = solucao_path[i+1]\n", + " print(f\" Ação {i+1}: Dirigir de {cidade_origem} para {cidade_destino}\")\n", + "\n", + "# Saída Esperada:\n", + "# Solução (Sequência de Cidades): ['Arad', 'Sibiu', 'Fagaras', 'Bucharest']\n", + "# Número de Ações (paradas): 3\n", + "# Execução das Ações:\n", + "# Ação 1: Dirigir de Arad para Sibiu\n", + "# Ação 2: Dirigir de Sibiu para Fagaras\n", + "# Ação 3: Dirigir de Fagaras para Bucharest" + ] + }, + { + "cell_type": "markdown", + "id": "66961fb7", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/search.ipynb b/search.ipynb index caf231dcc..8cf99be8d 100644 --- a/search.ipynb +++ b/search.ipynb @@ -123,119 +123,130 @@ "text/html": [ "\n", - "\n", + "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "

\n", "\n", - "
class Problem(object):\n",
-       "\n",
-       "    """The abstract class for a formal problem. You should subclass\n",
+       "
class Problem:\n",
+       "    """The abstract class for a formal problem. You should subclass\n",
        "    this and implement the methods actions and result, and possibly\n",
        "    __init__, goal_test, and path_cost. Then you will create instances\n",
        "    of your subclass and solve them with the various search functions."""\n",
        "\n",
-       "    def __init__(self, initial, goal=None):\n",
-       "        """The constructor specifies the initial state, and possibly a goal\n",
+       "    def __init__(self, initial, goal=None):\n",
+       "        """The constructor specifies the initial state, and possibly a goal\n",
        "        state, if there is a unique goal. Your subclass's constructor can add\n",
        "        other arguments."""\n",
        "        self.initial = initial\n",
        "        self.goal = goal\n",
        "\n",
-       "    def actions(self, state):\n",
-       "        """Return the actions that can be executed in the given\n",
+       "    def actions(self, state):\n",
+       "        """Return the actions that can be executed in the given\n",
        "        state. The result would typically be a list, but if there are\n",
        "        many actions, consider yielding them one at a time in an\n",
        "        iterator, rather than building them all at once."""\n",
        "        raise NotImplementedError\n",
        "\n",
-       "    def result(self, state, action):\n",
-       "        """Return the state that results from executing the given\n",
+       "    def result(self, state, action):\n",
+       "        """Return the state that results from executing the given\n",
        "        action in the given state. The action must be one of\n",
        "        self.actions(state)."""\n",
        "        raise NotImplementedError\n",
        "\n",
-       "    def goal_test(self, state):\n",
-       "        """Return True if the state is a goal. The default method compares the\n",
+       "    def goal_test(self, state):\n",
+       "        """Return True if the state is a goal. The default method compares the\n",
        "        state to self.goal or checks for state in self.goal if it is a\n",
        "        list, as specified in the constructor. Override this method if\n",
        "        checking against a single self.goal is not enough."""\n",
@@ -244,16 +255,16 @@
        "        else:\n",
        "            return state == self.goal\n",
        "\n",
-       "    def path_cost(self, c, state1, action, state2):\n",
-       "        """Return the cost of a solution path that arrives at state2 from\n",
+       "    def path_cost(self, c, state1, action, state2):\n",
+       "        """Return the cost of a solution path that arrives at state2 from\n",
        "        state1 via action, assuming cost c to get up to state1. If the problem\n",
        "        is such that the path doesn't matter, this function will only look at\n",
-       "        state2.  If the path does matter, it will consider c and maybe state1\n",
+       "        state2. If the path does matter, it will consider c and maybe state1\n",
        "        and action. The default method costs 1 for every step in the path."""\n",
        "        return c + 1\n",
        "\n",
-       "    def value(self, state):\n",
-       "        """For optimization problems, each state has a value.  Hill-climbing\n",
+       "    def value(self, state):\n",
+       "        """For optimization problems, each state has a value. Hill Climbing\n",
        "        and related algorithms try to maximize this value."""\n",
        "        raise NotImplementedError\n",
        "
\n",
@@ -315,103 +326,114 @@
       "text/html": [
        "\n",
-       "\n",
+       "\n",
        "\n",
        "\n",
        "  \n",
        "  \n",
        "  \n",
        "\n",
        "\n",
        "
\n",
        "\n",
-       "
class Node:\n",
-       "\n",
-       "    """A node in a search tree. Contains a pointer to the parent (the node\n",
+       "
class Node:\n",
+       "    """A node in a search tree. Contains a pointer to the parent (the node\n",
        "    that this is a successor of) and to the actual state for this node. Note\n",
        "    that if a state is arrived at by two paths, then there are two nodes with\n",
-       "    the same state.  Also includes the action that got us to this state, and\n",
-       "    the total path_cost (also known as g) to reach the node.  Other functions\n",
+       "    the same state. Also includes the action that got us to this state, and\n",
+       "    the total path_cost (also known as g) to reach the node. Other functions\n",
        "    may add an f and h value; see best_first_graph_search and astar_search for\n",
        "    an explanation of how the f and h values are handled. You will not need to\n",
        "    subclass this class."""\n",
        "\n",
-       "    def __init__(self, state, parent=None, action=None, path_cost=0):\n",
-       "        """Create a search tree Node, derived from a parent by an action."""\n",
+       "    def __init__(self, state, parent=None, action=None, path_cost=0):\n",
+       "        """Create a search tree Node, derived from a parent by an action."""\n",
        "        self.state = state\n",
        "        self.parent = parent\n",
        "        self.action = action\n",
@@ -420,31 +442,29 @@
        "        if parent:\n",
        "            self.depth = parent.depth + 1\n",
        "\n",
-       "    def __repr__(self):\n",
-       "        return "<Node {}>".format(self.state)\n",
+       "    def __repr__(self):\n",
+       "        return "<Node {}>".format(self.state)\n",
        "\n",
-       "    def __lt__(self, node):\n",
+       "    def __lt__(self, node):\n",
        "        return self.state < node.state\n",
        "\n",
-       "    def expand(self, problem):\n",
-       "        """List the nodes reachable in one step from this node."""\n",
+       "    def expand(self, problem):\n",
+       "        """List the nodes reachable in one step from this node."""\n",
        "        return [self.child_node(problem, action)\n",
        "                for action in problem.actions(self.state)]\n",
        "\n",
-       "    def child_node(self, problem, action):\n",
-       "        """[Figure 3.10]"""\n",
+       "    def child_node(self, problem, action):\n",
+       "        """[Figure 3.10]"""\n",
        "        next_state = problem.result(self.state, action)\n",
-       "        next_node = Node(next_state, self, action,\n",
-       "                    problem.path_cost(self.path_cost, self.state,\n",
-       "                                      action, next_state))\n",
+       "        next_node = Node(next_state, self, action, problem.path_cost(self.path_cost, self.state, action, next_state))\n",
        "        return next_node\n",
-       "    \n",
-       "    def solution(self):\n",
-       "        """Return the sequence of actions to go from the root to this node."""\n",
+       "\n",
+       "    def solution(self):\n",
+       "        """Return the sequence of actions to go from the root to this node."""\n",
        "        return [node.action for node in self.path()[1:]]\n",
        "\n",
-       "    def path(self):\n",
-       "        """Return a list of nodes forming the path from the root to this node."""\n",
+       "    def path(self):\n",
+       "        """Return a list of nodes forming the path from the root to this node."""\n",
        "        node, path_back = self, []\n",
        "        while node:\n",
        "            path_back.append(node)\n",
@@ -456,10 +476,14 @@
        "    # with the same state as equal. [Problem: this may not be what you\n",
        "    # want in other contexts.]\n",
        "\n",
-       "    def __eq__(self, other):\n",
+       "    def __eq__(self, other):\n",
        "        return isinstance(other, Node) and self.state == other.state\n",
        "\n",
-       "    def __hash__(self):\n",
+       "    def __hash__(self):\n",
+       "        # We use the hash value of the state\n",
+       "        # stored in the node instead of the node\n",
+       "        # object itself to quickly search a node\n",
+       "        # with the same state in a Hash Table\n",
        "        return hash(self.state)\n",
        "
\n",
        "\n",
@@ -523,128 +547,139 @@
       "text/html": [
        "\n",
-       "\n",
+       "\n",
        "\n",
        "\n",
        "  \n",
        "  \n",
        "  \n",
        "\n",
        "\n",
        "
\n",
        "\n",
-       "
class GraphProblem(Problem):\n",
+       "
class GraphProblem(Problem):\n",
+       "    """The problem of searching a graph from one node to another."""\n",
        "\n",
-       "    """The problem of searching a graph from one node to another."""\n",
-       "\n",
-       "    def __init__(self, initial, goal, graph):\n",
-       "        Problem.__init__(self, initial, goal)\n",
+       "    def __init__(self, initial, goal, graph):\n",
+       "        super().__init__(initial, goal)\n",
        "        self.graph = graph\n",
        "\n",
-       "    def actions(self, A):\n",
-       "        """The actions at a graph node are just its neighbors."""\n",
+       "    def actions(self, A):\n",
+       "        """The actions at a graph node are just its neighbors."""\n",
        "        return list(self.graph.get(A).keys())\n",
        "\n",
-       "    def result(self, state, action):\n",
-       "        """The result of going to a neighbor is just that neighbor."""\n",
+       "    def result(self, state, action):\n",
+       "        """The result of going to a neighbor is just that neighbor."""\n",
        "        return action\n",
        "\n",
-       "    def path_cost(self, cost_so_far, A, action, B):\n",
-       "        return cost_so_far + (self.graph.get(A, B) or infinity)\n",
+       "    def path_cost(self, cost_so_far, A, action, B):\n",
+       "        return cost_so_far + (self.graph.get(A, B) or np.inf)\n",
        "\n",
-       "    def find_min_edge(self):\n",
-       "        """Find minimum value of edges."""\n",
-       "        m = infinity\n",
+       "    def find_min_edge(self):\n",
+       "        """Find minimum value of edges."""\n",
+       "        m = np.inf\n",
        "        for d in self.graph.graph_dict.values():\n",
        "            local_min = min(d.values())\n",
        "            m = min(m, local_min)\n",
        "\n",
        "        return m\n",
        "\n",
-       "    def h(self, node):\n",
-       "        """h function is straight-line distance from a node's state to goal."""\n",
-       "        locs = getattr(self.graph, 'locations', None)\n",
+       "    def h(self, node):\n",
+       "        """h function is straight-line distance from a node's state to goal."""\n",
+       "        locs = getattr(self.graph, 'locations', None)\n",
        "        if locs:\n",
        "            if type(node) is str:\n",
        "                return int(distance(locs[node], locs[self.goal]))\n",
        "\n",
        "            return int(distance(locs[node.state], locs[self.goal]))\n",
        "        else:\n",
-       "            return infinity\n",
+       "            return np.inf\n",
        "
\n",
        "\n",
        "\n"
@@ -808,9 +843,9 @@
    "outputs": [
     {
      "data": {
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AAAAAAAC7UTgCAAAAAAAAAAAAsBuFIwAAAAAAAAAAAAC7UTgCAAAAAAAAAAAAsBuFIwAAAAAAAAAAAAC7UTgCAAAAAAAAAAAAsBuFIwAAAAAAAAAAAAC7UTgCAAAAAAAAAAAAsBuFIwAAAAAAAAAAAAC7UTgCAAAAAAAAAAAAsBuFIwAAAAAAAAAAAAC7UTgCAAAAAAAAAAAAsBuFIwAAAAAAAAAAAAC7UTgCAAAAAAAAAAAAsBuFIwAAAAAAAAAAAAC7UTgCAAAAAAAAAAAAsBuFIwAAAAAAAAAAAAC7UTgCAAAAAAAAAAAAsNv/Az9zkNgkn2ODAAAAAElFTkSuQmCC",
       "text/plain": [
-       ""
+       "
"
       ]
      },
      "metadata": {},
@@ -847,103 +882,117 @@
       "text/html": [
        "\n",
-       "\n",
+       "\n",
        "\n",
        "\n",
        "  \n",
        "  \n",
        "  \n",
        "\n",
        "\n",
        "
\n",
        "\n",
-       "
class SimpleProblemSolvingAgentProgram:\n",
-       "\n",
-       "    """Abstract framework for a problem-solving agent. [Figure 3.1]"""\n",
+       "
class SimpleProblemSolvingAgentProgram:\n",
+       "    """\n",
+       "    [Figure 3.1]\n",
+       "    Abstract framework for a problem-solving agent.\n",
+       "    """\n",
        "\n",
-       "    def __init__(self, initial_state=None):\n",
-       "        """State is an abstract representation of the state\n",
+       "    def __init__(self, initial_state=None):\n",
+       "        """State is an abstract representation of the state\n",
        "        of the world, and seq is the list of actions required\n",
        "        to get to a particular state from the initial state(root)."""\n",
        "        self.state = initial_state\n",
        "        self.seq = []\n",
        "\n",
-       "    def __call__(self, percept):\n",
-       "        """[Figure 3.1] Formulate a goal and problem, then\n",
+       "    def __call__(self, percept):\n",
+       "        """[Figure 3.1] Formulate a goal and problem, then\n",
        "        search for a sequence of actions to solve it."""\n",
        "        self.state = self.update_state(self.state, percept)\n",
        "        if not self.seq:\n",
@@ -951,19 +1000,19 @@
        "            problem = self.formulate_problem(self.state, goal)\n",
        "            self.seq = self.search(problem)\n",
        "            if not self.seq:\n",
-       "                return None\n",
+       "                return None\n",
        "        return self.seq.pop(0)\n",
        "\n",
-       "    def update_state(self, state, percept):\n",
+       "    def update_state(self, state, percept):\n",
        "        raise NotImplementedError\n",
        "\n",
-       "    def formulate_goal(self, state):\n",
+       "    def formulate_goal(self, state):\n",
        "        raise NotImplementedError\n",
        "\n",
-       "    def formulate_problem(self, state, goal):\n",
+       "    def formulate_problem(self, state, goal):\n",
        "        raise NotImplementedError\n",
        "\n",
-       "    def search(self, problem):\n",
+       "    def search(self, problem):\n",
        "        raise NotImplementedError\n",
        "
\n",
        "\n",
@@ -1186,9 +1235,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e698e3059b6b47e5b1568dda5083a6a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b40c7e334f15493ba970903f97ee5992",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
@@ -1267,9 +1345,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bf121b583e14883903a5835d5187968",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c848eec0b00423faabba63301e52e99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
@@ -1352,9 +1459,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dd1e31b085d4df6bb158fcf7d922998",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06f73a78d0bd45fb907c3d1891425846",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
@@ -1437,9 +1573,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ceb8bb1cacb4df7bdd3777ed7a0b50e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dca1cb57c0c146dd98cb814cd4e7185c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
@@ -1557,16 +1722,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
-   "source": [
-    "all_node_colors = []\n",
-    "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
-    "display_visual(romania_graph_data, user_input=False, \n",
-    "               algorithm=uniform_cost_search_graph, \n",
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba6b505db00744f199258b36757edbda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "120b4fcf503c4d9db6007c6458aee832",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "all_node_colors = []\n",
+    "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
+    "display_visual(romania_graph_data, user_input=False, \n",
+    "               algorithm=uniform_cost_search_graph, \n",
     "               problem=romania_problem)"
    ]
   },
@@ -1656,9 +1850,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2eb6e7564dc840a2abcac7f1211acc29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a44ca8e40774472b44111feda278cc6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
@@ -1691,9 +1914,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01c5599fd141460bbe615ce52b193686",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fce9f1769df4c93917f839e2a0792c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
@@ -1729,7 +1981,36 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "299e50eb59de45b6be210093837f38bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9432929f89db40779accae8a38832221",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
@@ -1765,9 +2046,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4593cc88d1a648fda923792ceb346b10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26e5fdcc292d4033ae86c9c4bf686565",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
@@ -1856,9 +2166,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e63133eec82f46a59fee2a6984880cd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fa018c25d564569866d6dbd8a0c97f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
@@ -1873,7 +2212,78 @@
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91c8cc79cb1d447c8fc06679a0d2d947",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Dropdown(description='Search algorithm: ', index=3, options=('A-star Search', 'Best First Graph Search', 'Brea…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4991523751224edea06158bd9d5e5ad9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Dropdown(description='Start city: ', options=('Arad', 'Bucharest', 'Craiova', 'Drobeta', 'Eforie', 'Fagaras', …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfda4ca1a3f144549af27e3a7e895d30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Dropdown(description='Goal city: ', index=5, options=('Arad', 'Bucharest', 'Craiova', 'Drobeta', 'Eforie', 'Fa…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffe41e56ce4644aa85d8fd98cd993474",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80c56dfcae2547a7a382abdf70435d1a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "all_node_colors = []\n",
     "# display_visual(romania_graph_data, user_input=True, algorithm=breadth_first_tree_search)\n",
@@ -1916,124 +2326,131 @@
       "text/html": [
        "\n",
-       "\n",
+       "\n",
        "\n",
        "\n",
        "  \n",
        "  \n",
        "  \n",
        "\n",
        "\n",
        "
\n",
        "\n",
-       "
def recursive_best_first_search(problem, h=None):\n",
-       "    """[Figure 3.26] Recursive best-first search (RBFS) is an\n",
-       "    informative search algorithm. Like A*, it uses the heuristic\n",
-       "    f(n) = g(n) + h(n) to determine the next node to expand, making\n",
-       "    it both optimal and complete (iff the heuristic is consistent).\n",
-       "    To reduce memory consumption, RBFS uses a depth first search\n",
-       "    and only retains the best f values of its ancestors."""\n",
+       "
def recursive_best_first_search(problem, h=None):\n",
+       "    """[Figure 3.26]"""\n",
        "    h = memoize(h or problem.h, 'h')\n",
        "\n",
-       "    def RBFS(problem, node, flimit):\n",
+       "    def RBFS(problem, node, flimit):\n",
        "        if problem.goal_test(node.state):\n",
-       "            return node, 0   # (The second value is immaterial)\n",
+       "            return node, 0  # (The second value is immaterial)\n",
        "        successors = node.expand(problem)\n",
        "        if len(successors) == 0:\n",
-       "            return None, infinity\n",
+       "            return None, np.inf\n",
        "        for s in successors:\n",
        "            s.f = max(s.path_cost + h(s), node.f)\n",
-       "        while True:\n",
+       "        while True:\n",
        "            # Order by lowest f value\n",
        "            successors.sort(key=lambda x: x.f)\n",
        "            best = successors[0]\n",
        "            if best.f > flimit:\n",
-       "                return None, best.f\n",
+       "                return None, best.f\n",
        "            if len(successors) > 1:\n",
        "                alternative = successors[1].f\n",
        "            else:\n",
-       "                alternative = infinity\n",
+       "                alternative = np.inf\n",
        "            result, best.f = RBFS(problem, best, min(flimit, alternative))\n",
-       "            if result is not None:\n",
+       "            if result is not None:\n",
        "                return result, best.f\n",
        "\n",
        "    node = Node(problem.initial)\n",
        "    node.f = h(node)\n",
-       "    result, bestf = RBFS(problem, node, infinity)\n",
+       "    result, bestf = RBFS(problem, node, np.inf)\n",
        "    return result\n",
        "
\n",
        "\n",
@@ -2060,7 +2477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -2069,7 +2486,7 @@
        "['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest']"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2087,7 +2504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -2096,7 +2513,7 @@
        "['UP', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT', 'DOWN']"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2133,7 +2550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2157,7 +2574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2218,7 +2635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -2227,7 +2644,7 @@
        "True"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2249,7 +2666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -2258,7 +2675,7 @@
        "['UP', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT', 'DOWN']"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2277,7 +2694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -2286,7 +2703,7 @@
        "['UP', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT', 'DOWN']"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2319,7 +2736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -2328,7 +2745,7 @@
        "['LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'RIGHT']"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2339,7 +2756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -2348,7 +2765,7 @@
        "['LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'RIGHT']"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2366,7 +2783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -2375,7 +2792,7 @@
        "['LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'UP', 'DOWN', 'RIGHT']"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2398,7 +2815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2416,14 +2833,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.24 ms ± 190 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "718 μs ± 1.34 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -2436,14 +2853,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.68 ms ± 368 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "665 μs ± 2.66 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -2456,14 +2873,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.12 ms ± 88.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "538 μs ± 1.29 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -2476,14 +2893,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "22.7 ms ± 1.69 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "6.25 ms ± 30.9 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -2496,14 +2913,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.91 ms ± 434 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "581 μs ± 2.02 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -2535,14 +2952,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "140 ms ± 9.89 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "23.2 ms ± 226 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -2581,7 +2998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -2589,100 +3006,114 @@
       "text/html": [
        "\n",
-       "\n",
+       "\n",
        "\n",
        "\n",
        "  \n",
        "  \n",
        "  \n",
        "\n",
        "\n",
        "
\n",
        "\n",
-       "
def hill_climbing(problem):\n",
-       "    """From the initial node, keep choosing the neighbor with highest value,\n",
-       "    stopping when no neighbor is better. [Figure 4.2]"""\n",
+       "
def hill_climbing(problem):\n",
+       "    """\n",
+       "    [Figure 4.2]\n",
+       "    From the initial node, keep choosing the neighbor with highest value,\n",
+       "    stopping when no neighbor is better.\n",
+       "    """\n",
        "    current = Node(problem.initial)\n",
-       "    while True:\n",
+       "    while True:\n",
        "        neighbors = current.expand(problem)\n",
        "        if not neighbors:\n",
        "            break\n",
-       "        neighbor = argmax_random_tie(neighbors,\n",
-       "                                     key=lambda node: problem.value(node.state))\n",
+       "        neighbor = argmax_random_tie(neighbors, key=lambda node: problem.value(node.state))\n",
        "        if problem.value(neighbor.state) <= problem.value(current.state):\n",
        "            break\n",
        "        current = neighbor\n",
@@ -2716,7 +3147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2766,7 +3197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -2798,7 +3229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2823,7 +3254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2855,7 +3286,7 @@
     "        neighbor = argmax_random_tie(neighbors,\n",
     "                                     key=lambda node: problem.value(node.state))\n",
     "        if problem.value(neighbor.state) <= problem.value(current.state):\n",
-    "           \"\"\"Note that it is based on negative path cost method\"\"\"\n",
+    "            \"\"\"Note that it is based on negative path cost method\"\"\"\n",
     "            current.state = neighbor.state\n",
     "        iterations -= 1\n",
     "        \n",
@@ -2871,7 +3302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2888,35 +3319,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['Arad',\n",
-       " 'Timisoara',\n",
-       " 'Lugoj',\n",
+       "['Iasi',\n",
        " 'Mehadia',\n",
-       " 'Drobeta',\n",
+       " 'Timisoara',\n",
+       " 'Vaslui',\n",
+       " 'Neamt',\n",
        " 'Craiova',\n",
-       " 'Pitesti',\n",
        " 'Giurgiu',\n",
+       " 'Eforie',\n",
        " 'Bucharest',\n",
+       " 'Lugoj',\n",
+       " 'Oradea',\n",
+       " 'Arad',\n",
        " 'Urziceni',\n",
-       " 'Eforie',\n",
-       " 'Hirsova',\n",
-       " 'Vaslui',\n",
-       " 'Iasi',\n",
-       " 'Neamt',\n",
+       " 'Drobeta',\n",
+       " 'Zerind',\n",
        " 'Fagaras',\n",
        " 'Rimnicu',\n",
+       " 'Hirsova',\n",
        " 'Sibiu',\n",
-       " 'Oradea',\n",
-       " 'Zerind']"
+       " 'Pitesti']"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2963,7 +3394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -2971,92 +3402,104 @@
       "text/html": [
        "\n",
-       "\n",
+       "\n",
        "\n",
        "\n",
        "  \n",
        "  \n",
        "  \n",
        "\n",
        "\n",
        "
\n",
        "\n",
-       "
def simulated_annealing(problem, schedule=exp_schedule()):\n",
-       "    """[Figure 4.5] CAUTION: This differs from the pseudocode as it\n",
+       "
def simulated_annealing(problem, schedule=exp_schedule()):\n",
+       "    """[Figure 4.5] CAUTION: This differs from the pseudocode as it\n",
        "    returns a state instead of a Node."""\n",
        "    current = Node(problem.initial)\n",
        "    for t in range(sys.maxsize):\n",
@@ -3068,7 +3511,7 @@
        "            return current.state\n",
        "        next_choice = random.choice(neighbors)\n",
        "        delta_e = problem.value(next_choice.state) - problem.value(current.state)\n",
-       "        if delta_e > 0 or probability(math.exp(delta_e / T)):\n",
+       "        if delta_e > 0 or probability(np.exp(delta_e / T)):\n",
        "            current = next_choice\n",
        "
\n",
        "\n",
@@ -3097,7 +3540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
@@ -3105,93 +3548,105 @@
       "text/html": [
        "\n",
-       "\n",
+       "\n",
        "\n",
        "\n",
        "  \n",
        "  \n",
        "  \n",
        "\n",
        "\n",
        "
\n",
        "\n",
-       "
def exp_schedule(k=20, lam=0.005, limit=100):\n",
-       "    """One possible schedule function for simulated annealing"""\n",
-       "    return lambda t: (k * math.exp(-lam * t) if t < limit else 0)\n",
+       "
def exp_schedule(k=20, lam=0.005, limit=100):\n",
+       "    """One possible schedule function for simulated annealing"""\n",
+       "    return lambda t: (k * np.exp(-lam * t) if t < limit else 0)\n",
        "
\n",
        "\n",
        "\n"
@@ -3218,7 +3673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3236,16 +3691,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'E': (1, 0), 'N': (0, 1), 'S': (0, -1), 'W': (-1, 0)}"
+       "{'W': (-1, 0), 'N': (0, 1), 'E': (1, 0), 'S': (0, -1)}"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3263,7 +3718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3279,7 +3734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3288,7 +3743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -3297,7 +3752,7 @@
        "9"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3323,7 +3778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3339,14 +3794,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       ""
+       "
"
       ]
      },
      "metadata": {},
@@ -3367,23 +3822,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'E': (1, 0),\n",
+       "{'W': (-1, 0),\n",
        " 'N': (0, 1),\n",
-       " 'NE': (1, 1),\n",
-       " 'NW': (-1, 1),\n",
+       " 'E': (1, 0),\n",
        " 'S': (0, -1),\n",
+       " 'NW': (-1, 1),\n",
+       " 'NE': (1, 1),\n",
        " 'SE': (1, -1),\n",
-       " 'SW': (-1, -1),\n",
-       " 'W': (-1, 0)}"
+       " 'SW': (-1, -1)}"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3403,7 +3858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3412,14 +3867,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "533 ms ± 51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "47.1 ms ± 395 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -3430,7 +3885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
@@ -3439,7 +3894,7 @@
        "9"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3459,36 +3914,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "206 µs ± 21.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+     "ename": "AttributeError",
+     "evalue": "'PeakFindingProblem' object has no attribute 'two_opt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[102]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# %%timeit\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m solution = problem.value(\u001b[43mhill_climbing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[84]\u001b[39m\u001b[32m, line 23\u001b[39m, in \u001b[36mhill_climbing\u001b[39m\u001b[34m(problem)\u001b[39m\n\u001b[32m     21\u001b[39m current = Node(problem.initial)\n\u001b[32m     22\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m iterations:\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m     neighbors = \u001b[43mfind_neighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     24\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m neighbors:\n\u001b[32m     25\u001b[39m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[84]\u001b[39m\u001b[32m, line 12\u001b[39m, in \u001b[36mhill_climbing..find_neighbors\u001b[39m\u001b[34m(state, number_of_neighbors)\u001b[39m\n\u001b[32m      9\u001b[39m neighbors = []\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(number_of_neighbors):\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m     new_state = \u001b[43mproblem\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtwo_opt\u001b[49m(state)\n\u001b[32m     13\u001b[39m     neighbors.append(Node(new_state))\n\u001b[32m     14\u001b[39m     state = new_state\n",
+      "\u001b[31mAttributeError\u001b[39m: 'PeakFindingProblem' object has no attribute 'two_opt'"
      ]
     }
    ],
    "source": [
-    "%%timeit\n",
+    "# %%timeit\n",
     "solution = problem.value(hill_climbing(problem))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "AttributeError",
+     "evalue": "'PeakFindingProblem' object has no attribute 'two_opt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[103]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m solution = problem.value(\u001b[43mhill_climbing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m      2\u001b[39m solution\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[84]\u001b[39m\u001b[32m, line 23\u001b[39m, in \u001b[36mhill_climbing\u001b[39m\u001b[34m(problem)\u001b[39m\n\u001b[32m     21\u001b[39m current = Node(problem.initial)\n\u001b[32m     22\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m iterations:\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m     neighbors = \u001b[43mfind_neighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     24\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m neighbors:\n\u001b[32m     25\u001b[39m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[84]\u001b[39m\u001b[32m, line 12\u001b[39m, in \u001b[36mhill_climbing..find_neighbors\u001b[39m\u001b[34m(state, number_of_neighbors)\u001b[39m\n\u001b[32m      9\u001b[39m neighbors = []\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(number_of_neighbors):\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m     new_state = \u001b[43mproblem\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtwo_opt\u001b[49m(state)\n\u001b[32m     13\u001b[39m     neighbors.append(Node(new_state))\n\u001b[32m     14\u001b[39m     state = new_state\n",
+      "\u001b[31mAttributeError\u001b[39m: 'PeakFindingProblem' object has no attribute 'two_opt'"
+     ]
     }
    ],
    "source": [
@@ -3516,7 +3980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3529,14 +3993,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       ""
+       "
"
       ]
      },
      "metadata": {},
@@ -3565,7 +4029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3581,18 +4045,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 107,
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'PeakFindingProblem' object has no attribute 'two_opt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[107]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m solution = problem.value(\u001b[43mhill_climbing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[84]\u001b[39m\u001b[32m, line 23\u001b[39m, in \u001b[36mhill_climbing\u001b[39m\u001b[34m(problem)\u001b[39m\n\u001b[32m     21\u001b[39m current = Node(problem.initial)\n\u001b[32m     22\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m iterations:\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m     neighbors = \u001b[43mfind_neighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     24\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m neighbors:\n\u001b[32m     25\u001b[39m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[84]\u001b[39m\u001b[32m, line 12\u001b[39m, in \u001b[36mhill_climbing..find_neighbors\u001b[39m\u001b[34m(state, number_of_neighbors)\u001b[39m\n\u001b[32m      9\u001b[39m neighbors = []\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(number_of_neighbors):\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m     new_state = \u001b[43mproblem\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtwo_opt\u001b[49m(state)\n\u001b[32m     13\u001b[39m     neighbors.append(Node(new_state))\n\u001b[32m     14\u001b[39m     state = new_state\n",
+      "\u001b[31mAttributeError\u001b[39m: 'PeakFindingProblem' object has no attribute 'two_opt'"
+     ]
+    }
+   ],
    "source": [
     "solution = problem.value(hill_climbing(problem))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3619,7 +4097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3759,7 +4237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3911,7 +4389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4034,7 +4512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4164,7 +4642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4331,7 +4809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -4351,7 +4829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -4377,7 +4855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -4395,7 +4873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -4413,7 +4891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -4438,7 +4916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -4456,7 +4934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
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@@ -4467,7 +4945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
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@@ -4486,7 +4964,7 @@
   },
   {
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+   "execution_count": null,
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@@ -4508,7 +4986,7 @@
   },
   {
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-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {
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@@ -4526,7 +5004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "metadata": {
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@@ -4544,7 +5022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4568,7 +5046,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4597,7 +5075,7 @@
   },
   {
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-   "execution_count": 68,
+   "execution_count": null,
    "metadata": {
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@@ -4621,7 +5099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
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@@ -4652,7 +5130,7 @@
   },
   {
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+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4781,7 +5259,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4834,7 +5312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -4859,7 +5337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4886,7 +5364,7 @@
   },
   {
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-   "execution_count": 74,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -4905,7 +5383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4930,7 +5408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4975,7 +5453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5006,7 +5484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -5038,7 +5516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5085,7 +5563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5267,7 +5745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -5287,7 +5765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5305,7 +5783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -5316,7 +5794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5343,7 +5821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5361,7 +5839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -5372,7 +5850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5399,7 +5877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5417,7 +5895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -5428,7 +5906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5464,7 +5942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5583,7 +6061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5608,7 +6086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -5619,7 +6097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -5667,7 +6145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5834,7 +6312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5844,7 +6322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5865,7 +6343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5880,7 +6358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -5934,7 +6412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6124,7 +6602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6305,7 +6783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6332,7 +6810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6348,7 +6826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6364,7 +6842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6392,7 +6870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6422,7 +6900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6452,7 +6930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -6489,7 +6967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6515,7 +6993,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -6529,7 +7007,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.12.3"
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   "pycharm": {
    "stem_cell": {

From e12f4b4503c154a3fc168fe205bcf394673c8e23 Mon Sep 17 00:00:00 2001
From: reginaldo 
Date: Thu, 23 Oct 2025 06:36:18 -0300
Subject: [PATCH 2/4] initial 2

---
 neural_nets.ipynb | 415 ++++++----------------------------------------
 1 file changed, 48 insertions(+), 367 deletions(-)

diff --git a/neural_nets.ipynb b/neural_nets.ipynb
index 1291da547..9939a4df7 100644
--- a/neural_nets.ipynb
+++ b/neural_nets.ipynb
@@ -15,7 +15,22 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/home/reginaldo/aima-python/aima-data/orings.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlearning\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnotebook\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m psource, pseudocode\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/aima-python/learning.py:1138\u001b[39m\n\u001b[32m   1132\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[32m1.\u001b[39m - (np.sum(np.square(y_pred - y_true)) /  \u001b[38;5;66;03m# sum of square of residuals\u001b[39;00m\n\u001b[32m   1133\u001b[39m                  np.sum(np.square(y_true - np.mean(y_true))))  \u001b[38;5;66;03m# total sum of squares\u001b[39;00m\n\u001b[32m   1136\u001b[39m \u001b[38;5;66;03m# datasets\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1138\u001b[39m orings = \u001b[43mDataSet\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43morings\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mDistressed\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattr_names\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mRings Distressed Temp Pressure Flightnum\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m   1140\u001b[39m zoo = DataSet(name=\u001b[33m'\u001b[39m\u001b[33mzoo\u001b[39m\u001b[33m'\u001b[39m, target=\u001b[33m'\u001b[39m\u001b[33mtype\u001b[39m\u001b[33m'\u001b[39m, exclude=[\u001b[33m'\u001b[39m\u001b[33mname\u001b[39m\u001b[33m'\u001b[39m],\n\u001b[32m   1141\u001b[39m               attr_names=\u001b[33m'\u001b[39m\u001b[33mname hair feathers eggs milk airborne aquatic predator toothed backbone \u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m   1142\u001b[39m                          \u001b[33m'\u001b[39m\u001b[33mbreathes venomous fins legs tail domestic catsize type\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m   1144\u001b[39m iris = DataSet(name=\u001b[33m'\u001b[39m\u001b[33miris\u001b[39m\u001b[33m'\u001b[39m, target=\u001b[33m'\u001b[39m\u001b[33mclass\u001b[39m\u001b[33m'\u001b[39m, attr_names=\u001b[33m'\u001b[39m\u001b[33msepal-len sepal-width petal-len petal-width class\u001b[39m\u001b[33m'\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/aima-python/learning.py:59\u001b[39m, in \u001b[36mDataSet.__init__\u001b[39m\u001b[34m(self, examples, attrs, attr_names, target, inputs, values, distance, name, source, exclude)\u001b[39m\n\u001b[32m     57\u001b[39m     \u001b[38;5;28mself\u001b[39m.examples = parse_csv(examples)\n\u001b[32m     58\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m examples \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m59\u001b[39m     \u001b[38;5;28mself\u001b[39m.examples = parse_csv(\u001b[43mopen_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m.csv\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m.read())\n\u001b[32m     60\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m     61\u001b[39m     \u001b[38;5;28mself\u001b[39m.examples = examples\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/aima-python/utils.py:469\u001b[39m, in \u001b[36mopen_data\u001b[39m\u001b[34m(name, mode)\u001b[39m\n\u001b[32m    466\u001b[39m aima_root = os.path.dirname(\u001b[34m__file__\u001b[39m)\n\u001b[32m    467\u001b[39m aima_file = os.path.join(aima_root, *[\u001b[33m'\u001b[39m\u001b[33maima-data\u001b[39m\u001b[33m'\u001b[39m, name])\n\u001b[32m--> \u001b[39m\u001b[32m469\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43maima_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/home/reginaldo/aima-python/aima-data/orings.csv'"
+     ]
+    }
+   ],
    "source": [
     "from learning import *\n",
     "\n",
@@ -67,142 +82,11 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "  \n",
-       "  \n",
-       "  \n",
-       "\n",
-       "\n",
-       "
\n",
-       "\n",
-       "
def NeuralNetLearner(dataset, hidden_layer_sizes=None,\n",
-       "                     learning_rate=0.01, epochs=100, activation = sigmoid):\n",
-       "    """Layered feed-forward network.\n",
-       "    hidden_layer_sizes: List of number of hidden units per hidden layer\n",
-       "    learning_rate: Learning rate of gradient descent\n",
-       "    epochs: Number of passes over the dataset\n",
-       "    """\n",
-       "\n",
-       "    hidden_layer_sizes = hidden_layer_sizes or [3]  # default value\n",
-       "    i_units = len(dataset.inputs)\n",
-       "    o_units = len(dataset.values[dataset.target])\n",
-       "\n",
-       "    # construct a network\n",
-       "    raw_net = network(i_units, hidden_layer_sizes, o_units, activation)\n",
-       "    learned_net = BackPropagationLearner(dataset, raw_net,\n",
-       "                                         learning_rate, epochs, activation)\n",
-       "\n",
-       "    def predict(example):\n",
-       "        # Input nodes\n",
-       "        i_nodes = learned_net[0]\n",
-       "\n",
-       "        # Activate input layer\n",
-       "        for v, n in zip(example, i_nodes):\n",
-       "            n.value = v\n",
-       "\n",
-       "        # Forward pass\n",
-       "        for layer in learned_net[1:]:\n",
-       "            for node in layer:\n",
-       "                inc = [n.value for n in node.inputs]\n",
-       "                in_val = dotproduct(inc, node.weights)\n",
-       "                node.value = node.activation(in_val)\n",
-       "\n",
-       "        # Hypothesis\n",
-       "        o_nodes = learned_net[-1]\n",
-       "        prediction = find_max_node(o_nodes)\n",
-       "        return prediction\n",
-       "\n",
-       "    return predict\n",
-       "
\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Object `(NeuralNetLearner)` not found.\n"
+     ]
     }
    ],
    "source": [
@@ -253,47 +137,15 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/markdown": [
-       "### AIMA3e\n",
-       "__function__ BACK-PROP-LEARNING(_examples_, _network_) __returns__ a neural network  \n",
-       " __inputs__ _examples_, a set of examples, each with input vector __x__ and output vector __y__  \n",
-       "    _network_, a multilayer network with _L_ layers, weights _wi,j_, activation function _g_  \n",
-       " __local variables__: Δ, a vector of errors, indexed by network node  \n",
-       "\n",
-       " __repeat__  \n",
-       "   __for each__ weight _wi,j_ in _network_ __do__  \n",
-       "     _wi,j_ ← a small random number  \n",
-       "   __for each__ example (__x__, __y__) __in__ _examples_ __do__  \n",
-       "     /\\* _Propagate the inputs forward to compute the outputs_ \\*/  \n",
-       "     __for each__ node _i_ in the input layer __do__  \n",
-       "       _ai_ ← _xi_  \n",
-       "     __for__ _l_ = 2 __to__ _L_ __do__  \n",
-       "       __for each__ node _j_ in layer _l_ __do__  \n",
-       "         _inj_ ← Σ_i_ _wi,j_ _ai_  \n",
-       "         _aj_ ← _g_(_inj_)  \n",
-       "     /\\* _Propagate deltas backward from output layer to input layer_ \\*/  \n",
-       "     __for each__ node _j_ in the output layer __do__  \n",
-       "       Δ\\[_j_\\] ← _g_′(_inj_) × (_yi_ − _aj_)  \n",
-       "     __for__ _l_ = _L_ − 1 __to__ 1 __do__  \n",
-       "       __for each__ node _i_ in layer _l_ __do__  \n",
-       "         Δ\\[_i_\\] ← _g_′(_ini_) Σ_j_ _wi,j_ Δ\\[_j_\\]  \n",
-       "     /\\* _Update every weight in network using deltas_ \\*/  \n",
-       "     __for each__ weight _wi,j_ in _network_ __do__  \n",
-       "       _wi,j_ ← _wi,j_ + _α_ × _ai_ × Δ\\[_j_\\]  \n",
-       "  __until__ some stopping criterion is satisfied  \n",
-       "  __return__ _network_  \n",
-       "\n",
-       "---\n",
-       "__Figure ??__ The back\\-propagation algorithm for learning in multilayer networks."
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'pseudocode' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mpseudocode\u001b[49m(\u001b[33m'\u001b[39m\u001b[33mBack-Prop-Learning\u001b[39m\u001b[33m'\u001b[39m)\n",
+      "\u001b[31mNameError\u001b[39m: name 'pseudocode' is not defined"
+     ]
     }
    ],
    "source": [
@@ -315,186 +167,11 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "  \n",
-       "  \n",
-       "  \n",
-       "\n",
-       "\n",
-       "
\n",
-       "\n",
-       "
def BackPropagationLearner(dataset, net, learning_rate, epochs, activation=sigmoid):\n",
-       "    """[Figure 18.23] The back-propagation algorithm for multilayer networks"""\n",
-       "    # Initialise weights\n",
-       "    for layer in net:\n",
-       "        for node in layer:\n",
-       "            node.weights = random_weights(min_value=-0.5, max_value=0.5,\n",
-       "                                          num_weights=len(node.weights))\n",
-       "\n",
-       "    examples = dataset.examples\n",
-       "    '''\n",
-       "    As of now dataset.target gives an int instead of list,\n",
-       "    Changing dataset class will have effect on all the learners.\n",
-       "    Will be taken care of later.\n",
-       "    '''\n",
-       "    o_nodes = net[-1]\n",
-       "    i_nodes = net[0]\n",
-       "    o_units = len(o_nodes)\n",
-       "    idx_t = dataset.target\n",
-       "    idx_i = dataset.inputs\n",
-       "    n_layers = len(net)\n",
-       "\n",
-       "    inputs, targets = init_examples(examples, idx_i, idx_t, o_units)\n",
-       "\n",
-       "    for epoch in range(epochs):\n",
-       "        # Iterate over each example\n",
-       "        for e in range(len(examples)):\n",
-       "            i_val = inputs[e]\n",
-       "            t_val = targets[e]\n",
-       "\n",
-       "            # Activate input layer\n",
-       "            for v, n in zip(i_val, i_nodes):\n",
-       "                n.value = v\n",
-       "\n",
-       "            # Forward pass\n",
-       "            for layer in net[1:]:\n",
-       "                for node in layer:\n",
-       "                    inc = [n.value for n in node.inputs]\n",
-       "                    in_val = dotproduct(inc, node.weights)\n",
-       "                    node.value = node.activation(in_val)\n",
-       "\n",
-       "            # Initialize delta\n",
-       "            delta = [[] for _ in range(n_layers)]\n",
-       "\n",
-       "            # Compute outer layer delta\n",
-       "\n",
-       "            # Error for the MSE cost function\n",
-       "            err = [t_val[i] - o_nodes[i].value for i in range(o_units)]\n",
-       "\n",
-       "            # The activation function used is relu or sigmoid function\n",
-       "            if node.activation == sigmoid:\n",
-       "                delta[-1] = [sigmoid_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]\n",
-       "            else:\n",
-       "                delta[-1] = [relu_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]\n",
-       "\n",
-       "            # Backward pass\n",
-       "            h_layers = n_layers - 2\n",
-       "            for i in range(h_layers, 0, -1):\n",
-       "                layer = net[i]\n",
-       "                h_units = len(layer)\n",
-       "                nx_layer = net[i+1]\n",
-       "\n",
-       "                # weights from each ith layer node to each i + 1th layer node\n",
-       "                w = [[node.weights[k] for node in nx_layer] for k in range(h_units)]\n",
-       "\n",
-       "                if activation == sigmoid:\n",
-       "                    delta[i] = [sigmoid_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])\n",
-       "                            for j in range(h_units)]\n",
-       "                else:\n",
-       "                    delta[i] = [relu_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])\n",
-       "                            for j in range(h_units)]\n",
-       "\n",
-       "            #  Update weights\n",
-       "            for i in range(1, n_layers):\n",
-       "                layer = net[i]\n",
-       "                inc = [node.value for node in net[i-1]]\n",
-       "                units = len(layer)\n",
-       "                for j in range(units):\n",
-       "                    layer[j].weights = vector_add(layer[j].weights,\n",
-       "                                                  scalar_vector_product(\n",
-       "                                                  learning_rate * delta[i][j], inc))\n",
-       "\n",
-       "    return net\n",
-       "
\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Object `(BackPropagationLearner)` not found.\n"
+     ]
     }
    ],
    "source": [
@@ -507,10 +184,14 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
+     "ename": "NameError",
+     "evalue": "name 'DataSet' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m iris = \u001b[43mDataSet\u001b[49m(name=\u001b[33m\"\u001b[39m\u001b[33miris\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m      2\u001b[39m iris.classes_to_numbers()\n\u001b[32m      4\u001b[39m nNL = NeuralNetLearner(iris)\n",
+      "\u001b[31mNameError\u001b[39m: name 'DataSet' is not defined"
      ]
     }
    ],
@@ -539,7 +220,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -553,18 +234,18 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.12.3"
   },
   "pycharm": {
    "stem_cell": {
     "cell_type": "raw",
-    "source": [],
     "metadata": {
      "collapsed": false
-    }
+    },
+    "source": []
    }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}

From e51faeaf1bbc7d582987bb0d7032263519035d41 Mon Sep 17 00:00:00 2001
From: reginaldo 
Date: Thu, 23 Oct 2025 06:37:46 -0300
Subject: [PATCH 3/4] slides 1

---
 slides/main.pdf | Bin 0 -> 620043 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 slides/main.pdf

diff --git a/slides/main.pdf b/slides/main.pdf
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r
Date: Thu, 30 Oct 2025 06:54:23 -0300
Subject: [PATCH 4/4] Slides 2

---
 agents.ipynb                                  |  44 +--
 aspirador.py                                  | 216 +++++++++++++
 aspirador_reativo.py                          | 193 ++++++++++++
 search.ipynb                                  | 288 +++++++++---------
 search4e.ipynb                                |  79 ++---
 .../Resolu\303\247\303\243o de problemas.pdf" | Bin 0 -> 890601 bytes
 6 files changed, 615 insertions(+), 205 deletions(-)
 create mode 100644 aspirador.py
 create mode 100644 aspirador_reativo.py
 create mode 100644 "slides/Resolu\303\247\303\243o de problemas.pdf"

diff --git a/agents.ipynb b/agents.ipynb
index ff9e4f049..f9dd627af 100644
--- a/agents.ipynb
+++ b/agents.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -535,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -589,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -636,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -659,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -697,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -723,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -767,7 +767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -833,13 +833,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
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+       "
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       ],
       "text/plain": [
        ""
@@ -905,7 +905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -976,7 +976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1041,13 +1041,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
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+       "
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       ],
       "text/plain": [
        ""
@@ -1090,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1132,13 +1132,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
"
+       "
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       ],
       "text/plain": [
        ""
@@ -1151,7 +1151,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[], [], [], [], [, None]]\n"
+      "[[], [None], [], [None], [None]]\n"
      ]
     }
    ],
diff --git a/aspirador.py b/aspirador.py
new file mode 100644
index 000000000..b7aa3ca5e
--- /dev/null
+++ b/aspirador.py
@@ -0,0 +1,216 @@
+import random
+from collections import deque
+import time
+
+# Constantes para o estado do grid
+LIMPO = 0
+SUJO = 1
+ASPIRADOR = 2 
+PAREDE = 3
+
+def inicializar_ambiente(M, N, num_sujeira):
+    """
+    Cria um ambiente M x N com sujeira e posição inicial aleatória do aspirador.
+    Retorna: (grid, pos_aspirador)
+    """
+    grid = [[LIMPO] * N for _ in range(M)]
+    
+    # 1. Posicionar a sujeira aleatoriamente
+    todas_posicoes = [(r, c) for r in range(M) for c in range(N)]
+    random.shuffle(todas_posicoes)
+    
+    sujeira_posicoes = todas_posicoes[:min(num_sujeira, M * N)]
+    for r, c in sujeira_posicoes:
+        grid[r][c] = SUJO
+
+    # 2. Posicionar o aspirador em um local aleatório não ocupado
+    if len(todas_posicoes) > num_sujeira:
+        pos_aspirador = todas_posicoes[num_sujeira] # Próxima posição livre
+    else:
+        pos_aspirador = (0, 0) 
+    
+    return grid, pos_aspirador
+
+# --- FUNÇÃO DE EXIBIÇÃO CORRIGIDA (ALINHAMENTO DO CABEÇALHO) ---
+def exibir_ambiente(grid, pos_aspirador, custo_total, acao_anterior="INÍCIO"):
+    """Exibe o grid e a posição do aspirador no terminal com layout de tabela."""
+    M = len(grid)
+    N = len(grid[0])
+    
+    # Largura de cada célula para o conteúdo
+    LARGURA_CONTEUDO = 4 
+    
+    # Define a linha separadora horizontal.
+    # Cada célula tem 4 traços (para o conteúdo) e é separada por um '+'.
+    # Exemplo: N=5 -> +----+----+----+----+----+
+    separador_horizontal = ("-" * LARGURA_CONTEUDO + "+") * N
+    separador_horizontal = "+" + separador_horizontal
+    
+    # A largura interna total do grid, usada para alinhar o cabeçalho.
+    largura_interna_total = len(separador_horizontal) - 2 
+    
+    # --- Exibição de Status ---
+    print("\n" + separador_horizontal)
+    
+    # Formatando as linhas de status para preencher a largura interna, 
+    # mantendo os separadores verticais '|' nas pontas.
+    print(f"| {'ESTADO DO AGENTE':<{largura_interna_total-2}} |")
+    print(f"| {'Ação Anterior: ' + acao_anterior:<{largura_interna_total-2}} |")
+    print(f"| {'Custo Total: ' + str(custo_total):<{largura_interna_total-2}} |")
+    
+    # --- Exibição do Grid ---
+    for r in range(M):
+        # 1. Desenha o separador horizontal antes da linha
+        print(separador_horizontal)
+        
+        # 2. Desenha o conteúdo da linha
+        linha_conteudo = "|"
+        for c in range(N):
+            conteudo = ""
+            if (r, c) == pos_aspirador:
+                # O emoji ocupa 2 espaços no terminal, usamos 1 espaço antes e 1 depois.
+                conteudo = " 🤖 " 
+            elif grid[r][c] == SUJO:
+                conteudo = " 💩 " 
+            else:
+                conteudo = "    " 
+            
+            # Garante que a string tenha o tamanho fixo da célula, seguido do separador vertical
+            linha_conteudo += conteudo[:LARGURA_CONTEUDO] + "|"
+        
+        print(linha_conteudo) 
+    
+    # 3. Desenha o separador horizontal final
+    print(separador_horizontal + "\n")
+# -------------------------------------------------------------
+
+def tem_sujeira(grid):
+    """Verifica se ainda há sujeira no ambiente."""
+    for linha in grid:
+        if SUJO in linha:
+            return True
+    return False
+
+def bfs_caminho_para_sujeira(grid, inicio):
+    """
+    Usa BFS para encontrar o caminho mais curto do 'inicio' até o 'SUJO' mais próximo.
+    Retorna: (caminho_de_acoes, pos_sujeira) ou (None, None)
+    """
+    M, N = len(grid), len(grid[0])
+    
+    # Fila: (posição_atual, caminho_de_acoes_ate_aqui)
+    fila = deque([(inicio, [])])
+    visitados = {inicio}
+    
+    # Direções de movimento: (dr, dc, ação_nome)
+    movimentos = [
+        (-1, 0, 'Cima'), (1, 0, 'Baixo'), 
+        (0, -1, 'Esquerda'), (0, 1, 'Direita')
+    ]
+    
+    while fila:
+        (r, c), caminho = fila.popleft()
+        
+        # Teste de Meta (encontrou sujeira)
+        if grid[r][c] == SUJO:
+            return caminho, (r, c)
+
+        # Expansão
+        for dr, dc, acao in movimentos:
+            nr, nc = r + dr, c + dc
+            
+            # Verifica limites do grid
+            if 0 <= nr < M and 0 <= nc < N:
+                proxima_pos = (nr, nc)
+                
+                if proxima_pos not in visitados and grid[nr][nc] != PAREDE:
+                    visitados.add(proxima_pos)
+                    novo_caminho = caminho + [acao]
+                    fila.append((proxima_pos, novo_caminho))
+                    
+    return None, None # Nenhuma sujeira encontrada
+
+def agente_aspirador_grid(M, N, num_sujeira):
+    """
+    Função principal que simula o aspirador de pó usando o BFS para cada tarefa.
+    """
+    grid, pos_aspirador = inicializar_ambiente(M, N, num_sujeira)
+    
+    custo_total = 0 
+    
+    print("--- 🏁 Início da Simulação do Aspirador de Pó (Grid) ---")
+    exibir_ambiente(grid, pos_aspirador, custo_total, "INÍCIO") 
+    
+    
+    while tem_sujeira(grid):
+        print("🔎 Buscando o caminho mais curto para a sujeira mais próxima...")
+        
+        caminho_acoes, pos_sujeira = bfs_caminho_para_sujeira(grid, pos_aspirador)
+        
+        if caminho_acoes is None:
+            print("🛑 Não há mais sujeira acessível.")
+            break
+
+        # 1. Executar as ações de movimento
+        print(f"✅ Solução encontrada (Caminho: {caminho_acoes})")
+        
+        for acao in caminho_acoes:
+            r, c = pos_aspirador
+            dr, dc = 0, 0
+            
+            # Mapeamento da ação para a mudança de coordenadas (dr, dc)
+            if acao == 'Cima': dr = -1
+            elif acao == 'Baixo': dr = 1
+            elif acao == 'Esquerda': dc = -1
+            elif acao == 'Direita': dc = 1
+            
+            # Atualizar a posição e o custo
+            pos_aspirador = (r + dr, c + dc)
+            custo_total += 1
+            
+            exibir_ambiente(grid, pos_aspirador, custo_total, acao) 
+            # time.sleep(0.3) 
+
+        # 2. Executar a ação de Aspiração (Suck)
+        r_s, c_s = pos_sujeira
+        if grid[r_s][c_s] == SUJO:
+            grid[r_s][c_s] = LIMPO
+            custo_total += 1
+            exibir_ambiente(grid, pos_aspirador, custo_total, "Aspirar") 
+            # time.sleep(0.5)
+
+    print("--- ✅ Meta Atingida! Ambiente completamente limpo. ---")
+    print(f"Custo Total (Número de Ações): {custo_total}")
+
+
+# --- Configuração de Entrada do Usuário ---
+
+# Recebendo o tamanho do ambiente
+while True:
+    try:
+        M = int(input("Informe o número de linhas (M) do ambiente (ex: 5): "))
+        N = int(input("Informe o número de colunas (N) do ambiente (ex: 5): "))
+        if M > 0 and N > 0:
+            break
+        else:
+            print("M e N devem ser maiores que zero.")
+    except ValueError:
+        print("Entrada inválida. Por favor, digite um número inteiro.")
+
+# Recebendo a quantidade de quadros sujos
+max_sujeira = M * N
+while True:
+    try:
+        num_sujeira = int(input(f"Informe a quantidade de quadros sujos (Máx: {max_sujeira}): "))
+        if 0 <= num_sujeira <= max_sujeira:
+            break
+        else:
+            print(f"A quantidade de sujeira deve estar entre 0 e {max_sujeira}.")
+    except ValueError:
+        print("Entrada inválida. Por favor, digite um número inteiro.")
+
+# --- Execução ---
+if M * N > 0:
+    agente_aspirador_grid(M, N, num_sujeira)
+else:
+    print("O ambiente não pode ser de tamanho zero.")
\ No newline at end of file
diff --git a/aspirador_reativo.py b/aspirador_reativo.py
new file mode 100644
index 000000000..fcf4b54a6
--- /dev/null
+++ b/aspirador_reativo.py
@@ -0,0 +1,193 @@
+import random
+import time
+
+# Constantes para o estado do grid
+LIMPO = 0
+SUJO = 1
+ASPIRADOR = 2 
+PAREDE = 3 # Não utilizado neste agente, mas mantido por consistência
+
+# Mapeamento de direções (dr, dc)
+DIRECOES = {
+    'Direita': (0, 1),
+    'Esquerda': (0, -1),
+    'Cima': (-1, 0),
+    'Baixo': (1, 0)
+}
+
+# --- Funções Auxiliares (do código anterior, ajustadas para simplicidade) ---
+
+def inicializar_ambiente(M, N, num_sujeira):
+    """Cria um ambiente M x N com sujeira e posição inicial aleatória."""
+    grid = [[LIMPO] * N for _ in range(M)]
+    todas_posicoes = [(r, c) for r in range(M) for c in range(N)]
+    random.shuffle(todas_posicoes)
+    
+    sujeira_posicoes = todas_posicoes[:min(num_sujeira, M * N)]
+    for r, c in sujeira_posicoes:
+        grid[r][c] = SUJO
+
+    pos_aspirador = todas_posicoes[min(num_sujeira, M * N)]
+    return grid, pos_aspirador
+
+def exibir_ambiente(grid, pos_aspirador, custo_total, acao_anterior="INÍCIO"):
+    """Exibe o grid e a posição do aspirador no terminal com layout de tabela."""
+    M = len(grid)
+    N = len(grid[0])
+    LARGURA_CONTEUDO = 4 
+    
+    separador_horizontal = ("-" * LARGURA_CONTEUDO + "+") * N
+    separador_horizontal = "+" + separador_horizontal
+    
+    largura_interna_total = len(separador_horizontal) - 2
+    
+    # --- Exibição de Status ---
+    print("\n" + separador_horizontal)
+    print(f"| {'ESTADO DO AGENTE (PATRULHAMENTO)':<{largura_interna_total}} |")
+    print(f"| {'Ação Anterior: ' + acao_anterior:<{largura_interna_total}} |")
+    print(f"| {'Custo Total: ' + str(custo_total):<{largura_interna_total}} |")
+    print(separador_horizontal)
+    
+    # --- Exibição do Grid ---
+    for r in range(M):
+        print(separador_horizontal)
+        linha_conteudo = "|"
+        for c in range(N):
+            conteudo = "    " 
+            if (r, c) == pos_aspirador:
+                conteudo = " 🤖 "
+            elif grid[r][c] == SUJO:
+                conteudo = " 💩 "
+            
+            linha_conteudo += conteudo[:LARGURA_CONTEUDO] + "|"
+        
+        print(linha_conteudo) 
+    
+    print(separador_horizontal + "\n")
+
+def tem_sujeira(grid):
+    """Verifica se ainda há sujeira no ambiente."""
+    for linha in grid:
+        if SUJO in linha:
+            return True
+    return False
+
+# --- O Agente Patrulhador (Sem BFS) ---
+
+def agente_patrulhador(M, N, num_sujeira):
+    """
+    Simula um aspirador de patrulhamento simples (reflexivo com estado).
+    Ele limpa o local e depois tenta seguir a direção atual, virando nas bordas.
+    """
+    grid, pos_aspirador = inicializar_ambiente(M, N, num_sujeira)
+    custo_total = 0
+    
+    # Estado Interno do Agente: Direção de patrulhamento
+    direcao_atual = 'Direita'
+    
+    print("--- 🏁 Início da Simulação do Agente Patrulhador (Não-BFS) ---")
+    exibir_ambiente(grid, pos_aspirador, custo_total, "INÍCIO") 
+    
+    MAX_PASSOS = M * N * 20 # Limite de segurança para evitar loop infinito
+    passos = 0
+
+    while tem_sujeira(grid) and passos < MAX_PASSOS:
+        r, c = pos_aspirador
+        passos += 1
+        
+        # 1. Percepção da Sujeira
+        if grid[r][c] == SUJO:
+            # Ação: ASPIRAR
+            grid[r][c] = LIMPO
+            custo_total += 1
+            exibir_ambiente(grid, pos_aspirador, custo_total, "Aspirar")
+            # Continuar no mesmo ciclo para verificar o próximo movimento
+            continue # Volta ao topo do while loop sem incrementar passos
+
+        # 2. Percepção da Borda (e Planejamento de Movimento)
+        dr, dc = DIRECOES[direcao_atual]
+        nr, nc = r + dr, c + dc
+        
+        proxima_posicao_valida = (0 <= nr < M) and (0 <= nc < N)
+
+        if proxima_posicao_valida:
+            # Ação: MOVER
+            acao = direcao_atual
+            pos_aspirador = (nr, nc)
+            custo_total += 1
+            exibir_ambiente(grid, pos_aspirador, custo_total, acao)
+
+        else:
+            # Ação: MUDAR DE DIREÇÃO (Regra da Borda)
+            
+            # Lógica simples de Zig-Zag:
+            # Se bate na lateral (Direita/Esquerda), desce/sobe (muda a linha)
+            if direcao_atual in ['Direita', 'Esquerda']:
+                # Mudar de linha
+                direcao_atual = 'Baixo' if r == 0 or (r < M - 1 and grid[r+1][c] != SUJO) else 'Cima'
+                
+                # Se não conseguir mudar de linha (ex: última linha), inverte a direção horizontal
+                if not (0 <= r + DIRECOES[direcao_atual][0] < M):
+                    direcao_atual = 'Esquerda' if direcao_atual == 'Direita' else 'Direita'
+            
+            # Se bate no topo/base (Cima/Baixo), inverte a direção horizontal
+            elif direcao_atual in ['Cima', 'Baixo']:
+                # A próxima direção horizontal será o oposto da direção anterior à última mudança vertical
+                direcao_atual = 'Direita' if direcao_atual == 'Esquerda' else 'Esquerda'
+
+            # Ação de Movimento após mudar a direção
+            # Tenta se mover uma unidade na nova direção
+            dr, dc = DIRECOES[direcao_atual]
+            nr, nc = r + dr, c + dc
+            
+            # Se o novo movimento for válido, execute. Caso contrário, apenas exiba a mudança.
+            if 0 <= nr < M and 0 <= nc < N:
+                 acao = direcao_atual
+                 pos_aspirador = (nr, nc)
+                 custo_total += 1
+                 exibir_ambiente(grid, pos_aspirador, custo_total, acao)
+            else:
+                 # Se a mudança de direção falhar, apenas reporta
+                 exibir_ambiente(grid, pos_aspirador, custo_total, f"Virar p/ {direcao_atual} (sem mover)")
+                 # Note: Não há custo se a ação foi apenas mental (virar), mas sim se mover
+
+    # --- Resultados ---
+    if tem_sujeira(grid):
+        print("\n--- 🛑 Simulação interrompida: Limite de passos atingido. ---")
+    else:
+        print("\n--- ✅ Meta Atingida! Ambiente completamente limpo. ---")
+        
+    print(f"Custo Total (Número de Ações): {custo_total}")
+
+
+# --- Configuração de Entrada do Usuário ---
+
+# Recebendo o tamanho do ambiente
+while True:
+    try:
+        M = int(input("Informe o número de linhas (M) do ambiente (ex: 5): "))
+        N = int(input("Informe o número de colunas (N) do ambiente (ex: 5): "))
+        if M > 0 and N > 0:
+            break
+        else:
+            print("M e N devem ser maiores que zero.")
+    except ValueError:
+        print("Entrada inválida. Por favor, digite um número inteiro.")
+
+# Recebendo a quantidade de quadros sujos
+max_sujeira = M * N
+while True:
+    try:
+        num_sujeira = int(input(f"Informe a quantidade de quadros sujos (Máx: {max_sujeira}): "))
+        if 0 <= num_sujeira <= max_sujeira:
+            break
+        else:
+            print(f"A quantidade de sujeira deve estar entre 0 e {max_sujeira}.")
+    except ValueError:
+        print("Entrada inválida. Por favor, digite um número inteiro.")
+
+# --- Execução ---
+if M * N > 0:
+    agente_patrulhador(M, N, num_sujeira)
+else:
+    print("O ambiente não pode ser de tamanho zero.")
\ No newline at end of file
diff --git a/search.ipynb b/search.ipynb
index 8cf99be8d..113a40935 100644
--- a/search.ipynb
+++ b/search.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -318,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -539,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -705,7 +705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -752,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -777,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -802,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -836,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 85,
    "metadata": {
     "scrolled": false
    },
@@ -874,7 +874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -1060,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1101,7 +1101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -1169,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1235,13 +1235,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e698e3059b6b47e5b1568dda5083a6a2",
+       "model_id": "6cf3b5f99b3945549c968ed157798bfc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1255,7 +1255,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b40c7e334f15493ba970903f97ee5992",
+       "model_id": "5289e52e44f0402296e660733f0e79dd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1286,7 +1286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1345,13 +1345,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3bf121b583e14883903a5835d5187968",
+       "model_id": "283b08c44556462087b6ecda363c8cc9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1365,7 +1365,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5c848eec0b00423faabba63301e52e99",
+       "model_id": "ff35d612716248388495a47b8f609b7f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1398,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1459,13 +1459,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1dd1e31b085d4df6bb158fcf7d922998",
+       "model_id": "c50faa7e50394cd1ac394fddef82640e",
        "version_major": 2,
        "version_minor": 0
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@@ -1479,7 +1479,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "06f73a78d0bd45fb907c3d1891425846",
+       "model_id": "f0c5528f793c4bf494d9c88e5a7f09fc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1509,7 +1509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1573,13 +1573,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ceb8bb1cacb4df7bdd3777ed7a0b50e",
+       "model_id": "4b4f122b17fa468c908a98fe9962e76c",
        "version_major": 2,
        "version_minor": 0
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@@ -1593,7 +1593,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dca1cb57c0c146dd98cb814cd4e7185c",
+       "model_id": "9e29803a347c46e1a0ceec5ad04c5f92",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1624,7 +1624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1709,7 +1709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1722,7 +1722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 99,
    "metadata": {
     "scrolled": false
    },
@@ -1730,7 +1730,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba6b505db00744f199258b36757edbda",
+       "model_id": "7a69a9bcee014f25b8c0c285b11001e5",
        "version_major": 2,
        "version_minor": 0
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@@ -1744,7 +1744,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "120b4fcf503c4d9db6007c6458aee832",
+       "model_id": "eb773e5c15a14bf5baf99d912ca83b90",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1776,7 +1776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1850,13 +1850,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2eb6e7564dc840a2abcac7f1211acc29",
+       "model_id": "22c4a8017ae848179f60fd497827be2c",
        "version_major": 2,
        "version_minor": 0
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@@ -1870,7 +1870,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5a44ca8e40774472b44111feda278cc6",
+       "model_id": "ec886fa3804343929294fa8f3c3c618b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1901,7 +1901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1914,13 +1914,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "01c5599fd141460bbe615ce52b193686",
+       "model_id": "5130f1b98825406e864a58173fdded95",
        "version_major": 2,
        "version_minor": 0
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@@ -1934,7 +1934,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4fce9f1769df4c93917f839e2a0792c6",
+       "model_id": "dac36db2ab6041ccb782a55b16e32d27",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1964,7 +1964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1979,13 +1979,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "299e50eb59de45b6be210093837f38bd",
+       "model_id": "ff150caf0d5d4906b4ef442a7def070d",
        "version_major": 2,
        "version_minor": 0
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@@ -1999,7 +1999,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9432929f89db40779accae8a38832221",
+       "model_id": "1dc97b8578fc4dc28aa3ebd08a87206c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2030,7 +2030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2046,13 +2046,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4593cc88d1a648fda923792ceb346b10",
+       "model_id": "bccaca5530494ffbbe8447799bacd86b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2066,7 +2066,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "26e5fdcc292d4033ae86c9c4bf686565",
+       "model_id": "26143abca5694130850d677155e6584a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2096,7 +2096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2166,13 +2166,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e63133eec82f46a59fee2a6984880cd2",
+       "model_id": "a9c9ca5947014cbd9b751b9bfe62ea85",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2186,7 +2186,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6fa018c25d564569866d6dbd8a0c97f9",
+       "model_id": "305b08ec2c244172a6ba0435a348f18a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2208,7 +2208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 110,
    "metadata": {
     "scrolled": false
    },
@@ -2216,7 +2216,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "91c8cc79cb1d447c8fc06679a0d2d947",
+       "model_id": "a289e3d794c14b52afb6b566394f10fe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2230,7 +2230,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4991523751224edea06158bd9d5e5ad9",
+       "model_id": "25ef96b563f04541822435fb85b595a5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2244,7 +2244,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bfda4ca1a3f144549af27e3a7e895d30",
+       "model_id": "84485654c94a48e9bb098a534664b019",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2258,7 +2258,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ffe41e56ce4644aa85d8fd98cd993474",
+       "model_id": "3b12e86a96344e8396d20ca64122cfd5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2272,7 +2272,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80c56dfcae2547a7a382abdf70435d1a",
+       "model_id": "71ea253b4a204333818c8d8b6a3828e8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2318,7 +2318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -2477,7 +2477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -2486,7 +2486,7 @@
        "['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest']"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2504,7 +2504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -2513,7 +2513,7 @@
        "['UP', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT', 'DOWN']"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2550,7 +2550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2574,7 +2574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2635,7 +2635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -2644,7 +2644,7 @@
        "True"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2666,7 +2666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -2675,7 +2675,7 @@
        "['UP', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT', 'DOWN']"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2694,7 +2694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
@@ -2703,7 +2703,7 @@
        "['UP', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT', 'DOWN']"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2714,7 +2714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 119,
    "metadata": {
     "scrolled": true
    },
@@ -2725,7 +2725,7 @@
        "['LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'RIGHT']"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2736,7 +2736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
@@ -2745,7 +2745,7 @@
        "['LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'RIGHT']"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2756,7 +2756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -2765,7 +2765,7 @@
        "['LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'RIGHT']"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2783,7 +2783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -2792,7 +2792,7 @@
        "['LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'UP', 'DOWN', 'RIGHT']"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2815,7 +2815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2833,14 +2833,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "718 μs ± 1.34 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "705 μs ± 1.53 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -2853,14 +2853,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "665 μs ± 2.66 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "657 μs ± 1.43 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -2873,14 +2873,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "538 μs ± 1.29 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "529 μs ± 3.54 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -2893,14 +2893,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6.25 ms ± 30.9 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "6.13 ms ± 29.9 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -2913,14 +2913,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "581 μs ± 2.02 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "569 μs ± 2 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -2952,14 +2952,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "23.2 ms ± 226 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "23.7 ms ± 298 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -2998,7 +2998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
@@ -3147,7 +3147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3197,7 +3197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -3229,7 +3229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3254,7 +3254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3302,7 +3302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3319,35 +3319,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['Iasi',\n",
-       " 'Mehadia',\n",
-       " 'Timisoara',\n",
+       "['Fagaras',\n",
+       " 'Hirsova',\n",
+       " 'Urziceni',\n",
+       " 'Rimnicu',\n",
        " 'Vaslui',\n",
-       " 'Neamt',\n",
+       " 'Sibiu',\n",
+       " 'Lugoj',\n",
+       " 'Timisoara',\n",
+       " 'Eforie',\n",
+       " 'Drobeta',\n",
        " 'Craiova',\n",
+       " 'Mehadia',\n",
+       " 'Neamt',\n",
+       " 'Pitesti',\n",
        " 'Giurgiu',\n",
-       " 'Eforie',\n",
+       " 'Zerind',\n",
+       " 'Iasi',\n",
        " 'Bucharest',\n",
-       " 'Lugoj',\n",
-       " 'Oradea',\n",
        " 'Arad',\n",
-       " 'Urziceni',\n",
-       " 'Drobeta',\n",
-       " 'Zerind',\n",
-       " 'Fagaras',\n",
-       " 'Rimnicu',\n",
-       " 'Hirsova',\n",
-       " 'Sibiu',\n",
-       " 'Pitesti']"
+       " 'Oradea']"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3394,7 +3394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -3540,7 +3540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -3673,7 +3673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3691,7 +3691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
@@ -3700,7 +3700,7 @@
        "{'W': (-1, 0), 'N': (0, 1), 'E': (1, 0), 'S': (0, -1)}"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3718,7 +3718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3734,7 +3734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3743,7 +3743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -3752,7 +3752,7 @@
        "9"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3778,7 +3778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3794,7 +3794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -3822,7 +3822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [
     {
@@ -3838,7 +3838,7 @@
        " 'SW': (-1, -1)}"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3858,7 +3858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3867,14 +3867,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "47.1 ms ± 395 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "46.9 ms ± 109 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -3885,7 +3885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [
     {
@@ -3894,7 +3894,7 @@
        "9"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3914,7 +3914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
@@ -3924,9 +3924,9 @@
      "traceback": [
       "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
       "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[102]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# %%timeit\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m solution = problem.value(\u001b[43mhill_climbing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[43m)\u001b[49m)\n",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[84]\u001b[39m\u001b[32m, line 23\u001b[39m, in \u001b[36mhill_climbing\u001b[39m\u001b[34m(problem)\u001b[39m\n\u001b[32m     21\u001b[39m current = Node(problem.initial)\n\u001b[32m     22\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m iterations:\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m     neighbors = \u001b[43mfind_neighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     24\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m neighbors:\n\u001b[32m     25\u001b[39m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[84]\u001b[39m\u001b[32m, line 12\u001b[39m, in \u001b[36mhill_climbing..find_neighbors\u001b[39m\u001b[34m(state, number_of_neighbors)\u001b[39m\n\u001b[32m      9\u001b[39m neighbors = []\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(number_of_neighbors):\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m     new_state = \u001b[43mproblem\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtwo_opt\u001b[49m(state)\n\u001b[32m     13\u001b[39m     neighbors.append(Node(new_state))\n\u001b[32m     14\u001b[39m     state = new_state\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[150]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# %%timeit\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m solution = problem.value(\u001b[43mhill_climbing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[134]\u001b[39m\u001b[32m, line 23\u001b[39m, in \u001b[36mhill_climbing\u001b[39m\u001b[34m(problem)\u001b[39m\n\u001b[32m     21\u001b[39m current = Node(problem.initial)\n\u001b[32m     22\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m iterations:\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m     neighbors = \u001b[43mfind_neighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     24\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m neighbors:\n\u001b[32m     25\u001b[39m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[134]\u001b[39m\u001b[32m, line 12\u001b[39m, in \u001b[36mhill_climbing..find_neighbors\u001b[39m\u001b[34m(state, number_of_neighbors)\u001b[39m\n\u001b[32m      9\u001b[39m neighbors = []\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(number_of_neighbors):\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m     new_state = \u001b[43mproblem\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtwo_opt\u001b[49m(state)\n\u001b[32m     13\u001b[39m     neighbors.append(Node(new_state))\n\u001b[32m     14\u001b[39m     state = new_state\n",
       "\u001b[31mAttributeError\u001b[39m: 'PeakFindingProblem' object has no attribute 'two_opt'"
      ]
     }
@@ -3938,7 +3938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3980,7 +3980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3993,7 +3993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4029,7 +4029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4045,7 +4045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
diff --git a/search4e.ipynb b/search4e.ipynb
index 7c636f2e7..31572b322 100644
--- a/search4e.ipynb
+++ b/search4e.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 356,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,18 +302,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['N', 'I', 'V', 'U', 'B', 'F', 'S', 'O', 'Z', 'A', 'T', 'L']"
-      ]
-     },
-     "execution_count": 236,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'r2' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m path_states(depth_first_recursive_search(\u001b[43mr2\u001b[49m))\n",
+      "\u001b[31mNameError\u001b[39m: name 'r2' is not defined"
+     ]
     }
    ],
    "source": [
@@ -329,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 412,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -360,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 413,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -462,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 398,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -527,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 400,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -603,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -644,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -696,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 397,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1808,7 +1809,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1827,7 +1828,7 @@
     },
     {
      "data": {
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IeDcWBzg/UBsrqgCsqdaAi1L9riEQp4Y+sg2tjsVaz5sptXtMWIEVVZqzfJjS07abm7W2Durmm3WAaU6dNwWr0TorqrRImBIAAFRMoUqLhCkBAEDF3PpLc4QpUbOp4TknXiPS7WCCNDZgxpgLPT5yzEcALmNFFbjUqXCHmkMfImjtQ3Fr27O21udT9PERvX9TOGdTgvHFxayo0hxhSuuIvAoCrZgzz4KtrFNYxHP2lGsz8+12u650H2AKK6q0SJgSAMTn2gqcpFClRcKUACA+11bgJIUqzen71Pd9+vzxLUNj23I8HwA4z7UVGOJvVAFiuU3bCHBheTWMpejjo4Z92LqixyD333ofeb3QyddQkkIVIBAfWMjFWJqvhn3YemjWsWPQ2Db7IgROcOsvVeu61HVd+vA+JXB2W47nAwDvWuLaDLRNoUrt5iT0Sv0FgHUscW0GGqZQpXZzEnql/gLAOpa4NgMN8zeqVO0+FfDzXG1jHrvf71+mlN6/ucmwAZV42OZCby9ogqoVnj9zmHtkc+m1eeo1t7G/X4VNs6IKI3SfdF33Sff97pOuS/E/cJ5K0ZyTrllym6Pvbzin1jFca78PLXFOZD2tjMNTjEM4wYoqVbsPVfggpfTFw2+uzWk71t590nWpT3+WUvpRSumnfd+nroub5WAFBOBLzonrW+LaHMlutxv8EDC0qnvuucCXrKhSu0XDlO5XUP8s9c9+L3WpS/2z3/vp//pp6vuQ104AiGCJazORdN1vpq7754/++c3SXaI9VlSp3XJhSt//yRcppT9LKf0wPXvz9ZRSSs/efP2zf/gspZTSj/7pj0KvrAJAIUtcm4mi6347pfTXKaWvPGr9f6nr/kXq+/9WqFc0SKFK1ZYKU+o+6e6K1F9/4/fTb/zynf//q/5X6T//779JKSlWAVomSO4yS1ybW7T2+BoZNDU87rrut/8hPf/F++nlO7dlvkkp3abnv/hW1/2OYpVc3PoLB97e7pvSDw+L1Ld+45fps3/4LG3oNuCSYQ+CJqhdrWO41n7nJEiuvGjjcEx/xgZ4RTzGp/t0d3vvXx8WqSndFRTvp5cppfTXbgMmFyuqVC13YMPb4KT+2e+9vd33hF/1v0pbuQ241m/1IQLzh5Zlvw4ftD3Mn8ftNzf7N6f6c329ezb1fYZe75Lwo4bn/D9JKX3l1CrXs5RSn9JXfpJ+8r1/26X/UFNAFjFZUaV2uQMbvpdS+tG5IvXBr/pfpZ///c/T3/7yby/oOgBUL/d1+GzQ4YL9YaY36dmzT9NHf5LsazJQqFK73IEN/zWl9NP05tn/HfPmX+u+ln7wj3+Qfusbv3VB1wGgermvw8NBh+dDloQ2FfQsvXnzUfr0j5J9TQZu/aVquQMb+h/3ffdJ9wepe5NSSj9MKf2jU+/9te5r6bvf+m7zt/1OUTh4ZA1Fwk02sF9Tqjg45kHQ41T9fiW27NfhgaDDh/b9Pm9/hl4vl6Dnh6NOhS699xd/kf7VH/5h+uovf3l0petNSull+tb7f5L+6NOUUrr/aHTb9+l52khAFnlZUYUD/Y/7PqX0Bymlv0y//sbxB/36G4rU46q4CM9Qavta368ptbGNEbchYp9qIkiOXKqfi6+/+c30808+SbfpeTr8o977IjV9N32W/j69k6VU/XZTjhVVqpY7xOHL9v6D9P2f/EF6/3/+fvqdv0zvpP/++hsp/eKH6Uc//JeKVICGWY1+19LBSafaDttvbvL2cej1eNfLb387/c2/+zfpBz/+cerefFmu/p9XX0vfTZ+l/55++8lzhClxKSuq1G65wIb/8pMP0s/+PKVf/DC9XVm9L1LTz/5ckQrA1qwRnCRMKbiX3/52+o9/+qfpP/3xH7/955+l/3G0SL1nX3MRK6rUbuHAhi6ln/35Xet3/uJtkZqSIhWAzVkjOEmYUgVef/Ob6fU3v/n2vw9u9z1kX3MRhSpVWzqw4W7R9L5Y/cW/TunvvpcUqQBs0RrBSa2GKY318ccfpVev3hv56D7V8Jnk1DGFc9z6C6N0Kf3d99MFF4SthWG0vr2ltq/1/ZpSG9sYcRsi9gm2aNRcHF+kplRDkZqcg5jBiipVWzqwYWp/drtdFVeNpawRPHIqNv/+/Zvc/wJd6uA40ZJSwUkthSm9+75354dzfUnpSaBubZ6lkccUzrGiSu1KBjYAQKtKBSe1FKYUqS9raX37WJFCldqVDGwAgFaVCk5qKUwpUl/W0vr2sSK3/lK1dcKUlrXf71+m4z+IfetWwu0ZGA+1anIcm7f1aHBOraJUcFJLYUoHnyeejMMWf+VuyjGFc6yoQnmnPkD5YLVNrR331rbngXlbD8eECBYYh+H+3FNwElkpVGlO16Wu69KH93+8P6ltqB0AWjTnurlG21B7zm2ZY4H3eJZS+k5K6dnNzT4d/+evUt+n7vFj+z51Bdue+wxFTgpVWiRMCQDGixScVGuYUu73mPLcSMfKZyiyUajSImFKADBepOCkWsOUcr/HlOdGOlY+Q5GNMCWac2lgw7Ggg6mGfuMzwusdkTX4pXRoSW37i9NKjyVowchz4u39b3yGCE66NEzp448/Sq9evZfSo98hvQ8ruu37NLh9OcKUzgUnzXm9c/2LdKwEJ5GTFVX40uiLytXV6yX7sabchUDrhcUa29daGMWl29P6WGI9rc2p3Gqca0+O6X2RekyJ7Zv7nsYsJCuqNOj+D/g/SCl9cf/N3ui2U25uFsquhwNWbCGv6HNqhTtBzppz3Vyj7Wn73TF93JYeraRO3b6bm7z768zTn43Z5jn9u7Tfl3w+giVZUaVF/uAfAMaLFLwzJUwpd8DSWGv0ZYnPKD4fURWFKi3yB/8AMF6k4J0pYUq5A5bGWqMvS3xG8fmIqnR9b0X/nKHbcna7nd+JakTXjb+95erqdXrx4tMlu0M9BCwtIMLtkJdyXWCKCGO9tjGbIazoIWAppTTvc97Uvtz//ugkEcbIClxLZ2i1VrGiCl8aHV4wENrA9tQYRFIDYSJsRemxXvr9LzH3vJvzvD3ltWrc12txLeUJYUo059KwgIdvV8eGMxxz7Fur8yEJ+5PvketbsI18G7vot4Zb2YclnQtLKR3oMnbewhTnVpFyj8Xr693kMJ/SbYftQ9v3OPzw+np38nFjw4rWCE4CjrOiSouWCGxYoz+wdZHCW8xboigVBBSpbaj9UmsEMTmPwAwKVVq0RGDDGv2BrYsU3mLeEkWpIKBIbUPtl1ojiMl5BGZw6y/Nub+V5vNL2g7buww3kp57772faIWU0ry5u3abectaco/FCPNnSluG4KRT3t4yfX29OxmSePCZYFJfHt+W/XCcHt9m7DwCw6yoAgDUq/WAntGF4dXV68H/HjIyJPHivnBW6+OYC1hRZROWCGyY8z5jQxxKiRxlLtSoXZHCW2qct7Tp/Ph8Gjw2FLAUYf5cEqZ0wtuwomPb/LBC+jhAKk0MSbwkxKlFkT8X0C4rqmzFWoENQlngcpHCW8xbosg9PiPNn7nzbM42j2XeQyEKVbZircAGoSxwuUjhLeYtUeQen5Hmz9x5NmebxzLvoRC3/rIJa4UptRLKcuT22ttzv/UHc52fK/uXKaX3j4WRrN1W0sN+KN2PTJxbzsh9XYkwp77+9Y+e/E3osevtuWvwJds857qeI2ARGM+KKjBGKx+KqZtxeKel/dDSttSoyP4fGVx0jvCd9djXFGFFlU0QpgSxmCvwrjHXpBxzpcY51fepy7XNU9977HMfBzYd79/pgKtzQUVDIYJCjmiZFVW2QpgSxGKuwLumXJNyhynVoNQ25w6pAkZSqLIVwpQgFnMF3jXlmpQ7TKkGpbY5d0gVMJJbf9kEYUoQi7nyVGMhSUw05po0tm18mNK4vn388dPwo7Xl2ualwpS2fv6CJVhRBaAWrQd6bLFIbf2YRjdq/xcuUo0R2CgrqmyWMCUo55K50vd3P2Ny2XPHzsdxgSdD4SacJvjltBJhSmPnVErp5LyYaTCAaMp+KB2m5FoP+VlRZcuEKUE5c+bKGm2wtpJhSqXmxdygQ2FK0DCFKlsmTAnKmTNX1miDtZUMUyo1L+YGHQpTgoa59ZfNmhumdH29e+e/r65eC4iBkS6ZK2u0mY+UUiJM6Vhb16XVQr0uDTo81pY7TOlRgNSo256dWyA/K6owbHSIQ+lERKB6rYfGtL59rVgr1Cv0eJh4TQ+9LVArK6pw4CAQ4UnQRBr4dlWYEoyzZCDSvDClNbb+uN3uLtiG9o0dm1Mee1mY0tHgpCGjwo9yt+Xc5jPbN9bk/eBaD9NZUYWnWgmpgMgiBSeZj6xtiRChNUJ/Ss7HUmFKY1/PuQUyU6jCU62EVEBkkYKTzEfWtkSI0BqhPyXnY6kwpbGv59wCmbn1Fw6MCJoY8va24PvH3d7fPlx9wELB3428dUvkOPv9/lQIytl9OPDcRTy+De5hDhxpu73/nceqw5TW3rcXMs9WlDNE6FjbhGChSWNz2bl3N0+OnRtSOn/OGPL495EPgxAfG/p/j0U5t0DrrKjCdFNCE6J/OK2BfTjeqX01Zh9G3M8R+3SJGrajhj6S35TjvnRg0Cpj8Orq9dyXEJwEK1Gowghdl7quSx92Xer6Pj3v+9Slu/nznXRmHj1+7lDbkn1e6j1gaWPnT+42WNKUcZh7HE8Y72+vcX1/d+1rYe69ePFpurnZv/PPGVn3AzCeQhXGKRVcMYcQB1ogTIkWRQtTmtPH1ude69sHYSlUYZxSwRVzCHGgBcKUaFG0MKU5fWx97rW+fRCWMCUYIWfA0tXV63Rz8+mo950TYOQ322hB7lCWOYEsY+djweAxMlsqCGvM2Pz444/Sq1fvvfO8Y9eaY23X17t0dfU6vXjx5bXm0eud/C3wx5YMTjrWFjVsqPXtg8isqI5z6g/n/UE9D0aPhcMPHpxlnq0j4n6+tE+CgaaLePwjKDaW5l4rDp8/8fVKjIdiY3AgYMm8oBZN1ipWVEcQ2c8x9+EIH6SUvrj/CZp32tLIb63XdH29e5bu+/c4rn+M3W4nDKJhQ+e5g7Hen2qb8tgl21q8m8D8Y2Fvrw2l5u39T1FdfG4ZuqYdmz9z+g3RtFqrWFGFy9UYnBC9f8S0VvCL0BIoI9K8nXtuGcu5BYJTqMLlagxOiN4/Ylor+EVoCZQRad7OPbeM5dwCwSlU4UJ9n/q+T58/vhXoWFsk0ftHTFPG+tjHrtEGjBNp3s49t6yxzcA6/I0qLOc2nQjiuL7enX3yYWJjpv604NR+Db19S6WHTnj/pT9k3Qb6G5mTc69Socd24y4eS8dSe6fpU0rz/jR5zLUmZRxfpc9zp4w8/0U6hx06OQ5PbFvkbYHRFKqwkIeApce6bvw3sq9evSdA5YiKL77hPrxlFmb75oyRoQ+0j+fj2MfNfR/KmjOWrq/nrsB1qe/PV6rHxtJQgTrmNWcIcx64QNi+nxqHA+eRsNsCU7j1FzLqutR1XfrwPiXwZFvu15vTBjmtMWbNC9ZWciy5rgBbpVCFvCKlEkovpIRISaHmBbmUHEuuK8AmKVQhr0iphNILKSFSUqh5QS4lx5LrCrBJXd8LMYO1TPkb1Qluj/097GNRAy5oSwt/W7lC6NTahKqsoOvSIufY2v5Gtfb5s+Q5bIm/S/e37rTOiiqsa4n0zjEfjhSpLE0ybUzm/jqW2M8Xz6mrq9fZX3Okms8DNfcdmiT1FxZ2HzDxQUrpi4eVz4O2/rAtpfRmxns8eb2bm0wbw2ou+TZ86Nv16+vdszQwRnK0LfGaa7eZKxwz4pw95OK5N+axx8bsw0+brTHv323bjbrGLbMf9ievm1YXoU5WVGF5awRSCLjgnLUCVCIFIgmDIZc542atOZX7vSO1TX0s0ACFKixvjUAKARecs1aASqRAJGEw5DJn3Kw1p3K/d6S2qY8FGuDWX1jY/S1Kn09p66bfpPT2lqf75z4ELH2eUkr7/eTXozGXjMOpbWu9z5Jt5grHHJyfJwUnLT2nhsZshDmVo23MY4f2Q+0hT7BVVlQhprmhDocfooREwDitzZXWtieCKcFJ9j/nGCNwghVVKGBEgMTs0KVzARdDwRPHCKOYbk74x9p9Eaa0bhh2ULfIAAAPIUlEQVSM4Je4SgUn5QxTGvvcWtrm7odIzG8Yz4oqlLFG8IvgifIiHYNogSdbaxtqJ5Y1zrtrhQhFmgMl9wNQoa7v3bYPa8v1bXMa/hmbwW/2ragub+2fU/DzNHHbDtutqMa15Hl37Z9lWf/naUquqK53h8ocOef30DnfeYQWKFShYl2Xpkzgh4CllNL0cAkXvfxyf8jwoaUeCxz7UwE/t7vd7vmRdka4IDhp9Xk2ZywNjBsWolCF8dz6C3WbEsLgwwi069T8Nu/naT04yfhYV41jBIoRpgSBXRK6lEYGLNUSPFGz3OEf0UNV1nqfGtumHCvWEyk4qUSYUovj8Nztzm67h3pYUYXY5oZPjHk9lpM7/CP3sS8ZeLK1tqF2ymlpTs3pY0u2uM3QJIUqxPZFSul307vf7I9tG/t6LGfOscr9enPG0pT3XeN9amwbaqeclubUnD62ZIvbDE0SpgSNmRKwdHX1Or148emoxy59S9RGQj3eCbYRpnQn4LFfPIDIsY+nhuCkY8Ye+4DzbBEjAqSamSstbQscY0UV2jM6rOHVq/eyv+YMzX+AStvYxktE2y/R+sM6BCfVr8bjApwgTAkqkztg6ZhjYRQj3/vithZDPY4RplTHsY8WplTjPowmenDSEmFKkSyxwtfK+QY4zooq1Cd3wNLY95j73gIu7uQ+LrUGv0Q/9tHClGrch9FEnz9LhCm1rpXzDXCEQhXqkztgaex7zH1vARd3hClN63cp0cKUatyH0USfP0uEKbWulfMNcIQwJdiAKQFLTNWndCRT5VRQ1UG4iUCdNNzvLdrSsS+l1uCkY2qZP1HGYUtzpaVtgWOsqMI2CJhYzPHPAieCqhyH4+wX1tZ6cFI09iEwmTAlaFTugCWmOxZKtWT4Ua1hSn2/ezI+5wVz7ase2wJi5qsxOGmtMKXWV9rMFWiHFVVol0CJ8kqFt+R+vbXClJboT422uM25RZ8rwpSWY39BIxSq0C6BEuWVCm/J/XprhSkJ5rqzxW3OLfpcEaa0HPsLGiFMCQLa7/eTgj4ucX29W/LlOe724TbslIQpLaWWcJlTajr2a5yr5vr4449O/c34UWsHJ9WwDzO43e12z88/bHktnSdb2hY4xooqxLT4h5arq9cTHl315/5Ilj6upwJLthZksrXtLSl8gTWlSE1lxk74fZjBFrYRyEyYEmzUsZ9OWdK5YKGa2w7b00BQ1bLhR3lDidYLU8rdtvx+GApsGrOSMbQSIkxpVaPOS2M5dgD5WFEF1hIpoGStwJNjagx0WSL4pZW2JUTrT8ty71fHDiAThSqwlkgBJWsFnhxTY6BLrWFKtQY2RetPy3LvV8cOIBO3/gKreHyr5H7/0Pbl/79vu72/bfPzx8+9v10ubNthezd84+fb/XB9vUtXV6+P3oYdafsu3Q8ttz2M4SVE608EUwORxjq/X08GHR0NB3LsAPKxogoxbTUMppXAjdHHb4kP37CiVc5VC82TMX0/dU7Kea7awvl+C9sIZGZFFQKaE+Nf+09zRDIjROhJmE8aCFia896R2qL1Z9kwpSlHc5po/RmS6ydHzm1fmjh/TsganJTLEj/b4mdLgBZYUQU4bYkQodzvHaktWn+EKdVjje1rfR8CNEWhCnDaEiFCud87Ulu0/ghTqsca29f6PgRoilt/AU7IGSI0FLB0fb071vz2Vsdjz43eduaxt/e3R4cLSRrbJkwpr4O5cirAKNt7DLUBEIMVVSCSlgM3Wt62qVoJzZrj1HgwTpYZH/YrQGWsqMKGCNFYzogAnNkBSy2JEIhUMkzpIUAnSn/WMiaE68xLjApEmhIABkBMVlQB8thiAM4ckQKRSh67aP1Z2pQQrrHPb3l/AWyWQhUgjy0G4MwRKRCp5LGL1p+lTQnhGvv8lvcXwGa59Rc4a7/fLxJuMsPtEr89OMclAThDAUsbcBgWVVXAUq7womj9WdrhdkwNTsp9TCMbOO+GO/8NCXj9OGnG75BXdUygFlZUgTGifciI1p9LCXj5UivHlGmmHPetzZdT+6a2uVJbfy+xhW2E1SlUAVbUdanruvRh16Wu79Pzvk9dujsXfyel9KzvU1dr25jHjt03tbRNEa0/S8vQ58Ox9Dz6NgOQj0IVYF2RgoCWCKaZE2ITaVuWCOOJ1p+lze1zjdsMQCYKVYB1RQoCWiKYZk6ITaRtWSKMJ1p/lja3zzVuMwCZCFOCFZUOlZgRFLEpSx6nx793+RCAs0Lbbd/vFg8qOmx/2I+P+3N9vTt8ymPVBCxdEl4UrT9LOwgOezKnzoWJtRCIBMDlrKjCugQu5LF0qEprx6nU9jx536ur17OeT7WmHsutBSe1bAvHstQ2nnrfLexzNsCKKrCK3W4n/KSg+/CZD1JKX9yvSmVvO2x/vJL64MWLT1NKKV1f7549PC49Wkkt0e9L2o5t21CfS/ZnDef6N/Tcvk/dlDFGXfxsy3LsW1pnRRVgG0qGKY3tT6l+5w4vmhs0VWOYkuAkALJSqAJsQ8kwpbH9KdXv3OFFc4OmagxTEpwEQFZu/QXYgDXChg7bhwJ+DoJ2hoQMWBq7baef/zRoak5o1pA1QtQe9+XrX/8ovXr13qTnC04C4JAVVVjXVgMOatvu2vp7TvTtmdK/VgKWWtmOJyYWqdHHJgCFWFGFFa0RfDC0eiLQaJzaAypKhQ0dtk8IHHp+2JaCByzNDVMqHX5U0NsQrXNBUwBsmxVVgPaUDBbKHZI057k1hCltjX0DwCgKVYD2lAwWyh2SNOe5NYQpbY19A8AoXd+7wwZa4tZfopgzFrtu0u2fDwFLq5k7z9YIOBrr44+nhx9dqu9T2HPQQ8BV6X5syG3tf2YBLMuKKgDhXF29nvJwxcUMaxWpKX5wknG0LvsbGKRQBdiorktd16UP74NsZrcNtU997xcvPk03N/t3/llzW6Zs85i+THl+Q56llL6TUnrW96nr+/R8g/sAgAspVAG2q9YwpTmvJ0xpPbmPMQAbolAF2K5aw5TmvJ4wpfXkPsYAbIgwJWiMMKU7DQajVBc8MmcsHnvu9fXu5OPP3Rq8pshhSoKTTosUcLUVI+dKyXN5deddaIkVVaBVLRWpKbW3PVtXJFhIcNKgGvtcs7H7u+S5z3kXCvpq6Q4AENt98M0HKaUv+v7uZ2OOtR2239xc/ppDz41uzL7p+7tVmrH79vz+2r851Z/r692zh8ellE4+Lt19eT27L+fGSFTHVs5K3qHi7hhg66yoAnBODWFKkUzZNyXDncY8bo3+AcATClUAzqkhTCmSKfumZLjTmMet0T8AeEKhCsCgvk9936fPH9++eaxtqH3Oa9Zmyr7J3TalP2Met0b/AOAYf6MKhNVgcu8cmw96ubp6fTIMaCgReG3X1yUKs126unqdXrz49G3LQ8LvyP5sfnydcJuOn4Psr/WcOgZrvTdQiEIViKyKIlWwyToeirDH+7vrrNY9OCzihxJ+a/vpmFL8NEl5jgFsl1t/AahK16Wu69KH96myXODYPrRfAYhEoQpAbaTJzielF4DQFKoA1Eaa7HxSegEIzd+oAlCV+xTZz1NKqXOT6jvGhko93odDbZEJWwNomxVVILIaEhdr6GPL7P/pWtlnilSAhllRBcKS9sgx92E/H6SUvuj79PxIWx+tben3SSm9Gdhlz8b2EQCisKIKQG3GBgFFalvzfQ4JTgKgOgpVAGozNggoUtua73NIcBIA1XHrLwBVGRsEFKlt6fcZCpVqITgJgO2xogrtORWU0kqACvDUFud9y9sGsHlWVKExAohgG+aESrWg9XPdfr9v5lgBXMKKKgDUKXfoEgCEoVAFgDrlDl0CgDDc+gsAA/b7/cuU0vul+3Hgtu93z9OMcCcAiMyKKgAMi1akphSzTwCQjUIVAACAUBSqAAAAhKJQBQAAIBRhSkA2QUNncrpt/bcbAciv8uujax9FWFEFcqr1IjxW69uX2+3E9qgi9jdin8irlfnDnZqvHzX3nYpZUQVgEa18A9/KdlAX4w7YOiuqAAAAhKJQBQAAIBSFKgAAAKEoVIGcWg/5aH37IhEkA7Sk5nNXzX2nYl3f96X7AAAAAG9ZUQUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEMr/B8wj0o1qhF8ZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1846,7 +1847,7 @@
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     {
      "data": {
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3zc0Xt/tcX++q3ea+/XBKrjmZ+3xaw/k5WrggnGJFla2YEtgg8AFgu6KFCK1xrYm0za6tQEpJocp2TAlsEPgAsF3RQoTWuNZE2mbXViCl5NZfNmJKYMMlgQ8CloAlTA2fKnRb8G1LtxGeCy/qTty4uUbbkiIFJ00JUzox3psai7k5P1AbK6oArKnWgItS/a4hfKqGPl6qim27unpdugtRVHG8LlDreTOldo8JK7CiyibkDVN62nZzs+bWQL38ZZ1Izp3bS/btjGcpY5hfpOCkKWFKW9F33hSsRuusqLIVwpQAOFbruT13HyMFJ9Ww/4EVKFTZCmFKAByr9dyeu4+RgpNq2P/ACtz6yyYIU6IWU8Nzel4j0u1ggjQ2YMaYKzo+zgUnRTXndt+e31h9+3r3QU63h0N6noKHKQFts6IKXKov3KHm0IcIqvigPEFr27O21udTpPERqS8p9Xw9c0xwUoZwpWD7goq1fg5jQVZU2QRhSvlZJYPlzZlnwVbWQxobnHRzU9dtMy9efJJSSun6evc2dCk9WjUdQ5hSe3a73co/fATzWFFlK4QpAXCs9fP4nO0TpgQUpVBlK4QpAXCs9fP4nO0TpgQUpVBlEw6HdDgc0mePbyM61TblsX3PB6AOrZ/H52zfnGth7jZgm3xHFSCW29RWkIkgjXJqGEsVjI9m6qVJ46Hrnmz4QxLw2oqO49zf9T7xepLRoYdCFSAQH1jIxVjKpQsdQjO2kBpbZJ4oUB8UKRZPjePGgsKi/zEJinHrL5vQdanruvTN+zTB3rYpj+17PgB1GHsen3NdmHtNmXP9yX09i7YfgLYpVNkKqb8AHBt7Hi+Zdjvn+pP7ehZtPwANU6iyFVJ/ATg29jxeMu12zvUn9/Us2n4AGuY7qmzCfXrgZ+faxjx2v9+/TCm9f3OzWHfDedjmQm8vaIKqFZ4/czQ79z766MP06tV7KaX0Zszjx15DcreNeex+n7ff3fCNtW/31/3jHgKWFtsPU6+5jX1/FTbNiiqM0H3cdd3H3Xe7j7suxf/A2ZeiOSdds+Q2R9/fcE6tY7jWfh97cu67L1Ivfn7jpmzvGmOklXHYZ2vjC0azokpz7oMWPkgpff7wO2xj2061dx93XTqkn6SUfpBS+tnhcEjdmT85l9TqCgjAWO+ex+/OiY/b0vBK6rN04TUkd9uYx+a4u+foPSbtr7X2QyTnUqCHVnUjJ0hDNFZUaVG2wIb7FdSfpMOz76cudenw7Ps/+18/S4dDyGsnAHdqDAxaIkxprOj7i2i67vdT1/3TR//8fuku0R4rqrQoT2DDd3/8eUrpJyml76Vnb34vpZTSsze/9+mvPk0ppfSDf/yD0CurABtWY2DQEmFKY0XfX0TSdd9IKf0ipfQ7j1r/X+q6f5YOh/9WqFc0SKFKc3IEV3Qfd3dF6m+/8sfpd3/9zn//zeE36T//779JKSlWASI6CgeaFGZ1LjzvIbxohbbb3W73fE6Y0ljRA5ZatXbQ2sigqeEQta77xq/S81++n16+c1vmm5TSbXr+y6913R8qVsnFrb9w5O3tvil977hIfet3f50+/dWnaUO3AZcMexA0Qe1qHcO19vvYlELgeJsFyd0pGbAUbRyO6c/YUMNIx/hBf5/ubu/9xXGRmtJdQfF+eplSSr9wGzC5WFGlObNCHB6Ckw7Pvv/2dt8evzn8Jm3lNmABTXA582dZ587vZ54+GJwU4WfI1ghTGvG+xQKWHubPu9u8733v6+vd5DCsode7JPyo4Tn/j1JKv9O3yvUspXRI6Xd+nH78nX/Tpf9QU0AWMVlRpUVzAhu+k1L6wbki9cFvDr9Jf/33f53+9td/O6/HAFxqjSCgkkr1MVLA0lrvzUxv0rNnn6QP/zzZ12SgUKVFcwIb/mtK6WfpzbP/O+aNvtx9Of3RP/yj9Adf+YN5PQbgUmsEAZVUqo+RApbWem9mepbevPkwffKnyb4mA7f+0pw5gQ2HHx0O3cfdD1P3JqWUvpdS+gd97/Pl7svp21/7dvO3/U6xdjBEAcMhEwvZwH5NqdC+zSnocap+v56TKzjpVFuOoKK5SvUxUsDScfvQNl/yGWCN4xz0/HBSX+jSez//efoXf/In6Uu//vXJla43KaWX6Wvv/3n6009SGj72MIYVVThy+NHhkFL6YUrpL9Nvv3L6Qb/9iiL1tCouwjOU2r7W92tKbWxjxG2I2KclzQlOuvQxS4kWInSsZMBSjarfB6+/+tX01x9/nG7T8ydfUL4vUtO306fp79M7WUrVbzflWFGlOXNCHL5oP3yQvvvjH6b3/+cfpz/8y/RO+u9vv5LSL7+XfvC9f65IhQ0pFWozV85gmyXacr7mmV0xOWTncHga5BNt36wUpnSqj6sFLB23D23zJe8Tde5G9PLrX09/82//dfqjH/0odW++ONz/59WX07fTp+m/p288eY4wJS5lRZUW5Qls+C8//iD91U9T+uX30tuV1fsiNf3VTxWpsD21BrLkDrZZKygndyhOpG1eYt/kFmn/r/k+nPHy619P//Ev/iL9pz/7s7f//JP0P04Wqffsay5iRZUWZQxs6FL6q5/etX7r52+L1JQUqbBBtQay5A62WSsoJ3coTqRtXmLf5BZp/6/5Pozw+qtfTa+/+tW3/350u+8x+5qLKFRpzpwwpeP2u0XT+2L1l/8qpb/7TlKkwjadC+25vt71PTMVPm8cB9u8I1Jbjuf3mRW0V6htzGNLhSmdapsYsPSOgbbbwyE9rz1M6ZSPPvowvXr13oxXKH5uGaVvbMM5bv2FUbqU/u676YILQvQwjNxa395S29f6fk2pvm2cEBAS/4PkBtQ2vmq1xH5uJYznyb6ZV6SmVMm5xdzjYlZU2YRLAxumvs9ut6viqrGUNX4Goy82//79m9z/rf+8yINIAT8zQ3so74LgpHhtYx67ZhBQ7oClS9+7ljClc8FcKdO+CWbU3IMxrKiyFUsENgB5RQqxEb5St0hjpNYwpVNKzpUaw5QiHbu1bHGbWYhCla1YIrAByCtSiI3wlbpFGiO1himdUnKu1BimFOnYrWWL28xCusPBCvw5W7zVkDtd13+Lys3N0/SFS8bDfr9/Espy73Yrt3xO0fp8HBgPtbpoHJ8KK4KxDocvvrzX4JwaLcI5ceg6OsfV1ev04sUnT9ovvA4vfl3Zyjnt8dxjPa1+NrKiCuX1Xbiav6BxUmvH/dLtaW0/sJ7j8BZjqaxFwnTmBxGtboFxGG6xSXASWSlU2YSuS13XpW/ef6G/t22oHchnypyM7OZm/+SfdHdt/VZK6dnhkLqHf061a1vkNZ/XOJZqdW4u3/+0zMXHM3d/htrmmPkeg/vh1Hnm7p9/l0rO3RNt5h5ZKVTZCmFKEEvLgRtzQ3G21rbm+5BftGNXaozMeY+5/Ys0n809slGoshXClCCWlgM35obibK1tzfchv2jHrtQYmfMec/sXaT6be2QjTGmEVr+gzLumBh2cClOqUNbApg2EliwecDV0vqnVufNkzSEjuULVWE6Lcyqz0MF9GcKYbh9+37VP7s95U89p525x9jmUc1odI1ZU4QujLypXV6+X7MeachcHVRYbE6yxfa2FUYzZnirHTUPngda1Nqdyiz7/5h6/Ets35T2NT+jxpdIdgDXcf6n/g5TS54fD3V9nT7X1aWT1lApEXtnI6fH8G3qcucdc0edU7Su+Y6+vl7Y9rIYenTPe5OzjzU3ebT7z9GdDzz33eQS2xIoqW+EL/xCL+QdtKBm4lbuPa7yezyMwkkKVrfCFf4jF/IM2lAzcyt3HNV7P5xEYya2/bML9rTSfnWvrc329e+ffr65epxcvPsnWv5Jqv+1sbSf2V+ggkqgez79uIOah5bkHLRh7fZ3Tdtw+dM7o8fZW4fvnPgQsfZZSSvuJ3zA46svU4KRZn0eONXQNdy3lCSuq8IXRgQavXr23ZD+oS/QgkhqYe2xZ6TCd0u9/iUgBS4KT8nAt5QkrqmzCWoEN19e7USEJ74Y47HvfI1ekeEN/cR20ZAT7VvbhWo7mxaS5d8k8y902NG9hirmrSFPPTWv8VEXuuXei/ck549ScPL4jo6+PQ2FKgpOgHCuqbEW0wAbBCWzdGmEka4W8AO9aK0wp9/V67OMEJ8EKFKpsRbTABsEJbN0aYSRrhbwA71orTCn39Xrs4wQnwQrc+ssmrBXYcElYxNQQB2jBuXkxNPfWCG8xb+FyS4cpnWq7YE6+vVX4+nrXG9Q2Jzjp8e3ID/17fJux8wgMs6IKAFAvAT09rq5ej37syKC20UXqlPcmpWQcc4IVVTZrTGBDjtc8H8oyd0vyWyNs41JCjepzybzI/Xr5w5Ry7BmY71QYU8TzZOYwpVFz8mGF9HEAW5oYkjj2vHRz0/byaOTPBbTLiipbJkwJ1pF7XghTgvqUDFNaI3QJyEyhypYJU4J15J4XwpSgPiXDlNYIXQIyc+svmyVMqd+J28Zu5/7WH9t1ybwYmntjA0qWbCtpv99PCnQJzrmloDXH0ok5dXs47J6nFcKUcl3Xpz4XmMeKKjBGKx+KoQUtzceWtqVGJfe/Y18PQUcUYUWVzRKmBOvIHaYEtGHpMKVTz53Tx6HHPQ5sOt2/fW+I07mgoqFwLCFHtMyKKlsmTAnWYV4Ap7QUpuScBpkpVNkyYUqwDvMCOKWlMCXnNMjMrb9sljAlWEfuMKVWNRaSBGeNPTeMeWzpMCXXesjPiioAxLDFIlVIS1kl979jDwyyogpHhClBXrnDlM6FlsxpGxt4MhRuQj/BL7FE/GmgWsOUXOshPyuq8JQwJcgr97yY83rmI8QmTAlIKSlU4RRhSpBX7nkx5/XMR4hNmBKQUnLrLzwxNjjh+nr3zr9fXb0WpgQn5A5TuuT1xraZj1BWlDCljz76ML169V5KKfV+HSBX/4DTrKjCsNFhD/cXNIBLtR4u0/r20ZCJ13RjGxZgRRWOHAUiPD9uSwN/XRWmBE/lDlPKFZwUbT5GDLaBKEqFKfWYHOjmWg/TWVGFp0qFvECrhCkBc5UKU5rTF+cWmEGhCk+VCnmBVglTAuYqFaY0py/OLTCDW3/hyJyQl/TotuD7x93e3z5cfcBCwd+NvHVL5Dj7/f5lSun9E//p7D4ceO5sj295exjv59qOw8oeqy1Macl9m5F5RlHn5sm5c8aQx7+PPHRuGfpvj0U5t0DrrKjCdFNCE6J/OK2BfThe374asw/t5+XUsG9r6CNtW2UMXl29nvsSgpNgJQpVGKHrUtd16Ztdl7rDIT0/HFKX7ubPt9KZefT4uUNtS/Z5qfeAEsbOqTltQJtevPgk3dzs3/nnjLfX+sPh7jOAcwasQ6EK46wR/JKbEAdaJUwJWItzBhSiUIVx1gh+yU2IA60SpgSsxTkDChGmBCPkDFi6unqdbm4+GfW+cwKM/GYbtfjoow/Tq1fvjX78+dCSu1CWS0KchoydjwWDx8iscBCWgKsAxgYnAflZUR2n74vzvlDPg9FjYcoHclJK5tlaiu3niXNiTD8FA01nnp1WcixtbRwXG4MDAUvmBbVoslaxojqCv2hyyn2Iwgcppc/vf4Lmnbb0aCU1iuvr3bN037/Hcf1j7HY7oRENm3qeOxr/h6H2c21peK48G3ruqbYW7yYw/2jd3M9aQ3cynJo/l5yrHp/rIJJWaxUrqnC5GgMWovePevSNpdwBRgKRgCU4t0BwClW4XI0BC9H7Rz36xlLuACOBSMASnFsgOIUqXOhwSIfDIX32+FagU22RRO8f9egbS2PnxdixmPv1AFJyboEa+I4qLOc29YRhXF/vsr3J1dXr9OLFqBThqr9Q/0jffg29fYXTQ9dIoh2VUNp1acp+uPSY9s69SoUe240rOZZGHfeJ55ZFx1Lp81yfkee/yCnLveOwZ9sibwuMplCFhTwELD3Wdfn/Ivvq1XubClqp+OIb7sNbZmO3r/dxh0PKMo7njJGxgSxTg1sufR/KquR80zunCoylms9zYfveNw4HziNhtwWmcOsvZNR1qeu69M37lMDetlLvu0Zf2LY5427OOM7dBgCUpVCFvEolBkovJIpSCb+52wCAghSqkFepxEDphURRKuE3dxsAUJDvqEIuwSwPAAANJ0lEQVRG96mAn/W1dcvdWPjm4f/cv8ft/XdkP0vpi4CLm5vL32CFMJ5q2TdfOBrvk4JVzs2fNdr2+/7+jT3Oc8dD5vEkVAWAKllRhXWtld55XBwIVmBpp8b2lHEn2XYZ5j5rqnke19x3aJIVVVjYfUDLBymlzx+SgI/aDpe2pUcrqUPvO2cllTIuSevMkSQ7Z2wet595q2eXvs+SbeYKXK7k6r0kbWiPFVVYXqngFwExXGLuOKwxOMlcAYBgFKqwvFLBLwJiuMTccVhjcJK5AgDBuPUXFrZk8MuZcKa3twVfX+/S1dXr9OLFJ9M3gE25dGzWGJx0qm0oTAmok8A7qJMVVajb6PCHV6/eW7If0EpwUuS+XaK17YHWmKPQw4oqVOZcOFMaCFi6vt69DbC5udn3Pu4UYRTTnQ/umXYM1u7fJWFKPUIGJ51u22UNPOsLmho69uYatMv8hvGsqEJ9BCzVI3pwz1oBRJFCkkqGM0U69gAQmkIV6iNgqR7Rg3vWCiCKFJJUMpwp0rEHgNDc+guVEbBUj+jBPRcGeD0JTToz5kKEJJVuO27Pfez3+31fmNVtyd+2JLaBcQNQnBVVaI+AJZY09UOtoJB19B0XRQhDjI91OR/CBFZUoQFzApZYzvkwpZK9uywcaOj1DofUxQhEitd23F762EOrzoUVDf1UjaAjiMWKKrQhemjPVkU/LoKT1msbagcAjihUoQ3RQ3u2KvpxEZy0XttQOwBwxK2/0ICZAUshtBjq8fj2zvvwnNv73+msIkzpVHDS3Nc81fZw7E/sr1P7cI22d47TnG3ray997NmeFs+xQNusqAJjrBEAsYUPULVt45T+zhkj0fZLtP5ADlsY18KKoCFWVKFRYwNwTrm+3j17eG5fGEzu0JmthMvM2eY5+zB3cFK6+0NnljES8dgLU4K8BBUBU1lRhXbNCW6ZEgazROhMy3Ifl7Gvl/uYLDFGIhGmBAAFKVShXXOCW6aEwSwROtOy3Mdl7OvlPiZLjJFIhCkBQEHd4dD7c1JAI7oumeiLOaSUnt7RdnX1Or148cmT9se3v835Pb++oKPj9/3oow/Tq1fvDb3URQ6HExt9oaH9sEWX3CLptyG3q5b5E2UctjRXWtoWOMWKKmyDgInFnP4s0FMc5jwOJ4NRjt93iSI15R9Pxie0zRwHJhOmBI06CnR5ftyWUnpTsHubcCqUKlf40bI9f2JwO+YHbu2ejM95wVx7Yxt6WGkDamFFFdpVY4BNa9YIP1rD3MCgUm0AQKUUqtCuGgNsWrNG+NEa5gYGlWoDACrl1l8IaL/fnwzKmeLxbaX7/dO26+vdnJdnnLe3oHZ3N9vd3t+G/VlKXxyXU+5vaf3s/rmTxkPuY/u4L0NtUx67ZNvQfiWvHOeqldzudne3mEez9j4sFL4Udv8DcVlRhZgW/9BydfV6wqOrCJWswaXHtWQhUGMISo19rlUNRWpKsfsZuW+5bGEbgcysqMJGnfrplCWdCxaque24PQ0EVV0SpjS4Yx8FHQ29bxoZiJRzP5Q7LnnDmaYGNo0Jq6nlJ0UAoBQrqsBaIoXsLBHaMyckqdTj1toPLbcBAAtQqAJriRSys0Roz5yQpFKPW2s/tNwGACzArb/AKh7fKnkq3Om+7fb+ts2iYTxT247bu+EbP9/uh+vrXbq6ev3ObdgfffRhevXqvXceN2Ts+5bYDy23CWzahoGgI+FAAAuzogoxbTUMppXAjdHH774o7f33ie/T975bHU8sr5axdWk/+85JOc9VtezDObawjUBmVlQhoDl/qRfSUsZR+M6TMJ80coX0jHOBSIuHCNURprR821AQ1pZYVZxviX04dB0YE/YFEIEVVYA81gjfiRQiJEwJAFiMQhUgjzXCdyKFCAlTAgAW49ZfgAzOhe8MBR1dX++yvEfptmj9EaYEAPWyogpE0nLgxtxta3nfbJHgKwAYYEUVNkSIxrpmBiydC05aJDRLmNI6YUpCiABgmBVVgOXMCeQpFeYjTGlaGwCwAIUqwHLmBPKUCvMRpjStDQBYgFt/gbP2+/3LlPcH7ue6reHWyTkBS1MCjHISpjSuTZgSSxs471Zx/nsQ8PrRa8bvkFd1TKAWVlSBMaJ9yIjWn0sJ1AH69J3najv/1dbfS2xhG2F1ClWAFXVd6roufbPrUnc4pOeHQ+rS3bn4WymlZ4fDXfvjxxXucjrVl77+jX1sK20AwDIUqgDrqjG4R5hSfxsAsACFKsC6agzuEabU3wYALECYEqyodKjEjKCITVnyOD3+/c2HQJ6xbTPMCvq4NEzpYT+e2Jbbw2H3fMxrRm0TpgQAy7KiCusSuJDH0mFDrR2nUtvTShgMcLkthMOV2kaBfDTNiiqwit1uJ4CmIfeBQh+klD6/X2l80v54JXXM82tqG9o24At+tmU59i2ts6IKwCWmhCmNfX6NbQDAAhSqAFxiSpjS2OfX2AYALMCtvwBMNi5Madrza2qbG6a0ZrBaoRC1WQFeAGBFFda11YCD2ra7tv6e09r2tKD1QKnWtw+AhVlRhRWtscIwtHoi0GgcK0HLixCIJEwJAOKyogpACZECkYQpAUAwClUASogUiCRMCQCCcesvAKs7H1Z0Fzb0+Bbb+wCj28Nh93zouWu0zQ1TIp6pAVeFQqpmv3fJfh8RuAUMsqIKQER9BYOQnjrUGOBlbK3L/gYGWVEFYHVzwoqEKS1D2BoAkVhRBaCEOWFFwpQAoHEKVQBKmBNWJEwJABrn1l+gSVODUSrQVPDInLCikaFLack2YB2Fz+VNnXehNlZUgVa1VKSm1N725FLrfokWNhStPyXYB+sau79LzvFazy/QBCuqAFSlhVCj3Ks0Qz85IiRpnFPHpOR+dUyBrbOiCkBthBoBQOMUqgDURqgRADROoQpAVQ6HdDgc0mcPv28KALTHd1SBsBpM7p1D0MtptynWGHGc2tI3vhzn9ZSc444zFKRQBSKLVID0EmxSjp+OYEnGV3mOAWyXW38BAAAIRaEKAABAKApVAAAAQvEdVQCgOsLWANpmRRWIrIbExRr6SPv6xmHL41ORCtAwK6pAWNIeYRxzBYDWWFEFAAAgFIUqAAAAoShUAQAACEWhCu3ZYqgKsD3OaQANE6YEjRGqAmxB6+e6/X5/KN0HgJKsqAIAABCKQhUAAIBQ3PoLAAP2+/3LlNL7pftx5Lb1W18B2DYrqgAwLFqRmlLMPgFANgpVAAAAQlGoAgAAEIpCFQAAgFCEKQHZBA2dyUmADQCTVX59dO2jCCuqQE61XoTHan37crud2B5VxP5G7BN5tTJ/uFPz9aPmvlMxK6oALKKVv8C3sh3UxbgDts6KKgAAAKEoVAEAAAhFoQoAAEAoClUgp9ZDPlrfvkgEyQAtqfncVXPfqVh3OBxK9wEAAADesqIKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACAUhSoAAAChKFQBAAAIRaEKAABAKApVAAAAQlGoAgAAEIpCFQAAgFAUqgAAAISiUAUAACCU/w8XqOddMO5L7QAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -1865,7 +1866,7 @@
     },
     {
      "data": {
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\n",
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zGnGL812tt3fudrvbtMz3vmF2R9eQk7nddL3Y7ZYd4xKW+DrH1O/R8/V6n4svHd8KzonVXs/WREcV5tXnIhAtYKlmwli61Ty/al431q1tbpvz5Vpi39U+P2pfv1XQUYUjU4cpnXuPqQOWBGa0i/bXVeFOwDnnzvl9n9t1bYgctOc8CeulowqnlgifELAEQB9TXy8AiqBQhVNLhE8IWAKgj6mvFwBFcOsvHBkTpnTJe1zwvp0BSzUGZlCPFQR4wKTOBSed8Xi9uL7epqurV+n58xcTjxBgHjqqEJOApXWIFu60xHjMV7jcqOPn5ct3phoH81riXBzt+jO12tdvFXRU4UjfkIrr6+2z9BhSsWsNP7rkfccELAlTKseYcKeugJHIwSgli7JdhcvM61x40ZLLjpd3jfvm5u3tNNfX25FboQxTHZNTn09LOD9HCxeEJjqqcCpXSIXADID8+p6Ll1jWtRygagpVOJUrpEJgBkB+fc/FSyzrWg5QNbf+wpG+oUaHj5siwGiqgCWBGVCPoeFTmW4LvqvpNsJz4UVN5+IllnGqYb5XNRen5vxAaXRUoRy9gwEEZhBYqQEXucZdQvhUCWO8VBHrdnX1KvcQoihif12g1PNmSvXuExagowpH+gZXLBFgNCZgCSLyl3UiORdqlHNsZ8wZ5iesK5i286ZgNWqnowqnIgUYjQlYAqBbqefYJcL8ALJSqMKpSAFGYwKWAOhW6jl2iTA/gKzc+gtHcoUpnXuPoeOjTEPDc1peI9LtYII0VmDEnMs6P84FJ0U15nbf499Yvbp61XitAchNRxW4VFu4Q8mhDxEU8UF5gNrWZ2m1H0+R5keksaTU8lXRPsFJQ8KVhO8xs9rPYcxIRxWORApTOve+y73rKV0ymN+Y4yxYZz2kvufTm5uZbpuZycPPk11fbx9Dl1JH+N65UCkBS3XYbrfuw6IoOqpwKlKYUqT3BahN7efTMdez2rcNEJxCFU5FClOK9L4Atan9fDrmelb7tgGCU6jCkf0+7ff79Pm5W536Pm5qud4XoDa1n0/HXM9q3zZAfL6jClXyuaJgdylcqMsogjTyKWEuFTA/qjmfts6HzeZkJe/2+xQlhyDrPJ76u94NrycZHVooVKFK8hJK5QMLUzGXprIJHULTt5BqKjwbCtQHYf7A0TSPKwsKC7OtIRq3/sKRzSZtNpv07fvEw9GPm9qY9801ZoCIxpzvl1g29LGXjnvq5wJMQaEKp2pO/ZXiCPDW1Km4Uy8b+thLxz31cwFGU6jCqZpTf6U4Arw1dSru1MuGPvbScU/9XIDRfEcVjtwnHH6eUkqbjpubbm52rT+ePqe+47u+3j7596urV0+eO8Rut7tN+b5HI2iComU+fsao/tjrez5tOncusazPY3e7y8fdtc4ppcdr3P3jHgKWBl9Dxhh6/FT2/VVYNR1V6GWf0j/6eQqY/tg7MfPly3fGvE/OD9klfsCHQ6XO4VLHfaztPFlA4vDshmyDXPOhlnnYxjyEFjqqcOQ+JOLD9Hhr0z6l3/vDlP7pT1L67/8qpf/871LOVN3D8T2kOB6NubXTe/Tc/ZBlOY0Z9xLLoo3Hdoi3rFR1zJvtyXmy6XG5t8Ol2+bmpmvkZ9dlsWtITcfPuRTorq5u5ARpiEZHFU4dhEXcF6nf+mlKz16/+efv/WHK3FldIhwjWmBGriCTOQJPal4WbTyRlpVqjfOmSQnHVF+lbgci2Wx+M202/+Tgf7+Ze0jUZ7Pfh/xjVSj+MrYuj3/N/c4nv0zv/+/X6Vs/Tek3vnj7gC/fS+mvvp/+w/d/N23OfMEnpennSI+uQNd3Z591PbdtWe7v/Fxfby8a91LLoo3Hdoi1LPfxM8YSx94cr3nJsjTDuXOJbdOVl9B0/SltO0Q8fqJ0VIdum8nee7P5ZkrpFymlrxws/X8ppX+e9vv/Mcl7MEittYpCtYdadz7tNp9uNimlP09fvvcHT4rUB1++l3737/+z9MN/+MOzxerSc2TT/gPuI+xTSqY6XMbxU7qbm47Eovn0CrOa+jPKPNeQy11dvUrPn7/IPYwnpipUgwatdc+7zeabv0of/NX76fbJbZmvU0p36YP09XT7LcXq8mqtVdz6C0cei9SUvt9YpKaU0m98kT771Wfpx//nxyngH3tmCGYo9hwHATh+SnZ19SrXW2dLWs/0vo1GBgHOoc/26RvgFa1ITalrTG9u7/3FcZGa0puC4v10m1JKv3AbMFMRpgQHNp9uNmmf/jztn/3L9Oz13+l67K/3v06f/eqzlFLq1Vmd05hwDACeeLy9NdfPkB2aOkypx3uEu4Zst9tN39udL7ldfejt0z3GW+vPOv2DlNJX2rpcz1JK+5S+8kn65Hf+zSb9p5ICsohJRxWe+p2U0g/PFakPfr3/dfrZ3/ws/fUXfz3zsM4SSAEwjWjnziXO7yVcQ5YIfGKk1+nZsxfpe3+abGsmoFCFp/4ypfTj9PrZ3/Z58Lubd9N3/95302+/99szD+usX6aUfj89/YmFpmUAdIt27lzi/F7CNaTvePquS7T1q8Kz9Pr199KLP062NRNw6y8c2H+8328+3fwobV6nlNL3U0p/t+2x727eTR99/aPst/2mlNL9rTSfty3LPDyAYhyeO3dZMpSeOnd+n2KM0a8hxwFP19fb1pClc+vStGyJ/Rw0OKlRWzDPOz/5SfoXf/RH6atffNHY6XqdUrpNX3//T9Mfv0jpcd7c3d9O/nnDU6CTjioc2X+836eUfpRS+mn68r3mB335XpgitadQ4RjUyNeO2tk2BTk+V+Y8d0Y6b0caS0opZMjSOUUUqV1efe1r6Weffpru0gcnX1q+L1LTR+mz9DfpSZZS8etNPjqqcOTNl/73H6bvfPKj9J1PUzrqrL67eTd91POnaXI6F46x9O/51aIpqCOleX9LcA3bNaX4v5db6n6Kvl2jjefpsu3oc+f8592uvX+5qa8hly4b+nM5lx27l2yhdbr9xjfSf/u3/zp99+OP0+b121Pe/335bvoofZb+Z/rmyXOEKXEpHVU49eZL/z//5MP00Fl9+M7q62d/W1AndUygRFvwgUCK8dtG0Ee7JbbhGvdTpG2T8/gpcdnQx04p2nZYYtz0cPuNb6T/8md/lv7rn/zJ4//+cfpfjUXqPduaiyhU4dTjl/4fbwPevP6LtE/7tHn9F4UUqSmNC5RoCz4QSDF+2wj6aLfENlzjfoq0bXIePyUuG/rYKUXbDkuMm55efe1r6e63fuvxf0e3+x6zrbmIW3/hyHHowmPAUkr/PqX0l5vN5g+yDW6ASwIlupb1eWyE4JG5Xbptxixbw3ZNad5tuMSyqPspwrbpWhZtPJGW9XnsXPMu2naYc9y5jt0f/OB7I79ru08pxf/D+dh9ynrpqEIP+4/3+/3H+5/fd1iHCBdAMbPa1zfX+tW+XVOqYx0jrkPEMcEanRyL4wOh4hepyTmIEXRU4ciY0IXtdlvEVWMu2+2b4JE5tcXm379/ldt/ie3KePZTeWIENsUMU8plbPBOpDClc8FcKZ2E59agV3gb9KGjCqeELgCsQ6TAoGhhSrnUFKa0tn2X0jrXmZkoVOGU0AWAdYgUGBQtTCmXmsKU1rbvUlrnOjOTzX7wV+7WZ423GtJu6vmw2+1uU/MPYt+5lfBU7cdjx3woVZXz2HFbjgqPqd5qOCd2/Y7qzc3bFKSOYKK7h9+BbbPEdWWzSauYh/t9GV+crU2tn410VCG/tgtX9Rc0GtW232tbnweO23LYJyvQEUwUZf/PMI5wzSbBSUxKmBIcWVtwBQBEMUfwzhLX9XPvcebpnQFENze71tCl7Xa7iRQAJjiJKemowilBAACQxxzX2yWu62PeY+z4IgWA+bzEZBSqcEoQAADkMcf1donr+pj3GDu+SAFgPi8xGbf+wpH7W1U+T+ltCEff24K6vsx+ialfr8GkwS+5Q0tK2160yz2XoAY9z4mhzmuH1+Bzrq+3fV/28dbZzZtYmYeApftr/aAhNjoc99DgpKZ1fvpZZNzzcy+DS+moQrfaPyhPvX6213i1hVFcuj61zyWWU9sxNbXox9oc+2/udR7y+uYntNBRhSOCk8gpUmcDahD9mFrgTpCiHIfxPPy0zFEwUWu4UDoIJup63Jhr/ZzBSQKI4C0dVTglCAAA8mi7Bo8JJhrzuL7PXSo4CVZDoQqnBAEAQB5t1+AxwURjHtf3uUsFJ8FquPUXjgwJMKiB286GadheoYJIAErWFsZzFFbU6vA3R8+ELT153NXVq/T8+YvBY5w6OGmoiq7hrqWc0FGFbkIOOCd6EEmpHHusRe65nvv9L9E45qurV53/3uXly3cuHYvgpGm4lnJCRxU6NP11r+uvl9vttuPvvM2mfr2h71GTqbZXk7Vswyii/2XdfGAqY+f60Lk453lyTkeBQycBS4ed1AcPHdLr622vgKUe7ys4CRakowoAQHRTBxjlel/BSdCTQhUAgOimDjDK9b6Ck6Ant/4CABDauRCirvDDvkFMPZ47KDjp8Hbkh/Ed/mbrGgIbYQwdVQCAcgnoWU7vInVIkBMpJfOYBjqqwInIYRtCbADeGhr6t0YDwo8ufu7NTd3t0cifC6iXjioAADWbOkwJWIBCFQCAmk0dpgQswK2/wImG28buov+uJfXb7XaDgkxqVdl2cG7JKPNcWmzfTxim1Or6evvk36+uXj3+jitwGR1VoI9aPhRTNvPwjZq2Q03rUqKc27/Efd878Ofly3fmHMfSBB2RhY4qAADVmipMab9PHzS83uu2515fb589PO7wp2qOnQsq6grHEnJEzXRUAQCo2dRhSn1fTxATjKBQBQCgZlOHKfV9PUFMMIJbfwEggMpCkiCMCcOUBh2jh8/d1f0zqzALHVUASlF7oMcai9Ta92l0Obd/ift+yDFa4vpBKDqqABRhiZ+y6Bta0vU42gl+iWUtPw00VZjSmYc+Bifdd1KfPPfmZug7AzqqAADUbOowpb6PE6YEIyhUAQCo2dRhSn0fJ0wJRnDrLwAA1bokTOkHP/heevnynZQ6fie17T2alglTguF0VGG4toAEwQnAGLWfQ2pfPypyX6T2ZW7DDHRUYaC1hE8Ay3JugXmMCVNq0RmcJEwJpqGjCgBAzaYONeobnCRMCUZQqAIAULOpQ436BicJU4IR3PoL9JLxdyPv3BLZz263u03NP0h/dht2PDenKvZ90G17rIptTbnmPE5ubnaPgUjX19vWx3X9t0PngpOalglTguF0VIHoon/Aj6RtW/XZhhG3c8QxXaKE9ShhjNRtkTl4dfVq7EsIToKF6KgCALAKz5+/OFl2ppM6ODhJmBJMQ0cVAACajQlOEqYEIyhUAQCg2ZjgJGFKMIJbf2FBQ8MiMgYYQbHmCmXpezw6buuROQhLwFUAlwQnNS0TpgTD6aj20/bFeV+oZyiBJcM5zpYRcTtfOibH2XAR938EOefS2uZxtjnYEbDkuKAUVdYqOqo9+Ismtdhut5uH/z+063P4XOrjPBeb44/ajT0HdV3Tmo6fSwKRHpZBNLVew3VUAQBYG4FIEJxCFQCAtRGIBMG59RcAgFUZE5IELEOhCsu6SxkTJDO979TatmHo9cucHrpEEm2khNKcx9kcQs/tyoU/Zw88t8w6l3Kf59r0PP9FOocda52HLesWeV2gN4UqLMiFY7yCt2G4D28TC7N+Y+ZI30CWocEtl74PeRVyvmk99jLMpTDngQuEHXvbPOw4j4RdFxjCd1QBAAAIRaEKAABAKApVAAAAQvEdVViBKQIuFgjjKZZtsw599/PY+TDxfBKqAkCRdFRhHQQrMDfJtDE59llSyeeBkscOVdJRBQjokrROSbJATjm7985/UB8dVQAAAEJRqAIAABCKW38BAKiWwDsok44qrIOQCOintmOltvWB2jhGoYWOKqxAU8DF0L8wC6OYnr/yx7NUGIzgF1gnxzf0p6MKAABAKApVAAAAQnHrLwAUbrfb3aaU3m/4T3c5f9uS2DrmDUB2OqoAUL62YkMRQhfzY1mCk2AAHVUAAKpwLqxIkBmUQ0cVAACAUBSqAAAAhOLWXyCElYR6CLZpEHDf209UJ+BxBtBJRxXoY4l03D+5AAAHrklEQVQAiDV8gFrDOl4i2naJNh6YwhrmtbAiqIiOKnBCoAQAU3JdAYbSUQUAACAUhSoAAAChuPUXAI50/dYilMI8Bkqmowrr1RY6kSuMYg0hGGtYx0vYLlA3xzgwmI4qrFS0n9+INh6WM/W+10WCdkKNgFLoqAIAABCKQhUAAIBQ3PoLAe12u9tU14+z37m1F+pT0Lkq7Dlo6W2Y6db4sNsfiEtHFWIq4YPfELWtT1TRArJyWdv65lTKsR15nJHHNpU1rCMwMR1VgEroWLyxxHbo6kr1CasR+AQA3XRUAQAACEWhCgAAQChu/QUW0fNWR4EbQBgdQUfOVQAz01GFmNYaBiNwA8pSyrnq0nG2nZOmPFeVsg3HWMM6AhPTUYWAxvylXkgLsBRdxfHm2IZjw74AItBRBQAAIBSFKgAAAKEoVAEAAAhFoQpEsrbAjbb1Xdt2WCP7HgA6CFOCFRGiEYsgmvWy7wGgm44qAAAAoShUAQAACMWtv8BZu93uNk37A/dj3bl1EqhZx3m3qPNfwOtHqxG/Q17UPoFS6KgCfUT7kBFtPABTazvPlXb+K228l1jDOsLiFKoAAACEolAFAAAgFIUqAAAAoQhTggXlDpUYERSxKrn30wyyBH3UEgYDACxPRxWWVVPxk9PdzK9f237KtT61hMEAl5v7fB1BrnVse981bHNWQEcVWMR2u93kHgMAy3L3xHxsW2qnowoAAEAoClUAAABCcesvACxsycCuTCFqArMAGEVHFZa11oCD0ta7tPGeU9v61KD2QKna1w+AmemowoKW6DB0dU8EGvWjEwQAkJeOKgAAAKEoVAEAAAjFrb8AhNMRNiSkh1kMDbjKFFI1+r1zjvuIYxnopKMKQERtBYOQnjKUGOBlbi3L9gY66agCAMLWAAhFRxUAAIBQFKoAAACE4tZfoEpDg1EKIHikQYX7GTiQ+Rh33oWMdFSBWtVWvNS2PlMpdbtECxuKNp4cbINl9d3eOY/xUs8vUAUdVQBY2NRdmq6fHBGS1E/TPsm5Xe1TYO10VAEAAAhFoQoAAEAoClUAAABC8R1VICyJrk8Ieml2l2LNEfupLm3zy35eTs5j3H6GjBSqQGSRCpBWgk3y8dMRzMn8ys8+gPVy6y8AAAChKFQBAAAIRaEKAABAKL6jCgAUR9gaQN10VIHISkhcLGGM1K9tHtY8PxWpABXTUQXCkvYI/ThWAKiNjioAAAChKFQBAAAIRaEKAABAKApVqM8aQ1WA9XFOA6iYMCWojFAVYA1qP9ftdrt97jEA5KSjCgAAQCgKVQAAAEJx6y8AdNjtdrcppfdzj+PIXe23vgKwbjqqANAtWpGaUswxAcBkFKoAAACEolAFAAAgFIUqAAAAoQhTAiYTNHRmSgJsABis8Oujax9Z6KgCUyr1ItxX7es3tbuBy6OKON6IY2JatRw/vFHy9aPksVMwHVUAZlHLX+BrWQ/KYt4Ba6ejCgAAQCgKVQAAAEJRqAIAABCKQhWYUu0hH7WvXySCZICalHzuKnnsFGyz3+9zjwEAAAAe6agCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAhFoQoAAEAoClUAAABCUagCAAAQikIVAACAUBSqAAAAhKJQBQAAIBSFKgAAAKEoVAEAAAjl/wPl7yXh8I1XfgAAAABJRU5ErkJggg==",
       "text/plain": [
        "
"
       ]
@@ -1896,7 +1897,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1915,7 +1916,7 @@
     },
     {
      "data": {
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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1934,7 +1935,7 @@
     },
     {
      "data": {
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M/VnSsZxSOsAIgAO25gLASENCiFp3aTvuoRHbZGcN4SgVYHRzs43nzz+PV68+mWCU7Wk1eAkoT0cUOBfCIfQHhpkgPKKu9+5JwUS1h3B0vkZ+9tl7U44DoEk6orByQojKGRJY83jb2N9f8m21jad0CNHtbYHv9yDFmACjMeYIfBpD1xPoQkcUoJyxgTVjfn/Jt9U2nqWHEHGecw9QiEIUoJyxgTVjfn/Jt9U2nqWHEHGecw9QiK25MIPdbvckzGTCx+qyRWrWUJClGBJYcy7wpOvPru222sazxBAizhsZYATAAR1RKKtr8E9tIRy1jWcJzClXJYX+tEpgWrucO+AqHVEoSFexLddCZ25vh//utCOnBadCiG5uts/inXXzXmw22+ZDmy59lUtrQTt9jXned73etODdNbJ9EuR0bW33JRAJ2qcjCqzZmOARoSUMsfTQpjUaMw9LmkPrBuhFIQqs2ZjgEaElDLH00KY1GjMPS5pD6wboxdZcoJPMgKUJnAxjuhYudHOzvXSfb7ciCiihq2thN5duG/v7Q257fN4fbhvd3e84vnvYfvnpwW2r1DWs6Mz15M3hvz9//nm8evVJ4RHm6BjWdvfw/atP1jawPjqiMI/aghy6jKfVIjSi29hbPj4qs6AQonPPC8+X00Zd2z/77L1S45ibdQNcpSMKE7ge0NAvyEFQyHhzhQt1C6zpNsa13lbbeMY8H1ubh66BXa0H7YxxNIdPru1x0PUs9BjVr5sSxwIsn44oTENQSH1qmsNWg2jmXNc1jWfMWmp1HroeyxplXFuWvm6sJVghhShMQ1BIfWqaw1aDaOZc1zWNJyPkqrZ56Hosa5RxbVn6urGWYIU2+/26d0Bc+h4qWxop4VRoQ0+P4Q5nZazjKb6z7YMPvr+kz0SddGpr7oxOhjZlayj4atB8Lel1peux1HbMY8ZzYX0OWg+bja2mV1x9jTtlijXX93Uu4/W1w3qt6rnHNJZ6nnVEYXpj33DX8oa9eMDS0ovQCgNrallLtYzjmlbGSVmlg3ZqC6erTavPM+cVRhJWBCN0CYaY6nGyg0L6dgI6zs3oEI/SKutgsgI1h84IKxrmWoDRmYCf6q6HYx1eTy99HVbptTmFlrtOUCsdURinTzBE6cepPdwha26gdUsKneFe6bleOvMFK6QQhXH6BEOUfpzawx2y5gZat6TQGe6VnuulM1+wQgpRGGG/j/1+H58ef+/ZudtLPk7pxygta26gdX2e311/tvRtXcfNvdJzvXTmC9bJZ0ShoAIJuefu9/hFd1DK4JymmpuSKgwXKq2WcI27qHwtPKhlvujv3Bqr+Zy28rzo5Ph6+vz552cD6oYlC2/j+fPP49WrT4YM75wW1w00SyEKZZ19E3EqBOda+MGFF+cW36wUnZtpvBcR5wM1+lhq1HoJNXyFDMvW4hpr7Y+Ll5y6/j0WjIfXv7FfbVM6eb3FdQMtszUXRthsYrPZxL9+SPIr8vtd73PsYw/VdcxZc1P7bXBNn7VU0zq23sfLOndrWzdTzA1QnkIUxhmb4NdiYmVWuuFc6aAZaaNwSGruemWdu7WtmynmBihMIQrjjE3wazGxMivdcK500Iy0UTgkNXe9ss7d2tbNFHMDFLbZ79cdPOZzXJR06fMuhT8j+sS50IYp1/GmUABRPZ8RLce1Zb26nvvdbld9gNdE7mr+LJ7n7nhd57DPa1yWCQKQRrm25qzXdVjqedYRhbp1TuorHdrQ0eg30StIqoVz1liERqz3uHmqujTamV5LYZUUotBRVqDB4X3u9/Fiv49N3D9XvxPJz9nSx3x8LLe3u1n/8iysCCDXtde4/T42XW6b7QCAYjyRobusQIOaghOyQojmIqwIIJdrLBARClHoIyvQoKbghKwQorkIKwLI5RoLRETEl+ceALRiv499RHwacTqkZ1NoM+bh45x57DTXjrmv3W63v73t/rNjHquDu/1++yIuzPWY23ZP85d6uRBkU3XQy7GVBPI0dU74Qt/1mXBdKq26tVnqGjvla+HNzXa6Oz/jXEjSmDU3wXqtbj3RNh1RGKbXG+uFBPKMKiYqnIPai6Nz46t93MdaG+8QQ4+xuqCWJDUd99LX55KPr6Z1NFojIUlLXk/MQEcUTngIm/l2RPzq4S+w79x26Xf3+9gc/uzt7e5NyfGM+d1Tx9L1tkuPcerrV1pVar66dn5ZrxY6C0v9ygDadHTdfXHitl7X7OPbI2L06zXQnY4onDY2JKF0oMKY+xMM0Y/5AqjTFNdn122YiUIUThsbklA6UGHM/QmG6Md8AdRpiuuz6zbMxNZcOGFsSE/J0JoT4znrTMDC261Gp363621LdGrb4eGW2sdzN+Q21mXqEJsaQ586HrNwk0ZNtebGPFc6Xos7B9Ed3z73a1+XkKRzoUbXfPDB909+DnXo/UEJClFWb/PxZhMR342IX+w/2p96gezzQpwRnnAXFb0hrTCEiLpUtV5nNub60Ooc1j7upa9Pa66f6tfD0FCjc7/X8/4WFRDF/BSirMpxWMHm480m9vHTiPjriPibzcebD/cf7fc9woGeHd7fqccp0SW7FtAQuQELR8f8Xmw220HBEJeCnIYEoTT41QqLt/RumDCftp1an32vI85z20oHIA0MA+z1Gn5zs337OnzpdfTw5y49xna73fQJeIJSfEaUtXkbSvDQCf1p7J/9MDaxif2zH0bETx9u7xpekBV+UFMwjmAIAJaidABSRrjd2Pco1392s/nGj+Pjv/xW/I//9uP4+C9js/nGgHHCRTqirM19KMH3Xv4qIn4aET+IZ2++GhHx8N8fRETE915+GD9/2SW8ICv8oKZgHMEQACxF6QCkjHC7rr87LGRxs/mziPjly3j5pR/HT549izf/OSL+EJvNX8R+/48DxgsnKURZlYftuPdF6O+/9qP4yu+Of+SP4vdf+1G8/79/FLGPiMs7rg63xDyGJGy32xdThhWdui0zYOHaWC7ddnx7ibkBgKHGvKaVum3Aa/jb9x43N9sngUMHwUSdtvxuNl9su/3T+Mf4p/h6vIh/jmcR8aWHu3gTEXfx4tdf32y+pRilFFtzWZW323EjfnCiCL33ld9FfOtnEf/mP0Rc+EjEmZCeuUIOsgIEag8qqH18MFSra7vVcdPuuWtx3KPGfBw4NDTQ6I/jt/H38edvi9BDzyLi/XgdEfFL23QpRUeU1XgbTLR/9sO323HPeSxGIyL++3+Mh85op3CAiPJhRdceY66Ahb63TT03NQXjCE6ipLnWtuCe9Rqz5kqHeC0xFGzIa3gUCCa8vX13K9LhV8Z8M34Tz+LN2S7Vw+1fiohvRsRvx44FdERZk+9GxF9fLUIffeV3Ed/5TxH/8hePtwz/0P80agpYEFYEAN1lhBpB1RSirMkvIuJv4s2z/9fpp3//tYh/+PcR/+u7j7cM+9D/dGoKWBBWBADdZYQaQdVszWU19h/t95uPNx/G5k3EfTruH5394d9/LeLXPzjclhvXtuO+81gJgTw1BCz0ve349jFzs9vtXkflXzx+zoltZnclt15WODdZx1f0cZiPc0zNSqzP0qFGh1tsh/pNfDPexLN4E6c7VQ+3/yEifjP6wSB0RFmZ/Uf7fUR8GBE/i99/7fQPnShCzwQTMa+aCq2xSh9LbXOTdXy1HTfDzXmOWwy7Iddc63PU2jz1Xubwtn+Kb8Sfx9/H6/j6kw+jPqTmRkT8Rez3Ph9KETqirMr9h/73347vvfwwvvdxxNPO6P+Nr/zuZ3H3Lz6M2HQKJjr/ONOGFWVoLawIYKxTHS3hY8zlWqhRXA4wuhqy+OrVJ7HdbjeP9/k/489+9cfxz38aEb/cR3zpTTx79izevHkW8Yevx2vfI0pROqKszf2H/n/+8tvx2Bl9/Mzo/X9/FhEfPvz7mHCApYQLCCsCgPmMCTAaFrJ4X2z+yct4+VffiX/4Py/j5V9FxJ8oQilNIcravP3Q/9ttups3fxv72Mfmzd9GxIcPt48NB1hKuICwIgCYz5gAo+Ehi/v9b38SH/3dr+Nf/dufxEd/ZzsuU7A1l1U5DgJ4G2AU8V8i4hcPReioQJ3dbrfvuuW09u1eh8fxOA9jbju+HRiuwlCqyYO4zjzmrPPQ8Tp+dR6yApoama8x97eoQKsxAUZ93sv0CR2EUhSirN5D8fnzucdBb3dR2ZvwEUqHo9Q2N8JfplHTOT4nY4xLmYesAJwW5muMpR/fsXPXe9ddqqcQBZpU+i/el/5Kv91uL/zNuT5L6gYA8K5rAUanQgPtRqJGPiMKAADtGBsaCFVQiAIAQDvGhgZCFWzNBaq09AAKgCHmDhtamxrnu2uw0JjgRcigIwq0oqo3AlCBFsJIMsbYwjyU5FqYy3zDRHREobDjYJslheCUVvvX10DNMnYI9H2OznFNOzUPpa+7Xe9vLde0a3OYOf9Au3REAQAASKUQBQAAIJWtuXCgxlACoH2uLQDwLh1ReJc3isAUXFsoZe5wpi6Pf+5n5h47UBEdUQCARrTwNVYtjBGYn44oAAAAqRSiAAAApLI1F2bku9H6mWu+Cj/u3Rzb1i6E5cwynq76hvzU9JyqaSxZMo75xGNUvYbJV/MaEVwGX9ARBcg11xuQc49b+xui2sfH/KwRrqlpjdQ0FpiVjigAzGi73W6G/F7f7uOQx1ljVxeAHDqiAAAApFKIAgAAkMrWXACKaDUQqSbmcJg+W4htN16nrPNec1AS1EZHFCDX3dwDmFCrgUhzOl4P5hCW5fi5u+TXAOhFRxRmNDSkZMku/dV6yvma63EpK/tcWTfvKnXMupYs1VzdUc8paqQjCgAAQCqFKAAAAKlszYXCGgjNEJzAYtW0/az0WMbeX01zsyRd53XM/E9w7rwOALPTEYX1EXrShnOBFoIugLG8DuRxzYYzdEQBKqRbAad1CUQaEyKlc7xMQ4O0+q6HNYaUwVA6ogAAAKRSiAIAAJDK1lxYocJbz4RejLTb7V7H6c9smVsAIuLia8XY+x3znsDrFIPpiAJjCb0Y79wcmlvIt4ZwmdaOsbXxTqXG14Qax0QjdEShsOOgAsEXkGfKoJASz+UxQTljry1D5maN169T3Z2u52WqkKS1B+D0PSdAG3REAQAASKUQBQAAIJWtuQAAXDVVWE4GW3mhPjqiwFhCJIAI14I1aLIITTT1c6DG51iNY6IROqLQUddAiqH3CbRriutD7Vy/WLvs54CvSWFpdEQBAABIpRAFAAAglUIUAACAVD4jCnDvLk4HcVQdxHAhxfJuhZ8nqvpcHWs5gZRJtHANOjdGgN4UogDRdAjEuTeFi3+zuICwnMWfI7pr4RpU0xj7hoINvV6sJXwM5mBrLgAAAKkUogAAAKRSiAIAAJDKZ0ShMQ0HnKwxPAcobOxn9nzmj1JOrCWvc9CDjuj5NLqaUuqoz5zrpsUiNKL7uD0np2FeAabV6usz9Vvka/jqO6L+csUQ1s10zO00zCsAtGmpr+E6ogAAAKRSiAIAAJBq9VtzoaS+QUJrC82Y8XhXHyBxYW2ufm64lxGE1nDY2lldr2uFr3+et0DzdEThXWM/DL6oN1gL4rycn4Pa52aRAQ2VylgLta+3VpjHPK41MBEdUTjgL8zDbLfbzbWfWVv3lzI8J4E5nboGeT2DMnREAQAASKUQBQAAIJWtuQAAVGGqQCvbaaE+OqJQ1hpDDboe85xzs8bzAjXyXCxjyfMoiAlWQkcUChKscp65AVq9DlzqpnUJawPgKR1RAAAAUilEAQAASGVrLtDJhQCJu1a32wHtWNI1aEnHwrtObON2TuEMHVGgq3MBEoIleHQuQGXJwSqnmIdpLOkatKRjKW1pzxPnFM7QEQWgCH/1v2ceYLgxzx9f0QJt0REFAAAglUIUAACAVLbmAtA84S/1uXBOpnis0lsyrRuAiemIArAEawx/qT3UpeW5b3nsa1bjc6LGMUEVdEQBoEGnOnbCWlizPl3sS8+V7Xa7mep3gS/oiAIAAJBKIQoAAEAqW3OB0U5sUxL0AQDAWTqiwBQEfeQ5F4QhIKN+Sz9HLR9fy2MHaIKOKEDDdJ7btfSwodJrU0AMwLLoiAIAAJBKIQoAAEAqW3MBetjtdq9CzRnhAAAHpElEQVTj9GdgBTQBVblwvcpQ7JrougvLpCMK0M+5N3UCmliTtYX5tBoKNud1qeRju+7CAumIAgAXrT0MSNcNoDwdUQAAAFIpRAEAAEhlay5AxTK+V/LEY1QTACKkZJ36huw08P2rnddrA8fSWc3Xlgwzh0VlWdU5pSwdUQCO1fTGSUjJOi3t/C7teIZa2zys4XjXcIxMREcUAIDOroVXLamrC0xHRxQAAIBUClEAAABS2ZoLUMjagzngkKApTim9bXeKbcC2FkMOHVGA6XQNcbibdBT91TYeujt37rqc0zG/e8qYoKmlrcElHc+SjqW047lZw1yt4RiZiI4owMx0hyhlzFqqaR2eGsulLtW18JyWjAkCWtI8tODafNf0nLJuqJGOKAAAAKkUogAAAKSyNReA2V0ItgGo0ohQI4FdEDqiANShhSK0dJgPsE4tXO9gcjqiANCBDgaMNyQYx9epwDLpiAIAAJBKIQoAAEAqW3MBuEiQUDtaOVe2WgKgIwrANdUXNrzlXLVB8NW6Oc8QOqIAAKkEX5UzJPwo4nJXfuh9Av3oiAIAAJBKIQoAAEAqW3NhYhfCQ+6GbM9aSRjJoLkBgFo0/nrtdZjJ6YjC9M69CA19car+Ra2ANRxjS1oI1hD+cm9txws1a/m1rOWx0wgdUQAuyvir+Niv8/CX+3s1zUPpMBhf+QKwLDqiAAAApFKIAgAAkMrWXACABbB9GWiJjii0Zw1hJDUfY81jA6CbjICzll8vWh47jdARhcbMFUbS9y/tQ8JIWnBq/nUhANqS8Vq65PAwKEFHFAAAgFQKUQAAAFIpRAEAAEjlM6IAFLHb7V5HxPuJjzf0s7l3NXx2q+98+Sxy3MXp+RKqAtAghSgApaQVoSPVMs5axtGEGv54ULtaQmf80QTowtZcAAAAUilEAQAASKUQBQAAIJXPiMIVUwawzPQ5miqCWmBOnnv0ceF1wDkFGEhHFK5bWqDI0o6Hekgvvez4uWe+2nHuujnH9fTcurGegKboiAJQxJjO0JAO5bWE0NqTO0/N16Ux15KI2lXt898qHVhgKXREAQAASKUQBQAAIJWtuQALM2XAFgBACTqicN3SAiCWdjw8pQi9V9tar208wHoIuaI6OqJwxdhgiCWFj0BLhLoA3HM9pEY6ogAAAKRSiAIAAJDK1lwoqG9IzEzfs3dniw7QmlpDuFzHAYbREYWyqnuTdELGGIUfzGsN87/UYxQocl4L19csa5sLzwtYIB1RYDShS3VpsVMi1Otei+cOpuZ5AcukIwoAAEAqhSgAAACpbM0FgInVGrRzghAcWCDXIGqkIwpltRCcMHSMwiLOMzfjLX0OW3gDGFH3OJeyFkqofS6W/nxuUc3P7UOtjJMCNvv9HKnjsB5CWICZvuJjkFLXpSVd+5Z0LKzTGq9B1E9HFAAAgFQKUQAAAFIJKwIAmEhDITFjCJgBetMRhekJbQBaeb63Ms6WLL0IjVjHMbauled2K+OkAB1RmJi/EgOuA8CcXIOokY4oAAAAqRSiAAAApFKIAgAAkEohCgAwnTWEr6zhGIHChBUBAExESAzAaTqiAAAApFKIAgAAkEohCgAAQCqFKAAwhXMBNi0G2yzpWACqsNnv93OPAQAAgBXREQUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASKUQBQAAIJVCFAAAgFQKUQAAAFIpRAEAAEilEAUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASKUQBQAAIJVCFAAAgFQKUQAAAFIpRAEAAEilEAUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASKUQBQAAIJVCFAAAgFQKUQAAAFIpRAEAAEilEAUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASKUQBQAAIJVCFAAAgFQKUQAAAFIpRAEAAEilEAUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASKUQBQAAIJVCFAAAgFQKUQAAAFIpRAEAAEilEAUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASKUQBQAAIJVCFAAAgFQKUQAAAFIpRAEAAEilEAUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASKUQBQAAIJVCFAAAgFQKUQAAAFIpRAEAAEilEAUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASKUQBQAAIJVCFAAAgFQKUQAAAFIpRAEAAEilEAUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASKUQBQAAIJVCFAAAgFQKUQAAAFIpRAEAAEilEAUAACCVQhQAAIBUClEAAABSKUQBAABIpRAFAAAglUIUAACAVApRAAAAUilEAQAASPX/AeIfP+8eZiEsAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1953,7 +1954,7 @@
     },
     {
      "data": {
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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1993,7 +1994,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -2012,7 +2013,7 @@
     },
     {
      "data": {
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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -2031,7 +2032,7 @@
     },
     {
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\n",
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+Vf16rPrWszo7O6tnz4aqP1TVX9Y4/nbhe6X6bRWhOkGvX3x2u3ol9ZtX/rpe/vr2N/jmlap/f6fqn/62yjtegA4YTjo9/nzThiFonOn79dv6Tf2oHtXvX3hb5rOquqhH9Z16+oZYXV6vv5a99RduuIrUqne2RmpV1ctfV73xUdVf/U1Vzu8fAA9lOAlyRfz6fK2+2BqpVc+D4tV6WlX1a28D5liEKlxzNZz07OzdevHtvreJVaBdqwwn7RpfWeva6WdLXocs179Od40zLTna9N36rzqrZzu/4eb8W1X13QN+6nBFqMKLflxVv6izZ3806Vu//HXVW39f9aefzntXAMe11jjQroGYpPtJOlvyOmRZ8zkCEXxGdYJe3/fNbf//V9GcvTspVn1WFWjTKsNJxpSyHhtjSrnWeI7UPaNNf16/q3+pH9aju9+JfFFVP6xx/N3+P2seqtdWEaoT9PrFZ7sXPqN619t/RSrQKMNJVPnzDS+6b7Tptfqi/rP+bOtnVKueV+5Z1ZdV9b0axy9muUm26vXXsrf+wg3j++NYVe9V1Uf1zSvbv5FIBdoVMcwCxLnz3w1f1uv1o/pNPa3v3Hrp9XL1t57/FTUilaMQqnDDMNRQT8Y365P336uXv/5VPf97U6/7ql7++ld18SdnVcPB4wWtnKXdT9JZ2v14HPLOwu5nteEkY0qZjw2na+po0+XZf9RfnL1Wv3/jrOrLseriD3X21Vh1cVb1pb+ahmMTqnDb82GBT568WZevrD47+5+qqs0/P6qq9zb/f8rgRk+jHi2epd2PxyHvLO1+ks7S7ifpbMnrcJr2f948j9HvPaknP3+r/vW/n9STn9fzt/uKVI7KZ1Qn6PV932x3c2zgamCp6hdV9Xc11Hvj++OYNLjRw6hHy2dp9+NxyDtLu5+ks7T7STqb+zrGlJjjOcvyem0VoTpBr198ptsMLP24qj7dfIYVAJrmzzfQh15/Lb+09g1ACzZx+sna9wEAAKfAZ1QBoGNLjPEcOuSTdD9JZ0teByCNUAWAviWNA605GNTi2ZLXAYjiM6oT9Pq+bwD6lzQOZEwp67ExpgR96LVVvKIKAB0bxxrHsT67Hj9JZ2n3k3S25HUA0ghVAAAAoghVADgxpzgY1OLZktcBSCNUAeD0nOJgUItnS14HIIoxpQl6/YAyAKfplAaDWj6b+zrGlKAPvbaKV1QB4MSc4mBQi2dLXgcgjVAFAAAgilAFAAAgilAFALpftm3xbMnrAKQRqgBAVf/Lti2eLXkdgChWfyfodUkLAC71umzb8tnc17H6C33otVW8ogoAdL9s2+LZktcBSCNUAQAAiCJUAYDuB4NaPFvyOgBphCoAUNX/YFCLZ0teByCKMaUJev2AMgBc6nUwqOWzua9jTAn60GureEUVAOh+MKjFsyWvA5BGqAIAABBFqAIAW/U0GNTi2ZLXAUgjVAGAXXoaDGrxbMnrAEQxpjRBrx9QBoC79DAY1PLZ3NcxpgR96LVVvKIKAGzV02BQi2dLXgcgjVAFAAAgilAFACZrdTCoxbMlrwOQRqgCAPtodTCoxbMlrwMQxZjSBL1+QBkA9tXaYFDLZ3Nfx5gS9KHXVvGKKgAwWauDQS2eLXkdgDRCFQAAgChCFQA4SAuDQS2eLXkdgDRCFQA4VAuDQS2eLXkdgCjGlCbo9QPKAHAMyYNBLZ/NfR1jStCHXlvFK6oAwEFaGAxq8WzJ6wCkEaoAAABEEaoAwNGlDQa1eLbkdQDSCFUAYA5pg0Etni15HYAoxpQm6PUDygAwl5TBoJbP5r6OMSXoQ6+tIlQn6PWLDwBrOj8/f1pVr659H9zmzzfQjl5bxVt/AYC1iFQAthKqAMDRGfIB4BBCFQCYgyEfAB5MqAIAc/i8qn62+eddZwBwy0tr3wAA0J/Nyuxn950BwDZeUZ3mYs9zAOB+fh/N5OsCbemyVfz1NAAAAETxiioAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABRhCoAAABR/g/qIHhYi9KlRgAAAABJRU5ErkJggg==",
       "text/plain": [
        "
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       ]
@@ -2050,7 +2051,7 @@
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     {
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",
       "text/plain": [
        "
"
       ]
@@ -2088,7 +2089,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -2107,7 +2108,7 @@
     },
     {
      "data": {
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",
       "text/plain": [
        "
"
       ]
@@ -2126,7 +2127,7 @@
     },
     {
      "data": {
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",
       "text/plain": [
        "
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@@ -2145,7 +2146,7 @@
     },
     {
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\n",
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@@ -2630,7 +2631,7 @@
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  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
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    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.12.3"
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  },
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