8. Tabu Search.ipynb

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    "# Tabu Search\n",
    "Tabu (\"taboo\") search algorithm is based on the idea that you want to prevent the search from going back to some regions. This forces the algorithm to exploit the search space. It is most commonly used to solve combinatorial (finite solution set) optimization problems. To apply Tabu search, a dynamic neighbourhood search algorithm needs to be implemented first. Tabu search has the following 3 main strategies:\n",
    "\n",
    "1. Fobidding strategy: controls what enters the Tabu list\n",
    "2. Freeing Strategy: controls what exists the Tabu list\n",
    "3. Short-term strategy: manages interplay between the forbidding strategy and freeing strategy to select trial solutions.\n",
    "\n",
    "## Basic Elements of Tabu Search\n",
    "* **Memory** is exploited by classifying a subset of moves in a neighbourhood as **forbidden** (or *tabu*).\n",
    "    * This classification depends upon the search history and on the frequency that certain move (called attributes) have participated in generating past solutions.\n",
    "    * **Tabu tenure** is the numbe rof iterations for which a move is considered to be tabu.\n",
    "* A **neighbourhood** is constrcted ot identify adjacent solutions that can be reached from current solution.\n",
    "* The **Aspiration Criterion** provides exception to Tabu restrictions. When a tabu move has *sufficiently attractive* evaluation (better than any other move visited so far, then its tabu classification may be overridden).\n",
    "\n",
    "## Basic algorithm\n",
    "1. Choose initial solution $i\\in S$. Set $i^* = i$ and $k=0$\n",
    "2. Set $k = k+1$ and generate a subset $V^*\\in N(i, k)$ where $N(i,k)$ is the neighbourhood such that either one of the Tabu conditions is violated or atleast one of the aspiration conditions holds.\n",
    "3. Choose a best $j\\in V^*$ and set $i=j$\n",
    "4. If $f(i)<f(i^*)$ then set $i^*=i$\n",
    "5. Update Tabu and aspiration conditions\n",
    "6. If stopping condition met, then end. Else goto 2.\n",
    "\n",
    "Some possible stopping criterion could be:\n",
    "* $N(i, k+1)=\\phi$ (no feasible solution in the neighbourhood of solution $i$)\n",
    "* $k$ is larger than the maximum numebr of allowed iterations\n",
    "* The number of iterations since the last improvement in $i^*$ is greater than a specified number\n",
    "* Some evidence avaialbe that optimum solution has been reached.\n",
    "\n",
    "## Advantages and Disadvatnages of Tabu Search\n",
    "**Advantages:**\n",
    "* Allows escaping the local optimum by accepting non-improving solution.\n",
    "* Can be applied to both discrete and continuous solution space.\n",
    "* Can be used for complex problems on scheduling, quadratic assignemtn and vehicle routing. Obtains competitive solutions when compared to other approaches.\n",
    "\n",
    "**Disadvantages:**\n",
    "* Too many parameteres to be determined\n",
    "* Number of iterations can be large\n",
    "* Like all heuristic methods, the global minimum is not guaranteed.\n",
    "\n",
    "## Data structure for holding Tabu information\n",
    "For examples in which there is pair switching (example the travelling salesman problem), the Tabu structure can be maintained by using a triangular table where the element $T(i,j)$ denotes the number of iterations for which transition between solution $i$ and solution $j$ is tabu.\n",
    "\n",
    "To show some implementations of Tabu Search, we will be looking at two problems: the travelling salesman problem (similar ot the one explored in `4. Genetic Algorithms Examples.ipynb`), and the vehicle routing problem.\n",
    "\n",
    "## Tabu Search for Travelling Salesman Problem\n",
    "The travelling salesman problem (TSP), as mentioned before is a problem where given a random set of cities, and a starting city, the aim of the saleman is to figure out the optimal path such that all cities are visited (compleeting the loop back to the startin city) by travelling the least amount of distance."
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   "source": [
    "import numpy as np\n",
    "from random import randint\n",
    "from itertools import combinations\n",
    "from matplotlib import pyplot as plt"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "class TravelSalesPerson(object):\n",
    "    def __init__(self, n_cities, max_coor = 1):\n",
    "        self.city_position = np.random.rand(n_cities, 2)*max_coor\n",
    "        self.iter = 0\n",
    "        self.n_cities = n_cities\n",
    "        self.max_coor = max_coor\n",
    "\n",
    "    def cost(self, visit_order):\n",
    "        '''Calculates the total distance travelled when visiting the cities in the order provided.\n",
    "        :param visit_order: (Nxd) matrix of N possible permutations of d cities\n",
    "        :return dist: (1xN) vector of total distances travelled for each permutation \n",
    "        '''\n",
    "        if visit_order.shape[1] != self.n_cities:\n",
    "            raise Exception(\"visit_order should have dim[1] = number of cities\")\n",
    "        dist = np.zeros(visit_order.shape[0])\n",
    "        for i in range(self.n_cities-1):\n",
    "            dist += np.linalg.norm(self.city_position[visit_order[:, i], :] - self.city_position[visit_order[:, i+1], :], axis=1)\n",
    "\n",
    "        dist += np.linalg.norm(self.city_position[visit_order[:, -1], :] - self.city_position[visit_order[:, 0], :], axis=1)\n",
    "        return dist\n",
    "\n",
    "    def plotting(self, order, cost, save=False):\n",
    "        plt.cla()\n",
    "        plt.scatter(self.city_position[:, 0].T, self.city_position[:, 1].T, s=100, c='k')\n",
    "        lx, ly = self.city_position[order, 0], self.city_position[order, 1]\n",
    "        plt.plot(lx.T, ly.T, 'r-')\n",
    "        plt.text(-0.05, -0.05, \"Total distance=%.2f\" % cost, fontdict={'size': 20, 'color': 'red'})\n",
    "        # plt.xlim((-0.1, 1.1))\n",
    "        # plt.ylim((-0.1, 1.1))\n",
    "        if save: plt.savefig('Images/travelling_salesman/travelling_salesman_{:03d}'.format(self.iter))\n",
    "        if self.iter%100==0: plt.show()\n",
    "        self.iter += 1"
   ]
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   "execution_count": 13,
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   "source": [
    "class Tabu_TSP():\n",
    "    def __init__(self, tsp, tabu_tenure=3, n_neighbors=10, neighbour_method='basic', max_iter=500):\n",
    "        '''\n",
    "        :param tsp: TSP problem of class TravelSalesPerson\n",
    "        :param tabu_tenure: no. of iterations for which move is Tabu\n",
    "        :param n_neighbors: Number of neighbours to consider at every iteration\n",
    "        :param neighbour_method: Method used to calculate neighbours\n",
    "        :param max_iter: Maximum number of iterations allowed for Tabu Search\n",
    "        '''\n",
    "        if neighbour_method not in ['basic', 'two_opt']:\n",
    "            raise Exception(\"neighnour_method must be one of 'basic', 'two_opt'\")\n",
    "        self.tsp = tsp\n",
    "        self.tt = tabu_tenure\n",
    "        self.n_neigh = n_neighbors\n",
    "        self.get_neighbours = self.basic if neighbour_method=='basic' else self.two_opt_swap\n",
    "        self.tabu_list = np.zeros((self.tsp.n_cities, self.tsp.n_cities), dtype=np.int)\n",
    "        self.max_iter = max_iter\n",
    "        self.draw = False\n",
    "\n",
    "    def basic(self, order):\n",
    "        '''\n",
    "        Perform random swap of 2 citites to get n_neigh neighbors.\n",
    "        :param order: intial order for which the negighbors are found\n",
    "        :returns neighborhood: (Nxd) numpy matrix of N neighbors, of each length d cities\n",
    "        :returns swap_ids: (Nx2) numpy matrix of ids swapped for each of the N neighbours\n",
    "        '''\n",
    "        # get n_neigh number of random swaps on citites\n",
    "        combs = np.array(list(combinations(np.arange(1, len(order), dtype=np.int), 2)))\n",
    "        sample = np.random.randint(len(combs), size=self.n_neigh)\n",
    "        swap_ids = combs[sample]\n",
    "        neighbors = np.tile(order, (self.n_neigh, 1))\n",
    "\n",
    "        # swap indices\n",
    "        n_aranged = np.arange(self.n_neigh)\n",
    "        temp = neighbors[n_aranged, swap_ids[:, 0]]\n",
    "        neighbors[n_aranged, swap_ids[:, 0]] = neighbors[n_aranged, swap_ids[:, 1]]\n",
    "        neighbors[n_aranged, swap_ids[:, 1]] = temp\n",
    "        return neighbors, swap_ids\n",
    "\n",
    "    def two_opt_swap(self, order):\n",
    "        '''\n",
    "        Perform stochastic two-opt swap to get n_neigh neighbors.\n",
    "        :param order: intial order for which the negighbors are found\n",
    "        :returns neighborhood: (Nxd) numpy matrix of N neighbors, of each length d cities\n",
    "        :returns swap_ids: (Nx2) numpy matrix of ids swapped for each of the N neighbours\n",
    "        '''\n",
    "        # get n_neigh number of random swaps on citites\n",
    "        combs = np.array(list(combinations(np.arange(1, len(order), dtype=np.int), 2)))\n",
    "        sample = np.random.randint(len(combs), size=self.n_neigh)\n",
    "        swap_ids = combs[sample]\n",
    "        neighbors = np.empty((self.n_neigh, len(order)), dtype=np.int)\n",
    "\n",
    "        # swap indices\n",
    "        for i, (n1, n2) in enumerate(swap_ids):\n",
    "            neighbors[i] = np.concatenate((order[:n1], order[n1:n2][::-1], order[n2:]))\n",
    "        \n",
    "        return neighbors, swap_ids\n",
    "\n",
    "    def run(self, initial_solution=None):\n",
    "        '''\n",
    "        Run the Tabu search.\n",
    "        :param initial_solution: Optional initial solution to start the search.\n",
    "        :returns best_sol: Best permutation of cities found\n",
    "        :returns best_cost: Cost of the best permutation found\n",
    "        '''\n",
    "        # Initialize\n",
    "        if initial_solution is None:\n",
    "            best_sol = np.arange(self.tsp.n_cities, dtype=np.int)\n",
    "            np.random.shuffle(best_sol[1:])\n",
    "            curr_sol = best_sol\n",
    "        else:\n",
    "            best_sol = initial_solution\n",
    "            curr_sol = best_sol\n",
    "        best_cost = self.tsp.cost(np.expand_dims(best_sol, axis=0))[0]\n",
    "        curr_cost = best_cost\n",
    "        iter_num = 0\n",
    "\n",
    "        while iter_num < self.max_iter:\n",
    "            neighborhood, swap_ids = self.get_neighbours(curr_sol)\n",
    "            neighborhood_costs = self.tsp.cost(neighborhood)\n",
    "            neigh_ordered_id = np.argsort(neighborhood_costs)\n",
    "            found = False\n",
    "            idx = 0\n",
    "\n",
    "            while not found:\n",
    "                id2check = neigh_ordered_id[idx]\n",
    "                if self.tabu_list[tuple(swap_ids[id2check, :])] == 0 \\\n",
    "                    or neighborhood_costs[id2check] < best_cost:   #best move is not tabu -> proceed OR aspiration criteria passed\n",
    "                    curr_sol = neighborhood[id2check, :]\n",
    "                    curr_cost = neighborhood_costs[id2check]\n",
    "                    self.tabu_list[self.tabu_list>0] -= 1\n",
    "                    self.tabu_list[tuple(swap_ids[id2check, :])] = self.tt\n",
    "                    if curr_cost < best_cost:\n",
    "                        best_sol = curr_sol\n",
    "                        best_cost = curr_cost\n",
    "                    found = True\n",
    "                else:\n",
    "                    idx += 1\n",
    "                    if idx >= self.n_neigh:\n",
    "                        print(\"No neighbours pass Aspiration Criterion. Getting new neighbors...\")\n",
    "                        break\n",
    "            if self.draw: self.tsp.plotting(best_sol, best_cost)\n",
    "            iter_num += 1\n",
    "        return best_sol, best_cost\n",
    "\n",
    "    def visualize(self):\n",
    "        self.draw = True\n",
    "        self.run()\n"
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     "text": "Starting Tabu Search with initial cost:  4.665817225861911\n"
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XYJzLJYXxlCaAkGAMPPccnHGGrND0zTdOR6RC2aJFMpW4RQv5vn17pyMKCampqbhcrnLbvwcGAycB/zOGB3bvlgq9//lP1I+9aQIIFfXry+36rVrJoLCuKxzVKprLvikjA/r3l8/JwoXQurXToYaMMWPG+EwAJZYCFzRsyMbXX5cbMW+6Sa6cpk+XFdKikbU2bB7du3e3Ee/nn61t2tTaTp2szctzOhrlgDlz5tjY2FjrcrkssP9xXp06dhfY/HbtrN282ekwQ1JF753L5bKxsbF2zpw5suO+fdZ++qm1p5xiLVjboYO1r7xi7d69zv4DAgTIsj7aVL0CCDVHHSVXAmvWyLJ90XpmEqUqmss+CMgsKuIn4NjNm8ndudOxGENZcnIy2dnZpKWllbl6SktLIzs7u/QmMGOkSuo338g9OU2aSJnsY4+VqqnRUrDRV1YI1UdUXAGUeP55OTO59lo5W1FR4frrry939poCdg/YJWCbFJ/N3nDDDU6HGln27bP2nXesPfZY+bvr0sXa99+PmL899AogzIwYAbffLkv3TZ7sdDQqSLznsg8F3gC+Bc4G8tG57AFhDAwZIvdVvPqqrJ18/vlw0knw8ccRe4+OJoBQ9tBD8qEcMwY++MDpaFQQeM9RzwVmA/2BnZXsp2pJnTowbBisWAEvvihlWpKT4fTTYf58p6OrdZoAQlnJusLdu8tKTsuWOR2RCjDvuexZwBDAex057/1ULatbV6Zkr1oFzzwjVVZ795Z7LyJomrYmgFAXG1u6rvDAgVI7SEWsiuaye3K5XAwfPjxIEUW5evXguusgJ0e6Yn/8USr5nnsufPed09EdME0A4eDww2WmQn4+DBqk6wpHsKrmsoMkgNGjRwcpIgVIuY2bb5aS2488At9+C0lJ0kX7ww9OR1djmgDChee6wpddBkVFTkekAiAhIYHMzExiY2PLJQKXy0VsbCyZmZkkJCQ4FGGUa9RIJmf88guMHy+LOh13nHTRrlrldHTVFpAEYIzpb4xZZYzJMcb4XADXGHOxMWaFMeYnY8xrgYgj4gwYAJMmSRlpXVc4Yvk9l105Jz5eanf98guMGyeTNI45Bq64Qq4SwkStrwhmjKkDrEZmrW1A7sC+1Fq7wmOfROAtoLe1Ns8Y08Jau6WqY0fMimAHwloYNQqmTpVb2K+5xumIlFJbtsCjj8rf5d69Mo37zjtDplRHMFcEOwnIsdautdbuQaYxD/ba5xrgaWttHoA/jb8qZgw8+aTUgxk5Ej77zOmIlFItWkB6OuTmwrXXyhTSI4+UcYM//nA6ugoFIgG0BNZ7PN9QvM1TR6CjMeZrY8wSY0z/ig5mjEkzxmQZY7K2bt0agHDDUN26crv60UdDSgqsXOl0REopgCOOgClTpJTLv/4FTz8NHTrAbbeF5NKvgUgAxsc2736mukAicCZwKfC8Maapr4NZa6dba5OstUmHHHJIrQYa1uLjS9cVPu880OSoVOho21ZKvP/8s5ykPf64lO2+5x7Yvt3p6PYLRALYAHh2fLUCNvnYZ5a11m2t/QVZu0FXtKiutm1lQHjTJrjgArl9XSkVOo48EmbMkPsHkpNl9b/27eHBB8GjoF9F5b8DvoC9rwJBB/JAzu7XAu2Besh6DMd67dMfeKX4++ZIl9HBVR07qorBVcebb0oBq2HDIqZ4lVIRadkyawcNkr/X5s2tffxx+/G77/pXwvoAEKxicNbavcAoYB6wEnjLWvuTMWaCMWZQ8W7zgD+NMSuA+cCt1to/azuWqHHxxbKe8GuvyRmGUio0HX+8XLV/+62UeBk7luOGDOHKwkKMV+l3t9tNYWEhKSkpAbsSqPVpoIGk00ArYa3MQZ4xQxLBpZc6HZFSqgrpF1xA0qxZ9LKW34D7gZeBvR77uFwu0tLSmDJlSo1/T0XTQDUBRJLdu2Wt2G+/hS++gFNPdToipVQlDmncmE4FBdwL9Cne9hRws9d+8fHx5Ofn1/j3VJQA6tb4iCr0lKwrfMopUst8yRKZgqaUCg2FhbB4MSxaBAsXsr6ggAbAPmSwdCEw3cePBar8tyaASHPwwTI9tEcPmR76zTfQ1OcMW6VUoBUUwNdfw8KF8li6VJZ5jYmBE07gOZeLz9xuvgTyKjlMoMp/azG4SKTrCivljPx8+OgjufHr5JPl5Kt/f5g4UQo4jh4tr//1F2RlsfLqq5nrclXa+Aey/LeOAUSyl16Cq66SW9OfeUbKSCilas9ff0l3TnGXDsuXy4LyLpcsJ9mrlzxOPRV8nMXn5ubStWtXCgsLK/wVsbGxZGdnH1AFWB0DiEZXXgmrV0v98o4d4ZZbnI5IqfC2ZUtpY79wYelaAPXrS7fr3XdDz54yDhcbW+XhSsp/p6Sk4Ha7y6wH7XK5cLlcAS3/rQkg0j34oHQFjR0rdyUOGlT1zyilxO+/lzb2CxeW1t2KjZWz+osvljP8k06SJFADJeW/J02aREZGBgUFBcTFxTF8+HBGjx4d0LUftAsoGhQWyod0xQr48kvo1s3piJQKTb/9Jg19yVn+mjWyPS5OFoYv6dLp3l2WiwwT2gUUzUrWFT75ZFlX+L//hZbeBVqVCi+5ubmkp6czc+bM/WfNqampjBkzxr+zZmtlQRfPM/x16+S1pk3hjDNk/KxnTzjhBKnCG2H0CiCaZGfLgtaJiXKGE6CpZUoF2ty5c6vsNy+3cpq1ckbv2eBv2CCvHXywNPQlZ/hdukCdOkH8FwWW3gmsxEcfyTjAwIHwzjsR9SFX0cHvmTPff0/C7t1lB21LFmdp0aK0se/VS5ZzjIncWfHaBaTEgAEweTLcdJOsK/zYY05HpFS1pKenlznrL2GALkAv4Kxdu2jRpUtpifSWLaF3b2nse/aUe2V0WrQmgKh0442wapUsUpGYCGlpTkeklN9mzpxZJgEcBTwKnAEcVLxtnbV8UFTEsBdekEa/Qwdt8H3QBBCtJk+W9UtHjpQ/jr59nY5IKb9418U5BVl0fD7wElJP5zcgpqiIYVddFfT4wknkdnqpypWsK9ypk64rrMKKd12cN5AlBusBGUjj72s/VZ4mgGhWsq5wgwYyNqDrCqswkJqaisvl2v98N1JH/zRkqUEIbP2cSKIJINqVrCv8++9SQlrXFVYhbsyYMWUSAEjXz1rggeLnLpeL0aNHBzu0sKMJQMkNYjNmSOnoESNkvrRSIaqkfk5sbOz+ROAGxgPdgaH16gW0fk4k0QSgxEUXSd2g116DCROcjkapSpXUz0lLSyM+Pp6YmBg+aNyYP5o2ZUbbtiSfc47TIYYFTQCq1LhxcPnlcN99kgiUCmEJCQlMmTKF/Px8ioqK+GvHDg575hnqrVkDb73ldHhhQe8EVmXt2SPrCi9eLOsKn3aa0xEp5b99++C442R97BUrIrJ+T01UdCewXgGosurVkxIRbdvKoPDatU5HpJT/YmLg/vul5k9GhtPRhDxNAKq8knWFi4pkXeHt252OSCn/DR4s5ZrHj5crWlUhTQDKt44d4b33ICdHBoh1XWEVLoyBBx6AX3+FF15wOpqQpglAVaxXL5g+HT77DEaN0umhKnz06yfjVw88ALt2OR1NyNIEoCp3xRUyO2j6dJg0yelolPJPyVXApk0wbZrT0YQsTQCqag88IPWCxo6Vu4aVCgdnngl9+sDDD4NXATklNAGoqsXEwCuvQFISDBsG//uf0xEp5Z/774ctW2DKFKcjCUmaAJR/StYVbt5cVhPbuNHpiJSqWo8eUuhw4kTIz3c6mpCjCUD577DD4IMPYMcOSQJ6Wa3CwYQJkJenY1g+aAJQ1dO1q6wj8P33cNllcq+AUqGsWze48EJ44gn480+nowkpAUsAxpj+xphVxpgcY8wdleyXYoyxxphytymrEHXuufDkk9IldPvtTkejVNXGj5crVl0Du4yAJABjTB3gaSAZOAa41BhzjI/9GgM3Ad8GIg4VQKNGySM9XafZqdB37LFw6aXw1FPwxx9ORxMyAnUFcBKQY61da63dg6zaNtjHfvcDEwFdhSQcTZoEyclwww3w6adOR6NU5e69V0pDPPKI05GEjEAlgJbAeo/nG4q37WeMOQFoba39sLIDGWPSjDFZxpisrbpkYWipWxfeeAOOOUbKRaxY4XRESlWsY0cpd/7MM7B+fdX7R4FAJQDjY9v+OgLGmBhgEjCmqgNZa6dba5OstUmHHHJILYaoaoXnusLnnafrCqvQdvfdUtLkwQedjiQkBCoBbABaezxvBWzyeN4Y6AwsMMasA04BZutAcJhq00YGhHVdYRXq2rWDa66RInFa6jxgCWApkGiMaW+MqQdcAswuedFam2+tbW6tbWetbQcsAQZZa3W1l3B10klSf/2bb+Cqq7RwnApdd94p3Zf33+90JI4LSAKw1u4FRgHzgJXAW9ban4wxE4wxgwLxO1UISEmBhx6C11+XaXdKhaIjjoCRI2HGDFi1yuloHBWw+wCstXOstR2ttQnW2geLt91jrZ3tY98z9ew/Qtxxh1QQHT+ezU88wciRI/cv2h0fH8/IkSPJzc11OkoV7W6/nX0NGrD0vPOi+vOpawKr2rdnD38mJRH3ww+cU6cOizzuFna5XLhcLjIzM0lOTnYwSBXN5s6dyw+DBnHb3r10BX4o3h6pn09dE1gFTe769RyXk8OvQGZREe09XnO73RQWFpKSkhJVZ1oqdOTm5pKSksLDe/eyHfDsrIy2z6cmAFXr0tPT2bJ3LwOQD9hHQBOvfdxuN5O0OJdyQHp6Om63m+1AOnAB0N1rn2j5fGoXkKp18fHx7Ny5E4CewKfAs8DNPvbL1xK9Ksg8P5+NgbXItMVzfewXKZ9P7QJSQVPgUSZ6EVLo6fgq9lMqWEo+d72Bl4CmwOmUbwyj4fOpCUDVuri4uDLP1wCJfuynVMBt386t9eqxEvgcuUJ9HOgC7PPaNRo+n5oAVK1LTU3F5XLtf74GOBzw/HNyuVwMHz482KGpaLV8OaSlQcu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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "tsp = TravelSalesPerson(10)\n",
    "tabu_search = Tabu_TSP(tsp, n_neighbors=5, tabu_tenure=1 ,neighbour_method='two_opt')\n",
    "tabu_search.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have implemented the Tabu search for TSP, let's compare the two neighborhood search strategies: `basic` and `two_opt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0, 0.5, 'Final solution cost')"
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
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og72NFRtOquYeRSkPzBr4hRC2aEH/Jynlr/riKCFEXX19XeCmOctgCtUd7XiknTvrg64RFZ9s9vzuJKayZP9lBrasw5xHfIlLSuObPSFmz3fvxVsERyUwpZvXfT14nOxt6Nu8NptORZKe8WCXV0VRyhZz9uoRwGLgnJRyrsGqDcAE/fEEYL25ymBKk7o2JCNTsswCzS6L9oZyNzWdF/s1paVbVQJ83Vhy4DI3zfyj892+UGo52xPg6/bAumG+bty6m1qmLnLfTEhm1dGr6tqEouRgzhp/F+AJoLcQIkj/Gwx8DPQTQlwE+unPSz2PGo4M9KnDT4evcDcl3Wz5xNxNYdnBMIa2dsO7jjMAL/dvSnqGZMHOi2bL9+z1ePZdvMWEhzyxs3nwsOjp7YqzvU2Zau55Y+0pXlt7iq8tcLakKGWJOXv17JdSCillaymln/63SUoZI6XsI6Vsov+PNVcZTG1qNy/ik9NZfTTcbHks3BNCcloGM/o0yV7mUcORxzo0YOWRcMJu3TNLvt/tD6WSrTXjOjbIdb2DrTUDfOqw9fQNi/YyKqr9F2/x5/mb1K3qwJxtwRwOLTtnKopibhVrHIJiatOgOu09q7PkwGWztHXfjE/m+0NXGO5Xj8a17h8b5/k+jbG1tmLO9gsmzzcqPpnfT17nkXbuVKtsl+d2Ab5uJKSkszu45HpZGSMjU/LBxrO4V6/Exn93w7OGI/9ecYLohJSSLpqilAoq8BfS1G5eRNxOYsuZGyZP+6vdIaRnSv5tUNvPUsvZgcldG/L7yeucvhZn0nyXHQwjI1MyqWvDfLd7qFENajrZ8Xspb+5ZeyyC8zcSeH1QM1wc7fhqvD9xSWm8sOoEGaq9XzFSYmo6X+8O4fqdpJIuismpwF9IfZvXpmFNR7418TAO1+8k8fNfVxnt757nuELTenhRrbItn2wNNlm+91LS+enwFQa0rINHjfzHM7KxtmJwq7rsOBdl1uscxXEvJZ1PtwXj36AaQ1rVBaBZnSr852EfDlyK4XMzXidRypcVR8L535bzDPhsL6uOXi1Xw7aowF9IVlaCKd0acjIijiOXTXd54otdl5BInu/TOM9tqjjY8q+ejdl7IZqDIbdMku8vgeHEJ6czxeCGrfwE+LqRkp7J9rOmP+MxhW/2hBCdkMLbQ1vc1yV1TDt3Rvm7M//Pi+y/aJrPTim/pJSsOnqVZnWcaeFWhdfWnmLi0qNExpWP2r8K/EUwyt8dF0c7k93QFR6byOqj4YxtXx/36pXz3faJzh7UrerA/7YEF7sGkpEpWXzgMv4NqtHWw7ix8vwbVKdetUpsCCp9zT3X7ySxaF8oAb5u+De4//0IIfjP8JY0dnVixsoTFrkfQym7gsLvcCHqLk929mTF1E7MGtaCI5dj6f/ZXlYHhpf52r8K/EXgYGvNk5092HHuJpdu3i12ep/vvIiVleC5Xg+27eeW94t9m3Iy/A5bz0QVK9+tZ24QHpt03/AMBbGyEgxtXZd9F29x+15qsfI3tdlbg8mU8OpA71zXV7az4evx/iSmZvD8ihPqZjQlT6sDw6lka80w37pYWQkmdmnIlhe60bxOFV5d8zeTlh3lRpz5Kw8JyWlm+ZFRgb+Inujkgb2NFYv3F6/WH3brHmuPX2NcxwbUqepg1GtG+tejkasjn249X6zg9e2+UBq4VKZ/yzqFet0wXzfSMyWbTkcWOW9T+zviDr+euMbkrg3zPWtqXMuZD0f6cORyLHPN0EPKlMJjE5m34wIp6aW/+2x5ci8lnQ1B1xnSui7ODrbZyz1qOLJyWifeG9aCQ6Ex9PtsD2uORZg8MGdmSg6G3OLFVUG0/+8Os4yRpQJ/EdVwsmd0W3fWHr9WrG6C8/+8iK21YHrPRka/xsbaipkDvAmJvsevx68VKd9jV2I5cfUOk7s2LPT8wy3dquDl6lhqevdIKflg4zlqONrxrBGf44g27jzWoT5f7Q5hV3DpHDEkNT2T6T8dY96Oi7lOH6qYz8ZTkdxLzWBs+/oPrLOyEjzVpSFbZnSnWR1nXvnlJJOXB5qk6TDidiLzd1ykx+xdPP7tX+w4G6U3K9sXO+2cVOAvhsldG5KWkckPh8KK9PpLNxP4LegaEzp7UsvZuNp+lgEt6+Bbvxqf7bhQpBuqvt17maqVbBnTzr3QrxVCEODrxl+XYy1yuluQrWeiOHI5lpf6N72vhpaf94a1pHndKry4KqhUdtebu10bmdW9eiW+3HWJxNTS2YuqPFp9NBwvV0fa5XPdy7OmI6umdeadoS04GHKLfnP3sLYItf/ktAzWB13jicV/0e2TXXy24wINXCoz/1E/jr7dl/+OaEXDAmYPLAoV+IvBy9WJfs1r8/3hKySlFj74frbjIpVtrXm6h/G1/SxCCF4b6E1kXDI/HLpSqNdeibnH1rM3GNexAZXtijb7ZoCvG1LCHyU8QUtqeiYfbT5H09pOjG33YA0tLw621nz5eBvSMyTP/XyctFLU3n8oJIZv9obwWIf6zH+0DbfuprL0QFhJF6tCuHQzgcArtxnbrn6BU41aWQkmd23I5hndaVrbmZd/OcnU7wMLHFNLSsnfEXd457fTdPjvDmasDCI0+h4z+jRh36u9+GlKJx72q4eDrbUp39r9ZTdbyhXE1O5e3ElMY82xwg3jcC4yno1/R/JUl4a4OOZ9t2x+HmpUk+5NXfly9yXik40ftnnxfm0+3QkPeRYpX9B+9HzqVSnx5p7vD4VxJSaRt4a0wMa6cIezl6sTH49qxfGrd/hky3nzFLCQ4hLTeHl1EJ41HHlnaAvaelSnT7NafLPHMkNzV3SrAyOwsRKM9Df+TLhhTUdWPd2Zt4c0Z9/FW/T7bC/rTjxY+4+5m8Li/ZcZNH8fAV8cYHVgOL2b1eKnKR3Z92ovXujblPou+ffqMxUV+IupnUd1/OpX47v9lwt1V+hn2y/g7GBTqB41uXl1gDd3EtNYtMe4i8xZ8+kG+NajdpXCNS/lFODrxsmIOLONH1SQ2/dSWfDnRbo3daVH06LN2TC0tRtPdvbg232X2WaGu7ELQ0rJ2+tPczMhhXlj/bLPxl7q35T45HS+LYFZ4CqS1PRMfj0eQe9mtXB1Lly7urWVYEo3LzbP6EbjWk68uOokU78/RmRcEjvPR/HMD8fo9NGf/OePs9jbWvPBcB+OvNWXeY+2oUvjmlgV8jpbcanAX0xCCKZ19+JKTKLRNzWdiohj29kopnT1ompl49qk8+JTrypDW9dl8f7L3EwouL09az7dqd3zH57BGENba8M3l1Stf8HOi9xNSeetwc2Llc5bQ5rTql5VXvnlJOGxiSYqXeH9FnSN309e54W+TfCtXy17eUs3bR8vOXCZW3fVeEPmsvN8FLfupvJoB+ObDHPycnVidXbtP5rOH+1k0rJAjobFMqGzJ1tf6M76f3VhfCcPqlYq3ne/OFTgN4EBLetQ36WS0fPyzt0eTNVKtkzq6mmS/F/p701aRiaf/3kp3+1yzqdbXG7VKtHB04UNJ69b/IaW0Oi7/HDoCo92aJA9fHVR2dtY89U4fyTwr5+Pl0j3yfDYRN797QztPaszveeDd2+/2K8pyWkZfLVLDTFtLquOhlO7ij3dmxRvxr+s2v+mGd2Y1t2Lb55oy6E3+vD20BbFPlZNRQV+E7C2Ekzp6sXxq3c4diX/YRyOXbnNruBopnX3MroHSkE8azoytn19Vhy5ypWYvJtdNuQyn25xDfNz4+LNu5y34HzEAB9tPp99M5sp1HepzOwxvvwdEceHG8+ZJE1jZWRKXlodBMDcR/xy7V7byNWJ0W3d+fGvK+Vm2IDS5EZcMnsuRDO6rXuhrxXlpZGrE28Obs6AlnVyneOiJJWu0pRhY9q5U7WSbYG1/s+2X6CGox0Ti3FhNTcz+jTBxlrkeVOSlJLvcplPt7gG+9TB2kpYdIKWgyG32H42imd7NSp0W2x+BrSsw+SuDVl+6Aob/7bczWlf777E0bDb/N/wlvle3Pt3nyZIKVlQwJmdUnhrjoWTKeGRQvQMK8tU4DeRynY2PNHJg21no/K82PlXaAz7L93imR6NcLQvWjfKvNSq4sCkLg1ZH3SdM9cfHLY5az7dyV0bFthNrTBqONnTtXFNfrdQc09GpuSDP85Rr1olJnUp/nWKnF4b2Iw2Darx2tq/uWyBi9Ynw+8wb8dFhvm6MdyvXr7bulevzLiOHqwONN+EPBVRZqZkVWA4nb1qFDhCbXmhAr8JPfmQB7ZWVizef/mBdVJK5my/gKuzPeM7eZgl/6d7NKJqJVs+zWXY5u/2heLqbE+A34Pz6RZXgK8bEbeTOH71jsnTzunX4xGcjYzn1YHeZunnbGdjxReP+2NjLXj2p+NmnW3sXko6L6wKopazPR8M9zHqB/nZXo2wtRbM21G6h5soSw6HxhAem5TrnbrllQr8JlTL2YERberxy7FwYnMMYHbgUgxHLsfyr56NqGRnnhszqlay5dmejdgdHH3fVIPnIrX5dCc+5Im9jenz7t+yNnY2Vmbv3ZOYms7sbcH41a+W64TwplKvWiXmPuLLuch4Zm04Y7YzmQ82niUs5h5zx/oZ3cOjlrMDT3VpyPqT1wm28HWV8mpVYDhVHGwY6FO4MavKMhX4TWxKt4Ykp2Xy4+F/7qbVavvB1K3qwKMdcp/T1lQmPORJnSoO/G/L+eyA9d2+y/nOp1tczg629PauxR9/R5p1xMtFe0OJik/hnaHNTdpclZvezWozvWcjVh4N5/Fv/zJ508rWMzdYcSScp7s3opNXjUK99unuXjjZ2TBnm+km5Kmo4hLT2Hz6BsPbmPdO2dJGBX4Ta1Lbmd7NarH8YFh2M8Hu4GhOXL3Dc70bm/3gcrC15oW+TThx9Q7bz0YRFZ/MhpPXCpxPt7gC/Ny4dTeFw6Gmm5zG0I24ZL7ZE8qQ1nVp6+Filjxymtnfmw9HtOL0tTgGzt/Lor0hJvlhuxmfzOtr/8anXhVe6lf4XknVKtsxrbsX285GERRu/ua18uy3oGukpmdWqGYeUIHfLKZ28yLmXirrTlxDSsnc7Rdwr16JMW0tc3CNbuuOl6sjn24NZsn+y6QbMZ9ucfVuVgsnexuzNffM3hZMRqbk9YHNzJJ+bqysBI93bMC2l7rTtXFNPtx0npFfH+RcZHyR08zMlLyy5m+S0jKYN7ZNkbv5PdVVG+pD1fqLTkrJyqPh+NSrQku3qiVdHItSgd8MOnm50KpeVb7dF8rWM1GcuhbHv/s0sVhfXhtrK2b29+bizbss2hfKgBYFz6dbXA621vRvUZvNpyNNfgPU6WtxrD0ewVNdPC02lomhulUr8e2T7fj8sTZcu53EsM/3M2dbcJHe5/JDYey9EM1bQ1rQuJZTkcvkZG/Dsz0bse/iLQ6FxBT8AuUBp6/Fcy4yvlCD+5UXKvCbgRCCqd29CI2+x6trTtKwpiMj2+TfVc/UBvrUobV7VaTUBpKzhGF+bsQnp7P3gunmtNXG2j9L9cp2PNsr7/mIzU0IwTBfN3a81IMAXzc+33mJwfP3ERhmfNNW8I0EPtp8nj7NajHeBNdbxnfyoE4VB2ZvK/40nBXRqsCr2NtYEVBAN9rySAV+MxnsU4d61SoRn5yu31xl2Y9aCMGno315Vx/h0RK6Nq5J9cq2Jr2Za8e5mxwOjeXFvk1KdGyTLNUd7Zg71o9lT7UnOS2TMd8c4r31p7mbkv94+SnpGcxYeYIqDjb8b3Rrk1ycdrC15vk+jfW7wUvnhDKlVVJqBuuDrjO4Vd1ScVxZmgr8ZmJjbcVrg5oxuFUdhpmx62F+vOs4m71t35CttRWDW9Vlx9kok0wckpqeyYebztG4lhOPmbk3VGH19K7F1he7M6GzJ98fvsKAz/ayO5/g++mWYM7fSODT0b7UdDLd3caPtKtPA5fKzN56gcxCjA5b0W0+HUlCcnqFuVM3JxX4zSjA142vxrUt9NSGZVmArxtJaRlsP1v0ieCllBwOjWHi0iNcvnWPNwc3s/gZkzGc7G2YFdCSNc90xsHWiolLj/LSqqAHJqHff/EW3+2/zBOdPOjVrJZJy2BrbcWL/ZpwNjKezadLdljpsmTV0XA8a1Smk5dlelEoQHEAACAASURBVIiVNqXv26SUae09Xahb1aFIvXuklOw8H8XohYd4dNFhLkQl8O7QFvTyNm2wNLW2Hi5smtGNf/duzIaT1+k7d0/2iKW376Xy8i9BNHJ15M1iDh+dlwDfejSt7cSc7cFmvY+ivLh86x5/XY5ljBGzbJVXph0wRqnwrKwEQ1vXZdnBMO4kphp170BGpmTjqUi+2nWJ8zcSqFetEv95uCVj2tUvMzfV2NtY81J/bwa1qstra//m3ytOsP7ENQBi76WyeEJ7s92xbW0leKmfN8/8eIx1J64xpoI2XxhrdWA4VkLr9lxRqcCvmFyAbz2+3XeZLadv5Hunckp6Br8ev8Y3e0IIi0mkkasjc8b4EuDnhm0pbNoxRvO6Vfh1+kMsPRDGnO3BJKdl8sagZvjUM28/8QEta9PavSrzdlwkwM/NLENzlAfpGZmsPabNslXcGejKMhX4FZPzqVeFhjUd2XDyeq6BPzE1nZ//usq3+7QhGFrVq8rC8W3p36K2xaegMwcbayumdveif8vaHAqJscgFRCEEr/T35sklR1h1NJwnO3uaPc+yaHdwNDcTUirsRd0sKvArJpfV5/3znRe5GZ9MLb1mdScxleUHr7Ds4GVuJ6bRycuF2WN86dq4Zrlsa/Wo4WjRYX67NalJx4YufL7zEmPa1jdb01JZtiownJpO9ia/yF7WlM3zaaXUC/Cti5Twx9+R3IxP5qNN5+jy8U4+23GBth7VWTv9IVZO60y3Jq7lMuiXBCEEMwd4E52QwvJDYSVdnFLnZnwyO8/fZHRb9zLblGgqqsavmEXjWs40r1uFL3dd4uMt50nPyGRoazem92xE87rFn+9XyV07Txd6ebvy9e4QHu/YgCommt6zPFh7/BoZmZJH2lXci7pZKvbPnmJWj3eoT0JyOqP83dn5ck8WPNZGBX0LeLm/N3FJaXy378EJgSoqKSWrA8Pp4OmCl2vRx0gqL1SNXzGb8Z08GNu+QambaLq886lXlSGt6rJ4XygTOntQw4R3CpdVRy7HcvnWPZ4rwfGeShP1jVTMRgihgn4JebFfU5LSMli4J6Ski1IqrAoMx9nehsGt6pZ0UUoF9a1UlHKocS0nRrRx5/tDV7h+J6mki1Oi4pPT2HQqkmF+bqqnk04FfkUpp17s1wSAjzefL+GSlKwNQddJTsvk0Qo2y1Z+zBb4hRBLhBA3hRCnDZb5CSEOCyGChBCBQogO5spfUSo69+qVebpHIzacvM7RQswbUN6sDgynWR1nWpn57umyxJw1/mXAwBzLPgHel1L6Ae/qzxVFMZNnenhRt6oDszacIaMCDtt89no8f0fEMbZ9xR2QLTdmC/xSyr1AzmqGBLL681UFzDNBq6IoAFS2s+GNwc05cz2eNcfCS7o4Frc6MBw7GytGWHgGvNLO0m38LwCfCiHCgdnAG3ltKISYpjcHBUZHR1usgIpS3gxrXZf2ntX5dGsw8clpJV0ci0lOy2DdiWsMaFnHqFFiK5ICA78Qoosxy4w0HXhRSlkfeBFYnNeGUspFUsp2Usp2rq6uRcxOURQhBO8Na0nMvVQ+//NiSRfHYpYeCCMuKU1d1M2FMTX+z41cZowJwK/6418AdXFXUSzAp15Vxrarz9IDYYRE3y3p4pjd6WtxzN0ezOBWdXioUY2SLk6pk2fgF0J0FkK8DLgKIV4y+JsFFLUz7HWgh/64N1Bxqh+KUsJeGeBNJVtrPvjjbEkXxayS0zJ4YVUQ1Svb8d/hrdRF3VzkV+O3A5zQhnVwNviLB0YXlLAQYgVwCPAWQkQIISYDU4E5QoiTwIfAtOIVX1EUY9V0smdG3ybsCo5m1/m8J4Yv6z7efJ5LN+8ye4wv1R1V235u8hyrR0q5B9gjhFgmpbwCIISwApyklPEFJSylfCyPVW2LVFJFUYrtyc6e/HzkKv/54yxdGtc065Aam05F8smW88wd64d/g+pmy8fQvovRLDsYxsSHPOneVF0bzIsxe/0jIUQVIYQjcBYIFkLMNHO5FEUxAzsbK94Z2oLQW/f4/lCY2fI5eOkWL6wMIiwmkanLA7kak2i2vLLcSUzllV9O0riWE68Pamb2/MoyYwJ/C72GPxzYBDQAnjBrqRRFMZte3rXo5e3K/B0XiU5IMXn6p6/FMe2HY3jWrMyvzz5EeqbkqWVHiEs0X1dSKSVvrTtN7L1U5o31w8FWjcmTH2MCv60QwhYt8K+XUqah3YilKEoZ9c7QFiSlZTBnW7BJ070Sc4+JS49StZIt30/qiH+D6nzzRFuuxiby9I+BpKZnmjS/LOtOXGPjqUhe7NfU7BPblwfGBP5vgDDAEdgrhPBAu8CrKEoZ5eXqxFNdPFkVGM6piDiTpBmdkMKTS46QnpnJ8kkdqFNVm2u5k1cNPhndmsOhsby+9m+kNG29MTw2kXfXn6GDpwtPd29k0rTLqwIDv5RygZSynpRysNRcAXpZoGyKopjR832aUMPRjvd/P1PsYJyQnMbEpUe4GZ/C0ontaVzr/lmuRrRx58W+Tfn1xDXmm/AmsoxMycurTwIw5xFfrK1U101jGHPnblUhxNys4ROEEHPQav+KopRhVRxsmTnAm8Art9lwsujDZqWkZ/DMj8c4fyOBr8b70yaPHjz/7tOYUf7uzNtxkXUnIoqcn6FFe0M5EhbL+wEtqe9S2SRpVgTGNPUsARKAR/S/eGCpOQulKIpljG5bH596Vfh483kSU9ML/frMTMlLq09y4FIMn4xqTS/vWnluK4Tgo5Gt6OxVg1fX/M3h0JjiFP2+u3NH+qtB2ArDmMDfSEr5npQyVP97H/Ayd8EURTE/ayvBrGEtiYxLZuGe0EK9VkrJ+7+fYePfkbw5uBmj2roX+Bo7GysWjm9LA5fKPP3DsSIPH5GclsGL6u7cIjMm8CcJIbpmPdEHaKvYc7kpSjnSztOFAF83vtkTQsRt4/vbf7U7hOWHrjC1W0OmFeKiatXKtix7qgM2VoKnlh4l5m7hu5T+b8t5Lqq7c4vMmMA/HfhSCBEmhAgDvgCeMWupFEWxqNcHNUMI+GiTcdM0rjxylU+3BjOiTT3eGNS80PnVd6nMdxPaERWfzJTvA0lOyzD6tfsuRrP0gLo7tziM6dUTJKX0BVoDraWUbaSUJ81fNEVRLMWtWiWe7dmYjaciORSSf9v79rNRvLnuFD2auvLJ6NZYFbEnTZsG1Zk31o+g8Du8vPokmUbMEKbuzjUNY3r1fCiEqCaljJdSxgshqgshPrBE4RRFsZxp3b2oV60S7/+e9zSNR8Niee7n47Ryr8ZX4/yxtS7eWD+DWtXljUHN2Hgqkk+25n8zmbo713SM2WuDpJR3sp5IKW8Dg81XJEVRSoKDrTVvDWnO+RsJrDhy9YH1wTcSmLzsKPWqV2LpxPY42uc5xmOhTO3mxbiODVi4JyTXfLOou3NNx5jAby2EsM96IoSoBNjns72iKGXUIJ86dGzowpxtwfeNrXPtThITlhyhkp0130/qgIsJL6gKIXg/oCU9vV15+7fT7Lnw4FSrEbcTeU/dnWsyxgT+H4E/hRCThRCTgO3AcvMWS1GUkpA1TWNcUhqf7bgAwO17qTy5+C/upaazfFIH3Kub/kYpG2srvnjcn6a1nfnXT8c5F/nPqDAZ+r0CEnV3rqkYc3H3E+ADoDnQEviPvkxRlHKohVsVHuvQgB8OX+Fk+B2eWnaU8NtJLJ7QnmZ1qpgtXyd7G5ZMbIejvTWTlh0lKj4Z0O/OvRzLLHV3rskIUw+YZA7t2rWTgYGBJV0MRakwYu+l0vPTXSSnZZKemcnX49syoGUdi+R9+locj3xziIY1HZkV0JLHvz1Mvxa1+fJxf3WjViEJIY5JKdvlXG6+6XcURSmzXBzteGWAN6kZmfx3RCuLBX3QJob/8nF/zkXGM/abQ+ruXDMwzWV5RVHKnSc7ezLIpy6uzpbvy9GrWS3ef9iH/248q+7ONQMV+BVFyVNJBP0sT3TyYGy7+madF7iiKjDw62PzzAI89O0FIKWUaqA2RVHMSgV98zCmxr8YeBE4Bhg/oIaiKIpSKhkT+OOklJvNXhJFURTFIowJ/LuEEJ8CvwLZ46dKKY+brVSKopSotLQ0IiIiSE5OLumiKEZwcHDA3d0dW1tbo7Y3JvB31P8b9gWVQO9Clk1RlDIiIiICZ2dnPD09VTfKUk5KSUxMDBERETRs2NCo1xQY+KWUamJ1RalgkpOTVdAvI4QQ1KhRg+joB8c4ykuRJlsXQqih8RSlnFNBv+wo7L5Sk60rilIqWVtb4+fnh6+vL/7+/hw8eLBI6UyZMoWzZ88atW1iYiLjxo2jVatW+Pj40LVrV+7evcudO3f46quvipT/hx9+WKTXmVOBY/UIIYKklH4FLTMnNVaPoljWuXPnaN688FMqmpKTkxN372qTsW/dupUPP/yQPXv2mDXPjz76iOjoaObOnQtAcHAwnp6eREZGMnToUE6fPv3AazIyMrC2zntSGMP3YU657bPijNWjJltXFKVExcfHU716dQDu3r1Lnz598Pf3p1WrVqxfvx6Ae/fuMWTIEHx9ffHx8WHVqlUA9OzZk6yK45YtW/D398fX15c+ffo8kE9kZCT16tXLfu7t7Y29vT2vv/46ISEh+Pn5MXPmTHbv3k2vXr14/PHHadWqFQDDhw+nbdu2tGzZkkWLFgHw+uuvk5SUhJ+fH+PGjQPgxx9/pEOHDvj5+fH000+TkaHdHrV48WKaNm1Kz549mTp1Ks899xwJCQk0bNiQtLS07M/B09Mz+3lRGdOrZzqwXG/XF0AsMLFYuSqKUma8//sZzl6PL3jDQmjhVoX3hrXMd5usgJmcnExkZCQ7d+4EtK6L69ato0qVKty6dYtOnToREBDAli1bcHNzY+PGjQDExcXdl150dDRTp05l7969NGzYkNjY2AfynDRpEv3792fNmjX06dOHCRMm0KRJEz7++GNOnz5NUFAQALt37+bIkSOcPn06uyfNkiVLcHFxISkpifbt2zNq1Cg+/vhjvvjii+zXnTt3jlWrVnHgwAFsbW159tln+emnn+jbty//+c9/OH78OM7OzvTu3RtfX1+cnZ3p2bMnGzduZPjw4axcuZJRo0YZ3W0zL4WdbL2VmmxdURRLqFSpEkFBQZw/f54tW7bw5JNPIqVESsmbb75J69at6du3L9euXSMqKopWrVqxY8cOXnvtNfbt20fVqvf3QTl8+DDdu3fPDtQuLi4P5Onn50doaCgzZ84kNjaW9u3bc+7cuVzL16FDh/u6Ty5YsABfX186depEeHg4Fy9efOA1f/75J8eOHaN9+/b4+fnx559/EhoaypEjR+jRowcuLi7Y2toyZsyY7NdMmTKFpUu1y6pLly7lqaeeKvyHmUOeNX4hxHgp5Y9CiJdyLAdASjm32LkrilLqFVQzt4TOnTtz69YtoqOj2bRpE9HR0Rw7dgxbW1s8PT1JTk6madOmHDt2jE2bNvHGG2/Qv39/3n333ew0pJRG9X5xcnJi5MiRjBw5EisrKzZt2sSoUaMe2M7R0TH78e7du9mxYweHDh2icuXK9OzZM9eb36SUTJgwgY8++ui+5evWrcuzPF26dCEsLIw9e/aQkZGBj49Pge+hIPnV+LPelXMuf07FzllRFMVI58+fJyMjgxo1ahAXF0etWrWwtbVl165dXLlyBYDr169TuXJlxo8fzyuvvMLx4/cPLtC5c2f27NnD5cuXAXJt6jlw4AC3b98GIDU1lbNnz+Lh4YGzszMJCQl5li8uLo7q1atTuXJlzp8/z+HDh7PX2draZrfJ9+nThzVr1nDz5s3sMly5coUOHTqwZ88ebt++TXp6OmvXrr0v/SeffJLHHnvMJLV9yKfGL6X8Rn+4Q0p5wHCdfoFXURTFbLLa+EGrKS9fvhxra2vGjRvHsGHDaNeuHX5+fjRr1gyAU6dOMXPmTKysrLC1teXrr7++Lz1XV1cWLVrEyJEjyczMpFatWmzfvv2+bUJCQpg+fTpSSjIzMxkyZAijRo1CCEGXLl3w8fFh0KBBDBky5L7XDRw4kIULF9K6dWu8vb3p1KlT9rpp06bRunVr/P39+emnn/jggw/o378/mZmZ2Nra8uWXX9KpUyfefPNNOnbsiJubGy1atLivqWrcuHG8/fbbPPbYYyb5bI3pznlcSulf0DJzUt05FcWySkN3zorm7t27ODk5kZ6ezogRI5g0aRIjRowAYM2aNaxfv54ffvghz9cXpjtnfm38nYGHANcc7fxVgLw7rSqKoiiFNmvWLHbs2EFycjL9+/dn+PDhADz//PNs3ryZTZs2mSyv/Lpz2qG15dugtetniQdGm6wEiqIoCrNnz851+eeff27yvPJr498D7BFCLJNSXjF5zoqiKEqJMOYGrmVCiAcuBEgp8x2WWQixBBgK3JRS+hgsfx54DkgHNkopXy1ckRVFUZTiMCbwv2Lw2AEYhRa0C7IM+AL4PmuBEKIX8DDQWkqZIoSoZXxRFUVRFFMwZjz+YzkWHRBCFDhSkpRyrxDCM8fi6cDHUsoUfZubRpZTURRFMRFjxuN3MfirKYQYANQpYn5NgW5CiL+EEHuEEO3zyXda1hwAhZlgQFGUsi88PJxevXrRvHlzWrZsyfz5802W9rx580hMTMx13R9//EGbNm3w9fWlRYsWfPONdjvTb7/9ZvTQzoZ2795d5OGkzcmYpp5jaFMtCrQmnsvA5GLkVx3oBLQHVgshvGQuNxNIKRcBi0Drx1/E/BRFKYNsbGyYM2cO/v7+JCQk0LZtW/r160eLFi2Knfa8efMYP348lStXvm95Wloa06ZN48iRI7i7u5OSkkJYWBigBf6hQ4fmmn96ejo2NrmH0t27d+Pk5MRDDz1U7HKbkjGDtDWUUnrp/5tIKftLKfcXMb8I4FepOQJkAjWLmJaiKOVU3bp18ffX7hF1dnamefPmXLt2Lddt586di4+PDz4+PsybNw+AsLAwmjVrxoQJE2jdujWjR48mMTGRBQsWcP36dXr16kWvXvfPKpuQkEB6ejo1atQAwN7eHm9vbw4ePMiGDRuYOXMmfn5+hISE0LNnT95880169OjB/Pnz+f333+nYsSNt2rShb9++REVFERYWxsKFC/nss8/w8/Nj3759REdHM2rUKNq3b0/79u05cEAbFCE6Opp+/frh7+/P008/jYeHB7du3eKdd96572znrbfeYsGCBcX+fPO7gWtkfi+UUv5ahPx+Q5ukfbcQoinavQK3ipCOoiiWsvl1uHHKtGnWaQWDPjZq07CwME6cOEHHjh0fWHfs2DGWLl3KX3/9hZSSjh070qNHD6pXr05wcDCLFy+mS5cuTJo0ia+++opXXnmFuXPnsmvXLmrWvL/O6eLiQkBAAB4eHvTp04ehQ4fy2GOP8dBDDxEQEMDQoUMZPfqfW5ju3LmTPTHM7du3OXz4MEIIvvvuOz755BPmzJnDM888g5OTE6+8ovWRefzxx3nxxRfp2rUrV69eZcCAAZw7d47333+f3r1788Ybb7Bly5bs8fwnT57MyJEjmTFjBpmZmaxcuZIjR44U6SM3lF9Tz7B81kkg38AvhFgB9ARqCiEigPfQpnFcIoQ4DaQCE3Jr5lEURQFtGINRo0Yxb948qlSp8sD6/fv3M2LEiOyRMkeOHMm+ffsICAigfv36dOmiDSs2fvx4FixYkB2A8/Ldd99x6tQpduzYwezZs9m+fTvLli3LdduxY8dmP46IiGDs2LFERkaSmpp633DNhnbs2HHftYL4+HgSEhLYv39/9gidAwcOzJ50xtPTkxo1anDixAmioqJo06ZN9hlJceR3A1exhoGTUuY1mtD44qSrKIqFGVkzN7W0tDRGjRrFuHHjGDlSa4AIDw9n2DCtTvrMM8+QX70x5xDMxk5I3qpVK1q1asUTTzxBw4YN8wz8hsMyP//887z00ksEBASwe/duZs2aletrMjMzOXToEJUqVbpveX7vY8qUKSxbtowbN24wadIko95DQYzp1VNVCDE3q4eNEGKOPhuXoiiKWUgpmTx5Ms2bN+ell/4ZKqx+/foEBQURFBTEM888Q/fu3fntt99ITEzk3r17rFu3jm7dugFw9epVDh06BMCKFSvo2lWbQTavIZbv3r3L7t27s58HBQXh4eGR72uyxMXFZU/ZuHz58uzlOV/Xv39/vvjii/vyAOjatSurV68GYNu2bdlDQwOMGDGCLVu2cPToUQYMGJDfx2Y0Y+bcXQIkAI/of/HAUpPkriiKkosDBw7www8/sHPnTvz8/PDz88t1kDJ/f38mTpxIhw4d6NixI1OmTKFNmzYANG/enOXLl9O6dWtiY2OZPn06oA2TPGjQoAcu7kop+eSTT/D29sbPz4/33nsvu7b/6KOP8umnn9KmTRtCQkIeKMesWbMYM2YM3bp1u+/awbBhw1i3bl32xd0FCxYQGBhI69atadGiBQsXLgTgvffeY9u2bfj7+7N582bq1q2Ls7M2RJqdnR29evXikUceyXdS98IwZljmICmlX0HLzEkNy6wollXWh2UOCwtj6NChnD59uqSLYpSUlBSsra2xsbHh0KFDTJ8+PftsIDMzE39/f3755ReaNGmSZxomGZbZQJIQomtWF059EpakQrwnRVEUJR9Xr17lkUceITMzEzs7O7799lsAzp49y9ChQxkxYkS+Qb+wjAn804Hleru+AGKBiSYrgaIoiol5enqWmdo+QJMmTThx4sQDy1u0aEFoaKjJ8zNmrJ4gwFcIUUV/Hm/yUiiKoigWY0yvnhl60E8A5gohjgsh+pu/aIqilCR1i03ZUdh9ZUyvnkl6Lb8/UAt4CiiZjr2KoliEg4MDMTExKviXAVJKYmJicHBwMPo1xrTxZ931MBhYKqU8KYy9E0JRlDLJ3d2diIgI1Mi4ZYODgwPu7u5Gb2/U6JxCiG1AQ+ANIYQz2uBqiqKUU7a2tnkOO6CUfcYE/smAHxAqpUwUQtRAa+5RFEVRyiBjevVkAscNnscAMeYslKIoimI+xlzcVRRFUcoRFfgVRVEqmPwmYnHJ74VSyljTF0dRFEUxt/za+A3n2s1JAl5mKZGiKIpiVvlNxKL6cimKopRDxnTnRAhRHWgCZN8aJqXca65CKYqiKOZTYOAXQkwBZgDuQBDQCTiENmm6oiiKUsYY06tnBtAeuCKl7AW0AdR93IqiKGWUMYE/WUqZDCCEsJdSnge8zVssRVEUxVyMaeOPEEJUA34DtgshbgPXzVssRVEUxVyMGbJhhP5wlhBiF1AV2GLWUimKoihmY2yvHmugNnBZX1QHuGquQimKoijmY0yvnueB94Ao/hmOWQKtzVguRVEUxUyMqfHPALz1UTkVRVGUMs6YXj3hQJy5C6IoiqJYhjE1/lBgtxBiI5CStVBKOddspVIURVHMxpjAf1X/s9P/FEVRlDLMmO6c71uiIIqiKIpl5Dce/zwp5QtCiN/RevHcR0oZYNaSKYqiKGaRX43/B/3/bEsURFEURbGM/AJ/NICUco+FyqIoiqJYQH7dOX/LeiCEWGuBsiiKoigWkF/gN5xyUU2zqCiKUk7kF/hlHo8VRVGUMiy/Nn5fIUQ8Ws2/kv4Y/bmUUlYxe+kURVEUk8tvsnVrSxZEURQzkBKEKHg7pUIxZqweRVHKor9/gTnN4MrBki6JUsqYLfALIZYIIW4KIU7nsu4VIYQUQtQ0V/6KUqHdPA+//xvu3oCfH4Ubp0q6REopYs4a/zJgYM6FQoj6QD/URC6KYh6p9+CXCWBbGab8CfZO8OMoiL1c8GuVCsFsgV9KuReIzWXVZ8CrqJ5CimIem16F6GAYuQjc28ET6yAjFX4YDglRJV06pRSwaBu/ECIAuCalPGnEttOEEIFCiMDo6GgLlE5RyoGgFRD0I3R/BRr30Za5esO4NXA3Wqv5J90p2TIqJc5igV8IURl4C3jXmO2llIuklO2klO1cXV3NWzhFKQ+ig2HjS+DRFXq8fv8693Yw9geIPg8rHoO0pJIpo1IqWLLG3whoCJwUQoQB7sBxIUQds+V46yJcP2G25BWl1EhNhNV6u/6o78A6l57ajfvAyG/g6iFYMwky0i1fTqVUsFjgl1KeklLWklJ6Sik9gQjAX0p5w2yZ7vkEFvWElePgxgOdixSl/Ng8U6vNj1wEVermvZ3PKBj8KQRvgt9naP38lQrHnN05VwCHAG8hRIQQYrK58srTkDnQ8024vA8WdtFqRDfPW7wYimJWQSvgRI52/fx0mKo1BQX9CNuNanlVyhkhy8Avfrt27WRgYGDRE0i6DYe+hMNfa13dWo2GHq9BzSamK2RO6SkQshPSk6HFcHX3ZFFEX4CD8yEjDWQmZGaAzNAfZ2r/s5/r/+97nAGOrjDwI6juWdLvxjyig7WzWjd/eHJ97k08uZESNs2Eo99Cv/+DLjPMWkylZAghjkkp2z2wvEIE/iz3YuDgAjiySAvIrR+FHjPBxUSDj6anQuguOLMOzm+EFH14o+YBMPxrrT+1YryV4+DiNnCuC1bWIKxA6P+trLUf0/ue57Lu2nGwsoJRi42rDZclqYnwbW+4Fw3P7M+/iSc3mZmwdjKc+RUe/hLajDdPOSuqqLOwbzb0egtqNCqRIqjAb+huNByYB0e/02qTbcZB95lQrUHh08pIg9DderD/A5LjwKEqNBsGLUdA9DntdNq1GTz6k+l+ZMq72FBY4A/dXoY+7xQ9nZgQWDVea//u/Q50fbH8nH2t/xec+AnGry36j1p6KqwYqx3DY3+EZkNMWsQK6+ph+PkRLR64NoMpO8De2eLFUIE/Nwk3YN9cOLZUO/X1fwK6vQJV6+X/uow0uLxXC/bnfofkO2BfVfvStBwBXj3Bxu6f7UN2wZqntOaH0UugcV/Tv5fyZtOrELgEXjwNzsXs+JVyjvra8AAAFhZJREFUFzY8p+2vFg9rtdsS+BKaVNAK+O0ZrcLS++3ipZVyF74P0DpAPPEreHY1TRkrqgtbteuJVetB15e0Y6/ZUHjke4tXOlTgz0/cNdg3B47rO6btU9DtpfsDTkY6hO37J9gnxYKdMzQbrAX7Rr3Bxj7vPG6HaU0XN89Cn3ehywvlp+Zpakl3YG4LaBEAIxaaJk0p4eDnsOM9qNkUxv4ENRubJm1Ly27XbwNPbjC+XT8/92Jg6UCtMjTxD6jrW/w0K6KgFdqZWN3W2k1zjjW1427b29r3vtvLFi2OCvzGuH1Fa5M78RNY20L7KeDVS2vCObcBEmPAzgm8B+nBvg/YOhiffuo9WP+c1qbacoRW87RzNN/7KasOLIDt78DT+7QvkCmF7oZfnoLMdBj5LXg/MJxU6Vbcdv38xEXA4gGQkQKTtpZYu3SZlRXgG/bQmnWzziqlhLVT4PRaGPcLNOlnsSKpwF8YsaGw51P4e6XWPGNbGZoOBJ+RWjONbaWipy0lHJgPf74Prs31dv+Gpit7WZeRDvN9tc9k4h/myePOVa3dP/Kk1q2xx2vaBeCywBTt+vmJvgBLBmhBa/K24jezVQRSameSB+ZrPfhGLnrw7D81ERb3h7irMHWXxX5UVeAvipgQiLkEnt3ArrJp0770p3b3JMCYpVpTUWmTmqg1UdVuYbk8T6/VPpfHVmpnVuaSlgR/vAgnV2g/6iMXaRflS7Osdv1urxTvgndBIo7B8mH6j+9GqFTNfHmVdRnp2o1wQT9Cu8nazXFWecxhdTtMa6JzqqNf7DV/L7+8An8ZqeaUkBqNoOkA0wd90Gpr03ZBlXrawFkH5pe+uyi3vQ0Lu1rupjcptfstXBpBkwHmzcu2ktbFdtCncGkHLOoFN8+ZN8/iyB6Hpwv0fMO8ebm3hUd/1PJc8ajWM0V5UFoSrH5CC/o9XtduGM0r6IN2L8nopXArGNY/W6LfdxX4S5KLl3Y63XyY1uVz7WTtOkBpkBCl3Q0qM7TTWEsIPwLXjkGn6ZZpehECOk6DCb9DSgJ82wfO/Gb+fAsrNRF+maiPw7PYNBdzC9Kot3YWFHFU+1yiL5g/z7Ik6Q78MBKCN8Pg2dDrDeM6azTqBX3fh7PrYf9n5i9nHlTgL2n2TjBmOfR5D07/ql1cux1W0qWCvxZqY7j7T4ALWyB0j/nzPPwlOFQDv8fNn5chj4fg6T1ak9YvE2D7e9rdv6XF5le1s5GCxuExNZ+RWq+h5DvaBeVzZrrmUtYk3IBlQ7QfxdGLtSEwCuOh57Uxk/78P7i4wzxlLIAK/KWBEFr30XFrtIs/i3pqff9LSnI8HF0MzYfCoE+gagPY9pZ2p6e53L6idZNtO7FkejpVcdPas9s+pd3c99NoSMxtHiELO7kSTvygdQMsiTuPPbvAtD3a8CarxsHO/5r3ODCUcAP+WgQXt/9/e+ceJUV5JfDflRGQ99sFBkQi4qIIAvJQVFyRh1EwiboqR4jIIYZHJLueaGLi8bUbXRM1uiIiqCQaxWCMbgQBNUY0DDjAIKAgEBF5OIggyJth7v5xa6Rpumd6pru6p6fv75w+VV31VX23v6669dX33YeZm1YHvlpvk7Q7PoURL5kCrywiMOwxOPlMeHm0GZOkGZ/crW58td7s/bevsRgq/Sak396/zJxyzNs23rtilg1DXTkFul8XTp1v/AIWPwm3fFixA13YLJkBs281i5arZ5hNe3ljt8lSctDMM/cUm1f5nmLYuw32bLPhtlTa61eVwwfg9f+08exOg+3tI6xJ3yMlFkPob/99NOwJQJNToG0PaNvTYhO17pbeMChbl9t8nJaaWWbbnsmdb8en1slr1AZumh/Kb3Grnmzi4Dfwlx9bD7jb9XDl5PQp/5KDZk7Z/LSj5pSlpTDtElNIEwpTP9l9YLc5bHUeYrHkqwObCmHmDfDNFvteu4GZONZpCHUaHb9eN3JbxPZaJ1pvde+2KMVepui32VBKLOo2hhad4ZoZphwyjSoUToc5t5kSvvaP0OqM1NaxcZE9YIpXmJ/MpXfb5PLmJRZ3afNSeysGi8vU8gx7GLQJHggnn2ltnmo+fRdeuN4edje8kroAj+vftodJl+E28Zvi+9wVf7aharb+7z1sN1i6Yqgs/YO5mI94GTpFhJbY8D48e5nFu7nw1tTWuXAyzP252Te37ZHacyfDni/NvHT/TnsYH9wdLL+J/T2RNNK1G0KDVkc/9VtBg5OhQUtb1i/b3rJyzoHp5LOF8NJIOLzPLKO6DEv+nHu3mxHBsufM0m3Iry24YSxFuOdL2BI8BDYvsfV9wVBQrTrm9Ff2IGjdzdqybuOqvzF99Jq98Tb7joW0SPVD+L1H7LcPvBv6T0rpqV3xZyNHDsMT59mr5biCcHoykZSWwuO9Ia8u3Lzg+JvuxRHm+fqTZaacUlLnEXi0u93so99IzTkzQWkpHN5rby+RD4aSg+a2X6bkwzANzgS7t9gb0eZCm3+4+I6qDYeVlsLSZ+HNu+HQHug7zhzqKjPsoQpffxbxIFgGW4rs/4ikdgN7ANRtEiwbWw++bD3Wvo0FNrnethdcPxPqNav8b0xE/lk3mqXPiFkpnctxxZ+trJljttRDHzTTwzBZ/Tq8eD18fxqcffXx+7evg8l9zNLn8odSU+dHr1rv8d+fM7NWJ3soOWhzIUt/bx7tP5gGJzVN/Pgty2xYZ/MSyxP83d9Aq39NjWylR8wPoXilvbEd2GUmmAd22dBa5HL/LjhYjq9Cp0E21xPmQ/vQXph2KezeDGPfSZk3vyv+bEXVvCiLV8EtReF5l6qatcKeL2DisvivxbN/ZuGsxy2Elp2Tr3f6IBvrnrg03AlUJzwKn7GkLo3zbViyIk/v/Tvh7fvMcqx+Sxj8X9D16swGLSw9Ym9pxzwYdtl90Xlo+G/bYNY9Uy+2t98x81Ni3eaeu9mKCAy6z6KBLkhRLzsWGxfCpsXQb2L5Y6EX3WYX5PwUOHVtWgKfL4I+N7vSz2Z63WimsIf3wbSBFsE2FqpQ9Ed4rJeF3O7zI5hYCGdfk/lItSfUsqGdpqfYvMCpF9obaJdh6VH6YA6dV023HB6vTgjVs9cVfzbQprtlCyt4wgKMhcF7j0C95hVnYarf3MZ0P5ljlg7JUPC4Wb945qfsp30fs/c/+UzzMo52giteBc8MNWu1ZqfacMbQB6p/fKR0c9pAC9+86s+WLTAkXPFnC5f8ynpFb92T+nMXfwRr50LvHyU2jtnnZmjczmL5VNWZ5+vPLTxCj5HZnxTFMRq1tp5/r9FHneC+3ghz74ApF9iY+7DHYPQ8j/dfHudPsiifb95l5p4h4Io/W2icD/3Gw4o/2WRYKnn/dxYHJlHX8xPrWoiJrctNnqqweCqg9rrv1BzyasPlD8MVj8KG9+CRrhZ4r8cNMHGJPeizJQR2phCxXB0tz7BItSGEcPF/IJs4fxLUawHzfpW68b+vP4eVs8xSpzKmamf9wDxK37rHohRWhoN7zDu2y/Cq5Tl2qj89R8GNc2zSdsybcMXvwjGFrKnUaWC5Ouo1Nye/FOOKP5uo28iiAH72PqyZnZpzLnzclv3GV+64E06wSefdm6BgcuWOLXrezOf6VrJOJ7vI72UmnvnHGZU4idCsI4xfDO16p/zUrvizjR4/tJyx8+80B69k2LcDls6As66CJu0qf3yH/tD5u7DgYfOmTITSIzZJnX8utDu38nU6Ti4RkrWbK/5so1YeXHqvZQYrfCa5cy1+ykzwzr+l6ue49G47x9/vT6z8J2/Azk8r/4bhOE7KcMWfjZw+2NJBvvPrqmdHOrTPomF2GpxcasUWncyKo/CZxJJ1LHzcwjyf4V66jpMpXPFnIyLm7bh/Z9WdupY9Z4GtUhEUasDtZhVUUaauLUU2P9FnbGZDDDtOjuOKP1tp3Q26BU5dOz+r3LFHSmDhY5DfG9r3S16W+i0skcya2fDpgvjlCiZboKweI5Ov03GcKuOKP5v5t19a7//teyt33KpXzLGm/6TUucr3/TE0yo/v1LV7i4U4PucG99Z0nAzjij+bqYpTl6o5bLXoDKcPTZ0sJ55kruZbi8wvIJrFT1l4aXfYcpyM44o/2+n/U4twmKhT17q3LLvR+T9JvQdl16uhdffjnboO7YUlz1gymRSFm3Ucp+q44s926jSEAYFT1+rXKy7//iPQsA10vSb1spQ5de36HBZNObp9+Qs2Ee0OW45TLXDFXxPoMcqGbipy6tq0BDYsgH7jLKZKGJx6AXS+zKyN9m638f6CJywVXvu+4dTpOE6lcMVfE6iVB4PuhR3ry3fqev9hm1jt+cNw5Rl4tw3vvHM/rJtvzmb9xmc+5rrjOIAr/ppDp0GWPCKeU9f2tfDxX+HcMeGHQW55uiXnKHzacqk2amsB2RzHqRa44q8pfJupaycs+O3x+//xKNSqbbH008FFgVPXtlXQe2z6shg5jlMhrvhrEq27QbfroGDKsU5d33wBy1+Ec0ZAg1bpkaVBS4skWq+Fheh1HKfa4Iq/phHLqatgMpSWwHkT0ytLv/Fw61o4qWl663Ucp1xc8dc0GreFfhOOOnUd2GUTvl2GW3zvdOPZlhyn2hHaXSkiT4vINhFZGbHtQRFZLSIfisgrItIkrPpzmv6TzKlr7i9tgvXgbsve5TiOQ7g9/meBIVHb5gNnqerZwCfAz0OsP3ep0xAu/gVs/IeZVHYcAG26Z1oqx3GqCaEpflV9F9gRtW2eqpYEXwuA/LDqz3nOGWlOXSUHvLfvOM4xZDIo+mhgZrydIjIWGAvQvr0n5K40tfLgyics41XHAZmWxnGcakRGFL+I3AGUAM/HK6OqU4GpAL169Uog+phzHPk97eM4jhNB2hW/iIwCLgcuUU0knKTjOI6TStKq+EVkCHAbcJGq7ktn3Y7jOI4RpjnnC8BCoLOIbBKRm4D/BRoC80WkSESmlHsSx3EcJ+WE1uNX1etibJ4eVn2O4zhOYrhbpeM4To7hit9xHCfHcMXvOI6TY7jidxzHyTEkG0zpReRL4LMKC8amBbA9heKkGpcvOVy+5HD5kqc6y3iKqraM3pgVij8ZRKRQVXtlWo54uHzJ4fIlh8uXPNkgYzQ+1OM4jpNjuOJ3HMfJMXJB8U/NtAAV4PIlh8uXHC5f8mSDjMdQ48f4HcdxnGPJhR6/4ziOE4ErfsdxnByjxih+ERkiImtEZJ2I3B5jfx0RmRnsXyQiHdIoWzsR+ZuIfCwiq0TklhhlBojIriBqaZGI3Jku+YL6N4jIiqDuwhj7RUQeDdrvQxHpkUbZOke0S5GI7BaRSVFl0tp+IvK0iGwTkZUR25qJyHwRWRssm8Y5dlRQZm2QnyJd8j0oIquD/+8VEWkS59hyr4UQ5btLRDZH/IeXxTm23Hs9RPlmRsi2QUSK4hwbevsljapm/QeoBawHOgK1geVAl6gy44Apwfq1wMw0ytca6BGsN8QSzUfLNwD4awbbcAPQopz9lwFzAAH6Aosy+F9/gTmmZKz9gAuBHsDKiG3/A9werN8OPBDjuGbAP4Nl02C9aZrkGwTkBesPxJIvkWshRPnuAm5N4P8v914PS76o/b8F7sxU+yX7qSk9/t7AOlX9p6oeAl4EhkeVGQ7MCNZnAZeIiKRDOFXdqqpLg/VvgI+BtumoO4UMB36vRgHQRERaZ0COS4D1qlpVT+6UoKrvAjuiNkdeYzOAK2McOhiYr6o7VHUnMB8Ykg75VHWeqpYEXwuA/FTXmyhx2i8RErnXk6Y8+QK9cQ3wQqrrTRc1RfG3BT6P+L6J4xXrt2WCi38X0Dwt0kUQDDGdAyyKsbufiCwXkTkicmZaBQMF5onIkiDRfTSJtHE6uJb4N1wm2w/gZFXdCvawB1rFKFNd2nE09gYXi4quhTCZEAxFPR1nqKw6tN8FQLGqro2zP5PtlxA1RfHH6rlH26kmUiZURKQB8DIwSVV3R+1eig1fdAMeA/6STtmA81W1BzAUGC8iF0btrw7tVxsYBvwpxu5Mt1+iVId2vAMoAZ6PU6SiayEsngC+A3QHtmLDKdFkvP2A6yi/t5+p9kuYmqL4NwHtIr7nA1vilRGRPKAxVXvVrBIiciKm9J9X1T9H71fV3aq6J1ifDZwoIi3SJZ+qbgmW24BXsFfqSBJp47AZCixV1eLoHZluv4DisuGvYLktRpmMtmMwmXw5MEKDAeloErgWQkFVi1X1iKqWAk/FqTfT7ZcHfB+YGa9MptqvMtQUxf8B0ElETg16hdcCr0WVeQ0os6C4Cng73oWfaoIxwenAx6r6UJwy/1I25yAivbH/5qs0yVdfRBqWrWOTgCujir0GjAyse/oCu8qGNdJI3J5WJtsvgshrbBTwaowyc4FBItI0GMoYFGwLHREZAtwGDFPVfXHKJHIthCVf5JzR9+LUm8i9HiYDgdWquinWzky2X6XI9Oxyqj6Y1ckn2Iz/HcG2e7CLHKAuNkSwDlgMdEyjbP2x19EPgaLgcxlwM3BzUGYCsAqzUigAzkujfB2DepcHMpS1X6R8AjwetO8KoFea/996mCJvHLEtY+2HPYC2AoexXuhN2JzRW8DaYNksKNsLmBZx7OjgOlwH3JhG+dZh4+Nl12CZlVsbYHZ510Ka5PtDcG19iCnz1tHyBd+Pu9fTIV+w/dmyay6ibNrbL9mPh2xwHMfJMWrKUI/jOI6TIK74HcdxcgxX/I7jODmGK37HcZwcwxW/4zhOjuGK38lqRKR5RMTEL6KiO9aOKju3zMa6nPNtihe1MgxE5DkRiRXTx3FCIy/TAjhOMqjqV5iLPyJyF7BHVX8TWSZw7BJVHZx+CcNDRPL0aNA1x0kY7/E7NRIROU1EVorIFCyOT+vI3ryI/F8QRGuViIyp4Fx5IvK1iNwfBIFbKCKtgn3H9NhFZE+wHCiWg2GWWNz9+0RkpIh8EAQh6xBRxWARWSAin4jI0Ig6HxKRxUH5MRHnfVNEXgSWpa7FnFzCFb9Tk+kCTFfVc1R1c9S+UaraEzgX+I84kSAjaQz8XS0I3ELM+7YiugHjga7AGKCDqp6LhWyeEFGuHXARcAUwVUTqAGOBbaraO5BxvIi0D8r3BX6mql0TkMFxjsMVv1OTWa+qH8TZ91MRWY4p8XwsKmR57FfVsjDGS4AOCdS/SC3w2AEs4UpZTJ4VUce/pKqlqroGC6nQCYvxcqNYlqdFQJNgO8BCVd2YQP2OExMf43dqMntjbRSRgViGpb6qul9E3sNiOZXHoYj1Ixy9d0oIOlAiUotj76mDEeulEd9Lo8pFx01RLDbSOFV9K4bsMX+X4ySK9/idXKQxsCNQ+mdiQylVZQPQM1j/HpYasLJcHUQ9PR0b9lmLvR2MC8IAl+UdPikJOR3nW7zH7+QirwNjg6Ge1cTOhpYoTwKvisilwDyO7eUnyjrgXSxj11hVPSQiTwLtgaIg2vQ2Qkgx6OQmHp3TcRwnx/ChHsdxnBzDFb/jOE6O4YrfcRwnx3DF7ziOk2O44nccx8kxXPE7juPkGK74Hcdxcoz/B44fEBYg11FbAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "n_trials = 20\n",
    "best_basic = []\n",
    "best_two_opt = []\n",
    "for _ in range(n_trials):\n",
    "    tsp = TravelSalesPerson(n_cities=100)\n",
    "    tabu_basic = Tabu_TSP(tsp, tabu_tenure=5, n_neighbors=20)\n",
    "    tabu_2opt = Tabu_TSP(tsp, tabu_tenure=5, n_neighbors=20, neighbour_method='two_opt')\n",
    "    init_sl = np.arange(tsp.n_cities, dtype=np.int)\n",
    "    np.random.shuffle(init_sl[1:])\n",
    "    _, basic_cost = tabu_basic.run(initial_solution=init_sl)\n",
    "    _, opt_cost = tabu_2opt.run(initial_solution=init_sl)\n",
    "    best_basic.append(basic_cost)\n",
    "    best_two_opt.append(opt_cost)\n",
    "\n",
    "plt.plot(np.arange(n_trials), best_basic)\n",
    "plt.plot(np.arange(n_trials), best_two_opt)\n",
    "plt.legend(['Basic Strategy', '2-opt Strategy'])\n",
    "plt.title('Comparison of neighbourhood selection strategies')\n",
    "plt.xlabel('Trial number')\n",
    "plt.ylabel('Final solution cost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Capacitated) Vehicle Routing Problem\n",
    "The [Vehicle Routing Problem](https://en.wikipedia.org/wiki/Vehicle_routing_problem)(VRP) is a generalization of the Travelling Salesman Problem which asks the question: \"What is the optimal set of routes for a fleet of vehicles to traverse in order to deliver to a given set of customers?\" The Capacitated VRP is a variant of the VRP where each vehicles can only carry a maximum capacity of $Q$. The problem can be formally defined using a graph $G = (V,E)$ with a set of vertices $V$ and edges $E$. Vertex $v_0\\in V$ is the **depot**, or the source of goods to be delivered. All other vertices $v_{i\\neq 0}\\in V$ are the customers to which the goods need to be delivered. Each $v_{i\\neq 0}$ is associated with a $q_i$ which is the quantity of the goods requested by the $i$th customer. The edges $e_{ij}\\in E$ is weighted with the cost $c_{ij}$ of travelling between the two vertices. The optimal set of routes has the following characteristics:\n",
    "* The start and end of each route is the depot vertex.\n",
    "* Each customer is visited exactly once by only one of the routes.\n",
    "* The total number of goods carried by the vehicles does not exceed $Q$.\n",
    "* The total cost of transporting the goods is minimized.\n",
    "\n",
    "The implementation below is adapted (with some trivial changes) from [afurculita's solution in Java](https://github.com/afurculita/VehicleRoutingProblem). The original code implements a Greedy search algorithm as well, which is then used as an intializatin for the Tabu search. I also added a random initialization to see how much benefit does doing the greedy search first accrues. This is importatnt because even doing the greedy search takes some computation. In the random initialization, beginning with the first vehicle, random customers are added until no more customers can be added due to the quantity constraint. This is continued until all customers are catered to.\n",
    "\n",
    "The Tabu search takes place as follows:\n",
    "\n",
    "* **Neighborhood definition:** For every pair of vehicles $v_a$ and $v_b$, and for all node pairs $(n_i, n_j) s.t. n_i\\in v_a, n_j\\in v_b$, we remove the node $n_i$ from $v_a$ and insert it after $n_j$ in $v_b$. Therfore, after the change we have:\n",
    "\n",
    "  $v_a: \\ldots\\to n_{i-1}\\to n_{i+1}\\to\\ldots$\n",
    "\n",
    "  $v_b: \\ldots\\to n_{j-1}\\to n_j \\to n_i \\to n_{j+1} \\to\\ldots$\n",
    "* **Tabu Constraint:** A move that fulfills even one of the following conditions is considered Tabu:\n",
    "\n",
    "  $T[n_{i-1}\\to n_{i+1}] > 0$\n",
    "\n",
    "  $T[n_j\\to n_i] > 0$\n",
    "\n",
    "  $T[n_i\\to n_{j+1}] > 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from third_party.VRP.read_vrp import read_input_cvrp # to read CVRP problem files\n",
    "from third_party.VRP.cvrp_afurculita import Node, Vehicle, Greedy_VRP\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "greedy_vrp = Greedy_VRP('data\\VRP\\A-n32-k5.vrp', n_vehicles=10)\n",
    "greedy_vrp.run()\n",
    "greedy_vrp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tabu_VRP():\n",
    "    def __init__(self, vrp_file, n_vehicles=None, tabu_tenure=5, initialize=None, max_iter_without_improvement=20):\n",
    "        '''\n",
    "        Class responsible for running Tabu search on a Capacitated Vehicle Rouitnig Problem.\n",
    "        :param vrp_file: Path to .vrp file that describes the CVRP problem\n",
    "        :param n_vehicles: maximum number of vehicles avaiable. Default n_vehciles=number of customers (upper bound)\n",
    "        :param tabu_tenure: no. of moves for which move is tabu (a random int between [0,5) is added for every element)\n",
    "        :param initialize: Method used to initialize solution. One of ['random', 'greedy']. If greedy, greedy optimization is run first\n",
    "        :param max_iter_without_improvement: stopping criteria. If no improvement after these many turns, algorithm stops.\n",
    "        :returns vehicles: list of class Vehicle indicating the optimal solution found\n",
    "        :returns cost: minimum cost of the optimal solution found\n",
    "        '''\n",
    "        self.n_cust, self.cust_locations, Q, self.dist_matrix, self.dist_warehouse, demands = read_input_cvrp(vrp_file)\n",
    "        if n_vehicles is None: n_vehicles = self.n_cust\n",
    "        self.vehicles = [Vehicle(Q) for _ in range(n_vehicles)]\n",
    "        self.nodes = [Node(i, demands[i]) for i in range(self.n_cust)]\n",
    "        self.max_iter = max_iter_without_improvement\n",
    "        self.tt = tabu_tenure\n",
    "        self.finished = False\n",
    "\n",
    "        if initialize in [None, 'random']:\n",
    "            self.random_init()\n",
    "        elif initialize is 'greedy':\n",
    "            greedy_vrp = Greedy_VRP(vrp_file, n_vehicles)\n",
    "            self.vehicles, self.cost = greedy_vrp.run()\n",
    "            self.n_vehicles = greedy_vrp.n_vehicles\n",
    "        else:\n",
    "            raise Exception(\"initialize must be one of ['random', 'greedy']\")\n",
    "\n",
    "        self.best_sol = [Vehicle(Q) for _ in range(n_vehicles)]\n",
    "        self.updateBestSolution()\n",
    "\n",
    "    def updateBestSolution(self):\n",
    "        '''\n",
    "        Takes the current value of self.vehicles and copies it int self.best_sol\n",
    "        '''\n",
    "        self.best_cost = self.cost\n",
    "        for v_id in range(self.n_vehicles):\n",
    "            self.best_sol[v_id].nodes = []\n",
    "            n_nodes = len(self.vehicles[v_id].nodes)\n",
    "            if n_nodes != 0:\n",
    "                for i in range(n_nodes):\n",
    "                    self.best_sol[v_id].addNode(self.vehicles[v_id].nodes[i])\n",
    "        return\n",
    "\n",
    "    def random_init(self):\n",
    "        '''\n",
    "        Randomly assigns customers to a vehicle until vehicle cannot accept anymore due to quantity constraint\n",
    "        '''\n",
    "        cust_assigned = np.zeros(self.n_cust, dtype=bool)\n",
    "        vehicle_id = 0\n",
    "        self.cost = 0\n",
    "        while not all(cust_assigned):\n",
    "            node_id = np.random.choice(self.n_cust, p=(~cust_assigned/sum(~cust_assigned)))\n",
    "            if len(self.vehicles[vehicle_id].nodes) == 0:\n",
    "                self.vehicles[vehicle_id].addNode(self.nodes[0])\n",
    "                cust_assigned[0] = True\n",
    "\n",
    "            if self.vehicles[vehicle_id].checkConstraint(self.nodes[node_id].demand):\n",
    "                self.cost += self.dist_matrix[self.vehicles[vehicle_id].nodes[-1].id, node_id]\n",
    "                self.vehicles[vehicle_id].addNode(self.nodes[node_id])\n",
    "                cust_assigned[node_id] = True\n",
    "            else:\n",
    "                self.cost += self.dist_matrix[self.vehicles[vehicle_id].nodes[-1].id, 0]\n",
    "                self.vehicles[vehicle_id].addNode(self.nodes[0])\n",
    "                vehicle_id += 1\n",
    "                if vehicle_id >= len(self.vehicles):\n",
    "                    raise Exception(\"Could not randomly initialize problem. Ran out of vehicles, with customers still remaining...\")\n",
    "\n",
    "        self.cost += self.dist_matrix[self.vehicles[vehicle_id].nodes[-1].id, 0]\n",
    "        self.vehicles[vehicle_id].addNode(self.nodes[0])\n",
    "        self.n_vehicles = vehicle_id+1\n",
    "        return\n",
    "\n",
    "    def run(self):\n",
    "        '''\n",
    "        Raun the Tabu search algorithm\n",
    "        '''\n",
    "        nodesFrom, nodesTo = [], []\n",
    "        movingNodeDemand = 0\n",
    "        swapIdA, swapIdB, swapNodeFrom, swapNodeTo = -1, -1, -1, -1\n",
    "        n_iter = 0\n",
    "        tabu_matrix = np.zeros((self.n_cust+1, self.n_cust+1), dtype=np.int)\n",
    "\n",
    "        while n_iter < self.max_iter:\n",
    "            bestNCost = math.inf\n",
    "            ##SEARCH FOR BEST MOVE\n",
    "            for vIdFrom in range(len(self.vehicles)):\n",
    "                nodesFrom = self.vehicles[vIdFrom].nodes\n",
    "                n_nodesFrom = len(nodesFrom)\n",
    "                for i in range(1, n_nodesFrom-1):   # We cannot move the depot (1st and last)\n",
    "                    for vIdTo in range(len(self.vehicles)):\n",
    "                        nodesTo = self.vehicles[vIdTo].nodes\n",
    "                        n_nodesTo = len(nodesTo)\n",
    "                        for j in range(0, n_nodesTo-1): # We cannot insert after last depot\n",
    "                            movingNodeDemand = nodesFrom[i].demand\n",
    "\n",
    "                            if vIdFrom==vIdTo or self.vehicles[vIdTo].checkConstraint(movingNodeDemand):\n",
    "                                # If moving within the same vehicle, no need to check for constraint\n",
    "                                # If moving between different vehicles, need to check for constraint\n",
    "                                if not (vIdFrom == vIdTo and (j==i or j==i-1)):\n",
    "                                    # Only continue for moves that change the overall cost\n",
    "                                    #Check if the move is Tabu:\n",
    "                                    if any(tabu_matrix[[nodesFrom[i-1].id, nodesTo[j].id, nodesFrom[i].id], [nodesFrom[i+1].id, nodesFrom[i].id, nodesTo[j+1].id]] != 0): break\n",
    "                                    \n",
    "                                    # below calculates the cost associated with the following changes:\n",
    "                                    # vIdFrom: ... -> i-1 -> i+1 -> ...\n",
    "                                    # vIdTo: ... -> j-1 -> j -> i -> j+1 -> ...\n",
    "                                    minus = self.dist_matrix[[nodesFrom[i-1].id, nodesFrom[i].id, nodesTo[j].id], [nodesFrom[i].id, nodesFrom[i+1].id, nodesTo[j+1].id]]\n",
    "                                    add = self.dist_matrix[[nodesFrom[i-1].id, nodesTo[j].id, nodesFrom[i].id], [nodesFrom[i+1].id, nodesFrom[i].id, nodesTo[j+1].id]]\n",
    "                                    neighbor_cost = sum(add) - sum(minus)\n",
    "                                    if neighbor_cost < bestNCost:\n",
    "                                        bestNCost = neighbor_cost\n",
    "                                        swapIdA, swapIdB = i, j\n",
    "                                        swapNodeFrom, swapNodeTo = vIdFrom, vIdTo\n",
    "            \n",
    "            ## UPDATE TABU MATRIX\n",
    "            tabu_matrix[tabu_matrix>0] -= 1\n",
    "            nodesFrom = self.vehicles[swapNodeFrom].nodes\n",
    "            nodesTo = self.vehicles[swapNodeTo].nodes\n",
    "\n",
    "            swapNode = nodesFrom[swapIdA]\n",
    "            tabu_matrix[[nodesFrom[swapIdA-1].id, swapNode.id, nodesTo[swapIdB].id], [swapNode.id, nodesFrom[swapIdA+1].id, nodesTo[swapIdB+1].id]] = self.tt + np.random.randint(0, 5, size=3)\n",
    "            nodesFrom.pop(swapIdA)\n",
    "\n",
    "            ## UPDATE VEHICLES\n",
    "            if swapNodeFrom == swapNodeTo:\n",
    "                if swapIdA < swapIdB:\n",
    "                    nodesTo.insert(swapIdB, swapNode)\n",
    "                else:\n",
    "                    nodesTo.insert(swapIdB+1, swapNode)\n",
    "            else:\n",
    "                nodesTo.insert(swapIdB+1, swapNode)\n",
    "\n",
    "            self.vehicles[swapNodeFrom].nodes = nodesFrom\n",
    "            self.vehicles[swapNodeFrom].load -= movingNodeDemand\n",
    "            self.vehicles[swapNodeTo].nodes = nodesTo\n",
    "            self.vehicles[swapNodeTo].load += movingNodeDemand\n",
    "\n",
    "            ## UPDATE SOLUTION\n",
    "            self.cost += bestNCost\n",
    "            if self.cost < self.best_cost:\n",
    "                n_iter = 0\n",
    "                self.updateBestSolution()\n",
    "            else:\n",
    "                n_iter += 1\n",
    "\n",
    "        self.vehicles = self.best_sol\n",
    "        self.cost = self.best_cost\n",
    "        self.finished = True\n",
    "        return self.vehicles, self.cost\n",
    "\n",
    "\n",
    "    def __str__(self):\n",
    "        if not self.finished:\n",
    "            print(\"Optimization not run yet. Run by calling Greedy_VRP.run(). Showing initial solution\")\n",
    "        string = \"=================================================================\\n\"\n",
    "        for v_id in range(len(self.vehicles)):\n",
    "            if len(self.vehicles[v_id].nodes) != 0:\n",
    "                string += \"Vehicle {}:\\n\\t\".format(v_id)\n",
    "                n_nodes = len(self.vehicles[v_id].nodes)\n",
    "                for i in range(n_nodes):\n",
    "                    if i == n_nodes - 1:\n",
    "                        string += str(self.vehicles[v_id].nodes[i].id)\n",
    "                    else:\n",
    "                        string += '{}->'.format(self.vehicles[v_id].nodes[i].id)\n",
    "                string += '\\n'\n",
    "\n",
    "        string += 'Best Cost: {}\\n'.format(self.cost)\n",
    "        return string\n",
    "\n",
    "    def plot(self):\n",
    "        '''\n",
    "        Plot the custmer locations and paths of vehicles\n",
    "        '''\n",
    "        plt.scatter(self.cust_locations[0, 0], self.cust_locations[0, 1], marker='*', c='r')\n",
    "        plt.scatter(self.cust_locations[1:, 0], self.cust_locations[1:, 1], c='b')\n",
    "        if not self.finished:\n",
    "            print(\"Optimization not yet run. Run by calling Greedy_VRP.run(). Showing initial solution\")\n",
    "        cmap = plt.cm.get_cmap('hsv', self.n_vehicles+1)\n",
    "        for v_id in range(len(self.vehicles)):\n",
    "            if len(self.vehicles[v_id].nodes) != 0:\n",
    "                c = np.random.rand(3,)\n",
    "                node_ids = [node.id for node in self.vehicles[v_id].nodes]\n",
    "                lx, ly = self.cust_locations[node_ids, 0], self.cust_locations[node_ids, 1]\n",
    "                plt.plot(lx.T, ly.T, c=cmap(v_id))\n",
    "        plt.text(-0.05, -0.05, \"Total distance=%.2f\" % self.cost, fontdict={'size': 10, 'color': 'red'})\n",
    "        plt.legend(['Vehicle {}'.format(i+1) for i in range(self.n_vehicles)]+['Depot', 'Customers'])\n",
    "        if not self.finished:\n",
    "            plt.title('Initial solution to Tabu Search for CVRP')\n",
    "        else:\n",
    "            plt.title('Tabu Search for CVRP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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++rJjXRrKO8qmtHNa32ZCsZKQ5RwHquBMAK40w5M+Nqu7vk2J18MBD7vIlj5pOmUKnDkDdesaCt/Y74QnvfGkN1YSieUHollOJO8TwTu4cDPejMCb4bjQyC7yaDSF4YZX7mlcREjBhcZlSukVFoVC4W6zSmsiWLGSkGHZmzkPnAdMtth6T5u7p+Qn1C3EZHOVZFXiVmKztDfhhTP1caExnvTAmfo2y9vY0t1U14MRI/KPjDHhjjdD8WYoaVwhhrVEs5yLTOMi03CjIz4E4cVQnGxRWBpNSXNDK3crKbZJVB8c8CptceyKsilxBzxxwh8hLcNfb0zOxmAGzFzhNG/gSXc86I4z9QAjDDB3izUnFuIyKe3QHC4UC9FZ2pvwyFDYleiaobTTre/SmtC2B45UwZdx+DKOVM4SwyqiWE44EwnnWTzohjdBeDGowv2b05Qtbmjlfq1J1K5duzJ58uQscePz5s3j2LFjLFiwINf+QkND6devH//++2+OY9OmTeOOO+7IMzPk9u3bmTNnDuvWrSuQ7Pv372fChAnExsbi4ODAlClTGDp0aJ7tFY444gP4AEZ0kJVYFEkksJ0YjDh+Zxpx/lh3lv/YnajYuxCpwqXLCbz17mk8/UK59a6c1reFyGxjuWco60rcmvHdKcPyrnJD+KOdqUM1JlGNSSRzmGiWE81yzvEQYTxGZfrZIm76ZEziajT24oZV7haisRBtm0TNGV0yfPhwVq5cmUW5r1y5ktmzZxdpvNdff73IsuaGu7s7X375JY0bNyY8PJx27drRq1cvvL0LZvWacMZEVRypShPCSeEQ8Wwmjs24+S1j5vKPsVoV0ZG+VKkWkXFeOKBwzbC23eiQJdrEmfo4UPWGUN6FwZWm1OQNavA6SewiiuXEsJIYvsKEF17cjzdBeNAVRf6L4TSa/LghlbsxiXoWE655TqIOHjyYV155hZSUFFxcXAgNDSU8PJzbb78dgNmzZ7N69WpSUlIYNGgQr732GgAWi4VHH32U33//HX9/f7777jvc3NwYM2YM/fr1Y/DgwezevZunn36ahIQEXFxc2LJlS5axExISeOqpp/jnn39IS0tj+vTpDBgwIEubm266KeO7n58f1atX5/LlywVW7plRKFxpjivNqcrT3FzFTPMOu+jUfTPV/cIzYr3DQ+uz548AHKlerucnShNjZqQT7nTCj3eIZyvRLCeGNUSxEEdq4p0RcdNePyQ1RaZcKPdwJpKE/VL+Cqk4UY/afJKnkvL19aVjx45s3LiRAQMGsHLlSoYOHYpSik2bNhESEsKuXbsQEfr378+OHTuoW7cuISEhrFixgs8++4whQ4bw1VdfMXLkyIx+SyLl765du0hNTaVhw4Z2uT9+fk7s//029v9+W5b99eqBk11G0IDhKvOkJ570xJ+PiOVHogkmkgVEMA9nGuNNED4E4cJN+Xeo0WTihjO/jCiSFEy45ht1ke6aAcMlk548LHPK37Zt23LkyBFCQkIAqF+/Pq1btwagXbt2hIaGZunz6NGj1KpViw4dOgDFT/l7/vx5Ro0axaJFizCZ7PPnnDED3LOFgru7G/s1JYMJN7wZTADf0JQL1OZznKnDJV7nKDcTQnsu865tlbJGkz/lwnL3wz4pfwUhleNYicOF3GudZmbgwIE8++yzhU75m46Dg0NGJaYMGURQ6tqv2gVN+RsbG0vfvn158803ueWWW/K9noJy7fhuTUnjiA9VeJgqPIyZcKJZRTTLOc9znGcSHtyFN0FU5j7bJLlGk5MbynI3VqLG4IhfgZboe3h4FDvlb3aaNGlCeHg4u3fvBiAuLo60tLQsbXr16pVvyt/U1FQGDRrE6NGjeeCBBwo0dmEYMQJCQ8FqNT61Yi8dnPCjGs/QmN3cxBGqM41UznCORzhMTUIZRDRrsGZbpavR3DDK3cj+dwYTbjhSvcDnDR8+nAMHDjBs2LCMfT179iQoKIjOnTvTsmVLBg8enKMOal44OzuzatUqnnrqKQIDA+nRowfJyclZ2kydOhWz2UyrVq1o0aIFU6dOzdHP6tWr2bFjB4sXL6Z169a0bt2a/fYuRagpU7hyMzWZzs0coxG78eVxEvmTMwzhEDU4yxji2ISQln9nmgrPDZPy10wYZs7jws04oNO1pnOjp/wt7wgW4tlui7hZi5VYHKmOF0PxJgh3OumImwrMtVL+3hCWu5VkzFzAgSpasWsqFAoHPOlGHb6gGRepx1dUogtX+JQTdOYojbjAVJI5XNqiaq4zFV65C2KrrmQqU+l8NRp7Y8IVL+6jHmtpxkVqswhnGnKJtzhGM47RhkvMJpVzpS2q5jpQ4ZW7sRI11jaJqqO0NTcGDnhRhTE0YBNNCcOPeSicucALHKEuJ+hKJJ+SxpXSFlVTQlRo5W5Mop4t9CSqRlORcKImVXmaxvzFzYRQg9dI4wJhjOcwNTlFf6JZiZXE/DvTlBsqpHKPjISDB+Hs+fMIqSTF1NWTShoN4EIjajCVmzhMI/biy/+RxF7OMJxDVOcMo4hlQ47Sg5ryR4VT7pGRttJopiR8a1wkOsKXUyc8iYzM91SN5obByHHTFj/m0JQzNGAb3gwnjnWE0odD+BPGkyTwO3Idcv5r7E+FU+5hYWC1CrXqnsFqNXHpXG2sVmN/YejatSs//fRTln3z5s3j8ccfz/Oc0NBQWrTIfeXrtGnT2Lx5c57nbt++nX79+hVYvtOnT9OuXTtat25N8+bN+fjjjwt8rkaTGYUDHnSlNp/RlAvU41s8uIsrfMEJbuMIDTjPyySTM5W1puxSLtIPFIbUVHB1T6JS5TgunKlDWppTxv7CUNZT/taqVYvff/8dFxcX4uPjadGiBf3798fPz8+u42huLEy44MUAvBiAhThi+ZYogrnM21xmJq60xJsgvBmeUdhFUzYpluWulHpGKfWfUupfpdQKpZSrUqq+UuovpVSIUmqVUir/df52xNkZXN2MiaH4GK8s+wvD4MGDWbduHSkpKQC5pvzt0KEDrVq14tVXX804Lz3lb/PmzenZs2dGbpkxY8awdu1aAHbv3s2tt95KYGAgHTt2zLG6NSEhgbFjx9KhQwfatGnDd999l8t1OmfksUlJScFqtRbuAjWafHDAEx9G0YCNtoib+Zjw4AKTOUIAx+lCJB/Z6vVqyhpFttyVUv7A/wHNRCRJKbUaGAb0AeaKyEql1MfAw8BHxRFyIsvYz+kCtTU3Bauk4ChmEgMMt4oCXFyzpqttTT3mMTLXPqB8pPw9e/Ysffv25fjx48yePVtb7ZoSw4kaVOVJqvIkKZwkhpVEEUwYjxPG/+FJT7wZQWX6261AuaZ4FNfn7gi4KaUcAXeMCsx3A2ttx5cAA4s5RqFwcgJHRwtiNS7NpGyKvQgh7mU95W+dOnU4ePAgx48fZ8mSJVy8eLHwF6nRFBIXGlCdl7mJf2nMfqrxLMn8w1lGcIganCGIWNZhpZC+UI1dKbLlLiJhSqk5wBkgCdgE7AWiRSQ9c9E5wL+4Ql7Lws6NJNN+HPDG2TOgWOOW9ZS/6fj5+dG8eXN27tzJ4MGDC3SORlNcFAo3AnEjkJrMJIFfM6pKRbMCB6rgxQP4MAJ3btPVu64zRb7bSikfYABQH/ADKgH35NI01zgqpdQ4pdQepdSey5cvF1WMHFgxI6ShcMu/cT6U5ZS/586dy3hwREVF8dtvvxX4YaDR2BuFCQ/uoDYf05TzBPADnvQiii85wR0cIYDzvEgSB3Ro5XWiONEy3YFTInIZQCn1NXAr4K2UcrRZ77Uh99IxIvIp8CkYWSGLIUfWfm15rU12UO5guGbuu+++DPcMGCl/Dx8+TOfOnQHjIbBs2TIcHPIvbJw55W9SUhJubm45QiSnTp3KxIkTadWqFSJCQEAA69aty9Lm8OHDPPfccyilEBEmTZpEy5Yt7XDFGk3xMOFMZfpRmX5YiCeW74hmOZd5h8u8jQvN8GGELeKmfmmLW2EpcspfpVQnYCHQAcMtsxjYA9wBfJVpQvWgiCy4Vl/2TPlr5gJmzuFGIErnkskXnfJXc71I4zIxrCWK5STyKwDudLaFVg7RKUKKQImk/BWRvzAmTv8G/rH19SnwIvCsUuo44At8UdQxiiQXSSictGLXaMoYjlTDlwk0YidNOEVNZmIlnnCe4hB+nKQ3USzFQsEK32iuTbEWMYnIq8Cr2XafBDoWp9/iYCXJbi4ZjUZTMjgTQHVeojovkcQ/RLOCaJZzltEoXKlMf7wJwpPemHDJv0NNDirU9LUgWEmyy2SqRqO5PrjRklq8RRNO0pBfqcJY4tnKaQZymFqcYxzxbEewLdSLiYHmzY1PTZ5UMOWeAoi23DWacojCRCVuw58PaUY4AfyIJ32IZjknuYsj1CWcSST+/gHJx47C+vWlLXKZpkIp9/R81Npy12jKNwonKtOHuiyjGRepywpc90IE73CHpRZuE9N4NmQzeHhAUFBpi1smqVDK3d5hkBqNpvQxUQk3aUOStGTiiuXs/WUsAM4pqVCvHrzxRilLWDapUMrdmEx1tctKuLKe8jed2NhY/P39efLJJwt9rkZTlhGEeLZx6vKtLD76JF03fMa2fUOMg8pK54M74LXXoGHD0hW0jFLhlLu9XDKZ88qkkzm/TGF5/fXX6d69uz1Ey8LUqVO588477d6vRlNaWEnhCksISWzKv6n9mPJzEI998TOxTg64jp9Oh9Sfodp54iubYNWq0ha3zFJhlLtgQUixm0umrKf8Bdi7dy8XL16kZ8+edrlmjaY0SSOCi7zJkbQ6nGMM+/7yZsTL+1mx70nq3P4Xac9N49u0QOL97wDfS8R4OUOmeguarJSLYh0Tv4P9uSYxuIoAws0o3ApULbW1H8wbkPfxsp7y12q18txzz7F06VK2bNlSgCvWaMomyRwignlEyVJEJeP8szNL18zkXd8XqOMt3DX8G7Y1/JqlPEaPxrcRGi3Q5AIxPi5w6lRpi19mqTCWO1gA7Jp5riyn/F2wYAF9+vShTp06drtejeZ6IQhxbOIkvTlGc6LMi/BZnEZq51YM+/k4c3xf4sEOinsmrWVbw6+ZzTBGchvhsZBkVjhUvUxMswDYuLG0L6XMUi4s92tZ2OmkcgELEbjSpkCWe0Eoyyl///jjD3bu3MmCBQuIj48nNTUVDw8PZs2aVdjL1GiuG1aSiCKYCOaRwn84JnhSY7473u8mM+/eJUy7IwgfdxPfPwCHm/3Ii6zjGXrzHH0AOG4rdO9RNYoY1wD4+we4eBFq1Ci9iyqjVBjLPX0yVdlNtZftlL/BwcGcOXOG0NBQ5syZw+jRo7Vi15RZzFzgAtM4TF3CeBQVFUft56vRpEoc8dsH0u3ZaF6qMpJ7m5v4dxJENfuVF1nFcDozh+EZ/69DbBX9vKvGExNgSzSWLapNY1AuLPf8EAQhEQeq2L3vspryV6MpDyRxkAjmEs1yhFQ8L3Wi2hQvKn1+AgID+eTDbUw61QzHBMWy4RDUBjaqA4zlM7rTnMWMw5TJBg25DM4O4OudSrTyMCz2jRth9OhSvMqySZFT/tqT4qb8tZJKMgdxoi5OFTRtaGQkhIVBaqpR7NvfH3x9i9+vTvmrsTeClTjWE8Fc4tmKwp0qF/tQ9fmzuCz9C+rXJ2zqHB5JG8TGY4oejWHhEKjtDbs4wV28RRP82M7LeGaLfhu0GI5ehlrPzyQZM7+NOQk//ACXLkEBjKuKRomk/C1LVPSVqZGRcPq0odjB+Dx92tiv0ZQVrCQQyUccpSmh3EsKR6l5+TmaPtQd/5prcdl4EnnvfVasOkaLU/ex45RiwSD46VFDsR/jPH15h5p4s55JORQ7GG6ZxlXBG3diSITeveHKFchmHGoqiFvGWsGVe1iYYHJIw61SEiYHC4hCRHElRuHtqwBjU6R/J2Nf1v2ZN+w6P6G5cTETRgQfcIVPsBCFG+2pE/UB3lP2oz6dB66u8OqrREyYxOObPFizGjrXgyVDoXE1o4/zRNOLt1HAT7xADbxyjGO1wolI6H0zXMGdGJKgRw8wmQzXTKdO1/fCyzgVRLknonBGVYDLESxYSUJIwmrb6jdNwsEpLdf2KcUaTZFKBP9yi63AiaOtyIljRsNtDLwAACAASURBVMETlfHdEbL8vnZ7Mp2X+Vj+fVw9VrA+8mpftl/Rg4NhyhQ4cwbq1oUZM2DEiNKWqnAkstfmT18FWKnMQKrFP4r7zB2oeS+A2QwTJsArr/DjlRo88glEJsLMe+D5ruBg8xvEkMg9zOYycWznZRqRe+RLWCwkp0GjqnAUN8Ny9/WFjh1hwwZ4NXtpiRub8q8NMdwy5c1qF6wIKVhJzKLMhdSMNgoTCjcSYr1JTHAjJckNS5ojKEEpwdlZaNDQWL5FRtlhK1drkqfvv9om8yYIDqRQhYcQ0hBbcXHj0wx57LOSnKM9Wdrl7COPOukliMrjAZH+MCnIAyLzwyS3Pgr2oMv+QPplmxNfrHakRgMnomJbc/p0FcaNM6Qu6wpesBDL90QwlwR2YsKTqjxJ1ZTxOH+4HmaMMNwkw4fDG28Q59+QZ3+Az3dBq1qw8REI9LvaXwpm7uM9/iOMH3mO9jTIc+z0SJnGVeEC7sSRjAUrDr17GzlmIiPtMxFVQSj3yl2wYiUZx1xe48oChgpNzWKJG9+TuarwFCZcMeGBCTcUbrZP5wwVFR1hvJamYzJBzeoU2z51IA4/5hWzl/wxHmZ5PzSENPJ7QOS3zz59mBESc/SbXx+FIeAumHeX8f3iOX9G3vInl8JqM2VK2VXuFuKIYiERvE8qJ3GiHrV4hyqWMTgs+wGm9TJeQ3r1gpkzoU0bfjkBY96FM9Hw0l0wvSe4ZNI4VqyM5hO2cohlPEZPrl3gPeSy8dm4Khy0GXNxJOF9zz0wfTr8/DMMG1ZCd6D8UQGUe9kp0CGYsylw4zOjggygcMaEG454YcpQ4tfOZJlujJREtMz1wngLcYEKWDLNeDOykvdDJusDolMnMyaHNKpUv8SMpaOY/8O9PHTHDs6c8SzlK8lJKqeJYD5X+AwrsbhzKzX5H14yAPXjTzD5Tvj3X2jfHhYtgrvvJtkMU36AuTuhQRXY+TjcGpC1X0F4hmBW8xdzGM4IbstXluORxsOhthd44Q5ANIl4t2tn/GfYsEEr98yISKlv7dq1k+wcOnQox77cMEuEJMhusUhCgdoXlDvvvFM2btyYZd/cuXNlwoQJYpU0SZN4MctlSZEzkixHJVH2y6FT30nT5g0kQXZLouyTJDkiKXJazHJJpkx9UTb9vDGP0US2bdsmffv2LZSMJpNJAgMDJTAwUO69994iXWdB77PGftSrJwLGdmuvDbLX7CDz1/WR+g3MpS1aBvHyu4TKA3JAHOSAOEioDJME+cs4+NtvIrffblxA48Yiq1eLWK0iIrLnrEiz2SJMEnn8K5H4lNz7nyU/CDJSnpXgAss0YJHRt4jIWtklyEjZL6HGjuHDRWrUELFYinjF5RNgj+ShV8u95W5EyigUrnbtNz2vTI9ed2RY4CtWLmbG7IkkkXnFqAkTrjjghSO1MOGCK4E2/+rVaJQ3X7f/6lE3Nzf2799v9341JcuMGTBuHCQmwu8/9WbmEx8y9ZPHaN74aYQPSi2KSUgjhq+JYC6J/IkJL6rxLL48iTN14dAheHkgfPcd1KwJH30EDz8MTk6YLTBzK7yxGWp4wk+PQM88smcsYScvsYogOjObglva6WGQYIRCAkbEDMA998CKFbB/P9jShNzolPs4d7FDgQ6jsHYqFmIwc4EUTtJvcAvWrfuWmJR9pHKS46F7CQ+/yO23344Tfsyf/SN3dhjPLa3GMPPVVTgTgBPVsFiE8Y8+TovmLUo85a+mfDJiBHz6qVFESCnY/dN4Lh+ahO9NC4i4DvMf2bEQzWXmcISGnGEoaUTgx3yaco5avI3zWQVjx0LLlrBtG7z5Jhw/Do89Bk5OHLkEt34Ar26CoYHwz7N5K/YNHOBhPqc7zVmUbfXptUgPg0xX7l42N2yMrbQm6WmvdSKxDMqF5X5kIsTlYaBaqQU4FEK1C4IFj9apNJp3OZNf3JLRQuFMVd9qtO/Ylm0bTzBwwP18u/Jnhg0diatqxKZNmzgREsauXXtKLeUvQHJyMu3bt8fR0ZGXXnqJgQMHFvguaEqXESOyTp4K/+MMpzjPczgTgBeDSlyGFE4QwXtEsQgr8VTiTvx4n8r0M0JJr1yBWS/A++8bXqSJE2HyZKhqaFirFd7/DSavh0rOsGYUDG6V93h/cZzBvE8gdfmap3EuhPo5FwMpaZmVezbLvUYNaNfOUO4vv1yk+1HRKBfKPTfMZkhNFVwrCWmpJkwKnJwytxDbRKbVprjTv6cHDCZi4QoKNxyoki1KxbgtI4Y/xJqVP3LfgFGsWrmKhQsXAllT/gLEx8cTEhJC3bp1i5TyNzubNm3i+++/Z86cOQAZKX+zpwk4c+YMfn5+nDx5krvvvpuWLVvSUJccK5coTNRhKWbOcYYRNOQX3Olg93EEIYGdRPAusXyPwhEvhlGVibhjc2ckJsL8+TBrFsTEGHlbXnvNeNWwcToKxqyC7Sfg3mbw6f1QM+c/5QwKsvr0WqSHQTbKy3IHY7XqrFkQHQ3e3oXqvyJSLpR7k2xvqunL8b3d4wlocpTzp+titThRo2YSLu6ZQw3TUVmUt/HdB0X1a/o3y3LKXwA/PyNguEGDBnTt2pV9+/Zp5V6OMeFGPb7jOLcQyr004k+cCbBL31ZSiWE1Ecwlib9xoArVmYwvT+CELfA8Lc2IeJk+HcLDoV8/eOstwx1jQwQW74GnbZ7CLx6AhzoY7qW8SF996oApz9Wn+ZE5xh1ysdzBUO4zZsCWLXD//YUeo6JRLn3uYWHGK6Gzq6HAa9U7g3+DEzi6hdtWq7riRC2caYArzXGjLa40w4X6OFETB7ww2WLIr0VZTvkbFRWVUQIwIiKC3377jWbNmhVIBk3ZxYka1Gc9VpI5RV8sRBervzQiucRbHKE+ZxmFlUT8+ZimnKUmMwzFLgJffw0tWhgzvfXqwY4dRkKuTIr9YhwMWAxjV0Nbfzj4LIzteG3Fnnn16Xom5bn6ND9CIsDVEfxtbwcuOOGCE9EkXG10yy3g5WWERGrKh+WenfQEWgmxlYkIr0VqigvJSW6kJrvStq19l52X1ZS/hw8fZvz48ZhMJqxWKy+99JJW7hUEV5oSwNecpBeneYD6rLctZSs4yRw1StexBCEJD3pQlc/xpFfW4INffoEXX4S//oKmTeHbb6F//xwa+6uD8NjXEJcCc/vD/91mLKS7FimYGci8jNWn7ahfqGvITEiE4ZLJPKYXblktd0dHI9fMxo3GAyufN+QKT14xktdzK2yc+4EDIrt359wOHChkkKhGx7mXYSJlsRwQ5Iw8LFax5tveKlaJlZ/lpPSRA4IcFBc5I2MlUQ7mbLx/v8g99xix6rVri3zxhYg5Z5x9VKLIyOVG3Hr7eSKHLhRMdotY5AF5X5CRskx+LdhJ16Dp2yKDFmfdd5NMkqEyP+vOzz83rumff4o9ZnmAihbn7u9v+NyzL8f39y89mTQae1OFB0nlBJd4AxcaUp3Jubazkkw0K4hgLsn8gyPVqcF0fJmAY/b6BqGhMHWqkbnM2xvefhuefBLcck5w/nwMHloNF+Jgeg94uRs4FeDFWBAmsow17Crw6tNrYbGFQfbLVnbAKz0zZGZ69zY+N2ww3Ew3MOXS5+7ra7gFnZ2N387Oxu/ytBxfoykINXgNb4K4wMus+3UlAQGGIRMQACvXXuIir3GEepxjLAC1+YImnKYGr2ZV7JcvG6GMN98Ma9fCCy/AiRPw/PM5FHtCKjzxNfT8DCq7wJ9Pwqs9C6bYAf7HOubzM89xT0bt0+JwNhpSLVfTA6fjlZ4ZMjP+/sY8gY53L5+WOxiKXCtzTUVHoajNQsIunaVG+zH4+NfBycOLkc/MpWG/YC6Sgid9qMozeNAtZ5BAfDzMnQuzZ0NCgrEY6dVXoXbtXMf7IxRGrzIs5We6wIx7wK0Q7v7F7GAyqxnBrbxdiNWn1yJ7pEw6XrgTntuE8z33GNccHw8eHnaRoTxSLi13jeZGwoQL43p+w4Uzdflkc3e++rclvYev4LtFDzGh22Hq8yOedM+q2M1mWLAAGjWCadOge3cjwddnn+Wq2FPT4OUNcPsCMFtg23h4t3/hFPt69vMIX9CDFizk0QKvPs2P43kq91wsdzBcM2YzbN1ql/HLK+XWctdobiT+O+jLE33W88rHj7Fr692s/WQ8MVd8cwaEWK2wZo1RCeTECbjjDiMC5pZb8uz7YDiMXgkHzsPDHeHde6FyIVM1/cVxHmA+ranHV/xfoVaf5kdIhPGQqZUtaaYX7kTnptxvu82w2DduNCJ/blCK9WhVSnkrpdYqpY4opQ4rpTorpaoopX5WSoXYPn3sJaxGc6NSty6cPdGI8T0288XMl4m54puxP4PNm6FDByPtrbs7/PgjbN+ep2K3WOF/26D9+8ak6fcPwecPFF6xH7WtPq2FNz/yXKFXn+ZHSAQ08s0ZeumFGwmkkJYpdQgAzs6cvbkbZz/dgEkJAQHG/PGNRnHfm94DNopIEyAQOAy8BGwRkcbAFtvvcomDgwOtW7emefPmBAYG8u6772LNHKJjJ9566y2796mpWMyYYejrzLi7G/vZu9eI7+7Rw1i+/eWXsG8f9OmTZ6z38Qi4YwG8tB4GNId/JxlpBApLOFHFXn2aH5mzQWbGGyPXUmy2iJngYJh9sDd1LKE05hinTxtrs244BZ9XjGR+G1AZOAWobPuPArVs32sBR/Prqzj53EuSSpUqZXy/ePGidOvWTaZNm1ai41xvysJ91hSMCRNEHByMMG4HB5FpQSEiQ4caO3x9RebNE0lOvmYfVqvIgt9E3CeLeE8VCf47IxV7oYmWBGklk8VDHpE9crJoneSDOU3E6UWRF3/MeWyR/CLISDkpF7Psr1dPpB6nRECeZm5G7vx69UpExFKFa8S5F0e5twZ2AYuBfcDnQCUgOlu7qDzOHwfsAfbUrVs3h9BFUjrR0SLNmhmfdiC70j1x4oRUqVJFrFarpKWlyaRJk6R9+/bSsmVL+fjjj0XEKLrRpUsXGThwoDRt2lTGjx8vFlsBgeXLl0uLFi2kefPm8sILL4iIyIsvvphRdCMoKMguchcGrdzLB8uWibi7G/9ja3BePuBxScVRzC7uIlOnisTE5NvHuWiRXp8aC5J6fmr8LipJkiJdZYY4yYOyKbdFUnbiZKQh7+d/5Tz2tewWZKTsSy/YYUMp4z7tI1D+pZkoLALG/opGSSn39kAa0Mn2+z3gjYIq98yb3Sz34GDjkpYvL/y5uZCbRe3t7S0XLlyQTz75RN544w0REUlOTpZ27drJyZMnZdu2beLi4iInTpyQtLQ06d69u6xZs0bCwsKkTp06cunSJTGbzXLXXXfJN998k+c41wut3MsH9eqJVOOivM4rEo+7pOIoH/C4tK99Pt9zrVaRZXsNS919smG5F9VaFxFJE4sMlvcEGSnB8lvROyoAPx0xlPv24zmPbZF/BRkp2yXrv+H0SldDWCkCMoSVN6TlXhyf+zngnIj8Zfu9FmgLXFRK1QKwfRYso1ZxCAoyZscffND4PXq08TsoyO5DGffTSMv75Zdf0rp1azp16kRkZCQhISEAdOzYkQYNGuDg4MDw4cP59ddf2b17N127dqVatWo4OjoyYsQIduzYYXf5NBWQPXt47fSDnKUOU3mTH7iXphzmST5kb1jNa54akQBDlsHIFdCsOhx4FibcWvS0K+mrT9eym3cIIohbi9ZRAckrxh3yyAzJ1fmJtQzmP5rxKq/h4WYx5iduIIqs3EXkAnBWKZWel7YbcAj4HrBpWR4ESr6M0OuvG2ED6QndnZyMJatvvGHXYU6ePImDgwPVq1dHRJg/fz779+9n//79nDp1ip62ajDZU/oqpTIeChpNgUhNheXLoXNn6NCB+9XXfMajNOUQw1nJCRoB2aJlsrHuELSYA9/9B7P6wI7Hr+ZDLyqz+IEPbKtPn+We4nVWAEIijEIgtXLJFZ+5SHZm0itd1annwOu8SjMOs2HsmizFUW4I8jLpC7Jh+N33AAeBbwEfwBcjSibE9lklv37s4pZZs0bE0VGkUiXjc82awp2fC5ndJZcuXZIePXpkTKh+8sknMmDAAElNTRURkaNHj0p8fLxs27ZNXF1d5eTJk2KxWKRnz56ydu1aCQ8Pl7p168rly5clLS1NunXrJt9++62IGK6e9H6uN9otU8Y4f15k+nSRmjUN30KjRiLvvSerPo3O8Lmnb+7uhi8+OzFJIg+vNtwZrd4RORBmH9HSJzBHyAKxyPUpRN33C5HAd3I/dkliBBkp78tPeXdgsYi0aCHSpIlIWlrJCFmKUBI+d3tudlHuDzwg4uUlMmeO8TlkSOHOz4X0ic5mzZpJq1atZPbs2RmToxaLRSZPnpwxQdq1a1eJjo6Wbdu2yV133SVDhgzJMaEaHByc0f7555/PGOeFF16QJk2a6AnVGxWrVeSPP0SCgkScnIz/lvfcI7J+vaGcbCxbZviNlTI+c1Ps24+L1JshYnpeZPJ6keSciR6LxDrZJw4yWnrK/yRF7NRpAbjpfyL3L8n9WIqYBRkpb8g31+5kzRrjngYH21/AUubGUO67dolcsOUjvXDByAFcCmzbtk369u1bKmMXBa3cS5HkZJEvvxRp3974r1i5ssjTT4scO1borhJTRZ75TkQ9L9Jolsjvp+wn5p8SIm4yVtrJVImVRPt1nA/mNBHHF0ReyiUMMh03GSuTJJ8ACotFpGVLkZtvrnDW+7WUe8VJP9AhU73JGjWMTaMpi4SFwccfG47hS5egSRP48EMYNQo8PfM/Pxt7zhrpAw5fgiduhf/1NfzU9iB99akf3qwvgdWn1+J0FKRZc59MTSfP/DKZMZmMZGmDB8PKldwoznedOMzOdO3aNUfVJI0GEfj1Vxg61MjXO2MGdOoEmzbBoUPw+OOFVuxmC0zfBLd8ALEpsOlR+GCQ/RR79tWn1Utg9WleBAfDrX2N7y8/nvfq0lxzuufGoEHQqpURfJGtrGVFRSt3jaYkSUqChQuhbVvo0sVQ5k8/DcePw/ffGykDihCXePgidP4AXvsZhreGf56FHjfZT+z02qeRxLOB52lYxNqnRSE42EgXcMls/L54JO/0AQWy3OGq9X7sGKxYYV+ByyhauWs0JcGZMzB5MtSpAw8/bKSg/eQTOHcO5syBBg2K1K3VCnN3QJt5EHoF1o6CpcPBxz3/cwtKMqkMYC6HCedr/o+2BNiv8wIwZQokOgItgGQgARITjf3ZyTMzZG4MHAiBgTeM9V5xfO4aTWkjYhScnj/fSLMLMGAAPPUUdO1a7ILNoVeMsnfbTxhJvj4bDDUK76K/JhasjOJjfuEIy3mcHrS07wAF4LQjMArD9Nxwdf+ZMznbeuHGWa4UrGOTCaZPN1w0y5cbix0rMFq5azTFJTERli2DDz6Af/6BKlWM8nUTJhiL6YqJCCzaDRO/N34vHAJj2hf7WZFzHISnWcpadvMuQQyns30HyAezBaZsAAYBF4EfIHOhpdwWbHlTqWBumXQGDIDWrY0FjkFB4FhxVaB2y+TDhQsXGDZsGA0bNqRZs2b06dOHY8eOFaqPb7/9lkOHDpWQhJpS49QpQ4nXrg3jx4ODA3zxheF6mTXLLor9Qiz0XwQPr4F2teGf5+ChDvZX7AAz+YEP2cwk+vDMdVh9mpmz0dD1I5j9C3TzBbdvyaLYM9IbZ6PAPvd0lDKs9+PHjQdyBUYr92sgIgwaNIiuXbty4sQJDh06xFtvvcXFixcL1U9pKHeLxZJ/I03hETGKYgwYAA0bGrU6u3eHnTvh77+NGqVu9gkXXHsQWrwDm0Ngbn/YMg7qlVDpm0XsYAprGMlt/I+hJTNIHmw4Am3mwsELsDwINr8En31kPBuVMj4//TT3CEYv3EkkFTOF8KH3729McL/xhjEXUkoEB5Ol4Lnd883nFQB/PTd7LGIqyOq9wrJlyxbp0qVLjv3ZFyo98cQTsmjRIhExUvg2bdpUWrZsKc8995z89ttv4uPjIwEBARIYGCjHjx+Xffv2SadOnaRly5YycOBAuXLlioiI3HnnnTJx4kTp0qWLNGnSRHbt2iWDBg2SRo0ayZQpUzLGW7p0qXTo0EECAwNl3LhxkmZbmFGpUiWZOnWqdOzYUXbu3JlDltzQi5gKSFycyIcfijRtaiw4qlZNZMoUkbNn7T7UlQSREcEiTBJpP0/k0AW7D5GFH+TvUll9ak4zVtEySaTlHJEjF/M/JzvvyUZBRkqExBbuxO+/N/6OCxcWflA7kDmFc37pJK4FFX2Fqr1uVHbee+89mThxYo79eSn3yMhIuemmm8Rqy6caFRUlIiIPPvigrMmU66Zly5ayfft2ERGZOnWqPP300yJiKPf0PO/z5s2TWrVqSXh4uCQnJ4u/v79ERETIoUOHpF+/fhm5aCZMmCBLlhjrswFZtWqViEiesmRHK/d8CAkRmTjRWD0KxmrSJUtEkpJKZLifjoj4v2GszHxtk0hqCS+o/MO2+rS9TJU4KZlryo1z0SJdPjQU+yOrjRW2RWGx7BBkpJyQQj4ZrFaJqN9OTjvWFydS7WMQpqWJREQY/2Z27RL56SeRVatEPv5YZOZMkRdeEHn0UZHBg2WnazfZQ1s5SAsJZF+RC4pcS7lXiNmEKVOMOa3MpIdOXc/FaJUrV8bV1ZVHHnmEvn370q9fvxxtYmJiiI6O5s477wTgwQcf5IEHHsg43t9W0Ldly5Y0b96cWrVqAdCgQQPOnj3Lr7/+yt69e+lgW5GblJRE9erVAaMs4P33319gWTR5YLUa8ejz58OGDYYvfcgQI+qlU6cScXgnpMLz6+CjP6BpdfhujOFjL0mOEE5f3sEfH37kOTwoZPHUIrLpqJGCOCEVlg6Dke2K3peXbcVsgcMhbQQvV3wdPp2v0u5lFF+y8PTDjBsHypJGUJ9oiIoq/BYbe+1BnZ3Bxwd8fDAl++BDFA04hVumRVi5RQQVlQqh3PO6IcW9Uc2bN2ft2rU59js6OmappZqcnJyxf9euXWzZsoWVK1fywQcfsHXr1kKN6eLiAoDJZMr4nv47LS0NEeHBBx9k5syZOc51dXXFwcHBbrLccMTGwuLFRtRLSAjUrAnTphmTpbaHbEnweyg8uApORMKzd8CbvcHNqcSGA66uPnW8jqtPLVZjRe2MrUZu+TWjoGkx10ZdzeleOOU+ZQqcTunLbtozj4lM43V8EqOo/GDctU90dc1Q0Pj4GJPpLVtm3efjA97eOfe5uWUYBkEBsPR0F1Jx5k+uFjC/VgrnwlIhlHvdunD6dO77i8Pdd9/Nyy+/zGeffcajjz4KwO7du7FYLBw6dIiUlBSSk5PZsmULt99+O/Hx8SQmJtKnTx9uueUWGjUycm57enoSF2f8o/Hy8sLHx4edO3fSpUsXli5dmmHFF4Ru3boxYMAAnnnmGapXr86VK1eIi4ujXrbIjLxk0eTCkSOGQl+yBOLj4ZZbjIiKwYMNa6uESEkzlN3b26GuN2wbD3c2LLHhMogmgd7M5goJ/MIUGlC9xMc8HwtBy40Y/Yc6wAcDwd0Ot9a7iMrdMPwUTzGfF3ibODyJwodofJj+nk9OxZy+udrn7ea9p47TZdKvvMRMwFD4eUUEFZUKodxnzDCWJ2d2zdjjRiml+Oabb5g4cSKzZs3C1dWVgIAA5s2bx5AhQ2jVqhWNGzemTZs2AMTFxTFgwACSk5MREebOnQvAsGHDePTRR3n//fdZu3YtS5Ys4bHHHiMxMZEGDRqwaNGiAsvUrFkz3nzzTXr27InVasXJyYkPP/wwh3LPSxaNDasV1q83XC+bNhlKfNgww/XSvn2JD38wHEathIPn4ZGO8O694HkdvCLJpDKQeRwhnPVMui6rT7eEwIjlRv6bRUNgTIf8zykoeVVjyo90g/AvbuF+vs7YX68eTP8/+8mXFwOiFmNVJrb5jUKFG/LMmGFnN3JezvjruZXVaJkbgRtuQjUqSuSdd0QaNDBmsPz8RN54Q+RiEUI1ioA5TeStLSJOL4rUmC6y7jre/jSxyP222qfL5feSH88iMv0nIw1x07dF/s2/3GuhiZBYQUbKe7KxUOeVVBBGgUhLE6ldW6R372J3RUWfUAXjiXeDZPLUFIX//jNcL19+abzi3X47zJxpLEV3KmEHt42Qy4Zv/Y/TMLgVfHQfVK10XYbOWH361XVafXoxzpg03RwCo9rCgvvAwyX/8wpLZduEamEt93RdMWWK4aIpEcs5L7ZuvZpjqASpMMpdo8mBxQI//GC4XrZuBRcX43/vk0+CzZV2PRAxomCeXwfOjhAcZGRyLIlVpnnxFt/zIZt5/jqsPt1+AoYHQ3QSfP4AjC2hFbUATjjijnOhfe5Qigbh4sXGhOuAASU6TJlW7iKSo9i0xn4Yb3UVkCtX4PPPYcECw7Fap45hpT/yCFQtZoXoQnIuGsauhp9DoNdN8MUQ8L9+adEBWMgvvMJaRnIbs0pw9anVCm9thVc3GYW4f3oEWvmV2HAZFCozZGkTHQ1ffw0PPWS3ydm8KLPK3dXVlcjISHz/v73zDo+qaPvwPSlA6F1QSCgqiDSliIiACBKaKFIFwQYiXQGlqi8Kn8BLEaUqSpUi/QUFBaSKdJAuTZp0SCjpyXx/PBvS+/ad+7r22t2Ts3Pm7Nn8zswzTylUyAi8DdBac/PmTXLY+AdmVw4elFH6/PkQFiaZGMePl3BzOyeI0hp+3A89l0tCrKmt4L1a9h2tA6xmP934nsZU4nvexctGGUeu3xMzzK9/y6xk+mv2WSCG2IIdLiLuixfLb/Ott2x+KKcV9xIlSnDx4kWuX7/u6K64LTly5KBECRtHytiaqChJr/v117Bli/gSd+4sppdK9k9XCyJ07y+DpYegdgDMbi8jWXuzg5O05RueIoAl9MHXRv/uW89A+/lwMwSmtYJudr6J9iJfLgAAIABJREFU5U9vNSZn4IcfoEIFu3hkOa24+/r6Urp0aUd3w+CsXL8O334LU6fK4lSpUjB2rCTuKljQYd1adQS6LhF78+im0L8eeDsgPd8xLtGc8TaNPo2JER/9YeugdEFY/TY89YjVD5Mm+fDjNvftf+CMcvw4/Pmn/E7tcPdzWnE3GJJl714ZpS9cCOHhkpFx8mRo1kzSBDiIO2GSb/2H3VClOKzvBpVsF9SaKpe4RSBj8cXbZtGnN+9LUe6fj0ObyrJwmtdBFr585OQfbjjm4Blh1iz5jXbqZJfDGXE3OD+RkbB0qYj6H39ArlxSuq5XL3jiCUf3jk2n4c1FkpN8SAP4tJF4xTiC2OjT2zaMPv3jH2g3D67dg8mvwvvP2n8tIT4ZzunuCKKjYe5caNJE0lrYASPuBufl6lWpOzptGly+DI8+KvnT33oL8tnZ5SQZQiNhyC8wcSs8Vhi29YBnSzmuP7G1T09wmZ8ZwFNWjj7VGsZthsG/SLqEP3rZPrlZesjnCjb3X3+Ff/+VAYqdMOJucD527pR/gsWLZdQeGCiujYGBUtnACdhzQdIHHL8GPWvD6GaQy3ZpaNIkmhg6MpUtnGABPWhIRau2fytEZif/OwqtKopLZ37r1CTJMvnwI5QIIogim5NJ2vz5Eig1+twsGnkVYt3d5nSw07Gd65sweC7h4fDTTyLqu3ZBnjxSg7RnT3j8cUf37gGR0TByA3yxAYrlgV+7QiMHd0+j6cMclrGHCXSkvZWjT3eeh7Zz4fJd+Kol9H7OsWaYxMTPDFmEvA7uTRzz50vOq1wh13iFFUyL6c6QHtmI8bFP8JQRd4Nj+fdfMbtMnw7XrkH58pImoHNnEXgn4uhVWUTce1FC6ie94hyj15GsZAob+Ihm9CPQau1qDV9tg4/WwMN5xexU04opaa1F/ORhTiHuWsPBg5zv9SurQtZRh234EskPvGXXOhNG3A32R2vYsQMmTZKF0uho8Xbp00e8X5xpWIi4/E3cCkPWQp7ssLQztHKMC30SvmMTw1nKGzzH/9HWau3eDpHI2hVHoOWTks2xQE6rNW9VMpv216pcuwa//Qbr1ol9/epVBgN/UYlJ9GEZrThIVcC6BTlSw6PEPdb+ZfdEQQYhLExcGL/+WopJ58sngt6jhxSbdkL+uSW25s1n4OUKMKM1POQkE4r/sY/3LNGnM60YfbrnArSZCxeDYVxz+KCu091vE5DvQfIwO4p7RIR4bq1bJ4/9+2V74cLQqBG89BI1h73E7ktJ8y9YsyBHaniMuMfav8JDoshFKP+ey063rr6AMgJvay5ckGCjb7+FGzfgySfFFNOpk7g1OiFaw/e7xXddISPXLtWdR+T+4G/a8g1PU8pq0adawzfbof9qKJ4HtvaAWgFpf87RZDane4bQGk6dihPz33+H+/clrUXt2jJSfOklePrpB4v+fX1tU2civXiMuA8dCoEhS5lMT4pxVTaGQninbNA9m2QMzBbvOVsy2+y9j7e386hJRtFa0gF8/bWkB9Bacrz07g0vvODU53XlDry7BNYcgxfKwg/tIKCAo3sVxzEu0YIJlKCg1aJPg0PlnJf8Bc2fkJQJBZ3UDJOYzNZRTZPgYMkmGivo//wj28uWhS5dRMxfeAHyJm/nd2haYTxF3K9dY8y5XrTlJ/byNOPojy+RZCOCHIQzqFuEeGtEJPMc+/rOnbT3iYqybr+Vcq6bTeJ9kosIDQmBH38UUf/rL0kF0L+/mF4CnH8Y+NNByQtzPwImviyeIU7ifQlI9GljK0ef7rsIbefBP7dhTDPoX9e5zjktMltHNQnR0RIBHSvmf/4p23LnhgYNYOBAaNw4QyZER9aZyLK4K6W8gT3AJa11c6VUaWAhUBDYB7yhtY7I6nEyhdbiXtezJy25wxBGMpaBRBFXnCEgAAaNs9LxYmISin1yN4DUnrOyz717ae8Tr6i3VfD2jhP8oKDk9yldWv5hevSw/w0pA7OD2yHQa4VkcqxREua0h/K2Ly2aIW5bok+DrBR9qjVM2yGmpyK5YXN3eM4F0znlzYrN/eJFWQBdtw7Wr5d00UpBtWrw8cci5s8+a7eCLtbEGiP3vsAxeOCDNBqYoLVeqJSaBrwDTLXCcTLG1aviI710KdSowW8DfuCrEU8SZUv7l5eX5Gh21jS60dHWvdmEh8PatbI4Gp+HHoKaNePaCAuTKW5a7VkbX9903QjWFXqWt0sO4JpPPkbcXc7gfb/gc9jHtrMgH58M3XziR5/+wsAsR5/eCYNuS2DRQQgsB3M72K8qlLXxwZtcZE+fzT00VMyFsaPzo0dle/Hi0KKFiHnDhlCkiG07bQeyJO5KqRJAM2Ak8KGSxOsNgNctu8wGPsPG4p7AC6akZl7zhdRZ1Bvu3oUvv4T+/Wnu48OMEh7uLePtLXe0nFk0pt67B/Pmienl6FHxEOjWDbp3l8IYmUFrMWvZamaTzD73orwYWPRtphVtSYV7p1m1uwfVru9Pvp3IyKx9Z8mRzptEdI5sdByWn621fVnwdQwv7h+XpZvNwdACtNn5GKfvZWdUzVt8/EwIXveyQWS8duyc/z6r5Ccn+06FUKphov/v17WUWIwV8y1b5Hpmzw7PPy+pLBo3hooVnXodKDNk9QpOBD4CYp3DCgFBWutY4/NFINkkoEqpbkA3AP8s+AbFesGEhMBDXGHC+fepM2UFN8o+Q+Et30vuZAumzmoWOX1aMjB+/72MxKtVk0x37dplfbailIy0fX3t4kGz/azUMz1zC/o/D18EliWH76KUP6B1UtG3taktIgIdHk7vd3KxrLYvE//vAu2/v5T8vtHRaZ6zBr6r9C69X/yagmG3+H11e+qO3Zr8zl5e9lm7yWx7iRcFgnKy7VAo0eegIDepde43YrqsI6T3r+S8/a/s88QTEvXcuDHUrZv1QY6TozJbak0p1RxoqrXuoZSqDwwA3gJ2aK0ftexTEvhZa51qyEf16tX1nj17MtWPUqWkkpo/59jH0+TiPsP4gmX+H3DmnONSwLoNMTESnPH11/DzzzL6b91a/NNrOaC0UBYJj5IycGM3SfKr2e2hbhlH9yplvmAFw1nKRzRjNO1T3jE6WmYXKdwk7t2PpPtuf+ZfLEqjAjeY99huiuq79lkfCg+Xm6M18fFJIPbVl9ck+z3NDy+d4FFO4YXmFgX4I2dDmk9qLJ4tmZ1VOjFKqb1a62Qrf2Rl5P4c8LJSqimQA7G5TwTyK6V8LKP3EsC/WThGmsRGe4WQk7vkwY9QzuPPPxeMsGeJu3dh9mwR9b//Fjv68OHw3nvwsB0KY9qAA5ckfcChK/BuTRjfwn6l4DJDbPRpZ+qkXfvU21seycygDl2WoKSTN+DzxjCkQWG8vGxbJDsJKZncrHQjCQ8K4VbhGA5QlXl0Yh2N2UN1dKg3Me/Y91SdBq11lh9AfWC15fVPQHvL62lAj7Q+X61aNZ1ZAgK0lmGB1kW4qrdQR2vQE/N9onV0dKbb9VhOnNC6d2+t8+SRL/WZZ7SeN0/rsDBH9yzTREZpPWqD1r4fa13sP1qvPuroHqXNSr1Xe+k3dKAeoyN0ZKbaiInReuZOrf0Ga/3QZ1pvPGnlTjoROVd9rTkx4IEWxD4CAhzdM9sC7NEp6XJKf8jII5G4lwF2AacsQp89rc9nRdznzdM6Z864i5mNMD3b+y1507q11vfuZbptjyE6WuvVq7Vu3Fi+N19frd94Q+tduxzdsyzz9zWta03SmgFat52r9Q0X+Dls1yd0Dv2WrqE/0Xd1aKbauBeudecFct4Npml9OdjKnXQyXvh7puZqjwTCnjOn6IM7Y3Nxz+ojK+KutVzAgACtlZLneXNjtB43TmsvL62fekrr8+ez1L7bcvu21uPHa122rPwUHn5Y6xEjtL5yxdE9yzLR0Vp/s01GrQWGa71gv6N7lD6O6ou6gH5PP6YH6Gs6c4p85IrWFcZqrQZq/ek6raM8YAI7UP+ofaPeSqgDbi7sWqcu7q7l75QCSb1gFPChpI/t0AFq1JAQ+Fq1HNRDJ+PoUUmrO2eO5Md47jnxG2vVyiWDNRJzIUgyGq4/CY0fl8ISjzi+cFOaXLREn2bHh3UMzFT62jl7JMI2d3b49V1o+LgNOuqE5CcXkd6RnPgnkuy4/m/YGrhQkHEmaNpUUsvmzg3160sNQ08lOhpWrpQAjSefFHfGtm0lenTbNnFndHFh1xrm7oVK42DHOZjWCn551zWE/Tb3aWKJPv2ZAZTOYPRpSAS8s1jcO2uUhP39PEfYwUGZIZ0ctxi5p0qFClK2rU0bKQBx5AiMGuVayTOywq1bMHMmTJkiiY9KlpTz79pVgo/chOv3oPtSWHYYnisFs9tBWRc5vdB40adrMxF9evyaeMMcuQpDX4TPGoGPhzmLxc8MaY18O+6A+4s7QKFCEp3Wpw+MHg3HjkmEpZNV+rEqhw6JG+O8eRJyXa8e/Pe/0LKly0UfpsWqI9B1CQSFwuim0L8eeLvIvTu29uk2/mYBPWjAkxn6/I/7JY2Any/88g40Lmejjjo5NssM6cK41395avj6Sk7xihWhb1+xM69aJVFQ7kJUlJhevv4aNm8GPz/Jmd6rF1Su7OjeWZ3gUEl6NWsPVH0Y1neDSsUd3av0o9H0YjbL2cNXdKId6V8TCo2Evivh251QpzQseB1K5LdhZ50cq2WGdCM8R9xj6dkTypUTM02NGrBsmeSYcGVu3JBCGFOnSmGMUqVg7Fh4+21JueuG/H5KKiRdDBZTxCcNIZuL/Zq/YCXT2MjHNKcPjdP9ub+vS8Hqg5dh0AsSmORpZpjExNncbViww8VwkcmrlWnYUOzwhQrBiy+KTdoV2b9fBLxECRgyBB5/XLyCTp2CAQPcUthDI6HfSmgwHbL7wPae8EWg6wn7t/zOJ5bo04zUPl10AKp9BReCYc3b8H9NjbCDGbknh2eKO4gQ/vmnVFJ591348EPrF9vIIvPnyyDcy0ue589H8ocsWgR16khJr8WLReCPHJF81C1bJl9Eww3YfQGenghfbYNez8GBD1yjDFxiVrGP7vxAIJX5jndQpJ2fJywSeiyD9vOhUjE596ZP2KGzLoJTFMl2MlxsvGNl8ueHNWtklDthgiy0LlwohZsdTPxslwCh565y+q0ZhPSeJlnuypaVPr/5ppyHGxMZDZ+vh1Ebpbbnb11d181vO3/Tjm+oRml+one6ap+evgFt5sH+SzCgHoxqAr7uef/ONHmMWSYJni3uIJ4jEyeK73ePHhLo9L//waOPOrRbQ4eKsFfhAB8ynnYsIntkBJtDG1Nv9Qxo0sQj3DmPXJFkX/suQedq8FVLyO/n6F5ljqNcogXjKUmhdNc+XfoXvP0TeCtY9Ra0qJDmRzwSb7zIQw4zco+H+6tDeunaVcwa169LFaGNGx3anfPnoQk/s5savMIKpvMe5TjOC+FroVkztxf26BgYt1nsy+eDYFlnSc/rqsIu0adj0h19Gh4FfVZA67lQvgjs/8AIe1rkI6dxhYyHGbnHp1492LULXn5Z8j9//bUk93cAbYpuZtbV1zhIFRrxG0EUACAg83VNXIazt6DLQth6Flo+CdNfg4dcOCRBap+OIZgQtjAszejTs7fEG2bPRej3vPjuu9qCsSPIh58xy8TDvYd/maFMGfjjDzF79OghrpO2KLGWGrt3My+4OWdVGQJZ+0DYrV7z1cnQGr7bCZXHi5vfrHawvItrC3soEbzMeE5ylRX0oypJV4DjL5wXrQ0Vx0ju9WWdYcLLRtjTSz5yGrNMPIy4J0fevOJS+NFHErYfGChh/Pbg8GEIDMS3eBGOT/qN3AGFUQoCAmDGDPctE3j5DrT4QSJNa5aEQx9Cl+ouV+gpAdHE8DpT2M5J5tI92ejT2IXzcxdA14Prz0HYZfi0NLyaav0yQ2LMyD0hmS6zZ02yUmbP5syZI/Z4f39ZaC1f3nbHOnVKAqq8vGDrVplFeACLD0omw5AIGN0MetV2/SUFjeZ9ZjGdjXxFpxSDlGLLRNIIqALsAzZDQAlJBWRIP68zhd2c4ST/dXRX7EZqZfZc/F/IDnTuDJs2wZ074kmzdq1tjnPxogRXRUZKzVIPEPZbIdBhPrSbB2ULyaJhnzquL+wAn7OC6WxkUBrRp+fPIwUqKwL7gY1AdFz5SEP6kZG7McvE4gb/Rnbg2Wdh924ZZjVrJq6T1pzxXL8OjRrB7duS4KyC+7tFrD0OFcfBkr8kfP6PnlA+Y1lunZYZbORTltGFOoxKI/rU3x+oYXmzK9F2Q4YQm3soGsdbI5wBI+7pxd9f8p63bAkffCCG0oiIrLcbFASNG8vcfPVqqFYt6206MffCJTVvk5lQ0A929oZhDd0nhH4le3mfWTShMt+mI/p0wGdAJeAIcFe2ufvCua3Ihx8RRBGGnR0gnBQj7hkhd25YsgSGD4fvvhMzyvXrmW/v/n2ZCRw+7B4JzNJg21moMh5m7JRIyz194ekSju6V9djO37RnMtUzEH36TyFQPvDwRTxi4dyWmPwyCTHinlG8vGDECFiwQEw1NWtK7vSMEh4Or74q+W0WLBCPHDclPAo+XgN1p4IGNnWHsc0hh2sXfkrAES7SnHH4U4g1DCBXOqJPb9yHqTvg9afg0hGIiZFFVCPsmcNkhkyIEffM0r49bNkiIl27tnjSpJeoKKnt+ttvkpHytdds108Hc+ASVP8KxmyCrjXh4AdQ183Wii9wk0DGkgNf1jKQwqTPMf+rrRASCUNetHEHPQQzck+IEfesUKOGjN7Llxdb/JgxaS+0xsRIFsfly+GrryTxlxsSFQ0jN0CNSTJCXfM2TG8NedIe0LoUEn06ljuEspaB6a59GhwKX2+HVhWhwkM27qSHYDJDJsSIe1Z55BEZwbdrBx9/DF26QFhY8vtqLaX+5s6Fzz+X127I39ehzhQYthZeqwSH+7tnetrY6NNTlujTKslEn6bE5D8gOEwKjRisw7Y1Iu4vtQmNS5HtwZjAZmvg5wc//iiZJYcPh5MnZWRerFjC/YYNg8mTJcXw0KGO6asNiYmBKTvgozWQwwcWdIT2VR3dK9sQRTQdmMx2TrKQnrxA+t1X70fA+C3QpLx7LSg7kvnz4T8j/KAZkDeEc+fEoQ08dw3DjNythVIi3kuXwl9/QY0a/Dxy/4OcIV8WGA2jRskvbswY146rT4YLQfDSt9B7BdQrI6N1dxV2jaYns1nJPr6iE215JkOfn/En3AyBYWbUbjWGDoWwKzJyp/pZ8I4mJMQtx1DpxozcrU2rVlCmDPcbvky9YXWoxhyacI1BQYNY7N2BqDpTeN2NhF1rmLsX+qyEqBjJ4Nj1Gbe7dyVgBMuZwe8Mojm9eSlDnw2LhLGb4YWyULuUbfrniZw/D2g/WFMF3t8ILx6FT1px7qdn8NQxrGeeta2pWpV6frs5SBWW0prJ9GQVLegYPZshw90kWge4fg9emwNdFkGl4uIJ062Wewv7dDbyGcvTFX2aHLP2SJI0Y2u3LhLRq6B5f3j5AwjzhYVT8D08jFXs88ioVZM4zEZ4eYGvDmci/SjMDd5gLuHkQCmxTbs6Kw9LBsfgMClQ/WFd8HbzocIK9vAakwikMivol64gpfhERsNjo6VU4B+93PsmaG8Sl6VExZCt804KTFrK1bxXeYayjKQNLyaTmdOVMYnDHIC/P0SQnR5MpS0/EW4JanH1nCHBofDmQnhlNjySD/b2g4H13V/Yt3GCDkyhOqVZTK8MCzvAj/vh3G0ZtRthty4dO0pkb0CAJdLX34vvGz3Lhbxf8i3v8C9BNORLGjCKHZx0dHftghm524gkIwkkZ4grh5ZvPAVvLoJLwTCkAQxv6BmFJI5wkTp8TlHysp1P0h2kFJ/oGKjwX/DzkeyXRtztSxgRzOB3RrKKa9yhOVX5gtYZcl91RszI3QEkGUm4cM6QkAjouxJenA5+vmJS+DzQM4Q9fvTpOj7KlLADLD0k/v9m1O4YcpCNPjTmNOMYRRu28TdVGUZ7vuEElx3dPZtgRu6GVNl1Ht5YKMLU+zn4sinkzOboXtmHW9zjeb7gIrfYwtBMj/K0hqoTICJaXETd3YTlCgRxn//yMxNZRygRvMnzfMKrBFDY0V3LEDYZuSulSiqlfldKHVNKHVFK9bVsL6iU+k0pddLyXCCzxzA4jogo+GQd1J4MoZGwvhtMesVzhF2iTydkKvo0MauPwV+XYfALRtidhfzk4gvacIZx9OEl5rODxxlIb+ZwhSBHd88qZOWnFgX011o/AdQCeiqlKgCDgA1a68eADZb3Bhfi8BWo9Q18vh46PSX1TF98zNG9sh+x0ad/cJJ5dM9Q9GlitIYv1kPpgtDhKSt20mAVipKPCXTiJGPpQh2msoEy9GcQi7jFPUd3L0tkWty11pe11vssr+8Cx4BHgJbAbMtus4FXstpJg32IjoH/boJqE+FiECzrDLPaQz4/R/fMfiSOPm2TwejTxGw4CbsuwMf1wdd9QhzcjpIUYgbvcJwxvEo1xrCG0nzI56zgroumELaKzV0pVQrYglSCPK+1zh/vb7e11klMM0qpbkA3AH9//2rnzp3Lcj8MmefMTfGE2XoWXnlSMjgWze3oXtmfz1jGf1jOYFpkKkgpMfWnwskbcGYwZPeABWh34RAX+ISlrGAvhcnDYFrwPi/ih3PZJW3qLaOUyg0sBfppre+k93Na6xla6+pa6+pFihTJajcMmURryXVSeTwcvAyz28GyLp4p7NPZyH9Yzps8z0jaZLm9bWdh8xmJAzDC7lpUoiTL6cdOPuMpAujPjzzKAKazkUiiHN29dJElcVdK+SLCPl9rvcyy+apSqrjl78WBa1nrosFWXL4Dzb6H95ZCLX+xrXeu7pmueivYQw9m0ZQqzODtNGufpoeRG6BILsm1Y3BNalKWX/mY3xlCKQrTnR8oz8fMZRvROHeoeVa8ZRQwEzimtR4f70+rgC6W112AlZnvnsFWLDoAFcfBptMwqSX82hX8PdSvaRsnaM8UalAm09Gnidl7EdaekLQMuZxrJm/IBPV5gm0MZw39yYsfnZlOZYawjN1Om7cmKyP354A3gAZKqQOWR1PgS6CRUuok0Mjy3uAk3AqBDvOh/Xx4tBDs7we960guHE/kMBdowXhKUZjV9E9X7dP0MHID5PeDHrWt0pzBCVAomlKVvYxgMb2IQfMak6jBp6zjL6cT+UwPUbTW2yDFuavJeeeE/HIc3vlJsjl+3hgGvQA+HuzBERt96ke2DNU+TYvDV2D5YfikIeR1s7KCBvDCizY8w6tUZx7b+YzlBDKW5ynHSNrwPOUc3UXApB/wCO6Fw3tLoOlMKOgHu/rAsIaeLey3uEdjxnCXMH5hAKWw3qL+/20UU0yfOlZr0uCE+ODNm9TlBGP4hs6c5Ap1+YImjGUvZx3dPSPu7s62s1BlPHy7CwbWgz194alHHN0rxxJKBC0Yz2musZIPrJo86tQNWHgAejwLhXJZrVmDE5MdX3rSiNOMYzTt2MUZqvMJrZnEUS45rF9G3N2I+fN5UNYvoAw0Hwt1p4IGNneHMc0hh6+je+lYooimPZPZwSnm0Z36WLdy95e/S7DSh3Wt2qzBBchJdj6iOWcYx6e8yjoOUYnBdGE6ZxzgNGjE3U2ITTF87hzoInC+Hqy5BvULSoWk58s4uoeOR6PpwSxWsY9JvJHl6NPEnL8Ns/eI62OxvFZt2uBC5CMnn9GKs4znQ5qwmJ2U4yPe5wcucctu/TDi7uKERcK+i9D3Wwh5BmgHdARyAEvhzDTIYxb1AIk+/ZZNDOFletHI6u2P3SzPA+tZvWmDC1KYPIylA6cZR1fq8x2beZQBDOBHbnA3wUy7VCkZoFkTEzfnQly5I1GkB/+Nez5+XXLCUAOIBK4D+4E/gTA474EBSckxjQ2MYAVvUZcvaG319q/cgW93QpfqnhsvYEiehynAFN5kAE35D8uZwFomR/5O1LlAom43AZ2Tc+dk5g3Wq/lg8rk7IZHRcOK6RcTjCfnVeEnqSuaHKsWhysPy3K8j/HskaVsBAfDPP3brulOynD20ZhJNqMIK+uGD9d2EPloN47bAiY/gUddKCW6wM8e4RPU1Swlpthtu5obGA2Gv2E0z+v+aWm4ZM3J3MLdDko7GD1+Rwg4A2bzhyWLQpLwIedWHoXJxKJgzYTsRg5Mv6zdypP3OxRnZaql9WoMyLKKnTYT95n2YsgPaVzXCbkibJ3iE0BZ9oO4x2DQKqp99IO7nz1vvOEbc7URMDJy+mVDID/wLF+LVBSiaW0bhfeqIiFd5GMoVSV+q2Nip3NCh8gPx9xdhd8WyftbiMBd42QbRp4mZtA3uR8DgBjZp3uCG+PvDuePFk91uLYy424B74XDocpyAH/wXDl0RAQCpxlOuCNQpFSfiVYpn3cOiY0fPFvP4nOfGg+jTrNQ+TYs7YTBpO7xaESoWs8khDG7IyJHQdTgJMsVbe6ZtxD0LaC0j7wPxbeOXZYQeu5SRL4eI9zs142zkTz5k/M1tyS3uEchY7hLGVobZtC7mlD8gKFQKXxsM6aVjRwjKAb0AlNjarT3TNuKeTsIi4cjVhLbxg5flHzuWsoVkJN75acto/GHwz++ZKXQdRQjhNGccp7nGOj6iMlac5yY+VgSM3wKB5aBaCZsdxuCmvPaaiPvUqdDdBu17lLjPn58+m3SqLodATl9Z1GxXJW40XqmY8Sd3NLHRp39ymsX0snr0aWK+3QnX78MwM2o3OCEeI+6xEZyx3iTnzkHX7nAhFEo+nbbL4SsV44S8bCFTxd7Z0GjeZxb/Yz/f0JnW1LTp8cKjYMwmqFcGnitt00MZDJnCY8R96FCLsJcBHgOKQGhhGHwCOJHU5TBWyBO7HBqch/gzsbwTlhHcdxNDeZmeNog+TczsPfDvHSlLaDA4Ix4MSoZiAAAQ3UlEQVQj7g/8R58GSsX7QygMbgbda5nIQlciwUys+3qC+67Ae3Zdyvu0lvQLNiQqWhKE1SwJLz5m22MZDJnFY4wLD/xHlwJzgU3AaVA+kn87YBQ8Phq6LYEf98OlYId11ZAOHszEWu6ByXPgRDGi/xvI4NEhWa6Ik1bOjwUH4OwtyYlvFssNzorHjNxHjow30rsqj5xHYdp0qFAfNp+GTWdg8UFZKAOJNqxfRuyq9ctCifwOPAFDAh7MxN7cCl4ayl2BQ0O4COTAh2Lkozj5KUY+ij14TrjtIfKSg4QFTpNbm4mf8yMmBkZtlAX15rZdrzUYsoTHiHtaEZzVSsCH9cQj5q/LUjh602lYcgi+2yX7lC0UJ/T1y8piq8Ex+PuL8NK2N5S5BsWCoVgQBZ4MouvwYK4QzGWCOMN1/uAk17mbbDsFyPVA+IuRn9Wh+QjpkR+u5LM88hNyOT9DhuWiY0cvlh2G49dgUSczajc4NyZxWBpEx0i06aYzMrrffAZuW3zbSxe0CH0ZqFcWAgqk393SkDUSj7BBIvxmzEj++44kimvc4QrBlkcQlwmK995yMwgJgpwRyTTgzcM+ebk9YTBeUdlpN2AFD3vlTXaGkJPstjtxg9twhSCK05upvEn3TJadNonDsoC3F1R9RB79npdp+aErIvKbTsPKI/DDbtm3SDa4dRCicwPF4FwIvDsQbkbA623Bzxf8fMSWa8gaGc2l44sPj1CQRyiYarsBFTTnb4ZB8SDLbEBmBHnLB/HEC3nZcLk4/u2WssZrH9e4k6x9Py9+CWYDxeOZheS9PBcmD96es+xlsDNm5J5FYmIkcnXTaRg0GUIKAmm4T2bzhpzZ4sTezzfukdM34fuUtqV3ew5zM8kQKc0Ipk+Hb27D1bvw98eSzC2KaG5w98GoP3ZGkPC9bLtLWJJjeaEoSt4kI//k3ue2UdIzg+MwI3cnx8sLKhWXR9+6SMHSwkBu5Nv1lcekyRAaaXlESej6g/eWbaGRYvL5907c+9BI2TcsKvN9zO6TwRuET7ybTxrb/HwTbs/h49q26JRmBMVqws4ZMLVVXJZOH7wtApyfqmkU2b5PWBIT0JV4N4DLBHGQC1wlmGhiknw+F9mTjPwTLhLL+6LktUlaY4PrYcTdijxY5LtheVgICIDedbLWttYi8PFvCCERCW8CD7ZHJt2W0vabIcnvm5WbSY7Eom/D2Ul2G9xM4mfXjF1DOfcMeBeB7KeAZzPeZi5yUJYclOWhVPeLIYab3Esy8o///jCX+I0jBBOS5PMKRRHypDATSGgmyosfChe+E7s4S5cCr8H778OXv5jEYU5NAndLC9ZK46lUnLDZg5iYpDeTFG8cKcxEktt2MwQuRMS9j/1beCZvJkrFu5lYYXYS/6a0YR38ZziE+QH+EP079PoBsnnZbpHcCy+KkJci5KVyGvuGEsHVVGYCVwjmGP9yhSAiiU7y+Rz4Jhn5J2cWeoh8ZDNSYVXmz4eBw4HXAJ3U5dYaGJu7lTHeMpkjJib5WUhKN5SMzE6S2x6RVOtSJwT4Foh0vdKFGs0t7qXpKXSFYG5xL9k2CpE7hQXihDeHAuQys4FUCCWC7fzNa1OPcOeZQ/D0OXj3HZhZH7BumT0j7gaPJNoyM0kyu4g362jRCvBG5rfXgCvyWaXkZuSOhBPJNe6kuUB8mWDCiUzy+WyWALK0zELJBZC5I9HEsI9/2MAR1nOEbfwt31uEN+x4FNZXhEkvwR3xwsjob8ssqBoMifD2glzZ5JESAWGWNZREWLMUmrORHV9KUoiSFEp1P40mmJBUF4jPcp0dqQSQ5SdnKgvEcdsKkRsvJ3QZTW6W/npHzSmusp7DrOcIGzlKkGVtpDIl6UlDGvIk71Utx4VjST2gTJk9g8EO2HINxdVRKPKTi/zkojwPp7pvegLIdnKaywQRStIAMh+8eYi8qcwE7B9AlsBltmgw5549Speow/S9f4SbuW4C4E8hXqMGDXmSBlSgKPkefP7/htr+t+Wa4n7zJrxo8Qu9cgW8vaFIEXm/axdkSzQcu3ULFi+G7mnUO4mKgsKFISgo9f2GDZP9+vWTW3fDhvDCC8nvu2wZVKgA5cunfV72on9/WLtW5n+BgTB+vMwH582D0aPldYkS8r5gQdi3T5b0w8PB11dKx1RPZiY4cyZ8+aW8/vRT6NRJXu/eDW+9BWFh0KIFTJhgv3PNAqbouHVIbwCZRnPP4jKaklnoErfZy1mucYeYZALI8pAjXXEDGQkgS26E/tGMq4T02Qmtd0O1fwCIvpWL0M0VmNq0BQ2pSFmKprj+YI/fluvb3D/7DHLnhgEDUt7n1Clo3RoOHEi9rcyIe1p06iTHfuWVtPe1B1u2SP83bRJxr11bxL1mTRH048dF0D/8UJ6HDYMGDWDwYGjUCFatgkmTYP36hO3euAHPPAN790J0tIj/gQOQLx9UqwbTpsm2xo1h4EBpy2DIJPEDyFLzFkorgCytuIHNi/LR920/GWE/dhna7EK13Y2uYrHX/VkWVj4Nv1WE/aVQ2suu6zGeZXMfMwbmzJHX770HvXvDoEFw4gRUrSoj1SFDRGyDgkTQR42C5s1Tb3fECLmF+/uL6BW2FF2OL94DB8KaNeDjA02aSJs//wzbt8tNaMUKGTHPnAkREfD449JXPz9pp1AhGeVeuQLjxsGrr8oxRo2CBQskYqp5c7nFnzwJvXqJqObKBd99J+2lhVIygo6IEBGOioKiRcWRXmu4fx8KFIC7d6FixbjP3Lkjr4OD4eFkpuG//CLfbX5LNrUGDeDXX6FWLTlejRqy/Y035Hsw4m7IAvEDyNIicQBZcmahFAPI2gHNskNwTnjkNgB6x6OoAa+jF9eACwmLr/unHstmV9xL3HftEgHetUuEq2ZNqFdPTAWnTsWN3CMjYeVKyJMHrl2D555LXdx37ZKIgwMHRBSrVoVnE0WyXL0qQn7kiIhhUJAIXdOmCUfubdrEmYcGDYJZs8TkAdKX7dvh0CFo21bE/X//E+HctUtuArduyb7duomgly0rn+nVS8R0zhwZiSemXDlYtAief15G68WKiZj36xd3U/jmGzEh5c4tZqTp02X7pEky4u7XTz6zY0fS9i9dgpIl496XKCHbUtpuMNiJjAaQxR/5d/k4CB4KhiJ3YV8ALKkJFwuhERt5/DAyZ1uPsYm4K6UCga8QR7LvtNZf2uI4Sdi6VUqK57Qkd3nlFdi2DV56KeF+WsPHH8vfvLzgwgUZAedPYRSwZYu06+cnjxYtku5TsKC01bUrNGuW8s3ir7/gk09E/O/eTbjfK6/IjaFy5TgBXL8e3n5bjht7nKAg+PNP6VMsUZYooM6d5ZESJ07A6dPSfnS0rBc0biwmk+nTpX8BAdCjh8yCBg2CyZPl0bIl/PijnOPatUm/08ShokrJ9sS4cn4Cg9sSP4CsEjIg+WRR8h5TAQEi5M68HmN1cVdKeQOTgUbARWC3UmqV1vqotY+VhPSuH8yZI+aFffvEhFKihJgOUiMtQfL1hT174LffYOFCWXT89dek+3XuLCPxihVl5P3nn3F/yx5vpT/2XJITTa3FLJTcGkJaI/dly2TkniuXbA8MlD4oJedQ2lLtuW1bmDhRXs+bB1OmyOt27UT4E1OiRMJzuXhRzrFECbl5xt+enFnHYHBCUvOYip+mwhmxhfNoTeCU1vqM1joCWAi0tMFxklK3LixfDqGhcO+emF6ef17ML3fj+doGB4ud2cdHxDgtM0HduiKKYWFie169Ouk+d+/K35o3F2+Q/ftle+Jj378vJpHISBkFp8VLL4mNPtSSRP7WLbGJFy8u5wqyMHrwoLzu3FlEP/Fj0SL5u78/bN4sI/3ISHn9xBMiwocOiScSyIzhCUupoYcekllO7PZy5ZL2MzBQblpBQdLGhg3S95Il5aa1e7fclObOlRmAweACdOwoNQICAmT8ExCQcs0AZ8MWZplHgHhDNS4CzyTeSSnVDegG4G8tz/2aNaFDh7jFu/ffh0qV5HX16vK6WTPxBGnRQrY9/TQ89lja7b76KlSpIkU169ZNuk9wMLRqJe6CMTFxo+cOHWRhd9w4WUgcMULa8/eXkW1aM4bmzUW4q1eXkXWLFvD55zI7eP99WaiNiJAF2SpV0v6O2rcXT5nKlswlzZrJ4i+IZ0ydOnKcUqVg9mzZPnMm9OkjZhw/vzhb/M6d8MMP4glTpIh41MS6SI4YIZ4yILOYN9+Uc23e3CymGlwKZx+hp4TVXSGVUm2Axlrrdy3v3wBqaq17p/QZk37AYDAYMk5qrpC2MMtcBOK5R1AC+NcGxzEYDAZDCthC3HcDjymlSiulsgHtgVU2OI7BYDAYUsDqNnetdZRSqhewDnGF/F5rfcTaxzEYDAZDytjEz11r/TPwsy3aNhgMBkPaOEVuGaXUdSCZUIEUKUyCQnYehTl3z8NTzxvMuad17gFa6yLJ/cEpxD2jKKX2pLRC7O6Yc/e8c/fU8wZz7lk5d+fLgG8wGAyGLGPE3WAwGNwQVxX3GY7ugAMx5+55eOp5gzn3TOOSNneDwWAwpI6rjtwNBoPBkApG3A0Gg8ENcTlxV0oFKqVOKKVOKaUGObo/tkIpVVIp9btS6phS6ohSqq9le0Gl1G9KqZOW5wKO7qutUEp5K6X2K6VWW96XVkrttJz7Ikt6C7dDKZVfKbVEKXXccv2f9ZTrrpT6wPJ7P6yUWqCUyuGu110p9b1S6ppS6nC8bcleZyVMsujeX0qpp9Nq36XEPV4hkCZABaCDUqqCY3tlM6KA/lrrJ4BaQE/LuQ4CNmitHwM2WN67K32BY/HejwYmWM79NvCOQ3ple74C1mqtywNVkO/A7a+7UuoRoA9QXWtdEUlf0h73ve6zgMBE21K6zk2AxyyPbsDUtBp3KXHHkYVA7IzW+rLWep/l9V3kH/wR5HwtidaZDbzimB7aFqVUCaAZ8J3lvQIaAEssu7jluSul8gJ1gZkAWusIrXUQHnLdkZQofkopHyAncBk3ve5a6y3ArUSbU7rOLYE5WvgTyK+UKp5a+64m7skVAnnEQX2xG0qpUsBTwE7gIa31ZZAbAFDUcT2zKROBj+BBOfpCQJDW2lIs1m2vfRngOvCDxST1nVIqFx5w3bXWl4D/AucRUQ8G9uIZ1z2WlK5zhrXP1cQ9uUKmbu3LqZTKDSwF+mmt7zi6P/ZAKdUcuKa13ht/czK7uuO19wGeBqZqrZ8C7uOGJpjksNiXWwKlgYeBXIg5IjHueN3TIsO/f1cTd48qBKKU8kWEfb7Wepll89XY6Zjl+Zqj+mdDngNeVkr9g5jeGiAj+fyW6Tq477W/CFzUWu+0vF+CiL0nXPeGwFmt9XWtdSSwDKiNZ1z3WFK6zhnWPlcTd48pBGKxMc8Ejmmtx8f70yqgi+V1F2Clvftma7TWg7XWJbTWpZBrvFFr3RH4HWht2c1dz/0KcEEpFVuF/EXgKB5w3RFzTC2lVE7L7z/23N3+uscjpeu8Cuhs8ZqpBQTHmm9SRGvtUg+gKfA3cBoY6uj+2PA86yDTrr+AA5ZHU8T2vAE4aXku6Oi+2vh7qA+strwuA+wCTgE/Adkd3T8bnXNVYI/l2q8ACnjKdQf+AxwHDgNzgezuet2BBcjaQiQyMn8npeuMmGUmW3TvEOJRlGr7Jv2AwWAwuCGuZpYxGAwGQzow4m4wGAxuiBF3g8FgcEOMuBsMBoMbYsTdYDAY3BAj7gaDweCGGHE3GAwGN+T/AcCabq/tjpwcAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "tabu_vrp = Tabu_VRP('data\\VRP\\A-n32-k5.vrp', n_vehicles=6, initialize='greedy')\n",
    "tabu_vrp.run()\n",
    "tabu_vrp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that Tabu search gives a much better result than Greedy search. Also, we notice that initializing Tabu search with the greedy algorithm has a much larger benefit. We compare the benefit for multiple example problems below. More complex problems can be found at [afurculita's repo](https://github.com/afurculita/VehicleRoutingProblem/tree/master/datasets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Solving A-n32-k5...\nSolving A-n33-k6...\nSolving A-n48-k7...\nSolving A-n80-k10...\nSolving B-n56-k7...\nSolving P-n22-k8...\nSolving P-n76-k5...\n"
    }
   ],
   "source": [
    "problems = ['A-n32-k5', 'A-n33-k6', 'A-n48-k7', 'A-n80-k10', 'B-n56-k7', 'P-n22-k8', 'P-n76-k5']\n",
    "n_trials = 10\n",
    "costs = np.empty((len(problems), n_trials, 2))\n",
    "for p, prob in enumerate(problems):\n",
    "    print('Solving {}...'.format(prob))\n",
    "    for i in range(n_trials):\n",
    "        tabu_vrp = Tabu_VRP('data\\VRP\\{}.vrp'.format(prob), initialize='random')\n",
    "        _, cost_random = tabu_vrp.run()\n",
    "        tabu_vrp = Tabu_VRP('data\\VRP\\{}.vrp'.format(prob), initialize='greedy')\n",
    "        _, cost_greedy = tabu_vrp.run()\n",
    "        costs[p, i, :] = [cost_random, cost_greedy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "random_means = np.average(costs[:, :, 0], axis=1)\n",
    "random_std = np.std(costs[:, :, 0], axis=1)\n",
    "greedy_means = np.average(costs[:, :, 1], axis=1)\n",
    "greedy_std = np.std(costs[:, :, 1], axis=1)\n",
    "x = 2*np.arange(len(problems))  # the label locations\n",
    "width = 0.8  # the width of the bars\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(x - width/2, random_means, width, yerr=random_std, label='Random')\n",
    "rects2 = ax.bar(x + width/2, greedy_means, width, yerr=greedy_std, label='Greedy')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Cost of optimal solution')\n",
    "ax.set_title('Problem Set')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(problems)\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "def autolabel(rects):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that in most cases (except that of `P-n22-k8.vrp`) having a greedy initial solution leads to better optimal solution being found. In all cases, the standard deviation of the optimal solution is much smaller when beggining with the greedy initial solution (as expected, since for every new random initialization, the initialization is different)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3-final"
  }
 },
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 "nbformat_minor": 2
}