diff --git a/ES1_START_HERE.ipynb b/ES1_START_HERE.ipynb index 149208e..cc9696a 100644 --- a/ES1_START_HERE.ipynb +++ b/ES1_START_HERE.ipynb @@ -1,170 +1,170 @@ { "cells": [ { "cell_type": "markdown", "id": "a56a45de-211d-46a1-abbc-b5668a6a7d9c", "metadata": {}, "source": [ "## Evolutionary Robotics Exercise Sheet 1: Evolutionary Algorithms\n", "\n", "Euan Judd (euan.judd@epfl.ch)\n", "\n", "Luca Zunino (luca.zunino@epfl.ch)\n", "\n", "# Goal. \n", "\n", "The first 3 laboratory hours introduce basic implementations of Genetic Algorithms, Evolutionary Strategies and NSGA-II. Each is written in Python and is intended to give you a quick understanding of how the theory introduced during lectures can be used to solve simple optimisation problems. Future exercise sheets will use the RoboGen software (https://robogen.org/) which will be used as a black box where you are only expected to work with the GUI (although you can look at the C++ code if you wish https://github.com/lis-epfl/robogen). For this reason, you should spend time reading the Python code in this exercise sheet and compare it with the theory before the implementation is hidden under a layer of abstraction.\n", "\n", - "As the functions we will optimise parameters for are simple and the algorithms are not computationally expensive, you should also use this opportunity to observe the effects of changing algorithm parameters such as the mutation rate, crossover rate, selection pressure, population size and number of generations. The future RoboGen problems will be more computationally expensive and so it will be significantly more time consuming and computationally expensive to gain an appreciable understanding of how each parameter influences the progression and result of the optimisation procedure. \n" + "As the functions we will optimise parameters for are simple and the algorithms are not computationally expensive, you should also use this opportunity to observe the effects of changing algorithm parameters such as the mutation rate, crossover rate, selection pressure, population size and number of generations. The future RoboGen problems will be more complex and so it will be significantly more time consuming and computationally expensive to gain an appreciable understanding of how each parameter influences the progression and result of the optimisation procedure. \n" ] }, { "cell_type": "markdown", "id": "09d8b725-c38a-4f07-8dca-faeac29a578a", "metadata": {}, "source": [ "# Fitness function\n", "\n", - "In the first two parts of this exercise sheet, genetic algorithm and evolutionary strategies, we will optimise the x and y parameters of the Ackley function. The Ackley function has a single global optima at [0, 0] and multiple local optima. We will look at the function with x and y in the range [-5, 5].\n" + "In the first two parts of this exercise sheet, genetic algorithm and evolutionary strategies, we will optimise the x and y parameters of the Ackley function. The Ackley function has a single global optimum at [0, 0] and multiple local optima. We will look at the function with x and y in the range [-5, 5].\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "3abbbb09-ea24-465d-90be-f929898af062", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Ackley function\n", - "def objective(x, y):\n", + "def ackley(x, y):\n", " return -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2))) - np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))) + np.e + 20\n", "\n", "# define range for input\n", "r_min, r_max = -5.0, 5.0\n", "# sample input range uniformly at 0.1 increments\n", "xaxis = np.arange(r_min, r_max, 0.1)\n", "yaxis = np.arange(r_min, r_max, 0.1)\n", "# create a mesh from the axis\n", "x, y = np.meshgrid(xaxis, yaxis)\n", "# compute targets\n", - "results = objective(x, y)\n", + "results = ackley(x, y)\n", "# create a surface plot with the jet color scheme\n", "fig = plt.figure()\n", "ax = fig.add_subplot(projection='3d')\n", "surf = ax.plot_surface(x, y, results, cmap='jet')\n", "ax.set_xlabel(\"param x\")\n", "ax.set_ylabel(\"param y\")\n", "ax.set_zlabel(\"Ackley\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f76561b5-9233-4576-8b1c-96a0dbc65538", "metadata": {}, "source": [ "## How to use this exercise sheet\n", "\n", "### Step 1 Genetic Algorithm Walkthrough\n", "\n", "We will first go through the contents of the folder\n", "\n", - "- es1_0_genetic_algorithm_walkthrough\n", + "- /es1_0_genetic_algorithm_walkthrough/\n", "\n", "where I will explain how to write the building blocks of a genetic algorithm in simple Python.\n", "\n", "### Step 2 Genetic Algorithm\n", "\n", - "Take a look at the code in \n", + "Look at the code in \n", "\n", - "- es1_1_genetic_algorithm\n", + "- /es1_1_genetic_algorithm/\n", "\n", "which is the same but written as functions so you can optimise the Ackley function parameters using a GA. First, look at the functions in \n", "\n", "- crossover.ipynb\n", "- fitness.ipynb\n", "- mutation.ipynb\n", "- selection.ipynb\n", "\n", "Then open \n", "\n", "- GA.ipynb\n", "\n", "Run the genetic algorithm and complete the exercises.\n", "\n", "Open \n", "\n", "- GA_param_search.ipynb\n", "\n", "Complete the exercises.\n", "\n", "### Step 3 Evolutionary Strategies\n", "\n", "Open the folder\n", "\n", - "- es1_2_evolutionary_strategies\n", + "- /es1_2_evolutionary_strategies/\n", "\n", "and open the file \n", "\n", "- es.ipynb\n", "\n", "Complete the exercises.\n", "\n", "### Step 4 NSGA-II\n", "\n", "Open the folder\n", "\n", - "- es1_3_nsga\n", + "- /es1_3_nsga/\n", "\n", "and open the file \n", "\n", "- es1_beam.ipynb\n", "\n", "Complete the exercises.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "92be6549-f891-4b6f-a59f-cf53ad1987e5", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_0_genetic_algorithm_walkthrough/step_1_fitness.ipynb b/es1_0_genetic_algorithm_walkthrough/step_1_fitness.ipynb index e8eb4bc..8fcf0d6 100644 --- a/es1_0_genetic_algorithm_walkthrough/step_1_fitness.ipynb +++ b/es1_0_genetic_algorithm_walkthrough/step_1_fitness.ipynb @@ -1,482 +1,482 @@ { "cells": [ { "cell_type": "markdown", "id": "c625c849-05f3-4992-baac-d01f6c1fd6bb", "metadata": { "tags": [] }, "source": [ "## Fitness function\n", "\n", "GA order of operation:\n", "1. **Calculate the fitness of each chromosome in the current generation.**\n", "2. Select the parents from the current generation that will be used to make the next generation.\n", "3. Perform crossover of the parents to make children that will be the chromosomes in the next generation.\n", "4. Mutate the children made in step 3.\n", "\n", "When a new generation of chromosomes is created while running a GA, the first thing to do is rank the chromosomes based on their fitness. To do this, we obviously need to define the fitness function which we will do here.\n", "\n", - "The fitness is usually decided by you, the programmer, depending on the type of problem you are working on. However, in this example, we will find parameters (x and y) of the Ackley function. The Ackley function has a single global minima of 0 at [0, 0] and multiple local minima.\n", + "The fitness is usually decided by you, the programmer, depending on the type of problem you are working on. However, in this example, we will find parameters (x and y) of the Ackley function. The Ackley function has a single global minimum of 0 at [0, 0] and multiple local minima.\n", "\n", "First, we'll import numpy for manipulating arrays and matplotlib for plotting the Ackley function again." ] }, { "cell_type": "code", "execution_count": 1, "id": "bf55cc5a-846c-4b5d-8327-6024c657eb9e", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib import cm" ] }, { "cell_type": "markdown", "id": "18bf9db4-3a0a-4156-838b-4af1561655ce", "metadata": {}, "source": [ "Let's plot the Ackley function again:" ] }, { "cell_type": "code", "execution_count": 2, "id": "ee1bc645-4e37-4f4c-bb8f-38d34770c3a7", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Ackley function\n", "def ackley(x, y):\n", " return -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2))) - np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))) + np.e + 20\n", "\n", "# define range for input\n", "r_min, r_max = -5.0, 5.0\n", "# sample input range uniformly at 0.1 increments\n", "xaxis = np.arange(r_min, r_max, 0.1)\n", "yaxis = np.arange(r_min, r_max, 0.1)\n", "# create a mesh from the axis\n", "x, y = np.meshgrid(xaxis, yaxis)\n", "# compute targets\n", "results = ackley(x, y)\n", "# create a surface plot with the jet color scheme\n", "fig = plt.figure()\n", "ax = fig.add_subplot(projection='3d')\n", "surf = ax.plot_surface(x, y, results, cmap='jet')\n", "ax.set_xlabel(\"param x\")\n", "ax.set_ylabel(\"param y\")\n", "ax.set_zlabel(\"Ackley\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "f04d3bb0-56e7-4775-a564-4beda7308eb0", "metadata": {}, "source": [ - "You can see the global minima: f(0, 0) = 0\n", + "You can see the global minima: ackley(0, 0) = 0\n", "\n", "## Chromosome\n", "The chromosome we have chosen is a binary list where the first 10 numbers are the x value (decoded later) and the last 10 are the y value." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "ef32aca4-d007-47d9-93cc-214856db6d63", "metadata": {}, "outputs": [], "source": [ "chromosome = np.array([0,0,0,0,0,1,0,1,1,1,\n", " 0,1,0,0,0,0,1,1,0,1])" ] }, { "cell_type": "markdown", "id": "486d5cfd-2dc0-4f36-812e-872a75e9aa2c", "metadata": {}, "source": [ - "## Precision the binary number\n", + "## Binary number precision \n", "\n", "We are looking at the Ackley function where the x and y values are both in the range -5 to 5. Therefore, with 10 bits for each value and a value within this range, the precision of the decoded x and y values is:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "b5bf3f98-235e-4aab-8f73-a327e2ef6ff7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Precision: 0.009775171065493646\n", "\n" ] } ], "source": [ "lower_bound_x, upper_bound_x = -5, 5\n", "len_x = (len(chromosome) // 2)\n", "lower_bound_y, upper_bound_y = -5, 5\n", "len_y = (len(chromosome) // 2)\n", "\n", "precision_x = (upper_bound_x - lower_bound_x) / ((2**len_x) - 1)\n", "precision_y = (upper_bound_y - lower_bound_y) / ((2**len_y) - 1)\n", "\n", "print()\n", "print(f\"Precision: {precision_x}\")\n", "print()" ] }, { "cell_type": "markdown", "id": "1ce5eed5-f564-4012-9e62-31056567f8d6", "metadata": {}, "source": [ "## Decoding the chromosome\n", "\n", "Now that we know the precision of the parameters, we want to decode the chromosome so that the decoded values can be used to calculate the fitness.\n", "\n", - "NOTE: For a numpy array, index 0 is the first item and index 19 is the last item with an array of length 20. If we want to go in the opposite direction then index -1 is the last item (i.e. index 19) and index -20 is the first item (e.g. index 0). As we are decoding the binary number from least to most significant bit, the least significant bit of the encoded x value is at index -11 and the most significant bit is index -20.\n", + "NOTE: For a numpy array, index 0 is the first item and index 19 is the last item with an array of length 20. If we want to go in the opposite direction, then index -1 is the last item (i.e. index 19) and index -20 is the first item (e.g. index 0). As we are decoding the binary number from least to most significant bit, the least significant bit of the encoded x value is at index -11 and the most significant bit is index -20.\n", "\n", "Starting from the least significant bit of the x value (-t=-11), we multiply this bit by 2^z (z=0), e.g. \n", "\n", " chromosome[-{t}]*(2**z) = chromosome[-11]*(2^0) = 1 * 2^0 = 1\n", " " ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "104bfdbb-12a6-4397-9429-d4c801e6e7da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Encoded x: [0 0 0 0 0 1 0 1 1 1]\n", "\n", "chromosome[-11]*(2**z) = 1*(2**0) = 1\t=> x_bit_sum = 1\n", "chromosome[-12]*(2**z) = 1*(2**1) = 2\t=> x_bit_sum = 3\n", "chromosome[-13]*(2**z) = 1*(2**2) = 4\t=> x_bit_sum = 7\n", "chromosome[-14]*(2**z) = 0*(2**3) = 0\t=> x_bit_sum = 7\n", "chromosome[-15]*(2**z) = 1*(2**4) = 16\t=> x_bit_sum = 23\n", "chromosome[-16]*(2**z) = 0*(2**5) = 0\t=> x_bit_sum = 23\n", "chromosome[-17]*(2**z) = 0*(2**6) = 0\t=> x_bit_sum = 23\n", "chromosome[-18]*(2**z) = 0*(2**7) = 0\t=> x_bit_sum = 23\n", "chromosome[-19]*(2**z) = 0*(2**8) = 0\t=> x_bit_sum = 23\n", "chromosome[-20]*(2**z) = 0*(2**9) = 0\t=> x_bit_sum = 23\n", "\n", "Decoded x: x = (x_bit_sum * precision_x) + lower_bound_x = 23 * 0.009775171065493646 + -5 = -4.775171065493646\n" ] } ], "source": [ "print(f\"Encoded x: {chromosome[:10]}\")\n", "print()\n", "z = 0\n", "t = 1 + (len(chromosome)//2)\n", "x_bit_sum = 0\n", "for i in range(len(chromosome)//2):\n", " x_bit = chromosome[-t]*(2**z) \n", " x_bit_sum += x_bit\n", " print(f\"chromosome[-{t}]*(2**z) = {chromosome[-t]}*(2**{z}) = {x_bit}\\t=> x_bit_sum = {x_bit_sum}\")\n", " t += 1\n", " z += 1\n", " \n", "x = (x_bit_sum*precision_x)+lower_bound_x\n", "print()\n", "print(f\"Decoded x: x = (x_bit_sum * precision_x) + lower_bound_x = {x_bit_sum} * {precision_x} + {lower_bound_x} = {x}\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "6c91012e-ff98-41f1-8442-8922a029675b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Encoded y: [0 1 0 0 0 0 1 1 0 1]\n", "chromosome[-1]*(2**z) = 1*(2**0) = 1\t=> x_bit_sum = 1\n", "chromosome[-2]*(2**z) = 0*(2**1) = 0\t=> x_bit_sum = 1\n", "chromosome[-3]*(2**z) = 1*(2**2) = 4\t=> x_bit_sum = 5\n", "chromosome[-4]*(2**z) = 1*(2**3) = 8\t=> x_bit_sum = 13\n", "chromosome[-5]*(2**z) = 0*(2**4) = 0\t=> x_bit_sum = 13\n", "chromosome[-6]*(2**z) = 0*(2**5) = 0\t=> x_bit_sum = 13\n", "chromosome[-7]*(2**z) = 0*(2**6) = 0\t=> x_bit_sum = 13\n", "chromosome[-8]*(2**z) = 0*(2**7) = 0\t=> x_bit_sum = 13\n", "chromosome[-9]*(2**z) = 1*(2**8) = 256\t=> x_bit_sum = 269\n", "chromosome[-10]*(2**z) = 0*(2**9) = 0\t=> x_bit_sum = 269\n", "\n", "Decoded y: y = (y_bit_sum * precision_y) + lower_bound_y = 269 * 0.009775171065493646 + -5 = -2.3704789833822093\n" ] } ], "source": [ "print(f\"Encoded y: {chromosome[10:]}\")\n", "z = 0\n", "t = 1\n", "y_bit_sum = 0\n", "for i in range(len(chromosome)//2):\n", " y_bit = chromosome[-t]*(2**z)\n", " y_bit_sum += y_bit\n", " print(f\"chromosome[-{t}]*(2**z) = {chromosome[-t]}*(2**{z}) = {y_bit}\\t=> x_bit_sum = {y_bit_sum}\")\n", " t += 1\n", " z += 1\n", " \n", "y = (y_bit_sum*precision_y)+lower_bound_y\n", "print()\n", "print(f\"Decoded y: y = (y_bit_sum * precision_y) + lower_bound_y = {y_bit_sum} * {precision_y} + {lower_bound_y} = {y}\")" ] }, { "cell_type": "markdown", "id": "4cd9c548-9b9e-4cc8-95f3-e24d72535266", "metadata": {}, "source": [ "## Note\n", - "The GA will not necessarily be able to find the values of x and y that result in exactly zero, i.e. [0, 0], due to the precision we get when encoding each as a binary number of length 10. Increasing the number of bits used to represent each parameter will improve the precision but increase the search space. However, finding the exact global optima is not necessarily important so if you can reduce the search space then you may improve the convergence rate.\n", + "The GA will not necessarily be able to find the values of x and y that result in exactly zero, i.e. [0, 0], due to the precision we get when encoding each as a binary number of length 10. Increasing the number of bits used to represent each parameter will improve the precision but increase the search space. However, finding the exact global optimum is not necessarily important so if you can reduce the search space then you may improve the convergence rate.\n", "\n", - "We can see if the global minima of [0, 0] can be found by rearranging the equation \n", + "We can see if the global minimum at [0, 0] can be found by rearranging the equation \n", " \n", " x = (x_bit_sum * precision_x) + lower_bound_x\n", "\n", "to find y_bit_sum\n", "\n", " x_bit_sum = (x - lower_bound_x) / precision_x\n", "\n", "Then the global minima is at x=0 \n", "\n", " x_bit_sum = (0 - -5) / 0.009775171065493646 = 511.5\n", "\n", "As x_bit_sum=511.5 is not an integer, the global optima cannot be found." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "69477081-47c1-441d-b0dd-202a9f5c6563", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x_bit_sum = (x - lower_bound_x) / precision_x\n", "x_bit_sum: 511.5 = (0 - -5) / 0.009775171065493646\n", "\n", "The x_bit_sum is an integer: False\n", "If False, the global minima cannot be found.\n" ] } ], "source": [ "print(f\"x_bit_sum = (x - lower_bound_x) / precision_x\")\n", "x_bit_sum = (0.0 - lower_bound_x) / precision_x\n", "print(f\"x_bit_sum: {x_bit_sum} = ({0} - {lower_bound_x}) / {precision_x}\")\n", "\n", "print()\n", "print(f\"The x_bit_sum is an integer: {int(x_bit_sum) == x_bit_sum}\")\n", "print(f\"If False, the global minima cannot be found.\")" ] }, { "cell_type": "markdown", "id": "1ff09333-446b-4585-8fcd-dddba30327de", "metadata": {}, "source": [ "## Fitness function\n", "Now we have the decoded values for x and y, we can use them as parameters to find the solution of the fitness function." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "31ec07e0-b11e-4c40-a9f4-e6f46463fa16", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ackley(-4.775171065493646, -2.3704789833822093) = 12.54062231936802\n" ] } ], "source": [ "fitness = ackley(x, y)\n", "\n", "print()\n", "print(f\"ackley({x}, {y}) = {fitness}\")" ] }, { "cell_type": "markdown", "id": "46a622eb-5578-4fa4-be1b-5710b70d9d8a", "metadata": {}, "source": [ - "## Fitness function global minima\n", + "## Fitness function global minimum\n", "\n", - "As stated, the global minima is:" + "As stated, the global minimum is:" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "id": "aa166d4e-f458-4368-ac23-fcc045cd63ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Global minima: ackley(0, 0) = 0.0\n" ] } ], "source": [ "fitness = ackley(0, 0)\n", "\n", "print(f\"Global minima: ackley(0, 0) = {fitness}\")" ] }, { "cell_type": "markdown", "id": "c6b74376-6eff-4ff5-9079-67d7e9e4f640", "metadata": {}, "source": [ - "If we print the Ackley function output for x values monotonically decreasing from 5 to 0 in steps of 0.5, we will also see that the output does not monotonically decrease. This makes the search space harder to find as the GA could get stuck in a local minima rather than continue searching for the global optima. This can be avoided by choosing good GA parameters." + "If we print the Ackley function output for x values monotonically decreasing from 5 to 0 in steps of 0.5, we will also see that the output does not monotonically decrease. This makes the search space harder to find as the GA could get stuck in a local minimum rather than continue searching for the global optimum. This can be avoided by choosing good GA parameters." ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 10, "id": "12c8393c-4b5a-4d0c-91f5-8c1d34273cd7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f(5.0, 0) = 10.138626172095204\n", "f(4.5, 0) = 11.134358629140673\n", "f(4.0, 0) = 8.640585759756158\n", "f(3.5, 0) = 9.526555806697585\n", "f(3.0, 0) = 6.914978162949289\n", "f(2.5, 0) = 7.674511801927853\n", "f(2.0, 0) = 4.927233671124704\n", "f(1.5, 0) = 5.5411239587646826\n", "f(1.0, 0) = 2.637531092108304\n", "f(0.5, 0) = 3.0836533599911533\n", "f(0.0, 0) = 0.0\n" ] } ], "source": [ "for x in np.arange(5, -0.1, -0.5).round(1):\n", " print(f\"f({x}, 0) = {ackley(x, 0.)}\")" ] }, { "cell_type": "markdown", "id": "cbb35524-db62-48c9-94c6-8145b9621111", "metadata": {}, "source": [ "# Precision\n", "\n", "Additionally, if we monotonically decrease x and y from 5 to 0 but in steps of precision_x, we can see the best possible fitness value with the given precision." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "456a7dc6-0341-4fae-8260-e6d375fa8d66", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ackley(0.00488758553275126, 0.00488758553275126)\t= 0.020822165450741892\n", "ackley(0.00488758553275126, -0.004887585532742378)\t= 0.020822165450720576\n", "ackley(-0.004887585532742378, 0.00488758553275126)\t= 0.020822165450720576\n", "ackley(-0.004887585532742378, -0.004887585532742378)\t= 0.020822165450702812\n" ] } ], "source": [ "best_fit, best_x, best_y = 10000, 10000, 10000\n", "for x in np.arange(5., -precision_x, -precision_x):\n", " for y in np.arange(5., -precision_x, -precision_x):\n", " if x < 0.01 and y < 0.01:\n", " fit = ackley(x, y)\n", " print(f\"ackley({x}, {y})\\t= {fit}\")\n", " if fit < best_fit:\n", " best_fit, best_x, best_y = fit, x, y" ] }, { "cell_type": "markdown", "id": "60cf5993-254e-47aa-892a-ca0aaabe7ebb", "metadata": {}, "source": [ "## Fitness function\n", "See /es1_1_genetic_algorithm/fitness.ipynb to see the above code written as a function that will be used in /es1_1_genetic_algorithm/GA.ipynb." ] }, { "cell_type": "code", "execution_count": null, "id": "39aac6dc-ce33-4844-a243-08b095a5b8cc", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_0_genetic_algorithm_walkthrough/step_2_selection.ipynb b/es1_0_genetic_algorithm_walkthrough/step_2_selection.ipynb index 00b83c1..6e1ff36 100644 --- a/es1_0_genetic_algorithm_walkthrough/step_2_selection.ipynb +++ b/es1_0_genetic_algorithm_walkthrough/step_2_selection.ipynb @@ -1,401 +1,401 @@ { "cells": [ { "cell_type": "markdown", "id": "ac247453-4955-4538-8f7e-67e357420e1f", "metadata": {}, "source": [ "# Tournament selection\n", "\n", "GA order of operation:\n", "1. Calculate the fitness of each chromosome in the current generation.\n", "2. **Select the parents from the current generation that will be used to make the next generation.**\n", "3. Perform crossover of the parents to make children that will be the chromosomes in the next generation.\n", "4. Mutate the children made in step 3.\n", "\n", "Now we have the fitness of each chromosome in the current population, we want to use a selection operator to select parents from the current generation that will be used to make the next generation. Here, we will use tournament selection with a tournament size of 3. \n", "\n", "First, we will write the fitness operator as a function:" ] }, { "cell_type": "code", "execution_count": 1, "id": "5c0640a1-92fb-4631-a594-c3e561b8ba1f", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import random\n", "\n", "\n", "# Fitness code as a function\n", "def ackley(chromosome):\n", " lb_x, ub_x = -5, 5\n", " len_x = (len(chromosome)//2)\n", " lb_y, ub_y = -5, 5\n", " len_y = (len(chromosome)//2)\n", "\n", " precision_x = (ub_x-lb_x)/((2**len_x)-1)\n", " precision_y = (ub_y-lb_y)/((2**len_y)-1)\n", " \n", " z = 0\n", " t = 1 + (len(chromosome)//2)\n", " x_bit_sum = 0\n", " for i in range(len(chromosome)//2):\n", " x_bit = chromosome[-t]*(2**z)\n", " x_bit_sum += x_bit\n", " t += 1\n", " z += 1\n", " \n", " z = 0\n", " t = 1\n", " y_bit_sum = 0\n", " for i in range(len(chromosome)//2):\n", " y_bit = chromosome[-t]*(2**z)\n", " y_bit_sum += y_bit\n", " t += 1\n", " z += 1\n", " \n", " x = (x_bit_sum*precision_x)+lb_x\n", " y = (y_bit_sum*precision_y)+lb_y\n", " \n", " fitness = -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2))) - np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))) + np.e + 20\n", " \n", " return np.array([x, y, fitness])" ] }, { "cell_type": "markdown", "id": "b5eaf494-a4c1-4f2f-8961-4ca44623112f", "metadata": {}, "source": [ "## Generate a new population\n", - "In order to perform selection, we need a population of chromosomes to work with. Here, we generate a population with 20 chromosomes by shuffling the chromosome we initiate the population with and then stacking them in a numpy array where each row of the population is a different chromosome." + "To perform selection, we need a population of chromosomes to work with. Here, we generate a population with 20 chromosomes by shuffling the chromosome we initiate the population with and then stacking them in a numpy array where each row of the population is a different chromosome." ] }, { "cell_type": "code", "execution_count": 2, "id": "8161851a-265b-4e4e-a02a-76b76883722b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "[[1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 1. 0. 1. 1.]\n", " [0. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 1.]\n", " [1. 0. 1. 0. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]\n", " [1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 0.]\n", " [1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1.]\n", " [0. 1. 1. 1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 1. 0. 1. 0. 1.]\n", " [1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 1. 1. 0. 1. 0.]\n", " [1. 0. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 0. 1. 0. 0. 1. 0.]\n", " [0. 1. 0. 1. 1. 0. 1. 1. 0. 0. 1. 1. 1. 0. 1. 1. 0. 0. 1. 1.]\n", " [1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0. 1. 1.]\n", " [0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 0.]\n", " [1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0.]\n", " [1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0. 0. 1. 0. 0. 1. 1.]\n", " [1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 1.]\n", " [0. 0. 0. 1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]\n", " [1. 0. 1. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]\n", " [1. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0.]\n", " [0. 1. 0. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1.]\n", " [1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1.]\n", " [1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]]\n" ] } ], "source": [ "tournament_size = 3\n", "population_size = 20\n", "\n", "chromosome = np.array([0,0,1,0,1,0,1,1,1,1,1,1,0,1,1,1,0,0,1,0])\n", "population = np.empty((0,len(chromosome)))\n", "\n", "for i in range(population_size):\n", " random.shuffle(chromosome)\n", " population = np.vstack((population, chromosome))\n", "\n", "print()\n", "print(population)" ] }, { "cell_type": "markdown", "id": "40cdd253-bc40-4e15-88d3-a817bfb1db45", "metadata": {}, "source": [ "## Choosing chromosomes for a tournament\n", "\n", - "A tournament is made to select a new parent. The number of chromosomes in each tournament is tournament_size. First, we randomly choose the indices of 3 chromosomes using np.random.choice. We do not want duplicates of the same chromosome in a single tournament so we set replace=False so that a chosen index is removed from the options before the next index is selected. However, when a new tournament is made to select the next parent, all index options are available once again. " + "A tournament is made to select a new parent. The number of chromosomes in each tournament is tournament_size. First, we randomly choose the indices of tournament_size (3 in this example) chromosomes using np.random.choice. We do not want duplicates of the same chromosome in a single tournament, so we set replace=False so that a chosen index is removed from the options before the next index is selected. However, when a new tournament is made to select the next parent, all index options are available once again. " ] }, { "cell_type": "code", "execution_count": 3, "id": "cc91865f-64f1-406c-aac2-85a738948397", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Population size: 20 - chosen tournament indices: [4 5 6]\n", "\n" ] } ], "source": [ "indices_list = np.random.choice(len(population), tournament_size, replace=False)\n", "\n", "print(f\"Population size: {population_size} - chosen tournament indices: {indices_list}\")\n", "print()" ] }, { "cell_type": "code", "execution_count": 4, "id": "39913524-606b-4da9-af48-408c45a1321c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "chromosome 4: [1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1.]\n", "chromosome 5: [0. 1. 1. 1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 1. 0. 1. 0. 1.]\n", "chromosome 6: [1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 1. 1. 0. 1. 0.]\n" ] } ], "source": [ "pos_parent_1 = population[indices_list[0]]\n", "pos_parent_2 = population[indices_list[1]]\n", "pos_parent_3 = population[indices_list[2]]\n", "\n", "print()\n", "print(f\"chromosome {indices_list[0]}: {pos_parent_1}\")\n", "print(f\"chromosome {indices_list[1]}: {pos_parent_2}\")\n", "print(f\"chromosome {indices_list[2]}: {pos_parent_3}\")" ] }, { "cell_type": "markdown", "id": "948f6c51-1aeb-4f79-a84a-3faeefc70180", "metadata": {}, "source": [ "### Fitness\n", "The fitness function is then used to calculate the fitness of each of the chromosomes in the tournament. The fitness function returns 3 values, x, y and the fitness, so we choose index [2] to get the fitness of the parent." ] }, { "cell_type": "code", "execution_count": 5, "id": "27fc3be1-8640-4e52-9576-b3f0a8c7ac94", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Fitness of chromosome 4: 14.104025320695866\n", "Fitness of chromosome 5: 8.183946443490017\n", "Fitness of chromosome 6: 10.854247817287186\n" ] } ], "source": [ "fitness_parent_1 = ackley(pos_parent_1)[2]\n", "fitness_parent_2 = ackley(pos_parent_2)[2]\n", "fitness_parent_3 = ackley(pos_parent_3)[2]\n", "\n", "print()\n", "print(f\"Fitness of chromosome {indices_list[0]}: {fitness_parent_1}\")\n", "print(f\"Fitness of chromosome {indices_list[1]}: {fitness_parent_2}\")\n", "print(f\"Fitness of chromosome {indices_list[2]}: {fitness_parent_3}\")" ] }, { "cell_type": "markdown", "id": "3c12e5a5-5194-4155-ae6b-9fd08eff5064", "metadata": {}, "source": [ "## Selecting the best chromosome\n", "In this case, the parameters to the Ackley function that result in the lowest result are the fittest. We therefore select the chromosome with the minimum value." ] }, { "cell_type": "code", "execution_count": 6, "id": "fa62abcd-a08c-435e-bc50-e499acea2dcb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "tournament winner: [0. 1. 1. 1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 1. 0. 1. 0. 1.]\n", "min fitness is: 8.183946443490017\n" ] } ], "source": [ "min_obj_fn = min(fitness_parent_1, fitness_parent_2, fitness_parent_3)\n", "\n", "if min_obj_fn == fitness_parent_1:\n", " selected_parent = pos_parent_1\n", "elif min_obj_fn == fitness_parent_2:\n", " selected_parent = pos_parent_2\n", "else:\n", " selected_parent = pos_parent_3\n", " \n", "print()\n", "print(f\"tournament winner: {selected_parent}\")\n", "print(f\"min fitness is: {min_obj_fn}\")" ] }, { "cell_type": "markdown", "id": "059910c2-2a7a-4489-a3f7-54faa89d27fd", "metadata": {}, "source": [ "# Selection of two parents\n", "\n", "The code above was written assuming the tournament size is always 3. When we write a selection function, we want to have the tournament size as one of the input parameters. We will therefore update the code to work with any tournament size. For this, we can make use of list comprehensions in Python, e.g. [i for i in range(3)] = [0, 1, 2]. We can also use numpy.argmin which returns the index of the minimum value in the array." ] }, { "cell_type": "code", "execution_count": 8, "id": "b8b2af87-1f59-4f45-83af-c45ef96a640e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Population\n", "[[1. 1. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 0.]\n", " [1. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 0. 0. 0. 1. 0. 1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 0.]\n", " [1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0.]\n", " [0. 1. 0. 1. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 0.]\n", " [1. 1. 0. 1. 0. 0. 0. 1. 1. 1. 1. 1. 0. 0. 1. 0. 0. 1. 1. 1.]\n", " [1. 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0.]\n", " [0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1.]\n", " [1. 1. 1. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 0. 0. 0. 0. 1.]\n", " [1. 1. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 0.]\n", " [1. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 1. 0. 1. 1. 0.]\n", " [0. 1. 0. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]\n", " [1. 1. 1. 1. 0. 0. 1. 0. 0. 1. 1. 1. 1. 1. 0. 1. 1. 0. 0. 0.]\n", " [0. 1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 1.]\n", " [1. 1. 0. 0. 1. 0. 1. 1. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 1.]\n", " [0. 1. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1.]\n", " [1. 0. 1. 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0. 0. 0. 1. 1. 0.]\n", " [1. 1. 1. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.]\n", " [0. 1. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]\n", " [1. 1. 0. 0. 1. 0. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 1. 1. 1. 0.]]\n", "\n", "Population size: 20 - chosen tournament indices: [11 15 10]\n", "\n", "\n", "chromosome 11: [0. 1. 0. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]\n", "chromosome 15: [0. 1. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1.]\n", "chromosome 10: [1. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 1. 0. 1. 1. 0.]\n", "\n", "Fitness of chromosome 11: 10.838148783921328\n", "Fitness of chromosome 15: 7.197947192140866\n", "Fitness of chromosome 10: 4.199441297232852\n", "\n", "Tournament Winner\n", "Chromosome 10: [1. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 1. 0. 1. 1. 0.]\n", "Chromosome 10 fitness: 4.199441297232852\n" ] } ], "source": [ "tournament_size = 3\n", "population_size = 20\n", "\n", "chromosome = np.array([0,0,1,0,1,0,1,1,1,1,1,1,0,1,1,1,0,0,1,0])\n", "population = np.empty((0,len(chromosome)))\n", "\n", "for i in range(population_size):\n", " random.shuffle(chromosome)\n", " population = np.vstack((population, chromosome))\n", "\n", "print(\"Population\")\n", "print(population)\n", "print()\n", "\n", "indices_list = np.random.choice(len(population), tournament_size, replace=False)\n", "\n", "print(f\"Population size: {population_size} - chosen tournament indices: {indices_list}\")\n", "print()\n", "\n", "chosen_parents = np.array([population[indices_list[i]] for i in range(tournament_size)])\n", "\n", "print()\n", "for i in range(tournament_size):\n", " print(f\"chromosome {indices_list[i]}: {chosen_parents[i]}\")\n", "\n", "parent_fitnesses = np.array([ackley(chosen_parents[i])[2] for i in range(tournament_size)])\n", "\n", "print()\n", "for i in range(tournament_size):\n", " print(f\"Fitness of chromosome {indices_list[i]}: {parent_fitnesses[i]}\")\n", "\n", "min_fitness_idx = np.argmin(parent_fitnesses)\n", " \n", "print()\n", "print(f\"Tournament Winner\")\n", "print(f\"Chromosome {indices_list[min_fitness_idx]}: {chosen_parents[min_fitness_idx]}\")\n", "print(f\"Chromosome {indices_list[min_fitness_idx]} fitness: {parent_fitnesses[min_fitness_idx]}\")\n" ] }, { "cell_type": "markdown", "id": "263e9e20-26e3-43cd-bef4-55b49402d96e", "metadata": {}, "source": [ "## Selection function\n", "See /es1_1_genetic_algorithm/selection.ipynb to see the above code written as a function that will be used in /es1_1_genetic_algorithm/GA.ipynb." ] }, { "cell_type": "code", "execution_count": null, "id": "c5e8c7d9-aacf-42e2-9a76-6c50adcbb43a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_0_genetic_algorithm_walkthrough/step_3_crossover.ipynb b/es1_0_genetic_algorithm_walkthrough/step_3_crossover.ipynb index fced5f2..0933bda 100644 --- a/es1_0_genetic_algorithm_walkthrough/step_3_crossover.ipynb +++ b/es1_0_genetic_algorithm_walkthrough/step_3_crossover.ipynb @@ -1,335 +1,335 @@ { "cells": [ { "cell_type": "markdown", "id": "036917e9-962f-44af-a51a-927c29ebc986", "metadata": {}, "source": [ "# GENETIC ALGORITHM CROSSOVER OPERATOR\n", "\n", "GA order of operation:\n", "1. Calculate the fitness of each chromosome in the current generation.\n", "2. Select the parents from the current generation that will be used to make the next generation.\n", "3. **Perform crossover of the parents to make children that will be the chromosomes in the next generation.**\n", "4. Mutate the children made in step 3.\n", "\n", "Here, we implement a 2-point crossover operator for two predefined parent chromosomes. " ] }, { "cell_type": "markdown", "id": "36d3cfde-537b-4a21-9bdc-a4fc6b4e8a93", "metadata": {}, "source": [ "## Parents\n", "We will use two parent chromosomes, parent_1 and parent_2, of length 10. For the GA, we will use length 20 for the parents, 10 for x and 10 for y, but to keep it simple, we will use 5 values each for x and y here." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "4d0a3b71-7765-4235-850e-961891b25b14", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "parent_1: [1 1 1 1 1 0 0 0 0 0]\n", "parent_2: [0 0 0 0 0 1 1 1 1 1]\n" ] } ], "source": [ "import numpy as np\n", "\n", "parent_1 = np.array([1,1,1,1,1,0,0,0,0,0])\n", "parent_2 = np.array([0,0,0,0,0,1,1,1,1,1])\n", "\n", "print()\n", "print(f\"parent_1: {parent_1}\")\n", "print(f\"parent_2: {parent_2}\")" ] }, { "cell_type": "markdown", "id": "3defa0d3-094d-487d-9504-b6e4f803d749", "metadata": {}, "source": [ "## Select the indices\n", "Next, we need to randomly select two indices, index_1 and index_2, where the parent chromosomes will be split. We need to check that index_1 and index_2 are not equal (or no crossover would occur) and that index_1 is less than index_2." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "6131a3a8-fb82-4908-966f-dcdc78480aba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "index_1: 4\n", - "index_2: 5\n" + "index_1: 1\n", + "index_2: 4\n" ] } ], "source": [ "index_1 = np.random.randint(0,len(parent_1))\n", "index_2 = np.random.randint(0,len(parent_2))\n", "\n", "while index_1 == index_2:\n", " index_2 = np.random.randint(0,len(parent_2))\n", "\n", "if index_1 > index_2:\n", " index_1, index_2 = index_2, index_1\n", "\n", "print()\n", "print(f\"index_1: {index_1}\")\n", "print(f\"index_2: {index_2}\")" ] }, { "cell_type": "markdown", "id": "305b7e2c-77c7-43c0-8a11-0c4cbc19a7d8", "metadata": {}, "source": [ "## Split the parents at the indices\n", "We then split each of the parent chromosomes into 3 segments at the two indices, e.g. parent_1 gets split into first_seg_parent_1, mid_seg_parent_1, and last_seg_parent_1." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "fe4104aa-ef1c-471e-82b8-6ccfe2680303", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "first_seg_parent_1: [1 1 1 1]\n", - "mid_seg_parent_1: [1 0]\n", - "last_seg_parent_1: [0 0 0 0]\n", + "first_seg_parent_1: [1]\n", + "mid_seg_parent_1: [1 1 1 1]\n", + "last_seg_parent_1: [0 0 0 0 0]\n", "\n", - "first_seg_parent_2: [0 0 0 0]\n", - "mid_seg_parent_2: [0 1]\n", - "last_seg_parent_2: [1 1 1 1]\n" + "first_seg_parent_2: [0]\n", + "mid_seg_parent_2: [0 0 0 0]\n", + "last_seg_parent_2: [1 1 1 1 1]\n" ] } ], "source": [ "first_seg_parent_1 = parent_1[:index_1]\n", "mid_seg_parent_1 = parent_1[index_1:index_2+1]\n", "last_seg_parent_1 = parent_1[index_2+1:]\n", "\n", "print()\n", "print(f\"first_seg_parent_1: {first_seg_parent_1}\")\n", "print(f\"mid_seg_parent_1: {mid_seg_parent_1}\")\n", "print(f\"last_seg_parent_1: {last_seg_parent_1}\")\n", "\n", "first_seg_parent_2 = parent_2[:index_1]\n", "mid_seg_parent_2 = parent_2[index_1:index_2+1]\n", "last_seg_parent_2 = parent_2[index_2+1:]\n", "\n", "print()\n", "print(f\"first_seg_parent_2: {first_seg_parent_2}\")\n", "print(f\"mid_seg_parent_2: {mid_seg_parent_2}\")\n", "print(f\"last_seg_parent_2: {last_seg_parent_2}\")" ] }, { "cell_type": "markdown", "id": "8e5aecc4-4c5f-408d-92d8-d8a39a25c236", "metadata": {}, "source": [ "## Combine the parent segments to create the children\n", "The children are then created by concatenating the first and last segments from one parent with the middle segment from the other. " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "43fd4800-25db-4445-8e65-d3e94984aeca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "first_seg_parent_1 + mid_seg_parent_2 + last_seg_parent_1 = child_1: [1 1 1 1] + [0 1] + [0 0 0 0] = [1 1 1 1 0 1 0 0 0 0]\n", - "first_seg_parent_2 + mid_seg_parent_1 + last_seg_parent_2 = child_2: [0 0 0 0] + [1 0] + [1 1 1 1] = [0 0 0 0 1 0 1 1 1 1]\n", + "first_seg_parent_1 + mid_seg_parent_2 + last_seg_parent_1 = child_1: [1] + [0 0 0 0] + [0 0 0 0 0] = [1 0 0 0 0 0 0 0 0 0]\n", + "first_seg_parent_2 + mid_seg_parent_1 + last_seg_parent_2 = child_2: [0] + [1 1 1 1] + [1 1 1 1 1] = [0 1 1 1 1 1 1 1 1 1]\n", "\n" ] } ], "source": [ "child_1 = np.concatenate((first_seg_parent_1, mid_seg_parent_2, last_seg_parent_1))\n", "child_2 = np.concatenate((first_seg_parent_2, mid_seg_parent_1, last_seg_parent_2))\n", "\n", "print()\n", "print(f\"first_seg_parent_1 + mid_seg_parent_2 + last_seg_parent_1 = child_1: {first_seg_parent_1} + {mid_seg_parent_2} + {last_seg_parent_1} = {child_1}\")\n", "print(f\"first_seg_parent_2 + mid_seg_parent_1 + last_seg_parent_2 = child_2: {first_seg_parent_2} + {mid_seg_parent_1} + {last_seg_parent_2} = {child_2}\")\n", "print()" ] }, { "cell_type": "markdown", "id": "25be5e5a-9073-40e0-98b3-6f5c2207d042", "metadata": {}, "source": [ - "## Combining all of the above\n", + "## Combining all the above\n", "\n", - "The only difference between the following code and the code above is the use of the crossover probability, PROB_CROSSOVER, which is initially set to 1 so that crossover always occurs. Each time this code is run, a random number is generated and, if that random number if less than PROB_CROSSOVER, crossover will occur. However, if PROB_CROSSOVER is set to a different value, such as 0.5, then the parents will only be crossed over if the random number is less than PROB_CROSSOVER. If it is greater than PROB_CROSSOVER then each child will be equal to the corresponding parent, e.g. child_1 = parent_1." + "The only difference between the following code and the code above is the use of the crossover probability, PROB_CROSSOVER, which is initially set to 1 so that crossover always occurs. Each time this code is run, a random number is generated and, if that random number is less than PROB_CROSSOVER, crossover will occur. However, if PROB_CROSSOVER is set to a different value, such as 0.5, then the parents will only be crossed over if the random number is less than PROB_CROSSOVER. If it is greater than PROB_CROSSOVER then each child will be equal to the corresponding parent, e.g. child_1 = parent_1." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "0e359ff8-7f4f-46ac-8664-0ae82fbe86d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "parent_1: [1 1 1 1 1 0 0 0 0 0]\n", "parent_2: [0 0 0 0 0 1 1 1 1 1]\n", "\n", - "rand_num 0.647 < PROB_CROSSOVER 1: True\n", + "rand_num 0.313 < PROB_CROSSOVER 1: True\n", "\n", "--- Crossover ---\n", "\n", - "index_1: 4\n", - "index_2: 8\n", + "index_1: 8\n", + "index_2: 9\n", "\n", - "first_seg_parent_1: [1 1 1 1]\n", - "mid_seg_parent_1: [1 0 0 0 0]\n", - "last_seg_parent_1: [0]\n", + "first_seg_parent_1: [1 1 1 1 1 0 0 0]\n", + "mid_seg_parent_1: [0 0]\n", + "last_seg_parent_1: []\n", "\n", - "first_seg_parent_2: [0 0 0 0]\n", - "mid_seg_parent_2: [0 1 1 1 1]\n", - "last_seg_parent_2: [1]\n", + "first_seg_parent_2: [0 0 0 0 0 1 1 1]\n", + "mid_seg_parent_2: [1 1]\n", + "last_seg_parent_2: []\n", "\n", - "first_seg_parent_1 + mid_seg_parent_2 + last_seg_parent_1 = child_1: [1 1 1 1] + [0 1 1 1 1] + [0] = [1 1 1 1 0 1 1 1 1 0]\n", - "first_seg_parent_2 + mid_seg_parent_1 + last_seg_parent_2 = child_2: [0 0 0 0] + [1 0 0 0 0] + [1] = [0 0 0 0 1 0 0 0 0 1]\n", + "first_seg_parent_1 + mid_seg_parent_2 + last_seg_parent_1 = child_1: [1 1 1 1 1 0 0 0] + [1 1] + [] = [1 1 1 1 1 0 0 0 1 1]\n", + "first_seg_parent_2 + mid_seg_parent_1 + last_seg_parent_2 = child_2: [0 0 0 0 0 1 1 1] + [0 0] + [] = [0 0 0 0 0 1 1 1 0 0]\n", "\n" ] } ], "source": [ "PROB_CROSSOVER = 1\n", "\n", "\n", "parent_1 = np.array([1,1,1,1,1,0,0,0,0,0])\n", "parent_2 = np.array([0,0,0,0,0,1,1,1,1,1])\n", "\n", "print()\n", "print(f\"parent_1: {parent_1}\")\n", "print(f\"parent_2: {parent_2}\") \n", "print()\n", "\n", "child_1 = np.empty((0,len(parent_1)))\n", "child_2 = np.empty((0,len(parent_2)))\n", "\n", "rand_num = np.random.rand()\n", "print(f\"rand_num {round(rand_num,3)} < PROB_CROSSOVER {PROB_CROSSOVER}: {rand_num < PROB_CROSSOVER}\")\n", "print()\n", "\n", "if rand_num < PROB_CROSSOVER:\n", " \n", " print(\"--- Crossover ---\")\n", " \n", " index_1 = np.random.randint(0,len(parent_1))\n", " index_2 = np.random.randint(0,len(parent_2))\n", " \n", " while index_1 == index_2:\n", " index_2 = np.random.randint(0,len(parent_2))\n", " \n", " if index_1 > index_2:\n", " index_1, index_2 = index_2, index_1\n", " \n", " print()\n", " print(f\"index_1: {index_1}\")\n", " print(f\"index_2: {index_2}\")\n", " \n", " first_seg_parent_1 = parent_1[:index_1]\n", " mid_seg_parent_1 = parent_1[index_1:index_2+1]\n", " last_seg_parent_1 = parent_1[index_2+1:]\n", "\n", " print()\n", " print(f\"first_seg_parent_1: {first_seg_parent_1}\")\n", " print(f\"mid_seg_parent_1: {mid_seg_parent_1}\")\n", " print(f\"last_seg_parent_1: {last_seg_parent_1}\")\n", "\n", " first_seg_parent_2 = parent_2[:index_1]\n", " mid_seg_parent_2 = parent_2[index_1:index_2+1]\n", " last_seg_parent_2 = parent_2[index_2+1:]\n", "\n", " print()\n", " print(f\"first_seg_parent_2: {first_seg_parent_2}\")\n", " print(f\"mid_seg_parent_2: {mid_seg_parent_2}\")\n", " print(f\"last_seg_parent_2: {last_seg_parent_2}\")\n", "\n", " child_1 = np.concatenate((first_seg_parent_1, mid_seg_parent_2, last_seg_parent_1))\n", " child_2 = np.concatenate((first_seg_parent_2, mid_seg_parent_1, last_seg_parent_2))\n", "\n", " print()\n", " print(f\"first_seg_parent_1 + mid_seg_parent_2 + last_seg_parent_1 = child_1: {first_seg_parent_1} + {mid_seg_parent_2} + {last_seg_parent_1} = {child_1}\")\n", " print(f\"first_seg_parent_2 + mid_seg_parent_1 + last_seg_parent_2 = child_2: {first_seg_parent_2} + {mid_seg_parent_1} + {last_seg_parent_2} = {child_2}\")\n", " print()\n", " \n", "else:\n", " \n", " print(\"--- No crossover ---\")\n", " \n", " child_1 = parent_1\n", " child_2 = parent_2" ] }, { "cell_type": "markdown", "id": "e6f602c6-e6da-4e80-bc18-97375b0a6a63", "metadata": {}, "source": [ "## Crossover function\n", "See /es1_1_genetic_algorithm/crossover.ipynb to see the above code written as a function that will be used in /es1_1_genetic_algorithm/GA.ipynb." ] }, { "cell_type": "code", "execution_count": null, "id": "f20cff55-34a1-46c6-8699-db712e329412", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_1_genetic_algorithm/GA.ipynb b/es1_1_genetic_algorithm/GA.ipynb index 9cd47c6..6a064fd 100644 --- a/es1_1_genetic_algorithm/GA.ipynb +++ b/es1_1_genetic_algorithm/GA.ipynb @@ -1,360 +1,360 @@ { "cells": [ { "cell_type": "markdown", "id": "22d803b1-586b-4787-a87e-06d216268b7b", "metadata": {}, "source": [ "# Genetic algorithm\n", "\n", - "Now we have functions for fitness, crossover, mutation, and tournament selection. We can import these and use them in our genetic algorithm to search for the global optima of the fitness function. The objective is to find the global minima of the Ackley function [0, 0].\n", + "Now we have functions for fitness, crossover, mutation, and tournament selection. We can import these and use them in our genetic algorithm to search for the global optima of the fitness function. The objective is to find the global minimum of the Ackley function [0, 0].\n", "\n", "## Exercise 1:\n", "\n", "Provide written answers for the following questions:\n", - "- 1.1. Run the GA code using the default parameters and see if the GA finds the global minima. How many generations does it take to converge?\n", + "- 1.1. Run the GA code using the default parameters and see if the GA finds the global minimum. How many generations does it take to converge?\n", "- 1.2. How does using mutation alone (PROB_CROSSOVER = 0) and crossover alone (PROB_MUTATION = 0) influence the results? What about if you use a small or a large population size?\n", "- 1.3. What happens if you use a larger magnitude mutation probability (PROB_MUTATION)?\n", "- 1.4. What happens if the tournament size is small (minimal is 2) or large (TOURNAMENT_SIZE must still be less than POPULATION_SIZE)?\n", "- 1.5. When the x and y value of each chromosome in the population is plotted, do you notice anything odd about it? Can you explain it? \n", "- 1.6. How does making ELITISM=True influence the results?\n", "\n", "NOTE: Once you have completed the above exercises, complete the exercises in GA_param_search.ipynb" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "id": "0f8b54c3-0122-4a93-b607-298ca63efb69", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "\n", "%run fitness.ipynb\n", "%run selection.ipynb\n", "%run crossover.ipynb\n", "%run mutation.ipynb" ] }, { "cell_type": "markdown", "id": "059c46eb-1ab4-4741-8bf7-bb39258cd3bd", "metadata": {}, "source": [ "## GA parameters\n", "\n", - "The user should first choose the GA parameters. IMPORTANT: The parameters we have set for you are by no means optimal. You should try different values to see what happens. We will show you a more systematic way to aid the choice of parameters later but for now just change one parameter while keeping the others constant and see if you can observe a difference in the convergence to a solution.\n", + "The user should first choose the GA parameters. IMPORTANT: The parameters we have set for you are by no means optimal (in fact, they are intentionally bad). You should try different values to see what happens. We will show you a more systematic way to aid the choice of parameters later but for now just change one parameter while keeping the others constant and see if you can observe a difference in the convergence to a solution.\n", "\n", "It is often a good idea to keep track of the best solution thus far so that it is not lost. If elitism is chosen, then this best solution will always be added to the new population without being mutated so that future generations can continue to search this area of the solution space." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "id": "99b5c5f0-3340-4ff2-b958-438a05ae1d94", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generation 0\n", - " --> Best: fitness(-0.39589, -0.42522) = 3.86528\n", - " --> Mean: 9.1731 and SD: 2.75698\n" + " --> Best: fitness(-0.02444, 0.96285) = 2.59918\n", + " --> Mean: 9.60973 and SD: 2.52694\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 10\n", - " --> Best: fitness(-1.06061, -0.00489) = 2.88199\n", - " --> Mean: 7.85442 and SD: 3.29364\n" + " --> Best: fitness(-0.01466, -0.01466) = 0.07007\n", + " --> Mean: 2.86645 and SD: 3.33056\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 20\n", - " --> Best: fitness(-0.08309, -0.02444) = 0.43354\n", - " --> Mean: 4.17859 and SD: 3.31775\n" + " --> Best: fitness(-0.00489, 0.00489) = 0.02082\n", + " --> Mean: 2.96191 and SD: 3.61013\n" ] }, { "data": { - "image/png": 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\n", 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bbbdp+vTpysrK0sGDB5WYmKjly5fX2K9Vq1Y12g4ePChJevbZZ2sEjOPB8fjwzuk43TpO16JFi9StWze1adMm7PnuvvtuLVu2TLNmzVJKSorcbrf+8z//s8Zk4JYtW9aod9y4cbrzzjtrHKtDtVPTmjZtGvaYw+FQMPjjdYzCLViwQHfeeafeffddLVq0SP/93/+tZcuWqV+/fnV9uQCAWlgm1KDuunXrpiVLlkiSevfurd27d6tJkybq1KnTKfdNSEhQ+/bt9e233+rGG2+sdZsePXroueee07/+9a9ae2uaNWumysrKsLa61JGamqo1a9bopptuCrWtXr36lDVLUnJysrp06VKj/dNPP1VWVpauueYaSVVhZevWrad8vt69e+vrr79WSsqZrz/UrFkzSarxXkhVk4d79eqlnJwc9e/fXy+//DKhBgDOkmWGn2qzfPlyzZ492+wyTLN3715dfvnlevHFF/Xll19qy5Yt+utf/6pHHnlEI0aMkCQNGzZM/fv318iRI/X+++9r69atWrlype699159/vnntT7vfffdpxkzZujJJ5/UN998ow0bNmjBggV6/PHHJUnXX3+92rVrp5EjR+rTTz/Vt99+q9dee02rVq2SVHVW0JYtW5SXl6fvvvtOFRUVdarjrrvu0v/8z/9owYIF+uabbzR9+nT985//PKv3qGvXrnr99deVl5en/Px83XDDDafsSZGke+65RytXrtSECROUl5enwsJCvfnmmzUmCp9MfHy83G633n33XZWWlsrn82nLli3KycnRqlWrtG3bNr3//vsqLCxUamrq2bxMAIAsHmoiXXR0tDIyMvTEE0/osssuU/fu3TV16lSNGTNGTz31lKSq4ZC3335bl112mW655RZdeOGF+sUvfqFt27YpISGh1ue97bbb9Nxzz2nBggW65JJLNHjwYOXm5oZOUW7WrJnef/99xcfH68orr9Qll1yimTNnhoanrr32Wg0fPlxDhw5V27Zt9b//+791qmP06NGaOnWqJk+erEsvvVTbtm3T7bffflbv0eOPP664uDgNGDBAV199tTIzM9W7d+9T7tejRw999NFH+uabbzRo0CD16tVL06ZNU/v27et87CZNmujJJ5/UvHnz1L59e40YMUItWrTQpk2bdO211+rCCy/U2LFjNX78eI0bN+5sXiYAQJLDMAzD7CIaSnl5uWJjY+Xz+eTxeMIeCwQC2rJlizp37hw6ZdwKVxSGvdX2cwkAkeZk39/VMafmJDp0qAoYdl/7CQAAOyDUnEKHDoQMAACsgDk1AADAFgg1AADAFgg1PxJB86ZhAfw8AkDdEWq+d/x05B9faRYw0+HDhyXVvHIxAKAmJgp/r0mTJmrRooXKysrUtGlTOZ3kPZjHMAwdPnxYe/bsUatWrSJ2bTMAOB2Emu85HA4lJiZqy5Yt2rZtm9nlAJKq1sVq166d2WUAgCUQaqpp1qyZunbtyhAUGoWmTZvSQwMAp4FQ8yNOp5MrtwIAYEFMHAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAEAALZAqAFgeYFAsfbt+4cCgWKzSwFgoiZmFwAAZ6Ok5HkVFIyRZEhyyOt9VomJ2WaXBcAE9NQAsKxAoLhaoJEkQwUFY+ixASIUoQaAZfl8K/VDoDnOkM+3yoxyAJiMUAPAso4e3Xta7QDsjVADwLKaNm1zWu0A7I1QA8CyYmMHSHL8qNWp2Nj+ZpQDwGSEGgCW5XIlyet9VlLU9y1R8nrny+VKMrMsACbhlG4AlpaYmK24uEz5/UVyu1MINEAEI9QAsDyXK4kwA4DhJwAAYA+EGgAAYAuEGgAAYAuEGgAAYAuEGgAAYAuEGgAAYAuEGgAAYAuEGgAAYAuWCTUzZsxQenq6YmJiFB8fr5EjR6qgoMDssgAAQCNhmVDz0Ucfafz48Vq9erWWLVumo0eP6mc/+5kOHTpkdmkAAKARcBiGYZhdxJkoKytTfHy8PvroI1122WV12qe8vFyxsbHy+XzyeDz1XCEAADgX6vr9bdm1n3w+nySpdevWJ9ymoqJCFRUVofvl5eX1XhcAADCHZYafqgsGg5o4caIGDhyo7t27n3C7GTNmKDY2NnRLTk5uwCoBAEBDsuTw0+2336533nlHn3zyiZKSTrwyb209NcnJyQw/AQBgIbYdfpowYYLeeustffzxxycNNJLUvHlzNW/evIEqA9CQAoFi+f2Fcru7yuU6+d8CAJHBMqHGMAzdcccdeuONN7R8+XJ17tzZ7JIAmKSk5HkVFIyVFJTklNc7X4mJ2WaXBcBklplTM378eL344ot6+eWXFRMTo927d2v37t3y+/1mlwagAQUCxdUCjSQFVVAwToFAsZllAWgELBNq5s6dK5/PpyFDhigxMTF0W7RokdmlAWhAfn+hfgg0x1XK7y8yoxwAjYilhp8AwO3uqqr/j1UPNlFyu1NMqghAY2GZnhoAkCSXK0le73xJUd+3RMnrncdkYQDW6akBgOMSE7MVF5cpv79IbncKgQaAJEINAItyuZIIMwDCMPwEAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADAABsgVADwPICgWLt2/cPBQLFZpcCwERNzC4AAM5GScnzKigYKykoySmvd74SE7PNLguACeipAWBZgUBxtUAjSUEVFIyjxwaIUIQaAJbl9xfqh0BzXKX8/iIzygFgMkINAMtyu7uq5p+xKLndKWaUA8BkhBoAluVyJcnrnS8p6vuWKHm98+RyJZlZFgCTMFEYgKUlJmYrLi5Tfn+R3O4UAg0QwQg1ACzP5UoizABg+AkAANgDoQYAANgCoQYAANgCoQYAANgCoQbAOcH6SwDMxtlPAM6a2esv+XxrVV6+Qh7PIMXGpjfYcQE0LoQaAGflROsvxcVlNshp1hs3Zqm0dGHofkLCzUpNza334wJofBh+AnBWzFx/yedbGxZoJKm0dKF8vrX1fmwAjQ+hBsBZMXP9pfLyFSdo/7Tejw2g8SHUADgrZq6/5PEMOkH7wHo/NoDGhzk1AM6aWesvxcamKyHh5hpzapgsDEQmQg2Ac8Ks9ZdSU3PVvv14lZd/Ko9nIIEGiGCEGgCWFxubTpgBwJwaAABgD4QaAABgC4QaAABgC4QaAABgC4QaAJZWVFSipUvXq6ioxOxSAJiMUAPAsh57bIW83niNGNFLXm+8Hnus9isMA4gMDsMwDLOLaCjl5eWKjY2Vz+eTx+MxuxwAZ6GoqEReb7yCwahQm9N5TAUFZUpJSTSxMgDnWl2/v+mpAWBJX3+9OyzQSFIw2EQbN5aaVBEAsxFqAAsJBIq1b98/FAgUm12K6bp1ayenszKszek8ptTUBJMqAmA2Qg1gESUlz2v16o7Kz79cq1d3VEnJ82aX1GBqC3MpKYl65JGVcjqPSaoKNI88soqhJyCCMacGsIBAoFirV3eUFKzWGqV+/baast5SQyopeV4FBWNV9dqd8nrnKzExO/R4UVGJNm4sVWpqAoEGsKm6fn+z9hNgAX5/ocIDjSRVyu8vsnWoCQSKqwUaSQqqoGCc4uIyQ687JSWRMANAEsNPgCW43V1V89c1Sm53ihnlNJiThbnqmGsEQCLUAJbgciXJ650v6fjZPlHyeufZupdGOh7mHD9qdYaFuZKS55Wbe43uvXepcnOviai5RgDCWW746emnn9ajjz6q3bt3q2fPnpozZ4769u1rdllAvUtMzFZcXKb8/iK53Sm2CjSBQLH8/kK53V3r8LqC8vlWShogSfqP/4jX6tWfqSr8GFqyJFdLlhTb6v0BItXxvw1Hj7ar0/aWCjWLFi3SpEmT9MwzzygjI0OzZ89WZmamCgoKFB8fb3Z5aERO70vSOlyupJO+nrKyt7Rv39uKi7tSbdte1YCVSfn5Hyg//yv17NldPXsOq/N+JSXP65NP7tNXX2VIcujKK4cqI+N2SceHn2qey/Dxx5NUXOzVn/40V9u2XaUfenMceu+9LL33Xq5GjLjlrF8TAPNUP0ng0KG67XPaZz/dfPPNys7O1mWXXXYGJZ6djIwMpaen66mnnpIkBYNBJScn64477tCUKVNOuT9nP0WGU50tY1dffDFQ5eUrQ/c9ngHq3fvTBjn2tGkz9NBDkxUMRsnprNS99z6i++/POeV+gUCxpk79gx57bL4Mo2o03OEIaubM9zV58vBaz/r6+99v1eOPz//+wnuGag5PSWlpn2v9+j7n6NUBaGg//t0/dEi66iqd+ysK+3w+DRs2TF27dtXDDz+snTt3nnHRp+PIkSNat26dhg374X+ATqdTw4YN06pVq2rdp6KiQuXl5WE32NuJzpax+wTSsrK3wgKNJJWXr1RZ2Vv1fuz8/A9CgUaSgsEoPfTQ75Sf/8Ep9924cb0ee2xeKNBIkmE4lZMzTEVFJTXmEpWVnV8t0Ei1BRpJysvrdVavCYC5aj9J4NROO9QsWbJEO3fu1O23365FixapU6dOuuKKK7R48WIdPXr0tAuoq++++06VlZVKSAi/WmhCQoJ2795d6z4zZsxQbGxs6JacnFxv9aFxqOvZMnazb9/bJ2h/t96PnZ//Va3LFXz55T9PuW9hYVCGEVWjvfpyB4mJ2erXb6tSU19VcfGFNY4FwH5qP+Pz1M7o7Ke2bdtq0qRJys/P15o1a5SSkqJf/epXat++vf7rv/5LhYWFZ/K051xOTo58Pl/otmPHDrNLQj2L1FOf4+KuPEH78Ho/ds+e3WtdrqBHj4tPuW/Xrk45HJU12h2OyrDlDlyuJCUkjNKAAdfVOFbtjtVhGwCNlcuVpAsumHna+53VKd0lJSVatmyZli1bpqioKF155ZXasGGDunXrpieeeOJsnrqG8847T1FRUSotDV+srrS0VO3a1T4runnz5vJ4PGE32Fuknvrctu1V8ngGhLV5PAMaZLJwz57DdO+9j4QtV3DvvY/WabJwamovjR17j2qbDFybjIxfa8aMZaFjnWi/e+/NrdPzAWi8mjfveNr7nPZE4aNHj2rp0qVasGCB3n//ffXo0UO33XabbrjhhlBoeOONN3Trrbdq3759p13QyWRkZKhv376aM2eOpKqJwh06dNCECROYKIwwVWc/2e/U51OpOvvpXcXFDTfl7Kcvv/ynevS4+LTOfnrxxWf0q1/9ukb70qV5uvrqtFr3Ob40wrJlhzVnTn+Fz60JKi/v/06rBgCNT2npq9q4cbSkuk8UPu1Qc9555ykYDOr666/XmDFjlJaWVmOb/fv3q1evXtqyZcvpvYJTWLRokW6++WbNmzdPffv21ezZs/Xqq69q06ZNNeba1IZQAzQ+RUUl8nrjw+bKOJ3HVFBQVqflD3r0WKcNG3rr+HVqLrnkC3355aX1VzCABlF1BlQHSUb9nf30xBNPaNeuXXr66adrDTSS1KpVq3MeaCRp9OjRmjVrlqZNm6a0tDTl5eXp3XffrVOgAdA4nc1q2ytWlFYLNJLk0IYNvbViRenJdgNgAVXTCZ7V6UQVVukG0CicyWrbDz5YqKlTu9Zof+ihIv3+9/aeHA5EikCgWKWl+erU6SpW6QZgDWey2vbgwR7VvACfoUGDYs5laQBM5HIlKS6ubh0RLGgJwLIGDUrQqFHVl1IwNGpUoQYNYkgaiET01ACwtFdfvVCLFhXo73/36+c/d2v0aK/ZJQEwCaEGgKU99tgK/e53A2QYUXrxxUoVF6/Qb387yOyyAJiAicIALKuoqEQXXpgQtnaUw1Gpb77Zc9rzcwA0XnX9/mZODQDL+r//2xIWaCTJMKL0j39sNacgAKYi1AAAAFsg1ACwrMsv7yyHI3xVdoejUkOHdjKnIACmItQAsKyUlETdf39u2NWI779/IfNpgAjF2U8ALCsQKNa//dsYvfLKNO3cmaLzzy9S27a7FQj8LKIWMgVQhVADwLL8/kJJQbVtu1Nt2+6s1l5EqAEiEMNPACzL7e6qmn/GouR2s+4TEIkINQAsq2oV3/mSor5viZLXO49eGiBCMfwEwNISE7MVF5cpv79IbncKgQaIYPTUAGj0AoFi7dv3DwUCxbU+XlFRooMHv1BFRUkDVwagMaGnBkCjVlLyvAoKxkoKSnLK652vxMTs0OMbN2aptHRh6H5Cws1KTc1t8DoBmI+eGgDnxKl6U870OX8INJIUVEHBuNAxfL61YYFGkkpLF8rnW3vOagBgHYQaAGetpOR5rV7dUfn5l2v16o4qKXn+nDzv8VO2w1XK7y+SJJWXr6h1v/LyT8/J8QFYC6EGwFk5VW/K2TjVKdsez6Ba9/N4Bp71sQFYD6EGwFk5VW/K2TjVKduxselKSLhZZWXna/36ISorO18JCTcrNjb9rI8NwHqYKAzgrPzQm1I92Jy7C+Cd6pTtt9/O1uTJzysYjJLTWalHHlmp1NRzcmgAFuMwDMMwu4iGUl5ertjYWPl8Pnk8HrPLAWyj6gylcZIqdbw3pfoZSvWlqKhEXm+8gsGoUJvTeUwFBWUsagnYSF2/v+mpAXDWzLoA3tdf71YwGB5egsEm2rixlFADRCBCDYBzwuVKavCr+Xbr1k5OZ2WNnprU1IQGrQNA48BEYQCWlZKSqEceWSmn85ikqkDzyCOr6KUBIhRzagBYXlFRiTZuLFVqagKBBrAh5tQAiBgpKYmEGQAMPwEAAHsg1AAAAFsg1AAAAFsg1AAAAFsg1AAAAFsg1AAAAFsg1AAAAFsg1AAAAFsg1AAAAFsg1ACwvKKiEi1dul5FRSVmlwLARIQaAJb22GMr5PXGa8SIXvJ64/XYYyvMLgmASVjQEoBlFRWVyOuNVzAYFWpzOo+poKCMtaAAG6nr9zc9NQAs64svPgsLNJIUDDbR+vVrTaoIgJkINQAs6/zzC+V0Voa1OZ3H1L59kUkVATAToQaAZXXvPliTJo2V03lMUlWgmTRpnLp3H2RyZQDM0MTsAgDgTMXGpuvWW59W376dtHNnis4/v0jduw9TbGy62aUBMAGhBoClpabmqn37tSov/1Qez0ACDRDBCDUALC82Np0wA4A5NQAAwB4INQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYINQAAwBYsEWq2bt2q7Oxsde7cWW63W126dNH06dN15MgRs0sDAACNRBOzC6iLTZs2KRgMat68eUpJSdFXX32lMWPG6NChQ5o1a5bZ5QEAgEbAYRiGYXYRZ+LRRx/V3Llz9e2339Z5n/LycsXGxsrn88nj8dRjdQAA4Fyp6/e3JXpqauPz+dS6deuTblNRUaGKiorQ/fLy8vouCwAAmMQSc2p+rKioSHPmzNG4ceNOut2MGTMUGxsbuiUnJzdQhQAAoKGZGmqmTJkih8Nx0tumTZvC9tm5c6eGDx+uUaNGacyYMSd9/pycHPl8vtBtx44d9flyAACAiUydU1NWVqa9e/eedJsLLrhAzZo1kyTt2rVLQ4YMUb9+/ZSbmyun8/QyGXNqAACwHkvMqWnbtq3atm1bp2137typoUOH6tJLL9WCBQtOO9AAAAB7s8RE4Z07d2rIkCHq2LGjZs2apbKystBj7dq1M7EyAI1BIFAsv79QbndXuVxJZpcDwCSWCDXLli1TUVGRioqKlJQU/gfLomekAzhHSkqeV0HBWElBSU55vfOVmJhtdlkATGDZ69ScCebUAPYSCBRr9eqOqgo0x0WpX7+t9NgANlLX728mpgCwLL+/UOGBRpIq5fcXmVEOAJMRagBYltvdVTX/jEXJ7U4xoxwAJiPUALAslytJXu98SVHft0TJ653H0BMQoSwxURgATiQxMVtxcZny+4vkdqcQaIAIRqgBYHkuVxJhBgDDTwAAwB4INQAAwBYINQAAwBYINQAavUCgWPv2/UOBQPFJ2wBENiYKA2jUalsGQRJLIwCogWUSADRaJ1oGoeq+EdbG0giAfbFMAgDLO9EyCOGBpqqtrOyvDEUBEY5QA6DROtEyCJKjxrabN0/S6tUdVVLyfEOUBqARItQAaLROtAyC1/tstbbqgiooGEePDRChmCgMoFE70TIIcXGZKiv7qzZvnvSjPapW6WZ+DRB56KkB0Oi5XEmKixsSFlRcriS1bTtKrNIN4DhCDQDLYpVuANUx/ATA0lilG8BxhBoAlscq3QAkhp8AAIBNEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAAIAtEGoAWF4gUKx9+/6hQKDY7FIAmKiJ2QUAwNkoKXleBQVjJQUlOeX1zldiYrbZZQEwAT01ACwrECiuFmgkKaiCgnH02AARilADwLL8/kL9EGiOq5TfX2RGOQBMRqgBYFlud1fV/DMWJbc7xYxyAJiMUAPAslyuJHm98yVFfd8SJa93nlyuJDPLAmASJgoDsLTExGzFxWXK7y+S251CoAEiGKEGgOW5XEmEGQAMPwEAAHuwXKipqKhQWlqaHA6H8vLyzC4HAAA0EpYLNZMnT1b79u3NLgMAADQylgo177zzjt5//33NmjXL7FIAAEAjY5mJwqWlpRozZoyWLFmiFi1a1GmfiooKVVRUhO6Xl5fXV3kAAMBkluipMQxDWVlZ+vWvf60+ffrUeb8ZM2YoNjY2dEtOTq7HKgEAgJlMDTVTpkyRw+E46W3Tpk2aM2eODhw4oJycnNN6/pycHPl8vtBtx44d9fRKAACA2RyGYRhmHbysrEx79+496TYXXHCBrrvuOv3tb3+Tw+EItVdWVioqKko33nijFi5cWKfjlZeXKzY2Vj6fTx6P56xqBwAADaOu39+mhpq62r59e9h8mF27dikzM1OLFy9WRkaGkpLqdtEtQg0AANZT1+9vS0wU7tChQ9j96OhoSVKXLl3qHGgAAIC9WWKiMAAAwKlYoqfmxzp16iQLjJoBAIAGRE8NAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwBUINAACwhSZmFwAgXCBQLL+/UG53V7lcSWaX02hVf58k8Z4BINQAjUlJyfMqKBgrKSjJKa93vhITs80uq9EJf58c37ca4j0DIpvDMAzD7CIais/nU6tWrbRjxw55PB6zywHCBAI7tXbtxar6cj7OqfT0r+RynW9WWY1O7e9TdbxngN2Ul5crOTlZ+/fvV2xs7Am3i6iemgMHDkiSkpOTTa4EqKugpG5mF2ExvGeAXR04cOCkoSaiemqCwaB27dqlmJgYORyOU+/QCB1Pq/Q2mY/PonHh82g8+CwaD7t8FoZh6MCBA2rfvr2czhOf4xRRPTVOp1NJSfaYROjxeCz9A2onfBaNC59H48Fn0XjY4bM4WQ/NcZzSDQAAbIFQAwAAbIFQYzHNmzfX9OnT1bx5c7NLiXh8Fo0Ln0fjwWfReETaZxFRE4UBAIB90VMDAABsgVADAABsgVADAABsgVADAABsgVBjExUVFUpLS5PD4VBeXp7Z5UScrVu3Kjs7W507d5bb7VaXLl00ffp0HTlyxOzSIsLTTz+tTp06yeVyKSMjQ5999pnZJUWcGTNmKD09XTExMYqPj9fIkSNVUFBgdlmQNHPmTDkcDk2cONHsUuodocYmJk+erPbt25tdRsTatGmTgsGg5s2bp3/+85964okn9Mwzz+j3v/+92aXZ3qJFizRp0iRNnz5dX3zxhXr27KnMzEzt2bPH7NIiykcffaTx48dr9erVWrZsmY4ePaqf/exnOnTokNmlRbS1a9dq3rx56tGjh9mlNAhO6baBd955R5MmTdJrr72miy++WOvXr1daWprZZUW8Rx99VHPnztW3335rdim2lpGRofT0dD311FOSqtZ4S05O1h133KEpU6aYXF3kKisrU3x8vD766CNddtllZpcTkQ4ePKjevXvrz3/+sx588EGlpaVp9uzZZpdVr+ipsbjS0lKNGTNGL7zwglq0aGF2OajG5/OpdevWZpdha0eOHNG6des0bNiwUJvT6dSwYcO0atUqEyuDz+eTJH4HTDR+/Hj9/Oc/D/v9sLuIWtDSbgzDUFZWln7961+rT58+2rp1q9kl4XtFRUWaM2eOZs2aZXYptvbdd9+psrJSCQkJYe0JCQnatGmTSVUhGAxq4sSJGjhwoLp37252ORHplVde0RdffKG1a9eaXUqDoqemEZoyZYocDsdJb5s2bdKcOXN04MAB5eTkmF2ybdX1s6hu586dGj58uEaNGqUxY8aYVDlgnvHjx+urr77SK6+8YnYpEWnHjh2666679NJLL8nlcpldToNiTk0jVFZWpr179550mwsuuEDXXXed/va3v8nhcITaKysrFRUVpRtvvFELFy6s71Jtr66fRbNmzSRJu3bt0pAhQ9SvXz/l5ubK6eT/DfXpyJEjatGihRYvXqyRI0eG2m+++Wbt379fb775pnnFRagJEybozTff1Mcff6zOnTubXU5EWrJkia655hpFRUWF2iorK+VwOOR0OlVRURH2mJ0Qaixs+/btKi8vD93ftWuXMjMztXjxYmVkZCgpKcnE6iLPzp07NXToUF166aV68cUXbftHo7HJyMhQ3759NWfOHElVQx8dOnTQhAkTmCjcgAzD0B133KE33nhDy5cvV9euXc0uKWIdOHBA27ZtC2u75ZZbdNFFF+mee+6x9ZAgc2osrEOHDmH3o6OjJUldunQh0DSwnTt3asiQIerYsaNmzZqlsrKy0GPt2rUzsTL7mzRpkm6++Wb16dNHffv21ezZs3Xo0CHdcsstZpcWUcaPH6+XX35Zb775pmJiYrR7925JUmxsrNxut8nVRZaYmJgawaVly5Zq06aNrQONRKgBzolly5apqKhIRUVFNQIlnaH1a/To0SorK9O0adO0e/dupaWl6d13360xeRj1a+7cuZKkIUOGhLUvWLBAWVlZDV8QIhLDTwAAwBaYxQgAAGyBUAMAAGyBUAMAAGyBUAMAAGyBUAMAAGyBUAMAAGyBUAMAAGyBUAMAAGyBUAMAAGyBUAMAAGyBUAMAAGyBUAPAssrKytSuXTs9/PDDobaVK1eqWbNm+vDDD02sDIAZWNASgKW9/fbbGjlypFauXCmv16u0tDSNGDFCjz/+uNmlAWhghBoAljd+/Hh98MEH6tOnjzZs2KC1a9eqefPmZpcFoIERagBYnt/vV/fu3bVjxw6tW7dOl1xyidklATABc2oAWN7mzZu1a9cuBYNBbd261exyAJiEnhoAlnbkyBH17dtXaWlp8nq9mj17tjZs2KD4+HizSwPQwAg1ACztd7/7nRYvXqz8/HxFR0dr8ODBio2N1VtvvWV2aQAaGMNPACxr+fLlmj17tl544QV5PB45nU698MILWrFihebOnWt2eQAaGD01AADAFuipAQAAtkCoAQAAtkCoAQAAtkCoAQAAtkCoAQAAtkCoAQAAtkCoAQAAtkCoAQAAtkCoAQAAtkCoAQAAtkCoAQAAtvD/GpBCspq4qcsAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 30\n", - " --> Best: fitness(-0.44477, -0.05376) = 2.94458\n", - " --> Mean: 7.76483 and SD: 2.7774\n" + " --> Best: fitness(-0.00489, 0.00489) = 0.02082\n", + " --> Mean: 2.38739 and SD: 3.37714\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 40\n", - " --> Best: fitness(-0.92375, -0.28837) = 3.89325\n", - " --> Mean: 7.58945 and SD: 2.48446\n" + " --> Best: fitness(-0.00489, 0.00489) = 0.02082\n", + " --> Mean: 2.0203 and SD: 3.0153\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 50\n", - " --> Best: fitness(-0.33724, -0.14174) = 2.67126\n", - " --> Mean: 5.52892 and SD: 2.61471\n" + " --> Best: fitness(-0.00489, 0.00489) = 0.02082\n", + " --> Mean: 2.31555 and SD: 3.25056\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# GA parameters\n", "NUM_GENERATIONS = 50\n", "POPULATION_SIZE = 10\n", "TOURNAMENT_SIZE = 5\n", "PROB_MUTATION = 0.15\n", "PROB_CROSSOVER = 0.5\n", "STAT_INTERVALS = 10\n", "\n", "# add the best chromosome in the current generation to the next generation\n", "ELITISM = False\n", "\n", "# initial population \n", "chromosome = np.array([0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0])\n", "population = np.empty((0, len(chromosome)))\n", "for i in range(POPULATION_SIZE):\n", " random.shuffle(chromosome)\n", " population = np.vstack((population, chromosome))\n", "\n", "# keep track of the best chromosome and it's fitness\n", "best_chromosome = np.empty((0, len(chromosome)))\n", "best_fitness = 100000\n", "gen_stats = np.empty((0, 9))\n", "\n", "# we will add the best individual to the population if ELITISM\n", "POPULATION_SIZE = POPULATION_SIZE - 1 if ELITISM else POPULATION_SIZE\n", "\n", "for i in range(NUM_GENERATIONS + 1):\n", "\n", " # evaluate the fitness for each chromosome in the current generation\n", " fitnesses = np.array([ackley(chrsm) for chrsm in population])\n", "\n", " # print information about the current generation\n", " if i % STAT_INTERVALS == 0:\n", " gen_best_idx = np.argmin(fitnesses[:, 2])\n", " gen_best_x, gen_best_y, gen_best_fit = fitnesses[gen_best_idx, 0], fitnesses[gen_best_idx, 1], fitnesses[\n", " gen_best_idx, 2]\n", " gen_mean_fit, gen_std_fit = np.mean(fitnesses[:, 2]), np.std(fitnesses[:, 2])\n", " gen_mean_x, gen_std_x, gen_mean_y, gen_std_y = np.mean(fitnesses[:, 0]), np.std(fitnesses[:, 0]), np.mean(\n", " fitnesses[:, 1]), np.std(fitnesses[:, 1])\n", " gen_stats = np.append(gen_stats, [[gen_best_fit, gen_mean_fit, gen_std_fit,\n", " gen_best_x, gen_mean_x, gen_std_x,\n", " gen_best_y, gen_mean_y, gen_std_y]], axis=0)\n", " print(f\"Generation {i}\")\n", " print(f\" --> Best: fitness({round(gen_best_x, 5)}, {round(gen_best_y, 5)}) = {round(gen_best_fit, 5)}\")\n", " print(f\" --> Mean: {round(gen_mean_fit, 5)} and SD: {round(gen_std_fit, 5)}\")\n", "\n", " next_gen = np.empty((0, len(chromosome)))\n", " collect_parents = np.empty((0, len(chromosome)))\n", " \n", " while len(next_gen) < POPULATION_SIZE:\n", " # select parent chromosomes\n", " parent_1 = tournament_selection(population, fitnesses[:, 2], TOURNAMENT_SIZE)\n", " parent_2 = tournament_selection(population, fitnesses[:, 2], TOURNAMENT_SIZE)\n", " collect_parents = np.vstack((collect_parents, parent_1))\n", " collect_parents = np.vstack((collect_parents, parent_2))\n", " # make child chromosomes using 2-point crossover\n", " children = crossover(parent_1, parent_2, PROB_CROSSOVER)\n", " # add children to the next generation\n", " next_gen = np.vstack((next_gen, children[0]))\n", " if len(next_gen) >= POPULATION_SIZE: break\n", " next_gen = np.vstack((next_gen, children[1]))\n", "\n", " # mutate genes in the next generation\n", " next_gen = mutation(next_gen, PROB_MUTATION)\n", "\n", " if ELITISM:\n", " if gen_best_fit < best_fitness:\n", " best_fitness = gen_best_fit\n", " best_chromosome = population[gen_best_idx]\n", " # add the best chromosome to the next generation without mutation\n", " next_gen = np.vstack((next_gen, best_chromosome))\n", " \n", " population = next_gen.copy()\n", " \n", " # plot current population and parents\n", " if i % STAT_INTERVALS == 0:\n", " plt.plot(fitnesses[:,0], fitnesses[:,1], '.y')\n", " par_fitnesses = np.array([ackley(chrsm) for chrsm in collect_parents])\n", " plt.plot(par_fitnesses[:,0], par_fitnesses[:,1], '.b')\n", " plt.xlim([-5, 5])\n", " plt.ylim([-5, 5])\n", " plt.xlabel(\"x\")\n", " plt.ylabel(\"y\")\n", " y_patch = mpatches.Patch(color='y', hatch=\".\", label='Population')\n", " b_patch = mpatches.Patch(color='b', hatch=\".\", label='Selected Parents')\n", " plt.legend(handles=[y_patch, b_patch])\n", " plt.pause(0.1)\n", " plt.close()\n", "\n", " plt.show()\n", "\n", "# plot GA statistics \n", "fig, axs = plt.subplots(3)\n", "\n", "x_axis_range = list(range(0, gen_stats.shape[0] * STAT_INTERVALS, STAT_INTERVALS))\n", "\n", "axs[0].plot(x_axis_range, np.zeros(len(x_axis_range)), '--r', linewidth=1.5)\n", "axs[0].plot(x_axis_range, gen_stats[:, 0], '-k', linewidth=1.5)\n", "axs[0].plot(x_axis_range, gen_stats[:, 1], '--b', linewidth=1.5)\n", "axs[0].fill_between(x_axis_range, gen_stats[:, 1] - gen_stats[:, 2], gen_stats[:, 1] + gen_stats[:, 2], color='b',\n", " alpha=0.1)\n", "\n", "axs[1].plot(x_axis_range, np.zeros(len(x_axis_range)), '--r', linewidth=1.5)\n", "axs[1].plot(x_axis_range, gen_stats[:, 3], '-k', linewidth=1.5)\n", "axs[1].plot(x_axis_range, gen_stats[:, 4], '--b', linewidth=1.5)\n", "axs[1].fill_between(x_axis_range, gen_stats[:, 4] - gen_stats[:, 5], gen_stats[:, 4] + gen_stats[:, 5], color='b',\n", " alpha=0.1)\n", "\n", "axs[2].plot(x_axis_range, np.zeros(len(x_axis_range)), '--r', linewidth=1.5)\n", "axs[2].plot(x_axis_range, gen_stats[:, 6], '-k', linewidth=1.5)\n", "axs[2].plot(x_axis_range, gen_stats[:, 7], '--b', linewidth=1.5)\n", "axs[2].fill_between(x_axis_range, gen_stats[:, 7] - gen_stats[:, 8], gen_stats[:, 7] + gen_stats[:, 8], color='b',\n", " alpha=0.1)\n", "\n", "axs[0].set_ylabel(\"Fitness\")\n", "axs[1].set_ylabel(\"x\")\n", "axs[2].set_ylabel(\"y\")\n", "axs[2].set_xlabel(\"Generations\")\n", "\n", "axs[0].legend(handles=[mpatches.Patch(color='k', label=f\"Best fitness\"),\n", " mpatches.Patch(color='b', label=f\"Mean & SD\"),\n", " mpatches.Patch(color='r', label=f\"Global minima\")])\n", "\n", "axs[0].grid(color='k', linestyle='--', linewidth=0.5, alpha=0.1)\n", "axs[1].grid(color='k', linestyle='--', linewidth=0.5, alpha=0.1)\n", "axs[2].grid(color='k', linestyle='--', linewidth=0.5, alpha=0.1)\n", "\n", "# axs[0].set_ylim([-0.5,14])\n", "# axs[1].set_ylim([-5,5])\n", "# axs[2].set_ylim([-5,5])\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "97d80dcc-c88b-4be1-ab4f-2e0ead3e0119", "metadata": {}, "source": [ - "## Fitness function global minima\n", - "Remember that the Ackley fitness function has a global minima:\n", + "## Fitness function global minimum\n", + "Remember that the Ackley fitness function has a global minimum:\n", "f(0.0, 0.0) = 0.0" ] }, { "cell_type": "code", "execution_count": null, "id": "05ade091-4ff4-4d2c-90aa-65822b252312", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_1_genetic_algorithm/GA_param_search.ipynb b/es1_1_genetic_algorithm/GA_param_search.ipynb index b1971b5..fc106e5 100644 --- a/es1_1_genetic_algorithm/GA_param_search.ipynb +++ b/es1_1_genetic_algorithm/GA_param_search.ipynb @@ -1,257 +1,269 @@ { "cells": [ { "cell_type": "markdown", "id": "22d803b1-586b-4787-a87e-06d216268b7b", "metadata": {}, "source": [ "# Genetic algorithm parameter search\n", "\n", - "The GA code here is the same as the code in GA.ipynb (do the exercises there now if you havent already) except that we will explore how changing the value of a single GA parameter influences the convergence to a solution. You can choose which GA parameter you wish to explore but for now it is set up to look at 5 different mutation probabilities. The plot at the end shows the mean and standard deviation of the best individual, i.e. if we are trying 5 different mutation probabilities and we run the GA 10 times for each of them then at each generation we get 10 best chromosomes at each generation which we use to find the mean and SD at that generation. This allows you to see which parameter value resulted in finding the global optima faster and how reliably it found it.\n", + "The GA code here is the same as the code in GA.ipynb (do the exercises there now if you haven’t already) except that we will explore how changing the value of a single GA parameter influences the convergence to a solution. You can choose which GA parameter you wish to explore but for now it is set up to look at 5 different mutation probabilities. The plot at the end shows the mean and standard deviation of the best individual, i.e. if we are trying 5 different mutation probabilities and we run the GA 10 times for each of them then at each generation we get 10 best chromosomes at each generation which we use to find the mean and SD at that generation. This allows you to see which parameter value resulted in finding the global optimum faster and how reliably it found it.\n", "\n", "## Exercise 2:\n", "\n", "- 2.1. Try different mutation probabilities. What does your initial observation of the results tell you? How would you know if the best mutation probability you found is actually close to the best for this problem, i.e. what would you do next in order to verify it or continue the search?\n", - "- 2.2. Set PROB_MUTATION as the best value you found and then do a parameter search for other parameters, e.g. POPULATION_SIZE, TOURNAMENT_SIZE and PROB_CROSSOVER. Which parameters have the biggest impact on finding the global minima and on the convergence rate and why do you think that is? NOTE: remember to change the variable name in the first *for* loop from PROB_MUTATION to whatever parameter you are searching next.\n", + "- 2.2. Set PROB_MUTATION as the best value you found and then do a parameter search for other parameters, e.g. POPULATION_SIZE, TOURNAMENT_SIZE and PROB_CROSSOVER. Which parameters have the biggest impact on finding the global minimum and on the convergence rate and why do you think that is? NOTE: remember to change the variable name in the first *for* loop from PROB_MUTATION to whatever parameter you are searching next.\n", "- 2.3. What is the benefit of increasing the number of repetitions?\n", "- 2.4. Why is this method better than the method you used in GA.ipynb and what would you do if it was too computationally expensive to run multiple options many times like this?\n", "\n", "Consider the following:\n", "- 2.5. Are there optimal parameters for an EA? \n", "\n", "NOTE: Once you have completed the above exercises, complete the exercises in /es1_2_evolutionary_strategies/es.ipynb" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "0f8b54c3-0122-4a93-b607-298ca63efb69", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "\n", "%run fitness.ipynb\n", "%run selection.ipynb\n", "%run crossover.ipynb\n", "%run mutation.ipynb" ] }, { "cell_type": "markdown", "id": "059c46eb-1ab4-4741-8bf7-bb39258cd3bd", "metadata": {}, "source": [ "## GA parameters\n", - "The default parameters provided here are by no means optimal. You should try different values to see what happens. However, to understand how changing one of the parameters influences the search for a solution, you should look at the mean and standard deviation of the fitness to see how the rate of convergence and best solution found are influenced. For instance, if you tried a mutation probability of 0.01, 0.1, 0.2, 0.3, and 0.4 ran each 100 generations and repeated this 10 times each, then you could plot the mean and standard deviation of the fitness against the number of generations to find a good mutation rate that results in finding the best x and y parameters in the lowest number of generations on average.\n", + "The default parameters provided here are by no means optimal. You should try different values to see what happens. However, to understand how changing one of the parameters influences the search for a solution, you should look at the mean and standard deviation of the fitness to see how the rate of convergence and best solution found are influenced. For instance, if you tried mutation probabilities of 0.01, 0.1, 0.2, 0.3, and 0.4, ran each 100 generations and repeated this 10 times each, then you could plot the mean and standard deviation of the fitness against the number of generations to find a good mutation rate that results in finding the best x and y parameters in the lowest number of generations on average.\n", "\n", "It is often a good idea to keep track of the best solution thus far so that it is not lost. If ELITISM=True, then this best solution will always be added to the new population without being mutated so that future generations can continue to search this area of the solution space." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "8134f0fe-037b-47eb-a2d3-8d5eaaf6e144", "metadata": {}, "outputs": [], "source": [ "# GA parameters\n", - "NUM_GENERATIONS = 50\n", - "POPULATION_SIZE = 10\n", - "TOURNAMENT_SIZE = 5\n", - "PROB_MUTATION = 0.15\n", + "NUM_GENERATIONS = 100\n", + "POPULATION_SIZE = 100\n", + "TOURNAMENT_SIZE = 3\n", + "PROB_MUTATION = 0.04\n", "PROB_CROSSOVER = 0.5\n", "STAT_INTERVALS = 10\n", "\n", "# add the best chromosome in the current generation to the next generation\n", - "ELITISM = False\n", + "ELITISM = True\n", "\n", "# we will add the best individual to the population if ELITISM\n", "POPULATION_SIZE = POPULATION_SIZE - 1 if ELITISM else POPULATION_SIZE" ] }, { "cell_type": "markdown", "id": "4181cd56-870e-4701-87b6-8571bcda31aa", "metadata": {}, "source": [ "## Statistical study of GA parameters\n", "\n", - "Choose which parameter you want to vary and which values you want to try. Here, we have selected PROB_MUTATION and the values of PROB_MUTATION that we want to test are 0.01, 0.1, 0.2, 0.3, and 0.4. The best value may not be any of these though so you should use the results you observe to update the searched values.\n", + "First, you should choose which parameter you want to vary and which values of this parameter you want to try. Here, we have selected PROB_MUTATION and the values of PROB_MUTATION that we want to test are 0.01, 0.1, 0.2, 0.3, and 0.4. The best value may not be any of these though so you should use the results you observe to update the searched values.\n", "\n", "You should also select how many times you want to repeat the GA for each chosen value (REPETITIONS). The results will be plotted at the end so you can try to visually see which mutation probability is, on average, likely to result in a faster convergence." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "f8ba3d8e-bae0-4c68-824f-81aacd9d29c1", "metadata": {}, "outputs": [], "source": [ "PARAM_TYPE = \"PROB_MUTATION\"\n", "PARAM_OPTIONS = [0.01, 0.1, 0.2, 0.3, 0.4] # e.g. 5 different mutation probs\n", "\n", - "REPETITIONS = 5" + "REPETITIONS = 10\n" ] }, { "cell_type": "markdown", "id": "14fc14fe-ed51-42b3-8cf0-edfe601214f1", "metadata": {}, "source": [ "Note: remember to rename the first *for* loop if PARAM_TYPE is not \"PROB_MUTATION\"" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "id": "99b5c5f0-3340-4ff2-b958-438a05ae1d94", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting PROB_MUTATION: 0.01\n", + "Starting PROB_MUTATION: 0.1\n", + "Starting PROB_MUTATION: 0.2\n", + "Starting PROB_MUTATION: 0.3\n", + "Starting PROB_MUTATION: 0.4\n" + ] + }, { "data": { - "image/png": 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P1SlXoWyF8An972KachFFFHEQoWioHBTQ+ilCmBS1VA5caBJtO4YRU3E0omrCfWsYKEehHIXhM+L/LqKIIoo4WFA0VA4CaDKtQgiz6FE5gOE4WkFSG5REn5fs8+elHAUuGF4DJEVDpYgiijio0KeGiuu63HHHHYwZM4aSkhLGjh3Lvffe2+cz0AMPMS+KflxF0bcDD1LauG4rppnQCtAGS98XkuxomBQNlSKKKOJgQp+SaX/5y1/yyCOPsHjxYo444ghWrVrFpZdeSlVVFT/5yU/6smkHGBRCECVAKvp64CsiHVIGkDKEx5Os0GkcEB4wGZEQ5c4KIVCRoqHSXdgWCrE3h0Jod2Ogx8PI3pbDLaKIPkafGioffPAB3/ve95gxYwYAo0eP5umnn2blypV92awDDjp8EBtcRNGjcoBBKYVtN8VJtDHEDMs+N1SiGT+AzvwJFd+f7sC2UIgJK1cSkr13P/2GwYbjjisaK0X8XaFPQz9Tp07l7bff5ssvvwR0Rcn333+fM844I+P24XCYlpaWlOXvA4rYlFgIccBocxShEVOiTZBoE9DPq+8MFSWV9qjEVPNNkLZEyaJXZX+x17Z71UgBCElZkAfnkksuQQiBEAKv18u4ceO45557cBzdh7zzzjvx34UQDBo0iDPPPJNPP/007Vjbt29n7ty5DBs2DK/Xy6hRo7j++uvZt29fynYnnXRSyjGHDBnCeeedx9atW/Nq85YtWxBCYJpmWuG9uro6LMtCCMGWLVtSrqGpqSntWKNHj+aBBx5g0aJFKW3KtMSOt3z5ckzTjE+gO97HTMvo0aPj196x0ODnn3/O+eefz6BBg/D5fIwfP54777yTQCCQ1lYhBCtWrEhZf8MNN3DSSSflde+SsWTJEmpra/H7/UyaNInXX3895/Z1dXVceOGFjB8/HsMwchZM7G30qaHyf/7P/+GCCy6gtrYWj8fD0UcfzQ033MBFF12Ucftf/OIXVFVVxZcRI0b0cov7BqkeFBMpDwxtjiI0XLeNRFZWKrQjrO88GLEsn2SPSjHz5+8Lp59+OnV1dWzcuJGbb76Z+fPn86tf/Splmw0bNlBXV8fSpUsJh8PMmDGDSCSRWr9p0yaOPfZYNm7cyNNPP81XX33Fo48+yttvv82UKVNoaGhIOd7ll19OXV0dO3fu5KWXXmL79u3Mnj27oHYPHz6cJ554ImXd4sWLGT58eIF3AGbOnEldXV18mTJlSryNsSU2nixYsIDrrruOZcuWsXPnTkAXPUzeFmDhwoXxvz/88MOM512xYgXHH388kUiE1157jS+//JL77ruPRYsWMW3atJR7DOD3+7nlllsKvr6O+OCDD5g1axaXXXYZH3/8Meeccw7nnHMOn332WdZ9wuEwgwYN4vbbb2fy5Mn73YbuRJ8aKs899xxPPvkkTz31FB999BGLFy/m17/+NYsXL864/a233kpzc3N82b59ey+3uK+QGOi00FvRo3KgQEoHx2nBMDK74vtaS6VoqBTh8/moqalh1KhRXHXVVZx66qm8/PLLKdsMHjyYmpoajjnmGG644Qa2b9/O+vXr479fc801eL1e/vjHP3LiiScycuRIzjjjDN566y127NjBbbfdlnK80tJSampqGDp0KCeccALXXnstH330UUHtnjNnDgsXLkxZt3DhQubMmVPgHYCSkhJqamrii9frjbcxtpimSVtbG88++yxXXXUVM2bMYNGiRQBUVVWlbAtQXV0d/3vQoEFp51RKcdlllzFx4kR+//vfc9xxxzFq1CjOO+88XnnlFZYvX85vfvOblH2uuOIKVqxY0an3ozM8+OCDnH766fzsZz9j4sSJ3HvvvRxzzDE89NBDWfcZPXo0Dz74IBdffHHGKtJ9iT41VH72s5/FvSqTJk3iRz/6ETfeeCO/+MUvMm7v8/morKxMWf4eoJVoY0qiBlLaxcyoAwRStiNlKKeh0pf1mWIGSez9if2/aKj8/aKkpCRtJh9Dc3MzzzzzDABer+ZcNTQ0sHTpUq6++mpKSlLDmzU1NVx00UU8++yzWfukhoYGnnvuOY4//viC2nn22WfT2NjI+++/D8D7779PY2MjZ511VkHHKQTPPfcctbW1TJgwgdmzZ/P44493ua9ds2YNX3zxBTfddBOGkTrUTp48mVNPPZWnn346Zf2YMWO48sorufXWW5FZwoqxUFcsVJUJy5cv59RTT01ZN336dJYvX96la+lr9KmhEggE0h6gaZpZH9DfKzTHITbQGGjOSvEe9TW0Em0LQnhySNL3rWEpI5nfk6Kh8vcHpRRvvfUWS5cu5ZRTTkn57ZBDDqG8vJzq6mqeeuopzj77bGprawHYuHEjSikmTpyY8bgTJ06ksbGRPXv2xNc9/PDDlJeXU1ZWxoABA9iwYQOPP/54Qe31eDxxYwHg8ccfZ/bs2Xg8noKOUwgWLFgQD1GdfvrpNDc38+6773bpWDHuZa77FtsmGbfffjubN2/mySefzLhfaWkpEyZMyHkf6uvrGTJkSMq6IUOGUF9fn2/zDyj0qaFy1llncd999/Haa6+xZcsWXnzxRf71X/+V73//+33ZrAMO2lDRj6oo+nbgQMoQjtOGaaaTaGMQwuhT0TcZTmT8xNtkimJxwr8jvPrqq5SXl+P3+znjjDOYOXMm8+fPT9nmvffeY/Xq1SxatIjx48fz6KOPph2nEGP7oosuYs2aNfEEiXHjxnHaaafR2tpaUNvnzp3LkiVLqK+vZ8mSJcydO7eg/QvBhg0bWLlyJbNmzQLAsixmzpzJggUL9uu4hU5SBg0axE9/+lPuvPPOjJ6v4447jvXr13eJq3Owok8NlX//93/nhz/8IVdffTUTJ07kpz/9KT/+8Y+59957+7JZByCclNBPrEhhEX2LBIk2e5Z/ouxB7xsqSqmU1OR4m6yiofL3hJNPPpk1a9awceNGgsEgixcvpqysLGWbMWPGMGHCBObMmcO8efOYOXNm/Ldx48YhhGDdunUZj79u3Tr69euXwtOoqqpi3LhxjBs3jm9961ssWLCAjRs38uyzzxbU9kmTJlFbW8usWbOYOHEiRx55ZNo2MQpAc3Nz2m9NTU158y0WLFiA4zgMGzYMy7KwLItHHnmEF154IeOxO8P48eMBct632DYdcdNNNxEMBnn44YcLPi/okNyuXbtS1u3atSvOrznY0KeGSkVFBQ888ABbt24lGAzy17/+lf/7f/9vPDZahIY2SmIeFYMDQe307x2dkWgTiHnAet8wUI4uQCjMdI9K7Lci/vZRVlbGuHHjGDlyJJbVuXTWNddcw2effcaLL74IwIABA5g2bRoPP/wwwWAwZdv6+nqefPJJZs6cmbMit2nqjLiO++eDuXPn8s4772T1phx22GEYhsHq1atT1m/atInm5uasxkAyHMfhiSee4P7772fNmjXxZe3atQwbNiyNS5IPjjrqKGpra/nNb36TRmdYu3Ytb731Vtx70xHl5eXccccd3HfffQV7oQCmTJnC22+/nbLuzTffZMqUKQUf60BAsdbPAQ5d5yfBUdEoir71NbQSbbBTQ6UvRd86ZvzE21TM/CkiB0pLS7n88su566674mGLhx56iHA4zPTp01m2bBnbt2/njTfeYNq0aQwfPpz77rsv5RiBQID6+nrq6+tZu3YtV111FX6/n9NOO63g9lx++eXs2bOHefPmZfy9oqKCefPmcfPNN/Pyyy+zefNmli1bxkUXXcQJJ5zA1KlTOz3Hq6++SmNjI5dddhlHHnlkynLuued2KfwjhGDBggV88cUXnHvuuaxcuZJt27axZMkSzjrrLKZMmZJTq+SKK66gqqqKp556KmX9ypUrqa2tTdOYScb111/PG2+8wf3338/69euZP38+q1at4tprr41vc+utt3LxxRen7Bcz0Nra2tizZ0+cENzXKBoqBzxSszb0v6FIpu07aBJtcyck2gT0Jn1jqKBAGOkeFdwioXZ/MdDjwW/0bhfqNwwG9iCZNIZrr72WdevWsWTJEkB7LVatWsWhhx7K+eefz9ixY7niiis4+eSTWb58Of3790/Z/7e//S1Dhw5l6NChnHzyyezdu5fXX3+dCRMmFNwWy7IYOHBgTm/Qgw8+yJw5c7jllls44ogjuOSSS/jGN77BK6+8ktc3umDBAk499dSMYaJzzz2XVatW8cknnxTc9qlTp7JixQpM0+SMM85g3Lhx3HrrrcyZM4c333wTn8+XdV+Px8O9995LKBRKWR8IBNiwYQN2DuG/qVOn8tRTT/HYY48xefJknn/+ef7whz+khM7q6urYtm1byn5HH300Rx99NKtXr+app57i6KOP5swzzyz4ursbQh3Eea4tLS1UVVXR3Nz8N5uqLKVDMLgZw7AwDP1S23YjPt/QDnVl8oP20Nhpcu9F5A8pwwSDWzAMP4bR+aCxP89rf2A32ITrwnj6pbfRbrDxj/BjVfVpFY2DAqFQiM2bNzNmzBj8HaTrO9b6UcpFKhswMPI0ZAtBsdZPEQcTcn07hYzfxV7qgIeKLomZm84k6Zrom5SB6MA5LMp3KaJQaGVgJy8jBfTz6gvRNxmWafyUOISW0i9i/zDS708xHKR0oro5AsPwFb+xIoroBhS/ogMeEp1Zkvyouj7wKeXiukGkDHdL6/4eoZRDIX7IvhJ9y5SaHENfFydUSmE32Ejnb81YStTlioVtexLaMDq47uGVV15JeXl5xuXKK6/s6+YVcQCi6FE5wKFDNcmdX2zg65pHRQ+yAaQM59T/KCI7pAwVOFM2owOK6vZQQDZIRyJtiWFlbqewBDIie7VNyVC2wm6yEZbAqPxbmi/FvtWeN1J0v+BGn+HBcw/vuecefvrTn2b87W81hF/E/qFoqBzwkNGlY9aPG5XWL6yDkjIc9aq04/FUd2M7/36gDZX8CY06VOein2N64cKeQDzjx5/Fo2IKXUXZUQhP7xsqMiyRAYkbdLEq/za6ocSkIvF3T9uA+hx9Z3B2BYMHD2bw4MF93Yw+ge63FYbxt/HO9xYOHjP87xa6s0vN+jG7LPomZRghvEgZRMqDp7jhgcL5ltJBSjunyFtH9IWasHIUSLJyVPo6RVmGtZHktrkoeWA82+5BssHQ09elj6+/jb+le/i3C91vH3zhur5G0VA5wKFf7NR12otSuNppbLA0jJKoZ+Xg4Km4bjvh8M4D4uNWykapwgyVhOhbLxoqdu6BSxgCZN+kKCulDRSjxEBG5N+QSm7yvRT0vISA6rAUceBD12nTxkrxmeWLoqFywCPTy9w1tVO9TyxbRR0UhFopHSKRPbhua59kznSEUjZ61pz/p6Nn2L1b70dGZJp+Sib0iaFiK2RYYvgNcOlTUm/3I8En6xgK6tGzHgBGfBG5od8HrTKeCAUXkQ+KhkoWKKmyVp7t1XZk6ICEEAhReAVlbcW7gIlheKO1ag5sOE4DrtsWbXt2gaPegk5N7ioXoBcNlQw1fjpCGKJTz0tPIBb2MTwGwiNw2/5WykEk7mVvhH8SRlCMs1acoR8M0O+GKHpVCkCR0ZMFbsDFaXbwDfP1MUktszGSkNbPH3p7FTV0vLhuCCkjB6z4m+O0EonsxTTLcd32qAeook/bJGUwWmiwUIhe86goV6Hs9Bo/aS3qoxRlGZZxW8/wGrghV2coeQ6+edO25m3sDewF9ERASif+fugwq7dbM3IGlg5kZNXI6F8x1Woj/m133YguoueR0MQSItYfuAWGkf8+UbxD2aBABiUyKDFLeydTI2MzlMxiKBVe7yc5pVl7VPTgfyAaKlLaRCK7o4q8XpSKIGXhBc26E0rJKBm58M9mf0T6CkWMJGuU5h4g45k/UuUVJuqWtsX4KVGjRHgFBLTxcrAZKtuatzHhoQmEnFDnG3cT/JafDdduiBorqYZJV7IAi+hNpHpPYn2CUkbxuXWC4t3JARmSuIG+dUunFyRM/q2wgU+TQFMfuZS918nmC6UUtr03WvRPl6MXwoqnVvddu+wkjk9hEMLsNY6NchS4dBr6waTXM39i/BTh1W0TQqCEOih5KnsDe3vVSAEIOSH2BvZm4b+k38NLLrkk6kEVeL1exo0bxz333IPj6L7jnXfeif8uhGDQoEGceeaZfPrpp2nH2r59O3PnzmXYsGF4vV5GjRrF9ddfz759+1K2O+mkk1KOOWTIEM477zy2bt2a1zVu2bIFIQSmaaYV3qurq8OyLIQQbNmyJeUampqa0o41evRoHnjgARYtWpTSpkxL7HjLly/HNE1mzJiR8T5mWkaPHh2/9o6FBj///HPOP/98Bg+uobS0mtraI7jrrrsJBILogqU6BDR69GiEEKxYsSJl/xtuuIGTTjopr3uXjCVLllBbW4vf72fSpEm8/vrrObf//e9/z7Rp0xg0aBCVlZVMmTKFpUuXFnzenkDRUMkFBU6zg3L7Lo6oB+b0x9QV0TftDUh4hwzDF+V/HFhxUtdtwXEasKzKuDdJp1TbfUqo1ffb6aKrVhsqvXGv8zU8+iJFOZmfEoPhNXBbixyLwqGSvg8jmiGYfg9PP/106urq2LhxIzfffDPz58/nV7/6Vco2GzZsoK6ujqVLlxIOh5kxYwaRSOJb27RpE8ceeywbN27k6aef5quvvuLRRx/l7bffZsqUKTQ0NKQc7/LLL6euro6dO3fy0ksvsX37dmbPnl3Q1Q0fPpwnnngiZd3ixYsZPnx4QccBmDlzJnV1dfFlypQp8TbGlhEjRgC6QOF1113HsmXL2LlzJ6CLHiZvC7Bw4cL43x9++GHG865YsYLjjz+eSCTCK6+8xLp1n3DvvfewePHvmD79TGw7xhvURqbf7+eWW24p+Po64oMPPmDWrFlcdtllfPzxx5xzzjmcc845fPbZZ1n3WbZsGdOmTeP1119n9erVnHzyyZx11ll8/PHH+92e/UXRUMkFASqkcIN96VVxMoZ+dMeUP7lUKRUd5JMNFS9Shg+o7B8pw0QiexDCl2IQaE+Q26eEWintggbT5G0TKeU97zmQEZkXVUEIAap3M3+S+SkxGF5DC8D9zaQp9wZifIfYzYyp4abfQ5/PR01NDaNGjeKqq67i1FNP5eWXX07ZZvDgwdTU1HDMMcdwww03sH37dtavXx///ZprrsHr9fLHP/6RE088kZEjR3LGGWfw1ltvsWPHDm677baU45WWllJTU8PQoUM54YQTuPbaa/noo48KusI5c+awcOHClHULFy5kzpw5BR0HoKSkhJqamvji9XrjbYwtpmnS1tbGs88+y1VXXcWMGTNYtGgRAFVVVSnbAlRXV8f/HjRoUNo5lVJcdtllTJw4kd///vccd9w3GTVqJOeddy4vvfR7li9fwQMP/DsxYi3AFVdcwYoVKzr1fnSGBx98kNNPP52f/exnTJw4kXvvvZdjjjmGhx56KOs+DzzwAP/0T//EN7/5TQ477DB+/vOfc9hhh/HKK6/sV1u6A0VDJRc0ORu3tS/DDTqdLR1mNAMl3+Noyz3ZoyKEFV3fM4aKcrVMet7bK0kksjcq71+aYQvRpx6Vjh6pnNs6ksjucFItG6PXtFTyyfiJQ/SeodKRnxJvQsyzEy56VPJH5nuVjyFdUlKS4i1JRnNzM8888wwAXq/mrjU0NLB06VKuvvpqSkpSy27U1NRw0UUX8eyzz+rJUAbeXENDA8899xzHH398p21Lxtlnn01jYyPvv/8+AO+//z6NjY2cddZZBR2nEDz33HPU1tYyYcIEZs+ezeOPP95lT9+aNWv44osvuOmmmzAMg2TDcvLkb/Cd75zCM888G/eGAYwZM4Yrr7ySW2+9FSkzG+6xUFcsVJUJy5cv59RTT01ZN336dJYvX553+6WUtLa20r9/71Z9z4SiodIJjBIDp83pk0qzicyeTB4VPYPKl1AbGyQ7hi2EMHHdQDe0Nh0yLLVLP8/QmeM04zhNWFbmeh8xAnBfQWf85MdPUa5ChlQ8/be31GnjafV58r+FKXrNk9GRn9KxHX3NBzv4IYjV/skEpRRvvfUWS5cu5ZRTTkn57ZBDDqG8vJzq6mqeeuopzj77bGprawHYuHEjSikmTpyY8bgTJ06ksbGRPXv24ERrWj388MOUl5dTVlbGgAED2LBhA48//nhBV+PxeOLGAsDjjz/O7Nmz8XgK54jliwULFsRDVKeffjrNzc28++67XTrWl19+Cej7k9BQSWDixFq+/HIjEPO4at7R7bffzubNm3nyySczHre0tJQJEybkvA/19fUMGTIkZd2QIUOor6/Pu/2//vWvaWtr4/zzz897n55C0VDpBIbPQIUVMtAXbunYIJdpdlzYwKddi24amVanKQd6ZABVUsXryXQG1w0SiezBMPxZvRaaUBvpE+n/hHR+nhaAG61nEzVwhTAQovCU8kIRr/GTp0dFWL1nqGTip8Rg+AzcQP5G7d87MhkjOuU1XaX21Vdfpby8HL/fzxlnnMHMmTOZP39+yjbvvfceq1evZtGiRYwfP55HH300r3Nm2kahmHXhLNasWcPatWt5//33GTduHKeddhqtra0FXefcuXNZsmQJ9fX1LFmyhLlz5xa0fyHYsGEDK1euZNasWQBYlsXMmTNZsGDBfh03NQSc7buMrXcZOHAgP/3pT7nzzjszer6OO+441q9f3yWuTr546qmnuPvuu3nuuecYNGhQn/PHioZKHhAegdOSLs7jukFse1+WvfYfiXo+mTkqhXEe3IwfiWH4eo6nEuU/dDb4KOVi23sBJ2dFZyE8cQn73kYs46cQjwouqKRsFv369KxRULChYoq8nlF3IJdBJLxa0+VgzP7pG2R6XjGeSupvJ598MmvWrGHjxo0Eg0EWL15MWVlZyjZjxoxhwoQJzJkzh3nz5jFz5sz4b+PGjUMIwbp16zK2ZN26dfTr14+BAweioueuqKxgzKFjGDduHN/61rdYsGABGzdu5Nlnny3oKidNmkRtbS2zZs1i4sSJHHnkkWnbxCouNzc3p/3W1NREVVVVXudasGABjuMwbNgwLMvCsiweeeQRXnjhhYzH7gzjx48HiN63jpwiWLduPePHH5a0R8zQlNx0000Eg0Eefvjhgs8LOiS3a9eulHW7du2K82ty4ZlnnmHevHk899xznHrqqXFF875E0VDJA4bfwG130zpa1w1g2009OMNPCARlaFVBHhVNBE1fHzN4esRQkdrd39kgaNuNOE4zppm7xHvCPdr7PBX9oeavU6Gkvm43mJqJ0RseFcg1c0tFb2X+xPkp3sz3L6bjUiTU5ovs4m4dQwxlZWWMGzeOkSNHYlmdZ6xdc801fPbZZ7z44osADBgwgGnTpvHwww8TDKZqGdXX1/Pkk09qwyZmJ0VhSzvOWTFN7YnsuH8+mDt3Lu+8805Wb8phhx2GYRisXr06Zf2mTZtobm6OGwy54DgOTzzxBPfffz9r1qyJL2vXrmXYsGE8/fTTBbf7qKOOora2lt/85jdImfrdr137CW+//b9ccMHMtP2ktCkrK+OOO+7gvvvuK9gLBTBlyhTefvvtlHVvvvkmU6ZMybnf008/zaWXXsrTTz/NjBkzon1X34dki4ZKHjA8BspRyGBHQ6UNKUM9RkaNeUwyDY6xgShfjkouoTIhPAVzP6R0cJzmnOdXUoGTm6zpOM1EInswzbK8jAAhRJ9kKRXsxbFVwgiI81QKy9TqCgot+yBMoT0/PW2o5OCnxGB4jL8hOf2eQya+QwIia5pyvigtLeXyyy/nrrvuih/noYceIhwOM336dJYtW8b27dt54403mDZtGsOHD+e+++7THlQUAkEwGKSuro6vd3zNmjVruOqqq/D7/Zx22mlZNGCy4/LLL2fPnj3Mmzcv4+8VFRXMmzePm2++mZdffpnNmzezbNkyLrroIk444QSmTp3a6TleffVVGhsbueyyyzjyyCNTlnPPPbdL4R8hBAsWLOCLL77ghz88j5UrP2Tbtm0sWfIC3/veD5gy5QSuv/66tH1ik7F58y6lqqqKp556CkiEkFauXEltbW2axkwyrr/+et544w3uv/9+1q9fz/z581m1ahXXXnttfJtbb72Viy++OP73U089xcUXX8z999/P8ccfT319PfX1dRn1aXobRUMlTxg+A6fJiZek11yJMEo5uG7PKKYmPuhsnXt+1q5OTc6esaJJqsGCPEOO00gotJ1IZFfW/ZSr4nLumdoUiewjFNqBYXgxDF9e59VGVc+Qf3PBdYMF6adIWyI8upZOgoht9HjWkgwXkPGThJ42VHLxU2IQXqHl9A+AGlv5YGDpQPyWv1fP6bf8DCwdCGT2miVCwvv3PK+99lrWrVvHkiVLAO21WLVqFYceeijnn38+Y8eO5YorruDkk09m+fLl9O/fPx72AXj8vx7n0JGHMmrEKE455RT27t3L66+/zoQJE6Jh1PwNUsuyGDhwYE5v0IMPPsicOXO45ZZbOOKII7jkkkv4xje+wSuvvJKXd3HBggWceuqpGcNE5557LqtWreKTTz7Ju80xTJ06lRUrVmAYJt/97vcZP/5wbrvtdi6+eDZLl76Oz5fe72nivcKyBHfffQehUCiuii1lhLa2FjZs2EAkkn3CNnXqVJ566ikee+wxJk+ezPPPP88f/vCHlNBZXV0d27Zti//92GOP4TgO11xzDUOHDmXo0KEMGzacG2+8ueDr7m4I1dcsmf1AS0sLVVVVNDc3x+OU3QWn1SG0LYSnn+YkKFfhtDqUjC7BLDVxnDZCoS0Yhg8hPPj9o7q9JpDrBggGN2NZ1RmPbduN+HzD8Hj65TyOlA6h0BaEMDMaBEopXLcJn28UllWeV7tCoW0I4UHKIKZZgc9Xk3bs8K4wkR0RPIM9+A9JdOg6DXkPtq09KfkaKfpaIkhpU1IyuksKsV2BUpJgcDNATg5NMoJbtfEqQxLvEC+e6piBZVBaemgPtVMR3KTPa5bkX/bBaXLwDPbgHejtMRn2yN4IkV2R+PeUDXajjX+kH6viwKnuEQqF2Lx5M2PGjMHvTzVMYrV+dGg1/d7F1nXXuzqwdCAjKg/JOfHQNYY8vV5DxpEOtmtjGol2xVKWvaYX0zCTJk3GAVm6o6eg3w8n6zPrHMleqEQmYU/fQy1SaSOEt0vvcK5vp5Dx+8DpDQ5wCFOLY7ntLmapiZQhAlIRkdDPDKNUBCHyH3Dzge7ksvMNEoWtOoMblX7P3D59HKIhldyGilIySiCWmGYJhuHHcZoJhb7G6x2SYugoV4GhwxFKqWjYxsG2d2PbDZhmRcEvvybUtkdDKL1lqDgolZvom7K9q0CBMKJZNUEXqj3R0I9K07PptnZGSbHZeCBZYWqDSimXUOhrDMOPx9Ov2zrBOD/Fl2doLyj7uvZk3hhZNZKRVSNx3TBaKTbdUAGiE5rumcjk883H+o6+hhACgcCWdvTfmncXC0/1bcHX3sT+FowUHe5Vz9/DXMkcvY1i6KcAGH4Dp8VBOpKQ00KDrQgqzTvomZo5KiMBNoH86sdkEnvrCB3+ae00duy6rVHiqx5JhBB4PNUo5RAO78C2mxLHcKL1ZmRUV0RGiETqsO0GLKuqizVzYjHc3sv80edyyFecJEakFabAsAQyEiMUx1LKeya0UWjGTwyxFGWdgh3CtvcQDG4lEtnXLUTxOD/F03m7hE/gtCZCrAcDYinBmUMxvd/J55LT70lkO58RNdAd10EpydVXX0dV1UAqKiooLy9PWa688spebXNvIDenqKvIrkTcfUjPVOorFD0qBcDwGziNDpG2AHtlG+3KpEKBwsB1A1hWfmlw+SP3S6hnb50PJIkS8LmO5Y0Sg7WbL2NrpE0ksjc6O0wdtC2rIqqFshOlbDyeAUgnytNwFG4kiCN247rt0VBW121kLVIX6oH7nRkxBeB8B50YNwdT7+O2u0hbYviStW+63xuknJgnp0BDxRTIiERG645YVj+kDBGJ1OE4TXi9AzHNii4/s3z4KTEY3qjAYlgWFL7qW+T6tgQJzkh3eVQ6M0Bi55TkrfzXDZBZK72DaZg40gGpmD//Tm666YZoeCq1fd0dwj+Q0BNGq/ac9cwz7ssCsB1RNFQKgBACYQoam1poKQ1R4R2AVAqEVkztbpd+cockba0xkRq7N+L1Z3J9BDq1NvdHYhgeXLcVKcNZXf6O04iUQTyezJLKplmClKYm2LoRpCzHsDzYwRZUsAXhd7Csfvv9wWpuTKDXXMc6HTr/8yhXgYx2TCY6q8ZWCL+JEJKeSvfLRFrOB8ISqIBCOdqg1ZVrdVhPygCh0NeYZjkez4BodlZh97yQlONYFtLBZahAdhmB5N+781x5bKVUr4V/YkJvuWAKA0eGGDh4EIMHD0IID4bxtzMExWQYhNnxpueSmdgfaIO0J/rBmBcoWd6/L/G385b0EiIexZ6GJnx+A8sQhFwFwoeULUgZwjTLOj9I3ki8IFqO3sEsNztUTI1V3szeqUsZycuAEsJAyiCZCAJaM6YB08zNYTEML0JU4UQaiTgteKwqbHcXwvXis3KTfvNFTKE2l/enO+G6gbyF3oA0R5gwBCqayZIoi9D9kGGZoZPsHMLQ/CvpOCRfpjZYyjCMEly3jVBoK5ZVHSVv6+wS3Ym50VCDGxfkUwp8vhqdpZUnPyV+Xo/2Qnmqe4eDtP/o7TBVPryBmFeldxAzUkSOdolo6rIrXYRhIHqxfb0B5SoQ2QyV7ofmFkqE6InwTMwLeGCwQ4qGSgFwlaJBuLiRNspsn+6qlYo6dRWu272Gikp2paqEaz/xThooFenUk5NvMT0hfDhOKx7PwBQ3f4JAq/IiWAphYogqXHc30mgHPBidkHQLQSqhtqdZ706UrZ//p9JR4E54dB0bz4Do7z1RrkDpKt9dSU2OQdoOKsNlCmFgWZUo5UTrMbXE9uiwnQCMqMEbQcpqDGkhwxLDn3+HZ3i1nL50JIZ1YHSU+4vu8m7kq0ESG8R6KosrE5RSGDnOpZAYwsBVEqUMVA95A/oCSimU7O1rSWhpdfczPhC8KMkoGioFoNl2aHXbKfUqVMBAVOmu2gUswxclo/bvRnZ/ImSjXIV0ox+DEfOomEnM7GzHkHmnxRmGF8dpQ8oIpplIJXOcFhynpSBOiEBgUYVhgTSALoYlMh47SqiV0sbs4eiAUrrGj1WAXoaKpHo2hAXS0Xoq2gtWGEE1n848xovp6sAuDIEbDmGUZr+hQlhR4nTM0DKytkupJq0vFCnNm58SP49XQEBnIhnlB76hkh9ptbve/3yVh5O5MT2P+D3I2iwFcU2o5DTbA4Osud+IXlLMkEx+Pj1Lau5+z1muYrh9hQO/FzhA0O44NDg2JcLF8ClU0EBFouJvSkXJqGG6U95dGyD6ESlbppX2SYg7ZZ+hxzJ+8iHVaWPGJVlpV8oItr0vSqDN/3VRMmZUGTqrpJurT+vwT88I7SVDe23Siznm3MdWqYaKR6AiMZn6/DK1YpCOJLQtRGRfJGcpgnjGTx6ZNZkgLIEbzjdEaEaXHG5+4cN123FDhWcNCSFQqIOo7k++5NbuPF+exO4DZWasEkZJLEk5+kOfNalbkVxiKe2Seu4akz1n3Qdt4PaWJy4fFD0qecCWkn22gwA8RJCWiQrqisqqPBrJSyGjdpeeihMfDJSj4oN/R+T2qLhRj0p+ISkhrJQMJtvOTaDNiuhkThgCTKVTdLvRzasVaoM97jrWhkoBRFqlkI5KsQtjWhLSlhhes6DUahmQuG0ubquL2+LiGeDBrEg3EpQTJfAWmPEThwHSDoPqHqVVw/DgOO2o1iBGBvXNTvf3ajl9NeDADg2EtoUI7w7ogThrO2PZWOH9vhZrgIH3kHwHkJ4jW3ZEZ0RaFRvJow4VFb1fPZm10puIe00y3oae9Bp1v+fsgDFuk1A0VDqBUoom2yEgXaoMA0dqzoXwGsg2F0oNnflDLG22HcvqnhS7ZI+KjKiMxZJ1/5PLUIkV08vvQ9F6KjqDSWtqdE6gzXzexIcjBDps5Rau8ZEN2qOSO526O1CodL6KhueMjoQ6E1RIIspjxSTziyu77S7CEJiVJrJdEt4exqw28fT3pGTFdDXjJwZhSWTQRblGt/QKQlhI24FwCE9p4caP4TNwgy4qohC+XBltCikDGEZJr88AQ9tCrJywslc9P4Zf8A9fTKZkVGmn2+rZtuohsmUqOg1vJIUSYh6z3ib89iiSLyPpVvSMhko6usvgS7T3wJocHDi+nQMUba5Lo+tQbpggbBRhhPAi/IYeeMIKNx439uK6gW4hSybHCeNeFEWaR6WzLJJCY42xEJbO8tmHEHRNoTQ5RGXplNPu7JO0WJzTo7Vz9CAYKshQiUXiOjL/hUdEKynnX/Va2hKnzcHway6IWW5iVpq4La4OB+2OxENqMiK77k0BlOGiXAfc7usSlA2OHSyInxKDsERcKC4bpHSIRHYTCn2N6xZeYXZ/Ye+1ez08JUMKZ2++4TTB3LmXYxjaA+f1ehk3bhz33HMPjqOP8c4772iPX3QZNGgQZ555Jp9++mna0bZv387cuXMZNmwYXq+XUaNGcf3117Nv374Ur820U6bhs3zxZcSwEVx4wY/YuvXrtPZlGsi3bNkSzTgz0wrv1dXVYVkWQgi2bNmScg2ZiueNHj2aBx54gEWLFqVcZ6Yldrzly5djmiYzZsyIH+eSSy7Jue+hhx0KAk6Zfgo33HhDShs+//wLLrhgNkOGDKekpILa2iO46667CQRSa5Ydeuh4TNPHihV/SVl/4403c8op09KuLfU+umnG4pIlS6itrcXv9zNp0iRef/31HMeA999/n3/8x39k8OBhlJf34/DDJ/HAAw/m3Ke3UDRUcsBWkn22jUcIPIYBKgxKpcbngy5O9AUxDF+0cFR3VPdNkObiAmIZPHyaUJu949LS/oUYKvqViBFoYwq0hSLlozESZM/uhFKx0EzPQKfZFp7xExN7S4YRraSMI/I3VIJSexSSKg4LU2BVWRheg8juCKFtIewmGxnsWjHCxIElKIlyunEmZVtI1d7lGaUwdZpyJrhugHD4a2x7DyCx7cYD0mV9IGD69OnU1dWxceNGbr75ZubPn8+vfvWrlG02bNhAXV0dS5cuJRwOM2PGDCKRxCRg06ZNHHvssWzcuJGnn36ar776ikcffZS3336bKVOmsK9hX8rx5s6by9avt7Jl+xaef+E5vv76ay695LIMrUsmd6Ri+PDhPPHEEynrFi9ezPDhwwu+BzNnzqSuri6+TJkyhcsvvzxl3YgRIwBdoPC6665j2bJl7Ny5E9BFD5O3BVi4cCF1dXXs3LGTFe+vyNjPrlixnKlTv00kEuGVV/7A+vWfce+997B48e+YPv3MlHsM4Pf7ufXW2wq6Nt1np97HDz74gFmzZnHZZZfx8ccfc84553DOOefw2WefZT1OWVkZV199FX/60x/5/PO1/PM/38odd8znt78tvHJ0d6NoqGSBVIrGiENYSkqjRbakDCKSRiDhMRDtCsfVHWTshekOOf0EQcqIS7JjpHtUOqvIq1OTC/Pla35By/6505OMqmSeTXdCh9p6jlDbVUMFlZ6VEfMQaEPAJR/Rt1jYJ1MHaPgMPP09oCD8dVinJndBQyXpbCihus3rpZRCBUzwOl0mmAuvTutONnCVUth2E6HQ10gZxLL6YZoVSBnok6rafYPCnrPP52HIkCGMGjWKq666ilNPPZWXX345ZZvBgwdTU1PDMcccww033MD27dtZv359/PdrrrkGr9fLH//4R0488URGjhzJGWecwVtvvcWOHTu46467UjRUSktLqampYejQoRx3wje58qorWPPx2mjrRcpEJlvYaM6cOSxcuDBl3cKFC5kzZ05B1w9QUlJCTU1NfPF6vfE2xhbTNGlra+PZZ5/lqquuYsaMGSxatAiAysoKhgwZHN8WoLq6Wv89pIZBAwclHktSBtC8eZczceIEXnjhOY477puMGjWK8847l5de+j3Ll6/ggQf+LaWdl19+GStW/IXXX/+fgq8x2VB/8MEHOf300/nZz37GxIkTuffeeznmmGN46KGHsu5/1FFHMWvWeRxxxBGMHj2a2bMv5LTTpvH++x8U3JbuRtFQyYKg69LqulSYepBSykWqAMmKWMIjEI7CjiQ+ND3Id14zp3Mkpe7FuCkC6KjRkcOjokMXEQqV0TaMEgzDzLsIX0a4KjUUkSFstb+IVW/usdo5yoEsNVyy7pPrGkWMS9J5McnksE8umKUmVrX2sOTicnQGXXHXQIa7R+NFOQplGwjLRaqueRgNn6FrEEXF8nQJh3rC4a8RwsSyqqKudwMQOE5zD6eCHozoMHqiB+2OM/kYmpubeeaZZwDwenXIt6GhgaVLl3L11VdTUpLaJ9TU1HDhhRfy/JLns9x7RcO+vbzw/It887hjs7Qx8zM7++yzaWxs5P333wd0aKKxsZGzzjory3H2H8899xy1tbVMmDCB2bNn8/jjj8fD69m+2UzXrZRizZo1fPHFF9xww08wjNTvePLkb/Cd75zCM888m7J+zJjR/PjHl3PbbXcgZeZ+7Z133sU0ffFQlUZsAqSxfPlyTj311JT9pk+fzvLly7NfPDE1Wv3OfPzxGpYvX8G3v/2POfbpHRQNlSxQAEphxrwBKqLDKEkCY8ly3wlCrS+apry/IQltnQhhpEgzp3slYqGE9Jc6QdoszFARwuwSgTbl3FEPUPyYpuhWLRXQRmFMCTWvNimJ47TlPZjFytEXBEdlnfAKSyADbrwtOc+dIeyTDcIQmCW504U7g1QOWImU+/1FvL6P5emyhzGmmKscheu2R0M9+7CsyjQj2jBKo1l3PVEc9ABDFx5zjMPw1ltvsXTpUk455ZSU3w855BDKy8uprq7mqaee4uyzz6a2thaAjRs3opRi4sSJGY89ceJEGhsb2bt3b3zdfz7yn/Sv6k+/yv4MqxnFl19+xX/+9pF4+xUqTqjNVkDR4/HEjQWAxx9/nNmzZ+Px9Jxi8YIFC5g9ezYAp59+Os3NzbzzzjtxD3fGviNpldAXB8CXX34JQG3thIznmjixli+/3Ji2/rbbbmXz5i08+eTTGfcrLS1lwoTxKfchRpyO9Sv19fUMGTIkZb8hQ4ZQX1+f+cJJ9EkjR46lpKSC446bwtVXX8lll12adZ/eQtFQyROK9DLujguRCDhhJ26o6MEzst8dZkKBMkqmjab6pqme5hB90xoq+Ym9dTdUB4+KMHtGSyUmyJYPbLuRcHgHrtvS+cbEMn4K6xSVo7KGYGKVlJExb02Oc+cI+/QIVATDMFAuuEEXN+zihpKWoIsbSFqi20hbIh2ZTvKOekEM4cHdD56KUpJwYG801BOO1opKD8Xp787tE1Jt76Owd+K11/6Hysp++P1+zjjjDGbOnMn8+fNTtnnvvfdYvXo1ixYtYvz48Tz66KNpx8lm4GdKTZ514SxWrl7Jh6v/wv++s5Sx4w7lu2d+j9bW1OcTE2/M5lWZO3cuS5Ysob6+niVLljB37ty8rrkr2LBhAytXrmTWrFkAWJbFzJkzefzxBUltTE9mSKlokO7AKtjLN2jQIG6++Ubmz787o+fruOO+yRdffNqBq5P7PuYHfSHvvvs2K1cu5+GHH+LBB/+dZ555bj+O2T0opifnCSUDKfwU24WGfRBsMSixFO5QRUwUVAgD1w3uZ5pywqOCdPV7aKTzPBKib5kNFSH6RqdAOakeFUy6XUtFQ+TFgdBZTHtRyolWgPbn1LuJ1a0plN8jI9nr7Qiv0NogjsjNK8oz7NOdkMpGeCxkqyS8I5yuCxHjG8WUktHPVxgi8X+0MYals1MMnxHNImuPkroLS1NWSuGIPRBowV9ZhWHk3t80S7DtZiyrumuZan+jOOmkE/mP/3gQn6+UQw4ZiWWlv9NjxoyhurqaCRMmsHv3bmbOnMmyZcsAGDduHEII1q1bx/e///20fdetW0e/fv0YNGhQfF1lVSXjxo1DyQhjx47i0cceZvSIsSx57gXmXnYJEDNwYv1X5gF20qRJ1NbWMmvWLCZOnMiRRx7JmjVrUraJVVxubm6muro65bempiaqqqryuk8LFizAcRyGDRsWX6eUwufz8eCDv44ep0M7o5owaX2agvHjxwOwfv0GjjnmH9LOt27desaPPyxjW2688XoeeeQ/eeSRdIMxF2Ie9JqaGnbt2pXy265du+L8mkz7xcI+Y8aMAWDSpCPZvXs399xzH7NmXVhQO7obRY9KHlDKQapgXK8jbqQEwPQb7Nut2LdHEgspalXOtv3iTuiXJvrv2OAnQMmOFrqZI36anrLWG9B1L0iZ+AlBghTcjYgJ1OVuj4tt7wVcPJ5+SBnEtvflvDdS2khZmKGir4+sX5UwRLSScm7jqpCwT3dAKYnCwTAsrCoTs9TAKjf1UqkXT7WFp9rCqjb1UqV/N/0GhpWgUcqIwm2TKFth+IQ2klXXeCpShXCNZoRT0qmRAmAYfpQK47rtBZ/rbxllZWWMGzeOESOGYeZRc+Kaa67hs88+48UXXwRgwIABTJs2jYcffphgMJW8Xl9fz9NPPc25552bYbBO8OxMU38UoeT98/Q6zJ07l3feeSerN+Wwww7DMAxWr16dsn7Tpk00NzfHDYZccByHJ554gvvvv581a9bEl7Vr1zJs2LC4VyGtnUlUwo6YPHkytbUTePDBf0/jm6xd+wlvv/2/XHDBzIztKS8v5/bbb+XnP/8lra1tnbZfI5GmPGXKFN5+++2UX998802mTJmSZd/MFyKlJBzujizW/UPRUMkDMX4KeIk40LAXQkEoq4CSMgOvJdlVJ9m3D1w3OU15f8I/ivik1lbRMAD6XUqR0RdRQad0o0iHRPpAuCc++06gJ7RUICb8Fs5pFNp2YzTVWs+8LKsK227AcZqz7qN5L4V5o1S8FlOO9hoC7Oy8Iuj9sI+KPhghDH1eU+j/GwmdiI4QIvq7JTC8BobfwPQbCSOnwkyE/oTZpXIHrmxHCAnSg3Tye3EMw49tN+SV/v33hEQKa+f3pbS0lMsvv5y77rorPjA/9NBDhMNhpk+fzrJly9i+fTtvvPEG06ZNY9jwYcy/Z37KMQKBAPX1O6mvr+eTTz7jJ9fegN/v59Rp34m1KMlOyS38dvnll7Nnzx7mzZuXsl7aEuUqKioqmDdvHjfffDMvv/wymzdvZtmyZVx00UWccMIJTJ06tdNrfvXVV2lsbOSyyy7jyCOPTFl+8INzWLhwceZ2ZrGvYl6Wxx57hC++WM8PfziTlSs/ZNu2bSxZ8gLf+94PmDLlBK6//roc1z2PqqpKnn76mZT1K1d+yOGHT0rTmEk8Y8n111/PG2+8wf3338/69euZP38+q1at4tprr41vf+utt3LxxRdH2+vy8MOP8sorr7Jx40Y2btzIggULuf/+33DhhRd0ev96GkVDJQ/Eat/YrqBxH4RCUFqeUMz2WlDi0YbKnj3gutqduX96KtodmiLJHk1Pzlf0TcpIwaGL7kC8jR3ItMpV3Z6ibBhelLKzhlJctx3b3otplsX5RUKYGEYJkcgeXDezMdkVMrSSKqPYWzK08JuKulozPLM+CPsQ98j1TIjQEF5cFSjIw6iUiyubMT0lOoMoT0+cYZQgZahXvCrWAAvh792JgPALPAO7SibNLAyWCddeey3r1q1jyZIlgPZarFq1ikMPPZTzzz+fsWPHcsUVV3DyySfz7nvvMmDAgJT9H/+vxxk14lBGjxzP9GlnsnfvPv7wyguMnzA+2hJIHuWzElXRXJGBAwemhaySPbQPPvggc+bM4ZZbbuGII47gkksu4Rvf+AavvPJKXgb/ggULOPXUUzOGiX7wg++zatVHfPLJp2ntzHovpb6+qVNP4IMP3osKyJ3N+PGHc9ttt3PxxbNZuvR1fDnKS3g8Hu6+ez6hUGofFQgE2LDhS2w7cx+llGLq1Kk89dRTPPbYY0yePJnnn3+eP/zhDxx55JHx7erq6ti2bVuchKsU3HbbHRxzzHEcd9xUHnnkUX7xi/u4++47s9+4XoJQB3E+X0tLC1VVVTQ3N8fjlN2FpsYQWze20G9QCbbzNaFwkObGciIRKClLGCkA7e1h+vv8VA4ro6UFKiuhf/82fD4/fv/ILp3ftpsIh3dgGdWEtocQhvZKuAGJf6Qfw2skbduIzzccj6c65RjB4BaUcjDNzuW2uxPS1oX0TL+RIkJmNzr4hvuwKrrXeLLtBvz+UVhWqjidUm6UhBnIWPnZtpuwrEp8vmFp2T2h0E5ct6UgnpEbcAltD+Gpzn59MiJxbQfvMJfSirFpPBmnxSG0PYRVbfWaR8WV7YTsrXjM6h45vjY62vF5RmEa+aW8u7KVkL0Ny6jGbZZ4D/FileX33jhOK6ZZgs83olvuYSgUYvPmzYwZMwa/PxGCUkoS2NKKsy9R5TwnlARhYRhdMwiljGANsCgZ1XXZAJ2G7u023ppSirCrJ2RGh29IxSYPGZ6BVBIDA4/pSWpXfoVPlVJRTSI9GdJcqJ75VrTEQ6xGk0hrpwxHayklJQ4oqb0pwquik8Xe4wjGeCZCePO+J7pcSvZ2KuUihBVVAy8M2b4dKGz8LpJpO4FSNqFwiKYGL7adbqQAKEsgbRdDSqqqDFpawHG8DBwYwuuNdJHYF/WoyKQaOVk8KrqdToe/Y8UI+4BIG2tjx+8kylPpfmQmp9p2A67bimX1y7iXZVXgOE0YRileb6Loou6cupDxk8esX3gEql2/L5k8Kr2e7UO0I+rB+Yp+B2U0fJrfIOu4LQgSKdeFcJtMsxTHacXjCWCa+RXj7Cr8I72IPA2H/enwUwfM/YOKEqm65VixgotpnWKM4J+5/9EVlDs+0zyfsYxmFZqJjMiei3DHuBtGh3WJzMw0ccdoqjB94APoSn2ng0HRuRj66QTBUISGfTa27clopEBMHl2ibIlhQFUVhEJe6utt2tq6Fv6JFa2LDfrCjPIFlMhQmDBd9E2nJrt9YqjESfwd3i7Nz+j+j9cwPEiZSqjVIZ990ZBPliycaAjItvemKNxqbRa34LBZPkZYrJKyctJDP30S9gF0vKqnz2GkPaNskDKMK9sTBFpBQe+NLnGhy0D0LAp9l2Mks944V5YWCDP63nXf4KRQKaq0sXX6hOnbX3v1TxjYr4aB/WqorOxPZWV/qqoGUVFRxZVXXpn7XCopfJwhFbj7kWlSGL13sX4u27fTJ7GKDETGHEiuKXcgo+hRyYGwDS17QrgOlJbneJCGwFFosaxSbcxUVUFLi2DHjiBQQaGRKW14aAKqkCLuWlSkx+uFMLJ6VHqKd5ATCoRK9woIU8RVRrsTCUKtNsx0sbo9ADlTkEGntDpOE7a9F8MYFjf6dGpygbNxN7/Ua2EJZCiDoRLL9int3U5DKpuenrMk81Q6c+9LFUBhYwgtOigskbM4YcbzGaU4TktUYr/w6s09hRjHoWvejAzWf5fb0T1elThzoONhcgx+d86/nRtu/AkKhcfw6ozA6D2prh6Y+4Qu8Ylb/FQyXbtIt0vG37dcE7bYBCNTUc90ZkQSoTabIRLbRMk+G/+Ts0ZzQ1tbfTKhLQBFQyULwhHY1wAlpe2UdBIbFwikpZBBF7M64dYtL/cRDLayY8dApDTokOKfE/E6P65K+Vg61snQMNNCH12Rf+8uKKkyuHXRWip292upCOHBdduQ0sY0TRynAddtyxrySWuWWRkNAZXg9Q6MEmkLb6OMyLzsQuEROCGZJlSXLewjlY1RYBiqEGijLL8BcEfDvxC2v2bM4PsLCo3pcgftSBXGFNlDJUpJHLcZQySFS2PvjVR5V4g2DC+u247rtvWYoVIovS8eEuja2bq4X6Z2GEkewx7oH5LSkjNh8ODBDB40GKkkXjPGpdD9Qm5tI6Wzv1LSCaP9Tfy+xhRkXRJhG09uQyXqocksB5BqICcUYFWnz1LldLf0HPTzzW0Q67a7SJlvJe6+RTH0kwV2BOxwhJKyMJCbY2IA0gQVkaikNEohvPj9ESwrzM6d0NhYSNjSSXRsSe+aQmUI/cQ+9MQPferOU6S5ggFEjGPT7VoqCQ6E47QRiezDNMvzNjSEMDDNsmgIqD1KLMv+aSilCIW2pBuHdnZV2mQYlgDHQCapTmYL+7iynYi9A1dm11JQUu2Xp0pzRzq3sML2DuqbHqWx/dWc7ckEIUwULp2J80kVxFUBjCRjRkuxFP7eGIYfx2nKW7m4cHQ19FP4+9+9OQ9R72w3pHBnnJAkqpF2sm/y/p3fm3jBz2TFawFIiZJ2VBIiEvcu637ByJnppDWfVI5syo7GRlJopYNWVIaD5/ixJ5E7/KONGDv+XRzo3hQoGio5IYQNwgFyzxwF4Hqis76kWLp+AVz8/jB+P9TVwb59+b2/MY8KTqpVLIRIK0yY+BgTHU+X6tR0E1JnOAnEaiN1t5ZKDK4bxLb3IIRRMIE5NpPTKcuBnN6CTz6Zxscff4v29rXxdbqj67RvBnQoQzkCN5JIO8wk8qaUg+3sxZXN2M6+tPAe6M77k9M/Yc2312A3dCGlWrko3BTV5WxobH8NgHL/CVhmft6qZBjC7JSn4rptCETKu5uoPF24odKzqcpdnTF3ZQDrbuM+1mfs38eYsSBfPh+40FumI8vgKqPPP3nSphwkEaSKxD0Dmp9kJkkRdGIAyaSf006tf8g04clGpE3et/tVuAtDx2erCdlO1JhzU+7TgY6Do5V9BRGCDFyLjjAQaI6ritc4iR9CeJCyDb8fSkqgvl5rrWQpjAmkEpzSpOgN0sSvYh6F5C9NGyp9FNnLMtPoKS0V0IRa7epv73JBRdOsiIaQcqcU+nw65by19aP4uviMP8/JiRAmMuLEO/qOYR+lFLazD1e1YpkDcGUrttuYdpy2j9oIfB4gvD1M+yeFD8jay5GfsF1j26sA9C+fUfB5AAQxnkrmmbxUNq5qxeggtS+EIFacsKDzCYFh+HCcph7JbOjdME53hxFiXtj986pklo/P4WpQEum2x29BWt5PNs9HB2+KQqspAzo7TGUbdHMzbpO9KOlk+BwhLClzPxIByL4J/SQ1IH4/Y16UmD7UweBFSUbRUMkCpRSCdvKh8QgEUoEyQIY6fvgWSunaKT4fVFTA7t0xYbisZ9fHFSItnCAMAWkT61SPin4pHfrs8bo5uASKbg/9gA6zuW4rplnR5VmMEALTLEepEEJk9sjU1y8mEFgPQGtrQrI7nkaeR+gHQFgmbkBnF2UK+7iyFVs2YBkV8dCU4zbgylRjpGFpAwCVUyqpPqm6kMvV7VYu2s2V+10J2VsIRD4DTCpL/j+a2t/EdvYUdC4houJ8WcI/UgZQMpz53ouuvTdaAK69270qSmWfbXe+b5fO2JWdOkFuheTOoFQGLlo8LTnzfXEiO3Ei23DjRnfy/qmDa/yQ0clNilZJLLyDkZfsQeairSreVGGIDOGf7GxZpWQnj6RzDktPIsZT0cKjB6cXJRlFMm1W2CAiQFWnW8bzcSyBCqcOWDqLxEYPBhYejxaEixkqQ4ZAx/IbiWrIlvaepHlUOmb9iPh++v86NbmzjJeegnRl9nGvh7RUDMOL1zuo8w3zOI5hDMj6+44d/04kUgdAW1vCo9IxO6szmJaFE2lHuQ4qaKVk+0gZxnZ3YwhPPARlCB9KhLGdvRgeH7pytKLhDW2oDLl4SNZz5YaLovNMnMY2HfapLJnKlj3/RFtoJSMH/pxBlfnLawthRD04YTrqqSilcGQrwsgsdCdMgQy7dBaGzXROMHGc5oJ4S51Dv8Oh0DZsu6GA/bQCcCFaKtrDGgEEHs/ALotIdkRy+Kerg1d6anL2tGQAKXX1ZNfZh+mtTPFKZNIAUSqJmyRiZ9DGdTxcmRzdyXjehAGU8vxVkkcomqmTTNjOZmgIIZBK5tYq6bRNvQM9Fmhr7GDzoiSjaKhkgVIRBE5emQ2GEHqWZAlUSOupiLj1YaJUiGRdDsvS6csNDXp2NXgweFJOEw2cShF1dyZ+iXrBs2RAxAyVmNhb7yrSxuFkTvUDekxLpTcQieyOGyn67zrC4Z34fMP0rK+AWa/wmqhWBzdso9oTdXWUktjuXpSKYHVQijVEBY7biO024bUGEvwqSGhzCOEVVJ+st21+vxnlKqpPrE47Zybk6/pvaHsFgH5lM7DdXbSFVtISeLcgQ0VfQ7TuT4drkyqElO1ZlWt1SnfX4v5aAK4F0yzFNCsxjO7p9kKhbaxa9Q1UFwoudhVC+PjmNz/rNmNFewccpGPEB9X4/RWkGBEdERN7S0RXOvHkdhj400Xf4nm9iVVJ4m6xFYlzxCaDIs6Ly1aXKqMIWsyQiDVXoNOfzdhxsnmGRDR2n+t7V3S8Pb0PkXdY90DHwecD6jUUGA8HZLQmVCpPxQBi7vUETBOqq3UmUH09hMPgOHpxXYWUEtcRuBGFROC60e88pzptLPQTS03uIzKtq7JPNMzCNTEOFLS3fwJAScl4ysomAdDWpsM/hXqJDFOL+bkBOyXs47iNuG4TplGRto8OTZXhuPtwZTuNb2j3edW3qrAqLPa+tJcvZn7Bpn/ahBvMzwDRRNrcXant7CHi7ETgobpsOpUl3wagJfhnCq2JlI2nokNa2UX29idjTKvB+giHdxIOb8dxmruBs6JwnL29aqQAKBXGtvflvf2ll87DNH2Ypg+/v5zx4ydy77334Tg6dPLuu+9hWSWYHhPTazJk6BDOPPNM1n60FhmWeonoZeumrVx66aUMGzYMr9fLmNFjuPmmm2loaEgyUmDaqWfg95THl5HDx3DhBbPZsmVjvF2xCWAmr8XmzZuj77rJ11u/TulLdu78Gn9ZBR5/KVu2bAHgnXffxVNWQlNjU9qxDj10PA8++O8sXvwEhqGVjmOLYRlYpRamz8T0mWzduhUlFcs/WI5pmnz3u+fEDZ/k+2iaPjwl5Vil3vi+h44/FIBTpp3CjTffmPzA+PzzL5g580KGDBlOSUkFtbVHcNdddxMIBNLaapo+Vqz4S8r6G2+8mVNOmZbH007F88+/yBFHHEVpaSWTJx/D66//T977/vnPH+D1lnLMMd8s+Lw9gaKh0k1QaO6UEKkDsYh6WzJlbBiGNlaam2HrVti8WS9btki+/lrx9Rao26HYWQc7d8DuXeBKkTbpSJwnZqi46Rv0ErSbmuzTCFOHrjqL3zpOK3v3vkR9/RPd3sauoq1NZ/mUl3+D8vJjgCRCrZ2/xkcMhiFwQ5F4to8r27HdfRhGadZZkCF8CKGw3X00/7kJgP7Ttfx/v9P64R3mJfx1mJ2P7syrDboeS+5uwGMNYvLoDxk/7Bkss4pS3yQsoz9StdEW+jjv64UYTyWSwlPRtYBaMET2UKXOlOp6artplmBZ/VDKIRTaTii0Hcdp61MeQW9h+vTT2LFjKxs2fM6NN97A3Xffy69//a8p23y+Zi07tn7N/7z6P0QiEc4+92xsx44Tmf/6179y3JTj2PjlRp584kk2frmR/3j4P3jnf9/h2//4bfbt202y+3fuZZewZftf2bztK5a88Cxfb9/B3EuuBATCKMPjG6XDwDkyf4YPH84T//1EEoHW5Yn/foLhw4ZlvtActuf55/+QHTu2U1dXR11dHVOmTGHe3Hl8vflrdmzdwY6tOxgxcgSgCxRee+21vPfe++zcqT2oDzxwPzt2bGXHjq18/fVWAP7rscfi+/7lz6nGhYp6Y1as+AtTpvwjkUiEV175A+vXf8a9997D4sW/Y/r0M4lEUvlafr+fW2+9LfuF5IkPPljORRf9iLlzL2H16r/wve+dzQ9+cB6fffZ5p/s2NTVxySVzOeWUk/e7Hd2FoqHSTdBeRAWeaPgnaYatjfLMM1zDgH79wOvVISHLAsNQGIbENAwMITA9AmFAaxs0t0bJZWkz+IToWyECXt2OqBGVzTufa2Zs2w3s3v0M69ZdzKpV32DjxqvZvv2XuJEI9r5Inw8qMUOlrGwy/fufzrBhV9Gv32mA1kHJl0gbh2UgQ060I3axnb0IITvlFhmiAum2MnbhYA5/5nD6z9CGilliMuqOUQDsfGgn4R2dz/Yl+b0rhvBR7j8a0NyGytJ/BKAluKzTfZOheSoyxRMhVRCpQgiRXZgtltq+PxljMY+UZVUjZZBQaBvh8E5cV89snWYnLaMuG/r6XSwEPp+PmpoaRo0axVVX/ZjvfOcUXnnl1ZRtBg0awJAhgzjm6GP4yXU/Yfv27az/cn2caHrdDdfh9XpZ+tpSvv2tb3NIzSFMP3U6r/zPK+zcsZO77ryb5OGktLSUmpohDB1aw/EnHMeVV/+YNWs+w+uvxeMdDpDFk5cQxfvRRT9i8e8WR9crlHJY/MR/86PZszNeZ7zuTwaUlJRSUzOYmpoaampq8Hq8lJaUMnTY0Pg60zRpa2/juSXPcdVVP+bMM09n8eLfAVBVVRXfrmaI5oNVV1dSUzOEmpoaBg3qyI/Tk7ErrriSiRNreeGF5zjuuG8yatQozjvvXF566fcsX76CBx74t5S9Lr/8Mlas+EtB3o9M+Ld/e4jp00/jpz+9mYkTJ3LPPfM55pij+Y//eLjTfa+66lpmzbqAKVNO2K82dCeKhko3wUBoG9oSKFt2CP+InC5yIbSh4vPFFonXCx5L4fEqvB7weaGsDJpbBG2tZBB9M4ilA/VlanI8LJXlzYoPOEmGyt69L/P55+exatVk/vrXm2lqehulIvj9Yxg8eDZOoB2nzU3RHeltKKXioZ/y8slUV3+bUaNup6pqitYniOSfmhyDYZk4bWGET0RTkdswRHrIpyOEEBhmKa7RQPm3PHj6JwhOA84aQOWUSmRIsvXerZ1ek1J2Tg0VXZogvfevLDkRgJZAYYYKaJ5KcvaS67Yi8pRz746MMSEMLKsSyyrHcZoJhbYRCtYTbmhDBvINCR08hkpHlJSUpM3khWGgsGls2sOzS54BwOvR2VcNDQ388c0/ctWPr6KktCRed0w6kiH9B3DBBefx/JIXs3K0GhoaeOH53/PN446N8l5S37cUlkpc9wTOOvMsGpsaef/P76OUw/t//jONTU18d0aW9HiVPaMqxv/qzMBc8vsl1I6vZfxhh3HhhRewcOGi9H1SXpHsx1v7ySd8sW4dN9xwPYaR2iFOnvwNvvOdU3jmmWdT1o8ZM5of//hybrvtDmQWDYt33nkX0/TFQ1+ZsGLFXzj11FNS1p122rS0sFJHLFy4mM2bN3Pnnbfn3K63UTRUugsCXBWt6KlIE37rTJEzFdHwiZs6Q7BMbbA0NkGgreMHYiCljVIy6lnZPwJVlzNzFCBzkGljom9JDqZAYB0tLR8AktLSIxgx4qdMnvy/HHXUe4wadSutTWtYt+t0Nn6Vu2BZT8K2d0fF5CxKSw9P/bETL1I2GF4Lo9JFedqjqcj5Z6XoMImKEm8TN1MIwei7R4MB+17ZR/Py5hxHiT6IHB6VnY0P8Pn2U+Jk2hgqS7RHJRD5DNvNnzMBIPAhowRzqSLaQDM6l7kXhoBI9xkIQlh4PNUYhh87tJdg2zbsYL6Ku32cztEFKKV46623+eMf3+Tkk0+KrtO/jR53GNUDhjBwSA1PP/MMZ333u9TW1gKw8auNKKWonVCbOJgADAeBZMJhtTQ2NrKnfk+8v/rPR3/LgOoh9K8azLAhI/nyy438528fAUC6bdihzbj2TtKrDCfuqcfr4aJZF7Fw8eMoXBYv/h0XzpqFx5MjwSGrIRIzgHIbogsXLeTCWReipOT000+jubmFd9/tYIyrxD+yE+gVG7/6CoCJE2szbjFxYi1ffrkxbf1tt93K5s1bePLJpzPuV1payoQJ43Peh/r6egYPTs0EHDJkMPX1u7Lus3HjRv75n2/niScWYlkHVp5N0VDpJggETpL+iYwkh3q0oZKvu1hFU9+0ZyK1M/Tp8YmGvWAnOWm0R0WRqPzbdUPFDbpE9mSeSXfa9pgqbY4+vGNhxYEDv8+oUXdw9NEfMHnyHznkkBspLZ2gZ0GuQtgVhN2vaGn7ACl7l7wYg9c7hGOP/Ywjjnge09SZKY7TQlPTMlpbPipIQyUGgQmWi+3uwRBWXhlmAG6r5LNTdrDzXhsn0JImBFd2RBlDZutOasudW7J6IeKpqVmMWqUUje2vEXa2pm3jsQYxZvBvOGLE/+Ixs6dzZ4IQHpyWMKFdbUgZQKpIam2fbPt1oThhPjAML4aqRDoRIq0Neb33B1Po57XXXqeysj+lpZXMmHE2559/HnfddYf+MToheeftt1m5fDmP//a3jD/sMP7j3x8gVvMqhuRBWevhOAjDjPdRytELwAWzZrJy1Qd8uHo5//vOm4w9dDQzTj+Txn1foT3MIaTs3EN66SWX8PwLL1BfX8/zv/89l86Zk33jWKJODsQl8zP0URs2bGDlhyu54PwLUK7Esjycf/4PefzxRUkHSH72IothlLqu0EnfoEGDuPnmG5k//+40zxfAccd9ky+++JThw4cXdNxccF2X2bPncNdddzB+/Pj4einDBIMb83pWPYmiodJNiJoJQDT8E0y4GTMpx+aGnq0pO3MFzJISCLQr9u1LVrg1krwp+2eoyLBEhlTXpO478ajEN0v6eEtLxzNs2JX4/aMytsUvJ2KJQUgZoKUlt+uyJ+Hx9KOiIsGCr69fxLp1s6irf6wgVdo4hIgasGFMI/9Kzc3vBgn/1aHlnRBmSWlUCC41g2DEz0ZQekQpw68bnj1bFDeqSZG54YHwWiLO1xiilKrSdGJd//Lv4feMzrvdMQhhsGluG+t+sJFwU2NeRgroiIF0Mme87S+UozBUCXakESecWxwuJvZ2sHhUTjrpRD76aCUbNnxOe3szixYtoKws+r5Fb+WY0aOZMH48F//oR8y99FIunD0HhYNUNmPHHooQgvXrtdChUk5UFVbnL29Yv55+/foxaMiguBe4qqqSsePGMnbcWKZ+awoPP/Iv/PWvW3n++ZcS3jMViSrMdnie0T+FEBx5xOFMmDCe2RdfSm1tLUcecUTa9VVGS9M3tzRHPTSJ35qamqisrEraOrtQ2+OLHsdxHEYcOgJ/eQU+XzmPPvoYv//9izQ3NyealrR/9nIBKj7gr4vet45Yt24948cflvG3G2+8nmAwxCOPPJrl+LlRU1PD7t2p3pNdu3ZTU5NZb6m1tZVVq1bzk5/cgNdbitdbyr333senn26guvoY/vSnP3epHd2FoqHSTdB0SIVUKlGbJO6mNkkogOYDHUvQ8vmZdAGgvByamvSi15nRGXI3GCrtrs6w6MKAkM8+whCE2rewdu1p7Nz5aM7ZaUzpt8L7/wHQ1PSngtvUU6io+AcA2to/joZ+CvWo6AE6UypyLjT/URsl1aeVYhp+IKa9kug0Pf09fGPpNxh49sAc7XIROXRJGto14bKq9BSMLPomXYHT7BJY7RLeZiNlIE0yPxv2N/MnF1RYYnp9yIjCDjV1tnW3n78nUVZWxrhx4xg5cmSKS19lkQK5+sor+fyLL3jppdcASf/+FZz6ne/wyH8+QiDYhsJBRXVMdu3azTPPPscPz/0BRkw4LQMMod2/oZAb5ahY0TZk8pAmsnwUDpfMmcO7y5Zl9aYcNm4chmHw8Zo1KTyVTZs20dzcnGQMRHkqGfoox3H43ZO/49e//DWrP1zFh8s/YPWHK/n441UMGzaUp5+OcklSDKFkVbf0azhq8mRqJ0zgwQf+LY1vsnbtJ7z99v9ywQUzM15TeXk5t99+Kz//+S9pbS2sACjACSccz9tvp/aVb731NieccHzG7SsrK1m79iM++ujD+HLFFZdw2GGj+OCDZznhhL4l1hYNlW6CJtPq+YGwtAAR8QwCXZwwU4pyJmjSl8iubC4EhlKUlcHevdDaGgv9yKhUctc7UmlL3LDsepXjfOqRmYKGplcJBD6nqelPWQdKpRRuu4vhNaj0a/JmXxgqSinWr7+ErVvvw3Fa4uvLy48CDCL2Dmw3e+w3G4QQWGZlQUalshXNfwoCUH2aNh5Moxwp25Eq1T2bfF8zVVdWykVlq3SgJI1trwPQv/ysrO3Z1/oif62/kkD4s7yvIfi5HrS8hxiYlQUoZpra89HdtaKUUrhBifAITFGKHWrCdYOd7cXB4lHJig7ehxhKS0u5bO5c7r73XlDaIHnwgV8TDoc5Y8aZvPfen9m+/WuW/vFNvnvW2QwbNoy759+VcoxAIEB9/S7q63fxydpPuf762/D7fUyb9h0ARCyzTYWzelRi3orL515G/ddfc9mll2a8jIqKCi679FJ+dsstvPLqq2zetIlly97jRz+6hBNOOJ6pU6foc8bC4276t/Dqa6/S2NjI3EvncuQRR3DkkUdwxMTDOfKII/jBD77PwoWLUtoGsawlzVORIZn0jSUoAP/58CN8sW4dP/zhTFau/JBt27axZMkLfO97P2DKlBO4/vrrMl4TwOWXz6OqqpKnn34mZf3KlR9y+OGT2LFjR9Z9f/KTa1m69I/867/+hvXr13P33feyatVqrrnm6vg2//zPtzNnzlwADMPgyCOPSFkGDqzG7/dy5JGTEh64PkKfGyo7duxg9uzZDBgwgJKSEiZNmsSqVav6ulkFI17vJ/53YnBIMNnzFOFSDkIKkKojQT56PFB2NDPIoysyh8OQsO673oHKsNT1hWJ6LVngShfbTc9k6uh6zQgzMVsfMOB72dsSkciwwvAKKkr+ETAIBjcSDn+dx5V0H8LhrTQ2vkld3X+lkD5Ns4zSUk2UCzhreqUtrX8J4bYorIEGZUfrzj6R8psez1ZKsevJXXx0wkcE1qeGh6Rysoq9tYVWYbv1GKKcytJvZ21PU/sfaQr8keZA/gZk4HPdTv/hAoE/b8M6Xiqiuw0VJ1b2AgzLiwzZKQZphj0A8HgGIHJov/QEhPDh8RTGCcqGXMKM11x5JevWr+f5F15AYDB+3ARW/Pk9xowZw6zZP6L28CO47rrrOfHb3+bdP71N//79ow0EFDy+YBGjR4xl9IixTJ92Jvv2NfD88w8woVaHbmIp+EqFc/QXmj9lWRYDBw7MSfD8zf3386PZs/nnO27nG0cdw9y585g06Uheeun3HWTzVZSXlXrhjy96nO+c8h2qqqog5mWU2pb7wQ++z6pVq/nkk08ziNXqPjdSFyG4Poi0U0NZU0+Ywp/fW4ZpmsyYcTbjxx/ObbfdzsUXz2bp0tfx+bK/Px6Ph7vvnk8olDoBCQQCbNjwJbadPZN06tQp/Pd/P8Fvf7uAo4/+Ji+88Ht+//slHHlkInRWV1fP9m3bs97/2MQ6H6J7T0OoPmSFNTY2cvTRR3PyySdz1VVXMWjQIDZu3MjYsWMZO3Zsp/u3tLRQVVVFc3NzPE7ZXdi2dQ+ffrSOQcNq8tpeKkVIOQwx/fgMAxmSYIFnqB8hBK7bgGUdgtlBOjwTbHsnbqgZt96D4TMQVupHJYMS4QHPMD2jbm6G0lIYOLARn68S123Hsrp2P+y9ESL79AfgG+bDqrCi1yeJOGFsaRO0gwSdIIZhMrxiOEZS1ojdZBPZFcFTnb1TaW/fwPpdZyCExT/8wxo8nn4Zt3NaHcI7wnj6WUhbsqF+JgF7NWPG/IKamou7dH1dwd69L7Fx49WUlR3FN77xWspvmzbdwq5d/82gkssYOXT/hZo6w7Y7GtjzRCsDLyhn1C/1gKWUItLWjLe8Hz7P0LR9Nly+gYbXG6j8ViWHP3t4vOOO2DtxVQumkf6ubNt7F3tafkf/8h8wZvCvs7ZnT8szbNv7z5T5/oHa4UvyuobN1++l4Q+aB+IZYjL+2SH4x+RHJLabHbyDvXiqC6v5kwtu0CW0LYRVZSLDCqkieIcLSkpGY5p+QqEQmzdvZsyYMfj9fp2tFK2wrWv9FJb1BIByEMKryag5IKWDlh3Q23k8A7pFPl9JPdkpVKQwBldJHGljdkw1Vvo/wiPi02AlbeywJtF6/RNACKTTjGPvBFGKzz8GK+k+aK9ZTJeosPbFhjPDm3kOLl0HbBPDyv7+aDFCBa5AWCKl/02dfBJVdjYJrrdRtsI/1o8oTRRMVFKT7Dv24QcKYiH+TPcrEPgSpUL4fCOxrH4F1aeKoeO3k4xCxu8+zUH65S9/yYgRI1i4cGF83ZgxY7JuHw6HCYcTMc2Wllyznt5FvN5PFMJCc1RsBV79weUb+gEHVJRRnqkfE9GOJmr5V1ZqroppwuDBTpc1VJRUOO0uht/QnXc4hPLKuGEScW1c5WIZFqYwcBwHVzoYZhIZUnZei6UppEMKlRXfzmqkALjtTvwDF6agn/csSivGUlo6sUvX11W0tSX0UzqivPwf2LXrvwk4a3u8HUopmt7UXpGq0xKcEWUrCJu4nnaUlV5gbtSdo2h8u5GWP7fQ+GYj/U/Ts1+pbLIRafuVzUCpCP3Kv5uzTVWlmjvUHv4Yx23GMqtybg8JjwqAvculdUUob0Olq8UJcyHmodGy6qBCHqQdxPW2YJrps8nkuZ3fP7JLhoOM2AjTxLC8OQX3tOSA0/31WvLxfOY+QObVGTJvVDQkKYQvnsMvDB9CeMHwpB1LyViuf+GDe6LuTxa5AClA5IpPJ3FOouKUQokUOkqqcSdQUsblKCK7IvhGi5SUfyU7Fm48cKCkylBPKUY/0M+tO/lpXUWfhn5efvlljj32WM477zwGDx7M0UcfzW9/+9us2//iF7+gqqoqvowYMaIXW9s59Dcay/wxouXJYxa4lbeWilIy/rELIXDVPly5J9FBajpK0jZQUQGNjQYNDTZtbQatrWRd2tp0TaGOkLaOszrCob69nq+bvmZH606aw9ogLPOW0s9fTYW3nFJPKY50sWXqgTQBONe1KRoD0bBP9dlZt5OOxA1KDG+sYxMM8F3ImOG/orKyd+tPtLfHpPMzGyoAAfvTFFn4noAMKqqnleIfa1H5rcQAqiQIy4MrIxmFBf0j/Aydpz0t236+Tc+ilEThZE1Nrig5jlGDfkFlybdytslrDcfvOQyQtAY7zwyQQUnor7qNA2eVA9D6Qf6pj8nFCbsLOrtOxI+vHIVw/dh2U5Z0+P08twIwUa7mreW+lp7hwuQK++S1f5Z7IBIbpK4XPkQ0hHDt1T9hYP9R1NRMpWbw0fSvHkxlZX8qK/tz1VXXdDhQlxoXT7tOXy9AZCPAxjbShkUi/KO3zRTWFghkOLFStklUJKn5B6Z9AiSRqUX0fUi5Nhn1opT1mXhoMvq0BZs2beKRRx7hpptu4p//+Z/58MMP+clPfoLX62VOBob3rbfeyk033RT/u6Wl5YAyVhQ6Oy8ZMiwxSiGmpdLpMbTSG0oa8RenNfIELfa/4zdPZKD/QYRRrg0CqSBqBVsWlJQY7N0bQQh/p+JjPp9Wui0rA79fS/mriAIXgipIWIWooBKfP3vqqELhyNSBUbm5a94EI18Qtjcj8FFddmrW7WRYQgREVeqxeiI1NReUknGPSlnZN9J+93lGc0jl/6XMPxmR9DmFt0XDZyO7b+ZvlhqMvLd/+g8umH4vTqQdRQRIj3sPv3Y4u5/aTXBjkN3P7GbQRf3ReeT7377K0m8Tat5Ic/Bd+pWfmXPb4AYbJFgDDfqfXcbep9toXR6Oewc7gzCiKcqu6jZ3ugzKtGMJ14tSrThOK1DeYY9uMB5ixop0ooa9J+36tXJwD2Q4ZfQMFHqMTrwESc02zAoMsyK+7s75t3PjTdfHjwPgMfV7WFlRud9t63j+RJuj64VCkVk/KGGAJZ1fEtOfyAChw/xJkGGF4UvmxdAtr0y3I2p4CVPEy7LEvCpCWPh8I6Kb5Zut2nPoU0NFSsmxxx7Lz3/+cwCOPvpoPvvsMx599NGMhorP58tJPuptSLkeIYYjorLn+n1MvM3CNFBBF/pBrIqyFnPL5ciKemRk4s121HYAQu67BJw3KTN+EBUuSn33/X4TrzeIaZo5DRWlNPm2oUFXb/b5tEfG0+qAoWgLt+K1fBhuboebKUzCTiRlXFRO7pmaEBb9yr4LtoWRNgAkHSckUSJ18BJCoMIubW2f4zgtVFdnJ3l2F4LBvyJlO4ZRQmlpuuaBUIIBvpmY/oQEvLIVG2buwt7pctTnIzDLe9ZxqRzdMaqwwJXhjOnOVqXFITccwpa7trD9/u30/145yisx0jgGNjsbf0N16WmU+iZnHDxd2YxpVMTDEVUl32Z38wJaAss6NThKJ3uZ9MFwInUOpUf6ED5w9riENzn4x3ZuNAkrWkvLVd3SeylXIe1U0rrhMZABF09lKbbdqEMUyfvsp/Ggkv+hzKSBIN1YgfwMuIKQoyZOvsh5D6JR67RWR1cMHjyYwYMHx4+jlMRnaW+LknQoP9IFxEI2HVsQa7PI1sAcx1IiA5E2ethwx7+TxoDOQlF9iBSvmkiaZB5g7YQ+Dv0MHTqUww9PlSOfOHEi27Zt66MW5Q/HfZNQ+HtE7ERaXsfwrPDqQUu7lnWKcqweT3bomI5yRfzNduX2+K8R+VnUuleJDy92PuHHNKs67diE0F6U6mptoCgFu+skOzYpduyxaWyOIJQX1849o7MMk5CTcNvHjKdcb1WJdwKHDvk3Rlb+KmuHpKTCaXPTCF7CEjQ0/Q+ffjqDLVvuyrhvdyMSqcOyqikrOzKjC1S5Kl3szQLDo59By/vdo+ho79ZcjkwZL0oqDMsAaSJVIMPeGkMuHoJvlA97t03Tu8069NPBUGkJfkB906N8tetyMmWpKewkvR6Ncv9xGKIcr3UIrmzOeR1CCLzDLcqP9WP4RTxzqXV5fvdJGFF3fDdl/khH6lCPJ8kgNkFGFEL5UCocL1qY8HBkNx50bL+Tbzx5hi2TNZA6hoF6aCqeZcDNF9ll46MQJDgwOaIs0mnGDn2Ja9cnjlmAEShVBrG4RCPTzpsYmEV2oTaVuj4e/smhuB03VKKfkszoOO+znJXM6PBqxb+rqMdaC4fuf5u7yyPYp4bKt771LTZs2JCy7ssvv2TUqFF91KL8YUfuBsB1E1kgmk+S9GBMEZWVVsRE3zp3o0XF3mwVt8AdlUjHjbifRUuviwxxYKPgdEnD0Eq3VWUKv0fS5obZ26DY02BSXwc7v4a9+6C5BdoDEApBxI5GnQwLR9o4MZ5KbMKSx3ljongZ70BEc2Vi/JQ4TCizTkCnKX9JOJxdR6C7UF39bY499jNqaxdn/F25Cle2sbf1Kbbv+7+Afg+qvhPNyHoru+FQCBpebufLmbv465V70n+M3iYDD0qFoyTZdBheg7G/Gsuk1yfR74xyHarvgMY2zR/qV3ZG5ti0cjEMPyrJ4DYMH5NHraR2+BKsPLLaklExVc+kW1cUZtB1l6GibJVGJBReoflatowSCVtRShEIBOis87btPQQC63JLjqvEhxKr+CuEETVUko2VHhjcVPSc3RBZyT4hSvRNUrYTCa3HiWT4VqOK3blTlLNDKolU6QaH7h87DJIpA3MSM7YDVCYrLuU5pV+z2d/AGmzg6R8VsQtnuJgDzU7JdD1GdOKlFMHgl7S3fxY1WLoO/c2Quz5THujT0M+NN97I1KlT+fnPf87555/PypUreeyxx3jsscf6slmdQqk2FHuS/rYRwpNS7wcS+ikqIjFKPKRV48t47CjR0TWiBQ7DuCohJmbLL6JCXSozYayL0AqwEuVtp5/HhyU1edOJKCKuQLqxawLD1BlGpmnRbodRLTY+y0I5CmdndHaa4c0KuX/CFIfgMQ6LzkIUVoZQgdsqcXeDUZlY7/FAdQWYqorysmNoa19FU9P/MmTIj7rtHmSDEALLqsr4W2wGsm3vnYCipurHeKxBVH2nhN2Pt9L8p6B2He9nzD2W7ZNMoo2f39CGH8qDVO3a25GFe1L1LX0djtuY1h9LFaYp8EcAHZ7LAO2F8SM7SPbno7WgHMWm6/ZSUuuh5seVGH6Dyql+mpYGKZmQn4w+RGd/WYzcQpFOIowe39VGjOUvQcoGKitL2b17N0pJ/H4LI0tacTgcQEqFlA14PBn4RIBy9b2IcQOEI+KhJ6XCCGFGjUSJlHYnoeJCrzdx7q5CKokjHQxhZLR3FICrEK7AlW1IVyEMF7cjMVmKaL2yMEIEMYShJ3Z5fi+OayMMgZnF0yncpPsq9fOMXbdOK3YRKXN1hcSOhoySM2AS3qGM982rUF6JCplEsCEEJBkr8Wfc95zUOLLdZ+UqVMQhYmuDzTRlNOvMwTDy56rEDPvdu3dTXV2Nae5f1lqf3rpvfvObvPjii9x6663cc889jBkzhgceeICLLrqoL5vVKVz3rfi/fb7niTmmkuv9xCAMgQy4mFV64MjHo6KkC64JIuFNEZSgXZYBbPlXTDW2o5eyy1BKoYIuETOC7YYpsSoQhva2GD5IdtIoBa6rF9s2iEQkActFSFARcCKaCd8xA1Aplyb5f5Dsocr4bzzuCShbIQOpbncAp9FFSYGRZMy3t4PPK/ArSVXlSbS1r6Kx8U+9YqjkhKMwjXL8nsMI2V/qNN0lU9j3gtYJcfZKAmsj8RBHl07R4NK2UnfyVdNKU36LDTrCayCMWCn77EJQMUjlEN6ucE0H3wjdDbQE3sOVrXjMIZT7j824n1IuhrCyutwdtwlDlMQFvZIR+qtN0+sBWpYJhl6nDabyb/o5/PV07ZdcEJbADbndkqAsQ25GUq4wBCosoUKnZ/brJzGMyqixkj1d2HVbcZxmDKMdjyez9Llyo5MMI8aVEEkDoIryfLQibHenJmvDbP8IE0pJXOWm6CelbSO1w8SVTSgZwjDDGEb67Nyx9wEKj8fAMLzxLMnO26dwpQtCpGm5RBuZcl9T7jmgPddmihGoPTAOGQMNsUlhVgNKgjSJ7LVBgNc0E9cQu9+G0iUjjNJuNT4LhkpSHe94n5VCqhCObEAIL17vtjivsivvYXV1NTU1+WmR5UKf23jf/e53+e53c2s1HGhwXF3y3mNdh2lMiq8XJOr9GLEXwCPi7uXYFrmhPyihtAXuRPkpljESgwrCchUR+RklHNp9HhVbIcOKMNG8+ehHJElvrhA6wygmEumY4PHZlPpBGgrbB4afNEXdkLMSGdqDQRWVJceCFMigxONTJE/GVURiGxJRlToDUQpa2wS+EqgqO4kd/Jrm5veRMoJh5D8bLwTt7Z+zfv2lVFX9I+PG/WvGbWKGQrn/GEL2l7SFPkKuO5a2DxOzx6a3AvtlqDT/bxAklBzuwXdI6ierBwSB8OhOWShLezs6CcHsXdLAtltbqTzZYdxvNbGxoU2/1/3Kzszakao4PyP93du06yc0tr/O2CH/SXXZd9J+j+mnlE707peHSRhRz0AXKlYnQymFDKUbyqDDP27QxVIKw/DjOI0MGjSQskoDJ7IHTwZBRaUUbW1/5a9/vR6PZzBHHPF8xvNG9oSRIYVZYugwp1T4hvgwPNHvTkaQMoRplkXFGwurBZUN0paE68MYloifqytoDwfYG2qk1JtdpMsNuFgDTHbvvQ3H3sHA4bfjLz06bbvd2x8kEvqCIcN/xpB+M4jsiugxPYtgWwwR12Z3eyNe4WFw2eC035PvqzAF4Tr9PZo+fVxXtWPgw+tJFOlz3FYibj0eI917KsM6c6Fju8Jf20TqXDyjHHyDyghXOXgGeTGT+iQ3WvF7t/vvNDa+QmXlP3LooT/PeX09CafdIbI7glVuZgxl1e1+mtbI0/TvfxaHHPIzHKcNy6rA6x1U0Hk8Hs9+e1Ji6HND5WCEZc7AwcY0Uw0sA4GDRKKIOUWFpT0qypYIr4mUEXI/u2idHalDDiY1VHguwxD9kWpPwlAR3+u2VF1lS6TjEDSDeJJnwopOjSFDmIST4/FKZZyQBBzNfSixTtdZFCYZeTbKllFyaOpN8vuhrR2CQJX3cDyegdj2XlpbV1JV9Y/5XadSOl3cZ2T8QDuire0TIpEdOSX7ZUQraJb5jmJv6zO0h9dg1Wv+RtnRXto/jtD8dpDhP+uXVxszoSmpCGEaXDBKojNHA5AWrgp1ml1WepSJktD8xyCtK0OUf9MXl8HvVz4jR2uE1sTATDuH5qdIWoLLMhsqn2lDpeSIdMNSBiXhrQ4ltZ0bnVpLRe6/oRJNc07jQhEl1Nq6rpDhMTAMP+HwHhrC7ZSZNuW+9K7zq69uoq3tY6TcSji8FSH24fWmV6uVrg0WmJaBMg2cZhcPBlb8mBauK5CyDY/Hi2l2TzftRBykY+Cp2L/jBWwXQyjMXLaEAcppJxJeDij8ZaOwMhiEXn8FocBWIuGP8Fpno6SBWWp0+lxdGUESAQwsU6R5d5Rp4LZIvIb2TLuuwCoz48eV0ocigte04p4CQbOW7M/UQVuZO+19r7Sx89dN9Pu+j9H/6mLWGJiGN8X7IKVAOoq9ex8CoKlpKz7fv+S8vp6EaHERhoXlyXxNEbUCKbdSXlKDz2dhmgaW5cHn6zsp/T6v9XMwwrJ+gN/3BBDGdhbhRENBHev9QNSFrBKEWuiMnKR02CTKPfCatfTz3UaV9ypKzO9Q6fkxpeY0TQfrpiqybsglLCM4MpxiqIiOaUwZYAmLiBvWpDYFKJEhpdUm4PwPAGVWsnGXXvjQDblkYvoZhu4rWlsFMiyorj4JgKamZXlepZ4VRXZFsPdGkE7ncbP29jVAZqE3IFq4ETCgzH+M3if8CeHdevY2cFYFRonAM9hEhrr2rGRQ0vyuNgQzGSrK0fLXwox6VKQHpeycmj1KufjGCgZeoAm/X9/XiCtb4/uUeg/Psp8WiDOET2eqdMhgqyw5EYCWQOZnEveodDBUAusirJm0nS9n7crP+O6m4oTKiZLWM4V+PEILREe5MIZRQtBpoj3UlLWNLS0rCAa/jP/d1rYm/ZxujMQYPY8Q8fBwMkyzBNOswDAyGKddRLLS8/7Alk5GXkgyhCmIBDYCCtMaiJWlPpG3RKf8h4Ib9PN0s/BAOsCV2mMilV7Szi+EnphERSw7HlcI/Z3IpNxiqUIFi5uFvtJhVv84r86Iy1C5Xpia85Sc5h4O1xV0nu6CchVuQCJ82e9x0FkHgF/V9lazOkXRUNkPuPIv2PYvcJ2XgdjwqtKcEEIIlK3zdvUgkn2Q1GXItZux44Dvt75Fte8W/NYUHSvthswHJRUqIAmJUJTEl3pO2UmkSmf+ODrzRypN8u2AkPtnJE0YYiA+s0O58KRrUK5uSyZXPGivSsgWtDdKhg69giOOeIERI36W34WiBx0ZlNj7HCJ1Ydxg7otLSOenC73F2ysVwgC/51BMoxKlQthePViV1HqYvGYEhz0xBMPftQGi9YMQKqTwHmJScng6K0NJBR492OlJpQnKzamSq9DE6aHXV2KUCgJrIrT8j8nRY9YxedSqrMRYpXQnbAh/9Dyp73FFyQkIPISdrYTsLR32VQS/0J16R0PFP9aDsAROgyT0Zef8mrhezX4a6srJnsmRPNDF/g45LiGnCVumf7+2vZdweCsg6N9fe6Ta2j5KP6dMT2c3vAI3INOMZ8PwdBuXQdqpSs/7A9uJYHbSLmEJbFt/B96SCVm385UcjqdsCiXlx+dVeT0GR9kIAS4uMsPziLVBBlxkwE3rUzoW8oyl3HfVUCkZ60Mqh/aVNpuu3sOOf2lMnMvUascTDv1dfF1Ly/sFnae7IMMS1VGQLgm2uzdaCV5ghcfihvte7A2KhkpBkHIntvMESumMH0No5T6ptkX/FhkphsIUyGDM0pbk5qkolIymHwMRd12qfH4MBt0S+lERiR2xCRpBvB25HlmyK5SS2IElOKE/YQoTKV1c6ehwVYYmxcI+pdaZqbMNIVI6Z2VLHSLLYqgYBlheaG5Q+HyHU1l5QkGFspStDUCrWns4wnVh7GY7Y66/lGECAT2zKCs7KvPxZGIGKIRBme8oQOBUbQbAW2N22UCJofLkEmpfqmHEPf0zh6tEYqYoPCKuFSE7qlAltzuaJu8Z7GXIlZpnsOOXTagIWGbmTBWIZvzgQQgLIbxpHhXTKIuTcDt6VSLbHdwWXUzTf1jqMzO8gvJvFqanAtHnuR9QkdxF+YQpotlwulp4wHFBRXAyTDRaW7VRUlIynurqE6PrPk4/p5shHdqnQ1kdFU73F0pJ2ts/p6VlpQ6VhRWGb/+6/FhKcKehUxMMOZiS8hO1EZIFvpJa+h3y/6gYcGH8+8wHtnSwDE+UPJ75vhmWlreXIRXXNUr5XZhIGQSITiCdFHXpzqCkipeDKDnMC0jcJmh8LUDLex3eY6EN+WHDdImA5uY+MlRCbk5RRoHBsH43M6hyNoZbhtucb326nkWRo1IAXPcVbOdfcd0/4fctRIiYxPD2+MPX0ZIOmT8ePZtXjkAZbnRmmm2AleDGijDA7uCFSJoZUvI6BuMwjDYi7qcYDMLjjs9bejwbVEQSiYSRngiWUZ36o0FGjoobfptwy/8DwPLPAP/V2NLGKzPM+JUk5P4FgDLrrJTfhJlqCKmQDh/lGjxKyqClQRFoUVT2L+y6ZVBieDQ/xao0cUOSSF0EFZZY/TwpBMNAYB1K2VhWP3y+QzIfsAOhc+TAe5F7yvnijy1ggjUwYZRF6hysfgaGv7CBQhiCsqMyE3HjqcnR8xuWgSslQniQMpDj3XC1hLgwGDKvkj2/ayOyzWHPk60MmZudIKmUixn1thjCiyNb07apLP02raHltATfZXBVosJ1eLuD8An84zwZZ/XlJ/hpWRaidXmIwZd2XvlbWCJeybarkKHsRjFow0+GtNcs7IaJSJsy/1AclR7bb21dDUBFxTH06/cdJk58KmPIUGefpL7j8fBP0IXyrnfJWvNCxj1i9fUL2bLlTgDGDV1ImZkflysXXClxlcTbyQRBmIIS4/+jcsjJGKW533lDGNjSQYbTSxlkglKKsBPCEiZh5eBmyaQUXoHb6iKUwKhKb4PAi6uC2tjBBpUetsmFyE4XGVRggW+UhTLK8I+1gQChTXba96dcxaBB51FRcSyVlSdkP3APQUmF0+rmNFYtsz9D+2ljSkYkTouLWebGkyf6CkWPSgbYDTaN/7Yb37+kdoSOqz0Dlqldu0LEBrA2oAkgY70foq4/XC2jn8ujogsSCu1tUC1ImvW55QAaQ/toDj/AntAc2tVzWU5WGGTQJUgws8vTQMfpk4wVpRSRtoT4mRN6DdH2z9gykikRBCEMhpW+xSD/ArxGB9a/QbzMuFIKGXAQORl6ugP0GIrGfTrDYtOm29i8+Y5OrzMulZ50mabfwKowsRuioaAknkBb21oAysrSZeSTj5kMn2cEtJRhDTTw1iSIe3/98R4+PWEHzX8qUNSsk2cbT02Ozc49Wq5bCA+KSJrHI76fcuMTV7PMYNhNVYiz32Df4TfT0PZyjjPKeIaVIbxkeuCVJbqsQUtweUpBv8pvlXD0FyM4bGHmzIGKKVHht7+E8/IUiuhsuavKl/H3IcfrZkRFCZWjCNpBlFKYhoVUbprQWFubNlTKy/8Br7eG6uoTsazq9INmsa2ET4d/CglnSWnT1LSMbdt+xeef/5APP5zI3r0vxX+vqEikmDc3v98tYR+pJC5uVh2ZjsjnegSCSKSeYODrbMW8U+AqzU/ROi4iI0cFSKitZlESjvFUlIpokcQCJ3wxb4p/tAfhERjCS8moUjBAtimcPYmH3WS/zN59v8eyqunf/zSsDFljPY2sQppZYHgNlFTIls4KZ/Y8ioZKBrS0wr4H92KtALlJv4xSbkCpLwEPpnkaAEL4EejUOKkSMvdpWipJNWC0oFsuQ8VB2foji6UmG2IAjrQIqSCGmAiALT/X3o79mFQqRxIJRAiJEL5MKb7RDz3ZrnIjHyKddYAPf/WvEEYNRuklhN1QUnn2VAjho8Q6OS3erqsKKHAVKqJTpEUnSR/CEPj8EGxTBAJt7Nq1iN27n+1UQTGmNJoWq7aEDgVFFOGdYezGCEoqTLOC8vKjc1ZqzjSgltR6mbx6BEcuGx5f5x2ue9/mt/NXqVVKsf6cerb80z7s3Znfl3hqcnQWKgydRSXwIHMQalUHQ3ng+eX0+9kWgoPfJhT5a852xQzabDPPEm8t1WWnM3rQL9KftyXwDMk8NSub5MUoE7hNkuC6PHgqMRXNLvK0pJP5fejYXuUq3LBDW6QVv+XDFCZuNNQZg1JOnDhbUfEPOc+brWqx4ROokIqSyfPDV19dz7p1s9ix4wFaWpYjZSjOqwIoK5vEoYfq7JJA5NOcBMp8IaWuV5ZLQ0Vv14bLbmQewnzt+37H7q/Oob7l33IajjG40sXFxTT0u5RJnTYGs9zAKs/8rgphgnKQKoKSoZzeFF3jqj1lXYJIm/AuGT6BNyohENqceI93BR5my+6baG//rJOr6znIcDqpuCNaAu8RtrfHDROz1MBucbW3uw9RNFQyYFe7h+WujtVH3tAxzJg3xTRORIiENSyMaPgnqR5PxkcqBDLiRo2WXJ2Rrj2CkShGaIkRBJ2AVmIUmpgWUV8gpdwvnoqyFZFwCGVGUvL+E21GpxsnWdN2+xMAeEq/h+U/hdJBL+LxnaAJtRGHCCtxVZM+vnJzW+KGiJMLY2nJwur8lRSAx1QEg5OwrIFI2U5r64edXqtyJGlKdGhD0qowMTyCyC6byO4wA/t/n0mTXuWQQ27IflAnfaa2q/lxNuw8n5ZIgqNRdUpUTj+qUpsPWt4NEfgkQuOr7dkHGFfXFIq3QWuExUOQWQ0VaZP86QtL4Hp3A+CxMoszqXgRp0QKrRYkSzfKxw55mP7l38sR3kyH8CTxVPKQ0xeWwI24BENBWiOtWvyrACg7GoLpLMNEQDAQJOSE8ZpeTGHgSDflfLpA5imUlIynpGQcAO3tX7B1633U1f1Xh/Nm5sUIIVBCoYL5DQjh8E727dO6NwMHfp9DD/0lkye/w5gx/xfpSNyQi9su8StNBA86n7Nfs5oo3DxVJgPhd6gPfY89e27udFuPfzQAIXtDXqEfVzpI5WIaZrRsSXYeheExch9TGEgZwFWhrPyUpvY3+WTbcWzefUPK+uppJYy+fwCDLkotruofEzVUopNcVwYIu5q3Vuo/nFBoO9u2/Qvbtv2ys0vtNiilcFudnPo0UgbZWH8pn20/EdvV/YHhMTQbob1vSbVFQyUDJk6ElWXaRR3+n5Ami0Zr+phWqnaKECMBzVPRf3eo9xPbzhKooEQpkVM5VEkblKENFan1OwwxnIhyEAhcdygCP4p2HLVpv67TDTkEnHaEmcWNYRCrkajbphRWyRkY1ng8pbP1dQlvPPMnbH/FXq6gPvBdwu5qAs6r1AVOpjWyOOPhtQdAewE02TjPGZ8Q+CxFMGhQVqaJi01Nf8p+nW6AnfX/wRfNJ7CvNbMIF4DhN7AqTZwmF6c5D3VXW6a5qoORDbSFVtEWWhlfV36cH6NC4OyVtK/Jr3bGrv9sAWDgBeVYGeLrEE1NToo3C1OgK9cqhDBxZWYPjiRdlj3i6DIN7qb+hLelX3usgKERNT4SMu+dk+3svS6fn7aTLTfvzWmoDbqwgkPu7Bevk5RyfqWwXYdAJEBzqIVd7bupa6lje8N26lvrCbvZycOZkK8nxrAMgq16smIIQ8u8o3CTBn2Ppz8TJjzGUUf9KX5fQ6FN7Nz5MHv2LEk9r51d+8XwGTjtbl7hkt27nwYkFeXHM2bYA/T3z8RqG0n46xCh7XoJfx3GbBqJQQmSdkL25ryuORey8UE6wnZ0xo/F8DRjVrqBlLCgzx9NUXY3ZpxIdIRMeocMTJwCjdRkCOFBqmBUATizoWKZA3HcfbSHPk65Ft9IDwN+WE7lt1PfV/+h+hsJb9LfRijyJaCwxAA81iBsew87djxIff3iPJTKuwcyIpE5sn0AgpEvAYllDsBjJkT0hCm6TbOrqygaKhkgBMjjKwli4Nnj4G5ahVI7gFJM4+SUbT3WPHy+57EsLfvfsd5P/JgWegbuGLoIVxYo19HuuSSPimAoStp4TT8haeMxouEfPu+yOq1SikhriLAI48sSbxFC6DCWm/jbUzKDkgFPYViJ0IYhDJRycZx2DAbgqp3sCl5AU+RXOGobrtqXqyWosEQFJSLP2KkwBEQkPh8IcQoAjY3phoqUIerqfsvHH09hx95/odx7fCdiZtHBviRAaG8LTnvuQTjToBP53/EANNetjq8zvIKqE/MvUhj4NEzrByEwYchlOcit0dTkeNsNkdBswItUobSOUNctsREdLCw7aqjsuMli35JUF7fe0Y3OOGPiWBY63T5zR+u4TdQ3/Sc7G35D4PMIoQ02bR+FcxKlq6eXMuSySvyjE56YQCRAY7CRupY6drbspK6tjj2B3YScEIZhUG6W46rUUEw+kGE3L70OZSpa29rwkfqNyE4Gx/Jyzcdqb1+H68YySzQvJhsPw/BGs3/CnQ/WsSyj/p5ZhLeHidRHcJpdlAuGKbDKTTz9LLz9/JT4tC5OIPxJrkPmBSfP2kMRWxeb9YjxaQ5kqWxUkkik1zsC8KEI///svXe4ZUWV/v+p2uGkm2/nTNNAN01OiiIIYg4YMDtmHdOM4DjiqKOMATGNiI5hDKNgALNiwAQiKCCZpolN0zndfE/coap+f9Q+6Z5wbzcofn8P7/Pw0PecffauvXeFVWut910E0dbZ29Cw0ZNIovjAC+dJkYRJiWnHLbEhn2qe4DhhvG3Wc6YO8hAe6JJ9j6XwbgDSzjpMbOjpOQrH6UWpqb9bKEgHuqNmUBXVdmb9wx8WQeNvgccMlQ445gmavzAPAPXgRqq5KUI0a0xIuQZHHokQVua6Xb0fwCbUKjC6qqXSjvZrMEpbj4oQxNoOCsNChJB4wiPWEY6wE0/IxgOu92NCQ6VSwTgxTpcMfmG36M2fderEZjUL3Z+RdZ8LKJTZBUDW7V4iwYQ23NUtX6AJieBXOm0w5lRsNeV7a9WUtQ7Zu/dSbrvtiWzZcj5RNIrvLGcg+1SksAaD0s27ukaMBz/irrHjeOjBD9R0NFraXFMPbv48/rMVSar4GzANLulaNeU/lGe9vT3/a70pQ8/J4i/tkm7fQE0GbOdLcjfqglYzJ3Ftc1QaFhttAmKdGJOj8yjc1vpcDHEi9JbkwwiZUJTbL9jl8H52jn+CPVNfpXjfKNCqnzIbymGZfcURxsvjKKPwHY9ev4/+1AA5P4fv+RDbPhqpuRsq3aTzZyISIWEQ4Jnmtjder1LZ3jKefX8JnrcAULXFaKbY20xUjTg9hzyVww6+hEPmX06vOBNv0MUbcHH7HJyMtMrLDQbhQO5pDPW8EN/twF7bD0Qqxpkl49UYQ5h4VDwObfJe1Zgwjd1WujjOKgDK4b2ztiFUUU1wTgqBNvqAkz1t4nmU/Lv1xSg9yaY9r6/9XahYyrma1uy7JN+WTj/vpT0ce+8KVnzMityVQytzkHHXJWPTpa/vZODAaMql0n37fb+62KolMxPlxFDJ+Ov2u01/azxmqHTAcceXuaqaKPv5Z5H2rsNzz5n1dzYDpZWNUKuPEkJnLRWd5ITYBSjnPp8e93XE6mAc4SOlkxSFs4vhw/Go6FBRDgpIfw41aBSEhW8QFi/D6PYLrcAliAIcp5fh1EUMpS5EkCElT8J3Du14aiFEYpzNnWZdTcIVypDJDOF5xwAwOflHyuWHuP3209i8+b2E4R58fzGrll/IYQNXMtz/AoRwiNUUD+x+DZv3/UuTMVGF3XkqHDVIPNHBqFStwl0AasMKKOQwTjlxpVr0n54BCeV7I4IdnRfVYEfMxC+t12XhP7fWHKldfwY12T4XUVNCFklBnJl5KsbEVvq+oeFRbHWBhPFgqp/i7a3MG0upb+4rEr+jR6UnfSJp71CMqTDp/ASYm6ES7okZvbzAxFVFJiuTaDR9qX7SXgbX8Zr6iHBsAqorHcs6myOq0vlzYaIGOrC5LA2vzGm4XhSNcdttj+eWW46peU7A9uuqV6Um/KZp0VCZCZmWxHnV1dWuI000EpJRx+L3t4bJZmLRwJs4aMGn6c2cNOux3WCMIVbRrGJvSu3CmCLg47ByxlQX0y5j1hEHATZ0OlsbQhXgVmXvhWX9zDV3ph2s2nKHathRswelGNwOQOnekO3/Oc6Wd4+2/EamRZPnohQkSq/O2lpYr1r2Y38NlWLxLu644ww2bHg2IyM/Ytu2C2f9zVzF/qrtzKbW71eb/h54zFDpgIPXFtmQ6mUPKUqHZRClPqRsrfJqTEQUf4sw/CjGhEis6JtqF/4RAh1UF4t2E7wBpTDJdiPnnUXOO4+YVbg1toWL1kczlLqAAT6w3/lxxgRW26RUJtQBKTG7oaKiScLi1wnzn0JFd7Q9xjUOkQoxyX32eC9hae4mFmS+3f3kjkh2t/vRFRuScDMZcJwz8Dyb1JxOL7feJ28+q1Z9mGOPvY75/S9HaK+2y6xEmyiFG5gq/Z6tI//RIhhVDDYA0NN7NNF4TJxvNSyq129cdIwxxLsN3Ls2OU9d8MsddFj8zn4Ounge7mDne933f9OgoPeUdNeFvYWaTJJEW9UUhCRRsHnHZ2pGcn1irlZK9dxFyIxE502N0dCImcmxlqrcQRVUCBb028rWlXU/BKHnZKhM/LzE1veMseebkxTjIjkv1/lg196KNJJQzZ5TVIWJOkvnN0IbTSEs4Pk+OqyPZ1c6tetV9VNcdwDHaTYaenurhortB+1UaWdC+gIddA7/hJUxKnsnUUWN29++qNzfCspo9Bw2FGFsjQ3fXY0UXq0MAZDUh3JBOLVNgjEGiTVUSrN4VGrUZJlUrJeOra72MOizrtOHI9sXfgzimYaKfZc1xs/B3RPGjdE1L1HWW1fTjaoaKvn8X5s8u0YZovGQqEOOXDXnSalpNm36V3bu/ALF4t1d26ArGhOarom0xqh6OzuU0Hg08Zih0gGuCysOmeCtfYdww+nLEX2dHpVLFF1ErC7FmF3Wo2LaBn+szkUgMTqmk0fFKI1oeC2RiTA6rlHxXOESmQEyzkvwxLr9SnKKom1s2XI0u3e9mEphDOOa2fUQHEEc/wBMBekehuO3V5l0hIvSMXGDASZFdnZJagfMHIWeqqi5tbVBCBgYeAfz5l3P8PArEcLlsMO+wbHH/oXFi9+AlOkWxcue9PGsXvB5wGGs8CN2jF9Qm+iUniaIbJJyLnsUMiOJxqJW2qgCoUWTi11NaysAdbcd6FU3cRVLzhlg6KwcTq7zsFv0tn4Wn9vP4rd39qZAMzXZGEM+zBOpqK5OSzVRsDRjEo9hhq5ELnUUx666h8OX/4Ls0daYKN5W91CYpD/PfJdCuG21c6oY6nk+UvRgFu2A429pW4xwJnpPtoZz+ZaYlEl1pcEKafWJpHJQOp4z86cegpltwQ0JVYif8jHlupdUCFnTUql6S3p6WmnJPT3HAPWaP3O5bpVirtuUdzDKsH3zxWzYfjIT4vL9MlKMiSkF9xCpbvli3aH13DRUqoaK5x1m1X0bKcpGIXCSopbJPcbgchR9mVcw3PP8rudWWhET1zwgjnDsu3gYCbXdUPWo9KStQFspuAetK22pyY3Y+ckJ7nnObqb/XGH98t9x8MKvkvZX1wzQTOZQPG8BWldqxq4KFOHegHBfRDwZt8ztWkeMjFjv5KpV5zM8/BzAsG3bx7vegy7Pno8VRFvRpoQQaVLeqq7HPhp4zFDpgpe/+jp+9JOVPOXMZ2A6JIQKIWrCb8ZsIyGGto3IVBMdLVGizURkqpOhQJkxAnUbQbirid/vSp9Yh7VaF2YOxfWqmJ7+NsbkqQQ3kFf/jkzP7vs2skJsrBXv5V7bcXJ0kJhYocTcd7ZgRYWcPmf/i6XpOnMjk/GoVATFImgN6fRhCJFFa/t3XNTgSrSup9sM5J7KqvmWHrhv6hvsmfwiAKVgIwC+uxTPGcZJS4ggHoua2BhGmZZcpGiPfady6xG4cgjnAArKecMOS84ZoPcJs1QqbaAmRypkpLCPXfmdlE251s56omD9nVgVzlYIIXBkL7ljraFQbMpTsYm0rUanY0lbHXazjszRVzrLnv+lP8Mbnr2/pda5iD4wBRAPdDdshGt1fhzjJJThueWpmKC7dH4VlbiCRuN4DkTUCE6NWip1RdpWQ8WWXhAoVSCO8zaENAeNeJmSqEJz+MdoQzCSZ2z6h2gK+G5rVeZu2LTnTdyz89lMFX+/X79rhDXOFM4sMbO0fxK92VeS8Z+UbEQa7gMFIm3ZBYmhYmKDZw5hoPccBnue2fXcjdRkqCf8z5WNtL+oelR6M4+nP3smC/tfjzazGyqVzTGlDSGV+2J8dzEDuacgXBcdm1qeTn//E3GcAcJwD3E+JtwdEhcUbq/TtqTC5OTVxPEYnjefgYEns3z5eYDD5ORVTE1d37YdOtao0uxhH8+dx0ELLmbZ0Hv3S53374XHJPS7YM3Bf0LKEsaMoB/oRbgRcnVrx5RiOcrcjzbb8YSYmXtag3AFVKyeR/vQj0ZHMQifSnwtY8G7EByLKz7XcC2JMYayeoiAv+IEQ/TzolnvxZiIQqFOlTTu9ThchDEf6LozU/IXICYRcilu+oyOx4mkNpFqV+xnFsxFO6UFstFzYAsWjo7C5GTzYSbWxLttPoLwwHFh/nzrMRvufSGxnmTH2EfZNfEZXKe/RunNpo6sncPplcRTCpmO8Ibt4tnOkxUmhoq/+2QOX3lT2+da2RQxcWWJnhNS9D6+bozsbykEWzXZTiihjoh0DAL2lPbSW+5loHcAz/EwFGyeSsLs0ibqulT2HJtiL82GSrUY4UydCSHc5DNFp6mkb/JlTGa+gznueoJoJylvadvjqijERZxjNfE1kuCvitSRs0+aUguUUcRGMZd0XVWZPbFQG00xLJKSKXDBlJJwkSdqWiqxCmvekp6e41rO4bq9HHfcjfj+ksSgjNon2s+8H18QFxU61DhpB6MN0VjI6N5foMwknrOY/uzps56nERl/LdPlaygGdzKPl+7Xb6tQWnVUeW1E2j+etG8NNxOapvpG1+34K7uLZV5w2LNwsWFJO47FnIwNpXVbY6+TOu3DRRBZ5mXKXcGSwX+tfV550EpHdDJU0qubtVTApuboROlYeIKDDvooBx/Ug5q0pTyEL/D6k3EkQJcUTrbe/0dGvg/AvHkvRAiXTGY1Cxe+kr17L2Hbto9xxBFXtLybWhHCNhIHqqjZ9/Vphs/uwV/Sx1BPd9LDo4nHPCpd0JOxVqrZcAbBq0eJvlloe1y95o/tvILWej81mGTwthUpMhBrpHRq1GRjFuLOyA0QwqEc/YVJcz75eJYckPqvmDfvAnK5s8iKT2CMRHEFkflKx18YExPL7wLgZV7ZPYyT7Bbj/dSzOFAIKZrUEjMZ8NrNGcoglAbXMg1KJag0pG0s7H89iwbeAcCuiYvJl/8C2HBI47WcnCQcj+qU5ci07MpNxeDOl/iL/I6T+ch38uz61CRjP2zuS2PfL3LfS/Ywfd3srCBIvBjJLilM6Jk5L0c2lWWyPMG+wl6KQTGRGG9INDWt9NKd4//Ng3vewnT5L+ROSLHsPwdZ8fHh+k+IEMJvVZrFQQing9FtMf+J6+nLPJnB3ucwm+ZKEIdMlidJn2Qn58pf5+YtrA6luSx0Vs2WWRNpwzgkUAGeU3+X1VyLqpZKsXwvWpdwnB6y2fYJ46nU0vrvQz0nSrRwrKdIl61gYjwREY3FjMeXATC/72X7veut9udSkn91INDGzMkjBNaouWfyfr639ceoSNW8n/93x48576oP8eP7fkktvym2CcphPEGhclNTEnrLeU3cMrYMpqs67cNBlS6d8lbU21DShDtsX8t0NFTs59Mrvsqu8YsIoq01pePqJkeoXqK9MeFohMwKnEx9fM3U1ImicSYmrDds/vwX145btuxcpMxQKNzG+PivW9phKhoj2huX5XtCJn9T5qFzWxOC/9HwmEelA7SeIpu+HYDv/PTVvApQ11YwFY2YUVhOyOW2QF2NTtyZjCMciQnooKWiQalE7K1uqMyMCXvSRZnVAERmI1oppNN94hLCJZd7Jin3THaObUW678K4n0XSmbKozNUYdoEZwPWe2/G4pKEIHII4gDkQiR42Eopyoyci1ea6KjZEHrhJFeMoglIRehrEJJcMnotAMtTzPArBrTiyn550M0NC+hITQTQaIX1p5ddnLDoDT88y8PRsk7dlsvh7xgo/YfWCixHCof8pGfZ9I19TqRVJYvDe/52msimidFdI3ymzMzmgzvgpqzKeY4ey57n0pHsJdMDe4l56fZeBTBYvqYqsiZmZzVmo3EChcjO59NOQuQrz39DTlBtiixG2Ptyql8UQ0u2lr1n0tVm1N7TRTFYmUSam5/E5ClQIblE1L0bHZ+AKG1rIMKfQT1U63811Hy8VFWAw9RCDa5O+nYbUoWK+mp9yzJwMh25ibzMhfRv+wUA0GhP6D1CcuAVwmNe7/x6RbNVQCe9F6wDZ5n3OhnZGQiP2lUf56UPfYmfxDn67ayv7Amt0H/fEozg8KaC6ZcpKCNw7tgmwYo06eS7F8rcZmfgu8/teyYp5H2l7jSAOcWYsWzLJGfpbYMnQuQTRVtKenW+Vnmb8oTuAFbhDEneo/XtPHWQNlfC4n7B7coS+7BNJeZYBZSJjVbxHQ0xkcPokiIjGMSRTAjWlUWWF2+MyNnYFxkTkckeSy9Xpw76/gMWL38zOnZ9j27YLGRp6eq0vGm2IC52LEHoLXUobbY22nZu/Qe+iQ+jJPC6p4/WPhccMlQ4IylciRMzY2Fq+ftWZPCfzFwbKEeraAPepzQuJTDwqjfV+OnpUXDCRRKsAZhjjRiUFCR2I46rSbaur3BE+oVqCkCmrUBtuwc8cPKf7qgRl4iAgk3kRiCcgRWdDRYoVOObJiPhQhJklZ8JYNkRsYpTWOPJv66wTjvVMETeLnrU0K9JNe8B0ynpVggqkklsSQrBk6Bz7vb+aeb1ntz2XzAriKUU8HlkWSIe1qeppidU4D+07F22K7J36XxYNvLVFpbbnuBTTV5epbIqQPYL5r2jPPmi6J21qGiqxjoniELeqhePY+8m6WWITMRlMUom3sZBBcn5fouEjCOKQSEXEOqIc2sVjKnSomFEWu4tJufXJyqA7Tl5S2Aq0HdtpmLXQJEAxKJEPp+nxepGHSWQ/6GmIHtT4a7sYAa6d+KWQNc9SN5jIzFrvxBhDMSjgN5aVcG2uRaNh7KXXsWTJ28h0GXtRNM7mzedRLj/IoQNXIOY4LmQqCf+UDTIjGMt/D7C5VZ67YJZft8J3l+DKYWI9Rjm8l1z66P0+R6giZBfK0gV3XESP+S1vXg0HpeGzD+Q4ZvgIAhXacAeKNx37Mt73x0+xdWo7IGzOVGCQjsDBGgOloD3zx6oThy30aIGtvvy3QONcoHWFO7aciMlErP7xVYiJ4Y6/Sx/sQt8UDFvqf8a3TEAhBCofo/JW4HJK/pSdO/6bwdwzWD6vXly1qjWjS7ai9sKFrySVWkq7IMiSJW+lWLyTJUve0mQwV9ljbm8HY2q5S9+TM0zfvpM9fJQ9ewTHrNrArAXXHgU8FvrpgErJVpGdLj4DEFzjWEl99bvWSbke+qkLP3VyRApXgBKYMGpVDVUKtG5SpXXaGCp2t+vgYKWng1kUJ6env8PExH8Tx7solvMIIZDSaTJSjBlDmWa3sBSHkXI+iRO/ZnYatAYpHJSJibvU3njE0EBR7gZTVk05MK4LsYLy3CIsTRDCKn5GE3FX4a7atZwhls/7EAA7xz9LsXJHs0ptUqSwKvA2/+W9dnc1C0xsqbXCsXkPoYrwElaYkMKOagWe49PnDxPpMrvyWxkp7mGsOMLu6b3syu9iT3E3o6VRVCL21ptahTLWFT324wIj38433Hv7PY2QqRZ6dxXljSG3r9/OptfZuiHl8AH2TLaGGsM4YqI8QVqmbf0WKZj/5TRL/5ztaKQoPYnWeYQUlCvXMb39XZSjqTk9u9miF2EcEugQr6G0hKhWUk5oyq50cNKHsHLl+1mw4GUdz+U4vUxMXEW5fB+V6KE5Fd2DuiaOTAnwI8YLPwNgfu8r5naCmecTgmzqCACKQXuJgW7QRhPGdf2Sdtgwfg+HJJ7KJy1+Gbc+//d867TPc8TAWkxsPXOr+u1cuWVymw0bJkrcjucgHWuolMP2gmaxVmhTpyZX4eAQ/40MlUZImSbt2zmXw+5h4MzOyfLugIM8xrIHPb28Tn92sPluSahHiDSR2k2+8pfW66UTYzWylO7BwTMZHGzNE3TdXtat+3aN8lyFLluPXLvEcR0a8n+tMPTcLKzZBEDKXX1ABIC/Bx4zVNogCHYRBTY/Rcw7BSEMPyssAUBdX8HkmydmIZaTSv2ATPoPyd/t6/1AklthJCZupSgbndQCEjHK7AHAFSs7nMfF1AyVznFnYzSTk19kYuIzTBf/SLFUxHeb3b7a7KGi30ig/xVtHmg5h0TOKixnNDVBOtWlltEjhUaKcsc2xdrmI8xYY1M+FAqWEbTf13UFMiNRRdXCVNr0pn3c99I9lO6p7+yHe17EYM7mZ2ze906ULjSp1BbvDCjcEIALC14/uzcFmqnJsY7QNFSzdRJvQfJYpHTIehlSjstEeYxCNAVIMm6G/tQAPT4kKoS4SX2PYEvElnPH2PXZSbSOEzppB0Oli1O2tDFEl60KbKymuGfHc9k5/omaTg3YXfJUZYrIhKS8utcudbSDM9DeojAmYnTyPPaMv56IzUxWPk1YvpWpkW/MSlHWldb3NhNlVcEYXQv7ALV6J9U8FadBS6UbpPTo6bGJ2aXgjjmHfgDcHgeZlkiRYt3SX7B06N/pzTxhzr+fiYeTpxKpmNBEuE77nIxSXGZrYQdrEkNlSe8T6mEzJwnPGc2qfquXsn16O7Ex6DhONIEkQixJKn8XCONdLdfQRhNTl2qoQgqBUvEBa6mYDhWey+EDTJf/UhNEBMiljgHqwm/d4J74EACpaG39s15b2kAmulG9mccn17qv6Tpg81RMYFrYP7NB69CKAk7FlrXYBpUHI+5/8V62/ec48lhrULkjh+zXdf6eeMxQaQPXHeIn3kf5Dq/gXdlhFj1rNw/RQ2G+CxGoPzaLaAnh4cijEGLI0pU71PtpPF5HbUTftLZpKmI3oDHGR4r5bc/hi7qhEkadJ55K5XrieAtC9IB7KroS4XjNrj3BAIL5QJFAn0NsriHUn0Kb+mQxWwjYlq8XWOl/e3C0NUJN/g2LbjVQlNu2KU4qJs9YHHwfgrA5qXZ/4KQl3pDbsugUb7ZGR6OnRQjBinkfxXeXEMbb2DZ6fl2l9p6I7R8cB2DouTn8JXOMxDZQk8tRpZkuKrD5Ow0GnMDBFYq+VA9ZL0Paz9UWEVWtkioGa4Ju/joX4UE8qgl2BNQLELZCCAdDe4pyaaNdyLPrfVynn8GeZwAwMnVp/ZkFJabDKXJeT8vv28EYw/j0Jwii21B6FAQMpM+15xq7jFJ5U9ff6qC70JsxhlJQxGvj/hYIqja4CndQyv+FMJqctc1VhdpSePsBz7gpbzmLBt46pzo7ndCfPYMlg+cyr2//c1wiFTXRgmfCFS6XnPYplicbct87rOFLOxa1iVnct4yUkyLWMbsL4xDHlmLuOiAcfK+qUNsa/lHaqirP1NYRwmoJHQjzZ/SyPLceso2JX7XWtxrNX84Du1/F3qmv1j7rSdt3Obn9ZuKpWeo9vcx6xfsWHdHxGM8ZrknW5yttKMZOzMb7n8O2bR+3FPcu0Dpg+/bPcNttJxNMjdiQWidD5T7bkTPrfPxTrUEVXX9Q1/M/mnjMUGkDx0nz1KGz+XX0WiaA3f92P7x6C7fNtzte9efuK1zHej/V7x2JqbTxqCiFkQIpevHEuxD8U2fdEuFjtHWVhtFdHXcT+bxlCuRyZ1EII1ydsgyYBgiRJiU/jWA1hhFC/e/E5gdE5kvVA2avNquweRPCJVQh8b6Y3WfvYtcLW3dGjxgaKMrtYJNtW12f0pZSotSm9t5cYIxGmemmZ64rhnjc7ny8Rc2Tuev0cdCCzwKS8cKPmfZ+Qc/xVb0S681Y+ObOxQdbrh9blUlLUy/jN+xyhUhE6Bq7lvDQlIGImexxpe0uznHqBrFJ6Zo4W/HWAIFPp4QcgZvI8bdO2qWN9t4y62375vdZpdrx4hXEaoJIxUxWJvCl33YBnL4kYu9rygS31s+dL32PYuXngGRe/8fw3YPJ9j2ZlDwZiNi65UMdx4I1XLtL54dxSEUH+G4bQ8UTmJI9d2XqN4xvfxdbtnyw88kSVIXfSvGd+60k2ymsdiDIpY9m8eC/0JM+wbZnY8jI97ovflVEOuwqvuc7HscN2j7syAU4crD2nZA2bEakcaTPin7LoNk2vRcdN4dsUq5lT7U3VHTbeVVKB4U+IObP6GUFMLDnf6ZbvquKvfnu8tpnWecY+11qIyrfPSeqVuNnlto5VS/ZdLk1/FMw11EKN7B37/eQsnueoBAe4+NXEoZ72Lnrf5CZzu+rfF8yNtd6mGXWix7+cVXt8380PGaodMBqz+cDo6M8HZvUxOu28JVXhuQ/lcL/6GDL8UpdTxh+jDj+Vcd6P1UID4g0Om52HRsVIYwEM4A2Z+HJ19jPjSH+dYR+sD4QhRBoVpALv8zSoevaF9RSkxSLvwTAyz6fOAzw8dq+dSH6SMnPIagn6nnCLixIZmOW2hwWYePFoa4Q3m87vMlr1HjnnYfdCZUPyG0rpMB0qTRrIt1xYUiloFiE6ADGpS1DEDcJqUX77AMSKYHTRrOgJ30iixMadCnYyKqL5nHMPcs56HPzWPC6XrKHzz2BrUpNjnVka6/McIXj06QYK2zGKdoEmBmPQ+sC4ONIa6gIIS3z5ti6ISVFZ7q1EG7iVZlpdBvKSQisKp2fSx1Hxl+HMQGj+R8yVZkk0AFprz3LKbxbE9ykKf3ePtty8GcmCxcDMNDzTjKpJyRtEAwO/BvgkZ++ti1NE6yLv5rf0wmBCjt6Dmp5KsoQlm2hwWzumI7nqqKqsVJR93UshNkJW0ffx6Y9b6YUdJdJnytUQTPyvTz3PHc39zxrN9s+ME40kgivRYY9X55CB81j0RhDOSzjzsz+n4GoqkjrzqBqJ1I71YJ8n3rqp/jtP/2Wk5Y9rsW+TXmdDZXYRG2NpSrrZ3/VaeMJRfF220dLd4WUNjS/m6qhkvIawu+7lsF0L6RC4uHWMHkVxsS18FU21d1Q6UsMlXwbQ2W8YpVoh/rOQnYpHgt27K5Y8T4ARouXoNzdHY8t32vnrtTamMBsAWDh04/Hm9/a7w26NX7+d8ZjhkoH7C7u4u78zZwrHN5S8KAi2fmEIq85PuD+NuqrWt9OrC5B6Wu71vsBapWUdTCjYJyKAUFsQmId4iTuZ3VlTPjxkMrrmjNAHZklDtcjab8bLxR+ijEBvr+OMitA2dfdSZVTioWk5MUIluGI5yFFMuFIQHU2vOwDMCDBFQ7KaLyTfdwVtnOHGztPzoYKEKOZ6qrH0RZOsvh02kF3kY72PYjiAwv/aBPZ3U3DdWtib4s6119ZPPgODll0CcuG30dqmYuTlQw9P8fy84f2uw1CiprQmzczZu82e5qs1L0VGZypg5HLPJ3lC65l3oCV4ZZIYqVqCrWl22JEFyqrpSh7Le8u2BKjSwaRFrV6KEKImldlZPrblIIiKafzubOn20mzfJUijB5kdOoDgCaXOYvebHMCq++tIOVZhsaWh85vKhBYhY6tWFin92OMoRDk8TvVv/Jsf9NBTFC2Csbp3OzsmVRqGa4zjCGiFG6c9fgqYjXJeOFnTJV+jzYHGKckYTHdGbD1vWPc8bQ72faLn1HStyA8GHxmFl22xv62D4yz8+OTPPDqvcRT9Q1ArBWRjnG7SCB878GfsGnyKgB8r9lQEdUKxwoQDicuPZH189eTcXMt7Ll06gksH/4vFg68ueUa7ajJUNe12V+PyvS1lSaDfvSyuraRrQCdiL15dY9KZZOCe6zhUQxvbzqfDjU61Mk9u6xL/ZXMhd/l3tO7z51WCsEljHc0FUGM1SRTRZv3OOi9YE6bub6eU+nxH48hYtf4RR2PKycbSXn4FsDgOQtY9qbVbenWAo18zFD5x8MDYw9w5k+fyJf2fJ5SXOL5vYYveYKVgI9hnmlN3Gpk/jTW+zHGoGZkbdqJ0sHMoFPqWCGEpBTfhOFuBHayVdfUF4HG6/rCJTARUYekvnzeirWlc2dTicukdGpWwSYpVpNxfkxKfqDxQ+sx6TAPGG1qHhUpXLSOUCYidZSd8IO7urktIiS9OPRjzFRLtd9uqFZRpk1YysQ6KTzX+fe+B/kDSKo1iaR84wOpyud7iztP5kK49GVP6fj9nK5dpSa7Vjq/7ftsM6qtJyVu+6UQAimsW9kRklhF5I61RnL5boWpdJ8mpPCYuTWuhn2y67wmY3Go53k4spcw3k45vr7VG9SA9CkOeBBvM4yPfBpjiqS84xjqfU9bYyOTexWOWEQY7WR0789bTxh2Z/yEKrJsH7f9zrV6zbC8GaNLIDL4qdWdT9jwu570SWTdo/arf48VfowxARl/HbnUsXP+3UyM/6jIvc/dw+j3Cpgn/xI+8kEy7/8VR964jNVfmE9qhb3fwedmkT2Cwg0B9714D+Fu68mKVURsojoFfgaMMXxmwxd57Q23ETgvIuM/seUYgUaoapgw+R0SIgmO7TtCSFx3FQv6/4lcgzJ09RrdKjdbGf39G8j9Z2Q4+KvzWfC6XmbaprEaRZsyIPHdOvOy8mAEVzyX7FXn0Z99ctNvVFGjivU2eIMe5d8uJtqmamHhdnBkjuGes1jQ91oarbbxwhUYQjLe4aT12o6FKhuhS5pFqX8DbP8ptkmcVvm6YN3AwUeydunPWJmUE2kHQ2fW398LjxkqbbBmaA2reg8iNhE3jll33EGu4HM4fCAKSf1PnsoL9qF2ReSTwSFko6Fi6/3EWjMZjDNa3ltLMK1BOKhgxq4vikFKCtGFSPlOFH8FQI80LMQNgqZSeBixlfHCRxkb+3DTqbSukEodiZSDkHqq1cIInZbE0jlBYr0HnRweVSNGVHerhhiNf4Qd/eHd3TwqCiGzOHIxUixEU0ab0hzb1ZmibOJqpdrO9+v71qMS7Ic33hhtJ1shk0rEFtVJ3V84N8XQMN7LA7tfR77817lfnGZqcikq4SYhCmN0LZ9BOKIlli9wEyn9WYQBsbtfb5mDO2xF7sr3zRL3E36LR8UdlPSfmaF3hnidI7MM95yNI4fQerJr3RiZE6QfZ79P/+FDZNNPZ97Ahcys4lyFJ3ro730Pq3q/TL9+HnpGHSxV7i6dH8QBysS4XYwnBARFO/m76bXEcywKumrwYg6d9+May2M2GGMYmbYbjfl9r9jv3JZG9D8lg9MnGDory9Kz7fX1sntaai/1nZLhsO8vwlvgULkv4t4X7KF8X1jTKOnUhn2VUSbCKcrKYcXgOaT8I1uOEY5CRwKEy2hplC/f/GUuuv4LoFyEtO9JIls2dVXEWqGMbqEm186P2O/wsdMjGXhalmUfHOSovy5jxcfquihBbBVpfXdxk4ZQZVMEfz6FgfLLmtRqk0Y0GcIyLfGX2mccbO7OEFu14FMsn/fBphITY4UfATDc90KbjNymUGUjdKyJJ2N6eo5lIPdMwHDfzrMZy/+06bhqHoq3yMEfyJJLHUl/9jR0aBj/RZFt76/XtbPzXZKh/yjiMUOlDYQQPG2FLY517cjVtc/TQrAgyKDviTB7FHf+ZpqPhZNAXfTNmL1gAmKtmaiMMR1OUI6LFKPmZC3puOhyWIuraq3RKrYly7GxTclSu+Duqg9es7s5TwWnTCX+Fvn85U0DVco08+d/hiXLb6QUS3xStsT4gfQ3m3TTFOpoQrVJstouSfGyPPnv2nsONoZta+PY9koEHkJIHDkPTy4BDLohWTXWMROVsVbPVBeKcqdE2kZUPdmlOdpFts0RQngNNW6q9yxw50u8OTJ39kx+ienyNTy4983sGLuASrh5btfXEDPOQ9veT7FwN67jsfuhf2XLxlMISjZvAkmLp0UID2OCpsrcAKNT/8nI5L8TxQ/V7kMbg8Zw8FeHWP/XIXqP7Z7oK4XXYhj1PSnDmq8vYOm7B1qOXzz4Tg5e9Dt8/6kIIciXfsjo1IeYLl5COfgzsdpbe/eZJPwT/Kafef0fwZGt56u1Qwo89wQGhs9EFTTxeFTXNYpt6KGbnVYMC23ZPo0QrqBStoaKn1lPpOcgMqeNFXLcj9m2ULmBINqMFDmGes6a+w/bwB10OOqmZRx08XzmHW3zZYJ4K7Fq1Z3Jrvc57CeLSK/xiHYr7jt7D5N/KeJ02VHfO2lzUw7qXdExlGcchQgdQFAIC3zkTx/hf275otVXcZNinlhvXiXczGj+B01GvNYKZVqpyY1QB6jfJKTAHWjuGLX8FLfZGOlUjLA290r7vreMnMdD+87FPcmGjyoP7V/byuEDlII7AZehnuch0xKVV23n0SpUSaHLGpkWrBg+n96M9d72Zk5saqe/3GX5R4ZY9Jbmca0Lmi3njjLy7QLFO6u7N2XlNMyjayo8Zqh0wNNWPBuAm8dvoByXyE+7vOnVx/HKF51C4Yl2YLm/C7hVh0knHQSygEHpLUwHo+SjKTJOL2k3w1Q0RSWue1CE61hqXpR04ISaHJlpwE4ggiUIV5D5WRbnVAfvrR4MzliAWAPGQ+tJ4ngbM1GJYyIV4BvfLugH4FER0ubcdPSoYJqMGCk8ottC4m31ujhqX7sfxwlrpJ7DIEU/rlwKpNFMYoyirCpMhRMUojYMhQ4U5W6JtI3wfcv+ieYo/WIIkCKNEKkmw3Dhm/s4+ublLD1vYE7nWTr0bjL+4Sg9zd6pr7Fxx5nct+sVjBd+jm5bXgGULrFn6gvcs+8M9u27hKl9X8KXHpgIYwKicIs9UNr/mj1NHkKkYEbZvkpwPeXgmpp3SErHGsvGkD3WwZtfNco6w7qF90MfxOnDNJyzEt5AqfJrJgtfYGTyXHaNPpcdI09hx76nop98BQDBbRo13n3H7AiH2Ci0sWqc5bF9TO21MvcmNlY4q4NHJVIxkYprpQg63qsrCGNrqKSyR85JS6Va30VI0LqMNgGVqNLqZa0eb2J2T3wBgKGes3Dk3KjbMxFPqXp9ooSm6jqD+Mni20lPJbXM5bAfLaTnxBRq2jD6rgBZ7rxURMH3+cKxcObCzrlWQhqIU6Bgae9SHOEQqIB9wTRCJH1PCJRWjBd/xdaR89iy7zzCsTLh7pjSloD4QYPuQCKUYv9E38Z+XGDnpydbWC7l+0LC3TE96RNYOe/jzO//p9p3xhgb+gHEmm2MTH+3nuSsbN8QrkCHmsnibxgv/IzUctvfKrN4VMDSi/Pl6wnjPUiRZn7fqxjuOQvPmWcl9Su6Y/jHKEM8ESNTEiEEnjufQxdfwvrlv2sKXT207xz2Ohcw+Ioy81+bY9vofzIy/W20CXCHHAaflQNg5NuF5J4V5ThgstLKivp74jFDpQPWDq5jgbeQUIf8dfx6enpjikWHOJLcu7Af48LBm2Fgi2afsYtiNU8lH95DKSqQ8XqQ0sGVKTAwFUzWvQKOC1rV6XnK1vmpJIUNoc9qnwDCF6Q+msZ7uY+c3/zKfDcDxvLfqwq1lcotBMEdKK0oRNM40oO4LhR2wOjkUTFYr0pVc0wI9KQ9tv+tAyy7ejnuotYFwLJmXGbWEpAiiyeX4DCANlMUg3GMgeloikDN2MF2oCh3S6RthO9ZTZWgIVex0wICJIyQLFI4QOt15+qid2QP65b+lIMXfjWphCspVG7goX3ncN/OFzcda0zMyPR3uWv76ewpXoQ2RdLZ9aQGX4wUEi9lWQlRYN3VVVXTRsPSqhHnmtqnTQVt7ATkJqwfKSQajdYKQ4wUqVm1O6wgXL04oS5rwj3dBbhiE9Xa0pt5Cf25t5BNPRXPWQ04GFNAmymm5IV4L9pI5kwHnZ/FUJFujf1RjG/nvqmnsWnrWwkLBWvMqs4eNqXjrnkYNbgw5H2U4QX/QSp3tL3eLLkRRttrPzT+Dm7bchTTpT8zUZ6gHLaXR54o/pp85XqESLGg/7Xd29MFuz45ye3HbGfkO80GfjX/oxR21l9yBxwO+fZC+p+bJneBxu9t72nSpsIy/xbW98GhvQMdz2ccDdrFxAbP8VjeZ+fKbcW9NSO5qocif/ciGB8kVFvZ8NEvseHxO9l85gT5s10mPzuDgJB4GByc/ZLRH728wJ7PT5G/ob4p2HHBBHc/bTf7vp4n5a1gXt9LGcxZ7R9jbELsEVcv4dDvLWSy7xtsG/0AE8Ur7fexrVckHUEY7ULpaQQeuaFElHMOhsrmfW/n/t2vZKLwS1LeclbM+zCrFnzKPhtpRRx1qf3cVPOmZJr7d2N4qhI+yETxCvZNf5MN209jy8i7GZn+DjvGLqxtRua/yq45Ez8vEk9pDIow1n+z6tRzxWOGSgcIITihxxamu3bf1QgBRx5lJ/WNDy0gOt4Orqf8Ae5LVKAMVr1W6+1kvT4ad5lpJ0tFFSkkISApHQwaEya/VQpQBGyx3yfnmg2O44NeA9QVasfHP87Onc9iYvIblKMSKScNsdmPPW8rhKUxtYeuJlzZKzh4kBgqqaNSXeigMZJM28VdCB9HLiY2A4RMkXFcjImZDqeaFsB2FOW5JNLWrwOuY5NqjYFCUGRfYS9h3Dqx2OuKxDMhu2rlzAVCuAzknsKaRV/nyBXXsHjgX/CcRQzknlY7JlYT3LX9DLaNfoBYjeDLZaxedjHL1nybVO54gJqhEiaGCo79b7b2KWWrpgpSCGE1gqSQGGM1KbSO2XNxgXtedQ/h3s4hDktRljWK8vSfK2x43E7uf9nejr+J4rhWNyadOon+ntczb+BjLJ53GcsXXMOioe8y3Pdh5vV/goX/dQLzP5fGWzmLwSQEGk2sYzL+YUiZI9I72bnl87bSdpcBECuFZnYvnJACT6yhJ/sCPHeQWMezF0NMqLlSZgFFvnIbgQopRu2FfAZzz2Hp0HtYveBiMv6a7ufugum/VNAFg7egOaxRLVBYrHQvvSHTgkWf6cF5fJ2uXfhxRPnPdSO0WP4ZWSdiVxl6s0/vfC4Jwjg1mYNVA6sA2FbeVdP2sUayYernAr75Wvvhqy+BgQIiC2LAIJKFON6pGf23CntfVUl+KxJBuNnHpCpoCjdbA6XvtLo2Sc9JNmw19qOCZSM1YCqYphAW8Ra69D4hTS5jk5uLQeKxMzasKNOy5mVJ+2tIH2QV8OYS+ulJPw6grZw+2HPHBdWyMbPelKjmTemElLeaNYsuIVU4GmOCWlmGjL+2ViMod0KK9KEeumwY/0kBpSOCuLsh/vfAY4ZKF5zYYzvO7ZO3EumI9YmhcveGfsIzbMc7+4ew58EKpajAdPwGpuLLcZyXghBNhQmlkPhOmqloikDZQSIE6Epi0RtDrGNirEdFsASjDJW3lgk/E2DGNPoBhbp7hrUgwahEoTa8kyjaTKVyPSBR3uMQQuBIxw68h5GQh4COjgYNjd4FKR2YTP492LmLGTSii4iREJIgzqL1II7UpESKUjRNUTUklchWivJcEmkbkUrb2j/TxYDx0jiFsEipzSJiCJHCS3RFZG2CNdqw8Sm7uO+le2ZVq+wE313KkqFzOXLFn1jY/4ba59YVvANHDrJ8+IMc2n8lQwPPoxIHNaG3Fo+KqNf76QalrSqt4yxomeC0sQv7xK+LTF49Sf7WbsJgicR+4lGpMn46Ke0aY4h02LFujBA+vreGXOYZZNOnd0ygbPtbBMpoHJll+bBlru3Nf4Xi1INd9VNCE3QVNJtxEUxkGVJK61ll+6u7/lzKUpmLlduJTUQlrjQZxNU+LIRg0cBbGMg9dW7taYNwZ0ywOQYHeh/XPMbm4lGpolHoTU0aJi4MGXlTwN5XVSj9JWCq+G0ALt8Oa/vXtj2HMRojJBi3FqatGyo7MMIWJxSJN2/oVVkWrX45vj4Y+qdZeNUVLP5riuE/SoY/Yo0JkROUfqcIb9eE91kD0+hmdVqlC0wUft0SSs3/pQIxpFa6pFfVPWj9T87gLXSIxzU7brycfPmG2m/LYYlSVJ93alL6lTsxRlmPii8QKVkTesv6h5Ne7eGvcEkfNPuuqaqnMlW6mnz5hhajS6YEpmJQleb+pkoK1cabMhNCCDL5xxM897Nw3ifIerY/9mfPaDpm/iutV2XkOwXiOCR+lPNT4DFDpStWpFbx/sM/zLce/wM86XHEUTZ35J6NvejTfHYfZ8hU4LDPl5iojAILSHtLEvYLxDM6mifTYBRTwaTVFXAFuhLZAaYUURxgEtl6IZZgHtTojZr49zHqHk3lDRXCz7TubKU+HLChn+lpq0SbypxKWedIOxnb4UPz8GplS9GWBmxhPQ21v2ID0/ZvmZGM/dcou1+xq1bQDRom5S4iUtpoyqqA7yxA0IeUAY70mQ4mavFo4WJzb+IZhsosibSNcB0I45hdk2PExGTdDIWg0BICsom06SQnw8Eki3s8qqlsiijcGOBkH96QEsJFymamzNKh/+DIFVczv/c1SOmjZEyk6hWTvdQqAKJwey38IlwxayFJpRJVWtlcpsFqUtjn2XOcjVkXbi3QCTbs6WOS7XK5Sk1e3ylckORs7KfhHG3RRNu635QQkjjxcA7knkFf5kkYInaMfxjhdt5tV8LKrIJmANPFSynqnxAXx2r6HWqWB10zVJKKxeVwg6XbmqhW8Xmy+Fs27Xk9Snd+zvuD6UQ9O3eU31LoMps6koMWfJZDFl/a7qf1drcReut5oYtIQXibZvQHv0DrvRAN8NzVF7I4u7DDmSxTTkinljOzst8a11vL22uJ6VXhtt6n+Cx91zxWLPkPAPZNf5Mw3N5ETXYGRC3RuvhTKwQ3U512z+QX2bzv7U0lGwCmr7Eht77TmseZcAXDL85BuszI0vdx/+5XoHXF1jlSEVPfC9h2wRilDQEZ/1CkyKJNgUq0yXrMfIlwBGVVVaRdS2q5y5HXLuXg/5294rVVsLX3eP/uV7SI3jVVVE5gtCGejJBed29KFVY6X5AaewJrl/2Yo1bcwKKBf246ZviFPciMoHJ/RP7m8FEP+8BjhkpXCCF40oLTybl2sl51UImenphy2WXLln62nBfw62fAx99n8KRLyq13fIloK/iWdnoox3mKcdEyf6IwKT2vEwbBU/DEu3DEaagNSZLZEQ5yiX1Vjayf5EJ4Zg3gYoCpKSt776bPQumkiFiE1RuZpRhb94eRnKMdZnrVp0ztN84Ch/J1ZaIHIsIHGo2syLJ96MyyCFSFQFXwZT004QuXUFXIhwljIclRaXSHzjWRttZ8o6kwwfh0mYzswXdTVHSlJYfAmBhH5pJbkwhsmCTcaxdod57Tlf56IBjseRaLBt6EI/ussqcriEUiwJV4JFxvkQ1HmYg4TAxdT3RlCEB7+Xx7bwJlIgQOvcfa597do0KSXKzRgamVBehkqCitiFHW8zZHTH4+ZPezyuS/0T3W7+AQJIu/EILlwx9C4JGP/8R9o88jX26tpxKpmFirroJmYD0DU8X/YzL4JHE4UutzsyqixtYosy52H22mkWYfjnApxSUKlVvYvO+dTJevYd/UN7ufa47IJ4bKTHo4WN2OoZ6zSHvda7vMFHpzBgSD702x5LcZev5JwsvtpohvvIT1551M9EAH4UViEA7CcTGVxKPSuwqArcVtCdVf1cZs1djoy5xOb/pkjAnJl7/aohacO8vuvIq/UAid5FY1GCquMw+AydLv6m0xhqk/JobKk1u9ufNe2gOLraKrQz+u01/TkSn/WjPylQLlByKEcJo8ZBgQrkR4gkpiqGRTh3d9vjMhhMR36yH/jN/qoZIpgSpqdGTvUxUVqji7N6WKqiJt5jA/SbpdUAv7VOH0SQaflyN3QopYK+K/Ybm2ueIxQ2U/IIRh/ZE2/HPXnf2sGHb45HmwvR8i6YEp4ZjP45r/7JjBIIXEk2mmgklCoYDAyumrmECXccRRePJlOOJo9J22hzhHSsSipCMWwBQacjSEQIosJvU9+gYvADRSziNwjsWrUgVVooHycN52Us6l7eI3w3Yyeazl0md/569P9FTuqrtg7e7bo5ubpxKXMZjE9ZwBcggRkHGy5KNpKnG5lmTWSFHWc0ykBTtx5cMpIjGFo3sIQ6sF44sU08FUbeIziYBfTVNBJNQadE3sze8i9vZIwNJrBXEi3V+d2IWQZHqfQLbvybU8EeEwq0fFmAArn9+82xNCEusKQjj0Hm8pjMXbi13rPUnhYjDs+8Y00T6FO1+SO6aTR0XZpORZNF0akTrWdt7yH7tTNB0hUSquvbe0v5qV8y/Akb2Uw3sJ41ZZcaVjpv87ZuT5MfG+zg8tUg9hTAFBBlcfVPMORKp7/oGONDi272T99fYzdR8pxydfuZdNe96EMQH92dNZNPCW7g9iDjDGMP3nZDE+pXt9mG7oJPTmzJdk3nULrNwKQQ9c+TyCOzSy1SZKGmQFEoUvbZhWG05aeBJXPPEKLj/zciQpSKjFaivk/xQQ7okRQrBs+D/oTZ9OKvXyFmpy5kkOcgj0mCH8i7GGcoPR2JNKSheED9Y8uMFDMeEOhfCh9/Gtzya1wiN9hs3dkhPWaAh1ZL2MlsFP+hD7PHLpYwAoBLdZr6IjMCJKcpGclho/s9ZLA1bOvxDXGWb1gv9pn7uXEuiKRle09aZMRQhPzNl73FjjpxtWfHSIw344D3F0xMPLbnxk8JihMgdcufsXvOPmN3DdyB954qmjPP1Ze1hzSJF5boYvI/mJcMkKgbzSwTE/xOGPCCYS6ZHWzuk7abSJyIdFlA5RgSKqhERG4YmqgJdB35l4VI5yEFkB/fb3Zu/McxqEGKJY+D4A6ezziJSySbRQC4s8HNEoqiySNvP4TI0muUri/96DS+yuLJXsrIONjR6Vzom0YHfdhbiAn+SwWIOsD+sitnTYGovK1CcBE2uYYyItQCEuMhVMkHGzeK5DMUlNSXkpKqruVTFESOEhEkNFYF2tBkW4O1GlXfS3NVRQVh6/oio1obcqFq38NItWfgY/CQPNRS+nv+cNLF9wLQM9zYujRBKpAIFHZk0Op89BVzSlezsLzgjhEu1R7L7YerqW/cdgx6Jo1ZyO/emP6ZMcRBbUPkN4dzeVT1vCoVFzZ7j3RRyx/I8sHTqvSZNkunStNV6mY4JLJPGDhqnPd/bYhElOh+8djlWWNrjSmVVLxYSmZjinEzE0pe5FMMVU/t9RepJc6hgOWvD5R0QBtHJ/RDyiEWlRK4XQci/xLvZM/i97Jr/c8TzdhN7S/snM67+AO6Pj+M0nfgcfKuIu77ScaIRI2YU8NhBDn9PH4X2H0+f3gUjVDOzoN4Idb5hm16cnAcimjmDp8MUIZ3lLO4QnyD078ar8NAk7hg/y0L53ofQ0mdQ6BD6xHq8JuAVbY5x+Sc+JaZxc+/Zmz7SeRr15sT1nVEZOeZiJ5B2uTgyVRC24WLkNI61GleunWbfoVxy16E5cZwCAkUvz3HHsdrb/10THZ11FX+YJHL3yJgZ7ntn2eyEsBVoVY8v0KZr9Cjc3elS6QfqCSIdE2tRKuTyaeMxQmQN2lLaxqXA/1438kac9cx/nvmcTRx9rJ+RVQuIJgfy5xvuEAyPWje6wE2hHYLVIyxzluEDZFDHliDAoY5jGcA3K3I3ZZTBjNq9Erktq9Cy0/9d7ZgifIZDGEEdWeMmkn5UwMRJXavQIxBgFNrW93W5WtSbqOq6H6YnRxHWPSkPNH2NU12qgga4QxjbsU0c2+a/cwKLKN1GU9yeRthKXmQjGcKSPI31SPpQrEIbW8yWFQz4sJNTECIHfkFNTpdYYoj37p0p7oDCxAR8CFXRXTyWhoc/BDrD5JTN2zEKiVGCpyVLQc6xNrusW/hG4TP1GoUuG3PEphl6Q63js/kqdg6Xop09Jav9c3dkXLaWDMnFLOMZ1Blk08M81N7fWFbaMvIe7dzybHbvfCwv32HP/LkaXm/t4rPZSqlxNMfgtACnvSKuEGlnDqJuWitHGygIk7yLjPw7PO52Udygjk+/EmH24zgrWLPoajszu30PpAKdfsuTf+lnw2l5kqn0nCOO97By/kL1T3+jIlKnElY5Cb0I4ZNNn8vG77+XC3Z9j5Ml7at/NPJ9B2z5W9cpWq1gnHVQKt9ZV1ZZmYwCa+4uZkRhbDf+UrlKoqTI7x89lvPBTto9+BClSZFNHAEl4Bug/PcPRty3joM/Na3tfAPJgy1ab9+RDbfgrDhFbbb+Ri7DOXWyNnkMWfYtD5l9eU4sGLPtG1Rd34QvicU3loTmKNc0CmRKokiaeirAi2XP0HitD+YHEUJnFowKg4oBwTKGuMLOGkf/WeMxQmQOeNP90AG4cu77G2JkJ/RSBXg5ih3UXSr0LW5S8/QuW0sGRGabVJEG5SBRXEO5uQvN+Qv2eWthHrpWIZLKRSfjH7GnjpRE5GPgu/fMupWLmk3YbjIDwYeanQDXK0T6c0CasVJXSj4zCTyoDx9ti1JSyE5kQzNRPaUQxLCaLaL3dQkik6McKxQk8mWY6miIkrFGUTWRsvHiWwRuqiPFgDIypeZ4cxzKbyklqSsbNUIqKVKIKxkRNOiRCCKsdgibaW63z87eth2GMIXYUoYrwnNZnZ4xBqUSYSdY/219YGf0YEgOm59genB4Hle8SrBYOC16bY813h1jxsaGuz79RQ2V/kD2jXqSwE2oJrrMUuFSmQE/6eMBQzv4KLvsn+K8Pot//HnbvegWV8NbasZXwRkanziMIbwYg5R9pKymXjWWqGNWR+VPLn0ps2FTqCeRy76Mc/pko3oQUQ/TlPgmifz+eRHf4i1wW/+sAy/5jsOMxWX8d4BKrUSLVLhymCeOgLTPLJGGaiWCSPWXLHDu0fzXBnYrdZ5XY90/NBRSFSbR2knduYoMONVfuu5IP3PwB/rz3plpdNL01MVQOrvfvSIdgCoxNf4w9Y6+pXR/AWyfJPt2h/19dKlxEGD+I68xn6dB7gHp4phjcVm+PI9pWCa4i1FY4M51aSawiIhNhtiSGykGGKNFycp0++rJPwqEPIZPQjzGIlGwK86RX23kheKQMFV9iQrPf3pRga4wJbKHQ1PLZ56ogCMi/OE3p0yXK17fX/Pl74TFDpSvsoDm0dy0LUouo6DI3jd+AiuG+e3vY/GCWvcbw30bxsawm/i8Hs8+6C907dqENXTOm004PSpaZKo9SDgvgjCVXXWwTVJcI5JH1ASVqhkqrtSCMkyxk61C6Xq/ExAZCDqzGT+P5RaJO285QMaZp9x7/OiI4v4K4RqJUiDPg4CYDI7w7pJ5I295QiXRESRXroasmZIEUUMZ30hgTk4+n0aFVUzWxbvHuzIRKajBFOiDrNqt+ep6V1Nfa7pYNxupdGIMzo4qwddNbyWp3vvM3z1EBiInQRrfkd4SVB9ly92nsuN9WEEZCrfRBGxij2TP+ekYm342ewTSRUiZGtp0elr59KSfefSJL37603akAEuaGQ+/JHtl13V3FjRoq+4P0qS5IiO7TxDu7e2U61YypwnPmsXrhF1iz6Id47vFADKdeCyfdhOp5kLghl8V1luF7R5BJnU5/7k2k/SfYZOXIILUk1jG6g2FUrUVV3W1Xogqu8OjPvQnXWcn8gYtQcoggOvDqyAcCKdNkfFvluBi06qnEOiY0STJ+A4LobnaNPo/p0mXcN/UgAMtzS+j1epA9gugBQ3iPru3ALTVZUIvFCqz4ZMXwp9E/cekDl/LXkVsSwcAYnXhUUg0elVBFuCJFuXINkdpMofzT2ndCCOZ9No18+RVE5irAYfWCz+O51rPdGJ7RlVkqwFfvMVH49r3lhFGECjTx5iQMf5BuzUlSNlQihGDjxrO556EXUFZ3176u3ku4U6Erj4wmicxIS4fej3ndW+RwyKULWPnxoTn9LpQl/GdIvFM9nP5Ht9bPo1sS8R8YrnSRSYE2KSRPmv9kfrTjMq4b+SNbfvkqvv3NlZx+5j7e8P77+G0ypVcOAideigPIvbtwbzKYx3W/TtrNUcxPInyN9PYmtf2W4j7bw32212SZO090EcMCecSMTiMEKIMQDuW41LzAK2P/6+AC3i/MSFqFZMc+w6OiN2rUVQqxwiWIA0hhCxS6AhMaDHHC9mnf/QJVIVYBGb91RyiEg2QAbfZgTMaG0MI8pSiLH6dnTaQ1xjAdTlCK8okoXzM8D8olW6gwk7FelXwwTc7rIz2DoWQnV82Kjw2z4mMdL/mIoFo1OaT9rsz1FmB0EaWLaFVAODn7HDrMi1pPEEZ3YQXsWg1CjQGTsD2y3SepyT9Okjk0g5jvY2YpKDmbhko3OAOC1HGS4GZN+WpF76s6FKgTokZRng2+u46enk/gcj9BcSPTn+yB3UPI9xwBNu+VtH8ci4a+0XwfrsFUDK5yUFgtFa/dLSkQ2iY7Kq2J4hBHSnz3UBYPX4YQDmE4TUVVyNE5XDZXVDZHlO8J6T05jTvU/RnnUkdSDu+mFGyoKbBWEakoUWFuPsd08VsoPUoU3cu9k3acHdZvRencFQI8MGVQuwzuMkGNmpwYpsIR6JKGGFb2JhTlwnbAQY8oTAlwrMYJWAaQUjGu00t/zxuZyH+aqeLXyKWfgUxKCwTRXUzk/xuAvtzb6M2cVGtvT/o4BrJPpyd9Ajs/OcHEr0osffcAw2d3Lkuwct7HqERbqPx2BbsunMA/24Xtdt5zV0sCVaEX+/tKtIV9U9/DCT1WLTqPQuE2jAmQ6Z6kbILAHZI4fRI1rQm2xGTWPvycDye9/z4GJyvpO7VTxnMzjDFU4gr95y1AFdOk5rfPdfp74TGPSgekHR9HusRJotwp858MwI1jf+aQ9dbdededfQxjc1w1sAXQh9hdp1iyi9zHDGpvdwvakQ6O56BUBSGrSqF1ilqjSJVzrIP3ch/nyBkTkMQKGDkpYh3hNxYGi234aa5xzG4Q7XbobcJBZsIOamdQUDEVKnGZ4fOHWfL9JWRPy2I9Kp0TaYtRcZakwhzWqxIgpYP0fKbLk4SVoGMirTaaWCumw2mmwwkybq6twJeUltBTTar1HB9lypTiuJZIW3se0mu9+b8RqtTksi7XhN4aIZ1eHNdWfw2DrbZPOJ1DP1VqspRDLc9aCoMxAm1a38/MWHU0GnH/W+/n9tNup3K3aamiPBMHqqFSRd+bPeZ9IUXuRZ37h4PbVlm4Hfb9T4HpVwj0H45hYOg1DJ96FovefxrZ9fO7/k7IpFpvTFctFaPqu/hYRUTEeE6SkJ0Ya77wKQWlWb1Ac8H4z4tsftso2z4wXvusFJbaMpNqCrVtPCqhbhXAi+ItlIM/AtCXezX3TW0C6oaKcAXeQfa9RpuSUGxCTa7Fvpx6HlmNolzYihAe4eaE5bgU8OwzU1oTJwyxnswLcZ3laD3OdOnS5PsJRiffC8S4o6difvPCpj7vu4s5eNGXWDjwBqavqRDtVrNSeXszj2d+38ug0ofaZQh/Ipj3uRRLfp8h9zSHSlSpscqUmmSk8lVGpy+jXN5kjRSZI+Uvr7HChBCkD7b9dS41f/4REGuF0nGdOfoo4zFDpQOkI/CdLFFSi2dt3+HMTy2grMqUl/wBx9GM7Euzb2+ag5O4xyZMTUbfLBpD5MHcOtvkI0mlXHIyh8EmpIlwSdv6NR0hAG3wHJ++1EDTImAiWirpPhy0zKUm+a/BEDKJfL4c9LA1jiaaJnJjNFK2HwCBCinFRdJdBogQLoJ+wLrL026GKKwwlZ+gUM4zrfJMBuOMlkfZV9zD7uIO9pR2sae0i8lgFN9J2/pHHZBKQalsk2oBfCEpBbql6Jk4gPDFgcIoUEIRi1Z3fBWNCrVCtNb7aUScGCqubF2QhTAYHHSDIuXeS/dy2ym3seOzO5qO3frxrahpRebgDLl1PXROH7c4EA2VRmROccme4XZdbBwhiVQ4Jzd//uoK6h6JKSaibM9y8Q+d27QohE2oBTrK6DcadlESIpppAPhuitCEBPHDD//kr6vqp1gvWaRixopjTFUmW46tKdQGG5qVnY1pK4A3XbwEMGRSp+G5q2uGytqBusy/t8beW9VQqVGTk3sWjrAy+gpW9iUelfxWhEgRP2R/46yqh8xV8swc6dqSEz3/Yu+z+F1itbdWBkJWVhC/5j0U/weUap1zw52xrXws689mNvQ+w4OcQW2H4BaNu0TiD/mEJiJKDGHLLPKI1ThjY7+2zzV3OI7n0JgznjrIPsv9raL8SGLv/04z/vMiqjC7QRzGFWJt8NwDp7c/knjMUOkAKcFzXLSxA1cIwZmLnsmp889gYc8gaw61W+677uyjOkwfxGBYQ8DPCPX3yF8o4JmzPWKJSClEDxhsXFz/cj7l55SIftRsfRtj0A8o4mttUlTDKWy9nTaZ2aYyNwbMnCCFTcxtRFU+v+ESVUNFDAgyTo6KKpEPJ237I42JOyfSBqqC1hGu7O4eFSIH+BgT2B2LlyVfmmS8PMJkNMp0OEkpLhAYq6woAHmNwL8pYxWCu2BmUm3K9QiNoRTODGtIgs2Ku07byaY37Ot6zocNhVWkNTFeB2+T51cNlS3JB3S0G5SyzAbHaaOYaazGjW40LpWh8lClifmTvy3PyGXW4DnoIwchXZeOhSsTHIiGyv5CSok2pmtxSYBoTBEkEgDpU1vboyvd70U4oiZgFqsO14rq3sxGOfqm8wiBwNLOHw5UUVO4LalhkyzGhTBPoAPyYYEgaiYCpP1DEcJH6VJTQu1MoTd7f3soVuxC3Jd7LcaYJGQDa/sPqR1XNVTCTdVnp5LaWAncupRA1aMyEU6Qj8qkTtEMfMwl9eK6J1BpZT3CyeYrkzqNlHcMhoCpwlfwvUNYNHwpC+Z/BuHk0Htg+i8zhRoNYzc+AEfdQe7YFG6XfIticCej+R9QDu9FpTT+M2w7Cj+wc7Ejbbi36qES+GRcGyPcu9eWE8hm1yNnJNTmjknRe0r675LH1g66otnx8Qke+pdRdHF2QyXWoVVY/ztuxrrhMUOlAxwJvuMjsXRHgNcc9Ebet/6/WNe/niOOtPTkm24cZHWySm/GWKaEGILFkvjE5no/7eFiCDEiwmAXD/3XRVAE0TdDN0AIKu+sEL4/wOxuNFRE+xCMMlZD5ZHqa5LWhcgYe91GQ2WibqgIIUg7OfLhFPsu2MfOM3ZQ+aNum0hrjKEQTuPMYqQACOEj6AXspORIl6zTR87vI+f2k3X7yLo9ZJwsKSeDs8snPl8RvTci/Fp33QsA37fhnzhWGCFJu71MViab2B1CSIJdNu4cbP3b7pRMbFCubpq0Z6LmUQmtZoTw2leWhgZV2jYeFatp4TftsnuOtzH5wm2FhHJreOgDVgFr/tnz6T2xF3CtymgXY+VANFRmIt6pmbw4ZPLi9u/RkS4xddG3Tpi4qggG3LXgLqxPhbpgGH1vwK6nlNDFLuPXsxopjumspaJjXRt/M+XoG+HLhx/+KdwYQAz+MofUCo8wjpiu5Em7aYzR5MNmerkUPuuW/pxjV21oUkSdKfQWxzsZnXofoEj5J5Dy1iOE4IbnXcmVT7+MlT3L6o9khkfF9tf6PddCZkDOyzE/bfvftsJu3KWGnud7uE+qC7fNpLILIRjofScApeBPaJ3HkYP42ZVkn2kf9MSPmjcUxeAWdh3zbPjgh+k7rXsoY7L4G7aOnMfI9HcJdIXUi+znpV8qpv+vKrQoCbQ1Kk1syHo2YTeKrEc8lzu8hfmz4DW9HPqdhQy/sHNuzN8S5Qci0OAMStyGIpWqpImnW43sclTAlWkeucXj4eExQ6UDHAekK3FFmki3xhVPeJwV7/njHxZw7ddWAfAQoBor+2Lr/ejtmvjaTguZJMlSxRcfxovPwdxsk0jlUW12X4vaSOkL7DlmznHVRNpHzKOCjck3em6S0E9112giAwmJRAzYzxzpIaVHSICpGMKNukW7AyDQARVVnqGd0hlC9AAuxlaIg6Dzvarr64PROWn2wed5NvQTBJahlHZ7qahKU2EycIiTAsF/a7E3Ywwh3fVTWooTVrtWG3Sq8wO2OwnhNi302bVZZFqiphSVzRX2Xb6P4u1FnB6HFe+3peSFcJJ8l85Gm2pQ+lUTqqn+01yh9hqmvxyR/25UywNohKxRhrsbj1N/sO8ye1rzMxVZCO9U6Im6kFg7CMcyfxwtO2qpVMXeZpPp91yfQAeEcXv5g7mgrkZrEyYLUZ7YRKTctE0Kb+NVyfiHtoRhZwq9CdlDFD8E+Azk6uKAnnQ5pH91U8Ktd6jEO0TUwmdVanIjhBQI3557ZY/ts9tLu0BIBCbxutl+EqqgFjaqIuWt567i03nOddNcu/eu2uc9z7dzytRvyk3hjYw4HGIHhsfJnj7Z9RkGkfUS+e5yymGZzOH1vlH8ZaKXJDzKYdm2UdUp0FXkcuvnrGP090Jd6M1r2iTU2JINGxqlNaGq4MpUy7N/tPCP0Yp/QAhhpUc8mWkRj9pafIjSiiv457dvRkrDUfNCssA8YAKQ5kpc8z5S4g/ouzWVV5UJPx5gSu0mZVvmVggHR5yKvOulEDmIBQK5sPX1yIWJiFujOq1Nrm/1dsR12fVHBFXPTePj0DMuO23sLUmshH6CtJNBr7UDPdzYfvIvR2WM0bOKmVUhRBpBD1CqVVHupBejbrSNdl/k4hxVnzjNaPsdrBAgHSgWYwRppHRxpctUZaqhoKIkSjRt/L+DoVIxFbwuz8ZPryHb+ySyvafaDxw66viAQZBqCf1USwU40msy0KUnyR1tWSkTV02w7QJL4Vz2rmX4C6pqvS4guybU1jRUYurvbD/hHy2Rg2CmIbitw/tDoLuIVJnIULg2kRN/8gxDRQp6X2UXvfy3o45iVzXdjFi01VKxibQJKU/HRCZskaOvohoS6qTTNBfU6vs8MU0Qh+QrBdJOGl3UOMpt61Vph+nKBoLKN2v93JH9zOv/MEvm/YCUf1TX33orJIt/lmX4glQrNTmB0+cgE/2Pi06+iDtfdCfPXPBsipc5VG6IQIuaoRKpCLcNO/Bfbv4NhRjOv/WTtc/8oyVyhcGUYeLX9Q1F6Q4JDx4MgD5oY9f2VxVsHbmMyMS4rsf8L6dwlgkG32v7ueO4RCYmUjFGQ09CgQbwvAVkMofZml+yNRyv8vqA+vzDhS1GSCvjyFiBOt2wYYh1TKgDPPnwWWiPFB4RQ0Upxe23387ExMQjcbp/GHgOuKQQOLWBc9/0PfzzTa/mM/dewLNftIUv/9+tPO/5e/geDl8XLj2Bg2AzDtficjf6MBDLBBQg/kW7BbpalMUAsi701sabAg1aKg2hn1q9m5n9P36EqzS0U6c12G1T9ZBhSeYPWTI/y7YwjVLr7E4vvDfERM0LjDaaYjy931nmQvQBAuMq60Fos46bikHfnhgqZ9UXCv2QpvzKMuGXwrZ1OFI+VIKYKLbtzrgZilGRcpTEwIUkTgxGb1HzhcM4ohgUD0hwraX92qCIiYXqmEgL4PlLWLTqIoYWvS1pnuiYSD3c/yGWLfgTufRMqW6FwMERKeIZTJHe42yBwuKGIgtevoDs+iyLXr+o9r0QMqmi3NlQqWqoGG2Fp+ZS/2QmhCPIJF6Q8tWdPR7dKMqFmwN0wSAGDf4RrWMtd5aL6IF4q6FyXef7EQhELNpqqVQ1VHBsDku3sB3Y8E8hLMwasmqHeFxRvsfeb+8T0hTCPJEJ8d0UOgpQpaitV0XpPFtH3s+9O19MObyfB/e8g73jr6Jc+Q6V8IbacZnUk3CdxbW/L7zjYt594/lsGL+nS6tUEzW5HZb3LKff7yfeopn8KIyeq0DYaUZpS/uWM+aRqXC69u9Uw3whhMB7rn12Yz+sawN5ww4ZjgagGN7epb11j4qUi1DG6lFlTnVZ+tss6ROSIqDSRRtFpCKMMqSyS/G8hbjuEIcffjmOk0E4Vuq+0Si557m7uf2I7RQ3zB56fqRRq/FzWPP8IYRAJvWDqrDU9Ai3rY7Vo4MDMlTOOeccvv71rwPWSDnttNM47rjjWL58OX/84x8fyfY9uvBAGg/P8YiT3eUhvYcx7M+jpIrcNv5nVqzYgxCQEoJC3uHtbzyWG/5is+klu1COwH1pEuv9ftQyMduJS6PMHcTmKuIdllUhj2o/uGuhnxm0ZwMtoR8dmCY2zsNFTZej8RaMaVkMhSMQ/a3XlSsk5AwEhsKmZqM2UBWCFsn8uSCNoBchK4gep+1CoG9TEFojT6ysf6/+qqAM8fcignMq6JHmByilxihBpZLspKQ9/3RgJ0qBJEzE9/xFDrFWFIMi+woj7MrvYqQ0Shg//EnJKEOMQjnx/umPJKJvHT0CQrRUTrXuMgcp/BaWU89xNr5eurfEyvet5KhfHYX0mqcQid/Ro9KkoRI3hAsPwJjLNKjUtvu9I9yu0vayR5B+hv2vncdR5gQ9CQU6/+0u4R9XIEOntqg2oqpKKxxBoCvI5FnHezXl6+KWdnuOT6jCA+oz7pDDkX9ZyuovzUMPKPJBgaybtcaSLGCcSluvihRZxgs/oxjcwt07nsFk6VcAZFJPwXWWdLocv915DT/Z+iumo/YeGqMNqlClJs/uIY0S1VZvlbEeKGz4J04YP424aeT22r/zUbNYYerZ1qMrRDL/YVVuF55yMlCX0m+HWE2j9KT9QyzoypYUQhDqAKMMMuVw9NG/5YQT7iSbtSJ60pVIVzQxf5yexGv2KFCU29X4sX3DINOO9f4k4Z9q0rf4Bwq4HFBLfvjDH3L00dZCveKKK3jooYe49957Offcc3n/+9//iDbw0YQrrUs27WSIk0lPCskT558GwLUj1zQdf+0189i5I81Pf3wCACbJpJdPc2BIYPYZVAf5b2V+TmT+E7PoKoCm8EQjRBcZ/cYYjDEGIvOIS/oZTEvoZ64QUiHX2vZP3DbStBCW41JSeHD/uqRdbHttyzoskNX8FOdxzYaM91IP/8MpyIG+U1N5Qxl1S+M5Ylzfp1DwSFjqZN0shbBAJaoAkigxVILBMnumd7OnuIdiVMQTLuFESDl8+JRToyAiRMjZn40xBhVPEEejdgG2kcW5X8sohPCQwnoRG3f3vcf1kl2Xpff4XhvyaBNmk9KnU6do1FAxyoCf6AQdwLydPtkBH+LthvjB1rHgCodQBR2NIP9wh94LBQPv6+yh6n2FBwIq1ymizR06ugdEoLVu0VIxyiC0zVUoR+UaW2v8fQEjbw4o/qDZAKoqIVcOMPzjL3UZfFaOfDiNMlavxUQxwkkhMrbi7kyvihAO2YSmDNCTPoOe3i8zf+DjeO7KttcpRiW2FeyGqpHxU0XhxxE7Tiwx8WGbNN/NizRaGeWDN3+Qn1zzEwDcg2z/UCZG6Rhl4haG2EhlrPbvj5/YvN44SyTLrs5w6GWLmuocVfNISsFdaNP++YaJIq3rzCOIBX6XpH4vyVPB2I2Z581ruc+ZzJ9UIqU/elmBeHw/BuXDRDyhiPbZ6zV6VKpGtJN1auEfYwyloIQrXP6R9GAPyFAZHR1l0SLr8v3Vr37Fi1/8Yg499FBe//rXs2HDhke0gY8mqg/Hc1O2ak8y6T0pEX+7YexGIq3BaMaM4Q/P3kn/L69jZNQmFxq1m02bMpAC72z70qPvttN3EDVqsly9FOcJDmJV+8FdrfejZxgqAprXhwibB/Bwa/y0tFQ0hX5meqnjP8YEH6oQ/7rd6hMjD09YAXcVmU4oy7GOKcZFvP32plRRL1bYDu6LPLy3ejhPax147pNd0l/NINZImITgXRWi71fbHuG7PmHgUknsDc/xUEaRD/NMV6bRgzFinqY4aHeWvX4fPX4PnvHxfZ/CZP7hh38UBAR4XvuJY2d+N1smrct6Yt9X2HrPmUzu+7otWOaIlpBgFO9gz9irGZ36z7YXEyKVUHybDRV/kc/Rvz+a1Reu7rL4OJ0TeBs1VIwtrobHAYV/ZE6QfrxdwEptwj9C2ByVTgUQlY6bmC3t4C6XZJ5sr5H/bntrqppQS2xak3e1NeyteJbNvTLG1CjRkxeFqMkZXhXhU3oYIcMgCsgHBTKuDVdqEyAcD6c3URmm1auyfPj9LOh/A2uX/pT5g5/Ec9e0PXcVD0xvxmCYnx5mON2qHi0HBaYM8QOmRSRxJjzp8a0HvkVpk80pcVcZy7TUVqCxXbjs5Qe/gPvO/gu3Pv/3nLro5KbvHBwYrv9d3BAw/ecynlqBK4cwhJSD9uGqIEqk893llp7dJR/MlS6Rjol02DEHcCbzZ96LexBpQeGmgLufvZvi7Qeej7Q/cPolR1y7hDWXLKh5dQCrmpyEqNxeWyHdUtODf6hEWjhAk2nhwoXcfffdLF68mCuvvJIvfelLAJRKJZwOWe3/L6I6PnzHr8UlHeFyeN86Bv1BJsIJbpu4g5OGT6EXh3sAlVX8+/mTKOXg+xU++Z+LOPuFMS966ihcGmE2G/RfFc7jGh+9g07E3rwzVuI8pXNsUCyTeG/1aiGgJjTOyVXGz36Gfiqqwqfu/xS7yrtqnz194dN5/tLnA7Av3MeHrj8fXMFTlz+VFw6/sLkJD2jU1QoxJKClUnmMc3Qv5kEQR/pMBuM1uf8wLtPjDexXW6sQQiDpQ5sixpSBdNPkJldK5MrOE6ZcJkl/KU34uRD1i5joiyHuU10YiJByCMeFfAFyOdsnMm6GsbLd1fV9XuBKg+f2N53TaEMq41PKlwiCgHT6wOO9cRQRuhGpNoZcqCLO+sFrmA4K/ObllzHPs+76KNxqrVfHtqXRja30bsL4XrRpNexUqJBp1zJn0Git92s7I4SLEXXtoUY0aqhotA2b5CRq9MB2l5nTHcI7VFuPkSNdQl1GaYU7Q1yuuCGgYgLUUj0rTbrvDR7+MZKes9sbNDVDMGrVUqmG3Kp034zMoHYbTKJ6rCdh6gshQx+ov1fP9SjHZcI4JOXNzXAPtkVs++AE/adnkGcHGKNr6rfEESKdRWRiZFZiKoZM2npVev1eUp6tMJxNHYExhqnp3XhtGHmNuHcyEXrrb2/Q+FWK8kOAcrsyXPv9fgb8AZaPLQfAXQXC2JwfZVTH8IsrXfr91hIYUogaVTwaVez46ASFGwIWvb2PpW95D47TR8pb3facvZkncMiiSwjiiKk4JOt0rmbtOh7FoIhydFvPItDC/Mkdk2Ldzxbx4FtGCB6Kue/Fe1j+oSHmv6q343W6wW4k7AZaa1XbWFjFCI0yMb70yaVypFZ4pFY0v1ejbZ6zkAKZcUBGRFFIbALSMsc/kkflgFryute9jpe85CUsXrzYCqGdeSYAN954I2vXrn1EG/hoQjggFAjj4TlpAlUmI10cEXLKvCdzxa6f8Ie9v+ek4SfiC8EKYynK0aoA9EJgF/PmbeELXzyFoOLx6uc+RHxlXNMZqcKGLKzktaRzXBhA5ATey9svulo3zAlJvHF/9Sou3345v9h9RdNnR/QfUft3YEJum7gNgDtH7+Ckx53EMlkvVteoodKmhbgn5fAeZwdmKS4yHozhi1RCbW3fVj0SI9IC0dvNCM4hxUK0mQSmMMYFMm1yMNpDpASp96SojBrEfIEONBIrTJdOW/G3IIB0GlJuCle6ONIhVgqt2ySRK3BSLiZnqOQrD8tQiVSM8hR+m91/rGNGStZo+vhfLubi0/4JgDDYUlOnnckyqFGTnWZqsgkNpqjAcXBSDkqr/U7stKrBLtZ6aJ5eajkcGmv8uCCNIJ5Va6g9ep7vUnjmBFvjUY7i8KbvHOlYw6iN6NuuT00yfU2FzHsdeHX3a6SOc0gdN0sfMuBot1VLJRF7a6b7Gnpe4RI9qAlu1BQui+k528Vf25yoGai5GyrT11aYvrpMXIhJPS+oeVMAjNY4ac9OZr0GXTI40sXEZfJhvukaVaE3z+m+LNw79QAAhw20hn0AnKUCkQFTFqjtAvfg7u1f1bOK5aPWUPFWS4zQliWlwlpeT72NdU/HzuJubhm9g+H0EE9caOv7CGENxz1fn2LnRydrm7e+0zL09r2kaztcZ4C+7ClMVqYQ8Wj3RgNCCUIZdDTkG5k/1XyszFqfdT9fzJZ3jzL5mzKljXPPR1JaE6mQUEWUohKxitDG2A0FGj3DsDMY0jJNyku3GOtgQz9V9pVMSWRKEpVjDDFSunOeO/8eOCBD5fzzz+eII45g+/btvPjFLyaVsp3dcRze+973PqINfDQhHatFYTRknHRS4djyKs9cdDZX7PoJ20s7qKgiaXeAgxE8hOFB4FS5GGPGeOmr76T8lWN54Vn78Fwf7w0+m3ZlyezULFuauP7EuN2VxVlMuQ8xcCCNFTYnJUEb6ZdZMRVNcsm2bwHw2pWv5ZAeOxGtzK2qHTOcGuZjh1/AD0d+yG0jt/KNh77BB1c3hBCqruwZhoox1dWpvthm3RzT4QSxjJqy9xuh7gsov20XYqFL9pJlnXcvQgL9yISurM0UUCS8RCDme7hPSiF6Zjfa0p9MJ+2NsMPDx0n6QKlkDRWgph0hhItpl5NhAB9S2RSFvQX64j6ke2Cu1DiOME773X/Wy/Dbl1/OMy57Ob/ZfDUbjjqLYUBFe9G6DL5XrTRQP18HsTcdWG8Kyk2qZZv9N1RwEMJJcl1mGCrVcymbmyJcUatDZeL2OS/dYHzD8678J8aDCa58+mUc0t+8UzaYFgE1VdTkr7cPJPO4/Z+I23mKhCsQgWhJ3tWRtom0KqgtuO4SWfOgjL6rgs6DmFEOwBM+xbBAb6pnThuNKi3ZOwkwpsYMMyZGGBfpZTGiiMgYdMqq6Wb8Zq8KNHt+uqEmnd/BoyKkwF0tiDYawk2a1CyGyjq1jlyYw0iDu1wSiQijHYI4aEke/9SGL/L7ndfwjsPfwGQ4zUdv/2+evvT0mqHiCIdQR+TWunUPsws9x809rFyOyrgzvEpaTwMCKeveD9d4lHW5s6HSwPwRDfkyTp9k9VfmM/aDIkPP60wB1kYTxRGhiijHZYKoQmQiNBpXeLjCwXWcJKdMtuT32QKsU2z/5BjpjM/wS3rwF9fHpFGmlgwvpA3/lMfLyLToWNn+0cIB+3bOPvvspr8nJyd5zWte87Ab9I8EKaxCrVbgummEkGgKSIY4rH81Hzzifzh+4GBSjn3ZaxD8HsODGCIuAJHh8BMMXz3xTnKOS9UP+N8Xr+D2O3o5/bQJ/unluzn0sCTMsnURlbeVyfw6a63xDtC7NHqzRi6RyNVJ56xqqVQR6P0Wevvmlm9RiAus6VnDP69+S9vE1qyX5SnDZ7Bk8RJe94fXcuXeX/Papa9hZWYV0M2jEmO7W1IVdV8MFU1uWV/C2W+dSExJUzl/L4QGsz1CXV/CfVJ3br/dBfQi6cGUisTf2geRQq6bRuZSbSsFt0cEeDVhulQKCgXo7bVicADF3xcZ+8QkqVMchj8wo+3KIB1Jqi9NYapAJV8hO9jZldwJRhtCHeG4nYfqYcNreNnhz+e7G3/MR/7yVS4+YgCtJomCrbjuIcQzap8oZeX+XdmsoaJVjEw5iDhhlmFQsxQZnAkhHAQupk2GbFVDpUrZFdLmqIjUgRkqN4/ezngwAQYeuHwHS1euIPv0+nMStFZRzv+5gglBLgP/4LkbKuU/xkx/LaL39R7ZM2a8CxdEZA1KpVUis27QoUFLTRBU2pY9GLoghUi1ej19xyNQIZGK8d3uC4bRhunEUNEnBuS8eh8z2FII0sughANSI3s81KjCzXgtXpWZQm+dEOkIgWiq8TMT3sHWUIk2KXh619OxcOFC/uV1/8LzM8/ntalnIePQetm0bpE4uHHfLWwpbEcKycFJraDN+S2170USssycWH/e3jynNp9Ol/9MsXIr8/pejufMazr33smvgegjUEfhOc3hGINBzMgbcpDErkKZGEmrl7vG/GkzhIQQzHtJXaXWKMPmd4wy/PwcqdMFxahIJaoQmqiWcuBJl5zTM2fCgRACD5+xS8uYQpmBZ2RhccP3CEQjay8tCHWIawTMklv098YBbfE+8YlPcPnll9f+fslLXsLw8DDLli3jzjtbK3H+vwpBYqgYcKWH6xhibUDY2OgT5j+VlJOhmj14UNKPH8RYecuqsmODzkglEKR9xQl6nLuuzvDaN6/nez9IevKuJZSWe7VBVQkEf76+n5tv7WXj3TmiyH4eXx4Rvi8g/n1D8p4EtI1XmthAyH4n0sZJhv3bD35H58GQFLpbP3gEpyx+EhrNN7Z/o/Z1rc7PYDtDxUMIl/BHU5RetI3gK+M40iHjtl/Ag8+MYnbU7zH62XTb49pBCIG+3TJKxGIHuWIIywyawJju7lajDfruCPNg3aipKtVWGrwT8Y6YeJtGj7U5Cdbb7kkXeg1BGOxfockEqqwJZAU/1bpgaaP54T1XsGVyO+963FvIeVnu2LeRvLb9M6pWUZ55Tt0a+jGhQboGXAejk36LOKCkTil82qnTRnGMg2s9Kunq2BCITGt4ai74yRZbe+b0u07niIuOZ/z8ANVAMXeEW1t8q5i6yubluKc05HHMAZVbNMGtmvylrQaYcASOclCRrmupJDR+bRSRiXGScEp4v67J8su0aC4gmjxr1/GITUQwB5Xa8sYQNakROXDWm6YEUKMDhMggPMfukI3G6ZF2lx+aFgZQJa7gdK1abvHDp3yDDS+8hkP7OrtKvDUJ1XXT7IbuiqEV3LXyLq468iqESCGEDWcodBM1OR8W2Dh5HwAnzT+Wg5NaQVsK22sMwmrID2FY8bEhAFZeMFQ7x46xj7Jr4rMUK7c2tcGYiB3jF7Jj7L3EJt/SN4QBWx6iQeHauCgnJuzivp7J/OmE0csLTP6qxINvHmHzBXuZKtm5Lutm6U8N0OP3kHLT+82K9EZTmALg1gsjVmEwTfOD9hSxEyMjiZxDP/h74oAMlS9/+cssX25jir/73e/43e9+x69//Wue8Yxn8O53v/sRbeCjDelWc1IlaWmIdRYai2zhok3Ed7Z8k5/ecz4Ywz5gumGCb6z3k04ZPrF4I59gA+ctfwDH0Xzvspdyywc+Dz96EQ+k6pb86KjPu847lLefs5bXv+VwXvm69WzfkaprqeyZIaOvSJRjk0Ta/fRs/9uh/8YPH/8jTh46ufNBVVl2BW8+/J8B+O3Ib9latIqOjQUJmxEhsAaJs8ZOAvruzhNxdGWe+LcFcCD1/vnIdSnc0/dPKTG+PmESPD6H48xDiqUIFgABxhQ6LsLRNyKCt7rEl9cnICHATZJqqz+L9yaT46LW8zROAl6PR9kvExf3zzuhI7sbj/sVXqp1Ub1v7EH+7Q/n88zLX85gup+3Hf86AG4asR6TKNhqWSkzckDq8vl1j4oJDWTtxCpMQy2Q/fSoAAiZSkJ9dVQ1VBwhE5dzQ7KzL/aL5g5WwfXXO/4AwBve+mK8dRI9BeMfqbPqHCGJGjRJjDF1Q+VJsyfSNqL35TYpNLhRt1CVhSsQRhKHcS0Ppyr2FmGF4FzpYpRh70vL7DixRLyzfg41aRg/P2Dqonp/c4VHMSrO2q6qN8U93pDNzDD4lUY66SSp0wrxCU8g+qzAl+t4NQaQ0powDtrmMrRDxk03SefPhH+kIXOGJH387B7Mqoz+RDCBEB5C2FDxTGryzaO3o41mZc9yFmcXsji7kIyTJtIx24s7a8fZkJ9i/qt6Oe7BFfQ/pf5ccomKbKFyW1MbgngnoBOP62CLAWmEgBmeQoEN7XQrezCT+dMJAy/K0PtqaxxU/k9Qep2H3Op3fcZzQZwUh3RXAd4M+QpBE2MpMjE6p3C0wz9KjZ8qDshQ2bNnT81Q+cUvfsFLXvISnva0p/Ge97yHm2666RFt4KMNzwEUGFPGc/vRZsZkIFy2l7bzva3f4vqRq1mw4wecgKDCKK75AL28i3imy/B02yGP3TvOT79yBy94ruDg246FO47BP65hAheGww4tsmplmZ6emK3bMrz+LYezpWLb0KSlUlWn1UBsarH//cWSzJLuE7iApKQ0a/vX8tJFL+U/136QZdlldleceBxaPSr14mTy0JRlo4wq9Eh7MS25xkes9PBfN4j3jF6y/7sU79mtWf6dYIxB3ZAULHy8fV5CeEg5hBRLsLkykxjTen15ov1//JegWQchDZUkqRbqhopcIFoWZoGoPf+0lyHMRkQqnLNXxRiDKmhK2TJkaSv0duMuuys8ftFRuNLljce8giU9C5kSa/D7X0Aqe5QVv5opyCe8Fvl8rTQyoxENoQZHOkRdRNM6QbSJKDdpqGAIRcRUJRHO80RXYbp2+MOua8lHBRZnF/K4xccx/DEfXCj/XlG6MlF3FhKVUIMByhsjor0KkQX/hP0bG+4SSerERLDr5lbjTSAsRTmxuKpib7Go9694u8EEINLgLK5fP7xdUfh+zPT/RUQPJQKCjkcQB5TDsqXpdjCqp69LDJXHzfCmGIXREuGmbNKy9Khag06SQGniulelEBYIZ6Fr7w9SJyrmf6GX/lf1z3rs6j+s5kZ9I7847hcI3Fr4BprDUDeO2P7+uPnW2JBCsrrXGjkPTm+tHWc9gcm/Z3iVc6ljACgGtzd9HiaKtI5c0uYZ2CRVITyYMV+4nkcpbi+LAK3Mn3YI4pDRYAR5bpnBT7uIXgg3aHa/qMzU/7ZXzZ4rwvuT57hGW92XBCa2GiqNhkoYh3ajgsSYfxxqMhygoTI4OMj27fbFXnnllTXWjzEGFHvJpgABAABJREFU1anc+f+jcLyq9RmQchbiyGxN/C05gpW5Fbxlzb8CMLr5f3np1AYWkMbhGjxxC4oyoVboZPTIYyRyrYQQBq4r8Ybn7mCgFIGA415R70xLloRc8rW7ufzSu/j+pRtYv67A9LTLBZccBMwwVAR2q28MJmpdnLrhm1v+j82FB+d0rK1+Sq220LmrzuGZC5+JI2wcOPOHLJkrstBQJHRmIq3ISORBiVflnva7EWdNiuzXluK9amDO99EIsyXC7I3BFzjHNe/qhMgixRIEw0AJY5qrrcr1MQwAeYO6ox7rSVKRKCYbXbU3keVf0JwgZLSpUYPBGhk6o4jTClWam+tA5TUqE1POlMmm2ofGbtpld4UnLTkOgLSb5sqXXca5T/4Wy1Z8gGzv42t1lxoNpIVD/8uyBX/Cd9fZ9obGxtNTIlnQkueAbFFbnQuEcFp4PDUNFSRCCF7z23/hlEufx7bpnQjfFqnbnwlZG8OKnmWcteIZSCHxDpP0vjmpnvvRADVmkNKxir7JPUxdbceW/ziBn97/BTl1hD1/uLH1HQrHMsWqWipGGdAQxEEtnFKtKOwdLJs2EenTHNKnWrXeiY9bj5Dn+IS6wO7CbnZN7WLn9E725vcxUZ4gXylQDIpUwgraVeAZep/Y3McNESgf4Xj2WsKpL95pSws3FVPzqpSjss2FmGUH/4GbP86L//BGrtl9fdfjDLolmboTCl8rUPpwiWhrBMKxiqhtkrhrhsqC42ufrU7yVB5syFMxGFQHlcNc2ho5peDOpk1KtcaPlIvaeJUMVq05XSsPUR3jnucSxkFHg75TzZ/avQdF9ub3UlZl+vx+ep+VYvHPM6RPcyCCqYsixs8/cHXrKDFU3EME02GDplNVQ6WhH5ZVGTcLpF2o/P/AUHnhC1/IK17xCp761KcyNjbGM59pBTNuu+021qzpLhb0/xrseyyBSeM6Q6ScmdWU7SN89pLnc8bCp6FRXHD3hxgLAww2jBPqXYzokBEdMKYqFE2Efpn9XfzjiHDvd+DUa+CwqCMzZXg45osX38uZp4+zS9msfDNm6tVnJbW4uKmYOSfS3jZ5G1/a/CVefdOrmQjb0GzbwiReFepUUyx1UEgrn9/slbH5KY2523KdDZ+pu5spKXpvffIQadlk8ZuCJvzRFPGNzYZFO8Q32GOcY9KIdJsq1MJFiHlIsQgQGDNZiz8LJ8Z5gl3I1LXN7nfft0m1YdgQ+lloaac1KKwno2ES8F2fcrpc22l3g65Y7ZJKT0As47a0ZGNMzaNy0pJja5/3p5u9TrXJaMa8bxV9bftMaBBZAW7iUUm8G1LItpXDZ4PAReA0xfOrGipSOxR0iRv33Mp0kOfTN3zR9pmU2C+F2ueseCpXPfNH/Ov6N/K/917KyVc8i++f+n28wyR6whorVapv1XhY+KY+DvrmMJlXc0AudX+97Ufh3W2MTQ+oQBQl/VdbLYtIhbWFL3ogMVQOae6PQiQF7zyrglu+2npQej2XtGNwpARjc0gmKhPsK+1ld3E3uwq7SH02ZPBPkDpkhlFgYqROW3l0qv29zlpy+mwIzmjrVSlFxTnlP9wydie3jt3ZUtdoJoQRYBzivTHxaOcSBDrQxDvt995BXuKNc5GCJmXYfFTgrol7AThp/nG1z6t5KpsbPCpSOKg2nlKAtHcwjuxFmzLl8L7a59UaP0IuaslPMSgQMgkLJXlbyULv+34i/NZZEHBmzR+w+WWT5Un2FW359V6/rzYe3YWS+V9MMfRxHzks6H31gXu5oiT0kznMpxyXanXKjEnkN5L5NdYxYRzgOQ4y62LC/x+Efj772c/yjne8g8MPP5zf/e539PTY7fPu3bt529ve9og28NGGsLEUpBxCCJeMm52hQGkXKSEE/3rou1mVW81EOM5H7/4Q2tgU66zcTVpYH0cITGrFxBMVagmg86i1n4f/Oh95ZKtxUYgLTEfWRZ5OGd5+3p9Z/J5XonxljZJ9db0UA9YmiOeWn2KM4X82fQGA5y55LoP+4ByfSbLwJZWThRD8eOePeOH1L+CBwgNtflFl0NQnU+dwa6g0elTiv5QovWwb4Xcn2143+sEU4UVjhN9u/30j9K7EiDi5M9OmKr9vvSv9QB5jKoDGfZI1BuNrm1VCfR+iCMplU/OoOAtFcyE+k0wADe/Ad1JU3Ao6092rYpRBlTWm31CUBTJee6ro1qkdjJTG8KXHMQvXt3y/c/IBPvHHf+H+8btsmK1LUqxWGpm1LmrhOjXZfSHspHxAWirCaZIqr3lmDKRSPu84/vUA/Gbz1YyXJ5AHUKBQCEHKSSEQjFTGuD1/lw0BOVD6jSK8216zSouWaUH2FBeOObAQx8RBVusovF/XNwjVtjgCaRyiyO5+jTIoEzeFU2qGyprWaddbJel7jT1u4sIQXbGJsI4w+G6KlJcm62fp9fvoS/XTJ/rJFHpIu2l6+nIt4VpDjDAppF+9lrQrUzWkkk2SmBOviis80rMUoQtUyObpLUBnDRWgVjV5/CMFtp26jenvdU6Cj7fGoK1Q5ps2vomfbPk5Qjjk/Azphr5fjiu89KCzeNLCx7Eku7D2+bOWn8lXT/kM71z/ptpnDg5R3N5QEUI2hH/qeSpBZA0dIRe1GmxGJ7R7PymwGFsPiWtDS8YYItXe69Gu5k+kYkaLY4yWR0m7aTJ+6xgXQtBzlsfS32fwD623J395RHjP3LycRhni7fbC6cMs6zQf2tw8o0wTszRSEZGKcYXAyaas53w/QrF/axyQoeJ5Hu9+97v53Oc+x7HH1ndz5557Lm984xsfscb9I0DIEkJk0dp6R9xkYtS1id/B+vYMaSfDuw7/CDhZ7pm6kweL1noV7EIKgScEaSHICUHWHUG/8zp4vWXM6Mog8RmppgVlU2ETr73pNZx/94dqi8X149dxT/aH7OiztTYuuu6z/Psvf8xN27bZCSKqJtLO7lH50+g1bJjeQFqmecOq/XtvRmGdCMllbpm4hWV3L2Py/RNEP5q5u4gRNA9Guc5Oiupey4bRozGVC/ZBDKbDDsx9Ti84oG+voB/q7g5N/9s8sj9bgff0nq7HAXYCEgsS74otyueckIO0wOxT6Pubr+V5UBg1eIf5OAsc3AXVDGMLoxNDseEVeNJFoYh77MTcyasS5xXugEMlXSFQIekO+jJVb8pRCw8n7bYuMHseej0vHv4L37vjU031fsrBDewZezUT+YttW6thn7TEGIFwnJraqhStMvpzgRAOnjPPJkRqK9Ve01CJraHy7se/jSPmr6USB1y64Qe1SXMuLKM/7/0rQcPCcMywNdTuHL8b/3CHwfN8FvxfGv9wJ7l2fWKfSxXjdijFZZ5918vYPrSd6JgSemqGoeIKpJYE5cRQCTWRiTFG17w3tdDPIe2v3ffPHs5CgdphyP9fZHfwwm9iqYX3KCY+HrDz9BKTF4a40m01UmqhVr/mxLRCfA5VrqwQAqdXopMq5hk/MysLanN+C7FR9Hm9LM4s6HKkrZrsrbDnix7o7CoLk3E8uWSSq3Zfxe1jtyPwmcnpXZCZx0dP+A++edrnmz4/uG8VZyx5Ekty9SreUohaEdl2qBkqlTpDNajV+VnW5hfVmkX2P2OieuhECFzpdE18bmT+lMMyewt7mA6n6PF6Z33mjfor4UbFxEdD9ry0wuTnwxZjueW3jmDZ9VkW/SyDs0TY6u/VOmWKBiMWYm31WYQwyHTKSgZU/h83VAAuvfRSTjnlFJYsWcLWrdYaveiii/jZz372iDXu0YdBOgqcQUiSizzpW/pgbaJMkgCSncoh2eVkDzsPhEchUZkVbKmdUZg78XkOKV6Md9J/wgt+aq8k1jB5mKFgIowx/HL3L3j9za9je3k7mwqbGAksU+PI/qN4xfJX8sun/4oPn/1hfpv9LX/KXMg7Np3Nc28+mz/t+hNGNWdzt0OsY7744BcBePnylzMvNa/r8U0Qye63YVF5w0FvZPXe1ay/bT2Td0w2Hw61RNoq5CoP7yX9pN49H2JD8JF9MKWRh/j4bxmmHeR8F+cJ1kMS/Xx2qrIccmdRs228JYkQ/UixBCmGEakUzuOscaVuaA41pVJQNpLery9l5bUrkb7fHFNXNuwzcwHxpEvRLSFzoq1XRZU0Mi0R/YJ8NE2mjQFSRTU/5XFLjmv7/WCPVWodz2/kutEba6GfWG0njO8lVtbQrYZ9hCcQQoOU1gOjDI5wUEZZGf39hOsMkPKWAC6xmrI1UZJ+I1L22fzzsVYW9pt3Xk5FBNZYmSX881B+G6++5h2c8ovnUo5t2HD94FqkkOwp72NPaR+9r/JIJ2JuUjhEKmTHBRPs+NgExa3l/aZ4AmTdDPm4wKv/9dVc877f4cxvPYcUEhVZ5o+JTFP4wYSGaIsdLzNDP7Xf5wQD/24XruIPBDLOIkWGeDRg+pKI3S8os+dFFfKXxugJCO7QHbxQkV1Y8RrmAWnptQ2eP5mTSE+iw7m936p0/mH9B3c19GyoxMFbY43s8IHOm4pq1WS9wrZhS2GLZY21obfPFbN5Aod7X8K6pb9g5fwLap8dtOBLDPR+hox7VIf7SfLrRBZDZDcayR7CdTzCOGypNl5rT0qiY810Jc/e4j4iHdPn9+93+NFZKG3V8BimvxSx5+WVmvHbCcIT+IfIxKByQUAhTLzEDYnGlThIGFYG6XjIPjmrIfT3xAGRpb/0pS/xwQ9+kHPOOYePfexjtQTagYEBLrroIs4666xHtJGPJlynF0f0oBU4ro1tp90shXAanxTJMoxdCawM/Lr5p3FL3+GQuhG4EcnVwHsAMCxEMI3BwbAaUzoUnT0UnTqdNIKRuMRnHvw8v05k7E8eOpn/Wv9f9Cd1cA7tPZRDew+FQ2BneSe/3nwj377lVsoL/8RYHPC7bz2TP0fD4Armz4t44+vqNXu++o0ljI55LF4UMrXm/9hS3kK/18+rVv7T/j0UCSaOMaoIpAGH1bnVHIv1rt3JHTyFaoK1wgaLmg0V4QhS/2INkvCSCdStFcgI0ucvQPidJ0HvrD7UtSWiKwv4/zzUNv/kQMTDau0S6eSewH/VILxkALm+2ashpTVWJichnanmOzQMagNt9J9IuWmCuILpBYrUqpcC6FCjI01qcYqiKFBRAQOpzoyJ9z/xHJ62+smsGljR9vv+nrVMVW5meQYu3PB5fnry8fj4LdRkrTRe1k124Q5C2qRoUzIH7FGpwpE9pFyXUO0jiB5CGjvdfOuByxnqH+C0FSezrHcJO/K7+NGmX/CyoRdYw6nL+//Z1isBOGJwbc2Qy7oZDu1bzb1Tm7hjfCOLsvXdvtgpye+qMPldjc4bhp/k4C2cW9inHFfYWx5hVa9lOJ6z/p+5aONXaloeM+E6DkE5ROkYHRvKulzbMRsFA+/2ibfoJKepPbLPdIj3OGSfr5CpNGMfLzL9HQFVmrUH2TMccs93ST/RadvPjYkQJotwnAaPikAIH6PrO3/hCmSvRI2rtv11JmqKtF3CPtWblSJD6tBESG5r1PG9Vg2VzGq7Kdia32rDLKZ+bCkuc9/UJo4YXGd1iWbgmt3Xc+f43Txj2ekc0r86ya2KUVojndb5IeUtBZY2fSblYqR7LL7bPtRalZOXwrfpeRpk8ux96ZGPCkQqalvIUEvFRGWCvJmuSdofCJx5gnkXpShdqZj4SEB0j2b32WUG/s2n95XunFieGSdDISqQVZla7TBjDKW4hO94GBNY71tGooT6hwn/HJBH5fOf/zxf/epXef/7399UhPCEE0444OrJF154IUIIzjnnnAP6/d8CghSuM4gjRWPBYFJOpiFRsNmjAnAwQGoet3AMBp8d5YW84+Y38N0t32JLqUjAVwn5LZH4BnHuvWjxQhCD7K3s4gO3v51f774CgeBNB72Z/z76szUjZSaWZpbyxvUv5BcvuoCTrnoQvnY9v/354fz01wv56RUL+MnOn1GM6xPT768e4qdXLOBL3xziu3u/DIDz5/fz2U8fwRW/2g+PigOoIkb3AJWaJskxiaGyUW7knum7k4OribTtFwe1oUL4DZvEmzp3HnJF9xnTOTGDWOxCQRP/odXdaoyh9KrtlN+5C737AOoINF5rbQrnqHRb71QqBXEMU5NgcJt2qo0GSCM86RKpmNAPcHpkzatijEEVNf6QB1mYrEySdlNdd62DmQGetvrJHDrUvsCal7JsiIN6HO6b2sSPtv8SgFjb5D3Xmd8U9rFVky01Ufx/7L13mGxVlf7/2XufVLFz3xy4wCVnFBUwooJZxxEFc5gxYNZRxzBmjGMY4xgwgzmjYkAByeGSM/fCzblD5XPO3r8/9qnqqq7Q1c1Fme+P93n6gdtddfLZe+213vddnmiREi/ES6UOKQM8tYSYFEKUiXSFT1zzBd70x/eytzLJ6457GWcc9hxOWnECIt2bp2KM4Zf3WpO3Z69q7Xh55PBM+aeO6rqYPc817HldjJ42qGGJOFT3tZLdUtrO6Rf+Gy/+21nsqU4AcOjQWgBunbgTPd1+nNJV6EpMFMbEsVU5KZn0UkkJ8i92GX5f7/sqhCD30ghnKEAIDzXqQSTwDhcMvddj2d/SjH4mIPUYp2swbogQOtUmPxXCb2v3IDOJKqUPw73bJusZlbkEEzEID7VIWXFAPBOQzEZ4j/398FpryrapuIlIgxEzZoNX7VzHc//8Cp7xx84Lqu/e9SM+e/NXuWrXOntOSdPL+ZgVhnFofVs6PBvCkHBTsJkVIdEmanDQ6g0823o9AZWwzPbyDqZqk2RUZsFBSuNYhCBzmsOSX6YITlZQg4mza+x6fbXtfPd+osru/6w2uFpgsz9axxTDUhORNiSKQ2S935pQtreaL4jLDw4V74IyKuvXr2/hptTh+z7FYvdaXTdcddVVfPWrX+XII9vTbv8sWELgIEqlUe6MdwbYbspSuMQ6ss6JZnagYgm217CMF3M+/33H27irsI67CnfwnQ1fZ1lqOY8afTSPHD2Zg/OHIoXEGMNHbn4vdxfuJO8O8KaD3suJIyfQTYRvpg3x9TFEkH2sw2c/upnf/2aYbbs2goAN2d9zwejreP4V47zjoHdw0ujJnP7c7ezZ43L3fYKrbnoj06t/xJ4/vIHfxD7rN6R4+lN2oTdpap+scmsmT/QvAUccXiDwW18AI0BEBhHnEeQwTAITZKZTaAwTmQm+tv5r/PdRn8EGKn7HBlemZqj+7x6IwXliFufUPvgkUuA+M0/tK3sIfzmF+9RWq2u9PsRsjoh3xojhB465XvvOXuT500yflid4tcKfNf506+flKodiWCQzkCUuWF+VuGAdQ9WgQzEqUI4qPbMp/cD1VwNwyEAGmOJzd/4vz1j75BlXWjluV7kZW/axKydLuGziXAIsOKNSh0EiGUVolw3lm6nEVQLHZ/XACvYfWj2zn6Im7rEovGb3DdxX3EzGSfPEZY9t+dvRI4fxo/W/5Po9Nzd+5x0iUUsE0T32+c0+xiOShTl72Vy9cx2vvfSd7K7uYdgfZFNxC8P+IIcMrmWoMMQHP/MRNpVLrLiqtdWFdCSmbIjKIXEtJiKck6DaCYYIJa25Yf65g/iPruHsX0TKuVsw2MlKgHbs6N70HIrZNxaQgURmJbqgUW7v92U8GGVJelFP63x7/DoZPwXegR7V66rU7qrhHdS+CFn0pUWE94Q4Bzr4f/KpxlW2lrezJEgMrHC4Yuc1ABw5dGjb9wHW5FZz4da/N4i+NhMYJ8qkzguk6fLl7J7+KWn/CNL+YWyfuoCY/cF/fPv5CKiv6QUeBgdBhGi6t450KNVK5JP31hhDoTbNrtIuaoTkUnmk3neSXzUmGfuKT+GHEROfrBGc1N7UtfSnmHiTIfOs1mk+kAEFXaBmaqRIUdMhkY7JuAJtpM1oSYHMS97/1/dz89TNvP24t7PigBX77PjniwUFKvvttx/r1q1j1apVLb///e9/zyGHHDKvbRUKBc4880y+9rWv8eEPf3ghh/OAQMoUUtgo31XQHFg60sNTHmFcSyyeLRO8jv2T4GI9EOPxzkM/yOW7/86lOy/iur1Xs7m8iR9v/AE/3vgDFgVL+ObDf4CSDm9c+x98454v87aD382IP0ZBa6BGXnrIWQ+hvk9T+88qYpHAeayDUvCUJ+2yRNqU5Lopzc23LmNzZTNvveGtnDJ+Cm857a2MeHX+x+PYtfsUbt3vXm6+JcvYWI34lpjqOyowCUso8MxLjsZ1NUccXuC4Y6Y5/tgpDjukiJJVDD4iSiXcjgzGTGIm9wKCqewUf9/9dzYUN7AqPYRgsPNFLmr0nVXEKhf/raN9Exzdp+SofWsvYkxhKrql/BMnbrTq2ADh3/+BQW8Nqf1gEqZigg/MqA30lgizOULWDBMTiuERqPNeZ1tTN8N3AsphmTgXobKSaCpGeAJ31KpVJoqTeMrteS3Ouf489lYmeMbaJ3PAUOKpY8IWHpDr2XczJaY5IL+Mu6Y2c86d5/KccRuo3Dq1m+nCpYhBW+YJozKR0Ryy6JGs9da27G8hNvrNiHVMjMYXo9yZdHo+eHj/9tWrS2N136nXVT2b8uTlj2vj7xw9cgTHjR7FsSMzix3hC4Y/4rHjzApoQfrRLqU5iLTn3f0L3n/dJwh1xKGDa/nKiZ9kWcaq95akxjFDMelKGmqWHOsd0nQOji07RpWYKIrQSjf4MJWrYmTOdgfuVdoyJkoUJkmj12FFMJgjiqY7NkRsR8JPiT1EqpUnZVV37Q+mykn0tG7p8tsJn3j4++bYd/0kaJj+eQckgUoXnooz5uCM2c+uzKzkzqk72TC9iSWp/WxmCIcrdiQy/PH2xTHQ6PlzV+KlUj/nXgF2JbyH3YWfUou3E8UTTBS+RuCdBpnWQKVeuia5j0JIpPaJ1HRLEOgql2pcbajbJioT7C7vxlMug+kBaq7ltczH32ouCCHIPd8l9WjVaiB4p0ZmId7UmRPl4lGmzHQ8TYpUQ7HUaCSarLJiN+ZXW37F3nAvtS6qpn8UFhSovOUtb+F1r3sdlUoFYwxXXnkl5557LmeffTZf//rX57Wt173udTz1qU/llFNOmTNQqVarVJtSG1NT/fd+uT9wZnElhRCknAx7KsWEzdC6UlmKZTlUgI3Aam+IU5c8jVOXPI1SVOLqPZfz910XcdXuy1iVXt3oZ7E2fzAfO+qzjRctJaGgNYYaA9JtIQHKpM5tdpoZToa0HioiA8cOHcsPTjiXr63/X87deC5/2vEnrtxzJa8/4A08fcnTbVOskZiTT5zk5BMniS+NqL6xCsnlzROxaqTEvbvTXHtdnmuvy/O1by4jCGJedMZ6XvHcSYRR7J4UXHtFGshx7JYiASEHpU7jUHMWd15+LGb/baxZbVdSE5OSK69unWDU6w/GKIG+XLJmv5AD1thUcKEguPSKmZXv4YdWWbokUSwMKTI/X4XItg+6ceKxUnejvd8QEP1iyl7bvTFiSCXX3Qam3lJFuSopFsH3QAhaXGlnw5UOBR1R0zVSA2niqsYddlGBolArUApL5P1cx+/Wcd4tP+e23XdxyOhaDhjaD6NDYj2JkgOIRAqrnGGkzKJ1gXc/7Plcvn4nLz7geUxOfBeAt175eTZ1MNT0lc9VZ15FVsxkt7qRBPtFbGJiY625b09aLRwyeiBxPImU1j/ixh238pVrv80Zi/+Fhw0d3RaoVOMav934JwCeNavsA5bg+aPHf63t98FRDqn3arzbMvhPMFS6rGpDHfGRdZ/hu3f9GLCy148/7L2kmzgLQggOGVrLHUvu4NgNx1K7pTVQEULYRoi10PY1Cmb+tuc9VaKNhvFzggbRtxOMqdl+N8zwourqH2tu0LsLsA1YA9CqRTFiN5Q40hqNaBpLREogAoEp2wzb/YEx2u4nmezSj00jc5LUCb2zWACrc6vZVt7GVDiNwMWYCsWoxI17bwXghCb/lGZ08lIxGHQPfkXdSr9YWYcSdvHmOp0yBsYGjs1RiQ5ATrWU3jzpMl0tUAqLTNemmapOk/EyDQ8kEQjM1APD93CWztxLXTTsfF0FnbQykaMCNcsl3GhD4AVM16YYjAZst2iZKMKE28h+X7TjIvaGexlxRzhx8YkPyLH3iwUFKq985StJpVK85z3voVQqccYZZ7B06VI+97nP8fznP7/v7Zx33nlce+21fdvun3322XzgAx9YyCHfL6gO44qXyJSNMQlRdOYhlEJwihF1gWAL0k6aR48/nkePP56arjEdtgZbzSsgKQQpCUWjMbrGoPRmgpVhYTdes8GKWCLAEwhnJgUYqIDXH/AGnrjoSXz01o9we+F2PnLbh3GE4rTFT2l8Lr4sovqfVdAgH64w92pIw7kfuZHNJsXV1+a55to811yXY++Ey0A+BpMGY1h/n8+732/5Lb/iNgLg/G++kS1JX5/Xv0azZrW9Nps3O43PdsK/vXyiEahs29H62bHRiF/+aEujc3GnIMUUNfENiaX4PgpU5GIXeaCHvrNGdGmpUWqqS6jFqEMqLShOQyowpKwnVE8fG1c6TNemyWZzeOMeKm277U5VJ1FS9VSlTFQmuW235Qo8bMnRAGhTRsp0sgKtqxMEg+MvRwiP5ekncKI3CKJGQY4QxTGD7lJyvovjKhypUAJu2nUXlajC3dN3cxRHYYxBSXX/A5W6h4qGW6ftpHPYomMRMsCYCkKk+MHNP+M3d/2RYqnM8UcexSw1O1fvWsdkbYpFqTEeMXYc84H/LMNoOkWxWsAVnUsB77zqQ/zi3t8hELz1iNfw6oNf0jF78YbDXknmhFHYkDjU/kvr36WjqNZqhHGtsS9dNkRdVrezYaghxWBbJkSKFNpMNTIt3b8fouQw6Hbln83U1EsqTYGKEKhBSbQt7kpEr8UhnuqHhBw3DP8AMqdkyJzSuUdX6ZIS5cvKpB6VIn1imi886gvWF0cIongP2hS4ZvctxCZmWXoJyzNLO25nTX41AJtLWylHFVJOgEB0dacFSHkHIkUabQpMlf8KgNcxUKlfq6aAVLsIa0vSQJ2nsqe8h0pUZcDPty4sXUEU6xlTyW614T4xUZ1koMkkrnG0Ow1CYRsRAt7aDoFnDF7Kp2AKTFanqMU1HOUCNYSYuVc/u/1nAJw2dlpHkvA/Egve+5lnnsmZZ55JqVSiUCgwPt5LV9+OjRs38sY3vpE//vGPBEF/ddx3vetdvOUtb2n8e2pqqtFz6IFEp6HFVT6udIl0iCtVi1QX4Kw+HkRPeozMIQuWwjqQlIwBXWNI2hdZSIEYF5hNxlrpL0mCnA67PTh3MN88/hzO23Qe31j/dS7edTGnLX7KzD6OVIj9JfJAifc2W4KovwArqbJyxU6e88ydaA133wMjIwYYBQ25nOa4Y2xw8EFzHKk4YqkSLBEljInIje8B7LORTs98thOWLJ4ZWFLBzGdvu8Nj5y6Hi/+e4vGPbU0D6M0hZk+MOiIgvrpseXwrXOSyfdOzBMA5OUPtzhrRJcVGoKJ3JgPOuINSmqqUTE5pPCntgrJHCt1zfCphxT47GRvKlsIShWqRrN+78eLVW68HYP+h1YymhxO1ju2jZGaR+QbHXgJYcm+oIoQJWDr6U4wx/PSRMe5iB5XIt+Noklv27mAwvZxlwTLMRuu+KZH7NlCZsIHK4eNHIWWOONoJpHjV0S/k3Jt/zoVbLuHO/e7h4HyrsuTERQ/nD6f+kM3FbT3JsMWwxM7K7oZSB2y63l7vGKfTqgN42doXMBaMcMzIETx5+eO6bv/hY8dSfFjE7h9WO1rpK0cR1mqEOsJPnFWje7T1pRkBNdyr7KMBgewQjEiZIY4mepZ/6vwUgYuRtAccSVrfZlRmbT8jkXmDntKofPs1evc1H+VvWy/jP448i+fu97Tu55C4uPYztZQvLjP5rUlMaEifmG7xA5LCRRvTKPucMN45mwIw7A8y7A+ypzrBPdP3ctjQQQghu7rTgg3+0v6RFCqXo80kAI7T7qFi+TaqNQMVu4iUAyZqyJbBNmrURjMYtPPLhGMNFbUpAAYlBrseWy8YYzj7+s/zjTu+z38c8Tr+/ZCXtPzdXS1Z/NMUE5+sUfhxROpx7ffBNgWVpNwUhdq05ad4aYyuopJndqo6xQV3XQDA05Z1v9//KNzvMCmdTpNOz3/1es0117Bjxw6OPXbmAYzjmIsuuogvfOELVKvVFkURWLKu7/deUTwQqB+GdWFNficVvpOmFE7jylbDr32NerBSNoas0XhJECQW1wMVKy3tBUc6vHDlCzljxRn2FxqMtIOeyAiCzwWQoesgCFaWe8D+E9YYbdqagh10YI2vfH5H22eNMXz6jvfz6S3nsnb6xxyWP4r9VkcdP9sJy5bGjc9+8asDfOt7A/ziN9mWQCX6W5HKe7Yj17ikvrW8YZvvPGLuNPN8oE5Owzf3El9ZxpS1XUlNJ26no9bZLfAlpZKhMA35bOeAsQ5PuhR1kXJYQpgSUuaYqkyCoKVbbCc0bPOX2NS1MVWE9EG4GLoEgcmC0Gb/rFfJjNongYAjFx2JcgaSwCa0GbYF2ug3IzQhQgj2hnvZVtoGwCFjhyCVIo4kxsSsGVrFk9Y8lj/ccyHnbDiXjy1+b1tG4ID8fhyQ36/rfv669VJedclbOHRwLb984ncav3dwiE1EaGoEsnNjy8OHDubwoYP7Oh/v0MRK/3bdxqeRniAuayJCMgkhttbDkbYZhhoCr2PWRIgg+X2v8o/ldGCSyXPWo2RVXU6LgdzM3wRqQGKKBl3TLWZgU7Vp/rDpQopRiSXpORakiTS5eRyJ98aWTLvGQ43MHFTd7M1b00HpJ1yMkE2NCHtn0dbkVrGnOsHd0xs4bOggG2DP0XcuGxxNoXJ549+OWtb+IRMnlgVN0A7S82yZrYms28l4cWbjCf9IC4RY2FwR6Yh3X302P9lgrSu+dvv3ePGBp7fxtWRaMPxfPkPv9NrLf2A5RA4EymdvOAHYcSfGNEp2V26+kkpc4cDhAzl0tDOJ+R+JBbENt2/fzote9CKWLl2K4zgopVp++sETnvAEbrzxRtatW9f4Of744znzzDNZt25d39v5R0AokApm92fznVTCLK97qbSibAy3GHO/yYhggxUDVJvSmWJJUnPe1v/2pZCIsqD6zirRuU0ty7PtBmWzYQc4F0jR6KLcJXMghKAQTVPVVb6z4ct9H18nPOOpVkl2+ZUB27bPPBfq2AB8gb4nRN9YRR0doB6RQp3YOysxX8j9PSuJrhniq8qYJJtCICBr5elCSPxAMzVpqIV0E2s14EiHQnUCY6BU2810rUDanTvgbzQiXGYDfGOqSJlHSp/Zig6ja1RLt1AuXGqPp+5iXzOWl9A0wdrFeDIc1AObOFm9m/un/AmjEGUUg+4gl7/wcn7wnB+Q9bJ28pUBRltiVN0A7pdbfs+26ZmAtt/358D8fmijuW3iTirRTNAmpUBrvSBH2k64QPyZWqpmCbX3tB6b4zrEtaglKxnemZR95ghUMDWkyHQsC9TLP6apLUHb1xNCtdBuo8dM+4Y86FISkb5EDkp0uVXae949v6AYlVg7sD+PGn9Y73MghlmB1vbXbWfrC7dS+nurcWJdsuzuZyf7ydokr7joFZz2u9MwxgZV/3nU63jr4a/hxEW99/u+Y97GH079Iacus2RYJeSs5rHtyPgzi2QhMkjRSWmn7aw+C9LNWIfaPiEcAU4NoX2MkG3d1vvBt+78IT/Z8GukkAx6efbWJvn5ved332enIIWE7J88Ylkv2zLu1EnQp6w5hStfeSWffNInkQNyzvHsgcaCMiovfelLue+++3jve9/LkiVLFvTy53I5Dj/88JbfZTIZRkZG2n7/z4aSNlCZ/WzZeqqDNqYt4ouM4XnEhMB3UPRchxjbT8gOIt3hCkHJaDLGqgnkIkEM6HkEKmaPofqOCvp2jb4hxnmSgxhtPfr4hpja52qIEUHwieZovYogZ42jpLY9hZJDjm+uUDtvEnWwj3fmIAAvWvVqfrvtp1y48/dsKN7N6sz+fR9nM1YsjzjumArX3+hz480+ixfZAU/kFM4pWaLfTrPuuxez4kNHMP7kJQvaRy8IIXBOzhD+aJLo4iLuctc2VfRE4oIpAYGjNKGGqaIgZUTPLga+41OqTaAZZbq6E611RzOrZpTCMjfuTIiFS4+xA6VwkCrdcZUc1u5j890vQsosS0f/RKH4I4rl80lxKkNjZ8w6R7hn77388o7fk/fzvGz8ZZjQID2bUYl13NE8ay4YY6jpGkorpCtZPrScFeMrkn0KW9JIJNPHLTmShy05mqu2ruM7d/2Idxz/egBe/LezGPTyvPWI17aUdGZjaXoxI/4wu6t7uGXiDo4dtQogJRQVXe3I/ZmoTnL29Z/n1BWP57GLH9XXWPb1O7/P4w7fwcmLHsFib3XTpG5N8qJ0iOPNvMv1Hj9eD36KSTqfS9U9GzhX+ccQIsWA9aKZJU2e2YZLFHefJFVeoosSU7LE2loc8u07fwjAK9aeMfdiBtMWaLkHulSuqRDeNTOxm5oh2jTTjBAg42T465a/EpmIHZXdjHoORw8fzLGj3cs+dRw2dFDLv22bE2tW2I3z5bkz84wSi7pcU93mqg2gHJ9oHrO3UAKcEBEPghNjVaJ9uOw14UUHPJdLt1/F6WueyebiVj5y/Wf51h3n8fw1z+rbbbmeVa2XputjjrF25jOLFWBZfhnL8suYLEzOKV1/oLGgQOWSSy7h4osv5uijj97Hh/PghJI2cRDr1gvmSA9f+dTiaQIlbcCR3GhHCFYYuAe4G8N4z4e6gmXFipaa52y4QEkbKkKTFhJ1soNYJpH79/mQThsqrytjNhsYAP/jQVuQUt+RuVPDnuYVt22V3CBbSTBV03iu9X0h8V+LUNKQBCr7Z9fy6NEnctGuP/K9+77Kew75RF/H2Qn/8eY9DA1qhoZaB9ntT9rLyG8d9rtqBZ+99mwesd9jeMqS5yx4P93gnJwmuqSIXOqi1nik/3cmTWyzDgoICTwoVWFqCoYGu2/Pky7TcZVCWKUYhgTu3K/i+on7SDspcn6WZbkl6HgaqdK2JNDBlM3xVgACrQsYtZcw3kAY30bgPrKl7FPnRmya3sJnLv8MB40cxMtOfRmU7SRfNe1mUv1CGyt7JcaOy7NOU6pUo/wjhOLfj30xV/12Hefe93Nee9TL2FOd4NIdVyGF5D3HvKXTLhoQQnD0yGH8ecvFXL/nlplARTrUatMtCp46/rTlIn6y4dfctPdWHrekt7LB2rJPcPDAaj731M9RPXgHR656Odq0vtvSrSblYIuZHj+93tMwUVx0L23PVf4Rxjqn2qRGF+VZh+xAy5+VwBlShFsjTGQ4f/Of2FbewVgwwtNXPrnndxsu1LPOwTvQTsjNEuXwPltaFBmBGrOToCMdVmRXsH56PRumNzA2eiDG7O25z26Q0pZ+tDHdkr7UdIps7kdkqqApdZRnWx1X07sSG5tdcF2EnHlu54LRIcL3kMUMsShhiPsKcwphkYyTpt6A8xsnfwYhBJPVrXzhlm+wNr+SqeoOBvwhwJs70K63gZr9GBhtzR6FQxiHuE3EaeEK5D6werg/WNDeV6xYsU/KGbPx17/+lc9+9rP7fLv3F0JYnspstZsQgkCliXT97rdOomuSR/GuOfkrGsgBpTZS7uz9KSkomwhjDHI/ifMEB7m6v9sYXRBhNhvEuCD4Ugp1aOcXTC5LSkq7DaZcP54qdqZJBntHNEi9YKW7AGKwdZsvWf0aAH679WfsqGzr6zg7Yc1+UVuQAvD91DncvuR2vNjjgJ+u4I87frPgffSCPCogfd4KvJcNdfy7TZlqBAY3LZichEr3LD1gW21UYoOWaZw+OE6HjR3Eulf+hZ885xuNXiZSJcx/YbM6zSllKX0c1yolovheYmMzF443Pkv+awPsFUm2YtPUJtsVNr7/NvqxjolMhDKKD9/8YT5z+WfYXtje+Pvs8s8TVp/MiUsfzmvXvBSJ5FeJZf6jxh/GotTYnPs7ctjW029oMn4TQpDzsvgdOATnb/wzAKeteMKc2zZUEQQcPmT5ErdPbsFVK3Cd1p/B1BpcZScxYwzD7/MYeLPbM1AxppZwO7ovVGz5J9Ox/GNMBEJZUnVTH5q2bfTRVl2krdlXXIj5xu3fB+DFBzwPf44GeoZ2aTVYLxWA2l1NgUriSOutaZ1cV2ZtS4gNhQ38723f4zcb/0KhR8O/OipRha/c+m3eceWHbPZPKGK696mKdEyhMk1aLML1VuJnDu7YhC8R+c78IrZlHOnabHq/5R9tKig/A9pD0nlhMRs7y7t43l9excdu+J/GfFu/Vjk3zcVP/wNfOumLDPqLMEajzRSxnkCbcpNz+izUmynOSvcaooSfonj6uU/nRT97Eev3ru/r3P4RWFCg8tnPfpZ3vvOdbNiwYR8fzoMXjtvOUQFr4CWESh7o1pfigCRQuQHT1G25E3RS100DpR6fs6FCxRjCBUwc8e9tqtV5gYtc0f3Wi7ywcRNgttSPu4Ig31g9CFc0PEUAzMSMx0kzjhw4jmMGTyAyIT/YOD+PnW7Yvcce+0S4l/O3/ZTfHmvt4Z9z5XPYcu+G++2k2gm2V0qv1YpDfVgL0vZZmZiAQhFKJahUoFqDKJ6JRX0nYKpaIO2PIKWH1t0VUXUoqViaW5xwUzxk4rIqqKdtWx/SupV+GN83E6hkZhUikzb2SwdsoFIMi0zGk43z1ugF2+jXPVSiMOI7d36HT1/26RbzKCEESuUw2N9JIfn+M7/EK9e+kJQO+Hli8vbMVaf2tb+jEiv965us9AFc1b7anKxNcemOKwF4yvJT5ty2MRWkzHP4iCUy37HzLmo3CETsI0XQ9JNHiTzGFK3n0mMdBl7lIbM9nh8T9eU8a2Xo7bw3k2RkwLMkaLfL+514nHSdyJgh1t5aupNbJu4gpQLO2H/uLKUxVaTItV1nb22iftoYocv23Qw3tPJT6lidXQ3AbRO38ekb/4c3X/FRdlfmzqq40uVzN3+Nn2z4NVtK2xK3b92VC1KqlajoCm7sIQJhS15hKzennmlstpk2seV3CEcmmcy5AxW7TYPj5e3mZDBn08VNxS2cfuG/c/vkXfzy3t+xuzpzDYwxGCDnDiNlHsdZjOssx1XLUHIMEGhTTIKW1jGl0dl99uNhbInr1l23cuOOG7lk4yUMpTovyv4ZWFDp5/TTT6dUKrH//vuTTqdx3daHbc+ePfvk4P7paHrfXKdzoOIqH0d4xEbjzGJzH5usX24EvoPmpT1XMx6IAMzWRPbW+dZIIcAYyibCQxFfFqG3GJxTHMRA94FQr9fo2zUocJ4w922XyyT6No3erBFrrL26EN0H0m4ZFYCXrHoN101cwW+3/pTX7v92PLkw5VaxJHjzf4xx0y0+5/9sMz+f+AFVXeXeEzcj7vP4s/gD92Tu5r7SPazOzNWPZGEwoaH0vPvAFfivHcZ5rDVGs6uruOFKm0pBqQjFxM8gaaGBFFY9JYXGcXykO0qxliWOQozegZSdlQOzeQk6riLUCMpxku0rpFD4nqaJHoHrr6JcuJQomsmouOlFrdtGQ2JiOJoeZVdpF5tLmzlI2Lp/vd6/ENSlyfcU7yHUIXk/z/J8qwxUSB9QVhUhHLvi8wTXbruRewsbSamAJy/rLhluRj2jcm9hIxPVyZ6tCP64+SJCHbF2YH/W5Fd1/RwkRmy4KJnh4IGDEUbwxY98kS3lLSz75TL8g5sN2gRSDaKjQmLgNkcmInnf5/JIsdv2Ext3m92Z2UiIlEPU+zN1G2qEcGwpw2i69nnAEmuPWHUIvzXf547o7jlbOthJXSJnK2Sw7rpyWKL3aMK7Q/zDfQZeOUD2aVk78TdhdWLe9rP1PyMyEUtS46yYHVh3gJKK/XIruX3yLu6a3sCK7LKufapirZmqTNqmfLFVyciMRPq2Y/AMCTUph8zOqKRFwq9KEcVzm47aRn8uwguwzHTZ0nRxNu6aWs9L/vZ6tpV3sCKzlG8/5guMBsNNn7DqLpHMEeun17Nu9zqevfrZSDIYM4ShijEVonintfyvzyf1sqCYnVGJkcLnZ7da75RT9juFwWBwznP7R2FBgcpnPvOZfcKefzBD+BIcialphCdRXa6UlSlnKIUxjtP64q8UgjcayX+jOQ/DuNE8pSvpSWIzKoPAHjD5GS30LLhCUEaTNZra52qYLQZ5oEQd2cPxcrtGjArkwRIxOPe9E8sF3AZmswZCbMmn+0A6k1FpP79HjTyWNx34Hk5d9KwFBykAmbShUhGEoeDXF3j8eOm3AfiXA15E5kvL+c01f4AJuGHymgckUDGxoXT6fZhdyeDXUs8WM/+VAiUh22QwawxobeeHWEMUx1RDBeW6734GtDVd6kSqvmnvDXx03Xs5cdFjed0hb0z2k2ns1hgFsUS5Gj+ATBo8b8ZKvxbejcYuIJygQ0ZF2IzD8txydpV2sam4iYPkQY0VZrcU+lyItS1/1I3eDh09tG3smCn/1BDJixb7Mf921VsBeOKyx5DpQxEFMODlecXaM1meWTInwfD3m2zZ5ynL5y77aFNGiUGE8Mm4Pvvl92P9+HqOvvdoajfXWgIVwGZW5CDFv+1BVCX+MRI11vl4ZoKZucmV9fKPNhN2YTOzEaTwZ8iSXbuHqySjMmMO2A1qQLJ2bH/WhnOT4G3Zp7O0Gmz5p3JlhdpdNfzDfYQUOEvbB9VVWfu8TiVGmCeMHwOiv2ze/rlV3D55F/dMbeBxS07s6k5bz6bkVB4jbGAiVNJJelfcNMzVbR9aMyp16ba9Z2LO1gbG1KxTtHARToiJHYwUbQ7BAJfvuIazLn0ne2uTHJjfj289+n9auoFD3atGAQ53Tt7JE89/Ip70OHnxyYwGthWJIMDgo0SV2Ew0fFuMNl3UQAZtBD+/7ecAPOeQfc/zuz9YsOrn/3VITyIytsmY8JhDwZGiWBPMLv0APElIthvDzzF09FWsNxGzekZgAEwJKAOdB2dXCIqxoSpi5GKB2ZKYvvXo6age4RD8WMF09880Qyy1ZQSz2QA1pBjp/TJO2HPvlFERQnDmylf1t+M58IynFrn1dp/zbv4DO0e2M+KNccqipwK2zHTdxBVcP3E1z1h6+j7ZXzOEEshDfOKdieporPlcFUliomNBtc5zQiVFIhMBCqmcJB71iaM8mL0I2T5h3bbpWraWN7Mr3EImWwGRQqqgJZaNQpc4LFGpQHEalAMOduCvlK7FlqYclDM7pTsjwVyWX8a67evYUtwCA9gVmBALLv2EJkRowa2FJFAZa/dkqJd/Ir2N+jNflmUmQlt+emYHy/xe+M+j3zjnZ6Zq01yy/Qpgbn6KvVcCKWciz8OGDuOOpXdw9L1HU725Su5f2tseKDlA4ZtTVK+qMvxRj+yzugQqhKgkG9IPpEwTx3sbE6RVbEhb+omZPbe2wJYwXYzu0D+hCdO1Ajkv20Ks7R782LKYkqNtE28d+TPyZE/LEhzb29xzVW4VGSfT6Pr+iLGHJxnmnl8DZhxq756+F0gygbPG5FhrpqqTeMK3QktfNCZumRHoCWGzKp5oZBpbXmhDg98lZGI3P8v4rRl1Q0Yp05YX4gKhQngOs5U/X7zlm3zmpq9iMBw5fCjfPPmzDPmDHTYaIUUWIQQH5A/gqJGjWLd7Hd+783u86Yg3NT5m36tBdFRCm4rNdmmQHSczwRVbrmFbYRuD/iCP3+/xHT7zz8OCOCpKKXbsaDfu2r1794PK/+T+QmQkOklNyh5Xyle2Rq3pXK98IZKvoDi640tcH1mSvwkXxLD9fQ9nRSUFJRMj6j1/ts694hVK9JVNAZCrJGKlgAFNC4m2C8xU99LPbEwlE9BC8ORTivi+ZsfVj+MpmbN48arXNLI0Rw0eD9iMygMF51FNngNjzXF+kwdJPxOOibFdpZvMwmQWu0Jrv+/rdlr/lGPGjsWYsNEjpxlCeDiuJpOG/AAEAWixFuO+GeO8FsxKHHdl22RiJZh2wKyXZTYXN9uBNTF9W6g7bRiFONrh1qnugQrYuj3CaZz7YHaA757wRT509Dt4zOJH9tyH1sWO16wXNpe2sjKznAPza3qayEHCTRGZFuOvtx/5ds58+pkAVG/pzJoWwiW8O5GBHtDF0yLhQcwmoPaCEEGj/AMz/BSR8FO6eqg0vu/ZVXkX7Cju4uHnnMobL3gPVa+KHJDoYvfxZabs032MyJ6WJX9GHnelS7wnZvsbtrPnc3vauDb75/bnmmdf02g/8LD8CT2PtfW7Niivd1EWQhDN4pCUwzKVuELgBpiaQaZl4z2SnkTkRYNHg7HtAKbDaX5w1w8ohAUrv04meiFcEF5PQq01ZLQZQwARCIiVLRXPOq9DB9diMJy+5ll87zFf6hykYImvje0JwSsOegUA373zu1TiVk6KED5SDoKuzFzrWemJ+u9/eps1knvaQU/Dd/7xxqq9sKBApZvip1qt4nlzpy//r0CkJNITtvyTcBU7mR060sNVKcK424AlGG+aVO4zhu2Na5gQtlqWQBkgD3Rnu3tA1Rj0Yrtdvd1urxN5TN9le3jMB84THVLfS+O+MkKQnrPOnj53BZnfrkKu7f6Ab6ts5rXXnsGLrnwK0QLdTnM5wxMeW4ap5bh/O5szVr6i8bcjBqzfQiGaphL3XjEuFM6jEnn2gJxFHJYYLTCiOz+gFXH7NRUBiCyY1mOPdcy6XesAOHr08GSAbJ8UpLTKozqUglR6kNzIC8kMvwid/jm5xT/ucCwzKehXHvtKLnnZJbzjpHdY0zdtkCzMnbbuoSJiMXegImywX1f/4MAjlhzH81c8u2emQZsyIDB6uhGsaKO5feIufrrhN13HqkMG1/KHU3/IeY/76hznoDEmQs0KDFflVrHoGMv1qd1a6/h+xbtj9B4rZ1X7dSZKdyuZXLr9Uj563UcphIW27wihkDI7o/4xIRLbybzuoTJb1dH6/dbeZLPx7Rt/RCksc9/UZlJuCjUgEa5AVzsHK9ZRt7e0uhnTt09T/EORwi8LHYJtwTXbryE0IUvSS1jhrrQu2n2oTGdnVCSKuKlkqY3lpjj17IcBGbTuX6WtuZm9nxqBy/uveT/vuupdvPmyN9sPNU30UqbnCFRqSJlrvF/Sl0kXZY8ornH7xF2Nzz5u6Un89knf56PH/2fPUqcwomHMBnDaitNYml7Kruoufrnhl22fVzKPkGmMsfNJu2w9ohKF/O6uPwDwL4fMamD1IMC8Sj+f//znAfswff3rXyebnemwWre/P/jg/myoH+wQgPAVBBpTMqhUk+lbmzW1IKWyTEThnOuiW43hvcQMA/9tFNl6HbSZ2CYEMGhLQKbceVJKSLXhuLalhK0xxkwAGmMGGy+GKRoqr6lARhB8LUB2qZN3gh0cYkRTJ91uEFJAhx4hzRh0R7ircBt7w938ccdvOG3xs/s+lmY882kFzv9Dhj/+Oc1bXr+XdNok2x/i1ydexiJ/YSaE/UAMKdI/XAFtEj8BWoDSfYX/xth+Ji3bFgIp8+h4usWf4a7JuyiGRTJOhgMHVoDIdgkcFcZ0biQvJfiB9XfJZMBr2fWMsmFpbqZAGTohpmpQamGNCeseKhOVCUpxCSUUa0fWdvysJaBmiXRx5t8pSbw37prMM0aDrqGcRRhdIdZ7kTJPqCOe+acXE+qIE8aO7drMTgjRB0m0gpApRId30F3tIjICUzSE94QNdUsdtTuskslZoRCpWoMs3Lr9KkoOt2S5dpR38O8X/ztT4RS7q7v59CM+3bZvKVLE1BUg8QwJW4Pwej/7QqiuLgilsMz3bvwJAK86+oV2X55EDhiiXRHC66B+M/VGir26QhuqN1Qp316mNm2vy2zFTx0377XS8keueCRuNqBWUpCNmWu6WpNkVPZWJ5isTeHhEkYzCrNyrUw5LpP1cta2xhFtfA0RWK6KLmjIaLZX9vLLe+3kf8HmC/jjkj/ytP1net8I6WG61KXqkvFmE786affOPVt427XvYENhC+c/+QcsSQjuBw8e2HFbM9usl/lmrrUrXV6y9iWcve5svnn7N3nemue13CMhFEoOUattBiJmEy5NQqx+z8nv5pKNf+f4pcf3PIZ/BuYVqHzmM58B7EP3la98paXM43keq1ev5itf+cq+PcJ/EgIp8YUgyirkVISS1vhNx9CJg+aqNAKVGAx1HyhGgQC4D/ggmg+j8TqR6IQHjIDZBsbryND3hKCyOCYLmG0awVCSDi5jszIQ/zWCKohFIEbnO3lXMcbDmKCXQKBvBCrgBStfzpfu/iTf3vBlTl30rHkHFNsqW/gu72HshLey84rT+NOF6YbFPsDioPOktC8hl3YaYBVoaVezc7YiMEl1qMPrJ1Ig0kmAagPE65J+J0eMHoESMikRdYKi00pZR1uIw5tw1CKKtaMoFsEbnLXbTtGVi32UhN1srOOeDQFno+6hMuqNcuuLbmWr3ErK7V4esOWfJvWP33slbUwRIbNIkQOVBQyxmcSTeQ4eOJAb997Kut03twUq20o7GPTyvXuzNPZRxVFLOk7C59x5DmuWrWHlHSup3lxtD1QSgzPvQB8lghZSo912UlaeFQR96NoPNcikF2y6gO2l7SyapdRqlH9MGZANAraJTEufno4QKuG2tJNAf3zrr5moTrIyv4wnr3ls4/dqQGJKCj2lEYFoBCw2UDJzSquFEGx/7XbiXTHB8fa6q9Wdn6VX7fcqnr7y6ehFGqUCKDjoKETNYYqYdlL8/snnsSKzlMAJqIYVGywn13mqNo0SjvUGCpPzcNozOion0dMatOF7d/2IyERknAxn7HcGj1786JbXVojuxm9GV5Ey1VIyNI7hO1u/w9lXn20JvW6We6bvbQQqcyMCnDYi9Av2fwGfu+lz3DZ5G3/f/ndOWnzSrPPKoMwAkZpEzHY/NjFpN8uLjnoxLzrqxX0exz8W8yr9rF+/nvXr1/OYxzyG66+/vvHv9evXc/vtt/OHP/yBE0444YE61n8oHCnJKEXVs43GRKRtoNJl3HRVgKsc4jlS5GNC8EEUaay/ymeQmK7s+yzW0KRzCcihRrzIpobNDgFmNOlXETZezqjunXKqM++goPbBkMrTDPrKdnv2Zuj1Ncrv2U7163PL0p+77EVkVJa7i7fz991/mdfxAPx403e4ZPefyZ72ET70vl086ZTevjP/OMgkUOlHHWOJtJ2iPyEkUg4kJQd7Dxv8lNEjkwaEXSZYIZPVcmt9Mqr8hurkuyjveTmu/AuFgvVzgfpkWTeMs43PPnXpp3jzH95MRVQwsbENy0w8b4ly3UNFSYXruaweXN3z87b8k2qUf4RrDQVN2MGIK0m3WxKqPW+lRpEijzZTHY3f6vivaz/Bw391Kr/d+Meex2NlpQGyiyz/r1v/yncP/y7rX7Ee/+j2XOpMoOKh1CACb5avRXsTwgu3XMiv7vsVUkjOOuwsLnjKBW1BCjSXf0q27NI0hvTipwBdPXdiHfON663B2yuOPrMlKBVS4Iwr1KiyQetUjC5qjK52baQ4G2p/u73K1ck16KIIN7Fh6fBSVg+uRmYlaiBAF3uPQXUcOLCmEYBKKdHGEBtNOSxTjkqNv5nIINOdr5NICUQgKE6X+f7dPwLgM4/8DP95yH+S8lMtpRNbtuts/KZNhFTZxri7s7iTM392Jv915X9R0RUeOXYMv33S9zlx0cP7OjcgCYjctqBowBvgeWuex4A3wI5yO3/UKoEGEI6DFrPLkDFztW/5Z2NBHJULL7yQoaEHjxnMA4WsUuBLCLApcNdKSztBSRdXpvqq5a8RgncjUcCFOHy7g1oIsCUgMYxtvdn0cJkYzDQQI8ZHqLx3AP9/6uWONFZfV0Vv0ejrbbdf9aT5CbyMiTGhgALozb3PSW8Jif9WJL5ibl5Izh3gOcssCfHb82xWWI5L/GLzDwB43ZEv5dQnlgj81klsOpzk7Tf8G8/8+4kL5sEsBEJIMAKh+uEC1dPYXe6JSFvlT3LPV+f344CBAzhm7FCEyHdc3VvUSdmtz5NQqxv/76ga1Yo1oWsci5DUhwIlFF+79mv86OYfsaW8xdbxE3fa+bpRxzq2ckgh+srd1ss/dTMs4Vnrbl3WxJPxzM90TFQsQDWHiIIGP0QIB0eNIkWOI4YsQfb6WYHKdFjgom2XU4xKHJDrTaLVppzwCzovJA4bPIw/Hfkn/vy4P+Pt1z7QNwKVtV5HUqMxYeJGay9OKSrxnqvfA8DL176ctx/5dpaku/eukiINwkWKoOWZ6MVPST5A3fenGRes/xv3Tm5iwM/zvEOe0f4119rru8sc3MUOuBBPVzBFH+LemTZjDGLNrOPqZl2jm5Q1QuAMpcGxXZ3nAyGkNSvUMVPVaUCgpGrY4Hdr2ieEQOYFP9t0AZO1KVZlV3HK0lPscQWCWMfcO3FvYx9CBMw2fjM6REqnYcg4WZnkBT99ARfdexGBE/D+Y9/HN4/9BEvTI/M6J4ho6+ac4E2Hv4nLn3k5z9mvs7RYah/lDGFMreVd/v09f+N7N/2UPeUHr/9Z37PXW97yFj70oQ+RyWR4y1t699z47//+7/t9YA8GpJLyT5iRqKkYV9HD+VjiqzTFcG4DIIDjhOSNhsRjJcSvTPICv4N3ivCAYTA7sO3ba8lPDsQQruNTepwho1wcAbExSAYw7CC+ICFwHSeR4/ONSSvIpQqNxswRqDQ8VAb728cLVr6C8zaew7rJq1g3cRVHD87VkdXi/K0/YyqaZFlqJSeNdpaUZpwc1+69nKloktsLt3BY/qi+tr0vIIxEqD64HCYGEXSVctrV8iBxtA1Bilcf8Rr+/fBXgqkieqbY67rU1vqkdFY1/f84roCpSctVkZikx0fiOJx4qdy2+za2lLewSqxqZFTmK1GOdQwxvPraV5O9O8s7Tn5Hm9lb27lLPyn/2E7AzhKFiaQVwSU9g+JaGRkFKD2IiQwmNLYfSVra76hRjhmxxOqb9t5GpCOcpPnahVsuoaZrrMmtYu1Ad38Q6zOikLJ7J+7DEhfcOqdiNsbOHqN2e60hyVUyhzbTNgsiMlZm2pSG//W9v2ZTcRPL0st4yxGtY+yfN/+ZydpkyyRUJyCLZDJs9KGZY1QXov6ctD6r37vJclNeePhzSfcq0SmByilERiAKClnJWlWQttmITn1hymGZYG1AlRnBQbAmoBpV2xUmojUrpAIfOSAwewzG7e1ZcuvEHXznzh8z6OX5jyPPQpuYclSiFBZJu/ZemtC0yJI7QWbgiJGDOGXx43nM8sehpCKOY3bUdvDaH76WLdNbuPAlF5Lzc0gZEMUTLd/XpmKzKYnVwBev+iK37rqVsfQYP/rXH3GAfwDl++5B6wg1R2uCZhh0G7etjiF/juSBBhXkQVUxuohQtoT81evOY92OW9F4vOLYV/Texj8JfQcq1113HbfddhvHHHMM1113XdfP/b9kBOdISU4pdnoRGRdkTWM75XaCwlV+svq0vSa6whgI4UlV2O7X+L7rscPRUDKQ6XT96uWfSSANYgmQASGTdbChbGKMrlKNKuS9DMa4RH+wAYZzam9jp84IkcsHgSJ601yBSncPlU4Y8xfxtCXP5edbfsAvNp/bV6Cijea8jd8E4PTlL7MdcauCH/4ky0WXpPnK57fjunb1f+TAcVyy+y/cMHHNPzRQMbgI2U8WJwbm4EeIDEL4lswpAptdEfVsWZev1El2JqSZ3yedlTOfkUMELhSmbVYlk27NqID1Urlt921Wotz0PM639BOakEqtwp+2/Qm9TfPex753zu/MlH8qCOW29SUxJgYd43hLLC9Cg57WRNuiRlM5ITwOHDyWrJOhEBW5Y/IeDh2yJN7zE5O3U5c/vudYZUwJKXIdnVbrOHTQlpem7ppisjRJ5pEZnEUzQ6q3n9eSaRHCwZFDhPFWq9iZ5Ub7vDXPI+tmybt5Mu5MgHTx1ot5+UUvJ6VSHDt6bMO91ZIkR2aI1UkfmrlKP/a7HmZWy4YvPflj/ODmn/Gcg5865/cBBBEq6+EMZ6DqEE/HmEmDjjQyM/M8aaOp6RrDhw4zibUmkBlJblmO6XC6JVBpONU2z0zCQeU84mKMKQtEl5INwGRtmh+t/yUrs8t5x1GvR6MphxWbVU5KWaZqUMOq5/0XwnDMiiP5avZU1ODMPRzMDLK7tJutha185OKP8LFTPsZs47e6Zb5SM946b33kW9lT3sNLj34pa0fW2mc1CNDlaVvh7wONMi1uIyPSseOzMVy87WKOGD6iJXgxscHxXIQzRFjbgjERGya3sG7HrSiheMZB7Vm0Bwv6XmZfeOGFPPzhD2fXrl1ceOGFXHjhhYyPj3Peeec1/n3hhRfyl7/Mn3fwYEZGKUSg7EqhQ618BhIlfXzltvQyaYE2UNYwbWx2fgBeOAhvzuU5Iz8ARkDVsMGE/NEUievpOSFBjACLbJAicg1OAUDq7ojop9NUriySctNWDXBbGrMZSIE6eX5MWGNqgItcZldVenPvLEEv+/xuePGqV/O+Qz7Fuw/5WF+fv3zPRWwo3U1GZXn60n8FwFGG836c44abfC66ZGYFeOSgbRo320/FRAYzGWNq+76hpoVDNMtL5we3f5//+PvbuWzrpU0TvUmkxN1hSXoDbJy+m2pUxRjd0TulHS6zuQdCpPCy/46Tei5CrUYIS/yfniLpQaVasjvL8rYz9KbippZK0rwDlShk/eR6NJrR9CjjfVihzy7/zIbWRaTKIqWt/QtlG+jJbKLUSKBkiqNGrAPiut32OSiERf629TIAntLD5K3eCqHZ4K0TVmVXkXbSvPHnb2T3O3ZTvmzu0qcQWZTIo/V0ksJvDmQET135VE5ecnLLd05cfCKPHH8k5bjMmy57U4sCS8pMozRV70PTjzxeSq/NymAgyPOa417KoszczR+h7hGSsj2nUhJ33EWNqDZOUTEsknEzDB8+YwN/xG+OIOtniZPGjQ10CLaEcJGugxzQmNDYzFoX1L1UNhW3UI2rCATlqEzKSSfHnBCYg97vkSFGZR2Eq2bGCwNpP80nnmi7wH/3hu9y6cZLE+M3t+F7VbfMJ3EKBvAdn0896VMcPn64PScpcAZT6DicR0k1TqzzFZU9Fcp7Oj9vb7jsDbzory/ie3d9r/2PCoTMIFUerYv8/HbbJ+3Rq05irM/7/s/AvOoBsy/o7373O4rFuTtb/l9GSimCpPwj4u4PlBASgSJQfqO/SQM1A4UkY+JKGHdgsQNDEhEonpzOMpb1YEhAxfBtPcWnzQSvNju4yJTsZCJ8kMMdSU/uxRXS/zOFuKiGKzxqcYg6ZAD/axr3rQ4i1X+Wy97jIoIccnmSUt4atvXkaPlOl4aEvbA8vYqnL/1XXOlhjOGqPX+nGs8mec2gnk15xtLTyTp2AnEcePpT7PP3y9/OLEuOHOgcqFDWkJVQ1ZjSvm1caLRhKp7itZe9lXPvsDwaYww/vesnXLjpL7zhotfz7N8+k2/e8g12lHbRLZlpjGFrcRN/2Xg+X7rxy7zsz2fxhJ8/llv23kUnmXod9cnLru7az83L/hvBwLsagU4QQLkM5XLcJpldnktM3wqJ6VtMYkfe/zWre6jcMXUH0N0/pRNm1D/tdX+ERDmtcl4hBWrIkjybJ8nXHfp6vvWYr/GU5U9A6wIXbv07NV1jVXYFBw+0y0B1WaOLmrhYglKAKQbogm77qb8LSioOHjyYO5bYc6zePFPWKP2txMT/TrT8DuqB2CBCpBvB1kVbL2JPtTs/QArJpx/xaXJujut2X8eXbvlS5w/GCQG5n6y2UI2sWzWuzWOynIHt3t2aDhCuaMnmRTpCa81Qaghv0MNbbMeveDImcAMCacs/dZjIgMusQMU2AJQZbQPSHuZzo8EIOTeLNpoN0xupi/Xrpb9usuRm3LTzNj58yefYXNmJGnQwJTMTBDpw4soTOfMIy7N7+wVvpxJZZ9r682pMDSGzfPzST/H+v72/a4CvMh7SlZhqf9e+3uE4KggqskIlqnQM2h635HEAfOfO77QtmoWyz4dyBgHFL24/H3jwWebPxoLItHUs5OH+vwYlBDnHoeaB8kDEhq7jtXBQyk2auCXXpqTtAjcvbXCySNnJ0hHQ6NmQTPADCpMTHFp1yCHYSMRHzV7OMju53JS7Xm+zOElpbtcEbhpjIqRModZmcZ7Y2YSuO0pAGiEGrfOqC0RgdnTPqiwko9KMe0t389rrzuCUi47mrde/kp9vPpcdlW0z2zeGx4+dxgHZg3neipe2fLcuTb78yoBt2+3+D80fhRKKHdWtbKtsttuIbLNAOaSQi+2gZabjffYM31dYzytueQnX77mJb9z8daZqUwgh+MRJn+L5B76AnJtjS3ELX77xSzzz/Bfwtktex+VbL2p8/7odV/K6v5zBKT87kqf98gTefvGrOOeWLzFVm0JJh9X5w9sCijpCHTJZ3Us1riT8g7nPSUrrCzQ9rZmtt2+40xY2z5i+CTkvjkpsLJH29sLtABw2dljf3xXCdoVumL9hnwFtiig12CAotnwnLZBDrVmVExefyOOWPonh1P6A4vyNvwdsb5/Zk7kua3vZ0iBSEU5uAJmTiKxo+cEDU565vocNHcb65esBqN08MykUzi+w59N7KP21XZUmRZCQflNsLm7m3y/5dx7/m8ezfnp912uyLLOMDx73QQA+d9PnuHHPjW2fMXG3Pi6t0EZz1571/PDW83nbnz/AIV85iWf95CVcsfnaOb/b2FedLDpb7VP3UkuCuWKtyIA/QDoxMFv9odUccu4hpA9J40iHbJCl2myUGXcmuUoZgNCoAdkWkDbD2sqvBuDu6Q1kvRxZdyaYMqHpKEtuxteu+x7fuOFHfPqK/0VmpX0HKqalDPmeR7+HxdnFbJjcwCcv/WTD+K1umX/ODT/iC1d+ga9f+3Uu3Xhp52N1XUROoSt9vlcmQsQ+xVKR3KIc0pPE1fbvPm3l0xhPjbOjvINn/fFZnP7n0zn9z6fz9fVfb0w1xTDkjF+9lXuntpB2Ujx5//m1qfhHY15SkE6t7v9f4qR0Q0YpdgUKmdWwQ2O06tzPBRdXKpxEpixx7eJ2XEG6U0xYN3ur268LxLDDv27L85Ryhp+nS/zMFLiHkPebPZxGmjeKdsJUPVBR2wHpIqVDpKOk1fxUR41/J1hnzxgpxm3qUoE6LpkUepS9zHTCUZlHRqUZ2ytbGfMXsbO6nYt2/ZGLdlnZ6NrsoZw0+gSetuS5PGvZC3jm0ue3PW/Ll0Ucf2yFq68NOPfHOU7/l2mWLkmzNnsYt07fwF/uXscjM6swRQ1Zh2VLJKmsQToCvTuCKY3JyL7q+t2wbuIq3nr9K5mKJlicXsTnHv0F8l4egP0H9uetx76N1x15Fn/Z9Gd+dvdPuX7X9fx10x8JVJZHLHk0AJEJuXzb3wBQwuGAwYM4ePhIDhk8mIcvOpSc371sEsY1Ml6WalzBc5yupm+zEaSgXDBUKg5uU6KuUfqZSko/IUh3fjb6WmtCHTYClXllVIRAyixhNN14zYwuI2SAcjobtAkhUIMKM23QFY0MZt43KdM4YjH/ccSbOHxwLU9Y+sgW8zVTs4RcZ7FCpGsgsrjeYMd3JtodEe+cmRzecdQ7cHIOW3+6leqtVRssKNGi+OkEKXMYY3jfNe+jFJU4dPTQRkO+bnj26mfzp81/4rcbf8sbL3sj5z/5/FYvGEPP5/iCuy/gezd8j2u2XsNEZaLlb3ft2YCn+ueyGVNFqFRbXyrhCoQrMJGhZmooqRgIBhrv7chTWlUuaSfNbnbb7Iywrq3K6yTbdwFLmJaDEr1XowY6jzdrcqu5bvdN3D11b9t4YSJj3We7YGthO7+56wIAXnnMS5ApicxL4h0xckA2Jvq8n+fjp3ycl/ziJXzt2q/x1AOewGFDoxhT5Zd3/o0PXPRRAN550js5aeVJHfclhINKu8STMabmzGnUZ4gIp0ENKUYWj1Ar1wgnQpxU6zTuKY9XrH0FZ19/dgvRe01qTeP5CHXI5ZuvBuBpBz6RjNedNP5gwLwCFWMML33pS/F9G0VXKhVe/epXk8m0nuTPfvazfXeEDwLUzd/CnESZGB23mfslcJBC4KsUxbCAW3FgQEK30ovRMHt16AkYUWS2wwvDHM9wM/zEFPgxBX5HiaebLGtmsb7NIvv2yO0xFSPhg9NUvQLBS0ZgWQbr2tWbsWUzC9MIRqibxQGkPtldHtn4zFeX2tLWHC9aN5wwcjK/PfEK7ijczCW7/sIlu/7CzVPruKNwC3cUbuGIgWNYkV7dNSh+xlMLXH1twA9+mOemm32+8eXtHD/0SFzp8vWvL+IzV88Yfq1cHXHur3YQBBK5yEXviTCTGnxhO2bPE3/Y9ks+cMvbCE2NQ7NH8KnHvo+xzJq2zwVOwFNWP5XTVj6ee6Y28qsNf+YJK2ccLg8ZPpJ3PexsDhk+kgMGD8ZXid+DMQmRtjupMzQ18nKQalTBGIk1Le6tjgBrYAgwPS3J5mYEZ4eNHcbFL7uYpbmliJ22SZtCzctGPzYxYS3k9un5BypA0h+l7k8hMYQ4zpKuUmGw9uRySBLtiBqt7C/eejGX7biMp658KocNPYzXDx6BjieJzRTCACaFLgucYQeZkWhdQ6mxroG99CRRU1+hnJvDHGAQKYEpGcINIe5ql/Bue628A7srOn636Xf8afOfcKXL2Q8/e85uz0IIPvKwj3DVrqu4e+pufr/p9zxr9bMafzfYxoHlsMxVW67isk2X8fzDns+qQRsAbS1s5c/rLZk4cAKOGj+Y4xYfxfFLj+G4xUcyGPR26W2GNjGuaufwCGXN4EzVUDIlRtOjBG73ZzdwA2vOFlVJuSkMpvOsJBwQ1lhN5SWmYNBV3VFh1Oj5M72h5fdzyZIBvn3DD4l0zAlLj+LIRZbjpPIKPanbXHlPWXMKzz742dy9925SbhYhFX/ZcBFv/8tHANuO4qyHndV1X0I4yMAnzlTQ07pjgNYMXdRUZcSSZaMMBAPsze6lsLtAqoN186sOfhWHDB3SaMEQhzErUysbgVbaTfO5Uz+HIwyPW3Vy2/cfbJhXoPKSl7yk5d8vfOEL9+nBPFihhCDvOGwLIqQLcWhwOzzsQkiM0XgqoDA1AX5iK991wuhic5uRMKRhlyavJC9XA+zVmiyCoQ6pnHBU4wkQNUPxjiLibxWEBvESgRB5tCn0MXGVgJQt+cwzSyaEgNz9s64VQnBQ7nAOyh3OK/Z7A3tqu7hs99+4bPdfOX7oUT2/+/jHlPjlbyrcdbdLJmOzO2848D8BeN7nFiNyMQgolSX3bXD4wbeyvPzVBYQjkKMOxo0xe2JMrBE9Vluzcc6GL/Kluy2x7rHDT+YDh36MVGqSTu3bZxCxZuBg3nrco1t+m3VzPPfAdldIIURPbooNZLAZFV2hqqt4DYny3K93KiUoFBWVCqSS3aTcFGuGbLAVeRE61o3GhP0EQGClyZOVSRanFrO5vJn9h7pLgTtBSt+Wf+KylWSqXA9H3hmoAWV5JEWDyAq+d9f3+P2m35P38hw2dBhSBEgnQOo8Wk8STU/astFgDozl6yjVYz+uvSf1zAnYydk72KN6XZXqTVWEI2ypwBc4Kzrfg6naFP91zX8B8OpDXs3agc6tBWZjyB/iv0/4b4pRkVNXnApAJapwza5ruGzjZVyx7gqu235dI6hckl3Ciwftc/XYVY/l/Y99P8cvOZ5Dxw5FxFutT46cQ4E2C8aECKm6mryJlKA8USbIBwzM0aJACknez7OjuIPACWzW3u00trqJ90uE9Pyelv71nj+TtdZW8XPJkou1Et+/2S6yX37kvzYy3SItkBnZ8XsfO+VjBE6AEpIr7juf113wASId8ZyDn8N/Pea/5nxXhPBR6TLxNC3P1GzoMKJSq5JeuoyRAZuVSmfTTKrJRrfnZiipeMySx8x8v6jts5uY1QVOwHMPfW7PY3swYV6ByjnnnPNAHceDHmmlkIFEZjS1KQ3ZThOzDUo8PJzYIc4bjDLUtCbdzXq8C++AAQVVYCqGQcVbegQQNRmSGVGwK8b57k60hvAQiVnuIEhjpbBlrBlcO9pKPp0+U9ULyjgsFMPeKE9d8i88dcncDbJ8H77y+XY3RoAfnrMVqhq51OV3f8jw3rcP8a2vZnnWv5YYHtFWzjrkYFyJ3h1hpmPIyA6NuzrsNxngz1z5Ks5a/A5koIEpbJDQ7VpppOzfN2EuhLqGqzzSToaaW2VPuYgn203fOsEYg1QGjGRqaiZQaYZQwnI3hOWqaKNRfZQRYx0zpIb406l/wiw1uLPKCtpo9pb3kvNzeF18JKTKEUZTCOminKEewV/T8TqWWBttjjCx4ZiRY/j9pt9z9rqzWZJawtNWPg0llQ2CSh6On0WMFjEUraeFM9ZWzmjZvptkDKKZSeWTN3ySoewQj+fxVG+uNqS57v5u14nn49d/nB3lHeyX24+zDuu+6u6EZlXQ1Tuv5oy/nEFVt3LRlmSX8KgVj2oJEFcNruJVx76q8e8wdjGmO4G9G4yuJdbwXa6TY4Onpamlbfe9E1JOygbCtQilVMfylRAKIdxEUu2j8hJdlJiSaZHQAzx68SO44dl/JeOmE1M7YReRc8iSf3zbr5mqTrN6YAVPWH2CdfAlKSuOzZCPm5H1bFA7XZ3mVb99B5WoymNXP5ZPP/nTc2bIwKqv4pSVc5tK+7nUERbKmJxkfMnSBjE4lU4hfNExUGlDHz2gHsyYn13p/4+RkpJASibzMiGWdhqsJSCQZYE7EFD2q5hYIoUgNBp39oMrDCCpRGVCXSPnNa0+pIBhZRVDpd4rfWNixBIXdsXoP00AUHiiSxgWGXEzSDGINtvoFKjYkk8BwTDNJZ864hsqlN+5DTnmkP52u1mX3hFR/fwu5LiD/4bRrsf4z0KhMEmcNQwHY5z69DLnfjvDLTd5nPvtDK97y8yKS2Ql0nXQu2PLucnLOVdDL1jxcg7JHcExQw/HTMVJLaU3mdVyd/fda1fTNVKOJSamnDQYhQYk/RD0LEcqSEmmCzAwYNVAAD++5cdcuvFSnrnymTyCR6CEokbNBip96F9DEzacPDtNVtWoiitdwjjsHqiIACkDpMp3JNB2g8xZXoGe1hw1MuOj84kbPsEzVlmvCFOz5nHu0hwyO4DRZeJ4GuX0liQ3Shtl07C02VzczO8O/R25E3P8y9P+hekf2+eqW9nnml3X8P27rFX9R47/CIGaX0ajGWsH1hKakPFgnEeOPpITDz6RE1eeyKqBVXOv5qWP1vNvQWGIUCrTdfslSqTcVN+8B9/xSTkpqsUqaS/d3bBZpNBJOxGhBM6wItwWtXGSfOXjJ4+oNtNJHyt7X7vJkmMd883rrVrv5Uc935qqNY3XzdvvhJyf46OP/yDfuv57fO3pX+v6TLefk2NVTTlBVNQdM5a6pCmLAsOjixgIZjiKnvJwcg7h7hB/jna4Ju6jB9SDGP93j/wfDCkEA46DyYJRomNbd5CYigZXkBrOUtQRKSXJSEV1tlTIGDCCUlQm1jGOcKjFsxQ6CV+FWNiABbjV1Hiv3sW9iRQuikOUdPFevxT/P1Y2vuc+YYA9UZkJXSM2KcDvsnoqAUHXko8YUjCt0Vs66/3Njoj4byWiSx4sPXdm8PnbP8op1xzDT3daPwEp4S3/OcWb3znJq86abvu88CVyLCG1daFjXLH7Yiqx9S8QQnDM0EyfDuHUy3ydsxm2JCTYJx0eE0Q6JJOoGgKVwnMDarG2/Kc5YUMa15XEEUw3XZJLN17Kj27+Edftug6BaNjo9+ulEkYhSquOaXywK+6Ml+nJexHSQ7mjXQm0Xb8nErmygMOzhzd+f8LYCbZsow1xKUaNKqvsEQKp0rjeor7KICLV+v4fOngody+5mwsPvBBnzGnp8dMJB+QP4AX7v4B/3e9fOXHxifM6t9nIe3kufvrFXP7Ey/nsCZ/lzCPPZPVgdz5Xy3kIh9meO3Oh3hFYdLlOsY6JZMRQbggZ9ze9CCHI+3lqtVrn7sz1z0m30fXZGMOUnKY2ULNk6A7eSMbYPkQYPacsuRrXeOJ+j2F5bgnPPfgp1PtHzQdPP/hf+MnzftZQOPUF4QACkbJlKVNpPQ8TGirVCt6IYmRoacu1cZWLm3YbCrs5dzVXa4UHMR4KVOaBtFK4aUnsC6i0D9hGC6gJ5KCClIcEssIhKxUSQdQyyGvKYQmJy3hmKRkvTyXqYOCTkTAsrAeLNvzYTHMVVc41dlap6Rq+k8I7egh9nw1EnJMHyQzn8NBMxzGTxqBNDmgNVGZKPiNdSz5isWOTBBWD2d0+qN1fafIDiXG5GI3mxr0zsstjjq/xwpcX8boseIQrwBXQIRC9c/pW3nT9yzjzitPYW9vd8jdDvRSgegQJMQ0zhn2AulLCV3Y1paQi5+aT56z/QAUUqRRMTUE1iZXrXipbilssAVGLvhsT1j1UnnPFc3jWb57F3Xvvbjtugc20zCUPVyrXVZbdCzJtibXpWpoTxk5ACskrDrb24Hpao3IKNdTbnbTrtj1pSZ8JDhtqtdIf+9gYy36xjOwzOnNdBrwBzn742XzihE/Me9+dsDyzHGFsI715QageTtudYXQNIfyu/JRCrUA+kyeTynRZzHVG4AQ4xiF0ewSuwk2cXzVT1WlSboooHSKHJXE5btnfeXf/gjP/ehY/3fAnjJDoWtxTlpx2U7z3pLdw0Yt+ScrxW20j5oH58/ucpKylLUm4KeAyxhCXIsJMjaHhQVKJkrAOKSSZbIbIieb2YhG9FWEPdjwUqMwDgZSkXUUtIzFRhwG7BKQlJm2IcBhy0igT4UnVklUxxlAMJ5HKZySznLSbJe3mkEjiThLQAWV9WKY1ZwqbwvwbZTaakFhHpFQaExmiP1jTKOepI7jSQwlJWkoKOmbSuGgzY6I1U/IZpFPJpw7hCsR4IuPs0PNnps/PgytQMaHhyLzt93LDrms6TrBhCFu3dDjuoD1QiXTIB259K5EJ2S9zIIPu8KwvCVuuw6F7kBAlA+C+CVRqcRVP+XhyZtKwDpwe2vQhJTbaro6FxPPs9aj7NzZ7qQglENoOcv0EKtWoyu7ibu4s3sm1O65lOGi9VpWoYksD7gMriXQGHYQv+PLDvsyfn/JnDhs6DF3UCFegRtXCV5gJKbHuFXLokFU05W/Js/WzW6muq+If4rfY6QPsre5tCcz64TD0CxMbpDu/7dlJ0szLS8gQopo6AjdDG43BMJwexkk78wpUPMcj7aSpie5dkuuE2unqBL7jMZoetcFKNkQNKWuxn2QWNhU3ccXO67lp710IXExU69otuRnWZt8G8P1wou4vhHCsWRyx7VXlikbzRVMylEWZ7GiOnJ/vGLCn/TRxurfbduP+PriG6HnhoUBlHrDlH0UYALPKP6ZmH2454FMWMVnpMubnCbV98TJSIRCEOqYYTuNLj5FgCamkJh6oFCkvSyXqUEKRAgYlSMn+2uWRBBjgB2YKKRw8FWAmI+ShacRKH/WIPI7ycKWLNiFpKSkah0njo03dSbh3yadl98tttkVvap/8ZlxpH2SPUllz4PihBCrFdDjJ+sk7W/58520Opz9tjLe8eph4VqJI+qJlxQxwzoYvcfv0zQw4g7zz4I+0XDPbY4ZkIHDpylExMeDvM++hUNdIqXSjhwlA4KTwnHR7GbEjEh+f+ncD2LsXtm2DjLZeKvdObGKqCFOThmLBMDmtKRZp+SmVWn92Tha4YfttACzNLmUo1er9U42q5P28rbEnnj/3B5Wo0nGyFZ5ADSsGzAD7ZfezJYLIoMbUnJyDXmj2CgGrxFmaXsrjbnoc5S+XKf2l/R0OdcgZfzmDF/31RWwrbWv7+z7BvOPfpDdUn+UfS0yVXcs+laiCr3zSbhoZyJ5u1u3bNmS8DLHobsIohKISRQhixjLjpN00OS9PVddwhhUqp9DTluexJmctCe6evhe0gxFR17LPN9b9gMs3XzOzX9Pu1vxAwvb0iuxzlbP8JxMawrCGHFaM5AZQ0u+Y9Xali0zJ3sFmwu1/qPTz/yPkXIXjCyJvpvxjjMGUYhj0iH0JOmbQcck6mUY/DV8qUkKwpzZB2skwHIzgOTMrEyEEWTdPNLv3RR2edcUkNJxRz6qYCjulxFUeYthBHZ8neN9qhCNwpIPrBIRxDSmEzayYNBMmItYl5ir5NEMsSwKVLR0yKj1KP5EOKYRT/zAH4/pq34TW7tobDDh89BgArt91dctnxxbF7N6luOM2l9/+YhZR0xENLgPA7dM3840Nnwfg7Qd9kNHZ5mv1CooQyMaKrBPirinzhUCbmNSsergUkrw7SKj7DVRmamBBYHk8hSJktc2obC1sZtcew64dsGOnYMtWzaZNNH42bmz9WX9vyB0bprl+sy33rM4eysaNsHMnTEzAxFRErSpxSeNI934HKtpoCrUChVqh499lXiLT1rG2mZdyfyBUwnVoeh0OGzqsYaU/+a1JCr9vPZ6v3PoVbpm4hRv33NiXamqhxzWvzwvHKlv65B1Za3iv6zNcjaoM+ANIYTMDfRgkzyCGwA/wfb/VqbYJlahCZFxG0oMNHkjatYqhWGicEduTLZ4K2S8pXd41dReEAcLTHQOVLdPb+Oiln+X0n/8bd+yxz6zBdFc0PQCwKkB7D1RGgoS4EFNJVxgaGSJwvIbqaTY85eGnfSIRdc2qNGTP/4czKg+pfuaJjCvJuA7VwOCUbSt4ihqRUogBl4KWDEpDWjrUlIeSLlEcWnmcLpF18uSDMRxRhlkPXsrJ4KsU1bhM4MwiZEkBaQF7DQcGHieYgCtEhZ+IGsclwY535qLW7ak0xdpU8nVBRqQpmRQwQcosJpAp+ilbymVJ6adDF+VG5+QOrrSlqICSLqGp4vUwLNsXiHSNqXCKQW8YUTYwKBGB5KjR47l6+6XcsOtqnnPAmY3PDw4ZXvHaaT738QG+9Jk8TzytQipd79wq7E8EoVPjA7e8ldhEPG7sVJ60qEOHUY0l0SroFfsbA3IfrdRiHaOk01L2qSNwc0ihiOIQp6c8VCeB1QzqEuX9g6UIBFVdIcztYTgcQXiCrB8x2ONWTtZKpESVzbHNYB04eChhCJUKaA2lqIKvUmTKAY4j2FJwqcYVMgtp8A3U4pDYpKnpKnm3k8eLwJQc2BriDCpSRuEVBI4DSoHr9rA56gGZkkRTMwHWEcNHcO2BM1yo8kVlsqdajsodk3fw+ZtsoPv+497PWGrfNn8zsW0PMd/RXAjVMFHr5xIYU0Wp8Y4lkUhHljORKH2ka92eTWT64kaYyOD6Ltl0lolwotVxF6jFNcphmcW5ZQRihvXtOwEpN0U1qpBxMzhjitq2Imu0lWXvrOxkulQmN5r43xjDHXvu5qL7LufijZdz+eZriXTMI5Ydx0EjByRb1f/gjIol1II1o5NZSblQJj2SZjAYxJhKwtVqv46ucvF8j7Jf7i5TTiyV/i9nVB4KVOYJxxHklMMONyTrCKvy0QY57FFSBt945FTC+lcevgooR0W00Yx5w0ROngrgGY2cFbUrqcj7g+wsbW0PVACCGX+MF5DlCir8hQL36QorO6RjXeUjUWitkVIipSStB6hiKOk0LiEZYgIUnugux5UH+KgTUsiD2ydFM9U5o1KOSpbkK3yK0RTePE2l5otQRzhCEddCHMdBJgZ0R44eD8D1O69q+87zX1TkJ9/PsHmTw3e/meHfzrKrYOEkhNqa4dubv8ydhVsZcId4x0Ef7nyNdNKwTAJGYky75YKVHcI+46foGq70OsogAyeDJ9PU4vIcgQp0W2a5ymM0tYid5W3sjLYzrIcBSdgj+xHrmKnaBK4KuLN4KwCHjB5KuvlRrtYYS43hKUEcgy+ti/NCk26hjvCkh5CtXXKbYQKBGXEoe4LSXoHWNjipByueZ7NJrguZjM0qzYXZzffecNgb4GBY/ynbr6e+eo91zNuveDs1XeMJS5/As1Y9a2En2gv1jsMLmIiE8DG6czaqGUbXQDhI1VnRUokqpN10I8Col8d0qFFOH+07YoP0Jfl0nr0Te1tkupGOKNQKLMosYtDPUKkUG6aKQggG/Dybp7eQcZNy35Ahv3sJi4JFbK9sZ31xE0ctW4TWEU867wXcueeeln0vzy3hvSe9ZdaF+QcWG5JApd7qRAwIYj9m8cBiXOUShgVUDwl7xstQSBcwe7q8RJqu6rt+8GDo6fdQoDJPCAFZV7IzFMS+QE5GiDGPMA2R0Sx2UjhmJnWZdjOUwgJDwQhDwQhVY9gaVtAYZIcUcNrJ4kqvQZRsgZus9EPDGgFPFj5He2Ms7pKmdJWHo1xCXWuYk0mZJWVSGCWpGZiIIwQRKSlJC4dASNSsl9Q5PoVzfGcfi+CTi619ftOLEOuYSNcYTy1FIiiEk307mi4UsYlwlU88FeOMeIiEg3Dk2HGctvo5HD32sLZj8Dw4621TvOtNw3zn61me/bwSY+NJGjwQUNY8Y8np3DR5HU9d8lxG/C4rYQ0kXWuN6TYoJ4qffbRSC3WV4WC0IyFTCpeMO8Duym4g3/7lBMYIemWAvvWkXzEUjOAVXPT2CCVUZ7J3gnJcohaVCVSWe8q2DHLQ4EwzwlBbKX3GS1N3C88rjyKaYIEVsVotZDAYQAmHHaUtBH6niVRAqvW+GANxDFEEpbKVZhsDixfD4GAfO7atZxppdSEEOOAf41O9rkr22Tab8o3bv8G63evIuTk++rCPPiDvQKOz7wJS+0J46DmaTdqGkGWUM9pVvl2LaoxnxmdK2VIgPWmbPfYBExnkgCTlpAicoEG41kYzVZ1iJDXCSHoEjLbGbyZslKACJ4WvPKpxDVfEqFwKqfKsyaxhe2U7G6tbOSZYDsTsP7iajVObecSy43n0ikdw8ooTOHB4Tdt9Ef/AOoklNTtgYqo6pkSJwYGBpmaKpmeJ3nd8jGtansdm1IPAhaAaVdFG4zv7rmS9EDwUqCwAWU/iFh3CrCGIBXLQYdrEDEuXjPCJm0hkKSfNeGYJWdem7gIMKakoRzG5Di+DqzyyXp7Jyp72QMUT9qemCZ0ab/UWMeR1TyNLIUk7GaZqe/FpGmCEQmAd/n0UsTFUjKEU13ClJCulzbJ0c9NtQif7/FI8TdYbIOtkiU2M5wTUdLXRv+aBgDEaJ1KEMmxkUwDy3iAfftT/dP3eE0+rcO63a9xwnceXP5vjfR+dBCyhNjaG8WAxnznqnN4TjGbmTRKikWJu/U7d0n7fvHLGaALVOXgUQhE4GaTYTaRjnC73UQhDr9ltccYSao2KMcIghCSms42+MYap6iRSuhTiaR4+cBIbo3tZnlvd+Ew1rpBy0nhNzq9OYzW5sEDWtqzwCVQKV/mdA/wOqGdUnKbbUS5bHk022/r7jt/3EkJt2DoxjH15jHB7SHBwwPrp9Xzqxk8B8J5j3sPi9OJ5n19fiK2x3kKun5BOR8fVZhhTQUgf5XQOemtxDU95bf4h9fJYPwaBRtuyhc0qz1jqT1QmGPAHGMuM2aBcSKR0E/Wivc+ucsl6OfZW9uI4BqXGUIMeD1/+cC7bfRlHLDoS6fmYuMQHH/MfDAYD+F0M2Wz2QPxDMypCOGgjKFT34jgZxtPj5IN8Q4oNnfkpdXjKQwUK7WpETSBm95YzCyv7VKIKlajC4uxiBoPBeX9/X+IhMu0C4PuCNA5hWiAXeZQdQyAkg47bVtv0lEfOy7eQZvNSAYK4i4dB2rVk2Vh3WOlkrGWyMQbfmZmouqXnPCdoEHq7QSVk20zSpW5PHLFLh1Rn7d8UtC119UA1ruBIjwF/yBpvCUXGyfRJ7lw4hABRFuisaWRT+v3em99pg5Otmx2iJGFwX2VDg1A75wSgm+vwMvmZbfAXg3D3ieSxnpnoNSH7Tg5feYRdiIlWwdGrD1UTpAAESgi0MR0lyuW4RCUqEcgUg2aQTx/6dX7ylL+2ZHxiHba8CwCOdFHSIZ5jVd/5HOyk4krXvmdunnK8cOPBILBcmsLclRDbesG3PKY63n3Vuznij0fwO+93AJSiEsszyzlp0Umcvub0eR9PrLsrYJphYtOz0V4vzJU5MEZjTIhSQ10ny3JYJutl28qQ0pP9E2qbOj9nvAwCwd7KXrJulkXZRQ3beACl0g2bhToyXgajaxhUQz79+pNfzw+f+EMWjy22LROIWZQZ6xqkWMQg5D8so2KMsWTwsEbWS7Est8zKu5PzNSZEyvZ5pRmudPFdnzgTd/RTmfF46h/lsEwlrLAku4Th1PADmg3vBw8FKguA44CPxFGSmgehMYwoL+F5yDnr7SkBgfToNqQGKkXazVLpNOh6gsiEOCg85WOM4dfhLk4v38LmDsGAJz2kcHum7OsQQuBLQU4pImPYq8OGSV35ndsonraB6OKZYzJFTfnd26h8amejD0wtLjPoDxMoH1PTxHtCAlJg+vPgWAhiHUMokK7EZNsvvjaauyZu48KNv+/4/SOPCfnuT3fypW/txnHg5t3r+Nc/Po4PrH87Ua2/jsGtgUond9oI5rC57hdhXMNXPq7s0UlY+qTdVEMe3w6dHGf3Afnm3et4/+Vv5iu3fcouMLWw7rQdVE3TtWkQiQ9FCKRbzbXCuIYjvbYskCOdRPnTf2fmOiIdJsohex2yXh6JXLCKSAgbrExMWE+ZuSBTssWiwJMelbjCLRO3AFYJdP6p5/O5R31uQQP9VHWKYlic+4MLXDEDSeZAdl3MGF1EygyyQ6dkSEzJTEzO79BJ2RUgmdM1tV66qj8vQUKQTTkpFucWtwdA0m873sAJ8CTUjNfo1ZTyUpx0xEkMjg32r+Ix2gYpD5AyqxmVqMLe8l5c6bJ8cD/GM6MEbuv7YUyEEA5Sdg9UlFT4yid24+Q7raZxgvkpfsphmVpcY2neWgv8s4MUeChQWRCUAg/b+2daRwxI6z5rIZOUencIockpD2MUupP/gxBkvYHOKypPEKoQz6RwpHVqvCDey3pT4Zyw3Z9BJavNMO5upNQJaSmoGs1eXSM2ukGWbVb+mN0R8UUloj8WEFJQigqknRy5JCNkIoPMKJyyg6M8al0nzfsHTYSsKfyBFKZDLLBxej2nn/8E/vPvr+3qL3LoESFC2IzQ+y9/M7GJCanh6LlLNQbT9CbVyQKzB36N7BBYLCR4C02NjNPZdKsOIVx86aGkQ9QpM9fwUOk+BOyp7OLX9/yIi7b9CaRAaIk2MfGsY67EFUpRgSAhWk7W9ra5pFbiCmk30z7pCIkrvQVlVCIT2UAnWW0GKiDr5SlHfUzuXeD7NqvS3E6gG2YTamc71NaPaTSYfw8sbTRKqp4tBmYOZOGuo/VeM528VIwJMRiUM9g1E1iJKqScFCm3vQzZ8JsJ5whUEmVQ/RykkIxnxlmaW9qm/rHHbAlCzWOjQDPgDxKabo2CnMSJt3cQa5KMygM5NUY6Ym95L1EcsTi7mBUDK8j7I3Sqwdls1tyW/GkvTeRaL5aW9h91M+w+A5VSWCKMQ5bmlv7Tyz3NeChQWQCktMFETjrkpGJIuU2Thn0qepVbjNGkhEtaOpS6DNApJ4OnAqrxrP48ShAHMSk9MzC80l0CwG+iXWydlVURQpByMvNeZYqkHFTSmkkdIhKJcrOXSrM0OdRVhBAMpYZn0v3aIDyJk3HJ1jKEev6dWvtBFMa4yiEYTHcst6/MrWHQH6amq9y+96au25mqTfDxyz/EPZN3MOSN8bbDPwR9mVbVXWmxA3qHfj+WuNo6iIZxjb2V3e33uAeMMWCsLLM3VIO7Ueu0faMTPk33EWxJxnpRbCttAgnK1FferedWCgtoE+NIFx1qTr/pSTzxgmO4Z/KOxjFrE5F1O6/KUyq14IxKSqVby6rewLx6Es1GPasyOdlHVsXDEhijVofay3dczldu+UrPd06XNLqku5qihXGIIxx86VONupdNG9mKBScAVDKBt49DWpeQagAhu0+UlahCzs+1lGbqkI60Wc453iETG/tZZ2Y6yniZjsEP1B1q3ZbyTxwXyQSjuCpLGLffuLqr7VyBis2oOA9YFqEUlijUCgwFQ6wcXMloZhRHJmRaZIf7YJAdLAhmw1OelYSnRaufik58f/rIuBVrRWIdszS/lIFgfv21Hmg8FKgsAErZYCVlHBY5Pn6TnlGILhyFFsQo6TOoPGJjOmZVlFTkvAGqUeskE+sImfbwmoy6jlJZHiZzxNAxq+Ipv0HwnA+kEKSkZDqOqS61D7puzqg02edXohKD3lBLat9oEIHEGXEJZIAIu/Bu7ifiYoibC1CpGWJmM4QQHNWQKV/d9v0f3fEtzvzdqTz+J4fzy3u/BUD6wi/hmJE5r1mrK20dDrSkX+MkgJkl4Y5LDPrDVKJS3/cm1DVc5c1JGLUGUYKMmyXuGARorHSlO+qBylRtkqIu2EFPtN7DWhwyHU43iNL3Tt3NnnAXpajI0swK+xldxVU+gdN54nGUOyehsxOsGqE1YEs5adJuhnInh+c+EQS259FcWZXZDrUH5A9o/O3s689mS2lLx++Z2DRMCXVBE0/E6IJucM/q5yalJO2muxqgAZZSobr3sJkLQkgEbpvpm9ZVhHBQzkDXSbves6lXKwSZkn1lVObjFFxXydSDjnrwnPbHyfk5yh16plkpcwBmruhTt/lb7SvU4hq1qMay3DKW5Ja0ZItsecdtCaSMMdbqoA+lYN3lOQ5aex71qwgr1AoYY1iaW0re764U/GfhoUBlAVDK/ggjCNoUFfUeEb0yKjFCeGSkIi0U5S6rv7SbxZ1VtqnFVbxUgON5LQ/kv3nWMvqX0S5un8VtcZSHIxbGA1BCECjJ9GL7qJjNTS9SEqjoAUPgpMl5s6JwY6xMMeMQjGXxKj61fUyqNZGxGaqhNEo6KKk6rqaPHLOByl82ns/n132U6cQID2BneRu37b0Rg2FpcACpiz7N5j8+l7POGqdYkr1r7E2utDOYbUverviJdIRAkPcH8J1U3xNrTdfwVdCTnzJzDBJPuciO7q/NUqXOyLhZ8t4gANuiLaCT7EgTQ7Icl4jiasMn5+o9fwfgqLHjG4FJNa6QcbJdj9mVLpiF+TXMXslLIcl7g0S6Nu/thXqmQ3g/WRUhk0aAyaX1lMexo7a/1JsPfzMrsys7fs9UDCIlcFe4uKtcnCWO7cgcGvSkJp6MCUshvvStrXw3t2qSicjh/rmOJr1mGts0Bq3LSDXUs5t0OSxbLkmXzAdgZbFzJLfmK58VQrQQarUuIWUGpdLk/TyRjjpeLykDzBwHY0znEu39RZ00O5oZJe/n24I/KevBV/N9iJKgbO7jcaWLq1zLU5HMZLHiJKDukSGaqk4hECzNL+3INXow4KFAZQGoByqze8RY1MlpvTIH1gtACsGAcom07vhiecon7WRbuiqHOiSTySF8BbWZl+5oleWJaggNfLx2X0uWpmGnv4BABcARolH6MXtjTEk3/h+AQcGgP9w2acTGUEv4Ou6gR2YgRzS1j8s/FQNpcNIeSiikUB19IRoZlV1X8+1bvsh1O69o/O3UVc/mw4/6Ar9/1jX8+jl/45w3v5CBwZhbbvJ4w/sWU5icQ5rccKW1sMS9pvtpYqh7JSQoRyXSbpasm2M4GCUytb7KFZEOyTT8FbrDZlQUvnIInFSH8lJ/A/KSRKK8Nd5sje2EaBxns8Eb2OzSVdOXAvCwRSfa3xmbJeh1zEo4lkvTTyPFBJGOUNJpEGmbkXGzncumPVCOypTCAtOhVYD1m1WRQSuh9r8f8d986cQv8YbD39D1O6ZmUHnbFFGmJGpQ4S63QYu73EWNKGIT45Qc3KKLX/WpVLucS5SYvcmFlyrsczDz7BldRqqgqxy5jmpcZTAY7Nlcsd4oca6gcb4ZISkDTBLAaV3DdYcQwmagUk6KStR+veoTfs9jEfBATItT1SmybrangsaeU1PGeh6Bis2eZgidEBGIhvrHxF3cahMUagUc4bA0t5SsN/e48s/CQ4HKAiClDVR0h3lFNOoAc63m7MyWkcr6qnSZpLJJa+9Yx41Uq+ekEBkJs3o7vMlbThrJjbrItbPcJlMq3ZfyByx3olidohyWCGO7MvXzCj1gH5fqRpsVqWdUvJEUaWcm/VvVmr1xyLaoyjZTpao1wpGkF+cRUhKWFxYwzYaJDVrHiEGFo9wkUJEdiZmHjxzDMWMnsDSzgqeveR4jwYz/zP6DB3Ha6mczlvhcHHhQxFe+vZuBQc1Ntwa8+V3jlEpdXvZmV9oE9hlovjcRzT11LIciZsAbtMRpN0fGyVEMe+titdFIIfH78AmZ4UrFZN0cuu3eG/p5/RenbaCyvbrFqksQjVJS3eCtbiaoazHXTF8OwMMXnwTYbIrvBB0dY+uoK3/6fT6hrvhxOmZpHOkw4A1SidtLAJ1QicpEusZYehFKOI2FQT2rUuvBAZ89CeyX24+nrnxq18nbRInCZbbXBXblK3MSZ8xBrVRk1mRILU2RzWSpTFWI9yYloubUvjb3X0wmnEal0pjYdkh2hnuWHOocml7ZlPo54dC1k7JJbJzn65wqEyGB1hWkTCGlHX+UVAz4A90DFaFo0ZS3HQ89eVsLQSWqWIJwdrwjl6eO2WomYyKkDPrmywROgEYjs3KGp6K7B4FWpVljPDveaH3wYMVDhm8LhOvaFVcn2BReb4VL/WVQQjCgHLZHVaZi3TK9SQRID2TAZFjAlQ6u8vFVgAgMWoS2vJI8yOPS453+SsaFx3Gz5ISz7fS7oRKWiLUm7w8TmpBaVKYWW3fC3ONd4giMF+EYTTRhzzE1msMAZR1R1JqCjoi1xpUQSsNUHDImfdLZDP5omtq2Mo7v3K9VoD1Yg/Y1Ku0lhDSBK1yqHcpLrvL4+hN/1vem1x4S8cVzdvOaF49w/c0BH/74MB/9wO72Dza50s5gto2+ThqPJYcdlUm7GdJJbV8KyWAwTLFQINJR18Es1DU85Xfs7zMbMwFziK+CRs+puqW+MW3Emo5YnPBUJqI9GOxzGyVBc93grX7ud07cwlQ0ScbJcsjwUYDlpwwHoy0dnmdDComnfEr9SHETRCYi42S7BgQZL4dT3U0YW05PN1TjCjVdZTy9hEF/CCkU24ubcaVHECgmJqyvyvBwlw242FR7vz1tKgaZlj19T4wxSCnxMh6u6zKUHWJy6yQCgSgIdEnPTEDxTNZioRCoRuXSEmizSNl7dV2JKmS8zJxBc13NYyLTkRI1W/HT9zELF3CI4yK+v6xFvpvxMsiSbH+XGiTcqGOWotHmYh8GKrGOKYUllmSXtBnizcbsYzImQnUxdewEV7lIZGNNZIzBCNP1NS+FJbJu9kGdSanjoUBlgXDdbqUfAKdr6cdGzK2TRF46eK5EY9AG+19s6SQyGh0MsblwH5U4ZEl6EVJIjGcVNYTGutUmOM0Z6Xy8Hez0Z6NUKyCFZCS9iIxnA51IR0RxjVDXqLyxTC0sM6kLlMslBt8RMPKOVSg3zdaoSsVoBBBIiTIKHE3sKCZ1RFY7pKQiPzrMjumNUNRtjrbzgdEGExniYXDUjETVlR6l+0GkbMYhh4V84Us7+cB7hnj1Kyc7f6gj1aN14rCKn5m0c6irjKTGWibZtJNhwBtioraHAW+o465qcY28N9Bz0m+GEB7GVPBUhpSbplgrkk0CFSuhn3uCe82Rb+eNx7wbr+IRbwkTn5KQSlymEpVIOTOD3JWTlp9y7PgjcKRjS0SGlmxbNwQyYFp3ucYdEJuoqzMvgK98cm6eieoeBroEKrW4SjWqMJpaxKBvr3neG6ASlZmo7rF+QMGMW63XYTPCFQiv/+Z7OtS4Y27PVXJ9gq1ni1JuinQ+TahD0kNpTNVgygZd0CAXLk2eOQmr/NGJKs+au/V+NkIdduRatG1aCmQgiQtdxsPILIgMXFfxSClwnNaJNnACMl6GSlRpmYSFEAgRoPXU7M0l0EmQsu8KDVPVKQaDQYZSnd/pZjQrf+xCtrd1/mx4ysNVLpGIkJ7Nqgg6K36MMdTiGosyi3qW7h4seChQWSBct0XY0QIpXXSnuhAAcYM/UIcQglSPKH5UOXjRNNuqU8TJylw4wjYpLOrmqkILtukaRWL2l7YVespJM12baLXTxz60xdo0rvIZTo23qDPqafmANDkGiXTEcFRlIiqhBUx6HoYYB0lGKmQycJnYDqKuo6iYmIk4JBCSlJdGjvhEOyKcqkAssAcFFYPICOJUTFplGgOmq+buXTIfHHZUyA++uBmZqxu5zYI2CGf2OcjGCnX2Ks2WQlJtnA3bXG2IQjhtP9Oh3YA2Eak5VmWt2/TQiUIn42QpVCeT+nwSpPQxQOWS0qNR2rr/GkFMnBi8iUbQZCLDcUMn8MLUv3HI2FHJuVbxnO5qn2bYrEf/5FcBcxKKc16eydrepNN06/sVxjXKUYmx1CKGgpl0iRSS4WCUSlymFBZJB5meWZW6Q60p9uEgWzNWQtqh7NMMy79RjWyAFJKBYIAt01usa2sgIAA5KK1nxv0cxYVQCCRaF1HOWNfGg3VUoyq+8ufMENShUopoonO5xUQGlVHzlgPXCbUg2+S79l0aYKraHpBIGaDjvZ03ajQCuc9KP6WwhK98xtJjfQUDrcofa8Y4ny7OjnTwHZ9KVMFP+ehJ3bWrdikskXEz/yeyKfAQR2XBUD2f5e5/bFiXz4Omb2vuQ4z5eXJOmlIy+ciUwkSdA6K/R5P8a/lm/qu6njiJqDzVbqevtaZQnSLtZhlLL55zUlFlQbBdMZ4axfOH8KVDTrmkmoIUu2EDypL8MlIxrWOKJrYDXDZDbSCyK8O+fEpaYYzBRBqZV2gRt0zq/WYb+oYjkO6MsuPvlwe87V2jLWW/ttVgi9tnhL3XdrSoxGXy7kDH8k7gBAx1kSvHOkYK1VfZp3EYwqE++ftOgOcEFGqTTFf3UAzLVKJq//460hKGpbHyb2vw1vSs1AyHDh/Dm45/H6eufpb9la6Qdwf6GqTt9ehPQh/rGIHsSKRtRspJk3Fzbbb6oQ4pRYUkSBlpmyQ95TESjBGZkFCHpFI2q9Kt1NuPBBfAVG1wPZfCJdIRfmIpUEfaTeMIp8UjRAibzbnfJdTES0XKFMqZ2z+jHFnLfHfOztzJcbqiaww6X2lyM1x3BNftXJNLu2l81e5B08ksrnEs+9DsLdIR1ajKWGas74Z+M8qfqMk6f34KpLSTJoojZEbONMyc3aTQGKpxlaHU0L4fLx8gPBSoLBC9W8H3+mO9ydT8Ln3Oz7MoNc64myY0xgYfvkRI0XGyP0SlcRHcrsv8NNoJJKZATXb6sY4o1qbI+gMMpcZx56g3xzcXKT5uHeXX3IEyoN51L/HHNmKKHTIYMbbTM5aHIwXsjUI0dqWrMxEil2SE5ouKsZmYtARjWiZ92ZTN2BcQjrCltchQKAre98ER/nZJmnf91yhxPNuVduYoaNjox0k2xaEWV3Gl1yirdULeH+woV641/FP6tALHrpTr47ErXRall7Iku4LR9DhpNwtCUYnLTFb3Mlndy3RtqkVhBpZP8/7L38xrLjqdmqkhtCQ2UcPgrYHIILOyMbnaYEL0nQFSwip4+lH+RMZa58+VUakbwMVNsuNQh5TCaUZS4x2DlDqybo4hf4RSOI3nGWq17j2AZjvUdoOOLdFxLkQ6anNk9ZXfKGfsa9iSSAqpBuc0F9PGKhTnI2MVrg1yOy5KzMI5NlJ6XZVrrnLJ+3lKYet7JOp9czo9Z2ZhY3PbZoxhqjrFcGp43p4kVvkT9WWd3wm+46PRCF80rvvsNXE5KpNyUv9nsinwUKCyYKikn1t35U+3FVZM330nmuBKl5yXJycVeako6CjpppzwVGZhWLi8JvFW+XJtC7t0iCO9hp1+GNcohyUGg1GGU73Z6HXIpfa4zY4QsyskvnCC6Oe7WjgyDWgDTUz+tFCUdExBW26B4/jEAxpcgemzFTwk2ZRQIweUrV4I2dqwLGn4uBBPjq4IBMSGbMbw8Q/vwvc0F/89zTe/k6fZlXYGTaZ/xip+hBBU4jI5L9+TgOhIp6NcOdRVsm5unvVk1WL0ZzvcZsh7OcbTS1mZ258VudUsz61iUWYpOS9PZKIWWa+vAi6491dctePv7Ii2IY2kFtdmGfsZrpz6O5dPXNww26rGFQKV6rtjtitdlFR9ZXgiHeFJr6/VYNrJ4DspKnGZSEcUw2mGgzGGg9Ge5QYhBEPBCGk3SyGc7p1VcbETcRdlC8yUfWRq7vtnMG3ZCiEEeT+/YIuBueC4Iyhnbh5FNaraXjx9lPPqkG7iUDtrnKp3zL7fHJsuyHq2zUSr7N8B4WE6Kn/0vDMYnVCoFci4GUbTvZ+xTqgrfyyRtv8ybx1e4pkVu7HtqO20Ev2NMVTCCsOpdjuJBzMeClQWiLqXSmcqiiVkdUwvmoUFKnUIIRhSLq4QVNGQkVDtPNE/xxnjEJmmQMzna5sQQhCoNJW4Qi2qMRyMMxCM9D/5DTp2f4C+OVFoZBWi04pIt66UpBB4UrI3DhHSIe1mqKoqckRBZPoPVqoJiTgjiUzUWInP7Md6qSykd0w3SH9msj/+2CrvfJutcX/tnAGuuj7oUMVTSQrZZlSECBoGb3XORy90kisbo3uSRzujvpyafS1i6zqaNDPLuFkG/SEWZ5YmdvYzg7gQgsVpG/BujbYgjWTIH8FtXnmHhm9s/R/O+vuZnL/+J4BV++S8/so+9f34KiDuJ6Oiw55y52YoqRj0h6lGFYrhFMPBaBuRuRsc6TASjNmB3qkQhp19VWY71HaCrmhEVswpw60/Z50mkZSb6ljO2BeoOxnPhXJUZsDvn9ANiXOu2yHzm/ggPlCBSr2xYTmcyRLWCbWzuy8DGPT9GpvBBnLaaMYyY32XxpphAyUDCzwWV1njt0hHNsM5u+dWVCFwgv9T2RR4KFBZMPoxfetsyWjmRZDqhEAqhpVHVWvwZdfcjRKCd3grEcDv4j1cE0/jOT4ZN8dIehG5YHBeEb8QArncTlDxjTZQEUOdz8UI2p6uQEiqRjMd274v2kSYjEAuSszkpuK5+4JUDSIvEY4grpMOm65n3UtlXxJqSVYldYfap51W5JlPLWCM4H2fGGfnztZB215TBU0W2OWoRMbN9RVs1OXKBk2kI0IdoqTbU2bbCTOk7dnXQtONgR04qbZgoS5R3hZu6tj7qFIuceP0dYD1T4l1jBKqLxJtM3wZ9HnfzLyuRdrJELgpBvxhRoL+gpTGd90Mw/4olahEkNJMTrZnVRoOtV2SHcYY0KAyc0/usYlxpdsxUPGUR9bLPiDln35Qv68L8dxQadWeUVmgNLlfSCEZSg1RTewVGr9XPp0z3uZ+lX200RTDIqPp0QUHAnZuUIlEff6BjhSSlJuyY8agQg3NatsRlRlKDS0oiPpn4qFAZYGQ0v50K/1070i6bwyF8tIhqxyKjrY+BWHnjMRhKsOzHdu99RPV+3BUitH04p48iV6Qy20qP77RrvbFcLegy5JpmyGEIBCKvToE6eEqn8iEiJxCLnUgJzFFjemSITJVbVevyYAfmajRx6iOGdO3hTWl6whH2HNpmr/f9ua9HLCmxp4Jxbv/Y5ioLREwEyBoYwOnvNe9b8ps1OXKxWiaMK7hKx9PzneFVc/szW6QGHcNll3ltX2+3vNne21rx/H9uumriUzIovRSlmdXNzgk8z1eV7k9m3lC0l9GzE2kbYanPBallzKWWrQg8uBgMETOG6QqJomizlyV2Q61zTA16w46l9oHZqTJ3dLyWS/b4InsS5TCElPVKaar0xRrRUphiXJYphJVqMU1wjikFJZIOamOHY3ngvTaW1GYOFFB9dEwb6HI+/k2BVA3Qq0x3C8PlanKFHk/z3Cqm+nO3JDSbZBoF1qGSjk2Kzpb9l2JKla2/yC1ye+FhwKVBUKIXl4q1ielfYJIpKr3qzFHsgchGFYu0pOEHm0utc14rbeMceHyGGcQLZjXID8bYoXNqOh6RmWwg3GSTkzoOjxdvpRExlAygoyTbbiHCk8ixx3UuAMxmOm4vcdOxSAGZMMNNNYRwSxPmLrp277MqDQTausIfMPZ791BJq1ZuV/U4TlwgBAhFJU4bDF462ufiVzZES7luETGyS5IwmkHu/Zr0TVQke2DeMNGv7K5TZJvQsM1hcsAm00RQhDruG8OSTMc6SJE55JpHbGO+iLSzkbKSS1Y4SCFZCQ1hid9jFPq6Fbbi1BrygaR6y9zEOsYV7ldjzXtpgmcYJ9mVWIdU42qDPgDZL0svuM3spTGGMI4bJSbBlPzy8LWUb8+zff2/ih++oUUktH0KEqoxjWre7DMblAohDW/WwgmK5P4js94Zvx+KWlsFtRp/CwEnvIQHQKxUlhiKBiaFyH/wYL/O2yaByFct/PqymZMBO3LT8sq3xeBCkBKKoaUy650iFPUXV+yAeHw09ThBPvA2Kde+qlXtTpmVOqedl1kk2mpmNIhAyqFMbtnSHVSwIBCBgK9O8YUYqts8hNLaFcgmlUTgobTajP2pelbA4GAWTyaVUsjzj1nC8uOab+uQjiJbX1AqCNGvd49UTruMpEr767sbOsS3D88jGl1fLUDWOdjaVbfuMmKrp5R2Vbe3D4Z19r7+0QmZMAZnPeROtJBJc0zu5V2IhN1LY08kPCVz0hqjK3xJspFj0LBafVVaSLUNgck9WD7/2vvvOMkKev8/36eCp0np43sskvYJSxIEjjROzAfAiZUPBE5I3iIOZwieiecepgOUU8PTn8qnh7giaIiEgygkl0yy7I5zO7kjlX1PL8/qrqne6ZnpmemZ6Znqffr1a/dqX66++mq6qe+9Q2frxGv7TfvKpfmSUqEDWnQFGlib3rvlPL1tZJ1sySsBD3JnnGJlxpd8uAorWYcMijl8TijvWe0mrwPTb2IWTHaY+3sTu8OKh9NEBZaOYggBFoS4pzBGjmcH8aS1riOyDNFylhQ9TOz9do2bCxp4SinZJTk3Ty2tBelNwVCj8qssO2JkmmLd6xjnxwv9jZbmg2TaMQkg5q0y2+5kZKdpBvrVMjD45jndPhic0yQoxJoqIwN/RSxhF++mxUmloyMax4nIhLZYyI7TPA0etjzjYSU8BNpCS62urrol2XYU3ZJnS7SrlJJpKBn2ai3wvNgcMD/zkVjNa88olaypkaC1WiKtNBst9ZcPTNu3tJm7Hnoe1qqn4PVqm+KhkrWyyDG3BUP5QZ4YngjUN6IUE07nwbAFH7Iw53EG+YqZwZJxfUhZTXRFGlBmWmGhqgI95VfiMvRee1XX9QQ9gE/R2WqO96EnQh6LtXHa1jwCrTGWsd5SoQQyKCqzjIsImZkxiqmJSn98hwnPXeJtGNpjbWSslOMFPw7SynjYyp/PD9kP81LYrqQRiDoSfXULIA3FYYRH6e2Ox1MaQbVnaMeo7STpjnaXLOmS6MRGiqzYHLRN5OxLndf7K22Hiu1YgpJWzwCFrj5qReurSrH+dnHuc7ZPaPPMw6NE/3YQSTuPIbEb4/BfnPP+EHFjsKTnF1xaZBDIMw4hSq9eYQUyFYT2WNBTIItkGWS+572MII78HFzlEbdY/jFfj4VxqAavXvu75P809vbeO8/tuMUoJhQ7Sg9ocBbLZjSpCvRMwsPQmWDzNEWDtUPTrXqm6M6juOP527iutN/VlFEpD3Ng5m/oFAc1LSGrviSYL+LaYdmJvrssSitsGtqylh/hBCkrCYs2yOb1aTLHFWlhNoxU9d5jWyaXh7GVMc6akaJWdU7BE+XnJsjakTrdpGdCCEERmw0oVZ7QXPGeTJUDGnQEe8A7XsXhLQrw1Alj0rta3PWyeIpj55UT12raEwzhVmD8N5ECCGI2/FSKXvBK2BJi+bozN9zoQkNlVlgmr6xMjw8Xk7f77My9q5eBfHR+v44k6ZJKh4hm5+6tPNBb4TNOsc1zk7ucgdm/JlCCETSQCSr/LA9v//QZN/TEAJTCPLCRik9Ruug7HNigXely6yQ2y92z622qBtitMla3aiSUAujC202I3jkYZtHHrb5yheaAImrFKaRmHHicj0Y7znxF+TJvHpRo7Lyx5KW79ExCMSDgicczd90ns71L7uVjx7/OcAPzZjSmnEe1GSGitZ+75KZGEH1ImbGiVoxtJFjcLAyR01GZYXHQCu/M2Ut2ikwWlUzlaFSlNTPe7MvU846WZqiTfNSBVK+f+a64qcaCTtBe7ydtJOmKOw2ukYrELXL5+fcHHk3T0+qZ9qibvNB1IyW1tRMIUNztLkuYamFIjRUZkEiAcuX+yGg/n5wynKzik2lKvGYsDHPLBBC0NoUwVaC3BTu4FdZHbzW7EQDn8pvZrPKTjq+Gjrr4T2VQe2foB5zjNjbRMSFxBM2BWlQmGTRFYZAxCtPVb+M067qipai/qJv1RJqy1Vply73uPzffH2VH/53ktt+naKgPJrstik7zM4tYyt/ig3PJl6QLWkhqu26ojOw6FUqaGTS5JC29ZzY8zdAWdXKDBMBJ/xsfCPIkOasksFniyENmqxmhJ0lm4VMWSrUWI0UndOImBinZTERU1X8lFNNUn+6uMpFCjlvmhrlUvra00hzbit+qtEaayVhJUg7hUCuPth/unaxt4JXIOtk6U520xJtmbvJzgJLWhjCIO/mfcM2sni9KRAaKrMmmfSNlc5OSKf95Fr/+jhenVZrL8gZqD92xKQ5YuM4HmqKC/QH7BUcK5OkUXwwt4mhGkS2ysl9cjPZ8x4j8/KHqxsrHsga7pSEEMRNizw2I+70DCa/e271OwRjAtE3rTX52cT1A4XaUSpVaV94ep63/KMfA//MxzvZsbWJpN0y88+rA6NGif+9fYOlishNGYY0YYyi53WPXs27f3sufxi4wxfcDTRixvatKeaQzNRrWO2zR9/b181ZSI8KQMxKYEoDLV0GB0fz1IQdSMUXwxuFoK1Ajb14JtNQGUs9JPWzTpaEnZiWyuxskJavf6RdXar4qbd3eSpMadKZ6ERpcJUoSenXKvbmeA4jhRG6E92zKkOea4oJtYP5QZoiTXVLvF4owqqfOmBZ0NUF8Tjs3et7V5LJCbrt1jE/pRxhC5JRi+GsS95Sk3ZjNoXg36IH85bs42zVeT6R38yXI2sxalw05NLIaPZN1fYdesKKn7HEpIFpRunL9tOqdc1z0Hpi0S9DGBjCCEqU/VNcac2Acsgojx4RwZxBUqC0BZ7Wfj2X0n6BwJjd/J5Lh3j4AYsH74vwrjc9n9NPF3z966PPf+tb/vnS1uY/4nFKYapkEg49dHTsww9TRZ/FJx6Hww8f/dt1/VDkeIyg548X3DEqhIhMeoGwgtCNq5xSPsjTA4/zlz1/4KSmF3Bq/EXgwO+Gf8NvHvwFL111FqctfwngG5Azr1Aqduv2+1HJMcfXVQ4pu2neL25jiRpR4maCtM6STqfIZPxjh8WoQm3w85fx2s8zV7kkIrWVsBcl9QfzgzP6DlprHOXQE+mZt/1ZruCrPT1lc8a5ImH5IaDdg/tJmW5gsqspwz6uchkuDNMZ76Q9PnGfqEbAkAZRM4qjHFpiLQs9nVkTGip1QghIpSAahf37Yf9+P08imRw7bo4MFSEwkgaJEcn+nEvUCNysgSpm6f9+riOtKYsvRtZwYe5x7vGGuNbZzT/aS2r7rKbR7yBaqpxCYuKKn2q0WUn2ZSW9hRG67dr1QiYKL0ghK0TflNb0ewUGPBcEuFozo9B4eUJtMSd1zFwtCz7++S380z8czO6d5jjJ9X//94mb2518MvzkJ6N/v/nN/rlUjQ0b4Be/GP37LW+BSAQuuAD+5m9Gm2b651u5SvKo8TYRhvC9FpWVP76Wyp78Tj/048CdQ7/hl7tuojPRUzJUpJhdDokpTAxh+GXIY8KknnanrXY7FwghSEWaGXGGMAwYHPLDwEL41T16SPuG7DTCPuDnqEynKqNcUn+61RzFJNqZqMzOFCEF0paooMx/PvNTKuYhBG2xNoayTWQK22kqJq5OsjZ7ymMoN0R7vJ3ORGdDGylF4nYcIcS8eczmktBQqTOWBd3dEI1Kdu+W9PcrEgmJEBql/LJQKccn38Ko2u1MfwMyahCPmQw6LgXPw5aGbzBEhP+mpi8ToIc8dE5xWDTOZZFVXO/s5exAvbYWykXexi42JVG7adhjETNCi5Vgv5MhacVITpHfUAwBGBO4yIUQmNIi52bxAiNl0HOJG35jRAdFdCaerbEJtUJU7Uy6ZIngN7c77HjWxCq7ZisF55wDfX3+Y/9+yJV57ru6Kt9r+XL/AliN7u7R/+/YAXfd5Z9Tv/41rFkD558Pr3sdNDUVE7v9EhWt1ZThRyEEERkl5/aXthVLlHfltvvv42ruGwiE3rr9/BRPeUhhVK3EqhW/H1WMocJ4T4Ggejn6QhAz41hGBCHzpEciZLO+l0tGJK7nggdmuzntC9p0vp9t2DRFmujN9PoiX9P4rKybpWdW1WQzQ8Yk7qDrF9jUkMc2V1iGRWdyCVv2bcNRBSRinA6V4zkUvAIFr+DnAcZa6Up0zbhEe75pjjTTFFl4D2Q9CA2VOUAIaGryS0CHhjxGRiRae2ht4HmjFSnl54/WgfND+Y/ic77kfnBNNHyPjTXBWibjBvHlcZqVRZ8qEDWrVxgp6aD2OBARvNhs43SjFTmNk9l8eRvO/+7FeF6VapZiBXaNoZ8izXYTQ4VB+rwCJoLoJOqOXrGyZJILoi1thtUQaS/PsPJIGAaGEEgEBaVmlJ1VSqgt6NEK37L3cZRDwcuxJLmcJjtG65GVr5cSrryy9s8r95hMxrJlcMcd8N3vwo9+BJs2wac+5X/Wa18Lb31rjFWrihLiU7u4AWwzgiprZdATDwyVrK9Ou9PZys7sNgxhckzniQAl6fzZGhMRM4oq9FVs843ThU2kLceSFimrif78fiDC0FAQxrMDT6ZJzdopELQGQEzbcGiLtTFSGCHtpGtOinWViynMefWmFJG29Ats7Pmt+KlGU6SNllgb/dl+miMxFJB3sqXeQH61W4S2WFupJHyxGCnArBRyG43QUJkjhDCwbYOODkVbGyjlGyrRaGXpbLlBUm6ojP2/50E26yfsDg9DLOYbLWPtC2FJksr3qkzk5BdJEzHo+iJqcaPCSPmN288xMknHJBcEkTCI//CI6k8Wxd6maajYRpS4tMkrj35RoEtEMCZYFDzli2JN+kMUBr1uHiFsktIo5b6YQpALlDZndKdRUqj1PVXF99Bak3GGaYt2krLmv1xx7Vr4zGfgwx/2w0fXXQdPPeUbLxs2RFm1qmh01Obu8o2Nchn9MnVaNH/J+N6UozqeV2oN4Co/wXm2C6QpzHEux6Jx2igeFYCElaQ/t59oTDEyIslmIWr5nksZl4hI7efXdCp+yrEMi65EF9sGt1HwCjXJo2ecDAk7sSDlqsISuFL7nuMFNlSkNGmN9ZDJDzKQG8JWLUTtFO2x9lJYbbqeqpC5ITRU5oxiMq0KegL5SQ22LWcc2tHaDxWk0zA46CftWpZvtJQnUkaFJCENRpRHyhh/iIUpkC0W3u4CRHWpKuEHzh6+VNjOBpngmuihgYLsNCkWlUzzWmUbEUzDxtAuGSXpx6F9gkXCz1WYuNyuoBX7lesnzspKQ8wQ4GiNh8acqDnLJJQSapWo+PWMOEPErSSt0YVNsksm4a1v9UM/f/gD/M//wJlneqXrvu+hm/q4mtLCkKav7SENeoIclbQ7wrAY4t5h31ApqtGCn+wai8y+EsKQJlIYKK1Kd7Cu8vNTGumONmrGgm7TOTwvzsgIRDt8vRCZml5FS3E/zyQUk4qk6Eh0sHdkb1WF2XK01njKozlae4PMeiIsQR8uBQ3drkvKMDDlwh3TmN1CW6wZhUVzcg1RK9FQ51iIT3hE5ohRmfLinaFfmjybxUEI3yjp6ICDDoKVK313czrtGy3ZbPFCJEgZJgomLlVOGr42SVn/mlOMZpIYPKTSXFXYPrNJehrMycXeqiGFJGmlcJVDXBoMKo+RCcqmixoq1cgrxR4nT05DYoyRAmAicLXGmanGSpBQizeqSpt1s0hh0BHrmveY/0QI4SfVfvWrEIsZCCHYtQvOPXc1Dzww9Rz9pFYTN9CZiJkxWiPttEc76aePe/v9/j5F/RT/Q5mRdP5YrMBIctVo6bunXWILJJ0/EVJImiIt5L0csRgMDUGhIDA6DWRyekurb4hFZ7w+tMXaSNrJii7B1ci5OaJmlMQ0GmTWE1dCTmqULdiRz7Mll2PQdaeUVJgrhLBI2E00R9uIWcnQSGlQwqMyhwhhBbL59ddQMU0/UXL5cli1yk/E1BoGBnyjJT9gUBiW7B1QDA5Q8Rjoh6FhwYgwGelXDPdrRoahMxvlU2I1AP/r9vK0N30xODRgzey0ipgxNBoDsIWgz3OqCtgJqsuMZ5XHHjdPVns0G35fkrF6HP6FQODOtBdQkFCrA0PFVS4FL0dHrIuYObcy5DPHr/y56qoU994b58ILI+zYMcUrpIEt7YrKn1vOvpdfv/pBWtra6YovIW4mOLL9WGBUNbYehlqx6qhCB2eScvSFJGbG/a7PpoPj+DcNMjZ9ITNXubMSBjSlSVfST/ScTFsl62ZpibYsWP5CTimcuKApYdNq+jdT23M5tuZyDLtu/VtfTIF/82giZf0Vw0PqR2Pc/h2gSGkFIR+/2qJW5cPpUPSyxGLQ2gr5ksCroM012VHI02ZWLkrFfBjPM3BjBt6wQscNPA2nuc28KN/CHQxwdWYn/yLWELHBrPUa4WnkDDui2kYUW9o4qkDUiDDiefR5fr5KUffEd5GP9vhxtKKgFTmlGFIujtakpImLDsIH3ri7JInveUnNJqE2D1poRpwhWiPtNNmNq/xYbIT5yU/u4aGHbJ54IsL558NNN40vny8nasZIu6O11EVDoa29k++//Jdk3UxJZ8VVTqnz8uznK7BlpHTBVVohhGyYRNpyIkaEhJVkpDBMLGYxMODvU3sGNtVsZezjVpyOeAe7RnYFXYIrT3DHc7CktSBJtEXSnofdaSODNSlhGMSkJO15bM3naTIM2iyLuJwfMTi/pYk1J2tzSP0IPSpziC/RrMr+ntu7GNP0y1mLj66UQVtCIqOKeJzSI5HwF9PmZkHrSovmJk1bs6arA5b0wCeWLEUCv5cDPBtNU3BhaMCXC/emFLGtXext3PyliW3GcAI5/YSUZLRiwHNKd1qucnC1IKNhl5Njm5Nlu5Nnv1cAoMkwg66vBkYVjwpUJtTOiKifLDzijZCwkrTFOhr8bsxXp00k8nznO7vp7NQ89hi85z2VvWrGYhnV+lWNUu5B8nv8zFw6fywRc7Tnj9/XqbESactJWik0HratKRQm1smZiOJ5WA9vVGusleZIM0O58SGgjJMhaScXrOeLqxRpzyMyJidFCkHKNGkyDEY8jy3ZLLsKBTLezLu814oQEiljoaHS4ISGypwyKqPvlxjPr7vVkpJm0yQzydVIJCSyyUClR8ccasc4J+EnRX5H7GDJEujqAdvy82CGByGX9S9yxcqk0YdAIapsr+0RM5I4nhdUOwliGPS7LgPKYcArsM1J06s8epXLiPYwhaRJGjQZFrEyd7YhfS+C0uO/uyHAQ+NWk9WtAWkLCjKPMCUdsa6GvYAWEYGErtYuy5Zprr1WEI3CbbfB5ZdP/Drf6BjtmXTPrrt4x29ey2f/9KFxY/2Kn5lL51f77GJ+kavcmqXlF4KYGcc2ouS9HNGon6uSzU5uBJbjaQ9TmnVpDCiFpDPRiWVYZJ3R0K3SCqXVgjbQy2tNXmsiE5wjhhA0myZJ02TAcXg2m2VLLseA4/iSAnOEZbVgGPPT7yhkZiyooXLFFVdwwgknkEql6Orq4uyzz+aJJ55YyCnVFf8CIdBaoXUVvfV5IGkYmELgTPBDF0JgtJogRnuUALyveSnrrBhvTHZim5qmpO9tWboU2jp8wyubgcxI2WNYk81o0hkYGZ7Zo5CJks9aDA25pEcgnxaojGRzv8M+x8FRLk1mnGbDIiVNbDGxi9g07OoeFQSe9hVqZ4JnKBwjT0e8s4HzUirx+5h4CGFz7LHwla/427/zHfj+96u/plj54waejayb4b69d3PTph/w/rveVjFWaW9W0vnVPlsg8ZSHq52GUKSdCFOapKwm8ipHJOJX5u3YAdu2wY6dvrjf8LBvvFRrieB4E3cCnwlRM0pnopOsm8ULcryKSbRxa+HO16znIWqQBTCFoMWySJkmBa3Zns/zbDbLrnyeEdfFq7OXxTDiGBP0DQtpDBb0FuXOO+/koosu4oQTTsB1XT7+8Y/zkpe8hEcffZTERJKciwg/N4Cgz4ox7x4VgJhhkDIMhjyP5gnKAGXcQDabqH4XI5DEX2lFuGXJuopFRQiIRvxHcwoKLhW9frQHOiMwDxKIGeYFam0TGYrgqDxxq3h6Snp7pS8mJzSxGi+ItrTIVjFUhBAodJBQO/1j4pgOdnOcpkTLtF+7UBQbrokgNPP3fw8f+Qj89Kdw2mnVX1O8eHqBR6OopQIgqpR219OzVOr5o120VqVcmEYlbiWQOYnSHi0tBp4C1/E9jyNBGwUh/PBsUVKgqcnPZfG0R9SM1rXipDnSTCaaoS/XR1usjZybY2lq6YIl0SqtGXJd7GmUIhtCkDQMMAwKSjHguvS5LtHA85IwDCJSTkusMmRxsqCGyi9/+cuKv6+77jq6urq47777OG2i1XNRIdFaoLW7YIYKQLNpMuj5XZUn+lEbrSZq2EXnFSJoFjbZnY+UEB2TMKgLGh0TWAnBzFMVBO06SW96D9Gya1Mq6UvOi6iYUDp/LKa0qnpUAAzEjBNqPeGRaEsuKuVH30AxKmLx730vvP3t/kWzGlJIIkaUtOMnXfQklpae29BxfOn/xZYG9Ux2LRpJjucAs+sfNB9EjRgxM07Oy5KQSQwJRsTvv1REKd+j4jh+HotSfrVesTS5nggh6Eh0kHWz9Gf7saW9YCXJ4Cev55UiWb1z5pTYUmJLidaanFLsKRQwhMASovSvJSWWEEjhi+HL4DlbTF8uIaSxaKgclcFBv79HW1t10ah8Ps/Q0FDFo5ERQgada53AUFmY3R03DOJSkpkkziujEqPFRGUqA+s5rfjO0B7evOfJKbUOdNFBMcvrd8zycx3KjYxIxG/FjpaYNSa+ySk6SM80odbTfj7GYsI//yzKf/LFirEiv/kN9PZWvi5qxEpJrc12a2n7iT0vKP1/rpJdI9LP+zCDbs6NjBCClN2MqwoTjpHS96DE434yezGXRWlVk6LsdLENm85EJ+CLwk23cWE9ySmFBzV3R58IIQQxw6DVsogb/u/b1ZoRz2O/47Azn2dbLseWXI7N2Sybs1n2Oc4U7xrS6DRMdppSive9732ceuqpHHnkkVXHXHHFFVw+WfZfw+HrV2idL7neFwIpBC2myfZ8Hj1J2Z/RYqIGPVROIaP+BS2rFF8a2MmwVvw808+ZiUmURz2NiMz+7sUOpKsLXqF0p2lHQJoOrjtxM8Jx32cSQ6WoUOuisaapUNtIzfFqxwjKMKvvkx/+ED70IV+X57DDoLkZWlogmkxAtI1XvlLQ3aP47kt/zvb9/cSHNrA7r4lEwJWKpkR9Qxfg9xsqJprWq5poLvE1VWwKXn7KUJUVJKYPDECkuT76M9VI2Sl6kj0LmpuiZxD2qQVTCExj4t+4DpJ3+xyHpGEQm2RsSGPTML/+iy66iI0bN/L73/9+wjEf+9jHeP/731/6e2hoiBUrVszH9GaE71GRKOXUVextJiQMg6iU5JSa8AcrbIlsNfD2OOjA4Gg1TN7e1M1Vg7u4amAnL4+3Yk5kiHjMWOytHEMaJOwk/Zm+kqFiSLCjHs6QOakBUo4UBhJZIcVexESQ1RpXa6bTxLXYIbjR7/DHM96jUs4JJ/jGybZt/mOUCLCEYzfspbtHcUT7MTzw0wQfv7JcN6YH8PMvIhG/z9App/jPpNO+J2Gi8NJkFCt9YkZ81sbvgOeQkMbM2kLUiG3YJK0Ug4X+mnJqEgkYGPJoicxMOr8WhBC0x9vn5L1rpRCEa6LzLJUvhCAq/BDvfsdh2Txps4TUn4YwVC6++GJuvvlm7rrrLpYvXz7huEgkQiTS2El15RSrfkCUkhgXCktKWkyTPYXCpHcWRrPvVdFZhYj74y5s6ua64V6ecfP8b3o/5yY7qr5WK029rt8xM04/+yu/g+1iGgmo0QMipYGU1UXfhBDoGSTU1qtD8Hzjn3/WhOfh2rVw111wzz1+H6nBQf9uf2BAsXNfmvauPMX9JCUkkopCXuA4o8fCdf1HeRrCj34En/0snHgivPCF/mP9+vHNNKthCBNL2rOuJnK0YtBzMBBYxtxeLJN2ioF8X1XjeCyGARqXkUETY9niOp+mQ06pQIhxYULfScNg0PNo8jyaZpgjE7KwLOhR01rz3ve+lxtvvJE77riD1atXL+R05gQpLZRauETacpKGwf6gVNmaYNEQlsRos3B2FpCBtzgpDd7T3MO/9G/nywM7OTvRRmSCRViY9VmMImYUQ1o4XqGkiGraimQ0QiEPkRquXYYwqsrojz4//YTaYnO8xZRIC36Oiml2T2owt7fDK19ZuU1rwbbhfYGcvX9CvOmtad701jQAjqvoTw/Taa9CuDFyOejuHn39ww9DoQC//73/+Nd/hc5OeMEL4Ljj/AaKRX79ayqk/ZW2Gcp3BJ1s4YILRp/77W9hy5aJv++b3+yHVwBuv0uzMyM48ww15woBMTNO1IyRc7OlrtKTEYl5ZEZMMmmD5sYVN54VI647sRd2Higm2+4rFIhLuaBNEENmxoIaKhdddBE/+MEP+OlPf0oqlWL37t0ANDc3E5uJr7gBKbrbG8FQiRoGKdNkwHVpmeTHKhMSYfm6KiKIi/xDspNvD+1hp+fwg+F9XNDUNe51Gqbd42QiLMMiasbIuumSoWKZEEtaFDK1GSpSSAxh4unqyXQmowm1tbqEXeUQM6buEKy1JqM9IkKW5P8Xmpl49YQQRI0Yg4WBCQa4JBMmnSmLajbql74EF18Md97pP/74Rz9h94Yb4JZbKg2V733PN0BGkcDovi43VK6/Hn7+84nnfe65o4bKjTfA//0kxr1vdPnKv/mejLlCCklrtJ3d6e0oPXW3Z4VDKpqkr88PBR1oN/yOUmSqqNHONwkpfeFI16VjJv0NQhaUBf1ZXHPNNQC86EUvqth+7bXX8tbyFWwRI4S5oKXJY2kyDAYC0aQJM/AtgbAk2h01VKJS8k/NS/h431auHtrFeakO7LHhFDSe1PQWCnTXYTFIRpKMFPxKMN8rIkjFTfYP1/4ehjQpTNCkzRBQUNNNqK2tOV5OK3a7eQwETdIkaZjj9tdiwTYjqHx1mVVX+xorE+UNCeGHldauhQsv9HtR3Xefb7Ts2lU59qST/It1LRx/vB+Cmojic1prelb64b0bf2iSG9D8x3/4yrxzRdJKkTBTpJ0RUvbkSrCe9uhIRUinfVG41tZJhy86ckqR15r4AueGCCGIG0YpsTYaJtYuKhY89HOg4xspvoZFI5AIBODSk8RrhRCImEQNuBAbvRq8PtnBxkKGf0h1jrvobsvn+FF+Hz95rJ9VsSg/OeIIAJ7OZulzHE5smr50d8SM+h4R5aG0hyFM4jGTIRucAlg12EK2tElPEPoxEWSpPaF2Os3x8tqPy0sh6PUKDCiHZmmRlOaC311OFz8fp/pv1VUOSStVs0cqEvETbYvJtuVcfHHtc3rHO2obl9eK8y4ucNBaxaffZ3PLLYI3vxmuvRZSqdo/bzpIIWmJtpEeGcFRzqT5TH4FmYkR83WCkslRT9CBQMbzkA2iYxKVkgHPo891WRIm1i4qFteKuQgp6lc0ikdFBKXKHkyqiyJjErzK5y0huKL9INbbfq6CozW3ZPp5y56neMHuR/habg+7nAJPZDIMuC6/7uvjbx98kPdv2jQjufqIEcE2IxS8PK7yMA2TeMQkHi/vEj05hjQnzFHxFyqNQ219RGrVC9FaM6JcLCGJSYNmw8JAss8rsN3N0uvkyaoaG8E0AIYwMaRZkmMvx9NeQ6vGFrTG05qXvRKu+E6ORFJz993w2teO14ypJ3EzQbPdSqas+/RYioavIU1iMV96f2Bg7uY033iBvslEvX0WgmQQ+h6ptRET/u95wHEYqdb/4ABFaU3O8xh2Xfoch+EF/u4HWES08RDCQMpIQ1nvCcMgGbRWT03kVbH8DsET5W/sdAu8de9TPOGMhlVOsZKcd1APL29vJyIlpzQ302KabM7l+PHevbyxPMuyBoQQJO0kvem9GNIgaiQRQpBIwOBAbe8hhTFpUEcK4Tc8q8Fkd5WLbdhTlpIWyzHLPScRKYkgKWhFn3IYVC5JaZIyDAR+BZLSoHxZO7zgAusFnoxO057T0trJ8EM7Jq52MMZ4BkWDq8ZmlVdSYz7uVI//ut7lordabNzoe3B+9KO5+VwhBC3RNkacYfJejkiVXjKecgMxO/98isehv9/39MxlaGq+yClFTimaGyjxxgxUa/c7DnHDmFKAzlGKfY7D/iBkFDeMA06yX2mNozWOUhS0Jut5pUotL5Bw6LbtCa8V80HjnEEHKFLGse3GSgyWQtBqWWzL5yeU1Re2RJgCChoilc/nlOLqwd1scnJ0SpPXJTt4ndnKqmgUu3N0hU0aBhcvW8ZntmzhS9u38+rOzmmHPaKmr1LrKre02EciYFp+LxVzimuknzsx8cLi66nUllDraocmc+rSjLxWeFpXNSxsIbENiaMVQ8plUDloyvts+wgEQoBE4GpF1JO0mQuTBGhIA1va5L18RQTTl843axbgm288rclqDys4roaQrD7S46abLN77Xr8CaS6JGBFaI+3szezCrnKz4moPQxglMbto1DdUBgagp2du5zYf5AKvRaNd2JNG0JHdcWifJJcu43nsLRQY8TyShkE6SAyeaRuARiPneQx7HkOu64tfao0gENITgkRgyA01gCfpwNjjDYy/ODXWDxX8H2sikNVPVkksE6ZARAQ6r8c1GIwIwSnRFC+MNfG3sWYsIVDDHqKK2Ntburv51s6d7CgU+P6ePbxtyZJpzdM2I1jSIutksAzfKrEtiEUhl5/aUJlM9A2ml1Bba3O8tHKRcvL3soTEMmRNBlJeCQaUQ0ItXH5LxIySHhPG8ENhZsN6VApaUdCKZGBImQgKWrFyleZnPxMVWi79/XOTyNoUaWbIGSDrZsaVK3vaJWomK45/MukbKk1NvodlsaK1ZrgBqn2qIYQgJqWfWGuO/01prRl0XfYWCnhAq2kihCCrFIOuS8IwGspDPh201qXvUTRQYlISN4wFLSGfisY7i0LmBSkEbZZFQakJc1VEXKKd8c8JIXhlopWXxFtKd6taaap1CYgZBpcEIn5f3bGDzDRiw+A3p4tbCYygSZ3/+ZBI+gm1U1Eu+lYNS0g8fFGwyfATv6cOczhakQ3KkmuhlgUvIiWO1gx5C9ezxDYi6DH7yNUuthGpu3R+vchrhWL0jt4UvnfK0arCSLnzTnj+8/1y6XpjSpP2aCeOKozL8VHaG9czyrL8ZoX9/bCYaw3yQdin3rL59SJmGBQCef3yog43aHi4I2h62BwYKeCHzIeDsMhiQ2nNiOuyI59nSzZLv+MQkZI2yyLW4EYKhIbKc5qEYZAwjAmbFcqIUftiqUFMoPr5hq4uVkYi9DoO1wZaOdMhbicCAbhRB2A06mtOeFN4JacSfStO3p2gqqVIMZF2qoqfvFZ+FVGdvWgxaTCoXHILlITrhydExaLuaZeobKywZjlp5VUswIYQKM24Y/2Tn/jdjN/xDrj77vrPI2ElSdpN4xJrtVZV851SKV8ZeGTiPNyGJ6f830EjXwBTpkm/45AObp6ynseOfJ59jkOqSm8gUwi8oG/RYqE43225HFvzeYY9j4Rp0mJZDWtEVmPxzDSk7hhlXpWqpeKWQJigvamtFa2ZsALblpL3r1hBS2AYTZeoGSMVSVV4MyK2n6uSn8KrUhR9m8ij4o8R5KcwAIp6IVM1x8t6Hn7LhPou0LaQeGgGp7LM5ghTWhjSxNWjny80pXBco+FoRV5747V+BH7ydBlf+hKcdZbvybj0Ul/PpJ5IIWmLtiOEwPH8E7booatmqBiG/+jr81V9HWfyRyN6Xoa90dygRsUUAikE+x2HAcdhey5HWilaTXNCAytuGAy67rhzqBHJeB5bczm25XLklCJlGDRP8t0amTBH5TlOssyrMtaIELZAmMJXqJ1CcVaIyce8uqODl7S2zqgCwDIs2uOdYz7PD//07qWo7D4hprQmFH2DokKtnjRfxFUOCTs5qQHiaU1ae9hztBDEpcGQcmhSJrF5lvA3g9Cbp3yDbTqaMhPhajVnqr0FrXA0xMYcC0NIsmO8a6YJn/883H+/35Dx8svhi1+s73xiZpxmu5W+3D6aDRtXB4nIExi+iYSfqzJVGb6UfsuCudKEmQkFpcg2aH7KWJKBAGZaKWwhaJniRioSVEsOu+6kibiNwKDrkvE8WsrCV4uVxj+TQuYUQwhaTZN8Fa+KkAIRlVAlT6UcrTRI4WemTvI59S5TjEV9g2WqtBdL2pOGfoouXWeSW1NPe1M2x8trv7yv1vyU6WIJiUYw4DnzLpYohcQ2IiWPiqscjFkk0rpascctzJmeTF4pYLzhaSJwgqqscpJJ+PKX/fPphz/0+w7Vm+ZIK7YRIedmg9Jkc8JSdymhpcX3Gk72yOVq1xSaL3JBmetiCC1IIWgyzVLpcS3EpGTAdXEb2KviaU3G84gt4sTfchr/TAqZc1KmSUxKslV+eDJuTB368fweP7Vo2mmtubW/n+tmkKsyluJiXZgk/ONqjRQGOtAEeLSQY2TM9zSFwIOgk3L1OdeiF5JTHrrKxbGe+F4Vl8wkoay5IipjpRCaG3hWptKUmYisUowol8IUScwzQWtNWo0P+4B/rJ0goXYsz3/+qOLthz7kK8XWE9uwaY22k/eyQSjRnjQRWUo/uXayRyTSeLksac+bUp+kkSiW49ZKNFgr0w1sqBQ1bObKuzvfhIZKSClXJVfNq2L76q2T3cH7HhUm9agU+dPwMG99/HE+++yz7J7MwqgBKX2Xd2GCO8r7s0O8ecfD/DI7hAA+0b+X9/ft4e58psroiRNqi276ycIcSmuGlTvn/XyKcfUBz51UWXgusAyrVPnjaZeYObP6WR3sq4LWZObAo1LQmrxWVXVsJkqoLfLhD8O6dX5Tw2Sy7lMjZTURt5Jk3QzRKiJw08WyRnNVGgFXKdKLJOwzU4QQ2FLS7zjz/huslUbVsJkpB+7ZFDItUoZBTMpxpXciaFDIZDmcngZDIKbQDgE4KZXiuGSSnNZ8dfv2Wc56VMGzfNp73Dwf3/Mkb9/1CE8VMlw/vB9Pa46wfA2Ue3LZce8zWUKtG7jpJ/Oo5APNjvloPJgQBiPKIz3PFUCmtBBBBZXSCkvOLEaf14qM8ohLoySOV08KwXtOeJdcJaG2SDTqd2X++Md9b0W9MaRBa6SdmBmrqbnlVBQNlVna/HWj2ISwkWTz54JYoEE1XbmF+aCoYbMYQm+1cuB8k5BZYQY19dmxXhVLgCXQziRuTo9Sl+WpEELwkZUrAfjB3r1sy02c5FoLkQhEov5CXdCK/+rfwau3Pciv0vsRwGtS3Xyz+1BMaXKC7V957itkKYy5OBYTaqvdIbnKIWrEJg3p5LXCg3lxecvAVT3gOXW/yE+GGXiVXOXMSjo/ozxcNDEhS6Js9aRcNr8aZpWE2nLKDRTX9TVN6knCStJit83Y0CtHCL/qp1EMlaxSiBpEDBc7hhAI/ITVRqOgNfkxLTwWOwfONwmZNckqXpViJ+Vqwm9FtNY1GyoApzY38zfNzThac9UsvSqGAckE/G6on9dte5Cr+7eS04oNkRT/b9nRfLzzYFqtCIaQHGxIOqRBTmseLFQaSMWE2mrNE30F0Yn1QsqbEM4XMSHJKI8RNX8LpSlMDGGQ9/JIacyo4qcYIosIiRQCDXU1VJTWZPTkpbETJdSOZfNmOPtsePe7Kz12s0UIQUe8i9gk59R0ME3IVItmzjNOoHh6IF0gJyMuJcOeF0gSNA7FPj2LsQx5Ip4bZ1RITVhS0mKa45JqZVSCmmRRV1SVz5+Mj6xYAcBPent5Ojs+FDMdYjHI4leRdBgWn+1cy3eWHsHhEV+y3BAGQhhoNM+P+BeHe3KVK7ufUKurJtQKmNR7UGxCOJ+Ja1IILCnp9xzcOUhIrYYQgqgRI+/lZiydn9VeEBrwzxcpRF2VPmsJwfkKtXpKNWKl4LHH4He/g+9+t25TrDuW5Vf/LPT1ciRQbR0rlHagYkmJF4RZGomM5x1QRgqEhkrIGJpM089qL/vxCTvopDyZsTLNtel5qRQvaW1FAV/bsWNmkw2IRODFqXa+1XEkN6w4llekOitcz77omy+j//yonwB6Tz47LsyjGZ9kWWy8N5n3YLImhHNJTEjySjE8jyJwxRLtiBGdkXt/ZEySn4Ugq726hbDGyuZXwyh6z6ZQI16zBv75n/3/f/azsGlTXaZYdywrCH0uYPjH07oky/5cImYYDDpOwwjAFZOZD6T8FAgNlZAxVPOqCEv6oZ0Jwz+6pkTasXxoxQraTJN3TrNRYZGN6TQ78nlM02/gtpokiQmE0ExpobTH0XaUmBD0KY+n3MqV3RRinER9UTp/Mu9BLU0I54Ji9cGAcqf0DtSLYkny2B41tVDQirR2iZYtorbwO0nXK/wzVjZ/QiZJqC3n/PPhBS/wPRaXXOLnrDQahuF7UxbSUEl7HhmliB9gF8ipiEpJXuuSDP9Ck9f6gExmfm6dVSE1kTJNbCFKC3mpk3IVQ0Ur7Wf0zcDbuz6R4PfHHsv6xGhX2VqFzJ7N5Tjvscd41caNbMpmicf8xXpkpPqjkLUZTiu8nODiVBtXtfWw1qxMZjQQ5Mck1E7VeG+6TQjrTSyonJkvr4ohTKJmbGZhH+XhKF0RlpFCoGBSsb1amUg2vxpTJdSW5ifh3//d72b8wANw9dWznuacIOXCCb8Vuw1bov6tIxYD0aAT83wmtk9E1vPgAExmDg2VkHFEpCQ51qsSk2i3yg9RAQZTSuxPRLla7Z+GhnjzY49N2fSrt1DgTY8+yj7HocOy6LIsYjFob/e1L6o9mlMmsZjGkHCCk2SdFRlXoVMtodbTLjFjYr2QYhPC+ShLnoiYMNjvFdhayLLLybHPLTDgOQx7LhnlkVMejp64S/Z0sKRFREannUhb1E4xq9xxS0RdFGqLsvm1NISsNaEWYNky+Jd/8f9/1VWaP/xx4S9IY7Ft3yBfiGtlRimGXbdmZdcDjWIBwkJ7VYplyQdi+C3s9RNSlZRhlFqgCyGQEQOvmpiKF4R9ZmioFCkoxT89/TTb83le+8gj/GD9ejqs8RfDYdflzY8/zpZ8noMiEb53+OGkAmOns3Pc8BLJvIEagpgB+3pheAhSTb4zqIgpBJkgodYu2vBaTx72CZoQLiQRKZHaV+EtaIVSHgodzEog0UghMBAkhN8VNirkjPrsGNKgM949bUXanFZklUe8yuss4eepKK1nJVA1kWx+NUwhyCpfodaoQVL51a+G236r+eujmpWHe0BjNWMsJtQ6jm+0zCdDrgtCLCo12noiArmA/qDr8kJ5M/JKkVfqgDQYQ0MlpCrxslLlmGGA7Yd3tKsR5ugPUSt8I2WWRrwtJd857DDOe+wxHslkOGfjRq5fv55lZaIWeaX4xyefZGM6TYdl8f116+iqcVU2pAEILFPT0Sn4/a48v9o3wtGJCC+JV0qQOkGSpac8pJi4DNfTekHDPuVYQjJRhbjSGg+/hUC/culTDraQxIXfkDIi5LQSgWcim59Vnu98q7KIW0KSUS55rYjV0oehCkXZ/Fq/R60JtUWEgM9e5fJ4bwGVFHjaxBACrSuN3YXCsvyuz4XC/BoqOc9jyHWfc7kpY4kbBkOuy0AQAgOqGiwCsIWo6lmcLfnAG3ygVfxAGPoJmYBiE8Fi6aivUCvGh3887XdZrsOP48hEghuOOIJlts0zuRxnbdxYKl1WWvO+p5/m94ODJKTke4cfzupY7QmdUhhB5Y/CtmBbJM9vvBF+PZKucJcbZQq1rp48kbbYhLDR+2lIIbCEJCoNmgyTJmkiEQwpl51Ojm1Olt1OnmFvbnrvuFoxFGinVKMoaz+bPJVR2fxpHIsaE2oh0GcRLt1dvncoozz+8z/hoosapymgEPM/lxHP80Ofz3FDxQg8Sjvzebbkcjxb5bE5m2VzNsuOfH5OqoRGXPeANFIgNFRCJiFhGEh8z4GQAhEzoDCmpFdpMOv341gTi3HTkUeyNhZjV6HAORs38vDICEOex9PZLJYQfPuwwzh6mo1YDGkgpSw11fu7Ft/IeVTl2D3klYyVYkJtTnm4ysWWduCNqcR156cJ4VwghCAiJSnDJCVNTCEZDoyW7U6W/W4hCKPUh1xQ1TOZ50lWqbiaDoUZlIgbQpCr0TDLao+sVsSFgSEET+5wuOIKzU9/Cm96EwwMzHDidcSyIJ2ev89zlGLAraziei7TZJq0WhatlkWbZdFqmhWPNsuixTQZ9jx25fM4dfyNuYGc/4GYnwKhoRIyCVEpiRtGKalWRiXaG/PjUiCnoUpbC0sjEW484gg2JBL0uS7X7t5Ni2nyv0ccwXcPP5zTWlqm/Z6GMJDCwAsuTCsiFmsiFgrYKHOMDPuJiJGgu+5uN8+eQhrkaOhJKf9C0NcHQ8Oavem5b0I41wghsIVvtDSbFgaSXq/ADjdLn1uoi4dl2HN9heNJZe0FmSBPZSbklDdtg9FClgycqRjyXAS+QRUTBsluj29cq0gm4Z57fAXbOrSumhVFPZX5KqFOP8cE3uqBEIJW02TE89hdKNTNWMkpRQEa3rs7Uxb3Khsyp4gg/FMqU7b9XJSKEmKtZ51IW402y+JH69dz8bJlXHnwwYB/xzITIwX872JJC1V2135ayveqPCgzRCIwMgwgSBkmthQMKYd9SrE9k2fPgMfQkH8xWL4cOpYqMq5C5Q+sn1BESloM32DZ6xXY7szOYMkrRVp5RKcw6GwhS8nA00VpTXoK2fxq1KpQmwsaQBY9B4YQSARHneJwww2anh546ik480zYuHHa068b8yn85mlN/3NILr+eFNfVQddld6GAWwdjJXeA91gKz7KQSYkbBrYQ5JXyc1FMWSn8JsSMS5OnImWafGzlyrothpZh4elRQ+WFTYFKbTpLS6cuGStag9QQ8WyckQi9OYfhWA67J0/nMo+WFrCTio5OUI5gln0VG5KJDJbpCstltd+AcKqQTDG5dWyzyFrIa796Z7reLUMIFFMn1GaUhzfmO0SlJK08Dl6n+NnP4PDDYe9evzrojjum/RXqgpTz16Aw43mkPY9YaKjMCCkELXUyVpTWDLnuAZ0ndOB+s5C6YEtJqqipYgqwR4XftNZ+Gvsi8fxaho0qu9AeEbNpNw3SSvPXQo6OTl+Of2gQ+gccDGGysifCupUWK3skBdthWyHHrnyeYdelvUnS2QWFvP84ECkaLBJBr1dgu5Or2WBRWjPk1R4eE2WJzNOhEMjmz6Q8VjN5Eq+jFYNVEoEtIfHQjHguS5fCjTfCKaf4ocGnn572NOqGYcAsW2dNidaagSBxczbl5M91isbKgOuyp1CYsWBcIShLPpC9W2F5csiUpAyDviDwLWMSLxMEwT18I2UB5ONngiGMCskTKQSnpWLcm86RUZqIDR2dMDQE2nRJxW262os/EYktJY5S9DsOHr5YXbzJz13p3etXXVjzrGExX0SlQRSDnPLo9QoMKZcWwyIlzQkNhFyg2puqsZzZEoK09mifpp5KRnkz1vAwhC8212pUr+zKKI+CUjQZ479DTBoMaZcmZdHUJPn+9+EXv/DzVYp897t+d+NzzvGbZ841luV3UlbK97DMBUWBt5QZXj5miwzCQP2un8fVbdvTPpdzSvlL8QFsNIZnWsiU+AJhfpdbOyop3Ux7fkx0rkI/9UYKiRhz0/LBJa1EyhI9IzZ0dsBQziUVbR73HpaUtEhZEsIDaG3x9WR6eyEpwGwsLbC6EpUGES3JacUeJ8+Q9A2WpDTGGRfpsgTUWrCEJKc8CloRrVFPJRvkj8z0brKYUFtNbK7oEbKkrBr7t4UkozzSyiUibWy70kjJZuHzn4f+fvjc5+C88/zeQUuXzmiqNWHbvlenUIBodG4+o6gcfSBfGOeTohREn+MggK5pGivD3vTzsxYbB66vKKRulGuqCEsgDL8sWQfy+Ysl9GNIA4SoSAaOTnAR0ihsIzJue5Hy1wgBra2+hP/ICMxjM+MFQQhBLNBkcdHsdnPscHKMKLdUteNoxbB2iUxDwK3YwqBWPRUdGBJj80emQzGhtloSb1Z7ZLSaNBE4KiSDysWdIBR28cWwYoVvrPzHf8Dznw/vehfcd9+MpjslhuFX/cxVnkpJ4K1OlT6PpdP8dWSELbkcA65blzYPi5HiGrvfcegtFGreD45S5A7AbsljCT0qITVR0lSxBMIM8lSURkQXTyMyU1rYRoThwhBJO1XRaNBRmr2uyzLbCgwZgTVBOKAaUvqGitLQ3we/l70862VpN6yyh027aZEUE8tsO1oxojxGlMuw8hj2XJZZUZZbc3R7PAuEEMSFgdKSjPbY4eRJSoMWw/SraZQmbk5vARWBnkqqSqhlLDmtGFIO8Qk6ZtfCZAm1Q56LZHKPUERIhjyXtPJoNiq/ayzmGyVvfzvceit8+9tw993ws5/5j3/7N3jzm2c89QkRYu4MlaLAW2oGF8aCUvy4t5c3dHWVPAaf3rKF3w8OlsYI/FBzj23zT8uWcXZHx6JZX2aLIQRNpsk+x9fG7rSsKRVsc0qR15r4HOyjEc/j37Zu5dhkktdO1p9kHggNlZCaiEpJwjTJeh7RqECntZ+JaC0eS94yLHpSS9mf3sdwfpC4lcAybP4ykuMDW/ey3Lb4wdoluMpXpJ1u4z0poaMdPE+za4/Dd7M78apcACNCcGn7Kl7X1APA3ZkBPtO7iWHlVu3o+5JEO1d0HzqzLz0PSCFIChNP61IoxBYSYwa5S1agp6KnKLXUWjPgOWjEjL0ppfdifEJtdkxJ8kT4Ze+SQc8lOUG+jmHAy17mPx591O/AfOedcPTRs5r2hBQbFLa311fef7YCb/++bRv/sXMntw8M8O3DDgOgxTTpsSwGPY+sUmhgyPMYymb5+ObNvLClhbYqPb8OVMzAWNnvOLha02Xbk4Y1s0ohqC7XPxvuHBjgg5s2sbNQoMuy+Pv29rq+/3QJDZWQmijW/g+5LvG4gTvkAGDYi+tuJ2pG6UktYSAboS+7n4JXYG00RkZpnsgV2F1waRYupmHOqKeNYUBXp+ACtQTRC4+rEfp0gT7PoU85pLVHXmsi2ij1iTGEYK9XeQucEAZJ6T8+3LG6tH2qC/hCYghfg8bVmpz2iM+gb89onoomMsn3zGiPIeWSmMExGku1hNqM8suqkzUYQTEhGVJ+p+qpPEHr1/shoD17oKdn1lOvSrnwWz2v8SOBwNtMDId7hoa4eudOAF7T0VHa/s1DRw3wglIMeR4DrsvP9u1jVTT6nDJSiphlpcsFpeiJREhUCbUprRmeI2Xg/Y7DzkKBlZEIlx100IKrD4eGSkjNxKUkIgQFUyOLpcmLpOKnHEMatCc6iZhR9mV6Ee4QR8dsHsoWuGs4y8uTmmY7OW2DIK8UrtYkTIPuLsE7rKU4jl+B4Slf4yKrPPa7DilMhof8bctUgmtSR5EUpm+giDF35kMwgG+kXJl5moONOK+NLB13926aEE/MXbVHrZiBh2Wmr/WCnJHIBCl0SmsGPBcZdK2dLSaiIqHWCXoT1dogUQR9XoaUS1JO3T1XiEoj5b77fA/IC184m28xSlFKv1Coj6HiKkWf67K/UJiRbsqQ6/JPTz2FBt7Q2cnLJ7g7t6WkQ0o6LItLV6yoeO43/f38Yv9+Ll+16jlRbSSFoNWyGHJdduRydEciNI3pzJxXioJSJOuwP7TW7CgUWB40gT2no4OsUpzT0YHbAHlDB/4RD6kblpQkTZM+o0DcEui8XjQVP9VIRlLYZoS+zD5Oiu/moSzcOZzhJQmbiDn9nJDPbtnCXQMDfOPQQ1mfSNDVNfqc1r7BorWBCrwpSgXdpzFZx9S9i/44PMRvBvYBcL/s58oVazko4s9TKb/fzNAg2BE/P6JBHS9TIoQgrxWpCZ5PK48R5ZGaRW5KOZaQZMuqjYolyc3TKN+KCYMR5ZLV1rQ8SY88Am94g3/8vv99P9l2OhS0QgJmmeeneNwLBUgkpvd+YxlxXfY5DiOeR9IwZpS0+YnNm9lRKHBQJMLlq1dP/YIx5JTiI5s2sdtx+MPQEFetWcOpzeMr8g5EmoJw+/Zcjm7bps2ySjlT9SpL3pLL8ZFnnuHxTIY7jjmGFtNECMF53d3AaJXXQrJ4EgxCGoKUYaBtgTb86p/FUvEzEbZh05Xs4VVdfs3ovSM5MooJOyZPxM/37+fa3bvZlMuxq0omoxB+WMg0wbb8MuhYFOLx2h+ndzXxxYMPJiEl96aHedWTD/GTod3EYppk0i97XboMDAmDAzSsYq7Wms/0buJtOzYyXKVEyhKCtHIrWzUEeEFuSj3FxsoTapXWDAYlydN9D4Fg0HOm9bpDDoGTT/aP1fnnw4MP1v5apTV73XxJhC+nvNI+M01fT2WmuEqxt1BgWy5HXilaTXNGRspP9+3jhn37kMBXDzmE5AyqhaJScvWhh7IyEmF7Ps/rH32UT23eTNabeRPL6fDLvj7e/eSTPDA8PC+fN5aYYZAwDHYXCuwpU7EdmWVZsqs139i5k7976CF+NzjIsOty/wJ9x6kIDZWQaRE3DGJSUoji9/iZxKOitZ6x2uJ8IoXk2JYeVkciuMD9WY05jYqfLbkcH9i0CYD3LF3K6a2tczJPIQRv7O7mNxs2cHJTExml+NjmzfzD44+zI59HSmhKwbJl0LPE9+IM9EO+wQyWnwzv4afDe3koP8x/DewY97wlJM4Ecvpp5ZFRHrEaE2g9rfn3fc/yzp2P0DeJEVFMqC12SZ6qN1E1YoGsfnYa6rq2Dd/8pq9qOzLia608/nhtr81oj3SQgFpUDd7t5hlRLtLU5HIwk2v5iOuyPZ9nb6FAzDBIBXfY0yWnFJ9+9lkALlm+nONTE/nIpub5TU3cumED5wVuyu/s3s0ZDz/Mx555hn3O6HHNKVXVwJ0JSmve/sQTXPjEE/zf/v287tFHuWOB2mTbUpbKl3cEytjZWXRLfjyT4ay//pXPbtlCTilODvbv383R2jVbQkMlZFoUZZ8dm0BTZfwC5gZJXv2uy6DrNkSMsxZeFsTO783Jmj0qeaV495NPMux5HJ9K8eExsfW5YGU0yv+sX89lBx1ERAhuHxjgs1u2lJ43DE0uVmDZMujsBNfzPSzu9G7254RnC1m+tH90rtXuCEt6KlRWQLla0ecVsKSsyZuitOZfejfxg6Fd3Jsb4pq+rROOLSbU1lKSPBHlsvrTIRaDa6+FY4/1w3dveAM888zkr9GB50cKXyG32bCISMlIUCa+R2T9HlW52nvI1MuLUiQqJT9Yt47Xd3ZyybJlM36fIknD4PNr1vDdww+n27J4Npfju3v2VDh1/3XLFtb+6U+c9sADvOnRR/n4M8/w+8HBGemzyEAp1hSCdfE4WaV46+OP89N9+2b9XWaCEXReTnseewsFHK2nfXy01nx5+3Ze9vDDPJhO02QYfOHgg/nx+vUcPB/SyTMkzFEJmTYJw8CMSLy8xgpWCaU1OaXIKYUpBAnDoNk0GXZdhjyP5kWQAHd2RwdJw+DlbW0130H+65YtPJRO02KafP2QQ6YdMpgpUgjesXQpf9vSwgefeYaTyu5WN+VyvPDBB1kZiXBCKsXz4k0cbqeQ2RjeiEBKX+rfMudXRdfRik/1PkVeK06MNXN55xq6zAlE9YRvBCbLdueI8shPIGc/Fq01X9i/mf8b6UUCCrhpeC/nNi9hrR0fN76YUOtqPasKh6KsflKZxKaRQ5NMwv/7f/Da18Jjj/nGyk9/CkuWVB+f1Yr0mKonS0gsIVFak9eK3W4ec0SwzDR9YcOy11cIFuJ7lPpnmYtSjXWJBF9au7Yu71Xk9NZWfnvMMfyyr48d+TwtZWvLtnyenNZsyuXYlMtx5+Ag/71nD6siEd7Y3c2FPT3EJgk/PZpOE5OS1cFF+yMrVnBeVxdrYjHe9/TT/HT/fi566in6XZe3zlXZ1iQIIWixLEZm6E0RQrA5l8PRmjNaW7ly9WqWRCYWtmwUhK6Xn2wBGBoaorm5mcHBQZqamhZ6Os8ZtNZsz+dJ510itlHSP4gG3paEYRAJFF/TnseWbJaUOXFPmEblqUyGG/ft451Ll1Y1tH7V18fbnngCgOsOP5wXL6DbtFwC/uf79/OuJ59k7L30MjvCOzqW8cp4J4WMxHF9L4sIZP+Lhstc2Vrf6NvGfw5sJyUNfrR8A90TGSn41VEmguVWFBFU4uxwcmioyQB4Mp/mzTv+ikLzmc613JHp47Z0H69MdvKZrvEXTk9rPy8GaJ5G2K8aw4Gno1VaNBvWtM773l6/A/OaNfCNb/gy+L/7HVxzTeW4vFK4KDraBB/+jEtzS5V5DEGiWRFvqwyHaEYNFa11yYAxhSBpTF21NBWPpdPklOLYWYR6ZkpBKXYVCmzL59mRz3P/yAg37dvHiOfRY1n86bjjqlaKZT2PL2/fzjd27eKEVIofr18/bj8orfnk5s1ct2cPJ6VS/M8RR9Sl6myuyStF2vNKpd79jsNdg4O8qr29pmM95Lq+3k2dDZrpXL8b/zY3pOEQgSjRkOviBC7ilGEQM4xxi3I86L480gBeFa01HtS8uHx+2zZ+0dfHf+/ezXuWLeNtY+7Gjk0mObWpiaMSiQU1UqAyVPHK9nYePeEE7h8Z4U9DQ/x5eJgHhofZUchz2c5naFkrec3KThwXnALkC5DNQMGB3LCf2wL+v1L6DxH8K8XoNoL/17I7tdbscv0W0x/rOLjCSNmYG+bW9H7e13ZQaeE0hfDLLwM9lWHPJa9VzUbEoZEEX+w+jH1egVekOjkqmuJwO8Gbmqu7KAwh0DArldsiKcMkrxS9boG08mg1LRKTqBGX09kJN9wALS2jpcV79/oCcZVIipH7gw7WvON945NRTAucrKTZkPNWAZZTioueeoqns1muPvRQzpxnoTBbSg6KRjkoaHR0blcXlx10ED/bvx/N6G/f1ZpzNm7khS0trIvH+dyWLTyb98/PFtMko9Q47RIpBP+yejWHxuOc3dExbSNl2HV5PJNhfSJRVRdlLrh/eJj3b9rEQdEo1x12GCIoez6rTMtmMRB6VEJmhKc1ac9X7pzKTTziumzN50lVMWTmEhUkZDqBvkmReODxmYpb9u/n89u28WQ2C0CXZXHJ8uW8qaur9J097VeKzFfIZ6ZkPY/v793Lzfv38+P160vz3ZTNsjISwZIS1wXHBeWNar8oj9J21/HvxpXyH5RKrimJ1xX/Bb9MOhKp9NA8lBtmQ3T0TnvQc3j51vvIa82Xug/ntMSowTfkOSy1olhIdrhZDOSUxy2rvGmFXOYSrTVp7aE0NEuTliCPZLps2QL33jv697DnMKI9nrzf4pDDFWefq6p27fZcyOb8XkOReerqfdmzz/LtXbvosCxu27CBjgYVbLu1v5+3jsla7rFt/nX1al7W1jat9/q/fft4WVvbpOvg13bs4N+3bcPRmpiUvKKtjdd1dXFKU9OcrIl5pfi3rVv5z127UPhy/L846iiWzsAr0ggeldBQCZlzSqEiz6NpDr0qntYUlMLRGldrJP4dVkxK4kHcPeN57CkUaK2xksHTmhv27ePft21jW3DHtcS2+dXRR9PeoItwrRSU4gUPPoiBX5Xx6o6OKQ2uopGi9Kg2TLnBUtzuuTA07FccCaGJxUTViynAV/dv4b8Hd7LainH98g2lO9VBz6HDsNHAPq9AyxTelP8d2sN1Azv4+pL1rJikN5KnNducHKvs+UkedIOwkiUELTMIB5WTV4rtbhZLSOwaKpMG+n1DZbZ6KrXw674+LghCod89/PA5q36rBzml+FVfHz/Yu5c/Dw3xpq4uPrpy5bTF5P5z1y4+/eyz/G1LC9869FDihsHWXI5b+vp4aVsbqwLPzk379nHRU0/RbBgMlpViLbFtXtPZyUVLl9ZtbfS05p1PPsktfX2ArwT86VWrZqzyGxoqsyQ0VBYPw4FXpdkw6qZ/UU5eKTKBh6dYQh0JvD3lFwVXKbbn8+SVmtaiVFCKH+zdy5e3b6fXcXhLdzefW726YeXsa+HxTIY3PPoovUF556pIhPN7ejg0HmdVNMrySGRWMXjPg1/s9S8GH0isIela2BE/76LcHhr2XM7a9gCDyuUTHQfz6iZfaCoT5KkUtMKWk1+Ybx7u5dO9T6OBi1pX8rbW6lUmu5w8l+55nH1ugZtWHkuyDhL8tZILkoHj0qDN9IXhpnv+9LkF9lYx2lzXFw8cawwODkBnF7TNoc1QUIovbNvGNTt3ooHzu7v53MEHz90H1pny/K7pcnt/P//45JPklOKIeBwNPBoI2HxkxQr+aflyANKex858nrWxGPeNjPCT3l7+b98+Bj2PFsPg/uOPL3nbXK1n/LvTWvOJzZv57z17sIXgG4ceykun6SEaSyMYKo3trw45YEgYBkkpSc+RSFPa8+iybVbHYiyJRGixrKo5M2Yg0e0FIaFasaXkrT09/PHYY/nX1as5ualpURspAIfH49x97LF88qCDaDdNns3nuXzLFs577DFOfeABbt6/vzT28UyGb+3cya/7+tgeeJamol85/POOZ7gr08+txi56loBpwMiwr6BbyPuel7g2ubDJX9Cv6dvGcMHDc8HwBGnHI+9qDE/iuVR9/GpoP5cHRsq5yR7eklw6flxw2nWYFgWt6Fcu11bRcJlLotKgyTDJ4ycGD0xTHM7RikHljtN4+fXNklf/rc0NPxi/nJuWn380V7ha8+pHHuHrgZFyXlcXn1q1au4+cA6YzY3T37a28qP162kxDB7JZHg0k8EATmlqqij3TRgGh8TjCCE4PpXiyoMP5v7jj+ebhx7KRw86qGSkaK0546GH+PqOHTPSg/nGzp389549CHyBvdkaKY1CmEwbMi8Ue1dszeVmdQdTjaznEQmaJtbyvgnDoN2y6HUc2qaZMxA3jAUpS5wrYobBu5Yu5S3d3fy/PXu4e2iILbkcW3K5ktsa4I+Dg1xeptXyvGSSV3d08KqOjqohMK01H9q0iX2Ow+GxGJcetJxoIEiXzcJIOuhHE1yrX2F18yNjFzu8PNf17eSC5ApAklOKmDDITnBYf5fv47LBp1DAK6NdvCu2ilx+/GCtwDAhHpe8r+0gLt3zBD8Y3MVrUt0snSRMVG9E0AcprxT7vAKmkFM2MiySDqT9x5ZnD/bDjq2C737L5NXnFSr6+1imnyztur5abb0xheCM1laezeX44po1087vOBA4PpXixiOP5Lrdu9mQTPLi1taawixRKcd1JX4oneapbJZ/3bqVZ3I5rli9elr5byc3N9NumlyyfPm8JzLPJWHoJ2TeUFqzNZejoPWMpLQnos9xWGLbtNu1ZwwWlGJbLoeCecvAX0wUBbKKht+t/f38pLeXZ3M5Hk2nS6XPBvDClhauOPjgUkMzgB/u2cMHn3kGWwhuPuoojqiSJOEEyblFftG3n4ueeZKYlNxx5LF0TXI8c8rj01uf5Uf79gJwVlsH/7567YS5H7kc7NoJyRRIqXn3rkf5S26Ilyba+Vz3oVVfM5b/G97L1/u28vqmHl7f3DPrsFFGeYBmiRmdMgHY05rtThbF+PLsXA7OPNWmb5/g8qsc/v41o55CrX3v1dKlvlZLPe4P9jsOg65b8hh4WrPfcSY9XiG1c+2uXXzq2WdRwKlNTXzrsMMqtGKmos9x6tp1Ogz9hDynkELQZlk4Ss1IKbIa2SAvZbqJaLaUdNg2hUUi8z/fyDG9dF7c2so3Dz2UXx19NPcedxyfXrWKDYkEHvCnoSHay/b/XQMDfGLzZgA+vGJFVSMF/PJbu+xxVlcbr2hr4/JVq+iJWxXPjX0kbcmWvF+NdUFPD185ZA0xW0w4PpWEpmZIj/hejUvbVyGAX6X383Cutv4mL060YwjB1f3bOHPr/fxn//aqvYpqJS4NPGCP6+dMTUZGeeQnkPaPRuFNF/qxrf++xqD8rYTwPUm7d8OOHTA4NOrFmgm39/dz+kMP8Y4gLwP80u7QSKkfFyxZwnWHH05CSv4wNMRZGzeyZZLGXRvT6Yo+RPU0UhqF0KMSMq94gVfF1XrWngytNf2uO21vSvnrd+bzDLgurQfgj3s+2JTN8kQmwysCN7PWmuX33APA8wNRrHqVX/Y5DhEpS+fN5myWPY7D82v87Wdz/sXaMiEShc/0Ps1Ph3s5KpLk2qVHjss56vMcrh/cxTtbV2AEsv63jOzj2oEdPOv4RlJSGrypaQlvbF5Sk2JuNQY9h4Q06DEjFV2Qiyit2enmyGk1oRdneAheeYpNeljw7//p8KKXVBo+nud7XpyCXzaeSEAy4cv31xJZyCnF57Zs4Tu7dwNwWCzGd9etq/CihdSXR9Npzn/8cXYWCrSZJtcefvi4fklbczletXEjI57HD9et44Q5uA6GHpWQ5xxG4FXJ16F5WFYpYkGzrpkghKDdsrCFmLdOrAcaa2KxkpEC0Os4dFgW3ZbFVw45ZFZGSqHMNfDz/fv524ce4sqto/16VsdiNRsp4Herbmv1c2S0hne3riQmJBLBoKr0jPw2vZ/XbXuQ7wzs4PuDOwH/3P37VCf/s3wDV3Qdwhorxojy+NbAdv5+6/0lQbvp0iRNRpTHPtep6mnMao9MkKszEakmeP1b/HP42q8bjH0bw/CNk5ZWP6F5cMA32rZtg75+yE0y9bsHB3nJQw+VjJS39fTw86OOCo2UOWZ9IsHNRx3F0YkEOaXGtXbocxzOe+wxeh2H1dEoh8fHt4Y4UAiTaUPmnWRQPlxN/bFWdNBbaFkkgjmbxmlBYu3OQoFIjc3uQiamy7Z54Ljj0DArI+Wn+/bxua1b+djKlfxi/35+HmhC/HFwkKznTdqvZTKamvwuxZkMdCZsvrfsaFYFMv3gezc+v/9ZfjniN55ba8c5MdZS8R6GELwk2cEZiXZ+m+7jPwe20yQNlpSp7fa6BTrN2rx8QghS0mTQczCADtOukLgf8lwEU+/PN17g8YNvG2x8QPLow4IjNlS/EbAj/kMpyOdh7x6/OqilGVpbfaMGfC/KJzdv5gd7/TygLsvii2vWNLQ+yoFGt23zv0ccwWOZDEeWhVCznsf5jz/OM7kcy2yb761bN20NmMXEgfvNQhoWI/BkbM/niQc9gaZLJvCm1EMkqdmyGFGK4QaQ+T8QqIex95fhYbbn81z01FOAn7R78bJlXLJ8+Yxb24Nf+dLaCjt3+uGQ1WWib7/P9PPZ3k3s8xwk8NaWZby9dfmE+i1SCM5ItvN3ibYKj0y/5/D3W+/nsEiCVyY7eEmyg9YpxOoMIYgbBn3KxfAEbYGRk9OKEeUSqyFxt70TPnCZy8GH6gmNlIr5Sz/0E4v5IaF9+3zDpa3d9z5FggZ24Jcdf+Kgg8LfxwIQNwyOKwv5/HloiHMeeQSAFsPg++vW0XOA5wiFOSohC4KrFFtyfpO5+DTvjou5KSui0botnBnPY2suR0ROLdMeMvfsdxxOfeABhj2PI+Jxrlq7tuKOcjZoDbv3+HkdTc3+tp1OjjO3PQDAQVaUyzvXclR0Zk317kz38aE9T1AMJhoITom38IpkB6fFW4lOUuHj9zfy6DGjpAyTPU6eQeXQNMbQ0VpzX26I46L10/NRCp4ZzNNsGazsNGlKweZclr3TyAMKmVuynsfzH3iAfY5DRAh+tH79nOSllNMIOSqhoRKyYPQ7Djvy+Zrl7IuMeB6WEKyMRuvaJ6O3UJiWvH7I3PLXkRGeymY5s7297r2Ucnk/R8M0/MTaD+x+nDsz/byxaQnvaVsx635B+90Cv07v5xcjvTyaT5e2J4TBF3sO48RY84SvzSoPhabdsNnn+iFJq8yr0+sWuHLfM9yR6edfutby8mQnALeN7Oc7Azs4IpLgiEiStTrJ4U3xmlROPa350dBuvt63lVfEu7gosprWVmhrmxv9lZCZc9fAAN/YuZN/XLKEv5uHMFwjGCrhKRiyYKSCXJWcUjXnHKign09PnY0UgNagy/OI5x3Q8d7FwlHJJEclk3Py3tEItLb4nYntCHyu61BGlEt7jXklU9Fu2ryx2a8G2lzIcstIL78Y2cdet8Ah9mjS47BySY6R0o9Jg2HlMuA5eOiSkaK15uaRXv59/7MMKw8DwX53tNb4ofwwTxTSPFFIc8Own1cS2SdZG40REwaf6Vpb6lr92/R+7skMEhESWwr+kh3kkcCg2uyliSYUfX2SfB7a2+EAztNcdJzW0sJpLS0LPY15JVyNQxYMU0paTJNdhcK4njwTkfY8koZRV8G48vl0WBbbczmGXZfEHPUlCmkMyhNrEwlJRM5NnH+1HeM9bSt5V+sKNjvZinyVD+x+Aldr3tG6nJNizSWDJSVNRpRLPPDs7HLzfK73Gf6YHQBgnZ3gss41HBIZDYf9Q/NSNkRSPJIf4aaH0gx2jpBPeCUDRJX5zh/ODfO/w3sq5pkQBpe0H8Q5qS6kENgtvu7Mzl2+Z6WlubZS5pCQehMaKiELSrNpklOKQc9D4lcETWQcKK1xgaWWNWcGRNIwWB6Nst9xGHBdokH35TAUdOBhmv4FeOdOvx/QDGVQakYKwZoyb8oOJ8fG/DB5rblo92NsiKR4Z+tyTgwMlqJmyq9H9vEvvc+Q1h62ELyrdQXnNS8dF9LpNG1OT7ZzerKdFzULLjjTwlid5mPfGybWomgp+4LPj7UQEwZ57Xcbj0nJ65p6KiqVhPCVfPM56N0LhQIk4vVRt60FIXzDSBpgyOD/oaH0nCTMUQlZcLTWjHgefY7DiOdhS1m1GmjIdYkbBssjkTn3dHhaM+y67HccskoRN4xxOgaNgtKavFJYUs6q2/FzEa1hz15fV6S5Zf4/v9ct8N8DO/jf4T0UgqX4mGiKd7au4IQgUfbe7CDv3PUoGyIpPtW5hlVllUqT8Y5zLe67R/KmC10+8KnZ6QR5nt9Mcr4pN1ak9A0Ww/TVhg1z/oym5zLDnktXzGR1S5hMOyNCQ+XAQmnNsOexv1AolR8Xc1e84LkVkci85o84SjHouvQ7DvmgR5HdIAaL0pq05+ECUSFwtMbVmoiURGsMpYWMT6xdCHrdAtcO7ODGMoPlS92HcVrCb/L35+wgx0WbpnVM/3in4L1vsYnGNBe+1+NvX6pYvXZxLfda+9VIygv+HfP34r16LR6GlcvqDpOjV4TJtCEhyKADckJK32BxHPoch4RhkFOK1BzlpkyGFfQESpkmA45Dv+uSUYqkYSyY96JooDiB4bTEskgYBo5SZJViwHUZdl00fofWaBi6mpSxibULsas6TZsPd6zmrS1LuW5gJ/dmBzklPlrRMVmV0EScfJrm8CMVj2+UXP15k4NWOyVD5Xe3Sb52pcHSFZqlKzQHrdaceKpm1VrdUF4KIXwBuudS39Ci76BRfrO5wYWeQWiohDQgppS0Bj1dit4MtF7QsuGIlHQH3px+x2HQ8zBgXhNuldZklKIQGEpLLKsip8cwDKKGUcr7SXseQ67LgOchgNgi0ogpKg/nlcKehzyhpiZIp31tlVTTwoUUuswIH+5Yjav1rA1hIeDz1zjc/L8GO7YK1hw26n7Y8oxg05OSTU9Wvmbpcs2pf6t404UeK1eH7or5wtWaglY42m8boTUkDTMM5QaEoZ+QhifneeS1pskwGuIuo5hTsz/IqYlKOW3Ruukw1kBpG2OgTIanNVnPY9jzGHZdHK1pMs2GDQuVf9eYlKQMg2HPm5c8oVzeTxrNZHwhuAbdRXVh31548jHBzm3+44lHJff/SVDI+1/6h7cUOHS9f2l4dpNASkLDpY5orXHQ5LXfSd5AEBGShDSISoO059KvHGLSqNDQWQh2DbqsaAlDPyEhkxI1DBYodaAqQghSpkncMEoJt8UQ1XQ8Fm6gCaPwFy4FaPyLtfA/yL+1EoKElPREozUbKEUMIUiaJknTpNU0K6uZGsif7ihFRilfqVhKuqNRElL63rUgT6jPcegPytPrLQAHfgiou8fvfTM06HtWFokDatp0dEFHl8Y/4wA8shn4y92SB/4kOGTdqFHyna8Z/OJGg85uTTRWaazYEfifX49quXz2Iyb33TPx+XnjHU7JAPz8ZQZ/vKP6DhYCvvczh2QgDvyD7xj85Y+CSNT/zEgE7MhomOo9H/SIB5Xat/5c8tC9E8/h7Zd4pcTpO34tuffuice+9d0eHV3+//9wu+TuuyYee94/eixZ5v//z78X3HXb+O/maXC14py3eKxarUkKg8fuNbntlxIDARTfX5LXJlmlOOdNLusO89/rrw8IfvV/E5+UZ75WcdgR/jF6fKPg5v+deOzLzlIceYw/dtOTght/WH2+Bx2ueft5C7tWhIZKSMgMMYSgJcgPGQgupFnPI2lWd9kqrSkEFTqe1lhCYEtJRAgM/BwdUwikEEh8g0gG22N1aJgYNQyWBN6ffYUCA45Dk2kuqFZMTikynocpBE1B2Co+xhgr5gklDYP+IBQogpyles/dtqC7G/b2+sZK0wJqh2it59WDGIvDaacrTju9crvrgmlpeveUX0h9ItFKw6V3D2x7trYdtr9XTDrWKusa8PhGwV2/mfhieeHFo4bKvX+U/OT/TTz2vAtHDZUH/yL44X9NfBl89RtVYND5RsJkY19+tmLJMn/sYxvl5O/7CsmKQ33F4V89Cdd9Z+wIgd/hyuDEF3gcdIhHXBpsfmryOWw43ikZKlufnXzsYUc4JUNl57aJx575GkHqLaGhwtVXX80XvvAFdu/ezYYNG/ja177GiSeeuNDTCgmpCUtKOssupAOuW8pf8cqME4Gf3NpimiXviy3EvF6MpBC0WhYxKekNvCvTDal4WpcMqeniBV6kgtZ4WhOVkm7bJhXk10xG1DDoCRpR9gVzj0xQyj4bLAu6u3wDZXDA96zU6nzSQXUaAqTw+/xIBEZgjFabp6c1Hv7+cIN/hR4NPaXkwrZ0uOI/XNIjsOkJgVKVz409bd73cY+3XVRbKfS73u/xxguqj9Xa95wUOfsNHseeqMjnoJAX5PNQyI8+Hy2r2D7lRYqm5onDVImyFk4nnKKxLHfCsS1to+9z7ImKt1008diiQQNw1LGjYx2tUYHCcExITCFZs8LECg7pUUfBe9874dty9BoTyDOsXA5ZZ006h1VrRueweq2edOzaspylFasqx3r4FYRxYXDUYQvvVlzwHJUf/ehHvOUtb+Eb3/gGJ510El/+8pf58Y9/zBNPPEFXV9ekr53zHJV00KMjXqZyVCiA4/hqUeW9D4pjY7HRX6/j+OMNA6LRmY3NZPxfbTQ6ulq6rt/mtNj+dCZjs1m/vi8SGW3m4XmQy01vrBCV+tq5nP+cbY/eEk1nrFL+5wGUN6HL5/3vYln++OmO1drfP1D9eE5n7CTHXgvha8Kk06RzOUzLIhKLkTJNolISyWb9/JCZHvt6nCfB8VSRCP1Ksd9xcF2XZtdFGEbFWJ3J4HoeBdvGkRINGEqhslm0EIhEAjPwBNm5HIbW6LLzRLkuTjZLQWucWAwDfJ0cxyEOxKNRrOK+nOh4VjlPlOcxPDLil7LHYjQVvVj5PMJ10WOOpwiOpx5znkw21o3E6d0vGOiHpkgBEwdtmP73C5AZ/9h70Tj5gmB/xiXqucQdhdUs8WIWCt8YITNSGosUaEA6DqbjIE0TIxILJO0lVjaDRLDfMhhB0yRNpOsinQJaGuiyYy+yGYTWqEjlsZeFPFpKdNlVfFpjc1mEUii78ncv87npjRUCHYuXjc0hlIeyKteImscqhcz554mKjx5Pkc8jPBdtWuiyNaLmsVojs/6xV7HR370oFBCuM72xholj26SViy0k7fmCb3DG4qO/+7Kx5euJCM4pHR1dI7L5HL3ZYQpSkIglS4Zr1ePpOLM6T1ytyaaHaZMmbfEUwxmTlhbo6aGuTOf6veCm0lVXXcXb3/52LrjgAtavX883vvEN4vE4//Vf/zVubD6fZ2hoqOIxpyST/mPfvtFtX/iCv+3iiyvHdnX527duHd129dX+tgsvrBy7apW//bHHRrddd52/7Q1vqBy7fr2//f77R7f96Ef+tle9qnLsCSf423/3u9FtN9/sbzvjjMqxp53mb//Vr0a3/fa3/raTT64c+/KX+9tvvHF02z33+Ns2bKgc+5rX+Nu///3RbX/9q7/tkEMqx/7DP/jbv/Wt0W2bNvnbli2rHPvOd/rbv/KV0W27dvnbxva9eP/7/e2f+9zotsHB0ePplt1lfOIT/rZPfGJ0m+uOjh0sq8373Of8be9/f+XntbT423ftKuWvrPjmN1nf08OaSy9lZTRKm2URNwyM5cv9sZs2jb7+W9/yt/3DP1S+7yGH+Nv/+tfRbd//vr/tNa+pHLthg7/9nntGt914o7/t5S+vHHvyyZBMIm+/nXbbZkU0Ssdvf8uyjg7aTj+dnFIMByGW1le8goM6O2m+5Ra6bJuV0SgH/+UvrO/p4fC//Vs6LYuolCitaX3Na1jS3o66/vpSTknh3ntZ3dnJmuOPZ0U0yqpYjNWxGEvOP5/mlhas7353dF6PPebPd9WqyvleeKG//eqrS5vktm00t7SwetUqOiyLoeCYNr/vfSxpbyd51VWjY/ftY0l7O0va2yvetukTn2BJezupf/kXwO9Mq9Lp0lgzn6Gzw1ev7fjiv3DMunaWXfmJivc4Zl07x6xrJ/vsPrLKo7MdTrnpq5x6chuHfPKDdDhxVloxVtox/uaEgzntyB5W7tlDlxlhiRnhiB/8F39zZDcbPnoxK+wYXVaEFsPi4JPXsuawZpZvfoZmaTLkubT++Lscs66d1e+tPE/Wn3EMx6xrJ77xgdK21p/9mGPWtbPmwsrz5PAzT+WYde0k//z70rbm237BMevaOeRNlefJoa87g2PWtdN0562AH7pM/uF2jlnXzmHnvLBi7NrzX8Ux69pp+dVPS9sS9/+JY9a1s+5lJ1SMPfhdb+CYde203fTD0rbY4xs5Zl07R7zwyIqxqy59G8esa6fjB6OxkciWZzhmXTtHnnRwxdiVH7+IY9a10/lf/1HaZu3dxTHr2tlwVHfF2OWf/TDHrGun5+p/K20zhgZLx7N8jVj6hcs4Zl07S79w2egbuG5prDE0ukb0XP1vHLOune7PfIi08miWFkvNKIcd2cmyQ1PIPbtG9893vsKSQ5K0fOSdFXPrPm4ZSw5JYjw7uka0//A7PP+ILo54/zsYUi4q8C8c8cIjOWZdO7HHN5bGtt30Q45Z187B76q8lqx72Qkcs66dxP1/Km1r+dVPOWZdO2vP968lntakPZcTX3sGR61rI/rH22kEFjT0UygUuO+++/jYxz5W2ial5IwzzuDuu+8eN/6KK67g8ssvn88phoTMiFLJsBANXz4SMwzssoQAHeizxA2DSDD3rkhk1OsQ3I0ZQGewzdMaEdz9tZomlmn6wnPBnaIZaOTUGwG0WRZpzyPjeUxfbcQnG+jS5FyX5WXbDQM6OsAKnAdeIDSWy1WGHtq7Fe5Sxcp4lETU3w+JBPRJGOwXNDWNngNJwyQW9Puxhb8vJwrtmELSZUYwEOTV7NRlZ4pGk1YertYYCzSHxYJX1EABlpoRkrK+lYpRKUlJk2HlkpD1/T0prRlRLs2G1XBVgQsa+tm5cyfLli3jj3/8IyeX3cV/+MMf5s477+RPf/pTxfh8Pk8+P7o6DA0NsWLFijD0M5OxYejH/3+dQz/lbt1ZnydzHPqpNrYAGPH46EI1nWM/2/NkGqGfsWMHXZdtuRzNnofpedMK/bimyaCULI1EyHsefYODfgl3IlE69jpfoH+vw74BE21HiMX8RNu4SmNYmkHbpicSocO2K459QUTYtw/6+yHqpYnHK136OA4icNOXH8+x7n+lNQO5DH25tG8ExsrCGHMY+im4LhnTJGZHaDUs0k6B4cwwMcvCLJ/Dczz04wJZ7UE+T5uCpkgMq3xfVgnnTCf0U36eeJEI+9wCA55Dc6GAhFmHfpQQDNgWTdKky7Qxc3mEVuhIlKG0seChn0VlqIwl1FEJCQkB/25wVz7PoOvSUl4uUgP9jkOzabI0uFjsLRTY5zikxpRBa+13Wy7aiFL6nzvgurRbFt22XbUKSWsYGoLeXt9ubJpGcm7l+2iGlEuvW0AKUeqsPBd4WpNWLoYQpJRFXJsIJfGUZkA59CkHSwiiYu7mULoyaf//5Q8Yrd5fSApakcNDIogLgyZpEmPu9Z48relVeUa0S4rZJVtrrRnGJSFMOqQ9TrdFKb8Srrt7gjeYIYtGR6WjowPDMNizp7Ld+J49e+ipt/kWEhJywCKFoD0IAeWUqrmKKeP5HYk7yjpyd9k2Auh1HFJQMlaEgFSq8vVDrkuTadI5SUdvIaC52Tduit6VWKzSyVULQoiSW36v61eBpOrk/lfK7yDtepphR5H3NElMmgyLqGkgTTAtiJmChLZIKcE+r4CHW+ryXG+KUVNZbEwooXgNXciIqtKarPJVZJuFpMnwK/6iYj5bVQiWKJvdjiarPZpm2Ppba82Q59IpDXose8I+ZuVO4YVgQQ0V27Y57rjjuO222zj77LMBUEpx2223cfHYZNWQkJCQSYgaBu2Wxa5CgUgNZd+u1uS1ZpltV5RGSyHotG2EEPQWCiSg6gI+HJRHd9s2Zg2GUSQCS5f6Ua19+6Cvz/es2Lb/XK0V4klpYpiCPW6eIc9XL50SLdCeb4z4jf4EXllOuRDgGoqC8GiJGyxJ2LRGDCxLYJp+NGd0FwnAIu1Jdufz5JXvkWoE1ei5pBCIEgqgQ0pazAiJORIfrA1JyrPZnsuB8EhM002ntWbQdemSkmXRSEO311hwHZX3v//9nH/++Rx//PGceOKJfPnLXyadTnPBBRcs9NRCQkIWGS2W5bcL8DyapkjeHXJd2iyrapKvFIJOy0IAewoFklQaK1nPTyrttu1pLfBC+EVi8bifepNO++lFQ0O+AVFMrSmmQkxETBosMaP0enkKelTcxPeK+Ck9ngueGv1caYA0NNIAI+KL25kmGBKEAVFT0B21abXMmgyvhGGwPBpldz5Pv+vS3MCtGWZDvkyUsMU0aQoSzRdSKLFIzDDojkTYkcuRF6Lmc1EFRkrCMOiZ5jm8ECy4oXLuuefS21KzjYIAABQoSURBVNvLpz71KXbv3s0xxxzDL3/5S7rrHRALCQk54DGCMM62XI5C0NCwGmnPIyYlHZY1oSdABO8lgD2Og8ZvTllQirzWLLVtkjOsZCoaI01NvlGRz/uP4eFRA6bYOXhiJEkVxfF0sdMChhEYH7YfairmNUsJhhkYKpJS24aiiL4GIkJMKbo3loiULItE2Bu0kUgaxoT7fLFRNFCswMPWVIMo4ULQZPqaLbsLBYxA02gyXK0Zcl1aTZMu215Aj1DtLLjg22wIk2lDQkKqsSefp9dxqnbcdoOmkssjkSm9LuBf0Pc7Dnsch6gQZJWi27YnNXJmQ6HgGy3ZbKXkz0QUi6GKIZrKMM38oIJ91Os4mEIQn0XLh2LnbFOIBbmI5oPO43bQIqM5KLVvZLTW7AmSwFsmaYtR/G6dlkWHbS+oB2zRJNOGhISEzAVtlsWI55FRqiJ2r4O7yQ7LIlXj1VwEiboCvyKo1TT9v+dokS96W8Ym7jYyMvA+RaSkz3EY9DwkfoPJWo0NV2synldqrTCi1IT5QXNBuYHSZduLwkApUvT+OUFIp7VK5VvG8ygoxRLbpm0Oz9+5IDRUQkJCDjiKjQy35/NEtS7dOaaVIibltA0NIQRtwYU4WocGkQciQgiaTJOkYZDxPAZdl2HPY8TziE3QT6roPckphQSSpuk3ppSSAdetCLnNFQWlGAkMlM7Ag9KIIZ6pMKWky7YpKFWqRisy5LpIYFk0OifCi3PN4ptxSEhISA00GQYtQWfrVsuioBSuUiyJRmcUUhBCzDgn5bmEDPZTwjD8dgyex6Dj0Od5RKQkFvSMyipFISgl7yw2pixrMNkeeAX2OA4wN8ZK1vPIa72oDZRyIlLSEyTXZj2PqJQMBtVpPZHItCuDGoXwVxcSEnJAUgzZpD2PrOeRVYpOyyK5SBfrxYYQgphhEDMMWk2TEc9jwHUZ9DyE1sQNg+5odMLwUPH4wdwYKxnPw1GKJZEILQdQeXWiWAmUz5N1XVKGQU+kscuPpyI0VEJCQg5YitoqOwoFmoL/HygXpMWEJSWtUtJkmmQ9DxEk3E51LMrzg4phoFrF/CZj2HUR+KGQWhKqFxtNhoFjWRQCb9FiqOyZjAPvCIWEhISU0RIs2E1mbfogIXOHMYPwWTE/CEY9KzM1VnwlVr/kuGcW5eWNjhCCDttGa31AGOYH5lEKCQkJCTCEYMlCa4CHzIp6GCtFJdZYIHIWew6EAA8EIwVCQyUkJCQkZBFQNFakEOzK54HajZWiEmvyAMjXeC4SGiohISEhIYsCIURJI2RXPo8TCMMVFVmrlY17gZHSbJr0LBIl1pBKQkMlJCQkJGRR0Rp4VkZcl7xSOEqR1dpvDYBfIm0KgYFfBt1mWXRZVpijtEgJDZWQkJCQkEVHcyAOp7XG0xq37FHQmnyg09JpWXTadijSt4gJDZWQkJCQkEWLCLwn1S5mB0rVy3Od0A8WEhISEnJAEhopBwahoRISEhISEhLSsISGSkhISEhISEjDEhoqISEhISEhIQ1LaKiEhISEhISENCyhoRISEhISEhLSsISGSkhISEhISEjDEhoqISEhISEhIQ1LaKiEhISEhISENCyhoRISEhISEhLSsISGSkhISEhISEjDEhoqISEhISEhIQ1LaKiEhISEhISENCyhoRISEhISEhLSsFTrjL1o0FoDMDQ0tMAzCQkJCQkJCamV4nW7eB2fjEVtqAwPDwOwYsWKBZ5JSEhISEhIyHQZHh6mubl50jFC12LONChKKXbu3EkqlUIIUdf3HhoaYsWKFWzbto2mpqa6vnfIKOF+nh/C/Tw/hPt5fgj38/wxV/taa83w8DBLly5FysmzUBa1R0VKyfLly+f0M5qamsIfwjwQ7uf5IdzP80O4n+eHcD/PH3Oxr6fypBQJk2lDQkJCQkJCGpbQUAkJCQkJCQlpWEJDZQIikQiXXXYZkUhkoadyQBPu5/kh3M/zQ7if54dwP88fjbCvF3UybUhISEhISMiBTehRCQkJCQkJCWlYQkMlJCQkJCQkpGEJDZWQkJCQkJCQhiU0VEJCQkJCQkIaltBQqcLVV1/NqlWriEajnHTSSfz5z39e6Ckteu666y7OPPNMli5dihCCm266qeJ5rTWf+tSnWLJkCbFYjDPOOIOnnnpqYSa7SLniiis44YQTSKVSdHV1cfbZZ/PEE09UjMnlclx00UW0t7eTTCZ5zWtew549exZoxouXa665hqOPProkgnXyySdzyy23lJ4P93P9ufLKKxFC8L73va+0LdzP9eHTn/40QoiKx+GHH156fqH3c2iojOFHP/oR73//+7nsssu4//772bBhAy996UvZu3fvQk9tUZNOp9mwYQNXX3111ec///nP89WvfpVvfOMb/OlPfyKRSPDSl76UXC43zzNdvNx5551cdNFF3HPPPdx66604jsNLXvIS0ul0acyll17Kz372M3784x9z5513snPnTl796lcv4KwXJ8uXL+fKK6/kvvvu49577+Xv/u7vOOuss3jkkUeAcD/Xm7/85S9885vf5Oijj67YHu7n+nHEEUewa9eu0uP3v/996bkF3886pIITTzxRX3TRRaW/Pc/TS5cu1VdcccUCzurAAtA33nhj6W+llO7p6dFf+MIXStsGBgZ0JBLRP/zhDxdghgcGe/fu1YC+8847tdb+PrUsS//4xz8ujXnsscc0oO++++6FmuYBQ2trq/72t78d7uc6Mzw8rA855BB966236he+8IX6kksu0VqH53M9ueyyy/SGDRuqPtcI+zn0qJRRKBS47777OOOMM0rbpJScccYZ3H333Qs4swObzZs3s3v37or93tzczEknnRTu91kwODgIQFtbGwD33XcfjuNU7OfDDz+clStXhvt5Fniex/XXX086nebkk08O93Odueiii3jlK19ZsT8hPJ/rzVNPPcXSpUs5+OCDOe+889i6dSvQGPt5UTclrDf79u3D8zy6u7srtnd3d/P4448v0KwOfHbv3g1Qdb8XnwuZHkop3ve+93Hqqady5JFHAv5+tm2blpaWirHhfp4Zf/3rXzn55JPJ5XIkk0luvPFG1q9fz4MPPhju5zpx/fXXc//99/OXv/xl3HPh+Vw/TjrpJK677joOO+wwdu3axeWXX84LXvACNm7c2BD7OTRUQkIOQC666CI2btxYEWcOqS+HHXYYDz74IIODg/zkJz/h/PPP584771zoaR0wbNu2jUsuuYRbb72VaDS60NM5oHn5y19e+v/RRx/NSSedxEEHHcT//M//EIvFFnBmPmHop4yOjg4MwxiXzbxnzx56enoWaFYHPsV9G+73+nDxxRdz8803c/vtt7N8+fLS9p6eHgqFAgMDAxXjw/08M2zbZu3atRx33HFcccUVbNiwga985Svhfq4T9913H3v37uV5z3sepmlimiZ33nknX/3qVzFNk+7u7nA/zxEtLS0ceuihPP300w1xPoeGShm2bXPcccdx2223lbYppbjttts4+eSTF3BmBzarV6+mp6enYr8PDQ3xpz/9Kdzv00BrzcUXX8yNN97Ib3/7W1avXl3x/HHHHYdlWRX7+YknnmDr1q3hfq4DSiny+Xy4n+vE6aefzl//+lcefPDB0uP444/nvPPOK/0/3M9zw8jICJs2bWLJkiWNcT7PS8ruIuL666/XkUhEX3fddfrRRx/V73jHO3RLS4vevXv3Qk9tUTM8PKwfeOAB/cADD2hAX3XVVfqBBx7QW7Zs0VprfeWVV+qWlhb905/+VD/88MP6rLPO0qtXr9bZbHaBZ754ePe7362bm5v1HXfcoXft2lV6ZDKZ0ph3vetdeuXKlfq3v/2tvvfee/XJJ5+sTz755AWc9eLkox/9qL7zzjv15s2b9cMPP6w/+tGPaiGE/vWvf621DvfzXFFe9aN1uJ/rxQc+8AF9xx136M2bN+s//OEP+owzztAdHR167969WuuF38+hoVKFr33ta3rlypXatm194okn6nvuuWehp7Touf322zUw7nH++edrrf0S5U9+8pO6u7tbRyIRffrpp+snnnhiYSe9yKi2fwF97bXXlsZks1n9nve8R7e2tup4PK7POeccvWvXroWb9CLlbW97mz7ooIO0bdu6s7NTn3766SUjRetwP88VYw2VcD/Xh3PPPVcvWbJE27atly1bps8991z99NNPl55f6P0stNZ6fnw3ISEhISEhISHTI8xRCQkJCQkJCWlYQkMlJCQkJCQkpGEJDZWQkJCQkJCQhiU0VEJCQkJCQkIaltBQCQkJCQkJCWlYQkMlJCQkJCQkpGEJDZWQkJCQkJCQhiU0VEJCQkJCQkIaltBQCQkJOSC57rrrxrWmDwkJWXyEhkpIyHOc3bt3c8kll7B27Vqi0Sjd3d2ceuqpXHPNNWQymYWeXk2sWrWKL3/5yxXbzj33XJ588smFmVBISEjdMBd6AiEhIQvHM888w6mnnkpLSwuf+9znOOqoo4hEIvz1r3/lW9/6FsuWLeNVr3rVgsxNa43neZjmzJapWCxGLBar86xCQkLmm9CjEhLyHOY973kPpmly77338vrXv55169Zx8MEHc9ZZZ/Hzn/+cM888E4CBgQH+8R//kc7OTpqamvi7v/s7HnroodL7fPrTn+aYY47he9/7HqtWraK5uZk3vOENDA8Pl8YopbjiiitYvXo1sViMDRs28JOf/KT0/B133IEQgltuuYXjjjuOSCTC73//ezZt2sRZZ51Fd3c3yWSSE044gd/85jel173oRS9iy5YtXHrppQghEEIA1UM/11xzDWvWrMG2bQ477DC+973vVTwvhODb3/4255xzDvF4nEMOOYT/+7//Kz3f39/PeeedR2dnJ7FYjEMOOYRrr7129gciJCRkQkJDJSTkOcr+/fv59a9/zUUXXUQikag6pnjRf93rXsfevXu55ZZbuO+++3je857H6aefTl9fX2nspk2buOmmm7j55pu5+eabufPOO7nyyitLz19xxRV897vf5Rvf+AaPPPIIl156KW9+85u58847Kz7zox/9KFdeeSWPPfYYRx99NCMjI7ziFa/gtttu44EHHuBlL3sZZ555Jlu3bgXghhtuYPny5XzmM59h165d7Nq1q+p3ufHGG7nkkkv4wAc+wMaNG3nnO9/JBRdcwO23314x7vLLL+f1r389Dz/8MK94xSs477zzSt/zk5/8JI8++ii33HILjz32GNdccw0dHR3T3PMhISHTYt76NIeEhDQU99xzjwb0DTfcULG9vb1dJxIJnUgk9Ic//GH9u9/9Tjc1NelcLlcxbs2aNfqb3/ym1lrryy67TMfjcT00NFR6/kMf+pA+6aSTtNZa53I5HY/H9R//+MeK97jwwgv1G9/4Rq211rfffrsG9E033TTl3I844gj9ta99rfT3QQcdpL/0pS9VjLn22mt1c3Nz6e9TTjlFv/3tb68Y87rXvU6/4hWvKP0N6H/+538u/T0yMqIBfcstt2ittT7zzDP1BRdcMOX8QkJC6keYoxISElLBn//8Z5RSnHfeeeTzeR566CFGRkZob2+vGJfNZtm0aVPp71WrVpFKpUp/L1myhL179wLw9NNPk8lkePGLX1zxHoVCgWOPPbZi2/HHH1/x98jICJ/+9Kf5+c9/zq5du3Bdl2w2W/Ko1Mpjjz3GO97xjoptp556Kl/5ylcqth199NGl/ycSCZqamkrf493vfjevec1ruP/++3nJS17C2WefzSmnnDKteYSEhEyP0FAJCXmOsnbtWoQQPPHEExXbDz74YIBSIurIyAhLlizhjjvuGPce5TkglmVVPCeEQClVeg+An//85yxbtqxiXCQSqfh7bBjqgx/8ILfeeitf/OIXWbt2LbFYjNe+9rUUCoUav+n0mOx7vPzlL2fLli384he/4NZbb+X000/noosu4otf/OKczCUkJCQ0VEJCnrO0t7fz4he/mP/4j//gve9974R5Ks973vPYvXs3pmmyatWqGX3W+vXriUQibN26lRe+8IXTeu0f/vAH3vrWt3LOOecAvtHz7LPPVoyxbRvP8yZ9n3Xr1vGHP/yB888/v+K9169fP635dHZ2cv7553P++efzghe8gA996EOhoRISMoeEhkpIyHOYr3/965x66qkcf/zxfPrTn+boo49GSslf/vIXHn/8cY477jjOOOMMTj75ZM4++2w+//nPc+ihh7Jz505+/vOfc84554wL1VQjlUrxwQ9+kEsvvRSlFH/zN3/D4OAgf/jDH2hqaqowHsZyyCGHcMMNN3DmmWcihOCTn/xkycNRZNWqVdx111284Q1vIBKJVE1w/dCHPsTrX/96jj32WM444wx+9rOfccMNN1RUEE3Fpz71KY477jiOOOII8vk8N998M+vWrav59SEhIdMnNFRCQp7DrFmzhgceeIDPfe5zfOxjH2P79u1EIhHWr1/PBz/4Qd7znvcghOAXv/gFn/jEJ7jgggvo7e2lp6eH0047je7u7po/67Of/SydnZ1cccUVPPPMM7S0tPC85z2Pj3/845O+7qqrruJtb3sbp5xyCh0dHXzkIx9haGioYsxnPvMZ3vnOd7JmzRry+Txa63Hvc/bZZ/OVr3yFL37xi1xyySWsXr2aa6+9lhe96EU1fwfbtvnYxz7Gs88+SywW4wUveAHXX399za8PCQmZPkJX+0WHhISEhISEhDQAoY5KSEhISEhISMMSGiohISEhISEhDUtoqISEhISEhIQ0LKGhEhISEhISEtKwhIZKSEhISEhISMMSGiohISEhISEhDUtoqISEhISEhIQ0LKGhEhISEhISEtKwhIZKSEhISEhISMMSGiohISEhISEhDUtoqISEhISEhIQ0LP8fotRHDMowacwAAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# save statistics for each parameter option\n", "all_options_mean = []\n", "all_options_std = []\n", "\n", "#######################################\n", "# NOTE: Change PROB_MUTATION to the name of the parameter you are testing.\n", "#######################################\n", - "for PROB_MUTATION in PARAM_OPTIONS:\n", + "for po in PARAM_OPTIONS:\n", + " exec(f\"{PARAM_TYPE}={po}\")\n", + " print(f\"Starting {PARAM_TYPE}={po}\")\n", " option_best = []\n", - "\n", " for _ in range(REPETITIONS):\n", "\n", " # initial population \n", " chromosome = np.array([0,0,1,0,1,0,1,1,1,1,1,1,0,1,1,1,0,0,1,0])\n", " population = np.empty((0,len(chromosome)))\n", " for i in range(POPULATION_SIZE):\n", " random.shuffle(chromosome)\n", " population = np.vstack((population, chromosome))\n", "\n", " # keep track of the best chromosome\n", " best_chromosome = np.empty((0,len(chromosome)))\n", " best_fitness = 100000\n", " repetition_best = []\n", "\n", " for i in range(NUM_GENERATIONS + 1):\n", "\n", " # evaluate the fitness for each chromosome in the current generation\n", " x_y_fit = np.array([ackley(chrsm) for chrsm in population])\n", " \n", " gen_best_idx = np.argmin(x_y_fit[:, 2])\n", " gen_best_x, gen_best_y, gen_best_fit = x_y_fit[gen_best_idx, 0], x_y_fit[gen_best_idx, 1], x_y_fit[gen_best_idx, 2]\n", "\n", " # save information about the current generation\n", " repetition_best.append(gen_best_fit)\n", "\n", " next_gen = np.empty((0,len(chromosome)))\n", " while len(next_gen) < POPULATION_SIZE:\n", " # select parent chromosomes\n", " parent_1 = tournament_selection(population, x_y_fit[:, 2], TOURNAMENT_SIZE)\n", " parent_2 = tournament_selection(population, x_y_fit[:, 2], TOURNAMENT_SIZE)\n", " # make child chromosomes using 2-point crossover\n", " children = crossover(parent_1, parent_2, PROB_CROSSOVER)\n", " next_gen = np.vstack((next_gen, children[0]))\n", " if len(next_gen) >= POPULATION_SIZE: break\n", " next_gen = np.vstack((next_gen, children[1]))\n", "\n", " # mutate genes in the next generation\n", " next_gen = mutation(next_gen, PROB_MUTATION)\n", "\n", " if ELITISM:\n", " if gen_best_fit < best_fitness:\n", " best_fitness = gen_best_fit\n", " best_chromosome = population[gen_best_idx]\n", " # add the best chromosome to the next generation without mutation\n", " next_gen = np.vstack((next_gen, best_chromosome))\n", "\n", " population = next_gen.copy()\n", " \n", " option_best.append(repetition_best)\n", "\n", " option_best = np.array(option_best)\n", " \n", " m = np.mean(option_best, axis=0)\n", " s = np.std(option_best, axis=0)\n", " \n", " all_options_mean.append(m)\n", " all_options_std.append(s)\n", "\n", "fig, ax = plt.subplots()\n", "\n", "x_axis_range = list(range(m.shape[0]))\n", "\n", "COLOURS = ['b', 'c', 'g', 'm', 'y', 'r']\n", "\n", "ax.plot(x_axis_range, np.zeros(len(x_axis_range)), ':r', linewidth=1.5)\n", "\n", "for i in range(len(PARAM_OPTIONS)):\n", " ax.plot(x_axis_range, all_options_mean[i], '--'+COLOURS[i])\n", " ax.fill_between(x_axis_range, all_options_mean[i]-all_options_std[i], all_options_mean[i]+all_options_std[i], color = COLOURS[i], alpha=0.1)\n", "\n", "ax.set_ylabel(\"Fitness\")\n", "ax.set_xlabel(\"Generations\")\n", "\n", "patches = []\n", "for i in range(len(PARAM_OPTIONS)):\n", " patches.append(mpatches.Patch(color=COLOURS[i], label=f\"{PARAM_TYPE}: {PARAM_OPTIONS[i]}\"))\n", "\n", "ax.legend(handles=patches)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "97d80dcc-c88b-4be1-ab4f-2e0ead3e0119", "metadata": {}, "source": [ "## Fitness function global minima\n", "Remember ackley(0, 0) = 0.0" ] }, { "cell_type": "code", "execution_count": null, "id": "05ade091-4ff4-4d2c-90aa-65822b252312", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_1_genetic_algorithm/crossover.ipynb b/es1_1_genetic_algorithm/crossover.ipynb index f0cae10..e49bae0 100644 --- a/es1_1_genetic_algorithm/crossover.ipynb +++ b/es1_1_genetic_algorithm/crossover.ipynb @@ -1,78 +1,86 @@ { "cells": [ { "cell_type": "code", - "execution_count": 42, + "execution_count": 1, "id": "87063d74-5a67-40b1-bf21-f78592e39a8f", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "\n", "def crossover(parent_1, parent_2, prob_crossover=0.5):\n", " \"\"\"\n", " This function takes two parent chromosomes and returns two child chromosomes used to \n", " populate the next generation. If the crossover probability is 0, the returned child \n", " chromosomes are duplicates of the parent chromosomes. If it is 1 then all child \n", " chromosomes are generated using 2-point crossover of the parents. \n", " \n", " :param parent_1:\n", " :param parent_2:\n", " :param prob_crossover: The crossover probability [0,1]\n", " :return: child_1, child_2\n", " \"\"\"\n", " \n", " if np.random.rand() < prob_crossover:\n", "\n", " index_1 = np.random.randint(0,len(parent_1))\n", " index_2 = np.random.randint(0,len(parent_2))\n", "\n", " while index_1 == index_2:\n", " index_2 = np.random.randint(0,len(parent_2))\n", "\n", " if index_1 > index_2:\n", " index_1, index_2 = index_2, index_1\n", " \n", " first_seg_parent_1 = parent_1[:index_1]\n", " mid_seg_parent_1 = parent_1[index_1:index_2+1]\n", " last_seg_parent_1 = parent_1[index_2+1:]\n", "\n", " first_seg_parent_2 = parent_2[:index_1]\n", " mid_seg_parent_2 = parent_2[index_1:index_2+1]\n", " last_seg_parent_2 = parent_2[index_2+1:]\n", " \n", " child_1 = np.concatenate((first_seg_parent_1, mid_seg_parent_2,\n", " last_seg_parent_1))\n", " child_2 = np.concatenate((first_seg_parent_2, mid_seg_parent_1,\n", " last_seg_parent_2))\n", " else:\n", " child_1 = parent_1\n", " child_2 = parent_2\n", " \n", " return child_1, child_2" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26c0637f-9fe2-49b9-8028-74b81523a021", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_1_genetic_algorithm/fitness.ipynb b/es1_1_genetic_algorithm/fitness.ipynb index 642b8f9..ef2bf5d 100644 --- a/es1_1_genetic_algorithm/fitness.ipynb +++ b/es1_1_genetic_algorithm/fitness.ipynb @@ -1,86 +1,86 @@ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4ea6d5c6-1750-4365-b5ce-7eead06002d9", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "\n", "def ackley(chromosome):\n", " \"\"\"\n", " The fitness function first decodes the chromosome and then calculates the Ackley function. \n", " There is a single global minima at [0, 0] and multiple local minima. We will look at the \n", " function with x and y in the range [-5, 5].\n", " \n", " :param chromosome: chromosome to evaluate\n", " :return np.array([x, y, fitness]): which are the decoded x and y values and the ackley function output.\n", " \"\"\"\n", " lb_x, ub_x = -5, 5\n", " len_x = (len(chromosome)//2)\n", " lb_y, ub_y = -5, 5\n", " len_y = (len(chromosome)//2)\n", "\n", " precision_x = (ub_x-lb_x)/((2**len_x)-1)\n", " precision_y = (ub_y-lb_y)/((2**len_y)-1)\n", " \n", " z = 0\n", " t = 1 + (len(chromosome)//2)\n", " x_bit_sum = 0\n", " for i in range(len(chromosome)//2):\n", " x_bit = chromosome[-t]*(2**z)\n", " x_bit_sum += x_bit\n", " t += 1\n", " z += 1\n", " \n", " z = 0\n", " t = 1\n", " y_bit_sum = 0\n", " for i in range(len(chromosome)//2):\n", " y_bit = chromosome[-t]*(2**z)\n", " y_bit_sum += y_bit\n", " t += 1\n", " z += 1\n", " \n", " x = (x_bit_sum*precision_x)+lb_x\n", " y = (y_bit_sum*precision_y)+lb_y\n", " \n", " fitness = -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2))) - np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))) + np.e + 20\n", " \n", " return np.array([x, y, fitness])" ] }, { "cell_type": "code", "execution_count": null, - "id": "901d4046-7b4a-4875-bfe6-b20f805442da", + "id": "97319d06-4978-4fc5-8828-5c56aab1abdf", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_1_genetic_algorithm/mutation.ipynb b/es1_1_genetic_algorithm/mutation.ipynb index fb008a4..3471568 100644 --- a/es1_1_genetic_algorithm/mutation.ipynb +++ b/es1_1_genetic_algorithm/mutation.ipynb @@ -1,72 +1,72 @@ { "cells": [ { "cell_type": "code", "execution_count": 2, "id": "59fc498c-93e1-4017-a6e6-c2645375bec8", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import random\n", "\n", "\n", "def mutation(population, prob_mutation=0.05):\n", " \"\"\"\n", " This function takes the entire next generation, created by crossover of parents from \n", " the current generation, and mutates each bit of each chromosome with a probability \n", " of prob_mutation.\n", " \n", " :param population: 2D numpy array with each row being a single encoded chromosome.\n", " :param prob_mutation: The mutation probability [0,1], default=0.05, is the probability \n", " that each bit in each chromosome is mutated.\n", " :return: mutated_population which has the same shape as population.\n", " \"\"\"\n", "\n", " mutated_population = population.copy()\n", "\n", " for i in range(len(population)):\n", "\n", " for j in range(len(population[i])):\n", "\n", " if np.random.rand() < prob_mutation:\n", "\n", " if mutated_population[i][j] == 0:\n", " mutated_population[i][j] = 1\n", " else:\n", " mutated_population[i][j] = 0\n", "\n", " return mutated_population\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "b0e8b64c-8e3c-4482-b3e8-f32e53a9855f", + "id": "52515fb6-32f2-4916-9ae6-a834e7bd622e", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_1_genetic_algorithm/selection.ipynb b/es1_1_genetic_algorithm/selection.ipynb index 4521306..8dab69b 100644 --- a/es1_1_genetic_algorithm/selection.ipynb +++ b/es1_1_genetic_algorithm/selection.ipynb @@ -1,57 +1,65 @@ { "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "b4461ad9-e7cd-489e-9e6f-0f8c55a1a87a", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "def tournament_selection(population, fitnesses, tournament_size=3):\n", " \"\"\"\n", " This function takes the entire current population, randomly selects \n", " tournament_size (default=3) chromosomes from the population as candidate \n", " parents and returns the candidate with the highest fitness.\n", " \n", " :param population: current population \n", " :param fitnesses: array of fitnesses for all chromosomes in the current population.\n", " :param tournament_size: number of candidate solutions to compare based on their fitness.\n", " :return: chosen_parents[min_fitness_idx]: the single parent candidate with the highest fitness.\n", " \"\"\"\n", "\n", " indices_list = np.random.choice(len(population), tournament_size, replace=False)\n", " \n", " chosen_parents = np.array([population[i] for i in indices_list])\n", "\n", " parent_fitnesses = np.array([fitnesses[i] for i in indices_list])\n", "\n", " min_fitness_idx = np.argmin(parent_fitnesses)\n", "\n", " return chosen_parents[min_fitness_idx]" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "831b2acb-cdd0-4314-a85d-0d2dae2768c3", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_2_evolutionary_strategies/es.ipynb b/es1_2_evolutionary_strategies/es.ipynb index f1da252..c3c3097 100644 --- a/es1_2_evolutionary_strategies/es.ipynb +++ b/es1_2_evolutionary_strategies/es.ipynb @@ -1,399 +1,399 @@ { "cells": [ { "cell_type": "markdown", "id": "eee45af8-3514-44b2-a37d-c7c256193e86", "metadata": {}, "source": [ "# Evolutionary Strategies\n", "\n", "We will now use evolutionary strategies to optimise values for the Ackley function. Here, we start with an initial guess of the x and y parameters that are not encoded like they were in the GA. This guess is mutated LAMBDA times to generate the next population by adding a random number multiplied by the standard deviation to the current guess. The selected parents in each generation are then used to update the guess by finding the fitness-weighted mean of the parent parameters. Therefore, the only information carried over to the next generation is this single x and y pair that is the current best guess.\n", "\n", "## Exercise 3:\n", "\n", "- 3.1. Look at the evolutionary strategies code below, what are the primary differences between this algorithm and the GA?\n", "- 3.2. Run the ES with the default parameters and observe the distribution of the population on the [x, y] plot. Compare it to what you saw when running the GA. What do you think causes this difference?\n", "- 3.3. What happens when you change the standard deviation magnitude for the mutation operator, MUTATION_SD? How does it influence the rate of convergence when you have a small vs large magnitude?\n", "- 3.4. Why should the number of parents (MU) be less than the number of children (LAMBDA)? \n", "- 3.5. How does a large MU/LAMBDA ratio influence the convergence rate?\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "id": "54eb0129-8ef2-4144-8e2e-bec91b260d69", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generation 0\n", - "Best parent: ackley(0.98788,-2.22903) = 6.79298\n", - "Updated m: 0.46265,-2.65643\n" + "Best parent: ackley(1.21168,4.351) = 11.32149\n", + "Updated m: 1.74055,4.56594\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 5\n", - "Best parent: ackley(0.1116,-1.04229) = 3.10011\n", - "Updated m: 0.02947,-1.6814\n" + "Best parent: ackley(2.00125,3.9979) = 9.37247\n", + "Updated m: 1.58549,3.58921\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 10\n", - "Best parent: ackley(0.00075,-0.92433) = 2.59738\n", - "Updated m: -0.07056,-0.72506\n" + "Best parent: ackley(0.95669,2.9815) = 7.21412\n", + "Updated m: 1.21986,2.96895\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 15\n", - "Best parent: ackley(0.01454,-0.02003) = 0.08625\n", - "Updated m: 0.05764,-0.00699\n" + "Best parent: ackley(1.23107,2.05872) = 6.78029\n", + "Updated m: 0.99357,2.62847\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 20\n", - "Best parent: ackley(0.01454,-0.02003) = 0.08625\n", - "Updated m: 0.01251,-0.01585\n" + "Best parent: ackley(0.93633,1.96306) = 5.43556\n", + "Updated m: 0.69851,1.8017\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 25\n", - "Best parent: ackley(0.01454,-0.02003) = 0.08625\n", - "Updated m: -0.09728,-0.01631\n" + "Best parent: ackley(0.10669,0.27162) = 2.14416\n", + "Updated m: 0.45662,0.70213\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 30\n", - "Best parent: ackley(0.01454,-0.02003) = 0.08625\n", - "Updated m: -0.11292,-0.01494\n" + "Best parent: ackley(0.01739,0.19414) = 1.30994\n", + "Updated m: 0.06214,0.27964\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 35\n", - "Best parent: ackley(0.01593,0.01706) = 0.08046\n", - "Updated m: 0.02983,0.03683\n" + "Best parent: ackley(0.04026,-0.01893) = 0.17778\n", + "Updated m: 0.08319,0.07409\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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2mtQRExOjjRs3aty4cY5tGzZsOG/NkhQeHq6OHTtW2r5u3TrFx8frlltukVQeMLKzs897vp49e+qHH35QVFRUjfqvipeXlyRV+ruQyhew9ujRQ0lJSerfv7+WLFni0iDi1lu8r1mzRs8995w7uwQAGHD06FENGTJEb775prZt26Y9e/borbfe0lNPPaURI0ZIkoYOHar+/ftr5MiR+vjjj5Wdna2vvvpKf/rTn/TNN99Ued7HHntMycnJeuGFF/Tjjz/qu+++U2pqqp555hlJ0u23367Q0FCNHDlS69at008//aR//vOfWr9+vaTyq0n27NmjLVu26MiRIyopKalRHQ8++KBee+01paam6scff9Ts2bO1ffv2i/o76tSpk9555x1t2bJFW7du1R133HHeEQtJevjhh/XVV18pMTFRW7Zs0a5du7RixYpKi1WrExwcLF9fX3300Uc6dOiQ8vPztWfPHiUlJWn9+vXau3evPv74Y+3atcsx1eMqfNcMAOCS8/f3V2xsrJ599lldd911uuqqqzRz5kxNmjRJL774oqTyqYIPPvhA1113nSZMmKArrrhCt912m/bu3auQkJAqz3v33Xfr1VdfVWpqqq6++moNHDhQaWlpjstRvby89PHHHys4OFg33XSTrr76as2bN88xdTNq1CjFxcVp8ODBat26tf73f/+3RnWMGTNGM2fO1IwZM9SrVy/t3btX995770X9HT3zzDNq0aKFBgwYoJtvvlnDhg1Tz549z3tc165dtXbtWv3444+69tpr1aNHD82aNUtt2rSpcd9NmjTRCy+8oEWLFqlNmzYaMWKE/Pz8tHPnTo0aNUpXXHGFJk+erISEBE2ZMuVifszzslmWZbm0h4tQUFCgoKAg5efnKzAw0HQ5AGBEcXGx9uzZo8jISMftEOrDnVXRsFX1vqxQm89v1ogAQD0UEVEeChr6d82g4SOIAEA9FRFBMED9xxoRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMZwHxEAqK9ycrijGeo9gggA1Ec5OVKnTtJZXxnvUl5e0q5dNQ4j8fHxSk9Pl1T+lfQREREaN26cHn30UTVpYvbjJz4+XseOHXN8E3B17dLT0zVlyhQtXLjQaV9CQoL++te/avz48UpLS3NdsQ0cUzMAUB8dOeLeECKV91fLEZi4uDjl5uZq165dmjZtmubMmaP58+dfUPdlZWU1+nbaSy08PFxLly5VUVGRY1txcbGWLFmiCEaILhpBBADgMt7e3goNDVW7du107733aujQoVq5cqWk8m+fvfrqq9WsWTOFh4frvvvuU2FhoePYtLQ0NW/eXCtXrlSXLl3k7e2tnJwclZSUaPr06br88svVrFkzxcbGas2aNZWOW7VqlWJiYuTv7+8IRJI0Z84cpaena8WKFbLZbLLZbE7Hn61nz54KDw/XO++849j2zjvvKCIiQj169HBqa7fblZycrMjISPn6+qpbt256++23HfvLyso0ceJEx/7o6Gg9//zzTueIj4/XyJEjlZKSorCwMLVq1UoJCQk6depUrf/+6wOCCADAbXx9fVX660iOh4eHXnjhBW3fvl3p6en6v//7P82YMcOp/cmTJ/Xkk0/q1Vdf1fbt2xUcHKzExEStX79eS5cu1bZt2zR69GjFxcVp165dTselpKTojTfe0Oeff66cnBxNnz5dkjR9+nTdeuutjnCSm5urAQMGVFv3XXfdpdTUVMfz1157TRMmTKjULjk5Wa+//roWLlyo7du3649//KPuvPNOrV27VlJ5UGnbtq3eeust/fDDD5o1a5YeffRR/eMf/3A6z2effabdu3frs88+U3p6utLS0hru9I9Vh+Xn51uSrPz8fNOlAIAxRUVF1g8//GAVFRX9tjEjw7Ik9z8yMmpc9/jx460RI0ZYlmVZdrvdWr16teXt7W1Nnz69yvZvvfWW1apVK8fz1NRUS5K1ZcsWx7a9e/danp6e1v79+52Ovf76662kpCSn47Kyshz7X3rpJSskJKTK2mryMxw+fNjy9va2srOzrezsbMvHx8fKy8uzRowYYY0fP96yLMsqLi62/Pz8rK+++srpHBMnTrRuv/32c/aRkJBgjRo1yqnPdu3aWadPn3ZsGz16tDVmzJjz1utOVb4vf1Wbz28WqwIAXOa9996Tv7+/Tp06JbvdrjvuuENz5syRJH3yySdKTk7Wzp07VVBQoNOnT6u4uFgnT56Un5+fJMnLy0tdu3Z1nO+7775TWVmZrrjiCqd+SkpK1KpVK8dzPz8/dezY0fE8LCxMhw8fvuCfo3Xr1ho+fLjS0tJkWZaGDx+uyy67zKlNVlaWTp48qRtuuMFpe2lpqdMUzksvvaTXXntNOTk5KioqUmlpqbp37+50zJVXXilPT0+n+r/77rsLrr8uI4gAAFxm8ODBevnll+Xl5aU2bdo4rpbJzs7W73//e9177716/PHH1bJlS3355ZeaOHGiSktLHUHE19dXNpvNcb7CwkJ5enoqIyPD6YNakvz9/R1/btq0qdM+m80my7Iu6me56667lJiYKKk8TJytYn3L+++/r8svv9xpn7e3tyRp6dKlmj59up5++mn1799fAQEBmj9/vjZu3OjUvqr6TSzUdQeCCADAZZo1a6aoqKhK2zMyMmS32/X000/Lw6N8ueLZ6ySq0qNHD5WVlenw4cO69tprL7guLy8vlZWV1eqYuLg4lZaWymazadiwYZX2n7mgduDAgVWeY926dRowYIDuu+8+x7bdu3fXrvgGhiACAHC7qKgonTp1SgsWLNDNN9+sdevWVbpPR1WuuOIKjR07VuPGjdPTTz+tHj16KC8vT59++qm6du2q4cOH16j/9u3ba9WqVcrMzFSrVq0UFBRUaRTibJ6entqxY4fjz2cLCAjQ9OnT9cc//lF2u13/8R//ofz8fK1bt06BgYEaP368OnXqpNdff12rVq1SZGSk3njjDW3atEmRkZE1qrsh4qoZAIDbdevWTc8884yefPJJXXXVVVq8eLGSk5NrdGxqaqrGjRunadOmKTo6WiNHjtSmTZtqdU+PSZMmKTo6Wr1791br1q21bt26Gh0XGBiowMDAc+6fO3euZs6cqeTkZMXExCguLk7vv/++I2hMmTJF//mf/6kxY8YoNjZWR48edRodaYxs1sVOmrlQQUGBgoKClJ+fX+0LDwANWXFxsfbs2aPIyEj5+PiUb8zJkaKjpeJi9xXi4yNlZnKbd0g6x/vyV7X5/GZqBgDqo4iI8lDAd82gniOIAEB9FRFBMEC9xxoRAABgDEEEAAAYQxABAADGEEQAoJ6owxc5ohG6VO9HgggA1HEVN9o6efKk4UqA31S8H893I7jz4aoZAKjjPD091bx5c8eXtvn5+Tl9/wrgTpZl6eTJkzp8+LCaN29e5V1ma4MgAgD1QGhoqCRd1DfIApdS8+bNHe/Li0EQAYB6wGazKSwsTMHBwTp16pTpctDINW3a9KJHQioQRACgHvH09LxkHwBAXcBiVQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgjEuDSHJysvr06aOAgAAFBwdr5MiRyszMdGWXAACgHnFpEFm7dq0SEhK0YcMGrV69WqdOndLvfvc7nThxwpXdAgCAesJmWZblrs7y8vIUHBystWvX6rrrrjtv+4KCAgUFBSk/P1+BgYFuqBAAAFys2nx+u3WNSH5+viSpZcuW7uwWAADUUU3c1ZHdbtfUqVN1zTXX6KqrrqqyTUlJiUpKShzPCwoK3FUeAAAwwG0jIgkJCfr++++1dOnSc7ZJTk5WUFCQ4xEeHu6u8gAAgAFuWSOSmJioFStW6PPPP1dkZOQ521U1IhIeHs4aEQAA6pHarBFx6dSMZVm6//779e6772rNmjXVhhBJ8vb2lre3tytLAgAAdYhLg0hCQoKWLFmiFStWKCAgQAcPHpQkBQUFydfX15VdAwCAesClUzM2m63K7ampqYqPjz/v8Vy+CwBA/VOnpmYAAADOhe+aAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAXrbj4Z/3yy2cqLv7ZdCkA6pkmpgsAUL/l5v5dmZmTJdkleSg6+hWFhU00XRaAeoIREQAXrLj45zNCiCTZlZk5hZERADVGEAFwwYqKdum3EFKhTEVFWSbKAVAPEUQAXDBf306q/M+Ip3x9o1zeN+tSgIaBIALggvn4tFV09CuSPH/d4qno6EXy8Wl7zmMuRYDIzf27Nmxop61bh2jDhnbKzf37BZ8LgFk2y7Is00WcS0FBgYKCgpSfn6/AwEDT5QA4h+Lin1VUlCVf36hqQ8ilWNhaXPyzNmxoJ+cpIU/165ddbd8A3Kc2n9+MiAC4aD4+bdWixaDzjoTUdmFrVaMnrEsBGhYu3wXgFtUFiKoCzLlGT35bl+I8IlKxLqV8dGaXfH07MUIC1AOMiABwi9osbK1u9KS6dSmsHQHqH0ZEALhFRYDIzJwiqUzVLWw93+hJWNhEtWgxzGldyrnCS4sWwxgZAeowgggAt6kqQFTlfNMvUnmwOfP42k79AKgbmJoBcMnU5NLcmixsvZDLgk3e0wTAhWNEBMAlcebi0ry8tiop+ZP69RuhqKiwCzrfiRM36ejR1erQ4YRiYrqfd1SjNlM/AOoOggiAi3bm+oz3379Lzzzziux2T3l4lOmpp77QtGnX1ug8WVm5+uGHg9q4MV/z5l0ruz3s13N8pWnTzh8oajr1A6Du4IZmAC7aL798pq1bhygv73Lddtte2e2ejn0eHqeVmZl33pGRN6f+TW2eX6IZmq8M9ZJkczrHyy9/rSFDIi94hAWA+3BDMwBuVbE+4+efOzmFEEmy25vos8+ytXLlZmVl5VZ5fFZWrn55fpuGaI3+W2/ozBBScY4pUwYoOjpYTz/9hYt+CgAmuDyIvPTSS2rfvr18fHwUGxurr7/+2tVdAnCzivUZbdvulodHmdM+m61Mkyf31YgRPXTFFWcFib17pYwMbU37SLfqH5Kk27RUPfSteipDEdrrdC673VMzZvQ/Z6ABUP+4dGpm2bJlGjdunBYuXKjY2Fg999xzeuutt5SZmang4ODzHs/UDFC/FBf/rGef/Up//vN/ym5vIg+P07LbPXTm7zw2W5l+/PFw+RSL7beRD7ts8pDl+K+jvSr/EzVz5hqNGxfNNA1QR9WZqZlnnnlGkyZN0oQJE9SlSxctXLhQfn5+eu2111zZLQBDfHzaKinpVmVm5mnlyi2aO/dLnf3PjGV56rPPssufvPmm1KR8zXxF+Kj47yk10cpb/ySbzXmERbI0d+4gpmmABsJlQaS0tFQZGRkaOnTob515eGjo0KFav359lceUlJSooKDA6QGg/omKCtPNN3fXZZd5Vd9w7Fhp48Yqdy2d+rL+sOwvmj//K3l4nP51q6WK9SNM0wANg8uCyJEjR1RWVqaQkBCn7SEhITp48GCVxyQnJysoKMjxCA8Pd1V5ANxgyJBI2WzOdzu12co0eHD7yo09yv85sn6drvnv/+4pSZo27VplZuZp5sw1qmoR644dhy512QDcqE5dNZOUlKT8/HzHY9++faZLAnARoqLCNH/+OseIhofHac2f/5Xz2o7gYCk0VOrVS1q4ULbevcufn7GOLCoqTOPGRVdaCOvhcVoxMc6/7ACoX1x2Q7PLLrtMnp6eOnTI+beVQ4cOKTQ0tMpjvL295e3t7aqSABgwbdq1GjEiVzt2HFJMTIiios66uVnbtlJ2tuTlVb54dfJkqbRUOuvfgqioMD311BeaMaO/YyHsU0+tr3w+APWKy0ZEvLy81KtXL3366aeObXa7XZ9++qn69+/vqm4B1EEVa0YqRkKysnKd7yvi7f3bFTQ2W6UQUqFimmblyi3KzMyr8R1bAdRdLr3F+0MPPaTx48erd+/e6tu3r5577jmdOHFCEyZMcGW3AOqwp5/+QjNmDDjj9u01vwW8VB5quGwXaDhcfov3F198UfPnz9fBgwfVvXt3vfDCC4qNja3RsdxHBGhYsrJyFR0dfEG3gAdQf9Tm89vlX3qXmJioxMREV3cDoB744YeDstudA0fFlS8EEaBxqlNXzQBo2Lp0CeXKFwBOCCIA3Kb8ypevnC7nLb/yhdEQoLFy+RqRi8EaEaBhyso683LeqkNIcfHPKiraJQ8Pf9nthfL17SQfn7ZVtqlq34W0A3Bp1Kk1IgBwtvNd+ZKb+3dlZk6WdOZdWT0UHf2KwsImVtHGed+5z3XudgDMYGoGQJ1SXPxzFSFEkuzKzJyi4uKfq2jz277qz1V1OwDmEEQA1ClFRbtUOYRUKFNRUdY52pTvO/+5KrcDYA5TMwDqFF/fTir/HamqMOIpX9+oX/98dpsz91V3rsrtAJjDiAiAOsXHp62io1+R5HnWHk9FRy+Sj0/bKtr8tq/6c1XdDoA5jIgAqKN+G8UID5+uyy9/0ClAhIVNVIsWw1RUlCVf36hzhouatgNgBpfvAqhTiot/1oYN7XT2dEq/ftmECKCeqM3nN1MzAOoUFpgCjQtBBECd8tsC0zOxwBRoqAgiAOoUFpgCjQuLVQHUOSwwBRoPggiAOqniMl0ADRtTMwAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGNcEkSys7M1ceJERUZGytfXVx07dtTs2bNVWlrqiu4AAEA91cQVJ925c6fsdrsWLVqkqKgoff/995o0aZJOnDihlJQUV3QJAADqIZtlWZY7Opo/f75efvll/fTTTzU+pqCgQEFBQcrPz1dgYKALqwMAAJdKbT6/XTIiUpX8/Hy1bNmy2jYlJSUqKSlxPC8oKHB1WQAAwCC3LFbNysrSggULNGXKlGrbJScnKygoyPEIDw93R3kAAMCQWgWRRx55RDabrdrHzp07nY7Zv3+/4uLiNHr0aE2aNKna8yclJSk/P9/x2LdvX+1/IgAAUG/Uao1IXl6ejh49Wm2bDh06yMvLS5J04MABDRo0SP369VNaWpo8PGo3AMMaEQAA6h+XrRFp3bq1WrduXaO2+/fv1+DBg9WrVy+lpqbWOoQAAICGzyWLVffv369BgwapXbt2SklJUV5enmNfaGioK7oEAAD1kEuCyOrVq5WVlaWsrCy1bdvWaZ+brhYGAAD1gEvmS+Lj42VZVpUPAACACizcAAAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgDEEEAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgjMuDSElJibp37y6bzaYtW7a4ujsAAFCPuDyIzJgxQ23atHF1NwAAoB5yaRD58MMP9fHHHyslJcWV3QAAgHqqiatOfOjQIU2aNEnLly+Xn59fjY4pKSlRSUmJ43lBQYGrygMAAHWAS0ZELMtSfHy87rnnHvXu3bvGxyUnJysoKMjxCA8Pd0V5AACgjqhVEHnkkUdks9mqfezcuVMLFizQ8ePHlZSUVKtikpKSlJ+f73js27evVscDAID6xWZZllXTxnl5eTp69Gi1bTp06KBbb71V//rXv2Sz2Rzby8rK5OnpqbFjxyo9Pb1G/RUUFCgoKEj5+fkKDAysaZkAAMCg2nx+1yqI1FROTo7T+o4DBw5o2LBhevvttxUbG6u2bdvW6DwEEQAA6p/afH67ZLFqRESE03N/f39JUseOHWscQgAAQMPHnVUBAIAxLrt890zt27eXC2aAAABAPceICAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwhiACAACMIYgAAABjCCIAAMAYgggAADCGIAIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgAgAAjCGIAAAAYwgiAADAGIIIAAAwponpAqpjWZYkqaCgwHAlAACgpio+tys+x6tTp4PI8ePHJUnh4eGGKwEAALV1/PhxBQUFVdvGZtUkrhhit9t14MABBQQEyGazmS7nghUUFCg8PFz79u1TYGCg6XIaNV6LuoPXou7gtag7GsprYVmWjh8/rjZt2sjDo/pVIHV6RMTDw0Nt27Y1XcYlExgYWK/fWA0Jr0XdwWtRd/Ba1B0N4bU430hIBRarAgAAYwgiAADAGIKIG3h7e2v27Nny9vY2XUqjx2tRd/Ba1B28FnVHY3wt6vRiVQAA0LAxIgIAAIwhiAAAAGMIIgAAwBiCCAAAMIYgYkhJSYm6d+8um82mLVu2mC6n0cnOztbEiRMVGRkpX19fdezYUbNnz1Zpaanp0hqNl156Se3bt5ePj49iY2P19ddfmy6p0UlOTlafPn0UEBCg4OBgjRw5UpmZmabLgqR58+bJZrNp6tSppktxOYKIITNmzFCbNm1Ml9Fo7dy5U3a7XYsWLdL27dv17LPPauHChXr00UdNl9YoLFu2TA899JBmz56tb7/9Vt26ddOwYcN0+PBh06U1KmvXrlVCQoI2bNig1atX69SpU/rd736nEydOmC6tUdu0aZMWLVqkrl27mi7FPSy43QcffGB17tzZ2r59uyXJ2rx5s+mSYFnWU089ZUVGRpouo1Ho27evlZCQ4HheVlZmtWnTxkpOTjZYFQ4fPmxJstauXWu6lEbr+PHjVqdOnazVq1dbAwcOtB588EHTJbkcIyJudujQIU2aNElvvPGG/Pz8TJeDM+Tn56tly5amy2jwSktLlZGRoaFDhzq2eXh4aOjQoVq/fr3BypCfny9J/H9gUEJCgoYPH+70/0dDV6e/9K6hsSxL8fHxuueee9S7d29lZ2ebLgm/ysrK0oIFC5SSkmK6lAbvyJEjKisrU0hIiNP2kJAQ7dy501BVsNvtmjp1qq655hpdddVVpstplJYuXapvv/1WmzZtMl2KWzEicgk88sgjstls1T527typBQsW6Pjx40pKSjJdcoNV09fiTPv371dcXJxGjx6tSZMmGaocMCshIUHff/+9li5darqURmnfvn168MEHtXjxYvn4+Jgux624xfslkJeXp6NHj1bbpkOHDrr11lv1r3/9SzabzbG9rKxMnp6eGjt2rNLT011daoNX09fCy8tLknTgwAENGjRI/fr1U1pamjw8yOauVlpaKj8/P7399tsaOXKkY/v48eN17NgxrVixwlxxjVRiYqJWrFihzz//XJGRkabLaZSWL1+uW265RZ6eno5tZWVlstls8vDwUElJidO+hoQg4kY5OTkqKChwPD9w4ICGDRumt99+W7GxsWrbtq3B6hqf/fv3a/DgwerVq5fefPPNBvs/eV0UGxurvn37asGCBZLKpwUiIiKUmJioRx55xHB1jYdlWbr//vv17rvvas2aNerUqZPpkhqt48ePa+/evU7bJkyYoM6dO+vhhx9u0NNlrBFxo4iICKfn/v7+kqSOHTsSQtxs//79GjRokNq1a6eUlBTl5eU59oWGhhqsrHF46KGHNH78ePXu3Vt9+/bVc889pxMnTmjChAmmS2tUEhIStGTJEq1YsUIBAQE6ePCgJCkoKEi+vr6Gq2tcAgICKoWNZs2aqVWrVg06hEgEETRSq1evVlZWlrKysiqFQAYJXW/MmDHKy8vTrFmzdPDgQXXv3l0fffRRpQWscK2XX35ZkjRo0CCn7ampqYqPj3d/QWiUmJoBAADGsDIPAAAYQxABAADGEEQAAIAxBBEAAGAMQQQAABhDEAEAAMYQRAAAgDEEEQAAYAxBBAAAGEMQAQAAxhBEAACAMQQRAABgzP8D6XuRv+oNFlwAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 40\n", - "Best parent: ackley(0.01216,-0.00983) = 0.05074\n", - "Updated m: 0.02805,-0.02048\n" + "Best parent: ackley(0.00096,-0.00017) = 0.00278\n", + "Updated m: -0.01755,-0.00379\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 45\n", - "Best parent: ackley(0.01216,-0.00983) = 0.05074\n", - "Updated m: 0.03804,0.01968\n" + "Best parent: ackley(0.00096,-0.00017) = 0.00278\n", + "Updated m: -0.04053,-0.11775\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Done!\n", - "f([ 0.01216326 -0.00983279]) = 0.050742\n" + "f([ 0.00095805 -0.0001675 ]) = 0.002776\n" ] } ], "source": [ "import numpy as np\n", "import sys\n", "from time import sleep\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "\n", "\n", "BOUNDS = np.asarray([[-5.0, 5.0], [-5.0, 5.0]])\n", "NUM_ITERATIONS = 50\n", "MUTATION_SD = 0.3 # define the maximum step size\n", "MU = 9 # parents\n", "LAMBDA = 20 # children\n", "STAT_INTERVALS = 5\n", "\n", "if MU >= LAMBDA:\n", " print(f\"ERROR: The number of parents (MU) must be less than the number of children (LAMBDA)\")\n", " sys.exit(1)\n", "\n", "# ackley function\n", "def ackley(v):\n", " x, y = v\n", " return -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (x ** 2 + y ** 2))) - np.exp(\n", " 0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))) + np.e + 20\n", "\n", "\n", "# check if a point is within the BOUNDS of the search\n", "def in_bounds(point, BOUNDS):\n", " # enumerate all dimensions of the point\n", " for d in range(len(BOUNDS)):\n", " # check if out of BOUNDS for this dimension\n", " if point[d] < BOUNDS[d, 0] or point[d] > BOUNDS[d, 1]:\n", " return False\n", " return True\n", "\n", "\n", "# evolution strategy\n", "def es(ackley, BOUNDS, NUM_ITERATIONS, MUTATION_SD, MU, LAMBDA):\n", " best, best_eval = None, 1e+10\n", " # calculate the number of children per parent\n", " n_children = int(LAMBDA / MU)\n", " # initial population\n", " population = list()\n", " m = None # initial candidate guess\n", " while m is None or not in_bounds(m, BOUNDS):\n", " m = BOUNDS[:, 0] + np.random.rand(len(BOUNDS)) * (BOUNDS[:, 1] - BOUNDS[:, 0])\n", " # make LAMBDA offspring from m\n", " for _ in range(LAMBDA):\n", " candidate = None\n", " while candidate is None or not in_bounds(candidate, BOUNDS):\n", " candidate = m + np.random.randn(len(BOUNDS)) * MUTATION_SD\n", " population.append(candidate)\n", " # perform the search\n", " gen_stats = np.empty((0, 3))\n", " for i in range(NUM_ITERATIONS):\n", " # evaluate fitness for the population\n", " scores = [ackley(c) for c in population]\n", " # rank scores in ascending order\n", " ranks = np.argsort(np.argsort(scores))\n", " # select the indexes for the top MU ranked solutions\n", " selected = [c for c, _ in enumerate(ranks) if ranks[c] < MU]\n", " #### NORMALISE SCORES\n", " sum_scores = sum([scores[c] for c in selected])\n", " # create new m from parents\n", " m = np.asarray([0., 0.])\n", " for c in selected:\n", " # check if this parent is the best solution ever seen\n", " if scores[c] < best_eval:\n", " best, best_eval = population[c], scores[c]\n", " # create new m\n", " m += (scores[c] / sum_scores) * population[c]\n", " # make LAMBDA offspring from m\n", " children = list()\n", " for _ in range(LAMBDA):\n", " child = None\n", " while child is None or not in_bounds(child, BOUNDS):\n", " child = m + np.random.randn(len(BOUNDS)) * MUTATION_SD\n", " children.append(child)\n", " # print information about the current generation\n", " \n", " # plot current population and parents\n", " if i % STAT_INTERVALS == 0:\n", " gen_stats = np.append(gen_stats, [[best_eval, np.mean(scores), np.std(scores)]], axis=0)\n", " print(f\"Generation {i}\\nBest parent: ackley({round(best[0],5)},{round(best[1],5)}) = {round(best_eval,5)}\\nUpdated m: {round(m[0],5)},{round(m[1],5)}\")\n", " for i in range(len(population)):\n", " plt.plot(population[i][0], population[i][1], '.y')\n", " for i in selected:\n", " plt.plot(population[i][0], population[i][1], '.b')\n", " plt.plot(m[0], m[1], '*r')\n", " plt.xlim([-5, 5])\n", " plt.ylim([-5, 5])\n", " y_patch = mpatches.Patch(color='y', hatch=\".\", label='Population')\n", " b_patch = mpatches.Patch(color='b', hatch=\".\", label='Selected Parents')\n", " r_patch = mpatches.Patch(color='r', hatch=\"*\", label='Parent Mean')\n", " plt.legend(handles=[y_patch, b_patch, r_patch])\n", " plt.pause(0.00001)\n", " plt.close()\n", " \n", " population = children\n", " \n", " plt.show()\n", " \n", " # plot best, mean and standard deviation of the current population\n", " x_axis_range = list(range(0, gen_stats.shape[0] * STAT_INTERVALS, STAT_INTERVALS))\n", " plt.plot(x_axis_range, np.zeros(len(x_axis_range)), '--r', linewidth=1.5)\n", " plt.plot(x_axis_range, gen_stats[:, 0], '-k', linewidth=1.5)\n", " plt.plot(x_axis_range, gen_stats[:, 1], '--b', linewidth=1.5)\n", " plt.fill_between(x_axis_range, gen_stats[:, 1] - gen_stats[:, 2], gen_stats[:, 1] + gen_stats[:, 2], color='b', alpha=0.1)\n", " plt.ylabel(\"Fitness\")\n", " plt.xlabel(\"Generations\")\n", " plt.legend(handles=[mpatches.Patch(color='k', label=f\"Best fitness\"),\n", " mpatches.Patch(color='b', label=f\"Mean & SD\"),\n", " mpatches.Patch(color='r', label=f\"Global minima\")])\n", " plt.grid(color='k', linestyle='--', linewidth=0.5, alpha=0.1)\n", " plt.show()\n", " \n", " return [best, best_eval]\n", "\n", "\n", "# perform the evolution strategy (MU, LAMBDA) search\n", "best, score = es(ackley, BOUNDS, NUM_ITERATIONS, MUTATION_SD, MU, LAMBDA)\n", "print('Done!')\n", "print('f(%s) = %f' % (best, score))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "cc7cf351-cdf1-4e77-9250-1e6c2351025b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "33a04af9-ef57-4826-a12b-f3d823fb073d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_3_nsga/es1_beam.ipynb b/es1_3_nsga/es1_beam.ipynb index 053d8d3..ab22642 100644 --- a/es1_3_nsga/es1_beam.ipynb +++ b/es1_3_nsga/es1_beam.ipynb @@ -1,233 +1,233 @@ { "cells": [ { "cell_type": "markdown", "id": "aa192955-35a1-4dd2-9f19-00cebc828d5e", "metadata": {}, "source": [ "## Non-Dominated Sorting Genetic Algorithm II (NSGA-II)\n", "\n", "NSGA-II can be used to solve a multivariate, multi-objective optimization problems. For instance, here the objective is to minimize two objectives, a beam’s weight and deflection (functions f1 and f2), by finding a good combination of two variables, the beam's diameter, d, and length, l. \n", "\n", "When you run the code below, NSGA-II will look for a good solution for the two objectives. \n", "\n", "While setting up the optimisation algorithm, the user will have an idea about the possible range of each variable (d and l). For instance, here the diameter can be between 0.01 m and 0.05 m while the length can be between 0.2 m and 1.0 m. \n", "\n", "## Exercise 4\n", "\n", "- 4.1. Run the code below and observe the results you get from this type of genetic algorithm. What is the benefit of obtaining the first Pareto front rather than a single optimal solution?\n", "- 4.2. Try different values of ROW, P and E. How does this affect the first Pareto front? \n", "- 4.3. In the next block of code, \"Constraint handling\", try changing the the maximum stress and see how this changes the subset of the first Pareto front that the designer can choose from." ] }, { "cell_type": "code", "execution_count": 1, "id": "dee066fa-9a4d-42d5-9f61-084dccfb4cc1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:06<00:00, 14.58it/s]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import math\n", "import numpy as np\n", "\n", "%run problem.ipynb\n", "%run evolution.ipynb\n", "\n", "ROW = 7800 # [kg/m^3] density of beam\n", "P = 2000 # [N] force applied to the end of the beam \n", "E = 207e9 # [Pa] young's modulus of beam\n", "\n", "def f1(d, l):\n", " # minimize weight\n", " return ROW * l * (np.pi * d**2) / 4.\n", "\n", "\n", "def f2(d, l):\n", " # minimize deflecton\n", " return 64. * P * l**3 / (3. * E * np.pi * d**4)\n", "\n", "# variable range:\n", "## d between 0.01 m and 0.05 m\n", "## l between 0.2 m and 1.0 m\n", "problem = Problem(num_of_variables=2, objectives=[f1, f2], variables_range=[(0.01, 0.05), (0.2, 1.0)], same_range=False, expand=True)\n", "evo = Evolution(problem, mutation_param=20, num_of_generations=100)\n", "func = []\n", "features = []\n", "for i in evo.evolve():\n", " # print(i, i.objectives, i.features)\n", " func.append(i.objectives)\n", " features.append(i.features)\n", "\n", "function1 = [i[0] for i in func]\n", "function2 = [i[1] for i in func]\n", "plt.xlabel('Function 1: weight [kg]', fontsize=15)\n", "plt.ylabel('Function 2: deflection [m]', fontsize=15)\n", "for i in range(len(features)):\n", " plt.plot(function1[i], function2[i], '.g')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "61edaaea-3bfe-48de-8020-032531d60eb9", "metadata": {}, "source": [ "## Pareto front\n", "\n", - "The green points in the plot above are the first Pareto front of the solutions found. This means that none of the green points are dominated by any of the other green points but they all dominate every other solution that was found. The first Pareto front is therefore the set of solutions that are all as good as each other, i.e. there is no optimal point. This is because we are trying to minimise two objectives at the same time and, as you can see, reducing the deflection results in a higher weight and vice versa. \n", + "The green points in the plot above are the first Pareto front of the solutions found. This means that none of the green points are dominated by any of the other green points, but they all dominate every other solution that was found. The first Pareto front is therefore the set of solutions that are all as good as each other, i.e. there is not a single optimal point. This is because we are trying to minimise two objectives at the same time and, as you can see, reducing the deflection results in a higher weight and vice versa. \n", "\n", "## Constraint handling\n", "\n", "But then how does the designer choose from this set of good solutions? The choice can be narrowed down by looking at the constraints such as maximum stress and maximum deflection of the beam. If the maximum stress is 300 MPa and the maximum deflection is 0.005 m then we can use these to eliminate some of the first Pareto front solutions. \n", "\n", "Run the code below:" ] }, { "cell_type": "code", "execution_count": 2, "id": "957cbfe5-5066-440f-87f3-cda58f635dcb", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "S_y = 300e6 # [Pa] # maximum stress\n", "DELTA_MAX = 0.005 # [m] maximum deflection\n", "\n", "def stress(d, l):\n", " return 32. * P * l / (np.pi * d**3)\n", "\n", "function1 = [i[0] for i in func]\n", "function2 = [i[1] for i in func]\n", "plt.xlabel('Function 1: weight [kg]', fontsize=15)\n", "plt.ylabel('Function 2: deflection [m]', fontsize=15)\n", "plt.plot([0, 3],[DELTA_MAX, DELTA_MAX], '--r')\n", "plt.text(1.75, DELTA_MAX*1.1, f'Max Deflection={DELTA_MAX}m', color='r')\n", "\n", "# check max stress constraint. max stress <= S_y\n", "for i in range(len(features)):\n", " if stress(features[i][0], features[i][1]) > S_y:\n", " plt.plot(function1[i], function2[i], '.r')\n", " else:\n", " plt.plot(function1[i], function2[i], '.g')\n", "g_patch = mpatches.Patch(color='g', hatch=\".\", label='Stress(d,l) <= max')\n", "r_patch = mpatches.Patch(color='r', hatch=\".\", label='Stress(d,l) > max')\n", "plt.legend(handles=[g_patch, r_patch])\n", "# plt.scatter(function1, function2)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "79a1f964-beaf-4970-8aa3-f8d276da7c06", "metadata": {}, "source": [ "In the above code, we first calculated the stress in the beam for each pair of variables in the first Pareto front. The stresses were compared with the maximum stress and coloured red if they are greater. The maximum deflection was also plotted as the red dashed line. All solutions that met the stress requirements also met the deflection requirements and are coloured green. \n" ] }, { "cell_type": "markdown", "id": "7bdcf61c-146e-489e-b387-4fefb782df1a", "metadata": {}, "source": [ "# Zoomed in\n", "\n", "Zooming in to the 2-objective Pareto front in the desirable stress region (green), we can see that there is still not a single optimal combination of the diameter and length of the beam. Although the deflection difference is small, there is still a trade-off between reducing the deflection and increasing the weight of the beam." ] }, { "cell_type": "code", "execution_count": 3, "id": "a6ba7304-a663-43d9-95ec-ad8cf0c3715a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xlabel('Function 1: weight [kg]', fontsize=15)\n", "plt.ylabel('Function 2: deflection [m]', fontsize=15)\n", "\n", "# check max stress constraint. max stress <= S_y\n", "for i in range(len(features)):\n", " if stress(features[i][0], features[i][1]) > S_y:\n", " plt.plot(function1[i], function2[i], '.r')\n", " else:\n", " plt.plot(function1[i], function2[i], '.g')\n", "g_patch = mpatches.Patch(color='g', hatch=\".\", label='Stress(d,l) <= max')\n", "r_patch = mpatches.Patch(color='r', hatch=\".\", label='Stress(d,l) > max')\n", "plt.legend(handles=[g_patch, r_patch])\n", "plt.ylim([0,0.002])\n", "# plt.scatter(function1, function2)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "b1bce709-0e0b-45a7-b711-e884c39fe2cd", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_3_nsga/evolution.ipynb b/es1_3_nsga/evolution.ipynb index 1ef7fb5..ef19d6d 100644 --- a/es1_3_nsga/evolution.ipynb +++ b/es1_3_nsga/evolution.ipynb @@ -1,85 +1,85 @@ { "cells": [ { "cell_type": "code", "execution_count": 2, "id": "dee066fa-9a4d-42d5-9f61-084dccfb4cc1", "metadata": {}, "outputs": [], "source": [ "%run utils.ipynb\n", "%run population.ipynb\n", "\n", "from tqdm import tqdm\n", "\n", "\n", "class Evolution:\n", "\n", " def __init__(self, problem, num_of_generations=1000, num_of_individuals=100, num_of_tour_particips=2,\n", " tournament_prob=0.9, crossover_param=2, mutation_param=5):\n", " self.utils = NSGA2Utils(problem, num_of_individuals, num_of_tour_particips, tournament_prob, crossover_param,\n", " mutation_param)\n", " self.population = None\n", " self.num_of_generations = num_of_generations\n", " self.on_generation_finished = []\n", " self.num_of_individuals = num_of_individuals\n", "\n", " def evolve(self):\n", " self.population = self.utils.create_initial_population()\n", " self.utils.fast_nondominated_sort(self.population)\n", " for front in self.population.fronts:\n", " self.utils.calculate_crowding_distance(front)\n", " children = self.utils.create_children(self.population)\n", " returned_population = None\n", " for i in tqdm(range(self.num_of_generations)):\n", " self.population.extend(children)\n", " self.utils.fast_nondominated_sort(self.population)\n", " new_population = Population()\n", " front_num = 0\n", " while len(new_population) + len(self.population.fronts[front_num]) <= self.num_of_individuals:\n", " self.utils.calculate_crowding_distance(self.population.fronts[front_num])\n", " new_population.extend(self.population.fronts[front_num])\n", " front_num += 1\n", " self.utils.calculate_crowding_distance(self.population.fronts[front_num])\n", " self.population.fronts[front_num].sort(key=lambda individual: individual.crowding_distance, reverse=True)\n", " new_population.extend(self.population.fronts[front_num][0:self.num_of_individuals - len(new_population)])\n", " returned_population = self.population\n", " self.population = new_population\n", " self.utils.fast_nondominated_sort(self.population)\n", " for front in self.population.fronts:\n", " self.utils.calculate_crowding_distance(front)\n", " children = self.utils.create_children(self.population)\n", " return returned_population.fronts[0]\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "a6ba7304-a663-43d9-95ec-ad8cf0c3715a", + "id": "23a7a039-ea68-4c43-ade8-ddcb86bf44a1", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_3_nsga/individual.ipynb b/es1_3_nsga/individual.ipynb index a7cc6ba..62995bb 100644 --- a/es1_3_nsga/individual.ipynb +++ b/es1_3_nsga/individual.ipynb @@ -1,64 +1,64 @@ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "dee066fa-9a4d-42d5-9f61-084dccfb4cc1", "metadata": {}, "outputs": [], "source": [ "class Individual(object):\n", "\n", " def __init__(self):\n", " self.rank = None\n", " self.crowding_distance = None\n", " self.domination_count = None\n", " self.dominated_solutions = None\n", " self.features = None\n", " self.objectives = None\n", "\n", " def __eq__(self, other):\n", " if isinstance(self, other.__class__):\n", " return self.features == other.features\n", " return False\n", "\n", " def dominates(self, other_individual):\n", " and_condition = True\n", " or_condition = False\n", " for first, second in zip(self.objectives, other_individual.objectives):\n", " and_condition = and_condition and first <= second\n", " or_condition = or_condition or first < second\n", " return (and_condition and or_condition)\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "a6ba7304-a663-43d9-95ec-ad8cf0c3715a", + "id": "2e080966-15e9-4000-843c-808c6894f5d4", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_3_nsga/population.ipynb b/es1_3_nsga/population.ipynb index 5535700..6faf6b9 100644 --- a/es1_3_nsga/population.ipynb +++ b/es1_3_nsga/population.ipynb @@ -1,59 +1,59 @@ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "dee066fa-9a4d-42d5-9f61-084dccfb4cc1", "metadata": {}, "outputs": [], "source": [ "class Population:\n", "\n", " def __init__(self):\n", " self.population = []\n", " self.fronts = []\n", "\n", " def __len__(self):\n", " return len(self.population)\n", "\n", " def __iter__(self):\n", " return self.population.__iter__()\n", "\n", " def extend(self, new_individuals):\n", " self.population.extend(new_individuals)\n", "\n", " def append(self, new_individual):\n", " self.population.append(new_individual)\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "d5113774-819c-4a41-b46a-a06f047f9ee3", + "id": "1184e275-44d0-4bd3-a362-a0a2dc1b4058", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_3_nsga/problem.ipynb b/es1_3_nsga/problem.ipynb index 1e6bba4..7bed62f 100644 --- a/es1_3_nsga/problem.ipynb +++ b/es1_3_nsga/problem.ipynb @@ -1,70 +1,70 @@ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "dee066fa-9a4d-42d5-9f61-084dccfb4cc1", "metadata": {}, "outputs": [], "source": [ "%run individual.ipynb\n", "import random\n", "\n", "\n", "class Problem:\n", "\n", " def __init__(self, objectives, num_of_variables, variables_range, expand=True, same_range=False):\n", " self.num_of_objectives = len(objectives)\n", " self.num_of_variables = num_of_variables\n", " self.objectives = objectives\n", " self.expand = expand\n", " self.variables_range = []\n", " if same_range:\n", " for _ in range(num_of_variables):\n", " self.variables_range.append(variables_range[0])\n", " else:\n", " self.variables_range = variables_range\n", "\n", " def generate_individual(self):\n", " individual = Individual()\n", " individual.features = [random.uniform(*x) for x in self.variables_range]\n", " return individual\n", "\n", " def calculate_objectives(self, individual):\n", " if self.expand:\n", " individual.objectives = [f(*individual.features) for f in self.objectives]\n", " else:\n", " individual.objectives = [f(individual.features) for f in self.objectives]\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "a6ba7304-a663-43d9-95ec-ad8cf0c3715a", + "id": "3e8c28fb-0af2-420f-a43a-4a000d45711c", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/es1_3_nsga/utils.ipynb b/es1_3_nsga/utils.ipynb index 870f4af..5e9b56b 100644 --- a/es1_3_nsga/utils.ipynb +++ b/es1_3_nsga/utils.ipynb @@ -1,185 +1,185 @@ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "dee066fa-9a4d-42d5-9f61-084dccfb4cc1", "metadata": {}, "outputs": [], "source": [ "# from nsga2.population import Population\n", "%run population.ipynb\n", "import random\n", "\n", "\n", "class NSGA2Utils:\n", "\n", " def __init__(self, problem, num_of_individuals=100,\n", " num_of_tour_particips=2, tournament_prob=0.9, crossover_param=2, mutation_param=5):\n", "\n", " self.problem = problem\n", " self.num_of_individuals = num_of_individuals\n", " self.num_of_tour_particips = num_of_tour_particips\n", " self.tournament_prob = tournament_prob\n", " self.crossover_param = crossover_param\n", " self.mutation_param = mutation_param\n", "\n", " def create_initial_population(self):\n", " population = Population()\n", " for _ in range(self.num_of_individuals):\n", " individual = self.problem.generate_individual()\n", " self.problem.calculate_objectives(individual)\n", " population.append(individual)\n", " return population\n", "\n", " def fast_nondominated_sort(self, population):\n", " population.fronts = [[]]\n", " for individual in population:\n", " individual.domination_count = 0\n", " individual.dominated_solutions = []\n", " for other_individual in population:\n", " if individual.dominates(other_individual):\n", " individual.dominated_solutions.append(other_individual)\n", " elif other_individual.dominates(individual):\n", " individual.domination_count += 1\n", " if individual.domination_count == 0:\n", " individual.rank = 0\n", " population.fronts[0].append(individual)\n", " i = 0\n", " while len(population.fronts[i]) > 0:\n", " temp = []\n", " for individual in population.fronts[i]:\n", " for other_individual in individual.dominated_solutions:\n", " other_individual.domination_count -= 1\n", " if other_individual.domination_count == 0:\n", " other_individual.rank = i + 1\n", " temp.append(other_individual)\n", " i = i + 1\n", " population.fronts.append(temp)\n", "\n", " def calculate_crowding_distance(self, front):\n", " if len(front) > 0:\n", " solutions_num = len(front)\n", " for individual in front:\n", " individual.crowding_distance = 0\n", "\n", " for m in range(len(front[0].objectives)):\n", " front.sort(key=lambda individual: individual.objectives[m])\n", " front[0].crowding_distance = 10 ** 9\n", " front[solutions_num - 1].crowding_distance = 10 ** 9\n", " m_values = [individual.objectives[m] for individual in front]\n", " scale = max(m_values) - min(m_values)\n", " if scale == 0: scale = 1\n", " for i in range(1, solutions_num - 1):\n", " front[i].crowding_distance += (front[i + 1].objectives[m] - front[i - 1].objectives[m]) / scale\n", "\n", " def crowding_operator(self, individual, other_individual):\n", " if (individual.rank < other_individual.rank) or \\\n", " ((individual.rank == other_individual.rank) and (\n", " individual.crowding_distance > other_individual.crowding_distance)):\n", " return 1\n", " else:\n", " return -1\n", "\n", " def create_children(self, population):\n", " children = []\n", " while len(children) < len(population):\n", " parent1 = self.__tournament(population)\n", " parent2 = parent1\n", " while parent1 == parent2:\n", " parent2 = self.__tournament(population)\n", " child1, child2 = self.__crossover(parent1, parent2)\n", " self.__mutate(child1)\n", " self.__mutate(child2)\n", " self.problem.calculate_objectives(child1)\n", " self.problem.calculate_objectives(child2)\n", " children.append(child1)\n", " children.append(child2)\n", "\n", " return children\n", "\n", " def __crossover(self, individual1, individual2):\n", " child1 = self.problem.generate_individual()\n", " child2 = self.problem.generate_individual()\n", " num_of_features = len(child1.features)\n", " genes_indexes = range(num_of_features)\n", " for i in genes_indexes:\n", " beta = self.__get_beta()\n", " x1 = (individual1.features[i] + individual2.features[i]) / 2\n", " x2 = abs((individual1.features[i] - individual2.features[i]) / 2)\n", " child1.features[i] = x1 + beta * x2\n", " child2.features[i] = x1 - beta * x2\n", " return child1, child2\n", "\n", " def __get_beta(self):\n", " u = random.random()\n", " if u <= 0.5:\n", " return (2 * u) ** (1 / (self.crossover_param + 1))\n", " return (2 * (1 - u)) ** (-1 / (self.crossover_param + 1))\n", "\n", " def __mutate(self, child):\n", " num_of_features = len(child.features)\n", " for gene in range(num_of_features):\n", " u, delta = self.__get_delta()\n", " if u < 0.5:\n", " child.features[gene] += delta * (child.features[gene] - self.problem.variables_range[gene][0])\n", " else:\n", " child.features[gene] += delta * (self.problem.variables_range[gene][1] - child.features[gene])\n", " if child.features[gene] < self.problem.variables_range[gene][0]:\n", " child.features[gene] = self.problem.variables_range[gene][0]\n", " elif child.features[gene] > self.problem.variables_range[gene][1]:\n", " child.features[gene] = self.problem.variables_range[gene][1]\n", "\n", " def __get_delta(self):\n", " u = random.random()\n", " if u < 0.5:\n", " return u, (2 * u) ** (1 / (self.mutation_param + 1)) - 1\n", " return u, 1 - (2 * (1 - u)) ** (1 / (self.mutation_param + 1))\n", "\n", " def __tournament(self, population):\n", " participants = random.sample(population.population, self.num_of_tour_particips)\n", " best = None\n", " for participant in participants:\n", " if best is None or (\n", " self.crowding_operator(participant, best) == 1 and self.__choose_with_prob(self.tournament_prob)):\n", " best = participant\n", "\n", " return best\n", "\n", " def __choose_with_prob(self, prob):\n", " if random.random() <= prob:\n", " return True\n", " return False\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "f9578f6f-5d17-496c-a0b6-80092c4c0f5c", + "id": "117a96a6-c20d-4f5b-8628-5d22dfbf3e94", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }