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b/Code/All Qiskit, PennyLane QML Nov 23/02a Classifier & Regressor 85% kkawchak.ipynb |
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "intense-ecology", |
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"metadata": {}, |
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"source": [ |
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"# Neural Network Classifier & Regressor\n", |
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"\n", |
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"In this tutorial we show how the `NeuralNetworkClassifier` and `NeuralNetworkRegressor` are used.\n", |
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"Both take as an input a (Quantum) `NeuralNetwork` and leverage it in a specific context.\n", |
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"In both cases we also provide a pre-configured variant for convenience, the Variational Quantum Classifier (`VQC`) and Variational Quantum Regressor (`VQR`). The tutorial is structured as follows:\n", |
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"\n", |
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"\n", |
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"1. [Classification](#Classification) \n", |
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" * Classification with an `EstimatorQNN`\n", |
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" * Classification with a `SamplerQNN`\n", |
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" * Variational Quantum Classifier (`VQC`)\n", |
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" \n", |
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" \n", |
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"2. [Regression](#Regression)\n", |
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" * Regression with an `EstimatorQNN`\n", |
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" * Variational Quantum Regressor (`VQR`)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 160, |
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"id": "functioning-sword", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import matplotlib.pyplot as plt\n", |
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"import numpy as np\n", |
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"from IPython.display import clear_output\n", |
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"from qiskit import QuantumCircuit\n", |
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"from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, ADAM\n", |
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"from qiskit.circuit import Parameter\n", |
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"from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap, ZFeatureMap\n", |
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"from qiskit.utils import algorithm_globals\n", |
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"\n", |
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"from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier, VQC\n", |
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"from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor, VQR\n", |
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"from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN\n", |
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"\n", |
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"algorithm_globals.random_seed = 42" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "compact-divide", |
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"metadata": {}, |
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"source": [ |
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"## Classification\n", |
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"\n", |
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"We prepare a simple classification dataset to illustrate the following algorithms." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 161, |
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"id": "short-pierre", |
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"metadata": { |
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"tags": [ |
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"nbsphinx-thumbnail" |
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] |
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}, |
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"outputs": [ |
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{ |
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"data": { |
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\n", |
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"text/plain": [ |
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73 |
"<Figure size 600x400 with 1 Axes>" |
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74 |
] |
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75 |
}, |
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76 |
"metadata": {}, |
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77 |
"output_type": "display_data" |
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78 |
} |
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79 |
], |
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"source": [ |
|
|
81 |
"num_inputs = 2\n", |
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|
82 |
"num_samples = 20\n", |
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|
83 |
"X = 2 * algorithm_globals.random.random([num_samples, num_inputs]) - 1\n", |
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84 |
"y01 = 1 * (np.sum(X, axis=1) >= 0) # in { 0, 1}\n", |
|
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85 |
"y = 2 * y01 - 1 # in {-1, +1}\n", |
|
|
86 |
"y_one_hot = np.zeros((num_samples, 2))\n", |
|
|
87 |
"for i in range(num_samples):\n", |
|
|
88 |
" y_one_hot[i, y01[i]] = 1\n", |
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|
89 |
"\n", |
|
|
90 |
"for x, y_target in zip(X, y):\n", |
|
|
91 |
" if y_target == 1:\n", |
|
|
92 |
" plt.plot(x[0], x[1], \"bo\")\n", |
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93 |
" else:\n", |
|
|
94 |
" plt.plot(x[0], x[1], \"go\")\n", |
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|
95 |
"plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n", |
|
|
96 |
"plt.show()" |
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97 |
] |
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|
98 |
}, |
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99 |
{ |
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|
100 |
"cell_type": "markdown", |
|
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101 |
"id": "religious-history", |
|
|
102 |
"metadata": {}, |
|
|
103 |
"source": [ |
|
|
104 |
"### Classification with an `EstimatorQNN`\n", |
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|
105 |
"\n", |
|
|
106 |
"First we show how an `EstimatorQNN` can be used for classification within a `NeuralNetworkClassifier`. In this context, the `EstimatorQNN` is expected to return one-dimensional output in $[-1, +1]$. This only works for binary classification and we assign the two classes to $\\{-1, +1\\}$." |
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107 |
] |
|
|
108 |
}, |
|
|
109 |
{ |
|
|
110 |
"cell_type": "code", |
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|
111 |
"execution_count": 162, |
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|
112 |
"id": "recognized-musician", |
|
|
113 |
"metadata": {}, |
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|
114 |
"outputs": [ |
|
|
115 |
{ |
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|
116 |
"data": { |
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|
117 |
"image/png": 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\n", 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"text/plain": [ |
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"<Figure size 621.739x200.667 with 1 Axes>" |
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] |
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}, |
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"execution_count": 162, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"# construct QNN\n", |
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"qc = QuantumCircuit(2)\n", |
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"feature_map = ZZFeatureMap(2, reps=1) # Default 2 reps\n", |
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"ansatz = RealAmplitudes(2, reps=2) # Default 3 reps\n", |
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"qc.compose(feature_map, inplace=True)\n", |
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"qc.compose(ansatz, inplace=True)\n", |
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"qc.draw(output=\"mpl\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "formed-animal", |
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"metadata": {}, |
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"source": [ |
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142 |
"Create a quantum neural network" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 163, |
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"id": "determined-hands", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"estimator_qnn = EstimatorQNN(\n", |
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" circuit=qc, input_params=feature_map.parameters, weight_params=ansatz.parameters\n", |
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")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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159 |
"execution_count": 164, |
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"id": "acute-casting", |
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161 |
"metadata": {}, |
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162 |
"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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166 |
"array([[0.38180374]])" |
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] |
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}, |
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169 |
"execution_count": 164, |
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"metadata": {}, |
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"output_type": "execute_result" |
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172 |
} |
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], |
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"source": [ |
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175 |
"# QNN maps inputs to [-1, +1]\n", |
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176 |
"estimator_qnn.forward(X[0, :], algorithm_globals.random.random(estimator_qnn.num_weights))" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "stone-holiday", |
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"metadata": {}, |
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"source": [ |
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"We will add a callback function called `callback_graph`. This will be called for each iteration of the optimizer and will be passed two parameters: the current weights and the value of the objective function at those weights. For our function, we append the value of the objective function to an array so we can plot iteration versus objective function value and update the graph with each iteration. However, you can do whatever you want with a callback function as long as it gets the two parameters mentioned passed. " |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 165, |
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"id": "similar-controversy", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# callback function that draws a live plot when the .fit() method is called\n", |
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"def callback_graph(weights, obj_func_eval):\n", |
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" clear_output(wait=True)\n", |
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" objective_func_vals.append(obj_func_eval)\n", |
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" plt.title(\"Objective function value against iteration\")\n", |
|
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199 |
" plt.xlabel(\"Iteration\")\n", |
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200 |
" plt.ylabel(\"Objective function value\")\n", |
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" plt.plot(range(len(objective_func_vals)), objective_func_vals)\n", |
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" plt.show()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 166, |
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"id": "lesser-receiver", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# construct neural network classifier\n", |
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"estimator_classifier = NeuralNetworkClassifier(\n", |
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" estimator_qnn, optimizer=COBYLA(maxiter=60), callback=callback_graph\n", |
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")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 167, |
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"id": "adopted-editor", |
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222 |
"metadata": {}, |
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|
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"outputs": [ |
|
|
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{ |
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|
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"data": { |
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"image/png": 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\n", 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"text/plain": [ |
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"<Figure size 1200x600 with 1 Axes>" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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}, |
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{ |
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"data": { |
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"text/plain": [ |
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"0.85" |
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] |
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}, |
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"execution_count": 167, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"# create empty array for callback to store evaluations of the objective function\n", |
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"objective_func_vals = []\n", |
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"plt.rcParams[\"figure.figsize\"] = (12, 6)\n", |
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"\n", |
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"# fit classifier to data\n", |
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"estimator_classifier.fit(X, y)\n", |
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"\n", |
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"# return to default figsize\n", |
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"plt.rcParams[\"figure.figsize\"] = (6, 4)\n", |
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"\n", |
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"# score classifier\n", |
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"estimator_classifier.score(X, y)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 168, |
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"id": "civilian-analysis", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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268 |
"image/png": 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\n", |
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"text/plain": [ |
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"<Figure size 600x400 with 1 Axes>" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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"# evaluate data points\n", |
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"y_predict = estimator_classifier.predict(X)\n", |
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"\n", |
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"# plot results\n", |
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"# red == wrongly classified\n", |
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"for x, y_target, y_p in zip(X, y, y_predict):\n", |
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" if y_target == 1:\n", |
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" plt.plot(x[0], x[1], \"bo\")\n", |
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" else:\n", |
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" plt.plot(x[0], x[1], \"go\")\n", |
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" if y_target != y_p:\n", |
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" plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n", |
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"plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n", |
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"plt.show()" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "japanese-seattle", |
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"metadata": {}, |
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"source": [ |
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"Now, when the model is trained, we can explore the weights of the neural network. Please note, the number of weights is defined by ansatz." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 169, |
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"id": "offshore-basket", |
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"metadata": {}, |
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"outputs": [ |
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|
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{ |
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|
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"data": { |
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"text/plain": [ |
|
|
311 |
"array([-0.143509 , -1.27510925, 1.74182181, 0.66736135, 0.23563014,\n", |
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" -1.26956137])" |
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] |
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}, |
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"execution_count": 169, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"estimator_classifier.weights" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "determined-standing", |
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|
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"metadata": {}, |
|
|
328 |
"source": [ |
|
|
329 |
"### Classification with a `SamplerQNN`\n", |
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|
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"\n", |
|
|
331 |
"Next we show how a `SamplerQNN` can be used for classification within a `NeuralNetworkClassifier`. In this context, the `SamplerQNN` is expected to return $d$-dimensional probability vector as output, where $d$ denotes the number of classes. \n", |
|
|
332 |
"The underlying `Sampler` primitive returns quasi-distributions of bit strings and we just need to define a mapping from the measured bitstrings to the different classes. For binary classification we use the parity mapping." |
|
|
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] |
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|
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}, |
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|
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{ |
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|
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"cell_type": "code", |
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"execution_count": 170, |
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"id": "discrete-factor", |
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"metadata": {}, |
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|
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"outputs": [ |
|
|
341 |
{ |
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"data": { |
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343 |
"image/png": 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\n", |
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344 |
"text/plain": [ |
|
|
345 |
"<Figure size 621.739x200.667 with 1 Axes>" |
|
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346 |
] |
|
|
347 |
}, |
|
|
348 |
"execution_count": 170, |
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349 |
"metadata": {}, |
|
|
350 |
"output_type": "execute_result" |
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351 |
} |
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352 |
], |
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|
353 |
"source": [ |
|
|
354 |
"# construct feature map\n", |
|
|
355 |
"feature_map = ZFeatureMap(num_inputs, reps=4)\n", |
|
|
356 |
"\n", |
|
|
357 |
"# construct ansatz\n", |
|
|
358 |
"ansatz = RealAmplitudes(num_inputs, reps=1)\n", |
|
|
359 |
"\n", |
|
|
360 |
"# construct quantum circuit\n", |
|
|
361 |
"qc = QuantumCircuit(num_inputs)\n", |
|
|
362 |
"qc.append(feature_map, range(num_inputs))\n", |
|
|
363 |
"qc.append(ansatz, range(num_inputs))\n", |
|
|
364 |
"qc.decompose().draw(output=\"mpl\")" |
|
|
365 |
] |
|
|
366 |
}, |
|
|
367 |
{ |
|
|
368 |
"cell_type": "code", |
|
|
369 |
"execution_count": 171, |
|
|
370 |
"id": "young-sensitivity", |
|
|
371 |
"metadata": {}, |
|
|
372 |
"outputs": [], |
|
|
373 |
"source": [ |
|
|
374 |
"# parity maps bitstrings to 0 or 1\n", |
|
|
375 |
"def parity(x):\n", |
|
|
376 |
" return \"{:b}\".format(x).count(\"1\") % 2\n", |
|
|
377 |
"\n", |
|
|
378 |
"\n", |
|
|
379 |
"output_shape = 2 # corresponds to the number of classes, possible outcomes of the (parity) mapping." |
|
|
380 |
] |
|
|
381 |
}, |
|
|
382 |
{ |
|
|
383 |
"cell_type": "code", |
|
|
384 |
"execution_count": 172, |
|
|
385 |
"id": "statutory-mercury", |
|
|
386 |
"metadata": {}, |
|
|
387 |
"outputs": [], |
|
|
388 |
"source": [ |
|
|
389 |
"# construct QNN\n", |
|
|
390 |
"sampler_qnn = SamplerQNN(\n", |
|
|
391 |
" circuit=qc,\n", |
|
|
392 |
" input_params=feature_map.parameters,\n", |
|
|
393 |
" weight_params=ansatz.parameters,\n", |
|
|
394 |
" interpret=parity,\n", |
|
|
395 |
" output_shape=output_shape,\n", |
|
|
396 |
")" |
|
|
397 |
] |
|
|
398 |
}, |
|
|
399 |
{ |
|
|
400 |
"cell_type": "code", |
|
|
401 |
"execution_count": 173, |
|
|
402 |
"id": "hybrid-orlando", |
|
|
403 |
"metadata": {}, |
|
|
404 |
"outputs": [], |
|
|
405 |
"source": [ |
|
|
406 |
"# construct classifier\n", |
|
|
407 |
"sampler_classifier = NeuralNetworkClassifier(\n", |
|
|
408 |
" neural_network=sampler_qnn, optimizer=COBYLA(maxiter=30), callback=callback_graph\n", |
|
|
409 |
")" |
|
|
410 |
] |
|
|
411 |
}, |
|
|
412 |
{ |
|
|
413 |
"cell_type": "code", |
|
|
414 |
"execution_count": 174, |
|
|
415 |
"id": "adult-newman", |
|
|
416 |
"metadata": {}, |
|
|
417 |
"outputs": [ |
|
|
418 |
{ |
|
|
419 |
"data": { |
|
|
420 |
"image/png": 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7HcDbug2rHGmN4eHhOvvss/Xuu+/q2WefVWFhob788ks9/PDDLc678MIL9dVXX+m2227T8OHDFRMTI6fTqWnTph3xSPLBz/fRvB5LS0s1ceJExcXF6f7771ffvn0VERGhlStX6re//e0R1fi3v/1Nl19+uc4++2zddtttSk1NVUhIiObMmaOtW7d26DYO93eJjIzU559/riVLlujDDz/UwoULtWDBAp188sn66KOPfN593tPnvPmsBxdvPE/t6eiMlkD4NwoAniLIA0CAOOOMM/TCCy/oiy++aDGl2mXp0qXKy8vTL3/5y1aXbd68ucXo15YtW+R0Og+5l3ffvn1VWVmpKVOmHLKuvn37atGiRdq7d+8hR+U9mUYeHR2tM844Q2+99ZaeeOIJLViwQOPHj1dGRkaL+zVNU9nZ2V79UKFbt25tTrk9eJSvo1wd+F0d3tvjyfPTu3dvbdy4sdVx1xTx3r17e1Dhoc2YMUOvvPKKFi9erPXr18s0zRbT6vft26fFixfrvvvu09133+0+vnnz5g7dfrdu3VRaWtriWF1dnXbv3t3iWEdfj2359NNPtWfPHr3zzjuaMGGC+3hubm6L81zP25YtWzRp0iT38YaGBuXl5bVovvb222+rT58+euedd1r87dpaYnI0bDabJk+erMmTJ+uJJ57Qww8/rN///vdasmSJpkyZ4pVGgu3dxtE85y4dfZ48ff07nU5t3rzZPQtFamwCWVpa6tXXPwD4K9bIA0CAuO222xQZGalf/vKXrbb12rt3r6699lpFRUW5tzFr7plnnmnx+9NPPy1JOvXUU9u9vwsvvFDLli3TokWLWl1WWlqqhoYGSY3rYk3T1H333dfqvOYjXtHR0a0C26HMmDFDu3bt0osvvqg1a9a0CI+SdO655yokJET33Xdfq5E10zQ7tPVZW/r27asNGzaouLjYfWzNmjXuTuGeSklJ0YQJE/Tyyy8rPz+/VZ0u0dHRktSh5+i0007TN998o2XLlrmPVVVV6fnnn1dWVpYGDx58RLW2ZcqUKUpMTNSCBQu0YMECjR49usWHQq7RzoP/BofrIu/St2/fVmutn3/++VYj8h19PbalrRrr6ur07LPPtjjv+OOPV1JSkl544YUWt/f3v/+91TTstm7z66+/bvE3OVp79+5tdczVDd+1zZonr5v2tPdv82iec5eOPk9RUVHu2z2c0047TVLr19gTTzwhqXHHBQAIdozIA0CA6N+/v1555RVdcsklOuaYY3TllVcqOztbeXl5eumll1RSUqLXX3+9zW3dcnNzNX36dE2bNk3Lli3T3/72N/2///f/NGzYsHbv77bbbtO//vUvnXHGGe6tmqqqqvT999/r7bffVl5enpKTkzVp0iRdeumlmjdvnjZv3uyeTr106VJNmjRJ119/vSRp5MiR+vjjj/XEE08oIyND2dnZba7zdjnttNMUGxurW2+9VSEhITrvvPNaXN63b189+OCDuvPOO91bhMXGxio3N1fvvvuurrnmGt16660eP89XXHGFnnjiCU2dOlVXXnmlioqK9Nxzz2nIkCGt1lZ31Lx58zRu3Dgdd9xxuuaaa9x/tw8//FCrV6+W1Pj8SNLvf/97XXTRRQoLC9OZZ57pDmrN3XHHHXr99dd16qmn6sYbb1RiYqJeeeUV5ebm6p///KdX10+HhYXp3HPP1RtvvKGqqir98Y9/bHF5XFycJkyYoMcee0z19fXq0aOHPvroo1aj3e256qqrdO211+q8887Tz3/+c61Zs0aLFi1yb8Hn0tHXY1tOPPFEdevWTTNnztSNN94owzD02muvtfrwwW63695779UNN9ygk08+WRdeeKHy8vI0f/589e3bt8Wo8RlnnKF33nlH55xzjk4//XTl5ubqueee0+DBg1VZWdmhx344999/vz7//HOdfvrp6t27t4qKivTss8+qZ8+e7lk5ffv2VUJCgp577jnFxsYqOjpaY8aM8Wj9+ciRI/XnP/9ZDz74oPr166fU1FSdfPLJR/Wcu3T0eYqMjNTgwYO1YMECDRgwQImJiRo6dGibvSWGDRummTNn6vnnn3cvm/jmm2/0yiuv6Oyzz24xmwIAglYnd8kHAByltWvXmhdffLHZvXt3MywszExPTzcvvvjiNrcSc23b9OOPP5rnn3++GRsba3br1s28/vrrzf3797c49+DtvkyzcUuzO++80+zXr59pt9vN5ORk88QTTzT/+Mc/mnV1de7zGhoazMcff9zMyckx7Xa7mZKSYp566qnmihUr3Ods2LDBnDBhghkZGWlKct9XW9thuVxyySWmJHPKlCntPh///Oc/zXHjxpnR0dFmdHS0mZOTY1533XXmxo0bD/k8trf9nGma5t/+9jezT58+pt1uN4cPH24uWrSo3e3nHn/88VbXVxvbaP3www/mOeecYyYkJJgRERHmwIEDzbvuuqvFOQ888IDZo0cP02aztXhO2vrbbN261Tz//PPdtzd69Gjz3//+d4ceo6v2jm5Z9r///c+UZBqGYe7YsaPV5T/99JP7scXHx5sXXHCBuWvXrlbPQ1t/a4fDYf72t781k5OTzaioKHPq1Knmli1bjur12JYvv/zSPOGEE8zIyEgzIyPDvP32281FixaZkswlS5a0OHfevHlm7969zfDwcHP06NHml19+aY4cOdKcNm2a+xyn02k+/PDD7vNGjBhh/vvf/271OjHN9refO3hbuYOfn8WLF5tnnXWWmZGRYdrtdjMjI8O8+OKLW20H9/7775uDBw82Q0NDD/t3betvUFBQYJ5++ulmbGxsq232OvKcH+rfgifP01dffWWOHDnStNvtLZ6zg7efM03TrK+vN++77z4zOzvbDAsLMzMzM80777zTvZWgS+/evc3TTz+9VV3tbTMJAIHCME06fQAApMzMTE2dOlUvvvii1aUAfsXpdColJUXnnnuuXnjhBavLAQCANfIAAKm+vl579uw57DRZINjV1NS0mnL/6quvau/evTrppJOsKQoAgIOwRh4AurhFixbpjTfe0P79+917hQNd1fLly3XLLbfoggsuUFJSklauXKmXXnpJQ4cObbH9IQAAViLIA0AX98gjj2jLli166KGH9POf/9zqcgBLZWVlKTMzU/PmzXNvqXjZZZfpkUcekd1ut7o8AAAkSayRBwAAAAAggLBGHgAAAACAAEKQBwAAAAAggLBGvg1Op1O7du1SbGysDMOwuhwAAAAAQJAzTVMVFRXKyMiQzXboMXeCfBt27dqlzMxMq8sAAAAAAHQxO3bsUM+ePQ95DkG+DbGxsZIan8C4uDiLqwEAAAAABLvy8nJlZma68+ihEOTb4JpOHxcXR5AHAAAAAHSajizvptkdAAAAAAABhCAPAAAAAEAAIcgDAAAAABBACPIAAAAAAAQQgjwAAAAAAAGEIA8AAAAAQAAhyAMAAAAAEEAI8gAAAAAABBCCPAAAAAAAAYQgDwAAAABAACHIAwAAAAAQQAjyAAAAAAAEEII8AAAAAAABhCAPAAAAAEAAIcgDAAAAABBACPIAAAAAAAQQgnwA+3FXud76boeKK2qtLgUAAAAA0EkI8gHs9n+u0W1vr9WK7XutLgUAAAAA0EkI8gEsJz1OkrR+d4XFlQAAAAAAOgtBPoAN6t4Y5DcUlFtcCQAAAACgsxDkA9ig9FhJjMgDAAAAQFdCkA9gOU0j8vl7q1VZ22BxNQAAAACAzkCQD2CJ0XalxYVLkjYWMCoPAAAAAF0BQT7AHWh4xzp5AAAAAOgKCPIBjoZ3AAAAANC1EOQD3KDujQ3vNtDwDgAAAAC6BIJ8gHNNrd9QUCGn07S4GgAAAACArxHkA1yflGjZQ2yqrG3QztL9VpcDAAAAAPAxgnyACwuxqV9qjCTpRxreAQAAAEDQI8gHgRzWyQMAAABAl0GQDwKD6VwPAAAAAF0GQT4IsJc8AAAAAHQdBPkg4NqCbvvealXVNlhcDQAAAADAlwjyQSApJlwpseEyTWlTIevkAQAAACCYEeSDRE5646j8ehreAQAAAEBQI8gHCRreAQAAAEDXQJAPEq4t6Gh4BwAAAADBjSAfJFyd6zfsrpBpmhZXAwAAAADwFYJ8kOibEqOwEEMVtQ3aWbrf6nIAAAAAAD5CkA8S9lCb+qbESKLhHQAAAAAEM4J8EHE3vGOdPAAAAAAELYJ8EHE1vNtQwIg8AAAAAAQrgnwQcTW8o3M9AAAAAAQvgnwQGdQ0tT53T5X21zksrgYAAAAA4AsE+SCSEhuu5Bi7TFPaWMj0egAAAAAIRgT5IDOIhncAAAAAENQI8kEmJ52GdwAAAAAQzAjyQcbV8O5HRuQBAAAAICgR5INM86n1pmlaXA0AAAAAwNsI8kGmb2q0Qm2GymsatLusxupyAAAAAABeRpAPMuGhIeqbEiOJ/eQBAAAAIBgR5IPQoO40vAMAAACAYEWQD0I53Wl4BwAAAADBiiAfhNhLHgAAAACCF0E+CA1q2ks+t6RKNfUOi6sBAAAAAHgTQT4IpcSGKzHaLqcpbSpknTwAAAAABBOCfBAyDONAw7vdBHkAAAAACCYE+SCVk964Tn59AevkAQAAACCYEOSDVE7TOnn2kgcAAACA4EKQD1LuzvUFFTJN0+JqAAAAAADeQpAPUv1SYxRiM1RaXa+C8hqrywEAAAAAeAlBPkhFhIWob0q0JBreAQAAAEAwIcgHMRreAQAAAEDwIcgHsZzuroZ3jMgDAAAAQLAgyAcxd8M7OtcDAAAAQNAgyAexQU1T67eVVKmm3mFxNQAAAAAAbyDIB7G0uHAlRIXJ4TS1pajS6nIAAAAAAF5AkA9ihmG4R+XXM70eAAAAAIICQT7IuRrebSig4R0AAAAABAOCfJBzNbxjRB4AAAAAggNBPsg1n1pvmqbF1QAAAAAAjhZBPsj1T4uRzZD2VderqKLW6nIAAAAAAEeJIB/kIsJC1CclRhLT6wEAAAAgGPhFkH/mmWeUlZWliIgIjRkzRt988027586fP1+GYbT4ioiIaHGOaZq6++671b17d0VGRmrKlCnavHmzrx+G38pJp+EdAAAAAAQLy4P8ggULNHv2bN1zzz1auXKlhg0bpqlTp6qoqKjd68TFxWn37t3ur+3bt7e4/LHHHtO8efP03HPP6euvv1Z0dLSmTp2qmpoaXz8cv0TDOwAAAAAIHpYH+SeeeEJXX321Zs2apcGDB+u5555TVFSUXn755XavYxiG0tPT3V9paWnuy0zT1Ny5c/WHP/xBZ511lo499li9+uqr2rVrl957771OeET+Z5BrC7rdjMgDAAAAQKCzNMjX1dVpxYoVmjJlivuYzWbTlClTtGzZsnavV1lZqd69eyszM1NnnXWW1q1b574sNzdXBQUFLW4zPj5eY8aMafc2a2trVV5e3uIrmOQ0da7fWlyp2gaHxdUAAAAAAI6GpUG+pKREDoejxYi6JKWlpamgoKDN6wwcOFAvv/yy3n//ff3tb3+T0+nUiSeeqJ9++kmS3Nfz5DbnzJmj+Ph491dmZubRPjS/0j0+QvGRYWpwmtpSVGl1OQAAAACAo2D51HpPjR07VpdddpmGDx+uiRMn6p133lFKSor+8pe/HPFt3nnnnSorK3N/7dixw4sVW88wjAMN75heDwAAAAABzdIgn5ycrJCQEBUWFrY4XlhYqPT09A7dRlhYmEaMGKEtW7ZIkvt6ntxmeHi44uLiWnwFGxreAQAAAEBwsDTI2+12jRw5UosXL3YfczqdWrx4scaOHduh23A4HPr+++/VvXt3SVJ2drbS09Nb3GZ5ebm+/vrrDt9mMHI3vGMLOgAAAAAIaKFWFzB79mzNnDlTxx9/vEaPHq25c+eqqqpKs2bNkiRddtll6tGjh+bMmSNJuv/++3XCCSeoX79+Ki0t1eOPP67t27frqquuktQ4jfzmm2/Wgw8+qP79+ys7O1t33XWXMjIydPbZZ1v1MC3nani3oYAReQAAAAAIZJYH+RkzZqi4uFh33323CgoKNHz4cC1cuNDdrC4/P18224GJA/v27dPVV1+tgoICdevWTSNHjtRXX32lwYMHu8+5/fbbVVVVpWuuuUalpaUaN26cFi5cqIiIiE5/fP5iQFqsbIZUUlmnoooapcZ23ecCAAAAAAKZYZqmaXUR/qa8vFzx8fEqKysLqvXyJ//fp9pWXKVXrxitCQNSrC4HAAAAANDEkxwacF3rceQGMb0eAAAAAAIeQb4LcTW8W88WdAAAAAAQsAjyXYir4V2gbUG3ZkepRj/0sd5e8ZPVpQAAAACA5QjyXUhO04j81uJK1TU4La6m4/76Za6KKmr1/uqdVpcCAAAAAJYjyHchPRIiFRsRqnqHqa3FlVaX0yF1DU4t3lAkSdpWXGVxNQAAAABgPYJ8F2IYRsA1vFu2bY8qahokSbvK9qum3mFxRQAAAABgLYJ8FxNoDe8W/lDg/tk0pfy91RZWAwAAAADWI8h3MTndA6fhncNp6n8/FkqS7CGNL1Wm1wMAAADo6gjyXUxOeuOI/IYC/x+RX5m/TyWVtYqNCNXPB6dJknJLCPIAAAAAujaCfBczMD1WhiEVV9SqpLLW6nIOaVHTtPrJOakakNb4AURuSWA06QMAAAAAXyHIdzFR9lBlJUVLkjb48Tp50zS1cF1jkJ86JF1ZyVGSpLwS1sgDAAAA6NoI8l2Qa3q9P6+T/3F3uX7at1/hoTZNHJiiPskxkqRtTK0HAAAA0MUR5LugQa6Gd368Bd2idY1N7iYMSGmcRdA0Il9SWauKmnorSwMAAAAASxHkuyB3wzs/nlrvWh8/bUi6JCk2IkzJMeGSmF4PAAAAoGsjyHdBrhH5LUWVqnc4La6mtdySKm0srFCIzdDkQanu432SG9f2b6PhHQAAAIAujCDfBfXsFqmY8FDVOZx+uS/7oqYmd2P7JCkhyu4+nt0U5NmCDgAAAEBXRpDvggzDaLafvP+tk1/k7laf1uJ4dkpjkM8jyAMAAADowgjyXZRrev2Pfta5vqCsRqvySyVJpzStj3dxbZvHiDwAAACArowg30XldPfPhnf/+7FxNH5ErwSlxUW0uKxPimuNfJVM0+z02gAAAADAHxDku6ic9KYt6PxsRH7hupbd6pvrlRglw5Aqahq0p6qus0sDAAAAAL9AkO+iXGvkiypqtaey1uJqGpVW12n5tr2SpKltBPmIsBBlxEdKYp08AAAAgK6LIN9FRYeHqndSlCRpY4F/TK9fvL5IDqepnPRYZTV1qD9Y8+n1AAAAANAVEeS7sEHp/tXwzjWt/uAmd82xBR0AAACAro4g34W5G975wYh8dV2DPt9ULKn1tnPNuTrXM7UeAAAAQFdFkO/CXA3v/GEv+c82Fqu2wanMxEgNbtoary2uveQZkQcAAADQVRHkuzBXYN5UWKkGh9PSWhY1TaufOjhdhmG0e16fZlPrnU62oAMAAADQ9RDku7Ce3SIVbQ9RXYPT0hHuuganFm8okiRNG9r++nhJ6pEQqbAQQ7UNTu0ur+mM8gAAAADArxDkuzCbzdDApm3o1lu4Tn7Ztj2qqGlQcky4juvV7ZDnhobYlJnY2G2fdfIAAAAAuiKCfBc3qGl6/XoLO9cv/MHVrT5NNlv70+pdXNPr2YIOAAAAQFdEkO/icpqC/AaLgrzDaep/PxZKkqYeYtu55txb0BUT5AEAAAB0PQT5Lm5w0xZ063dbM7V+Zf4+lVTWKjYiVGP7JHXoOllNQT5vD0EeAAAAQNdDkO/iBqQ1BvmC8hrtq6rr9Ptf1DStfnJOquyhHXs5ZiezBR0AAACArosg38XFRoQpMzFSkrShkxvemaapRT82BvnDdatvrk9yjCQpf2+16i3eNg8AAAAAOhtBHhqUbk3Dux93l2vH3v0KD7VpwoCUDl8vLS5ckWEhcjhN/bRvvw8rBAAAAAD/Q5DHgYZ3BZ0b5Beta2xyN2FAiqLsoR2+nmEY7nXyuSWVPqkNAAAAAPwVQR4a1LSXfGdPrXetj5/WwW71zbm3oKNzPQAAAIAuhiAP917yGwsq1NBJa85zS6q0sbBCITZDkwelenx9Gt4BAAAA6KoI8lCvxChF2UNU2+BU3p7qTrnPResaR+PH9klSQpTd4+uzBR0AAACAroogD9lshgamu/aT75x18q4gP3VI2hFd3z0iz9R6AAAAAF0MQR6SpJz0zmt4V1BWo1X5pZKkU45gfbx0YI38rrIa7a9zeKs0AAAAAPB7BHlIkgZ1b2p4t9v3De/+17R3/IheCUqLizii2+gWbVd8ZJgkafteRuUBAAAAdB0EeUg60PCuM6bWu7adO5Ju9c0xvR4AAABAV0SQhyS518jvKqtRWXW9z+6ntLpOy7btkSRNPcog796Cjs71AAAAALoQgjwkSXERYeqRECnJt+vkF68vksNpKic91t15/ki5RuTzCPIAAAAAuhCCPNw6Y3r9wqZu9Ufa5K65LPaSBwAAANAFEeTh5m54V+CbhnfVdQ36fFOxpKNfHy81WyNPkAcAAADQhRDk4ebrEfnPNhartsGpzMRI94cGR8MV5PdU1alsv+/W9QMAAACAPyHIwy2nqeHdxsIKOZym129/UdO0+qmD02UYxlHfXnR4qFJjwyWxTh4AAABA10GQh1vvpGhFhNlUU+/U9j3eDcZ1DU4t3lAkSZo29Oin1bswvR4AAABAV0OQh1uIzdDAdNf0eu+uk1+2bY8qahqUHBOu43p189rt9klhCzoAAAAAXQtBHi0MSnc1vPPuOvmFP7i61afJZjv6afUuWUlsQQcAAACgayHIowXXOnlvjsg7nKb+92OhJGmqF7rVN8fUegAAAABdDUEeLfiic/2q/H0qqaxVbESoxvZJ8trtSgem1ueWVMk0vd+gDwAAAAD8DUEeLeQ0rZHfWbpf5TXe2dLNNa1+ck6q7KHefcllJkbJZkiVtQ0qqazz6m0DAAAAgD8iyKOF+Kgw9UiIlCRt8ML0etM0tejHxiDvzW71LuGhIerRrbFeptcDAAAA6AoI8mglx4sN737cXa4de/crPNSmCQNSjvr22pKdHCNJyi2p9MntAwAAAIA/IcijlZzu3mt4t2hdY5O7iQNSFGUPPerba0ufZLagAwAAANB1EOTRijcb3i1qWh/v7W71zWUlRUliCzoAAAAAXQNBHq24Gt5tLKiQ03nkneBzS6q0sbBCoTZDkwelequ8VrJTXFPrCfIAAAAAgh9BHq1kJ0crPNSm/fUO5e+tPuLbWbSucTT+hD5JSoiye6u8VlxT6/P2VB/VBw8AAAAAEAgI8mglxGZoYLprnfyRT693BfmpQ9K8Uld7MhIiZQ+xqa7BqV1l+316XwAAAABgtSMK8kuXLtUvfvELjR07Vjt37pQkvfbaa/riiy+8Whys4+pcv77gyBreFZbXaFV+qSTpFB+uj5caP3jo1bROnun1AAAAAIKdx0H+n//8p6ZOnarIyEitWrVKtbW1kqSysjI9/PDDXi8Q1jjahncfNY3Gj+iVoLS4CK/V1Z7spun1BHkAAAAAwc7jIP/ggw/queee0wsvvKCwsDD38Z/97GdauXKlV4uDdVwN7450L3nXtnPTfDwa79KHIA8AAACgi/A4yG/cuFETJkxodTw+Pl6lpaXeqAl+YFDTXvI79u5XRU29R9ctra7Tsm17JPl227nmsgjyAAAAALoIj4N8enq6tmzZ0ur4F198oT59+nilKFgvIcqu7vGNU+I3erhOfvH6IjmcpnLSY90B29eYWg8AAACgq/A4yF999dW66aab9PXXX8swDO3atUt///vfdeutt+pXv/qVL2qERY604d3CpvXxvm5y15xrav2OvdWqa3B22v0CAAAAQGcL9fQKd9xxh5xOpyZPnqzq6mpNmDBB4eHhuvXWW3XDDTf4okZYZFD3OC3ZWKwNHjS8q65r0OebiiV13vp4SUqJDVe0PURVdQ7t2FetvikxnXbfAAAAANCZPB6RNwxDv//977V371798MMPWr58uYqLi/XAAw/4oj5YKOcIOtd/trFYtQ1OZSZGutfZdwbDMA6sky9mej0AAACA4OXxiLyL3W7X4MGDvVkL/Mygpqn1Gwsq5HSastmMw15nUdO0+mlD0mUYhz/fm7KTo7VuVznr5AEAAAAENY+D/KRJkw4Z0D755JOjKgj+Izs5WvZQm6rqHPpp3371Soo65Pl1DU4t3lAkqfO61Tfn3oJuD0EeAAAAQPDyOMgPHz68xe/19fVavXq1fvjhB82cOdNbdcEPhIbYNCAtRj/sLNePu8sPG+SXbdujipoGJceE67he3TqpygOYWg8AAACgK/A4yD/55JNtHr/33ntVWVl51AXBv+Skx+mHneXaUFCuaUMPPcq+yN2tPq1D0/C9jS3oAAAAAHQFHje7a88vfvELvfzyy966OfiJQR1seOdwmvpoXaEka6bVSweCfEF5jarrGiypAQAAAAB8zWtBftmyZYqIiPDWzcFPuBrebTjMXvKr8veppLJWsRGhGtsnqTNKayUhyq5uUWGSpLySaktqAAAAAABf83hq/bnnntvid9M0tXv3bn333Xe66667vFYY/INrC7rte6pVVdug6PC2XzILf2icVj85J1X2UK99PuSx7ORo7csvVW5JlQZnxFlWBwAAAAD4isdBPj4+vsXvNptNAwcO1P33369TTjnFa4XBPyRG25UWF67C8lptKKjQyN6tm9iZpqlFPzZtO3eYdfS+lp0co5X5pcotoV8DAAAAgODkcZD/61//6os64Mdy0uNUWF6sDQXlbQb5H3eXa8fe/QoPtWnCgBQLKjwgO7mxs34uU+sBAAAABCnr5kAjYLga3m3Y3fY6+UVNTe4mDkhRlN3jz4a8Kjs5RpIYkQcAAAAQtDqUurp16ybD6Nh2Ynv37j2qguB/BnVvbHjXXuf6RU3r463qVt8cW9ABAAAACHYdCvJz5871WQHPPPOMHn/8cRUUFGjYsGF6+umnNXr06MNe74033tDFF1+ss846S++99577eGVlpe644w6999572rNnj7Kzs3XjjTfq2muv9dljCHY56U0j8gUVMk2zxYc6eSVV2lhYoVCbocmDUq0q0S2raWr9vup6lVbXKSHKbnFFAAAAAOBdHQryM2fO9MmdL1iwQLNnz9Zzzz2nMWPGaO7cuZo6dao2btyo1NT2Q2FeXp5uvfVWjR8/vtVls2fP1ieffKK//e1vysrK0kcffaRf//rXysjI0PTp033yOIJdn5Ro2UNsqqxt0E/79iszMcp92aJ1jaPxJ/RJ8ovQHGUPVXpchArKa5RbUqURvayvCQAAAAC86ajWyNfU1Ki8vLzFlyeeeOIJXX311Zo1a5YGDx6s5557TlFRUXr55ZfbvY7D4dAll1yi++67T3369Gl1+VdffaWZM2fqpJNOUlZWlq655hoNGzZM33zzjcePD43CQmzql9q49vzg6fULm4L8VIu71TfH9HoAAAAAwczjIF9VVaXrr79eqampio6OVrdu3Vp8dVRdXZ1WrFihKVOmHCjGZtOUKVO0bNmydq93//33KzU1VVdeeWWbl5944on617/+pZ07d8o0TS1ZskSbNm065NZ4tbW1R/WBRFfgbnhXcKDhXWF5jVbll0qSThmcZkVZbcpOaQzyeQR5AAAAAEHI4yB/++2365NPPtGf//xnhYeH68UXX9R9992njIwMvfrqqx2+nZKSEjkcDqWltQyAaWlpKigoaPM6X3zxhV566SW98MIL7d7u008/rcGDB6tnz56y2+2aNm2annnmGU2YMKHd68yZM0fx8fHur8zMzA4/jq6irYZ3HzWNxo/olaC0uAhL6mpLdlJjkN9GkAcAAAAQhDwO8h988IGeffZZnXfeeQoNDdX48eP1hz/8QQ8//LD+/ve/+6JGSVJFRYUuvfRSvfDCC0pOTm73vKefflrLly/Xv/71L61YsUL/93//p+uuu04ff/xxu9e58847VVZW5v7asWOHLx5CQGve8M7Fte3cND/oVt8cU+sBAAAABDOPN/3eu3eve216XFyce7u5cePG6Ve/+lWHbyc5OVkhISEqLCxscbywsFDp6a2D4datW5WXl6czzzzTfczpdDY+iNBQbdy4URkZGfrd736nd999V6effrok6dhjj9Xq1av1xz/+scU0/ubCw8MVHh7e4dq7IteIfN6eKlXXNaiuwall2/ZI8o9t55pzTa3PLalq1WUfAAAAAAKdxyPyffr0UW5uriQpJydHb775pqTGkfqEhIQO347dbtfIkSO1ePFi9zGn06nFixdr7Nixrc7PycnR999/r9WrV7u/pk+frkmTJmn16tXKzMxUfX296uvrZbO1fFghISHu0I8jkxQTrpTYcJmmtLGgQovXF8nhNJWTHqusphFwf5HZLUo2Q6quc6i4otbqcgAAAADAqzwekZ81a5bWrFmjiRMn6o477tCZZ56pP/3pT6qvr9cTTzzh0W3Nnj1bM2fO1PHHH6/Ro0dr7ty5qqqq0qxZsyRJl112mXr06KE5c+YoIiJCQ4cObXF91wcHruN2u10TJ07UbbfdpsjISPXu3VufffaZXn31VY9rQ2s56bEqrqjVhoIKfbKhSJJ0ip+NxkuSPdSmzMQobd9TrW0lVUr1o/X7AAAAAHC0PA7yt9xyi/vnKVOmaMOGDVqxYoX69eunY4891qPbmjFjhoqLi3X33XeroKBAw4cP18KFC90N8PLz81uNrh/OG2+8oTvvvFOXXHKJ9u7dq969e+uhhx7Stdde69HtoLXB3eO0dHOJVmzfp883FUvyv/XxLtnJ0dq+p1q5JVU6oU+S1eUAAAAAgNcYpmmanlxhx44dQd/Vvby8XPHx8SorK1NcXJzV5fiNd1f9pFsWrJE9xKY6h1OZiZH6/LZJfrkG/b4P1umvX+bplxP66M7TBlldDgAAAAAckic51OM18llZWZo4caJeeOEF7du374iLROBx7SVf52jsNzBtSLpfhnjpQOd6tqADAAAAEGw8DvLfffedRo8erfvvv1/du3fX2Wefrbffflu1tTQVC3Z9kmMUFnIguPtbt/rm2IIOAAAAQLDyOMiPGDFCjz/+uPLz8/Xf//5XKSkpuuaaa5SWlqYrrrjCFzXCT9hDbeqbEiNJSokN13G9ullcUftcQT5/T7UcTo9WjwAAAACAX/M4yLsYhqFJkybphRde0Mcff6zs7Gy98sor3qwNfmhIRrwk6eeD02Sz+ee0eknKiI+UPbRxLf+u0v1WlwMAAAAAXnPEQf6nn37SY489puHDh2v06NGKiYnRM888483a4IeuP7mffnFCL908pb/VpRySzWYoKylKEuvkAQAAAAQXj7ef+8tf/qJ//OMf+vLLL5WTk6NLLrlE77//vnr37u2L+uBnspOj9eDZx1hdRodkJ0drU2GlcosrNXFAitXlAAAAAIBXeBzkH3zwQV188cWaN2+ehg0b5ouaAK/ITo6RVKi8PdVWlwIAAAAAXuNxkM/Pz/fbLceA5rKTmVoPAAAAIPh4vEaeEI9A0TgiL+WWVFpcCQAAAAB4zxE3uwP8nWsLup379qu2wWFxNQAAAADgHQR5BK3kGLtiwkPlNKUde1knDwAAACA4EOQRtAzDcI/KbytmnTwAAACA4ECQR1BzBflcGt4BAAAACBIeB/nCwkJdeumlysjIUGhoqEJCQlp8Af4kqynI5+0hyAMAAAAIDh5vP3f55ZcrPz9fd911l7p3704Xe/i1PkytBwAAABBkPA7yX3zxhZYuXarhw4f7oBzAu5haDwAAACDYeDy1PjMzU6Zp+qIWwOtcU+uLKmpVVdtgcTUAAAAAcPQ8DvJz587VHXfcoby8PB+UA3hXfGSYkqLtkhiVBwAAABAcPJ5aP2PGDFVXV6tv376KiopSWFhYi8v37t3rteIAb8hOjtaeqjrlllRpaI94q8sBAAAAgKPicZCfO3euD8oAfCc7OVrfbd+nPEbkAQAAAAQBj4P8zJkzfVEH4DNZNLwDAAAAEEQ8DvKS5HA49N5772n9+vWSpCFDhmj69OnsIw+/5N6CjiAPAAAAIAh4HOS3bNmi0047TTt37tTAgQMlSXPmzFFmZqY+/PBD9e3b1+tFAkcjO4UReQAAAADBw+Ou9TfeeKP69u2rHTt2aOXKlVq5cqXy8/OVnZ2tG2+80Rc1Akeld2JjkC/bX699VXUWVwMAAAAAR8fjEfnPPvtMy5cvV2JiovtYUlKSHnnkEf3sZz/zanGAN0TaQ5QRH6FdZTXaVlKlkU3b0QEAAABAIPJ4RD48PFwVFRWtjldWVspuJyDBPzG9HgAAAECw8DjIn3HGGbrmmmv09ddfyzRNmaap5cuX69prr9X06dN9USNw1LKbGt6xBR0AAACAQOdxkJ83b5769u2rsWPHKiIiQhEREfrZz36mfv366amnnvJFjcBRy0piRB4AAABAcPB4jXxCQoLef/99bd68WRs2bJAkDRo0SP369fN6cYC39ElhCzoAAAAAweGI9pGXpP79+6t///7erAXwmezkGEmNU+tN05RhGBZXBAAAAABHpkNBfvbs2XrggQcUHR2t2bNnH/LcJ554wiuFAd7Us1ukQmyG9tc7VFheq/T4CKtLAgAAAIAj0qEgv2rVKtXX17t/BgJNWIhNvRKjlFtSpW0llQR5AAAAAAGrQ0F+yZIlbf4MBJLs5GjlllQpt6RKJ/ZNtrocAAAAADgiHnetv+KKK9rcR76qqkpXXHGFV4oCfIEt6AAAAAAEA4+D/CuvvKL9+/e3Or5//369+uqrXikK8IWsZLagAwAAABD4Oty1vry8XKZpyjRNVVRUKCLiwBpjh8Oh//znP0pNTfVJkYA39ElmCzoAAAAAga/DQT4hIUGGYcgwDA0YMKDV5YZh6L777vNqcYA3uabW79hbrQaHU6EhHk9IAQAAAADLdTjIL1myRKZp6uSTT9Y///lPJSYmui+z2+3q3bu3MjIyfFIk4A3pcREKD7WptsGpnaX71Tsp2uqSAAAAAMBjHQ7yEydOlCTl5uaqV69eMgzDZ0UBvmCzGcpOjtaGggptK6kiyAMAAAAISB7PLf7kk0/09ttvtzr+1ltv6ZVXXvFKUYCv0LkeAAAAQKDzOMjPmTNHycmt9+BOTU3Vww8/7JWiAF+hcz0AAACAQOdxkM/Pz1d2dnar471791Z+fr5XigJ8JZsgDwAAACDAeRzkU1NTtXbt2lbH16xZo6SkJK8UBfiKewu6YoI8AAAAgMDkcZC/+OKLdeONN2rJkiVyOBxyOBz65JNPdNNNN+miiy7yRY2A17hG5HeV7VdNvcPiagAAAADAcx3uWu/ywAMPKC8vT5MnT1ZoaOPVnU6nLrvsMtbIw+8lRtsVGxGqipoG5e+t1oC0WKtLAgAAAACPeBzk7Xa7FixYoAceeEBr1qxRZGSkjjnmGPXu3dsX9QFeZRiG+iRHa81PZdpWXEWQBwAAABBwPA7yLgMGDNCAAQO8WQvQKbKbgnzeHtbJAwAAAAg8Hgd5h8Oh+fPna/HixSoqKpLT6Wxx+SeffOK14gBfcG9BR8M7AAAAAAHI4yB/0003af78+Tr99NM1dOhQGYbhi7oAn2ELOgAAAACBzOMg/8Ybb+jNN9/Uaaed5ot6AJ/rkxwjScplaj0AAACAAOTx9nN2u139+vXzRS1Ap8hKjpIkFVfUqqKm3uJqAAAAAMAzHgf53/zmN3rqqadkmqYv6gF8LjYiTMkx4ZKkvJJqi6sBAAAAAM94PLX+iy++0JIlS/Tf//5XQ4YMUVhYWIvL33nnHa8VB/hKn+RolVTWaltJpY7pGW91OQAAAADQYR4H+YSEBJ1zzjm+qAXoNNnJ0fomby8j8gAAAAACjsdB/q9//asv6gA6lXsLupJKiysBAAAAAM94vEYeCAZsQQcAAAAgUHk8Ip+dnX3IveO3bdt2VAUBnaFPyoEgb5rmIV/TAAAAAOBPPA7yN998c4vf6+vrtWrVKi1cuFC33Xabt+oCfKpXYpQMQyqvadDeqjolNXWxBwAAAAB/53GQv+mmm9o8/swzz+i777476oKAzhARFqKM+EjtLN2v3JIqgjwAAACAgOG1NfKnnnqq/vnPf3rr5gCfc02v38Y6eQAAAAABxGtB/u2331ZiYqK3bg7wOVfDuzyCPAAAAIAA4vHU+hEjRrRoDGaapgoKClRcXKxnn33Wq8UBvpSVROd6AAAAAIHH4yB/9tlnt/jdZrMpJSVFJ510knJycrxVF+Bz2SkEeQAAAACBp0NBfvbs2XrggQcUHR2tSZMmaezYsQoLC/N1bYBP9XFNrd9TJafTlM3GFnQAAAAA/F+H1sg//fTTqqyslCRNmjRJ+/bt82lRQGfokRCpUJuhmnqnCsprrC4HAAAAADqkQyPyWVlZmjdvnk455RSZpqlly5apW7dubZ47YcIErxYI+EpoiE29kqK0rbhKuSVVykiItLokAAAAADisDgX5xx9/XNdee63mzJkjwzB0zjnntHmeYRhyOBxeLRDwpT7J0e4g/7N+yVaXAwAAAACH1aEgf/bZZ+vss89WZWWl4uLitHHjRqWmpvq6NsDn6FwPAAAAINB41LU+JiZGS5YsUXZ2tkJDPW54D/gdOtcDAAAACDQep/GJEyf6og7AEtnJBHkAAAAAgaVDXeuBYNUnOUaStGNvteodTourAQAAAIDDI8ijS0uLC1dkWIganKZ+2rff6nIAAAAA4LAI8ujSDMNQlnt6faXF1QAAAADA4R1xkN+yZYsWLVqk/fsbRzFN0/RaUUBn6uMO8tUWVwIAAAAAh+dxkN+zZ4+mTJmiAQMG6LTTTtPu3bslSVdeeaV+85vfeL1AwNeykqMkMSIPAAAAIDB4HORvueUWhYaGKj8/X1FRUe7jM2bM0MKFC71aHNAZspsa3tG5HgAAAEAg8Hj7uY8++kiLFi1Sz549Wxzv37+/tm/f7rXCgM7i2oIuj6n1AAAAAAKAxyPyVVVVLUbiXfbu3avw8HCvFAV0JleQ31m6XzX1DourAQAAAIBD8zjIjx8/Xq+++qr7d8Mw5HQ69dhjj2nSpEleLQ7oDN2iwhQfGSZJytvD9HoAAAAA/s3jIP/YY4/p+eef16mnnqq6ujrdfvvtGjp0qD7//HM9+uijHhfwzDPPKCsrSxERERozZoy++eabDl3vjTfekGEYOvvss1tdtn79ek2fPl3x8fGKjo7WqFGjlJ+f73Ft6BoMw3CPyucWE+QBAAAA+DePg/zQoUO1adMmjRs3TmeddZaqqqp07rnnatWqVerbt69Ht7VgwQLNnj1b99xzj1auXKlhw4Zp6tSpKioqOuT18vLydOutt2r8+PGtLtu6davGjRunnJwcffrpp1q7dq3uuusuRUREeFQbuhb3FnSMyAMAAADwc4Zp4QbwY8aM0ahRo/SnP/1JkuR0OpWZmakbbrhBd9xxR5vXcTgcmjBhgq644gotXbpUpaWleu+999yXX3TRRQoLC9Nrr712xHWVl5crPj5eZWVliouLO+LbQeCYt3iznvjfJl0wsqcev2CY1eUAAAAA6GI8yaEej8j369dP9957rzZv3nzEBUpSXV2dVqxYoSlTphwoxmbTlClTtGzZsnavd//99ys1NVVXXnllq8ucTqc+/PBDDRgwQFOnTlVqaqrGjBnTIui3pba2VuXl5S2+0LW4p9azBR0AAAAAP+dxkL/uuuv04YcfauDAgRo1apSeeuopFRQUeHzHJSUlcjgcSktLa3E8LS2t3dv74osv9NJLL+mFF15o8/KioiJVVlbqkUce0bRp0/TRRx/pnHPO0bnnnqvPPvus3VrmzJmj+Ph491dmZqbHjweBzb0FHVPrAQAAAPg5j4P8Lbfcom+//VYbNmzQaaedpmeeeUaZmZk65ZRTWnSz97aKigpdeumleuGFF5ScnNzmOU6nU5J01lln6ZZbbtHw4cN1xx136IwzztBzzz3X7m3feeedKisrc3/t2LHDJ48B/iurKciXVNapbH+9xdUAAAAAQPs8DvIuAwYM0H333adNmzZp6dKlKi4u1qxZszp8/eTkZIWEhKiwsLDF8cLCQqWnp7c6f+vWrcrLy9OZZ56p0NBQhYaG6tVXX9W//vUvhYaGauvWrUpOTlZoaKgGDx7c4rqDBg06ZNf68PBwxcXFtfhC1xITHqrU2HBJUh7T6wEAAAD4sSMO8pL0zTff6Oabb9Y555yjTZs26YILLujwde12u0aOHKnFixe7jzmdTi1evFhjx45tdX5OTo6+//57rV692v01ffp0TZo0SatXr1ZmZqbsdrtGjRqljRs3trjupk2b1Lt37yN/oOgSmF4PAAAAIBCEenqFTZs26e9//7tef/115ebm6uSTT9ajjz6qc889VzExMR7d1uzZszVz5kwdf/zxGj16tObOnauqqir3yP5ll12mHj16aM6cOYqIiNDQoUNbXD8hIUGSWhy/7bbbNGPGDE2YMEGTJk3SwoUL9cEHH+jTTz/19KGii+mTEq2vc/dqG3vJAwAAAPBjHgf5nJwcjRo1Stddd50uuuiiVs3qPDFjxgwVFxfr7rvvVkFBgYYPH66FCxe6bzM/P182m2eTBs455xw999xzmjNnjm688UYNHDhQ//znPzVu3LgjrhNdQ1YSnesBAAAA+D+P95HfvHmz+vfv76t6/AL7yHdNH60r0DWvrdAxPeL1wQ188AMAAACg8/h0H/lgD/HouvqkNK2RL6mSh59vAQAAAECn6dDU+sTERG3atEnJycnq1q2bDMNo99y9e/d6rTigM2UmRslmSBW1DSqprFNKUxd7AAAAAPAnHQryTz75pGJjY90/HyrIA4EqPDREPbpFasfe/cotqSLIAwAAAPBLHQryM2fOdP98+eWX+6oWwHLZyTHasXe/8kqqNDo70epyAAAAAKAVj9fIh4SEqKioqNXxPXv2KCQkxCtFAVbJToqSJG2jcz0AAAAAP+VxkG+vCVhtba3sdvtRFwRYKTvZtQVdpcWVAAAAAEDbOryP/Lx58yRJhmHoxRdfVExMjPsyh8Ohzz//XDk5Od6vEOhE2SmNr+u8kmqLKwEAAACAtnU4yD/55JOSGkfkn3vuuRbT6O12u7KysvTcc895v0KgE/VxjcjvqZLTacpmo7EjAAAAAP/S4SCfm5srSZo0aZLeeecddevWzWdFAVbJSIiUPcSmugandpXtV89uUVaXBAAAAAAteLxGfsmSJYR4BK0Qm6FeTQ3vcml4BwAAAMAPeRzkzzvvPD366KOtjj/22GO64IILvFIUYCVXw7s8gjwAAAAAP+RxkP/888912mmntTp+6qmn6vPPP/dKUYCVXEGeLegAAAAA+COPg3xlZWWb28yFhYWpvLzcK0UBVjqwBR1BHgAAAID/8TjIH3PMMVqwYEGr42+88YYGDx7slaIAKzG1HgAAAIA/63DXepe77rpL5557rrZu3aqTTz5ZkrR48WK9/vrreuutt7xeINDZXFvQ7di3X3UNTtlDPf68K6As3VysfdX1mj4sw+pSAAAAAHSAx0H+zDPP1HvvvaeHH35Yb7/9tiIjI3Xsscfq448/1sSJE31RI9CpUmLDFW0PUVWdQzv2VatvSozVJflMTb1D17y6QvvrHUqIDNOEASlWlwQAAADgMDwO8pJ0+umn6/TTT/d2LYBfMAxDWcnRWrerXHklVUEd5NftKtf+eock6cEPf9R/+o5XaEhwz0AAAAAAAt0R/Rd7aWmpXnzxRf3ud7/T3r17JUkrV67Uzp07vVocYJWu0vBu9Y5S98+bCiu14Lsd1hUDAAAAoEM8DvJr167VgAED9Oijj+rxxx9XaWmpJOmdd97RnXfe6e36AEt0lS3oVuXvk3SgL8ATH21SeU29lSUBAAAAOAyPg/zs2bN1+eWXa/PmzYqIiHAfP+2009hHHkHDHeSLKy2uxLdW5ZdKku6ZPkR9kqO1p6pOzy7Zam1RAAAAAA7J4yD/7bff6pe//GWr4z169FBBQYFXigKsNiAtVlLjGnKn07S4Gt8oKq/RztL9MgxpZO9u+t1pgyRJL3+Rqx17qy2uDgAAAEB7PA7y4eHhKi8vb3V806ZNSkmh4zWCQ056rKLsIaqoadCWIB2VX9W0Pn5gWqxiwkM1eVCqftYvSXUOpx5ZuMHa4gAAAAC0y+MgP336dN1///2qr29cR2sYhvLz8/Xb3/5W5513ntcLBKwQGmLT8MwESdKK7fusLcZHXI3uXI/TMAz94fTBMgzpw7W79V3eXuuKAwAAANAuj4P8//3f/6myslKpqanav3+/Jk6cqH79+ik2NlYPPfSQL2oELHFcr26SgjfIuxrdjeiV4D42qHucZhyfKUl64MP1QbusAAAAAAhkHu8jHx8fr//973/64osvtHbtWlVWVuq4447TlClTfFEfYJmRvRuD/Mr84AvyDQ6n1v5UJkka0fSBhcvsUwbogzW7tGZHqf61ZpfOHtHDihIBAAAAtMPjIO8ybtw4jRs3zpu1AH7FNVK9rbhKe6vqlBhtt7YgL9pUWKnqOodiw0PVLyWmxWWpsRH69aR+enzRRj26cIOmDklXpD3EokoBAAAAHKxDQX7evHm65pprFBERoXnz5h3y3JiYGA0ZMkRjxozxSoGAVRKi7OqXGqMtRZValb9PkwelWV2S17jWxw/LTJDNZrS6/Mpx2frH1/naWbpfLy7dphsm9+/kCgEAAAC0p0NB/sknn9Qll1yiiIgIPfnkk4c8t7a2VkVFRbrlllv0+OOPe6VIwCrH9UrQlqJKrdgeXEHetT7e1ejuYBFhIbp92kDd9MZq/fmzrbpwVKbS4iI6sUIAAAAA7elQs7vc3FwlJSW5fz7U165du/Tf//5X8+fP92XdQKcI1nXyrq3nmje6O9j0YRka0StB1XUO/d9HGzunMAAAAACH5XHX+o4YN26c/vCHP/jipoFO5Qrya3aUqd7htLga7yjbX68tRZWS2h+Rlw5sRydJb634ST/sLOuM8gAAAAAcxhEF+cWLF+uMM85Q37591bdvX51xxhn6+OOP3ZdHRkbqpptu8lqRgFX6JMcoLiJU++sd2rC7wupyvGJN02h876QoJcWEH/Lckb276cxhGTJN6aEP18s02Y4OAAAAsJrHQf7ZZ5/VtGnTFBsbq5tuukk33XST4uLidNppp+mZZ57xRY2AZWw2Q8cF2fR6V6O7Q43GN/fbaQNlD7Vp2bY9+t+Phb4rDAAAAECHeBzkH374YT355JN6/fXXdeONN+rGG2/UP/7xDz355JN6+OGHfVEjYKmRTfusr9geHEHe1ehuRAeDfM9uUbpqXLYkac5/N6iuITiWGAAAAACByuMgX1paqmnTprU6fsopp6isjDW0CD6udfLBEORN02zW6K5bh6/360n9lBxjV25JlV5bvt1H1QEAAADoCI+D/PTp0/Xuu++2Ov7+++/rjDPO8EpRgD8ZlpkgmyHtLN2vwvIaq8s5Knl7qlVaXS97qE2Dusd1+Hox4aH6zSkDJUnzFm9WaXWdr0oEAAAAcBgd2kd+3rx57p8HDx6shx56SJ9++qnGjh0rSVq+fLm+/PJL/eY3v/FNlYCFosNDlZMepx93l2vl9n069ZjuVpd0xFbvaJxVcEyPeNlDPfsc78LjM/XKV3naUFChuR9v1r3Th/iiRAAAAACH0aEg/+STT7b4vVu3bvrxxx/1448/uo8lJCTo5ZdfZts5BKWRvbvpx93lWhHgQX5Vfqmkjje6ay7E1rgd3S9e+lp/W75dl47trb4pMd4tEAAAAMBhdSjI5+bm+roOwK8d1ztBry3frhUB3rneFeRH9Eo4ouuP65+syTmpWryhSHP+s14vzhzlveIAAAAAdMgR7SMvSSUlJSopKfFmLYDfGtkrUZL0w84y1dQ7LK7myOyvc2j97nJJnjW6O9idpw1SqM3Qx+uL9OUW3gMAAACAzuZRkC8tLdV1112n5ORkpaWlKS0tTcnJybr++utVWlrqoxIB62UmRio5Jlz1DlPrdgXm7gw/7CpTg9NUamy4MuIjjvh2+qXG6Bcn9JYkPfDvH+Vwmt4qEQAAAEAHdGhqvSTt3btXY8eO1c6dO3XJJZdo0KBBkqQff/xR8+fP1+LFi/XVV1+pW7cjH+kD/JVhGBrZO0GL1hVqxfZ9Gtk70eqSPLa62fp4wzCO6rZumtxf76z8SRsKKvTWdzt00eheXqgQAAAAQEd0eET+/vvvl91u19atW/WXv/xFN998s26++WY9//zz2rJli8LCwnT//ff7slbAUsf1Cuz95Fc1daw/mmn1Lt2i7bpxcn9J0h8/2qTK2oajvk0AAAAAHdPhIP/ee+/pj3/8o9LS0lpdlp6erscee6zN/eWBYDGyd2MAXplfKtMMvOnkR9vo7mCXjc1SVlKUSipr9edPt3jlNgEAAAAcXoeD/O7duzVkSPv7Rg8dOlQFBQVeKQrwR0N7xCssxFBxRa1+2rff6nI8UlBWo91lNbIZ0rE9471ym/ZQm+48rXGJzQtLc/XTvmqv3C4AAACAQ+twkE9OTlZeXl67l+fm5ioxMfDWDQMdFREWoiEZjSE40KbXr26aVp+THqcoe4dbYxzWKYPTdEKfRNU1OPXYwo1eu10AAAAA7etwkJ86dap+//vfq66urtVltbW1uuuuuzRt2jSvFgf4G9f0+kAL8q5p9cO9NK3exTAM/eH0wTIM6V9rdmllfmA9LwAAAEAg8qjZ3caNG9W/f3899thj+te//qX3339fjzzyiPr376/169frvvvu82WtgOUOrJMPrMDqXh+fmeD12x7aI17nH9dTUuN2dIHYPwAAAAAIJB2eY9uzZ08tW7ZMv/71r3XnnXe6/2PdMAz9/Oc/15/+9CdlZmb6rFDAH7iC/Prd5aqqbVB0uPemqftKvcOptTtLJXmnY31bbp06UB9+v1ur8kv1wdrdmj4swyf3AwAAAMCDEXlJys7O1n//+1+VlJRo+fLlWr58uYqLi7Vw4UL169fPVzUCfiMtLkI9EiLlNKU1O0qtLqdDNhZUqKbeqbiIUPVJjvbJfaTFRejaiX0lSY/+d4Nq6h0+uR8AAAAAHgZ5l27dumn06NEaPXo0De7Q5RwXYNPrVzV94DAsM0E2m+Gz+7l6fB91j4/QztL9eumLXJ/dDwAAANDVHVGQB7qykU0N4wKl4d2qpg8cfDWt3iXSHqLbpw2UJD27ZIuKK2p9en8AAABAV0WQBzx0YES+VE6n/zd2W+1qdOfljvVtOWtYDw3rGa+qOoee+B/b0QEAAAC+QJAHPDSoe5wiwmwq21+vbSWVVpdzSKXVddpWUiVJGt4zwef3Z7MZ+sMZgyVJC77dofW7y31+nwAAAEBXQ5AHPBQWYtOwplC8cnuppbUczuqm9fF9kqPVLdreKfc5KitRpx/TXU5TeujD9WxHBwAAAHgZQR44Aq5t6Px9nbxr//jhPtg//lB+Oy1H9hCbvthSoiUbizr1vgEAAIBgR5AHjsBxTY3jVvh553pXx/rOWB/fXK+kKM0alyVJevDD9ap3ODv1/gEAAIBgRpAHjoCr4d2WokqVVtdZXE3bnE5TqzupY31brpvUT0nRdm0rrtLfl2/v9PsHAAAAghVBHjgCidF29UmOlnRg1NvfbCupUnlNgyLCbBqYHtvp9x8XEaZbfj5AkjR38WaVVdd3eg0AAABAMCLIA0fIvQ2dn66TdzW6O6ZHvMJCrPmnftGoTA1Ii1Fpdb3mfbLZkhoAAACAYEOQB46Qe528nwb5VRZOq3cJDbHp96c3bkf36rI85TZthQcAAADgyBHkgSPk6ly/ZkepGvywmZurY/2ITu5Yf7CJA1J00sAU1TtMzfnPektrAQAAAIIBQR44Qv1TYxQbHqqqOoc2FlZYXU4L1XUN2lBQLsnaEXmX3582SCE2Qx/9WKhlW/dYXQ4AAAAQ0AjywBGy2QwNb9rWzd/Wya/9qUxOU+oeH6H0+Airy1H/tFj9v9G9JEkPfvijHE7T4ooAAACAwEWQB46Ca3q9v62TdzW6G27xtPrmbp7SX7ERoVq3q1zvrPzJ6nIAAACAgEWQB46CK8ivbFqP7i8ONLpLsLaQZpJiwnXDyf0kSY8v2qiq2gaLKwIAAAACE0EeOArDMxNkGFL+3moVVdRYXY4kyTRN9wcL/rA+vrmZJ2apV2KUiipq9ZfPtlpdDgAAABCQCPLAUYiNCNPAtFhJ0srtpdYW02RXWY2KK2oVajM0NCPe6nJaCA8N0Z2n5kiSnl+6TQVl/vHhBwAAABBICPLAUTrOPb3eP9bJr24ajc/pHqtIe4i1xbRh2tB0HdcrQTX1Tv396+1WlwMAAAAEHII8cJRGNk1f95fO9e718Zn+Na3exTAMXTmujyTp9W92qK7BaXFFAAAAQGAhyANHyTUiv3ZnmWobHBZXI61q6ljvT43uDnbKkDSlxYWrpLJWC9cVWF0OAAAAEFAI8sBRykqKUmK0XXUNTq3bVW5pLXUNTn2/s0yS/zW6ay4sxKaLm/aVf21ZnrXFAAAAAAGGIA8cJcMwdJyfTK9fv7tcdQ1OJUSFKSspytJaDufi0b0UajP0bd4+rd9t7QcgAAAAQCAhyANeMNJPGt6tbppW37gtnmFpLYeTFhehqUPSJUmvLqPpHQAAANBRBHnAC45rWo++Yvs+maZpWR3+3ujuYJeO7S1Jem/VTpXtr7e4GgAAACAwEOQBLzi2Z4JCbYYKy2u1s3S/ZXUEQqO75sZkJ2pAWoz21zv0zsqfrC4HAAAACAgEecALIu0hGpIRJ0la2bSPe2fbU1mr7XuqJUnDMhMsqcFThmHo0rFZkqTXlm+3dDYDAAAAECgI8oCXjLC44d2an0olSX1TohUfGWZJDUfinBE9FBMeqm3FVfpyyx6rywEAAAD8HkEe8BJXw7sVFgX5VU0zAfx527m2xISH6tzjekiSXmUrOgAAAOCwCPKAl7iC/I+7y1Vd19Dp938gyCd0+n0frUtPaGx69/H6Qkt7DAAAAACBgCAPeElGQqS6x0fI4TS19qeyTr1vh9N0bz0XKB3rm+ufFquxfZLkNKV/fM1WdAAAAMCh+EWQf+aZZ5SVlaWIiAiNGTNG33zzTYeu98Ybb8gwDJ199tntnnPttdfKMAzNnTvXO8UCh3BcL2um128trlRlbYOi7CEakBbTqfftLZc1bUW34Nsdqm1wWFwNAAAA4L8sD/ILFizQ7Nmzdc8992jlypUaNmyYpk6dqqKiokNeLy8vT7feeqvGjx/f7jnvvvuuli9froyMDG+XDbTpuN7WNLxb3TSt/pge8QoNsfyf9RGZMjhNaXHhKqms08IfCqwuBwAAAPBblv8X/xNPPKGrr75as2bN0uDBg/Xcc88pKipKL7/8crvXcTgcuuSSS3TfffepT58+bZ6zc+dO3XDDDfr73/+usLDA6eCNwOZaJ78yf1+nbqW2akfjBweB1uiuubAQm/7f6MZR+VeXMb0eAAAAaI+lQb6urk4rVqzQlClT3MdsNpumTJmiZcuWtXu9+++/X6mpqbryyivbvNzpdOrSSy/VbbfdpiFDhhy2jtraWpWXl7f4Ao7E4O5xCg+1aV91vXJLqjrtfgO50V1zF4/OVKjN0Irt+7RuV+f2GQAAAAAChaVBvqSkRA6HQ2lpaS2Op6WlqaCg7am1X3zxhV566SW98MIL7d7uo48+qtDQUN14440dqmPOnDmKj493f2VmZnb8QQDN2ENtOrZnvKTOWydfWdugjYUVkqQRmQmdcp++khoXoWlD0yVJrzEqDwAAALTJ8qn1nqioqNCll16qF154QcnJyW2es2LFCj311FOaP3++DMPo0O3eeeedKisrc3/t2LHDm2Wjizmu2fT6zrD2p1KZptQjIVKpcRGdcp++dNnYLEnSe6t3qmx/vbXFAAAAAH4o1Mo7T05OVkhIiAoLC1scLywsVHp6eqvzt27dqry8PJ155pnuY06nU5IUGhqqjRs3aunSpSoqKlKvXr3c5zgcDv3mN7/R3LlzlZeX1+p2w8PDFR4e7qVHha5uZC9Xw7vSTrk/17T64QE+rd5lVFY35aTHakNBhd5e8ZOuHJdtdUkAAACAX7F0RN5ut2vkyJFavHix+5jT6dTixYs1duzYVufn5OTo+++/1+rVq91f06dP16RJk7R69WplZmbq0ksv1dq1a1uck5GRodtuu02LFi3qzIeHLso1Ir+pqKJTRpTd6+MDfFq9i2EY+sUJjU3v/rZ8u5zOzmsaCAAAAAQCS0fkJWn27NmaOXOmjj/+eI0ePVpz585VVVWVZs2aJUm67LLL1KNHD82ZM0cREREaOnRoi+snJCRIkvt4UlKSkpKSWpwTFham9PR0DRw40PcPCF1ecky4eidFafueaq3eUaqJA1J8dl+maWp1EHSsP9g5I3ro0f9uUG5Jlb7YUqIJPnwOAQAAgEBj+Rr5GTNm6I9//KPuvvtuDR8+XKtXr9bChQvdDfDy8/O1e/dui6sEPOOaXu/rhnc/7duvkso6hYUYGpIR59P76kzR4aE6b2RPSWxFBwAAABzM8hF5Sbr++ut1/fXXt3nZp59+esjrzp8//7C339a6eMCXRvTupndW7dQqHze8W7WjVFLjtncRYSE+va/O9osTemv+V3n6ZEOhftpXrZ7doqwuCQAAAPALlo/IA8HINSK/Kr9UDh+u8XZ9UBBM0+pd+qXG6Gf9kuQ0pX98nW91OQAAAIDfIMgDPjAwPVbR9hBV1jZoU9Me777gbnQXJB3rD3ZpU9O7Bd/uUG2Dw+JqAAAAAP9AkAd8IMRmuEfJfbWffG2DQz/uKpckjcgMvhF5SZoyKE3d4yO0p6pO//meXhkAAACARJAHfOa4plFyXzW8+3FXueocTiVG25WZGOmT+7BaaIhN/290L0k0vQMAAABcCPKAj7j2k1/poyDffP94wzB8ch/+4KLRvRQWYmhVfql+2FlmdTkAAACA5QjygI+4ptbn7alWSWWt12/f1bE+WNfHu6TEhuvUod0lSa8xKg8AAAAQ5AFfiY8MU//UGEkHRs+9KZg71h/s0rGNTe/eX7NTZdX1FlcDAAAAWIsgD/jQyKbp9d5eJ19cUauf9u2XYUjH9oz36m37o+N7d1NOeqxq6p16a8UOq8sBAAAALEWQB3zIV+vkVzdNq++fGqPYiDCv3rY/MgxDl43NkiS9tny7nE7T2oIAAAAACxHkAR9yjciv+alUdQ1Or92ue1p9kG4715azR2QoNiJU2/dU6/PNxVaXAwAAAFiGIA/4UJ/kaCVEham2wan1u8u9drvujvVB3uiuuSh7qM4f2VMSTe8AAADQtRHkAR8yDEPH9fLuOnmH09Tan0oldY1Gd8394oTGpnefbCzSjr3VFlcDAAAAWIMgD/iYu+FdvneC/OaiClXVORRtD1G/pq74XUXflBiN65cs05T+/nW+1eUAAAAAliDIAz7mmv6+yksj8q5p9cMyExRiM7xym4HEtRXdgm/zVVPvsLgaAAAAoPMR5AEfG9azMXDvKqvRrtL9R317B/aPTzjq2wpEk3NSlREfoX3V9fpw7W6rywEAAAA6HUEe8LHo8FAN6h4rSVrphen17kZ3XahjfXOhITZd0rRW/tXlNL0DAABA10OQBzrBSC81vCuvqdeW4kpJ0vAuOiIvSTNGZSosxNCaHaXuxn8AAABAV0GQBzrBcU0N71Y2jaYfqbU7ymSaUmZipJJjwr1QWWBKjgnXacd0l8RWdAAAAOh6CPJAJ3BtQbduZ9lRNWhzr4/votPqm7usqendv9bs0r6qOourAQAAADoPQR7oBD27RSo1NlwNTlNrfyo74ttZtaNUUtdtdNfccb26aXD3ONU2OPXWih1WlwMAAAB0GoI80AkMw3CPyh9pwzvTNJt1rGdE3jAM96j835bny+k0La4IAAAA6BwEeaCTjOx9dA3v8vdWa191vewhNncX/K7urOE9FBsRqvy91fpsU7HV5QAAAACdgiAPdBJ3w7vt+2Sano8eu7adG9IjTuGhId4sLWBF2kN0wchMSdJrbEUHAACALoIgD3SSoT3iZA+xaU9Vnbbvqfb4+jS6a9ulTdPrl2ws0o69nj+vAAAAQKAhyAOdJDw0REN7xEk6snXyNLprW3ZytMb3T5ZpSn9jVB4AAABdAEEe6ERHuk6+pt6hH3eVSyLIt+WysVmSpAXf7Tiq7f0AAACAQECQBzrRkQb5dbvK1OA0lRwTrh4Jkb4oLaCdnJOqHgmRKq2u1wdrdlldDgAAAOBTBHmgE7m2oNtYWKGKmvoOX8/V6G5ErwQZhuGL0gJaiM3Q/xvTSxLT6wEAABD8CPJAJ0qNi1DPbpEyTWnNjrIOX695kEfbLhqVKXuITWt+KtOapn4CAAAAQDAiyAOd7Eim19Ox/vCSYsJ1+rHdJUmvLmNUHgAAAMGLIA90MneQ72Dn+sLyGu0qq5HNkI7tGe/L0gKeayu6D9bu0t6qOourAQAAAHyDIA90Mtc6+VX5++R0moc93zWtfkBarKLDQ31ZWsAbkZmgoT3iVNfg1Jvf7bC6HAAAAMAnCPJAJ8tJj1VkWIgqahq0pbjysOev2tE0rb4X0+oPxzAMXXpC46j835Zvl6MDH5QAAAAAgYYgD3Sy0BCbhmcmSOrYOnka3Xlm+rAeio8M00/79uuzTUVWlwMAAAB4HUEesMBxvRMkHT7INzic+v6nxu72xxHkOyTSHqILRvaURNM7AAAABCeCPGABV8O7lYdpeLexsEL76x2KjQhVn+SYzigtKPyiaXr9Z5uKtX1PlcXVAAAAAN5FkAcs4NpGbltx1SG7q7um1Q/PTJDNZnRGaUEhKzlaEwekyDQb18oDAAAAwYQgD1igW7RdfVOiJR3YI74t7vXxTWvq0XGupndvfveT9tc5LK4GAAAA8B6CPGAR1zZ0h1onT8f6IzcpJ1U9EiJVtr9eH6zdZXU5AAAAgNcQ5AGLHG6dfFl1vbYVN67vHsaIvMdCbIZ7rfxry7bLNNmKDgAAAMGBIA9YxBXk1+woU73D2ery1T+VSpKykqKUGG3vzNKCxoxRmbKH2vT9zjKt3lFqdTkAAACAVxDkAYv0TYlRXESo9tc7tGF3RavLXWvnmVZ/5BKj7Trj2O6SGkflAQAAgGBAkAcsYrMZ7pDe1vR6d6M79o8/KpeNzZIk/Xvtbu2prLW2GAAAAMALCPKAhVzT6w9ueGeapnsquGurOhyZYT3jdUyPeNU5nHrzu5+sLgcAAAA4agR5wELtBfnckiqV7a9XeKhNOd1jrSgtaBiGoUvHNja9+9vy7XI4aXoHAACAwEaQByw0LDNBNkPaWbpfheU17uOuafXH9IhXWAj/TI/W9GEZSogK087S/bplwWptK660uiQAAADgiJEQAAvFhIdqYHqcJGlls1H5A/vHJ1hRVtCJCAvR9ZP6SZL+tWaXpjzxmW58fZU2FbZuMggAAAD4O4I8YLGRvRMktZxef6DRHevjveWq8X307q9P1JRBqXKajYH+lCc/17WvrdAPO8usLg8AAADoMII8YDH3OvmmzvX76xzaUNA4Ujw8M8GqsoLSiF7d9OLMUfrwxnE6dWi6JGnhugKd8fQXunL+t+w1DwAAgIAQanUBQFd3XNOo+w87y1RT79D3O8vkcJpKiwtX9/gIi6sLTkMy4vXnX4zUpsIK/emTLfr32l1avKFIizcUaXz/ZN1wcn+Nzk60ukwAAACgTYzIAxbrlRil5Bi76h2m1u0q06qmkfkRmd1kGIbF1QW3AWmxmnfxCH08e6LOH9lTITZDSzeX6MK/LNOMvyzTl1tKZJp0uQcAAIB/IcgDFjMMwz0qv2L7vmbr4xOsK6qL6ZMSoz9eMEyf3nqSLh7dS2Ehhr7O3atLXvxa5/35Ky3ZUESgBwAAgN8gyAN+4Lhm+8kf6FhPo7vOlpkYpTnnHqPPb5+ky0/MUnioTSvzSzVr/rea/qcvtWhdgZzsQw8AAACLEeQBP+BqeLd0c4kKy2sVYjN0TI94i6vqurrHR+re6UO09LeTdPX4bEWGhej7nWX65WsrdNq8pfpgzS45CPQAAACwCEEe8APH9IhXWIih6jqHJCknPVaR9hCLq0JqbIR+f/pgffHbSfr1SX0VEx6qDQUVuuH1VTrlyc/0zsqf1OBwWl0mAAAAuhiCPOAHIsJCNCTjwAg86+P9S1JMuG6flqMvf3uybp7SX3ERodpaXKXZb67Ryf/3md74Jl91DQR6AAAAdA6CPOAnjmu2Jn5EJuvj/VF8VJhunjJAX95xsm6fNlCJ0Xbl763WHe98r0l//FSvLctTTb3D6jIBAAAQ5AjygJ9wrZOXpOGMyPu12Igw/fqkfvrit5P0h9MHKSU2XDtL9+uu99dpwmNL9OLSbdpfR6AHAACAbxDkAT8xOjtR0fYQ9UqMUnZStNXloAOi7KG6anwfLb19ku6bPkTd4yNUVFGrBz9cr3GPfqI/f7pVlbUNVpcJAACAIGOYbI7cSnl5ueLj41VWVqa4uDiry0EXkldSpUh7iNLiIqwuBUegtsGhd1bu1LOfbtGOvfslSfGRYRrfP1kjenXTcb0SNDgjTuGhNDIEAABAS57kUIJ8GwjyAI5GvcOp91fv0rNLtmhbSVWLy+yhNg3NiGsK9t00oleCMhIiLaoUAAAA/oIgf5QI8gC8weE09XXuHq3KL9XK7fu0akep9lbVtTovPS5CI3olaESvBB3Xq5uG9ohXRBij9gAAAF0JQf4oEeQB+IJpmtq+p1qrduzTyu2lWrVjn9bvrpDD2fJtOCzE0ODujaP2rnDfs1ukDMOwqHIAAAD4GkH+KBHkAXSW6roGff9TmVbml2pV/j6tzC9VSWVtq/OSY8LdoX5ErwQd2zNeUfZQCyoGAACALxDkjxJBHoBVTNPUT/v2a2X+Pq1qCvfrdpWr4aBR+xCboZz0WHewP65XN/VOimLUHgAAIEAR5I8SQR6AP6mpd+iHnWWNa+3z92ll/j4VlrcetU+MtmtEZuNa+xP7JWtYzwSF2Aj2AAAAgYAgf5QI8gD83a7S/e5gvyp/n37YWa46h7PFOd2iwjRxQIom5aRqQv8UdYu2W1QtAAAADocgf5QI8gACTW2DQz/uKteq/FJ9t32vlm4uUUVNg/tymyGN6NVNkwam6KSBqRqSEcc0/A5ocDiVW1Kl9QUV2rC7XBsLKlTvNDW2T5ImDEjWoPQ42Zj1AAAAvIAgf5QI8gACXb3DqZXb92nJxmJ9urFIGwoqWlyeGhuuSQNTNSknRT/rl6zYiDCLKvUfxRW12lBQrg27K7ShoEIbCsq1uahSdQ3Odq+THBOu8f2Tm75SlBIb3okVAwCAYEKQP0oEeQDBZlfpfi3ZWKQlG4r15ZYS7a93uC8LtRkalZWok3Mag33flJigHq2vqXdoS1FlY1jfXe4O7SWVdW2eH20P0cD0WA1Mj9Og7rFyOk0t3VyiZdv2qLrO0eLcQd3jNGFAsib0T9HxWd0UHhrSGQ8JAAAEAYL8USLIAwhmtQ0OfZO7V0s2FGvJxiLlllS1uLxnt0j3aP3YPsmKtAdmGDVNU7vKatxhfX3T99ySKjmcrf+vzzCk7KRoDUyPVU56nHK6x2pQepx6dotsc/p8XYNTK7bv0+ebi7V0c7F+2Fne4vKIMJtO6JOkCf1TNGFActB/QAIAAI4OQf4oEeQBdCW5JVX6dGORlmws1vJte1pMJQ8PtWls36TGYD8wVb2SoiystH2VtQ3a2DSy3jg1vjG0N+8T0FxCVJhymgL7oO6N3/unxSjKHnrENZRU1urLLSX6fFOJPt9crOKKljsLZMRHaHz/FI0fkKxx/ZKVEEXzQQAAcABB/igR5AF0VdV1Dfpqyx4t2VikTzcWa2fp/haX902JbhqtT9WorETZQ21er8HpNFVV16DK2gZV1jSowvW9pkGVtfVN3xt/z99brY0FFcrfW93mbYXaDPVLjWkM7d3jlJMeq0Hd45QaG+7T0XHTNLWxsEKfbyrW0s0l+jp3b4sPSAxDOrZngib2T9b4ASkanpmgsBDvP5cAACBwEOSPEkEeABrD6OaiSi3ZUKRPNhTpu+37WkxJj7aH6Gf9kjUpp3G0PjU2XFV1DS2CtjuM19S3PtYslFc2/17XoCP5f6a0uPAWU+JzuseqT3KMTz5s8NT+Ooe+ydvbFOyLtamwssXlseGhGts3SRMGpGhC/xS/nfkAAAB8hyB/lAjyANBaeU29vthcoiUbGqfhl1TWHv5KRyHUZig2IlQxEaGKDQ9r+t74e0x4qGIjwpQaG66cpqnxidGBM1V9d9l+Ld1cos83FeuLLSUqra5vcXlWUpTG90/RhAEpGts3STHhRz7lHwAABAaC/FEiyAPAoTmdptbtKm/shL+xSKt3lLpH0cNCDMVGhCkmvDFwuwK4K5THhIc1/uw6Fn5QWG86Fh5q6xLN4RxOUz/sLNPSzcX6fFOJVubvU0OzmQ+hNkPDMxM0qHucBqTHakBqjAamx7LGHgCAIEOQP0oEeQDwTNn+etU7nF0qgPtKRU29lm3d0zhiv7lY2/e0vf4/NTZcA9JiNSAtVgPTYzQgLVb902IZvQcAIEAR5I8SQR4A4C+276nSiu37tKmwUpsKK7SpsEI/7dvf7vk9EiI1MD1W/dNiNLAp6PdLjVFEWGBuIwgAQFdBkD9KBHkAgD+rrG3Q5sIKbS6s1MamcL+xoEJFFW33LbAZUu+kaA1Ii2k2ih+rrKRov2gGCP/X4HC6G1aW1zQ2qqxoamTZ/Hv5QcfqHE6Fh4YoIszW5vfwMJsimn2PCAtReKitxffm13EdD3d9ZwYQgCBCkD9KBHkAQCAqra7TpqZwv7kp3G8qrNC+g5rpuYTaDPVJiXaHe1fA75UYpRDbocORaZpqcJqqdzhV32Cq1uFQvcNUfYNT9Q6napu+1zsaz6lrcKrO4Trm+r3x/DqH0309U2qxa4Eps+n+XL+rxe/Nz1Grcw6cdKjrh4YYsofYZA+1Kazpuz3UpvA2jrnOszc/5vq92bHDPX/eZJqmnGZjvwWnabq/O52So+l30zRVU+9sFsIP+l7b+HN5OwG9us7RaY/HE4ahpkDfMvCH2mwKC7UpzGYoLMTm/huHhjT+3vhlKLTp7xZqMw46v/Fy17mtr990ns0me6ihUFvj3zzEZii06fuB322y2dTqHJvR9L0TXytHwnS/piSnaQZM3UAgCrgg/8wzz+jxxx9XQUGBhg0bpqefflqjR48+7PXeeOMNXXzxxTrrrLP03nvvSZLq6+v1hz/8Qf/5z3+0bds2xcfHa8qUKXrkkUeUkZHRoXoI8gCAYGGapkoq69yj9puLXAG/UpW1DW1eJzzUpt5JUTJkNIbupuDtCuauQG79f0H4rxBbY/ALCzFkD20cPW78UMCQPdQmQ0aL0N0Ytg8Eb6fTlKMpoLt+dp/juqzpe2f+HSLCbIqNaGxYGRsRprimBpWx4QeOxbqORYQpPMym2nqnahscqq13qsb1vd5x4OcGh2rqGz/8qal3qKbeodoGp2rrXccbvzc/P5hee4YhhRjNAr77wwCbQpo+AGjxQYBx4EMCSS1eP06z8fV04Oem782CuNN0ffjT9uXNb8d5iOfZMOT+0MJVW2jzDzNCjGaPq+nyEOOg8w66Xkjj425xO00fetgMyTAO/GyzGTIMHfjdMJouV9vnGwedbzNkqP1zDEPu58o0Gz8wbPy96XnSgcuaP2+uv0nj743XM80DH7gdOL/xNpqf63peDR2owVVj48+ux9XOMRnuy2zNrmtr/KHF7dqazm/6X7O/a+NvLY+1/tlodkZbk2Kaz5Qx2jjPUBtXaud11qHzOnBOfGSYTuyX3LEbtEhABfkFCxbosssu03PPPacxY8Zo7ty5euutt7Rx40alpqa2e728vDyNGzdOffr0UWJiojvIl5WV6fzzz9fVV1+tYcOGad++fbrpppvkcDj03XffdagmgjwAINiZpqldZTWN6+4LKppG8Su1uahCNfXOI7rN5iPVrhFL17GwkAMhNizE1mIEvPG8xvNtbfxX28H/4Xjg9zbOOfg/Qtv4D8fm1zfVGIzrGg7MIqhrODCDoMX3hpYfariO1Tb97M9shhQeGtIiZMdGhCruoOB9cBiPO+hYWIj1SzFMs/HDJFfwr20e9ps+BHB96NTgcKre2Tjzo8HZOAukwdFytkhD0/d6h6kGZ+vL6hzOpus0HXOaB53T+LPD2ThLxeF0Nn7Y0vS70zRV7wiiTx6AAHVMj3h9cMM4q8s4pIAK8mPGjNGoUaP0pz/9SZLkdDqVmZmpG264QXfccUeb13E4HJowYYKuuOIKLV26VKWlpe4g35Zvv/1Wo0eP1vbt29WrV6/D1kSQBwB0VQ6nqR17q5W/t1ohNqNFAHcH8mZTycNCG0eeQ2xGl16r7FpqcLgPAeqbgr/MxhFBW9NorM098tg4YuYahTzwveVxW9PIrM11vNlthDSNuIXYDhyH9ZrPrGhoCvqNPzvldEoNzT4AaH1O4wcCDU0fGDTejtM9Aut6XRwYcT4wIt38ddX88hBb26PR7d2WawTblORwHKjPVbf7d0fr487mlzd92NH6/IOON32vdzhbjIy3N6vg4JkEZtPyksOdf6jbbDmyL6nZ6Ld7xLzVSPeBkf7mI98224HrNz9Xajn63nyU/uBR+7ZmAbguV4uZAq1nDbR5TAdmEUgHL1lq9nM7cfHAkqV2bqOtJVAHHW/zdo/4wpb3c7A+yTF69PxjD30DFvMkh1q6R01dXZ1WrFihO++8033MZrNpypQpWrZsWbvXu//++5Wamqorr7xSS5cuPez9lJWVyTAMJSQktHl5bW2tamsPNAgqLy/v+IMAACCIhNgMZSVHKys52upSAophGO5ZCNHhVlcDf2SzGbLJEBtIAPAGS+dHlZSUyOFwKC0trcXxtLQ0FRQUtHmdL774Qi+99JJeeOGFDt1HTU2Nfvvb3+riiy9u91ONOXPmKD4+3v2VmZnp2QMBAAAAAKCTWL/QyQMVFRW69NJL9cILLyg5+fCNCurr63XhhRfKNE39+c9/bve8O++8U2VlZe6vHTt2eLNsAAAAAAC8xtKp9cnJyQoJCVFhYWGL44WFhUpPT291/tatW5WXl6czzzzTfczpbGwuExoaqo0bN6pv376SDoT47du365NPPjnkGoPw8HCFhzMPDgAAAADg/ywdkbfb7Ro5cqQWL17sPuZ0OrV48WKNHTu21fk5OTn6/vvvtXr1avfX9OnTNWnSJK1evdo9Jd4V4jdv3qyPP/5YSUlJnfaYAAAAAADwJUtH5CVp9uzZmjlzpo4//niNHj1ac+fOVVVVlWbNmiVJuuyyy9SjRw/NmTNHERERGjp0aIvruxrYuY7X19fr/PPP18qVK/Xvf/9bDofDvd4+MTFRdru98x4cAAAAAABeZnmQnzFjhoqLi3X33XeroKBAw4cP18KFC90N8PLz82WzdXziwM6dO/Wvf/1LkjR8+PAWly1ZskQnnXSSt0oHAAAAAKDTWb6PvD9iH3kAAAAAQGfyJIcGVNd6AAAAAAC6OoI8AAAAAAABhCAPAAAAAEAAIcgDAAAAABBACPIAAAAAAAQQgjwAAAAAAAGEIA8AAAAAQAAhyAMAAAAAEEAI8gAAAAAABBCCPAAAAAAAAYQgDwAAAABAAAm1ugB/ZJqmJKm8vNziSgAAAAAAXYErf7ry6KEQ5NtQUVEhScrMzLS4EgAAAABAV1JRUaH4+PhDnmOYHYn7XYzT6dSuXbsUGxsrwzCsLqdd5eXlyszM1I4dOxQXF2d1OQhQvI7gDbyO4A28jnC0eA3BG3gdwRuO5HVkmqYqKiqUkZEhm+3Qq+AZkW+DzWZTz549rS6jw+Li4niTwVHjdQRv4HUEb+B1hKPFawjewOsI3uDp6+hwI/EuNLsDAAAAACCAEOQBAAAAAAggBPkAFh4ernvuuUfh4eFWl4IAxusI3sDrCN7A6whHi9cQvIHXEbzB168jmt0BAAAAABBAGJEHAAAAACCAEOQBAAAAAAggBHkAAAAAAAIIQR4AAAAAgABCkA9gzzzzjLKyshQREaExY8bom2++sbokBJB7771XhmG0+MrJybG6LPi5zz//XGeeeaYyMjJkGIbee++9Fpebpqm7775b3bt3V2RkpKZMmaLNmzdbUyz80uFeQ5dffnmr96Zp06ZZUyz81pw5czRq1CjFxsYqNTVVZ599tjZu3NjinJqaGl133XVKSkpSTEyMzjvvPBUWFlpUMfxNR15DJ510Uqv3o2uvvdaiiuGP/vznP+vYY49VXFyc4uLiNHbsWP33v/91X+7L9yGCfIBasGCBZs+erXvuuUcrV67UsGHDNHXqVBUVFVldGgLIkCFDtHv3bvfXF198YXVJ8HNVVVUaNmyYnnnmmTYvf+yxxzRv3jw999xz+vrrrxUdHa2pU6eqpqamkyuFvzrca0iSpk2b1uK96fXXX+/EChEIPvvsM1133XVavny5/ve//6m+vl6nnHKKqqqq3Ofccsst+uCDD/TWW2/ps88+065du3TuuedaWDX8SUdeQ5J09dVXt3g/euyxxyyqGP6oZ8+eeuSRR7RixQp99913Ovnkk3XWWWdp3bp1knz8PmQiII0ePdq87rrr3L87HA4zIyPDnDNnjoVVIZDcc8895rBhw6wuAwFMkvnuu++6f3c6nWZ6err5+OOPu4+Vlpaa4eHh5uuvv25BhfB3B7+GTNM0Z86caZ511lmW1IPAVVRUZEoyP/vsM9M0G997wsLCzLfeest9zvr1601J5rJly6wqE37s4NeQaZrmxIkTzZtuusm6ohCQunXrZr744os+fx9iRD4A1dXVacWKFZoyZYr7mM1m05QpU7Rs2TILK0Og2bx5szIyMtSnTx9dcsklys/Pt7okBLDc3FwVFBS0eG+Kj4/XmDFjeG+CRz799FOlpqZq4MCB+tWvfqU9e/ZYXRL8XFlZmSQpMTFRkrRixQrV19e3eD/KyclRr169eD9Cmw5+Dbn8/e9/V3JysoYOHao777xT1dXVVpSHAOBwOPTGG2+oqqpKY8eO9fn7UOhR3wI6XUlJiRwOh9LS0locT0tL04YNGyyqCoFmzJgxmj9/vgYOHKjdu3frvvvu0/jx4/XDDz8oNjbW6vIQgAoKCiSpzfcm12XA4UybNk3nnnuusrOztXXrVv3ud7/TqaeeqmXLlikkJMTq8uCHnE6nbr75Zv3sZz/T0KFDJTW+H9ntdiUkJLQ4l/cjtKWt15Ak/b//9//Uu3dvZWRkaO3atfrtb3+rjRs36p133rGwWvib77//XmPHjlVNTY1iYmL07rvvavDgwVq9erVP34cI8kAXdeqpp7p/PvbYYzVmzBj17t1bb775pq688koLKwPQlV100UXun4855hgde+yx6tu3rz799FNNnjzZwsrgr6677jr98MMP9HnBEWvvNXTNNde4fz7mmGPUvXt3TZ48WVu3blXfvn07u0z4qYEDB2r16tUqKyvT22+/rZkzZ+qzzz7z+f0ytT4AJScnKyQkpFXHw8LCQqWnp1tUFQJdQkKCBgwYoC1btlhdCgKU6/2H9yZ4U58+fZScnMx7E9p0/fXX69///reWLFminj17uo+np6errq5OpaWlLc7n/QgHa+811JYxY8ZIEu9HaMFut6tfv34aOXKk5syZo2HDhumpp57y+fsQQT4A2e12jRw5UosXL3YfczqdWrx4scaOHWthZQhklZWV2rp1q7p37251KQhQ2dnZSk9Pb/HeVF5erq+//pr3Jhyxn376SXv27OG9CS2Ypqnrr79e7777rj755BNlZ2e3uHzkyJEKCwtr8X60ceNG5efn834ESYd/DbVl9erVksT7EQ7J6XSqtrbW5+9DTK0PULNnz9bMmTN1/PHHa/To0Zo7d66qqqo0a9Ysq0tDgLj11lt15plnqnfv3tq1a5fuuecehYSE6OKLL7a6NPixysrKFiMRubm5Wr16tRITE9WrVy/dfPPNevDBB9W/f39lZ2frrrvuUkZGhs4++2zrioZfOdRrKDExUffdd5/OO+88paena+vWrbr99tvVr18/TZ061cKq4W+uu+46/eMf/9D777+v2NhY93rT+Ph4RUZGKj4+XldeeaVmz56txMRExcXF6YYbbtDYsWN1wgknWFw9/MHhXkNbt27VP/7xD5122mlKSkrS2rVrdcstt2jChAk69thjLa4e/uLOO+/Uqaeeql69eqmiokL/+Mc/9Omnn2rRokW+fx866r73sMzTTz9t9urVy7Tb7ebo0aPN5cuXW10SAsiMGTPM7t27m3a73ezRo4c5Y8YMc8uWLVaXBT+3ZMkSU1Krr5kzZ5qm2bgF3V133WWmpaWZ4eHh5uTJk82NGzdaWzT8yqFeQ9XV1eYpp5xipqSkmGFhYWbv3r3Nq6++2iwoKLC6bPiZtl5Dksy//vWv7nP2799v/vrXvza7detmRkVFmeecc465e/du64qGXzncayg/P9+cMGGCmZiYaIaHh5v9+vUzb7vtNrOsrMzawuFXrrjiCrN3796m3W43U1JSzMmTJ5sfffSR+3Jfvg8ZpmmaR/9xAAAAAAAA6AyskQcAAAAAIIAQ5AEAAAAACCAEeQAAAAAAAghBHgAAAACAAEKQBwAAAAAggBDkAQAAAAAIIAR5AAAAAAACCEEeAAAAAIAAQpAHAACdLisrS3PnzrW6DAAAAhJBHgCAIHf55Zfr7LPPliSddNJJuvnmmzvtvufPn6+EhIRWx7/99ltdc801nVYHAADBJNTqAgAAQOCpq6uT3W4/4uunpKR4sRoAALoWRuQBAOgiLr/8cn322Wd66qmnZBiGDMNQXl6eJOmHH37QqaeeqpiYGKWlpenSSy9VSUmJ+7onnXSSrr/+et18881KTk7W1KlTJUlPPPGEjjnmGEVHRyszM1O//vWvVVlZKUn69NNPNWvWLJWVlbnv795775XUemp9fn6+zjrrLMXExCguLk4XXnihCgsL3Zffe++9Gj58uF577TVlZWUpPj5eF110kSoqKnz7pAEA4IcI8gAAdBFPPfWUxo4dq6uvvlq7d+/W7t27lZmZqdLSUp188skaMWKEvvvuOy1cuFCFhYW68MILW1z/lVdekd1u15dffqnnnntOkmSz2TRv3jytW7dOr7zyij755BPdfvvtkqQTTzxRc+fOVVxcnPv+br311lZ1OZ1OnXXWWdq7d68+++wz/e9//9O2bds0Y8aMFudt3bpV7733nv7973/r3//+tz777DM98sgjPnq2AADwX0ytBwCgi4iPj5fdbldUVJTS09Pdx//0pz9pxIgRevjhh93HXn75ZWVmZmrTpk0aMGCAJKl///567LHHWtxm8/X2WVlZevDBB3Xttdfq2Wefld1uV3x8vAzDaHF/B1u8eLG+//575ebmKjMzU5L06quvasiQIfr22281atQoSY2Bf/78+YqNjZUkXXrppVq8eLEeeuiho3tiAAAIMIzIAwDQxa1Zs0ZLlixRTEyM+ysnJ0dS4yi4y8iRI1td9+OPP9bkyZPVo0cPxcbG6tJLL9WePXtUXV3d4ftfv369MjMz3SFekgYPHqyEhAStX7/efSwrK8sd4iWpe/fuKioq8uixAgAQDBiRBwCgi6usrNSZZ56pRx99tNVl3bt3d/8cHR3d4rK8vDydccYZ+tWvfqWHHnpIiYmJ+uKLL3TllVeqrq5OUVFRXq0zLCysxe+GYcj5/9u5Q5ZIwjiO479TMWvYYlrE4uIiiMmgZfEtiAiCTbAYfA+LQYsvQQwKBqPBaFDRaFPErYsYbLKzBmE57+TOIBxz+/nEYeB5ZtJ8ef5MUXzrGgBQBkIeAPrI8PBwOp3Oh2szMzM5Pj5OtVrN0NDXPw2ur69TFEV2dnYyMPA+5Hd0dPTX9X41OTmZVquVVqvVO5W/vb3N8/NzarXal/cDAP3CaD0A9JFqtZqLi4s8PDyk3W6nKIpsbGzk6ekpy8vLubq6yt3dXU5PT7O2tvbHCJ+YmMjr62v29vZyf3+f/f393k/wfl7v5eUlZ2dnabfbn47cNxqN1Ov1rKys5ObmJpeXl1ldXc3CwkJmZ2e//R0AQNkJeQDoI1tbWxkcHEytVkulUsnj42PGxsZyfn6eTqeTxcXF1Ov1bG5uZmRkpHfS/pnp6ens7u5me3s7U1NTOTg4SLPZ/HDP3Nxc1tfXs7S0lEql8tvP8pL3EfmTk5OMjo5mfn4+jUYj4+PjOTw8/PbnB4D/wY9ut9v915sAAAAAvsaJPAAAAJSIkAcAAIASEfIAAABQIkIeAAAASkTIAwAAQIkIeQAAACgRIQ8AAAAlIuQBAACgRIQ8AAAAlIiQBwAAgBIR8gAAAFAib6K6hiIVNGDeAAAAAElFTkSuQmCC\n", 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"text/plain": [ |
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422 |
"<Figure size 1200x600 with 1 Axes>" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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}, |
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{ |
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"data": { |
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"text/plain": [ |
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"0.5" |
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] |
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}, |
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"execution_count": 174, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"# create empty array for callback to store evaluations of the objective function\n", |
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"objective_func_vals = []\n", |
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"plt.rcParams[\"figure.figsize\"] = (12, 6)\n", |
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"\n", |
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"# fit classifier to data\n", |
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"sampler_classifier.fit(X, y01)\n", |
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"\n", |
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"# return to default figsize\n", |
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"plt.rcParams[\"figure.figsize\"] = (6, 4)\n", |
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"\n", |
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"# score classifier\n", |
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"sampler_classifier.score(X, y01)" |
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452 |
] |
|
|
453 |
}, |
|
|
454 |
{ |
|
|
455 |
"cell_type": "code", |
|
|
456 |
"execution_count": 175, |
|
|
457 |
"id": "angry-bulgarian", |
|
|
458 |
"metadata": {}, |
|
|
459 |
"outputs": [ |
|
|
460 |
{ |
|
|
461 |
"data": { |
|
|
462 |
"image/png": 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\n", |
|
|
463 |
"text/plain": [ |
|
|
464 |
"<Figure size 600x400 with 1 Axes>" |
|
|
465 |
] |
|
|
466 |
}, |
|
|
467 |
"metadata": {}, |
|
|
468 |
"output_type": "display_data" |
|
|
469 |
} |
|
|
470 |
], |
|
|
471 |
"source": [ |
|
|
472 |
"# evaluate data points\n", |
|
|
473 |
"y_predict = sampler_classifier.predict(X)\n", |
|
|
474 |
"\n", |
|
|
475 |
"# plot results\n", |
|
|
476 |
"# red == wrongly classified\n", |
|
|
477 |
"for x, y_target, y_p in zip(X, y01, y_predict):\n", |
|
|
478 |
" if y_target == 1:\n", |
|
|
479 |
" plt.plot(x[0], x[1], \"bo\")\n", |
|
|
480 |
" else:\n", |
|
|
481 |
" plt.plot(x[0], x[1], \"go\")\n", |
|
|
482 |
" if y_target != y_p:\n", |
|
|
483 |
" plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n", |
|
|
484 |
"plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n", |
|
|
485 |
"plt.show()" |
|
|
486 |
] |
|
|
487 |
}, |
|
|
488 |
{ |
|
|
489 |
"cell_type": "markdown", |
|
|
490 |
"id": "assisted-individual", |
|
|
491 |
"metadata": {}, |
|
|
492 |
"source": [ |
|
|
493 |
"Again, once the model is trained we can take a look at the weights. As we set `reps=1` explicitly in our ansatz, we can see less parameters than in the previous model." |
|
|
494 |
] |
|
|
495 |
}, |
|
|
496 |
{ |
|
|
497 |
"cell_type": "code", |
|
|
498 |
"execution_count": 176, |
|
|
499 |
"id": "indonesian-bulletin", |
|
|
500 |
"metadata": {}, |
|
|
501 |
"outputs": [ |
|
|
502 |
{ |
|
|
503 |
"data": { |
|
|
504 |
"text/plain": [ |
|
|
505 |
"array([2.10016069, 1.02099559, 1.50297438, 1.71696324])" |
|
|
506 |
] |
|
|
507 |
}, |
|
|
508 |
"execution_count": 176, |
|
|
509 |
"metadata": {}, |
|
|
510 |
"output_type": "execute_result" |
|
|
511 |
} |
|
|
512 |
], |
|
|
513 |
"source": [ |
|
|
514 |
"sampler_classifier.weights" |
|
|
515 |
] |
|
|
516 |
}, |
|
|
517 |
{ |
|
|
518 |
"cell_type": "markdown", |
|
|
519 |
"id": "champion-approval", |
|
|
520 |
"metadata": {}, |
|
|
521 |
"source": [ |
|
|
522 |
"### Variational Quantum Classifier (`VQC`)\n", |
|
|
523 |
"\n", |
|
|
524 |
"The `VQC` is a special variant of the `NeuralNetworkClassifier` with a `SamplerQNN`. It applies a parity mapping (or extensions to multiple classes) to map from the bitstring to the classification, which results in a probability vector, which is interpreted as a one-hot encoded result. By default, it applies this the `CrossEntropyLoss` function that expects labels given in one-hot encoded format and will return predictions in that format too." |
|
|
525 |
] |
|
|
526 |
}, |
|
|
527 |
{ |
|
|
528 |
"cell_type": "code", |
|
|
529 |
"execution_count": 177, |
|
|
530 |
"id": "legislative-dublin", |
|
|
531 |
"metadata": {}, |
|
|
532 |
"outputs": [], |
|
|
533 |
"source": [ |
|
|
534 |
"# construct feature map, ansatz, and optimizer\n", |
|
|
535 |
"feature_map = ZFeatureMap(num_inputs, reps=4)\n", |
|
|
536 |
"ansatz = RealAmplitudes(num_inputs, reps=1)\n", |
|
|
537 |
"\n", |
|
|
538 |
"# construct variational quantum classifier\n", |
|
|
539 |
"vqc = VQC(\n", |
|
|
540 |
" feature_map=feature_map,\n", |
|
|
541 |
" ansatz=ansatz,\n", |
|
|
542 |
" loss=\"cross_entropy\",\n", |
|
|
543 |
" optimizer=COBYLA(maxiter=30),\n", |
|
|
544 |
" callback=callback_graph,\n", |
|
|
545 |
")" |
|
|
546 |
] |
|
|
547 |
}, |
|
|
548 |
{ |
|
|
549 |
"cell_type": "code", |
|
|
550 |
"execution_count": 178, |
|
|
551 |
"id": "geographic-adjustment", |
|
|
552 |
"metadata": {}, |
|
|
553 |
"outputs": [ |
|
|
554 |
{ |
|
|
555 |
"data": { |
|
|
556 |
"image/png": 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\n", 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557 |
"text/plain": [ |
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558 |
"<Figure size 1200x600 with 1 Axes>" |
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559 |
] |
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560 |
}, |
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|
561 |
"metadata": {}, |
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|
562 |
"output_type": "display_data" |
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563 |
}, |
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564 |
{ |
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|
565 |
"data": { |
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566 |
"text/plain": [ |
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567 |
"0.5" |
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568 |
] |
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569 |
}, |
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|
570 |
"execution_count": 178, |
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571 |
"metadata": {}, |
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|
572 |
"output_type": "execute_result" |
|
|
573 |
} |
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574 |
], |
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|
575 |
"source": [ |
|
|
576 |
"# create empty array for callback to store evaluations of the objective function\n", |
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577 |
"objective_func_vals = []\n", |
|
|
578 |
"plt.rcParams[\"figure.figsize\"] = (12, 6)\n", |
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|
579 |
"\n", |
|
|
580 |
"# fit classifier to data\n", |
|
|
581 |
"vqc.fit(X, y_one_hot)\n", |
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|
582 |
"\n", |
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|
583 |
"# return to default figsize\n", |
|
|
584 |
"plt.rcParams[\"figure.figsize\"] = (6, 4)\n", |
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|
585 |
"\n", |
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586 |
"# score classifier\n", |
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|
587 |
"vqc.score(X, y_one_hot)" |
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588 |
] |
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589 |
}, |
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590 |
{ |
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591 |
"cell_type": "code", |
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592 |
"execution_count": 179, |
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593 |
"id": "stopped-heavy", |
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594 |
"metadata": {}, |
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595 |
"outputs": [ |
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596 |
{ |
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597 |
"data": { |
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598 |
"image/png": 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\n", |
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599 |
"text/plain": [ |
|
|
600 |
"<Figure size 600x400 with 1 Axes>" |
|
|
601 |
] |
|
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602 |
}, |
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|
603 |
"metadata": {}, |
|
|
604 |
"output_type": "display_data" |
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605 |
} |
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|
606 |
], |
|
|
607 |
"source": [ |
|
|
608 |
"# evaluate data points\n", |
|
|
609 |
"y_predict = vqc.predict(X)\n", |
|
|
610 |
"\n", |
|
|
611 |
"# plot results\n", |
|
|
612 |
"# red == wrongly classified\n", |
|
|
613 |
"for x, y_target, y_p in zip(X, y_one_hot, y_predict):\n", |
|
|
614 |
" if y_target[0] == 1:\n", |
|
|
615 |
" plt.plot(x[0], x[1], \"bo\")\n", |
|
|
616 |
" else:\n", |
|
|
617 |
" plt.plot(x[0], x[1], \"go\")\n", |
|
|
618 |
" if not np.all(y_target == y_p):\n", |
|
|
619 |
" plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n", |
|
|
620 |
"plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n", |
|
|
621 |
"plt.show()" |
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|
622 |
] |
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|
623 |
}, |
|
|
624 |
{ |
|
|
625 |
"cell_type": "markdown", |
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|
626 |
"id": "grave-testament", |
|
|
627 |
"metadata": {}, |
|
|
628 |
"source": [ |
|
|
629 |
"### Multiple classes with VQC\n", |
|
|
630 |
"In this section we generate an artificial dataset that contains samples of three classes and show how to train a model to classify this dataset. This example shows how to tackle more interesting problems in machine learning. Of course, for a sake of short training time we prepare a tiny dataset. We employ `make_classification` from SciKit-Learn to generate a dataset. There 10 samples in the dataset, 2 features, that means we can still have a nice plot of the dataset, as well as no redundant features, these are features are generated as a combinations of the other features. Also, we have 3 different classes in the dataset, each classes one kind of centroid and we set class separation to `2.0`, a slight increase from the default value of `1.0` to ease the classification problem.\n", |
|
|
631 |
"\n", |
|
|
632 |
"Once the dataset is generated we scale the features into the range `[0, 1]`." |
|
|
633 |
] |
|
|
634 |
}, |
|
|
635 |
{ |
|
|
636 |
"cell_type": "code", |
|
|
637 |
"execution_count": 180, |
|
|
638 |
"id": "plastic-dividend", |
|
|
639 |
"metadata": {}, |
|
|
640 |
"outputs": [], |
|
|
641 |
"source": [ |
|
|
642 |
"from sklearn.datasets import make_classification\n", |
|
|
643 |
"from sklearn.preprocessing import MinMaxScaler\n", |
|
|
644 |
"\n", |
|
|
645 |
"X, y = make_classification(\n", |
|
|
646 |
" n_samples=10,\n", |
|
|
647 |
" n_features=2,\n", |
|
|
648 |
" n_classes=3,\n", |
|
|
649 |
" n_redundant=0,\n", |
|
|
650 |
" n_clusters_per_class=1,\n", |
|
|
651 |
" class_sep=2.0,\n", |
|
|
652 |
" random_state=algorithm_globals.random_seed,\n", |
|
|
653 |
")\n", |
|
|
654 |
"X = MinMaxScaler().fit_transform(X)" |
|
|
655 |
] |
|
|
656 |
}, |
|
|
657 |
{ |
|
|
658 |
"cell_type": "markdown", |
|
|
659 |
"id": "forced-disclosure", |
|
|
660 |
"metadata": {}, |
|
|
661 |
"source": [ |
|
|
662 |
"Let's see how our dataset looks like." |
|
|
663 |
] |
|
|
664 |
}, |
|
|
665 |
{ |
|
|
666 |
"cell_type": "code", |
|
|
667 |
"execution_count": 181, |
|
|
668 |
"id": "premier-drill", |
|
|
669 |
"metadata": {}, |
|
|
670 |
"outputs": [ |
|
|
671 |
{ |
|
|
672 |
"data": { |
|
|
673 |
"text/plain": [ |
|
|
674 |
"<matplotlib.collections.PathCollection at 0x7fdb6009e3b0>" |
|
|
675 |
] |
|
|
676 |
}, |
|
|
677 |
"execution_count": 181, |
|
|
678 |
"metadata": {}, |
|
|
679 |
"output_type": "execute_result" |
|
|
680 |
}, |
|
|
681 |
{ |
|
|
682 |
"data": { |
|
|
683 |
"image/png": 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\n", |
|
|
684 |
"text/plain": [ |
|
|
685 |
"<Figure size 600x400 with 1 Axes>" |
|
|
686 |
] |
|
|
687 |
}, |
|
|
688 |
"metadata": {}, |
|
|
689 |
"output_type": "display_data" |
|
|
690 |
} |
|
|
691 |
], |
|
|
692 |
"source": [ |
|
|
693 |
"plt.scatter(X[:, 0], X[:, 1], c=y)" |
|
|
694 |
] |
|
|
695 |
}, |
|
|
696 |
{ |
|
|
697 |
"cell_type": "markdown", |
|
|
698 |
"id": "deadly-response", |
|
|
699 |
"metadata": {}, |
|
|
700 |
"source": [ |
|
|
701 |
"We also transform labels and make them categorical." |
|
|
702 |
] |
|
|
703 |
}, |
|
|
704 |
{ |
|
|
705 |
"cell_type": "code", |
|
|
706 |
"execution_count": 182, |
|
|
707 |
"id": "exposed-bailey", |
|
|
708 |
"metadata": {}, |
|
|
709 |
"outputs": [ |
|
|
710 |
{ |
|
|
711 |
"name": "stdout", |
|
|
712 |
"output_type": "stream", |
|
|
713 |
"text": [ |
|
|
714 |
"['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']\n" |
|
|
715 |
] |
|
|
716 |
} |
|
|
717 |
], |
|
|
718 |
"source": [ |
|
|
719 |
"y_cat = np.empty(y.shape, dtype=str)\n", |
|
|
720 |
"y_cat[y == 0] = \"A\"\n", |
|
|
721 |
"y_cat[y == 1] = \"B\"\n", |
|
|
722 |
"y_cat[y == 2] = \"C\"\n", |
|
|
723 |
"print(y_cat)" |
|
|
724 |
] |
|
|
725 |
}, |
|
|
726 |
{ |
|
|
727 |
"cell_type": "markdown", |
|
|
728 |
"id": "instructional-headquarters", |
|
|
729 |
"metadata": {}, |
|
|
730 |
"source": [ |
|
|
731 |
"We create an instance of `VQC` similar to the previous example, but in this case we pass a minimal set of parameters. Instead of feature map and ansatz we pass just the number of qubits that is equal to the number of features in the dataset, an optimizer with a low number of iteration to reduce training time, a quantum instance, and a callback to observe progress." |
|
|
732 |
] |
|
|
733 |
}, |
|
|
734 |
{ |
|
|
735 |
"cell_type": "code", |
|
|
736 |
"execution_count": 183, |
|
|
737 |
"id": "latin-result", |
|
|
738 |
"metadata": {}, |
|
|
739 |
"outputs": [], |
|
|
740 |
"source": [ |
|
|
741 |
"vqc = VQC(\n", |
|
|
742 |
" num_qubits=2,\n", |
|
|
743 |
" optimizer=COBYLA(maxiter=30),\n", |
|
|
744 |
" callback=callback_graph,\n", |
|
|
745 |
")" |
|
|
746 |
] |
|
|
747 |
}, |
|
|
748 |
{ |
|
|
749 |
"cell_type": "markdown", |
|
|
750 |
"id": "proper-bookmark", |
|
|
751 |
"metadata": {}, |
|
|
752 |
"source": [ |
|
|
753 |
"Start the training process in the same way as in previous examples." |
|
|
754 |
] |
|
|
755 |
}, |
|
|
756 |
{ |
|
|
757 |
"cell_type": "code", |
|
|
758 |
"execution_count": 184, |
|
|
759 |
"id": "reported-pioneer", |
|
|
760 |
"metadata": {}, |
|
|
761 |
"outputs": [ |
|
|
762 |
{ |
|
|
763 |
"data": { |
|
|
764 |
"image/png": 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\n", |
|
|
765 |
"text/plain": [ |
|
|
766 |
"<Figure size 1200x600 with 1 Axes>" |
|
|
767 |
] |
|
|
768 |
}, |
|
|
769 |
"metadata": {}, |
|
|
770 |
"output_type": "display_data" |
|
|
771 |
}, |
|
|
772 |
{ |
|
|
773 |
"data": { |
|
|
774 |
"text/plain": [ |
|
|
775 |
"0.6" |
|
|
776 |
] |
|
|
777 |
}, |
|
|
778 |
"execution_count": 184, |
|
|
779 |
"metadata": {}, |
|
|
780 |
"output_type": "execute_result" |
|
|
781 |
} |
|
|
782 |
], |
|
|
783 |
"source": [ |
|
|
784 |
"# create empty array for callback to store evaluations of the objective function\n", |
|
|
785 |
"objective_func_vals = []\n", |
|
|
786 |
"plt.rcParams[\"figure.figsize\"] = (12, 6)\n", |
|
|
787 |
"\n", |
|
|
788 |
"# fit classifier to data\n", |
|
|
789 |
"vqc.fit(X, y_cat)\n", |
|
|
790 |
"\n", |
|
|
791 |
"# return to default figsize\n", |
|
|
792 |
"plt.rcParams[\"figure.figsize\"] = (6, 4)\n", |
|
|
793 |
"\n", |
|
|
794 |
"# score classifier\n", |
|
|
795 |
"vqc.score(X, y_cat)" |
|
|
796 |
] |
|
|
797 |
}, |
|
|
798 |
{ |
|
|
799 |
"cell_type": "markdown", |
|
|
800 |
"id": "weighted-renaissance", |
|
|
801 |
"metadata": {}, |
|
|
802 |
"source": [ |
|
|
803 |
"Despite we had the low number of iterations, we achieved quite a good score. Let see the output of the `predict` method and compare the output with the ground truth." |
|
|
804 |
] |
|
|
805 |
}, |
|
|
806 |
{ |
|
|
807 |
"cell_type": "code", |
|
|
808 |
"execution_count": 185, |
|
|
809 |
"id": "employed-patient", |
|
|
810 |
"metadata": {}, |
|
|
811 |
"outputs": [ |
|
|
812 |
{ |
|
|
813 |
"name": "stdout", |
|
|
814 |
"output_type": "stream", |
|
|
815 |
"text": [ |
|
|
816 |
"Predicted labels: ['B' 'A' 'B' 'C' 'C' 'B' 'B' 'B' 'B' 'B']\n", |
|
|
817 |
"Ground truth: ['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']\n" |
|
|
818 |
] |
|
|
819 |
} |
|
|
820 |
], |
|
|
821 |
"source": [ |
|
|
822 |
"predict = vqc.predict(X)\n", |
|
|
823 |
"print(f\"Predicted labels: {predict}\")\n", |
|
|
824 |
"print(f\"Ground truth: {y_cat}\")" |
|
|
825 |
] |
|
|
826 |
}, |
|
|
827 |
{ |
|
|
828 |
"cell_type": "markdown", |
|
|
829 |
"id": "guided-secret", |
|
|
830 |
"metadata": {}, |
|
|
831 |
"source": [ |
|
|
832 |
"## Regression\n", |
|
|
833 |
"\n", |
|
|
834 |
"We prepare a simple regression dataset to illustrate the following algorithms." |
|
|
835 |
] |
|
|
836 |
}, |
|
|
837 |
{ |
|
|
838 |
"cell_type": "code", |
|
|
839 |
"execution_count": 186, |
|
|
840 |
"id": "iraqi-flavor", |
|
|
841 |
"metadata": {}, |
|
|
842 |
"outputs": [ |
|
|
843 |
{ |
|
|
844 |
"data": { |
|
|
845 |
"image/png": 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\n", |
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|
846 |
"text/plain": [ |
|
|
847 |
"<Figure size 600x400 with 1 Axes>" |
|
|
848 |
] |
|
|
849 |
}, |
|
|
850 |
"metadata": {}, |
|
|
851 |
"output_type": "display_data" |
|
|
852 |
} |
|
|
853 |
], |
|
|
854 |
"source": [ |
|
|
855 |
"num_samples = 20\n", |
|
|
856 |
"eps = 0.2\n", |
|
|
857 |
"lb, ub = -np.pi, np.pi\n", |
|
|
858 |
"X_ = np.linspace(lb, ub, num=50).reshape(50, 1)\n", |
|
|
859 |
"f = lambda x: np.sin(x)\n", |
|
|
860 |
"\n", |
|
|
861 |
"X = (ub - lb) * algorithm_globals.random.random([num_samples, 1]) + lb\n", |
|
|
862 |
"y = f(X[:, 0]) + eps * (2 * algorithm_globals.random.random(num_samples) - 1)\n", |
|
|
863 |
"\n", |
|
|
864 |
"plt.plot(X_, f(X_), \"r--\")\n", |
|
|
865 |
"plt.plot(X, y, \"bo\")\n", |
|
|
866 |
"plt.show()" |
|
|
867 |
] |
|
|
868 |
}, |
|
|
869 |
{ |
|
|
870 |
"cell_type": "markdown", |
|
|
871 |
"id": "talented-capitol", |
|
|
872 |
"metadata": {}, |
|
|
873 |
"source": [ |
|
|
874 |
"### Regression with an `EstimatorQNN`\n", |
|
|
875 |
"\n", |
|
|
876 |
"Here we restrict to regression with an `EstimatorQNN` that returns values in $[-1, +1]$. More complex and also multi-dimensional models could be constructed, also based on `SamplerQNN` but that exceeds the scope of this tutorial." |
|
|
877 |
] |
|
|
878 |
}, |
|
|
879 |
{ |
|
|
880 |
"cell_type": "code", |
|
|
881 |
"execution_count": 187, |
|
|
882 |
"id": "perfect-kelly", |
|
|
883 |
"metadata": {}, |
|
|
884 |
"outputs": [], |
|
|
885 |
"source": [ |
|
|
886 |
"# construct simple feature map\n", |
|
|
887 |
"param_x = Parameter(\"x\")\n", |
|
|
888 |
"feature_map = QuantumCircuit(1, name=\"fm\")\n", |
|
|
889 |
"feature_map.ry(param_x, 0)\n", |
|
|
890 |
"\n", |
|
|
891 |
"# construct simple ansatz\n", |
|
|
892 |
"param_y = Parameter(\"y\")\n", |
|
|
893 |
"ansatz = QuantumCircuit(1, name=\"vf\")\n", |
|
|
894 |
"ansatz.ry(param_y, 0)\n", |
|
|
895 |
"\n", |
|
|
896 |
"# construct a circuit\n", |
|
|
897 |
"qc = QuantumCircuit(1)\n", |
|
|
898 |
"qc.compose(feature_map, inplace=True)\n", |
|
|
899 |
"qc.compose(ansatz, inplace=True)\n", |
|
|
900 |
"\n", |
|
|
901 |
"# construct QNN\n", |
|
|
902 |
"regression_estimator_qnn = EstimatorQNN(\n", |
|
|
903 |
" circuit=qc, input_params=feature_map.parameters, weight_params=ansatz.parameters\n", |
|
|
904 |
")" |
|
|
905 |
] |
|
|
906 |
}, |
|
|
907 |
{ |
|
|
908 |
"cell_type": "code", |
|
|
909 |
"execution_count": 188, |
|
|
910 |
"id": "velvet-marks", |
|
|
911 |
"metadata": {}, |
|
|
912 |
"outputs": [], |
|
|
913 |
"source": [ |
|
|
914 |
"# construct the regressor from the neural network\n", |
|
|
915 |
"regressor = NeuralNetworkRegressor(\n", |
|
|
916 |
" neural_network=regression_estimator_qnn,\n", |
|
|
917 |
" loss=\"squared_error\",\n", |
|
|
918 |
" optimizer=L_BFGS_B(maxiter=5),\n", |
|
|
919 |
" callback=callback_graph,\n", |
|
|
920 |
")" |
|
|
921 |
] |
|
|
922 |
}, |
|
|
923 |
{ |
|
|
924 |
"cell_type": "code", |
|
|
925 |
"execution_count": 189, |
|
|
926 |
"id": "working-mongolia", |
|
|
927 |
"metadata": {}, |
|
|
928 |
"outputs": [ |
|
|
929 |
{ |
|
|
930 |
"data": { |
|
|
931 |
"image/png": 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ERG3YsEEzZ8602+/mm2/WTz/9pMcff1wdOnSQn5+frFarrrnmmnKPAF/4el/Mz2NKSop69+6tgIAAPfPMM4qJiZGXl5c2b96sJ598slwZP/zwQ911110aNmyYHn/8cdWtW1eurq6aNWuWDhw4UKbH+Lvz4u3trfXr12vNmjX6+uuvtWLFCi1ZskRXXnmlvv3220q/S7ujr/n5sxUKVcTrVJKyzkSpDu9RAHAUJR0AnMDgwYO1YMEC/fjjj3bTnAv98MMPiouL03333Vfka/v27bMbtdq/f7+sVmupa1XHxMQoIyND/fv3LzVXTEyMVq5cqeTk5FJH0x2Z2u3r66vBgwfrk08+0csvv6wlS5aoV69eioiIsPu+hmGocePGFfoHg+Dg4GKnwV44OldWhXeqL7wTekkceX2ioqK0Z8+eItsLp21HRUU5kLB0I0aM0KJFi7R69Wrt2rVLhmHYTXU/ffq0Vq9erWnTpmny5Mm27fv27SvT4wcHByslJcVuW25uro4fP263raw/j8VZu3atTp06pc8//1xXXHGFbXtsbKzdfoWv2/79+9W3b1/b9vz8fMXFxdndyOzTTz9VkyZN9Pnnn9udu+Iu+7gYLi4u6tevn/r166eXX35ZM2fO1MSJE7VmzRr179+/Qm7KV9JjXMxrXqisr5OjP/9Wq1X79u2zzR6Rzt5QMSUlpUJ//gHAWXFNOgA4gccff1ze3t667777iixtlZycrPvvv18+Pj62pbzON2/ePLvPX3vtNUnStddeW+L3u/nmm7Vx40atXLmyyNdSUlKUn58v6ex1qIZhaNq0aUX2O3+kytfXt0gZK82IESN07Ngxvf322/rjjz/siqEkXX/99XJ1ddW0adOKjIgZhlGm5b+KExMTo927d+vkyZO2bX/88YftjtqOCgsL0xVXXKF3331X8fHxRXIW8vX1laQyvUYDBw7Ur7/+qo0bN9q2ZWZm6q233lJ0dLRat25drqzF6d+/v0JCQrRkyRItWbJE3bp1s/uDT+Eo5YXn4O/utl4oJiamyLXNb731VpGR9LL+PBanuIy5ubl644037Pbr0qWL6tSpowULFtg93n/+858iU6OLe8xffvnF7pxcrOTk5CLbCu8aX7jUmCM/NyUp6b15Ma95obK+Tj4+PrbH/TsDBw6UVPRn7OWXX5Z0dmUCAKjpGEkHACfQrFkzLVq0SLfddpvatWune+65R40bN1ZcXJzeeecdJSUl6b///W+xS5vFxsZq6NChuuaaa7Rx40Z9+OGH+r//+z+1b9++xO/3+OOP64svvtDgwYNtyxVlZmZq27Zt+vTTTxUXF6fQ0FD17dtXt99+u+bOnat9+/bZpjj/8MMP6tu3rx588EFJUufOnfXdd9/p5ZdfVkREhBo3blzsddWFBg4cKH9/fz322GNydXXVDTfcYPf1mJgYPfvss5owYYJtmSx/f3/FxsZq6dKlGjVqlB577DGHX+e7775bL7/8sgYMGKB77rlHJ06c0Pz589WmTZsi1zKX1dy5c3X55ZerU6dOGjVqlO28ff3119q6dauks6+PJE2cOFG33HKL3N3dNWTIEFsJO9/48eP13//+V9dee60efvhhhYSEaNGiRYqNjdVnn31Wodcru7u76/rrr9fixYuVmZmpF1980e7rAQEBuuKKK/T8888rLy9PDRo00LfffltklLok9957r+6//37dcMMNuuqqq/THH39o5cqVtmXoCpX157E4PXv2VHBwsO688049/PDDslgs+uCDD4r8YcHDw0NTp07VQw89pCuvvFI333yz4uLitHDhQsXExNiN9g4ePFiff/65hg8frkGDBik2Nlbz589X69atlZGRUabn/neeeeYZrV+/XoMGDVJUVJROnDihN954Qw0bNrTNpomJiVFQUJDmz58vf39/+fr6qnv37g5d7925c2e9+eabevbZZ9W0aVPVrVtXV1555UW95oXK+jp5e3urdevWWrJkiZo3b66QkBC1bdu22Hs5tG/fXnfeeafeeust26UMv/76qxYtWqRhw4bZzYIAgBqriu8mDwAoxZ9//mnceuutRv369Q13d3ejXr16xq233lrsclqFSxft3LnTuPHGGw1/f38jODjYePDBB40zZ87Y7XvhkleGcXZZrwkTJhhNmzY1PDw8jNDQUKNnz57Giy++aOTm5tr2y8/PN1544QWjZcuWhoeHhxEWFmZce+21xqZNm2z77N6927jiiisMb29vQ5LtexW3JFSh2267zZBk9O/fv8TX47PPPjMuv/xyw9fX1/D19TVatmxpjB492tizZ0+pr2NJS7AZhmF8+OGHRpMmTQwPDw+jQ4cOxsqVK0tcgu2FF14ocryKWUpq+/btxvDhw42goCDDy8vLaNGihTFp0iS7faZPn240aNDAcHFxsXtNijs3Bw4cMG688Ubb43Xr1s346quvyvQcC7OXddmuVatWGZIMi8ViHD58uMjXjxw5YntugYGBxk033WQcO3asyOtQ3LkuKCgwnnzySSM0NNTw8fExBgwYYOzfv/+ifh6Ls2HDBuPSSy81vL29jYiICOOJJ54wVq5caUgy1qxZY7fv3LlzjaioKMPT09Po1q2bsWHDBqNz587GNddcY9vHarUaM2fOtO3XsWNH46uvviryc2IYJS/BduHSahe+PqtXrzauu+46IyIiwvDw8DAiIiKMW2+9tciSaMuXLzdat25tuLm5/e15Le4cJCQkGIMGDTL8/f2LLDVXlte8tPeCI6/TTz/9ZHTu3Nnw8PCwe80uXILNMAwjLy/PmDZtmtG4cWPD3d3diIyMNCZMmGBbTq9QVFSUMWjQoCK5SlpqEQCqC4thcGcNAKjpIiMjNWDAAL399ttmRwGcitVqVVhYmK6//notWLDA7DgAAHBNOgDUdHl5eTp16tTfTl0Farrs7Owi0+Dff/99JScnq0+fPuaEAgDgAlyTDgA12MqVK7V48WKdOXPGthY2UFv9/PPPeuSRR3TTTTepTp062rx5s9555x21bdvWbglAAADMREkHgBrsueee0/79+zVjxgxdddVVZscBTBUdHa3IyEjNnTvXtqzgHXfcoeeee04eHh5mxwMAQJLENekAAAAAADgJrkkHAAAAAMBJUNIBAAAAAHASte6adKvVqmPHjsnf318Wi8XsOAAAAACAGs4wDKWnpysiIkIuLqWPlde6kn7s2DFFRkaaHQMAAAAAUMscPnxYDRs2LHWfWlfS/f39JZ19cQICAkxOAwAAAACo6dLS0hQZGWnro6WpdSW9cIp7QEAAJR0AAAAAUGXKcsk1N44DAAAAAMBJUNIBAAAAAHASlHQAAAAAAJwEJR0AAAAAACdBSQcAAAAAwElQ0gEAAAAAcBKUdAAAAAAAnAQlHQAAAAAAJ0FJBwAAAADASVDSAQAAAABwEpR0AAAAAACcBCUdAAAAAAAnQUkHAAAAAMBJUNIBAAAAAHASlHQAAAAAAJwEJR0AAAAAACdBSXdSuflWzV93QEkZOWZHAQAAAABUEUq6k3rskz/03P92a8bXu8yOAgAAAACoIpR0J3XP5Y1lsUhLtxzVhv1JZscBAAAAAFQBSrqTah8ZpNsvjZIkPb1su7LzCkxOBAAAAACobJR0J/bYgBaq6++p2KRMzV93wOw4AAAAAIBKRkl3YgFe7po8pLUk6Y01BxSblGlyIgAAAABAZaKkO7lB7erriuZhyi2w6ull22QYhtmRAAAAAACVhJLu5CwWi569rq083Vy0Yf8pLd96zOxIAAAAAIBKQkmvBhrV8dHD/ZpJkp79eqdSs/JMTgQAAAAAqAyU9Grin72aqGldPyVl5Gr2yt1mxwEAAAAAVAJKejXh4eaiGcPaSpI++iVemw6dNjkRAAAAAKCiUdKrke5N6ujGzg0lSROXblNegdXkRAAAAACAikRJr2aeGthKwT7u2p2Qrvc2xJodBwAAAABQgSjp1UyIr4cmDGwlSXpl1T4dOZ1lciIAAAAAQEWhpFdDN3VuqG7RITqTV6CpX+w0Ow4AAAAAoIJQ0qshi8WiGcPbyt3Vou92JWrljgSzIwEAAAAAKgAlvZpqFu6vUVc0kSRN/WKHMnPyTU4EAAAAALhYlPRq7MG+zRQZ4q3jqdl6ZdVes+MAAAAAAC4SJb0a8/Zw1fTrzq6d/t5PcdpxLNXkRAAAAACAi0FJr+b6tKirQZfUV4HV0FNLt6vAapgdCQAAAABQTpT0GmDy4Nby93TTH4dT9NGv8WbHAQAAAACUEyW9BggP8NJjA1pIkp5fsVsn0rNNTgQAAAAAKA9Keg3xj0ujdEnDQKVn5+vZr3aZHQcAAAAAUA6U9BrC1cWimcPbycUiffHHMa3fe9LsSAAAAAAAB1HSa5C2DQJ1Z89oSdKk5duVnVdgbiAAAAAAgEMo6TXMuKuaKzzAU4dOZemNNfvNjgMAAAAAcAAlvYbx93LX1CFtJElvrjug/ScyTE4EAAAAACgrSnoNdE3berqyZV3lFRh6etk2GQZrpwMAAABAdUBJr4EsFoumDW0jL3cX/XwwWZ9vPmp2JAAAAABAGVDSa6jIEB+N6ddckjTjm106nZlrciIAAAAAwN+hpNdg9/ZqrObhfkrOzNXsFbvNjgMAAAAA+BuU9BrM3dVFM4e3kyQt/u2wfotLNjkRAAAAAKA0lPQarkt0iG7pGilJmrh0m3LzrSYnAgAAAACUhJJeCzx5TUuF+Hpob2KG3vkx1uw4AAAAAIASUNJrgWBfD00c2EqS9OrqvTqcnGVyIgAAAABAcSjptcT1nRro0iYhys6zavLy7aydDgAAAABOyNSSvn79eg0ZMkQRERGyWCxatmzZ3x6zdu1aderUSZ6enmratKkWLlxY6TlrAovFomeHtZO7q0Vr9pzUiu0JZkcCAAAAAFzA1JKemZmp9u3ba968eWXaPzY2VoMGDVLfvn21detWjR07Vvfee69WrlxZyUlrhqZ1/fRA7xhJ0tQvdyg9O8/kRAAAAACA81kMJ5n3bLFYtHTpUg0bNqzEfZ588kl9/fXX2r59u23bLbfcopSUFK1YsaJM3yctLU2BgYFKTU1VQEDAxcaudrLzCjRgznodOpWlkZdFa8qQNmZHAgAAAIAazZEeWq2uSd+4caP69+9vt23AgAHauHFjicfk5OQoLS3N7qM283J31bPD2kqSFv0Up+1HU01OBAAAAAAoVK1KekJCgsLDw+22hYeHKy0tTWfOnCn2mFmzZikwMND2ERkZWRVRnVqvZmEa2j5CVkN6auk2FVidYjIFAAAAANR61aqkl8eECROUmppq+zh8+LDZkZzC04Nbyd/LTX8eSdWHPx8yOw4AAAAAQNWspNerV0+JiYl22xITExUQECBvb+9ij/H09FRAQIDdB6S6/l564pqWkqQXVu5RYlq2yYkAAAAAANWqpPfo0UOrV6+227Zq1Sr16NHDpETV223dGqlDZJAycvL1zJc7zY4DAAAAALWeqSU9IyNDW7du1datWyWdXWJt69atio+Pl3R2qvodd9xh2//+++/XwYMH9cQTT2j37t1644039PHHH+uRRx4xI3615+Ji0YzhbeXqYtHX245rzZ4TZkcCAAAAgFrN1JL++++/q2PHjurYsaMkady4cerYsaMmT54sSTp+/LitsEtS48aN9fXXX2vVqlVq3769XnrpJb399tsaMGCAKflrgjYRgRrZM1qSNHn5dp3JLTA3EAAAAADUYk6zTnpVqe3rpBcnMydf/V9ep+Op2RrdN0aPD2hpdiQAAAAAqDFq7DrpqBy+nm6aOrSNJOmt9Qe1LzHd5EQAAAAAUDtR0iFJGtCmnvq3CldegaGJS7fLytrpAAAAAFDlKOmwmTq0tbzdXfVrXLI+3XzE7DgAAAAAUOtQ0mHTMNhHj1zVTJI065tdSs7MNTkRAAAAANQulHTYGXlZY7Ws56/TWXma+c0us+MAAAAAQK1CSYcdd1cXzRjeThaL9OmmI/r54CmzIwEAAABArUFJRxGdo4J1a7dGkqSnl21Xbr7V5EQAAAAAUDtQ0lGsJwe0VKifh/afyNCCHw6aHQcAAAAAagVKOooV6OOupwe1liTNXb1Ph05lmpwIAAAAAGo+SjpKdF2HCF3eNFQ5+VZNWr5DhsHa6QAAAABQmSjpKJHFYtH0YW3l4eai9XtP6uttx82OBAAAAAA1GiUdpWoc6qt/9YmRJE37cqfSsvNMTgQAAAAANRclHX/rgT4xahLqq5PpOXpx5R6z4wAAAABAjUVJx9/ydHPVs8PaSpI++PmQ/jicYm4gAAAAAKihKOkok55NQzW8YwMZhvTU0m3KL2DtdAAAAACoaJR0lNnEQa0U4OWmHcfStGjjIbPjAAAAAECNQ0lHmYX6eWr8ta0kSS9/u0fHU8+YnAgAAAAAahZKOhxyS9dIdY4KVmZugaZ9sdPsOAAAAABQo1DS4RAXF4tmDG8rVxeLVuxI0OpdiWZHAgAAAIAag5IOh7WsF6B7L28sSZq8fIeycvNNTgQAAAAANQMlHeUypn8zNQjy1tGUM3p19T6z4wAAAABAjUBJR7n4eLhp2tA2kqR3fojV7oQ0kxMBAAAAQPVHSUe59W8drgFtwpVvNTRx6XZZrYbZkQAAAACgWqOk46JMHdpGvh6u2nTotJb8ftjsOAAAAABQrVHScVHqB3rrkauaS5Ke+99uJWXkmJwIAAAAAKovSjou2l09o9W6foBSz+Rp5te7zI4DAAAAANUWJR0Xzc3VRTOvbyeLRfp8y1H9dCDJ7EgAAAAAUC1R0lEhOkQG6R/doyRJTy/drpz8ApMTAQAAAED1Q0lHhXn8mhYK8/fUwaRMzV970Ow4AAAAAFDtUNJRYQK83DV5cGtJ0ry1+xWblGlyIgAAAACoXijpqFCDL6mvXs1ClZtv1aRl22UYrJ0OAAAAAGVFSUeFslgsenZYW3m4uejH/Un64o9jZkcCAAAAgGqDko4KF1XHVw/1bSpJmv7VTqVm5ZmcCAAAAACqB0o6KsWo3k0UE+arpIxcPb9yt9lxAAAAAKBaoKSjUni6uWrG8HaSpI9+jdfm+NMmJwIAAAAA50dJR6W5tEkd3dCpoQxDeurzbcovsJodCQAAAACcGiUdlWrioFYK8nHX7oR0vbchzuw4AAAAAODUKOmoVCG+Hnrq2laSpFe+26ujKWdMTgQAAAAAzouSjkp3Y+eG6hodrKzcAk39YofZcQAAAADAaVHSUelcXCyaMbyd3FwsWrUzUd/uSDA7EgAAAAA4JUo6qkTzcH/984omkqSpX+xQZk6+yYkAAAAAwPlQ0lFlHr6ymSJDvHUsNVtzvttrdhwAAAAAcDqUdFQZbw9XPTO0rSTp3Q1x2nkszeREAAAAAOBcKOmoUn1b1tXAdvVUYDX01NJtsloNsyMBAAAAgNOgpKPKTRnSRn6ebtp6OEUf/RpvdhwAAAAAcBqUdFS58AAvPXZ1c0nS7BW7dSI92+REAAAAAOAcKOkwxe09otWuQaDSs/M14+tdZscBAAAAAKdASYcpXF0smjm8nVws0vKtx/TDvpNmRwIAAAAA01HSYZp2DQN1R49oSdKkZduVnVdgbiAAAAAAMBklHaZ69OrmCg/wVNypLL2x9oDZcQAAAADAVJR0mMrfy11ThrSRJM1fe0AHTmaYnAgAAAAAzENJh+mubVtPfVqEKbfAqqeXbpdhsHY6AAAAgNqJkg7TWSwWTb+urbzcXbTx4Ckt3XLU7EgAAAAAYApKOpxCZIiPHu7XTJI04+tdSsnKNTkRAAAAAFQ9Sjqcxr2XN1Gzun46lZmr2St2mx0HAAAAAKocJR1Ow8PNRTOvbydJ+u+vh/V7XLLJiQAAAACgalHS4VS6RodoRJdISdLEpduVV2A1OREAAAAAVB1KOpzO+GtbKsTXQ3sS0/XOj7FmxwEAAACAKkNJh9MJ9vXQUwNbSZLmfLdXh5OzTE4EAAAAAFWDkg6ndEOnBureOETZeVZN+WIHa6cDAAAAqBUo6XBKFotFM4a3k7urRd/vPqGVOxLMjgQAAAAAlY6SDqfVtK6f7u8dI0ma+sVOZeTkm5wIAAAAACoXJR1ObXTfpoqq46OEtGy9/O1es+MAAAAAQKWipMOpebm7avp1bSVJC3+K1fajqSYnAgAAAIDKQ0mH07uieZiGtI+Q1ZCeWrpNBVZuIgcAAACgZqKko1qYNKiV/D3d9OeRVP3nl0NmxwEAAACASmF6SZ83b56io6Pl5eWl7t2769dffy11/zlz5qhFixby9vZWZGSkHnnkEWVnZ1dRWpilboCXnrimhSTphRV7lJjGOQcAAABQ85ha0pcsWaJx48ZpypQp2rx5s9q3b68BAwboxIkTxe7/0Ucfafz48ZoyZYp27dqld955R0uWLNFTTz1Vxclhhv/rHqX2kUFKz8nX9K92mh0HAAAAACqcqSX95Zdf1j//+U+NHDlSrVu31vz58+Xj46N333232P1/+uknXXbZZfq///s/RUdH6+qrr9att976t6PvqBlcXSyaMaytXCzSV38e17q9J82OBAAAAAAVyrSSnpubq02bNql///5/hXFxUf/+/bVx48Zij+nZs6c2bdpkK+UHDx7UN998o4EDB5b4fXJycpSWlmb3geqrbYNAjbyssSRp0rLtys4rMDkRAAAAAFQc00p6UlKSCgoKFB4ebrc9PDxcCQkJxR7zf//3f3rmmWd0+eWXy93dXTExMerTp0+p091nzZqlwMBA20dkZGSFPg9UvUeuaq76gV6KT87S69/vNzsOAAAAAFQY028c54i1a9dq5syZeuONN7R582Z9/vnn+vrrrzV9+vQSj5kwYYJSU1NtH4cPH67CxKgMfp5umjKkjSTp3+sPaP+JdJMTAQAAAEDFcDPrG4eGhsrV1VWJiYl22xMTE1WvXr1ij5k0aZJuv/123XvvvZKkdu3aKTMzU6NGjdLEiRPl4lL0bw6enp7y9PSs+CcAUw1oE67+rerqu10n9NTS7Voy6lJZLBazYwEAAADARTFtJN3Dw0OdO3fW6tWrbdusVqtWr16tHj16FHtMVlZWkSLu6uoqSTIMo/LCwulYLBZNHdpG3u6u+jU2WZ9uOmJ2JAAAAAC4aKZOdx83bpwWLFigRYsWadeuXXrggQeUmZmpkSNHSpLuuOMOTZgwwbb/kCFD9Oabb2rx4sWKjY3VqlWrNGnSJA0ZMsRW1lF7NAz20dj+zSRJM7/ZpeTMXJMTAQAAAMDFMW26uySNGDFCJ0+e1OTJk5WQkKAOHTpoxYoVtpvJxcfH242cP/3007JYLHr66ad19OhRhYWFaciQIZoxY4ZZTwEmu/vyxlq65ah2J6Rr1je79MJN7c2OBAAAAADlZjFq2TzxtLQ0BQYGKjU1VQEBAWbHQQXYdChZN7x5dtm+JaMuVfcmdUxOBAAAAAB/caSHVqu7uwPF6RwVolu7NZIkTVy2Xbn5VpMTAQAAAED5UNJRI4y/pqXq+Hpo/4kMLfjhoNlxAAAAAKBcKOmoEQJ93PX04FaSpLmr9yn+VJbJiQAAAADAcZR01BjDOjRQz5g6ysm3atLy7SzLBwAAAKDaoaSjxrBYLJo+rK08XF20bu9JfbMtwexIAAAAAOCQcpX0H374Qf/4xz/Uo0cPHT16VJL0wQcf6Mcff6zQcICjYsL89ECfGEnStC93KC07z+REAAAAAFB2Dpf0zz77TAMGDJC3t7e2bNminJwcSVJqaqpmzpxZ4QEBRz3QJ0aNQ311Ij1HL63cY3YcAAAAACgzh0v6s88+q/nz52vBggVyd3e3bb/sssu0efPmCg0HlIeXu6umX9dWkvT+z4f055EUcwMBAAAAQBk5XNL37NmjK664osj2wMBApaSkVEQm4KJd3ixUwzpEyDCkp5ZuU34Ba6cDAAAAcH4Ol/R69epp//79Rbb/+OOPatKkSYWEAirCxEGtFeDlpu1H0/TBz4fMjgMAAAAAf8vhkv7Pf/5TY8aM0S+//CKLxaJjx47pP//5jx577DE98MADlZERKJcwf089eW1LSdJL3+5VQmq2yYkAAAAAoHRujh4wfvx4Wa1W9evXT1lZWbriiivk6empxx57TA899FBlZATK7daujfTppiPaEp+iaV/u0Jv/6Gx2JAAAAAAokcUwDKM8B+bm5mr//v3KyMhQ69at5efnV9HZKkVaWpoCAwOVmpqqgIAAs+OgCuw6nqbBr/2oAquhd+/qoitbhpsdCQAAAEAt4kgPLdc66ZLk4eGh1q1bq1u3btWmoKN2alU/QPdc3liSNHn5Dp3JLTA5EQAAAAAUz+Hp7n379pXFYinx699///1FBQIqw9j+zfT1n8d15PQZvbp6n8afu1YdAAAAAJyJwyPpHTp0UPv27W0frVu3Vm5urjZv3qx27dpVRkbgovl4uGnq0DaSpLd/OKg9CekmJwIAAACAohweSX/llVeK3T516lRlZGRcdCCgslzVOlxXtw7XtzsTNXHpNn18Xw+5uJQ8KwQAAAAAqlq5r0m/0D/+8Q+9++67FfVwQKWYOrSNfDxc9fuh0/r498NmxwEAAAAAOxVW0jdu3CgvL6+KejigUkQEeWvcVc0lSbP+t1unMnJMTgQAAAAAf3F4uvv1119v97lhGDp+/Lh+//13TZo0qcKCAZXlrp7R+nzzUe08nqYZ3+zSyzd3MDsSAAAAAEgqx0h6YGCg3UdISIj69Omjb775RlOmTKmMjECFcnN10YzhbWWxSJ9vPqqfDiSZHQkAAAAAJEkWwzAMs0NUJUcWkUfN9vSybfrw53g1CfPV/8b0kqebq9mRAAAAANRAjvTQCrsmHahuHh/QUqF+njp4MlP/XnfQ7DgAAAAAULZr0oODg2WxlG2pquTk5IsKBFSVQG93TRrcSmMWb9Xra/ZraPsIRYf6mh0LAAAAQC1WppI+Z86cSo4BmGNo+wh9uumIftiXpEnLt+v9u7uV+Q9SAAAAAFDRuCYdtV5cUqaunrNeuflWvXpLB13XoYHZkQAAAADUIFV2TXp2drbS0tLsPoDqJjrUVw/2bSpJmv7VLqWeyTM5EQAAAIDayuGSnpmZqQcffFB169aVr6+vgoOD7T6A6ui+3k3UJMxXSRk5emHlbrPjAAAAAKilHC7pTzzxhL7//nu9+eab8vT01Ntvv61p06YpIiJC77//fmVkBCqdp5urZgxrJ0n6zy/x2hJ/2uREAAAAAGojh0v6l19+qTfeeEM33HCD3Nzc1KtXLz399NOaOXOm/vOf/1RGRqBK9Iipo+s7NZBhSE8t3a78AqvZkQAAAADUMg6X9OTkZDVp0kSSFBAQYFty7fLLL9f69esrNh1QxSYObKVAb3ftOp6mhT/FmR0HAAAAQC3jcElv0qSJYmNjJUktW7bUxx9/LOnsCHtQUFCFhgOqWh0/T024tqUk6eVVe3Us5YzJiQAAAADUJg6X9JEjR+qPP/6QJI0fP17z5s2Tl5eXHnnkET3++OMVHhCoajd3iVSXqGBl5RZo6hc7zI4DAAAAoBa56HXSDx06pE2bNqlp06a65JJLKipXpWGddJTFnoR0DZr7g/Kthhbc0UVXtQ43OxIAAACAaqpS10k/fPiw3edRUVG6/vrrq0VBB8qqRT1/3dvr7L0XpizfrsycfJMTAQAAAKgNHC7p0dHR6t27txYsWKDTp1mmCjXXmH7N1DDYW8dSs/Xq6n1mxwEAAABQCzhc0n///Xd169ZNzzzzjOrXr69hw4bp008/VU5OTmXkA0zj7eGq6de1lSS982Osdh5LMzkRAAAAgJrO4ZLesWNHvfDCC4qPj9f//vc/hYWFadSoUQoPD9fdd99dGRkB0/RtWVfXtq2nAquhicu2yWq9qFs4AAAAAECpHC7phSwWi/r27asFCxbou+++U+PGjbVo0aKKzAY4hSlD2sjP001b4lP039/izY4DAAAAoAYrd0k/cuSInn/+eXXo0EHdunWTn5+f5s2bV5HZAKdQL9BLj17dXJI0+3+7dTKdSzsAAAAAVA6HS/q///1v9e7dW9HR0Xr//fc1YsQIHThwQD/88IPuv//+ysgImO6OHtFq2yBAadn5mvH1TrPjAAAAAKihHC7pzz77rLp3765NmzZp+/btmjBhgqKioiojG+A0XF0smjm8nSwWadnWY/pxX5LZkQAAAADUQG6OHhAfHy+LxVIZWQCndknDIN1xaZQWbTykScu3639jesnL3dXsWAAAAABqEIdH0inoqM0eHdBCdf09FZuUqTfXHjA7DgAAAIAaptw3jgNqowAvd00Z0kaS9ObaAzpwMsPkRAAAAABqEko64KCB7eqpd/Mw5RZYNWnZdhkGa6cDAAAAqBiUdMBBFotF069rK083F/104JSWbT1qdiQAAAAANQQlHSiHRnV89HC/ZpKkZ7/apZSsXJMTAQAAAKgJHC7piYmJuv322xURESE3Nze5urrafQC1xT97NVGzun46lZmr2Sv2mB0HAAAAQA3g8BJsd911l+Lj4zVp0iTVr1+fu72j1vJwc9Gzw9pqxFs/67+/xuvGzg3UOSrE7FgAAAAAqjGHS/qPP/6oH374QR06dKiEOED10r1JHd3UuaE+2XREE5du15cPXS53V64iAQAAAFA+DreJyMhI7mYNnGfCwFYK9nHX7oR0vftjrNlxAAAAAFRjDpf0OXPmaPz48YqLi6uEOED1E+LroacGtpIkzflun46czjI5EQAAAIDqymI4OCweHBysrKws5efny8fHR+7u7nZfT05OrtCAFS0tLU2BgYFKTU1VQECA2XFQQxiGoRFv/axfY5PVr2VdvX1nF+7XAAAAAECSYz3U4WvS58yZU95cQI1lsVg0c3hbXfvqD1q9+4RW7kjUNW3rmR0LAAAAQDXj8Eh6dcdIOirTiyv36PU1+1UvwEvfPdpbfp4O/x0MAAAAQA1TqSPpklRQUKBly5Zp165dkqQ2bdpo6NChrJOOWu/BK5vqiz+OKT45S6+s2qtJg1ubHQkAAABANeLwjeP279+vVq1a6Y477tDnn3+uzz//XP/4xz/Upk0bHThwoDIyAtWGl7urpg9rK0l6b0Osth9NNTkRAAAAgOrE4ZL+8MMPKyYmRocPH9bmzZu1efNmxcfHq3Hjxnr44YcrIyNQrfRuHqbBl9SX1ZAmLt2mAmutuqIEAAAAwEVwuKSvW7dOzz//vEJCQmzb6tSpo+eee07r1q2r0HBAdTV5cGv5e7rpjyOp+uiXQ2bHAQAAAFBNOFzSPT09lZ6eXmR7RkaGPDw8KiQUUN3VDfDS49e0kCQ9v2KPTqRlm5wIAAAAQHXgcEkfPHiwRo0apV9++UWGYcgwDP3888+6//77NXTo0MrICFRLt3WP0iUNA5Wek6/pX+8yOw4AAACAasDhkj537lzFxMSoR48e8vLykpeXly677DI1bdpUr776amVkBKolVxeLZg5vJxeL9OUfx7R+70mzIwEAAABwcuVeJ33fvn3avXu3JKlVq1Zq2rRphQarLKyTjqr2zJc79e6GWDUK8dG3j1whL3eWKgQAAABqk0pfJ12SmjVrpmbNmpX3cKDWGHd1c32z7bjik7M0b81+PXp1C7MjAQAAAHBSZSrp48aN0/Tp0+Xr66tx48aVuu/LL79cIcGAmsLP001Th7bW/R9u1vx1B3Rdhwg1retvdiwAAAAATqhMJX3Lli3Ky8uz/RuAYwa0qad+Letq9e4Tmrh0uxaPulQWi8XsWAAAAACcTLmvSa8o8+bN0wsvvKCEhAS1b99er732mrp161bi/ikpKZo4caI+//xzJScnKyoqSnPmzNHAgQPL9P24Jh1mOZycpateWafsPKtevKm9buzc0OxIAAAAAKqAIz3U4bu733333cWuk56Zmam7777bocdasmSJxo0bpylTpmjz5s1q3769BgwYoBMnThS7f25urq666irFxcXp008/1Z49e7RgwQI1aNDA0acBVLnIEB+N7d9ckjTzm106nZlrciIAAAAAzsbhkXRXV1cdP35cdevWtduelJSkevXqKT8/v8yP1b17d3Xt2lWvv/66JMlqtSoyMlIPPfSQxo8fX2T/+fPn64UXXtDu3bvl7u7uSGwbRtJhprwCqwbP/VF7EtM1okukZt94idmRAAAAAFSyShlJT0tLU2pqqgzDUHp6utLS0mwfp0+f1jfffFOkuJcmNzdXmzZtUv/+/f8K4+Ki/v37a+PGjcUe88UXX6hHjx4aPXq0wsPD1bZtW82cOVMFBQUlfp+cnBy7rGlpaWXOCFQ0d1cXzby+rSRpye+H9WtsssmJAAAAADiTMpf0oKAghYSEyGKxqHnz5goODrZ9hIaG6u6779bo0aPL/I2TkpJUUFCg8PBwu+3h4eFKSEgo9piDBw/q008/VUFBgb755htNmjRJL730kp599tkSv8+sWbMUGBho+4iMjCxzRqAydI4K0a3dzv4cTly6Tbn5VpMTAQAAAHAWZV4nfc2aNTIMQ1deeaU+++wzhYSE2L7m4eGhqKgoRUREVErIQlarVXXr1tVbb70lV1dXde7cWUePHtULL7ygKVOmFHvMhAkT7JaNS0tLo6jDdE9e01Lf7kjUvhMZevvHg/pXn6ZmRwIAAADgBMpc0nv37i1Jio2NVaNGjS56+ajQ0FC5uroqMTHRbntiYqLq1atX7DH169eXu7u7XF1dbdtatWqlhIQE5ebmysPDo8gxnp6e8vT0vKisQEUL8vHQxEGtNO7jPzR39T4NuSRCkSE+ZscCAAAAYDKH7+7+/fff69NPPy2y/ZNPPtGiRYvK/DgeHh7q3LmzVq9ebdtmtVq1evVq9ejRo9hjLrvsMu3fv19W61/Tg/fu3av69esXW9ABZza8YwP1aFJH2XlWTVq+XSavhggAAADACThc0mfNmqXQ0NAi2+vWrauZM2c69Fjjxo3TggULtGjRIu3atUsPPPCAMjMzNXLkSEnSHXfcoQkTJtj2f+CBB5ScnKwxY8Zo7969+vrrrzVz5kyHroUHnIXFYtGzw9vKw9VFa/ec1P+2F38vBgAAAAC1R5mnuxeKj49X48aNi2yPiopSfHy8Q481YsQInTx5UpMnT1ZCQoI6dOigFStW2G4mFx8fLxeXv/6OEBkZqZUrV+qRRx7RJZdcogYNGmjMmDF68sknHX0agFOICfPT/X1iNHf1Pk37cod6NQuVv1f5lhcEAAAAUP05vE56o0aN9Prrr2vo0KF225cvX67Ro0fryJEjFRqworFOOpxNdl6BrpmzXnGnsnRXz2hNHdrG7EgAAAAAKlClrJNe6NZbb9XDDz+sNWvWqKCgQAUFBfr+++81ZswY3XLLLeUODdRWXu6uenZYO0nS+xvj9OeRFHMDAQAAADCNwyV9+vTp6t69u/r16ydvb295e3vr6quv1pVXXunwNekAzrq8Waiu6xAhqyFNXLpdBVZuIgcAAADURg5Pdy+0d+9e/fHHH/L29la7du0UFRVV0dkqBdPd4axOpGer30vrlJ6dr6lDWuuuy4re+wEAAABA9eNIDy13Sa+uKOlwZh/+fEhPL9suP083fTeut+oFepkdCQAAAMBFcqSHOnx394KCAi1cuFCrV6/WiRMn7NYsl86uow6gfP6vWyN9tvmItsSn6JmvduiN2zqbHQkAAABAFXK4pI8ZM0YLFy7UoEGD1LZtW1kslsrIBdRKLi4WzRjWTkNe/1HfbEvQmt0n1LdlXbNjAQAAAKgiDpf0xYsX6+OPP9bAgQMrIw9Q67WOCNDdl0VrwQ+xmrR8u1Y16S1vD1ezYwEAAACoAg7f3d3Dw0NNmzatjCwAzhnbv7kiAr105PQZzf1+n9lxAAAAAFQRh0v6o48+qldffVW17H5zQJXy9XTT1KFtJEkL1h/U3sR0kxMBAAAAqAoOT3f/8ccftWbNGv3vf/9TmzZt5O7ubvf1zz//vMLCAbXZ1W3q6arW4Vq1M1ETl27TklE95OLCPSAAAACAmszhkh4UFKThw4dXRhYAF5g6tI027E/Sb3Gn9cmmwxrRtZHZkQAAAABUItZJB5zcgvUHNeObXQrycdfqcb1Vx8/T7EgAAAAAHOBID3X4mnQAVWvkZdFqVT9AKVl5mvnNbrPjAAAAAKhEDk93b9y4calrox88ePCiAgGw5+bqohnD2+qGN3/SZ5uP6MbODdUjpo7ZsQAAAABUAodL+tixY+0+z8vL05YtW7RixQo9/vjjFZULwHk6NQrW/3VrpP/8Eq+nl23TN2N6ydONtdMBAACAmsbhkj5mzJhit8+bN0+///77RQcCULwnrmmplTsSdOBkpt5ad1AP9WtmdiQAAAAAFazCrkm/9tpr9dlnn1XUwwG4QKC3uyYNbi1Jem3NfsUlZZqcCAAAAEBFq7CS/umnnyokJKSiHg5AMYa2j9DlTUOVm2/VpOXbVcsWZwAAAABqPIenu3fs2NHuxnGGYSghIUEnT57UG2+8UaHhANizWCyaPqytBsxZrx/2JenLP49raPsIs2MBAAAAqCAOl/Rhw4bZfe7i4qKwsDD16dNHLVu2rKhcAErQONRXo/s01Svf7dX0r3aqd/MwBXq7mx0LAAAAQAUoU0kfN26cpk+fLl9fX/Xt21c9evSQuzulADDL/X2aaPnWozqYlKkXV+7R9GFtzY4EAAAAoAKU6Zr01157TRkZGZKkvn376vTp05UaCkDpPN1c9ezws8X8w18OaevhFHMDAQAAAKgQZRpJj46O1ty5c3X11VfLMAxt3LhRwcHBxe57xRVXVGhAAMXrGROq6zs20Odbjuqpz7fpiwcvk5trhd0LEgAAAIAJLEYZbg+9bNky3X///Tpx4oQsFkuJd5S2WCwqKCio8JAVKS0tTYGBgUpNTVVAQIDZcYCLkpSRo34vrVPqmTw9PaiV7u3VxOxIAAAAAC7gSA8t07DbsGHDlJCQoLS0NBmGoT179uj06dNFPpKTkyvkCQAom1A/T42/9uwNG19etVfHUs6YnAgAAADAxXBobqyfn5/WrFmjxo0bKzAwsNgPAFVrRJdIdYkKVlZugaZ9ucPsOAAAAAAugsMXsPbu3Vtubg6v3Aagkri4WPTs8LZyc7Fo5Y5Efbcz0exIAAAAAMqJu0wBNUDLegG6p1djSdKUL3YoKzff5EQAAAAAyoOSDtQQY/o1U4Mgbx1NOaNXv9tndhwAAAAA5UBJB2oIHw83PXNdG0nS2z/GatfxNJMTAQAAAHBUuUv6/v37tXLlSp05c/Zu0mVYyQ1AJevXKlzXtKmnAquhiUu3yWrlfQkAAABUJw6X9FOnTql///5q3ry5Bg4cqOPHj0uS7rnnHj366KMVHhCAY6YMbS1fD1dtjk/R4t8Omx0HAAAAgAMcLumPPPKI3NzcFB8fLx8fH9v2ESNGaMWKFRUaDoDj6gd6a9zVLSRJz/1vl5IyckxOBAAAAKCsHC7p3377rWbPnq2GDRvabW/WrJkOHTpUYcEAlN+dPaLUJiJAadn5mvH1LrPjAAAAACgjh0t6Zmam3Qh6oeTkZHl6elZIKAAXx83VRTOHt5PFIi3dclQb9ieZHQkAAABAGThc0nv16qX333/f9rnFYpHVatXzzz+vvn37Vmg4AOXXPjJIt18aJUl6etl2ZecVmJwIAAAAwN9xc/SA559/Xv369dPvv/+u3NxcPfHEE9qxY4eSk5O1YcOGysgIoJweG9BCK7YnKDYpU/PXHdDY/s3NjgQAAACgFA6PpLdt21Z79+7V5Zdfruuuu06ZmZm6/vrrtWXLFsXExFRGRgDlFODlrslDWkuS3lhzQAdPZpicCAAAAEBpLEYtW+A8LS1NgYGBSk1NVUBAgNlxgEpnGIbufO83rd97Upc1raMP7+kui8VidiwAAACg1nCkhzo8kt60aVNNnTpV+/btK3dAAFXHYrFo+nVt5Onmog37T2n51mNmRwIAAABQAodL+ujRo/X111+rRYsW6tq1q1599VUlJCRURjYAFSSqjq8eurKpJOnZr3cqNSvP5EQAAAAAiuNwSX/kkUf022+/affu3Ro4cKDmzZunyMhIXX311XZ3fQfgXEZdEaOmdf2UlJGr2St3mx0HAAAAQDEcLumFmjdvrmnTpmnv3r364YcfdPLkSY0cObIiswGoQB5uLpoxrK0k6aNf4rXp0GmTEwEAAAC4ULlLuiT9+uuvGjt2rIYPH669e/fqpptuqqhcACpB9yZ1dGPnhpKkiUu3Ka/AanIiAAAAAOdzuKTv3btXU6ZMUfPmzXXZZZdp165dmj17thITE7V48eLKyAigAj01sJWCfdy1OyFd722INTsOAAAAgPM4XNJbtmypFStWaPTo0Tpy5IhWrlypO+64Q35+fpWRD0AFC/H10ISBrSRJr6zapyOns0xOBAAAAKCQm6MH7NmzR82aNauMLACqyE2dG+rT34/o17hkTf1ip96+s4vZkQAAAACoHCPpFHSg+rNYLJoxvK3cXCz6bleiVu5gGUUAAADAGZSppIeEhCgpKUmSFBwcrJCQkBI/AFQPzcL9NeqKJpKkqV/sUEZOvsmJAAAAAJRpuvsrr7wif39/278tFkulhgJQNR66spm+/POYDief0ZxVe/X04NZmRwIAAABqNYthGIbZIapSWlqaAgMDlZqaqoCAALPjAKZbu+eE7nrvN7m6WPTFg5epTUSg2ZEAAACAGsWRHurwNemurq46ceJEke2nTp2Sq6urow8HwGR9WtTVoHb1VWA19NTS7Sqw1qq/2wEAAABOxeGSXtLAe05Ojjw8PC46EICqN3lIa/l7uumPwyn66Nd4s+MAAAAAtVaZl2CbO3eupLN3hX777bft1kUvKCjQ+vXr1bJly4pPCKDShQd46bEBLTTlix16fsVuDWgTrrr+XmbHAgAAAGqdMpf0V155RdLZkfT58+fbTW338PBQdHS05s+fX/EJAVSJf1wapc82H9GfR1L17Fe7NPfWjmZHAgAAAGqdMpf02NhYSVLfvn31+eefKzg4uNJCAah6ri4WzRzeTkNf/1Ff/HFMN3ZuqCuah5kdCwAAAKhVHL4mfc2aNRR0oIZq2yBQd/aMliRNWr5d2XkF5gYCAAAAahmHS/oNN9yg2bNnF9n+/PPP66abbqqQUADMM+6q5goP8NShU1l6Y81+s+MAAAAAtYrDJX39+vUaOHBgke3XXnut1q9fXyGhAJjH38tdU4e0kSS9ue6A9p/IMDkRAAAAUHs4XNIzMjKKXWrN3d1daWlpFRIKgLmuaVtPfVuEKa/A0MSl20pcehEAAABAxXK4pLdr105Lliwpsn3x4sVq3bp1hYQCYC6LxaJnrmsrL3cX/RKbrM83HzU7EgAAAFArlPnu7oUmTZqk66+/XgcOHNCVV14pSVq9erX++9//6pNPPqnwgADMERniozH9mmv2it2a8c0uXdmyroJ9i86iAQAAAFBxHB5JHzJkiJYtW6b9+/frX//6lx599FEdOXJE3333nYYNG1YJEQGY5d5ejdU83E/JmbmavWK32XEAAACAGs9i1LKLTdPS0hQYGKjU1FQFBASYHQdwer/HJevG+RslSZ/c30Ndo0NMTgQAAABUL470UIdH0iUpJSVFb7/9tp566iklJydLkjZv3qyjR7luFahpukSH6JaukZKkiUu3KTffanIiAAAAoOZyuKT/+eefat68uWbPnq0XXnhBKSkpkqTPP/9cEyZMqOh8AJzAk9e0VIivh/YmZuidH2PNjgMAAADUWA6X9HHjxumuu+7Svn375OXlZds+cODAcq+TPm/ePEVHR8vLy0vdu3fXr7/+WqbjFi9eLIvFwrXwQCUL9vXQxIGtJEmvrt6rw8lZJicCAAAAaiaHS/pvv/2m++67r8j2Bg0aKCEhweEAS5Ys0bhx4zRlyhRt3rxZ7du314ABA3TixIlSj4uLi9Njjz2mXr16Ofw9ATju+k4NdGmTEGXnWTV5+XbWTgcAAAAqgcMl3dPTU2lpaUW27927V2FhYQ4HePnll/XPf/5TI0eOVOvWrTV//nz5+Pjo3XffLfGYgoIC3XbbbZo2bZqaNGni8PcE4DiLxaJnh7WTu6tFa/ac1Irtjv9RDgAAAEDpHC7pQ4cO1TPPPKO8vDxJZ/+Pe3x8vJ588kndcMMNDj1Wbm6uNm3apP79+/8VyMVF/fv318aNG0s87plnnlHdunV1zz33/O33yMnJUVpamt0HgPJpWtdPD/SOkSRN/XKH0rPzTE4EAAAA1CwOl/SXXnpJGRkZqlu3rs6cOaPevXuradOm8vf314wZMxx6rKSkJBUUFCg8PNxue3h4eIlT53/88Ue98847WrBgQZm+x6xZsxQYGGj7iIyMdCgjAHv/6ttUUXV8lJiWo5dX7TU7DgAAAFCjOFzSAwMDtWrVKn355ZeaO3euHnzwQX3zzTdat26dfH19KyOjTXp6um6//XYtWLBAoaGhZTpmwoQJSk1NtX0cPny4UjMCNZ2Xu6ueHdZWkrTopzhtP5pqciIAAACg5nAr74GXX365Lr/88ov65qGhoXJ1dVViYqLd9sTERNWrV6/I/gcOHFBcXJyGDBli22a1nl2z2c3NTXv27FFMTIzdMZ6envL09LyonADs9WoWpqHtI/TFH8f01NJtWvqvy+TqYjE7FgAAAFDtlamkz507V6NGjZKXl5fmzp1b6r5+fn5q06aNunfv/reP6+Hhoc6dO2v16tW2ZdSsVqtWr16tBx98sMj+LVu21LZt2+y2Pf3000pPT9err77KVHagCj09uJXW7DmhP4+k6sOfD+nOntFmRwIAAACqvTKV9FdeeUW33XabvLy89Morr5S6b05Ojk6cOKFHHnlEL7zwwt8+9rhx43TnnXeqS5cu6tatm+bMmaPMzEyNHDlSknTHHXeoQYMGmjVrlry8vNS2bVu744OCgiSpyHYAlauuv5eeuKalJi3brhdW7tE1bespPMDL7FgAAABAtVamkh4bG1vsv0uyatUq/d///V+ZSvqIESN08uRJTZ48WQkJCerQoYNWrFhhu5lcfHy8XFwcvnQeQBW4rVsjfbbpiLYeTtEzX+7UvNs6mR0JAAAAqNYshmEYFf2gZ86c0VtvvaUxY8ZU9ENftLS0NAUGBio1NVUBAQFmxwGqvR3HUjX09Q0qsBp6b2RX9W1R1+xIAAAAgFNxpIeWa4h69erVGjx4sGJiYhQTE6PBgwfru+++s33d29vbKQs6gIrXJiJQI89djz55+XadyS0wNxAAAABQjTlc0t944w1dc8018vf315gxYzRmzBgFBARo4MCBmjdvXmVkBODkHrmqueoHeulw8hm99v0+s+MAAAAA1ZbD090bNmyo8ePHF7n7+rx58zRz5kwdPXq0QgNWNKa7A5Vj5Y4E3ffBJrm5WPTNmF5qHu5vdiQAAADAKVTqdPeUlBRdc801RbZfffXVSk1NdfThANQQA9rUU/9W4cq3Gnp66XZZrRV+uwsAAACgxnO4pA8dOlRLly4tsn358uUaPHhwhYQCUD1NHdpa3u6u+jUuWZ9uPmJ2HAAAAKDaKdMSbHPnzrX9u3Xr1poxY4bWrl2rHj16SJJ+/vlnbdiwQY8++mjlpARQLTQM9tEjVzXTzG92a9Y3u9S/VbhCfD3MjgUAAABUG2W6Jr1x48ZlezCLRQcPHrzoUJWJa9KBypVXYNWQ137U7oR03di5oV68qb3ZkQAAAABTOdJDyzSSHhsbWyHBANR87q4umjG8nW6c/5M+3XREN3ZuqEub1DE7FgAAAFAtlGuddElKSkpSUlJSRWYBUEN0jgrWrd0aSZKeXrZduflWkxMBAAAA1YNDJT0lJUWjR49WaGiowsPDFR4ertDQUD344INKSUmppIgAqqMnB7RUqJ+H9p/I0FvrD5gdBwAAAKgWyjTdXZKSk5PVo0cPHT16VLfddptatWolSdq5c6cWLlyo1atX66efflJwcHClhQVQfQT6uOvpQa01dslWvfb9fg1pH6GoOr5mxwIAAACcWpluHCdJY8eO1erVq/Xdd98pPDzc7msJCQm6+uqr1a9fP73yyiuVErSicOM4oOoYhqF/vPOLNuw/pSuah2nRyK6yWCxmxwIAAACqlCM9tMzT3ZctW6YXX3yxSEGXpHr16un5558vdv10ALWXxWLR9OvaysPVRev3ntRXfx43OxIAAADg1Mpc0o8fP642bdqU+PW2bdsqISGhQkIBqDmahPnpX31jJEnPfLVTadl5JicCAAAAnFeZS3poaKji4uJK/HpsbKxCQkIqIhOAGuaBPjFqEuqrk+k5enHlHrPjAAAAAE6rzCV9wIABmjhxonJzc4t8LScnR5MmTdI111xToeEA1Ayebq56dlhbSdIHPx/SH4dTzA0EAAAAOKky3zjuyJEj6tKlizw9PTV69Gi1bNlShmFo165deuONN5STk6Pff/9dkZGRlZ35onDjOMA8jyzZqqVbjqpNRICWj75Mbq4OrQIJAAAAVEuO9NAyl3Tp7JT2f/3rX/r2229VeJjFYtFVV12l119/XU2bNr245FWAkg6YJykjR1e+uFZp2fmaNLi17rm8sdmRAAAAgEpXaSW90OnTp7Vv3z5JUtOmTavVteiUdMBcH/0Sr6eWbpOvh6u+e7S36gd6mx0JAAAAqFSVsgTb+YKDg9WtWzd169atWhV0AOa7pWukOjUKUmZugaZ9sdPsOAAAAIBT4YJQAFXKxcWiGcPbydXFohU7ErR6V6LZkQAAAACnQUkHUOVa1Q/QveeuR5+8fIeycvNNTgQAAAA4B0o6AFOM6d9MDYK8dTTljF5dvc/sOAAAAIBToKQDMIWPh5umDW0jSXrnh1jtTkgzOREAAABgPko6ANP0bx2uAW3ClW81NHHpdlmtDi82AQAAANQolHQAppo6tI18PVy16dBpLfn9sNlxAAAAAFNR0gGYqn6gtx65qrkk6bn/7VZSRo7JiQAAAADzUNIBmO6untFqXT9AqWfyNPPrXWbHAQAAAExDSQdgOjdXF828vp0sFunzLUf10/4ksyMBAAAApqCkA3AKHSKD9I/uUZKkp5dtV05+gcmJAAAAgKpHSQfgNB6/poXC/D11MClT89ceNDsOAAAAUOUo6QCcRoCXuyYNbi1Jmrd2v2KTMk1OBAAAAFQtSjoApzLkkvrq1SxUuflWTVq2XYbB2ukAAACoPSjpAJyKxWLRs8PaysPNRT/uT9IXfxwzOxIAAABQZSjpAJxOVB1fPdS3qSRp+lc7lZqVZ3IiAAAAoGpQ0gE4pVG9mygmzFdJGbl6fuVus+MAAAAAVYKSDsApebq5asbwdpKkj36N1+b40yYnAgAAACofJR2A07q0SR3d0KmhDEN66vNtyi+wmh0JAAAAqFSUdABO7amBLRXk467dCel6b0Oc2XEAAACASkVJB+DU6vh5asK1LSVJL6/aq6MpZ0xOBAAAAFQeSjoAp3dT50h1jQ7WmbwCTf1ih9lxAAAAgEpDSQfg9FxcLJoxvJ3cXCxatTNR3+5IMDsSAAAAUCko6QCqhebh/vrnFU0kSVO/2KHMnHyTEwEAAAAVj5IOoNp4+Mpmigzx1rHUbM35bq/ZcQAAAIAKR0kHUG14e7jqmaFtJUnvbojTzmNpJicCAAAAKhYlHUC10rdlXQ1sV08FVkNPLd0mq9UwOxIAAABQYSjpAKqdKUPayM/TTVsPp+ijX+PNjgMAAABUGEo6gGonPMBLj13dXJI0e8VunUjPNjkRAAAAUDEo6QCqpdt7RKtdg0ClZ+drxte7zI4DAAAAVAhKOoBqydXFopnD28nFIi3fekw/7DtpdiQAAADgolHSAVRb7RoG6o4e0ZKkScu2KzuvwNxAAAAAwEWipAOo1h69urnCAzwVdypLb6w9YHYcAAAA4KJQ0gFUa/5e7poypI0kaf7aAzpwMsPkRAAAAED5UdIBVHvXtq2nPi3ClFtg1dNLt8swWDsdAAAA1RMlHUC1Z7FYNP26tvJyd9HGg6e0dMtRsyMBAAAA5UJJB1AjRIb46OF+zSRJM77epZSsXJMTAQAAAI6jpAOoMe69vIma1fXTqcxczV6x2+w4AAAAgMMo6QBqDA83F80Y3k6S9N9fD+v3uGSTEwEAAACOoaQDqFG6NQ7RzV0aSpImLt2uvAKryYkAAACAsqOkA6hxJlzbSiG+HtqTmK53fow1Ow4AAABQZpR0ADVOsK+HnhrYSpI057u9OpycZXIiAAAAoGwo6QBqpBs6NVD3xiHKzrNqyhc7WDsdAAAA1QIlHUCNZLFYNGN4O7m7WvT97hNauuUo16cDAADA6VmMWja8lJaWpsDAQKWmpiogIMDsOAAq2Uvf7tFr3++XJLm5WNSojo+ahPqqSZifGof6qkmorxqH+SrMz1MWi8XktAAAAKiJHOmhblWUCQBMMbpvUx04maHvd59Qdp5VB09m6uDJTGnXCbv9/D3d1DjsXGkP9VOTMN+zJT7MVz4e/KcSAAAAVYORdAC1gtVqKCEtW7FJmTp4MkMHk86W9dikTB05nSVrKf8lrBfgdV5p9zs3Eu+rBkHecnPlqiEAAACUzpEe6hQlfd68eXrhhReUkJCg9u3b67XXXlO3bt2K3XfBggV6//33tX37dklS586dNXPmzBL3vxAlHcCFsvMKdDg5SwfOlfaDJzPO/m9SppIzc0s8zt3Voqg6f424nz+Nvo6vB9PnAQAAIKmaTXdfsmSJxo0bp/nz56t79+6aM2eOBgwYoD179qhu3bpF9l+7dq1uvfVW9ezZU15eXpo9e7auvvpq7dixQw0aNDDhGQCo7rzcXdUs3F/Nwv2LfC0lK/e8UfcM2+h7bFKmcvKt2n8iQ/tPZBQ5LsDLTY3D/BQT+tcIfONz//b2cK2KpwUAAIBqyPSR9O7du6tr1656/fXXJUlWq1WRkZF66KGHNH78+L89vqCgQMHBwXr99dd1xx13/O3+jKQDqAhWq6FjqWdspf38KfTHUs+otP+yRgR6/XXjunPT6GPC/BQR5C1XF0bfAQAAappqM5Kem5urTZs2acKECbZtLi4u6t+/vzZu3Fimx8jKylJeXp5CQkKK/XpOTo5ycnJsn6elpV1caACQ5OJiUcNgHzUM9tEVzcPsvpadV6C4U5mKPZlpK+4Hz43Cp57J07HUbB1LzdaP+5PsjvNwc1F0HR+7kfeYsLM3sgvx9ajKpwcAAACTmFrSk5KSVFBQoPDwcLvt4eHh2r17d5ke48knn1RERIT69+9f7NdnzZqladOmXXRWACgrL3dXtawXoJb1iv6VNDkzV7FJGUWuf49LylJuvlV7EzO0NzFDUqLdcUE+7ueWjPOzXf/eOMxX0XV85eXO9HkAAICawvRr0i/Gc889p8WLF2vt2rXy8vIqdp8JEyZo3Lhxts/T0tIUGRlZVREBwE6Ir4dCfEPUOcp+9k+B1dCxlDM6UHjTuvNK/LHUbKVk5WlLfIq2xKfYHWexSBGB3kVuXNckzFcRgd5yYfo8AABAtWJqSQ8NDZWrq6sSE+1HjBITE1WvXr1Sj33xxRf13HPP6bvvvtMll1xS4n6enp7y9PSskLwAUFlcXSyKDPFRZIiP+rSw/9qZ3ALbzeoKR94PnPt3ena+jqac0dGUM/phn/30eU83F9vN6pqE/bX+e5NQXwX5MH0eAADAGZla0j08PNS5c2etXr1aw4YNk3T2xnGrV6/Wgw8+WOJxzz//vGbMmKGVK1eqS5cuVZQWAMzh7eGq1hEBah1hP33eMAydyswtdu33Q6fO3n1+d0K6diekF3nMEF+Pc9Pnz06bL5xGH1XHR55uTJ8HAAAwi+nT3ceNG6c777xTXbp0Ubdu3TRnzhxlZmZq5MiRkqQ77rhDDRo00KxZsyRJs2fP1uTJk/XRRx8pOjpaCQkJkiQ/Pz/5+fmZ9jwAoKpZLBaF+nkq1M9TXaPtp8/nF1h1NOXMuZvWnbf2+8lMJaRlKzkzV8mZudp06LTdcS4WqUGwt5qE2t+4rkmYr+oFeDF9HgAAoJKZXtJHjBihkydPavLkyUpISFCHDh20YsUK283k4uPj5eLiYtv/zTffVG5urm688Ua7x5kyZYqmTp1aldEBwGm5ubooqo6vour4qu8FX8vMyT9v+vy59d/P/TsjJ1+Hk8/ocPIZrdt70u44L3eXs4X9vKXjCq+BD/R2r7onBwAAUIOZvk56VWOddAAonmEYOpmRY1s67vxp9PGnspRvLfnXRaifh+3u841tN7HzVaMQX3m4uZR4HAAAQG3gSA+lpAMA/lZegVVHTp/568Z1hSPwJzN1Ij2nxONcLFJkiM/Za9/Pu3FdkzA/hQd4ymJh+jwAAKj5KOmloKQDQMXKyMk/N/qe8dfScUkZij2ZqczcghKP8/FwPe/u8/bT6P29mD4PAABqDkp6KSjpAFA1DMPQifScczevy7CbRh+fnKWCUqbPh/l7nnfjur+m0TcK8ZG7K9PnAQBA9UJJLwUlHQDMl5tv1eHTWX/duM52F/pMJWWUPH3e1cWiRrbp83/duC4mzFdh/kyfBwAAzsmRHmr63d0BALWPh5uLYsL8FBPmJync7mtp2XmKPZlZ7PrvZ/IKbHemv5Cfp9t50+d9z5V3P0WH+srPk193AACgemAkHQBQLRiGoYS0bMWezNSBpEzbdfCxSZk6nJylUmbPKzzAs5hr3/0UGewtN6bPAwCASsZ091JQ0gGg5snJL9Dh5Kxzd53PtN2F/uDJTJ3KzC3xODcXixrV8VGT8+48X1jmQ/08mD4PAAAqBNPdAQC1iqebq5rW9VfTuv5FvpaalWcbcS+cNn/gZIbiTmUqO8969nr4k5nSLvvj/L3cilz7XjiN3seDX58AAKByMJIOAKiVrFZDx89Nnz9od/O6DB1NOaPSfjvWD/Q6r7T/NQrfMNhHri6MvgMAAHtMdy8FJR0A8Hey8wp06FSWYpMyikyhP52VV+JxHq4u56bP+6pxmK9izi0d1yTUVyG+TJ8HAKC2Yro7AAAXwcvdVS3q+atFvaLT509n5tpG3M+fQh97KlO5+VbtP5Gh/ScyihwX4OVW5MZ1TcJ8FV3HV94erlXxtAAAQDXASDoAABWgwGroWMoZHUzKVOy5peMKS/zRlDOlHtsgyNvumvfCMh8R5M30eQAAagCmu5eCkg4AqGrZ563vfv7a7wdPZigtO7/E4zzcXBR97u7zhdPmz17/7qdgX48qfAYAAOBiMN0dAAAn4uXuqlb1A9Sqvv0vZcMwlJyZaxtxP38a/aFTWcrNt2pvYob2JhadPh/k437u7vN/3biuSZifour4yMud6fMAAFRXjKQDAOCECqyGjp4+owNJGbY70BeW+eOp2SUeZ7H8NX0+5oKl4yICveXC9HkAAKoc091LQUkHAFR3Wbn5502fP28a/clMpeeUPH3e083F/tr3c9PoY0L9FOjjXoXPAACA2oXp7gAA1GA+Hm5qExGoNhGBdtsNw1BSRq7dknFnl5DLUHxylnLyrdqdkK7dCelFHrOOr0exa783quMjTzemzwMAUFUYSQcAoBbIL7DqyOkzOpiUYbv+vXAafWJaTonHuVikhsE+ttH3pnX91CEySC3C/eXm6lKFzwAAgOqL6e6loKQDAGAvM+fs9Pni1n/PKGH6vI+Hq9o3DFKnqCB1ahSsjo2CFcId5wEAKBYlvRSUdAAAysYwDJ1Mz7EtGReblKFdx9O19XBKseW9caivOjY6W9o7NQpWi3r+rPMOAIAo6aWipAMAcHEKrIb2nUjX5kMp2hx/WpvjT+vgycwi+/l6uKp95LnSHhWkjpHBrO8OAKiVKOmloKQDAFDxUrJytSX+bGnfEp9S4mh7k1BfdTxX2js1ClbzcEbbAQA1HyW9FJR0AAAqX1lH2/083dQ+MtA2Rb5joyAF+TDaDgCoWSjppaCkAwBgjtOZudp6+K/SvjU+RZm5BUX2axLmayvsjLYDAGoCSnopKOkAADiHAquhvYnpZ0v7oRRtiT+tg0mMtgMAah5Keiko6QAAOK/Tmbnacvi0bZr8H4dLH20vvClds7qMtgMAnBclvRSUdAAAqo8Cq6E9Cem2KfJb4lMUW8xou7+n27k7yQepY1SwOkUGK9DH3YTEAAAURUkvBSUdAIDqLTkzV1vOlfbNh1L0x5EUZRUz2h5TONoedXbEvVldP7kw2g4AMAElvRSUdAAAapb8Aqv2JKZrc3yKthw6W97jTmUV2c/f000dGgWdXQKu0dl12xltBwBUBUp6KSjpAADUfKcycmzrtp+9tj1VZ/KKjrY3reunTufuIt8pKlhNwxhtBwBUPEp6KSjpAADUPvkFVu1OSD83Tf5seT9U3Gi7l5s6RP5V2jtEBinQm9F2AMDFoaSXgpIOAAAkKen80fZDp/XnkaKj7RaL1DTMz3YX+U6NghXDaDsAwEGU9FJQ0gEAQHEKR9sLS/vm+BTFJxcdbQ/wclOHc9e1d2oUrA6NghTgxWg7AKBklPRSUNIBAEBZJWXk2Ar75vjT+vNIirLzrHb7WCxSs7p+duu2NwlltB0A8BdKeiko6QAAoLzyCqzafdx+3faSRts7nlfaO0QGyZ/RdgCotSjppaCkAwCAinQyPcfuhnQljbY3r+uvTlFBtvLeJNSX0XYAqCUo6aWgpAMAgMp04Wj75vjTOpx8psh+gd7u6li4/FujYLWPDGS0HQBqKEp6KSjpAACgqp1Iz7bdSX7LoRT9cSRFOflFR9tbhPufG2k/O+IeE+Yri4XRdgCo7ijppaCkAwAAs+UVWLXreJrdTemOnGa0HQBqKkp6KSjpAADAGZ1Iy9bm+JRz17efXbf970bbO0Wdvbad0XYAcG6U9FJQ0gEAQHWQm39utL3wpnSHTutoStHR9iAfd3WMPDfaHhWs9pFB8vN0MyExAKAklPRSUNIBAEB1dXa0/a/S/ufRVOVeMNruYpGah/urU9S5JeAaBakxo+0AYCpKeiko6QAAoKbIzbdqp+3a9rPrthc32h7s4/7XFPlGZ0fbfRltB4AqQ0kvBSUdAADUZIlp2bbSvjk+RdtKGG1vUS/AVto7RQUruo4Po+0AUEko6aWgpAMAgNokN9+qHcdSbXeR33LotI6lZhfZL8TX4+y17VHB6tgoSO0bMtoOABWFkl4KSjoAAKjtElLPXdt+bsR9+9E05RYUHW1vWS9AnaL+WgIuitF2ACgXSnopKOkAAAD2cvILtOPY2Wvbt5wbcT9ezGh7HV8PdWwUdO769rPrtvt4MNoOAH+Hkl4KSjoAAMDfO556RpsPpZy7tv20dhQz2u7qYlHLev7nrms/O+LeKITRdgC4ECW9FJR0AAAAx+XkF2j70TRtOVfaNx9KUUJaSaPtf5X2Sxoy2g4AlPRSUNIBAAAqRllH21vV97dd196pUbAiQ7wZbQdQq1DSS0FJBwAAqBzZeWevbf+70fZQv7Oj7R0L121vGCRvD1cTEgNA1aCkl4KSDgAAUHWOpZyxFfbN8ae141iq8grs/+8no+0AajpKeiko6QAAAOY5O9qeajdNPjEtp8h+oX6etpH2To2CdAmj7QCqMUp6KSjpAAAAzsMwDB1Lzbat2b45PkU7ixltd3OxqFX9AHVqFKROUWdH2xsGM9oOoHqgpJeCkg4AAODcsvMKtP1oqt00+RPpxY+2n1/aL2kYKC93RtsBOB9Keiko6QAAANWLYRg6mnJGm+NTtPnQaW2JP60dx9KUby062t46IkCdzrspHaPtAJwBJb0UlHQAAIDqLzuvQNuOptpNkz9ZzGh7mP+50fZGweoUFax2DRhtB1D1KOmloKQDAADUPIZh6Mjps3eS3xJ/dor8zmJG291dLWpdP0Adz5X2To2C1CCI0XYAlYuSXgpKOgAAQO1wJvfcaHv86XMj7ilKyig62l7X3/PcSPvZEfe2jLYDqGCU9FJQ0gEAAGonh0bbIwLtpslHBHox2g6g3CjppaCkAwAAoFBZR9vDA86Ntp8bcW8TwWg7gLKjpJeCkg4AAICSnD/aXljadx5PU0Exo+1tIgLtpslHBHmblBqAs6Okl4KSDgAAAEecyS3Qn0dSzi4BF392CbikjNwi+9UL8LIt/cZoO4DzUdJLQUkHAADAxTAMQ4eTz422n/vYdTy9yGi7h6uLbd12RtuB2o2SXgpKOgAAACpaVm6+/jxSeG17irbEn9apzOJH2ztFBaljZLACvd3L983Kcf+68tzyrjw3yivvrfXKc0++ch1TjoRVeb/AqnrNy/ucqur1K0+8q1qHy83VpRxHVg1Keiko6QAAAKhshmEoPjnLVto3x5/W7oSio+0AKsaOaQPk6+lmdowSOdJDnfdZAAAAANWUxWJRVB1fRdXx1fCODSWdHW3/4/DZ0fbtR1OVk2916DHLO7ZWnqPK863K++eHqhozLN9zKudrXp7vVUX5yvtyl+uwKnzNXWrQEolOUdLnzZunF154QQkJCWrfvr1ee+01devWrcT9P/nkE02aNElxcXFq1qyZZs+erYEDB1ZhYgAAAMAxPh5u6hFTRz1i6pgdBYATM33S/pIlSzRu3DhNmTJFmzdvVvv27TVgwACdOHGi2P1/+ukn3Xrrrbrnnnu0ZcsWDRs2TMOGDdP27durODkAAAAAABXL9GvSu3fvrq5du+r111+XJFmtVkVGRuqhhx7S+PHji+w/YsQIZWZm6quvvrJtu/TSS9WhQwfNnz+/yP45OTnKycmxfZ6WlqbIyEiuSQcAAAAAVAlHrkk3dSQ9NzdXmzZtUv/+/W3bXFxc1L9/f23cuLHYYzZu3Gi3vyQNGDCgxP1nzZqlwMBA20dkZGTFPQEAAAAAACqQqSU9KSlJBQUFCg8Pt9seHh6uhISEYo9JSEhwaP8JEyYoNTXV9nH48OGKCQ8AAAAAQAVzihvHVSZPT095enqaHQMAAAAAgL9l6kh6aGioXF1dlZiYaLc9MTFR9erVK/aYevXqObQ/AAAAAADVhakl3cPDQ507d9bq1att26xWq1avXq0ePXoUe0yPHj3s9pekVatWlbg/AAAAAADVhenT3ceNG6c777xTXbp0Ubdu3TRnzhxlZmZq5MiRkqQ77rhDDRo00KxZsyRJY8aMUe/evfXSSy9p0KBBWrx4sX7//Xe99dZbZj4NAAAAAAAumuklfcSIETp58qQmT56shIQEdejQQStWrLDdHC4+Pl4uLn8N+Pfs2VMfffSRnn76aT311FNq1qyZli1bprZt25r1FAAAAAAAqBCmr5Ne1RxZnw4AAAAAgItVbdZJBwAAAAAAf6GkAwAAAADgJCjpAAAAAAA4CUo6AAAAAABOgpIOAAAAAICToKQDAAAAAOAkKOkAAAAAADgJSjoAAAAAAE7CzewAVc0wDElnF5MHAAAAAKCyFfbPwj5amlpX0tPT0yVJkZGRJicBAAAAANQm6enpCgwMLHUfi1GWKl+DWK1WHTt2TP7+/rJYLGbHKVVaWpoiIyN1+PBhBQQEmB0HxeAcVQ+cp+qB8+T8OEfVA+epeuA8OT/OUfVQXc6TYRhKT09XRESEXFxKv+q81o2ku7i4qGHDhmbHcEhAQIBT/8CBc1RdcJ6qB86T8+McVQ+cp+qB8+T8OEfVQ3U4T383gl6IG8cBAAAAAOAkKOkAAAAAADgJSroT8/T01JQpU+Tp6Wl2FJSAc1Q9cJ6qB86T8+McVQ+cp+qB8+T8OEfVQ008T7XuxnEAAAAAADgrRtIBAAAAAHASlHQAAAAAAJwEJR0AAAAAACdBSQcAAAAAwElQ0k00b948RUdHy8vLS927d9evv/5a6v6ffPKJWrZsKS8vL7Vr107ffPNNFSWt3Rw5TwsXLpTFYrH78PLyqsK0tc/69es1ZMgQRUREyGKxaNmyZX97zNq1a9WpUyd5enqqadOmWrhwYaXnrO0cPU9r164t8l6yWCxKSEiomsC10KxZs9S1a1f5+/urbt26GjZsmPbs2fO3x/G7qWqV5zzxu6nqvfnmm7rkkksUEBCggIAA9ejRQ//73/9KPYb3UtVy9BzxPnIOzz33nCwWi8aOHVvqftX9/URJN8mSJUs0btw4TZkyRZs3b1b79u01YMAAnThxotj9f/rpJ91666265557tGXLFg0bNkzDhg3T9u3bqzh57eLoeZKkgIAAHT9+3PZx6NChKkxc+2RmZqp9+/aaN29emfaPjY3VoEGD1LdvX23dulVjx47Vvffeq5UrV1Zy0trN0fNUaM+ePXbvp7p161ZSQqxbt06jR4/Wzz//rFWrVikvL09XX321MjMzSzyG301VrzznSeJ3U1Vr2LChnnvuOW3atEm///67rrzySl133XXasWNHsfvzXqp6jp4jifeR2X777Tf9+9//1iWXXFLqfjXi/WTAFN26dTNGjx5t+7ygoMCIiIgwZs2aVez+N998szFo0CC7bd27dzfuu+++Ss1Z2zl6nt577z0jMDCwitLhQpKMpUuXlrrPE088YbRp08Zu24gRI4wBAwZUYjKcryznac2aNYYk4/Tp01WSCUWdOHHCkGSsW7euxH343WS+spwnfjc5h+DgYOPtt98u9mu8l5xDaeeI95G50tPTjWbNmhmrVq0yevfubYwZM6bEfWvC+4mRdBPk5uZq06ZN6t+/v22bi4uL+vfvr40bNxZ7zMaNG+32l6QBAwaUuD8uXnnOkyRlZGQoKipKkZGRf/sXWVQ93kvVS4cOHVS/fn1dddVV2rBhg9lxapXU1FRJUkhISIn78H4yX1nOk8TvJjMVFBRo8eLFyszMVI8ePYrdh/eSucpyjiTeR2YaPXq0Bg0aVOR9Upya8H6ipJsgKSlJBQUFCg8Pt9seHh5e4vWWCQkJDu2Pi1ee89SiRQu9++67Wr58uT788ENZrVb17NlTR44cqYrIKIOS3ktpaWk6c+aMSalwofr162v+/Pn67LPP9NlnnykyMlJ9+vTR5s2bzY5WK1itVo0dO1aXXXaZ2rZtW+J+/G4yV1nPE7+bzLFt2zb5+fnJ09NT999/v5YuXarWrVsXuy/vJXM4co54H5ln8eLF2rx5s2bNmlWm/WvC+8nN7ABATdKjRw+7v8D27NlTrVq10r///W9Nnz7dxGRA9dKiRQu1aNHC9nnPnj114MABvfLKK/rggw9MTFY7jB49Wtu3b9ePP/5odhSUoqznid9N5mjRooW2bt2q1NRUffrpp7rzzju1bt26Eksgqp4j54j3kTkOHz6sMWPGaNWqVbXqRn2UdBOEhobK1dVViYmJdtsTExNVr169Yo+pV6+eQ/vj4pXnPF3I3d1dHTt21P79+ysjIsqhpPdSQECAvL29TUqFsujWrRulsQo8+OCD+uqrr7R+/Xo1bNiw1H353WQeR87ThfjdVDU8PDzUtGlTSVLnzp3122+/6dVXX9W///3vIvvyXjKHI+foQryPqsamTZt04sQJderUybatoKBA69ev1+uvv66cnBy5urraHVMT3k9MdzeBh4eHOnfurNWrV9u2Wa1WrV69usTrYHr06GG3vyStWrWq1OtmcHHKc54uVFBQoG3btql+/fqVFRMO4r1UfW3dupX3UiUyDEMPPvigli5dqu+//16NGzf+22N4P1W98pynC/G7yRxWq1U5OTnFfo33knMo7RxdiPdR1ejXr5+2bdumrVu32j66dOmi2267TVu3bi1S0KUa8n4y+851tdXixYsNT09PY+HChcbOnTuNUaNGGUFBQUZCQoJhGIZx++23G+PHj7ftv2HDBsPNzc148cUXjV27dhlTpkwx3N3djW3btpn1FGoFR8/TtGnTjJUrVxoHDhwwNm3aZNxyyy2Gl5eXsWPHDrOeQo2Xnp5ubNmyxdiyZYshyXj55ZeNLVu2GIcOHTIMwzDGjx9v3H777bb9Dx48aPj4+BiPP/64sWvXLmPevHmGq6ursWLFCrOeQq3g6Hl65ZVXjGXLlhn79u0ztm3bZowZM8ZwcXExvvvuO7OeQo33wAMPGIGBgcbatWuN48eP2z6ysrJs+/C7yXzlOU/8bqp648ePN9atW2fExsYaf/75pzF+/HjDYrEY3377rWEYvJecgaPniPeR87jw7u418f1ESTfRa6+9ZjRq1Mjw8PAwunXrZvz888+2r/Xu3du488477fb/+OOPjebNmxseHh5GmzZtjK+//rqKE9dOjpynsWPH2vYNDw83Bg4caGzevNmE1LVH4VJdF34Unpc777zT6N27d5FjOnToYHh4eBhNmjQx3nvvvSrPXds4ep5mz55txMTEGF5eXkZISIjRp08f4/vvvzcnfC1R3PmRZPf+4HeT+cpznvjdVPXuvvtuIyoqyvDw8DDCwsKMfv362cqfYfBecgaOniPeR87jwpJeE99PFsMwjKobtwcAAAAAACXhmnQAAAAAAJwEJR0AAAAAACdBSQcAAAAAwElQ0gEAAAAAcBKUdAAAAAAAnAQlHQAAAAAAJ0FJBwAAAADASVDSAQAAAABwEpR0AABQoaKjozVnzhyzYwAAUC1R0gEAqMbuuusuDRs2TJLUp08fjR07tsq+98KFCxUUFFRk+2+//aZRo0ZVWQ4AAGoSN7MDAAAA55KbmysPD49yHx8WFlaBaQAAqF0YSQcAoAa46667tG7dOr366quyWCyyWCyKi4uTJG3fvl3XXnut/Pz8FB4erttvv11JSUm2Y/v06aMHH3xQY8eOVWhoqAYMGCBJevnll9WuXTv5+voqMjJS//rXv5SRkSFJWrt2rUaOHKnU1FTb95s6daqkotPd4+Pjdd1118nPz08BAQG6+eablZiYaPv61KlT1aFDB33wwQeKjo5WYGCgbrnlFqWnp1fuiwYAgBOipAMAUAO8+uqr6tGjh/75z3/q+PHjOn78uCIjI5WSkqIrr7xSHTt21O+//64VK1YoMTFRN998s93xixYtkoeHhzZs2KD58+dLklxcXDR37lzt2LFDixYt0vfff68nnnhCktSzZ0/NmTNHAQEBtu/32GOPFclltVp13XXXKTk5WevWrdOqVat08OBBjRgxwm6/AwcOaNmyZfrqq6/01Vdfad26dXruuecq6dUCAMB5Md0dAIAaIDAwUB4eHvLx8VG9evVs219//XV17NhRM2fOtG179913FRkZqb1796p58+aSpGbNmun555+3e8zzr2+Pjo7Ws88+q/vvv19vvPGGPDw8FBgYKIvFYvf9LrR69Wpt27ZNsbGxioyMlCS9//77atOmjX777Td17dpV0tkyv3DhQvn7+0uSbr/9dq1evVozZsy4uBcGAIBqhpF0AABqsD/++ENr1qyRn5+f7aNly5aSzo5eF+rcuXORY7/77jv169dPDRo0kL+/v26//XadOnVKWVlZZf7+u3btUmRkpK2gS1Lr1q0VFBSkXbt22bZFR0fbCrok1a9fXydOnHDouQIAUBMwkg4AQA2WkZGhIUOGaPbs2UW+Vr9+fdu/fX197b4WFxenwYMH64EHHtCMGTMUEhKiH3/8Uffcc49yc3Pl4+NToTnd3d3tPrdYLLJarRX6PQAAqA4o6QAA1BAeHh4qKCiw29apUyd99tlnio6Olptb2X/tb9q0SVarVS+99JJcXM5OvPv444//9vtdqFWrVjp8+LAOHz5sG03fuXOnUlJS1Lp16zLnAQCgtmC6OwAANUR0dLR++eUXxcXFKSkpSVarVaNHj1ZycrJuvfVW/fbbbzpw4IBWrlypkSNHllqwmzZtqry8PL322ms6ePCgPvjgA9sN5c7/fhkZGVq9erWSkpKKnQbfv39/tWvXTrfddps2b96sX3/9VXfccYd69+6tLl26VPhrAABAdUdJBwCghnjsscfk6uqq1q1bKywsTPHx8YqIiNCGDRtUUFCgq6++Wu3atdPYsWMVFBRkGyEvTvv27fXyyy9r9uzZatu2rf7zn/9o1qxZdvv07NlT999/v0aMGKGwsLAiN56Tzk5bX758uYKDg3XFFVeof//+atKkiZYsWVLhzx8AgJrAYhiGYXYIAAAAAADASDoAAAAAAE6Dkg4AAAAAgJOgpAMAAAAA4CQo6QAAAAAAOAlKOgAAAAAAToKSDgAAAACAk6CkAwAAAADgJCjpAAAAAAA4CUo6AAAAAABOgpIOAAAAAICToKQDAAAAAOAk/h+64EC/teLqogAAAABJRU5ErkJggg==\n", 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932 |
"text/plain": [ |
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933 |
"<Figure size 1200x600 with 1 Axes>" |
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934 |
] |
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935 |
}, |
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936 |
"metadata": {}, |
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937 |
"output_type": "display_data" |
|
|
938 |
}, |
|
|
939 |
{ |
|
|
940 |
"data": { |
|
|
941 |
"text/plain": [ |
|
|
942 |
"0.977113000417064" |
|
|
943 |
] |
|
|
944 |
}, |
|
|
945 |
"execution_count": 189, |
|
|
946 |
"metadata": {}, |
|
|
947 |
"output_type": "execute_result" |
|
|
948 |
} |
|
|
949 |
], |
|
|
950 |
"source": [ |
|
|
951 |
"# create empty array for callback to store evaluations of the objective function\n", |
|
|
952 |
"objective_func_vals = []\n", |
|
|
953 |
"plt.rcParams[\"figure.figsize\"] = (12, 6)\n", |
|
|
954 |
"\n", |
|
|
955 |
"# fit to data\n", |
|
|
956 |
"regressor.fit(X, y)\n", |
|
|
957 |
"\n", |
|
|
958 |
"# return to default figsize\n", |
|
|
959 |
"plt.rcParams[\"figure.figsize\"] = (6, 4)\n", |
|
|
960 |
"\n", |
|
|
961 |
"# score the result\n", |
|
|
962 |
"regressor.score(X, y)" |
|
|
963 |
] |
|
|
964 |
}, |
|
|
965 |
{ |
|
|
966 |
"cell_type": "code", |
|
|
967 |
"execution_count": 190, |
|
|
968 |
"id": "diverse-conservative", |
|
|
969 |
"metadata": {}, |
|
|
970 |
"outputs": [ |
|
|
971 |
{ |
|
|
972 |
"data": { |
|
|
973 |
"image/png": 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\n", |
|
|
974 |
"text/plain": [ |
|
|
975 |
"<Figure size 600x400 with 1 Axes>" |
|
|
976 |
] |
|
|
977 |
}, |
|
|
978 |
"metadata": {}, |
|
|
979 |
"output_type": "display_data" |
|
|
980 |
} |
|
|
981 |
], |
|
|
982 |
"source": [ |
|
|
983 |
"# plot target function\n", |
|
|
984 |
"plt.plot(X_, f(X_), \"r--\")\n", |
|
|
985 |
"\n", |
|
|
986 |
"# plot data\n", |
|
|
987 |
"plt.plot(X, y, \"bo\")\n", |
|
|
988 |
"\n", |
|
|
989 |
"# plot fitted line\n", |
|
|
990 |
"y_ = regressor.predict(X_)\n", |
|
|
991 |
"plt.plot(X_, y_, \"g-\")\n", |
|
|
992 |
"plt.show()" |
|
|
993 |
] |
|
|
994 |
}, |
|
|
995 |
{ |
|
|
996 |
"cell_type": "markdown", |
|
|
997 |
"id": "false-india", |
|
|
998 |
"metadata": {}, |
|
|
999 |
"source": [ |
|
|
1000 |
"Similarly to the classification models, we can obtain an array of trained weights by querying a corresponding property of the model. In this model we have only one parameter defined as `param_y` above." |
|
|
1001 |
] |
|
|
1002 |
}, |
|
|
1003 |
{ |
|
|
1004 |
"cell_type": "code", |
|
|
1005 |
"execution_count": 191, |
|
|
1006 |
"id": "terminal-turner", |
|
|
1007 |
"metadata": {}, |
|
|
1008 |
"outputs": [ |
|
|
1009 |
{ |
|
|
1010 |
"data": { |
|
|
1011 |
"text/plain": [ |
|
|
1012 |
"array([-1.58549121])" |
|
|
1013 |
] |
|
|
1014 |
}, |
|
|
1015 |
"execution_count": 191, |
|
|
1016 |
"metadata": {}, |
|
|
1017 |
"output_type": "execute_result" |
|
|
1018 |
} |
|
|
1019 |
], |
|
|
1020 |
"source": [ |
|
|
1021 |
"regressor.weights" |
|
|
1022 |
] |
|
|
1023 |
}, |
|
|
1024 |
{ |
|
|
1025 |
"cell_type": "markdown", |
|
|
1026 |
"id": "offensive-legislation", |
|
|
1027 |
"metadata": {}, |
|
|
1028 |
"source": [ |
|
|
1029 |
"### Regression with the Variational Quantum Regressor (`VQR`)\n", |
|
|
1030 |
"\n", |
|
|
1031 |
"Similar to the `VQC` for classification, the `VQR` is a special variant of the `NeuralNetworkRegressor` with a `EstimatorQNN`. By default it considers the `L2Loss` function to minimize the mean squared error between predictions and targets." |
|
|
1032 |
] |
|
|
1033 |
}, |
|
|
1034 |
{ |
|
|
1035 |
"cell_type": "code", |
|
|
1036 |
"execution_count": 192, |
|
|
1037 |
"id": "offensive-entry", |
|
|
1038 |
"metadata": {}, |
|
|
1039 |
"outputs": [], |
|
|
1040 |
"source": [ |
|
|
1041 |
"vqr = VQR(\n", |
|
|
1042 |
" feature_map=feature_map,\n", |
|
|
1043 |
" ansatz=ansatz,\n", |
|
|
1044 |
" optimizer=L_BFGS_B(maxiter=5),\n", |
|
|
1045 |
" callback=callback_graph,\n", |
|
|
1046 |
")" |
|
|
1047 |
] |
|
|
1048 |
}, |
|
|
1049 |
{ |
|
|
1050 |
"cell_type": "code", |
|
|
1051 |
"execution_count": 193, |
|
|
1052 |
"id": "cooperative-helmet", |
|
|
1053 |
"metadata": {}, |
|
|
1054 |
"outputs": [ |
|
|
1055 |
{ |
|
|
1056 |
"data": { |
|
|
1057 |
"image/png": 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\n", |
|
|
1058 |
"text/plain": [ |
|
|
1059 |
"<Figure size 1200x600 with 1 Axes>" |
|
|
1060 |
] |
|
|
1061 |
}, |
|
|
1062 |
"metadata": {}, |
|
|
1063 |
"output_type": "display_data" |
|
|
1064 |
}, |
|
|
1065 |
{ |
|
|
1066 |
"data": { |
|
|
1067 |
"text/plain": [ |
|
|
1068 |
"0.9771130005061327" |
|
|
1069 |
] |
|
|
1070 |
}, |
|
|
1071 |
"execution_count": 193, |
|
|
1072 |
"metadata": {}, |
|
|
1073 |
"output_type": "execute_result" |
|
|
1074 |
} |
|
|
1075 |
], |
|
|
1076 |
"source": [ |
|
|
1077 |
"# create empty array for callback to store evaluations of the objective function\n", |
|
|
1078 |
"objective_func_vals = []\n", |
|
|
1079 |
"plt.rcParams[\"figure.figsize\"] = (12, 6)\n", |
|
|
1080 |
"\n", |
|
|
1081 |
"# fit regressor\n", |
|
|
1082 |
"vqr.fit(X, y)\n", |
|
|
1083 |
"\n", |
|
|
1084 |
"# return to default figsize\n", |
|
|
1085 |
"plt.rcParams[\"figure.figsize\"] = (6, 4)\n", |
|
|
1086 |
"\n", |
|
|
1087 |
"# score result\n", |
|
|
1088 |
"vqr.score(X, y)" |
|
|
1089 |
] |
|
|
1090 |
}, |
|
|
1091 |
{ |
|
|
1092 |
"cell_type": "code", |
|
|
1093 |
"execution_count": 194, |
|
|
1094 |
"id": "genetic-cambridge", |
|
|
1095 |
"metadata": {}, |
|
|
1096 |
"outputs": [ |
|
|
1097 |
{ |
|
|
1098 |
"data": { |
|
|
1099 |
"image/png": 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