{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "a48e9306-f459-4d8a-8608-9bd71a7600ae", "metadata": {}, "source": [ "# 6. Exploring advanced features\n", "\n", "This notebook is targeted at advanced users that want to access MOA objects directly using CapyMOA's Python API. \n", "\n", "In this notebook, we include:\n", "* Examples on how to use any MOA classifier or regressor from CapyMOA.\n", "* An example of how preprocessing (from MOA) can be used.\n", "* Comparing a sklearn model to a MOA model.\n", "* A variation of **Tutorial 5**: `Creating a new classifier in CapyMOA` which uses MOA learners, thus accessing MOA (Java) objects directly.\n", "* How to log experiments using TensorBoard alongside the PyTorch API. This extends **Tutorial 3**: `Using Pytorch with CapyMOA`.\n", "* Creating a synthetic stream with concept drifts using the MOA CLI directly.\n", "* An example utilising a multi-threaded ensemble.\n", "\n", "---\n", "\n", "*More information about CapyMOA can be found at* https://www.capymoa.org.\n", "\n", "**last update on 28/11/2025**" ] }, { "cell_type": "markdown", "id": "d2bb536e-4716-48fe-bf9b-05455b9e5a85", "metadata": {}, "source": [ "## 6.1 Using any MOA learner\n", "\n", "* **CapyMOA gives you access to any MOA classifier or regressor**.\n", "\n", "* For some MOA learners, there are corresponding Python objects (such as the `HoeffdingTree` or `AdaptiveRandomForestClassifier`). However, MOA has over a hundred learners, and more are added constantly.\n", "\n", "* To allow advanced users to access **any** MOA learner from CapyMOA, we included the `MOAClassifier` and `MOARegressor` generic wrappers." ] }, { "cell_type": "code", "execution_count": 1, "id": "ded154ef", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:12.975145Z", "iopub.status.busy": "2024-09-23T00:29:12.974567Z", "iopub.status.idle": "2024-09-23T00:29:12.994851Z", "shell.execute_reply": "2024-09-23T00:29:12.993070Z" }, "nbsphinx": "hidden" }, "outputs": [], "source": [ "# This cell is hidden on capymoa.org. See docs/contributing/docs.rst\n", "from util.nbmock import mock_datasets, is_nb_fast\n", "\n", "if is_nb_fast():\n", " mock_datasets()" ] }, { "cell_type": "code", "execution_count": 2, "id": "3d1a9e23-a272-4c01-ab9b-e7f3ec5f7395", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:13.001384Z", "iopub.status.busy": "2024-09-23T00:29:13.000816Z", "iopub.status.idle": "2024-09-23T00:29:15.524383Z", "shell.execute_reply": "2024-09-23T00:29:15.523734Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cumulative accuracy = 83.38629943502825, wall-clock time: 0.7772889137268066\n" ] }, { "data": { "text/html": [ "
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instancesaccuracykappakappa_tkappa_mf1_scoref1_score_0f1_score_1precisionprecision_0precision_1recallrecall_0recall_1
0500.086.071.762808-9.37500068.88888985.88608284.58149887.17948785.93939485.33333386.54545585.83283683.84279587.822878
11000.089.278.45687428.94736878.98832789.44118989.28571489.11290389.40829494.14225984.67433089.47410784.90566094.042553
21500.095.886.82757966.12903283.06451693.43570189.44723697.37827794.26338591.75257796.77419492.62242687.25490297.989950
32000.077.054.896301-47.43589741.32653178.79401575.47974478.34275078.23256064.83516591.62995679.36358890.30612268.421053
42500.086.271.98310925.00000068.63636485.99185284.28246087.70053586.00968584.47488687.54448485.97402684.09090987.857143
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8744000.077.435.265811-32.94117628.48101374.11711944.33497585.82183287.582418100.00000075.16483564.24050628.481013100.000000
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" ], "text/plain": [ " instances accuracy kappa kappa_t kappa_m f1_score \\\n", "0 500.0 86.0 71.762808 -9.375000 68.888889 85.886082 \n", "1 1000.0 89.2 78.456874 28.947368 78.988327 89.441189 \n", "2 1500.0 95.8 86.827579 66.129032 83.064516 93.435701 \n", "3 2000.0 77.0 54.896301 -47.435897 41.326531 78.794015 \n", "4 2500.0 86.2 71.983109 25.000000 68.636364 85.991852 \n", ".. ... ... ... ... ... ... \n", "86 43500.0 84.4 66.158171 20.408163 62.679426 85.193622 \n", "87 44000.0 77.4 35.265811 -32.941176 28.481013 74.117119 \n", "88 44500.0 72.0 39.008452 -105.882353 36.073059 74.346872 \n", "89 45000.0 77.6 52.642706 -77.777778 45.365854 76.539541 \n", "90 45312.0 76.4 52.842253 -38.823529 47.555556 76.613251 \n", "\n", " f1_score_0 f1_score_1 precision precision_0 precision_1 recall \\\n", "0 84.581498 87.179487 85.939394 85.333333 86.545455 85.832836 \n", "1 89.285714 89.112903 89.408294 94.142259 84.674330 89.474107 \n", "2 89.447236 97.378277 94.263385 91.752577 96.774194 92.622426 \n", "3 75.479744 78.342750 78.232560 64.835165 91.629956 79.363588 \n", "4 84.282460 87.700535 86.009685 84.474886 87.544484 85.974026 \n", ".. ... ... ... ... ... ... \n", "86 77.058824 88.181818 89.430894 100.000000 78.861789 81.339713 \n", "87 44.334975 85.821832 87.582418 100.000000 75.164835 64.240506 \n", "88 53.947368 79.885057 81.729270 96.470588 66.987952 68.187653 \n", "89 70.526316 81.935484 77.362637 76.571429 78.153846 75.733774 \n", "90 75.105485 77.566540 76.446493 70.634921 82.258065 76.780738 \n", "\n", " recall_0 recall_1 \n", "0 83.842795 87.822878 \n", "1 84.905660 94.042553 \n", "2 87.254902 97.989950 \n", "3 90.306122 68.421053 \n", "4 84.090909 87.857143 \n", ".. ... ... \n", "86 62.679426 100.000000 \n", "87 28.481013 100.000000 \n", "88 37.442922 98.932384 \n", "89 65.365854 86.101695 \n", "90 80.180180 73.381295 \n", "\n", "[91 rows x 14 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.base import MOAClassifier\n", "from capymoa.datasets import Electricity\n", "\n", "# This is an import from MOA\n", "from moa.classifiers.trees import HoeffdingAdaptiveTree\n", "\n", "stream = Electricity()\n", "\n", "# Creates a wrapper around the HoeffdingAdaptiveTree, which then can be used as any other CapyMOA classifier\n", "HAT = MOAClassifier(schema=stream.get_schema(), moa_learner=HoeffdingAdaptiveTree)\n", "\n", "results_HAT = prequential_evaluation(stream=stream, learner=HAT, window_size=500)\n", "\n", "print(\n", " f\"Cumulative accuracy = {results_HAT['cumulative'].accuracy()}, wall-clock time: {results_HAT['wallclock']}\"\n", ")\n", "display(results_HAT[\"windowed\"].metrics_per_window())" ] }, { "cell_type": "markdown", "id": "3c102052-1a19-4f30-b3d1-f0163cab6af0", "metadata": {}, "source": [ "### 6.1.1 Checking the hyperparameters for the MOA CLI\n", "\n", "* MOA objects can be parametrized using the MOA CLI (Command Line Interface)\n", "* Sometimes you may not know the relevent parameters for a `moa_learner`, `moa_learner.cli_help()` presents all the hyperparameters available for the `moa_learner` object." ] }, { "cell_type": "code", "execution_count": 3, "id": "3fbca563-e87f-41f2-98f2-dcad2ab65fb6", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:15.526653Z", "iopub.status.busy": "2024-09-23T00:29:15.526425Z", "iopub.status.idle": "2024-09-23T00:29:15.536747Z", "shell.execute_reply": "2024-09-23T00:29:15.536082Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-l treeLearner (default: ARFHoeffdingTree -e 2000000 -g 50 -c 0.01)\n", "Random Forest Tree.\n", "-s ensembleSize (default: 100)\n", "The number of trees.\n", "-o mFeaturesMode (default: Percentage (M * (m / 100)))\n", "Defines how m, defined by mFeaturesPerTreeSize, is interpreted. M represents the total number of features.\n", "-m mFeaturesPerTreeSize (default: 60)\n", "Number of features allowed considered for each split. Negative values corresponds to M - m\n", "-a lambda (default: 6.0)\n", "The lambda parameter for bagging.\n", "-j numberOfJobs (default: 1)\n", "Total number of concurrent jobs used for processing (-1 = as much as possible, 0 = do not use multithreading)\n", "-x driftDetectionMethod (default: ADWINChangeDetector -a 1.0E-3)\n", "Change detector for drifts and its parameters\n", "-p warningDetectionMethod (default: ADWINChangeDetector -a 1.0E-2)\n", "Change detector for warnings (start training bkg learner)\n", "-w disableWeightedVote\n", "Should use weighted voting?\n", "-u disableDriftDetection\n", "Should use drift detection? If disabled then bkg learner is also disabled\n", "-q disableBackgroundLearner\n", "Should use bkg learner? If disabled then reset tree immediately.\n", "\n" ] } ], "source": [ "from moa.classifiers.meta import AdaptiveRandomForest\n", "\n", "arf = MOAClassifier(schema=stream.get_schema(), moa_learner=AdaptiveRandomForest)\n", "\n", "print(arf.cli_help())" ] }, { "attachments": {}, "cell_type": "markdown", "id": "55d070de-8697-4f98-a11b-eab4e3d5c281", "metadata": {}, "source": [ "## 6.2 Using preprocessing from MOA (filters)\n", "\n", "We are working on a more user friendly API for preprocessing, this example just shows how one can do that using MOA filters from CapyMOA.\n", "\n", "* Here we use `NormalisationFilter` filter from MOA to normalize instances in an online fashion.\n", "* MOA filter syntax wraps the whole stream, so we are always composing commands like `FilteredStream`.\n", "* We obtain the MOA CLI from the `rbf_100k` stream. Since it can be mapped to a MOA stream, it is possible to obtain it. Comment out the print statements below if you would like to inspect the actual creation strings (and perhaps try to copy and paste that into MOA)." ] }, { "cell_type": "code", "execution_count": 4, "id": "ae9bb646-e0d1-4de6-b5a1-cff0f0a1b172", "metadata": { "ExecuteTime": { "end_time": "2024-04-29T11:52:48.998749Z", "start_time": "2024-04-29T11:52:45.889095Z" }, "execution": { "iopub.execute_input": "2024-09-23T00:29:15.538820Z", "iopub.status.busy": "2024-09-23T00:29:15.538644Z", "iopub.status.idle": "2024-09-23T00:29:18.121257Z", "shell.execute_reply": "2024-09-23T00:29:18.120745Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-s (ArffFileStream -f data/electricity.arff) -f NormalisationFilter\n", "\tAccuracy with online normalisation: 80.53937146892656\n", "\tAccuracy without normalisation: 82.06656073446328\n" ] } ], "source": [ "from capymoa.stream import MOAStream\n", "from capymoa.classifier import OnlineBagging\n", "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.datasets import Electricity, get_download_dir\n", "\n", "from moa.streams import FilteredStream\n", "\n", "stream = Electricity()\n", "\n", "# If we are running with low resources then we use a smaller dataset\n", "elec_file = f\"electricity{'_tiny' if is_nb_fast() else ''}.arff\"\n", "cli = f\"-s (ArffFileStream -f {get_download_dir() / elec_file}) -f NormalisationFilter\"\n", "print(cli)\n", "\n", "# Create a FilterStream and use the NormalisationFilter\n", "rbf_stream_normalised = MOAStream(CLI=cli, moa_stream=FilteredStream())\n", "\n", "# print(f'MOA creation string for filtered version: {rbf_stream_normalised.moa_stream.getCLICreationString(rbf_stream_normalised.moa_stream.__class__)}')\n", "ob_learner_norm = OnlineBagging(\n", " schema=rbf_stream_normalised.get_schema(), ensemble_size=5\n", ")\n", "ob_learner = OnlineBagging(schema=stream.get_schema(), ensemble_size=5)\n", "\n", "ob_results_norm = prequential_evaluation(\n", " stream=rbf_stream_normalised, learner=ob_learner_norm\n", ")\n", "ob_results = prequential_evaluation(stream=stream, learner=ob_learner)\n", "\n", "print(\n", " f\"\\tAccuracy with online normalisation: {ob_results_norm['cumulative'].accuracy()}\"\n", ")\n", "print(f\"\\tAccuracy without normalisation: {ob_results['cumulative'].accuracy()}\")" ] }, { "cell_type": "markdown", "id": "f74c58fb-dd90-49f4-8b4f-81a9e36e47ff", "metadata": {}, "source": [ "## 6.3 Comparing a MOA and sklearn models\n", "\n", "* This example shows how simple it is to compare MOA and sklearn regressors. \n", "* We use wrappers for the sake of this example.\n", "* `SKClassifier` (and `SKRegressor`) are parametrised directly as part of the object initialisation.\n", "* `MOAClassifier` (and `MOARegressor`) are parametrised through a CLI (a separate parameter)." ] }, { "cell_type": "code", "execution_count": 5, "id": "afe7193c-5bab-4b46-8627-c74b28a3b7c5", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:18.123102Z", "iopub.status.busy": "2024-09-23T00:29:18.122818Z", "iopub.status.idle": "2024-09-23T00:29:20.988442Z", "shell.execute_reply": "2024-09-23T00:29:20.987622Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.base import SKClassifier, MOAClassifier\n", "from capymoa.datasets import CovtypeTiny\n", "from capymoa.evaluation import prequential_evaluation_multiple_learners\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "\n", "from sklearn.linear_model import SGDClassifier\n", "from moa.classifiers.trees import HoeffdingTree\n", "\n", "covt_tiny = CovtypeTiny()\n", "\n", "sk_sgd = SKClassifier(\n", " schema=covt_tiny.schema,\n", " sklearner=SGDClassifier(loss=\"log_loss\", penalty=\"l1\", alpha=0.001),\n", ")\n", "moa_ht = MOAClassifier(schema=covt_tiny.schema, moa_learner=HoeffdingTree, CLI=\"-g 50\")\n", "\n", "results = prequential_evaluation_multiple_learners(\n", " stream=covt_tiny, learners={\"sk_sgd\": sk_sgd, \"moa_ht\": moa_ht}, window_size=100\n", ")\n", "plot_windowed_results(results[\"sk_sgd\"], results[\"moa_ht\"], metric=\"accuracy\")" ] }, { "cell_type": "markdown", "id": "df198282-7a87-4e03-ba0b-b3de3ccf9163", "metadata": {}, "source": [ "## 6.4 Creating Python learners with MOA Objects\n", "\n", "* This follows the example from `05_new_learner` which shows how to create a custom online bagging implementation.\n", "* Here we also create an online bagging implementation, but the `base_learner` is a MOA class instead." ] }, { "cell_type": "code", "execution_count": 6, "id": "a0a0906a-c953-4d50-8be8-c1c94e3eac4d", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:20.990976Z", "iopub.status.busy": "2024-09-23T00:29:20.990635Z", "iopub.status.idle": "2024-09-23T00:29:21.000499Z", "shell.execute_reply": "2024-09-23T00:29:20.999858Z" } }, "outputs": [], "source": [ "from capymoa.base import Classifier, MOAClassifier\n", "from moa.classifiers.trees import HoeffdingTree\n", "from collections import Counter\n", "import numpy as np\n", "\n", "\n", "class CustomOnlineBagging(Classifier):\n", " def __init__(\n", " self,\n", " schema=None,\n", " random_seed=1,\n", " ensemble_size=5,\n", " moa_base_learner_class=None,\n", " CLI_base_learner=None,\n", " ):\n", " super().__init__(schema=schema, random_seed=random_seed)\n", "\n", " self.CLI_base_learner = CLI_base_learner\n", "\n", " self.ensemble_size = ensemble_size\n", " self.moa_base_learner_class = moa_base_learner_class\n", "\n", " # Default base learner if None is specified\n", " if self.moa_base_learner_class is None:\n", " self.moa_base_learner_class = HoeffdingTree\n", "\n", " self.ensemble = []\n", " # Create several instances for the base_learners\n", " for _ in range(self.ensemble_size):\n", " self.ensemble.append(\n", " MOAClassifier(\n", " schema=self.schema,\n", " moa_learner=self.moa_base_learner_class(),\n", " CLI=self.CLI_base_learner,\n", " )\n", " )\n", "\n", " def __str__(self):\n", " return \"CustomOnlineBagging\"\n", "\n", " def train(self, instance):\n", " for i in range(self.ensemble_size):\n", " for _ in range(np.random.poisson(1.0)):\n", " self.ensemble[i].train(instance)\n", "\n", " def predict(self, instance):\n", " predictions = []\n", " for i in range(self.ensemble_size):\n", " predictions.append(self.ensemble[i].predict(instance))\n", " majority_vote = Counter(predictions)\n", " prediction = majority_vote.most_common(1)[0][0]\n", " return prediction\n", "\n", " def predict_proba(self, instance):\n", " probabilities = []\n", " for i in range(self.ensemble_size):\n", " classifier_proba = self.ensemble[i].predict_proba(instance)\n", " classifier_proba = classifier_proba / np.sum(classifier_proba)\n", " probabilities.append(classifier_proba)\n", " avg_proba = np.mean(probabilities, axis=0)\n", " return avg_proba" ] }, { "cell_type": "markdown", "id": "c971ac60-0aa5-4295-be56-bcb6ee1ccb40", "metadata": {}, "source": [ "### 6.4.1 Testing the custom online bagging\n", "\n", "* We choose to use an HoeffdingAdaptiveTree from MOA as the base learner.\n", "* We also specify the CLI commands to configure the base learner." ] }, { "cell_type": "code", "execution_count": 7, "id": "0740765c-35c0-416a-99ca-f8e55f921032", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:21.003376Z", "iopub.status.busy": "2024-09-23T00:29:21.003149Z", "iopub.status.idle": "2024-09-23T00:29:26.177264Z", "shell.execute_reply": "2024-09-23T00:29:26.176754Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 86.01694915254238\n" ] } ], "source": [ "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.datasets import Electricity\n", "from moa.classifiers.trees import HoeffdingAdaptiveTree\n", "\n", "elec_stream = Electricity()\n", "\n", "# Creating a learner: using a hoeffding adaptive tree as the base learner with grace period of 50 (-g 50)\n", "NEW_OB = CustomOnlineBagging(\n", " schema=elec_stream.get_schema(),\n", " ensemble_size=5,\n", " moa_base_learner_class=HoeffdingAdaptiveTree,\n", " CLI_base_learner=\"-g 50\",\n", ")\n", "\n", "results_NEW_OB = prequential_evaluation(\n", " stream=elec_stream, learner=NEW_OB, window_size=4500\n", ")\n", "\n", "print(f\"Accuracy: {results_NEW_OB.cumulative.accuracy()}\")" ] }, { "cell_type": "markdown", "id": "62e3e70a-2422-4b3a-b2bf-b8f96a3efdeb", "metadata": {}, "source": [ "## 6.5 Using TensorBoard with PyTorch in CapyMOA\n", "\n", "* One can use TensorBoard to visualise logged data in an online fashion.\n", "* We go through all the steps below, including installing TensorBoard." ] }, { "cell_type": "markdown", "id": "8fda8006-e0e9-4547-a2c9-8fc43d16ca57", "metadata": {}, "source": [ "### 6.5.1 Install TensorBoard\n", "\n", "Clear any logs from previous runs.\n", "\n", "```sh\n", "rm ./notebooks/runs/*\n", "```" ] }, { "cell_type": "code", "execution_count": 8, "id": "f11baceb-2c77-4636-8e91-d19aadf7b3b3", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:26.178950Z", "iopub.status.busy": "2024-09-23T00:29:26.178803Z", "iopub.status.idle": "2024-09-23T00:29:27.705372Z", "shell.execute_reply": "2024-09-23T00:29:27.703057Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tensorboard in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (2.20.0)\n", "Requirement already satisfied: absl-py>=0.4 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (2.3.1)\n", "Requirement already satisfied: grpcio>=1.48.2 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (1.76.0)\n", "Requirement already satisfied: markdown>=2.6.8 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (3.10)\n", "Requirement already satisfied: numpy>=1.12.0 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (2.3.4)\n", "Requirement already satisfied: packaging in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (25.0)\n", "Requirement already satisfied: pillow in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (12.0.0)\n", "Requirement already satisfied: protobuf!=4.24.0,>=3.19.6 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (6.33.1)\n", "Requirement already satisfied: setuptools>=41.0.0 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (80.9.0)\n", "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (0.7.2)\n", "Requirement already satisfied: werkzeug>=1.0.1 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from tensorboard) (3.1.3)\n", "Requirement already satisfied: typing-extensions~=4.12 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from grpcio>=1.48.2->tensorboard) (4.15.0)\n", "Requirement already satisfied: MarkupSafe>=2.1.1 in /home/avelas/code/CapyMOA/.venv/lib/python3.13/site-packages (from werkzeug>=1.0.1->tensorboard) (3.0.3)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install tensorboard" ] }, { "cell_type": "markdown", "id": "b1e64bd6-b0c7-4296-aee7-a29985a9da21", "metadata": {}, "source": [ "### 6.5.2 PyTorchClassifier\n", "\n", "* We define `PyTorchClassifier` and `NeuralNetwork` classes similarly to those from **Tutorial 3**: `Using Pytorch with CapyMOA`." ] }, { "cell_type": "code", "execution_count": 9, "id": "ea9a4d94-7515-424c-a9fd-76c78ddf52d1", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:27.713429Z", "iopub.status.busy": "2024-09-23T00:29:27.712681Z", "iopub.status.idle": "2024-09-23T00:29:27.755315Z", "shell.execute_reply": "2024-09-23T00:29:27.754066Z" } }, "outputs": [], "source": [ "from capymoa.base import Classifier\n", "import torch\n", "from torch import nn\n", "\n", "torch.manual_seed(1)\n", "torch.use_deterministic_algorithms(True)\n", "\n", "# Get cpu device for training.\n", "device = \"cpu\"\n", "\n", "\n", "# Define model\n", "class NeuralNetwork(nn.Module):\n", " def __init__(self, input_size=0, number_of_classes=0):\n", " super().__init__()\n", " self.flatten = nn.Flatten()\n", " self.linear_relu_stack = nn.Sequential(\n", " nn.Linear(input_size, 512),\n", " nn.ReLU(),\n", " nn.Linear(512, 512),\n", " nn.ReLU(),\n", " nn.Linear(512, number_of_classes),\n", " )\n", "\n", " def forward(self, x):\n", " x = self.flatten(x)\n", " logits = self.linear_relu_stack(x)\n", " return logits\n", "\n", "\n", "class PyTorchClassifier(Classifier):\n", " def __init__(\n", " self,\n", " schema=None,\n", " random_seed=1,\n", " nn_model: nn.Module = None,\n", " optimiser=None,\n", " loss_fn=nn.CrossEntropyLoss(),\n", " device=(\"cpu\"),\n", " lr=1e-3,\n", " ):\n", " super().__init__(schema, random_seed)\n", " self.model = None\n", " self.optimiser = None\n", " self.loss_fn = loss_fn\n", " self.lr = lr\n", " self.device = device\n", "\n", " torch.manual_seed(random_seed)\n", "\n", " if nn_model is None:\n", " self.set_model(None)\n", " else:\n", " self.model = nn_model.to(device)\n", " if optimiser is None:\n", " if self.model is not None:\n", " self.optimiser = torch.optim.SGD(self.model.parameters(), lr=lr)\n", " else:\n", " self.optimiser = optimiser\n", "\n", " def __str__(self):\n", " return str(self.model)\n", "\n", " def cli_help(self):\n", " return str(\n", " 'schema=None, random_seed=1, nn_model: nn.Module = None, optimiser=None, loss_fn=nn.CrossEntropyLoss(), device=(\"cpu\"), lr=1e-3'\n", " )\n", "\n", " def set_model(self, instance):\n", " if self.schema is None:\n", " moa_instance = instance.java_instance.getData()\n", " self.model = NeuralNetwork(\n", " input_size=moa_instance.get_num_attributes(),\n", " number_of_classes=moa_instance.get_num_classes(),\n", " ).to(self.device)\n", " elif instance is not None:\n", " self.model = NeuralNetwork(\n", " input_size=self.schema.get_num_attributes(),\n", " number_of_classes=self.schema.get_num_classes(),\n", " ).to(self.device)\n", "\n", " def train(self, instance):\n", " if self.model is None:\n", " self.set_model(instance)\n", "\n", " X = torch.tensor(instance.x, dtype=torch.float32)\n", " y = torch.tensor(instance.y_index, dtype=torch.long)\n", " # set the device and add a dimension to the tensor\n", " X, y = (\n", " torch.unsqueeze(X.to(self.device), 0),\n", " torch.unsqueeze(y.to(self.device), 0),\n", " )\n", "\n", " # Compute prediction error\n", " pred = self.model(X)\n", " loss = self.loss_fn(pred, y)\n", "\n", " # Backpropagation\n", " loss.backward()\n", " self.optimiser.step()\n", " self.optimiser.zero_grad()\n", "\n", " def predict(self, instance):\n", " return np.argmax(self.predict_proba(instance))\n", "\n", " def predict_proba(self, instance):\n", " if self.model is None:\n", " self.set_model(instance)\n", " X = torch.unsqueeze(\n", " torch.tensor(instance.x, dtype=torch.float32).to(self.device), 0\n", " )\n", " # turn off gradient collection\n", " with torch.no_grad():\n", " pred = np.asarray(self.model(X).numpy(), dtype=np.double)\n", " return pred" ] }, { "cell_type": "markdown", "id": "2b166ade-23b3-445c-a382-6f0cb6231d66", "metadata": {}, "source": [ "### 6.5.3 PyTorchClassifier + the test-then-train loop + TensorBoard\n", "\n", "* Here we use an instance loop to log relevant information to TensorBoard.\n", "* This information can be viewed while the processing is happening using TensorBoard." ] }, { "cell_type": "code", "execution_count": 10, "id": "9e93527d-26cb-4a0b-a4e4-2f3399724502", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:29:27.759393Z", "iopub.status.busy": "2024-09-23T00:29:27.759034Z", "iopub.status.idle": "2024-09-23T00:30:14.994165Z", "shell.execute_reply": "2024-09-23T00:30:14.993711Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed 10000 instances\n", "Processed 20000 instances\n", "Processed 30000 instances\n", "Processed 40000 instances\n" ] } ], "source": [ "from capymoa.evaluation import ClassificationEvaluator\n", "from capymoa.datasets import Electricity\n", "from torch.utils.tensorboard import SummaryWriter\n", "\n", "# Create a SummaryWriter instance.\n", "writer = SummaryWriter()\n", "# Opening a file again to start from the beginning\n", "stream = Electricity()\n", "\n", "# Creating the evaluator\n", "evaluator = ClassificationEvaluator(schema=stream.get_schema())\n", "\n", "# Creating a learner\n", "simple_pyTorch_classifier = PyTorchClassifier(\n", " schema=stream.get_schema(),\n", " nn_model=NeuralNetwork(\n", " input_size=stream.get_schema().get_num_attributes(),\n", " number_of_classes=stream.get_schema().get_num_classes(),\n", " ).to(device),\n", ")\n", "\n", "i = 0\n", "while stream.has_more_instances():\n", " i += 1\n", " instance = stream.next_instance()\n", "\n", " prediction = simple_pyTorch_classifier.predict(instance)\n", " evaluator.update(instance.y_index, prediction)\n", " simple_pyTorch_classifier.train(instance)\n", "\n", " if i % 1000 == 0:\n", " writer.add_scalar(\"accuracy\", evaluator.accuracy(), i)\n", "\n", " if i % 10000 == 0:\n", " print(f\"Processed {i} instances\")\n", "\n", "writer.add_scalar(\"accuracy\", evaluator.accuracy(), i)\n", "# Call flush() method to make sure that all pending events have been written to disk.\n", "writer.flush()\n", "\n", "# If you do not need the summary writer anymore, call close() method.\n", "writer.close()" ] }, { "cell_type": "markdown", "id": "9da96643-1900-41e8-96aa-6af460194ac6", "metadata": {}, "source": [ "### 6.5.4 Run TensorBoard\n", "\n", "Now, start TensorBoard, specifying the root log directory you used above. Argument `logdir` points to directory where TensorBoard will look to find event files that it can display. TensorBoard will recursively walk through the directory structure located at `logdir`, looking for `.*tfevents.*` files.\n", "\n", "```sh\n", "tensorboard --logdir=notebooks/runs\n", "```\n", "Go to the URL it provides.\n", "\n", "This dashboard shows how the accuracy changes with time. You can use it to also track training speed, learning rate, and other scalar values." ] }, { "cell_type": "markdown", "id": "38b1f9ce-c3a1-4944-8cb1-f2a84cd4ff25", "metadata": {}, "source": [ "## 6.6 Creating a synthetic stream with concept drifts from MOA\n", "\n", "* Here we demonstrate the level of API flexibility that is expected from experienced MOA users.\n", "* To use the API like this, the user must be familiar with how concept drifts are simulated in MOA.\n", "\n", "For example:\n", "* EvaluatePrequential \n", " * -l trees.HoeffdingAdaptiveTree \n", " * **-s (ConceptDriftStream -s generators.AgrawalGenerator -d (generators.AgrawalGenerator -f 2) -p 5000)**\n", " * -e (WindowClassificationPerformanceEvaluator **-w 100**)\n", " * **-i 10000**\n", " * **-f 100**" ] }, { "cell_type": "code", "execution_count": 11, "id": "7b79ac8e-d7ac-48fb-b983-22301d272364", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:30:14.997825Z", "iopub.status.busy": "2024-09-23T00:30:14.997552Z", "iopub.status.idle": "2024-09-23T00:30:15.375085Z", "shell.execute_reply": "2024-09-23T00:30:15.374632Z" } }, "outputs": [ { "data": { "image/png": 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9FmvXrkVLS4ukayuFDHKCIAiCIAiCIAgNsW/fPvA8j0WLFsX8/aJFizA2NoahoSEAwIoVK3DDDTcAAO644w78+te/xptvvinZIPd6vXjkkUfQ2toKALjxxhvxs5/9TPz9j3/8Y6xduxZf/vKXAQAtLS3YtWsXfv/730cY5FdccQX0ej18Ph/cbje+8IUv4K677hJ/f9xxx+G4444Tf77nnnvwt7/9DS+88AJuvPFGdHR0YOPGjdi2bRva29sBCJ75efPmiX+zYcMGdHV1YdOmTZg9ezYA4L777sP555+f9HPed999OOusswAAd955Jy666CJMTU2hoKAADz74IL7xjW/g6quvBgDcfffdeO2112C32yX1oVLIICcIgiAIgiAIYkZgMeqx62fLJL//Vxv24vHN3fDHCDfXcxyuXtqM286fL/necpEa5r548WLx/zmOQ21tbYRXORmFhYWiMQ4AdXV14t87HA50dnbiG9/4Bq677jrxPT6fb5ow3K9//Wucd9558Pv92L9/P2677Tb8x3/8B5599lkAgof8Jz/5CV588UX09/fD5/PB5XKhp6cHALBnzx4YDAaceOKJ4jXb2tpQXl4u/rx3717MmTMnIkf8lFNOkfQ5w/uprq4OAHDkyBE0NjZiz5494qFG+HXfeOMNSddWChnkBEEQBEEQBEHMCDiOkxw2DgBfPbUJj23uivk7Hjy+dmqTrOtJpa2tDRzHYffu3bj00kun/X737t0oLy9HdXU1AMBoNEb8nuM4BAIByfeL9ffsMIB5iP/3f/8Xp556asT79PrIQ4ba2lq0tbUBABYsWACbzYYrrrgC9957L9ra2vDd734XGzZswC9/+Uu0tbXBYrHgK1/5Cjwej+S2pkL45+Q4DgBk9VM6IFE3giAIgiAIgiCIGLRUWbFm5WLoOECv4yL+XbNyMZqrrGm5b2VlJc4//3z87ne/g8vlivjdwMAAnnrqKVx22WWiUZlOampqMHv2bBw4cABtbW0R/yXLr2YGO/sMW7ZswVVXXYVLL70Uxx57LGpra9Hd3S2+f8GCBfD5fPjoo4/E1/bv34+xsTHx5/nz5+PQoUMYHBwUX9u2bVvKn3PBggXTrqPGdZNBHnKCIAiCIAiCIIg4rGpvwMnNFXhuey/6xlyoL7fgsvaGtBnjjIceeghLlizBsmXLcO+990aUPZszZw7uu+++tN4/nJ/+9Ke4+eabUVpaiuXLl8PtdmP79u0YGxvDbbfdJr5vfHwcAwMDCAQC2LdvH372s59h/vz5Yi78vHnz8Ne//hUXX3wxOI7Dj370owgP9cKFC3Heeefh+uuvx8MPPwyj0YjvfOc7sFgs4uHD+eefj5aWFlx11VX4xS9+AZvNhh/+8IcAkNIBxU033YTrrrsO7e3tWLJkCZ577jns3LkTc+fOVXxNKZCHnCAIgiAIgiAIIgHNVVbcsXwhHrziBNyxfGHajXFAMF63b9+OuXPnYvXq1WhtbcX111+Pc845B1u3blVUg1wp1157Lf7whz/g8ccfx7HHHouzzjoLf/zjH6d5yK+++mrU1dWhvr4eV1xxBY4++mi8/PLLMBgEP/CvfvUrlJeXY8mSJbj44ouxbNmyiHxxAHjiiSdQU1ODM888E5deeimuu+46FBcXo6CgAIDgdf/zn/8Mh8OBk08+Gddee61YAo69Rwlf/epXcdddd+G73/0uTjzxRHR1deGqq65K6ZpS4Hi5BfFyjMnJSZSWlmJiYgIlJSVZa4fX68VLL72EFStWTMvRIAitQOOUyBVorBK5Ao1VIlfI17E6NTWFrq4utLS0pN2wItJDX18fGhoasHHjRpx77rkIBAKYnJxESUkJdDrBv7xlyxYsXboU+/fvjxCnS5Xzzz8ftbW1ePLJJ2P+PtH4kmqHUsg6QRAEQRAEQRAEoQneeOMN2O12HHvssejv78f3vvc9NDc348wzzxTf869//QvV1dVYsGAB9u/fj1tuuQWnn356Ssa40+nEI488gmXLlkGv1+OZZ57Bxo0bsWHDBjU+VlzIICcIgiAIgiAIgiA0gdfrxfe//30cOHAAxcXFWLJkCZ566qmIyA273Y6f/exn6OnpQVVVFc477zysXbs2pftyHIeXXnoJ9913H6amprBgwQKsX78e5513XqofKSFkkBMEQRAEQRAEQRCaYNmyZVi2LHGt+MsvvxzXX3+9GLKuBhaLBRs3blTtelIhg5wgCIIgCIIgNEjXsAPrwpS9V7c3oCUDYmIEQWQOMsgJgiAIgiAIQmOs296LO9fvBMdx4HkeHMfh9291Ys3KxVjV3pDt5uUcea5jTWQJNcYVlT0jCIIgCIIgCA3RNezAnet3IsAD/gAf8e8d63eie9iR7SbmDCzv2Ol0ZrklRD7CxlUqlQnIQ04QBEEQBEEQGmLd9l5wHAfE8L5xHIfntvfijuULs9Cy3EOv16OsrAxHjhwBABQWFgp9S+QsgUAAHo8HU1NTquaQy4HneTidThw5cgRlZWXQ6/WKr0UGOUEQBEEQBEFoiL4xV9xQWJ7n0TfmynCLcpva2loAEI1yIrfheR4ulwsWiyXrhytlZWXi+FIKGeQEQRAEQRAEoSHqyy0JPeT15ZYstCp34TgOdXV1mDVrFrxeb7abQ6SI1+vF22+/jTPPPDOlUPFUMRqNKXnGGWSQEwRBEARBEISGWN3egN+/1RnzdzzP4zISdVOEXq9XxYAisoter4fP50NBQUFWDXK1IFE3giAIgiAIgtAQLVVWrFm5GLqwaFwOgI4D1qxcjGYqfUYQeQN5yAmCIAiCIAhCY6xqb0CpxYjrn/w3AKC2tADPXPc5MsYJIs8gDzlBEARBEARBaBCLKRRebdTryBgniDyEDHKCIAiCIAiC0CCjDo/4/wOTU3GV1wmCyF3IICcIgiAIgiAIDTLuDCmCe3wBjDlJIZwg8g0yyAmCIAiCIAhCg4R7yAFgYGIqSy0hCCJdkEFOEARBEARBEBpk3BlpkA9OkkFOEPkGGeQEQRAEQRAEoUGiQ9QHyCAniLyDDHKCIAiCIAiC0CBjQQ95YVBtvZ9C1gki78iqQW6z2XDrrbeiqakJFosFS5YswbZt28TfX3XVVeA4LuK/5cuXZ7HFBEEQBEEQBJEZmEG+oLYYADBIBjlB5B2GbN782muvxaeffoonn3wSs2fPxp///Gecd9552LVrF+bMmQMAWL58OR5//HHxb8xmc7aaSxAEQRAEQRAZY8whhKwvqivBRz3jFLJOEHlI1jzkLpcL69evxwMPPIAzzzwTbW1t+MlPfoK2tjY8/PDD4vvMZjNqa2vF/8rLy7PVZIIgCIIgCILIGMxDvqiuBACJuhFEPpI1D7nP54Pf70dBQUHE6xaLBZs3bxZ/3rRpE2bNmoXy8nJ8/vOfx7333ovKysq413W73XC73eLPk5OTAACv1wuvN3u1G9m9s9kGgkgGjVMiV6CxSuQKNFYJpbi9fjg9fgDAvCoLAKB/wpW2sURjlcgVcmWsSm0fx/M8n+a2xGXJkiUwmUx4+umnUVNTg2eeeQZf//rX0dbWhj179uDZZ59FYWEhWlpa0NnZie9///soKirC1q1bodfrY17zJz/5CX76059Oe/3pp59GYWFhuj8SQRAEQRAEQaTMuBv48YcG6Dge97X7cdc2wY/2i1N8MMXeBhMEoSGcTif+3//7f5iYmEBJSUnc92XVIO/s7MQ111yDt99+G3q9HieeeCLmz5+Pf//739i9e/e09x84cACtra3YuHEjzj333JjXjOUhb2howPDwcMKOSDderxcbNmzA+eefD6PRmLV2EEQiaJwSuQKNVSJXoLFKKGV3vw1f/N1WVBWZ8O73zsJx97wOlzeAjbcuRVOl+k4mGqtErpArY3VychJVVVVJDfKsirq1trbirbfegsPhwOTkJOrq6nDZZZdh7ty5Md8/d+5cVFVVYf/+/XENcrPZHFP4zWg0auIL00o7CCIRNE6JXIHGKpEr0Fgl5GL3BAAA5YUmmEwm1JZa0DXswLDTh7ba9I0lGqtErqD1sSq1bZqoQ261WlFXV4exsTG8+uqruOSSS2K+r6+vDyMjI6irq8twCwmCIAiCIAgic4wGBd3KC00AgNoSQXdpgEqfEURekVUP+auvvgqe57FgwQLs378ft99+OxYuXIirr74adrsdP/3pT7Fy5UrU1tais7MT3/ve99DW1oZly5Zls9kEQRAEQRAEkVbGnIIgVLlV8LLVlgYNclJaJ4i8Iqse8omJCXzrW9/CwoULceWVV2Lp0qV49dVXYTQaodfrsXPnTnzxi1/E/Pnz8Y1vfAMnnXQS3nnnHapFThAEQRAEQeQ1Y45ID3kNecgJIi/Jqod89erVWL16dczfWSwWvPrqqxluEUEQBEEQBEFkH1aDvNzKQtYFhxTVIieI/EITOeQEQRAEQRAEQYQYZyHrhSxkndUiJ4OcIPIJMsgJgiAIgiAIQmOMRoWssxxy8pATRH5BBjlBEARBEARBaIzxOCrrR2xu+AN81tpFEIS6kEFOEARBEARBEBpjNCqHvKrIBB0H+AM8hu3ubDaNIAgVIYOcIAiCIAiCIDTGuCMyh9yg12FWMSmtE0S+kVWVdYIg1KFr2IF123vRN+ZCfbkFq9sb0FJlzXazNI0afUb9ThAEQaQDjy8Am9sHAKgIesgBoKa0AAOTUxiYnMJx2WocQRCqQgY5QeQ467b34s71O8FxHHieB8dx+P1bnVizcjFWtTdku3maRI0+o34nCIIg0sW4SwhX13FASYFRfL22xIwdIGE3gsgnKGSdIHKYrmEH7ly/EwFeyCkL//eO9TvRPezIdhM1hxp9Rv1OEARBpJOxYLh6WaEJOh0nvl5Hpc8IIu8gg5wgcph123vBcVzM33Ech+e292a4RdpHjT6jficIgiDSyVhQ0K2s0Bjxek1QaX2QDHKCyBvIICeIHKZvzAWej136hOd59I25Mtwi7aNGn1G/EwRBEOlkLFiDvKLQFPF6bakZADBAIesEkTeQQU4QOUx9uSWhp7a+3JLhFmkfNfqM+p0gCIJIJ2POUMh6OMxDTgY5QeQPZJATRA6zur0hoaf2MhIXm4YafUb9ThAEQaQTFrJeHhWyznLIByam4q5DBEHkFmSQE0QO01JlxZqVi6GLctbqOGDNysVophJc01Cjz9g1wp3knMxrEARBEEQ8xJB1a1TIetBD7vT4xbJoBEHkNlT2jCBynFXtDSgrNOK6J/4tvvbKrWdgfk1JFlulbVa1N6C80IRrn9gOACgyG/Cvm5bKMqRXtTdgxOHBf7/cAQBoqizEH68+hYxxgiAIImVGRVG3SIPcYtKjpMCAySkfBiemIkqiEQSRm5CHnCDyAH2Uu9ego0c7GRaTXvx/p8eH2WXy876dHr/4/1VFZjLGCYIgCFUYD+aQV1inG9y1pZRHThD5BO3aCSIPGJhwR/x8cNSZpZbkDiw/DwACPHBoXL4yes9IqN54+PUIgiAIIhXG4njIAaCWapETRF5BBjlB5AHRp+S9ZJAnheXnMXoU9Fn43zBFXIIgCIJIlXg55ABQWyKUPqNa5ASRH5BBThB5wMCE4N1lkes9I2SQJyPagFZmkIe86uNODwIBUrwlCIIgUoetUdEq60BI2I1C1gkiPyCDnCDygIFJIWT96NmlAJQZlzON6BBzuVEFTo8Pw/ZQqkCAB2xTpHhLEARBpIbPH8CEixnk8UPWB8kgJ4i8gAxygsgDWNjaKS0VAMgglwILB5wTFHOTG1XQG/SOl1qMKDILBStGKY+cIAiCSBFmjAPCGhNNbakQsk455ASRH5BBThB5QH8wZP3kZsEg7x11gucpfDoRLBzwuAZlUQXs/Y0VhSgLhhSSsBtBEASRKmwtKbUYYdBP36rXBEPWyUNOEPkB1SEniBzH5fFjMhgq3d5cDo4DHB4/Rh0eVBaZs9w67cI2PMfVl+GlTwbEQwyO45L8pUC4Qc5xQN+Ya5pQHEEQBEHIJVH+OBDKIR+2e+DxBWAykH8tnK5hB9Zt70XfmAv15Rasbm9ASx6XJc3E551pfZppyCAniByHiboUmvSotJpQW1KA/okp9Iw6ySBPADPIj60XPOQ2tw/jTi/KYyjaxoLlnDdUFMLm9gWvSUrrBEEQRGqMBg93461HFVYTTHodPP4AjtimUF9emMnmaZp123tx5/qd4DhOPGT//VudWLNyMVa1N2S7eaqTic870/o0G9CRGkHkOAPBHLLa0gJwHIeGCmFhpjzyxIw5BOO5rtSCmmAJGTl9Fu4hr2Ah6+QhJwiCIFJkPHhgHEvQDQA4jkNNMI98gPLIRbqGHbhz/U4EeMAf4CP+vWP9TnQPO7LdRFXJxOedaX2aLcggJ4gcZ2BSyB9nIWyNQYOcapHHx+MLwB70apcXGsU+U2qQlwU3TZRDThAEQaTKqCO+wjqDSp9NZ9323rhpZxzH4bntvRluUXrJxOedaX2aLcggJ4gcZ2BCKL0VbZCThzw+zPug44CSAqPsqIJAgBcPPBorClFhJYOcIAiCUIeQhzx2DjkQEnYjD3mIvjFXXEFbnufRN+bKcIvSSyY+70zr02xBBjlB5DhMZbWmlAxyqbBc77JCE3Q6TnZUwZDdDbcvAL2OQ11ZgbhpYmHwBEEQBKGUZDnkAFBXSkrr0dSXWxJ6c+vLLRluUXrJxOedaX2aLcggJ4gch52Os8W5QTQu6dQyHuJmJ2hIyz3EYO+bXVYAo14nbpqoDjlBEASRKiGV9fgGOfOQUy3yEKvbGxJ6cy/LMwGyTHzemdan2YIMcoLIcfqZhzwqZP3whAseXyBr7dIy0YI5sg3ykVC4evh1xskgJwiCIFJESsh6LXnIp9FSZcWalYsR7tDVccJ/a1YuRnOelelinzcctT8vu4cuykmer32aLcggJ4gcZ5CprAcN8qoiEyxGPXge6BujsPVYME92WZRBfnhc2iHGwdFIg7wsuGkapZB1giAIIkXYGpUoZJ1E3WKzqr0BD/2/E8Sfz5pfjTe+c3belue6+LjZ4KJ+VvvzrmpvwBvfORtlltAB0VPXnpq3fZoNyCAniBzGH+AxZA+KugVPyzmOozzyJIwHwwErrMLiUl1sRoFRhwAvGOXJCK9BLlwn5CGPF9pFEARBEFIYlxCyLnrIJ9y07kRRUhAyHE9vq8prL+6+QTvCv/0Lj6lNy+dtrrLCoA+ZjQEacqpCBjlB5DDDdjf8AR56HYeqIrP4emMllT5LRCiHXNjsyD3EYO9pqrBGXMcX4GELllMjCIIgCLkEAnwoZN0aP2R9VrFgkHv8AXFNIwTsU6F12DaV32tyx8BkxM9MfyAd2KZC1yaHj7qQQU4QOQwTc5lVbIY+LMGHPOSJGYsRDqjEIGd/U2DUw2LUAwDGKWydIAiCUMjklFf0PpZZ4nvITQYdqoqE31PYeiThB+OTU/m9JncM2CJ+TtfhjNvnhzsspY/2l+pCBjlB5DBMYZ0JujHIIE/MmGO6YE6DxNJnLo8fQzYhTYD1c/i1SGmdIAiCUAozqIrNBpgMibfpJOwWG4d75njI9wQNcqZlky5x2eh+pP2lupBBThA5DFuEa+Ma5FT6LBaxSspIPcToDQrllRQYUBpm0DNv+xgZ5ARBEIRC2PpUliBcnVFLpc9iEhmynu8eciFk/XMtlQDSJy4bbZBTSqS6kEFOEDkMC1Njp+SMcG8vib1MJ5WQdbHkWWVhxOvMuB+jXD6CIAhCIWwNqUgg6MZg0XGDZJBHYPfMDA/5kM2NYbsHHAec0lIBIJ0e8khDnzzk6kIGOUHkMCxkPdogry+3AADsbl9aBT5ylbEoUTcgzCAfSXyIEZ0/zgh5yKm/CYIgCGWMRZXlTASVPovNTBF1Y+HqTRWFmF0m7PvSlTbH+nFO8D7jTi8mXLTfUQtDthtAEIRyBiZih6wXGPWoLSnAwOQUekadYlkuAvD5A5gMLizhOeT15YKBbXP7MOHyxt0M9USVPGOUpzl/iyCIzNI17MC67b3oG3OhvtyC1e0NaMnj8kmENmAGuZR1mx3GD0y609qmXMPuTj1kPVPPfyr3YeHqC2qLw/Yg6QpZF65bU2KG2xfAsN2N3lEnSueUqnqfmTrvkkFOEDkMyyGPFnUDBA8uM8iPbyjLcMu0y3jwRJfjgFJLyCC3mPSYVWzGEZsbPaPOuAZ5bzwPefD9VH6GIHKfddt7cef6neA4DjzPg+M4/P6tTqxZuRir2huy3TwijxFzyAsl5JAzg3yC9GLCSVXULVPPf6r3YR7yhbUl4gFOunRsmCOjxGJEY4VQdrdn1IljVDTIZ/K8SyHrBJGj8DwvCrnUlU43yKWqhs80mAe7pMAIgz5yCpSSRx43ZD3Np9MEQWSGrmEH7ly/EwEe8Af4iH/vWL8T3cOObDeRyGPk5JCLIeuUQx5BuBE+OeWVpaWTqedfjft0iAZ5sehEmHB54Q+orx3E+rS4wJiWSj4zfd4lg5wgcpTJKR9cXj+A6TnkQGRONBGCKZDGCgdMtsjwPJ80h5w85ASR26zb3guO42L+juM4PLe9N8MtImYSYg65hJD1muDaPznlg8vjT2u7conwkHWvn4+on52MTD3/qd7HH+CxdzBokNeViBEVPI+05HZPBq9ZXGBIi0E+0+ddMsgJIkdh4eqlFiMKjPppv2+sFIQ3SAkzkpBgzvRwwGRRBUM2N9y+AHQcRAEVhqiyTjnkBJHT9I254nrUeJ5H3xiFBxPpI1SWM3nIerHZAKtJWP9J2C1EeMg6IHjJpZKp5z/V+3SPOOD2BVBg1KGxohBGvQ7FBUImcjocAyEPuSEtEZgzfd4lg5wgcpR4gm6MdJxg5gOJwgGT9Rl7fXaZBcaocPd0528RBJEZ6sstCT01rIoFQaQDOSHrHMeJXvJ+yiMXsUcZ5HLyyDP1/Ce6jpT7sPzxBTXF0OuE9rJ9SDrEZZmoW0maQtZn+rxLBjlB5CjxSp4xGisEVcr+CRc8MsK18p2QYM70zU5TsLb4wThh/uz1pqga5ML1jOL1qfY7QeQuq9sbEnpqLstzcSEiuyRao2LBDuUHyUMuwgzwoJ0qyyDP1PO/6qR6xEv1lnKfjv6QwjqjLI3isuEe8sbgHujQmAs+vzr7y5k+75JBThA5CgtPi+chryoywWLUI8ADh8bp5JwRKikzPRyQnfoeHnfBG2ORiZc/LlxPWAg9vgCclMtHEDlLS5UVa1YuRrizRscJ/61ZuRjNM6AED5EdeJ4XvZtSy5WGlNap9BkAeP0BMWd8VrHQN3JKn7HnXxflrFX7+Q8/+I+ea6Tchwm6LagtEV+rSKO4rM0d8pDXFBfApNfBFwiJC6eKOO+GvTaT5l0yyAkiR2EGeU0cDznHcRS2HgMWDhjL+1BdbIbZoEOAF4zyaHrj1CAHAItRD5NBmFIpbJ0gcptV7Q24+wtHiT8vP7oWb3zn7LwvvUNkF5vbB1/QbSql7BlAHvJowvPH68qYQS6v9Nmq9gY88rWTxJ85AK/ceoZqz38gwGPNKx0AgMtPbsDXPtck/u4f31oq6T7MIF8U5iEXy6+mJWQ95CHX6TjUVwgh5Grmka9qb8CXT5wj/nzGvOoZM++SQU4QOcpgkhxyIGQ4kkEeghnL5TEM8mSHGIk85BzHiSI8Yw4qfUYQuU6hKSSWed5RNXnvoSGyDzswLjTpY4q1xqKWcsgjYPnjZoNOzMOfVKA6bjUbxP/nIai1q8ULOw6jY8CG4gID7rxwIe655Bi0Vgvzi5SIRofbJ+5HwkPWy9OoZRNe9gxIn05RuHf/tNbKGTPvkkFOEDlKohrkjEaqRT4Nlp8XK2QdSLzIJDLIAVJaJ4h8wu4OpZ6ko4wQQUQTUliXFq4OADWsFvkkhawDIYO8uMAgqo7L9ZAD05/5jn5b6o2DkNa2dsMeAMB/ndUqRuudMa8aALB5/1DSa7ByZ9XFZlQWmcXXQ04B9fcg4WXPgPQZ5OHXk5NqkOuQQU4QOQoLT6tJ4CFvDIYUUS3yEIlC1oH4UQUujx9HbMKGhwxygsh/7GGbeDLIiUzA1qfyOAfGsWCH8oMq5fLmOuy5LTIbRG+uEsMu+pnfM6iOQf7sth70jrpQXWzG1ac3i6+f3lYFANiyfyTpNVi4+sIw7zgQ7iFPQw55WMg6kB6DnOf5KINc/kFKrkIGOUHkIG6fHyPBhTueyjoAUQmTQtZDjCURzIkXVdA3JvxcXGBAqSX2ZkksfZaG02mCIDKL3R3a1JJBTmSCRClV8WBpa0N2t2qK17kM85BbzSEP+WQKHnJDUN1td1DVPBUcbh/+5/X9AICbz52HQlMoLP7UuRXQ6zh0DTvE/UY89sQzyAvTsweZ8vrhCY4tdsiRjlrkQza3KMgHkEFOEITGORIMTTMZdGKIUizCjUsqxQX4AzzGXaykjLyQ9fBw9Xi1Mtk1R9NwOk0QRGahkHUi07ByVXIM8soiM/Q6Dv4Aj2E7HQYzgzzSQy7fsGO5zMfWlwIIGcGp8NjmLgzb3WiqLMTlJ0cKlZUUGHFc8F7vJvGS7xZLnpVEvB4qv6ruOAjvvyJz+jzk0deikHWCIDRNeMmzeMYhANSXCxOmze1LSxmMXGPS5QU7l4i34RGjCkbiG+TxYB7ycQpZJ4icxx6m1qxEFIog5DIu5pBLD1nX6zjMKhbyiAdIaT0qZJ3lkCsPWT+luQIAcMTmTqm+96jDg0ffPgAA+M4FC2DUTzfBlgbD1jfvH457HZ7nxfD5aA95RZpC1ln/FZsN0AcjBpiHfMzpxaRKhnO0Qa4ksiFXIYOcIHKQAQkK6wBQYNSjpkRYqClsPVQKpNhsiLkYAkBD8BBjcsqHibBFTYpBzvLSU1m0CYLQBvawTSYdaBKZgK1R5RJrkDNCtcjJIBc95CmKurFDuLrSAnHd7xhQHrb+8Kb9sLl9OKquBF84ti7me0J55MMIBGJHNQ5OujHu9EKv49A2qyjid8zRMO70xP17JUTnjwPCgUdlcJyqpVPE9llzyiwR950JGJK/hSDUpWvYgXXbe9E35kJ9uQWr2xvQMkPKGqjFYJIa5OE0VhRicNKNnlEnjmsoS3PLtM24hM2OxaRHdbEZQzahz44tFELIEtUgZzDldjmbd3oeCEKbOChkXXWkzHczeU4cV5BDDgiHzADw0Jv7sKNvfEb1WTThIeslQb0XJR5c9syXFhqxoLYYPaNO7BmwYUlrleRrsLG8b9CGNzqOAAC+t3wBdLrYkY0nNJaj0KTHiMODPYM2LKormfYedijQXFk4rTQeC1kP8IIxWyoj0iIR0SXPGA0VhRhxeNA76sQxc0pTvg8zyI+aXYJD464ZFZlEBjmRUdZt78Wd63eC4zjwPA+O4/D7tzqxZuVirGpvSH4BAkCo5FltiTnJO4UJc1v3GHnIAYw6pIUDNlYUhgzyYE6XHA+51Pwteh4IQrvY3KSyriZS5ruZPieKOeQyPOTrtvfinX1CiPNnhyaxu982o/osGke4Qa5C2bNSixELa4uxYdegrNJn4WPZH+atHrLFL09nMuhwaksF3twzhC37h2Ma5KKgW4zfmQ16WE16ODx+jDo9qhnk7EAj3EMOAE2Vhfi4d1y1/SVzfBw9uwQbdg1SDjlBpIOuYQfuXL8TAV4Q1wr/9471O9E97Mh2E3MGMYe81JL0vU0Vwik51SIPU7BNstlpihIrCS/F0VSZwEMuQ+GUngeC0DYOMshVI9l8t3dwEnsGJmf8nCg3h5z1KzP3eMy8PosmtqhbCh5yixELg+JpHRJLn0WP93CSfS8sbJ0dskQjljyrKY75+1DpM/VS52xxDHK1hd16RINccITY3b4ZI0hMBjmRMdZt740rQMZxHJ7b3pvhFuUugxJzyAGgsVIw2g9SLXLJ4YDRtciH7G5MeQPQccDssviHIKE65MkXf3oeCELbhIu6uX0BTHn9Cd5NJCLRfBfggQt+/Q6W/eYdxEt7nSlzotyyZ7SOTId5w6NzyOUaduEG+YKgeNreAZuk3OxUvpel8wSD/IOuUbh90+ecjgQeciA9pc8ShawD6hjkU14/BoMVhI6eLXy2AA84PDNj3iWDnMgYfWOuuBMiz/PoG3NluEW5S8hDnjxkPR2lKXKVUMh64s1OqM+EU2wmWDK7zBJXDA4AyoM55C6vP+nmnZ4HgtA29qgw15mUz6g2ieY7KcyEOZHneYyxNUpiyDqtI9NxRNQhF9ZkX4DHlFd6jfZAgBfDtEssRjRXFsJs0MHl9UvaS6XyvSyoKUZVkQkurx8f9YxH/M7rD2D/kdgK64zyNCitT8YQdQMiS+umCqu9Xmw2oK60QKz/PlPC1skgJzJGfbkl4YlhfXny8GtCWChEUTcJHnJ2gtk/4YLHJ31BykdCHvIkOeSVkYcYUvLHASFEji0iycLF6HkgCO3i9vnh8QvzJXumKWxdOYnmOz0HfGNpC76xtAX6GTwnOj2hMSc1ZJ3WkemwyJZiswFWkx5MP02OYWdz+8QSqaUWIwx6HebVCIrmHRLqkafyvXAcF6G2Hk7XsANeP48is0FUIo+GjR11PeShw4lw2J6ob8w1LTRfLj1hwrkcx4n3milK62SQExljdXtDwhPDy2ag+IgSRp0eeP08OA6YVZzcIK8uMqPAqEOABw6Pz7zT8nCkCuawRebw+BS8/oBkg5zjOPHayUqf0fNAENolXGGdlZQig1w5Cec7AP/xuSZ87XNN4DFz50S2ZpgNOlii1LPjQevIdJgBZzUbwHEcioIK9HJqWrNomAKjDmaD8F2IeeQSSp+l+r2cHqce+e5+4d7za4riKrWXyxSXlUKssmeA4BQy6XXwBXj0T6S2v2RplUynJ5Ua8rkIGeRExmipsmLNysWIdWj4ky8ejeYZWqJDLqzOaKXVDJMh+SPMcRyFrQcJCeYkNsiri8wwG3TwB3j0j09FnNwmg51OJyt9xp6H6DVVxwFrVi6m54EgsggLVy806cX5ggxy5bD5jsEB0Ou4iPkufE7kwt43U+bE8PUpnnc1mljriI6bOX0WC4cnlEMOQFHps/D8cQYLEd8jwUMuZbwnYmnQIN/ROx7RbnbvBbWx88eBdBnkTNQt0kOu14W8/anuL6MdH8wgn3SRh5wgVGdVewPu+eLRAISJjhkvzMgkkjMoI3+cQQa5wKiosp44HFCn4yLESnolesiB0GKYzEMOCM/Df4ct2gDw8i1nzMhSNQShJcKVmtmmnAzy1Fh5Yr3Yl0vaKnH9mXPxxnfOjpjvVrU34I3vnI1zFs4CANSVFUx7T74yKrEKSDSsz1h1kDPmVc+YPosFO0xjtdlDSuvSDbtYBjkTdpMSsg4Ay46pFQ+Wzl00K+Z4j8fsMgvmVlsR4IH3OkfE15lBvqgudv44AFRYWci6ijnkQaO4pGB6tewGlfLIe6McH8Vm5TXkcxEyyImMUxScHI+dU4oHvnIcAOCxLV04MklGuRT6ZSisM9SaMHMdqSrrQOQhhtSQ9fBrj0s8nXZH5fUbEojGEQSRGcggV59d/ZOYcHlhNenxx6tPwR3LF8b0FDZXWXHb+fMBAC6PP2GpyXxCqsZJLJqrrOIhxqK6khnpGWewdBOraJDLD32O7SEXvNLdIw64JCh/b+0cAQ9gbpUVf/j6yXHHezyWxsgjZ4cBC+KUPAOAMuYUUNND7o5d9gxQz+ETz0NOOeQEkSbYRqfQpMd5i2bhxMYyTHkD+J839mW5ZblByEMu3SAnD3lQwTYYElghwQPB+mzvoE0sxSHJIBdzyKUt/tGHJDP5OyIIrWAPbkCLCgxiyCsZ5KnBDItT51YmrFYBAG2ziqDjBKXoIzZ3JpqXdaRqnMSDDt4jxRjFkHUFhl0sg7y62IxKqwk8L+wLksHGO8sHl4tYjzx4nckpLw4FdYAWJghZZ/sbqU4BKcQrewaE7y+V55DzPB/DICdRN4JIK05PyPPAcRzuWL4QAPDsB73oHnZks2k5wYACDzkZ5IKgC1MBLZPggWCbm63BcLFis0HS34kKpxIXw56o+vAzeTNFEFrBzrxsJvKQqwUTqFoqwUApMOrREvQmSg0RznXGxBxy+R5ygNZ5ILJUodUUHbIu30MerSq+sE56HvnmFA3yz82thI4DDgw5cHjcJd6zrrQApQnGCNunSHUKSCGeqBugTi3yIbsbU94AdJwQrh9+LxJ1I4g0wcKJCs2CcuWpcytx9oJq+AI81m7Ym82m5QQDMkqeMcSFesSZUi3YXIaVACk06UXV1ESwPtsTPAlnpTiSIVdQhS1irdXC5jPaQCcIIvOwjX1RARnkajDl9WNb9ygAYOk8aQaKqGrdn1zVOh9ga1SFhJSqWJBBHra/NOmhDyrdKQl9juUhB4AFNUxpPbFBfnjchQNDDug44LTWSsn3DafUYsTi+jIAgred3TNe/XFGeNqcGvs9nudDZc8SechHlDvUmCOirtQiihVT2TOCSDOOYMg6O70EgNuXLQAA/HPHYXx6aCIr7coVRA+5jJD1+nJhwrS5fTN2UzkmI38cmB6eLiVcHQiFG44lUVkHhIWOLUTMazSTN1MEoRUcYbWM2aZ8cobOnWrwYc8YprwBVBebMW9WkaS/kaNqnQ+wNapMoUHeUCF4FidcXkxIWH/yEZbrzEqdAeoa5MxDnqz0GQtXX1xfNu0acgjPI2cHU4kU1oHQHscX4MUU0VRw+wLw+gXDPraHXBh3Y06vYgG2WDo9JeQhJ4j04vBECm4AwNGzS3HJ8bMBAL94dU9W2pUrMA95nQyD3GLSY1axoMo+Uw2+MYkK6wy2yDAaJQoLiSHrElTWx51e2IIL5mmtZJAThFZgz6WVRN1UYUtYuLrUkl5yVa1zHbZGSdE4iUWhyYCqImGd7x2bmeuIGNkStr9kXl05B2pxDfKwMZnI+7xFRnpGIlg0yeb9I5I95BaTHgVGwbxTQ2mdGdkcF+lIYxQXGMUxqzTlrmdEyD8PN8jFsmfkISeI9OAIE3UL57bz58Og4/DW3iExb5eIxOH2iae8ckLWAYhKtTPV4GMLk1QPeaHJgOriUGk5+R7y5AY5+y5qSsyYX1MkvjZT0woIQitQyLq6bN4vrOly8mkX1QmewP1H7PD6A0nenfuwNUqKVkk8Zvo6H12DHAjlkMsx7CbjGOTzZhWD4wQBviF7bLFBnucVjfdYnNBYBotRj2G7Gx/1jAEIeekTUaFiLXJW8qzIbIBOF/swLVVBQdFDXhlukMvP/c9lsmqQ22w23HrrrWhqaoLFYsGSJUuwbds28fc8z+Puu+9GXV0dLBYLzjvvPOzbR0rcuU64qFs4TZVWXHFKIwDglmc/wk1Pf4g1r3SgK4bQW9ewA2te6cBNz3wU9z35CPOOW036mGqXiWCG6ENv7J9RfcaQG7IOQIwqAID3DoxI6jMxh1yCh/xgWJjWnHILOA5wevwYkfC3BEGkD0cGy57l+3o24fTik75xAMDpbdLzaeeUWWA16eHxB2aE4Ot4ih5yQPt55Oke67YYHnK1yp4Bgve5pVLQe4mXSrFn0IZhuxsFRh1ObCqTfM9YmA16HDNHOJgK8AAHQIfkESZqlj5LlD/OSHXcRdcgB6jsWUa59tprsWHDBjz55JP45JNPcMEFF+C8887DoUOHAAAPPPAA/ud//gePPPII3n//fVitVixbtgxTU1SvOpcJibpND31prhIexiM2N/61sx+Pvn0A567dhL9s7xXfs257L85duwmPvn0AL+48HPM9+cpgMH+8Rka4OiD02YZdgwCEUKuZ1GcMueGA67b34rPDoTyxlz7pl9Rn7GTa4fHD40vs1QlfhMwGPeqCUQ9a3UwRxEwhU3XIZ8J6tvXAMAK8IFxZV2pJ/gdBdDpODFvfPQPC1kcVHBpHo4bidbrIxFi3h6WaMNTMIQfCUin6Y4/JzfuEcPVTWiolCcgmYt32XmzvHhN/5gEs/+3byfchKpY+S6SwzmgMpvgpHXexcsip7FmGcLlcWL9+PR544AGceeaZaGtrw09+8hO0tbXh4YcfBs/z+M1vfoMf/vCHuOSSS7B48WI88cQTOHz4MP7+979nq9mECrCQImtUyHrXsAP3vbhb/JkH4A/wCPDAHet3onvYga5hB+5cvxMBPvS76PfkM0ryx1mfhQdBz6Q+Y4zKCAdkfRZOgIekPisuMIBFdSVbDJmiOluEqI4sQWgDWwyDfMobgNvnV+0eM2U9k1PuLBomYLUniYhWruPy+DHlFQ5wldYhB0JridbWkEyN9XAxRoZo2LlT95ADYer/cQ6JQvnjytTVGbH2boC0fYiapc+YQSzNQy6/FvmU1y/ubWPnkM+MkPX4xx1pxufzwe/3o6Ag0rCwWCzYvHkzurq6MDAwgPPOO0/8XWlpKU499VRs3boVl19+eczrut1uuN2hvI7JSWES93q98Hqz96Wye2ezDVqB5eaZ9ZH98ez7B8GBA6ZNP8IEdM4vNwExfyvAAXjm/YP47gXz1G2whjgcFGqZVWSSPJYS9Wt0n+XzOB21CxN+aYE+6eeT02exKCs0YtThxZEJJ8ot8U/ID47YAQBzSs3wer2oLy/A+11A15A9L78DNcnnsUpkH3twE1hgAAr0PDgO4HlgZNIVoS0hhXhjNdV5JldgHsPTWsplP6/zqoUN+u7Dk3n9rA8FDRKjnoOJCyj+rLNLBGP+4IhD0TXSNa9maqxPOFl5U534GSxBS8fm8kn6XIEAL+aQFxq5aX/TVi14g3f3T0z7nccXwPtdQnm/U5vLUurHVPqsLPihR2xTKX+X445gqqRZF/dac0qDosEKxl3XEWEfVGQ2oMgYGnuFwe/N7vbB7fZMy1/PlT2A1PZlzSAvLi7GaaedhnvuuQeLFi1CTU0NnnnmGWzduhVtbW0YGBgAANTU1ET8XU1Njfi7WNx///346U9/Ou311157DYWF0kSZ0smGDRuy3YSsMzKpB8Dh423vYSTkEMe2vToEeA6Ikx+TTOYqwPPYtqsTL/nyV2fg/QM6ADrYhg7hpZekhXkl6td4fZaP47SzTxh33Xs+w0sjnyZ8r5I+C8cYEO71ypub0Vkaf+TuPSy8r6/jY7x0+GO4hjgAemzduRctzg5Jn2umk49jlcg+/UPCs/nZjg/h6+ZRoNPD5efwz1dfR63CrUT0WE11nskFRt1A94gBOvAY37cdL3XJ+/uxSQAw4OPuI3jppZfS0URN0OcAAAMsugBefvllxdcZdwvX6Rtz4p8vvgS9NEH7aag9r2ZqrH/aLeyRBnoP4qXgYGN9Muny4MUXX0IykX+XDwjwgnm09a3XYYyKJR6eEq63d2ByWh93TgJOjwFFBh4HPtyMboX9D6TWZ0OHhH7Y0bEfL3n2Km8EgA8OC/uSyZH4z+BosI97Rx3414svIY72W0w+GxOuX6r3Rox9IWDEAJ4H/v6vlxEvYl7rewCnU1q0StYMcgB48skncc0112DOnDnQ6/U48cQTccUVV+Df//634mvedddduO2228SfJycn0dDQgAsuuAAlJYlr96UTr9eLDRs24Pzzz4fRqFxBMx/44UdvAPDhgs+fhZYqq/j6LsM+7NjcDX8MhWkdB/y/UxoAAM980At/DBtHx3E4+ai5WJEHHoV4/Ovpj4HBIzj9hKOw4tRGSX+TuF8j+yyfx+n/1/kuMGnHOaefgtNbE4eSyemzWDx5+AMMHhzH/GNPwIXH1MZ8j8cXwLff2wgAWH3RuZhVbIZvRz9e6v0EvLUSK1acLOPTzTzyeawS2WfNrrcB1xTOPWMJFteX4hcd76BvzIXjT1mCExvLZF0r3ljdZdiHjzd3IVZRhXxZz/7y70PAh5/huIYyrPziqbL/fsLlxYOfvYlRN4czPn++bDHTXGFL5wiw89+oLS/GihVLFF8nEOBx387X4fEFcPySs9FQLu/0KF3zaqprqlTe+dtnQP8hHHfUfKw4ay4AQUj4xx++gQA4nH3eBRH55bHoG3MB295BgVGHS76wYtrvAwEeaz97A06PH0edchZaq0P72N++vh/AAZy1qA5fuGhxSp8llT4bfq8Hr/Z1oKSqDitWHJdSO/a+vh84eAAL5zZhxYpFMd/jD/D4+Y6N8PqBE08/B7PLpGtFDL/XA3R04OjmGqxYcXzE7+7avgFeP4/PnTn9mrmyB2CR2snIqkHe2tqKt956Cw6HA5OTk6irq8Nll12GuXPnorZW2MQODg6irq5O/JvBwUEcf/zxca9pNpthNk8PJzMajZr4wrTSjmzB8zycwTrkpdaCiL64/NQm/O/m+Mfn157RCh7A0x/E9gzzAK44tSmv+/eITUjHmFNulfw5E/VrvD7Lx3E6HgxBqyq2JP1sSvosnHKrMAfZPIG47zs04UCAB8wGHWaXW8FxHFpmCWIxfWOuvOv/dJGPY5XIPo6odaqs0Ii+MRcc3vjPdDKix+qlJ9Xj9+8on2dygfe6BEGqM+ZVK/osVUYj6koL0D8xhQMjU2hvzn6kYzqwuUP546l+5w3lFnQOOdA/6cXcWeqM1VRJdU2VitMrPLclhWbxeiUGA/Q6Dv4Ajyk/h7Ik93F4BY9mqSV+H8yrKcaO3nHsH3Zi4ewy8fWtwfF+5nxl4z2cVPqsqlhIBx53+VJuh8MjjM3Swvhj0wigvrwQXcMOHJ70oqlaugP0kBDCgOaqomnXLykwYsThgcuP+PfW+B5Aats0UYfcarWirq4OY2NjePXVV3HJJZegpaUFtbW1eP3118X3TU5O4v3338dpp52WxdYSqeD2BeAPCKd90XXIW6qsWLNyMXQcoNdxEf+uWbkYzVXWiPeEhx2FvyefYcIXtTJE3aL7NTyS6N4vHZP3fQYIB0FyVNaljMVEVEgofRauKsoFBzMTNOmfnFJVPIogCOnwPC+qNTNhoXQorbPc6nB0XP6sZ4EALwpcpVKPWVS1zmOldblVQBKhxdJnbE2NDhdXe6zbg1V8wsuecRwnq/RZIkE3xqLgmAwvfWab8uLj3nEAqdcfB1Lbh5SrWYc82GfJolOUitL2xCh5xphJpc+y6iF/9dVXwfM8FixYgP379+P222/HwoULcfXVV4PjONx666249957MW/ePLS0tOBHP/oRZs+ejS996UvZbDaRAkwBEwAKTdOH36r2BpzcXIHntveib8yF+nILLmtviJh42Ht+s3Ev/v7xYRSZ9fjXTWfk/OYlGT5/AENBD3ltibyyZ+H9enDEgbf2DMHh8YvRCvmOw+OHN5jnILWkjJSxGI8yq7BwjTnjL/6xynxUWk0oNOnh9PhxaMyFudVFktpKEIR6hB8cs419mUWYNyYSPNNymJzy4v97cz8A4HvLFuC5bb04OOrE6W1VuOeS/Dgo7RiwYcThgcWoxwmN5Yqvs7C2BJv2DKEjj5XWx8QqIPlpkAPCmjowMYW1G0I5zfd+6Risam9Q7R5MjLEoKuG4uMCAcacXkxIMOykGuViOL6z02XsHRuEP8GiuLES9zFSBeCjdh6hpkEspewYoL33WG2MvxAiVPtO2cJsaZNUgn5iYwF133YW+vj5UVFRg5cqVuO+++0T3/ve+9z04HA5cf/31GB8fx9KlS/HKK69MU2YncgdWg9xi1EMfR/WhucqKO5YvTHid5iorfvrFY/D3jw/D7vanVCYkVxiyuxHghRPSyiJ5Kr9AZL8++0EP7vzrJ/j/3tyPy05uyNu8PAbzVBcYdbCYpNcFlTIWYyHFQy4uQpWhRYjjODRWFKJjwIaeUScZ5ASRBdgGlONCkVwloodcHU/NH94+gDGnF63VVlx/5lzY3T78blMnGioK88IYB0Lln06dWwGTQXlA5sIY3sh8I+QhT30t1nIt8mG7O+LnRIfWSrCHlSsMp9hsBOBSzUPOSp/tGQwdEqkRDRILJfuQ8jCnAM/zYhSeEliflSToD0DZQRDP8zGdE4yZ5CHPasj66tWr0dnZCbfbjf7+fjz00EMoLS0Vf89xHH72s59hYGAAU1NT2LhxI+bPn5/FFhOpItYgN0s3iuJRWmjE7GDodj4v1IyBCSFcvabYHPcwQypfOakec6utGHN68b9xchjzCbbZkeodTxUpp9PxFiGt1pEliJmCuKk3GcSNrJoh60M2N/4QzA29fdkCGPQ6LKwL1jbuzx8vcCr1x8MJD1nnYyng5QFqrlFaXkN2B/dq82YJh81qpyE4YoSsA/IMO/aMJzJA2SFR76hLnC/UGu9qwMaRxxdIORJSuodcGHcHZYy7YbsHLq8fHAfMiSEEx2qfS4lsyHU0kUNOzBycokGuTnDGAvHkPH82MfEYDOaP18jIH4+HQa/D7RcsAAD84Z0D006t841RR4YN8mDExqjMkPXwn7Xo3SCImQBLrQpfp9Q0yB96Yx+cHj+OayjDsqMFAVu2wd87aEcgkPtGp9vnxwfBesxL56VmoLRWF8Gg42Cb8uFw8GA631BzjWJRV1pbQ3ieF50nXzphDgD19262uCHrLPRZnZD1cqsJs4qFSMU9AzYMTExh/xE7OA5Y0pp9g7zQpIdJL5h4qYatsz4rSWKQK8khZ2N0dqklZhSNnNz/XIcMciKjMMGNWPnjSmBehd0zwEPeH9yIyM0fj8fyY2qxuL4UTo8fD72xX5VrapXxoGFcrkI4oBTKC43B+8ZeCHmeR89IHINco5spgpgpsA1o+KZeLYO8Z8SJpz/oAQDcsXyB6IFvqbLCpNfB7vbh0LgrpXtogY96xuHy+lFVZMKCmuKUrmUy6NAaTN/J18N3NdcoVups3OlVVYQwVQYmpzDh8kKv4/CFxUL1pM4hh2oCpjzPi9URoj3kJRbh50mVQtaB0P5zz4BNDFdfPKcUpYXZTwHkOE4cS+MppgXYZIq6jTo8kg3oXlHQLXaZNHbPSZVShbQMGeRERnGK+T2ph6wDMyO3jMEU1mtUMsg5jhPzkp56/6Amw9vUImse8jg55BMuL2zBZyFa/CWU/5f7m3KCyEVi5aGyzflkigbOrzfuhdfP44x5VRGeNKNeh9Y0hfFmg/B82lTyVxkL66aLaOUTaoasW80GVBUJ19HSus7G9dwqKxorClFSYIA/wKPziEOV6095p4sxMkpkiINJNsjFVIrJtOWPpwIbS/H2IVLgeV5yyHpJgVF0RvRK3L8kyh8Pvyd5yAlCZdjppWoe8trQCWW+5pYxBoMe8joVQtYZp7dV4Yx5VfD6efw6TPk03xjPUg65bcoHrz8w7fdsEZpVbJ4mMieGrI848n5ME4QWcSQwyFPxOHYM2PD3jw8BAL63bLpI08I8SsF6Z5+6BsqCPD98H1P50FhpCap0wr67hXUl4DgupjBaKtjcwrMZLsbIkJNDPinXIO+3aSp/nKGG0rrL64cveMghRfxXbsqddIOcPOQEoSqh3Dx1PORzq60w6jnY3T70jeW3R1EMWVfRIAcEUSEA+NvHh/LCMxOLUdEgz0woWanFKNZbjRUulmgRmlNmAccJh1epnGwTBKEMWwKDfNyl/Jlcu2EfeB64aHEdjq0vnfZ7tsHP9RSsCZcXO/vGAahnkC8KGm/5WPrM7fOLzgq1KsZoUYuECRaycb4gzKBVA1HQLUyMkaFE1C2ZQc7a/++eMRyxuWE26HBik/LyfmojKq2nsI9g/aXjAKuECjVyD4JY6l6sGuSAvMiGXCerZc+ImYeosq6Sh9yoF3LLOgZs2DNgi/tQ5zpdww5xMXuj4wgW15ehRaXSOIvry3DRsXV48ZN+fOuZj1HF6bDLsA+Xn9qk2j2yzZiYn5cZD7lex6HUYsS404txpwfVxZFl6g7GyR8HgAKjHrUlBeifmELPqFNSibuuYQfWhdUpXd3eoNnvLpfamgmoP7SHXcUc8q5hB559/yDe/EyHvZPD0HHAd4OCmtHkgxe4a9iBNS93IMADZRYjPL7pEUJKYH1zYMgBjy+QUhk1rbGzbwIAwAF4ZNN+rD65MeU5oEmBQc7G6ra96dkDsAN/ZpCzNAS1HAGxnluGnHrWUg3ytllF0HEQw+Sri83on5jSzPwd8pArN2bD88elpJ4w4ben3j+IUacn6XrGxmdTZez3kIecINJELPXaVAnP48lH1m3vxblrN2EiOCH9c8dhnLt2E/6yvVe1exw1W/A+9Iy68NEIhz9s7lb9HtlE7XBAKSTK3woJmcQ+QJJTR5aNj0ffPoAXdx7Go28f0Ox3l0ttzQTUH9okUcj6lDcgWYSKfb9/2NyNvZPCZjbAA9u7R2O+f1FQJKpr2IEprzpCV5mEfd5XPxsAIBg2ao3nutICFBcY4Avw6Byyp3w9rbBuey9W/34rAIAH8Og7Xar0mdxa5OFjNR17AK8/IH5v7HBF7b0bC1mPtb9khp2U8llSDfJ/fHwY4QURDo27NDV/qxGyPikxfxwQxtAzHwifvXvEmXQ9m/L6RW2keCHrrPQcGeQEoTIspEitkHUAWCCGsuWuVyEeXcMO3Ll+Z8SkH+CF/+5YvxPdw6mLoXQNO7D2tT3izzw4+Hle1Xtkm0x7yIFQeHys0+lkeVNS68iGjw9/gI/4V2vfXS61NRNQf2iXWKJuxQUGMQ1Fipc84vvleQj+T4F43++sYjPKCo3wB3jsP5JbRmf452XLFQ/11iqO4/IubJ31WbhUiFpzgJyQ9eixmo49wIEhB7x+HsVmg1hven5QfX9w0p1SWDUjXg1yQHrZs0CAl5RDzvosHF7lvVmqsP1Oah5yZpAnPpwQx3LYa8nGMkszLTIb4qYTkqgbQaQJVodcLVE3QP2wJy2xbntv3DAhjuPwnAonsZm4R7YJecgzV44k0em0aJBXJjbIk22mcum7y6W2ZgLqD+1ijxHJpdNxKA7+LEVpXcn3Kwhd5eZ6lonxvCBH+yYe6ewztrYcGnPBF0NYNFPtYLBDlAW1xeK9iguMqC+3BH+f+ndqd7Pw6un7SxZKnezZtXt8ogOkJIFBngvzt+gUSCmHPH6fhqOkP8IjBeP9rVj2jDzkBKEuoodcgjiEVNgGpmtYvXqWWqFvzBVXaZvneVWE7DJxj2zC87yqJWWkEjqdjlwMvf4ADgfrDCfzkCczyHPpu8ultmYC6g/tEi8XldUXluIhV/r9hiqH5JYXOBPjWTx8z5PSZ+nss5riApj0OvgCvCgIm412MJjBzQ5VGGqOd7u4v1SeQz4R9CabDToUGOPvU3Nh/o63B5ED85CXJDHIlfRHKFIwdg1yIHQQYHf7xFz9fIUMciKjiKJuKuaQ15YUoNSSm2F+yagvtyQ8dWSny1q/RzZxef1wB4WFshGyHq2yfnjchQAvLPjVcQTbQkqliRf1XPrucqmtmYD6Q7swD3lx1DolR9hN6febq17gTIznhXkgehdOOvtMp+NQHzR0kqU+ZeK7Cy95Fo74nQ6q4CFPIOpWEmbYJSonKjV/PBfmbzFKLwUPOYsoSBayrqQ/kqXuCfcNfZdsXs5XyCAnMko6RN04jlO9fIZWWN3ekPDU8bL2hpy4RzZh+VMmvU7VyIxkMOM/WtStJyxMS6eLvYCxBerwhCuhSnEufXe51NZMQP2hXWKFrAPyDHKl32+uhqxnYjyznOOBySmMp+D10wrp7jOpkVaZ+O6iS54x2N5ttwp7NxayniiHPMBDLDEXC6k1yHNh/q5QRWVdmqibkv6QYpCbDXqxokK+55GTQU5kFBayXqiyYbRIxVNWLdFSZcWalYvFnzlOKKml44A1KxejWYXyGuweOk6oNclQ8x7ZhJ0OlxVKK9uhFux0OnrjKJb5SLAIVRWZUGjSg+cF5dZ4sO8u+lNp8bsLH2cMDtpsayaI1R/AzO0PLRFL1A0IM8glbHCnP5s89Fzy75cZnUM2N0bsbkXtzwYtVVbcet588WddGtYqtXOOs02613epBnlLlRXfiVGKT612TLi8OBwMm48OWV8UTEPYO2hDIMWQ5ESibgVGHQzByTaRYSfVQx4+f7PvTO3xniplwTrkLq9fcdUG1lclSTzk0f0RvtX6ycVHx+yPZNVmGCUzpPQZ1SEnMgoTdYs1YaYCU1rf3Z9beXdS+MLi2bj9eUHN8/xFNWidVYTL2htUnfBXtTfg5OYK/HrDHvxjRz+KzXr886YzNLGopArLn6rIYLg6EL/sWY+ERYjjODRWFKJjwIaeUWfCOp6r2hvw6DsHsG9QSNfQccDrt52FluqiVD+C6qxqb8Cs4gJ8/fEPAAh1W9f952l5Mc6UsKq9Ae1N5Tj3V28hwAMWow4v33LmjO0PrWCP4xUKecilbQxXtTdgV/8kHt/SjUoz8JVTWnDFqU0Jv1+r2YCmykIcHHFiz4ANS9pip7VoEXbQ3lBuwfGN5agvt6i+Vi2sLUbfmAt7Bmz43NxK1a6bLb5yUj1+8LdP4PHz+PyCWZhfW6xan8lRWmd7sgqrSVyz1n9zCU5oLE+5HSxcfU6ZZZph11xphcmgg9PjR++YM249ainYEoSscxyH4gIDxpxe2KZ8qCuNfQ2pBjkQ2jc9t70XfWOutIz3VCg2G2DQcfAFBB2dulL5YfRSPeRAZH/0jjrxbucIRh0eHJqY7lTgeV6ShxwQDgOG7R4yyAlCTeyih1xtgzy/csvC2Rv0+ldaTXj0yva03ae5yoqbPt+Kf+zohzfAoymOAniuwcK1yjKosA7EzyHvlbgINYQZ5IkYdXgitBMCPFCWQfE6uZRYQs++1x/QzOYlW1QWm0VVX7cvkNRbQKSfeKlVJTJC1hlMv6K9msd3L5gHozH5PLSgphgHR5zoGLBhSVuV5Htlmy37hwEAV57WjOvOnJuWeyysLcHG3UfypvTZkM0Nj5+HXsfhkf84CUa9eoGrDRLLZwLA5uB3d82SJjyzZS8OOTn0jrlUMshDCuvRGPQ6zJtVhM8OT6JjwJaSQW5PUIccECIsxpzehErrcgxyQNg33bF8ocyWZgaO41BWaMKw3Y1RhzKDfFJi2TNGeH+8vnsQ3/jTdvxxSzeuXtKC2tIC8X0jDg+cHj84DpiTJN9+ppQ+o5B1IqOkz0MuTPRHbO5pHslcJySGMn0xU5uqoBd5yhuAM0GeVS7BQtYz7SFn9xuNE7KezCCXWot8a+cIeF7YxLN7DkwmVtXNJuGn3GNOLybzfJFNxog9ND4CfGqKuETqBAI87HHWKTk55IzBYKhumUl6OC4TvsqlA2aPL4D3u0YBAKen8RAhV0Xv4sHWg9llBaoa44B0D7nPH8B7nSMAgCWtFVhQKozVLfuGVWnHbraHiWGQA1BNA4iFrEeLMTLYYXAiTyt7thOVPMslKqyxHQNSkVr2LBafXzgL7U3lcPsC+O3r+yJ+x8ZkXUkBzIbEKayh0mf5vVcgg5zIGIEALxp5hWZ1c8iLzAY0VLDcsvw4OWfsZqfLNSVJ3pk6hSY9jDphMR7OofzFRDADJ9NeY3a/CZc3olxHz0jiGuQMcTM1kngzxTwbp7dVoaZEOIEeSFLmJptEb4akeG/ymejnLF+eu1zF6fWDaROpYZCzklOlMqafkLBb7qxlH/eOw+nxo9Jqimt4qQHLOd4zkHrOsRaQekCrBOYhT3bwufPQBGxuH0otRhxVV4L5ZUK/bt4/nFCRXCp74pQ8Yyxipc8GUxvvtjjaD4xic3LDTq6HXOuUicJuyg565YSsR8NxHO64UPCWr9veiwNDoUg+qfnj4ffO95B1MsiJjOEME5WIVScyVUL1LPPj5JyxJ8npsppwHIfi4DqUL4aB6CHPuEEudCTPhxb5CadXDAFrKJdokCcxWFmY6NJ5lagLhoRp20MeO4R/pjJsizLIbeQhzyYsXF2v41BgjNwisU16opDXaAYn5XvImeGyd9CeM7V32cHgkraquNUj1CA851gLtZ5T5eBI+gzyIrMBlcGoqUTzLPOEL2mthF7HYW4xD6Oew6FxF7qTHAgng+d5cQ+zqC62U0GtqAf7VLKQdeke8nwxyFnqnNLSZ5Oih1xZf5zcXIFzF86CP8Bj7Wt7xdfljHsyyAlCZZzBjY6Ow7SNjhoszMPSZzzPi4tUJkLWAYgG+VCeGAbZyiE36nXiQsJOp5lxXV1shiVJpYGGMIM8npeiZ8SJnlEnDDoOp7RU5qSHXIrgUD4zHLVRypeDsFxFFIYyG6ZVZZDrIXf7/BgJfr9yPOTNlVaYDTq4vP6ceT427xsCACxtS6/QmkGvQ1tQsDKXIgjiIcdTqAQpeeTvhEVZAYBZD5zYWAYgdNCilL4xF+xuH4x6Lq44Kdu7dQ87FKuBA2Eh63G8ucyonEkGOUtjU1r6jPVVqUW5E+27yxaA44AXP+nHzr5xAGHVZiRoFVHIOkGojFjb1TR9o6MGzEPekUelz4aCYhwcB8yblSmDXDD+Rhz5YRhkS2U9/J7sdFpOeCIr72N3++IupmyzdGJjOYrMBtQGDfLBHPKQ54rBkS6mecjJIM8qjgRhr3IN8iOTwndpMuhglbGf1es4sfzZnhwwOienvNjRNwEgvfnjDHY4nQ955OkMWQ+/brx51uH24aOeMQDA0rDvbklQwT7VPHLmHW+tLoqbI19dbEaF1YQAD7FaiBLilStkSBEHk1qHPFcoi1PtRQo8z4t9qtRDDgiREV86fg4A4Bev7gEgrdoMgzzkBKEyLH88XjhRqohhfnmSWwaEFrOWSmtSj6paiCHreeMhFz5HeRaUx0P5W8IiL2fzVWDUiwZ2vM3UlijPBgtZ79ewh5yF7FcXC+WcDqYYEpnrTM8hz4/nLldJtKmXa5Cz1JFZxWbIPYNmXsPdORDx9f6BUfgDPFqqrKhPkoqjBgvzqKpKpgzyePPsB92j8Pp51JdbIryVS1orAADvdg6nlDbBohjihasDQqrcguAB1G6FB1CBAA+HJ3Z1BIaUetbjruxE1KULlqo3riCH3Onxi9+9khzycG47fz6Meg7v7BvGlv3DkqvNAKEa6GSQE0QYXcMOrHmlAzc98xHWvNKBrmGH5L9lnge1Bd0YzZWFORfmlwwWfh9PDCUd5F8OefYW2Oj8LTmnwkBi70YgwGNLZyh/HABqSrXvIWdhZ0fPFjZoMz6HPPicscOXdD13qczdM4lEtYyZQe7y+uEJljNLBEsdqS2RX0s8l0p5hg4GM1MXvMwiGBmb9h7J6bHs8vhxJBghkzaDvDKxh5x5wJe2VUVELh4zuwTFBQZMTvnw6aEJxffvSCLoxlhYl9p4DxdjTBayPrNE3YTPMaogZJ3NhXodB4sxtX17Q0UhvnpqEwDgxy98KjoN/vHx4aTPL5U9I4go1m3vxblrN+HRtw/gxZ2H8ejbB3Du2k34y/ZeSX8vnl6mQdANCNazrMmf3DIgtJixcPxMQCHr6lERpXDaMyosPFI3X4ny/3b1T2Lc6UWR2YDF9WUAQkadtkXdhHmAGeR9Y66cEa5KB6zsGduQjqTBIE917p5JxKtBDkSGbUrxkrODMabtIAdRpDQHUrBY6szSDISrr9veizv/uhOAkDP86Fu5O5b7xoR5vbjAkDYDMFn5zM1RUVYMg16HJa2VEe9RglRR2lQrC9jDjEezIbZpk6zsWSDA513IOtv3KPGQh5c8UyPN9FvntMGk57D/SMgAf/K9g0mfX/EgRYaYZi5CBjkhia5hB+5cvxMBHvAH+Ih/71i/E90STqiZ4IY1TR5yIFQaLB9yy4DQ4pQVD3kehKxPef1iqkSmy56F33PUKT+HPPx9sUqfsU3S5+ZWiLl5zCAfd3pTEsdJJ2yRn19TDKOegy/Ao38i99WSlcI84uwZVztkXY25eyYh5kzGMMj1Ok4Me5VikKfiIWcHNN0jDjg92g3VHJiYwv4jdnAccNrc9Brk4WOZ4edzdyyHrwfp0NVh1wZiH3wO2dziXokZ3+GwA5YtCg1yt8+PA8HvJJlTIdUqOeGpJvH6MiTqFvvZtXt84tjKlzrkqeSQT6ZQ8iwWdrcPXn/kGJSyFklJNcgHyCAnJLFue2/cSY7jODwn4XTa4U6vhxyIrFGa6/j8Aew7IgicZKLkGaMoj0LWx4NhWuEb6UxSYRU6c9zhhdcfwOFxYYMu2SCvFITdYoUbRuePA4IHgIWWaVVpnS2qJRajmG+aLykmSmAG+ELRIFf3uVNj7p5JiOKjcQ6OSwul55H3p+Ahryoyo6rIBD5Foat0w+ahxXNKxb5JF/k2ltOdPw4IY8+k18U8+Hw3mPJ0VF0JKoumHxqxtWV79xhcHvkHvPuPCGX7Si1G1CQ5lJpfUwyOE+bDIZv8OTCZoBuQXBxsIrhfMBt0KEgxRFsrhDzk8r3LYskzszrP9brtvdApeH6lqOPnA2SQE5LoG3PFLb3E87ykeqCONIu6AerVs9QC3SNOeHwBWIz6tC7Y0bCQ9aE8MMjZqXB5oTFtHohEhHvI+8en4A/wMBt0mFUszWPWWCGUiYk2WKe8fnzQNQogMkyU4zjUarwWuWiQFxgkleTJZ6a8fnEjyTxEI3ZP3LlWCWrM3TOJ0MY+9iZUTi3ywRQ85EBY5RANp2DFOhhMF/k2lkWDXELpJ6XodZxYsSN6HdnM8sfnxf7uWqqsmF1aAI8/gG3do7LvzTRwFtYWJ11/LSY9miuF9U6JQ8U+JcUgT2zY5Vv+OBDSsbG7fZJ0L8IJHZ6rs2fvG3OBh/znl3LICSKM+nILOMQ/2WITfiKcSTwPasA2MN0jDkUnulqCbcLm1xZDp8ucMclC1m1TPrh9ud2H41lUWAci87fCBd2kfp/sIKZ/whWxmP774BjcvgBmFZvRNqso4m+YJ0Krwm5sUS0pMKKxIn4EwEyAecNNep1Yo9fjD4ihgmpQX25J6FWUMnfPJOwJRN0AeUrrAyl4yAHtHzDzPJ/R/PF8G8tylKZTIdbBJ8/zSQ9TOI4Tf6ckbJ3pH0iN8GNK60oOoMSDtASRcMywiyfqlm/544CwzrLthtw88lAOuTr9ofT5Zd+bI0z1PR8hg5yQxOr2BgQSnExf1t6Q9Br2YB5cYRpD1quLzai0CmF+e3NADCcR7JR4UQbD1QHAYgAMwRl8JMdLMI1m2SBnCqdjTq+i8MSqIhMsRj0CPHB4PHR6HL4Jjl7gWB65Fkuf8TwflpdmDFORzy3PllqwcPXKIhMKjHrRu6Nm2Prq9oaEXkUpc/dMIlSHPE7IukSDPBDgw0TdlHrIgwaKRkuf7TtixxGbGwVGHU5sKk/7/fJtLGciZD38+uEHn13DDhyemIJJr8MpzRVx/5Z5z5UIu+3uFwzrhQlKnoWTygGUnJB1u9sXszRuPnrIdTpumpaNVGwq55ArfX7DDwTseRy2rsggf/PNN9VuB6FxDDpuWu6HjhP+W7NyMZqD3p1EON3pD1kHUi+foRV2Z6HkGSB8p5VBz26u55Gz+t/l1uwssMxDPubwKNp8cRwXczOVyLNRWyqcMmsxh9zljaxrmqis20xgOJgrWRXM36wqMkW8rgYtVVasWbk4Ir5J7tw9k7BJDFlPZpCPOj3w+nlwHFAdIz9XCuFK62qmMagFC3k+ubkiIzm3bCyz8cvIxbHM83wWDPLQwSdbQ05qKofFFP+7W9IqrDGfHZ6ULQy2R2LJM0YqGkAOCQY5q2fN8yEHUTj5aJAD4eVX5YV8h0ezqUH486vXcRH/Jnp+TQYdCoyCuZqoZF2uo8ggX758OVpbW3Hvvfeitze3RDQIZfx64174eR4nNZaLnoNLT5iDN75zNlZJPJUOibqld+FmSuu7NZx3J4U9g8HT5QyWPGNUBg2DXPeQjzmy6yFn9x13eXFwRFAQlVqDnNEQZbSOOz34JFgXNlbuX62GQ9bD65oWmvRijvxMzSFnpQWZIc4M8xEFiriJWNXegNUnh+bpJa2VsubumUSykPUSiQY5OxCrtJphilOGKRnzaoqg4wQtDC1qemzJYLg6Y1V7A974ztm4ZmmL+No/blyac2N5yO7GlDcAHQfMLktvqH30GgKERVnFyR9nVBebxUgNJgInhVGHR6yxPr9GYsh6cK+zd9AmOzRZiofcbNDBFKxIEiuPPH8N8sjyq1JR20MOhJ7f68+ci4sWz8b1Z86VtBZJqSGf6yhaJQ4dOoQbb7wRzz//PObOnYtly5Zh3bp18Hhye/NOxGbPgA1/++gQAODui48SlZEvOX6OrBNpVoe8kDzkSbG7fegNnmZnUmGdwQwELW4C5cAWoPIs1CAHQiHr/gCPzw4LByxyvSHRdWS3do6A54F5s4pi5qZqWdSNnbiz0jQNwRzyUYcn7wVbYsFC1kMecnPwdfWfu4kwld2TmytzypuYSdg6lWrIOjsQqy1V5h0HgAKjXvyetBa27vUH8N6BEQCZEXQLp7nKih9edJT4vGRerjN12Hw+u8wilq1MF9FriD/A491O6d8dO3BhERFSYHngjRWFCY3k6HZajHq4fQF0j8grYceMx0QRmBzHJRQIY890vpQ8Y7D9jxYMckB4fu9YvhAPXnEC7li+UNJalEwhPx9QNAtUVVXh29/+Nj7++GO8//77mD9/Pm644QbMnj0bN998M3bs2KF2O4ks8otXO8DzwIpja3FcQ5m4IRmXIGoTDqsHHW+joxYLw/KQtBjmJwV2mDCr2JwVYzJvQtbDVNazgdmgFyNClIYnRgufbU4ixMOMdC2GrEfXNS0uMIph/b0zMI+clfdhJYcq0xCyzgj3jo276PA8HiG15tRC1vtFhXVlgm4Mtp5p7YB5R+84HB4/yguNOEpijrDa5LIo5MGRzISrA5h28PnJoQnYpnwoLjDg2DmlSf/+9KAX/Z19w5L3VHLD1QEhcmp+TVHE30vFIUHUDUhs2OWvhzy4Z5dZ+oyJ3Kkl6pYKM6H0WcrHcieeeCLuuusu3HjjjbDb7Xjsscdw0kkn4YwzzsBnn32mRhuJLLK9exQbdx+BXsfhOxcsACBPZTYcFlKUTlE3AJg3S6hnqdUwPymwxUiqGIraMANh2JbbG3cxhzxLIevAdO882xxJhZXEYZvOZGGidcEc8iM2t+YUSW1hgm6MWOGUMwUWmh4dsj6kcqoIz/MRaQFy5+6ZhC1ZHXLZHvJUDXJtpmCxg8ElbVUZrQISTi5rUGQqfxyYfvDJ1pAlrZXQS/juTmmugFHP4dC4S3Jfs4gOuaK0Yqm/fnnjne0vi5N440OGXXwPef4Z5EFRN5mpUKESpdnvj5IZUPpMsUHu9Xrx/PPPY8WKFWhqasKrr76Khx56CIODg9i/fz+ampqwatUqNdtKZBie57HmlQ4AwOr2erRWCyeXcuqwhsNE3aSGLynFYtKjJYV6llqAhXtlI1wdCBkILMc1V8l22bPoe1cVmWUfSImbzhEneked6B5xQq/jcOrc2Mq4VUUm6DghLHFEYwdSIZGYUB9Eh1POJJgnvDpYl74q+K/a39u40ysamoD8uXsm4RA39qnVIR9QyUO+QKMecmbUnZHhcPVw8sEgl6spopTQwacD7+wbAgAsnVct6W+tZgNOaBRU9KWqrXcMMg+5PKeCUqV1KSHrwAz1kCsMWZ8Uy56ld88uBQpZj8NNN92Euro6/Od//ifmz5+Pjz76CFu3bsW1114Lq9WK5uZm/PKXv0RHR4fa7SUyyJt7jmBb9xjMBh1uPnee+HrqHvL0q7Eu0Hi5mGSwxShbBnm+hKyPZjmHPPrejTK94wBEzQab24d/7ewHABzfUBY3jMyg14kGntbyyCdd0z3kuRx2mirs+WKe8eqi9Dx30X1LHvLY+AN8KLUqxTrkqdYgZywKGjT7jtjh8wdSupZa2N0+fNQzDiDz+ePhxKqvnStkqgY5g92nY8CGDw+OA5AnxrdURj3yQIDHXjHKT66HXJlBLjdkfTKGYZePdciBcJV1beSQK6EkQWRDvqDIIN+1axcefPBBHD58GL/5zW9wzDHHTHtPVVUVlUfLYQIBHg+8sgcAcNWSZjEMFgjbkMjMR3F6pJ1gqoEY9qQxr4IUeJ4Xw7UyXfKMEcplze2Q9XEHC1nP3gIbfm8lm68Co16sY/zsth4AyTfBrPSZ1mqRJ/KQz2SDnD1vYqqIyiHrrG9ZdCoZ5LGxh0URpBqyLnrIUwxZry+3oNCkh0eB0FW6eP/ACHwBHk2VhRnz8MYil+eOTIasC/cR1oS/fXQIHn8Ac8osaK6Ufm+25rzbOZI0Fapn1AmX1w+zQYfmSnnikWzP0zPqFI1sKcgNWY8V4SJ6yLO4X0gHIZV1ZWXPtJFDTh7ymLz++uu44oorYDbHVw81GAw466yzFDeMyC4v7DiMjgEbigsM+ObZrRG/Y5OV3E2dw5OZOuRAWJjfoLby7qQwMDmFySkf9DoObbOKstKGKmv61J4zhccXEMN0K7LpIS8M95Ar23yxv2NCQMk8G1otfRbrxH2m5pD7/AFxgxStsq52yDrrWzafkEEeG2YAmPQ6mA2JDXKnxw+PL77HmnnI61I0yHU6TiwbpZUD5mTCkpmC6WscGnNpJnpAClNePwYnhWc80x5ytoac3lYJjpOe+39cfSmKzAaMO73YdTjxvoqN03k1RZJy1MOpLDKLEV57B6WPd7GsbpL9ZUkCcTAKWQ/B87x4yFGiAQ85lT2Lw/3334/HHnts2uuPPfYY1qxZk3KjiOzQNezAmlc68K2nPsSP/vEpAOC/zmpFWVT+rZKQda8/IG5e0l2HHAAWBcOkdh2exI1Pf4g1r3Sga1gb3oVksMVsbpU17qYw3bAc8lGnR3PCYEBorN70zEdxv9udh8bF/3/krU5NfP+fHJpQ1I5wo96g41CWZMNQq1Gl9Vgn7myj2Dfm1ORYSxdMYEfHhb5f9tw5PH64ggeYasDCY48JKiorNcilPHe5jF1C2Gv42I3Xj06PT9zwpxqyDgD15cI1frtxX1b7nX3/z2/vAwDMz9KBMaOmuAAmvQ6+AK+5aKBE9I0Jz2Ox2SCWxkw3hqjSavMk1gYP//vF9cL8cfvzO+KOw65hB/6w+QAAwOvnFY3VpuCa8OMXPpM83m0S6pADiFv2jOd5MYw97wxy5iGXEbLu8PjBlmMtechjpRrkC4oM8t///vdYuHDhtNePPvpoPPLIIyk3isg867b34ty1m/Do2wfw0if94mYi1mJRosAgZ4JuQPpV1gGI9VEDPPDSJ/149O0DOHftJvxle2/a750qLO89W+HqgBBmzXEAz8tX5kw34WP1xZ2HY36367b3YtUjW8Wf//edrqx8/+u29+JP73aLP7+1d0h2O9Zt78WGXYPiz74Aj+W/fTvhNWo0Wos8loe8rtQCg46D189rrr3phFWAqLCaRC9SkdkAk0FYltWMTmEeclbiaMobgNsnz+CX8tzlOvYkCuuAUJqJjd94ayA7CLOa9ClvZtdt78WLOwcACHnk2ep38ft/64Bo/PzsX7uy+v3rdBzqg6HYuZRHHi7oJsdLrZR123tx+18iyxHf/9Ju2evQ1mDt8o4BW9x199y1m/Dv7jEAgodbyXr374PC33/SNyF5vNsl5jvHC322u33igXD+GeTMu+yTHEnCDiyMeg4FxpQLcqUMlT2Lw8DAAOrq6qa9Xl1djf7+/pQbRWSWrmEH7ly/EwFeELUJ91H96O+fojvqdFKJh9zuCYUCsg1nuugaduCuv34i/sw+V4AH7li/c9rn0Rp7ggrri7JU8gwQTsPZqaqWwtajx2qs75a9J7xcaja+f7EdYa8FeMhqR6xrsOskugYLk9Wah3wyRtkzvY5DfXlQ2G0kdzbVqTJiZyXPQqlfHMehWix9pr5BfvTsUnAK8silPHf5QLIa5IxkayB77mpSDFeP9fxncy4L8IA/bGLVwvefi3nkPRmsQR7+3YWT6joUb90N8BDfx6e43vEx7hMLf4CHyystJTKeOBh7lk0GHQqM2YlMTBelFqM4749LnPfDBVgzcWiUjHiRDfmEIsuooaEBW7Zsmfb6li1bMHv27JQbRWSWddt74z5wHMfhuaiTSSVlz5xMYT2B50Et5H4ercFC1hfIDClTG7H0mcoCU6mQ6LsN8MAXH9qMSx7aPG3zwcjk96/GOFR6DRYmqzWPsyjqZoncNOWyWrJSohXWGWo/d15/AIfHXQCA5spCUfRIzvyd63OqVMSQ9STrVLI1UK38ca30u1baEYucNMhHheexSYaomlLSvQ6pue4qbasUMUZGPA95vuaPA4KDhR1ESA1bt2mo5BkwM0TdFPX0ddddh1tvvRVerxef//znAQhCb9/73vfwne98R9UGEumnb8wFno89k/I8j74xV8RrbMKyBUN8pIh2iIJuGQhXl/t5tITHF0DnkB2A/HIhalNpNQOwa8pDnui7BZLnF2Xy+1djHCq9BsshH8wBDzkgbEzf2Zdbm+pUCRnkkRodVUXqCioeHnchwANmg1AOr7TQiMkpnywPeS7PqXKwS8xDTeohV6nkmVb6XSvtiEVuGuSZq0Ge7nUIUG/dVdpWuwQxRka80Od8NsgBITVqwuWVrLSupZJnwMwoe6aop2+//XaMjIzghhtugMcjnLYUFBTgjjvuwF133aVqA4n0U19uEU4lY0yEHBcKJ2WET1iTLq+kGs8OCbl5aiH382iJA8N2eP08is0GzCnLbjurirWntJ7ou9VxwCXHzwEA/OPjQzFP6zP5/asxDpVeg5Vacnj8sE15NSHKAsQ/dc/FTXWqMA94ZZSHPFRyUJ3n7mBYeCzHcSi1GNELlyyDPJfnVDmIIetJnpdkBjk7CKtN0SDXSr9rpR2xyMUqDZmsQZ7udUjNdVdpW6XWIAfCxcEin918rUHOYHpQUjWBWP8UJ0nfyRSJ1PHzBUUh6xzHYc2aNRgaGsJ7772HHTt2YHR0FHfffbfa7SMywOr2hoSnkpe1N0S8ZtTrRKV0qZs6NmFmQtBN7ufREntYuHptcdbzdpjnTs1c1lRZ3d6AQIKT+lvOnYebz50X9/eZ/P7VGIdKr1FoMoilSrSUR84W0+gyKjPRIB+KG7IeLH2mkphidL1jJRoguTynykFuyHq8PuxXqQa5VvpdK+2IBQv7zpW5g+f5jNYgT/c6BKi37iptq21KWmQLEBIlnnEe8qAm0LjE0mda85Czdjg9fnhzqMShHFJS1yoqKsLJJ5+MY445JmFNckLbtFRZsWblYoSbf3odBx0HrFm5GM1V1ml/I3dT5wyGrEuZMFOFfZ7wSHo9h4SfRyt0hBnk2SZUE1k7OeRNFYWi14mD8J1Gj9Xw75/9Ltl4TgdqtCOVa9RqTGk9vK5ptMd+ZuaQM1G32CHrah2Eid64yiiDXGLoIhB7TmXPn9bnVDk4VApZH5xUx0Me8fyHHdBmay5jcMjOnBqLhnJhXI87vYrL+WWSYbsHLq8fOg6YnYEouEytQ2rfh412KfOMXWINciBk2IWrqgP5b5Cz8sWjsg1ybfRHePSDPU+95Iqto+3bt2PdunXo6ekRw9YZf/3rX1NuGJFZVrU3YNfhSTz+bjfqyy24+LjZuKy9Ie4EWGIx4vDElOQF0C56yDOjXrmqvQEnN1fgwv95By6PH5eeUI8bP9+m+Y1jR7+gsL5QEwa59lTW/7nzMPonpmA16bG6vQHDDg/qyy3Txir7/p/b3ou+MVfM92QCNdqh9Bo1JQXYO2jXjIfc6fGLG6DoU3dmkI84PLC7fRk5uMs2LCQ92kOudsh6fA+5vE3NqvYGFBcY8F9//hCAoHHx8FdP0vycKodQLePEm9BkpT/ZIViqHnIg8vn/83sHYZvy4UdfOAqrMuyVvvSEOfjxC5/B6fHjjHlVOHpOaVbm1GisZgOqikwYtnvQO+pEabC0n1bpGRWUwutKLWmvOMPI1Dqk5n1uf34HtnWPYUFtMR75WuJ5hh2kFcswyAFhX8rmw3FnfhvkFdbIz5mMeAKs2cKo18Fi1MPl9cM25ZOUKptrKOrpZ599FldeeSWWLVuG1157DRdccAH27t2LwcFBXHrppWq3kcgQbBPx9dOacd2ZcxO+V76HXHpIkVo0V1lxVF0J/n1wDOcsnJX1jYMUWMj6wiyWPGOoLS6VKh5fAGtf2wsA+ObZrbjx8/FD5ADh+79j+cJMNC3t7VByDVHYTSMecnbirtdxsESVlSkpMKK80Igxpxe9o86slvzLFCOO2AZ5tcrPXbRBnsyYTER4OaA5ZYU5MafKgXlekmmdJFr/fP4AhoKHKal6yBns+ff6AvjD5i7sHbSpcl057OibgNPjR1mhEX+8+hToJIi5ZoqGikLRID9G8wZ55sLVw8nUOqTWfb56ahO2dY+hrNCYdJ4JaT8k31+aDXqYDDp4fAHYprzTnuV8NciZh1yqyrqYQ64RDzkgHKa4vP5p+f/5gqLjuZ///Of49a9/jX/+858wmUz47W9/i46ODqxevRqNjY1qt5HIECFjMLl3Vq5BbncLIeuZKHsWTi7lpk44vTgc9GbOz3LJM0B7IevPbetBz6gTVUVmXLO0JdvN0Tys5FK/Rjzk4ol7gSGmPkIuPaupEgjwoTrkxVEh68Xq5ZDzPD+t5rGSHHJGuHeFHSjkE6KnLcnGPlEfDtndCPCAQcdNE+xLldPnVQEA3tk3nDCnNx1s2T8stKG1SlPGOJBbc0fPiKAUnmmDPNcIpTElV2eXE7IOhDRMwvPI890gL2cGucyQ9Wi9l2yS76XPFBnknZ2duOiiiwAAJpMJDocDHMfh29/+Nh599FFVG0hkBpfHj+4RIZRKSv6ybA85mzAzIOoWTi4psO4Jej3mlFk0sSiIhoHdk/HNXzROjw+/fX0/AOCWc9syIg6Y69SUastDnuzEfSblkU+4vPAFw/eF8oIh2EHYuNObsnjNhMsrhmHXl6dukIcr9GolckZNbBI39kyxOFYdcpYiMqvYLKkkqBxOaa6AUc+hb8yV8TVtMzPI26oyel8p5JRBHqXpQMSGfaeHJ1zw+BLPg1LLFTJilT7Ld4OchaznatkzIFyQjzzkIuXl5bDZgsbDnDn49NNPAQDj4+NwOrU/IRLT2XfEhgAPVFpNYshkItikFWtDEguxDnmGc0Mbc2iTv2dAyB/XgqAbIIwFAPD4A5iUmW+qNo9v6caw3Y3GikJcdjJF4UiBhctqRdRtMskCn0ub6lRh3uWSAsO0PNIyi1E05FKNTmF9OavYDEtQv0Pu3B1OuELvsE0bkTNqooaoGzsAq1Ehfzwaq9mAExrLAYQM5EzgcPvwUc8YAGCpBg3yXDp4781gDfJcpqrIhEKTHjwP9I0l/l6lVkdgiKXPwp7f/C97Ji9k3abJkPX8Ln2myCA/88wzsWHDBgDAqlWrcMstt+C6667DFVdcgXPPPVfVBhKZoUNmuS25XhZHhkXdGLm0yd/NUgY0YpAXGPWiSEo2S5+NOTx4ZFMnAOA7F8zPmBBOriOqrGsmZJ0McsaQjYWrTz/81Ok4VFjVEVSMla+akoc8zCB3ef3ivJ4v2FUIWe9XqQZ5PJhBvCWDBvkHXaPw+nk0VFg06dnNpYP3bOWQ5xocx0leE+wSxRgZYk1rd+j5FT3khdoxQNWErSlyQ9a15CGPV0M+X1C0s33ooYdw+eWXAwB+8IMf4LbbbsPg4CBWrlyJ//u//1O1gURm6OiXV26LTVpaFnUDQjVKD427NF+7cI+GSp4xQmHr2TPIH36rEza3Dwtri3Hx4tlZa0euwQyCYbsnachfJkh24j6TDPLhODXIGWoJKqptkEeHO2pFX0ItQqJu0gzyWDVx1VRYjwULGX+3cySibFM6Yd54LXrHgdD47htzZaxPlDDl9Yvjgwzy5EhNY5Ij6gbEzkXO95B1lmYz7vJKeka0VvYMiJ37n0/INsh9Ph/+9a9/Qa8XPJ06nQ533nknXnjhBaxduxbl5eWqN5JIP3sGhXDpRbXS1I3li7oFPeQZNsiri8wwG3TwB3j0j2vDUxgLnudDonoSv4NMUCl66rKz8e6fcOGP73YDAO5YvlBzYkJapsJqgkkvTPFHbNkf+0k95MHDs75RFwIa3lSrQcggj126JVRyMLXnLlZ4bEoGeVS4YzYjZ9KB1FzU8E1qdD8OptlDflx9KYrMBow7vdh1eDIt94hmi4bzxwGhxKNJr4MvwKN/IrkIWLboGxPaVmQ2oDxPPbFqIt9DLi9kna1JPM+LKVX5apAzUTeel5auNBkmwqoVQiHr+ekhl93TBoMB//Vf/4Xdu3enoz1ElpDrIZdbOsfJcsgzHLKu03FoqCjE/iN29Iw6ZYfbdQ07sC6spubq9ga0yCz1k+waXcMO/OGdA7C7fdBxgJZsznSUPpPSp+w9L+7sh8cXwOL6Upy9oFq1NswEOI7DrBIz+sZcGJycEkW9skVIZT32hqeu1AKDjoPHH8DA5BRml1lSul8mnl2liArrWfSQu7x+eHwBWSkg0R7yfBJ28/gCcAcjSYqThL7qdRyKCwywTfkw4fJGfI/p9pAb9Dp8bm4lNu4exDv7h3BsfXrLfB2xTaFjwAaOA5a0atMg1+s41JdbcGDYgZ5RZ9bnuniEH5BJSQ2c6aQrZJ0ZdszotLt9otc4Xw3yvjEXTHoOHj+PB17twPVntsZdywIBPix9Rzv9wVIo89VDrujo45RTTsHHH3+MpqYmtdtDZIEhmxsjDg84Tnq5LaU55JkWdQOESZ0Z5HJYt70Xd67fCY7jwPM8OI7D79/qxJqVi7GqvUGVa7DfMwI8sOw3b8u6RzphJZnUClmX0qfhfcIcpZ8cmsDz/+7TRJ/kEnWlBegbc2mi9FmyMipsU9094kTPqDMlgzwTz24qJA9ZV+e5i6XoHO3drY6Rxx4P5iGvKTFjcNKdVyHr4fnwyeqQA8IayAzycJhmQ02aPOQAcMa8KmzcPYgt+4dxw9ltabsPAGztHAEAHD27RMxD1SINFYU4MOwQjN7WbLcmNux5bKJwdUmEDPLEUQ+phqyzZ9hk0KHAmFmnUSZgaxnbTz23rRfPbeuNu5bZPT6wwjpazCHPV4NcUQ75DTfcgNtuuw0PPfQQtm7dip07d0b8R+QWLFS6udIqKvEmQ75Bzjzk2THIAXm5qV3DDnEC8wf4iH/vWL8T3cOOlK+xed+Q+PvwCF0590g3zGAYUmHjLaVPw98T3ie8hvokl2BGgRaE3ViYXKITdzXUkjPx7KY6DqXnkCt/7rz+AA6PT89X1es48VBEbtg6EwSaN6s42L788ZAzj1CBUQeDPvnWKNYayPO86CGvS5OHHAiFjm/rHsOU15+2+wDA5n3aDldnsDF+cES7GhRU8kwe4TnkiUqvOjxyQ9Yj1brzOX88fC1jsP1VvLWM9YtJr60DChaZS6JuYVx++eXo6urCzTffjNNPPx3HH388TjjhBPFfIrfoYOW2JHrHgdDEZZvySRKIYBNmocQJU02U1Ddet703bkgZx3F4bntvStcI8MC1T2xHvK6Teo90U6liyHqy/vjyw+9i5cPvar5PcgmWx6qFWuRSVFvVUEtO97OrxjhkhnZlnBxyNZ67/vEp+AM8zAbdtFKWckU5AcDl8Ysh3W2zilJun9aQW8s4Vvm4SZcPU16hj9LpIW+ttqK2pAAeXwDbu8fSdh+e5zUv6MbIBVHIHip5Jov6ciFKyu72JayfLXrIJYesR5Y9y2eDXMlaFhJg1Y53HMj/smeKerurq0vtdhBZhJU8W1gn3yAHhIeX1TiMBc/zkuu7pgMlC3XfmCvuiSzP86I4i9JrABA3bqncI91Ui+JSqW+8k/XHaJL6mFrpk1xCLH02mX3DSYpqqxqb6nQ/u2qMQ6kh66l4yMM3/9FiiKUWI3rhklWLnJU8M+o5sXoFGeSRhxrMO15WaEyrZ4njOJzeVoX1H/Zh8/5hLJ2XHmP5wLAD/RNTMBl0OLm5Ii33UAslB++ZppdKnsmiwKhHbUkBBian0DPqjJsyYRNTIqU9cyVR4mD5XINcyVqmxZJnQP6XPVPU25Q7nl8wD7mc+tdGvQ6FJj2cHj8mXIkNcrcvIHo9M12HHFC2ya8vtwinijEmMo7jxJNbpdfQccDcKisODDtieoSl3iPdMINBjVzRZP1x0bF1AIAXP+nXdJ/kEqFa5Nk/yJiUcOquhkGu2rMLDoDya8SD53nRkI32XDPUEHVLVO+YbTzHXdKfa5Y/XlZoUiWkXmuIBrnETahokId57pjCd7oU1sNZOq8S6z/sS2s9cnbt9qZyTYWuxkLrHnKe56kGuQIaKwpFg/z4hrJpv/f4AmJZz2RijIzo8ln57CFXsh4mK1GaLfI9h1yRQf7EE08k/P2VV16pqDFE5vH5A9g3aAcgv9xWqcUoGuSJCBfLKcxCDnlDhTDhTLi8mHB6xXDNRKxub8Dv3+qM+Tue53GZBFGnRNcAgJ988Whc+dgHKd0j3agZsp6sP75zwQLwEAzyWGilT3IJZhgM5EjIuhpeLrWe3Uc2pXaNeDg9fjE6Jl7IOjN4Rx0eBAK8onJ/B0eF3MBEBvlEgjDQaFj+eEWEQZ5HHnJWg1ziGhXLQz6YZoX1cE4PKp5/engCYw4PytMguJYr+eNAKC97zOnF5JQ3bjWHbDFs98Dp8YPjgDkpVpCYSTRUFOKD7tG4a4JcMUZgZuWQK1kPRQFWi7Y85NGRDfmGohzyW265JeK/G264AVdddRWuv/563HrrrSo3kUgn3SNOuH0BWIx62ae2opclyaaOCbpZjHros1DTq9BkEDeQvWPSNvotVVb8/MvHRrym5zjoOGDNysVollD6qKXKijUrF4s/cxAEldg1ls6rxpqVi6HjQq+H/17KPdINC511evxwelI7lUzWH81VVvE9Wu6TXKJGzCF3J0wXyARSTt0bxVBoT8RGSw5sDMWaaaSOoQKjbpoRzAGqjENmxFqM+rhVJ5ih7g/woiEsl0ThsSFjUnofsxzOskIjqoPVF4Zt+WOQO9zywjRjlf4cmBD6IxMe8lklBZhfUwSeB94NKqGric8fwNYDwnXPSFNIvJoUmQ2oDB5KaDFsnXnHZ5daZJUanOmIkQ9xxPrkijEC4Z7W/M8hj95TMTjEX8tEAVaJEQeZgn1vU94AvP74KZ+5iqLjj7Gx6SIi+/btwze/+U3cfvvtKTeKyBxMYX1+bbFsL4zUWuRM0E3q6WU6aKywYNjuRs+oE8fMkVa39aTG8oifrzytCV9f0ixrM76qvQF/+6gP73aO4tj6UpzeVoXL2hvEa6xqb8DJzRV4LqzWcfjvs02R2QCzQQe3L4ARuweFFamdmK5qb8Cf3u3Gp4cncUJjGU6dWznt82q9T3IJZpB7fAGMOb1ZK1sUXtc00al7SYERZYVGjDu96B1zyo7aYaxqb8BLn/TjzT1DaKosRM+IEzwguYb4bzfugz/AY3F9KYpMBrx7YAQt1VY89vWTUx6HYv54cfzvwqjXif0w4vCIkSpySBQeK3XuDoeFrFdYQx7yySmf7FrmWkXNHPJ0CrqFs7StGnsH7di8fxgXLa5T9dqfHJqAbcqHUosRR89Ob61ztWioKMSIw4PeUafm2hyqQU7ecTkwvYp4qQhya5ADIcPO4fHDH+DFZ7gkDw1yIHJP9eqnAzgw7MCKY2vjlu+c1GgOefjcbJvyodiUeQdfOlGtt+fNm4f//u//xte+9jV0dHSodVkizexh+eMyFNYZUkufOUWDPHsPd2NFIT7sGZeVX7Y7eFjBuOLURkWbcYdHOMn71jltWHZ07bTfN1dZccfyhbKvmwk4jkNVkRmHxl0YsrtTVofleV4sS3P/lxdjQRzdAi33SS5hMuhQVWTCsN2D/glX1gxyh8cn6gIkCyVtrCjEuHMCPSPKDXIAcAXLQd12/ny8d2AEz3zQizWvdGDdf54WV3UWAPYfsWNdUHn2xxcfBafHj3cPjAA8VDkUGrIJhm08QTdGVZEZ404vhm1uzFcwPzOPUqwSS3LLVgKhkPWyQhNKCoww6Dj4AjxGHG7Ulea+kcHCNKWuUzENcpZDnoGQdUDII39sS1da8sjZNZe0VmYlsk0JjRWF+LhX3jqfKSh/XBnJSmHaZUa2CO8NrUH2KZ8YKZSPHnIG21Od0lKBqx/fho97J8DzfMy1UIoAazYw6HWwmvRwePywTXlRbMrOfiZdqHqsbTAYcPjwYTUvSaQZZnTGM4wSIXVTZw+GrGcjf5yhRPCFHVYwlIZnsr9LtgHXKqLiswrhqYfGXbC5fTDqOcytJo93JqjRQOkztsAb9RzMSbypatQiB0KCY1VFZtxy7nyYDTps6x7Dm3uOJPy7ta/tQYAHzltUg5OaKsS5sXvEAZcn9ZrPIw7hOaq0Jp4PWPjtkII87QmnV/RyNJSrZJCLHnIjdDpODKsftuWHsJtDqahbhIc8GLKeIYP8lJZKGHQcekadcUN6lfJODuWPM7Qs7EYGuTJYf/VPuETxtnDsMhXWAeGgmq1Dk1PevA5Zj+aU5goY9RwOjbviPidaLXsG5HfpM0W9/cILL0T8zPM8+vv78dBDD+H0009XpWFEZtijoOQZI1Yd1lg4xZCi7IWsKxGL6uiP9JAPJynLFQue58UNeDxFZa2jpqIy69PW6iIYJeZ7EalRW1KAzw5Pivmt2SD8xD2RdxoAmlQqXzQSVlqstrQAV53ejN+/dQAPvLIHZ8+fFTNFZ0fvOF7+dAAcB9y+bAEA4bmttJow4vBg3xEbFteXpdQuZsBWJwhZB4CqYuUVDthGq7rYDEuMyhZS5+5wWA55ebCiRlWRGYOT7rwRdhNDX2WKuk3GEnXLUMh6kdmAExrLsK17DFs6h9FY2ajKdZ0eHz7sEVITtV5/PJyQQZ79qhLRUA1yZVQVmWAx6uHy+nFo3DUt7ShUg1yeOVNiMWLI5p5xBrnVbMAJjeX4oGsUm/cPo6lyumNEq2XPAKFNA5Py1q5cQdGO+Etf+lLEf1/+8pfxk5/8BIsXL8Zjjz2mdhuJNGF3+8RFQkloqHQPufBw55qHnNVnrw5ujJV4iB0SFJW1Tqj0Weob7z2DwQMgBREZhDJCtciz6SGXfuKuhpfL6w+IBiSL8PjmWa0oKTCgY8CGf+w4NO1veJ7HmleEdKsvn1AvesY5jhP/vyMqjUUJyWqQM6pTUDJP5o1LJWSdGeRqVmDQArYUPeRTXj9Gg4e2mTLIgZAHmymiq8EHXaPw+nnMKbOIOby5gJZrkVMNcmVwHJdwTZCr/cAIL6GVz3XIY7E0yZzB1mst5tSHapHnn4dckUEeCAQi/vP7/RgYGMDTTz+Nujp1hUWI9LE3aBzNKjYryi2VnkMuhHlmU9SNnQIeGnPBJ0GdcXLKi0Pjwin76a2VAJRtPJkRn0hRWeuIoakqbLx39wtpAAtSyA0m5CGWPstiLXI5J+5qGOTMMNJxIQOyrNCE/zq7FQCw9rW908IfN+8fxrudIzDpdbj1vHkRvxMN8v7UDXIWMZM8h1z5c5dWg9xqjGpfnoWsy8whd3j88PoDOBIMVzcZBEG+TME211s6hxEIqFNJgeWPL22rShrRoiWYXkLfmBN+lfpCDaa8fvFAlAxy+SRKY5L73DLCQ59nkoccCB3ivds5EvM5EcueadJDnr+lzyhmdAbDNpdK8scB6Zs6UWU9ix7yWcVmmAw6+AI8+ieSewr3Bj1hdaUFaK0uAqAsdFTcfCcJT9Uyaoasp5IiQSijRvSQZ8+TOck85BKUcEUv15hLsYHBjNgKqzkiNP3qJS2YVWxG35gLz3zQI74eCPB44JU9AICvfq5xWljpouAB0p7BSF0JRW0Lhqwni5ipTOG5SxYeqyyHPDJkPRUPvhaRG/oa7j2adHlFg6uutCCjRuxxDWUoMhsw7vRiV3/q4xMANu8Xyp2dngPlzsKpLSmAUc/B6+ezGhEUzaFxF3gesJr0WRPWzGUaE0Q+MONRamQLoySs9NlMM8iPqy9FsdmACZcXnx2emPb7SQklSrNFeGRDvqHIIF+5ciXWrFkz7fUHHngAq1atSrlRRGZgomWL6pR5KyUb5KLoRvYMcp2OQ0O5oAQsxfPWESZ2x3I5lWw8pSoqa5lUPn84bp8fB4YdAChkPZMwD/mghIOodCGeuCcoecaoKy2AQcfB4wvgiFIhRVHQLXLzazHpcfO5gvf7wTf2iXPTy58O4JNDE7Ca9PjWOW3TrscOLfdkMGQ9lVSRZOGxbO52ef0xhZJiMT1k3aS4fVrELjNkXa/jUBxc0ybCDPJMlTxjGPU6fG5uBQAhyiNVhu1uMZKJRYflCnodh/qgiOHBEUeWWxMi/IAslyIOtEJjsFRcLOFCu8L9JTPsBianRC/xTDHIDXodPhd8tmPNGdrOIc9fUTdFBvnbb7+NFStWTHv9wgsvxNtvvy35On6/Hz/60Y/Q0tICi8WC1tZW3HPPPeD5kFfkqquuAsdxEf8tX75cSbOJKESjU0FJHUBGHXJ39kPWAXmhsB2sHFxtSZiHWEHIul2aorKWSSV0Npz9R+zwB3iUWowZzbGc6dQFPeT9KoSsdw07sOaVDtz0zEdY80oHuoalbXrlnLgb9DrMKhGel9vWfSzrPgyWKsL0H8K57OQGNFcWYtjuwVWPf4BvPf0hvv+3TwAA1505N6ahPL+mGBwnGPpDKVYbGJJskCsPCU8Wsh7+PUjxkk95/WLqUbk1JOqmtH1aREkuKlsDx11e8cArG3MbC0H983sHZT+b4XQNO3DneuFZqLSacjJPU2t55F3DDjy2uQuAcCit5HuZ6TQmqEXODlWL5RrkwWit3qAAoEmvQ4Fx5gQNi6kuCQ1y7R1QsEP9fAxZV3T8YbfbYYpR/81oNGJyUnrI1Jo1a/Dwww/jT3/6E44++mhs374dV199NUpLS3HzzTeL71u+fDkef/xx8WezOXeNG63A83yEF1gJcj3k2RR1A+QZ5GJodW1xWA61gpB1uzRFZS2j1sZ7T9h4Iy9B5mAh65NTPrg8/piq21JYt70Xd67fCY7jxPqlv3+rE2tWLsaq9oaEfyvnxH3d9l4cHheMm62dI3i/a1TyfRih0mIx1im9Dqe3VaF7pAfbusfAAWBHwPGMZItJj+ZKK7qGHdgzYItp6EvB7fOLfRHtvY+GtWXI7o5bLzYWPn9A1L+IZ5DrdRyKCwxi/mSyzzMeFMjT6zgx1DOVg0otosQgL7UYcWjchQmXV0yFqstQybNwWNv7xlw4PO6S9Wwy2PPN/CGjDg/OXbtJ1jW0gOhN1YBBHt2n3cPOnOzTbBMesh49F9pSFHXrGxPGSYkleQWQfIId4m3rHsOU148Co7Av8Ad4RbXdM0UJecgjOfbYY/Hcc89Ne/3ZZ5/FUUcdJfk67777Li655BJcdNFFaG5uxle+8hVccMEF+OCDDyLeZzabUVtbK/5XXl6upNlEGIOTbky4vNDrOLTNKlJ0DWaQ26Z8CQVURFE3hYaAWkitbxx+WLGwrjgiVzI8ekMKUsNTtQxr+4TLKzm8NRYdA6Swng2KzQYUBp89pXmVgudsJwK8sGCH/3vH+p3oTuL1sUn0kLP7MHjIuw8jvAZ5rHuE54+HP9F3/+PTuPdYKCqtK8/TZQd0Rj2XNDyStd3jC4gbJCn0TwghmCaDDrMSGNpy8shD4eqhTWs+GeQ8z8uuQw5Elj4bzFLIetewA7/esFf8We6zya7Bnm/2PPDBa8l57rSAVkqf5VOfZhuWhmBz+8TDQQbTfpAbss6iW/rGhHFSKiGdKp9orbaitqQAHl8A27pHxdfD1xotGuQhlXXykAMAfvSjH+HLX/4yOjs78fnPfx4A8Prrr+OZZ57BX/7yF8nXWbJkCR599FHs3bsX8+fPx44dO7B582b86le/injfpk2bMGvWLJSXl+Pzn/887r33XlRWxs5tcrvdcLtDGwTmsfd6vfB6s/cFsntnsw3hfNonPIDNlYXQIwCvV76hVRg2ekZtrrjKsvbgg1Ng4LL6+eeUChvInhFHwnYcHnfBNuWDQcehodQMX0DoG7cvgDH7lKxJ6sikMNmXWwya+e4TEWucWg2CZ8wf4DE44VAckrk7KB4yr9qaE32RT9QUm9E14sShUTvqS+VHazz7/kFwEb7kEByAZ94/iO9eMG/a7xgTQYPOakw8B8i5T6I59UgwPL+8cPpzp/SzzKu24mUAuw5PKB6/A+PCJrzCaoLPl9jINnDCIabD48fAuAMFMerFxuLAEWHNqy+zwO/3we+P/T7m6R61u+D1Jj6UHZoUDjFLLUbxs5cWCOf5ow4Pptwe6GPUdc8V3F4/vH5hPJh1vOTvt7hAOOgatU+JKSFV1uljLp3rf6rPplrX0Aqzg+kuB0fsWV1ncrVPtbZXBQA9hDVs0ObGgSOTWFxfKv6O7S8tMveXhUZhvhI95AW5sUdTk9NaK/C3jw7j7T1H8LnmMgDAmF2Yx8wGHXS8MtsgnRQahO9twuXR5FiNhdT2KTLIL774Yvz973/Hz3/+czz//POwWCxYvHgxNm7ciLPOOkvyde68805MTk5i4cKF0Ov18Pv9uO+++/DVr35VfM/y5cvx5S9/GS0tLejs7MT3v/99XHjhhdi6dSv0+uke1/vvvx8//elPp73+2muvobAw++UmNmzYkO0mAABeP8QB0KMkYMNLL72k+DomnR6eAIcXXtmAqjh2Wu+AHgCHPZ/txEsDOxTfK1UOOwDAgM7BiYSf+dMxoW+qCwLY+NorAEKfc/2Lr2GWRfo99/UKn71n72d4afTTVJqfUaLHqVWvx2SAwz9eeQMNygIqsOOg0BcjB3bipaGdSd9PqIfBqwOgwytvv4+R3fKVy7ft1SHAcxC2kpEEeB7bdnXiJd++uH/f2SPcv2vvbrw0sUvV+8SaUzu6hfsd7uzAS7bdqnwW+4gwL3yw9xBeeqln2u+l8FlwbjH6pyTNuxZODwc4/HPDW2iVqL357qBwjwJf4rnd6xD66O2t2+Hcn3hMfBT87Jiyi9f08wAHPQI8h+f/+TKKtZduKBmbF2Dbobde3wCpZwu2YaEPt+34DN1HdAA4HPjsQ8QbHulY/1N9NtW6hlboY+v8QOJ1Pt3kep9qZa/KsELYP7zwxrvoqwrNV4eHhdd37fgQ/oPS17auI8Kcxg7i3LaxrI6XbGC1CX3w8kddOMa/HwBwKPj8mDi/Jvtjz6jQ5r6BEXGMam2sRuN0SkufURyPcNFFF+Giiy5S+ucAgHXr1uGpp57C008/jaOPPhoff/wxbr31VsyePRtf//rXAQCXX365+P5jjz0WixcvRmtrKzZt2oRzzz132jXvuusu3HbbbeLPk5OTaGhowAUXXICSkuzVPvZ6vdiwYQPOP/98GI3Z37m88fwnQE8/zj5+PlacPVfxde7/7C0MTLpxwqmn49g5pTHf80jXVsBmw9LPnYwzs1hGxeH2Yc3ON+D0cVh6zvkRZWvC6XnrANCxH+1tdVixYjEA4Jcd76B3zIVj20/DSU3SUyZ+s3czACfOO+NUnNpSocbHSCvxxunDXVsxOWDDwuNPxlnzq2Vfd8zpweTWTQCAr3/pAtn5XkRqvOn8BPt29KO2ZSFWnNki++93GfZhx+Zu+GOkbOg4DicfNRcrEnh7njz8ATA+jiUnn4ALj6lV5T6J5tRHurYCEzacs6R92nhV+lmOHnHisb2bMeTWY9nyCxR5hJ0fHgI6PsPc2VVYseKkpO//Y9/7GO6dwLxjT8Lyo2sk3WPXa/uAA104aUETVqxYFPd9L018jH27jqBlwdFY8bnGhNcc+6AX2Lsbc+trsGLF8eLr9+x8E2NOL44/9QzFWiRa4OCoE9i+GVaTHl+46ALJf/fJq3ux9Ug3ZtW3YPJQLwAeX17++Wl55Olc/1N9NtW6hlawTfnwi51vwOHjcMbnL8ha2G2u9qnW9qqMTVOf4sBHh1HVtAArzgrtWX+x+23AOYVzzjgNxzeUSb6ecdcRPNX5sfjzvKY5WLHiWBVbrH3abW78+YG3cMjJ4XNnnYcKqwnbuseAndtQVWLFihVLs93EaVR1j+J/92yHvsCK888/VZNjNRqp2mqKZqpt27YhEAjg1FNPjXj9/fffh16vR3t7u6Tr3H777bjzzjtFo/vYY4/FwYMHcf/994sGeTRz585FVVUV9u/fH9MgN5vNMUXfjEajJr4wrbRjz6AdAHDUnLKU2lNWaMLApBsOLx/3Ok6vEDNZWmjO6mcvMxpRVWTCsN2DfpsXlSWxIyb2DQmnWYtml4rtrS42o3fMhfEpv6zPMOwQQnVry6ya+N6lEj1Oq4vN6BiwYXwqoOhzdA4LE1JDhQXlRTJCDAhVqAvm4A07vIq+v8tPbcL/BpWCo+EBXHFqU8Lr2oOVFiqKLAnfp+Q+sebUkQTPndLPMndWCSxGPVxePw5NetBaLT9UZMwlhKlXFyfuB0Z1cQGACVnzTl9QXKy5ujjh35QHKz/YPcmfadtU8PuzRs7hVUVmjDm9mHArmxe0AtMHspoNsj4H68OesSn4Ajw4Dqgrt8Kojy3Pk471P9VnU61raIUKoxEVVhNGHR4M2LyoKM7OenP5qU14NIf7VCt7VUZzpTDfHppwR7TLzqo/FBXIam9ZlL5IuTW7+9NsMKfCiAU1xdgzaMP2nklctLgOTq9wgFRi0db3zyi3Cs+zzR1aE7U2VqOR2jZFom7f+ta30NvbO+31Q4cO4Vvf+pbk6zidTuh0kU3Q6/UIBOLnLPT19WFkZAR1dXXSG0xE4PUH0DkkGOSpCmxJKX3Gyp5lW2UdkFYShYk2LaoNRVSEFI+lK41PeaUrKmud6hQFnMLLyBGZh+X9DyisRd5SZcWalYsRLkLLcYCOA9asXIzmqsT5zZMuJuqWeA5g9wl3Putl3AcAAgFeNMhjibqF30Ov4yL+TXQPnY7DfCbs1q+sHjkTdauSWHWhKijKNiyj1FqyGuQMOaJuo0zULUq1Pl+E3eTWIGewPmQVJKqKzHGN8XQRPp7ZY8NB3jPTUmXFjee0iT9LfSa0ilQB13TSXFmIxnLBeGDfRy73abZprBT68mBYLXKe55WLukUJjMaLmMx3mNo6q0duc0svUZoNqOxZFLt27cKJJ5447fUTTjgBu3bFzw+M5uKLL8Z9992HxsZGHH300fjoo4/wq1/9Ctdccw0AobzaT3/6U6xcuRK1tbXo7OzE9773PbS1tWHZsmVKmk4AODDkgNfPo8hsQH15aqfHUjZ1To+yshTpoLGiEB/1jMddqN0+Pw4MCcJL4SGYlUXyN8ajDumKylpHLP2msAbzHlJYzyq1rBa5QpV1AFjV3gC9jsNt6wQdiGNml+LBK06QtLGUU/ZsVXsDTm6uwIW/fQcurx+XnliPG89pk7yBnXB5xaoPFTHKnoXf47ntvegbc6G+3ILL2huS3mNhTTF29I5jz4DgTZCLWHXBKq3qQpWVlVyU/tyxDWsyg1zKYSqDKRuXRwl3sgODVGuzZxultYzZvM7KzGWjBjkQGs9rXunAy58OoKTAgH/cuFSW0WcOlj1qqSrEMXPKJD8TWqSxohA7esezWot84+4jODjqgsnA4YqTGzHq9OZ0n2abWGVr3b4AfMG5XmnZM0au79GUsnReJR7b0iXWI5ezVmcDdlDg9gXgTqHqjxZR1ONmsxmDg4OYOzcy97i/vx8Gg/RLPvjgg/jRj36EG264AUeOHMHs2bPxn//5n7j77rsBCN7ynTt34k9/+hPGx8cxe/ZsXHDBBbjnnnuoFnkKMG+lGvWgkxnkgQAvlj0rNGe37BmQvBZ55xEHfAEeJQWGiDzA6qBByuobS4Ftoiut5pyvb5mqJ2x3ijXvidRghsKgQg85ozCsdOHssgLJHmu7hy3y0jY9zVVWtM0qwieHJnDhMbWyNrBsjJZajDAZ4nsrm6usuGP5QsnXBYQyiEBoPMtFNMhleshHJEbmTDi94lzcUJH4sFWWh9wRz0PODgykRw5pEeYhl+tli97E12ahBjmjucqKB76yGK/tGsTElE+2xsHmfcKG/JrTW/AfpzWnoYWZI9u1yP0BHr94tQMAcO3SufiezHmGmA6LeuifcMHjC8Bk0EWU6LLKjMCM9pDPVIP8lJZKGHQcekad6Blxat4gDz94seeZl1xRj19wwQW466678I9//AOlpYKQ1/j4OL7//e/j/PPPl3yd4uJi/OY3v8FvfvObmL+3WCx49dVXlTSRSECHisZRsk0dyx8H5E+Y6SBZKNuewVBodbgRHQodlb7xlLv51jLMIGehwHIIBHjsG2QecgpZzwbMUBiyu+EP8IpLVIU/5wOT0g5n7B4fmK6RnEW+pqQAnxyakF07fYg9d2lIE2Fz5h6lBrktfih9LOQehPUGS/hUFZmTpgjJMcjHxTrksUPWR/IlZD1VgzxLHnJGcYERxzeU4d8Hx7Bl/zAuPyWxWB/D5fHj3wfHAIRCWHOZZAfv6eZvHx3C3kE7Si1G/OdZrVlpQ75RXWRGgVGHKW8Ah8ddaK6yhsLVTXroZK5p0ekpM9UgLzIbcEJjGbZ1j2Hz/mGxvnf0gYVW0Os4FJkNsLt9sLkTlw7NNRQlO/3yl79Eb28vmpqacM455+Ccc85BS0sLBgYGsHbtWrXbSKgM20wuUtEgn4yzqWOhgDoOKDBmNrcuFo1JcshZbijzhDEqrfI9xGzzXSkxPFXLpBKa2jvmhNPjh8mgQ3Nl9ksPzkSqisxiLflU8n3DjTep3nZ24m7S61BglB4lU1sqjDm5ee9inrZEo1cO7ECpZ9Qpzm1yYBE26TLIe8T88eSpSMnm7nBYDnmFNSpkvUh+SL0WYRv7lA3yLHrIGdE5oVLY1j0Kjz+A2aUFaMmDcGopWjHpwu3z49cb9gIAbji7dcYaemrDcdy0gxal2g8AYNTrYAlbj2by98TmjC37h8M85NrtD3awz9qaLyiykObMmYOdO3figQcewFFHHYWTTjoJv/3tb/HJJ5+goaFB7TYSKrNH9JCn7q1M5mVhm1aryaCJsG02ofeNueDzT88/iRc9oGTjOSxz861lKq3KQ1N3Bw855tcUwZBhwSNCQK/jMCt4qNKfQth6+HM+ZHfHfIaiYeIrckPglArRiZEpaXjuKqwmsR/3DMrzkvsDvBj6XSnRe18pMyS8R6KgGwCUFcrwkDu8wb+JJ+qWHyHrSkXdGDVZ9pADwNLg5vrdzhEEAtLqMrP80dPbqjSxTqeKePA+5hT1JDLFU+/14NC4CzUlZnx9SXNG753vxDXIFeoTha9JM9kgZ3PGls5hcT3Qasg6QAb5NKxWK5YuXYqLL74YZ555JsrKyvDyyy/jhRdeULN9hMpMuLyiAM2CmgyErAfzx+Xm5qWL2pICmPQ6+AJ8TMMknviY3FxOICw8NQ9C1quDn3/U4Za9wREPgGooXD2b1KSotA6ExL0ABL3tyZ8HtmjKVbGtLRW8vHJD1ofTGLIOKA9bH3V4EOAFdfqKQok55EGD1+72YSos/ScecgxyqSHrHl9ADA2MbnflDA9Zjx7T0fXHs8EJjWWwmvQYdXiwe0Ba/VvmTV86L/fD1QGgrtQCg46D18/Lnj9SwTblxUNv7gcA3HrefFkRQURyoiMfHGSQq8JxDWUoMhsw7vTig65RAFo3yIXvKt8MckU9fuDAAVx66aX45JNPwHEceJ6POFX1+5NvHIjssDfo1ZldWoDSwtQnoGSbOrbR0YKgGyCULqqvsODAkAO9o05xggeEPEm2eM+vifaQCxtPW3BjLGWhlauorGWYWnWAF/qpUob3USwjV0eCbtlEFHZLYYMa/ZwPTE4lDdOVWvIsGqXtlZunLZdFdSV4Z98wOvqlGTsMFq5eUWiSHClSUmCASa+Dxx/AsN2N+vLEhjbbqDbIMMidHj+8/kDccl3jLqE/OW66ARou6ha9D8gllJZO0us4FJsN4oGFFjzkRr0Op86txBsdR7Bl/zCOnl2a8P0jdjc+OyyM5SWt+WGQ63Uc6sst6B4RhKrmlGWmFvkf3unCqMODuVVWrDqpPiP3nEmoGbIORIZlz2SD3KjX4XNzK7Bx9xExLVHLIeslzEPu9iGfkiAVechvueUWtLS04MiRIygsLMSnn36Kt956C+3t7di0aZPKTSTUhG0i1VK7TlY6h5U804KgGyOe4AsLV68vt0ybjNjGGJAeti7mi+aBh9yo14khrnLDU/eQwromYIZzKh6jaQa5BG+7UtVWlkMuN8SePXdyDo3kwCKLOmR6yEVNCRmee47jZIWty/GQh89xibzkLCqizGKcJgbIDj08/gAmc9hbwTb2SrxC4YcUWsghB8LzyEeSvvfdTuE9C2uLxUiofCDTeeTDdjf+8M4BAMB3ly2g9Kw0EL13s02ltr9kz7ugbzKzv69oMccS8pBnHEUjcOvWrfjZz36Gqqoq6HQ66PV6LF26FPfffz9uvvlmtdtIqETXsAPPfNALABh3edE17Ej5mqKH3Bkvh5yFrGvDQw4kMMj7Qwrr0YRvjKWGrafbU5dplJQ+c3n86BqZXtedyDysBNg/Pz6MNa90KHr+mbfbHLyWFO+1mENulnfizryNtimfeLAnhSFR1C29IesdAzbwvPT0DaW57VKVzPcP2tATrEH+r0/6k36/eh0nbkjH48zfQFjJsxhh9gVGvVi7O5eF3ZSGrHcNO8RUAqOe00w9dpYT+kHXCNy+xBGLLH98aR6oq4fDDpD/950Diue7rmEH1rzSgZue+SjuNdh7Vj2yFQ6PH/NrinDhMbUpt5+Yjrh3G3GC5/lQyLpC41E8YOSAB17do8qeOFeJfv7/+uEhzfZHKIc8v8qeKTLI/X4/iouFTUlVVRUOHz4MAGhqasKePXvUax2hGuu29+LctZuwK2h07ugdx7lrN+Ev23tTui4zyG1uX0wBmXBRN60QzyDfMxg7f5wh1yBNp7hUNlAibLfviA08L4jCVedJP+Qi67b34n/fFrw3feMuPPr2AUXPP/OkzqspAiDNez2p0ENeXGAUDSQ5ee/DNhaZkp7x1jarCHodhwmXF4MSS78BqRjkyZ+7ddt7ccFv3gabgZ9+v0fS9yslj1wseWaNfcAh96BSiyipQ87WVFYK0uvnVVlT1WB+TRGqisyY8gbEcmax4Hke7wTrj5+eJ/njgPDd/GtHPwBg3xG7ovmOfb+Pvn0AL+48HPMa4nve6hSNl31H7Hj+333qfiACQCjqweb2YcLlDUW2KMghX7e9F5v2/P/t3Xl8XHW9P/7XmTWTfW2SkrQp3VK6QCGlUhC5tlCgD0StUJHLRfCLC/jT6hWLDwEVRSiKl+UKilfxelmLIPeqFSzQAi3QEuhCoXvTJilN2jT7Psvn98fM58xMMsuZ9cycvJ6PBw+azGTmzMznnDnv835/3p+TALx9MuL9TjSK7c3Bx4kXtrdm7PvBDHmAefPmYefOnQCAxYsX47777sOWLVtw11134fTTT0/qBlLimjoGcNvzuxAYL3uE9781z+/CkQSugskTOiFC7xwDvqZuuRnS1A0IX8omu4GHy+SWxRCQuj1CXSoolhLVTBZPR+XArvXZOr8028n9P/Bymdsj4tr/ZeAmeyxoy5DHv4xKZaFv6TONZfZCCLVkPVUXgHKsZnVpqL0am2YB/v0m3gx5uP0u1PFd6+erZemzTl+H9ZIwPUfiqZzJNHIOudYT+1DvOZCc79RkUBQFF8woA+DPgIfS3DmIY91DsJoVnFtXmq7NS6lkHO8CP1/5t2MfI+g+AU8mMmQMGFGO1ax+JzR3DvpL1uOobLnt+V1Bv4v3O9EImjoGcNsLHwT9LlkxQioUBMwhN5K4AvLbb78dHo93uZu77roLTU1N+OQnP4n169fjoYceSuoGUuLWNbaEDYYURcGzCVwBs1n8azmGyrL4u2Bmdsm6xyPUhnfhmo/FEpB2DoxCxNhROdPFc+KtruuehCX2KD7J2v+FEGq2W1aRaJtDHv8yKuq8d40Z8oFRN4ad3u+mVF4Iq6+KfR653G9i3S45Fz5cOXQin6+WDHnXYPiSdcAYAflAjBnyVH6nJouWeeSyu/rCKSUZsxJKopLx2UR6DI8Arv2vd/Cv//XOuAsysT4PxS7w/C3ekvVs2H/TKdvej0KDLnsW1xF4+fLl6r9nzJiBvXv3orOzEyUlJcyCZaDWrqGwcx2FEGjtGkro8YscVgw53egeGsWUMT0PB3xzP3MzqGRdZsi7Bp3oHXaiMMeK1q4hDI66YbOYUFeWF/LvYjnxlPeJpaNyplNLZ2OYJ7mvXc7L5/xxvSRr/+8fcalL3sWTIY912TMgYKk2jRlyOTZzbeaUHnPqqwrwt13HY1r6TB4TYs3cy/1OlkaPlcjnqykgH9BWsp7Na5H3xXhin+rv1GSQS5h90NqNnkFnyFVVZPb8kwaaP56MzybSYwDAse7Ix6NMGQNGVFuai3ePdKG5czDukvVs2H/TKdveD1lt1z/sAiIvIpFVkhYplJaWMhjPUDUljohXv2pKElsSJNJJ3eBIZq1DDngb95T5Ti5l2bpcr3XmpPywAXR5DCee8WbDMpnaXCpMYBCKmiHnkme6Sdb+L/dvm8WEqb6LVsd7hqM2NutNIEMu13Vu15ghT1ffBlnxsSeGpc/kHOtYV12Qna/DXQiL9PlF+3y1ZchlyboxM+SBzaG0ntin+js1GaqLHJhekQePAN4+PD5L7vYIbPFlz400fzwZn02kxzApwLI5k7BsziSYwpzyZsoYMKLAxm7x9H4AsmP/Tadsez8KHSxZpyx1dUNtxKtfqxpqE3r8SCd1/qZumVOyDoyfR65laS71xFNDhvhUnPNFM1lZjCfeJ/tGcGpgFIoCzJzEgFwvydr/5f5d5LCqa4QPOd1Rl7tSM+TxlKzHmiHvT0/fBnmcOHSyH063R9PfqBfp8mI7Jsj7h9vvrjqnJmzpbLTPN7aS9TBzyKNcMMh0Q063+v5pPbFP9XdqssjOyaHmkX/4cQ96hpwosFuw4DTjpJmS8dlEegwAuH3FGfjhijPC3p5JY8BoAkvW410dIVv233TJtveDTd0oa00rz8PalQsQeP3LbFJgUoC1Kxegrjx0ibZWkdYiV0vWMyhDDoyfRy6bM82JMNfZnyHWXrJupIA81pJ1+Z7WleXBkWEXZCYSuf8HZnNMCmLe/wMDcofNrAZz0crW/XPIEyhZz7AMeU2JA/l2C5xugcMnoze8EUIEZMhjLFkviFyyHjiP3ax4j+taj++Rjt2SDMiLw2TIK+JYfSGTyIZuiuKd6qBF4D4l3+tkfqcmy/kRAnI5f/wT08sMM60KCP5sxh7ztH428jEkJcTnmy1jwGiCAvLh+AJyfnbBsu39kNV2vQZb9iyzoiRKmasaavF+czee3taMaeV5uHReFVY11CZlR4ucIfeWrGdSUzcgVECuIUNeoL1k/aQhA3JfJmxgFEKIqFNUZNUB54/r76qGWiyqK8W1//UOjnUP41/qJ+GOFWfEtP/3BgTkgDd73TPkRFvPsDqnPJS+OJc9AwKaumnOkKdnv1MUBbOrCvDe0S7sbeuNeNwAgN4hF0Z9mfSyMHOxw5GvpWtwFC63Jyh4crk9+OXL3qVGrz9vKnLtFrR2DaGmxKHp+B7LHPLSsHPIY5/KkkkCs2yxTLuT+9SzjS0xvefp9InpZTApwOGOARzrHsJpxf7SU6OuPw74P5sntx7F7zc3wSOAP3x5ES6aPUnzYzT4us6bFODSeVWYWpY37vPNhjFgNPLc7ePuIfXYE8865PzsgmXT+2HUDDkD8gnoyrMmY/WyWUl7vIgBeQY2dQMCA/IhDDvd6rIOkYJHWToa6sR4rFNpKp1NJxkYjLo86BtxoTBKxlPLRQ5Kn7ryPCycUoJj3cdx/vTymL9oe8YG5EU52NfeFzV7nciyZzIgP9k3EnWfA/z7XUUa9jsZkGtp7Nbhq6opyLEgxxrbxcmSXBtMire7c+fAKCb5qgYA4Ln3WnG4YwCleTZ8b/nsmN/j2OaQR1n2LEtL1uMtewW8+9SaS+uTvUlJU5hjxZm1xdje3I0tBzpw9SJv6emw0413j3jXHT7fgAE54P1sfrjiDOxt68ObBzrQ1DGAi2Zr//vNB7xrVJ87rRSPXHtOxOfJ5DFgNBUFdtgtJoy4POrKE/HsuwA/u7Gy5f2QF/edbgGnthljWcE4dUoU1dgMV7JEWst2UM2QZ1ZAHjiH/EB7PzzCmwGqiFBOWprnPTEWvhPjSOLtqJzJHDaz2gtAy8m3LFnnkmeZQzbm6h6MPZs5LiDXML/b7RH+TrhxZDHK8+ywmBR4hL/qJBJ/M8XU73dzYlj6TO4v8WTuzSZFzU4HvgfDTjceeGU/AOCb/zIjrgse0dYhd7k96ucersu6nMoyMOrG0Kg75m3QW7xlr9niAnX5M3/ZeuORLoy6PKgqzMH0iszLgCWTuvzbgfDrsYey2cAVBNlMURQ1oSIZdd+l0PJtFshipiEDJckZkE8g8sSqOEymI15Fvo6HobIs8mRc69y8dJlS5j2gt3YN4qPjPQCA2ZUFEUsWA0+Mo5Wtq6WzMXZUznRqA6cor9/l9uBAez8AlqxnEpnl7ExCQF6poZy8P6ALajwBucmkYJJvzGmZR57O3g2zfReaNGXI1SaP8R0P1P4VAfvdH986gvbeEZxW7MC1n5gS1+NGy5AH/r44zIXcfLsFNov3VCIb55HH26k5WwTOI/f4ute9efCkepvRV8eRAfU7h09pbsDo9gi8dcjbgf6CmRUp2zaKz7iAPI7vFspeJpOCfF/V7XD2XQMOiwH5BDL2hDpZ5PqmIZc9G83Mk52qwhxYzQqcboGNe70nJ1qW5tK6xE9Hn69kPcaOypnOHxhEfv1HTg1ixOWBw2oe9+VJ+pFZTlmGHAu5fxeOyZBHWpJMZl7tFhPslvguysnAX8ua56cSDHxjIadiHOseiljyDfgbQcZ7oWDscadn0IlHNh4EAHzn4llxv7fRAnI5TgpzLGGnCyiKolYCZXNAHs8Fo2xw9pQSOKxmnBoYxb5278Ujdf1xAy13Fs4Z1YUoybViYNSNnS3dmv7mg2M96Bt2oSDHgvkG6kBvFLUB5xQmBXDEOA2Isp88DxliQE7ZKGUBuYambpkWkJtNCmpLvAf1N3xzxbRkcss0dBQWQvhPwGPsqJzpyjV2VJZZw1lVBTCFW6yV0k6WrHfF0YCrx1cbJvd3uUb48QgBeSLzxyX5PFoy5CfTWLJe5LBism/b9rdHzpInUrLu/bvg/e63bxxC77ALsyrz8bmFp8X1mID/sxwcdYfMHqpLnkVpROffvuxr7JbIHPJsYLOYsPh0b4OyLQc70Dkwig8/9k4nWjKjTM9NSwuTScGSEGX7kcgLFkuml8HM76+ME3iRPy/GZoxkDPIC6rDLOJ89A/IJJN0B+ajLo3YWzrR1yAH/VdZB37zH2RrmOocqHR2rd8gFp9tbGhhrR+VMJwOdk1FOvNX54xG6b1P6JSNDrpasF0bPXMslz+JZg1ySz3M8SoZ82OlWLwCkq3eDzJLvPd4b8X4nE2zyWBZw3DnRO4w/bGkCANy6vD6hgKEw4Lsg1AVVeeGmJMySZ5LWyplMZPSSdSB4Hvnbh05BCO8UrUkFOVH+0hgircceipxvzvnjmWlqmT8gLzDwfkvhyYCcGXLKOh6PUNfsK0xVQD7mJD+wwU+mdVkHgq+yKgowqzI/6t9oKVmXWbp4OipnOq0n3rLRlZZpAJQ+cg55fBny8V3WAe9yVyOu0N+KiSx5JmkpjZfbAQA2swmFjvQcb+qrvRfxojV2O5Xg3PZy9ULYCB589QCGnR6cPaUYy+ZoX8YpFLNJUU9oQwbkMkMepe+IlsqhTGX0pm6Afx751sOd2LjvRNDvJgIZWG9v7g7qaxHK0Kgb7x01dgf6bBd47sb54xOTrLpjUzfKOn0jLghv0jYFGXKb+hyyaQwA9Pvmj9vMJrXpTyYJPAErtFvQ3hv9ZFKeeEbq+JzoyXcmM8H7+b66px1rX9qLJt9ycYGaOgaw9bC3Ic725q6Q9yF9qCXrg9615GMxdpWGklyrul+fCLPv9I14/yaRknWta5GfUsvVbWkrYZRNHv/xwfGI+8PO1m4AUJdfipX8rP75YRue2toMAFhzaX1SXmdhhClH6pJnUUvWtTV7zEQDBp9DDniz4SW5Vgw53Xj+/VYAwMxKY3dXD1RbmosppblweQS2NZ2KeN93j3Ri1O3B5KIcTMvANZgJqCnxB+Qn+kbCHnvJuORX35ttCn75zwOG+PwzL0qilJAn0znW+JsrhSNP0IXwZ8QAYFB2WLdnXpZ4XWMLfvP6IfXn3mEXlt6/Cc81tkT8Oy0nnol2VM5U6xpb8OCr3kZSbb0jeOyNw+Pes3WNLVh6/yb0+sbB33e1aXpfKT1kYDXi8mDIGVut19gMuaIoUZc+S2qGPMoFs46AgDwd1jW24Ofr9wAAOgedEfcHue0bPop9f1jX2IJf/HMfAKB/xA15GaW5czApryNSD5BYS9a1LE2XafomQMn6n99vVS+uyOtwP/zL7gl1XPYvfxY5IJfzzCdCB/ps9dddH6v/7g5z7CXjWtfYglf3eCt9jg0q+K/NRwzx+TMgnyBSNX8c8DaNkV0uA0/qBnwl63kZVq7e1DGA257fhcD8oADgEcCa53fhSIQrbRUaSrbTufRSuoR6z9weEfSeyfsEFEnALYSm95XSI89mhs3XLTuWeeRCiJDHEDUgD1NOnpSAXG0eNxQxqy9XNkjHfqfuD4FjXcP+4BHajjORnkdK1j4VaS1yWbJeGiVDLi+CZOUccoOXrMsxNNZEOy7759GfjHg/df74BOhAn41Cjeexx14yrvGfv2KY80wG5BNEKgPywMcNCsjVzENmZcjXNbaEvfKtKAqejXCVTcscciOWrEd6zzwC+PLj23DD49tCBg5A9PeV0kNRFBTHMY98YNQNty+yDDyGRFuSTAZ5hQmUrMumbsNOD3ojTBjrSHBpsVika39I5FilVaQMeeeA93fFUeaQV2RzyfqosQPydIyhbLBkehkUBdjf3o8TYY5Xp/pH8JGvQeOS6QzIMxHH88Rm5M+fAfkEoWdAnmkN3Vq7wmfahBBo7RoK+7f+TNBo0Hz5QIl2VM5Ekd4zwLvu+JFTgwh3j2jvK6VPaZ5/HrlWcr+2mU3Isfq/NqoKvYFYuAx5bxKWPcuxmtWAMNI8cpkhT8d+l679IZFjlVbhmnICQLfMkEcrWS/I4nXIDZ4hT8cYygYleTbMm+xdU3zLodDd1t865C1nr68qQIXBliw1Co7nic3Inz8D8glCl4A8QzMPNSWOiFfYakocYf9Wnuy7ArrWj2XEkvVI75lJAS6aVYGLZlUg3ApM0d5XSh81Qx5DyboM1god1qBxUFXk/UzDLUkmlz1LtGFWtLnqgH+/S8eSZ+naHxI5VmlVlBshQ+4LyIs1ziHvHnSGXM88k8k55Ebt1pyOMZQtos0jl8uicbmzzMXxPLEZ+fNnQD5ByJOtZC95JoXq1Dsw4p1Dnptha5Bf3VAb8QrbqobasH9rt5jVNZXDZYOMGJBHes8A4MefmYsffWZu2Nujva+UPmqGPIaSdf8FveCgJdqSZMmYQw4EdFrvCX/1O537Xbr2h0SOVVpFKlnv9l2IiTaHvNhhVddD74xjST09yUquTLtwnCzpGEPZInA98rHviRACb/rmj5/P+eMZi+N5YjPy58+AfILQI0M+mKEZ8mnleVi7cgFMincd3sD/r125AHVRljpROwr3hT7xPGXALuta3rNE31dKj+Lc+EvWxx4/qop8JetRM+SJHXf8zeMi9W5IX1O3dO0P6dinwgXkHo9QS9ajrUNuMilq0H6yL7vK1o1ess7jsl9DXQlsFhPaeodx6GRw86fmzkEc6x6C1azg3LpSnbaQouF4ntjGfv4KBMwKDPH5G/MbiMbRIyDvlxnyDGvqBgBXNdRiUV0pnm1sQWvXEGpKHFjVUKtpZy7Pt+Nwx8CEypAD2t6zRN5XSg85HziWDPnYNcilykJ/UzePR8A0pkZbZsgLk5Uh11Cynq7eDenaH1K9T4ULyHuHnWqH+Ggl64D3eHeybySr5pF7PEJdDcSoJesAj8tSjtWMRXUl2HLwFLYc7MCMSfnqbXK5s4VTSgy9BJ4RcDxPbPLzf3rrUbz70SEsOuN0XLN4atZ//jzqTBC6ZMhll/UMa+om1ZXnYc2l9TH/XXlB+CV+BkddGPSd4JUbsCmMlvcs3veV0iOeOeTdQ97gfezxY1JBDhQFcLoFOgdHx12E6ktCUzcgMEMeumTd5fao853TeSEsXftDKvepcAG5HB/5dgtslujFdOUBDS+zhexzAhg3Qy7xuOx1/oxybDl4Cm8e6MD1S+rU36vLnXH+eFbgeJ7Y6srz8L1LZmK96wAuv2QmrNbUxDbpxJL1CSJchitZ5NzS3hBN3Yx2tbk8whI/8mQ0x2pCXobNnScCEuuyPvb4YbOYUJYXvtO6bHxY6EjsGFCpZshDZ1+7Bp0QAlCU6POdKVi4dcjlXPBoS55JWpaEzDT9vovGFpMCu4aLDpT9ZMD9zuFTcPkaELo9Qu2wzvXHiUgP/AaaIFKeIQ/RqTdTm7olSgYgoU48T8qy2Tx72E6QRHoqSeIccsA/j3zsWuQut0etFklWhjzceudyXyzNtanNxUgb+Zl2jwnI1SXPNF7gkBnybArIZUO3PLuFx+sJYu7kIhTnWtE/4sLO1h4AwIcf96BnyIkCuwULTivSeQuJaCJiQD5BsKlb8siS9VAZ8g5fQyMjlquTMagl6wMxLHs25JsLHiog9wXLx8dkyGX2EUi8y3q1L0PeOTCKYad73O1G7duQDvLYPTjqDlqyzJ8h1xqQe9/7bCpZ7zN4Qzcaz2xSsGR6GQD/Mmdy/vgnppfBYuZpMRGlH488E4Q+Td28Jzu5BjvZiVSaKYP0CgN1WCdjSWbJOuBvuDY2ey2DnRyrCdYET3KLHFa1pPhEiLJ1tcN6Afe7WAVeZAk8fqtLnmksWS+Tq09kUYZcfkclesGIsou6HrkvEOf640SkNwbkE4DHI9IwhzxUhtybyTLaXOpIpZmnAkrWiTKRzHgOjrpDZptDiRiQqw3XggPy3iQteQYAiqJE7LTODHn8zCYFBb6LpoHHb9kkT3uGPHzlUKYKLFmniUMG3tubu9A5MIp3j3QB8AfqRETpxoB8AugbcanL14QqOU0G+bi9w054fE/Wb9CTncAMuRAi6DY1MGCmjjJUYY5FnWfdrbHTurygFyo4k0ufjQ2U/R3Wk7P/h3seILh3A8WuMMQF1djnkGdfUzeWrE9MU0pzUVPigNMt8MjGgxh1eVBVmIPpFdm9bBIRZS8G5BOAPJm2W0zIsaYmWy0zZ0J4LwAAwOCIzJAb62RHnngOO/1NqySZHWKmjjKVoigo8ZUhd2pcizxShry6yAFgfIY8WUue+Z/HVxofopt7Rx9L1hMRqsJJjo2SGLusdw6Mqhdl49XUMYC1L+3F//f0dqx9aS+aOgYSerxwz/HijmMAgNauwZQ8B2UmRVHULPkftjQB8O4DR04N6rlZRDSBMSCfAFI9fxwA7BYzcqze4dTjy7rJZc9y7cYqWc+zW+DwXdgYmw1i6SxlA9lpvVvDPHIhhKYu62Mz1/JCYGGSMuThmscBwKkB7neJCLX0mVyHvERjhrzMV7Lu9ohxHdtjsa6xBUvv34TH3jiMv+/6GI+9cRhL79+E5xpb4n7McM8hl7o6fHIg6c9BmU1WCclrRwdO9HEMEJFuGJBPAOkIyAMfv2fICSGEOj/PiOWAZWHmkcufy9jUjTKYDMg7NQTkA6NuuH1nraGOIbKUvG/Ypa6s4P1ZBuTJOe5URlj6zH8hjPtdPEJlyLvUDLm299RqNqkd/OMtW2/qGMBtz++CR3gD+8D/r3l+F44kIYsd+BxyxpEAkvoclNmaOgbw9LbmoN95BMcAEemHAfkEoEdAPuLyqFeejbYOOeDPxJ3sCw5o/F3WmamjzFWS51v6TMMccnn8sJlNahVMoIIcq9q4MbBsPdlzyCM2devjVJFEqMfuwRAZco0BOZD4PPJ1jS1h1wNXFAXPJiF7mY7noMzGMUBEmYYB+QTQozZkSl9AHrgGca7B5pADAWvuDvhPPEddHvW9ZmBAmUwtWdcwh1wGaYUOa9iTWDVYDgzIk7ykVKjnALwl9SxZT0xRbnCGXAihTmeQF2+0KMtLrNN6a9fQuEaZkhACrV1DcT1uup+DMhvHABFlGgbkE4A8yUpVh3UpMCCXDd0cVrM6V8tIKnzNozoCMuSyCZLZpKS8GoEoEXJesJaSdX+FTfjAOlT2ui+Jy54B/jnk7b3DQU3DeoddcLq9P3OqSHzGlqz3jbjg8r3HMWXIC3wZ8r74MuQ1JY6ImcuaEkdcj5vu56DMxjFARJmGAfkEkK6S9cClc2RDtzyDNXST5PJKgaWZ6vzxPBtMBrwIQcYhO2drWfZMy/Ej1JJkvUkuWa8osENRAJdH4FRAZl9mYwtyLLBbjHm8SbWxy57J+eMOqzmmlTkqEixZv7qhNmLmclVDbVyPm+7noMzGMUBEmYYB+QSgxxzyAYOuQS7J5lGBJesn2WGdsoTa1E1DyXqvhuOHmr0OOYc8Occdq9mk7luBjd3kPsi+DfEbmyGX88e1rkEuyZL1U3GWrE8rz8PalQvG/d6kAGtXLkBdeeLrRE8rz8MVZ04OemyzSUnqc1Bmk+Ms8LPnGCAiPRkzWqIgugTkvvW5jTh/HAgszfSfeMqTUHkbUaaKZdkzLccPuUZ44JJkyV72TD7Pyb4RHO8ZxuxJuQAC9jsG5HEbH5B739NY+46ox8U4M+QAcFVDLX77xmEcPNEPALCaFPxj9ScxY1JB3I8ZqH/Ehc0HOgAAF84qR5HDhpoSB1Y11DIQm0CuaqjForpSPNvYgtauIY4BItKVMaMlCqIlw5UMgWvZDqpLnhmzhDRSyXp5jFklonSLbw559JL19hTOIfc/T09QabwsWef88fiNXYdclqzHmiFPtMs64C0ZlpUWZpMCp0domlqh1e/fbMKpgVHUleXi99cvgtXMQsGJqq48D2surdd7M4iIWLI+EeiRIZdd1o2aIZdN3U4GBuS+RkbMkFOmU+eQDyRnDnnopm7JnUMOhC6N72CGPGHhStaLY2joBvin8sTbZR3w9jWQHfr/ZXYFAGDzwY64Hy/Qqf4R/O7NwwCAf79kNoNxIiLKCPw2mgD0CMgHfSXrRm3qJk/++4ZdGHF5X6tsNFXOTB1lOJn57BtxYdTliXhfLas0yED5ZN8IXG7v48mAvDCJGfJQgb9/v2NAHi957B4YdcPp9qgZ8pJYS9YDMuThmmZF09w5CACoLLRj6ZxKAMCWJAXkv954CP0jLsw7rRAr5lcn5TGJiIgSxYB8AtAzQ55n0Ax5kcMKi6+TupzD6u+yzsCAMlthjhVyIYDuocjZTC3Hj7J8OywmBR7hrRpxuj0YcnovVKUiQx64FvkpOVWkgBfC4hU4z793yKnOIY9lyTPAH5CPuDzqd0CsZEA+pTQXF8woBwBsb+6O+/Gk1q5BPPHOUQDA95fXcyUMIiLKGAzIDc7jEWmbQy4bAHkz5Mbusq4oijpnVQbiJ1myTlnCZFLU40FXlLJ1LQG52aRgkm/ct/UMo3/YHzzlJzMgD5Eh7/Blc3khLH4Wswn5vmN1T1BAHtt3hsNmRp7NWxUVb9m6DMhrS3NRW5qLKaW5cHkEtjWdiuvxpAdeOYBRtwfnnV6GT84sT+ixiIiIkokBucH1j7rg8VUORio5TQb5+L3DTvWE3Kgl64A/G+TPkLNknbKHbOzWFaWxm9YLepVF/sZuslzdYTUndZ5uZYg55HL/q2CGPCGBFU7yIk1JHA0qy9TjYnyN3VoCMuQAcL4vS/7mgfjL1ve39+GF91sBAN+/dDYUhdlxIiLKHAzIDa7H15zHbjEhx5ra4Fie0AkBtPd6T8aM2tQN8AfkJ/tH4PEIdHI9ZMoishy5K8pa5GqGPEq2NLCcvHdYzjtP7v4vM+R9Iy61hJlzyJOjMDAgj7NkHQhs7BZfQN48JiCXZeuJzCP/xcv74BHApXOrsHBKSdyPQ0RElArGjZYMpqljAOsC1su8uqEW0zSsl5mu+eMAYLeYkWM1Ydjpwcc9QwCgli8aUWDJetfgqFqJEE9WiSjd1IA8wpJSQgjNxxAZLB/v9QfkyVzyDADy7RYU2C3oG3GhvXcEI26oDSTLGJAnpDhEQB7rsmeAtyoC8DZQ29nao/m7ShobkC+ZXgZFAfa39+NE7zAm+S78RCO/M3e19mDLwQ4oAL63fFZsL4aIiCgNGJBngXWNLbjt+V1QFAVCCCiKgt++fghrVy7AVQ21Ef82XfPHpSKHFcPOEXzc7S0pNeoccsCfCe/oG1XL1UtyrVxKh7KCnB8cqWR9cNQNl+9KU9SAPKCcPBVLnkmVRTnoO9HvLY33XUvIsZoMffEvHQLXIpcl68UxziFf19iCLYe8c713H+vBR8d7NX9XAYDT7cHH3d6LuVPKvAF5SZ4N8yYX4YNjPdhyqAOfW1ijaTvkd6bbN34FvM3hZkwqiOk1ERERpRojhwzX1DGA257fBY8A3B4R9P81z+/CkY6BiH+fzgx54PPIckUjB+TqHPKBEX+nZ2bpKEvI7GekknV5/LCaFTXzGU5gwzV/QJ78444a+PeOoN8XkJfn2zkvOEHy2P1xzzBGfUvXxZIhl99VkkBs31UA8HH3EDzCe4ElcOqPnEe++UD0xm5jvzMDad0OIiKidGJAnuHWNbaEPdFUFAXPNrZE/Hu9AnIp18BZK7nMUkf/CE4yIKcsU+wrWe+MkCEPPH5EC3grAwLlPrVkPQUZ8sKA5nFO7zZxv0uc7BFw9JQ3YLVZTFEvwgRK9LsKCC5XD3yswHnk0dY3T8Z2EBERpRMD8gzX2jUU9gRECIHWrqGIf693QJ5v4Ay5XGYpsGS9jB3WKUuU5nn31e4Ic8jlbVpWaKiWc8h7htQMeWEKAvJqNRM/opasc2WDxMlj95EOb1BcmmuLqeog0e8qYPz8camhrgQ2iwltvcM4dDJyhjsZ20FERJRODMgzXE2JI+LV/poSR8S/lwF5qpc8k8Y+z0Toss6SdcpGxbnRlz2L5YKezFwPOz1o7fIGVqkoWQ9aXi2gZJ0SI4/dR3wZ8ljnjyf6XQUEr0EeKMdqxqI6b3f0aN3Wa0ocUJDYdhAREaUTA/IMd3VDbcSr/auiNMphhjx1ZMl658CousxbRQEDA8oOWuaQx9IUMsdqVoO4Ayf6AaQmQ67OIe8bYcl6EsnPWHatj7XDeqLfVcD4NcgDqfPIowTkVzfUwpPgdhAREaUTA/IMN608D2tXLgj6nVkBTAqwduUC1EVZTkbvgDzXbtw55N6STsAjgIMn+gAAZVzyjLKEv8t6+JL1WI8fMlg+2O4NyFORIVdL1nv8GXJOFUnc2M841jXI5XeVKSA5bYrhuwoAjp4KH5DLeeTvHDoFl6/pXCiVhXbk+y4EKb7nN5uUmLaDiIgonYybvjSQS86owq3wd6/93Nk1+Oa/zNB0YqF3QJ5n4JJ1i9mEklwbOgdGsa/dG5AzU0fZQgZcPUNOuNweWEIs1xdzQF6Ug71tfegbSeGyZ76gv2NgFPkKM+TJMi4gz4v9O+OqhlosqivF1/7nPexr78M5U0vwiy+cqem7SgiB5ggB+dzJRSjOtaJ70ImdrT04Z2pJyMd5fMsR9A27UFVkx2fOPA3He4ZRU+LAqoZaBuNERJSRjBstGcjett6gny+dW6X5xELPgNykeJevMbLyfG9APuz0ZmzKWbJOWSJwX+0ZcqIsRFAbb4ZcSkWGvCzPBqtZgdMt8LE3fmNAngSJZsiluvI8fOGcGty9fg8qCuwxfVfJCzk1JeMDcrNJwZLpZVj/QRu2HOwIGZB3DYziN5sOAQBuu3QOPrvwtLheAxERUToZO1oyiL1tfUE/t/UOa/5b9YQ6xgY98Qo8qcuzWQy/NvDYQIDdnilbWMwmdX8N19gt1oC8clxAnvxrviaTgkkF3ucZcXuPLxUF3O8SlayAHADqqwsAAHuP90W5p59s6DapwA5HmOUyo80jf/T1Q+gbcaG+qgCfOXNyLJtMRESkGwbkWWBcQN4TR0CuQ4Y8z8AN3aSxWUVm6iibRJtHHusqDXJ+t5SKgBzwlsYHkksQUvzGNuCLp2Rdml3lDciPnBrAkK9JXDThljwLJOeRb2/uwoAvmy4d7xnCH986AgBYc2k9TCZjXwwmIiLjYECeBWTJer3vJEdrhtzjEWqX5GIdAnIjN3STAjPi+XYLcqzGf81kHCV5/pUCQok5Qz4mUC5MQck6EFwabzEpabvgaGQWsyloVYziBDLkFfl2lOXZ4BHAgRPasuRaAvIppbmoKXHA6RbYdqQz6LYHXzmAUZcH59aV4qLZFXFvOxERUboxIM9wHo/Afl+G/KLZkwB419/Von/UBY9v9Zd0rUM+tmTd6AIz4ixXp2wjy5K7w5Ssx7LsGTB+DnmqAvLA0viyPBuzoUkS+DmXJhCQK4qiZsnHVniF0xJmDfKxjyuz5FsO+MvWD57ox7rGFgDAmstmG36qFBERGQsD8gx3rHsIA6Nu2MwmfOL0UgDaS9Z7fGWodospbZnbwqCSdeNniwOD8FBNsYgymVw3vHMgcsl6vAF5fspK1v37WqzrZVN4gcfvROaQA/6y9X0aA3ItGXIg9Dzy+/+5Dx4BLJtTiXOmlsazuURERLoxfgozy+057i1XnzEpX+08qzkgT/P8cQDIsZpht5gw4vIwQ06U4UojZMiFEOoxpFhjU8jiXGvA/m+GOUWZ66oih/pv7nfJU+TwH7MTmUMOAHOqCgGMXyUkHDUgL9MWkO9t68PJvhF83D2Ef+xug6IAty6fncAWExER6YMZ8gwnswv11QVqI6O+Ede4hjahxFpumgxNHQPqSfjRzkE0dQyk7bn1MOryqP9u6Rwy/OslY4k0h3xw1A2Xb86L1mOIoigo8wXIbo/A2pf2pmSfEEKo/27vHeZ+lyQW37FbAfDIxoMJva+xZMidbg8+7vZeaJ4aJUNemmfDjEnepdS+9j+NuPnJ9wAAn1t4mvqcRERE2UTXgNztduOOO+7AtGnT4HA4MH36dPz0pz8NOtkSQuDOO+9EdXU1HA4Hli1bhgMHDui41ekl59/VVxUg325Rm+5oaeyW7gz5usYWLL1/EwZ9XXUPnejH0vs34Tnf3D6jWdfYglueel/9eU9br6FfLxmPLEsO1WVdHj+sZgUOjVNe1jW2qIHVsMuDx944nPR9Yl1jC77z7A715wMnBrjfJcG6xhZsPngKACAAPPZmU0Lv66zKAigK0NE/ipN9IxHve7x7GG6PgN1iQkVB5Kk/6xpbcOiE90LB+83dOOYbb7MrGYwTEVF20jUgX7t2LR599FH853/+J/bs2YO1a9fivvvuw8MPP6ze57777sNDDz2E3/zmN9i6dSvy8vKwfPlyDA9rX/orm8lyv9m+8r/KQu/JSruGsvV0BuRNHQO47fldahM5wHtS5xHAmud34YjBMlghX68w7uslY/IvezY+Qx54/NDSJEvuE4HcHpHUfWKiHWfSJRWfncNmRl2ZN5MdLUseOH880liT2ylC3Lb2pb38/ImIKCvpOsn3rbfewpVXXokVK1YAAOrq6vD0009j27ZtALzZ8QceeAC33347rrzySgDAn/70J1RWVuLFF1/EF7/4xXGPOTIygpER/9X43l5vQOt0OuF0hm5clA7yuWPZhmGnWy0ZnFHugNPpRGWhHYdODqC1cwBOZ1HEv+8c8AbtBXZzyl/7M1uPQoEChDhVUgA8vfUovnfJzJRuQzoZ9fXGM04pexXYvddkuwZGxn3mp/qGAHjXp9YyHtKxTxh1v9Nbqt7XWZPy0NQxgA+PdWFxXfjvq6aT3oC9piQn4ljL1s+fx1XKFhyrlC2yZaxq3T5dA/IlS5bgsccew/79+zFr1izs3LkTmzdvxq9+9SsAQFNTE9ra2rBs2TL1b4qKirB48WK8/fbbIQPye+65Bz/5yU/G/f6f//wncnMjz01Lhw0bNmi+b0s/4BEW5FkE3n3jVSgK4Oo1ATDhjcadsB/fEfHv32/23rez/RjWr09tOee7+03wCAXe06JgHiHw7keHsN5lnKkGRn+9sYxTyl4fDwKABe3dA1i/fn3Qbbs6FQBmeIbH3xZKOvYJo+93eknV+6r0esfQq+/tRVXPR2Hvt+mo97vK3XMi4ljL9s+fx1XKFhyrlC0yfawODg5qup+uAfltt92G3t5e1NfXw2w2w+124+6778a1114LAGhrawMAVFZWBv1dZWWlettYP/jBD/Dd735X/bm3txe1tbW45JJLUFhYmKJXEp3T6cSGDRtw8cUXw2rVVkL+/PvHgA8+xPzaUqxYsQgAsHfDAWw72YTi6jpcfvmciH//zv99BBxrxZlzZuDyT89I+DVE8pHlAHZuPgK3GJ+5MCkKFp1xOi7PwMxFvIz6euMZp5S9TvaNYO3O1zHkVnDppZcFrec9+P4xYN+HmFpdgcsvPzvqY6VjnzDqfqe3VL2v5g/b8dIzOzFoLcbll38i7P3+8cxO4ON2fPLsObj8vKlp385U43GVsgXHKmWLbBmrslI7Gl0D8nXr1uHJJ5/EU089hblz52LHjh1YvXo1Jk+ejOuvvz6ux7Tb7bDbxzeFsVqtGfGBxbIdB096r6rUVxepfzPZt/TZib7RqI/TN+JtrlaSl5Py1/7FxVPxu81NIW8TAK5ZPDUj3v9kMfrrzZT9hVKrvNDbrM0jgCE3UGz3f+YDo94VBIrzbJrGQjr2CaPvd3pJ1fs6r6YEAHDgRD9MZkvYZfBau73TI6aVF0R8nmz//HlcpWzBsUrZItPHqtZt07Wp26233orbbrsNX/ziFzF//nxcd911+M53voN77rkHAFBVVQUAaG9vD/q79vZ29TYj29funVc3p9rfPVauv5tpXdanledh7coFMCmA2aQE/X/tygWoK89L+Tak00R7vWRMNosJBb6VG8YufRbr8SMd+8TY51AgYFbA/S5BqfrsppTmwmE1Y8TlwZFT4RuuNZ/StgY5j7tERGREumbIBwcHYTIFXxMwm83weLyZmWnTpqGqqgqvvvoqzjrrLADe1P/WrVvxjW98I92bm3Z7jnsDctlhHQCqCr1rkbdp6LKe7nXIr2qoxaK6Ujzb2ILWriHUlDiwqqHWsCdJE+31kjEV51nRN+Iat/RZPBf00rFPyOd4eutRvPvRISw643Rcs3gq97sEpeKzM5kUzKoqwM6Wbuw93ofpFfnj7tMz6ETvsAsAUFsSvc8Lj7tERGQ0ugbkV1xxBe6++25MmTIFc+fOxfbt2/GrX/0KN954IwBAURSsXr0aP/vZzzBz5kxMmzYNd9xxByZPnozPfvazem56ynX0j6CjfwSKAsyq9J/EVBZ5y/FP9o/A6fbAag5f5KCeUOemr5SjrjwPay6tT9vz6W2ivV4yntJcG1o6h9CVYIZcSsc+UVeeh+9dMhPrXQdw+SUzM7pcLZuk4rOrr/QG5PvaerFiQfW42+WSZxUFdjhs2ta753GXiIiMRNeA/OGHH8Ydd9yBm2++GSdOnMDkyZPxta99DXfeead6n+9///sYGBjAV7/6VXR3d+OCCy7ASy+9hJycHB23PPXkuq1TS3ORa/N/TOV5dlhMClwegZN9I5hc7Aj7GOksWSei7FScawMwfi1yefwo5PGDEjC7yjvlak+YtcgD1yAnIiKaiHQNyAsKCvDAAw/ggQceCHsfRVFw11134a677krfhmWAvW2yXL0g6Pcmk4LKwhwc6x5CW+9w2IBcCKGWATIgJ6JwSvMiB+Q8flAi6n09UPYxICciIgpJ16ZuFN7e4942+fVV45dqqyz0lq23R5hH3j/igtvjXRqGJ9REFE6xb0pLMuaQE40lv8OaOwcxMOIad7sMyGsZkBMR0QTFgDxDyQ7r9WMy5ABQVeRr7Bah07o8mbZZTMixapuXR0QTT4ksWR8zhzzdTSHJmErzbJhU4L2ILL/XArUwQ05ERBMcA/IM5PYI7JcBefX4DHlVoW/pswgZcma3iEiLkhAl60IIHkMoaeTUq1Bl6yxZJyKiiY4BeQY6emoAw04PcqymkCcpVb5O61oy5DyZJqJISmTJ+oC/ZH1w1A2nm1NeKDnm+C4sy6lYksvtwbHuIQDA1ChrkBMRERkVA/IMJLMIsyoLYDYp426v1LAWec8gA3Iiiq40RJd1eUHPYlKQq3EpKqJwZld6M+R7x2TIj/cMw+0RsFtMqMi367FpREREumNAnoHk8jCh5o8DQJUvIG9nhpyIEhRq2bPA44eijL8oSBQLWbK+t60PQgj190dP+Ru6mUJcfCYiIpoIGJBnoH1t3rK+2SE6rANAdZF3DvnxnuGgk5tADMiJSAv/smdO9XjC4wcl04xJ+TCbFPQMOdHeO6L+nvPHiYiIGJBnJFnWNydMhnySb9mzEZdHPXEeiyfURKSFXPbM7RHoHfYuSyWPH4U8flAS5FjNmFaeBwDY2+afR86AnIiIiAF5xhkcdaknKbPDBOQ5VrPaiClcYzcG5ESkRY7VrM4T7/aVrfP4QclWXzV+HnkL1yAnIiJiQJ5p9rf3QwigosCOsghNbqI1duMJNRFppa5F7msGyTXIKdnqQyx9xgw5ERERA/KMI5eFCdfQTaouYkBORMlRkieXPmOGnFJD9kTZc5wl60RERIEsem8ABZPlfHKZmHCqZEAepmSdGS4i0qpkTKd1BuSUbPIi86GT/XC6PRgccavjrLbUoeemERER6YoBeYaRDW/qq0N3WJcqoyx9pp5Q5/KEmogikwF5JzPklCI1JQ7k2y3oH3Hh8MkBON0eAEB5vh25Np6KEBHRxMWS9QwihFDn10UrWa/iHHIiShLZJLLbN4ecxw9KNkVRAtYj7w0oV2d2nIiIJjYG5BnkRN8IugadMCnedVsjkSXrx0ME5EL4ly/iCTURRVPiW4u8c0zJOpc9o2SaHdDYjfPHiYiIvFgnlkHk/PFp5XnIsZoj3lcG5KFK1vtHXHB7BAAG5EQUnSxZ57JnlEpzApY+k99hDMiJiGiiY0CeQfbJ+eNVkeePA/6S9a5BJ4ad7qAAXp5M2yymqIE9EZGaIffNIWdTSEoF2Wl9X1ufOoeca5ATEdFEx5L1DLL3uLb544D3RNlu8X58J3pHgm5jdouIYhE4h1wIwaaQlBJy9ZBj3UP46GPvBWhmyImIaKJjQJ5B1CXPNATkiqKoa5Ef7xkKuo0BORHFIrDL+pDTDaebU14o+YpyrZjs+9465avGmFqWp+cmERER6Y4BeYZwuj04eKIfADAnypJnklz6bOxa5Cw3JaJYyJL17kGnekHPYlKQZ+OUF0quwAvONosJkwrsOm4NERGR/hiQZ4gjHQMYdXuQZzPjtGJty8CEa+zGDDkRxUKWrI+6Pfi423s8KXJYoSiKnptFBlQfcMG5tsQBk4ljjIiIJjYG5BmgqWMAa1/aBwDIz7HgqG85mGj8a5FzDjkRxc9hNas9KY50DADg8YNSo9RXjQF4K8OafOONiIhoomJArrN1jS1Yev8mvLqnHYC3QdvS+zfhucaWqH8rM+RtvZxDTkTxUxRFnUd+9JQ3QOIa5JRs6xpb8PP1e9SfWzqHNH/fERERGRUDch01dQzgtud3wSMA4fudAOARwJrnd6mZqnD8GfLQJes8oSYireQ88qZT3godXtCjZJLfd0L4fxfL9x0REZFRMSDX0brGlrBzNBVFwbNRsgaV6hzysSXrLgA8oSYi7eQ8cpkh5/GDkinR7zsiIiKjYkCuo9auIYjAdEEAIQRau4ZC3ibJDHl77zA8Hv/jsGSdiGKlZsg5h5xSINHvOyIiIqNiQK6jmhJHxIxBTUnkbusVBXaYFMDlEegY8GfJGZATUaxkhrxvmBU2lHyJft8REREZFQNyHV3dUBsxY7CqoTbi31vNJpTne9dwbQ/otM51yIkoVqW5tqCfefygZEr0+46IiMioGJDraFp5HtauXACTAphNStD/165cgLryvKiP4e+07m/sxgw5EcWqmAE5pVAyvu+IiIiMyKL3Bkx0VzXUYlFdKZ5tbEFr1xBqShxY1VCr+eSksjAHQA/aerzz74QQDMiJKGaB60MDXKWBki/R7zsiIiIjYkCeAerK87Dm0vq4/rZ6TIa8f8QFt6/BGwNyItKqODf4eMHjB6VCIt93RERERsSS9SxXqa5F7p1DLrPjNrMJOVZ+vESkzdgMOQNyIiIiotRjxJblApc+AwLmj+daw3a0JSIaq2TsHPJcBuREREREqcaAPMvJpm7HfXPIOX+ciOJRwgw5ERERUdoxIM9yMiBv7/WWrHPJMyKKR57NDKvZW1VjNinIs5l13iIiIiIi42NAnuVkyXr/iAv9Iy5myIkoLoqiqEufFTk45YWIiIgoHRiQZ7k8uwUFdm+z/LaeYQbkRBS30oCAnIiIiIhSjwG5AVTKpc8YkBNRAuTKDF2Do1j70l40dQzovEVERERExsaA3AAC1yKXAXkhA3IiisG6xhbsbO0BAHQPOvHYG4ex9P5NeK6xRectIyIiIjIuBuQGUBmw9FnPkAsAM+REpF1TxwBue35X0O/cHgGPANY8vwtHmCknIiIiSgkG5AYgG7uxZJ2I4rGusSVsEzdFUfAss+REREREKcGA3AAq1bXIGZATUexau4YghAh5mxACrV1Dad4iIiIioonBovcGUOKqA0rW+0dYsk5EsakpcXgz5CGCckVRUFPi0GGriIiIiIyPGXIDqArR1I0BORFpdXVDbcQM+aqG2jRvEREREdHEwIDcAGRTt47+EQbkRBSzaeV5WLtyAUwKYDYpQf9fu3IB6srz9N5EIiIiIkNiyboBlOXZYDUrcLoF3L4sFwNyIorFVQ21WFRXimcbW9DaNYSaEgdWNdQyGCciIiJKIQbkBmAyKZhUkINj3d7GSzazCTlWFj8QUWzqyvOw5tJ6vTeDiIiIaMJg1GYQch45ABQ6rGGXMCIiIiIiIqLMwIDcIORa5ABQ5GDhAxERERERUaZjQG4QgRlyzh8nIiIiIiLKfAzIDSI4Q86AnIiIiIiIKNMxIDeISmbIiYiIiIiIsgoDcoNghpyIiIiIiCi7MCA3CJfbo/5798c9aOoY0HFriIiIiIiIKBoG5AawrrEF//r7rerP7zd3Y+n9m/BcY4uOW0VERERERESRMCDPck0dA7jt+V3wCP/vhAA8Aljz/C4cYaaciIiIiIgoIzEgz3LrGlugKErI2xRFwbPMkhMREREREWUkBuRZrrVrCEKIkLcJIdDaNZTmLSIiIiIiIiItGJBnuZoSR8QMeU2JI81bRERERERERFowIM9yVzfURsyQr2qoTfMWERERERERkRYMyLPctPI8rF25ACYFMJuUoP+vXbkAdeV5em8iERERERERhWDRewMocVc11GJRXSmebWxBa9cQakocWNVQy2CciIiIiIgogzEgN4i68jysubRe780gIiIiIiIijViyTkRERERERKQDBuREREREREREOmBATkRERERERKQDBuREREREREREOmBATkRERERERKQDBuREREREREREOtA1IK+rq4OiKOP+u+WWWwAAF1100bjbvv71r+u5yURERERERERJoes65O+++y7cbrf68+7du3HxxRfjqquuUn9300034a677lJ/zs3NTes2EhEREREREaWCrgF5RUVF0M/33nsvpk+fjk996lPq73Jzc1FVVZXuTSMiIiIiIiJKKV0D8kCjo6N44okn8N3vfheKoqi/f/LJJ/HEE0+gqqoKV1xxBe64446IWfKRkRGMjIyoP/f29gIAnE4nnE5n6l5AFPK59dwGomg4TilbcKxStuBYpWzBsUrZIlvGqtbtU4QQIsXbosm6devwpS99Cc3NzZg8eTIA4LHHHsPUqVMxefJk7Nq1C2vWrMG5556LF154Iezj/PjHP8ZPfvKTcb9/6qmnWO5OREREREREKTc4OIgvfelL6OnpQWFhYdj7ZUxAvnz5cthsNvz1r38Ne5/XXnsNS5cuxcGDBzF9+vSQ9wmVIa+trUVHR0fENyLVnE4nNmzYgIsvvhhWq1W37SCKhOOUsgXHKmULjlXKFhyrlC2yZaz29vaivLw8akCeESXrR48exSuvvBIx8w0AixcvBoCIAbndbofdbh/3e6vVmhEfWKZsB1EkHKeULThWKVtwrFK24FilbJHpY1XrtmXEOuSPP/44Jk2ahBUrVkS8344dOwAA1dXVadgqIiIiIiIiotTRPUPu8Xjw+OOP4/rrr4fF4t+cQ4cO4amnnsLll1+OsrIy7Nq1C9/5zndw4YUXYsGCBTpuMREREREREVHidA/IX3nlFTQ3N+PGG28M+r3NZsMrr7yCBx54AAMDA6itrcXKlStx++2367SlRERERERERMmje0B+ySWXIFRfudraWrz++us6bBERERERERFR6mXEHHIiIiIiIiKiiYYBOREREREREZEOGJATERERERER6YABOREREREREZEOGJATERERERER6YABOREREREREZEOGJATERERERER6YABOREREREREZEOLHpvQKoJIQAAvb29um6H0+nE4OAgent7YbVadd0WonA4TilbcKxStuBYpWzBsUrZIlvGqow/ZTwajuED8r6+PgBAbW2tzltCREREREREE0lfXx+KiorC3q6IaCF7lvN4PPj4449RUFAARVF0247e3l7U1taipaUFhYWFum0HUSQcp5QtOFYpW3CsUrbgWKVskS1jVQiBvr4+TJ48GSZT+Jnihs+Qm0wm1NTU6L0ZqsLCwoweOEQAxyllD45VyhYcq5QtOFYpW2TDWI2UGZfY1I2IiIiIiIhIBwzIiYiIiIiIiHTAgDxN7HY7fvSjH8Fut+u9KURhcZxStuBYpWzBsUrZgmOVsoXRxqrhm7oRERERERERZSJmyImIiIiIiIh0wICciIiIiIiISAcMyImIiIiIiIh0wICciIiIiIiISAcMyNPg17/+Nerq6pCTk4PFixdj27Ztem8SGdg999yDRYsWoaCgAJMmTcJnP/tZ7Nu3L+g+w8PDuOWWW1BWVob8/HysXLkS7e3tQfdpbm7GihUrkJubi0mTJuHWW2+Fy+UKus+mTZtw9tlnw263Y8aMGfjjH/+Y6pdHBnXvvfdCURSsXr1a/R3HKWWKY8eO4V//9V9RVlYGh8OB+fPno7GxUb1dCIE777wT1dXVcDgcWLZsGQ4cOBD0GJ2dnbj22mtRWFiI4uJifOUrX0F/f3/QfXbt2oVPfvKTyMnJQW1tLe677760vD4yBrfbjTvuuAPTpk2Dw+HA9OnT8dOf/hSB/Zs5VkkPb7zxBq644gpMnjwZiqLgxRdfDLo9nePyueeeQ319PXJycjB//nysX78+6a83ZoJS6plnnhE2m0384Q9/EB9++KG46aabRHFxsWhvb9d708igli9fLh5//HGxe/dusWPHDnH55ZeLKVOmiP7+fvU+X//610Vtba149dVXRWNjo/jEJz4hlixZot7ucrnEvHnzxLJly8T27dvF+vXrRXl5ufjBD36g3ufw4cMiNzdXfPe73xUfffSRePjhh4XZbBYvvfRSWl8vZb9t27aJuro6sWDBAvHtb39b/T3HKWWCzs5OMXXqVPHlL39ZbN26VRw+fFi8/PLL4uDBg+p97r33XlFUVCRefPFFsXPnTvGZz3xGTJs2TQwNDan3ufTSS8WZZ54p3nnnHfHmm2+KGTNmiGuuuUa9vaenR1RWVoprr71W7N69Wzz99NPC4XCI3/72t2l9vZS97r77blFWVib+9re/iaamJvHcc8+J/Px88eCDD6r34VglPaxfv1788Ic/FC+88IIAIP7yl78E3Z6ucbllyxZhNpvFfffdJz766CNx++23C6vVKj744IOUvweRMCBPsXPPPVfccsst6s9ut1tMnjxZ3HPPPTpuFU0kJ06cEADE66+/LoQQoru7W1itVvHcc8+p99mzZ48AIN5++20hhPfAaTKZRFtbm3qfRx99VBQWFoqRkREhhBDf//73xdy5c4Oea9WqVWL58uWpfklkIH19fWLmzJliw4YN4lOf+pQakHOcUqZYs2aNuOCCC8Le7vF4RFVVlfjFL36h/q67u1vY7Xbx9NNPCyGE+OijjwQA8e6776r3+cc//iEURRHHjh0TQgjxyCOPiJKSEnXsyueePXt2sl8SGdSKFSvEjTfeGPS7z3/+8+Laa68VQnCsUmYYG5Cnc1xeffXVYsWKFUHbs3jxYvG1r30tqa8xVixZT6HR0VG89957WLZsmfo7k8mEZcuW4e2339Zxy2gi6enpAQCUlpYCAN577z04nc6gcVlfX48pU6ao4/Ltt9/G/PnzUVlZqd5n+fLl6O3txYcffqjeJ/Ax5H04tikWt9xyC1asWDFuLHGcUqb4v//7PzQ0NOCqq67CpEmTsHDhQvzud79Tb29qakJbW1vQOCsqKsLixYuDxmpxcTEaGhrU+yxbtgwmkwlbt25V73PhhRfCZrOp91m+fDn27duHrq6uVL9MMoAlS5bg1Vdfxf79+wEAO3fuxObNm3HZZZcB4FilzJTOcZmp5wQMyFOoo6MDbrc76GQRACorK9HW1qbTVtFE4vF4sHr1apx//vmYN28eAKCtrQ02mw3FxcVB9w0cl21tbSHHrbwt0n16e3sxNDSUipdDBvPMM8/g/fffxz333DPuNo5TyhSHDx/Go48+ipkzZ+Lll1/GN77xDXzrW9/Cf//3fwPwj7VI3/VtbW2YNGlS0O0WiwWlpaUxjWeiSG677TZ88YtfRH19PaxWKxYuXIjVq1fj2muvBcCxSpkpneMy3H30HrcWXZ+diFLqlltuwe7du7F582a9N4UoSEtLC7797W9jw4YNyMnJ0XtziMLyeDxoaGjAz3/+cwDAwoULsXv3bvzmN7/B9ddfr/PWEfmtW7cOTz75JJ566inMnTsXO3bswOrVqzF58mSOVaIMxgx5CpWXl8NsNo/rCtze3o6qqiqdtoomim9+85v429/+ho0bN6Kmpkb9fVVVFUZHR9Hd3R10/8BxWVVVFXLcytsi3aewsBAOhyPZL4cM5r333sOJEydw9tlnw2KxwGKx4PXXX8dDDz0Ei8WCyspKjlPKCNXV1TjjjDOCfjdnzhw0NzcD8I+1SN/1VVVVOHHiRNDtLpcLnZ2dMY1nokhuvfVWNUs+f/58XHfddfjOd76jViFxrFImSue4DHcfvcctA/IUstlsOOecc/Dqq6+qv/N4PHj11Vdx3nnn6bhlZGRCCHzzm9/EX/7yF7z22muYNm1a0O3nnHMOrFZr0Ljct28fmpub1XF53nnn4YMPPgg6+G3YsAGFhYXqiel5550X9BjyPhzbpMXSpUvxwQcfYMeOHep/DQ0NuPbaa9V/c5xSJjj//PPHLR25f/9+TJ06FQAwbdo0VFVVBY2z3t5ebN26NWisdnd347333lPv89prr8Hj8WDx4sXqfd544w04nU71Phs2bMDs2bNRUlKSstdHxjE4OAiTKfjU3mw2w+PxAOBYpcyUznGZsecEuraUmwCeeeYZYbfbxR//+Efx0Ucfia9+9auiuLg4qCswUTJ94xvfEEVFRWLTpk3i+PHj6n+Dg4Pqfb7+9a+LKVOmiNdee000NjaK8847T5x33nnq7XI5qUsuuUTs2LFDvPTSS6KioiLkclK33nqr2LNnj/j1r3/N5aQoIYFd1oXgOKXMsG3bNmGxWMTdd98tDhw4IJ588kmRm5srnnjiCfU+9957ryguLhb/+7//K3bt2iWuvPLKkEv2LFy4UGzdulVs3rxZzJw5M2jJnu7ublFZWSmuu+46sXv3bvHMM8+I3NxcLiVFml1//fXitNNOU5c9e+GFF0R5ebn4/ve/r96HY5X00NfXJ7Zv3y62b98uAIhf/epXYvv27eLo0aNCiPSNyy1btgiLxSJ++ctfij179ogf/ehHXPZsonj44YfFlClThM1mE+eee65455139N4kMjAAIf97/PHH1fsMDQ2Jm2++WZSUlIjc3Fzxuc99Thw/fjzocY4cOSIuu+wy4XA4RHl5ufj3f/934XQ6g+6zceNGcdZZZwmbzSZOP/30oOcgitXYgJzjlDLFX//6VzFv3jxht9tFfX29eOyxx4Ju93g84o477hCVlZXCbreLpUuXin379gXd59SpU+Kaa64R+fn5orCwUNxwww2ir68v6D47d+4UF1xwgbDb7eK0004T9957b8pfGxlHb2+v+Pa3vy2mTJkicnJyxOmnny5++MMfBi0DxbFKeti4cWPIc9Prr79eCJHecblu3Toxa9YsYbPZxNy5c8Xf//73lL1urRQhhNAnN09EREREREQ0cXEOOREREREREZEOGJATERERERER6YABOREREREREZEOGJATERERERER6YABOREREREREZEOGJATERERERER6YABOREREREREZEOGJATERERERER6YABOREREREREZEOGJATERFliJMnT8Jms2FgYABOpxN5eXlobm6O+Dc//vGPcdZZZyVtGy666CKsXr06aY9HRERE4TEgJyIiyhBvv/02zjzzTOTl5eH9999HaWkppkyZovdmERERUYowICciIsoQb731Fs4//3wAwObNm9V/x+LLX/4yPvvZz+KXv/wlqqurUVZWhltuuQVOp1O9zyOPPIKZM2ciJycHlZWV+MIXvqD+7euvv44HH3wQiqJAURQcOXIEbrcbX/nKVzBt2jQ4HA7Mnj0bDz74YMzPOzIygjVr1qC2thZ2ux0zZszA73//e/X23bt347LLLkN+fj4qKytx3XXXoaOjQ739z3/+M+bPnw+Hw4GysjIsW7YMAwMDMb9HREREmcKi9wYQERFNZM3NzViwYAEAYHBwEGazGX/84x8xNDQERVFQXFyML33pS3jkkUc0P+bGjRtRXV2NjRs34uDBg1i1ahXOOuss3HTTTWhsbMS3vvUt/M///A+WLFmCzs5OvPnmmwCABx98EPv378e8efNw1113AQAqKirg8XhQU1OD5557DmVlZXjrrbfw1a9+FdXV1bj66qs1PS8A/Nu//RvefvttPPTQQzjzzDPR1NSkBtzd3d349Kc/jf/3//4f/uM//gNDQ0NYs2YNrr76arz22ms4fvw4rrnmGtx333343Oc+h76+Prz55psQQiTlcyAiItKDIvhNRkREpBuXy4XW1lb09vaioaEBjY2NyMvLw1lnnYW///3vmDJlCvLz81FeXh7y73/84x/jxRdfxI4dOwB4M9WbNm3CoUOHYDabAQBXX301TCYTnnnmGbzwwgu44YYb0NraioKCgnGPd9FFF+Gss87CAw88EHG7v/nNb6KtrQ1//vOfNT3v/v37MXv2bGzYsAHLli0b93g/+9nP8Oabb+Lll19Wf9fa2ora2lrs27cP/f39OOecc3DkyBFMnTo16vtKRESUDViyTkREpCOLxYK6ujrs3bsXixYtwoIFC9DW1obKykpceOGFqKurCxuMhzN37lw1KAaA6upqnDhxAgBw8cUXY+rUqTj99NNx3XXX4cknn8Tg4GDUx/z1r3+Nc845BxUVFcjPz8djjz02ruFcpOfdsWMHzGYzPvWpT4V8/J07d2Ljxo3Iz89X/6uvrwcAHDp0CGeeeSaWLl2K+fPn46qrrsLvfvc7dHV1xfS+EBERZRoG5ERERDqaO3cu8vPzcd1112Hbtm3Iz8/H0qVLceTIEeTn52Pu3LkxP6bVag36WVEUeDweAEBBQQHef/99PP3006iursadd96JM888E93d3WEf75lnnsH3vvc9fOUrX8E///lP7NixAzfccANGR0c1P6/D4Yi4zf39/bjiiiuwY8eOoP8OHDiACy+8EGazGRs2bMA//vEPnHHGGXj44Ycxe/ZsNDU1aX1biIiIMg4DciIiIh2tX78eO3bsQFVVFZ544gns2LED8+bNwwMPPIAdO3Zg/fr1SX9Oi8WCZcuW4b777sOuXbtw5MgRvPbaawAAm80Gt9sddP8tW7ZgyZIluPnmm7Fw4ULMmDEDhw4diuk558+fD4/Hg9dffz3k7WeffTY+/PBD1NX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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.stream import MOAStream\n", "from capymoa.classifier import OnlineBagging\n", "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "from moa.streams import ConceptDriftStream\n", "\n", "# Using the API to generate the data using the ConceptDriftStream and SEAGenerator.\n", "# The drift location is based on the number of instances (5000) as well as the drift width (1000, the default value).\n", "stream_sea1drift = MOAStream(\n", " moa_stream=ConceptDriftStream(),\n", " CLI=\"-s generators.SEAGenerator -d (generators.SEAGenerator -f 2) -p 5000 -w 1000\",\n", ")\n", "\n", "OB = OnlineBagging(schema=stream_sea1drift.get_schema(), ensemble_size=10)\n", "\n", "results_sea1drift_OB = prequential_evaluation(\n", " stream=stream_sea1drift, learner=OB, window_size=100, max_instances=10000\n", ")\n", "\n", "plot_windowed_results(results_sea1drift_OB, metric=\"accuracy\")" ] }, { "cell_type": "markdown", "id": "0f2f9fb6-0994-4f3f-aaf3-c73b09847019", "metadata": {}, "source": [ "## 6.7 Drift, multi-threaded ensembles and results\n", "\n", "* Generate a stream with 3 drifts: 2 abrupt and one gradual.\n", "* Evaluate utilising test-then-train (cumulative) and windowed evaluation.\n", "* Execute a multi-threaded version of `AdaptiveRandomForest`.\n", "* For more on multi-threaded ensembles, see the **parallel_ensembles.ipynb** notebook." ] }, { "cell_type": "code", "execution_count": 12, "id": "3142e7e7-7175-40da-a89c-b528d71eb00c", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:30:15.376980Z", "iopub.status.busy": "2024-09-23T00:30:15.376832Z", "iopub.status.idle": "2024-09-23T00:30:25.563962Z", "shell.execute_reply": "2024-09-23T00:30:25.563432Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n", "Cumulative accuracy = 89.316\n", "Wallclock = 24.386993646621704 seconds\n" ] }, { "data": { "text/html": [ "
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instancesaccuracykappakappa_tkappa_mf1_scoref1_score_0f1_score_1precisionprecision_0precision_1recallrecall_0recall_1
05000.088.2473.65508374.28946267.04035987.01072182.43727691.16055388.23871888.23529488.24214285.81643477.35426094.278607
110000.089.0475.82498676.86787770.15250588.08478184.15268991.62335789.21260389.70406988.72113786.98511979.24836694.721871
215000.089.2076.24412276.97228170.68403988.27842084.48275991.71779189.33937489.74359088.93515887.24236979.80456094.680177
320000.088.5874.76554575.71246368.67800387.53019183.43487191.28643488.57138588.54679888.59597286.51319378.88096594.145420
425000.089.8877.40940377.68959471.49295888.82084985.02072292.35880489.80132089.58203490.02060687.86155780.90140894.821705
530000.089.2275.97473176.59574569.87143788.12773884.08621291.84938889.19120389.11138989.27101787.08933479.59754194.581127
635000.089.3675.90539175.92760268.87068588.02179183.81010392.07625988.80929987.31769290.30090687.24812780.57343593.922820
740000.089.6877.06106877.48691171.28547688.66757684.85026492.17470489.71345789.80733489.61958187.64579980.41179794.879800
845000.090.1278.16378179.12087972.96113889.25335385.65621492.46491890.40667091.21830689.59503488.12909180.73344395.524740
950000.089.8477.37945477.78749571.54061688.80790285.04122592.30769289.78590189.63376889.93803587.85097980.89635994.805599
\n", "
" ], "text/plain": [ " instances accuracy kappa kappa_t kappa_m f1_score \\\n", "0 5000.0 88.24 73.655083 74.289462 67.040359 87.010721 \n", "1 10000.0 89.04 75.824986 76.867877 70.152505 88.084781 \n", "2 15000.0 89.20 76.244122 76.972281 70.684039 88.278420 \n", "3 20000.0 88.58 74.765545 75.712463 68.678003 87.530191 \n", "4 25000.0 89.88 77.409403 77.689594 71.492958 88.820849 \n", "5 30000.0 89.22 75.974731 76.595745 69.871437 88.127738 \n", "6 35000.0 89.36 75.905391 75.927602 68.870685 88.021791 \n", "7 40000.0 89.68 77.061068 77.486911 71.285476 88.667576 \n", "8 45000.0 90.12 78.163781 79.120879 72.961138 89.253353 \n", "9 50000.0 89.84 77.379454 77.787495 71.540616 88.807902 \n", "\n", " f1_score_0 f1_score_1 precision precision_0 precision_1 recall \\\n", "0 82.437276 91.160553 88.238718 88.235294 88.242142 85.816434 \n", "1 84.152689 91.623357 89.212603 89.704069 88.721137 86.985119 \n", "2 84.482759 91.717791 89.339374 89.743590 88.935158 87.242369 \n", "3 83.434871 91.286434 88.571385 88.546798 88.595972 86.513193 \n", "4 85.020722 92.358804 89.801320 89.582034 90.020606 87.861557 \n", "5 84.086212 91.849388 89.191203 89.111389 89.271017 87.089334 \n", "6 83.810103 92.076259 88.809299 87.317692 90.300906 87.248127 \n", "7 84.850264 92.174704 89.713457 89.807334 89.619581 87.645799 \n", "8 85.656214 92.464918 90.406670 91.218306 89.595034 88.129091 \n", "9 85.041225 92.307692 89.785901 89.633768 89.938035 87.850979 \n", "\n", " recall_0 recall_1 \n", "0 77.354260 94.278607 \n", "1 79.248366 94.721871 \n", "2 79.804560 94.680177 \n", "3 78.880965 94.145420 \n", "4 80.901408 94.821705 \n", "5 79.597541 94.581127 \n", "6 80.573435 93.922820 \n", "7 80.411797 94.879800 \n", "8 80.733443 95.524740 \n", "9 80.896359 94.805599 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.stream.generator import SEA\n", "from capymoa.stream.drift import DriftStream, AbruptDrift, GradualDrift\n", "from capymoa.classifier import AdaptiveRandomForestClassifier\n", "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "\n", "SEA3drifts = DriftStream(\n", " stream=[\n", " SEA(1),\n", " AbruptDrift(10000),\n", " SEA(2),\n", " GradualDrift(start=20000, end=25000),\n", " SEA(3),\n", " AbruptDrift(45000),\n", " SEA(1),\n", " ]\n", ")\n", "\n", "arf = AdaptiveRandomForestClassifier(\n", " schema=SEA3drifts.get_schema(), ensemble_size=100, number_of_jobs=4\n", ")\n", "\n", "results = prequential_evaluation(\n", " stream=SEA3drifts, learner=arf, window_size=5000, max_instances=50000\n", ")\n", "\n", "print(f\"Cumulative accuracy = {results.cumulative.accuracy()}\")\n", "print(f\"Wallclock = {results.wallclock()} seconds\")\n", "display(results.windowed.metrics_per_window())\n", "plot_windowed_results(results, metric=\"accuracy\")" ] }, { "cell_type": "markdown", "id": "2ce76f5f-a427-4ef1-a8ba-14dc4b65a4ac", "metadata": {}, "source": [ "## 6.8 AutoML with AutoClass\n", "\n", "The following example shows how to use the `AutoClass` algorithm with CapyMOA. \n", "* AutoClass is configured using a json configuration file `settings_autoclass.json` and a list of classifiers `base_classifiers`.\n", "* AutoClass can also be configured with a list of `base_classifier` strings representing the MOA classifiers. This approach is only enticing for people that are very familiar with MOA.\n", "* In the example below, we also compare it against using the base classifiers individually." ] }, { "cell_type": "code", "execution_count": 13, "id": "1b658289-9802-469f-b607-fc9f1ae12118", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:30:25.565685Z", "iopub.status.busy": "2024-09-23T00:30:25.565537Z", "iopub.status.idle": "2024-09-23T00:31:37.785055Z", "shell.execute_reply": "2024-09-23T00:31:37.784289Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[HT] Cumulative accuracy = 53.396, wall-clock time: 0.30961084365844727\n", "[HAT] Cumulative accuracy = 57.676, wall-clock time: 0.39025139808654785\n", "[KNN] Cumulative accuracy = 86.956, wall-clock time: 2.8920769691467285\n", "[AUTOCLASS] Cumulative accuracy = 86.21600000000001, wall-clock time: 116.48931813240051\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.datasets import RBFm_100k\n", "from capymoa.automl import AutoClass\n", "from capymoa.classifier import HoeffdingTree, HoeffdingAdaptiveTree, KNN\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "\n", "rbf_100k = RBFm_100k()\n", "\n", "max_instances = 25000\n", "window_size = 2500\n", "\n", "ht = HoeffdingTree(schema=rbf_100k.get_schema())\n", "hat = HoeffdingAdaptiveTree(schema=rbf_100k.get_schema())\n", "knn = KNN(schema=rbf_100k.get_schema())\n", "autoclass = AutoClass(\n", " schema=rbf_100k.get_schema(),\n", " configuration_json=\"./settings_autoclass.json\",\n", " base_classifiers=[KNN, HoeffdingAdaptiveTree, HoeffdingTree],\n", ")\n", "\n", "results_ht = prequential_evaluation(\n", " stream=rbf_100k, learner=ht, window_size=window_size, max_instances=max_instances\n", ")\n", "results_hat = prequential_evaluation(\n", " stream=rbf_100k, learner=hat, window_size=window_size, max_instances=max_instances\n", ")\n", "results_knn = prequential_evaluation(\n", " stream=rbf_100k, learner=knn, window_size=window_size, max_instances=max_instances\n", ")\n", "results_autoclass = prequential_evaluation(\n", " stream=rbf_100k,\n", " learner=autoclass,\n", " window_size=window_size,\n", " max_instances=max_instances,\n", ")\n", "\n", "print(\n", " f\"[HT] Cumulative accuracy = {results_ht.accuracy()}, wall-clock time: {results_ht.wallclock()}\"\n", ")\n", "print(\n", " f\"[HAT] Cumulative accuracy = {results_hat.accuracy()}, wall-clock time: {results_hat.wallclock()}\"\n", ")\n", "print(\n", " f\"[KNN] Cumulative accuracy = {results_knn.accuracy()}, wall-clock time: {results_knn.wallclock()}\"\n", ")\n", "print(\n", " f\"[AUTOCLASS] Cumulative accuracy = {results_autoclass.accuracy()}, wall-clock time: {results_autoclass.wallclock()}\"\n", ")\n", "plot_windowed_results(\n", " results_ht, results_knn, results_hat, results_autoclass, metric=\"accuracy\"\n", ")" ] }, { "cell_type": "markdown", "id": "dbeb8d3b-9527-49dc-8550-e3ceb0f09965", "metadata": {}, "source": [ "### 6.8.1 AutoClass alternative syntax\n", "\n", "Another way to configure the learners is by using a list of string `base_classifiers` representing the MOA classifiers." ] }, { "cell_type": "code", "execution_count": 14, "id": "77ef4a93-7e3b-4429-8715-78f00ea47dda", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T00:31:37.787261Z", "iopub.status.busy": "2024-09-23T00:31:37.787098Z", "iopub.status.idle": "2024-09-23T00:31:38.147333Z", "shell.execute_reply": "2024-09-23T00:31:38.146850Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from capymoa.automl import AutoClass\n", "from capymoa.datasets import RBFm_100k\n", "from capymoa.classifier import KNN, HoeffdingTree, HoeffdingAdaptiveTree, OnlineBagging\n", "from capymoa.evaluation import prequential_evaluation\n", "from capymoa.evaluation.visualization import plot_windowed_results\n", "\n", "rbf_100k = RBFm_100k()\n", "\n", "autoclass = AutoClass(\n", " schema=rbf_100k.get_schema(),\n", " configuration_json=\"./settings_autoclass.json\",\n", " base_classifiers=[KNN, HoeffdingTree, HoeffdingAdaptiveTree],\n", ")\n", "\n", "autoclass_MOAStrings = AutoClass(\n", " schema=rbf_100k.get_schema(),\n", " configuration_json=\"./settings_autoclass.json\",\n", " base_classifiers=[\"lazy.kNN\", \"trees.HoeffdingTree\", \"trees.HoeffdingAdaptiveTree\"],\n", ")\n", "\n", "results_autoClass = prequential_evaluation(\n", " stream=rbf_100k, learner=autoclass, window_size=100, max_instances=500\n", ")\n", "results_autoclass_MOAStrings = prequential_evaluation(\n", " stream=rbf_100k, learner=autoclass_MOAStrings, window_size=100, max_instances=500\n", ")\n", "\n", "results_autoclass_MOAStrings.learner = \"AutoClass_MOAStrings\"\n", "\n", "plot_windowed_results(\n", " results_autoClass, results_autoclass_MOAStrings, metric=\"accuracy\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "c0b04d99-1142-4f07-b7a1-7de6bba3843f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }