Untitled2 (2).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Untitled2 (2).ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"metadata":{"colab_type":"code","id":"GQ4vpIWlbMXw","scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":493},"outputId":"493366d7-aefb-4469-edc2-1e5c18d7f99b","executionInfo":{"status":"ok","timestamp":1544693183109,"user_tz":-120,"elapsed":38770,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["!pip install --upgrade pip #upgrade pip package installer\n","!pip install scikit-learn --upgrade #upgrade scikit-learn package\n","!pip install numpy --upgrade #upgrade numpy package\n","!pip install pandas --upgrade #upgrade pandas package\n","import warnings\n","warnings.filterwarnings('ignore')\n","from IPython.display import HTML"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Requirement already up-to-date: pip in /usr/local/lib/python3.6/dist-packages (18.1)\n","Requirement already up-to-date: scikit-learn in /usr/local/lib/python3.6/dist-packages (0.20.1)\n","Requirement already satisfied, skipping upgrade: scipy>=0.13.3 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.1.0)\n","Requirement already satisfied, skipping upgrade: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.14.6)\n","Collecting numpy\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ff/7f/9d804d2348471c67a7d8b5f84f9bc59fd1cefa148986f2b74552f8573555/numpy-1.15.4-cp36-cp36m-manylinux1_x86_64.whl (13.9MB)\n","\u001b[K    100% |████████████████████████████████| 13.9MB 2.9MB/s \n","\u001b[31mthinc 6.12.1 has requirement msgpack<0.6.0,>=0.5.6, but you'll have msgpack 0.6.0 which is incompatible.\u001b[0m\n","\u001b[31mfeaturetools 0.4.1 has requirement pandas>=0.23.0, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n","\u001b[31mcufflinks 0.14.6 has requirement plotly>=3.0.0, but you'll have plotly 1.12.12 which is incompatible.\u001b[0m\n","\u001b[?25hInstalling collected packages: numpy\n","  Found existing installation: numpy 1.14.6\n","    Uninstalling numpy-1.14.6:\n","      Successfully uninstalled numpy-1.14.6\n","Successfully installed numpy-1.15.4\n","Collecting pandas\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e1/d8/feeb346d41f181e83fba45224ab14a8d8af019b48af742e047f3845d8cff/pandas-0.23.4-cp36-cp36m-manylinux1_x86_64.whl (8.9MB)\n","\u001b[K    100% |████████████████████████████████| 8.9MB 4.3MB/s \n","\u001b[?25hRequirement already satisfied, skipping upgrade: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.7)\n","Requirement already satisfied, skipping upgrade: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.5.3)\n","Requirement already satisfied, skipping upgrade: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from pandas) (1.15.4)\n","Requirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.5.0->pandas) (1.11.0)\n","\u001b[31mcufflinks 0.14.6 has requirement plotly>=3.0.0, but you'll have plotly 1.12.12 which is incompatible.\u001b[0m\n","Installing collected packages: pandas\n","  Found existing installation: pandas 0.22.0\n","    Uninstalling pandas-0.22.0:\n","      Successfully uninstalled pandas-0.22.0\n","Successfully installed pandas-0.23.4\n"],"name":"stdout"}]},{"metadata":{"colab_type":"text","id":"oCWfnBMfbQze"},"cell_type":"markdown","source":["# **                              Εργαστήριο “Νευρωνικά Δίκτυα & Ευφυή Υπολογιστικά Συστήματα” 2018-2019**\n","\n","#                 Άσκηση 1. Επιβλεπόμενη Μάθηση: Ταξινόμηση.\n","**Μελέτη datasets του UCI Machine Learning Repository**\n","**Dataset = ISOLET (B12)**\n"]},{"metadata":{"colab_type":"text","id":"26-RVyqPbqlv"},"cell_type":"markdown","source":["# Α. Στοιχεία ομάδας\n","**Μέλη ομάδας:**\n","\n","- **Ονοματεπώνυμο: Γεώργιος Σουλιώτης - ΑΜ: 03114201**\n","\n","- **Ονοματεπώνυμο: Νικόλαος-Ιωάννης Τιτομιχελάκης - ΑΜ: 03114805**\n","\n","**Αριθμός Ομάδας: Α10**"]},{"metadata":{"colab_type":"text","id":"clsUCIF5bwWY"},"cell_type":"markdown","source":["# Β. Εισαγωγή του dataset\n","\n","\n","**1. Περιγραφή του Dataset**\n","\n","Το συγκεκριμένο dataset περιλαμβάνει ηχογραφήσεις 150 εκφωνητών, οι οποίοι είπαν τα 26 γράμματα του αγγλικού αλφαβήτου 2 φορές.\n","Το dataset είναι χωρισμένο σε 5 αρχεία και το κάθε αρχείο περιέχει 30 εκφωνητές. Λόγω μάλλον αρκετού θορύβου στις ηχογραφήσεις, **3** εκφωνήτες έχουν αποσυρθεί.\n","\n","**2. Αριθμός δειγμάτων και χαρακτηριστικών, είδος χαρακτηριστικών**\n","\n","Ο αριθμός των δειγμάτων του dataset είναι **7797** και των χαρακτηριστικών είναι **617**.\n","\n","Το είδος των χαρακτηριστικών που περιλαμβάνονται είναι integers, floats καθώς και ints. Το dataset δεν περιέχει μη διατεταγμένα χαρακτηριστικά.\n","\n","**3. Υπάρχουν επικεφαλίδες; Αρίθμηση γραμμών;**\n","\n","Όχι δεν υπάρχουν επικεφαλίδες ή αρίθμηση γραμμών στο dataset.\n","\n","**4. Ποιες είναι οι ετικέτες των κλάσεων και σε ποια κολόνα βρίσκονται;**\n","\n","Η ετικέτα των κλάσεων του dataset είναι το χαρακτηριστικό το οποίο βρίσκεται στην τελευταία κολόνα και παίρνει τιμές απο 1 έως 26 όσα δηλαδή και τα γράμματα του αγγλικού αλφαβήτου.\n","\n","**5. Χρειάστηκε να κάνετε μετατροπές στα αρχεία text και ποιες?**\n","\n","Χρειάστηκε να κάνουμε ένωση των 2 dataset καθώς ήταν χωρισμένα σε δύο διαφορετικά αρχεία. Το concatenatιον έγινε με την εντολή:\n","***cat *.data > all.csv***\n","\n","**6. Υπάρχουν απουσιάζουσες τιμές; Πόσα είναι τα δείγματα με απουσιάζουσες τιμές και ποιο το ποσοστό τους επί του συνόλου;**\n","\n","Όχι δεν υπάρχουν απουσιάζουσες τιμές.\n","\n","**7. Ποιος είναι ο αριθμός των κλάσεων και τα ποσοστά δειγμάτων τους επί του συνόλου; Εκτιμήστε την ισσοροπία του dataset**\n","\n","Στο παρών dataset υπάρχουν 26 κλάσεις."]},{"metadata":{"colab_type":"code","id":"4ljcFTY3f3Xs","colab":{"resources":{"http://localhost:8080/nbextensions/google.colab/files.js":{"data":"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13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","\n","# from ibm_botocore.client import Config\n","# import ibm_boto3\n","# #Εισαγωγή του dataset στο Notebook απο την πλατφορμα IBM WATSON.\n","# def __iter__(self): return 0\n","\n","# # @hidden_cell\n","# # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n","# # You might want to remove those credentials before you share your notebook.\n","# client_587927642c4e46b4843aa8bb64ed179a = ibm_boto3.client(service_name='s3',\n","#     ibm_api_key_id='8eFSxPtWnz967BGWbR9ogxxi7ShLebS-Bq7psPMxVK80',\n","#     ibm_auth_endpoint=\"https://iam.bluemix.net/oidc/token\",\n","#     config=Config(signature_version='oauth'),\n","#     endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n","\n","# body = client_587927642c4e46b4843aa8bb64ed179a.get_object(Bucket='ask1-donotdelete-pr-q8u2z4arfnlp9r',Key='all.csv')['Body']\n","# # add missing __iter__ method, so pandas accepts body as file-like object\n","# if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n","from google.colab import files\n","\n","uploaded = files.upload()\n","\n","for fn in uploaded.keys():\n","  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n","      name=fn, length=len(uploaded[fn])))\n","CSV_filename = \"all.csv\"\n","df = pd.read_csv(CSV_filename, header=None)\n","labels = df[df.columns[-1]].copy() #Εισαγωγη των κλασεων στον πινακα labels\n","features = df[df.columns[:-1]].copy()#Εισαγωγη των χαρακτηριστικων στον πινακα features\n","counts = labels.value_counts().to_dict()\n","print(counts)"],"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","     <input type=\"file\" id=\"files-74856df7-8ac3-40ea-9c6a-704b82a12280\" name=\"files[]\" multiple disabled />\n","     <output id=\"result-74856df7-8ac3-40ea-9c6a-704b82a12280\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script src=\"/nbextensions/google.colab/files.js\"></script> "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["{25.0: 300, 23.0: 300, 2.0: 300, 3.0: 300, 4.0: 300, 5.0: 300, 7.0: 300, 8.0: 300, 9.0: 300, 10.0: 300, 11.0: 300, 12.0: 300, 1.0: 300, 14.0: 300, 15.0: 300, 16.0: 300, 18.0: 300, 20.0: 300, 22.0: 300, 24.0: 300, 26.0: 300, 17.0: 300, 19.0: 300, 21.0: 300, 13.0: 299, 6.0: 298}\n"],"name":"stdout"}]},{"metadata":{"colab_type":"text","id":"14nXETuLnWrn"},"cell_type":"markdown","source":["Από πάνω βλέπουμε ότι υπάρχουν **300** (**3,08%** του δείγματος καταλαμβάνει κάθε γράμμα) δείγματα για κάθε ένα από τα γράμματα της αλφαβήτου. Εξαίρεση αποτελούν τα γράμματα **F** και **M**, τα οποία έχουν 298 και 299 δείγματα αντίστοιχα, καθώς όπως προαναφέρθηκε, 3 δείγματα έχουν αφαιρεθεί από το dataset λόγω θορύβου στην ηχογράφηση. Επομένως **κάθε** κλάση καταλαμβάνει το **3,08%** του δείγματός μας, άρα το δείγμα μας **είναι ισορροπημένο**"]},{"metadata":{"colab_type":"text","id":"SapbrLYHnWro"},"cell_type":"markdown","source":["\n","**8. Διαχωρίστε σε train και test set. Εάν υπάρχουν απουσιάζουσες τιμές και μη διατεταγμένα χαρακτηριστικά διαχειριστείτε τα και αιτιολογήστε τις επιλογές σας.**\n","\n","Στο δείγμα μας δεν υπάρχουν απουσιάζουσες τιμές ή διατεταγμένα χαρακτηριστικά. Θα χωρίσουμε το δείγμα μας σε train set (70%) και test set(30%). Για το train set δημιουργήσαμε τον πίνακα **features** και για το test set τον πίνακα **labels**"]},{"metadata":{"colab_type":"code","id":"vUcdm41lnWrp","colab":{}},"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","# Split our data\n","train, test, train_labels, test_labels = train_test_split(features, labels, test_size=0.3)\n","from sklearn.dummy import DummyClassifier\n","dc_uniform = DummyClassifier(strategy=\"uniform\")\n","dc_constant_1 = DummyClassifier(strategy=\"constant\", constant=1)\n","dc_constant_2 = DummyClassifier(strategy=\"constant\", constant=2)\n","dc_constant_3 = DummyClassifier(strategy=\"constant\", constant=3)\n","dc_constant_4 = DummyClassifier(strategy=\"constant\", constant=4)\n","dc_most_frequent = DummyClassifier(strategy=\"most_frequent\")\n","dc_stratified = DummyClassifier(strategy=\"stratified\")"],"execution_count":0,"outputs":[]},{"metadata":{"id":"dOgfPpe6Q_t-","colab_type":"code","colab":{}},"cell_type":"code","source":["#Βιβλιοθηκη για τη σχεδιαση των Confusion Matrix\n","import matplotlib.pyplot as plt\n","import itertools\n","def plot_confusion_matrix(cm, classes,\n","                          normalize=False,\n","                          title='Confusion matrix',\n","                          cmap=plt.cm.Blues):\n","    if normalize:\n","        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n","        print(\"Normalized confusion matrix\")\n","    else:\n","        print('Confusion matrix, without normalization')\n","\n","    print(cm)\n","\n","    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n","    plt.title(title)\n","    plt.colorbar()\n","    tick_marks = np.arange(len(classes))\n","    plt.xticks(tick_marks, classes, rotation=45)\n","    plt.yticks(tick_marks, classes)\n","\n","    fmt = '.2f' if normalize else 'd'\n","    thresh = cm.max() / 2.\n","    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n","        plt.text(j, i, format(cm[i, j], fmt),\n","                 horizontalalignment=\"center\",\n","                 color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n","    plt.ylabel('True label')\n","    plt.xlabel('Predicted label')\n","    plt.tight_layout()"],"execution_count":0,"outputs":[]},{"metadata":{"colab_type":"text","id":"qNQeylFtnWrs"},"cell_type":"markdown","source":["# **Γ. Baseline classification**\n","\n","Θα εκπαιδεύσουμε τους Classifiers στο train set με default τιμές."]},{"metadata":{"colab_type":"code","id":"LxMJQRLMnWrt","colab":{"base_uri":"https://localhost:8080/","height":170},"outputId":"a49dab16-8372-44db-d9d0-b40acf52f075","executionInfo":{"status":"ok","timestamp":1544694552436,"user_tz":-120,"elapsed":4775,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["isolet_accuracy = {}\n","model = dc_uniform.fit(train, train_labels)\n","isolet_accuracy['uniform (random)'] = dc_uniform.score(test, test_labels)\n","model = dc_constant_1.fit(train, train_labels)\n","isolet_accuracy['constant 1'] = dc_constant_1.score(test, test_labels)\n","model = dc_constant_2.fit(train, train_labels) \n","isolet_accuracy['constant 2'] = dc_constant_2.score(test, test_labels)\n","model = dc_constant_3.fit(train, train_labels) \n","isolet_accuracy['constant 3'] = dc_constant_3.score(test, test_labels)\n","model = dc_constant_4.fit(train, train_labels) \n","isolet_accuracy['constant 4'] = dc_constant_4.score(test, test_labels)\n","model = dc_most_frequent.fit(train, train_labels)\n","# Χρησιμοποιουνται ταξινομητες για τις 4 πρωτες κλασεις. Οι υπολοιποι παραλειπονται καθως ειναι εμφανες πως τα αποτελεσματα θα ειναι ιδια\n","#για οποιαδηποτε αλλη κλαση καθως το δειγμα μας ειναι απολυτως ισορροπημενο\n","isolet_accuracy['most frequent label'] = dc_most_frequent.score(test, test_labels)\n","model = dc_stratified.fit(train, train_labels)\n","isolet_accuracy['stratified'] = dc_stratified.score(test, test_labels)\n","print(\"Classification Accuracy on the isolet Dataset (20% test set)\\n\")\n","sorted_accuracy = [(k, isolet_accuracy[k]) for k in sorted(isolet_accuracy, key=isolet_accuracy.get, reverse=True)]\n","for k, v in sorted_accuracy:\n","  print(k,v)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Classification Accuracy on the isolet Dataset (20% test set)\n","\n","stratified 0.04401709401709402\n","uniform (random) 0.04358974358974359\n","constant 3 0.0405982905982906\n","constant 2 0.039316239316239315\n","constant 1 0.03547008547008547\n","constant 4 0.03376068376068376\n","most frequent label 0.03376068376068376\n"],"name":"stdout"}]},{"metadata":{"colab_type":"text","id":"B7x8--9XnWrx"},"cell_type":"markdown","source":["# **Confusion Matrix για τον Dc Uniform Classifier**"]},{"metadata":{"colab_type":"code","id":"4ZjHgErlnWrx","colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"421dffa8-5bc3-4f32-9d45-a776a312c9f6","executionInfo":{"status":"ok","timestamp":1544694555435,"user_tz":-120,"elapsed":7767,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import precision_recall_fscore_support\n","from sklearn.metrics import classification_report\n","import matplotlib.pyplot as plt\n","preds = dc_uniform.predict(test)\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro1 = micro[2]\n","macro1 = macro[2]"],"execution_count":9,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[ 4  6  2  3  1  6  2  3  2  3  4  2  5  2  2  3  5  3  3  5  3  3  2  3\n","   4  2]\n"," [ 2  7  3  2  2  2  8  4  4  0  7  4  3  4  9  6  0  3  3  3  4  2  3  1\n","   2  4]\n"," [ 3  4  3  4  7  2  7  7  2  3  1  5  1  4  4  0  3  4  6  7  2  3  2  8\n","   2  1]\n"," [ 4  7  5  1  4  3  3  5  1  3  6  4  0  2  1  4  3  3  3  3  4  1  3  1\n","   3  2]\n"," [ 2  4  4  3  4  1  1  2  1  3  5  6  2  3  4  3  5  8  5  4  1  2  5  0\n","   2  7]\n"," [ 4  5  4  4  5  2  4  4  4  1  3  3  4  4  7  1  4  6  7  0  5  5  1  6\n","   3  1]\n"," [ 3  1  5  2  3  3  5  4  3  6  3  3  3  5  6  6  5  3  4  2  2  4  6  2\n","   1  2]\n"," [ 3  6  6  1  1  4  4  4  3  0  2  6  2  3  3  2  5  5  3  2  1  2  6  4\n","   4  1]\n"," [ 4  4  7  3  1  5  5  2  5  3  4  2  6  4  1  4  3  4  5  3  4  3  5  4\n","   6  6]\n"," [ 5  5  7  1  4  2  6  1  3  4  1  2  2  3  5  4  5  2  3  5  1  4  4  0\n","   1  5]\n"," [ 4  2  7  2  4  3  0  4  4  5  3  5  3  7  1  2  2  0  3  4  3  1  6  2\n","   5  6]\n"," [ 4  6  4  2  6  2  6  1  4  2  6  4  4  2  1  5  3  3  2  3  6  2  4  4\n","   8  3]\n"," [ 3  7  3  2  5  6  5  4  4  3  1  3  2  1  0  4  3  5  3  5  4  6  3  8\n","   4  1]\n"," [ 3  3  3  5  1  2  1  4  8  5  5  3  4  8  1  0  1  1  4  4  4  3  8  2\n","   1  7]\n"," [ 3  5  4  2  3  5  5  6  7  3  3  3  1  4  5  4  3  5  5  3  2  4  2  2\n","   3  3]\n"," [ 3  3  3  4  5  4  3  2  2  0  4  2  4  2  4  4  4  2  7  1  2  6  5  2\n","   6  3]\n"," [ 1  2  3  2  6  3  2  3  0  4  2  4  3  7  3  1  6  5  8  4  1  3  2  1\n","   5  1]\n"," [ 3  5  8  5  2  2  3  3  2  1  2  2  3  7  3  3  4  3  2  5  6  5  5  4\n","   1  1]\n"," [ 1  1  3  7  4  2  5  2  1  3  4  1  2  2  3  5  1  4 10  4  5  2  9  0\n","   5  5]\n"," [ 3  2  2  3  2  6  5  1  4  3  6  1  5  8  5  1  2  7  3  7  3  3  3  3\n","   3  4]\n"," [ 4  5  1  6  4  2  7  5  4  4  3  1  7  0  2  1  3  1  3  6  3  3  4  2\n","   3  3]\n"," [ 8  4  3  6  2  7  5  5  6  1  2  5  0  4  8  6  1  2  5  1  4  2  5  3\n","   7  6]\n"," [ 6  4  3  0  5  5  4  0  3  0  2  6  4  1  5  3  2  5  3  1  6  3  3  6\n","   0  2]\n"," [ 4  3  3  5  3  3  1  3  2  3  1  1  2  5  2  3  2  3  3  1  2  4  5  6\n","   3  7]\n"," [ 3  1  4  3  7  2  2  2  4  2  0  3  4  1  5  3  4  2  4  3  6  5  2  4\n","   5  4]\n"," [ 3  7  2  1  4  4  2  5  3  2  2  5  2  3  3  3  2  3  6  3  4  6  6  4\n","   2  4]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.04      0.05      0.05        83\n","         2.0       0.06      0.08      0.07        92\n","         3.0       0.03      0.03      0.03        95\n","         4.0       0.01      0.01      0.01        79\n","         5.0       0.04      0.05      0.04        87\n","         6.0       0.02      0.02      0.02        97\n","         7.0       0.05      0.05      0.05        92\n","         8.0       0.05      0.05      0.05        83\n","         9.0       0.06      0.05      0.05       103\n","        10.0       0.06      0.05      0.05        85\n","        11.0       0.04      0.03      0.04        88\n","        12.0       0.05      0.04      0.04        97\n","        13.0       0.03      0.02      0.02        95\n","        14.0       0.08      0.09      0.09        91\n","        15.0       0.05      0.05      0.05        95\n","        16.0       0.05      0.05      0.05        87\n","        17.0       0.07      0.07      0.07        82\n","        18.0       0.03      0.03      0.03        90\n","        19.0       0.09      0.11      0.10        91\n","        20.0       0.08      0.07      0.08        95\n","        21.0       0.03      0.03      0.03        87\n","        22.0       0.02      0.02      0.02       108\n","        23.0       0.03      0.04      0.03        82\n","        24.0       0.07      0.07      0.07        80\n","        25.0       0.06      0.06      0.06        85\n","        26.0       0.04      0.04      0.04        91\n","\n","   micro avg       0.05      0.05      0.05      2340\n","   macro avg       0.05      0.05      0.05      2340\n","weighted avg       0.05      0.05      0.05      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAasAAAGACAYAAAATek2YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSHOx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DqTmPitNMfWr5j4rTRmTw0daxlgcSGMyF5kYnkz8Ts19Vlx90t1nBbAfc6Lus+q1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EeA77VIFu6U1SbenTX/62oWQXuBja0p66+AU5uBmjhkqYQT0rFxcXoXOUhst//AHNmdNK/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\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b5837048>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"text","id":"s7VIshbmnWr1"},"cell_type":"markdown","source":["# **Confusion Matrix για τον Dc Constant 1 Classifier**"]},{"metadata":{"colab_type":"code","id":"o6563O67nWr1","colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"6a4924ca-73da-4800-938c-3890a80c085e","executionInfo":{"status":"ok","timestamp":1544694557525,"user_tz":-120,"elapsed":9846,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import precision_recall_fscore_support\n","preds = dc_constant_1.predict(test)\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro2 = micro[2]\n","macro2 = macro[2]"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[ 83   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 92   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 79   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 87   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 97   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 92   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 83   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [103   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 85   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 88   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 97   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 91   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 87   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 82   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 90   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 91   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 87   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [108   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 82   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 80   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 85   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [ 91   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.04      1.00      0.07        83\n","         2.0       0.00      0.00      0.00        92\n","         3.0       0.00      0.00      0.00        95\n","         4.0       0.00      0.00      0.00        79\n","         5.0       0.00      0.00      0.00        87\n","         6.0       0.00      0.00      0.00        97\n","         7.0       0.00      0.00      0.00        92\n","         8.0       0.00      0.00      0.00        83\n","         9.0       0.00      0.00      0.00       103\n","        10.0       0.00      0.00      0.00        85\n","        11.0       0.00      0.00      0.00        88\n","        12.0       0.00      0.00      0.00        97\n","        13.0       0.00      0.00      0.00        95\n","        14.0       0.00      0.00      0.00        91\n","        15.0       0.00      0.00      0.00        95\n","        16.0       0.00      0.00      0.00        87\n","        17.0       0.00      0.00      0.00        82\n","        18.0       0.00      0.00      0.00        90\n","        19.0       0.00      0.00      0.00        91\n","        20.0       0.00      0.00      0.00        95\n","        21.0       0.00      0.00      0.00        87\n","        22.0       0.00      0.00      0.00       108\n","        23.0       0.00      0.00      0.00        82\n","        24.0       0.00      0.00      0.00        80\n","        25.0       0.00      0.00      0.00        85\n","        26.0       0.00      0.00      0.00        91\n","\n","   micro avg       0.04      0.04      0.04      2340\n","   macro avg       0.00      0.04      0.00      2340\n","weighted avg       0.00      0.04      0.00      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAbEAAAGACAYAAAAj9ly5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHPxU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8OF05cqVfOvXGrOfVh8NpKcR7+hJRDz5h0RT/xUX5LsTX8cmyncu5rU7MSmNs+XK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\nRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b5818a58>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"text","id":"Q8aRHFvnnWr5"},"cell_type":"markdown","source":["# **Confusion Matrix για τον Dc Constant 2 Classifier**"]},{"metadata":{"colab_type":"code","id":"k_jBI26tnWr6","colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"c1b0f8e2-1454-48d1-fd23-279998c104ba","executionInfo":{"status":"ok","timestamp":1544694559721,"user_tz":-120,"elapsed":12028,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import precision_recall_fscore_support\n","preds = dc_constant_2.predict(test)\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro3 = micro[2]\n","macro3 = macro[2]"],"execution_count":11,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[  0  83   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  92   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  79   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  97   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  92   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  83   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0 103   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  85   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  88   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  97   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  82   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  90   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0 108   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  82   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  80   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  85   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.00      0.00      0.00        83\n","         2.0       0.04      1.00      0.08        92\n","         3.0       0.00      0.00      0.00        95\n","         4.0       0.00      0.00      0.00        79\n","         5.0       0.00      0.00      0.00        87\n","         6.0       0.00      0.00      0.00        97\n","         7.0       0.00      0.00      0.00        92\n","         8.0       0.00      0.00      0.00        83\n","         9.0       0.00      0.00      0.00       103\n","        10.0       0.00      0.00      0.00        85\n","        11.0       0.00      0.00      0.00        88\n","        12.0       0.00      0.00      0.00        97\n","        13.0       0.00      0.00      0.00        95\n","        14.0       0.00      0.00      0.00        91\n","        15.0       0.00      0.00      0.00        95\n","        16.0       0.00      0.00      0.00        87\n","        17.0       0.00      0.00      0.00        82\n","        18.0       0.00      0.00      0.00        90\n","        19.0       0.00      0.00      0.00        91\n","        20.0       0.00      0.00      0.00        95\n","        21.0       0.00      0.00      0.00        87\n","        22.0       0.00      0.00      0.00       108\n","        23.0       0.00      0.00      0.00        82\n","        24.0       0.00      0.00      0.00        80\n","        25.0       0.00      0.00      0.00        85\n","        26.0       0.00      0.00      0.00        91\n","\n","   micro avg       0.04      0.04      0.04      2340\n","   macro avg       0.00      0.04      0.00      2340\n","weighted avg       0.00      0.04      0.00      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAbEAAAGACAYAAAAj9ly5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsnXdYVEfbxm+qWFAJKmoUX8VYohI1\nGmMHBEGsn1FQBGNiLFGxYU2sr5qoMRpBjehrNK69i70BtmBBbBgVFQsiItIU6fB8fxA3Agu7Zxd2\nZ4f5fddcn7tn5jn3PeRlOGfn3GtARASBQCAQCPQQQ10LEAgEAoFAXcQiJhAIBAK9RSxiAoFAINBb\nxCImEAgEAr1FLGICgUAg0FvEIiYQCAQCvUUsYgKBQCDQW8QipkOICBs3bkSvXr3g7OwMR0dHzJs3\nD2/fvtWo7pQpU9C1a1ecP39e8thbt25h+PDhGp2/pDl69ChSUlIUHvv111+xffv2Ujv3tGnTEBgY\nWEjHjBkzsGbNmhI7T2RkJK5evVpi9UoDLy8vHDx4ELGxsejVq5fadT70eurUKcycObOkJArKIMa6\nFlCWWbZsGa5cuYINGzbAysoKqampWLRoEUaNGoWtW7fCwMBArbpHjhzBiRMnYG1tLXmsra0tNmzY\noNZ5SwtfX1+0bt0alSpVKnTMx8enVM+9dOlSlXRoyunTp5GdnY22bduWeO2SxsrKCocPH1Z7/Ide\nnZyc4OTkVILqBGUNcSWmI5KSkiCTybB48WJYWVkBACpUqIA5c+bgu+++AxEhIyMDc+bMgbOzM3r0\n6IHFixcjJycHAODg4IAdO3ZgwIAB6NSpExYvXgwg76/l3NxcDB8+HGfPnoWDgwNCQ0Pl533/Ojs7\nGz/++COcnZ3h5OSEcePGISUlBZcvX5b/UlHn/AXx8vLCunXr4O7uji+//BJbt27FmjVr4OLiAldX\nV0RFRQHI++t88ODB6NGjB5ycnOS/JGfOnInHjx/Dy8sLoaGhmDFjBn7++Wf07t0bx44dk18R3bp1\nC3Z2dnj37h0AYO3atRg/fnyR85+eno5WrVohPT0dALBu3Tp07txZfnzhwoXYuHGj/OqjoA4ASE5O\nxogRI2BnZ4fhw4fLr9Lu3buHQYMGwcXFBX379pVfEe/btw/Dhg2Tn+P968DAQPj7+2Pz5s0K57G4\nuT527Bh69eoFFxcXDB06FM+ePQMA+Pn5YdasWRgwYAA2bdqEffv2Yfz48fDx8YGdnR2++eYbhIaG\nYtCgQejQoQN27twJAMjNzcX8+fPh7OwMBwcHTJ06FVlZWfn0PH/+HJ9++imAvD8iXFxc4OLiAgcH\nBzRu3BgpKSlF1ino9cM5SUpKwoQJE+Ds7AxXV1esW7dOfs7GjRvjwIED6NevHzp16oRNmzYV+bMV\nlDFIoBOCg4PJycmp2D7+/v40YsQIysrKorS0NPrqq6/owIEDRERkb29PkydPpuzsbHr58iU1a9aM\nYmJiiIioUaNG8n/b29vT1atX5TXfvw4KCqKhQ4dSbm4u5ebm0ooVK+jcuXN06dIlcnR01Oj8H+Lp\n6UnfffcdZWVlUWBgIH322We0d+9eIiLy9vamFStWEBHRqFGjyN/fn4iIrly5Qra2tpSZmVnIz/Tp\n06l3796Unp4uf7169WoiIlqwYAH9+uuv9PLlS+rcuTPFxsYWO78eHh7yuRk5ciQNHDiQoqKiiIio\nb9++dPfuXfL09JR7LqijZ8+elJiYSFlZWdS3b1/av38/5eTkUI8ePejQoUNERHTr1i1q27YtvX37\nlvbu3Utff/21/Pwfvv7QR0GKmuvo6Gj6/PPP6cmTJ0REtGHDBnk9X19f6tSpE8XHx8vP1bJlS4qM\njKSMjAzq3LkzjRo1irKzsykwMJC6dOlCRETHjx+nXr16UWZmJqWnp1OPHj3k/t/PRVRUFDVt2rSQ\nzh9//JEWLFigtM6HXj+cg9mzZ9Ps2bOJiCgxMZHs7OzkP59GjRrRL7/8QkREN2/epBYtWlB2dnax\nP19B2UBciemIpKQkWFpaFtsnODgYbm5uMDY2hpmZGXr37o2LFy/Kj/fu3RtGRkawsrKCpaUlYmJi\nVD7/Rx99hEePHuHUqVNIS0vDxIkT812JlOT57e3tYWxsjEaNGiEtLQ3Ozs4AgEaNGuHVq1cAgDVr\n1sg/i/v888+RkZGBuLg4hfXat2+PcuXKFXp/0qRJOH78OGbOnIkxY8agRo0axc5Bu3btcP36deTm\n5uLFixewt7dHWFgYUlJSEBcXh8aNGxc7vkuXLqhatSqMjY3xySefIDY2Fs+fP8fr16/Rs2dPAECL\nFi1Qu3Zt3L59u9haylA01xcvXkS7du1Qr149AMDAgQNx+fJlZGdnAwA+++wzfPTRR/IaDRs2RP36\n9WFqaop69eqhU6dOMDIyyvdzcHZ2xt69e2FiYoJy5cqhRYsW8qvl4jh+/Dhu376NadOmqV3n7Nmz\n8PDwAABUrVoVTk5O+f5769u3LwCgWbNmyMjIQHx8vKrTJ+AYsYjpCAsLC8TGxhbbJyEhAVWqVJG/\nrlKlSr7/4X742YyRkZH8Vp8q2NraYtasWZDJZOjYsSN8fHzw5s2bUjl/xYoV5X0+fG1oaIjc3FwA\nwPnz5zFkyBD5rSQikh8ryIeaCp6nR48euHbtGnr37l2sfyBvEbtx4wbu378PGxsbtGzZEmFhYQgL\nC0Pbtm2VfiapyH9CQgLMzc3zja1cuTISEhKU6pF6rsTERFSuXFn+vrm5OYgIiYmJAArP0/t5f1+j\nQoUK8n+/n+uEhARMnz4dzs7OcHFxwZkzZ0BKMsKjo6Px008/Yfny5TA1NVW7TkJCQj4/lStXzvff\nm7m5uVwvgCL/+xCULcQipiNatmyJ+Ph43LlzJ9/7WVlZWLFiBdLS0lCtWjUkJSXJjyUlJaFatWqS\nzvPhQgHkfY7zHhcXF8hkMgQFBSEtLa3Qho6SOL8qZGVlYeLEifj+++9x4sQJBAQEqLWpJTY2FocO\nHULPnj2xatUqpf1btWqFu3fvIjQ0FK1bt4atrS1u3bqFa9euoX379upYgaWlJZKTk/P9wn5/1W1o\naJhvoS/4R4M65/rw55OcnAxDQ0NYWFioXXPFihUwNjbGoUOHcPz4cXTt2rXY/jk5OfDx8YG3tzds\nbGzUrgNo7783AV+IRUxHVK5cGd999x2mT5+Op0+fAgDS0tIwZ84c/P333yhfvjzs7OywZ88e5OTk\nIDU1FQcPHlTpl8GHVK9eHffu3QOQt0U8IyMDALB3716sXr0aQN6tmwYNGhQaWxLnV4W0tDSkpqai\nefPmAIA///wTJiYmSE1NBQAYGxur9At/0aJF+O677/DDDz/g2LFjuHv3brH9TU1NUadOHRw6dAit\nW7dGxYoVYWBggAsXLihcxFTRUadOHdSsWRNHjx4FAISFheH169ewtbVFjRo18PjxY2RkZCAtLQ3H\njx/PV1vqoxUdO3ZEaGio/Dbdjh070LFjRxgbq7/pOD4+Ho0aNYKpqSnu3buH69evy38OivDz80PN\nmjUxcOBAlesU5dXOzk6+wSQhIQGnTp2CnZ2d2l4EZQOxiOkQb29vuLm54fvvv4ezszP69+8PS0tL\n+VWEl5cXatasiZ49e+Krr76CnZ0devToIekcY8aMwaZNm9CrVy88evQIDRs2BAB069YNd+7cQffu\n3dGjRw88fPgQ33zzTb6xJXF+VXi/oPfr1w/9+vWDtbU1HB0dMXr0aKSmpsLFxQWDBg2SLwyKCA4O\nxvPnzzFo0CBUqlQJkyZNwqxZs5CTk1Pss2Tt2rXDgwcP0KRJEwB5t1kTExMVPp6gig4DAwMsX74c\nW7ZsQY8ePbBw4UKsXLkSFSpUQLt27fDZZ5/B2dkZI0aMQLdu3eTj7O3tsWPHjmJ3VBakZs2aWLhw\nIcaMGQMXFxdcvXoV//3vf1Uer4hvv/0WO3bsQI8ePbB161ZMnz4du3fvxrFjxxT29/f3x82bN+U7\nFF1cXBAaGlpsnaK8Tpw4EW/evIGLiws8PT0xcuRI2NraauRHwD8GpOxGtUCg54SFheHOnTvw8vLS\ntRSBQFDCiCsxAfdkZGTAxcVF1zIEAkEpoHeLWGBgIFq3bo1GjRrByckJz58/13l9qWNY68+ippL0\n0L59e1SvXp0pTfrSn0VNZdWDoAh0+IyaZFJSUqh69ep07do1IiJauXIl9ezZU6f1pY5hrT+Lmnjw\nwKIm4YENTaX9e6ysoVeLWEBAALVr107++u3bt2RiYkJv3rzRWX2pY1jrz6ImHjywqEl4YENTaf8e\nK2swHQCcnp3/9Z17EfhPfRukZwOmRoCxWSVYWlrizr2HaNmqFRpNCoBV5XI4NsMOg/z+QkTMW4x1\naojmdauioVUlOP0cDACoUbkcto1rj/l772DD6PYq11fE+zG5BGTmKB8j9Ryl3Z83Dyxq4mFeefDA\niiZV+5tp8bdz+VbjNBqfdl35c5mlBdOLWEHSUlNhZmYGADD851lYs/Ll5aGvABD7JgNLD93F8eld\n8S4jG6kZOXDzvYjzcx0BALsndMRn1lWxLvARzt+Pk1y/KE2GHzybW9wYqeco7f68eWBREw/zyoMH\nVjSp46HUMdC77RFydKI8IiICjo6O2LJli6RxFSpUlKeOvyctNTVfJE+zOpUxzvkTdJp/Bi2mH8fi\ngLvYMPIL+fGBKy+i9Y8n0LBmJczs01RyfXU0sdyfRU08eGBRk/DAhiZ1PAiKRuuLWGpqKhYsWKBW\nrE/j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wKIm4YENTSxmJ+rz7UStbuzQFFUyx2KT0vHTvtsInNsdKenZSM3IRr+lQejTpi4A\noGPj6jAwMEBIRP40D1XrazqGtf4sauLBA4uahAc2NLGZnai7U2uKVhexy5cvY8KECfjkn3iVRo0a\nYfbs2SqPVyVzrLl1VUzs2RRtZxxFdEIqBnxpjc3eneTH+39ZD/uvPFO7vqZjWOvPoiYePLCoSXhg\nQxOL2Ym6vprSBK3fTvziiy8gk8kgk8kkLWCAapljXZpaIfRRPKITUgEAB65GocnH/+Y0OtnWwulb\nMWrX13QMa/1Z1MSDBxY1CQ9saGIyO1GP0avPxFTJHHv48g3a2FjCoqIpAMCxRS3EJuV9n1g183Ko\nZl4Oj2LfFi6uYv2ixvCQLceDBxY18TCvPHhgRZPITixhtLmL5NKlS9SjRw8aNWoUDRo0iC5cuFBs\nf0U7e4rLHHu/+3DpgXB6GPOGHsQkU1hkPPX66QwRETnMO0EvE1Pl/QruTlRWv6jGS7YcDx5Y1MTD\nvPLggSVNrGUnWnhu1ajpEq1mJ8bGxuLatWvo0aMHoqKiMHToUJw8eRKmpqYK+4vsRIFAICh9LIdu\n12h8/ObBJaREOlrd2GFlZQVXV1cAgLW1NapVq4bY2FjUrVtXYX+p2YnWo3YV2f/VBjfUGF74+DN/\nN5XrFwfvOW5Ck/b6s6iJBw8sahLZiZqj1c/EAgICsGHDBgBAXFwc4uPjYWVlpU0JAoFAIOAIrV6J\nOTg4YMqUKThz5gyysrIwb968Im8lCgQCgUA76HxzhgZo9UqsUqVKWLt2LbZt24bdu3eja9eukmuo\nEpzp1r4ezv3XGWFLe2L1d1/A1DjP5sWFLvJ2+9fe+GNM4QT9shyQyoMHFjXxMK88eGBJkwgALkF0\nuq1ECQV36ygLzqz+7U7qPPsYvX6TTp/5BFD1b3fSnpAn9PO+20RE+XYlnrr5goasPCepvqLGU0Aq\nDx5Y1MTDvPLggRVNLAYAV/9mp0ZNl+jVIqYsONPM1Zcm/x5Me85FkJmrL5m5+lKniTvpxqNXRETy\n9/rMPkiB15+RmauvpPqKGk8BqTx4YFETD/PKgwdWNLEYAPzhH/jqNF2iVw87qxKcSQCMPtiXn5KW\nCZtaVT4sg9lD2uGn7VfUqq/pGNb6s6iJBw8sahIe2NDEYgCwPqNXi5gqwZlBN6Lg0Moan9b7CEaG\nBhjVyxZmpv/uX+li+zEMDIAL4S/Uqq/pGNb6s6iJBw8sahIe2NDEYgCwPn8mpvVFLCAgAH369EH/\n/v0RHBwsaawqwZn3ohIxee1ZbJ7mgnMr3HDvWQKSUjLkx927NsausxFq19d0DGv9WdTEgwcWNQkP\nbGhiNQBYLGIqkJiYiNWrV2Pbtm1Yu3Ytzpw5I2m8qsGZW8/cQ5ux29Bxwk6EP4nHnafx8mM92tbD\n8dCnGtXXZAxr/VnUxIMHFjUTK5qdAAAgAElEQVQJD2xoYjEAWCxiKhISEoL27dujUqVKqFGjBhYs\nWCBpvCrBmQ1qVcElv0GoUtEUxkaGmObeBrLTdwEA1auUR/WqFfAgOlHt+kWN4SEglQcPLGriYV55\n8MCKJhEAXMJocxeJv78/TZ8+nUaNGkWDBw+mv/76q9j+ObmF3wsKygsjtbGxIWdnZ4qJiSlRjerU\nlzqGtf4sauLBA4uahAc2NJX27zGp1Bq5V6OmS7QaALxu3TqEhYVh1apVePHiBYYOHYqgoKAiV/Li\nMsgUZY5Z9PUrsn/aEW+U71n4eOJBb5XrFwfvOW5Ck/b6s6iJBw8samIlO7H26H0ajX+xtn8JKZGO\nVm8nWlpaolWrVjA2Noa1tTUqVqyIhIQEbUoQCAQCQQH0+XaiVhexTp064dKlS8jNzUViYiJSU1Nh\nYWGhTQkCgUAgKIBYxFTEysoKzs7OcHNzw4gRIzBr1iwYGkqToErmmIdDE1xb44GIjcOwwcdJnp24\nfHQX3FjriVvrPOE71i7fQ9FS6isaw0O2HA8eWNTEw7zy4IElTSI7sQTR6SdySigY16Isc8zM1Zda\nf7+F4pJSqeHQP8jM1Zd2BN2jeZtDiIjo1LWnZN5nFZn3WUUXw6Pp+5VnJNVX1HjKluPBA4uaeJhX\nHjywoonF7MQ6Yw5o1HSJXi1iyjLHKrltoil/XKK9fz2mSm6bqJLbJuo68zDdfBxPREStJ+6Tv+93\nKJxWHbkjqb6ixlO2HA8eWNTEw7zy4IEVTSxmJ9YZe0Cjpkv0KnZKrezE9Cw0qGkOAIh48Qb457i9\nbW2EPngtub46mljuz6ImHjywqEl4YEMTi9mJ+nw7Ua8WMVUyx4Jvx8C+RS00rVsVRoYGGOncBGYm\nRvnGrBj+JaLj32FfyBPJ9dXRxHJ/FjXx4IFFTcIDG5pEdmLJotVvdt69ezcCAgLkr8PDw3H9+nWV\nx6uSOXY/OhlTN17BpgldkJGVC1nQAyS/y4RlZTMYGRpgzfcdUc28HIb8GozcAo/IldVcNtY08eCB\nRU3CAxuaWM1O1Fe0eiU2cOBAyGQyyGQyeHt7o1+/fpLGq5o5tu3cI7SbEoAuMw/j76gk3InKi5la\nNaoDypsawf2XQKRn5ahdX5MxrPVnURMPHljUJDywoUlkJ5YsOruduHr1aowZM0bSGJWyE63McXFJ\nb1SpYAJjIwP49GuBrcGPAACNP66Cb33PITtHcUhJWc+W48EDi5p4mFcePLCiicXsRL1GF7tJbt68\nSdOnT1faT2Qnaqc/i5p48MCiJuGBDU2sZSf+Z+JhjVpxpKSk0NixY8nT05Pc3d3p3LlzdPfuXXJ3\ndyd3d3eaM2eORtq1mp34njlz5qBnz55o165dsf2kZidWH/Jnkf3f7vwa5u6Fj8dt/Vrl+sXBe46b\n0KS9/ixq4sEDi5pYyU5sMPmoRuMjl7sWeWzLli2IjY2Fj48PYmNj8fXXX6N69eqYOnUqbG1t4ePj\ngz59+qBr165qnVsntxMvX76MVq1a6eLUAoFAIChAaX4mZmFhgaSkJADAmzdvULVqVURHR8PW1hYA\nYG9vj5CQELW1a30Ri42NRcWKFWFqaqrtUwsEAoFAAQYGmrXi6NmzJ168eAEnJyd4enpi2rRpqFy5\nsvy4paUl4uLi1Nau9UUsLi4OH330kdrjVckc87RriKu/9kXo8r44+KMTGtbKm7Dlw9vh2vJ+uL6i\nH3777ksYG4nsRN48sKiJh3nlwQNLmljLTixNDh48iNq1a+PUqVP4888/MXXq1HzHNf5ES6NP1EqZ\ngnEtyjLHKrltotYT99Hr5DT6ZNQuquS2icav+4v+uvuSiIj2hzymyu5/kuWQzXT5fixN/F+IpPqK\nGk/Zcjx4YFETD/PKgwdWNLGYndhwyjGNWnHMmTOHjh8/Ln/dsWNH6tKli/z1vn37aPHixWpr16tF\nTFnmWN1xB2nE+isUGhlPdccdpLrjDlKX+acp6V0mERF1mnda/v7a0w9oQ9AjSfUVNZ6y5XjwwKIm\nHuaVBw+saGIxO/GTqcc0asWxYcMGWrJkCRERPX/+nLp3707ffPMNXb16lYiIRo8eTRcvXlRbu17F\nTqmSOXb9cQLqVauIRrXy8hJdW9bChft591ufvs6LdalRuRzsP62BM+Gxkuuro4nl/ixq4sEDi5qE\nBzY0lbXsRHd3d0RHR8PT0xM+Pj6YN28efvjhByxfvhyDBg2CtbU1OnTooLZ2rcZOaYoqmWOxbzKw\n9NBdHJ/eFe8yspGakQM334vo2ao2AGD3hI74zLoq1gU+wvn7cZLrq6OJ5f4sauLBA4uahAc2NLGZ\nnVh6tStWrIiVK1cWen/btm0lUl+ri9i7d+8wffp0JCcnIysrC2PHjkXnzp1VHq9K5lizOpUxzvkT\ndJp/Bi8S0/B/bepgw8gv5McHrryISmbGWDakJWb2aSq5vjqaWO7PoiYePLCoSXhgQxOL2Yn6jFZv\nJ+7fvx/169eHTCbDypUrsWjRIknjVckc69ioOq5FJuJFYhoA4FBYNBr9szuxtkV5AEBKejb2XI5C\nl6Y1JNdXRxPL/VnUxIMHFjUJD2xoYjE70dDQQKOmS7S6iBV86M3CwkLSeFUyxyJfpeDzBhaoWsEE\nAGDfzAqvkvP+6pnUo7H8stmhmRXu/fP9YlLqF6WJh2w5HjywqImHeeXBAyuaWMxOLM3nxEodtbeE\nqMm3335Ljo6O9MUXX9D169eL7atoZ8+J00HUokVe5phTd2d6HBUjP/Z+5+HyI/foUexbevjyLd14\nkkD9l58nIqIDoVH08OVbiox9S8dvvqBWM49Lql9UO3E6LwetgYpjpJ6jtPvz5IFFTTzMKw8eWNKk\nSn9t0uzHkxo1XaLV7MSDBw8iNDQUCxYswL179/DDDz9g3759RfbPJUDHV6oCgUDAPS1mn9Jo/O0F\nTiWkRDpa3dgRFhaGTp06AQCaNGmCV69eIScnB0ZGRgr7Zxb+yi85ioIzG00KUNwZwDO/PrD2Lnw8\nYkUflesXB+9hpEKT9vqzqIkHDyxqYiUAWJ/R6mdi9erVw82bNwEA0dHRqFixYpELmEAgEAi0gz5/\nKaZW13p3d3f88MMP8PT0RHZ2NubNm6fN0wsEAoFAAbpeiDRBq1di7x9627JlC3bs2IH27dtLrlFc\ncOas/2uGZ359ULPqvw8SDrdrgDM/2gMAlgz+DCb/hP6amxlj1bDPYWoEmBoBxobK6xeniYeAVB48\nsKiJh3nlwQNLmlgLABa7E0uJgrt1igvOzM4hWrjhDBER2fRbQmYdf6QuI3+nx9EJZOW8gIiI9gXe\nphmrjpJZxx9p/YEr5Lvjgrx2Ti5RQnLZDkjlwQOLmniYVx48sKKJxQDglvPOaNR0iV4tYsUFZ6Zn\nEZl1/JGI/l3Eftt+nn6RnZW/39fnT7py5xmZdfyRek7cSPV6/yyvnZ1DtK+MB6Ty4IFFTTzMKw8e\nWNHEYgCwPi9i3AQAK3pOoGHdaoiMTpC/joxOQCPr6gCAM1cfIjYhBQBggLyt/BFlNFyUNU08eGBR\nk/DAhiY2A4D193aiXm3ilBqcWaGcCdIz/92/mp6ZhYpmJvLXhoYGMDXKW8Syc4HUMhouypomHjyw\nqEl4YEMTmwHAuv5gS320uojl5uZi7ty5ePDgAUxMTDBv3jzY2NgoH/gPUoMz36Vnwsz0X4vly5kg\nJS3zAz0kfxbN1AioVLFshouypokHDyxqEh7Y0MRiALAer2Ha3Z145swZvH37Fjt27MCiRYuwdOlS\nSeOlBmdGPH0NmzofyV83rGuJe0/yvn5lsHNLVKn0719DObl5D2CXxXBR1jTx4IFFTcIDG5pYDADW\n5+fEtLqIPXnyBLa2tgAAa2trvHjxAjk5xcRyFEBqcObewNtwc7RFDYu842MHdsCu07cAAENdW2Oc\n279fxGZoANjZl+2AVB48sKiJh3nlwQMrmlgMANZrtLmLJDg4mL755hvKzs6mR48e0WeffUZxcXFF\n9s/JLfxeUFBeGKmNjQ05OztTTExMiWpUp77UMaz1Z1ETDx5Y1CQ8sKGptH+PSaXtoiCNmi7RagAw\nAKxYsQKXL19G48aNcfv2bfj7+6N69eoK+xaXQaYoc8zCblaR/dMuLET5ToWPJwYvVLl+cfCe4yY0\naa8/i5p48MCiJlayE9v9fFaj8Zdndi0hJdLR+u7ESZMmyf/t6OgIS0tLbUsQCAQCwQeIjR0qcu/e\nPcycORMAcO7cOXz66acwNNSrR9UEAoGAO8TGDhVp1KgRiAgDBgyAv7+/fEGTgiqZY16urREmG4/r\nW8bj8IphaFg372ov6vBM3Ng6Qd76dGmqVn1FY3jIluPBA4uaeJhXHjywpIm17ES9RqefyCmhYFyL\nsswxs44/ku3gFRSXmEIN+i4ms44/0rilB+jizSdERLRgwxky6/hjvialvqLGU7YcDx5Y1MTDvPLg\ngRVNLGYntl9yVqOmS/RqEVOWOVZr1F769vcQuvroNdUatZdqjdpLHWefoKR3GUREtOzQ3/L33zcp\n9RU1nrLlePDAoiYe5pUHD6xoYjE7scPScxo1XaJXH0ipkjkW9jgB/6lWEY1rVwYAuLaqjXN3XwEA\nOjepjoCpXXF+nhPmfNUCpsaGkuuro4nl/ixq4sEDi5qEBzY0iezEkoW77MTY5HQsPvg3Tv3ogJT0\nbKRm5uCrX8+h9+d1cOzGC2wMjkQFUyNs/L49xnZvJLm+OppY7s+iJh48sKhJeGBDk8hOLFlK9Uos\nIiICjo6O2LJlCwAgJiYGXl5e8PDwwIQJE5CZmamkQn5UyRxrXrcKxvdojPazTuBTn8P4aX84No3J\n+/JN/9MPkZmdi6TULKw78xCOtjUl11dHE8v9WdTEgwcWNQkPbGhiMTtRnym1RSw1NRULFizI9+3N\nvr6+8PDwwLZt21CvXj3s2bNHUk1VMsc6NamB0Mh4RCemAQACQp/Lby1W+uDpQWMjA2Tn5H/Ou6zm\nsrGmiQcPLGoSHtjQJLITS5ZSW8RMTU2xfv161KhRQ/7e5cuX0a1bNwCAvb09QkJCJNVUJXPs0cu3\naNPAEhYVTQEA3VrURGxy3l89M/s1AwCUMzaEZ+f6OH37peT6RWniIVuOBw8sauJhXnnwwIomFrMT\n9fkzsVLfnejr60symYyIiL788kv5+0+fPiV3d/dix4rsRO30Z1ETDx5Y1CQ8sKGJtezErisuatR0\nSalnJ/r5+cHCwgKenp5o3769/Orr6dOnmD59Onbs2FHkWKnZiQ3G7Suy/4u1/VF7dOHjkav6q1y/\nOHjPcROatNefRU08eGBREyvZifYr/9JofNCEDso7lRJa3WJfoUIF+QeasbGx+W41CgQCgUAgFa0u\nYh06dMCJEycAACdPnkTnzp21eXqBQCAQKEBs7FBAeHg4vLy8sH//fmzevBleXl4YN24cDhw4AA8P\nDyQlJaFfv36S66qSOebevh6C5zri7FxH7JjQEQ1q5G1d/e3rz3F+nhOC5zpizlctYKhg7stythwP\nHljUxMO88uCBJU2sZSeKjR2lRMG4FmWZY7VG7aXOc05Q/Nt0ajX9CNUatZembQmjyw/yvnhz/5Vn\nVHv0Xqo3dj/9df8V+Wy+Jqm+osZTthwPHljUxMO88uCBFU0sZic6+oVo1HSJXi1iyjLHLIdtp6F+\n5+nKgziyHLadLIdtpy+mH5ZnJ07edEX+/vJDd+j3E/ck1VfUeMqW48EDi5p4mFcePLCiicXsRKdV\nIRo1XcJdduK1R6/xnxqV0OTjKgCAXm3qIPhO3vNgrq3rwMzECOblTWDXzApn77yUXF8dTSz3Z1ET\nDx5Y1CQ8sKGJxexEfYa77MSXSelYtPcWguc752UnZmSjz+JA9G1rDRMjQ9zz7QcTI0McufYcp27F\nSK6vjiaW+7OoiQcPLGoSHtjQJLITSxatZicCwObNm9GsWTO1fmCqZI61sK6KSb0+xefTDqPhuH1Y\nsOcmtkzI2wX57HUKGo7bh4bj9qFCOWN492giub46mljuz6ImHjywqEl4YEMTi9mJhgaaNV2i1ezE\nAwcOID4+Xu3nw1TJHOvyqRWuPnyN6IRUAMD+K8/ktxb3X36G7BxCWmYOjt+IRofGNSTXV0cTy/1Z\n1MSDBxY1CQ9saBLZiSWLVrMTHR0dMWnSJLVNq5I59jDmLdo2rCbPTnSyrY3YpLww4O4tawMADA0M\n4NC8Fu5GJ0uuX5QmHrLlePDAoiYe5pUHD6xoEtmJJUxp7xz5MDvxPfb29pSSkqJ0rKKdPSdOB1GL\nFnmZY07dnelxVIz82Pudh0v236aHMcn04EUyhUW+JtdFp4iI6MT15/To5Rt69PIN7f7rMdUbvVtS\n/aLaidN5OWgNVBwj9Ryl3Z8nDyxq4mFeefDAkiZV+msT17WXNWq6RKvZie9xcHDAoUOHlP7lkUu6\nv98qEAgEvNPL/6pG4w+PaltCSqTD9O7EzJyijykKzqwzougw4dcbB6HaN4WPP18/SOX6xcF7GKnQ\npL3+LGriwQOLmlgJANbniwWmFzGBQCAQlD663pyhCVrNTvz999/h5eWFuLg4jBgxAkuXLpVcV5XM\nMbcO/8GFhT1wY1lvrBnxJUyN82yG/OQqb3dW9MXGsR3Vqq9oDA/Zcjx4YFETD/PKgweWNInsxBJE\np5/IKaHgB53KMscsh22njj8epddv0qnF5INkOWw77Ql5Qj/tvUVE/278sBy2nU7ejCaPFWcl1VfU\neMqW48EDi5p4mFcePLCiicXsxL7rr2rUdIleLWLKMsfMXH1p8u/BtOdcBJm5+pKZqy91mriTbjx6\nRUQkf6/P7IMUeP0Zmbn6SqqvqPGULceDBxY18TCvPHhgRROL2Yn/979QjZou4S47kQAYffApZUpa\nJmxqVclXZ/aQdvhp+xW16ms6hrX+LGriwQOLmoQHNjSxmJ2oz7cTi9zYsWfPnmIHDhgwoMTFKEOV\nzLGgG1GYN7Q9Pq33Ee5HJWJUL1uYmf5rs4vtxzAwAC6Ev1CrvqZjWOvPoiYePLCoSXhgQ5PITixZ\nilzErl27VuxAVRaxiIgIjBkzBsOGDYOnpydiYmIwc+ZMZGdnw9jYGL/88guqV6+uslhVMsfuRSVi\n8tqz2DzNBRnZOdh88m8kpWSgWpXyAAD3ro2x62yE2vU1HcNafxY18eCBRU3CAxuaWMxO1OM1rOjd\niT///LO8LVq0CJMnT873njIUZSf+9ttvcHNzw5YtW+Dk5ISNGzdKEqtq5tjWM/fQZuw2dJywE+FP\n4nHnabz8WI+29XA89KlG9TUZw1p/FjXx4IFFTcIDG5pYzE4sbQICAtCnTx/0798fwcHBiImJgZeX\nFzw8PDBhwgRkZmaqXVvpZ2IhISFwdHSEl5cXAOCnn35CcHCw0sKKshPnzp0LZ2dnAICFhQWSkpIk\niVUlc6xBrSq45DcIVSqawtjIENPc20B2+i4AoHqV8qhetQIeRCeqXb+oMTxky/HggUVNPMwrDx5Y\n0cRidqKhgYFGrTgSExOxevVqbNu2DWvXrsWZM2fg6+sLDw8PbNu2DfXq1VP68VWxKNv5MXDgQIqL\niyNPT08iIoqPj6eBAweqvHNEUXZidnY2DRkyhP76669ixyra2VNc5tj73YcLt16mp7HJ9ORlMi3d\neZXMXH2JiOhL7+30Ij5F3q/g7kRl9YtqvGTL8eCBRU08zCsPHljSxFp2ovumMI1acRw5coTmzp2b\n7z17e3vKyMggIqKwsDAaN26c2tqVZicOGzYMmzZtgpeXF2QyGQBgyJAh2Lp1q0qLZMHsxJycHEyb\nNg3169fHuHHjih0rshMFAoGg9Bm8+YZG47cPbVnksXXr1iEyMhJJSUl48+YNvL29MXnyZISEhAAA\nnj17hmnTpmHHjqJjA4tDaeyUmZkZrlzJ246enJyMI0eOoFy5cmqdDABmzpyJevXqKV3AAOnZiRZ9\n/Yrsn3bEG+V7Fj6eeNBb5frFwXuOm9Ckvf4sauLBA4uaykp2YlJSElatWoUXL15g6NCh+PDaScl1\nlFKUfiY2d+5cbNiwAbdv34aTkxPOnz+P//73v2qdLCAgACYmJhg/frxa4wUCgUCgX1haWqJVq1Yw\nNjaGtbU1KlasiIoV/92hGRsbq/YXJQMqXInVqlUL/v7+kguHh4djyZIliI6OhrGxMU6cOIH4+HiU\nK1dOvknExsYG8+bNk1xbIBAIBCVHaT4n1qlTJ8yYMQMjRoxAcnIyUlNT0alTJ5w4cQJ9+/bFyZMn\n0blzZ7XrK70Su3r1Kr766iu0bNkSrVq1gru7u9JnyACgefPmkMlkCAwMxMmTJyGTyXD06FHs378f\nMpkMMplMrQVMleBMD4cmuLbGAxEbh2GDj5M8ADhq23e4sdZT3vq0b6BWfUVjeAhI5cEDi5p4mFce\nPLCkqSwFAFtZWcHZ2Rlubm4YMWIEZs2aBW9vbxw4cAAeHh5ISkpCv3791BevbOdHr1696K+//qL0\n9HRKS0ujCxcuUO/evdXeSSKFgrt1lAVnmrn6Uuvvt1BcUio1HPoHmbn60o6gezRvcwgRES3Ycinf\nzsSCuxPLekAqDx5Y1MTDvPLggRVNLAYAe229qVHTJUoXsa+//rrQe6NGjSoNLYUo+ENVGgDc158m\nr7tAey48JLO+/mTW1586TdlHNyLjiIhowfZQ+fvvm5T6ihpPAak8eGBREw/zyoMHVjSxGAD89bab\nGjVdUuTtxKioKERFRaFFixb4448/cO/ePURERODPP//Ep59+qv6lnwaoHgD8r62UtCzY1MwLAHaw\n/RhBi/vg5mo3LP7m3+8Zk1JfHU0s92dREw8eWNQkPLChicUAYH2myI0dX3/9NQwMDOTbH7ds2SI/\nZmBgoJMdhioFAN+MxrwhbfGptQXuP0/CKNdmMDM1AgAEXH6M34/cQUUzE+z+oTt8+reUXF8dTSz3\nZ1ETDx5Y1CQ8sKFJBACXLEUuYoGBgUUOCgsLU6l4wQDg69evY+nSpTA2NoapqSl++eUXfPTRRyqL\nVSkA+HkSJq+/iM0+3fICgE/fR9K7TFSrbIaVB28DADJTMuAXcBtTCixiZTVclDVNPHhgUZPwwIYm\nJgOAdXZmzVG6OzElJQVbt27FqlWrsGrVKqxYsUKlqzBFAcAbN27E0qVLIZPJ0KpVK+zatUuSWJUD\ngIMeoM2EPejosx/hTxNw52kCAMC8vIm8j7GRIbJyctWqr8kY1vqzqIkHDyxqEh7Y0MRiAHBpZieW\nunZlHSZOnIj79+9j3759ePfuHYKCglTaGq8oANjX1xd169YFESE2NhY1a9aUJFalAOCalXFpRf9/\nAoANMG1AK8gC7wMA/uv5BQCgnIkRhjs3xbFrzyTXL0oTDwGpPHhgURMP88qDB1Y0sRgArM9fiql0\nd+L74N/3/z8xMZEmT56s8s6RggHAZ8+epe7du9Po0aMpJyen2LE5uYXfCwrKCyO1sbEhZ2dniomJ\nUVmLKqhTX+oY1vqzqIkHDyxqEh7Y0FTav8ek8t3O2xo1XaI0AHjQoEH4448/MGLECKxatQoWFhZw\nc3NT+VZgwQDgfxZOLFu2DObm5hg9enSRY4vLIFOYnThgXZH90w6MRPl+hY8n7hmpcv3i4D3HTWjS\nXn8WNfHggUVNrGQnjtx9R6Px6wY2KyEl0lF6O7Fv377YtWsXBg4cCFdXV/Ts2ROWlpZqnezUqVMA\n8nbCODs7q5T8IRAIBILSRZ9vJypd6wcPHiz/d/v27REfH6/2c2J+fn6oU6cOmjZtips3b6J+/fpq\n1REIBAJByaHrzRmaUOSV2MqVKwu1HTt24NSpU1i5cqXSwuHh4fDy8sL+/fuxefNmeHl5YeHChZg/\nfz6GDBmC4OBgjBo1SrJglbIT7T7BNd8BiFjvgQ0T7eUPNdevaY6/fu2PI/NdNaqvaAwP2XI8eGBR\nEw/zyoMHljSVpezEUqeoD8v8/PyKbdqgYFyL0uzEvv7U2nsXxSWnUcPhW8isrz/tOPuA5m29QkRE\nd58l0Prjf9OZG1EKY6fKerYcDx5Y1MTDvPLggRVNLGYnjtn3t0ZNlyjdnahLCv5QlWWOVR68maZu\nukL7Qh5T5cGbqfLgzWQ36wjdfBxPRESffL+bRv9+gYJuv5Afl1JfUeMpW44HDyxq4mFeefDAiiYW\nsxP1eRFTurGDJVTKTiSC0QdfU/ouPRsNapoDAGKT0jSur+kY1vqzqIkHDyxqEh7Y0MRidqKhhk2X\n6Pr8klAlc+zsnZewa14LTetUhZGhAUY4NYaZiVGJ1dd0DGv9WdTEgwcWNQkPbGhiNTtRk6ZLVFrE\nEhMTcft2Xu5gbm6ukt7/EhERAUdHx3zhwQBw/vx5NG7cWILMPFTJHLsfnYxpf17FH96dEbigB+5F\nJyE5NbPE6ms6hrX+LGriwQOLmoQHNjSxmJ1oaKBZ0yVKF7HDhw/D3d0dM2fOBAAsWLAAu3fvVlpY\nUXYiAGRkZGDdunWoXr26ZLGqZo5tPx+J9tMPoeuPR/F3VBLuPEsq0fqajGGtP4uaePDAoibhgQ1N\nbGYncryIbdy4EQcPHoSFhQUAYPr06SqldSjKTgSAtWvXwsPDA6amppLFqpSdaGWO8z/1RJUKJjA2\nMoBP3+bYdu5RidUvagwP2XI8eGBREw/zyoMHVjSxmJ2o1yjb+fH+m53fZycSEQ0ePFjlnSMfZidG\nRkbS6NGjiYjI3t5e6ViRnaid/ixq4sEDi5qEBzY0sZadODngnkZNlyjNTpw0aRK6dOmC7du3Y+7c\nuTh69CiePn2KVatWqbRIfpidOHLkSMyaNQvW1tZwcHAo9jvLAOnZiVZDZUX2T97mhSoehY/HbvZS\nuX5x8J7jJjRprz+LmnjwwKImVrITpx6+r9H4X3pJ3+NQUii9nTh//nzcvn0b7969w6xZs5CRkYGF\nCxdKPlFsbCwiIyMxZbnvhrQAACAASURBVMoUuLm54dWrV/lCgQUCgUCgG/Q5sUPpWl+5cmXMmTNH\n4xNZWVnh9OnT8tcODg6Fdi0KBAKBQPtwmZ34nq5du8LOzq5QU4ai7MSkJNV2CRaHKpljgzo3wKWl\nvRHu2x/+33eUZydG+g/E1WV95K1Xm7pq1Vc0hodsOR48sKiJh3nlwQNLmljLTtRrlH1o9vz5c3l7\n/Pgxbdu2jTZu3FjqH9YRSc8cqzx4M7WbGkCv36RR07F7qPLgzbTrQiQt2HWdiIh+2nNDHjelKHaq\nrGfL8eCBRU08zCsPHljRxGJ24swj9zVqukSt7MRvv/22pHUopOAPVVnmmFlff5q87gLtufBQHvDb\naco+uhEZR0REC7aHyt9XFABc1rPlePDAoiYe5pUHD6xoYjE78Yej9zVqukTp7cSQkJB8bf/+/Xj2\n7Jk2LhILoVJ2IgAjw39tpaRlwaZmFQCAg+3HCFrcBzdXu2HxN1/KbzNKqa+OJpb7s6iJBw8sahIe\n2NDEZHaigYFGTZco3dixZs0a+b8NDAxQqVIlzJ8/v1RFFYUqmWNBN6Mxb0hbfGptgfvPkzDKtRnM\nTPOyEwMuP8bvR+6gopkJdv/QHT79W0qur44mlvuzqIkHDyxqEh7Y0MRmdqLOTq0xShexGTNmoFmz\nZmoVj4iIwJgxYzBs2DB4enpixowZuHPnDqpWrQoAGD58uEqbRN6jSubYvedJmLz+Ijb7dENGdg42\nn76PpHeZqFbZDCsP5uU/ZqZkwC/gNqYUWMTKai4ba5p48MCiJuGBDU0sZifqM0pvJy5ZskStwkVl\nJ06ePBkymQwymUzSAgaonjm2NegB2kzYg44++xH+NAF3niYAAMzLm8j7GBsZIisnf5hxWc1lY00T\nDx5Y1CQ8sKFJZCeWsHZlHWrXrg0vLy8sW7YMK1eulDdlFJWdqAkqZSfWrIxLK/qjSkVTGBsZYNqA\nVpAF5j2N/l/PLwAA5UyMMNy5KY5deya5flGaeMiW48EDi5p4mFcePLCiicXsRK4/E6tTpw7q1Kkj\nvbCxMYyNC5ffsmULNm7cCEtLS8yePRsfffSRyjXLly+PzVt3YNL4sUhNfYcGNg2xbsOmfH0iX77B\n4ctPcWXFVyAAu849xNagB/jfBHt8XK0ibq9xR04u4cS1Z1h54BZ+GPSFpPpFaRo7dixS3r2DjZIx\nUs9R2v1588CiJh7mlQcPrGhSx0Npo8+fiRWZnRgQEIA+ffpofIIPsxNDQkJQtWpVNG3aFOvWrcPL\nly+LTQPJJd1fqgoEAgHvLDqj2c7IH7s1LCEl0inySmzPnj0lsoh9yIefjzk4OGDevHnF9s/MKfqY\nouBMiwHriuyfdmAkyvcrfDxxz0iV6xcH72GkQpP2+rOoiQcPLGpiJQBYn1Hpm51LCm9vb0RFRQEA\nLl++jE90+EGmQCAQCPIw0PD/dEmRi9j169cVZia+z1JUhqLsxF69emHixInw9PTE2bNnMW7cOMmC\nVckc83JohDC/gbjuNxCH57miYe28h51vrHKTt8g/PLF9upNa9RWN4SFbjgcPLGriYV558MCSJtay\nE/V5d2KRsVNubm75chMLNm1QMK5FWeaYWV9/sh2zg+KS06jBNzIy6+tP49aco4t38r5w7sO4qWOh\nT6n/wmOS6itqPGXL8eCBRU08zCsPHljRxGJ24pLAhxo1XVLkIvbhNznrioI/VFWyE91/PkGX7r6U\nL1bNv99BiSnpRPTvItZn/lEKvPlcZCdy6IFFTTzMKw8eWNHEYnbi0qBHGjVdUuTtRFtbW21eEKqE\nsswxc8uquBOXjga1q6CtrTXMLavCvVtTnLv7Sn7c3LIq5np+gd+ORcDcsqqk+upoYr0/i5p48MCi\nJuGBDU0sZifqM0Xuf5k6dao2daiEKpljsUnp+GnfbQTO7Y6U9GykZmSj39Ig9Pnnu8M6Nq4OAwMD\nhETEqVVf0zGs9WdREw8eWNQkPLChicXsRJ1/rqUBpbo7MSIiAo6OjvJvcM7KyoKPjw8GDBiAr7/+\nGsnJyZLqqZI51ty6Kib2bIq2M46i0fgDWLj3FjZ7d5If7/9lPey/ojiFv6zmsrGmiQcPLGoSHtjQ\nxGJ2ooGBZk2XlNoipig7cdeuXbCwsMCePXvg6uqK0NBQSTVVyRzr0tQKoY/iEZ2QCgA4cDUKTT6u\nIj/uZFsLp2/FqF1f0zGs9WdREw8eWNQkPLChic3sRP2NnSq1RUxRdmJQUJD8AWp3d3d069ZNUk1V\nMscevnyDNjaWsKhoCgBwbFELsUlpAIBq5uVQzbwcHsW+Vbt+UWN4yJbjwQOLmniYVx48sKKJzexE\n/d1iX2rPhCvKToyOjsa5c+fwyy+/oFq1apg7d678a1lUQZXMsZM3Y/BZvY9w9IduIBDepmXju99D\ncGimA2pZlEf82wwoDtoS2XI8eGBREw/zyoMHVjSxmJ2ozxSZnVhSfJid6OLiAm9vb/Ts2RNr1qzB\n27dvMX369CLHiuxEgUAgKH38Lj7WaLx3x/olpEQ6Wk3nqlatGtq2bQsA6NSpE/z8/IrtLzU70XrU\nriL7v9rghhrDCx9/5u+mcv3i4D3HTWjSXn8WNfHggUVNrGQnGuo4OkoTtJqd2KVLF5w/fx4AcOfO\nHdSvr7vVWyAQCAR56PPuxFJb68PDw7FkyRJER0fD2NgYJ06cwLJly7Bo0SLs2bMHFSpUUPtbowUC\ngUBQcujzxzaldiXWvHlzyGQyBAYG4uTJk5DJZLCysoKvry+2b9+ODRs2oFq1apLrqhKcOajjf3B+\ngTMuLHDB7sld0MAq7/mLnzxa4eJCF4Qs6oFfvD6HkYKfXFkOSOXBA4uaeJhXHjywpIm1AGC9Rqeh\nV0oomDmmLDiz+rc7qf0PR+n1m3RqMTmAqn+7k6b8GUqXIl4REVFQeAzVHrGbao/YTZciXtGkTVcl\n1VfUeApI5cEDi5p4mFcePLCiicUAYP+QJxo1XaJXi5iy4Ezr8QE08n9X6FpkPFmPDyDr8QHUdcEZ\nSnqXSURE9gsD5e+vD3xI/wt6JKm+osZTQCoPHljUxMO88uCBFU0sBgCvu/REo6ZLtLqxQ1NUCc4M\ne5II62oV0aiWOQCgx2e1cOF+Xk7io1cpAAAjQwN0alwdN54mSq6vjiaW+7OoiQcPLGoSHtjQxGIA\nsDYSO9LT0+Ho6Ih9+/YhJiYGXl5e8PDwwIQJE5CZmam+drVH6gBVgjNfvcnAL4fv4djULrj1szOG\ndv4PFh+6m2/MwoEt8DIpHYevv5BcXx1NLPdnURMPHljUJDywoYnFAGBt7E78/fffUaVKXgSgr68v\nPDw8sG3bNtSrVw979uxRW7tWA4DHjx8PLy8veHl5oXfv3pg9e7akeqoEZzb7uDLGdf8EnRcEwnbm\nCSw5dBcbRnwBIO8KbPmQlqhdtTxG/nEVuQUe8y6r4aKsaeLBA4uahAc2NLEYAFzaPHr0CA8fPoSd\nnR0A4PLly/LYQXt7e4SEhKhdW6sBwL6+vpDJZJDJZGjevDkGDhwoqaYqwZkdG1XDtccJeJGYl5d4\nKOyF/Nbi4kG2MDMxwvD1V5CRlatWfU3HsNafRU08eGBRk/DAhiYmA4A1bMpYsmQJZsyYIX+dlpYG\nU9O8fFtLS0vExRX+aiwp2ksFRQHA74mMjMTbt28lf/GmKsGZj169w+f1P0LVCiYAAPtPa+BVct5f\nPZ9YmWP85jBkF7wEk1C/qDE8BKTy4IFFTTzMKw8eWNHEYgCwgYGBRq04Dhw4gJYtW6Ju3boKj5Om\nyYelvXPE19eXZDJZvvfmzp1LISEhSsfm5BZ+LygoiGxtbcnGxoacnZ0pJiampKSqXV/qGNb6s6iJ\nBw8sahIe2NBU2r/HpPLn1WcateKYMGEC9e/fnwYOHEidO3embt26kYODA6WlpRER0eXLl8nb21tt\n7VoNAAaAzMxMfPXVVzh06JDSscVlkCnKHGvsU3TNpyt7o96Ewsfv/9pb5frFwXuOm9Ckvf4sauLB\nA4uaWMlO3HJNs4etPT+vo1I/Pz8/fPzxx7h+/TratGmDvn37YuHChWjcuLHkj5feo/XdiVevXpV8\nG1EgEAgE/ODt7Y0DBw7Aw8MDSUlJ6Nevn9q1tJpiDwC3b99GkyZNtH1agUAgEBSBtqITvb295f/e\nuHFjidQstSux8PBweHl5Yf/+/di8eTO8vLyQlJSEuLg4WFpaql1Xlcyx/m3r4NQMO/w1zxErPFvB\n1CjP5vTeTRD4gz3O/GCPab0UL6RlOVuOBw8sauJhXnnwwJIm1rIT9TnFXq9ip5RljlmPDyDHn4Mo\n/m0GtZtzkqzHB9CB0ChadvguERFde5xADScdJptJh+haZDyN3iCyE3nzwKImHuaVBw+saGIxO3Fb\n2HONmi7Rq0VMWeZYlSEymvbnFdp36QlVGSKjKkNkZD/7KN16Ek9ERBM3XJK/P3njZdob8lhSfUWN\np2w5HjywqImHeeXBAyuaWMxO3BH2XKOmS/QqdkqVzDEiwOiD69t36dmob5X3sPPjV2/l7z+OfYtG\ntapIrq+OJpb7s6iJBw8sahIe2NDEYnaiPqNXi5gqmWNn77yEXYuaaFqnCowMDfCdUyOYmRgBADKy\ncuT90jNzUKHAHtaymsvGmiYePLCoSXhgQxOb2Yml97BzaaPV7MSrV69i8ODB8PLywqhRo5CcnCyp\nniqZY/dfJGP65lBsGNsZZ+a74H50MpJT8xKSy/2zmAFA+XJGeJeeJbm+OppY7s+iJh48sKhJeGBD\nE4vZiQYaNl2i1ezEn3/+GYsWLYJMJkOrVq2wc+dOSTVVzRzbfj4SHWYeht3sY/g7Kgl/RyUBABr8\nc1sRAGysKuNedP5FtKzmsrGmiQcPLGoSHtjQxGJ2orgSU4Ci7EQLCwskJeUtKMnJybCwsJBUU5XM\nsfpWlXB+kSuqVDCBsZEBJvdtjm3nIwEAw+w/QYVyRqhYzhhfOzTE3pAnkusXpYmHbDkePLCoiYd5\n5cEDK5pYzE4s7QDgUqW0d458mJ348OFD+vLLL6l79+40cOBAysoqfguOyE7UTn8WNfHggUVNwgMb\nmljLTtx744VGTZdoNTtx2LBh8Pb2xueff44lS5agVq1aGDp0aJFjpWYn1hy2pcj+SVs8UdWz8PGX\nmzxVrl8cvOe4CU3a68+iJh48sKiJlezE/bdeajT+/2xrlpAS6Wj1SvD+/fv4/PPPAQAdOnRAeHi4\nNk8vEAgEAgWIjR0qUq1aNTz851mI27dvo169eto8vUAgEAgUoM+xU1rNTpw/fz5mzZoFLy8v/P33\n3/Dy8pJcV5XMsUGd6iNk8f+3d+ZhTVxtG7+TALIIiAiKiCjUKlYR665Vcdf2q1p9FVForRtaN6zW\nve4baLWiXbBuFUvVIlVsVSxFERQBFVxQQEBxYd9kJySc7w/e5BVIQgIGTsbz6zXXVZjnPHM/52BO\nZubMPf+HB99/Bu/5A6GjVVVmB/PmCNn2Mc6tHtGg/LLacMFbjgs10KiJC/3KhRpo0kSbdyIfvAZt\nTUqT3pGrg5p2LXV5jhnP8CH9VwWQ7IJS0nXxWWI8w4ecuZFMtv0RQwghJO5lPjn6bwK5+iBVaj+l\nSn5ZG5e85bhQA42auNCvXKiBFk00eicG3E9v0NaUaNQk1lDvxPcX+pEFP9+QO4m9695yXKiBRk1c\n6Fcu1ECLJhq9Ey88SG/Q1pQ0+RJ/VajLc4xUElRWEgh4Vf9PKgmKSiuk3onpuSUg/12MKdmvSv76\naKI9nkZNXKiBRk2sBjo00eidyGvgf02JRk1iSnsndrOAXbsWEPB5mDuqs9Q78W3kb2gb2uJp1MSF\nGmjUxGqgQxOd3olsYYdManonJiUlYcaMGXBxccH69eshEqn2kIdS3omvXmPlr1E4ungwgreOQ9yr\nfKl34tvI39A2tMXTqIkLNdCoidVAhyYavRM1eWFHo3on7tmzB/PmzcPJkydhYWGBS5cuqZRTFe/E\nAasuYOi6i3j0Ih+xz/Pfav6GtKEtnkZNXKiBRk2sBjo00eidqMk0qndiSkoK7O3tAQCDBw/GjRs3\nVMqpjOeYTWtDhO74ROqduHxCN/heT3pr+eW14YK3HBdqoFETF/qVCzXQoolG70RNvpzYqN6JS5cu\nJX/++SchhJCDBw+S2bNnK2wra2VPYNBV0r17lefYqNFjyNMXadJ9Rs4niJHzCbLTL4akZBaSZ5mF\n5LtzD4iR8wlCCCHxr/JJWm4xKSqtIPGv8onv9USV8svbAoOqfNBslGyj6jHUHc+lGmjUxIV+5UIN\nNGlSJr4xCXyU2aCtKWlU78S0tDRs2rQJpaWl6Nu3L6Kjo3HkyBG5bSsJwG/qWZ7BYDA4zj+PsxvU\nfpRdq7ekRHUa0WISsLCwgLe3NwAgNDQUmZmZCuOFYvn7ZBlntv7cR278a19XGE+vvT/jhGzXEGZG\nSscxuKCJ1UBHPI2aaDEA1uSThUZdYu/l5YVr164BAPz9/TF8+PDGPDyDwWAwOEajeicOHToUBw8e\nxOTJk2Fubg5HR0eV8yrlnTjYBrc8P8VDr0nwXjBI6p2Y7D0FUXvGS7f/621Vr/yy2nDBW44LNdCo\niQv9yoUaaNJEm3eiJj/srFG2U3V5jhk5nyD9vqnyTrRb6EeMnE+QM2HJZOuZaEIIITv8YqSLPySb\nKvllbVzyluNCDTRq4kK/cqEGWjTR6J0YHJfdoK0p0ahJrC7PMSPnE+Sb45HEP/ypdJJyXP83ufe0\nyjuxrknsXfeW40INNGriQr9yoQZaNNHonXg1LqdBW1OiUbZTdXmOCUtKIRIKwSMEwpJSCEtKkZ9X\nBJv/eicOtWuNwA2jELX7U2xx6gEIy1XKXx9NtMfTqIkLNdCoidVAhyYavRP5vIZtTYlGTWLKeI5d\nvfcKw3tYomt7Ewj4PLh9/AF0daq8EwMinmLM+r/guOo8encyw/JJDirnr48mmuNp1MSFGmjUxGqg\nQxON3omajFoXcXp6euLOnTsQiURwc3ND9+7dsXLlSojFYpiZmWH37t3Q0dFROp8ynmNxL/Px9S83\ncGL5CJSLxDgRFI/8YiFaGeli//kHAABhUTkOBDzAihqT2Lvqy0abJi7UQKMmVgMdmmj0TmzyxRkN\nQG1nYrdu3cKTJ09w+vRpHD58GDt27ICXlxemT58OX19fWFtbw8/PT6WcynqO/Xb1CXov9cOg5X/i\nYUouYlNyAQCGetrSGC0BHxXiynrlb0gb2uJp1MSFGmjUxGqgQxON3omabDultkmsT58+2L9/PwDA\nyMgIpaWliIiIwIgRIwAAw4YNQ3h4uEo5lfJObGOEW/smwdhAB1oCHlb+pyd8guMBAFtc+gIAmmkL\nMHuMHS7dea5yfnmauOAtx4UaaNTEhX7lQg20aKLSO7GBW5PSGKtHTp06RVasWEH69+8v/V1KSgpx\ncnJS2E7Wyh5FnmO6E7yJ7gRvsu332yQlo4A8yyggnn/cJboTvAkhhATcekqevMoncS/yyP5z94jR\n5F9Uyi9v44q3HBdqoFETF/qVCzXQpIk278SbT/IatDUlavdODAoKgre3N44ePYrRo0dLz75SUlKw\natUqnDp1Sm5b5p3IYDAY6ic8UbnXVcljwHst3pIS1VHrwo7Q0FD8/PPPOHz4MAwNDaGvr4+ysjLo\n6uoiIyOj2mtaZKGqd6LJfw7JjS89Nw96E2vvz/Obp3R+RXDdx41parx4GjVxoQYaNdHinajJ5wpq\nuydWWFgIT09PeHt7o0WLqll64MCBCAwMBABcuXIFgwcPVtfhGQwGg6EsGnxTTG1z/cWLF5GXlwd3\nd3fp73bt2oX169fj9OnTaNu2LSZOnKiuwzMYDAZDSdgSexk4OTkhLCwMPj4+0s3S0hLHjh2Dr68v\n9uzZA21t7boT1UAZ40zX4e/j7oEpiD4wBX9t+hjvtTUGAMQcnCrdko+64PdVo+qVX1YbLhikcqEG\nGjVxoV+5UANNmqgzANbgJfYa5Z1Yl3Gm7gRvYv/VKZL1upTYfOlDdCd4k0U/Xic3YtMIIf9bvag7\nwZtcup1CJm27pFJ+WRuXDFK5UAONmrjQr1yogRZNNBoARyblN2hrSjRqEqvLONNs1mky82AYiUrM\nImazThOzWadJvzV/k/zickIIkf7OaW8ICXmUTsxmnVYpv6yNSwapXKiBRk1c6Fcu1ECLJhoNgDV5\nEtMo70RljDNvJ+fA2qw5ulgaAQD+r1c7hDzKqJZn5YQP8F3Ao3rlb2gb2uJp1MSFGmjUxGqgQxON\nBsBsYUcjoYxxZkZ+GXb4P0DwxtEoKhOhpFyEiZ5XMf6/L8Ac1NkMPB4P4QlZ9crf0Da0xdOoiQs1\n0KiJ1UCHJhoNgNnCDjl4enrCyckJkydPxpUrVwAAJ06cwAcffFCvAVPGOLNb+xZw/8QOfVZfxPtL\nzmHb2fs4sfgj6f5J/a3xZ2R1uylV8je0DW3xNGriQg00amI10KGJSgNgDV7Y0agGwOfOnUNOTk6d\nDznLQxnjzCF2rXE7KQevcksAAOeiXqCLpbF0/yh7CwTdT6t3/oa2oS2eRk1cqIFGTawGOjRRaQDc\nwK0paVQD4BEjRmDZsmXg1XPqVsY4MzG9AL1tTWFiUPWKl5HdLZCRXwoAaGXYDK0MmyEpo7De+eW1\n4YJBKhdqoFETF/qVCzXQoolGA2CNpjFWj0gMgCUMGzaMFBUV1dlOXFn7d1evVpmR2trakjFjxpC0\ntLS3KbVe+VVtQ1s8jZq4UAONmlgNdGhS9+eYqtx59rpBW1PSqAbAhoaGAIDhw4fjwoULdX7zUORB\nJstzrL3bGbnxmUemwnx27f3PvacqnV8RXPdxY5oaL55GTVyogUZNtHgnRqfIvjqlLD2tDd+SEtVp\nVANgBoPBYNBHUy/OaAhqm8QkBsDHjx+XGgAzGAwGgz40eA5T38KONw2AXV1d4erqioMHD8LV1RVZ\nWVmYO3cuPD09Vc6ryHNs89QeyDwyFRYmetLfzRvZCWFbxwIA9n7RG9qCqpLNjXTx66JB0BEAOgJA\nwKs7vyJNXPCW40INNGriQr9yoQaaNNHmnajRNOkduTqoadeiyHNMJCZk9/mHhBBC7JcHELNZp8nY\nbf+QZ5mFxHahPyGEkICo52Tj6RhiNus08Qt/Rg5cfCzNLa4kJPf1u+0tx4UaaNTEhX7lQg20aKLR\nOzHmeUGDNmXw8PAgU6dOJZMmTSKBgYEkNTWVuLi4EGdnZ7JkyRJSXl5eL+0aNYkp8hwrqyBE12Eh\nIYQQ29HriK7DQvL9iSCy+2ig9PcTFv1AIu8/JboOC8n9+Jfk/xYclOYWiQnxf8e95bhQA42auNCv\nXKiBFk00eifee17YoK0uwsPDyZw5cwghhOTm5pKhQ4eS1atXk4sXLxJCCPnuu+/Ib7/9Vi/tnPFO\nlLXE8r325kh+kS39OflFNt7v0BoAcC0yHpNH95Tu4/GAhHfUl402TVyogUZNrAY6NNHonahuxw5Z\nzw1HRERgxIgRAIBhw4YhPDy8Xto1ahJT1XNMX1cHZcIK6c9l5RUw0Kt6CHqb90X06mqNZgKgmQCo\nJEDJO+rLRpsmLtRAoyZWAx2a6PROVK9jh0AggL6+PgDAz88PQ4YMQWlpKXR0qj6PTU1NkZVV289W\nGdS6xN7T0xN37tyBSCSCm5sbunfvjjVr1kAkEkFLSwu7d++GmZmZ0vlU9RwrLhNCV+d/L97U09VB\nUWk5AODQJhec+zcGnWwsAVQt7jAweDd92WjTxIUaaNTEaqBDE43eiY1FUFAQ/Pz8cPToUYwePVr6\ne9KAx5Ub1Tvx+++/x9SpU3Hy5EmMGjUKx44dUymnqp5jCU/TYWv1v0nyvfZmiEtOBwCMGGCH05dv\nS/dVEsDuHfVlo00TF2qgUROrgQ5NNHonNoZ5ouS54V9++QWGhobQ19eXTuYZGRn19tRtVO/EjRs3\nYsyYMQAAExMT5Ofnq5RTVc+xs/9EY+rYXjBvWfWg9cLpjjhz+Q4AIOFZBj4Z0l0ay+cxbzku1ECj\nJi70KxdqoEUTjd6JvAb+VxeS54a9vb2lzw0PHDgQgYGBAIArV65g8ODB9RNfr+UgKlLTO1EkEpEZ\nM2aQmzdvKmwna2VPYNBV0r17lefYqNFjyNMXaaS0omqJfFxylf9YYkomiUtOIzaj1pKvPc6QhGcZ\nhBBCfj4dQgx6LSa6DgvJh5O3kZCoBCKurGpbIVacX9EWGFTlg2ajZBtVj6HueC7VQKMmLvQrF2qg\nSZMy8Y1J7KuiBm11cerUKTJo0CDi4uIi3V6+fElmzpxJnJ2dyfLly4lQKKyX9kb3ThSLxVi5ciU6\nduyIRYsWKWxbSarOkBgMBoOhPh6nNmxRiV3bpjuLbHTvxDVr1sDa2rrOCQwAhGL5+2QZZ5r0kZ+z\nNPog9HrW3p8XdVDp/Irguhkp09R48TRq4kINNGqixQBYk2lU78SAgABoa2tjyZIl6josg8FgMFRF\ng694Nap34v79+/Ho0SPpz5s2bVI5rzKeY67j++Pu2XWIPrsef/20CO+1r1r1snfVFMT4r8f9cxvg\ntW4aBILa5b/L3nJcqIFGTVzoVy7UQJMm2rwT1b2wQ63U605aI1HzRmddnmO6DguJ/cQtJCu3kNiM\nWkt0HRaSRdt+JzfuJhJCCPnn5iNi2HsJMey9hNy4m0gWbP5NpfyyNi55y3GhBho1caFfuVADLZpo\n9E6MSytu0NaUaNQkVpfnmK7DQuL09SFy614y0XVYSHQdFpJu4zeTvIKqTrafuEX6+/0+/5IDJ4NV\nyi9r45K3HBdqoFETF/qVCzXQoolG78T4tOIGbU2JRtlO1eU5Zj5kDJL0rWHbsS0+cvoPzIeMgcuc\nKbiRWPU8Wn57B5gPGQMLx7EYPbw34kVGKuWvjyba42nUxIUaaNTEaqBDE43eiZqMRk1iyniOZRaU\nY/dfcbj0zRDcpoTh5QAAIABJREFU3zkGnw/ugF0XHldrs21Kd6Tnl+Gv6FSV89dHE83xNGriQg00\namI10KGJRu/ExnDsUBeN6p1oZmYGT09PaGlpQUdHB7t370bLli2VzqeM59gHlkZYNLoTBm8NRmpe\nKT7rbYkjc/sCAAR8HnY794Bp82aYdzQKlTWekHtXfdlo08SFGmjUxGqgQxON3olNvjijATSqd+Kx\nY8fg6ekJHx8f9OzZE2fOnFEppzKeY4Peb4U7T3ORmlcKALhwNxXvW1Q9o7Zrmj10tQWY/Uskyisq\n65W/oW1oi6dRExdqoFETq4EOTTR6J6r7VSzqpFG9E/ft2wcrKysQQpCRkYE2bdqolFMZz7GkzGL0\n6tgSLfSr3OuHdTVH5uuqbz2dWhtiyYm7ENU8BVMhv7w2XPCW40INNGriQr9yoQZaNNHpnaixVxMb\n3zsxJCSEjB49msyfP5+IxWKF7WSt7FHkOdZ+SQBpvySA7L0YR5IyCklieiGJSckjk74PI4QQkllQ\nRp6kF0q30+EpKuWXt3HFW44LNdCoiQv9yoUaaNJEm3diYkZJg7ampNG9E/87cWLPnj0wNDTE/Pnz\n5bZl3okMBoOhfpKyShvU3tZM7y0pUZ1G9U78559/MGrUKPB4PIwZMwYHDhxQ2F5V78TOyy/IjU/Z\n/ymsl9beH//dp0rnVwTXfdyYpsaLp1ETF2qgURMt3olsYYcMZL0/5sCBA3j8uGq5+71799CxY0d1\nHZ7BYDAYSqLJCzvUNte/6Z0o4dtvv8XmzZshEAigq6sLT09PdR2ewWAwGEqiuedhajwTc3JyQlhY\nGHx8fKRbnz59cOrUKfz22284cuQITE1NVc6rjHHmlH5WCFrjiH/XOOLkV/3R0axq1c+qT7sgeO0w\n/Lt2GFb+X5d655fVhgsGqVyogUZNXOhXLtRAkybaDIA1miZdVlIHNVfr1GWc2X5JABm2LZjkFJaT\nPt9eIe2XBJA1p++RyKQcQgghd57mkveW/UVsl10gd5JzyPwjUSrll7VxySCVCzXQqIkL/cqFGmjR\nRKMB8NPs0gZtTYlGTWJ1GWfaLr9IFhy7Q+4+zSW2yy8S2+UXyYid18jrkqrXXq/3eyD9/YazD8lf\n0akq5Ze1cckglQs10KiJC/3KhRpo0USjAfCz7LIGbU2JRnknKmOcGfM8H+1b6aNTmyoLl7H2bXAj\nIRsA8Dy7RBr3PLsENubVHy58V81FadPEhRpo1MRqoEMTjQbAbGFHI6GsAfB3FxNwYdkgFJeLUSoU\nY/pPERjXwwLlov9ZTZWJxNDXEaicvz6aaI6nURMXaqBRE6uBDk00GgBr8sKORjUAHj16NICq58fm\nzJmD+Ph4lfIpY5zZta0RFoy0xbCdIUjLL8OED9vC+8sPAQDNtP534qmnLUBxefUH0d5Vc1HaNHGh\nBho1sRro0ESjAbAm06gGwABQXl6OQ4cOwczMTOWcyhhnDuhkirvP8pGWX/VH8ndMGjq1qXIKsW71\nv8uH1q0MkJhRpHL++miiOZ5GTVyogUZNrAY6NDED4LdLoxoAi8Vi/Pzzz5g+fTp0dHRUzqmMcebT\nrGJ82KGF1ADY0c4MmQVVE9q0/lbQ0xFAX0eAaf2t8FdMmsr55WnigkEqF2qgURMX+pULNdCiiUYD\nYI22AG6M1SMSA+Dk5GQyf/58Qgghw4YNq7OduLL2765erTIjtbW1JWPGjCFpaWlvVWt98qvahrZ4\nGjVxoQYaNbEa6NCk7s8xVXmZV96grSlpVAPg5cuXY/369Wjfvj2GDx+O4OBghW0VeZDJ8hzrtvqS\n3PjEPePw3ora+x/uGqd0fkVw3ceNaWq8eBo1caEGGjXR4p2Ymi9sUPu2LVS/sva2UOsSe4kB8C+/\n/IKSkhIkJydjxYoVmDp1KjIzM+Hi4qLOwzMYDAaD46htrpcYAB8/flxqABwUFCTdP3z4cJw8eVJd\nh2cwGAyGkjT14oyGoLYzsTcNgF1dXeHq6orU1NQG51XGc2xyH0tc/mYwLn8zGMfn9UGHVvoAgMjN\nIxC4crB0G9Wtdb3yy2rDBW85LtRAoyYu9CsXaqBJE23eibwG/tekNOkduTqoaddSl+eY7fKLZNSu\nEJJTVE4Gbv6X2C6/SNb7PSBRyVXeifsDE6S2U5JNlfyyNi55y3GhBho1caFfuVADLZpo9E5Mey1s\n0NaUaNQkVpfnmIXbWTLrp3ASlZRNLNzOEgu3s2TQt4Ekv7hq9cyeC4+kv5dsquSXtXHJW44LNdCo\niQv9yoUaaNFEo3di+mthg7amhHPeiXef5qJDKwN0bmsEAPi4Z1tcf5wJABjcxQwB3wxF6KZR2DC5\nO3S0+Crnr48mmuNp1MSFGmjUxGqgQxON3omaDOe8EzNel2HX+Uf4Z91wFJWJUCIUY/J31/Fpr3a4\nFJOKY9eSoa8jwLEFA7Bw9Psq56+PJprjadTEhRpo1MRqoEMTld6JGrywo1G9E4ODgxEbGytdrTh7\n9mw4OjoqnU8Zz7FuVsZYMq4zBqwPxKu8Ukzqa4XjXw0AAHgHVX3TEYoqcejfRCwaW30Se1d92WjT\nxIUaaNTEaqBDE43eiU2+OKMBNLp34tdffy1907MqExignOfYR13McTs5B6/ySgEAAbdfSi8tNn/j\n6UEtAQ8icfXnvN9VXzbaNHGhBho1sRro0ESjd6Imu041undiQ1DGcywpvRC9bUxhYlD1BPmI7m2Q\n8brqW8+aiR8AqHKzdxncEUEP0lXOL08TF7zluFADjZq40K9cqIEWTTR6J2rwHNa43omrVq0is2bN\nIq6ursTd3Z3k5OQobMe8ExsnnkZNXKiBRk2sBjo00eadmFVY0aCtKWlU78SHDx+iRYsWsLOzw6FD\nh5Ceno4NGzbIbauqd6LNIn+58ak/T0Lb+bX3Jx+cpHR+RXDdx41parx4GjVxoQYaNdHinZhTrGJH\n1sDUoOnWCDaad6KhoSEGDBgAOzs7AFW2UwkJCeo8PIPBYDCUQJMdO9Q2iUm8E729vaWrERcvXowX\nL14AACIiItCpKW9kMhgMBgMAeymmTGR5Jw4fPhzu7u5wcXFBSEgIFi1apHJeZTzH/tOvPa5uGImo\nHWPhNbO39KFm61YGCFw7HKeXftSg/LLacMFbjgs10KiJC/3KhRpo0kSbd6JG06R35Oqgpl1LXZ5j\nFm5niePmf0hOYRnptfoisXA7S/wjnhOP87GEEEISUl+TEyHJ5PqjDJm2U++6txwXaqBRExf6lQs1\n0KKJRu/E3GJRg7amRKMmMWW8E9efiiHnb7+QTlLjdgaTh8/zCCGE9Pjmb7L0eJTcSexd95bjQg00\nauJCv3KhBlo00eidmFciatDWlHDKO7G4oBhlpeWAWIzigmIUFxQjO6cQ1mZVz188fZmD8tJyiN/Y\nr0r++miiPZ5GTVyogUZNrAY6NNHoncgWdjQSyniOhcSmw7GbBezatYCAz8PcUZ2hqy14a/kb2oa2\neBo1caEGGjWxGujQRKt3oqYu7GhU78Rhw4Zh9erVSElJgYGBAby8vGBsbKx0PmU8x+JfvcbKX6Nw\ndPFgCEVi+FxLxOsSIUwNdWumq1f+hrahLZ5GTVyogUZNrAY6NNHpnai5NKp34pkzZ2BiYgI/Pz98\n/PHHuH37tko5lfUc+z00GQNWXcDQdRfx6EU+Yp/nv9X8DWlDWzyNmrhQA42aWA10aKLSO1GDaVTv\nxKtXr2L8+PEAACcnJ4wYMUKlnMp4jtm0NkTojk9grK8NLQEPyyd0g+/1pLeWX14bLnjLcaEGGjVx\noV+5UAMtmmj0TtRo88TGWD0i8U4cO3YsOXDgAHFxcSHu7u4kLy9PYTtZK3sCg66S7t2rPMdGjR5D\nnr5Ik+4zcj5BjJxPkJ1+MSQls5A8yywk3517QIycTxBCCIl/lU/ScotJUWkFiX+VT3yvJ6qUX94W\nGFTlg2ajZBtVj6HueC7VQKMmLvQrF2qgSZMy8Y1JYVllg7ampFG9E6dMmYLFixfjk08+wY8//ojC\nwkKsWrVKbttKAvCbepZnMBgMjlMsbNg0YKDTdB/Ual3YIfFOPHz4MAwNDdGqVSv06dMHAPDRRx/h\nwIEDCtsLFby5RZZxZuvPfeTGv/Z1hfH02vszTrgqnV8RXDcjZZoaL55GTVyogUZNtBgAa/K5QqN6\nJw4ZMgShoaEAgNjYWHTs2FFdh2cwGAwGJezYsQNOTk6YNm0a7t+//1Zzq22uf9M7UYKHhwd27doF\nPz8/6Ovrw8PDQ12HZzAYDIayqPFULDIyEikpKTh9+jSSkpKwdu1anD59+q3lV9sk5uTkBCcnp1q/\n9/LyUtchGQwGg1EP1Om6ER4ejpEjRwIAbG1t8fr1axQVFb215+I0yrGDwWAwGG8fdTp2ZGdnw8TE\nRPpzy5YtkZWV9da0N93rOJWgrhubNfe/9pW9SEPZ/aoev6HxjXGMd7GGxjgGq4GOY3BBU2Mu4KBB\nw9teEM/OxBgMBoOhNszNzZGdnS39OTMzE2ZmZm8tP5vEGAwGg6E2Bg0ahMDAQABVq9LNzc3fqk8k\nBSeyDAaDweAqH374IT744ANMmzYNPB4PGzdufKv51e7YwWAwGAyGumCXExkMBoOhsbBJjMFgMBga\nC5vEGAwGg6GxaMwkJhYrcAOWQ2ZmJl68eKF0fFZWFtLS0pSOT0pKwvPnz5WOv3v3Lq5du6Z0PFBV\nQ3p6utLxwcHB2LVrl9LxOTk5yMjIUDq+qKgIQqFQ6fimQp23elXJXVpaqjYdElT5++Ai79JYM2oj\n2LRp06amFlEXkZGRuHnzJtq2bQs9PT2l2ly7dg1btmxBaGgowsLCMHr0aIXxoaGh2Lx5M8LCwnDn\nzh0MGzZMbmxlZSUKCwsxY8YMFBUVoXXr1jA1NVWY/9atWzhw4ABGjBiB1q1bK1VDUFAQtm7disTE\nRBgZGcHS0lJhfGRkJH766SdkZmbCwcEBLVu2VBgfFhaGrVu34saNG4iMjMTw4cMVxoeEhGDnzp14\n+PAhoqOjMWDAAKXqAIBHjx4hIyMD5eXlMDY2BlD1AcGT87i/WCwGn89HZWWl3Jg3iY6ORmJiIrKy\nstC2bVul2ty7dw/JycnIy8tTakyuX7+OJ0+ewNbWVqF2CeHh4QgJCUGXLl2gpaXcQuDo6GikpKSA\nECLtJ0VERUXBw8MDdnZ2df4NAsD9+/eRkpKCvLw8mJub1xmfkJCA3NxciEQi6bJoRbWrOs6A+sda\n1XEG1D/Wqo4zQz4aMYnt2rULqampqKiogIWFRZ0TWXp6Ovbt24dt27Zh5syZ+Pnnn1FRUQF7e3uZ\n8fHx8di7dy82bNgAZ2dnBAYGwtHRUe4fI4/HQ7NmzZCUlITi4mKIxWJoaWnJfYAvPDwcmzZtgqen\nJ+zs7FBSUgKhUAgdHR25NZSUlOCHH37A0qVLMX36dFhaWkIoFEIgEMiMj4iIwL59+7Bq1SqYmZlB\nW1sb7dq1k5s/Li5OWvPMmTNx/vx5fPTRR2jWrJnM+JSUFOzZswdr167F+PHjcejQIYSGhmLYsGFy\nNUkICwvD3r178fz5c9y7dw8JCQno3bs3eDyezA+IyMhILF26FJ988gmaNWtW54fbzZs3sXfvXohE\nIty+fRvFxcXo0qVLnZr279+PwsJCPHz4ELa2tnV+mPz+++/w8PBAv379YGlpKf2WLktbeHg4Dh48\niIkTJ6J9+/bV9sn7UIyIiMDWrVtRXl4OKysrtGnTRmGb8PBwfP/992jevDlsbW1rHUdezQUFBXj4\n8CGsrKwUftGRxL969QoJCQlISEiAg4OD3HFTdZwB9Y91fcYZUO9YqzrODMVoxOXEZs2aoU2bNkhK\nSsKVK1eQm5urMF5bWxvl5eXg86vKmzt3LkQi+S/50dHRgY2NDbp06YLU1FQ8fvwYe/fuxebNmxUe\nx8bGBnw+H7m5uYiJiUFwcDDi4uKqxRBC8OLFC7Ro0QK6urooKyuDu7s7Vq5cibVr18q9BMHj8ZCX\nlweRSISioiLMnz8f7u7uWLduXa1YoVCI6OhorFmzBg4ODjAzM4Ovr6/CftLW1oaNjQ3atm2LvLw8\nPHjwAF5eXti2bZvMeF1dXRgYGEBXVxfa2trYvn07YmNj8eOPP8o9BiEEQqEQvr6+cHNzw86dO+Hs\n7IyHDx/C09NTWueb8UDVZd2MjAysWLECRUVF0m/psvKLRCL8/vvv+Oqrr7Bq1Sr079+/zku8RUVF\nOH78OFavXo1vv/0WFRUVKCkpQV5eXi0tb2Jvb4+uXbti5cqVCAoKAo/Hk/mB8/DhQ+zYsQOzZ89G\nr169kJubi7i4ODx58kRa85v5Jf8fGBgIZ2dnrF+/Hp06dUJSUhKSkpJktomIiMAPP/yAbdu2YfXq\n1fjzzz9RUFAg9/JXYWEhjh07htWrV2PdunUQCARSI1ZZ/VpWVoaTJ0/Czc0NGzZswIgRIxAcHIzD\nhw9L9byJUCjEqVOnMGfOnDrH+U1ycnLw6tWrOscaqDpjO3XqFNzc3JQa66KiIpw4cQIrV65UaZwB\noFu3bkqNdWxsLHbu3ImZM2cqNdYA8M8//2Dq1KlKjTOjbjTiTKxbt24YN24chEIhHj16hOzsbFha\nWkJPT0/mNxfJWcgHH3wAAEhMTMStW7cwZswYAIBIJJJOcACgpaUFY2NjWFlZISAgANbW1vj8889x\n9uxZ3Lp1S+rALEFyTG1tbQgEAsyaNQu+vr44fPgwHBwc0KlTJ2ksj8eDra0tDAwMcPjwYfj7+2Py\n5MmYM2cOLl++XM3huWYNurq6CA0Nxb///ouRI0di9uzZOHv2LCIiIjBixAhprEAggL29PSwtLSEW\ni2FpaYkXL17A2toaLVq0kF6ueROBQIAHDx7gypUrOHz4MKZMmQJXV1ccPnwYDx48qHVpUUdHB8+f\nP8eTJ0/A5/MREREBGxsbREZGIjU1FX379q1Vg1gshra2Nh4/fgwbGxu0a9cOpqam6NGjBy5duoTU\n1FR8+OGH1eL5fD4SEhIwceJEZGdn49dff8Xo0aNlfkuXnAGHhYWhb9++MDc3h7a2Ns6fPw9HR0do\na2vX+tuQaAoJCcGUKVMgFouxe/dupKam4saNG0hKSpKePbypCQD4fD7atGkDZ2dnbNy4EaWlpYiN\njUWPHj2q/R1Kzlx69+6NkpISbN26FbGxsbh58ybi4+MxYMCAWnXw+XxkZ2ejpKQEbdu2xdKlSxEb\nG4vIyEg8ffoUffr0qdbm1atXGDx4MLp27Qo+n4+4uDh8+OGH0NPTk9lPfD4fQUFBGDJkCMrLy+Ht\n7Y2EhATcu3cPqamp1a5SVFZWQkdHB5GRkbC3t0ebNm1gZmaGJ0+e4MmTJyguLpb+2wKAjIwMFBYW\noqCgAJ06dUKbNm0UjrOkTXFxMQoLCzFp0iS8fPkSPj4+csc6IyMD+fn5SElJQb9+/WBqaqpwrDMy\nMpCZmYmUlBQ4OTmhuLgY3333ndxxlrTJzc2FsbEx9PX1YWpqiunTp8sd65ycHERGRkIkEqFv377I\ny8vDtm3b5I615DKrlpYWSktL0apVK7i7uyscZ0bdaMQkZmBgAB6PBxsbGxQXF+Px48cQiUSIjIxE\ndHQ0HBwcqsULBAJYWVlJf3758iWSkpIwatQonDt3DmFhYfjwww+lfyzNmjWTxjs4OKBv374wMDDA\nhAkTcPr0aXz00UfVLmG++QF3+fJl8Hg8/P333+jZsyf09fWl/wAkaGlpwdraGiKRCEKhEF988QUM\nDAwwduxYnDlzplZ+Caampnj8+DGeP3+OsWPHwsrKSqpp4MCB0NfXr1YzUPVBq6WlhYiICAQEBODj\njz+Web9BV1cXPXr0QPfu3REbG4svv/wS5ubmmDhxIi5cuFDr0qJAIIC1tTWePXuGwMBAFBUVYfXq\n1RgwYABiYmIwcODAatol9zE7deqE169fY/fu3RgxYgQMDQ1hYGAACwsL3LlzB927d4eenp40vkOH\nDrCwsMD777+PoUOHIiYmptqHW0pKClq0aCGN79y5Mzp06AA7OztUVlaiqKgIN27cwMSJE8Hn8/Hs\n2TPpS1kjIyMRHh6Orl27omPHjrCwsEB8fDwcHBwwd+5ctG3bFsHBwbCysoKZmVmte7GVlZX44Ycf\nMG/ePIjFYuzfvx92dnbo379/tfyOjo4wMzPDmTNncO3aNYwZMwbu7u7o0qULAgMD0a5dO+m9mTfr\n5vF4OHXqFPLy8jB48GAsWrQIVlZW+Pfff9GuXTuYm5sjIiIC4eHhGDBgAKytrVFZWQldXV1ERkYi\nICAAY8eOrXb5TqKpW7dueP36NQICAvDrr79i7NixWLduHfT19XHz5k3Y2NjAxMREGt+pUycUFBTA\ny8sLWlpaOHv2LABg/PjxePToEXr16gWBQIBr165h69atuHPnDnx9ffHs2TMMGTIEBgYGMscZgLTN\njRs3EBYWhsWLF2PYsGG4e/cuTp48WWusJfHR0dG4ffu29B2FBQUFMsdaEv/48WNERUVh5syZePr0\nKezt7WWO85uawsLCEB4ejgkTJuDAgQOYPXu2zLEOCQnBli1bkJOTg6tXr4LP50u/bC5btqzWWEvy\nh4aGws/PT/qlZeDAgVi8eHGtcWYoj0ZcTuTz+dJT7DFjxmDkyJE4c+aM9MO8LkxNTfHee+8hJiYG\n586dw/Dhw+V+2xEKhUhPT0dWVhZCQkJQUlIi996Vubk5BAIB9u/fj7Vr12LNmjUoKytDq1atasXq\n6upi4sSJcHd3h66uLkpLS3H9+nWUlpZCW1tbZv6WLVvC2dkZHTp0wLVr1xAdHY2QkBCUlpbKvXcl\n6adFixZBT08Pq1atAoBaZ2IA0Lx5c7Rr1w7W1taIiopCTk4Obt68icLCQpn3udq0aYNZs2Zh//79\n2L59O8rLyxEREYGEhAQIhcJql0F8fHxw8+ZNBAQE4NNPP4WTkxNcXFyQnp4OgUAABwcHZGdnIz8/\nXxofHh6OS5cuVdO6ZcsW2NjYwN3dHefOncNPP/2EwsJCaf5z585J+5vP50NPTw/a2trg8/m4ePEi\n9u7di9evX0uPcePGDfj7+0s/KLp27YqRI0dCS0sL3bt3R7NmzVBWViaNv3XrFoKCgpCdnY1WrVqh\nd+/eCAgIwPXr17Fo0SL4+voiNDQUPB4PJ0+eRFhYGPz9/dG3b1/MmDED3bt3x+DBgwFUXX42MjKq\ndmn7zbp79OiBzz77DP7+/qioqAAAfPDBBzAwMJBqOnnyJG7cuIFLly4hNzdX2lfu7u5o3bo1Ll68\nCOB/X7QkNZ89exbTpk3Dd999h48//hgfffQRdHR00K9fP1RUVKC4uLha/Pnz5zFp0iQsWrQIpaWl\nMDU1xbp16zBgwADExcUhLy8P6enp8PHxgYeHB3788Uc4OjoiKSlJ4Ti/2eaXX36BUCjE8ePHAQDb\ntm2rNdaJiYnw8fHBrl27cPDgQTRv3hy//fYbAEBfXx86OjrVxjohIUEa7+XlBWNjY5w+fRqdO3eW\nO84STZ6enjh+/Dji4+Ph7++P3r174/z587XGOiMjAz4+Pti5cye8vLzQo0cPvHz5Evb29hgyZEit\nsa5Zc+fOnREXFwd/f3/p30LNcWYoj8Z4J7757bKoqAiZmZk4ePAgbGxs6mxramqKH3/8EZcvX8ae\nPXsUthEKhTh37hwePHiAsrIybNq0Sa5ZpUAgwPTp0zFu3Dj07t0bADBr1iy5k562tjZatmyJoKAg\nXLp0CZmZmdi4caNCM8zWrVvDzc1N+i2Vx+Nh3bp1ctvweDxUVlaCz+fj66+/xuHDh5Gbmyv3Bj6P\nx8PEiRNx4sQJXL16FQUFBdJv6PLg8Xi4e/cuDh48CLFYjPXr19equVmzZjAzM0N8fDzOnDmDmTNn\norKyErNnz8ayZcuQnZ2NgoICGBgYVItPTEyEWCzG6NGjpZq3bNmCKVOm4Pvvv8fRo0dhaGhYK37M\nmDEwMTGBtra29NJtWFgYNmzYIL2RL2mTlJRU7RghISFo164dEhIS8OLFC+m38zfjKysrMXnyZJiY\nmGD79u3w8PCAo6Mj7O3tYWFhUS0+ISEBZ8+exfjx4+Hg4AAtLS3k5eUhKioKKSkp1RYAvdnmzJkz\nmDp1KsRiMfz9/WFgYID09HQ8efJEOunW1CSpQSQSoWvXrrh79y7at2+Pbt261cr/xx9/YPz48TAx\nMcGyZctw6NAhPHnyBFlZWdK+rjluH3/8sfRv7enTpwgPD0dFRQW0tbVBCEF5ebn0C89nn32G8ePH\nIy8vD3PmzMGyZcuQlZVVbZwl96slbebOnVvtEY+aY21sbCy9BFczXkdHBwYGBjhy5AiuX7+OjRs3\nyozPzMwEULUCuW3btrXGueY9dDc3NxQXF8PU1BQ7d+6Eh4cHhg4dKh1rSbzkGE5OTigoKMDUqVMB\nAPn5+dK3GUsWWb1Z85QpU1BRUQE+nw8/Pz8YGBggIyOj2jgzVIBoGCKRiAQHB5OnT58q3UYsFhMv\nLy/y7NkzpeILCwtJeno6yczMVPoYlZWVSscWFhaS58+fk7S0NKXbEEJIaWkpKSkpUalNeXm5UnFF\nRUXk5cuXJD09Xenc2dnZJDs7W+a+V69eEUIICQoKIlu3biWnTp0ihBBy8+ZNcuzYMbJu3TqSmJgo\nM37nzp3E19eX5OTkEEIIiY2NJV999RV58uSJzPgdO3YQX19f6Xh99dVXxNnZmSQnJ8vVJDlGUVER\nuXr1Klm8eDFZvHgxSUhIkBm/fft2curUKZKdnU3KysoIIbXHvGa8pIbExETi7u5O5s2bVy2/on56\n/PgxOX/+PNm1axdJSkpSWINkDHJycsiPP/4o7Td5NRBCyB9//EFWr15dZ82SGrKyssj+/fvJokWL\nSFxcHCGEEKFQSG7evClt+9dff5EVK1YQQggJDAwkx44dI2vXrq02zjXbXLx4kXz99dfSnx8+fFht\nrOuKnz8KxotfAAAIAklEQVR/Ppk2bZr080BW/PLlywkhhFy4cIEsWrSILF68uNrfUs02f//9t7QO\nSd+KxWKF8ZJjxMTEkMWLF5M5c+bIreHNfoqKiiLnz58nO3furDbODOXRmDMxCQKBAI6Ojird/OTz\n+ViwYIHSz+o0b95c5VcFqKKnPvmBqkuSqqJoGf+bSO5hqIKi55Iky4ZHjBgBkUiEqKgoXLp0CUVF\nRTA2Nq61CrJm/N27d3Hr1i0UFBQgPj4eGzdurPYtVVZ8VFQUhEIhXr9+je3bt6Njx44Kj3Hnzh2E\nhoZCKBSiX79+cHZ2rnYpU1a8oaEh8vLyUFFRgS+++KLOGiIiIlBRUYEuXbrgiy++qDWGNdvcvn0b\nly5dQmFhIfh8vvRycF3HyM7OhoGBAebNm1ftUrCscbh8+TK0tbXRtWtXODs7V/t3IS9/SUkJWrZs\nib1790ovf2tra1d7VlBfX1+6slAkEsHAwADbt2+vpr9mG11dXaneCxcuIDIyEhs2bJDeM1QUHxQU\nhNzcXOzYsQMdOnSQGy/5t6mvr49evXrB1dW1Wh/VbPPm/enQ0FBkZmZi1qxZ0r8NWfGSfTk5ObC1\ntcXcuXOlVzNk9RP576X3jIwMlJaWYtWqVWxBRz3RiIUdNanPYMu6J8RQH29e/n3vvfegp6eHo0eP\nIiIiAi4uLrUmwJrxurq6OHbsGG7fvo1vvvmm2kIdefFHjx7FrVu34OHhIfOSsaw2R44cwc2bN/Hl\nl1/WupcpK/748eO4ffs25syZo1QNkvzz58+X+aCtvH6KjIzE559/rtQxjh8/jjt37uDzzz+vswY9\nPT0cOXIE4eHhmD17dq1nG+XVEBERgVmzZil8mWFhYSHKysrA5/Nx5MgRTJ06tc4HsAsLC1FSUgI+\nn49jx45h6dKltcZaVjyPx8Phw4exZcsW2NraKowvLS0Fj8eDt7c3XFxcZN6zlnUMLS0tHD9+HF98\n8YXCNm9qOnToEObMmaPwoWqJJkk/OTk5KfWgOkMOTXQGyHhHkFxyCw4OJuPGjavzkknN+DcvRb2N\n+Leh6W3H06ipPjW8fPmS9OjRg3z22WdKXxpTtY2642nVxJAPm8QYakfV+5jqjmea1BOv6r3n+rRR\ndzytmhjyYS/FZDQKREU7HXXHM03qiReJRErfe65vG3XH06qJIRs2iTEYDAZDY2GrHRgMBoOhsbBJ\njMFgMBgaC5vEGAwGg6GxsEmMQQUvX75Et27d4OrqCldXV0ybNg3Lly9HQUFBvXP+8ccfWL16NQBg\n2bJlCt9gfffuXZXeAi4SidC5c+davz9w4AD27dunsO3w4cORkpKi9LFWr16NP/74Q+l4BuNdgk1i\nDGpo2bIlfHx84OPjg1OnTsHc3Bw//fTTW8m9b98+hQ+g+vv7qzSJMRgMOmBrPBnU0qdPH5w+fRpA\n1dnLuHHj8OLFC3h5eeHixYs4efIkCCFo2bIltm3bBhMTE/z222/4/fff0aZNm2o2VcOHD8exY8dg\nZWWFbdu24eHDhwCAL7/8ElpaWrh8+TLu37+PNWvWwNraGps3b0ZpaSlKSkrw9ddfY+DAgUhOTsY3\n33wDPT099OvXr079vr6+OH/+PLS1tdGsWTPs27cPRkZGAKrOEh88eICcnBx8++236NevH1JTU2Ue\nl8FgyIdNYgwqEYvF+Oeff9CrVy/p7zp06IBvvvkGaWlp+Pnnn+Hn5wcdHR38+uuv8Pb2xsKFC+Hl\n5YXLly/DxMQECxYsqPUq+oCAAGRnZ+PMmTMoKCjAihUr8NNPP8HOzg4LFizAgAEDMG/ePMyaNQv9\n+/dHVlYWnJyccOXKFfzwww+YPHkypk+fjitXrtRZQ3l5OY4cOYLmzZtjw4YNCAgIgIuLCwCgRYsW\n+PXXXxEeHg4PDw/4+/tj06ZNMo/LYDDkwyYxBjXk5ubC1dUVQNXbhXv37o2ZM2dK9/fs2RMAEB0d\njaysLMyePRtA1etz2rVrh5SUFFhaWsLExAQA0K9fP8TFxVU7xv3796VnUUZGRjh06FAtHRERESgu\nLsYPP/wAoOqlpjk5OUhISMC8efMAQPpyREW0aNEC8+bNA5/Px6tXr6r5Dg4aNEhaU2JiosLjMhgM\n+bBJjEENknti8pC4p+vo6MDe3h7e3t7V9j948KCau4TEUf1NJO9bU4SOjg4OHDhQ6x1shBCpkbRY\nLFaYIz09HR4eHvj7779hamoKDw+PWjpq5pR3XAaDIR+2sIOhcXTv3h33799HVlYWAODSpUsICgpC\n+/bt8fLlSxQUFIAQgvDw8Fpte/bsidDQUABAUVERpkyZAqFQCB6PJ32bcq9evXDp0iUAVWeHkteJ\n2NraIiYmBgBk5n6TnJwcmJiYwNTUFPn5+QgLC4NQKJTuv3XrFoCqVZGdOnVSeFwGgyEfdibG0Dha\nt26NdevWwc3NDXp6etDV1YWHhweMjY0xf/58zJgxA5aWlrC0tKz1uvdx48bh7t27mDZtGsRiMb78\n8kvo6Ohg0KBB2LhxI9auXYt169Zhw4YN+PvvvyEUCrFgwQIAwMKFC7Fq1SpcvnwZPXv2VOh9Z2dn\nB2tra/znP/9B+/btsWTJEmzatAlDhw4FUPX2Xzc3N6SmpmLjxo0AIPe4DAZDPsw7kcFgMBgaC7uc\nyGAwGAyNhU1iDAaDwdBY2CTGYDAYDI2FTWIMBoPB0FjYJMZgMBgMjYVNYgwGg8HQWNgkxmAwGAyN\nhU1iDAaDwdBY/h/DsBuSrk8fdAAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b0c9f198>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"text","id":"9cDuTj56nWr9"},"cell_type":"markdown","source":["# **Confusion Matrix για τον Dc Constant 3 Classifier**"]},{"metadata":{"colab_type":"code","id":"_-T0Cux0nWr-","colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"5ebc0a30-d90c-4f52-a7de-d64536084c29","executionInfo":{"status":"ok","timestamp":1544694562071,"user_tz":-120,"elapsed":14369,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import precision_recall_fscore_support\n","preds = dc_constant_3.predict(test)\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro4 = micro[2]\n","macro4 = macro[2]"],"execution_count":12,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[  0   0  83   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  92   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  79   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  97   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  92   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  83   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0 103   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  85   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  88   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  97   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  82   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  90   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0 108   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  82   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  80   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  85   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.00      0.00      0.00        83\n","         2.0       0.00      0.00      0.00        92\n","         3.0       0.04      1.00      0.08        95\n","         4.0       0.00      0.00      0.00        79\n","         5.0       0.00      0.00      0.00        87\n","         6.0       0.00      0.00      0.00        97\n","         7.0       0.00      0.00      0.00        92\n","         8.0       0.00      0.00      0.00        83\n","         9.0       0.00      0.00      0.00       103\n","        10.0       0.00      0.00      0.00        85\n","        11.0       0.00      0.00      0.00        88\n","        12.0       0.00      0.00      0.00        97\n","        13.0       0.00      0.00      0.00        95\n","        14.0       0.00      0.00      0.00        91\n","        15.0       0.00      0.00      0.00        95\n","        16.0       0.00      0.00      0.00        87\n","        17.0       0.00      0.00      0.00        82\n","        18.0       0.00      0.00      0.00        90\n","        19.0       0.00      0.00      0.00        91\n","        20.0       0.00      0.00      0.00        95\n","        21.0       0.00      0.00      0.00        87\n","        22.0       0.00      0.00      0.00       108\n","        23.0       0.00      0.00      0.00        82\n","        24.0       0.00      0.00      0.00        80\n","        25.0       0.00      0.00      0.00        85\n","        26.0       0.00      0.00      0.00        91\n","\n","   micro avg       0.04      0.04      0.04      2340\n","   macro avg       0.00      0.04      0.00      2340\n","weighted avg       0.00      0.04      0.00      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAbEAAAGACAYAAAAj9ly5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npFqy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TSXrInbx4kWMGzcOH/+DV2nYsCFmzJhR6v5SMMqaWVfF+B5N0GbqYcQmpqH/F9bY\n5N1RebzfF3Wx99IjWT3JWc+iJx4ysOhJZGDDE4vsRF1fTWki2W8nfv7551AoFFAoFGotYIA0jLLO\nTawQGpWA2MQ0AMC+yzFo/OG/XEcn21o4eSNOVk9y1rPoiYcMLHoSGdjwxCQ7UY+lV5+JScEou/f0\nJVrbWMKioikAwLF5LcQn53+fWDXzcqhmXg5R8YXBv++OwQNbjocMLHriYV55yMCKJ8FO1LLk3EVy\n4cIFcnV1pZEjR9LAgQPp3LlzxdYXtbNH24yy6t/soEX7Iuhe3Eu6G5dCYdEJ1PPnU0RE5DD7GD1N\nSlPuUixqd2LBGDyw5XjIwKInHuaVhwwseWKNnWjhuUWjpkvJyk6Mj4/HlStX4OrqipiYGAwZMgTH\njx+HqalpkfWCnSgkJCQkvSyHbNOof8KmQVpyor5k3dhhZWWF7t27AwCsra1RrVo1xMfH46OPPiqy\nXg52ovXInUXWP1vnhhrDCh97tNpNK37K0oe1euFJmnoWPfGQgUVPgp2ouWT9TCwgIADr1q0DADx/\n/hwJCQmwsrKS04KQkJCQEEeS9UrMwcEBkyZNwqlTp5CdnY3Zs2ervJUoJCQkJCSPdL45QwPJeiVW\nqVIlrFq1Clu3bsWuXbvQpUsXtc8hBWjTrV1dnPmvM8IW9cCKbz+HqXH+tJyf56Js4b/1wvrRRRP3\neQGk8pCBRU88zCsPGVjyJADAWpROt5WUoHd360gB2uw04wi9eJlBn/oEUPVvdpB/yAP6ZU84EdFb\nuxJPXH9Cg5edUTkGD4BUHjKw6ImHeeUhAyueWAQAV/96h0ZNl9KrRUwK0ObEP4LJ/0wkmXX3JbPu\nvtRx/A66FvWMiEj5Xu8Z+ynw6iMy6+6rcowCv/oCF+U1A4ueeJhXHjKw4olFAPCbf7CXpelSevWw\nsxSgTQJg9MY+/tT0LNjUqvLmaTBjcFv8vO2SbJ7krGfREw8ZWPQkMrDhiUUAsD5LrxYxKUCbQddi\n4NDSGp/U/QBGhgYY2dMWZqb/7nfpbPshDAyAcxFPZPMkZz2LnnjIwKInkYENTywCgPX5MzHZF7GA\ngAD07t0b/fr1Q3BwsFp9pQBt3o5JwsRVp7FpsgvOLHXD7UeJSE7NVB5379IIO09HyupJznoWPfGQ\ngUVPIgMbnlgFAItFrBRKSkrCihUrsHXrVqxatQqnTp1Sq79UoM0tp26j9Zit6DBuByIeJODmwwTl\nMdc2dXE09KHsnuSqZ9ETDxlY9CQysOGJRQCwWMRKqZCQELRr1w6VKlVCjRo1MHfuXLX6SwHarF+r\nCi74DUSViqYwNjLEZPfWUJy8BQCoXqU8qletgLuxSSV64gGQykMGFj3xMK88ZGDFkwAAa1ly7iJZ\nvXo1TZkyhUaOHEmDBg2iv/4BVcYQAAAgAElEQVT6q9j63LzC7wUF5cNIbWxsyNnZmeLi4oo9h7r1\nZZHUnuTIzJonHjKw6ElkYMOTHL+X1FGtEbs1arqUrADgNWvWICwsDMuXL8eTJ08wZMgQBAUFqVzJ\ni2OQaYtRZtHHr8j69EPeKN+j8LGk/d5a8VOWPqzVC0/S1LPoiYcMLHpihZ1Ye9Qejfo/WdVPS07U\nl6y3Ey0tLdGyZUsYGxvD2toaFStWRGJiopwWhISEhITekT7fTpR1EevYsSMuXLiAvLw8JCUlIS0t\nDRYWFnJaEBISEhJ6R2IRK6WsrKzg7OwMNzc3DB8+HNOnT4ehoXoWpGCUeTg0xpWVHojcMBTrfJyU\n7MQlozrj2ipP3FjjCd8xdm89FP3uGDyw5XjIwKInHuaVhwwseRLsRC1Kp5/IlaB3cS1SMMpafbeZ\nnienUYMh68msuy9tD7pNszeFEBHRiSsPybz3cjLvvZzOR8TSd8tOqRyDB7YcDxlY9MTDvPKQgRVP\nLLIT64zep1HTpfRqEZOCUTZp/QXa/dd9quS2kSq5baQu0w7S9fsJRETUavwe5ft+ByJo+aGbKsco\n8KsvXDZeM7DoiYd55SEDK55YZCfWGbNPo6ZL6RV2ShZ2YkY26tc0BwBEPnkJ/HPc3rY2Qu++kMWT\nnPUseuIhA4ueRAY2PLHITtTn24l6tYhJwSgLDo+DffNaaPJRVRgZGmCEc2OYmRi91WfpsC8Qm/Aa\ne0IeyOJJznoWPfGQgUVPIgMbngQ7UbuS9Zudd+3ahYCAAOXriIgIXL16tdT9pWCU3YlNwQ8bLmHj\nuM7IzM6DIuguUl5nwbKyGYwMDbDyuw6oZl4Og38LRl4Rj9Sxxll7X9lywpPIwHMGqaXrhUgTyXol\nNmDAACgUCigUCnh7e6Nv375q9ZeKUbb1TBTaTgpA52kH8XdMMm7G5GOmlo9sj/KmRnD/NRAZ2bmy\nepKrnkVPPGRg0ZPIwIYnwU7UrnR2O3HFihUYPXq0Wn0kYSdameP8wl6oUsEExkYG8OnbHFuCowAA\njT6sgm98zyAnVzXUhCe2HA8ZWPTEw7zykIEVTyyyE/VauthNcv36dZoyZUqJdYKdKE89i554yMCi\nJ5GBDU+ssRP/M/6gRq04paam0pgxY8jT05Pc3d3pzJkzdOvWLXJ3dyd3d3eaOXOmRt5lZScWaObM\nmejRowfatm1bbJ0c7MTqg/8ssv7Vjq9g7l742PMtX2nFT1n6sFYvPElTz6InHjKw6IkVdmL9iYc1\n6h+9pLvKY5s3b0Z8fDx8fHwQHx+Pr776CtWrV8cPP/wAW1tb+Pj4oHfv3ujSpUuZxtbJ7cSLFy+i\nZcuWuhhaSEhISOgdSfmZmIWFBZKTkwEAL1++RNWqVREbGwtbW1sAgL29PUJCQsrsXfZFLD4+HhUr\nVoSpqancQwsJCQkJFSEDA81acerRoweePHkCJycneHp6YvLkyahcubLyuKWlJZ4/f15m77IvYs+f\nP8cHH3xQ5v5SMMo87Rrg8m99ELqkD/b/5IQGtfIneMmwtriypC+uLu2L37/9AsZGgp3IegYWPfEw\nrzxkYMkTa+xEKbV//37Url0bJ06cwJ9//okffvjhreMaf6Kl0SdqEutdXIsk7MTxe+hFSjp9PHIn\nVXLbSN+v+Yv+uvWUiIj2htynyu5/kuXgTXTxTjyN/1+IyjF4YMvxkIFFTzzMKw8ZWPHEIjuxwaQj\nGrXiNHPmTDp69KjydYcOHahz587K13v27KEFCxaU2bteLWJSMMqGr71EodEJ9NHY/fTR2P3Uec5J\nSn6dRUREHWefVL6/6uRdWhcUpXKMAr/6wmXjNQOLnniYVx4ysOKJRXbixz8c0agVp3Xr1tHChQuJ\niOjx48fUrVs3+vrrr+ny5ctERDRq1Cg6f/58mb3rFXZKCkbZ1fuJqFutIhrWyucldm9RC+fu5N+f\nffgiHwNTo3I52H9SA6ci4mXxJGc9i554yMCiJ5GBDU/vGzvR3d0dsbGx8PT0hI+PD2bPno0ff/wR\nS5YswcCBA2FtbY327duX2bus2ClNJQWjLP5lJhYduIWjU7rgdWYO0jJz4eZ7Hj1a1gYA7BrXAZ9a\nV8WawCicvVP4w0fWOGvvK1tOeBIZeM4gtaSEblSsWBHLli0r9P7WrVu1cn5ZF7HXr19jypQpSElJ\nQXZ2NsaMGYNOnTqVur8UjLKmdSpjrPPH6DjnFJ4kpeP/WtfBuhGfK48PWHYelcyMsXhwC0zr3UQW\nT3LWs+iJhwwsehIZ2PDEIjtRnyXr7cS9e/eiXr16UCgUWLZsGebPn69WfykYZR0aVseV6CQ8SUoH\nABwIi0XDf3Yn1rYoDwBIzciB/8UYdG5SQxZPctaz6ImHDCx6EhnY8MQiO9HQ0ECjpkvJuoi9+9Cb\nhYWFWv2lYJRFP0vFZ/UtULWCCQDAvqkVnqXk/5U0wbWR8jLboakVbv/z/WJFjcEDW46HDCx64mFe\necjAiicW2YlSPicmucq8JaSM+uabb8jR0ZE+//xzunr1arG1Re3sOXYyiJo3z2eOOXVzpvsxcSp3\nJZWm/qOx+2nJodsUFf+K7j19RdceJFK/JWeJiGhfaAzde/qKouNf0dHrT6jltKMqx7C1taX6WvIk\ndz1PGVj0xMO88pCBJU+lqZdTTX86rlHTpWRlJ+7fvx+hoaGYO3cubt++jR9//BF79uxRWZ9HgI6v\nVIWEhIS4V/MZJzTqHz7XSUtO1JesGzvCwsLQsWNHAEDjxo3x7Nkz5ObmwsjIqMj6rKK/wguA9kCb\nDScEFH4TwCO/3rD2LnwscmlvrfgpSx/W6oUnaepZ9MRDBhY9sQIA1mfJ+plY3bp1cf36dQBAbGws\nKlasqHIBExISEhKSR/r8pZiyrvXu7u748ccf4enpiZycHMyePVvO4YWEhISEipCuFyJNJOuVWMFD\nb5s3b8b27dvRrl07tc+hTdCmsWH+bcOaVf998HCYXX2c+skeALBw0Kcw+Qf6a25mjOVDP4OpEWBq\nlN/3zTF4AKTykIFFTzzMKw8ZWPLEGgBY7E6USO/u1tEmaDMnlyg7N38cm74LyazDT9R5xB90PzaR\nrJznEhHRnsBwmrr8MJl1+InW7rtEvtvPKc+dm0eUmcMXIJWHDCx64mFeecjAiicWAcAtZp/SqOlS\nerWIaRO0mfHGfygFi9jv287Sr4rTZNbhJyIi6uPzJ126+YjMOvxEPcZvoLq9flGeOyeXKCuHL0Aq\nDxlY9MTDvPKQgRVPLAKA9XkRe28BwEU9V9Dgo2qIjk1Uvo6OTURD6+oAgFOX7yE+MRUAYID8rf95\nxB4s9H0FpApPIgPPGaSWPt9O1KtNnFKDNiuUM0FG1r/7XTOyslHRzET52tDQAKZG+YtYTh5AMngS\ngFT9zMCiJ5GBDU9sAoB1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VaH3Ro4cKYWXQnr3hyoJAHjNOfI/d4/M+qwmsz6rqeOkPXQt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\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b0bb7cc0>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"text","id":"oJ6hdZX-nWsB"},"cell_type":"markdown","source":["# **Confusion Matrix για τον Dc Constant 4 Classifier**"]},{"metadata":{"colab_type":"code","id":"E7qYCzvmnWsC","colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"6f66c17e-13b5-421d-baf9-a3a48a901235","executionInfo":{"status":"ok","timestamp":1544694564297,"user_tz":-120,"elapsed":16590,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import precision_recall_fscore_support\n","preds = dc_constant_4.predict(test)\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro5 = micro[2]\n","macro5 = macro[2]"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[  0   0   0  83   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  92   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  79   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  97   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  92   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  83   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0 103   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  85   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  88   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  97   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  82   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  90   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0 108   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  82   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  80   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  85   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.00      0.00      0.00        83\n","         2.0       0.00      0.00      0.00        92\n","         3.0       0.00      0.00      0.00        95\n","         4.0       0.03      1.00      0.07        79\n","         5.0       0.00      0.00      0.00        87\n","         6.0       0.00      0.00      0.00        97\n","         7.0       0.00      0.00      0.00        92\n","         8.0       0.00      0.00      0.00        83\n","         9.0       0.00      0.00      0.00       103\n","        10.0       0.00      0.00      0.00        85\n","        11.0       0.00      0.00      0.00        88\n","        12.0       0.00      0.00      0.00        97\n","        13.0       0.00      0.00      0.00        95\n","        14.0       0.00      0.00      0.00        91\n","        15.0       0.00      0.00      0.00        95\n","        16.0       0.00      0.00      0.00        87\n","        17.0       0.00      0.00      0.00        82\n","        18.0       0.00      0.00      0.00        90\n","        19.0       0.00      0.00      0.00        91\n","        20.0       0.00      0.00      0.00        95\n","        21.0       0.00      0.00      0.00        87\n","        22.0       0.00      0.00      0.00       108\n","        23.0       0.00      0.00      0.00        82\n","        24.0       0.00      0.00      0.00        80\n","        25.0       0.00      0.00      0.00        85\n","        26.0       0.00      0.00      0.00        91\n","\n","   micro avg       0.03      0.03      0.03      2340\n","   macro avg       0.00      0.04      0.00      2340\n","weighted avg       0.00      0.03      0.00      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAbEAAAGACAYAAAAj9ly5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLOMyAAAAAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b55c7518>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"text","id":"s5WOwJPLnWsG"},"cell_type":"markdown","source":["# **Confusion Matrix για τον Dc Most Frequent Classifier**\n","\n"]},{"metadata":{"colab_type":"code","id":"0X8RN-0InWsI","colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"effcc89a-7f42-4c6b-c113-20b275093340","executionInfo":{"status":"ok","timestamp":1544694566644,"user_tz":-120,"elapsed":18927,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import precision_recall_fscore_support\n","preds = dc_most_frequent.predict(test)\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro6 = micro[2]\n","macro6 = macro[2]"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[  0   0   0  83   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  92   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  79   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  97   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  92   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  83   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0 103   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  85   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  88   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  97   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  82   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  90   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  95   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  87   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0 108   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  82   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  80   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  85   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0  91   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.00      0.00      0.00        83\n","         2.0       0.00      0.00      0.00        92\n","         3.0       0.00      0.00      0.00        95\n","         4.0       0.03      1.00      0.07        79\n","         5.0       0.00      0.00      0.00        87\n","         6.0       0.00      0.00      0.00        97\n","         7.0       0.00      0.00      0.00        92\n","         8.0       0.00      0.00      0.00        83\n","         9.0       0.00      0.00      0.00       103\n","        10.0       0.00      0.00      0.00        85\n","        11.0       0.00      0.00      0.00        88\n","        12.0       0.00      0.00      0.00        97\n","        13.0       0.00      0.00      0.00        95\n","        14.0       0.00      0.00      0.00        91\n","        15.0       0.00      0.00      0.00        95\n","        16.0       0.00      0.00      0.00        87\n","        17.0       0.00      0.00      0.00        82\n","        18.0       0.00      0.00      0.00        90\n","        19.0       0.00      0.00      0.00        91\n","        20.0       0.00      0.00      0.00        95\n","        21.0       0.00      0.00      0.00        87\n","        22.0       0.00      0.00      0.00       108\n","        23.0       0.00      0.00      0.00        82\n","        24.0       0.00      0.00      0.00        80\n","        25.0       0.00      0.00      0.00        85\n","        26.0       0.00      0.00      0.00        91\n","\n","   micro avg       0.03      0.03      0.03      2340\n","   macro avg       0.00      0.04      0.00      2340\n","weighted avg       0.00      0.03      0.00      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAbEAAAGACAYAAAAj9ly5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLOMyAAAAAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b0a47e80>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"text","id":"16llNDJrnWsL"},"cell_type":"markdown","source":["# **Confusion Matrix για τον Dc Stratified Classifier**\n"]},{"metadata":{"colab_type":"code","id":"gx9Wv7mXnWsO","colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"bbaa7326-eca7-4727-b1af-00e36f8c1ce9","executionInfo":{"status":"ok","timestamp":1544694568961,"user_tz":-120,"elapsed":21237,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import precision_recall_fscore_support\n","preds = dc_stratified.predict(test)\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro7 = micro[2]\n","macro7 = macro[2]"],"execution_count":15,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[ 2  5  3  3  3  1  4  4  1  4  4  6  1  2  6  2  2  3  5  2  3  1  2  8\n","   2  4]\n"," [ 3  5  4  6  4  4  2  4  3  4  2  3  3  5  2  3  2  2  8  4  3  1  3  2\n","   5  5]\n"," [ 6  4  6  4  0  3  2  7  1  4  5  1  2  4  3  2  6  6  2  1  6  2  8  5\n","   0  5]\n"," [ 3  5  3  4  2  4  4  3  6  0  0  4  0  5  2  2  3  0  3  5  4  3  2  4\n","   5  3]\n"," [ 2  5  5  5 10  1  2  4  4  3  6  2  2  2  1  2  2  2  4  4  4  1  5  4\n","   4  1]\n"," [ 6  2  3  3  3  7  4  3  1  4  4  5  6  2  5  2  5  5  5  2  5  5  2  4\n","   2  2]\n"," [ 7  5  8  5  1  6  4  4  4  1  0  4  3  2  3  6  2  2  3  4  2  1  6  2\n","   4  3]\n"," [ 3  1  3  1  4  2  1  2  4  4  6  5  6  3  3  1  3  1  2  1  5  2  4  4\n","   3  9]\n"," [ 1  2  2  3  3  5  4  5  2  9  4  3  2  3  3  5  2  5  7  4  8  1  6  5\n","   3  6]\n"," [ 1  4  4  4  4  2  3  4  6  0  2  3  4  3  6  5  6  5  2  3  3  2  2  3\n","   3  1]\n"," [ 5  3  3  3  5  6  6  5  4  2  4  2  4  0  5  2  2  4  2  3  6  2  2  2\n","   5  1]\n"," [ 3  5  4  6  6  3  3  1  1  5  2  4  8  2  2  5  6  6  2  1  4  2  2  7\n","   4  3]\n"," [ 4  1  0  4  2  4  2  3  3  3  4  4  4  9  5  2  3  4  3  6  3 10  1  2\n","   4  5]\n"," [ 5  2  4  3  9  3  4  4  3  3  3  2  3  5  3  3  4  0  3  2  5  6  4  5\n","   3  0]\n"," [ 3  2  3  2  5  2  2  1  2  9  4  3  9  4  4  4  2  4  2  4  5  7  3  4\n","   4  1]\n"," [ 2  2  4  3  3  3  5  6  5  2  3  3  3  3  3  4  4  2  1  4  3  4  4  2\n","   5  4]\n"," [ 2  3  4  2  1  4  1  3  1  2  3  2  3  4  2  6  6  4  4  1  5  3  2  6\n","   5  3]\n"," [ 1  1  0  5  4  1  4  2  2  4  3  4  3  5  0  5  9  4  6  4  4  3  4  4\n","   4  4]\n"," [ 4  3  4  6  4  3  3  5  4  5  2  4  3  4  1  3  3  6  8  4  2  4  1  2\n","   2  1]\n"," [ 5  3  3  4  5  3  3  2  1  5  4  2  3  6  4  7  2  3  4  3  4  4  3  4\n","   6  2]\n"," [ 4  6  2  3  6  4  4  3  2  2  1  4  4  4  5  1  1  4  3  2  4  3  7  4\n","   3  1]\n"," [ 3  5  3  4  3  3  3  9  5  3  5  3  2  5  2  6  7  3  4  1  1  4  6  5\n","   9  4]\n"," [ 3  5  3  4  1  3  1  5  4  3  4  4  2  5  2  2  2  2  3  7  2  4  4  2\n","   1  4]\n"," [ 0  2  3  6  1  2  3  3  3  3  2  7  2  4  7  5  2  3  4  2  1  2  4  4\n","   1  4]\n"," [ 1  1  5  4  2  6  4  1  2  2  4  6  3  4  1  5  3  5  2  3  4  1  7  2\n","   5  2]\n"," [ 1  4  3  1  1  2  4  8  2  6  3  5  2  3  7  3  3  4  2  8  2  2  2  1\n","   6  6]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.03      0.02      0.02        83\n","         2.0       0.06      0.05      0.06        92\n","         3.0       0.07      0.06      0.07        95\n","         4.0       0.04      0.05      0.05        79\n","         5.0       0.11      0.11      0.11        87\n","         6.0       0.08      0.07      0.08        97\n","         7.0       0.05      0.04      0.05        92\n","         8.0       0.02      0.02      0.02        83\n","         9.0       0.03      0.02      0.02       103\n","        10.0       0.00      0.00      0.00        85\n","        11.0       0.05      0.05      0.05        88\n","        12.0       0.04      0.04      0.04        97\n","        13.0       0.05      0.04      0.04        95\n","        14.0       0.05      0.05      0.05        91\n","        15.0       0.05      0.04      0.04        95\n","        16.0       0.04      0.05      0.04        87\n","        17.0       0.07      0.07      0.07        82\n","        18.0       0.04      0.04      0.04        90\n","        19.0       0.09      0.09      0.09        91\n","        20.0       0.04      0.03      0.03        95\n","        21.0       0.04      0.05      0.04        87\n","        22.0       0.05      0.04      0.04       108\n","        23.0       0.04      0.05      0.04        82\n","        24.0       0.04      0.05      0.05        80\n","        25.0       0.05      0.06      0.05        85\n","        26.0       0.07      0.07      0.07        91\n","\n","   micro avg       0.05      0.05      0.05      2340\n","   macro avg       0.05      0.05      0.05      2340\n","weighted avg       0.05      0.05      0.05      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAasAAAGACAYAAAATek2YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6Piu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OBQs6mKVl5eHFStWYOfOnRILUJIkSZKqiqoByFbU5fTYsWPIycnBpEmTEBoaitDQ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5hR/gdWmisIQT2VV4u1s+chIiXSci3q6NXP1AxQ9HNoTHkTRq/+bcdvr91Bb5t3yqD69Jl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\nAAAAAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b6fec7f0>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"text","id":"iNzcSQDUnWsU"},"cell_type":"markdown","source":["# **Confusion Matrix για τον KNN**"]},{"metadata":{"colab_type":"code","id":"Xkxlu3eSnWsW","colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"31cd840c-99e9-4a01-c67e-09a51474e962","executionInfo":{"status":"ok","timestamp":1544694586339,"user_tz":-120,"elapsed":38610,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","knn = KNeighborsClassifier()\n","model_knn = knn.fit(train, train_labels)\n","preds = knn.predict(test)\n","knn_pred = preds\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro8 = micro[2]\n","macro8 = macro[2]"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[ 83   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0  82   0   7   1   0   0   0   0   0   0   0   0   0   0   1   0   0\n","    0   0   0   1   0   0   0   0]\n"," [  0   0  93   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   2]\n"," [  1  10   0  65   1   0   1   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   1   0   0   0   0]\n"," [  1  10   0   6  70   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0  89   0   0   0   0   0   0   1   0   0   1   0   0\n","    3   0   0   0   0   2   1   0]\n"," [  0   0   0   2   1   0  81   0   0   3   0   0   0   0   0   0   0   0\n","    0   5   0   0   0   0   0   0]\n"," [  2   0   0   0   0   0   0  80   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   1   0   0]\n"," [  0   0   0   0   0   0   0   0 102   0   0   0   0   0   0   0   0   1\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   2   0   0  81   2   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0  14  74   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0  96   0   0   1   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   1   0   0   0   0   0   7  74  13   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  4   0   0   0   0   0   0   0   0   1   0   0  11  75   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   2   0   0  91   0   0   2\n","    0   0   0   0   0   0   0   0]\n"," [  0   7   0   8   1   0   4   0   0   0   0   0   0   0   0  60   0   0\n","    0   3   0   3   0   0   0   1]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  82   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0  89\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   8   0   0   0   0   0   0   0   0   0   0   0   0\n","   83   0   0   0   0   0   0   0]\n"," [  1   0   1   8   0   0  14   0   0   0   1   0   0   0   0   9   0   0\n","    0  60   0   1   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1   1   0\n","    0   0  85   0   0   0   0   0]\n"," [  2  15   0  14   3   0   0   0   0   0   1   0   0   0   0   4   0   0\n","    0   1   0  68   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   1   0\n","    0   0   0   0  80   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    1   0   0   0   0  79   0   0]\n"," [  0   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0  84   0]\n"," [  0   1   9   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   1   0   5   0   0   0  75]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.88      1.00      0.94        83\n","         2.0       0.66      0.89      0.76        92\n","         3.0       0.90      0.98      0.94        95\n","         4.0       0.59      0.82      0.69        79\n","         5.0       0.91      0.80      0.85        87\n","         6.0       0.91      0.92      0.91        97\n","         7.0       0.79      0.88      0.84        92\n","         8.0       1.00      0.96      0.98        83\n","         9.0       0.97      0.99      0.98       103\n","        10.0       0.82      0.95      0.88        85\n","        11.0       0.95      0.84      0.89        88\n","        12.0       0.91      0.99      0.95        97\n","        13.0       0.86      0.78      0.82        95\n","        14.0       0.85      0.82      0.84        91\n","        15.0       0.99      0.96      0.97        95\n","        16.0       0.79      0.69      0.74        87\n","        17.0       0.98      1.00      0.99        82\n","        18.0       0.97      0.99      0.98        90\n","        19.0       0.95      0.91      0.93        91\n","        20.0       0.86      0.63      0.73        95\n","        21.0       1.00      0.98      0.99        87\n","        22.0       0.86      0.63      0.73       108\n","        23.0       1.00      0.98      0.99        82\n","        24.0       0.96      0.99      0.98        80\n","        25.0       0.99      0.99      0.99        85\n","        26.0       0.96      0.82      0.89        91\n","\n","   micro avg       0.89      0.89      0.89      2340\n","   macro avg       0.90      0.89      0.89      2340\n","weighted avg       0.90      0.89      0.89      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAbEAAAGACAYAAAAj9ly5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX+JZ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L/88Rp+WrljQ1wMAcPZhBnZdeY4iIswJaI6JXZtAV3T1ZTNEG6zF8zlVHg3awJoG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7WItbW9CqVis8++4yNGzcyaNCgdvV6vR6VSsXu3buZP3++1Tlra3XY29sTFxfH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\nOmT2AAAAAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b0e38278>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"text","id":"WdxzGXxBnWsZ"},"cell_type":"markdown","source":["# **Confusion Matrix για τον GNB**"]},{"metadata":{"colab_type":"code","id":"vDCMVouRnWsa","scrolled":false,"colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"610e8b78-db5e-444b-cb0a-2236b1129d95","executionInfo":{"status":"ok","timestamp":1544694588770,"user_tz":-120,"elapsed":41035,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.naive_bayes import GaussianNB\n","gnb = GaussianNB()\n","# κάνουμε εκπαίδευση (fit) δηλαδή ουσιαστικά υπολογίζουμε μέση τιμή και διακύμανση για όλα τα χαρακτηριστικά και κλάσεις στο training set\n","model_gnb = gnb.fit(train, train_labels)\n","preds = gnb.predict(test)\n","gnb_preds = preds\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro9 = micro[2]\n","macro9 = macro[2]"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[81  1  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n","   0  0]\n"," [ 6 43  0  4 36  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0\n","   0  1]\n"," [ 0  0 92  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n","   0  3]\n"," [ 3  3  0 55 10  0  0  0  0  0  0  0  0  1  0  0  0  0  0  1  3  2  1  0\n","   0  0]\n"," [ 1  7  0  3 72  0  0  0  0  0  0  0  0  1  0  1  0  0  0  0  2  0  0  0\n","   0  0]\n"," [ 0  0  0  0  0 77  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0  0  3 14\n","   0  0]\n"," [ 0  0  0  0  0  0 70  0  0  4  0  0  0  0  0  1  1  0  0 15  0  1  0  0\n","   0  0]\n"," [ 0  0  0  0  0  0  0 81  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  1\n","   0  0]\n"," [ 0  0  0  0  0  1  0  0 95  0  0  0  0  0  0  0  0  3  0  0  0  0  3  0\n","   1  0]\n"," [ 0  0  0  0  0  0  3  0  0 70  7  0  0  0  0  0  2  0  0  2  0  0  1  0\n","   0  0]\n"," [ 0  0  0  0  0  0  0  0  0  2 84  0  0  0  0  1  0  0  0  0  0  0  1  0\n","   0  0]\n"," [ 0  0  0  0  0  0  0  0  0  0  0 89  2  2  1  0  0  1  0  0  0  0  2  0\n","   0  0]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  5 15 71  0  0  0  0  0  0  1  0  3  0\n","   0  0]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  0  3 85  0  0  0  0  0  0  0  0  3  0\n","   0  0]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  8  0  1 83  0  0  1  0  0  0  0  1  0\n","   1  0]\n"," [ 4  1  0  1  9  0  0  0  0  0  0  0  0  0  0 58  0  0  0  8  1  2  3  0\n","   0  0]\n"," [ 0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0 80  0  0  0  0  0  1  0\n","   0  0]\n"," [ 0  0  0  0  0  0  0  0  3  0  0  0  0  0  0  0  0 84  0  0  0  0  1  0\n","   2  0]\n"," [ 0  0  0  0  0  3  0  0  0  0  0  0  0  0  0  0  0  0 88  0  0  0  0  0\n","   0  0]\n"," [ 1  0  0  1  0  0  4  0  0  1  2  0  0  0  0  3  0  0  0 79  1  2  0  0\n","   0  1]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  3  0  0  0 82  0  1  0\n","   0  0]\n"," [ 0  8  0  4  5  0  1  0  0  1  0  0  0  0  0  0  0  0  0  3  2 79  3  0\n","   0  2]\n"," [ 0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0  0  0  0  0  0  0 79  0\n","   0  0]\n"," [ 0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 79\n","   0  0]\n"," [ 0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  4  0\n","  80  0]\n"," [ 0  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  5  0  0\n","   0 84]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.84      0.98      0.91        83\n","         2.0       0.68      0.47      0.55        92\n","         3.0       0.98      0.97      0.97        95\n","         4.0       0.81      0.70      0.75        79\n","         5.0       0.55      0.83      0.66        87\n","         6.0       0.94      0.79      0.86        97\n","         7.0       0.90      0.76      0.82        92\n","         8.0       0.98      0.98      0.98        83\n","         9.0       0.96      0.92      0.94       103\n","        10.0       0.89      0.82      0.85        85\n","        11.0       0.90      0.95      0.93        88\n","        12.0       0.87      0.92      0.89        97\n","        13.0       0.75      0.16      0.26        95\n","        14.0       0.52      0.93      0.66        91\n","        15.0       0.99      0.87      0.93        95\n","        16.0       0.91      0.67      0.77        87\n","        17.0       0.93      0.98      0.95        82\n","        18.0       0.94      0.93      0.94        90\n","        19.0       0.96      0.97      0.96        91\n","        20.0       0.73      0.83      0.78        95\n","        21.0       0.89      0.94      0.92        87\n","        22.0       0.86      0.73      0.79       108\n","        23.0       0.72      0.96      0.82        82\n","        24.0       0.84      0.99      0.91        80\n","        25.0       0.95      0.94      0.95        85\n","        26.0       0.92      0.92      0.92        91\n","\n","   micro avg       0.84      0.84      0.84      2340\n","   macro avg       0.85      0.84      0.83      2340\n","weighted avg       0.85      0.84      0.83      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAasAAAGACAYAAAATek2YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\navxJ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4hsaVSYwWZkUbulh0jNycXGh+MSnRCTjgrVr30EtZkWbeDlOyb7jbLLrMI1gXY1s\nW40iIiJr955k6eRRLqYiS1BeiMqzBCuW4JBQ+nreAgJAj54kk0hMdPnyZXJv3JjSs3JJWCKlgYMG\n0w/LVpSr97bw3wzlwZhtyM/jggXlz+Pb0K/aelA31vV5j1YsiU9TyMnJie7fv08iMdHqtRuoY6fO\nBsvJWP0qxyilZr0iF1dXiv3nGhERLf1pFQWH9K5a3FK/X/QqbIjTk1XFk6ctU4tJfNnBpA3ja8zv\ntynzdREN3HK9XCEiOvswS2n78+wiep5dRIdPxtLz7CICQFfvJNLz7CKaPn06fTFlpiJmx74j5Nu2\nA+Oc9IkvW4dI9rqqeW5c4b9VBIsWFksp+tw/REQEgBKSnquFp5pbv2ryoKqPCoulKvuosFjK+D2a\nX1Rarjx+nk6HomSr5/KLSuny9VtUq1atcjFMc+LieWBT9v036VXYkEnhlpgytZjGa8v4crCxwpxA\nD6wb0gLzg99DAycZaqVJnWpY3McTG4a2xMQu7qhm82Y1T7sOHZXaS0hIgHuTJorX7o2bIulR+QcD\n30Z2Ghc9yOXXsZPG/WzmxMV+BZj1kS6cPNe6dRHUK0Tx+vSpk2jf4X2NdUz5vPGSifVfCjaENm5Y\nixU/fo8mTT2wZ/8hg8Vrw/gqEpfiQlIOjtzNwMuCEvRr5Ya5Qc0AANee5eHI3QxIpcDUbo3xaceG\nGvMSCoWws3vTnr2DPYTC8jyxt5GdxkUPuohr/cRGv+oqpu9puWKjz2LDutU4fvKMwXLh4nkwuvgf\nX1StoqIiBAYG4tAh7QdlWTFlamkbrw3jK7+4FFsuP0dWQQkIQNTdTNRykM35+26moUgsRUmpFAdv\nZ6B9o1oa83J0dERx8Zv2REIRHB3L88TeRnYaFz3oIq71Exv9qqt04eQdOXIE48d8ij8ORcHLu7nB\ncuHieTC6TGCBRZVMVhs3bkStWpr/kKsSU6YW03htGF+OtlaoW922XD3L//9UUtP+zYWqlaUFJCrA\nt2Xl5eWFp48fK14/eZyI9zy9GeekTzwbbZiDB13EtX5io1+ZSldOXszZM5g6dSqOHD+Jtu3aGyQX\nubh4HowuE8AtsT5ZJSUlITExEd27d2dclylTi2m8NoyvZq6O+K6Pp2JiCvJ0wcsC2XLzCf7usLKw\ngKUF0Lt5XdxIfqXRz7BhwxB1aD+yXmRCIpHgt83r0X/QMMY56RNftg7Pf9PcBlOZU79q64GpdOHk\nCYVCfDH2Mxw6dAheXt5q43QVF8+DsWVhYaFXYUWsLOMoozFjxtDz589p7dq1dPDgQY2xqlb4rfh5\nLXl6yZhaLVr50P8ORaldDahNfMWVOqfOxFCrVjLGV1CvYHqSnF5u/8At12nHleeUmiei5Fwh3U55\nRZP+uEtEstWAqXkiSskV0Zlnpt3OAAAgAElEQVSHWTRix016nl1ENx48I49mAvJoJiAA5N6kKXk0\nE1BKSgot+mElNfFoRo2belDEJ2PpcWYB45z0jZfX8fHxoaZGaoMtD4Zso+KKscfP00kg8CRPT08C\nQE2bepBA4EmPHierXA1oTv2qyYOqPhIIVPdRxdWA2rxHK64G/G3nbrKzsyNPT096T/CmJD1LU7ka\nUJucuHge2JTDoG16FTbEKhvwyJEjSEtLw4QJE7Bu3Tq8++67GDRokNr4UinBypL7X/zx4sWLlynL\ncch2veoXHvjEQJmoF6urAWNjY5GcnIzY2FhkZGTA1tYW9erVQ+fOnVXGFxSr/2kOTQBIJvF2Nqph\nkeqAlB/tiFMZf/jz9vhwq/K+tYNaqc2pYW07JOcUK213rWnHKCd1MncwqLHbUPVjmwBQzdYCwhLl\nfZZqPliZQ79qimfaT2I1P7mj7j2q7gNrdTtLtX8jLNXcmjL2uTNkvD2bf51N4JqA1clq9erViv/L\nr6zUTVS8ePHixYsdsfa9kx4yqYeCARnj65s5M2FhYVEpT+vPwwfRwbcFOvi2gJeXFzr4toBTNWvk\n5+erraMLOy31RjR8fX1xZv4QnF/2OV6nVnyI2BL1atmUKw1ry66enKpZK7Y5V1P92YGL/DeuMuzY\nYgM62llqxXMzNlePq2Ojs187CAQC9A3tVWk/nTh2FP5+7eDt7Y2Qnl3x333NnL9zMdHw79geAoEA\n/XtXfnxdcuLi+Dam+AUWekrVgomgXiE0a+48AkD3E55qXFxRkbO1PfJ36jfgQ7ULLLRhfFXEKfVa\nepRsqzvR06dPaeCW69QqbDo5NW6uhFsqW7LyS6iwWJaTqKRUsb2opJSyC0oY56RPfNk65sCwM2Qb\nqnBLvYLfsAErwy0Zm6vHlbFRtg9e5OSTi6srXbwSR0Qy1l9IaB+1uKX/Hj2jWk5OdOXGHVlOq9aR\nX8dOahdYZGS/JhdXV7pwWYY4W75yNQWH9lZahME0Jy6ObzZVfdgOvYomFRQU0MSJEyk8PJzCwsLo\n/Pnz9ODBAwoLC6OwsDBasGCBVjlyerJSx2ZTx/jKeFWitohEImrq0Yyu3HpQfnuZwaENs0sdf5BI\ntq8i26zRlKhy5b3pxygpM5/8vz1DREQB30cr9m2JTqStMUmMc9InvmwdItnrqmbSvU1sQC4z7Iw1\nNlRx/vKLSomIVHL+uDg22IhnUzXCdupVNCkyMpJ++uknIiLKyMig4OBgCg8Pp9u3bxMR0fTp0yk2\nNrbSHE3uNiATxldZbdu2DR06dkbjMkyuitKF2cWUbRbWsRHinuTgebYQAJD0ogCA7Etkf09X/Pss\nV6+cuMjVMwcPchmTMWfs43OxX5ly/rjowRzYgMa8Dejs7Iy8vDwAwOvXr+Hk5ITU1FT4+PgAAAIC\nAnD58uVKczS5yUoXSaVSrFy5El9MnqYxTh9m15o1a/Be43dw+Z+L+HbxjypjLCyAMQFN8Wt0ktK+\n74e2QkZeEY7dStMrJy5y9czBgy4ydhvm1q9nz8o4f0tX/GywfHSpw7V4VmShZ9GgPn36IC0tDUFB\nQQgPD8esWbNQs2ZNxf46deogKyur0hRZnayuXr2Kjh07IiIiAhEREVi8eDEr7cZdu4Lq1avDy7uF\nxjh9mF1Tp07F4+RMjWyzdo2dUVhSikcZBYptVpYW+PljX7zj5ICxv11HxVXAXOS/cS2erTaYytht\nmFO/Ho06gtGjR1fK+eOiBy6OPaYy5pXVn3/+iXfeeQd///03du7cia+++qrcftLyUV/Wr6zef/99\nREZGIjIyEvPnz2elzb9PHkfv3r0rjdOF2cWEbdajhRti/ssst23pcB/Y21jhsy3XUCxWfmaEi/w3\nrsWz1QZTGbsNc+nXmLNnMHvGNJw+fbpSzh8XPXBx7HFJN2/ehL+/PwAZD7W4uBi5uW++7sjMzETd\nunUrPc5bcRvw/r278PaunCGmC7NLzjZL04Jt1vzdmkgsc1UFAO+51cCUXTfVQm+5yH/jIjuNi3w2\nY3P1zGFsyDl/e/YfMNp7lGvj9W1jA7q7u+P27dsAgNTUVDg6OsLDwwNxcTKIwunTp9GlS5dKc2T9\n96wSExMxfvx4vHr1CpMmTcIHH3ygdd3MzEyEBHZXvA4JCoC1tTWOnzyDd959V2299LQU1KtXr9Lj\nOzg4YNeefZg2ZSKEwkI09WiGX7ft0FjnA/+umDFrLgIDAyEplcLWzg7bdu4pd09WrnpODsjKL0+s\naFCnGk7NeePp5pMcvXLSxYO8zsSJE1FQWAgPA7fBpgdjtiEff/L3ZmXjj2m/Mh3f5jA2jh/9Ey9f\nZuGz0RGwtIDiNvjJv2NQ183NoB64Ml518WBsGfNZqbCwMHz99dcIDw+HRCLBokWL4OrqigULFkAq\nlaJ169ZawSFYZQNmZmbixo0bCA0NRXJyMkaOHInTp0/D1tZWZbxUSmoxKLx48eLFyzCqM/J3vepn\n7/rIQJmoF6tXVm5uborvjho1agQXFxdkZmaiYUPVv6grY2Zpzx3L1wDlcqtpg8zXYqXttarZqIxX\nx+wqFqvmEarjmvnMOaE2p2dr+sF96lGl7Q9X9mOUkzqZEoePizmxwQasKv5gVZ4HiRo2oDrWn7WV\n6m8rzGG88mxA7cXqd1ZRUVHYtm0bACArKwvZ2dlwU3Gpz4sXL168eJUVq1dWPXr0wMyZM3H27FmI\nxWIsWrRI7S1AXrx48eLFjniQbQVVr14dmzZtwt69e/HHH3+gW7dujI/BFEh58VwMgrq8D4FAgGED\nQpGWanjA5LlYGeiznY83BvYNrjSnouTbSP/9SwgEArw4PB+S/Jfl9ltaALZW5Yv8lkDZbXZWgI2K\nM8iDbI2XE5OxJ69jTAgsG/16+NBB+Pr6onVLL/To5o/79zSDZpm2wRRMq6sHv3a+8PJS70EOEXaw\nUQ0RnjNrJjybNTZITjzIVgfpiZQyqipy0SoDUlbkASal5VIdF1c6fe4qERF9v+xnCgzurZYNqA1g\nsiIbMDXrFbn8P+gzTyihpT+touCQ3mrZgA2++B9ZOtSiesNXERGRc9cxZN+4fbmYiiyxYgmRRIZO\nK7ddUirbx9RDxcKDbA0DaNWlX7kMXBWJiR4mPSMXFxd6+vQpicSynNq176BXG0zBtIbyEJ/4VNGv\nqjyoggjL33NX4/6lph4e1MjdnbXxzaZcP9mvV2FDnJ6sKp48rgAmVdWR52uINlw+2aco7475HyWm\nv6b2s44S0Zt9YT/H0rn7GeTyyT5Oeqiq88DFnEytX5mAmiv+cgGXPch/fUGVh4SMQtp/LJoSMgoJ\nAJ2/mUAJGYVERBSflk9t2vvRz5t20LsNGlFCRiErHtiU66f79SpsyKQeCuYiYNLYbXzctSmuJb7E\n06zy3LBZA1piZdR9k/BgDufBHHJiA9Rsyh7atPdTWX/frm0QeLeAb1vVgF1zANmagkxqsuIiYNKY\nbVhYAF8Ee2LDyfhy2z/wqgsLCwtcSlCGP3LNAxvxfE7G8yCXNqBmrnvYuGEt3NzcKvVQVhkZGdi5\nZQNmfvOd2hhzANmawndWrE9WUVFR6N+/PwYNGoTY2FhGdbkImDRmGx08XFBYJMHDtNfltg/2a4RD\nV5+ZhAc24vmcjOdBLm1AzVz38MXEKXj58mWlHspq2rRpmDh9Dmo5OauN4UG2ZjhZ5ebmYsOGDdi7\ndy82bdqEs2fPMqrPRcCkMdvo1bo+zt5NV9oe1PodnLmjvJ2LHtiI53MyngcmoGZz8VBWx44dw9JF\nc9G5VRMMDumK9LQUdG7VBMXFb7Bp5gCy5SerCrp8+TI6deqE6tWro27duox/IoSLgEljgj5bNHRC\nQoWrKpcadnCpYYekzHyT8MBGPNdzMuV+ZQJq5rqHdC09lFV+fj4u3X2CS3ef4ODJ86j/TgNcuvsE\ndnZ2rHowtkxhsmJ1NeDmzZtp9uzZNG7cOProo4/o0qVLGuNLpcrbYmJiyMfHhzw8PCg4OJjS09M1\nHsPY8Wy1wURc9GAO58EcctLFw/r168nb25s8PT3Jx8eHjh8/bnYeMjIyyNPTkzw9PQkAeXh4kKen\nJ6WkpChinjx5Qu7u7lXmwZiqP/agXoUNsQqy/fXXX3Hz5k2sX78eaWlpGDlyJGJiYtTOzJoYW6bE\n+GJap+HY/Srjs34Lg+unyvuSfw0zSE7mwFrjYk6m5kEV/1Id+xIA7GysjJ4T03imDM8Xr4tVRAPv\nuVXDo0yh0vaGdaoxzolpPJtswHfGH9KrftqmQQbKRL1YvQ1Yp04dtGnTBtbW1mjUqBEcHR2Rk5NT\neUVevHjx4mU0mcJtQFYnK39/f1y5cgVSqRS5ubkQCoVwdla/yoYXL168eBlf/GRVQW5ubggODsaw\nYcMwZswYzJs3D5aWzFLgKpPOmFy9krR7yP1zDgQCAV6dWoLSwuxy+zWxBK0t32yz1tDVxvYg1/Hj\nx+FgY4FnT58a/PjmMDbkfDoLC9V8On3jjcW+VMXV27FjB9zq1ELrll6KsnHDeoPkJBaLMWOGeo6f\nKg9dO3WAQCDQit95+WIsBgZ1hkAgwOhhfZGRlqoxnmcDGl+sP2c1fPhwHDhwAAcOHEDPnj0Z1S0s\nLMTIj4fjl81bkZCQgN59+mHKxPFVFl+2ztatW3H3P8O3QeIi5J9bixofjENCQgJsG7ZFwaWt5WKk\nBJSUvikSKSD/ySBLizfbLS1khW0PcgmFQsyZMwe1a9fWGKfPeTDlsQEAQwcNYPS8DZN4XT18Nupj\nbN26FTfuPEBI776YPmWC1nn0H/Ahbt+LV5QvJk7SOydN7WnysPaXzUhISFDrQS5hYSGmjRuFJSs3\nICEhAT169caCWVM0Ht/YY4kXuA2y5So7jWv8t2YzTyhK8zkn6fGLAurxYywREc0/cFexb8HBe3Ts\nVhorHnILJUpl8pczaNmyZdSwkTvd/i+x3D5DnQdTGxsVYc3R5/6hwmIpAaCEpOdK+5nGs+HhQVoB\n7Y06Sw/SCggAxcQ9pAdpBbR9+3YaOOxjepBWUK4wzUmdbyIyim+uxLOpBhOP6FXYkEnhlrjIHeNa\nTkPfb4CbT3PxPFu2gkn+r/z/TesqP8vBRj/dv3cXsdFnMW3aNLUx+hyfa+dB1zp+HTup3advvDE9\nqOPqxd+7g5GDQxDi74tvpk9A/utXeucEGNc31+LZEH8b0MDiIneMSzlZWACfdW2CreeeKLYVS978\nTHixuBTVbJWXGRvbAxFh+tQJWLZyNWxsbFTG6HN8NjywkZOxxbYHgUCAHsF9sHHnHzh8+hIKC15j\n6cI5Bju+tuLa2ODauABMY7Ji9ZeC//jjD0RFRSle37t3D7du3dK6Phe5Y1zKqY27EwpLJEjMLFBs\nsyuzqsLe1grCEuVnTIztYcdvW+Dl1RydOvur3K/v8XWpw8WxYWyx7aFz586o3aS14vWYSTMx9uOB\nBju+tuLa2ODauAD4XwpW0tChQxEZGYnIyEhMnjwZAwcOrLxSGXGRO8alnAK86+Lcg/IkdneXN7f9\nGrs4lpvI2PJw4lgUThyLgmeTd1GvXj2kpiSjR9eOuHAuxiDHZ8MDGzkZW2x7SE5ORk72m/FYWiqB\ndYUrazb6iGtjg2vjAjCNK6squw24YcMGTJigfkWOKnGRO8Yl/ptX/RpIelH+VkKYX0M42Fihmq0V\nwvwa4tgtZQCusT38cfgYHj1Lx8MnqcjIyMC7DRoi+vwVdOkWYJDj61KHi2PD2GLbw8aNG7Fg5mSI\nxWKUlpZi92+b0K1nsN45MRXXxgYX2YAmIVaWcVTQ7du3afbs2ZXG8WxA3XNiKjZzcnd3pydPnhj8\n+Fw8D0zqaMOn0yfeWB405TFq1Chq1qwZCQQCGj16NOXl5emdExu+uRZvbDX+8phehQ2xygaUa8GC\nBejTpw/8/FSvIJLrbWUDMo1vNfcvlfGPVoTiva+U9939MdQg+WiqU6TiuzEAcKpmhTyh8j57FQs/\nDJ0TF+OlUtVvv2q2FhCWaP/WVBdvqerBukpyYhpf8Ves5fKq74j4dOV9jV1VX0Goa0OXPmLDt7Hj\n2WQDNp1+Qq/6j3/ubaBM1KtKbgNevXoVbdq0qYqmefHixYtXBZnCd1asrgYEgMzMTDg6OsLW1pbt\npnnx4sWLlwqZwGJA9q+ssrKyKsXtaJI58N+MnZPw+b9I3jUZAoEAqX98DUn+S6UYqzI8QRtLQD5W\nLfBmmyFzOh8bjW6dZWy2D/sGIzXVOOfBHMZGZ792EAgE6Bvaq1KGnS7xxubwXbkYi0G9PoBAIMCn\nYf2MwtVj4lmXNozNXOQaG9AkxMo3YzqqIqbkZV4Bubq60qWrN4iI6Kef11Bo7z5qUTHGji9b58aN\nGyQSV01OZXFLTaccIiuHWtQgYi0REbn0GEfVmnYoF1MkJpJK39QvkbxZzFIqJRKXEklKK/esKaey\nKKWUF6/IxcWVYi9eIyKipStWUa+Q3mpxS/qcB1MbG2URQS9y8snF1ZUuXokjIqIVK1dTSGgftagl\nbeL19SASEwWHhNKCBQsIAD16kqy0vyxK6UZiJtWu40IHTl4kIqKvv1tO3QJD1OKWtMlJlz7S13dw\nSCh9PU+9ZzbGEptqNvMvvQobMqnJiivMLlV15PlWdU7axI/4KZquPsyk6sN2UPVhO8h36iHKLSgm\nIiKPsftp3IYLFH0nVbGfix5MKSdTGhsisWpeIZFpcvi0rcMmc5GLbMD3vvpLr8KGTAq3xEVmF9dy\n0ib+2qMsNHGrAe+GTgCAAX6NEHM3DQCQmSdS65VLHvic2GPSmTKHT9c6XPLMhvgFFgYWF5ldXMtJ\nm/iMXBG+3XcLl5b1Q75IDGGxBKHfnsKHHRurPCYXPfA5cZNJx0UPxvbNxfPAVKawwILVyaqwsBCz\nZ8/Gq1evIBaLMXHiRHTp0kXr+lxkdnEtJ23ifRrXxswPW6HV5ENIyS5EmH9T7P+qh2qDHPXA58RN\nJh0XPRjbNxfPgzmK1duAhw8fRpMmTRAZGYk1a9ZgyZIljOpzkdnFtZy0ie/esj6uJmQhJVv2Se7g\n5SeKW4LaiAse+Jy4yaTjogdj++bieWAqS0sLvQorObLSyv/L2dkZeXl5AIDXr1/D2dmZUX0uMru4\nxAbUNv5R2iv4CVxRu7odACC4TQNk5ApVHo+rHkwpJ1MaG/qKix6M7ZuL54GpLCz0K6yIlWUcZfTp\np59SYGAgvf/++3Tr1i2NsapW0pw6E0OtWsmYWkG9gulJcrraVUBsxMvr+Pj4UFOO5FRZfPVhO2jJ\n/27Ro9Q8SkjNo7jELAqaf4KIiB6m5FF6TiEViEroYUoe7TmXyEkPppSTKY0Nkbj8yrjHz9NJIHjD\n4Wva1IMEAk969DhZ5co4rnhgWkeVZ4Ggaj2zqRbfnNarsCFW2YB//vkn4uLisHjxYsTHx+Prr7/G\noUOH1MZLCWDpCpMXL1683lq1mv+3XvXvLg4yUCbqxeoCi5s3b8LfX/YDfF5eXnjx4gVKS0thZaUa\nYqqGhQrAtICUXMvJ9eOdKuPz949CjTDlfVl7Rhk9J0PFczEnU/OgChxrDtBYTXWYwnLZ8MwmyNYU\nxOp3Vu7u7rh9+zYAIDU1FY6OjmonKl68ePHixY7456wqKCwsDF9//TXCw8MhkUiwaNEiNpvnxYsX\nL14qxP+sfQU5OjpizZo12L17N/bt24dOnbR/Slwuc4GVcgmqCQCSzP/Qtm1bFJ6YDdG5FZAKc5Ri\nLP8ffmtXAXRrZ/UGimtrpfp7RrbOw9s2NpjGHz50EH7tfOHl5YUe3fxx/949rT20buFpFFguG+eB\nqW9dYLlMgL9cA9nyqwH1VMXVMaYKK2XaRkUOWa/gUJr7zXytOGXG8GDvO5HaDv6esnLyqVnwN2Tv\nO5H2nbhOizYcJSKixRuPk73vxHKlqs6DuY8NpvF5Qomi3H34mOq4uNCd+CQiIvpxxc/Utl37cjF5\nQtVQ4ar0oEs8U9/6vkfl71M58FfT+5SLIFvfRWf1KmzIpCYrLkMy5fkaog02oZraeLD3nUjTl/2P\nDpy6oZiM/D9eTv/GJxNR5ZMVV2Cl5jA2mMaX/YMcn5RMh4/+RXlCCRERXbr+L9WsWVPjZMUFD7rE\nM/Wt73uUCfCXiyBbU5iseJCtHvFstWFMqKa2dYgAK6s3w6VAVAyPhq4AgB5+nojZMR23D8/H0ukf\nwtam/Feh5nAezCGnevXrI6CnbImxRCLB3t07Edq3v9r8uehBl/PA1Lexgb/cBNly/zagSU1W5gDJ\n5BpUU9s6MdceooefJ5p71IeVlSXGDesKe1vZpBQVcxvBY9ai+6iVaN/CHTM+CdQrJy6eB3PJCQA2\nblgLNzc3XP7nIr5d/KPGWK550Of9o61vLr5HjS1TWA3I6mQllUoxf/58DB8+HBEREUhKSmJU3xwg\nmVyDampbJ/5xBqYvP4BdSz/B+V0zEf84A3n5sp8TWRMZjRKxBLmvhVi3Jwa9u7TUKycungdzyQkA\nvpg4BS9fvsQXk6aiV48uEInU/ywM1zzo8/7R1jcX36PGFn9lVUFnz55Ffn4+9u3bhyVLlmD58uWM\n6psDJJNrUE0mdfYcvYr2Q3/ABx8vx73EVNxPlP0GVg3HN58Sra0sIZaUf5rbHM6DOeT0MP4BYqPP\nAJB9kh4ybDjy81/jUcJDk/Ggy3lg6puL71Fji7+yqqCnT5/Cx8cHANCoUSOkpaWhtFQDpqKCuAzJ\nNBaslKmM5aFpQxdc2TcHtao7wNraErM+DUbk0asAgO8m9QMA2Nla47PB/vjrwn29cuJBtsaJf/ky\nC+M//wTpabIPGVcu/wOJWIzGTZqajAddzgNT31x8j/ICu0vXY2Nj6ZNPPiGJREJJSUnUunVrysrK\nUhtfKlXeFhMjA4N6eHhQcHAwpaena2zT2PHGbiMjI4M8Pd+ARD08PMjT05NSUlKq1ANTmfp5MJec\n1q9fT97e3uTp6Uk+Pj50/Phxk/Ogy3lg6ptpG0zfp8Z+vzFVhyUxehU2xCrIFgBWrVqFq1evwtPT\nE3fv3sXmzZvh6uqqMlYTY4tr3DFz5pQ5d5ikto7o1no4tFHen3t9vVFzMmQdrsUbso1iseo7F7Uc\nrPBKpHqfnY0yAs3UzgNT36o8a2qD6XsUUP0+5Qob0O/Hc3rVvzq3m4EyUS/WUYnTpk1T/D8wMBB1\n6tRhOwVevHjx4lVGJkBbYvc7q/j4eMydOxcAcP78eTRv3hyWlia1ep4XL168zE78AosKEggEICIM\nGTIEmzdvVkxcTMRF7hgb/DcmnDJ9+G/aeijNT0Hxw/0QCAQoSfwTVFJQbr+cI1i2yG9rWFu+2Wat\nZgQeOngArVt6wcvLC61byoqDjQXy8/M1enjbxsaxo1Hwa+cLb29vrc71udhodO3UAQKBAAP7BmvF\nvGPqgWlObJwHsViMb+bMhIWFhdaejfke5Rob0CTEyjdjOqoiaoUr3DFVdYzFBnyRk08urq508Uoc\nERGtWLmaQkL7qMUtPUx6Ri4uLhSf+FQR3659B708VEQp2bUaS7B2IFvBMCIisn7XnyxruqvFLYnE\nRMUSIkmp7LyWSt9sL5XK9qnLTT4OIvfupwEfDqqyc82VsVG2JD5NIScnJ7p5+z4REa1eu4E6duqs\nFjuUmvWKXFxdKfafa0REtPSnVRQc0tugbEBtcmLjPFT0FNQrhGbNnUcA6H7CU608G/I9ynU2YKdl\n5/QqbMikJiuucMdU1ZHnawr8NzY8NJt5QlGazzlJj18UUI8fY4mIaP6Bu4p9Cw7eo2O30ijjVYnK\nQkT0NPM1NfVoRlduPVBsf1vHRn5RqaI8fp5Oh6KOU36R7FPA5eu3qFatWuVi2PDAZk7axrPJ1zRW\nPJvqvPy8XoUNmdQXRlzkjnEtJy7y34a+3wA3n+biebYQABT/yv/ftK7m50v2Rm5Hh46d0bhMm2x7\n4OLYcK1bF0G9QhSvT586ifYd3jdYPlzMydjcPjZy4tmAusmkfjiZi9wxLuYEyDhoK378Hk2aemDP\n/kMaY42Zk4UF8FnXJhi7/YZiW7FE+ub/4lJUs1X/a9FSqRSb1q3Grir0oEs8W23IdfbsWWxYtxrH\nT54xWD5czIkNrh4Xx5Kx9db/+GJCQgICAwOxe/duAEB6ejoiIiIwYsQITJ06FSUlJYyOx0XuGBdz\nArjDf2vj7oTCEgkSM98swLArs6rC3tYKwhL1FJPLly/DsbojvLxbqI0xtgdd4tlqAwCORh3B6NGj\n8cehKHh5NzdYPlzMiQ2uHhfHEi8jTlZCoRCLFy8u92vAa9euxYgRI7B37164u7vjwIEDjI7JRe4Y\n13LiGv8twLsuzj3IKrfN3eXNbb/GLo7lJrKKOnbsGHoGhajdz4YHXeLZaiPm7BnMnjENp0+fRtt2\n7dXGseXB2DmxwdXj4lgytoy9dD0qKgr9+/fHoEGDEBsbq9OFi9EmK1tbW2zZsgV169ZVbLt69Sp6\n9uwJAAgICMDly5cZHZOL3DGusdO4xn/zql8DSS/K394I82sIBxsrVLO1QphfQxy7la42t9u3b+M9\nTy+1+3XJiY34snWMNTaEQiG+GPsZ9uw/AG9vb7V5sOnB2DmxwdXj4lgytoz5nVVubi42bNiAvXv3\nYtOmTTh79qxuFy7GXsGxdu1aioyMJCKijh07KrY/e/aMwsLCNNbl2YC6xXOR/6arWrVqRSdPntQq\nlmvnwdht7N27l+zs7BRMOnnJyMioMg9s5GRsbh8bOXGNDdht1T96FU06fvw4LVy4sNy2gIAAKi4u\nJiKimzdv0qRJkyrN0ehswHXr1sHZ2Rnh4eHo1KmT4mrq2bNnmD17Nvbt26e27tvKBmQab2wOmi45\ntZr7l8r4RytC8d5Xyvsuzg9UEQ241bRB5mux0vZa1WwY52Qq8ZrqSEqlyhsBVLezREGx8j5rK9U3\nTwzpgYs5cY2vqUs8m4l2yHkAACAASURBVGzAgDWX9KofM7Wz2n2//vorHj9+jLy8PLx+/RqTJ0/G\n9OnTFXPB8+fPMWvWLI1zAcDyasBq1aqhqKgI9vb2yMzMLHeLkBcvXrx4mafy8vKwfv16pKWlYeTI\nkSh7jaTt9RKrz1l17twZp06dAgCcPn0aXbp0YbN5Xrx48eKlQsZcYFGnTh20adMG1tbWaNSoERwd\nHeHo+GZFpLYXLkabrO7du4eIiAgcPnwYu3btQkREBCZNmoQjR45gxIgRyMvLw8CBAxkf923kvzGN\nZ4P/xjSnwsQreL5zEry9vZHy+0wUZz1VirG3sUAdR2vUcbSGUzUrVLxbVMvBCs7V1D+TZQ5jQywW\nY/ZXM2BhYaHV8c/FRMO/Y3sIBAL0720cJh1TD8bOSVc24JxZM4zKBjS2B2PKmAss/P39ceXKFUil\nUuTm5kIoFOp24aLTt3EsqSKm5G3kv2kTz5T/xoaHsrilxuN2kaVddWo0ehMREbn2nED273iXi8nK\nL6HSUim9eC3DKb0SSqhYLEP2ZLwqoZxCMUlKpVQsLlWJWzKHsSESEwWHhNLX8xYQAHr0JFlpf1ls\nUUb2a3JxdaULl68TEdHylaspOLS3WrSRsTywmZO28RVxSr2CQ2nuN/O1wi1xZSyxqcB1l/Uqlen3\n33+nwYMH0+DBg+nMmTOUmZlJo0ePpo8++ohmzJhBJSUllR7DpCYrrjC7VNWR51vVOXHFA1NeXNgP\nJ+nKg3Sy77+R7PtvpJbj91JufhERETkP2UL/Pc+mz1dH07k7qWTff6PZ9CuXGXamPL653K9cZAMG\nrb+sV2FDPBtQj3gu5sRFD9rw4q4lZKJJvVpo3sgZADCwU1NE35bdGvlmeDvsjXmEZy/U/zyIOfQr\nwC2GHRtt8GxAbrABTUE8G1CPeC7mxEUPZaWOF5eeI8TC3VdxdfVQ5IvEEBaJEfRNFAZ94IHANg3h\nP/MQOnnXqzIPbHP1tBEXPXAtXheZgwem4tmAFdiAALBr1y60aNFCpxPDRWYX13Lioge5NPHiWjep\ng9lD26L52L145+PtmLfrKg58I7sam7HlotpnedjywCZXT1tx0QPX4nWROXhgKksL/QorORrrwKrY\ngEeOHEF2drbOz1dxkdnFtZy46AGonBcX0LoBrsRnIvmljBN44GISmjeqDQDYPasXnuwYiX1zeqGj\nlxuurRnKuge2uHpMxEUPXIvXRebggane6p+1V8UGDAwMxLRp03Q2x0VmF9fYgFz0oA0vLiE1Dx29\n3FC7hh0AIKRdI6TnyK6+m4zehSajd2H40tO4Ep+J96f+oXdOXOxXpuKiBy6eB6YyBw9M9Vb/npW1\ntTWsrcsfXt/LXAcHB+zasw/TpkyEUFiIph7N8Ou2HVUWX7bOxIkTUVBYCI8qzomLHo4f/RMvX2bh\ns9ERsLQA5CSck3/HoK6bGwDgxPVnaOPhithlH4IA5AtLEL78b5xdqt2zeObQr5mZmQgJ7K54HRIU\nAGtraxw/eQbvvPuuSXjg4nngWr/q4oEXwCobUK4ePXrg6NGjlX6SkBJ790N58eLF621V383X9ap/\nbFwHA2WiXpxeDajhN/k4Bx+tSpBtVcVrqsMUbuo67FeV8aI/x8NhwCal7bkHxzPOiYvx5gBcrao2\nzL1f2QTZmsJFAacnK168ePHiZXy91UvXVbEBN27ciIiICGRlZWHMmDFYvnw54+NykdnFNTYgFz0w\n5cWVZsWj+NwPEAgEKLm0BiTKVYqxsgBsrWTFxhIo+3aTb9f0ifHY0Sj4tfOFt7c3enTzx/179zTm\nxDQekDHpZsyYAQcb7Vh/sTHR6OzXDgKBAH1DjcP60yUnUx/fx49GoWOHNvD29kZgQBfcv6/53HHR\ngzFlCgssTAq3xBVml6o6VckG5KIHprw4OWbJvv9Gsuu9mmBbnWy7ziUiIuuWQ8nSrWW5mCIxkVT6\npr0SiezHOv8fJ6jYLpUSFanwkPg0hZycnOjm7ftERLR67Qbq2KmzWs9M4+UlOCSUFixQz/ori/x5\nkZNPLq6udPFKHBERrVi5mkJC+xiMYadtTuYwvsv22aPHyeTk5ERx/94jIqJVa9ZTx06deTZgGQ3Y\ncl2vwoZMarLiCrNLVR15vlWd09viIWz+/+jKvWSy776Y7LsvppbhGyg3X0Q3H6YRESm2bzpynWau\nO0X23Rcz5hUyja/Im5Mz6Yioyph0hsrJlMaGSMycTclFD2zqw61xehU2xLMB9YjnYk5vi4dr/6Wi\nyTvOaN7YFQAwsKsXouMeo+JX54WiEni866zUhja8Qn3i5eISk46LObHhmem546IHY8sUbgOqXWBx\n4MABjRWHDBli8GQqExeZXVzL6W3xkJ5dgIVbY3B16xjkC4tlLMEvI/FpH1+0FdSHnY0V6jo7or+/\nFy7cfqba+P9LHa/QUPHaiouMOS6ca33iK0qbc8d1D8aQKSywUDtZ3bhxQ2NFbSarhIQETJgwAaNH\nj0Z4eDjS09Mxd+5cSCQSWFtbY8WKFXB1ddU6WS4yu7iW09vioXUzN8wO90fzEeuR/OI1hge2xIEl\nw9Dli98wc8QHuL5tLJJSc3H6WiJKxOqfgTgadQSzpk9VySs0RDwTcZExx4VzrU98WWl77rjswVgy\ngblK/WrAH3/8UVGWLFmC6dOnl9tWmVSxAVevXo1hw4Zh9+7dCAoKwvbt2xkly0VmF9dyels8BLRt\ngiv3U5D84jUA4EDMf2je2BXV7GwAAD4jN+LDufvg6GCLe0+yVLZTGa9Q33im4iJjjgvnWp94uZic\nO656eNtV6XdWly9fRmBgICIiIgAAP/zwA2JjYys9sCo24MKFCxEcHAwAcHZ2Rl5eHqNkucjsMgd2\nmil6SEjORscWDVC7pgMAIKRjM6Rn52NUaGtFjJe7C3q0bYJjFx8qtaENr1CfeF3ERcacKY6NimJ6\n7rjowdiytLDQq7ChSh8KXrVqFf73v/9h2rRpAIDx48dj/Pjx6N69u+YDq2ADVqtWDQBQWlqKvXv3\nYuLEiYyS5SKzyxzYaabo4cTlR2gjqI/Y9aNBBOQLixG+6BASU3Pw/bie+G/PRIhKJPjsxyi8KixW\nakMbXqE+8cAbJp38vVzVTDp9cjKlsVFRTM8dFz0YWyZwF7ByNuDo0aOxY8cOREREIDIyEgDw8ccf\nY8+ePVo1UJENWFpailmzZqFJkyaYNGmSxro8G5AXL168jK+Pdv2rV/3fR/oaKBP1qvTKyt7eHteu\nXQMgu7d6/Phx2NnZ6dzg3Llz4e7uXulEBfBsQK7HV2VOzkHfq60jipkHhwDl/Vknv1YZr45XqE7q\n4tXdDlHHowOMz6RjyshTl5MpjQ2AOZvS2kr1NyI8G5A7qvQ7q4ULF2Lbtm24e/cugoKCcOHCBXz3\n3Xc6NRYVFQUbGxtMmTJFp/q8ePHixevtVKVzd/369bF582bGB7537x6WLVuG1NRUWFtb49SpU8jO\nzoadnZ1isYaHhwcWLVrE+Ni8ePHixctwMoXnrCq9srp+/ToGDx4MX19ftGnTBmFhYZU+gwUALVu2\nRGRkJKKjo3H69GlERkbixIkTOHz4MCIjIxEZGanTRMVFwKQ5gD5N3YNUlIuicwtRfHUNvLy8UHx1\nDUoeKD/YLhaLMXf2TNSwt1JAYnfs2IF36zqjrU9zRdm8cQOsLIFqNhblSnU72VvGAoCDjQXsbTS/\nyeX92rqFp1HAtLrEMwHllvVgrLEBMIPr6nL8Pw8fROf328LLywtBAV3xHw+yLSdTIFhUygbs27cv\nXbp0iYqKikgkEtHFixepX79+xgdBEXchmarqmBro09Q9yNl/8mLrN51g50T23RcTkfJ+ORuwV3AI\nzfl6PgGg+MRnlF9UStu3b6cR4SPVcgEV/LiSUhJLpEREVFoqpRKJlMSlUrVsQDmY9saNG1RYLDU4\nmFabeKagXHU5GWtsyIu2cF1tj1/2vP2X8ITquLjQ/YePiYho2U+rqF37DmrZgFwY3yKxYf6GaquI\nPbf1Kmyo0slq1KhRStvGjRtnjFyUVPHkcQUwqaqOPN+qzult8fCyQFyu3Lz/iBo2cqeXBbJKFfe/\nLBDTg7QC2ht1lh6kFRAAiol7SA/SCmj79u00cNjH9CCtoFxpMu24onh+9Rc9ziygbt/HEBHR+wvO\n0My9/9LFh1mKGHPoVzY8MIXr6uIhTyhRlPikZDp89C/KE0qIiOjS9X+pZs2a5WK4eB7Y1Ki9t/Uq\nbEjtbcDk5GQkJyejVatW+O233xAfH4+EhATs3LkTzZsbFjOjrbgImORaTm+rBwDIz3+NiOGD4eXl\nhWED+yAh/oFSTJv2firrxt+7g5GDQxDi74tvpk9A/utX5faH+TXEjae5eJ4tBABk5Ss/u6WvD67F\ns9UGoD1cV5fj16tfHwE9gwAAEokEe3fvRGjf/gZrwxxAtqYgtQssRo0aBQsLC9D/P4a1e/duxT4L\nC4sqWdHHRcAk13J6Wz1Ur14Dg4cOx8Sp09Haqym+X7YS4cMH41LcHaWH0ytKIBCgR3AffPrFVFha\nWmHul2OxdOEc4J3hAGT35D/r3gRjtsZpPI6+PrgWz1YbTKTP8TduWIsVP36PJk09sGf/IYO1wbU+\n0kWmsMBC7bs4OjpabaWbN29qdfCKINtbt25h+fLlsLa2hq2tLVasWIHatWtrnSwXAZNcy+lt9VC7\nTh0s+3ktgP9j78zjmrjWN/4ECIgKCgH3pYiyqKVuuNUNVHD7WVuruHGvvd62ttaqtbfa1qqttlXc\n0S5aq1bUuu9aV0ClRVxqrTuIgIjsArJDkvf3R5pIINskmWES5unnfO515j3nvM+ZSQ6ZOfMdwMbG\nBu/PnI2Vy5YiMSEe3nqAs3379oWrxwtM09sffIx3Jo9Bo38pJqtubV1QUi5FQmaRznZM9cG3eK76\nYCJT2n9vxoeYN3c2tmzfhaDA/oj78xYcHR1N7oNvY2SM+D9VGbAasKioCDt37sSGDRuwYcMGrFmz\nxqBfVZpAtlu3bkVYWBgiIiLQtWtX7N27l1GyfARM8i2nuuohPy8PKclJatvkMhnsxGKtdZRKTU3F\ns9wXsFuZTKpWL7BTE0Tf0wzD1SW+jRMfzw2mMqb9B/fvITpS8UoQkUiEN8dPQGHhcyTE12RGGtMH\n38bIGFkCG1DvZDV79mw8ePAABw8eRHFxMaKiogxacq4JZBseHo7WrVuDiJCZmYlmzZoxSpaPgElr\nAH1ag4cbf17D6yODkJOtmFS2b92Mlq3a4CWPdlrrKPXDDz9g4cczUVlZCZlMhh1bfsTAwcGq/b4t\nnPCQ4a+qqj4seVzZ9sBUxrSfk5ON6f99C+lPnwIALsf+DmllpdZzg4/HgW1ZxdL1KVOmqP1vXl4e\nffTRRwav4AgPD6eIiAjVvy9cuEBBQUE0ffp0kslkOuvK5DW3RUVFkZ+fH3l6elJwcDClp6frbIPt\neD7mVFc9hIWFUYcOHcjb25sCAwPp7t27avszMjLI29ubvL29CQB5enqSt7c3PXnyhP79739T+/bt\nycvLi6ZOnUr5+fl6+zNEfBsnPp4buo6LuTxs2LCBfH19ydvbm/z8/OjEiRNm9cDFcWBT/91zy6TC\nhfSCbCdMmIAtW7bg7bffxoYNG+Di4oLx48cbfAmvOsj2nwkSK1euhJOTE6ZPn661ri7GFt84eZbG\nTjNHfG3mVFyuvRFJAzvkFtfcn/1c8wo+n+YNcD+95s3tESsuaIx/tHoE2n10ssb2u2EjNMZb0ria\nK15XHaa8QmP4ieVaXrjZyNEWBaU19zmIbRn3wXY8l2zAd/bdMan+pnGdzJSJdum9DPjaa69h7969\nGDduHEaMGIGRI0dCIpEY1dnZs2cBKK4bBwcHG0TCECRIkCBB7MoSLgPqnbsnTpyo+v99+vRBbm6u\n0c9ZrV+/Hq1atYKvry9u3rwJDw8Po9oRJEiQIEHmE1eLJEyR1l9W69atq1F2796Ns2fPYt26dXob\nvn37NkJDQ3Ho0CFs374doaGhWLp0Kb788ktMnjwZ0dHRePfddxknzEdml6Vz9azFw8XoKAS86g8v\nLy+M/b9heJqmu86ZE4fx+pA+8PHxweTXhiL+fs1LIW/6t8LpT/rjzLwB2P5uT3i4K26CX/tqCM7O\nG6AqQS9rfgGjNYwrFx6Y8AqNYgMePoh+vbrDx8cHwwYLbMDqsoRfVloXWKxfv15n4ULVMSV8YXZp\nqmNJXD1r8FAdpZSSmU9ubu50PiaOiIi+CVtNQ4eNqIFbUpbzV+6Ri6uEzl1RLML49Kvl9HKX7mox\ng7+NptzCcuq9+Bx5zDlBn++9RVcTc4mIaO2peDUckybckiWOKxcemPIKjfFQFaV068Ejkri50d/3\nE4mI6NsVq6lb9x5acUt8OQ5c6v2Dd00qXEjvasDaVPWDxxdml6Y6ynxrO6e67kFXHaa8uJClJ+jy\n3adUb0Q41RsRTp3/u53yCsuIiGjJjsuq7cpizeNqaR4ENiAzWcJkpXeBBZ/ER2YX33ISPBhWxxBe\n3JX7GfBo3ggd2yooK2Ne9UTkX48BAIFdWiNq5Zu4uXEKlk3rB3u7mh8lvo1TXT03BDagftmYWLgQ\nh4sjTRcfmV18y0nwYHgdQDcvLv1ZMRb9Eou49RNRWFqBkjIphs47gDf6dcDR2Ef44dhNNKgnxr4v\nRmHuuO6c++bjuPItvqoENqB2WQIb0KBJMS8vD7du3QIAyOVygxuPj4/HkCFD1CC4AHDp0iV4e3sz\nSFMhPjK7+JaT4MHwOoCCF5eTk4P3PpiFoMD+KC0tVe17pZ0b5oX0QMdpv6BFyE9YsO0P7F84CgCw\n7tANVEjlyCsqx/ojf2GEf82VrXwbp7p6biil61hbige2ZCMyrXCSo76A48ePIyQkBJ9++ikAYMmS\nJdi3b5/ehjWxAQGgvLwcmzZtgru7O+Nk+cjs4ltOggfD6hjCiwvo0hqX72UgNVuBWtp/MQEd2yqe\nMXRyfMEOtLMRoVJW80FTvo1TXT03BDagflnFZLV161YcOXIELi4uAIB58+YZRK/QxAYEgB9//BGT\nJk2Cvb0942T5yOwS+G+1H29MHUN4cfFP8tDbtxlcnRSXbIb5t0X6M8Wlmq/+3ReAgnwwbXhn/HY1\nRWtO1jCuluxBYANaifStwFC+KVjJBiQimjhxosErOKqyAR89ekTTp08nIqKAgAC9dQU2oOCBTQ9M\neXHGiG/jVFfPDYENqFsfHb1vUuFCetmAc+bMwYABA/Drr79i0aJFOHnyJFJSUrBhwwaDJsOqbMB3\n3nkHCxYsQJs2bRAYGKjznVmAwAbke7yl5cSUF9fsze81xpeemAnHketrbM87MpNRPrrEt3G1NA8C\nG5CZ/ndc8yVRQ7ViFPM1CEyl9zLgl19+iVu3bqG4uBgLFixAeXk5li5dyrijzMxMPHr0CB9//DHG\njx+PrKwsNbitIEGCBAmqHVkCwULv3O3s7IyFCxea3FHTpk1x7tw51b8DAwNrrBIUJEiQIEHcy6LZ\ngEoNHDgQgwYNqlH0SRMbMD8/3+SE+cjsEvhvtR8PANu3bUVXv47w9fXFyGFDkRAfrzP+QnQkBvRR\nsATHjArWy6QDAJLLMHfuXJSd/BBUmldjv60IsLdVFLHNi9eF29m82K7h+WGVuBinyspKzJ07F45i\nUZ05NyorK/H5/I8hEokMOs5sjyvf2IAWIX03tZ48eaIqSUlJtGvXLtq6dSvrN9OIuGNwGRtftY7A\nf6tdD3/dukcSiYQeJite2Be+4Qfq0/dVrQietOwCcnN3p+jfrxAR0bKVayh42Ai1mOo4pXojwsnG\nvSMtXLiQAJBDwJdq+8oqieTyF/1VSF8sEpLKXmyXyRX7amOcSiuJgocNV3lISEq1ynOj6nHML5HS\n0KBh9MmnCwgA3YlPrrGfy3HlIxvw0xMPTCpcyCg24H/+8x9z56FR1Q8eX5hdmuoo863tnOqKh6f5\n5Wpl8/Y91N2/Fz3NLyciokvXbpGzcyO1GKZ9VIWpKkvkhd+JiAgAxSc+VtsXsuwsXb6fSfVe30z1\nXt9Mnd/fS3lFCpZgx/f2qLavOfw3bTh2m+q9vpn1cWLqoTo41hLPDU2+Iy/8TsXlckae2RpXPrIB\nPzv5wKTChfReBoyNjVUrhw4dwuPHj9n8sadVfGR28S2nuuqhm39PJCc9wv27d0BEOHn0MAYEDNYa\nbyyfrVfvPlr3XYnPhkczJ3Rso3gmcUyflxB5U/Fsz6OMQgBAMxdHBHVthZPXa36GuGLM6fLAdU7W\n4JlpH7xkA4pEJhUupHeBxfffv1i+KxKJ0LBhQ3z55ZesJqVNfGR28S2nuuqhWfMWmL/wKwzt7w8n\nJyc41m+AAyfOao1ng8+WnleCRTuvIW7VGBSWVqKkXIqhC07gjb4KFNPZJSPRvb0b1h29rZrETMmJ\nC8Yc3441Hz3zrX1jZAHrK/RPVvPnz0enTp2Majw+Ph7vv/8+pk6diilTpmD+/Pm4c+cOGjduDACY\nNm2aQYs1lOIjs4tvOdVVD7du/oXwlcsQ+9d9+Hdujw2btmHqxLGIir2hEdLJBp/tFQ8J5o3tgo7v\n7UVqTjEmDPDE/k+HqvYP/eIEnBzF2PRBfywN9ceCiKsm5cQFY45vx5qPnvnWvrVK72XA5cuXG9Ww\nNjbgRx99hIiICERERDCaqAB+Mrv4llNd9RBzMRI9evVBq9ZtAACj3xiH+Pv38Cw3x2x96FOAX3Nc\nfpCJ1BzFX8j7f3+kuiTY2k2B0iksrUREVAKGdGlpck5cMOb4dqz56Jlv7Rsjq2ADtmjRAqGhoVi5\ncqXaK+71SRsb0BTxkdllDew0a/Dg2d4L1+Ji8exZLgDg/Jnf0KRpM7hK3MzWhz7FpxWgt3dTuDZ0\nAAAM69Ya6XklAIDPQ7qpLrUM694at1OemZwTF4w5azg3mIrtPvjIBrSKe1atWrVCq1atmDdsZwc7\nu5rN79ixA1u3boVEIsEXX3wBV1dXg9t0dHTE9p27MefDGSgpKUY7z/bY9PO2WouvWmfGjBkoKi6G\nZy3nVFc9BA0fhb//uoHRQwfAztYGjg2csHHbLq3v6TGmj8zMTAwbMujFpDM0AHZ2djhx6hxatGyJ\nk9dS0dXTDdHL/g9EhMLSSkxZEYnz34yCo70t/gofCxuRCPdS8/HBjzG1Mk76PGjrw5LPDaVnpQz1\nzNa4GtM+27KEe1Za2YBHjx7F6NHa36ZpqKqyAWNjY9G4cWP4+vpi06ZNyMjI0EnHkBN3PzEFCRIk\nqK7q6/OmrUT8fHB7M2WiXVp/We3fv98sk1VVVb1/FRgYiMWLF+uMr9DMogTAP3CnpYE+zRFfmznl\nFVdordO8kT3SC2rud2mg+bU02vqQyzUznuvbi1BSUXOfZMIWjfGlB6fB8Y2fNe7L2zuNUU7aZC4P\nAGCj4S9ESzo3AOa+NXnW1QcX48olyNYSZNCbgs2lmTNnIjU1FQAQFxeHDrV4Q1GQIEGCBCkkMvE/\nLqR1srpx44ZGJqCSFahPmtiAo0aNwuzZszFlyhRcuHABH3zwgdGJnzhxAo5iEVKSk3XGVVZWYt7/\n5kIkMpyDxjZrbWfEdnR7pRPatGmD//w7FOXl5WbNqa6yAWMuRCFoQC94eXkhZMxwPE0zf059e3WH\nl5cXRg0PMogxJ8u+h27duqH8zOeoiFkFKn2xsKKNe0M42L5gBip5gkqxxRJk6oHpucGU0Qgw/wwd\nP3YUXbp0QZeXfRE4sB/u3L6tM/7woQPo7d8VPj4+GBLQH3fu6I5ne1z5xgbkYjVgWVkZhgwZgoMH\nDyI9PR2hoaGYNGkSZs2ahYoK7VdKVNKGthg/frwaF7B64ULa8EC5BcXUuXNncnV1pfsJSVrjSisV\nvK7PFjDjoLHJWrt24xZJJBKKf/SY5HI5jZ8wkRYu/spsOdUVNmB13NLDtGckcXOnU9GXiYjoq2Wr\naEjwcK24JUP6qIrKyXpWSG7u7hRz+RoREa1YtZaGDR+pFqPEKSmLw/99R7B3ouvXr1O91zeTnd8E\nsmnqp9rv9c5uNZagslRI1c9/qYyoUmbcODH1UB09xPTcMITRaOpn6GHyE2rcuDHduXOHSiuJ1oZ/\nR7379NXq+35CMrm5udG9+CQiIgpbuYa69/DX65mtceUjG3B55EOTiiFavXo1vfHGG3TgwAGaP38+\nnTx5koiIVq1aRTt37tRbX+vfbPb29mjZsqXWUpta+tVihIaGoqGTk97Y+Z99gS8WGUbciI6KxEse\n7dC1WzcAwL/f+g/OnT2DwsJCvX0YSvWIjorEwIBAtG7dGiKRCB/MnI3DBw+YLSdjPCjrdGOpDy48\nxFyMQtuXPODXpSsAYOKUqbgQeQ5FZurjQlQkPDzaoWtXRfy/pv4H58/pzkmefQ+iBu6qcbVt2w/y\nrDugyjKtdYCaK7PkBI0XWrjwwPTcuHfvLjzbd1B9RwwKCMRdPb9iAGafIbFYjF92/IqOHTsCAPq+\n2g/37t7RGm8nFmPLLzvRpm1bRU6Bg5EQr/1lg2yPq7HfM2xKJBKZVPQpMTERDx8+VF2Vi4uLw+DB\nChxaQEAAYmNj9bahdbLy8/Mz0CZ7KquQ1SjX//wL586exZw5c0CkeCOocp9cTjVKz169VTdDScP+\nqjKE2aWtD6Wq95FbWK5WSipkKC2rRG6h4tKfVGSPh4kP1WKY5mRKPBd9sBHv0sBeraSnJqFD+/aq\nRRStmrpCIpEgN/2xKoZpHzY2IlV5mJiAdu08VTfJnZ2dIJFIkPQoURWTt3eaWvnqjZcwPlhxbuTt\nnYb8gzPQrKk7ohf2Rd7eafj7uxAUlUvx5+NnyC4sxe20fHyw9y8sPaX4Ip287Sre3ByHa4+f4YdL\nSXht42XWPVRfBGBIH3nFFari/XI3JCYmIvbqDRARft2zF/0HDVaLYfoZksrkasVV4obAIUEAAKlM\njt9+O4ke/j3V60KHYAAAIABJREFUYiqrFLcmTTEgYDAqZXJIpVJs/2Urho8arRbD5bjykQ3ItpYv\nX4758+er/l1aWgp7e8VnUiKRIDs7W28bWteb/O9//zNDiuYVEeGjWe9j+aq1EIvFZm+fC2ZXvwEB\nCFu6CPfv3kHTXn745ecfUV6m/S9tgQ1omR4MqUMAouJzsP+vdGQVluONLs2xeIQ33t51EwDw61vd\nIZMTHmYX47e7Wbz0UF1MGY2mKjryPL5bvxYnTp3TG/vDd+FY8e1SeLTzxM49B7XG8fFcYltsPiJ0\n+PBhdOnSBa1bt9a4nzQ/PVVDrK4GjI+Px5AhQ1RvBFbeRH3zzTfx73//GwUFBYza27blJ/j4dESf\nvv3YSJcTZpeXjy+WLF+NGf8NRa9evdDB2xfOjRqbLae6yga01Jy+u5SMzMJyEIADf6XDxVGM/3u5\nGQDgzc3X8MZPV/H4WSmm92vLWw9VVZXRmJeXh88WLcXUiWMN/kJiosOHD2P62//BvoNH4ePbUW/8\nezM+RE5ODt77YBaCAvujtLRUYxwfzyW2xeZr7aOjo3H+/HmMHz8e+/btw/fff4/69eurxiAzM9Mg\n0hFrk5UmNuDevXvh4uKC/fv3Y8SIEbh27RqjNk8eP4qTx4/C26MlmjVrhrQnqQgc0BuXLkSZJWeu\nmF3jJobi/B9/4vr16/Dp2Ak+HbWDggU2oGV6MLROUycHtTo2NiJ0b90IAFAulUNOwKXEXPi1dOat\nh6piymg0VlHnz2HWrFk4fOIUunXvoTP2wf17iI5U/PISiUR4c/wEFBY+13rfio/nEttiE7e0du1a\nHDhwAHv37sW4cePw/vvvo2/fvjh9+jQA4MyZM+jfv7/+HM3iVIM0sQGjoqJUDxqHhISobrAZqn2H\njiMhJR0PktKQkZGBlq1aI/LiZfQfGGCWnLlgdiU9SkTQgJ4oKMhHZWUl1q8Ow/iJoWbLqa6yAfmc\nk7ZxtREBYWN80eifpz+Hd2yCrMJypOWXqfYDQM+2LkjOrfkrgA8eqospo9EYlZSU4L13puHgwYPw\n8fHVG5+Tk43p/30L6U8Vr2W5HPs7pJWVeMmjncZ4Pp5LbItrkO3MmTNx+PBhTJo0Cfn5+RgzZoze\nOqw9I62JDZiWloaLFy9ixYoVcHNzw6JFi1SvC2FDfGOCAYBHO08EDR+FoP7+sLO1wajXx2GcjslK\nYAMy88DHnLSNq5yAY7czsXpsJxABOcUVWHIqHs+KK/FGl+b4eXIXyImQll+GddGPeOmhupgyGgHm\nn6ETx44gJycbkydPRtU1UqfORqFJ06Y14l/tNwBzP/kUY0YFAySHnb0Dfv5lJ5yda/5areqZT+eS\ntWjmzJmq/79161ZGdbWyAc2lqmzAYcOGYebMmRg5ciS+//57FBYWYt68eVrryuQEWwEOKEiQIEGs\nav3vSSbVn/mqh5ky0S5O6VNubm7w9/cHAPTr1w/r16/XGV9YJte6r3F9W+SXqMMD7XU86l9bTDBd\nDLuWLg5Iy6tJr5BUu4+hLydtsmY2oCXlpCu++nJ0pU7P6I3g72ruO/Jubw3RtetB2zmujdHYyFHz\nSl5tnyG5lr+nGzrYoKhc83eETMvntJGjLQpKa0JHHcS2GuNrc1y5ZAPacIRMMkWcsgEHDBiAS5cu\nAQDu3LkDDw/2Z2NBggQJEqRbbK4GNJdYm7tv376N5cuXIy0tDXZ2djh9+jRWrlyJr7/+Gvv370f9\n+vWNfguxIEGCBAkynyzhbgtrv6w6d+6MiIgIREZG4syZM4iIiEDTpk0RHh6OX3/9FT///DPc3Jiv\nELoYHYmBff3h5eWF10cFI80AWCnbgEkl6POVTt4G9fH7xSgMG9QbXl5emPj6CFaAq3wD2QLMYKXG\neuAbXJfpuOY+uIbYZf+Gl5cXrq3/EGV5NR8GthWpg2+V3zN2Nvrht1x4MAYozOQzeiEqEv1694CX\nlxdGjzAMxltZWYnP538MkUhkMLyXb+dSnZeR3ENOlFcsVStPsgrIzc2domOuEBHRshVrKGjYCNX+\n6uBIQwCTpgAsq/Zx/fp1Ki6X1+jjybMytRKfmksSN3f6LSqWiIi+/HYVDQ4arhZjbSBbprBSUzzU\nJlzXmHEN2hCrKoGrIknc0IV6z9tGRETeb84ht0591WLKKkkNflshJZLJFZ8XWZXtMjlRuZQbD0yB\nwkw/o4VlMlXJyH1Obu7udCn2KhERha1aS8HDR6jFFJbJKL9EqlaGBg2jTz5dQADoTnxyjf18PJe4\n1MbYZJMKF+L1ZFX94O0/dJT8e/ZSHcjsvEISi8WU9ey5xpOC7fiqdZT51nZOfPFQfVIvLpdT5IXf\niYgIAMUnPtb6R4MpHqx9XCevjqK4+CxynridnCdup65zDlFeUTkREc3eHKva/tHPl+nAH0m89MBF\nvKZzr7hcrvHcM/X8YyueS226nGxS4UKcLrAwVXwAqPI9Jz56UKpX7z469/PZA19yupqQA48mTvBt\npXg+8bWebRF9Ox0AkJRZpIpLyipEhxbqzxHxxQOX8UoZeu7x2QObYpNgYS5Z1IuT+QiY5FtOfPTA\nVHz0wJecMvJL8dWeG4j5diSKyipRXCbFyCVnMKZXW5RVvliSXVohQwMH9Y83XzxwGW+MrMEDU3G1\nos8UsTpZxcfH4/3338fUqVMxZcoUfPjhh8jLywMA5Ofno0uXLliyZInB7fERMMm3nPjogan46IEv\nOfm1dcHHY17GK7MP4UluCca/6oFfP1bgxupVeVbI0d4ORdUe4OGLBy7jjZE1eLBGcQqyDQ8PR0RE\nBCIiItC5c2eMGzeOUZt8BEzyLSc+emAqPnrgS04DOzdHXEI2nuSWAAAOXk5WXRJs1+zFy0g9mznh\nQZr6Ww344oHLeGNkDR6YysbEwlWOrEgTyFapR48eobCwkPELHvkImLQmCCxbHpiqroBsjekjIf05\nenVwh0tDxYvrgrq0REaeYuKaGtgB9R3s0MDBDlMDO2B/bDIvPXAZb4yswQNTsf2mYLOI7RUc4eHh\nFBERobZt0aJFFBsbq7euckluVUVFRZGfnx95enpScHAwpaen62yD7Xg+5sRHDxkZGeTt7U3e3t4E\ngDw9Pcnb25uePHliMR74mBNT8dED3849PnpgW79cfWxS4UKcgmwBoKKiAmPHjsWxY8f01tXF2LIk\n/pul5GROD0wZikwZjcbkZCnxuuo0/VeExviCXaFoNKnmvsztmon+1n5+19b5Z6lswB3XTXsoeUr3\nVmbKRLs4X7p+9epVxpf/BAkSJEhQ3RbnS9dv3boFHx8frrsVJEiQIEFaZAEr19n7ZXX79m2Ehobi\n0KFD2L59O0JDQ5Gfn4/s7GxIJBKj2+Ujs4ttrh4fPRw6eAC9uneBj48PAgf2w53bt/W2zwWj0dLH\nlWkf0oy7KDr1Bby8vFAcuRzykmc1YrSxBJUS/8MUrC0PXByHyspKzP9kLmtswOPHjqJX9y7w9fU1\n+PPAJzagJVDXLQq3xBdml6Y6bHH1+OjhQWIKubm50f2HCszKilVrqXsPf624GzYYjdY4rob0ocQp\nOU/cTk7jfiKRgxM1CP6KiIgcuk0huxavqMXoYgmWVir4gXK5OlfQGs7v6jiloODh9OnnXxiEW2Ka\n08PkJ9S4cWP68+YdIiJaG/4d9e7T12QPXGrXn09MKlzIoiYrvjC7NNVR5lvbObHhoTr0835iKh06\n9hvll0iJiOiPq3+Rs7OzVjAolx4saVy5yCnkm1N0+V461Rv9A9Ub/QN1nr6L8grLiIjI5c2f6O7j\nXPrv2ki68HeaKoZvHoyJZ5sNWBWa++hxOh08eoIKy2RERBR79QY1atSoBlyXqQcutfvPJyYVLiSw\nAU2I52NOXHho1rw5AgYPBQBIpVLs2vELho8abbb2reE48CWnK/GZ8GjWCB3buAAAxvRph8ibiktO\nn0/ojl1RCUjJKuS1B1PilWKTDejepAmGBg1T/fvM6VPo4d/TbO0LUkhgA5oQz8ecuPCg1A/fhWPF\nt0vh0c4TO/ccNFv71nAc+JJT+rMSLNoRh7i141BYWomSskoM/fwo3njVE0O6tka/jw+ij28zXnsw\nJd4YmdLH+fPn8d36tThx6hwr7bMlzh7sNUGs/rKKj4/HkCFDsGPHDgCKZesTJ05EaGgo3n33XRQU\nFOhpQV18ZHbxLScuPCj13owPkZOTg/c+mIWgwP4oLS21GA91JadXPCSYN64bOr6zCy0mb8WC7XHY\n/7niV8Dcn2Iglcm15s8XD6bEGyNj+zh29DCmTp2KfQePwse3o9nbZ1MiEwsX4pQN+O233+Lrr79G\nREQEunbtij179jBqk4/MLr7lxIWHB/fvITpS8ZejSCTCm+MnoLDwORLiH1iMh7qSU8ArrXD5fiZS\ncxSvD9kfk4iObVwBADs+CULStn9h9/wg9PZpiivrarI6+eDBlHhjZEwfUefPYd7cOThz5gy6de9h\n9vbZliXgljhlA7q4uCA/Px+A4gC5uLgwapOPzC5rYKcx9ZCTk43p/30L6U+fAgAux/4OaWUlXvJo\nV+seLHlc2cgpPi0fvX2awtXJAQAwrHsbpD9TXG7ymLodHlO3Y8KyM7h8PxM9Z+3jpQdT4o0R0z5K\nSkrw3jvTsHPPfvj6+pq9fS5kCSBbTtmADx8+pN69e1NQUBCNGzeOKit1L3kR2ID89bBhwwby9fUl\nb29v8vPzoxMnTlich7qaE1PxzQPf2IC7du0iBwcHVR/KkpGRYTYPbOvAX09NKlyIUzbg1KlTMXPm\nTHTv3h3Lly9H8+bN8a9//UtrXYENyI/48iov9auuRo62KCitud9BrPkpU2Fcue3DZeyPGuNLj0yH\n42ua9+UdmG5yPsbUsSQ2oLZ7fQ0dbFBUrnmfnW3N3yB8YQMe+jvDpPqv+2lfpGMucbp0/cGDB+je\nvTsAoG/fvrit5ylvQYIECRLEvur0AgtNcnNzw8N/niW4desW2rZty2X3ggQJEiRIgywBt8QpG/DL\nL7/EggULEBoairt37yI0VPPrC3SJj9wxttlpxnD42PZwIToSA/r4w8vLC2NGBZud9SewAdmJl2Xf\nR/mFb+Dl5YWKP9aBSvNqxGhjCSoZgva2gJ2Obw6+Hgc22ZQXoiLRr3cPeHl5YfQIdtiXbMoGIpMK\nJ+LkzpiRqo5a4Qt3TFMdtthphnD42PZQHbeUll1Abu7uFP37FSIiWrZyDQUPG6EVt8TlcahL54Yh\n8UqEUr3RP5DDiLUE+4ZkP+BTIiKy6zyObJp2Vovxe+9XrSxBqezFdplcsY+vx4FtNmVVjFJG7nNy\nc3enS7FXiYgobNVaCh4+QituiY9swKN/Z5hUuJBFTVZ84Y5pqqPM19x9PHr8lI7/dkYVf+3GLXJ2\ndtabjzk9VJ+sft13iHr491SxAZ9k5ZNYLKbUzDyBDcjhuWFIfNWJSNzzPRK5vET1Rv9AREQOI9YQ\nRLbkMGKNKibkm1NqUFslCJdIfXKqlCkKX49D1Ylo34Ej5N+zFxWXK4xk5j4nsVhMGTkFZmED7jlw\nmHr07KViA6bnFJBYLKan2fkWwwY8divDpMKFBDagCfFs9VFeKVMVV7cm6DcwEOWVMkilUmzbtgXD\nR41Wi2Hbg4PYVq0kJyXC07O9asWfxKURJBIJnqQkqWLYHiNT69SVnPIOTFeVr970xPjgvqrVfvlH\nZqNZU3dELx6oitn2v2Dk5Bfj1Xd/gkvwt1i6LRqHLt4DAERdf4Tm/7cCbd9YjZvx6Ri3YC9cgr/l\nnWdAsbpPWR4mJqBdO0/Vij9nZydIJBIkPUpUxTDtw87WRlUeJT6EZztP1Wq/xo2cIZFIkJL0SC3O\nWM9cSGTif1xIYAOaEM9VHwB7HD4uPFjDcbCGnAyNX7TlAuI2TUNhSbmCJThnJ94Y6AuxnQ1SD86C\n2NYGR2LicSoukbce+JwTP9mAtda1weKUDZiYmIjJkydjypQpWLBgAaRSZg9u8JE7xhXbjC0OHxce\nrOE4WENOhsSLAMyb1Bcdp/yAFmPWYsHmaOxf+iYAIDkjH81fW4PmY9agfj0xPgrpxUsPfM+Jj2xA\nS1hgwSkbcOXKlXjnnXewY8cONG/eHL/99hujNvnIHWO7D7Y5fFx4sIbjYA05GRJvIwIu30lDatZz\nAMD+qHvo+JK74v9H34NUJkdpuRQnYhPQz68NLz3wPSc+sgEtQZyyAVNSUuDn5wcA6N+/P37//XdG\nbfKRO8Y2O41tDh8XHgQ2ID9yMiSeAPTu1BKuzo4AgGG9PJGeq4DgjujdHoDiflCQfzvcTc7mpQe+\n58RHNqAlPGfFKRtw1qxZdOjQISJSsOWmTZums66m1UCnz0XRyy8rmFpDg4IpKTVd62osLuKVdfz8\n/Kidmfqovvpuxepw8vZRcPg6vexHew8e1bpMnC8eaus48DEnSxvXJdsuUnxqLj14nEPX7j2lwA+3\nExHRidgESkjNpYTUXPr17G1yH7WS6gV+w0sPfM/JkHgudfpulkmFC3HKBkxPT8fixYtRWlqKnj17\n4saNG/j555+11pWT4rKEIEGCBAliT2fv5ZhUf6ivm5ky0S5OVwM2b94cGzduBABcunQJWVlZOuMr\ntPNTeQcfNSfoUxs4VoDG8qMPa/dQfTk6AJSe/xSOg2tuB4C805+ynpM54vmYE19Atpbwo4DT56zC\nw8MRHR0NADh48CACAwO57F6QIEGCBFmoOGUDDhw4EBs2bMDYsWPRpEkTDBo0iHG7lZWVmPe/uRCJ\nRLzgxSlzmjt3LhzF7OTElMOn1IkTJ+AoFiElOdmgnNhk2AHMx+n4saPo0qULurzsywoTkY9sQLY5\nkMZ4kD1LRLdu3VAeuwoVN7aAygrU9ttUYQkqi/JXgV0VnqCuv97r4rnB1DObsoSHgi0Kt1RaSRQ8\nbDh9tmAhAaCEpFSdNzG54L8pc1q40Hw5mcLhU5bcgmLq3Lkzubq60v2EJINyYothZ8w4PUx+Qo0b\nN6Y7d+5QaSXR2vDvqHefvrV2rLlgA7LNgTQ0vl7gN6riMHAxQdyArl+/TvUCvyG7DqPIRuKtFlO9\nfrlUwREkUucJyuUKfJNwbhjmmUtF3s8xqXAhi5usoi7+QaWVZNBJzQX/TZkTkflyMoXDpyxz5v6P\nli9fTm3attU7WbHNsDNmnFLSMunI8d9UOV25fpMaNWpUa8eaCzYg2xxIQ+OrTkRiv1ASObcmIsV2\nhwGLFDzBAYu0TlayfyYlIsXEpdxeKdMMv62L54YhnrlU1P1ckwoX4jUbUC6nGqVnr96qt4BStX3V\nxQZ3TFtOSpkjJ6YcvrIKmVq5/udfOHf2LObMmQMixYKNqvv5OE5SmVytuErcEDgkCIDiray//XYS\nPfx7qvZz4cGUeEPrcMmBNDQ+7/SnqvLVlFcwfkR/1fb884sVPMGVr6limr+1U1W+2HkVv15KRLO3\ndgIA/r0uWrXv+9/uYlvkA9bPjernB1/ODSaetb3pmC3ZiEwrnOTITTe1Iz4yu9jOiYjw0az3sXzV\nWojFYl7kZKqiI8/ju/VrsWzFaq0xfOS5mcKBbNq0KWJ/j8GXSzSvwOOjB5EImDHcBxtO3lNtmzbE\nCw5iG7SS1MfI7q20rlw1VpZ6bghiLlYXR4aFheH69euQSqV499138fLLL+OTTz6BTCaDu7s7VqxY\nAXt7e9b65yOzi+2ctm35CT4+HdGnbz/e5GSKDh8+jA8+mIl9B4/Cx7ej1jg+8txM4UDOmzsbW7bv\nQlBgf8T9eQuOjo6899CzvTuKy6S4n/ZiAUZ6Xglivh6JpKxCnPv7KSqkNX8VGytLPjf4Js4WSZgg\n1n5ZXb58GQkJCdizZw82b96Mb775BuHh4Zg0aRJ27dqFtm3bYv/+/Wx1D4CfzC62czp5/ChOHj8K\nb4+WaNasGdKepCJwQG9cuhBVazkZq6jz5zBr1iwcPnEK3br30BnLR54b3ziQbHsI7toSZ28+Vdv2\n4eY4+H9yDONXRqOBgxh3U/O19sVEln5u8E2WgFtibbLy9/fHunXrAADOzs4oLS1FXFwcBg8eDAAI\nCAhAbGwsW90D4Cezi+2c9h06joSUdDxISkNGRgZatmqNyIuX0X9ggN6c2GLYGaOSkhK89840HDx4\nED4+vnrj+chzYzqubHMg2fbQuU1jPHiqvqx9ycRuAADvFs4Y1KkZTv5p+hJtazg3+CaRiYULsXYZ\n0NbWFvXr1wcA7N+/HwMGDEBMTIzqsp9EIkF2dk0Qpi5lZmZi2JBBqn8PGxoAOzs7nDh1Di1atqwR\n7+joiO07d2POhzNQUlKMdp7tsennbVrbZxpfNSflXxd8yImplH3MmDEDRcXF8OTBOJ04dgQ5OdmY\nPHkyqt5rPnU2Ck2aNjU5Jy6OA9NxfbXfAMz95FOMGRUMkBx29g74+ZedcHZ2tggPLVzqI6tA/XLY\nKy+54Maq0SitkGH6xj/wvKSyRr26eG4w9cy2bCzghVasswHPnTuHjRs3YsuWLQgKClL9mkpJScG8\nefOwe/durXXlcqrxFk9BggQJEmRexT407fJsn/aNzZSJdrG6wOLSpUv48ccfsXnzZjg5OaF+/foo\nKytDvXr1kJmZqfb6EE1SMLM0z6X17UUoqVDfp2tiMxezS9uSUk35cJWTpuXoANC4vi3yS2ruq2dv\nHpagrjpMx0mu5W+mhg42KCqveVO+6mvCDc2Jj/GWwoHUFd/8n2Xq1ZUXMRkuoTX3pf08SWO8uc4N\nQPv5UZvjpOkzoc2zch9XsoSfBKzdsyosLERYWBg2btyIxo0Vs27fvn1x+vRpAMCZM2fQv39/troX\nJEiQIEGGygJuWrH2y+rkyZPIy8vD7NmzVduWLVuGBQsWYM+ePWjRogXGjBnDVveCBAkSJMhA1eml\n6yEhIYiJiUFERISqtGzZElu3bsWuXbuwcuVKgx9araroqEj07dUdXl5eGDU8SC/UlUtY6SudvHmR\n08XoSAzsqwDfvj4qGGlp5geuGuOByXG7EBWJfr17wMvLC6NH6I83NicuoMVMxpUptJht8K0xHioz\n7qDwxOfw8vJC0bllkBfn1oixtQHqiRXFwe7FH+di2xfbtT0/bMy5wTcANtPPA9uyhKXrvGYDFpfL\n1UrWs0Jyc3enmMvXiEgB+hw2fKRqf3W2FhtASm05Xb9+nYrL5bWSU16xVFWeZBWQm5s7Rcf8A75d\nsYaCho1Qi9HWhzlBtkyOW3G5nArLZKqSkfuc3Nzd6VLsVSIiClu1loKHj1CL4WJcNRVjoMW6xtUU\naDHb4FtDPTSeskNVGk3YTCIHZ2o4fAkRETn2CCW7ll3UYkrK5SSXy6nkn2NfXiknqUxORERS2Ytz\nQiqTU1kl83ND0/nBBwA2k89DcbncPF+iBupKYr5JhQvxerIyFRjJR1ip4IFdD+bso/qXR3G5nCIv\n/E5ECvhofOJjtX1cjGvViet+YiodOvabCnL8x9W/yNnZWevkxpdzY0LYebr8IJPqv7mF6r+5hfxm\n7qe8ojIiIury4QHV9vBjt2jD8dtGedB03IrL5RqPW/Vjx5fzm0tZwmRlUWxAvgAp+ZyT4IG9PgCg\nV+8+OvdzmVOz5s0RMHgoAEAqlWLXjl8wfNRos+XDlocrCVlo19QJHVsrFl6N6dUWkX8rHoSO/+eh\nYlsbEQL9WuJqgvqzmNZw3Iz1wKosYIGFRU1WfARS8i0nwQN7fTAVVzmxBb5ly0NGXikW7bqO2BWv\n4cnWSXhnmC8W7ryuVmft232QlluMA7HJJntgKj6e32zLEl6+yOpkFRYWhpCQEIwdOxZnzpwBAGzf\nvh2dOnUy6sDwEUjJt5wED+z1wVRc5fTejA+Rk5OD9z6YhaDA/igtLTVb+2x4eOUlV3wy9hV0+mA/\nWr21Cwt3XsPeeQoMm62NCD990B+tJA0wcWVkjWeTrOG48RF8y8UCi+rzQXp6OkJDQzFp0iTMmjUL\nFRUVOutzCrI9fPgwcnNz9T4MrE18BFLyLSfBA3t9MBXbObENvmXLw6CXWyDuQRae5Cj+YN3/RxI6\ntnYBAHw3/VXUs7fFuOXnND7sbg3HjY/gW7avApoDbM4pyHbw4MGYM2cOREaudeQjkJJtCKzggZkH\nPsFH2R5XtsG3bHmIf1qAXt5N4NrQAQAQ3LU1MvJKAAA+rRrjrXUXIJVppjpYA2zaGsC3TGUWsDkX\nqzh2795NH3/8serfAQEBVFRUpLeeTMPqzaioKPLz8yNPT08KDg6m9PR0nW2wHc/HnAQP7PSRkZFB\n3t7e5O3tTQDI09OTvL296cmTJ7WW04YNG8jX15e8vb3Jz8+PTpw4Ydb2ufDAVNZw3NgeI6a6nlxg\nUmEi5XzQu3dv1baUlBQKCQnRWY9TkK2TkxMAIDAwEMeOHdP7l4QuJpcl8d8sJSdr8GDOPszFgTSn\nB0thCeqqI5m4VWN88b630GBczX25v77FOKfaOnbmjK/HKrlVXTdSCk2q37Wtk0FxpoDNOQXZChIk\nSJAg/okLCoWpYHNOQbaCBAkSJIh/YnuBhTnA5qxNVlVBtqGhoQgNDcWGDRsQGhqK7OxsvP322wgL\nC2PcLlMG1/ZtW9HVryN8fX0xcthQJMTHm7V9pU6cOAFHsQgpyck641KSk+HkKMYrnX3g4+ODVzr7\nYNrUf5k9J7YZdmznZCzDjm88N7bHlSlLkAs2IFNeofTpDZSeXQRfX1+URX0DeYF6+zYiwN5WvSgv\nkdnZvNim69V3hw8dQG//rvDx8cGQgP64c8e8DEVjxvX4saPo0qULurzsa9A4WbI0zQfTp0/H4cOH\nMWnSJOTn5+sHmxtxL44zVceUMGV2/XXrHkkkEnqYrLiRGr7hB+rT91Wt8cby4nILiqlz587k6upK\n9xOSdMbeT0iiNm3bqnAq+trmC8OOy5xMYdixxYE0hOfGxbiawhJkiw1YtRjCK1TilOq/uYXqjVxF\nENenekHiCCsqAAAgAElEQVQKlqC46xSykbRXi6neR7mUSCpTfH6kshfb5XKiMg3H7n5CMrm5udG9\n+CQiIgpbuYa69/DXeuy44Ew+TH5CjRs3pjt37lBpJdHa8O+od5++ajFc6q/Hz00qXMiiJit9TK2n\n+eVqZfP2PdTdvxc9zS8nIqJL126Rs3MjtRgm7ZdWqkNjlWXm7Lm0fPlyat2mLd28+1BtX05RpVr5\n804CtW7TlnKKFJ1U359TVMk4Jz4y7EzJiS9sQHPlZM5x5aMHprzCqsDZR4/T6eDRE1RYpph9Yq/e\noEaNGqnFVIXgNn1rFyU8LaAuHx0mIqJxKyJV+zaffUDzI65S4yk7OGUoGhqvyTeRYrsm31zq5uNC\nkwoXsijcElOmVjf/nkhOeoT7d++AiHDy6GEMCBhstvYB4M7tW4iOPI85c+YY7KOw8DlCJ4yFj48P\nxo8Zifj798yaE8AvFhrTnPjCsLO2nLjwwJRX6N6kCYYGDVP9+8zpU+jh31NrfOjA9ohLyEZyVhEA\nBfFCqeJyKdo1rbmQi22GojHjytQ327KEV4RY1GTFlKnVrHkLzF/4FYb294erqyu2bf4Rny1earb2\niQgfzXofy1etNfjdXA0bOmHsuAn4evkq3L17FwMDh2DKhLGQSjWvX7UGFhrb+RhTxxp4hXz0oJSh\nvMKqOn/+PL5bvxbLVqzWuF8kAmYM98GGky/+uJs2xAsOYhu0ktTHyO6ttC7VZ5IT15+H6EjdvrmQ\nBXBsuWUDpqenY+rUqZgyZQqmTp2K7Oxs/Y1UEVOm1q2bfyF85TLE/nUfeXl5+GzRUkydOBak5dEy\npu1v2/ITfHw6ok/ffgZ7cJVIsHx1ONq0fQk2NjZ4f+ZsZGdlIjFB88IPa2ChsZ2PMXWsgVfIRw9K\nGcorVOrY0cOYOnUq9h08Ch/fjhpjerZ3R3GZFPfTClTb0vNKEPP1SKx+qyfO/f0UBSXa+XJsMRRN\n+TwcPnwY09/+j07fghTilA24du1ajB8/Hjt27MDQoUOxdavmhwO1iSlTK+ZiJHr06oNWrdsAAEa/\nMQ7x9+/hWW6OWdo/efwoTh4/Cm+PlmjWrBnSnqQicEBvXLoQpdVDfl4eUpKT1LbJZTLYafllZg0s\nNLbzMaaONfAK+eiBKa8QAKLOn8O8uXNw5swZdOveQ2tccNeWOHvzqdq2DzfHwf+TYxi/MhoNHMS4\nm5pvck5cfR6izp/DrFmzcPjEKZ2+OZEF/LTilA24aNEiBAcHAwBcXFyQn1/zxNIlpkwtz/ZeuBYX\ni2fPFK/VPn/mNzRp2gyuEjeztL/v0HEkpKTjQVIaMjIy0LJVa0RevIz+AwO0erjx5zW8PjIIOf/8\nqty+dTNatmpjVp4bU7HNsDM2Hz6xAfnC1eO7B6a8wpKSErz3zjTs3LMfvr6+WvMAgM5tGuPB0wK1\nbUsmdgMAeLdwxqBOzXDyz5pLxtlmKBozrkrfBw8ehI+Pbt9cyBJeEcIawcLW1hb169cHAOzfvx8D\nBgxQ/Vsmk2HXrl2YMWMGozYdHR2xfeduzPlwBkpKitHOsz02/bxNa3zQ8FH4+68bGD10AOxsbeDY\nwAkbt+3SCtJl2r4xChg8FG+9PR0jhg6E2NYGTZq1wLade2Brq/lauzE5ZWZmYtiQQaobn8OGBsDO\nzg4nTp1Di5YttfYxY8YMFBUXw1NPH2znZEz7TOuwHV+1DlvjykcPr/YbgLmffIoxo4IBksPO3gE/\n/7ITzs7OGuNPHDuCnJxsTJsaChsRoKQknTobhSZNm6rFtnCpj6wC9cttr7zkghurRqO0QobpG//A\n85JKk3PiYlyVvidPnoyqZChNvrkQV4skTBHnbECZTIZPPvkEHh4e+OCDD3TWlZPuB/0ECRIkSJDp\nuvfUtMVRvi3YJ8Zzzgb89NNP0bZtW70TFQBoeJ2NSpoAkHnF2m+uNm9kj/SCmvtdGtgb3D4Aje/Y\nAYDG9W2RX1Jzn0zH3wKSBnbILa7ZSQMHzYfFkoCrfMzJUuL5mJOueKZwXVstx7qhgw2KyuU1trf+\n768a4/MiJsMldKfGfcmbJjDKiQvgr1RW05s2z8p9gl6ItclKyYLatm2bigV19OhRiMVifPjhh2x1\nK0iQIEGCmMoCrmBxygZct24d7t69q/r34sWLGbfLlMEVcyEKQQN6wcvLCyFjhuNpmvkZXxejFey0\nHq/44vVRwUjT08fF6CgEvKrguY39v2Fmz4mPDDumOfGRDciUeafsg+1xZZthx9QDU17hhahI9Ovd\nA15eXhg9Qv+5UZlxB4UnPoeXlxeKzi2DvDi3RoydjeKXSdXSyFHx68lRLEJDBxs4OdhAbKv5W5rt\ncWXqmW1ZwgILi8It6WNwVcctPUx7RhI3dzoVfZmIiL5atoqGBA/XilsyhPFVHbX0JKuA3NwU7LS8\nYiktW7GGgoaN0IpbSsnMJzc3dzofE0dERN+Eraahw0ZoxS1ZKsPOlJz4wgasWgxh3nExrlzGG+qB\nKa+wKlIoI/c5ubm706XYq0REFLZqLQUPH6EVt9RowmYSOThTw+EKlqBjj1Cya9lFLaY6bim/RErF\n5TKqkCre5lpeKaP8EikVlEhJJpdTQalpDEVD45l45hq3dD+92KTChSxqsmLKBtz26wHq1qOnig2Y\n8CSXxGIxxafmmI0NuGvfIerh35OIFPtSM/NJLBbT44w8jZPVjr2HqLt/TxUbMDkjj8RiMSWlPzML\nG3DfgSPk37MXFZcrPpiZuc9JLBZTRk5BrbEBmebERzbgo8dP6fhvZ1Tx127cImdnZ51f9HWRDfjr\nP58HJYfvSZbi85Camadxstpz4DD16NlL9eWcnlNAYrGYnmbna5ysGgz6iGzdPKnxlB1ERNQoZDPB\nxpYahfykc7KSyuT0vFSRU1HZi+1llTIqKZdxMq5MPHM9WT1ILzapcCGLuoOnj8Hl0sBeraSnJqFD\n+/aqRRStmrpCIpEgN/2xKoZJ+wBQz95WraQkJcLTs71qn5trI0gkEqQ9TkI9e1s0cLBTK6nJiWjv\n2V61iKKppDEkEgkyUpNVMUxzsrERqcrDxAS0a+epWrDg7OwEiUSCpEeJqhim42pMvCk58YXDV14p\nUxVXtyboNzAQ5ZUySKVSbNu2BcNHjVaL4SInLuMNreMgtlWV5H8+D8oFCxIXxefhSUqSKsbO1kZV\nHiU+hGc7T9jZKr6KGjdyhkQiQUrSI1VM+tbJqrJgRAu8OaQX0rdOBgBk7JiGZk3ccWpud7W4l97Z\nrSpL9/6J3TGP0OZtxVto//vdJdW+jafvY8eFBE7GlYln5T5BL2RRI2IN7DS+xfMxJz56UIoJ845v\n48THcWU73hieIN88cCILIFiwunQ9LCwM169fh1Qqxbvvvgt3d3eEhYXBzs4O9vb2WLFiBVxdXQ1u\nzxrYaXyL52NOfPSg1HszPsS8ubOxZfsuBAX2R9yft+Do6FgrOVnDuLIdr4snmJRViHN/P0WFVH3p\nON88cCHOFkmYIE7ZgFu3bkVYWBgiIiLQtWtX7N27l1Gb1sBO41s8H3PiowdjmHd8Gyc+jivb8cbw\nBPnmgQvV6VeEaGIDrlmzBq1btwYRITMzE82aNWPUpjWw0wQP7Hpgqw+mfLmqfVjDuFqqB2N4gnzz\nwIUs4CogN0vXd+/eTR9//DEREV24cIGCgoJo+vTpJJPpXvGiaeXN6XNR9PLLfuTp6UlDg4IpKTVd\n54ostuOVdfz8/KgdT3Kqyx7M2Uf1FWUrVoeTt48veXt7U6eX/WjvwaNa3zZrbeNqaR6UqwJvpzyj\nN5afV/2biOjinXRKynxOd1PzaOTSM9R4yg5eeuBSDzNLTCpciHM24D8TJFauXAknJydMnz5da12B\nDShIkCBB7CsxW/f7xvTJ013zfVtzilM24NmzZzF06FCIRCIEBwdj/fr1OuszZQPqEt/i+ZiTNXgw\nZx9MmXfm4ssZU4dv8bWdU/O3NDMDtfEElUvh2cyJaXw9Vr+d1VWnF1go2YAbN25UsQHXr1+Pe/cU\nS0hv3rwJDw8PtroXJEiQIEEGyhIWWLA2d1dlAyr1xRdf4Msvv4StrS3q1auHsLAwtroXJEiQIEEG\niv+/q1j8ZRUSEoKYmBhERESoir+/P3bv3o2dO3fi559/hkQiYdyuNYA+mfZRWVmJef+bC5FIxArQ\nlQsPXB0HVuGjDAGtyj6sYVwt3YM++K2NCLC3VS/Ky3B2Ni+2abuHzoWHOi9OlnEYqeqrYywV9Glq\nH8HDhtNnCxYSAEpISjUoHz554PI4mLMPpoBWax5XS/PAFH5bvX65lEj6z2JlqezFdrmcqIwjD1wq\nKafUpMKFLGqyslTQJ9M+qkJgi8vlFHnhdyoulxMAik98XGM/Hz3U1nHgY07WMK7W4EFXnaoTV9O3\ndlHC0wLq8tFhIiIatyJStW/z2Qc0P+IqJx64VHJOmUmFC1kUG9BSQZ+m9tGrdx+t+yzBgzUcB2vI\nSfBgWJ3Qge0Rl5CN5KwiAOpvNi4ul6JdUyfOPbAtS1hgYVGTFR8Bk3yDWPLRgzUcB2vISfCgvw4f\nwbdcyBIIFqxOVmFhYQgJCcHYsWNx5swZ1fZLly7B29ubcXt8BEzyDWLJRw/WcBysISfBg/46usC3\nq9/qiXN/P0VBSQXnHgRxDLIFgPLycmzatAnu7u6M2+QjYJJvEEs+erCG42ANOQke9NfhI/iWC9Xp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X/iRMnmDJlCqampqxYsYIDBw6QkZFBYWGhuq+bxm3y5Mnqa+3BgwfEx8dTV1eH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\nAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b5371a58>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"text","id":"jJZ6b20nnWsd"},"cell_type":"markdown","source":["# **Confusion Matrix για τον MLPC**"]},{"metadata":{"colab_type":"code","id":"_8L2yNmInWsd","colab":{"base_uri":"https://localhost:8080/","height":1863},"outputId":"9b55ebb6-fb61-45c0-986d-f628d8b4bf7d","executionInfo":{"status":"ok","timestamp":1544694611131,"user_tz":-120,"elapsed":63392,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["from sklearn.neural_network import MLPClassifier\n","mlp = MLPClassifier()\n","model_mlp = mlp.fit(train, train_labels)\n","preds = mlp.predict(test)\n","mlp_pred = preds\n","cnf_matrix = confusion_matrix(test_labels, preds)\n","labels = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n","plot_confusion_matrix(cnf_matrix, classes=labels,\n","                      title='Confusion matrix, without normalization')\n","micro = precision_recall_fscore_support(test_labels, preds, average='micro')\n","macro = precision_recall_fscore_support(test_labels, preds, average='macro')\n","print(classification_report(test_labels, preds))\n","micro10 = micro[2]\n","macro10 = macro[2]"],"execution_count":18,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[ 82   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   1   0   0]\n"," [  0  82   0   0   3   0   0   0   0   0   0   0   0   0   0   2   0   0\n","    0   0   0   5   0   0   0   0]\n"," [  0   0  94   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   1   0  72   2   0   0   0   0   0   0   0   0   0   0   1   0   0\n","    0   0   0   2   0   0   0   1]\n"," [  0   8   0   3  75   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   1   0   0   0   0]\n"," [  0   0   0   0   0  91   0   0   0   0   0   0   0   0   0   1   0   0\n","    2   0   0   0   0   2   0   1]\n"," [  0   0   0   1   1   0  86   0   0   1   0   0   0   0   0   1   0   0\n","    0   2   0   0   0   0   0   0]\n"," [  1   0   0   0   0   0   0  81   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   1   0   0]\n"," [  0   0   0   0   0   0   0   0 103   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   1   0   0  83   1   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   1  87   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0  93   1   0   3   0   0   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   3  84   7   0   0   1   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   1   0   0   0   0   0   4  85   0   0   0   0\n","    0   0   0   0   0   1   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  94   0   0   1\n","    0   0   0   0   0   0   0   0]\n"," [  0   3   0   1   0   0   1   0   0   0   0   0   0   0   0  81   0   0\n","    0   0   0   0   1   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0  81   0\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0  89\n","    0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   4   0   0   0   0   0   0   0   0   0   0   0   0\n","   87   0   0   0   0   0   0   0]\n"," [  0   0   0   1   0   0   1   0   0   0   0   0   0   0   0   5   0   0\n","    0  88   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1   0\n","    0   0  85   0   0   0   0   1]\n"," [  0   3   0   1   0   0   0   0   0   0   0   0   0   0   0   1   0   0\n","    0   0   0 100   0   0   0   3]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0  82   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    1   0   0   0   0  79   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0  85   0]\n"," [  0   0   1   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   5   0   0   0  84]]\n","              precision    recall  f1-score   support\n","\n","         1.0       0.99      0.99      0.99        83\n","         2.0       0.85      0.89      0.87        92\n","         3.0       0.99      0.99      0.99        95\n","         4.0       0.91      0.91      0.91        79\n","         5.0       0.91      0.86      0.89        87\n","         6.0       0.96      0.94      0.95        97\n","         7.0       0.95      0.93      0.94        92\n","         8.0       1.00      0.98      0.99        83\n","         9.0       0.99      1.00      1.00       103\n","        10.0       0.97      0.98      0.97        85\n","        11.0       0.99      0.99      0.99        88\n","        12.0       0.97      0.96      0.96        97\n","        13.0       0.94      0.88      0.91        95\n","        14.0       0.92      0.93      0.93        91\n","        15.0       0.97      0.99      0.98        95\n","        16.0       0.88      0.93      0.91        87\n","        17.0       0.98      0.99      0.98        82\n","        18.0       0.99      0.99      0.99        90\n","        19.0       0.97      0.96      0.96        91\n","        20.0       0.98      0.93      0.95        95\n","        21.0       1.00      0.98      0.99        87\n","        22.0       0.88      0.93      0.90       108\n","        23.0       0.99      1.00      0.99        82\n","        24.0       0.94      0.99      0.96        80\n","        25.0       1.00      1.00      1.00        85\n","        26.0       0.93      0.92      0.93        91\n","\n","   micro avg       0.95      0.95      0.95      2340\n","   macro avg       0.96      0.95      0.95      2340\n","weighted avg       0.95      0.95      0.95      2340\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAbEAAAGACAYAAAAj9ly5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nggft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ZVaDWGnMMXUMwTd5cpUMweI/6NowzbxnnlaW97+PomfJb6j7EgWnsNfyi8p1Wy48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at 0x7f22b0e3a080>"]},"metadata":{"tags":[]}}]},{"metadata":{"colab_type":"code","id":"IR43iBaVnWsg","colab":{"base_uri":"https://localhost:8080/","height":1506},"outputId":"fd11d10e-6664-47e1-cf98-fee5a4fa17a1","executionInfo":{"status":"ok","timestamp":1544694611133,"user_tz":-120,"elapsed":63387,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["index = ['F1 Micro Average Score']\n","microall = pd.DataFrame({'Dc Uniform': micro1, 'Dc Constant 1': micro2, 'Dc Constant 2': micro3, 'Dc Constant 4': micro4, 'Dc Constant 4': micro5, 'Dc Most Frequent': micro6, 'Dc Stratisfied': micro7, 'KNN': micro8, 'GNB': micro9, 'MLPC': micro10}, index=index)\n","import matplotlib.pyplot as plt\n","ax = microall.plot(kind='bar', title =\"\", figsize=(9, 10), legend=True, fontsize=16)\n","plt.show()\n","\n","index = ['F1 Macro Average Score']\n","macroall = pd.DataFrame({'Dc Uniform': macro1, 'Dc Constant 1': macro2, 'Dc Constant 2': macro3, 'Dc Constant 4': macro4, 'Dc Constant 4': macro5, 'Dc Most Frequent': macro6, 'Dc Stratisfied': macro7, 'KNN': macro8, 'GNB': macro9, 'MLPC': macro10}, index=index)\n","import matplotlib.pyplot as plt\n","ax = macroall.plot(kind='bar', title =\"\", figsize=(9, 10), legend=True, fontsize=16)\n","plt.show()"],"execution_count":19,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAiEAAALmCAYAAACU+kSLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nM2JWrPhQGzd+JUlq0+YJ9ezZWxERE1WtmqcOHfpRyck/a8KEqap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AjCBCAACAEdwnBABQ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\nrkJggg==\n","text/plain":["<matplotlib.figure.Figure 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RhAhAADACCIEAAAYQYQAAAAjiBAAAGAE9wkBANxRRu88XKzbmxpS+JNwe/bsooCABpIkJycn9ejR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\n","text/plain":["<matplotlib.figure.Figure at 0x7f22b54bf630>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"QjYmWY7qQ_u_","colab_type":"text"},"cell_type":"markdown","source":["  Όπως είναι αναμενόμενο βλέπουμε από τα παραπάνω πως την καλύτερη επίδοση στο δείγμα μας παρείχε ο **MLP**. Ταυτόχρονα γίνεται εμφανές πως σε ένα τοσο ισορροπημένο δείγμα, ο ταξινομητής **DC_Constant**, είναι καταδικασμένος να αποτύχει. Αυτό γιατί ο αριθμός των κλάσεών μας είναι αρκετά μεγάλος(26), με αποτέλεσμα κάθε φορά να μας επιστρέφει την συχνότητα εμφάνισης της κλάσης, δηλαδή το ακριβές ποσοστό 3,08% που βρήκαμε και αρχικά.\n","Για τον ίδιο λόγο, παρατηρούμε εξίσου χαμηλά ποσοστά στους ταξινομητές **DC_Most_Frequent** και **DC_Stratisfied**. Συγκεκριμένα ο **DC Most Frequent**, προβλέπει πάντα την πιο συχνή κατηγορία στο training set, ενώ ο **DC Stratified** κάνει προβλέψεις διατηρώντας την κατανομή των κλάσεων στο training set. Έτσι από τη στιγμή που έχουμε 26 ομοιόμορφα κατανεμημένες κλάσεις, οι 2 αυτοί ταξινομητές θα έχουν πολύ χαμηλές τιμές τόσο στο Precision(4% όποια κλάση και αν επιλεχθεί) όσο και στο Recall(4%). \n","    Ως τελευταία παρατήρηση θα μπορούσαμε να πούμε πως όσο προχωράμε σε πιο εξελιγμένους ταξινομητές, οι οποίοι χρησιμοποιούν και υπερπαραμέτρους που επιδέχονται βελτιστοποίηση, όπως είναι οι **KNN** και **MLP**, τόσο αυξάνεται ο αρμονικός μέσος της ακρίβειας και της ανάκλησης, δηλαδή το f1-score. Κάτι που είναι και το ζητούμενό μας. Αντιθέτως, ο **GNB**, καθώς δεν έχει υπερπαραμέτρους, δεν παρέχει το ίδιο ποσοστό επιτυχίας όσο οι προηγούμενοι δύο. Το score του όμως είναι εμφανώς ευξ"]},{"metadata":{"colab_type":"text","id":"VZto7-jLnWsj"},"cell_type":"markdown","source":["# **Δ. Βελτιστοποίηση ταξινομητών**"]},{"metadata":{"colab_type":"code","id":"ew_cIUTtnWsl","colab":{"base_uri":"https://localhost:8080/","height":85},"outputId":"6bf9fad9-661f-46f7-98f7-e82bb0176b60","executionInfo":{"status":"ok","timestamp":1544694615098,"user_tz":-120,"elapsed":67345,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["!pip install --upgrade imbalanced-learn"],"execution_count":20,"outputs":[{"output_type":"stream","text":["Requirement already up-to-date: imbalanced-learn in /usr/local/lib/python3.6/dist-packages (0.4.3)\n","Requirement already satisfied, skipping upgrade: scipy>=0.13.3 in /usr/local/lib/python3.6/dist-packages (from imbalanced-learn) (1.1.0)\n","Requirement already satisfied, skipping upgrade: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from imbalanced-learn) (1.15.4)\n","Requirement already satisfied, skipping upgrade: scikit-learn>=0.20 in /usr/local/lib/python3.6/dist-packages (from imbalanced-learn) (0.20.1)\n"],"name":"stdout"}]},{"metadata":{"colab_type":"code","id":"zO3c1NrnnWso","colab":{"base_uri":"https://localhost:8080/","height":1088},"outputId":"6fcd2787-1abd-4baa-f911-5b3847cee255","executionInfo":{"status":"ok","timestamp":1544694615099,"user_tz":-120,"elapsed":67338,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["#from imblearn.pipeline import Pipeline\n","from imblearn.pipeline import Pipeline\n","# φέρνουμε τις γνωστές μας κλάσεις για preprocessing\n","from sklearn.feature_selection import VarianceThreshold\n","from sklearn.preprocessing import StandardScaler # φέρνουμε τον StandarScaler ως transformer που έχει .transform kai ΄όχι ως scale()\n","from imblearn.over_sampling import RandomOverSampler\n","from sklearn.decomposition import PCA\n","train_variance = train.var(axis=0)\n","print(train_variance)\n","print(np.max(train_variance))"],"execution_count":21,"outputs":[{"output_type":"stream","text":["0      0.055709\n","1      0.101463\n","2      0.107125\n","3      0.095576\n","4      0.213445\n","5      0.324920\n","6      0.312418\n","7      0.283852\n","8      0.275785\n","9      0.262447\n","10     0.228212\n","11     0.197342\n","12     0.201948\n","13     0.226857\n","14     0.232734\n","15     0.231147\n","16     0.204766\n","17     0.176885\n","18     0.152459\n","19     0.136522\n","20     0.131814\n","21     0.136559\n","22     0.147906\n","23     0.153045\n","24     0.151316\n","25     0.149339\n","26     0.147706\n","27     0.147322\n","28     0.151197\n","29     0.165580\n","         ...   \n","587    0.124711\n","588    0.144238\n","589    0.140852\n","590    0.137313\n","591    0.135856\n","592    0.127130\n","593    0.115694\n","594    0.113392\n","595    0.109368\n","596    0.103793\n","597    0.093620\n","598    0.092034\n","599    0.086781\n","600    0.085015\n","601    0.090707\n","602    0.095002\n","603    0.096746\n","604    0.104639\n","605    0.105884\n","606    0.113498\n","607    0.114562\n","608    0.111310\n","609    0.110602\n","610    0.112762\n","611    0.112764\n","612    0.112815\n","613    0.115737\n","614    0.115931\n","615    0.111521\n","616    0.126590\n","Length: 617, dtype: float64\n","0.8800800392512094\n"],"name":"stdout"}]},{"metadata":{"id":"lJkO_UePQ_vI","colab_type":"text"},"cell_type":"markdown","source":["# Βελτιστοποίηση για τον KNN"]},{"metadata":{"colab_type":"code","id":"0ts99zILnWsr","colab":{}},"cell_type":"code","source":["from sklearn.model_selection import GridSearchCV\n","n_components = [10, 30, 50]\n","k = [1, 5, 11, 41] # η υπερπαράμετρος του ταξινομητή\n","weight = ['uniform', 'distance']\n","metric = ['manhattan', 'minkowski']\n","vthreshold = [0.1, 0.15, 0.2] #προσαρμόζουμε τις τιμές μας στο variance που παρατηρήσαμε"],"execution_count":0,"outputs":[]},{"metadata":{"colab_type":"code","id":"mCuZ-UNfnWst","colab":{}},"cell_type":"code","source":["# αρχικοποιούμε τους εκτιμητές (μετασχηματιστές και ταξινομητή) χωρείς παραμέτρους\n","selector = VarianceThreshold()\n","scaler = StandardScaler()\n","ros = RandomOverSampler()\n","pca = PCA()\n","clf =  KNeighborsClassifier(n_jobs=1)\n","pipe = Pipeline(steps=[('selector', selector), ('scaler', scaler), ('pca', pca), ('sampler', ros), ('KNN', clf)])"],"execution_count":0,"outputs":[]},{"metadata":{"colab_type":"code","id":"EzmE08sinWsv","colab":{}},"cell_type":"code","source":["estimator = GridSearchCV(pipe, dict(selector__threshold=vthreshold, KNN__n_neighbors=k, KNN__metric=metric, KNN__weights=weight, pca__n_components=n_components), cv=5, scoring='f1_micro', n_jobs=1)"],"execution_count":0,"outputs":[]},{"metadata":{"colab_type":"code","id":"IL3PgNJWnWsx","colab":{"base_uri":"https://localhost:8080/","height":595},"outputId":"8f5653ed-6b35-4f21-ff1f-0dd555ed2422","executionInfo":{"status":"ok","timestamp":1544695737520,"user_tz":-120,"elapsed":937821,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["import time\n","start_time = time.time()\n","estimator.fit(train, train_labels)\n","preds = estimator.predict(test)\n","print(\"Συνολικός χρόνος fit και predict: %s seconds\" % (time.time() - start_time))\n","print(classification_report(test_labels, preds))"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Συνολικός χρόνος fit και predict: 1121.669153213501 seconds\n","              precision    recall  f1-score   support\n","\n","         1.0       0.84      0.98      0.91        83\n","         2.0       0.72      0.95      0.82        92\n","         3.0       0.91      0.98      0.94        95\n","         4.0       0.72      0.86      0.78        79\n","         5.0       0.93      0.75      0.83        87\n","         6.0       0.88      0.95      0.92        97\n","         7.0       0.76      0.79      0.78        92\n","         8.0       1.00      0.93      0.96        83\n","         9.0       0.99      0.98      0.99       103\n","        10.0       0.77      0.95      0.85        85\n","        11.0       0.87      0.86      0.87        88\n","        12.0       0.92      0.98      0.95        97\n","        13.0       0.88      0.83      0.85        95\n","        14.0       0.87      0.85      0.86        91\n","        15.0       0.98      0.94      0.96        95\n","        16.0       0.85      0.78      0.81        87\n","        17.0       0.91      1.00      0.95        82\n","        18.0       0.94      1.00      0.97        90\n","        19.0       0.98      0.90      0.94        91\n","        20.0       0.83      0.66      0.74        95\n","        21.0       0.93      0.91      0.92        87\n","        22.0       0.86      0.70      0.78       108\n","        23.0       1.00      0.87      0.93        82\n","        24.0       0.97      0.97      0.97        80\n","        25.0       0.98      0.99      0.98        85\n","        26.0       0.99      0.84      0.90        91\n","\n","   micro avg       0.89      0.89      0.89      2340\n","   macro avg       0.90      0.89      0.89      2340\n","weighted avg       0.90      0.89      0.89      2340\n","\n"],"name":"stdout"}]},{"metadata":{"colab_type":"code","id":"u7lfvDSLnWsy","scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":139},"outputId":"67f7cd24-ab18-466e-9c45-5a951222ebdf","executionInfo":{"status":"ok","timestamp":1544695737522,"user_tz":-120,"elapsed":48,"user":{"displayName":"Νtdark 13","photoUrl":"","userId":"09958224867973618888"}}},"cell_type":"code","source":["print(estimator.best_estimator_)\n","print(estimator.best_params_)"],"execution_count":26,"outputs":[{"output_type":"stream","text":["Pipeline(memory=None,\n","     steps=[('selector', VarianceThreshold(threshold=0.1)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('pca', PCA(copy=True, iterated_power='auto', n_components=50, random_state=None,\n","  svd_solver='auto', tol=0.0, whiten=False)), ('sampler', RandomOverSampler(random_state=None...tan',\n","           metric_params=None, n_jobs=1, n_neighbors=41, p=2,\n","           weights='distance'))])\n","{'KNN__metric': 'manhattan', 'KNN__n_neighbors': 41, 'KNN__weights': 'distance', 'pca__n_components': 50, 'selector__threshold': 0.1}\n"],"name":"stdout"}]},{"metadata":{"id":"W9EyCD0bQ_vV","colab_type":"text"},"cell_type":"markdown","source":["# Βελτιστοποίηση για τον MLP"]},{"metadata":{"colab_type":"code","id":"c1k2uzmI4_Me","colab":{}},"cell_type":"code","source":["from sklearn.neural_network import MLPClassifier\n","from sklearn.metrics import classification_report\n","from sklearn.model_selection import GridSearchCV\n","scaler = StandardScaler()\n","ros = RandomOverSampler()\n","activator = ['logistic', 'tanh', 'relu']\n","\n","solver = ['lbfgs', 'adam']\n","lrate = ['constant', 'invscaling', 'adaptive']\n","max_iter = [100, 200, 300]\n","alpha = [ 0.0001,  0.001]\n","pca = PCA()\n","clf = MLPClassifier(hidden_layer_sizes=(100,))\n","pipe = Pipeline(steps=[('scaler', scaler), ('sampler', ros),  ('pca', pca), ('selector', selector), ('MLP', clf)])"],"execution_count":0,"outputs":[]},{"metadata":{"colab_type":"code","id":"9lVytkR1HJ7a","colab":{}},"cell_type":"code","source":["estimator = GridSearchCV(pipe, dict(selector__threshold=vthreshold, pca__n_components=n_components, MLP__activation=activator, MLP__solver = solver, \n","                                    MLP__max_iter=max_iter, MLP__alpha=alpha, MLP__learning_rate=lrate), scoring = 'f1_micro', cv=5, n_jobs=1)"],"execution_count":0,"outputs":[]},{"metadata":{"colab_type":"code","id":"vcgwCASVHYtl","colab":{}},"cell_type":"code","source":["import time\n","start_time = time.time()\n","estimator.fit(train, train_labels)\n","mlppreds = estimator.predict(test)\n","print(\"Συνολικός χρόνος fit και predict: %s seconds\" % (time.time() - start_time))\n","print(classification_report(test_labels, mlppreds))"],"execution_count":0,"outputs":[]},{"metadata":{"colab_type":"code","id":"pycdo2ROyxNv","colab":{}},"cell_type":"code","source":["print(estimator.best_estimator_)\n","print(estimator.best_params_)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"8kxBFgoYQ_vf","colab_type":"text"},"cell_type":"markdown","source":["# Βελτιστοποίηση για τον GNB"]},{"metadata":{"id":"d8zgTeCPQ_vf","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.naive_bayes import GaussianNB\n","gnb = GaussianNB()\n","selector = VarianceThreshold()\n","scaler = StandardScaler()\n","ros = RandomOverSampler()\n","pca = PCA()\n","pipe = Pipeline(steps=[('selector', selector), ('scaler', scaler), ('pca', pca), ('sampler', ros), ('GNB', clf)])"],"execution_count":0,"outputs":[]},{"metadata":{"id":"6l3Vz-KkQ_vj","colab_type":"code","colab":{}},"cell_type":"code","source":["estimator = GridSearchCV(pipe, dict(selector__threshold=vthreshold, pca__n_components=n_components), cv=5, scoring='f1_micro', n_jobs=1)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Jb2yf9c1Q_vm","colab_type":"code","colab":{}},"cell_type":"code","source":["import time\n","start_time = time.time()\n","estimator.fit(train, train_labels)\n","gnbpreds = estimator.predict(test)\n","print(\"Συνολικός χρόνος fit και predict: %s seconds\" % (time.time() - start_time))\n","print(classification_report(test_labels, gnbpreds))"],"execution_count":0,"outputs":[]}]}