end_to_end_notebook.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":2},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython2","version":"2.7.6"},"colab":{"name":"Flower_notebook4_3.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"code","metadata":{"id":"HmKQGcbsEa5e","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":575},"executionInfo":{"status":"ok","timestamp":1597551052367,"user_tz":-600,"elapsed":25342,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"90bd8c1f-fe69-4f5c-c284-fa08796707f3"},"source":["import numpy as np\n","!pip install opencv-python==3.4.2.17\n","!pip install opencv-contrib-python==3.4.2.17\n","import cv2 as cv\n","from matplotlib import pyplot as plt\n","from sklearn.cluster import KMeans\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.svm import SVC\n","from sklearn.ensemble import AdaBoostClassifier\n","from sklearn.metrics import classification_report,confusion_matrix, accuracy_score, plot_confusion_matrix\n","import os\n","import pandas as pd\n","import random\n","import matplotlib.pyplot as plt\n","!pip install kneed\n","from kneed import KneeLocator # used for finding the optimal value of k\n","\n","np.random.seed(22)"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting opencv-python==3.4.2.17\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/53/e0/21c8964fa8ef50842ebefaa7346a3cf0e37b56c8ecd97ed6bd2dbe577705/opencv_python-3.4.2.17-cp36-cp36m-manylinux1_x86_64.whl (25.0MB)\n","\u001b[K     |████████████████████████████████| 25.0MB 170kB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from opencv-python==3.4.2.17) (1.18.5)\n","\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n","Installing collected packages: opencv-python\n","  Found existing installation: opencv-python 4.1.2.30\n","    Uninstalling opencv-python-4.1.2.30:\n","      Successfully uninstalled opencv-python-4.1.2.30\n","Successfully installed opencv-python-3.4.2.17\n","Collecting opencv-contrib-python==3.4.2.17\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/61/29/fc60b2de1713aa92946992544329f20ccb5e4ba26290f403e04b7da44105/opencv_contrib_python-3.4.2.17-cp36-cp36m-manylinux1_x86_64.whl (30.6MB)\n","\u001b[K     |████████████████████████████████| 30.6MB 175kB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from opencv-contrib-python==3.4.2.17) (1.18.5)\n","Installing collected packages: opencv-contrib-python\n","  Found existing installation: opencv-contrib-python 4.1.2.30\n","    Uninstalling opencv-contrib-python-4.1.2.30:\n","      Successfully uninstalled opencv-contrib-python-4.1.2.30\n","Successfully installed opencv-contrib-python-3.4.2.17\n","Collecting kneed\n","  Downloading https://files.pythonhosted.org/packages/c3/6b/e130913aaaad1373060e259ab222ca2330672db696b297b082c3f3089fcc/kneed-0.7.0-py2.py3-none-any.whl\n","Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from kneed) (1.4.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from kneed) (3.2.2)\n","Requirement already satisfied: numpy>=1.14.2 in /usr/local/lib/python3.6/dist-packages (from kneed) (1.18.5)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->kneed) (2.4.7)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->kneed) (2.8.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->kneed) (0.10.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->kneed) (1.2.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->kneed) (1.15.0)\n","Installing collected packages: kneed\n","Successfully installed kneed-0.7.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AxWbW20rmpUX","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":124},"executionInfo":{"status":"ok","timestamp":1597551078721,"user_tz":-600,"elapsed":23330,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"5c4713b6-b0c8-4a87-f681-3876524136eb"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"Dx_KlNDnmNTb","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"executionInfo":{"status":"ok","timestamp":1597551091196,"user_tz":-600,"elapsed":3990,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"755fb319-3442-4d80-9c08-87465143d626"},"source":["import os\n","flowers = ['Daisy', 'Rose', 'Sunflower', 'Tulip']\n","path = '/content/drive/My Drive/2D Geometric Shapes Recognition System/Flowers/'\n","training_file_names = []\n","training_flower_labels = []\n","for i in range(0, len(flowers)):\n","    sub_path = path + 'Train/' + flowers[i] + '/'\n","    sub_file_names = [os.path.join(sub_path, f) for f in os.listdir(sub_path)]\n","    sub_flower_labels = [i] * len(sub_file_names) #create a list of N elements, all are i\n","    training_file_names += sub_file_names\n","    training_flower_labels += sub_flower_labels\n","\n","print(len(training_file_names))\n","print(len(training_flower_labels))"],"execution_count":3,"outputs":[{"output_type":"stream","text":["160\n","160\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"WMSBDUt8wtBC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":137},"executionInfo":{"status":"ok","timestamp":1597417926549,"user_tz":-600,"elapsed":13856,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"fa588380-52c4-4058-d0b1-d5d25f352483"},"source":["# code to find optimal value of k\n","'''training_data = []\n","\n","sift = cv.xfeatures2d.SIFT_create()\n","num_keypoints = [] #this is used to store the number of keypoints in each image\n","#load training images and compute SIFT descriptors\n","for filename in training_file_names:\n","    img = cv.imread(filename)\n","    img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n","    list_des = sift.detectAndCompute(img_gray, None)[1]\n","    if list_des is None:\n","        num_keypoints.append(0)\n","    else:\n","        num_keypoints.append(len(list_des))\n","        for des in list_des:\n","            training_data.append(des)\n","\n","def kmeans_method(training_data):\n","  ssd = []\n","  K = []\n","  val = [i for i in range(10,101,5)]\n","  for k in val:\n","    kmeans = KMeans(k)\n","    kmeans.fit(training_data)\n","    K.append(k)\n","    ssd.append(kmeans.inertia_)\n","\n","  # plot\n","  plt.figure(figsize=(15,7))\n","  plt.plot(K, ssd, 'bx-')\n","  plt.xlabel('k')\n","  plt.ylabel('Sum_of_squared_distances')\n","  plt.title('Elbow Method For Optimal k')\n","  plt.show()\n","\n","  # find optimal value of k\n","  k = KneeLocator(range(10,101, 5), ssd, curve='convex', direction='decreasing')\n","  print(f'Optimal value of k is {k.elbow}.')    \n","\n","  return k.elbow # retuns optimal k vlaue\n","\n","kmeans_method(training_data)'''"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["\"training_data = []\\n\\nsift = cv.xfeatures2d.SIFT_create()\\nnum_keypoints = [] #this is used to store the number of keypoints in each image\\n#load training images and compute SIFT descriptors\\nfor filename in training_file_names:\\n    img = cv.imread(filename)\\n    img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\\n    list_des = sift.detectAndCompute(img_gray, None)[1]\\n    if list_des is None:\\n        num_keypoints.append(0)\\n    else:\\n        num_keypoints.append(len(list_des))\\n        for des in list_des:\\n            training_data.append(des)\\n\\ndef kmeans_method(training_data):\\n  ssd = []\\n  K = []\\n  val = [i for i in range(10,101,5)]\\n  for k in val:\\n    kmeans = KMeans(k)\\n    kmeans.fit(training_data)\\n    K.append(k)\\n    ssd.append(kmeans.inertia_)\\n\\n  # plot\\n  plt.figure(figsize=(15,7))\\n  plt.plot(K, ssd, 'bx-')\\n  plt.xlabel('k')\\n  plt.ylabel('Sum_of_squared_distances')\\n  plt.title('Elbow Method For Optimal k')\\n  plt.show()\\n\\n  # find optimal value of k\\n  k = KneeLocator(range(10,101, 5), ssd, curve='convex', direction='decreasing')\\n  print(f'Optimal value of k is {k.elbow}.')    \\n\\n  return k.elbow # retuns optimal k vlaue\\n\\nkmeans_method(training_data)\""]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"FiL3PLvlmNTY","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1597551097897,"user_tz":-600,"elapsed":968,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}}},"source":["class Dictionary(object):\n","    def __init__(self, name, img_filenames):\n","        self.name = name #name of your dictionary\n","        self.img_filenames = img_filenames #list of image filenames\n","        #self.num_words = num_words #the number of words\n","        self.training_data = [] #this is the training data required by the K-Means algorithm\n","        self.words = [] #list of words, which are the centroids of clusters\n","\n","    def learn(self):\n","        sift = cv.xfeatures2d.SIFT_create()\n","        num_keypoints = [] #this is used to store the number of keypoints in each image\n","        #load training images and compute SIFT descriptors\n","        for filename in self.img_filenames:\n","            img = cv.imread(filename)\n","            img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n","            list_des = sift.detectAndCompute(img_gray, None)[1]\n","            if list_des is None:\n","                num_keypoints.append(0)\n","            else:\n","                num_keypoints.append(len(list_des))\n","                for des in list_des:\n","                    self.training_data.append(des)\n","        #cluster SIFT descriptors using K-means algorithm\n","        self.num_words = 40 #optimal value\n","        kmeans = KMeans(self.num_words)\n","        kmeans.fit(self.training_data)\n","        self.words = kmeans.cluster_centers_\n","        #create word histograms for training images\n","        training_word_histograms = [] #list of word histograms of all training images\n","        index = 0\n","        for i in range(0, len(self.img_filenames)):\n","            #for each file, create a histogram\n","            histogram = np.zeros(self.num_words, np.float32)\n","            #if some keypoints exist\n","            if num_keypoints[i] > 0:\n","                for j in range(0, num_keypoints[i]):\n","                    histogram[kmeans.labels_[j + index]] += 1\n","                index += num_keypoints[i]\n","                histogram /= num_keypoints[i]\n","                training_word_histograms.append(histogram)\n","        return training_word_histograms\n","        \n","    def create_word_histograms(self, img_filenames):\n","        sift = cv.xfeatures2d.SIFT_create()\n","        histograms = []\n","        for filename in img_filenames:\n","            img = cv.imread(filename)\n","            img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n","            descriptors = sift.detectAndCompute(img_gray, None)[1]\n","            histogram = np.zeros(self.num_words, np.float32) #word histogram for the input image\n","            if descriptors is not None:\n","                for des in descriptors:\n","                    #find the best matching word\n","                    min_distance = 1111111 #this can be any large number\n","                    matching_word_ID = -1 #initial matching_word_ID=-1 means no matching\n","                    for i in range(0, self.num_words): #search for the best matching word\n","                        distance = np.linalg.norm(des - self.words[i])\n","                        if distance < min_distance:\n","                            min_distance = distance\n","                            matching_word_ID = i\n","                    histogram[matching_word_ID] += 1\n","                histogram /= len(descriptors) #normalise histogram to frequencies\n","            histograms.append(histogram)\n","        return histograms"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"3QCeew05mNTd","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1597551101345,"user_tz":-600,"elapsed":719,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}}},"source":["#num_words = 50\n","dictionary_name = 'flower'\n","dictionary = Dictionary(dictionary_name, training_file_names)"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"GHXTw7DWmNTg","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1597551339899,"user_tz":-600,"elapsed":232326,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}}},"source":["training_word_histograms = dictionary.learn()"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"OiEpCAvXdFsl","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597551339906,"user_tz":-600,"elapsed":231596,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"a2527ac5-331e-46cc-9c85-5c142b6b3b29"},"source":["len(training_word_histograms)"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["160"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"OYH8jukymNTj","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1597551341479,"user_tz":-600,"elapsed":232786,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}}},"source":["import pickle\n","#save dictionary\n","with open(os.path.join(path,'flower_dictionary.dic'), 'wb') as f: #'wb' is for binary write\n"," pickle.dump(dictionary, f)"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"xhu3K9B7mNTl","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1597554772096,"user_tz":-600,"elapsed":1035,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}}},"source":["import pickle \n","with open(os.path.join(path,'flower_dictionary.dic'), 'rb') as f: #'rb' is for binary read\n","  dictionary = pickle.load(f)"],"execution_count":48,"outputs":[]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"covKB_k-mNTv","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1597554772098,"user_tz":-600,"elapsed":1027,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}}},"source":["# store values for plotting\n","n_neighbours = []\n","n_accuracy = []\n","def knn_classifier(n):\n","  num_nearest_neighbours = n #number of neighbours\n","  knn = KNeighborsClassifier(n_neighbors = num_nearest_neighbours)\n","  knn.fit(training_word_histograms, training_flower_labels)\n","  # test all images\n","  validation_file_names = []\n","  validation_flower_labels = []\n","  for i in range(0, len(flowers)):\n","      sub_path = path + 'Validation/' + flowers[i] + '/'\n","      sub_file_names = [os.path.join(sub_path, f) for f in os.listdir(sub_path)]\n","      sub_flower_labels = [i] * len(sub_file_names) #create a list of N elements, all are i\n","      validation_file_names += sub_file_names\n","      validation_flower_labels += sub_flower_labels\n","  \n","  word_histograms = dictionary.create_word_histograms(validation_file_names)\n","  predicted_flower_labels = knn.predict(word_histograms)\n","\n","  print(f'When No. of Nearest Neighbours (K) is {n}')\n","  accuracy = accuracy_score(validation_flower_labels, predicted_flower_labels)*100\n","  n_neighbours.append(n) # will be used for plotting\n","  n_accuracy.append(accuracy) # will be used for plotting\n","  print(f'Accuracy score is {accuracy:.2f}%')\n","  cm = confusion_matrix(validation_flower_labels, predicted_flower_labels)\n","  recognisation_accuracy = (cm[0][0] + cm[1][1] + cm[2][2])\n","  print(f'Recognition accuracy is {recognisation_accuracy}/{len(validation_file_names)}')\n","  print(f'Confusion Matrix:\\n{cm}')\n","  print(plot_confusion_matrix(knn, word_histograms, validation_flower_labels, display_labels=flowers))\n","  plt.title(f'{n}-NN Classifier')\n","  print(f'Classification Report:\\n{classification_report(validation_flower_labels, predicted_flower_labels)}')\n","  return f'Success when N={n}'"],"execution_count":49,"outputs":[]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"CL0qPFDMmNTx","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1597554910980,"user_tz":-600,"elapsed":139894,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"6f733c86-3203-4f77-8699-2c9b4e3b4edc"},"source":["print('KNN Classifier')\n","n =  [10, 15, 20, 25, 30]  #nearest neighbours\n","for k in n:\n","  knn_classifier(k)\n","  print(f'{k}-NN completed!')"],"execution_count":50,"outputs":[{"output_type":"stream","text":["KNN Classifier\n","When No. of Nearest Neighbours (K) is 10\n","Accuracy score is 56.67%\n","Recognition accuracy is 55/120\n","Confusion Matrix:\n","[[14  8  6  2]\n"," [ 2 25  1  2]\n"," [ 3  7 16  4]\n"," [ 3 12  2 13]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4ed81b70>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.64      0.47      0.54        30\n","           1       0.48      0.83      0.61        30\n","           2       0.64      0.53      0.58        30\n","           3       0.62      0.43      0.51        30\n","\n","    accuracy                           0.57       120\n","   macro avg       0.59      0.57      0.56       120\n","weighted avg       0.59      0.57      0.56       120\n","\n","10-NN completed!\n","When No. of Nearest Neighbours (K) is 15\n","Accuracy score is 56.67%\n","Recognition accuracy is 55/120\n","Confusion Matrix:\n","[[11 10  6  3]\n"," [ 1 28  0  1]\n"," [ 3  8 16  3]\n"," [ 2 13  2 13]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4d4a4048>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.65      0.37      0.47        30\n","           1       0.47      0.93      0.63        30\n","           2       0.67      0.53      0.59        30\n","           3       0.65      0.43      0.52        30\n","\n","    accuracy                           0.57       120\n","   macro avg       0.61      0.57      0.55       120\n","weighted avg       0.61      0.57      0.55       120\n","\n","15-NN completed!\n","When No. of Nearest Neighbours (K) is 20\n","Accuracy score is 55.00%\n","Recognition accuracy is 53/120\n","Confusion Matrix:\n","[[12  9  6  3]\n"," [ 1 26  1  2]\n"," [ 4  7 15  4]\n"," [ 2 12  3 13]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4eb99390>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.63      0.40      0.49        30\n","           1       0.48      0.87      0.62        30\n","           2       0.60      0.50      0.55        30\n","           3       0.59      0.43      0.50        30\n","\n","    accuracy                           0.55       120\n","   macro avg       0.58      0.55      0.54       120\n","weighted avg       0.58      0.55      0.54       120\n","\n","20-NN completed!\n","When No. of Nearest Neighbours (K) is 25\n","Accuracy score is 55.83%\n","Recognition accuracy is 55/120\n","Confusion Matrix:\n","[[11 10  7  2]\n"," [ 2 27  1  0]\n"," [ 2  8 17  3]\n"," [ 0 14  4 12]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4ea0fa20>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.73      0.37      0.49        30\n","           1       0.46      0.90      0.61        30\n","           2       0.59      0.57      0.58        30\n","           3       0.71      0.40      0.51        30\n","\n","    accuracy                           0.56       120\n","   macro avg       0.62      0.56      0.55       120\n","weighted avg       0.62      0.56      0.55       120\n","\n","25-NN completed!\n","When No. of Nearest Neighbours (K) is 30\n","Accuracy score is 56.67%\n","Recognition accuracy is 55/120\n","Confusion Matrix:\n","[[11 10  7  2]\n"," [ 2 25  2  1]\n"," [ 3  6 19  2]\n"," [ 0 13  4 13]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4eda77b8>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.69      0.37      0.48        30\n","           1       0.46      0.83      0.60        30\n","           2       0.59      0.63      0.61        30\n","           3       0.72      0.43      0.54        30\n","\n","    accuracy                           0.57       120\n","   macro avg       0.62      0.57      0.56       120\n","weighted avg       0.62      0.57      0.56       120\n","\n","30-NN completed!\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"aXKvCfgmmNT0","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":623},"executionInfo":{"status":"ok","timestamp":1597554910987,"user_tz":-600,"elapsed":139880,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"99211482-595b-4830-9b6c-a8f67fc10860"},"source":["# plot\n","import matplotlib.pyplot as plt\n","plt.figure(figsize=(10,10))\n","plt.plot([int(i) for i in n_neighbours], n_accuracy)\n","plt.title('No. of neighbours vs accuracy', fontsize=15, fontweight='bold')\n","plt.xlabel('No. of Neighbours', fontweight='bold')\n","plt.ylabel('Accuracy', fontweight='bold')\n","plt.show()"],"execution_count":51,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"NlEsvPpsIvjz","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1597554910993,"user_tz":-600,"elapsed":139877,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}}},"source":["# store all test classifier accuracies for plotting\n","classifier_accuracy = [] # format (classifier_name, accuracy)"],"execution_count":52,"outputs":[]},{"cell_type":"code","metadata":{"id":"Pap96ybBBy0b","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":679},"executionInfo":{"status":"ok","timestamp":1597554942890,"user_tz":-600,"elapsed":171745,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"4c2595f8-1362-4d58-8eb5-40f0229dd34f"},"source":["''' For test set '''\n","# dynamically find best accuracy n value\n","\n","values = zip(n_neighbours, n_accuracy)\n","\n","def Sort_Tuple(tup):  \n","    return(sorted(tup, key = lambda x: x[1], reverse=True))   \n","\n","neighbors = Sort_Tuple(values)[0][0]\n","\n","def knn_classifier(n=neighbors):\n","    num_nearest_neighbours = n #number of neighbours\n","    knn = KNeighborsClassifier(n_neighbors = num_nearest_neighbours)\n","    knn.fit(training_word_histograms, training_flower_labels)\n","\n","    # test all images\n","    test_file_names = []\n","    test_flower_labels = []\n","    for i in range(0, len(flowers)):\n","        sub_path = path + 'Test/' + flowers[i] + '/'\n","        sub_file_names = [os.path.join(sub_path, f) for f in os.listdir(sub_path)]\n","        sub_flower_labels = [i] * len(sub_file_names) #create a list of N elements, all are i\n","        test_file_names += sub_file_names\n","        test_flower_labels += sub_flower_labels\n","    \n","    word_histograms = dictionary.create_word_histograms(test_file_names)\n","    predicted_flower_labels = knn.predict(word_histograms)\n","\n","    print(f'When No. of Nearest Neighbours (K) is {n}')\n","    accuracy = accuracy_score(test_flower_labels, predicted_flower_labels)*100\n","    classifier_accuracy.append(('KNN', accuracy))\n","    print(f'Accuracy score is {accuracy:.2f}%')\n","    cm = confusion_matrix(test_flower_labels, predicted_flower_labels)\n","    recognisation_accuracy = (cm[0][0] + cm[1][1] + cm[2][2])\n","    print(f'Recognition accuracy is {recognisation_accuracy}/{len(test_file_names)}')\n","    print(f'Confusion Matrix:\\n{cm}')\n","    print(plot_confusion_matrix(knn, word_histograms, test_flower_labels, display_labels=flowers))\n","    plt.title(f'{n}-NN Classifier')\n","    print(f'Classification Report:\\n{classification_report(test_flower_labels, predicted_flower_labels)}')\n","    return f'Success when N={n}'\n","  \n","knn_classifier()"],"execution_count":53,"outputs":[{"output_type":"stream","text":["When No. of Nearest Neighbours (K) is 10\n","Accuracy score is 42.86%\n","Recognition accuracy is 37/119\n","Confusion Matrix:\n","[[ 7 17  3  3]\n"," [ 1 21  1  7]\n"," [ 1 11  9  8]\n"," [ 0 15  1 14]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d440f2dd8>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.78      0.23      0.36        30\n","           1       0.33      0.70      0.45        30\n","           2       0.64      0.31      0.42        29\n","           3       0.44      0.47      0.45        30\n","\n","    accuracy                           0.43       119\n","   macro avg       0.55      0.43      0.42       119\n","weighted avg       0.55      0.43      0.42       119\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'Success when N=10'"]},"metadata":{"tags":[]},"execution_count":53},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"DgMcALtmmNT6","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1597554942893,"user_tz":-600,"elapsed":171739,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}}},"source":["# store values for plotting\n","c_values = []\n","svm_accuracy = []\n","\n","def svm_classifier(c):\n","    svm_classifier = SVC(C = c, kernel = 'linear')\n","    svm_classifier.fit(training_word_histograms, training_flower_labels)\n","\n","    # validtion on all images\n","    val_file_names = []\n","    val_flower_labels = []\n","    for i in range(0, len(flowers)):\n","        sub_path = path + 'Validation/' + flowers[i] + '/'\n","        sub_file_names = [os.path.join(sub_path, f) for f in os.listdir(sub_path)]\n","        sub_flower_labels = [i] * len(sub_file_names) #create a list of N elements, all are i\n","        val_file_names += sub_file_names\n","        val_flower_labels += sub_flower_labels\n","    \n","    word_histograms = dictionary.create_word_histograms(val_file_names)\n","    predicted_flower_labels = svm_classifier.predict(word_histograms)\n","\n","    print(f'When C value (C) is {c}')\n","    accuracy = accuracy_score(val_flower_labels, predicted_flower_labels)*100\n","    c_values.append(c) # will be used for plotting\n","    svm_accuracy.append(accuracy) # will be used for plotting\n","    print(f'Accuracy score is {accuracy:.2f}%')\n","    cm = confusion_matrix(val_flower_labels, predicted_flower_labels)\n","    recognisation_accuracy = (cm[0][0] + cm[1][1] + cm[2][2])\n","    print(f'Recognition accuracy is {recognisation_accuracy}/{len(val_file_names)}')\n","    print(f'Confusion Matrix:\\n{cm}')\n","    print(plot_confusion_matrix(svm_classifier, word_histograms, val_flower_labels, display_labels=flowers))\n","    plt.title(f'C = {c}')\n","    print(f'Classification Report:\\n{classification_report(val_flower_labels, predicted_flower_labels)}')\n","    return f'Success when C={c}'"],"execution_count":54,"outputs":[]},{"cell_type":"code","metadata":{"id":"FboGxIVhWf7B","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1597555107665,"user_tz":-600,"elapsed":336492,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"d65c0ac9-0b31-4ba3-910e-49b5635f1052"},"source":["c = [10, 20, 30, 40, 50, 100]  # values\n","for c_value in c:\n","    svm_classifier(c_value)"],"execution_count":55,"outputs":[{"output_type":"stream","text":["When C value (C) is 10\n","Accuracy score is 58.33%\n","Recognition accuracy is 50/120\n","Confusion Matrix:\n","[[13  5  6  6]\n"," [ 1 20  1  8]\n"," [ 5  1 17  7]\n"," [ 0  5  5 20]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4cfc2be0>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.68      0.43      0.53        30\n","           1       0.65      0.67      0.66        30\n","           2       0.59      0.57      0.58        30\n","           3       0.49      0.67      0.56        30\n","\n","    accuracy                           0.58       120\n","   macro avg       0.60      0.58      0.58       120\n","weighted avg       0.60      0.58      0.58       120\n","\n","When C value (C) is 20\n","Accuracy score is 59.17%\n","Recognition accuracy is 53/120\n","Confusion Matrix:\n","[[13  7  6  4]\n"," [ 2 22  1  5]\n"," [ 5  1 18  6]\n"," [ 2  6  4 18]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4dc0a2e8>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.59      0.43      0.50        30\n","           1       0.61      0.73      0.67        30\n","           2       0.62      0.60      0.61        30\n","           3       0.55      0.60      0.57        30\n","\n","    accuracy                           0.59       120\n","   macro avg       0.59      0.59      0.59       120\n","weighted avg       0.59      0.59      0.59       120\n","\n","When C value (C) is 30\n","Accuracy score is 57.50%\n","Recognition accuracy is 50/120\n","Confusion Matrix:\n","[[13  6  6  5]\n"," [ 2 21  1  6]\n"," [ 6  2 16  6]\n"," [ 2  6  3 19]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4e149a20>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.57      0.43      0.49        30\n","           1       0.60      0.70      0.65        30\n","           2       0.62      0.53      0.57        30\n","           3       0.53      0.63      0.58        30\n","\n","    accuracy                           0.57       120\n","   macro avg       0.58      0.57      0.57       120\n","weighted avg       0.58      0.57      0.57       120\n","\n","When C value (C) is 40\n","Accuracy score is 58.33%\n","Recognition accuracy is 51/120\n","Confusion Matrix:\n","[[13  8  5  4]\n"," [ 2 22  1  5]\n"," [ 6  2 16  6]\n"," [ 3  6  2 19]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4d529208>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.54      0.43      0.48        30\n","           1       0.58      0.73      0.65        30\n","           2       0.67      0.53      0.59        30\n","           3       0.56      0.63      0.59        30\n","\n","    accuracy                           0.58       120\n","   macro avg       0.59      0.58      0.58       120\n","weighted avg       0.59      0.58      0.58       120\n","\n","When C value (C) is 50\n","Accuracy score is 58.33%\n","Recognition accuracy is 50/120\n","Confusion Matrix:\n","[[13  8  5  4]\n"," [ 2 22  1  5]\n"," [ 6  2 15  7]\n"," [ 2  7  1 20]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4d098f98>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.57      0.43      0.49        30\n","           1       0.56      0.73      0.64        30\n","           2       0.68      0.50      0.58        30\n","           3       0.56      0.67      0.61        30\n","\n","    accuracy                           0.58       120\n","   macro avg       0.59      0.58      0.58       120\n","weighted avg       0.59      0.58      0.58       120\n","\n","When C value (C) is 100\n","Accuracy score is 59.17%\n","Recognition accuracy is 51/120\n","Confusion Matrix:\n","[[14  7  5  4]\n"," [ 2 21  1  6]\n"," [ 5  3 16  6]\n"," [ 1  7  2 20]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4cfc2358>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.64      0.47      0.54        30\n","           1       0.55      0.70      0.62        30\n","           2       0.67      0.53      0.59        30\n","           3       0.56      0.67      0.61        30\n","\n","    accuracy                           0.59       120\n","   macro avg       0.60      0.59      0.59       120\n","weighted avg       0.60      0.59      0.59       120\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAWcAAAEWCAYAAAC6xlbpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZgU1dn38e9vhmHfQXADkUgM6qtgEDUuAWNwSaKJGpcYt2iQRNQYk2ji80RfeKNJfIJxfQzuG0aMGjUaEFGiiSgiIoJLMIjKIjhsssPM3O8fdQbapmemmKmerm7vj1ddVp3a7pqh7zl96tQpmRnOOefSpazQATjnnNuWJ2fnnEshT87OOZdCnpydcy6FPDk751wKeXJ2zrkU8uTsnHMp5MnZpZqk70maLmmNpMWS/i7p0ITPcbKklyStkzQlx/oBkl4L61+TNCDJ8zuXiydnl1qSfgr8Ebga6An0Bm4Bjk/4VMvDeX6bI4aWwOPA/UAX4B7g8VDuXN7InxB0aSSpE7AQOMfMHm6mc54HfN/MhmSUDQPuAna18GGR9CEw3MwmNEdc7vPJa84urQ4GWgOPxd1B0uWSVtY1NTKOvYFZ9tlazKxQ7lzetCh0AM7VoRtQaWZVcXcws9+So2miidoDq7LKVgEdEj6Pc5/hNWeXVsuA7pIKXYFYA3TMKusIrC5ALO5zxJOzS6upwEbg23F3kPSr0Ksj59TIOOYA+0pSRtm+ody5vPHk7FLJzFYBvwZulvRtSW0lVUg6RtLv69jnajNrX9dU17kklUtqTdTMVyaptaSKsHoKUA1cJKmVpJGh/LnELta5HLy3hks1SacDlwD9iZoSXgN+Y2YvJXiOs4l6ZGS6x8zODusHArcDewFvA+ea2etJnd+5XDw5O+dcCnmzhnPOpZAnZ+eci0FSL0nPS3pL0hxJF4fyrpImSZob/t+ljv3PCtvMlXRWg+fzZg3nnGuYpJ2AncxshqQORPc/vg2cDSw3s99KuhzoYmaXZe3bFZgODAIs7PtlM1tR1/m85uycczGY2WIzmxHmVxPdHN6FaKyXe8Jm95C7++dRwCQzWx4S8iTg6PrOV+gO/kWrRae21rJHp0KHkbiyytL9J2HlhY7Aba91yxZUmtkOjd3/qKHtbNny6ljbvjZr4xxgQ0bRWDMbm2tbSX2AgcArQE8zWxxWfUw0SFe2XYCPMpYXhLI6le4nMc9a9ujEF687t9BhJK7dXZ0LHULebGrvXxSLzfR7L/2gKfsvW17NtIm9Y21bvtPcDWY2qKHtJLUHHgF+YmafZj6fZGYmKZG2Yv/X6pwrWQbUxPwvjvBw0iPAA2b2aCheEtqja9ull+bYdSHQK2N511BWJ0/OzrmSZRibrTrW1JDwCP8dwNtmNiZj1RNAbe+Ls4jG/842ERgmqUvozTEslNXJmzWccyUtbq04hkOAM4A3Jc0MZb8iGglxvKRzgQ+AkwEkDQJGmNl5ZrZc0mjg1bDfKDNbXt/JPDk750qWYVQn1F3YzP4JqI7VX8ux/XTgvIzlO4E7457Pk7NzrqTVUJzPcnhyds6VLAOqPTk751z6eM3ZOedSxoDNRTpEhSdn51zJMsybNZxzLnUMqoszN3tyds6VrugJweLkydk5V8JEdZ1dk9PNk7NzrmRFNwQ9OTvnXKpE/Zw9OTvnXOrUeM3ZOefSxWvOzjmXQoaoLtKRkT05O+dKmjdrOOdcyhhiU5G+PNKTs3OuZEUPoXizhnPOpY7fEHSJaH/9YlpNX0tNp3JW3LQ7AG3vr6TVK6uhTNR0Kmf1xTtR0624f3XjR41j3YYKaqyM6mrxw9+fUOiQEtO+9UauOOkf9O25HAP+38NDmP3hjoUOq8mK8brMRLV5zblZSKoG3gQqgCrgXuA6M6vzEXpJOwM3mNlJzRNl4238Wic2fLMLHa5bvKVs/QldWPf97gC0eXIFbR+qZM2P0/2hiOPi67/FqrWtCx1G4n563L+Y+m4vfnn/MFqUV9O6oqrQISWiWK+rJqGas6Q7gW8CS81sn1D2ELBn2KQzsNLMBuTYdz6wGqgGqsxsUEPnK7rkDKyvvXhJPYBxQEfgyrp2MLNFQOoTM8DmfdpStmTzZ8qsbcYNjQ3FOozL50O71hsZuPtiRo0fCkBVdTlrqovzhlSmYr2u6IZgYmnubuAmogphdHyzU2rnJf0BWFXP/kPNrDLuyYoxOW9hZkslDQdelXQVsBtwH9AubDLSzF6S1Af4m5ntI2lv4C6gJVAGnEj0Rt3lZvZHAEm/IfrreH1zXk992t73Ca2f/xRrW8bK3/QqdDhNZibGjHwKQzz+z/48+a/+hQ4pETt3Wc2Kta357+8+T7+dlvHOwh0Y88QhbNhcUejQmqRYryvJG4Jm9kLIJduQJKK3bh+RyMmgSG9jZjCzeUA50ANYCnzdzPYHTgFuyLHLCOD6UPseBCwgeiPumQCSyoBTgfvzH318687YgeV3foENX+1Im6dWFjqcJrtgzHGc+7sT+dnNx3DC4XPYb4/FDe9UBMrLathz50oefXlvzrzhu2zY1IKzhr5e6LCarJivq9oUa2qiw4AlZja3jvUGPCPptVChbFDRJ+csFcBtkt4EHgb2yrHNVOBXki4DdjOz9WY2H1gmaSAwDHjdzJZl7yhpuKTpkqZXrVqXv6uox8YhHWn10uqCnDtJlauiLzcr17ThhTf60H+3pQWOKBlLV7Vn6ap2zPmoJwDPvfkF9tw59jfZ1CrW66p9QjDOBHSv/XyHKVYSDU4DHqxn/aGh0ngMcIGkwxs6YNEnZ0l9iRrZlwKXAEuA/YhqxS2ztzezccBxwHrgaUm1X0NuB84GziGqSW/DzMaa2SAzG9SiU9uEr6Ru5Ys2bZlv+coaqnfd5rKKSuuWm2nTatOW+QP6L2Te4q4FjioZy9e0Zemq9vTuHn27GbTHAt5f2qXAUTVdMV9XjZXFmoDK2s93mMbGOb6kFsAJwEN1bWNmC8P/lwKPAYMbOm5RtzlL2gG4FbjJzExSJ2CBmdVIOououSN7n77APDO7QVJvYF/gOaIf2Cii2vf3mu0isnS4dhEVs9dR9mk1Xc/5D+tO60bL19ZSvnATCKp7VLDmxz0LFV4iunRYz9XDnwGgvNyY9OoXmPZW8bej1/qfxw9l1GmTaVFezaLlHRn98NBCh5SIYryuaOCjvNdBjwTeMbMFuVZKageUmdnqMD+MKNfUqxiTcxtJM9nale4+YExYdwvwiKQzgQnA2hz7nwycIWkz8DFwNYCZbZL0PFFXmOo8X0OdVv98523KNgzrXIBI8mfxso6cc01RdJ5plLmLu3P2jScWOozEFeN1GWJzQo9vS3oQGELU/LEAuNLM7iC6R/Vg1rY7A7eb2bFAT+Cx6J4hLYBxZjahofMVXXI2q/snHRrj980ouiyUzwf2CfO/BX6bvW+4EXgQ8N0Ew3XOFZAZiT2EYman1VF+do6yRcCxYX4eUVPrdin6NuckSNoLeA+YXM/dVudc0RE1Mae0Kbqacz6Y2VtA30LH4ZxLlpFczbm5eXJ2zpU0H2zfOedSxpAPtu+cc2ljwObkxtZoVsUZtXPOxSIfz9k559LGoPbpv6Ljydk5V9K85uyccyljJq85O+dc2kQ3BNP/UoBcPDk750qYv0PQOedSJ7oh6G3OzjmXOv6EoHPOpYw/IeiccymV1Atem5snZ+dcyTKDzTWenJ1zLlWiZg1Pzs45lzr+hKBzzqVMMXelK876vnPOxRI1a8SZGjySdKekpZJmZ5RdJWmhpJlhOraOfY+W9K6k9yRdHidyT87OuZKW4DsE7waOzlF+nZkNCNPT2SsllQM3A8cAewGnhfeW1subNRqp4v0qdjxraaHDSNzTcx4qdAh5c+zeQwsdQl58OLx/oUNIrai3RjJja5jZC5L6NGLXwcB74S3cSPozcDzwVn07ec3ZOVeyah9CiTMB3SVNz5iGxzzNSEmzQrNHlxzrdwE+ylheEMrq5TVn51xJi9lkAVBpZoO28/D/C4wmuvc4GvgD8IPtPEZOnpydcyUr3701zGxJ7byk24C/5dhsIdArY3nXUFYvb9ZwzpW0pHpr5CJpp4zF7wCzc2z2KtBP0u6SWgKnAk80dGyvOTvnSpaZqEroCUFJDwJDiNqmFwBXAkMkDSCqpM8Hzg/b7gzcbmbHmlmVpJHARKAcuNPM5jR0Pk/OzrmSllSzhpmdlqP4jjq2XQQcm7H8NLBNN7v6eHJ2zpWsYn5C0JOzc66keXJ2zrmU8cH2nXMupbajn3OqeHJ2zpUsM6jywfadcy59vFnDOedSxtucnXMupcyTs3POpY/fEHTOuZQx8zZn55xLIVHtvTWccy59vM3ZOedSxsfWcM65NLKo3bkYeXJ2zpU0763hnHMpY35D0Dnn0smbNVzifjL6HQZ/dRkrl1fw428PLnQ4TbJ0YQXXXtyblZ9UgIxjv7+M75xXyacryrl6RB+WLGhJz103ccWf5tOhc3Whw220UvqdZTtj4BucuPfbmMHcZd34r0lD2VSd/hRSrL01irO+n0FStaSZkmZLelJS50LHlJRn/7oj/33+voUOIxHlLYzhv17Ebf94h+v/Npcn7+7OB/9uxfibejDw0NXc9a+3GXjoah66qUehQ22SUvqdZerRbg2n7/cmpzx4Et954FTKZBzzxfcKHVaDzKLkHGdqiKQ7JS2VNDuj7FpJ70iaJemxuvKPpPmS3gy5anqc2Is+OQPrzWyAme0DLAcuKHRASZn9WmdWr0p/zSSObj2r6LfvegDatq+h1x4bqVxcwdSJnTjy5OUAHHnycqZO6FTIMJuslH5n2VqU1dCqRRXlqqFNRRWfrG1X6JBiqTHFmmK4Gzg6q2wSsI+Z7Qv8G/hlPfsPDblqUJyTlUJyzjQV2AVA0gBJL2f8ResSyi+S9FYo/3Moaxf+Kk6T9Lqk4wt4DSXv449a8p/ZbfjS/utYUVlBt55VAHTtUcWKyooCR+dyWbq2PXfPGMCzP7iP58+7h9UbW/LSh70KHVYsZvGmho9jLxBVADPLnjGzqrD4MrBrUnGXTHKWVA58DXgiFN0LXBb+or1J9BpzgMuBgaF8RCi7AnjOzAYDQ4FrJW1TLZA0XNJ0SdM32YY8Xk3pWr+2jNHn9WHEqIW061DzmXUSSEV696bEdWy1kaF93+eou7/PEXecSZuKzXxzz38XOqwGGaKmpizWBHSv/XyHafh2nu4HwN/rDAWekfRa3OOWQnJuI2km8DHQE5gkqRPQ2cz+Eba5Bzg8zM8CHpD0faD2L94w4PJwnClAa6B39onMbKyZDTKzQS3VOm8XVKqqNsPo8/pwxAkrOPTYVQB06b6ZZUuiZoBlS1rQuVtVfYdwBXJQrwUs/LQjK9a3oaqmnMnv9WXAzh8XOqxYLOYEVNZ+vsM0Nu45JF1BlE8eqGOTQ81sf+AY4AJJh9ex3RalkJzXm9kAYDdANNzm/A3gZmB/4FVJLcJ+J4b2oAFm1tvM3s5r1J8zZjDm0t706reRE8//ZEv5QcM+5dnxXQF4dnxXDj5qVaFCdPVYvLo9++64hNYtNgPGgb0WMG95l0KH1bAEbwjWRdLZwDeB081yN5CY2cLw/6XAY0CDXXlKITkDYGbrgIuAS4G1wApJh4XVZwD/kFQG9DKz54HLgE5Ae2AicKEkAUga2Nzx5/KLa99izLjX2bXPeu6d/BLDTlhc6JAabc60dkz+S1fe+Fd7fnTknvzoyD2ZNrkDp4xcwowXO3DOIf2Z8WIHTh65tNChNkkp/c4yvbmkJ5Pe68v40/7CY6c/RJng4dl7FTqseLaj6ry9JB0N/AI4LuSgXNu0k9Shdp7om/rsXNtmKqnbymb2uqRZwGnAWcCtktoC84BzgHLg/tDsIeAGM1spaTTwR2BWSODvE/0lLKjf/7xI/vHHsM+Ba5m4aGbOdb8b/59mjiZ/Sul3lu3mlwdz88vF13c7qX7Okh4EhhC1TS8guo/1S6AVUXMqwMtmNkLSzsDtZnYsUXPrY2F9C2CcmU1o6Hx1JmdJN1LP3xMzuyjuReWTmbXPWv5WxuJBOXY5NMcx1gPnJxyac67ADKipSSY5m9lpOYrvqGPbRcCxYX4esN/2nq++mnOsjtLOOZdaBhTpE4J1JmczuydzWVLbutpUnHMurYp1bI0GbwhKOljSW8A7YXk/SbfkPTLnnEtCHm8I5lOc3hp/BI4ClgGY2Rts7TPsnHMpFq8bXRoHR4rVW8PMPgp3GmsV77BhzrnPlxTWiuOIk5w/kvQVwCRVABcD/oCGcy79DCyh3hrNLU6zxgiip+52ARYBAyihkd+cc6VOMad0abDmbGaVwOnNEItzziWvSJs14vTW6BsGsf8kDDT9uKS+zRGcc841WQn31hgHjAd2AnYGHgYezGdQzjmXiNqHUOJMKRMnObc1s/vMrCpM9xMNqemcc6mX1GD7za2+sTW6htm/S7oc+DPR36FTgKebITbnnGu6Iu2tUd8NwdeIknHtlWUODGTU/64s55xLhWJ9uU59Y2vs3pyBOOdc4lJ6sy+OWE8IStoH2IuMtmYzuzdfQTnnXDLSebMvjgaTs6QriQaY3ouorfkY4J9EL1B1zrl0K9Kac5zeGicRvdX6YzM7h2jQ6E55jco555JSE3NKmTjNGuvNrEZSlaSOwFKgV57jcs65piviwfbj1JynS+oM3EbUg2MGMDWvUTnnXEJk8aYGjyPdGZ6Snp1R1lXSJElzw/9zvpJc0llhm7mSzooTd4PJ2cx+bGYrzexW4OvAWaF5wznn0i+5x7fvBo7OKrscmGxm/YDJYfkzwjMjVwIHAoOBK+tK4pnqewhl//rWmdmMhg7unHOlwsxekNQnq/h4og4TAPcAU4DLsrY5CphkZssBJE0iSvL1DoNRX5vzH+qLEziivgOXuuoOrVk95IuFDiNxx+5d6Ajy58SXSnMY8ptu7F/oEFJtOx5C6S4p88XWY81sbAP79DSzxWH+Y6Bnjm12AT7KWF4QyupV30MoQxva2TnnUs3Ynse3K81sUKNPZWZScs8jxrkh6JxzxSu/Q4YukbQTQPj/0hzbLOSzPdx2DWX18uTsnCtpSfXWqMMTQG3vi7OAx3NsMxEYJqlLuBE4LJTVy5Ozc660JVRzlvQgUTfiPSUtkHQu8Fvg65LmAkeGZSQNknQ7QLgROBp4NUyjam8O1ifO49siek1VXzMbJak3sKOZTWv4cpxzrsASagU2s9PqWPW1HNtOB87LWL4TuHN7zhen5nwLcDBQG9hq4ObtOYlzzhVC3CaNNA4rGufx7QPNbH9JrwOY2QpJLfMcl3POJaMEB9uvtVlSOeHLgaQdSOUwIc45t6001orjiNOscQPwGNBD0m+Ihgu9Oq9ROedcUor07dsN1pzN7AFJrxE1egv4tpmV5qNWzrnSktL25Dji9NboDawDnswsM7MP8xmYc84lolSTM/AUW1/02hrYHXgXKOFRGJxzpUJFeocsTrPG/8lcDqPV/ThvETnnnIv3gtdMZjZD0oH5CMY55xJXqs0akn6asVgG7A8syltEzjmXlFK+IQh0yJivImqDfiQ/4TjnXMJKMTmHh086mNnPmike55xLVqklZ0ktzKxK0iHNGZBzziVFlGZvjWlE7cszJT0BPAysrV1pZo/mOTbnnGuaEm9zbg0sI3pnYG1/ZwM8OTvn0q8Ek3OP0FNjNluTcq0ivVzn3OdOkWar+pJzOdCezyblWkV6uc65z5tSbNZYbGajmi0Sl9P4UeNYt6GCGiujulr88PcnFDqkRPxk9DsM/uoyVi6v4MffHlzocJpk2hUdWTylFa261nD0k8sAmHpJJ1bPjz5emz4to2XHGoY9tqyQYTbJbl1X8LsTJm1Z3qXLp/zvPw5g3LT9ChhVTCWYnJs8QrWkK4DvAdVEY0Cfb2avNOI4FwE/AmYAk4BBZjayqfEVi4uv/xar1rYudBiJevavO/LkuF249JriH+Bw92+vp9/31vHK5Z22lB183aot8zN/14GK9kXaZSD4YHkXTr39ZADKVMPEi+/l+Xf7FjiqGCy53hqS9gQeyijqC/zazP6Ysc0Qope8vh+KHm1sJbe+5LzNe7G2h6SDgW8C+5vZRkndgca+QeXHwJFmtkDS2U2JK67aroTNca7Po9mvdabHzusLHUYidjhgM2sXludcZwYfTWjNkLsafJ9n0Ri8+0IWrOjE4lUdGt44DZJ7h+C7wADY8gzIQqKx7rO9aGbfbOr56hxsP87bYRuwE1BpZhvD8SrNbJGk+SFR176hdkqYv0rSnZKmSJoXastIupXoL9TfJV2SeQJJfSQ9J2mWpMmSeksql/S+Ip0lVUs6PGz/gqR+ktqFc02T9Lqk48P6syU9Iek5YHITrz8RZmLMyKe4/bJH+dYhxV/L/LypnF5B6241dOhTXehQEnPUXu8xYc4ehQ4jtjy9Q/BrwH/M7IPkI47EeRNKYz0D9JL0b0m3SPpqjH2+BBwFDAaulFRhZiOIxvIYambXZW1/I3CPme0LPADcYGbVREOa7gUcStQUcpikVkAvM5sLXAE8Z2aDgaHAtZLahWPuD5xkZtvEK2m4pOmSpm/euGa7fhiNdcGY4zj3dyfys5uP4YTD57DfHoub5bwuGR8+1Ybe3yiNbwgALcqq+eoX5zPp7S8UOpT44r8JpXvt5ztMw+s56qnAg3WsO1jSG5L+LqnRQyvnLTmb2Rrgy8Bw4BPgoRhNEk+Z2UYzqwSWAj0b2P5gYFyYv48oGQO8CBwepmtC+QHAq2H9MOBySTOBKUR9uXuHdZPq+tZgZmPNbJCZDapo1b6B0JJRuSr6m7FyTRteeKMP/Xdb2izndU1XUwULnm1Fr2M2FDqUxBy6x4e883F3lq9tW+hQ4ombmKPkXFn7+Q7T2FyHDC+4Po7owbxsM4DdzGw/osrjXxsbej5rzphZtZlNMbMrgZHAiUSDJ9WeN/su18aM+WoaMaRp8AJwGFEN/GmgMzCEKGlDdLPzRDMbEKbeGa/eWpt9sEJp3XIzbVpt2jJ/QP+FzFvctcBRubiWTG1Jx92rabtjcd8MzHT03u8xYU6/QocRm8hLs8YxwAwzW5K9wsw+DRVTzOxpoKK2GXd75S05S9pTUuZvcQDwATCfqEYNUbJuipeIvl4AnM7W5DsN+ApQY2YbgJnA+URJG2AicKEkhVgHNjGOvOjSYT23/PQJ7vrlXxj7i78ydXYvpr3Vq9BhJeIX177FmHGvs2uf9dw7+SWGnVC8zTVTL+3E5FO7snp+C54csgPz/tIGgI+ebkOvEmrSaF2xmQN3/4jn3tm90KFslzwk59Ooo0lD0o4ZeWUwUY5tVB/KxtZM42gP3CipM1Ft+T2iJo7+wB2SRhM1KTTFhcBdkn5O1HRyDkDoHfIR8HLY7kWiH+ibYXk08EdglqQyom4vTb67mrTFyzpyzjUnFTqMvPj9z/cqdAiJOfgPq3KWD74md3mx2rC5gqFjflDoMLZfgv2cw72prxNV9mrLRgCY2a3AScCPJFUB64FTzaxREeQtOZvZa0S112wvAl/Msf1VWcv7ZMz3yZi/G7g7zH9ANOZHrvMfljE/jq1t05jZejJ+uLmO7ZwrEQkmZzNbC3TLKrs1Y/4m4KYkzpXPmrNzzhVWiY9K55xzxcuTs3POpU8pDrbvnHNFz5s1nHMubbY+YFJ0PDk750qbJ2fnnEuX2icEi5EnZ+dcSVNNcWZnT87OudLlbc7OOZdO3qzhnHNp5MnZOefSx2vOzjmXRp6cnXMuZRJ8+3Zz8+TsnCtZ3s/ZOefSqnFj3RecJ2fnXEnzmrNzzqWNP4TinHPplOQNQUnzgdVANVBlZoOy1gu4HjgWWAecbWYzGnMuT87OuZKWh94aQ82sso51xwD9wnQg8L/h/9utrHGxOedcETCiG4JxpmQcD9xrkZeBzpJ2asyBvObcSGUr19L2sVcKHUbiVn+nUX/ki8Ldv9rmpe8l4fWbbyl0CHlTfnPTj7EdNwS7S5qesTzWzMZmbWPAM5IM+FOO9bsAH2UsLwhli+NHHPHk7JwrbfGTc2V2G3IOh5rZQkk9gEmS3jGzF5oUXx28WcM5V7JqH0KJM8VhZgvD/5cCjwGDszZZCPTKWN41lG03T87OudJlhmriTQ2R1E5Sh9p5YBgwO2uzJ4AzFTkIWGVm292kAd6s4Zwrdcn1c+4JPBb1lqMFMM7MJkgaAWBmtwJPE3Wje4+oK905jT2ZJ2fnXElL6glBM5sH7Jej/NaMeQMuSOJ8npydc6XLAH+HoHPOpVBx5mZPzs650uYDHznnXArF6YmRRp6cnXOly0elc8659IkeQinO7OzJ2TlX2vwdgs45lz5ec3bOubTxNmfnnEujeONmpJEnZ+dcafNmDeecSxnLy2uqmoUnZ+dcafOas3POpVBx5mZPzs650qaa4mzX8OTsnCtdhj+E4pxzaSPMH0Jx+TFoyKeMGL2I8jLj7w92ZfxNPQsdUiLGjxrHug0V1FgZ1dXih78/odAhJaZUrm3pwgquvbg3Kz+pABnHfn8Z3zmvkk9XlHP1iD4sWdCSnrtu4oo/zadD5+pCh1s3T86FIakbMDks7ghUA5+E5cFmtilj2z7A38xsH0mDgDPN7KJmDHe7lJUZF1y9kF+e2pfKxRXc+PRcXp7YiQ/nti50aIm4+PpvsWptaVxLtlK4tvIWxvBfL6LfvutZt6aMkUd/kf0PX82kh7oy8NDVnHLhUh66sQcP3dSD8/6rUe8wbR4JJWdJvYB7id4laMBYM7s+a5shwOPA+6HoUTMb1ZjzFf3bt81smZkNMLMBwK3AdbXLmYk5x37T05yYAfYcuI5F81vy8YetqNpcxpTHO3PwUasKHZb7nOjWs4p++64HoG37GnrtsZHKxRVMndiJI09eDsCRJy9n6oROhQyzfrVtznGmhlUBl5rZXsBBwAWS9sqx3YsZOahRiRlKIDnnIuluSSdlLK/Jsc0QSX8L81dJuk/SVElzJf2wOeOtS7cdN/PJopZblisXV9B9p80FjCg5ZmLMyKe4/bJH+dYhbxc6nESV4rV9/FFL/jO7DV/af3tRWFYAAAzeSURBVB0rKivo1rMKgK49qlhRWVHg6OqnmppYU0PMbLGZzQjzq4G3gV3yFXfRN2skaF+iv4btgNclPWVmiwocU8m6YMxxVK5qR+f267nuwqf4cEln3nhvp0KHlYhSu7b1a8sYfV4fRoxaSLsOn01iEijV74GyvLQ5hybSgcArOVYfLOkNYBHwMzOb05hzlGTNuZEeN7P1ZlYJPA8Mzt5A0nBJ0yVN38zGvAe07OMKdth5a8tM9502U7k43bWUuCpXtQNg5Zo2vPBGH/rvtrTAESWnlK6tajOMPq8PR5ywgkOPjZrUunTfzLIlUb1u2ZIWdO5WVcgQ62dEyTnOBN1rP99hGp7rkJLaA48APzGzT7NWzwB2M7P9gBuBvzY29FJNzlWEa5NUBrSsf3Ng2+eItvlza2ZjzWyQmQ2qoFXTo2zAuzPbssvum+jZayMtKmoYcvxKXn4mxe17MbVuuZk2rTZtmT+g/0LmLe5a4KiSUUrXZgZjLu1Nr34bOfH8T7aUHzTsU54dH13Ts+O7pv8+SPw258raz3eYxmYfSlIFUWJ+wMwezV5vZp+a2Zow/zRQIal7Y8Iu1WaN+cCXgfHAcUCc6ubxkq4hatYYAlyer+DiqqkWN1+xC1ePm0dZOTzz56588O/i7gEA0KXDeq4e/gwA5eXGpFe/wLS3ehU4qmSU0rXNmdaOyX/pyu791/OjI/cE4JxfLuKUkUv4zYg+TPhzN3rsEnWlS7Ok+jlLEnAH8LaZjaljmx2BJWZmkgYTVRKXNeZ8pZqcbwMeD+0+E4C1MfaZRdSc0R0YnZb25lef68irz3UsdBiJWrysI+dcc1LDGxahUrq2fQ5cy8RFM3Ou+934/zRzNE2QXJvzIcAZwJuSan8wvwJ6R6exW4GTgB9JqgLWA6eaNS6AkkrOZnZVxuJBGfOXhfXzgX3C/BRgSsY2s8zszLwG6JxrXmZQnczz22b2T6J3xta3zU3ATUmcr6SSs3PObcOfECxeWTVu51wp8eTsnHMpY4C/Q9A559LGwIpzzFBPzs650mUkdkOwuXlyds6VNm9zds65FPLk7JxzaZOfgY+agydn51zpMsBf8OqccynkNWfnnEub5B7fbm6enJ1zpcvAvJ+zc86lkD8h6JxzKeRtzs45lzJm3lvDOedSyWvOzjmXNoZVVxc6iEbx5OycK10+ZKhzzqVUkXalKyt0AM45ly8GWI3FmuKQdLSkdyW9J+nyHOtbSXoorH9FUp/Gxu7J2TlXuiwMth9naoCkcuBm4BhgL+A0SXtlbXYusMLM9gCuA37X2NA9OTvnSppVV8eaYhgMvGdm88xsE/Bn4PisbY4H7gnzfwG+JqneN3bXRVak3UwKTdInwAfNdLruQGUznau5leq1+XUlYzcz26GxO0uaQBRzHK2BDRnLY81sbMaxTgKONrPzwvIZwIFmNjJjm9lhmwVh+T9hm+3+mfkNwUZqyj+Y7SVpupkNaq7zNadSvTa/rnQws6MLHUNjebOGc87FsxDolbG8ayjLuY2kFkAnYFljTubJ2Tnn4nkV6Cdpd0ktgVOBJ7K2eQI4K8yfBDxnjWw79maN4jC24U2KVqlem19XiTGzKkkjgYlAOXCnmc2RNAqYbmZPAHcA90l6D1hOlMAbxW8IOudcCnmzhnPOpZAnZ+ecSyFPzs1MUrWkmZLmSHpD0qWS6v09SNpZ0l+aK8amyrjG2ZKelNS50DHlIumK8HuYFeI9sJHHuUjS25IekHS2pJuSjjWfJHUL1z9T0seSFmYst8zatk/oy4ukQZJuKEzUpc/bnJuZpDVm1j7M9wDGAf8ysysLG1lysq7xHuDfZvabAof1GZIOBsYAQ8xso6TuQEszW9SIY70DHGlmCySdDQzKfDAhHyS1MLOqPBz3KmCNmf1PHev7AH8zs32SPrf7LK85F5CZLQWGAyMV6SPpRUkzwvQV2Ka2srekaaFWM0tSP0mjJP2k9riSfiPp4sJc1TamArsASBog6eUQ92OSuoTyiyS9Fcr/HMraSbozXOvrkrIfk22qnYBKM9sIYGaVZrZI0vyQqGtrhlPC/FUhnimS5km6KJTfCvQF/i7pkswThN/bc+G6JkvqLalc0vvh9905fMs4PGz/Qvh95rz2UCt/QtJzwOSEfx6fIeluRU/E1S6vybHNEEl/C/NXSbpP0lRJcyX9MJ/xfR54ci4wM5tH1C2nB7AU+LqZ7Q+cAuT6yjgCuN7MBgCDgAXAncCZAKGJ5FTg/vxHXz9FA8V8ja19Qe8FLjOzfYE3gdpvC5cDA0P5iFB2BVEf0cHAUOBaSe0SDO8ZoJekf0u6RdJXY+zzJeAoojEWrpRUYWYjgEXAUDO7Lmv7G4F7wnU9ANxgZtXAu0QD5xwKzAAOk9QK6GVmc6n/2vcHTjKzOPE2t32BI4CDgV9L2rnA8RQ1T87pUgHcJulN4GGiD3C2qcCvJF1GNO7AejObDyyTNBAYBrxuZo16KikhbSTNBD4GegKTJHUCOpvZP8I29wCHh/lZwAOSvg/UflUfBlwejjOFaNyD3kkFaGZrgC8TfXP5BHgoNEnU5ykz2xjGSVhKdG31OZio2QrgPqJkDPAi0bUfDlwTyg8gesgB6r/2SWa2vKHrK5DHw7/HSuB5oj9irpE8OReYpL5ANdGH/RJgCbAfUa24Zfb2ZjYOOA5YDzwt6Yiw6nbgbOAcopp0Ia0PNfvdAAEXNLD9N4iGYtwfeFXRY68CTjSzAWHqbWZvJxmkmVWb2ZTQ3j8SOJHoj0Pt56J11i4bM+arafxDXC8AhxElr6eBzsAQoqQN9V/72kaec3tt+TmEb2Pb/FvMIfsGlt/QagJPzgUkaQfgVuCm8IhnJ2CxmdUAZxA1d2Tv0xeYZ2Y3AI8TfZUEeAw4mqgGNrEZwm+Qma0DLgIuJUoqKyQdFlafAfwjfPB7mdnzwGVEP4P2RNdwoRQNtxi+FSRG0p6S+mUUDSAaZXA+UY0aomTdFC+x9Qmx09mafKcBXwFqzGwDMBM4nyhpQ56vPab5bP05HEf0ra4hx0tqLakb0R+bVxvY3tXDH99ufrVf+SuIaif3EfUaALgFeETSmcAEcteSTgbOkLSZqNngagAz2yTpeWBlaNdMBTN7XdIs4DSiMQduldQWmEdUyy8H7g/NHiJql10paTTwR2BWSODvA99MMLT2wI2KuvlVAe8RNXH0B+4I55/SxHNcCNwl6edETSfnAITeIR8BL4ftXiT6+bwZlvN97XHcBjwu6Q3q/reYbRZRc0Z3YHRjer64rbwrXYkIH+IZwHfDTSXnmo0a6ILntp83a5QARa/KeQ+Y7InZudLgNWfnnEshrzk751wKeXJ2zrkU8uTsnHMp5MnZ5Y0+Ozrdw6ELXWOPtWWsB0m3h5ugdW07RGFcku08x5ZxNeKUZ22zzdgTDWx/laSfbW+M7vPDk7PLp/XhCbd9gE1sHTcD2PICzO1mZueZ2Vv1bDKE6CEP54qWJ2fXXF4E9gi12hclPQG8FUZpu1bSq2H0tvMBwqhtN0l6V9KzRANDEdZNkTQozB+taAS/N8LIb32I/ghcEmrth0naQdIj4RyvSjok7NtN0jOKxnS+neghmHpJ+quk18I+w7PWXRfKJ4enP5H0BUkTwj4vSvpSEj9MV/r8CUGXd6GGfAzRk2YQjaGxj5m9HxLcKjM7IIzM9i9JzwADgT2JBn/qCbxF1pghIQHeBhwejtXVzJYrGsZzywMRksYB15nZPyX1Jno8uj/RqHj/NLNRkr4BnBvjcn4QztGGaByQR8IgU+2IXvJ5iaRfh2OPJHoh6ggzm6toMP9biEZuc65enpxdPtU+qg5RzfkOouaGaWb2figfBuyrrWMHdwL6EY3Y9mB4FH2RojGMsx0EvFB7rHpGazsS2CsMVQHQUVL7cI4Twr5PSVoR45oukvSdMN8rxLoMqAEeCuX3A4+Gc3wFeDjj3K1inMM5T84ur2pHp9siJKnMcRoEXGhmE7O2OzbBOMqAg8IgQ9mxxCZpCFGiP9jM1ikaiD975LpaFs67Mvtn4Fwc3ubsCm0i8CNJFQCSvqhoYPkXgFNCm/RORIPOZ3sZOFzS7mHfrqF8NdAhY7tniAYhImxXmyxfAL4Xyo4BujQQaydgRUjMXyKqudcqA2pr/98jai75FHhf0nfDOSRpvwbO4RzgydkV3u1E7ckzFL2K609E3+geA+aGdfcSvWTgM8zsE6KR5B4No6fVNis8CXyn9oYg0bClg8INx7fY2mvk/xIl9zlEzRsfNhDrBKCFpLeB37J1VDmIvg0MDtdwBDAqlJ8OnBvimwMk/botV6J8bA3nnEshrzk751wKeXJ2zrkU8uTsnHMp5MnZOedSyJOzc86lkCdn55xLIU/OzjmXQv8fbABfGmOzNNQAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 2 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aiyo7JC3PPLg3l3Xkfata9g7GNvMOPlvVn6bmnSoTVaUbFx2XUr6Nt/Kx9tacUVZx7KwJM3M/DkzfznT1ZQVAz3/LQHf7ytG5f+18qGT5hSkx/twhN/KOOaW5c2vHMe+uFPz2TT5rZJhxFbdLPPRwgmZWvo/H0UUR/E7yQdULasL2/Du/M6ArD1o2LeX9Sesm7bE46qabp2r6Bv/60AtO9QRe9DtlO+soRjh26mKFQRjjj2I8pXliQYZfPNebUDm9fnfZ2noGRrhGBLK4TknOllQv9BSQMkvRL6H06o7n8o6SpJb4ftfwzbSkMfxdckvSmpwQ7iLa1bz60cfMQW5s/aK+lQmu2Dpa15d047Dh/40ce2T3qkC8edujmhqFxDzMRN107mdz/7K8NPfSfpcGJrRFe6VCmYj/gwRPI0drcB3Q9caWZTJV1P1Hj/PWAkcKCZbZfUOew7CnjWzP4zbHtN0j/M7MMaZVwGXAbQtlWH3F9U0LZ9BaNumctdNx3C1g/z+0+29cNW3HDpAYy4fjmlHat2bX/41u4UFRunfnl9gtG5+lw95izWri+l815bueknk1m6ohOz5++bdFj1MkRVnk62n59Rf1w7STOBD4DuwBRJnYDOZjY17DMeODmszwIekvR1oCJsOwMYGc7zPNAW6FOzIDO7q7qDemu1TLtbUXEVo26Zy/NPduelf+zTImXmSsVOuOHSAzj1y+s5cfjGXdsnP9qF1/6xFz++/T2Uvm+XLli7PrrXsWFTO/75eh8OO7g84YjisZhL2hRCct5qZgOA/QHRcJvzvxFNQjIQeF1ScTjunIzJS/qY2bycRh2L8b3r32HpovZMGN+74d1TzAx++4M+9O67nXMuX7Nr++vPdeSxO7oxZtwi2rZP438RB9C2zU7atd25a/3Y/itYsqxzA0elgGVvbo2Wlt/fkTOY2UeSrgL+AtwBrJd0kplNA74BTJXUCuhtZs9JepGoM3gHov6HV0q60sxM0jFm9mZS11Kt38CNnHb2Kha/U8ptj78OwPhbDmL6tK4JR9Z4c18r5Zn/68KBR2zlW8OiLmbfvHYFd/x3L3ZuF9eeewgAhx/7Id/9xbIkQ22WkXe8R/8hW+jUpYIHp7/NA7/pzqRH8u/vVVPnTtsY8/1nASgqMp7754FMfytPunXm6Wd+wSRnADN7U9Is4HzgIuBOSe2BRcA3iTqAPxiaPQSMNbMNkm4AbgFmhQS+GPh8IheR4e0ZnRl+5NCkw8iKo47/kEkrZu6xffBpKfiCkkU3fXv/pEPIiQ9Wd2TEyNTdJ48ljbXiOOpMzpJuo57PHDO7KicRNZKZdajxOrNn/Am1HHJiLefYShjZ45wrHAZUVRVYcgamt1gUzjmXCwYUWs3ZzMZnvpbU3sw+qmt/55xLozT2YY6jwd4akoZIehuYH14fLemOnEfmnHPZkKd96eJ0pbsF+BywFsDM3mJ3n2HnnEuxeN3o0njTMFZvDTNbqo+PDsjfacOcc58sKawVxxEnOS+V9GnAJJUA3wUKq/+Tc64wGVie9taI06wxgmjU3X7ACqJnZRXMzG/OuUKnmEu6NFhzNrNy4GstEItzzmVfnjZrxOmtcZCkv0paI2m1pImSDmqJ4Jxzrtmy1FsjTCu8WtKcjG2/kjQ/Y2riWicckbRE0mxJMyXFGkMSp1njYeBPQA+gJ/AY8EickzvnXKKqB6HEWRo2DjizxrYpwFFm1h/4F3BtPcefEiZWGxSnsDjJub2ZPWBmFWF5kGhKTeecS71sTbZvZi8QPW0pc9tkM6ueevgVIGuzQdU3t0aXsPp3SSOBPxJ9Dp1LIx9U6JxziYnfW6OsRpPDXWZ2VyNK+k/g0TreM2CyJAN+H+e89d0QfCOcsPrKMicGMuqvvjvnXCoo/g3B8rhNDnuUIY0ienjHQ3XscqKZLZfUjeiBIPNDTbxO9c2tcWBTgnTOudRogaHZki4mmmL4NLPaG0jMbHn4uVrSBGAw0LTkXKPwo4B+ZLQ1m9n9sSJ3zrnExL7Z17SzS2cCPwI+W9fEcJJKgVZmtjmsnwFc39C5G0zOkkYDQ4mS81PAWcCLRA9Qdc65dMtSzVnSI0S5sEzSMqKHRl8LtCFqqgB4xcxGSOoJ3GNmw4mebTohvF8MPGxmTzdUXpya81eAo4E3zeybkroDDzb6ypxzLglVDe8Sh5mdX8vme+vYdwUwPKwvIsqhjRInOW81sypJFZL2AlYD+f20UefcJ0MhTrafYXoY9XI3UQ+OLcDLOY3KOeeypBG9NVIlztwa3w6rd0p6GtjLzGblNiznnMuSQkvOkgbW956ZzchNSM455+qrOf+mnvcMODXLseQVa9eanf0PSDqMrPtczwFJh5AzJ83alnQIOTHlJyVJh5BqBdesYWantGQgzjmXdUZjhm+nSqxBKM45l7cKrebsnHOFoOCaNZxzriDkaXKO8yQUSfq6pOvC6z6SBuc+NOecy4IsPQmlpcWZbP8OYAhQPXRxM/C7nEXknHNZIou/pE2cZo3jzWygpDcBzGy9pNY5jss557KjgHtr7JRURKj4S9qHrE0l4pxzuZXGWnEccZo1xgITgG6SfkY0XeiNOY3KOeeyJU/bnOPMrfGQpDeA04geWfVFM5uX88icc665UtqeHEecyfb7AB8Bf83cZmbv5zIw55zLijxNznGaNZ4E/hZ+PgMsAv6ey6Cccy5bVBVvafA80n2SVkuak7Gti6QpkhaEn3vXcexFYZ8Fki6KE3eDydnMPmVm/cPPvkQPJvT5nJ1znzTjgDNrbBsJPBNy4zPh9cdI6kL0SKvjifLn6LqSeKY4NeePCVOFHt/Y45xzLhFZuiFoZi8A62psPhsYH9bHA1+s5dDPAVPMbJ2ZrQemsGeS30OcNufvZ7xsBQwEVjR0nHPOJS73NwS7m9nKsP4B0cNca9oPWJrxelnYVq84/Zw7ZqxXELU9Px7jOOecS1785FwmaXrG67vM7K7YxZiZlL2PgnqTcxh80tHMfpitAp1zrkXFT5flZjaokWdfJamHma2U1IPoAdg1LQeGZrzuBTzf0InrbHOWVGxmlcBnGherc86lg8heb406PAFU9764CJhYyz6TgDMk7R1uBJ4RttWrvprza0TtyzMlPQE8BnxY/aaZ/Tle7M45l5AstjlLeoSoBlwmaRlRD4ybgD9JugR4D/hq2HcQMMLMLjWzdZJuAF4Pp7rezGreWNxDnDbntsBaomcGGtGHkQGenJ1z6Zel5Gxm59fx1mm17DsduDTj9X3AfY0pr77k3C301JjD7qS8q6zGFOKcc4nJ02xVX3IuAjrw8aRcLU8v1zn3SVOIc2usNLPrWywSV6sHxj7G1q0lVFWJyqpWfGfUvycdUlZ8/7fvc/ywzWwoL+byUw9LOpxm+dd1xaybWkRJF+PYCTsA2DJfLLyhhKodoCI4ZNROOn4qT7NE0KHddq65cBoH7rcOTPxi/MnMXVRbt96UydNfe33JudkzVEsaBVwAVBLNAX25mb3ahPNcBXwLmEE0umaQmV3R3PjyxQ9/eiabNrdNOoysmvxoF574QxnX3Lq04Z1TrvsXKul5XiXvjCrZtW3xzcX0GVFBl5OqWDetFYtvLqH/fTsSjLL5rjz3ZV6b24vRvx9GcVElbVtXJB1Sw6xZPTESVd/w7T0auRtD0hDg88BAM+sPDOPjo2Qa49vA6Wb2tebE1BiS/OG3OTTn1Q5sXl8Yv+JOg4ziTjU2CipD36aKzdB6nzytvgWl7XZw9KErefLF6FtORWURW7a2STiqmAptPuc4XT0a0IOoU/f2cL5yAElLiGq+5aG7ya/NbKikMUAf4KDw8xYzGyvpzrDt75LuA9ZXFyDpAKI7oGXAGuCbRB2+F4ZjOhH1NDnFzF6Q9AJwCdHw89uAo4ASYIyZTZR0MfBlorb2IuCzzfwdNJuZuOnayZiJJ585lKeeze8mgE+Kg39UwZwRrVn0G8Dg6Pu3Jx1Ss/ToupkNm9sx8uKpHNJrHe+8V8Ztjw5h246Shg9OWL62OTd64qNGmAz0lvQvSXdIipPoDieaJKR65qYSMxtBlExPMbOba+x/GzA+1MwfAsaGgTPvAP2AE4maQk6S1AbobWYLgFHAs2Y2GDgF+JWk0nDOgcBXzGyPeCVdJmm6pOk7dn5Y8+2cuHrMWXz7J19g1C+G8YUz5vOpwz9okXJd86z8UxEHXbOT46ds56BrdrJgdPqTWH2Kiqro26eciVP7celPv8y2HcVccOZbSYcVT57WnHOWnM1sC3AscBlRrfbRUDOtz5Nmtj3UsldT+yQimYYAD4f1B4iSMcA04OSw/DxsP47dncDPAEZKmkk0jLItUW0dwuxRdVzTXWY2yMwGtS4prW2XrFu7Pipnw6Z2/PP1Phx2cHmLlOuaZ9UTRXQdFjV2lp1RxeY5uawH5d6a9aWsWV/KvMXdAJj6xoEcun8e/FuMm5g/SckZwMwqzex5MxsNXAGcQzR5UnW5Ne9yZX73qyTeIJnavACcRFQDfwroTDSyZ1p4X8A5ZjYgLH0yHr3VMlXiGNq22Um7tjt3rR/bfwVLlnVOOCoXR+t9jI3To3/mG15tRbs+Kfzf3wjrNrVnzfpSenffAMDAI1awZEWDUxInTkTNGnGWtMnZHRlJhwFVoRkBYADR8MZ2RDXqvxMl6+Z4CTiPqNb8NXYn39fCtkVmti3UkC8nukEJ0bj2KyVdGWaSOsbM3mxmLFnXudM2xnz/WQCKiozn/nkg09/qlXBU2THyjvfoP2QLnbpU8OD0t3ngN92Z9EjXpMNqkvk/KmHD9FZUbIBXh7Vh/29X0Hf0Thb9ogSrhFat4ZDRO5MOs9lufeQz/Nclz1FSXMWK8o7cNC7xWzKxpDHxxpHL2+UdgNskdSaqLS8kauI4Arg3jDV/vpllXAn8QdI17L4hiJltl7QUeCXsNw04H5gdXt8A3ALMktQKWMzuxJ0aH6zuyIiRZycdRk7c9O39kw4haw7/Ze2J95hH87vrXE0Ll3Xl8hu/lHQYjefJ+ePM7A3g07W8NQ04tJb9x9R4fVTG+gEZ6+OIHheDmb1HNOdHbeWflLH+MLvbpjGzrUQ16ZrH7Dq3c65AeHJ2zrmUSWl7chyenJ1zhc2Ts3POpU++Dt/25OycK2jerOGcc2mT0gEmceT3sCXnnGtIlkYISjpM0syMZZOk79XYZ6ikjRn7XNfUsL3m7JwrWNUjBLPBzN4hGkyHpCKiSdYm1LLrNDNr9rgJT87OuYKmqpy0a5wGvBvGWuSEN2s45wpX4yY+KquedTIsl9Vz5vOAR+p4b4iktyT9XdKRTQ3da87OuYLWiGaNcjMb1OD5pNbAF4Bra3l7BrC/mW2RNBz4C9A3dgQZvObsnCts2Z8y9Cxghpmt2qMos01humTM7CmgRFJZU8L25OycK2g5mDL0fOpo0pC0rySF9cFEOXZtU+L2Zg3nXGHL4v3A8MSk08mYOE3SCAAzuxP4CvAtSRXAVuA8M2tSBJ6cnXOFK8tP3zazD4GuNbbdmbF+O3B7Nsry5OycK1jZ7Ofc0jw5O+cKW9NaFRLnydk5V9C85uycc2mTxxMfeXJ2zhU0n8/ZOedSyJOzc86ljeE3BD9ptHUHJbOWJB1G1n34+cFJh5Az/7xwS9Ih5MTp97+QdAg58+Jfm38OvyHonHNp5MnZOefSxQehOOdcGpnlarL9nPPk7JwrbPmZmz05O+cKmzdrOOdc2hjgzRrOOZdC+ZmbPTk75wqbN2s451wK5WtvDX+GoHOucMV9uGvM/C1piaTZkmZKml7L+5I0VtJCSbMkDWxq6F5zds4VrGgQStZrzqeYWXkd750F9A3L8cD/hp+N5jVn51xhq4q5ZMfZwP0WeQXoLKlHU07kydk5V9BkFmsByiRNz1guq+V0BkyW9EYd7+8HLM14vSxsazRv1nDOFa7GPQml3MwGNbDPiWa2XFI3YIqk+WaWk2kBvebsnCtg0dwacZZYZzNbHn6uBiYANefYXQ70znjdK2xrNE/OzrnCZhZvaYCkUkkdq9eBM4A5NXZ7Argw9No4AdhoZiubErY3azjnCpdl9TFV3YEJkiDKnQ+b2dOSRgCY2Z3AU8BwYCHwEfDNphbmydk5V9iy1JXOzBYBR9ey/c6MdQO+k43yPDk75wpbfg4Q9OTsnCtsqsrPx297cnbOFS4jmwNMWpQnZ+dcwRKWi+HbLcKTc4qV7bsQIcYAAA5FSURBVLuNH/x8Pnt33YEZPP1YTyY+2CvpsLKiQ7vtXHPhNA7cbx2Y+MX4k5m7qHvSYWVFq1ZVjL11EuVr2zNmzGeTDqfJ/nVdMeumFlHSxTh2wg4AtswXC28ooWoHqAgOGbWTjp9KefLz5JwMSV2BZ8LLfYFKYE14PdjMdmTsewDwNzM7StIg4EIzu6oFw22Uygpxzy8P5t15HWnXvoKxj73BjJf3Zum7pUmH1mxXnvsyr83txejfD6O4qJK2rSuSDilrzj77X7y/tBPt2+9MOpRm6f6FSnqeV8k7o0p2bVt8czF9RlTQ5aQq1k1rxeKbS+h/3456zpICeZqc834QipmtNbMBZjYAuBO4ufp1ZmKu5bjpaU7MAOvL2/DuvI4AbP2omPcXtaes2/aEo2q+0nY7OPrQlTz54mEAVFQWsWVrm4Sjyo6yrh8x+LgVTJp0UNKhNFunQUZxpxobBZUfRqsVm6H1PilPfNVtzi038VHW5H3NuTaSxhHVkP8vvN5iZh1q7DMU+KGZfV7SGOBg4BCgDPilmd3dokE3oFvPrRx8xBbmz9or6VCarUfXzWzY3I6RF0/lkF7reOe9Mm57dAjbdpQ0fHDKXX75DO69bwDt2uV3rbkuB/+ogjkjWrPoN4DB0fenv7KQr7018r7mnEX9gVOBIcB1knomHM8ubdtXMOqWudx10yFs/TD/P0+Liqro26eciVP7celPv8y2HcVccOZbSYfVbIMHL2fDhjYsXNgl6VByZuWfijjomp0cP2U7B12zkwWj0/6BGnPodgqbPjw57zbRzLaGSbSfY88JTZB0WfV0gjtsW4sEVVRcxahb5vL8k9156R/7tEiZubZmfSlr1pcyb3E3AKa+cSCH7l/X3OX5o1+/NZxwwnLG/eEJRv74JY7uv4prfvhS0mFl1aoniug6LKqJlp1RxeY5KU8hRt4m5/yvhtWugvDBI6kV0DrGMTX/Onv8tczsLuAugE7FLdHYZnzv+ndYuqg9E8b3bnj3PLFuU3vWrC+ld/cNLF3VmYFHrGDJir2TDqvZxo0bwLhxAwD41KdWcc458/nVrz+dcFTZ1XofY+P0VnQ+rooNr7aiXZ/0JbU95GerRsEm5yXAscCfgC8Acb57nS3p50ApMBQYmavg4uo3cCOnnb2Kxe+UctvjrwMw/paDmD6ta8KRNd+tj3yG/7rkOUqKq1hR3pGbxuVvl7NCNf9HJWyY3oqKDfDqsDbs/+0K+o7eyaJflGCV0Ko1HDI6/W3r3s85Xe4GJkp6C3ga+DDGMbOImjPKgBvMbEUO44vl7RmdGX7k0KTDyImFy7py+Y1fSjqMnJk9uzuzZ+d3v+3Df1l74j3m0ZR3navJk3PyzGxMxssTMtZ/HN5fAhwV1p8Hns/YZ5aZXZjTAJ1zLcsMKvOzXaOgkrNzzu3Ba875q0aN2zlXSDw5O+dcyhgQ8/mAaZPyTorOOdccBlYVb2mApN6SnpP0tqS5kr5byz5DJW2UNDMs1zU1cq85O+cKl5HNG4IVwA/MbEZ40OsbkqaY2ds19ptmZp9vbmGenJ1zhS17zxBcCawM65slzQP2A2om56zwZg3nXGGLP3y7rHp6hrBcVtcpw/TDxwCv1vL2EElvSfq7pCObGrbXnJ1zBaxR82aUm9mghnaS1AF4HPiemW2q8fYMYH8z2yJpOPAXoG9jIq7mNWfnXOEyoKoq3hKDpBKixPyQmf15j+LMNpnZlrD+FFAiqawpoXtyds4VtizNSidJwL3APDP7bR377Bv2Q9Jgohy7tilhe7OGc66AZXX49meAbwCzJc0M234C9AEwszuBrwDfklQBbAXOM2vaHUlPzs65wmVgMfowxzqV2YuAGtjnduD2bJTnydk5V9jydISgJ2fnXGHzuTWccy5lzGL3xEgbT87OucLmNWfnnEsbwyorkw6iSTw5O+cKVx5PGerJ2TlX2LLUla6leXJ2zhUsA8xrzs45lzJmXnN2zrk0ytcbgmrisO9PPElrgPdaqLgyoLyFympphXptfl3Zsb+Z7dPUgyU9TRRzHOVmdmZTy8o2T855QNL0OPPM5qNCvTa/LtdcPmWoc86lkCdn55xLIU/O+eGupAPIoUK9Nr8u1yze5uyccynkNWfnnEshT87OOZdCnpxbmKRKSTMlzZX0lqQfSKr37yCpp6T/a6kYmyvjGudI+qukzknHVBtJo8LfYVaI9/gmnucqSfMkPSTpYklZeUxRS5HUNVz/TEkfSFqe8bp1jX0PkDQnrA+SNDaZqAuftzm3MElbzKxDWO8GPAz808xGJxtZ9tS4xvHAv8zsZwmH9TGShgC/BYaa2fbw+PrWZraiCeeaDwwzs2WSLgYGmdkV2Y14jzKLzawiB+cdA2wxs1/X8f4BwN/M7Khsl+0+zmvOCTKz1cBlwBWKHCBpmqQZYfk07FFbOVLSa6FWM0tSX0nXS/pe9Xkl/UzSd5O5qj28DOwHIGmApFdC3BMk7R22XyXp7bD9j2FbqaT7wrW+KensLMfVg2hE2HYAMys3sxWSloREXV0zfD6sjwnxPC9pkaSrwvY7gYOAv0u6OrOA8Hd7NlzXM5L6SCqStDj8vTuHbxknh/1fCH/PWq891MqfkPQs8EyWfx8fI2mcpK9kvN5Syz5DJf0trI+R9ICklyUtkPT/chnfJ4En54SZ2SKgCOgGrAZON7OBwLlAbV8ZRwC3mtkAYBCwDLgPuBAgNJGcBzyY++jrJ6kIOA14Imy6H/ixmfUHZgPV3xZGAseE7SPCtlHAs2Y2GDgF+JWk0iyGNxnoLelfku6Q9NkYxxwOfA4YDIyWVGJmI4AVwClmdnON/W8DxofreggYa2aVwDtAP+BEYAZwkqQ2QG8zW0D91z4Q+IqZxYm3pfUHTgWGANdJ6plwPHnNk3O6lAB3S5oNPEb0H7iml4GfSPox0bwDW81sCbBW0jHAGcCbZra2pYKuRTtJM4EPgO7AFEmdgM5mNjXsMx44OazPAh6S9HWg+qv6GcDIcJ7ngbZAn2wFaGZbgGOJvrmsAR4NTRL1edLMtptZOdEHafcG9h9C1GwF8ABRMgaYRnTtJwM/D9uPA14P79d37VPMbF1D15eQieHfYznwHNGHmGsiT84Jk3QQUEn0n/1qYBVwNFGtuHXN/c3sYeALwFbgKUmnhrfuAS4GvklUk07S1lCz3x8Q8J0G9v834HdEtcLXJRWH484xswFh6WNm87IZpJlVmtnzob3/CuAcog+H6v8XbWscsj1jvZKmz+r4AnASUfJ6CugMDCVK2lD/tX/YxDIba9fvIXwb2+PfYi1q3sDyG1rN4Mk5QZL2Ae4EbrfozmwnYKWZVQHfIGruqHnMQcAiMxsLTCT6KgkwATiTqAY2qQXCb5CZfQRcBfyAKKmsl3RSePsbwNTwH7+3mT0H/Jjod9CB6BqulCSA8K0gayQdJqlvxqYBRLMMLiGqUUOUrJvjJaImJoCvsTv5vgZ8Gqgys23ATOByoqQNOb72mJaw+/fwBaJvdQ05W1JbSV2JPmxeb2B/Vw+fz7nlVX/lLyGqnTxA1GsA4A7gcUkXAk9Tey3pq8A3JO0kaja4EcDMdkh6DtgQ2jVTwczelDQLOB+4CLhTUntgEVEtvwh4MDR7iKhddoOkG4BbgFkhgS8GPp/F0DoAtynq5lcBLCRq4jgCuDeU/3wzy7gS+IOka4iaTr4JEHqHLAVeCftNI/r9zA6vc33tcdwNTJT0FnX/W6xpFlFzRhlwQ1N6vrjdvCtdgQj/iWcA/xFuKjnXYtRAFzzXeN6sUQAk9SOq+T3jidm5wuA1Z+ecSyGvOTvnXAp5cnbOuRTy5OyccynkydnljD4+O91joQtdU8+1a64HSfeEm6B17TtUYV6SRpaxa16NONtr7LPH3BMN7D9G0g8bG6P75PDk7HJpaxjhdhSwg93zZgDRzGpNOamZXWpmb9ezy1CiQR7O5S1Pzq6lTAMOCbXaaZKeAN4Os7T9StLrYfa2ywHCrG23S3pH0j+IJoYivPe8pEFh/UxFM/i9FWZ+O4DoQ+DqUGs/SdI+kh4PZbwu6TPh2K6SJiua0/keokEw9ZL0F0lvhGMuq/HezWH7M2H0J5IOlvR0OGaapMOz8ct0hc9HCLqcCzXks4hGmkE0h8ZRZrY4JLiNZnZcmJntn5ImA8cAhxFN/tQdeJsac4aEBHg3cHI4VxczW6doGs9dAyIkPQzcbGYvSupDNDz6CKJZ8V40s+sl/RtwSYzL+c9QRjuieUAeD5NMlQLTzexqSdeFc19B9EDUEWa2QNFk/ncQzdzmXL08Obtcqh6qDlHN+V6i5obXzGxx2H4G0F+75w7uBPQlmrHtkTAUfYWiOYxrOgF4ofpc9czWNgzoF6aqANhLUodQxpfDsU9KWh/jmq6S9KWw3jvEuhaoAh4N2x8E/hzK+DTwWEbZbWKU4ZwnZ5dT1bPT7RKSVOY8DQKuNLNJNfYbnsU4WgEnhEmGasYSm6ShRIl+iJl9pGgi/poz11WzUO6Gmr8D5+LwNmeXtEnAtySVAEg6VNHE8i8A54Y26R5Ek87X9ApwsqQDw7FdwvbNQMeM/SYTTUJE2K86Wb4AXBC2nQXs3UCsnYD1ITEfTlRzr9YKqK79X0DUXLIJWCzpP0IZknR0A2U4B3hydsm7h6g9eYaiR3H9nugb3QRgQXjvfqKHDHyMma0hmknuz2H2tOpmhb8CX6q+IUg0bemgcMPxbXb3GvkfouQ+l6h54/0GYn0aKJY0D7iJ3bPKQfRtYHC4hlOB68P2rwGXhPjmAtl+3JYrUD63hnPOpZDXnJ1zLoU8OTvnXAp5cnbOuRTy5Oyccynkydk551LIk7NzzqWQJ2fnnEuh/w9QhWX5xULV0QAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"RmdcZMDUbibB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":623},"executionInfo":{"status":"ok","timestamp":1597555107668,"user_tz":-600,"elapsed":336471,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"22af500f-b298-4c0a-8219-099558a0c5d2"},"source":["# plot\n","import matplotlib.pyplot as plt\n","plt.figure(figsize=(10,10))\n","plt.plot([int(i) for i in c_values], svm_accuracy)\n","plt.title('C value vs accuracy', fontsize=15, fontweight='bold')\n","plt.xlabel('C', fontweight='bold')\n","plt.ylabel('Accuracy', fontweight='bold')\n","plt.show()"],"execution_count":56,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"TzKDbOxPPuFp","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":679},"executionInfo":{"status":"ok","timestamp":1597555139345,"user_tz":-600,"elapsed":368131,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"89c10eea-a2e7-489e-d4a7-9a98ab967b54"},"source":["# for test set\n","values = zip(c_values, svm_accuracy)\n","\n","def Sort_Tuple(tup):  \n","    return(sorted(tup, key = lambda x: x[1], reverse=True))   \n","\n","c_value = Sort_Tuple(values)[0][0]\n","\n","\n","def svm_classifier(c=c_value):\n","    svm_classifier = SVC(C = c, kernel = 'linear')\n","    svm_classifier.fit(training_word_histograms, training_flower_labels)\n","\n","    # test all images\n","    test_file_names = []\n","    test_flower_labels = []\n","    for i in range(0, len(flowers)):\n","        sub_path = path + 'Test/' + flowers[i] + '/'\n","        sub_file_names = [os.path.join(sub_path, f) for f in os.listdir(sub_path)]\n","        sub_flower_labels = [i] * len(sub_file_names) #create a list of N elements, all are i\n","        test_file_names += sub_file_names\n","        test_flower_labels += sub_flower_labels\n","    \n","    word_histograms = dictionary.create_word_histograms(test_file_names)\n","    predicted_flower_labels = svm_classifier.predict(word_histograms)\n","\n","    print(f'When C value (C) is {c}')\n","    accuracy = accuracy_score(test_flower_labels, predicted_flower_labels)*100\n","    classifier_accuracy.append(('SVM', accuracy))\n","    print(f'Accuracy score is {accuracy:.2f}%')\n","    cm = confusion_matrix(test_flower_labels, predicted_flower_labels)\n","    recognisation_accuracy = (cm[0][0] + cm[1][1] + cm[2][2])\n","    print(f'Recognition accuracy is {recognisation_accuracy}/{len(test_file_names)}')\n","    print(f'Confusion Matrix:\\n{cm}')\n","    print(plot_confusion_matrix(svm_classifier, word_histograms, test_flower_labels, display_labels=flowers))\n","    plt.title(f'C = {c}')\n","    print(f'Classification Report:\\n{classification_report(test_flower_labels, predicted_flower_labels)}')\n","    return f'Success when C={c}'\n","\n","svm_classifier()"],"execution_count":57,"outputs":[{"output_type":"stream","text":["When C value (C) is 20\n","Accuracy score is 46.22%\n","Recognition accuracy is 39/119\n","Confusion Matrix:\n","[[10  7  3 10]\n"," [ 0 16  2 12]\n"," [ 3  2 13 11]\n"," [ 3 10  1 16]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d440f2d68>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.62      0.33      0.43        30\n","           1       0.46      0.53      0.49        30\n","           2       0.68      0.45      0.54        29\n","           3       0.33      0.53      0.41        30\n","\n","    accuracy                           0.46       119\n","   macro avg       0.52      0.46      0.47       119\n","weighted avg       0.52      0.46      0.47       119\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'Success when C=20'"]},"metadata":{"tags":[]},"execution_count":57},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"pycharm":{"name":"#%%\n"},"id":"P1ZuRULnmNUA","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1597555139347,"user_tz":-600,"elapsed":368122,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}}},"source":["'''Adaboost'''\n","\n","# store values for plotting\n","n_values= []\n","ada_accuracy = []\n","\n","def adaboost_classifier(n_estimator):\n","    adb_classifier = AdaBoostClassifier(n_estimators = n_estimator, random_state = 0)\n","    adb_classifier.fit(training_word_histograms, training_flower_labels)\n","\n","    # test all images\n","    val_file_names = []\n","    val_flower_labels = []\n","    for i in range(0, len(flowers)):\n","        sub_path = path + 'Validation/' + flowers[i] + '/'\n","        sub_file_names = [os.path.join(sub_path, f) for f in os.listdir(sub_path)]\n","        sub_flower_labels = [i] * len(sub_file_names) #create a list of N elements, all are i\n","        val_file_names += sub_file_names\n","        val_flower_labels += sub_flower_labels\n","    \n","    word_histograms = dictionary.create_word_histograms(val_file_names)\n","    predicted_flower_labels = adb_classifier.predict(word_histograms)\n","\n","    print(f'When n_estimators is {n_estimator}')\n","    accuracy = accuracy_score(val_flower_labels, predicted_flower_labels)*100\n","    n_values.append(n_estimator) # will be used for plotting\n","    ada_accuracy.append(accuracy) # will be used for plotting\n","    print(f'Accuracy score is {accuracy:.2f}%')\n","    cm = confusion_matrix(val_flower_labels, predicted_flower_labels)\n","    recognisation_accuracy = (cm[0][0] + cm[1][1] + cm[2][2])\n","    print(f'Recognition accuracy is {recognisation_accuracy}/{len(val_file_names)}')\n","    print(f'Confusion Matrix:\\n{cm}')\n","    print(plot_confusion_matrix(adb_classifier, word_histograms, val_flower_labels, display_labels=flowers))\n","    plt.title(f'n_estimators = {n_estimator}')\n","    print(f'Classification Report:\\n{classification_report(val_flower_labels, predicted_flower_labels)}')\n","    return f'Success when n_estimetors={n_estimator}'"],"execution_count":58,"outputs":[]},{"cell_type":"code","metadata":{"id":"nQG-q3B3d82k","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1597555277711,"user_tz":-600,"elapsed":506467,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"9c715ace-60a0-4f82-b17c-5c868630dc9a"},"source":["n_estimators = [50, 100, 150, 200, 250]  # values\n","for n_estimator in n_estimators:\n","    adaboost_classifier(n_estimator)"],"execution_count":59,"outputs":[{"output_type":"stream","text":["When n_estimators is 50\n","Accuracy score is 40.00%\n","Recognition accuracy is 32/120\n","Confusion Matrix:\n","[[ 8  6  5 11]\n"," [ 4 18  3  5]\n"," [ 4  6  6 14]\n"," [ 4  6  4 16]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4e9fc940>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.40      0.27      0.32        30\n","           1       0.50      0.60      0.55        30\n","           2       0.33      0.20      0.25        30\n","           3       0.35      0.53      0.42        30\n","\n","    accuracy                           0.40       120\n","   macro avg       0.40      0.40      0.38       120\n","weighted avg       0.40      0.40      0.38       120\n","\n","When n_estimators is 100\n","Accuracy score is 43.33%\n","Recognition accuracy is 42/120\n","Confusion Matrix:\n","[[12  5  6  7]\n"," [ 3 19  2  6]\n"," [ 4  9 11  6]\n"," [ 4 12  4 10]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4f0ebd30>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.52      0.40      0.45        30\n","           1       0.42      0.63      0.51        30\n","           2       0.48      0.37      0.42        30\n","           3       0.34      0.33      0.34        30\n","\n","    accuracy                           0.43       120\n","   macro avg       0.44      0.43      0.43       120\n","weighted avg       0.44      0.43      0.43       120\n","\n","When n_estimators is 150\n","Accuracy score is 43.33%\n","Recognition accuracy is 43/120\n","Confusion Matrix:\n","[[12  4  6  8]\n"," [ 4 18  3  5]\n"," [ 3 10 13  4]\n"," [ 4 11  6  9]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d4d7e2978>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.52      0.40      0.45        30\n","           1       0.42      0.60      0.49        30\n","           2       0.46      0.43      0.45        30\n","           3       0.35      0.30      0.32        30\n","\n","    accuracy                           0.43       120\n","   macro avg       0.44      0.43      0.43       120\n","weighted avg       0.44      0.43      0.43       120\n","\n","When n_estimators is 200\n","Accuracy score is 50.00%\n","Recognition accuracy is 48/120\n","Confusion Matrix:\n","[[14  6  5  5]\n"," [ 4 20  2  4]\n"," [ 4 10 14  2]\n"," [ 6  8  4 12]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d44122438>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.50      0.47      0.48        30\n","           1       0.45      0.67      0.54        30\n","           2       0.56      0.47      0.51        30\n","           3       0.52      0.40      0.45        30\n","\n","    accuracy                           0.50       120\n","   macro avg       0.51      0.50      0.50       120\n","weighted avg       0.51      0.50      0.50       120\n","\n","When n_estimators is 250\n","Accuracy score is 48.33%\n","Recognition accuracy is 45/120\n","Confusion Matrix:\n","[[12  7  4  7]\n"," [ 3 21  2  4]\n"," [ 3 11 12  4]\n"," [ 6  7  4 13]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d51e1ce48>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.50      0.40      0.44        30\n","           1       0.46      0.70      0.55        30\n","           2       0.55      0.40      0.46        30\n","           3       0.46      0.43      0.45        30\n","\n","    accuracy                           0.48       120\n","   macro avg       0.49      0.48      0.48       120\n","weighted avg       0.49      0.48      0.48       120\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"8jsagpfheM-F","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":624},"executionInfo":{"status":"ok","timestamp":1597555277713,"user_tz":-600,"elapsed":506453,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"ced513d2-cc82-4de2-90fa-ea90a5749baa"},"source":["# plot\n","import matplotlib.pyplot as plt\n","plt.figure(figsize=(10,10))\n","plt.plot([int(i) for i in n_values], ada_accuracy)\n","plt.title('n_estimators vs accuracy', fontsize=15, fontweight='bold')\n","plt.xlabel('n_estimators', fontweight='bold')\n","plt.ylabel('Accuracy', fontweight='bold')\n","plt.show()"],"execution_count":60,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"otaPxqdrQzE2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":679},"executionInfo":{"status":"ok","timestamp":1597555309455,"user_tz":-600,"elapsed":538188,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"aa742a8c-e499-4c6e-8654-a99b0098c88b"},"source":["'''Test set only'''\n","# for test set\n","values = zip(n_values, ada_accuracy)\n","\n","def Sort_Tuple(tup):  \n","    return(sorted(tup, key = lambda x: x[1], reverse=True))   \n","\n","n_value = Sort_Tuple(values)[0][0]\n","\n","def adaboost_classifier(n_estimator=n_value):\n","    adb_classifier = AdaBoostClassifier(n_estimators = n_estimator, random_state = 0)\n","    adb_classifier.fit(training_word_histograms, training_flower_labels)\n","\n","    # test all images\n","    test_file_names = []\n","    test_flower_labels = []\n","    for i in range(0, len(flowers)):\n","        sub_path = path + 'Test/' + flowers[i] + '/'\n","        sub_file_names = [os.path.join(sub_path, f) for f in os.listdir(sub_path)]\n","        sub_flower_labels = [i] * len(sub_file_names) #create a list of N elements, all are i\n","        test_file_names += sub_file_names\n","        test_flower_labels += sub_flower_labels\n","    \n","    word_histograms = dictionary.create_word_histograms(test_file_names)\n","    predicted_flower_labels = adb_classifier.predict(word_histograms)\n","\n","    print(f'When n_estimators is {n_estimator}')\n","    accuracy = accuracy_score(test_flower_labels, predicted_flower_labels)*100\n","    classifier_accuracy.append(('Adaboost', accuracy))\n","    print(f'Accuracy score is {accuracy:.2f}%')\n","    cm = confusion_matrix(test_flower_labels, predicted_flower_labels)\n","    recognisation_accuracy = (cm[0][0] + cm[1][1] + cm[2][2])\n","    print(f'Recognition accuracy is {recognisation_accuracy}/{len(test_file_names)}')\n","    print(f'Confusion Matrix:\\n{cm}')\n","    print(plot_confusion_matrix(adb_classifier, word_histograms, test_flower_labels, display_labels=flowers))\n","    plt.title(f'n_estimators = {n_estimator}')\n","    print(f'Classification Report:\\n{classification_report(test_flower_labels, predicted_flower_labels)}')\n","    return f'Success when n_estimetors={n_estimator}'\n","\n","adaboost_classifier()"],"execution_count":61,"outputs":[{"output_type":"stream","text":["When n_estimators is 200\n","Accuracy score is 33.61%\n","Recognition accuracy is 32/119\n","Confusion Matrix:\n","[[ 7 12  5  6]\n"," [ 4 15  3  8]\n"," [ 4 10 10  5]\n"," [ 3 18  1  8]]\n","<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f5d456b96a0>\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.39      0.23      0.29        30\n","           1       0.27      0.50      0.35        30\n","           2       0.53      0.34      0.42        29\n","           3       0.30      0.27      0.28        30\n","\n","    accuracy                           0.34       119\n","   macro avg       0.37      0.34      0.34       119\n","weighted avg       0.37      0.34      0.33       119\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'Success when n_estimetors=200'"]},"metadata":{"tags":[]},"execution_count":61},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"v5FmCtP4fBeK","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":519},"executionInfo":{"status":"ok","timestamp":1597555310066,"user_tz":-600,"elapsed":538793,"user":{"displayName":"Pradeep Thapa","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggx2Jn4575eOS7zcaBWc-GpBgdcYkQFvNePa2vilA=s64","userId":"02020823281178009959"}},"outputId":"ff8a0123-9b2d-4696-aef4-9338dfbb93e4"},"source":["# plot all accuracy -- this is accuracy only \n","plt.figure(figsize=(12,8))\n","plt.bar([i[0] for i in classifier_accuracy], [i[1] for i in classifier_accuracy])\n","plt.title('Classifier Accuracy', fontweight='bold', fontsize=16)\n","plt.xlabel('Classifiers', fontweight='bold', fontsize=14)\n","plt.ylabel('Accuracy', fontweight='bold', fontsize=14)\n","plt.xlim()\n","plt.show()"],"execution_count":62,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}