all the 10 programs

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    "1. Aim: Illustrate and Demonstrate the working model and principle of Find-S algorithm.\n",
    "\n",
    "\n",
    "Program: For a given set of training data examples stored in a .CSV file, implement and demonstrate the Find-S algorithm to output a description of the set of all hypotheses consistent with the training examples."
   ]
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     "text": [
      "['Sky AirTemp Humidity Wind Water Forecast EnjoySport']\n",
      "['Sunny Warm Normal Strong Warm Same Yes']\n",
      "['Sunny Warm High Strong Warm Same Yes']\n",
      "['Rainy Cold High Strong Warm Change No']\n",
      "['Sunny Warm High Strong Cool Change Yes']\n",
      "Most specific hypothesis is\n",
      "[['0', '0', '0', '0', '0', '0']]\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "# Open the CSV file\n",
    "with open('/kaggle/input/tennis-csv/tennis.csv', 'r') as f:\n",
    "    reader = csv.reader(f)\n",
    "    your_list = list(reader)\n",
    "\n",
    "# Initialize the hypothesis\n",
    "h = [['0', '0', '0', '0', '0', '0']]\n",
    "\n",
    "# Process each row in the dataset\n",
    "for i in your_list:\n",
    "    print(i)\n",
    "    if i[-1] == \"True\":\n",
    "        j = 0\n",
    "        for x in i:\n",
    "            if x != \"True\":\n",
    "                if x != h[0][j] and h[0][j] == '0':\n",
    "                    h[0][j] = x\n",
    "                elif x != h[0][j] and h[0][j] != '0':\n",
    "                    h[0][j] = '?'\n",
    "                else:\n",
    "                    pass\n",
    "            j += 1\n",
    "\n",
    "# Print the most specific hypothesis\n",
    "print(\"Most specific hypothesis is\")\n",
    "print(h)"
   ]
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   "source": [
    "2.Aim: Demonstrate the working model and principle of candidate elimination algorithm.\n",
    "Program: For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples."
   ]
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     "text": [
      "Steps of Candidate Elimination Algorithm 1\n",
      "[]\n",
      "[]\n",
      "Steps of Candidate Elimination Algorithm 2\n",
      "[]\n",
      "[]\n",
      "Steps of Candidate Elimination Algorithm 3\n",
      "[]\n",
      "[]\n",
      "Steps of Candidate Elimination Algorithm 4\n",
      "[]\n",
      "[]\n",
      "Steps of Candidate Elimination Algorithm 5\n",
      "[]\n",
      "[]\n",
      "\n",
      "Final specific hypothesis:\n",
      " []\n",
      "\n",
      "Final general hypothesis:\n",
      " []\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "# Opens the CSV file \"tennis.csv\" and reads its contents\n",
    "with open(\"/kaggle/input/tennis-csv/tennis.csv\") as f:\n",
    "    csv_file = csv.reader(f)\n",
    "    data = list(csv_file)\n",
    "\n",
    "# Initializes the specific hypothesis 's' and the general hypothesis 'gh'\n",
    "s = ['?' for i in range(len(data[0])-1)]\n",
    "gh = [['?' for i in range(len(s))] for j in range(len(s))]\n",
    "\n",
    "# Iterates through each data instance in 'data'\n",
    "for instance in data:\n",
    "    # Checks if the last attribute of the instance is \"Yes\"\n",
    "    if instance[-1] == \"Yes\":\n",
    "        # Updates 's' and 'gh' based on positive instance\n",
    "        for j in range(len(s)):\n",
    "            if instance[j] != s[j] and s[j] == '?':\n",
    "                s[j] = instance[j]\n",
    "            elif instance[j] != s[j] and s[j] != '?':\n",
    "                s[j] = '?'\n",
    "    elif instance[-1] == \"No\":\n",
    "        # Updates 'gh' based on negative instance\n",
    "        for j in range(len(s)):\n",
    "            if instance[j] != s[j]:\n",
    "                gh[j][j] = s[j]\n",
    "            else:\n",
    "                gh[j][j] = '?'\n",
    "\n",
    "    # Prints the steps of the Candidate Elimination Algorithm\n",
    "    print(\"Steps of Candidate Elimination Algorithm\", data.index(instance) + 1)\n",
    "    print(s)\n",
    "    print(gh)\n",
    "\n",
    "    # Removes inconsistent hypotheses from 'gh'\n",
    "    g = []\n",
    "    for i in gh:\n",
    "        if i not in g:\n",
    "            g.append(i)\n",
    "\n",
    "    # Ends the loop if 's' becomes the final hypothesis\n",
    "    if len(g) == 1:\n",
    "        break\n",
    "\n",
    "# Prints the final specific and general hypotheses\n",
    "print(\"\\nFinal specific hypothesis:\\n\", s)\n",
    "print(\"\\nFinal general hypothesis:\\n\", g)\n"
   ]
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   "source": [
    "3)Aim: To construct the Decision tree using the training data sets under supervised learning concept.\n",
    "Program: Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample"
   ]
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   "source": [
    "4)Aim: To understand the working principle of Artificial Neural network with feed forward and feed backward principle.\n",
    "Program: Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets."
   ]
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  {
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     "text": [
      "Constructed Decision Tree:\n",
      "{'Outlook': {'Overcast': 'Yes', 'Rain': {'Wind': {'Strong': 'No', 'Weak': 'Yes'}}, 'Sunny': {'Humidity': {'High': 'No', 'Normal': 'Yes'}}}}\n",
      "\n",
      "Classification Result for the Sample:\n",
      "No\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "# Defining the dataset\n",
    "data = [\n",
    "    [\"Outlook\", \"Temperature\", \"Humidity\", \"Wind\", \"PlayTennis\"],\n",
    "    [\"Sunny\", \"Hot\", \"High\", \"Weak\", \"No\"],\n",
    "    [\"Sunny\", \"Hot\", \"High\", \"Strong\", \"No\"],\n",
    "    [\"Overcast\", \"Hot\", \"High\", \"Weak\", \"Yes\"],\n",
    "    [\"Rain\", \"Mild\", \"High\", \"Weak\", \"Yes\"],\n",
    "    [\"Rain\", \"Cool\", \"Normal\", \"Weak\", \"Yes\"],\n",
    "    [\"Rain\", \"Cool\", \"Normal\", \"Strong\", \"No\"],\n",
    "    [\"Overcast\", \"Cool\", \"Normal\", \"Strong\", \"Yes\"],\n",
    "    [\"Sunny\", \"Mild\", \"High\", \"Weak\", \"No\"],\n",
    "    [\"Sunny\", \"Cool\", \"Normal\", \"Weak\", \"Yes\"],\n",
    "    [\"Rain\", \"Mild\", \"Normal\", \"Weak\", \"Yes\"],\n",
    "    [\"Sunny\", \"Mild\", \"Normal\", \"Strong\", \"Yes\"],\n",
    "    [\"Overcast\", \"Mild\", \"High\", \"Strong\", \"Yes\"],\n",
    "    [\"Overcast\", \"Hot\", \"Normal\", \"Weak\", \"Yes\"],\n",
    "    [\"Rain\", \"Mild\", \"High\", \"Strong\", \"No\"]\n",
    "]\n",
    "\n",
    "# Creating a DataFrame from the dataset\n",
    "df = pd.DataFrame(data[1:], columns=data[0])\n",
    "def entropy(target_col):\n",
    "    elements, counts = np.unique(target_col, return_counts=True)\n",
    "    entropy = np.sum([(-counts[i]/np.sum(counts)) * np.log2(counts[i]/np.sum(counts)) for i in range(len(elements))])\n",
    "    return entropy\n",
    "def information_gain(data, split_attribute, target_attribute):\n",
    "    total_entropy = entropy(data[target_attribute])\n",
    "    values, counts = np.unique(data[split_attribute], return_counts=True)\n",
    "    weighted_entropy = np.sum([(counts[i]/np.sum(counts)) * entropy(data[data[split_attribute] == values[i]][target_attribute]) for i in range(len(values))])\n",
    "    information_gain = total_entropy - weighted_entropy\n",
    "    return information_gain\n",
    "def ID3(data, original_data, features, target_attribute, parent_node_class=None):\n",
    "    if len(np.unique(data[target_attribute])) <= 1:\n",
    "        return np.unique(data[target_attribute])[0]\n",
    "    elif len(data) == 0:\n",
    "        return np.unique(original_data[target_attribute])[np.argmax(np.unique(original_data[target_attribute], return_counts=True)[1])]\n",
    "    elif len(features) == 0:\n",
    "        return parent_node_class\n",
    "    else:\n",
    "        parent_node_class = np.unique(data[target_attribute])[np.argmax(np.unique(data[target_attribute], return_counts=True)[1])]\n",
    "        item_values = [information_gain(data, feature, target_attribute) for feature in features]\n",
    "        best_feature_index = np.argmax(item_values)\n",
    "        best_feature = features[best_feature_index]\n",
    "        tree = {best_feature: {}}\n",
    "        features = [i for i in features if i != best_feature]\n",
    "        for value in np.unique(data[best_feature]):\n",
    "            value_sub_data = data[data[best_feature] == value]\n",
    "            subtree = ID3(value_sub_data, original_data, features, target_attribute, parent_node_class)\n",
    "            tree[best_feature][value] = subtree\n",
    "        return tree\n",
    "def classify(sample, tree):\n",
    "    for attribute in list(sample.keys()):\n",
    "        if attribute in tree.keys():\n",
    "            try:\n",
    "                result = tree[attribute][sample[attribute]]\n",
    "            except:\n",
    "                return None\n",
    "            result = tree[attribute][sample[attribute]]\n",
    "            if isinstance(result, dict):\n",
    "                return classify(sample, result)\n",
    "            else:\n",
    "                return result\n",
    "# List of features\n",
    "features = list(df.columns[:-1])\n",
    "\n",
    "# Target attribute\n",
    "target_attribute = df.columns[-1]\n",
    "\n",
    "# Constructing the decision tree\n",
    "decision_tree = ID3(df, df, features, target_attribute)\n",
    "\n",
    "# Sample to classify\n",
    "sample = {\n",
    "    \"Outlook\": \"Sunny\",\n",
    "    \"Temperature\": \"Cool\",\n",
    "    \"Humidity\": \"High\",\n",
    "    \"Wind\": \"Strong\"\n",
    "}\n",
    "\n",
    "# Classifying the sample\n",
    "classification_result = classify(sample, decision_tree)\n",
    "\n",
    "print(\"Constructed Decision Tree:\")\n",
    "print(decision_tree)\n",
    "\n",
    "print(\"\\nClassification Result for the Sample:\")\n",
    "print(classification_result)\n"
   ]
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   "outputs": [
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     "text": [
      "Epoch: 0, Loss: 0.3037\n",
      "Epoch: 1000, Loss: 0.5000\n",
      "Epoch: 2000, Loss: 0.5000\n",
      "Epoch: 3000, Loss: 0.5000\n",
      "Epoch: 4000, Loss: 0.5000\n",
      "Epoch: 5000, Loss: 0.5000\n",
      "Epoch: 6000, Loss: 0.5000\n",
      "Epoch: 7000, Loss: 0.5000\n",
      "Epoch: 8000, Loss: 0.5000\n",
      "Epoch: 9000, Loss: 0.5000\n",
      "Accuracy: 55.00%\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Activation function and its derivative\n",
    "sigmoid = lambda x: 1 / (1 + np.exp(-x))\n",
    "sigmoid_derivative = lambda x: x * (1 - x)\n",
    "\n",
    "# ANN class\n",
    "class NeuralNetwork:\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        self.W1 = np.random.randn(input_size, hidden_size)\n",
    "        self.b1 = np.zeros((1, hidden_size))\n",
    "        self.W2 = np.random.randn(hidden_size, output_size)\n",
    "        self.b2 = np.zeros((1, output_size))\n",
    "\n",
    "    def forward(self, X):\n",
    "        self.a1 = sigmoid(np.dot(X, self.W1) + self.b1)\n",
    "        self.a2 = sigmoid(np.dot(self.a1, self.W2) + self.b2)\n",
    "        return self.a2\n",
    "\n",
    "    def backward(self, X, y, output):\n",
    "        d_output = (y - output) * sigmoid_derivative(output)\n",
    "        d_hidden = d_output.dot(self.W2.T) * sigmoid_derivative(self.a1)\n",
    "\n",
    "        self.W2 += self.a1.T.dot(d_output)\n",
    "        self.b2 += np.sum(d_output, axis=0, keepdims=True)\n",
    "        self.W1 += X.T.dot(d_hidden)\n",
    "        self.b1 += np.sum(d_hidden, axis=0, keepdims=True)\n",
    "\n",
    "    def train(self, X, y, epochs=10000):\n",
    "        for epoch in range(epochs):\n",
    "            output = self.forward(X)\n",
    "            self.backward(X, y, output)\n",
    "            if epoch % 1000 == 0:\n",
    "                loss = np.mean(np.square(y - output))\n",
    "                print(f'Epoch: {epoch}, Loss: {loss:.4f}')\n",
    "\n",
    "# Create and preprocess dataset\n",
    "X, y = make_moons(n_samples=1000, noise=0.2)\n",
    "y = OneHotEncoder().fit_transform(y.reshape(-1, 1)).toarray()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Initialize and train network\n",
    "nn = NeuralNetwork(X_train.shape[1], 10, y_train.shape[1])\n",
    "nn.train(X_train, y_train)\n",
    "\n",
    "# Test network\n",
    "output = nn.forward(X_test)\n",
    "predictions = np.argmax(output, axis=1)\n",
    "accuracy = np.mean(predictions == np.argmax(y_test, axis=1))\n",
    "print(f'Accuracy: {accuracy * 100:.2f}%')\n"
   ]
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   "source": [
    "5)Aim: Demonstrate the text classifier using Naïve bayes classifier algorithm.\n",
    "Program: Write a program to implement the naive Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets."
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Unnamed: 0     150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    object \n",
      " 2   SepalWidthCm   149 non-null    float64\n",
      " 3   PetalLengthCm  149 non-null    object \n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        149 non-null    object \n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 7.2+ KB\n",
      "None\n",
      "   Unnamed: 0 SepalLengthCm  SepalWidthCm PetalLengthCm  PetalWidthCm  \\\n",
      "0           1           5.1           3.5           1.4           0.2   \n",
      "1           2           4.9           NaN           1.4           0.2   \n",
      "2           3           4.7           3.2           1.3           0.2   \n",
      "3           4            ??           3.1           1.5           0.2   \n",
      "4           5             5           3.6           ###           0.2   \n",
      "\n",
      "       Species  \n",
      "0  Iris-setosa  \n",
      "1          NaN  \n",
      "2  Iris-setosa  \n",
      "3  Iris-setosa  \n",
      "4  Iris-setosa  \n",
      "['Iris-setosa' nan 'Iris-versicolor' 'Iris-virginica']\n",
      "Accuracy: 93.33%\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from collections import defaultdict\n",
    "\n",
    "class NaiveBayesClassifier:\n",
    "    def __init__(self):\n",
    "        self.priors = {}\n",
    "        self.likelihoods = defaultdict(dict)\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.classes = np.unique(y)\n",
    "        total_samples = len(y)\n",
    "\n",
    "        for cls in self.classes:\n",
    "            X_cls = X[y == cls]\n",
    "            self.priors[cls] = len(X_cls) / total_samples\n",
    "\n",
    "            for column in X.columns:\n",
    "                self.likelihoods[column][cls] = X_cls[column].value_counts(normalize=True).to_dict()\n",
    "\n",
    "    def predict(self, X):\n",
    "        results = []\n",
    "\n",
    "        for i in range(len(X)):\n",
    "            posteriors = {}\n",
    "\n",
    "            for cls in self.classes:\n",
    "                prior = np.log(self.priors[cls])\n",
    "                likelihood = sum(\n",
    "                    np.log(self.likelihoods[col].get(cls, {}).get(X.iloc[i][col], 1e-6))\n",
    "                    for col in X.columns\n",
    "                )\n",
    "                posteriors[cls] = prior + likelihood\n",
    "\n",
    "            results.append(max(posteriors, key=posteriors.get))\n",
    "\n",
    "        return results\n",
    "\n",
    "    def accuracy(self, y_true, y_pred):\n",
    "        return np.mean(np.array(y_true) == np.array(y_pred))\n",
    "\n",
    "# Load data from CSV file\n",
    "data = pd.read_csv('/kaggle/input/iris-dataset/Iris_data_sample.csv')\n",
    "\n",
    "# Inspect data for inconsistencies\n",
    "print(data.info())\n",
    "print(data.head())\n",
    "\n",
    "# Check for NaN values and data types in target column\n",
    "print(data.iloc[:, -1].unique())\n",
    "\n",
    "# Separate features and target variable\n",
    "X = data.iloc[:, :-1]\n",
    "y = data.iloc[:, -1]\n",
    "\n",
    "# Clean target variable to ensure consistent data type\n",
    "y = y.astype(str)\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize and train the Naive Bayes Classifier\n",
    "nb_classifier = NaiveBayesClassifier()\n",
    "nb_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = nb_classifier.predict(X_test)\n",
    "\n",
    "# Compute the accuracy\n",
    "accuracy = nb_classifier.accuracy(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce4ad80",
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   "source": [
    "6)Aim: Demonstrate and Analyse the results sets obtained from Bayesian belief network Principle.\n",
    "Program:- Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You can use Python ML library classes/API."
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting pgmpy\r\n",
      "  Downloading pgmpy-0.1.25-py3-none-any.whl.metadata (6.4 kB)\r\n",
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      "Downloading pgmpy-0.1.25-py3-none-any.whl (2.0 MB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m35.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hInstalling collected packages: pgmpy\r\n",
      "Successfully installed pgmpy-0.1.25\r\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy pandas pgmpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b63ea4a",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample instances from the dataset are given below:\n",
      "   age  gender  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
      "0   63       1   1       145   233    1        2      150      0      2.3   \n",
      "1   67       1   4       160   286    0        2      108      1      1.5   \n",
      "2   67       1   4       120   229    0        2      129      1      2.6   \n",
      "3   37       1   3       130   250    0        0      187      0      3.5   \n",
      "4   41       0   2       130   204    0        2      172      0      1.4   \n",
      "\n",
      "   slope ca thal  heartdisease  \n",
      "0      3  0    6             0  \n",
      "1      2  3    3             2  \n",
      "2      2  2    7             1  \n",
      "3      3  0    3             0  \n",
      "4      1  0    3             0  \n",
      "\n",
      "Attributes and datatypes:\n",
      "age               int64\n",
      "gender            int64\n",
      "cp                int64\n",
      "trestbps          int64\n",
      "chol              int64\n",
      "fbs               int64\n",
      "restecg           int64\n",
      "thalach           int64\n",
      "exang             int64\n",
      "oldpeak         float64\n",
      "slope             int64\n",
      "ca               object\n",
      "thal             object\n",
      "heartdisease      int64\n",
      "dtype: object\n",
      "\n",
      "Column names in the dataset:\n",
      "Index(['age', 'gender', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
      "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'heartdisease'],\n",
      "      dtype='object')\n",
      "\n",
      "Learning CPD using Maximum likelihood estimators\n",
      "\n",
      "Inferencing with Bayesian Network:\n",
      "\n",
      "1. Probability of HeartDisease given evidence= restecg\n",
      "+-----------------+---------------------+\n",
      "| heartdisease    |   phi(heartdisease) |\n",
      "+=================+=====================+\n",
      "| heartdisease(0) |              0.1386 |\n",
      "+-----------------+---------------------+\n",
      "| heartdisease(1) |              0.0000 |\n",
      "+-----------------+---------------------+\n",
      "| heartdisease(2) |              0.2403 |\n",
      "+-----------------+---------------------+\n",
      "| heartdisease(3) |              0.2174 |\n",
      "+-----------------+---------------------+\n",
      "| heartdisease(4) |              0.4036 |\n",
      "+-----------------+---------------------+\n",
      "\n",
      "2. Probability of HeartDisease given evidence= cp\n",
      "+-----------------+---------------------+\n",
      "| heartdisease    |   phi(heartdisease) |\n",
      "+=================+=====================+\n",
      "| heartdisease(0) |              0.3791 |\n",
      "+-----------------+---------------------+\n",
      "| heartdisease(1) |              0.1944 |\n",
      "+-----------------+---------------------+\n",
      "| heartdisease(2) |              0.1533 |\n",
      "+-----------------+---------------------+\n",
      "| heartdisease(3) |              0.1355 |\n",
      "+-----------------+---------------------+\n",
      "| heartdisease(4) |              0.1377 |\n",
      "+-----------------+---------------------+\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pgmpy.estimators import MaximumLikelihoodEstimator\n",
    "from pgmpy.models import BayesianModel\n",
    "from pgmpy.inference import VariableElimination\n",
    "\n",
    "# Load the dataset\n",
    "heartDisease = pd.read_csv('/kaggle/input/heart-disease-csv/heartdisease.csv')\n",
    "heartDisease = heartDisease.replace('?', np.nan)\n",
    "\n",
    "# Print sample instances and data types\n",
    "print('Sample instances from the dataset are given below:')\n",
    "print(heartDisease.head())\n",
    "\n",
    "print('\\nAttributes and datatypes:')\n",
    "print(heartDisease.dtypes)\n",
    "\n",
    "# Ensure the column names are correct\n",
    "print('\\nColumn names in the dataset:')\n",
    "print(heartDisease.columns)\n",
    "\n",
    "# Define the model structure with the correct column names\n",
    "model = BayesianModel([\n",
    "    ('age', 'heartdisease'),       # 'heartdisease' corresponds to the target variable\n",
    "    ('exang', 'heartdisease'),\n",
    "    ('cp', 'heartdisease'),\n",
    "    ('heartdisease', 'restecg'),\n",
    "    ('heartdisease', 'chol')\n",
    "])\n",
    "\n",
    "# Learning CPD using Maximum Likelihood Estimators\n",
    "print('\\nLearning CPD using Maximum likelihood estimators')\n",
    "model.fit(heartDisease, estimator=MaximumLikelihoodEstimator)\n",
    "\n",
    "# Inferencing with Bayesian Network\n",
    "print('\\nInferencing with Bayesian Network:')\n",
    "HeartDisease_infer = VariableElimination(model)\n",
    "\n",
    "# Queries\n",
    "print('\\n1. Probability of HeartDisease given evidence= restecg')\n",
    "q1 = HeartDisease_infer.query(variables=['heartdisease'], evidence={'restecg': 1})\n",
    "print(q1)\n",
    "\n",
    "print('\\n2. Probability of HeartDisease given evidence= cp')\n",
    "q2 = HeartDisease_infer.query(variables=['heartdisease'], evidence={'cp': 1})\n",
    "print(q2)\n"
   ]
  },
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   "source": [
    "7)Aim: Implement and demonstrate the working model of K-means clustering algorithm with Expectation Maximization Concept.\n",
    "Program: Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Python ML library classes/API in the program."
   ]
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     "text": [
      "   5.1  3.5  1.4  0.2  Iris-setosa\n",
      "0  4.9  3.0  1.4  0.2  Iris-setosa\n",
      "1  4.7  3.2  1.3  0.2  Iris-setosa\n",
      "2  4.6  3.1  1.5  0.2  Iris-setosa\n",
      "3  5.0  3.6  1.4  0.2  Iris-setosa\n",
      "4  5.4  3.9  1.7  0.4  Iris-setosa\n",
      "Index(['5.1', '3.5', '1.4', '0.2', 'Iris-setosa'], dtype='object')\n",
      "Number of columns: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Adjusted Rand Index of K-Means:  0.6150051194844962\n",
      "The Silhouette Score of K-Means:  0.45655541185952575\n",
      "The Adjusted Rand Index of GMM:  0.9025775147114491\n",
      "The Silhouette Score of GMM:  0.37020510233493803\n"
     ]
    },
    {
     "data": {
      "image/png": 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zdepUbr31Vjp37kxycjKWZTFkyBCfPBtM/rz11lu55JJLWLBgAUuXLuX+++/nscceY+XKlXTo0KHcMZbF4MGDWbt2LRMmTOD0008nISEBt9tNr169KuxeP5jXIyL+qJAtUk3Z7XYee+wxzj//fCZPnsw111wDQFRUFBdeeGHAvvPmzeP888/n//7v/3yOHzx40PtFDyIiIuJ5E3nBggX07duXiy66iE8//dTvlyFdddVVPPDAA+zcuZPp06f7HfOUU05h06ZN9OjRw+fjwMf64IMPyMvL4/333/dZ3VR065HySElJYfTo0YwePZrs7Gy6du3KpEmTVMgWETlB9e3bl9dff51169bRsWPHco0RbA481sknn8w555zD6tWrGTduHA5H8GWsuLg4LrjgAlauXMlvv/0W1FZgx5o3bx4jR47kmWee8R7Lzc3l4MGDxdoGkz9POeUUbr/9dm6//Xa2bt3K6aefzjPPPFPqqvbSnHLKKQBkZGT4vd8/cOAAK1as4KGHHuKBBx7wHi9pO5RArz+O1bhx4xI/mfbdd995nxcJFW0tIlKNde/enY4dO/L888+TlJRE9+7deeWVV9i5c2extn/88Yf3b7vdXuzd0Hfeece7h7aIiIgc1aNHD2bPns0PP/xAr169vHtyHuuUU07h+eef57HHHgtYCBg8eDA7duzgtddeK/ZcTk4Ohw8fBo6uaCqaszMzM5k6dWq5z2Xfvn0+jxMSEmjevDl5eXnlHlNERKq2O++8k7i4OK655hp2795d7PlgVtIGmwNL8sgjj/Dggw9y8803l6kfwIMPPogxhhEjRpS4PcpXX33Fm2++6bd/SffGL7zwAi6Xy+dYafnzyJEj5Obm+rQ55ZRTSExMDEmOPeOMM2jatCnPP/98sSJ7YfwlvW4AeP7554uNFx8fD1Biwf5Yffr0Yd26dXz++efeY4cPH+bVV1+lSZMmpKenl+FMRALTimyRam7ChAkMGjSIadOmMWXKFM4991zatWvHddddR7Nmzdi9ezeff/4527dvZ9OmTQD069ePhx9+mNGjR9OlSxc2b97MzJkzadasWSWfjYiISGS6/PLLee2117jmmmu49NJLWbJkSYntbrnlllLHGjFiBHPnzmXs2LGsWrWKc845B5fLxXfffcfcuXNZunQpZ511FhdffDHR0dFccskl3HDDDWRnZ/Paa69Rp06dEt+0DkZ6ejrdu3fnzDPPJCUlhQ0bNjBv3jxuuummco0nIiJVX4sWLZg1axZDhw6lVatWDBs2jPbt22OMYdu2bcyaNQubzUbDhg0DjhNMDixJt27d6NatW7n6dunShSlTpnDjjTfSunVrRowYQYsWLTh06BCrV6/m/fff55FHHvHbv1+/fkyfPp3k5GTS09P5/PPP+fjjj0lNTfVpV1r+/N///kePHj0YPHgw6enpOBwO3nvvPXbv3s2QIUPKdW5F2Ww2XnrpJS655BJOP/10Ro8eTf369fnuu+/YsmULS5cuJSkpybs3d0FBASeddBLLli1j27ZtxcY788wzAbj33nsZMmQIUVFRXHLJJd4Cd1ETJ05k9uzZ9O7dm7/+9a+kpKTw5ptvsm3bNubPnx/0NjIiwVAhW6SaGzBgAKeccgpPP/001113HRs2bOChhx5i2rRp7Nu3jzp16tChQwefjxbdc889HD58mFmzZjFnzhzOOOMMFi9ezMSJEyvxTERERCLb6NGj2b9/P3fccQeDBg2iffv25RrHZrOxYMECnnvuOd566y3ee+894uLiaNasGbfccov3Sx9btWrFvHnzuO+++7jjjjuoV68e48aNIy0tzbulWFn99a9/5f3332fZsmXk5eXRuHFjHnnkESZMmFCu8UREpHq47LLL2Lx5M8888wzLli3jjTfewLIsGjduTN++fRk7dmy581643XDDDZx99tk888wzvPXWW/zxxx8kJCRwxhlnMHXqVIYPH+637z//+U/sdjszZ84kNzeXc845h48//rjY906Vlj8bNWrE0KFDWbFiBdOnT8fhcNC6dWvmzp3LwIEDQ3KePXv2ZNWqVTz00EM888wzuN1uTjnlFK677jpvm1mzZnHzzTczZcoUjDFcfPHFfPTRRzRo0MBnrLPPPpu///3vvPzyyyxZsgS32822bdtKLGTXrVuXtWvXctddd/HCCy+Qm5vLaaedxgcffEDfvn1Dcm4ihSyj3dRFREREREREREREJIJpfb+IiIiIiIiIiIiIRDQVskVEREREREREREQkoqmQLSIiIiIiIiIiIiIRTYVsEREREREREREREYloKmSLiIiIiIiIiIiISERTIVtEREREREREREREIpqjsgMINbfbze+//05iYiKWZVV2OCIiUg0ZYzh06BANGjTAZtN7wuWlnC0iIuGkfB0aytciIhJOZcnX1a6Q/fvvv9OoUaPKDkNERE4Av/32Gw0bNqzsMKos5WwREakIytfHR/laREQqQjD5utoVshMTEwHPySclJVVyNCIiUh1lZWXRqFEjb86R8lHOFhGRcFK+Dg3laxERCaey5OtqV8gu/KhTUlKSkqyIiISVPl57fJSzRUSkIihfHx/laxERqQjB5GttFCYiIiIiIiIiIiIiEU2FbBERERERERERERGJaCpki4iIiIiIiIiIiEhEUyFbRERERERERERERCKaCtkiIiIiIiIiIiIiEtFUyBYRERERERERERGRiKZCtoiIiIiIiIiIiIhENBWyRURERERERERERCSiqZAtIiIiIiIiIiIiIhFNhWwRERERERERERERiWgqZIuIiIiIiIiIiIhIRFMhW0REREREREREREQimgrZIiIiIiIiIiIiIhLRHJUdgIiIlI0xhtWrV/PRRx+Rn5/P2WefzcCBA4mNjS3zWG63mxUrVrBs2TKcTid/+ctfGDBgANHR0WzdupXZs2ezd+9eGjduzPDhw6lbt24YzkhERKSa+uUXmDkTdu2CBg1g+HBo2LB8Y/30E8yaBXv2eMYYMQLq14fsbJg9GzIyIC4OLr8cOnYM7XmIiIhUYznkMJe5fM3XxBBDP/pxLudiYZV5rMMcZg5z2MQmalCDS7mUznQGYBWrWMIS8snnbM7mCq4ghphQn061ZhljTGUHEUpZWVkkJyeTmZlJUlJSZYcjIhJSO3bsoF+/fmzcuBGHw4FlWRQUFJCamsr8+fPp1q1b0GP98ssv9O3bly1btviMlZaWRqdOnfjggw+w2+3YbDZcLhc2m41HHnmEu+66K4xnWDUo14SGrqOIVFtuN0yYAM89Bzab59ftBmNg4kR45BGwgrw5drngllvgxRePjuVyefoPGgSLFsHhw+BweMZ3OuH882H+fKhVK7znGeGUZ0JD11FEqrPlLGcwgznIQaKIwmBw4qQjHXmf96lL8Iu5FrGIq7iKQxzyGetszuYIR9jCFhw4sLAooIBUUnmXd+lK1zCeYeQrS55RIVtEpIrIz8+nffv2/PDDDzidTp/nbDYb0dHRbNy4kVatWpU6Vk5ODm3btuXXX38tNlZpXnvtNcaMGVOmPtWNck1o6DqKSLX10EMwaZL/55980lPoDsbEiZ72Zblts9vhnHNg9ergC+bVkPJMaOg6ikh19Q3fcDZnU0ABBt88a8dOW9ryFV9hx17qWBvYQGc648JVbCx/bNiIIYaNbKQlLct1DtVBWfKM9sgWEaki3n33Xb777rsSC89utxun08mzzz4b1Fhz5szhp59+KnMRG2DSpEm4XK4y9xMRETkhZGd7Cs+BPPoo5OaWPtaBA/D882UrYoNnxfaaNfDvf5etn4iIyAnkSZ7EjbvEwrMLF5vYxId8GNRYj/EY5s+fYLlxU0ABz/Fc0H1OdCpki4hUEfPnz8dm8//PttPpZM6cOUGN9c477wQcK5AdO3bw1VdflauviIhItbd8ORw5ErhNZiZ88knpY330EeTllS8OhwPmzStfXxERkWrOYJjHPJz4X9zlwME8Ss+lTpwsZCEuyr7gy4mTt3m7zP1OVCpki4hUEYcOHcLtdgdsc6S0G+c/ZWVllTpWabGIiIhICbKzQ9cu2LHC1V9ERKSacuEij8BvFrtwcYjS733zyCtXEbvQEYK7jxcVskVEqow2bdrgcDj8Pm9ZFi1bBrev1qmnnhpwrEDKMo+IiMgJp3Xr0LVr06b8cbjdx9dfRESkGnPgoClNA7axYaMNpefSOOKoT/1yxWFh0YrSv+dKPFTIFhGpIq6//vpS97QeP358UGPdcMMN5dof226307t3bxo1alTmviIiIieEs86C007zfOFiSex26NwZTj219LHOPRdatoTybAdmt8PIkWXvJyIicoIYz3hsAUqjbtyMYUyp41hY3MiNAccqLQ4JjgrZIiJVRJs2bXj44YcBiu1vbbPZuOCCC7j22muDGqtDhw7cfffdgGeFdVGWZWGz2bAfcwPucDioVasWL7zwQnlPQUREpPqzLJg6FWJjixez7XaIj4dXXw1+rDffhJiYksey24sXuQsfv/gipKWV7xxEREROAOMZT2c6FytAFz5+kidLXbVd6DZu40zO9DvWsX8XPu5BD67hmvKEf0JSIVtEpAq5//77mTlzJunp6d5jaWlpPPjggyxevJjo6Oigx3r00UeZNm0arVod/RhTvXr1ePTRR/n888/p06ePt2AeHR3N8OHD2bBhA82aNQvdCYmIiFRHZ5wB69bBgAFHC9AOBwweDOvXQ9u2wY/VqRN88QVccsnRInVUFAwdCl99BbfdBsnJR9t37AiLF8OY0leQiYiInMhiiWU5y7mHe0ghxXu8Pe15h3e4gzuCHiuOOFaykju5k5rU9B4/kzNZwAJmMMNnm5I61GESk1jEIqKICsn5nAgsY4yp7CBCKSsri+TkZDIzM0lKSqrscEREwsIYw+7du8nPz6dBgwbl3u+6cKxdu3bhdDpp0KCBz0rszMxMDhw4QFpaGvHx8aEIvVpQrgkNXUcROSEcOgT79kHt2pCQcHxjZWXB/v2eldZF83J+PuzaBTVqaBV2EcozoaHrKCInggIK2MlOYoihLnVDMlYssdShjve4wbCb3eSTTwMa4KD89/HVSVnyjK6YiEgVZFkW9erVC9lY9euX/MUUycnJJBdd5SUiIiJlk5jo+Q2FpCTP77Gio+Hkk0Mzh4iIyAkoiihOJjS51N9YFhb1CM19/IlKW4uIiIiIiIiIiIiISERTIVtEREREREREREREIpoK2SIiIiIiIiIiIiIS0VTIFhEREREREREREZGIpkK2iIiIiIiIiIiIiEQ0FbJFREpx5MgRDhw4gDGmwud2u91s27aNvXv3VvjcIiIiVYnT6WT//v3k5+dXTgB79sAvv4DbXTnzi4iIVAEGQyaZZJNdKfMf4Qg/8iNHOFIp88vxUSFbRMSPFStW0KNHD+Lj40lJSaFhw4Y8/vjj5OXlhX3urKwsevXqRVRUFM2aNSMtLY2UlBSeeuqpsM8tIiJSlezYsYObbrqJ5ORkUlNTSUxMZPTo0WzdurViAnjkEahZE+rWhSZNIDoaLrkEjugGWUREpJALF5OZTAtaUJOaJJJIJzqxgAUVMv/nfE466cQTT3OaE088rWjFGtZUyPwSGpapjCWGYZSVlUVycjKZmZkkJSVVdjgiUkVNnTqVa6+9FpvNhsvl8h632Wx07dqVJUuWEBMTE5a5s7KyaNy4MQcPHizx+dGjR/PGG2+EZW4JjnJNaOg6isjx+vnnn+nUqRP79u3D6XR6jzscDuLi4lizZg3t27cPXwBDh8Lbb5f8XO3anhXacXHhm18CUp4JDV1HETlebtwMYQjzmAd4VmUD2LDhxs1jPMZEJoZt/qUspTe9vfMWZWHxPu/Tj35hm18CK0ue0YpsEZFj7N69mxtuuAFjjE8RGzxbfaxZs4Z//etfYZv/qquu8lvEBk+R/Ztvvgnb/CIiIlXF2LFj2bt3r08RGzzbjBw+fJjhw4eHb2uwzz/3X8QG2LsXRo0Kz9wiIiJVyCxm8Q7vYP78KeTGsx3X3dzNFraEbf7BDC6xiA2eovoQhoRtbgktFbJFRI7xxhtvFCtgF+V2u5k8eXJYbozdbjdLliwptd2ECRNCPreIiEhV8vPPP7N06VK/OdvlcpGRkcGXX34ZngDuuqv0NgsWhGduERGRKmQyk7EFKEHasfMKr4Rl7kUsIousgG0Oc9i7WlwimwrZIiLH2Lx5M5ZlBWzz66+/kpubG/K59+/fH7CIXuj7778P+dwiIiJVyZYtwa3cysjICE8AP/xQepuCAjhmtbiIiMiJJoMM7+rrkrhwsYlNYZl7JSuDareKVWGZX0JLhWwRkWPExcWVWsi22WxERUWFfO6EhISg2oVrf24REZGqIi7Ivadr1KgRngCio4NrZ9Mtl4iInNhqEDgXW1jEEx+WuZMIbm//BIK7F5fKpVdVIiLH6N+/f7G9NotyOBz069cPh8MR8rljY2OpW7duqe2GDh0a8rlFRESqki5dulCzZs2AbaKioujVq1d4AhgwoPQ2J5+sQraIiJzwruAKHPi/fzYYBhBEXi2HG7kxqHY3c3NY5pfQ0qsqEZFj9O7dm7Zt25ZYqLYsC7fbzV3B7ItZTg8//HDA52NiYrjnnnvCNr+IiEhVEBMTw9133+33ecuyGDduHKmpqeEJ4JFHoLRPZ/3jH+GZW0REpAq5lVtx4Chxn2w7dhrSkKGEZ7FWHerQjW4B23SmMw1pGJb5JbRUyBYROYbdbmfp0qWkp6cDnhXYDocDy7KIjo5m9uzZdOnSJWzzX3/99fztb38r8bmYmBjWrl1LdLAfZxYREanGJkyYwG233QZ48ndhzgYYPnw4Tz/9dPgmj4uDTz7xv8XIPffAsGHhm19ERKSKaEUrFrGIBBKwsHD8+QNwMiezkpVh21oE4GM+pi1tS3yuNa1ZzeqwzS2hZRljTGUHEUpZWVkkJyeTmZlJUlJw++CIiJTE7XazfPlyFi5cSG5uLqeddhpXX301KSkpFTL/jz/+yIQJE8jIyCA6OpohQ4YwceLEsGxpImWjXBMauo4iEipbt27lzTffZPv27dSpU4cRI0bQrl27ipk8P9+zOnv+fM+XO7ZvD88849lWRCqV8kxo6DqKSKhkk81MZrKOdThw0JOeXMqlAbcdCaUP+ZBHeIQ97CGNNO7mbi7l0gqZW/wrS54JayF7zZo1PPXUU3z11Vfs3LmT9957j/79+/ttv3r1as4///xix3fu3Em9evWCmlNJVkREwq065hrlbBERqW6qY55RvhYRkeqmLHkmrFuLHD58mPbt2zNlypQy9fv+++/ZuXOn97dOnTphilBERERAOVtERKQqUL4WEZETWVjX7vfu3ZvevXuXuV+dOnVK/QZyERERCR3lbBERkcinfC0iIieyiPyyx9NPP5369etz0UUX8dlnn1V2OCIiIuKHcraIiEjkU74WEZHqIKK+Max+/fq8/PLLnHXWWeTl5fH666/TvXt3vvzyS84444wS++Tl5ZGXl+d9nJWVVVHhioiInLCUs0VERCKf8rWIiFQnEVXIbtWqFa1atfI+7tKlCz/++CPPPfcc06dPL7HPY489xkMPPVRRIYqIiAjK2SIiIlWB8rWIiFQnEbm1SFEdO3bkhx9+8Pv83XffTWZmpvf3t99+q8DoREREpJBytoiISORTvhYRkaoqolZkl2Tjxo3Ur1/f7/MxMTHExMRUYEQicqIwxvD555/z/vvvk5OTQ/v27RkyZAhxcXFs376dGTNm8Pvvv1OvXj2GDRtG48aNycnJYe7cuXz99ddER0fTr18/zjvvPAA+/fRTFi9eTF5eHh06dGDw4MHUqFGjXHGtXr2ajz76iPz8fM4++2wGDhxIbGys3z7Z2dnMnj2bjIwM4uLi6N+/Px07dsSyLL99tm7dyuzZs9m7dy+NGzdm+PDh1K1bt8zxVjan08nixYv55JNPsCyLbt260bdvX+x2e2WHVu0oZ4tIpfn9d5gxA7Zvhzp1YNgwaNoUcnNh/nxYvx6io6F3b+je3dPn88/h/fchJwdOPx2uvBLi4jxjzJjhGbNePRg+HE4+uXxx/fILzJwJu3ZBgwaesRo29N/eGFi1CpYsgfx8OPtsuOIKCPRvZ3Y2zJ4NGRme+C+/HDp2LF+8lW3LFpgzBw4ehObNPdcrJaWyo6p2lK9FpLLkk8985rOOdThwcDEXcyEXArCOdSxgAUc4QjvaMYQhJJDA7/zODGawne3UoQ7DGEZTmpJLLvOYxwY2EE00velNd7pj4f8e158ccpjLXL7ma2KIoR/9OJdzA471C78wk5nsYhcNaMBwhtMQ/zneYFjFKpawhHzyOZuzuYIriKHq/Xt7kIPMZCb/438kksggBtGe9hUzuQmjQ4cOma+//tp8/fXXBjDPPvus+frrr80vv/xijDFm4sSJZsSIEd72zz33nFmwYIHZunWr2bx5s7nllluMzWYzH3/8cdBzZmZmGsBkZmaG/HxE5MTxxx9/mHPPPdcAxuFwmKioKAOYxMREM3jwYGOz2YzdbjdRUVHGbrcby7LMwIEDTc2aNQ1goqKijMPhMIA5/fTTTYcOHYqNVatWLbNs2bIyxbV9+3Zz+umnFxsrNTXVrF69usQ+7777rklISDCWZfnEdf7555v9+/cXa19QUGDGjBljAO852mw243A4zOOPP16u61lZNm/ebE4++WTvf5PC69W0aVOzZcuWco9bHXONcraIVElutzGTJhljtxtjsxkTFeX527KM6d/fmJQUY8Bz3OHw/N22rTFnneX52+HwPAfGJCYaM3iwZxy73Xes2283xuUKPi6Xy5jbbvP0LTqWzWbMPfd44j7W9u3GnH568bhSU4355JOS53n3XWMSEjzzFD3H8883poQcH7FycjzXHo5eL5vNmOhoY156qdzDVsc8o3wtIlXVWrPWpJk0g8FEmSjjMA6DwbQxbUwn08lgMA7jMFEmyljGMgkmwVxprjR2Yzc2YzNRJsrYjd1YxjKXm8tNikkpNtbp5nSz3WwvU1zLzDJT09QsNlZH09HsMruKtXcZl7nN3GYsYxm7sXvjshmbudfca9ymeI7/zfxm2pv2PueIwaSaVPOJ8ZPjI9Q0M83EmlhjGcvnel1mLjPZJrtcY5Ylz4S1kL1q1SoDFPsdOXKkMcaYkSNHmm7dunnbP/HEE+aUU04xsbGxJiUlxXTv3t2sXLmyTHMqyYrI8XK5XObss882dru9xH/DAv1alhV0W5vNZqKjo80333wTVFx5eXmmdevW3kL0sWPFxsaa7777zqfPZ5995i20H9vHbreb8847z7iPuZn+61//GvA8XnvttZBd63Das2ePqV27don/He12u6lTp47Zt29fucaujrlGOVtEqqR//ctT/PT3a1mBny/L70MPBR/XpEmBx3rySd/2eXnGtGp1tBBd9NdmM6ZGDWOOyfHms8+OFtqP7WO3G9O1a8kF80g0bJjnPP1dr3nzyjVsdcwzytciUhX9aH408Sbe2IzNEKIfy1jFjjmMw7QxbUyeyQsqrk1mk4k20SWOZTd20960N07j9OnzoHkwYFxPmad82ueZPNPStPQWfIv+2IzN1DA1zPfm+5Bd63BabBb7PW+bsZnLzeXlGrcsecYyxpiyruKOZFlZWSQnJ5OZmUlSUlJlhyMiVdCyZcvo2bNnhczlcDgYOnQob731Vqlt3377bYYOHRpwrGuuuYZXXnnFe6xv374sXboUl8vlt98nn3xC165dAdizZw8nnXQSTqfTb/uTTjqJX375JeK35nj00Ud54IEHcLvdJT5vs9l47LHHuPPOO8s8tnJNaOg6ishxyc+Hk06CvXsrZr74eM8WIQkJgdtlZ0PdunDkiP82ycmesQq3BZs9G666yn97hwOuvRZefvnosT59YNkyCJDjWbMG/tziLGL9+CO0aOEpWZfEsqBNG8/WKQG2RCuJ8kxo6DqKyPH6K3/lJV7Cif/7zFCawxwGM7jUdsMZzhzmBIzrfd7nEi4B4BCHqEc9juA/x9ekJjvZSSyeHD+b2VyF/xzvwMEYxvASL5Uab2X7C39hAxtwU/I9NkAGGZzKqWUatyx5JuK/7FFEpKLNmzcPh6NivkLA6XTyzjvvEMx7ivPnz8dm8//PttPpZM6cOd7HOTk5fPTRRwGL2A6Hg3nz5nkfL168OGARG2DHjh189dVXpcZb2d5++22/RWwAt9vN22+/XYERiYhISH3+ecUVsQEOH4aPPy693fLlgYvYAJmZ8MknRx/Pnw8BcjxOJxTNWUeOePbRDlTEdjigSI6PWO+9F7hAbQx8+62n4C0iIlVSacXiULJjZx6l5z+DYR7zAsblwOEz1nKWByxig2f/6DWs8T6exzxsAcqvTpy8TeTfl+5kJ+tYF7CIbcfOfOaHNQ4VskVEjpGdnR2wABpqubm5Qc136NChUtsdKXLjnJOTE1SBPDs722eOQMXyou0iXTAxZmVlVUAkIiISFkXyV0TNGWxcRdtlZUFprwWKFsdzcvyvYC5PLJUpOxuC+ZRXVTgXEREp0WEOV9hcLlwcovR7QRcu8sgr01jZBJeLirbLIitg8RcotTgeCYI5dxu2oK798VAhW0TkGK1bt8Yq40dXy8uyLJo1axbUNh1t2rQJuFLcsixatmzpfVyzZk1SU1MDjul2u2nTpo3PHKUVy4+dJ1K1bds24HV1OBy0a9euAiMSEZGQatWq4ucskjP9at06uLGKtktP96yg9seyfM+3Zk1ISQk8vtsdXLyVrXVrKCgI3CYqCpo0qZBwREQk9FrRKuCq5FCyYyed9FLbOXDQlKYB29iw0YYi98sEl1dbczTHp5OOgwD38Vi0ohJe05TRSZxEDWoEbFNAQdDXqLxUyBYROca1114b1ErmULAsi/HjxwfV9vrrry9124+iY9lsNsaNGxewmGu32xk5cqT3cY8ePWjcuLHfVdl2u53evXvTqFGjoGKuTOPGjQu4rYrT6WTcuHEVGJGIiIRU8+bQvXtwq3mPl90O7dvDGWeU3vass+C00/zHZbdD585wapH9I6+/3rN9SCBFXy/Y7TBuXOBzt9uhSI6PWJdf7inK+1tE4HDA0KGe4r2IiFRJ4xlf6qrkUHHh4jquC6rteMYHLLC7cTOGMd7HZ3EW7WiHnZLzrx07XejiU0i/nutL3VZlPMHVBCpTHHGMYpTfc7ewSCCBK7kyrHGokC0icoyTTjqJ559/HqBYQddms2Gz2YoVh+12OzabrdhK7qL9SxqrS5cu3HjjjUHF1aZNGx5++GG/Y11wwQVce+21PsfvvPPOElcmF/afMmUKderU8Tn+1ltvERUVVayPw+GgVq1avPDCC0HFW9n69OnjU6QvVPjf6Nprr+Wiiy6q6LBERCSUXn4ZkpKKr2a22Ty/xxZHi+bPY9+0tdk8xd9ji8N2u+dLGadODe7LBi3L0zY2tuSx4uPh1Vd9j6enw0MP+Y+rRw+45hrf43fdBW3bFp+jsP+LL0JaWunxVraYGHjzzaPXvyi7HerXh8cfr5zYREQkJK7mavrQp1jR2OJoXj32ORs27H/+HHvchs2nb9H+f+fvPiuiAxnPeDrTucS5AZ7kSZ9V2xYWU5lKDDHF4nLgIIEEXsU3x5/KqUxikt9z7EEPruGYHB+hHuZhmtCk2LnbsWNh8QZvEE98WGNQIVtEpAQ333wzCxYs4Mwzz/Qeq1mzJnfeeSdfffUVgwcP9m7zYbfbGTBgAOvXr+e+++7z2c6jXbt2zJ07l7lz5/psY5Gamsq9997LsmXLiI2NDTqu+++/n5kzZ5KefvQd3rS0NB588EEWL15MdHS0T/vExETWrFnDbbfdRnJysvd4x44dWbRoEdddV/yd6q5du7J27Vr69OnjLXhHR0czfPhwNmzYQLNmzYKOtzJZlsUbb7zBP//5Txo3buw93rhxYyZPnsyrr75aYVvIiIhImLRqBevXw5AhR4vZNhv07w9ffgmTJvkWc9PTYdYszxcMduhw9HitWp7C8IYNMGjQ0bHsdhgwwDNH0falOeMMWLfO07ewOOtwwODBnrHati3e54EHYOZM3+1A6tTxnMOiRXBMjicxEdasgb/9DYrkeDp2hMWLYcwYqox+/Txffnn++UePxcZ6zmH9ek8xW0REqiwHDhawgL/zd+pS13u8Na15kzf5gA/oSEfv8WSSuZ3b+YqvGMpQ79YcNmz0pz/rWMckJpHG0RyfTjqzmMV93Bd0XLHEspzl3MM9pHB0y672tOcd3uEO7ijW50zOZD3rGcAAb0HXgYNBDGI96zmVU4v1eZAHmcEMn2036lCHSUxiEYuIIiromCtTbWrzBV8wjnE+BevzOI8VrGAQg8Ieg2Uq6vPzFSQrK4vk5GQyMzNJSkqq7HBEpBrYs2cPubm51K9fn6ioowkmOzubvXv3kpqaSmJiovd4QUEBO3fuJCYmhrp16/qMtXv3bvLy8oqNVVbGGHbv3k1+fj4NGjQIuHd2ofz8fHbt2kWNGjVIC3KFVmZmJgcOHCAtLY34+PC+sxpObreb33//HcuyqF+/flBfaBmIck1o6DqKSEhlZ8PevZ5tKor+m+J0wu+/ewrBdev6rqreswdycz2F0qJ5+dAh2LcPUlM9BePjUThW7dqQkFB6e2Ng927Iz4cGDQLvnV0oPx927YIaNarGKuxADhzwfPllnTqe8zkOyjOhoesoIqHkwsXv/I4DB/Wo57Oy+g/+IIcc6lGPaI6+gZtNNnvZSwopJHH03yEnTn7nd6KJpi51i63SLosCCtjJTmKI8Sm2B3KIQ+xjH7WpTQKl53iDYTe7ySefBjQIuHd2pMsll93sJpFEnzcByqMseUaFbBERkTJSrgkNXUcREQkn5ZnQ0HUUEZFwKkue0dYiIiIiIiIiIiIiIhLRVMgWERERERERERERkYimQraIiIiIiIiIiIiIRDQVskVEREREREREREQkoqmQLSIiIiIiIiIiIiIRTYVsEZEIZozhwIEDHDlyJOg++fn57N+/H6fTGcbIKldeXh779+/H5XJVdigiIiIehw/DwYNgTHDtjYEDB6AMOb7Kcbs955ibW9mRiIiIAFBAAfvZTwEFQfc5zGEOchBDkDm+Csomm0wyI/4cVcgWEYlAeXl5PP744zRs2JCUlBTi4+Pp0aMHK1as8Ntn69atjB49msTERFJTU0lOTuamm25ix44dFRh5eG3cuJFBgwYRHx9PamoqtWvX5q677mL//v2VHZqIiJyoliyBbt0gIQFq1YLGjeGZZ6DAzw1yXh48/jg0bAgpKRAfDxdeCAFyfJWTlQX33QdpaUfP8bLLYN26yo5MREROUL/yK2MZSzLJpJJKEkmMYQzb2Oa3zxKW0I1uJJBALWrRmMY8wzNlKoJHujnM4UzOJJFEalKTNrThVV7FjbuyQyuRZUywSwaqhqysLJKTk8nMzCQpKamywxERKbO8vDx69erFmjVrcLuPJg+73Y7b7eb//u//GD16tE+fTZs2cd5555GTk+OzEtvhcJCamsoXX3xBkyZNKuoUwmLVqlX06tULt9vtc452u51mzZqxdu1aateuXSGxKNeEhq6jiFR5L70EN94IdjsU/ZSQZUHPnvD++xAVdfR4Xh706gVr1nhWKxey2z2P/+//4JgcX+VkZsK558J//+t7Tex2z3VZsAD69q2QUJRnQkPXUUSquv/xP7rQhUwycVLkfhkHiSTyGZ/RhjY+fV7kRcYzHjt2XBzNZxYWvejFQhYSRRRV2f3czyM8gg2bt3BtYWEwjGIUb/AGFlbY4yhLntGKbBGRCPPCCy8UK2IDuFwujDHccMMN7Nq1y3vcGMPw4cM5cuRIse1EnE4ne/fuZezYsRUSe7gUFBQwZMgQnE5nsXN0uVz89NNP3HXXXZUUnYiInJB+/RVuusnz97FbXRkDS5fCq6/6Hv/Xv4oXsQv7GwM33AC7d4cv5oowaVLxIjZ4HrtcMGwY5ORUSmgiInJiGsMYDnLQp4gN4MRJFlmMYpTP8V/4hZu5GcCniA1gMCxhCa9yTI6vYtaznkd4BMBn9XXh1iLTmMZCFlZKbIGokC0iEkGMMbzwwgvFithFuVwupk6d6n385ZdfkpGR4Xe/aJfLxbJly/j5559DHW6F+eCDD9izZ4/f6+JyuZg5cyZZWVkVHJmIiJywXn/ds8I4kBdeOPq3MTB5cvEidlEuFxTJ8VVObq7nuvj7DgtjPCu233mnYuMSEZET1n/5L5/yabGCdCEXLtaxjm/4xnvsdV4vdSXyZCaHNM6K9hIv4cDh93k79og8RxWyRUQiSE5ODr/++mup7TIyMrx/b968udT2xhi+/fbb44qtMm3evBmHw3+SBc+WLD/99FMFRSQiIie8zZv9F2zBU7T9/vujheucHM8q7kAsyzNuVfXbb5CdHbhNVBQUeR0jIiISThkEl3OKttvMZr+Fb/CsWv6e7yN2H+lgfM3XxVaoF+XC5VPcjxQqZIuIRJDo6GhstsD/NNtsNmrUqOF9HBcXF9TYRftUNXFxcQTzlQ5V+RxFRKSKiYvz7PscSHT00VXb0dFQSo7HsqAq57JgXpMYU7XPUUREqpQ4grxfpsg9NnHYCZzjo4mukP2jwyWBhFLbFL0mkUKFbBGRCOJwOOjXr1/A1cdOp5PLL7/c+7hnz55ERQX+kolatWrRpUuXkMVZ0S699FK/W6cAWJZFixYtaNmyZQVGJSIiJ7T+/QOvyHY4YMCAo4Vsh8PzJYeBit9Op6dPVXXSSdChQ+CCvdMJRV7HiIiIhFN3uhNPfMA2NahBD3p4H/enf8AV2Q4cDGBAlS5kD2RgwPjt2BnM4AqMKDgqZIuIRJiJEyfidruxSth30+Fw0LZtW3r16uU9Vrt2bcaNG1di+6JjxsTEhCXeitCqVSsGDhyI3c/NvzGGBx54IOA1EBERCan+/aFFC0+B+liF+WjCBN/jEyd6ViSXlK8cDmjXDnr2DHmoFer++/3vA263w8UXw+mnV2hIIiJy4oonnju4w+/zFha3citJJHmP9ac/LWhR4h7ShcXfQGNWBaMYRW1ql7jy3IaNWGIZz/hKiCwwFbJFRCJM586defvtt4mJicGyLBwOh3eFdps2bVi6dGmxgu7TTz/N8OHDAbztC9vcdtttTDj2RroKmjZtGhdffDHge442m40nn3zSe/4iIiIVIioKPv4Ymjf3PHY4PL+F24PMnw9nnOHbp0sXmD376JYjhX0A0tNhyZLStyuJdJdf7vlSS7vdszK76Dmeey7MnVu58YmIyAnnAR7gRm4EPKupC38AxjCGv/N3n/bRRPMxH9Oc5j59LCxqUIP5zOcMjsnxVUxNarKKVdSjHoDPNUkmmSUsoQlNKjHCklkmmE1Hq5CsrCySk5PJzMwkKSmp9A4iIhFq//79TJ8+nU2bNhEbG8ull17KxRdfHHAP7c2bNzN9+nT27NlDw4YNGTVqFM0Lb7CrAWMM69at4+233+bgwYOccsopjBo1ioYNG1ZoHMo1oaHrKCLVgssFH30EixdDXp6neD1iBCQn+++zfz+89RZ88w3ExsJll8FFF5W+h3ZVsmsXTJvm+cLLpCQYNAjOOafk1ehhojwTGrqOIlJdfMd3vMVb/M7v1Kc+IxhBOul+27tw8REfsZjF5JHHGZzBCEaQTIAcX8Xkk897vMcKVuDGTRe6MIQhQe8tHgplyTMqZIuIiJSRck1o6DqKiEg4Kc+Ehq6jiIiEU1nyTDV6y19EREREREREREREqiMVskVEREREREREREQkoqmQLSIiIiIiIiIiIiIRTYVsEREREREREREREYloKmSLiIiIiIiIiIiISERTIVtEREREREREREREIpqjsgMQETkRGGP49NNPWbx4MXl5eXTo0IHBgwdTo0YNfv31V2bMmMGuXbto0KABw4cPp2HDhhw5coQ5c+awceNGatSowaWXXkrnzp0BWL16NR999BH5+fmcffbZDBw4kNjYWL/zZ2dnM3v2bDIyMoiLi6N///507NgRy7Iq6hKUicvl4qOPPmLVqlUYYzjvvPO45JJLcDiUtkREJMy2b4cZM+D336FePRg+HE4+GXJyYO5c+PpriImBvn3hvPM8fT79FBYtgvx8OOMMGDQIatSAX36BmTNh1y5o0MAzVsOG/uc2BlatgiVLPGOdfTZccYVnvkj13Xfw9tuwfz80beo5x7S0yo5KRESquVxymcc8NrCBaKLpTW+60x2AtazlAz4gl1za054ruZI44viN35jBDHayk3rUYzjDOZmTySGHuczla74mhhj60Y9zORcL//fLv/ALM5nJLnbRgAYMZzgNCZDjK1kWWcxiFv/lvySQwEAGcgZnVHZYZWYZY0xlBxFKWVlZJCcnk5mZSVJSUmWHIyLC7t27ueSSS1i/fj0OhwPLsigoKKBWrVpccMEFvPvuu9hsNmw2G263G2MMV1xxBUuWLCErK4uoqCiMMTidTs466yxyc3PJyMjwGSs1NZX58+fTrVu3YvO/9957XH311Rw+fBiHw+Ed6/zzz2f+/PnUqlWrEq6Kf9999x19+/blp59+IioqCoCCggIaNWrEBx98QPv27Ss5QuWaUNF1FJGIYgzcdx88/jhYFths4HZ7fgcOhOXLITMToqI8bZ1OOP10z9+bNoHD4elXUAC1asEFF8C773rGKRzLGJg4ER55xNO2qB07oF8/2LjRd6zUVM84XbtWxlXxLz8frrsO3noL7HbPObpcnr+fegpuuaWyI1SeCRFdRxGJNJ/wCQMZyD72EUUUBoMTJ21pSwwxfMVXOHBgYVFAAYkk0pvezGMeFhY2bLj//BnIQJaznEwyfcbqSEfe533qUtdnbjduJjCB53gO258/btwYDHdzN3/n7wEL4JXhbd7mWq4lhxwcOLzn2JOezGUuSVTuv+1lyTMqZIuIhJHT6eTMM8/k22+/xel0+jxnWRah+ifYZrMRHR3Nxo0badWqlff42rVr6dq1q7dAXpTdbqdLly588sknEbMy+8CBA6Snp/PHH3/gcrl8nrPb7SQlJfHf//6XunXr+hmhYijXhIauo4hElCefhLvu8v+8ZXkK0cEore2TT8KECUcf5+fDaafBjz96CuRF2WyeFdlffw1Fcnylu/56+L//8xToSzJjBgwbVrExHUN5JjR0HUUkknzHd3SgA/nk48ZPDgoBO3ba0pav+Ao7du/xSUziIR7y2+8pnuIO7ghbXGW1kpVcyIUYir8usWPnQi5kCUsqIbKjypJntEe2iEgYLV68mG+++aZYERsIWREbwO1243Q6efbZZ32OP/roo37ncrlcfPrpp3z66achi+N4TZ06ld27dxcrYoMn3szMTF555ZVKiExERKq1nBz4xz8CtylL3i6t7aOPQm7u0cfz58P33xcvYoOnUFxQAM89F/z84bZ9O7z+uv8iNsADD5TtmomIiAThWZ7FiTOsRWwAFy42sYkP+dB77BCHeIqnAvZ7lEfJJTdgm4r0MA9j81P+deFiKUvZwIYKjqr8VMgWEQmj+fPnV9i+zk6nkzlz5ngf5+Tk8NFHH5VYFC7kcDiYN29eRYQXlDlz5gQs8LvdbmbPnl2BEYmIyAnhk08824ZUlMxMz5yF5s/3rLz2x+n07EMdKRYuLL3NTz9BRkb4YxERkRPKHObgpIQ3fsPAgYN5HL1fXs5yjnAkYJ+DHGQNa8IdWlD2s59P+AQXAWoCOJjP/AqM6viokC0iEkbZ2dklrsYOlyNHjibVnJycoFZ9Z2dnhzOkMsnKyiq1zaFDhyogEhEROaFURi4sOmdWVuDVzQBHAt84V6jsbM9e2MG0ExERCaHSCsmh5MLFIY7ef2YTXF4Ltl24HeZwqW0srIiJNxgqZIuIhFHr1q2xB3OjFwKWZdGyZUvv45o1a5Kamhqwj9vtpk2bNuEOLWjt2rULuILdbrdz6qmnVmBEIiJyQmjdunLnTE/3fMGjP5YVWftjt25d8jYoRdntcMopFROPiIicMFrQosK+TNGGjTYcvV8u+ncgramE1xUlqEtdEkkM2MaJM+jzigQqZIuIhNGYMWNwl7bCKoTGjx/v/dtmszFu3LiAhXS73c7IkSMrIrSgjB07NuAKdpfLxY033liBEYmIyAmhbVv4y1+CW2V8vOx26NwZir4xe/31pReGi+T4Ste3L9St6387FIcDBgyAOnUqNi4REan2buKmCpvLjZsxjPE+PouzaEc7ny9/LMqOnS50IZ30igoxoGiiuY7r/MYLEEMMw6jcL2cuCxWyRUTCqFmzZjzxxBOAp7BclM1m8/4Ge7ykvwsfX3DBBVx77bU+x++8807atm1brJhd2H/KlCnUiaCbzPPPP58bbrgB8KwwL8qyLIYNG8Yll1xSGaGJiEh19/rrEB9ffGW03e4p2B6Tl3yKuMcWdG22o7/HjhUfD6++6ns8PR0eesj/WD16wDXXlO18wsnhgOnTPbEdW/x3OCAtDZ55pnJiExGRam0MYzif84t9gWHRxyU9Z//zpyg7dmzYiq3wLuz/JE/SlKbe4xYWU5lKDDHFxnLgIIEEXuWYHF/J7uM+WtCiWLyF5/0ar5FMciVFV3YqZIuIhNmECROYO3cu7dq18x5LTU3l3nvvZf369QwcONBbaHY4HFx55ZV89dVX3HXXXdSsWdPb54wzzuC9995j5syZpKcffYc3LS2NBx98kMWLFxMdHe0zd2JiImvWrOG2224jOflocurYsSOLFi3iuuuuC9NZl49lWbz00ku8+OKLNG169AVDo0aNePbZZ3nzzTeLFfFFRERCom1bWL8errjiaDHbbvesLN6wAe67D4pu2XXaaTB3rue3SI4nNRXuvdcz1sCBRwu9DgcMHuw53rZt8fkfeABmzoSiW37VqQOTJsGiRXBMjq90F10En30GF198tMgfGwsjR3quV6NGlRufiIhUS9FE8yEf8iAPkkaa93g66cxiFu/yLh3o4D1ei1rcxV1sYAODGIQDT463Y2cgA9nABu7lXlJI8fZpT3ve4R3u4I5i85/JmaxnPQMY4C0OO3AwiEGsZz2nEllbYdaiFmtZy83cTAIJ3uNd6MJSljKc4ZUYXdlZJphvAqtCsrKySE5OJjMzk6SkpMoOR0TEx+7du8nLy6N+/fpERUV5jx86dIh9+/ZRu3ZtEhKOJpeCggJ27txJbGysz8ppYwy7d+8mPz+fBg0aBNxXulB+fj67du2iRo0apKWlldq+shlj+P333zHG0KBBg4gqYCvXhIauo4hErEOHYN8+T1E6scjekgUFsHMnxMR4ttYoavduyMuD+vWhSI73jlW7NhTJ8X4Z4xkrPx8aNAi8d3akOHjQ81unDsTFVXY0XsozoaHrKCKRyomT3/mdaKKpS12fldV72EMuudSnPlEUuffmEPvYRyqpPvtHF1DATnYSQwx1OSbH+1E4Vm1q+xSJI1UeeexiF/HEU5valR2OV1nyjArZIiIiZaRcExq6jiIiEk7KM6Gh6ygiIuFUljwTOcvbRERERERERERERERKoEK2iIiIiIiIiIiIiEQ0FbJFREREREREREREJKKpkC0iIiIiIiIiIiIiEU2FbBERERERERERERGJaCpki8gJxel0sn//fvLz832OG2M4ePAghw8fLtbnyJEjHDhwAGNMiWMVFBSENebqIi8vj/379+NyuSJqLBERiTzGGDIzM8nOzi72XE5ODgcOHMDtdvs+4XTC/v1wTI7HGDh4EErI8VICtxsOHIDc3MgaS0REIlIOORzgAG5887ITJ/vZTz7H3HtjyCSTbErI8X7GkpJlk00mmRhM6Y0rcKxwUiFbRE4IO3bs4KabbiI5OZnU1FQSExMZPXo0//3vf/nnP/9Js2bNqFWrFgkJCZxzzjksWrSIFStW0KNHD+Lj40lJSaFhw4Y8/vjjbN26lbFjx5KUlOQd69prr2Xbtm2VfZoRKSMjg6FDh5KQkEBqaiopKSncfvvt7N27t8xjbdy4kUGDBhEfH09qaiq1a9fmrrvuYv/+/WGIXEREKprL5WLKlCm0aNGCmjVrkpiYSKdOnXjvvff45JNP6Nmzpzcvn3TSSTzyyCPk/PAD3HQTJCdDaiokJsLo0fDf/8Lzz0OzZlCrFiQkwLnnwqJFlX2akSk7GyZNgnr1ICUF4uKgb19Yu7bsY2VlwX33QVqaZ6z4eLjsMli3LuRhi4hI5fiMz+hDH+KJJ4UU6lGPSUziJ37iFm6hFrVIJZUEEhjBCLawhclMpgUtqElNEkmkE51YwAI+4RN60tM71kmcxKM8Sg45lX2aEeld3qUTnUgkkZrUpCUtmcIUXJR9odcc5nAmZ3rHakMbXuXViH0zwTLHLjGs4rKyskhOTiYzM5OkpKTKDkdEIsDPP/9Mp06d2LdvH06n03vc4XAAnpXVlmV5V1zbbDbvKi+73e6z6teyLOx2u7df0bESEhL47LPPSE9PD/s5VRWfffYZF154IU6n0+d62e12GjVqxOeff069evWCGmvVqlX06tULt9tdbKxmzZqxdu1aateuHfJzKIlyTWjoOopIUW63m6FDh/LOO+8ABJWXbTYbnex2Pna7qVH0Uzp2O1gWuFyeFdlFj7tc8NxzcOutYT+nKiM7G7p1g40bPauoC/35moe5c2HAgODGysz0vGHw3/96rnXRsSwLFizwFMgrgPJMaOg6isix5jOfwQzGwvIpntqwYceOC5dPIdSBA4PBhQsLy7vq14YNN24sLGzYio3ViU58zMfUoEbFnVyEe5RHuY/7vNcOwMIC4EquZCYzsQW5bvl+7ucRHik2lsEwilG8wRvescOpLHlGK7JFpNobN24ce/fu9Sl+Aj7F1aLv6RX9qPKxW1cYY4oVZQvHOnToEKNGjQpx9FWXy+ViyJAh5OfnF7teLpeL7du3c/vttwc1VkFBAUOGDCnx2rtcLn766SfuuuuukMUuIiIVb/bs2cydOxdjTNB52e1280VBAU8fu9WUy+XZauTYNTuF7W67DX74IaTxV2n/+Ads2uRbxAbP9XK7YcQIOHQouLEmTSpexC4cy+WCYcMgRyvsRESqqiyyuJqrvYXpoty4KaCgxG1GCtsW3bqisJ2/sb7gC57hmXCcRpW0mc3cx30APtfY/PnzNm8zhzlBjbWe9TzCIyWOBTCNaSxkYahCDxkVskWkWvv5559ZunRpheyl7HK5WL9+PRs3bgz7XFXBsmXL2L59e/E9TP/kdDqZO3duUFuMfPDBB+zZs8fvWC6Xi5kzZ5KVlXVcMYuISOWZMmUKNlvZb0/cwJQ//zdoNhu8+mqZ56qWCgrgpZeKF54LGeMpPM+aVfpYubnw+uuBx8rMhD9X3YuISNUzk5nkkFMheym7cTOFKRG7zUVFe5mXceDw+7wdO1OYEtRYL/FSqWNNZnKZYww3FbJFpFr79ttvi31JY7hlZGRU6HyRavPmzd5tWPxxOp1s3bo1qLEKt4LxJy8vj59++qlMMYqISOTYvHmz3zcsS7MbyCxLB5cLNm8u11zVzu7dni/DDMThgGBe3/z2m2ebkkCiooIbS0REItJmNgcsgIbaLnaRWbYsX21tZCNOnH6fd+HiG74Jaqyv+TpkY1UkFbJFpFqLi4ur8Dlr1ND+XeC59sG8iRDM9QrlWCIiEpmO99/wmLI0ttk8X2YoEMx1Nya4dsFc02DHEhGRiBRHXIWsxi5kYRFLbIXNF8niiS91z+pg9xNPIKHUNpG4N7kK2SJSrXXu3JlatWpV2HyxsbFceOGFFTZfJLvkkktKLT43atSI0047rdSxLr300oDbw1iWRYsWLWjZsmWZ4xQRkchwxRVXlPrpm5LYgYuAMpWl3W64/PIyz1UtpaZCly6e4r4/Tmdw1+ukk6BDh9CMJSIiEak//QOu5A0lO3Yu5uKILKhWhoEMDPi8AweDGRz0WIGK4nbsQY9VkcJayF6zZg2XXHIJDRo0wLIsFixYUGqf1atXc8YZZxATE0Pz5s2ZNm1aOEMUkWouJiaGiRMnVshclmVxyy23kJycXCHzRbrGjRszbNiwgPud3n///UHth9qqVSsGDhzod6sSYwwPPPAAlhX+b1SurpSzRaSy3XLLLTgcjjLvk+0G7ilLB4cDmjSBK64o0zzV2n33Ff+ix0J2O5x3HnTqFNxY998feKyLL4bTTy9XmKJ8LSKV75w/f+wE3kYyFNy4mUjF3M9XBcMYRn3ql3jtbdhw4OCv/DWosUYxitrU9jtWLLGMZ/xxxxxqYS1kHz58mPbt2zNlSnAbjW/bto2+ffty/vnns3HjRm699VbGjBnD0qVLwxmmiFRzEyZM4PbbbwfAbrfjcDi8K77OP/984uLisCzL53iTJk1o1aoVgPe4ZVlER0fTs2dPn+OFfa655hoeeeSRij69iPbqq69yySWXAEevl91ux7IsHn74YcaMGRP0WNOmTePiiy8uNpbNZuPJJ59k+PDhYTmHE4VytohUtlatWrFo0SISEhKK5eVGjRpx6qmnAkdzgM1mIzo6mrd69aI7eIqkDofnF+CCCyA+HizL93jTprByJcTqY8pevXvDa6959q+22Xyv11/+AgsWeK5jMC6/HCZP9vz3OHasc8+FuXPDcgonCuVrEalsFhYLWUhHOgKeVcAOHNiwEUUUfeiDhYUdu/c5gO50J5FELCyf4ydzMqdyarGxoolmOtPp7snygmc7kJWspCENAXyuYwIJLGYxLWgR1Fg1qckqVlGPesXGSiaZJSyhCU1CfxLHyTIV9C1olmXx3nvv0b9/f79t7rrrLhYvXuzzRWlDhgzh4MGDLFmyJKh5srKySE5OJjMzk6SkpOMNW0SqkR9++IE333yT3377jTp16jBixAjatWvHoUOHmDFjBuvXryc6OprevXvTt29fbDYby5cvZ+HCheTm5nLaaadx9dVXk5KSwvfff89bb73Fjh07qFevHldffTXp6emVfYoRa/369cyePZv9+/fTtGlTRo0aRePGjcs8jjGGdevW8fbbb3Pw4EFOOeUURo0aRcOGDcMQtX/VPdcoZ4tIZcrOzmbWrFl8+eWXOBwOevbsySWXXILdbmflypUsWLCAI0eO0LZtW0aOHElqaips3Qpvvgnbt0OdOjBiBLRrB1lZMHMmrF8P0dHQp4/ntxxbmJwQ9uyBadPgv/+FhAQYOBC6dQu+iF3Url2esb7/HpKSYNAgOOec8o1VTtU9zyhfi0hlMhhWs5p3eZdsskknnVGMIo00fuIn3uRNfuEX0khjGMM4ndPJJpuZzGQd63DgoCc9uZRLsWFjBStYyEKOcIS2tGUkI0kltbJPMyIVUMD7vM8yluHESSc6MZShQe17fax88nmP91jBCty46UIXhjCEuLJt2nZcypJnIqqQ3bVrV8444wyef/5577GpU6dy6623kplZ8jeU5uXlkZeX532clZVFo0aNlGRFRCRsqvsNnXK2iIhUB8rXytciIhL5ypKvI+rLHnft2kXdunV9jtWtW5esrCxycnJK7PPYY4+RnJzs/W3UqFFFhCoiInJCU84WERGJfMrXIiJSnURUIbs87r77bjIzM72/v/32W2WHJCIiIiVQzhYREYl8ytciIhKpImpzuHr16rF7926fY7t37yYpKYkaNWqU2CcmJoaYmJiKCE9ERET+pJwtIiIS+ZSvRUSkOomoFdmdO3dmxYoVPseWL19O586dKykiERERKYlytoiISORTvhYRkeokrIXs7OxsNm7cyMaNGwHYtm0bGzdu5NdffwU8H1m6+uqrve3Hjh3LTz/9xJ133sl3333Hiy++yNy5c/nb3/4WzjBFREROeMrZIiIikU/5WkRETmRhLWRv2LCBDh060KFDBwBuu+02OnTowAMPPADAzp07vQkXoGnTpixevJjly5fTvn17nnnmGV5//XV69uwZzjBFREROeMrZIiIikU/5WkRETmSWMcZUdhChlJWVRXJyMpmZmSQlJVV2OCJSBeTm5jJ//nzWr19PdHQ0vXr14vzzz8cYw2uvvcbUqVPJzc2lQ4cOPPXUU9SuXdvvWDk5OcydO5evv/6a6Oho+vXrx3nnnYdlWRV4RqGxdetWZs+ezd69e2ncuDHDhw8v9q33JyrlmtDQdRSRsjDGsG7dOhYsWMCRI0do27YtQ4cOJSEhgf/85z/cd9997Nixg7S0NB544AG6du0aaDD4/HN4/33IyYHTT4crr4S4uAo7n5DJzobZsyEjwxP/5ZdDx46VHVVEUJ4JDV1HESmr3exmOtP5lV+pTW2u4iqa05xssrmbu/mMz7BjZyADuZM7sQVYZ/s7vzODGWxnO3WowzCG0ZSmFXg2oWEwrGIVS1hCPvmczdlcwRXEoO8kKEueUSFbRE5oa9asYcCAAezbt4+oqCiMMTidTtq0acOvv/7K4cOHi/V58MEHmTRpUrHjH3/8MYMHD+bAgQM+Y5199tl88MEHVaYI7HQ6GTduHK+//jp2ux2bzYbL5cJms/HII49w1113VXaIlU65JjR0HUUkWAcOHGDgwIGsWrUKh8OBZVk4nU7i4+Np3LgxW7ZsKdanXbt2bNiwgejoaN8n9u6F/v3hs8/A4QDLgoICSEryFIT79KmYkwqF996Dq6+Gw4c952IMOJ1w/vkwfz7UqlXZEVYq5ZnQ0HUUkbJ4nMe5n/tx48aOHTduXLg4l3P5N/8u1j6GGFaxis747t1vMDzMw/ydv2Mw3rHcuBnPeJ7neezYK+q0jst2ttOPfmxiEw4cWFgUUEAqqbzLu3QlwJvvJwAVspVkRSQI33//PR06dCAvLw+3212mvtOnT2f48OHex5s3b+ass87C6XQWG8vhcJCens5XX32Fw+EISezhdMstt/DCCy/gLz289tprjBkzpoKjiizKNaGh6ygiwTDG0K1bN9auXYvL5SpT386dO7N27dqjB9xu6NQJvv7aU/AtyrLAbocvvoAzzwxB5GG2di107eo5p2Nztt0O55wDq1d7zusEpTwTGrqOIhKs13iN67m+zP0cONjNblJI8R77F//iFm4psb2Fxd3czaM8Wu5YK0o++bSjHT/xE058X3vYsBFDDBvZSEtaVlKEla8seSase2SLiESyZ599loKCgjIXsQEmTpzo8/ipp57C7XaXOJbT6eSbb75h8eLF5Y61ouzZs4cXX3zRbxEbYNKkSWUuJIiIiJTXp59+yqefflqu3PP555/zyy+/HD2wfDmsX1+8iA1Hi8FPPFHOSCvYI494/reknO1ywZo18O/iK99ERETCwYWLB3mwXH2dOLmLo5/8zSefh3nYb3uD4Vme5SAHyzVfRZrPfP7H/4oVsQHcuCmggOd4rhIiq5pUyBaRE9acOXNwlnQjG4QdO3aQn58PeFaKvfPOOwHHstvtzJs3r1xzVaTFixeXek127NjBV199VUERiYjIiW7evHnH9Ymmp59++uiD+fM9W3D443R6tuuI9DdsjxyBJUsCx+lwQBV47SEiItXDBjawk53l7r+Qhd6/17KWfewL2D6XXJawpNzzVZR5zAu4B7gTJ2/zdgVGVLWpkC0iJ6wjR44cV/+srCwA3G43ubm5Adu6XC6ys7OPa76KcOjQIWy20lPDoUOHKiAaERERjjt/ZmZmHn1w6JBnK45AnE7IyzuuOcMuJ6fkldjHqgKvPUREpHrI5vhyTh5Hc2+wYx3vnBUhiyzcBH7tcYTjq02cSFTIFpETVsuWLbHKuW+kzWYjJcWzf5fdbqdZs2YBx7Lb7bRp06Zcc1WkNm3alLrVimVZtGx54u7fJSIiFat169bl2gasUKdOnY4+aNOm9D2jGzSAGjXKPV+FqFkTUlICt3G7PecrIiJSAVrSEovyfy9DIxp5/25Fq6D6tCHy81w66Tjw/2kwCyvo8xUVskXkBDZ+/Phy9+3Ro4fPyuWbbropYCHb7XZXiS9I7NGjB40bN/a7Kttut9O7d28aNWpU4vMiIiKhNmrUKOx2e7n6RkVFMXbs2KMHrrkm8Epmmw3Gj4/8L0i022HcOM//BmozcmTFxSQiIie0RjSiF72wU76cfT/3e/9uQQu60c3vWHbstKY1XehSrrkq0vVcX+L+2EWNp/y1iRONCtkicsK69tprueCCC4oVbUvbWiM+Pp5Zs2b5HBs3bhxdunTxO9YTTzxBs2bNQhB1eNlsNt566y2ioqKKFQ0cDge1atXihRdeqKToRETkRFSnTh2mTJkCFM/RpeXsF1980bdNw4bw/POFnX0b2+1w1llw663HGXEFuesuaNu2eDG78LxefBHS0io+LhEROWFNZjK1qFVsBXJpxe0udOFKrvQ59gqvkERSiWNFE800ph3XCvCKciqnMolJAMX2yrZhowc9uIZrKiGyqkmFbBE5YUVHR7N48WImTZpEWpEbvfT0dGbOnMndd99NXFycT59OnTrx008/Ubt2bZ/jsbGxLFu2jHvvvZfU1FTv8Xbt2jF37lwmTJgQ3pMJoa5du7J27Vr69OnjvfmPjo5m+PDhbNiwoUoU5EVEpHq57rrrWLRoER07dvQeS05O5vbbb+f999+nadOmPu0bNGjAO++8U/KnoW6+2fOFjh06HD1WqxbceSesXAnH5P6IlZgIa9bA3/4GyclHj3fsCIsXQxX4JJiIiFQvzWjGBjYwnOFEEw14irV96MNnfMZFXORTzI0mmuu4jk/5tNhYrWjFetYzhCHeYrYNG5dxGV/yJX/hLxVzUiHwIA8ygxk+W6HUoQ6TmMQiFhFFVCVGV7VYxgTzLSFVR1ZWFsnJyWRmZpKUlFTZ4YhIFeF0Ovn999+Jjo6mbt26PtuEbNmyhaysLNq3b1+ssF2SgoICdu7cSUxMDHXr1g1n2GGXmZnJgQMHSEtLIz4+vrLDiRjKNaGh6ygi5fHHH3+Qk5NDvXr1iI6O9h7ftWsXP/zwA02aNKFhw4bBDbZnD+TmQv36EFWFbyLz82HXLs/e3lqF7aU8Exq6jiJSHoc5zB/8QS1qkczRN1zzyedrviaGGE7jtGKrlEuSTTZ72UsKKSRRdf8dMhh2s5t88mlAg4B7Z59IypJndMVERPBsm3HyySeX+Nypp55aprGioqL8jlXVJCcnk1x0lZeIiEglS/NTqK1Xrx716tUr22B16oQgoggQHQ3V5LWHiIhUD/F//hwrmugyr6ZO+POnqrOwqEcZX6uID20tIiIiIiIiIiIiIiIRTYVsEREREREREREREYloKmSLiIiIiIiIiIiISERTIVtEREREREREREREIpoK2SIiIiIiIiIiIiIS0VTIFpFqacf+HWzctpF8Z77P8dzcXH788Ueys7ODHuvIkSMcOHAAY0yow/SZ48cff+TIkSM+x40xHDhwoNjxQPLz89m/fz9OpzPoPtnZ2WRmZobkHEM5loiIVG+52dn8+NVXZO/f73PcuN1k/vor2bt2BT2W0+lk//795Ofnl964vNxu+OUX2Lu3+HOHD8PBgxBs/jMGDhyAMuR48vNh/34oQ46vkLFERKRac+Eigwx+5Mdiz+1nP9vYhpPg8onBkEkm2QR/T14ee9nLL/yCG7fP8QIK2M9+CigIeqzDHOYgBzEEl+MNhgMc4AhlyPF+uHFzgAPkkHPcY1UHKmSLSLVyywu3EF07moapDenQrAMxMTGccuEpfLjiQ0477TRq1KhB8+bNSUxMpHnz5ixfvtzvWCtWrKBHjx7Ex8eTkpJCw4YNefzxx8nLywtZvGvWrKFVq1bEx8fTvHlz4uPjSU9P55NPPuHxxx+nYcOGpKSkEB8fT48ePVixYoXfsbZu3cro0aNJTEwkNTWV5ORkbrrpJnbs2OG3z3vvvUenTp1ITEykZs2atGjRgilTpuByucp8LnPnzuWss87yjtW6dWteeeUV3G536Z1FROSE8s3KlZyelkaNxESan3UWiampNE1I4IPJk/lk8GB+jYkhuXFjEurXJyMhgS/uusvvWDt27OCmm24iOTmZ1NRUEhMTGT16NFu3bg1dwNnZ0K8fREdDkyaQlgY1a8Kjj8KSJdCtGyQkQK1a0LgxPPMMFPi5Qc7Lg8cfh4YNISUF4uPhwgshQI5n61YYPRoSEyE1FZKT4aab4Pffy34u//0vDB/uiTc11RPzrbfC7t1lH0tERKq1fPI5m7Nx4KAd7WhOc2zY6Ec/XuIl0kgjlVSa0YxooulOd/awp8SxXLiYzGRa0IKa1CSRRDrRiQUsCGnMT/IktahFGmk0oQlRRNGLXmxhC2MZSzLJpJJKEkmMYQzb2OZ3rCUsoRvdSCCBWtSiMY15hmf8FsFzyeUxHqMhDUkhhXjiuZALWcnKMp/HEY7wCI/QgAbesXrRizWsKfNY1YqpZjIzMw1gMjMzKzsUEalgF990sQHK/Pv2228XG+uNN94wlmUZu93u09Zms5nu3bub3Nzc44534cKFxrKsoOO02+3GsizzxhtvFBtr48aNJjEx0TgcDp8+DofD1K1b12zbtq1Yn3/84x/ecypsb1mWsSzLXHnllcblcgV9Lvfff3+JYwFm5MiRxu12H8+lijjKNaGh6yhyYlr3wQfG5ifXxYD5AozLs17ZGDAFf/7vqosvLjbWtm3bTN26dUvMf0lJSWbjxo3HH/Dhw8akpnrjKfHXbvd9bFnG9OplTH6+71i5ucZ0726MzVa8v2UZU0KON19/bUxiojEOh28fh8OYunWNKSHH+7VunTFxccXHstuNadjQmN9+O54rFXGUZ0JD11HkxOQ0TpNgEgxl/IkzcWa32e0zlsu4zCAzyFh//hS2tRmbwWAeM4+FJOZRZlTA2ArnK/xxGIepZWqZb823xcaaYqYYDMZu7D59LGOZ3qa3yTe+OT7X5JpupluxOezGbixjmalmatDncdgcNp1MJ79jzTazj/dSRZSy5BkVskWkWtjww4ZyFbEBExMT41O03bVrl4mKivLb3mazmSeffPK4Y46LiytXvFFRUWbnzp3ecdxut2nbtm2xonvRAnjPnj195s7IyCh1nhkzZgR1HuvXry91rPnz5x/39YokyjWhoesocmKqHxPj/01bMM3BuP0UjLcuWOAzVs+ePQPmv7Zt2x7/m6lXXBG4iO3v17KMmTzZd6wnnyxexC76GxVlzK5dR9u73cacemrxQnnRAnSvXsGdh8tlzCmn+B/L4TBmwIDju1YRRnkmNHQdRU5Ml5pLy1zELvzpYrr4jDXdTC+1T4bJOK54vzZflytWu7Gbjqajz1g/m5+LFZGL/ljGMpONb45/wjwRsE+UiSpW4PdnkpnkdyzLWCbGxJh9Zt9xXa9IUpY8o61FRKRaGHXHqHL3zcvL44033vA+fuONNwJureF2u5k8efJx7QE9Z86cMu17XZTL5WLq1Knex19++SUZGRl+Y3a5XCxbtoyff/7Ze+zll1/G4XD4ncNmszFlypSg4nnppZcCjmW324MeS0REqrf/LF3KzgBbdLmAH4BPSniuANhx//3exz///DNLly4NmP8yMjL48ssvjytmFi4sf98XXjj6tzEwebJnn21/XC4okuP54gvYssVz3F/7pUs9+3aX5pNP4Mcf/Y/ldMKCBbBzZ+ljiYhItfchH5a77+d8Ti653seTmYwtwO7Gduy8wivlng/gTu4sVz8XLtaxjm/4xnvsdV7HwgrYbzKTvX8bDJOZXGw/7mPneYM3/D5fyI2bKUzxO5bBkE8+05le6ljVkQrZIlIt/Lrl1+Pqv2bN0X2mNm/ejGUFTlq//vorubm5AdsEsnr16nL3BcjIyPD+vXnz5lLbG2P49ttvvY83btwY8Msg3W53UOMGM5bL5eKbb77x+7yIiJw4VsyfX2obG5BRwvEooGaRgu2WLVuCmrNoziyz/Hz/e12Xxhj4/vujheucHPi1lNcrlgVF828wudgYKJLj/dq8GWyl3P653fDdd6WPJSIi1V6wX95YEoPhO47mkwwySi3ybmJTuecD+J7vj6t/RpFXH5vZjAv/i9sMhu/53ntORzjCb/wWcHwLy2cOf/aznz/4I2AbO/agxqqOVMgWkWohKjbquPrHx8d7/46Liyu1kG2z2YiKKv+ciYmJ5e5rs9moUaOG93FcXFxQ/Yr2iY+PL/Uci7YPJJRjiYhI9ZZcq1apbdxASVnDBRRER3sflyf/lVmATxwFJTraU5wu/Lu0QrJlQdF4gzxHgjnHuLjAq8HLMpaIiEgpkkn2/l2jxMx+lIVFPPEB25Qmhpjj6l80xjjisGMP2D6aaO+q7WiiA644B885lnYdAGKJDSLa0q9pdaVCtohUCwNGDDiu/rfddpv37/79+wdcYexwOOjXr1/A7TRKc9NNN5W7r9Pp5PLLL/c+7tmzZ6lF9Vq1atGlSxfv4wEDAl8vh8PBoEGDgopn4MCBAZ+32+0MHjw4qLFERKR6u/r++0v5oC7Ygb4lHLcBR3r39j7u0qULNWvWDDhWVFQUvXr1KmOURSe1QcOG5evrcMCAAUcL2Q4H9O0L9gA3xk6np0+hXr2gtDfOU1Kgc+fS4+nTJ/DcAHXrwllnlT6WiIhUe3WoU+6+SSTRlKbex1dwBQ783z8bDAM4vnv6K7my3H1rUIMe9PA+7k//gCuyHTgYwABvITuKKPrQJ2Dx24mTy7nc7/OFEkjgAi4IyVjVkQrZIlItvHT7S1hxpd0al+y0006jRYsW3se9e/embdu2JRaqLcvC7XZz1113lTtWgJNPPplOnTqVuZ/D4aBt27Y+N+W1a9dm3LhxAVdFT5w4kZiYo+9QDxs2jAYNGmAv4YbWZrPhcDi45ZZbgopp1KhRpKWl+R0rJiaG8ePHBzWWiIhUb7EJCVx+2ml+n7cBo4F6xxwvAHbZbJz59NPeYzExMdx9991+x7Isi3HjxpGamnpcMfPYY2XvU5iTJ0zwPT5xomcrkJJytsMB7dpBz55Hj9WuDWPHlty+6JgxQaxCa9AArrkm8Krwe+89/lXoIiJSLfyLf5W777H7Vd/KrThwlLhq2Y6dhjRkKEPLPR/A/dxfrlXZFha3citJJHmP9ac/LWhRYvG9sHh9B3f4HJ/IRAymxL217dhpRzt60rPYcyW5l3v9bsXiwMHZnE13ugc1VnWjQraIVAt2m53V61b7LWYnpCWUeLxJkyZ8/vnnvmPZ7SxdupT09HTAUzx2OBxYlkV0dDSzZ8/2Wd1cXp988gmtWrUq8blGjRoRHR2NZVne+QHatGnD0qVLixWNn376aYYPH+4Tb2Gb2267jQnH3EjHx8ezcuVKGjVqVOwcExISWLx4MS1btgzqPJKTk1m5ciX169f3GavwuSVLltC0adNAQ4iIyAnknf/8hy4nnVTic80dDv6BZ3uRgj9/AXY5HOR++CHxdXxXh02YMMH7qSq73e6Tg4YPH87TRQrf5TZ8OPgrmEdFQePGnr8dDs9v4fYg8+fDGWf4tu/SBWbPPrrlSGEfgPR0WLKk+KrpZ56BYcN85yhsc/vtcIfvjXRAL7wAhZ+kKjqWZXmK2MfxiTEREaleruRKbsH/4qZooks8PpKR3Mu9Psda0YpFLCKBBCwsHH/+AJzMyaxk5XFvLRJNNGtZ67eY3Z72AN65C+cfwxj+zt+LjfUxH9Oc5j59CrcHmc98zsA3x5/DOcxilnfLkaJztKUtS1la6nYlhS7gAt7kTe+WJUXH6kAHFrO41C+jrK4sY4yp7CBCKSsri+TkZDIzM0lKSiq9g4hUK/nOfMY/O553Z7yLM9dJozaNeOPpN+jYoiMrVqxg0qRJ7Ny5k5SUFO66666A22K43W6WL1/OwoULyc3N5bTTTuPqq68mJSUlpDG///77/OMf/2Dv3r3UqVOH+++/n969e7N//36mT5/Opk2biI2N5dJLL+Xiiy/GFmAl1ebNm5k+fTp79uyhYcOGjBo1iubNm/ttX1BQwAcffMDSpUtxOp385S9/4aqrriIhoeTCfyD5+fksWLCAjz/+GJfLxTnnnMOQIUOC3sO0KlGuCQ1dR5ET25o5c7j/zjvZceAANePiuP2WWxh6991k79rFf+64A2vdOozdTvQll3DmpElEBcgnW7du5c0332T79u3UqVOHESNG0K5du9AG/MsvnqLxpk2eAvagQXDPPZ5C8EcfweLFkJfnKV6PGAHJyf7H2r8f3noLvvkGYmPhssvgoosCr5b+5huYMQP27IFGjWDkSAiQ4wP6+muYORP27vUU4keNgmr4prPyTGjoOoqc2LaxjeEMJ4MMLCxvwTaRRJ7neaYxjRxyaElLnuZp2tDG71jZZDOTmaxjHQ4c9KQnl3JpwG1HysqJk8d5nLd5m3zyaUtbnuEZmtKU7/iOt3iL3/md+tRnBCNIJ93vWC5cfMRHLGYxeeRxBmcwghE++38faz/7eYu3+IZviCWWy7iMi7io1D20S7KXvbzJm2xhC3HEcTmXcwEXVLsidlnyjArZIiIiZaRcExq6jiIiEk7KM6Gh6ygiIuFUljyjrUVEREREREREREREJKKpkC0iIiIiIiIiIiIiEU2FbBERERERERERERGJaCpki4iIiIiIiIiIiEhEUyFbRERERERERERERCKaCtkiIiIiIiIiIiIiEtEclR2AiAjAV199xfz58zl8+DBt2rThqquuIikpqULmziWX+cxnPeuJJppe9OJ8zsfCqpD5RUREqoo//viDGTNmsG3bNlJSUhg6dCitWrWqmMmNgXXrYMECOHIE2raFoUMhIaFi5hcREakinDh5n/f5N//GwuJ8zqc3vbFjr5D5d7Ob6UznV36lNrW5iqtoTvMKmVuqN8sYYyo7iFDKysoiOTmZzMzMCiuCiUj5ZWVlMXjwYJYuXYrD4cCyLJxOJzVq1OCNN97gyiuvDOv8a1jDAAawj31EEYXB4MTJ6ZzOIhZxEieFdX6pmpRrQkPXUaRqef7557nzzjtxuVzY7Xbcbjcul4urr76a1157jejo6PBNfuAADBwIq1aBwwGWBU4nxMfDW2/B5ZeHb26pspRnQkPXUaRq2cQm+tGP7WwniigACijgFE5hMYtpRXjfgH6cx7mf+3Hjxo4dN25cuLiO63iRF3FoTa0coyx5RluLiEilGjRoEB9//DEATqeTgoICjDHk5OQwdOhQVq1aFba5v+d7etGLAxwAPMndiROADDLoQQ/yyQ/b/CIiIlXFjBkz+Nvf/kZBQQFut5uCggJcLpf3uZtvvjl8kxsDl10Ga9Z4HjudUFDgOX74MAwaBJ9/Hr75RUREqohd7OJ8zmcnOwHPPW4BBQD8wi90pzsHORi2+V/jNe7mbpw4ceOmgAJceF4vvM7rTGBC2OaWE4MK2SJSadavX8+yZcu8N8JFGWOw2Ww8/PDDYZv/WZ6lgALcuIs958TJ93zPu7wbtvlFRESqArfbzQMPPBDw+ddff50dO3aEJ4BPP/X8lvB6gcIPlz76aHjmFhERqUJe5mUyyfQWj4ty4mQ3u5nGtLDM7cLFgzzo93mDYTKT2cvesMwvJwYVskWk0syfPx+Hw//HilwuF6tXr+bAgQNhmX8Oc7wrsEtiw8Y85oVlbhERkaoiIyODbdu2BWxjjGHhwoXhCWDePM92Iv64XPDRR5CTE575RUREqoi3ebvEhVqFDIY5zAnL3BvY4F0J7o8TJ4tYFJb55cSgQraIVJrs7Gwsq/QvVDx8+HBY5j/CkYDPu3FziENhmVtERKSqyM7OLrWNzWYLql05Ayi9jdutQraIiJzwgrl/zSQzLHNnU3q+trB0jy3HRYVsEak0rVu3xun0vyIaICkpiTp16oRl/pa0xMJ/Id2Bg3TSwzK3iIhIVXHKKadgswW+bXC5XLRp0yY8AbRu7SlUB5KaCjVrhmd+ERGRKuJUTsWO3e/zDhy0o11Y5i7t/ho8K8LbEKbXC3JCUCFbRCrN8OHDiYmJ8fu83W7nuuuuIzo6Oizzj2d8wOedOLme68Myt4iISFVRt25dBgwY4Hc7MJvNRr169ejdu3d4Ahg1Cuz+b8qx2+HGG6GUYruIiEh1dyM3lrg/diEnTsYyNixzN6IRvejlt5Buw0YTmnABF4Rlfjkx6NWeiFSamjVr8tprr2FZVrGVXna7nZYtW3LfffeFbf5ruZYLuADbMf8UFj5+iIf0brGIiAjw7LPPUrt27WLFbLvdjt1uZ/r06QG/9+K41KkDU6Z4/j62WG23Q9u2MGFCeOYWERGpQi7lUoYytNjK6MLHN3AD3eketvknM5la1MLBMa8XsBNFFG/xVrH7b5Gy0P/1iEilGj58OEuWLKFLly7eY4mJifz1r39l7dq11Azjx4SjiWYxi5nEJNJI8x5PJ52ZzOQBHgjb3CIiIlVJo0aN2LBhA1dffbX301SWZXHxxRfz73//mwsvvDC8AVx3HSxaBB07Hj2WnAy33QZr1kBiYnjnFxERqQJs2JjOdJ7hGRrRyHu8KU15kRd5iZdK3f7jeDSjGRvYwHCGE020N6Y+9OFzPuc8zgvb3HJisIwxprKDCKWsrCySk5PJzMwkKSmpssMRkTLYu3cvhw8fpl69egG3HAkHJ05+53eiiaYudcOa3KXqU64JDV1HkarpyJEj7Nmzh5o1a4b1DWe//vjD88WO9epBmLYfk+pBeSY0dB1FqiY3bn7ndywsGtCgwu9xD3OYP/iDWtQimeQKnVuqlrLkmTB9/k9EpOxq165N7dq1K2VuBw5O5uRKmVtERKQqiYuLo0mTJpUXQFpa6W1EREROcDZsNKRhpc0f/+ePSChpaxERERERERERERERiWgqZIuIiIiIiIiIiIhIRFMhW0REREREREREREQimgrZIiIiIiIiIiIiIhLRVMgWERERERERERERkYimQraIRIxccjnAAdy4g2pvMGSSSTbZYY3rCEc4wAEMJqzzhEp+fj779+/H6XRWdigiIlIduVywfz/k5QXfJycHDhwAd3A5vjycTif79+8nPz8/bHOElDGea3LkSGVHIiIi1ZAxhqysLA4dOhR0H5fLFfZcWlH38aF0mMMc5GCVqQlUZypki0ilW8ta+tKXOOJIIYV61GMSkzjM4RLbu3AxhSm0oAU1qUkiiXSiE+/xXkjjWsEKetCDeOJJIYWGNORxHiePMty4V6CtW7cyevRoEhMTSU1NJTk5mZtuuokdO3ZUdmgiIlId/PEH3H47pKRAaiokJMDQoZCR4b/P2rXQty/Ex3v61asHkybB4ZJzfHns2LGDm266ieTkZFJTU0lMTGT06NFs3bo1ZHOEVF4ePP44NGzouSbx8XDhhbBiRWVHJiIi1YAxhv/7v//j1FNPJTk5maSkJE4//XRmzZqFMSUXYv/44w9uv/12UlJSSE1NJT4+nqFDh5IRKMeXkQsXk5lc7D5+AQtCNkeoLWEJ3ehGAgnUohaNacwzPEMBBZUd2gnLMv7+r7iKysrKIjk5mczMTJKSkio7HBEpxbu8yyAGYWHhwuU9bsPG6ZzOJ3xCAgne427cXMVVzGUugPcdURs23Lh5lEe5h3uOO66pTOVarsWGrVhcXenKEpYQQ8xxzxMqmzZt4rzzziMnJ8dnJbbD4SA1NZUvvviCJk2aVF6A1YxyTWjoOopUIbt2QefO8NtvnhXZhRwOiIqC5cvhnHN8+8yfD4MHg2X59rHZ4IwzYNUqTzH8OPz888906tSJffv2Fct/cXFxrFmzhvbt2x/XHCGVlwe9esGaNb6r0+12z+P/+z8YPbry4qtmlGdCQ9dRpOowxjB27FheffVVLMvyFq5tNhtut5s777yTJ554wqfPrl276NSpE9u3b8dVJF87HA6ioqL4+OOP6dKly3HF5cbNEIYwj3meOI+5j3+Mx5jIxOOaI9Re5EXGMx47dp+agIVFL3qxkIVEEVWJEVYfZckzWpEtIpUmiyxGMAKD8UkM4El0m9jEP/iHz/G3eZs5zMH8+VO0PcC93EsGx/eu8W52cwM3+I1rDWv4F/86rjlCyRjD8OHDOXLkSLHtRJxOJ3v37mXs2LGVFJ2IiFQLt99evIgN4HR6irNDh/o+l5UFV1/t2T7j2D5uN3z9NfzDN8eXx9ixY9m7d2+J+e/w4cMMHz7c7+qzSvGvfxUvYoPnGhkDN9wAu3dXTmwiIlLlffTRR7z66qsAPvnP/WfeefLJJ/nss898+tx2223s2LHDp4gNnlyal5fH0KFDiz1XVrOYxTu84/c+/m7uZgtbjmuOUPqFX7iZmwGK1QQMhiUs4VVerYzQTngVUsieMmUKTZo0ITY2lr/85S+sW7fOb9tp06ZhWZbPb2xsbEWEKSIVbDazySHH7z5TLly8zMs+H9uZzGRsAf7pcuDgZV4+rrje4I1iyaooN24mMzli9sf68ssvycjI8PviwuVysWzZMn7++eeKDUyqHOVrESnR3r0wd27xgnQht9tT5F627OixWbM8+2L7KyK7XPDyy55CeDn9/PPPLF26NGD+y8jI4Msvvyz3HCFlDEyeHHifcJcLpk6tuJikSlK+FhF/XnzxRex2u9/nHQ4HL730kvfx3r17eeedd/x+v5Lb7ebXX39l+fLlxxVXaffxduy8wivHNUcovc7rWFgB20xmcgVFI0WFvZA9Z84cbrvtNh588EH+85//0L59e3r27MmePXv89klKSmLnzp3e319++SXcYYpIJdjMZhw4ArY5wAH2cPTfi81sDvhlkE6cbGLTccdVWtL6lV/JJfe45gmVzZs3l9rGGMO3335bAdFIVaV8LSJ+/e9/pRec7XbfvbI3b/ZsOxLIgQMQ4N+Y0mzZEtzKrVDu73lccnLg118Dt7Esz7UT8UP5WkQC2bhxY8DV006nk6+//tr7+Pvvv/dbxC5kt9uPO5dmkBHwPt6F67jv40NpM5sDLm4zGL7n+4DnJOER9kL2s88+y3XXXcfo0aNJT0/n5ZdfJi4ujjfeeMNvH8uyqFevnve3bt264Q5TRCpBHHFBrWquQY0S/y6JhUUccccdV2mFbBu2iNkPKy4uuPOtUSPwtZMTm/K1iPgVTJ5xu6FonomL878au6jjyE1VLv9FR3v2Bw/Eso7rmkj1p3wtIoEEkxuLtgmmvdvtPu5cGsx9fDzxxzVHKMURhx3/K9sBookutW4goRfWQnZ+fj5fffUVF1544dEJbTYuvPBCPv/8c7/9srOzady4MY0aNeKyyy4LuNoiLy+PrKwsn18RqRr60x8n/t/9tWPnHM4hhRTvsUEMKnUV90AGhjUuBw760a/UOCpKz549iYoKXFSvVavWcX9Bh1RfFZGvQTlbpMpq1w4aNSq93SWXHP27f//Aq7htNjj3XKhVq9xhdenShZo1awZsExUVRa9evco9R0g5HNC3r2f1uj9OJwwYUHExSZWifC0ipRk0aFDArUVsNhuDBw/2Pj7ttNNoFESOv6Roji+HK7gi4P2zwTCAyMl//ekfcEW2AwcDGKBCdiUIayF77969uFyuYu/41q1bl127dpXYp1WrVrzxxhssXLiQGTNm4Ha76dKlC9u3by+x/WOPPUZycrL3N5j/BxSRyNCZzpzHeX7f6XTh4j7u8zl2C7fgwFHi/lp27NSnPldx1XHF1ZvetKVtiYnWwsKNm7u467jmCKXatWszbtw4LMt/Ep04cSIxMTEVGJVUJRWRr0E5W6TKstvh/vv9P2+zwbBh0Ljx0WNdungK1f5upt1uuPfe4worJiaGu+++2+/zlmUxbtw4UlNTj2uekJo40bNSvaSc7XB43jTo2bPi45IqQflaREozbtw44uLisJXwCSC73U7NmjW55pprfI7dd999xdoWstlsDB8+nJNPPvm44rqVWwPexzekIUMZelxzhFJ/+tOCFn5rAgB3cEdFhyVU0Jc9lkXnzp25+uqrOf300+nWrRvvvvsuaWlpvPJKyZu+33333WRmZnp/f/vttwqOWETKy8JiAQvoSEfA865mYXKLIorXeI1e+K6iaklLFrOYBBKwsLx9ABrRiFWsIoGE44rLjp2lLCWddJ+4LCyiiWY2s+lCZK1ufvrppxk+fDjg+QIPh8PhfSf+tttuY8KECZUZnlRDZc3XoJwtUqWNGQMPP+wpwNrtnqJr4R7Yl1wCr77q296yYMEC6OjJ8d72NhtERcHrr0MIVkpPmDCB2267DfDcjBfmQIDhw4fz9NNPH/ccIdWlC8ye7dlmxLJ8r2N6OixZEnjFtkgZKV+LnFgaNmzIsmXLqPXnJ56K5sU6deqwcuXKYm/wXnfddTz00ENYllUsl1566aUB/70IVitasYhFJd7Hn8zJrGRlRG0tEk00H/MxzWkO+NYEalCD+cznDM6o5ChPTGH9XHzt2rWx2+3s3r3b5/ju3bupV69eUGNERUXRoUMHfvjhhxKfj4mJ0SpDkSoshRQ+4zM+4RPmM59ssmlDG0YxijrUKbHPBVzADnYwi1l8yZc4cNCTnlzCJSHbt7oBDfiar1nOchaykFxyOY3TuJqrfbY6iRRRUVG89dZbTJgwgenTp7Nnzx4aNmzIqFGjaN68eWWHJxGuIvI1KGeLVGmW5VmVffXVMG0abNsGKSlw1VVw1lkl90lNhc8+g08+gfnzITvbU6wdORLqlJzjyx6WxTPPPMPYsWN588032b59O3Xq1GHEiBG0a9cuJHOE3ODBcOGF8NZb8M03EBsLl10GF11U+h7ackJTvhaRYHTq1InffvuNuXPn8umnn2JZFueffz4DBw4s8f+3LcvigQceYOTIkUybNo1t27aRmprK0KFDOctfji+HHvRgBzuYyUzWsc57H38pl0bMtp1FnczJZJDBR3zEYhaTRx5ncAYjGEEyyZUd3gnLMiaYb2Epv7/85S907NiRF154AfBsEn/yySdz0003MXHixFL7u1wuTj31VPr06cOzzz5bavusrCySk5PJzMwkKSnpuOMXERE5VnXMNRWdr6F6XkcREYkc1THPKF+LiEh1U5Y8E/a3PG677TZGjhzJWWedRceOHXn++ec5fPjw/7N33/FRVekfxz937qRDKi0BpEsRWMWKuqCCggVBBWmKWLD/XF0bsoqLZV3Luroq4ooKioJSbLi6i2BDmgUUFVGRolQhkBBCSGbm/P4YGTJkJplJZjKT8H3nlZeZM+ee+9wb9cl57p1zufTSSwEYNWoUzZs354EHHgDgnnvu4YQTTqB9+/bs2rWLhx9+mPXr13PFFVdEO1QREZFDlvK1iIhI/FO+FhGRQ1nUC9lDhw7lt99+Y/z48WzZsoUjjzyS9957z/eAig0bNvgtQr9z507GjBnDli1byMrK4uijj2bRokV06dIl2qGKiIgcspSvRURE4p/ytYiIHMqivrRIbdPHnkREJNqUayJD51FERKJJeSYydB5FRCSawskzepqIiIiIiIiIiIiIiMQ1FbJFREREREREREREJK6pkC0iIiIiIiIiIiIicS3qD3sUkfrrK75iJjPZzW460pERjCCTzGqNNZjBvMVbePCQTjqzmc2pnMpWtjKNaaxnPY1oxHCG04EOlFLKHOawlKU4cXIGZ9CHPjgquT63ghWMYxwb2UhjGjOe8fSiFyUlJcyePZvPPvuMxMRE+vfvz6mnngrA4sWLeeutt9i7dy9/+MMfGDZsGKmpqdU6RhERkVjYsWMH06ZNY82aNWRmZjJs2LBqP+ht7ssvM+aaa9hVXEyS08nNV13FXY8/TllZGW+88QaLFi3Ctm369u3LGWec4X3o3BdfwOzZsGcPdOkCw4dDJesflhYVMWfcOJYuXIjTtjnjggvoc8stWLbNsmXLeOONNyguLqZr164MHz6cBg0asGnTJqZNm8avv/5KkyZNGDlyJG3atKnuKRMREal1brebd955h48++ghjDL179+bss8/G6Qy/dLd913ZOGHEC675bh2VZHH7k4Sx6aREZDTJYsWIFs2bNYvfu3XTs2JGRI0eSkZHBb7/9xrRp01i7di3Z2dkMHz6cjh07VrqfF797kUnbJlFsFXOEdQQPHvkgLdJbsJWtvMRLbGADjWjECEbQnvaUUspsZrOMZb55fF/6YmFV97TJocbUMwUFBQYwBQUFsQ5FpN4qMkVmgBlgMBincZoEk2AsY5lkk2ymmqlhjbXALDAE+co0mcZpnMZhHCbBJBjb2AaDGWAGmCamicFgEkyCcRqnwWCOMEeYdWZdhX24jducak4NuI+2H7U1OTk5BjAJCQnG6XQawHTt2tUce+yxBjBOp9MkJCQYwKSnp5u5c+dG6lRKHaVcExk6jyLR9/TTT5vExETjcDhMQkKCsW3bAGbo0KFm7969YY3VrGFDA1T4doBp3KhRhVzao317s+ePfzQGjHE6jUlIMMayjElNNWbGjID7WPTMM6axZXnHAuP8fR+dExNNz6OO8svLlmWZtLQ0M3ToUGPbtt8xWpZlrrvuOuNyuSJxGqWOUp6JDJ1Hkej79ttvTevWrX25dP/8s1WrVmblypVhjTVi3AiDo2K+xolpfXLrCrk0JSXFjBgxwiQkJFT4e2HUqFFm3759FfbxS8EvJuezHO+suhTDPgxuDEWYE9edGHAef6451zQ2jSvM47uarma9WR+pUyl1UDh5xjLGmFqpmNcSPVFZJPoGMYi5zMWNu8J7Fhb/4T/0p39IY1X3yquFhcH/f19OnLSkJd/yLSmk+NoHM5jZzK44yGrgKGAf4Alxv5aFbdssWbKEo48+ulqxS92nXBMZOo8i0TVr1iyGDBkS8D2Hw8HIkSN58cUXQxrriObN+W7TprD2/1/gNAJ8BNT6PffPnw+/fwIK4OcPP6T7qaeyl5DTcqUsy2Ls2LH87W9/i8BoUhcpz0SGzqNIdO3YsYPOnTuTn5+P2+0/x7Ztm6ysLFatWkWjRo2qHOvZOc9y5eArIVilzwGkAbtDi83hcDBmzBgmTZrka/MYD42/bEz+H/IrJnkPlS5iHGwe34pWrGSl3zxeDh3h5BmtkS0iYVnJSt7kzYBFbPAmpglMCGmsgQysdhwHJz8AFy7WspYZzPC1FVPMHOYEHuRRoIywZsv7r/39/e9/DyNaERGR2mWMYfz48VhW4AvGHo/H9/HhqpQUF4ddxD4WOIMg6xgaAw4H3HuvX/PjN95ICZEpYnt3Y/jnP//Jrl27IjSiiIhI5E2ePJkdO3ZUKGKDd7mR/Px8nn322ZDG+r9x/xe8iA3eJNs49Ng8Hg/PPvssm8r9HTD1u6nkHx2giA3eKmMl+w82j1/DGl7jtdADk0OWCtkiEpY5zMFZyfL6HjwsYQmb2VzlWO/ybiRDA8CBg5nM9L1+jucCJksAXgVc4e/D5XLx+uuvB/xDQ0REJB789NNPrFq1iso+fGlZFnPmBLnYW85frr467P1fgPdacVBuN3zwAZQrMs/4+usgl8mrr6SkhHffjfzfGyIiIpEyY8YMPJ7gl3E9Hg8zZswI+n55+37eV3WnqqfqfowxvPnmm77XT//2dOVJvhofuj54Hi8SjArZIhKWIopCWg6kiKIq+wS7q7smPHgopND3egc7gncurv5+3G43+/aF8EeCiIhIDBQVVZ2HHQ5HSP22bNkS9v4bUPkNYT579vh+LI7SioehHKOIiEisFBYWRqQPQEhT7DBv5jr474ViR3GIST50B8/jRYJRIVtEwtKJTriqyHyppNKc5lWO1ZCGkQrLx4mTrnT1ve5Fr+CdD6daV4sB8vLySEnR+l0iIhKfWrduTUJCQqV9XC4XnTt3rnKsC4YNC3v/3xNkWZHyMjKg8YHPN3dMTY3K5CSUYxQREYmVrl274nQGz5q2bdOtW7fQBksLoU+Y03C32+2XSzuajiEk+fAcPI8XCUaFbBEJy1CGkkpq0LuybWxGM5pUUqscazrTIx0eLlxcxVW+16dxGplkBu58XfX24XA4uO6664KuOyoiIhJrWVlZDB06NOjE2LIssrOzGTiw6udVnH/ZZWFPGqYBpVSy3rVtw5gxkJjoa7pu6NCIrY/t3YXN4YcfzkknnRTBUUVERCLr2muvxeUKfrOY2+3mmmuuCWmsw3sfXnWnrFAj8859c3Nz6d+/v6/tkT88UnmSr8bd2i5cXMmV4W8ohxwVskUkLA1owAu8AHiL1uXZ2LShTcgPezyTM8kjL6z9O3Bg/f5V3v7XYxnLURzl994rvBK48H45cBpYDv/3HA5HwJ/BOyk++uijufHGG8OKW0REpLY99NBD5ObmYtsH5WvbxrZtXnzxRZKSkkIa698PPRTWvgssizH8/sGng3Iptg0dO8Jf/uLXfPHEiZzduHGFCUr5LH1wXnY4HL7j8d+FTWJiIlOnTtWFZxERiWtnnHEGl112GUDAnDV69Gi/QnJlls9cXvnDHJsBa/ybKsultm3z0ksv+V0Yb5PZhjGfj/G+OLj+7gLcYBv/saqax49jHEdyZOUHJ4IK2SJSDUMYwnzmczIn+9rSSONarmUJS2hEo5DH2shGetCjQruNzRSmMJrRJOK9W8uBg7M5m8Us5j7uoxnNfP070YkpTOFv/K3CWGdyJgtYQBva+LXnJubyyjuvMOGvE2hc7qPNXbp04eWXX+aNN97g6KOP9rVnZmZy66238sEHH5CaWvUd5yIiIrGUm5vLsmXLuOKKK0hOTva1n3baaXz00UecffbZIY91+a23MvWf/yTBOngCCqPPPpuHHnqIvLwDF6fbt29P72efhffeg549D2zQsCHccAN8+ilkZvqN5UxO5vV167j39NNpVq5g3SkxkSlXXMHbb73Fcccd52vPyMjg5ptv5vPPP2f48OG+SbbD4WDgwIEsWbKEE044IeRjFBERiQXLsnj22Wd54oknaNWqla+9VatWPP744zz33HMhX5RNTU5l18+7aPiHhv4VPxuyjs3ivanv0bt3b19zWloa119/PStWrOCSSy7xXeC2LIt+/fqxcOFC+vTpU2E//z7539z35X00/K7cOiW7ocfCHiwsXsjF1sUV5vFLWMJ93EdTmvo26UQnpjKV+7gvpOMTsUxljzKvgwoLC8nIyKCgoID09PRYhyNS7+1gB0UU0ZSmJJNc9QaVeJVX+YmfGMYw2tHO176HPfzGb2SRRQYZvnY3bjaxCSdOmtEspIdQbmMbP/ADrWlNC1r42l0uF5s2bSIxMZGmTZv6/aGwbds2SkpKyM3NrXK9UTk0KNdEhs6jSO3Zu3cv27ZtIz09naysMD5THMDqFSt4/623+MOxx3LymWf62t1uN5s2bcK2bXJzc/0n3du3ex/s2KwZhHAXuLu0lE3Ll+NMSqJZ9+5Y5Qrbv/32G3v37qVZs2YklluapKioiO3bt5Odna3/pwigPBMpOo8itcfj8bB582bAe0H64E8ihaO4pJhJsyZ5l8a88DoSnAfmsjt27KCoqIimTZv6XewuLi5m27ZtZGZmknnQBedgVu9Yzfa92+nWuBvpSQf+HxHJebzUb+HkGRWyRUREwqRcExk6jyIiEk3KM5Gh8ygiItEUTp7R0iIiIiIiIiIiIiIiEtdUyBYRERERERERERGRuKZCtoiIiIiIiIiIiIjENRWyRURERERERERERCSuqZAtIiIiIiIiIiIiInFNhWyRQ9A+9pFPPm7ctb5vDx42sIEtbAl5Gxcu8smnlNKQx9rLXnayEw+eGsccS8YYdu7cSXFxcaxDERGRWuZ2u8nPz6ekpCQ2AezaBWvXgssVUndjDAUFBRQVFVV4b29+PjvXrsVz8FguF+TnQ2lphW3qmj179rBr1y6MMbEORUREatnu3bspLCyMSQ4oLS1lzZo1FBYWhrxNSUkJO3fuxOPxny/vH+vgXF5Zjq9ryigjn3zKKIt1KFINKmSLHEK+5EsGM5hUUskhh0Y0Yixj2cnOqO/bhYuLuIhkkmlFK3LJJZVUbuKmoNtsZSt/4k9kkUUOOTSgARdxEV/xFRdyIUkk+cZqQANu53Y+5mP605800sgmmzzyuI/72MveqB9jJO3bt4+///3vtGjRguzsbNLS0ujTpw/z58+PdWgiIhJlBQUFjBs3jsaNG5OTk0NaWhrnnXcen3/+ee0E8MIL0KwZZGVB27aQmAgnnwy//hqwu8fj4ZlnnqFTp05kZmbSsGFDjjnmGF577TUWPfMMZzdpQlpODtlt29IsMZG/nnIKxd98A3/6k3cfOTnQoAFcdBF8/33tHGMEvffee/Tu3ZsGDRqQlZVFq1at+Mc//kFZmSbIIiL1mTGG6dOnc9RRR5Genk5GRgZdunRh8uTJtVLQXrNmDccddxxJSUm0b9+ejIwMWrRowWuvvRZ0m2XLlnHuueeSlpZGdnY2TZo04a677uLzzz/nmGOO8Y3VsGFDWrVqxezZs5k0aVKFHD9z5syoH1+kbWADV3M1GWSQQw7ppHMFV7CWtbEOTcJh6pmCggIDmIKCgliHIhJX5pl5JsEkGKdxGsp92cY2HU1Hs8PsiNq+3cZt2pl2fvst//VH88cK2/xifjHNTXNjG9uvr9M4jWWsoGPtP6byrx3GYXqanqbYFEftGCOppKTEnHLKKcbhcBjA923btrEsyzz//POxDvGQp1wTGTqPIhXt3LnTdOnSxdi2XSEHOJ1O8+6770Y3gLvvNgYCfyclGbN+vV93j8djLrnkEgMYy7J88ZbPYXa54wBMSzBbwHhs2398p9OY1FRjli2L7jFG0MSJE32/n/LHaFmW6d+/vyktLY11iIc05ZnI0HkUCWzs2LEVct7+XHjFFVcYj8cTtX1///33JiEhwS/3lP/+xz/+UWGbt99+2zidzgo56+B5Z6DvQDn+7rvvjtrxRdpqs9rkmJwK9RCncZosk2W+M9/FOsRDWjh5RndkixwCSillOMNx48aF/0d63bj5iZ8Yx7io7f8O7mANa4K+/wmf8CIv+rXdwA1sZWuF5U9cuDBUfnX74G08eFjKUh7m4TAjj40nnniCjz/+uMLHvNxuN8YYrrrqKrZsCX1pFhERqTvuuusuVq9ejdvtn8vcbjcej4cRI0ZEb6mR7dthwoTg7+/bB+ed59f0+uuvM3XqVAC/u8/K57CDFzJ7DMgGrIOOEZfLu48RI7yl7Ti3YcMGrr/+eoAKvy9jDP/973/597//HYvQREQkyhYvXszf//53wD/n7c+FkydPZu7cuVHb/4ABAyr95M+tt97qtzxlcXExI0eOxO12V8hZB887AwmU4ydMmMCXX34ZbugxcQVXsItdFeohLlwUUshoRscmMAmbCtkih4A3eIPtbA+6XrQbN1OZShHRWe/qWZ6tss993Of7eTObeZM3KySZmvDg4Smeivs1s40xPPHEE5X+MeF2u3nhhRdqMSoREakNxcXFPP/88xUmmPt5PB527tzJ7NmzoxPAHXdU3efLL71rZ//uqaeewrbtkHeRCwwCEoJ1cLvhp5/go49CHjNWJk+ejGVZlfZ54oknaikaERGpTU8//TROpzPo+7ZtM3HixKjse8OGDfz444+V9vF4PEwod3H6tddei/ga3k6nM2rHGEmrWMUnfBL0GWFu3CxjGV/zdS1HJtWhQrbIIWAlK0kIPmUEoIQS1rEuKvvfxa4q+/zKgXU3V7EqKgXnbWwLKZZY2rt3Lxs2bKiy3zfffFML0YiISG3asGFDlQ/3TUhIiF4OWL48tH7l7r766quvghbeA+lMCBMQhwPqQJ5buXJlpcdujGH16tUh3ekmIiJ1y/Lly3FV8jBkt9vNV199FZV9f/LJJyH1K/9sjZUrV5KQUHlNIFwulytqxxhJ3xDa3xSh9pPYUiFb5BCQSmpIheEUUqKyf0cI/6spX2hPJTUqcQAkkRS1sSMhMTERh6Py8+VwOEhJic7vSkREYic1ter85/F4opcDQtg/AJmZ5TYJL2dXXqb/nccDdSDPpaamVnk3emJiYpV3bYuISN2TlpZWZZ9o5eusrKyQ+pXff2pqasQfQGlZVth/B8RCqPWFaNVDJLJUyBY5BAxkYNCP0QBYWHSmM21pG5X9H8mRVfbpS1/fz8dwDE1oEtEYbGz60pc0qv6DI5acTifnnHNOpR9Tc7lcnHfQGqUiIlL3tWzZkm7dulVa+HS73QwaNCg6AVx1VdV9UlKgRw/fyyFDhoS1tMhnwNaqOtk2nH12yGPGyqBBgyq9I9vpdHL++eerkC0iUg8NHjy40huQbNvmwgsvjMq++/fvX+l8cb8//elPvp8HDRpU6R3k1TV48OCIjxlpp3BKlXWAFFLoQ59aikhqQoVskUNAF7pwLudiE3iiaTCMZzwW0ZloPcZjlb5vYfEoj/peO3FyJ3dGNAYPnqg+0DKSxo4di8fjCTjxdTqddO3alf79+8cgMhERiSbLshg/fnzQO6Zs2+bMM8+ke/fu0Qlg5EjIzq68T7lJMcD1119PUlJSlZ8m2s8N3F9ZB4cDLr8cmjULabxYGjRoEB06dAhYTNifw2+99dbaDktERGrBZZddRlZWVsCLufs/QXvttddGZd8Oh4PLL7+80j7Nmzfn9NNP970++uij6du3b1gXnytj2zZNmjRh1KhRERkvmtJI4xZuCfq+hcWN3Eg66bUYlVSXCtkih4hpTOM0TgO8hWInTmxsHDh4hEcYxrCo7ftkTuYJnghYKLexeZ3XaUUrv/bruZ6/8BcsLGxsX8wA3Qk8gbex6UAH4MAxOnCQSCIv8iKncmqEjyw6evbsyYwZM0hKSsKyLJxOp2+S3LlzZ/773/9G7A8QERGJL4MHD+bxxx/Htm0cDodfDujduzczZsyIbgBffgkZGYHfGzoUHnjAr6lNmza89957ZPy+Tfl485o14w+/f+TY+fu3A5gErOjWDSzLe/e10+n9Bhg8GP71r8gfVxQkJCTw/vvv0759e+DAsVuWRUpKCrNnz6ZHubvXRUSk/sjOzuaDDz6gadOmgH/+y8rK4n//+x8tW7aM2v4nTZrEmWeeGfC9xo0b82W551nsN3PmTE466SS/ePf/rXH44YcHHCsnJydgjs/NzWXBggW+9+LdeMZzLd4LC85yXwBXcAX3cm8sw5MwWCbSi+TEWGFhIRkZGRQUFJCerqspIuUZDEtYwqu8SgEFtKc9oxlNc5rXyv63s51buZVFLMKBgzM4g/u5nwY0CLrNWtYyhSmsZz2NaMRIRnIUR7GFLdzCLSxjGTY2AxjAX/krySSzgAW8wRsUU0xXunIJl5BDTq0cYyTl5+fz0ksv8dVXX5GcnMy5557LGWecEfJdbxI9yjWRofMoEtzmzZuZMmUKP/zwA+np6Vx44YWceOKJtbNMhccDEyfCs89CcTG0awcPPQSV3Am+Z88eZsyYwaJFi7Btm759+zJo0CASnE4++te/mP388xTt3UuXww/nkoceoskRR8DatTBlCqxfD40aee8IP+qo6B9fhLndbt59913eeecd9u3bR48ePbj44ovrzOS+PlOeiQydR5HgSktLmT17NgsWLMAYw8knn8zQoUNr7ZlGn332GXfccQfr16+nYcOGXHfddVx66aVB54zGGBYuXMjMmTPZvXs3HTt2ZPTo0TRr1oxPP/2UO++8k19++YXMzExuuOEGRo0aFTDHn3feeRF/eGRt+J7veZEX2cQmcsnlYi6mC11iHdYhL5w8o0K2iIhImJRrIkPnUUREokl5JjJ0HkVEJJrCyTO6rU9ERERERERERERE4poK2SIiIiIiIiIiIiIS11TIFhEREREREREREZG4pkK2iIiIiIiIiIiIiMQ1FbJFREREREREREREJK6pkC0iIiIiIiIiIiIicc0Z6wBEpGqrWMUMZrCTnbSlLRdxEY1oFNF9LGMZYxjDRjaSSiq3civ/x/9RQgl3czfv8z4WFmdxFndyJ4kk8hqv8S/+RRFFdKITD/IgrWjFKlZxB3ewlrVkkcVt3MZZnEUZZbzBGyxiETY2fehDP/rhwMEXfMFsZrOHPXSmMyMYQTrpbGUr05jGetbTiEYMZzgd6BDRYxcREYmEgoICXnnlFb7//nsaNGjA4MGDOeqooyK6j7179zJq1Cg+/vhjAP74xz8yZcoUGjRowOuvv86jjz5KYWEhHTp04OGHH6ZNmzb8+OOP3H777axZs4aMjAxuueUWzj33XEpKSrj77rt5//33sSyLs846izvvvJPExERWrFjBrFmz2L17Nx07dmTEiBFkZmby22+/MW3aNNauXUt2djbDhw+nY8eOlJWV8cYbb7Bo0SJs26Zv376cccYZOBy6b0ZEROKLx+Nh3rx5zJs3D7fbTc+ePRk0aBCJiYkR3c/TTz/N3//+d/bs2UNeXh6TJ0/muOOOY+3atdx222388MMPpKenc+ONN3LBBRdQWlrKhAkT+O9//4sxhn79+jF+/HiSk5OZPXs2jz32WIUcv2PHDqZNm8aaNWvIyspi6NChdOnSBZfLxVtvvcXChQuxLItTTz2VM888E9u2K+T4kSNHkpGREdFjF4kWyxhjYh1EJBUWFpKRkUFBQQHp6emxDkekRvaxj8u5nJd5GRsbBw7cuLGxeZRHuZ7rI7KfYzmWz/m8QnsCCbhx48Hj125j05CG7GJXhW1a0Yr1rK/Q3pzmuHCxla0kkABAGWW0pz255PIJn+DEiYWFCxcppDCIQbzGa3jwYGPjwYMbN1dwBU/zNE5di5MYUa6JDJ1HqU9efvllxowZQ0lJCU6nE2MMLpeLM888k1dffZWGDRvWeB9PPvkk//d//xfwvfT0dAoLCyu0t27dmnXr1lVoz8nJYefOnXg8/jne6XRy9NFHs3TpUpxOJ5Zl4XK5SE5O5rzzzmPmzJm43W5s28bj8eB2uznrrLP48ssv2bJlCwkJCb5j79ixI++88w7t2rWr8bGLVIfyTGToPEp98vPPP3P22Wfz/fff+/JcWVkZzZo148033+S4446r8T5+++03WrduTXFxcYX3srOzyc/Pr9CelZVFQUFBhbzscDjIzMwMuM3xxx/P8uXLcblcfnm5X79+fPPNN2zcuJGEhN/n3mVltGnThtatW/PBBx9UyPHPPPMMF198cY2PXaQ6wskzKmSLxLHLuZwpTKlQSN5vOtMZxrAa7WMgA3mLt2o0Rk1YWBjC+9+QhcX/8X88zuNRikqkcso1kaHzKPXF+++/zxlnnEGgP6tt26Zfv3688847NdrHsmXLOP7442s0RjRZllXh+G3bJi8vj++++44GDRrEKDI5lCnPRIbOo9QXe/bs4YgjjmDjxo24XC6/92zbJjU1lW+++YbDDjusRvtp2LAhRUVFNRqjtlmWxbvvvku/fv1iHYocgsLJM/qsn0ic2sAGXuCFoEVsC4vxjA+7CFyeG3dMi9hAteI3GCYykW1si0JEIiIi4ZkwYQKWZQV8z+1285///Ifly5fXaB+jRo2q0fbRFqiI73a7+fXXX5k2bVoMIhIREfH38ssvs379+gpFbPDmrOLiYp544oka7WPmzJl1rogN3kL2hAkTYh2GSJVUyBaJU2/wBhaBJ8XgLeb+yI+sYlW19/EyL1d721hz4WIuc2MdhoiIHOK2b9/OwoULK3wUuDyn08ns2bNrtJ8ffvihRtvH0quvvhrrEERERJg5c2bQC8/gLWZPnz69Rvu45557arR9rHg8HhYvXszWrVtjHYpIpVTIFolTRRThCOE/0SKqf7V3M5urvW2sOXDU6NhFREQiIZS7rizLqvHdWXV1NUBjTMC1u0VERGpbYWFhlfl0z549NdpHoHWx65K6eDe5HFpUyBaJU53ohIuKH3kqz4mTtrSt9j7O5MxqbxtrHjx0pnOswxARkUNcs2bNqlz/2eVy0blzzXJWUlJSjbaPFafTSdeuXWMdhoiICEcccQROpzPo+w6Ho8b5ui7nvNTUVPLy8mIdhkilVMgWiVMDGEBjGgddXsSJk8EMphGNqr2P7nSnAXXv4UsOHBzGYfShT6xDERGRQ1xycjKXX345tm0H7ZOSksLw4cNrtJ8RI0bUaPtYcblcXH311bEOQ0REhKuvvjrg+tj7eTwerrvuuhrt44UXXqjR9rFi2zaXXXYZKSkpsQ5FpFIqZIvEqQQSeImXsH//Ks+JkyY04REeqfF+5jCnxmNUV/ki/cHLqDhwBDx2G9t3bkJZekVERCTaxo8fT/v27SsUs23bxrIsJk+eXOUT2Kvy7LPP1niMcDgc/jnWtm3f98H9LMuqsObo/tc33ngjPXv2jG6wIiIiITjuuOO45ZZbAALmrYEDBzJs2LAa7SM7O5uLLrqoRmOEI1C+DrQOePm2QH+vtG3blr/+9a9RiVEkklQFEolj/ejHJ3zC6ZzuK/omk8ylXMrnfE5zmtd4H6dzOvOZTyaZfu1OnPyJPzGIQX7FZCdOhjKUh3mYDDL8tulOd+Ywh8M53K+9EY14lmd5iIf8Ym5Pe/7Nv3mP9ziRE33tDWnIn/gTy1nOxVxMIomAt7h9FmexiEX0oleNj11ERCQSsrOzWbRoEddff73fMiMnnngi//vf/2p8NzZ4J5m//fYbRx11VIX3unfvzj//+U+ysrL82o844gjmzJlT4WPS2dnZPPHEEwwcONBvMut0Ohk2bBjz5s3jj3/8o689LS2Na6+9lhUrVnDJJZf4ljmxLIt+/fqxcOFCHnzwQb+PI7dv355nn32WRx99tMbHLiIiEikPPfQQzz33HB06dPC15ebm8sADDzBr1qxKP2EVqpdeeonbbruNhIQEv/bMzEwmT55Mt27d/NqzsrL45z//yeDBg/2WPrFtm/PPPz9gju/atSsffvghY8aMITk52dfep08fPvroIx599FFatmzpa2/Tpg0TJ07k/fff5+STT/a1p6Wlcd1117F48WJycnJqfOwi0WaZuvrkmCAKCwvJyMigoKCgVu9aEYm2neykgAKa0pQUovNxn41sZAlLaEc7juRIX7sLF8tZjoVFD3r43Qn9Iz+yjW0cwRF+xfDtbOd7vqclLWlFK1+7Gzeb2YwDB7nk+t2VvZ3t7GEPzWhGEgfWAt3DHn7jN7LIqlA8F4kF5ZrI0HmU+mjfvn1s2bKFBg0aRG1CWFpayvvvv4/H4+GMM84gMTHR996aNWvYsmULRxxxBJmZmb72/Px8Vq1aRV5eHm3atPG1u1wuli9fjmVZ9OjRw+/Orh07dlBUVETTpk39JsnFxcVs27aNzMxMv3243W42bdqEbdvk5uYGvCNMpDYpz0SGzqPUR8YYtmzZgsvlIi8vLyIF7EBWrFjBmjVrOOGEE2je/MBNXbt27eLbb7+lWbNmtGvXztfucrlYsWIFAEceeaRfYTtYjt+7dy/btm0jPT3dr+Dt8XjYtGkTlmWRl5fnl5eD5XiRWAgnz6iQLSIiEiblmsjQeRQRkWhSnokMnUcREYmmcPKMlhYRERERERERERERkbimQraIiIiIiIiIiIiIxDUVskVEREREREREREQkrqmQLSIiIiIiIiIiIiJxTYVsEREREREREREREYlrtVLIfuqpp2jdujXJyckcf/zxLFu2rNL+M2fOpFOnTiQnJ9OtWzf+85//1EaYIoeMfewjn3zcuP3a3bjJJ5997At5rBJK2MlOPHhC6m8wFFBAEUVhxRztsURE+Vok3rjdbvLz8ykpKanw3u7duyksLAx7rH37wsjxJSXs3LkTjye0HF+ZvXv3RmwskUOd8rVI/Nmfl40xfu379u0jPz8ft9sdZMvgY4WqOjm+qrFKS0trPJZINES9kP3qq6/y5z//mbvvvpsvv/ySP/zhD/Tr149t27YF7L9o0SKGDx/O5ZdfzvLlyxk0aBCDBg3im2++iXaoIvXel3zJYAaTSio55NCIRoxlLD/zM7dzO41oRA45pJHGEIawghVBx1rGMgYykDTSyCabxjTmTu6kkMAJ14OHf/NvOtOZTDJpSEOO5mhe47Wwj8ONm6d4ig508I11AifwOq+HPZaIeClfi8SPgoICxo0bR+PGjcnJySEtLY3zzjuPzz77jBdffJHu3buTnp5ORkYG3bp1Y+rUqRUmzvvl5+dz++2306hRI99YQ4YMYcWKFUH3v2zZMgYOHEhaWhrZ2dk0btyYO++8M6xJ9X6LFi3i7LPP9o3VrFkz/vrXv7Jnz56wxxIR5WuReGKMYfr06Rx11FG+vNylSxcmT57MihUrGDp0KGlpaeTk5JCTk8Ott97Kjh07go5VnRx/2223+cbfn+O/+uqrsI/lt99+4+abbyY7O9s31vDhw/X/Cok/JsqOO+44c9111/leu91uk5eXZx544IGA/S+88EJz9tln+7Udf/zx5qqrrgppfwUFBQYwBQUF1Q9apB6aZ+aZBJNgnMZpKPflMA6TYBKMbWy/dqdxmkSTaOab+RXGmmvmGqdxVtjGNrbparqaXWaXX3+P8ZhLzCUGg7GM5bdvDOYuc1fIx+E2bjPUDDXW718Hj3W/ub/G50qkKvUx19R2vjamfp5HkZrauXOn6dKli7Ft2wC+b4fDYSzL8v28v31/2/XXX288Ho/fWL/99pvp0KFDhbGcTqdJTEw0CxYsqLD/uXPnGqfTWWEb27ZNt27dzK5duypsE8ysWbOMw+EIeCzHHHOM2b17d43Pl0hl6mOeUb4WiR9jx44NmpcdDodxOp0Vcmm7du3Mtm3b/MbxeDzmhhtuCDvHt2/fPmC+TkpKCpjjg9m8ebNp1apVwL8XUlJSzKeffhqR8yUSTDh5Jqp3ZJeWlvLFF1/Qt29fX5vD4aBv374sXrw44DaLFy/26w/Qr1+/oP1FpGqllDKc4bhx48Ll954HD2WUVVhmxPX713CGU0aZr72YYkYyEvfvX+W5cbOKVfyVv/q1v8EbTGUq4F0OpPy+Ae7lXj7n85COZQYzeJVXfdXzg8f6C3/hG3TVWCQcytci8eOuu+5i9erVFT6C7PF4fHdklV+eY3/bk08+yfvvv++3ze23387PP/9cYSyXy4XL5WL48OGUlZXL8cXFjBgxArfbXWEbt9vNd999x4QJE0I6jsLCQkaNGoUxJuCxLF++nL/97W8hjSUiXsrXIvFj8eLF/P3vfwcC52WPx4PL5T/3drvdrF+/nttuu82v/cMPP+Rf//pX0LGefPJJ5s+f77fN7bffzrp16wLm67Kysgo5vjJ//vOf2bhxY8C/F/bt28fw4cPDWhpFJJqiWsjevn07brebpk2b+rU3bdqULVu2BNxmy5YtYfXft28fhYWFft8i4u8N3mA720Nex3o/Dx62sY23eMvXNotZFFDgV0Quz42byUymhAPreT7Jk9jYQffjxMnTPB1STE/yJI5K/tflxMkkJoU0loh41Ua+BuVskaoUFxfz/PPPV2uy6HQ6mThxou91QUEBL7/8ctCxPB4PW7du5e233/a1zZw5M+D6nvu53W4mT54ccM3ug73yyivs3bu30rEmTZpUYZIvIsEpX4vEj6effhqn0xn2di6Xi1deeYVdu3b52iZOnFjpWIFy/LRp04Lm0P05fu7cuVXGs337dmbOnFnpWBs2bGDevHlVjiVSG2rlYY/R9MADD5CRkeH7btmyZaxDEok7K1lJAgnV2taJ0+8O51DGKqKIX/nV9/orvqpw93Z5LlyVrsdd3kpWVlqQd+HiK8JfE0xEok85W6RyGzZsoLi4uFrbulwuv3Wvf/755yof+uR0Ov3Wvly5ciUJCZXn+N27d7Nx48Yq41m5cmWVE/ydO3cGXddXRGJH+VqkasuXL6/2xdjS0lJ++umnkMc6OMevWbOmyocxHpzjg1m9enWVx2HbttbKlrgR1UJ2o0aNsG2brVu3+rVv3bqVZs2aBdymWbNmYfW/4447KCgo8H3/8ssvkQlepB5JJTXsu7H3MxhSSPEbK9jd2OUdvE1V0kgLKZ7y4wZiYYW0PxE5oDbyNShni1QlNbVm+av89qGMZYwhJaVcvk5NDXoHdXnlt6kslkiNJSJeytci8SMtLbT5azDl83QoY4Wb4z0eT8j5OlJjidSGqBayExMTOfroo/3W8vF4PMyfP5+ePXsG3KZnz54V1v6ZN29e0P5JSUmkp6f7fYuIv4EMrPSO6Mq4cTOQgX5jHbzOdnkOHPSgB81p7mu7kAsrXVrEwmIwg0OKZwhDcFL5HV4XcEFIY4mIV23ka1DOFqlKy5Yt6datG5Zlhb2tbdtceOGFvteHH3447du3r3Qst9vNwIEHcvygQYMqvSvL4XDQo0cP8vLyqownlLFOPvlksrKyqhxLRLyUr0Xix+DBg3E4qldSa9euHZ07dw55rINzfMeOHWnXrl2lOd7j8fjl+GC6d+8e0qcuBgwYUGUfkVoRtUdO/m7GjBkmKSnJTJkyxXz33XfmyiuvNJmZmWbLli3GGGMuvvhiM3bsWF//Tz/91DidTvPII4+YVatWmbvvvtskJCSYlStXhrQ/PVFZJLBzzbnGNrYhjC/b2OZ8c36FsU43p1c61uvmdb/+a81ak2pSjcM4Au6jiWlidpqdIR3HarPaJJvkoGPlmTyz2+yOwBkTCa4+5praztfG1M/zKFJTM2fONEBY37Ztm4yMDLNp0ya/sV566aVKt7ngggsq7P/00083tm0H3e71118P6Tg8Ho85+eSTKx3r3XffjcQpEwmqPuYZ5WuR+LBjxw6Tk5NTaZ4L9j1lyhS/sbZs2WIyMzMDjmXbtsnMzDSbN2/22+bFF18MOr7D4TCDBw8O+VieeeaZSse6+OKLI3LORIIJJ89EvZBtjDFPPPGEOeyww0xiYqI57rjjzJIlS3zv9e7d21xyySV+/V977TVz+OGHm8TERHPEEUeYd955J+R9KcmKBFZoCs3p5nSDwTh//7KNbRzGYc42ZxuHcRjb2L73MJj+pn/AovAus8v0Nr39xnIYh3Eap3nSPBlw/x+bj02WyfLbBoNpYVqYb8w3YR3LfDPfpJt0YxnLb6zWprVZbVZX6/yIhKO+5prazNfG1N/zKFJTjz/+uLFt2zgcDuN0Oo3T6TSAOfbYY03jxo0N4NfeuHFj89lnnwUc66GHHjIOh8PYtu23zZlnnmmKiooq9N+5c6fp1auX3z72x/HUU0+FdRzbt283PXv2rDBWQkKCmTx5crXOjUg46mueUb4WiQ9ff/21ycvLq5CXs7OzzYknnujXvj+v/+1vfws41ueff26aNGkSVo5/8MEHA+b4s846K2COD8bj8ZgJEyYYy7IqjDVo0CBTXFxcrfMjEqpw8oxlTAiL19UhhYWFZGRkUFBQoI9AiRzEYFjCEl7lVQoooD3tGc1omtOcX/mVKUxhDWvIJJNhDOM4jsMi8MeVDIaFLGQWsyikkI50ZDSjaUbw9faKKWYGM1jEIhw46EMfzuM8EkkM+1iKKOIVXmEpS3HipB/9GMCAaj/UUiQcyjWRofMoEtzmzZuZMmUKP/zwA+np6Vx44YWceOKJlJaWMnPmTD766COMMfTu3ZshQ4aQnJwcdKxff/2VKVOmsGbNGjIzMxk2bBjHHXdc0I8kG2NYuHAhM2fOZPfu3XTs2JHRo0dXuqZuMMYYPvroI2bPnk1RURFdunThkksuoUmTJmGPJRIu5ZnI0HkUCa60tJTZs2ezYMECjDGcfPLJDB06lOTkZD777DOmT5/Orl27aNu2LaNHj650GY+SkhJmzZrFRx99BECvXr0inuMrs379eqZMmcLatWvJyclh+PDhHHPMMWGPIxKucPKMCtkiIiJhUq6JDJ1HERGJJuWZyNB5FBGRaAonz0T1YY8iIiIiIiIiIiIiIjWlQraIiIiIiIiIiIiIxDUVskVEREREREREREQkrqmQLSIiIiIiIiIiIiJxTYVsEREREREREREREYlrKmSLiIiIiIiIiIiISFxzxjoAkXiyhjW8wiv8xm+0pCUXcRG55MY6rLC5cfMO7/ARH2Ew9KIX53AOTpx8wze8xmvsYhcd6MBIRpJNdqxDFhERCdmePXuYMWMGX3/9NSkpKQwcOJATTjgBy7JiHVrYVq1axYwZM9i5cydt27bloosuolGjRuzcuZOXX36ZH374gYyMDC688EK6desW63BFRERCZozho48+4j//+Q+lpaUcffTRDBkyhOTk5FiHFraCggJeeeUVvv/+exo0aMDgwYM56qijcLvdvPfeeyxYsABjDCeddBIDBw7E6VS5TSQaLGOMiXUQkVRYWEhGRgYFBQWkp6fHOhypI9y4uZ7reYZncPz+5caNhcUEJjCOcVjUjcnxKlZxNmezlrUkkABAGWW0pCWd6MQ85uHEiYWFCxeJJPIETzCGMTGOXKTuUK6JDJ1HqY63336bkSNHsnv3bhISEjDG4HK56NWrF6+//jrZ2XXj4uy+ffu47LLLeOWVV7BtG4fDgdvtxrZthgwZwuzZsyktLcXpdPqOcdCgQbz88sukpqbGOnyROkF5JjJ0HqU6Nm/ezDnnnMOXX36J0+nEsizKysrIzs5m1qxZnHrqqbEOMWQvv/wyY8aMoaSkxC8v9+7dm19++YWff/6ZhITf595lZTRv3py5c+dy5JFHxjZwkToinDyjQrYIcAu38CiPYgj8n8NEJnIN19RyVOHLJ5/OdGYHO3Dj9nvPwgp6fACv8zqDGBTlCEXqB+WayNB5lHAtXbqUk08+GbfbzcF/wtq2zfHHH8/ChQvrxJ3Zl112GVOnTsXj8YS8jW3bnHfeecycOTOKkYnUH8ozkaHzKOEqKyvjyCOP5IcffsDlcvm953A4SExM5Msvv6Rz584xijB08+bNo1+/fhX+7qiMbdukp6fz7bffkptb9z7hLVLbwskzWiNbDnk72METPFFpkXcCE3DhCvp+vJjMZLazvUIRG6j0+CwsxjO+0j4iIiKx9re//Q0g4GTS7XazaNEiPvjgg9oOK2zr169nypQpYRWxwXuMs2bNYtWqVVGKTEREpObefPNNvvvuuwpFbACPx4PL5eKRRx6JQWThmzBhQtgXyN1uN4WFhTz99NNRikrk0KVCthzy3uVdSimttM9WtrKUpbUUUfW9yqt4CG9SDN4i90pWso51kQ9KREQkAkpLS5k7d27ASfF+TqeTWbNm1WJU1fPGG29U+65x27aZPXt2hCMSERGJnFmzZmHbdtD3XS4Xr732Wi1GVD2//fYbn376adgXnsFbzJ4+fXoUohI5tKmQLYe8IopCWv96N7trIZqaKaSwRtsXURShSERERCKrpKSkyomkMYaiovjPZUVFRTgc1fsz3OFw1IljFBGRQ9fu3btxuyt+Sri84uLiWoqm+vbs2VOj7ZWvRSJPhWw55HWiU0hLanSkYy1EUzNd6YqT6j0dOZFEWtEqwhGJiIhERsOGDWnSpEmlfYwxdWK9zU6dOlV6Z3llysrK6sQxiojIoatz5844ncHnpZZlcfjhh9diRNXTrFkz0tLSqrWtbdscccQREY5IRFTIlkNeb3rTjnY4gvznYGPTl760oU0tRxa+a7imWmt5O3EykpGko4e3iIhIfLIsi2uvvbbSO5kdDgeXXnppLUZVPQMGDKBx48Zh35VtWRbp6ekMGTIkSpGJiIjU3JVXXlnlBdvrrruulqKpvuTkZC6//PJKl0kJxu12c+2110YhKpFDmwrZcsizsHiRF0kkERv/BOXESQYZTGRijKILz+mczuVcDhB0uZSDC/Y2Ns1pzgM8EPX4REREauKWW27hyCOPrDCh3F8Q/te//kWzZs1iEVpYEhMTefHFF3E4HBWOxbZtbNuuUOS2bRvLsnjhhRdITU2tzXBFRETCcvjhh3P//fcDVMhnDoeD3r17c+WVV8YitLDdfffdtGvXLmC+3u/g515YlsWwYcMYNGhQbYQockhRIVsEOJETWcxizuEcX6E3gQRGMpLP+ZwOdIhxhKGxsPg3/+ZJnvRbJqQVrXicx/mIjziN03ztKaRwJVeyjGU0pWksQhYREQlZWloaH330EbfccgsZGRm+9mOOOYa33nqLa665JobRhad///588sknnH766b4JcHJyMpdddhkrVqzgmmuu8StY9+rViwULFnD++efHKmQREZGQjRs3junTp/str9GoUSPuuusu3n33XRITE2MYXeiys7NZvHgx119/PQ0aNPC1n3TSScybN49JkybRtm1bX3uLFi145JFHmDZtWrWfhyEiwVnGmKoXB65DCgsLycjIoKCggPR0LZMg4SuggJ3spDGNSaN662HFAw8eNrMZgyGPPL87sfPJZze7aUITUkiJYZQidZNyTWToPEpNlJaWsmXLFlJSUmjcuHGsw6mRnTt3UlhYSJMmTUhJOZCXS0pK2Lp1Kw0bNiQ7OzuGEYrUTcozkaHzKDVhjGHr1q2UlpaSl5dX6drZ8W7fvn1s2bKFBg0akJOT42s3xrBp0yaMMeTl5amALRKmcPJM3f0/iEiUZPz+Vdc5cNCc5gHfy/79S0REpK5KTEzksMMOi3UYEZGVlUVWVlaF9uTkZFq10oOYRUSk7rIsq04s+xWKpKSkgHnZsiyaNw889xaRyNJlIhERERERERERERGJaypki4iIiIiIiIiIiEhcUyFbREREREREREREROKaCtkiIiIiIiIiIiIiEtdUyBYRERERERERERGRuKZCtkgUFVHEGtZQSmnI22xhC7/yKx48IfV34yaffEooqW6YFcbaxz6/doOhkEJ2s7vG+xAREYk3LpeLtWvXkp+fH/I2RUVFrFmzhtLS0HP87t27KSwsxBhTnTADjnWwffv2kZ+fj9vtrvE+RERE4s22bdvYsGEDHk9o8+Xq5PjS0tKI5dJgedntdpOfn09JSc3n8SKHEhWyRaLgLd6iNa1pSEPa054kkuhBD77l24D9PXi4gRtIJZVccmlJS1JIYRSjcOEKuE0BBfyFv9CEJuSQQxppDGIQn/N52PHmk8/t3E4jGvnGGsIQlrOc53mernQlgwzSSedIjmQa0zDUfBIuIiISS9u3b+e0004jMTGRtm3bkpOTQ+PGjXnyySeDbvPWW2/RunVrGjZsSPv27UlKSqJHjx58+23gHG+MYfr06fTo0YP09HQyMjLo0qULkydPDrugbYzhxRdfpHv37r6xunXrxtSpU/niiy+44IILSE1N9R3H2LFj2bVrV1j7EBERiUfjxo2jYcOGNG3alFatWpGUlMTgwYODXlDetm1bwBw/ceLEoPv49ttvGTFiBA0aNCAnJ4esrCxuuukmtm3bFna8X331FUOHDiUtLY2cnBxycnK49dZbWbt2LePGjaNx48bk5OSQlpbGeeedx+efhz+PFzkUWSYSt4TEkcLCQjIyMigoKCA9PT3W4cgh6Dme4wquCPiejc0yltGDHn7tJ3ESi1gUcJsOdOB7vsdR7rrTLnZxEiexmtW4OXBl18bGwuIt3uJMzgwp3u1s50RO5Gd+rjCWweDBg4XlK1w7cODBwy3cwsM8HNI+ROob5ZrI0HmUWNq2bRtt27Zlz549Ad+/8cYb+ec//+nXNnnyZMaMGROwv23bLFu2jB49/HP8uHHjeOCBB3A4HL67xyzLwhjD5ZdfzrPPPotlWVXGa4zhpptu4vHHHw84lsPhwOFw4HIduABu2zbt27dn0aJFZGdnV7kPkfpGeSYydB4l1s466yzefffdgO81b96cn3/+mcTERF9bVTn+pptu4tFHH/VrW7x4MX369KGsrKxCLm3evDlLliwhNzc3pHg/+ugj+vXrh9vt9hvL4XBg2zYej8fvDm3btrEsi7lz59KvX7+Q9iFSn4STZ1TIFokgFy5SSAl6FzVAC1rwC7/4XldW+N5vHOO4n/t9r2/gBiYy0a/wvJ+FRSaZbGITySRXGfMVXMEUpgQcqyof8zF/5I9hbydS1ynXRIbOo8RS7969+fjjjyvts27dOlq1agV4P5qckpLiNyE9WIsWLfjllwM5fsmSJfTs2bPSfbz11lsMGDCgyng//PBDTj311Cr7Hcy2bcaMGcPTTz8d9rYidZ3yTGToPEoszZ07t8o8OWbMGP7973/7Xvfq1YtPPvmk0m3Wr1/PYYcdBoDH46Ft27b88ssvAZcscTqdnH/++bz66qtVxutyuTjssMPYunVryMufgLfInZmZyaZNm0hKSgp5O5H6IJw8o6VFRCLoUR6ttIgN8Cu/8g3f+F4/wANVjjuJSb6fiynmOZ4LWng2GHayk9nMrnLcQgqZxrRqFbGdOHkaTYpFRKTuKS0trXKCC3DLLbf4fn7kkUcqLWID/Prrr3zzzYEc//TTT+N0OoP2t22bp556KoSIYeLEiZWOFYzb7WbKlCkUFRWFva2IiEisjR8/vso+r7zyiu/n0tJSFi5cWOU25XP8+++/z/r164MWnl0uF7Nnzw5piZF33nmHzZs3h1XEBm8xPT8/n9mzq57HixzKVMgWiaBgy4Mc7AM+8P28kY1V9t/FLt/PG9hAMcWV9k8ggZWsrHLcn/m5woMdQ+XCxXKWV2tbERGRWPrhhx9CWp+6/LrXixcvDmnsDz/80Pfz8uXLKy1+u91uvvrqq5DG/fLLL6sspAdTUlLCunXrqrWtiIhILK1du7bKPuWXEPn+++/DzvErV67Etu1K+7vdbn744Ycqx125cmW1LjwDJCQk+F0QF5GKVMgWiaA00kLql86Bj0okkFBlf4sDa2emklplfw+ekPqF0qcyoR6viIhIPMnMzAypX3LygSW60tJCzPHlPg7ZoEGDKvunpKSENG4oY0ViPyIiIvGk/NrXwZR/1kR1cnxqampId1CHkktDHSsQj8ejfC1SBRWyRSLoRm6sso8DB8MZ7nt9KlWvd3k0R/t+bklLutPdr7h9MDduBjGoynE7/P5V2VjBOHAwhCFhbyciIhJrLVq0ICMjo8p+l156qe/nm266qcr+DoeDYcOG+V5fcMEFOBzB/9y2bZsLL7ywynEBBg8eXOlYwViWRZcuXWjbtm3Y24qIiMTaWWedVWWfTp06+X4+7LDDws7x55xzTpX9mzdvzpFHHlllv4EDB1a7kO12uxk0aFC1thU5VKiQLRJBx3IsHehQaZ8hDCGRA1eVH+OxKgvJj/GY72cLi/GMxxD441I2Nv3pT3e6VxmvhcXd3B10rGBsbDLJ5HIuD2s7ERGReDFu3LhK32/QoAHXXXed7/Wxxx5L+/btK93mwgsv9Ltz7NJLLyU7Ozvgx5UdDgcpKSlce+21IcV75ZVXkpGRUeVHnw9mjOGuu+7yu1tNRESkrnj44YerzH0PPfSQ3+uxY8dW2r9BgwZ++bdly5aMGjWq0gvGd955Z0g5uF27dlx44YVh52vbtjnrrLPo1q1bWNuJHGpUyBaJsGUsoznNA773R/7IK7zi19aGNsxkJo4A/zlaWExiEj3p6dd+ARfwOI9jY+PAgfP3L4Be9GIGM0KOdyQjeYiHcODAxvYb6wROIIccAL/2xjRmPvNpRKOQ9yMiIhJPbrvtNi6/PPAF2bS0ND7//PMKE9rPPvuM5s0D5/hevXrx8ssv+7VlZ2ezYMECmjZtCoDT6fStm5mZmcl///tfDjvssJDibdKkCe+//z45OTkVxsrJyeH444/3a7dtG4fDwT/+8Q+/u8RFRETqkkaNGvHf//436LrTDzzwQIU7qseOHRt2jp80aZLvbujyudSyLMaPH89VV10VcszPP/88/fv3rzCWw+Hg7LPP9v1cPpf37t2b6dOnh7wPkUOVZUJZBb8OKSwsJCMjg4KCAr81CkVq2zSm8RiPsYtdtKAF93EfJ3Ny0P5FFDGOccxjHh48nMRJPMRDlRaLN7OZKUzhB34gnXQu5EJO5MRqLRXyK78yhSmsYQ2ZZDKMYRzHcZRQwmu8xid8goXFqZzKBVxAEklh70OkvlCuiQydR4kHq1at4tZbb2X16tWkpKRwySWXcNNNN1V6V9a0adN47LHH2LVrFy1atOC+++7j5JOD5/jS0lLmzJnDggUL8Hg8nHTSSQwbNqxa62Du27ePmTNn8tFHH2GMoXfv3gwZMoSkpCSWLFnCq6++SkFBAe3bt2f06NFBC+8ihwLlmcjQeZR4UFJSwt13383bb7+Ny+Xi2GOP5eGHHyYvLy/oNvtz/A8//EBycnJIOf6LL77glVdeYceOHbRu3ZrRo0fTunXrsOM1xvDZZ58xffp0du3aRdu2bRk9ejQtW7Zk06ZNTJkyhR9//JH09HSGDh1Kz5499ekpOWSFk2dUyBYREQmTck1k6DyKiEg0Kc9Ehs6jiIhEUzh5RkuLiIiIiIiIiIiIiEhcUyFbREREREREREREROKaCtkiIiIiIiIiIiIiEtdUyBYRERERERERERGRuKZCtoiIiIiIiIiIiIjENRWyRURERERERERERCSuqZAtIiIiIiIiIiIiInFNhWwRERERERERERERiWsqZIuIiIiIiIiIiIhIXFMhW0RERERERERERETimgrZIiIiIiIiIiIiIhLXVMgWERERERERERERkbimQraIiIiIiIiIiIiIxDUVskVEREREREREREQkrqmQLSIiIiIiIiIiIiJxTYVsEREREREREREREYlrKmSLiIiIiIiIiIiISFxTIVtERERERERERERE4poK2SIiIiIiIiIiIiIS11TIFhEREREREREREZG4FtVCdn5+PiNHjiQ9PZ3MzEwuv/xyioqKKt3mlFNOwbIsv++rr746mmGKiIgc0pSvRURE4p/ytYiIHOqc0Rx85MiRbN68mXnz5lFWVsall17KlVdeySuvvFLpdmPGjOGee+7xvU5NTY1mmCIiIoc05WsREZH4p3wtIiKHuqgVsletWsV7773HZ599xjHHHAPAE088wVlnncUjjzxCXl5e0G1TU1Np1qxZtEITERGR3ylfi4iIxD/laxERkSguLbJ48WIyMzN9SRagb9++OBwOli5dWum2L7/8Mo0aNaJr167ccccdFBcXB+27b98+CgsL/b5FREQkNLWVr0E5W0REpLqUr0VERKJ4R/aWLVto0qSJ/86cTrKzs9myZUvQ7UaMGEGrVq3Iy8vj66+/5vbbb2f16tXMmTMnYP8HHniACRMmRDR2ERGRQ0Vt5WtQzhYREaku5WsREZFqFLLHjh3Lgw8+WGmfVatWVTugK6+80vdzt27dyM3NpU+fPqxZs4Z27dpV6H/HHXfw5z//2fe6sLCQli1bVnv/IiIi9UG85WtQzhYRETmY8rWIiEjowi5k33zzzYwePbrSPm3btqVZs2Zs27bNr93lcpGfnx/W+lzHH388AD/99FPARJuUlERSUlLI44mIiBwK4i1fg3K2iIjIwZSvRUREQhd2Ibtx48Y0bty4yn49e/Zk165dfPHFFxx99NEALFiwAI/H40ueoVixYgUAubm54YYqIiJyyFK+FhERiX/K1yIiIqGL2sMeO3fuTP/+/RkzZgzLli3j008/5frrr2fYsGG+Jypv3LiRTp06sWzZMgDWrFnDvffeyxdffMG6det46623GDVqFL169aJ79+7RClVEROSQpXwtIiIS/5SvRUREoljIBu/TkTt16kSfPn0466yzOPnkk/n3v//te7+srIzVq1f7npqcmJjI+++/zxlnnEGnTp24+eabueCCC3j77bejGaaIiMghTflaREQk/ilfi4jIoc4yxphYBxFJhYWFZGRkUFBQQHp6eqzDERGReki5JjJ0HkVEJJqUZyJD51FERKIpnDwT1TuyRURERERERERERERqSoVsEREREREREREREYlrKmSLiIiIiIiIiIiISFxTIVtERERERERERERE4poK2SIiIiIiIiIiIiIS11TIFhEREREREREREZG4pkK2iIiIiIiIiIiIiMQ1FbJFREREREREREREJK6pkC0iIiIiIiIiIiIicU2FbBERERERERERERGJaypki4iIiIiIiIiIiEhcUyFbREREREREREREROKaCtkiIiIiIiIiIiIiEtdUyBYRERERERERERGRuKZCtoiIiIiIiIiIiIjENRWyRURERERERERERCSuqZAtIiIiIiIiIiIiInFNhWwRERERERERERERiWsqZIuIiIiIiIiIiIhIXFMhW0RERERERERERETimgrZIiIiIiIiIiIiIhLXVMgWERERERERERERkbimQraIiIiIiIiIiIiIxDUVskVEREREREREREQkrqmQLSIiIiIiIiIiIiJxTYVsEREREREREREREYlrKmSLiIiIiIiIiIiISFxTIbuuMQYKCqCoKNaRiIiISCVKKGEnO/HgiXUoIiIiEozbDfn5sG9frCMREZEqqJBdV3g88PTT0LEjZGZCw4Zw3HEwa1asIxMREZFylrCEAQwgjTSyyaYpTRnPeHazO9ahiYiIyH47dsCtt0JOjvc7LQ2GDoWvv451ZCIiEoQK2XWBxwOjRsG118JPPx1o/+ILGDIE7rkndrGJiIiIz5u8ycmczLu867sTezvb+Rt/44/8UcVsERGReLBtm/fGsH/+0/uJZ/DemT1nDhx/PHz8cWzjExGRgFTIrgtmz4aXX/b+bMyBds/vH1W++25YsaLWwxIREZED9rCHi7kYDx7cuP3ec+PmG77hHnTxWUREJOZuvRU2bPAWr8tzuaC0FIYP9/4sIiJxRYXsuuCpp8C2g7/vdHqXHREREZGYmcEMdrMbgwn4vhs3z/IspZTWcmQiIiLis3MnTJ8evFDt8cCmTfDuu7Ubl4iIVEmF7Lrg668rXikuz+XSHdkiIiIxtpKVJJBQaZ8CCtjM5lqKSERERCr48UcoK6u8j9MJ33xTO/GIiEjIVMiuC1JSKn/fsrwPphAREZGYSSU16N3Y5aVQRV4XERGR6ElNrbqPx1P1PFxERGqdCtl1wZAh3ivClbnggtqJRURERAIaxCBcBF9P04GD4zmeJjSpxahERETET5cu0KaN94awYIyBc8+tvZhERCQkKmTXBf/3f5CQAI4Avy7bhqZN4eKLaz8uERER8TmWYzmVU7EJ/FwLDx7u5M5ajkpERET8OBxw113eYnUgtg1Dh0LbtrUbl4iIVEmF7LqgXTvvgybS072vnc4Dd2jn5cGCBQfeExERkZiwsJjNbHrSEwDn718OHDhxMolJnMM5MY5SREREuPRSuP9+b1Hbtv3n2GeeCc89F9v4REQkoCrWq5C40bs3/Pqr9+nKixd7k2zfvjBokPdubREREYm5LLL4mI/5hE+YxSx2s5tOdGI0o2lK01iHJyIiIvuNG+f9ZPOUKfDzz5CVBcOHw7HHxjoyEREJQoXsuiQtDa64wvstIiIiccnCotfvXyIiIhLHWrb0LjMiIiJ1gpYWEREREREREREREZG4pkK2iIiIiIiIiIiIiMQ1FbJFREREREREREREJK6pkC0iIiIiIiIiIiIicU2FbBERERERERERERGJaypki4iIiIiIiIiIiEhcc8Y6AImQzz+HOXNgzx7o0gVGjICGDWMdlYiIiJSzjW1MYxrrWEcOOQxnOIdzeKzDEhERkfLKyuCtt+DTT8HhgNNOg/79vT+LiEjMqJBd1xUUwJAhMG8eOJ1gWeBywZ//DFOmeN8TERGRmPsH/2AsY/HgwcbGg4e/8lcu5VKe4RkSSIh1iCIiIrJ8OQwYABs3QsLvufkf/4AOHeCdd7z/FBGRmNDlxLrMGDj/fFiwwPva5fJeOTYG9u6FYcPg449jG6OIiIgwhSncwi24cOHBQxlluHH73ruRG2MboIiIiMCmTd67r7ds8b4uK/N+A/z8M5xyChQWxiw8EZFDnQrZddnSpd4itttd8T1jvHdn33tv7cclIiIiPh483M3dQd83GJ7hGbawpRajEhERkQomToTduwPPsd1u2LwZpk6t/bhERARQIbtumzXLu5xIMG43vP++NxGLiIhITKxgBRvYUGkfDx7e4q1aikhEREQCmjEjcBG7vFdfrZ1YRESkAhWy67KiIu9d11XZsyf6sYiIiEhARRRV2ceBg93owrOIiEhMFVWRs43R0iIiIjGkQnZd1qlT1VeLMzOhUaNaCUdEREQq6kAHHFX8yeXGTRe61FJEIiIiElCXLmDbwd93OqFbt9qLR0RE/KiQXZeNGlX50iK2DVddVXkfERERiapcchnAAJwEzscOHDSnOWdwRi1HJiIiIn6uvbbym8VcLrj66tqLR0RE/KiQXZdlZ8Mzz3iXF3Ec9Ku0bejcGe64IzaxiYiIiM9jPEY22RWK2TY2Tpy8yIvYVHIHmIiIiETf+efD0KEVl/Dc//q66+Dkk2s/LhERAVTIrvtGj4b//AdOOOFAW3o63HgjLFwIGRmxikxERER+15rWfM7nXMzFJJEEgIVFf/rzKZ9yGqfFOEIRERHB4YCXX4aHH4YWLQ60t2vnvYnsiSdCe06ViIhERdQK2ffffz8nnngiqampZGZmhrSNMYbx48eTm5tLSkoKffv25ccff4xWiPVH//7w6afw22+wbh1s2waPPKIitoiIhEQ5u3a0pCXP8zz55LOWteSTz1zmcgzHxDo0ERGpA5Sva4ltw803w/r18MsvsHEj/PADXHmlitgiIjEWtUJ2aWkpQ4YM4Zprrgl5m4ceeoh//etfTJo0iaVLl5KWlka/fv0oKSmJVpj1S6NG0KoVJCXFOhIREalDlLNrVyqptKY1mWTGOhQREalDlK9rmcPhvSs7L08FbBGROGEZY0w0dzBlyhRuvPFGdu3aVWk/Ywx5eXncfPPN3HLLLQAUFBTQtGlTpkyZwrBhw0LaX2FhIRkZGRQUFJCenl7T8EVERCqor7lGOVtEROqT+ppnlK9FRKQ+CSfPxM0a2WvXrmXLli307dvX15aRkcHxxx/P4sWLYxiZiIiIlKecLSIiEv+Ur0VEpL5xxjqA/bZs2QJA06ZN/dqbNm3qey+Qffv2sW/fPt/rwsLC6AQoIiIigHK2iIhIXaB8LSIi9U1Yd2SPHTsWy7Iq/f7++++jFWtADzzwABkZGb7vli1b1ur+RURE4pFytoiISPxTvhYREQldWHdk33zzzYwePbrSPm3btq1WIM2aNQNg69at5Obm+tq3bt3KkUceGXS7O+64gz//+c++14WFhUq0IiJyyFPOFhERiX/K1yIiIqELq5DduHFjGjduHJVA2rRpQ7NmzZg/f74vqRYWFrJ06dJKn8qclJREUlJSVGKKGGNg927vk44bNgxtm7174bvvoE0byM72H6uwEGwbGjSouE1JCWRkeJ+wHEpcwcYSEZE6TTm7ekoooZhiMsjAxq6yvxs3P/ADNjaHc3iFsfaylwwycJT7EJwbNwUU0IAGJJIYUlx72UsJJRXGEhGRuk35uprcbigogLQ0CDXWLVtg40bo0gVSUqoea/883uEIfb68f6wGDSAxtBwvIiKhi9pMaMOGDaxYsYINGzbgdrtZsWIFK1asoKioyNenU6dOvP766wBYlsWNN97Ifffdx1tvvcXKlSsZNWoUeXl5DBo0KFphRpcxMHkyHHGEt7icng5HHQXTp3vfC+Tjj6FpU0hNhWOOgZwcb0J9/HF4+mno2BEyM70F8eOOg1mzYOFCOOssb7/sbMjNhXvugT17Au/D7YYnnoD27Q+M1bMnvPlmtM6EiIjEMeVs+JzPOY/zSCONHHJoTGPGMY4CCgL2d+PmRE7EiZMudKEjHXHgoB/9+JRPGcAA0kgjm2ya0pTxjOdnfuYmbiKLLHLIoQENGMEIvuO7oHEtZCFncZZvrFxyuYd7KKY4WqdCRETilPI1sGsXjB0LjRp558qpqXDBBfDll8G3mTABkpO98+RjjvFu06KFdx59663ecfbPu4cOhRUr4NlnvQXvjAzvfLlHD5gxI/g8fts2uOkmyMryjtWgAYwc6b05TUREIsdEySWXXGKACt8ffPCBrw9gXnjhBd9rj8dj7rrrLtO0aVOTlJRk+vTpY1avXh3WfgsKCgxgCgoKInQk1eTxGHPFFcaAMZbl/ScY43B4/zl2bMVt/ve/A/2CfQcay7KMsW3/fg6HMccdZ0xRkf8+XC5jLrjAu02gsR58sHbOj4hIHRY3uSZCDvWc/Z55zziN09jGNpT7so1tupguJt/k+/V3GZfJMBl+fQ/+Ongsh3GYBJNgHMbh1+40TpNiUswis6hCXK+aV43DOAKOdZw5zhSZogrbiIjIAfGSZyLlUM/XZscOYzp2rDj3dTqNSUgwZt68ituMGlX5/Hr/PLj8WPvHDzRfHjeu4j42bjSmZcvAcaWkGLN4cfTPjYhIHRZOnrGMCXZJsW4qLCwkIyODgoIC0tPTYxfI3LkwYEDlfRYt8t4JvV9qqnd5kEhxOGDcOLj33gNtL74Il1xS+XbffQedO0cuDhGReiZuck0dFw/nsYQS8sijgAI8eCq8b2NzDdfwBE/42oYylNd4LWIxOHBwGIexhjW+ZUN2sYs88iihBEPFP9UcOPgLf+Ee7olYHCIi9U085Jn6IG7O4zXXeO+UdrsrvudweO+G3rTpwJIeW7Z478KOtCVL4PjjD7y+8EJ4/XVwuQLH1aoV/PRTaMt/iogcgsLJM/o/abQ89ZR37elgnE7vUiH7ffxxZIvYAB6Pdx/lE/2TT1aeQG0bnnkmsnGIiIjEqTnMYSc7AxaxwbuEyPM877eUx+u8HtEYPHhYxzrmM9/XNo1pQYvY+7d5mqdxE2AyLyIiUt8UFcGUKYGL2OCd++7YAW+8caDtoosiH4fTCRMnHni9dSvMmRO4iL0/rrVrYf78wO+LiEhYVMiOlhUrgidZ8Ca65csPvJ4zJzpx7NgB27cfeP3NN95kGozbDV99FZ1YRERE4sxKVpJAQqV9iilmAxt8r8soi3gcNjYrWekXl7OKZ3JvZzs72BHxWEREROLO2rVQUlJ5n4QE73x3v2isT+1yeef6+61eXfm8H7w3i5WPS0REqk2F7GhJTa26T1ragZ9zcqIXS3LygZ/LP505EMvyj0tERKQeSyU16N3Y5aVQRf6sIQ8eUjnwt0MqqUHvxi4vmeQq+4iIiNR5ocyvPR7/+W5SUnRiKT9frk5cIiJSbSpkR8uFF1a+tIjDAUOGHHh9yy2Rj8G2oXdv75OW9xs82PtxqGCMgfPPj3wsIiIicWggAytdnsPCohvdOIzDfG25RH69TQuLczjH93oQg3AR5GPKeO/gPoVTSEdrvoqIyCGgbVvo1Ml741UwbjcMGnTg9TXXRD4Oy/LOqfc76ijIy6t6m3POqbyPiIiERIXsaLn2Wu9V10DrUdu290EUl156oC0lBY45JrIxeDzwl7/4t914o3f/gf4AsG1o2RKGDYtsHCIiInGqO905i7OwCXzx2WAYz3gsDuTNp3gqojE4cHAxF9OCFr62XvTiBE4IGpcHD3/hLwHfExERqXcsC+6+23vjVSC2DeeeC507H2i77TbvciORYtveT1KXn8fbNtx5Z/BtHA4YNQpatAjeR0REQqZCdrS0bAn/+5+3YA3eu6D33wndpAksWADZ2f7bLFnifaJxIAkJsP/JneXHat4cunf3b3c4vP2few5OP91/nM6dYe5caNDA+8dA+bFatfLGFcrHo0REROqJ6UynN70BcP7+5cCBjc3jPM5gBvv1P4/zuJVbg47XmtYVxnLi5GzOxsLCxva9B967rycxyW8MC4u3eZtjOKbCWAkk8DzP05e+kToFIiIi8W/YMPjHP7zzXdv2n8uedhpMm1Zxm6++Cv6J5KZNA4914omB5/FNm3rny/vf2+/qq2H8eO/8+uCxzjsPnn66ZsctIiI+ljHBLmnWTYWFhWRkZFBQUEB6ehx83HbvXnj1VVi40JvYTjsNLrgAEhODbzNzJtx6K+Tne+/Uvv56uOsu2LMHpk+HxYu9ibFvX+9Hp5xO+OAD7wMj9+yBI46ASy6Bxo2D72P3bnjlFVi2zLt9//4wYEDly46IiAgQh7mmjoqn82gwLGIRr/EahRTSgQ5cyqWVLiOygQ2MZCRf8zUWFidwAq/wCllk8QmfMItZ7GY3nejEaEbTlKasYx1TmMI61pFDDiMYwdEcXWlcH/ABc5hDMcV0oQuXcAmNqSTHi4gIEF95pi6Lu/O4cSNMmQI//eRdRnPoUDjhhODLjrjdcPvtMHUq7NvnLUhPnOi96euXX7xj/fyzt0A9fDgceywUF3vn8Z9+6i12n3aadwnOyubx69Z5x1q3znvn9siR0KNHxA9fRKS+CSfPqJAtIiISJuWayNB5FBGRaFKeiQydRxERiaZw8oyWFhERERERERERERGRuKZCtoiIiIiIiIiIiIjENRWyRURERERERERERCSuqZAtIiIiIiIiIiIiInFNhWwRERERERERERERiWsqZIuIiIiIiIiIiIhIXHPGOoB6r7gY7r4bFiwAy4JzzoFx4yAxER54AJ56Cvbtg8MOg6lToWtX2LYNpk2DdesgJweGD4fDD698P59/DnPmwJ490KULjBgBDRvWyiGKiIjUB7OYxeM8TiGFHM7hPMRDtKENS1jCGMawmc2kkcZYxnIN11BGGW/xFp/yKQ4cnMZp9KMfNnbQfWxjG9OYxjrWkUMOwxnO4VSR40VEROSANWvg9tvhxx8hPR3+/Gc47zwoKoJLLoGFC739evWCF1+ElBT4+muYORMKCrxz65EjISsr+D7KyuCtt+DTT8HhgNNOg/79vT+LiEjMWMYYE+sgIqmwsJCMjAwKCgpIT0+PbTCvvuotKHs8/u227S1qu1wVt+nQAdau9W5j295/ut0wejT8+9+QkODfv6AAhgyBefPA6TwwbkoKTJnifU9ERCIqrnJNHRYv53E72+lOdzazucJ72WSTT36F9mSSySKLzWwmAW9uLqOM9rTnHd4JWJz+B/9gLGPx4MHGxoMHN24u5VKe4RnfOCIiEhnxkmfqurg6j6NGwUsvVWxPT4fCwsDbdOsGK1f6z5eTkmDiRLj00or9ly+HAQNg48YD8++yMu9c/Z13vP8UEZGICSfP6HJitKxY4b2T+uAiNngL04GK2OC9quxyebcrK/P2Be/d2n/6k39fY+D88713e4N3u7Iyb/vevTBsGHz8ccQOSUREpD46kiMDFrGBgEVsgBJKfNuU/f4FsJa1nMqpFFDg138KU7iFW3DhwoOHMspw4/a9dyM3RuhoRERE6qk77ghcxIbgRWzwFrHBf75cUgKXXQZz5/r33bTJe/f1li3e12Vl3m+An3+GU06pfF8iIhJVKmRHyw03eBNkpBjjvSN7c7mJ9tKl3iL2/mL3wf0tC+69N3IxiIiI1DNzmctGNkZsPDduNrOZqUz1tXnwcDd3B93GYHiGZ9jClojFISIiUq94PPDPf0Z2TIcDJkzwb5s4EXbvDjzHdru98/GpUyu+JyIitUKF7GhZsiTyY3o83nW69ps1y/vxqGDcbnj/fW8iFhERkQoe5/GojPsqr/p+XsEKNrCh0v4ePLzFW5X2EREROWQtXOh9tlQkeTzeZ039+uuBthkzAhexy3v11crfFxGRqFEhO1qqSn7V4XB4H2CxX1GR967rquzZE/lYRERE6oEiiqruFCaDoZADHzsOZR8OHOxGF55FREQC+u236I198By7MsZoaRERkRhSITtaMjIiP6bbDZ07H3jdqVPVBfPMTGjUKPKxiIiI1ANd6RrxMZ046UY33+sOdMBRxZ9cbtx0oUvEYxEREakXjj8+OuMmJ0OLFgded+kCth28v9PpfXikiIjEhArZ0XLFFZEdz+GAvDzo1+9A26hRlS8tYttw1VWV9xERETmEPcADER/ThYurudr3OpdcBjAAJ4HzsQMHzWnOGZwR8VhERETqhRYtoEOHyI5p23DJJdCgwYG2a6+t/GYxlwuuvjr4+yIiElUqZEfL3/4Ghx1WvW0dB/1abNv7/dJL/leHs7PhmWe8y4sE2qZzZ++TnUVERCSgRjTiNm6LyFgW3uW+ruVa/sgf/d57jMfIJrtCMdvGxomTF3kRm0ruABMRETnUzZlT+d3SlQk0X27VCu6917/9/PNh6NCKS3juf33ddXDyydWLQUREakyF7GhxOuHHH+Hcc/2TrdMJI0Z479g+OAk3agTvvOO9KpyU5G2zLO9d2J9+CqedVnE/o0fDf/4DJ5xwoC09HW680ftAjGgscSIiIlKPPMiDPMZjZJLp196NbkxmcoX2BBK4lVt5hEdowYGPI7elLZOYxJM86Stq79ea1nzO51zMxSThzfEWFv3pz6d8ymkEyPEiIiJyQNeu8NVXcMQR/u1ZWfDPf0L37hW3OeoomDcPevc+0Jaa6r2reulSaNzYv7/DAS+/DA8/7L/kSLt23pvInngitOdUiYhIVFjGGBPrICKpsLCQjIwMCgoKSE9Pj3U4Xi4XLF/uTXg9evhfDV62DH75BXr18k+ixcWwbZt3jevMzND2s32798GOzZodKISLiEjExWWuqYPi8TyuYQ1b2MIRHOFXwN7IRpawhHa040iO9LV78LCJTThwkEtuhQJ2IMUUs41tZP7+JSIi0RGPeaYuisvzmJ8Pq1Z5577t2h1oLy2F//3PO+fu2xcSE/232b0bmjb1ro1dFY8HNm3yjpWbqwK2iEiUhJNnVMgWEREJk3JNZOg8iohINCnPRIbOo4iIRFM4eUZLi4iIiIiIiIiIiIhIXFMhW0RERERERERERETimgrZIiIiIiIiIiIiIhLXVMgWERERERERERERkbimQraIiIiIiIiIiIiIxDUVsiuze7f3O1SlpbBmDRQW+re7XPDZZ/DllxW3+ewzeO65itvk58NLL8FPP/m3l5XBvHmwZEnFsfbuhZ07weMJLV5joKAAiopC6y8iIhKH9rGPfPJx4w55m1/5lU1sqtC+jnUsYAFF+OfG3/iNp3maFazway+llLnM5b/8t8JYq1nNO7xDMcV+7W7c5JNPKaUhx7uXvexkJx5CzPEiIiLxxuPxzldLSkLfprDQO8cuPShnFhXBggXw668Vt3ntNXj99YrtX3/tnWPn5/u3FxTAG29491OeMd79hzNfdru94x8cr4iIRIQK2QczBqZOhW7dID3d+929O7z4ove9QH78EY49FpKSoH17yMiAli1hxgxo1w4SEuC44+Doo8GyvON17er9+bjj4IorvNtYFlx+OTgckJMDo0ZBhw7e9oEDITsbEhPhjDOgZ09ve9u2sHAhnHUWpKV5++Tmwj33wJ49geN1u+GJJ7yxZmZCw4be8d58M2qnVUREJNJWsIIhDCGNNHLIoRGNuI3byCc/YH8PHq7gCpJJpiUtaU5zkknmyt+/HDhoQxv60IeGv3/9mT9jYdGEJlzLtRzFUVhYnMRJNKQhSSQxgAH0pz8WFjnkcAZnYGHRiU6cwzmkkUYSSbzFW9zETWSRRQ45NKABIxjBd3wX9BgXspCzOIs00sgmm1xyuYd7KhTHRURE4lZhIdx5JzRu7J2vpqV557fLlgXf5tVXoUUL7zy5fXvvXPv4473tDRp457B9+njn3bYNY8ZASop3jjx0KJx/vvfntDT44x+9P//hD945dk6Od5tbb/XO1TMz4bzzvPuxLLj4Ynj2WejSxbv/hg2hRw/v/D5YTWDbNrjpJsjK8o7foAGMHAnfBc/xIiISPsuYYP8nrpsKCwvJyMigoKCA9PT08DY2Bm64AZ580ltM3n9n8/6fb7gBHnvMm9z2W7XKW5h2uSJ2DNVi294C9X4OBxxzjPcqdVragXa325vY58zxvt7/699/jA8+CLfdVntxi4jUQTXKNeJTk/P4AR/Qn/548ODiQA62sWlDGxazmEY08rV78HAkR7KSlRGLPxKcOEkggfnMpyc9/d57jdcYznAsLL+7zR04OIZjWMAC0kg7eEgREfmd8nVk1Og8FhR4C8nffec/X7Vt77z6jTfg7LP9t/nHP+CWW2ocd41ZVsX58rhxcP/9/v02bYITTvD+s/wxOp3eQvmCBd73RUQkoHDyjO7ILm/BAm8RG/yX59j/87/+BR984L/NgAGxL2KDf8IEb8yffw5//7t/+8svw+zZ3oRc/hrG/mO8/XZvcV5ERCROlVHGMIbh+v2rPDdu1rGO27ndr/0RHom7IjaACxf72McIRvgtG7KLXYxmNAZTYckUDx4+53Me5MHaDldERCQ8f/1rxSI2eF+73d67lvfuPdBeXOy9UzoeBJov/+1vsHSpf78bb4TNmyseo8sF+/bBiBGhL/8pIiKVUiG7vKee8l41DcbphKefPvB6/fqK62jFE4/HG2/5hLr/bvNgbBueeSb6sYmIiFTTXOayjW1B14t24eJlXqaAAl/bYzxWS9GFz4OHdaxjPvN9bdOYRgklGAJ/cM6Dh6d5Oqx1wUVERGpVSQlMnlyxwLvf/mc2vfbagba//jX48h3xwOmEiRMPvN661ftp52A3t3k8sHYtzJ8f+H0REQmLCtnlrVhR+d3VLhcsX37g9UcfRT2kGtuxA7ZvP/D6m28qvxrsdsNXX0U/LhERkWpayUoSSKi0zz728TM/+15vY1u0w6oRG9vvjvGVrMRJJRfXge1sZwc7oh2aiIhI9fz6a9UPSkxI8M5R9/vss+jGVFMul7dusN/q1cEL9fvZtv8xiohItamQXV5aCOtMlu+TmRm1UCIqOfnAzykplffd/0AMERGROJVKatC7sctL4UDOs7GjGVKNefCQSqrvdSqpQe/GLi+Z5Cr7iIiIxERqatV9jPGfo4ayTayVny+HEq/HU/U8XEREQqJCdnmDB3uvlgbjcMCQIQden3VW5UuRxJptQ+/e3ict7zd4cOUxG+N9wrOIiEicOpdzK11Sw8KiAx3oSEdf24mcWBuhVZuFxTmc43s9iEEV1v8uz8bmFE4hHT28TERE4lReHhx1VOVLW7pcMGjQgdfXXx/1sGrEsrxz6v2OOsp7nFVtc845lfcREZGQqJBd3lVXQcOGgYvZtu0tCI8Zc6DN6YTRo2stvLB5PPCXv/i33XjjgSdEH8y2oWVLGDasVsITERGpjsM5nAu4IOhd1gbDXdyFxYFc9ziP11Z4YXPg4GIupgUtfG296MUJnBD0GD14+At/CfieiIhI3Bg/PvjSlrYNp58OPXocaDvzzKoLw7Fi25CTA5de6t92553Bt3E4YNQoaNEieB8REQmZCtnlNWsG8+ZBdrb3tdN54O7lnBzve02b+m/z7LPQr1/g8ZKSohdreY0be/+5P16Hw7vW2HPPef8wKK9zZ5g7Fxo08Bazyx9jq1awYEHd+DiXiIgc0qYylTM4AwDn7182Ng4cPMiDXMzFfv27052pTPUrbte2ZJKxsLCxfTGD9+7rSUzy62th8TZvcwzHAAeO0YGDBBJ4nufpS99aPwYREZGwDBoETz11YJ5afv558sn+D3rc74svoFGjwOMlJkYtVD8NG3r/WT7epk298+WsLP++V1/tLdhblrewXX6b886Dp5+unZhFRA4BKmQf7JhjYMMGePFF793Wo0fDSy/B+vVw9NGBt3nvPViyBE47Ddq393686PnnobgYfvwRDj/cW1hOSIBu3WDzZu8TnNu2PTCGwwETJniX9th/1zR4k+Fpp3nbly3zJnTL8vZv0wY2bfI+KXn+fO8d5RddBA8+CBs3+l8pLq9vX+/7Tz/tvTp82WXeJy2vXu2NX0REJM6lkcY7vMMSlnA913MRF/FX/sp61nMbtwXcZhSjyCefq7iKjr9/Xc3VFFBAGWUMYxjJJOPESTbZTGc6BsP1XI+j3J9M3elOCSUsYhENaehrzyGHH/mRUkrpRS9sbCwsUknlOZ5jL3v5mZ+5kzsZyUhu4AY+53NmMzvgWteNaMRiFjOf+VzFVVzMxTzIg2xkI6MZHfFzKiIiEhXXXgu//AL33++dr15zDXz8MXzwQeDnTjVr5p3jPvusd27dvj306eOdD+/bB1Onem8+czq9z4MaORLKyrzvl78pKzUVli/3bnP00Qc+lex0wgMPeOfYDz3kHWN/Efrcc73tW7Z45/SXXOKdV0+fDmvXeufzB7Ms71z+55+9d2ePHAk33OAtyM+a5f/MKhERqRHLGFP1k4TqkMLCQjIyMigoKCA9XetGiohI5CnXRIbOo4iIRJPyTGToPIqISDSFk2d0R7aIiIiIiIiIiIiIxDUVskVEREREREREREQkrqmQLSIiIiIiIiIiIiJxTYVsEREREREREREREYlrKmSLiIiIiIiIiIiISFxTIVtERERERERERERE4poz1gHUOStXwmuvQUEBHH44jBwJWVnVG2v5cpg1C4qKoGNH71gZGfDmm3DVVd59JCfDLbfAX/4S2eMQERGpx3ayk2lM40d+JIMMhjKUrnSt1ljb2c40pvEzP5NFFsMYRmc6s53tnMmZfMu3WFgcy7HMZS4NaBDhoxEREamn3G5491344AMwBv74RxgwAJzVKFW4XDB3Lnz8MVgW9O4NZ58Ntg033wyTJ0NpKeTkwCuvQK9ekT8eERGJKssYY2IdRCQVFhaSkZFBQUEB6enpkRu4uBguughef92bVC3LmygTE2HiRLjsstDHKiqC4cO9Sbb8WMnJkJAAhYUVt3E6YfNmaNQocsckIiLVErVcc4iJ1nmczGSu53pKKcWJE4PBhYsLuICXeIkUUkIe6yme4iZuwo0bGxsPHty46UAHfuTHgNvcx338BV2AFhGJNeXryIjaefz+e2+h+eefvfNggLIyaNkS3n4b/vCH0Mf65hvvWBs2+I+Vl+edRwcqe3ToAD/8UPPjEBGRGgknz2hpkVCNHu29Uxq8ReeyMm8y3LcPLr/cm2hDNXy496rzwWPt3Ru4iL2/X/PmNToEERGR+u5N3mQMY9jHPgyGMspw4QLgdV7nUi4NeaxXeZXruZ4yyvDgoYwy3LgBghaxAe7kThazuGYHIiIiUp/t3Amnngrr13tfl5V5vwE2bYLTToOtW0Mb67ff4JRTYOPGwGMFu3fvxx91V7aISB2jQnYoVq2CmTPB4wn8vsMBf/1raGMtX+69E9vtDj+O0lKYMSP87URERA4BBsNd3IUjyJ83Hjy8yqv8QNV3XxkM4xmPhVWtWAYzuFrbiYiIHBJeeMFbqA40L3a7YdcueOaZ0Mb697+9hfHqzLE/+ST8bUREJGZUyA7F669719UKxuOBL7/0foypKnPmVG+9r/3Gjq3+tiIiIvXYetazkpV4CHLhGbCxmcOcKsf6nu/5gR8wVG8Ftk1sqtZ2IiIih4QZM4LfKQ3eOfb06aGPFeyms1BMmVL9bUVEpFapkB2KoiLvXdeh9Aulj1W9u7sA7/IjIiIiUkERVedhB46Q+oXSR0RERKpp9+6q+4Qyvw51rMrsX95ERETingrZoejU6cAaW8EkJ3sfShHKWC5X9WPp3Ln624qIiNRjh3EYSSRV2qeMMjpTdS5tS1ucVP8TVDaVfJJLRETkUNe1a+WfVLZtOOKI0Meq7BPUVTnvvOpvKyIitSpqhez777+fE088kdTUVDIzM0PaZvTo0ViW5ffdv3//aIUYusGDIT09+J3Utg0XXwwNG1Y91vDhkJJS/buy586t3nYiIiJB1JecnU46IxgRtIhsYZFJJudzfpVj5ZDDEIZUu5g9iEHV2k5ERCSY+pKvAbjmmspv8HK74dprQx+rOutjAyQmQvfu1dtWRERqXdQK2aWlpQwZMoRrrrkmrO369+/P5s2bfd/TQ10XK5pSU73rZllWxSu9tg2HHQb33RfaWOnp8NxzB7YNxznnQIMG4W0jIiJShfqUsx/gAVrSskIx28bGgYMpTCGFlJDGepiHaUrTsIvZSSQxAz2cWUREIqs+5WtOPRWuvNL788E3eVkWjBwJAwaENtZZZ8GoURXbQ7l5bPbs0PYhIiJxIWqF7AkTJnDTTTfRrVu3sLZLSkqiWbNmvu+srKwoRRim886DDz6A3r0PtKWmwlVXwdKl0KRJ6GMNGwbz5sFJJx1oa9AA/vQn+Ne/Kn7EyrLg8svh7bdrdgwiIiIB1Kec3ZSmLGUpV3GVX8H6VE7lAz5gIANDHqs5zfmMz7iMy0gmGfDe1d2PfvyP/9GEirm/Fa0ooqhGy5KIiIgEUp/yNZYFkybBxInQps2B9pYt4dFHYerU0D/FbFnwwgvw+OPQqtWB9tat4cknYdCgitskJcG773pvFhMRkToj7mZZH374IU2aNCErK4vTTjuN++67j5ycnKD99+3bx759+3yvCwsLoxdcr14wfz7k53sfKNG0qXdt7Oro08f7vWOH9yEWzZp5kynA//0frF4N778Pf/gDnHxy5I5BREQkQuI1ZzehCU/xFP/gH2xlKw1pSDbZ1Rorl1ye4Rke4zG2sY100snCWwDYylZKKGEGM0gkkQu5UAVsERGJO/Gar7Es77IgV18NmzaBMZCXB45q3G/ncMANN8D113vHsizIzfW2X3edt8+CBfDDD9C/v7fILSIidU5czbb69+/P+eefT5s2bVizZg3jxo3jzDPPZPHixdhBluF44IEHmDBhQu0Gmp3t/Y6EnBzv98E6dvR+i4iIxKG6kLOTSaYVraruGIIUUgKOlUwyoxkdkX2IiIhEWl3I11gWNG8embEcDmjRIvB7p53m/RYRkTorrEudY8eOrfCgiIO/v//++2oHM2zYMM4991y6devGoEGDmDt3Lp999hkffvhh0G3uuOMOCgoKfN+//PJLtfcvIiJSXyhni4iIxD/laxERkdCFdUf2zTffzOjRoyvt07Zt25rEU2GsRo0a8dNPP9GnT5+AfZKSkkjavySHiIiIAMrZIiIidYHytYiISOjCKmQ3btyYxo0bRyuWCn799Vd27NhBbm5ure1TRESkPlDOFhERiX/K1yIiIqGrxlMUQrNhwwZWrFjBhg0bcLvdrFixghUrVlBUVOTr06lTJ15//XUAioqKuPXWW1myZAnr1q1j/vz5DBw4kPbt29OvX79ohSkiInLIU84WERGJf8rXIiJyqIvawx7Hjx/P1KlTfa+POuooAD744ANOOeUUAFavXk1BQQEAtm3z9ddfM3XqVHbt2kVeXh5nnHEG9957rz7WJCIiEkXK2SIiIvFP+VpERA51ljHGxDqISCosLCQjI4OCggLS09NjHY6IiNRDyjWRofMoIiLRpDwTGTqPIiISTeHkmagtLSIiIiIiIiIiIiIiEgkqZIuIiIiIiIiIiIhIXFMhW0RERERERERERETimgrZIiIiIiIiIiIiIhLXVMgWERERERERERERkbimQraIiIiIiIiIiIiIxDUVskVEREREREREREQkrjljHUCkGWMAKCwsjHEkIiJSX+3PMftzjlSPcraIiEST8nVkKF+LiEg0hZOv610he/fu3QC0bNkyxpGIiEh9t3v3bjIyMmIdRp2lnC0iIrVB+bpmlK9FRKQ2hJKvLVPPLk97PB42bdpEw4YNsSwr6vsrLCykZcuW/PLLL6Snp0d9f7WtPh+fjq3uqs/HV5+PDerP8Rlj2L17N3l5eTgcWqWrumozZ9eXf/eCqc/HV5+PDer38dXnY4P6fXz15diUryNDc+zIqc/HBvX7+OrzsUH9Pj4dW/wLJ1/XuzuyHQ4HLVq0qPX9pqen1+l/aapSn49Px1Z31efjq8/HBvXj+HRnV83FImfXh3/3KlOfj68+HxvU7+Orz8cG9fv46sOxKV/XnObYkVefjw3q9/HV52OD+n18Orb4Fmq+1mVpEREREREREREREYlrKmSLiIiIiIiIiIiISFxTIbuGkpKSuPvuu0lKSop1KFFRn49Px1Z31efjq8/HBvX/+CR+1fd/9+rz8dXnY4P6fXz1+digfh9ffT42iX/1+d+/+nxsUL+Prz4fG9Tv49Ox1S/17mGPIiIiIiIiIiIiIlK/6I5sEREREREREREREYlrKmSLiIiIiIiIiIiISFxTIVtERERERERERERE4poK2SIiIiIiIiIiIiIS11TIrob777+fE088kdTUVDIzM0PaZvTo0ViW5ffdv3//6AZaDdU5NmMM48ePJzc3l5SUFPr27cuPP/4Y3UCrKT8/n5EjR5Kenk5mZiaXX345RUVFlW5zyimnVPjdXX311bUUcXBPPfUUrVu3Jjk5meOPP55ly5ZV2n/mzJl06tSJ5ORkunXrxn/+859airR6wjm+KVOmVPgdJScn12K0ofv4448ZMGAAeXl5WJbFG2+8UeU2H374IT169CApKYn27dszZcqUqMdZHeEe24cffljh92ZZFlu2bKmdgKXeq8/5Gup3zq5P+Rrqd85Wvj6gruRrUM6W+KJ8XZHydWwoX3vVpXwN9TtnK19XpEJ2NZSWljJkyBCuueaasLbr378/mzdv9n1Pnz49ShFWX3WO7aGHHuJf//oXkyZNYunSpaSlpdGvXz9KSkqiGGn1jBw5km+//ZZ58+Yxd+5cPv74Y6688soqtxszZozf7+6hhx6qhWiDe/XVV/nzn//M3XffzZdffskf/vAH+vXrx7Zt2wL2X7RoEcOHD+fyyy9n+fLlDBo0iEGDBvHNN9/UcuShCff4ANLT0/1+R+vXr6/FiEO3Z88e/vCHP/DUU0+F1H/t2rWcffbZnHrqqaxYsYIbb7yRK664gv/+979RjjR84R7bfqtXr/b73TVp0iRKEcqhpj7na6jfObu+5Guo3zlb+fqAupSvQTlb4ovydUXK17VP+dpfXcnXUL9ztvJ1AEaq7YUXXjAZGRkh9b3kkkvMwIEDoxpPJIV6bB6PxzRr1sw8/PDDvrZdu3aZpKQkM3369ChGGL7vvvvOAOazzz7ztb377rvGsiyzcePGoNv17t3b/OlPf6qFCEN33HHHmeuuu8732u12m7y8PPPAAw8E7H/hhReas88+26/t+OOPN1dddVVU46yucI8vnP8W4wlgXn/99Ur73HbbbeaII47waxs6dKjp169fFCOruVCO7YMPPjCA2blzZ63EJIeu+pyvjal/Obs+5Wtj6nfOVr4+oK7ma2OUsyV+KF97KV/HhvL1AXU1XxtTv3O28rWX7siuRR9++CFNmjShY8eOXHPNNezYsSPWIdXY2rVr2bJlC3379vW1ZWRkcPzxx7N48eIYRlbR4sWLyczM5JhjjvG19e3bF4fDwdKlSyvd9uWXX6ZRo0Z07dqVO+64g+Li4miHG1RpaSlffPGF3zl3OBz07ds36DlfvHixX3+Afv36xd3vCKp3fABFRUW0atWKli1bMnDgQL799tvaCDfq6tLvrrqOPPJIcnNzOf300/n0009jHY5IvczXUHdydn3J11C/c7bytb+68nurKeVsiSfK17GlfF03/r+vfF1RXfnd1UR9ztfOWAdwqOjfvz/nn38+bdq0Yc2aNYwbN44zzzyTxYsXY9t2rMOrtv3r7DRt2tSvvWnTpnG3Bs+WLVsqfJzC6XSSnZ1daawjRoygVatW5OXl8fXXX3P77bezevVq5syZE+2QA9q+fTtutzvgOf/+++8DbrNly5Y68TuC6h1fx44def755+nevTsFBQU88sgjnHjiiXz77be0aNGiNsKOmmC/u8LCQvbu3UtKSkqMIqu53NxcJk2axDHHHMO+ffuYPHkyp5xyCkuXLqVHjx6xDk8OUfU1X0Pdydn1JV9D/c7Zytf+6nO+BuVsiT/K17GnfK18XVfV55x9KORrFbJ/N3bsWB588MFK+6xatYpOnTpVa/xhw4b5fu7WrRvdu3enXbt2fPjhh/Tp06daY4Yq2scWa6EeX3WVX+OrW7du5Obm0qdPH9asWUO7du2qPa5ETs+ePenZs6fv9Yknnkjnzp155plnuPfee2MYmVSmY8eOdOzY0ff6xBNPZM2aNfzzn//kpZdeimFkEs/qc76G+p2zla9F+bruUs6WcClfK18Ho3wd/5Sv665DIV+rkP27m2++mdGjR1fap23bthHbX9u2bWnUqBE//fRT1BNtNI+tWbNmAGzdupXc3Fxf+9atWznyyCOrNWa4Qj2+Zs2aVXiYgcvlIj8/33ccoTj++OMB+Omnn2KSaBs1aoRt22zdutWvfevWrUGPo1mzZmH1j6XqHN/BEhISOOqoo/jpp5+iEWKtCva7S09Pr9NXioM57rjjWLhwYazDkDhWn/M11O+cfajla6jfOVv52t+hlq9BOVsqp3ytfB0q5evoUr6u6FDL2fUtX6uQ/bvGjRvTuHHjWtvfr7/+yo4dO/wSU7RE89jatGlDs2bNmD9/vi+pFhYWsnTp0rCfOl1doR5fz5492bVrF1988QVHH300AAsWLMDj8fiSZyhWrFgBUCu/u0ASExM5+uijmT9/PoMGDQLA4/Ewf/58rr/++oDb9OzZk/nz53PjjTf62ubNm+d3lTVeVOf4DuZ2u1m5ciVnnXVWFCOtHT179uQ///mPX1u8/u4iYcWKFTH7b0vqhvqcr6F+5+xDLV9D/c7Zytf+DrV8DcrZUjnl6+pTvq59yteVq0/5Gg69nF3v8nWsnzZZF61fv94sX77cTJgwwTRo0MAsX77cLF++3OzevdvXp2PHjmbOnDnGGGN2795tbrnlFrN48WKzdu1a8/7775sePXqYDh06mJKSklgdRkDhHpsxxvz97383mZmZ5s033zRff/21GThwoGnTpo3Zu3dvLA6hUv379zdHHXWUWbp0qVm4cKHp0KGDGT58uO/9X3/91XTs2NEsXbrUGGPMTz/9ZO655x7z+eefm7Vr15o333zTtG3b1vTq1StWh2CMMWbGjBkmKSnJTJny/+3asUubeRzH/ugO1wAAAslJREFU8ZxtEimliCDZWqjFpYuToxEEB/+CdpDQoV3rEshSxFEodZDOvdnJRVAQnQQX2yFoKFJLO3XpIAXHfm66HOK1dxbaPH14vSBTfoHfd3jyDl/yZ46Pj/PkyZOMjIzk06dPSZKFhYV0Op3++f39/Vy/fj3Pnz9Pr9fL0tJSqtVqut3uoEb4rqvOt7y8nO3t7bx79y6Hh4d58OBBhoeHc3R0NKgRvunLly/956pSqeTFixd58+ZNPnz4kCTpdDpZWFjonz89Pc2NGzfSbrfT6/Xy8uXLXLt2LVtbW4Ma4ZuuOtvq6mo2NjZycnKSbrebp0+fZmhoKDs7O4MagZIpc6+Tcje7LL1Oyt1svf49e51oNsWi13qt1z9XmXudlLvZen2ZRfYPaLVaqVQql157e3v9M5VKJa9evUqSnJ+fZ25uLmNjY6lWq7lz504eP37c/9IokqvOliRfv37Ns2fP0mg0Uq/XMzs7m7dv3/76y/8Pnz9/zsOHD3Pz5s3cunUrjx49uvAD4v379xfm/fjxY6anpzM6Opp6vZ579+6l3W7n7OxsQBP8Y21tLbdv306tVsvU1FQODg767zWbzbRarQvn19fXMzExkVqtlvv372dzc/MX3/hqrjLf4uJi/2yj0cj8/Hxev349gFv/t729vX99xv6ep9VqpdlsXvrM5ORkarVa7t69e+H5K5KrzrayspLx8fEMDw9ndHQ0MzMz2d3dHczlKaUy9zopd7PL1Ouk3M3W64uf+R16nWg2xaLXeq3XP19Ze52Uu9l6fdkfSfKDf+YGAAAAAICfbmjQFwAAAAAAgO+xyAYAAAAAoNAssgEAAAAAKDSLbAAAAAAACs0iGwAAAACAQrPIBgAAAACg0CyyAQAAAAAoNItsAAAAAAAKzSIbAAAAAIBCs8gGAAAAAKDQLLIBAAAAACg0i2wAAAAAAArtLwRCyT5iszlSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score, adjusted_rand_score\n",
    "\n",
    "# Load the dataset\n",
    "csv_path = \"/kaggle/input/iris-2-csv/iris 2.csv\"\n",
    "dataset = pd.read_csv(csv_path)\n",
    "\n",
    "# Inspect columns\n",
    "print(dataset.head())\n",
    "print(dataset.columns)\n",
    "print(f\"Number of columns: {len(dataset.columns)}\")\n",
    "\n",
    "# Update column names based on inspection\n",
    "# Ensure the number of names matches the number of columns in the dataset\n",
    "names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']\n",
    "dataset.columns = names\n",
    "\n",
    "# Prepare features and labels\n",
    "X = dataset.iloc[:, :-1]  # Features\n",
    "y = dataset['class'].map({'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2})  # Labels\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "# REAL PLOT\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.title('Real')\n",
    "colormap = np.array(['red', 'lime', 'black'])\n",
    "plt.scatter(X_scaled[:, 2], X_scaled[:, 3], c=colormap[y])\n",
    "\n",
    "# K-Means Clustering\n",
    "kmeans_model = KMeans(n_clusters=3, random_state=0).fit(X_scaled)\n",
    "y_kmeans = kmeans_model.labels_\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.title('KMeans')\n",
    "plt.scatter(X_scaled[:, 2], X_scaled[:, 3], c=colormap[y_kmeans])\n",
    "print('The Adjusted Rand Index of K-Means: ', adjusted_rand_score(y, y_kmeans))\n",
    "print('The Silhouette Score of K-Means: ', silhouette_score(X_scaled, y_kmeans))\n",
    "\n",
    "# Gaussian Mixture Model (GMM)\n",
    "gmm_model = GaussianMixture(n_components=3, random_state=0).fit(X_scaled)\n",
    "y_gmm = gmm_model.predict(X_scaled)\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.title('GMM Classification')\n",
    "plt.scatter(X_scaled[:, 2], X_scaled[:, 3], c=colormap[y_gmm])\n",
    "print('The Adjusted Rand Index of GMM: ', adjusted_rand_score(y, y_gmm))\n",
    "print('The Silhouette Score of GMM: ', silhouette_score(X_scaled, y_gmm))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121637d2",
   "metadata": {
    "papermill": {
     "duration": 0.012286,
     "end_time": "2024-07-22T17:34:44.611478",
     "exception": false,
     "start_time": "2024-07-22T17:34:44.599192",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "8)Aim: Demonstrate and analyse the results of classification based on KNN Algorithm.\n",
    "Program: Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02acde35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-22T17:34:44.637916Z",
     "iopub.status.busy": "2024-07-22T17:34:44.637477Z",
     "iopub.status.idle": "2024-07-22T17:34:44.665513Z",
     "shell.execute_reply": "2024-07-22T17:34:44.663968Z"
    },
    "papermill": {
     "duration": 0.044413,
     "end_time": "2024-07-22T17:34:44.668078",
     "exception": false,
     "start_time": "2024-07-22T17:34:44.623665",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "\n",
      "Correct predictions:\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 2 Actual: 2\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 1 Actual: 1\n",
      "Predicted: 0 Actual: 0\n",
      "Predicted: 0 Actual: 0\n",
      "\n",
      "Wrong predictions:\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Load Iris dataset\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# Split dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Initialize k-NN classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# Train the classifier\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Predict the labels for test set\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Print correct and wrong predictions\n",
    "print(\"\\nCorrect predictions:\")\n",
    "for i in range(len(y_test)):\n",
    "    if y_pred[i] == y_test[i]:\n",
    "        print(\"Predicted:\", y_pred[i], \"Actual:\", y_test[i])\n",
    "\n",
    "print(\"\\nWrong predictions:\")\n",
    "for i in range(len(y_test)):\n",
    "    if y_pred[i] != y_test[i]:\n",
    "        print(\"Predicted:\", y_pred[i], \"Actual:\", y_test[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c21d13e",
   "metadata": {
    "papermill": {
     "duration": 0.011854,
     "end_time": "2024-07-22T17:34:44.692078",
     "exception": false,
     "start_time": "2024-07-22T17:34:44.680224",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "9. Implement the non-parametric Locally Weighted Regression algorithm in order to fit data\n",
    "points. Select appropriate data set for your experiment and draw graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e114461",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-22T17:34:44.718460Z",
     "iopub.status.busy": "2024-07-22T17:34:44.718014Z",
     "iopub.status.idle": "2024-07-22T17:34:50.701827Z",
     "shell.execute_reply": "2024-07-22T17:34:50.700632Z"
    },
    "papermill": {
     "duration": 6.000241,
     "end_time": "2024-07-22T17:34:50.704577",
     "exception": false,
     "start_time": "2024-07-22T17:34:44.704336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rbutf/o/Q9S8drHscxyE9PT0CvRMRkXApKCgIGUlPAqZjFup+iSklY6sO61yCKXiJYRMnTmTEiBEh154B5gHJwEygvnXP3LlzVf9IRCSO2XWLRgPdgYOYdS6HrPY1a9asFutcgil4iXGTJ0+mR48eIdduBf4HtMZk4LWp/pGISHyy6xZ1ITSfi1uIUl3WuQRT8BIHFi1axIknnhg4/wVT/6gQk8juFpd7BgwYoPUvIiJxxK5b1BAzwl4TeAN4zeWeRKoUXREKXuLEli1bQs5XUrzq/GngDKt9QUEB/fv3j0DPRETkWNl1i8DsLG0JfA0Mdbnn+uuvT6hK0RWR5PV67TpOcS0vL4/U1FRyc3NJSUmJdneqVHp6eolFXO8DlwGfA50ouec/Pz+f5OTkiPVRREQqxnEc6tevz+HDxX/B7wCew+ww7YTZsBHM4/GQn5+fUNNFFXn/1shLHMnKygrJ/+LFVBHdBfwemORyj73wS0REYkv//v1DApczgUzf8b2UDFzAvB8kUuBSUQpe4ohb/pddmAAG4M/A1dY927Zto2fPnhHonYiIVFR2dnbIiHoyJodLXczI+j9c7qlO+VxKo+Alzrjlf1kK+GdKX8XUvAi2ePHiEveIiEh0uWXRfRw4G9iN2Yxhr+uobvlcSqPgJQ651T8ag1n30gT38gGZmZnaPi0iEkO6du0akkW3G8WJSP8I7LTaV5e6ReWh4CVOzZo1K2S+Mx+zbfowZurIbWW6tk+LiMSGjIwMVq1aFThvBLzuO34WWOxyz9ixY6v1OpdgCl7ilNv6ly+A+3zHk9D2aRGRWOS2LfoVoAUmCd09LvfUrVuXsWPHRqB38UHBSxxLT08vsRj3H8ASzGKvGUAt657Zs2dTUFAQmQ6KiEgIx3EYOHBgyLU/YQru5mMSkNrp/wGmTZumUZcgCl7i3MKFC2nTpk3g3ItZ5PUTpnDjeJd72rdvH5G+iYhIqPHjx4dsi25D8YaLMbhvi66uWXTLouAlAWzYsCEk/8sOYIjveBRwkUv7kSNHRqh3IiICZtTl0UcfDZx7gGlAPeADYIrLPSNGjKi2WXTLouAlAbitf5kPvIz5HzwNsHMVTpkyRdWnRUQiqG3btiGbJu7FZM/dCwym5Lbozp07l1gbI4aClwTRt2/fEsOKI4DNwEkUD0sGU/VpEZHI6NmzJ19//XXgvB0wznd8J7DNau/xeFixYkWEehd/FLwkkJkzZ4Ys6DqAWf9ShMkZYGffBW2fFhEJN7tadDIw3ff6FqZitO2BBx7QAt0yKHhJIG7TR/+iuEbGS5gS68G0fVpEJHzctkU/DPwO+BG43eUebYs+OgUvCSY9PZ0uXbqEXHsAkzugOfC0yz3aPi0iUvUcx+HWW28NudYZs5ECzBbpn1zu07boo1PwkoCWL18esvsoHxgEFAI3Am4b7rp37x6ZzomIVBM5OTns27cvcF4Ps4HCg8mmu8DlnoyMDG2LLgcFLwnIbfpoDTDBd/wcJYs35uTkaPGuiEgVGjo0tFDLE8BpwFbgLpf2PXr0YNKkSRHoWfxT8JKg3KpPjwfWAycAz7vc079/fy3eFRGpAhkZGXzzzTeB80uBYb7jwUCu1b5NmzYsWrQoQr2LfwpeEphdffoIZvqoALgWU8gx2JEjR7jwwgsj10ERkQRkL9Ktj8m7BWbd4YdWe1WLrjgFLwnOrj79OWalO5hfohZW+5UrVzJ79uwI9U5EJLE4jsOAAaGPhhOAk4HvKC6eG0zVoitOwUuC83g8PPDAAyHX/h/wKXAcpvS6TdNHIiKV079/f44cORI4vwAY7ju+DZN/K1hycrK2RVeCgpdqYOzYsdSpUydw7mCS1hVgKpn2s9o7jsMNN9wQuQ6KiCSAgoKCkJHrusCrvuMXKTldBDB9+nSNulSCgpdqwOPxMG3atJBrXwKP+Y6fAhpZ98yZM0e7j0REKqB9+/Yh5w8DrTGp/90qyXXp0oV+/ezHRykPBS/VRN++fUtUkp4A/AezbXqKyz2aPhIRKZ+MjIyQRbd/ADJ8x3cAeVb7WrVqsXz58gj1LvEoeKlGMjMzadu2beD8CHArZhrpJuAqq712H4mIHJ29uygZM13kwdQwetvlnqysLE0XHYOwBi/Lly+nZ8+etGjRgqSkJObPn19m+5ycHJKSkkp87Ny5M5zdrFY+++yzkPN/U1xx+nmggdVeu49ERErnOA4DBw4MufZX4CxgFzDC5Z4+ffooi+4xCmvwcuDAAdq1a8czzzxTofs2bdrEjh07Ah9Nmtj5YKWykpOTS8yxPghsBlpidiLZVHlaRMRd//79OXz4cOD898AY3/EwYI/V3uPxMHPmzAj1LnGFNXi58sorefTRR7n22msrdF+TJk1o1qxZ4KNGDc1uVaWsrKyQ2keHgCG+46GAPVFUWFioytMiIpbs7OyQkWkP8ApQC5jr+7BpuqhqxGRUcPbZZ9O8eXMuu+wy/vWvf5XZNj8/n7y8vJAPKZtb7aMc4AXf8ctAHeseVZ4WESnmNl00HDgH+IXiUgDBtLuo6sRU8NK8eXOef/555s6dy9y5c2nZsiVpaWmsW7eu1HsmTJhAampq4KNly5YR7HH8ctt9NBqzpa81Zs7W1qFDhwj0TEQk9o0fPz5kuuhE4FHf8b2Y9S7BtLuoaiV5vV5vRL5QUhLz5s2jd+/eFbrvoosu4qSTTmL69Omu/56fn09+fn7gPC8vj5YtW5Kbm0tKSsqxdLla6NKlC6tWrQqcXwu8hUlgdzbwldV+1qxZenIQkWrNcRzq1q0bkkl3Pibp5z8xU+/2G2t2drYW6R5FXl4eqamp5Xr/jqmRFzfnnnsumzdvLvXfa9euTUpKSsiHlN+KFStISkoKnM8DFmK2+j0PJFntlftFRKo7uwRAb0zgUgDcTsnApV+/fgpcqljMBy/r16+nefPm0e5GwnKrfXQnpv7GhcAtVnvHcZT7RUSqLXuRbgNMlnKAJwC7NnStWrXIysqKUO+qj7AGL/v372f9+vWsX78egO+++47169ezdetWAMaMGROy4GnKlCksWLCAzZs388UXXzBixAg+/PBDhg1zW/okVWXcuHEhq9+3AuN8x08CJ1jtlftFRKojt0W6j2LWu2ymuORKsPvvv1+7i8IgrMHLmjVraN++faDeQ0ZGBu3bt+fBBx8EYMeOHYFABkxRq7vvvpvf/e53XHTRRfzf//0fH3zwAZdeemk4u1ntue0++juwHlPzaKLLPcr9IiLVjZ3T5RyKK0bfARy22tetW1cVo8MkYgt2I6UiC34k1Pnnn8/KlSsD5+cCqzAR7sWY7dTB+vXrx6xZsyLWPxGRaMnOzg7ZrODBZChvjykBMLCUe7TWpfwq8v6t4EUCHMehdu3aISMqzwB/BjZhMkfamV7y8/NJTk6OXCdFRCLMcRzq168fMuqSAUwCfgbOBHZb9+jhruISareRRI7b9NH9wA7gdOA+l3uU+0VEEp2d06U58JDveDQlA5c6depokW6YKXiREOnp6XTp0iVwnktxYbH7gVOt9l9++aUW74pIwnIch8cffzzk2kTMLqNVwGsu90yfPl2LdMNMwYuUsHz58pDcL7OBJUBtiitQB1PuFxFJVHZOlzSgP1CEKQGgnC7RoeBFSnDL/fIXzHqXnsDVVnvlfhGRRGTndKkJPO07fg74zGqvnC6Ro+BFXNm5XzZRPOryd8woTDDlfhGRROKW0+UvwG8xa1wecLlHOV0iR8GLuHJbvPsosB2z7uUel3sGDx6s6SMRSQhlLdK9D9hrtVdOl8hS8CKlshfv7qc4aLkfOMlqf/DgQXJyciLTORGRMHFbpPskZpHuatwX6U6bNk2jLhGk4EXKtHz58pBfyJmYZHX1MDkObEOHDo1Mx0REwmT8+PEhi3QvAgagRbqxRMGLlMlt+uhOoBDoA3Sz2n/zzTda+yIiccsedQlepPsCsM5qr5wu0aHgRY4qPT2d1q1bB86/oPiX+SmgltVeW6dFJF7ZW6OHAWcBPwF/dWmvnC7RoeBFyuW5554LOX8I2AWcQXESOz9tnRaReGRvjW5E8SLdMcAvVvtOnTppuihKFLxIuaSlpVGvXr3AeS4mLTbAWKCp1V5bp0UknrhtjX4EOA5YD7zqcs9jjz0W9n6JOwUvUi4ej4dXXw399Z0OfIJZgT/e5Z6bb75Z00ciEhfsrdG/BW73HY/ALNYNlpKSQlpaWkT6JiUpeJFys7dOe4GRvuNbgXZW+4KCAsaPdwtrRERih+M4PProoyHXJgMeYC7wscs9r7zyita6RJGCF6kQe+v0KuBNzA+SW92jxx57TKMvIhLT7E0GPYHLgHxglEt7bY2OPgUvUiFuW6fvAw4BFwPXWO0LCwt5+OGHI9Q7EZGKKSgoCFmfV4viHFaTge+s9toaHRsUvEiF2dNHWyn+ZX+SklunH3/8cY2+iEhM+tOf/hRyfifQGtgJPO7SXlujY4OCF6mU5cuXk5SUFDj/G7AD80s/3GrrOA79+/ePYO9ERI7OcRymTZsWOD8BeNB3/Fdgn9Ve00WxQ8GLVIrH4wnZVngAU+8IzC//CVb72bNnM2fOnAj1TkTk6C688EK83uJk/+OBVEwW3alWW4/Ho+miGKLgRSrtxRdfDDl/HfNLfxzFiZ2CDRw4UNNHIhITZs2axcqVKwPnZwJDfMcjKbk1+v7779d0UQxR8CKVlpycTL9+/QLnwVun7wDaWu0PHTqkrdMiEnWO43DzzTeHXPsbZmv0PGC51b5mzZqMGzcuQr2T8lDwIsckKysr5GlkOfAW5o/ABJf2EyZM0OiLiESVXb/oQqAXpuDsGJf2f/3rXzXqEmMUvMgx8Xg8PPDAAyHXxmD+CPQCLrDaK3GdiESTXb8IzC5JgJeATVb75ORkxo4dG4muSQUkeYNXKyWAvLw8UlNTyc3NJSUlJdrdqRYcx6F+/fohqbWfBYZikth1sdonJydz8OBBPcmISEQ5jsPxxx/Pvn3F+4j6ArOB/cBpmIKzwWbNmhUyPS7hU5H3b428yDHzeDwh2w3BFDQ7AHQGrrXaa/RFRKIhJycnJHCpRfH09pOUDFy6dOmiwCVGKXiRKtG3b1/OO++8wPlOihPXTQBqWu219kVEIu3ZZ58NOb8DOJXQv1d+Ho+H5cvtpbsSKxS8SJWxC5s9CfwInI4p3BhMoy8iEkmO47BgwYLAeQrFCenGYUaKgz3wwAOa2o5hWvMiVcZtPnkY8DTmyeY0Qv9AeDwe8vPz9QdCRMIuPT09ZKHuY5jEmhuBs4DgcWCty4sOrXmRqPB4PLzyyish114ENgPNgAyrveM4XHjhhRHqnYhUV/YOoxbACN/xvYQGLgBjxoxR4BLjNPIiVc5+wukHzMLUCTkV2G2112p+EQkXt92QzwO3A/8EulrtNeoSPRp5kajKysqiVq3i2tLZwL+BBoBbtoTBgwdr8a6IhMX48eNDApdTgD/6jt0S0mnUJT4oeJEq5/F4uP/++wPnXszQLJinnZOs9gcPHiQnJycynRORasNxHJ544omQaw9htki/ixl5CaaEdPFDwYuExdixY0NGXz4ClgHJuI++2FsYRUSOVU5ODocOHQqc/xYY4Dt+wKW9Rl3ih4IXCQuPx8P06dNDrvn/WNyC2XkUbN68eZo6EpEqZZcueQTzpjcHWGe1rVu3rkZd4oiCFwmb9PR0WrduHThfDSzGJKx7yGrr9Xq180hEqkx2djarV68OnHcErgOKKM7vEmzatGkadYkjCl4krJ577rmQc/9zzY2YIdxgK1euLFEwTUSkohzHYeDAgSHX/Ck0pwNfWe07depEnz59ItE1qSIKXiSs0tLSqFu3buB8PWb3UQ3MEK7t5ptv1vSRiBwTe4dRV+AK4AjwsEv7xx57LEI9k6qi4EXCyuPxMHr06JBrD2KSQl2HGcoNprIBInIsHMfh8ccfD7nmD01eBr6z2tetW5e0tLQI9EyqkoIXCTt759FGYIbv2C1MUdFGEams8ePHc+TIkcB5d8zIyyGKp46CjR49Wmtd4pCCFwk7t51HD2OGcK8Ezrfaa/RFRCrDbdTF/5fkGeAHq712GMUvBS8SEenp6XTp0iVw/i3wqu/YLUx54oknNPoiIhVij7pcCfwBUxD2/7m01w6j+KXgRSJm+fLlIX8oHgMKgIuBC6y2hw4dUtZdESk3t1GXcb7XZ4CfrPb9+vXTDqM4puBFIsbj8YQkjfoeeM137DZwO3To0Eh0S0QSgNtal07AQWCi1bZWrVpkZWVFsHdS1RS8SESNHTs2ZPRlAmbty+XAeVbbb775RnlfROSoyhp1eY6Slezvv/9+TRfFOQUvElEej4drrrkmcP4/4HXfsVvWS+V9EZGjsUddLgM6Y3YYPWm1rVmzphbpJgAFLxJxf/7zn0POJwCFFC+uC6adRyJSlrJGXZ4Hdlnte/XqpVGXBKDgRSIuLS2NevXqBc6/Bd7wHbs9Dynvi4iUxh51uQSTfuEw8IRLe/vhSeKTgheJOI/Hw6uvvhpy7TFM1t2eQHurvUZfRMRNWaMuLwI7rfYpKSnKppsgwhq8LF++nJ49e9KiRQuSkpKYP3/+Ue/JycmhQ4cO1K5dm9NOO42pU6eGs4sSJXbel82Af+2/29oX5X0REZs96pIGXAjk457X5ZVXXtGUUYIIa/By4MAB2rVrxzPPPFOu9t999x1XX301F198MevXr2fEiBHcdtttvP/+++HspkSJW96XIqA30M5qq7wvIhLMcRyeeCJ0Ysg/7fwSJbPpKq9LYknyer3eiHyhpCTmzZtH7969S21z77338vbbb/PFF18Ert1www3s3buX9957r1xfJy8vj9TUVHJzc0lJSTnWbkuYPfTQQzz8cHGd1yzgRkzl6X5W2+uuu465c+dGsHciEquWLVtGt27dAufnAp9gUi+cAmwLalurVi0OHTqkUZcYV5H375ha87Jq1aqQH0aA7t27s2rVqlLvyc/PJy8vL+RD4oed98Vf/fV64DSr7cKFCzV1JCIAPPvssyHn9/le3yA0cAHldUlEMRW87Ny5k6ZNm4Zca9q0KXl5eRw6dMj1ngkTJpCamhr4aNmyZSS6KlXEzvvyJbAI84M5ympbWFiohbsiguM4LFiwIHB+JnAtZtrZXuuivC6JKaaCl8oYM2YMubm5gY/vv/8+2l2SCrK3Lv7N9zoIaG611bZpERk/fnzI34F7fa/zgE1WW+V1SUwxFbw0a9aMXbtCUwrt2rWLlJQU6tat63pP7dq1SUlJCfmQ+JKWlhby/3clsAKoDYyw2mrbtEj1Zm+PPgno7zv+m0t75XVJTDEVvHTu3Jlly5aFXFu6dCmdO3eOUo8kEjweD6NHjw655v8jNBQ4zmqv0ReR6sveHn03UAv4AFhjta1bt67yuiSosAYv+/fvZ/369axfvx4wW6HXr1/P1q1bATPlM3DgwED7O+64g2+//ZbRo0ezceNGnn32WWbPns3IkSPD2U2JAWPHjqVWrVqB83eA/wANMAFMMI2+iFRP9qjLCcBtvmO3UZfRo0dryihBhTV4WbNmDe3bt6d9e5MzNSMjg/bt2/PggyYN2Y4dOwKBDECrVq14++23Wbp0Ke3atWPSpEm8/PLLdO/ePZzdlBjg8Xi4//77Q675/xiNAOpY7TX6IlL92KMufwHqAf8Gllltk5OTtVA3gUUsz0ukKM9L/HIch7p16wb+OHmAb4BWwJ8xpe2DjRs3joceeiiifRSR6LD/PjTAVKU/HpNa4S2rvf4+xJ+4zfMi1Zs9+uIAE33HozDBTDCVDBCpPuxRl9sxgctGzC6jYBp1SXwKXiSm2GtfXgN+xIy+pFttVTJApHqwSwHUongn4hOAPX0wZswYrXVJcApeJKbYoy+HgH/4ju92aW9n2RSRxJOTkxOSqLQf8GtgBzDDaqtRl+pBwYvEHLtkwHPAQaADcJHVViUDRBKf/ZCS4Xt9Giiw2mrUpXpQ8CIxxy4ZsAeY6jvOsNqqZIBIYrNLAaRhHmQOAs9bbVUKoPpQ8CIxyc6KOcX32gtobbXVtmmRxGWXAvA/wEzFPNgEUymA6kPBi8Qku2TAN8BC3/EIq62S1okkJjsp3elAT0wBxiku7VUKoPpQ8CIxya1kQKbv9RagodVeoy8iicfeHj3C97oI80ATTKUAqhcFLxKz7G3THwPrMBk1b7faavRFJLG4lQIY5Due5NJepQCqFwUvErPcSgb4R1/uBJKt9hp9EUkc9qjLHUBdTPHFFVZbbY+ufhS8SEyzR19mA9uB5pRMWqfRF5HEYI+61AaG+47dRl20Pbr6UfAiMc0efTlC2UnrVDJAJP7Zoy7pQFPge2CO1VajLtWTgheJefboy4vAAaAdcKHVViUDROKbXQoAzDQxwLNAodVeoy7Vk4IXiXn26MteYLrveLhLe5UMEIlfdimA84BzgMPAS1ZbjbpUXwpeJC7YJQOe9r1ei6lxEkwlA0Til/3w4X9AeRP42WqrUZfqS8GLxAW7ZMCXwEdATUpum1bJAJH4ZJcCaAr09R0/ZbVVKYDqTcGLxA07e6Z/9OVPaNu0SCKwSwHcjvnd/hfwmdVWpQCqNwUvEjfskgELMLsPgp/O/LRtWiS+2Nuja2Fyu0Dxg0owlQKo3hS8SNywSwY4FFeVdVu4q9EXkfhhb4++HpPPaQcw12qrUgCi4EXiir1t+iUgn+IdCcE0+iISH+xRFyjeHv08Jr9TMJUCEAUvElfsbdO7MVl3AYa5tFfSOpHYZ4+6dAC6AAXAC1ZbbY8WUPAiccgeffHPh9+AKd4WTEnrRGKbW1I6/4NINrDLaq/t0QIKXiQO2aMvn/o+6gC3ubT/8MMPI9QzEakoOyndccCNvuNnrLYadRE/BS8Sl+ykdf4/ckOAJKvtP//5z0h1S0QqyB4ZvQlTPfpzYJXVVqMu4qfgReKSnbRuNvALcArQzWq7evVqrXsRiVEbNmwIOfcnnXzRaqdRFwmm4EXiVnCeh8MU1zv6k9VOu45EYpPjOCxcuDBw3gU4CzgIvGG1vfHGGzXqIgEKXiRupaWlUadOncC5/0ntGkziumDK+SISe8aPH09hYXGdaP+oy0wg12p72WWXRapbEgcUvEjc8ng89OjRI3D+JSaNeC1gsNVWoy8iscXO7XI80M93bG+PBvj1r+0SrFKdKXiRuHbHHXeEnPtHX9wW7mr0RSR22LldBmJ2DK7H7B4MlpKSQteuXSPXOYl5Cl4krtn1jrKBvbgv3NXoi0hscMuo61+r5jbqMnLkSK13kRAKXiSu2fWODgHTfMf2wl1Qxl2RWGCPulwAtAUOADOsttplJG4UvEjcc6t3BO4Ld5VxVyS6HMchMzMz5Jp/oe6bwD6rvXK7iBsFLxL37Iy7XwArcV+4C/D888+7XBWRSFixYgX79hWHKA2BPr5j1TGS8lLwIgnBHn3x/xF0W7j79ttva+pIJErmzZsXcj4As1D3M2CN1VajLlIaBS+SEOzRl+CFuxdbbTV1JBIdjuPw8ssvh1zzj46+YrXVqIuURcGLJIyxY8dSs2ZNwCzcfdN3/RaXts8++2yEeiUifjk5ORw8eDBwfjbQHsgHsqy2Q4cO1aiLlErBiyQMj8dDly5dAudTfa/XAylW24ULF2rqSCTC7IcG/6jLAkxtsmC9e/eOQI8kXil4kYRywQUXBI4/BTYA9YC+VrvCwkLlfBGJIMdxWLx4ceA8GbPeBeBVq229evWUlE7KpOBFEsoll1wScv6a79Vt19HkyZM1+iISITk5ORQUFATOewKNgG3AUqvtFVdcoSkjKZOCF0kodsbdN4BC4HygtdU2Ly+PFStWRLB3ItWXnaLA/0AxDSiy2gZXjBdxo+BFEoqdcXcn8J7v+BaX9vPnzw9/p0SqOcdxWLBgQeC8OXCF7/g1q23dunVJS0uLUM8kXil4kYRj53yZ6nsdSMkf+Oeee05TRyJh5laE0QOsADZbbUePHq0pIzkqBS+ScDweD0OHDg2cLwJ+Bk5ExRpFIs2tCKN/ysgedVFuFykvBS+SkK699trAcQHFxd7cFu6qWKNI+NijLucBp2OKMGZbbXv16qVRFykXBS+SkLp27UqDBg0C51N9r72B46y2yrgrEh5uRRhv9r3OAfZb7e+4445IdEsSgIIXSUgej4eMjIzA+WfA55gaKn1c2qtYo0jVs4sw1gLSfcdvWG21UFcqQsGLJCx74a7/j+UAl7Yq1ihS9ewijN0xuV12AB9abbVQVypCwYskLLtY45uYfBJpmMW7wTR1JFK13Iow3uR7zSI0t4sW6kpFRSR4eeaZZzj55JOpU6cOnTp14tNPPy217dSpU0lKSgr5qFOnTiS6KQkouFjjNmC57/qNLm1VrFGk6thFGFOAXr7jGVZbFWGUigp78DJr1iwyMjIYN24c69ato127dnTv3p0ff/yx1HtSUlLYsWNH4ON///tfuLspCcou1uj/o3mTS9v33ntPU0ciVeTDD0Mnhq4D6mLqjX1mtVURRqmosAcvmZmZDBkyhMGDB9O2bVuef/556tWrx6uv2qW4iiUlJdGsWbPAR9OmTcPdTUlgwcUa5wD5wO+Bs6x2Bw8eVLkAkSryz3/+M+Tc/8BgL9RVEUapjLAGLwUFBaxdu5Zu3YpTg9WoUYNu3bqxatWqUu/bv38/v/nNb2jZsiXXXHMNX375Zalt8/PzycvLC/kQCRZcrHEv8I7v2G3hrsoFiBw7x3FYuXJl4LwFcLHvOMtqqyKMUhlhDV5++uknHMcpMXLStGlTdu7c6XrP6aefzquvvsqCBQt44403KCoqokuXLmzbts21/YQJE0hNTQ18tGzZssr/OyS+paWlUa9evcC5f+qoP5BktX3ppZc0dSRyjMaPH09hYWHg/EbMm80KwF4EoCKMUhkxt9uoc+fODBw4kLPPPpuLLrqIt956i8aNG/PCCy+4th8zZgy5ubmBj++//z7CPZZY5/F4uO222wLni4Fc4CTgAqvtwYMHtetI5Bi4JaYrbcpIuV2kssIavJxwwgl4PB527doVcn3Xrl00a9asXJ+jVq1atG/fns2b7fJdRu3atUlJSQn5ELEFlwvIx6x9AfepIyWsE6k8OzHdb4GzMWU67HIAV199taaMpFLCGrwkJyfTsWNHli1bFrhWVFTEsmXL6Ny5c7k+h+M4/Oc//6F58+bh6qZUA3a5AP/UUT8g2Wq7cOFCTR2JVJKdmO4G3+u7wC9WW5UDkMoK+7RRRkYGL730Eq+//jpfffUVQ4cO5cCBAwwebErkDRw4kDFjxgTaP/LIIyxZsoRvv/2WdevWcdNNN/G///0vZNhfpKLscgEfA9uB44ErrLaqNC1SOW6J6fr5XmdabevVq6cpI6m0sAcv6enpTJw4kQcffJCzzz6b9evX89577wUW8W7dupUdO3YE2v/yyy8MGTKEM888k6uuuoq8vDxWrlxJ27Ztw91VSXDB5QKKgNm+631d2k6ePFmjLyIVZCemawe0AQ5h1poFGzJkiKaMpNKSvF6vN9qdqEp5eXmkpqaSm5ur9S9SQt++fZkzx6x4OQ9YBeQBTTBrYYJ99NFHejKMooKCAv7xj3/w1ltvsX37drxeL4cPH8ZxHDweD3Xq1KFGjRrUr1+fdu3accstt3DJJZfoDTGKgn+/AB4HxmDWmNkPCfr9EltF3r8VvEi1smzZskDeoSTgv5hdR9cAC622b7zxBgMGuC3plapmByp79uxh//79lfpcqampNGnShEsvvZTMzEzq1q1bxb0VN47jUL9+fQ4fPhy4thk4FTN1FLxYNyUlhT179ijQlBAVef+Oua3SIuGUlpYWeDPzUvwHtZ9L26VLl0aqW9VSQUEBEydOpEWLFtSuXZtRo0axatUqtm7dWunABSA3N5dvvvkmkM27QYMGDBgwgKVLl2oqMIxycnJCApcOmMDlIPC21XbkyJEKXOSYKHiRasXj8XDllVcGzv3rXnoBdvnPefPm6c2uijmOw7vvvstJJ50UCFiC17yFw/79+8nKyuLyyy+nZs2a3HzzzRQUFIT1a1ZHdi0j/wPBYkwA46cK0lIVFLxItRO8+PtTTMbPBpTcdZSXl6daR1XEcRwefPBBkpOTueqqq6KaTPKNN96gdu3aXHTRRQpiqpBdy8gfvMy22p133nkadZFjpuBFqh17kaD/j6vb1JFqHR2b4KBl/PjxFBUVHfWeNsBAYAIwF1iHCTDzgELgCGZx9W7gS+BD4AXgLuAS4Ffl7Nvy5csVxFQRu5bROUArYD/FtcT8ggulilSWFuxKteM4DikpKYEtnecA/8b8oW2C2dbpV69ePfLy8vSkWAmzZ89mwIABITVu3JwAXI2ZursIaHSMX7cQE/B8ALwFrC3nfX369GHmzJn6f10JDz30EA8//HDg/EngHuBNTA2xYB988AGXXnppBHsn8UK7jRS8yFHcdddd/OMf/wicf4t5Urwe84YXTH9sK6agoID27duzYcOGUtskA72BW4FLgeBw4RAmmPwc+BrYAvwI7MGsnUjCDBkfhwk2mwNnAG0xgehvrK/1P0xNnZcoWRTQzdixYxk3bpyCmHJyHIfjjz8+pCTA/zC7+K4F5ge1rVu3Lvv27dP3Vlxpt5HIUQTXOoLiXUfpLm1V66j8Ro4cSe3atUsNXBoBD2OyG88CLscELuuAcUAnIBUzAnMn8BRm2mENJsDcCezw3f8l8BGQBTwI9AFOxrxp3oyZDtyPCWb+6rv/bd/nLsv48eOpU6cO2dl2JR5xY9cy6oT5f7APeM9qq1pGUlUUvEi1ZNc68q97uZqSu45U6+joHMehRYsWTJkyxfXfGwGTME/kD2Kmir4HHgFOATr6jj/FrGk5Ft9jRlrSgcaY5GhLMX/srgJygOWYEZ/SFBYW0q9fv5CSEuLOrmXUx/e6CDhstVUtI6kqCl6kWrJrHa0FtmIWe15mtVWto7LNnj2bmjVrum55TgbuxiQry8B8f9dgpudOxoy2fFfOr1OrVi0aN25Mw4YNady4MfXr1z/qPYcx2V0vB04DnsEs9u2KWROzGLNAuDSTJ0+mc+fOCl5L4VbLyD+maU+/qpaRVCUFL1JtBdc6guK5+Wtd2qrWkbtevXqRnu422WYChP8AEzHrUz4DugN/wLyxHW3fUWpqKq1bt+aOO+7g4MGDFBQU8OOPP/Lzzz/z448/sm/fPvLz83nyySfp3LkzJ510UpkBzRZgOGZt098xIzxXA18AfwNql3Lf6tWrSU5O1jSSC7uW0e8wiekOU3LKSLWMpEp5E0xubq4X8Obm5ka7KxIH+vTp48Uk2/WmgdcL3t3g9fiuBX989NFH0e5uTOnQoUOJ7xHgrQ/eZ3zfSy94t4N3EHhruLQN/mjUqJG3f//+3iVLlngLCwsr3a/8/Hzvk08+6W3Tpk2ZX681eBcG9XMDeDsdpY933313FX4H41/w7w/gHev7Xi7Q749UQkXevxW8SLX2wQcfBP64enyBixe8F7n88b3rrrui3d2Y0apVK9c397PB+3VQQPACeFOOEhDcdNNN3vz8/LD0s7Cw0PvOO+94W7ZsWerX7wneH3z9LQTvuKMEWhkZGWHpa7wpLCz01qlTJ+R7s873fbzF+p6lpKQcU0Aq1UNF3r81bSTVWlpaGvXq1QPAwSwyBPepo9dff73aTx05jkOzZs347ruSK1X+DKwGWmPWD10C3I5JLmdLSkpi7NixFBYWMn36dJKTk8PSX385iK1bt5Kfnx+SXdlvEfBbYBpm59NDmCmPxqV8zszMTEaOHBmW/sYTu5bRyUB7Qn+P/FTLSKqaghep1jweD7fddlvg3L9vordL271791brcgFz5syhZs2a7Nq1K+R6LeAVzGLY2sAC4GzMNmY3ffr04ciRIzzyyCMRfUNLTk7myy+/ZNasWdSsWTPk334BBgE3AQcwi7Y/wxQXdDNlyhR69uwZzu7GPDuFQG/f63Lg56DrqmUk4aDgRaq94JwvSynODeL2xrV9+/YI9Sq2jBo1ir59+5a43gjzPfsj5ok7A/Mm9ovL57jwwgvJz88nOzs7qk/h/fr14/Dhw65vqDMwC4o3AL/GvBH3KuXzLF68mHPOOSds/YxljuOwePHikGv+36J5VttevXpp1EWqnIIXqfaCc74E75JwmzqyRx2qg3vuuYeJEyeWuH4SsBKT9C0Ps3Nnssv9xx9/PPn5+Xz88cdhmx6qKI/HwyOPPEJhYSGdO3cO+bevgPOA9zFbu+dhdim5Wbt2bbUcgbGnjBoD/opF8622yu0i4aDgRao9j8fDZZcVZ3fxPzm6BS//+te/ItKnWJGdnc2kSZNKXG8NrMDkSPkv0BnzZm/r0aMHe/bsiZmgxebxeFi5ciWzZs0iKSkpcH0f0ANT8LEGJtPv/aV8jsWLF1e7NTA5OTkh570w36c1mCSBfnXr1lVuFwkLBS8iELKQ821MDpDfYt6kg7377rvVZtGu4zjccMMNJa6fhZlOOQkzSnE+ZpolWFJSEjNnzmTRInvpZmzq168fR44coVOnToFrhcAdmIzAAI9hsgC7mTJlCnfffXd4OxlD7PIPpU0ZXXnllZoykrBQ8CICIU+HuRQvNu1ttTt06FCJp85EdeaZZ1JUFJpKrg2wDGiGWdB6EfCDdV+zZs04cuRIqcnrYpXH42H16tX06NEj5Pp4YJTveCzwaCn3Z2ZmVosAxnEcli5dGjivD3TzHc+32p5//vkR6pVUNwpeRDDBS506xVWNFvhee7i0rQ6FGjt27Mg333wTcu0kTEr9JphyCpcAu637WrVqxY4dO+L6aXvRokUl1rFMxBSKBFPksbSKR5mZmYwaNaqUf00MdiHGbphdZlsoOQLXtGnTCPZMqhMFLyKYp+7gJ+63fa/nA8dbbRO9UOM555zDunXrQq41xQQuLTHVnLsDe6372rdvz7fffhuJLobdwoULSxRlfBq4z3c8CbillHsnTpzInDlzwte5KLMLMfp/a9wmCH/961+HvT9SPSl4EfEJ3hXxP0xdHg9whdUukQs1ZmRksHbt2pBrdYGFmPU/32JyoPxs3dejR48SAU+8mzRpEjNnzgy59v+AJ33HL2OqVLvp379/Qga4diHGJMwuMzBFLoMdd9xxdO3aNVJdk2pGwYuIjz115P9jfLVL20Qs1FhQUMDkyaGbnZOA14FzMQHL5YBdO/ovf/lL3CzMraj09PQSAcxo4FVMYPsmUDJnL3G55qc87EKMHTHrn/ZhFnEHGzRoUFxPH0psU/Ai4mNPHfmDlysxb1TB8vLyEi7b7qmnnlri2nigL1CA2VGyxfr3Hj168Pe//z38nYui9PT0Egtx7wBygBTMdEkjl/vmzp2bcOtf7PVe/t+W9zE79IL17t07Aj2S6krBi0iQ4Kmj1ZjRhoaYPCa2RMq227NnT7Zt2xZy7XrM4lSA2zB5XYJ17NgxYUdcbBMnTgxZA3ME8/3ZDJwCzAVqlnJfoqx/cRyH998PzebjD17sKaOUlBRNGUlYKXgRCRJcqLEIeMd33W3X0bJlyyLVrbDKyMgokeq9NWZqBMwaj+nWPR06dGDNmjUR6F3smDRpUkgAswfoidlafxFmlMpNoqx/sXcZNcdMGxUB71ptVYhRwk3Bi0gQu1Cj/y3dLXjJzs6O+zel7OzsEutc6gLZmCmR5ZTMLHvaaaeVWNRbXUyaNIkRI0YEzjdi6jqB2YnktoD3yJEjXHjhheHvXJjZu4z8/62fAj8GXa9Tp44KMUrYKXgRsQQXanwfk2n1t8DJVrv9+/fHdcI6x3EYMGBAiev/ANoBu4AbMP/9wTZu3Bj+zsWwyZMnh9RDegvzPQOYBpzocs/KlSuZPXt2BHoXHo7jMHXq1JBrpU0Z3XjjjRp1kbBT8CJiCS7UmAv803fdbddRPCes69+/P0eOhC6zvAazvqUI6E/JnUWzZs3SGxNmCqVWrVqB81HAvzELd2fg/oc1nqePVqxYQV5eXuC8NmbLPBTnRPK79NJLI9UtqcYUvIhYPB4P3bt3D5yXNXX09ttvx+UbUkFBQYmRgMbAi77jJ4EPrXt69uxJv379ItC72OfxeJgxY0bgvABIx2wZvhC4y+We0mpFxQN7yigNU3F7O7DeaqvEdBIJCl5EXATvOvIHLxdj/mAHi9daR+3bty9x7UVM6v/PKS5G6NemTRsWLlwYgZ7Fj759+4Zsof4O8J89Dpzhcs+cOXPibvdRRaaMlJhOIkXBi4iL4IR1mzCZZWtjnjht8Ra8ZGRklKgKPBBThLIAuNn36lezZs0S7cWYOHFiyALel4D3gDqY5H5uE2zxNn1kTxlBcdbpd6y2SkwnkaLgRcSFx+Ph6quLV7n4s1t0d2kbT2/sbruLTgAyfccPYkZegr355pt6QyrD5MmTadu2OM/ubZi6T+fiXsAx3nYf2fmMTgFOw+S6+chqq8R0EikKXkRKEbyjpKzg5cMPP4yLJ2nHcRg4cGCJ609iFpquxxQcDNanTx/69OkT/s7Fuc8++yxwvB0Y6Tseh6nGbYun3Uc7d+4MOff/DqzErPHx05SRRJKCF5FSNGvWLHD8IeZJsw3Qymq3d+/euCgVMH78eA4fPhxyLQ1THbkIuJ3QbdEej6dEXR9xl5ycHLKYeSomR86vgNKKJwwePDgugt5Vq1aFnPuDl/esdpdccolG6CRiFLyIlCJ418Q+zJMmuI++xHqpAMdxePzxx0OuJQP+jd7PYZKNBcvKytKbUQVkZWWFbJ8eigl4e+O+U+3gwYMxv17KcRyWLFkSOK8FXOI7ft9qGzx1JhJuCl5EStG1a1dSUlIC52VNHcV6qYDx48eXyOlyF3A6JpeLnUW3S5cu2hZdQfb26Q0UryV6CpO52DZ06NAI9Kzy7JIAnYEGmIy66622aWlpEeuXiIIXkVJ4PB5uueWWwLk/eLmEkkX4YrlUgNuoS2OKiy7eCwTvJalVqxbLly+PUO8SS9++fRk5cmTg/BFgKyY78wiX9t98801Mr32x87v4dxktAbxB1+vXr6/gRSJKwYtIGYJLBXyGeeJMoWSV6VguFeA26vIQkAqsBd6w2mu66NhkZmbSunVrAA4CY3zX78MEjbabb745JgNft/wu/lFHe8qob9+++pmRiFLwIlKG4FIBXmCp77rb1FEslgpwG3Vpi1mcC2ZXTPATdKdOnbS7qAo899xzgeM3gTWYoHecS9uCggLGjy+tJnX02PldmgAdfMdLrLYqCSCRpuBFpAx2qQD/E+cVLm1jsVSAW/2iiZjkaXMBe4/UY489FqGeJba0tDTq1asHmODwHt/12zHrjGyPPvpozP3s2FNG/lpG/hHIYCoJIJGm4EXkKIJLBfifODtScgog1koFZGdnl1hPcQFwJWYXzL1W+5SUFK1bqCIej4dXX301cP4xsACzVupvLu0dx4mpxHUVmTJSfheJBgUvIkcRXCpgF8W7LC5zaRsrU0elJaR7xPf6MrDF+rdXXnlF6xaqUHp6Ol26dAmc3ws4mK3THVzax1LiOnvKKAm43HdsBy8qCSDRoOBF5Cg8Hg89ehRn6vAn53Jb97JkyZKYGP4vLSHdxUA+pnBgsH79+mmtSxgsX7488Ma+CfBvpHZb+wKxk7jOzlvUDmgK7Af+ZbVVSQCJBgUvIuUQPHXkX7TbzaVdXl5e1LPtOo7DE088UeK6f9TlRWBb0PVatWqRlZUVia5VOx6PhwceeCBw/ihm9KUX7qMvsZK47oMPPgg59y/HzcFMOfppykiiRcGLSDmkpaXxq1/9CjCZdg8DLYAzXNpGO9tuTk4Ohw4dCrnWDeiK6fcEq/3999+vYf8wGjt2bCDz7jccffTl2WefjUS3SuU4DgsWLAi55g9e7FSMmjKSaFHwIlIOHo+Hvn37AiYA+KfvutsG0d27d0eqW67c3vwe9L0+h8mo61e3bl3Gjh0biW5VWx6Ph+nTpwfOjzb6snDhwqhOHa1YsYJffvklcF4L8C8ltoMXTRlJtEQkeHnmmWc4+eSTqVOnDp06deLTT+0qKqGys7M544wzqFOnDr/73e945513ItFNkTJdcsklgWP/H3G34OXbb7+NSH/cuD01n4cZdSnAVJAONm3aND05R0B6enogcV3w6MtfXdoWFhZGNe+LPXLYCVNg8kfgi6DrDRs21JSRRE3Yg5dZs2aRkZHBuHHjWLduHe3ataN79+78+KOdKcBYuXIlN954I7feeiufffYZvXv3pnfv3nzxxReu7UUi5eeffw4c+4OXNEr+Ek2fPj1qT87jx48v8bVH+V6nEzrqooR0kRWcuM6/Xbo3cKpL2wkTJkTtZ2jnzp0h5/6Q/SNCExr27NlTga9ETdiDl8zMTIYMGcLgwYNp27Ytzz//PPXq1QvJgRDs73//O1dccQWjRo3izDPPZPz48XTo0IGnn3463F0VKVPjxsWZXdYCe4HjKTn0v3fv3qgs2nXLptsa8wYJJjldMCWki6y0tDTq1jXlGb8C3sb8AR7h0jaaWXdXrVoVcl7aehclppNoCmvwUlBQwNq1a+nWrXhfRo0aNejWrVuJXxC/VatWhbQH6N69e6nt8/PzycvLC/kQCYfgP9ZFmJ0X4D51FI1Fu241jO7G/JIvBDYGXa9bt64S0kWYx+Nh9OjRgXN/MPlHoKFL+2iMvjiOw5Ilxcn/62GmHaFk8FKjhpZMSvSE9afvp59+wnEcmjZtGnK9adOmJYYm/Xbu3Fmh9hMmTCA1NTXw0bJly6rpvIila9eupKSkBM7LWveybJn9pz683EZdmgKDfMf2WpfRo0dryD8Kgnce5WBG8OoBQ13aRmP0ZcWKFezbty9w3hVIBv4L2Cu5FPxKNMV96DxmzBhyc3MDH99//320uyQJyuPxcMsttwTO/eHJBUBtq+2CBQsi+tTsNupyB1AHWE3x7iiA5ORk7TCKEo/Hw/333x84n+R7vZOSP0MQ+dEXe8TQv97lQ6td/fr1FbxIVIU1eDnhhBPweDzs2rUr5PquXbto1qyZ6z3NmjWrUPvatWuTkpIS8iESLtdee23g+CvgB6Au0MVqt2fPnoite3EbdakJ/Ml3PMVqP2bMGI26RFHw6Es2sBUzSpbu0jbSoy+lJaezxxH79u2rnyGJqrAGL8nJyXTs2DFkCL2oqIhly5bRuXNn13s6d+5cYsh96dKlpbYXiaSuXbty/PHHB879T6RuU0fz58+PRJdcR116Y5Lo7QTeCrquUZfoCx59KcTk3gH3qSOAJ554IiKjL47jkJ2dHTg/HmjvO7ZHXi691O0nXiRywj5tlJGRwUsvvcTrr7/OV199xdChQzlw4ACDBw8GYODAgYwZMybQ/q677uK9995j0qRJbNy4kYceeog1a9YwfPjwcHdV5Kg8Hg/XXHNN4LysdS+vv/562N90HMchMzOzxPVhvteXCE3nrlGX2BA8+vIKJgfPecDZLm0jVa08JyeHAwcOBM4vxrxBfIkJgoNpp5FEW9iDl/T0dCZOnMiDDz7I2Wefzfr163nvvfcCi3K3bt3Kjh3F2Se6dOlCVlYWL774Iu3atWPOnDnMnz+fs846K9xdFSmX4N1w/uDlD4A9YRmJLdP2AkuAtpj8M4XAC0HXNeoSO4JHX3YDc33X7yilfSSCF7siemnrXVJSUpScTqIuIgt2hw8fzv/+9z/y8/P55JNP6NSpU+DfcnJymDp1akj7vn37smnTJvLz8/niiy+46qqrItFNkXIJfur8Hvga8AAXubQN95Zpt8//Z9/rQiD4XzXqElvGjh1LzZo1geKpowFAA5e2GzZsCGtfHMfh/fffD7lW2nqXyy+/XD9HEnVxv9tIJNJiacv00qVLQ85/BQz0HT8TdF2jLrHH4/EwYMAAAFZgpmfqAze7tF28eHFYpyDtETx/0VGH4nxGfsEV1kWiRcGLSAWVtmXaLXgJ55Zpx3GYMWNGyLW+mCf3rwkd7u/Ro4eelmPQZZddFjj2T9pEI+eLPYLn/1leC+QGXdcWaYkVCl5EKiF4y/RHmIy7Z2G2vAYL55bp8ePHU1hYGHLtj77X16y2bdu2DUsf5NgET0FOAw5ifo7OcWkbzpwv9hbp0ta7aIu0xAoFLyKVELxleg+w3nf9Epe24Vj34jgOTzzxRMi10zAZUR3MG2EwPS3Hpq5du9KggVnlkkfxtvZbXNqGa/TFrRJ5aetdtEVaYoWCF5FKsLdM+59Q3YKXcKx7ycnJ4dChQyHXbvG9LsEkz/NTHaPY5fF4yMjICJxP9b3eiHvG3cmTJ1f56MuKFSv45ZdfAuetgZZAPvAvq622SEusUPAiUkluW6Yjte7F3tZag+I6RvaUkeoYxbbgnC8fYTLuNgR6urTNy8ur8mnI0ta7rASCw+OGDRtqi7TEDAUvIpUU/BS6ApMMrpXvI1hVr3txG+bvBpyImcJaGHRdu4xiX3DOlyKKp/xuKaV9VWduLu96l2uuuUZBsMQMBS8ilRS87uUApgAiuI++VOW6F7dyAIN9r1mY4X4/5XaJD8GjL6/7rl0BuFV0e+mll6psJM8OhJMwmXVB610ktil4EamkipQK2L17d5V8TbdyAA0Afy+mBl3XqEv88Hg8DB1qNklvxlQB9wA3ubQ9ePBglWXctde7tANOAPYB/7baar2LxBIFLyLH4JJLipfolrVo99tvv62Sr+dWDuAaTGXrTZi8HH69evXSqEscCd5+7x99cUtYB1VXLqC09S4fY8pL+Gm9i8QaBS8ix+Dnn38OHK/GTB81weTqCDZ9+vQqGep3m37q73t907quTKjxpWvXrtSvXx+AOZhijb/H1KqyVVW5gJ07Q0sulrZFumfPngqEJaYoeBE5Bo0bNw4cH8Es3IWSU0dVVaTRLgdwAuDP0RocvNSrV0/bo+OMx+Ph+uuvB2Av8K7v+o0ubd97770qCYZXrVoVOK4FXOg7toMXTRlJrFHwInIM7D/qZa17OdZFu47jMHfu3JBrfYCamOmir4OuX3HFFXpSjkPB5QL8wahb8FIV614cx2HJkiWB806Y2li7gS+stjVq6K1CYot+IkWOQWlFGi/CLLgMdqzJ6lasWMH+/ftDrvnf2LKstioHEJ+Cg+FFmGnIU4FzXdrauX4qyl4/FbxF2mu11SiexBoFLyLHwC7SuB6TayWFkvVpjjVZ3bx580LOW2KG+YuAWVZbvdnEp+ByAQeB+b7rbqMvb7/99jH9PJW2WNcOsVWMUWKRgheRYxS8S8SLyZIKJaeOjiVZneM4TJ06NeRauu91BRD8NqT1LvHLLhfgnzpKp+Qf60OHDh3T1FFwcrp6wHm+Yzt4UTFGiUUKXkSOUXCyOgjPupcVK1aQl5cXcq20KaMhQ4bozSaOBSesW4IZyWuOmYq0VXbqyE5O1xVIBv4L2Jv6lZxOYpGCF5FjVFqyui5AHattZde92FNGpwMdMDuc5lhte/fuXamvIbEh+Ocp+P9vf5e2lZ06spPTlTZlBNppJLFJwYtIFQgu0vg1sA0TuJxvtavMuhe3KSP/qIv/ydzvuOOOUzKxBBCco8c/dXQ9ZnQkWGWnjkpb72LXM1JyOolVCl5EqkBpW6btbLuVWfdSkSmjQYMGacooAaSlpVGnjhm3Ww78ABwPdHdpW5mpo+D1Lg2Bs33HKsYo8ULBi0gVsNe9+N8EqmLdiz1l1AFog9mNssBqqymjxODxeOjRowdgdpPN9F2vil1H9nqXSzBvBF8CO622Wu8isUrBi0gVKG3dyzlAqtW2IkUay5oy8ucB8dOUUWJxmzq6BpNILlhFp47s9S5X+l7fd2mr9S4SqxS8iFSR4HUv24GNmER19rPrf//733J/TnvKKAm4wXesKaPEFjx1tAb4BrOluZdL24pMHe3YsSPk/Arf67tWu0aNGikYlpil4EWkithPqe/4Xq+22mVlZZV7mN+eMuoKnAj8ArxntdWUUWIJnjqC4tGXY9111KRJk8Dx74EWmBG85Va74cOHKxiWmKXgRaSKdO3alRNOOCFw/rbv9UrMiInf7t27y7Vot6wpo7cwVYf9NGWUmNymjrpjFtkGq8jUUfDPnn/K6ENCf54A/TxJTFPwIlJFPB4P/fsXPxf/E9iHSTDW3mpbnkW79pRRTUwhRgitIA2aMkpUwVNHG4HPMNWf+7i0LU/w4jgOTz31VODcH7zYo3gAP/74Y8U6KxJBCl5EqlCrVq0CxwWAf0PqVVa78izatQOcy4ATMDtCPrLaasooMXk8Hq6+unjisaxK00VFRUf9fCtWrGDPHpMZKIXiPET2eheA5s2bV6SrIhGl4EWkCjVq1Cjk3D91ZAcvdjs3doDjf8Oajdk+66cpo8TWuXPnwLF/y/SFgL0PqDyjecFtumFG8zYB31ntlJxOYp2CF5Eq9PPPP4ec+59oO2FGTfw++sgeOylpy5YtgeO6gL/8oz1ldPPNN2vKKIE1a9YscPw9phBnDYoLc/rNnTv3qIt2g5PT+ZcCu426KDmdxDoFLyJVqHHjxiHnPwDrMb9oVwZdP1qZAMdxmDZtWuC8B1Af84S82mp7yimnHEuXJcbZu9j8W+TtqaP9+/eXue4lODldTYq3XM93aavkdBLrFLyIVCG3pF4Lfa/XB107WpkAe7HuIN+rPeoCJQMmSSxdu3alQYMGgfM5QCEmAWJrq21Z+V6Ck9NdBDQCfsSM5NiUnE5inYIXkSpklwkAs0YFTDKwlKDrZa1RCP63FhQnEnvNpa3eaBKbx+Ohe/fiqkY/YQpyQnFQ67dkyZJSR/SCf6b8gfQCQtdPgda7SHxQ8CJShewyAWBqxmwAahOaHXXZsmWUJnhtwkBMpt7lwGarnRbrVg/B+V4AXvW9Dsb8bPjl5eWVOqLnXwBeg+L1U3Nd2mm9i8QDBS8iVSy4TIDfLN9rv6Brpa17sQvnDfa9uo26KL9L9ZCWlsavflVc1WghZsqnBSV3spU2ouff4dYFaAbspWQVaYCLL774WLsrEnYKXkSqmNs0TrbvtTtwnO+4tHUvwWsTumIqSO8P+hzBlN+levB4PPTt2zdwfgR43Xc8xGpb2oiefyfcQN/5fN/nKa2dSCxT8CJSxbp27UrDhqEJ3L8CPgeSCd0lYhfJg9An5zt9r1mEVpAGrU2obuwRvVd8r1dh6l35lTai991331GP4i3WbiN5oAXgEh8UvIhUMY/Hw5133lni+su+19uDrgUXyfPzr3dpSfHahH+4fB2tTahe7BG9TZhMyx7gL0HX3Ub0HMchKyuL6zCLxrdQshBjaV9HJBYpeBEJA7cRkenAIaAdJmkd4Pomk51tJojuxOTjWIZZ9GtTLo7qxW0n20Tf6+2E7mSbP39+SLsVK1bw008/4V/2O7WUr9G4cWON5klcUPAiEgZuRe32Urxwd4Tv9emnnw4Z4s/JyeHAgQM0Af7su5ZZytfQE3L14raT7V3MTrYUIHg/0uuvvx7yc7V9+3bOx9Qyyqd4FNDWv39/jeZJXFDwIhIGpRW18wci/YC2mMWRwaMv/iRj9wK/Aj4B3nH5PCkpKXpCrobsdS9e4P/5ju8D/OMye/fuDfm5+uCDD7jXd/w6prinm5NPPrmquioSVgpeRMLAbYgf4D+YDKk1gId81/wLdB3H4f333+c0ikddHizl819++eV6Qq6G3Ebb3gD+DxO4jA26HvxztWPuXHpiEtJNLPEZimmxrsQLBS8iYeA2xO/3MOZNpC9m67R/a+uKFSs4uG8fLwF1MFlUl7h+hpJJy6R66Nq1KykpKSHXioBRvuM7gfN8x/6fq38uW8aEffsAs8PomzI+v6YiJV4oeBEJE7dkdQBfAH/3HU8DPn/rLRzHYfv33zMZSMNsix5ayuetX78+aWlpVdtZiQsej4dbbrmlxPWlmBGYmpiRvZPxbZk+coRfP/447YGfgTFlfG5tvZd4ouBFJEzKeor9K7AOaAIsyc1l+4ABdBg9OpDXZTDwbSn39u3bV1NG1di1117ren0YZlfar4E1wH179pB37rmc9vHHANwM7C7j82rrvcQTBS8iYeKWrM7vECa52BqgIXDSrFmcuXMnBcAtuGfT9dMW6eqttPVUecDlwFpMxehRwPHr11NYowY3YXYmlUU/VxJPFLyIhElpyer8dmHqzNwCfN6pE+OB0ylO+14arUuo3spaT/UD0BlTbfpFYHW3bpyXmsqMcnxe/VxJPFHwIhJGR1tD4K9R8/Cvf82DwH+P8vkaNWqkdQlS6noqMD9T0zCJ6xadey5rfXWyyqLkdBJvFLyIhJFbsjo3S5cuLVe74cOHa12C0KxZs3K1KyoqKlc7JaeTeBO24GXPnj0MGDCAlJQUjjvuOG699Vb2799f5j1paWkkJSWFfGhLqMSz0pLV2fb5trIejZ6OpSI+++yzcrVTcjqJNzXD9YkHDBjAjh07WLp0KUeOHGHw4MH86U9/Iisrq8z7hgwZwiOPPBI4r1evXri6KBJ2/kW7e/bsqZLPt3NnablRpTop74jeRx99VK52Sk4n8SYsIy9fffUV7733Hi+//DKdOnXiggsu4KmnnmLmzJn88MMPZd5br149mjVrFviwEzKJxJOjLdqtqN27y9rsKtVFeUf0CgoKytVOi3Ul3oQleFm1ahXHHXcc55xzTuBat27dqFGjBp988kmZ986YMYMTTjiBs846izFjxnDw4MFwdFEkYqpyqkdPyAKlb5euDC3WlXgUlmmjnTt30qRJk9AvVLMmDRs2LHPYu3///vzmN7+hRYsWfP7559x7771s2rSJt956q9R78vPzyc/PD5zn5eUd+3+ASBWqyqkePSELFG+Xnjp16jF/Li3WlXhUoZGX++67r8SCWvtj48aNle7Mn/70J7p3787vfvc7BgwYwLRp05g3bx5btmwp9Z4JEyaQmpoa+GjZsmWlv75IOFTVVI/St0uwsrZLV4QW60o8qtDIy9133+1aVyPYKaecQrNmzUosKCssLGTPnj3l3uIH0KlTJwA2b97Mqaee6tpmzJgxZGRkBM7z8vIUwEhMqaqpHqVvl2BVNQqnqUiJRxUKXho3blyuH/TOnTuzd+9e1q5dS8eOHQH48MMPKSoqCgQk5bF+/Xqg7MVptWvXpnbt2uX+nCKRVlVvMkrfLsH8615+KUcSurJU5IFSJFaEZcHumWeeyRVXXMGQIUP49NNP+de//sXw4cO54YYbaNGiBQDbt2/njDPO4NNPPwVgy5YtjB8/nrVr1/Lf//6XhQsXMnDgQC688EJ+//vfh6ObIhHRtWtXTjjhhGP+PFrvIsHKKhMgkujClqRuxowZnHHGGVx66aVcddVVXHDBBbz44ouBfz9y5AibNm0K7CZKTk7mgw8+4PLLL+eMM87g7rvv5vrrr2fRokXh6qJIRHg8Hvr3739Mn0NlAcRNVax7KW/OGJFYErYkdQ0bNiwzId3JJ5+M1+sNnLds2ZKPfaXbRRJNq1atjul+lQUQN1UxGlfenDEisUS1jUQi4FgXRWrURdwc65SkRvQkXil4EYmAY31C1tC+uDnWKUmN6Em8UvAiEgHH+oSsoX0pzbFMSWrUReKVgheRCDiWJ2QN7UtZjmVKUiN6Eq8UvIhESGWfkDW0L2U5lilJjehJvFLwIhIhlX1C1qiLlKWyU5Ia0ZN4puBFJEIq+4SsoX0pS2WnJDWiJ/FMwYtIhFT2CVlD+3I0lZmS1KiLxDMFLyIR4vF4uOmmmyp0j4b2pTwqMyWpET2JZwpeRCKoR48eFWqvoX0pj8oUV9SInsQzBS8iMUyjLhIOjRs31s+WxDUFLyIRVNGheg3tS3lU9OdkwIABGtGTuKbgRSSCKjpU36RJkzD1RBJJRX+uKjp9KRJrFLyIRNCxlgkQcaOfK6luFLyIRFBFc3Jo2kjKQz9XUt0oeBGJsIrk5NCOECkv/VxJdaLgRSTCypuTo2HDhtoRIuWmnyupThS8iERYecsE3HXXXdoRIuWmnyupTpK8Xq832p2oSnl5eaSmppKbm0tKSkq0uyNSguM4nHzyyWzbtq3UNo0aNWLXrl16k5Fy08+VxLuKvH9r5EUkwjweD3//+99JSkoiKSnJtc2LL76oNxipEP1cSXWi4EUkCq677jrmzJlTYqi/ZcuWzJ07l+uuuy5KPZN4pp8rqS40bSQSRY7jsGLFCnbs2EHz5s3p2rWrnozlmOnnSuJRRd6/FbyIiIhI1GnNi4iIiCQsBS8iIiISVxS8iIiISFxR8CIiIiJxRcGLiIiIxBUFLyIiIhJXFLyIiIhIXFHwIiIiInFFwYuIiIjElZrR7kBV8ycMzsvLi3JPREREpLz879vlSfyfcMHLvn37AFOITEREROLLvn37SE1NLbNNwtU2Kioq4ocffqBBgwalloWvrLy8PFq2bMn333+vuklHoe9V+el7VX76XpWfvlcVo+9X+YXre+X1etm3bx8tWrSgRo2yV7Uk3MhLjRo1OPHEE8P6NVJSUvTDXU76XpWfvlflp+9V+el7VTH6fpVfOL5XRxtx8dOCXREREYkrCl5EREQkrih4qYDatWszbtw4ateuHe2uxDx9r8pP36vy0/eq/PS9qhh9v8ovFr5XCbdgV0RERBKbRl5EREQkrih4ERERkbii4EVERETiioIXERERiSsKXiqpV69enHTSSdSpU4fmzZtz880388MPP0S7WzHnv//9L7feeiutWrWibt26nHrqqYwbN46CgoJody0mPfbYY3Tp0oV69epx3HHHRbs7MeeZZ57h5JNPpk6dOnTq1IlPP/002l2KOcuXL6dnz560aNGCpKQk5s+fH+0uxawJEybwhz/8gQYNGtCkSRN69+7Npk2bot2tmPTcc8/x+9//PpCYrnPnzrz77rtR64+Cl0q6+OKLmT17Nps2bWLu3Lls2bKFPn36RLtbMWfjxo0UFRXxwgsv8OWXXzJ58mSef/557r///mh3LSYVFBTQt29fhg4dGu2uxJxZs2aRkZHBuHHjWLduHe3ataN79+78+OOP0e5aTDlw4ADt2rXjmWeeiXZXYt7HH3/MsGHDWL16NUuXLuXIkSNcfvnlHDhwINpdizknnngif/vb31i7di1r1qzhkksu4ZprruHLL7+MToe8UiUWLFjgTUpK8hYUFES7KzHviSee8LZq1Sra3Yhpr732mjc1NTXa3Ygp5557rnfYsGGBc8dxvC1atPBOmDAhir2KbYB33rx50e5G3Pjxxx+9gPfjjz+OdlfiwvHHH+99+eWXo/K1NfJSBfbs2cOMGTPo0qULtWrVinZ3Yl5ubi4NGzaMdjckjhQUFLB27Vq6desWuFajRg26devGqlWrotgzSSS5ubkA+vt0FI7jMHPmTA4cOEDnzp2j0gcFL8fg3nvv5Ve/+hWNGjVi69atLFiwINpdinmbN2/mqaee4vbbb492VySO/PTTTziOQ9OmTUOuN23alJ07d0apV5JIioqKGDFiBOeffz5nnXVWtLsTk/7zn/9Qv359ateuzR133MG8efNo27ZtVPqi4CXIfffdR1JSUpkfGzduDLQfNWoUn332GUuWLMHj8TBw4EC81SRhcUW/VwDbt2/niiuuoG/fvgwZMiRKPY+8ynyvRCSyhg0bxhdffMHMmTOj3ZWYdfrpp7N+/Xo++eQThg4dyqBBg9iwYUNU+qLyAEF2797Nzz//XGabU045heTk5BLXt23bRsuWLVm5cmXUhtEiqaLfqx9++IG0tDTOO+88pk6dSo0a1SdurszP1dSpUxkxYgR79+4Nc+/iQ0FBAfXq1WPOnDn07t07cH3QoEHs3btXo56lSEpKYt68eSHfMylp+PDhLFiwgOXLl9OqVatodydudOvWjVNPPZUXXngh4l+7ZsS/Ygxr3LgxjRs3rtS9RUVFAOTn51dll2JWRb5X27dv5+KLL6Zjx4689tpr1SpwgWP7uRIjOTmZjh07smzZssAbcVFREcuWLWP48OHR7ZzELa/Xy5133sm8efPIyclR4FJBRUVFUXvPU/BSCZ988gn//ve/ueCCCzj++OPZsmULY8eO5dRTT60Woy4VsX37dtLS0vjNb37DxIkT2b17d+DfmjVrFsWexaatW7eyZ88etm7diuM4rF+/HoDTTjuN+vXrR7dzUZaRkcGgQYM455xzOPfcc5kyZQoHDhxg8ODB0e5aTNm/fz+bN28OnH/33XesX7+ehg0bctJJJ0WxZ7Fn2LBhZGVlsWDBAho0aBBYP5WamkrdunWj3LvYMmbMGK688kpOOukk9u3bR1ZWFjk5Obz//vvR6VBU9jjFuc8//9x78cUXexs2bOitXbu29+STT/becccd3m3btkW7azHntdde8wKuH1LSoEGDXL9XH330UbS7FhOeeuop70knneRNTk72nnvuud7Vq1dHu0sx56OPPnL9GRo0aFC0uxZzSvvb9Nprr0W7azHnj3/8o/c3v/mNNzk52du4cWPvpZde6l2yZEnU+qM1LyIiIhJXqtfiAxEREYl7Cl5EREQkrih4ERERkbii4EVERETiioIXERERiSsKXkRERCSuKHgRERGRuKLgRUREROKKghcRERGJKwpeREREJK4oeBEREZG4ouBFRERE4sr/B1JhJfKaDIWKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def local_regression(x0, X, Y, tau):\n",
    "    x0 = [1, x0]   \n",
    "    X = [[1, i] for i in X]\n",
    "    X = np.asarray(X)\n",
    "    xw = (X.T) * np.exp(np.sum((X - x0) ** 2, axis=1) / (-2 * tau))\n",
    "    beta = np.linalg.pinv(xw @ X) @ xw @ Y @ x0  \n",
    "    return beta    \n",
    "\n",
    "def draw(tau):\n",
    "    prediction = [local_regression(x0, X, Y, tau) for x0 in domain]\n",
    "    plt.plot(X, Y, 'o', color='black')\n",
    "    plt.plot(domain, prediction, color='red')\n",
    "    plt.show()\n",
    "\n",
    "X = np.linspace(-3, 3, num=1000)\n",
    "domain = X\n",
    "Y = np.log(np.abs(X ** 2 - 1) + .5)\n",
    "\n",
    "draw(10)\n",
    "draw(0.1)\n",
    "draw(0.01)\n",
    "draw(0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62498d0a",
   "metadata": {
    "papermill": {
     "duration": 0.014189,
     "end_time": "2024-07-22T17:34:50.733452",
     "exception": false,
     "start_time": "2024-07-22T17:34:50.719263",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "10. Implement and demonstrate the working of SVM algorithm for classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0b5f56c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-22T17:34:50.765172Z",
     "iopub.status.busy": "2024-07-22T17:34:50.764275Z",
     "iopub.status.idle": "2024-07-22T17:34:51.061534Z",
     "shell.execute_reply": "2024-07-22T17:34:51.060340Z"
    },
    "papermill": {
     "duration": 0.3166,
     "end_time": "2024-07-22T17:34:51.064629",
     "exception": false,
     "start_time": "2024-07-22T17:34:50.748029",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7333333333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing necessary libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Load the iris dataset\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2]  # Taking only the first two features for simplicity\n",
    "y = iris.target\n",
    "\n",
    "# Splitting the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Feature scaling\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "\n",
    "# Training the SVM model\n",
    "svm_classifier = SVC(kernel='linear', random_state=42)\n",
    "svm_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the test set results\n",
    "y_pred = svm_classifier.predict(X_test)\n",
    "\n",
    "# Calculating the accuracy of the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Visualizing the decision boundary\n",
    "def plot_decision_boundary(classifier, X, y):\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                         np.arange(y_min, y_max, 0.1))\n",
    "    Z = classifier.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, alpha=0.4, cmap=plt.cm.coolwarm)\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolors='k', cmap=plt.cm.coolwarm)\n",
    "    plt.xlabel('Feature 1')\n",
    "    plt.ylabel('Feature 2')\n",
    "    plt.title('SVM Decision Boundary')\n",
    "    plt.show()\n",
    "\n",
    "# Plotting decision boundary\n",
    "plot_decision_boundary(svm_classifier, X_train, y_train)\n"
   ]
  }
 ],
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     "sourceType": "datasetVersion"
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