huquach_pca.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "4881ab2d-bea5-49fd-b2d6-7b1bd4b9e369",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb23c8c-66e9-49d0-9d1b-56f67bab9b0e",
   "metadata": {},
   "source": [
    "### Problem 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8303e276-0358-4217-8031-3d0e9d30cdb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = np.random.multivariate_normal([0,0], [[1,0],[0,1]], 100)\n",
    "data2 = np.random.multivariate_normal([0,0], [[1,-.9],[-.9,1]], 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "513ba310-12e8-4fd3-8fb4-45f0e97c1839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n",
    "ax1.scatter(data1[:, 0], data1[:, 1], s=30, alpha=0.7)\n",
    "ax1.set_title(\"Data 1: No Correlation\")\n",
    "ax2.scatter(data2[:, 0], data2[:, 1], s=30, alpha=0.7, color='red')\n",
    "ax2.set_title(\"Data 2: Negative Correlation\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed71e2e1-ea08-4878-8a53-e70eceec18b5",
   "metadata": {},
   "source": [
    "Data 1:\n",
    "- The scatterplot shows no clear pattern or trend.\n",
    "- Points are scattered randomly without any discernible relationship between X and Y.\n",
    "- This suggests no correlation between the variables.\n",
    "\n",
    "Data 2:\n",
    "- The scatterplot shows a downward-sloping pattern.\n",
    "- As X values increase, Y values tend to decrease.\n",
    "- This indicates a negative correlation between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ce88766d-059f-4098-87de-433852913133",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of total variability explained by the first principal component is 54%\n"
     ]
    }
   ],
   "source": [
    "pca1 = PCA(n_components = 1)\n",
    "princ1 = pca1.fit(data1)\n",
    "print(\"The percentage of total variability explained by the first principal component is\", \"{:.00%}\".format(princ1.explained_variance_ratio_[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bda08f2-f08b-4763-b4ec-47e2520a584d",
   "metadata": {},
   "source": [
    "The first principal component captures the direction of maximum variance in the data.\n",
    "- For data1, with no dominant direction of spread, PC1 might not explain a large portion of the total variance, as points are distributed equally in all directions.\n",
    "- For data2, where points are concentrated along a diagonal direction due to the negative correlation, PC1 is likely to align with this direction and capture a significant portion of the total variance.\n",
    "\n",
    "Therefore, we can predict that data2 has a **higher percentage** of total variability explained by its first principal component compared to data1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "1faa4e87-036b-40f7-abc9-5ab685c7f417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of total variability explained by the first principal component is 96%\n"
     ]
    }
   ],
   "source": [
    "pca2 = PCA(n_components = 1)\n",
    "princ2 = pca2.fit(data2)\n",
    "print(\"The percentage of total variability explained by the first principal component is\", \"{:.00%}\".format(princ2.explained_variance_ratio_[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "b29868a8-df9c-4202-bf2a-83fb8a7119e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.72327683,  0.6905582 ]])"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca2.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54d7db04-ec17-43bc-9256-623b2705eb3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def line(point1, point2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "479c6db8-afea-493e-a487-baf36746b4dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first component for data 2 is [-0.72327683  0.6905582 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_pc = pca2.components_[0]\n",
    "fig, ax = plt.subplots()\n",
    "print(\"The first component for data 2 is\", first_pc)\n",
    "plt.scatter(data2[:, 0], data2[:, 1], s=30, alpha=0.7)\n",
    "plt.plot([-first_pc[0]*5, first_pc[0]*5], [-first_pc[1]*5, first_pc[1]*5], color='g', label='Line Containing First Principal Component')\n",
    "plt.title(\"Data2 Scatterplot with First Principal Component\")\n",
    "ax.set_aspect('equal')\n",
    "ax.quiver(0, 0, first_pc[0], first_pc[1], angles='xy', scale_units='xy', scale=1, color='r', label = 'First Principal Component Vector', zorder=10)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "6c6d3c7f-2711-4b54-a055-f7c2e3d80690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.72327683,  0.6905582 ])"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_pc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dccdb6a0-1a70-4284-8dc5-b8ef4961972c",
   "metadata": {},
   "source": [
    "### Problem 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "76052119-5d3f-434e-9b89-0ca212b350ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "data3 = pd.read_csv(\"imdb-dataset.csv\")\n",
    "rev = data3.loc[:,'review']\n",
    "sent = data3.loc[:, 'sentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "fb5e5133-35b6-41fa-b2ee-a4a70d0e50c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import string\n",
    "from bs4 import BeautifulSoup\n",
    "import nltk \n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "87ce0ab2-c096-4780-bd57-cb0c657e87f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    # Remove HTML tags\n",
    "    soup = BeautifulSoup(text, \"html.parser\")\n",
    "    text = soup.get_text()\n",
    "    # Remove URLs\n",
    "    text = re.sub(r\"http\\S+\", \"\", text)\n",
    "    # Remove stop words\n",
    "    stop_words = set(stopwords.words(\"english\"))\n",
    "    # Remove punctuation and special characters\n",
    "    text = re.sub(r\"[^\\w\\s]\", \" \", text)\n",
    "    # Remove numbers\n",
    "    text = re.sub(r\"\\d+\", \"\", text)\n",
    "    # Convert to lowercase\n",
    "    text = text.lower()\n",
    "    # Tokenize the text\n",
    "    words = nltk.word_tokenize(text)\n",
    "    # Remove stop words\n",
    "    stop_words = set(nltk.corpus.stopwords.words('english'))\n",
    "    words = [word for word in words if word not in stop_words]\n",
    "    # Apply lemmatization\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    lemmatized_words = [lemmatizer.lemmatize(word) for word in words]\n",
    "    # Join the processed words back into a string\n",
    "    clean_text = ' '.join(lemmatized_words)\n",
    "    return clean_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "28be42aa-c6ed-4901-a8b7-cd187f9bbe0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9j/63cw8wmd6qg8x_p0ffm9yqk00000gn/T/ipykernel_17149/496528003.py:3: MarkupResemblesLocatorWarning: The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.\n",
      "  soup = BeautifulSoup(text, \"html.parser\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review  37306:  ending made heart jump throat proceeded leave movie theater little jittery nearly midnight movie better expected know last long theater make much money anticipated definitely would recommend\n",
      "\n",
      "Review  22629:  tried rent many occasion always girlfriend general rule usually reject heist flick ensemble comedy comment uhm look good mood movie thus entereth almighty solo movie night anyway found welcome collinwood rather enjoyable movie ultimately fairly forgettable moment fun laugh loud moment unfamiliar fact remake general rule watch movie trying ignore fact watch merit anyway george clooney put humorous brief cameo wheeled safe cracker part left wondering two thing every comedy better mr clooney put strange minute cameo make fake tattoo look old faded easily wash cast fine actor right put great job get impression good time working together fairly important movie like needle say go planned movie plot wise humor wise made check special feature consider watching original consider success rent one good time maybe grab friend pizza good time side note soundtrack spectacular great hear far appreciated paolo conte used left humming snippet score long credit rolled\n",
      "\n",
      "Review  31092:  man named walt disney mission satisfy family child world one thing entertainment made cartoon whoo made character great mouse mickey mouse ha ha oh boy two film drawn mr disney plane crazy steamboat willie review focus feature steamboat willie ever since learned movie little boy always wanted see movie well rented ancient vhs lot old disney cartoon starring mouse mickey mouse guess short loved shortly took tape back playing old disney channel note old disney channel early one saturday morning yes great cartoon paved way great disney stuff star indeed\n",
      "\n",
      "Review  209:  film well cast often silly always funny lemmon matthau work tag team magic perfection brent spiner riot egotistical tyrant cruise director first hare krishna last ought pay fifty buck calling two stud thought totally entertaining fun comedy\n",
      "\n",
      "Review  25440:  film suck big one many hole plot devil invincible require protection arnies bodyguard fathom arnie turn work several day suddenly appears take entire armoury disappears nice work get sad last hour result standard arnie uzi mm finale arnies interpretation depressed cop bow head sniff thought bad cold film dreadfully scripted arnie called buddy dude etc film suddenly hear called real name jericho cane get name ending twee better get barf bag ready shame gladiator pulled ending class\n",
      "\n",
      "Review  47254:  dutiful wife norma shearer katherine kitty brown wait husband rod la rocque bob brown hand foot making breakfast bed helping dress sunday golf outing m shearer suggests joining mr la rocque day noting infrequently two see la rocque put saying presence adversely affect game unexpectedly shearer meet real reason husband frequent absence pretty blonde mistress three year later shearer glamorous flirty divorcée summering paris struck friendship wealthy older socialite marie dressler mr boucci bouccicault m dressler invite shearer long island home socialize friend ask favor dressler worried granddaughter relationship suave worldly man want young sally eilers dionne marry raymond hackett bruce instead aware shearer flirtatious conquest dressler asks lure undesirable man away m eilers shearer stunned discover man la rocque ex husband shearer dressler make cute entertaining play top form giving guaranteed popular performance enthusiasm professionalism story silly predictable yet way help humorous situation ending quite clever fact comic let u gay may aged better shearer larger produced serious divorcée released around time cast uniformly fine la rocque better film lilian gish role endearing gilbert emery towney tyrell davis wallace funny supporting suitor familiar norma shearer may realize appears dowdy wife opening scene shearer kitty make watch close ups shearer light natural make good look intriguingly beautiful woman let u gay robert z leonard norma shearer marie dressler rod la rocque\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cleaned_text = np.vectorize(clean_text)(rev)\n",
    "for index in indices:\n",
    "    content = cleaned_text[index]\n",
    "    print(\"Review \", index, end=\"\")\n",
    "    print(\": \", content)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "0cebb5f2-cb81-4f12-b3f0-7dfcce9aac7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 92331 columns in the term frequency count vector\n"
     ]
    }
   ],
   "source": [
    "mycount = CountVectorizer()\n",
    "bag = mycount.fit_transform(cleaned_text)\n",
    "print(\"There are %d columns in the term frequency count vector\" %(bag.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "88a33aa8-db7e-4d9f-9585-7fa6c118a3bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfTransformer()\n",
    "bagX = tfidf.fit_transform(bag)\n",
    "X_train = bagX[:25000]\n",
    "y_train = sent[:25000].to_numpy()\n",
    "X_test = bagX[25000:]\n",
    "y_test = sent[25000:].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "c769c033-d32e-47f8-978c-7cd54808a851",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score using Logistic Regression is 89%\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "lgModel = logreg.fit(X_train, y_train)\n",
    "predictions = lgModel.predict(X_test)\n",
    "print(\"The accuracy score using Logistic Regression is\", \"{:.00%}\".format(accuracy_score(predictions, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa59f169-04b1-4a4c-8cc8-3d07c744f488",
   "metadata": {},
   "source": [
    "### Problem 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "c2bfa199-5365-43c8-88d9-567ffb0a3aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ucimlrepo import fetch_ucirepo \n",
    "  \n",
    "# fetch dataset \n",
    "breast_cancer_wisconsin_diagnostic = fetch_ucirepo(id=17) \n",
    "  \n",
    "# data (as pandas dataframes) \n",
    "data4X = breast_cancer_wisconsin_diagnostic.data.features \n",
    "data4y = breast_cancer_wisconsin_diagnostic.data.targets \n",
    "  \n",
    "# metadata \n",
    "# print(breast_cancer_wisconsin_diagnostic.metadata) \n",
    "  \n",
    "# variable information \n",
    "# print(breast_cancer_wisconsin_diagnostic.variables) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "6e73ee06-53f6-4a06-bcfe-f374f84767b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius1</th>\n",
       "      <th>texture1</th>\n",
       "      <th>perimeter1</th>\n",
       "      <th>area1</th>\n",
       "      <th>smoothness1</th>\n",
       "      <th>compactness1</th>\n",
       "      <th>concavity1</th>\n",
       "      <th>concave_points1</th>\n",
       "      <th>symmetry1</th>\n",
       "      <th>fractal_dimension1</th>\n",
       "      <th>...</th>\n",
       "      <th>radius3</th>\n",
       "      <th>texture3</th>\n",
       "      <th>perimeter3</th>\n",
       "      <th>area3</th>\n",
       "      <th>smoothness3</th>\n",
       "      <th>compactness3</th>\n",
       "      <th>concavity3</th>\n",
       "      <th>concave_points3</th>\n",
       "      <th>symmetry3</th>\n",
       "      <th>fractal_dimension3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.71190</td>\n",
       "      <td>0.26540</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.24160</td>\n",
       "      <td>0.18600</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.45040</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.68690</td>\n",
       "      <td>0.25750</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>0.16250</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>19.59</td>\n",
       "      <td>25.00</td>\n",
       "      <td>127.70</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>0.10320</td>\n",
       "      <td>0.09871</td>\n",
       "      <td>0.16550</td>\n",
       "      <td>0.09063</td>\n",
       "      <td>0.1663</td>\n",
       "      <td>0.05391</td>\n",
       "      <td>...</td>\n",
       "      <td>21.44</td>\n",
       "      <td>30.96</td>\n",
       "      <td>139.80</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>0.1528</td>\n",
       "      <td>0.1845</td>\n",
       "      <td>0.39770</td>\n",
       "      <td>0.14660</td>\n",
       "      <td>0.2293</td>\n",
       "      <td>0.06091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>12.00</td>\n",
       "      <td>28.23</td>\n",
       "      <td>76.77</td>\n",
       "      <td>442.5</td>\n",
       "      <td>0.08437</td>\n",
       "      <td>0.06450</td>\n",
       "      <td>0.04055</td>\n",
       "      <td>0.01945</td>\n",
       "      <td>0.1615</td>\n",
       "      <td>0.06104</td>\n",
       "      <td>...</td>\n",
       "      <td>13.09</td>\n",
       "      <td>37.88</td>\n",
       "      <td>85.07</td>\n",
       "      <td>523.7</td>\n",
       "      <td>0.1208</td>\n",
       "      <td>0.1856</td>\n",
       "      <td>0.18110</td>\n",
       "      <td>0.07116</td>\n",
       "      <td>0.2447</td>\n",
       "      <td>0.08194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>14.53</td>\n",
       "      <td>13.98</td>\n",
       "      <td>93.86</td>\n",
       "      <td>644.2</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.09242</td>\n",
       "      <td>0.06895</td>\n",
       "      <td>0.06495</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>0.06121</td>\n",
       "      <td>...</td>\n",
       "      <td>15.80</td>\n",
       "      <td>16.93</td>\n",
       "      <td>103.10</td>\n",
       "      <td>749.9</td>\n",
       "      <td>0.1347</td>\n",
       "      <td>0.1478</td>\n",
       "      <td>0.13730</td>\n",
       "      <td>0.10690</td>\n",
       "      <td>0.2606</td>\n",
       "      <td>0.07810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>12.62</td>\n",
       "      <td>17.15</td>\n",
       "      <td>80.62</td>\n",
       "      <td>492.9</td>\n",
       "      <td>0.08583</td>\n",
       "      <td>0.05430</td>\n",
       "      <td>0.02966</td>\n",
       "      <td>0.02272</td>\n",
       "      <td>0.1799</td>\n",
       "      <td>0.05826</td>\n",
       "      <td>...</td>\n",
       "      <td>14.34</td>\n",
       "      <td>22.15</td>\n",
       "      <td>91.62</td>\n",
       "      <td>633.5</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.1517</td>\n",
       "      <td>0.18870</td>\n",
       "      <td>0.09851</td>\n",
       "      <td>0.3270</td>\n",
       "      <td>0.07330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>13.38</td>\n",
       "      <td>30.72</td>\n",
       "      <td>86.34</td>\n",
       "      <td>557.2</td>\n",
       "      <td>0.09245</td>\n",
       "      <td>0.07426</td>\n",
       "      <td>0.02819</td>\n",
       "      <td>0.03264</td>\n",
       "      <td>0.1375</td>\n",
       "      <td>0.06016</td>\n",
       "      <td>...</td>\n",
       "      <td>15.05</td>\n",
       "      <td>41.61</td>\n",
       "      <td>96.69</td>\n",
       "      <td>705.6</td>\n",
       "      <td>0.1172</td>\n",
       "      <td>0.1421</td>\n",
       "      <td>0.07003</td>\n",
       "      <td>0.07763</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>0.07675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>456 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     radius1  texture1  perimeter1   area1  smoothness1  compactness1  \\\n",
       "0      17.99     10.38      122.80  1001.0      0.11840       0.27760   \n",
       "1      20.57     17.77      132.90  1326.0      0.08474       0.07864   \n",
       "2      19.69     21.25      130.00  1203.0      0.10960       0.15990   \n",
       "3      11.42     20.38       77.58   386.1      0.14250       0.28390   \n",
       "4      20.29     14.34      135.10  1297.0      0.10030       0.13280   \n",
       "..       ...       ...         ...     ...          ...           ...   \n",
       "451    19.59     25.00      127.70  1191.0      0.10320       0.09871   \n",
       "452    12.00     28.23       76.77   442.5      0.08437       0.06450   \n",
       "453    14.53     13.98       93.86   644.2      0.10990       0.09242   \n",
       "454    12.62     17.15       80.62   492.9      0.08583       0.05430   \n",
       "455    13.38     30.72       86.34   557.2      0.09245       0.07426   \n",
       "\n",
       "     concavity1  concave_points1  symmetry1  fractal_dimension1  ...  radius3  \\\n",
       "0       0.30010          0.14710     0.2419             0.07871  ...    25.38   \n",
       "1       0.08690          0.07017     0.1812             0.05667  ...    24.99   \n",
       "2       0.19740          0.12790     0.2069             0.05999  ...    23.57   \n",
       "3       0.24140          0.10520     0.2597             0.09744  ...    14.91   \n",
       "4       0.19800          0.10430     0.1809             0.05883  ...    22.54   \n",
       "..          ...              ...        ...                 ...  ...      ...   \n",
       "451     0.16550          0.09063     0.1663             0.05391  ...    21.44   \n",
       "452     0.04055          0.01945     0.1615             0.06104  ...    13.09   \n",
       "453     0.06895          0.06495     0.1650             0.06121  ...    15.80   \n",
       "454     0.02966          0.02272     0.1799             0.05826  ...    14.34   \n",
       "455     0.02819          0.03264     0.1375             0.06016  ...    15.05   \n",
       "\n",
       "     texture3  perimeter3   area3  smoothness3  compactness3  concavity3  \\\n",
       "0       17.33      184.60  2019.0       0.1622        0.6656     0.71190   \n",
       "1       23.41      158.80  1956.0       0.1238        0.1866     0.24160   \n",
       "2       25.53      152.50  1709.0       0.1444        0.4245     0.45040   \n",
       "3       26.50       98.87   567.7       0.2098        0.8663     0.68690   \n",
       "4       16.67      152.20  1575.0       0.1374        0.2050     0.40000   \n",
       "..        ...         ...     ...          ...           ...         ...   \n",
       "451     30.96      139.80  1421.0       0.1528        0.1845     0.39770   \n",
       "452     37.88       85.07   523.7       0.1208        0.1856     0.18110   \n",
       "453     16.93      103.10   749.9       0.1347        0.1478     0.13730   \n",
       "454     22.15       91.62   633.5       0.1225        0.1517     0.18870   \n",
       "455     41.61       96.69   705.6       0.1172        0.1421     0.07003   \n",
       "\n",
       "     concave_points3  symmetry3  fractal_dimension3  \n",
       "0            0.26540     0.4601             0.11890  \n",
       "1            0.18600     0.2750             0.08902  \n",
       "2            0.24300     0.3613             0.08758  \n",
       "3            0.25750     0.6638             0.17300  \n",
       "4            0.16250     0.2364             0.07678  \n",
       "..               ...        ...                 ...  \n",
       "451          0.14660     0.2293             0.06091  \n",
       "452          0.07116     0.2447             0.08194  \n",
       "453          0.10690     0.2606             0.07810  \n",
       "454          0.09851     0.3270             0.07330  \n",
       "455          0.07763     0.2196             0.07675  \n",
       "\n",
       "[456 rows x 30 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX = data4X.loc[:455,:]\n",
    "trainy = data4y.loc[:455, 'Diagnosis'].T\n",
    "testX = data4X.loc[456:,:]\n",
    "testy = data4y.loc[456:, 'Diagnosis'].T\n",
    "trainX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4e88cd6e-ff79-4521-85f8-187249c75edd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for 5-Nearest Neightbor Model is 0.9380530973451328\n"
     ]
    }
   ],
   "source": [
    "neighModel = KNeighborsClassifier(n_neighbors=5)\n",
    "neighModel.fit(trainX, trainy)\n",
    "print(\"The accuracy score for 5-Nearest Neightbor Model is\", neighModel.score(testX, testy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "6a7f73a2-b175-4a62-82c9-2dd380f398d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98204467, 0.99822116, 0.99977867, 0.9998996 , 0.99998788,\n",
       "       0.99999453, 0.99999854, 0.99999936, 0.99999971, 0.99999989,\n",
       "       0.99999996, 0.99999998, 0.99999999, 0.99999999, 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ])"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Without Normalization\n",
    "pcaModel = PCA(n_components = 30)\n",
    "prinComp = pcaModel.fit(data4X)\n",
    "cumPercentage1 = np.cumsum(prinComp.explained_variance_ratio_)\n",
    "cumPercentage1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "ea48ad0d-2bc7-4f39-a54d-470a50f6820d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.76709045, 0.94500855, 0.98475325, 0.99688563, 0.99877802,\n",
       "       0.99939466, 0.99986228, 0.99993073, 0.99996344, 0.99998911,\n",
       "       0.99999581, 0.99999768, 0.99999856, 0.99999915, 0.99999939,\n",
       "       0.99999958, 0.99999971, 0.99999982, 0.99999989, 0.99999992,\n",
       "       0.99999995, 0.99999997, 0.99999998, 0.99999999, 0.99999999,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With Normalization using Normalizer\n",
    "transformer = Normalizer().fit(data4X)\n",
    "newX = transformer.transform(data4X)\n",
    "normalizedPCA = PCA(n_components = 30)\n",
    "normalizedComp = normalizedPCA.fit(newX)\n",
    "cumPercentage2 = np.cumsum(normalizedComp.explained_variance_ratio_)\n",
    "cumPercentage2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "f26fe06d-7f88-4a0e-8d1c-51e67bfa6c13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of principal components necessary to explain 90% of the total variability is 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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QjRo1EgDETz/9pLb92n4+PD091Y4VIfI/35aWlmLixIlS2cyZMwUAsWnTpgKxqc65Rf09x4wZo1a/S5cuwsLCQty7d08qf/6zmpOTI3x9fUXbtm3Vyp//bGnSo0cPAUA8evSoyHoqqs/5gwcPpLIzZ84IExMTMWDAAKnMUN/R7u7uQqFQSOWqz+ySJUukbXdychK+vr4iMzNTqrd9+3YBQMycOVMqU+3j2bNnq21To0aN1L47f//9dwFAfPfdd2r1du3aVaBc2+OisPOaoc45pbZFUKFQAADs7e1LbB3/93//J/1uamqKgIAACCEwZMgQqbxChQqoXbs2/v777wLzDxgwQK3/WGBgIIQQBZplAwMDcevWLYPdsflsP6G0tDTcv38frVq1QkZGBi5duqTXMsPDw3Hy5EnpP18AiI2NhaWlJbp37w4AiI+Px5UrV9CvXz88ePBAaslLT09Hu3btcOjQIa0uZQ4bNgx79uwp8HrttdekOjY2NlizZg0uXryI1q1bY8eOHfj000/h4eFRYHlBQUHw9/eX3nt4eKB79+7YvXt3oZcmgeLvx/DwcKmlFMhvuQEgHSsPHz7Evn370Lt3b2n59+/fx4MHDxAaGoorV65Ilx927tyJZs2aoWnTptLyqlSponW/vooVK6Jz587YunUr0tPTAQBCCGzYsAEBAQGoVatWgW1+8uQJHjx4gJo1a6JChQpS6+uzhg4dqlV/J132pZmZGYYPHy69t7CwwPDhw5GcnIyTJ09qtb26MjExQf/+/bF161akpaVJ5d999x2aN28Ob2/vQueVyWTo1asXdu7cicePH0vlsbGxcHd3R8uWLQE87YO2fft2jZegi6N169Z4/fXXDd4q+Ow5UHWus7W1Re/evaXy2rVro0KFChrPgcOGDVM7B7777rswMzPDzp07AQB79uxBSkoK+vbtq9b6b2pqisDAQOzfv7/AMt99990Xxp2Xl4dff/0VYWFhqF69ulTu6uqKfv364Y8//pC+Q/QRFRWFKlWqwMXFBW3atMG1a9cwf/589OzZU62etp8PAHjttdekcwSQ//l+/rvlp59+QsOGDdGjR48C8z8/dJImz94Mo+o2k5OTg99++00qf/az+ujRI6SmpqJVq1YaP/8vosv39N27dxEfH4+BAweiUqVKUnmDBg3Qvn176Zh5liG+o5+N7a233oKrq6u0rhMnTiA5ORkjR45U63fZpUsX1KlTR2Mr6fNXpFq1aqW27o0bN0Iul6N9+/Zqx7y/vz/s7OwKHPPaHBeFMdQ5p9Qmgg4ODgCgdtI2tOeTCtVt746OjgXKNfV70TQ/kN9n6/lypVKJ1NRUQ4SNCxcuoEePHpDL5XBwcECVKlWkS1v6rqNXr14wMTFBbGwsgPwkYuPGjVL/GyB/LC4gfyiGKlWqqL2+/PJLZGdna7V+Hx8fhISEFHip1qPSokULvPvuuzh27BhCQ0ML7ffg4+NToKxWrVrIyMiQOoprUtz9+PzfX5UUqo6Vq1evQgiBGTNmFNhfqss1ycnJAIB//vlH43YU1Xftef3790d6ejq2bNkCADh8+DBu3LihlkxmZmZi5syZqFatGiwtLeHo6IgqVaogJSVF4zYXlSA9S5d96ebmVuAGAFWiWpLDvgwYMACZmZnSJfmEhAScPHlSq8ta4eHhyMzMlC6FPX78GDt37kSvXr2kL+jg4GC8+eab+PDDD+Ho6Iju3btj9erVav2Di2PWrFlITEws0J9SX1ZWVqhSpYpamVwuR9WqVQskHYWdA58/Zu3s7ODq6ir9HVXnjLZt2xb4DPz666/S8a9iZmYm9bUryr1795CRkaHx81G3bl0olUqNfcG0pfpnde/evTh58iSSk5MxefLkAvW0/XwABc8XQP4549n9eu3aNa3G2dTExMRELSkGNH+utm/fjmbNmsHKygqVKlVClSpVsGLFCr2+O3T5nlb1QS/sb6ZqVHhWcb+jnz8+ZTIZatasKe2PomKqU6dOgX7zmj4zz/8Nr1y5gtTUVDg5ORU45h8/flzgmNfmuCiMoc45pfauYQcHB7i5ub2wn5dKYf8tFdUipOk/ucL+uxMaxmgrrO6LlqFPrCopKSkIDg6Gg4MDZs+ejRo1asDKygqnTp3C+++/r/cwJ25ubmjVqhV++OEHTJs2DUeOHMHNmzfV+jaqlr1gwYJCh1Cws7PTa/2aZGdnSx1ir127hoyMDNjY2Bhk2YbYjy/6O6uW8d577yE0NFRj3Zo1a+q5BQV17doVcrkc69evR79+/bB+/XqYmpqiT58+Up0xY8Zg9erVGD9+PIKCgqQBdvv06aNxm7W5S7WkjklDe+211+Dv749vv/0WAwYMwLfffgsLCwu11q/CNGvWDF5eXvjhhx/Qr18/bNu2DZmZmQgPD5fqyGQy/Pjjjzhy5Ai2bduG3bt3Y/DgwVi0aBGOHDlS7M9G69at0aZNG8TExBRolVCtX5PCziv6nr90ofrbr1u3Di4uLgWmPz+ckaWlpdpwQMai+mf1RXS5i9uQ+1Vfv//+O9544w20bt0a//vf/+Dq6gpzc3OsXr260BuCilKnTh0A+X2Rn23VMpTifkcbmjatv0qlEk5OTvjuu+80Tn8+kSzO9hjqnFNqE0Eg/4tt1apViIuLQ1BQUJF1Va0xz3fu1HQnrLEVJ9YDBw7gwYMH2LRpk9pQIZo6cmtzKeFZ4eHhGDlyJBISEhAbGwsbGxt069ZNml6jRg0A+Um6NifJ4oqKisLFixexcOFCvP/++5gyZQqWLl1aoJ6q1eFZly9fho2NTYEPnYou+1Ffqv/Ozc3NX7i/PD09NW5HQkKC1uuztLTEW2+9hW+++QZJSUnYuHEj2rZtq/YF/OOPPyIiIkLtLrusrKwCx6IudN2Xd+7cKTAsiGrQ2eKOkfWiY37AgAGIjIzE3bt3sX79enTp0kXt8n5RevfujSVLlkChUCA2NhZeXl5o1qxZgXrNmjVDs2bN8PHHH2P9+vXo378/NmzYoHaZS1+zZs1CmzZtNA5hYoxz4JUrV/D6669L7x8/foy7d+9K44SqzhlOTk4GPWdUqVIFNjY2Gj8fly5dgomJSYErM2VBjRo1tG78eJ5SqcTff/8ttQICBT9XP/30E6ysrLB79261oZdWr16t1zq7deuG6OhofPvtty9MBFV3bxf2N3N0dCz2UEHPe/6cKoTA1atX0aBBgwIxqe4MV0lISJCm66JGjRr47bff0KJFC4MN9/Si81pxzznG/9erCJMnT4atrS3+7//+D0lJSQWmX7t2Tbqr0sHBAY6OjgXuHPrf//73UmLVRXFiVf338Ox/Czk5ORrntbW11am5/80334SpqSm+//57bNy4EV27dlX7YPr7+6NGjRpYuHChWl8plaIuw+rq6NGjWLhwIcaPH4+JEydi0qRJ+Oyzz3Dw4MECdePi4tT6t9y6dQtbtmxBhw4dXtjqoc1+1JeTk5P0pX337t0C05/dX507d8aRI0fU7ry+d+9eof9VFqZ///548uQJhg8fjnv37hXoY2hqalrgP81ly5Zp1RpdGF33ZW5urloik5OTg88//xxVqlRR6+upD9XxWlhi27dvX8hkMowbNw5///23TuNZhoeHIzs7G2vXrsWuXbsKtCQ+evSowL5VtZw/e6nm2rVran1xdREcHIw2bdpg/vz5yMrKUpvm6ekJU1PTl3oOXLVqlVrfpBUrViA3NxedOnUCAISGhsLBwQFz587V2IdJ33OGqakpOnTogC1btqhd9kxKSsL69evRsmXLAl1NyoI333wTZ86cURtRQEWbFqLPPvtMrf5nn30Gc3NztGvXDkD+fpPJZGqf9xs3buj9yL2goCB07NgRX375pcZl5OTk4L333gOQ33/Tz88Pa9euVft8nj9/Hr/++qvGhwwU1zfffKN22frHH3/E3bt3peMzICAATk5OWLlypdpn9JdffsHFixfRpUsXndfZu3dv5OXl4aOPPiowLTc3V69/ugs7r2l7znmRUt0iWKNGDaxfvx7h4eGoW7eu2pNFDh8+jI0bN6o9C/H//u//MG/ePPzf//0fAgICcOjQoVL7eBt9Y23evDkqVqyIiIgIjB07FjKZDOvWrdN4kvD390dsbCwiIyPRpEkT2NnZqbXwPc/JyQmvv/46PvnkE6Slpald9gLy+6B8+eWX6NSpE+rVq4dBgwbB3d0dt2/fxv79++Hg4IBt27a9cBtOnTqFb7/9tkB5jRo1EBQUhKysLERERMDHxwcff/wxAODDDz/Etm3bMGjQIJw7d04tQfX19UVoaCjGjh0LS0tL6Yvvww8/LDQGXfZjcSxfvhwtW7ZE/fr1MXToUFSvXh1JSUmIi4vDv//+izNnzgDI/6dn3bp16NixI8aNGwdbW1usWrUKnp6eOHv2rNbrCw4ORtWqVbFlyxZYW1sX6NzetWtXrFu3DnK5HK+99hri4uLw22+/FRgWQxe67ks3NzfMnz8fN27cQK1atRAbG4v4+HisWrWq2IN3+/n5wdTUFPPnz0dqaiosLS3Rtm1bODk5AchvSerYsSM2btyIChUq6HSib9y4MWrWrIkPPvgA2dnZBT4fa9euxf/+9z/06NEDNWrUQFpaGr744gs4ODiofcmpvpT17Q8ZFRWl1gqnIpfL0atXLyxbtgwymQw1atTA9u3bC/RJMqScnBy0a9cOvXv3RkJCAv73v/+hZcuWeOONNwDk/9O7YsUKvPPOO2jcuDH69OmDKlWq4ObNm9ixYwdatGihlrzoYs6cOdizZw9atmyJkSNHwszMDJ9//jmys7MRExNjyM18aSZNmoQff/wRvXr1wuDBg+Hv74+HDx9i69atWLlyJRo2bFjovFZWVti1axciIiIQGBiIX375BTt27MC0adOkKyNdunTBJ598go4dO6Jfv35ITk7G8uXLUbNmTZ3OM8/65ptv0KFDB/Ts2RPdunVDu3btYGtriytXrmDDhg24e/euNJbgggUL0KlTJwQFBWHIkCHIzMzEsmXLIJfLXzh+rj4qVaqEli1bYtCgQUhKSsLixYtRs2ZNDB06FED+1Zr58+dj0KBBCA4ORt++fZGUlIQlS5bAy8sLEyZM0HmdwcHBGD58OKKjoxEfH48OHTrA3NwcV65cwcaNG7FkyRK89dZbOi2zsPPa+vXrtTrnvFCx7jl+SS5fviyGDh0qvLy8hIWFhbC3txctWrQQy5YtUxuaIiMjQwwZMkTI5XJhb28vevfuLZKTkwsdPubZW+qFyL893NbWtsD6g4ODRb169aT3qlvTN27cqFZPNWzA87dya1qftrFqGorgzz//FM2aNRPW1tbCzc1NTJ48WezevbvA7eWPHz8W/fr1ExUqVBAApCFQihq+5osvvhAAhL29vdrt9M86ffq06Nmzp6hcubKwtLQUnp6eonfv3mLv3r0a66u8aPgY1e3/EyZMEKampmpDqQghxIkTJ4SZmZl49913pTIAYtSoUeLbb78VPj4+wtLSUjRq1KjAbfbF2Y+FDR+jaTiK5/9+Qghx7do1MWDAAOHi4iLMzc2Fu7u76Nq1q/jxxx/V6p09e1YEBwcLKysr4e7uLj766CPx1VdfaTV8zLMmTZokAIjevXsXmPbo0SMxaNAg4ejoKOzs7ERoaKi4dOlSgaEiCjuWn52mz75UfZZOnDghgoKChJWVlfD09BSfffaZ2jr0HT5GiPxjuHr16sLU1FTjkAuqISSeHZpCWx988IEAIGrWrFlg2qlTp0Tfvn2Fh4eHsLS0FE5OTqJr165qQxupYtY0rNPzCjtPCfF0GJ5nh48RQoh79+6JN998U9jY2IiKFSuK4cOHi/Pnz2scbkSbc92zMT+7LtUxcPDgQTFs2DBRsWJFYWdnJ/r37682NIjK/v37RWhoqJDL5cLKykrUqFFDDBw4UG3fFBZTUU6dOiVCQ0OFnZ2dsLGxEa+//ro4fPiwWh19ho95UV1dPx/P7z+V4OBgERwcrFb24MEDMXr0aOHu7i4sLCxE1apVRUREhLh//75ajJr+nteuXRMdOnQQNjY2wtnZWURFRYm8vDy15X/11VfSubJOnTpi9erVWn+2CpORkSEWLlwomjRpIuzs7ISFhYXw8fERY8aMEVevXlWr+9tvv4kWLVoIa2tr4eDgILp16yb++usvtTqG+o7+/vvvxdSpU4WTk5OwtrYWXbp0KTAEmRBCxMbGikaNGglLS0tRqVIl0b9/f/Hvv/9qtW5N+04IIVatWiX8/f2FtbW1sLe3F/Xr1xeTJ08Wd+7ckeroclxoOq9pe855EZkQL7GnKpGByWQyjBo1Su9WBSp/tmzZgrCwMBw6dKhEOrgTvWwDBw7Ejz/+qLHLTnl04MABvP7669i4caPOrW/lUanuI0hEZGhffPEFqlevLo3/R0RUnpXqPoJERIayYcMGnD17Fjt27MCSJUt0vqueiOhVxESQiMqFvn37ws7ODkOGDMHIkSONHQ4RUanAPoJERERE5RT7CBIRERGVU0wEiYiIiMop9hEsQUqlEnfu3IG9vT07phMREZURQgikpaXBzc2tVDz/uiQxESxBd+7cKZPPuyQiIqL8R5ZWrVrV2GGUKCaCJcje3h5A/oFUFp97SUREVB4pFApUq1ZN+h5/lTERLEGqy8EODg5MBImIiMqY8tCt69W+8E1EREREhWIiSERERFROMREkIiIiKqeYCBIRERGVU0wEiYiIiMopJoJERERE5RQTQSIiIqJyiokgERERUTnFRJCIiIionGIiSERERFROMREk0sLZf1PQd9URnP035aXPb8x1F3d+rpvrLivzc93la930FBNBKjeKc9LYdOo24v5+gE2nbuu17uLMb8x1F3d+rpvrLivzc93la930lJmxAyDS1tl/UxC98xKmdq6DBlUr6Dz/sycNbeb/91EGHqU/gUwGbDtzB0D+z7f8q0IIoKKtOapWtCl0/lsPM/AwPUeaT/WzW0PX/+a3gJvcGgICQgDiv/mEELidkimte2t8/rxbz9xGh9ecoQRQwdocrnKrZ+YBhOqdAO6kZiIl4wkAYEv8belnm9pVIISA3MYcrnJrad7n3U3NRGrmE8ggU5u/da0qEBCoYG0Ol//mf15iaiZS9Jy3uPO/iutu5eMIAfw3v1Uh82b9Ny90nre48xtz3WU5dq7bcOvW5bxMBcmE0PQ1QIagUCggl8uRmpoKBwcHY4dT5s3aegFrDt/AwOZemPVGvRfWz1MKXE5Kw+2UTGQ+ycMHP5+DIjMX9pZm+L9W3sjMVcIEgLmpCTKf5CEjJxcZOXnIzMlDRk4eDl6+98J1WJubQinyUzAh8hM6pRBQ8lNFRGQUN+Z1KfYyytP3N1sE6aXStVVP1SqXnp2LTaf+BQBsPHELT/KUSM/ORZ5SQAkgLSsXaVlP8DgrV/o9PSdP4zLTsnPx6W9XDLI9mU80r8NYZDJABqi1MGpiKgNMTGSQQaZhIflJdF4R2aypiQymJhrmxYvnNStiXtX8uXrOX5x5ue6yt+7izs91v1rrNjORYWGvhoXOS5oxEaSX6kWXZ7Nz83A1+TESEtOQkJiGzw/9XaBOek4evjt6s9ixyAD4e1ZELRd72FqYwtrCDDYWprCxMIW1uSlsLMyQnJaFD7f9VWDeVe/4o7aLPUxksvzkSyaDiQz57/H0/aXENPT/8miB+X8YHoR6bvn/ZeYnb/nLkWKTAX/dUaDH/w4XmHfrqBaoX1X+X73CT5jnb6ei67I/CpRvH9MSvu7yF+yd4s3PdXPdL2vdxZ2f63511r15VAut5id1TASpxBXW165FTUfcuP8YyYps3FFkISExDdfvpxfZmvQsmQzoWM8FTb0rwd7KHHaWZnCwMsv/3coM9v+9riQ91njS2KblCUu1LiGe/nSrYA3PyrYvjFFunaVxfhsLU9haFv3xMzc10TiviYmsyATwec/Pr6vizM91c91lZX6uu3ytm55iIkglruX8/QXKHqTnYOg3JzTWl1ubo7aLPWo726O2iz0sTGWY/NO5AvW2jdbuv0cVfU4ale0sUMXOEq4VrBDepBpij9/C3ZQsVLazKPH5jbnushw7112+1l2WY+e6jXO8kDreLFKCylNn06JsPn0bEzeeKbSlL8CzItq/5ozaLvao4+IAZwdLtRYv1WWA5xM5bS8j3E3NxBvL/ixw0tg6poV052xRsnPzYGFqAplMBiEEcvKUsDQz1Xr7izO/MdddlmPnusvXusty7Fy3cY6XFylP399MBEtQeTqQiiKEwOA1x7E/oeBduNokc8VN5ICSP2kQEdGrozx9f/PSMJW4mN0JUhIoQ/7drLpcnnWVW+OPKa9LiVy/ph46J3LP1pXJZEwCiYiIwESQStg3cTew4sA1AIC9pRm8q9jq1aeDiRwREZHhMRGkErPrfCKitl4AAExsXwvDgqsXq1WPiIiIDIuJIJWIEzceYtyG0xAC6NvUA6Pb1lS7AYStekRERMZnYuwA6NVzNTkNQ9aeQHauEiF1nfBR93o6jXtHRERELwcTQTKoZEUWIr4+jtTMJ/CrVgHL+jaGmSkPMyIiotKI39BkMGlZTzBw9XHcTsmEt6MtvooIgLUFL/8SERGVVkwEySBycpV499tT+OuuAo52Flg7qCkq21kaOywiIiIqAhNBKjYhBKb8dBZ/XL0PGwtTfD2wCTwq2xg7LCIiInoBJoJUbAt2J2DT6dswNZFhef/GaFC1grFDIiIiIi0wEaRiWRd3A//7b8Do6J718XptJyNHRERERNpiIkh623U+ETP/GzA6sn0t9A6oZuSIiIiISBccUJp0cvbfFETvvISwRm6YueXCfwNGV8OYtjWNHRoRERHpiIkg6WTTqduI+/sBTt58hJxcJdrVccJH3X05YDQREVEZxESQXujfRxl4lP4EMhmwJf42gPzhYnyc7TCiTXUkKrJQtSLvEiYiIiprmAjSC7Wcv19j+ZWkx+i18ggA4Ma8Li8zJCIiIjIA3ixCL7Q43A9mJpov/ZqZyLA43O/lBkREREQGwRZBeqGwRu6o6WSHrsv+KDBt86gW8HWXGyEqIiIiKi62CJJeeG8IERFR2VcqEsHly5fDy8sLVlZWCAwMxLFjx4qsv3HjRtSpUwdWVlaoX78+du7cqTZdCIGZM2fC1dUV1tbWCAkJwZUrV9TqeHl5QSaTqb3mzZuncX1Xr16Fvb09KlSoUKztLMsq2ZpDdXV4YHMv1HeXo4qdJSrbWRg3MCIiItKb0RPB2NhYREZGIioqCqdOnULDhg0RGhqK5ORkjfUPHz6Mvn37YsiQITh9+jTCwsIQFhaG8+fPS3ViYmKwdOlSrFy5EkePHoWtrS1CQ0ORlZWltqzZs2fj7t270mvMmDEF1vfkyRP07dsXrVq1MuyGlzEZOUooBWBuIsP7Hetgy6gW+GPK63CVWxs7NCIiItKT0RPBTz75BEOHDsWgQYPw2muvYeXKlbCxscHXX3+tsf6SJUvQsWNHTJo0CXXr1sVHH32Exo0b47PPPgOQ3xq4ePFiTJ8+Hd27d0eDBg3wzTff4M6dO9i8ebPasuzt7eHi4iK9bG1tC6xv+vTpqFOnDnr37m3wbS9L/rhyDwDQxLsSrC1MIZPJYGlmauSoiIiIqDiMmgjm5OTg5MmTCAkJkcpMTEwQEhKCuLg4jfPExcWp1QeA0NBQqf7169eRmJioVkculyMwMLDAMufNm4fKlSujUaNGWLBgAXJzc9Wm79u3Dxs3bsTy5cu12p7s7GwoFAq116vi9yv3AQCtfKoYORIiIiIyFKPeNXz//n3k5eXB2dlZrdzZ2RmXLl3SOE9iYqLG+omJidJ0VVlhdQBg7NixaNy4MSpVqoTDhw9j6tSpuHv3Lj755BMAwIMHDzBw4EB8++23cHBw0Gp7oqOj8eGHH2pVtyx5kqfEkb8fAABa+TgaORoiIiIylHI7fExkZKT0e4MGDWBhYYHhw4cjOjoalpaWGDp0KPr164fWrVtrvcypU6eqLVehUKBatWoGjdsYTt9MQXpOHirZWuA1V+2SYiIiIir9jHpp2NHREaampkhKSlIrT0pKgouLi8Z5XFxciqyv+qnLMgEgMDAQubm5uHHjBoD8y8ILFy6EmZkZzMzMMGTIEKSmpsLMzKzQ/ouWlpZwcHBQe70Kfv+vf2CLmo4wKWRgaSIiIip7jJoIWlhYwN/fH3v37pXKlEol9u7di6CgII3zBAUFqdUHgD179kj1vb294eLiolZHoVDg6NGjhS4TAOLj42FiYgInJycA+X0R4+Pjpdfs2bNhb2+P+Ph49OjRQ+9tLoue9g/kZWEiIqJXidEvDUdGRiIiIgIBAQFo2rQpFi9ejPT0dAwaNAgAMGDAALi7uyM6OhoAMG7cOAQHB2PRokXo0qULNmzYgBMnTmDVqlUAAJlMhvHjx2POnDnw8fGBt7c3ZsyYATc3N4SFhQHIT/KOHj2K119/Hfb29oiLi8OECRPw9ttvo2LFigCAunXrqsV54sQJmJiYwNfX9yXtmdIhNeMJzv6bAoCJIBER0avG6IlgeHg47t27h5kzZyIxMRF+fn7YtWuXdLPHzZs3YWLytOGyefPmWL9+PaZPn45p06bBx8cHmzdvVkvQJk+ejPT0dAwbNgwpKSlo2bIldu3aBSsrKwD5l3A3bNiAWbNmITs7G97e3pgwYYJa/z7Kd/jafSgFUKOKLccMJCIiesXIhBDC2EG8qhQKBeRyOVJTU8tsf8Gpm87h+2M3MbC5F2a9Uc/Y4RAREZW4V+H7W1tGH1CaSrc/rubfKMLLwkRERK8eJoJUqH8epOPWw0yYm8rQrHplY4dDREREBsZEkAp16L+7hRt5VIStpdG7kxIREZGBMRGkQqmeL9yal4WJiIheSUwESaPcPCUOX81/rFxLPl+YiIjolcREkDQ6828q0rJzIbc2R313ubHDISIiohLARJA0+uO//oEtalaGKR8rR0RE9EpiIkgaqZ4v3LImLwsTERG9qpgIUgFpWU9w+lYKAI4fSERE9CpjIkgFxF17gDylgFdlG1SrZGPscIiIiKiEMBGkAv64mt8/sBXvFiYiInqlMRGkAn7/70aRlrwsTERE9EpjIkhq/n2Ugev302FqIkNQDT5WjoiI6FXGRJDUqIaN8atWAQ5W5kaOhoiIiEqSzomgl5cXZs+ejZs3b5ZEPGRk0mXhmrwsTERE9KrTOREcP348Nm3ahOrVq6N9+/bYsGEDsrOzSyI2esnylAJ/XstPBFvXYiJIRET0qtMrEYyPj8exY8dQt25djBkzBq6urhg9ejROnTpVEjHSS3L+dipSMp7A3tIMDatWMHY4REREVML07iPYuHFjLF26FHfu3EFUVBS+/PJLNGnSBH5+fvj6668hhDBknPQSqIaNCapRGWam7D5KRET0qjPTd8YnT57g559/xurVq7Fnzx40a9YMQ4YMwb///otp06bht99+w/r16w0ZK5Uw1WPl+DQRIiKi8kHnRPDUqVNYvXo1vv/+e5iYmGDAgAH49NNPUadOHalOjx490KRJE4MGSiUrPTsXJ/95BABoyYGkiYiIygWdE8EmTZqgffv2WLFiBcLCwmBuXnCIEW9vb/Tp08cgAdLLcez6QzzJE6ha0RpelflYOSIiovJA50Tw77//hqenZ5F1bG1tsXr1ar2Dopfv0DOXhWUymZGjISIiopdB5zsCXn/9dTx48KBAeUpKCqpXr26QoOjlUw0kzecLExERlR86J4I3btxAXl5egfLs7Gzcvn3bIEHRy3U3NRNXkh9DJgOa87FyRERE5YbWl4a3bt0q/b57927I5XLpfV5eHvbu3QsvLy+DBkcvh6o1sEHVCqhgY2HkaIiIiOhl0ToRDAsLAwDIZDJERESoTTM3N4eXlxcWLVpk0ODo5VCNH9iKj5UjIiIqV7ROBJVKJYD8O4KPHz8OR0cmDa8CpVJILYItOX4gERFRuaLzXcPXr18viTjISC4mKvAgPQc2FqZo7FHR2OEQERHRS6RVIrh06VIMGzYMVlZWWLp0aZF1x44da5DA6OX4/b/WwGbVK8PCjI+VIyIiKk+0SgQ//fRT9O/fH1ZWVvj0008LrSeTyZgIljFPh43hZWEiIqLyRqtE8NnLwbw0/OrIepKHYzceAmAiSEREVB7xWmA5duz6Q+TkKuEqt0KNKnbGDoeIiIheMq1aBCMjI7Ve4CeffKJ3MPRyqYaNaVmTj5UjIiIqj7RKBE+fPq3VwphMlC2HLuc/X5jDxhAREZVPWiWC+/fvL+k46CVLTsvCpcQ0APktgkRERFT+sI9gOfXnf5eF67k5oLKdpZGjISIiImPQqkWwZ8+eWLNmDRwcHNCzZ88i627atMkggVHJ+l0aNqaKkSMhIiIiY9EqEZTL5VL/P7lcXqIBUckTQnD8QCIiItIuEVy9erXG36lsupz0GMlp2bAyN4G/Jx8rR0REVF7p/KxhleTkZCQkJAAAateuDScnJ4MFRSUr9vhNAEBdVwdYmZsaORoiIiIyFp1vFlEoFHjnnXfg7u6O4OBgBAcHw93dHW+//TZSU1NLIkYysN0XkgAAZiYc7oeIiKg80zkRHDp0KI4ePYrt27cjJSUFKSkp2L59O06cOIHhw4frFcTy5cvh5eUFKysrBAYG4tixY0XW37hxI+rUqQMrKyvUr18fO3fuVJsuhMDMmTPh6uoKa2trhISE4MqVK2p1vLy8IJPJ1F7z5s2Tph84cADdu3eHq6srbG1t4efnh++++06v7SsN/n2UgXP/puLUzYe4nZIJALiS9Bjnb6fi3L+p+PdRhpEjJCIiopdNJoQQusxga2uL3bt3o2XLlmrlv//+Ozp27Ij09HSdAoiNjcWAAQOwcuVKBAYGYvHixdi4cSMSEhI0Xm4+fPgwWrdujejoaHTt2hXr16/H/PnzcerUKfj6+gIA5s+fj+joaKxduxbe3t6YMWMGzp07h7/++gtWVlYA8hPBIUOGYOjQodKy7e3tYWtrCwCYO3cuMjMz0alTJzg7O2P79u2IjIzEli1b0LVrV622TaFQQC6XIzU1FQ4ODjrtF0PzmrKjQJkMwLN//Bvzury0eIiIiEqr0vT9XdJ0TgQ9PDywY8cO1K9fX6387Nmz6Ny5M/7991+dAggMDESTJk3w2WefAQCUSiWqVauGMWPGYMqUKQXqh4eHIz09Hdu3b5fKmjVrBj8/P6xcuRJCCLi5uWHixIl47733AACpqalwdnbGmjVr0KdPHwD5ieD48eMxfvx4rWPt0qULnJ2d8fXXX2tVvzQdSJtP38Z7G88gV1nwz21mIsPCXg0R1sjdCJERERGVLqXp+7uk6XxpePr06YiMjERiYqJUlpiYiEmTJmHGjBk6LSsnJwcnT55ESEjI04BMTBASEoK4uDiN88TFxanVB4DQ0FCp/vXr15GYmKhWRy6XIzAwsMAy582bh8qVK6NRo0ZYsGABcnNzi4w3NTUVlSpVKnR6dnY2FAqF2qu0CGvkjs2jWmictnlUCyaBRERE5ZBWdw03atRI7TnCV65cgYeHBzw8PAAAN2/ehKWlJe7du6dTP8H79+8jLy8Pzs7OauXOzs64dOmSxnkSExM11lclpqqfRdUBgLFjx6Jx48aoVKkSDh8+jKlTp+Lu3bv45JNPNK73hx9+wPHjx/H5558Xuj3R0dH48MMPC51e2shkgG7twURERPQq0SoRDAsLK+EwXr7IyEjp9wYNGsDCwgLDhw9HdHQ0LC3VH7m2f/9+DBo0CF988QXq1atX6DKnTp2qtlyFQoFq1aoZPng9VbazgKWZCbJzlejwmjMSFVm4m5KFynYWxg6NiIiIjECrRDAqKqpEVu7o6AhTU1MkJSWplSclJcHFxUXjPC4uLkXWV/1MSkqCq6urWh0/P79CYwkMDERubi5u3LiB2rVrS+UHDx5Et27d8Omnn2LAgAFFbo+lpWWBJLI0cZVbo1VNR/x2KRmtalXB24EeyMlTwtKMYwkSERGVRzr3ETQkCwsL+Pv7Y+/evVKZUqnE3r17ERQUpHGeoKAgtfoAsGfPHqm+t7c3XFxc1OooFAocPXq00GUCQHx8PExMTNTuVD5w4AC6dOmC+fPnY9iwYXptY2mTnpMHAHCwMoNMJmMSSEREVI7p/GSRvLw8fPrpp/jhhx9w8+ZN5OTkqE1/+PChTsuLjIxEREQEAgIC0LRpUyxevBjp6ekYNGgQAGDAgAFwd3dHdHQ0AGDcuHEIDg7GokWL0KVLF2zYsAEnTpzAqlWrAAAymQzjx4/HnDlz4OPjIw0f4+bmJl3ijouLw9GjR/H666/D3t4ecXFxmDBhAt5++21UrJj/yLX9+/eja9euGDduHN58802pf6GFhUWRN4yUdoqsJwAAB2tzI0dCRERERid0NGPGDOHq6ioWLlworKysxEcffSSGDBkiKleuLJYsWaLr4oQQQixbtkx4eHgICwsL0bRpU3HkyBFpWnBwsIiIiFCr/8MPP4hatWoJCwsLUa9ePbFjxw616UqlUsyYMUM4OzsLS0tL0a5dO5GQkCBNP3nypAgMDBRyuVxYWVmJunXrirlz54qsrCypTkREhED+MHtqr+DgYK23KzU1VQAQqampuu2QEtRq/j7h+f52ceLGA2OHQkREVCqVxu/vkqLzOII1atTA0qVL0aVLF9jb2yM+Pl4qO3LkCNavX2/wZLWsKo3jEPnN/hUpGU+wZ0Jr+DjbGzscIiKiUqc0fn+XFJ37CCYmJkqDSdvZ2UnPF+7atSt27Cj49AoqPYQQSMvKHyvR3oqXhomIiMo7nRPBqlWr4u7duwDyWwd//fVXAMDx48dL9R2zBGTk5CHvvyeLOFjr3D2UiIiIXjE6J4I9evSQ7sgdM2YMZsyYAR8fHwwYMACDBw82eIBkOKrWQFMTGazNebcwERFReadzs9C8efOk38PDw+Hh4YG4uDj4+PigW7duBg2ODEu6Y/i/oWOIiIiofCv29cGgoKAix+ej0iPtv0SQ/QOJiIgI0DIR3Lp1Kzp16gRzc3Ns3bq1yLpvvPGGQQIjw1Nk5l8aZv9AIiIiAnR41nBiYiKcnJyKfO6wTCZDXl6eoWIjA1NdGra3ZIsgERERaZkIKpVKjb9T2aLIYosgERERPaXTXcNPnjxBu3btcOXKlZKKh0oQ+wgSERHRs3RKBM3NzXH27NmSioVKmNRHkIkgERERQY9xBN9++2189dVXJRELlbCnLYK8NExERER6DB+Tm5uLr7/+Gr/99hv8/f1ha2urNv2TTz4xWHBkWE/7CLJFkIiIiPRIBM+fP4/GjRsDAC5fvqw2jYMUl25sESQiIqJn6ZwR7N+/vyTioJdAkal6sghbBImIiEiPPoJUdqmeNezAFkEiIiKCno+YO3HiBH744QfcvHkTOTk5atM2bdpkkMDI8KRnDbOPIBEREUGPFsENGzagefPmuHjxIn7++Wc8efIEFy5cwL59+yCXy0siRjIQVYsg+wgSERERoEciOHfuXHz66afYtm0bLCwssGTJEly6dAm9e/eGh4dHScRIBvAkT4mMnPzH/7GPIBEREQF6JILXrl1Dly5dAAAWFhZIT0+HTCbDhAkTsGrVKoMHSIbx+L/WQACwY4sgERERQY9EsGLFikhLSwMAuLu74/z58wCAlJQUZGRkGDY6MhhV/0AbC1OYm/IeISIiItLjZpHWrVtjz549qF+/Pnr16oVx48Zh37592LNnD9q1a1cSMZIBsH8gERERPU/rrOD8+fPw9fXFZ599hqysLADABx98AHNzcxw+fBhvvvkmpk+fXmKBUvFwDEEiIiJ6ntaJYIMGDdCkSRP83//9H/r06QMAMDExwZQpU0osODIcBVsEiYiI6DladxY7ePAg6tWrh4kTJ8LV1RURERH4/fffSzI2MiCOIUhERETP0zoRbNWqFb7++mvcvXsXy5Ytw40bNxAcHIxatWph/vz5SExMLMk4qZie9hFkIkhERET5dL591NbWFoMGDcLBgwdx+fJl9OrVC8uXL4eHhwfeeOONkoiRDOBpH0FeGiYiIqJ8xRpHpGbNmpg2bRqmT58Oe3t77Nixw1BxkYGxRZCIiIiep3fz0KFDh/D111/jp59+gomJCXr37o0hQ4YYMjYyoKd9BNkiSERERPl0ygru3LmDNWvWYM2aNbh69SqaN2+OpUuXonfv3rC1tS2pGMkA0v5LBNkiSERERCpaJ4KdOnXCb7/9BkdHRwwYMACDBw9G7dq1SzI2MiBFZv6lYfYRJCIiIhWtswJzc3P8+OOP6Nq1K0xNTUsyJioBadkcUJqIiIjUaZ0Ibt26tSTjoBImtQiyjyARERH9p1h3DVPZwT6CRERE9DwmguWAEEJ6xBwvDRMREZEKE8FyIPNJHvKUAgCfNUxERERPMREsB1T9A01NZLCx4I0+RERElE+r5iFdbhThY+ZKn6f9A80gk8mMHA0RERGVFlolgmFhYWrvZTIZhBBq71Xy8vIMExkZjPRUEfYPJCIiomdodWlYqVRKr19//RV+fn745ZdfkJKSgpSUFOzcuRONGzfGrl27Sjpe0oNCes4w+wcSERHRUzpnBuPHj8fKlSvRsmVLqSw0NBQ2NjYYNmwYLl68aNAAqfgUmWwRJCIiooJ0vlnk2rVrqFChQoFyuVyOGzduGCAkMjS2CBIREZEmOieCTZo0QWRkJJKSkqSypKQkTJo0CU2bNtUriOXLl8PLywtWVlYIDAzEsWPHiqy/ceNG1KlTB1ZWVqhfvz527typNl0IgZkzZ8LV1RXW1tYICQnBlStX1Op4eXlBJpOpvebNm6dW5+zZs2jVqhWsrKxQrVo1xMTE6LV9xqa6WcTBmi2CRERE9JTOieDXX3+Nu3fvwsPDAzVr1kTNmjXh4eGB27dv46uvvtI5gNjYWERGRiIqKgqnTp1Cw4YNERoaiuTkZI31Dx8+jL59+2LIkCE4ffo0wsLCEBYWhvPnz0t1YmJisHTpUqxcuRJHjx6Fra0tQkNDkZWVpbas2bNn4+7du9JrzJgx0jSFQoEOHTrA09MTJ0+exIIFCzBr1iysWrVK5200NtXwMWwRJCIiomfJxLO3/2pJCIE9e/bg0qVLAIC6desiJCREr6FJAgMD0aRJE3z22WcA8m9MqVatGsaMGYMpU6YUqB8eHo709HRs375dKmvWrBn8/PywcuVKCCHg5uaGiRMn4r333gMApKamwtnZGWvWrEGfPn0A5LcIjh8/HuPHj9cY14oVK/DBBx8gMTERFhYWAIApU6Zg8+bN0na/iEKhgFwuR2pqKhwcHLTeJ4b2wc/n8N3RmxjXzgcT2tcyWhxERERlQWn5/n4Z9BpQWiaToUOHDhg2bBjGjBmD9u3b65UE5uTk4OTJkwgJCXkakIkJQkJCEBcXp3GeuLg4tfpA/s0qqvrXr19HYmKiWh25XI7AwMACy5w3bx4qV66MRo0aYcGCBcjNzVVbT+vWraUkULWehIQEPHr0SOdtNSb2ESQiIiJNdE4ElUolPvroI7i7u8POzg7Xr18HAMyYMUPnS8P3799HXl4enJ2d1cqdnZ2RmJiocZ7ExMQi66t+vmiZY8eOxYYNG7B//34MHz4cc+fOxeTJk1+4nmfX8bzs7GwoFAq1V2nAPoJERESkic6J4Jw5c7BmzRrExMSotZb5+vriyy+/NGhwJSkyMhJt2rRBgwYNMGLECCxatAjLli1Ddna23suMjo6GXC6XXtWqVTNgxPp7OnwMWwSJiIjoKZ0TwW+++QarVq1C//79YWr69Lm1DRs21LrvnIqjoyNMTU3V7kAG8u9CdnFx0TiPi4tLkfVVP3VZJpDfVzE3N1caAqew9Ty7judNnToVqamp0uvWrVuFru9lSvvv0jDHESQiIqJn6ZwI3r59GzVr1ixQrlQq8eTJE52WZWFhAX9/f+zdu1dtOXv37kVQUJDGeYKCgtTqA8CePXuk+t7e3nBxcVGro1AocPTo0UKXCQDx8fEwMTGBk5OTtJ5Dhw6pbdOePXtQu3ZtVKxYUeMyLC0t4eDgoPYqDRTSs4aZCBIREdFTOieCr732Gn7//fcC5T/++CMaNWqkcwCRkZH44osvsHbtWly8eBHvvvsu0tPTMWjQIADAgAEDMHXqVKn+uHHjsGvXLixatAiXLl3CrFmzcOLECYwePRpA/o0s48ePx5w5c7B161acO3cOAwYMgJubm/TM5Li4OCxevBhnzpzB33//je+++w4TJkzA22+/LSV5/fr1g4WFBYYMGYILFy4gNjYWS5YsQWRkpM7baGxSi6A1Lw0TERHRUzpnBjNnzkRERARu374NpVKJTZs2ISEhAd98843akC7aCg8Px7179zBz5kwkJibCz88Pu3btkm7MuHnzJkxMnuarzZs3x/r16zF9+nRMmzYNPj4+2Lx5M3x9faU6kydPRnp6OoYNG4aUlBS0bNkSu3btgpWVFYD8lrsNGzZg1qxZyM7Ohre3NyZMmKCW5Mnlcvz6668YNWoU/P394ejoiJkzZ2LYsGE6b6MxPclTIiMnDwBbBImIiEidXuMI/v7775g9ezbOnDmDx48fo3Hjxpg5cyY6dOhQEjGWWaVhHKJH6Tlo9NEeAMCVjzvB3FSvEYOIiIjKjdLw/f2y6HWtsFWrVtizZ4+hY6ESoOofaG1uyiSQiIiI1OjdaSwnJwfJyclQKpVq5R4eHsUOigyH/QOJiIioMDpnB1euXMHgwYNx+PBhtXIhBGQyGfLy8gwWHBWfagxB9g8kIiKi5+mcCA4cOBBmZmbYvn07XF1d9Xq0HL08CmkMQbYIEhERkTqds4P4+HicPHkSderUKYl4yMAUfLwcERERFUKvcQTv379fErFQCVD1EeSlYSIiInqezong/PnzMXnyZBw4cAAPHjyAQqFQe1HpwucMExERUWF0zg5CQkIAAO3atVMr580ipRNbBImIiKgwOieC+/fvL4k4qIQ87SPIFkEiIiJSp3N2EBwcXBJxUAlJy+LwMURERKSZVong2bNn4evrCxMTE5w9e7bIug0aNDBIYGQYikwOH0NERESaaZUd+Pn5ITExEU5OTvDz84NMJoOmRxSzj2Dpk5atulmELYJERESkTqtE8Pr166hSpYr0O5UdUosg+wgSERHRc7TKDjw9PTX+TqUf+wgSERFRYfRuJvrrr79w8+ZN5OTkqJW/8cYbxQ6KDEMI8cwj5pgIEhERkTqdE8G///4bPXr0wLlz59T6CqqeOcw+gqVH5pM85Cnz/z72vFmEiIiInqPzk0XGjRsHb29vJCcnw8bGBhcuXMChQ4cQEBCAAwcOlECIpC9V/0BTExlsLEyNHA0RERGVNjo3E8XFxWHfvn1wdHSEiYkJTExM0LJlS0RHR2Ps2LE4ffp0ScRJenjaP9BMarElIiIiUtG5RTAvLw/29vYAAEdHR9y5cwdA/k0kCQkJho2OikV6qgj7BxIREZEGOrcI+vr64syZM/D29kZgYCBiYmJgYWGBVatWoXr16iURI+lJIT1nmP0DiYiIqCCdM4Tp06cjPT0dADB79mx07doVrVq1QuXKlREbG2vwAEl/iky2CBIREVHhdE4EQ0NDpd9r1qyJS5cu4eHDh6hYsSL7oZUyaWwRJCIioiIYJEOoVKmSIRZDBib1EbRmiyAREREVpFUi2LNnT60XuGnTJr2DIcNiiyAREREVRasMQS6Xl3QcVALYR5CIiIiKolUiuHr16pKOg0oAWwSJiIioKHpnCMnJydK4gbVr14aTk5PBgiLDYB9BIiIiKorOA0orFAq88847cHd3R3BwMIKDg+Hu7o63334bqampJREj6UnVIujAFkEiIiLSQOdEcOjQoTh69Ci2b9+OlJQUpKSkYPv27Thx4gSGDx9eEjGSnthHkIiIiIqic1PR9u3bsXv3brRs2VIqCw0NxRdffIGOHTsaNDgqnqd9BJkIEhERUUE6twhWrlxZ413EcrkcFStWNEhQZBhP+wjy0jAREREVpHMiOH36dERGRiIxMVEqS0xMxKRJkzBjxgyDBkf6y81TIiMnDwBbBImIiEgznZuKVqxYgatXr8LDwwMeHh4AgJs3b8LS0hL37t3D559/LtU9deqU4SIlnaguCwMcPoaIiIg00zlDCAsLK4EwyNBUiaC1uSnMTXVu+CUiIqJyQOdEMCoqqiTiIANj/0AiIiJ6EZ2bivbv31/otGcvC5NxqRJB9g8kIiKiwuicCHbs2BGTJk3CkydPpLL79++jW7dumDJlikGDI/0pMjmYNBERERVNrxbBn3/+GU2aNMFff/2FHTt2wNfXFwqFAvHx8SUQIukjjS2CRERE9AI6J4LNmzdHfHw8fH190bhxY/To0QMTJkzAgQMH4OnpWRIxkh4UqsfL8TnDREREVAi9bie9fPkyTpw4gapVq8LMzAwJCQnIyMgwdGxUDKrHy3HoGCIiIiqMzongvHnzEBQUhPbt2+P8+fM4duwYTp8+jQYNGiAuLq4kYiQ9qIaP4XOGiYiIqDA6J4JLlizB5s2bsWzZMlhZWcHX1xfHjh1Dz5490aZNmxIIkfTx9K5htggSERGRZjongufOnUOnTp3UyszNzbFgwQL8+uuvOgewfPlyeHl5wcrKCoGBgTh27FiR9Tdu3Ig6derAysoK9evXx86dO9WmCyEwc+ZMuLq6wtraGiEhIbhy5YrGZWVnZ8PPzw8ymazAjS67d+9Gs2bNYG9vjypVquDNN9/EjRs3dN4+Y0mTxhFkiyARERFppnMi6OjoWOi0unXr6rSs2NhYREZGIioqCqdOnULDhg0RGhqK5ORkjfUPHz6Mvn37YsiQITh9+jTCwsIQFhaG8+fPS3ViYmKwdOlSrFy5EkePHoWtrS1CQ0ORlZVVYHmTJ0+Gm5tbgfLr16+je/fuaNu2LeLj47F7927cv38fPXv21Gn7jInDxxAREdELCS1ZW1uL5ORk6X3nzp3FnTt3pPeJiYnCxMRE28UJIYRo2rSpGDVqlPQ+Ly9PuLm5iejoaI31e/fuLbp06aJWFhgYKIYPHy6EEEKpVAoXFxexYMECaXpKSoqwtLQU33//vdp8O3fuFHXq1BEXLlwQAMTp06elaRs3bhRmZmYiLy9PKtu6dauQyWQiJydH6+1LTU0VAERqaqrW8xhKl6WHhOf728W+i0kvfd1ERERlmTG/v182rVsEs7KyIISQ3h86dAiZmZnPJ5VaJ6A5OTk4efIkQkJCpDITExOEhIQUetNJXFycWn0ACA0Nlepfv34diYmJanXkcjkCAwPVlpmUlIShQ4di3bp1sLGxKbAef39/mJiYYPXq1cjLy0NqairWrVuHkJAQmJsXfqk1OzsbCoVC7WUsqhZB9hEkIiKiwug1fExhZDKZ1nXv37+PvLw8ODs7q5U7OzsjMTFR4zyJiYlF1lf9LKqOEAIDBw7EiBEjEBAQoHE93t7e+PXXXzFt2jRYWlqiQoUK+Pfff/HDDz8UuU3R0dGQy+XSq1q1akXWL0nsI0hEREQvYtBEsCxYtmwZ0tLSMHXq1ELrJCYmYujQoYiIiMDx48dx8OBBWFhY4K233iqy1XPq1KlITU2VXrdu3SqJTXghIYQ0oDRbBImIiKgwWmcJMplMrcXv+fe6cnR0hKmpKZKSktTKk5KS4OLionEeFxeXIuurfiYlJcHV1VWtjp+fHwBg3759iIuLg6WlpdpyAgIC0L9/f6xduxbLly+HXC5HTEyMNP3bb79FtWrVcPToUTRr1kxjfJaWlgWWawyZT/KQp8xPWDmOIBERERVG6xZBIQRq1aqFSpUqoVKlSnj8+DEaNWokva9Tp45OK7awsIC/vz/27t0rlSmVSuzduxdBQUEa5wkKClKrDwB79uyR6nt7e8PFxUWtjkKhwNGjR6U6S5cuxZkzZxAfH4/4+Hhp+JnY2Fh8/PHHAICMjAyYmKjvGlNTUynG0k7VP9DURAYbC1MjR0NERESlldYtgqtXrzb4yiMjIxEREYGAgAA0bdoUixcvRnp6OgYNGgQAGDBgANzd3REdHQ0AGDduHIKDg7Fo0SJ06dIFGzZswIkTJ7Bq1SoA+a2U48ePx5w5c+Dj4wNvb2/MmDEDbm5uCAsLAwB4eHioxWBnZwcAqFGjBqpWrQoA6NKlCz799FPMnj0bffv2RVpaGqZNmwZPT080atTI4PvB0NKeGUy6OK22RERE9GrTOhGMiIgw+MrDw8Nx7949zJw5E4mJifDz88OuXbukmz1u3ryp1jLXvHlzrF+/HtOnT8e0adPg4+ODzZs3w9fXV6ozefJkpKenY9iwYUhJSUHLli2xa9cuWFlZaR1X27ZtsX79esTExCAmJgY2NjYICgrCrl27YG1tbbgdUEL4VBEiIiLShkzoMuYL6UShUEAulyM1NRUODg4vbb37E5IxaPVx1HNzwI6xrV7aeomIiF4Fxvr+NoZyd9dweaDIZIsgERERvRgTwVdQWpbq8XK8Y5iIiIgKx0TwFfS0jyATQSIiIiqc3olgTk4OEhISkJuba8h4yACkFkFrXhomIiKiwumcCGZkZGDIkCGwsbFBvXr1cPPmTQDAmDFjMG/ePIMHSLp72keQLYJERERUOJ0TwalTp+LMmTM4cOCA2pAsISEhiI2NNWhwpJ+nfQTZIkhERESF0zlT2Lx5M2JjY9GsWTO1wYrr1auHa9euGTQ40o+qjyBvFiEiIqKi6NwieO/ePTg5ORUoT09P51MsSgn2ESQiIiJt6JwIBgQEYMeOHdJ7VfL35ZdfFvqMYHq52EeQiIiItKFzk9HcuXPRqVMn/PXXX8jNzcWSJUvw119/4fDhwzh48GBJxEg64jiCREREpA2dWwRbtmyJ+Ph45Obmon79+vj111/h5OSEuLg4+Pv7l0SMpCOpjyAvDRMREVER9MoUatSogS+++MLQsZAB5OYpkZGTB4CXhomIiKhoOrcIhoSEYM2aNVAoFCURDxWT6rIwwGcNExERUdF0TgTr1auHqVOnwsXFBb169cKWLVvw5MmTkoiN9KBKBK3NTWFuyicIEhERUeF0zhSWLFmC27dvY/PmzbC1tcWAAQPg7OyMYcOG8WaRUoD9A4mIiEhbejUZmZiYoEOHDlizZg2SkpLw+eef49ixY2jbtq2h4yMdqRJB9g8kIiKiFylWs1FiYiI2bNiAb7/9FmfPnkXTpk0NFRfpSZHJx8sRERGRdnRuEVQoFFi9ejXat2+PatWqYcWKFXjjjTdw5coVHDlypCRiJB2ksUWQiIiItKRzs5GzszMqVqyI8PBwREdHIyAgoCTiIj0ppMfLMREkIiKioumcCG7duhXt2rWDiQnvSC2NnrYI8tIwERERFU3nbKF9+/YlEQcZyNM+gmwRJCIioqJplQg2btwYe/fuRcWKFdGoUSPIZLJC6546dcpgwZHu2CJIRERE2tIqW+jevTssLS2l34tKBMm4no4jyBZBIiIiKppWiWBUVJT0+6xZs0oqFjIA1ZNFOHwMERERvYjOd3xUr14dDx48KFCekpKC6tWrGyQo0p/UIsg+gkRERPQCOieCN27cQF5eXoHy7Oxs/PvvvwYJivSnahFkH0EiIiJ6Ea2zha1bt0q/7969G3K5XHqfl5eHvXv3wtvb27DRkc4UmewjSERERNrROhEMCwsDAMhkMkRERKhNMzc3h5eXFxYtWmTQ4Eg3Qgi2CBIREZHWtM4WlEolAMDb2xvHjx+Ho6NjiQVF+sl8kodcpQDAPoJERET0Yjo3G12/fr0k4iADULUGmprIYGNhauRoiIiIqLTT6/pheno6Dh48iJs3byInJ0dt2tixYw0SGOlO1T/Q3sqMYz0SERHRC+mcCJ4+fRqdO3dGRkYG0tPTUalSJdy/fx82NjZwcnJiImhECvYPJCIiIh3oPHzMhAkT0K1bNzx69AjW1tY4cuQI/vnnH/j7+2PhwoUlESNpiWMIEhERkS50TgTj4+MxceJEmJiYwNTUFNnZ2ahWrRpiYmIwbdq0koiRtMQ7homIiEgXOieC5ubmMDHJn83JyQk3b94EAMjlcty6dcuw0ZFOpDEE2SJIREREWtC56ahRo0Y4fvw4fHx8EBwcjJkzZ+L+/ftYt24dfH19SyJG0pLq0rA9E0EiIiLSgs4tgnPnzoWrqysA4OOPP0bFihXx7rvv4t69e1i1apXBAyTtqS4NO1jz0jARERG9mM4ZQ0BAgPS7k5MTdu3aZdCASH9Ph49hiyARERG9mM4tglR6SS2CvFmEiIiItKBVxtCoUSOtByg+depUsQIi/XH4GCIiItKFVolgWFhYCYdBhsA+gkRERKQLrTKGqKioEgtg+fLlWLBgARITE9GwYUMsW7YMTZs2LbT+xo0bMWPGDNy4cQM+Pj6YP38+OnfuLE0XQiAqKgpffPEFUlJS0KJFC6xYsQI+Pj4FlpWdnY3AwECcOXMGp0+fhp+fn9pyFi1ahFWrVuGff/6Bo6MjRo4ciQ8++MCg229I7CNIREREujBqH8HY2FhERkYiKioKp06dQsOGDREaGork5GSN9Q8fPoy+fftiyJAhOH36NMLCwhAWFobz589LdWJiYrB06VKsXLkSR48eha2tLUJDQ5GVlVVgeZMnT4abm5vGdY0bNw5ffvklFi5ciEuXLmHr1q1FJqilwdM+gkwEiYiI6MVkQgihywwmJiZF9hfMy8vTelmBgYFo0qQJPvvsMwCAUqlEtWrVMGbMGEyZMqVA/fDwcKSnp2P79u1SWbNmzeDn54eVK1dCCAE3NzdMnDgR7733HgAgNTUVzs7OWLNmDfr06SPN98svvyAyMhI//fQT6tWrp9YiePHiRTRo0ADnz59H7dq1td6e5ykUCsjlcqSmpsLBwUHv5WjrtZm7kJGThwPvtYGXo22Jr4+IiOhV9LK/v41J585kP//8s9r7J0+e4PTp01i7di0+/PBDrZeTk5ODkydPYurUqVKZiYkJQkJCEBcXp3GeuLg4REZGqpWFhoZi8+bNAIDr168jMTERISEh0nS5XI7AwEDExcVJiWBSUhKGDh2KzZs3w8bGpsB6tm3bhurVq2P79u3o2LEjhBAICQlBTEwMKlWqVOg2ZWdnIzs7W3qvUChevCMMJDdPiYyc/CTcwZotgkRERPRiOieC3bt3L1D21ltvoV69eoiNjcWQIUO0Ws79+/eRl5cHZ2dntXJnZ2dcunRJ4zyJiYka6ycmJkrTVWWF1RFCYODAgRgxYgQCAgJw48aNAuv5+++/8c8//2Djxo345ptvkJeXhwkTJuCtt97Cvn37Ct2m6OhonZJhQ1JdFgb4rGEiIiLSjsH6CDZr1gx79+411OJKzLJly5CWlqbWEvk8pVKJ7OxsfPPNN2jVqhXatGmDr776Cvv370dCQkKh802dOhWpqanS62U+e1mVCFqbm8LclMNDEhER0YsZJGPIzMzE0qVL4e7urvU8jo6OMDU1RVJSklp5UlISXFxcNM7j4uJSZH3Vz6Lq7Nu3D3FxcbC0tISZmRlq1qwJIP+JKREREQAAV1dXmJmZoVatWtIy6tatCwC4efNmodtkaWkJBwcHtdfL8vQ5w2wNJCIiIu3onAhWrFgRlSpVkl4VK1aEvb09vv76ayxYsEDr5VhYWMDf31+tFVGpVGLv3r0ICgrSOE9QUFCBVsc9e/ZI9b29veHi4qJWR6FQ4OjRo1KdpUuX4syZM4iPj0d8fDx27twJIP8O5o8//hgA0KJFC+Tm5uLatWvSci5fvgwA8PT01HobXyZpMGn2DyQiIiIt6dx8tHjxYrX3JiYmqFKlCgIDA1GxYkWdlhUZGYmIiAgEBASgadOmWLx4MdLT0zFo0CAAwIABA+Du7o7o6GgA+UO6BAcHY9GiRejSpQs2bNiAEydOYNWqVQAAmUyG8ePHY86cOfDx8YG3tzdmzJgBNzc3aVBsDw8PtRjs7OwAADVq1EDVqlUBACEhIWjcuDEGDx6MxYsXQ6lUYtSoUWjfvr1aK2FposjMvzTMFkEiIiLSls5Zg+ryqSGEh4fj3r17mDlzJhITE+Hn54ddu3ZJN3vcvHkTJiZPGy2bN2+O9evXY/r06Zg2bRp8fHywefNm+Pr6SnUmT56M9PR0DBs2DCkpKWjZsiV27doFKysrreMyMTHBtm3bMGbMGLRu3Rq2trbo1KkTFi1aZLBtN7Q0Pl6OiIiIdKTzOIIAkJWVhbNnzyI5ORlKpVJt2htvvGGw4Mq6lzkO0Vd/XMdH2/9C1wau+Kxf4xJdFxER0auM4wgWYdeuXXjnnXfw4MGDAtNkMplOA0qT4aSxjyARERHpSOebRcaMGYPevXvj7t27UCqVai8mgcbDPoJERESkK50TwaSkJERGRhYYtJmMi30EiYiISFc6J4JvvfUWDhw4UAKhUHFIw8ewRZCIiIi0pHPW8Nlnn6FXr174/fffUb9+fZibq7dAjR071mDBkfZUTxZhH0EiIiLSls6J4Pfff49ff/0VVlZWOHDgAGQymTRNJpMxETQSPlmEiIiIdKVz1vDBBx/gww8/xJQpU9TG+CPjkloE2UeQiIiItKRzJpeTk4Pw8HAmgaWMIlPVIshEkIiIiLSjczYXERGB2NjYkoiF9CSEeKaPIC8NExERkXZ0zhry8vIQExOD3bt3o0GDBgVuFvnkk08MFhxpJ/NJHnKV+Q+IYYsgERERaUvnRPDcuXNo1KgRAOD8+fNq0569cYReHlVroIkMsLUwNXI0REREVFbonAju37+/JOKgYni2fyCTcSIiItIW7/h4BSjYP5CIiIj0oHPmkJWVhWXLlmH//v1ITk6GUqlUm37q1CmDBUfakcYQtGT/QCIiItKezongkCFD8Ouvv+Ktt95C06ZNeSmyFOAdw0RERKQPnTOH7du3Y+fOnWjRokVJxEN64BiCREREpA+d+wi6u7vD3t6+JGIhPfGpIkRERKQPnRPBRYsW4f3338c///xTEvGQHlR9BHlpmIiIiHShc+YQEBCArKwsVK9eHTY2NgUGlH748KHBgiPtpGXx0jARERHpTudEsG/fvrh9+zbmzp0LZ2dn3ixSCigyVZeG2SJIRERE2tM5czh8+DDi4uLQsGHDkoiH9KBqEWQfQSIiItKFzn0E69Spg8zMzJKIhfTEAaWJiIhIHzongvPmzcPEiRNx4MABPHjwAAqFQu1FLx/7CBIREZE+dG5C6tixIwCgXbt2auVCCMhkMuTl5RkmMtLa0z6CTASJiIhIezongvv37y+JOKgYnrYI8tIwERERaU/nzCE4OLgk4iA95eYpkZ6T3wrrYM0WQSIiItKezongoUOHipzeunVrvYMh3T3OzpV+Z4sgERER6ULnzKFNmzYFyp4dS5B9BF8uVf9Aa3NTmJvqfO8PERERlWM6Zw6PHj1SeyUnJ2PXrl1o0qQJfv3115KIkYqgYP9AIiIi0pPO2YNcLi9Q1r59e1hYWCAyMhInT540SGCknafPGWb/QCIiItKNwa4lOjs7IyEhwVCLIy2l/TeYNFsEiYiISFc6Zw9nz55Vey+EwN27dzFv3jz4+fkZKi7SkiKTj5cjIiIi/eicCPr5+UEmk0EIoVberFkzfP311wYLjLTDFkEiIiLSl87Zw/Xr19Xem5iYoEqVKrCysjJYUKQ99hEkIiIifemcCHp6epZEHKQn1fAxbBEkIiIiXWl9s8i+ffvw2muvQaFQFJiWmpqKevXq4ffffzdocPRiqsfLsY8gERER6UrrRHDx4sUYOnQoHBwcCkyTy+UYPnw4PvnkE4MGRy8mXRpmiyARERHpSOtE8MyZM+jYsWOh0zt06MAxBI1AdbMI+wgSERGRrrROBJOSkmBuXniyYWZmhnv37hkkKNIenyxCRERE+tI6EXR3d8f58+cLnX727Fm4uroaJCjSntQiyD6CREREpCOtE8HOnTtjxowZyMrKKjAtMzMTUVFR6Nq1q0GDoxdTDShtz0SQiIiIdKR1Ijh9+nQ8fPgQtWrVQkxMDLZs2YItW7Zg/vz5qF27Nh4+fIgPPvhAryCWL18OLy8vWFlZITAwEMeOHSuy/saNG1GnTh1YWVmhfv362Llzp9p0IQRmzpwJV1dXWFtbIyQkBFeuXNG4rOzsbGmQ7Pj4eI11rl69Cnt7e1SoUEGfzSsxQohn+gjy0jARERHpRutE0NnZGYcPH4avry+mTp2KHj16oEePHpg2bRp8fX3xxx9/wNnZWecAYmNjERkZiaioKJw6dQoNGzZEaGgokpOTNdY/fPgw+vbtiyFDhuD06dMICwtDWFiY2mXrmJgYLF26FCtXrsTRo0dha2uL0NBQja2ZkydPhpubW6HxPXnyBH379kWrVq103raSlvkkD7nK/Ce8sEWQiIiIdCUTzz8rTguPHj3C1atXIYSAj48PKlasqHcAgYGBaNKkCT777DMAgFKpRLVq1TBmzBhMmTKlQP3w8HCkp6dj+/btUlmzZs3g5+eHlStXQggBNzc3TJw4Ee+99x6A/HEOnZ2dsWbNGvTp00ea75dffkFkZCR++ukn1KtXD6dPny7wvOT3338fd+7cQbt27TB+/HikpKRovW0KhQJyuRypqakah90priRFFgLn7oWJDLg2tzNkMpnB10FERFTelPT3d2midYvgsypWrIgmTZqgadOmxUoCc3JycPLkSYSEhDwNyMQEISEhiIuL0zhPXFycWn0ACA0Nlepfv34diYmJanXkcjkCAwPVlpmUlIShQ4di3bp1sLGx0biuffv2YePGjVi+fLne21iSnu0fyCSQiIiIdKVXImgo9+/fR15eXoFLys7OzkhMTNQ4T2JiYpH1VT+LqiOEwMCBAzFixAgEBARoXM+DBw8wcOBArFmzRuv/BrKzs6FQKNReJUnB/oFERERUDEZNBI1l2bJlSEtLw9SpUwutM3ToUPTr1w+tW7fWernR0dGQy+XSq1q1aoYIt1DSGIKW7B9IREREujNqIujo6AhTU1MkJSWplSclJcHFxUXjPC4uLkXWV/0sqs6+ffsQFxcHS0tLmJmZoWbNmgCAgIAARERESHUWLlwIMzMzmJmZYciQIUhNTYWZmRm+/vprjbFNnToVqamp0uvWrVu67A6d8Y5hIiIiKg6jJoIWFhbw9/fH3r17pTKlUom9e/ciKChI4zxBQUFq9QFgz549Un1vb2+4uLio1VEoFDh69KhUZ+nSpThz5gzi4+MRHx8vDT8TGxuLjz/+GEB+X0TV9Pj4eMyePRv29vaIj49Hjx49NMZmaWkJBwcHtVdJ4hiCREREVBxGb0qKjIxEREQEAgIC0LRpUyxevBjp6ekYNGgQAGDAgAFwd3dHdHQ0AGDcuHEIDg7GokWL0KVLF2zYsAEnTpzAqlWrAAAymQzjx4/HnDlz4OPjA29vb8yYMQNubm4ICwsDAHh4eKjFYGdnBwCoUaMGqlatCgCoW7euWp0TJ07AxMQEvr6+JbYvdMWnihAREVFxGD0RDA8Px7179zBz5kwkJibCz88Pu3btkm72uHnzJkxMnjZcNm/eHOvXr8f06dMxbdo0+Pj4YPPmzWoJ2uTJk5Geno5hw4YhJSUFLVu2xK5du2BlZfXSt68k8TnDREREVBx6jSNI2inpcYimbz6Hb4/cxNh2PohsX8vgyyciIiqPOI4glQmKTNWlYbYIEhERke6YCJZhaf9dGmYfQSIiItIHE8EyTDWgNPsIEhERkT6YCJZhUougNVsEiYiISHdMBMswVR9BtggSERGRPpgIlmHsI0hERETFwUSwjMrNUyI9Jw8AWwSJiIhIP0wEy6jH2bnS73zEHBEREemDiWAZpeofaGVuAgsz/hmJiIhId8wgyigF+wcSERFRMTERLKP4nGEiIiIqLiaCZVTaf4NJcwxBIiIi0hcTwTJKkalqEWQiSERERPphIlhGSS2CvDRMREREemIiWEY97SPIFkEiIiLSDxPBMuppH0G2CBIREZF+mAiWUao+ghw+hoiIiPTFRLCMYh9BIiIiKi4mgmUU+wgSERFRcTERLKPYR5CIiIiKi4lgGcUWQSIiIiouJoJl1NM+gkwEiYiISD9MBMsgIcQzTxbhpWEiIiLSDxPBMijriRK5SgGAzxomIiIi/TERLINU/QNNZICthamRoyEiIqKyiolgGZT2zI0iMpnMyNEQERFRWcVEsAxKzeTQMURERFR8TATLIKlF0JL9A4mIiEh/TATLIAUHkyYiIiIDYCJYBqVxMGkiIiIyACaCZZAik4NJExERUfExESyDnrYI8tIwERER6Y+JYBmkGkeQg0kTERFRcTARLIOeXhpmiyARERHpj4lgGaS6NMw+gkRERFQcTATLoLupWQCAh+nZRo6EiIiIyjImgmVQ4n+J4OlbKcYNhIiIiMo0djIrI/59lIFH6U8gkwGp/10aPvL3Q5y/nQohgIq25qha0cbIURIREVFZwkSwjGg5f3+BstTMJ+i67A/p/Y15XV5mSERERFTG8dJwGbE43A9mJjKN08xMZFgc7vdyAyIiIqIyjy2CZURYI3fUdLJTawFU2TyqBXzd5UaIioiIiMoytgiWQTKZ+k8iIiIifZSKRHD58uXw8vKClZUVAgMDcezYsSLrb9y4EXXq1IGVlRXq16+PnTt3qk0XQmDmzJlwdXWFtbU1QkJCcOXKFY3Lys7Ohp+fH2QyGeLj46XyAwcOoHv37nB1dYWtrS38/Pzw3XffFXtbi6OynQWq2FmivrscH/fwRX13OarYWaKynYVR4yIiIqKyyeiJYGxsLCIjIxEVFYVTp06hYcOGCA0NRXJyssb6hw8fRt++fTFkyBCcPn0aYWFhCAsLw/nz56U6MTExWLp0KVauXImjR4/C1tYWoaGhyMrKKrC8yZMnw83NTeN6GjRogJ9++glnz57FoEGDMGDAAGzfvt1wG68jV7k1/pjyOraMaoH+gZ7YMqoF/pjyOlzl1kaLiYiIiMoumRBCGDOAwMBANGnSBJ999hkAQKlUolq1ahgzZgymTJlSoH54eDjS09PVErJmzZrBz88PK1euhBACbm5umDhxIt577z0AQGpqKpydnbFmzRr06dNHmu+XX35BZGQkfvrpJ9SrVw+nT5+Gn59fobF26dIFzs7O+Prrr7XaNoVCAblcjtTUVDg4OGg1DxERERlXefr+NmqLYE5ODk6ePImQkBCpzMTEBCEhIYiLi9M4T1xcnFp9AAgNDZXqX79+HYmJiWp15HI5AgMD1ZaZlJSEoUOHYt26dbCx0W78vdTUVFSqVKnQ6dnZ2VAoFGovIiIiotLKqIng/fv3kZeXB2dnZ7VyZ2dnJCYmapwnMTGxyPqqn0XVEUJg4MCBGDFiBAICArSK9YcffsDx48cxaNCgQutER0dDLpdLr2rVqmm1bCIiIiJjMHofQWNYtmwZ0tLSMHXqVK3q79+/H4MGDcIXX3yBevXqFVpv6tSpSE1NlV63bt0yVMhEREREBmfURNDR0RGmpqZISkpSK09KSoKLi4vGeVxcXIqsr/pZVJ19+/YhLi4OlpaWMDMzQ82aNQEAAQEBiIiIUJvv4MGD6NatGz799FMMGDCgyO2xtLSEg4OD2ouIiIiotDJqImhhYQF/f3/s3btXKlMqldi7dy+CgoI0zhMUFKRWHwD27Nkj1ff29oaLi4taHYVCgaNHj0p1li5dijNnziA+Ph7x8fHS8DOxsbH4+OOPpfkOHDiALl26YP78+Rg2bJhhNpqIiIiolDD6k0UiIyMRERGBgIAANG3aFIsXL0Z6errUF2/AgAFwd3dHdHQ0AGDcuHEIDg7GokWL0KVLF2zYsAEnTpzAqlWrAAAymQzjx4/HnDlz4OPjA29vb8yYMQNubm4ICwsDAHh4eKjFYGdnBwCoUaMGqlatCiD/cnDXrl0xbtw4vPnmm1L/QgsLiyJvGCEiIiIqK4yeCIaHh+PevXuYOXMmEhMT4efnh127dkk3e9y8eRMmJk8bLps3b47169dj+vTpmDZtGnx8fLB582b4+vpKdSZPnoz09HQMGzYMKSkpaNmyJXbt2gUrKyut41q7di0yMjIQHR0tJaEAEBwcjAMHDhR/w4mIiIiMzOjjCL7KytM4RERERK+K8vT9XS7vGiYiIiIiJoJERERE5ZbR+wi+ylRX3fmEESIiorJD9b1dHnrPMREsQWlpaQDAJ4wQERGVQWlpaZDL5cYOo0TxZpESpFQqcefOHdjb20MmkxlsuQqFAtWqVcOtW7de+U6shsT9ph/uN91xn+mH+00/3G/6KWq/CSGQlpYGNzc3tZFLXkVsESxBJiYm0riEJYFPL9EP95t+uN90x32mH+43/XC/6aew/faqtwSqvNppLhEREREViokgERERUTnFRLAMsrS0RFRUFCwtLY0dSpnC/aYf7jfdcZ/ph/tNP9xv+uF+y8ebRYiIiIjKKbYIEhEREZVTTASJiIiIyikmgkRERETlFBNBIiIionKKiWAZtHz5cnh5ecHKygqBgYE4duyYsUMq1WbNmgWZTKb2qlOnjrHDKlUOHTqEbt26wc3NDTKZDJs3b1abLoTAzJkz4erqCmtra4SEhODKlSvGCbYUedF+GzhwYIFjr2PHjsYJtpSIjo5GkyZNYG9vDycnJ4SFhSEhIUGtTlZWFkaNGoXKlSvDzs4Ob775JpKSkowUcemgzX5r06ZNgeNtxIgRRoq4dFixYgUaNGggDRodFBSEX375RZrOY42JYJkTGxuLyMhIREVF4dSpU2jYsCFCQ0ORnJxs7NBKtXr16uHu3bvS648//jB2SKVKeno6GjZsiOXLl2ucHhMTg6VLl2LlypU4evQobG1tERoaiqysrJccaenyov0GAB07dlQ79r7//vuXGGHpc/DgQYwaNQpHjhzBnj178OTJE3To0AHp6elSnQkTJmDbtm3YuHEjDh48iDt37qBnz55GjNr4tNlvADB06FC14y0mJsZIEZcOVatWxbx583Dy5EmcOHECbdu2Rffu3XHhwgUAPNYAAILKlKZNm4pRo0ZJ7/Py8oSbm5uIjo42YlSlW1RUlGjYsKGxwygzAIiff/5Zeq9UKoWLi4tYsGCBVJaSkiIsLS3F999/b4QIS6fn95sQQkRERIju3bsbJZ6yIjk5WQAQBw8eFELkH1vm5uZi48aNUp2LFy8KACIuLs5YYZY6z+83IYQIDg4W48aNM15QZUTFihXFl19+yWPtP2wRLENycnJw8uRJhISESGUmJiYICQlBXFycESMr/a5cuQI3NzdUr14d/fv3x82bN40dUplx/fp1JCYmqh13crkcgYGBPO60cODAATg5OaF27dp499138eDBA2OHVKqkpqYCACpVqgQAOHnyJJ48eaJ2vNWpUwceHh483p7x/H5T+e677+Do6AhfX19MnToVGRkZxgivVMrLy8OGDRuQnp6OoKAgHmv/MTN2AKS9+/fvIy8vD87Ozmrlzs7OuHTpkpGiKv0CAwOxZs0a1K5dG3fv3sWHH36IVq1a4fz587C3tzd2eKVeYmIiAGg87lTTSLOOHTuiZ8+e8Pb2xrVr1zBt2jR06tQJcXFxMDU1NXZ4RqdUKjF+/Hi0aNECvr6+APKPNwsLC1SoUEGtLo+3pzTtNwDo168fPD094ebmhrNnz+L9999HQkICNm3aZMRoje/cuXMICgpCVlYW7Ozs8PPPP+O1115DfHw8jzUwEaRyoFOnTtLvDRo0QGBgIDw9PfHDDz9gyJAhRoyMXnV9+vSRfq9fvz4aNGiAGjVq4MCBA2jXrp0RIysdRo0ahfPnz7PPro4K22/Dhg2Tfq9fvz5cXV3Rrl07XLt2DTVq1HjZYZYatWvXRnx8PFJTU/Hjjz8iIiICBw8eNHZYpQYvDZchjo6OMDU1LXBHU1JSElxcXIwUVdlToUIF1KpVC1evXjV2KGWC6tjicVd81atXh6OjI489AKNHj8b27duxf/9+VK1aVSp3cXFBTk4OUlJS1OrzeMtX2H7TJDAwEADK/fFmYWGBmjVrwt/fH9HR0WjYsCGWLFnCY+0/TATLEAsLC/j7+2Pv3r1SmVKpxN69exEUFGTEyMqWx48f49q1a3B1dTV2KGWCt7c3XFxc1I47hUKBo0eP8rjT0b///osHDx6U62NPCIHRo0fj559/xr59++Dt7a023d/fH+bm5mrHW0JCAm7evFmuj7cX7TdN4uPjAaBcH2+aKJVKZGdn81j7Dy8NlzGRkZGIiIhAQEAAmjZtisWLFyM9PR2DBg0ydmil1nvvvYdu3brB09MTd+7cQVRUFExNTdG3b19jh1ZqPH78WK3V4Pr164iPj0elSpXg4eGB8ePHY86cOfDx8YG3tzdmzJgBNzc3hIWFGS/oUqCo/VapUiV8+OGHePPNN+Hi4oJr165h8uTJqFmzJkJDQ40YtXGNGjUK69evx5YtW2Bvby/1xZLL5bC2toZcLseQIUMQGRmJSpUqwcHBAWPGjEFQUBCaNWtm5OiN50X77dq1a1i/fj06d+6MypUr4+zZs5gwYQJat26NBg0aGDl645k6dSo6deoEDw8PpKWlYf369Thw4AB2797NY03F2Lctk+6WLVsmPDw8hIWFhWjatKk4cuSIsUMq1cLDw4Wrq6uwsLAQ7u7uIjw8XFy9etXYYZUq+/fvFwAKvCIiIoQQ+UPIzJgxQzg7OwtLS0vRrl07kZCQYNygS4Gi9ltGRobo0KGDqFKlijA3Nxeenp5i6NChIjEx0dhhG5Wm/QVArF69WqqTmZkpRo4cKSpWrChsbGxEjx49xN27d40XdCnwov128+ZN0bp1a1GpUiVhaWkpatasKSZNmiRSU1ONG7iRDR48WHh6egoLCwtRpUoV0a5dO/Hrr79K03msCSETQoiXmXgSERERUenAPoJERERE5RQTQSIiIqJyiokgERERUTnFRJCIiIionGIiSERERFROMREkIiIiKqeYCBIRERGVU0wEicqhGzduQCaTSY+gKg0uXbqEZs2awcrKCn5+fgZdtpeXFxYvXmyw5Q0cONDgT1U5cOAAZDJZgeeeEhGVJCaCREYwcOBAyGQyzJs3T6188+bNkMlkRorKuKKiomBra4uEhAS1Z38+S7XfZDKZ9CD52bNnIzc3t8hlHz9+HMOGDTNYrEuWLMGaNWsMtjxdnD59Gr169YKzszOsrKzg4+ODoUOH4vLly0aJp7QydPJP9KpiIkhkJFZWVpg/fz4ePXpk7FAMJicnR+95r127hpYtW8LT0xOVK1cutF7Hjh1x9+5dXLlyBRMnTsSsWbOwYMGCIuOpUqUKbGxs9I7teXK5HBUqVDDY8rS1fft2NGvWDNnZ2fjuu+9w8eJFfPvtt5DL5ZgxY8ZLj4eIyj4mgkRGEhISAhcXF0RHRxdaZ9asWQUuky5evBheXl7Se9Vlyrlz58LZ2RkVKlSQWskmTZqESpUqoWrVqli9enWB5V+6dAnNmzeHlZUVfH19cfDgQbXp58+fR6dOnWBnZwdnZ2e88847uH//vjS9TZs2GD16NMaPHw9HR0eEhoZq3A6lUonZs2ejatWqsLS0hJ+fH3bt2iVNl8lkOHnyJGbPng2ZTIZZs2YVuk8sLS3h4uICT09PvPvuuwgJCcHWrVvV9sXHH38MNzc31K5dG0DB1iGZTIYvv/wSPXr0gI2NDXx8fKRlqFy4cAFdu3aFg4MD7O3t0apVK1y7dk1tPc/vh9GjR0Mul8PR0REzZszAs0/wXLduHQICAmBvbw8XFxf069cPycnJhW7n8zIyMjBo0CB07twZW7duRUhICLy9vREYGIiFCxfi888/l+oePHgQTZs2haWlJVxdXTFlyhS1VtM2bdpgzJgxGD9+PCpWrAhnZ2d88cUXSE9Px6BBg2Bvb4+aNWvil19+keZRXbresWMHGjRoACsrKzRr1gznz59Xi/Onn35CvXr1YGlpCS8vLyxatEhtupeXF+bOnYvBgwfD3t4eHh4eWLVqlVqdW7duoXfv3qhQoQIqVaqE7t2748aNG9J01f5fuHAhXF1dUblyZYwaNQpPnjyRtu+ff/7BhAkTpBZkAPjnn3/QrVs3VKxYEba2tqhXrx527typ9d+A6FXERJDISExNTTF37lwsW7YM//77b7GWtW/fPty5cweHDh3CJ598gqioKHTt2hUVK1bE0aNHMWLECAwfPrzAeiZNmoSJEyfi9OnTCAoKQrdu3fDgwQMAQEpKCtq2bYtGjRrhxIkT2LVrF5KSktC7d2+1ZaxduxYWFhb4888/sXLlSo3xLVmyBIsWLcLChQtx9uxZhIaG4o033sCVK1cAAHfv3kW9evUwceJE3L17F++9957W225tba3WErl3714kJCRgz5492L59e6Hzffjhh+jduzfOnj2Lzp07o3///nj48CEA4Pbt22jdujUsLS2xb98+nDx5EoMHDy7yEvTatWthZmaGY8eOYcmSJfjkk0/w5ZdfStOfPHmCjz76CGfOnMHmzZtx48YNDBw4UOvt3L17N+7fv4/JkydrnK5qobx9+zY6d+6MJk2a4MyZM1ixYgW++uorzJkzp0C8jo6OOHbsGMaMGYN3330XvXr1QvPmzXHq1Cl06NAB77zzDjIyMtTmmzRpEhYtWoTjx4+jSpUq6Natm5SAnTx5Er1790afPn1w7tw5zJo1CzNmzChwGX3RokUICAjA6dOnMXLkSLz77rtISEiQ9lNoaCjs7e3x+++/488//4SdnR06duyo9nfev38/rl27hv3792Pt2rVYs2aNtJ5NmzahatWqmD17Nu7evYu7d+8CAEaNGoXs7GwcOnQI586dw/z582FnZ6f134DolSSI6KWLiIgQ3bt3F0II0axZMzF48GAhhBA///yzePZjGRUVJRo2bKg276effio8PT3VluXp6Sny8vKkstq1a4tWrVpJ73Nzc4Wtra34/vvvhRBCXL9+XQAQ8+bNk+o8efJEVK1aVcyfP18IIcRHH30kOnTooLbuW7duCQAiISFBCCFEcHCwaNSo0Qu3183NTXz88cdqZU2aNBEjR46U3jds2FBERUUVuZxn95tSqRR79uwRlpaW4r333pOmOzs7i+zsbLX5PD09xaeffiq9ByCmT58uvX/8+LEAIH755RchhBBTp04V3t7eIicn54VxCJG/H+rWrSuUSqVU9v7774u6desWui3Hjx8XAERaWpoQQoj9+/cLAOLRo0ca68+fP18AEA8fPix0mUIIMW3aNFG7dm21WJYvXy7s7OykYyQ4OFi0bNlSmq46Pt555x2p7O7duwKAiIuLU4tvw4YNUp0HDx4Ia2trERsbK4QQol+/fqJ9+/Zq8UyaNEm89tpr0ntPT0/x9ttvS++VSqVwcnISK1asEEIIsW7dugLxZ2dnC2tra7F7924hxNNjPjc3V6rTq1cvER4erraeZ//mQghRv359MWvWrCL3H1F5wxZBIiObP38+1q5di4sXL+q9jHr16sHE5OnH2dnZGfXr15fem5qaonLlygUuRQYFBUm/m5mZISAgQIrjzJkz2L9/P+zs7KRXnTp1AEC6RAoA/v7+RcamUChw584dtGjRQq28RYsWem3z9u3bYWdnBysrK3Tq1Anh4eFql5Lr168PCwuLFy6nQYMG0u+2trZwcHCQ9k98fDxatWoFc3NzreNq1qyZ2o0+QUFBuHLlCvLy8gDkt5Z169YNHh4esLe3R3BwMADg5s2bWi1fPHOZuSgXL15EUFCQWiwtWrTA48eP1VqEn91+1fHx7DHj7OwMAEUeM5UqVULt2rWlv+PFixc1/p2f3Q/Pr1smk8HFxUVaz5kzZ3D16lXY29tLx12lSpWQlZWldtzVq1cPpqam0ntXV9cXXmofO3Ys5syZgxYtWiAqKgpnz54tsj5RecBEkMjIWrdujdDQUEydOrXANBMTkwIJgOoy3LOeT1hkMpnGMqVSqXVcjx8/Rrdu3RAfH6/2unLlClq3bi3Vs7W11XqZhvD6669LcWRmZmLt2rVqMWgbT1H7x9ra2nABA0hPT0doaCgcHBzw3Xff4fjx4/j5558BaH+DTa1atQDk9+s0hBcdM6pEUpdjpjjrVq3n8ePH8Pf3L3DcXb58Gf369dNqGYX5v//7P/z999945513cO7cOQQEBGDZsmUG2iqisomJIFEpMG/ePGzbtg1xcXFq5VWqVEFiYqJaMmjIsf+OHDki/Z6bm4uTJ0+ibt26AIDGjRvjwoUL8PLyQs2aNdVeuiR/Dg4OcHNzw59//qlW/ueff+K1117TOWZbW1vUrFkTHh4eMDMz03l+bTRo0AC///67xqS7MEePHlV7f+TIEfj4+MDU1BSXLl3CgwcPMG/ePLRq1Qp16tTR6UYRAOjQoQMcHR0RExOjcbpq/MG6desiLi5O7Zj5888/YW9vj6pVq+q0Tk2ePWYePXqEy5cvS8dM3bp1Nf6da9WqpdZ6V5TGjRvjypUrcHJyKnDcyeVyreO0sLBQa4VUqVatGkaMGIFNmzZh4sSJ+OKLL7ReJtGriIkgUSlQv3599O/fH0uXLlUrb9OmDe7du4eYmBhcu3YNy5cvV7uTs7iWL1+On3/+GZcuXcKoUaPw6NEjDB48GEB+x/qHDx+ib9++OH78OK5du4bdu3dj0KBBGr9gizJp0iTMnz8fsbGxSEhIwJQpUxAfH49x48YZbFsMafTo0VAoFOjTpw9OnDiBK1euYN26ddINDZrcvHkTkZGRSEhIwPfff49ly5ZJ2+fh4QELCwssW7YMf//9N7Zu3YqPPvpIp5hsbW3x5ZdfYseOHXjjjTfw22+/4caNGzhx4gQmT56MESNGAABGjhyJW7duYcyYMbh06RK2bNmCqKgoREZGqnUf0Nfs2bOxd+9enD9/HgMHDoSjo6N0B/XEiROxd+9efPTRR7h8+TLWrl2Lzz77TKebf/r37w9HR0d0794dv//+O65fv44DBw5g7NixOt1U5eXlhUOHDuH27dvSne7jx4/H7t27cf36dZw6dQr79++Xklii8oqJIFEpMXv27AKXturWrYv//e9/WL58ORo2bIhjx47p9KX6IvPmzcO8efPQsGFD/PHHH9i6dSscHR0BQGrFy8vLQ4cOHVC/fn2MHz8eFSpU0DmhGDt2LCIjIzFx4kTUr18fu3btwtatW+Hj42OwbTGkypUrY9++fXj8+DGCg4Ph7++PL774osg+gwMGDEBmZiaaNm2KUaNGYdy4cdIg1lWqVMGaNWuwceNGvPbaa5g3bx4WLlyoc1zdu3fH4cOHYW5ujn79+qFOnTro27cvUlNTpbuC3d3dsXPnThw7dgwNGzbEiBEjMGTIEEyfPl2/nfGcefPmYdy4cfD390diYiK2bdsm9cls3LgxfvjhB2zYsAG+vr6YOXMmZs+erdPd0TY2Njh06BA8PDzQs2dP1K1bF0OGDEFWVhYcHBy0Xs7s2bNx48YN1KhRA1WqVAEA5OXlYdSoUahbty46duyIWrVq4X//+59O20/0qpEJbXsgExGRRm3atIGfn98r/SSLAwcO4PXXX8ejR4+MMpg2EZUMtggSERERlVNMBImIiIjKKV4aJiIiIiqn2CJIREREVE4xESQiIiIqp5gIEhEREZVTTASJiIiIyikmgkRERETlFBNBIiIionKKiSARERFROcVEkIiIiKicYiJIREREVE79P5YkdRB2v8W/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(0,30), np.cumsum(normalizedComp.explained_variance_), marker='*')\n",
    "plt.title('Cummulative Explained Variability vs. Number of Principal Components')\n",
    "plt.ylabel('Cummulative Explained Variability')\n",
    "plt.xlabel('Number of Principal Components')\n",
    "#plt.bar(range(0,30), normalizedComp.explained_variance_ratio_, alpha=0.3)\n",
    "index = np.where(cumPercentage2 > 0.9)[0][0]\n",
    "print('Number of principal components necessary to explain 90% of the total variability is', index+1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ad2dec4-e7e2-48a8-8ed0-933891cd1426",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}