07-working-with-text-data.ipynb

{
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      "/var/folders/l_/499_8k_5387b9tz3wp5g8lg00000gn/T/ipykernel_2693/2603895367.py:13: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
      "  set_matplotlib_formats('pdf', 'png')\n"
     ]
    }
   ],
   "source": [
    "#from preamble import *\n",
    "#%matplotlib inline\n",
    "#=================================\n",
    "from IPython.display import set_matplotlib_formats, display\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import mglearn\n",
    "from cycler import cycler\n",
    "\n",
    "set_matplotlib_formats('pdf', 'png')\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['image.cmap'] = \"viridis\"\n",
    "plt.rcParams['image.interpolation'] = \"none\"\n",
    "plt.rcParams['savefig.bbox'] = \"tight\"\n",
    "plt.rcParams['lines.linewidth'] = 2\n",
    "plt.rcParams['legend.numpoints'] = 1\n",
    "plt.rc('axes', prop_cycle=( cycler('color', mglearn.plot_helpers.cm_cycle.colors) +  cycler('linestyle', ['-', '-', \"--\", (0, (3, 3)), (0, (1.5, 1.5))])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> You now possess all the required skills and know the necessary mechanisms to apply machine learning in practice. In the next chapter, we will dive in more detail into one particular type of data that is commonly seen in practice, and that requires some special expertise to handle correctly: text data. <br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Text Data\n",
    "\n",
    "- We will need to process the data before we can apply our machine learning algorithms to it. <br><br>\n",
    "- We have seen two kinds of features that can represent properties of the data: \n",
    "  - continuous features that describe a quantity,  \n",
    "  - categorical features that are items from a fixed list.  <br><br>\n",
    "- There is a third kind of feature that can be found in many applications,  <br><br>\n",
    "  - text features e.g email, tweets, etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Text data is usually represented as strings, made up of characters. This feature is clearly very different from the numeric features that we’ve discussed so far, and we will need to process the data before we can apply our machine learning algorithms to it\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "\n",
    "![](./images/featurersandtarget.png)\n",
    "\n",
    "<center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "\n",
    "![Iamge](https://cdn.numerade.com/ask_images/fc5cecce79914177808ec147b0eddb54.jpg)\n",
    "\n",
    "<center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<center>\n",
    "\n",
    "![Iamge](https://onlinelibrary.wiley.com/cms/asset/f0957aa1-a8f4-4372-9b7c-a97185821b1b/cpe4209-fig-0002-m.jpg)\n",
    "\n",
    "<center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types of data represented as strings\n",
    "\n",
    "There are four kinds of string data you might see:  <br><br>\n",
    "- Categorical data <br>\n",
    "- Free strings that can be semantically mapped to categories   <br>\n",
    "- Structured string data  <br>\n",
    "- Text data\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Data\n",
    "\n",
    "- Categorical data is data that comes from a fixed list. <br><br>\n",
    "- Say you collect data via a survey where you ask people their favorite color, with a drop-down menu that allows them to select from “red,” “green,” “blue,” “yellow,” “black,” “white,” “purple,” and “pink.”  <br><br>\n",
    "- This will result in a dataset with exactly eight different possible values, which clearly encode a categorical variable.  <br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Free String\n",
    "\n",
    "- Imagine instead of providing a drop-down menu, you provide a text field for the users to provide their own favorite colors.  <br><br>\n",
    "- Many people might respond with a color name like “black” or “blue.” Others might make typographical errors, use different spellings like “gray” and “grey,” or use more evocative and specific names like “midnight blue.” <br><br>\n",
    "- You will also have some very strange entries. “my dentist’s office orange. <br><br>\n",
    "- This belong to the second category of strings called free strings that can be semantically mapped to categories. <br><br>\n",
    "- It will probably be best to encode this data as a categorical variable, where you can select the categories either by using the most common entries, or by defining categories that will capture responses in a way that makes sense for your application. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Structured\n",
    "\n",
    "- Often manually entered values do not correspond to fixed categories, but still have some underlying structure, like addresses, names of places or people, dates, telephone numbers, or other identifiers. <br><br>\n",
    "- These kinds of strings are often very hard to parse, and their treatment is highly dependent on context and domain. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Data\n",
    "\n",
    "- The final category of string data is free form text data that consists of phrases or sentences. <br><br>\n",
    "-  Examples include tweets, chat logs, and hotel reviews, as well as the collected works of Shakespeare, the content of Wikipedia, or the Project Gutenberg collection of 50,000 ebooks. \n",
    "- In NLP, In the context of text analysis, the dataset is often called the corpus, and each data point, represented as a single text, is called a document. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<center>\n",
    "\n",
    "![Iamge](https://miro.medium.com/max/1838/1*TDF6jWl2PBdZySfHg_DkeA.png)\n",
    "\n",
    "<center>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<center>\n",
    "\n",
    "![Iamge](\n",
    "https://www.researchgate.net/profile/Hiroki-Ouchi/publication/311669623/figure/fig4/AS:439788613443585@1481865184595/The-flow-of-the-corpus-and-dataset-creation-From-the-original-logs-we-extract-addressee.png)\n",
    "\n",
    "<center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example application: Sentiment analysis of movie reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: command not found: wget\n"
     ]
    }
   ],
   "source": [
    "! wget -nc http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz -P data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "\n",
    "![Iamge](https://miro.medium.com/max/886/1*xLoPv538-PCpRGmkIkgPtw.png)\n",
    "\n",
    "<center>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aclImdb/train/unsupBow.feat: truncated gzip input\n",
      "tar: Error exit delayed from previous errors.\n"
     ]
    }
   ],
   "source": [
    "! tar xzf data/aclImdb_v1.tar.gz -C data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: command not found: tree\n"
     ]
    }
   ],
   "source": [
    "!tree -dL 2 data/aclImdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -r data/aclImdb/train/unsup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: command not found: tree\n"
     ]
    }
   ],
   "source": [
    "!tree -dL 2 data/aclImdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a helper function in scikit-learn to load files stored in such a folder structure, where each subfolder corresponds to a label, called load_files. We apply the load_files function first to the training data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is helper function?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type of text_train: <class 'list'>\n",
      "length of text_train: 25000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_files\n",
    "\n",
    "reviews_train = load_files(\"data/aclImdb/train/\")\n",
    "# load_files returns a bunch, containing training texts and training labels\n",
    "text_train, y_train = reviews_train.data, reviews_train.target\n",
    "print(\"type of text_train: {}\".format(type(text_train)))\n",
    "print(\"length of text_train: {}\".format(len(text_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text_train[6]:\n",
      "b\"This movie has a special way of telling the story, at first i found it rather odd as it jumped through time and I had no idea whats happening.<br /><br />Anyway the story line was although simple, but still very real and touching. You met someone the first time, you fell in love completely, but broke up at last and promoted a deadly agony. Who hasn't go through this? but we will never forget this kind of pain in our life. <br /><br />I would say i am rather touched as two actor has shown great performance in showing the love between the characters. I just wish that the story could be a happy ending.\"\n"
     ]
    }
   ],
   "source": [
    "print(\"text_train[6]:\\n{}\".format(text_train[6]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- You can see that text_train is a list of length 25,000, where each entry is a string containing a review. We printed the review with index 1. You can also see that the review contains some HTML line breaks `(<br />)`.<br><br> \n",
    "\n",
    "- While these are unlikely to have a large impact on our machine learning models, it is better to clean the data and remove this formatting before we proceed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I like apples'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txt = \"I like bananas\"\n",
    "\n",
    "x = txt.replace(\"bananas\", \"apples\")\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'kiwi', 'mango']\n"
     ]
    }
   ],
   "source": [
    "fruits = [\"Apple\", \"Banana\", \"Cherry\", \"Kiwi\", \"Mango\"]\n",
    "\n",
    "newlist = [x.lower() for x in fruits]\n",
    "\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text_train = [doc.replace(b\"<br />\", b\" \") for doc in text_train]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text_train[6]:\n",
      "b\"This movie has a special way of telling the story, at first i found it rather odd as it jumped through time and I had no idea whats happening.  Anyway the story line was although simple, but still very real and touching. You met someone the first time, you fell in love completely, but broke up at last and promoted a deadly agony. Who hasn't go through this? but we will never forget this kind of pain in our life.   I would say i am rather touched as two actor has shown great performance in showing the love between the characters. I just wish that the story could be a happy ending.\"\n"
     ]
    }
   ],
   "source": [
    "print(\"text_train[6]:\\n{}\".format(text_train[6]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we want predict?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "<p>25000 rows × 1 columns</p>\n",
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "target = pd.DataFrame(y_train) # 0 = negative, 1 = positive\n",
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset was collected such that the positive class and the negative class balanced, so that there are as many positive as negative strings:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples per class (training): [12500 12500]\n"
     ]
    }
   ],
   "source": [
    "print(\"Samples per class (training): {}\".format(np.bincount(y_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets observe test set data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of documents in test data: 25000\n",
      "Samples per class (test): [12500 12500]\n"
     ]
    }
   ],
   "source": [
    "reviews_test = load_files(\"data/aclImdb/test/\")\n",
    "text_test, y_test = reviews_test.data, reviews_test.target\n",
    "print(\"Number of documents in test data: {}\".format(len(text_test)))\n",
    "print(\"Samples per class (test): {}\".format(np.bincount(y_test)))\n",
    "text_test = [doc.replace(b\"<br />\", b\" \") for doc in text_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Representing text data as Bag of Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> One of the most simple but effective and commonly used ways to represent text for machine learning is using the bag-of-words representation. When using this representation, we discard most of the structure of the input text, like chapters, paragraphs, sentences, and formatting, and only count how much each word appears in each text in the corpus\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<center>\n",
    "\n",
    "![Iamge](https://dataaspirant.com/wp-content/uploads/2021/01/1-Bag-of-words.png)\n",
    "\n",
    "<center>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "\n",
    "![Iamge](https://miro.medium.com/max/1400/0*ahNY0R4Lm5oqr5yy.png)\n",
    "\n",
    "<center>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing the bag-of-words representation for a corpus of documents consists of the following three steps\n",
    "\n",
    "\n",
    " 1. Tokenization. Split each document into the words that appear in it (called tokens), for example by splitting them on whitespace and punctuation. <br><br>\n",
    " 2. Vocabulary building. Collect a vocabulary of all words that appear in any of the documents, and number them (say, in alphabetical order). <br><br>\n",
    " 3. Encoding. For each document, count how often each of the words in the vocabulary appear in this document.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![bag_of_words](images/bag_of_words.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying bag-of-words to a toy dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bards_words =[\"The fool doth think he is wise,\",\n",
    "              \"but the wise man knows himself to be a fool\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bag-of-words representation is implemented in CountVectorizer, which is a transformer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vect = CountVectorizer()\n",
    "vect.fit(bards_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the CountVectorizer consists of the tokenization of the training data and building of the vocabulary, which we can access as the vocabulary_ attribute:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 13\n",
      "Vocabulary content:\n",
      " {'the': 9, 'fool': 3, 'doth': 2, 'think': 10, 'he': 4, 'is': 6, 'wise': 12, 'but': 1, 'man': 8, 'knows': 7, 'himself': 5, 'to': 11, 'be': 0}\n"
     ]
    }
   ],
   "source": [
    "print(\"Vocabulary size: {}\".format(len(vect.vocabulary_)))\n",
    "print(\"Vocabulary content:\\n {}\".format(vect.vocabulary_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create the bag-of-words representation for the training data, we call the transform method:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bag_of_words: <2x13 sparse matrix of type '<class 'numpy.int64'>'\n",
      "\twith 16 stored elements in Compressed Sparse Row format>\n"
     ]
    }
   ],
   "source": [
    "bag_of_words = vect.transform(bards_words)\n",
    "print(\"bag_of_words: {}\".format(repr(bag_of_words)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bag-of-words representation is stored in a SciPy sparse matrix that only stores the entries that are nonzero. The matrix is of shape 2×13, with one row for each of the two data points and one feature for each of the words in the vocabulary. A sparse matrix is used as most documents only contain a small subset of the words in the vocabulary, meaning most entries in the feature array are 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Storing all those zeros would be prohibitive, and a waste of memory. To look at the actual content of the sparse matrix, we can convert it to a “dense” NumPy array (that also stores all the 0 entries) using the toarray method\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dense representation of bag_of_words:\n",
      "[[0 0 1 1 1 0 1 0 0 1 1 0 1]\n",
      " [1 1 0 1 0 1 0 1 1 1 0 1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Dense representation of bag_of_words:\\n{}\".format(\n",
    "    bag_of_words.toarray()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bards_words =[\"The fool doth think he is wise,\", \\\n",
    "\"but the wise man knows himself to be a fool\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s take a look at how to read these feature vectors. The first string (\"The fool doth think he is wise,\") is represented as the first row in, and it contains the first word in the vocabulary, \"be\", zero times. It also contains the second word in the vocabulary, \"but\", zero times. It contains the third word, \"doth\", once, and so on. Looking at both rows, we can see that the fourth word, \"fool\", the tenth word, \"the\", and the thirteenth word, \"wise\", appear in both strings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag-of-word for movie reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we’ve gone through the bag-of-words process in detail, let’s apply it to our task of sentiment analysis for movie reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b\"Zero Day leads you to think, even re-think why two boys/young men would do what they did - commit mutual suicide via slaughtering their classmates. It captures what must be beyond a bizarre mode of being for two humans who have decided to withdraw from common civility in order to define their own/mutual world via coupled destruction.  It is not a perfect movie but given what money/time the filmmaker and actors had - it is a remarkable product. In terms of explaining the motives and actions of the two young suicide/murderers it is better than 'Elephant' - in terms of being a film that gets under our 'rationalistic' skin it is a far, far better film than almost anything you are likely to see.   Flawed but honest with a terrible honesty.\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:\n",
      "<25000x74849 sparse matrix of type '<class 'numpy.int64'>'\n",
      "\twith 3431196 stored elements in Compressed Sparse Row format>\n"
     ]
    }
   ],
   "source": [
    "vect = CountVectorizer().fit(text_train)\n",
    "X_train = vect.transform(text_train)\n",
    "print(\"X_train:\\n{}\".format(repr(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of X_train, the bag-of-words representation of the training data, is 25,000×74,849, indicating that the vocabulary contains 74,849 entries. Again, the data is stored as a SciPy sparse matrix. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s look at the vocabulary in a bit more detail. Another way to access the vocabulary is using the get_feature_name method of the vectorizer, which returns a convenient list where each entry corresponds to one feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features: 74849\n",
      "First 20 features:\n",
      "['00', '000', '0000000000001', '00001', '00015', '000s', '001', '003830', '006', '007', '0079', '0080', '0083', '0093638', '00am', '00pm', '00s', '01', '01pm', '02']\n",
      "Features 20010 to 20030:\n",
      "['dratted', 'draub', 'draught', 'draughts', 'draughtswoman', 'draw', 'drawback', 'drawbacks', 'drawer', 'drawers', 'drawing', 'drawings', 'drawl', 'drawled', 'drawling', 'drawn', 'draws', 'draza', 'dre', 'drea']\n",
      "Every 2000th feature:\n",
      "['00', 'aesir', 'aquarian', 'barking', 'blustering', 'bête', 'chicanery', 'condensing', 'cunning', 'detox', 'draper', 'enshrined', 'favorit', 'freezer', 'goldman', 'hasan', 'huitieme', 'intelligible', 'kantrowitz', 'lawful', 'maars', 'megalunged', 'mostey', 'norrland', 'padilla', 'pincher', 'promisingly', 'receptionist', 'rivals', 'schnaas', 'shunning', 'sparse', 'subset', 'temptations', 'treatises', 'unproven', 'walkman', 'xylophonist']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "feature_names = vect.get_feature_names()\n",
    "print(\"Number of features: {}\".format(len(feature_names)))\n",
    "print(\"First 20 features:\\n{}\".format(feature_names[:20]))\n",
    "print(\"Features 20010 to 20030:\\n{}\".format(feature_names[20010:20030]))\n",
    "print(\"Every 2000th feature:\\n{}\".format(feature_names[::2000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, possibly a bit surprisingly, the first 10 entries in the vocabulary are all numbers. All these numbers appear somewhere in the reviews, and are therefore extracted as words. Most of these numbers don’t have any immediate semantic meaning—apart from \"007\", which in the particular context of movies is likely to refer to the James Bond character\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Before we try to improve our feature extraction, let’s obtain a quantitative measure of performance by actually building a classifier. We have the training labels stored in y_train and the bag-of-words representation of the training data in X_train, so we can train a classifier on this data. For high-dimensional, sparse data like this, linear models like LogisticRegression often work best\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross-validation accuracy: 0.88\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "scores = cross_val_score(LogisticRegression(max_iter = 1000), X_train, y_train, cv=5)\n",
    "print(\"Mean cross-validation accuracy: {:.2f}\".format(np.mean(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain a mean cross-validation score of 88%, which indicates reasonable performance for a balanced binary classification task. We know that LogisticRegression has a regularization parameter, C, which we can tune via cross-validation:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.89\n",
      "Best parameters:  {'C': 0.1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10]}\n",
    "grid = GridSearchCV(LogisticRegression(), param_grid, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))\n",
    "print(\"Best parameters: \", grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain a cross-validation score of 89% using C=0.1. We can now assess the generalization performance of this parameter setting on the test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.88\n"
     ]
    }
   ],
   "source": [
    "X_test = vect.transform(text_test)\n",
    "print(\"Test score: {:.2f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may notice that we violate our lesson from Chapter 6 on cross-validation with preprocessing here. Using the default settings of CountVectorizer, it actually does not collect any statistics, so our results are valid. Using Pipeline from the start would be a better choice for applications, but we defer it for ease of exposure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How can we improve performance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- One way to select important feautures is to select only use tokens that appear in at least two documents (or at least five documents, and so on). <br><br>\n",
    "\n",
    "- A token that appears only in a single document is unlikely to appear in the test set and is therefore not helpful. We can set the minimum number of documents a token needs to appear in with the min_df parameter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train with min_df: <25000x27271 sparse matrix of type '<class 'numpy.int64'>'\n",
      "\twith 3354014 stored elements in Compressed Sparse Row format>\n"
     ]
    }
   ],
   "source": [
    "vect = CountVectorizer(min_df=5).fit(text_train)\n",
    "X_train = vect.transform(text_train)\n",
    "print(\"X_train with min_df: {}\".format(repr(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By requiring at least five appearances of each token, we can bring down the number of features to 27,271, as seen in the preceding output—only about a third of the original features. Let’s look at some tokens again\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 50 features:\n",
      "['00', '000', '007', '00s', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '100', '1000', '100th', '101', '102', '103', '104', '105', '107', '108', '10s', '10th', '11', '110', '112', '116', '117', '11th', '12', '120', '12th', '13', '135', '13th', '14', '140', '14th', '15', '150', '15th', '16', '160', '1600', '16mm', '16s', '16th']\n",
      "Features 20010 to 20030:\n",
      "['repentance', 'repercussions', 'repertoire', 'repetition', 'repetitions', 'repetitious', 'repetitive', 'rephrase', 'replace', 'replaced', 'replacement', 'replaces', 'replacing', 'replay', 'replayable', 'replayed', 'replaying', 'replays', 'replete', 'replica']\n",
      "Every 700th feature:\n",
      "['00', 'affections', 'appropriately', 'barbra', 'blurbs', 'butchered', 'cheese', 'commitment', 'courts', 'deconstructed', 'disgraceful', 'dvds', 'eschews', 'fell', 'freezer', 'goriest', 'hauser', 'hungary', 'insinuate', 'juggle', 'leering', 'maelstrom', 'messiah', 'music', 'occasional', 'parking', 'pleasantville', 'pronunciation', 'recipient', 'reviews', 'sas', 'shea', 'sneers', 'steiger', 'swastika', 'thrusting', 'tvs', 'vampyre', 'westerns']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "feature_names = vect.get_feature_names()\n",
    "\n",
    "print(\"First 50 features:\\n{}\".format(feature_names[:50]))\n",
    "print(\"Features 20010 to 20030:\\n{}\".format(feature_names[20010:20030]))\n",
    "print(\"Every 700th feature:\\n{}\".format(feature_names[::700]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are clearly many fewer numbers, and some of the more obscure words or misspellings seem to have vanished."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  Let’s see how well our model performs by doing a grid search again:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "grid = GridSearchCV(LogisticRegression(), param_grid, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The best validation accuracy of the grid search is still 89%, unchanged from before. We didn’t improve our model, but having fewer features to deal with speeds up processing and throwing away useless features might make the model more interpretabl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stop-words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Another way that we can get rid of uninformative words is by discarding words that are too frequent to be informative. <br><br>\n",
    "- There are two main approaches: using a languagespecific list of stopwords, or discarding words that appear too frequently. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 5, 7, 9]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1,2,4,4,5,6,7,8,9,10]\n",
    "\n",
    "#a[1:5:2]\n",
    "#a[:5] # first 5 elements\n",
    "a[::2] # every other element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stop words: 318\n",
      "Every 10th stopword:\n",
      "['are', 'toward', 'about', 'this', 'every', 'became', 'on', 'yet', 'has', 'around', 'somewhere', 'thereupon', 'those', 'anyway', 'me', 'rather', 'if', 'from', 'own', 'forty', 'upon', 'whereafter', 'fill', 'under', 'whoever', 'nothing', 'five', 'whenever', 'into', 'twelve', 'we', 'co']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS\n",
    "print(\"Number of stop words: {}\".format(len(ENGLISH_STOP_WORDS)))\n",
    "print(\"Every 10th stopword:\\n{}\".format(list(ENGLISH_STOP_WORDS)[::10])) # every 10th stopword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Every 10th stopword:\n",
      "['are', 'fifty', 'two', 'still', 'herself', 'describe', 'four', 'whether', 'therefore', 'together']\n"
     ]
    }
   ],
   "source": [
    "print(\"Every 10th stopword:\\n{}\".format(list(ENGLISH_STOP_WORDS)[:10]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Clearly, removing the stopwords in the list can only decrease the number of features by the length of the list—here, 318—but it might lead to an improvement in performance. Let’s give it a try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train with stop words:\n",
      "<25000x26966 sparse matrix of type '<class 'numpy.int64'>'\n",
      "\twith 2149958 stored elements in Compressed Sparse Row format>\n"
     ]
    }
   ],
   "source": [
    "# Specifying stop_words=\"english\" uses the built-in list.\n",
    "# We could also augment it and pass our own.\n",
    "vect = CountVectorizer(min_df=5, stop_words=\"english\").fit(text_train)\n",
    "X_train = vect.transform(text_train)\n",
    "print(\"X_train with stop words:\\n{}\".format(repr(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There are now 305 fewer features in the dataset, which means that most, but not all, of the stopwords appeared. Let’s run the grid search again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.88\n"
     ]
    }
   ],
   "source": [
    "grid = GridSearchCV(LogisticRegression(max_iter = 1000 ), param_grid, cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The grid search performance decreased slightly using the stopwords—not enough to worry about, but given that excluding 305 features out of over 27,000 is unlikely to change performance or interpretability a lot, it doesn’t seem worth using this list”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rescaling the Data with tf-idf\n",
    "> Instead of dropping features that are deemed unimportant, another approach is to rescale features by how informative we expect them to be. One of the most common ways to do this is using the term frequency–inverse document frequency (tf–idf) method\n",
    "\n",
    "\\begin{equation*}\n",
    "\\text{tfidf}(w, d) = \\text{tf} \\log\\big(\\frac{N + 1}{N_w + 1}\\big) + 1\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> where N is the number of documents in the training set, N_w is the number of documents in the training set that the word w appears in, and tf (the term frequency) is the number of times that the word w appears in the query document d (the document you want to transform or encode)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Article on [tf-idf](https://medium.com/analytics-vidhya/tf-idf-term-frequency-technique-easiest-explanation-for-text-classification-in-nlp-with-code-8ca3912e58c3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Because tf–idf actually makes use of the statistical *properties* of the training data, we will use a pipeline, as described in Chapter 6, to ensure the *results* of our grid search are valid. This leads to the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.89\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "pipe = make_pipeline(TfidfVectorizer(min_df=5, norm=None),\n",
    "                     LogisticRegression(max_iter= 1000))\n",
    "param_grid = {'logisticregression__C': [0.001, 0.01, 0.1, 1, 10]}\n",
    "grid = GridSearchCV(pipe, param_grid, cv=5)\n",
    "grid.fit(text_train, y_train)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there is some improvement when using tf–idf instead of just word counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We can also inspect which words tf–idf found most important. First, we extract the TfidfVectorizer from the pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with lowest tfidf:\n",
      "['poignant' 'disagree' 'instantly' 'importantly' 'lacked' 'occurred'\n",
      " 'currently' 'altogether' 'nearby' 'undoubtedly' 'directs' 'fond'\n",
      " 'stinker' 'avoided' 'emphasis' 'commented' 'disappoint' 'realizing'\n",
      " 'downhill' 'inane']\n",
      "Features with highest tfidf: \n",
      "['coop' 'homer' 'dillinger' 'hackenstein' 'gadget' 'taker' 'macarthur'\n",
      " 'vargas' 'jesse' 'basket' 'dominick' 'the' 'victor' 'bridget' 'victoria'\n",
      " 'khouri' 'zizek' 'rob' 'timon' 'titanic']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "vectorizer = grid.best_estimator_.named_steps[\"tfidfvectorizer\"]\n",
    "# transform the training dataset:\n",
    "X_train = vectorizer.transform(text_train)\n",
    "# find maximum value for each of the features over dataset:\n",
    "max_value = X_train.max(axis=0).toarray().ravel()\n",
    "sorted_by_tfidf = max_value.argsort()\n",
    "# get feature names\n",
    "feature_names = np.array(vectorizer.get_feature_names())\n",
    "\n",
    "print(\"Features with lowest tfidf:\\n{}\".format(\n",
    "      feature_names[sorted_by_tfidf[:20]]))\n",
    "\n",
    "print(\"Features with highest tfidf: \\n{}\".format(\n",
    "      feature_names[sorted_by_tfidf[-20:]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Features with low tf–idf are those that either are very commonly used across documents or are only used sparingly, and only in very long documents. Interestingly, many of the high-tf–idf features actually identify certain shows or movies. These terms only appear in reviews for this particular show or franchise, but tend to appear very often in these particular reviews. This is very clear, for example, for \"pokemon\", \"smallville\", and \"doodlebops\", but \"scanners\" here actually also refers to a movie title. These words are unlikely to help us in our sentiment classification task (unless maybe some franchises are universally reviewed positively or negatively) but certainly contain a lot of specific information about the reviews. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also find the words that have low inverse document frequency—that is, those that appear frequently and are therefore deemed less important. The inverse document frequency values found on the training set are stored in the idf_ attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([3, 1, 2]) #One dimensional array:\n",
    "np.argsort(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.71995186, 5.7285084 , 8.35408238, ..., 8.48761377, 9.33491163,\n",
       "       9.33491163])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.idf_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with lowest idf:\n",
      "['the' 'and' 'of' 'to' 'this' 'is' 'it' 'in' 'that' 'but' 'for' 'with'\n",
      " 'was' 'as' 'on' 'movie' 'not' 'have' 'one' 'be' 'film' 'are' 'you' 'all'\n",
      " 'at' 'an' 'by' 'so' 'from' 'like' 'who' 'they' 'there' 'if' 'his' 'out'\n",
      " 'just' 'about' 'he' 'or' 'has' 'what' 'some' 'good' 'can' 'more' 'when'\n",
      " 'time' 'up' 'very' 'even' 'only' 'no' 'would' 'my' 'see' 'really' 'story'\n",
      " 'which' 'well' 'had' 'me' 'than' 'much' 'their' 'get' 'were' 'other'\n",
      " 'been' 'do' 'most' 'don' 'her' 'also' 'into' 'first' 'made' 'how' 'great'\n",
      " 'because' 'will' 'people' 'make' 'way' 'could' 'we' 'bad' 'after' 'any'\n",
      " 'too' 'then' 'them' 'she' 'watch' 'think' 'acting' 'movies' 'seen' 'its'\n",
      " 'him']\n"
     ]
    }
   ],
   "source": [
    "sorted_by_idf = np.argsort(vectorizer.idf_)\n",
    "print(\"Features with lowest idf:\\n{}\".format(\n",
    "       feature_names[sorted_by_idf[:100]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> “As expected, these are mostly English stopwords like \"the\" and \"no\". But some are clearly domain-specific to the movie reviews, like \"movie\", \"film\", \"time\", \"story\", and so on. Interestingly, \"good\", \"great\", and \"bad\" are also among the most frequent and therefore “least relevant” words according to the tf–idf measure, even though we might expect these to be very important for our sentiment analysis task” "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Investigating model coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- let’s look in a bit more detail into what our logistic regression model actually learned from the data. <br><br>\n",
    "-  Because there are so many features—27,271 after removing the infrequent ones—we clearly cannot look at all of the coefficients at the same time. <br><br>\n",
    "-  However, we can look at the largest coefficients, and see which words these correspond to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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MOpZyee4tDgyG0wiNQoZ1w1ekHZAd50R4xEgnNO/xuq0e2YUhNm//eNhfTiV0uTIivRar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\nJUVPRgo=\n",
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9833a6160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will use the last model that we trained, based on the tf–idf features.\n",
    "mglearn.tools.visualize_coefficients(\n",
    "    grid.best_estimator_.named_steps[\"logisticregression\"].coef_,\n",
    "    feature_names, n_top_features=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> “The negative coefficients on the left belong to words that according to the model are indicative of negative reviews, while the positive coefficients on the right belong to words that according to the model indicate positive reviews”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the terms are quite intuitive, like \"worst\", \"waste\", \"disappointment\", and \"laughable\" indicating bad movie reviews, while \"excellent\", \"wonderful\", \"enjoyable\", and \"refreshing\" indicate positive movie reviews. Some words are slightly less clear, like \"bit\", \"job\", and \"today\", but these might be part of phrases like “good job” or “best today.”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bag of words with more than one word (n-grams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the main disadvantages of using a bag-of-words representation is that word order is completely discarded. Therefore, the two strings “it’s bad, not good at all” and “it’s good, not bad at all” have exactly the same representation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From a BoW approach all words are broken into count and frequency with no preference to a word in particular, all words have same frequency here (1 in this case)and obviously there is no emphasis on \"its good\" and \"not good\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The words are just broken down and if we were talking about importance, ‘it’ is as important as ‘good’ or ‘bad’.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, another major drawback is say a document has 200 words, out of which ‘a’ comes 20 times, ‘the’ comes 15 times etc.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bag of words needed an upgrade to incoporate this !\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many words which are repeated again and again are given more importance in final feature building and we miss out on context of less repeated but important words like \"good\", ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, there is a way of capturing context when using a bag-of-words representation, by not only considering the counts of single tokens, but also the counts of pairs or triplets of tokens that appear next to each other. Pairs of tokens are known as \"bigrams\", triplets of tokens are known as \"trigrams\", and more generally sequences of tokens are known as \"n-grams\". We can change the range of tokens that are considered as features by changing the ngram_range parameter of CountVectorizer or TfidfVectorizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bards_words:\n",
      "['The fool doth think he is wise,', 'but the wise man knows himself to be a fool']\n"
     ]
    }
   ],
   "source": [
    "print(\"bards_words:\\n{}\".format(bards_words))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the ngram_range parameter, which is a tuple, consisting of the minimum length and the maximum length of the sequences of tokens that are considered. Here is an example on the toy data we used earlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default is to create one feature per sequence of tokens that is at least one token long and at most one token long, or in other words exactly one token long (single tokens are also called unigrams):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 13\n",
      "Vocabulary:\n",
      "['be', 'but', 'doth', 'fool', 'he', 'himself', 'is', 'knows', 'man', 'the', 'think', 'to', 'wise']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "cv = CountVectorizer(ngram_range=(1, 1)).fit(bards_words)\n",
    "print(\"Vocabulary size: {}\".format(len(cv.vocabulary_)))\n",
    "print(\"Vocabulary:\\n{}\".format(cv.get_feature_names()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 14\n",
      "Vocabulary:\n",
      "['be fool', 'but the', 'doth think', 'fool doth', 'he is', 'himself to', 'is wise', 'knows himself', 'man knows', 'the fool', 'the wise', 'think he', 'to be', 'wise man']\n"
     ]
    }
   ],
   "source": [
    "cv = CountVectorizer(ngram_range=(2, 2)).fit(bards_words)\n",
    "print(\"Vocabulary size: {}\".format(len(cv.vocabulary_)))\n",
    "print(\"Vocabulary:\\n{}\".format(cv.get_feature_names()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformed data (dense):\n",
      "[[0 0 1 1 1 0 1 0 0 1 0 1 0 0]\n",
      " [1 1 0 0 0 1 0 1 1 0 1 0 1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Transformed data (dense):\\n{}\".format(cv.transform(bards_words).toarray()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Using longer sequences of tokens usually results in many more features, and in more specific features. There is no common bigram between the two phrases in bard\\_words:”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- “For most applications, the minimum number of tokens should be one, as single words often capture a lot of meaning. Adding bigrams helps in most cases” <br><br>\n",
    "- Adding longer sequences—up to 5-grams—might help too, but this will lead to an explosion of the number of features and might lead to overfitting, as there will be many very specific features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 39\n",
      "Vocabulary:['be', 'be fool', 'but', 'but the', 'but the wise', 'doth', 'doth think', 'doth think he', 'fool', 'fool doth', 'fool doth think', 'he', 'he is', 'he is wise', 'himself', 'himself to', 'himself to be', 'is', 'is wise', 'knows', 'knows himself', 'knows himself to', 'man', 'man knows', 'man knows himself', 'the', 'the fool', 'the fool doth', 'the wise', 'the wise man', 'think', 'think he', 'think he is', 'to', 'to be', 'to be fool', 'wise', 'wise man', 'wise man knows']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cv = CountVectorizer(ngram_range=(1, 3)).fit(bards_words)\n",
    "print(\"Vocabulary size: {}\".format(len(cv.vocabulary_)))\n",
    "print(\"Vocabulary:\\n{}\".format(cv.get_feature_names()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Let’s try out the TfidfVectorizer on the IMDb movie review data and find the best setting of n-gram range using a grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.91\n",
      "Best parameters:\n",
      "{'logisticregression__C': 100, 'tfidfvectorizer__ngram_range': (1, 3)}\n"
     ]
    }
   ],
   "source": [
    "pipe = make_pipeline(TfidfVectorizer(min_df=5), LogisticRegression())\n",
    "# running the grid-search takes a long time because of the\n",
    "# relatively large grid and the inclusion of trigrams\n",
    "param_grid = {'logisticregression__C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              \"tfidfvectorizer__ngram_range\": [(1, 1), (1, 2), (1, 3)]}\n",
    "\n",
    "grid = GridSearchCV(pipe, param_grid, cv=5)\n",
    "grid.fit(text_train, y_train)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))\n",
    "print(\"Best parameters:\\n{}\".format(grid.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from the results, we improved performance by a bit more than a percent by adding bigram and trigram features. We can visualize the cross-validation accuracy as a function of the ngram_range and C parameter as a heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fa987ed74e0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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NxOaevt0/wjmf6/sXFriD4AplbmRzdHJlYW0KZW5kb2JqCjM3IDAgb2JqCjw8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\ncnR4cmVmCjExMTgzCiUlRU9GCg==\n",
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa983b4a400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract scores from grid_search\n",
    "scores = grid.cv_results_['mean_test_score'].reshape(-1, 3).T\n",
    "# visualize heat map\n",
    "heatmap = mglearn.tools.heatmap(\n",
    "    scores, xlabel=\"C\", ylabel=\"ngram_range\", cmap=\"viridis\", fmt=\"%.3f\",\n",
    "    xticklabels=param_grid['logisticregression__C'],\n",
    "    yticklabels=param_grid['tfidfvectorizer__ngram_range'])\n",
    "plt.colorbar(heatmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heat map we can see that using bigrams increases performance quite a bit, while adding trigrams only provides a very small benefit in terms of accuracy” "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-22, 22)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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XccnEBVN12MiZw0+mjAvtXM8NyLkR1mUYpJuVxoyEI00hUkih6iapM0GQBKOr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\nMTY4MzAKJSVFT0YK\n",
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa983822588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract feature names and coefficients\n",
    "vect = grid.best_estimator_.named_steps['tfidfvectorizer']\n",
    "feature_names = np.array(vect.get_feature_names())\n",
    "coef = grid.best_estimator_.named_steps['logisticregression'].coef_\n",
    "mglearn.tools.visualize_coefficients(coef, feature_names, n_top_features=40)\n",
    "plt.ylim(-22, 22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-22, 22)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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yNDggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVE5kgNBCMvn\nFXpCc9PvscuR9//pCsoBg4ZDIDotcVDGTxCWK97yyFW04e+ZGMF3waHfynUbFjkQFUjSGFRNqF28\nHr0HdhxmAvOkNSyDGesDP2MKN3pxeEzG2e11GTUEe9drT2ZQMisXccnEBVN12MiZw0+mjAvtXM8N\nyLkR1mUYpJuVxoyEI00hUkih6iapM0GQBKOrUaONHMV+6csjnWFVI2oM+1xL29dzE84aNDsWqzw5\npUdXnMvJxQsrB/28zcBFVBqrPBAScL/bQ/2c7OQ33tK5s8X0+F5zsrwwFVjx5rUbkE21+Dcv4vg9\n4+v5/AOopVsWCmVuZHN0cmVhbQplbmRvYmoKMjEgMCBvYmoKPDwgL0xlbmd0aCAyNDcgL0ZpbHRl\nciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTVG7bUQxDOvfFFzgAOtreZ4LUl32b0PJCJDCIKEv\nKaclFvbGSwzhB1sPvuSRVUN/Hj8x7DMsPcnk1D/muclUFL4VqpuYUBdi4f1oBLwWdC8iK8oH349l\nDHPO9+CjEJdgJjRgrG9JJhfVvDNkwomhjsNBm1QYd00ULK4VzTPI7VY3sjqzIGx4JRPixgBEBNkX\nkM1go4yxlZDFch6oCpIFWmDX6RtRi4IrlNYJdKLWxLrM4Kvn9nY3Qy/y4Ki6eH0M60uwwuileyx8\nrkIfzPRMO3dJI73wphMRZg8FUpmdkZU6PWJ9t0D/n2Ur+PvJz/P9CxUoXCoKZW5kc3RyZWFtCmVu\nZG9iagoyMiAwIG9iago8PCAvTGVuZ3RoIDIzMiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJl\nYW0KeJw1UTtyBTEI630KXSAz5m+fZzOvSu7fRrCTZmEBCQnnPdiIxJcY0h3lim9ZnWYZfieLvPhZ\nKZy8F1GBVEVYIe3gWc5qhsFzI1PgciY+y8wn02LHAqqJOM6OnGYwCDGN62g5HWaaBz0h1wcjbuw0\ny1UMab1bqtf3Wv5TRfnIupvl1imbWqlb9Iw9icvO66kt7QujjuKmINLhY4f3IF/EnMVFJ9LNfjPl\nsJI0BKcF8CMxlOrZ4TXCxM+MBE/Z0+l9lIbXPmi6vncv6MjNhEzlFspIxZOVxpgxVL8RzST1/T/Q\nsz5/mjBURwplbmRzdHJlYW0KZW5kb2JqCjIzIDAgb2JqCjw8IC9MZW5ndGggNzQgL0ZpbHRlciAv\nRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzU3VTBQsLQAEqaG5grmRpYKKYZcQD6IlcsFE8sBs8xM\nzIAsQ0tklomxIZBlYmGGxDI2sYDKIlgGQBpsTQ7M9ByuNAADcRiTCmVuZHN0cmVhbQplbmRvYmoK\nMjQgMCBvYmoKPDwgL0xlbmd0aCAzMDQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnic\nPZI7ksMwDEN7nYIXyIz4k+TzZCeV9/7tPjLJVoBJiQAoL3WZsqY8IGkmCf/R4eFiO+V32J7NzMC1\nRC8TyynPoSvE3EX5spmNurI6xarDMJ1b9Kici4ZNk5rnKksZtwuew7WJ55Z9xA83NKgHdY1Lwg3d\n1WhZCs1wdf87vUfZdzU8F5tU6tQXjxdRFeb5IU+ih+lK4nw8KCFcezBGFhLkU9FAjrNcrfJeQvYO\ntxqywkFqSeezJzzYdXpPLm4XzRAPZLlU+E5R7O3QM77sSgk9ErbhWO59O5qx6RqbOOx+70bWyoyu\naCF+yFcn6yVg3FMmRRJkTrZYbovVnu6hKKZzhnMZIOrZioZS5mJXq38MO28sL9ksyJTMCzJGp02e\nOHjIfo2a9HmV53j9AWzzczsKZW5kc3RyZWFtCmVuZG9iagoyNSAwIG9iago8PCAvTGVuZ3RoIDIz\nNyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFUUlyBCEMu/cr9IGpwivwnk7NqfP/\naywzSU4WYGsxaYGBLXiJIdbAzIEvuXxN6DR8NzLb8DrZHnBPuC7cl8uCZ8KWwFdUl3e9L13ZSH13\nh6p+ZmR7s0jNkJWVOvVCNCbYIRE9IzLJVixzg6QprVLlvihbgC7qlbZOO42SoCMU4W+UI+HpFUp2\nTWwaq9Q6oKEIy7YuiDqZJKJ2YXFq8ZYhIp91YzXH+ItOInbH4/6sMOtRJJLSZwfdcSajTZZdAzm5\neaqwVio5iD5e0caE6nSqgWO817b0E2ngufZf4Qc+ff+PGPq53j/G7lwiCmVuZHN0cmVhbQplbmRv\nYmoKMjYgMCBvYmoKPDwgL0xlbmd0aCAyMzAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFt\nCnicNVFJbsMwDLzrFfOBAOIuv8dBT+3/rx3SCWBgaEuczREbGxF4icHPQeTGW9aMmvibyV3xuzwV\nHgm3gidRBF6Ge9kJLm8Yl/04zHzwXlo5kxpPMiAX2fTwRMhgl0DowOwa1GGbaSf6hoTPjkg1G1lO\nX0vQS6sQKE/ZfqcLSrSt6s/tsy607WtPONntqSeVTyCeW7ICl41XTBZjGfRE5S7F9EGqs4WehPKi\nfA6y+aghEl2inIEnBgejQDuw57afiVeFoHV1n7aNoRopHU//NjQ1SSLkEyWc2dK4W/j+nnv9/AOm\nVFOfCmVuZHN0cmVhbQplbmRvYmoKMjcgMCBvYmoKPDwgL0xlbmd0aCAyMjcgL0ZpbHRlciAvRmxh\ndGVEZWNvZGUgPj4Kc3RyZWFtCnicNU87sgMhDOs5hS6QGYxtYM+zmVQv92+fZLINEv5I8vRERyZe\n5sgIrNnxthYZiBn4FlPxrz3tw4TqPbiHCOXiQphhJJw167ibp+PFv13lM9bBuw2+YpYXBLYwk/WV\nxZnLdsFYGidxTrIbY9dEbGNd6+kU1hFMKAMhne0wJcgcFSl9sqOMOTpO5InnYqrFLr/vYX3BpjGi\nwhxXBU/QZFCWPe8moB0X9N/Vjd9JNIteAjKRYGGdJObOWU741WtHx1GLIjEnpBnkMhHSnK5iCqEJ\nxTo7CioVBZfqc8rdPv9oXVtNCmVuZHN0cmVhbQplbmRvYmoKMjggMCBvYmoKPDwgL0xlbmd0aCAy\nNDUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRVC7jUMxDOs9BRcIYP0se553SJXb\nvz1KRnCFIVo/kloSmIjASwyxlG/iR0ZBPQu/F4XiM8TPF4VBzoSkQJz1GRCZeIbaRm7odnDOvMMz\njDkCF8VacKbTmfZc2OScBycQzm2U8YxCuklUFXFUn3FM8aqyz43XgaW1bLPTkewhjYRLSSUml35T\nKv+0KVsq6NpFE7BI5IGTTTThLD9DkmLMoJRR9zC1jvRxspFHddDJ2Zw5LZnZ7qftTHwPWCaZUeUp\nnecyPiep81xOfe6zHdHkoqVV+5z93pGW8iK126HV6VclUZmN1aeQuDz/jJ/x/gOOoFk+CmVuZHN0\ncmVhbQplbmRvYmoKMjkgMCBvYmoKPDwgL0xlbmd0aCAxMzMgL0ZpbHRlciAvRmxhdGVEZWNvZGUg\nPj4Kc3RyZWFtCnicTY9BEsMwCAPvfoWegLEB8550ekr+fy2QNu4F7YyAkYYwCDxiDOswJbx6++FV\npEtwNo75JRlFPAhqC9wXVAVHY4qd+Njdoeyl4ukUTYvrEXPTtKR0N1Eqbb2dyPjAfZ/eH1W2JJ2C\nHlvqhC7RJPJFAnPYVDDP6sZLS4+n7dneH2Y+M9cKZW5kc3RyZWFtCmVuZG9iagozMCAwIG9iago8\nPCAvTGVuZ3RoIDMzOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFUktyxTAI2+cU\nXCAz5mfj87xOV+n9t5VwOt089AwICTI9ZUim3DaWZITkHPKlV2SI1ZCfRo5ExBDfKaHArvK5vJbE\nXMhuiUrxoR0/l6U3Ms2u0Kq3R6c2i0Y1KyPnIEOEelbozO5R22TD63Yh6TpTFodwLP9DBbKUdcop\nlARtQd/YI+hvFjwR3Aaz5nKzuUxu9b/uWwue1zpbsW0HQAmWc95gBgDEwwnaAMTc2t4WKSgfVbqK\nScKt8lwnO1C20Kp0vDeAGQcYOWDDkq0O12hvAMM+D/SiRsX2FaCoLCD+ztlmwd4xyUiwJ+YGTj1x\nOsWRcEk4xgJAiq3iFLrxHdjiLxeuiJrwCXU6ZU28wp7a4sdCkwjvUnEC8CIbbl0dRbVsT+cJtD8q\nkjNipB7E0QmR1JLOERSXBvXQGvu4iRmvjcTmnr7dP8I5n+v7Fxa4g+AKZW5kc3RyZWFtCmVuZG9i\nagozMSAwIG9iago8PCAvTGVuZ3RoIDE2MyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0K\neJxFkLl1BDEMQ3NVgRJ4gDrqGT9Hs/2nC2m83kD6eIR4iD0Jw3JdxYXRDT/etsw0vI4y3I31Zcb4\nqLFATtAHGCITV6NJ9e2KM1Tp4dVirqOiXC86IhLMkuOrQCN8OrLHQ1vbmX46r3/sIe8T/yoq525h\nAS6q7kD5Uh/x1I/ZUeqaoY8qK2seatq/CLsilLZ9XE5lnLp7B7TCZytX+30DqOc6gAplbmRzdHJl\nYW0KZW5kb2JqCjMyIDAgb2JqCjw8IC9MZW5ndGggNjggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4K\nc3RyZWFtCnicMzK3UDBQsDQBEoYWJgrmZgYKKYZcQL6piblCLhdIDMTKAbMMgLQlnIKIW0I0QZSC\nWBClZiZmEEk4AyKXBgDJtBXlCmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0xlbmd0aCAx\nMjYgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPY5BEgQhCAPvvoIPWGUUQd4zW3Oa\n/f91QdY5pSuYmK6LGlUo1d6UsJhUGn1QjGmy0reEF/RsqnbAk9DIdgd2Ax68CqILBlL+d0rqVd5v\nnkQMOMoBbkkyonSQTtcdXP7YG31SnWuLuWtnl+XonBqUC4Uljy/k7f4B7g0uHgplbmRzdHJlYW0K\nZW5kb2JqCjM0IDAgb2JqCjw8IC9MZW5ndGggODEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3Ry\nZWFtCnicPcy7FYAwCAXQPlO8EUJ8gOzjsdL9W8FEG7h81QMdIRnUDW4dh7SsS3eTfep6tYmkyIDS\nU2pcGk6MqGl9qX1q4Lsb5kvViT/Nz+cDh8cZawplbmRzdHJlYW0KZW5kb2JqCjM1IDAgb2JqCjw8\nIC9MZW5ndGggNDUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzK3UDBQsDQBEoYW\nJgrmZgYKKYZclhBWLhdMLAfMAtGWcAoingYAn30MtQplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2Jq\nCjw8IC9MZW5ndGggMjU1IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWRS5IDIAhE\n956CI4D85DyZmlVy/+00mEw2dpeo/YRKI6YSLOcUeTD9yPLNZLbptRyrnY0CiiIUzOQq9FiB1Z0p\n4sy1RLX1sTJy3Okdg+IN566cVLK4UcY6qjoVOKbnyvqq7vy4LMq+I4cyBWzWOQ42cOW2YYwTo81W\nd4f7RJCnk6mj4naQbPiDk8a+ytUVuE42++olGAeCfqEJTPJNoHWGQOPmKXpyCfbxcbvzQLC3vAmk\nbAjkyBCMDkG7Tq5/cev83v86w53n2gxXjnfxO0xru+MvMcmKuYBF7hTU8z0XresMHe/JmWNy031D\n51ywy91Bps/8H+v3D1CKZogKZW5kc3RyZWFtCmVuZG9iagozNyAwIG9iago8PCAvTGVuZ3RoIDE2\nMSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkEsSwyAMQ/ecQkfwRwZ8nnS6Su+/\nrSFNs4CnsUAGdycEqbUFE9EFL21Lugs+WwnOxnjoNm41EuQEdYBWpONolFJ9ucVplXTxaDZzKwut\nEx1mDnqUoxmgEDoV3u2i5HKm7s75R3D1X/VHse6czcTAZOUOhGb1Ke58mx1RXd1kf9JjbtZrfxX2\nqrC0rKXlhNvOXTOgBO6pHO39BalzOoQKZW5kc3RyZWFtCmVuZG9iagozOCAwIG9iago8PCAvTGVu\nZ3RoIDIxNCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9ULsRQzEI6z0FC+TOfO03\nz8uly/5tJJykQjZCEpSaTMmUhzrKkqwpTx0+S2KHvIflbmQ2JSpFL5OwJffQCvF9ieYU993VlrND\nNJdoOX4LMyqqGx3TSzaacCoTuqDcwzP6DW10A1aHHrFbINCkYNe2IHLHDxgMwZkTiyIMSk0G/61y\n91Lc7z0cb6KIlHTwrvnl9MvPLbxOPY5Eur35imtxpjoKRHBGavKKdGHFsshDpNUENT0Da7UArt56\n+TdoR3QZgOwTieM0pRxD/9a4x+sDh4pS9AplbmRzdHJlYW0KZW5kb2JqCjM5IDAgb2JqCjw8IC9M\nZW5ndGggMjM2IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1QS25EIQzbc4pc4Ekk\nIQHOQ9VV5/7bscNU7SqGGH9ID+myVR7rU2J1iezypU2XyjJ5FajlT9v/UQwCbv/QyEG0t4ydYuYS\n1sXCJDzlNCMbJ9csH487TxtmhcbEjeOdLhlgnxYBNVuVzYE5bTo3QLqQGreqs95kUAwi6kLNB5Mu\nnKfRl4g5nqhgSncmtZAbXD7VoQNxWr0KuWOLk2/EHFmhwGHQTHHWXwHWqMmyWcggSYYhzn2je5QK\njajKeSsVwg+ToRH1htWgBpW5haKp5ZL8HdoCMAW2jHXpDEqBqgDB3yqnfb8BJI1dUwplbmRzdHJl\nYW0KZW5kb2JqCjQwIDAgb2JqCjw8IC9MZW5ndGggMTU3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+\nCnN0cmVhbQp4nEWQuRFDMQhEc1VBCRKwCOqxx9F3/6kX+Uq0bwAth68lU6ofJyKm3Ndo9DB5Dp9N\nJVYs2Ca2kxpyGxZBSjGYeE4xq6O3oZmH1Ou4qKq4dWaV02nLysV/82hXM5M9wjXqJ/BN6PifPLSp\n6FugrwuUfUC1OJ1JUDF9r2KBo5x2fyKcGOA+GUeZKSNxYm4K7PcZAGa+V7jG4wXdATd5CmVuZHN0\ncmVhbQplbmRvYmoKNDEgMCBvYmoKPDwgL0xlbmd0aCAzMzIgL0ZpbHRlciAvRmxhdGVEZWNvZGUg\nPj4Kc3RyZWFtCnicLVI5jiQxDMv9Cn5gAOvy8Z4eTNT7/3RJVQUFqmzLPORyw0QlfiyQ21Fr4tdG\nZqDC8K+rzIXvSNvIOohryEVcyZbCZ0Qs5DHEPMSC79v4GR75rMzJswfGL9n3GVbsqQnLQsaLM7TD\nKo7DKsixYOsiqnt4U6TDqSTY44v/PsVzF4IWviNowC/556sjeL6kRdo9Ztu0Ww+WaUeVFJaD7WnO\ny+RL6yxXx+P5INneFTtCaleAojB3xnkujjJtZURrYWeDpMbF9ubYj6UEXejGZaQ4AvmZKsIDSprM\nbKIg/sjpIacyEKau6Uont1EVd+rJXLO5vJ1JMlv3RYrNFM7rwpn1d5gyq807eZYTpU5F+Bl7tgQN\nnePq2WuZhUa3OcErJXw2dnpy8r2aWQ/JqUhIFdO6Ck6jyBRL2Jb4moqa0tTL8N+X9xl//wEz4nwB\nCmVuZHN0cmVhbQplbmRvYmoKNDIgMCBvYmoKPDwgL0xlbmd0aCAxMzEgL0ZpbHRlciAvRmxhdGVE\nZWNvZGUgPj4Kc3RyZWFtCnicRY/LDQQhDEPvVOES8hk+qYfVntj+r+swmkFC+EEiO/EwCKzz8jbQ\nxfDRosM3/jbVq2OVLB+6elJWD+mQh7zyFVBpMFHEhVlMHUNhzpjKyJYytxvhtk2DrGyVVK2DdjwG\nD7anZasIfqltYeos8QzCVV64xw0/kEutd71Vvn9CUzCXCmVuZHN0cmVhbQplbmRvYmoKNDMgMCBv\nYmoKPDwgL0xlbmd0aCAxNzEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTZBNDkIh\nEIP3nKIXMKHzA4/zaFzp/bd28PnigvRLIUOnwwMdR+JGR4bO6HiwyTEOvAsyJl6N85+M6ySOCeoV\nbcG6tDvuzSwxJywTI2BrlNybRxT44ZgLQYLs8sMXGESka5hvNZ91k35+u9Nd1KV199MjCpzIjlAM\nG3AF2NM9DtwSzu+aJr9UKRmbOJQPVBeRstkJhailYpdTVWiM4lY974te7fkBwfY7+wplbmRzdHJl\nYW0KZW5kb2JqCjQ0IDAgb2JqCjw8IC9MZW5ndGggNzIgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4K\nc3RyZWFtCnicNYyxEcAwCAN7ptAINlhg75NLRfZvQ3xOAy8dD5eiwVoNuoIjcHWp/NEjXbkpRZdj\nzoLhcapfSDFGPagj497HT7lfcBYSfQplbmRzdHJlYW0KZW5kb2JqCjQ1IDAgb2JqCjw8IC9MZW5n\ndGggODggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNYy7EcAwCEN7T8EIBouP98ml\nSvZvg+3QgKR394KDOkHyuBspnC5u2Vd6G4+TniYAsfRMQ+3fYEXVi1oULV9uY9BiKr4/+iQglnXy\nXjj0kBLeH8UXHXsKZW5kc3RyZWFtCmVuZG9iago0NiAwIG9iago8PCAvTGVuZ3RoIDg3IC9GaWx0\nZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVNuRHAMAjrmYIRzKPY7JNL5ezfBuy4QTp9IJQb\na+QBguGdbyH4pi8ZhHUITyq7JTpsoYazCpKJ4Vc2eFWuiva1konsbKYx2KBl+tHOt0nPB6XeG5gK\nZW5kc3RyZWFtCmVuZG9iago0NyAwIG9iago8PCAvTGVuZ3RoIDEzOCAvRmlsdGVyIC9GbGF0ZURl\nY29kZSA+PgpzdHJlYW0KeJw9j0EOAzEIA+95hT8QKXZCWN6zVU/b/19Lmt1e0AiMMRZCQ2+oag6b\ngg3Hi6VLqNbwKYqJSg7ImWAOpaTSHWeRemI4GNwetBvO4rHp+hG7klZ90OZGuiVogkfsU2nclnET\nxAM1Beop6lyjvBC5n6lX2DSS3bSykms4pt+956nr/9NV3l9f3y6MCmVuZHN0cmVhbQplbmRvYmoK\nMTQgMCBvYmoKPDwgL1R5cGUgL0ZvbnQgL0Jhc2VGb250IC9EZWphVnVTYW5zIC9GaXJzdENoYXIg\nMCAvTGFzdENoYXIgMjU1Ci9Gb250RGVzY3JpcHRvciAxMyAwIFIgL1N1YnR5cGUgL1R5cGUzIC9O\nYW1lIC9EZWphVnVTYW5zCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnRN\nYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0KL0NoYXJQcm9jcyAxNSAwIFIKL0VuY29kaW5n\nIDw8IC9UeXBlIC9FbmNvZGluZwovRGlmZmVyZW5jZXMgWyAzMiAvc3BhY2UgNDggL3plcm8gL29u\nZSAvdHdvIDUzIC9maXZlIDY3IC9DIDcwIC9GIDk3IC9hIC9iIC9jIC9kIC9lIC9mCi9nIC9oIC9p\nIC9qIC9rIC9sIC9tIC9uIC9vIC9wIDExNCAvciAvcyAvdCAvdSAvdiAvdyAveCAveSBdCj4+Ci9X\naWR0aHMgMTIgMCBSID4+CmVuZG9iagoxMyAwIG9iago8PCAvVHlwZSAvRm9udERlc2NyaXB0b3Ig\nL0ZvbnROYW1lIC9EZWphVnVTYW5zIC9GbGFncyAzMgovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3\nOTQgMTIzMyBdIC9Bc2NlbnQgOTI5IC9EZXNjZW50IC0yMzYgL0NhcEhlaWdodCAwCi9YSGVpZ2h0\nIDAgL0l0YWxpY0FuZ2xlIDAgL1N0ZW1WIDAgL01heFdpZHRoIDEzNDIgPj4KZW5kb2JqCjEyIDAg\nb2JqClsgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAw\nIDYwMCA2MDAgNjAwIDYwMCA2MDAKNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAg\nNjAwIDYwMCA2MDAgNjAwIDYwMCAzMTggNDAxIDQ2MCA4MzggNjM2Cjk1MCA3ODAgMjc1IDM5MCAz\nOTAgNTAwIDgzOCAzMTggMzYxIDMxOCAzMzcgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYz\nNgo2MzYgNjM2IDMzNyAzMzcgODM4IDgzOCA4MzggNTMxIDEwMDAgNjg0IDY4NiA2OTggNzcwIDYz\nMiA1NzUgNzc1IDc1MiAyOTUKMjk1IDY1NiA1NTcgODYzIDc0OCA3ODcgNjAzIDc4NyA2OTUgNjM1\nIDYxMSA3MzIgNjg0IDk4OSA2ODUgNjExIDY4NSAzOTAgMzM3CjM5MCA4MzggNTAwIDUwMCA2MTMg\nNjM1IDU1MCA2MzUgNjE1IDM1MiA2MzUgNjM0IDI3OCAyNzggNTc5IDI3OCA5NzQgNjM0IDYxMgo2\nMzUgNjM1IDQxMSA1MjEgMzkyIDYzNCA1OTIgODE4IDU5MiA1OTIgNTI1IDYzNiAzMzcgNjM2IDgz\nOCA2MDAgNjM2IDYwMCAzMTgKMzUyIDUxOCAxMDAwIDUwMCA1MDAgNTAwIDEzNDIgNjM1IDQwMCAx\nMDcwIDYwMCA2ODUgNjAwIDYwMCAzMTggMzE4IDUxOCA1MTgKNTkwIDUwMCAxMDAwIDUwMCAxMDAw\nIDUyMSA0MDAgMTAyMyA2MDAgNTI1IDYxMSAzMTggNDAxIDYzNiA2MzYgNjM2IDYzNiAzMzcKNTAw\nIDUwMCAxMDAwIDQ3MSA2MTIgODM4IDM2MSAxMDAwIDUwMCA1MDAgODM4IDQwMSA0MDEgNTAwIDYz\nNiA2MzYgMzE4IDUwMAo0MDEgNDcxIDYxMiA5NjkgOTY5IDk2OSA1MzEgNjg0IDY4NCA2ODQgNjg0\nIDY4NCA2ODQgOTc0IDY5OCA2MzIgNjMyIDYzMiA2MzIKMjk1IDI5NSAyOTUgMjk1IDc3NSA3NDgg\nNzg3IDc4NyA3ODcgNzg3IDc4NyA4MzggNzg3IDczMiA3MzIgNzMyIDczMiA2MTEgNjA1CjYzMCA2\nMTMgNjEzIDYxMyA2MTMgNjEzIDYxMyA5ODIgNTUwIDYxNSA2MTUgNjE1IDYxNSAyNzggMjc4IDI3\nOCAyNzggNjEyIDYzNAo2MTIgNjEyIDYxMiA2MTIgNjEyIDgzOCA2MTIgNjM0IDYzNCA2MzQgNjM0\nIDU5MiA2MzUgNTkyIF0KZW5kb2JqCjE1IDAgb2JqCjw8IC9zcGFjZSAxNyAwIFIgL3plcm8gMTgg\nMCBSIC9vbmUgMTkgMCBSIC90d28gMjAgMCBSIC9maXZlIDIxIDAgUgovQyAyMiAwIFIgL0YgMjMg\nMCBSIC9hIDI0IDAgUiAvYiAyNSAwIFIgL2MgMjYgMCBSIC9kIDI3IDAgUiAvZSAyOCAwIFIKL2Yg\nMjkgMCBSIC9nIDMwIDAgUiAvaCAzMSAwIFIgL2kgMzIgMCBSIC9qIDMzIDAgUiAvayAzNCAwIFIg\nL2wgMzUgMCBSCi9tIDM2IDAgUiAvbiAzNyAwIFIgL28gMzggMCBSIC9wIDM5IDAgUiAvciA0MCAw\nIFIgL3MgNDEgMCBSIC90IDQyIDAgUgovdSA0MyAwIFIgL3YgNDQgMCBSIC93IDQ1IDAgUiAveCA0\nNiAwIFIgL3kgNDcgMCBSID4+CmVuZG9iagozIDAgb2JqCjw8IC9GMSAxNCAwIFIgPj4KZW5kb2Jq\nCjQgMCBvYmoKPDwgL0ExIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDAgL2NhIDEgPj4KL0EyIDw8\nIC9UeXBlIC9FeHRHU3RhdGUgL0NBIDEgL2NhIDEgPj4gPj4KZW5kb2JqCjUgMCBvYmoKPDwgPj4K\nZW5kb2JqCjYgMCBvYmoKPDwgPj4KZW5kb2JqCjcgMCBvYmoKPDwgL0RlamFWdVNhbnMtbWludXMg\nMTYgMCBSID4+CmVuZG9iagoyIDAgb2JqCjw8IC9UeXBlIC9QYWdlcyAvS2lkcyBbIDEwIDAgUiBd\nIC9Db3VudCAxID4+CmVuZG9iago0OCAwIG9iago8PCAvQ3JlYXRvciAobWF0cGxvdGxpYiAyLjAu\nMiwgaHR0cDovL21hdHBsb3RsaWIub3JnKQovUHJvZHVjZXIgKG1hdHBsb3RsaWIgcGRmIGJhY2tl\nbmQpIC9DcmVhdGlvbkRhdGUgKEQ6MjAxNzA2MDMxNzI3NDMrMDInMDAnKQo+PgplbmRvYmoKeHJl\nZgowIDQ5CjAwMDAwMDAwMDAgNjU1MzUgZiAKMDAwMDAwMDAxNiAwMDAwMCBuIAowMDAwMDE2NjA0\nIDAwMDAwIG4gCjAwMDAwMTYzODUgMDAwMDAgbiAKMDAwMDAxNjQxNyAwMDAwMCBuIAowMDAwMDE2\nNTE2IDAwMDAwIG4gCjAwMDAwMTY1MzcgMDAwMDAgbiAKMDAwMDAxNjU1OCAwMDAwMCBuIAowMDAw\nMDAwMDY1IDAwMDAwIG4gCjAwMDAwMDA0MDMgMDAwMDAgbiAKMDAwMDAwMDIwOCAwMDAwMCBuIAow\nMDAwMDA2NTcyIDAwMDAwIG4gCjAwMDAwMTQ5ODYgMDAwMDAgbiAKMDAwMDAxNDc4NiAwMDAwMCBu\nIAowMDAwMDE0MzY0IDAwMDAwIG4gCjAwMDAwMTYwMzkgMDAwMDAgbiAKMDAwMDAwNjU5MyAwMDAw\nMCBuIAowMDAwMDA2NzYzIDAwMDAwIG4gCjAwMDAwMDY4NTIgMDAwMDAgbiAKMDAwMDAwNzEzNSAw\nMDAwMCBuIAowMDAwMDA3Mjg3IDAwMDAwIG4gCjAwMDAwMDc2MDggMDAwMDAgbiAKMDAwMDAwNzky\nOCAwMDAwMCBuIAowMDAwMDA4MjMzIDAwMDAwIG4gCjAwMDAwMDgzNzkgMDAwMDAgbiAKMDAwMDAw\nODc1NiAwMDAwMCBuIAowMDAwMDA5MDY2IDAwMDAwIG4gCjAwMDAwMDkzNjkgMDAwMDAgbiAKMDAw\nMDAwOTY2OSAwMDAwMCBuIAowMDAwMDA5OTg3IDAwMDAwIG4gCjAwMDAwMTAxOTMgMDAwMDAgbiAK\nMDAwMDAxMDYwNCAwMDAwMCBuIAowMDAwMDEwODQwIDAwMDAwIG4gCjAwMDAwMTA5ODAgMDAwMDAg\nbiAKMDAwMDAxMTE3OSAwMDAwMCBuIAowMDAwMDExMzMyIDAwMDAwIG4gCjAwMDAwMTE0NDkgMDAw\nMDAgbiAKMDAwMDAxMTc3NyAwMDAwMCBuIAowMDAwMDEyMDExIDAwMDAwIG4gCjAwMDAwMTIyOTgg\nMDAwMDAgbiAKMDAwMDAxMjYwNyAwMDAwMCBuIAowMDAwMDEyODM3IDAwMDAwIG4gCjAwMDAwMTMy\nNDIgMDAwMDAgbiAKMDAwMDAxMzQ0NiAwMDAwMCBuIAowMDAwMDEzNjkwIDAwMDAwIG4gCjAwMDAw\nMTM4MzQgMDAwMDAgbiAKMDAwMDAxMzk5NCAwMDAwMCBuIAowMDAwMDE0MTUzIDAwMDAwIG4gCjAw\nMDAwMTY2NjQgMDAwMDAgbiAKdHJhaWxlcgo8PCAvU2l6ZSA0OSAvUm9vdCAxIDAgUiAvSW5mbyA0\nOCAwIFIgPj4Kc3RhcnR4cmVmCjE2ODEyCiUlRU9GCg==\n",
      "image/png": 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mTps2rd4qIiIiIiIiUjHDIBAYmlL6z+JFSuk/gIVmZqdm9rSZvVPnZ1vgWmAssCLwe+CSettIKU1OKXWklDqGDh06M8kRERERERFpG418RcQ/zWxkSunfAMxsFHUeFPOvSClt3Mh6ZnYD8OjM7EtERERERERKjQSBpwM/NLMXAAPWASY2K0FmtkhK6ffxcnvgnWbtS0REREREpN008j2Bj5vZysDqseiY+O7AZrnIzFbErzb+Gji4ifsSERERERFpK119T+BSKaWfRwAI8Lv4PTKmh/6kGQlKKe3djO2KiIiIiIhI11cCj8OnfdZ7MEsCNmxKikRERERERKRpugoCn4rfB6SUfjUrEiMiIiIiIiLN1dVXRJwav++dFQkRERERERGR5uvqSuBfzOxJYKyZPVx9M6W0TfOSJSIiIiIiIs3QVRC4BbAy8F06+cJ2ERERERERmbN0FQTelFLa28xuSCm9MMtSJCIiIiIiIk3T1T2Bq5jZosCeZja/mQ3Jf2ZVAkVERERERKT7dHUl8DrgGWAs8AZg2XsplouIiIiIiMgcpNMrgSmlK1JKSwM3p5TGppTGZD8KAEVEREREROZAXU0HBSCldKiZrW1m+wGY2YJmNqb5SRMREREREZHuNsMg0MzOAk6m/N7AfsDtzUyUiIiIiIiINMcMg0Bge2Ab4L8BUkq/AwY2M1EiIiIiIiLSHI0Egf8vpZTwh8FgZv2bmyQRERERERFplkaCwHvM7HpgsJkdBDwN3NDcZImIiIiIiEgzdPUVEQCklC42s02AvwFLAmemlJ5qesp6go8/bnUKREREREREajRyJRDgbeAF4HngrZndqZntbGbvmtnnZtZRee9UM/vIzD4ws01ndl8iIiIiIiJSauTpoLsArwM7A7sAr5nZTjO533eAHYAXK/taBtgNmABsBlxjZr1ncl8iIiIiIiISZjgdFDgdWDWl9EcAMxuK3xd475fdaUrp/dhW9a1tgbtSSp8BH5vZR8BqwKtfdl8iIiIiIiJSamQ6aK8iAAx/bvD/vozhwG+y17+NZdMxs4lmNtXMpk6bNq1JyREREREREelZGrkS+LiZPQF8L17vCjw2o38ys6eBheu8dXpK6aHGk1hfSmkyMBmgo6Mjzez2RERERERE2kEjTwc90cx2ANaORZNTSg808H8bf4n0fAoslr0eEctERERERESkG3Q6rdPMljCztQBSSvenlI5LKR0HTDOzxZuUnoeB3cxsLjMbA4zDH0ojIiIiIiIi3aCre/u+jX83YNVf470vzcy2N7PfAmsAU2K6KSmld4F7gPeAx4HDU0r/nJl9iYiIiIiISKmr6aDDUko/qy5MKf3MzEbPzE5jOmndKaUppUnApJnZvoiIiIiIiNTX1ZXAwV28N093J0RERERERESar6sgcKqZHVRdaGYHAm80L0kiIiIiIiLSLF1NBz0GeMDM9qQM+jqAfsD2zU6YiIiIiIiIdL9Og8CU0h+ANc1sA2DZWDwlpfTsLEmZiIiIiIiIdLtGvifwOeC5WZAWERERERERabKu7gkUERERERGRHkZBoIiIiIiISBtRECgiIiIiItJGFASKiIiIiIi0EQWBIiIiIiIibURBoIiIiIiISBtRECgiIiIiItJGFASKiIiIiIi0EQWBIiIiIiIibaQlQaCZ7Wxm75rZ52bWkS0fbWb/x8zejJ/rWpE+ERERERGRnqpPi/b7DrADcH2d936ZUlpxFqdHRERERESkLbQkCEwpvQ9gZq3YvYiIiIiISNuaHe8JHBNTQV8ws3VanRgREREREZGepGlXAs3saWDhOm+dnlJ6qJN/+z0wMqX0ZzNbBXjQzCaklP5WZ/sTgYkAI0eO7K5ki4iIiIiI9GhNCwJTSht/if/5DPgs/n7DzH4JjAem1ll3MjAZoKOjI81cakVERERERNrDbDUd1MyGmlnv+HssMA74VWtTJSIiIiIi0nO06isitjez3wJrAFPM7Il4a13gbTN7E7gXOCSl9JdWpFFERERERKQnatXTQR8AHqiz/D7gvlmfIhERERERkfYwW00HFRERERERkeZSECgiIiIiItJGFASKiIiIiIi0EQWBIiIiIiIibURBoIiIiIiISBtRECgiIiIiItJGFASKiIiIiIi0EQWBIiIiIiIibURBoIiIiIiISBtRECgiIiIiItJGFASKiIiIiIi0EQWBIiIiIiIibURBoIiIiIiISBtRECgiIiIiItJGFASKiIiIiIi0kZYEgWb2LTP7uZm9bWYPmNng7L1TzewjM/vAzDZtRfpERERERER6qlZdCXwKWDaltDzwC+BUADNbBtgNmABsBlxjZr1blEYREREREZEepyVBYErpyZTSP+Llj4AR8fe2wF0ppc9SSh8DHwGrtSKNIiIiIiIiPdHscE/g/sBj8fdw4DfZe7+NZdMxs4lmNtXMpk6bNq3JSRQREREREekZ+jRrw2b2NLBwnbdOTyk9FOucDvwDuONf3X5KaTIwGaCjoyPNRFJFRERERETaRtOCwJTSxl29b2b7AlsBG6WUiiDuU2CxbLURsUxERERERES6QaueDroZcBKwTUrpf7K3HgZ2M7O5zGwMMA54vRVpFBERERER6YmadiVwBq4C5gKeMjOAH6WUDkkpvWtm9wDv4dNED08p/bNFaRQREREREelxWhIEppSW6OK9ScCkWZgcERERERGRtjE7PB1UREREREREZhEFgSIiIiIiIm1EQaCIiIiIiEgbURAoIiIiIiLSRqz8ir45l5lNAz5pdTq60YLAn7phHW2rtfvUtnrGtlqxT22rddtqxT61rZ6xrVbsU9tq3bZasU9tq2dsq5lGpZSGNrRmSkk/s9kPMLU71tG2ek76tS3lHW1L51vbmr23NaenX9ua/fepbfWMbc0uP5oOKiIiIiIi0kYUBIqIiIiIiLQRBYGzp8ndtI621dp9als9Y1ut2Ke21bpttWKf2lbP2FYr9qlttW5brdinttUztjVb6BEPhhEREREREZHG6EqgiIiIiIhIG1EQ2IOZ2ZpmtnCr09FsZjbOzAa1Oh0iIiIicwIzm2Bmg1udDmkdBYGzmJn1M7O5ZsV+gJv8TxtnZr27YXuzlJkt0sA6CwLfBv5uZn2an6rp9m+z8v/NrG93bberdcxsHTPbyswa+66ZBjX6eTtbz8xWNbMDGskbzUhXF//fq97vZuxrZnRxXK2r97/kvhoqj928z5nelpmN6460ZNubqTzfzH3OYBtDuiMtM5mGmeqjtLqszW7ntBXMbGAX77VdH3Q2O48/AEab2Qiofz66s20ws+XMbGwD6x1oZsvM7P6aoafl2R71YWZnZrZE/HkIsHIX6xUFrn8EjPUKZa/s7846WssBPwNGAqellP75ZdMeDjaz0/6VzndnAUsjhcjM5gbOin2O7mLV3YA+wLrAaf/C9uuu02Dw1Dt+D0id3FRbOUf9O1lnA2BfM1tgBgHZ8Oz3VY0Egp2lq5F1Iu0bAnsBu5nZSjMauDCzsY10+rvYZ3FMB5vZ4C7S3wGsChxgZhtEPukqXQ0FY40cr3r/l/3/5/HnfmZm2et6aSk+67BG91lnW/2K32a2RCPBnZnN1cXnnDd+n2tmG3WyrSLALfY9byfrFfn9a2a25Iw+S0op1Ut/lif6mtmiZjagq3QV25rR/iKPdXa8BgInmdl5Zrb8jLY1g/1YkSYz62VmI6wyGJd9xvHAQTPKz7HuPPG7XtuwaPy5s5nt3mA6Oysjh5jZ5Wa2aiPbmcE+is+5et6G5OchW2ceMxsf9Wu9ctQnOwb7F39X1vni2MfrzurgesdwugHTrBwNirZ5gXrpytLXP4U657yRNiZvP/rln6WTdK3W1T4srvaY2brVdav7tErnu3KO8nR1FdwNM7MBZrYccHgn5btXcX7N7DjrZNAhS9cSZrZmF/ucqYHPZsjS3reo7zs5j/l6Q8xshZncb5EvtjCzMZ2sMwZ4BRgF3GlmfeuVtyy9Z9bL95Vt9jazdePvXpX3DNghtrN3vXIb680HLAlcFPmibjuTrb94F+/1jd8LmNlKXW1nRiz6YcBOZrbZzGxrttLqLypshx9gGHAAcBfwy2x5r8p6veL30sB1wHPARcCgbJ3iYT59gROAScC5QN/KtuYGbgQ+A27uIm294/diwIrAfkC/arqAPYBLgWuB3YG5utjWOsCxwCnAVpV1LPv7UDxw26/OthbAK4zz8SuaOwED6qw3INI0Dbiok+NZpGtR4KvA/HWOZ5/suI2KczCozv7y4/8iMH4G5/4q4Ft4QD6+8t4OsY3LgPWLNFT3F3nn57GNbWP5XJX1is/YgQ807AsM6WSdccDFwDeA5YGBnaT9YLyReAc4EFgqz7PZ9jaN/Nq/izyxdOShrTrbX6z3feBd4JrIR/PVWWcP4EHgR3gZWK2Tfa6Gl43vxLGe7vjGersDuwL7A4M7WWccsFalTOR52YD5geeBW6vHvrKti+L43wps0MV6SwCLA8PrvLcGPph0D7B7nt87KQPHAk8CVwMrkJVxYHicv8uAn1SPY2U78wNX4k8/mwQMq5Pms4HLgVc72VZxfpaK8zMJ2IcsT1eO7QPAhcAfgJW7OF5HRJ6dBCzUSZ44DLiCshxbZb1BwCbAOcDteL5fpJNtDYzPO129VOfYXxfbO6STdZ8HvhZ/Lwas1Mk+t4z0P4nXC/lxnS+O/SPAB8DcsbxanxfbmgDsDZwObFZZZ25g43jvFryuXqyLzzgB2AbYrc46+bl8E9g0/u7sfD8Wn/EfwJp1ttcB7BJ57OlOjmfxGfcEvou3EZOAjk7WnwRcVy/d2WccHdu6G6+fhnWS/qvxtnsyWbucbafY7rZ4vj+9OFd10nUS3oY8nx9/vL4pPuPywEtU8nwlX+yE15sfAmt3cbzGAS8Ai3Z2DuP1N4GH8Dp4tzz9+CDXV4Ez8DbrhE7StT5e5xwD3FtJh9VZ/3vAwZ1sy7J9bxb7Xp6sfNQ59kfhdXGfyjpFGkbHZzsX2LOLY7ZXrDNdmrN1rwJexdvT9btY76bY1h+Ao7oob0OAZWeQ99cFXifqJ2BkZb2+wNHA74D7gUWAeTvZ3/7ADZ2dm2z99YDvdJYuYAxel9wV536Tep8x1j0E+GGkbfdOtrlYpH/fOuXQIi+siLchZ9XLN9nxWgv4Ol63rEhtGzl/5NPbgY8o+4t966VrTvppeQLa4Qfoh1+9mBaZ+si8AANrVjLvE3hH5ALKynGB+F0UygujEJ0E/DCWDa7sdym8on4ZbyQ26iKNzwDH4x3wgyvvbRC/F8U7WdfiAWHdDizwYzyY/BlwYCwbEr+LSvg0YCpwOF65PwR8NV8n/r4JrzgfwRv9jbL3im0dDLyFN9C3UqfjEOu9Htv5M97Jqxe03Bjp/w7eodqY+h3Yy4DL4u8l8AZlico52h+v7JYEPgYWwjuOg7J1Von9fYZPa12mk7R/I9L9IjAhW/5FUBXbfx/v2H3e2fkGngLOA+4E7sMbgqWYvrP4PN55OCmOx23AjlQ6g3ieXjf+3hoPcDbIzs9APJA8MtK1SeX/i2NxWJzvBSJ9jwAnAytVzsEzeANxKt55uwHPb+Mq230LDyTfBM6JZfNW9rkPHlBehjfSg4F58IaoWOeIOBY/jzy0SjUPZq/7ApcQHd06n3GX+FzrAr/COxp9gIUr6+0Z5/JuvB7YnRi8wDt1OwJTYhurVvY1T5164IM4jtfgAcRxeIBZ5OflgF8D/0bWScQb43HZubwzztMFwFNF2SbrSOEN739Gvlg1Wz4vMCp7/RLeMf0hZVkaUjkOJ+MBwXi8PpkHGEp0aLL19sbrkCPjfC8A9Ke2E94Pr3OWjNf74fXPinXqnbvwjvwDeH23A9OXjxfwuutd4PjKe8XxGge8DYyN87lFLF88S/smwOPx95bAw8B7wD51yu57sa0fASfHssGVz/g63jE6lSxABdarbKvIB88AZxR5q7LOBcDj8Vmvwzu881SO/Qi8rB2I12NrdFLvfBO4vEhLHJdIXadqAAAgAElEQVQL8TJTHK9j43wPjOM1AM9fi2fbGYXn4//F6+iRRbrj//pk630EbI6Xp6PwdmvBOmkbj+fH3xAdyjp54hFge+As4OFYtjAwF96G98UHPu+P8/4gXmcflW2jKG9r4+3ynni5G5+9VxzXr+J5cFHgb8Ay1A7CFcfseWCn+HvneL1Jtl4fPED/BZ5XVycL8oAFs209TgzK4uX4PqIMZ+naFs9/A/Fy9yheT+VB/XC8Lf0r3tZvSgyq4kF88felwD+B+yvnozqAuXwcr6GdnJsibVfgeepZ4LlO6uDV8DZ3Y+Dv+KBYvaDzEbxtOQHvk91PJZCK9V4D1smO2bp4W1zsb/1Ie5/Y1k/x/Lt05TyuBzyND8C8Cqwey1eupH8pvIw/gNdnO3VS3qYU+QAvV1OA66kNbkbgdfr1kabdgTGV7QzABzYuqXecKuvOjw9MPkjU9VQGJ/GB3tuAe/G69UxgqcpnHBPn8HQ8oL8Dr39Wr7PPdfC+4cLVOhjPq+8DfwKWL8pDsa9KHvpJ7OPu+Kx7Zenqg9cnH8T2jqQ2L64+o2Mzu/60PAE9/YfazmsHXrFPwkcJvwbcDJyYrbNUFMr+eIe/yLjXAdvH38OAH8XfDwF7xN9fowzYqgVvf7wDcR/TjwgdGYVxUbyCGoJ3JjbHK99nYv9FWlbAG7vr8U7CmGxb+wLfiL9/UlQ4eGdre7wR2hwPwkbHe4sCB+GdwfMpK8Wi8M+DBxWn4h2XS8hGPbOKYyjeaXwQbwwWydZZH7gl/p6AV4g/xYOJQ2L/I/CKflCcp1PwRuocajsh/SMd8+Kd2BtjW9+nDNYNr+QWxRul82L55mQjUvio93Z4w3Qd3oH9Ot6xySuodWLZ6XgH+ya84nmbslN2QXyWkcDzsWwcHugUx3Qt4PvZdjfAA+fHqA1utgSeyV4vFMfmPbLR9Nje83gH5YQ4rmcCZ2brnIt3NhcAXollo4j8HK/nje3vlC3rwMvCVdmyNYvPFq9H4x3xHwMbZ8u3Aq6Pv1/Pzssl1ObXl/C8fg5wQbaP1eLvPnjQPC5en4o3KA/iwViR9wZRNnrr453PrYu8kO3v4jhW+xefCx8gOpfauuL2yBMr4GXn0ji/RfmeC893j+Kd60PxvDYvPpiTN/SjgOOy12vgDd1T8X8T4nPuggf97+D1ymh8BLtI5yLAffH3g5QBzSl42b4Urzvmw8vhAbGdq/A661zKumoN4I74+0eUAyinZce6D34Van68M3Z8LN8FOL9Shz2Pd1bOAS6MZesCW1by9B34KO+ZeAf7GuCIyrYuBK7Nzs1xeD6ZTHSe8Q7CXVnZfAbPZztVtrVPHIflgcdi2UC8E1QE/l/By82UOFab4UHC5dTmne3wwYoF45j1zfJK0Vnph1+9WCn2MRXv2B0ITM62tSNlffg2cbUZD4yHZfn4bTyvrRHvPYnXPStm27oFDwbWzz7j0njHP0//SXgbdQ4+4LU3XmfuXNTj8fmXxPN2EeTuCtyUlyW8Xjsevyp9FZ5vR0T6OrJz9O1s/2PxjvO+lXOUX8XaCg+gf0x2NTa2/V18UORpyg76t/D8/zBwIt5ZXjP7v3XwgZUfVPb5RJyjQ4BbY9nKeLBW1Cl3xnE/NPv86+IDZHPF6wXiM43A+xVX4uXsMaa/mrcfHsDegrcjy+IDqDdn23oIz5+n4X2C6/HAcOFsO5fit5gUr/uRBYvZ8sXwNuo4vJwdHZ/ljSz9i8fxexAfJD88lt9BdnU68sDU+Fzr5/upnKMX4+/biKt3eF27RLbeA3jAtQdwZyxbBS+rRSD+VaKtKvII3qfYsbLPXfD2cxDej3orzu3OlfbvrOz1kDiub5JdeYv/3yTO0+3ZMbyHbHAGLzsnxd+74X2Px6nNrwMjXWfi+fZiYCO8XI2tlKWFsmN8W6y7afb+CpSDYnsU/1/n+Oef5fT8GGTL9yD6FXh7tQ0eID9Gbbm5gnJgalCcr1fxem90ne2ehQ9U1ZutsE8ciyl4HbQQ3rY9QjkovA9lH21ByosdVxJ9P7x+2gnvM12Bl5Ud8fx9Xr1jMif8tDwBPfknK0S98A5X0YEYhTeAF0fBql6CPxHvVBSjpgvijXExSj43XuGfCdyT/d/PKBvAvaPw34xXvPPhwdR3gR0q+9sbb6y+RUzdwO8JewDvAEyINE3BG/AFY52N8U7J2Gxb60XBeQbYO5btDrwcfx8X6Xyf2hHXfrGf8cWxwzu3+2XrDIuCe052bLeP15dTdl5WiwL8lXg9L96YXkftFJ6d8cZ7a7yT91NqO0oL4ZX8RXgDs2P23iX4FcqHKK8k/JBstJ2YQgG8kC17Atg//l4Rb9jyDvtE4L/wyqboDJxGTBWL14PwhvwJYvoV3jk5mHKK6ZZZXsrzyGn4aPfB1F692ZPaztAoPM/uShlArUk0mpX8c3Kc00vwfLY2XlkX5+gIvHP1FGWH7yg8f/WP16vE65/g+XGebPtfXCWLdL2G5+liNHkFojNJjLrHufs6PppYNJhrAVMrx3ES3vi+li1/hnJq3o54BydvYAfgwVcR0IzGy8YVkQe2j9cvUpnKGef1KXxaVnF+76Xs8Bp+pe1VygGUufAG/HziimuWr/viHYEr8CDxp3Eeis7M1ngD+Ge8ocyvDK1OXC3Gp7rskuWlcyMN36e23jkd7wTfm6X3HTyw3TZeX0mMkuLBz3l4WXkzS9fieGfyCcqgbTl8kGi1LI37xznMByReAbbLXvfFB4xOo3YK6lNEWYvXg/E64EWiE4vn+/uzdXrh9Wp+9WZuPGguztF8eL03sXJuJ+KdmfwYrxB54beU9dHZeP03gnKq1gZx/ooO2e1E4E7WWcPruqmUMyy2IDq+8fpqslF1vDP3dByzpbL1VsZnF9xADELi5fanlPlyV2IgJaujr8DblCHZ8lPwMvA00UGP83FRts48+NWuF/EOcBF0vkRcRYnX28QxfCpb9iJeDvOp5UXbOC8+SPJdfLbCE9n/jcavsu2WLTsV+Gb2ehk8QF462/4IfLDru5V8cQoeHBZB23x4vTccD84uxx+0cSuel/MrYyPwQY3i2J4Y5+YVyrJyCzFjIasrToh1iqDpNrydzgfLjgf+He9PDIr9vEk20wWvQ4Zln/niOPdvUA7a9sLz5jSy6a54nVzUtX3wQYvb8QHrIv8+SG0btXOku7hCti5e195BlBs8CNglO+4bxHF/O/JAdWrfeLwTfz1eDy1XKX8j8LboeGBKtvwtamckHBzH/mUiaMDLzdnZOqPwwcX8GO6NB0P51dgl8LrhCbx+7osHFLdl79+Ft1n7ACOq7VqlrngJ+G227HqiHMUxWAkfjFqvcm4viv2cmi1fC8+TZ8frJakdNN4J7yfdHPlhMbyv+S2y+jXb3uZx3i/E6/p8BkKRpy7D2/JJ+ODHYdQObm6O95nyY3hS7DM/33vgg8J5n+RCysB+rvhsB+Jt17A4X8WMnyJPrU/ZF1oTrycexvuRV2b1+734TJ9lK5/p4Oz1TZT11iJ43r0zjumY6vGaU35anoCe/ENZ4Z9PeVViON5pK0a+56Fs5PNpAS/jDemZeIfz1HhvTbxCPRj4YxTiwXiH6sZYZ0ngl3jH8Wi8Yp9upCLb30p4Y/mn7L2nsgLXC29sV4+CNwUfrepFnXsZoiL4MR6QjMMbmq9m7/eNNH2Md0T6d5KuLaNgHkBZcT1N3JuFN0bv4w3O7fGZj4j38grqdrxD+xI+arwb099DORQPVv+KVyZ5Zb0QfkVggyj828TytSg7bafjwcOO8fkWxacKTcFHlrfHO8OPV/Z7LX4VcnC8HhH775u9fpdyKuDu+Ai34Q3V17NtLYN3mJ7GK8lV8ApqQqzfC++UH4uPCF5I1gHLj338vS/euByFN6yvUXb0e8XnGxLHJj9eL5DN4Y/z9CzwZLwei3c2j49tF+d2AN6RvgZvUKa7khavNyNGV+P/n8Ubg1fwSn9bfCDgDLwDs0Uct+co75/L72d8C+8E9I/zV4wm98Y7fk/i+X4tIiCupOcivJwNxUdyT8DL53Nx/C8iK+Px2e7Cg9RTqQ1Mi/sYfomXoTzoK4KxufBG87I4hvPijeD2cc56Zdt6KdKyfXzG+8g6a7FeP7w83oY33mtkdVOR74pO4Dp4B+0MPB/dAlxdSf/9eNA5KU870VBmx/7g+Ix7xzkqroRtiY9kLxOf9Qq8Y3oG3im6u845WB2vZ+7Hy8xeZMFRVu/MT3nFdl48H65dqXfWim0dQzkA8QrltKy18UDsWXxwKe8oLkYEyNmyA/BO8gV4ff4OfpW7bxzLA6kNqrYFXq+0IevgHfyiHO6J581XiSsm8Zmfjb8HxjEtAs9qh3MuPGj6lLj6hNcb+2ZlcT48/55JOf32CuDIyrZ2Jab/Zu3PW5Rt3OJ4R3OZyv9dQGVQifKq/N/wvHhnnXWmEFeMsmUj8ClkRT16FV6ed8IHJe7Ay+UvqL0qtANeJi6O4zUMr9euwvPa+pTB0Pg4j5PwqzH3U3bQB0Ze2DqO66V4mSsGNZchu30i9jWNcsbGRnhAeVS2vcXxfPcRHlQW53s9vA5dFa8vjTIY640HoidQe6/TQ3EcrqEc+BmBt4VL4e3CXkV7l6Xz3jhPRRB6fuz7MrxdPQsPSp6vnI9FKAdFrqdyP2P8vo+YMRLHvehgr0LM4onjsC9ed22Al68tKGdGFJ9xfjxAPQ/P05vi+fcbxIyDOsf+p/F6K7y8F4Pba+Jt/p3EYHVsfwp17unFy/LGsX+Lc/ZVPC/PR9muXRnHa1s8b9abgroV3he7Jz7fy9lnNLyueBvvIyxJ7UDD3HF89sPzZ14v9cavFh6WHa938XZxK7wuv4Pp64kz8LbvMbyO7YfnrbuprbPWwvP7xXif5uv4QOfd1Ja3/rHsKsp+1JNU7iXG257JeL49GB8w/w3lhZSv4QOUe+N10pQ4Zp8B52bHI78wMiiO4Qp4HW7Zvjpif1PwwcRienlR/06knOXQH68XVijybvU8zkk/LU9AT/+JjPh4ZJzt8NGED6PAzU/tdLKD8FH8UXhDdCJe8e+abe92ynne6+ANzhv4VbNiRPFQYmpbvF4Gv0JRjPQXc6L74Y1/f7wRuAav9G4j62jF+0WG74VXYnfjHdmicZifcgrdcHxE7ja80cxH1femvMdgLN4QvI9XbsWxKAKgufAO4dmU0y3zEb6bgWOy12vgAcgIakd1b41tLYdXKFfiVwTy6Qffic8wIs7Bm3hjX31wzC7x2c+mvAK4CB40rxrney+84vxKHONd8atcB5GNxsf/roU32sUDfn5EdoUBDxKuxjsgX8cbmMvxRm6xOG8TYll/fIrPM7G/uymn0PWN90fF6w48cL0Lr+Dy47Vv7GtjfJTuFDyQOTw7RxPwSve+OA9D8cp0HWIKVpy7i+PvbfEO/91456lo/Isrvm9R3le4dHzu2yiD7AUiXcfgo4QnRronR7rmieN3RWyrN55XT8Dz9ZXAodlxXTJ+VsLLy7fwfHgT0wfGC+Cdidvwkc1Vs+PQQQS3nZT/ayM/nIAHDWPwDsbesc8jKe8NqXkQS3zWN/BAaxRlXjwfb5R3iM/2DNM3ooZ3dq7JjsVwPHh4ipjKWfmfpfFO1C14Q5hfOXqV8l66DSNPXIBfqetXZ1tL4FM0f03ZUTW8Q7Q6ZV2xD95xuDuOxfyR1oPxKxXbxP90EPda4h3GojyuhAf8a+KdkOPxjv4llMHdRpHWh2MfxRXW3eK8Fnl/cPyMxgPx8/ArE1OIkf3s8/XBp/K9gOe7CVmahsRn/B1eBubGR8CPwPNtPu16F7ys3hPrDMXzyBcBc7z+cfY/+0UeOJVsVgcexG0ex+pqPO9cjneki/w6L+X0ppF42Xk88sS3sn3eG8d1JXyw6CW8Lv9i5gJenywc52dNvFP5Cl6n5NMFL8DL5BS8nls1+xxFcHkGnqcOj/QuhQ+srE3tvTdLUzsdvHj4zTp4vizuV380lhczEw6P47UuTHerxNx4Hr0dLy9vUV5RMPw+oGfwqyRD8XqomAK9GV4fvU35wJuhkfY7I2/MHflgFB7AbY+3C6vjAdIneB16JOWDib5LzD7A25qH8fo6v2p6MN6OnYd35Ifg5efyyud7mXIq4GNxfr5FDBDEOofj5e3sOI4D4nPcGuf2Zrz+Pj/7n13w4GF7svvUs/f74m38hfjAxSRqH2D2Rvy9Gn4l7QHKGRhFXXcbXmbup3Y2wFBqBx5vogxwzqO8X/cOyuByOF6X942/i/t5J+NtwMZ4O/Vxtv9t8XJ0Dx74FulfOc7xRdTef3kE5YD/rngQsk/W5nw9PlN+D+ihsZ178Pp0vjgfW+D1aC9q24bBePl/Nvb3RdsQ7y+H1wVvZsdzCNH/i9fbUU4x7o23SdcSQS6e7/eI47g2Xj4+pbxlYmyW/pFk9+NTTrEcTvmgquGU9c54vB19Es+bt2TbWgEvK3vE+dgEH0y9gfJe1VFMf7/nfHFex8RxXBqvU4opnnvg7dFPI729s8+ePyhwE7xMvUB560VvfHB9BbwsXIsP0l5B5f77OfGn5Qno6T94h/G6KJSPUt5L8CLRqYrX36e81P6feCem+iCCQ6JAVEfUB2brGB5YvB2FqWiwv0l272Esuw6vXL8XhWyVyOjjsoJcXDV6Eu8YrBnLh1I7beuJKNCf4KM0xRTCuai96vJeFKBjiUocr2hPyLb1DbzRuyKOw2Z4QLUB5QhN79jPpPi72Med1AbN9+FBVhFMzBfbOQNvvF7HO0DFfU9F5b9apOGLaZmxfECk5Sy84TmBeOBL9nlXwhvsa/AGbFS8V3TEFsErwW/jQdtgvEE/grjCmu1vbByHX8Q6fYnpr9k6q8X5e5Dy6aFDIy8Un2cynrfewBucYtRzO8p7sAwfLChG4C/H81z1CWu9Ij9siDdqxdTAhfHR66JjthQxdSo7Z6vgAVveqM2DDwL8GG8giyk6S2TpejSO5x2xzxOyvFB8xuF4IPdzaqexVh8Gs0Gc20fxDtFQPLCYn3J66lqUwVaRx1bGy+m+WdrPxBuWdSvHqC8eKNwe57V3HPeP8AGSmlHa+L0kftXoi6db4kH5TZSB9VC8YS1GROfHA6Un8WBqUcqrd7fg5e0oyisFfZn+npDq0/E2juNc3AP8DeCa+HtxvKOycf4/2f9+MeUtXm+D30NZXEW7Fy+T/47n13x6dh5EjcLz3tV4R3rV7P0i3evj+fkpvCxdhQ+K9Kuk4SW8Drkl8sa7RB1BbT68I/b3KF53LoSXv2UpB7uOiuN/Jd7RWADPl89RXoXqi5eRXfGrfndSBtDFANcAaqdjHxif46bY53CyJxLiHanVs9f9iOA+Xo/EBxuuxDt/W+Ll6mlq7626Cc+vz+J125J4+RpY2fahxJVcvM5cBg8wiw71XHid/028zBV13Frx2YpzdCQ+mr84XtecE6/zAHCbOCdH42XuTvwKRU35zuqYV/FOXbGPsXi9NRBvV/4br/PzKxUDi/OSHf/z8WDrDsoAYVM8UCzO05g4PlfjVyfzmRfz4p3G5/F8tTm1V9E2IbsnNZYdjdchp8axmhuveyw7rmvj7cbNeDtS96mfsf4qsa3L8cC0o/L+upRl9228A15MMd21su7aeH32bfy2jQlZfjgW+A+8v7B1pe3Jn16+ahyrYuZAbzxv3k7tVMWRkea74/jvHMfvQcr2Yxzx9Fc8QNwq/j6RypMx8c75S5QzPZbA29mi/zMaD8gexoO5XSM/5DNwzgV+H+dnQWrr6OqDap7By8gDeLt1H+VV96INGYyXw7vjcxUDXysRV8XxfPgRPji3DV42bsuOQZFf+8f5vRhvH4bgZXIK3rZ/kX+yNG6JD9o8Se2At8Wx/ZDaW1yOIgLDeH0/tX2phfDykufXQfjA9buxnx1iWV6v/hoPzH5KOVtqqTj+wyjr1gF4H/K7+CDFg5RTh/NpoRNje9V2q2gfHsbL2QmUAwEXRx45tfKZbsMHYt6mnDY6Nz7Qk5fl02O9l/F82huv8+s+hHBO+ml5AnriT1ZA1sEDqF74qFnRuT0Wrzy++AoDakdOF8A7S59Tez/Dtvg0q59RO/LSu04aDsJHxIrG4ed4B3FfvHOwQhTI5fCg5gS8MskrkUGUIyf34JXjh/hVswXxqxnbxu8iECieaDaF6W+ifhXvgO8f+3qUcsppccwOwDsoq+JB2olkDxmpbG+F2Ne38Ab1a/j0vy0pR1G3xyupp8geL08ZBO2Ed1I/oPaBJxPicxfnqB/e4G6Ndyh64yN1VwGXZP/3MOUV163wjsbVeABXdPYfwjukp8Z5uZ5stDvbVtFRHUV5JXYBvGNZDARsFOmy+Czfwyu2rSmnba4Xx2kBvMG6Pz7vyZX9rUPtaOsqeCNyHLUdscXwDsogfMSsCBauwfP1ffgod2/KG+/nxTs4X4vjV28qzDC8gfsIOCVbvhxxT2m87ojzWdyjdSjeWeuD58UN4xjfG+doUCX9L+KdodMoH46xBLVTiF/EG5LV8Xz/Ad7p7085fXVu/GrSzXjney9qR6d74QHEBZT3dy2Ll433iPu6svVfxzu/n+ADJWdQBrBFZ+ZC/Il2+aBJ7zgnw/Ayf1Z2TraMY3Uj3gj2o2wsh1aOS72v/5gbL6dL4XXHjXiH+zt4fZJ/3cPc1f+Pv7eL47Qh2SP98brpz8S9V5Rl7WXKe5PXiOMwGe+kLIQH7mviZaiY2rYy3on7ZuWYFudnAB4czR37/X/UBhJH4wMpy+P5byCeXxfL0rUnnt/3w+vwNyg7KRMq+yweetML74B8FNvvi5fLO/FO68V4gGp4x+5CvPO6Dt6xehjPM9vjZboDr9vvouwUL4YHeyvFekVdsVmcq3xK6RPx9354PXAlXk8NqBy3RfGy/ANq7/kujtdl+GDMppT3ey9K2cYV611C7f1rRcf1Hrx9uwxvn4pBgnF4J3EyPkjZG6abDr5dHKej8fbnfsrbJRbEO/gX40/OPTfO42t4B7VI1+V4QLxKrPNHpn9gzCC8Li0C+PFxnP83O/YWy74dafo6XmesjZdFy35OxevgpeJz34gPBi6bpetMYMP4ewN8wOFmfOCob/U8xN998Xb3cuJqWPbeCLz92Ij4CozIJ9/N9plvq38c06upncGzWJyXHfB69QZ8sOh5ymDeYn8/wNuoQ7Jz8jSVqfRxng6jvEJ0KdNPIT+eGBjIlr9Lnadox3G/jbhXkPJJzf3i+B2H56+Ds/O1PWX5WCA+13fw/LNDHI9jiOcb5OUbL9s/w/PQ9/F6e4tKmoqrbCfhgca1kZ538SvO51LewmJ44HgDcc9fdo6uwAPp3fE28g3KmTNFX2DNWOcOau8ZPIu4/62StmIWwvfxGRk/xwO5IkDdm6xvE8uep/ZhW8dQTr+cGOf5GnzwoR/exlzI9PXOFXi9k7cZ55E9JRpvL+6i9km2C8Q5fp14Iju1+bcfMXAdx/JAvI/WO45v/iTXA/H+0kL4V3L8Ga+TJ1BpC/H+yy6Us7+2I3vWw5z80/IE9OSfKPhHVZYtiQc5o7Nlx+AV66TK8rWoc8NprPdXvBHr7PuFiu/ZOzwqgfWigNyEV1xPUz5coOjs3EPtExv3wIPD8cSDM/BK+QO8QSie8nZ1UXiz/y3u1SoqsW2ovdm/Pz7V5EVqb8g+jwgeKa++PEvtNJi80C8UlU7+WN+7opLZibLzfAoewN5MGWzMg48Qrot3cKfhna5F8MAxHz27Ea8Ab8Y71fvE8qHUPm1yUzwIKiqaEfhoeHHfzhhqHz4zP954/xe192AuCvwFr+zy76c7jrLD0zuO148or+aNxTuo+ZXCC/EKdW/KG9Zvi/M4PFtvWTxY3Igy0PkK2YMcsnVPxDvVk7Pz8Dbl0xSLqV9P4w3Nd+I8PpyfQ+p/t10HtU/6HIePXG+VLSs6Mn0pn1J5AuVorOGN/ct45yK/YnJ1fNbXKO89uZvyfqgNmX66ybHx04syuO5P+VUEh8V+LqLy1Rx4h/A2avPTztQ+FfhAPDAoGqpt8BHUD5j++yX3i+V3UHlSGx70fQMvExMjfXPh5fbt2M9qeJDzQ+LKcWUb1RHWE/ArUY8TI/BxPvInRP6QOl8ZgwfmxdS4b+OdlJHZ+4vhV5Tye4aWwuuGoswMwOui1fErNqdR3pC/SWVbPyQC4Fi2BN4JPojynumtmf4rHSbhgw3nAKfHsq2Ip4TG6yfJnkyIB6i3MH0ANRovM0dmy8biwfSilHXEMnin/wY8QChmifSPfDAIr7fejLzwfyJ9V0T+KYL5k8keKJLl89uo/Uqdb1NbLwyivOe8uHqxM2Vd0g8vQwdUtt0vO5bfo5xydhDZ1Z5YtieeV/MH+TwU5/BpvL79R+U4z43XAV90xuKY7Iy3O9vGvs7E65b8OOdtw7J4nn2S2hkJC+JlLK/71sPrhfz/B+BXAJ6lNk9tQ3mV7GzKhwxtinfqv40P8hyepwkvk9dR1q3FQ6f2zbZ9MB5AFVclxuAd9Pxpk/07+bwLUhlgo5wNMRif5nglXnaLqZNzd7KtxajzXadxbpaP8/ci3v4U5yi/sl2028/j5buYydCH8vt/N6F8iNEGcU76ZvufGx8A+l2c8+L7k/N7kHfEBzuKGTCn4wHWWnggcTc+kPkjsmn38f7ZlH2gvNM/EK8vb8cHmD+l9r62DfAr20dRziDaBy9vg/Hg+Elqp9MPwgc2DsiWHY/3OX5J7ZPMbyV7hgPlU9vzQYB9Im1DKae9P4/nnwvwMjaJGBAqzi/ebuXnaRxex5xP9kAdvM5fMc7JT/D690x8EKwY+BuAl5uTs+0Nxvsz+VN569U7Z5HVO7F8L+IhYdmyW6h/H+b6cbwvxMvjIlm6lsP7OgdRO/A/iXJQuhdeN4yN43hoLP8Af4hXkadPxwP3L562iyp8BKkAACAASURBVNetU+jkq3DmtJ+WJ6Cn/kRF8Qs8sKo+7n+RLJPtggdlxRexf53y6kUxejEcv3p2LOVI6xA8GHnkS6Rtdbzx/G9qG5dHqb0fbYEo1PsAl8ayA4Gjs3V2w6cEPYdXivlTFL8Yxc0Kzh6UU902wAO2pykbvT3xYC1/kMzD+JS6sZVt55VZ/h1q8+Ed4DvxTvm62ee5P/axRhzXy8m+PwbvkE2NiuFAyqsPd8R2B+Ed7bvwRm6FOsd3r9hPvccV34pXrFtQ+1TQFaj/0JFj8GDwYSrfb5Ud21PxDkYR0AyinP5X5J8B8VmLJ5NeBGwef+cN4LF4Bb0L3pg9iDcIl+INZzGVcDG8gb0Qr6gfofbJfKvhQfl7eKem+r1u/9KXrOJB+vn4KN8aeAB4Ht5QFWXlOuI7zyinGi/E9A/+OCTSdUm8XhYfWc0btzupfaLcNnhnJr8Schl+Rak4pivjDc1K+f7i74lxHPbIlxfHJPLU0sWxjuU74Z3E4piNo8znxRPhPsWDvsGUQWzxgJvL47xtG8dnFD4I9U28o/QTKgFfUbayv4vgYAJluT0Bz9/5A5wezj9PnW2uhneQn8A7HV+hnDLWq/p/xEgrdRraOD+r4QMyH0R6BsTnezs7j9tRPthlebxTtwU+4r1T5fysE3ng/WzZk2T3KOHlrBpsvUBcuaks78A729/A642BeHl4BK+382lYxZMlL4v9fB/v+BUDWIvHuf6Q6b/OYmSci9eofAk4tZ3QxfCrG+/g5Tu/FWEjvDO0cKThV3gQeyM+Mv5fVL6zEK8n3qWcrmdk06Irx3ZPvCP4NN4RLq4KvIUHORviQe6bVKZXZXnsO3H8fkJ572JnX2zdq5KXRjJ9oH4x2VWzOD9vUqmn4u8T8fpmQiVNQyJdm2brFg/Kmu6LvPF66jJqp5TOV+dzbBX5Yf7s84yJ/59I5etRKmkqjsFYyunND8Wy8XiAcCQ+ALor0+fnut91Rp16orK/fnFuiytRRV20Dl7nFOtdi3fer8bb2gvxQGY0tVfT7yKu6uMD2c/Gsb4cny20QhyXb1IGPa/g7fVfya58xbpP4U+r3KZyPqrfqZqf9wn4FdaV6nxuo7x/bGG8D/devDcqlv8Cb6MGRxpexa8o5f2WeeN4/V88QN8zXhczO4rj9l1qZ4Utipf5g/E+5sXUPsdh6ThWL9NgsBL54+d4EHRptvzI2Mfh1PbvnsPr81/G583vdS6meHZV71T7M2MivTfj9XvvOK7FLIEd8UD1B5HWRfGy+RhZG48Hw6fg9dGR2fFak9rZPovHuZlCOWh/EeVg3GZ4vlsYn32zVHY+RjdyTOeEn5YnoCf/4CODD+Od5PWoNESxztmUQchGeAf2WrwyKyr25+L1r/ARrRMpO1DFqOIgsnutsv/trFLvhQeWU+PncmJ0t866S+EB4mn4vTyb4lew5sc7AwvhweC38Ar54Ch0RRqKp6vtjDcUp0SF8CZe+Z9D7feY7RHbegTv+E+l/H6j6tcH9M3200F5FbF4QufX8Q79iUz/dLp+eKP8TKSpeLDOgHhvg6hI3sYr5uL93rHtIynvWytGKosnUZ2ONzzVL0FdnvLR+HvHsaueswnUjjz2i/P+OeWVmAHUXjX+onNRPeeUncmt8fx4Fj66WlTU8+KN9ZaU30NVPKxjUmVb7+BTShePNOyAB1Wb4lM9F6V2KueWeAPxeGy7+Iw30fmX3H4RKEa6NsI7RidF2osvu18hPs8x2XGegAfaj0a65sq2t15sc5lI0yWxrceYfirY8XjwfTkeTLxK+fUWX0xPJbtCyfRXS7fEy9hc+Kj2V/F8NrDyOfN7l3YjAlC8sStGuA/HG+JXIv8UjdZSeMC+Xnz2JbPPPSz+76r4HKOy/dyJdyKuoJx6tCy15fA6PCB5l/LK9wJ4WRiVlYUz8HtpvkHtQzzmIwJXvHHth18VLa6ITCQrk3HMN41z3Q/PT8+RjeZWju8AvKG+Db9f6fU4VxbpuoryXi3Dg9DL8Dq3mA4+JI5nf8r7sG7HO6hPVPa3Ah4oPhPbnEjtNOWVYh+j4rOvjAeOq+D17WB8BPs6vPzlj9TvQ/m9qUPwuvyX1D7MaO04/3dSO21uQ7wDeQl+lbjmns/KZyi+o/U6vP4aRfkkwi3wfNoPL3dr4p302ymD6ZGUAyyX4HnoWjxgzO8nOirOy074QM2qeN7eg/ieRzxfnVo5/3/GO3F5vb4UMfUK75QVg1fbZmnZkOkD1XqDEWvhdfBKeHv6KD4l8Day+8Lj2BxGmYfPJHsIWay3D14X3hPnuhrMFfX6Qtl2F8Hr/svw/FvUAcvh5a8INK/GBxjG43l591j2IZ3Um5V93xt54WTiK1DwctiXsn19n+wpzpX/n9GXglefaH5V5IP18XbzhjrHayi13703PPLW7XW2Pxqv2w/KykRRb26Bt1lHUTswvDpe/vemfIL4dsQTk+P3q9n5yvPYhOpnauAYD43880Ccq+fwwY7iXtrx8d6rxHfU5XmT2it7K+Fl+3O8fOxJ+X19ffG25H28TV4Dr0OL73beLs7zh3h/Z4Fsu+tRTrc9gWzWVTWfxt9r4HX534jBlsp5zh/s80D8XVwZfojKg6iyfL853j5cS/kApGJbw/AytnJsaz+8bN5NORAwCB80WhmvNw7Jtp9/h/NexJViPIAsnqi9K7X3ruYD8MfiZesa4qnMsfxOfLDySMoZTxvgfY/pbsGaU39anoCe9ENZoY/HG71ialpxn9tVZPcn4SMbn1P7XUwDIyMXoxHbAQ/G36/hHY/38cZnlVi+LB5cvYA3zIP+hTQPwSubP0T6lqV+47kdfnn9q3gH4UI8OMq/12h8FJgrKRvnsfEZi6sbK1B+bcXOeDDxEzxwvAJvbM/AO3cH4xX3mPg5DB8hvIxsGlhst08U5oF4x+Pi7PisEtu+l/Jq2dZR8C3eL27ePpDsvqnsPL0X/59P/Syu1PbFR+NvxRu11yItfyK+JDbSNxpvJHvjlfqj+JWedSk7pfPhjdQ38Y5NMQVhG2rvG70MH61/Dm/0Dsev7E6idprY1fHZ9ollu+GdlzWzz3AXPtL4RpzXHSlvTO9D+aCL4isbvok3EpcTAU6WrqXxq1PvUT4tb744d0We2Biftlfs40Bqv5+nKB9XxnF9Cc8vR+IdaaMMbDeJ9W7CA64iUN8SH1Us5vCPx6dDFed/ON6JXL/evuPvkXhQcDa103gOi2O+G+Uo+0J4oLtQdtwOjfPzKN74/Rj4H2rvBx4f/1c0VoviAdMt/H/y3jPcqiLd9/2ttcg5g6IESZIEEwqCJBVUUBDMSDKLiSDamAAVUCSoIIhIUBAjRsyKiti2bc7d3lY7x7139+5z77Pvued47of/W9RbNccE/MSW/jCeteaYY45Ro+qN/zeURflt/j6zdXgJOYcvIcO9I1L0q2xexiC6uIPY3ewgRAte8YXnXYXkxmzkRIVU40nIOa6LFPjPbZxVZPWryDE8Fcme5QgYCKnc05FR7yOFFciofYLoIFUgw/QO5KB8be/3PyjdGN6vURUyIi7F0jjDefs7BDl5ASmuY8+6ANHgNuJ+j/vbu4633zXz93L3vhLx+PWkzbGOR/SyDdHkWwhI+DNyrmrZfLdGPLgRRTeOsd+H6ExYm0DD20i3FxiK0swPtbVuZe81gdggx6dSjrbzi5Gx2RXR7zrkoDW2dQsNdnqR0ooH3V4l1qw1Q/oiNDkJRvpwRC9TkfF7FzJm69haX4ZSW//kfhPWqzqlztyhRgs3YdEOe/ZXiFemotTEofl6UQqIhY67Qa7Ox7oiErMn2iPA6Ukb/0JklH5PaTOVbsh4XIVoOu/+XB0Zr48juTkLyaw3iGns1ZEBvQrJi4eQE/BXIo9X2Dx+S9SPLdz6+o2693PztJ1oS8xG+rQWkpe/R47BYCLNHUMEllvafD2BHMeaZea0pr1fAHvr2H02k6bcNrJzF5Fufv48qeMS5H9PpAuHFHxXA4EZHyNd6A38+kjfP45AXO+EN7XfbSV2v11DVvtWdLhn72i4FmQpkvc3IHm1hrSpSI/ss3eQ8qj1MLv/0cQmPdMReNAIgUwvGB2FQMAIRFPj7fq7iOBhHcQ3P0d1yUE/FnV0ro7s09ZIJrxuNHJuwbWrEb2G/gj1kS5dRMweOQfJnOWIhjvbufWkpQRPIsB/HdKVJ9l8epoIWxL1BLY5vXhTtvbH2rkHsYw1BBr47srX2hytJwJ2M5Bd6tNnT0GybTtRPiUNjvaGY48PYG85iAq3I4rarEaI3wtIqbQ1xj2etMZrILFj3aCC+56M0JnJxMLuYXbf0DQidGqch20MThZqz+5ZVBA+FNtoGSmYkqilu7YpUoz/NGY6hag4DqW0U2JIBf0zthdRmDPkIC9FQuoi5MzMIaultOsfQYjtY0hx30CMAC1GTmroNDnHxnYFMWrh0/saIoF1njs3GUU+6iAjIxjd1ZDivBMZp7dSBvW1/4dgRinRCVlo43kZuNe9/0yb7+vtGdWNVm6wd52ODPKviGmGlUZnHey3K5Cy9jV6jVE6ykjkrGy2+x2Rjbs3Kfo1GjlwEzO67gl87a4L9RbfIwH+U9JUkfNtvbdQWrcW9oILWxJ8jgxdrxgOAD63/6sjmn8ZaxOdzz0ymtYjJ+JEHMpr1zyFKTNkCG0xOqmDHKdm2f3yujivqAOC/RXRcJxHbA2+I13M/ja39wn7KIUW+ZVIWU3L5rotMqwCf5+HIog9iDx6HTJme9h7D0fgzBhk2FyGlPRc3KbKNva7EH2NQfyyP+K7s9x191LaZGINci6CQmyCjJUJiMePQI7vgwjAqYGcrX/amp9NbDrQjNSBqiCti2lj4zuNLMKayy77XIsog0YhNHuxvf8MxCO5gb4cASebs/U/jAKkN39mtma1bD6q7L1bIef7J0jO3INkdtiLrQGi/6lIT7S3575vc++dvimIJ59Dhl4AB44wenoGyc5qRjt+K5deSGaNIDbjCg07/KbyrxC74d5n69jV3sV3+lxv/x9CTGGtmc3JvVg6PwIRpiM5eisRoJqNHKEPiXVZTRFtFqUo/wSBn2ONVsKz29j5/6QUGGyb38fOX0oaSd3R0dbWryYx8tnS5vQY0qjlifZep7nPD9k75l2966Io1YnIYXzGxjwuu646oqNJNsY7jT6C3BqAQKJpmNyw9f84u089RG9vEcHXNijl9ir73Je4ZcEGo5/ZwKfuPo8h/byeuEdaYQSEmN3iG2O9QynPnWRzcAbi05uBx7O1mUCMwl6CnJfhpGDjtfZ9F2RrfYDLYrBr3kdgw9Ts/KHIoRqJePIvWMdHd02Nnb1vWK9AP/Z3NJIzP0H6ZQZZ1pL9nUFp2nMONFWgSOeLSI/fhXg/8OL+SOdfQrqvbV+bw/Wk6c4PINBlJWnkbCKlstQD4COQjPTNa2oj3ttu69DOXR90Vm/koPdAJRNe1vv676uBTe6dxhBrK73+PpIoR8I+tufhwMXwfKOvz5EdVh0BRKEGdojN5yAkl79BwFxlwTwcgBzhb41mrsNlfuwtxx4fwN52IPQpbPhaaYy6xn0/GBnoF5AWy1+CkL83cYrXfT/GBEJvpACC4uxKrFfbRkwXXIXbQ8rOFeb123f9ULRhDZZPT9qCPB9PUyT0zjbBMh8JqW1EhKo5pXuw/AfqouWbQywlovEhxWsraTH+WVjnSqQIT0JC6HnkSG9ETtw9CAmtg5DZ25AyHe/eJSCjQ5BwvNQ9pxIZOCuRIH82e++uCA0PivkEm7OvkOAtauTTFTm5dVFEOKB0IWWlCULJ9rG1DelOhyHn4lbSiOJYO388bhsEG/s2O3Y0JSI2/rkcKWavBI5CBqDP1R+EBKRXisuxdKns3Y5EBuyNKH0t6XSKBPIfSFNfTrVxvEk0iu8jbYkf9jv0XXCHIgPTGwNeYbVAimKTzU9ArmsgGhtkazQfGVdP2ZysQ4p7YHa/ImP0JrvfBFR7stTm6m2i09cHGVAP4RrOFNyrOaLfgFTXc/PTzv6v7d7hNGK79xDxq45kx3s2z0EJN7X5mmdjDq39VyGavwEZkAvtuqRBBOLB9aT8+waWbmmfXzK6eBvJvZAuO8i9UxUyXs9GhvtNyPD4ltjFcrL9/gukdAdn8+Q7lXZBAE5JHaG7vpHdby5S3FsRIPF7UqemOzFL4B/IIG2PMhyCDOuLA8RwaVxuDE2QI/k4ApHmk259EZz9f6DIjueFfqS8Vws5/K8j2RUctdrEKOl4m8OJiO+HI/55lKzRD6JNn0HQ197Pp+HeSNq4YQIyjDcQ6aYS0c5M5DDcg4C2fL+5g5Hz8Qopqt4Zyf8qO8L2DycQ29g/juSFN/7aI8DgSGQI34kAp6eIm02PQzT0EZLZHe1ZP3VrFAz1usSOt6uxRiqOJm60sTxqYznOfRfocArihekoev4RSlltjUCRkCGyP4p8LCWtRatPjDL3Qg7Ul4gnQ3lBGHdzRLOjEAh6MtIB3REv3YzAgDDGC+3a3oiGnkX08gSiy9p2j4eIEd0jEZ0sJUZTuwPP2f8vEFPG55HVnjr6WIBslJUI9Hkk0ymn2P8hU+dBJD98xOdq5CTdgxy7WYhvliP5vAbJJZ89VYGc9G3ITqhyz+lrdPAdknOjsewNu+YylHZ6t/3fFfHUnaT1Y0uQPLmIgj4ABfLnROQQvobSuNvbnC8F3nHXluv42gKBN/UR2BuyyTYgfm+MHOm/Ilr0IFZzZPu0dOMZhWg/dM29xt7nlWzsVf5vfj471xvZXU8hh8zP1zIUHDiGmDYa6sK9jr2ItLFTDbunT5U/AvHUs2gz+H6Izz4glt80QDS7H7INwx7IoXPv23ZuJqmd0R3JjC+J+ncIkq1hX8LLEP/MYC9pBpOs454ewN5yEIX2YqLArEJOzdMmCHY4VAit2UramKQKuNDdM2nQgJC6J4AXsmfPsHsF1K8lUvSNHFMfY896E0Vpevlx2/+TjMk+Jq1JCwp0v4L3boAE/Ewb20z3LkuRs9uDaJyehpS9f7fzjEk7u3ObSJvWHI+EjR/vTGSEVCDh/Z8ItfJGVUeE9IRUxibIGHwGGSDLiZ1WPYo5HCmOF0n3x9qXVNi9jZRqS6RIf09WZ0HcbHUoUSBWIQHVJhvr5Tam20kjY3lh/TJkxCwkS6VBKP6XCGX3xfa1kJDMBfytyFg+GimQu4m0FLoUzkfprYtInTy/Hq2Qg/E1aefLuu6dT3FzEtJ1g2EajOtg0NyMlO9IZLzchJzAfLPnBMVDTvzI7JrJiD5vsusrkfHWDAn9Vcj4S5obZc+ohQyi4Ii1Qem3JxLbklehVMGJyLgaautVrv5xNa4jGjIkvyE6UTcg/q6OkMmPkeP4JbFgvtLO/REZCT7y1w45YH9BNOU70x6JQKr1pE0HKoiO87vI2F+NdY6za4aQbtp9AnLsTit6T7vmUMSzC7AUT3vOe+5dLkKNCS4t+P2TSJZ+jRR6CR0SnaZ+yODoZmtykD0/yMPOpI0MOiJj6i0ianwpStcM9Fi/zHtttGcdiAyj23GOl10zGjlXq1CEYgLiq2eIe5V5mdgB8foryAhpYut8vI3xr8Ruh5VGNxdS2l1vjL3D/kRQZAUuoo7k/lOkNaPXUdoevicyzt8hOiuv27W+lnQg0WGdguRCzrM5qDgOpcJ64PE6xG+/Qs7BSfaeLRG9tUROmm/ffy0CE54n1R+LEP+vRo7MSATcnejWt4+da04ELlaT6oWayPj0KeQzcN1NM3q9G8mUr2zdOmTXbEX6+Agk998ii2jadT2MZpYhB8U73j4avZIoi9obTdxPurffIYjPViCgoWij97oo0riCmF1SHQHFbbNrA081sDm809YqOPrXIkDvEwTcDqEg0mZ00oVYJrAf0hNHE/XvVJRZ8CwWrbXz++KAl4L3OQvJ1ZeJpRAVRJCtH9ItyxEvPEm0yybYOs002lmC9MlhCIArbK6F9MNkYglCT2J6uN+OqiYZeGz08HJ2bqKNI2xZ0h4BA9fZfFxn588hleczkY4P4HdnJPOXELeXKNsQyOZpElktu7vmFGQHedDnJMT77xOzP27DdKeb/8ORvL/O3f9NZK9W2Bz+gWg3XIZq/RYRO3pWIHvxLeTQXWfrHTK6QmnCvUh2bSKzg9ycHgz8FoHCryAbsAT02JuOPT6Ave0whvg9EUXYBxmb+xDR5TVExb3EiG0Wad54kWBpYgJjx0as9vdohIJdj4yR53D7iNk1XyPHZhIyUtaS5spXIAE8GSmtvyPjJqAjZ2KF/xRsS0FakxCU0jjkXN1p89INGXE7ajeINTo3IaR3HkK4fkWKGNVFUZVlRAT4JVJhtw4ZH/+OkNxGBeOsRCmiXyADuB8SoL8iLTKvjYT26bY+K5Gz+0diJO8IhM75cY4iFoYH46IVEj7/RTR25wNri+YRKb6ZyAC5jtgsoFxh/SZs82g7fyRCEJcT95vLU7bCdiHNkPKej+jyFVv3vFlNpY3rdqQgfapJBalA7Y+U1S9Q5DvQw0nIQVpImhK9gdiFsQoZVc2QkXw1Et7vI+S4kRvP9ZRBUovOud9WQ85q2N9ohn2+B9Fg2Ey4XcH9AppakpZq37fBdey19TzP3qFWPkYEXryODMurkVPgtwA50sYTUPuWiD9CM5qwToNsTuYhZ+xWUrDiCFvv/yQ12OsigzrMzTSb16tt7L1QxCPsUxfS3w4xOmhHdC6GkTq0gXY8fwQH3Ddzep60RqgfcHt2j4uIe5H2QLT6MWkKaQ3ktD6O5Nz9NhfTED+HezVFAMnHyCicjIugunu9h3i3BgKSHsMh1HZdTXtOqDGrZWv2DHIsiyKVJyKj6Uu31h4g9MDKIOQIrXbnQgfjB1C0IxhP1clkM0plXIcc+VMRGPUJaYS3MXL4L0OA1qGIHntm96pm9BJ0wqUIJGtqxyWIN6rZfJ+B5PEDmByjNFUyT7ve0UwM6c2aCPy4CvHJoEzuXI3k3wJi3Wd9HLjm1ulYG8NcpG8eRQb5WCe7fWv7+ohnrnS0W4Hkqt+yqI6NLc+w+Hk2hqWk3bd74Fru27kLsbKPArqpiZysyxH930Iq/xaiTJuji36fzW9d5OjORLbAjUTHrAsCr6ciA/1Mu3YjcJu7186cLq831hIjnxOREf+yvX+QXxchPfB/IRshL7e4HsnDzsi5uA7R8Tz7/nFcuYf7XQ54+j3n1iLZugDJuSrkfAy3z7+2eV5D5O9uSJ4vRzJkJFYbW2YeghMV3nOyzefvkWNcD4H7eZfWashRu424yfw4ZBPk7xSajq1AcuU7G1OQKX2JQN5VxNr8AO63s/l9DQHIfq/oCgTGzrHPft/UpLYxG9PBtsavI3DsAiRvQ7ZMKywTjhgN/w3S8Y9m9zoK2X3tsvMTUZT7JCwwgvjjMiSr22bv0hnpqGeQTBqGAiUNkQN5DKItXyI0GtnTJdul7C3HHh/A3nIgo/FnRlD9jKg+RY7LLGJx+2qyTSaNyJ8jtppvQ9r9qGRDZ3vOGca8+xtTTUcG7djs/sNwaY1IAay13wbhVBMZAwGFq4+E0Pf2nNXIoD+bNLWlaGw1kXALXerGIUNkkzFgUBAXGDP2RIpnFFK6txC7H9YjdrfqjAyNlxCiuC57z0o39geQMxi2AQh1dGOQYBuBFEeos+vj75XdtwESondjaJv7bjVpLdWxwFsF9zgACen3bC62U2bLB/u/BTIWQ8pVPcoX1j+HFNcwpFBed9/1Qkb2n3BNf1BE4W92XGPnQl1TU6KxfhIymm5EBnsrW6dnKG7O49/hEmL+fmdi5DtECW7AIhRuXKNRNPffjD7qICO8NdFAr0S0+gjZnnxl1m9HzY993pcYsW6ADN5A951snX6BwIJAq42INZtdEYhwDaVGbB17zzVEx6qnncs7pvnPZyDHze/rNtr+noVkyWUF71bk+LZDDtrXnk7su0nIUdpCrE0MreiPQWj5eKSUX0coeDAWtiH+6W7HMpuDg4jpTrPKvF8l5euJVpFuO3M6lvrtzs0BbsrO3YXbFNmdPxEZ0xfZvP3W1tWn/E1zz9qM5M4oojw8wuYoNDdYY9feRqlTOw+BFB7d/4BYi1yS3h94Il9H9zc39NphUTJitHMiMiRDqly5+d0XGXJ3IVkyquCaPvbdKmSE5/v95eBFC2Rs+bTSw4nbVQSwoK3NYZD9kyhtLFRRcP/JSG55I+5i5EhVz65tY2u6ETmFByP92KFoPvz8Il0dNpPuinjmaqLTsogYYQkG7DijqQuQsX0FLkru5mc9KbB7rM2Bd9xWIqeioXuXd4k8VwQiNEb6azCpvG2CHNukDq6I9rJxjiU2qamwe6xwcuk5o7E5yDlvbnT4M1w0LpvXIM/7EJt6+bEudc+shvRhLcRfs+38kUje1kR6oRVyTEPGyCFIh/8S2Oju3TyfN0pTuMcjp2c0cU/AWaTlB+3snf+M7a/rvjsG2XarkOMV6KUQ1LD/OyAeaWLzPM/o6EWirvCpySHl9z6bH7/XYbWMjioRLfYkNiTcEVW3z8cjvbCdtDfD0wggWkncx7emG88cm+d3ieCjB/J2JnfWYuABqd24hFi3PdDG3gXJ6br+vvau0xFAMMjO1ULy4C4kW+7Lnr0KgX99kSx/mLi1TKXR0asIUGuEAgKfIL0+P3v+FrLSqr3p2OMD+DEflCqumchgDkXlg0jbv5+DjNunkKLa0amJtIB4kDH8iTt59kPGmIuMOeaSRXvsukrk9D1rzw/PHI4V5Nrn+1EU6H2EsrW184cjA+9shDD9lYial0shWI2E6sfI+G2PkOG6jsHb25i+Mua7lLgHohec9yHE7VVkgB6IFENLUgOhuv9r/x+Ki7YhZbfB7ncuEkSbcah4mDM3dx6lrU2pcdYfGXxrUdrLO8RI5YW21vMR+tcN2co9awAAIABJREFUOWVHkxlC7n65EtmHtKFEYWE9EqDPIQNgChLefiPWAUSh3QUZ+CFd8XVk/AenI1zXFjlDQ5FjFiK4tciU/87ewc6tIt2zZ7zRxypibWQro5fDUeT4PZR+dFyZ54y1+S7Zp63M9UV1DY2REpxOWhf3ErG+th1KSVmM+G4SMgS/REZOLgea2Zq/jgzMV7E07wL6KadAh6Go3UYkD+YjVH56tq4+Ap87Xn7T7ztJtxe5GsmNlQiVvgrxYKhHbUg0dJ4ndhG+GBl/TZDBuhop4UcxUAaBMLchkOkysvo4TydGSx2Q7HmemILa113XglgL3YeYwbCdtK66Kn8OcspHZ/z0Muk2JrURIJFvh3K+XRuaIpyCDMeqgne5ABkLLyIZ82/ICUjet2CdK/Ox74qXsu8bImPyNU8Xjh68LC3aoqgquz4YqCXOR8Fva5W5z1iU0vUcaTOuexEY9QGKONbPx+vkyy2olmtH1gCSa4/a/30QvU4ipj8eisC/iUaTw40WSzZED3ODZPGH7vu+SJduJXZMDjprEZI3TexZbyOnbj2xvGIHbdj136ISiE5I18zI1n0A0oM3EusRr3TjGcZu7KmKIkE+tfF9xN/e8B6wm/dqYO8e5HUNolPQy977F8CdZWRahbv2j0Rb5SYsJTG7vheSF0NIm5S9iTmK9rkzig6/R5rq25YIZF2Nq13Pxxb4AMmykGHTGum4hUhO+i2KaiKQ/nlb+9Oye32HdK5Pr/aRv/B3GpJxL6CMkwCWNcHV3fr5s//3R85gfwTIvYw5guH+lLfDxiDd41OBm6EARdjPsxdyDKsw3Wvnl5J2ZX0c2X7zSBvBFM1v0ljNrY3PEHsIgZv3IttzFtHmrEB6+TV751ORY38O0lfBYa6N+HUmagSz0I3/QRQxP97m7K8IHPT13Z2AKe5z6PD6HbKrDra1TzIn9rZjjw/gx3wQhd1pyIAdYEwyk2IEL7ROnoYMxHnIMfia0jqy0SikHfK4fa1Ea1wUDBmpD6BUkJA7HVKErkIKaxxCTa5BwvhtIhJ3JrHpypEoIvEmMW0gvOfjyGl7A0OE/Pf2/zAkZBq4539CVneAjMaQMtvPGP4bY1SvIMPGwpOQEr0ToaAlKanu3oVRB2K051Cbg4WoBnJavl7ZO4VC/yDQe9va3W/vWw0ZHpOJSFl9ZMyeZnN+O1LyQ5AzHO7VEAl6H0WowEXVkGI7mV0U1iPn6S6ksO62zzWREOzk7j0L61DqnvkgMtDWEJ382fZOHYmbQre18QSjoL/Rz9GUQd7tmTNQBNQ77svt3N22ZpfjOsW5cf07AgPCMz2wMgw5IqG2wXfYPAcp3Anl1tY+H02sXxqBUMK861hbRKeDkWN2B3ICQ1SmHeLBm5CDfhixBs47NC1RRGn/7P6JMkfo5EKEcs9GBm1ooOFbYi/B1YRm9OqV7h+QsXInkTe72PodhcCK32F7LDk+akU0svsg5HpHPaKd70gK8CxBhu0U4nYXZyOaDnQ/zM5vwRQx4vdjEcLfDjmH7W0NG9hcPkuMVnkQyzvGeSOPpsSUqo7ION+GwC5f2xWApEORcXQUMfWxHjI0fBrzJkS7lxhdtENRnT7IcX0OVxrgeKFINl2HjLHzbYx5tCuPFOat5Zu574aQyo8a+X0K6KV6Nsbw2/4o0nUocSP1nH8GIqPpIaIhVw3LILDPw4kpsjOQjthsc5ynqp9A7Ag83+Z5m9FKSMt7FwEinyJdcg0RRNzPrm+I+LQwbdHm8ADEX1sQ7w6zMR2OUv3yWq2L7J7hWW3dvSqMTvrbb2sgAGcbkqt3OBkwHcm07sgRuMzOTUY6eKzN56L/U2btiPQ9EMnuT3GNgZA83YB06n3AkjL3KUnrQwbyE7isIveuIV34a5uPkEZ6Mqkeu5QIXh6LaDyUOORpwbcg2yLouFOQjVTkGJ2KbIpnSGVrAwTwhrW5Eumqw7JnnYQynJ7Pzncm3dLiLlL76zREt68S5cJ19t4fIFldkpli6/stsv+a2PquxxqU5OvhZVF2rhnis4+Rs+IDB0UAUxsE2j2Mslu6ImfXlyvUQvx4P7EPQNiSqCVRDlyAgNvZSK5dQXFZUAkQV3DNFtKMl0lIPy0iLcm5HNlZtxitbbF165/drwGyq29EwOB7wEPu+8koa2g5svV62bstceuY12NvRunAOxzmvfXY4wP4sR/IWPuTEUtoPvFPhGL2QMZjEQLbERk360iLZQ8lGrvTjAlyg2AFcsSGkCrvocihaEfsAvhT9/0pxlhX4hAtZFwtzp5xLxKWXvi2s79noLD5Q2S58AjdCfU8wai6jtQQqm9MlqetvmTMHvLPl5J2rWtojP4s0ckdSsE+NvbdrmrEapPVaSHBvxQpmgPL3Pd1ZJycbv8/QUwr9dE2v4fi0ShSuJa06c7LyIgJgrpXPlYk8HdaWE9q9Nc3utmKjI+t2fgPQkLRpypNRQr9LmL67ShUIP0hsdnRtcRtSKYgZPNFm7M5ZLU47v7NET0uRMq0s611E1v3g5DwXUdaWzPB6GctAkw6IoR7hb3jfKOHB7LnPYWicBchhbad0qYMhxCjDKPtOS8jQ6VDvg7Zb+vgUraQEr3D5mchMoCHkiKPFUYzDxH3CyvZ0xOh+iOQITzAxuX3ERyADKA3iIo7dwoC7XRCsmh/m+OnUArweaSyoz4yZj6xa3rn43K0eBUCbZZgKcbuee2QMR3osjsyHh4kTVF7w9amL6LF7aQG56E2p7/BajftfDdbqy5ImTdEBsr8ovHab4YgWdaRaCAONdq5B8m/BvYeI2xun8VqQZCDewRp067QhXYSosOFxK0yAl1MQI7dn5C8HJiNK/DrKOQonI2Av/uRrG5N6vTVtHHmaWAeoDrC7lWCXLvnhVKCuaTpnNWy625CcvppZLTOIgJEjd1cfomyHu7COuYW0OFK4Hp3vhUxU2RHZgkxHTLojjY21vsQOBn27lpEbFRyFpJRTyMar2nv9r9wWx54eZmN4xPkKC1BevNyFAXw0YxFRjOjkF6eQXHmzYNIVoetIMYUXPMMke7+huvCa+t5BHLe/omMeN/5sQml9sAWm4N5CLx9CMm2KgRc3Y+AtAtJMwcaZO94IzLur7d5n4BliCAeuBnpzN6Il0JN40+RXPs1sdnXUAQY+Q3S2yBAaow7d5WNdR97j81IB2zFbaOAnLlFpM2cZiPHN9DKcKQnBiDe3oKyN2YUrEEbpHf+SsrXniffLvhdM+L+t/ledpNRA65tpMBLGyRTvS4YgXS+B/Suw8kxDAz2dIvk+DLkMH+AKxvKxlkX6dxGCJSaifTtNkq7L1+I5Oy5SE4+geS8r1MOAFB1BNKstjUKtkIvxBdbsLKmgjEFWXAtsi/bu+82oSDDYzjn1ssmRMtH5vfL5nkskhU93fiDrTQUyaG7kXx/JPt9FakzeAwOaNxbjz0+gL3hMKHzIhKEByBluB0ppCdIa57yToZNiU7fSUj4f2KMcgdCPtYYM4d7DME2k0aGW+MChuiJ0JVf4tJK7T6+I2UFMsgft+e3s/PPk7bpHYOMgFHIOKmy8X1FjLD1QCjo26QpWBuA6dn4TjOG7Gpz0MqeOcoETHskwD9Hysl3/PRKcRux/qQLqVANQqd19rkQCbU5fwwJ47DtxcmkxdD1SZui1EFO9e+JzU16IYPi78AN7tqAEodxdEOKoJb95lpkNC6yayvYRWF9wbt6A7GrPS9ECn2OfWigcwuxI2MdpBhDJ9V9kAHxElIkY5DRdYDdY0dNiI1/LbFzZnhWR2TUVEfo+oXIUHkKOdpH2n1C982FaDuNKxE6/h2i74eJqHIb4ga5dyChvhVFnSoobspwuz07rON5iEd/j2j/CCIf+mhlkyJayeimPqnyDikqD5ABKfb9EchgW4mMyT5EZd8A8dlSZFwvNZrYmM3rBGRAbiONTh1MimZfhkNF7dxi++3bKL16A2nDh7nI0X6AMuljCNxaikCQGW78QWb5bVca2JyEVPSmthY+ejca8foOdBzR5W+MVq51vx+J5MOZCN39mjLdV7Mxz0KG6ixEb3VQtHgR0VB4C8mi64m03AXxgpfhVxAjpB2IzX9CE5GQqhwM1CkoU+MFmw+vA9Zi9bX23uchfbLIzetVSC4+ikuFK3jHt4lG6iTkaJ2aXRPS826zubuL0lTSBogPA0/0QPomNDWbjXj1fFwjE6OL540GehLl0jBEZ52ITt+tNsacPx4mlbGhOcx0m4PL7PN8Yqrudajmvp373cdIl/6KVB8lKdOkW2gcYzS1yda8wp71V6QbbrD7/h2lRLZ179gYGejVkJ4dYWv7ErFpTRUpGFINOZ7fkwKlg1HUfj6SFScgo34taXr1WbgukijC8SZyisN+kBNszecjoG4Icd/bVm78kxHthRrXOYhmlzl90A7JhcnEtLzxNmeTsve6FcmReUTQNqThhmdOs2uCDh+FANMdTZUQGBE6uz6LdeK173yqb0MkU7cSNwqfQLpVRRfS/WwH2zp+QyqP1gLnB15wfB62JBiE6PTXSP+c4n67o7GIkxeBd09wc507IYehdNevSdPcPb1uszU6COm/ECEfRNokbJONbRHmdCObIth3tRHYONvW9QRE34uQPA9z38bm/jXES+FeLUhTjV9CMm8dcS/RksZ8dr4zkjlTkVy8lrgP5TZiSnrZmkNS+ZnXaB9Q9Fz7vjoCfAaQNR8qmu9/hWOPD+DHfBjT3oQU5lgj4OH23X5G4C8iVDvvAleU5lAXKZIDkGAdjBT1JqIBXJeYljHC7r+W0nTCeihNaYQTFJ3sfvm+eDVMsNxj93oGGZ5hjDcjhG4VEuy3E1HgoAjqI+FbCxlpv0UG4l24DTYdYzexsTyBkJufoZSeh3BNU5CgW4CE+2WkTt45wIP2/xhkNH9AmiLSiazbGgXOIEL4niQ6tF0Q+vggpgzs/J3IONpRN2nnWxMNvueQIB2ODNsviYZZnjp7E1ZraHM4CAnikFq3y8L6ArpMgIbsO490HWTrfRlynIba3J+LOa/ISbjLaOMeLG0ZGWZPkDbFqW60E5zuhgjV/wwZ8mNIlVAdZBjmG+cORZG+K20O+9h9BiLHcTRpV8i6xE6pB9lariVtIX4cMiBCt7bn3VhmIqNpDeK9A23sPyHuX7mz5grTEf3el83FCaSGxVnZ78YhJ+dWUuc90OAByBlZiQAdnyrTBQEUZyJ6fNDOvUYKDuyDRQbcuctRqtYLCCT6I6UR/daUAjcBlPBGyWCba78B8ARUE/eym+PAcw2QsfEV4uuuRL6pnT2vCjkfhxs9PIV44X0EOlQg2vwW8eNwYr3gaAqUPHKSN9icha6QgdfaIid2gH0fjL9nkKwJ7zAd0f7npIBUHyJifi6xKVVwMGcZnfjOnIcgOfkWaeSjFbGpUm9ih+nfEut5AgCzI00aOXe9kHy4HemgLcTU/qJSgnXI2D/ZvWPY2ug0UlDkcaSjuiG9sBErDyDtRuu7Z4b09iUo6jXd/v8wW5uRKPJ1IwKl7kcG9mdEh/tSN59TEC3VQ87vcKQPW5A2zjoX8c/riN6CsdyfGL28MZOfg8P/bm1XInpsZWuZZ8+cgHjhSPfebRDgEWTibKQfFpMCm13ceh6J+L4SAZDXICfrNUoBnW4okn4okY6H2jq9R1qT2d2evwzVpz3tvtvX5iPQfD3EK12zda1EDswaxCsjKJO6bP93QLzyCdExCzTmO27OJgWr/R6cvplcb6OJV3GdwZ0eqEUEPZshwDTUiZ1h8/KZrYEHBgZnYzsLF9G2c08T92d+G+nvJigT4SVsy4LsNwG4amDXPWjP30K6xYuPHJ6N5OdW0kY1rXENs5B8HIVkynXufBhbANteIDq04f1WIz57FddIrmD864Fr7f+TkS0TtgwJa3SsvVc1JDdDQ8DZuL2es/sehmT2QlvPToin7sfVClPejqlHaYlRRTauk0j1ntdbQQa0QcDPGLL9HylwPvfGY48P4Md8IONkNTJKpiDh+ilxH5qmyMH5M1m6EhnagJywq+3I64UONkZfQNzD5i0k7GsihR+iN0W54U2RQfu5MX25JgS9EUrYl7i3TV17VjBM2xvTPkzadn00Lq3Vzp1jcxQMkGTbAfv/EOT4dEMC/GOEDv4NQ0yRc3SOCYgTiEjyEQhduwcpyQ7ISFji7h82YC9Epdx1IxDau47USBtM3BNrJjI6ZtpYbjYB4nPnD0ZKy6d+XIhFR4gC6hqk1P+GkLB27vrabj0KC+uzOTwORaFPyt6pkmKwIekaZudqIdAgNLoJz+mHDAGfOrovMh5HIaNnHDIwTkF06tM+rrb/JyDavRshcSHC0Y2Y6lhUR1sNKc+TEVp7L6L3e5BxF7rq1UEpd1MQ0rjCrj8TOXVPE+npeKRgB7vnNENgy3iby/uQg3ZwPqfZ38uRcXm8zdt3FKeAhcY9G7HmK3Z+PbFVtk/r8wqrNjIiQnfIExCPBGO4NULHPyStIQqNfs5FvL8Y0ew3tt5bkEP0KuKjiSiC0Rgp56K6lIqC8YXnzCbdv2wh8P/ZeoXfzUHG4xgkO8P2MXntVVJbYu841tb/BiwFHDls+9p8PGh/b0dypNLN35E2b+3s/U6ya54k3W/wIuSghg5xRyAAItyrH4ocHIxkxX+S7c1n17VDBtFkd2411uTIrXN9Iph4NwKP8rTlKxGPHU3cZ7SNPf9g0tT+8xENzrE1bogM8EArOy0lsP8PQPR6tq3VCTbGS5HzEO7VA9H9nchBmohtA+HmaxRy+sJ6HY8cnGmkTm89RJvrkLH+M1vH41BUqALpugcRrbewd3sJRVx9LWtzYgaNf8d7gQ3u82soGjECyYe3KC1RCKnonW3cHxCbJ9XA6s+RfHsbOetP4AAgoqwbh2jpRuR8zkW0Hzb1DjzSB/Hjs5hjhIzk3pRGbEMTnVWIp05Dzl9nRFOvIn5ZTtye6GDEN8FZmmHv/xECx/JIVhFo2gJXS4bLPnA6xvPwSGCz+zze1ugBJAMXI/2Q7+96OjGa6AHgMcR9/E5CMuZ5e9fwXqeQpqNuR7r6ZsQD79l7e5lVBwH4oYHKT5EOmYt1dUc20Iuk200MQrRZn8jbYxGNvW3rchARQNmh8xB9jyN1+DogcGtDNqcPk4KNXZCuCjx5QJmxbSLWv3UB3rT/NxP1z2RcCYyNdUs2P41szT0w0BTRtN9O5UAkd4LdGGyavPFdSGdvjkDDzVgpAuVrYCchXv4Gt52V10tInmwnq5cvkNNPIxD2XSJIUbbfxN547PEB7A0HQuvGGRN9iYvaIMP6K2TI30PxBuDDkVEaEMvOxhxBidRBKQJfmgBphpCelynotmW/qVFwrjrRuetI7I5VNvyNhNrjKB3Jp148T0Q3WyCB+jNkqOSIfpgLX2dVJAxqIsFdGxlbm0kb2OyDlHZzYqOTi5GD29iN6ylcFy37HLq39UBOWR59aoAMofVIiU/Ivm9h89DM3SfUzExw101FRsnduIikfRcQ+8bIeKmLUqY2mDA6j1S47qywPtxrBhLwy5GTUUVxwfatlG4kXeQITkAKucLe5XMkzE9B6GhzJIDPsWddgMCJ9xCNB9S1pb2jT31qiAyWBewk2lYwrgoUBQwd1brbOJcio7ETchJD+lEwusbYWqwgbQ890eZsLVLOuQGzHzJ2P0R0f7Kb75Hu/5r2/IHut2chx+CJMnLifHv2wwgIeK/gump+Pgq+f4toHB6FZEFjbDsNx9/bED13sHWbjuTUYfabbUTk/xTEO2uRAfqoW49Lbd2S/aNy2ja6eAXRZ0D52xKdxE7IKArftUBplU8j2Vc22po9qzkywEehKMjJCCwILdVvRs5bGP9GZKB9iPgkpJzWQ85SR6IhVAPR1XKUVreFtFZxDmln0c7EzdvrErsxN0Wy+itEg/eGtXbjao8i2x3t2lOQ47ORdIPyw5Gx/C1Rjt2G9Ek/RK8DEL/WIkZWayGA4mJ2UUpg6zfRnvUIMhxrYN0WbY0etucch+j4Lcef45EReA8RQOqJQKxQO/wecMxurvFge2Zj94wKJFduAiZmsrkWqTG4D5LX/066L6Z35jcSjdMqe4dfkW4+3wbJww2ID7YiHT0gG+8mYtQ7OMuriI5XDSzy7Z5/tc3Xldm9KhGNn4Xo725cLVzBXFUhMOoeRLtTUbTydzYHY5H98BqSkX67lLq2LsERPRY5gyXAhl8H9/lgnKNVcH1JnbL7vACBAacix+IzBLBMJ9ZqN0GyYa2tQ0n2C3LU+iNZFxq0XOyfieySZQgw+sjOHYoAEw/KnY54Izj65xqdXE5aj3gz4sGwnu0RqOS7z76N5NA4JE+XIcAh74p7OgKj5tv8N0W6cSnRWQwOUGObi69t/t4kAq2VuxobMb35MmSb+uZa7+Oi03buCqz+1j3/l6RZNtXsmu8Rr3dBut/XWk4hBdd9vWGYs9uIHWe7ku2r6XjjbSQHlhL3ZN6Rem6fZyH+a0RGs+6aseH9EYAcsjiOJwNb9uZjjw/gx3oQU6N2la9cnYhkz0aKbQFph8NQyDqKmE99CHIqK43JTqF0L5RLEUJWgQze7zEDx77fMT77vzYSOH2RYxQUVFlBbecGELvgjUTI9Kv+emQMLUIo1PlI2Pr7NkfKZnyZ+QyG72pi/UQNhMK9g4yIWsiQro2UxhzS4uKw2XY1pDB+ZWO9DRlRTxBrC3ykrjGxsUtNpHwfQ05NMPiPRd0VnyctBh9OmtZTEwn+21HK07nE1vw+evRJdp/TkDILRt4uC+uRgPvM/n8Eq6NBRlrnbH47IbS+e9H8u+sOQ87Bc9i+bPYOi5Hj9z0yeJPW80hpNXbj74IiBNuRs+PnqDmiwzXsPNoWaLcZitA8TXRaaiHFf7p9f67NV9iE2DdA2K/gPUNHvuUIxTyJUiOynY19GTKsHsM1oLG1fwXR3CBSeu9BlBG9bfz9kZHdy565nLin0/WUZgsUyZbhiBeCcfQhabp44MdqNv5pyPi7FhkXXVHU/gxKu+ZVQ871jlRKW6tHUcrct6QGcknaMZJbG2xOLiKt1+iHajA/JE3T6WnzHcZ+MpKbeXpP/qweNt5liNaOKJivg3D1ofZuX+CcCDv/JKLrkO7cGclJ38TpQCQT3kFyyUfHu9nfO5BM/jNxq5gTbaz7YBESo4V3bW7fRRGMsO1GyZYoCCx5FoE+8yjd8H2SzcNtyKjeBwF4s901ZUsJbO2HIwfnD5Q2I2lp690d0e6fkGPr052DjgsOxSWkm6OfQWxutWO7HeRIXIGc08Pdej9CbEh1CpLdvezaL2zNQ/qvb07kaS5kNvwDycDQOXcRAi2vJ60/awylYBTigxBBezqji+G23hdk16/EunUiuvseK1+wc9XtvQ526+P5ogbSA++QdU1213g57GXedKT/GiE9NAs5Ql+SpoEeiGR9b6IObolkcy03L8eRdvdNMkvs705T69y8jkCAVV9bwwBoNUOAVQe7/m4iD3W2d1qLa5aHdPUyRKMf2rkFwP/A0kWRTq5h9zwK2zMZZQds9O+D6Hscou3luOhcJosOsu8XISD4VVIweGw2z3UQ7/4URXpzoLM+slU2IBnyPrDAvmtBbDo2yt71MKSbBuW0UGZsE7O1ehD4f2wOKpBDlXQMt+vaI7573eb1BeI+jo0QT3S3Oe6LdM39xBTSCnu3h5C8KdHFjva3IPBnlN1jPgKefFSzv42jG8q0CKDnE4g3K+0IJRS3U2abHSSXp2ON3OzcUbjGVP8Kxx4fwN5wUGqshL/DkGNyEzFtbQASZEcRkZmLkKD+nIj8bCDtitccpZrOc+euAe5xn49HyOurpF0mQx1UPbvmF0hBtMjeo6SLqZ2viQyLaxHCcwfmZCCB3ZOYMtkfGcuvYHUc7j5DTRgclM2TzwG/hWx/RHt331K4HrGoeQ0SrvvYdWGrgG72vM0oMvMqaSQyCMOLkfHxEaYM7XwbStHeDsiB/JjS/Z6aovSpa+y66kRHZxpuY3VkNLyGUOVz8jG5NStbWG//t7DvRpI2B3iHmJLskcnLEYqWOF0F6z2M6GjWRkIx1CEtJqaxBgQyNBkYhpRfW6IhOMzeYyVypOogIb1b0Tb7/BJSyp8h5HN4wTo2QilWoVPj5faM84jO8eFI6C8i1u52RYriPHfP0USnty6i6Wvs/iG1OUSuQ5fJhYiPg0Hntzl5D/H8E0gWlHSdZSdNAdwa1kbG0O3I2b0T0fxpyEHY0f3R/b4WUvTbkfx4FynufZHjXhh9db9/BPFIla3RrxF9HZhdl3ddPAXRWu/sfAMUKfwIGW8t/O9trJ9SkCJMMUBVExlVlyNeu4U0BbAfojNvtA81GgjPnOf48mt7v3IZFj2RvFliNLFjHzejrzeRfHqHmPrZtoB3HyM2w2iNQKrt2fsehRyeYAifjOh3OgWRISTzpiGD7gZk9AeamMEuSgnsujVEeRjmpRGiuQo3py8hY+wmoqyYTAQW+yK6DwZ6hVuvEdm4P0TOwzektXknEiOB45BuW2br9DByqm4jlRW+Y6+fywnZ5xrECNRCe9Z+lBrnO5yXgvn22yn8hLgvaI8y1/dChuuvKU4ZH4n2Y11BCjCsRoZ2oNdDSJvDVCsYd1OUPdECpT6GVMD7EP/5pm83IhnWwdZqPK6jNKLPfMuckv0t2UVqHaL/UDf+EqLV1Qh8uRM5ga8afdVA8vcdBHKFLpRDcSAJAoVbo4yUsMH9aNIMlDOQrRDsrRXI4fglWfqpXV/P5vgWBIjeSnFmVQujm+GkNbA1kE77yt6rezZezwshu2cFAiBa2/hPcNfda3MwF/HhbUiv5HNbuRtjuwE5ZdVtzb9DNtkmXB1tLm+R4z6ZtLHdwwhI2YBkwcAiunf3uBnXPKjg+zOQLHiDCCa/i6srRHwd6oTDvsUjcX0n3LWeAGpMAAAgAElEQVRtbJ3fI9vf0b5vhuTwt8SGP8/iIpj/CsceH8CP5WA30C77PgjHXiYEJiPl+wti98h8X692ds16JOROQ4ZQgoAjZfM0MlQWI1S1LREN6YAE1jaEfG6iNPWgPhLCq5BRGlLs9sOlOfnnus8hv9sbWe8jwX2fCZjQtetsUmM0KOcpCP1u6t79e6LBMQRLd0SI5Ako4hHmv7a9Q0D3TkZC8RGiA9fYxuUbAGyjtJNiTaSUWyIn5XUU7dtAmc5R9rsByGF8l5hO9KQJlFuQATUfOTwt7R23o/bRwfCrQEruYYTKDXXnyxbWE3Po26CUn/nIyQ3reAVyfAPtHIIcnc7IoDnb1r4k5TBfbzsXGpMkDjsyUD9CdP086XYY16MIn6/pnIQMTr+3UdloG5HmD8elViL6+RwZES0L5qsBsePjMmQ4hrTn11CU+glb90eJRmtYx4uNDs63NduOQ+fL8EVLZDgtscOn9D6IdWe06xbYvHkjbqdNAdxcLEXOzmAkgwLfv0Kkq/1QtG0WKZ/Osvl91NbzO/f7cg5gUxuHT4NqjXjrj7huqzmf7+y+9l1H5HDsQHPt/FuI56vZWn9BQUpQwf0aI0NlcMF381Ed79HIyL0La/qAIrhbs+uvIOtwmr8jAqHmIrkXZN4MG/tpwGOOxjeSRu3qIcPuwuzdHyLuZ9jY1nE24qWtZNF997vcAR+AAzaQrPiC3SglIAIxh9l4ClvRIznU0+jqDnufPxP5qIbR21YEzPSAwi0VTkcGcBWSp03s/7nEqH9orhGaIJ1l83OzzfeHODDUz0nB3CTZO4ieZyL57es3f0JxfX25evrqCFy5ERmS84nga7XsmWegVM2PkY7w8quRzecvkRxdjuSR3ypkOxGU25192VYTt7V4B9Hnu8iW6GljWIJk76NInx+J9GZo/LamiF7cM35Qap2t37G2/i8iUPooFCnrjqXqGj3MRTJzEanjcw4R9O2OdOnpyKj3+xgfiG1xYp+bIEfzuGz8n5PWxO2P7Iqwd2E/ZDMUphdm79fc3m8u0rdTiMBo0KUL7T0H2pon9oH97YE1YnFjmGrvEyKdvglSdQpo3v7OBda4860QbTamfBfofD/HmYgue2D1irZuU5HDexuSM37LoNDg6jBb33OycVV3/zcm2oZzyLZ+cvLtK2THzrV7DrHvhtvvVhCj8GNR5kJIEa5GLOvpbXT1FJJV63a2rnvjsccH8GM72M1CUqQUZ7jPxxmxtnYE384Rb0D4H0ZGyiATNqEZhEd5hiC0KG+m8Ckxlz00fvgnQo49iljHjmHGLGF/vnG7OQf32fNHEGutRhAL3mcSjYkKhBZPRemr+yBnbAVRyXdHiuc7pKheRwbvVpsPn+L6MkKqnyWmbrVATkYwQOaTdfay87NI0yfGmMBoj6WMISP8n8Dp9rlcG+EaxD2RapPugdXJ1v93uBoY0khKiIbua2M4P7v/rgrrnyPWMV1h8/QZUlrd3D0eQYb1UmS8r0do88oy75UjioFWCls2IzR9QsF9jkJC+m1iuk9Y70p2P9r2ExQh8M0vapHWSxQ1LuiIaj7CPJ+FUP8q5FD3QgbPb9z4qqNIju+od6W9R7VsTkoMAZSGMzQ7t8AOr6g3OdrZaVMA926tgVfcNSFN/CxMcZE6zk8i4/h0ZOj9AhlDjZHB9QvEMz6dryj9dBqlNSH3I36/DYFc+2W/KUyTL0NvvvatHjJ6ByIgYA4y/HdE6O26HdG3Ivp1/3dBhtxgo6vQVfAh9z6bKK7frEFxt1JPB81I64k621h/RZS3S+3ZhyFQIqTZnogMwCORvDgMRf1vRzLoatKOf6Fd/zOUiU6R8m09ojwcwM5LCRK+zuawP6LPq8i2S3E8MxQZvCG12YN/reydXkKZJDko2RuljT6B1e4h2fG+/V/f1m0D0hMnIaPab4MSsgq+w3TC7tBh9p59iBHb0GW1ZOPvgnvkBncjZIiWRBMKrj3VzflEYp1WU6OlO2ydOrg1upXYdbYfskNCKlug6YvsuBjRaG9ktywhgl6NkGz9BDnTNZEuao94ZiJyCi9BfDQPgbEtkb65j7Quc2epdcEWGGrvFPZR9DKxN5Kr1yJH/1lSXuuKNY4hRvQWIJ4JujqkSl/pfjfO3mkfo5H1uEZQ2XosQQDcvUY7nZHzOQLpqzWZnE0yv8rcsyOSYfeRZps0tff0XZWH4Roc2bnZlG65U9/mYS7SoY8jGePTgatlY21ArEnugepH/454yTdyqmvrn+ux8H1/JIM+Ja0nDGOaQwQO9iNmFvwc6Yp3EMDh9/u7EeniOTa2erZedxDrbPsYbVxLrDk+D+k/f693bK1ex9UCZ3MSAiXPIzugNbIhD6AAqNrbjz0+gB/TwQ9Au4zg55owCcR3P7F2pzMSwK8gxNdvoOrr+Hznup21qW+IjBtvTO6PBHmIyHRFdVOhFXVtI/z+xlCB0UcTN+I+NHtOBTIetyI0xjdJaI2Ux0yEPk6w81MR+rkZCZ+N9s7TSB2KU5Aj8z0wxZ0P6VBn2vu0tTFuRs7PQDdflXb/7ylT3O7nGRm1ZxBrMU8jNb6WsOsOUxchQXw+aa1n2PQ7j7DORpHaNcTmPyWOTHinfP2N3h5355shIdwNCfSgJKfaulYhpb8vyrc/1uYuNMgIRke+TUCVjbVZwRiqFZwL+x369I2zEcK6nEjXO422ufFXQwbs9UiJzqE03bLSrqleNH/ungMRrV8O3GXnJiCl4o3BW3EpunZuq71XaKo0N1vncs/siUCXdcjgC513vyUa6DtrChBS4apszj5DgIqPIrYiopr7Gg0cYvMy1OZ5DTL0KmyN5iCFvcrooNweatURf2xAQMItCMi6HBl4v8SiaPa8HV1ks/Wpk9/bPcNHwvojxHwbtj2FPf9jW+OxSMYtKjf37rnziRGGn9j7tkG8Eua1PqLBjxFYkqd/e7poWO47+7zA5vcKZEguQxkN7yLjthMxfSo0QhpHbLjyAgJELjN6CV0Tfc1zIwSUBD4aikszD3yZzzO7UUqwC/k2AVht/x9NsbPo0+JDx+bFxP0P+wB3FNy7ib3vN4h2ByJ+O9PdrxdxX8+7EWD5K1u/oFvPRqDn35Dh7jsahvF2oTSqX5LmZ+dPRQDW0bs5R34ufC3dQhy/etlJNJZPsTHPQ/z9HCl4uIN/7H5nEOuZLzDaCfM8zObyegTe3I/kSjkZ1YnY4fzS7LuTkUN0MwJLvkKyagHSvz7TYJepdbZ+5yND/167dlj2zINQ5PkxBIQMJOM9u85voTSfCKZ7eXIoqgv7FvHlqYjX7yLdd9mn3jZFsuB3CLgIUaV/A27OxlAU2Q739PqxGpLJua6/nXRvxTr27m39/ZCceAfJiqD/eiMbdKDdeyOi/Wn5mMI7Iuct9DqYaPfeTNqb4FGjnb8AV+3kXsNtjp4n3XexIVEfTSNuw9MX0fmJqDTiDaSvpiD9cjzwX4iWr0KAZQCDK4x2ViK5tRarC8zGdSai1WqIngP4MMeefRDSW+8RMwk+Nvpptzt8vjcee3wAP6aDH1BIigTQ6wjlONiI8GPkIPVEgiiEp49HiNYHSMn2QcrxHuD2gnGUS0m5zpilnX0+lrTF8BYs5QU5qVsxIU0avv8KGV3/xJQRpYbFvkiw/gMpZi9I6pmQeRPbv8x9F5ojHIoMyn75+9h9/47Q4f2REN+K0g1Cp9AaCGmajjlw2T0OtLn4htTIrXC/D3WaDZEQnU/sZvcMElSLw/uTGsiVbh7fRUpyA0JOexGN/CNtLOOxjYTdMzchZzUI9t3as4aYrraOLGXJvV9dJDSP8ON11w1GAERLe88bkFG/oz2zjXELAisml3lOBdEBewChtX+1tQsOQT1iamMNdhJtoziq3gSlfd1oczwZRQVORM7hEj+mnfBvhc3vvQiw+Bmle02diBTuhSg6MwMZA70RKv8ktrHtLp41gLif0nDE74/YvISUH79NQLmmAGGeuyJH8F7kuA5GxqY3Pp+x435bt9Bhz9fcVhL3cWqOAJ0n7DdBadZCBvydNq5qSCZNQDKlCiG7v0SyrKFd+xxmKLlxz7Q1eoZ0Sw4ftQzreLudC0ZvPXuPKajZ0gYkky6mtHGJbyTTmhhJ2gfxS9g7y3elC+/bBTm2m5Es24/U0boZOcHXZWscUrNrIQNtkpOhdyAHvxexGUwT5Hj/mpiKV8PG2DSjnatsXa4ibecf3rEOcuyC7OhIFmXLxlpUShBS6ZoiWm+LS1t183Q+kr+biClo5eTOA0gGXoPAn/uQc1O2DtnWeTFyGlZiTSXKXHsYov2p7pmjES/XQbR6FdIfm3HpligdPNRpNihzfx/FHGHjb+vWO9DFjr1UC+4x1Ob6XmBtmC9S/dEO6bWrkJMR1rExMvrvs99UQyBjDfu/N9LPm9z6vYe2YpmJ+DWk33VHwMI6RNehSV0RGDMIyb0ziPK8oT33IuQ8rLe5buzmYbdS62zNXsmeeRGuds+dPxDJpoUIwLqE1NE4AzV+WUmsf70fS6XO7nWS0cFaBBTcg5yXwPv7G128afMXOmq2JXYxHkesfbuP2AH7ctLeC8eTNZwqwyOHoOhuPwTKb0JydRXWnMXW6SjkPHVCTvY1iH9D/fn67P6HIZtzPsVZHW0Rnw20z+chpyuM60Qkp2vY+4a9OY+29fANm0IjuFnIbl1t59ramj0PvLOTubgTyeWfIXl7O+Lp4agkZz7in+9I9y1uavNyNdINQ9w92yMafZMYHQ6pz4sQoH8Hlhbs1n8Dss0Le2Ls7cceH8CP6eAHFpIiQb8eRYmWI4G/CQnkNRga466fi4TUJGOs3xPrmcp2IUWKLnSgvBKhOA8hwyWgzrXt/kON6G9BxtVTOOGJUJNJWJtfO9cWCdrQ/KUN0cANXSffx4r63bga2e8+sWvyWshVuEgMKYrXCGs8Yp/HI+RrGxaBtfM1iXVqhyNBOZO4b+Io4H8jAz4ormHI6Xsd6x6FDLKxRCdzAkLW8jrCg4lbY1TZeoWI8Mn2eY3daypWK4eMtFcR4uuL8n3R+G7vWYNQrXUo5W9ywffTEFq+gGIkNaRj3mPjPAHbNBbRbVd37TJUd/gL0g19K5DiOwVFcd6w89WRsvwbseXz7kTbDrR1KYy8IiE/ESHJ/ZDi/3fksPlUmAaU7wjWFBnXm7FGAna+B4rUNUW0vxHxySqb6wY2p/8v4t8THB20pnRvz5ZIST2I+LMxUqQBHNjdpgChLf4QIngyx9Yt33dyoxvPGBu/N9jOQvLnSxtbr+zdfcrZs8iACptUn+7erbXN+RJEXy8jhd4P1zkRASA/tzn9K9F4Ck5eVbaOF5OmiHUgzTQYjIyH+cixOMHmdC1pdPQCG5OvzRxvvw0G7tWIT59CBnBd5Oguw3VNRQbF0bZOgZb39Wtt5w6zOS1qNOGbwVxj456C5OJixG+dbR0uJXbl7WNzfC8Cu3zq7A1Eh/scJFs+IRqxvoZsp6UENu6VSG+Mzsfvxv4/ydLISSMeB2AZCjaekxDNfYXL7Mjkh/998+x773TtLK3zAiJYF+pcb0CyMTgolxCzPY5C0deXEZgZ9kdsiYzs5Sgid5PRx9psTmsh/RwAthORA+SBustQ2v12UjpsT9RXQ5DT/BdcEzj77j2ju3Y23hYIBGuTXbcEyar+Nuf/kzTiW99+fz6uURsFKcWktVltEd8H8GF/FCF6HFe6wG6m1iH+eoQUCDoFOSFhjfsgx2QecfP241F0alI2zjXIdrgM0e9v7Agg6yDkAPRCfHUGERz341qBbUmBHIS1Nld5d+3qSIdPRWDXU2Tb+yC+/ozY+6EcWH8NspeaGS0tt+fOcPO9wN7xPWCZ45euSGY2R7ZlvtVVO6R7fUfNEscf0eGXpHr+PJu3K7AsAXvnJ5GT+gVuL1pHK53tmSPcb75A9sc4d/+m2NY9SBafh+yp5ojWAh1sIka2pyGn/deU1mt6B/AsZMfNRnLwMiST30fAb2ukX+5COulsUp3Rrpx82duPPT6A/84HPxDtcr+rRqrcfLpBFTKgNiDFejwu/O+um4OE60MozF0XITSLkUPnG9BsQQ7B53auMUKQ/HNnIWG3meisVUNOiUehRyLh+jFxq4YbkKKZjJTkF8QUzSB0ByPjuk3BuwQh8T6xdXBNu18JIuvuGdIneqO0h7pIYP4KGdC+yUil3X8+cZ+c2e77fYgdTd9A6FJvZAx8iqs7tGu6IKcuNCN40M69RnS4R2Id6tzv6iBFcbTN2Vxb64DOnmpz/jKxuL+Cnacah85uDZDinGTzvT8Szj9HhmLSthspvteRou5J6mRXIDq60+hgMzF14zIs6oEU6YNIkV6OFO1zKHWrEjncm5AzvY6sbT5yaA/I5rZctG2ivUcNpIiLNrr3Bu14e/Z8xC9DiJ1KW/nfUtpAxjtHoxBdr0SG1BSKm0LUMpo4DxkP1yFA4V3S9DNvZMwmTfXeZVMAR88ViG+fQlHzT4hNCs7EparZfF2RyaCNxJTsShvnAGTQXI1SpSZk79jKxhxSXxvZer1AVP6hQVQ/ZNAE1PUY4DV3r5sRvZ8EPOTk1TpSp73cOt5ClLtHIiO0EtH9NUQH9SF3rwZ2j+1IToc0sWlE/vLO6d+IgF5dUh4Zjhz+eki2hAYli3FNqNz1R9t4QgTW64D9seikW4+JKAPkDsTHdyH+ujhbkzORc9qQdJuZh+1YiAzoq4lplDstJSDyxUSsXhBFdpsj2RGiAOF5pyLDaRNy2C8v4I+Gtr4HkDZzehm3BUgBT9ck4/V8vDs7EBD5DgZ42rm7iYBeFXLoFiMduALJ0JuQ3L0DyaIuCOA8xdZ+GQKBQodIP2dhL9Urka78FMkGbxifSWzcFVLQXyTtdF0TyZOvEZ8PRWUEedSsv9HHUsTroe57KtGJrUS89ldEh7405GqkN28iba5SzlF5HNueiNS5PRgZ3D84tc5+9zfkvJyNePAk9/3rtg5bcdFgJAuCzAmNk/ramgRH/ABig6YqJB+uRTbWhwjM+cLG7+26i9zvKpHOfAI4185NRvp7rdFKN7vHpThw2I11gK1T2ai8XXclaSdcrzP2J2538RIx+n4U0tkByLqA2DHXr9FHRCfay1kPDNWmtNSnl83VX9y5B4hRuKEoMPF70uyqvhmtjUGARX9E768hmXs3MfOsJ+LFBjaeFUQ7wkcn97N1Cr0Mbslo8RzkfId6x8bIZlmP7JaL3fVnEe2NxUi3Hs+/YB1gsu57egA/hoMfUEiaMaM3WA8wYj4TKfwmyIB+AAmr4aRNGkIq3THGSKuMSZ7Knvc0EvwTsMYhCIlpSRR2E4nITqhDqI6E2/XZ/Zrb+ZeNuc9A6FZb+34Oyo//KWmKxvFEFKsSKbaVpIr5WGRkh3v1QYJsElkqh41vvDH5dqzNv313EBKOvySiqucA99v/te3eLxI7RF2NUsJmIkXqDbThSEGGVNkTkNEYUOXWyJnbgYK5345GTuVWSoup2yIU7Q9kLcGRk3q3+7zLVGNk8K1HxsICpOiqI8fTGwKHI4MkdAO8AynYq8gQYGRE/AYzOmzuPiY2LnkEZ/AZfXyJUr1quHEuQOmlM1C0ql72vpXsOtp2DFLW7xLrFZOaSKKRVemefyByXJbZGPJ25v59S2oHkeIZbnNzJHJE30B0GfYVa2Z0EOalt9HEvbi0HBS9utm+62Z/v7Z7BcW2u00BmiCaC2lJQxHNX5G/G3LIfoP4M9S8bSN2nT0c8Yw3CoYgOqwkpkfNQ/tH+Qhclb17cII2IHryzTn2t3PHuN/0R7z2JbHF+12k2zPsbB23uPv3QRGBZ4mNfDrZOhR1H+xh7/IoSjl6xo3/Fkqd04MR8u6BpUY2/ueJGRX9iAZaR9R1bhkyOK5DoN1zFIMIm0izG1ojR7ahzcNcJGvnoshVyAIZac9ahHWaRMbTJQjwCLXQ7yJwa7dLCZA8PN3e4UY7dzKSZ4HXapBGs45AkbD/wDUtyebtHSR/NyL+9vw2ztZmTvY7n6ofat5KOqJCYTpjqFF9G+mdj5EMD3S9D6K914n65xWkNy9FcncB0mM+fTo0yniOCNp1QzrpBdK9VEPH2e62Nj7l8FkkM9flY7drgiP1XyjqUbQFSCuiA3oLMmoDH52OwJUwh4tRSuyDREDpaMQL72JOTtGcGm09SmzkFWTm+ciI3p3UulBq0QU5dlOMVuog+v4JaROf0zH5Z78PtsTlRCCoBpJpz9o916Io0WyKweTgOIYO4q9hYKydfxzR+V9IgbrmCPg5FMnuw5HDcDep4xbmuiGKrAXgZAWyn9p5niPyUy0UoX0F0XhdUv7oi/hjIvCcnauNnLu8Dq4VsjG+QXbqw0jOB9p7GSend4OPhiAZ9BqSCWE/6Cqkt7ojXfUd4rXDkI4ajuyRI+z5Ld3cXIJo7jn3nK5ILoy2z+1sjlcjuRzKaL5w89YN6eX/suc1IpbUlOxx6OT1DCT/Q7lBdQS0zEGyomTLpn+lY48P4L/rgcLiu0S7HIP3QkpnR92du1cDhE4sRkLLRw16mSC41Z1biBTWUyhKUYmU1Vmk6VKtjJGqI6Ub0OklxAhAiPbkzsu+pF1D2yAUqz0SgLei6N0KZDAFRuyOFOpVyAhYh5TLp0ShOxEh5+ebsHjXjW1HYxFkSFyGnKSAYHsjrKdd8zei4+wRs47ZPL6Pa26CHJz57vOJKOL1AZZ6RgHijNJog6F5FBJ6jZECCwZrW5uLfYnK+T27f033nitsHm+3w+/Z45G5naYaY5ujut/2RQ51SBsN6zgOCbxbgU+y68+hAPm1tdyOFMj9WPqJfTcaKT+/LmFD6vZIoc1GiuwYRI/LEb12ce+4u9G2BcQuiIfkc+U+32/32kCMvPRCNBycoLuIac+H5M+yayYjRRnuUYno7Gws6ubWYh6x0UqyqXNYA6ScQu3JKqRofmdz61PwdqcpQDek4FsRo+Ohs1tQ8t6p62lr81ts76fsXVeSpnKdjJTq2aSpNqcjx+1hYi1noK+DjLbeRxGOe4gdjAdnz6uDZM8r9ox59jtPS7tcR7cuzZEM3IJo8tBsbGOR7NyGeLYucpxX2pjPtrkcgGTkl8TU1buwlMLsHaYiBPpWRN/bsS0/7PvQVKmPzf0sm9NHKG0W1geBZ28hXnyG2F05NEJqY++4BNHu/cDHds2ByJB+lNI0sPnEetkPSLdGyR2/wJM1EY8+jGR0AAhfJKa01UaO3MdIH/lGH5Movy1BbUT7M0kjfVcj52kC4qnfEiN2YVzLkXF3L4p+z8Mi+9l1oXxiGRHsOA459J2Q89wUyacAQgSHZirworvnQSg6cw/KfDmOmLpdZfM5DBnJtZGxG7Jh6pDupfoiqdzua+u7H7uOEHUn20utYP26IYPb67b6Nk8eZNifNDX0ARShudnG+CLF0awK5OA9QuT/IJ9as+vUus7u/zeQLLrZnn8NMUvDA+YHIfvoEeK+rscCHxSM72ijn4nIqf0NBXsAF/yuCZEOT0fgTgMENm9HEcAjicDfdaTZRF1tnY/J7jsH8eRzSF5fiHol5AD7bYiuVyAneDhuv7vs2nnIdgrbC82yeVyHq6d31x+GgPeB2L7BCFANDZ2KHL7AR1chsGuG3acWslePIAKQRXbDzUgfXmOfL0cO2ZdlaCpkNIWtKQ42mrqdtMlQB+Sc/5MIrHhaOY5YQxuAw3fsfQMIeR+yUdoiWfp/I9vC02ZvLPvpX/nY4wP473ggJ+gHFZIi9GOsMWmoPfDo6YEIOfkMISNDHRP69tQzkTAajJyWV4gIbREjL0U1Fze553xJzM/ujJyLrUjwtXO/Dc+vjeq91tjf2yhTi5Y9uwESTM8ThVUVMvz6ueuuB/4XDq2xOZht/79PRLjHk6arjLA5eQEJz54oUvcR0fg7GRl8s5DBNxMZy58gRRKcgoEognseQrruRGifj8AOt2c1Qejuh0hwjyMqh+OQIfopcUuMhsiAG18wR7VsPWfZO8xHBm0w/HansP5AZNz6FMDDEb0Fh6DSxruvvVuY36FkRhRx64KRNledkEO+I23U3rcGQpNfQE79Iluvg5HR9BIpwleLuDemrzUtG23LaLE3UraXInTzbiKqGK45A2sugxTuS7YO3YhK/iLkbE5DxvQSW/c8NXUoMmR+SooSV7nnTTIaCPutzUbGxypkZFZSDCYEumuPlPpmZMCUbQpARO3DGqy0dQpNLy7FRTpt/bfaO45EBlo7pPwCXQQ+OQYZMY/YPd9FdDncvr+aiMxWtzH/gdSQ/IAYXW9ua/g7ZDSE+eqPDLQApIxHaWjTkWFRuat1dM+rS+Zw25q9g5B83wjpOyQLzkXyeTPWlAgZKKtsXpohx+FFZMDORzQdjJTWSMYMsvU9AEXg7yCmifW35yyjeL+91cR0+jwV+WLk3E10489rdrpjKb6k+zTWsbV71dYnbPtxBDFyE7bZ2WRznJQS2DUtkBNVZXPwCrG5z0Y3rtuxrp5Ivz1ArAcP1+RN0crV49ZH8smDdxNtzSvcuJ5y33dC+vZrDBC18x2x9GjE1/fa+gzKxtaJWMM1EWXINEYGok8fDPW9vZBRvwRl6ISMnJFIxq9BMmmoG8u1bk6ORrKkOopovIV05PlFc/JDDkqBsDC2K5BM6YpkwTM4p8jp0Q+cnmiEHJd/ILsg2e4AybrrEfjxGJIZS9z9yqbWIUf6XcRDwS5phRzoObYeg5Ec7YN1U0X09w3KlmqK9J0HXMptCzOk4NyOOjhKeas6ouuZXlZjDaDcuWGIL/oR65jvRPybr0UA6cYiPXc5ivxeT6TFRkYTh9uc3Ypspz8RexiEbIV2RmvLkN34DHG/4J8hvVC41YX9/liby49I90Psi3jHR9c+Mxq6CfHiVCJvJLLL6xM3l/7z3UYr/05MJz6DtOP6JmolIQwAACAASURBVGR/DUZy63WbJ99xuw2xkdV9xCZIoQSjLeLlD4lR58tRRtejSKeHNTkKyfhpSC5PsfuvpUyU9F/p2OMD+O968AMKSY3hNiEj7efE7nuzSdvld0PCeDZy3s4zpnnQvq9Cysp3TuyBDIaAngXmPQw5Kvsa0X9LbIse0loaIGOiEgnWDUjQjbDfBuF0LrGwfj9kWP6CmJa4I13MPldkjO+d4TFI8S0mjWo0IqI0FcS88ZeIUcvxWHORgvUIQvolhLYFtLA+Mu7ONcFwms3XZuRotrVnXQz8xt2vJRLCH5A2zwn7Nb6MBH5zu+ez7rfvoGhCJXII15NtA1HmHfZFxoevd/ghqcZXICdsvI1zJWn30ibIMOxqaxCE4LMIUDjE3WeLPTtEq4biIqMIab0VGVa1kUG6FDmKh9u5KSh1cLm9V0CNG5I6VDuNthFpuh1SUl2QsdsICe1/EJG/GgipP8atWRcb6xOBHpECDTxzoI1hOQ7QyeZ2GqrPWkNW04LoroM9N6SUPID4xNN+yebrpMb/NyhSVa4pQBM3P0tsbkIkZpvNxVvIWWtK7EJ4KFLgKxCq248yre/depxMur9SaBy10d43NHHpQKxDDPun+cZMbYl1tech5fpHRFMhJbBHwRh2uY52fiRCcVeQ1puuRrLNR5qXZs9Yh2gs0NdKt351kUyYjeuoaN+9gIEwSI6NwTb+tu9rIuPjGnuHPyOZ48GktyiNGJSLyBXWyrlzSY2045W7kUE9lejAdiZG8U5G8vIeslIC+/1biN5rINkzAumbAF7UsjkY637XBMmOYCS2QiBJg2zM5YCRhaR7K9ZGTlPQmecjmXw1aQfYkaRNLG4gpow3RjJpKnK49il47gk27rX2fzDqOyF9ssGeWx3JwVNxaddh/RDNTUFO5wJ7biXRuW6CQNRX7Vl9kNH6MmWcmIKxBtBmPxQx9qBoJWkpQ0dinf7Zdn3IPMg37X6MNNX1ZKSDQ3S0JuLb2xBPHENsYjSEH5Bah/TU35Fc8ABkB1vLRsjBed7WbV+UYXQesoGeo0yXWPdOVdnfkM3km60VNcA5BOnEF5BT1trN0fe4NGpEZwuRo389csI8uD/J5nBiwTj3Qc5OozLvEeb9WKRbz0W0+CVyJJvZGh6E6HS9fd4f6Y1PiFG4+khn+14RvZFT+jnSE32MVnxznvlYKqyNdyyi6xXETIJxFDd2yjsE1yJmyB2OnMsvEF8d6q65GTlr223tQ0pnaAazo5Muovd7kO68zX43FOnfj7BmaG4MdYwGOhWMtx0CLxYj3VSyEf2/4rHHB/Df7SBVtLtVSIoMsuuRYXKlnTsQCcADCp7RAhnyc5EDFza0rkSGxYbs+rcRmhEYoxVCTzxa0xsZ6EORI1AdpfI9ioR4Q+TQzELKIESf9kOG31WkxutA5AAHw/Q23Ebrbrw5ytYaRWEetXcZQAFihYTWfcgQ6YM1TiDt+BSiIl7ptaHUSD8IKdwz3LkQBWli4/gPG9MBpMbFQGITlqUITRqMFGAw6l8htvVvhhA5v3l1N6QYS4yPsD7Z5+NtzP0pk2qMU3Q2xnZIUV6OnNS3bH2CgRhScW9HHeKCA3+W0UoX5EgsQQ5yaLrTC9Hug8AFdi40zuiOhHOvMu9VAxk4E7HII0KVvyJtnb2zaFug6dYoKvQaUgq3EenbR4JGog6dT5M6YPWINH2ijTvno6NwUQg752mrAeKF0GUzrEFzpFwWERXVo8gw+pBsvzVKjf1KBDqE/ToLmwLgUFcMuSduoH0Y4vFONtfPIgdrlLvPMciwWkQpX1ZRGrEpMtJbIYPuARTh9l3mKlDE8UU31wOQcRJSoa5wY65EzvUvbb1quPfb2TrmXSIbITDil4hWl2OdbN36vIfkxyBinfA0Yp1w+/+fvfMOs6q6/v5n01HBggJiARRFsAt2EHvX2HvHRMWGvSti7A0bFlQsKIqNWLBENBYswdg1lrzRJMYSf2nG9Oh6//iuzdn3cO/MHbjDnWHOep77zJ1zT9lnl7VX+a61SARSH4NdyCCQ5yF+tAWeNRZ5xQ9Ha2Y8GaT9OEpL73RB6ydFPwxF+8LOJHyBHD/zY6MoVXDblTlnMSQcT0V8fqAf3wT3Qvn/h6F96DjE2zsh4XBfysdNjcPXfYU1vitSrFJP4/tkAuJ9OOzNxyGPrEi9q+t4+75AwvUKZFC6yAfWQfvtBDT/RlC6RiOs8u/Ik57CmJdM+iWiHS5Ga6mb92tEL0R+OQUZSPfFS7+gPaYjWjMRobIMMq5FZXUI2kunkiUriUL91ki5iHFtd1GmFEJjH6S0xjJSPUjg5LnztkUywkS0Hi9Ee8mpaD1GPnoDWkOH+f1+Rqk3+kak0FxNVv+ze9KffWkEWkepcbg34gt/prTubypnTECJRj70cYjww65kyt1mzF4Ps6RWJRma6WakRN1EBeXLz18crZE7kXdpc58jK/r8+IJsz98RwTfPQestPvt4tDaORsl4XsdRFf77w4gvLUBpCZv832FIUfsI7bl7IV51H6VomoUoLWEyHPGE6QgFcULSX1eQrb2AUBhTKIUL90Xxp7PKxfjxwd4fV6N59Usyo0mXCuOwPZIrJ5FkEPb+iknxOpLJWqsguXQ4WmPblhmjdN2v7v0cDclDvL+fR/M5rssR3n9XVxj39mh99yEJS2jLn7o3oKV9qNLalSyKWEPleiR4HoEE7p/6AtrRmcAsASh5VlrsNjKE7ogx/RJtMucDT+Sum0AWC7YdEgjPQZvcBCS4LOmLYSckUE8giy9MBbtVnJG8iISiHklblkVM/KfAS8k1lQrs9kIwwR5I0f0xEhBHkwlmi5J5GQYiofVOf4eK3jRKLfGp9f9WsjiaT/1d80LkAkjouNrH72CyRAExFnEpkoxs3k8d/L635e53LBmMrz3aON6mcQtue5oINUZK3XMo7uFs799FkeCZwkCvRMp/B8TwX0JK3yOIMXZHStBxaLO8gdLYrG3J0svf7e+2B1lGxfXRfKw09hsga+mVVGbADXnbriWzaq7i7zqN0riwOO79kaX7PcqUx/Bz1kWb3kxy5Sj891RYyRsaooDYBQnfUfjfHwnA1+Pxlj7WT6O5lyY6iOOdh1k1lhQgeF92R1bz0ZQaN6IXdTMk8HxGafmGrmRZcHv4fFk1d//8OhqN1sZPkODYzsf7HjLvcUj6ZQwSeu7w/t0YCQBf+JhdTKnHoSe5RBQNjSPZOjoIbfyjyGLvrkKQ2OVz7zDYf3scCWyjvS2R1+yLFMhlEI94DBcgEl50IxK+nsz198Z+bXz/DZm9VMI5eG3H5Nhe/n4n+z0WKvOOR1Oa3bRv7h77+d9zES/YHa2x67x/lsj1Q2+kuF2A1tSuJEYvP2dNMsFsXcTfS2DsubEb6317t8+RqCQPBd707zER1xQyT2TcN3dD8MAID17Zx+ZBJKxGAW7W3oTm3/lorY2CsjFJl5DVi4v9WQ7tMAEpRjFusGvSV3f695eSPrmIJGsu4rcfIb71U7SvLeLtjdlnF0M86xbkzeyG1v9WVFFXtMy7bYRn2kX8ewquKCXnjEC8fnUkg5yMe/fRejnS58wlSEjv7Pd9wfs+jf1eHE/EhPagiI45jFLPbaPQOmZHCm2G9sfPfM6kGWenIgP1/sj7/jwyEMUxarAeJpXRTLf4mJ2bO6/EcO19dylapynSa3uErnqBXDIW/7078nR1IysrcRpCxlyPZKFX0Pq5iAaSsyC+tja54uyIz/6NpGRNhXvcgGeG9/9fIDNWbo940wAkg+YNlMsgfvI2Wud54+FzSOm/jFI02Na5897297iPzAC9Uu6c/fBEaoh/nYXW/8LJ+OSh8+1zcyn9bXlk1J+C9p/9UP6Ix5EsuyalUNiqMw63pU/dG9CSPlRv7eqDNrddkPAXmdpWZBa5C5C7u1OeCZSbjEh5HOn3XZisftZBlMIqu/uEPwRBZm5BFrSJvtDPQUrG82Su+ZWRNXiCL+ZyG2rMQjoBeTiikrsIEty/oTSRRYxDiZvvCMR87kIY9wv9+I6UZvZ8CjH7z7zd3dDmtACZQhzTa29AzvqZYwInerseQdCW65AX7PjknDxT28/79VokfC6W9P+7yCOQbga9ySy6+yEFpbc/exyyRj1O6ebcoAWXKqHGPg+iINATCeqPI8tlz9w9B/j9Yu23bnEcyRjsAGSl3h4JdPdTJjAaCY77I89dLBUwntL0zF3z/Yvm+qwYAbJ1UdHb5sfaI4EvH0h/H4n3GQkQQ8m8mJt6/80kU5pX9eODyGK7XkAexvS6HckptMzufbkNbYx/AI5K1snWaPOMXoJ9fcz/D0FU1i3Tp1UlBUAGiu8R7HKaX/MqEvAWwuMFkRC2LIJP3YPW7fBkrLsi/nMmKr67fu458by1kAdtO2SRnuj93pFM6RmI5t2NaH30QXxhBPJQd0dGq83IyjMcjzbo2SBwjYxjFCR38b68wJ85jQReSTbHevs4x7jRjcnSwkePZEDr9h3EF6PH+yhKy7ssg4SxN5AAFxN/dEICWWfE9zp5f7yKhOohyOK/AbMLwCOQkDnen7cGpVk330CGgKXRPvEnxFcC4oOPkmX8i3N8EbI41cOT8U5huiv6/d6m1NC0MZpb93p/H4iQDX8nQTfkxqs9MgptQ+nesChSsu5Bhrzd/ZyHkLGjq/8/A8G4tqK0HNGKyTgehebwPUgwHOTP3cefncaqDUuu6+nv8z1a0+XQDqshgXMyuZq+aK+ciSvwPg4fkHnEuyLUzkXenrUQH56OFPi4ju5ByupNZPV1uyLlrKq4I++zuAeOICv9NB4ZFDZGfCT4Z3uk7L7s8+MhksRiSX8djfjOGMoUVPfzFvLfp1Aa4/02Od4R9ygqQOuSPplVd9D/P4nSmPbxePmB5P1fRLw6rfdYsR5mMmYNoZnKyVt5g/xq+XP8+KloTT5JqSG6L/Ly98VLv/hYj0NImgWQrPVvShO0lWS49r+rIK/sB8jQMCw5f7Fy7cq18TmymMINEI9b3ufG40hpH5N7Zn+0l63i31dDyv+nyMkR19dGZFnOv0Z851A8E72fszpaEwsinhj51CRKkT9d/DmrIaPSDMR37kza9UOSrPPJte2SNg1H8tddZKEKseTLoWjveQJBX3ul7118ysyfejegJX5oxNqFhKBzkXB4P9qIUivvgk1kArshIWq8L46LkSW0UparYQhqcB+eMRN5KPojxvsOUo72Ta7p6gs6LetwmT/zYbzGEVIsn0HC7wBfuNshq8pbiFGtg6xPqXDxLBm2vDsSXq7MtXtTPC4FKbuvkhUVTpWVGCP3iC/sHciUkVke06TvnkAWy36IGe2QnDerMHXSjiWQMLZMct4gpJhNQB6tTfzd4xit6kwlBiEvgLwPq1Ka6KCiBZcmQo0R872C0rjS4SQxOcnxjggq+j6lNXw6I0PCOkhQiRnslkMC4P1+vEty/y3JCsMuihjrL8gyKe5MkjkzHZfcsYa8bfmkJWshIeYgtEl0QHEA0ePzA6QIPItn3yMT8qLVc2//7VW0+Z+JlPDulFoE3yVLILE8pdl643zYFE+1jYScaGFfMXfezv68mLjlJBQL8yClc7qhpAA75vrtSDQXhyIhKMao7Yv4w8N4DIq/2ypISXyazKtyOVI2VwNmJPNpzdyzziKD88XSAzeSeU0C4hExzvgUZEA4Aa23Ad7OG8gEjbX9nKloU18wGeedGhjHfZAQexISWqP1f1FkgLmFRBDz3x5BgvKjaA3FBAJRYLyRLMatN5mRZCVcwCXzpk1DxpOOaG3M8DaujRSW3v5eW/r5hyEF4mqcFybjndY/64hK4fzOxz3O/aX9mZcgw9QJyINwD5q3ndD83B0pmdMp9equQAbL3QPFJo6hdN39hBycGxmX2iMh8Bh/j7S+4xrkPIzJb+2Sdz/bx2w02bq4gsRY5MdOQuvxQsRb10Zz5iq05yyKeM1gtObuQYLsOT4eKWT8OTT/30PKTdwHhqC5uz4Nox0inGxftI+uhLx775MVAj8VKUVPoX15KhlipL33305k870/8Ih/fxjYyr+fQaL0NPZBBo9lyRSgHyCZI67pm8iSxOXHZUe0lzxJNj8HkiWIW83781q05geUucfGaO89DRkCx6F1NVfQOirLMcORAjCIDCl0FVK6G6uHmaIjGkIzpbxif0pDQQJVxGki/nV8vr/8t56ILw3xfptMaQzkL5ER/W1K60Omyup4xLs28Xe7ye81MP8OuWcHPC9BGZ7+vo/f4kgxfIXZ19EzSFG7gIzXpl7f5SnlNxshfvcTMjh4NFrcgPhbTAazFfByI/3aE8naUVEbguTL1HgzWyiKt303tMdeXG6OIf7xJ58zJXD8atdjW/nUvQEt5VOOUVHG2pUwl8V8gt2KBK7d0MZxEqV11aphAlcmC34YEgYiI0gtIEsixr42pRbVW8kKObdDzG53JMTMKPdcb2elLKSLkxVhvRrfSPz/I/xd7krutTBifqky1Asx7j64dRwx+wsoFVKOR5bMs/ydf0hWL2gQWUbN88k8ZIsjwekK769N/fpZsVX+dwl/zxSWWW5z6OX3aI8Y0blIeDwqecebgBP9+4LJey9B9RbcaqDGEZK5IxKIn0EM7SRmTzkfkPdllsKKrJBXkXlUOiCv0+cIrpdugu0QEz7E58AtZJCaWLLiaSS87YHm+PnIWjibtytdR1Tpbcu9y5Y+P170PjwtaecLSGk/09s0FQk8qcL7atJ/GyDYSdwk4rgfQxbztyeCqd1BLsMjEto3RwpwjCvri7wO3XPnxT5LrddvkWygZd43xg9thjbrdmjdr+xjcou3tZOPYVROpiADT77G2rrJu7dHBp7FfAz3T9p6We66Db1vl0uOTcBjR9DGfw3ZOljG59P1SFmNKIRPmL0W5n5IoE/XWhzHs8qNo8+bO5GBbWzufq95e+JYnuZ9NwjBpa9AQmuMvVwareG8ASKg9XQK4qW/QsiEC3PPW8LbOdz/XwFZ129E67lc8oF2SLF4CvGAEclvt5FlUY7vMNTHNMYf7YDmf/S0r4vW3SrIMPcIWgPl4qyHojn/Kpq3xyBDQxy7g8kKX+9OqYLUEwnQq1EZhp0W2Z5GxmNSr8Gr5EIckj7fBkEpY+3IGNJwGFIkY9H3gPadmZR6jm5Ge9vaSMh9DK2zvZM2VkQ7IH59MOLBn1AaaxxLmGyUa/Pq/q6/pNSQ1D55zwWQ4jiZLMlbQIrqwEo8oMz4Dfb5cy/it6nRcBu8VEi+b8vxFf++KzIUX4onUfK+uZzS5CZDyDxJw7wfJiPD0t00AVqHFNc9vL3dK53vbVnM2zIZGS8mIn5cTT3McqUt8mimzmRz8zRgYnJuv2rHpcJzDiUzKJyOlP+nvJ+mI1ktlTuOR0iRR4Clk+MboXkdY+WWRQaG8VSAaJdpzyzDE1LweiMjb1z300lCIigfevGI93VaauohHB5PaXKoFL55j7d3DcRbrkTy6HNkWaSXZfbQmJQ3xHbej9BwHRGPm4FkonSuxr5pjwxH0Qh2NlJa47229b64HvH346hgjGjrn7o3oCV8cIhHhd/KWruQALQYUvyOQoxsHLKGzPCFWA0T2B6VT0iLM3dHG/96yWJrhywwP0MC4o2IofdBzDp6cvLMdhSyij5Kad2jcllI7ySX3AQJSj91xpF6EaPlLjKNC5DAEeGV6yHvUWz/tYh5v+mMIrU4dUTCzhjvn+mU4v43ozSRRm/vt/2QoDANCXEfII9nGus0psK4RmZxuvfFy3jKcT++N6UCwSgSmKkfu9jHvioLLk2oWeP9tQ2ZcnSjj2G0SKdKzXtoI3mQLLvYQ7m23oqE0DdxSz1i5pchReAOZDk7nNLg81Q5WNPH8EO0CaYetCGUCiAVvW2NrMUFkfCdemmHeDuXIIvFi56caPXu4e+/RXKvnmhzTpP4bOr3et7HYCMkFF6SzIklvU9eBD5Lrp1ABhtLU9C/gsfQ+LHxSCDYEfGHxpICvOvfxyHB9lQEbf4L2hhTmN/uSJm7Dc39PZEBYCqlgvtItPlFr3RXBF3eMDknWn/PQnC965En9kMy79npCGp3S3JdhCrG+R5r7o1Bm3QUaLaiVBBsdBz9WGc0vz5Ga3MztAbSmN0uaM4vgfjhEUgYf4mc8pi2uwH+/4H3d5o44QeUhyat5v1yG/L2rFzmnAGIN0xBa+8Ikppn3hdrUbrWlvC+39TnwMloDkY+0gsZlMb7vIiGhFWRIWhlpPhuRVbCZFDyvI+RJ+U0JFyl8O7eSMH9PV4guhy/9O/b+3w5NDcnVqE0M2M5g9viaG9IFbCeaC89mszwcySe6CJ59yloPT1LluzkdaQcH0TDaIe7fc5sgBTIX3o/pYll+idtDGhtxZIgP/Q5Mtn7O7/P7ul9fhhZqairquF5Zfr6SLSXTkR8aDG0Tlbx3xsUZsn4WA9/31OR0fZIMg9z3L9P8/583fslhSJ2pApoXfK8o5ACOwUpAQuSS6JXob07oX1sX59DjdXDTKGnDaGZIgpoIaQwdEby0qVkMXBVeYbI5vJ+iK+ei/bC8chAN8vQ5N97IL55VdpP3j/3JcdGo313MqVoskFk/LXBNsZnIwPla4j3x2tHAOOTcyuFXtxPaejFS4gf/dj7MspZGyR9sRIwNblmI2TEOgZ5eaMjYSJlkhp5W1IF72i0J/8C8aLVkWKZGnqXRbLRVLK1uTEem+z/D/TnL+N9swba70vKuhUf7696N6AlfBDDbtTaRabwHENSiBkJ0wsihh0zfVbLBDohoe5dJMRWwqafR1aLaHUkrOfhLpUK9y7A7LEQlbKQRqv3kmSbaW/EfJ9BCk+vCs8Zhyz4d6CNJSY16OVMpjdS9kYjgfMyShWhDZHA9xRi6vtVGovcczdEwvFRybHVkdfhyEp9gzbXN8iSEWyGgsmPLXPuWmgjPB0xx23RRhLTOFdrwa0msH435CXZM747WRzhrhX6YHUk9IxGnsAd0/cm85IORYLSB/7uMTHGAWgjuhBtcFv6vLmK0kRCK/s7nufjva9/n+XxoUpvW5VrsxPZZr4SUpLaIWHh8dy5ByFDxs5IsBzu7zgdrccI5d3b2xiF6OlktfK6IMWjC/I6/A4JI5cggbwkK10yph+h9XMDUsrLxgOXeb8VKYXcLIo202F+rzd8js0mePl7zvSxHFOmXYcjD/BkJARfm8yHrXGYm9+/L1qTx6KNd9lkzgz0d/s9pbGvcbwXR8rHcX7tacho8eacjGOuL34M/AtlsRuS+70PmqO3ksGGn8QFRzTXt0zb7MdTr2BHPCMpUtx+jrxE+yGheEVklJvg/TOL/yF+cT4SKmMczCC0Jtbyz1DE5yaSGUV2IYPEPoH4/0pojsaSGQPQnPqKJKW7/zacTDHMQ6BTREQKw80Xvl7Jr0mzMQ9G8+02NOejd3JTSuu1rY/m9UN+30G59sXnjyBRciuM8aFkkM5hyKByJEIt5KHLC+B1BJEi2c77sS+Nox2OIytKPwKt7dvQuh7m73MXMpYc5f+/iXszkrkyDinqEXmxEeK5y/u73ODz5BQaKNNSbj5SGgrQhQzifTmJIaiK+3WktOba0kjAfsPbNgTxvyE+/+J8PgSt8VcozRZZEVpHxgM6AC/49wvJUEUj8vMjbWcD79BQPcyY0bdBNFNyrxWQcfkSxCvORDztVhJjc5V9eziZB3Apnw8f48aL3LkD0dr8kiQxFtm+k8a13uHzalRT2lPmmdsgBfw5tCYWwvluck6l0IvIU2J20aXIEj+t4eek2U9PQ4azncu0ox+SIz7EM83GOZN8X44k6SHisyPJagcvhRTCfPmZ0xFfPIGsfnKUlfZGTotXkYJ+Dtn62qCx/muLn7o3oN4fqrR2JefHYP6FEHO91BfC+cyuNFZiAnFSprDIxZw5vYUs27Pq25DBl9I0y52RELdjle+ZFyIbzEKKNtfDk//bo9iHB0gUt/R9/HtMxpEqDrFOVRSUevo5F6NNNApAk5Dg0gfBcO/yd1yr3LtQuXhscAbzmI/p3hXOG+z3752MySre3mhJ64csTat7u55FDHYCudpAVLbgRsNAOt79qBxYv4H38+PIstY938/puJQ5lk9wUk5xGUYGE2yXvO9KyKNzLdpU08LkN5HFuyyJhN1L0NrZNrlXRW/bHKzP3ZGwtrSP6/UIovImLqTm1uahSGh6C210MV5wMWSUGZP2ExKO7k/m1PY4tDpZJ6cgZaI/GbR5rM+Ta8ni57ZEhqQVaCQeuNz6rDCWqRewUmbWVIm8Bq2rw729i6PNPs1ovCRZgqaHfIxSyHg0akyh1BCzExIGf1KuHX7OGkgROZGkPmBTxrHMPVdGML44j1dAXtYoTJ2AEkJNxXkY4r0z0Zr8DWWMa+Sy0CXfD0BGjSiMXOp9sQWa7z/z+3b0efUkUhrX9mfeh4xY55DLYurfy0FiH0HGn/S8UUghecHHaWkkuKeW/XIQ6Ag1T63sFQtf5/qkCxK29/d3fgB5/WIM0AL+zjFWfCzihceh9bQc4lmjSWKCyvU1MiY9STKvve/PICl9UmbcDkOC3kzg0mRtVUI7XOpj0wkZ0sai/bsHWtsTEA9bK7nmCWTQPBSto4i06Unm6b7cx+1hP/+HVIC7V8nrxnt/jME9cmifuIUmwBcRX/4eT6iSHJ+E+NNWaD++wcd4BKXz/0a0vqqG1iGecjHip68kx2dQinBZiJznnESR9L/V1MPsRJVoJv9tc2QIi3kL9sMT+FTRn3G+buJzaSylidw2pEyJg+T37ZCh7nN/n/ZojZ2P1ng0Wq2DZMv7q2xX7LflEG9K5bArfA7sXu46Ggi98L9dEa94zNs7jNmdBisiA/BTCA0SPelRhl4Tyb5PIWU5Ik9WJjO6dvb59T1JngHvn4l4xlQko22JjMobe5/f5XZWqgAAIABJREFUjZfoSK4rxw8vr6Y/2+qn7g1oKR+qDCSlcjD/FJI4vdw1KRPoSwNwDl8gx+afj6y/byJLfVxkr9FE7wpVZCFFDPNp/74W2jB+SmmCkny/dMi/FxnzPAEpm1MohcL2RELjT9GGe0HyWxckjB5FmdibCu9WLhvlpt5vj3kfzsrM539vQEJwTOxxFK74IO/A60ig+Y4s+UHP9Dk0bMHdljkMrEeW2afR5jsrFsp/KxePVJJeuQlz4g7vh0lkVs7Vvb8ivHdXpDR09H46yNvdATHypnjbGoW3JN+7oc0yhYatTgMJF9C8XprZoc0DvT+/IEtjvwxZ9tdeyML4NElwf5n5NQCHDiMhcQLymG6MNqu4ZhuNB27gGfFvuTqEeSU/nnu49/PxyBp/NTI0RE93bNcEssyZa/m57zN7XcBo1LgdRwjEfsy/R7kxzf3e5HFsoI+eJysUvoh/Bvqci2tzKlLo98IhS4ifbISUyCXz7aRMKnKfH2PJElN1R5byn5BBg09BynaaWr8vEsRmksTvURkS+w65BA/JNYujmJdf+5yK66gSBPonyGNSTeHruL5HeHufRArgut5P++PlEtA+dpu341q07vsiPnVA8i7nAH9EfCv1SHWkFLnyMlmNvbLhDBX6ozOat5uSGLhoHO3QCQmRlyIUxh6IB3chUy7iXFgX7ZGxLMYOSMH5nb/fpt5Xkb+tgpT1G2mCIuht6Ip47VNI0bgYGdxGUwZeW+E+5QzQTyDP3v5+rxeS3xdE6+UBH8vNKK88NQStG0Up1H4fhII4E+2n+yL0UByjgxHv+TUy8JYzejVYD5PSkJSKNZWT/wchPpOu7SWRh6ps/dty/Yr4+kfIYPgE2tvWoQnwQr8myh3XI8POVYhPn0kGcV46nYtV3Pd479dZ44H2wIkkIQJlrisXepEvzxBDFD4mUXT9ml7el+sjr990b8N0pMR19HmwJOL90xCyYyaz15z+gT/jF3h229xYT/N2XO1jEI08qezVED+sKkNvW/zUvQF1ffnSTJ1VB5JSIZi/iufNYgJVnBsZ50L+WRy5909FAtuDJIHOjdyrSVlIkcB/GxIKrnEmsy8SImK7lkNM8HBKLXcNeVAv8L49z/+PcUdbow37LaSQRc/XnmQW0QWR8no3EuyiVSkfY9UOKTUXICYbN/fT/f5RcbsSCfFd0Yb0AhIMXiRT9m7291uPTCleMWlTfGZDFtz7aWLNGkqV2O5ImYwJMPr7+7/SwPVxjHqgudqX3GaQzIm90Ka+NrKwP+VjPjh3/gQkGA1AQtRbPpcGJ+c06G2bg/V5DIJZDUIK6E+AZWqw7qNR5hNmVxTXQ5v8S7jXs8z1Z5MpIYt63x2HBM8XqCIeOOn/NSkDWUyubbAOYfL/QsgTEouhr4SE8+spzeLbEc3903LPucvbdwHljRrTUZxiqiRWlW2vFuNIttZ2wlPYI0H8UW9bGr/bDglV/ZDXLvKKS/34eUhJGUUj3nXEj/5JkojH79+LTIGKnrXL8bICybmTyTxMTYbE5to1q8ZqcuwgykCg/bdGC18n9/nQ+3aUz4/xzM4znkP85yK8TiKZ0p0q/EPQ/ngRMpbujQTKNMnOAt7uHXLPuJMKynBDc4PKaIf9ypy/KPI4XoeMX1s3cv/9kFJyK5rLN/n3p/3d4h7Txce0e5XtXgitz0f9ujSj7Hb+27hyc7SBe0blNQrSWyCeNI7SOMwDyNA9Y5Gh5yTkYYl7ZIPQOrRvL4L2vVX9+HHep68go0is09kO8dRe3pYoxK/q49doPUy03mMdxv7IsDQJ7UOn4WimZC7sh5Tcd8lqx/VEfKGqOUbGd47HY2CRkfw6nwejyCVsq+Je3YBxyfFt/b3voHpkVzrPF0Xrehxab2cgnnhCNfdq5DmLof1tE7I9awySd37tc2crf6dd8ERYSF79nqR2KjLAXUpi7C/zvJNxtAlSGG/x+9+RvOvKaL5uVub6g6jAD4tPhT6vdwNawocqA0lpOJh/tlo6DTyv6polPqGfRRbGa5xhDEBY7s5NuR+NZCFNzuvizO1+Mtf6zcAZyTnPIa/ke8web5jCfSYihfIytAEsj6w9J+KKgTOQbkhZmYQs1OciD16PpB+uQ8rcdcgilELlIoO9FHk2DkaW5k5kwloKO9oeefmiR2Soj3sq5J6OBPppZKm2T/RnNGrBpelQ4wWS73khvzOy1j+NvFWH59+pzP0eQxvyHyiP2++EFJoYh9QVrYWL8cQyZGmoR6HkOy+QJae5gxwMhsa9bdXO1QHeZ+8jA8T+yKo9hVxB7blY96MpjXs7PPn/YCQ8PJWMW0BK2d+RRyBNKrEk2dpqMB6YbDNtELJIJnBVU4dwO39m3jK+IUnW3mSuP4A2zFWQov46mv9T0VorZ9S4mQzeWFW2vVqMI6XrdlvET+5BPGB/ZMi4IHfNSJRFdbr/v5L3XRzLzZFi/TGlcLXZvOnIS/IGUpIr1dKLBZ9vR8rU+t63n5IZteYYEttQ31ABAp2cU7bwNRnP3AIvceD/x2zPZ5LN1SGIt62AEChxPT/mcy9vjOuM1sExiF/fTlJTN5nXk72/+iClosG08nPQP6kX6gJ/h4392PJIMd3Cx69P7tp87Gi67+/kY3krEjiHISXq0aa0DQmqWyNe+htKFcGF43jl52SFd9wByQrTkKxwPdl8TzNl7oH4WmqoGuZjdEByrCK0LjeOY4C/IsNye++rHpTCkWPc5WDE86JH7CGkbDZaD9PnyEH+nJgBdCG/9yRmr6lcLrnJQ5TJLNrIOC2F9twHcnziUGD0HMzJXZFSmhrnlkBJjHpWcX1ckwugGMCR3t/d0Jo6B89YXcN1lHpE30B7xrJI6Z9BkgU5uWYgMiL8ntLEaSF9jzLXLYhk04FI7n0HyZvpejwBrZl8mFOj/LD45Pq73g2o24tnDLExa1e5+lZlg/mboW37IGFpKWTdG42nvJ+DezaYhbRSG/x7PrnJaCSALYWEx+5ok9mB0kLC75MVED4WMfPFcs8f7kxiz9zzbiaDVvXBa535/wsjCMsUSgXErjijRsrnD/37gWRKU/Bx7Y6EgNEkNYeSe62BoLNf4oHN/r5vIWGvagsuDQfWR4a+MhKY+lPK7PJe2h3QhvYEUjD7+vELkYAWGexBeEZHBGNZAm3Qq+bu9W8U05IaOxaifAr61cgyfm2NF8mtMN8qetuaMGe3RRv5RJ8nF3r/V6xJ1cT7p97j25Egcy+wnR/vhBTi2Qr2Iovrt0jJLgdrajQemIYhi02qQ+jnrovWxEySlODl3hvxkwuRp+w5JMyuhjz1jcHSmpRtb27HEXkpl0r+j16cCNGcjJflodQLt4P38btIaTwh6f+LkMX8Up+jT/n7p/25IxKq1vf/xyDjxrhc+4aRlbWJKcwfQEpjNIS0p4aQ2DJ9VBYCnTsnLXzdJ5ljy/r770Om3G1BltypA1ojpyKBL8IrhwCvxTXkf09AyuNtZOtoRVwpzq2BDkiAvRzxirupci+lOrRDWs7pUyQcRojz9WSIj24+x87ysWzIGJcqP4ugtTEZzeebqJA0rUz7Z9WvRSEIyyH+fx+5TNxNmAOvkHneBiHlZ7Z4KEphuDFjeEmeAqqE1pHVheuJ9sIv8RItuWe2Q+viXbIY6h3IEmg1Vg+zV3LNY4jH3YbktXKhEZWSm3xCBYRHI327GdrHXyJJzkY27xs0bibnHeB9cLz31UtUSAZYRZvuRjLpdciQdAINGAtq8UHr9cncGjkUyYOVyj9tjxwln9FIOFSF6zfy93sd8df2aN+t6OmkCn5YfLyv6t2Aen+oMpCUKoP5a9iujkjQPzA5tpw/uyrIQO5+1WYhjcxqVrF15IGK8JL2SHHeGFnkYq2nrShNF7wTcHO8J9okxpPV7QpIiGznjP11Z2hDknukAtkkZ5xpfcSn0UacnncaEmqnJ8deJ9t4DkYekwf8eZN9DpzsbQloM4qZWPcnExLvRUJcVRbc5H5loca5dt+NlJD7kKWwT74f/H4dkaWsnz/7J96m3+TG8WQEp72WLGPaD0gydfmx/v5e7wGHlHnmGCTczyArxxCD8mPNs4ZiXEdTJQQ6uWYE2tBWR8rryWTF2quC3lT5nFRwvQF5bsag9XEzGcSpHVn85jAyQamnz6PvqRBfQoV4YBqBLCbXV6pDmNZIWhVZxQeRJWZ6ARkLGirivCDyzq+CBNivUHbMxowaVWXbq9U4os28ExK0d879tjvwqn9P44mORF7QRZDRJjVyrAl8lLtPHMc4p09BisF44KfJeX3IxbaifWG6v38n79M+lJbHaBZo8xzM+Vj4+kNvc8xIvJeP80k+ni+SGdGOQErcLj7vxiFF/kXv/7iOBiEhPsavP434a4NKLjIq9KKRTKIVrm0Q7eDn7Ea2T3VFSuk53sYBfnywv//9CO2xMpUTMc2Cn/r/y+EQxia0ex8ES56c9PMiPjfPRjxhQJX3ismq7qS09FIPJLCniIVKMNxJ5OB1NAKtQ2v6OjyBkh/bEBlKjizTzuGIFz6C9pSXyfaUquph+vyLiItNyfbvo5ndK9RocpMmzrX2CGL6POKvS+afWcU9bqG0duhYZJy/scrrI8/pigxZcT9ZGxkAf0UT4dRNaHt3ZFh40ed7hHYfDdxbxfVVh0OVuTYajH4N/K3a/io+VfRtvRtQ15ev3trV5GD+GrRteeTK/prSLJ3PUGGzq/K+FbOQNuEeayHF6OuEKT1DJkC1QwLv/yPxsiFv4HXJ/xEeFYPrz3UmdjcZhGoDZ/bbog1nJLI8XevXRvjehkjxXMI3hvuRAnIFcE+u/UciwWYo2nCfoDSN8RmUxl91QopZL7JC8VVZcGkYatwted7V3mcn+5y8ndljD0cjq/nDSFiLxX8PIYutiIlaBiLl7udkKbWfJPNMDPVPfJ9NkXV1JhkcZwhi+IORwDQiWTdN8sZRPQw0IMvhsUhIeAqtg09wQ0SN11lHJHBsnhwbgYSVO8jKpCyFjAsPI4X56GSODqniObNtgDQCWfRjleoQxmys+1BaIuBMH5/uVB/30h7BfM6gSlgajWTbq8U4+lzehmydHIyEwGlkSvMawIn+fSyl8URXIUE2n804JnFJk1r092O9EYrhHT9+HxmKYHtyCQYorY35Y5KSAsnxZoc2z8G8b4d4zjdk8VlrIwXxPAT/HokngkmuW9rnyX4kyYL8t+sQn9wCoST6IP7xFnNRnLtM2xtFO5CtzaWQ93MGpUnJFkLrLfXsPepz4BXvmz3IQUTz62Yu3mFBZKz4G0ltSv9tKElm3Sru1RkpkDshxftHiAesSVKmJTm/Khgu1UGNN0WG5cvI1mkao7m2z5czyAxAhyK+tQlNq4fZC639NMlWb8SXR1QaIyokN5mLsVsM7b8VS1xUuG4zb+sUSpOeLEGWdbvauoV7obW5D6XGpr1JavvW+uNzbXMErZ6MPJmPksWiN7omaKLinLt2ccRni3IPtRrTejeg3h8at3bNVTB/E9uSj0XpjKxjE5HgeQfuXavBs/JZSHdHluyjvA+icpUWQU0TRqyO8NofIYv5Hclv0YOyK7KwP4w2pg8pDU7v5szk5ORYH2SlPdUZ7QxKa2KNRcLu+c6Mj0dK0HSy2nirIS/YJL8mQpxO8PfeECnAx/j4diDbwHZElrkpJDETSfuqseCumLS3wZo1PsYPk0BUkBDwNhKyo9dhLRRLuR2yaN7mbcxDAnt6H7VHm/3TSKCZBtzt56TQ5idRQpioDKYb7LmI4W9LFiO4JBKwa+aRa2Se7ogs80/SfAaXUWhtpZDHh5AB4T6fpzcj4X1tJMw/hgSj2ermNfCcCKtrCLJ4Ypnr8nUIU2h2OSRD1ZBxmghLYw6z7c3JOPp7v428o0OT42f58yaidddYPNEPy9w7FpTexsf0PlwY9zV0AfJ8PZlc83MS4QN5hs9CQuxyyIv8LmWS/NDM0Oa5mPvLI571W5KyDIgvrYcMfS8hHpF6lPLzohMynnRH+0HkxRfhMZeNrY85aHsltMMLKAQg9dTehtbtbHMhmQ8RudLd5+pXaI9bJr4zjdSdbGL7h/tzpqB9Lhp27iNJuFTFfWJx+mWQHHM1MuTcS/lY8CbBcEmgdUmfDkh+XwrtFdFomcZOzkA861Sf+xdRGpfaWD3MfrljB5PJa4ujvfCZRvpnVnKTZlg/Tcnv0IPMmH0B2p/7NfVZPuffRLDmmNl9pTmdh014fsrze3s7bkRhA3tQJoSk1s8tPs3Qv/VuQL0/NGLtohmC+ato0x5I0dkPL7aKFIxHnaHWBEaUMJX9nFkf6u93PWVw8378CW/LVr4prOR/Y2zBvs7sY8rvwQgHP4bSbHR5eNQjeNIJ/z1uDl+SFDT235ZJvq/q5/2NnHWIrFxB3LiiRfBUZ8R/QZbGZXPXrYOUypdJ4DX+W9UWXKqHGh+OFNbVyCyoDyNlbgoSSs4EzvLf2vuxG8v0TT9kVY3QtNWRB2MVsmys5aDNTzI71G4EWg8fkHkTr8QzA87jddokq2sT790XKXxjkXB+C5nC/AbyEk1B3oNnyVJ5v44Ln014VoOQxWSuNlSHMAqlS1AeyfAwVabEpmlGjVpk22uq9XxBpNx9idZvzK7aGwmvfWk8nijCZjuSFQ5fBPGlpxBPnUipV+g8BM8+3f8/Abgv17YhyLh1FzISXICUpnuTMRrBPIA212AN7ID46O98vrdHBost/LcLvZ/i3jRbcezkXkcjePGWCBq4hB+fayUwWR8NoR2uRfx2EA659t82QbzxfWZPlnQ0MsIsmhw7D7jevzdad3Iu3qkDMjC9jTJdXthYf+Xmaow1PSo51ocKJauSc+YIhuvX3IrkpVORQvkIMlKui5K2fOrHz/ZrevjaOwXtwZsm92u0HmZybieUHfQKf/5TNJLdtc7rKjVEbI0U5t4IiXQdgtQ3OE5l7nktGSR2Px+L65iDMKFq2t7QcSTfHYf4X78aPrsviVER50f1Hs/58VP3BrSUD6XWrmapb9XI86NC9gMkeJ6ClM8ryWq3DUFW8btq/OwplMLhRiJFdykqZ8Ecj6BeQ5BwFZndq2TQsQOdQQ3LPa8cPOoztKH3Tc5LN4epzkiH+iaTWqWORBbVXyIr6pIomHhGmXdd1H8bhoSaN8g8hR1yY3+I3zMfCN2oBZeGocZL59rUERkhLkWb22QklHZAgkFXJEw+TwL18H44Jf+O/ts44EdljleCNr9NGUEewXg+QJlSD0Qbb4RazTdMGa39HyLI7Yk+fteRCTELJOO3HhKQp1FlTScqp0CfBVkk4wEV6xD67+kmfBBzkRKbphk1apJtr8p2pZlRb/H3nIqU8KOREhANT+vRQDxR0q/XIWVmBlmyp3wcURpzExWD9/zviuWu8WMxccUgtJbvQUL2PIM216jf01pmT6A9ICAExUgkgL6KlOh43g6IVx+PDFnd0N51HVmNwZpZ82kY7fA0cK2ftysyrt2AhOWIdhhJgmrxYz2QkfMgtD/0QPw27r2NJnGqwXsFZAwqicuvYryiMfU5tOf0a+Y50t6fOQQZio4hSwoTUUDHoz39N5R6kJchZ7hMfitbD7PCuVFem+dxtU3pJ/+7CYIY34WMLBegPX8oTXQk+Bz8jlL0VE9kZG9ywptGnhXn4d6U8fzn/h9cg+dF9NkhaH/5fyRGyLRNxaeG41zvBrTkD3UI5sfrovj3zkgo/SVZAP/ilClkPQfPSZWd45GylpZceATh8itlwdzef7ucTGBbn0xRHIu8bEciJToPda0aHpXbHN5HFrV1yAT1mLa6C1J+folgmAem70sZQYTyBXJTBbM9Sdrs3HkNWnBpHGo8EgkoO/j/W5Mpvgv7mJyUnH+mM8YorHxAppAF5L2JmQzX9T5IU343CG1OxnFr5J08ydu+trflBDLIT6uGaCABomKBap9bt5U5fpj360zg0krX565ZkSZAFmm4DmGv3L3nOiU21Rk1apptr8p2tUcGoLRQ/Y8QXPsqmhZPtBxuFPJ+3Mu/702CQMg9vxtS/gcm96mqYDeKbc7HIjY7tLkGfR551+nA5NxvkXd0I/O8dUCC/mXIODDJ5+OC5e5bozb2ozLa4Vqfp/shL2Z7nzN3ehsjTLU94pk90F7Sz+fCeASxmw5c6edWlcSpDmPVkDF12Ro/Kyo0KyMP+o+8f9NM1v2T+bO0z43bUUmd85vwrNnqYbbmDzJO7JK821Tg4dw51cYCpgn+HqKKgvdz2OaoAA5GnvYof66QO69WBpABPodj8sPV/fhFKKb3NpoZ7tpWP3VvQEv9VGCwzRrMj4SxGQhylVo/plHBejaHz4kW9C7Iu7MKUqaOQjCFY5CHrMl1jJDS+ou4OSLY6IvJNSOYQ3gUmUDcCQnPryPr4yq583pVGqOEubWjNKnDteSU62qZDhUsuJQX0GM2tIO8n37k/fosDvHw36OCu5OPyW4I4rOsHz+WLFFLeyT4PoAEgJk+rg+hDXgNP68haPNIP2chBAu7EgmCU0gU0db88fkaS1xMBPqXmRcNjjkyzKyFEiLMVqC6wjXVpEBfnOrrEJZTWuc6JTaNGzVqmm2vsTXlf8f4J4XpTUKxfFXFE3m/roj42kF4fKv/9nMk2AY8Jo65S/gRENzrrAbOaTZoc436vgNSoKMgFo0WG5LB299DcNx9gFF+rD/yCl6BIzeauZ2zoR18ncz0NZQmJeuLPDAX5c5/DMGuv0B7U1fEYxehNAa+0SROdRqreRpr6s+63sf4CoQUiQaBFZBXfkUkN6WKxHPAv2jB0M1m6Kt2aD+9Pf/e5FBPc3DvNMHfrcxBgr8qnzOJLKb3cGTcepmkBmSNnrM7klcuQwabNG60FzL6HFTvMZ0fP3GjLagMhRC2RQvtQ7TQtvXPemb2zxo9I5gPQghhAQRh2RxZ2L9FSs5oMxtai+f5c/ZBm9gA4DkzuzGEMAJ58BZBcIMHzWxGCKEj8n5MQ9bW4UjousXM3ihz784IKvC3EEI7JGSdZWaPhxACSmyyPILV/A1XUlBm0dea+B5jkDD+BWIg04D/Ik/KWDP7ZwhhTaRs9jCzybnrhyGL8T9QjMiOcTySv+3M7PumtKtMOxdG1vPvzOwLP3Y1ylr6kv9/DorRux041J/dC22601E9v/8hZvi4mf0jnTvJc75F4zjYr9nTzDb137vhtcLM7HI/tjpSIM5FRo/fAt+Y2dV+v8FIsT/bzD6cm36oN4UQ+iHjwV7A52a2iR8v6Uc/VtW4l7s293tXn4frIijUBwhivSaC1d1oZl+Xue4S5BWehNKtf1fdW849+TodiupAfp/jUe2RUWMFJOwda2bTmrEtw5DB4zlUBmAwiv3d0n8fhYStPYH/QxDOXkhoP9rMvkvG4B7Ew0aY2S9CCBchpfnAEMJqyNt6tJl921zv01oohHA+UuoONLP/+rHXyHj5Ymge7A08n4xHB4Sc6W1mP22Gdq2JPPgv+5r6MXCnmd2R8OxL0Z6yEDKwnW5mv/bruyEeGZBHa7iZjQwhfIxQDl/5XHgf7YNd4l4fQtgBzasByBMzM/LReU2+Xw9FXpPPEV973sxeCSEsaGZ/b4Zn7ouy3+7h/bgako8uNbNnQgjLo9iwoUg+GB1CaB95VwhhS+BLM3u71m1rSVRmXz4MyXSnI3luNWR4WH1u+XoIYWUUynPV3Nynwr27oNCfrsgw9CWCXf8Y7Vsza/y8vkgZ3AIZp+8DPjSzv9TyOQXlqN5aaEv8UIdgfgQTOo2sHlmEOU0lqd9Wo2elcUCn5n5bwf/OVR0jxDQ2AM5o4Jw5yRiYQjVj8oVtEETvem/vw3680WB+JPj9HcEh00L2fSiTHbRG/b8bUu6uoDQGchEEu4ulHo7BrV8oAc5hyPp9DfLQRkvrwUhZmIiYaDlIbYPQZiTYXIYsfdNy195FhdjD1vIhi9laE21mT3lfRmjXytTYSo2E0GogizG2qsl1COvUl82Wba/C89bxOX8V8thHz3bKCyrGEyFoezfE927ycyYhw1GEuy6MBLPnybxdzVp4uSV98u/qvGgCMqydhWBZMUNwhI13db7xuvfpDvn71bIPaRjtMFtxdbR3X+rtuwoZC9IwiHLZRXciixetqu5kPcaKOsSaIgPw85SGjZwIXJD8vyxKTPUxCm3p78fPpI2k9Sfbl0/GY0YRmuF1hKj6KRkcvUWHVThfvQ4Z1UEohw+BnjV8RtzvYjmmyO8nIKPLmm2JF8/zMa53A1rapx4M1p+5mTOIacB2fnxVZHW5BkECaxlcn8YBvQRs78cfRlDEua5jRK6obgPnNRkehSyOY1EA8WJI4D4UQXdjnFyjwfxIkNnDmfQn3t+dvU92qWF/jyCLR+mGoBVTUAKg4cxeFHcppKw8mDu+CUoYEts/BG246yAjwg3k4i9oArTZ++YVMiFrJe/jmBSj1TFjn4fTEZy4I4rzWhLFqk7zMZ+JJ16p8bMbgyxGIWmu6hDODx8y4SkKBR1zvzcKcSODjKc14SZQWpdreQQPjLD4tJD8KJK6rG3tg6zwEa0xAGX4nIyMgn2S8wb5XF3e/z8UQZjfQcpXTRI45PkNUtbbI8/KMcgw9kzy+2gEJzsfN+r5fHgAGV+qyS56g3+vmMSp3uOU65N5FmuKlOrrfZ30RvD2GNM+K7EaUrhvRMaBS1As+xxD1VvLJ3n/FRBUMw05WBJ5qKsu/1GH9kfeuyKC9i6Z+/1hslCfuZZHKU2e8xylNQ93Q7L3yHr3y/z8KeCgjVAIYUe0ILZACUieaqbntEPK16bI+vEp2ni+RNbKj81sejM8twOC9JyEmPhUMzvdIWHDUIKRdZCQer6ZTQsh3AccYWb/V+v2NNLWdiZ42kmIyd6PIEkbIhiomdm/4rkIfjEJxcEdZ2ZvOFToGzM7L4SwCNrIlkLJuI1pAAAgAElEQVTxeQsgq3A3BBc8sIZtH44sgbv6c25FVtNDvQ1vATdZAuMJIWyG1xYELjOzB/14QJvs9ygmo4OZjfHfVkKQ0tPM7JnkXlVDmx3ytx9SjpZAELkJtYDG1pNCCIsjSOElZnaqH9sIxTH918xOb6bnVoQsmtkoP+dmtAl+gMbvExTjd5E5jLkx6On8QiGE44HrzOzf/n9c91W9fwhhFbLyJ/cg/vYXM5viMMb+qC7ozxwifD4al2nICLId4huXt5H+bm+Czf4QGc06I6Pag8j794mfl4e5jUKJHC42hzQ7xPZ8M/tbjdoWx/5gZCj9LxqnRy2DacZzjkSC61SkgPQH/p7yLN8XFkfx7+ciA+HBeNkIBLlvh7yLiyJD4Le+V+yMlOJnzGxCLd6vlhRC6GgO3a3R/eK8WArlDfgYlSZaGe0dPRAE9YLcdR3M7H/+fXW0pl43sxm1altLJ5czvjazSyI8N4TQCSFSftcSeXmyjhZFHvAvkDHofoT8+hChqZ7z82v2DiGEFxAfeTSE0CWR47ogxfoftXhOQbNToQRWSbVmsMl9h6FYsZf9/85IUTgaeYEurfUzK7SjoTigVFHsiBTF0+rByLwtU5D3bhzwOzO7OISwC4KpjEvOHYkU6ZlmtpkrSPegbIZfhRAmIgXsS6TsTDOz8Y5N/6PVODYoKL7yAJRYxID7XRDdAkFrry9zTXvU9z/yQ3sC/zKzP/vvW6FxORtlbfxHULzhL83s+rmJHXGBeU9ggpn9ryVuXE2lEMJApOgPRglv7vbjNYsBbeDZiyML+kEoXvM4M3siKPbzGpQa+xFkYPkghPA68BMzO7c52tPSyHnQQsg78zfgGF+nTZp3IYSFEH9YHQn17ZHQ+hjyIP0WxYWdieB9C6EEVt8gwX87BIu+qjkMby2RvO9noH44DSlE7VEc69VmNikREndB/fcyMsKtj2JDU6NTLWKp45ocgvj2vkgRjPz5jKTtHZDydzgyrH1nZmNDCIcgWP/45L79kJL/kZ8/yN/zU2Qk6oZ49CmoVuRoM3vHr10B+PO8NoDWk0IITyDD5T9R2MSbCCnzPernKCfkY4exeRjL3JIoxk+i7NxRobkQzcXj6tq4RiiEcA0yiryAFMDnESptMnCrG0VqqQAuhLzLV5rZ68nxq5AR6me1eE5BFaiSi7D4zJsPgnl+jQTTWNdqBQR3jHVTWgQED5pex6iZ2nE8giU8lxybgdfcIYGNIS/PVBTIfw9e7xF5XF/370sgmMbdeImFZm5/PwRlmoBibdKsoJUKtC6GlL0BaPO9LPntbATTOQN5QN5Gwm3NoM3MZ/V5vF8+RCnV+zIPYzMokwKduaxDOL99UEKCnebgupEI5hdjq4/29f+mr40uyPM/xXlIWj8uxrr1QcaSB8nBtOfXD4KVn4q8z5EvLo6gkmmJjs4+R/+ElKRtUIKjX6Tn1ahNESp2JjAmOb6SP3PT3PlHIPRHui88DexT4f4l2UXJ4MHr0kDdybb0QQbEu/37ht5nE3wv6kUmC/yQ8nGZbYpvJe+9MIo/vgIZcYcglEdv/71FyHQV2n2L/30cj+NE8tXJNXxOHuZ9HIJsb4zkltWcp7TJ+TMvP+0oaJ6TQ1IIIaxg8v4MRjCLF91adDeCf/7bLaotwvtiopnmFt551a7YXwlNRELIgiGEQ4Iy2f2fZXCve0IIUx0e9CfkeTkYZd2M2dy+RYIhZva1mb2J4ln2KPO8uWl7B/+7TQhhtLe1i5ldjeL3OiBhA29L2T41sz+Z2aVm9itkuV4thPBFCGEPMxuLrHbfI+XhWDP7i4/Xo2Z2lZmtjwwNM5AFfLGmvIe1YhhoOfJ+GYjiVb6weWixNrM/Wy6zmpn9w8z+gBS/85CQ+56ZfeZW1/mq/1NyTw4hhJ4hhIH+/4PAFSGEPdNzqqDPUbzrqUhovwYJ8vcieOA5SLnYAyU82BO4LYSwDbCA89vPzWwiEnqXqtmLtjBK9qE+yPs1DsWC/zGEsBwyjP3BzF6I15gguqeh5FKbofIn1yF+Ukso4iLJmpwJDAshbBBCWMDMPkDrY+cQwuMhhE39vI+RwP1+CGHNEMJYNVnefr/vmiGE9f3fycDuIYQD3BtxbAjhZOTdfN3M3vV3uxwZBq53NMd8Tbm19j/gP0EZdmeY2WikWPdFWaa/DyGshRLpfeTXr+Yw0Plu36hE0fMZQhji83ETxMM+QkmFDkPJVb4Mgtm2CJmuDHU1s5FoLf8FGdNA8+AhKCuPzTGFELZ3/nM9ykWwCzLYnweMayvzp55UwEHnMSWQmqVQAPsPzez/+W8roJp8vzGzh+vZzpZEIYtNOAAJKx2QwtYBweseQEkArgohnIsEmVdRdtJ2qObhS2b2UQIxWgJZeD8BrjCzmSGE24F3zOyyGrW7m6lURgcUGH8LYqrDUfzXVSg+cY4YXQhhewQj/Aw4zMzer/K6ZoE2t2ZqThhoE9vRGcXcLII8Gt/NDzDcSpTww0HISPE+Ur6uRPFaPZCH/7dV3GsR83TibkzbBkGZzkECza4owclfkBfwcTe0/QgpD28ixeBNxDf2MrPbavi6LZJCCFcigf48//9M1FeGINPT/fgyqJ/eQsmiDgT+kSpZNWpPP5RQ5AozO9GPnY14/1/Q2Ozuz98Qxem9iebMwiiW+X/IIHCHCVod+e7R/ttyaL7t5NdviAyD0/wee5nZs0mbhqPQg09r+a4tkZI1OQ7BYrujuXBVXA+uDH/n592PvEZ3kHmH30GZx+f79P6JTLEwmj/PoT44Gskd36f7S0vl5yGEFZFR9EIzezUoRvgUBJF+3cyOqlXb3dDQHqGzlkMe0+lIpuuBjPqfze1zCmqcCiWwThRCuAV42xWXrVHQ+RtmdkNyTotkFvOSomAXFIdyNmKy/0bwiutNNatWREykoWD+Z83sJhcGFnDlbAkUO3Q4Ehj+Y2Y71LDtz6BMj0+j4qcXusV5JSTADPe2/nounxPLEMxE8UzftfV5M79QW+EBQXGsbyKY5kAEQ/srUjqWQtmLv2zg+n7Mrjj0R/DAoUgZuDKE0APFlq2DYgOnmdmLjiA4B0FCj2yWl2yBFEIYihJFHWyqnRjj6xZHGTN/mZy7CoLRD0MhC+8gHnaFmV1TS0OK8/RrUSzSsY7y2BEZSDqhuOZnQwgbo2zHa6EMvFOAiXHN5NdPaLyWaqNJnOZnShSaRZGRel8z+2sIYWeUTGcBZHj9JDG4HI3W6BZIEfwZgos+YGYP1eE16kIhhMuR5+95xG/W9n5cDXhxXqJNqqUy62MU8vJeZmZfhxAGIFj9DMuQaTU1ljoK4zS0Fq+0Zqw7W9DsVCiBdSAXOO5Fm9xySKF5A2ViOtvaUNB5Q+SC3bOontpyKHvmG95/2yCB5DC0aS9DFcH8IYQL/LoPkOUp1rjqhQrZ/quG7e+PoGibIAF3fzP7swtaiyDFsCYZ00IICyJv4BW1uF9BBTU3JR6H9VGdtq0rnHcV8LKZ3dPI/VLF4bTEa7ExsnBfamb3+bGByCu4AuIBj5jZhyErLN++JQpttaYQwqooe3JPlIinKqE9qFD7emjv+o2ZndVM7WsQ7RBCeB+VR/gcGQCPRTFFk8zsvlBFdtEKzy2bxKnW79dSKYRwLCo9cRnKhvpvN2Aeh2LOj4jrNYTQHXlmvzeziY5yehiVxPmmPm8w7ymEMBpB0Q8AxruB+kRU43X/+rauPIXKyZ7WQ2Nc04yuiZFhOSTvPmtZJtkr0frdpzFeX1DtqFAC60RuWRsJ/AfPZoYw0VtVA31qKxSErz8YZa27wczOTn57DUFOnnahZBmk3O2BCoy+ilKF/9EZXUy4cwhSIgcDf0YByD8zsz/VsN0dTBk1F0bJXK5A8IfzzOzJ3LltwttTUEHlKIRwHoLlnQI8kcCmOprZf4Ng2k9WCzsMIWwHXI2gg4eboN4RUt4Lxc6aG4XWRp6kZZBH64XKd54/qBy/CYLaH4a8aRdaLma1gXul5QCaM7PubGgHNGbjzWx7P6crSuiyH0pe8oALnA1mF23kuYuizM1V9cf8QkGxuFsj+WQaggP+Lvm9M+rLR4HdLCtftCja6961LP5+vic3QH0P3IXm5jYImfQacJCZvdmc62NuyMdyCvLqX4yU/DEIETC6OXhiUBmgQWShOm+6rDQOGVzmexhxS6FCCawTOSyxMxJG/hlUJ+zPZnZSS2UW9SJnUvsjIfF9JOC1RxCCJxHz3RGlY/61C3qDkDLYE9jbhckrkLUywsUGIqFhJZQ19A81au8g4F8ozuQYM1vHjx+BIDWfI69gRXhbQQXN75RAD7fyT0Ae82cti5MOSAj/eA7ufzJKEPNzlA32O5Ss4RsU5/QycB8qDbAT8LTVqL5dS6XEEt8Jlc1YG5WA+dSFsBOBYWa2SV0bWobyaAffFx5FhryTzOw3IYR1kBC5t++xVddSLaiUgsIpDkIIm9+hONBncvDBHyBv4TdobF4LIaxuZm/VocnzlBLD0s7AWWa2Vghhd5SLYH1Ueuo9MzujpRt6QwiDUTKWbigZ0sKojubeZvZKjZ6RlhBZFCmdmyJDw1/9+7S2ZDxoCVQogXUmF3L6IWZ7vpn9p6UzjHqRM44TkKDyFcok9VeqCOb3De105AUcjwpB/9XPW2FOhMwG2jkCpaNfFcHcbkg8g12QMntNLT2PBRXUWiiBIHUkq9P3OfLUDEaK4RO1gN+lioNDs/qj9f8kqoHVD8Xs3mxm/5rfeW/S99cj3rk6io+8HbjIzP4QPKFVXRtaJYUQFkB8fWOkqPRH8NHH8KRboZFaqvVpeculoOyPIcKhXWk+EiWqur/CNdHg8gKKc5/vodSRQgiTUc3fB9zwsByqqfg9CjEpSQzTkijMu2RPke8sAIxA2XanIgVwR4SWWtjMjq/lcwtqnAolsIVQAn1qkcyiJVEIYWVgHVMa96qD+T12YW2ULGYp4OF4j2Zo4w4ofuI/qD7fg66MnoUs718WY11QW6TEG3Uz8uIcjzzm17kXfRdgqpm9VyulzL1G45FX6BxUI/I2pAz+1MzOnNtntBZy6NpkMxsSlNnxGYSaWAMlSXmywRu0EEo8Md0QP98ElcD5huqyi25UwM4qU2hiXKwbXA5vS54cDzGZgIxJo6yVJTUJ8zDZkz/vbmT4+wQhpe70ZxWKSJ2oUAILmm8olAnmRwrYrHpFbuVcEjG6g1Da68dr9Pwo3LZDcNVOqBD9WOSpfBdBVpepxfMKKqi1UbJG1gEuNrNNQggzUEKs6SGEFc3soxo/M1qh+yIEwU3AmWb22xDC4wiB8WJbMcoE1XXrD3wB/NjMNvW+uZUkoVZLpWqUk1BldtHmb23Lp2R9rIn2qx5mNjn5fVbcZ0Gl5LDqJZHhajjyRt9kZu/VtWFzQKGZkj0lPL8rMsCd4cabtZGHfi2kQD9Vi+cV1DQqlMCC5jty2OhuwCuWZQjtQFI6wT0Dfczskxo+NzK70ShpwQoIHnMZinnqA8w0s3eKjbWgtkxBNai+QVDQrcxsj6CiwXcib9RcZ0jOQZC+R7GF74YQTkdw7YeRwLv53D6rpVM5j2oIYQMkhF0O7IBK5DRLls+5pcTrtymaM0+Y2X/yv5e5bo5qqbYVStbIQGAS8o6fDOxgZm/XtXGtiJzHrISgjZsDB5rHNbc2Cs2U7CmEsBdKNvQUys77jR/fG3jV5rJUVkFzRoUSWNB8SSGEU4AfAXcjS/+//HizKF+JArgWgoecjWJuDkW1lQ4C/lnAHgpqq5QK6iGEJRE8czVgoMfLjgMws9G1jM0LIUxEBY+HAleb2U9CCBuiOJSnzOyLpkLfWisFZcpcxsym+v9noVTtqwLbmtlX9WxfOUrnQgjhXeAoM/tZCGF5YHEze7WKexS1VBugEMJUlDl7IRRbv1NQspDFgReKvhIl+3xnU9mMEr4RVId0GTN7s47NbDGUGBn2RIn8vgC+RhD0V4BfFcbw+lK7ejegoIJqTc54LkalJbYEng8hjARILVy1fGaySW6L4pkeQ9kHj0FxT+sVG2lBbZnckxNCCNciL+DtKEvnzSGE8aisy6m1eFYIIfjffVEs2O3IE/+OowB+Z2a3m9kXsW21eG5LJI+ZI4RwGkIlnBVC+FUIYXMzOw/xyS1aogKYUgjhGOAtVwD3BCYDR4YQ1mvsWjO7CKEznjSz/xW8OCPfC19D4QqHo6yQoHmxUdFXGSV9sX8IoXueb5jZHwsFMKPEizgcITy2QZ7AjVDuhm3r1baCRIUSWNB8Rwnj+SHKHPoUsEcI4Un3AKTn1JpeALYIISxnZt855KEdSkhTUEFtllzYDMi7cBWCYy4PvIfquB1iytDZfm4Fz+T6figmd1MEIfw18nqd4RDx+Zrc83ew/x2BvDxDgB8Dt4cQXgT6WQvNVBxC6J2M5bvAFyGE55HB4ERU02yXau5lZn83Ly9RUEa+F36B+vI7M3sjKCPoFgjVUlBCQQnmtgEmBZWjKqgBckj2EcAPAMxsEjL2fY1yNxRURyrgoAXNl+SMZ6ypdk875HGYgDyDPzKze2v0nHKxNmciK+pTqGD9qSib6Te1hLkVVFBrpKAyKWOBz8zs6ma4f4QgdUX1usYB3c2sn//+EIK4XTG/r8eg8ggHIWj6YqgG2IsJLPdG4DEze7hujaxA7s19GmUu/Z+Z/dXjh5YArjdl054OXGpmT8zvY1lLClnJoq5m9k8/tgMwEsGk30Xx620m02dTKYRwKfCOmd1R77a0ZPLkOQchVNTHwBhrA3UkWwsVSmBB8w057Olbx+z3Q9Cng81rXgUVtl0DuMDM/lujZ8aEBTuhLGFfAy+hWmenAB+h2lTPtZW4o4IKKkchhKURDPQ7oAeqE/UoitmtiUU4idnpgxSFH4QQjgY2A/4G/B4YbmYb1uJ5rYGCUvfviJJlfQE8BLwfobAtnUIIi6HSP2MtK/jeAXkXRpjZbnVsXqsl79eJgAE/Bd5EXvkVUA3Fb+vYvBZFyT6/FIpDfSsow/HlwDgze6DOTWzx5PNtFCrP8guE1Pq+MNzUlwolsKD5hoKKQf8XxX18EEK4AcHALkeFoR8AbjOz22r0vChw9kKK33QEb/gf8CLwuJn9oxbPKqig1kwhhP6ohtv9KDbrJWQwGQPsY2Yv1eg5cU0eB3Qxsws9CU1/lP78E+Th+KytGGVCCAcAdwHLIoRCX+TpeQl4uRmh8TWjoOyV16ESD6eY2R1Bha7/YWZ/DG2kvEctKISwn5lNCiGcCyyI0CoboLCFN4CXrMZlWlozhRD6mtlv/Pu9KNPwMsD1wLEo5vgAM5tZeKMbp6A6z5ub2VX1bktBhRJY0HxCDv06BAl73wPPI+vmusD5SOD83MyOquEzo8B5DPCNmd0WQlgVbaiDUK3A88zsD7V6ZkEFtVYKKt3SF8UFjgT+jhIG7B2FrBo9ZykkzL5sZj+o1X1bEyWeiz1QhuIdLcuQPAxZ4ae3NihbCGE7ZNTrDGzQWryZLYWC6idegdbeosAuZvZtCGERYGcULvGMmRWxgMyKY34P+Bb1z9emrKDboaLn/0WxbosD+1oNStsUVNC8pEIJLGi+ohDCsogpD0aFoaeZ2c+b8XlLIWjDS2a2S3J8E2BRM3uwuZ5dUEGtgTy2q73lUoGHEDqZ13qrtScnhLAZynLYARWlf6hW925NFEJ4GRVL/3mM/0riwVqtJzSoFut4S2oFFtQ4eXzWMqhA9ykoVnS0ZfV0VwD+XCgzpRRCuABls7wTraf/5n6/AYV93FCP9hVU0JxSkR20oPmGHDK0mpldg6AaBhwQQjgrhDCgOZ5pZr8H9gUWDyG8FELYxY8/i2JvZqWrL6igtkLpnDfR/5Lf2vnx/yTn1BTKZ2bTUTbMa4HjQgjPhRB6t6W1GFTA+ldAL4CYAAS4LYSwWWtVAAHMbJyZ/SfUuNTP/ExuBPgP8loZStYxA7gghHBmCGEJM/u4UAAz8thTzOx0YEWkQH/lRoiU/osMTgUV1Kqo8AQWNN9QCGFXYC/gU+A24H1gK5Tq+jwz+0szPrs9sDcqUA+wJ/BVEadSUFukJEPn3sAHZvZG8ts89UB5QoKRKIFDTRJCtRYKqpO4PXAv8HNgNeAcM1u/rg0raJ5SCGEh4CgUKrEjimH7tcezD0IZWHsiaHabWiOVKPGYLw4sYma/8uPDgatR+ZmhwG+APmb227o1tqCC5pAKJbCgVk+JwNkDGIgKka6KEh/cA/xtXsGG2rLAWVBBULIeByNjzM5m9vsQwgpm9nFLaFs92zAvyT0ZByJ4/HbA68CtZvZ0XRtW0DwnR8NMAxZG9SKfTX4bDvzOzD6tU/NaFCUKYGfgJ6hsxq9ReYOX/JwDgTvbEj8paP6jQgksqNVTCKEj0M284LGnot8ZJYp5xcyOqFO72pTAWVBBKYUQJgFTzez+EMLhqLj318A2zemVL2h2ck/QgsC/zOyv9W5PQfWhEMIoFAa0Jyq7cTSCC480s1H1bFtLohDCIcArqKzKwmZ2QgjhbFTe4Fngiqgwt+bY2oIKKvD0Bc0PtAfwfyGEHwOY2WceF/gecFO9GlUogAW1VQoqCP9HYMsQwq2oPMEwsjpkBc1DMrNvzeyrQgFs22Rm483sWmQkfQNl0Z4EPFzXhrUgCiEsD6yNYiYHoBIamNlYYBugDzA2nl8ogAW1Zio8gQW1SsrX4/GkMFchGOipqBj1rmY2vE5NLKigNk0eS3Mu8EczOzuE0Bt4DhVrL8qmFFRQncnLtgwws5n1bktLohBCXxRWshXylN4AvGhmX/nvC3lpjQLtU1CrpkIJLKhVUwhhXRTj8Cczey2EsAVwNirbcIeZvV7XBhZUUBuhpDbdisBiwG/SOm4hhIeBn5vZjwsIVUEFFdTSKOFhPYG/INliP2B54HPgbeAJ4PtC+StofqBCCSyo1VHCqHcAjgf+Cfw/lKJ5jJl9FULobGb/rmtDCyqojVCSDGZRlEjhCyQ43Q/8DPgQlW95zs8v8eQXVFBBBbUUCiGMB5YDLjOzp0MIa6KwkwWAU8zsX3VtYEEF1YgKJbCgVkshhFeAo81sZghhEKrX19XMTqhz0woqqE1SCOEaVJrlBaQAPo8g2pNRVspvCwWwoIIKammUGJdHIijoIsB3wJ+AS1B20KXM7DcFDytofqEiMUxBrY6CqD3wMSp8i5n9ErgSWCWEsFI921dQQW2RQggLI0v53cClwCFm9iNUm6yLmX0LKh5fv1YWVFBBBc1OrgB2Q3HMJyOj8ulAR2AicDhSCAseVtB8Q4USWFBrpE5AN+AB4IQQwo+8RuCyQC8z+6CurSuooLZJXc1sJPBfFE/T3o//D3gIBButU9sKKqigghqjLsBM4O+eUfcD4EbgA2BdYI16Nq6ggmpNxYZcUGukHYBxKPnLdcAqqAjyqciKV1BBBc1D8mQwN4QQ1jWzfwDPABNDCC8B75jZxw6hKpIpFFRQQS2GQgibhRAGOn/6GvgMeCqEsLufMhjBQl8A9nEUUkEFzRdUxAQW1CooTcXskI1TgS+85hEhhD7Av83sj3VsZkEFtRkqU6ZlFNAXJVP4OoQwAHnnZ5jZv4t06gUVVFBLIkcmjARuB3ZECuCbwHbAIYif/QEViT8T+IOZXVif1hZUUO2pUAILalUUQjgGeAxBQm9ABamPN7NP69muggpqa5RkBN0F+BZ4GbgcWA84wsxm1LWBBRVUUEFVkBuWrwYC8BLwc+A9lOH4dyhT6NVmtkndGllQQc1AhRJYUKsh9yw8jmKOzkdw5guBGcBJZvbbOjavoILaHIUQOgNTgOHAxaiO1hhUrmW0mb1Qv9YVVFBBBZWnchk+QwibA3sD/0DK4HNm9rlnH29nZu/VoakFFdRsVMQEFtRqyMx+BRyLkk5sDnwK3AmsBHxdv5YVVFDbJK/FeRrwLLAZ8tBfh4rF/7eOTSuooIIKaog6AIQQdgohjAoh7IG8f0eg5DD7A/1A2ccLBbCg+ZEKT2BBrYJCCCOAocDTwOfAwcDzZvZKCGFBM/t7XRtYUEFtjEIIyyCr+VsoEcyBwD/M7O66NqygggoqqAEKISzhccsLIf41Hfg3SgDzMvAIsICZ/V8dm1lQQc1OhRJYUIunEEJAgdrLA3sBfwP+jtI1725mr9WxeQUV1CYphLAKsCkwDFgBeAfBQq/4/+3dT4hWVRzG8e+T2hQZWRQtWiRIEmF/yEJwIVrUMjGSiCCCoDJo36K2UQxBUARBLVtU4CJcpGCbCIJosmaKoEDFNgVDZUWLtF+LewZG58Vy/viOne8HXu597z33nnN37/Oec8+pqtecCEbSatN+T3wEfM+w1vBsVb2d5FZgO8PIokuB56vq5/G1VFp5hkBddJI8AGwG7gNeqapDY26S1LUk2xgmhNkKHK+qF8bcJEkaqc0vsI9hRtDpqnpw3rldwNVVtX9c7ZMuFEOgLlpJ1lWV7x1Jq0SStVV1qu3bEyhp1Uqyg2FyuTAsbbO/HU9V1ajJY6T/E0OgJEmSutMWf38EeLIdehj40T+w1ANDoCRJkrqV5BqGheNfdYSRemEIlCRJknAou/phCJQkSZKkjrhYvCRJkiR1xBAoSZIkSR0xBEqSJElSRwyBkqSuJTmd5Mi8z8ZF3GNDkmeWv3WSJC0/J4aRJHUtye9VtX6J99gIHKiqLed53ZqqOr2UuiVJOl/2BEqSdJYka5JMJvksyVdJnmrH1yc5nGQqyXSS3e2Sl4BNrSdxMsnOJAfm3e/1JI+3/WNJXk4yBexNsinJh0k+T/Jxkpsv9PNKkvqydtwNkCRpzC5PcqTtH62qPQwLR/9aVXcnmQA+SXIIOAHsqaqTSa4FPk3yAfAcsKWq7gBIsvNf6pytqjtb2cPA01X1XZJtwBvAPcv9kJIkzTEESpJ69+dceJvnfuC2JA+17xGJggAAAAEiSURBVFcBNwE/AC8m2QH8DdwAXL+IOt+FoWcR2A68n2Tu3MQi7idJ0n9mCJQkaaEAz1bVwTMODkM6rwO2VtVfSY4Bl424/hRnvnJxdpk/2vYS4JcRIVSSpBXjO4GSJC10ENiXZB1Aks1JrmDoEfypBcBdwI2t/G/AlfOuPw7ckmQiyQbg3lGVVNVJ4GiSva2eJLl9ZR5JkqSBIVCSpIXeAr4BppLMAG8yjJ55B7gryTTwGPAtQFXNMrw3OJNksqpOAO8BM237xTnqehR4IsmXwNfA7nOUlSRpyVwiQpIkSZI6Yk+gJEmSJHXEEChJkiRJHTEESpIkSVJHDIGSJEmS1BFDoCRJkiR1xBAoSZIkSR0xBEqSJElSRwyBkiRJktSRfwBLyx1c6tIqDQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa987421668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find 3-gram features\n",
    "mask = np.array([len(feature.split(\" \")) for feature in feature_names]) == 3\n",
    "# visualize only 3-gram features\n",
    "mglearn.tools.visualize_coefficients(coef.ravel()[mask],\n",
    "                                     feature_names[mask], n_top_features=40)\n",
    "plt.ylim(-22, 22)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Advanced tokenization, stemming and lemmatization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The feature extraction in the CountVectorizer and Tfidf Vectorizer is relatively simple, and much more elaborate methods are possible <br><br>\n",
    "-  \"replace\", \"replaced\", \"replace ment\", \"replaces\", and \"replacing\" <br><br>\n",
    "-  This problem can be overcome by representing each word using its word stem, which involves identifying (or conflating) all the words that have the same word stem.  <br><br>\n",
    "-  If this is done by using a rule-based heuristic, like dropping common suffixes, it is usually referred to as stemming. <br><br>\n",
    "-  If instead a dictionary of known word forms is used (an explicit and human-verified system), and the role of the word in the sentence is taken into account, the process is referred to as lemmatization and the standardized form of the word is referred to as the lemma <br><br>\n",
    "-  Both processing methods, lemmatization and stemming, are forms of normalization that try to extract some normal form of a word. Another interesting case of normalization is spelling correction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> SpaCy does not provide a built-in function for Stemming as Stemming is not accurate and SpaCy is mainly created for Production level use. Inaccurate process is not obviously not useful for Production.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However it provides Lemmatization. Lemmatization does the same job but more accurately with the help of language specific dictionary and returns the exact root word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apples apple\n",
      "and and\n",
      "oranges orange\n",
      "are be\n",
      "similar similar\n",
      ". .\n",
      "Boots boot\n",
      "and and\n",
      "hippos hippo\n",
      "are be\n",
      "n't not\n",
      ". .\n"
     ]
    }
   ],
   "source": [
    "# Lemmatizer: https://spacy.io/api/lemmatizer/\n",
    "# make sure your downloaded the english model with \"python -m spacy download en\"\n",
    "import spacy\n",
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "doc = nlp(\"Apples and oranges are similar. Boots and hippos aren't.\")\n",
    "\n",
    "for token in doc:\n",
    "    print(token, token.lemma_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contextual spell correction using [BERT(bidirectional representations)](https://spacy.io/universe/project/contextualSpellCheck)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: 100%|██████████| 29.0/29.0 [00:00<00:00, 3.62kB/s]\n",
      "Downloading: 100%|██████████| 570/570 [00:00<00:00, 157kB/s]\n",
      "Downloading: 100%|██████████| 208k/208k [00:00<00:00, 696kB/s] \n",
      "Downloading: 100%|██████████| 426k/426k [00:00<00:00, 883kB/s] \n",
      "Downloading: 100%|██████████| 416M/416M [01:43<00:00, 4.23MB/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "Income was $9.4 million compared to the prior year of $2.7 million.\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "import contextualSpellCheck\n",
    "\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "contextualSpellCheck.add_to_pipe(nlp)\n",
    "# million is spelled incorrectly\n",
    "doc = nlp('Income was $9.4 milion compared to the prior year of $2.7 milion.') \n",
    "\n",
    "print(doc._.performed_spellCheck) #Should be True\n",
    "print(doc._.outcome_spellCheck) #Income was $9.4 million compared to the prior year of $2.7 million."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us compare lemmatization and steeming Porter Stemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conda install nltk spacy\n",
    "import spacy # You need to install spacy first\n",
    "import nltk # You need to install nltk first\n",
    "\n",
    "# load spacy's English-language models\n",
    "en_nlp = spacy.load('en_core_web_sm') # spacy's English model\n",
    "# instantiate nltk's Porter stemmer\n",
    "stemmer = nltk.stem.PorterStemmer()\n",
    "\n",
    "# define function to compare lemmatization in spacy with stemming in nltk\n",
    "def compare_normalization(doc):\n",
    "    # tokenize document in spacy\n",
    "    doc_spacy = en_nlp(doc)\n",
    "    # print lemmas found by spacy\n",
    "    print(\"Lemmatization:\")\n",
    "    print([token.lemma_ for token in doc_spacy])\n",
    "    # print tokens found by Porter stemmer\n",
    "    print(\"Stemming:\")\n",
    "    print([stemmer.stem(token.norm_.lower()) for token in doc_spacy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lemmatization:\n",
      "['our', 'meeting', 'today', 'be', 'bad', 'than', 'yesterday', ',', 'I', 'be', 'scared', 'of', 'meet', 'the', 'client', 'tomorrow', '.']\n",
      "Stemming:\n",
      "['our', 'meet', 'today', 'wa', 'wors', 'than', 'yesterday', ',', 'i', 'am', 'scare', 'of', 'meet', 'the', 'client', 'tomorrow', '.']\n"
     ]
    }
   ],
   "source": [
    "compare_normalization(\"Our meeting today was worse than yesterday, \"\n",
    "                       \"I'm scared of meeting the clients tomorrow.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Stemming is always restricted to trimming the word to a stem, so \"was\" becomes \"wa\", while lemmatization can retrieve the correct base verb form, \"be\".  <br><br>\n",
    "> Similarly, lemmatization can normalize \"worse\" to \"bad\", while stemming produces \"wors\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In general, lemmatization is a much more involved process than stemming, but it usually produces better results than stemming when used for normalizing tokens for machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Technicallity: we want to use the regexp based tokenizer\n",
    "# that is used by CountVectorizer  and only use the lemmatization\n",
    "# from SpaCy. To this end, we replace en_nlp.tokenizer (the SpaCy tokenizer)\n",
    "# with the regexp based tokenization\n",
    "import re\n",
    "# regexp used in CountVectorizer:\n",
    "regexp = re.compile('(?u)\\\\b\\\\w\\\\w+\\\\b')\n",
    "# load spacy language model\n",
    "en_nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])\n",
    "old_tokenizer = en_nlp.tokenizer\n",
    "# replace the tokenizer with the preceding regexp\n",
    "en_nlp.tokenizer = lambda string: old_tokenizer.tokens_from_list(\n",
    "    regexp.findall(string))\n",
    "\n",
    "# create a custom tokenizer using the SpaCy document processing pipeline\n",
    "# (now using our own tokenizer)\n",
    "def custom_tokenizer(document):\n",
    "    doc_spacy = en_nlp(document)\n",
    "    return [token.lemma_ for token in doc_spacy]\n",
    "\n",
    "# define a count vectorizer with the custom tokenizer\n",
    "lemma_vect = CountVectorizer(tokenizer=custom_tokenizer, min_df=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform text_train using CountVectorizer with lemmatization\n",
    "X_train_lemma = lemma_vect.fit_transform(text_train)\n",
    "print(\"X_train_lemma.shape: {}\".format(X_train_lemma.shape))\n",
    "\n",
    "# standard CountVectorizer for reference\n",
    "vect = CountVectorizer(min_df=5).fit(text_train)\n",
    "X_train = vect.transform(text_train)\n",
    "print(\"X_train.shape: {}\".format(X_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score (standard CountVectorizer): 0.721\n",
      "Best cross-validation score (lemmatization): 0.731\n"
     ]
    }
   ],
   "source": [
    "# build a grid-search using only 1% of the data as training set:\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10]}\n",
    "cv = StratifiedShuffleSplit(n_splits=5, test_size=0.99,\n",
    "                            train_size=0.01, random_state=0)\n",
    "grid = GridSearchCV(LogisticRegression(), param_grid, cv=cv)\n",
    "# perform grid search with standard CountVectorizer\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Best cross-validation score \"\n",
    "      \"(standard CountVectorizer): {:.3f}\".format(grid.best_score_))\n",
    "# perform grid search with Lemmatization\n",
    "grid.fit(X_train_lemma, y_train)\n",
    "print(\"Best cross-validation score \"\n",
    "      \"(lemmatization): {:.3f}\".format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topic Modeling and Document Clustering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> One particular technique that is often applied to text data is topic modeling, which is an umbrella term describing the task of assigning each document to one or multiple topics, usually without supervision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A good example for this is news data, which might be categorized into topics like “politics,” “sports,” “finance,” and so on. If each document is assigned a single topic, this is the task of clustering the documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://www.frontiersin.org/files/Articles/539160/frai-03-00042-HTML/image_m/frai-03-00042-g002.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.ibb.co/zV5rjX6/Screen-Shot-2020-09-24-at-11-21-38.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Latent Dirichlet Allocation\n",
    "Often, when people talk about topic modeling, they refer to one particular decomposition method called Latent Dirichlet Allocation (often LDA for short)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Intuitively, the LDA model tries to find groups of words (the topics) that appear together frequently.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Let’s apply LDA to our movie review dataset to see how it works in practice. For unsupervised text document models, it is often good to remove very common words, as they might otherwise dominate the analysis. We’ll remove words that appear in at least 20 percent of the documents, and we’ll limit the bag-of-words model to the 10,000 words that are most common after removing the top 20 percen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vect = CountVectorizer(max_features=10000, max_df=.15)\n",
    "X = vect.fit_transform(text_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#rent ordering, and changing the number of topics will change all of the topics.10 We’ll use the \"batch\" learning method, which is somewhat slower than the default (\"online\") but usually provides better results, and increase \"max_iter\", which can also lead to better models:\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "lda = LatentDirichletAllocation(n_components=10, learning_method=\"batch\", \n",
    "                                max_iter=25, random_state=0)\n",
    "# We build the model and transform the data in one step\n",
    "# Computing transform takes some time,\n",
    "# and we can save time by doing both at once\n",
    "document_topics = lda.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LDA has as a components_ attribute that stores how important each word is for each topic. The size of components_ is (n_topics, n_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lda.components_.shape: (10, 10000)\n"
     ]
    }
   ],
   "source": [
    "print(\"lda.components_.shape: {}\".format(lda.components_.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand better what the different topics mean, we will look at the most important words for each of the topics. The print_topics function provides a nice formatting for these features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for each topic (a row in the components_), sort the features (ascending).\n",
    "# Invert rows with [:, ::-1] to make sorting descending\n",
    "sorting = np.argsort(lda.components_, axis=1)[:, ::-1]\n",
    "# get the feature names from the vectorizer:\n",
    "feature_names = np.array(vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "topic 0       topic 1       topic 2       topic 3       topic 4       \n",
      "--------      --------      --------      --------      --------      \n",
      "between       war           funny         show          didn          \n",
      "family        world         comedy        series        saw           \n",
      "young         us            guy           episode       thought       \n",
      "real          american      laugh         tv            am            \n",
      "us            our           jokes         episodes      thing         \n",
      "director      documentary   fun           shows         got           \n",
      "work          history       humor         season        10            \n",
      "both          years         re            new           want          \n",
      "beautiful     new           hilarious     years         going         \n",
      "each          human         doesn         television    watched       \n",
      "\n",
      "\n",
      "topic 5       topic 6       topic 7       topic 8       topic 9       \n",
      "--------      --------      --------      --------      --------      \n",
      "action        kids          role          performance   horror        \n",
      "effects       action        cast          role          house         \n",
      "nothing       animation     john          john          killer        \n",
      "budget        children      version       actor         gets          \n",
      "script        game          novel         cast          woman         \n",
      "minutes       disney        director      plays         dead          \n",
      "original      fun           both          jack          girl          \n",
      "director      old           played        michael       around        \n",
      "least         10            mr            oscar         goes          \n",
      "doesn         kid           young         father        wife          \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print out the 10 topics:\n",
    "mglearn.tools.print_topics(topics=range(10), feature_names=feature_names,\n",
    "                           sorting=sorting, topics_per_chunk=5, n_words=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Next, we will learn another model, this time with 100 topics. Using more topics makes the analysis much harder, but makes it more likely that topics can specialize to interesting subsets of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lda100 = LatentDirichletAllocation(n_topics=100, learning_method=\"batch\",\n",
    "                                   max_iter=25, random_state=0)\n",
    "document_topics100 = lda100.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at all 100 topics would be a bit overwhelming, so we selected some interesting and representative topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "topics = np.array([7, 16, 24, 25, 28, 36, 37, 41, 45, 51, 53, 54, 63, 89, 97])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "topic 7       topic 16      topic 24      topic 25      topic 28      \n",
      "--------      --------      --------      --------      --------      \n",
      "thriller      worst         german        car           beautiful     \n",
      "suspense      awful         hitler        gets          young         \n",
      "horror        boring        nazi          guy           old           \n",
      "atmosphere    horrible      midnight      around        romantic      \n",
      "mystery       stupid        joe           down          between       \n",
      "house         thing         germany       kill          romance       \n",
      "director      terrible      years         goes          wonderful     \n",
      "quite         script        history       killed        heart         \n",
      "bit           nothing       new           going         feel          \n",
      "de            worse         modesty       house         year          \n",
      "performances  waste         cowboy        away          each          \n",
      "dark          pretty        jewish        head          french        \n",
      "twist         minutes       past          take          sweet         \n",
      "hitchcock     didn          kirk          another       boy           \n",
      "tension       actors        young         getting       loved         \n",
      "interesting   actually      spanish       doesn         girl          \n",
      "mysterious    re            enterprise    now           relationship  \n",
      "murder        supposed      von           night         saw           \n",
      "ending        mean          nazis         right         both          \n",
      "creepy        want          spock         woman         simple        \n",
      "\n",
      "\n",
      "topic 36      topic 37      topic 41      topic 45      topic 51      \n",
      "--------      --------      --------      --------      --------      \n",
      "performance   excellent     war           music         earth         \n",
      "role          highly        american      song          space         \n",
      "actor         amazing       world         songs         planet        \n",
      "cast          wonderful     soldiers      rock          superman      \n",
      "play          truly         military      band          alien         \n",
      "actors        superb        army          soundtrack    world         \n",
      "performances  actors        tarzan        singing       evil          \n",
      "played        brilliant     soldier       voice         humans        \n",
      "supporting    recommend     america       singer        aliens        \n",
      "director      quite         country       sing          human         \n",
      "oscar         performance   americans     musical       creatures     \n",
      "roles         performances  during        roll          miike         \n",
      "actress       perfect       men           fan           monsters      \n",
      "excellent     drama         us            metal         apes          \n",
      "screen        without       government    concert       clark         \n",
      "plays         beautiful     jungle        playing       burton        \n",
      "award         human         vietnam       hear          tim           \n",
      "work          moving        ii            fans          outer         \n",
      "playing       world         political     prince        men           \n",
      "gives         recommended   against       especially    moon          \n",
      "\n",
      "\n",
      "topic 53      topic 54      topic 63      topic 89      topic 97      \n",
      "--------      --------      --------      --------      --------      \n",
      "scott         money         funny         dead          didn          \n",
      "gary          budget        comedy        zombie        thought       \n",
      "streisand     actors        laugh         gore          wasn          \n",
      "star          low           jokes         zombies       ending        \n",
      "hart          worst         humor         blood         minutes       \n",
      "lundgren      waste         hilarious     horror        got           \n",
      "dolph         10            laughs        flesh         felt          \n",
      "career        give          fun           minutes       part          \n",
      "sabrina       want          re            body          going         \n",
      "role          nothing       funniest      living        seemed        \n",
      "temple        terrible      laughing      eating        bit           \n",
      "phantom       crap          joke          flick         found         \n",
      "judy          must          few           budget        though        \n",
      "melissa       reviews       moments       head          nothing       \n",
      "zorro         imdb          guy           gory          lot           \n",
      "gets          director      unfunny       evil          saw           \n",
      "barbra        thing         times         shot          long          \n",
      "cast          believe       laughed       low           interesting   \n",
      "short         am            comedies      fulci         few           \n",
      "serial        actually      isn           re            half          \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sorting = np.argsort(lda100.components_, axis=1)[:, ::-1]\n",
    "feature_names = np.array(vect.get_feature_names())\n",
    "mglearn.tools.print_topics(topics=topics, feature_names=feature_names,\n",
    "                           sorting=sorting, topics_per_chunk=5, n_words=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The topics we extracted this time seem to be more specific, though many are hard to interpret. Topic 7 seems to be about horror movies and thrillers; topics 16 and 54 seem to capture bad reviews, while topic 63 mostly seems to be capturing positive reviews of comedies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " If we want to make further inferences using the topics that were discovered, we should confirm the intuition we gained from looking at the highestranking words for each topic by looking at the documents that are assigned to these topics. For example, topic 45 seems to be about music. Let’s check which kinds of reviews are assigned to this topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'I love this movie and never get tired of watching. The music in it is great.\\n'\n",
      "b\"I enjoyed Still Crazy more than any film I have seen in years. A successful band from the 70's decide to give it another try.\\n\"\n",
      "b'Hollywood Hotel was the last movie musical that Busby Berkeley directed for Warner Bros. His directing style had changed or evolved to the point that this film does not contain his signature overhead shots or huge production numbers with thousands of extras.\\n'\n",
      "b\"What happens to washed up rock-n-roll stars in the late 1990's? They launch a comeback / reunion tour. At least, that's what the members of Strange Fruit, a (fictional) 70's stadium rock group do.\\n\"\n",
      "b'As a big-time Prince fan of the last three to four years, I really can\\'t believe I\\'ve only just got round to watching \"Purple Rain\". The brand new 2-disc anniversary Special Edition led me to buy it.\\n'\n",
      "b\"This film is worth seeing alone for Jared Harris' outstanding portrayal of John Lennon. It doesn't matter that Harris doesn't exactly resemble Lennon; his mannerisms, expressions, posture, accent and attitude are pure Lennon.\\n\"\n",
      "b\"The funky, yet strictly second-tier British glam-rock band Strange Fruit breaks up at the end of the wild'n'wacky excess-ridden 70's. The individual band members go their separate ways and uncomfortably settle into lackluster middle age in the dull and uneventful 90's: morose keyboardist Stephen Rea winds up penniless and down on his luck, vain, neurotic, pretentious lead singer Bill Nighy tries (and fails) to pursue a floundering solo career, paranoid drummer Timothy Spall resides in obscurity on a remote farm so he can avoid paying a hefty back taxes debt, and surly bass player Jimmy Nail installs roofs for a living.\\n\"\n",
      "b\"I just finished reading a book on Anita Loos' work and the photo in TCM Magazine of MacDonald in her angel costume looked great (impressive wings), so I thought I'd watch this movie. I'd never heard of the film before, so I had no preconceived notions about it whatsoever.\\n\"\n",
      "b'I love this movie!!! Purple Rain came out the year I was born and it has had my heart since I can remember. Prince is so tight in this movie.\\n'\n",
      "b\"This movie is sort of a Carrie meets Heavy Metal. It's about a highschool guy who gets picked on alot and he totally gets revenge with the help of a Heavy Metal ghost.\\n\"\n"
     ]
    }
   ],
   "source": [
    "# sort by weight of \"music\" topic 45\n",
    "music = np.argsort(document_topics100[:, 45])[::-1]\n",
    "# print the five documents where the topic is most important\n",
    "for i in music[:10]:\n",
    "    # show first two sentences\n",
    "    print(b\".\".join(text_train[i].split(b\".\")[:2]) + b\".\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, this topic covers a wide variety of music-centered reviews, from musicals, to biographical movies, to some hard-to-specify genre in the last review. Another interesting way to inspect the topics is to see how much weight each topic gets overall, by summing the document_topics over all reviews. We name each topic by the two most common words. Figure 7-6 shows the topic weights learned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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gMzM4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVSOa7dQAzr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\nbmZvIDUyIDAgUiA+PgpzdGFydHhyZWYKMTgyMTkKJSVFT0YK\n",
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9a0b74dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 10))\n",
    "topic_names = [\"{:>2} \".format(i) + \" \".join(words)\n",
    "               for i, words in enumerate(feature_names[sorting[:, :2]])]\n",
    "# two column bar chart:\n",
    "for col in [0, 1]:\n",
    "    start = col * 50\n",
    "    end = (col + 1) * 50\n",
    "    ax[col].barh(np.arange(50), np.sum(document_topics100, axis=0)[start:end])\n",
    "    ax[col].set_yticks(np.arange(50))\n",
    "    ax[col].set_yticklabels(topic_names[start:end], ha=\"left\", va=\"top\")\n",
    "    ax[col].invert_yaxis()\n",
    "    ax[col].set_xlim(0, 2000)\n",
    "    yax = ax[col].get_yaxis()\n",
    "    yax.set_tick_params(pad=130)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary and Outlook"
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    "- In this chapter we talked about the basics of processing text, also known as natural language processing (NLP), with an example application classifying movie reviews. The tools discussed here should serve as a great starting point when trying to process <br><br >\n",
    "\n",
    "- Natural language and text processing is a large research field, and discussing the details of advanced methods\n",
    "\n",
    "- For more advanced text-processing methods, we recommend the Python packages spacy (a relatively new but very efficient and welldesigned package), nltk (a very well-established and complete but somewhat dated library), and gensim (an NLP package with an emphasis on topic modeling)\n",
    "\n",
    "- There have been several very exciting new developments in text processing in recent years and relate to neural networks. The first is the use of continuous vector representations, also known as word vectors or distributed word representations, as implemented in the word2vec library. \n",
    "\n",
    "- Both Gensim and Spacy provides word vectors \n",
    "\n",
    "- Another direction in NLP that has picked up momentum in recent years is the use of recurrent neural networks (RNNs) for text processing. RNNs are a particularly powerful type of neural network that can produce output that is again text, in contrast to classification models that can only assign class labels"
   ]
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