word_sim_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook records the experiments I have done in the article of \"Computing Semantic Similarity of Concepts in Knowledge Graphs\". If someone is interested in reproducing the experiments, one can install Sematch and use this notebook for reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T07:34:17.921452Z",
     "start_time": "2018-03-11T07:34:17.914448Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sematch.semantic.similarity import WordNetSimilarity\n",
    "from gensim.models import Word2Vec, KeyedVectors\n",
    "from nltk.corpus import brown, treebank\n",
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:44:55.442297Z",
     "start_time": "2018-03-11T05:43:54.563697Z"
    }
   },
   "outputs": [],
   "source": [
    "word2vec_model = KeyedVectors.load_word2vec_format('./GoogleNews-vectors-negative300.bin', binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T06:42:24.953030Z",
     "start_time": "2018-03-11T06:28:56.002783Z"
    }
   },
   "outputs": [],
   "source": [
    "glove_model = KeyedVectors.load_word2vec_format('./glove.840B.300d.w2vformat.txt', binary=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A simple example of word similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:44:56.035529Z",
     "start_time": "2018-03-11T05:44:55.443298Z"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>artist</th>\n",
       "      <th>musician</th>\n",
       "      <th>scientist</th>\n",
       "      <th>physicist</th>\n",
       "      <th>actor</th>\n",
       "      <th>movie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>artist</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.809924</td>\n",
       "      <td>0.359417</td>\n",
       "      <td>0.296175</td>\n",
       "      <td>0.359417</td>\n",
       "      <td>0.135239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>musician</th>\n",
       "      <td>0.809924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.296175</td>\n",
       "      <td>0.251859</td>\n",
       "      <td>0.641697</td>\n",
       "      <td>0.123384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scientist</th>\n",
       "      <td>0.359417</td>\n",
       "      <td>0.296175</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.790743</td>\n",
       "      <td>0.456999</td>\n",
       "      <td>0.149615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>physicist</th>\n",
       "      <td>0.296175</td>\n",
       "      <td>0.251859</td>\n",
       "      <td>0.790743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.359417</td>\n",
       "      <td>0.135239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor</th>\n",
       "      <td>0.359417</td>\n",
       "      <td>0.641697</td>\n",
       "      <td>0.456999</td>\n",
       "      <td>0.359417</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.149615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie</th>\n",
       "      <td>0.135239</td>\n",
       "      <td>0.123384</td>\n",
       "      <td>0.149615</td>\n",
       "      <td>0.135239</td>\n",
       "      <td>0.149615</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             artist  musician  scientist  physicist     actor     movie\n",
       "artist     1.000000  0.809924   0.359417   0.296175  0.359417  0.135239\n",
       "musician   0.809924  1.000000   0.296175   0.251859  0.641697  0.123384\n",
       "scientist  0.359417  0.296175   1.000000   0.790743  0.456999  0.149615\n",
       "physicist  0.296175  0.251859   0.790743   1.000000  0.359417  0.135239\n",
       "actor      0.359417  0.641697   0.456999   0.359417  1.000000  0.149615\n",
       "movie      0.135239  0.123384   0.149615   0.135239  0.149615  1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wns = WordNetSimilarity()\n",
    "words = ['artist', 'musician', 'scientist', 'physicist', 'actor', 'movie']\n",
    "sim_matrix = [[wns.word_similarity(w1, w2, 'wpath') for w1 in words] for w2 in words]\n",
    "df = pd.DataFrame(sim_matrix, index=words,columns=words)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluations on Word Similarity Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have collected some well known word similarity datasets for evaluating semantic similarity metrics. Several python classes can be used to separate the dataset for specicial purpose and evaluate the metric function automatically. \n",
    "\n",
    "We put them together and provide a uniformed framework to evaluate different semantic measures. The word similarity datasets include:\n",
    "\n",
    "- [Rubenstein and Goodenough (RG)](http://www.cs.cmu.edu/~mfaruqui/word-sim/EN-RG-65.txt) \n",
    "\n",
    "Herbert Rubenstein and John B. Goodenough. 1965. Contextual correlates of synonymy. Commun. ACM 8, 10 (October 1965), 627-633. DOI=10.1145/365628.365657 \n",
    "\n",
    "- [Miller and Charles (MC)](http://www.cs.cmu.edu/~mfaruqui/word-sim/EN-MC-30.txt) \n",
    "\n",
    "Miller, George A., and Walter G. Charles. \"Contextual correlates of semantic similarity.\" Language and cognitive processes 6.1 (1991): 1-28.\n",
    "\n",
    "- [Wordsim353 (WS353)](http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/) \n",
    "\n",
    "Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin, \"Placing Search in Context: The Concept Revisited\", ACM Transactions on Information Systems, 20(1):116-131, January 2002 \n",
    "\n",
    "- [wordsim353 similarity and relatedness (WS353Sim)](http://alfonseca.org/eng/research/wordsim353.html) \n",
    "\n",
    "Eneko Agirre, Enrique Alfonseca, Keith Hall, Jana Kravalova, Marius Pasca, Aitor Soroa, A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches, In Proceedings of NAACL-HLT 2009.\n",
    "\n",
    "- [SimLex-999 (SIMLEX)](http://www.cl.cam.ac.uk/~fh295/simlex.html) \n",
    "\n",
    "SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation. 2014. Felix Hill, Roi Reichart and Anna Korhonen. Preprint pubslished on arXiv. arXiv:1408.3456\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:44:58.682982Z",
     "start_time": "2018-03-11T05:44:56.037234Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sematch.evaluation import WordSimEvaluation\n",
    "from sematch.semantic.similarity import WordNetSimilarity, YagoTypeSimilarity\n",
    "from nltk.corpus import wordnet as wn\n",
    "\n",
    "data_word_noun = ['noun_rg','noun_mc','noun_ws353','noun_ws353-sim','noun_simlex']\n",
    "data_word_graph = ['graph_rg','graph_mc','graph_ws353','graph_ws353-sim','graph_simlex']\n",
    "data_word_type = ['type_rg','type_mc','type_ws353','type_ws353-sim','type_simlex']\n",
    "\n",
    "sim_methods_noun = ['path','lch','wup','li','res','lin','jcn','wpath']\n",
    "sim_methods_graph = ['path','lch','wup','li','res','res_graph','lin','jcn','wpath','wpath_graph']\n",
    "sim_methods_type = ['path','lch','wup','li','res','res_graph','lin','lin_graph','jcn','jcn_graph','wpath','wpath_graph']\n",
    "\n",
    "ws_eval = WordSimEvaluation()\n",
    "wns = WordNetSimilarity()\n",
    "yagosim = YagoTypeSimilarity()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To produce the TABLE 2 in the article \"The illustration of Semantic Similarity Methods on Some Concept Pair Examples\". We manually create the word to synset mapping and compute their semantic similarity scores using different semantic similarity metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:45:08.822928Z",
     "start_time": "2018-03-11T05:44:58.684480Z"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>lch</th>\n",
       "      <th>wup</th>\n",
       "      <th>li</th>\n",
       "      <th>res</th>\n",
       "      <th>lin</th>\n",
       "      <th>jcn</th>\n",
       "      <th>wpath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beef-octopus</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>2.028148</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.441994</td>\n",
       "      <td>6.109280</td>\n",
       "      <td>0.484051</td>\n",
       "      <td>0.071308</td>\n",
       "      <td>0.494238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beef-lamb</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.538974</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.667005</td>\n",
       "      <td>6.725370</td>\n",
       "      <td>0.591049</td>\n",
       "      <td>0.097025</td>\n",
       "      <td>0.691593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meat-seafood</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.538974</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.659377</td>\n",
       "      <td>6.109280</td>\n",
       "      <td>0.759623</td>\n",
       "      <td>0.205488</td>\n",
       "      <td>0.661525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>octopus-shellfish</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.538974</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.667005</td>\n",
       "      <td>9.359654</td>\n",
       "      <td>0.729141</td>\n",
       "      <td>0.125726</td>\n",
       "      <td>0.801453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beef-service</th>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.998529</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049523</td>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beef-atmosphere</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.152680</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052383</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beef-coffee</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>1.440362</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.168312</td>\n",
       "      <td>3.336706</td>\n",
       "      <td>0.319125</td>\n",
       "      <td>0.065625</td>\n",
       "      <td>0.208354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food-coffee</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.691676</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.251092</td>\n",
       "      <td>3.336706</td>\n",
       "      <td>0.410819</td>\n",
       "      <td>0.094601</td>\n",
       "      <td>0.259764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       path       lch       wup        li       res       lin  \\\n",
       "beef-octopus       0.200000  2.028148  0.714286  0.441994  6.109280  0.484051   \n",
       "beef-lamb          0.333333  2.538974  0.857143  0.667005  6.725370  0.591049   \n",
       "meat-seafood       0.333333  2.538974  0.833333  0.659377  6.109280  0.759623   \n",
       "octopus-shellfish  0.333333  2.538974  0.857143  0.667005  9.359654  0.729141   \n",
       "beef-service       0.071429  0.998529  0.133333  0.000000  0.000000  0.000000   \n",
       "beef-atmosphere    0.083333  1.152680  0.153846  0.000000  0.000000  0.000000   \n",
       "beef-coffee        0.111111  1.440362  0.428571  0.168312  3.336706  0.319125   \n",
       "food-coffee        0.142857  1.691676  0.500000  0.251092  3.336706  0.410819   \n",
       "\n",
       "                        jcn     wpath  \n",
       "beef-octopus       0.071308  0.494238  \n",
       "beef-lamb          0.097025  0.691593  \n",
       "meat-seafood       0.205488  0.661525  \n",
       "octopus-shellfish  0.125726  0.801453  \n",
       "beef-service       0.049523  0.071429  \n",
       "beef-atmosphere    0.052383  0.083333  \n",
       "beef-coffee        0.065625  0.208354  \n",
       "food-coffee        0.094601  0.259764  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aspects = {'beef':wn.synset('beef.n.02'), 'lamb':wn.synset('lamb.n.05'), 'octopus':wn.synset('octopus.n.01'),\n",
    "          'shellfish':wn.synset('shellfish.n.01'), 'meat':wn.synset('meat.n.01'), 'seafood':wn.synset('seafood.n.01'),\n",
    "          'food':wn.synset('food.n.02'), 'service':wn.synset('service.n.02'),'atmosphere':wn.synset('atmosphere.n.01'),\n",
    "          'coffee':wn.synset('coffee.n.01')}\n",
    "aspect_pairs = [('beef', 'octopus'), ('beef', 'lamb'), ('meat','seafood'), ('octopus', 'shellfish'),\n",
    "               ('beef','service'),('beef','atmosphere'),('beef', 'coffee'), ('food','coffee')]\n",
    "aspects_sim_matrix = [[wns.similarity(aspects[w1], aspects[w2], m) for m in sim_methods_noun] \n",
    "                      for w1, w2 in aspect_pairs]\n",
    "aspect_index = [x+'-'+y for x, y in aspect_pairs]\n",
    "aspect_df = pd.DataFrame(aspects_sim_matrix, index=aspect_index, columns=sim_methods_noun)\n",
    "display(aspect_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WPATH method with different K in Word Noun Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data_word_noun contains word pairs that can be mapped to WordNet noun taxonomy. The k settings are varied with interval 0.1 started from 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:45:37.579855Z",
     "start_time": "2018-03-11T05:45:08.824929Z"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>noun_rg</th>\n",
       "      <th>noun_mc</th>\n",
       "      <th>noun_ws353</th>\n",
       "      <th>noun_ws353-sim</th>\n",
       "      <th>noun_simlex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.747</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.746</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.776</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0.785</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.790</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>0.791</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.794</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.795</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.781</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     noun_rg  noun_mc  noun_ws353  noun_ws353-sim  noun_simlex\n",
       "0.1    0.747    0.703       0.278           0.535        0.486\n",
       "0.2    0.746    0.696       0.324           0.616        0.497\n",
       "0.3    0.776    0.737       0.343           0.635        0.550\n",
       "0.4    0.785    0.740       0.347           0.643        0.573\n",
       "0.5    0.790    0.738       0.347           0.644        0.582\n",
       "0.6    0.789    0.732       0.345           0.642        0.589\n",
       "0.7    0.791    0.723       0.345           0.644        0.596\n",
       "0.8    0.794    0.728       0.341           0.645        0.603\n",
       "0.9    0.795    0.726       0.332           0.636        0.601\n",
       "1.0    0.781    0.724       0.310           0.609        0.584"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wpath_cors = [ws_eval.evaluate_wpath_k(dataset) for _, dataset in enumerate(data_word_noun)]\n",
    "cors_matrix = [[cors[i] for _, cors in enumerate(wpath_cors)] for i in range(1,11)]\n",
    "wpath_index = list(map(lambda x: str(x/10.0), range(1, 11)))\n",
    "df_wpath = pd.DataFrame(cors_matrix, index=wpath_index, columns=data_word_noun)\n",
    "display(df_wpath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WPATH method with different K in Word Graph Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In word graph dataset, we performed the evaluation of wpath with different k using corpus-based IC and graph-based IC respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:45:37.835868Z",
     "start_time": "2018-03-11T05:45:37.581356Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>graph_rg</th>\n",
       "      <th>graph_mc</th>\n",
       "      <th>graph_ws353</th>\n",
       "      <th>graph_ws353-sim</th>\n",
       "      <th>graph_simlex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.734</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.732</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.769</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0.780</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.794</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.796</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     graph_rg  graph_mc  graph_ws353  graph_ws353-sim  graph_simlex\n",
       "0.1     0.734     0.673        0.286            0.512         0.479\n",
       "0.2     0.732     0.664        0.340            0.604         0.490\n",
       "0.3     0.769     0.711        0.361            0.625         0.546\n",
       "0.4     0.780     0.714        0.366            0.634         0.569\n",
       "0.5     0.786     0.714        0.366            0.636         0.579\n",
       "0.6     0.786     0.708        0.365            0.634         0.586\n",
       "0.7     0.789     0.696        0.367            0.636         0.593\n",
       "0.8     0.794     0.703        0.364            0.639         0.600\n",
       "0.9     0.796     0.700        0.354            0.631         0.599\n",
       "1.0     0.782     0.699        0.331            0.602         0.581"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#evaluate with corpus-based IC\n",
    "wpath_cors = [ws_eval.evaluate_wpath_k(dataset) for _, dataset in enumerate(data_word_graph)]\n",
    "cors_matrix = [[cors[i] for _, cors in enumerate(wpath_cors)] for i in range(1,11)]\n",
    "df_wpath_graph = pd.DataFrame(cors_matrix, index=wpath_index, columns=data_word_graph)\n",
    "display(df_wpath_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:46:13.828647Z",
     "start_time": "2018-03-11T05:45:37.837867Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>graph_rg</th>\n",
       "      <th>graph_mc</th>\n",
       "      <th>graph_ws353</th>\n",
       "      <th>graph_ws353-sim</th>\n",
       "      <th>graph_simlex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.707</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.729</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.736</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0.757</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.774</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.781</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>0.781</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.778</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.794</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     graph_rg  graph_mc  graph_ws353  graph_ws353-sim  graph_simlex\n",
       "0.1     0.707     0.687        0.161            0.385         0.375\n",
       "0.2     0.729     0.722        0.249            0.474         0.404\n",
       "0.3     0.736     0.742        0.301            0.545         0.436\n",
       "0.4     0.757     0.757        0.313            0.566         0.468\n",
       "0.5     0.774     0.754        0.320            0.573         0.479\n",
       "0.6     0.781     0.761        0.327            0.588         0.501\n",
       "0.7     0.781     0.725        0.330            0.591         0.519\n",
       "0.8     0.778     0.712        0.332            0.599         0.542\n",
       "0.9     0.794     0.716        0.339            0.616         0.571\n",
       "1.0     0.782     0.699        0.331            0.602         0.581"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#evaluate with graph-based IC\n",
    "wpath_cors = [ws_eval.evaluate_wpath_k(dataset, 'graph') for _, dataset in enumerate(data_word_graph)]\n",
    "cors_matrix = [[cors[i] for _, cors in enumerate(wpath_cors)] for i in range(1,11)]\n",
    "df_wpath_graph = pd.DataFrame(cors_matrix, index=wpath_index, columns=data_word_graph)\n",
    "display(df_wpath_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WPATH method with different K in Word Type Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:46:14.105362Z",
     "start_time": "2018-03-11T05:46:13.830148Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type_rg</th>\n",
       "      <th>type_mc</th>\n",
       "      <th>type_ws353</th>\n",
       "      <th>type_ws353-sim</th>\n",
       "      <th>type_simlex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.632</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.632</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.672</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0.683</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.689</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.688</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.598</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>0.688</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.691</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.689</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.679</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type_rg  type_mc  type_ws353  type_ws353-sim  type_simlex\n",
       "0.1    0.632    0.608       0.280           0.478        0.482\n",
       "0.2    0.632    0.584       0.327           0.563        0.503\n",
       "0.3    0.672    0.659       0.357           0.595        0.556\n",
       "0.4    0.683    0.646       0.361           0.600        0.582\n",
       "0.5    0.689    0.669       0.363           0.602        0.594\n",
       "0.6    0.688    0.667       0.363           0.598        0.603\n",
       "0.7    0.688    0.638       0.367           0.602        0.613\n",
       "0.8    0.691    0.627       0.365           0.606        0.621\n",
       "0.9    0.689    0.620       0.359           0.606        0.625\n",
       "1.0    0.679    0.621       0.353           0.601        0.616"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#evaluate with corpus-based IC\n",
    "wpath_cors = [ws_eval.evaluate_wpath_k(dataset) for _, dataset in enumerate(data_word_type)]\n",
    "cors_matrix = [[cors[i] for _, cors in enumerate(wpath_cors)] for i in range(1,11)]\n",
    "df_wpath_type = pd.DataFrame(cors_matrix, index=wpath_index, columns=data_word_type)\n",
    "display(df_wpath_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:46:39.515959Z",
     "start_time": "2018-03-11T05:46:14.106361Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type_rg</th>\n",
       "      <th>type_mc</th>\n",
       "      <th>type_ws353</th>\n",
       "      <th>type_ws353-sim</th>\n",
       "      <th>type_simlex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.653</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.692</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.696</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0.698</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.713</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.714</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>0.703</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.687</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.692</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.679</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type_rg  type_mc  type_ws353  type_ws353-sim  type_simlex\n",
       "0.1    0.653    0.670       0.175           0.398        0.387\n",
       "0.2    0.692    0.726       0.248           0.436        0.431\n",
       "0.3    0.696    0.729       0.304           0.504        0.477\n",
       "0.4    0.698    0.729       0.308           0.521        0.512\n",
       "0.5    0.713    0.759       0.318           0.529        0.527\n",
       "0.6    0.714    0.766       0.327           0.546        0.550\n",
       "0.7    0.703    0.732       0.331           0.553        0.567\n",
       "0.8    0.687    0.709       0.334           0.565        0.589\n",
       "0.9    0.692    0.656       0.343           0.585        0.611\n",
       "1.0    0.679    0.621       0.353           0.601        0.616"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#evaluate with graph-based IC\n",
    "wpath_cors = [ws_eval.evaluate_wpath_k(dataset, 'graph') for _, dataset in enumerate(data_word_type)]\n",
    "cors_matrix = [[cors[i] for _, cors in enumerate(wpath_cors)] for i in range(1,11)]\n",
    "df_wpath_type = pd.DataFrame(cors_matrix, index=wpath_index, columns=data_word_type)\n",
    "display(df_wpath_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline semantic similarity metrics on Word Noun Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T06:42:25.527509Z",
     "start_time": "2018-03-11T06:42:24.954532Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  del sys.path[0]\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:23: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>noun_rg</th>\n",
       "      <th>noun_mc</th>\n",
       "      <th>noun_ws353</th>\n",
       "      <th>noun_ws353-sim</th>\n",
       "      <th>noun_simlex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>0.781</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lch</th>\n",
       "      <td>0.781</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wup</th>\n",
       "      <td>0.755</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.624</td>\n",
       "      <td>0.542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>li</th>\n",
       "      <td>0.787</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>res</th>\n",
       "      <td>0.776</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lin</th>\n",
       "      <td>0.784</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jcn</th>\n",
       "      <td>0.775</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>word2vec</th>\n",
       "      <td>0.762</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glove</th>\n",
       "      <td>0.769</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          noun_rg  noun_mc  noun_ws353  noun_ws353-sim  noun_simlex\n",
       "path        0.781    0.724       0.310           0.609        0.584\n",
       "lch         0.781    0.724       0.310           0.609        0.584\n",
       "wup         0.755    0.729       0.344           0.624        0.542\n",
       "li          0.787    0.719       0.334           0.628        0.586\n",
       "res         0.776    0.733       0.346           0.634        0.535\n",
       "lin         0.784    0.752       0.307           0.603        0.582\n",
       "jcn         0.775    0.820       0.284           0.581        0.579\n",
       "word2vec    0.762    0.789       0.706           0.772        0.452\n",
       "glove       0.769    0.788       0.745           0.804        0.428"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = lambda x, y: wns.word_similarity(x, y, 'path')\n",
    "lch = lambda x, y: wns.word_similarity(x, y, 'lch')\n",
    "wup = lambda x, y: wns.word_similarity(x, y, 'wup')\n",
    "li = lambda x, y: wns.word_similarity(x, y, 'li')\n",
    "res = lambda x, y: wns.word_similarity(x, y, 'res')\n",
    "lin = lambda x, y: wns.word_similarity(x, y, 'lin')\n",
    "jcn = lambda x, y: wns.word_similarity(x, y, 'jcn')\n",
    "miss_values = []\n",
    "total = 0\n",
    "def word2vec(x, y):\n",
    "    ans = 0\n",
    "    try:\n",
    "        ans = word2vec_model.wv.similarity(x, y)\n",
    "    except KeyError:\n",
    "        pass\n",
    "    return ans\n",
    "\n",
    "def glove(x, y):\n",
    "    global miss_values, total\n",
    "    total += 1\n",
    "    ans = 0\n",
    "    try:\n",
    "        ans = glove_model.wv.similarity(x, y)\n",
    "    except KeyError:\n",
    "        miss_values.append((x, y))\n",
    "    return ans\n",
    "\n",
    "methods = {'path':path, 'lch':lch, 'wup':wup, 'li':li, 'res':res, 'lin':lin, 'jcn':jcn, 'word2vec': word2vec, 'glove': glove}\n",
    "cor_dicts = [ws_eval.evaluate_multiple_metrics(methods, dataset) for dataset in data_word_noun]\n",
    "baseline_cors_matrix = [[cors[m] for _, cors in enumerate(cor_dicts)] for m in sim_methods_noun[0:7] + ['word2vec', 'glove']]\n",
    "df_baselines_noun = pd.DataFrame(baseline_cors_matrix, index=sim_methods_noun[0:7] + ['word2vec', 'glove'], columns=data_word_noun)\n",
    "display(df_baselines_noun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T08:38:00.965200Z",
     "start_time": "2018-03-11T08:37:42.976094Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  del sys.path[0]\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:23: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>mean_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alpha*path + word2vec</td>\n",
       "      <td>0.7708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>path + alpha*word2vec</td>\n",
       "      <td>0.7672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alpha*lch + word2vec</td>\n",
       "      <td>0.7538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lch + alpha*word2vec</td>\n",
       "      <td>0.6738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alpha*wup + word2vec</td>\n",
       "      <td>0.7388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>wup + alpha*word2vec</td>\n",
       "      <td>0.7116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>alpha*li + word2vec</td>\n",
       "      <td>0.7570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>li + alpha*word2vec</td>\n",
       "      <td>0.7272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>alpha*res + word2vec</td>\n",
       "      <td>0.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>res + alpha*word2vec</td>\n",
       "      <td>0.6348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>alpha*lin + word2vec</td>\n",
       "      <td>0.7554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>lin + alpha*word2vec</td>\n",
       "      <td>0.7204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>alpha*jcn + word2vec</td>\n",
       "      <td>0.7712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>jcn + alpha*word2vec</td>\n",
       "      <td>0.7718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>alpha*glove + word2vec</td>\n",
       "      <td>0.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>glove + alpha*word2vec</td>\n",
       "      <td>0.7310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               description  mean_score\n",
       "0    alpha*path + word2vec      0.7708\n",
       "1    path + alpha*word2vec      0.7672\n",
       "2     alpha*lch + word2vec      0.7538\n",
       "3     lch + alpha*word2vec      0.6738\n",
       "4     alpha*wup + word2vec      0.7388\n",
       "5     wup + alpha*word2vec      0.7116\n",
       "6      alpha*li + word2vec      0.7570\n",
       "7      li + alpha*word2vec      0.7272\n",
       "8     alpha*res + word2vec      0.7130\n",
       "9     res + alpha*word2vec      0.6348\n",
       "10    alpha*lin + word2vec      0.7554\n",
       "11    lin + alpha*word2vec      0.7204\n",
       "12    alpha*jcn + word2vec      0.7712\n",
       "13    jcn + alpha*word2vec      0.7718\n",
       "14  alpha*glove + word2vec      0.7300\n",
       "15  glove + alpha*word2vec      0.7310"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jArpjPcRSq/NbBGEZEYF9PjNvSkdNTD9tDF30ocLsoBHRu15OtfsSxpNbOOFs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\nu3Ytb775JgMHDmTo0KH86Ec/4rvf/a5Xxx3ElNntUN9UT/5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rKwNg7NixHGuWSktLSU1N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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2408c6be320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/NTgO33lxiPd9+SF+eGKCV17Qwx/csZnzVyRmeHpNeWgInc+fVrVmCxUO43Z0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2408c4f1828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C9IuhTIFoqi5+iaUb8WTn2ul1sZy+aL4XSXJitsihC4u8cGGRQ+ftenKp2ZWj\nKx1jpCvK/TtTvP3AECNdaXYG1ujebFxvFEVRFGXDrCWcJ5eKTIQE9ORikanlIu3eAkMyxZBMsyNY\n3+POknPyVCRqF6JUxNpQKxKj6kSpEKMqNt1zovVwtR6P4Tm2TNWJ40kUz6ml2TKeE8ePxPAliuM4\nwfcqkHSFnLMciON5Ovx52rxZstUZ0uVZkuVp4qUZooUp3OLs6h/AAG47ZHoh0weZvcG6Fu+DdE+w\n7iYRiZK41n9SC+3Boly/qMCusTwFRz8Dhx+xw3OVFuy4xLteAfe/C/a+3g5DF01uyjBcy6UqL44v\ncujEIocuTHLowiIvji9SrNgPq6IRYU9vllfu7WZfX5ZdXSmGc1ZEZxPqyqEoiqJcnJpwnlxqFsoT\nIQFthXOJimeI4NHPLEMyzZBMszs+x8vdWYadafrMFJ2xSaKm2eBjEu1I+wikeqxxqlqE6lLzpFOb\nPtmXNGaLjbhQmF97Yis32RDJHXsh88ogHhLOmV5I99rJzRRlE7l1Bbbvw/iz1kJ95BE49zRg7PjO\nd7wV9r0Bxh6+6mG1jDFcWCjWrdE16/TJmUbXU3syyh0DbXzPAzu5faCNOwba2NObsb6fiqIoitKC\nMYap5RLn5gqcmy9wbq7A+fkC44GAnlqyIrriNQRtnDKDMsO++Bz7k/O8yZ1lKDFNX2yKXGWcVGmy\nebQGA8R6oWMY2u8P1iPBehg6hpHEZdhRLznZ1xqCfNUMsy3lvYrtPc70QaanWTjHMjouvbJt3FoC\nu7RkrdNHHoEjn4HlCUBg6H745p+Dvd8C/fdc8Uxt5arP0cnlZheP8UXm85V6mV1dKW4faOMd9+2w\nYnqwjYH2hLp1KIqiKHWqns/4YrFJQJ+bbw6Hh5KM4NGfqHBnZplXxecY7ZhlqHOaXn+SzvIE6cJ5\nosXpxgEKNGYu7RyGjofqopmOESuk24Ne281CBCJRu1zVh8OKcv2zpQJbRN4I/CYQAX7XGPO+lvx2\n7PgnI0Fdfs0Y8/ubWonpo4Ev9SNw6ot2/MV4O+x5Dex9A+x5nX3rvUzmVsp1i7QV1EscnVyqWwsS\nUYf9/W286a5+7giE9P7+NjLxW+udRlGUG4fros2+RShWPM7PFzg3l2d8Zo7pmWnmZ2dYnJ8lvzRH\nJT9PmjxZCmTJk5U8D0bL9ESLdLol2tsLpMmT8FeIVZZxqkGv6HKwgHWhaN9hBfSulwTCORDQHcOQ\nHbQuFopZjqIrAAAgAElEQVSibDpbdmeJSAR4P/B64CzwpIh80hhzKFTsR4FDxphvFZEe4EUR+Ygx\npnzFB66W4NQXGq4fs8dtes9t8OAPW9eP4QfsG/QGWShU+NKxGZ4/v1B39Ti/0JhAojcb5/aBNh7e\n31N38RjtThPZzjEwFUVRLoNta7NvJrwqTL0AUy9SXJphcWGW/OIcxeV5KvkFTHERp7xItLJC0l+m\nUwoMUyAqa/gPtzyiTDSNJNqs22K8DeK9kGgLwm2NcLYPOnZaIZ3uueIeWUVRro6tfHV9GXDUGHMc\nQEQ+BrwNCDfWBsiK9Y/IALPAZY5rByyety4fRx61LiCVFXATsOtV8OCPWNePzp0b3p0xhufPL/LY\n4Sk+9+IkT5+ex/MNEUfY3ZPmpaM57hho4/Zg6clqX5eiKDc8167NvhkwBn/+LLOHv0T+xBO448/Q\nvXCImLHGl0SwVI3DEimWSVGK2PGFvewg+UQblXQ7+UwnmbZOMu05Isn2kFjONsKxLKKWZkW5odjK\nO3YIOBOKnwUeaCnz28AnsXNvZ4HvNMasmj9ZRN4NvBtgZGQEfA/OfcW6fRx5BMa/Zgu27YCXfKd1\n/Rh9yA6Bt0EWChX+/sg0n3txkscOT9WHxrtzsI33vHqMh/f3cvdQO4mojiOtKMpNyda12Tc4nm84\nNzHJ1ItPUDn1JOmpZxhcOUSXmaUbKBmXQ2YXn3dfw0zH3fi9d9HR1UtXdw8DXZ0M5VIMpePbO7Of\noijXlO1+JX4D8FXgNcBu4DMi8rgxZjFcyBjzQeCDAAf39Bh+ba+d9EUi1t3jde+1VureOzb8xfB6\nVuq2hMur9vXw8L4eXr2/h97s5Q3dY4wBz7ODzlerGM+zcc/DVD3wbbiWV88P5ZlqFXzfpnlVjOdj\nvGp9P03lqx7G96Aa5NXSvCo0befbfYXLr7md13QcRGwXoyPBrFM2LsHMU3ZGKmzeGuXq8Xq4sV19\nHyI2z6nNciV21qpYFHFdJBqFYC1u1K6jtfga+bXtWvNqi+siEX1RUpQr4PLb7IMHN2NctmtC1fM5\nNZvn6IV5Zk8+i5z/Cp2zzzFaeoE9nGNE7KmckQG+kbyXxa57cYYP0r37PnYPdHEgFdvmM1AU5Xph\nKwX2OWA4FN8RpIX5QeB9xhgDHBWRE8BtwD+su9fiIuz5diuo97zWDs+zQS5mpf7RBwZ4dZewN1LE\nzFyg+tyzVD87ybmpKaqTk1RnZjClUrNgDovRatWKam8NX7rtJGKnV6UmKiORYO0gkeY0cSPgBPmu\nWx/ZxAQzTuH7waxVQdz4GL8WtjNXEcTrYWMa2wX7sGXX3mftBQV/lVFsc3GcugAX14VYSLy77up1\nrCbUY6vzo+u8BITyL/WCIK6LxONIPIGTiCPJJE48jiQS9v9TlK1na9rs65By1efUzAqHJ5Y5MrHI\n1PkTJCaeoX/pee6RI7xKTpAS+3xYdrKMd9zFod63ktj1UvrueAXDnX1NP5SiKEorWymwnwT2isgo\ntpH+LuCdLWVOA68FHheRPmA/cPyie+2/G97+wQ1VwBjDoWMX+PJTR3jha8eYPn2ezvwCA9Vl/l20\nxFB1hWx+AR6dws/bL7DDTxNJJHB7e3F7e4jv24cTj4Mbsb5wawhU3Ii1vNaEqhvkOZH6dhJxIOIG\nZYJ9tJavi+CgfDjPdZsFc5DXXI+gTCRyww7/Z4yxLy2VCqZ1HSyr8iu1+Br55fB+WvIraxynVibI\n8wvFljLN+eFtN/slS6JRK7QTcZxE0grweBCPJ5BEAifRGg/EejLREO21berlE0jcxiUWs0s8bo93\ng143ylWxNW32NlKseJyYXuHI5DJHJ5Y4MrnM2YlJ2me/zt0c5V7nKN/tHKVP5gGoulEW228jP/jd\nOLsfJLHrZWRyY+zR+0FRlMtkywS2MaYqIj8GPIId8un3jDHPi8h7gvzfAf4T8CER+RogwM8aY6bX\n3WkIb3mF6tQk1ckpqjUr8+QkhfEJZk+fpzwxSWxhlmS1xMuBl4e2DQvn6NgduD09Nl5bB2Enk1Gh\nsU2ICAQuHTcaxvfrop5AiK9+SahiKuWQyK/gl8uYYhG/WMQUS5hSEb+2LhQb8WIRv2TLeEtLmKmp\netzm2fXVINGoFdt14R3DicWsBf9y0mMxJNZId+KxRplYHCeVxEmn7ZJK4aTT6r6zTWx1m72VLOQr\nHJ1a4tjkCsemljk2tczRyWXOzS6xh7Pc6xzlXucY3xY9wS7/NE7UunqU2nbhjrwehl8KQwdx++8i\n5+pH64qiXD1ie/puHO5ubzd/vntP3eIcpuzGmI5nmUm0sZRqJzXYT//oDvbcvovczqG6gFbhrNzs\nGGOsS1NIcFtxXmiI9mIRUyrZdblsrfylkg1Xyna7Wnq53JJXDvIa6X6leR9X6uYjicQq0e2kUy3x\nNJFVZdaIp1LWBeg6QkS+Yow5uN31uFYcPHjQPPXUU1e9H983nJsv1MXzsSkrpo9PLTO93BglcDQy\nzXdkv8Y3y9PsKT1P1LcvmybZiQwdtBOL7QjWqdxV10tRlJubK22zr68nzwaQRILkt7+d06T4ejHK\nlxaE4ybJTKKN0ZFeHr6tl4f393JguAM3or6ryq2JiCCJBCQSbJc92FSrVsDXRXqpIcrLZfxiCb+Q\nx+TzeCsr+PUlb9f5fD3Nm52jcuZsUxobNA5IIrG2CE8mcZJJJJXESQbxVNL6v4fiTjKJJFP1sN0m\npa40W0Sx4nF8qmGJPja1wtHJZU5ML1OsNF7aOlJR9vRkeO3+Xh5InuZA4UsMjf8dsZlDUAS698Pd\nP1AX05Ib02mzFUW5ZtxwAvtsIsc3Fw7g+YZswuWhl/fw4/vtqB+9bZc34oeiKFtHbSQXJ53e9H0b\nYzCFQkOUB8K7SajXxXh+dbn5eSoXzmPyBfyCXS7brSYSWVukXyKu2P9vdqVcF88NMb3M2blC/d1J\nBHZ0JtnTk+EVu7vY3Zthd0+G3Z0uXdP/AN/4c3jx07B03o5ANPwg3P+LsP/N0LV7e09SUZTrhosa\nfNZJr/XiXik3nMD2jKmPS61WakW5NRERJJXCSaWgp2dT9ml834r22pIvYAr5prhfyNsydWG+Ou6v\nrOBPTzeEe97uY6MW95uNctXnb16YsAJ6coWjgZCez1fqZRJRh7HuDPcOd/KO+3awJxDSo93pxtwD\nhTk7odiTfwVH/wbKSxBNwe7XwG3/wc5/kO7aprNUFOVKMMYERpBl/KUlvKUl/OUV/OUlvOVl/KVl\n/OUl266Wy5hW98RKec30zXJZvBpuOIG9tzfDv37DbdtdDUVRbjLEcZDAhWSzqfnE+4UC5G4tv98X\nJ5b4Z39gfbC7MzF292R4890D1hLdk2Z3T4ahjuTak7DMnbQW6m/8FZz6IhgPMn1w9zuslXr01RDV\nnktF2Q5MuWx7DZeW8JeX8WpieM3wMt7yUiCYQ+GVlQ2JXyeVCoayrX00H8UJf0Df3h7kRYMP7Js/\nupdY7OLptf1GWz/Sj12xEeeGE9iKoig3GjWfeCdx64nBHR1J/vSHv4ndPWk6LjURi+/DhWfgG5+y\nwnryeZveczu88ietqB68z05cpSjKJTHG2JGq8vm1e+cu0hsXdqGrx2uueEtLmFLpkseXWAwnk8HJ\nZoikMzjZLNGRYRJB2MlmiGQyOJksTiZNJJttDmezVlzfgPe8CmxFURRly+hMx7h/50UmBKuW4MTn\nrZX68F/D0gXrTz3yTfCGX4b9b4Lc2LWrsKJsM8b3rZV3cRF/cREvWGx4CW9pMRDMxYuL4SB+uXMz\nSDy+5rckTk83sV07AwGcIZLNtIRtvB7OZnFit+7spiqwlTrGGKrlEuVCgVJ+hXKhgB9M++77tbWP\n8Ty7Dhbfb8Rr5WtlG/FQmdq+vHB68z7FcYhEY0RjMSLRGG4shhuNEYlFcWNx3GhjXc+PhcoFac4N\nPNmOoty05GfhyKNWVB/7WygvQzRtZ+e97R/ZmXp1CD3lBqXmEuYtLOIvhQTy0lIjbWERbykkmkNi\n2l9evvg3GyL10Yzq4jcQxNHOTjt5WGqt0ZCCtHA8PKJSMmUnJ9O5CDYFFdg3Ac3COE+5kA8Ecr6R\nll+hVMiH0oL8fJ5SoVDPN9fwQwARB3EcK4IdB8excXGc+jlVy+Wr+jhMxMGNxYjEYoEotwLcDQv3\nIGzFfJSIG8VxXSKuixOx69riuNEgPRKkNcrW8yORIC0oW8+P4riR+n5V+Cu3FLPHA3/qT8HpL1l/\n6uwA3PNPrOvHrlepP7Vy3WE8D29hAW9mhurMLN6sXVdnZ/Dm5/FbxLG3tIS/sGBn+r0IkkwSaWsj\n0pbFaWsn2teHs3cPkbb2IK2NSLaNSHsbTrCOZG26k07fkC4TtxoqsLcZr1oJBHBDFJdawg3BXGik\n5VesMA5Es7+BLiA3GiOWShFLJomn0sSSKdp6+4knk8RSaeKpFLGkXeKpFNFEkkgw/XpY/DpOI14T\nx7Vwa7l6+UgtPVRmAwLTGIPvVamWK1TLJbxKhWqlTLXcWLxKuZEWrL1ymWql0pxWadmmXKZcyJNf\nmKdaqdTT/GoVL1h8r7oZf/OahMW7ExLtTsSGHde1Yj3i4riRenkn4tZFvBOppdfKrZFXj7v1l4Pw\nviOuG7xsRJt6C9zghcP2COiYz8oVsnQB3v8gTL1g4713wqt+yrp+DBxQf2rlmlIbtcIK5Rm82dnQ\netYK6dnQem5u7Y/wHIdIWxtOe5sVxdks7sBAk2iOtGVtmbpQtvFINms/nlNualRgXyHGGCrFgrUK\n1wRxPk+psFa4ZjFeoZQvBGu7XbVy6TEWI65rBXAyRSyVIp5M0dbbVxfCDVGcDsRyI27zksRTKSLu\njTftuIgEluAo8VTqmh+/JvD9qheI7ooV3nURXsH3vCCt0hDmLXmr8r1qc9lKBc+r4nseftWuW+OV\nYoFSEPc8z9YrHK9W7TZVb0teDMJiu94DUEuLRRuuOfX8Wl5DsIddeiLRaL2MG4Tt9rVto/Weh0g0\niuNot+UNydIEpA/A/e+zorpz13bXSLnJMOUy1bm5VVbmJmtzaL3ex3lONoubyxHp6iK2ayfJAweI\ndOVwc124XTkitXVXF5H2dnWlUC6KCux1KBfyTJ85xdSpk0ydPsns2dMUlpdCrhUFjLmEO4UIsYS1\nFteEcKqtjY7+gSaxXLcep1LEk6FwIJDd6I0njG8WwgL/RvoXjDEY328S3DVh71ebxbtXrQQ9A7Z3\noGb9txb9IK0SpAU9Al69d6Bh+S+uLDd6DkJlvEoZr3r1gt+JRIiExHdNiDcJ/0Ck14V7LNq0TeMF\nIL7+PZdIavfrZtJ/F7zr/213LZQbCOP7a7plNK1n5+pWZn9xcc39SCxGpKsrEM054nv21AVzpCuH\n29VFJNdY38of5Cmbzy0vsH3fY378AlOnTjJ95qRdnz7BwuREvUwsmaRreCcdfQOhh3Kqya2iJobj\nIWEcSyT0Qa1sCyJiXXsiEbgOnhnG96lWK3hhwR4S6E0Cv1JpiPNqTaRX6uFqkF9zDapt61XKFIqF\nhuhv2Y93CZ/IMLGa21Sy+WW3cX8n17znw2FXH9YW55Z/zNzyrOmWMTvbZFVurGfxZmfXd8vo7Kxb\nmRN33E6kM4fb3dWwLoeszE46ra5tyrZxS7V8+cUFpk+fDKzSJ5g6dYKZs6frD14RIdPfS2Z4gO6X\n3U1ioJt4XxemLY6HRzaWJZfIkUvk6Ih34OqDQ1E2hDgO0VicaCy+bXUwxli3nEqZSrHY7N61xrcP\n4XBxaZGFyQn77UM+T7V86fFfI67bePkOvZQrys2Ct7xCdWKc6sQE1ampK3PLyGTqVuXoyDDJe+9V\ntwylCWNMYGRpuF6GXSKbXDW9ZjdMv+pZ98ymbULumV5zfmO72n4ub4jDMDecQpwuTPP+r76fsldu\nLH5zuFIuITMFojMlYnMVknMe6XlDvNh4ky3EPOayZWZ3VJjLlpnLVljIVPAiJ20BHzgXLGsgCB3x\nDiu4k7m68F5zSebIRrP6Jq0o24iIBK4lUeKpNBmufFptr1pd9RFy0wfJwTcY5Zb0hcnxTTwjRdka\njO/jzc5SmZigOjFJdWLchscnqE5OUJmYpDo+bmfha0GiUSLd3Wu7ZeRyzaI5l8OJb99Lt3JlGN9v\nGWyg1BSutMSb8kpr5FVC4Xp+qNwGvlW7GuoDALgRO9pXy4ADV4qYqxgCbTtIjibNnvfuIepEiTtx\n2stxOpdidC5GaVt0SM9DctEgwWmZiFDNxfG7k9CTwelpI9rXQawtQ8yJEYuEFmd1OB6JE41EccVl\nqbzEbHF21TJTmKmHF8tr+4K5jksukaMr0bVKfNfCtbzORCcJV4erUpSbERH5ijHm4HbX41px8OBB\n89RTT213NZQAUy5TmZyiOjlBdXzciuWJCSoT41ZMj49TmZqCVpeqSAS3pwe3r5doXz9uXx/R/j7c\n3mDd00Oku1vdMq4jfN+jUixRKRYoF4tUSkUqxQKVIFwuFut51Za4Lbs6Xi2Vrkrw1j6Sd4MezcYc\nFq3hlng0GhoqtzYSltuyjuC0DIfbtF5HQF/qer3SNvuGs2CPRnfw69UfYfr0SaZPn6SUb7xBt/X0\n0r1nJz0jo/Ts3EX38C46Bwav6g3kcql4FeZKc8wV55gpBsK7sFqUn1w8yUxhhqJXXHM/KTdVF+Dd\niW4GMgP0p/rpTzeWnmQPER1ZQVEURaHZZaNSszaPB8J5YoLKxATezMyq7SSRINrXh9vXR/Lg/bT1\n9eH29Vsx3d+P29uH292l7hlXgO97DfeGsJtDtVr/TsSvVqkGabXvTbxwOLRdw82tRLlYoFIqrSma\nK8XiZQlhEYdoIk40kSSWSBCNJ4km4iSybbR19xJNJHDjCaLxhviNxmK48RYxHG2Eo6vyYrfUd2k3\nnMBemp7ihcf/lu6RUW57xavpHtlFz8guukd2Er8O/BujkSi9qV56U70bKp+v5C9pFT+1eIovj3+Z\nlUpzd1xEIvSkeuhP9TOQHqA/3U9fuo/+dCPeGe9Ua4KiKMoNjjEGb36eyrnzVM6fC9bNi7+wsGq7\nSHs7br8Vy4k772xYngNBHe3rw2lr0+dEQLVcpri8FCzLFFZsuLS8THFlmeLyEoVlu64UCi3CuCaC\nK3gVG97syduciEskGrUiOJEgGk8QTSSJp9Nkcl02LZEkGo8TSyTrZWK19LXiiYQVv3oNbCo3nMDu\nHtnJj/3+n940F0IqmiIVTbEju+OSZZfKS4yvjHNh5QLjK+OMr4wzkZ/gwsoFvj7zdT57+rNU/OZu\nvXgkTl+qj4H0QF181wV4YBHPxDJbdXqKoijKBjC+T3Vqisq5tcVz5fx5TKHQtI2TShEdGsQdHCR5\n70uIDg4S7R8IWZ57cRK3nrthbZ6K4vIyhZBYLi4v1UVyIx4KLy9f9ANmcRwSmWywZIilUiQD14Xa\ncK6RqFufEbg2m28jXguvTnNcF9eNNsLRaH1GYBu2ZW8W7XMrcMMJ7Ih7684ol41lycay7O3cu2a+\nb3xmi7NMrExYAZ4f58LyBcbzVow/ceEJpgvT+C3jd2eimSbXk/5UPwOZAQbSA4y1j9GVvPKPwRRF\nUZTA93l83IrltQT0+Pgqv+dIRwfRwUHiY6NkXvkKooNWTEeDJdLRcVM8D40xVIPRfaqlkvX3ra+L\n1o+4FPILDvKqQdyK5mYBfbHRH9xojEQmUxfLHX39JHbvtfF0pklEh+OxZPKm+L2Va8MNJ7CV9XHE\noTvZTXeymzu771yzTNWvMpWfWiW+a8vz088zV5pr2qYz3smezj3s6bDL3s697O7YTVus7VqclqIo\nynWPMYbq5BTlE8cpnzzZLKLPnaM6NQXhQQVEcHt6iA4NkbznHtre+EaiQw3xHB0YwElvv9vjehhj\nKC4vsTI3y/L8HIWlxUAgh8Rx7eO4i8ZLVEulS0/c1oIbj1v3iHiiLoS7R3Y1CedaOJluhOOZzLYO\nF6rcOqjAvsVwHddapzMDHOg9sGaZYrXIRH6Cc0vnOLZwjKPzRzk6d5RPHP0E+Wq+Xq4v1WeFd/se\n9nTuYW/HXkbbR0lFr/2U5oqiKNcCU6lQPn2a0vHjlI+foHz8eBA+3jxsXTRKtL+f6OAg6Ve8oiGc\nayK6vx+5DicjMr5PYWmR5bnZQDzPsjI3V4+vzM+yMj/HytzsRWdoFccJ/IPth3E1MRxLpkh3dNbj\n0YTNc1vi0Xh4u3jD3ziewI3dWh/LKTcmKrCVVSTcBDvbdrKzbSffNPRN9XTf+IyvjHN0/ihH5o5Y\n4T1/lCcvPEnZt18rC8KO7I4ma/eejj3sattFNHIjTTauKMqtjLe4SPnECUrHjlM+cZxSIKbLZ85A\nSFi6/f3Ex0Zp/7ZvIzY2Snz3bmK7duH29FxXo274nkd+Yd4K5bBonp8NxPMcK/Oz5Bfm13SvSKQz\npDtzpDs62XHbnUE4R7qzk0xnjmS2vf7BXDSeUH9h5ZZHBbayYRxxGMwMMpgZ5KEdD9XTq36VM0tn\n6pbuI/NHODZ/jM+f/TyesQ21Ky4723bWXU32duxlT+cedmR26FCDiqJsC8b3qY6P18Vz6cRxysfs\n2puabhSMRontHCG+dy/ZN3wL8bExYqNjxEZHiWS2143Dq1YD4TxjhfJsTTTPhcTzLPnFhWYXlYBk\nto1MZ450Z47u4Z11wVwT0JnOTtIdOdzr0NquKNczKrCVq8Z1XEbbRxltH+X1O19fTy97ZU4snKhb\nuo/OHeXr01/nkZOP1MvEI3HG2sfqft018d2f7lfrh6Iom4JfKlE+dSrkzhEI6pMnMfmG25vT1kZ8\nbIzMqx4iPjZKbGw38bFRojt2IO61fVz6vkdhcZHl2ZmGu8ZcKDxr42sJZxGHVHt7IJBz9I/tWWVx\nTnfkSHd0EHG1Z1FRtgIV2MqWEYvE2J/bz/7c/qb0fCXPsfljDeE9f5Qnzj/BJ499sl4mE82wr3Mf\nezv3sq9zH/s697GnY48OKagoykWpzs1ReOarFJ55htLhw5ROnKBy9iyExiOODg4S272bzpcetJbo\nsVHiY2NEurq2/MXe+D6F5SWWZ2cC0WyFcj08O8vK3AwrC/Orx1AWIdXWTqazi3RnJ31ju0l3dpHp\nzJHJ5eqCOtXecU0nWFMUZTUqsJVrTiqa4u6eu7m75+6m9IXSQpN/9+G5w/zV8b/iTyp/Ui8zlBlq\nEt17O/cykh3BdfRSVpRbDWMM5RMnKDz9NPlnnqHw9DOUT5ywmdEo8dFREnfeQftb3kJs95h17di1\nCyeZ3Jr6+D6L05PMjV8IrMxrWZ/n8L3VHwcmAleNTGeO7pGdZALhnM7lgvQuUu0dRK6xJV1RlCtD\n71TluqE93s79ffdzf9/99TRjDBdWLnB47jBH5o5weO4wh+cO8/mzn6+P5x2PxNndsdsK7o697MtZ\n8Z1L5LbrVBRF2QL8YpHi175G/ulnKDxjFy+YvTDS0UHywAHav/3bSd13gMRdd23ZJCvVcpm58fPM\nnjvDzNkzzJ4/y+y5M8ydP7dqeup4Oh1YnHMM33F34N/cZX2bayK6o1N9nBXlJkMFtnJdIyL1Dysf\nHn64nl7yShyfP14X3EfmjvD42cf5+NGP18t0J7ut4O7cx76cFd9jHWPEIzoGqqLcCFQmJykEYjr/\nzDMUDx2qj+ARGxsj87rXkrrvPpIHDhAbHd10947i8jKz588wc+4Ms+esiJ49d5aFyYnGuM0itPf0\nkhvcwchdLyE3tIPOgSGyuW7SnXY4OkVRbj1UYCs3JPFInNu7buf2rtub0mcKMxyZP8Lh2UB4zx/h\nYy9+jJJnp7+NSIRdbbtW+XfrR5WKsr0Yz6N05Aj5p5+2PtRPP03l3DkAJB4ncfdddP3gD5K87wDJ\ne+/F7ezcnOMaw9LMtBXPgSW6JqjzC/P1cpFolM6BIXpHd3PbKx+ma2gHuaFhOgcGVUQrirIKFdjK\nTUVXsouuZBcPDjxYT6v6VU4vnW5yM3lu+jk+ffLT9TLZqJ2Cfm+nnSxnrH2M0fZR+lJ9KrwVZQvw\nllcoPPvVuoW68Oyz9YlaIj3dpA7cR+f3fS+pAwdI3H77VU/K4lUrzI+PNwT0+YZFulIq1svF02ly\nQ8OM3fdScoNWRHcNDdPW24ujQ4oqirJBVGArNz2u4zLWPsZY+xhv3PXGevpyebn+MWVNfH/q+KdY\nqizVy6TcFLvad9UF92j7KKNto4y0jRCLqM+komwEYwyVc+cpPPO0dfd42o7wge+DCPH9+2l767fW\n3T2iQ0NX/GJrfJ+58fNMHDvC9JlTzJ4/y8y5syxMXGiaQCXb1UNuaAd3veb1dA0N18V0qr1DX6oV\nRblqxKwx8Pz1zMGDB81TTz213dVQblKMMcwUZzixcIITCyc4vnC8Hr6wcqFeLiIRdmR3MNo22hDe\nwdIeb9/GM1Cud0TkK8aYg9tdj2vFPX195i/27ac6NQWAk0qRvPclJA9YMZ289yVEMlc2/KYxhqXp\nKcaPHWb82BEmjh9h4vgxSnlrCXciETr6B62ADlw6rJDeQSyxNSOJKIpyc3GlbbZasBUlhIjQneym\nO9nNS/tf2pSXr+Q5uXiyLrhrAvwL579Axa/Uy+USuSY3k9oykB7AEedan5KibCumUCD1wAMk7ztA\n6sAB4vv2XfEU4ivzc4GYPsrEscOMHz9KYdGOIuJEXHp2jnLbKx6ib/de+sf2khsa1mHtFEXZFrTl\nUZQNkoqmuKPrDu7ouqMp3fM9zi+f58TiCY7PH+fEohXfj556lIXSQr1cIpJgV/uuVVbvnW07Sbj6\nkZRycxLft4+hX/svl71dYXmJieNHmTh2xIrq40dZnrHTl4s4dO2wftL9u/fRP7aH7p2juFGdlVBR\nlOuDLRXYIvJG4DeBCPC7xpj3rVHmYeA3gCgwbYx59VbWSVE2m4gTYbhtmOG2YR7a8VBT3mxxdpW7\nyUUs7r8AACAASURBVHPTz/HXJ/8ag3XPEuxQhLvadjHSNsJIdqS+HsoOEXVUNCjXhu1qs8vFApPH\nj9WF9MSxI8xPNFyyOgcG2XHbnfTv3kvf7r307dpNdIvGuFYURdkMtkxgi0gEeD/weuAs8KSIfNIY\ncyhUpgP4APBGY8xpEendqvooynaQS+TIJXJNk+cAFKtFTi2eahLfpxZP8dWpr7JSWamXi0iEwcxg\nk+geaRthZ9tOBjODKr6VTeNatdnVcpmpUycYP34ksE4fYebcGQi+B8p299A/tpe7XvMtVlCP7iFx\nhT7aiqIo28VWWrBfBhw1xhwHEJGPAW8DDoXKvBP4P8aY0wDGmMktrI+iXDck3AT7c/vZn9vflG6M\nYbY4y5mlM5xaPMWpxVP18KXE9862nQxnh1V8K1fKlrTZ1XKJ5/7mESumjx9h+vSp+lThqfYO+nfv\nZd+Dr7RiemwP6Y7NGd9aURRlO9lKgT0EnAnFzwIPtJTZB0RF5HNAFvhNY8wftu5IRN4NvBtgZGRk\nSyqrKNcDIlIfy/ve3nub8lrF9+ml05xePK3iW9kstqTN3tHZzmc++FvE02n6xvZy8C3fRv/uffTt\n3ku2q1uHxFMU5aZkuz9ydIH7gdcCSeBLIvKEMeZwuJAx5oPAB8EO03fNa6ko1wFXIr5PL53m2WPP\nslxZrpeti+/A5WQ4O8xAeoD+dD/96X5yiZyOdqKsx2W32Xfdtt/809/4n3T0D6qYVhTllmErBfY5\nYDgU3xGkhTkLzBhjVoAVEfk88BLgMIqibJhLie+50lzd2t0kviebxTdA1InSl+qrC+6w+O5L9TGQ\nGSAbzapYuvnYkjY7kcnSOTC02XVVFEW5rtlKgf0ksFdERrGN9Hdh/ffCfAL4bRFxgRi2O/LXt7BO\ninLLISL1jy3XEt8LpQXG8+OMr4xzYeUC4yvj9eXpiaeZzE9SNdWm7VJuqkmA96X76E81C3IdevCG\nQ9tsRVGUTWLLBLYxpioiPwY8gh3y6feMMc+LyHuC/N8xxrwgIn8NPAf42GGhvr5VdVIUpRkRoSPR\nQUeig9tyt61ZxvM9Zoozq8R3bTk8d5jpwvSq7TriHXURXhPfYQHek+pRX/DrCG2zFUVRNg+dKl1R\nlKum7JWZyE/URfdEfoILyxfqlvHxlXEWy4tN2zji0JXooi/VR2+ql95UL33pPvpSfU1pqWhqm85q\na7jVpkrXNvvGwfcNftXH9wyeF6yDuF81+L6PVzVBvLmc8W8sLbEZiAhORHBcwYk4RCJ2beNCJOLY\ntWvXtmwQdkTd7G4QdKp0RVG2jVgk9v+3d+/xUZZ3/v9f18wkMzkTcjAQAkkwIIdAkCh85Vipitpq\n8UDVPlp01cqu5bfVn7V2H/XUb3/d1aUe1mOxy6F0tVTxwNd2bWW3/cqqVUQCiqgIhpMoJBByPkzm\n+v0xkyFHEmCGmcy8n49HHjNzzz33fV0ZeOcz11z3fVOQVkBBWkGf6zS2NR4b+W70T0c52HiQrxq+\nYk/dHjZ+tZG61roer0tLTOtSdJ+RErhNPlaMZ7gz9MdKBgVfu79Ibff6/D9tvuB9b5sPn9dHe5sN\nPu5tvXav7fq423O+douve4Hc7XF7x7LA+u3tPoi/GjmiOgpuZ0fx7XTgDBTrHQX5seI8sF7gcddi\nvtM2gs/3Utz3tv3O23AdW6/zazu21bGew6GsHQgV2CJyWiQnJFM8pJjiIcV9rtPY1sjBxoP+wrvx\nK/9Pw1fBZR3TUWy3SsDtdAdHvHOTc8lLzutRjGcnZeNyKPJk4NrbfbQ2eWltag/cemlt9t+2NLUH\n7/uXtx97vrm9jwI4dCO9HQWQ0+XAmeAvjpwuR6eCzb8s0eP0F12OTsVWjyKs76Ku10Krl20YhyHe\nPuP6fB0fZo4z8h98fOx+x4eszuv7guv3XOZ/7KOtpfM2+v7w5GsP7yclY+h1pL7zv72e/666FvDO\nXv4NOTqt3+PfZi/7OqEPEoHnT+dAjP7aiEjUSE5IpjCjkMKMwj7XafO1Ud1U3aX47lyMf3DoA9Y3\nrqfN19bldU6cZCfmkJc0jBx3LrmeM8h2Z5OVmENWYjaZiZkMScgk0biPjfJ1+oN17I9izz9wPf8g\ndh4d9AUe+5+X08Pb1k5zfdeiuKMIbum8rHNxHLjfErjf3tb/++VwGdxJLhI9LhKTXCQmOUkb6sGV\nECh+XQZngjNYADuDyzvdT+heLPt/OrbhcBlcnbfhcmA0iih9sNYGpvt0LeZ7fosxsNzqXsh3nTrk\no73z1KJePkh4W3342r3+7fXYd9d9hXuqkcPR/QNit28CXKbHh9GTpQJbRELG1+7/Wtvb6sPb5h/F\n87b68La2+5e3+e+3tx37+rvr3M6uX1kHA7zPkaChJHozyW8fw7COkA4EuNfbfmx+aLsF27MgaQUO\nAAdoBBrpeVa6getv9K9jmZwaay0tjV4aalqor2mh4UjHbTP1Na2B5c20NHj73Vaixxkoil0kepx4\nUhNIz0kKFsvuJCcJHpe/gA6sk9ipmHYnuXAm6D2V6GKMv3B0OsF/vPLgYX1d87/fbwLau32Q8HX6\nG3ISAyBd/9b48Db1nyN9UYEtEges9Y8itLX4v9Zua26nrcU/WtfW0o631Ud7W3uX4tjb5qP9OMVx\ncHnrsXV9pzj6YDpGF3r52tDZy9eMrkQn7uTuBW0fBxz1Uvh6aaO+vZY6bx213qPUtNVQ03aYI62H\nOdxWTXVLFYfbDtNu2vEZLz7Tjs+043Q6GJoylJzUbLJTs8hLOYPclK4HavY5JeUnp/Qrimk+n6Xx\nqL9I7iiUG2paqD/S0uXW28vIclJ6IqlD3KRleRg2OoOUIW48qQk9i+OOH7dTo8AiUcY4DE6Hia4P\nrned3MsGXGAbY2YCJdbaFcaYHCDVWvv5ye1WRI7HWou3zUdbc+8FcXB5S3unZR3Pd6wfeC6w3omc\nMMgYcCY6cSX4v6Z2JTpxJjhISHTgTHCSnOwKPOfEmXhsnY5lrkT/V92uxMDjjuc7lnes08s8zmjT\nMSXly4Yvg9NROg7O/KrxKz6o3sr6vV/1mJLScZaUjjngHcX36RJtme1tbfePNHcqlIOj0IFljbWt\nPb4idjgNKUPcpA5xkzMyjcLJ2aQOcQeXpWS6Sclw4zyFr3JFRKy11LXVUd1UTVVTVfD2ZA2owDbG\n3AuUA2OBFUAC8FtgxknvWSQGWWuDBXBLl4Oi2jsdHOWlramdls5zQJu8J10QOxMcJHqcJLj9X2cn\nuv1fdadlJfmXB55L9LgCt04S3C7/8sBzHQVvRyF8ug8GiWYJjoTgObz7Yq2lpqUmWHx3FOMdBfme\nuj1s/HIjdW09z5ISDtGU2YcPNPDr//eNXqdsJHicwWK54KxMUjI7imZPcHlSakJUfvASkcGhydsU\nLJg7iuaq5ir/bVMVh5sOB++3+lpDtt+BjmAvAKYA7wNYa78wxqSFrBUiUcDns7Q2ersVxl0PjGpr\nDpw9oMtBUl0fD6QwTvA4u3x17UnpWhB3FMPBgtjjOlYoB4rjjqJa83ojzxhDpieTTE9mnxfsAf9Z\nUlKuTzkdTYqazHa6HJw59Yxjo86Zx0afE5M0S1FETlxbexvVzZ0K5sBPdXPX0efq5moa2hp6vN7g\nz+yspCyyPdmMSh9FdlI2WUlZ/mVJ2WR7/I+HXj/0pNo40HRrtdZaY4wFMMaclr8QIifD57O0NLbR\n0uCluaGN5oY2WhraaG7w0tzYRkt9G82N3sCywPONXloa+z+YoeOMAccOfHKSnu3pcqBUx0FQmvsp\n3Z3Gi+ZETWZn5CQx97qxkdq9iAwiDW0NHGo8xKGmQz1uO48+H2052uvr0xLT/MVxUjYTsiZ0LZiT\nssny+O9nejLDftrWgW7998aYXwFDjDE3A38HPBO+ZomEqVA24E524UlOwJ2SgCc1gSFnJPvvJ7tw\npyR0OuWWzhggg5YyW0SiRm+F88HGg8HHVU1VHGw8SKO3scdrPU4P2UnZ5CTnUJRRRHleeY+COTsp\nm6FJQ3E73RHoXe8GVGBba5caYy4AavHP6bvHWvt6WFsmMand66OprpXG2lYajwZugz8tweUdxXKf\nDLiT/AWxJ1AoZ+Qm40k9Vih7Aj/uFFfwfmKSS1ehkpinzBaR0+FUC+ec5BxyknIYO3QsM/NnBh/n\nJOeQm5RLTnIOqQmpg/KYoH4LbGOME1hvrf0aoICWHnw+S3N927ECudfi2V9A93VuWneyi+T0RJLT\nE8kZlUZSaiKelF4K5WR/Ma1CWaR3ymwROVWhGHHOTc7ts3DOTs4mLSFtUBbOA9VvgW2tbTfG+Iwx\nGdba3ie9SMzpuJhD5+K4qTZQRHcUz3X+QrqprrXXA/tciY5A0ewmMy+Z/DFDgkV0x/LkjESS0hJw\nJQyuk+GLRCtltoj0pbGtsUuh3HF7sOlgv4Wz2+kmJymH3ORcxmSOidvCeaAGOge7HvjAGPM6EDwc\n01r7/4SlVRJWnS/m0P1CDv1dzMHhNMECOTXTQ+7INJIz3MFlSZ0K6ESPzhAgEiHKbJE4EurCuWME\nunMBrcL5xAy0Anox8CNRrreLOXR/3OvFHFyGlAz/KbRyRqVRNDmblCH+Eebk9GMFtDvZpf9gItFP\nmS0SA3zWx+Hmw3zZ8GWXn4NNB6lqqvIX0Sqco9JAD3JcZYxJBMYEFn1irW073msktDqmbHS9bHBg\n9LmmlYaaZuprep/jnOhxBs8/O3RYJqmZHlI6XwlNF3MQiSnKbJHo13HlwO7F85cNX3Kg4QBfNnzJ\nV409rxKrwnlwGOiVHOcCq4BKwAAFxphF1to3wte0+GKtpbG2lbrqZmqrmqitaqa22n9bf7i5zykb\nSemJpA5xk5aVxLDRQ45dCa3TBR00VUMkviizRSKv2dvsL5gbv+RA/QG+bPySrxq+6lJAdx95dhon\nucm55KXkUZpTygUpF5CX7L+S7LCUYeSl5DHEPUSF8yAw0Mrrl8CF1tpPAIwxY4DngKnhalgsam5o\n67WArqtuora6mfZuBXRSWgLp2UnkjEyjcHL2scJ5iJuUTDcpGW6cLp2XWUR6UGaLhJHX5+Vg48Fj\no869FNFHWo70eF2WJ4u8lDyKMoo4b/h55KXkcUbKGeQl+wvo7KRsnA4d9B8LBlpgJ3QENYC19lNj\nTEKY2jRotbW2U1vVRF1VM7XV/gK6rlMh3drUdfpGYpKL9GwPmXkpjJyYRXpWEunZHtKzkkjL9pCQ\nqP9kInJSlNkip8BaS1VTFfvr97Ovfh/76/azv/7Yz5cNX9Ju27u8Ji0xjbyUPPKS85iYPTE44tyx\n7IyUM0h0JkaoR3K6DbTAfs8Y82vgt4HH3wHeC0+Told7u4/6w83+0ecq/6hzXeC2tqqJprqu86Sc\nCQ7SszykZyeRV5xxrIDOTiIty4MnRX/vRCQslNkix2Gtpba1tkfx3PH4QMMBWtpburwmOymb/NR8\nJudM5pKiSxieOrxLEZ2SkBKh3kg0GmiB/ffArUDHKZ42AE+GpUVRwPostdVNVO2rp2pvPVX76qne\nX0/94eYu53s2DkPaUP/858JJ2T0K6OT0RM2TEpFIiKvMFulNY1tjl1Hn/fX7uxTT9W31XdZPT0wn\nPzWfM4ecyZwRc8hPyyc/NZ8RqSMYnjocj8sToZ7IYDTQAtsFPGqtfQiCVwqLngu+nwJvazvVXzRQ\nva+eqr11VO33F9Rtzf6vfoyBIWckc0ZROmOn5ZGe7SEtK4n0LA+pmW4cTs2BFpGoE7OZLdLB6/Ny\noP4Ae+v39iie99fv53Dz4S7re5we8lPzyU/L5+wzzg4Wz/lp+QxPHU56YnqEeiKxaKAF9n8BX8d/\n8QKAJODPwHnhaFS4NNa2+ovoffWB0ek6ar5qDI5KJ3icZOencta0PLJGpJJdkMbQ4SmaCy1h09bW\nxr59+2hubo50UyTEPB4PI0aMICEhIlPBYiKzRay1VDdXU3m0kt21u6msraSy1n9/b91evL5jxza5\njIthqcMYnjqcrxV8zV9MBwrq/NR8sjxZp/ytsjI7doU6swdaYHustcHvUqy19caY5JC0IAx87T5q\nDjZRta+Oqr31VO+r59C+eppqW4PrpA51kz0ijdFn55I9IpXsglTSs5J0Lmg5rfbt20daWhqFhYWa\nThRDrLVUV1ezb98+ioqKItGEQZXZIo1tjeyu3c3u2t18Xvu5v5gOFNWdp3IkOBIYlT6K4oxivlbw\nNQrTCxmRNoIRqSPITc4N+xk4lNmxKRyZPdACu8EYc7a19n0AY0w50BSSFpyi1mavf3pHxxSPffVU\nf9EQPOWdw2kYOjyFUROGkj0ijewRqWSNSNUBhhIVmpubFdQxyBhDVlYWhw4dilQTojazJX55fV6+\nqP/CPwrdbUT6YOPBLusOSxnGqPRRfKP4GxRmFFKYXsio9FEMSxkW0dPYKbNjUzgye6AF9g+B540x\nXwQeDwO+HbJWnICWJi8b//B5cJpH7aFjfzM8KQlkjUhl4px8ckakkjUijcy8ZJ0rWqKagjo2Rfh9\njZrMlvhyIlM60hPTKcwoZPqw6cECelT6KEamjyTJlRTBXhyfMjs2hfp9PW6BbYw5B9hrrd1ojDkL\nuAW4AngN+DykLRmgowebePfVz8nISSKnII1x/2sY2QWpZI9IJWWIW//wRSRuRWNmS2w62nKUPbV7\n2FO3hz21e9hd1/uUjkRHIiPTR1KcUcz5BeczKn0URRlFjEofpSsSSkzrbwT7V/gPlAH4X8A/AUuA\nMmAZcFX4mta7zLxkbn54ti7/LSLSU9Rltgxe3Yvozrc1LTXB9QyGvJQ8CtMLo25Kh0ik9FelOq21\nHee5+TawzFq7FlhrjKkIb9N6l+B2qrgWiSHt7e04nfoDHCJRl9kS3Y62HGVv3V521+4eUBE9Mm0k\nF4y6gFHpoyhIK2BU+ihGpI3A7dRZIOOJcrt//RbYxhiXtdYLzAO+fwKvFZEoV1lZycUXX8zMmTN5\n6623yM/P55VXXuGTTz5h8eLFNDY2Mnr0aJYvX05mZiZz585l6dKllJeXU1VVRXl5OZWVlaxcuZJ1\n69bR2NjIzp07WbBgAQ8++GCf+01NTeWWW25h/fr1PPHEE9TW1nL77beTkpLCjBkz2LVrF6+++upp\n/E3EDGW29KAiOrYotweH/gL3OeD/GmOq8B+BvgHAGHMmcDTMbROJK/f/n2189EVtSLc5fng6935z\nwnHX2bFjB8899xzPPPMMCxcuZO3atTz44IM89thjzJkzh3vuuYf777+fRx555LjbqaioYPPmzbjd\nbsaOHcuSJUsoKCjodd2GhgamTZvGL3/5S5qbmykpKeGNN96gqKiIa6+99qT7K8rseNXkbWLX0V1U\nHq08VkCriA6rSGU2KLcHg+MW2Nba/88Y81/4j0D/s7XBC4U78M/rE5FBrqioiLKyMgCmTp3Kzp07\nqampYc6cOQAsWrSIq6++ut/tzJs3j4yMDADGjx/P7t27+wxqp9PJlVdeCcDHH39McXFx8Nyj1157\nLcuWLTvlfsUjZXbs81kf++v3s+PIDj498imfHvmUHUd2sKduDz7rPz1t9yJ6ZNpIRqaPVBEdQ5Tb\n0a/frwyttX/rZdmn4WmOSPwayKhFOLjdx/7YOp1Oampq+lzX5XLh8/n/iHe/kln37Xi9Xvri8Xg0\nfy9MlNmxo7a1NlhId75t9DYC/kK6IK2AkswSLi66mJLMEoozilVEnyaRymxQbg8GmpMnIl1kZGSQ\nmZnJhg0bmDVrFqtXrw6OihQWFrJp0ybOPfdcXnjhhZDsb+zYsezatYvKykoKCwtZs2ZNSLYrMlh4\nfV521+4Ojkh3FNIHGg4E10lPTGdM5hi+dea3KMksYUzmGM4ccibJCbpApyi3o5EKbBHpYdWqVcGD\nZYqLi1mxYgUAd9xxBwsXLmTZsmVceumlIdlXUlISTz75JPPnzyclJYVzzjknJNsViTYdF2H59HCg\niK7xj0rvrNlJm68NAJdxUTSkiCm5U/h25rcZkzmGkswSzkg+Q+eMluNSbkcXc2yKXhg2bsx84FHA\nCfzaWvsvfax3DvA2cI219rgfr8rLy+17770X8raKRML27dsZN25cpJsRcfX19aSmpmKt5dZbb6Wk\npITbbrst0s06Zb29v8aYTdba8gg16biU2aHT7G1m59Gdx4rpIzvYUbODw82Hg+vkJuVSMtQ/Gl0y\nxH9bnFFMgjMhgi2X41FmHxOLuR3KzA7bCLYxxgk8AVwA7AM2GmPWWWs/6mW9B4A/h6stIhLdnnnm\nGVatWkVraytTpkzhlltuiXST4o4y++TVtdbx8eGP2Va1jY+qP2L74e1dDjr0OD2cOeRM5hbMZUzm\nmGBBPcQzJMItFzl5yu3jC+cUkXOBz6y1uwCMMb8DLgc+6rbeEmAtoO8XRGLMtGnTaGlp6bJs9erV\nlJaWdll22223DfqRjxigzB6A7sX0R4c/Ynft7uDzeSl5jBs6jvlF84PF9IjUEbqaoQwayu3QCGeB\nnQ/s7fR4HzCt8wrGmHxgAfA1jhPWxpjvE7hgwsiRI0PeUBEJj3feeSfSTZCBU2Z3M5BiekLWBC4b\nfRnjs8YzPms8Qz1DI9hikVOn3A6NSB/k+AjwY2ut73gHb1hrlwHLwD+f7zS1TUREuorZzFYxLSKh\nFM4Cez/Q+WzlIwLLOisHfhcI6mzgEmOM11r7chjbJSIiPcVNZg+kmB4/dLyKaRE5aeEssDcCJcaY\nIvwhfQ1wXecVrLVFHfeNMSuBVwdbUIuIxIiYzGwV0yISCWErsK21XmPMD4A/4T/l03Jr7TZjzOLA\n80+Ha98iInJiYiGz233t7KjZweaDm6k4WMG26m29FtPfLP4mE7InMG7oOLKSsiLYYhGJVWGdg22t\n/SPwx27Leg1pa+314WyLiIgc32DL7LrWOj449AGbD/kL6q2HtgYvI56TlENpdqmKaRGJiEgf5Cgi\nItIvay376/ez+eBmthzawuaDm9lxZAcWi8M4KBlSwjdHf5Oy3DKm5E5heMpwXflQRCJGBbaIiESd\ntvY2th/e3qWgrmqqAiAlIYVJ2ZP4+uSvMzl3MpOyJ5GamBrhFouIHKMCWyRa/Odd8OUHod1mXilc\n3OvVrgGorKzk4osvZubMmbz11lvk5+fzyiuv8Mknn7B48WIaGxsZPXo0y5cvJzMzk7lz57J06VLK\ny8upqqqivLycyspKVq5cybp162hsbGTnzp0sWLCABx98sM/9pqam8vd///f88Y9/ZNiwYfziF7/g\nzjvvZM+ePTzyyCNcdtlltLe38+Mf/5jXXnsNh8PBzTffzJIlS0L7+5GocaT5SLCQ7pg/3dLuv9hF\nfmo+04ZNY0rOFMpyyzhzyJm6cItEXgQyG5Tbg4Uj0g0QkcjasWMHt956K9u2bWPIkCGsXbuW733v\nezzwwANs3bqV0tJS7r///n63U1FRwZo1a/jggw9Ys2YNe/fu7XPdhoYGzj//fLZt20ZaWho//elP\nef3113nppZe45557AFi2bBmVlZVUVFSwdetWvvOd74SszxJZ1lp2Hd3Fizte5J437+GbL32T2Wtm\ns+S/l/Cbbb+hzdfGwrEL+eWcX/JfV/8Xr135Gv8y61/49lnfZuzQsSquJe4pt6OfRrBFokU/oxbh\nUlRURFlZGQBTp05l586d1NTUMGfOHAAWLVrE1Vdf3e925s2bR0ZGBgDjx49n9+7dFBQU9LpuYmIi\n8+fPB6C0tBS3201CQgKlpaVUVlYCsH79ehYvXozL5Y+poUN16rTBqsnbxLaqbVQcqqDiYAUVhyo4\n2nIUgAx3BmU5ZVx+5uWU5ZQxMXsiHpcnwi0WGYAIZTYotwcDFdgicc7tdgfvO51Oampq+lzX5XLh\n8/kAaG5uPu52vF5vn9tJSEgIHoDmcDiCr3U4HMd9nQw+nx/9nPOePQ+v9b+vRRlFnF9wPlNypzA5\ndzJF6UU6GFHkBCm3o5+miIhIFxkZGWRmZrJhwwYAVq9eHRwVKSwsZNOmTQC88MILYW3HBRdcwK9+\n9atgcB8+fDis+5PwMBgWTVjE4+c/zhvffoN131rHz2b8jAUlCyjOKFZxLRICyu3oowJbRHpYtWoV\nP/rRj5g0aRIVFRXB+XV33HEHTz31FFOmTKGqqiqsbbjpppsYOXIkkyZNYvLkyTz77LNh3Z+ER2FG\nIT+c+kPmFMwh05MZ6eaIxCzldnQx1tpIt+GElJeX2/feey/SzRAJie3btzNu3LhIN0PCpLf31xiz\nyVpbHqEmnXbKbIklyuzYFsrM1gi2iIiIiEgI6SBHEQmbadOm0dLS0mXZ6tWrKS0tjVCLRETkeJTb\noaECW0TC5p133ol0E0RE5AQot0NDU0REREREREJIBbaIiIiISAipwBYRERERCSEV2CIiIiIiIaQC\nW0QGhaeffprS0lLKysqYOXMmH330UfA5p9NJWVkZZWVlXHbZZcHlN954I5MnT2bSpElcddVV1NfX\nR6LpIiJxKZ5zWxeaEYkgXbRg4Gpra0lPTwdg3bp1PPnkk7z22msApKam9hrCnV9z++23k5uby113\n3XXa2qwLzSizJbYos0/MYMvtUGa2TtMnEiUeePcBPj78cUi3edbQs/jxuT/u8/nKykouvvhiZs6c\nyVtvvUV+fj6vvPIKn3zyCYsXL6axsZHRo0ezfPlyMjMzmTt3LkuXLqW8vJyqqirKy8uprKxk5cqV\nrFu3jsbGRnbu3MmCBQt48MEHe93n888/z9tvv81DDz3Eo48+yqOPPsquXbvYtWsX3/3ud3nzzTe5\n6667WLduHS6XiwsvvJClS5cGAxegoaEBY0y//e94jbWWpqamAb1GRGQgIpHZoNweLDRFRCTO7dix\ng1tvvZVt27YxZMgQ1q5dy/e+9z0eeOABtm7dSmlpKffff3+/26moqGDNmjV88MEHrFmzhr17Xbz8\nFQAAF3BJREFU9/a63qxZs9iwYQMAGzZsICsri/3797NhwwZmz55NdXU1L730Etu2bWPr1q389Kc/\nDb72iSeeYPTo0dx5553827/9W3B5c3MzZ599NtOnT+fll1/usr8bbriBvLw8Pv74Y5YsWXIyvyIR\nkaii3I5+GsEWiRL9jVqES1FREWVlZQBMnTqVnTt3UlNTw5w5cwBYtGgRV199db/bmTdvHhkZGQCM\nHz+e3bt3U1BQ0GO9vLw86uvrqaurY+/evVx33XW88cYbbNiwgSuuuIKMjAw8Hg833ngj3/jGN/jG\nN74RfO2tt97KrbfeyrPPPsvPf/5zVq1aBcDu3bvJz89n165dnH/++ZSWljJ69GgAVqxYQXt7O0uW\nLGHNmjXccMMNp/YLExEhcpkNyu3BQCPYInHO7XYH7zudTmpqavpc1+Vy4fP5AP/ow/G24/V6+9zO\neeedx4oVKxg7dmxwZOTtt99mxowZuFwu3n33Xa666ipeffVV5s+f3+P111xzTZcRj/z8fACKi4uZ\nO3cumzdv7rK+0+nkmmuuYe3atX22SURksFBuRz8V2CLSRUZGBpmZmcGvA1evXh0cFSksLGTTpk0A\nvPDCCye9j1mzZrF06VJmz57NlClT+Mtf/oLb7SYjI4P6+nqOHj3KJZdcwsMPP8yWLVsA/1eiHf7w\nhz9QUlICwJEjR2hpaQGgqqqKN998k/Hjx2Ot5bPPPgP8c/nWrVvHWWedddJtFhGJVsrt6KMpIiLS\nw6pVq4IHyxQXF7NixQoA7rjjDhYuXMiyZcu49NJLT3r7s2bNYu/evcyePRun00lBQUEwROvq6rj8\n8stpbm7GWstDDz0EwOOPP8769etJSEggMzMz+DXj9u3bueWWW3A4HPh8Pu666y7Gjx+Pz+dj0aJF\n1NbWYq1l8uTJPPXUU6f4mxERiU7K7eii0/SJRJBO+RTbdJo+ZbbEFmV2bAtlZmuKiIiIiIhICGmK\niIiEzbRp04Lz7DqsXr2a0tLSCLVIRESOR7kdGiqwRSRs3nnnnUg3QUREToByOzQ0RUREREREJIRU\nYIuIiIiIhJAKbBERERGREFKBLSIiIiISQiqwRWRQePrppyktLaWsrIyZM2fy0UcfBZ9zOp2UlZVR\nVlbGZZddFlx+4403MnnyZCZNmsRVV11FfX39Ce3vN7/5TUj7ICIST+I5t3WhGZEI6nxS+y9/8Qta\ntn8c0u27x51F3j/9U0i3GSm1tbWkp6cDsG7dOp588klee+01AFJTU3sN4c6vuf3228nNzeWuu+46\nbW3WhWaU2RJblNknZrDl9qC50IwxZr4x5hNjzGfGmB6/HWPMd4wxW40xHxhj3jLGTA5ne0Skq8rK\nSsaNG8fNN9/MhAkTuPDCC2lqaqKiooLp06czadIkFixYwJEjRwCYO3cuHcVSVVUVhYWFAKxcuZIr\nrriC+fPnU1JSwp133tnnPp9//nluv/12AB599FGKi4sB2LVrFzNmzAAIXjZ30qRJ3HHHHQDBwAVo\naGjAGNNv/zpeY62lqampz9f0tr/77ruPpUuXBvt92223UV5ezrhx49i4cSNXXHEFJSUl/PSnP+23\nHYOFMlsk+im36XN/UZXb1tqw/ABOYCdQDCQCW4Dx3dY5D8gM3L8YeKe/7U6dOtWKxIqPPvooovv/\n/PPPrdPptJs3b7bWWnv11Vfb1atX29LSUvvXv/7VWmvt3Xffbf/xH//RWmvtnDlz7MaNG6211h46\ndMiOGjXKWmvtihUrbFFRka2pqbFNTU125MiRds+ePb3u88CBA7a8vNxaa+2VV15py8vL7b59++zK\nlSvtXXfdZauqquyYMWOsz+ez1lp75MiR4Gsff/xxW1xcbEeMGGE//fTT4HKn02mnTJlip02bZl96\n6aUu+7v++uttbm6unTt3rm1oaOjRnr72d++999p//dd/Dfb7zjvvtNZa+8gjj9hhw4bZL774wjY3\nN9v8/HxbVVXVa197e3+B92yYcvdUfpTZIv2LdGZbq9y2Nny5HcrMDucI9rnAZ9baXdbaVuB3wOWd\nV7DWvmWtPRJ4+DdgRBjbIyK9KCoqoqysDICpU6eyc+dOampqmDNnDgCLFi3ijTfe6Hc78+bNIyMj\nA4/Hw/jx49m9e3ev6+Xl5VFfX09dXR179+7luuuu44033mDDhg3MmjUruI0bb7yRF198keTk5OBr\nb731Vnbu3MkDDzzAz3/+8+Dy3bt38/777/Pss8/ywx/+kJ07dwafW7FiBV988QXjxo1jzZo1Pdpz\nvP111jFHsLS0lAkTJjBs2DDcbjfFxcXs3bu339/PIKDMFhkklNvRn9vhLLDzgc6t3xdY1pcbgf/s\n7QljzPeNMe8ZY947dOhQCJsoIm63O3jf6XRSU1PT57oulwufzwdAc3Pzcbfj9Xr73M55553HihUr\nGDt2LLNmzWLDhg28/fbbzJgxA5fLxbvvvstVV13Fq6++yvz583u8/pprruHll18OPs7P90dLcXEx\nc+fOZfPmzV3WdzqdXHPNNaxduxaAiy66iLKyMm666aYB7a9z/xwOR5e+OhyO4/Z1EFFmiwwSyu3o\nz+2oOIuIMeZr+MP6x709b61dZq0tt9aW5+TknN7GicSZjIwMMjMz2bBhAwCrV68OjooUFhayadMm\nAF544YWT3sesWbNYunQps2fPZsqUKfzlL3/B7XaTkZFBfX09R48e5ZJLLuHhhx9my5YtAOzYsSP4\n+j/84Q+UlJQAcOTIEVpaWgD//MI333yT8ePHY63ls88+A/xT4datW8dZZ50FwJ/+9CcqKir49a9/\n3ef+pG/KbJHootyOvtx2hXHb+4GCTo9HBJZ1YYyZBPwauNhaWx3G9ojIAK1atYrFixfT2NhIcXEx\nK1asAOCOO+5g4cKFLFu2jEsvvfSktz9r1iz27t3L7NmzcTqdFBQUBEO0rq6Oyy+/nObmZqy1PPTQ\nQwA8/vjjrF+/noSEBDIzM1m1ahXgP+r7lltuweFw4PP5gge++Hw+Fi1aRG1tLdZaJk+ezFNPPdWj\nLX3tLw4ps0UGMeV2dAnbafqMMS7gU2Ae/pDeCFxnrd3WaZ2RwH8D37PWvjWQ7eqUTxJLejslkMSO\nwXSaPmW2SP+U2bEtlJkdthFsa63XGPMD4E/4j05fbq3dZoxZHHj+aeAeIAt4MnAaFm80/uEREYl1\nymwRkdAJ5xQRrLV/BP7YbdnTne7fBNwUzjaISORMmzYtOM+uw+rVqyktLY1Qi+R4lNkiotwOjbAW\n2CIS3955551IN0FERE6Acjs0ouIsIiIiIiIisUIFtoiIiIhICKnAFhEREREJIRXYIiIiIiIhpAJb\nRKLCPffcw/r16we8fmVlJRMnTgxji0RE5HiU233TWUREosSG339K1d76kG4zuyCVWQvHhHSb4fKz\nn/0s0k0QERmweM9sUG4fj0awReJYZWUl48aN4+abb2bChAlceOGFNDU1UVFRwfTp05k0aRILFizg\nyJEjAMydO5eOq/JVVVVRWFgIwMqVK7niiiuYP38+JSUl3HnnnX3us729neuvv56JEydSWlrKww8/\nDMD111/PCy+8AEBhYSE/+clPKCsro7y8nPfff5+LLrqI0aNH8/TTT/e6zR/96Eecc845TJo0iV/9\n6lcAvPTSS8ybNw9rLQcOHGDMmDF8+eWXIfv9iYicbsrtwUEj2CJRIlKjFjt27OC5557jmWeeYeHC\nhaxdu5YHH3yQxx57jDlz5nDPPfdw//3388gjjxx3OxUVFWzevBm3283YsWNZsmQJBQUFva63f/9+\nPvzwQwBqamp63d7IkSOpqKjgtttu4/rrr+fNN9+kubmZiRMnsnjx4i7r/vu//zsZGRls3LiRlpYW\nZsyYwYUXXsiCBQtYu3YtTzzxBK+99hr3338/eXl5J/mbEhE5JpIjzcrt6KcCWyTOFRUVUVZWBsDU\nqVPZuXMnNTU1zJkzB4BFixZx9dVX97udefPmkZGRAcD48ePZvXt3r0FdXFzMrl27WLJkCZdeeikX\nXnhhr9u77LLLACgtLaW+vp60tDTS0tJwu909wv3Pf/4zW7duDY6kHD16lB07dlBUVMRjjz3GxIkT\nmT59Otdee+0AfysiItFLuR39VGCLxDm32x2873Q6+xyZAHC5XPh8PgCam5uPux2v19vrNjIzM9my\nZQt/+tOfePrpp/n973/P8uXL+2yXw+Hosm2Hw9Fj29ZaHnvsMS666KIe29m3bx8Oh4OvvvoKn8+H\nw6GZcSIyuCm3o9/ga7GIhFVGRgaZmZls2LABgNWrVwdHRQoLC9m0aRNAcNThRFVVVeHz+bjyyiv5\n+c9/zvvvv3/Kbb7ooot46qmnaGtrA+DTTz+loaEBr9fL3/3d3/Hcc88xbtw4HnrooVPel4hItFFu\nRx+NYItID6tWrWLx4sU0NjZSXFzMihUrALjjjjtYuHAhy5Yt49JLLz2pbe/fv58bbrghOKLyz//8\nz6fc3ptuuonKykrOPvtsrLXk5OTw8ssv88tf/pJZs2Yxc+ZMJk+ezDnnnMOll17KuHHjTnmfIiLR\nRLkdXYy1NtJtOCHl5eW242hYkcFu+/btgy40ZOB6e3+NMZusteURatJpp8yWWKLMjm2hzGxNERER\nERERCSFNERGRsJk2bRotLS1dlq1evZrS0tIItUhERI5HuR0aKrBFJGzeeeedSDdBREROgHI7NDRF\nREREREQkhFRgi4iIiIiEkApsEREREZEQUoEtIiIiIhJCKrBFREREREJIZxERiRJ/WbmMg7t3hXSb\nuaOK+dr13+/z+crKSubPn8/06dN56623OOecc7jhhhu49957OXjwIP/xH//BhAkTWLJkCR9++CFt\nbW3cd999XH755VRWVvLd736XhoYGAB5//HHOO+88/vrXv3LfffeRnZ3Nhx9+yNSpU/ntb3+LMSak\nfRMRiaRIZDYotwcLFdgice6zzz7j+eefZ/ny5Zxzzjk8++yz/M///A/r1q3jF7/4BePHj+f8889n\n+fLl1NTUcO655/L1r3+d3NxcXn/9dTweDzt27ODaa6+l44p9mzdvZtu2bQwfPpwZM2bw5ptvMnPm\nzAj3VEQkNii3o58KbJEo0d+oRbgUFRUFLyAwYcIE5s2bhzGG0tJSKisr2bdvH+vWrWPp0qUANDc3\ns2fPHoYPH84PfvADKioqcDqdfPrpp8FtnnvuuYwYMQKAsrIyKisrFdQiElMildmg3B4MVGCLxDm3\n2x2873A4go8dDgderxen08natWsZO3Zsl9fdd999nHHGGWzZsgWfz4fH4+l1m06nE6/XG+ZeiIjE\nD+V29NNBjiJyXBdddBGPPfYY1lrA/zUiwNGjRxk2bBgOh4PVq1fT3t4eyWaKiEiAcjvyVGCLyHHd\nfffdtLW1MWnSJCZMmMDdd98NwD/8wz+watUqJk+ezMcff0xKSkqEWyoiIqDcjgam49PNYFFeXm47\nJuSLDHbbt29n3LhxkW6GhElv768xZpO1tjxCTTrtlNkSS5TZsS2Uma0RbBERERGREFKBLSIiIiIS\nQiqwRSJssE3TkoHR+yoSm/R/OzaF+n1VgS0SQR6Ph+rqagV2jLHWUl1d3eUUWCIy+CmzY1M4Mlvn\nwRaJoBEjRrBv3z4OHToU6aZIiHk8nuBFG0QkNiizY1eoM1sFtkgEJSQkUFRUFOlmiIjIACizZaDC\nOkXEGDPfGPOJMeYzY8xdvTxvjDH/Fnh+qzHm7HC2R0RE+qbMFhEJjbAV2MYYJ/AEcDEwHrjWGDO+\n22oXAyWBn+8DT4WrPSIi0jdltohI6IRzBPtc4DNr7S5rbSvwO+DybutcDvzG+v0NGGKMGRbGNomI\nSO+U2SIiIRLOOdj5wN5Oj/cB0wawTj5woPNKxpjv4x8tAWgxxnwY2qZGvWygKtKNOM3U5/gQj30e\nG+kG9EGZHTrx+O9afY4P8djnk8rsQXGQo7V2GbAMwBjzXjxdZhjU53ihPscHY0zMXzdcma0+xwP1\nOT6cbGaHc4rIfqCg0+MRgWUnuo6IiISfMltEJETCWWBvBEqMMUXGmETgGmBdt3XWAd8LHJk+HThq\nrT3QfUMiIhJ2ymwRkRAJ2xQRa63XGPMD4E+AE1hurd1mjFkceP5p4I/AJcBnQCNwwwA2vSxMTY5m\n6nN8UJ/jQ1T2WZkdUupzfFCf48NJ9dnocp8iIiIiIqET1gvNiIiIiIjEGxXYIiIiIiIhFLUFdjxe\nsncAff5OoK8fGGPeMsZMjkQ7Q6m/Pnda7xxjjNcYc9XpbF+oDaS/xpi5xpgKY8w2Y8z/Pd1tDLUB\n/LvOMMb8H2PMlkCfBzKvN6oZY5YbYw72df7nOM2veOyzMnuQZzYot+Mht8OS2dbaqPvBf4DNTqAY\nSAS2AOO7rXMJ8J+AAaYD70S63aehz+cBmYH7F8dDnzut99/4D7C6KtLtDvN7PAT4CBgZeJwb6Xaf\nhj7/E/BA4H4OcBhIjHTbT7Hfs4GzgQ/7eD4e8yse+6zMHsSZfQLvs3J7kOd2ODI7Wkew4/GSvf32\n2Vr7lrX2SODh3/Cfg3YwG8j7DLAEWAscPJ2NC4OB9Pc64EVr7R4Aa2089NkCacYYA6TiD2rv6W1m\naFlr38Dfj77EXX4Rh31WZg/6zAbldlzkdjgyO1oL7L4ux3ui6wwmJ9qfG/F/mhrM+u2zMSYfWAA8\ndRrbFS4DeY/HAJnGmL8aYzYZY7532loXHgPp8+PAOOAL4APgH621vtPTvIiJx/yKxz53pswenJTb\nym04ifwaFJdKl66MMV/DH9YzI92W0+AR4MfWWp//g3LMcwFTgXlAEvC2MeZv1tpPI9ussLoIqADO\nB0YDrxtjNlhrayPbLJHQUGbHPOW2cruHaC2w4/GSvQPqjzFmEvBr4GJrbfVpalu4DKTP5cDvAkGd\nDVxijPFaa18+PU0MqYH0dx9Qba1tABqMMW8Ak4HBGtQD6fMNwL9Y/0S3z4wxnwNnAe+eniZGRDzm\nVzz2WZk9uDMblNug3IaTyK9onSISj5fs7bfPxpiRwIvAd2Pkk3G/fbbWFllrC621hcALwD8M4qAe\nyL/rV4CZxhiXMSYZmAZsP83tDKWB9HkP/pEfjDFnAGOBXae1ladf3OUXcdhnZfagz2xQbiu3/U44\nv6JyBNuG75K9UWuAfb4HyAKeDIwOeK215ZFq86kaYJ9jxkD6a63dbox5DdgK+IBfW2t7PW3QYDDA\n9/h/AyuNMR/gP0L7x9baqog1OgSMMc8Bc4FsY8w+4F4gAeI6v+Kxz8rsQU65HR+5HY7M1qXSRURE\nRERCKFqniIiIiIiIDEoqsEVEREREQkgFtoiIiIhICKnAFhEREREJIRXYIiIiIiIhpAJbYpox5lvG\nGGuMOSvwuNAYc9zTJw1kHRERCT1ltsQKFdgS664F/idwKyIi0U2ZLTFBBbbELGNMKjATuBH/lai6\nP3+9MeYVY8xfjTE7jDH3dnraaYx5xhizzRjzZ2NMUuA1NxtjNhpjthhj1gau2iUiIqdImS2xRAW2\nxLLLgdcClyiuNsZM7WWdc4ErgUnA1caYjquslQBPWGsnADWBdQBetNaeY62djP9SuDeGtQciIvFD\nmS0xQwW2xLJrgd8F7v+O3r9yfN1aW22tbQJexD96AvC5tbYicH8TUBi4P9EYsyFwedjvABPC0nIR\nkfijzJaY4Yp0A0TCwRgzFDgfKDXGWMAJWOCJbqvaPh63dFrWDiQF7q8EvmWt3WKMuR6YG7pWi4jE\nJ2W2xBqNYEusugpYba0dZa0ttNYWAJ8DBd3Wu8AYMzQwX+9bwJv9bDcNOGCMScA/GiIiIqdOmS0x\nRQW2xKprgZe6LVsL/KTbsncDy7cCa6217/Wz3buBd/CH+schaKeIiCizJcYYa7t/2yISHwJfF5Zb\na38Q6baIiMjxKbNlMNEItoiIiIhICGkEW0REREQkhDSCLSIiIiISQiqwRURERERCSAW2iIiIiEgI\nqcAWEREREQkhFdgiIiIiIiH0/wOxYrCFRgC38gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2408bd28400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MyAMzCO7zmB/d14Df1Vr/bfsObIf6Bnt+S5iO6g1tr9m3IgV8AvPq5WnMk+5uT4XzXzD+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\n2URtlee/DTWeBrr1xJn3lTUGz5pNp2fdZsrgeM/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sWrXKe7FMVlYWK1asAOCBBx5g/vz5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2408c9c00b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rm8nnD+5Y1XqS42uBx4GzgSOBe0TkAWNMTzCTMeZ64HqAkxZOMXzzFXZUdNkF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2408cc2f240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NNGy31I+rMsnaIsnaArHqPNHyPOHyPOHyLE5xDqs4ByszsDJr9rUuYjHWC4kB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\nEREREfEjFdgi0iu88sor5OfnU1BQwLRp09i1a5fvNafTSUFBAQUFBVx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2408cc8e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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P8804QRAWnANbjaje+gMY32teODr9JXDmH8P6Z/K/Dw/xrmvvxXMVV7/xAp5+\natdS11gQBOGQiMA+htFaMzRVrBHQUW/0vtEcpUATw2elGqFXDXFa0yQvSk3Q1zxKb3qEznCQTHGA\neK4fFZYgj1kAUGb8nRs3j9/cODgx89a6G6u+2OLGzXa8yabPMb8bmzl/rMmK4kxVJCcy4tUVhOMB\n+7Ii930P+h80duGU58EffAhOuxTiTTVTnm9Z3cqXXnM2a9oP8yVGQRCEJUIE9jInW/R5YnCK3cPl\noRxZ9oyUh3PkcEsTrFQj9KhhetUwJ8XHeWZijNXuKCtah2j1B0gVh6sF+sAERsBmeqF1FbScBi2r\nILMKWnqr4fSKpX1BQRCE44cGLyuy5ny49FOw6eXQ3FnJWj/l+Ycu2yzfeBYE4ZhCBPYyIQw1u4az\nPHRgnG37xtmxv5+DB/aSG+uni1F61TAr1QhPcUe4LDZGrxqmIzZIws3WFqQB1QHp1dDSB5kLoWV1\nrXBuWWVepJFhEsJRQpc/1xWGEIZmlHw5HEZeJg1DkzeaRmS7nKY1OozsE4bm2tczlFneLpdxAr78\nv2TkR+G7/6fysiKdp8Kz/wa2/BF0nDwte3TK849dvoVXnb9WxlsLwgnEtPaiJkzEzldtvGkT9Nza\ngEi7os2UmXXtQ13aPBGBfTQo5c33Ge0yNdpP/4F9jA3tIzc6QDA1iJsfoU2Pc5aa4NlMkFD2+8CR\nERFauZDpQbWsgsxZVeFcFs0tvcYrLd/aXZboIED7PrpUqiyUtyvx5e1G6bOkFSNl+D66VET7PtSU\nWb8ul2O+mqLDiKHS1rCEYTXcSADPagh1jfETTlCGn4DdysysuOUVsOopM3buy1OedzTH+d5bn8pZ\na9uOcmUFYWnQYWhsehCggxACEyYIbNthPhWo/cCkNbT/Ebs/rU1p0H5Mazci6dH9InnwfUBX24t6\nu4+e3nZU2pXGzpL6Mo4XRGAfLoFvJgqICObqYuOnBiE7hM4OoaeGcPxaL3MzcBIQasWYSpP1WgnS\nHTjpDcRaV+B09kC6C5o6obnLLJlVKBmyYW7CskCdRZxyiPQagVpjSGZJi4jXmY1TpA516YtuOGIx\nlOehGq1jnk2PVeKdVArleeC5ZlZFx7GTqTl2tkVVDZsPg09PUwocB+WUZ1Qrh8vbCuU4deHqV1SU\nW/2ySrSMSl5UXRm2jjOVGc1bzme3a+q1lB7RCy9YumMvBZ2nwLsfmPU7ykU/5MM/eZBv37GTp57c\nyb+8+inKdlmoAAAgAElEQVR0peVza8LioYtFwlzOLNkcYS6LzuUIc/lIuC4tm6vuk8uii0Xwg1oh\nHAnj++gwRAf+IfMdVZSqtAPEYo3bDLsm5qEScZzmZhPnuXW23mncdkRsNKpB2+HMYPcr7cpc7H6k\n/EZp5fKjbUB9u1Kpx0ztiJq3zV5Uga2UeiHwOcyEm/+qtf5EXXor8O/AOluXT2mtv7mYdTokgQ9j\nu2D4cRh6HIYfM+HhJ8xEH/mxGXf1vWayXhsjZDjoN7OnuJ6hcAsjOsOYypBsXUFbVw8rVq5i7Zq1\nnLJuLd2tTbQv08efWmvwfcJC0fSMi0V0oWDWxSJhoWBEZrGILs4hvlhEF4qVbV0smLj6+EJhBtHq\nL74hchwrSOsMTJ04rYjUpqZZ02vFrVebJx45hldXRqzOyFXKanwMPE8eowtHzKLY7ERmVnF9YCzP\nn33nbn63a5S3PPNk3vuC0/DcxtPOCycuYT5PMD5OOD5OMD5BMD5GODFh4rL1Ani6INbZbEQc5w67\nLVGJhHFKNKVwUk0mHI8bG5yIo1wPXAfleijXBddFua4RpI5du9aZ4bk2nwOV/E5tGQ32M2Gz31za\njRoBHY13T2xH3dFi0QS2UsoFvgD8AbAHuEspdb3W+sFItrcDD2qtX6KU6gYeVkp9R2u9QHO7zsBM\nInroMRjdaadwtsSaofNkWHE6ZJ5NMdFOv9/MznyK7ZMJHhj1uGfQZWcuSREz6cGq1iSnrc6wsbeF\njT0ZntnTwsndzcSOYqOhi0WCqSljgCYmCCenCCcnCCcnCSYm68KTBJMThPXhqakF87qqeNwsiYQN\nx3DiiZp4J92Misdx4nFUzOSZZkAiorWhgfG8GcRpvKHxMemxqth1pGEXTkyWwmbf8fgQ77jmd2SL\nAV949dm86MzeIz4PYXmig6AiiIPxCcKJcYKxcYKJOtE8PlEV0hPVsC4e4hJTCpVK4USWshiOtbZO\nE8dOk82TsnHR7aamahmpJpxUUkSpcNgspgf7fGC71vpxAKXUd4HLgKix1kBGGddbGhjGfOfiyJkm\noq2Qnk1E92yGMy4zL950bkC3n8QT+TS3PzbErx8f4sEHxtk5nK0MJ22Ku5zWk+HcLS28tjfDaSsz\nbOxpobXpyGYXC4tFa3CqRsaI4bLwrQrjcHKiGp6YqIhqXSgc8jgqHsdJp3EyadzmNE4mQ2zdWpI2\n7KSbcRIJVI0QtgK4slTTnES8gZCOGzEr3lVBWO4cNZuttebrv3yCj//PQ/R1NPEfb7qQU1dmjvwM\nhKNGMDmJv38/pf37Ke0/gD84UBXKE+OEY2WBbERzODk5e4Gui5vJ4LS24GZacFta8Hp7cVtacFsy\nOC2tuC0Z3JYWnEwLbmsLTsZuNzWhkklpZ4RlxWIK7NXA7sj2HqB+IMvngeuBfUAGuELr6TOQKKXe\nDLwZYN26ddWEmUT08OMwstNMalIm1myEc52IpuNkM1uYvTH7J/L8avsQv7xzkNu3P8j+MfNR6NVt\nKZ68tpXLn7KGjb0ZTu9pYU17CseZfkPrMCScmiIYG59bL318vMYg6Xx+Wpl1PwhOc7MxLulmnHQG\nt72d+Lq1OOkMTjqNm0nXhc12JZzJ4MTlm9KCIFRYfJuN+fTo+39wPz/+/T5esGnl8TXl+XFCWCjg\nHzhAaf8BSvv34x8wIrp0YL8V1QcaCmanqQmnxYhjpyVDbNUqkqedVhXNrVVx7GZqRbNqahKBLBxX\nLPVLji8A7gWeA2wAfqGUuk1rPR7NpLX+KvBVgHNP7dV85xWzi+iVm8xMYB0bGoroKBP5Er/Z1s/t\njw1y+/ZBHjlojEZbU4ynbejknatTXNhcois3Rji2n+DgQ4TbJwjGxjkQEcVVoTxBODEx+9AKpYwR\nKve+W1pIdG/AaclEjFAG1xof401OG4OUyZjeugxlEATh6HP4NvvccyufkHlicIq3fvtuHu2f4H0v\nlCnPlwLt+/gDA8brfKDqgTbi2QjqYHh42n5uezux3l5i6/poOv8CYr09eL29Jq6nB6+72wy5EwQB\nWFyBvRdYG9leY+OivAH4hNZaA9uVUk8AG4E7Zyw1OwTj+2tFdNkbPYOIjlL0Q+7ZNcLt2wf5zcMH\n2PvoTjqnhuktjPPyZIFTVZbe/CiJkUH8600vPY9x5URRqZQRyLZH7nV3Ez9lQ20v3fbiq0LZpjU3\ni0AWBGG5sTg22/K/Dx6UKc8XGa01wfBwRDzXe6AP4Pf3QxDU7Oc0N+P19hDrXUXyjDNMuKeX2Cor\nnnt6cJLJJTorQTg2WUyBfRdwqlLqJIyRfiXw6ro8u4DnArcppVYCpwGPz1pq75nwtl8e8uDlXnpx\n3352bnucHdueYOiJ3QQHDtAxNcxTc6NcUpyatp/b2WkMyvo+mi+80PTOe42BcdvaK95lGV4hCMJx\nxuLYbODTP39YpjxfYLTWlPbuJX///eTu30r+/vvJP/AAYbb2s7AqHsfr6SHW00Pz+edXxXPEA+1m\nZPy7ICw0iyawtda+UuodwM8wn3z6htb6AaXUW236l4EPA99SSt0PKOD9WuvBOZRd20vft9/0zG0v\nPb93H+HAAMoODVSY7073xJIUOrpJnLyGzpOfSvOaVXg9vTUi2knIt1cFQTjxWCybvWNwin+5cbtM\neX6E+AMDRkhvtYJ661aCkREAVCxG4vTTaX3ZZcTXn1TjgXY7OmQYjiAsAUofYzOsbWlt1d/vWz/t\nkz1hLMZkSyf74i3s8TL0p9opdHTRc0ofT9q8gXPO20jv6u4lqrUgLB/CIKBUKOAXC5QKBYJS0UyG\nYGfaKs/AWI0zszyWZ+jS2sbbMJH9Knl1dSZHXVOWneIcqnF2xi9dU3Y4bZ+a+pSPHwa1eWYtr7Ze\nKjK5gFKOmWNAVSdPUHaSAoWqi7Nru09NXhs2ZdbldUzc2Ze89G6t9blLexUcPZK9p+pv/Oj/kynP\nD4NgfJz81q01gto/cMAkOg6JU04huWUzqS1bSG7eQvJJp6LkqephEYYBYRASBj5hEDRcdBgQ2Mli\nAt8nDM3EMIGdICYMo3YuBI21MVRtozkYZobviO0rz4aIbrxf1H7WhSuz5ML0fGibVI7HxtWWE923\nvE/1HCLlVMKN6mnLbnh+VGd2nMv5VfbXlfam/PtoO125shPAOI5jvgteCTsoZcJE4ippDcNuxUbX\nprnT9jnn0svmZbOX+iXHw8ZJJsm86tXsjbfwgJ/i1xMev5nyGIunySRjXLihk4s2dHLJqV1s6E6L\nQReOKWrEbz5PqVjALxghXCrkK6LYLxQo2TyVuGIkn033G2wHR3vWsMWgLGArRrUqbo0RtsYzKnKt\nEVblL/9UhHmDjkJd56Ji9Os6EeWGQ5iZ03oyvPqCdYfOeIIS5nLkt22rGepR3Lmzkh7rW0fTOedU\nBfXpp5sJro4xwiDALxbwi0X8UtGsi8VZ4ko1acFM+UslQr9kxHIYEFohPJNoDoOAMAwqAvW4RhkH\nQXmWReMQKMfZeCIOgbo4k5dKOpjZFBWYSdnKzgfqnBGoWkdDJByNq2yXZ1asK88pzxZpjlixzX7J\nR4cF88W2svPF2myzHUxLmxbWtduLcT0ccwJ7d7KDZ0+eSTEIibsOZ/e18aZTunjaKV2cubpVZgAT\nZkVrjV8qGvGaz1Mq2KUSLhD6PkHgE/pBxbsR+L6Nr8bVbPsBgV+yBjyyT026TxjU7lPZ9n38YpEw\nOHzx68ZixOIJvGTSrBMJYvEE8WSKppY2YokEXjxBLGGWcrqXSBJLJHBjMWPIytOLV7ytTp1xrHph\nVXk62bJhjXh36w1ojZd4mhF1rCF1q8exgrhSp0j5TqT85USt8K4T7HWe9Pd8r22pq3tUOZoTbC13\ndKlE4dFHq57p++6nsH175aVDb+VKkls203r55UZQb96M29q6dPXVmmIuR35ywixTk+QnJ+u2TbiQ\nzUbEb0QcF4sEpSJh3YuVh4PjenjxuF0SZh0rb8fMpDCui+O4Zl1ZPBzXiYQjac5h5PPK+U0+5bq4\nrtfQ5tWEZxOgESE6kxit2uQGIrUijKnmi4hl4fAo2+dGAv0935vfPXjMCexAa95w0XouOqWL89Z3\nkIrLeL7jEePJNcK32EgIRwRyOd0vh/P1ojkaVzhij2ONEfY83PK2Z4yu45k018aZxiGB63k2X+0+\njmu2vXjciF8rfMuiuBJXFs9220uYhsZx5B5YakwD6IJoScGiw5Dijh21LyE+9FBlEjC3tZXk5s2k\nn30xqTPPJLlpM7GVKxalLmEYUJiasmJ4sk4c1wrmXF1c+bF/I7x4gmQ6TTKdIZ5qIpZMkWppNbYr\nHseNxaqCuCyO6+NidcI5Hse1cbF4AjceExsnLDpKKZTtVMHCfG7ymBPYp65I89eXnr7U1TihqR/D\n6xfydrhCYQ7DFRoMdWgw9CEolQ5dkQhewnhsy+IzlkwSSyRJtbTUbMeTSeu5NeFyfGWdSFgBXBXQ\n9WJZvAOCIDRCl0qM/fgnjF1/PfmtWyuTsaimJpJnnE77q15VGeoRW3tkY9JL+TwTw0NMDg8yOTxk\nw0PkJsarwnnKepenpn8xK0o81UQynamI5UxHVyUcjU81V8OJdJpYXD4KIAgzccwJbGFhMI/+skyN\njpIdGyE7NsrU2CjZsVGyoyacGx+rit/IeOD5jOGtDGOoDFOoemTLIrjWY5uwItiIZi+ZJJ5IEUuW\nBbSJjydTePG4fFdcEIQlIywWGfvhfzL0ta9R2ruX+IYNtLzkxaQ2byG5ZTOJDRtQ7ty8sFprchPj\nTA4PMTkyxOTQUK2QHhpkcmSooWhONDXT1NpKsjlDU0sLHatWk2i24riBYE6mMySamnE9kQKCsNDI\nXXUcobWmMDXFlBXM2bFRK6CNiK4IaCui/VJxeiFKkcq00NzaRqqllVRLixW+yZrxuxWRXB7KkEzU\njP+NJSOCOZ6wj10EQRCOH8JcjtHvX8fQ17+Of/AgqSc/mZ6/+780P/OZDb3TYRAwNTpSEcmTQ4MV\nz7PxQhsRPe0JnlI0t7WTbu+kvXcVazdtId3RRaajk3RHZyUck8lgBGHZIAJ7mVP2ZlQEsxXH9YJ5\namyU3NhoQ++yUg6pFiOam9raae9dTVNrm9kur9vaaW5rJ5VpETEsCIIwC8HkFKPf/Q+GvvktgqEh\nms47j1Wf+DipCy5gZN9edv3yZiOi64ZwZEdHp70D4sZiRiS3d9J7ymmkOzprhHO6o5PmtnbxMgvC\nMYbcsUuI1pr81CQTgwNMDA3aZaBmPTk02Fg0O44Vx+00tbXRtbaPpqhgtvHNrW0kMxl5SUQQBOEI\nCcbHGf73f2fk364mGBuj6aKL4OWXMaRCfv+rm9jz9X8hNzFeyZ9obibd3kmms4uudeuteO6y4tks\nqUyLvNchCMchIrAXkUI2O00wTwxGwsOD+PaN8jLKcYwR7uym5+RTyZz/NNLtnRWxXBbRqXRGxh0L\ngiAcBfyREYb/7d8Y+vfvMBKWmHryZsZWreTAvj0Urv4KAK0rVnLy2eex+vRNrDp1Iy1dK2TIhiCc\nwIjAnielQr5WMA8PVr3Q1iNdzGVrd1KKdFs7mc5uutet5+Szz6W5vZN4WwuxtjRuSxO6OUY+LJD1\ns+T8HBN+juGghK/7CcL9BJMBwXhAoO0Szj3shz6hDmvCvvYJwsCEQ59AV8PhEk+g4ZS/fYxTCStU\nNV45OFTDyn5TdF7xkW37af2jTvT4lTpFzh9FzW9RPp+Z4mf7vaLlOsrBczxiTgzP8WrCMSfWOOzG\n8JRd2zRHSYdPOL7I793LI1/6Ajt//UuGEh6jp/QQaA3ZUTqyaU576jNYc8ZmVm/cREuXzBQsCEIV\nEdizkJ+cZHD3DgZ37WT/ru2MDRxkcmiI7MgIpanstPyqOQGZBDodxz+thWJTK/kmTTYVMpksMREv\nkA1Hyfn7yfk5cqUcxeEiDB95XR3l4CrXLI4757DneJV9PccjruK4jounvCUVm+XpVUNCQjtJR6hD\nQsKa+HKHIKQ6rXY5PGu8tlPV2vjoMZaKaH2idY7Wrb7OywlHOTVifC7r8rUWvSbLgn+u4UbX9Yx5\nI3kadRIadSqia+lEHN8Uc1n2PfIQu+66gx2338bQ5Diho6Crha7e1Zx51tmsOX0zazZuoqn1xJow\nSBCEw+OEFdg5P8dIfoTh/DAD4wc5uOtxhvfsJru/n1L/KGowSyxbFTBFL2Qy5TOV8pnqDJha7TOV\nCsgmfaaSZh3YYc4JN0HKS01bOrwMq6NxMbNu8poa5i8vcTdeadw95VUFRURYyBi+E5OZhHc0vrI9\ng1j3tY8fmqUUlua2DkqV/UphySw2rhTMrYycnzusJzDRpyvl8NGm3BE93I5E1PMvLB/yk5PseegB\n9mzbyt5tWzn4xGPoMERpTWuuyGmr17LhZX9I39OfRTKdXurqCoIwA1primGRYlCkEBQoBSWK4fRw\nMShSCkombPMXg2JtuG57vhw3AtsPfUYLowzlhhjOD09fcsMMZ4coDIygB6doGgtpn4jTPhEjk/Uq\nntrQ0eRboNQTR3W3kuzpJN3bQ293D5l4Zpo4ji5NXhNJLyleLuGooZTCVSfmC6xa6+kifC6C3Q6R\natRhOJxOwlw6I1k/Oy1eWDqmRkfYs60qqAd27wStcT2PDjfOhgPDdOSLrH/+pfS85c3EVq1a6ioL\nwjGF1ppSWCIf5Cn4hcq6EJhw3s9Pi6vkCwo16eW4crihGLbhUnh4k9PNhKMcEm6CmBMj7saJO/F5\nl3XMCeyB3AAfveOj0wT0aGG0mklDOufSNhGnczLBimwz3RMx1o1rnNAFWkBBrKuV9Ckr6Vizlp71\np7DupNPpWXOSfHFDEI4BlFJ4ysPDg2Poll2qYVcnIuOD/RVBvWfbA4zs2wNALJFk1Wmnc8Gpp9N8\n7/3Ebr4NL5Gg/Yor6HjjG4itWJwpywVhueCHPlk/S7aUZao0xVRpiqxvwvVx0e25iOT5Pl30HI+k\nmyThJkh6SRP2EiTdJCkvRVuizYheK3wPGZ5jWlRQe850WTxfm33MCez+bD8/feKndCQ76Eh2cGq8\nj47ik0gXITZcRA9MUuwfIShU3fqZrm66Tuqja916utb20bW2j45Va/Di8++ZCIIgCMuT/h2P87Mv\nf47+Jx4DzOfyVm/cxJbnPJ81p28iM5ln5GtfY/Lb1+E0N9P+pjfR8bor8To6lrjmgtAYrbX58EFx\noiJ2p/ypGkE8F4FczlMICoc+KEZcNseaafKaaIqZ4axJL0mT10RHsqNGECfcRGWIbE2cFckV0RyJ\nK+efSdweyxxzZ3NSbA0fzl/J4KM7Gdy9k9z44wCUAC/TYkT0pvONkF7XR+eadSSampe20oIgCMKi\nEwYBd/7Xdfz6uv8glclw8ZVvYu2mLXSt68NxXLK/+x2Dn/w0u2+7Dae1la4/fwcdr3kNbmvrUldd\nOI4phSUmi5NmKdnFhsuCeaI0wVTRrCeLk9PipkpTc/IMVwRxrKkijJtjzfSme2u2m2JNNHvNDfOW\n48rvh8k7XvPjmBPYE4MDbL3pF3St7WPDORfQva6PTuuVbmptkwtBEAThBGR4315u+MJn2L/9YU57\n6jN47p+8jVSmBa012d/8hsEvfonsnXfidnTQfdW7aX/Vq3HT4nwR5obWmvHiOP3ZfgZyAwxkBxgt\njFYFcnGiRjxH4+biLY47cdLxNOlYurJem147La68FkG8/DnmBHbXuj7+/Fvfk0lWBEEQBHQYcs/P\n/pvbrvkWXjzOi/7ifWx82jPRWjN5yy0MfunL5O69F2/FClZ+4K9pe8UrcFKppa62sEwoC+eB7AD9\nuX4GsgMVAV2/LobTvyhR9hhXBHAsTXuynbUZI44zsUxtejku3kwmlqnEx10Zsnq8ccwJbNeLibgW\nBEEQGB/s52df+iy7tt7HSU85l+e/5Z2k2zso7tnDvve+j9w99xBbtYqev/8grZdfjpNILHWVhaNE\nvXAezA0a7/MchXMmlqGrqYsVqRWcteIsVqRW0N3UTXeqm+6mblakVtCebKcp1iRfDhMacswJbEEQ\nBOHERmvNg7feyI3f/Apaa/7gze9gy3NegFKK8Z/9nP1/+7cA9Hz4Q7S97GWomHx//HhjrDDGzvGd\n7BzfycHswYYCupFwTsfSFYHcSDh3p7rpSnXRFGtagrMSjidEYAuCIAjHDNmxUX7+1c/z2G/vYM3p\nm3nhn/0lrSt6CAsFDn7yHxm55hqSZ57J6s98mviaNUtdXeEIKAZFdo3vYuf4Tp4Yf6IiqHeM7WCk\nMFKTtyycu1PdFeHclepiRdMKEc7CkiACWxAEQTgmePQ3v+IXX/s8xXyOZ732Tzjn0stQjkPhiSfY\n++6rKGzbRscb3sCKd/0lSj7DekwQ6pCDUwfZMb6DHeM7KgJ6x/gO9k/tr/lyRleqi/Ut63nOuuew\nvmU961vXs65lHT1NPSKchWWHCGxBEARhWZOfmuSmb36FB2+7iRUnbeDSd1xF55p1AIz9+Ccc+OAH\nUbEYa770RTLPfvYS11ZoxFhhbJqA3jm+k13ju8gH+Uq+Jq+JvpY+zuw6k5dueCl9LX2sb1lPX0sf\n6bhMVy8cO4jAFgRBEJYtO+67h599+XNMjQzz1D96FRdcfgWu5xHmchz46EcZu+4HpM45h9Wf/hSx\nnp6lru4JTSEosHt8d82Qjh1jRkhHh3S4ymVNZg19LX1c2HshfS19nNR6En0tfXSnuuUzc8JxgQhs\nQRAEYdlRyue55Tvf5Pc//286Vq/lso98mp4NpwJQ2L6dve96F4Xtj9H51rfQ/Y53oDxpzo4mxaDI\ntuFt3Nt/L/f238u24W3sm9yHRlfyNBrS0dfSx5rMGmKOvHgqHN+IRRIEQRCWFXsf3sYNX/wMowcP\ncM6LLuOiV15JLJ5Aa83YD/+TAx/+ME5zM2v/9WukL7poqat7QjCSH+He/nu5Z+Aeft//e7YObq18\npWN1ejVburbIkA5BiCACWxAEQVgW+KUSv/7+d7jr+h+S6ermj//uY6w9YwsA4dQU+//hHxi//sc0\nXXghq/7xk8RWrFjiGh+fhDpkx9gO7um/h3sHjId6x/gOADzH44yOM3jlxldy1oqzOKv7LLqbupe2\nwoKwDBGBLQiCICw5/Tse54YvfIaBXTvY8pznc/GVf0o8Zb4MkX/oIfb+5bso7tpF15+/g663vhXl\nuktc4+OHnJ9j6+BWM9xj4F5+P/B7xgpjALQl2jir+yxedsrLOGvFWWzq3ETSSy5xjQVh+SMCWxAE\nQVgywiDgrut/wK++fw2pTIbL3/9BTj77PMBMKDN67bUc/NjHcVtbWffNb9J8wflLXONjn4NTByue\n6Xv77+Wh4YfwtQ/Aya0n89x1z+Ws7rM4a8VZrG9ZLy8dCsI8EIEtCIIgLAnD+/Zywxc/w/5HH+ZJ\nT30Gz/uTt5HKtAAQTEyw///+HRM33EDzM57Bqk9+Aq+jY4lrfOzhhz6PjjzKvQP3ck+/GT+9b2of\nAEk3yeauzbx+8+t5yoqncGbXmbQl25a4xoJwfCACWxAEQTiq6DDk3p//N7d+51t4sRgveud72XjR\nsyrpufvvZ++7r6K0bx8r3nMVHW98I8pxlq7CM6BDTakYUCoElPIBxbxPKR/g+yGOUigHHFehHMeE\nHWW2lV07CsepW0fiTRwoR83ZizxRnOD+gfu5Z+Ae7u2/l/sG7iPrZwEqsxy+5ozXcFb3WWzs2EjM\nla95CMJiIAJbEARBOGqMDw7wsy99ll1bf8/6s87hBW95J+mOTsAMCRm5+moOfurTeN1d9H372zSd\n/ZQFO/ZMgrhUCCgWfBsXUMr7Ns7kK+V9E1+wecv7FQMiX6VbVJQV2069GFcKHMiHOab8SfI6T6B8\ntEpzUuzZbEpcSiaZpjXVQlM2hTOqcB5z6HcVg95jOK7CdR0cV+F4Csd1cD2F4zg4nsJ1FY5n02vy\nOjXbrufgxR3cmIMXc/HiDl7MwXGXX8dIEI4GIrAFQRCERUdrzYO33siN3/wKOgz5gze9gy3PfUHF\nMxuMjrLvA3/D5I03kn7Oc1j1sY/ith3+cIXJkTw7tw6x64FhJobz8xbEXtwhlvSIJ1xiSZd40qOp\nJW7CCZdY0iOWdIklTJpZm3gv5qBDTRjqyjoMNFqDDmrja9LDBmnT4qtljOXH2D78GLvGdhOEAW3N\nbaxM9dAaayPtZnC0Q+CbMsJcyPhkntAPCQNNEJh16GvCICQIqsdaSBxX4cUc3LiLFzOi24tXBbgb\nEeNeOU/ctUI9mte15VTDXtzm9Yywdz0j9GXMuLAcEIEtCIIgLCrZsVF+8bXPs/2uO1i9cRMv/LN3\n0bayOuti9nf3sPeqq/AHB1n5gb+m/bWvnbNICkPNwSfG2Xn/IDu2DjG0ZxKAdEeCzlXpOQniSjjh\nEU+6eAkXx1meIq0Ulrhp101c+/C13HngTmLdMZ5/3vN55Wmv5MndTz5icVkW9WUBHlhBHla2q+Ew\nCCsCPvBD/FKAXwzNUgoIStWwXwrxiwFBMTThknla4E+UTHxd3iN5MuBYoV1ZYuWwFfvT4q04j7kV\nke42yhdTNXGeN1NnQDz3wiILbKXUC4HPAS7wr1rrTzTIczHwWSAGDGqtn1WfRxAEQVh8FsNmF6Ym\n+dZ73k4xO8UzX/NGznnRZTiO+cSeDkOGvv51Bj77OWKrVrH+mmtIbdl8yHrmp0rsenCInfcbT3V+\nqoRyFL0bWnnq5Rvo29JJR2/zceXJ7M/2c90j13HdI9cxkBtgVfMq/uLsv+DyUy6nM9W5YMdRjsJ1\nFG5s6QSi1sazXioL77JwL4UEpYBSMbRCPbBxIYFvl3K4ZEW/jQttum/TS1m/dr+afUP0ETryHUdZ\nb3utZ9547WvFuBdzq3nLHv36vDVrGYpzLLBoAlsp5QJfAP4A2APcpZS6Xmv9YCRPG/BF4IVa611K\nKXYKD6cAACAASURBVJk1QBAEYQlYLJs9evAAmc4uLvm7j9G1tq8S7w8Ps+/9f8XUbbeReeEL6f3w\nh3AzmYZlaK0Z3jfFjvsH2bl1iAOPjaE1JNMx+jZ30relk7Wnd5BsPr5e2NNac9eBu/juw9/lxl03\nEuqQi1ZfxAdP+yBPX/10XOf4/Ba4Usp4i5dQ5Je98zXCOyLSQ7uueN2LYUOPvV+q7Qz4xZBSISA3\nWbL5bbwNz9dzP+NQnIogn4egj3QERNAfPovpwT4f2K61fhxAKfVd4DLgwUieVwM/1FrvAtBa9y9i\nfQRBEISZWRSbnens4tUf+TSuV21upu68k33veS/B6Cg9f/9B2q64Ypq3uVQM2PvQCDu2DrFz6yCT\nwwUAutamOeeS9fRt7mTF+pZlO5TjSJgoTnD9Y9dz7cPX8sTYE7QmWrnyjCt5xZNewdqWtUtdvRMC\nx3VwXIgljl4npuy5j4rxqnivevEPNRQnKEa9/tWhODWCvnhkQ3EqHvoGgn7eHvro+Pv4sS/oF1Ng\nrwZ2R7b3ABfU5XkSEFNK3QxkgM9pra+uL0gp9WbgzQDr1q1blMoKgiAsNFrb8akRD9gyZtFsdllc\n6yBg8MtfZvALXyS+bh1rv/oVkhs3VvYbH8yxc+sQO+4fYu8jIwSlEC/hsnZjO+ddehLrNnWSbk8s\n4CkvLx4efpjvPvxd/vvx/ybn5ziz60w++vSP8vy+58vsiScAUc/90bjKZxL0FSFeJ+iDOtE/TdAf\nwkMfFIN5D71xHIVjx72b8fINxtHHouPpG4+19+aQp76c+bLULzl6wDnAc4EU8Gul1B1a60eimbTW\nXwW+CnDuuecepY8iCYJwLKNDPb1xiI7lrH/0Wzd2Mxrn+yFhJa+u7B9GxnTWllcV1ccZ87bZpf5+\n9r3v/WTvuIOWl76E3g9+EJ1MsfeREXbeP8SOrUOM7J8CoLU7xaZnrGL95i5Wndq2pEMFFptiUOQX\nO3/BtQ9fyz3995BwE1x60qVcsfEKNnVuWurqCccxy0vQ1wn3BkNuGtvs6jCeYs6fNpbet/Y4LIWE\nC/yFnEOxmAJ7LxB9lrXGxkXZAwxpraeAKaXUrcCTgUcQBOG4IwhC/EJAqWA8G5WXmIpVgxs0eDxa\n9n5ExypWvkpQFxeUQkrFgNA/QmOqqPNoqDoPiImPp7xpeWb0jHgKvrIwv+UisGg2e/L229n3vvcT\nTk3R/sGPMbL+QrZe8wS7HhymmPNxXMWqU9s446Je1m/pom1l00Ke17Jk3+Q+vv/I9/nhoz9kOD9M\nX0sf7z33vVx2ymW0JlqXunqCsOAcbUFfTxjqaQK8Ktanj7cvi/r52uzFFNh3AacqpU7CGOlXYsbv\nRfkv4PNKKQ+IYx5H/vMi1kkQhFnQWlcEaqkQ4BdM2C8E1bhiRCDbeL+ytvkrecPIPgFhMD/R2+j7\nt64NJ9OxmjF8jb+3Wzuur/xZraoIni6MK5N4nDgsis0uHTzIg3/5D4ye+kJGT7uYgVuLcMs2mlri\nbHhKt3lBcWMH8dRSP1BdfEId8qt9v+Lah67llj23oJTi4jUXc8XGK7iw90Icdfx66gVhqXEchRN3\nicWPzrj6RbNoWmtfKfUO4GeYTz59Q2v9gFLqrTb9y1rrbUqpG4D7gBDzWaiti1UnQTjeCIOwOsNc\nvjobnZmdrjr7XM2MdTatMmtdwadUNJ5lfx5j5Ly4Qyzh4sXdyNoh3ZbASxhjVl7HEk5NPq9GNDd+\nW92NycQRR4PFstljuQR3nfNXoGBFIsn5L15N3+ZOutdmUMfhC4qNGM2P8qPtP+J7j3yP3RO76Ux2\n8qYz38QrnvQKepp7Dl2AIAjHHEof6ccejzLnnnuu/u1vf7vU1RCEeaO1plQIKGR9u/z/7d17XJVl\nvv//18UCQQWRTNNQAxxSUJAC08kDlpNi9qttB7fVt8xtTe5xHEczc+/poE3TTEaaYwfHGtHNdHDS\nDu6msak9OTLqmJmomWdDxSzFQOUoh+v3B0igIGhrsU7v5+Phw7Xuda/7/lxg7z5c3Ou+yikrrqi3\nHPPpmqWa6zXBdR/XNMqV5c27xteY6k/DB4UE1iyo4ahZdKN6kY2g2ka4frP8fXMcUKdJrtNMBwX4\nTZPkLMaYTdbaFHfX0VJiI3va/12+mu69O9CmXSt3l9Oith3bxpu73mTVV6s4XXWa5MuSGdtzLMO6\nDyPI4Vu3FBTxVReb2b7/OzkRF6isqKrXHJeVVHD6zPOSCsqKqrfVvl78/fPTxRVNzhI33BAHEtYh\nqHb1uVa1TXLNynS1j2sa55DvtwdqFljcJLxLGL1+3MXdZbSY8spy3t//Pst2LWP78e20CWzD6NjR\n/HvPfyc2Itbd5YlIC1GDLX6tvKySklOnKT51mtJT5ZQUnq4zs1xBWUl1g3y6uILS4gpO1zTMFU3M\nHDuCAghuHUhwm+o/bdq1ov1lbQhpE0irNoEEtw4iuG1gnX2C1BCLeLkjhUeYtnoaXxz/gh+1/xG/\n6v8rboq5idBWoe4uTURamBps8SmVlVWUFpZTcuo0JSfLKT51uvrxqZptp05TXOdxxelGGmVDvea3\nVetALuncpro5bhNUr3lu1TqQkLbV+5zZFhjkmyusiUjD1n29jkfWPEJ5VTnpqekMv2K4fkgW8WNq\nsMWj2SpLWUnF983xye+b4zNNc3Gdx2XFFQ0eJyDA0DosiNbtWtE6rBXtL2tN67BWtAlrVb09rHp7\n69AggtsG0SrYoWuLRaRJVbaKV7e9ygubX6BH+x7MGzqPqPAod5clIm6mBlvcwlpLaVE5RQVlFOaX\n1fu7qKCMopPVTXTpqfJGbw4f0jaotjnuEBlKmzoNdOvQM01z9d/BbQI1myQiTnXy9El+lfUrVueu\nZmT0SGb9eBZtgnz/Ht4i0rRmN9jGmEFArLU2wxjTEQi11n7lutLEW1VVVlF04nT95rng++a5ML+U\nooLT565yZ6BNu1aEtg8mLCKYTleENTzLHBZESGgQDofuGSvSGGW2a+36bhdTV0/lSOERZl4zk7t6\n3aUf4kWkVrMabGPME0AK0BPIAIKAPwEDXVeaeKLyssrvG+b80pq/v2+gCwvKKDl5+py7ZDgCA2jb\nvhVt2wdzWXQ4bdsHE9o+uPrviOq/24S3UtMs4gTKbNdauW8lv17/a9q1akdGWgZJnZLcXZKIeJjm\nzmCPBq4CPgew1n5tjAlzWVXiFmcu2zhxtIQTR4s5cazknAa6oWucg9sE1jbMHSJD6zXNZ/4OaRuk\n2R2RlqPMdoHTlaeZs3EOy3YtI+WyFJ5NfZZLW1/q7rJExAM1t8E+ba21xhgLYIxp68KaxIXObqIL\njpZw4tj3DXW9BrrOJRvhHVsTGduethF1Z55DaNs+mKBg3TFDxMMos53sm6JvmLZ6GtvytjG+93h+\ncfUvCAzQx5hEpGHNTYc/G2P+ALQ3xjwA/AfwiuvKkh/iQppoYyCsQwjhndoQG9WO9p3aEN6xNeGd\nWtOuQ2scQbpkQ8QLKbOd6F9H/sWMf8zgdNVp5g6dyw1X3ODukkTEwzWrwbbWphtjbgBOUn1N3+PW\n2o9cWpmc1zlN9LGSepd2qIkW8V/KbOeoslUs/mIxCzYvILpdNPOum0d0eLS7yxIRL9Bkg22McQAf\nW2uvAxTQLayspIL8I0VqokWkWZTZznHy9El+9c9fsfrQatKi0ph97Wzdgk9Emq3JBttaW2mMqTLG\nhFtrT7REUf6qrKSCvIOnOHrgFMcOnuTowVOcOFpS+7qaaBFpijL7h9udv5upn0zl68KveaTfI9wd\nd7c+pC0iF6S512AXAtuMMR8BRWc2Wmt/4ZKq/EBTzXToJcF06t6OXgO6cGnXUDXRInIhlNkX6f39\n7zN73WzCWoXxxxF/5OrLrnZ3SSLihZrbYL9d80cuQllJBccOnuJYM5rpjleE0al79QIrIiIXSZl9\ngcory5mzcQ5v7nqT5MuSSU9N1y34ROSiNfdDjkuNMa2AK2s27bLWlruuLO+lZlpE3E2ZfWG+KfqG\nh/7xEFuPbeXe+Hv5ZfIvCQoIcndZIuLFmruS41BgKZADGKCbMWactXaN60rzfGqmRcQTKbOb79Mj\nn/LwmocprSglPTWdEVEj3F2SiPiA5l4i8hww3Fq7C8AYcyXwBpDsqsI8TZPNdEQwna5QMy0iHsHv\nM7sp1loytmcw//P5RLWLYt6IecS0j3F3WSLiI5rbYAedCWoAa+1uY4xP//7sdGkFR/ad4PDOfHJ3\n5XPs0Cmw1a+FRgTTsXuYmmkR8VR+l9kX4tTpUzy29jH+7+D/MfyK4Tw58EnaBmmxSxFxnuY22J8Z\nY14F/lTz/G7gM9eU5B4V5ZV8s/8kh3flk7szn6M5J6mqsgQ4DJ1jwul3YxSXxYSrmRYRb+DzmX2x\n9uTvYerqqeSeymV6ynTujb9Xt+ATEadrboP9n8Ak4MwtnrKAl1xSUQuprKziaM6p6oZ6Vz7f7DtB\nZUUVxkDHK9qRdEN3uvaMoPOPwglq5XB3uSIiF8LnMtsZPtj/AbPWz6JtUFteHf4qKZ1T3F2SiPio\n5jbYgcB8a+1cqF0pLNhlVblAVZXleG4huTWXfHy9t4CKskoAOnQNpU9qJF17RtAltj3BrZv7ZRER\n8Uhen9nOVF5ZTvpn6by+83Wu7nQ16anpdGzT0d1liYgPa24n+X/AT6hevACgNfA34FpXFOUM1lq+\nO1LE4V35HN5VwOHd+bVLi0d0bkOv/p2J7BlBZM/2tA7VJR/iHuXl5eTm5lJaWuruUsTJQkJC6Nq1\nK0FBbrn02esy21W+LfqW6f+YTvaxbO6Jv4epyVN1Cz65aMps3+XszG5ugx1irT0T1FhrC40xbZxS\ngZNYazlxrKSmoc4nd3cBJSdPA9XLi8ckdSSyZwRde0bQtr3fTuSIh8nNzSUsLIyoqChdB+pDrLUc\nP36c3NxcoqOj3VGCx2d2S9j4zUam/2M6JRUlPDvkWdKi09xdkng5ZbZvckVmN7fBLjLGXG2t/RzA\nGJMClDTxHpcrzC8ld1d+7Z0+CvPLAGgT3opuvSJqG+p2l7Z2c6UiDSstLVVQ+yBjDB06dODYsWPu\nKsEjM7slvbbjNZ7d+CzdwrqxeMRierTv4e6SxAcos32TKzK7uQ32L4G3jDFf1zzvAvy706q4AGXF\n5ax+bSe5u/Jr70Md0jaIyCvbc/WICLr2iqD9ZW30j1+8hv6t+iY3f189JrPd4cOcD/ndp7/jum7X\n8fSgpwltFeruksSHKLN9k7O/r+dtsI0x/YBD1tqNxphewIPArcAq4CunVtJMJ46Vsnvjt0TGtqfP\nkEi69oqgw+WhmAD9gxcR/+aJmd3Sdufv5rG1j9G3Y1/SU9Np5dBnbESk5TU1g/0Hqj8oA/Bj4L+B\nyUASsAi43XWlNSyicxvuf24wAY6Alj61iIin87jMbkknyk4w5e9TCA0KZd7QeWquRcRtmmqwHdba\n72oe/zuwyFq7AlhhjMl2bWkNCwp2qLkW8SGVlZU4HLrXvJN4XGa3lIqqCh7+x8N8W/wtGWkZug2f\niAspt5vWVKfqMMacacKHAX+v85puFi3i5XJycoiLi+OBBx6gd+/eDB8+nJKSErKzsxkwYACJiYmM\nHj2a/Px8AIYOHcpnn1UvCJiXl0dUVBQAS5Ys4dZbbyUtLY3Y2FhmzJhx3vOGhoby0EMP0bdvX9av\nX88HH3xAr169SE5O5he/+AU33XSTS8ftw/w2s3//+e9Zf2Q9jw2ovjxExFcpt71DU4H7BvAPY0we\n1Z9AzwIwxvwIOOHi2kT8yuz/3c6XX5906jHjL2/HE/9f7/Pus2fPHt544w1eeeUVxowZw4oVK5gz\nZw4LFiwgNTWVxx9/nNmzZ/P888+f9zjZ2dls3ryZ4OBgevbsyeTJk+nWrVuD+xYVFdG/f3+ee+45\nSktLiY2NZc2aNURHR3PnnXde9HjFPzP7g/0fkLE9g7E9xzI6drS7yxE/4a7MBuW2NzjvDLa19jfA\nQ8ASYJC11tZ532TXliYiLSE6OpqkpCQAkpOT2bdvHwUFBaSmpgIwbtw41qxZ0+Rxhg0bRnh4OCEh\nIcTHx3PgwIFG93U4HNx2220A7Ny5k5iYmNp7jyqoL54/ZvaO4zt4Yt0TJF+WzIxrzj8DJ+IrlNue\nr8lfGVpr/9XAtt2uKUfEfzVn1sIVgoO/X3jJ4XBQUFDQ6L6BgYFUVVUBnLOS2dnHqaioaPQ4ISEh\nun7PRfwps78r/Y4pn0whPDic51Kf0wqN0qLcldmg3PYG+rSgiNQTHh5OREQEWVlZAGRmZtbOikRF\nRbFp0yYAli9f7pTz9ezZk/3795OTkwPAsmXLnHJc8W3lVeU8/I+HOV5ynPnXzadD6w7uLknEbZTb\nnsenP/QiIhdn6dKlTJw4keLiYmJiYsjIyABg+vTpjBkzhkWLFjFq1CinnKt169a89NJLpKWl0bZt\nW/r16+eU44pvm/vZXD795lN+M+g39L7UfTOJIp5Cue1ZzPeX6Lng4MakAfMBB/CqtfZ3jezXD1gP\njLXWnvfHq5SUFHvm07Ai3m7Hjh3ExcW5uwy3KywsJDQ0FGstkyZNIjY2lqlTp7q7rB+soe+vMWaT\ntTbFTSWdl7dk9nt73+PRtY/y/+L+H49c84hTjy1yPsrs7/libjszs112iYgxxgG8CIwE4oE7jTHx\njez3DPA3V9UiIp7tlVdeISkpid69e3PixAkefPBBd5fkd7wls7/I+4In1z9J/879eSjlIXeUICIo\nt5viyktErgH2Wmv3Axhj3gRuAb48a7/JwApAv18Q8TH9+/enrKys3rbMzEwSEhLqbZs6darXz3z4\nAI/P7LySPKZ8MoVLW1/Ks6nPEhigqxxFnE257RyuTKdI4FCd57lA/7o7GGMigdHAdZwnrI0xPwV+\nCtC9e3enFyoirrFhwwZ3lyDN59GZXV5ZzkOrH+Jk2Ukyb8wkIiTCKccVkfqU287h7ruIPA88Yq2t\nOt9O1tpF1toUa21Kx45a/lZExE3cltnPbHyGz49+zpMDn6TXJb2cckwREVdx5Qz2YaDuckBda7bV\nlQK8aYwBuBS40RhTYa1914V1iYjIuTw2s5fvXs6yXcsY32c8I6NHuvJUIiJO4coGeyMQa4yJpjqk\nxwJ31d3BWht95rExZgnwvpprERG38MjMzj6azW82/IZrL7+WKVdNceWpREScxmUNtrW2whjzc+BD\nqm/5tNhau90YM7Hm9YWuOreIiFwYT8zso8VHmbp6Kl3admHOkDk4ArSKnIh4B5d+BNta+wHwwVnb\nGgxpa+19rqxFRETOz5My+3TlaaaunkpReRGLblhEeHC4K08nIuJUuseRiIh4FGstv9nwG7Ye28q8\nofOIjYh1d0kiIhfE3XcRERERqefPu/7M23ve5qeJP+UnV/zE3eWIiFwwzWCLeIq/zoRvtjn3mJ0T\nYGSDq10DkJOTw8iRIxk0aBDr1q0jMjKS9957j127djFx4kSKi4vp0aMHixcvJiIigqFDh5Kenk5K\nSgp5eXmkpKSQk5PDkiVLWLlyJcXFxezbt4/Ro0czZ86cRs8bGhrKf/7nf/LBBx/QpUsXnn76aWbM\nmMHBgwd5/vnnufnmm6msrOSRRx5h1apVBAQE8MADDzB58mTnfn3E42z6dhO/+/R3pHZNZVLSJHeX\nI9I4N2Q2KLe9hWawRfzcnj17mDRpEtu3b6d9+/asWLGCe++9l2eeeYatW7eSkJDA7NmzmzxOdnY2\ny5YtY9u2bSxbtoxDhw41um9RURHXX38927dvJywsjEcffZSPPvqId955h8cffxyARYsWkZOTQ3Z2\nNlu3buXuu+922pjFM31T9A3TVk+ja1hXfjv4twQY/S9KpCHKbc+nGWwRT9HErIWrREdHk5SUBEBy\ncjL79u2joKCA1NRUAMaNG8cdd9zR5HGGDRtGeHj1B9Hi4+M5cOAA3bp1a3DfVq1akZaWBkBCQgLB\nwcEEBQWRkJBATk4OAB9//DETJ04kMLA6pi655JIfNE7xbKUVpUz5ZApllWXMv34+Ya3C3F2SyPm5\nKbNBue0N1GCL+Lng4ODaxw6Hg4KCgkb3DQwMpKqqehG/0tLS8x6noqKi0eMEBQVRs1gJAQEBte8N\nCAg47/vEN1lr+fW/fs2Xx7/k99f9npjwGHeXJOLRlNueT79/E5F6wsPDiYiIICsrC4DMzMzaWZGo\nqCg2bdoEwPLly11axw033MAf/vCH2uD+7rvvXHo+cZ/XdrzGyn0r+VnSz7iu+3XuLkfE6yi3PY8a\nbBE5x9KlS3n44YdJTEwkOzu79vq66dOn8/LLL3PVVVeRl5fn0hruv/9+unfvTmJiIn379uX11193\n6fnEPTYc2UD6Z+lc3+16Hkx80N3liHgt5bZnMdZad9dwQVJSUuxnn33m7jJEnGLHjh3ExcW5uwxx\nkYa+v8aYTdbaFDeV1OLOl9mHCw8z9v2xdAjpwGujXqNtUNsWrk7kwiizfZszM1sz2CIi0uJKKkqY\n8vcpVFZVMv/6+WquRcSn6EOOIuIy/fv3p6ysrN62zMxMEhIS3FSReAJrLU+sfYLd+bt5cdiLXNHu\nCneXJCI1lNvOoQZbRFxmw4YN7i5BPNCS7Uv4a85fmXL1FAZ3HezuckSkDuW2c+gSERERaTFrD6/l\n+c+fZ/gVw5nQZ4K7yxERcQk12CIi0iIOnjzIw2sepkf7Hvx64K9r76krIuJr1GCLiIjLFZcXM+WT\nKQSYAOZfN582QW3cXZKIiMvoGmwREXEpay2Prn2U/Sf2s/AnC+kW1vBSzCIivkIz2CLiFRYuXEhC\nQgJJSUkMGjSIL7/8svY1h8NBUlISSUlJ3HzzzbXbJ0yYQN++fUlMTOT222+nsLDQHaX7vVe3vcpH\nBz5iWvI0fnz5j91djoi0EH/ObS00I+JGWrSg+U6ePEm7du0AWLlyJS+99BKrVq0CIDQ0tMEQrvue\nadOm0alTJ2bOnNliNWuhGeiV2Mu2eqgVN8bcyG8H/VbXXYtXU2ZfGG/LbWdmti4REfEQz3z6DDu/\n2+nUY/a6pBePXPNIo6/n5OQwcuRIBg0axLp164iMjOS9995j165dTJw4keLiYnr06MHixYuJiIhg\n6NChpKenk5KSQl5eHikpKeTk5LBkyRJWrlxJcXEx+/btY/To0cyZM6fBc7711lusX7+euXPnMn/+\nfObPn8/+/fvZv38/99xzD2vXrmXmzJmsXLmSwMBAhg8fTnp6em3gAhQVFTWrUTvzHmstJSUlau7c\nILcwlxsvuZFZP56lr7/4FHdkNii3vYUuERHxc3v27GHSpEls376d9u3bs2LFCu69916eeeYZtm7d\nSkJCArNnz27yONnZ2Sxbtoxt27axbNkyDh061OB+gwcPJisrC4CsrCw6dOjA4cOHycrKYsiQIRw/\nfpx33nmH7du3s3XrVh599NHa97744ov06NGDGTNm8Pvf/752e2lpKVdffTUDBgzg3XffrXe+8ePH\n07lzZ3bu3MnkyZMv5kskP0BgQCDzr5tPSGCIu0sR8RnKbc+nGWwRD9HUrIWrREdHk5SUBEBycjL7\n9u2joKCA1NRUAMaNG8cdd9zR5HGGDRtGeHg4APHx8Rw4cIBu3c79MFvnzp0pLCzk1KlTHDp0iLvu\nuos1a9aQlZXFrbfeSnh4OCEhIUyYMIGbbrqJm266qfa9kyZNYtKkSbz++us89dRTLF26FIADBw4Q\nGRnJ/v37uf7660lISKBHjx4AZGRkUFlZyeTJk1m2bBnjx4//YV8wuSA/av8juoR2cXcZIk7nrswG\n5bY30Ay2iJ8LDg6ufexwOCgoKGh038DAQKqqqoDq2YfzHaeioqLR41x77bVkZGTQs2fP2pmR9evX\nM3DgQAIDA/n000+5/fbbef/990lLSzvn/WPHjq034xEZGQlATEwMQ4cOZfPmzfX2dzgcjB07lhUr\nVjRak4iIt1Buez412CJST3h4OBEREbW/DszMzKydFYmKimLTpk0ALF++/KLPMXjwYNLT0xkyZAhX\nXXUVn3zyCcHBwYSHh1NYWMiJEye48cYbmTdvHlu2bAGqfyV6xl/+8hdiY2MByM/Pp6ysDIC8vDzW\nrl1LfHw81lr27t0LVF/Lt3LlSnr16nXRNYuIeCrltufRJSIico6lS5fWflgmJiaGjIwMAKZPn86Y\nMWNYtGgRo0aNuujjDx48mEOHDjFkyBAcDgfdunWrDdFTp05xyy23UFpairWWuXPnAvDCCy/w8ccf\nExQURERERO2vGXfs2MGDDz5IQEAAVVVVzJw5k/j4eKqqqhg3bhwnT57EWkvfvn15+eWXf+BXRkTE\nMym3PYtu0yfiRrrlk2/TbfqU2eJblNm+zZmZrUtEREREREScSJeIiIjL9O/fv/Y6uzMyMzNJSEhw\nU0UiInI+ym3nUIMtIi6zYcMGd5cgIiIXQLntHLpERERERETEidRgi4iIiIg4kRpsEREREREnUoMt\nIiIiIuJEarBFxCssXLiQhIQEkpKSGDRoEF9++WXtaw6Hg6SkJJKSkrj55ptrt0+YMIG+ffuSmJjI\n7bffTmFh4QWd73/+53+cOgYREX/iz7mthWZE3KjuTe2/efppynbsdOrxg+N60fm//9upx3SXkydP\n0q5dOwBWrlzJSy+9xKpVqwAIDQ1tMITrvmfatGl06tSJmTNntljNWmhGmS2+RZl9Ybwtt71moRlj\nTJoxZpcxZq8x5pyvjjHmbmPMVmPMNmPMOmNMX1fWIyL15eTkEBcXxwMPPEDv3r0ZPnw4JSUlZGdn\nM2DAABITExk9ejT5+fkADB06lDPNUl5eHlFRUQAsWbKEW2+9lbS0NGJjY5kxY0aj53zrrbeYNm0a\nAPPnzycmJgaA/fv3M3DgQIDaZXMTExOZPn06QG3gAhQVFWGMaXJ8Z95jraWkpKTR9zR0vlmzKft0\nRgAAEtBJREFUZpGenl477qlTp5KSkkJcXBwbN27k1ltvJTY2lkcffbTJOryFMlvE8ym3afR8HpXb\n1lqX/AEcwD4gBmgFbAHiz9rnWiCi5vFIYENTx01OTrYivuLLL7906/m/+uor63A47ObNm6211t5x\nxx02MzPTJiQk2NWrV1trrX3sscfslClTrLXWpqam2o0bN1prrT127Ji94oorrLXWZmRk2OjoaFtQ\nUGBLSkps9+7d7cGDBxs855EjR2xKSoq11trbbrvNpqSk2NzcXLtkyRI7c+ZMm5eXZ6+88kpbVVVl\nrbU2Pz+/9r0vvPCCjYmJsV27drW7d++u3e5wOOxVV11l+/fvb995551657vvvvtsp06d7NChQ21R\nUdE59TR2vieeeMI+++yzteOeMWOGtdba559/3nbp0sV+/fXXtrS01EZGRtq8vLwGx9rQ9xf4zLoo\nd3/IH2W2SNPcndnWKretdV1uOzOzXTmDfQ2w11q731p7GngTuKXuDtbaddba/Jqn/wK6urAeEWlA\ndHQ0SUlJACQnJ7Nv3z4KCgpITU0FYNy4caxZs6bJ4wwbNozw8HBCQkKIj4/nwIEDDe7XuXNnCgsL\nOXXqFIcOHeKuu+5izZo1ZGVlMXjw4NpjTJgwgbfffps2bdrUvnfSpEns27ePZ555hqeeeqp2+4ED\nB/j88895/fXX+eUvf8m+fftqX8vIyODrr78mLi6OZcuWnVPP+c5X15lrBBMSEujduzddunQhODiY\nmJgYDh061OTXxwsos0W8hHLb83PblQ12JFC3+tyabY2ZAPy1oReMMT81xnxmjPns2LFjTixRRIKD\ng2sfOxwOCgoKGt03MDCQqqoqAEpLS897nIqKikaPc+2115KRkUHPnj0ZPHgwWVlZrF+/noEDBxIY\nGMinn37K7bffzvvvv09aWto57x87dizvvvtu7fPIyOpoiYmJYejQoWzevLne/g6Hg7Fjx7JixQoA\nRowYQVJSEvfff3+zzld3fAEBAfXGGhAQcN6xehFltoiXUG57fm57xF1EjDHXUR3WjzT0urV2kbU2\nxVqb0rFjx5YtTsTPhIeHExERQVZWFgCZmZm1syJRUVFs2rQJgOXLl1/0OQYPHkx6ejpDhgzhqquu\n4pNPPiE4OJjw8HAKCws5ceIEN954I/PmzWPLli0A7Nmzp/b9f/nLX4iNjQUgPz+fsrIyoPr6wrVr\n1xIfH4+1lr179wLVl8KtXLmSXr16AfDhhx+SnZ3Nq6++2uj5pHHKbBHPotz2vNwOdOGxDwPd6jzv\nWrOtHmNMIvAqMNJae9yF9YhIMy1dupSJEydSXFxMTEwMGRkZAEyfPp0xY8awaNEiRo0addHHHzx4\nMIcOHWLIkCE4HA66detWG6KnTp3illtuobS0FGstc+fOBeCFF17g448/JigoiIiICJYuXQpUf+r7\nwQcfJCAggKqqqtoPvlRVVTFu3DhOnjyJtZa+ffvy8ssvn1NLY+fzQ8psES+m3PYsLrtNnzEmENgN\nDKM6pDcCd1lrt9fZpzvwd+Bea+265hxXt3wSX9LQLYHEd3jTbfqU2SJNU2b7NmdmtstmsK21FcaY\nnwMfUv3p9MXW2u3GmIk1ry8EHgc6AC/V3IalwhP/xyMi4uuU2SIizuPKS0Sw1n4AfHDWtoV1Ht8P\n3O/KGkTEffr37197nd0ZmZmZJCQkuKkiOR9ltogot53DpQ22iPi3DRs2uLsEERG5AMpt5/CIu4iI\niIiIiPgKNdgiIiIiIk6kBltERERExInUYIuIiIiIOJEabBHxCI8//jgff/xxs/fPycmhT58+LqxI\nRETOR7ndON1FRMRDZP15N3mHCp16zEu7hTJ4zJVOPaarPPnkk+4uQUSk2fw9s0G5fT6awRbxYzk5\nOcTFxfHAAw/Qu3dvhg8fTklJCdnZ2QwYMIDExERGjx5Nfn4+AEOHDuXMqnx5eXlERUUBsGTJEm69\n9VbS0tKIjY1lxowZjZ6zsrKS++67jz59+pCQkMC8efMAuO+++1i+fDkAUVFR/Nd//RdJSUmkpKTw\n+eefM2LECHr06MHChQsbPObDDz9Mv379SExM5A9/+AMA77zzDsOGDcNay5EjR7jyyiv55ptvnPb1\nExFpacpt76AZbBEP4a5Ziz179vDGG2/wyiuvMGbMGFasWMGcOXNYsGABqampPP7448yePZvnn3/+\nvMfJzs5m8+bNBAcH07NnTyZPnky3bt0a3O/w4cN88cUXABQUFDR4vO7du5Odnc3UqVO57777WLt2\nLaWlpfTp04eJEyfW2/ePf/wj4eHhbNy4kbKyMgYOHMjw4cMZPXo0K1as4MUXX2TVqlXMnj2bzp07\nX+RXSkTke+6caVZuez412CJ+Ljo6mqSkJACSk5PZt28fBQUFpKamAjBu3DjuuOOOJo8zbNgwwsPD\nAYiPj+fAgQMNBnVMTAz79+9n8uTJjBo1iuHDhzd4vJtvvhmAhIQECgsLCQsLIywsjODg4HPC/W9/\n+xtbt26tnUk5ceIEe/bsITo6mgULFtCnTx8GDBjAnXfe2cyvioiI51Juez412CJ+Ljg4uPaxw+Fo\ndGYCIDAwkKqqKgBKS0vPe5yKiooGjxEREcGWLVv48MMPWbhwIX/+859ZvHhxo3UFBATUO3ZAQMA5\nx7bWsmDBAkaMGHHOcXJzcwkICODbb7+lqqqKgABdGSci3k257fm8r2IRcanw8HAiIiLIysoCIDMz\ns3ZWJCoqik2bNgHUzjpcqLy8PKqqqrjtttt46qmn+Pzzz39wzSNGjODll1+mvLwcgN27d1NUVERF\nRQX/8R//wRtvvEFcXBxz5879wecSEfE0ym3PoxlsETnH0qVLmThxIsXFxcTExJCRkQHA9OnTGTNm\nDIsWLWLUqFEXdezDhw8zfvz42hmV3/72tz+43vvvv5+cnByuvvpqrLV07NiRd999l+eee47Bgwcz\naNAg+vbtS79+/Rg1ahRxcXE/+JwiIp5Eue1ZjLXW3TVckJSUFHvm07Ai3m7Hjh1eFxrSfA19f40x\nm6y1KW4qqcUps8WXKLN9mzMzW5eIiIiIiIg4kS4RERGX6d+/P2VlZfW2ZWZmkpCQ4KaKRETkfJTb\nzqEGW0RcZsOGDe4uQURELoBy2zl0iYiIiIiIiBOpwRYRERERcSI12CIiIiIiTqQGW0RERETEidRg\ni4iIiIg4ke4iIuIhPlmyiKMH9jv1mJ2uiOG6+37a6Os5OTmkpaUxYMAA1q1bR79+/Rg/fjxPPPEE\nR48e5bXXXqN3795MnjyZL774gvLycmbNmsUtt9xCTk4O99xzD0VFRQC88MILXHvttaxevZpZs2Zx\n6aWX8sUXX5CcnMyf/vQnjDFOHZuIiDu5I7NBue0t1GCL+Lm9e/fy1ltvsXjxYvr168frr7/OP//5\nT1auXMnTTz9NfHw8119/PYsXL6agoIBrrrmGn/zkJ3Tq1ImPPvqIkJAQ9uzZw5133smZFfs2b97M\n9u3bufzyyxk4cCBr165l0KBBbh6piIhvUG57PjXYIh6iqVkLV4mOjq5dQKB3794MGzYMYwwJCQnk\n5OSQm5vLypUrSU9PB6C0tJSDBw9y+eWX8/Of/5zs7GwcDge7d++uPeY111xD165dAUhKSiInJ0dB\nLSI+xV2ZDcptb6AGW8TPBQcH1z4OCAiofR4QEEBFRQUOh4MVK1bQs2fPeu+bNWsWl112GVu2bKGq\nqoqQkJAGj+lwOKioqHDxKERE/Idy2/PpQ44icl4jRoxgwYIFWGuB6l8jApw4cYIuXboQEBBAZmYm\nlZWV7ixTRERqKLfdTw22iJzXY489Rnl5OYmJifTu3ZvHHnsMgJ/97GcsXbqUvn37snPnTtq2bevm\nSkVEBJTbnsCc+enGW6SkpNgzF+SLeLsdO3YQFxfn7jLERRr6/hpjNllrU9xUUotTZosvUWb7Nmdm\ntmawRUREREScSA22iIiIiIgTqcEWcTNvu0xLmkffVxHfpP+2fZOzv69qsEXcKCQkhOPHjyuwfYy1\nluPHj9e7BZaIeD9ltm9yRWbrPtgibtS1a1dyc3M5duyYu0sRJwsJCaldtEFEfIMy23c5O7PVYIu4\nUVBQENHR0e4uQ0REmkGZLc3l0ktEjDFpxphdxpi9xpiZDbxujDG/r3l9qzHmalfWIyIijVNmi4g4\nh8sabGOMA3gRGAnEA3caY+LP2m0kEFvz56fAy66qR0REGqfMFhFxHlfOYF8D7LXW7rfWngbeBG45\na59bgP+x1f4FtDfGdHFhTSIi0jBltoiIk7jyGuxI4FCd57lA/2bsEwkcqbuTMeanVM+WAJQZY75w\nbqke71Igz91FtDCN2T/445h7uruARiiznccf/11rzP7BH8d8UZntFR9ytNYuAhYBGGM+86dlhkFj\n9hcas38wxvj8uuHKbI3ZH2jM/uFiM9uVl4gcBrrVed61ZtuF7iMiIq6nzBYRcRJXNtgbgVhjTLQx\nphUwFlh51j4rgXtrPpk+ADhhrT1y9oFERMTllNkiIk7isktErLUVxpifAx8CDmCxtXa7MWZizesL\ngQ+AG4G9QDEwvhmHXuSikj2ZxuwfNGb/4JFjVmY7lcbsHzRm/3BRYzZa7lNERERExHlcutCMiIiI\niIi/UYMtIiIiIuJEHttg++OSvc0Y8901Y91mjFlnjOnrjjqdqakx19mvnzGmwhhze0vW52zNGa8x\nZqgxJtsYs90Y84+WrtHZmvHvOtwY87/GmC01Y27Odb0ezRiz2BhztLH7P/tpfvnjmJXZXp7ZoNz2\nh9x2SWZbaz3uD9UfsNkHxACtgC1A/Fn73Aj8FTDAAGCDu+tugTFfC0TUPB7pD2Ous9/fqf6A1e3u\nrtvF3+P2wJdA95rnndxddwuM+b+BZ2oedwS+A1q5u/YfOO4hwNXAF4287o/55Y9jVmZ7cWZfwPdZ\nue3lue2KzPbUGWx/XLK3yTFba9dZa/Nrnv6L6nvQerPmfJ8BJgMrgKMtWZwLNGe8dwFvW2sPAlhr\n/WHMFggzxhgglOqgrmjZMp3LWruG6nE0xu/yCz8cszLb6zMblNt+kduuyGxPbbAbW473QvfxJhc6\nnglU/zTlzZocszEmEhgNvNyCdblKc77HVwIRxpjVxphNxph7W6w612jOmF8A4oCvgW3AFGttVcuU\n5zb+mF/+OOa6lNneSbmt3IaLyC+vWCpd6jPGXEd1WA9ydy0t4HngEWttVfUPyj4vEEgGhgGtgfXG\nmH9Za3e7tyyXGgFkA9cDPYCPjDFZ1tqT7i1LxDmU2T5Pua3cPoenNtj+uGRvs8ZjjEkEXgVGWmuP\nt1BtrtKcMacAb9YE9aXAjcaYCmvtuy1TolM1Z7y5wHFrbRFQZIxZA/QFvDWomzPm8cDvbPWFbnuN\nMV8BvYBPW6ZEt/DH/PLHMSuzvTuzQbkNym24iPzy1EtE/HHJ3ibHbIzpDrwN3OMjPxk3OWZrbbS1\nNspaGwUsB37mxUHdnH/X7wGDjDGBxpg2QH9gRwvX6UzNGfNBqmd+MMZcBvQE9rdolS3P7/ILPxyz\nMtvrMxuU28rtahecXx45g21dt2Svx2rmmB8HOgAv1cwOVFhrU9xV8w/VzDH7jOaM11q7wxizCtgK\nVAGvWmsbvG2QN2jm9/jXwBJjzDaqP6H9iLU2z21FO4Ex5g1gKHCpMSYXeAIIAr/OL38cszLbyym3\n/SO3XZHZWipdRERERMSJPPUSERERERERr6QGW0RERETEidRgi4iIiIg4kRpsEREREREnUoMtIiIi\nIuJEarDFpxlj/s0YY40xvWqeRxljznv7pObsIyIizqfMFl+hBlt83Z3AP2v+FhERz6bMFp+gBlt8\nljEmFBgETKB6JaqzX7/PGPOeMWa1MWaPMeaJOi87jDGvGGO2G2P+ZoxpXfOeB4wxG40xW4wxK2pW\n7RIRkR9ImS2+RA22+LJbgFU1SxQfN8YkN7DPNcBtQCJwhzHmzCprscCL1treQEHNPgBvW2v7WWv7\nUr0U7gSXjkBExH8os8VnqMEWX3Yn8GbN4zdp+FeOH1lrj1trS4C3qZ49AfjKWptd83gTEFXzuI8x\nJqtmedi7gd4uqVxExP8os8VnBLq7ABFXMMZcAlwPJBhjLOAALPDiWbvaRp6X1dlWCbSuebwE+Ddr\n7RZjzH3AUOdVLSLin5TZ4ms0gy2+6nYg01p7hbU2ylrbDfgK6HbWfjcYYy6puV7v34C1TRw3DDhi\njAmiejZERER+OGW2+BQ12OKr7gTeOWvbCuC/ztr2ac32rcAKa+1nTRz3MWAD1aG+0wl1ioiIMlt8\njLH27N+2iPiHml8Xplhrf+7uWkRE5PyU2eJNNIMtIiIiIuJEmsEWEREREXEizWCLiIiIiDiRGmwR\nERERESdSgy0iIiIi4kRqsEVEREREnEgNtoiIiIiIE/3/7h9K4/jaC/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2408ddb0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G6qs2S0ReiJun+GpV/XFjZVV9wvfjctzw7z0+/RHcA8knRWRIRAIROVJEKudPK3uAiPQA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\n3/72tznttNOm1D/rrLPqvB4bNmwA4IgjjuCUU07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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2408deedb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_composed_method(func):\n",
    "    data = []\n",
    "    alphas = []\n",
    "    for alpha in range(11):\n",
    "        alpha = alpha / 10\n",
    "        my_method = {'composed': func(alpha)}\n",
    "        d = [ws_eval.evaluate_multiple_metrics(my_method, dataset) for dataset in data_word_noun]\n",
    "        d = [x['composed'] for x in d]\n",
    "        data.append(d)\n",
    "        alphas.append(alpha)\n",
    "    df = pd.DataFrame(data, index=alphas, columns=data_word_noun)\n",
    "    df['mean'] = df.mean(axis=1)\n",
    "    return df\n",
    "\n",
    "def combine(model_a, model_b):\n",
    "    df = calculate_composed_method(lambda alpha: lambda x, y: alpha*model_a(x, y) + model_b(x,y))\n",
    "    return df\n",
    "\n",
    "\n",
    "def plot_chosen(chosen):\n",
    "    info = []\n",
    "    chosen_model = eval(chosen)\n",
    "    for name, model in methods.items():\n",
    "        if name == chosen:\n",
    "            continue\n",
    "            \n",
    "        # ====================\n",
    "        # Left * alpha\n",
    "        df_l = combine(model, chosen_model)\n",
    "        info.append(['alpha*{0} + {1}'.format(name, chosen), df_l['mean'].max()]) \n",
    "        # plot\n",
    "        fig = plt.figure(figsize=(12, 4))\n",
    "        ax = plt.subplot(121)\n",
    "        plt.ylim(0, 1)\n",
    "        df_l.plot(title='Scores of Similarity (Alpha*{0} + {1})'.format(name, chosen), ax=ax)\n",
    "        plt.xlabel('Alpha')\n",
    "        plt.ylabel('Score')\n",
    "        \n",
    "        # ===================\n",
    "        # Right * alpha\n",
    "        df_r = combine(chosen_model, model)\n",
    "        info.append(['{0} + alpha*{1}'.format(name, chosen), df_r['mean'].max()]) \n",
    "        # plot\n",
    "        ax = plt.subplot(122)\n",
    "        plt.ylim(0, 1)\n",
    "        df_r.plot(title='Scores of Similarity ({0} + alpha*{1})'.format(name, chosen), ax=ax)\n",
    "        plt.xlabel('Alpha')\n",
    "        plt.ylabel('Score')\n",
    "\n",
    "    info = pd.DataFrame(info, columns=['description', 'mean_score']) \n",
    "    return info\n",
    "\n",
    "plot_chosen('word2vec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline semantic similarity metrics on Word Graph Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:47:02.428732Z",
     "start_time": "2018-03-11T05:47:00.559231Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
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       "      <th>graph_rg</th>\n",
       "      <th>graph_mc</th>\n",
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       "      <th>graph_ws353-sim</th>\n",
       "      <th>graph_simlex</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.602</td>\n",
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       "      <td>0.738</td>\n",
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       "      <td>0.776</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jcn</th>\n",
       "      <td>0.762</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           graph_rg  graph_mc  graph_ws353  graph_ws353-sim  graph_simlex\n",
       "path          0.782     0.699        0.331            0.602         0.581\n",
       "lch           0.782     0.699        0.331            0.602         0.581\n",
       "wup           0.738     0.711        0.362            0.612         0.537\n",
       "li            0.779     0.696        0.349            0.615         0.583\n",
       "res           0.765     0.713        0.363            0.622         0.530\n",
       "res_graph     0.723     0.717        0.310            0.540         0.366\n",
       "lin           0.776     0.736        0.320            0.589         0.578\n",
       "jcn           0.762     0.794        0.301            0.578         0.576"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_graph = lambda x, y: yagosim.word_similarity(x, y, 'res_graph')\n",
    "methods['res_graph'] = res_graph\n",
    "cor_dicts = [ws_eval.evaluate_multiple_metrics(methods, dataset) for dataset in data_word_graph]\n",
    "baseline_cors_matrix = [[cors[m] for _, cors in enumerate(cor_dicts)] for m in sim_methods_graph[0:8]]\n",
    "df_baselines_graph = pd.DataFrame(baseline_cors_matrix, index=sim_methods_graph[0:8], columns=data_word_graph)\n",
    "display(df_baselines_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline semantic similarity metrics on Word Type Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:47:04.883199Z",
     "start_time": "2018-03-11T05:47:02.430620Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type_rg</th>\n",
       "      <th>type_mc</th>\n",
       "      <th>type_ws353</th>\n",
       "      <th>type_ws353-sim</th>\n",
       "      <th>type_simlex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>0.679</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lch</th>\n",
       "      <td>0.679</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wup</th>\n",
       "      <td>0.613</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>li</th>\n",
       "      <td>0.673</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>res</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>res_graph</th>\n",
       "      <td>0.676</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lin</th>\n",
       "      <td>0.642</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lin_graph</th>\n",
       "      <td>0.627</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jcn</th>\n",
       "      <td>0.676</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jcn_graph</th>\n",
       "      <td>-0.367</td>\n",
       "      <td>-0.324</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>-0.372</td>\n",
       "      <td>-0.334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           type_rg  type_mc  type_ws353  type_ws353-sim  type_simlex\n",
       "path         0.679    0.621       0.353           0.601        0.616\n",
       "lch          0.679    0.621       0.353           0.601        0.616\n",
       "wup          0.613    0.606       0.357           0.589        0.538\n",
       "li           0.673    0.614       0.361           0.612        0.612\n",
       "res          0.667    0.679       0.355           0.595        0.540\n",
       "res_graph    0.676    0.704       0.294           0.490        0.387\n",
       "lin          0.642    0.696       0.321           0.539        0.592\n",
       "lin_graph    0.627    0.661       0.324           0.547        0.527\n",
       "jcn          0.676    0.805       0.340           0.546        0.594\n",
       "jcn_graph   -0.367   -0.324      -0.211          -0.372       -0.334"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_graph = lambda x, y: yagosim.word_similarity(x, y, 'lin_graph')\n",
    "jcn_graph = lambda x, y: yagosim.word_similarity(x, y, 'jcn_graph')\n",
    "methods['lin_graph'] = lin_graph\n",
    "methods['jcn_graph'] = jcn_graph\n",
    "cor_dicts = [ws_eval.evaluate_multiple_metrics(methods, dataset) for dataset in data_word_type]\n",
    "baseline_cors_matrix = [[cors[m] for _, cors in enumerate(cor_dicts)] for m in sim_methods_type[0:10]]\n",
    "df_baselines_type = pd.DataFrame(baseline_cors_matrix, index=sim_methods_type[0:10], columns=data_word_type)\n",
    "display(df_baselines_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Steiger's Z Significance Test on Word Noun Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:47:16.593217Z",
     "start_time": "2018-03-11T05:47:04.884699Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wpath_rg = lambda x, y: wns.word_similarity_wpath(x, y, 0.9)\n",
    "wpath_mc = lambda x, y: wns.word_similarity_wpath(x, y, 0.4)\n",
    "wpath_ws353 = lambda x, y: wns.word_similarity_wpath(x, y, 0.5)\n",
    "wpath_ws353sim = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)\n",
    "wpath_simlex = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)\n",
    "\n",
    "methods = {'wpath_rg':wpath_rg, 'wpath_mc':wpath_mc, 'wpath_ws353':wpath_ws353, \n",
    "           'wpath_ws353sim':wpath_ws353sim,'wpath_simlex':wpath_simlex}\n",
    "\n",
    "cor_dicts = [ws_eval.evaluate_multiple_metrics(methods, dataset) for dataset in data_word_noun]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:47:16.608226Z",
     "start_time": "2018-03-11T05:47:16.594215Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>noun_rg</th>\n",
       "      <th>noun_mc</th>\n",
       "      <th>noun_ws353</th>\n",
       "      <th>noun_ws353-sim</th>\n",
       "      <th>noun_simlex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>metrics</th>\n",
       "      <td>0.795</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         noun_rg  noun_mc  noun_ws353  noun_ws353-sim  noun_simlex\n",
       "metrics    0.795     0.74       0.347           0.645        0.603"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wpath_dic = {'noun_rg':'wpath_rg', 'noun_mc':'wpath_mc', 'noun_ws353':'wpath_ws353',\n",
    "            'noun_ws353-sim':'wpath_ws353sim', 'noun_simlex':'wpath_simlex'}\n",
    "\n",
    "cors_matrix = [[cor_dicts[i][wpath_dic[dataset]] for i, dataset in enumerate(data_word_noun)]]\n",
    "df_cors = pd.DataFrame(cors_matrix, index=['metrics'], columns=data_word_noun)\n",
    "display(df_cors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To perform the Steiger's Z Significance Test, one can use the implementation integrated in Sematch framework, or use the R, cocor package. The example scripts using cocor package to perform statistical test in Simlex dataset is shown as:\n",
    "```\n",
    "require(cocor) # load package\n",
    "#j means dependent sample, k and h means comparison sample\n",
    "#we have wpath with human (jk), jcn with human (jh), and wpath with jcn (kh)\n",
    "#simlex\n",
    "#wpath with path Pass\n",
    "cocor.dep.groups.overlap(r.jk=+0.603, r.jh=+0.584, r.kh=+0.955, n=666, alternative=\"greater\", alpha=0.05, conf.level=0.95, null.value=0)\n",
    "#wpath with lch Pass\n",
    "cocor.dep.groups.overlap(r.jk=+0.603, r.jh=+0.584, r.kh=+0.955, n=666, alternative=\"greater\", alpha=0.05, conf.level=0.95, null.value=0)\n",
    "#wpath with wup Pass\n",
    "cocor.dep.groups.overlap(r.jk=+0.603, r.jh=+0.542, r.kh=+0.946, n=666, alternative=\"greater\", alpha=0.05, conf.level=0.95, null.value=0)\n",
    "#wpath with li Pass\n",
    " cocor.dep.groups.overlap(r.jk=+0.603, r.jh=+0.586, r.kh=+0.965, n=666, alternative=\"greater\", alpha=0.05, conf.level=0.95, null.value=0)\n",
    "#wpath with res Pass\n",
    "cocor.dep.groups.overlap(r.jk=+0.603, r.jh=+0.535, r.kh=+0.913, n=666, alternative=\"greater\", alpha=0.05, conf.level=0.95, null.value=0)\n",
    "#wpath with lin Pass\n",
    "cocor.dep.groups.overlap(r.jk=+0.603, r.jh=+0.582, r.kh=+0.944, n=666, alternative=\"greater\", alpha=0.05, conf.level=0.95, null.value=0)\n",
    "```\n",
    "The example of using the integrated Statistical Test is illustrate in the following codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-11T05:47:16.799773Z",
     "start_time": "2018-03-11T05:47:16.610227Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>noun_rg</th>\n",
       "      <th>noun_mc</th>\n",
       "      <th>noun_ws353</th>\n",
       "      <th>noun_ws353-sim</th>\n",
       "      <th>noun_simlex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>(0.988,0.12237761609)</td>\n",
       "      <td>(0.948,0.351528419054)</td>\n",
       "      <td>(0.922,0.0321679269393)</td>\n",
       "      <td>(0.959,0.0107682312797)</td>\n",
       "      <td>(0.955,0.0206179473294)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lch</th>\n",
       "      <td>(0.988,0.12237761609)</td>\n",
       "      <td>(0.948,0.351528419054)</td>\n",
       "      <td>(0.922,0.0321679269393)</td>\n",
       "      <td>(0.959,0.0107682312797)</td>\n",
       "      <td>(0.955,0.0206179473294)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wup</th>\n",
       "      <td>(0.956,0.0425083374454)</td>\n",
       "      <td>(0.931,0.409258605134)</td>\n",
       "      <td>(0.956,0.420612449275)</td>\n",
       "      <td>(0.958,0.0917875911784)</td>\n",
       "      <td>(0.946,1.38934863614e-09)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>li</th>\n",
       "      <td>(0.984,0.281665699419)</td>\n",
       "      <td>(0.948,0.308981784008)</td>\n",
       "      <td>(0.95,0.208040059233)</td>\n",
       "      <td>(0.973,0.0897631401424)</td>\n",
       "      <td>(0.965,0.0192252476075)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>res</th>\n",
       "      <td>(0.939,0.239356192156)</td>\n",
       "      <td>(0.972,0.410158670791)</td>\n",
       "      <td>(0.984,0.455938513267)</td>\n",
       "      <td>(0.948,0.264502694351)</td>\n",
       "      <td>(0.913,9.47692808673e-08)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lin</th>\n",
       "      <td>(0.942,0.336047605166)</td>\n",
       "      <td>(0.976,0.334545281366)</td>\n",
       "      <td>(0.916,0.0270064898595)</td>\n",
       "      <td>(0.899,0.0431967198107)</td>\n",
       "      <td>(0.944,0.0215151648688)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jcn</th>\n",
       "      <td>(0.866,0.302109070882)</td>\n",
       "      <td>(0.901,0.057509130222)</td>\n",
       "      <td>(0.818,0.0197407219215)</td>\n",
       "      <td>(0.843,0.0181782667967)</td>\n",
       "      <td>(0.916,0.0292250544495)</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                      noun_rg                 noun_mc  \\\n",
       "path    (0.988,0.12237761609)  (0.948,0.351528419054)   \n",
       "lch     (0.988,0.12237761609)  (0.948,0.351528419054)   \n",
       "wup   (0.956,0.0425083374454)  (0.931,0.409258605134)   \n",
       "li     (0.984,0.281665699419)  (0.948,0.308981784008)   \n",
       "res    (0.939,0.239356192156)  (0.972,0.410158670791)   \n",
       "lin    (0.942,0.336047605166)  (0.976,0.334545281366)   \n",
       "jcn    (0.866,0.302109070882)  (0.901,0.057509130222)   \n",
       "\n",
       "                   noun_ws353           noun_ws353-sim  \\\n",
       "path  (0.922,0.0321679269393)  (0.959,0.0107682312797)   \n",
       "lch   (0.922,0.0321679269393)  (0.959,0.0107682312797)   \n",
       "wup    (0.956,0.420612449275)  (0.958,0.0917875911784)   \n",
       "li      (0.95,0.208040059233)  (0.973,0.0897631401424)   \n",
       "res    (0.984,0.455938513267)   (0.948,0.264502694351)   \n",
       "lin   (0.916,0.0270064898595)  (0.899,0.0431967198107)   \n",
       "jcn   (0.818,0.0197407219215)  (0.843,0.0181782667967)   \n",
       "\n",
       "                    noun_simlex  \n",
       "path    (0.955,0.0206179473294)  \n",
       "lch     (0.955,0.0206179473294)  \n",
       "wup   (0.946,1.38934863614e-09)  \n",
       "li      (0.965,0.0192252476075)  \n",
       "res   (0.913,9.47692808673e-08)  \n",
       "lin     (0.944,0.0215151648688)  \n",
       "jcn     (0.916,0.0292250544495)  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats_tests = []\n",
    "for _, dataset in enumerate(data_word_noun):\n",
    "    stats = {}\n",
    "    for _, m in enumerate(sim_methods_noun[0:7]):\n",
    "        cor, p_value = ws_eval.statistical_test(wpath_dic[dataset], m, dataset)\n",
    "        stats[m] = '('+str(round(cor,3))+','+str(p_value)+')'\n",
    "    stats_tests.append(stats)\n",
    "stats_matrix = [[cors[m] for _, cors in enumerate(stats_tests)] for _, m in enumerate(sim_methods_noun[0:7])]\n",
    "df_stats = pd.DataFrame(stats_matrix, index=sim_methods_noun[0:7], columns=data_word_noun)\n",
    "display(df_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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