Physionet.ipynb

{
    "cells": [
        {
            "cell_type": "code", 
            "execution_count": 75, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "# !pip install biosppy"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 73, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/html": "<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>", 
                        "text/plain": "<IPython.core.display.HTML object>"
                    }, 
                    "execution_count": 73, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "from IPython.display import display\nfrom IPython.core.display import HTML \n\n\n\nfrom IPython.display import HTML\n\nHTML('''<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "<center><b><font size=\"6\">Detect Atrial fibrillation from EKG signals</font></b></center>\n\n<br>\n<img src=\"http://images.medicinenet.com//images/slideshow/atrial-fibrillation-s1-photo-of-atrial-fibrillation.jpg\"></img>\n\n\n\n"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Problem ####\n<p>\n    <ul> \n        <li> AFib detection is not accurate </li>\n        <li> Subtlelities in Afib causes signals to be detected as \"normal\"</li>\n        \n        <br/>\n    </ul>\n</p>"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "#### Status Quo\n<ul>\n    <li> AF is the most common sustained cardiac arrhythmia, occurring in 1-2% of the general population </li>\n    <li> More than 12 million Europeans and North Americans are estimated to suffer from AF</li>\n    <li> Prevalence will likely triple in the next 30-50 years </li>\n</ul>"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Solution\n   We will go through the following steps:\n   - Read data\n   - Feature Engineering (Accumulators, Broadcasters, ml/mllib APIs)\n   - Data Visualizations (PixieDust, Seaborn)\n   - Modeling (Spark ML)\n   - Evaluation and prediction (Spark ML)\n   - Deployment (Watson ML repository)"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "#### Machine Learning process ###\n\n<center><img width=\"600px\" height=\"600px\" src=\"https://raw.githubusercontent.com/martinkearn/Content/master/Blogs/Images/MLProcess.PNG\"></center>"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {
                "collapsed": true
            }, 
            "source": "### Architecture ###\n<img src=\"https://raw.githubusercontent.com/joshishwetha/dsx-spark/master/arch.png\" width=\"800px\" height=\"500px\"></img>\n"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 1, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "import scipy.io\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\n\nimport numpy as np\nimport pandas as pd\nfrom biosppy.signals import ecg"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 65, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "%matplotlib inline\nplt.style.use('ggplot')"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "## 1. Read data\n<img height=\"100px\" width=\"100px\" src=\"https://pbs.twimg.com/media/C5GJbFiVYAIl94t.png\"></img>\n<center>Data Connect </center>\n\n<img height=\"100px\" width=\"100px\" src=\"https://mark.trademarkia.com/logo-images/bigsql/bigsql-85822451.jpg\"></img>\n\n\n\n<img height=\"100px\" width=\"100px\" src=\"https://media.licdn.com/mpr/mpr/AAEAAQAAAAAAAASuAAAAJDk3NGUwNWJkLWJkZGEtNGQzMy05OTJmLTIwZDc3OWU2MzZlYw.jpg\"></img>\n"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 3, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "# The code was removed by DSX for sharing."
        }, 
        {
            "cell_type": "code", 
            "execution_count": 4, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "#get session cookie\nheaders = {'Content-Length': '32','Content-Type': 'application/x-www-form-urlencoded'}\nparams = {'username':serviceUsername, 'password':servicePassword}\nurl = serviceURL + '/_session'\n\nresp_cookie = requests.post(url,headers=headers,data=params)"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 175, 
            "metadata": {}, 
            "outputs": [], 
            "source": "# %%time\n# url = serviceURL+'/physionet/_all_docs?include_docs=True'\n# docs = requests.get(url,cookies=resp_cookie.cookies)"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 174, 
            "metadata": {}, 
            "outputs": [], 
            "source": "# data_dump = docs.json()['rows']\n# x = data_dump[0]['doc']\n# x['label']"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 176, 
            "metadata": {}, 
            "outputs": [], 
            "source": "# %%time\n# def convert_to_num(row):\n#     vals = list(map(lambda x:float(x),row['data']))\n#     return {'vals':vals, 'file':row['file'],'label':row['label']}\n\n# dataset = list(map(lambda x:x['doc'],data_dump))\n# dataset = list(map(lambda x:convert_to_num(x),dataset))"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 177, 
            "metadata": {}, 
            "outputs": [], 
            "source": "# %%time\n# def normalize_vals(d):\n#     vals_norm = list(map(lambda x:x/1000,d))\n#     return vals_norm\n\n# def process_signal(vals,sampling_rate):\n#     out = ecg.ecg(signal=vals, sampling_rate=sampling_rate,show=False)\n#     res = out.as_dict()\n#     res['vals_norm'] = normalize_vals(vals)\n#     return res\n\n# res = list(map(lambda x:{'vals':x['vals'],\n#                          'file':x['file'],\n#                          'label':x['label'],\n#                          'res':process_signal(x['vals'],300),\n#                          },dataset))\n"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 78, 
            "metadata": {}, 
            "outputs": [], 
            "source": "res = list(filter(lambda x:x['label'] in ['A','N'],res))"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 79, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>file</th>\n      <th>label</th>\n      <th>res</th>\n      <th>vals</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A08135</td>\n      <td>N</td>\n      <td>{'ts': [0.0, 0.00333296296296, 0.0066659259259...</td>\n      <td>[260.0, 302.0, 352.0, 405.0, 444.0, 459.0, 463...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A08150</td>\n      <td>N</td>\n      <td>{'ts': [0.0, 0.00333300375717, 0.0066660075143...</td>\n      <td>[-481.0, -616.0, -778.0, -961.0, -1146.0, -131...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A08186</td>\n      <td>N</td>\n      <td>{'ts': [0.0, 0.00333296296296, 0.0066659259259...</td>\n      <td>[-75.0, -92.0, -107.0, -114.0, -118.0, -121.0,...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A08189</td>\n      <td>N</td>\n      <td>{'ts': [0.0, 0.00333296296296, 0.0066659259259...</td>\n      <td>[644.0, 762.0, 879.0, 993.0, 1091.0, 1145.0, 1...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A08214</td>\n      <td>N</td>\n      <td>{'ts': [0.0, 0.00333296296296, 0.0066659259259...</td>\n      <td>[119.0, 155.0, 209.0, 258.0, 308.0, 341.0, 371...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>A08277</td>\n      <td>N</td>\n      <td>{'ts': [0.0, 0.00333296296296, 0.0066659259259...</td>\n      <td>[345.0, 417.0, 495.0, 579.0, 658.0, 705.0, 720...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>A08315</td>\n      <td>N</td>\n      <td>{'ts': [0.0, 0.00333296296296, 0.0066659259259...</td>\n      <td>[-55.0, -62.0, -66.0, -70.0, -74.0, -78.0, -80...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A08354</td>\n      <td>N</td>\n      <td>{'ts': [0.0, 0.00333289206601, 0.0066657841320...</td>\n      <td>[4.0, 3.0, 2.0, 1.0, 1.0, 0.0, -1.0, -2.0, -3....</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>A07732</td>\n      <td>A</td>\n      <td>{'ts': [0.0, 0.00333296296296, 0.0066659259259...</td>\n      <td>[26.0, 32.0, 40.0, 50.0, 58.0, 62.0, 62.0, 61....</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>A07791</td>\n      <td>N</td>\n      <td>{'ts': [0.0, 0.00333296296296, 0.0066659259259...</td>\n      <td>[-515.0, -622.0, -736.0, -851.0, -946.0, -982....</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "     file label                                                res  \\\n0  A08135     N  {'ts': [0.0, 0.00333296296296, 0.0066659259259...   \n1  A08150     N  {'ts': [0.0, 0.00333300375717, 0.0066660075143...   \n2  A08186     N  {'ts': [0.0, 0.00333296296296, 0.0066659259259...   \n3  A08189     N  {'ts': [0.0, 0.00333296296296, 0.0066659259259...   \n4  A08214     N  {'ts': [0.0, 0.00333296296296, 0.0066659259259...   \n5  A08277     N  {'ts': [0.0, 0.00333296296296, 0.0066659259259...   \n6  A08315     N  {'ts': [0.0, 0.00333296296296, 0.0066659259259...   \n7  A08354     N  {'ts': [0.0, 0.00333289206601, 0.0066657841320...   \n8  A07732     A  {'ts': [0.0, 0.00333296296296, 0.0066659259259...   \n9  A07791     N  {'ts': [0.0, 0.00333296296296, 0.0066659259259...   \n\n                                                vals  \n0  [260.0, 302.0, 352.0, 405.0, 444.0, 459.0, 463...  \n1  [-481.0, -616.0, -778.0, -961.0, -1146.0, -131...  \n2  [-75.0, -92.0, -107.0, -114.0, -118.0, -121.0,...  \n3  [644.0, 762.0, 879.0, 993.0, 1091.0, 1145.0, 1...  \n4  [119.0, 155.0, 209.0, 258.0, 308.0, 341.0, 371...  \n5  [345.0, 417.0, 495.0, 579.0, 658.0, 705.0, 720...  \n6  [-55.0, -62.0, -66.0, -70.0, -74.0, -78.0, -80...  \n7  [4.0, 3.0, 2.0, 1.0, 1.0, 0.0, -1.0, -2.0, -3....  \n8  [26.0, 32.0, 40.0, 50.0, 58.0, 62.0, 62.0, 61....  \n9  [-515.0, -622.0, -736.0, -851.0, -946.0, -982....  "
                    }, 
                    "execution_count": 79, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "pd.DataFrame(res[:10])"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Types of signals ###\n"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 80, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "['N', 'A']"
                    }, 
                    "execution_count": 80, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "conditions = list(map(lambda x:x['label'],res))\nlist(set(conditions))"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Alternatively, reading data with BigSQL \n\n    sampleDF = spark.read.format(\"jdbc\")\n      .option(\"url\u201d,\"jdbc:db2://yourHost.com:32051/BIGSQL\")\n      .option(\"dbtable\",\u201dyourSchema.t1\")\n      .option(\"user\", \"yourID\").option(\"password\", \"yourPassword\")\n      .load() \n"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "## 2.Feature Engineering \n- Convert raw EKG signals into R peak distributions (open source python package)\n\n\n- Spark / Python \n- Spark MLlib/ Scikit learn"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 83, 
            "metadata": {}, 
            "outputs": [], 
            "source": "# conditions.index('N'), conditions.index('A')"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "## 3. Visualization "
        }, 
        {
            "cell_type": "code", 
            "execution_count": 82, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<function matplotlib.pyplot.show>"
                    }, 
                    "execution_count": 82, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }, 
                {
                    "data": {
                        "image/png": 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hhBCHBCEsRQcOjk+FYyhcBRSWCCGEEEIIIYQQ4o4ghCXTY0kxFK4c6kqEEEIIIYQQQghx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\n34YkI6p8t2c0zawfdm2P3uuI3OYaUy+2tSq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r6IZfRe/rljE3Bi+9S12rQs+WR4GxEYgX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55KchtrkVyHHHu6VPADQHuf5HoZWgXvaZfCu+yF8EZfTWP/7S/B/XKgAqmbTibb3Ut7nK3V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RxHJTHnJnoZ5nuB6QSYSnit22mIbUKlnYjCl3jQfDUluhVOA7jj2WvCsUlJshLpNKS7Be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\nwK8AeALA1wD0+v3+w5zP/Ri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
                        "text/plain": "<matplotlib.figure.Figure at 0x7fcbe4081358>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "source": "### plot data ###\n%matplotlib inline\nto_plot = [{'values':res[0],'label':'Normal'},\n           {'values':res[8],'label':'AFib'}]\nfig, ax = plt.subplots(nrows=2,ncols=1,figsize=(20,15))\n\nfor i,item in enumerate(to_plot):\n    d, label = item['values'], item['label']\n    y = list(d['res']['vals_norm'][24:])[:9000]\n    x = list(range(len(y)))\n    ax.flatten()[i].plot(x,y)\n    ax.flatten()[i].set_title(label)\n    \nplt.show"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "## 4.Machine Learning "
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Vanilla statistics based\n\n- summarize each signal by its mean r-peak and mean rr-interval \n- generate a distribution of each of the above datasets (so you get 2 dist overall)\n- use the same function as above\n\n\n### Spark Mllib\n- Assemble a vector for each signal and label them\n- feed into a multiclass classifier\n\n\n### Deep learning\n- generate images of each signal and feed into deep learning classifier"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "## Statistics approach"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### 1) Collecting r-peak and rr_intervals distribution"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 93, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "CPU times: user 531 ms, sys: 56 ms, total: 587 ms\nWall time: 586 ms\n"
                }
            ], 
            "source": "%%time\nimport functools\ndef get_rpeak_vals(d):\n    vals = list(map(lambda i:d['res']['vals_norm'][i],d['res']['rpeaks']))\n    return np.mean(vals)\n\ndef get_rr_intervals(d):\n    r_peaks_seconds = list(map(lambda x:x/300,d['res']['rpeaks']))\n    rr_intervals = list(map(lambda x,y:y-x,r_peaks_seconds[:-1],r_peaks_seconds[1:]))\n    return np.mean(rr_intervals)\n\nr_peaks = list(map(lambda x:get_rpeak_vals(x),res))\nrr_intervals = list(map(lambda x:get_rr_intervals(x),res))"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 94, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "### casting data into DFs for downstream analysis\nr_peaks_df = pd.DataFrame(r_peaks,columns=['r_peak_mean'])\nr_peaks_df = r_peaks_df.reset_index()\n\nrr_intervals_df = pd.DataFrame(rr_intervals,columns=['rr_interval_mean'])\nrr_intervals_df = rr_intervals_df.reset_index()"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 95, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "def get_moving_average(d,window_size):\n    window = np.ones(window_size)/float(window_size)\n    result = np.convolve(d,window,'same') # for same number of moving avg points\n    return result\n\n\ndef get_anomalies(d,window_size,sigma=1):\n    moving_avg = get_moving_average(d, window_size)\n    residuals = np.subtract(d,moving_avg)\n    \n    #calc mean and std of residuals\n    res_mean, res_std = np.mean(residuals),np.std(residuals)\n    print ('Vanilla:',res_mean, res_std)\n    \n    #collect a list of points in the original signal more than 1 std away from residuals\n    anomalies = [(i,point) for i,avg,point in zip(range(len(residuals)),moving_avg,d)\n                           if point >(avg+(sigma*res_std)) or point <(avg-(sigma*res_std))]\n    return {'vals':d,'anomalies':anomalies,'moving_avg':moving_avg}\n\n\ndef get_anomalies_rolling(d,window_size,sigma=1):\n    moving_avg = get_moving_average(d, window_size)\n    residuals = np.subtract(d,moving_avg)\n    \n    #calc mean and std of residuals (rolling vals using pandas)\n    residuals_df = pd.DataFrame(residuals)\n    res_mean, res_std = residuals_df.rolling(window_size).mean(),residuals_df.rolling(window_size).std()\n    testing_std_as_df = pd.DataFrame(res_std)\n    rolling_std = testing_std_as_df.replace(np.nan,\n                                  testing_std_as_df.ix[window_size - 1]).round(3).iloc[:,0].tolist()\n    \n    #collect a list of points in the original signal more than `sigma` std away from residuals\n    anomalies = [(i,point) for i,avg,point,res_i in zip(range(len(residuals)),moving_avg,d,rolling_std)\n                           if point >(avg+(sigma*res_i)) or point <(avg-(sigma*res_i))]\n    \n    return {'vals':d,'anomalies':anomalies,'moving_avg':moving_avg}"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### 3) Test with a single signal and plot results"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 96, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Vanilla: 0.00934523809524 0.0672618952802\n"
                }, 
                {
                    "data": {
                        "text/plain": "<matplotlib.legend.Legend at 0x7fcd0502d940>"
                    }, 
                    "execution_count": 96, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }, 
                {
                    "data": {
                        "image/png": 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S77pKXAo1pFQDLujHxo6NvLUrzFFvjPCBv/mUCOhY9+TGv7qBKx9cxQ9EaaE1\ntqCeCWS1EtSFYuZs+YvFYgHgBuAkYBNwdiwWW1/huGuAh+Z6TTdun4ZhVBT8stnyu8ZQKFTRQugn\nGjZpwSEubXoRCIhiRLcxubZ1LYG6cUg1yaIvCLPEwKCtroU3Lz1GPBsmMTBY13YoLeEWre6J7M2m\ns7FjIz87/eeEggGtvAsW+n6hlFq4fR4LvBCPx3fE4/E0cCdwepnjPgX8N7Bnrhd04/ZpJ3cpJJvJ\n0HvDdbxtdT99N15HNlts5bOTwghCKXbA+blfekpc2jTAMGQynxqTmz+lxZjc2LGRj2z6KCcdfJJW\ni74gzDcCAfFsKIduc75u7dEBA4P1rRsJBQNaCeq5nFUjXLCohfC3Enit4PXrk+9NEYvFuoC/jcfj\n34G5j4R8ts/KlLPgDd78NVZ97mt0jx3EIVdcw+DN17v6np+YpmiXdGAqS97m74gfuwaI5a9gTF63\nX4sxaWCwvGE5h7YdosWir4NlVIc2CPMTEf6K0XEvpFt7dME0Z5aN3wtyOemvQrxKc/l14NKC1667\noKura9p7K1fC0BCU+WiK4eFh0un01OtAIEDooQcYoYkmRgAIPXg/gU9eSq5glm1oaGDp0qVum6ec\n1lYYH4eurma/m1KWcv1zIHL4yOFWnRggHAhz+JLD581vny/tnAltbZDJQFdXi99NmTOz7Z+pMZlu\n0mZMtrZCNqtHv/zmhd9wytU3kj3sHsKBMA9/+GHeedg7Z3Wu2d7X37zwGytF/WAH4da+ObWh1vg9\nVmrNgfR7mpqgvb2h6h5nvjHX/tm3DyIRvfq5vh4WLao7YPqpVvd2aAhCIb36KpWylMY6tclPaiH8\n7QRWF7xeNfleIccAd8ZiMQNYApwSi8XS8Xj8504nTyaT094bHAzT0xMgmazs+2mXdbDJ5XJkTjqF\n0S2308goAJmTTy0S/ADGxsYYHBx0apZnDA42MTwcJJnUp002XV1dZfvnQGRd07qiOjHrmtbNi99+\noPbR8HATQ0P+Phe1qGc0l/7RcUwODjYzMmKQTA752g6A7fu2k/3xz+DzBulcmu37tnNY02EzPs9c\n+mj7vu2WdfYru0h/PjjrNtSeA2teONDmubGxDnp7x0gmx/1uSk2oRf/09ITIZjtIJvfWqFVzJ5Va\nzL59w1X3ovOFWj5Dg4MGhrGcZLKnJuerBePjYBgrSCZ3+d2UWVNLwbUWwt8TwLpYLLYG2AV8GDi7\n8IB4PH7+5EuOAAAgAElEQVSI/XcsFrsNuNeN4FcJK+av+iarXOH21vMu4s/7l1B3U4aXP3sZrZ+4\n0NX3/MQynwt+I1ny9MIw/HeLmio8nEsTDnhfeFjHManT9FmYcMavBDR2Xb00krZfcI+OLo5+Y5qG\ndvfEMEyt5jxdsOLr/G5FMaapX5v8ZM63Ih6PZ4FPAr8AtgJ3xuPxbbFY7BOxWOzcMl+Z86PiJuYv\nm81OE+SCwRCRMz5OwyEr6fiXiwkGi2Vf0zTJZrNzbV5NkUVAEKajw3PRPdhN+nu/BNAi5k4X/O4X\nGzvhzC3vu8W3BDRSV0+YDTrMb7phbd71krQkMU95dIyv0zEO0U9qEvMXj8cfBKIl791c4dh/nuv1\n3JR6sGv2lRZvz+WgUkLPTCajneUPLO2SIAjF+L24rG1dC6+eCPhnWdINnaZP2wX3tDWn+doG3ayz\ngjAf0VGgkKzTlTC027eK5a8YrxK+1BQ3pR7AEuZK6/1ls+UHQCaT0a7AO8jEIgjl0MEdutCyZMf8\nCYIgzBXJZjwdHa2hIvyVR0e3Tx3b5CfzVvhzsvzZpNNpTNOcKuBeOgBsC6GOgh+ItkIQyqHDRkAH\ny5JuyEZIf6SP9EeEiunouHmXfiqPjn2lo+XYT+al8FdX55zwpZBMJkM2myUYDJJOW26UtotnudhA\nnZABKwjTsYQ/fZ/bhYoOQrlQHY2XO2GSQMD01buhFpmMa42Oc4sIf+XRUfgTQ0ox81L4i0SgoISf\nK2wLXypl+SKnZ3oCn9DxIRIWJrptCHTbCAgW0i96o2PiDKEYv7MZT2UyfuFEwoc94nkm43Lome1T\njxAE3dDRaKFjm/xkXooVlj/87HoxlzPm1cKno7ZLWJjYG4Jzv/g0p919Gs/1PedbW0TbqifSL/oj\nmyD98Tvmr3uw26pPefsvtclkrKPSwrLQ+t0K/TBNHffZ+iWh8ZN5KfzB7CfGatk+dURHbZewMJna\nENx/oxYbAnku9ET6RW/E/Ul//Lb82fUpQZ9Mxjp6QfndT7qiY1+J0qsYzbrHPbPtxPk4AOZbe4UD\nE502BKJtFYTZMR/XQJXkzBxb+7ayecdmtvZtxdSgFpjfQoWO9Sl1HLcS81ceHYU/UXoVMy9j/uaC\njoNSEOYD9obgfZ/TZ0Mg6IduGzShGNkEFbOtfxunfPUassFhwqv/pEV8m9/9o2N9Sh1DYET4K4+O\n+2wdlQd+oln3qMeK+fO7FYIw/yjdEPiZ7EUWXD2RftEfK15eOsqme7Cb7Ctvh+1/rYU7O9iWP9mp\nFqLj5l2Ev/Lo2Fei9CpmQVr+gkF5WgVhvqPb4iJIjPJ8QKdNkA4ZhNe2riXAbnJGxnd3dhtJJDId\nncatjd/uubqi4zqgi0Cqw5wHC1T4c5pAdOkcv5H7IOiKbIz0RYcFVqiMLpsgmHS5/NbnyLa8RLi1\n3xeXy40dG/nQYYcySi//esbbtXBnDwREqChFR4FCt/bogrXP1muR1mX8bOvfxinXfIvsunsJB8K+\nuZlrpkdRTzbr/MBu69/Gyf/vPZx7/yd9T2nvJ9v6t3HqTZ/m3HsuWdD3QdAPHbXAggjl8wGdnp3u\nwW6yWy6A7nf75nJpYNARXsrGJYf77s5eiAh/xehb6kGP8aITOs0xNrqMn+7BbrJ3/BzAVzdzzbpH\nPaZpOLp9dg92k/v1v8N4mzYxAH7QPdhN5t5vQs9RC/o+CPqh4+IiWEgtJb3RyfK3tnUtRt0oTLT4\n6nKZzTrvC7zE7zp/OqLTuLURt8/ySMKXyhTOcX7OefPW7XMudf6cBuXa1rUQGYF0ozYxAH6wtnUt\nBgYm5oK+D4J+6OLCIRQjG1b90UlxsrFjIycfsoFFh6zjnDM+5JvLpS4bQxsdEon4ff1SdBq3Njr0\nk47o9jyBPuPHnuNued8tU+FUfjBvhb/Z4kb429ixkVXt7Xz8uOs4/sgO32MA/JpcNnZs5MhlDfyf\nYz7Nye+u8/0+CIIgCHNDJzc1A4PF9e1sbG9mU8eob+3QrYyADpY/3eK2dBQoRPgrjy6CViG6jB/b\nrfy0Naf52o55K/zNthPdxPwZGDRGIpzQ+S6iHZnZXaiG+DVgDQyawk0cu/StbOpI+dMIjZBJXtAN\nGZPCTNEl9sVGB0FHP+HP9L3Ug26ue7r1Eejh9qljYj6rpJo+cwzoOX78ZN4Kf7PFNCEYdD4uGISM\n/3KfK558MszoaIATT5yo+bl1WJh1wdIcyc0Q9MFa0PQak7LA6o0uGnCbYNAkm/W/QTrdEx2EimxW\nN+FPP4FCB8vftv5tnHb3aaR3H0J4+cu+ZY8sRDfFAejXJr+FUY1uhTe41UhYqZY1Wg2qsHlzA7//\nfUTJua3JbX7cB9Xo6MogLGx028j7vRESnPF701FKIGAJGn6i2z3RQajQTdjSba4DPdrTPdhNOpOF\nG5/XJjGfboIW2HkC9BjPgYDp+5ynWfe4R2XCF7C1kbO7hpeMjhosXpxTNglZ7idqzj3f0HFCExY2\nuikk/N6wCs7IPDYdHYU/v9dd3caJbnMd6OEZtbZ1LaFcM4TGtEnMp6OgrtP4CYX8V3hpcivU8swz\n4am/3cT8geX26XfnuKG7O8jBB2eUPWi6PcB+ottiKAg6jkmZM/RGp02Qjd9jRrfswTr0j25zi44C\nhWH4H5u5sWMjd53yMxrqA2w+Y7MWifksq7HfrShGp/FjWf78bYxm3eOemXTiKacsnfp7JjF/fneO\nG/r6AixalFNmzrbcX5Wcet6hk9uAoA9+an51WtBs/NaEC9WR+Xw6usXO6hB2otvcopt1FvRwzzUw\nOKRxA031ITZ1bPI92QvolykW9FJ6hUL+5xTR5FbMnNn6zLqN+Zsvbp/9/QHa2nLK4vLcuJ9cdlkb\nr7ziQqKe5+imCRX0wM/FXzeFhDwf8wG9Yrl02NTr0IZCLIuSv23I5dwpyr1CN2EU9FGOZzKWQKEL\nOvaVTm1yM2727lW7mM7bpbq11WRgoHpP/uhHjfz2t8WJUNxu4HUIQnfDwIAl/KnCSbOVSlmfP/JI\nnbI26IJOk4egB4GA6bPwp5fAZSmL9HpI/NbM64Zu85gOgpduY0SHWDLdFEug11xn43c/gTujxiOP\n1PHjHzd60h7d1iXQY56xcfN8H3VUp9o2KD27Qjo6cvT1VW/+88+H2bYtXPSe25i/UEi/TUw5+vsD\ndHSom32szW3l+7B9e4i3v32C/fvn7VByjY4TmuAvfmt+dbNG67K42hiGv8K5jug4j+k2bnTAb4uS\nbnOLjrXjdHD7BGtf62SlffnlYFH+C5XopmAC/eJ6nbz16utNxsbUNVijR3tmdHTk6O+v3vxFi3Ls\n2xcgGMw/nW5dGYJB03efXDeMjRk0NFi/T8Uk5LS5TSTCbNiQnheC8lzRbTEU/MfvrHw6LrI64Xf/\n6IhuY0aHUkI63Q/Qw/KnW50/3cYtzC/hz8u26rhXspReGnQW7jyGOjpy9PaK8DeNRYtyrqxNr7wS\nYs2a7NSNdqv1nC9un2A91OGwSSpV+3M7CX8jIwatrWofqJyZ4y+9W7mvezNb+7Zi4s8D7PfikzNz\nbO3byuYd/t4HIY/fmzQdrTg6ocPmzO/rl6LbmNEh2Yrfz3EpOrRHt5g/ndz2bHR5jiyraPVjvJwL\ndasRCXoJpE776lQKlizJMjCgrsEahYjOjFWrsvzxjxFgouIxhgGdnVmamy0rXjhsZfC0pe5qE0kw\nOD/cPm0iEZN02qCurvYPXLVBOjFhEImofci39W/jlIsfJGdMED7+X9l8xmY2dWyadtwrrwQZGTF4\nwxvUmGz9ntC29W/jlK9fRXblFsLBcMX7IHiHtaAasEAVErqji/Dnt3BTiI6WUL/HsA4p+wvRIXZW\nt7lFt/aAHv0EtuWv+hyTzVp7YC9wK2j967+2c+ON/eobhF7jx8kjZWAgwIoVWcbHxfI3DbeWvy98\nYZDDD0+TTls3MZez4/mqfy8Umh9unzaRCKRStR8oThrIiQlL4FT5UHUPdpMbXA7pJtK5NN2D3WWP\nu/32Ju67r0FZO/zWHHUPdpO94x4YW1T1Pgje4XdMmd8Kidnw6KMR/u//bfPkWm4sKCo8Jgrxe94o\nRTfLHzj30SuvBNm5U50ZSgdLWyE6bFJ1m1t0HLd+z/82btw+x8cN6uu9aaybOW98HDZvVrdfK0Un\ny7HTfNPfH6CzMycxf+Vw04n2zQ2HYWLSQJjLWS6STi6d88ntEyzLnyq3T6dsn3WKE32ubV1LIGCA\naRAOhFnbunbaMWNjBm1tOaXpjv3WHK1tXQuhCchGKt4HwVt0SPiiy4Jm47QZ+tWv6uns9OamuYn5\nO/potVnVdOsj3bI45q3nlbnjjibuvFNdpkI34+R//qeOLVsi1Q+qIX4LFW428Dt3BnnqKW/MSTpt\n3m108CwAt8KWGs+wSu1x6qu9e4MsWeLd4ummTaYJDzxQr7wtTvPN6KjB4sUi/M2ZcNgkk7Fuomla\nlj8nwW6+uX1aMX8qLH/V3WEs7aDaCXBjx0Y+GD2LDxx2BpvP2MzGjo3TjhkdNWhtzWGa6trit+Zx\nY8dGFjd2cO2JX6l4HwRvcbNp7O1VN2jcjMmJCbjppiZlbSjFaYFtaFDrKTC9LdUvNjBgKFX02X2k\nwyYR3G2iJybgiSe8EXSCQeeadg0NauveuXmOf/ObOp55xjvhz4lsFn75S3WaVzcCxcMP1/Gf/+lV\n+QC9LJHgTvjbtSvAiy+qjbDK5QxHt0/rGKXNmMLNujQwYLBoUc5DDzvnuMg9ewJcfLF6rxQnhdfE\nhEF7uwh/FXGbyc1yibT+zmYNwmFn//755vZZV6fGfUmHbHkGBksblnB423o2dWzCKLOZS6UsC29L\nS46RETUPjN8afAODSDDMu1f8VcX74BW6bGT9xE6IUO1ejI/DW96yXGkbnBa0v/wlzD33eOdeUw3v\nx42zW1ZDg6k0tsLqIz3cw8Ddxuzhh+v52teavWkQ7tYYlXOvG6VeS4va1OulOP3e7u4gDz+szkrh\nZr3r7w+waJE3GwS/199yuNkf/fSnjdx+u1oBWb/MrM6C+tBQgNWrMwwOetNwa/xUb9PrrwdZs0a9\ny5/TejA+jgh/1Vi2LMuePZV/gj1RhMNmUcxfJOJs+dNB6JkJqix/urg1OPVHJmMJ9W5jQWeDDppH\nN1ryu+9Wu9HXJc7Bb6z44er9sX9/kPZ2dROJmw3Rtm1hNm5MK2vDTEilLBd1LzZxpuksnIP6ekqm\nabhqx/bt3uRfczNmXnghxBvf6M2Y0cEqqkvijpnw/PNhhoZUKi2cLSWZjKE01KIQvz1vyuFmf2Rb\nuFSin/Dn3J7BwQArV2YZHPTmuXMzfvr6vFFmOO1nx8ctTzYVe3objYbLzDn44Awvv2zNPLt3B/jB\nD8prV0qFPzdWPcvts6bNVYqd7bPWuI1pUr2Zc5pkbcvfokU5ZW52OiRucNMfF1zQTlrhvm2+KUZU\nkbf8VR78+/YFOOigDKOjah4QN4Vr9+4NsGyZHh1mJ4gC9Rt+u3+qjVXTtIQ/lZY/t0Lou9+9TOlz\nW9gep3ksm7Xqx05UTqZdM3QQ/txaZv1uZyEvvhhi1Sp1VgrbYu2EV9Y4N2vO88+H+OxnW9U3ZhI3\n+4FQiKmwI1W4cen0MmbSjYJpYMBg6VK1Ak5pm5z6q7c3QGdnlrExtW1xk0ixpUWNQcemJjqbWCx2\nMvB1LGHy+/F4/NqSz/8OuHTy5RDw/8Tj8Wfnet03vjHNHXc08ba3pXjuuTAPPVTPRz4yOvW5fXMj\nkXzCF9O0hARLy1f57lsWlvmjCVSV7dNJ6PJqMXSaSGzLX1OTqWyjrYvw52S1bmoyGRkxaG9X0zm6\nWIP9xs6wVu1e7NsXYP36DHv3BpS4k7gpXFu4kVednMmJVMoS/jIZS+BqaFA3kOzntVr/7N0bYPVq\ntSm17XZU27wODRkcdlianp4gBx2k1u3IreKmszPLnj3q2+PG6qZ6znFSaNkuYzoUpLexniV153db\n588rgcKN0uLRR+s8EybAvcVY9XrpptSDl7gR/gYHAyxdmmViwjvhz4m+PmudHh4O0NCgTmHqtB5Y\nwl9OaSbqOW9lY7FYALgBOAnYBJwdi8XWlxz2MnBiPB5/I/BF4LtzvS5Aa6s55fawfXuIww7Lm/MK\ntRzFlj+DUEiyfbrFGqT6LHiVSKctob6hQZ0Ll9uYg/PPb1dyfXBn+WtsNJXFPdptEOHPsro5uX32\n9QVYuzajzD3LrUKipSXH0JD/jh6W5U/dfFWItXmtbtHp7g6xfn1Gqdunm3bs2RPgqKPS7NrlTUYG\nNwWhVbrQF7fF2eqm2mrhJFyOjho0NnqXqMgNqhWRbucWlUnWSq/jxnKzdKl3Xg5ulBJejBk3bp/e\nFnl3bs/QkJXR0gvvArdtGh4OsGxZVpkBoRBnt0+1lr9aTB/HAi/E4/Ed8Xg8DdwJnF54QDwefzwe\njw9MvnwcWFmD604xNmbwyishWlvzI3v//gAdHdbdLXWJDAadBTu/U7jPFHUxf9UX5sKJTeX9clr8\nUynL8ldXp86Fy80GZHjY4LHHIlVjUeeCtVGqln0VmptzjI6q2xm4cfv8p39aRF+fRjslBdiWPyeL\njhXX4J8rsmFYySpUxge5ZWLCmo8tBZxqVyjL5cpJ6Fq9OqPc7dNJYTI2Zo2Tvj71wlYu51zqwTRh\n8eIc+/Z5Ifz5v9Y6tcEW/nRTeqlsj1tlp+U2ra4d+fY4u7iDt0lh3MS/e9EeLzN5usFNfoRczqCp\nyfTM8ucmRALynlMqceP22dycUxLKNdWGGpxjJfBawevXqS7cfQx4oAbXBWDFiizf+lYz5503DOQn\ncMsSaAVQFGb7BHcunTosSE5YcW7WCIpEUBIv4iZmBuxyGrW/fuF1qj24lttncVmPWuNmo/388yFO\nOmmcHTvUzMROFulUyrKIq4sxc2f5e+WVEHv2aLQaKcCuGVrtXuSD2tUJf24WtNZWnSx/JsEgyrMp\n22Voqs1fVkym2hgPN1lhx8ascaKyLIiN28QZjY3eZbes1ke2S5vK2oRuLH8qXZR1xG2Cs+bmHMPD\nXo3b6u0xDDuBk/LmAO7qIIdC6seNjm6fbuaYSMRL4c/N+FEbOlR4HadSDw2KE3R7uhuIxWLvBj5K\nPv5vzhxxRJonn4ywdm2W5cuz7N5t/SQrw521uyh0+7QXPuc6f+o103PF1kaCuofIfTkNNQlnbJwt\nf1Y/h8NqSl5YbXBeDHfsCPGWt6To7VUj+DgJ4xMTVrF7Ve4C9gTqFPvS2JjzxHXCT9wkFEmnLfcN\nVa4tbt3hmptNz7KqVcOO+bOSIKi9lu1uWa3OX19fgK4utTF/+Xmj8jXGxgw6O7MMDHjjbuQk7BiG\n7Zqrvj1OG2hb8FKZaMqNZdZeaxcKbjfwKkMtStvjZq5ra8sxMODd1taNxVg1thdKNXRL+AJQV+fN\nHAPu29TQoF74c7b8WfOvSmqR8GUnsLrg9arJ94qIxWJHArcAJ8fj8T63J+/q6nL4HN77XohEujj6\naBgbs94bGoIjjrCKNfb0WDe6qwtaWmDJEutftVO3tlrHdnWpL/hYjZYW6/+urpZpn+Vy1m/o6mpj\neBh27ar+m2ZDc7N1L7q6ysextba20NXVwuLFsHhxG4sW1fb609tRPpNXW5v1+UEHwdat0NXVUfM2\n9PVZFtZqY3JoCE4/HZ56qvZ9Adb1lyxZVvHcgQAsXw4tLfVTxzg9QzMhm7Vc6To7V9BcoQzYvn1w\n+OHQ2LhUyT2A6s+FV4TDUF8Py5d3VvydLS1w0EEtvPxy5fEwl/4ZHHQek3YbenvVjMnSa0Hlfnn5\n5bwrptMcPFf6+60EN8uXd7J0afljGhth7VoYHq7elrn0UTBo9VFn5wqamsofU19vzV0vvaR+zXnt\nNet6K1Z0Vdzct7TA6tUtJBJq5tJC2tqsMVFpbk8mYeVK6O2FpUtbKyY5mUsftbba5yjfhldfhVWr\nYOdOWLGiRfkm2uk5SqVg0SLL20fVHPj669bz4zS3rFhhr83VzzfXdSi/J6tcf7KlBQ4+2G73nC7n\nuk0NDZXHTS5n3R9rr6ZurWpvt+Yyp74KBKCzs6Xic1+rvUJbm7Vn6+qqXN/Qy3UJrOfF6qvKF2tu\ntuaavXvVtslpH9fUBAcd1Do53tWMm1oIf08A62Kx2BpgF/Bh4OzCA2Kx2GrgJ8A/xuPxl2Zy8mQy\n6frYxsYgTz0V4b77gjQ1mSSTIwD094fo6QmRTI4zPNzC4GCGXbvSNDVVVj2PjrbS15edOodfDA1Z\nHZ9MDk37bOvWMMFgkGRynP7+IHv2REgma+vvMDbWRn9/mmRydNpnXV1dDA0NkUwOMTrayGuvjTM+\nrkY1OzTUgmGYJJPDZT/ftauOXC5HY2OWPXvqy7Z3rvT0hMjlOti5c2/FxX/fvhZyuRFee01NG7LZ\nJfT0DJBMlvfxffXVIOFwM8nkOMnkBF1dXTN6hpxIpyEQ6GTnzt20tJTXTG3fHmL58kZ27JggmVRj\n8qr2XHjF3r0BYBG7dvURDJZ3JRgaaqG/f4Tdu8uPh7n2jz0mk8m9ZT83TasNAwNjU3OgSpz6ZefO\nCJEIDA0F2bkzRSSiLquWFXO6hF279pNOl5+XrHszyq5dlefOufbRnj0BTHMJyeRemprKPzOvv97A\n4sUpBgYalY/p3bvDZLNt7Ny5r6K1YGiohb6+yuO2lgwPW8JUpd/98stBUqkIo6MBXntttKwlZa59\n5DRud+yIEA7D+HiE7u5h5Vlzndqzb18Aw6hncDCobLz09ITJZNpIJvdVbefYWJpXX83S3l457qQW\n61BfXxMjI0GSycGKxwwPNzMxkaa7O0dHh/q6KcPDLaRSlfclr7xijd2hoZDS53rfvnpSqQaSycp2\nleHhFhobc7z8sppnqJDe3gbGxupIJvurtKeZvr4xJfvWcuzdW0cq1UQy2VvxGGu9Vr9WZrNL6enp\nY9Gi8jLI0FALyeTQ1P82tVTkz9k2Ho/Hs8AngV8AW4E74/H4tlgs9olYLHbu5GFXAIuAb8disT/F\nYrE/zPW65Vi1Kstf/hImlTL4+MfzQluh2ydYbmvObp/6Z7lMJoN0dVk/JBxW4/YZCLgreVEaV1lr\n3MX8mZ7E/DnFuzU355TFeLlx+1RZHNRN5sK+vgArV2aUJp1xw3XXtbB1q7oKxG5S+IPl2qIqrsHJ\nlcV2hw6F1D6fbpmYMDxL+GIXV/ezf8BtzF/etVE1Vt1B50QVXrl9Ov3m0VErMYRqV2E3bp+qa0K6\nZXDQqgOmMoOj/fw40dDg3T1xckM1TcPTODKn9njlLuzW7dMLl0aw9s5u+iocRmm4UPH13Lqiql8r\n3T63KhM61WRnFI/HHwSiJe/dXPD3x4GP1+Ja1YhELIHoH/5hdNr7hZ0ZCk2P+RsYMGhry99pNwKi\n3+zcGeSYY6wf5neR91IBu9ZYmZoqPwl2qQdrMlHVBrebJnUTmlN/2CmCVcWYuYlz6+8PsHx5zvfs\nks88E+boo1Ns2qRmx2i5wDqPB5XChVPyDjvmRKVSZCakUtbmzIuEL26ELsNAaYbgfDuqlwTxKjYI\n3CXCAfVCsVtGRwM0NloKA9VjphK2cJ4XiP2N/yt8rtNpa82pNW6KvNsJVrwQ/txm+6yvx7NERU51\n/uyxq1qp46bUA3gbn+kmAY09fr3ATaIrwzCV72Wt6/hfLsv/9G815oYb+jjhhOKdb6kmyCr1UNy5\nb33r8qLX8yHbZ29vgEWL7HIWarQVboPs1Qt/OAh/xqSFQ1073FoSVNejqqaUsIQ/lQlfDMcNdV+f\nwbJlWc80epVYskRtnTKrzl/18WAJF+o0iU6Wv/wmUQ/L38iIN1YccGelNk1rs6hSyHFb6qGhwZtS\nApYSq3p7TFNdBumZYiV8yZVdt73CtuCo9nCZeXvUWWfdJnzxqtSDU7bGTMb6PBLxpj3gvNaPjBg0\nNqrfSLop9WAY+iXn8ULQsnFX4sZQVjatEDclu0DtXvKAE/4ikek3rFSDWc6qV+qmp4vw57aGjKpF\nwEn4y5d6ULtRcDLZW8Kf1Q5Vm0o3lgTVWJvZalkDUVocNL+hrp5BcfnynO8bx46OHP396qY4N3X+\nwJ5L/LH8jY0FtLL8jYwYNDebnmRTdmOlBnt9UNcO29JWPSW8ocR6Uw4nS6SdNt5qs/9jptDtU+Wc\nUl2JEiix/PnL2JjdHv8US+m0NU6s0greCBTV5jorPb43LuVuGR625jtQX5OxmmBsX9tL4a9aX1lj\ny/RUweS2xI0Xbp86yBcHnPBXjlK3hNIi7+UeSjcbtv37A8oFgWqTb+FnliZdjdunG7yw/FXDjm1S\nuWFx476lGqdJIx/zp+b6bqwGo6MB2ttzvrqM5XIor9eTzTrX+VONkzbT1jyrVs64ZXg4QFNTzjO3\nTzcxuqqFHDcWSLDmc69i/qx5vfzF0mlrPfEKp/tijWFvFAbV25DTSPizn2v/LH+2tdo7t8/qn4+P\n58vI6DDXQamyS911nNw+7f2RV8Kfk6A1Pp4X1HUr9eCV26eTx5BqFoTwZ0nyxcJfoWBnu5UU4ubm\nv+99S+nv938hAHWDpZoQbGv+QH3CFycfaTvhi0qcLH+ZjPpCq041Km23T5UJRpwEUMtlzDt3jnLs\n3x9gyZKsB5v6yoJDYYFfVQWqZxLz57cbLljKifp6Jt1lvUn4Uq2+nhfo4DFQiJNQ7MVcWoibhC+2\n67JfMX9Wfcrpewm/GB01qK83lc6ztsW6El4nwXE71+lk+RsaCtDcnFOaBwDyc0wlxset8eJlwhen\ncISGBm+fabcJX1T3FbhTSqpmQQh/pR1e6vZpZ86aDaofpEoDxNL0qB89VrbP8p/19jIVc6gy1s5q\nR3XtvJ3wRSVOm/2hodmPI7c4aYzGx63ERSq1wW6SnPhtadq/P8DixWpNtLmcUfVeDA0FaGuz2qDS\nGryGDkoAACAASURBVO1O+PMuq5oThmEpSfyO+fOq6HFeK6/H/bcTV1WaR7y2/DkxNma5ffpp+QNr\nrFiWNt+aMEVhDKLK5F7V9hj5Dbw3LuVOz2te+PNPSVCK7fZpWbjUXceK+avcVxMTllXUW8tfdY+U\npiZvXcvdujFbmfPVtsVtLg2VLAjhr5RSt8+RkcCUX/ZMaGw0GRvz5xbu2RNg6VL1o6faIN27l6KE\nMyoXAMOo7m5pJ3xRiWVJqLyZHBwM0Nqqtk+qCZ9gTaptbTlfk94YhnpXOicGB/OClyrsgveV7sVc\nlEpucZ/wRY9Nq40Xrll2TGal53Viwor3A7VaWCsxkL/uwYU4JW1Kp62MrLpgCxk6uPPp4vZZKHip\nmuudXAnzGVC96Rcny5/tHqwqBGY2ZLNWUjDVnjBOfWVb/qw9q/8JX2zhz0vcu6Kq90qxPNn8HaMi\n/GFtAurrZx7L1diYU275q/QAFdb4U339SpuEffvywp/qzaWTmdwLy59TvNvQkFVmQSVOJUhsLbkq\n3MZR+U1/v0F7u9oNt20FrTRv9PdbsY+gzu3TrfCny4bIvg9W2n71CV+q9Y+9eVWN1Q59nhl7E1Tp\nvthWAl2w3Q/LlWjyGl3cPguzfaoSKtx6FXgVt2WVe3LXHl0sfzaqPWGchD/L3d47y5/T2LEFdS+x\n5pHK1/RqPQA726fzcSrXjAUp/JUWLk+nrU6f6YTR2OiN/3Q5fvGLejZtUq9uq7ZJKBX+VLuVVY8z\ncy4qWovrV7sfAwPqLX9O8XaqU8a7TZ/vN3ZfqHTrc4rlKizFoo7qmSTtTIleuDe6wdZ2Wp4Caq/l\n1D+Fi73qceI2u5sXz47TfbE3irphuQr7O5CtslG+NgHIZ4dV6fbpnEnY+7itaujo9mnPK14kxHOO\n+fMu26ebLNReW/6cFKVjY97Ne26z6KtkQQp/pZa/vPCXHxlubr5fNX/Gxix3peXL1bt9VttYFrp9\nqq4jpoO1ySnebWjIf+FPtRCct36Wn0X97iMb2+1TZXuyWaNqts9C4U91BtrKwl9gSsOqiwAIeGIt\ncCqx4JWm13IP1snts/qYGR+3LFy6oYOQ4Xciq0IMQ217cjlnS0ljoz4lQWyvFy+8CmaK6r1iNlu9\nr/xI+OJs+fM26M2t26cXuN3PSp2/GlB4o0vdR9Jpy4Wz0Czv5qb7lVXq8cfreNvbvJE6q/km9/Qw\nFXeoOujbKdunFzhb/rxw+/R30nBjTfHanaMcg4Pq+yJfR678zS4U/lS6fVYbE/airxteJIlwEoy9\nEv7clEexn1cvBPT8mCl/sUK3T7/n3EJ0sPx5UQB6JuhQ6kEXRkYCNDTktHAPLkW15c855s8KbbIU\nKF65fTqVIPLe8lcNL8dzNcufV3PughH+bOwsRIVCWzptDUT7Pbc334taVeX4wx8ivPnN/mdvKNSk\nqA76dhJ6vHhgrMQN1WL+ArS02JYeNW0oHbte41TrsK8vQEeHeo2e0/3NZq2kFio303bCl0ptGR6e\nXSKpmeCmjIBOFj8b1Z4C4Bxr55Wbj60F10WQchJGdYv5s9HBnc+LAtAzQWV7nOOJA1oJf7bbZzCo\nj3XWRrXw56bUg/1Me6VgcorP1C3hi1frgZ21ttIeKpXCk4RbC0b4KxyIpW6fqZTlC20LL05ai/x5\nvN+I798fmEw57ellHVGtEdUhO1LekuBfqQenTJuqccoU2NdnTAl/Kje7Ogg0dqmHSv1hGPlYO1Vj\n11ZmVStnoBO2BdQry181C1ehplete7Cebp9uEr7o8JzZ+F3qAeyYP31uip+WP2sD73O++gJsgUIH\n9+BSVCu7rFIPlT/32gPEbbIgL3EqFeKlJ0i19cArwXjBCH82Vo2pSjF/1msnE7qNV5pI04QHHqgH\n4Gc/a+Dv/35E/UUdsNOo2/jtDlP4UKvaZNmao0rnt4QBNde28bs+TN7Vsfznvb15y5+fG0evtJvV\nLEteKCt0KyDuFi8sf86xbd5l+9TL8lfdEimWv2IK75MupR5sJYraIu/OGRu9tt5UwypRYodm+N9H\nhahWdjnVffb6mXYaO7oKf954glTPm1AYpw8KvcjUnFZvpmf7pCjhS6lgUwmvfMv37w9wySVtU3+v\nXOm/tm3fvgDLl+df19frkQIb1Me7uRG+VLXB76yBTnFU/f3q6+vpgm3RqWb5K/d3LXHayOtkuSl0\nBfLG8lfdSu1ltk9rPSl/EaeNm4r2VBf+dE34Ur3MjSqsQt35erZ+1xqE4qy5qpQoTklEbGFLN3Sa\n82zUJ3xxV+cPvMooXL0sR2FiOlVrY7k2BYOVr+WVxc1JYWsJxoVZ9NW0Y0EKf06WP6dBYhMKeeNb\nPjJiTMUOqS5n4Jb9+wMsXZp/XVenRwpslbgpc6Aay13LecypWgDteKFqtdN00garJB/zVynzqVH2\n71qSyVA142hxe5Q0wTWFwpbqulcws1IPattR3T3Y68QZTs9wYXyQ32OmkGAQX2K5ChM3idtnHp3G\nho4UKnX8LvXgR/kWt3sQr8J5nATkQuHPz/rAhclw6upMxsfV3B9NRAlvcSr14DTpVTqPKmxzdCpl\nDRod2LevWPjz29XCm4Qv/rvYubX8qcLJiqFbBji1k7jheyyXU9IZnTZoIyOFwp96xZmTZdZLt08n\nTa+Xz4yTEiuVErfPQgqTWFnePvoIf34WedcNryxIbil0i1Wf8KW6pc2u8wd6Wka9wE32Wi9cUW2r\nZ6V1qb8/QHu79WF9vQh/NaU0Pbvt9mlrot3G/HnhumSa1oO7eHGO558Ps3q1HjmMe3uDRcKf33gT\n42X4nnDFMKorHOwFUF3co1E1WYRuwp9K7Jg/d27Aau5Jvtagf9ZgtxTGeViWP68SvpT/3DvLnxs3\nH/UxHoXnd1vqwW8ymbz1xK+EL8UlWzy/fFVUuhM6xW15jdNz4XdCuFIsd2E7NlN9jLOz8KfHMw3e\nhESU4ibbp3d1XyuP5337AixZkhf+VHkaaPRoe4eVLj//Op22NDR5y1/1zEk2qks92AGqY2MGb3lL\nis2b61mzRo80Vvv3B1i2zO9W5PHGj726xrzwfVXtcbI8ql4A3bjS6bXIqOsLJ8tSIar6xY5P1snC\nV4lCIceLTbRTZtrCsWoY6uLJnOaNQsufJeCoaUdxe9wlfPGmnyp/Njho0NZmHaCD5U83VCagcSry\n7jW6Cd5ODA8HprKhehUiVA0v799M1iPv3D6rj+fCwvSqY8CreQyVCn9i+ash090+oakp/3C6DcBX\nXeQ9lbKsi6OjBm99a4qf/ayBgw/Ww/JnLYjF782HDehccNKYezG5liYrKsRSFqjdtDklvXGrOPEK\nlQHTTglFvEC3MgLV8Lq2k9O9KUw6YNVLU7eJrjZOhocDtLbmLQRebBKrlQfRKeHL4GC+dmoo5E+R\n90LLnw4UWkNVWtCd3OS8yLA9W3RoT6nlT4dEQeDNvXHaf/hhpdUlhtVuR6V9XGF94Pp6lOXSWJDC\nX6mrViplaV5ts7zbbJ+qLX+2qX501KCzM8vixTnPF6FqVq7SB0mX1P6q3AicUvtXak8tCQQqu316\nkT7Zqa6cbqjVjLt3+1RFNmtUHJPj48XFYv3uM6/Te2cyuC69YmlY1bTDydI2NJTPJmkl8FDTjnx7\nqruvFwrFpV4yKqg2Vw4O5rMH+2X5GxoypoRzHShMCKHaQjGfrG3FroQ+NmSS4eF83V8vYpzdosO9\n8QN9hD+DSKT6Hsruo7o6k7ExsfzVjECgWIOYyUBjY3HCFyfhL5eztG4qF0Z74bMTE9x//76qx9d6\n8Fari6LzBOIUFzdbbPetypum2l+zlGqBwoODhVlhTSUbJT/qys1FuFKbCr2yO1867U1ypmrxA319\nARYv1sdiYdUvyrdH9RjK5ax4SDeJqKxsxeqUBNXcPks3ibVUVuzYEeTFF4sXs5k8w7XMyppKwSOP\nzKw2wP79ATo68m6ffpR6MM3qyTS8ZmiouJyOyrI+bmP+an1/ZjPn6xfzl3f79Fv481vxV4ilVNAv\n5q/0WFXYSkk3Y1xi/mpMqdunaVoxDvYm0Y2ve2ltQBXs2hVk+fKcZ1mISrHdTt2i8oGZyblV+Uk7\n1VTzgmpF3oeG8i5SDQ1qNEZ+uDpefHHbrK9Xq0X36qtb+POfw0XvZbOWcFlu0+FVBsdqro379xe7\nq/m9gS29J7Vuz/Bw8Qmd4iEL31cp/Dm1o/C5rbWy4q67Grnnnsai95xiTgrfr+Wm9U9/inDtta0z\n+s6uXUG6uqzFOhj0x+2zFL830v39Bu3t3iQq8ivhy9/8zRJ6e/Xang4MzGzseZ3wZb7Q35+P4wU1\nQvuzz4an7X+smL/yx3v5TGez7sszSamHGlOa7ROKA3LdZPu0XUVVuqG8/HKQQw7JeJ4K3KaS0On3\n4ufE0qU59uyp/dB2cvv0YnNdbuzaDA7m3ZMaGy134VqTn7i82YSZJjz+eB07d5Y3xZfzhy+MfaxV\nKvTRUYPf/77YamHXTionjHvl4mhl+yw/JnWLVVJ9T97whs6i105Z1QpRKfyZpl0SpFKMR97yV+vU\n/YVpw23yMSfO369lTFlPT4CVK2dmutu9O8CyZdmptvjh9qkb/f3Flj918d3+lbIZHTVmtIYXzvkq\nyGbhzW9ePqPvjIzo4fZZWGJHBc8/H5qRRb63N6jcI+WCC9p5+eVyHg/l74OlUCl+ptQlijNmZPkT\n4a+GlItjKAzIdRPzl07bGz91D/TOnSFWrsxiGLjKoFjrRcAWOnfuDPLUU3mrR1+foVX2s9KHdNWq\nLK+9FuTnP6+v6XWc3D6rtalWVLv+8HDegqBS+Ks2cdV6DO7fH2DTpjTd3dMfyNFRg5tvbp72/vh4\nPmFFOFybgOnWVpPBweLp0q6HVq6vvRL+LBeS8mOir69Y+LMsJ8qbVBHV96R0c2Utsu42ryoXWaeS\nE8PDgYISGLXdJDY15RgZKT6ftal36/ZZu4RJu3cHWb58ZsKfrdwAfSx/flvQywn0KqimJCj3Xi3X\nvJaW6fNtNQpDHlTw4oshVqyY/qOr/eZit0//Er5s2xZi/Xp1F//QhxbT0+O+rwoVOqBGaO/oyNHX\nV9ymajGse/YEi9oUiZjKEq3M1PInbp9zpPBGlyvOXrjoFsb8bdkS4YknpscpTEwUJ1NQgd2OP/95\nty8ZFO3N2m9+U8ePf2y5Du3eHeDVV0NTrjiF+LUolrqnHnRQlq9+tZXvfS8vGDzyyNzT1zlt4ryg\nmtunF5a/mWiDa5EsoqcnwNFHp+jpmf4ATEyUz9BYmKq+FpaUsbHyypdUqrLlr7RmkGG4UxpU47/+\nq2GaBdS2RsP039jbWxzzpzJ+wA0qhb+BAWNqo2UzM8sfSt0+ndphz521dg8rl8U0H4Po/HtrKYwO\nDlqWv8HB4vO5nU/9SvjiF5Xuy8CAd8JfJXdlq4B5vg2We1rtrt3WlpuRm2Vvr9pyHM88E+bII1PT\n7kW1PY+lHLT+rpbdUTXbt4dZv17dg9PZmWXfPvcb1NdfD3LQQWqDdysJf5X2b7t3B+jsLF4rVSkD\n7Zi/cvPv2BhFNVbF8ldDTLN8qvpC95ZczpjSRtxzTwPPPTc9ZVw6bcwoHm4u+FU0e3zc2qzZGRNN\nEz7zmTauvLJ1xu47c8WpgGnhPerszHLuucOcfvoY+/YFSCYD/Nu/tc+5DaZZ+aEtLQ8yV7cB04Rb\nb22a9n71hC/FMX9zFf4SiRBPPDE9zq2a5a84XmjuG9ldu4IcdVS6gvBnlD1/YUmBWgh/r74aYs2a\nzLQxmEoxJfyV9tfISKCorEE5hdNMufPOxmmuLJmMUTGZSKl7WCSiTsBxQ6EVp9Y89lgdb31r8WCY\nSR1G9dk+vY+xg/Lzpl8JXwC6urLs2lU8hqvN7YVt9GMDrWOIg2X5q23Dnn02zM03F6831SznpQJo\nU1OO0dHabCft7OYzsfyprsX46qshjjgiPU1xMR9IJoOsWKFuv7ZkSY59+9z3VTIZpLMz3x4VISQt\nLWZZj4dK816p5U+tJ4hRcV0qp7AV4W+OFC4w5Wq1Fbq3FLp9NjWVT5yRSqmthWRnEvSSz3ymrej1\n6GiAhgZzSuAYGjI49tgUH/3oCJs2aVK0humFxYNBOO20cTZuTPPccyGeeirCm988d3V6Nlt5o21l\nd8y/nqvVq6cnwDe/Od2lsVrMX2Fx0IaGuVt5nn8+xI4dxYoPp2QRhdSizMLu3UHWrs2U/S0TE8a0\n9ycmirNK1sLts3RCLry+raUbGjK45pqWqc+s4tT571jxSnO7F+3tOQYGiqdsy4Wk/IJmjdf861pr\n53Vi27YQb3hD8Zw0E/dGtQlfKrfDqtmWf+1Fnb9qz3Dpe7UWRru6siST7qwEpaVKVHqWuCnDoYLf\n/jbC5s3lwxMq/V67BFQtefbZMIlEsWYmnbaei3Kb1IGB4qQdTU3TN9uzJZkMsn59ZkaC1r59AZYu\nVddPuRy0tZkMD+cfVtOcmXLAL8+o0iyXtfBCKaTculR47VJKlYC1LidjmtDcnGNoaHqbKpWp6ukp\nFkhVJcwDa86vHKcfLBH+1ClsF4zw50ThIlcq/I2MTL9NqZTh2m93Nrz+enBW1rXZPtQTE3DHHcVZ\n4ayYv9zUYLWFi/e/f1yrQt5WrNv0jtiwIc22bdaiFo3O3e3B3miXu8elluC5asx7esr3v1XkvVL7\n8pOq5bM+t0kjmQyWFTaq1QgrXOBqIfzt3Vt+UTdN2/JXfP5jj13OwIBRFEM11zYMDgbK1vnKZIzJ\nzZFRlLQj/53axdtNTFiJjKa7slTWzpduNhob1S1os6GWc6et1S0kr0Cb/psHBoo39SqFP2v+LN9H\nL74YYt26/EQRDtc21qSSgFfJg6DUglIL630hK1a4F/7+8pfwNIG+FpT73dVigZYvVydUbN8e5rHH\nZlb+oloJptmye3dgWjxmNXflgYHi+a32wl+6okBRjtnEk84Ew7Csm4UZhZ0U9LpYjUvbUQsvlELa\n2036+90LWqUU5tuoBQMDBl1d2WnZn6Fy2MzEhEFDQ/61SoubHfNXyfJXGKdvrUtKmrHwhL9yk6Zp\nFm/WM5n8Rr7SJJtOW4UaVWlztmyp45hjZrbqziU4/7XXQhx+eKZoc2jHLaVS1m/dvz84ZVnSiYEB\no2ghsmlvt5LVpFLOCXzcUM19q9DVECzN+lwsPZWC+t3WMKzFZnZwMDBN+2pbU8ptqKE0ff7cN462\n1aTcNVKp6YtGQ4PJCy+EpoS/urq5W1Ls8VWq6bU3YaY5XRu/d2/xJnqu8UqvvRacdDsqnrLz8QPO\n56ila1YuBzfdNN0t2U9NeGnigGoaViuGJz94ain8lV6vmpvljh0hDj44/0B7kRWwmkU0mQwUxXOH\nw7VNsrJsWY69e/NjMJutnPDh6acjHHVU7YW///iPVtdK0tLkFLVmNoXUVQgV+Xm98DqVSxsNDBS7\nlNdW+AuwenV2RuOu0OtFBYYBzc1mkUBhzy/lmKlVsFaUXrNcxmdrLapNX6XTsHhxtmJ8pruMwrWt\nbbprlxVTWM5DqlI4Tul7aoU/o6IRwSrPlJ9vqnl6zZUFJ/yVwzCKF13LimN9VqnenyUQqWmPacJL\nL4U49NCZLTpzsbR0dwc57rgUu3fnh4SdoOH/Z++8A9uqzv7/vdqyLe+R2E6cRUJIgIQVAmG8QNk0\nvKWolAKlrDJLx0sJpVBa6Av8oHRTWsYLpW2KCi2bklBGaYAskjjD2R6JFTtesmVZsqR77++P4yvd\nqXltB/x8/okjy7pHd5xznvV9pPS27m4LKirGoctuGuR9stRcdVUIZ545DLs9/5S3VJu4YFBp/OUb\n+TMy/lJF3eSwvpX5TRp6E0+6Oir5RsaMSV3vXEtGuF7a57RpPLZutStq/vI1QEMhpiSn9sJxXDKC\nojb+enuVdTn5KhX291swaRKv07so8zTcggJR1xuaC83NVrz1llvzeqqNrJmpRpmQqrZi82YHjjxS\nafyZsdi/8IJbk64djzPnWSbGFnNW5D2MBMY1f/rnxe+3KsZjdhqq3c42PxKsXYr+e/v69NOt82XX\nLlvGz0Fbm21UxSnkveAyYTSNCr3PtViAcFi7TWT3SfLamFFjLsH6G/OGc4m63yqg7yQ0C6mNRFGR\nMgssHmfzix7q53os0Iu0bdmiTYdnjmlzjhkOs/tX/kwnx5PZvWp2e5sDB4xrHDMd02hH/ozum56e\n0Znz9CDjD9rIXyyWVI80KkaNRkdP7bO52ZqTNG8+G/7WVhtOOGEYnZ3JEFnS+GObgJ6e0fWu5Ypc\n5VLNrFk8TjghispKAQcPWtHamnsIUPKO6m2aWlpsmDIlOaPm68E3auSbqZAMyxXP+fCGpEoFYvVL\nyV+YkfZpNAaLhTlg1J8/bVoc27bZTU37ZOItgm4dgJRGIhcd2rlTq4abb4+ygQHmaVdviFLdk2rM\n9M5v2WJHdTV/SNS8SBtidUpPqnt1YECZpmvW89LVZdXUmqRSd+vpUdZ4mO0F1yNV82618Wez5e88\nScVo1K+lY98+7TUCWIS/p8eieW99vfJZNtP4UtflpmMs12DJ6DnyyEma36nVNc2MnDNnifGYzj+/\nSvP6aDqWpP2FOu1Teq71YKUmyj3caEcC9VIat2+3a/aSZqrmGvWglubj11/XOgjVOJ0wtRY9ncBN\nS4v2oqnXprGJ/Gk/X6/EZLTuGzL+RpCLwMgjf0ZINX+jQVOTPSdBFabml9sx+/osmDMnroj8adM+\nD62m0RLq+io9Kit5bNhgx+WXV+R8nFQ5/o2NdixcKK/dyS+1Qor86UlL620ih4eV7S7MimRoU9iM\noylqBTg9mflskEdT5alhUn+9SESp9hkKcWhoiKO11aaK/OV3HljkW799huRJlG9i//lPFy69NKx4\nn82m7x3NFHZu9e89PYeA3oJhpvG3Z48Nxx0XRV+f9vOMBJFGS8CKbc4EuFxK49woKiopPssxqw0G\ni2QpD8gWe2OBFXW03CwveDjMKWTDJaT2IHrzSHe31hg1I0VM3Y5HIhLRb6MSDus7V/PdCEl/L09n\nZ6mXIl57zY0HH/Ro3j+aQh3ZfvauXcoa0dEcC8cZj099XsxsI5PqnMTj+vOI+m/M3DD39THHmzrt\nU1JD1WPnThsOO2x02isYGW56dbx664aZ/TLDYRYg0LtmHAesXp0+Pc6MLCU5XV0WVFdr904AG+8Z\nZ1QrXotGtZG40RZ8SSW4ZeSYMxsy/qCdOKTIn1q+Xw7rczM6mxk2wWc/ceSz0RVFoKaGx8GD2sif\nJKbA6hxz+vhRJRg0jvxJVFQIaGmx5XXNBAGGHht1zUQuhbryyYDnOXg8gsbYMFoY1WmiZnhi9Y6V\nKprCxCKSv8h3Am1psWH6dO1zIKUhs/Yjyc8/eJBN+sxpwc6FGXWHADsXet9HbvxJG211/SeAkecn\n9+Mb1bVKY1NfD5ZOpnx/YaFoYs0fh+nT42hrS+9FBZiAlTqCYhZSmo869cwoTbutzYqpU5X3lVnO\nEj2yqcs0s89fe7t+6hkzfvUdOOrNh1mCL1LkWo303EQiyk3trl36Il35GmJdXRY0NMQV94mk1Kw2\nYIyye8w0BrM1UnbtYrX5o0GmY9FLPR1NwSQ5TI9B+Rq7fsoB5dtqSU4wmDT+5M4z5tBK9TfaMeXL\n4CCHM8+s0q2xS9UGSk6+WShyjCJ/UkmEWjNA71qZfe9I0XS9882cc8qT1Nxsw9SpynlyNI0/nmfz\nitH8O1aYshPwer3ner3e7V6vd6fX673T4D2/8nq9u7xe70av17vAjOOajXTiYzHm0dHz4kqMZqPV\nWCzZHDQbcs2djkTYQ6FWbdKbaA9FQiFL2ubRlZUCtm2zo6oq9w0oM4Izq6/KdjMpCMBVV5UrIike\nj4hgMPl/KQ1HD23UzZwJVT2BStFPo0apcnGEfCfQvXutCeNPntLComwYSUdODqSry4qqKh6hUFLt\n0wzVUwmmlqmeMplzREr77O/ndOeFfCMoRjWgADs3Pl+BYpPe1aVNDysoEEyL/AHAlCk89u3LLGft\n5ZfdprRb0ePAAZaqqI7MSvOX+l7dulWbWWGzIa/IrBy9ZybTukwzI39+f1IxWH5sQeAMFfbUYzdr\nPIFAsjWAfCxSxPzxxz3w+ZJq036/FfX12h1qvpuj1larRthMqhdTf9ft281RiTYTpmpp/r7D6Lzq\nRSH+8x8HTjhB+SyPhvFnVFqgNh70FFlZ+rQ542CONyntUy5WNHa9niWamuy47roQ/vEPbTqluoWZ\nVB6hJt8sFDlSgEAPjoNGoExvHTNzjU4H682rfO2TTxxYvFjpqdfL8skFvf2PNN+ojb+xFgnK2/jz\ner0WAL8BcA6AeQC+6vV6D1e95zwAM30+32EAvgngiXyPmwuZnljm8WObSyPjbzQarUrk6iHKNe1z\n9WonzjgjkjbNIx3p+scsX16A5csLjN+QB+nOWWWlgNWrHZg5M45333Xi3/9ObV03NtrR3Kzc2Bo1\nOM/XExoIcPjRj4rxla8M4YUXkuenqEi92Bh7NNX3I4sgmD+hJqOf2t8dOKDulSPkZfy1trLm6oDS\nK9/Xx6GsTBhxkrB7LhZjkb+qKgFXXhlKTPBmbqbd7mQkVroG6rRPtYqkhNOZX02ZVAej39JBxKOP\nFqOjw4r+fg5vvunSlT43M+0TYD3bOjqUzwiLHGn7NkWjHGbMGN3In3rBjkbZNVHfq6OZkqWHkRGq\n5wU387mVjD91f0eeB4qLBd0WRmrMkmGXp+Y7nSLCI1nR8nRp+Rze0cGi+Gazf79W1Vq6DuqISGOj\nHUcfrf3yFot5ghm5MFppqEape2o++siJ005TTmajVWOuRq/O7sABi2LdAcytNx8YYIJybA5X1vxl\nU69pxsZ+61Ybzj47osjQklC3gWputupmzuSbhSInHLboRv6k8ajbdTAHsfK5NjvtM9XzIe/NDQhM\nnAAAIABJREFUmxyT1nlgVuTvjDOqFOrGbAxsP6OnzDqWmhpmRP5OALDL5/O1+ny+GIC/Aliqes9S\nAH8EAJ/PtxpAidfrrTHh2BljtYopZaWB5E0jtXpgKSn6m5nRKlTPZ4LIdcJbt86RaCshP778IcpE\nISqdwmUgwGHXrlGS5EpDQYGIri4riotFfPCBE9u2pR7H888X4MMPlQaikQe/r4/T1EJmUwPx6KMe\nnH12BBdeGEFPjyVxDtWRPymNTQ8mEJMcg1lpL+rPSNY5aL8b24SbpwDHomnaz+rstKK6mh+puwVW\nrnRh2bKSRK7//fcPJD7DrLRPaQzSgsBUuXiF4IvLJWpUJJPjMMe76XBoVWs5DnC5BEQiHPbts+L/\n/q8QBw9aNAuaWcqNUn1pcbF2cec49qzJjcz2duuoKt9JGwq18Zeco5XfmdXCaT/HLK+rWiTFaN7Y\nvt2uSeEzM2IhqSYWFSkNPdawWlDMLYD+9zdL+EBeL15VxaO7m01k8s2YfG3u7R0d1bvubtZGQL6x\nk9Z7q1VZp60Wv5EwWwF1vNoCSMgzJeTo1TtKY1VHlMyY33bvtuGVV5JhGbmTQCIW4zQ1f+oG3dLf\nmmVQBIMsaq12jkvpwmoiEf36VjOM9q4uK6qrtcJf0ufLjT89sRdAKmky59zIW0kYCWvJ2b3brnG8\nuVzmCb6ke5b0tDr07nOzIn8NDTwaG5Xpc1IKvHqc6p6vo40Zxl8dgH2y/+8feS3Ve9p13jOqZKNw\nxGr+kpsH9SZcYjQ8cHq1OpmSbZQjEOBw5ZXlGBjQr+WT35w1NYKutymT40vnbmgofXrmaMFxwLJl\nzCjIpM+NXjqDJBShjiS0tCQjVBKZLIaCkEwpOeUUtuO79NIwrriiAvv3W0fuO2Xkz2LRv+8CAf2a\nmkwIBjkdI0+/3tUo+glIjVKTf5NNU/HeXg4dHcnvun27TbHBkBteUlRLap7d2WmBIHCaBtVAfpvX\njRvt4PnkcyBfEPbtY1EVeeSvokJAZ6dFoSIpkW9NmTSGyZOV0TZp4XK5mFFz8KAV06bFsWqVU7c/\nmRlzVmcn22ypPysSYcaper5ctcqBk0/WhgVSOeKyQaq3TSXIIyFPP9SOx5ThoLxcQF+fMj1Mr+Zv\n7VoHjj9eaemZKXsuqcoVFiqFKkSROZbUUeBgkNO0zCkqEnSVMfWIxfRl+IFkai7AsjAkb7hR5E+q\nMzebgQGWmq6N/CVbYADsHEmZFmpYmlr+Y2GCVgJKSwUEAsrrY/R+vZZG+RqOTU02zJ6t3HRK/QfV\n33/PHn09AjNqZltbrXj++cJEKn9FhYDeXm32jboURc/4y0f8Tk1/v34rKSPBF6N6VbOQron8uksG\nuXzeMdKOMDPy19HBoq4VFbxGKVcv7bO5ma1PcsxM+5SXXegJfbGyleRrbD3QK9PIvxeiIABHHBFD\nU5PyhpWcCWrtCKPrNVqR/gkj+CLlORu1bgCUNX92e7Lmz+MRNDfxaJFPs9JsU8tWrXLiiiuGcOGF\n6d0uJSWCobKVhJ5YgSgC119fnviZ49gDt2JFDkWNeXLbbYMAMovKOZ3QbCSlDYl6UmlttWHaNOXi\nY5QG8z//U4LGRju2b7fhj38swLe/XaqQJZ49O4677x7A0BA3svGSR/6Maw7VTXez4eyzqxSGF5BU\n2tS2Fsi8fild5O/73y9JfM4rr7jx0kss5XXLFhuef74Ql146lHivXMmxudmKSZOExHPa3W3BggVR\nNDXZNR7pfJqk3n13iUKtTDJAv/3tUvzmN0WYMSMOi4XdJ5EIh/p6bRqkRL6ecbnxd+CA8hgcl9x8\nHTxowQ03hLBihSutCFKudHQoI7wSUqSksFAZaWpuVjYyNxvp3JSUCJpIJKC8Vz/+2KlriGZCOMzE\nYtJRXi5oNkJ6/aWkSLWcfPuDypEcNGqhCkFgRoTaqNNbe4wcn3q0t1tx3XVlur9ra7OhqkqK/Ano\n7mbHlkSagMw2OfluhESRGcN6kT95C4zWVv2UOcC8lMKODva8NDQohZOk1OlMIsXyv8kVKfNH+ozh\n4aTAlNoB+PbbLpxyivb5yaZm9s03Xejt1e6npD2WtHmvr+c1z5ukxyAnEFAKjQHm1iAa9WI0avXw\nwQdOLFqkP8fkc50kgxyQoufJcygJrCxeXANRZNewt9ei69Q3U8yE9VgWUVvLo71d6ZRkgi/K66yn\npG9mlDadsJi6DnL9egeOPXZ0om0HDlgxc2Zcc66lFHj1vaDOnBptzMjBawcwVfb/+pHX1O+ZkuY9\nutTW1uY1OInycqCqqgQeD1Bb60n8C0DzWkEBMGUKu4Fra92JIlH5UPQ+J1/Wrwd27QJmzwZqa/UX\n0VTU1QHd3cpxpmLvXuCnP1U+DHrnxeEArrmmHFdeCbhchYrPkF+fqiqgoqIEkycnf79xIxAIAJWV\ntfB4JG+iBzt2AFdfnfl383ik42nPdTbXoLg4OWkb/U00CtTXA3Z7AWprSxOvFxWx71hQoDzHXV3A\nSScpF4JAAOjv178WnZ2F+Mc/2GbyhReAwkI37PbkcWprgXPPBVpb3WhrS35GTw87vsWiHbsoAnPn\nlig2R8XF0ufp3xDt7eyemTUL6O6ehGOPTf7O7weOOQb49FPlsYqLgZIS7TkAtNehooJtZmtrS3SP\n/9ZbwM9/XojKSna80lKgtrYYTz4J/Pa3QEFB8l6rr2fnf/Vq4PDDgSlTPIjFgLKyIhQWArfcApSV\nsedVTa7P6PnnA2vWVKK8nP29xQJ89BGwYAGweTNw0klu7N8P/OAHwMUXA888A/zoR/pj6Opiz5ve\npUg3x8Xj0rnxYOFCYM2a5Od4PGxuKywECgoqEQ4DixcDzc1AYaH2c82YryIR4Oij2Rjkn7dtG3u9\nv59dK/kY6+r0n9vJk4vz3tBLYygsZPON/Lg1NR4EAsnXWlvZvKMXVWJj1j83tbW1WL0auPVWYN06\n/XFccAEwcya7P4eHleOorGTXSG8N0R9HcYbfXp94nD2ntbUeNDQAbrdyPHPmeEbmkeSz2dzMxq5e\ne9i4049n925g+nTt/dzYCMybB9TXFwFgm9j9+9l4BgeRmHcqKsrQ3FyGqVPZPKN3bsrKgKoqj64Q\nWSZ7BY8HmDHDMzIvsTm3s5ONm+OAoiI7amvdeO014Mtf1l+Ha2qkuSbt4VKydStw5JHs2W5qSn5e\nPJ5cp+TnYO9e4Mor2b0kp7ISqKz05CQSB0gRiuTe6LvfBW66CZgxAxgYyaB3u2tRWsrWxoUL9e+F\ndHOLdH18PvZd589X/p7n2X06fboTtbWlOP984IkngEsuSX6+wyHtxZInX29+mTJFf43KhcJCYMqU\nYs139PvZXLhyJVBc7EFRERKZIvPna8/R5MmAx+NJrMvZsns3cNRR7PjHHsuenaOPRmJcZSO3allZ\nLV59lV3D2toizec0NDAHUyZrUapsI0C6Fmxd2r1bOceUlrLzM2lS7UhfXv17JBRiz6AZ12rduuTa\nVFkJlJd7EvX/evdPWxvwve9pRWCk9+vdz1dfzdb56dNTj+XTT4FTTwVefx0oKfFgeJit1U4nMG1a\n8cjnJ+ffVOukWTaGHDOMv7UAZnm93gYABwBcBuCrqve8CuAWAC94vd4TAQR8Pl9nJh/u9/tNGCIQ\nChVg374IgsFC+P1BBIMe+P1BAEj8PDjI/u3uLkRPzxACgXKEQuw9u3cD1dXJsJb0N/LPyZff/a4Y\nH3/sxGOPBeD3Z++NCARsOHDABr8/fSQvHAZisSJ0dAwqXpe+TzwOhEJF8PsHsXUrh85OrcuqtrZW\ncX2GhtzYty8KUUx6Xv785yJccYWA1auHMTjIIju7dkWwc2ch/P5Axt8tGJQmXO25zuYahEJFiERY\nmqvR3/j9FlRWutDRYYHfnzw/waAHPT3DGBwE/P7kvdDX58HBg8rP6u+3wu93wO9XFi2UlhZj5UoO\np54axWWXhTE0BAwNQZdQiMO+fQXw+0MAWIpFOOzC4KBVM/Zg0IMDB5SvDQx4AHh0nyGeBy65pAIv\nvdSDyZNLsGFDDEcemRzI6tVuHHlkDMGgG/v3BxMOgq6uAlitPIaGAL8/6d0URXY8+bhEEejp0b82\nrLVIFTZu7IPDAcyebUdnpxWbNg2hr68QgUAQAdntEY060Nhowfr1dvzgB0H4/UAsNhnR6CCCQWBw\nMIgvfpEtyGqyfUbDYQ7xOBCJFKG52YpTThmG3x+GIADLl1fivvsGcPXVUXR2ssgFMAnr18cRDh9E\nPD4Zfv8BzWf291tx4ID2flA/Q3q0tVlRXMz+1mLhsHVrYeK+DAY9CATisNmK0N4eRE+PHd3dwZFj\nZn4ubr65FI880p9RK5Rt2wqxcOEQ/H4RoVAR9u8fhMUCNDa6sWRJFH6/Ffv2AbW1Ufj9FhQVueD3\na2/yWKwIe/eGDAUDMkX+ndrbkz8PDHjQ3R1BX58lca8GAh50durfC0bnRrpGO3c64fEUwu/vTfxO\nXi9itZbhtdfsuPTSHmzf7sARR4QT4+jtjaK1lcO0aUxYq73d+LyYsaasWuXAlCkW+P0RxON27Npl\nxYwZkcTnd3UF0durPM727S5MnRqH36+MLmU6ns2b3aiocOHDDwfw2mtu3H77IDo6LHj88SLcffdA\n4tmMx4GdO9na0tPDxgLUoq+vD88+a4fLJaaYnwuxY0dYU2PtcNQiGk2/VxgY8KCvL4jOzqLEM7Rh\ngwt1dTyGh8sQjUbh9wewdasHF10U1J1PhobcaGuLwm7PL5q9ZYsbixdH4XIJ2LixECefzMYTiwGD\ng0WIxTi0twcT91dzswfRqHZMPF+I3buHNNEvOUb9CgUBiX3P4KAH+/cH0dhYju3bgwAs6O+3YvJk\nDz76qBdut4iGBrtmDpNIdZ/I57nq6hJs2hTDggXKe7+trQh1dVYA4cT6qn6eg0EbRNEGv78r5XGj\nUQeam4GamvwKaP/6Vzfa2+3w+wc0xzpwwI6BAQveeacUa9f2YM6cOFatcuDIIznF2ijhcrmxcWMU\ns2bldt+sXu1EWZkAvz8Gj8eKNWscmD49Occ4HDyAUuzZ04GPPy7EKafo37+RiA3792v3iXpr0R13\nlOCccyI46yzt9+nr4+B0sv2Jw8Fh8+YCLF7M9irBoAd2uwCgBM3NfrjdwDvvOLFggajYOwFAIGBF\nR4d2bcyFLVsK0NAQgd8voKzMhU8+iSei5QMDHsybJ6C315mYw3t6POjtzW492L+/HO+9NwSnM/U+\n+4MPinDjjSHYbAW4804LYjEOP/rRAAYHPejsHEQgkNzbAcnnMNU4zAqGASakffp8Ph7ArQBWANgK\n4K8+n6/J6/V+0+v13jDynjcBNHu93t0Afg/g5nyPmy2smDuz90qpBVI9QkmJMu0zHjfu/5cPRUUi\niosFVFTkNjlkI26xZo0Tixdr3yylCrW3J2XCM63Ts9uVqY5NTaxua+7cGPbutSVqfPr7LYqWEmNJ\nXR2vK8ghp7ub1RCo0wWl1AqlVLl+Lyi9tBNRZK/v3WtLnNtUFBUpU65Yn8X87ztBAE47rRrhMIfV\nqx2YPz+mqedsaWG90FgKn1JEw+3WKijqpVukiuhs327DiScOo6PDio8/dmDJkmFcdFEYv/51ES65\nRLshrq7m8Ze/FODii5MLRDTK6abd5Mtf/+rGL37BnA2TJ/MJxT+LBdi40YEFC5LPjcUClJfz2L+f\nnb8NG/R9Wvmo4bGaUnZu3W5RkzLFav7yS+XZs8eG3buTJzOVKujgYLKuUV6/Jam9ytOKPv3UgWOO\n0Z+UWOphfmG/cNhYkRlQCh+1tloxZYrxc5cuAtndrRUhOfVU1jA4EgFmz45h7twYqqqEEaeA/HNF\n3HBDeaI1xtq1Tk29n0Q2qWHRKEuDPnAgeTxRBP75TxfOPZdtTubOjWHLlvQ9ezo6tCJB2SCprv75\nz4WorBTwyCMe3HtvCZYuDSs863JFU/l3jcVYel1DA4+mJv0Hu7RUQG+v9kJNmpRZjzNAmwre0sLq\nkKS0TyOJfAmzFIQ7OljtsrpWVUrvY2rPXOI1o5o69vep19M77yzRva82bbLjiCPY/CYdr6XFmigj\nsFiA6dPj2LvXhk8+0a/dlci0hreyUkgI/sjheQ4zZsQVhr26d526ZssIdUN2OX/8YwHicW1Zh5p4\nHPjkEydeey2ZySE/h/E4K8MYHLQkjvXRR8Zp5ZMm8ejszL2QVd66pb4+ueZI9fkWC9uLSHO30Xym\nrgFOhSiy4+rR2JgUNysuFhV7ZLnwyvAwhx07bHjxxQJNixAgu3rRb32rVPf1ZctKMDTEoafHmpij\nDzssjp07k/MIxwHXXx/SVfDNhlmzlJ+rhyiy+ku3W8T06XE0NjoUZTnq+zochkaFdLQxZQfu8/n+\n6fP55vh8vsN8Pt9DI6/93ufz/UH2nlt9Pt8sn893tM/n+9SM42aDJOOsnqDkFyBZ8ycZMkyJjKna\nJW/OwUFOV9QhXziOqQPlqnJWUiIgELAoWhS8+KI2BQ0A1q+36/bdqq/n0dpqzalOp7pa2ST+u98t\nxfXXhzB9Oo/16x2J7zUwYNHNoc8FtmBn/lleb1i3xnH37uS49XqkSegpah1xhNaroFdX2N/PiotD\nIS6jhtdMhCD5Gayvnf579VsAaF8bGuLw2msufOc7QTzwwAD+8Q83ZsyIa9RspdYCpaVKx0c4rK8M\nJ1eMzYTXX3fjuutC6Oy04uBBVsPX0MDjJz8ZwMyZ2nMzebKA9993Yu5c5bk2s5mvBM9zCAaZoX33\n3UEcfnjymGvWdChSzjhOauDNTrbRs5uPIEJTk02h2nbEETF8/LFDIcogCdvkmkI5bRqPlpbkM3Db\nbaUZnVd5rz9J2MTtTn7X7dvtmmsm4fGIGjW4bGlvtykUGWMxVlN8773FcLvFxP3x4x8X43//txjn\nnZe5d3njRjt8vuT82d1tTdStSezdyzYBfr8VDQ08nnqqDx6PqGjRAiSfxd5eC0QR+PRTu+K+yoVP\nP7XjxhvL8N57Ljz1VBGWLSvBI4948P3vl+DccyMJAybTdg3yjVMuSKqRLpeIK64Ywty5MXzve0Ec\nd5xeuwRtX9BwmG2mL7lkCCtW6ORhwXgDPX06c2CkIhzWV+hmUv5iohxgyxb9di0SZimgGvXzlYzQ\nsrKkcFB7u1VXuRFg/TuNDJnBQQ5Ll1bivfecmjpUgDkJzj6brYclJaxW1e+3JuZ5jmPPeFubVbef\nXrawvZVWPT0eZ8/pJZeEFWqQs2fHFBttueL10BBnKMqjrnOV87vfFaG93YpjjqlJOMt/85sijeJk\nW5sVJ588rHCqcRwSY5ful+FhDqEQh0gk2QpJj5oa45rwVJx9dhVEkT0v0vm32ZIOFKkG22Jhc++W\nLXbMmGE8t6QyjNVMmiRgxw79L/TBB87Ems9xzDkhikmHXCwm9Za14Pe/L8K11w7qGu7Z1Py9/LI7\ncc02bbIntAo2bbKjqcmmUKKdOTOecGiqBezWrHHg7rv1y1FSMTTEBGWkvd199xXrOl39fmtC9Kem\nRsCuXTYUF4vo62N7QPW+Ze9eW8prNhpMGMEX1upBe5GkyQhI9kmR1OOiURZNKy5WFseHQpacFTmN\nGBhgRfi33x7MOXe/vFzAhg0OeL0Vidduv12/djAa1Vf4PO20CN5/34nt2/WVh1LR0MAMRwC46aYy\nXHppGAUFIsrKBGzZYsfkyTwsFhF9fanFSV580Z3xhn5w0LjJaDoEAdi8mU1sp59enYgMd3ezfnF6\nY1BH/rZssWP+fH1vltr4kz63tpZXiLykG6NELpE/9Xd46y22UbzggjCOPTaKFStcmDEjDodD1PWo\nFxeLCiW6ZG895Xt377Zh5szMmjKLIls46+r4RIQkndFSUCDiwQf7Ne8zUsDLh/5+C8JhTlcts65O\neSy1B8+IXI2/4WEmliHv4XjJJWGsXOlKbJ4l4y+fHn7MiGMLpSCwRuh+vxU//3mRRgxITn29stAf\nUG6OU7Un8XgEjZGUDnX2xpYtyobt114bwsMPF+P88yO45ZZBcBwTJSguFvD73/ehvDzz52ffPit+\n97sifPgh+7/a8SG/7nK1XfkGUYI5CZh3/KOPHDjmmJjhPZ/qWfj0UzuiUWZovfaaGw8+2I9rrgnh\nnnsG8NBD/Tj11GHceWcQJ5+snJOqqwXs3WtVODEzPXa6ubi93YI//pGl9LvdYqKf30UXRQwN3LIy\nAe++61IYhp2dTDbe6QRWrOjS/bvJk3lNJKK/n8MXvgBs2KC/SZXExaQIn5xwOHkdeZ6DxSLiX/9K\nLQrEohz5G39GbZUkARq58bd9u81QQVLdWkVi3z4rLrusAj/7WR9uu20Q+/ZZ8dJLbjzyiAcPPuhB\nIMAirdI+oLhYRHMzE+bp67MkepHZ7WJGyoepxPQk+vosisheW5sVL7zgxrp1TPm2okJQ7EuOPz6K\ntWuTL8hFT77znVL89rdFqKvTnhd1Q3Y506bF0dxsQ2WlgOZmG/bssWLvXhuuvbYcTz9diOFhFsn/\nxS88WLgwio0bOxJ/O3Vq0qCQBAEBtif897+dOOMM4/tm8mQhI+Ovp8eCN95gzg/pWW9utmoUcMvK\nWIZBc7MVDQ1MfMzlEvH22y4sWWI8DmYYG8+7/f0cXn016XyZPj2u6VW3bRtTiJUbuvPnx7B1qw3t\n7SwLKB5n99fKlU54vUM4/nh950WqtVEQgJUrnXjiiUJEImydkpw/zz5bmHASTZmSjIQmPzcZLe/q\nsigcd9u323RVztOxfr09kckSDHJoabHhzTdd6O62KJwHW7faEutSTQ2PQMCCaJQ5qCShuC1b7InW\nJqzNg/F+ezRawUwY48+o1YN8Q61WQbJa2SKuVjwbHOQyqo3JBkkKW0rvygWbjXk01J5WQQCeeKIw\n4f3dtMluGHmqq2Neiq4uq6auIh21tTx277ajv5/D9OlxXHMNy2fmOGZkzZsXR1GRiPZ25kHXS4OL\nx4Gf/9yj68UVRWjSxIJBi6F0eyosFhH//rcTd93F0mEmTRISKooHD1pRWamVspe+i3zTxxQOtedJ\nT3m1t5d51q+4Yki3wDgdUo8aPQljPZjapvI1tgHohsvFDJcbbxxEba2AL385jEceKcaPf1yMtWsd\niU2uOuVZqh3RG0OqVCk5kqSx3Z65QhwAXH658svcc0//SC9O855F1raBRWjUUR49pHNxzz06BXYy\nclWfe/RRD668MqR4jeOY2tu//uXC3LkxcBxrr9DXZ0mbeqX+vZSeIzfYHnnEg3PPjWDtWgc6Oqx4\n+WUW/Vq71q55/urr42hpUT6rbjczlNKlsmQb+duzx4oLLqhURLF271Y2bK+uFvDgg/048US2QFss\nwD/+4cYll6SP+GnVOK349a/7sHEjUygEmBNROr7Ulqe11YqLLqpSzEPqeYKl8bHvu3atA+eck319\ny89+5sH69Q7ccksZbryxDDfcMKiJxCxaFNXNWjj11GFcfXU5nnuuMPE9M40SW62pe7z6/Tb87W/M\n+PN4hIx6OtbU8HjvPSfmzmUnc/nyHhw8aE04OYyMRj2127Y2G84+OxmFBZLXUhSBb3yDOUPl3nXp\nu69b50hslAWB/b+93Zoys0dfVTYzpPtdFJX3h/wcS4qI8jVp505jpc/CQmXaqPTdX3zRjb/9rRuz\nZvE44YQo9uyxobHRjgMHrPjgAyfWrXPgpJOSi5THI2DnTpZl0NlpTaT3WyzMuSEZ9dny8stu3Hor\ne16kvnAFBSJ27LDhzjtLsGGDA888U4jFi7Ubgtpadg6k78TzyT5/hx0Wx4oVLt0MpVTRrWnTeOzd\ny4yXPXtsePddF+66awB/+lMvKit5XHttOf72twJcd10Is2bxirX6nHMieOKJIkSjykjy4CCHDRuM\nU9wB49ZHogiFcuebb7qwciU76I4ddlx22RA2bNB66s84YxgffujE5s12zJ8fg8XCou5tbalVI1NF\nrjdvtmP58gK8/HLSAX/GGRH861/KiMR777lw0UXKUOlZZ0Xwr3+50NRkx2GHxRCPs3tz1SonFi40\nPi/yNHA5PT0WnH9+JbZvt6Ojw4otW+w466wIDhywoqXFiuOPjyaej7o6rVMIYAGRzk4L9u+3Kkpt\nDh604uijY1mXH23YwNRBnU4RL73kxg9/OIC1ax244YYy3HVXaaLVTVOTHXPmsLmtooL14B0a4tDU\nZB+Z80Ts2GHH22+z69zSYtM4piRsNtE0BWg5E8b4Y5Or9gaTR8AkaXrppv/pT/sxZQqvqREwkv7N\nB6OGstkybVo8EYEJhznU1cXR3W3BihUurFrlxOAgh7/8pUCzkZbz9a8P4brrBg1/b4TVyiaWnTvt\nmtzuxx4LoLaWR3ExSy2prOR1F9D2ditOOmlYE00AmJfw3HOrFK9JEdNsKS0V8MEHTixdGk40oZYm\nEtavTv/v9AwfvU0Uiwgpf8EaHfP4r//KvPhL/tnRKEsTUh9P3mhVTnGxoBBNASRnR/L/N93EDIsp\nU3jcf38/vvKVIdx1FyvyBrTGnzQm+cZlaIjLWLSjo8OC224rxVlnsc/Pp8HxjTeG8I1vhHD22SY1\ndAJw//3F+OIXIzh4UJvip4fUaP7GG0Mp35dLY+hwmDmZ9DbCl102hHXr2EIk3Q96NWlq5N55UQTO\nOKNKET1tbbWis9OKm28exEcfOTBnThyrVzuwebMdzz5biN/8pkixCZQMvVgs2SONpX0Cmzc7UtZX\nsBqjzJagv//djR/9qAQPP9yPp59mBszBgxasW+dI6XTgOBEHDlgxdWr6uVVu2AEsSnHEEXHcdhsS\n0Ye6umSkc2CAne+tW9mCLz8v8+fH8PTThdi71zoyDqnG0TJSN5vR104gpcVdf30I9903gGef7c1K\nFnzWrDiWL+9BOJxMDVbW2yWvn5qaGiFlrVJPDzsPHMfmi0xS2mtq2KZeqtldsmQYXV1jlSOkAAAg\nAElEQVTp29W4XNqNYmenBXV1ye8kisADDxRj61YbhoaSG/SWFmUpQzjM4cc/LknUXvI8awf08MOp\nHTllZYJuq4JM+OlPiyEIwN69VkWaV20tj08+cWDZshK89ZYLNpuIWbOSUaahIWOHszryd9VV5Vi7\n1o5YLHmf1dfzePDBYlx++RAefTSAL3xhWJOSXVkpoLGRpSN3dFgU/RdLS4Ws0volRBFobLTj+98H\nnnuuEL297F45/PAYnn++APff34977x3A3LkxQ4coxyUjhmon49AQp5txojaI5VRUCNi0yY5TThlG\nc7MNPT3M0cdxwNKlEfzylwHs2mXT1QYoLhZx3XWDePlld0IT4sorQwiFOFgsmdUjqnn66UJ85SsV\n+OQTB/r6OGzdakdDQxzxODPGli4NKxwbErNmxbFrl20ks0HMuPbbyOkTDgPPPluAWIzDRRdFcMkl\nFdi+3YYZM3hs3WrHm2+6EhkNoZD2fiwqYg7I1asdmDcvDp5nmRLSviVbgkEOgQCbL4uLBaxZ48SF\nF4axf78Vb7zhxvnnh8FxbCwVFfprydKlYTz5ZFHCQAaSc8TXvhbCnXcOGB5fr5+npHkwZ04c777r\nwqxZcTzwQD9eeqkHX/96CH/6UwEiETYm6dmzWoFNmzogilyinYPFwuZ06X5JdY4yaU2WCxPG+JPS\nONUbhWg0KdihLrxevDiauMHlCyVL+zw0jb+//KVXpg5mxZIlUezbx7wk69Y58Pe/u3HLLYMpN0yH\nHRbPud9IZSWPjz5yKLzxAHDmmcPgODZ5BoMsb1rP+GtttWHRoqhuqpl0DeWwninZX4uSEhG7dtlw\n1FGxhACLOm1ATXLhYSc4Fksd7VIbNX5/9n1cqqr4hJiDlEuv/tw9e/RTLktKBPT1KV9L5e232ZjH\n/e9/706kIMjTPltaWDRYbQBv324zrEdRH2/lSheeeqovoUwXCHCaxuzZcPjh8YzEc/S4777ikTRv\nVjB+/fVlOPbYKI48MgaeZ/dyOkpLBfzv/6beLALZ9SeT0kdWr3YkIlhqyspE/OIXgUTNnyhKgiuZ\nn8u+Pgs6OqyJDRnAomT33dePmhoBTU12XHzxEB56qB8vvODGmWeyzZJaNMXjYV58af5iGxAL1q5N\n7QmX5oJU7N9vxT33FGPjRjuefLIPCxawVKONG+148MFiPP54b8q/z+a860VQpOe7oYGlPrHUdhv2\n7bNi0yY7Zs2K48MPnfB4BEUpwH/9VwQ8DzzzTCFWrnTCYmGbI7/fmtPa0dhox1FHsXNZV8fntMms\nqxMMDZf//MepmbOTf6fvVZfo6WFlEFariAsuiGDhwvRu6qoqXiGKIzWoziW61NtrQWUlE7ZparKh\nv5/Dhx86cdVVFdi2zZYwKKWaRIA98//+txOPPBJIbLp4nsMZZwynFZGqrhY0AllyfvpTDz75RL84\ne98+KwIBDmvWOBUO0sWLo1izhkUh33/fCauV7Ud27bJh1SpHSgeZ/L6Nx4Ejj4zhySeLFA65oiIR\nkyfzmDMnnihdUKcxNzTwWL2aRWM7OljkT8r0uOMO/dpNOXpZB6++6sIZZ0QwdSob289+5kF1tYB5\n82J45RU3Zsxggjff+Y6xs9liYU7hyZN5jRjP22936Ua6jfq7hsMsU2T3bmbcBQLaUoqKCgE/+cmA\n4dwxb14cTU32hGPhJz/pRyBgybneuqvLgpUru/DOOy68+GIBvvWtIOrrWS11ezsTqdKrbZTmfene\nsNtZim46IRsjvvtd4NvfHsRttw3ijDMi+N73golel8uWBdHba8Fvf1uE664r0y2JAICbbhrEEUfE\nwXEYifwJGRkuevd3MJjcZy9aFMWnn9pxzDFsvzYwwKG0VMTZZ0dw110lmDo1joEBbcP2mhoBLpeI\nbdvsip6qLEUWKfeOZWWCIjLIHGTs5y98IYInn2Rrj83GrsWCBTHcfPMgbr65TOM4qKxMlhFJOgFD\nQ8mspVT3Tj56AamYUMbf4KBFU/AtNYoGkmmf6R5ilvZpbp3RwYP5Ka2pEQSWXnfGGRHs3m1DYSFL\nK2lrs2XkBc+VqVN5rFnjMKxpq6hgdYklJcpaMomWFitOOCGqmxuvV7c5MMDltGGwWkW43SKmTYtj\n715WCyIXvNBDUu0TBOBf/3LizTddOPPM9G01JIwidKlYuDCGTZvYRkKqXVIj5ZGr0TP+MomyySdE\ned3JK6+4cemlQ5o6t6YmfdEbvePt369UXGxttWHq1LEtdJZ47TU3/H4r/vCHQlxxRQg33jiI889n\n1/PVV7szMtQLC8VElDQdRud+0ya7wnN/2WXM48oa0Kb3tktRxVmz4gol0nTj2LDBjpNOGkZbGzPq\nJaEW6fq/9lo3ystFVFcLeOmlApx66jB++ctAQiBC4qSThvHss4WJe9DlEvHJJw7s2GFLubiy+zP1\nEvS73xXhrLOGcf31yZYQV14ZwgMPFOPii8Npa/iM0v2NxmOUBjR9Oj+iusrmiWeeKcTLL7txxBEx\nbN9uw/339yvG4nazaPADDwxg82YHOI5t5j/4wFjlMxWffOI0dARkw4knRrF6NZtPpHWO59l8dt55\n+vexuoGzmu5uC048MQq7PbkRSkd9PY/nnlMa7vK6yWyQjL9TThnG975Xis5OK3ieRZZ377ajqorX\nTZtqarIllC4BSUky/QQp/37r19vxz3+6sHYti/7u2mXD66+7NampEh0dVvT0WNHWpoxGT53K47vf\nHcSFF0bQ0MAn7sOzz47g9dfdmhpSOZKoBsBEOE45ZRi33jqIG29UGlSvv96d+FnKclJ+joiODium\nT2d1VU4nM6ByVTYfHmbqyEuWsPv2mmtCuOuuAcycGcekSQLOPHM4o1KBigoejY121NXxibq3WIyl\nwxmJqxjR1GTDnDnMuD3ssDjWrs2tyfeiRcxYd7tZzWQmNZF6SCUQklgLy0ISElE9ICnqpZcWePLJ\nwwlVX49HAM9zKev5JNSG+r59VixciMTaXFIi4uSTo7j7btZioLCQiTgVFIj4+c8DuPpq/cyxmhoh\nkVXGnkFt9MwI9frY38+ibDabiMWLo/jVrwKwWoGdO20Jo3TBghiamuxoaGD3rJ5RetNNg4o9B8uI\nSn+9SkuV69OKFS6cdho71xYLdCN106fzeOaZPk1KLJAUcgKYETk4aMkoY8rtpshfXlitIvr7LZpQ\ntVxBUR35M2I00j7Vxbz5wAwaG/buteG004bxxhusZ9tJJ0UVOf6jwdSpzEtsNKkff3wUv/pVH0pL\n2UbrnXeUT9DBg6xYWC8HXF7sLREMWnJK+1y0KIplywZQXS1g924bysuFjOrPJMPnqqsq0NjoSOnl\ndji091O23sFZs+KJ+kcW5dR+V3lbDjnMwE7+PxdxFLlyYTjMJdJL5BN1plHrcFibHtrWZs2rzjVT\n9AyvadPiaG21YmDAgvnz44oUSrOfb6MxDA2xtB9JMKOry4JFi5gQz/CwsXKcHIdDRDQK3HvvQFoH\nkryuubHRjvPOi6CxkSnxLlkyjMsu0y7qHMc2GZWVzEBUz1NHHRVDW5stsYmyWJhgzLJlxik1QHrh\ngWCQw6RJPE47bVixeDscwOOP9+GUU9LPZUbp/nrU1CgjOvJntaGBtQKorhawerVjxAi04Zhjopg7\nN45LLw0bLuR/+1t3YgO3d68Vs2en3mzq3SeBAJdThoOaefPYZkl+rE8/deC004YN15+pU+MKBWn1\nWPv7LbjiiiHcckvmpQJ2u7aur7+fUwgbGaFOW2WZOEB5uYg77gji9dfdOPPMCIaHOezebcPs2Ww9\nlM99ZWUs7VS+gcu0jQDAVEd37LBhxQoX/H4rfL4CdHZa8Ne/FuDpp3sTkdK//tWN//s/9mx/8IET\nRx8dS9R3Ga0F8mf0gguYcNGllxqXadTUCIlMmfXrHVi0KIqjjoqlnDsmT+Z1I8A+X/eIw49LpPfn\nGtH65z9d+NKXkrWtHo+I446LJZ6FX/wisz6/dXU81q51oLaWT6h7HzxoUURyMuXDD5kT5e23u1BS\nIuLeewewaFH2e6KZM1n0T3rmcy1dePvtpNrqxRcPJWqT1e1Z7rwzqDiXEqeeGk2k1ktjyaTlj1qc\n5913nTjvvPTjve66UMZK9/E4h5oaHl1d6R+qSZN4RbsagBnGpaWshthiSbYcO/HEqMK42rePCd4c\nPGjRzXwpLBTxrW8l56bDD1fOgUbIHd9Ast4vV1pb2TglBgaYHZHuGXO5co/mpmLCGH8OB4s6SHU8\nybYOnCbtM92DPDhoftqnmRx1VAybN9sRj7OWFGvWOHD00VGceupwSjUqM6iri6eMbnEcsGRJNFE0\n/9xzhQolqVTCIfJibwkW+cv+WkyaJGDmTCbq0t1tTYTlpRQmI+SF5Om8NmeeGcELL7gTUbJcFlF5\nkThrFSGORECT7zE6Z+rIH5t8cjO0AoFkSoXesYy+m9yhsnq1Q9NbMhzmRjUSDRgXuNfX89i505Z3\nk/F8ePxx4H/+J4holMM77zjxs5958M1vhrB/vzWlYpscZvxldnNNn550JsRirF5m0yY7KioEHH10\nDNOm6V+Lp57q030dYBtmn69HcV/EYki7OZNSg9SsWePAo4968JOfFOO//1tfGKWmRsgoamCzaZU3\njaip4dHZaUFTkw1/+EOhYh2YNo3HCy/0gOOA008fxle+MoRAgMOcOfG0ab8nnRRNOEyqq4WUzbj1\n2LjRrpvWnQtWK/NoA8kWAZ984sBJJxlHFeWqeWrWrbMr5N7zQeotlw63m9W43XlnCdavtyuOfcwx\nUbzxhgtXXjmEP/2pJ2H8vfKKGwsWJDdu55wT0YhoZOOAveyyIfzqV0WorBRwzTUh3H33AJ55phBH\nHRXFEUfEE3N2a6sNAwMW3HlnCT791I4bbhjE/v3WlArVN96ojNrV1/MpG4PLZf/l0YVUnHtuBPfe\nq3XOnHxyVBEBN2oOr4e6z+/Wrck6q3yoqRGwbRubo6Tvx9bs7NYNUWSpdsXFYiJV9JhjjGsNU1FX\nx6O52Zb4W6YMn/65ttuZo+6jjxz4/e8LE2s6AMycmexB7HIxMasTT0ye0HTXobRUwOLFwzmtZ52d\nVkyZkvWfpYTnWSqxXt9eNfPna3uRdndb8ctf9uGLX1SuAddcE1IEca6+OoTSUhEzZvCor08/T86d\nGzfMcpDD0rvZc9DSYkV9fTyvOW7rVqUytSiyrgIsI8z4Xla3mjOLCWP8eTysyFe9iKrTPjOxsLu7\nLYo6pdGQYc2HGTPiigLhL395KCuJ83xwu5FyIyEhGX/Dw5yuF0bvIdPzzLIeTfmly3Z3MwGaujoe\nv/1tUSKNQo0osglWEj9IN8nOnx9HNMrhu98txYoVzoxSivSIxdixQyEWuZY3aY1GjVsNqI2/3l6r\nYf/CVIgisGqVM5G+w1Jfk6p1qSgvT3qY16/X1oB9+OHBUTe+jPpgTZ7M4+233YoJeTRRqyYKAtDb\ny6LlN900iK4uK267LYjycgH339+PU07JLM3P4UDGaSFHHx3Fxo32RNrUlCk8Nmxw5NXfTY+pU/m0\ntVOAsikxwASB3n/fiSuvDOHhh/szEg9JhdTfNROkyN+WLfaEapscaVPt9YbhdgN33x3Mok6b1U/l\nshl+/32nZgOUDxs2MOn64mIRL7xQgHXrHGnVq2fPjmPLFuUF7eiw4NlnCzNOe06HIHAZRbpra/lE\nLzpJEEWitJTdT5Mn85g0iWV1HH54DG+84VKkzU6ZwuO115TtJNS9wFJhtwOPPtqPq68OJY57111B\nLF2aPBebN9tRWirg5psH8a1vDeLb3x5EVRWLHKeSdS8sFLN2LhcWimhstBsqBqopKBANn/miomTT\n+GyMv+LiZB1/PM7mpXwdAgArFZHKcaQoiVq6X4+CAiGxaQ6HOWzdajPsOZotBQVKh+LgYGb7kMpK\nZsi+8YYbtbW8bs9hiTvvDMrW3PTMnx/H/fcP4OOPO9O+Vy20lqlSd7Y4HKnrOSXU0bgDB1g/1PJy\nMa041rJlLDX1sccCmlZMelRVCbj22tQCbUCyb3Usxtp+fPnL+c3B3/lOULeuuqNDP2IpkY+6cCom\njPFXXCxg82Z7or5ImpTUaZ+Z1PzJ/95i0TYsHW+sViYaYbOxp/uBB1KnX5nN974XTPuekhKmDDVv\nXgzbtrEFPB5PPQnpeTWjUS4nz50cqf+epDKqF/0IBFjKZFkZ21A4HCIKCtJPNN/8Zgg//OEAOjqs\nmt5bmTJvXixR9C8p6kl9J1991Y0LLtCflJjHiP0siixd2e3OfpPPcaxZt2QkWSysBUEoxGkaNasp\nL2fqrqIo9dRU/j7XnpbZYNQHy25n88JYGX9qUaG2NivmzGE/OxzAV786lFi89FRdU5HpeydPFrBn\njy1Rp1lRwdLfzDbApZqMdHi9YTz/fEHi/y+/7MZ11w1mHNlLRzZpn1Kaz7591oyMzosvDmdccyRF\n/m6/Pf3cCLD39vayJta5qIOmQlrvSksFrFjhwtNPpxbNAYCLLgrjjTfcCAZZQ+v333fi8ceLcP31\nIdPKFTJFMv6kf9UOqHPPjcBmYwZRS4sNJ58cxXHHRTXPiHruySbtE2DOP6PrLwgs7fG660Kw29mz\nz3HsHlu/3mF6Q+dzzong1ltLM0qFTof8PGXTwqeqSsDGjazZ9oED1oyiMJlQXp7si8vzrMa/rc2W\n1pF56qlMPIfngW99qxTXXltumqMCUNaHDg/r901WU1XFY/nyAtxwwyAuuiiSUg25oUG/3VQ6MhkH\ni+6aH02So6eMboTbncwu4Hng//2/4qzSyEeDkhJWKvbCCwX45jcH8872W7o0opgvZs5k154JtRmv\nN5WVQkaps9kyYYy/khIR3d0WzYSvTvsMh/XTPqUG8HJ1UIBtIEejB0e+tLXZchIWGCusVparPXdu\nLGHI7Nljw6xZ2pPJvH2ibtqnGVx11RDq63lMn87jiSeU6W3SvbBpkwMLFsTgcADNzUykRE9NTI/K\nSgFXXTWkaX+RKeedF8E77yQtXI+HpQEEgxw2bbJj/nz9RVbqUwkAf/5zAX7yk+KcNvnV1SwdTtoE\nVFUJKC8X8Le/uTVy4WoOOyyO3/ymCOedV5lxY3uzMarlFUXgoYf6c6odyYWGBh5tbclJfOtWOxYs\nyP9zs/HOcxxTKrv99lKccALbEOfScy4dF1+c2WfW1fEIhSwIBDgIAkv1MTNLwe0WceqpmW2IpchC\nPG5e/bX8swGW2pUOJjTA4YYbyvH6666sRaIy5fzzI1i+vCfDCC3bMC5bVoIlS6rxta9V4N57BxSp\nlPny/e9n5qSU1EeN+oTedx/7HI5jtUQFBSJ+9rP0irxm1t0XFzNHgvrzrFaWCqpWzM2X2bPj+POf\nezOKfGQDE3zJ7L11dTweeqgYPl8BWlrMq+V2u5EQ5uF55sBqbGRCPqk44ggm6PKHPxTi5psH8frr\n3aY6ueTN35kcf/q/qa4WsGaNY0zq3FNx5JExvP8+21OMVuZatoar3c723y++6Ma11w6a3ks7W6Sy\nhJYW8yLGcj74gGUedHSkNv6mTOHR1GTD8uUFhu/JhQlj/Hk82n5lQLJxNiBtEvVPSVERkyXft0+p\nVmizZe5ZHkt+/eu+rFIGxoPmZmXfpcZGeyLn3WYTE20dpIhRtp7ZTLnmmlDCqyOfsOTesU2bkmPb\nu9eGZcuCWSl95oPdzu5NaZKWxHL+8pcC3Hprau+Y9H0OHLBiaIhLWWtihCQgJOHxiHjttW7s329L\nqKcZMX06j9tuG8Qjj/Rj6VLzjYxMKCzURv6CQdagW2pdMRZMmcKjvT15HnfutGHu3Pw/lxl/mV/X\nk0+OYuXKroRR8cwzxvV8uXLmmZlHIK66KoQ//rEQ777rxOmnm/tMlZSI+OUvMxOWAJKGdKo+YbmQ\njRf8sMPi+O1vPTjxxCiWLy8ctci0JLyRKd/4Rgj33DOA11/vxpNP9mZkNGbD7bdn5umvqeEVrYBS\n9V1dvTp9CpzR3+bD3LlxQ+PkmGOiOc3D6TDToFyzhhk22cwtRx8dw4oVXXA6RbS22hTiFvlyzz0D\nifFMmxbHjh22tBk/HMf6YlZWCli4MJZR39ZskDupLr88lJGewvz5sUSa4nhy/PFRbNhgR2+vJSch\nuEyYNi2elcP3y18O4+67S9DTYzV0aI81rB3Y6Oyj5f15U92bHAecddZwIpPPLCaM8ed0srQqNSyS\nx36Wav70FgCp2bVamv5QjfxlEvofb5qbWYsFl4ttzltabImUS8nAAZgn3m5nkdexTDGS0orWr7dj\n61Z7YsG+444BnHtuxFAcYzSorEwqukm9xoLB9O1B5BsjqWl4tsycyes2mT399Aj+8x9nomDdiKOO\niuHII2MZKfmNBnqRv/379RVSRxP1ppVlHeT/uTNnxnHzzdmlyJi9cc+HqioBBQUi3nrLlXNqtFlI\nDpLy8twbeeuhbo+SipNOGkZVFY/bbw9ixw7bmM4zqXA6mVBWXR2faIkyHthsLCLFcZLytvGJHa/7\n/LTThg1rnV58sWeMR5M9UgQxm6wCgJ3v6dPjWL3akVXP0XRI674gAMceG1W06EhFQwOPSy8dfafj\n/PnxjKJ5NhtMTT3NFasVuOGGEJ5/viBt2mGufP3rQ4oa2HRMncrjsccCWa9lo8nGjQ5Tsxv04Hku\n7Tx16qnDpt/Hh9AWYPR56CFt6oc87ZOF7vXTPln+L4fWVisuvDB5MyQjf7lvbA81wZixQlLdWrQo\nilWrHIqm6VL9TXW1MKI0ydI+R6swWY/p01mvna1b7Yp00K98ZewjWOefH8bpp7NVePJk3rCPlB7x\nOLtPly/vyUkZdfJkHk89pa0LWrIkiiVL0tcLjTeFhaKmSXVz89hvqm02Nt+YTWWlgLPPHl0V39Hm\nuuvSF+CPBbNnxzFlShzz58dMTZvPJvJnt7MegQBw222Do7Ix+6wTjTKnTnu7DQsXHtoZLp9lcmn1\ncNppw3j++cJRWasFgT0fmaTxEqmpr+dRXCzA5yswVVDq88Qf/tBreor2ocKEifwZIU/75Di2qEj/\nlyOljUptASQkgzEfmOjMxLMA//Ofg+A4YOHCKB57zKPwJst7rOzZY8O0afGs1NjM4LDD4ti61Q67\n3bx0oFyZNEnAjBlsEpKiCJlsJjku2YfvuONy82BZLEwS+7OK261V+9yxI3W66mhhsbAI7IEDqYVy\niPHh8suHcMopUZSViSkVGbMlG+NPzjXXjL2gymcBptAsYM6czCIuRG7kYvxNmiTglVe6078xB449\nNpZROxAiM770pTBeftltaoru5wl579/PGxMq8qcH68+S/H9fnwX19dpNYVGRiM5ObXNWM4y/XGux\nPutI59FuZ32T5MpX5eUC9u1jt+e6dQ7cdFNozNM+bTZgxw477rhjbNVSM8FmEzOelDo7raam4HzW\n0Kv5i0bNSbnMlqVLw3j1VRdcLlEhPU98vsk2fY5IzcGDVlRVRfC1r6XvIZYphYUTd440Itf7drTW\n6XQ9NYnsKCkR0dFhHbeSDGL8mPCRP3mfP4AZf3rqakVFAgYHtaeLNcDNbwzhsGVCGn9yrr56SLHI\nlJWxVhCCwGSU3e6xT/sEgGef7cW8eYeeV+yKK4Zw+umZpfodPGhJq4z2eUYt3uH3WxKNrseaww6L\no63Nhr17babLvROHLqMlVjVR6eqy5NSzNBWNjR3p3zQBGes1lxhbvvrVQyPlfiIyPAw4neOz95+w\nj7WUgsP6syRPvt9vRU2NdqNcVMQaa6u9YC4Xk6fNBxb5I6+jHCnt8+OPHYkWCWwDlbxWuaSkfF6o\nqhIyKkS22YD2dmtaYZjPM/I+f6IIPPdcIbxe8yIGuWCW2Avx2YCMP3O5/vpQRr0YsyHffrGfR7Jp\n9UB8Nnn0UYqmjhft7dZxa4E14R9rdcrleedFMGeO1iPv8Yi6kT+z0j7NbrL8WcftZkb1Rx85ExEu\nddrn0BBHqTppKCtjfWrKyibuebLZmKJWNAp84QtVsNtZZHn8xiMekgrBxOhhsSR7bhL5c8kl4THr\nzzmRGes6e4KYSJxzTsTU2vJsmLA1f1LEKBrlFDV/Rn3T3G4RgQCX6Acn4XTqG4XZMFFr/lIhXR9R\nTBp8PM/qAyX6+y05qVdOJMrKgECAvLccB+zbZ8XXvx4ad6nthQtjeTuMiM8WU6fyOPvs8Zd4J4hs\niMeBAnN7SxMEMcIXvzh+a8IE3xJmXtDMcUBPj7ZWyOUSEYmQ4MtosHx5gaIvHc9zishfT48FFRXk\n/U1FWRkwMDDhH3MAQGurDUccERv3iMHppw+PuwFKjC1VVULGTcwJ4lBBroZOEMTnhwkb+cuFQEDf\n+DMj7bOujiZYNStXdinSFVnaZ/I89fRYUF5OuVSpKC1l9y3B0l+XLqV+RgRBEJkg74NMEMTnhwlv\n/GUjGFJUJCpSRAGW9plv5I+lL1IES41adZXVHyT/39Njof40aZg2DQgGKcUQAHp79ZV8CYIgCC13\n3DFAwlQE8Tlkwht/2fDoowHYVGfMjLRPqWEtkRp52ufOnTY89pgHb73VNb6DOsSZPBlYsYLOkaTu\nO1HVYQmCILKF+r8RxOeTCW/8iVnMbXKxEQm3G3mnfcbjJPueCVLa58GDFvz610W49toQCb5kgDpa\nTRAEQRAEQUxMJqzxx3EiBBOCbU6niOHMem0TeSKlfb71lgtXXDGERYui4z0k4jOC1Zp/hJ4gCIIg\nCOKzzoRVgvB4RAwMaJu2Z4vFwhqh5kM20ceJjJT22dlpJcOPyIqaGgEzZ1J9KEEQBEEQE5sJG/kr\nKRHQ3z/+tm8sxpo+E+mR0j6pbovIlssvHxrvIRAEQRAEQYw742/9jBOlpcz4G++o28cfO7BwYWx8\nB/EZQUr7JOOPIAiCIAiCILJnAht/IgIBc6yIfAzIbdvsOPZYSmHMBJ5n4joFBSC8OU8AACAASURB\nVBQpJQiCIAiCIIhsmbDGX00ND7/fOu5RpFDIgqIiMmYyQRA4RKMcCgqoLQZBEARBEARBZMuENf7q\n63k0NdlNaa6erwE53gboZwWeB6JRwO0mY5kgCIIgCIIgsmXCGn9WK9Dba0FJSf7G33jXDU4UeB6I\nxTi4XHTCCYIgCIIgCCJbJqzxBwB9fRaUlo5/5I/IDEHgYLOJFPkjCIIgCIIgiByY0MbfeedFUFfH\nj/cwiAzhecBup7RPgiAIgiAIgsiFvPr8eb3eMgAvAGgA0ALA6/P5+lXvqQfwRwA1AAQAT/p8vl/l\nc1yz+NrXqPfXZwmeBxwOkdI+CYIgCIIgCCIH8o38LQPwjs/nmwPgXQB36bwnDuC7Pp9vHoDFAG7x\ner2H53lcYgIiCBxF/giCIAiCIAgiR/I1/pYCeG7k5+cAXKx+g8/n6/D5fBtHfh4E0ASgLs/jfi4g\noZjsYGmfVPNHEARBEARBELmQr/FX7fP5OgFm5AGoTvVmr9c7DcACAKvzPO4hBceJEHLQjRkaop51\n2RCPs7RPMv4IgiAIgiAIInvS1vx5vd6VYPV6EhwAEcAPdd5uuCv3er1FAF4EcPtIBDAjamtrM33r\nuFFdDZSVFaOwMLu/27cPaGgAamtLRmdgY8BYXp9Vq4Dnny/EjBmFKC8fs8N+5vksPEMTGbo+hz50\njQ596Bod2tD1OfShazRxSGv8+Xy+Lxj9zuv1dnq93hqfz9fp9XonATho8D4bmOH3vM/neyWbAfr9\n/mzePi7E4wXYsyeCysrsong7dtggijb4/ZFRGtnoUltbO6bXp6EBiESKEQgMIPLZPGVjzlhfIyI7\n6Poc+tA1OvSha3RoQ9fn0Ieu0aGPmcZ5vmmfrwK4euTnrwMwMuyeAbDN5/P9Ms/jHZIUFIgIhbJv\n9tffb06T+YmEzQY4neM9CoIgCIIgCIL47JFXqwcADwPweb3eawC0AvACgNfrnQzW0uFCr9d7MoCv\nAdjs9Xo3gKWG/sDn8/0zz2MfMrhcIiKR3Iy/+vr4KIzo88uCBTFw2Z9qgiAIgiAIgpjw5GX8+Xy+\nXgBn6bx+AMCFIz+vAmDN5ziHOm63iHBYa5F88IETFRU85s/XN/D6+znMm0fiJdlw8cXh8R4CQRAE\nQRAEQXwmyTftk4Bx5O+dd5z45BPjHMVAgNI+CYIgCIIgCIIYG8j4MwEj408UkTJFMRTiUFREkT+C\nIAiCIAiCIEYfMv5MwO3WN/5iMQ5WKzPuBAHo7VWebkHgYKErQBAEQRAEQRDEGECmhwm4XPo1fxYL\ni/4BwK5dNvz3f1eM8cgIgiAIgiAIgiAYZPyZgNsN3chfQYGIcJid4n37rKiv5xW/FynjkyAIgiAI\ngiCIMYKMPxNgNX/Gr196aQX6+pTiLoEAh+JiEnshCIIgCIIgCGJsIOPPBFiTd+2p5DhAFDns3m3D\nrl02eDwihBF7b9MmBxYsiI3xSAmCIAiCIAiCmKiQ8WcCbreI4WFjWc/aWh5NTXYsWhRFUxNrrdjY\naMeRR5LxRxAEQRAEQRDE2EDGn0kY1e+JIlBRIaCz04rTT4/gww+dEEWgr8+CggIq+iMIgiAIgiAI\nYmwg489kHnzQA35E10Xq8VdRIaC314LychGBgAXvvefE6acPj98gCYIgCIIgCIKYcJDxZyKCAPz9\n7260tFgBsKgfzwPV1Xyix5/VCrzyihunnELGH0EQBEEQBEEQY4dtvAfweWLvXiu+9KUwNm92YNq0\nMCwWFumbNo1P1ARee20IFouYiAoSBEEQBEEQBEGMBRT5M5ENGxy49NIh7N5tQ1+fBWVlAgYGLPB4\nBDz+eC8AoLxcQGkp1foRBEEQBEEQBDG2kPFnEi6XiK1b7Zg5k4cgAF1dFlRVCRga4lBUJGLpUp1G\ngARBEARBEARBEGMEGX8mMX16HM3NNnAcYLeLaG+3oqpKQCjEkaonQRAEQRAEQRDjDhl/JnHccVEs\nWcJEXObPj+Hf/3aispJHOMzB7SbjjyAIgiAIgiCI8YWMP5OoqRFw/fUhAMAxx8Tw5ptuVFcLsNmA\nGPVyJwiCIAiCIAhinCHjbxSoqBBQUCDA4xFx5pkRVFYK4z0kgiAIgiAIgiAmONTqYZR4//0uAMBN\nN4XGeSQEQRAEQRAEQRAU+Rs1LHRmCYIgCIIgCII4hCAThSAIgiAIgiAIYgJAxh9BEARBEARBEMQE\ngIw/giAIgiAIgiCICQAZfwRBEARBEARBEBMAMv4IgiAIgiAIgiAmAGT8EQRBEARBEARBTADI+CMI\ngiAIgiAIgpgAkPFHEARBEARBEAQxASDjjyAIgiAIgiAIYgJAxh9BEARBEARBEMQEgIw/giAIgiAI\ngiCICQAZfwRBEARBEARBEBMAMv4IgiAIgiAIgiAmALZ8/tjr9ZYBeAFAA4AWAF6fz9dv8F4LgHUA\n9vt8vi/mc1yCIAiCIAiCIAgiO/KN/C0D8I7P55sD4F0Ad6V47+0AtuV5PIIgCIIgCIIgCCIH8jX+\nlgJ4buTn5wBcrPcmr9dbD+B8AE/leTyCIAiCIAiCIAgiB/I1/qp9Pl8nAPh8vg4A1Qbv+zmAOwCI\neR6PIAiCIAiCIAiCyIG0NX9er3clgBrZSxyYEfdDnbdrjDuv13sBgE6fz7fR6/WePvL3GVNbW5vN\n24kxhq7PoQ9do0Mbuj6HPnSNDn3oGh3a0PU59KFrNHHgRDH3YJzX620CcLrP5+v0er2TALzn8/nm\nqt7zvwCuABAH4AbgAfB3n893Ve7DJgiCIAiCIAiCILIhL7VPAK8CuBrAwwC+DuAV9Rt8Pt8PAPwA\nALxe72kAvkeGH0EQBEEQBEEQxNiSb83fwwC+4PV6dwA4E8BDAOD1eid7vd7X8x0cQRAEQRAEQRAE\nYQ55pX0SBEEQBEEQBEEQnw3yjfwRBEEQBEEQBEEQnwHI+CMIgiAIgiAIgpgAkPFHEARBEARBEAQx\nAchX7XNU8Hq95wL4BZhx+rTP53t4nIc0YfB6vU8DuBCsN+NRI6+VAXgBQAOAFgBen8/XP/K7uwBc\nA9bK43afz7di5PVjADwLwAXgTZ/P9+2x/SafT7xebz2AP4L13hQAPOnz+X5F1+jQwOv1OgH8G4AD\nbH590efz/Ziuz6GH1+u1AFgHYL/P5/siXaNDC6/X2wKgH2yei/l8vhPoGh06eL3eEgBPAZgPdo2u\nAbATdH0OCbxe72ywayGC9deeAeAeAM+DrtEhgdfr/Q6Aa8Gen80AvgGgEGNwfQ65yN/IgvwbAOcA\nmAfgq16v9/DxHdWE4v/Azv3/Z+/O4+SqysT/f24tva/Z0wmkIZEKCSDoALKqKBLAZYTxCoxfkRlF\nVBRFxQFEEXWAr7KoiEMGYSA/R7wqihAF4auAbIooCiFUAOmEpJKQpfet1t8ft253dXVX3dvdde49\nnX7erxcv0t3VVafvuct5znnOOYX+A3jIsqwY8DvgUgDTNFcBJnAwcCpws2maRv53fgD8u2VZBwEH\nmaZZ/J5iatLAxZZlrQaOAT6Vvz6kjjRgWdYw8HbLso4ADgdONU3zKKR+dHQR8ELB11JHesli7yN8\nhGVZR+W/J3Wkj+9gNzQPBt4IvIjUjzYsy9qUv3beBLwZ6Ad+gdSRFkzTbAM+DbwpP9ASAc7Gp/rR\nLvgDjgJesixrs2VZKeAu4H0Bl2nWsCzrMaCz6NvvA+7I//sO4J/z/34vcJdlWWnLsjqAl4CjTNNc\nBDRalvV0/nV3FvyOmAbLsnZYlvVs/t99wEZgKVJH2rAsayD/z2rsG3oOqR+t5EfQT8MeuXBIHenF\nYHwbRepIA6ZpNgEnWJZ1O0D+uHcj9aOrdwKvWJb1GlJHOgkD9aZpRoBaYBs+1Y+Owd8S4LWCr7fm\nvyeCs8CyrJ1gBx/Agvz3i+tqW/57S7DrzSF1qIBpmu3Yo0tPAQuljvRgmmbINM2/AjuAB/M3Zakf\nvdwAfBE7MHdIHeklBzxomubTpml+NP89qSM9HADsNk3zdtM0/2Ka5lrTNOuQ+tHVB4H/zf9b6kgD\nlmUlgOuALdjHutuyrIfwqX50DP6E/mRzyICZptkA/Aw777uP8XUidRQQy7Ky+bTPpdg9c6uR+tGG\naZqnY89pfhZ7dKkUqaNgHZdPWTsNO739BOQ60kUEeBPw/Xwd9WOnq0n9aMY0zSj2qNFP89+SOtKA\naZot2KN8y4A27BHAf8Wn+tEx+NsG7F/w9dL890RwdpqmuRAgP8T8ev7724D9Cl7n1FWp74sKyKcI\n/AxYZ1nWPflvSx1pxrKsHuBhYA1SPzo5DnivaZr/AH4MnGSa5jpgh9SRPizL2p7//y7gl9hTQuQ6\n0sNW4DXLsv6c//rn2MGg1I9+TgWesSxrd/5rqSM9vBP4h2VZey3LymDPxzwWn+pHx+DvaWCFaZrL\nTNOsAs4CfhVwmWYbg7E94r8CPpL/97nAPQXfP8s0zSrTNA8AVgB/yg9Vd5umeVR+QuqHC35HTN9t\nwAuWZX2n4HtSRxowTXNefhU8TNOsBU7Gnpcp9aMJy7Iusyxrf8uyDsR+vvzOsqz/A9yL1JEWTNOs\ny2c3YJpmPfAu7NXw5DrSQD4t7TXTXlES4B3ABqR+dHQ2dieXQ+pID1uAt5imWZM/ru/AXoDMl/rR\nLvjLR8AXAr/FvpncZVnWxmBLNXuYpvm/wBPYKwZtMU3zPOAa4GTTNOPYJ+g1AJZlvQBY2Cfsr4FP\nWpblDFF/Cvgh9tLPL1mWdb+/f8m+yTTN44B/xR6t+Gt+vsUa4FqkjnSwGPi9aZrPAn8EHrAs69dI\n/cwEcp/Tx0Lgsfzc2aeAey17WXO5jvTxGeBH+XvdG4H/ROpHK/l5mO8E7i74ttSRBizL+hN2Btdf\ngb9hD7isxaf6MXI5SfcVQgghhBBCiH2ddiN/QgghhBBCCCEqT4I/IYQQQgghhJgFJPgTQgghhBBC\niFlAgj8hhBBCCCGEmAUk+BNCCCGEEEKIWUCCPyGEEEIIIYSYBST4E0IIIYQQQohZQII/IYQQQggh\nhJgFJPgTQgghhBBCiFlAgj8hhBBCCCGEmAUk+BNCCCGEEEKIWUCCPyGEEEIIIYSYBST4E0IIIYQQ\nQohZQII/IYQQQgghhJgFJPgTQgghhBBCiFlAgj8hhBBCCCGEmAUk+BNCCCGEEEKIWUCCPyGEEEII\nIYSYBST4E0IIIYQQQohZQII/IYQQM1YsFsvGYrFzCr5+NRaLXVbw9e9jsdjaYEonhBBC6CUSdAGE\nEELMLrFY7Hbg3PyXWWA78Dvg0ng8nqjwx70fSFf4PSctFosdB/wBaI/H41sm+bsvAevi8fhVSgon\nhBBi1pDgTwghRBAeBT6A/RxaDtwMWMDxlfyQeDzeVcn3mwYDyAVdCCGEELObBH9CCCGCkIzH47vy\n/96eT838TiwWa4jH430AsVisAbgOe/SuCXgOuCwejz/o9UNisdjvgZfi8fj5BV+/DGwGPgVUAfcB\nn4jH4wP51xjAN4GPAtXAr4A/A9+Ox+PRMp/1PuCrQAxIAnHg40AXdrCbAzpisRjAw/F4/KRYLHZE\n/rPeDNQBG4Er4vH4AwXlXQ58NRaLXZl/jwOABPB/sQPo+cDe/HuOpMAKIYQQxWTOnxBCiEDFYrE2\n7CAmk//PcTtwMnAO8EbgceC+WCx20DQ/8kygFXgr8EHg3cCXCn7+OeBC4LPAm4BngCsoM3IXi8UW\nYo9c/ghYBbwFuBE75XQL8L78S/8JWASckf+6CbgrX5YjgPuBe2Kx2Ir8z88AOrCD4EXAYmAr8Bng\nX7CPzQrgPcBTkzsMQgghZhsZ+RNCCBGEt8disV7sTsha7MDq2/F4fBAgFostxw7STo3H4w/lf+ez\nsVjsBOAS7FG5qdocj8c/n//3plgs9hPgndijdgAXA9fH4/H/zX99YywWOzpfnlIWYz9Tf1owpy/u\n/DAWi+3N/3N3PB5/3fl+PB5/pOh9vhKLxd6LHQxfHY/HO2OxWAboK/y9WCy2P7ApHo//If+trdhB\nqhBCCFGSBH9CCCGC8BTwYezAz8QOvq4o+Pkq7IDwD0W/9yj2qNp0/K3o6wTwLoBYLNYEtAF/LHrN\nk5QP/v4O/BbYEIvFHgQeBu6Ox+NbyxUkFovNA64C3o49shfBTjVd5vI33A48GIvFXgYezP93bzwe\nT7n8nhBCiFlM0j6FEEIEYTAej78aj8dfiMfjV2KnNt7k02cni77OMf55OKnFWeLxeDYej5+KHcT9\nCTtQ3BSLxU5z+dU7gOOAL2AvdvNG7OC0yuXz/ga0A58HhrFTTJ/Nz5MUQgghJiTBnxBCCB1cCZwX\ni8XelP96Q/7/Jxa97kTgeVWFiMfjPdgjgccU/aj461K//+d4PH5NPB5/K/AIcF7+R07AGS76lROA\nm+Px+Pp4PL4B2AkcWPSa5AS/RzweH4jH4/fE4/HPAkcCB2PPHRRCCCEmJGmfQgghAhePx1+OxWL3\nAv8JrInH4/+IxWI/A26OxWIXYK/O+UlgNXC24uJcB1wZi8Xi2KN478ZeeKbcgi/HAO/ATv3cDhwE\nHAb8d/4lm7H3NDwtFotZwHA+0IwD/xqLxR7HfiZ/jfEds68Cx8Visf2AAeyVPT+PHaQ+m//eOdiL\ny2ya1l8uhBBinyYjf0IIIXTxLeDkWCzmjPb9O/AAsA47yDkGOD0ejxcGOMUB2WS/nsiN2CmoNwJ/\nAY7CDgiHyvxOd758v8QOwG7Nl/sbAPnFWi4F/gM7aPtl/vfOw34W/xG4G/gN8HTRe38VaMEOFF8H\n9gN6sBemeQJ7vuH7gDPi8fhLHv4+IYQQs5SRy01/z1nTNNdgPyRDwA8ty7q26OdNwP8H7I+dunKd\nZVn/M+0PFkIIIXwQi8VuAw6Nx+NHBl0WIYQQYqqmPfJnmmYIu4f0FPLpOKZprix62aeADZZlHY49\nGf460zQl5VQIIYR2YrHY4lgs9slYLHZwzPYF4EPA2qDLJoQQQkxHJQKwo4CXLMvaDGCa5l3Y6Scv\nFrwmBzTm/90I7LEsK12BzxZCCCEqLYO9z95VQA3wMnBBPB6/LdBSCSGEENNUieBvCfBawddbsQPC\nQjcBvzJNMwE0AB+swOcKIYQQFZefn/f2oMshhBBCVJpfqZenAH+1LOsk0zSXAw+apnmYZVl9Lr83\n/QmJQgghhBBCCDGzGZV4k0oEf9uwF3JxLM1/r9B5wNUAlmW9Yprmq8BK4M9ub55IJCpQRKFCW1ub\n1I/mpI70JvWjP6kj/Ukd6U3qR39SR/pra2ur2HtVIvh7GlhhmuYy7L2NzmL8HkybgXcCj5umuRB7\n/6N/VOCzhRBCCCGEEEJ4MO3VPi3LygAXYm9suwG4y7KsjaZpftw0zfPzL/sGcKxpmn8HHgQusSxr\n73Q/WwghhBBCCCGENxWZ82dZ1v1ArOh7txT8ezv2vD8hhBBCCCGEEAGY9sifEEIIIYQQQgj9SfAn\nhBBCCCGEELOABH9CCCGEEEIIMQtI8CeEEEIIIYQQs4AEf0IIIYQQQggxC0jwJ4QQQgghhNjnXHrp\npfT395d9ze23385f/vKXKb3/s88+y2WXXTal3w1KRbZ6EEIIIYQQQghd5HI5rr76atfXnXfeedP6\nHMMwpvX7fpPgTwghhBBCCDGjWJbF/fffj2EYnHrqqRx//PFccsklHHzwwbz00ktcffXVfPazn+WW\nW26hqamJO++8k4ceeojW1lbmzZtHLBbDNE2uvfZajjnmGE488UTOPvtsTjnlFJ544gkymQxXXnkl\n++23Hy+++CI33XQTyWSS6upqvvSlL7F06dKgD8GUSNqnEEIIIYQQYsbYtGkTDzzwAP/1X//FTTfd\nxK9//Wv6+vrYtm0b73//+7nttttYuHDhyKhcPB7nscce47bbbuOaa65h06ZNJd+7paWFtWvX8t73\nvpef/OQnACxbtozvfve7rF27lo985CP893//ty9/pwoy8ieEEEIIIYSYkvvvr2HDhmjF3m/16hRr\n1gyVfc1zzz3HCSecQFVVFQAnnHACf//731m0aBErV66c8PXHHXcckUiESCTCMcccU/K9TzjhBAAO\nOuggHnvsMQD6+vq4+uqr2bp1K4ZhkMlkpvrnBU6CPyGEEEIIIcSUrFkz5BqsqZbL5QCoqamZ9ntF\no3YgGwqFRoK82267jSOOOIKrrrqKHTt2cPHFF0/7c4IiaZ9CCCGEEEKIGeOwww7jscceI5lMMjg4\nyOOPP85hhx02EgQ6nK8PPfRQnnjiiZHXP/XUU5P6vP7+fubNmwfA/fffX5k/IiAy8ieEEEIIIYSY\nMd7whjewZs0aLrjgAgzD4PTTT6ehoWHcypvO17FYjGOPPZaPfvSjtLa2cuCBB1JfXz/ufUut3HnW\nWWdxzTXXsG7dOt7ylrdU/g/ykVEcIWsml0gkgi6DKKGtrQ2pH71JHelN6kd/Ukf6kzrSm9SP/mZL\nHQ0ODlJbW8vw8DAXXXQRX/jCF1ixYkXQxfKkra0NoCJ7SsjInxBCCCGEEGKfdt1117F582ZSqRSn\nnHLKjAn8Kk2CPyGEEEIIIcQ+7ctf/nLQRdCCLPgihBBCCCGEELOABH9CCCGEEEIIMQtI8CeEEEII\nIYQQs4AEf0IIIYQQQggxC0jwJ4QQQgghhJj17rjjDizLCroYSknwJ4QQQgghhJixNN+3XCuy1YMQ\nQgghhBBCif50P8/vfZ7uZDer56xmSd2Sab/njh07uOSSSzj44IPZtGkTmzdv5swzz+TPf/4zc+fO\n5YorrqC5uZlEIsF3vvMduru7qa6u5gtf+AL77bcfTz75JOvWrSOdTtPc3Mzll19OS0vLmM+47777\neOyxx7jqqqu49957uffee4lEIixbtowrrrhi2n9DUCT4E0IIIYQQQijxm82/4aLfXQTAofMOZd2p\n65hfM3/a77tt2zYuu+wyVq5cyUknncTBBx/Mpz71Ke68807uuOMOPvOZz3Dddddx8cUXs2TJEjZu\n3MgNN9zA9ddfz6GHHsrNN98MwPr167nrrru44IILRt77F7/4BX/5y1/45je/STgc5q677uLHP/4x\nkUiE/v7+aZc9SBL8CSGEEEIIISrPgJ/Gfzry5XO7n2P30O6KBH+LFi1i5cqVAIRCId72trcBcPLJ\nJ/PVr36VwcFBNmzYwNe+9rWRtNB0Og3Arl27+NrXvsbevXtJp9MsXrx45H0feOABFi5cyNe//nXC\n4TAAy5cv5xvf+AbHHXccxx9//LTLHiQJ/oQQQgghhBAVZ2Bw2oGn8di2xwA4oPkA5tTMqch719TU\nlP15LpejsbGRtWvXjvvZd7/7XUzT5JhjjuHZZ5/lzjvvHPnZ8uXLefnll9m1axeLFi0C4Oqrr+Zv\nf/sbTzzxBD/60Y+47bbbCIVm5tIpM7PUQgghhBBCCK3lcjn+efk/8+N3/5ib33kzPzr9RyysWVix\n93Zks1keeeQRAB566CEOPfRQ6urqWLRo0cj3AV555RUA+vv7mTdvHmCP9BVasWIFF198MZdffjl7\n9uwhl8uxc+dODj/8cM4//3z6+/sZHBysyN8QBBn5E0IIIYQQQijRHG3mxMUnVvx9DcMY+XdNTQ0v\nvvgi69ato7W1la985SsAXH755dxwww2sW7eOTCbDSSedxPLlyzn33HO58soraWxs5IgjjmDnzp1j\n3vuQQw7hggsu4NJLL+Vb3/oW//mf/zky1+/MM8+kvr6+4n+PXwzNl0bNJRKJoMsgSmhra0PqR29S\nR3qT+tGf1JH+pI70JvWjv32hjk477TR+/etfB10MZdra2gAMt9d5IWmfQgghhBBCiBmrcBRQlCfB\nnxBCCCGEEGLGWr9+fdBFmDEk+BNCCCGEEEKIWUAWfBFCCCGEEEJUnGEYhMPhMWmZ2WyWTCYTYKlm\nt4oEf6ZprgFuxB5J/KFlWddO8Jq3ATcAUWCXZVlvr8RnCyGEEEIIIfRSVVU1skl6sWw2SyqVIpvN\n+lwqMe20T9M0Q8BNwCnAauBs0zRXFr2mGfg+8G7Lsg4BPjCVz8pmYc2aedMssRBCCCGEEEKV6urq\nkoEfQCgUKhscCnUqMefvKOAly7I2W5aVAu4C3lf0mnOAn1uWtQ3AsqzdU/mgPXtCbNsmJ4kQQggh\nhBA6qqqqIhRyDzEMwyAajc7olTo/97nPsWnTJgAuvfTSkb0AdVaJtM8lwGsFX2/FDggLHQRETdP8\nPdAAfNeyrHWT/aCdO0MsXCjDw0IIIYQQQugmHA5POJrn7CteHOgZhkFVVRXDw8O+lE+lq6++Ougi\neOLXgi8R4E3ASUA98KRpmk9alvWy2y/mNzUE4NlnYf/9x35PBEvqQn9SR3qT+tGf1JH+pI70JvWj\nv0rVUW9vL+l0esz3qnM5aq67DqOjg9TFFzOwfPlIMAh2CujChQsnnQL6qU99ih07dpBMJvnwhz/M\nBz7wAY444gg+/OEP8/DDD1NbW8vNN9/MnDlz2LZtG5dddhldXV3MmTOHq6++mkWLFnHppZdSXV3N\nxo0b2bt3L9/85jf55S9/ybPPPssb3/jGkYDuyiuv5Pnnn2d4eJhTTjmFCy+8ELBHOefPn09bWxsn\nnXQSd999Ny0tLfzqV79i3bp1pNNpDjvsMK688kpyuRyXX345zz//PIZhcOaZZ3LuuedO84hPXiWC\nv23A/gVfL81/r9BWYLdlWUPAkGmajwJvBFyDv0QiMfLvV16ppba2mm3bupjBI8T7jLa2tjH1I/Qj\ndaQ3qR/9SR3pT+pIb1I/+qtkHdXU1Ixb2TP0nvcQevJJAKI//Snpv/yF8JIlY35v9+7dk14B9KKL\nLqKhoYFkMskFF1zAoYceyuDgIMuWLeMHP/gBt9xyC7feeisf+tCH+PKXv8xJJ53EySefzG9+8xsu\nv/xyvv71rzMwMEBXVxc33HADjz/+OBdccAE33XQTF154IR//+Mf5wx/+h9cdcgAAIABJREFUwPLl\nyznnnHNoaGggm83y+c9/niOOOIIDDjiAZDLJrl27SCQSZLNZduzYwYsvvsgvfvELrr/+esLhMDfe\neCN33nkny5YtY8uWLdxyyy0A9Pf3ez7ulexAqUTw9zSwwjTNZcB24Czg7KLX3AN8zzTNMFANHA1c\nP9kPGhw0aGnJMTwMNTXTLLUQQgghhBBCmWwmQ/VTT418baRShJ94Aj4wpbUfx/jZz37GY489BsCu\nXbvYunUr0WiUt7zlLQAcdNBBPPPMMwBs2LCBq666CoB3vetdrF27duR9jj32WAAOPPBA5syZQ3t7\nOwDt7e3s2LGD5cuX87vf/Y777ruPTCZDZ2cnHR0dHHDAAWPK44xmPvPMM2zatIlPfOIT5HI5kskk\nra2tHHPMMWzfvp3vfe97HH300Rx55JHTPgZTMe3gz7KsjGmaFwK/ZXSrh42maX4cyFmWtdayrBdN\n03wA+DuQAdZalvXCZD9raMhg/vwMfX0hampk7p8QQgghhBC6CoXDJA85hOrnngMgFwqROfJIprt8\n47PPPstf//pXbr75Zqqqqvjc5z5HMpkkEhkNbUKh0MhoYrlFZaLR6MhrnH8X/v6OHTuwLItbbrmF\n+vp6rr32WpLJ5Lj3KfyMU045hY9+9KPjXnPrrbfy9NNPc9999/Hwww9zySWXTP6Pn6aKzPmzLOt+\nIFb0vVuKvv428O3pfM7goMGcOVkGByXnUwghhBBCCJ1ks9kxc/dCoRA9v/gFdVdcQWjbNgY/+1lC\ny5ZN+HuT0d/fT0NDA1VVVWzZsoUXXrDHlArnEhZavXo1v/vd7zj55JN58MEHOfTQQyd83US/39/f\nT21tLXV1dezdu5c//vGPHH744SV/901vehNXXHEF//Iv/0JLSwu9vb0MDAxQU1NDNBrlhBNOYOnS\npYEtEOPXgi8VkUwaNDdnGRqS4E8IIYQQQgidZDKZcQu3RObMYfimm8jlchNuAZHNZksGbaUcddRR\n3HvvvXzkIx9h//33Z/Xq1UDpEb5Pf/rTXHvttfzkJz+hpaWFL33pSxO+bqLfX758OStWrODcc89l\nwYIFYwLHwtc7/162bBn/9m//xhe/+EWy2SzRaJSLLrqIqqoqrr32WnK5HIZh8LGPfWxSf3OlGJM9\n2D7LFU6E/Pa3Gzn00BRLlqQ55JB0mV8TfpBJ3PqTOtKb1I/+pI70J3WkN6kf/VW6jqqrqz3t8weM\nzImb7MjfbJNf8KUio1+V2OTdV9XVOUn7FEIIIYQQQkOTCeZSqZQEfj6bUWmfhgE1NblZkfZpGAbh\ncBjDMDAMg1wuRy6XI5PJTHpoXAghhBBCCD/kcjmGh4epqqoquXdfLpcjlUpNensHMX0zKvjL5ezg\nr7d3xg1YTkokEiESiUyYdxyJREin0+M20BRCCCGEEEIXyWSSUCg0MpgBdtCXzWYl6AvQjAr+wA7+\nhof33ZG/aDQ6ZpnaYs4ytIZhkEqlfCyZEEIIIYQQ3mWzWUnr1MyMG0Krrt530z6dEb9Kv1YIIYQQ\nQgghZlzwt6/O+TMMo2QwV6rHpFRqqBBCCCGEEEIUm1HB37684EthPrQjk07T+f3/S9/7TqLz+/+X\nTGbsPD9nURghhBBCCCGEcDOj8gadBV/2xTl/E43g9dxyPQdecY39xeN/5x+GQesnv+j6e0IIIYQQ\nQghRbEaN/AFUV8PwcNClqLziIC6bzRK+/9es5zR+xpkAhH+zflwKqAR/QgghhBBCCC9mVPBnGBAK\nQS637wU8xXv3hUIhMmtO4zkO5WmOBCBz6umEQqGyvyeEEEIIIYQQE5lRaZ/7somCuKYLLqb78afp\n/Psu/vHJS2n6+Oc8/Z4QQgghhBBCFJsxwd++HuNkMplxq3eGwxEajn8bncsMWj85NO53crmcbJIp\nhBBCCCGE8GTGpH0ODdl7/O2rcrkc6XR63PfDYchmJ05zTafTMvInhBBCCCGE8GTGBH+DgyFqa/ft\nQCedTo8LACMRSKfHB38TvVYIIYQQQgghSpkxaZ/d3QbNzRNvdr4vSaVS5HK5khu4OyOEEvgJIYQQ\nQgghJmMGBX+hkeBvX890TKfTZDIZwuEw6XQEMEZSPDOZjKR6CiGEEEIIISZtRgV/LS120DMbtrZz\nRviy2QzZbIhUKhV0kYQQQgghhBAz2IwJ/rq6DBYvnn0rW8ognxBCCCGEPrK5LBu7NtLR00F7Uzur\nWldhMAtGJsQ+YcYEfz09oVkx528is2GkUwghhBBiJtjYtZHT7z6dVDZFNBRl/RnrWd26OuhiCeHJ\njFnts3DOnxBCCCGEEEHo6Okg9fsvwZ4VpLIpOno6gi6SEJ7NmOBvaMigpiboUvgvl5NhPyGEEEII\nXbQ3tWNsfht0tRMNRWlvag+6SEJ4NmPSPmfr3LdcDgxjlv7xQgghhBCaWdW6isMX1PDWf/osp73j\nEla1rgq6SEJ4NmOCv9lM5vwJIYQQQujBwKA+0sDRC45mdWsy6OIIMSkzIu1z797ZscG7EEIIIYQQ\nQqgyI4K/W29t4LTThka+nk0poLPpbxVCCCGEEEKoo33a56ZNEVpasixdOrrHn2E4c+ECLJhPZsvf\nKYRXsr+SEEKIoEnn/MwgbYbxtA/+Lrywldtv3zPme9FojmQSqqsDKpSP5OYixFiyv5IQQgghvBhp\nMzx2IdHjPyVtBmZA2ue//3sfS5aMne9XVZUjmZzdUbsQs1VHTwepRz8Hz50l+ysJMQnZXJYNnRtY\nv3k9Gzo3kEN6F3UjdSREZXX0dJDKpuC310ubIU/7kb8PfnBw3Peqq53gT26KQsw27U3thAZ7yJKT\n/ZWEmISRHvDn3030kPukB1xDktkwns5pezItZywd66q9yd6LMQXSZsjTfuRvItEoJGfJyroy50+I\nsVa1ruLsQ9/Pe5Z9kPVnrJf9lTQhIxb6G+kBt+6WHnBNdfR0kHr4i7Dtn6SO8jZ2beS0//kY51/7\nBKfffTovdL4QdJFECRu7NnLaz0/n/Hu+qE1drWpdxfoz1gNImyFvRgZ/sy3tU4I/IUYZGCxumMfy\nxoNY3bo68F5FYXNGLM7/7fnaPPTFWE4POEgPuK7am9oxth0DvYuljvI6ejpI9zXDk5+TgFhzHT0d\npDMZuHavNnVlYIyMnkubwVaRtE/TNNcAN2IHkz+0LOvaEq87EngC+KBlWXdP9fNG0z73fbLgixAT\ny8rWn1rp6Okg9dTHINpP6og76OjpmPXparpxesDf9RXpAdfVqtZVvKUtyuFvrOL97/m01BF2QBwJ\nRUhjaBUQS/tsvPamdiJGFWkjq1VdibGmPfJnmmYIuAk4BVgNnG2a5soSr7sGeGC6n1lVBcPD032X\nmUNG/oInKW16CYXkwavbOdne1E4o2QK7D5aHvqakB1x/BgbN1Y28ef4/SR3lrWpdxfffeRNt9Uuk\n00Jzq1pXcc8/30s4bGhVV7O9vVCsEiN/RwEvWZa1GcA0zbuA9wEvFr3u08DPgCOn+4HRaI5Uauo3\nxJtuauDCC/umWwwxi4xMwh+oIVo3JJPwA+bs9Tmb6bYwxKrWVZx3aDtbXx/g82e8SZuHvhAzjWFI\nZkMhA4MVzctpiDZq9dyVjvnxDAxWNq0iEg5pVVf2+hmzvNFQoBJz/pYArxV8vTX/vRGmabYB/2xZ\n1g9g+t1YU53zl81l+fuuF7jm2gYtesq9khtM8EYWSrimS5s89tlMgr+Cc/IXt2txThoYLKhZyPLm\nA7UYsdBtZFQIr+T+NjNIHU0slzO0a7dms3bGkLD5dShuBL5U8PW0Tovq6qmlfW7s2sh7rA+Si/TP\nmAUJ5Oaih9GFEkKS0qYBSfssOCf/9hGtzkldHvobuzZy2k/fG+gCNBKAiqmQ4G88HRvvUk8Ts+tK\nrwOTzerzbNJBJdI+twH7F3y9NP+9Qv8E3GWapgHMA041TTNlWdav3N68ra1t3PeWLIFdu2CCH5X1\nwLYHSA9HITpAKpti68BWTl598uTexGeNjTA0BG1tDUEXZUIT1c++aMHCBTx41oO87Svw4FkPctyB\nxxEJa79NJrBv1lFzs73dS1tbY9BFmbap1o+O52Rjo/2Q1aFeHtj2AOlvbYFLFkz7fj/VOnrkpUfs\n1Nw7fkX03NN58KwHeesb3jql96q0fe2+sC/9PXV10NJSO+k2js6mWz+7dtnrPehUz7W1MHdu9T5T\nT5U6tj09EA7rVVfDw3bngU5lClIlWgpPAytM01wGbAfOAs4ufIFlWQc6/zZN83bgXi+BH0AikRj3\nve7uCNu3R0gkhiZV0KV1S4lkWkhH+4mGoiytWzrh++ukp6eBvr4QiURP0EUZp62tTfvjV0lvqH/D\nyP9f3/l6wKXxZl+to97eenp7w4FeF5XYzHa69aPbOdnT00B/v0Ei0Rt0UVhatxQG5gNM634/nTra\ntHuTnZr7yhpS2RSbdm8aqbNg7Vv3hX3tPjcw0Epn5+Ck2zi6qkT97NwZIZNpJZHYVaFSTV8yOYdd\nu/pJJGb+CoSVvIa6uw0MYyGJxI6KvF8lDA6CYSwmkdgedFGmrJKB67QH0S3LygAXAr8FNgB3WZa1\n0TTNj5umef4EvzLtseDqaqY0529V6yq+f+LtLG5t1WoVIiHE5AWdwrGxayOnXvZz2deugE4pUM79\nfe271gZ2v5d99cRUBX1/0002a2i3YIekfU5MxxRd0O/8CVJFcoQsy7ofiBV975YSr/236X5edXVu\nSnP+DAza65fTWtug1SpE5dgrFAVdCiH0kssFf1F09HSQ+dV/wZtuGVlwZabcV2YDZxT29GWnB1YG\n2VdPTIUEFOPlcvoFFDL3fGL2gi96HRiZ8zfWzJi0VKSqKsfw8NRqMZs1CIf1OinLkeVphZhY0Dfy\nwlEcGdUREyneV08IL4K+t+lIx8a7bMkxMR1H/nTsPAjSjA3+ppL2CXqelOXIyJ8Q4+nQ21qYVujM\n+ZvtdKgXIWa6UEiCimK6toXknjeejoH6TGv7qzYjg7+pzvkDPU/KcnS94QkRtKBHxHVIK9SR3K/0\nJo1V/dlzyYK7kCqxmFXFy6Rh203m/E1Mx1E2Hc+fIM3I4M9O+5za72Yy+p2U5UhvhdCFTg0CeeDq\nSepFf3YjSCpKZ6FQLtBraWPXRnuLknSaaCTC+jPWB562rGNAYc/5k4iimI7tVh3PnyDNyOAvErHn\n7k1FLjfz5vzJCSt0MNIg6K8jWj8QeINAevH0JPWiN3mm6C/ouWQdPR32FiU3vEbq8/tpsZiVvYhI\noEUYxzCCDdJ1pWPKsgTpY83YR8BULzgdeyTKsZc3DroUQhQ0CK7dO7K6ZVDkgasnqRf9SfqT/oJO\nJxzZoqR3qTaLWdmdFnrdYIKuJ13lcoZ2daXj+ROkGRQGVcZMC/5AHtRCD6N7loW0aBDIdaEnqRe9\nycjfWNlclg2dG1i/eT0bOjeQm/5WxNMW9IIvzhYloM8WJTq23WSrh4llsxAOB12KsaTTa6wZmfY5\nHZmMnABCTIVOe5bJA1eIqZFG0FgbuzZy2v+eRZpBojXpwNPZIfgRJR23KNHxvA06PVdXOgbq0uk1\n1qw7FPacv6BLIcTMU9wgCHr1N6En3Rpooph+GzAHqaOng/Tjn4HHvxR4OrtDRpTG03Xlc6mn8XQM\n1HUsU5Bm7MjfVCvR7pGQq1WImUweuHqSetGfTo0gHVYQbm9qJ2TsIJvLapHODvZCIlNd1G5fpdN5\n65AgfWIy8leaDvc8mMHB31R5OSl1qRzQt7dLiKDJdaEfHVfkE2Pp0giCfMrlT99LmiGi4WggKZer\nWldxVmw5fdm9XHjGsVrMb5OgYiKGNuetI+j0XF3puOCLLp0HI/c8Y4hoKJh7HszCtE8vc/42dm3k\ntP/5KOc/cD6n3306L3S+4E/hNJPNZXl+r14T4YUA6RTRmdSL3nRpBEE+5fKem+Gl0wJLuTQwmBOd\nz6q5B2mTzi5zycbTcX9KO/gL/nzRjZ4jf3qku3f0dJD+1maAQNPMNase76ba2+Kl17Ojp4P0fz0J\ng62BzwEI8iG9sWsjaz60k/Nv/Z9ZHQQL/eg0eiFGSS+4/nS6dtqb2jGIQP/8QFMu02m91gKQ4G88\nnc5bh+zzNzE9gz89ytTe1A4DCwACvefNwrRP9+Ho9qZ2CGUhVUc01KfFHIAgdPR0kOs6CDLRkSBY\nl5W/xOwm+1/qS4feVVGaTiN/q1pXsWb5wcxd3s6Hz/jXwFIudVsLQIe0z6A/v5hO561Dh3rSkY51\npUuZnHvc2netHZlWFoQZG/xNtRIzGff9R1a1rqKtpZVPHH09Rx/SqsUcgCC0N7XbPVsY2kyEF8Kh\nw41cjCUNIf3plDJtYDC3toXVrQ2sbh0IrBy6NAwdOswl0y3NUqfz1iEjtBPTsa50Gflz0spPX3Z6\noOWYscHfVHkZjjYwqKuKcvzCt3FQa9qfgpUR1ENgVesqDltQy0lv/gKnnnTZrA2CHUE/jIWYCXR7\n6IuxdJn74gh6Q3PQr7EaCuUCn0um20bdOqYSQvDtAp0WKHTIgi/6k+CvhEjEHiXUgdsJ29dnkE5D\nS0tlLzYDg8aqeo6afzSrW5MVfe+ZyG4g6HVDE7Nb0A2PYrqVR4ynSw+4ww7+gm+V6dQw1CEg1i3Y\n0nElYR3SPjd2beT0u08nlU0FunpkId3OHQi+nooF3eGkWfWo57VHIhTSJ/hzc+ONjfzgBw1K3ltW\nsxql4w1NzG66zVXS7QErxtOtBzwczgUe6IBexwQk+Ctmd1rodYPRIT23o6eDVCYFa/8U+AKFDt3O\nHbDbseGwHuePvY9nsGXQrHrUy2S8nZThcI5MRrOnwQQyGaitzRGNqnn/UCj4k1QXujWahJBzUkyW\nbiN/hhF8R2vQvfDFdBhR0i09WMd7nQ7BX3tTO5FsA+w8TJu1GXSsK53KpENmoUaPAHUKL06vPRLh\ncPCV48Vrr4VZujStzUm9L9Ot0SSEjuek3Iv0plMjCPQ4h3UL/nQIKnQbvdHhPCmmw9zMVa2r+Olp\nv6Km2mD9Geu1WJtBt3MH9Dp/7Pgi2PNGk0MxeZO5Mb797fNH/r2vBX+7doVZvFjd0JwOPZC60PGG\nJmY3Hc9JuV/oTbdAB4Ivj26jXDo8d3W7t+iYgaTDap8GBgfWHUxdTZjVrasDX+wFZMEXN3ZmYbBl\n0OjSnjyvN8eXXhrNifQa/c+UtM/OToPmZnV3Hy83t0cfraa/X/9jNV26NRCEHoJspOl2TurycBWl\n6bhwRtB0ahiCM90i+NU+dTomOo3cOHQYoQVIpVA29WcqdOs4AL3On3AY0gFvJKDJoZi8+vosAwPl\n70wbNkTYtm3sn5jJeOuRmCkjf93dIaXBn9uqY7kc3HJLPQ89VK2sDLrQ8YYmghX0xG3dGmg6jFgU\n0608QdNt4QwdRyKDpsOIUjZraPW80/E80SX481JXW7eGeeEFfxb417GtFPT1VMjLeXPOOXOUlkGz\n6vGutTVLZ2f54t9xRz0PPlgz5nv7Wtqn6uDPbbXPbdvCvOMdw2zZsu/vGqLjDU0EK+hgR6feTNBz\ndWAdGmc60a3DQIdGvW7niA5Bhb3Pnz4HRsdUQh3qCey2qltdPfRQNXfcUe9LeXS7xzh0eVZ6OW8e\nfbRa6eigJodi8lpbc67BX2trlr17x77Ge/CnR9qn2wnS02PQ1JTz9NqpsCc0l/75hg1R3vzmJMPD\nwR8r1XS9oYngBL0fl3RIlGcY5e9fs5EOwVYhHToLdDoeoMvIn173Ft3KA3rUEzjBX9ClGKVjXWWz\n+kyRsJ9L5W86ra1ZurrUHUTNqsc7LyN/0SgkEmHmzRsdwvM+529mjPxlswbhsB2squglcGvc7t4d\nYsECtQcqm8uyoXMD6zevZ0PnBnIEcwEHPcqiy3EQo4J++Ov4kNVJ0COzEPznFwv6PlbMDkaDPUi6\njOA4dDhvdbu36Nj5qkt5vGxh5uc5peO8Yp3ue251kc3CnDnuMc50zNhcvUWLMjz5ZJXr6155JcIh\nh6RGeka8zvkLukff4XYBOT+vqoJUyiAarezV7da4HR42qK5We0fZ2LWRU6+/mkwmR3T546w/Yz2r\nW1ePe102a/8XUXRWB724xsaujZz289NJ51JEQ9GSx0H4J+hGmo4NIp0EHZxD8I34YjqeM0GXR4cl\n+wsFfV8B/c4T3UasQZ9OA3sQoHxB/Dx+Xjt0Hnmkmre+ddiHEunVmeEWX/T0GCxa5L6uybTKoOyd\nFWtry5BIlB/nNgz44Q/3ctRRSZJJ+3t2Hrt7gyAS0SPt06uqqtzI31hJ7sEfVCte66Wjp4PMi2tg\n21Gksik6ejomfN13vtPA9dc3KitH0DePjp4O0t//MyTryh4H4R8v6RuKSxD4qMlkvf56iJ/8pNaX\nz9KhcSYLrLgLuo506CQopMPcWd3m2OkWjII+nQZe0j6Hhw1qavypT6ed7faac86Z60t5nM/T5fxx\nu990dYVYvDjD4KAEf+NEIpBOux+YOXNy1NbmSKXs12azdjqoW0pnKBT8UqyTEY3mSCYrf6K49UAO\nDakf+WtvaicUCkHOIBqK0t7UPu416TQkk2pXJws6+GtvaoeBBTDcWPI4CH8F3WgM+pycittvr+eF\nF/xZl9xL8Pcf/9GstAy61ZFuKVleAp3rrmvkhz9Ut1iFl/Nk164Qu3ZpVJGKeTlv02kYHPSnPKDX\ndQTB3/8dXtI+h4b8Df7c7jG7d4eYO9e/uVVeO72GfRiIdGtX9/UZzJ8vwd+UOQfXHhUrDP7cN1j0\nMjqok+rq0QC3ktx6trJZQ1mapWNV6yo+EDuT97/hDNafsZ5VravGvaanx2DePLUVFnQjblXrKubU\ntnLtideVPA7CX0GnZ3k5J3M5e1Vev7gdj3AYGhv9OWhe6mfdunqldejUUdCjWw6vI5F+dX66LSoG\n9jFUOf/FyzSP732vgZ//3J8Ra6927lR3TLzcW+69t5avf11t58loefTqtABvnQa5nPr1I3I597TP\nZFJ9W83h5dzp6grR0pLVah5if7/BsccuVF4Wt06DoSGD1tacBH/TFY3am2CCfQOJRt03UNVltU+v\nolE1PRZe9vkDtcPpBgbza+dxUPNKVreuxmD8hznzHe0RUDXlCDptwMCgKhzh7YvfUfI4+EWXhmyQ\nnAnk5a6PbBbOO69VWRm8nJOvvhrmAx/wL71Grwaae1pWdXWOoSF1JbAbQvqsOurlnHnuuSgf+pA/\n54yX0RPV6bte3r+xMUd/vz5Npt27Q3z/+w3K3t/LaNLWrWHq6vw5sXVLnwZv541l1fKtb6mbjgLe\n6gr8nPPnnjLc3W3Q1palr8+fQnk5fzo6wrS0qB/1cZsuMjxs0NKSleCvlIaGLL29pQ+Oc6JHo2PT\nPiMRb2mfM2G1T0dVlZqRP13SGtxusum0QVVVjjlzxm/vUSk6zJUJh90bsz096gupS0M2SM4+WOWO\nxd69If70J3WTYr2sYPb3v1dxyCEpZWWYjHTav73DnOvV7VytqckxNKTymjE0HPkr/5pHH61m1Sp/\nzhmvwZ/Ke6+X+gn63l9s48aIsmcdeBu9GR62n7t+CLrzdSJezt1//COifGqMlzl/qq+hQl7Ond7e\nEPvvn6a7258wxMv5s2dPiLlz1Td4vUynam7OKt1CbUYHf8uWZdi82T7jh4dLpzZVVTFmwZd9Me3T\n/hv9D/78upm4NeKSSTuonzs3y+7dqoK/4CfAe0lPOuaYhUrPXdk7zeZ0JLlthbJoUUZZCp2XB9rW\nrWH231+Pnixn0QE/7hu5nH0f9xL8qexhdRY/cCvHmjXzfLmuvJwzg4MG9fU5X56BXgIv1cfFfs6V\nPyi6BR7xeJTmZnUHxsvzzs+Awkvn6969Bg8+qHgFugJe/nb7GaH2IGWzatc7mCwvwV9PT4iFC7NK\nsy4Keen06uwMMW9eRvnAj5dV9Bsb1WWxQYW2ejBNcw1wI3Yw+UPLsq4t+vk5wJfyX/YCn7As67np\nfu7q1SmeeKKaQw5J87e/VXHVVU3cd9/ukZ87D4zikT97wRcDyuyTFg67p4b6xcuDT+Vqnzo09L2M\n/EWjORobs/T1qbkL6tDz6GVEOpkcbbypKoOX1bz2dU5vqz0SO/Gx3r07xMqVKXbtCrF4ceVb0l4a\naMPDBvX1WS02AnZWB04m7ftVlftuPVPmBOfl7hv9/QZz5mSVjvx5Cf5SKXtP2s7OEHPmqI24vGYw\nzJ2bye/jqrY8OoyKunVoOT8LupyF9u4N0dqqrm68znH3czTJ7bMeeaSG9etrOPlkf7YP8NI+8qMN\nZd/b3T/Er/PXS6dRd7fBvHkZpaNbhbycP52dIdrbM3R3G8yZo+5gud3zhocNFi/OkEyqm6Q57Vay\naZoh4CbgFGA1cLZpmiuLXvYP4ETLst4IfAP47+l+LsD++2fYssVuzbzySoQ3vGHi7vWxwZ/haeRv\npqV9qlrt00uPtQ6SSTuor61Vl8LlNeB54gl1LVovI3/19Tn6+9XdUHXpEAhaLmcQiZRvNO7ZE+KA\nAzL09ATXIZHL2eeEX3MrynHSxNSnWo7OtSt3vbz6apiDD04pLYuz51W582THjjBHHplk61b10bnX\n4G/+fHVZFIW8jrqpTvssvwADvq2U6JXqkZ6gFzgr5mXk5tVXw6xY4d8y7XYjPvj7qpeOPT+f2V7a\nSj09IebPzyppt5Yqk5fgr60tw+Cg2hPfy4IvjY1ZJVO5HJX4C48CXrIsa7NlWSngLuB9hS+wLOsp\ny7K6818+BSypwOeOqcjnn4/S1jYarQ0Oji5rW5j2Cc5m7+XfW5dN3sHbQ6+6GmWrfXodAVV5c3F7\nb2fkT2UKl5ebRzoNZ589V9nmnF72FaqrU9vQ99JT/5Of1PqWzhGUTMY97bOnJ8R++6WVBX9eGkQA\nTU1ZenuDb8klk/bWMNGo+tUkvYy4bd8e5sAD08rTPt1GIPv6DPaP96Q1AAAgAElEQVTfP8OePerr\nyGv6+ty5WV/Ko8NiOG4dWoODIerqNGkQ5KkOiL12LIXDOV9WhvVyr/Nj9fFChuFParQbrwu++MXL\nPSaZNGhqUjuvbWyZvJ0/jY1qN1cHb/tnNzWpTfusxOmyBHit4OutlA/uPgr8pgKfC9grcN1/fw3H\nHTc85kb1yithDjjAviMVj4rZ8/nKV65OwV8p9gVvX2DRaE7Jap9eF3xR/QBw6612Rv7slDI1ZfDy\nMOzoiHDyyUMjc1ErzW1EOp1G+c3Lyzlx/fWNbN++b+eFOgu+lDsWvb0hliyx00hUlcHtgWYY0NCQ\nK7s4ll+ckT8/VlPOZg3X4G/37jBLl2Z8Sfssd54MDhosWZJRup1BYXm8BA2Njf50GLjdT/wIDN2e\n9wMDhm+rWk6G6g5XLwFFY6M/WQX2Vg/ucxD9CkadzytXB046purUWPtep8/8TK+jxtXVOR+DP29b\nhdTVqc2cAqfdXvozhobs+43KqWe+9hWYpvl24DxG5/9N24oVKW69tZ41a4ZoaMiOrHT44otRVq60\nVyurqho7KhYKeVnwRf+tHgYHDWprndFNdat9epkLUVVlj76p4hb8OVs9RCJqjoPNvTfr5ZcjnHDC\nMImEmsDHrRE5PGzQ3Kzuhjq6XHL597cbBBp1RSrgZURnYMDefzLI0WiwexH1GPmDmhr7uPkx8men\n5ZY+QLt3h9hvP7XBHziNjtKfMTBg0NbmT/DnpVFvGE5HmvpnoJcUKHuRIHWjLO4jf/5tkK0LexER\n97/Zj8YyeA9Gm5tzyjItink5b5w2mko6pn16zVjzY1N18L5ViOppM+Dtnqd6Fd1KDJBvA/Yv+Hpp\n/ntjmKZ5GLAWWGNZVqfXN29rayv7c9OEZctg6dI2Dj8choaaWbkSXn8djj3WviCWLIEdO6CtDRob\nYd48+79yb93UZL+2rc2fDUxLaWiwT5S2tvH7xDh/U1tbMz09sGtX+b9pKhob7WPR1tZS4ueNtLU1\nMncuzJ3bRMvEL5u2hganPpom/HlzM9TVwX77QTwObW2V31utq8sOcsudkz098Na3wpYtla8LsD9/\n3rwFJd971y5YuBCamuaPvMbtGpoMJ9Vx4cLFNJTYYmpgABYvhrq6+UqOAdjnAkx8XfilqsoOZBYs\nWFTy72xshP33b2TbttLnw3Tqp6fHHvEu9x6NjfY9srtbzTlZ/FlQul42b7bPjVzO/R48Xd3ddh0t\nXLiI+fMnfk1VFbzhDc49o/R7TaeOIhH7cxYtWkx9/cSvqauzn1M7d6p/5rz2mt3oWry4rWQDrbER\n9tuvkX/8Q829tFBzs3Nfnfje7jzXurth4cImotGJ32c6ddTU5DxnJy5DImE/W+z6UX/PcbuOslm7\nzNmsuvJs3WqfJ273lsWL7Tp0O/zTfQ41NtrtgLa20nsbNjZCeztUVzcrv9eBXQd1daXPmx077OOj\n+rxpaXHKUb6uABYvbix53VeqrdDSAvX10NZWV7Y8++3XSE+P+ucSQGur/bwu9zc2NcHSpY10dakt\nU1UVzJ9fuh1XWwvLljXl27xqzptKBH9PAytM01wGbAfOAs4ufIFpmvsDPwf+j2VZr0zmzROJhOtr\nYjH75tzQEOGZZyJs2ZJhz55adu7sAaCrK8KOHRESiSF6exvp6UmTSCSpqys9/Dcw0MTevRkSif7J\nFLfienvtCzWR6B33s5dfjpDNRkkkBunqCrNzZxWJxGBFP39wsJnOzhSJxMC4n7W1tdHX10si0cvg\nYB2vvTbEwICartne3kYgRyLRN+HPt2+vZu7cLHV1GV5/vWbC8k7Xjh0RstlWEoldJV+zeXMjJ5zQ\nz5YtasqQycxnx44u5s+feA+ubdvCRKONJBKDJBLDtLW1ebqGvEqlIBRaRCKxk4aGiXumOjrCLF3a\nwObNQyxfrqZbzz4fJr4u/LJ7d4hcbg47dnQSDk98L+nra6Srq5+dOyc+H6ZbPzt3up+Tvb2NdHUN\nsXNniERCbTerW71s21aVf12YRCJJNKpuVa2uLoNcbj7bt+8mlZr4vtTb20h39wA7dpS+d063jl5/\nPQTMI5HYVXIF3tdeq6GhIc2ePbXKz+nXX4+SyTSzdevukqMF9jnTz44dau5jhfr7GxkcLH1v37Il\nTDJZxcBAmC1b+qitHf+a6dZRX1/583bz5ioyGejvr+K11/qUr5rrdh11dxvkcnX09YWUnS87d9rn\nSSKxu+RrensbGRxMsXlzmoaG0kP5lXgOdXXVMzAQJpHoKfmavr4G0ukUL7+cpb5e/T6VfX2NJJOl\nz93Nm8MMD1fR2xtRel3v3l1DMllHIrG35Gv6+hqpqsrx6qt91NSM/3kl2wp79tQyNFRNItFV8jW9\nvY10dg7w+uuVb7dOZPfualKp+rLHqLe3ge7uYbZvD5NIqFu0IJudz44dnbS2TnzN9PU1sn17Lz09\njWPOm0p25E97bNyyrAxwIfBbYANwl2VZG03T/LhpmufnX3YFMAe42TTNv5qm+afpfu5E9tsvQ0dH\nmJ/9rI6Pf3z0YrTTPkdf52URE11WuSw3dJ5IhEcWuVG12qfbSmyj22mom2vnfE65Y+Es+BKN5pSl\nnzp57G7nRVOTujkQbnPMhobIT6JW8vEjqRPlytDZGWLp0ozySdNunnyySulqhU6qjVsqWk2NutQW\nt7kVzrzgwhWPg+TM+fM37bP861TPO/Gy2fzQkJ0e5secHGeuqttxsbcP8ifts1xqrjPfLsipGIOD\ndhlqa9XuCelVT0+Ipib7xqOqneJ13pYfK/eCt1VqczmDmhp/0pXBvTzOuavLVg9+nb9eU3Sj0bFt\nc5W8pIzncka+La22LF7X0lCpIusiWZZ1PxAr+t4tBf/+GPCxSnxWOc3NOR57rJr3vndwzN5EEy34\n4r7ap/5z/hKJEMcfb5+l9mqflf8MryuxqW5c2pN1SxfEWfBFZRBauMBHqZ5fw7BvsOpW+3TPFW9u\nVrd8spd5bp2dIRYvzgTeSLrxxkY++ck+3vpWNZGXE1y4LVahMrhwm1sxMGAHFfacXCVFmJRk0p47\n5UdDPpczXINzw7CDc5Ur03oJtvxdUMR9IRz7vPWvEV3OwIC9Z2kk4l9DcaIyLF6cy68cblBuj2A/\n9Pfbx8S+jlCywqXXVWH9DP68BBRVVTnfVpp26wx2rmvVnTpeV/usq7ODv9ZWteevl/mihuFvp+Rk\nzh/VZfK6XZbS7W3UvXUwLr64lzVrxl75XoK/z3xm7GS1mbDP344dYRYuHB35U9HAdOuhcE5OXUb+\nIhF1I3/2vm7uF63KvX8Mo/x5OTRk0NSkcsEX9wZ1V1eIBQsygY80zZmTZe9edbc4L4GwYdgPE5XB\nX7kHmtP4iET8GcVxYy+c4d+CL15G6mtq1I78edlyYnAw5MvCEE55vKxmbZ+3vhSprP5+f0b+vDTi\ndTkmzkhk8WJ2laTbyJ+92mfpnzv7afq5gqTXUWvVvOyrl8v5N/Ln5dzJ5YxxWXkqee3M8KNMXrbs\nUm2fC/7e/OYUzc1jK9hOuyoM/sY/RH7+87ETU72kc/mh/DLCxsjkd1U9KF63vFCZbgmjN/ZSUik7\nAFV54XptNKlkNyJLH+fhYXvvHJUjf26jGJ2dIRYuzCpPnXAzd67afdOcrQTczgc70FG3+qrbyF9d\nndp06Mno6zOor88Sifix1YO39MbqatWNV/eRNj9Xk3T2ZitVHuecsvewDP6cGRy0R7lU7w1Z7jpy\nVm30KxXWjTOir2qLJ3BvwNs/z/k88lf6Ghkasp//utzrAPr7QzQ0qG8weF2Z1c/gz8uolarpShOZ\nTJlUdx7okPa5zwV/Eym+OXnf5D34G4jXYV9VQY/X4WnVQ+Xu+/zZI39e9nCcThm8NCZVcptv56R9\nqmwQuAWgPT0Gc+cGP/LX0KB2/6lMxn5QuN3EVaZuuPVmOulhqkfmverrM2hosK9Tf+b8eQvOVQai\nXjqNvKYkVao85a7hVMpOZ9bFwECI2lp/OgxK8WOkbWrlUffcdWss21tw+Dny56U8/swn9qq3177f\nedlebDrctnpIp+22ix38+bOXaLn7WTJJfu63f4G6Tmmfbhkpvuxtqv4jgmenAYx+XRz8DQ+Pf9h5\nabB9+tMtgS9q4VAVrJY7SQu/r7px6VYf6TQllwCvFLfNmr1MSJ8ut3TkoSGDxkZ1vWleUumyWYPa\n2mDnC/X12UGw6ka9/cCd+DPsxop9oNw2J55OGco90AYHQ1qN/BXO31If/Bll68cvXkYg/dp8GdwX\noLFT6P0tTzmjaZ/BzvnTKe2zcC6vunu94SGlPKtN8OeMztqdOcqL40l/vx38qZ6v6jbnb3jYPja6\nLPjiXNP2+au8OID3kT8/0j5l5M8nxaNBxat99vWFaGycfE08+mg1vb3BNCwmemCqaECUWxm1u5uR\n46Z2c3X3VUedkT+V3Br79iiL2ivay4IvjY1ZZXXhjH66jWLY6VFKiuDJzp126qlK2axRdsEXZ5QL\n1M0B9bLgix38BddwLmSPVE6cel9pXoIuP3pYR3vlgw++wX0Oot8jf15WTPSrw6AU57zVJe2zcORP\n9QJn5cpQW5sbN61GFbeAonBVWB1GZ8FuW9bXZ31YEK/8yN/QkEF1dY66uqxPaZ/lM1IGBuzjojJT\nq5hb57zTWetHKqpbRp0fnYGzIvgrVjzy19NjL5IxWXV16lZ1dNPTY0wpYJ2scsHG7t32ohqgvrfE\nbdVRZ8EXlezFTko39u06UVsGt+BvYMBeyUttg8C90eznTX0iPT0hmpvVXh9Oo77UsfCjDF4aRLW1\nWV/nVnjhx8qNbqPUhUGOyiDQ6zXjF7cOnFRK/b10MgoXLQp6BW7dgj+V17V7VsHovEM/Opbc5zc7\ngZY+I3/Dw3ZqrOr2USZTPtgqTNHVYc5ff7/h2wJXDrcyOXVld5SqT/v0tvWEwjKoe2t9FQd/zh5L\nkz3Q9fXBBX/bto3u8adaqZN01y6YO9f+oeqeLbdhcmerB5XcGnGFey+p4rZH5cCAQUNDVuG8R/fF\nK/xMYSulu9sOvFQ36svN+evuHu1UUpn26WXkT5etHhz+jfyVPledxqtqTqeRPsGfUTYoHh5mwk2g\ng+Kkoeowl8ve/iLYMsDouatqiyfwvo2M6qwfh73dk3t5dBr5A/sYqm4fuaV9Dg3ZgZ/KbagKeTl3\n6uv9vyG6d2ZkfWm/uK0S64dZG/wVNo5TKfumMdkHi18X0kSefz7KAQeofxKWG54eO/KnPvhzm2fm\nttTxdLmNJPT2hqY0gjwZbqtLql5aunCvQ511dxs0N6vdX8ltk/e9e0Mj14cqbg0iZ8EXHR42hfyY\nV+G2yXth8OfHeaJP8Ff+vB0etlPEnNfqIhwOft6qLiPozuqwKlcm9DLyN7oIjpIijOM1xT3oToJi\nqttHbls9OGmfOs358zv4c0tF9aszENwz2ZyfyT5/FVa88lIyafeKFN4wvDz0/JroXCyXg7/9rYpV\nq9Tf4co1WgqDv0hE/YIvQa6qCO7pUvZIj9rGvttxKNz+QwUve9vp0GB0Rv5Usuf8lf57C4M/VYGX\ns/x5qTL4u3m4d36MFjgjXKWuFyfjQ7XJpH36ce24zYUsDP50osPIny5pn865rXJEyW0l4dGRNj1W\nRndGk/zIKpgs1QviuS3O46yEqlPw5/dzyeuCQaD+PiwLvgSkOO0znXZG/kbPDK/7gQRxk3nxxQhH\nHpn0bXi61I09kYD580c3mVfZK+tls2aHqgvXLfDp7Z3awkGTEfQ+g6OjnxPXdTKpxzLx3d12Cq7q\ntE97ZGniY9HZORr8qUz7dEtt1DH482vkT4e0T7cRSL85DTMvwZ8OKdyOIBv1zvWrS9qnQ2V5vI78\n6cIJKPyYTzxZfqR9llucx7mm/Vqcx8som98jf25zRu3UWH/K4taelQVfFCnuqUom7cVbnBuG14e0\nH3tVTeThh6s58cQhXz6r/ATZ0YtF9ZK9bmmffvWYlwu+nFRDleyRx+BaZG57HXZ3h2hpUR+dutW3\nM3lbpUym/D5yXV1+jD6WD3D8SIeeCj9G/txGuPyc8+dlnz/w56Hvts+fbiN/TuClQzqfvdWDPhFx\nkAu+OCN/unCCUR0WBiqmerVl9zl/9sifX505budOf3+Iujp/e7HdR/5CvkwDcN6/1PPArkv119Us\nDf7Gpng6c/6cG4aTSuUmiJtMKmUv9jJnjj43XfBnwRevqXOqLtxczsiPak38AYUjf6qC0aBXiXIW\nfClVhs7OEK2t6m/qXutY5U3c2eqh1LHOZOy0UJXc5mDqMtrkcBryfixA4zZH1680H7uTYGaN/Om0\n4Itz39ch7VP1c26yVM4ly2S8zbHThb3ap74jfyrThd22erCvaf/qystqn36fO26pqP51BpZvx/nV\nqTJLg7+J0z6dG4bbTc/h58PI2Vj297+v5t3v9mfUr5zihoPqni23+vCrx7zcRVvYaFJVHsMIdhlr\nt5GmwuBPl8auKs6xCDIN160hX/xaXUQi6udOOWnapTpr/Ez71GvBl/Krjw4NIQu+FHECUN3SPlVm\n3LgFFAMDoYJ9TNWUYTKcFS3tTd71CdBB/bQYL1s9+HlNu4/86Zf26SyipJozDaD8ftES/ClRKu3T\nuWGMbsrr/j5+BH99fQbHHbcQgGeeqeLoo/17+pRO7zNobR39urpan3QYlXP+gm7EBR1suI002cFf\n8POF/PjsyYzoqFvwpfyy/TopLKPqxhCMjsyWOlf9Wu1ztCE08Ye4NUoqza3nWde0z6BG/oaG7BE2\n0Cftc3QEXWXap/u8LT9Hk9w4Kx/rNE/VoXpajJe0z9padZ9fzO2epnphuonostqn2yBC4Yh6KDT5\nXQi8mpXBX/Fqn07apzNy5XWejF8T0Lu7Q2PmPehwc9u9O8SCBaNfV1cHs/LpRNSlfZafQ+QHe2VH\n9z9Q1TFwW/Smr89QvuiNLtzm/Kla5GV8GUqfk4XnQdD3jeFhuxEE6le/g8mlfaotR/n0YGc+jl/c\nOrGGh42RYCfocwZGO06CWvClMJtBl9U+HUHO+YPR80OH80Q3xZ1dQW71MDyMr4s4uW1BFAS3Y2Sn\noqpvu7gtAFa4B2JNjbrOplkZ/BWnfaZSjJnzZ9/03B/Gfo38OTnAQQQdpS7gvXvDY4K/aDTYVAv/\nlkgPduQtFAo+7bPcap9+7pUTtGzWIBotHYwXfl9VIJhOu2+9oYuBgdBIj6Y/I3/uq336leajw9xD\nh9uWNbqN/DmCWvClcNVee5VafVq1QW7yLsor3EpG9erGXrd68JNu585kNp5Xu0q4UbbTuKcnREOD\n/UMJ/iqsuAFfPPLnNoTu8Cu33FkWd/v2EAsWBNjyL7BnT4j584Muhb+cxU6CHfkLesEXPVZQ9Er1\nVg/e929Tl5pVbl6bTkGhnc5in7yq5wjD5DZ5V12OcudJ4XHxg1swWjh3Oejzp/Dzg5rzt3fv6Mif\nLg1a534S9Mifn3K54M/HyejtNaivd/ZBVj9i7HUPO934kSED3uYh+rEIjdsgwp49IebNs39YXQ2D\ng2rKodGl7R877XP0SkmloL4+O/Jg8TrnLxJRl4/ryOXsC3f16hT/7//V0N6uR/C3e/fsC/7c5rv5\nwS34U30jdVvtU8eHjKoGg1vapx/skT+v8w7Vl6ecwlXM/NgY2m2LheIRN7Xnibcg1E5tVFMOx+hC\nHqX36tRlnz978QOnAa1u5K9c3ReO/OlGZfDntjqi34I+Fyerr88YWRAn6FViC6cy+ZVd4P21/hwX\nt9FRv+YhunVKFgZ/NTXqplNpdGn7pzjtM5m0I35nDop9obifvfbIn6JCYj/owuEcg4MGxxyT5J57\nalm2LOC1rvPsEzToUozyc3+sUo0mXzbmDJVvNDs3UpVz/sqNGqTT/k/kLsfejkXNezs3cS/Bn6qg\nPJOxR9FKzztU8rFTUphW4we3oKvwXLXncqkph1uHydhGovoRUbcFXwpXBgxa4d6pqp+3pRSO/OnA\nTl9Tn07otkBG4XXlV8eSTvczN/39o1s/qU771I2O9eR03nuhegGwcvusFs4xVrmQ4qwN/iZO+xwd\n+fOW9qk2DcXJGR8cNFi5MsWTT1azdKkeI3+plEF19djvBT2yoJoec/7KzdWxexhVcuu10o3KOTpO\nymWQaZ/2XoIT14e9Zc3oD4J+IPu9L5gzH7KUwuOhcm6FW9qnvT+ofyMEbls9FO9PGeS13tMz2oBW\nmfZZ7too3rs06Htf4VwylSPoXttBEPy9xS7DaMUEXUdgd+o4nV1Bj/zp9BwIitucP7/OGSezsPQm\n76OjtDLyVwHFcwfGp30WrvbpnvbppM6oTPvs7x8N/urqcjzyyOue0lEraTIXhE43FRUX8mT2VFOl\nXI99X1+IpqbRH6oIUt2WKdZNNJob2SOz0uyb+MTBuN1w8mu1z4nPye7uEC0tGrSC8ornVKi+jkaD\nc/cbk8oeVqfHuXTwZ4xM8K/0pt2ZzPjRssmkK9uN1ooVZ9Kjq11dIZqbR+eJBrHgSxDL0kO5zgJ/\nRtB1m/Pnxq/0Qa/s69qfBV/cqE6xnw5d5vz51a4bXSjO/bWy4EuFFQdt2ay9EpLz0HXLDQb796ur\n1Y787dpl5/46PX0rVpR/8lX65HUatxPRKdArZjfkKv++emz1UG6VKGNkBEHVTcPp9Ch1DFQcm3vu\nqZny71aqx/WRR6rZu3fsTcFpRE+UBlzYOw9q0z5Ljfzt3avXXKXikT/V9xDn/hX0Q9bp6S2/Pcpo\n2mcl009vu62em29uGPM9t2C0UCVHLF56KYJpzp3U7+zcGWbxYvUjf5MR9LOvu3s0IFZpMnP+Kn3f\n/973Gqa10EXQdQR22qfTqaNybuZMUzj3G9QE7ROdj25buPl1zqTT9vngpfNN5RZqsyb4K6zYiYZc\nCx9yXnrtUym7IaOyJ7KjI0J7e3rcxVJKpU9er5+rm6amLN3dlT+1nZuHrgu+2Olj9g/r6rIMDFT+\npmH3gpfe3kDFDfQ732mkp8f7Gxfe+KurK9OYvvvuWn7zm7FBaDJpdxpNVB8DA2OX1lab9jnxw84O\n/vRIEwdGMhhUueCC1jFfu82HLFRdbW/mrYLbAiuF122lG4mbN4fHLfHuloY6dn+yygWjr74apqlp\ncvW/fXuIRYvsczioTd6DUupe2tUVorXVj5E/I7+vrPKPGueuu+pIJHxOc3JRfH9xo8uCL35loXi1\nZ0+IuXPVPpc+9KE5vPzy2POn3BZug4OMmees8pwvl61TTNI+K6x4tc9cbuxEey+rfQ4PQ12d2k1n\nX301woEHZgJbQdH53Gx2bAPAr/2xpmrp0gxbt4YrHvzoMN/NTjOc+O/q6TFGGld1dTlFwZ/37Q0q\nob/foLU1y0svjZ+8lUzCr389flQwlRqd+1iph25bW4adO8feLpNJe2GMUsGfH/Pbyo387dkzduTP\nMIJdqbZwnz8V7r23dszXkxnhUtnD6iwgVj7tczQ9rJIjfxOlSjrBqNe0z0qNtu3YEWbhwsk1+uzR\nk8KVUGX0pKvLoKVF/RzEcp0EudzY4LTSnX5NTVl6erw3TwcH1S5S9PrrIf7wh+px3y937IsXclK1\noJSbV1+NKF0l/v3vn0tnp/cTYNeuEHPnjn0uVVp3d4iurrHnT7k5f6+/Hh6zjVokUtl090JOB7qX\n+29NDZL2WUnFq33C2EZi4Zy/7m6D3t7xBz+VMqiqUtu4c1LHvv3tLk9zDir9EHAasHffXcvX/n/2\nzju+reru/597dTVtyyO2EzvDCdkJZDAS9oZCWmb5ibSUp4sW2lKgpYWnpYUy2rK6KYUWHsoughYI\nlDBKw57ZxBmOMxwn8h6ybFmWdO/9/XF8pDvlK+nKDvi8Xy9eOLJ8dXTHOee7Pt+bS1Ovt7TwqKk5\neCIKyaTaozN1qoj//teDM86oUr0nX0aqdxsNg4jjrEX+vN7CGX9WJy4g/3PS0sLjiCPiaG3Ve2MG\nBzls3643CpVNqu2IpBCjX183QSXxjb6jUZQr33PR3s7rNhCZvIjatE+3GwWrf7QCySQojPWpnQPI\na6RWy6rxN1Zpn0qBFbtr/oqKZESj2k0QEQkyi0QqsXPT2tVF1g3t8aw+F+Mt8meGsg6ykGQSOBsa\ngmr/43DYu1kuLZWzMv66u7mCKrJu2uTE4sX6B2Gk3np0DSB7zrFxXGzbJmDevMIVHPb08Ghvtx6l\n3b/fUXDhwspKCZ2d6jFlEjBqb3dg0qT0/VPIiFt6zdYfX1tqRcZRkGGMX+NPOaFxnDryp5Q4vuuu\nEjz5pE93jHi88MYf5aKLBsckh50af6JIIjAAcNNNfjzzjA+1tfqHd6wiYtr6qilTRGzZ4sQFFwxi\ncJB8j5NOqs77c6gRYLRp0npC7UBbYwaQyctM7lxZ80eFgvL7fA6hkHoM2TS6J4ZXXkNAa6sDS5Yk\nDI2/oSHOcMOuXHTtSPsMhRyYPFm/6yTNsNOGcHd3eizalGk7+rddfnk51q51qV6jXkSje1Jr/Llc\nhTNwrBCLpZuHA/bOF5984sT8+eprNFKrB+XrY6n2qU6ztNf4M5qTshFtsjPyJ4ocJk8Wdc+y1Xlz\nrFo9jBVm9wsRclJGTvL/rK4uHp98ovYwZ8p00dYdEieDPfdJMgmUl0sIh60fT6vIajc7djixdGki\n6zWVXhuOG7s6RJJBVjivSWWlhI4O7T7B/P0HDhTe+CspkdDfrz7hsmzuKG1r41WRv8Iaf+aCLz09\n6mebpX3ajFGvNH3NH3mdeI/1VykeJyk6hSTbySLfyWX9evXkT9M+aepQLEbSCXfsEDB//sHTtEab\nhur1ynj44W4cemgCO3c6sXGjU/Vg5wr1mBttmhIJDHvTCfmm2PX3czj2WL3BmmkT2d2dTqewI+1z\n3ToXPv5Ya2xYVwq0I4rR2urAvHkJXQoHQIwvo+MrVSXt2LjRVW4AACAASURBVEy3tvKYOFH/hUmL\nGHI9hoaAI4+clPpdJKJWXrUjalFcLOsWNNrOwOh6kJrE9L8LGd2ySqE2QB984MJxx6nDmiMZXUoK\nrfaZqR+kcnx2p30aCx9YN0btjkTW1Ii6Wq5M41D+juNGX9Gx0O1zWlt5HDiQXW1bJJJ28tnFW2+5\ncc89amGgRMI8ct7Xpzf+tHNTrrS18Zg9O5FV3X53t0OVSmg3g4McJk4UszJIDxb1Ue06ANjreCsv\nlwzXZ7PPUaZyF2I8gPFaKcucaaustjZ1SrrXWzijK9Oarc/WYcafbZhFaJRSvMqaP7MbOx63Ltea\nC7k2ls11PJIEnHNOleo1Gvmj56yri8fUqSIefLDnoJOQN5K+XrAgga1bBWzc6MLixfkbq5nSt7TN\nzQUhP2nnpiYHJk/WG6xk8jKeDAYG+NR5cLvzT3c8cMChW4CzUVAkG9n8xqBU+9MSj+s3yj/5Sanq\nfrAj7VO70VF+Pkn75BAOp1NuAXVzaoA2m899HLIMlJVJ6OnR1zFYrUO1IxpsBbOxaOddOw3BaFS/\nocgkTpRIqNNrCmn8ZUo/bW1Ve5xHQxgiUy2ktr5cEOw1RmtrRbS0WDN29uxxYPr00cnzNLtnOzoc\ntjgOzXjhBS/uu6/I8Hdmz4cVNfJsOXDAgalT1d+TqhIanZveXvX8VlQk6dKLcyUUcmD+/GRWaZ/a\nyE0h9mXFxXIqAwrILCBSqDHkgnYcdmShKDHaI5NIp1VFYXvbYMRiwIQJEiIR6wapVkSp0GmfLpd5\nnb7SicFaPdiI2YQqCOlNIm2cTF437pFCav4K58nessWJuXOzeyKIFHZun9fWRlTVlH9P0+doDVVX\nlwOVleazxlilNZAoi/5JmjpVxM6dTpWUej5k8uCTc5X+Rb7pUmQy0n9QprRPJaS+K78L0t6uN/5I\njxpzr6Y2apBvjVkmsaOhIU5n2K1a5cX+/UIq8udy5V/n1tdnfH8pPYnacWqNwXydAaEQjwUL9BHQ\nZNJ6o3k7U7MAYN06fSFyNhseuzdHWuGATM6aHTsEzJ6dnuxIYb2946EQT6/xYt/QIGDevPQ47HCY\nKDGakzMJvhw4oK7ntjPtEwBqayWV8ZcpXX7jRheWLrU/w+Tf//boroXZGIhRoT5Rdt63ufSNLYRR\nQVPYldBaVKP7xCjypzSM8iEUcuCQQ5JZbb7b2x2G2Rl2wXFAcbGEgYH0/EscSOZ/czAYf0aRaztr\nZyWJOCWNAiQkUj/yMex2eLW2OlBXlzScR82eN+08VEhHaabnShv5I/sGZvwVDKL2mV7k4nGMKLBC\n0j4L93S/+aYbxxyTncs1n2hPU5OAI46Io7MzfUvQDS0p7gY6O/mCplbkSjjMqVLsKBwHLFyYQEWF\nBI7L39uVKV1qYEC9+c/Xm5XJ+DNLH1NOXiRdIPfPp5+lvZ+yi/wVprcRUe0ix9YauDNmJFFf70wZ\n4nakrZFaSknnXKF9sGSZ1rOlT8r+/erNSL6Rv+5uB6ZPF3UbrJHq2pTYuUHr6uLxu9+V6F7PVNti\ndcNtF1ScyOjcbN7swpIl6QfUrvSaDRucWLNGrQpIm4QbjaOlRR3ht6NOVov2c2nLGrPxKOu57VYp\n9PnUmyqtcIiS3bsLU6v00ksey9e6pcWRajVRCEjJQsEOnxVGkXnymv5cdXaSHsQUn8++uUV7D2ox\num9jscL2EOU4fWprut5aD1HwLZzTwAijSNuOHU6Vcwkg64VdDp1YjAjtGDmazcTptNfGDgetkkz3\nTybBPCUeDwos+GIt8lfINZIZfyAnWFlrYVbnp4SmfRaC7m4eVVXiiO0mtOTzEDU1ObB8eVyl2kSl\n2am4TVeXesI/WFCqXGq58MJBXHbZQMa8dKuQmj/jSVwrX0ykgnN/crWFvxSrnmI70j4B/Wdl6p2m\nfa/bnb/hRY+pjOpQwYpYjNMZ2MT4U0b+8j8P0SiR7NZuXDkubYwrjb/+fhIpV6ZmORz5eVvDYSLv\nrs1CyKaujfR+tGfK37LFmXVkeTQ84crPEEVzI2ffPrXRZZfx98EHLrz1ltr4o5E/I0IhtXFhd42d\nec2f8eYjFNIaf4VNQ9U6TZTQ59xumpoEy31DGxsFzJypfnDt3JAZRdwyMTgI3fvteq60x6HRkKuv\nLtO998ABhypCbGfadH8/b5qpI8vAySdX6V4vZAsbqimgrSNLJs0jfzt2CJgzZ3SlaY0Mm23bBCxY\noF4k812LlGTqBc1xpFf1SNi1V6G0tDhM1ejNopHaZ7qQaZ8kndqaQnchYcYflGkw1iN/iQQHt77t\niy3U1wtYtCj7sFE+G4dQyIElS+JobU3fElSaPZ32OXbGX6YFTtnfzowJEyTU1zvxzW9m16hVSaao\n19q1LhxxRNpFbkfkz8j4IxO8/hrTaBjFjsXYTCnQzGtF+hopU1/z8+iZbZoTCW64Fkmd9hmPA9On\ni2hocCpqH/OPXNAFzqh9Bl1wlYqzzz3nxTnnqMOuJNUm9+tB1fW014SknlpP+7TLO791q4DDD49j\ncFD/O7O0Gi129XeiqU1a51emNhg8r76/7aqtMOplSB0mxtLe6lphZe15vpg1d84kNd7aqjVG7RmP\nWX2Usi2L9v2F8nr39vKqeiB6f7z0kgf33qsWPSG9PAszjlzYtauwyo1KiHNLxr//7dX9jqabUwpZ\nm6REFElEeDShbSSKitRpn9pzoKShwYm5cwvnNDDCKCuouVnQKWs6nfb1y6Rro9kc++ijeqV8LXar\nUIdC5joBiQTwxS9Wql4zGrvHU7i0z7RwoP74Wu2IQsKMPwNo5I94vow3KLEYCtZUdNs2Z05qmvlE\nOUSRQ22tqIr80bRPOskNDBS2SXOukJqszEZpZaWExkYhr4Uj06YpElEXDOfrwY/FiEGrNZ7MFhCt\nwIhdnljt59FoipGnVSu3na/3rLk5LQmtPOexGN1sqPPhOztJ3Wpfn1rtM1+vIjWwjBRUjdI+9+93\nqOrJAJpqk/sYSI2N8bNnZPzFYvp0OjuNv4EBHvPnJ7F/v/55MrpHOzpINoMSu9TwqMGiNc7NanSJ\nWIX6RbueF6MaNvLMGG+QtK/Zcb9S2tqMVWqJ5LnxeLTGTr4ZDBRlI3slZpG/piYHpk3TPzD5Rrn6\n+zlUV4sGERwS1dmxI30/a9VG7RpDPsdqbHTqIkp2GRX5HKcQgklG54Y6UrTvK6RhResbjSJ/mSL6\nRoJt+SKKwI03+g3XXyPjj5YmKLEz8mfUz5bCcTDQDNC/z+7IH3XEGt0/Q0Mctm5V30CknEl9rXy+\nwvXXyxT5G806UWb8IT1x0BNPJY4ztXMoZKPVcFhtSFgl17RPKlVfVaXu10JSUsjPNFw+VqIumbAi\n6FJZKaGhQcgrpE43TVZSTHJJI3vqKa/q2KRXjd5DbYQ2UkgELOy/WJnqqLQ1odo0yWzZs0fAjBnp\nVYp+Jt0wanttdnY6UFUlqtQ+7aw7NCoCJ3UWXGpMiYRx1kC+gi/UYDFLWQmHedXvurr06SN2Gn8A\nMHVqEvv2WctNX7fOVbA0KJrmQ+43vWdee87q6526VCi7hFZ4XtbNkckkOb7Vvnp2pVkeOGBc+0Jr\n/qzUQLtc9tQHafvCUehz88gjPrz9dnqxbWoScMgh+gHmu/7s2+fA3LlJ1XNMve20pRFlNJpRZ0uh\nFFDNDF2j893crFdALaRgkpJEglOp9ALGPf54Pj9nmxJaVqJV+6SS/UYUaq+0a5eAgQEOb76pD0db\n7d2ZbxaKEhr5M/quPA9dejVpU6IeJHXkjgaDg/pMg/ffd+Goo9TpQcSRWBjziO6hrNRDFhJbvl0g\nEDgrEAhsDwQCDYFA4HqT9/wxEAjsDAQCGwOBwBI7PrdQkA0c2bybRfe0jVYPBnLd6DY0kLoG0kQ3\n/fe5TGCZDJT1653YssX+lA0ajcpEZaWEtWtdmDZNRHs7rxK2MSIe12+OMtUQack2DWb9eifeeceN\n115LV/8XF8uIRNSRDDNpae3myg6lTcC45i+TWIQy194oTTIbSHNacjxyb5PXaZ+goSG18dfeTpT5\niorkVDTBjrRPGv3XRv7o80HSPsln7dghYN48vZWXbySYtvEweh45Tsbpp1ejvZ1HIkE2uET9Tn0D\nE7VP+xa0TLL92vvjvfdcWL7cZiWTYdLGn6S6PmZS9Vu3OrFwofoaFXLRperRRjVVWuzs80ejD1qh\nIkkic4uVZ9MuwZe+vnRmAs+nxbdozVtnpwPNzem1gUQt9YZXvp7xffsEzJ2rbtZNe7Rqo66bNzux\neLH+y5v1ChsNRjMtDDB+Lp5/3osLLlDnexeyH5kSmn2jpKWF14nykHnfnvEQQTlZN/8nk3pDtNBs\n3erEt789oOu/C5D7UplN0dHBG6qz55uFooS2AzOC42Rduw6jmjaXq3DiKlqM0swbGwVdiq7XK9mS\n9nn99aW6CCLtFKCdQwYHjbMJC7U25b0TCAQCPIB7AHwOwEIAXwoEAvM07zkbwMxgMDgbwOUA7sv3\nc7OFLjiZ6kzoSSaRP1mVkqJddArRaFU7jmzJ1Wv8xhtunHnmyJbCSAvvSJPKBx+48cwzI+eAG5Hv\nA1BWJmHbNidqa0X87W9FePzxzOP49a/9ePJJ9XvMxE4GB/UpdtlE3u67rwiPPFKE3/ymFx9+SCZ1\njgNKStRpJpkEELSRaGUNq52QVg/GvdOMjL98JtDOzrRHV3ks2tOJRtk++cSJp57yoqPDgaoqCevW\ntaXOk531BMox0EVPmfbpdsvYtEmtIkmxqwbTSLWW48izH4lw2LfPgbvuKjGUPrerjoHWY1VXqzMF\nAHIutOm+0SiHqiopawErqxB1N0nnbKApSdp5q7OTR0WFfjKzK+WG49TGgVmaTyik37TamfZJxVu0\nKoWSBPj96qwCM5xOe54fpYBVebmE7m7y2WSNJe9ROrba2wvTX6+tjceMGaIm8kcMCq0EfkODsWiH\n3QqoYw3teWkWvdHS38/pUtDtmN+iUU7llFU6CdJj5XSplto6VcA+5ydAW0lJw3O91rmkf7/ZPGLH\n/LJ7t0MnQETRCr5s2yZg/nz9e+1s9ZBJnMco7XP3bnU2D2B/2mcmtNlCQFpDQEm+jmvK5s1O1Ner\nbxKSqabP4imUurEZdriBlwHYGQwGm4LBYALAPwCcp3nPeQAeAYBgMPghgNJAIDDRhs+2TDZ5zlTw\nhRp/Pp/eC0DrgOwmk/T1SGTr7UomgX/8w4ueHmOFIeViUFYmIRzOfOyRohtDQzBVhsoHK4YhzwOz\nZiXAcdYU1jo6eJ0XhkbetA9tc7OAujr9hGY1Z7yvj8fvf98LlwtYtCiBxx/3YWiI9BVSChMQAQfj\nY4TDnC5VOJ/Fhta76uuXzCN/2kapJG/e2v2oTTuSZbXogzKFlG4Mk0kykW/fLuD11z1obyeeTuWC\nnM+mhC6kdMFXjmH/fgcmT06mFtzBQZKW2dRknGpnl2e8slJSbZDoOaMGV1ubA21tDuzaJeg2z3Z5\nEIkctTicOqiPTJP7Nv36Rx8VLuoHpI08rWc+3WLBWqqWHcgy2Rgr50ozae+1a1048kj1eSECK/Zc\nqP5+EiEn6WrpeyZt/Kk/x6iOvaQku1RhszlHuUFXlhcoHazK+1NZcmAn3d08amu1xp/eoKBjMyr7\nsCuVPB4n94XLpV4rzM4hjVDaTXOzvj4trbKsfm9PD2eY8WSH8ffeey58//tlKSdAebmEnh71gmdU\n82dk/NlZg2gmKEdrebU0NzswdWphNvG0R5yZoa40/rZvdxpmoZBMAHvOTUsLidCbiaFp0z537hR0\n9fB2XiujWneKLOsjx+SZ0r/XrrKZhQsTOuMvEuENSzgaGwXMmvXpMv4mA2hW/Hv/8GuZ3nPA4D0F\nhSocZRIZoBeDCr7QELHfL+sMn0IVZmr7fGSDMjXOChs3OrFxo8tUKVP5HSsrJXR0ZHbdm3lE//xn\noqBGJ654HNi7174wgNVr8cYbHQCgy903wuHQb8LMWj3s3Ut6sCkxm9Cef96Dtjby2G3bJuDuu0tU\nhuj55w9i504Bq1d74fdr0z7N01t7enKvQb322lLd+aBpjFqIiIa1XoMjec/Wrk1Piq++6sHTTxNV\nue5uDldcUa7aGCujbnv3kkgP2TyRxX/WrCR6enjdmF2u3OsbAoEJ2LfPkbredAyPPebDmjVuTJsm\npiJ/Q0McampE9PTwhguzXYtJTY061ZL24qJRvfZ2B7797X789a9FBVPmbW01VlNrb+dRVSWhpER9\n365f78TSpfqJwS61T3p9tIIMw79VPa8bNxqn8pHx2DIcVFSkI1tkfMaKrA0NevGObOdwKxQVqQ09\nWYZubgFgqOasdUBlor2dx3nnVRr+bt8+QWX8dXaSe1jZo3U06l1EkXjdtWmfNKODzq+ZFKTtilS0\ntxOjZepUEfv3axvf650Fe/cKunWGvj8fPv6Y1Dwpj0PF3rTX5N133Tj+eH1ILZua2b4+zrDelKo3\n0/NeVSWl1kqKUc0fTfdXj8e+aFI4bNxKysxw+OgjF5YutUmy1wTt2kqFXb73vbSaeVcXj7Iy/c1h\nZ0psV5cDlZUSJk9O4sAB9eaEGH/q60eiqOox2Zmdc+BAZqEdbfaUUQkAYM9cFA5zmDUribY29XmJ\nRDiUlenVPsnzPXrG3+hq5uZAbW2tLcepqACqqkpRUgLU1pak/g9A95rPB0ydSv5u8mTvcG8dQDkU\nv5+8t7g4fZx8SSSA/fuBOXOA2lp9b52RqK0FwmH1ODPxySfAvfcCxcUA4AeQ/l4AVN/tnHPKsXw5\nUFurln1WXp+qKmDChFJMVMR0OzqARx4BbrvNj5IS8lCJYglefx34+c+tf7eSEvp5+nOtvJZWjkMn\nytpav+F7JAmorgacTp/qOpSUkO9YVKQ+x+3twIUXknNH6eoi6aDaa7F6NTBxIvCrXwHbtwO33QYs\nWqQey/33A62twMBAFTo60sfo6QEqK4HeXv33lSRgwQK1V5SOZ6RnqLER6OwswuzZ6dcaGsjxGhrU\nn+X3k/uCnHP1cbTXoayMjMfsXr7oIvKdioqAzZsBrxeorS3Hc88Bf/4zMG1a+l6bPJkc3+cjx5w1\nyw9RBMrLi+ByAZdeCvzoR0BtbbHuc3J9Rg8/HHjrrYmpZyKZJOejrQ3497+B668n9/ddd5Hv8rWv\nkfugtrZId6ypU8mzbXQpRro+spw+t4sXA3v2pI9TUkLmNp8PKC6uwuAg8JWvkHM6dar+uNk8K2as\nXUvvWfV80dREXo/F1PeH2w1Mn65/1sh7jJ/BbFB+pxdeUJ+b6uoS1Zy4dSvwve+l5xOz42ipra1F\nfT3w618Djz1mPI7/+z9y3ufMIfMc/cziYvLcap8Zco30n0fml/zPCzlOCerq6LOVHs/cuX688or6\n2WxpAebOVb8my2Rjb2U8oRCJrGrv595ecg8ccgj5rgsXAh98QMbT1wccdRR5X0VFOUpLy+HxqO8r\nJX4/MGlSiWEGhJW9QkkJMHNmCd58M/09+/rIWEgjbydqa734+GPgvPOMv3dVFXnm8t2a7NlDzoXf\nT+d08rooAqWl9Fymz8HrrwNnnEHmSCVlZcDEiSU5p1V3dQHHHgu89hr5vMceA048EZg+PS3iUllZ\nC5cL2LIFuOwy4wyUkeYWen0uvRS4+Wbg+OPVv5ckOle4UVtbijPOAP77X/Kd6fGdTvIe5bX2+YC6\nOvV1mjKFzvtZnw4dHg8wY4Zf9x1bW4FolFzHmpqSlMGwfz9w1VX640yYAEyYUJJz65CuLqCujnz+\n4YcD0agfs2aR3/n9QHk5OV+VlbXYsAE49FDj61FXR5wcuaxFWlwuYMoUsi5FIuo5prycvFZTU5s6\nN2QfrR5TXx/ZB9hxrbZuBZYsIc+I9rmg9w8ZA/mwf/4TuPhist/TYnY/P/MMuSdLSzOPZe9e4Oij\n6VyXPg7PAzNnlg7PcemDuN3p+8zKOPLFDuPvAIBpin9PGX5N+56pI7zHkFAolNfgKAMDRWhujiIS\nKUYoFEEkUoJQKAIA6OsjP/f3k/93dhahqyuK3t4KDAxEIMscGhsllJenPQT075XHyZebbvJjxw4n\nrr22D6FQ9p6jvj4nWlocCIVGzjeUZaClpQR9fRH09SmPQb6PLKe/429/64XDMYiaGrLAU2pra1XX\nJxr1Yd++GEQx7SV79FEfzj3XgQ0bBhCJFIHjgJ07Y9ixw4dQKGz5u0Ui5OY3Otf0ullhcLAY0SiR\nMzf7m+5uHhMmeNHSwiEU6leNobt7CNEoEAql3fMHDpSgvz+C/vRbEQ47EAq5EAqpC+MnTCjF229L\nWLBAwjXXxDBjhjj8vfTjiEZ57NvnQSgUBUBSUQcHPYhEHLqx9/SUoKND/VpfXwmAEtNn6Lbb/PjZ\nz/owZUoZ1q4dQl1deqzvv+9GVZWESMSDAwciqcm7q8sHWZYQi8kIhdQeYHrvUCQJaGszvzaTJ1dj\n/fouTJggweMpwsAAh+bmCD75xI/zz+9TnZPBQTf27JFxyy0+3HBDH0IhCfF4DeLxfkQiQFlZBA88\nYHwec31G3e4SbN7MY/78BEKhKBIJ4Pnny3HeeYO48MIEentJa5RQaCJeey2B667rwN69tYbnu69P\nQCgk6J5N7TNkRHc3D0HwIhQagCDwqK/3YvnygeHjliAcTkIQitHc3IdQyIVIJIJjjsnuXLz8sgdn\nnBGztIGsr/dh2rQYQiFJdc03bvRg/vwkenocaGqSUF2dQDTKQZKKVM8RJRotxt69/aaKylah30mS\ngIaG9HgikRJ0dsYQDvOpezUUKkEkEkHE4HYwOzf0Gm3d6sKuXSUIhboMx/GXv1RCkoATT+zFtm0C\nDjkkljpud3d8+Jkh88bAAAdRND4vdqwp+/c7UFTkRigUxdCQC3v3cpg2bUgxnoju2dy2zY2yMkm3\n9lgdz+bNHlRV+dDU1I3GxnS90YMPFuH882MIhchcJ0kcdu70IRQaSF0PoBa9vT24+monpk8XEYnw\nhp8py0XYuTOqqzOy8hzR79LbG0Fra3Hq3G/a5ILbDQwNlSGRiCMU6sWaNSW49tqIybzsxb59cfB8\nfjWJmzd7MG9eEj6fhDVrPFi8mMzziQQQiRQjmeRU52DduhKccop+TMlkERob9efEKn19JWhpIXuZ\nAwcieOaZMlRX94PjBITDPPx+Pz74oBMVFSLKynxobR0wPE6m+0R5ffz+MqxfP4RDDlGvjbt3F6O0\n1AmOiyIUGoLLBWzerN6nRSICACdCofaMnxuPu7F7t4SKivwicOEwl9ozaj8rFHIiHObx97+XYcWK\nThxyiIimJgcqK92pNVuJy+XD5s0xTJ6cW0bGhx+6MHEih1BoCH6/gI8+ElBZmZ5jXC4RQBkaG1vw\nz38W4fLL+w3v31jMiY4OHrNnq9dvo2fo6ae9OOywBObN00ek4nEgmSTPkdvN4513PDjsMPK9+/tL\nIAgSgFI0NYXgcpEa2poap25PFA470NKi3yvlwiefeHHMMXGEQiLcbh82bIihtpac70ikBPG4Axzn\nRChEssD27ClBMmn8nGv3M5Q775wAWY7guOMyp2isWePDihUxDA768Oc/i+ju5vHNbw4gEilBW1s/\nenrIHEgxe36Ur9sVDAPsSfv8GMCsQCBQFwgEXABWAlilec8qAP8DAIFA4GgAvcFgsM2Gz7aMw2G9\nsSVt9UCL0UtKJFX4WpkeYWeqSlGRjJ4eLud0LSIWYO2927cLWLDAvI+SUpXp4outPZRawZneXtJT\n5YQT4mhqElSvt7aOftonQFIAzfqlUTo6eNTUiIa1N9qiarPPNkv7LCuTsHmzEzNmJFOGnxnFxWpR\nBlKsnPFPLHPzzX48+6wXzc1E+ry5WX09aGE2qSVLvz40ZNxDJxJJt1egZGo+3tvLYeZMkhLx1ltu\nnHpqDMuXx3HddaU62WUAmDBBxEsveXDeeYOYNIlcAJr2aTevvurGE08QsZ+WFgdmziTXyesFXn/d\njWOPHUpdO46TUVREav04Drj1VmOHRj51DXv2OFI1peXlepEVjiNew1iMy1li/Kab/GhqsvZMKtMD\nlfV9Bw4ImDxZVAm+bNrkxOLFxhswraBRLpAaVKoiqb/flK8pW4AYMdI80tmpT4v8/e9JpFmSgCVL\n4ti5U9C1zKFCPddcU4bubvJ9N2wwToW1iyee8OHss8mDO2tWEtu2qR8Us16MRmuP1fm1pYXU0917\nbzF+9Ss//vMfN264oRT9/ZyqZUJxsYzeXnJ+lHOpKJJ7oqOD16WkUkpLJZ2IBACce661McoybemQ\nPj5Nt1KmfSp/1mJXSiFJnxaH63jVaZ88D10tYH+/sc6AlRYuL75oXEBJ6qTJRaB16nv3Cqn2CRwH\nzJiRxO7dDrz9thsnnmiuomI1jXvyZFGXIgiQtWXOnERq36G9R0nWkLW6R22dq5KRlL4psgx84xsV\neP1141AdrSnu6+NS6/Tq1Z7Uc6dl0iQxr30PSWkk68D06SL27lX3pOQ4MhdGo9zwOm18nEznRsvr\nr3uwbZuxvOyOHc6UUVhdLaG93XiPPDRE1qWHHirCGWfoz002aZ9vvWW8AXr3XRdEUV3/OWdOEo2N\n6rHffXcvPvc5a2IMZuvo3LlJNDSMvPFobHRi4kQJs2Yl8dJLnlQpAH2+ledIqW8wWuRt/AWDQRHA\nlQBeBVAP4B/BYHBbIBC4PBAIfHv4PS8B2BMIBBoB3A/gu/l+brZQhSPtBKXcLKX7/FGVM1JL4/er\njT+yibC/nobjgLo6MWfjj0i5q+8gs5qrt99244QT9BO510tqQXbvduhUmUaipERWLcx33unHFVf0\nY/r0JD75xIniYvK9+vr0Yiq5km29w+WX9+NrXzP2XFJIQ2rja6B9aJuajPsueb36vkdUUKG9nbfU\nAJZ4o9PXj/SdHLk+k2I2mYRCPGIxDldc0Y/nnvNil4hByQAAIABJREFU7tykbjNDipLJva+s+TFT\nUNywwYXDDzeq6zIewzPP+HDZZf1oa3Ng925SBH7yyUO47bYwzjpLPznX1Eh47jmvytuWqQYyH/bt\nE7BlixMcJ+N3v+vFMcekL+SVV/arRG14Xt1C4RvfML638jH+lIXgZIFHyoigr+Wr5DlnThK7d6cX\ntAce0KeuUiQpvQmdMiVdrxSNEuNKWZ+5YYPL1MgpKbFeT2aGcvMKpNuzvPyyB83NjpSz5l//8uLK\nK8tw6qnWO/d2d/OqPoa0j6SSu+4iaTqtrTxmz07i/ffbUFGhF6rgOLKpDoXI8T780I0jjsgvKpFM\nAqtWeXDjjX5cdVUZbr7Zj8ZGBz780IWKCilVBzVhgroGUTkmJVQdNFe6uhyoqCD1dI880o333nNj\n8eI4rr5aHd3kOPLcaKXN43GySbz00gH885/GO9eqKvUmk/L22yNv6s0Es9raiDIu/X0oxKtUi7XY\n0T4GoP3j9HV1dCNYXi6ljOTeXs50zdAKHSmRZeCee4px++1+w/nhX//y4pxziHPX7yeGdVOTMNxO\nR2n8Cdi506mTxM8WmkZsZDwnk8Dxxw9h2rT099Q6UoyMcqO1T1vnquRzn6tCVxePq64qS/1tfb1g\nqAr8uc/FsHlz2uDQqvg6HMSQ6u8n924mzYaJE0Vd/ZcV/vpXMheT1i3U6ZZ2nNEesDxP1oGWFkfG\n+v+iIsmyiNP06Uls3Ghs/L35pjvlqHU40nsV8hwRpVbiwODwk5+Uoq4uaeh4z6aG9ktfqkxdg8FB\nLtU797rryrBnj5BqowAAs2cnsGMHWdOooJzXS56t5mYHXn01+/xbSSLzKXXWbN7sNLz/IhEOU6aQ\nZ2XSJNJjurRUSgk6aveRBw44Uu8fLWzRqwwGgy8Hg8G5wWBwdjAYvH34tfuDweBfFe+5MhgMzgoG\ng4uDweB6Oz43GwTBOPJHH2CKLKebA1NVSK16W39/Zg9yLhCFURmf//wgiotzO/aECRJ27xawcuWE\n1GuHHTbJ8L0ktVE/QRx77BDefdeta7BthWnT0k2fn3zSh54eHnV1ImprRaxb50otqCSVxHxyMvP6\nGhGLYUTlTiUlJbLhtVuxojL1MHZ2OnQF5BRt5G/LFicOPdSatD85roiKCsmS8cdx6gkiHidCRFYx\nmpTefdeFM86oxve/H8Ell0Tx/PNezJhBvN5aTxSg97THYjTyp/5uREjD+ma2o4PHEUck0NrKD6t2\nktfNvJUVFRKmT0/q1GILoYAXi3GIRjlUVEiorJRUdZQ/+IF+I2vFAZGP2mdTk6DaEH3jG/24//5i\nhMNEvIJE/ojBlav3cNo0URX9feCBIvT3c6ivF3QbXeX3nTpV1EWNlZE/ImttfIK0wjC50NDgVKnH\nLVtGosc7dgi4++7e1PXZutWJP/6xFwsXWp/T3n3XhZ/+tDRlUJJ2Kuq1gtLdTYzQ8nJ5WFlar3wH\nkA1/U5NjeCNi3ZFj9J6f/awU+/YJuOWWPvzmN7348Y8j+NvfirF+vRNf+YraCaGN4BtBBbmyJRZD\nKpLjdMpwu4lBc+ONfQgEjLNGfD4ZH33kVrVF6ewkolU1NZLp32kFjwCydh53HIkyG0HvXyPjVpaJ\n04Lnad82YM0aD046yTzCRQwLe6S+jZ5XElGSUVaWdiI0NDgNU+/IeIyNv3gc+Na3yiEIMo46Ko7m\nZgcaGgS8+qobu3Y5IIrkvqUtYUpLJTQ3C+A4YlhQtdWiIhmRCJ8xGgpAtyYYQYR01EqHQ0NExKuu\nTsRRRyVU+5KjjoqnetpRtUZqwP/2t8XYsMFpqD5qLP5EqKkRsWePA2++6UZLC4ky/+QnZbj33mKV\nobN6tQfz5ydw//3dqdcqKiR0dZEBKDNx+vs5bN1qnE2V/lzJUuSPaD84Ut/5N78pQU8Pl1p7KUS5\nPL0+8DyZU157zYNjjzX3Tmhbv2hJJpGa0wWBiENpHdnhMIkuKh1vZWXk3JAenRKSSQ7FxRI2bSJZ\nDldckZtjtLFRwLp1xMgqK0s7sm691Y/XXyc36SGHJLFnj/rcVlTIqeeH3neUjz924fHHzR2cZuzY\nQfqEAmSPdPnl5fjkE/28s3WrM3UvTJxIIr4cR67V9OnJlBhOSwsZ386do6v0Cdhk/H0aIOqN+tcT\niXTTR3ITktfppEz6rakjf/39XM4Gmhk0BeS882I5b+B8PhnvvutWTax0E9bamh5/OKzvdUJZvDiB\nTZuc2LHDibq67LzAdXUiGhsFJBJEtvnmm0kKnCCQfO9DDiGSwK2tDpSV6Xv4AGSyO/nkasupGcR7\nmn2klONkNDY6UoZyU5OQmlRoc1SzaJrydbNeUEab/a4ukkq7dGlixNRTI+JxUqtoNconCLLunt+y\nxYlXX+1Aba2U6lM3fXoSy5bFcdNNfmzZIqC3Nx1R0zo+qBKdVu1TuzBloqeHQ3m5lFXTcY4DnntO\nXWu1YEFC1T/MLiIRDpEIZ+oAUELltRcsyGz45qr2+fTTXlRViaqIRUUFuW6NjWTR5ziyUezt5Uc0\nHMxSs0jvNXLRt20TMG9eEhs2OPHrX/uxejVZYI3mT2L8qS0Gr1dOpaBmItvIXzTK4Ve/Uhe+b97s\nxGGHpQd2yilDuPHGPlx5ZT+cTnLfvPGGByeeGBuxJkr7DEUiPBYuTOBrXyOpt9p0HSor3tvL4fbb\n/ap5yKgXI02RXbPGrTPOrPDyyx5897tl+Pe/Pbj++lKcfXYMV15JnBFOJ5n/77gjjO98Z0DXJmHZ\nsjhuuKEUzz/vyakPWabf1dc7cdll5SkVUSvRw+pqEatXe1JR4QsuiKK93YGyMuLM+PnP+wz/rqZG\nTEVPKc3NDlxwAbkXjFi6lDhA9+zR99HasUNIOe8kicz9//qX11BVk1JWJhumnmaL9h6h55ims5MI\nA/mc7dsFzJljPMeQyJ9+PE895cM110RwxRUDuPTSATQ3O/DMM178618+fP7zVdiwwalSVPb7ZTQ0\nkIbXbW0OeDxy6p634mQzm1t27hTwzDPkZxoZ43myIb/oogk4/fRqPPusF6edpvdOzJ+fUKUdkqwD\n8jmxGIdbb/UbOqkzpTbOmJHEnj0k22TXLgH//a8bv/hFGPPmJfDss15cd10pNmxwIhLhcdxxcXzh\nC+lxHXfcEP7yl+LU51PHczTKY80aj+F3oGhVgM14+2037ryTzHP79zuwYkUMGzfq0x2PPXYIH37o\nGu7nl0hF/tatcxk6pClmzgKAGH4bNzpx880ko4HjgKOPHsKHH6qjZC+84MX/+3/qusazz47h5Zc9\naGoSMHUq6cNbVCTj1Vc9OP10c2eKxwPDqHR/P4c//rEYb77pxlNP+bB3rwPHHjuElhYHBgY4VFWJ\nKcOrttY4lZgocasdP8QIc2DJknjWZQcffeTCsmVxcBzw/vsu3HBDHx56qAjf+lY5gkFv6pmur3em\n9gTV1SL6+3lEo9zwc5wEx8l4+WUPfvlLcp4bGwXT/o1G/S7tYNwYf06ncW8T2tMP0KdPnHbaEHw+\nCR6POk1hYIC3Pe0zFHJkTDWxSn29E4sWkQk9mUynP5x/fiV27hRSnqSvfEVfkAwQI3nyZBHLlw9l\n3ceQ9nXbvVvA6afHVJvnuXMTmDcvAb+fyDdPmiTqesAAxBN52GEJVQoapaODx/XXqyWW+vr4nAyp\noiIZ//mPB3PmJBAOc6itFVNemHCYHNNKLx3SFkT/PpJmrDf+KislXHddJCcD3yzt06jJPEA8ub29\n6tciEXXK6auvdsDlAk49dQjf+U4/Vq3y4sc/LkvJ4WtTno3y1TP1H9RuGmMx4LLLKnDKKfl34H3t\ntQ4cfnh8xNrJbM71K694MG0aSc/RpvgZQb/7a691ZHyf0rFkFVkGdu0S8PWv65/Vs86K4bbb/Dj5\nZHJQh4NEpmhqtVX+/OfilBEjy2QRvuyyClx2WT/WrnXhiCPiePFFLzo7eVx5ZTlWrfKoos8VFWSD\nqrzONAV11y7zhsRAdpG//fsduOeeYni9Mj7+mDxwokiFTdQ3WXGxnHJecBzx8h5//Mh5elqnRkcH\nj2uvjeDBB5HyEk+YIKY25H19xPGwbZsTa9Z4VKlWkyaJ2LlTUDkTaVo8qUsxv05m9+vGjU78+c+9\naGgQcNllAxmjU1qOOiqOWbOS2LrVqStz0P6shfRbM79O9DwAxKC3YvxNmiRh1y4hNXf/7ne96Ogw\nlqZXYmR4HTjgwJw5aufKq68SJ2gike41ZlTK8NOflqbKH0SRPKd/+1tPxjGQeTU3D+3f/05qiXt6\nONX9QnvaNTU50N7ugCDImDmTGCcASU01u2d8PnUa3333FaGvj9TVH3oo+b5Tpoh4/HEfZswQcc01\nEaxcGcWWLepsjdJSKeX4aWvjU7XdJE1O35/NKs8840U0Sq4J1RKYMSOJYNCH66/vw6OPduH11z2p\nCKQSQSDXJRolzkVl9NHlItfeyPjLlPY5ebKILVucOPzwOHbtErBtmxNLlyZw2mlDuPnmPgQCUdx4\nYym++c1+3bo2b14Shx6awMaNzlQ7DLebRNIGBszbgwDmz/WHH7pw0klVqbYWb7zhxuTJxPn8ySdO\nfPnLA4bRpSOOiGPdOheam4mxxfNyau7NtHdzOKBrM0D56U9LcfvtfixalMCDDxYhHOawbFkca9ao\ngwotLQ5Mnap+zqdPF9HQIOD990nrEFEk+6z2dkfG9mVm2TPhMI+HHy5K3TPr17uwYkUMoZADq1d7\nsGJFDJJE7o/qagldXXrj74wzYnjqKR8aGtT3bzLJ4ayzYjj1VPM51MjoamsjbY6mTk3iT38qwVln\nxfDtb/fjxz+OIBRy4Mc/JgrCytIhjwe48cYwEgkOu3aRCJ92Dg6HeV2fZorHY18rDNX3s/2IBykO\nB9k8ax9AZSodaeKcPiU/+1mfoTJTISJ/+dZbUD74oC3l9W1tdeCoo+LYv9+B004bwuuvu/HWW6TW\nL1NU4ytfieLMM3PbnPv9MjZu1NcG3HtvL5xOsjkIh/nhpvH622/vXgeOPnooZYgpicWI11xJXx+X\nU+SvrEzCBx+QAvbGRkHnUTaaqNP1oelfZppktRNaplpCMzyetEPCLO1zzx7j/jClpTJ6Mu9jVNTU\nSPjpTyP43OdiKaOC1oEAxHBzOGRd6uuePcZ1j0a8844bP/xhJHV/0Bz4XFm5cjCrTbAS5YL65JM+\nvPyyBxs3OnHJJVF0dlq7Vg4HUucqE8Rgzm4Cpx5dIxYvTuAvf+lJ9eKS5XT2QCaUY4jFgDvuKFGl\ngK9a5cHf/96N44+P45lnfDjjjBhWroxi1Sovli6NIxj0qVJQ6Xfr6CCN34F05I8aj2ZYifyJIrB1\nq4D77y/C3LkJXH11P55+2ofOTh5/+lOxKupnBO2XZsWRpaxVBNJpXS4XeQ6iUQ51dUTRL5Eg33na\nNBEffuiCIMiqjd/ppw/hhhtKceutfvT0cKkMknCYz6lOdf9+B6ZMEcFxJPXYKNsgEy4X8J3vDAzX\n1+h/39rKo7ra+N6prdVH25R0daWdZUcfHceiRSOnf1dXi6raPUFIp31mwmhe7unhh9sMEUfE4CDw\nwAPFuOCCCWhqStc/dXQ4Us+0JBHRna9+dSD1migCd9wRTgmOmDFxoj71VMl//+vWpUJT3nnHjWiU\nS/XWo8yfn8CPflSK732vHL/+tR8uF9nQtrQ4UvVkZsaD1mm9d6+AP/2pWBWpr6qS8MorXqxcGcW8\neUkUF5NNuXIfMHWqiI8+cmPevARaWtSRv1tvDRuKdYzEzp0CKiok/M//kB6BO3Y4MWGChIULEwgG\nfVi4MInp00WsWtVpegyHg4gJ1dSIuj5td94ZNozSut36Pr0Aue5OJ2mAfswx8VTvYuW5PfLIBJ59\ntlMXPad84QuDePFFb6rkZPPmVvT15fZcA0RE7LnnOvH44z6sXu3B0UfHUV0toquLx44dAhYvThhu\n/mlQgtaI8jz53pl662bi8cdJ5kQw2IUVK2LYtUvAe++54XQCn/tcDF/9agU2b3birrtKTPvZnnTS\nEFpbHfB6SbClqEiffWSVSCS9t5s9O4nnnvPi9NNjaGwUsHMniVBPm0acGpMnJzE4yOm0JA4/nAQS\nduxQlwdwHDB/fjKjgJHfL6sCFLRuECBr/vXX90EQyHHmzEnimmv6ccYZMfzqVyW6c3P55STTgwYL\neJ7sZ60I+FExN7sZN8afIJATqI3SKNM+tYu/GQMD9ht/LS2OnCWAlUycmM6nb2gQcMopQ9i3z5Eq\nHn/vPVdGb0e+TJuWxFtvuU3rBf1+ObXIGxl/TU0Cli+PG+bGG9VtGjUNtQKJDsiYMSOJvXtJOhD1\nsppBVfuoR7m+PrumnLlEdw87LJFKZ4pEjD2Lu3YZpwyUlkpZGX+Uiy4aTNX+EE87OedPP00UBLWe\nuu3bnaZGinYSXL/ehaOPTm96mpsFnTExWlx44QSEwxzefdcFSSLpNt/9Lkmju+mmPkvXqqpKwu9/\n3zvi+wDz6Eo8rv7dHXeQGg8zUSYKjQRQ73hrK49DDrF+LnfsIKkp1DPL8zK2bk0vkvff341DDyUL\n5GOP+XDiiUO46qp+LF+uth4WLUrghRe8KU+wxyNj3z4B//63N2PquHZxNeIPfyjGH/9YgiOOSOC8\n82LgeeCHP4zgN78pgc8nm2YwUIiX28rZIHOCMhKpvHdnzkyiqcmBadOSaGoS8OCDRXjssSIsWJDA\nunUunHzykCrqOmWKiGCwC1dcMYCHHioe7iEn44MPXIZNhUfinXdchs21s2Xx4oRheiT1YhsxeXJm\n46+zk8fEiSKGhogBYSUTo6ZGxLnnpj+P42hNZfZrIKm3BE48cQi33OJHZ6cD+/Y50NvLY/t2J8rL\n5dTGTXlN161z4YQT1AJSNKUwE8om2YODHPr7uVSEN5Eg9bJm9YcHDjjQ2cljyxZ1uvKxx8bxf//X\ngxdf7ERtrYiBAeIw6OriU6lhZijVPhsbBSxZEofHA1x8cfrZ4DjiGKbfn0Y1lOejslLC1q1kU93a\nSow/SeLAcTIqKyVTY4hi5Nx64gkfvv51svENBKJYs8aN2bOTqefJ55OHx2N+XJ9Pws6dTtTWiilD\nh17Lk04aMjW6jObbvXvJM9zYSCJBjY36VGAgs4q0IACTJxMlSY9HRnGxeRrlSBCxFhnl5UQpfcMG\nJ84+O4aZM0kJDa2H7+gwzjYrKZFT825xMbnPrYq5KBkY4LB2LUnd5HmiEPyrX4WxejXJaDn66Dge\neqgbt99eglNOiaVKerSceioRbQMwHPmTLEWtjEVT+NQ++7TTYrj66ghKSmS0tfGp+2blyiiee86L\nadOIM8nIgXXhhYOor3em7hOjchgjlDW3AEm9pnV5EyeS8h0tZ50Vw7nnDuLqqzO3xnE6SRCJ2h6Z\nspOoCq/djBvjz+Eg6SLaB0id9km8uyPVqvT325/2mU3N1EiUl5PC24YGkn65Zo0HM2YkUVpK6rwK\noZBImTaNTFpmjUyXLo2julpKpe9oa/uamx1YsCChSjXMRDjMZRSPMaOyUsKZZ8YwdSpJVaistFbE\nT6NeRx45EatWpZXSjFB6ioDcBBXmz09g+3byR319xkI5ZuI8fr867TORUIsbWYHUgpHzcuCAA/Pn\nJ3Vpn5mKlbWpZVrRgH37yGI8FsyaRVTs3nrLjUsuieKXvwyn6sLOPjuWczNeM8zmlRtuKMV//kM+\nTBSJMf/KK57hdJeRrxeVnn/sse6MtR4AWfho5GftWhfOOSeG+nrnsKCOiOOOSxsYNGXM6SStP+bP\nJ7WhWrGiY48dwh//WJyKZDidJHXpRz/KnN6sXVy1SBLZZN93Xw/OPz/9nE2aJOHQQxNYuTKz4Ue+\nr3EEwAiiLGg8OdbVEScRnS+6u3l88gkxnjs7eTz8cLfhtZoyRcTFF0fBceTcb9zoshQZ07Jnj5B1\nDbYRy5bF8dFHxN2cbkrtwLx5CcO0OwCYOjWpkpXX0tXF4+qrI6lNvhVKS2Vce622L2luxl9vL4/y\ncqJYO3NmEuvWEeXhri4ejY1kbuzq4lU1a4JAVJeVUb5k0nrrGEEgBtcf/lCMq64qw7XXlqWUNWfO\nTKYUKrduFVIiIt3dPDwe4vxUqhJqKStLpyzefXcYJ544lDHSq1QfXL3ag89/PoZrr42kWtRQlGl6\nNTWSLnrJccTxN2EC6UnmcMBy1NyITZucWLQokfqeCxcm8cADJFvB6QQ2brTW7au2VsTatS5MnixC\nFDk4HHLOtd4ffUTujR/+MILaWhHTp2eO/phx+OEJbNjgzEpszohXX/WkWg8cdlgCkyeT6P68eQnU\n16eVJH/wg4hhy63vfrcfX/4ymQeLiuSUgNFIaNeid991YeVK/fuUwRKHA3jiiW4ceWTCsNSFQq83\nbatlJbXfSITmwAEHHA6gqkpEWZmMI48k8+aePQLOPZecCyqiUlcnplR7tSxalMC996a94LNmEaN/\nJGgqNuXFF71YsSJz2zOOI+um0d6hu5u0T6HvI4GLke9hpYCanYwb44+mlWgjJ8pUOq/XmgenEGmf\n2bYsyASNFkUiPKZPF7FmjRtLlhC1pauu0jcVthOSe27++7IyGX/5S8/wxo/DL37hx6DieSKRWHMV\nNK1nNlfBl+XL47j44kG4XCRdrrJSHP78zAYSbb/Q2UkmpkwGwmGHJVTqYblc46qq9MKuNP60fXSM\nVDLLytRpn/v36/P0R4LWBxBVXPKaNvJHr5nx36e9bEZpjHv32rOpzYTS4FEyZYqIffscGReyQvPy\ny8AJJwzhnXfcSCZJ+um3v92PDz90W44S0z5JxcXGdapKqqullNx4ZyePJUvi2LTJiYoKERdcMGia\n7r1+favpsUtKZLz0Uqcq4tPTw49YI+RwwFCBWZJIQf699xabqtZdcknUUsRfEPTiRGZUV4toa+PR\n3c2hoUHQRf7uvrsXLhe5Z7/85Sh6e3ksXx7HY491mx8UwG9/25tK+5VljJjebyQ57/GMfG2tUFIi\nIxYjE7QgkDrUN95wZ8wGKS+XTY309naSMlxRIaeaKecKKQcY+Zo6HDKSSeCFFzw4cMCRavnCccTL\n//TTXlx6aRQ33NCHXbuIOt9bb7lVpQhHHx3HO++oJy3SM9LaWFeujOLuu0vgcAAPPtiDr351AD/7\nWSkSCQ633NKXEk967jkv/vlPLxobBdx2mx9XXtmPjg5Hxs859dSY6r4/++yYyvmhRalsOTBgrq6r\n5PDD46kNtJLf/74X5eXGfRhHQlsj9dprHpx9tvm4rTq7a2slrF/vxKRJ6bRPsyiPEqPnZfduATNn\nirjkkih4HvjJTyIZ62/NmDJFxI4dQioCpew3OtKYJIk801u3Cqo2PitWxPDVrxJDrrqaqGTSuaKm\nRhoxHZnnSf1hLlHITZtcOPLIrP8sI8kkUeE0qy9UMn9+Alu3qhfiXbsEfO97Ed0a8Mgj3ao6f6dT\nxqRJ4nCphvE9oawLnzUraSkaWVmZVnaNRkkk3kyN3ApbtwqqrA8q4DcwwKXuIyPsaIlkRAFaJB+c\nFBXJePNNp27CSyTSG3ifj0SjRlpku7vtVxi0s8HjggUJPPxwWsZ2YIDDjBniqDSRLC+XLEnW0rTP\n/fsdaGhIF57TjY92A0RlnrWLplkqZDZQBbLubhkvvOA1DOdTysulVNrUSG0XTj01hiuuKMeKFTF8\n/vO5xe1pY1uARjnl4drU9ISRNDndpaUStm5N/7ulJTdRIVkmYhNLlsSHx6Ru9ZDpvqI9z6qrJbzz\njhsXXaR+/n73u54RF7V8oTUx2trCKVNErFnjycnzmyva1LPXXgN++MMYFixI4HvfK8cpp8SGPasD\nGcVSlGTTd2zRImLs0abbU6eK2LSJ1OFkvo6Z73VtNLC6Wsy4oFESCfU5SSaB668vxfTpIk45JZaK\nPuYKifxZe+/EiSK2b3fi/feJupwyQuf1IjUv3H8/8ajMn59ATY1oabMNECN04cJE1pGUF1/0IBAY\nOcpplYceIqq5paUk+hMM+kZMn/V45GGVXvV3DQZ9+NKX7Bmb1eyXSZOIA+PZZ726e3byZHI/L16c\nwJw5STz8cBFOOmkIDz9chIcfTqsFL1tGoj9KrKZ9AsQgOe20WCpSc9RRCSxY0AenM22kDwyQTI+v\nf30Ajz5ahJ/9LIzBQR6PPurL2EaJRjiyQRRJCqG1e5E0qjbr2UfqvMmXyCbyR9Omy8rSpREjpYpa\nYcIEEe3txElHjb/OTt5SP2TlniFTD75sIeuaI3VuBgY4S+tYeTkRvLvzTj/cbjljpsappw7h9NOt\n7xuWLYvj0Ue7sHbtyIVk2ueG9o+1m9JSCa+8klkMDQAOPZSUDixblhYr7OnhDJ2R2rG/9147eB54\n6qkuS9dg7twkHnkks8MOIGsYTd9+4gkfzj8/v3muuloyLI9paeFTjemN0Cqu28W4ifz5/RI2bnTq\nJt2hIXXkz0rNH7XYAWPZ+7GGegLpQ7JuXVvOqRvZwnHA//5v5nxnIG381dWJOo8PPY7230Z9hojy\nan7GX0cHMU4WLYrjH//wpQRElNLViQTxrJWXS9i7lwxiJKPT7QYeeqgHggD84AdlGR/wTJSWEiED\nmjZKWo+QE7Rpk3n/J23a58BA7v0pP/zQjWOOocYfieLRFJNMGzalvDVV7VJy5plDBXdKmElbFxfL\n2LfPMWKqpF1om2339HCYOpWcz1mzRNx/fw9WriTG8eLFCcvZBTxvHEEzYs6cJOrrncMNg0kPzq1b\nnZYiLtlgVEdjxPHHx/H66+kIzIsvevHVr0bx/e/35234ASS6ZaTybARtIN7UZC67reSBB3osb7Zp\ntDxTBMeM1lZ76sEpdEPl9cpYsaIK99wzcmFwIBDFM8/4FGPi8b//W4qBAc62DTVgzQlKBWhob0ql\no5DngSOOSMDnI+1Pdu4UsGxZHKGQOqrocECZc6bOAAAaq0lEQVQnP59N5A8g964yAlFUJKdS3iQJ\neOihIqxcGcUhh4i46aY+VFTIqKggAkG5KmeasXx5HHfe6cexx+bvyBKEdP1eJqEZLVRwh2zceUut\ncqwelzrbJYlkOrS0OEYU5Fq8OI7168kFufvuEtxxRwlWrLCneEp7Tkgp0MhzQWWliOefJ8I7t98e\nzuh0Of/87Ho+u1zESWdFrI80X7d86JxI15eO/N6qqnSUDQB++1tzRXot9PiVlZLlfa6V91VWkvt5\n/XonEgmyRufDX//aY7jOEvXnTMaflIrE28m4Mv6amwWd5zKRUKt9mtX80cVbOxlm41keTXbvFjB1\nKllgrCgK2YmVBuYeD7npFy1KpBqatrUZh+3p9RBF4ybW+RoPZWUyJk4Ucdxxcdx0U1ghw5s+8Pbt\nxMjy+WTs3ElSUKx6iS+4YBD339+DSy/NzXN0wQWDuO++4tS//X6SBiDLxCP1hS8YbyhJbzny86ZN\nTgSDPkvRGC0lJRI6OtLF10VFMu65pwTvvutSNT01orZWxEcfuXDrrX6dEtdoYdYHS5aB73+/37Kh\nki91depGtPX1TixZYs+xrT4DRHFXxi9+4ceyZXEIAlIpc3aSqdmxkhNOGMJbb7lTDrT6emFEBc9s\nyOa7uVxkPejvz5yGkwt0DBdeOLLxRzdmP/+5H9u3CwVLSz7ttCG89lq7JcGlmhoSbfvDH4px441+\nnH9+Ja69NoLrrx/Z0WeVmTOtXXdi/PGmvdx+/nMiOOFwAIODRJH1jTdGjj6QerLsxmyGIJDUSe25\n9XpJw3a7Ra5OOGEIpaVSTvWkmcgm8lddLeKKKypw990lKXEkO/D75ZTTXpI4TJwoob7emSrVMOO4\n44bwxhtu1NcLmDhRxLRpYtZlD5n45S/TnlWn05qISHW1hNWrvRlVkEeDurqRBe7yxayFgxnJJFLt\nLerqkqZO7dGCtut67jlvSq3TTl56icxJIxl/M2Yk0dDgRH29vddr3Bh/paWyYW+eeFyd9mmWL03l\ny7W55mb9A8eayy4bwDnnFNi1kyd79zpUG29lw2ZlOwFSiC8b5tXbUSv55JNdmDSJpL0tXJgej8sl\np+oR1693YunS+HDPIwfOPnswlTpXaCZOlFSGk99P0gBeftmDc84ZtLQ5XL3ag48/duW0qaXy9srx\nfPJJC9as8WDbNifmzzefpA87LIFduwRUVoqpwvbRhgo5KaG1naecMpS1CE+uTJ2qbkRbX+/E4sX5\nHzcb7zwAfOc7/fjKV6Kp6MP69daEF7JBK+ZhBscBX/hCDC++6MGePel2BnZRVSXhP/9pt/x+OufQ\nujK7yGYjNG2aiB07nOjocOCvfy0uWGS6slLKKqJ46aUDmD07ibIykr6UbduakXjzzZENNIA2dE4/\ntNr7Remh/8c/SAsBqxkPdmXImDWdBkipgp3RUoA4da6/PmKb8Xr99X0AMKz2ae1vlixJ4K67iEHU\n1CQYtmDIBY4jcxYZDzm3DQ3CiKqyXi/JQLjnnhJ86UtRXHmlvXoHX/ta2pm7aFHC0n5gzpwkpk9P\njtqaY8aJJw7hvvuKkUwSB/FIJSy5kK3xt2xZHHfcUYJHH/XpykPGirY2HjNnJguSOUdLnUiLJvP5\nwO0m+19l5oUdjJuaP59PNhSliMfTvURoLZXRZEfz4PftU09qB2vkT9lD6GCFiH0ksXYtkdrfts2J\nb32LTNA0tbGsTB6WO5YN0z7twOzBrqkh/ZzKyyWsWuVVFGOLuOGGvryKf7PF40kX4dfWkmLzHTuc\n+PGPM2+y6eTrcJAU51yMP20bDI4j6SVTpybxzjuujDU/Lhdw8819WX+mnfh8sk7+uqXFnr6a2TBl\niojXXksXwfT1EZXCwTzXudJSKSsDgUqkUwpRc5mNAbdsWRw33ujHm2+6ceut9t4rHAfT5rlGSBKR\n1q6okAzT0fMZh9WN0HHHDSEQmIDHHuvCypWVuPzywop0WaWuTkRdnYgVKwpzfKv3jDKNm9SVmZ9Y\nZSuH0eTcc2M480xjZ9c777SPSv19PlBhOOJYsnbjFhfLWLo0gffeI9G2s86ybwNPMwkkiaS4Pvmk\nz9I5/OIXB/HFLxbekLjkEmtZPRUVEv7wB2utgQpJWZmML385itdf92DBgkTOJSmZuPrq7JwRp502\nhFmzkqiokAqqSJ8Nmza5cs7YsoqVWuerruofdkYWZ3xfNoybyB/HARs3tupeJ5LL6po/o0Xa75fQ\n18dj714H6urSLuGDNfL3aYCkhojDSk8CIpG0aqVSZjca5eDxkMjfaNUuAiRS09ws4JFHivCnP6Xr\nYp58sntUDT+AGPM09YXKzluF3s8XXjiYU1uMurokTjlFX0dw8cVRXHJJ9KDfyBjV/O3b5xi1yC1F\na4TapfA7d24Sv/ylcd+lTwvXXNOP73xnwPZ0y2yJxbhUD6clS+zz6mVj/FVVSVizpgOTJxOFv7Fq\nhXIwQ6PdY+HEsYLXK5s6Hcaqr2ku5NLq4fjjh/DKKx5bxF60iCJphfHuu9aj+Qxjli8n9dYffOCy\nNR2WUloqZ62KX1dnVUBrdDjmmCHMmzf20R2et798a9wYf4DxyYvH0687HMQYNKrlIql2PA4ccKhq\n2lyudF1VriSTsFw/9lli1apOCALpE3b//cUq9TGl8XfgAAmL0x4/o8WCBaTXTjTK5S1jni9HHx1P\nFUB7POkmwyPBcbQJsohf/jKcU7qJx2McvfN6geOOO/gjzEaRv61bzRvTFxJRJBuqWAw69dHxTEWF\nNZXgQnPddRFcdFEUixcnUv2z7CDbFCjK8893jrqj6dNAOMyhrExCJMKlemcx7Ic2Vc+Gww5L4He/\nK0x0iyqy2tUTeTzD8yQ69/DDRaqABiPNLbf02d7v92BhXBl/Rij7/AFAXx9nmEteXEwaryaTnKrG\nyuORLfUMyUQ0ar/AwKeBww8nm++KChldXbyqYbrS+Fu3zonFi+MFS/s0o6RExhtvuMe88NiIeNz6\nohwKOcbceB1LjCJ/vb3WGqjbzeGHJ/DRRy589JE7JWvNOHgoKjIuD8iXXI0/hjFtbQ5UVYn47W97\nDbMSGPaQi/FHFVcLwdNPd1oWk2KMTE2NhA0bXBlrzhifTca98adM+wSM5egBYghEIvpZ0O0mQjD5\nMF6NPyWPPNKt2nQpjT/Sn04a9bRPALjllnBO8uyF5vTThyyng7W3mzc/HQ9o1T6HhsZuI37aaTG8\n+65L1/CV8dlmtB1Xn3Voe57yctk2NdR77x2599d45GBK6y8rk0d9D/BZhudJYIOd07GB7GnHZjMy\n7i+5Mu0TADo6HIZKXFRWWjsRejzM+LMDbToiNf7270/XdGiluEdjAz9vXvKgWvwoy5bFEQiMbJRy\nHPGST5w4fj17Pp+kSvt8/nmvaXuMQiMI5D6ORPgR1eoYnx1Iav9Yj+KzQ02NZLtQ0XnnHdzq2GNB\nLjV/jE8XRloYjNGhs5O3XTXZKuP+sR4cVCvtxOOcYb+h4mLJNPLH0j7tp7RURl8fjxde8OC888hG\nnXjP0+dpaAhj1jvu04LfT3o+jtQT6bOM202ea1kG7rijBJs2uVQtPcYClgI4vpBljm2ibeTee3sw\nZw5L/ys02bR6YHw6YTXFY4fTCdvbvlhl3LR6MENr/D37bKfhIl1URIwRbYjW45ERieS3qg8O8sz4\n08DzZIPc38+nVNNIn7/0eyIRPtWmg2FMeTlJkfqsFi1bgW5e2tt59Paqa0vHgoqKdEozY3xQWipB\nENgcbxfMIBkdEgnrPRIZDEZ23HZbeMwcwePe+NN6ZM28s4IAdHXxOkU6j0dGRweL/BWCvXvVyqqi\nqK/PZEpvmSkrA8JhZmgApK/k2WfHcOyxYyu08j//M8A2r+OMOXOSCAa7xnoYDEZWJBIcnE62xjIY\nhYDjxs6RxXaFWdDTw6OsTD0RejywJe3T62UTrJbp00VV81Rt2mdXFz9mIfNPC+XlRMGWQZwJ06eP\nfaqY06mvcWV89mHXnPFpI5GAbYI6DAbj4IEtR1kwOEiajSshgi/5Hbe/n8u6GeZ44Ec/iqj+rU37\n7OriMXv22G/mD2aqqoiIEYO2vBi/tY8MBoORDaedNjSu68UZjM8qzPjLgmuuiaCmRj0R2qH22dXF\no7KSRbBGQhTTKbrd3RxefdWDo4/WNx9npJk3D/j5z8NjPYyDAlHkWPSFwWAwLHLyyayHIoPxWWTc\nb4WyKbZculSvAmpHn7/+fp4VVVuApn1KEvDaax7s3i2wmr8R4HngzDPZAs7UNRkMBoPBYDDGsfEn\ny/ZsCO2I/AFMvcwKNO1z9WoPtmxx4sUXO9l5Y1iC48j9w2AwGAwGgzGeGbeCL14vMdryNR6cTiCZ\nzO8gLCphDVEkkazt25246aY+ZvgxsoI9ZwwGg8FgMMY74zbyV1Ymoadn7K0HtiG1jihyqbRPVrvF\nyIbLLutnzgIGg8FgMBjjnnG7hS4tlRAO87YYX/lsKpuaHDoRGYYxpOaPpcgysqe0lHlZGAwGg8Fg\nMMZt2ic1/saaNWvcOPPMPHtFjBNEkfzndLKNPIPBYDAYDAaDkS1jb/2MERMmSOjsHPuv39vLM8VK\ni4gih6EhjimjMhgMBoPBYDAYOTD21s8YMX26iG3bnPB6x9aQEEUOTueYDuFTgyQB8Tg35teMwWAw\nGAwGg8H4NDJujT+fT0ZrK4+ysvyjbky0ZXQQRWBoiBl/DAaDwWAwGAxGLoxb4w8Aenp4lJaylMtP\nC6JIWnMw44/BYDAYDAaDwciecW38FRXJ8PnyNySY+uToIEmAyyUz44/BYDAYDAaDwciBvFo9BAKB\ncgBPAagDsBdAIBgMhjXvmQLgEQATAUgA/hYMBv+Yz+faxW9+08vq7T5FUKVPZvwxGAwGg8FgMBjZ\nk2/k738B/CcYDM4F8F8APzF4TxLAD4PB4EIAxwD4XiAQmJfn59qCy2VP1I7V/I0OxPhjaZ8MBoPB\nYDAYDEYu5Gv8nQfg4eGfHwZwvvYNwWCwNRgMbhz+uR/ANgCT8/xcxjhEkjiW9slgMBgMBoPBYORI\nvsZfdTAYbAOIkQegOtObA4HAdABLAHyY5+ceVHBcbtG/eBwQBGbIWIVF/hgMBoPBYDAYjNwZseYv\nEAi8BlKvR+EAyAB+ZvB20115IBAoBvAMgKuHI4CfGZxOGfE44HZn93fhsD2tJsYLX/5yFAAz/hgM\nBoPBYDAYjFzg5DwK1gKBwDYAJweDwbZAIDAJwJpgMDjf4H0CgBcBrA4Gg3/I4iM+Fbv8P/wBuPRS\noKIiu7/bvh3YsAH40pcKM67PIldeCdx+O1BcPNYjYTAYDAaDwWAwRg1b+gvkpfYJYBWArwG4A8BX\nATxv8r7/A7A1S8MPABAKhXIe3GiRTPqwa9cQYjExq79raHBCkniEQkMFGllhqa2tHfXrE4v50dPT\nh76+Uf3YTy1jcY0Y1mHX5+CHXaODH3aNDm7Y9Tn4Ydfo4Ke2tta2Y+Vr/N0BIBgIBL4BoAlAAAAC\ngUANSEuHLwQCgeMAXALgk0AgsAEkmvfTYDD4cp6ffdDg9coYHMzeGA+HeZSXs7TPbCgqkuFwjPUo\nGAwGg8FgMBiMTx95GX/BYLAbwOkGr7cA+MLwz+8C+Exv1z0eGbGY/vW+Pi6jQElfH4/p05MFHt1n\ni2uuiYz1EBgMBoPBYDAYjE8l+ap9MmAe+bv9dj+efNJn+nfhMIeysk9FWeNBQ7aiOgwGg8FgMBgM\nBoPAjD8b8HiMjT+HQ0YyQ2AvHObh97O0TwaDwWAwGAwGg1F4mPFnAyTtU2/8JZOkKbkZiQRJC2Uw\nGAwGg8FgMBiMQsOMPxswM/4kiTSAB4CODh5XX102yiNjMBgMBoPBYDAYDAIz/mzAzPhTvr5jh4Dm\n5s+07g2DwWAwGAwGg8E4iMm31QMD5oIvXq+MaJTDP/7hRVGRjAkT0vV9iQSpCWQwGAwGg8FgMBiM\n0YBF/mzAzPgTBEAUOdx4YymamwWVuMu2bU7Mm8faPDAYDAaDwWAwGIzRgRl/NlBUJGNgQH8qab3f\nlCki6usFzJghor2dvG/DBieWLo2P5jAZDAaDwWAwGAzGOIYZfzbA84BskMFJX6uqktDcLODMM2N4\n+23SqK65WcCkSazNA4PBYDAYDAaDwRgdmPFnM7t26UVdJkwQ0dXFY/bsJHbuFNDU5EBtrTgGo2Mw\nGAwGg8FgMBjjFWb82cyZZ1ajq0t9WidMkNDVxafSQJ94woeLL46OwegYDAaDwWAwGAzGeIUZfzbS\n1cXj1FNjqK93QpZJzd/AAIeaGgmRCDnVJ5wwhEWLEigqYkqfDAaDwWAwGAwGY/RgrR5sZONGJy69\ndACbN7uwdGkcRUUSWloEzJol4dRTYwCA445jIi8MBoPBYDAYDAZj9GGRP5vgeRmbNzuxbFkc0SiH\njg4elZUk4ldcLOHRR7vHeogMBoPBYDAYDAZjHMOMP5uYPFnErl0CPB7y785OB6qqJESjHHw+luLJ\nYDAYDAaDwWAwxhZm/NnE/PnJVF1fXV0S69Y5UVUlYnCQGX8MBoPBYDAYDAZj7GHGn00s+v/t3VuM\nVWcZh/GHKXIoVmxqoQcs9hAqYorFZKKSxiqgQA306rWk2qNXVq2HNClEL7xp4MLUGsWkFpASK33F\nKtQQKYQa05haUDBGsCU2yEHBNjZjWhMzOOPFt8aMULSyYfYH6/ndsPbH2rPX7P+sNfPu77Cu62f1\n6jK0c9asfrZsGcfFFw8wMFDuAyhJkiRJ3WRZchqd19zi7+qrj7Fjx1guumiAiRMH6O8f1d0DkyRJ\nktR6rvZ5BvT0wObNL9HTAytXvvLveYCSJEmS1C0Wf2fIzJn9AIwf3+UDkSRJkiQc9ilJkiRJrWDx\nJ0mSJEktYPEnSZIkSS1g8SdJkiRJLWDxJ0mSJEktYPEnSZIkSS1g8SdJkiRJLWDxJ0mSJEktYPEn\nSZIkSS1g8SdJkiRJLWDxJ0mSJEktYPEnSZIkSS1g8SdJkiRJLTC6kydHxIXA48BUYD8Qmdl3kn17\ngJ3Aocxc1MnrSpIkSZL+P532/N0PbMvMa4HtwNL/su+9wJ4OX0+SJEmSdAo6Lf4WA2ub7bXAza+3\nU0RMARYCj3T4epIkSZKkU9Bp8TcpM48CZOYRYNJJ9nsQuA8Y7PD1JEmSJEmn4H/O+YuIrcDkYU2j\nKEXcl19n9xOKu4i4CTiambsj4sbm+ZIkSZKkETRqcPDUO+MiYi9wY2YejYhLgKczc/px+zwAfAI4\nBowHLgCeyMzb3sBL2FMoSZIkqe1OSwdaR6t9ApuAO4AVwO3AxuN3yMxlwDKAiPgg8KU3WPiBvYSS\nJEmSdFp0OudvBTAvIp4H5gDLASLi0oj4SacHJ0mSJEk6PToa9ilJkiRJOjt02vMnSZIkSToLWPxJ\nkiRJUgtY/EmSJElSC3S62ucZERHzga9TitNVmbmiy4fUGhGxCvgY5d6M1zVtFwKPA1OB/UBkZl/z\nf0uBuyi38rg3M59q2mcB3wXGAZsz8/Mj+52cmyJiCvAo5d6bA8B3MvMbZlSHiBgL/BwYQ7m+bsjM\nr5pPfSKiB9gJHMrMRWZUl4jYD/RRrnP9mdlrRvWIiInAI8C7KRndBbyA+VQhIqZRshikrJx/FfAV\nYB1mVIWI+AJwN+X8+S1wJzCBEcinup6/5hfyN4GPAjOAJRHxzu4eVausobz3w90PbMvMa4HtwFKA\niHgXEMB0YAGwMiKGbs/xbeDuzJwGTIuI47+mTs0x4IuZOQN4P3BPc36YUQUy8x/AhzLzeuA9wIKI\n6MV8anQvsGfYYzOqywDlPsLXZ2Zv02ZG9XiI8ofmdGAm8HvMpxqZ+UJz7swC3gu8BvwIM6pCRFwG\nfBaY1XS0jAaWMEL5VFf8Ab3Avsz8Y2b2A+uBxV0+ptbIzGeAV45rXgysbbbXAjc324uA9Zl5LDP3\nA/uA3oi4BLggM3c0+z067DnqQGYeyczdzfarwF5gCmZUjcz8e7M5lnJBH8R8qtL0oC+k9FwMMaO6\njOLEv1HMqAIR8RbghsxcA9C8732YT63mAn/IzIOYUU3OAyZExGhgPHCYEcqnxuLvcuDgsMeHmjZ1\nz6TMPAql+AAmNe3HZ3W4abucktsQMzwDIuIdlN6lZ4HJZlSHiOiJiF3AEWBrc1E2n7o8CNxHKcyH\nmFFdBoGtEbEjIj7VtJlRHa4EXo6INRHx64h4OCLOx3xq9XHgsWbbjCqQmX8CvgYcoLzXfZm5jRHK\np8biT/Xz5pBdFhFvBjZQxn2/yomZmFGXZOZAM+xzCuWTuRmYTzUi4ibKnObdlN6lkzGj7prdDFlb\nSBnefgOeR7UYDcwCvtVk9BpluJr5VCYi3kTpNfpB02RGFYiIt1J6+aYCl1F6AG9lhPKpsfg7DFwx\n7PGUpk3dczQiJgM0Xcx/adoPA28ftt9QVidr12nQDBHYAKzLzI1NsxlVJjP/BvwMmI/51GQ2sCgi\nXgS+D3w4ItYBR8yoHpn55+bfl4AfU6aEeB7V4RBwMDN3No9/SCkGzac+C4BfZebLzWMzqsNc4MXM\n/Gtm/pMyH/MDjFA+NRZ/O4BrImJqRIwBbgE2dfmY2mYU//mJ+Cbgjmb7dmDjsPZbImJMRFwJXAM8\n13RV90VEbzMh9bZhz1HnVgN7MvOhYW1mVIGIeFuzCh4RMR6YR5mXaT6VyMxlmXlFZl5F+f2yPTM/\nCTyJGVUhIs5vRjcQEROAj1BWw/M8qkAzLO1glBUlAeYAv8N8arSE8iHXEDOqwwHgfRExrnlf51AW\nIBuRfKor/poK+DPAU5SLyfrM3Nvdo2qPiHgM+AVlxaADEXEnsByYFxHPU35AlwNk5h4gKT+wm4FP\nZ+ZQF/U9wCrK0s/7MvOnI/udnJsiYjZwK6W3Ylcz32I+sAIzqsGlwNMRsRv4JbAlMzdjPmcDr3P1\nmAw808ydfRZ4Msuy5p5H9fgc8L3mWjcTeADzqUozD3Mu8MSwZjOqQGY+RxnBtQv4DaXD5WFGKJ9R\ng4MO95UkSZKkc111PX+SJEmSpNPP4k+SJEmSWsDiT5IkSZJawOJPkiRJklrA4k+SJEmSWsDiT5Ik\nSZJawOJPkiRJklrA4k+SJEmSWuBfBsADHx92/4oAAAAASUVORK5CYII=\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fcbe4198be0>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "source": "### prep data to be fed into anomaly detector ###\nr_vals = [res[0]['res']['vals_norm'][i] for i in res[0]['res']['rpeaks'] ]\nrpeaks = pd.DataFrame(list(zip(r_vals,res[0]['res']['rpeaks'])),columns=['point_vals','occ'])\nd = r_vals\n\n# get anomalies with vanilla mean and std\nresults = get_anomalies(d,3,2)\nanomalies = pd.DataFrame(results['anomalies'],columns=['index','point_vals'])\nano = pd.merge(anomalies,rpeaks,on='point_vals',how='inner')\n\n# get anomalies with rolling mean and std\nresults_rolling = get_anomalies_rolling(d,10,2)\nanomalies_rolling = pd.DataFrame(results_rolling['anomalies'],columns=['index','point_vals'])\nano_rolling = pd.merge(anomalies_rolling,rpeaks,on='point_vals',how='inner')\n\n## plot results\nfig,ax = plt.subplots(nrows=2,ncols=1,figsize=(15,10))\nplt.style.use('ggplot')\naxes = ax.flatten()\n\naxes[0].plot(list(range(len(res[0]['res']['vals_norm']))),list(res[0]['res']['vals_norm']),linewidth=0.5,c='blue',label='original')\naxes[0].scatter(x=rpeaks['occ'],y=rpeaks['point_vals'],c='green',marker='o',label='rpeaks')\naxes[0].scatter(x=ano['occ'],y=ano['point_vals'],c='red',marker='o',label='anomalies',linewidth=10)\naxes[0].set_title('Vanilla stats')\naxes[0].set_xlim([0,8000])\naxes[0].legend()\n\naxes[1].plot(list(range(len(res[0]['res']['vals_norm']))),list(res[0]['res']['vals_norm']),linewidth=0.5,c='blue',label='original')\naxes[1].scatter(x=rpeaks['occ'],y=rpeaks['point_vals'],c='green',marker='o',label='rpeaks')\naxes[1].scatter(x=ano_rolling['occ'],y=ano_rolling['point_vals'],c='red',marker='o',label='anomalies',linewidth=10)\naxes[1].set_title('Rolling stats')\naxes[1].set_xlim([0,8000])\naxes[1].legend()"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### 4) Feed distributions in to check anomalies"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "- Checking R-peak distribution"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 97, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Vanilla: 3.9562634642e-05 0.305253073127\n"
                }
            ], 
            "source": "# get anomalies with vanilla mean and std\nresults = get_anomalies(r_peaks,3,3)\nanomalies = pd.DataFrame(results['anomalies'],columns=['index','r_peak_mean'])\n\n# get anomalies with rolling mean and std\nresults_rolling = get_anomalies_rolling(r_peaks,3,2)\nanomalies_rolling = pd.DataFrame(results_rolling['anomalies'],columns=['index','r_peak_mean'])\n"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 179, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.legend.Legend at 0x7fcd0162ac88>"
                    }, 
                    "execution_count": 179, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }, 
                {
                    "data": {
                        "image/png": 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+t1mzZti+fTsyb/V23rhxA+np6cjKyoLJZEL79u0xYMAAnDx50qfPHCiK9vxF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BUBj7eyX6+0elfvi+AmQGo65pLA7wsxux/7PRuIeFC5nSYs2aNejpCdfDqlWrMDQ0jCAoYtmy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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fcd0a9ec470>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "source": "## plot results\nfig,ax = plt.subplots(nrows=2,ncols=1,figsize=(15,10))\nplt.style.use('ggplot')\naxes = ax.flatten()\n\naxes[0].plot(list(range(len(r_peaks))),list(r_peaks),linewidth=0.5,c='blue',label='original')\naxes[0].scatter(x=list(anomalies['index']),y=list(anomalies['r_peak_mean']),c='red',marker='o',label='anomalies',linewidth=10)\naxes[0].set_title('Vanilla stats over R-peak distribution')\naxes[0].set_xlim([0,6000])\naxes[0].legend()\n\naxes[1].plot(list(range(len(r_peaks))),list(r_peaks),linewidth=0.5,c='blue',label='original')\naxes[1].scatter(x=list(anomalies_rolling['index']),y=list(anomalies_rolling['r_peak_mean']),c='red',marker='o',label='anomalies',\n                linewidth=10)\naxes[1].set_title('Rolling stats over R-peak distribution')\naxes[1].set_xlim([0,6000])\naxes[1].legend()"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 99, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "#### Make labelled data easy to consume ####\nref_data = pd.DataFrame(list(map(lambda x:x['doc']['label'],data_dump)),columns=['label'])\nref_data = ref_data.reset_index()\n\n# labels_count = ref_data.groupby('label').count()\n# labels_count = labels_count.reset_index()\n# labels_count"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Spark SQL\n    query = \"\"\"\n    select label,\n           count(1)\n    from physionet\n    group by label\n\n    \"\"\"\n    spark.sql(query).toPandas()"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 108, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>label</th>\n      <th>count(1)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>771</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>N</td>\n      <td>5153</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  label  count(1)\n0     A       771\n1     N      5153"
                    }, 
                    "execution_count": 108, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "df = spark.createDataFrame(ref_data)\ndf = df.filter(df.label.isin(['A','N']))\ndf.registerTempTable('physionet')\n\nquery = \"\"\"\nselect label,\n       count(1)\nfrom physionet\ngroup by label\n\n\"\"\"\nspark.sql(query).toPandas()"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 111, 
            "metadata": {}, 
            "outputs": [], 
            "source": "ref_data = df.toPandas()"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Evaluate"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 112, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Vanilla:\n   label  index_x  index_y\n0     A        2      771\n1     N       19     5153\n\n\nRolling:\n   label  index_x  index_y\n0     A       11      771\n1     N       73     5153\n"
                }
            ], 
            "source": "classified_data = pd.merge(anomalies,ref_data,on='index')\nclassified_count = classified_data.groupby('label')['index'].count()\nclassified_count = classified_count.reset_index()\nclassified_count = pd.merge(classified_count,labels_count,on='label')\nprint ('Vanilla:\\n',classified_count)\n\nprint ('\\n')\nclassified_data = pd.merge(anomalies_rolling,ref_data,on='index')\nclassified_count = classified_data.groupby('label')['index'].count()\nclassified_count = classified_count.reset_index()\nclassified_count = pd.merge(classified_count,labels_count,on='label')\nprint ('Rolling:\\n',classified_count)"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 180, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Vanilla: 0.000100604811949 0.143802148909\n"
                }, 
                {
                    "data": {
                        "text/plain": "<matplotlib.legend.Legend at 0x7fccf6d67940>"
                    }, 
                    "execution_count": 180, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }, 
                {
                    "data": {
                        "image/png": 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DhsGm18vfiyLujB7t80Qq3mRlZSE8PBxarRYXL17E6dOnPe7frl69evj5558B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\nAABJRU5ErkJggg==\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fcd12c324a8>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "source": "# get anomalies with vanilla mean and std\nresults = get_anomalies(rr_intervals,3,3)\nanomalies = pd.DataFrame(results['anomalies'],columns=['index','r_peak_mean'])\n\n# get anomalies with rolling mean and std\nresults_rolling = get_anomalies_rolling(rr_intervals,3,3)\nanomalies_rolling = pd.DataFrame(results_rolling['anomalies'],columns=['index','r_peak_mean'])\n\n## plot results\nfig,ax = plt.subplots(nrows=2,ncols=1,figsize=(15,10))\nplt.style.use('ggplot')\naxes = ax.flatten()\n\naxes[0].plot(list(range(len(rr_intervals))),list(rr_intervals),linewidth=0.5,c='blue',label='original')\naxes[0].scatter(x=list(anomalies['index']),y=anomalies['r_peak_mean'],c='red',marker='o',label='anomalies',linewidth=10)\naxes[0].set_title('Vanilla stats over RR-interval distribution')\naxes[0].set_xlim([0,6000])\naxes[0].legend()\n\naxes[1].plot(list(range(len(rr_intervals))),list(rr_intervals),linewidth=0.5,c='blue',label='original')\naxes[1].scatter(x=list(anomalies_rolling['index']),y=anomalies_rolling['r_peak_mean'],c='red',marker='o',label='anomalies',linewidth=10)\naxes[1].set_title('Rolling stats over RR-interval distribution')\naxes[1].set_xlim([0,6000])\naxes[1].legend()"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Evaluate RR distribution"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 115, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Vanilla:\n   label  index_x  index_y\n0     A        2      771\n1     N        8     5153\n\n\nRolling:\n   label  index_x  index_y\n0     A        4      771\n1     N       23     5153\n"
                }
            ], 
            "source": "classified_data = pd.merge(anomalies,ref_data,on='index')\nclassified_count = classified_data.groupby('label')['index'].count()\nclassified_count = classified_count.reset_index()\nclassified_count = pd.merge(classified_count,labels_count,on='label')\nprint ('Vanilla:\\n',classified_count)\n\nprint ('\\n')\nclassified_data = pd.merge(anomalies_rolling,ref_data,on='index')\nclassified_count = classified_data.groupby('label')['index'].count()\nclassified_count = classified_count.reset_index()\nclassified_count = pd.merge(classified_count,labels_count,on='label')\nprint ('Rolling:\\n',classified_count)"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "## Use spark ML for multiclass classification\n- Easy to use API\n- Define stages to transform your dataset\n- Select any estimator (model) for prediction \n- Each pipeline will model a single estimator onto your model\n\n\n<center><img src=\"https://image.slidesharecdn.com/mlwithapachespark-2-160818115257/95/introduction-to-ml-with-apache-spark-mllib-47-638.jpg?cb=1490306278\" width=\"500px\" height=\"500px\"></center>"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 116, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "#### Classification metrics ####\ndef calc_metrics(results,label_col,prediction_col):\n    metrics = {}\n    metrics['tp_0'] = results.filter((results[label_col]==0)&(results[prediction_col]==0)).count()\n    metrics['fn_0'] = results.filter((results[label_col]==0)&(results[prediction_col]==1)).count()\n    metrics['tn_0'] = results.filter((results[label_col]==1)&(results[prediction_col]==1)).count()\n    metrics['fp_0'] = results.filter((results[label_col]==1)&(results[prediction_col]==0)).count()\n    \n    metrics['tp_1'] = results.filter((results[label_col]==1)&(results[prediction_col]==1)).count()\n    metrics['fn_1'] = results.filter((results[label_col]==1)&(results[prediction_col]==0)).count()\n    metrics['tn_1'] = results.filter((results[label_col]==0)&(results[prediction_col]==0)).count()\n    metrics['fp_1'] = results.filter((results[label_col]==0)&(results[prediction_col]==1)).count()\n    \n    return metrics\n        \n### calc precision & recall ###\ndef precision_recall(results,label_col,prediction_col):\n    items = calc_metrics(results,label_col,prediction_col)\n    pre_0 = items['tp_0']/float((items['tp_0']+items['fp_0'])+1)\n    pre_1 = items['tp_1']/float((items['tp_1']+items['fp_1'])+1)\n    \n    recall_0 = items['tp_0']/float((items['tp_0']+items['fn_0'])+1)\n    recall_1 = items['tp_1']/float((items['tp_1']+items['fn_1'])+1)\n    \n    return {'pre_0':pre_0, 'recall_0':recall_0,'pre_1':pre_1,'recall_1':recall_1}"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 117, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "#### Classification metrics multi ####\ndef calc_metrics_multi(results,label_col,prediction_col):\n    metrics = {}\n    metrics['tp_0'] = results.filter((results[label_col]==0)&(results[prediction_col]==0)).count()\n    metrics['fn_0'] = results.filter((results[label_col]==0)&(results[prediction_col]!=0)).count()\n    metrics['tn_0'] = results.filter((results[label_col]!=0)&(results[prediction_col]!=0)).count()\n    metrics['fp_0'] = results.filter((results[label_col]!=0)&(results[prediction_col]==0)).count()\n    \n    metrics['tp_1'] = results.filter((results[label_col]==1)&(results[prediction_col]==1)).count()\n    metrics['fn_1'] = results.filter((results[label_col]==1)&(results[prediction_col]!=1)).count()\n    metrics['tn_1'] = results.filter((results[label_col]!=1)&(results[prediction_col]!=1)).count()\n    metrics['fp_1'] = results.filter((results[label_col]!=1)&(results[prediction_col]==1)).count()\n    \n    metrics['tp_2'] = results.filter((results[label_col]==2)&(results[prediction_col]==2)).count()\n    metrics['fn_2'] = results.filter((results[label_col]==2)&(results[prediction_col]!=2)).count()\n    metrics['tn_2'] = results.filter((results[label_col]!=2)&(results[prediction_col]!=2)).count()\n    metrics['fp_2'] = results.filter((results[label_col]!=2)&(results[prediction_col]==2)).count()\n    \n    metrics['tp_3'] = results.filter((results[label_col]==3)&(results[prediction_col]==3)).count()\n    metrics['fn_3'] = results.filter((results[label_col]==3)&(results[prediction_col]!=3)).count()\n    metrics['tn_3'] = results.filter((results[label_col]!=3)&(results[prediction_col]!=3)).count()\n    metrics['fp_3'] = results.filter((results[label_col]!=3)&(results[prediction_col]==3)).count()\n    \n    \n    return metrics\n        \n### calc precision & recall ###\ndef precision_recall_multi(results,label_col,prediction_col):\n    items = calc_metrics_multi(results,label_col,prediction_col)\n    pre_0 = items['tp_0']/float((items['tp_0']+items['fp_0'])+1)\n    pre_1 = items['tp_1']/float((items['tp_1']+items['fp_1'])+1)\n    pre_2 = items['tp_2']/float((items['tp_2']+items['fp_2'])+1)\n    pre_3 = items['tp_3']/float((items['tp_3']+items['fp_3'])+1)\n    \n    recall_0 = items['tp_0']/float((items['tp_0']+items['fn_0'])+1)\n    recall_1 = items['tp_1']/float((items['tp_1']+items['fn_1'])+1)\n    recall_2 = items['tp_2']/float((items['tp_2']+items['fn_2'])+1)\n    recall_3 = items['tp_3']/float((items['tp_3']+items['fn_3'])+1)\n    \n    return {'pre_0':pre_0,'recall_0':recall_0,\n            'pre_1':pre_1,'recall_1':recall_1,\n            'pre_2':pre_2,'recall_2':recall_2,\n            'pre_3':pre_3,'recall_3':recall_3}"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 118, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "### read and prep data ###\nimport pyspark.sql.functions as F \n\ndef make_float(row):\n    row = [float(x) for x in row]\n    return row\n\n#make dataframe\nr_peak_data = list(map(lambda x,l:(x['res']['heart_rate'],len(x['res']['heart_rate']),l),res,ref_data['label']))\nd = pd.DataFrame(r_peak_data,columns=['features','count','label'])\nd = d[d['count']>0]\nr_peak_records = d.to_records(index=False)\n\n#value imputation for time series\nrows = []\nhr_len = 35\nrows1 = list(filter(lambda x:len(x[0])>35,r_peak_data))\nrows1 = list(map(lambda x:make_float(x[0][:35])+[x[1]]+[x[2]],rows1))\nrows1 = list(map(lambda x:tuple(x),rows1))\n    \ncolumns = [str(i)+'_feature' for i in range(hr_len)]+['count','label']\ndf = spark.createDataFrame(rows1,schema=columns)\n\n#define transformers\nfrom pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler\n\nstringIndexer = StringIndexer(inputCol='label',outputCol='indexed')\nencoder = OneHotEncoder(inputCol='indexed',outputCol='encoded')\nassembler = VectorAssembler(inputCols=columns[:-2],outputCol='features')\n\n#apply transformers to your data\nindexed = stringIndexer.fit(df).transform(df)\nassembled = assembler.transform(indexed)\ndataset = assembled.select('features','label','count','indexed')\ndataset_copy = dataset"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 120, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "###### PCA Visualize data ######\nfrom pyspark.ml.feature import PCA\nfrom pyspark.ml.linalg import Vectors\nfrom pyspark.sql.functions import UserDefinedFunction\nfrom pyspark.ml.feature import VectorAssembler \n\npca = PCA(k=2, inputCol=\"features\", outputCol=\"pcaFeatures\")\nmodel = pca.fit(dataset)\n\npca_result = model.transform(dataset)\nresult = pca_result.select(\"pcaFeatures\")"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 122, 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "data": {
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bs543EVrUd3aq8fHsdXj0Bulg6KDiqbhiqZjCsbD8Hn9WuDIR0E0M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mAAAg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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fcbe07424a8>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "source": "import regex\nfrom pyspark.sql.types import StringType, FloatType\nimport seaborn as sns\nfrom matplotlib import pyplot\n\ndef g(point,co_ord):\n    if co_ord=='x':\n        value = regex.split('\\s+',point)[0][1:]\n    else:\n        value =  regex.split('\\s+',point)[1][:-1]\n    return value\n\nget_x_coord = UserDefinedFunction(lambda point:g(point,'x'), StringType())\nget_y_coord = UserDefinedFunction(lambda point:g(point,'y'), StringType())\nconvert_to_string = UserDefinedFunction(lambda point:str(point.toArray()),StringType())\n\npca_result = pca_result.withColumn('pcaFeatures_string',convert_to_string(pca_result['pcaFeatures']))\npca_result = pca_result.withColumn('x_coord',get_x_coord(pca_result['pcaFeatures_string']))\\\n                       .withColumn('y_coord',get_y_coord(pca_result['pcaFeatures_string']))\n                      \npca_result = pca_result.withColumn('x_coord',pca_result.x_coord.cast(FloatType()))\\\n                       .withColumn('y_coord',pca_result.y_coord.cast(FloatType()))\n    \nviz_df = pca_result.select('x_coord','y_coord','label').toPandas()\n\n\na = sns.lmplot(x='x_coord',y='y_coord',hue='label',data=viz_df,\n           fit_reg=False,palette={'A':'red','N':'green'},size=8,aspect=2)"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Evaluation\n\n- Classification models work towards making a Yes/No decision\n- Metrics to evaluate the model: Precision/Recall, AUC\n\n\n<center><img width=\"500px\" height=\"500px\" src=\"http://opexanalytics.com/cnt/uploads/2016/01/Red-Fish-High-Recall.jpg\"></center>"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 92, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "from mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nimport numpy as np"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 32, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "# from pyspark.mllib.feature import Normalizer\n\n# normalizer = Normalizer()\n# features = dataset_copy.rdd.map(lambda x:x.features.toArray())\n# labels = dataset_copy.rdd.map(lambda x:x.indexed)\n# dataset_copy = labels.zip(normalizer.transform(features))\n# dataset_copy = dataset_copy.toDF(schema=['label','features'])"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 173, 
            "metadata": {}, 
            "outputs": [], 
            "source": "# from pyspark.ml.linalg import DenseVector\n\n# #split data into test and train\n# dataset = dataset.filter(dataset.indexed.isin([0.0,2.0]))\n# dataset = dataset.withColumn('indexed',F.when(dataset.indexed==2.0,1.0).otherwise(0.0))\n# train_df, test_df = dataset.randomSplit([0.8,0.2])\n"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Decision Tree classifier"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 134, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Precision/Recall Tree classifier \n"
                }, 
                {
                    "data": {
                        "text/plain": "{'pre_0': 0.93026706231454,\n 'pre_1': 0.7448979591836735,\n 'recall_0': 0.9616564417177914,\n 'recall_1': 0.6083333333333333}"
                    }, 
                    "execution_count": 134, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# define tree model and get predictions\nfrom pyspark.ml.classification import DecisionTreeClassifier\n\ndt = DecisionTreeClassifier(labelCol='indexed',featuresCol='features',maxDepth=8)\ndt_model = dt.fit(train_df)\n\npredictions = dt_model.transform(test_df)\nprint ('Precision/Recall Tree classifier ')\nprecision_recall(predictions,'indexed','prediction')"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 135, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "image/png": 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Ozc3lmW3PGBdmiH02TfJ+xZB2ajWAI0dIHTeuT/YoOKO0ozjcId6Xn49lyxaU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\n7OgAAAAASUVORK5CYII=\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fcbe3287320>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "source": "### Visualize the results of tree classifier ###\npca_subset = pca_result.select('features','x_coord','y_coord')\nviz_df = pca_subset.join(predictions,on=['features']).select('x_coord','y_coord','prediction','indexed')\nviz_df = viz_df.dropna(how='any',subset=['x_coord','y_coord']).toPandas()\n\na = sns.lmplot(x='x_coord',y='y_coord',hue='prediction',col='indexed',data=viz_df,\n           fit_reg=False,palette={1:'red',0:'green'},size=5,aspect=1)"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Logistic regression classifier"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 136, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Precision/Recall LR classifier \n"
                }, 
                {
                    "data": {
                        "text/plain": "{'pre_0': 0.9004149377593361,\n 'pre_1': 0.9795918367346939,\n 'recall_0': 0.9984662576687117,\n 'recall_1': 0.4}"
                    }, 
                    "execution_count": 136, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# define LR model and get predictions\nfrom pyspark.ml.classification import LogisticRegression\n\nlr = LogisticRegression(labelCol='indexed',featuresCol='features',regParam=0.5,maxIter=80)\nlr_model = lr.fit(train_df)\n\npredictions_lr = lr_model.transform(test_df)\nprint ('Precision/Recall LR classifier ')\nprecision_recall(predictions_lr,'indexed','prediction')"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 137, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "image/png": 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n206SZk0j25aNX/OHW7V2EjykKCkE9ECHx62KlZAeirmVSUfHolrMFq1GbQbQ\nYZXMmFSPfHSE8bbx3FR4E/vc+2jyN1FeX96r1q6y71eMJGY2ICWFUYHwv9X+PrU5Ebcd9YZRu7Cp\napNZ4xDvqn2sbULxXHtkYUMkOwkchEgwqqJS21pLiPDqv8vnovRgqbm/ttJdiaZreHwevEEvjlQH\nNxfdzMPvPWzWMtitdspd5Ux3TCfHloPL5zLbo6qoKIqCzWJD0RVURQ1vIdJ1bKoNm2qjLdgWc7uS\nhoaKioYW9XhQD3dcMk6XjmRTbaQoKfh0XzjzgUKmLZNTM04l05YZtfqm6RrrK9aTkpJCIBBgWdEy\npjumm/3T3/j8jW77p7dv7brfvV9au4oRY0BObW4nEbcd9YaqqOYBmT0Va5uQUYdmiJW9kIUNkewk\ncBAiARkr8objrcfNVSqX14XL68If8qMoCgc9B9lZuxOrYg3f0GvQEmih0dfIb8t/y2eNn0Xd6Gto\n2BU7VsVKY6AxqgNSZmpmuOi63ecbdF3vEDSY76tHP25VrGSnZtPsbyaoh8+DcNgd2FQbjlQHiqLQ\n5G9i7SdrzcPrHHYHrYFWAm0BbIqNcld5dP/0QAu/3vtrKt2VFDgKAKh0V0al/Nu3dnX73NLaVYwY\ng5INSMBtR4Mt1jahyDq09llSY4tppbsSl9eFw+5AQZGCZpF0JHAQIkEYE0uFq4Kx6WPJteearf/G\npo8125y2BFpoC7ahoKCi0hJoYcuRLQBYFAsaGq3BVv5+7O/h8xu+OMTNoKCQl5HHseZj5g29ocHX\nYL5vLJ0FFLGeNyZ9DJOyJnGo6RC+oA+71c78ifMpzC3k1apXUVCoaa4JB0K+cFF3U1NT+CA5Swq6\nrtPkbzJbuIb0ECEtxOGmw2yv2c7mzzYD4LQ7o1L+RvBQerAUb9CL2+vGYXfIyp4YEYzVf9uRI7QY\nNQ6DoY8HvA0HRjARWTi9s24nADnHc1hXvs7MyjpSHVEd8IRIFhI4CJEg1u5Za042uq5zYf6FZNmy\norbwuL1umvxNQDhzoOs6TYEmsmxZNAW+eFzX8IV81LbWxqw5SFFT8If8WJTwydCxWq6GiN1VqbPH\n29PRqW2pNdu9plpTSbWmUphbiKqo5lkNLq8Lt9eNX/Oj6RoWxYJNtZFhy8Cm2MhMyWTOpDnsPbk3\n/HMr4Z/P7XNHdUOB6JR/yf4SqhqqaAm2mIWPsrInRoQvsgGO/Hza+rmLDxA7QACy77kH+5Yt6HY7\ntu3bgTiQ0p26AAAgAElEQVRqK/ox2OhL0XF/d2KLtR0pw5cBgKKEO+ZNc0yTDKhIShI4CJEg9tbt\nNf+sKApZtixWzV0FQFALsrNuJ28dfgtN16IOZQvpIRRdwaJY8Gt+8z06O3NBR+dk20kCWiCucxl6\nS0Mzty8FA+HTrnfU7iArNStqfAE9YBZMh/QQKDAhawJ1TXXs9+xnmmMao9NG0xZsM0+u9ga9pFpS\naQ22mgflFTgKzBuANZ+sQdd1nKlOvEEvadY0yl3xFVcLIToXqxUrgH3LFpSWFpSWcKCeUl4OxcVd\ndnfq62nSkWIVHRt1B0c+OsKE1Amd/tvv74LlWIXTOTk5bDm4JeoxIZKRBA5CJIiZY2d2mFiM7Uu/\n3vtralpqwqc/x6gxaAg0xH32QkALd1pRCR/wFusE6P6moxPUgmw9spV5E+cB4QPe6r316Hp4DIqi\nmKdfO9Oc1DXV4fF6eHbPs7QEWlCU8DjTrGkU5hYyNm0sfz38Vxr8DTQFmtheE17lXF+xHo/Pg8vn\nIteeS1pKGm3BNnbW7mRH7Q621243MzmxbiQiOzt1dbMhxEgUq/gaQLfbzaBB8XpRmpq67e7Un4Xc\nXbVHNZotQOyAoLcFy51lKoyaBmNLpqZr3HzWzXg8HjlzQSQ9CRyESBC3zLqlw8RSsr+EZz54hprW\nGnMrT6yuRt0FDRbFEpWpgHBGwKJY4np9f9DQCOkhMlIyWFa0jBc/eTF8GF0oRIgQKipW1cpF+Rfh\nCYY7Rrl0Fx6fh6AWJD0lHVVRKcot4qV/fIkbXr+BlmBLuD1sCEqrSvno5EekWlJx2B0A4SDE7sTt\nc6Ojc7TpKJ+6PyXXnsvrh16n9GCpuc/YCA7ivdkQYiTqrPja2J6keL14581Dz8yMel2soKA/C7l7\n2x61s9fG0j5QMLaQAh3qrCK3ZK6vWI/T4RzQMx2EGCwSOAiRIGJ16ahwVeD2us0tPyE9hE2xgYK5\n1SgyYxAre2BVrEzLmcaxlmM0+Buivhd5psJg8Pg8vFb1Gk3+JryB8OF2RtCi6Rpj08cy65RZ/PqT\nX9MSbMHtc5uH33lDXnLtuSyavMhs6xrZySmgBTjgOYDdamdyzmScdifLipYBsK58HS6vK1xsrSgc\nbzsOgC/kMw/FMwK1NZ+swePzMMY6BpB2iUJE6qoVa+RjaSUl2HbuNL8XKyjoz7au7dujLp66mHv/\n916qm6tJt6UTCoU6bc3c2QnM3QUK2anZUWOIvFa0v27srdvLVWOvinpMznQQyUgCByESWKGzMKpu\nAcBmsXHlaVey6eAmAnrHOgWrYkVHN2+q7VY7/zLzX3iv5j3+fODPUdkFqxK+BLTvrjRQdHSOtx3n\nzwf+jAVLdAtXBepa61hbvpbR6aNp9DbiDXrDXZ4UFRWVdGu6WavwjdO/wd6Te/H4PObvwOgotd+9\nn5mjZ3LtlGuxquGfcc0na8yOVEEtXBRut9qB6G0NHp+Hem89FouF7JRs2YssEtdQdDKK1YpV67h9\nMq7uTv3Y1rX9wovRoc5uteP2urEq1k5bM3d2AnP7G/uc1BzzfBhv0GvWWinKlwdaGtpnMWaOndnh\n/RP9TAfJiIhYJHAQIoG0v1BfO+VafvzOjwmGvryxz0rN4njbcUJEn9RsVazYrXby0sOtVr0hLxbF\nwlVfuYol05YQ1IL8z+H/CW8P0sLBg1FcPdhCeqjDdivjZOkWfwtZaVnhg+q+yKCEtBABArQEWthZ\nu5OdtTu5qfAm/v28f+fXe3/NQc/BqPfzh/x8Uv8J9/39Pp6+9GmWFixF0zVW715tZmrSrenmlqbI\nbQ3GY840J9+e9m3ZiywSVn8WFw/EOAa0u1M3KlwVKIQ7GPl1P1pIM7Or3d2gRzZZ8Pg85pkLOuGg\nwThXxh60M2vMrKiaKUP7LMYts26htqY26nPi3SIVr/6+0ZeMiIgl6QOHPXv2sHbtWnRd5/LLL2fR\nokVDPSQhei3yQr29djt/qPxD1A2xRbFw4bgL+ejkRx1u+K2qlUnZk7CpNj5v+jy8nUcJFx2risqn\nnk+ZmDkRl9dFTUvNkAUNXQlqQfIz8rljzh089rfH8Pg95qFzQS1Ia7AVJ04UFCrdlayau4qlBUv5\n5qvf5IPjH5i/K2OyLK8v7/AZdoudbFs2F+ZfSHZqdlQ9yfba7RxtOkpbsI2vOL7C4qmLZYVNJKy4\ni4sHODMxGKdVd6f9TXOBo8C82U2zphFSvrzWdXeD3j77COHzYhacvoDffPwbdD3cpMFhd0R1v4vU\nPosR6zrS2Rap3urvG/1Ez4iIoZHUgYOmafz3f/83P/nJT3A4HNx///3MmTOH8ePHD/XQhOiVyAuz\n2+vmZNtJFMLdhlRF5fSs05mTN4f3at+LqmdQUZmQOSFcdPzxi+b2nMj3NFa32gLhKd04iGioGKv+\nIS1EQAugKAqpllTaguHxXTz+Yo41H6M12Go+v8HXwKiUUUD4xGhjv/KSgiUcbDhIg68BDc2cpIty\ni8zPq3RX4rQ7za+zU7NZNXeVecNRXh8+7bXR34iqqHxQ8wH3/u+9PH3p04P1KxGiR+ItLh7ozMSg\nnFbdjfY3zTcV3sSyomVUuCo4//Tz8Xg8UafMd8XMPqZ+2WRhWdEyNF0LH76pKHhD4cMl+5Il6GyL\nVG/1941+f2dExPCQ1IHDgQMHGDduHKeccgoAF110ETt37pTAQSStyAu1cf6AcX6BRbEwa8ws9rv3\nk5Oaw8nWk3i18CFoqZZUbp1xK0sLlrK9djtVjVXmacu+kI/iymIWT10MwK8++hUN/oZBacMaqf3n\nWRRLOHhISccb8mK32PGGvDT4G/i/O/8vkzMnY7PYzAPcIFyvYRRLe7we80Zhv3s/EzInMH7UeI61\nHENVVK6YeAWPX/K4+drOJsHIG47allpUJdzdSVGUmBkLIRJFvMXFA50R6M8i595qf5NsZCQB8vPz\nqY5zu5SmazT6G83zYXJsORQ6Cyk9WMoBzwFz+5Mv6OP07NO7DEIisyBzJ81l/pj5A5rB7O8b/f7O\niIjhIakDB5fLRW5urvm10+nkwIEDQzgiIfom8kLd6G+k/GQ5x1qO0eRvIi0lzbyAe3we/Hq4aFpB\nIagH+ctnf+HmopvNm+V3jr1Da7AVu9UeVRBY7iqnNdiKx+uJuimHzg+N64qxb1hBwW6xmwe/RRZ1\n59pzsam2cG3GF52cxqSNwRsKFz/PyJ1BW6CNBn8DjlQHuq6z+/hu/CF/eKLVwaJaSLOmMSVnitkJ\nyfhdGROmqn6ZeWm/ktfZJBh5w5FmTTNP5obojIUQCSfO4uIBzwj0Y5Fzb/XXTbNxo2+32vEGvdhH\n2SmrKaPeW09QC9eajU0fS35Gvtnhrav3Mq69u+t345nqGdAagf6+0e/vjIgYHpI6cBBiuDEODzK2\nzhSNLkJXwlt0jAK9TFsmk7InUddaZ2430nSNupY6VpatpNBZyJNffZJV762KmkgrXBXh1TRfIx6v\nB5/mQ0VFVcMr7EZxcrx1DwoKVtXKKOsoAlqAUbZRZNuyaQu24Uh1cLT5KOnWdC4afxGPXfwYGz7d\nwKaqTdS01NAaaAUFvP5wi1V/yM+M0TPY59oHwOcNn9PoawQw9xOnW9O5fMLlQLh2wW61k5OaEw6w\n6suZ7pxOpi2TImdRzAmzs0kw8oZj/Kjx2HPCNwznTjiXVbM77l0WItn0KiMwFB2b+qA3N82xiokj\ni6oBs3sShLOkVsVqbl3qLttQerDUzFyMTRk74DUCcqMvBkNSBw5Op5OTJ0+aX7tcLpxOZxevCMvP\nzx/IYcUlEcYAiTEOGcOX8vPzeXH3i7z06UvmY3MmzGFv3V7z6wsnXciFXMjnb39OTXP4YLhUSyo+\n3cfu+t3srt9NTk4O559+Pv9z5H9oC7aRZk3j/NPP583jb7Lj+A78ut8MEDJtmQS0AKdmnUqOPYe9\ndXtpDbTGPKE6kkWxMHvcbPbU7sGv+Ql4A6RaUvnalK+Rk5rDzLEzuWXWLeaK3A8n/JAfzvshmq6x\nds9antvxHBbVgqIo1LbVMiYwhu9d8D02lG/gUOOhcG3HF98fZRvFf3ztP9B0jed3Pc+o1FG0Bdvw\nal62Ht1Keko6uWm5fO+873Hr2bfG9bs2xnHYd5hzTj2HbFs2Z+adGTVm0TOJ8O8oEcYAiTGOqDGs\nWGH+0RHPi198EV764jq0ezeOnBy4Nb5/W12OYwCtGL+i0+/FGkPktda4bs6dNJfd9bvN58wcO5Ot\nh7bSGmpFURRy03O5+6K7u73OvLj7RT5v/pzWUCutoVYsFgtz58xNvP9fjOAxiN5J6sBhypQp1NbW\ncuLECRwOB++88w7Lly/v9nXx7nUcKD3ZbzncxyFj6DiOsqoy88RiAEvAwg1TbzBXxeaPmQ+A60wX\nrx58lbrWOpr8TYRCIfwBPwoKZVVlFDgKCIVCaCGNkBIyiwNbfC1msbWu6viCPmwWG1as+EN+pjun\nc6jhEC3+FrOGIhZN16hyVeEL+QAI6AHcbW6qTlQxzTENj8dDdXV1zJvwq8ZehWeah6c/eNrsWrLv\nxD7+WvlXKo5XEAgFCGgBc1vTVzK/wvwx81n13ioCgQDZKdmEQiGONh5FURSa/c2EQiHKqsq4auxV\ncbUlLK4sNrcRACwrWsZVY68yWyYmwv8vkm1yTYTf11CPIVHG0dcxZJWVYfP7UV0u1IYGtJ/8hCaX\nK1xU3YPMQyL/Ltpfa8uqylh5wUo8Uz1RB8nNyJzBpqpN6OgsnLSQ+WPmd/szlVWVkWXNIpQawhv0\nMs05La7XDbRE/vsYinGInkvqwEFVVf7lX/6FRx99FF3XmTdvHqeeeupQD0uIPmm/V7cotyhm+vmG\n6TegKirrytfRGmyNahtonEsQ2UXI6Ciy2bqZlmALIUKgg6J+2SFkbMpYvEEv4zPGc6TpCF5fF4ED\nmnkCsyGgB6hqqMLj87C9Zjvba7dH9Thvf1pr6cHS8D5iqx2H3cG71e/i8rnM5+i6TlZqFt6Al5L9\nJebvRkfH4/Og6Zp5JoXH66HAUQDE15ZQWg0K0blAYSH2zZuxHD8OwSAWn4/M1avNeobhIFZdRKzt\nPjdMv4Ebpt/Qq/c2rsFLZy7tUSZTDl8TiSqpAweAWbNmsXr16qEehhD9pid7dTtrG2jUSeyo3WGe\ndLrfvZ8CRwF3zrqT1w69RpWnCkUJdzpytbnw+Dw0+BtIs6SRoqaYbVEjGZ2ROuvIpCoqmqaZn7nl\n8BbyM/LZXhsdRCyeupgNn24wT2826jfSU9Jx+VyoqopFs2C32JmQOcH8WVdesBKA0oOlHFWP4g16\n0fTwwU4plpQOv5fOvgZpNShEV9qWLCGttBT15EkUQLdaUbzeITmjYaAMZNegeA6A64ocviYSVdIH\nDkIMNz0pcDNvfpXw6rwv5KP0YCmarpkT18aDG3H73Li8LlbvXs2k7EksmrwITddYX7EeXddpDbTS\nGmxFRycQDJCWkka2LZuAN4Cmh2sdLIolfApryE9bsA2f5uswHl/IR11bHYqi4Av5sFvtQPhMCiOI\n2FG7g+2129nn2mcWdztSHVwz+RqCWpBn9zyLX/cTUAOkW9Ojflbjd1PhqsDtc3Oo4ZCZsTg181Qq\n3ZXRv5eI17YnrQaF6IKq0nbNNVirqlDrw9lM3W4fkjMaBspAFhPHcwBcVyQjKhKVBA5CDJH2qei7\nxt3V4/cwbnZLD5ZytOkoLq+Lo81HqWqoirrJbvA14PK6qPfW4w168fg83Dj9RqY7p1NeX87otNG4\nvC7aQm3ouo6qqEzJmUJWWxZHm48S0kKMto/GmRbeBvX20bc7bFMyfiYjGzFv4jxzsjNu7g3l9eXm\nydZOu5NpjmksLViKpmtYVStHfEcYbwufxxLr0CYjMMhJzaFerycnNQcFxQwQ4gkKpAOJGLY0DV58\nkayysj51RGpbsiTcXWnTJtB12hYuHJIzGkYiyYiKRCWBgxBDJDIVvb1mO3sb92INWuPaz9o+6Jia\nMzV8WFn4SAW8QS+lB0upcFXg8Xo40nSERn+4vamx5/b/Hfp/eHweVEWlLdhmZhaM9/+86XOcdifp\n1nTzXAaPz8OeE3to9Dd2ul1JRycjJYMnv/qkOcZGfyMV9V+umBU6CymrKTMDCqM2wbiZ7654zggE\nyuvLaQo0dWjDKkGB6DdJ1pYUwidF89JL2AKBvp0Uraq03XADbTf0bH+/6Fy8tQuSERWJSgIHIYZI\nZOrZ7XPzl0//Ql5aXqf7WSMnHONGXFEUdtTuYLpzOnar3TzQLaSHzCLlw42HaQo0oekaOjon206i\noJBtyzbf25Hq4PTs07Gn2sMF0Tp4/B50dNqCbbQF27CqVhr8Dei6br5XLAoKY9LHRN28t58sQ3qI\nspqyXv/uJDAQgyWtpIRR68IBfp9uwgfRQJ8U3a+SMDDri3hrF+QaJxJVl4HDs88+i6Io3b7J97//\n/X4bkBAjRWQq2hv0Mip1lPm9WPtZIycc41AhI3uQactk+dnLebXqVTMT4PGGT1c2Tme2KBaCetA8\n/TRvVB4N/gYAFEVh0eRFrLh8BdXV1WarUpfXZR5+FNAC7YcURUFBVVSybFlcM/maqO+1nwRXlq3s\n0PEpHl2t1kkXEjEQkuom/AuBwkLYvTv66wSVSIFZUAty7//eGz58M7eIxy95HKv65W1Sf1xjpHZB\nJLsuA4e8vDzzz01NTbz99tvMnj2b0aNHc/LkSd5//30uvfTSAR+kEMNRZCq60d/IwaaDZk/xWPtZ\nIycYu9Vu3tADFDnDLVuNloGRZxSkWdNoDjSb9QdZtiycdidZqVksK1oWMxVu/HnNJ2vCtQ8xOixB\nOFiwqTZS1BTGZYwjLz2PvFF57HPvo7iyuNOJtbf7d7tarZMuJGIgBAoLzRta4+tE17ZkCY6cHPyR\nNQ4JKpECs3v/9142VW0C4GDDQQCevvRp8/v9cY3p7NoXKygxPlMWQ0Qi6TJwuO6668w///SnP+VH\nP/oRhREXzX379vHyyy8P3OiEGMbab+V58/iblFWVdbqfNXLCcdgdFDoLo85IiBQZlNw4/UZ21e3i\n3ep3aQ22cmpm+KyT6Y7pcY3tZzt+RkALxMw45KTmYFEszJs4z6xpWFe+Dh2dNz57g40HN7Jo8qKY\nZzgY4+vJ/l0jeDLava75ZI35frKSJwaCcdMdtZUm0akq3HorjVddNdQj6VaPA7MB2tqk6RrvHHsH\nf8iPqqhYFEu4bixCf1xjOrv2xQpKAFkMEQkn7hqH/fv3M3Xq1KjHpkyZwv79+/t9UEKMNKqicuvZ\nt3LV2M4nerMg2FVOk7+JzJTMTlehVEU1z3Iory9HURSuOu2qcCFxSiZFuUVoutZhUloxfkWHz9xe\nu50th7fQ6G/Er/lRCXdDmpA5gSsmXmEWJauKak6kbq/b7ODU4Atvh2rfmrA3E6ARPLl9bvPAO+Nn\nkC4kYkB8ceBZom9PSlY9DcwGamtTyf4SWoOtaLoWbhShhg/fjNQf15jOrn3xBCWyGCISQdyBw+mn\nn84f/vAHrr/+emw2G36/n5KSEk477bQBHJ4QwmBMOJHbkHbW7QRir0IZK1hGG9Zcey5Ou5NlRctY\nWrCUlWUro55f4apA0zWKK4ujVsOe/OqT3PO3e/jr53/FGwofuHbG6DN4ecHLUft/4cuJ1dhGZbRg\n7a8JL3ILFYQzL8b7G4fDSRcSIZJIDwOzgdraVOGqMLOxbcE2JmRO4PFLHo96zuKpi9leu92sgVg8\ndXE/fHJYZ0GJLIaIRBN34PBv//Zv/OIXv2DZsmVkZGTQ3NzM5MmT+cEPfjCQ4xNCtBNvujzyDIXI\n/xqPx5qo1u5ZGzM1nmXLMidVgLNGn8WGTzd02HtrTKz1bfWE9BA5thzzvftD5GqdMU7j/aULiRDD\nX/TWJp13HI1sKFvZ5xoA43ponFS/rGhZh4WRDZ9uYJ9rH6qiss+1jw2fbui3a05X2zdlMUQkkrgC\nB13X0XWdVatW4Xa7cbvdOBwORo8ePdDjE2JEiadrR7zp8gJHAZs/24w36CWoBc3V/64OSXty75NR\n79E+yNDRcXvd/O3Y33jj8zdw2B1s/myzWcug6Rr7XPtwpjlRvAqONIdZ49CfpMe5ECNT5NamdxyN\n3DehAr1W6XMNQDzXlIGso+ps4aO/AhNjbjny0REmpE6QQmvRa3EFDoqi8MMf/pB169YxevRoCRiE\nGCDxdO3o6U2zTbWhEd63O9053Uyvx5qoZo6dyZaDW8yv2wcZpQdLcXvduLwuWoOttARa8Ia8Zi1D\nTmo4w6AQPhG6wFEwIFkAyS4IMUJFbG3aULYSvfbLlvF9uZGP55qSzHVUxtySkpJidu+Ta6jojbi3\nKp122mnU1NQwfvz4gRyPECNaPCta8d40V7orzbMS2rxt+EN+9rn2UbK/xCxkNk5srnRXUugs5Ptf\n/T5v7Hujwx5e4zMrXBV4fB4zcGgLtqEoipnNaH8oXDJNrEKI5NLVjXxk9nbupLnMHzO/zyvs/Z3p\nHMyzZ6TrnOgvcQcOM2bM4Gc/+xmXXnpph4zDvHnz+n1gQoxEPV3R6mriKXQWsr12Ox6fh0AoQMgS\nQkfn1apXzU5Hmz/bjI6OgsLGAxv586E/09jW2GEPr/E5le5KXF4XOak56OgEtACt/tbwdkZ0Fk5a\nGBWUaLrGyh7uP9Z0jRd3vxjVmlZS6mLEGGEnKfdFVzfykdnb3fW78Uz19HmFvb8znYN59kwyZ0tE\nYok7cKisrGTMmDFUVHSMUiVwEKJ/RE6E8dx4dzXxGK1UD3kOoSgK3qAXt9eNI9Vhvt4b9OINeQnp\nIYJakPeOvmcWQiso5qqUeT6DHs4oOO1OinKLKD9Zjkf14A16KXQWsrRgqTnGyO5PO2p3oOmaGVRE\n/jztgx9N1yg+UEwgEJDe5WLESaSTlBNdVzfyybDCPphjNOaWI74vaxzE0Lv//vvJy8tj+fLl7Nq1\niwcffJDXX3+9x++zcuVK8vLyuOOOOwZglNHiDhxWrlzZ/ZOEEH0SORG2v/GGjjfQXU08qqKSZcvi\n9OzTcfvceINeTs8+nasnXc36ivXAlydQ+0N+QnoIBYVGfyNurxun3WmuSlW4KsKF0V+8T01LDfVt\n9TT4G3CkOlDs4ROpIwOb9mPbVLUJj8/T4edpH/wYdRKdvY8Qw1kinaSczJJhhX0wx2jMLfn5+VRX\nVw/Y54jeO/fcc+MKGl555RX+9Kc/8dJLL5mPrVq1aiCHFiXuwAGgubmZ999/H5fLhdPpZPbs2WRk\nZAzU2IQY0eJZjepu4jG+b9Q6RJ7ibGQ1iiuL+eD4B0C4RiHVkkpOag7LipaZq1KFzkI2f7aZem89\n/pAfl9eF3WqPykB09tmG9vUPxs/T/ueSOgkxkvX4JOURqLMtmpGPFzgKuKnwJirdlWaNQ6KR7nDD\nSygUwmKxDPjn6LqOoijdP3GA9Ojk6J///OeMHz+e0aNH88EHH7B27Vruv/9+pk2bNpBjFGJEimc1\nqruJJ9b326f397n3UdVQRZO/CVVVSbOm8c8z/jnqOUumLWHjwY14fB40XUNHxxfykWXL6hBkdPbZ\nmq6ZmY7In6f9z7lw0kKcDmdUjYMQI0WnJylL7YOpsy2a7R9fVrSMVXNXJewqu3SHSw7z5s1j6dKl\nlJaWcvLkSa644goeeugh9uzZw7333su3v/1t1q1bx0UXXcRjjz3G1q1bWb16NceOHWPKlCk89NBD\nFBSEG5GUl5fz4x//mM8//5yvfvWrUQHAjh07uPfee3n77bcBqK2t5ac//Sm7du1C13UWLFjAt771\nLR566CFCoRBnn302KSkp7NixI2rLE0BJSQm/+c1vaGhoYPbs2Tz00EOMGTMGgOnTp/PQQw+xZs0a\n3G43CxYs4Cc/+Uncv4+4A4e1a9fyne98h4suush87N1332XNmjX8/Oc/j/sDhRDxiWc1qruJJ56J\nqchZxI7MHbi9bvy6n8vGX9bhs1RFZdHkRVTUV+AL+QhqQVRFxaJYOgQZnX12rBqHzn7OU8efylVj\nr+py3EIMS52cpCy1D1/qLBubSHUN7bMid427a8jGIvpu06ZNvPjii6SlpXHbbbfxy1/+krlz53Ly\n5EmamprYunUruq5TXl7OAw88wK9+9SvOOOMMSktLueOOO3jjjTcA+P73v88tt9zCjTfeyF//+lfu\nuecevvvd75qfYwQSmqZx2223ceGFF/LEE0+gKAoff/wxkydPZtWqVWzYsIHf//73McdaVlbGU089\nxdq1a5k8eTKPPfYYK1as4He/+535nG3btvHyyy/T1NTEtddey7x587j44ovj+l3EHTjU1NQwd+7c\nqMcuuOACXnjhhXjfQgjRA4O1GhV5495V20Kj2Pqtz99CIxwEzJs4L+6MQGc/j6y6CdG9XtU+9CRL\nkUQZjc6ysYXOQrbXbDdrsRr9jWi6NiRj7FC7lZMjiyFJ7KabbmLs2LEA3H777fz0pz9l7ty5qKrK\nnXfeSUpKChBe6V+6dCkzZ84EYNGiRTz//PN8+OGHAASDQW6++WYArrzyStasWRPz8z788ENOnDjB\nvffei/rFv8NzzjknrrG+9tprLF68mOnTpwOwYsUKzjvvPKqrq8nPzwfgtttuIyMjg4yMDM4//3wq\nKir6P3DIy8vj3XffjXrjsrIy8xcphEg88fQJN27cNV3jzeNvsuq9VTGfqyoqT371SUr2l1BeX05T\noIlMWyYl+0ukZaoQA6w3tQ89yVIkU0ajs2yssbix5fAW7FY7Fa4KSvaXsGL8ikEfY/tsx966vRI4\nJLHIe93x48dz/PhxAJxOpxk0AFRXV1NaWmqu7uu6TjAYNJ/f/p65s7PRamtryc/PN4OGnjh+/Dgz\nZswwv05PTycnJ4e6ujozcIg8ViEtLY3W1ta43z/uwOGWW27hP/7jP3j99dcZPXo0J06coKamhh/9\n6FAt7I0AACAASURBVEdxf5gQYnDF2ydc0zXu+ds9bDu2DZtiY3vt9qjnGgFIuaucJn8TNc01HGo8\nhMPuYGftzk7fVwjRPzqtfehCT7IUydTNqavsZZYti/yMfPOxodqu1D4rMnPszC6fP5iHwYmeq62t\nNf987Ngxs16gfZFyXl4et99+O7fddluH99i5cyd1dXVRj1VXVzNx4sQOzx03bhw1NTVomtYheOiu\nMHrMmDFRNT2tra14PB7y8vK6fF284g4cCgoKePbZZ/nggw9wu93Mnj2bc845R7oqCZHA4t3zW7K/\nhC2Ht9AaajU7JRnPNYKKLYe3ENJDtAXbsKpWQnoICHdUkpapQgywTmofutKTLMWAdnMaxG1QidKG\ntX1W5JZZt1BbU9vp8wfzMDjRc7///e+57LLLSE1N5Ve/+hVf//rXAcz50rBkyRLuvPNO5s6dy5ln\nnklrays7d+5kzpw5zJo1C6vVyvr16/nWt77Fli1b2Lt3LxdccEGHzzvzzDM55ZRTeOKJJ7jzzjux\nWCx8/PHHnHPOOeTm5lJbW0sgEIjKdhgWLFjAPffcw4IFC5g0aRJPPfUUZ511FuPGjeuX30WP2rFm\nZGRQVFRktmOVoEGIxBbvJFpeX05QC+INegHw+DwUOMJdIIygoiXYQiAUQFEUrHr40mE8X1qmCpF4\nepKl6E1GI16DuQ1qIFucmpnXiK2aRc6iLreARn7dlUQq7BYdLViwgFtvvZUTJ05wxRVXcMcdd/Dh\nhx92WP0/44wzeOSRR3j44Yc5fPgwqampzJ49mzlz5pCSksKzzz7Lj3/8Y5555hkuvfRS5s+P3SZY\nVVWef/55HnnkES677DJUVWXBggWcc845XHDBBUydOpWLL74YVVUpKyuLeu3cuXNZvnw5d955J42N\njZx99tk89dRT5vf72so17sDB7XbzzDPP8Omnn5KRkUFTUxPTpk1j+fLlOJ3OPg1CCDEw4p1EmwJN\nNAea0XUdHT2qoLDCVYHdaqcl2IKiKGi6RnZqNoqiMCl7EtdMvkZapgqRiFQ1HAB8sdpPSUnnq/1d\nZTT6mDEYzG1QA9lswcgKuLwu6r315Npz+22rZqJkSkRsM2fO5F//9V+jHjvvvPPYtm1bh+defPHF\nnRYaz5gxg1deeSXm99q/X15eHv/1X//V4XkpKSk8//zzUY+17256/fXXc/3118f8nIp2/x572hk1\n7sDhhRde4Ctf+Qr3338/drsdr9fLH/7wB1544QXuu+++Hn2oEGJwxDuJZtoySbWk0hZqQ9EVVFT2\nufYBX3Qqqd1OS6CFNr2N/FH5TMiYgKqqXD3patmLK0QC64/V/r6+x3A51M7IAhiZVuO//ZEdkMPg\nRLKIO3CorKxkxYoVWK3hl9jtdr797W9z++23D9jghEh2yVLwVuQswmax0RZqCx/upvloCjQBX3Yq\nqWmuYXTaaIJakM+aPsNpd7K+Yr20UxUigfXHan9f32Mgt0ENJiMrYGRg7Va7+XhfyXU0cQ3lKc2J\nKO7AYdSoURw9epTTTjvNfKy6upr09PSBGJcQw0KyFLwtmbaEjQc2sufkHnRdJyc1h0xbJtCxU0l1\nc/QJrJFF1MkQJAkxYmgaSmMjlupqdLsdzeHo1Wp/nzMGvSjsHiztr1uLpy5mw6cbYl7HjCxArBoH\nMXy99dZbQz2EhBJ34LBw4UIeeeQR5s2bxymnnMKJEyfYtm1bp3uohBDJUfBmTJwo4ExzkpWSBUCT\nv4mVZSspdBZS4CgwAx9jlc1grLYlS5AkxLCnafDiizjXrcN68CB6aiqK19vr1f7hkjGIpf11a3vt\ndnOb5vaa7Wyv3U6WLcsMIuSaJka6uAOHr33ta+Tl5fH3v/+dw4cP43A4+MEPfmCejieE6CgZCt6M\nidNoK+dIdZA3Ko+K+goURWFH7Q5uKryJZUXLqHBVmN2WKt2VUXtxkyFIEmIkSCspgZdeIuXzz1Fa\nWtBycwnl56NnZfWuDWoCZwz6qv11qry+3MwwuH1uthzeQn5GfsIshvRnZleyxKI3etSO9YwzzuCM\nM84YqLEIMez0teAtngt7Xy/+xsSpKAqj00czzTHN/NpQ6a5k1dxVUZ9ZXFnMxoMbKT1YytWTrjaz\nEjo6bq+bSnclxZXFMhkJMciMmgTdbkdpaUHxhot4B7woOVb3pQTXfnGnKLfIzDh4g96oDGsiLIb0\nZ2ZXssSiN+IOHJ544gm+8Y1vUBhx4amoqOAvf/kL99xzz4AMTohk19eCt8gLe6y0uaqofb74t584\nCxwF7KzbSXVzNXarHUeqo0OmpGR/Cat3r6beWw9AVUMVy89ezrKiZZQeLMXtdePxesxxyWQkxOAJ\nFBbC7t1oDgcAwdNPp23RogG/kY/VfYkVKwb0M/uq/eJOZI1Do78xKlhIhIxxf2Z2JUsseiPuwKG8\nvJwV7S4A06ZN4/HHH+/3QQkhwiIv5J2lzft68Y+cOOdOmvv/27vz6Kjq+//jr5sFQggwmYSEBEFK\nQiBUStCACoocqdaqvx60npguQopIqdW2WqpCBfSLspxWsS7YspTl6xJDLbW21dIiEqsBoQbEElGk\nSDAkmAxhTWDC3N8f+WbIhAmTZTJzZ+b5OMdzvDN3Zl65IZ8773s/ixxHHO61G+ob6jUsbZhcpss9\n3iEvK09ljjLVOevU4GqQy3Sptr5WHzs+1v+M/R+VOcpUe7q2w3kAdE5dXp4SbTadKSnp8pWam/M2\n+1KHBWi1aW8Xd5q2vd3NbY/mr79y8JW6PuX6Tt999Wf311DoSgvviouLtWDBApmmqW9/+9vnrTEh\nSY899piKi4vVo0cPLVq0yOPCf2e0uXCIjY1VfX29xyxK9fX1io6O9ksQAOdr3rC3dtvc2x2Dwj2F\nbe66FGVEKS8rT0WfFGlX1S59cPADSZI9rnFhx6pTVfrfsv+VdO6ORrY9W6/qVTW4GiTJY/pWTkZA\nkEVFSVOn6tgNNwT0Y/25XkMgV5tujT/vGJfWlKp2SG2n7776c70H1o4ITS6XS/Pnz9fq1auVkpKi\n2267TRMnTlRGRoZ7n82bN+vAgQPasGGDdu7cqXnz5qmoqMgvn9/mwmHkyJFatmyZpk+frvj4eJ06\ndUorV65UTk6OX4IAkcxluvT70t+rZF+Jx5f95g17a7fNm+/TNM5gU/kmxcXEaeuhrZJ8dxVqOsHF\nxsaq6niVpHOFgynTY98yR5nmXTFPf9r7J5V+WSpJHtO3+uNk1DSGgkF7QOjwNvtSYgffK5CrTXeV\nrugK5M/1Hlg7IjAOHD2gnZU7lWHP0PC+wzv9fh9++KEuvvhi9e/fX5J00003aePGjR6Fw8aNGzVp\n0iRJjd/fjx8/rurqaiUnJ3f689tcOEyePFnPPPOMpk6dqoSEBJ04cUI5OTm69957Ox0CiHRFnxTp\npU9fktPp9OiG1Lxhb+22efN9CvcUalP5Jp1sOKmTDSclte1k1XyfxO6JssXZNDRxqLLt2XKZLvcd\nB6mxYIkyojQpc5KOnjnqfny4ffh5eTpq9Y7VDNoDQo0fZ18Kh9WmufuK3V/u1vTXp6u+oV6GYejR\nCY/qxiE3duo9q6qqlJaW5t5OTU3Vrl27PPY5fPiw+vXr57FPVVVVYAuHhIQEzZo1S7W1te6qxWaz\neezz8ccfa9iwYZ0OBUSatlyZassX8qaxCScbTso0TdXW1+qTI5/4nN2o+QnOMAxNypjkUbBI0uv7\nXpcpUy7TJZfp6tLb3LuqPBtBxkkAkSUc1o5oOX7s+pTrg5wIgfbax6+pvqFxVjPTNFX0n6JOFw7B\n1q7pWCXJZrOdVzA0Wbhwodb8X59EAG2Xbc9WaU2px3ZHDE0cqjf2v6FoI1pOl1Mx0TE6cvqIz9mN\nmk5w5afLNaD7AI8iIMqIUpQR5R7wvLZsrbZVbXPP7jTvinl+70Y0InWE3vrsLfc2V+qACBMGa0c0\nv9iTnp6uioqKICdCoPWJ6+Ox3bt7706/Z2pqqse/paqqKqWkpHjsk5KSosrKSvd2ZWWlUlNTO/3Z\nUgcKhwtpWkAKQPvkZeXJZrN5jHHoKEOG4qIbB1H37NZThhrXY7jQVfumE1zTya3lGIPdNbvd+x6p\n7/pFkQpyClRbW8ugPQBAyJoycop2VO7QB4c+0MA+AzVz7MxOv+eIESN04MABffHFF+rbt6/++te/\n6sknn/TYZ+LEiXrxxRd14403aseOHerdu7dfuilJfi4cmi8YBaDtoowoTR01VTekdm4WlD1H9rgH\nNTvqHe5bpFL7rtq3XBtimP1cF8RALIrEoD0AQKjr2a2nlv2/ZTpz9oy6RXfzy3tGR0drzpw5mjp1\nqkzT1G233aaMjAwVFhbKMAzdfvvtuuaaa7R582Zdd9116tGjhxYuXOiXz5b8XDgAaF1nV3hui+Zj\nFRLjGhdua75gXFu1LAZ6deulKcOnnJvdqcZaiyIB6IQArZkQ6QJxDoA1+atoaDJ+/HiNHz/e47H8\nFtMVz50716+f2YTCAQiQzq7w3BbeBix35MTUcjaQ4fbhnVoUiRMm4Gd+/LJvhTUTIkEgzgFAV2tz\n4bB69WpNmDBBgwYNanUfxjgAreuKOb1b8lcXnwvNmNSRz+CECfiXP7/sh8OaCaEgEOcAoKu1uXBw\nuVx6/PHH1bt3b1199dW6+uqrlZSU5LHP2rVr/R4QCBehNKe3v8cYcMIE/MufX/bDYc2EUBBK5wCg\nNW0uHKZOnaqCggKVlpbqnXfe0R//+EcNGTJE48eP1+WXX664uDjfbwJEsK5c98DqOGEC/uXPL/vh\nsGZCKIjkcwDCR7vGOERFRemyyy7TZZddpvLycj399NNaunSpVqxYoXHjxikvL092u72rsgIhzaoz\nBTUff9C0SJG/xx9wwgT8y69f9sNgzYRQYNVzANAe7SocTp06pS1btuidd97R559/rssvv1x33nmn\nkpOT9Ze//EULFizQr3/9667KCqALNB9/UFpTqtohtX4/uXHCBPyML/sAgqDNhcMTTzyhnTt3Kjs7\nW9ddd51Gjx6t2NhY9/OTJ09WQUFBV2QE0IUYfwAAQOiYPXu23n77bSUlJen111/3us9jjz2m4uJi\n9ejRQ4sWLVK2n8YutblwGDJkiO68807ZbDavz0dFRWn58uV+CQUgcLLt2dp6aKuOnD6iM3VnlNEr\nQy7TxXSpAIKPNSaA89x6662644479MADD3h9fvPmzTpw4IA2bNignTt3at68eSoqKvLLZ7e5cPjW\nt77lc5/u3bt3Kow369at08aNG9WnTx9J0ne+8x3l5ORIktavX69NmzYpOjpaBQUFGjlypN8/Hwh3\neVl52lq5VW8deEs9u/dUmaNMRZ8U0bUIQNCxxgRC3oED0s6dUkaGNHy4X94yNzdXX3zxRavPb9y4\nUZMmTZIkjRw5UsePH1d1dbWSk5M7/dkhsQDczTffrJtvvtnjsYMHD6qkpERLlixRTU2N5s+fr6ef\nflqGYQQpJRCaoowo9e7WW+kJ6YqNjZXT6XR3V2LhNgDBxBoTCGm7d0vTp0v19ZJhSI8+Kt14Y5d/\n7OHDh9WvXz/3dmpqqqqqqvxSOITENwBvC8tt375dY8eOVXR0tFJSUpSWlqa9e/cGIR0Q+lpOj9q0\n3TRw+v3K97Vm9xoVfdK5W50u06XCPYWaVzJPhXsK5TJdnXo/AOGt5TSzrDGBkPLaa41FgySZpuSn\n7kLBFBJ3HN58800VFxcrIyNDkydPVnx8vBwOh7Kystz72O12ORyOIKYEQlfT9Kjlp8s1oPsAj+lT\nm2tt4HRb70ywgjTQDP33fWKNCYS0/+tm79a7d0A+NiUlRZWVle7tyspKpaam+uW9LVE4zJ8/X0eP\nHnVvm6YpwzCUn5+vb3zjG7rttttkGIYKCwu1du1azZgxI4hpgfATZUQpLytPGw5vUMm+EhV9UqS8\nrLw2L9zW1oKAGZyAc+i/3wZMO4tQNmWKtGOH9MEH0sCB0syZfntrb71xmkycOFEvvviibrzxRu3Y\nsUO9e/f2SzclySKFw5w5c9q038SJE7V48WJJjXcYqqur3c/V1NS0efG59PT09of0MytkkKyRgwzn\nBDPH70t/r6XblkpqXM/BZrPpZxN+JpvNpl1VuzQidYQKcgq83kko/7DcY3rm8tPlXn+WKwdfqdKa\nUo/t1n5mK/xOrJAhlFjheFkhg9TGHOXlUrO/m27l5Ur0Y/6QOhZkCBgr5LBChoDo2VNatkw6c0bq\n1s1vb/vzn/9cW7duVW1trSZMmKB7771XTqdThmHo9ttv1zXXXKPNmzfruuuuU48ePbRw4UK/fbYl\nCocLqa2tdU8Bu3XrVg0YMEBS44jyp59+WjfffLMcDocqKyuVmZnZpvesqKjosrxtkZ6eHvQMVslB\nBuvkKNlXIklyOp3u7RtSb3D/J0mVhyq9vnZA9wHu1zVte/tZrk+5XrVDat1dmq5Pud7rfsE+FlbK\nEEqscLyCnaE9OXoMGKCezf5uTg4YoDo/5Q+1Y0GGyMlhhQxNOQLGj0WD1Li2mi9z587162c2sXzh\n8MILL2j//v0yDEN9+/bV9OnTJUkXXXSRrrzySt13332KiYnRtGnTmFEJ6IRse7bH3YDWuiV503xM\nRNMYB29YQRo4h/77AEKN5QuHe+65p9XnbrnlFt1yyy0BTAOEr7ysPNlsNpXsK7ngl39vKAiADqD/\nPoAQY/nCAYD/tTYL0tRRU93dkgAAAJqjcABChD8XY2NaVAAA0F4UDkCI8OeX/fZOi8oK0gAAgMIB\nCBH+XAOhreszNOEOBQAAoHAALK7pav8nRz6Ro96hxO6JMgyjXbMetdTWWZCasHAbAACgcAAsrulq\nv6nGVSJtcTZNypjUrlmPWmrvLEjtvUMBAADCD4UDYHFNV/cNGbLH2TU0cWjAuwm19w4FAMATY8UQ\nDigcAIvz59X+jp64WKcBADqHsWIIBxQOgMX582p/Z05cXC0DgI5jrBjCAYUDYHH+vNrfmRMXV8sA\noOMYK4ZwQOEARJDOnLi4WgYAHcdYMYQDCgcggrR24mrqhlT+YbkGdB/gtRsSV8sAoOMYK4ZwQOEA\nRJDWTlxN3ZBiY2PldDolnd8NiatlAABENgoHAG3qhsTVMgAAIhtTogDw6HZkmqaOnTmmeSXzVLin\nUC7TFcRkAADAKrjjAMDd7aj8dLkqjlSozFEmQ4Z7TENeVh5TsQIAEOEoHAC4uyGlp6frrlfvkiHD\n/VyZo4ypWAEAAF2VAHhqOVtStj2bqVgBAAB3HAB48jZ7UtEnRUzFCgBAhKNwAOChqdtS09oOj255\nVEMTh+qO7Du058gepmIFACBCUTgA8KrluIYpw6fo0SsfDXIqAAAQLIxxAOAV4xoAAEBzFA5ABHCZ\nLhXuKWzX2gzeBkkDAIDIRVclIAJ0ZDpVb4OkAQBA5KJwACJAR7odNQ2S7qymQdYsHgcAQGijcAAi\nQLY9u03TqTZ1afLnl3wWjwMAIDxQOAARoK3djlbvWO33L/kMsgYAIDxQOAARoK3djnZV7fLY9seX\n/Lbe7QAAANZG4QDAbUTqCL312VvubX98yWeQNQAA4YHCAYBbQU6Bamtr/fol31+DrAEAQHBROABw\n40s+AABoDYUDECaY9hQAAHQlCgcgTDDtKYALcrnUo6hIsWVlcmZnqy4vT4ri4gKAtqNwAMIE054C\nuJAeRUXquabx4kK39xsvLtTlc3EBQNtxqQEIEy1nQGLaUwDNxZaVXXAbAHzhjgMQJpj2FMCFOLOz\n3XcamrYBoD0oHIAw0dkZkVymS78v/b1K9pUwuBoIQ3V5jRcTPMY4AEA7UDgAkNQ4uPqlT1+S0+lk\ncDUQjqKiVJefr7pg5wAQsricCEASg6sBAMCFUTgAkMTgagAAcGF0VQIgqXFwtc1m8xjj4A0LzQEA\nEJkoHABIahxcPXXUVN2QesMF92OhOQAAIhOFAxAh/HWngLEQAABEJgoHIEJ05E6Bt2Ij257tfr3E\nWAgAACIFhQMQITpyp8BbscFCcwAARCYKByBCdOROgbdio7MLzQEAgNBE4QBEiPbcKXCZLhXuKVTx\nwWJVnKyQrbtNiXGJdEsCACCCUTgAEaI9dwqKPinSb0p/o+q6ap01z6r2dK3Gpo+lWxIAABGMwgEI\nUV25nkKZo0z1DfUyDEMxRoziouPUu1tv1msAACCCUTgAIaor11MYmjhUZ82zOnP2jKKMKHWP7k43\nJQAAIpwlCoctW7Zo3bp1OnjwoBYuXKjBgwe7n1u/fr02bdqk6OhoFRQUaOTIkZKkffv2aenSpXI6\nnRo1apQKCgqClB4Ijq5eT6FnbE85XU5JopsSAACQJfodDBw4UDNnztTw4cM9Hj948KBKSkq0ZMkS\nzZo1SytWrJBpmpKkFStWaMaMGfrNb36jQ4cOaceOHcGIDgRNyzsA/rwjsOfIHtnj7BrcZ7AG9xms\nPt370E0JAIAIZ4k7Dunp6V4f3759u8aOHavo6GilpKQoLS1Ne/fuVd++fVVXV6fMzExJ0vjx47Vt\n2zbl5OQEMjYQVF25ngKLvAGIJF05ZgwIJ5YoHFrjcDiUlZXl3rbb7XI4HIqOjlZSUpL78aSkJDkc\njmBEBIKmK9dTYJE3AJGkK8eMAeEkYIXD/PnzdfToUfe2aZoyDEP5+fnKzc0NVAxJrd/hiLQMkjVy\nkOEcK+RoynB///stkSPSM4QSKxwvK2SQrJHDChkka+TwlaH8w3LFxsae2z5d7vfcVjgOkjVyWCED\nOiZghcOcOXPa/Rq73a7q6mr3dk1Njex2u+x2u2pqas57vK0qKirancWf0tPTg57BKjnIYK0cVshg\nlRxWyRBKrHC8gp3BKjmskMEqOdqSYUD3AXI6nR7b/sxtheNglRxWyNCUA+1n6Q58ubm5eu+999TQ\n0KDDhw+rsrJSmZmZstlsio+P1969e2WapoqLizV69OhgxwUAACEoLytPU4ZP0Zh+YzRl+BS6ZwKt\nsMQYh/fff1+rVq3SsWPHtGjRIg0aNEizZ8/WRRddpCuvvFL33XefYmJiNG3aNBmGIUm688479dxz\nz7mnY2VgNAAA6IiuHDMGhBNLFA5jxozRmDFjvD53yy236JZbbjnv8cGDB+uJJ57o6mhAxGO2EQAA\nIFmkcABgXcw2AgAAJIuPcQAQfF29QjUAAAgNFA5AgLhMlwr3FGpeyTwV7imUy3QFO1KbdOUK1QAA\nIHTQVQkIkFDt8sNicAAAQKJwAAImVLv8MNsIAACQ6KoEBAxdfgAAQCjjjgMQIHT5AQAAoYzCAQgQ\nuvwAAIBQRuEARLCWi7v9LO1nwY4EAAAsisIBiGAtZ3qy2Wy6IfWGIKcCAABWxOBoIIK1nNlpV9Wu\nICUBAABWR+EARLCWMzuNSB0RpCQAAMDq6KoERLCWMz0V5BSo8lBlkFMBAAAronAAIljLmZ6iDG5C\nAgAA7/iWAAAAAMAnCgcAAAAAPlE4AAAAAPCJwgEAAACATxQOAAAAAHyicAAAAADgE4UDAAAAAJ8o\nHAAAAAD4ROEAAAAAwCcKBwAAAAA+UTgAAAAA8InCAQAAAIBPFA4AAAAAfKJwAAAAAOAThQMAAAAA\nnygcAAAAAPhE4QAAAADAJwoHAAAAAD5ROAAAAADwicIBAAAAgE8UDgAAAAB8onAAAAAA4BOFAwAA\nAACfKBwAAAAA+EThAAAAAMAnCgcAAAAAPlE4AAAAAPCJwgEAAACATxQOAAAAAHyicAAAAADgE4UD\nAAAAAJ8oHAAAAAD4ROEAAAAAwCcKBwAAAAA+UTgAAAAA8Ckm2AEkacuWLVq3bp0OHjyohQsXavDg\nwZKkL7/8Uvfdd5/69+8vSRoyZIimTZsmSdq3b5+WLl0qp9OpUaNGqaCgIFjxAQAAgLBnicJh4MCB\nmjlzppYtW3bec/369dPixYvPe3zFihWaMWOGMjMztXDhQu3YsUM5OTmBiAsAAABEHEt0VUpPT1da\nWprX50zTPO+x2tpa1dXVKTMzU5I0fvx4bdu2rUszAgAAAJHMEnccLuTLL7/Ugw8+qPj4eN1+++0a\nNmyYHA6HkpKS3PskJSXJ4XAEMSUAAAAQ3gJWOMyfP19Hjx51b5umKcMwlJ+fr9zcXK+vSUxM1NKl\nS5WQkKB9+/bpV7/6lZYsWRKoyAAAAAD+T8AKhzlz5rT7NTExMUpISJAkDR48WP369VNFRYXsdrtq\namrc+9XU1Mhut7f5fdPT09udxd+skEGyRg4ynGOFHFbIIFkjhxUyhBIrHC8rZJCskcMKGSRr5CDD\nOVbIYYUM6BhLjHFozbFjx+RyuSRJVVVVqqysVGpqqmw2m+Lj47V3716Zpqni4mKNHj06yGkBAACA\n8GWY3kYfB9j777+vVatW6dixY+rZs6cGDRqk2bNna+vWrSoqKlJMTIwMw1BeXp4uvfRSSY3TsT73\n3HPu6Vh/8IMfBPmnAAAAAMKXJQoHAAAAANZm6a5KAAAAAKyBwgEAAACATxQOAAAAAHyy/AJw7bFl\nyxatW7dOBw8e1MKFCzV48GBJ0okTJ/TEE0/os88+04QJEzR16lT3a/bt26elS5e6B1kXFBRIkhoa\nGvTss89q37596tWrl+677z4lJyd3OIMkrV+/Xps2bVJ0dLQKCgo0cuTILsnQ0ueff67ly5fr9OnT\n6tu3r37yk58oLi6uQ5k6av/+/Vq+fLmcTqeio6M1bdo0ZWRkBDSDJD311FM6dOiQpMZ/FwkJCVq8\neHHAc7zxxhvasGGDoqKidOmll+p73/tewDOsW7dOGzduVJ8+fSRJ3/nOd5STkxPwHJL0+uuv64UX\nXtDKlSvdUzAHKsMrr7yi7du3yzAM9enTRz/+8Y9ls9kCmkGSXnjhBf373/9WTEyMUlNTdffddys+\nPj7gObyhbfXOCm2rZI32lbb1HNrWc6zQvlq5bQ1JZhj54osvzIqKCvORRx4xP/vsM/fj9fX1KqLk\nSAAACzhJREFU5scff2z+4x//MFeuXOnxmlmzZpmffvqpaZqmuWDBArO0tNQ0TdP8+9//bi5fvtw0\nTdN89913zSVLlnQqQ3l5ufmLX/zCbGhoMKuqqsx77rnHdLlcXZKhpYceesgsKyszTdM0N23aZBYW\nFnY4U0c99thj5o4dO0zTNM0PPvjAfOSRRwKeoaU1a9aYf/jDHwKe46OPPjLnz59vNjQ0mKZpmkeP\nHg14BtM0zaKiIvP1118/7/FA56iurjYfe+wx8+677zaPHz8e8Ax1dXXu///b3/5mLlu2LOAZTNM0\nd+7caZ49e9Y0TdN84YUXzBdffDEoObyhbfXOCm2raVqvfaVtpW1tYoX21cptaygKq65K6enpSktL\nO+/x7t27a+jQoYqJ8bzBUltbq7q6OmVmZkqSxo8fr23btkmStm3bpmuuuUaSdMUVV2jXrl2dyrB9\n+3aNHTtW0dHRSklJUVpamvbu3dslGVqqrKzUsGHDJEkjRozQ1q1bO5ypowzD0KlTpyRJJ0+eVGJi\nYsAztFRSUqKrrroq4Dk2bNigSZMmKTo6WpLUu3fvgGdoYnqZVC3QOdasWaM77rgjaBmarhBL0unT\np2UYRsAzSNLXvvY1RUU1NslDhgxxL3IZzL+RJrSt3lmhbZWs177SttK2NrFC+2rltjUUhVVXpfZy\nOBxKSkpybyclJcnhcJz3XFRUlHr27Om+/drRz8rKynJv2+12ORwORUdHd3mGiy66SNu3b1dubq5K\nSkrcfzQdydRRU6ZM0eOPP661a9dKkubPnx/wDM2VlZXJZrMpNTU14DkOHTqk3bt36+WXX1a3bt10\nxx13aPDgwUE5Fm+++aaKi4uVkZGhyZMnKz4+PqA5tm/frqSkJA0cONDj8UAfi8LCQm3evFk9e/bU\nvHnzgpKhuU2bNmncuHFBz9FRtK2B/Z1ZqX2lbW1E23qOldrXUG9brSDkCof58+fr6NGj7m3TNGUY\nhvLz85Wbm9tln9v86oEVMrR0oUw/+tGPtGrVKr366qu67LLLzrs66C8XyrBr1y4VFBRozJgx2rJl\ni55//nnNmTMn4Dmafj/vvvuuu/EIdIazZ8/q5MmTevzxx7V37149+eSTevbZZwOe4xvf+IZuu+02\nGYahwsJCrV27VjNmzAhohvXr1+vhhx/2+2e2J0Nubq7y8/OVn5+vP/3pT3rjjTeUl5cXlByS9Mc/\n/lHR0dHuK7aBYoV2zQoZWrJC2+orR6DaV9rWtuWIpLbVV45Ata9WblvDTcgVDv5sDO12u/sKkSTV\n1NTIbrd7PGe32+VyuVRXV+e+GtWRDHa7XdXV1ed9VkcztOQr0y9/+UtJjVdkSktLO5zpQi6U4dln\nn3Wv7n3FFVfot7/9bZdk8JVDklwul7Zu3eoeuNcVOS6U4R//+Icuv/xySVJmZqaioqJ0/PjxoByL\nJhMnTnQfj0AdiwMHDujw4cP6xS9+IdM05XA49OCDD2rBggUB/X00d9VVV2nRokXKy8sLyu/j7bff\nVmlpqebOnet+rCtydCRbe9C2BvbfTqDaV9rWtuVoLtzb1gvlaKkr21crt63hJqzGOLSXzWZTfHy8\n9u7dK9M0VVxcrNGjR0uScnNztXnzZkmN/TUvueSSTn1Wbm6u3nvvPTU0NOjw4cOqrKxUZmZmQDIc\nO3ZMUmOj/uqrr+q6667rcKaOstvt2r17tyRp165d7r7KgczQ5MMPP9RFF13k0RAEMsfo0aP10Ucf\nSZIqKirU0NCgXr16BfxY1NbWuv9/69atGjBggKTAHYuBAwdq+fLlevbZZ/Xcc8/Jbrdr8eLF6tOn\nT0CPRWVlpfv/t23bpvT09IAehyY7duzQn//8Zz3wwAOKjY11Px6Mv5HOom0N7O/MKu0rbWsj2tZz\nrNC+hlPbagWGeaF7tCHm/fff16pVq3Ts2DH17NlTgwYN0uzZsyVJP/7xj1VfX6+GhgbFx8fr4Ycf\nVv/+/bVv3z4999xz7im3mq7aOJ1OPfPMM9q/f7969eqln/70p0pJSelUhvXr1+utt95STEzMedN+\n+TNDS3/729/097//XYZhaMyYMfrud7/rfq69mTpqz549WrVqlVwul2JjYzVt2jR95StfCWiGJkuX\nLlVWVpa+/vWvezweqBwNDQ16/vnntX//fsXGxmry5MkaPnx4QDNIjVcp9+/fL8Mw1LdvX02fPt1j\nmrxA/k4k6Z577tGiRYs8pgwMRIYnnnhChw4dch+Hu+66yz24NJDH4Sc/+Yn7i47UOIhv2rRpAc/h\nDW2rd1ZoWyXrtK+0rY1oW8+xQvtq5bY1FIVV4QAAAACga0R0VyUAAAAAbUPhAAAAAMAnCgcAAAAA\nPlE4AAAAAPCJwgEAAACATxQOAAAAAHyicAAAAADgE4UDYEHr1q3TM888E+wYABBWaFuBzqFwACzK\nMIxgRwCAsEPbCnQchQMQZCzeDgD+R9sK+F9MsAMAgVJVVaVZs2Zp7ty5GjRokBwOhx544AHdf//9\nGj58uNfXnDhxQmvXrtXOnTvldDo1fPhwzZw5U5L0z3/+U3/+85918uRJDR06VHfddZcSExMlSXv2\n7NHq1atVWVmptLQ0FRQUKCsrS5L06KOPaujQofrPf/6j/fv369e//rUMw9DSpUv13//+V1lZWUpL\nSwvMQQGATqJtBSIHdxwQMVJTU/X9739fzzzzjM6cOaPnn39eEyZMaPXEJsm975IlS7RixQrddNNN\nkqSPPvpIL7/8su6//3797ne/U3Jysp566ilJjSfERYsW6aabbtLKlSt10003aeHChTpx4oT7fd95\n5x3NmDFDa9asUXJysp5++mllZGRo5cqVuvXWW7V58+auPRgA4Ce0rUDkoHBARLn22muVmpqq2bNn\nq7a2Vvn5+a3uW1tbq507d2r69OmKj49XVFSUsrOzJUn/+te/dO2112rQoEGKiYnRd7/7XX366aeq\nrq7WBx98oPT0dF111VWKiorSuHHj1L9/f/373/92v/c111yj/v37KyoqSkeOHNFnn32m22+/XTEx\nMcrOztZll13W5ccCAPyFthWIDBQOiDgTJ05UeXm5vvnNbyompvXeetXV1UpISFB8fPx5zx05ckR9\n+/Z1b8fFxSkhIUEOh0NHjhxRcnKyx/7JyclyOBwe283fKyEhQd26dfP6PACEAtpWIPxROCCi1NfX\na/Xq1br22mu1bt06nTx5stV9k5OTdeLECZ06deq85xITE/Xll196vO/x48dlt9vPe06SampqZLfb\nvX5OYmKiTpw4oTNnzrgfq66ubu+PBgBBQ9sKRAYKB0SUVatWKTMzUz/84Q81atQoLVu2rNV9bTab\ncnJytGLFCp08eVJnz55VWVmZJGncuHF6++239fnnn8vpdOrll19WVlaWkpOTdemll+rQoUN69913\n5XK59N577+ngwYOt3iJPTk5WRkaGioqK1NDQoI8//tjj1jsAWB1tKxAZoh955JFHgh0CCITt27fr\nzTff1EMPPaRu3brpq1/9qv7whz+oV69eGjhwoNfXjBo1Sh9++KFWr16t1157TUePHtXYsWOVmpqq\n+Ph4rVq1SuvXr1dcXJzuvvtu9ejRQ926dVN2drZeeeUVvfTSS6qoqNC9997rns1j8+bNuvjii/WV\nr3zF/TmXXHKJ3nzzTa1du1aHDx/WJZdcIqfTqTFjxgTk2ABAR9G2ApHDMJnoGAAAAIAPdFUCAAAA\n4BMLwCHiTZ48WYZhuLdN05RhGJo1a5aGDRsWxGQAELpoW4HwQ1clAAAAAD7RVQkAAACATxQOAAAA\nAHyicAAAAADgE4UDAAAAAJ8oHAAAAAD49P8Bz8nCxG8Bf3cAAAAASUVORK5CYII=\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fcc1cb0bc88>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "source": "### Visualize the results of tree classifier ###\npca_subset = pca_result.select('features','x_coord','y_coord')\nviz_df = pca_subset.join(predictions_lr,on=['features']).select('x_coord','y_coord','prediction','indexed')\nviz_df = viz_df.dropna(how='any',subset=['x_coord','y_coord']).toPandas()\n\na = sns.lmplot(x='x_coord',y='y_coord',hue='prediction',col='indexed',data=viz_df,\n           fit_reg=False,palette={1:'red',0:'green',2:'yellow',3:'blue'},size=5,aspect=1)"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### GBT"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 138, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'pre_0': 0.9352941176470588,\n 'pre_1': 0.8260869565217391,\n 'recall_0': 0.9754601226993865,\n 'recall_1': 0.6333333333333333}"
                    }, 
                    "execution_count": 138, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "from pyspark.ml import Pipeline\nfrom pyspark.ml.classification import GBTClassifier\nfrom pyspark.ml.feature import StringIndexer, VectorIndexer\nfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator\n\n\n# Train a GBT model.\ngbt = GBTClassifier(labelCol=\"indexed\", featuresCol=\"features\", maxIter=10)\ngbt_model = gbt.fit(train_df)\nresults_gbt = gbt_model.transform(test_df)\nprecision_recall(results_gbt,'indexed','prediction')"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 139, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "image/png": 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KirBu3oziDm+QqVutCc+ExPsOErn29OfARn8lWQvRGxI4CJFkPCUlbD/8Ltmv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\nSdu3b5ckbd++Xdddd50k6eqrr9bu3bu7FcOOHTs0ceJERUZGKjU1VQM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
                        "text/plain": "<matplotlib.figure.Figure at 0x7fcbe072bb00>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "source": "### Visualize the results of tree classifier ###\npca_subset = pca_result.select('features','x_coord','y_coord')\nviz_df = pca_subset.join(results_gbt,on=['features']).select('x_coord','y_coord','prediction','indexed')\nviz_df = viz_df.dropna(how='any',subset=['x_coord','y_coord']).toPandas()\n\na = sns.lmplot(x='x_coord',y='y_coord',hue='prediction',col='indexed',data=viz_df,\n           fit_reg=False,palette={1:'red',0:'green'},size=5,aspect=1)"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 171, 
            "metadata": {}, 
            "outputs": [], 
            "source": "# ### K-means clustering \n# from pyspark.ml.clustering import KMeans\n# kmeans = KMeans(k=4,maxIter=10,seed=1,tol=0.001)\n# model = kmeans.fit(dataset_copy.select('features'))\n\n# # Evaluate clustering by computing Within Set Sum of Squared Errors.\n# wssse = model.computeCost(dataset_copy)\n# print(\"Within Set Sum of Squared Errors = \" + str(wssse))\n\n# # Shows the result.\n# centers = model.clusterCenters()\n# result = model.transform(dataset_copy)\n\n# precision_recall(result,'indexed','prediction')"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 172, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Test Error = 0.10304\nPrecision recall:\n {'recall_1': 0.5379310344827586, 'pre_0': 0.9095816464237517, 'recall_3': 0.0, 'recall_2': 0.0, 'recall_0': 0.9810771470160117, 'pre_2': 0.0, 'pre_1': 0.8571428571428571, 'pre_3': 0.0}\n"
                }
            ], 
            "source": "### One v/s all - Logistic regression ###\nfrom pyspark.ml.classification import LogisticRegression, OneVsRest\nfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator\n\n# instantiate the base classifier.\nlr = LogisticRegression(maxIter=50, tol=1E-6, fitIntercept=True,\n                        featuresCol='features',labelCol='indexed')\n\n# instantiate the One Vs Rest Classifier.\novr = OneVsRest(classifier=lr, featuresCol='features',labelCol='indexed')\n\n# split dataset and train the multiclass model.\ntrain_df, test_df = dataset_copy.select(['features','indexed']).randomSplit([0.7,0.3])\novrModel = ovr.fit(train_df)\n\n# score the model on test data.\npredictions = ovrModel.transform(test_df)\n\n# obtain evaluator.\nevaluator = MulticlassClassificationEvaluator(metricName=\"f1\",labelCol='indexed')\n\n# compute the classification error on test data.\nf1 = evaluator.evaluate(predictions)\nprint(\"Test Error = %g\" % (1.0 - f1))\n\n\nprint ('Precision recall:\\n',precision_recall_multi(predictions,'indexed','prediction'))"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 132, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "image/png": 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FYGKfpmuc7DhJijmFTEsmXs0bLNXaTfCQpCTh031dnjcrZgJ6IOpWJh0dk2oy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\nwgEAAACATxQOAAAAAHz6/57HtflaKr28AAAAAElFTkSuQmCC\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fcce21acfd0>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "source": "### Visualize the results of tree classifier ###\npca_subset = pca_result.select('features','x_coord','y_coord')\nviz_df = pca_subset.join(predictions,on=['features']).select('x_coord','y_coord','prediction','indexed')\nviz_df = viz_df.dropna(how='any',subset=['x_coord','y_coord']).toPandas()\n\na = sns.lmplot(x='x_coord',y='y_coord',hue='prediction',col='indexed',data=viz_df,\n           fit_reg=False,palette={1:'red',0:'green',2:'orange',3:'blue'},size=5,aspect=1)"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "## 5. Deploy \n\n- Deploy your model with a few lines of code\n- Provides a link that can be embedded into applications (easy hand-off from data scientists to developers)\n\nIBM WML Reference: http://watson-ml-api.mybluemix.net/#/Online32Scoring\n"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 163, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "# The code was removed by DSX for sharing."
        }, 
        {
            "cell_type": "code", 
            "execution_count": 166, 
            "metadata": {}, 
            "outputs": [], 
            "source": "### Required imports ###\nimport urllib3, requests, json\n\n### Authenticate ML repo ###\nfrom repository.mlrepositoryclient import MLRepositoryClient\nfrom repository.mlrepositoryartifact import MLRepositoryArtifact\n\nml_repository_client = MLRepositoryClient(service_path)\nml_repository_client.authorize(username, password)\n\n### Save model ###\nmodel_artifact_gbt = MLRepositoryArtifact(gbt_model, training_data=train_df, name=\"GBT Classifer\")\nsaved_model = ml_repository_client.models.save(model_artifact_gbt)"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Get list of existing models, obtain scoring endpoint for the desired model ###"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 167, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "### get a list of existing deployments ###\nurl = service_path + \"/v2/online/deployments\"\nheader_online = {'Content-Type': 'application/json', 'Authorization': mltoken}\n\n# get scoring endpoint for this model\nresp = requests.get(url,headers=header_online)\nscoring_href = resp.json()['resources'][0]['entity']['scoringHref']"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 168, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "### example of getting prediction with online scoring ####\ntest_row = test.take(5)[3]\ntest_features = [test_row[col] for col in test.columns if col not in ['label']]\n\n# get prediction from online scoring endpoint (scoring_href from above)\npayload_scoring = {\"record\":test_features}\nresponse_scoring = requests.put(scoring_href, json=payload_scoring, headers=header_online)"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "#### prediction_endpoint = \"https://ibm-watson-ml.mybluemix.net/32768/v2/scoring/1751\""
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "## Iterate "
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {
                "collapsed": true
            }, 
            "source": "### 1. Deep Learning ###\n\n<img src=\"http://www.kdnuggets.com/wp-content/uploads/neural-networks-layers.jpg\"></src>"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Import Keras"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 141, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stderr", 
                    "text": "Using TensorFlow backend.\n"
                }
            ], 
            "source": "from __future__ import print_function\nimport numpy as np\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Activation, Dropout\nfrom keras.optimizers import SGD, RMSprop, Adam\nfrom keras.utils import np_utils\nfrom keras import regularizers"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 142, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "label_transformer = OneHotEncoder(inputCol='indexed',outputCol='label_index',dropLast=False)\ntrain_df = label_transformer.transform(train_df)\ntest_df = label_transformer.transform(test_df)"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 143, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "X_train = list(map(lambda x:x.features.toArray(),train_df.select('features').collect()))\nX_test= list(map(lambda x:x.features.toArray(),test_df.select('features').collect()))\n\nY_train = list(map(lambda x:x.label_index.toArray(),train_df.select('label_index').collect()))\nY_test = list(map(lambda x:x.label_index.toArray(),test_df.select('label_index').collect()))"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 169, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Train shapes\n"
                }, 
                {
                    "data": {
                        "text/plain": "((1868, 2), (1868, 35))"
                    }, 
                    "execution_count": 169, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "X_train = np.matrix(X_train)\nY_train = np.matrix(Y_train)\n\nprint (\"Train shapes\")\nY_train.shape, X_train.shape"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 170, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Test shapes\n"
                }, 
                {
                    "data": {
                        "text/plain": "((770, 35), (770, 2))"
                    }, 
                    "execution_count": 170, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "X_test = np.matrix(X_test)\nY_test = np.matrix(Y_test)\n\nprint (\"Test shapes\")\nX_test.shape, Y_test.shape"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "#### Build network"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 145, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "np.random.seed(1671) # for reproducibility\n\n# network and training\nNB_EPOCH = 200\nBATCH_SIZE = 128 #number of input instances trained before the network readjusts its weights\nVERBOSE = 1\nNB_CLASSES = 2 # number of output classes\nINPUT_LENGTH = 35\n\n#optimizer (choose between SGC(), RMSprop(), Adam())\nOPTIMIZER = Adam() # SGD optimizer, explained later in this chapter\nN_HIDDEN = 64\nVALIDATION_SPLIT=0.2 # how much TRAIN is reserved for VALIDATION\n\n#dropout(regularization)\nDROPOUT = 0.3"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 146, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "____________________________________________________________________________________________________\nLayer (type)                     Output Shape          Param #     Connected to                     \n====================================================================================================\ndense_1 (Dense)                  (None, 2)             72          dense_input_1[0][0]              \n____________________________________________________________________________________________________\nactivation_1 (Activation)        (None, 2)             0           dense_1[0][0]                    \n____________________________________________________________________________________________________\ndense_2 (Dense)                  (None, 100)           300         activation_1[0][0]               \n____________________________________________________________________________________________________\nactivation_2 (Activation)        (None, 100)           0           dense_2[0][0]                    \n____________________________________________________________________________________________________\ndense_3 (Dense)                  (None, 150)           15150       activation_2[0][0]               \n____________________________________________________________________________________________________\nactivation_3 (Activation)        (None, 150)           0           dense_3[0][0]                    \n____________________________________________________________________________________________________\ndropout_1 (Dropout)              (None, 150)           0           activation_3[0][0]               \n____________________________________________________________________________________________________\ndense_4 (Dense)                  (None, 100)           15100       dropout_1[0][0]                  \n____________________________________________________________________________________________________\nactivation_4 (Activation)        (None, 100)           0           dense_4[0][0]                    \n____________________________________________________________________________________________________\ndense_5 (Dense)                  (None, 2)             202         activation_4[0][0]               \n____________________________________________________________________________________________________\nactivation_5 (Activation)        (None, 2)             0           dense_5[0][0]                    \n====================================================================================================\nTotal params: 30,824\nTrainable params: 30,824\nNon-trainable params: 0\n____________________________________________________________________________________________________\n"
                }
            ], 
            "source": "model = Sequential()\n\n#input \nmodel.add(Dense(NB_CLASSES, input_shape=(INPUT_LENGTH,)))\nmodel.add(Activation('softmax'))\n#hidden layer 1\nmodel.add(Dense(100))\nmodel.add(Activation('softmax'))\n#hidden layer 2\nmodel.add(Dense(150))\nmodel.add(Activation('softmax'))\n#dropout layer (regularization)\nmodel.add(Dropout(DROPOUT))\n#hidden layer 3\nmodel.add(Dense(100))\nmodel.add(Activation('relu'))\n# output layer\nmodel.add(Dense(NB_CLASSES))\nmodel.add(Activation('softmax'))\n\nmodel.summary()"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 147, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "model.compile(loss='categorical_crossentropy', optimizer=OPTIMIZER, metrics=['precision','recall'])"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 148, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Train on 1494 samples, validate on 374 samples\nEpoch 1/200\n1494/1494 [==============================] - 0s - loss: 0.6615 - precision: 0.7651 - recall: 0.7651 - val_loss: 0.6263 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 2/200\n1494/1494 [==============================] - 0s - loss: 0.5943 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.5627 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 3/200\n1494/1494 [==============================] - 0s - loss: 0.5346 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.5184 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 4/200\n1494/1494 [==============================] - 0s - loss: 0.5001 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4996 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 5/200\n1494/1494 [==============================] - 0s - loss: 0.4868 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 6/200\n1494/1494 [==============================] - 0s - loss: 0.4846 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4982 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 7/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4982 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 8/200\n1494/1494 [==============================] - 0s - loss: 0.4851 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 9/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 10/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 11/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 12/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 13/200\n1494/1494 [==============================] - 0s - loss: 0.4850 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 14/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 15/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 16/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 17/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 18/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 19/200\n1494/1494 [==============================] - 0s - loss: 0.4850 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 20/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 21/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 22/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4980 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 23/200\n1494/1494 [==============================] - 0s - loss: 0.4862 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 24/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 25/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 26/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 27/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 28/200\n1494/1494 [==============================] - 0s - loss: 0.4852 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 29/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 30/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 31/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 32/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 33/200\n1494/1494 [==============================] - 0s - loss: 0.4861 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 34/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 35/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 36/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4981 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 37/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 38/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 39/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 40/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 41/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 42/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 43/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 44/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4981 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 45/200\n1494/1494 [==============================] - 0s - loss: 0.4850 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 46/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 47/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 48/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 49/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 50/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 51/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 52/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 53/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4980 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 54/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 55/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 56/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 57/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 58/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 59/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 60/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 61/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 62/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 63/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 64/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 65/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 66/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 67/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 68/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 69/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 70/200\n1494/1494 [==============================] - 0s - loss: 0.4849 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 71/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 72/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 73/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 74/200\n1494/1494 [==============================] - 0s - loss: 0.4865 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 75/200\n1494/1494 [==============================] - 0s - loss: 0.4864 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 76/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4981 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 77/200\n1494/1494 [==============================] - 0s - loss: 0.4850 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 78/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 79/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 80/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4983 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 81/200\n1494/1494 [==============================] - 0s - loss: 0.4869 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 82/200\n1494/1494 [==============================] - 0s - loss: 0.4851 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 83/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4981 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 84/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 85/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 86/200\n1494/1494 [==============================] - 0s - loss: 0.4852 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 87/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4984 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 88/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 89/200\n1494/1494 [==============================] - 0s - loss: 0.4862 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 90/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 91/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 92/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 93/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 94/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 95/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 96/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 97/200\n1494/1494 [==============================] - 0s - loss: 0.4861 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4980 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 98/200\n1494/1494 [==============================] - 0s - loss: 0.4850 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 99/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 100/200\n1494/1494 [==============================] - 0s - loss: 0.4852 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 101/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 102/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 103/200\n1494/1494 [==============================] - 0s - loss: 0.4852 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 104/200\n1494/1494 [==============================] - 0s - loss: 0.4861 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 105/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4980 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 106/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 107/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 108/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 109/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 110/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 111/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 112/200\n1494/1494 [==============================] - 0s - loss: 0.4866 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 113/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 114/200\n1494/1494 [==============================] - 0s - loss: 0.4848 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 115/200\n1494/1494 [==============================] - 0s - loss: 0.4861 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4983 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 116/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 117/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 118/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 119/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 120/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 121/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4980 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 122/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 123/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 124/200\n1494/1494 [==============================] - 0s - loss: 0.4862 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4981 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 125/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 126/200\n1494/1494 [==============================] - 0s - loss: 0.4863 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 127/200\n1494/1494 [==============================] - 0s - loss: 0.4864 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 128/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 129/200\n1494/1494 [==============================] - 0s - loss: 0.4850 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 130/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 131/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 132/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 133/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 134/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 135/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 136/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4983 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 137/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 138/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 139/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 140/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 141/200\n1494/1494 [==============================] - 0s - loss: 0.4862 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 142/200\n1494/1494 [==============================] - 0s - loss: 0.4850 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 143/200\n1494/1494 [==============================] - 0s - loss: 0.4866 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 144/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 145/200\n1494/1494 [==============================] - 0s - loss: 0.4848 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 146/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 147/200\n1494/1494 [==============================] - 0s - loss: 0.4867 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4984 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 148/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 149/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 150/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 151/200\n1494/1494 [==============================] - 0s - loss: 0.4861 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 152/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 153/200\n1494/1494 [==============================] - 0s - loss: 0.4863 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 154/200\n1494/1494 [==============================] - 0s - loss: 0.4862 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 155/200\n1494/1494 [==============================] - 0s - loss: 0.4850 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 156/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4981 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 157/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 158/200\n1494/1494 [==============================] - 0s - loss: 0.4852 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 159/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 160/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4980 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 161/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 162/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 163/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 164/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 165/200\n1494/1494 [==============================] - 0s - loss: 0.4853 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 166/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 167/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 168/200\n1494/1494 [==============================] - 0s - loss: 0.4850 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 169/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 170/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 171/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 172/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4981 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 173/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4980 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 174/200\n1494/1494 [==============================] - 0s - loss: 0.4863 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4974 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 175/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 176/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 177/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 178/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 179/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 180/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4981 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 181/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 182/200\n1494/1494 [==============================] - 0s - loss: 0.4854 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 183/200\n1494/1494 [==============================] - 0s - loss: 0.4856 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 184/200\n1494/1494 [==============================] - 0s - loss: 0.4862 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4980 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 185/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4978 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 186/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 187/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 188/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 189/200\n1494/1494 [==============================] - 0s - loss: 0.4851 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4980 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 190/200\n1494/1494 [==============================] - 0s - loss: 0.4851 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 191/200\n1494/1494 [==============================] - 0s - loss: 0.4861 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 192/200\n1494/1494 [==============================] - 0s - loss: 0.4858 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 193/200\n1494/1494 [==============================] - 0s - loss: 0.4857 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 194/200\n1494/1494 [==============================] - 0s - loss: 0.4860 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 195/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4975 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 196/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 197/200\n1494/1494 [==============================] - 0s - loss: 0.4855 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4979 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 198/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4976 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 199/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\nEpoch 200/200\n1494/1494 [==============================] - 0s - loss: 0.4859 - precision: 0.8106 - recall: 0.8106 - val_loss: 0.4977 - val_precision: 0.8021 - val_recall: 0.8021\n"
                }
            ], 
            "source": "history = model.fit(X_train, Y_train,\n    batch_size=BATCH_SIZE, nb_epoch=200,\n    verbose=VERBOSE, validation_split=VALIDATION_SPLIT)"
        }, 
        {
            "cell_type": "code", 
            "execution_count": 161, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "770/770 [==============================] - 0s     \n\n\nLoss: 0.434353736894\nPrecision: 0.845454545455\nRecall: 0.845454545455\n"
                }
            ], 
            "source": "score = model.evaluate(X_test, Y_test)\nprint (\"\\n\")\nprint (\"Loss:\",score[0])\nprint (\"Precision:\",score[1])\nprint (\"Recall:\",score[2])"
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "#### One v/s All Approaches\n- Bring in Additional data "
        }, 
        {
            "cell_type": "markdown", 
            "metadata": {}, 
            "source": "### Streaming data sources"
        }, 
        {
            "cell_type": "code", 
            "execution_count": null, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": "from pyspark.ml.classification import DecisionTreeClassifier, OneVsRest\nfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator\n\n# instantiate the base classifier.\ndt = DecisionTreeClassifier(labelCol='indexed',featuresCol='features',maxDepth=20,impurity='entropy')\n# instantiate the One Vs Rest Classifier.\novr = OneVsRest(classifier=dt, featuresCol='features',labelCol='indexed')\n\n# split dataset and train the multiclass model.\ntrain_df, test_df = dataset_copy.select(['features','indexed']).randomSplit([0.7,0.3])\novrModel = ovr.fit(train_df)\n\n# score the model on test data.\npredictions = ovrModel.transform(test_df)\n\n# obtain evaluator.\nevaluator = MulticlassClassificationEvaluator(metricName=\"f1\",labelCol='indexed')\n\n# compute the classification error on test data.\nf1 = evaluator.evaluate(predictions)\nprint(\"Test Error = %g\" % (1.0 - f1))\n\n\nprint ('Precision recall:\\n',precision_recall_multi(predictions,'indexed','prediction'))"
        }
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