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anshulkgupta93/association.ipynb

Created December 31, 2015 12:26
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Association analysis with R and Python
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association.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: Matrix\n",
"\n",
"Attaching package: ‘arules’\n",
"\n",
"The following objects are masked from ‘package:base’:\n",
"\n",
" abbreviate, %in%, write\n",
"\n",
"Loading required package: grid\n"
]
}
],
"source": [
"library(arules)\n",
"library(arulesViz)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append(\"/usr/local/lib/python2.7/dist-packages\")\n",
"import Orange\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"transactions in sparse format with\n",
" 9835 transactions (rows) and\n",
" 169 items (columns)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data(\"Groceries\")\n",
"Groceries"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data=Orange.data.Table(\"/home/anshul/Downloads/groceries\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" items \n",
"1 {citrus fruit, \n",
" semi-finished bread, \n",
" margarine, \n",
" ready soups} \n",
"2 {tropical fruit, \n",
" yogurt, \n",
" coffee} \n",
"3 {whole milk} \n",
"4 {pip fruit, \n",
" yogurt, \n",
" cream cheese , \n",
" meat spreads} \n",
"5 {other vegetables, \n",
" whole milk, \n",
" condensed milk, \n",
" long life bakery product}\n"
]
}
],
"source": [
"inspect(Groceries[1:5])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[[], {\"citrus fruit\":1.000, \"semi-finished bread\":1.000, \"margarine\":1.000, \"ready soups\":1.000},\n",
" [], {\"tropical fruit\":1.000, \"yogurt\":1.000, \"coffee\":1.000},\n",
" [], {\"whole milk\":1.000},\n",
" [], {\"pip fruit\":1.000, \"yogurt\":1.000, \"cream cheese\":1.000, \"meat spreads\":1.000},\n",
" [], {\"other vegetables\":1.000, \"whole milk\":1.000, \"condensed milk\":1.000, \"long life bakery product\":1.000}]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"itemFrequencyPlot(Groceries,topN=20,type=\"absolute\")\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 333 itemsets with min support of 0.01\n",
"Support Frequent_Itemset\n",
"(0.2555) whole milk\n",
"(0.1935) other vegetables\n",
"(0.1839) rolls/buns\n",
"(0.1744) soda\n",
"(0.1395) yogurt\n",
"(0.1105) bottled water\n",
"(0.1090) root vegetables\n",
"(0.1049) tropical fruit\n",
"(0.0985) shopping bags\n",
"(0.0940) sausage\n",
"(0.0890) pastry\n",
"(0.0828) citrus fruit\n",
"(0.0805) bottled beer\n",
"(0.0798) newspapers\n",
"(0.0777) canned beer\n",
"(0.0756) pip fruit\n",
"(0.0748) other vegetables whole milk\n",
"(0.0723) fruit/vegetable juice\n",
"(0.0717) whipped/sour cream\n",
"(0.0649) brown bread\n",
"(0.0634) domestic eggs\n",
"(0.0590) frankfurter\n",
"(0.0586) margarine\n",
"(0.0581) coffee\n",
"(0.0577) pork\n"
]
}
],
"source": [
"def frequent_itemsets(data, minsupport):\n",
" \n",
"\n",
" ind = Orange.associate.AssociationRulesSparseInducer(support=minsupport, storeExamples=True)\n",
" itemsets = ind.get_itemsets(data)\n",
" print 'Found {0} itemsets with min support of {1}'.format(len(itemsets), minsupport)\n",
" print \"Support Frequent_Itemset\"\n",
" itemsets = [{'itemset':itemset, 'tids':tids, 'support':len(tids)/float(len(data))} for itemset, tids in itemsets]\n",
" itemsets.sort(key=lambda x: x['support'], reverse=True)\n",
" \n",
" for itemset in itemsets[:25]:\n",
" print \"(%6.4f) %s\" % (itemset['support'], \" \".join(data.domain[item].name for item in itemset['itemset']))\n",
" \n",
"frequent_itemsets(data,0.01)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Parameter specification:\n",
" confidence minval smax arem aval originalSupport support minlen maxlen target\n",
" 0.8 0.1 1 none FALSE TRUE 0.001 1 10 rules\n",
" ext\n",
" FALSE\n",
"\n",
"Algorithmic control:\n",
" filter tree heap memopt load sort verbose\n",
" 0.1 TRUE TRUE FALSE TRUE 2 TRUE\n",
"\n",
"apriori - find association rules with the apriori algorithm\n",
"version 4.21 (2004.05.09) (c) 1996-2004 Christian Borgelt\n",
"set item appearances ...[0 item(s)] done [0.00s].\n",
"set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].\n",
"sorting and recoding items ... [157 item(s)] done [0.00s].\n",
"creating transaction tree ... done [0.00s].\n",
"checking subsets of size 1 2 3 4 5 6 done [0.01s].\n",
"writing ... [410 rule(s)] done [0.00s].\n",
"creating S4 object ... done [0.00s].\n"
]
}
],
"source": [
"rules.all=apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8))\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" lhs rhs support confidence lift \n",
"1 {liquor,red/blush wine} => {bottled beer} 0.001931876 0.9047619 11.235269\n",
"2 {curd,cereals} => {whole milk} 0.001016777 0.9090909 3.557863\n",
"3 {yogurt,cereals} => {whole milk} 0.001728521 0.8095238 3.168192\n",
"4 {butter,jam} => {whole milk} 0.001016777 0.8333333 3.261374\n",
"5 {soups,bottled beer} => {whole milk} 0.001118454 0.9166667 3.587512\n"
]
}
],
"source": [
"inspect(rules.all[1:5])\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 247 rules with minsupport 0.001 and minconfidence 0.3\n",
"Supp Conf Lift Rule\n",
" 0.0 0.8 3 cereals yogurt -> whole milk\n",
" 0.0 0.7 3 honey -> whole milk\n",
" 0.0 0.7 3 rice other vegetables -> whole milk\n",
" 0.0 0.6 3 cereals -> whole milk\n",
" 0.0 0.6 2 rice -> whole milk\n"
]
}
],
"source": [
"def association_rules(data, minsupport, minconfidence):\n",
" \n",
"\n",
" rules = Orange.associate.AssociationRulesSparseInducer(data, support=minsupport, confidence=minconfidence, max_item_sets=100000)\n",
" # sort by lift in descending order\n",
" rules.sort(lambda x,y: cmp(y.confidence, x.confidence))\n",
" print 'Found {0} rules with minsupport {1} and minconfidence {2}'.format(len(rules), minsupport, minconfidence)\n",
" print \"%4s %4s %5s %s\" % (\"Supp\", \"Conf\", \"Lift\", \"Rule\")\n",
" for r in rules[:5]:\n",
" print \"%4.1f %4.1f %5.0f %s\" % (r.support, r.confidence, r.lift, r)\n",
"association_rules(data,.001,0.3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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UstKfUHjgc0Xbv00YglbJHKRKYvqQUMAhfd0PaDwHaUbRNX8JrCMkXpW0KcxB+kZqf2EO0jOpbYuK2kDzc5BuJ5ScTrunKJ4ehJ6pHzdxnkqOael522sOUmuuXekxzbWpIfQ0XU7z/+5OIsyls9S3JEnSVm44YQjcg8BnCWW35xCSokLBgXSCNImQVFwCjC56DUzafI+QVFwCnEVYnDMmlM8u2A+4GzixlTHdT0iizkjOMYcwjKo4QVpJ6EH4LGG4Xgz8qAVtICQxmwgP3mcCNyZt0ovnlkqQrgReIczV6lbic/6MhkUaICSAdYTemOMIFfZWJPek4ELCvevdgmMqaZPWnglSVp/pk4Tv6bfUly9Pv3qlztWoSIMkSZK2XhOBxwiV25YBMwnD4grSCdLFlF8HqZDsRMD/Rxgmt5ZQBvxrNPzrfHPrIDUX00jCIqFrkut8nlBRrzhBmkQYXrWckKx8m8brIDXVBkLxge8QynGvJgwtK54PVSpBOg54nVDiu1+Jz3gcoYejuNfiVMKcq0Ip9P2L9l9O47WVmjum0jYF7ZkgVXLtzfGZvkD5f6vF63ZdS+g5lCRJkrqkQvJzUBvbbE6FhWL3ba6hNisXipUkSZI6gNWEeUqnZx3IVu5wwjDL+9IbTZAkSZKkLe9Swjyq7lkHshX7IvATiirqmSBJkiRJW94rwPXAuVkHspX6CLALDdfykiRJkiRJkiRJkiRJkiRJkiRJUufyLvD1DK7zAfVrPwHsCTwDbCDMWTkZ+HQF5/0h8OXk976ERWfnE9ZveopQoKFSP6P82kR7AX8lrBm1APguDesMVBPWaZqXXPtJYEoLrt3W+FrSBkLp7W8ADxNKoRfW0qpKtankfp5A6TWQLk/27wU8hDUZJEmS1ElklSDdQXi4LvhF0uZYoAehPPctzZxzD8JCsYUKddcRFs6dCnwGmE54WD+2gvhGEJKfUsnFXsD7wAzgPOC3yXn/LdXmYqAWuAQ4K4k/pvGCt63VVHwtaVMwjfpE5kzCZ8oTEr+CSu7nBYSFjr9R9BqfanM9cE4FMUmSJEmZyypBKvY/wN2p95UkSL8Dvp/8PoDw8P6V1P4coUfnmibOcQTwALApOb5UcvEn4H4a9q78HLgx9f7t5DMURMBjhF6ntqgkvkrapNUQevD+WLT914Seoioqv5//DdzbzPXGEXqoovRGu5QkSZLU3qoIPRfzgBXA34B9i/b/B/ACYRjVI8DxFZyzuWMWEXoULgaWE3ouDqPxA3VTllI/xG4WYa2co5JzvEvo1ZicvO9f4vjehJ6avyTvtyX08NyTapMn9PzUNBHHcuBW4FvJ78V6EhaavQyooz5J+gZwavJ7lFxjSeq4wufo0cS1K9FcfJW2SdsJ6AfMLtr+D2AbwhDHSu/n7oRePAjDDEv5B2G43pEVxCZJkiS12mXAasJQr9MJf8lfDeyc7P81oVfhh4T5PNcTHtwnp85R3LNTyTGLgLsIawz9HBgI7EfoATqxTKzF10knSEMIw7keJiRbwwkP/Hcn79M9NwXHJeeMSuzrQRh+dx6wHjilTEzFXqNx78sehM9/BuFBfwPwFvCdorguTeI5nJB8nA5sJCRx7aVUfK1p04OwPlHPou2XEj7fNiXal7ufrwD3EZL0PCFZuoDGHUTXAr9sJi5JkiSp1XYn9GicmdrWh5AgfRUYSkh0Lkztjwi9NU+ltqUTl0qPWUQYUrZtC+JtKkGClg+x+zlhHlMp/059sYD/pnQSVUqp5OLw5Dxrkn2TgP8kzDeammpXDcylYaGC31R43Uq1V4JUyjmEz3RpiX3l7mcVIQl8H/gXwtyk3yXtvlV0jn8lFK6QJEmSNoszCH+x7160fQDQi/AgHwM7Fu3/bHJcYZhUOnGp9JhFhAfhlmjvBOkOQm9XKUMJ817+g9Dj8bMKYyyVXBTuSXHi8P8IyWg1IWG4gfAZ/5kwlOw7wDrgm2WulUuOLbxK9ZJVEl9r2qQNJcQeA1cB3cq0KXU/awg9hrsWtf8TYXhm+jOdRCj48H/JqnOQJEmS1J52Icw32VC0fQWht2MI4aH3vaL97xAeUouTIFp4zIJWRd1+BhM+aylvE4Yb/pCQyHyV0g/+lVia/JxRtP1uQiK6G2H+1amERPL3hCFnPwB+RCgi0a/EeT9J6K0rvH7ayvja4jTgeeAAQln1s5NYipW7nxsJSWzxv4XbCXPEdkttW0YY0te7sMEESZIkSe1pMaF4QfGD/0GEstSFpGb7ov07JD+X0FhLjqltYbztLS56fxLwOI17Yl4l9HS0tljC4uRn8Xydwn1fRX0i8ERRm8cJPXxDSpx3LrB/6lVqaNvmdCqhbPfNwD40rrZXyf0cQugtK851Ct/NyqYCMEGSJElSeyo8vJ6c2tabUDxhImHR1VoazlGKkvfPEoZ/FWvNMVlZQhhOWPAacCBhzlDaEYQejlWtvM4iwtyZ4mILpwILCb1tLybbxhW1OZLQy/I6ja0Cnk693m5lfK1RQ5gf9Ufg84Rhc8UquZ97EHqWJhS1OQl4k4YJ9baEfz+rCxvKlbyTJEmSWuMFQuW3PxKqvi0gzH+BUPr6bcI8oR8THk6fJzy4jqdhRbq01hxTsB+hF+S/aX79okrkgb0Jpb//QeOhXy8TesoKXiAkGtcT5uC8SxjGdjah+lrBhYQE8kRSD+tNiAn34wZCUYzZSUyfSV4xoefoDsLcm30IycWhhO9jGiFJylr6cx9MGKK4kYYFOQp+R2X38wFCgnQVYYHcdwjJ0hk0TNwh9LK9TOOeP0mSJKnd1BAeTF8n/EX/PsIQu4IqQrGAFwnJwFxCeey04uIJlRyziLAOUFpz6yC1tEjDcanPVWoOz3GE3pt0hbodCMUdliaxPwZMKWpzOeXXVmqqwMEUwppQqwi9d8UJwDaEOTqvEIoRPENY26k9R5K1pUhD+nN/gYbV9opfhSGVldzP7sBPCAnVSuAh4JgS17fMtyRJkrQZ9SYkK/s211CZqyL0LjUYgugcJEmSJKn9rCb0bpyedSBq1uGE3qX7Mo5DkiRJ6tJGEobhFa8FpY7lOsJCtJIkSZI2sx8CX846CJX1EcK8JEfUSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSVKHcyFwO9Ajef+d5L3UpeSyDkCSJEmSOorqrAOQJElSp/Rb4I9ZByG1NxMkSZIktcbyrAOQNgcTJEmSJLXGN4HDgeNT27YH/gn4COE58yXgJuAo4FDg7KTd94D9gNOKzlkF3ALcB/witb1Xcuw+wA7A28DTwLXAxlS7rwMHA2cC5wHjgT8Dd7T2Q2rr4xwkSZIktYfdgUuBI4APgBeBvYBLgJFtOO8A4NfAsUAdMAfoBpySXG+bEsecDkwG1gKr2nBtbYXsQZIkSVJbRYQemz6Enp/7ku09ge8Seos+aOW5PwMMJvQE3QTEyfU+B5wEnEjoSSroTUievknowZJaxB4kSZIktdWuhCRoLvXJEcA64PdtOG81cAzwFvXJEcnPqwlJ16SiY6Jkn8mRWsUESZIkSW21U/Lz8RL75tP63qPBhHlJz1CfHBVsJCRB/Wg8zO7FVl5PMkGSJElSmw1KfparbLeswvNERe+3TX6WS7BWJD+3K9r+XoXXkxoxQZIkSVJbfZj8HFhm/4AKz9On6H0hsepfpn1h+4qi7XUVXk9qxARJkiRJbbU4+XlgiX07UzpxqqZxj9GIovdLCMnOfiXa1hDKia/CSnVqRyZIkiRJaqt5hEIKowllvgt6AF8q0X41IUEaldrWi7B+UVotcBehCMSJqe0RYV2kgcCdbQlcKmaZb0mSJLVVHvgDoaT3RcAJwPvA3kB3Qk9QTar9w8C4pP29wCbCAq/vJ6+064ADgHMJC9MuAoYTkqZCdTup3diDJEmSpPbwFPAN4DFC9bm9CFXm/o3GRRoeAX4FvAt8EjiMUCJ8GqHXKG058FVgJiHJGksYdnczcAFhMVhJkiRJ6jQuAa7KOgipEvYgSZIkSVLCBEmSJEmSEiZIkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpE5s5c2bfmTNn9s06DqlSuawDkCRJUtcVRdEJ1dXVo7OOQ6qUCZIkSZI2i9tuu22bKIqGr1+//tWsY5EqZYIkSZKkzaKqqmpkHMfrnnjiiQVZxyJVygRJkiRJm0UURftEUfTitGnT8lnHIlXKBEmSJEntbvr06T1zudzwKIpezDoWqSWqsw5AkiRJXU/fvn1HxnG8fs6cOQ6vU6diD5IkSZLaXRzHewHzHF6nzsYESZIkSe3qiiuu6AGMiOP4haxjkVrKBEmSJEntaocddhgZRdGm1atXz886FqmlTJAkSZLU3vbO5/PzpkyZUpd1IFJLmSBJkiSp3UyfPr0ml8vtXlVV9XLWsUitYYIkSZKkdtOrV689gbqamprXso5Fag0TJEmSJLWbKIr2zufzr4wbN64261ik1jBBkiRJUrt4/PHHu+Vyud2rq6tfyjoWqbVMkCRJktQulixZsgfAG2+84fA6dVomSJIkSWoXURTtA8z74he/uCnrWKTWMkGSJElSm1122WXdoijaA7B6nTq16qwDkCRJUuc3bNiw3evq6qLVq1e/mnUsUlvYgyRJkqQ2i+N4nyiKXpkyZcrGrGOR2sIESZIkSW1y7733VkdRtEc+n3d4nTo9EyRJkiS1ydq1a4fn8/nqKIpeyToWqa2cgyRJkqQ2yeVy+0RR9MrEiRM3ZB2L1Fb2IEmSJKnVpk+fXhVF0ci6ujoXh1WXYIIkSZKkVuvbt+9uURQ5vE5dhgmSJEmSWi2fz+8bx/GrkyZNcnidugQTJEmSJLXK1KlTc7lcbmQcx1avU5dhgiRJkqRWOeSQQ3aL47gmjuN5WccitRcTJEmSJLVKLpfbF3jN4XXqSizzLUmSpIrceeedJ0RRtLGqqurF6urqd9avX79XFEV3Zh2X1J5MkCRJklSROI6HRlF0cm1tbXU+n58TRdFOGzZs+Gscx1EURXHW8UntIco6AEmSJHUOM2fOPDGKos8Ar0dRdBAwMI7jVXEcz8jlci9FUXTf+PHj12Qdp9QW9iBJkiSpIlVVVcvz+XzPOI43Ef7Q/jiwPJfLfQLoWVdX9wxggqROzSINkiRJqkg+n/8gl8t1B/pFUVQVRdFSYGdgTi6Xu37SpEmLMg5RajMTJEmSJFUkn8+vz+fzVVEUbRfH8QfAEGDexo0brzvmmGNezTo+qT2YIEmSJKkiURTVAVVRFA2O43gjsFsURX894YQT3sw6Nqm9OAdJkiRJFcnlchHQPY7j6lwuNziXy/34mGOOeSHruKT2ZA+SJEmSKpLP57eL47ia8Ef26x566KG7s45Jam8mSJIkSarUDkCvKIq61dbW3jNt2rR81gFJ7c0ESZIkSZXaM4qibeI4/tNxxx33RtbBSJuDc5AkSZK2Dt2AjwJ7ACOBEUB3oD9hTSOA1cBG4C3g1eT1DLAMiKIo2iuO4w11dXWztnDs0hZjgiRJktR17QqcCnwSOALoXZOr2rBdjx7LBvXYZlmPXNXGbbpVb4iIYoB1tbX9NsVx9bIN6z6+dP3abVdt2tQXyANP9+zZ86GHH35474MPPvjm448/3t4jdVlR800kSZLUifQEzjWRx4QAACAASURBVAD+CTi8f033D/bsN+CNA7YdPP9TO+66cJ8B266s9ETL1q+vuWvxgiFzliwe9vLqD0YsXPnh0FxV1eLa2torgMsBy3uryzFBkiRJ6hr6Al8GLqjJ5QaMGjj4hVOHjXjqpF1GvNVeF3hp5Yq+V7zy3Kh/vPvOx1ZsXN8X+DPwQ2B+e11DypoJkiRJUud3FvDLnlXVvY7acZdHLtzngMeG9Oq9fnNe8Pfznt3z+jfnHf722jWDgV8B3yfMYZI6NRMkSZKkzmsY8PuqKDfumKG7PHjxR0c/3L97j01bMoA/vfrCHpfNe27CBxvXrwG+CtyyJa8vtTcTJEmSpM7pNOAPu/Xpt+wH+4+546DtdliRVSCrN22o/s5Tcw6d/faCI/Jx/AfgAmBdVvFIbWGCJEmS1LlUA7+OiL503E673fvzg494IOuACm5b+NpO339m7mmrNm1aCJwIvJ51TFJLuVCsJElS59ETuLlHVdU5F+8/+sqOlBwBnLDLiEU3jjvusmF9+vYCHgQ+lkEYlwBXldn3ZeCzye/XAj+o8JwXArcDPdoWWpPOT67RazNeo5zvJNcWJkiSJEmdRW9gdr9uNYf/bswn//Tp3facn204pQ3r3W/tzeOOv+ajAwe9D9wHHJZxSAURMBqYk3Ug6thMkCRJkjq+GuDmgTU99r7qiAl/HDt46NKsA2pKr+puddOPPPbmw7YfOo/QM7Ff1jEBI4EYeDXrQDqg3wL/nHUQHYUJkiRJUscWAX/uXd1t9G/HfvLqPfsN7DSltC8be/SsUQMGLQJmAbtmHM5o4BFCkqSGlgOLsw6io6jOOgBJkiQ16aKaXO7kSz5++J/2Hzj4g6yDaYnqKIr/9/Bjbjn5ntvPmr9m5Y3A9cCBwJnAecB4wmKzdySHVAGnAAcTSpivBp4BbgSKF7zdCTgb2DN5/xLwlybCGQP8rsT2nYHPA3sDawk9TFcCC5s41/cIvWKnFW2vIpQ5vw/4RbItAo4GJgJDgbrk3DcAT5Q4dw1wKjCOsPjv4uScdzURD4QFe0cR1sT6MNnWg3BPcoR5Rs+k2v8PsD3wGUJ59sOB45N95yfvzwSmAJ8C+iWx3FoilpZ8bx2ePUiSJEkd11HAj762zwE3HbXjLu9lHUxr9KruVnfFYcf8pV+3mhHAycnm04HJhIRkVbKtG+Eh/2wgDzwAvE1IFC4lJDAFeyfbxgJLCcnR3sBPge1KhLEz4QH/haLt2xGKOuwCPA4sAw5Jzr1vqz5wY2cQEpABhB6sZ4DdgamUHnr4DeDIpO29hCTmq4QErylPJT/TcY+k/nl/n9T2/oRk7QWgqQWFv0JIYh8D/p6KZWyqTUu+t07BHiRJkqSOqRdw+ZhBOz71+T32fSXrYNpiSK/e67+x30G3fO+pOZ+N47jQ2/BNQmJTMAnYC/g+4YG8YA/gR4QH8y8n275I6B35GfCPZFtP4LuEpKO4p20M8ChQW7R9KCGx+BH1icJRwNeBLyQ/2zIkLwKOA5YQemUK1ygkc0cBzxUdM5DwWQuJ4/2EBGQMTReYeJJQoW8/4KHUdWJCj046QdondUw5vQjfx9eo75G6H/gxcCjwcLKt0u+t0wxttAdJkiSpY/rBwJoefX4z5pOzsg6kPUwZNnLBsN593yQMA7uRhskRhHWTngdeI/S2FF5LCcnNUGAQ4cF7ODCX+uQIwsK0vy9z+XLV62LCULN0L8o9hMRhOLBbZZ+urCpgG0JP2abU9pcIiUepIYHXU58cQUigNhJ6wJryJiGRSfdK7UNYi+oZ4CPUd45UkiABTKc+OYL6Hqd0LJV+b52GPUiSJEkdz77A+eftue+Nvaq71WUdTHsZNWC7N95avXKX2jj+BA2Tg56E4W7bEeb/lDMAGJL8/niJ/fNp3Hu0HWEI3VONWsN7wDsltj8OHADsCLzRRDzNqSWsB3UEcBkh+XqakEyUO29xlb2YhslVOTEh4RlHSGBWE5KivxE+42HACOBlwr+v5cCCZs7ZXCwt+d6WVPAZOgQTJEmSpI7nB3v07T+/sw+tK1aTy9Vt26Pn0vfWrT0HmEZIUKC+h+ExYEYTp3iH+jkty8u0WQZsm3o/mpA4bCjRdkUT54AwV6clohLbLiUkGkcR5iOdQejtepCwoG1xDCtbeM20pwgJ0r6Eggo9CL1VhUIJ+wKLCIUU7qH5YW/NxdKS763TMEGSJEnqWA4ETvjynh+9POtANod+3WpWborj6uXr111EKEgA9UnCBkpXdksrrAE1sMz+AUXvxwB3V9i2eHtLez36lNhWS6hCdwshcduXkMR8ijBc8Ks0TFTaMlfn6eTnftTfnxcJ93cVIblcQEjkmrvPlcTSku+t03AOkiRJUsdywa69+i44dufdOtVf3Vti4pBd5xKKIBR6aFYlr48RylynRYSFTP84derUakKFNAiJZLGdaZg49SEMM3usRFuiKNrxjDPOOHDmzJn7z5o1K31c4dyLmvgY1TTuMRpR9H4IcC7w8eT9MkKhg4sJCcUwYHAT12ipFYS5SPsRkqF3CT1tMWGe0N6EOWAxDUt+t1ZF3xule9Y6LBMkSZKkjmMAcOqpw0Y8knUgm9O5e+z7fM+q6mrCGjsFM4DewL/S8Bn1RGDnffbZZ9Ho0aP/ffr06WuBeYShc0ek2vUAvlR0qYMJPSirZ86c2XfGjBkjZ82adfidd9552vDhw0fuuuuueyxcuPAXa9asObKurq5QHvwoQkLzOOUXT11NSJBGpbb1IqwblLYJOAn4HNA9tb2GkMjV0XjOVFs9SZhz9THCZy94PonxKOAVGhaCaIsmvzdCRb1OU8EOHGInSZLUkZyxTXW3TZ/dfe/iyfFdyjbV1XUHbbf9sw+89/bnqK88dxMh6TmU0OvwAjA4iqK9d955Z374wx8eCdT27dt3JnA5oaz0RcAJwPuE3pFtgOd69Oix+4033rjvRRdddPaoUaOWfe5zn/tmHMfb5HK5fBzHS4F3V65cuSKfzz85Z86cwQ8++OAowno+2xPWDvoQ+N8mPsLDhGFy3yWsVbSJkIy9n7wKliZtxxKq5b1ISIpGEYbb3UzpuVFt8RShjHpvGidIEHrV2nM4XMnvjVD6exFwXTtea4swQZIkSeo4Pn3AtoNf6F5dnc86kM3tpF1GPPfAe29/nlBK+01C+egLgc8A+wOje/Xqte7www//4LzzztuxZ8+eT9fV1Q0FdgBmAhfU1NSc271791HV1dUHDho06L2zzz770blz54597bXXhm/cuHHyqlWr9hkzZszPgVc3btz4bt++fZeMGzeusBbSqYTelr8TengOJiRG/wCuoH6uUymPAL8i9A59ElhDWCD1KuDXqXYxoUjDAuBwwiK0GwnDBK8kJFft7UVC0tWdhgnSm4Ser940X967JRp9b4ShhLcSkqN17XitLaJTjQeUJEnqwvoBS6ftP+aaT++25/xsQ9kyDrrtmn9dVbvpIkIJ7P8za9asgXEcH0Z44N6HUBY7iqJoZBzH83K53JOEEtwD8/l8FEXRCmBxFEXv5vP596qrq9/91Kc+9WHx9aRK2IMkSZLUMRzZLZeLj995+FvNN+0a9ug74PUnly85Grjs1ltv7dOtW7d9crncfnEcj8zlcgfX1dUtz+VyAB+P47hbFEXd4ziOamtr50ZR9FhVVdV7q1atenfKlCmdrpdCHZcJkiRJUsdwxNBevRd1pYVhm7P/doMWPLty+dG33Xbb93O53Ig4jg+M4ziOomhdPp9fHUVRTRzHKwjrJa2qq6vLR1F0SFVV1QMTJkwotw6S1CZWsZMkSeoYRg3v3a/LlvYuZcxOw1bU1tb2X7JkyeFJD9FDwGxgRhRFdwGPRVE0NIqiXYChVVVVPYHafD6/Q6aBq0uzB0mSJKljGLlbr75PZR3ElnRA7/6LoiiKv/e97/3iv/7rv+7fsGFD7YoVK2q/+MUvbgKYPXt2r1wud2Vtbe0uURTtVldXNySKolG5XG57GhYgkNqNCZIkSVL2egI77d53wD1ZB7Il9aruVte3utuHCxcu3GHy5MmN1uUZP378GuCN5HXf9OnTa3r16nVbVVVVUxXmpDYxQZIkScreICA3vG/flVkHsqX16Vaz6sNNG7evpO2UKVM2As9t5pC0lXMOkiRJUvb6AAyo6b4x60C2tB7V1RuAvlnHIRWYIEmSJGWvD8B23XtsfQlSVdUGks8vdQQmSJIkSdmrBugWVeWzDmRLy5HL47QPdSAmSJIkSdlbCbBsw/qarAPZ0jbUbepO8vmljsAESZIkKXurAVZs3LDVJUjr6/LdgUYV7KSsmCBJkiRlbwXAO2tXb5N1IFva2rpNPUk+v9QRmCBJkiRlbwWw9KUPV2ybdSBbUm1cFy3fsH5b4JWsY5EKTJAkSZI6hpffWP3BdlkHsSU9t2JZv7o4rgLmZR2LVGCCJEmS1DG8/NaaVYOyDmJLenLZksHAWmBh1rFIBSZIkiRJHcP9r6/6cFjWQWxJc5Ys3g14AKjLOhapwARJkiSpY7hnXW1tr/veXTQ460C2lHkrV+wG3Jt1HFKaCZIkSVLHsBiY97d35u+edSBbwrwPl/d+f93awcDfs45FSjNBkiRJ6jiuvf/dt/fPOogt4Y+vvvDROBRneCzrWKQ0EyRJkqSO489L16/b7m9vL9gx60A2t4eWvPNR4Jqs45CKmSBJkiR1HAuAB65+/aUDsw5kc7pp/mu7LF2/biBwbdaxSMVMkCRJkjqWHz+27L39n1q+pH/WgWwuf3j1uSOB64E3Mg5FasQESZIkqWOZnY/j5/77pWfGZB3I5nDHW28OfTOUM/9Z1rFIpZggSZIkdSwxcPHDS9456IF3F3WphWNr4zj65QtPTABuBp7JOh6pFBMkSZKkjueWfBzPvPjpR47POpD29ONn5h749trV/YGvZx2LVI4JkiRJUsd04aK1qwf/5NlHu0TZ75c/XN7npgWvjQN+CizKOh6pnKqsA5AkSVJJy4D1L3yw7EujBmz30i69+67NOqDWqo3rorPun/3ppRvWzQfOA+oyDkkqyx4kSZKkjusXm/L5v/37Ew+etnzD+m5ZB9NaFz76wCcWrFk5APg0sDHreKSmmCBJkiR1XDFwzvvr16056x93nr6mdlOnG/3zk2cf23/W2/MPA87Bst7qBEyQJEmSOralJ5100uffWL2y7z89MOuk2rguyjqgSv3u5Wf3+t/XXjwe+BpwS9bxSJXodH+FkCRJ2prMnj177IEHHnjkW2+9ddljL790zn2LF+1y/C7DX67JVcVZx9aUXz7/xKjfzXv25Jj4B8AlWccjVarT/AVCkiRpKxPNnj376DiOR9fV1d1y7LHHPgfsB9w5rE/fjVcdPv4vg3v02pB1kKV858mHRt8w/9WjgYuAS7OOR2oJEyRJkqQOZurUqbmxY8ceG8fxfvl8/i+TJk16PbV7Z2BG/5ruO128/+gbJw7d7Z2s4iy2ZP2a7ufPue+4Z1e8vzvwWeCGrGOSWsohdpIkSR3I9OnTq3bfffdTgJFRFF0zceLE+UVNVgJXrq+r2+OudxZ+duGaVXVHDB76dnUul+mQu78ufG3n8+fce9aCNSs3AOOBe7OMR2ote5AkSZI6iOnTp9f07dv39Hw+v31NTc1VRx999HvNHHIe8MtBPXquv2DvA2ecMmzEwi0RZ9qS9Wu6f/OxB4965P3FB8VwLfAvhCRO6pRMkCRJkjqA2bNn98rn82dFUdRt3bp1V5900kkfVHjoEOAXwOl79xv48lf2+tj9Rw3ZpbnEqs2WrV9f84sXHz/wzkULxq6t3fQ28BXg7s19XWlzM0GSJEnK2KxZswYCZwNrVq5cec2UKVPWteI0RwDfBY76SL+Br5ywy/Anz9rtI691r67Ot2esD7y7aNB1b74y6qElbx+4vq7ufeDnwO+BDlkwQmopEyRJkqQM3Xzzzdv26NHj7CiKVq1ateraViZHaQcDFwCTe1ZVx6MGDnrxsME7vnbirrvPb03Vu9q4Lpq1aOGQexe/NfyxZe/u/d66tTsAjwKXAVcDG9sYr9ShmCBJkiRlZMaMGTtUV1efBSz+8MMPb5gyZUp7Jhv9gFOA04DDc1HUc8eevd7ZcZve7++8Te+lw/v0Xdarutum/jU91lfliAFWbtzYfUM+X7Vg1cr+C9es3PbtdWsGLVqzauj6urruwLPAHcA1wMvtGKfUoZggSZIkZWDWrFkjgClxHD//yCOP3DFt2rR2HQpXpBswGjgU2AMYCYwgJFE9U+3ywAfAW8CryespQkW6pZsxPqnDMEGSJEnawmbNmrUPcHJdXd0jxx577N1AliW6uwPbALXAqgzjkDoEEyRJkqQtaNasWR8FJgMPTJgwwbWCpA6mOusAJEmSthZ33nnnGOBTURTNHj9+/Nys45HUWFXWAUiSJHV1cRxHo0ePngQcmsvlbhg/fvwzWcckqTSH2EmSJG1G06dPr+rbt++JcRyPBK6bOHHi/KxjklSeCZIkSdJmcu+991Zv2LDhZGC3KIquHT9+/FtZxySpaQ6xkyRJ2gymT59eU11d/ek4jnfM5XJXjh8/fnHWMUlqnj1IkiRJ7Wz27Nm98vn8WVEURRs2bLhm8uTJls+WOgl7kCRJktrRrbfe2qe6uvpsoC6KomuOPfbY1VnHJKlyuawDkCRJ6ipmzZo1sHv37ufW1dWtWbly5Z/Hjx+/JuuYJLWMCZIkSVI7mD179s7AF4D5c+fOvXrKlCkbs45JUss5xE6SJKmNZsyYsStwRhzHLz7yyCN3TJs2LZ91TJJaxyINkiRJbTBjxoyR1dXVp+Xz+UcmTpz4dyDOOiZJrWeCJEmS1Ep33HHHgdXV1ZPiOJ41ceLEx7KOR1LbmSBJkiS1wsyZM0fncrlP5fP5OyZNmvRU1vFIah/OQZIkSWqBOI6jsWPHTszlcofmcrm/TJgw4YWsY5LUfkyQJEmSKjR16tTc+vXrjwc+ms/n/zJhwoTXso5JUvtyiJ0kSVIF7r333up169adksvlhkVRdO348ePfyjomSe3PBEmSJKkZM2fO7B5F0aeBbXO53NXjx49fknVMkjYPEyRJkqQmzJ49uxdwJtC9pqbmqnHjxn2QdUySNp9c1gFIkiR1VHffffe2dXV1X8jn83Uffvjh5SZHUtdnD5IkSVIJM2fOHBRF0dnA8jiOr5s0adKGrGOStPmZIEmSJBWZOXPmTrlc7owoihbU1NTcNG7cuNqsY5K0ZVRnHYAkSVJHctddd+1VW1t7CvD4+PHjZwNx1jFJ2nJcB0mSJCkxc+bMveM4PiWO40cnTpz4N0yOpK2OQ+wkSZKAWbNmHQAcB9w7YcKEB7KOR1I2TJAkSdJWb9asWUcDY+M4vmXixInPZh2PpOxY5luSJG3NoiQ5GlNbW3uzyZEke5AkSdJWaerUqblDDjnk2Fwutx8wfcKECa9lHZOk7JkgSZKkrc5ll13Wbddddz0N2HHDhg1XT548+b2sY5LUMZggSZKkrcrMmTO7R1H0GaBvXV3dVccdd9yKrGOS1HE4B0mSJG01pk+f3jOKorOAXqtWrfqzyZGkYvYgSZKkLmfWrFkj4jg+bNWqVX+ZMmXKOoCZM2f2jaLorCiKNm3cuPGaE044YW3WcUrqeKqzDkCSJKm9xXG8fxRFn+3Tp0/V448/fuV77723LXBWHMeLBw0aNP2ggw7alHWMkjqmqqwDkCRJak8zZ87snsvljgNiYMyaNWsGRlF0MDBv7ty5N5155pl1GYcoqQNzDpIkSepSqqqqPhbH8Sfy+fw7wPxcLnc+MHDu3Ll3TJs2LZ91fJI6NhMkSZLUZUydOjUHHBzH8btRFG0XRdHIKIqejqLoY2PGjBmfdXySOj4TJEmS1GUceuiho/L5/OQoimJg7ziOX8/n8y8AL8Zx/I0777zzyIxDlNTBWaRBkiR1CdOnT++Zz+ePBOqiKNoljuN5URTl4jgeBfQH7omiaNtso5TU0ZkgSZKkTm/q1Km5Pn36nAJ8GqgFVgAj8vn8rFwud+umTZte2XHHHedbvU5Sc0yQJElSpzd27Nij4jj+Rj6fj3O5XB1wcxRFj65aterlKVOmWLVOUsVcKFaSJHVqd9111351dXXXxXHcI5fLTa+rq7th0qRJT2Udl6TOyR4kSZLUad15553DNm3aNDWKoh75fP5nq1atumbKlCmrs45LUudlgiRJkjqtXC73z/l8vgb4/LHHHns/YXFYSWo1EyRJktRprVmz5ldVVVXR5MmT38s6FkmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSOoFrgR9kHQRwIXA70KOdz3sJcFUF7TrKfZDUxeSyDkCSJHUIHyckPOMq3C5JXZIJkiRJkiQlTJAkSZIkKVGddQCSJKlVdgY+D+wNrAVeBa4EFha16wWcDewD7AC8DTxNmMOzMWkzDTgg+f1fk9eZhHlGpbavLBNTFXAKcDAwDFgNPAPcCLxV1HanJK49k/cvAX9p5jOXUul9aElslbb9etLmTOA8YDzwZ+COVnwOSR1EVdYBSJKkFjmF8P/v4wnJz7OEESGjgE8BLwJLkrYDgEuB/YFlwAvAdsCY5HUfsAn4gJAE7AnMJjzgv5EcU2p7HTCWkDzcANQC3QhFE44G3geeB/LAIUlczyfbISQzPwF2IyQcC5JtE5PzVAF/bcf70JLYWtJ2NDA0OeY0QuI4N/k8kiRJkraAawlFE75PwwpyRyXb/x8QJdv+Jdl2ampbBJybbD8jdXxLizQUV7GbDNyatE/bg5BE/U9y7SiJ8XbgiFS7nsCPku2VVrGr9D5UGltL234duC3ZvlcFMUvqBJyDJElS5xMTHtTXp7bdAzwJDCf0zFQDxxB6aG5KjikcezWh12hSO8Z0IqF35TVCz1XhtRR4lNDTMoiQaAwn9LT8I3X8OuD3LbxmJfehJbG1tC2EZOlqwhBBSV2Ac5AkSep83gPeKbH9ccKcoR0JSUMVYe5MXNRuI+GBfgywDWHuTlv0JAzd244w/6ecAcCQVKzF5hMSt0pVch8WtyC2VS1ouyT1/sXKQ5bU0ZkgSZKaEhHmYkwk/OW8jjD5/QbgiaK2lRQDAPgesB9hzkZaFXALYV7ML5Jt1xL+an8DYVjYfsAaYB5hMvziVsba2a0os31Z8rM/sG3ye7mEo3CO7Whc0KClCj0qjwEzmmj3DmGuEcDyMm2WUR97cyq5Dy2JrSVt095roq2kTsYESZLUlDOATxOGFz0C1AAHAVOB/wCeS9oNAH4ODAZeB+YAuxMm0n8cuIjW91JsD/yMUEzgieQahwIfA75LqFrWkli7ggHNbF9CwyShlML2cklGSxTOsYHmk9Glyc+BZfaX+2wtaZu+Dy2JraAlbSEk45K6CBMkSVI5EXAc4SHzfOrneewN/JQwGb6QdHyGkLj8mfr5LhHwOeAkwryOa1sZx76ECmX/mYrhk8AFyfm/3cJYu4LtCUPVinsyDkx+LiJUWqsj9LpFNBxmVwN8hDCkbFU7xFM4z8eSc6d7DCPgN0B3Qjnut1Oxzio6z86ExKnSYXaV3IeWxNaStsXDFiV1ERZpkCSVU0X9/JRNqe0vAV+jfs2aLVEM4DIaTsT/O+Ev/PsR1tOpNNauIgK+RHhYLziK0Fv3OGHoYS1wF7ArIUFNH3s2IRG5s8S5y/3xtLk/qs4AehPWSko/X5xISHweIvybeJMwRHI0DavY9Ug+U0tUch9aEltL20rqguxBkiSVUws8SHiIvYxQHexpQnWvN1LtBrN5iwEsp/QcmScIPQVDCD0FlcTaVbxEmOv1P8nv2wMjgQ+B/021u45QrOBc4HDCfRpOSJoKCW1BobfkBEJxg+mEpLTc9mI3EZKeQ4E/EtZcGkwof70oiQXCv5HLCeW5L0rO+z6ht28bQk/fzu18HyqNraVtJXVB9iBJkppyKeEhcR1hjs8lwDXAV6mf59GSYgBNicpsb2oyP9RPrK8k1q7iDeBbwLvAwYT5RP8grMuTXqR0OeHzzyQMGRtLGHZ3M2GIYjphfYlQensoMIGw+GlT24utJ6yNdCMhQRkN9COsKfQNwvdS8HJy/YcJ/34OJiQf/0bLCkZUeh9aEltL2kqSJGkrti3wCeBiwkKc/0VIaoYk779Y5rhvJfv7JO+/R6gsV2xA0u7C1LZrCfOaSjk2aT+2BbFKktQke5AkSeUMIQzN+njyfhlwPyHpeAIYRhh6tISGxQDSyhUDqC7RdkSZOAZSesjVAcnPd1oQqyRJTTJBkiSVs4lQge5zNJwEX0NIWuoIw+paWgxgNSFBGpXa1gs4s4lYvkSYxF8wjjCkah5hKFWlsUqS1CSLNEiSyllKmCMyljAJ/kVCojGKMITtZsJ6MdCyYgAPExKc7wL3EpKbgwkT9d8vE8eu1E/EHwTsSZgL8gfCpP+WxCpJUllVWQcgSerQHickGkMJ1cG2J0yIvwq4JdVuHaFy3DaESmd7ESa430tYQDZd9WwRYVjezoQEZkdCcvNrYDxheNycpO0pyfWmArsk5+0FPEUowvBmK2KVJEmSpE7pWkIiJEnSFuEcJEmSJElKmCBJkiRJUsIiDZIkqSLTp0/v2bt379OiKFo4ceLE+7KOR5I2BxMkSVJHdkbWASi44447BlRXV58Zx3Hco0ePp7OOR5I2FxMkSZLUpNtvv31ot27dPpPP59+N4/iGcePGWTJdUpflHCRJklTW7NmzP1JVVXVOXV3dvEceeeTaSZMmmRxJ6tKirAOQJEkd0+zZsw+J43h8HMd/nzhx4oNZxyNJW4IJkiT9/+zdeXxc1YHm/eecWyXJ+74AAUxYw2pjh8VAgsBWSSUjgUOKkHR3QjaSTndPJ+mZt7uTN2pPd2fmnZl0z/vO2512JonpJNBJJSFYtmqxNStbRAAAIABJREFUIWLHgI2BEHYIWzDe0WJbS91z5o+SGwPeXdKtKv2+n48+kq6q7n3KBkuPzrnnAHiXdDodTJw4cYlz7twgCH6ZSCSejToTAIwU7kECAAD/LpPJ1FprU8aYWWEY3pJMJn8fdSYAGEmMIAEAAElSZ2fn+P7+/hu992Pi8fitixYt2h51JgAYaRQkAACgfD4/03v/Se99j7X2p4lEYlfUmQAgCqxiBwDAKJfP58/y3n8+DMMX161bt4JyBGA0YwQJAIBRLJvNXm6Muco5tyaZTK6LOg8ARC2IOgAAABh53nuzcOHCxZKusNauamxsXB91JgAoB4wgAQAwyqxfvz6+ZcuWpdbaOd77nzU1Nb0SdSYAKBcUJAAARpG1a9dOcs590jlnwzC8bcmSJTujzgQA5YRFGgAAGCVWrlx5fBiGn5e0u6en54eUIwB4P0aQAAAYBbLZ7JnGmI8ZY57u6upalUqlwqgzAUA5ikUdAAAADK9cLnehpGbv/YONjY2/luSjzgQA5YopdgCAcmLWrFkzN51Os8pqaZhsNnu1pGbn3Oqmpqa7RDkCgINiBAkAUDY6OzuD/v7+lnHjxu2U9GrUeSrZ+vXr41u3bv2YpBONMbckk8nXo84EAJWAESQAQNmor68vSNoRBMGsqLNUsnQ6PX7btm2f8d7PMMb8IJFIUI4A4DAxggQAKDdbjDEzow5RqXK53FTn3Ke89wODg4O3tLa29kSdCQAqCSNIAICy4pzb7L2fHXWOSpTP50/03n/OWru9p6dnBeUIAI4cI0gAgLISBMFm7/1l3ntjjGFBgcPU0dEx13t/jaT7Ghsb7xGLMQDAUaEgAQDKyuDg4OYgCGo6OjomS2Ij00Pw3ptsNttorV0gqaOpqemxqDMBQCVjih0AoKw0Nze/bYwZCIKA+5AOIZ1OB/l8vsVaO897n25sbKQcAcAxoiABAMrK0LS6LdyHdHCZTKZ24sSJn5R0Wn9//y1NTU3PRZ0JAKoBU+wAAGXHGLOZlewO7Pbbb59mjPmUMaavr6/veyzGAAClwwgSAKDsFAqFLcYY9kLaj/b29pPGjh37OWPMtq6uLpbxBoASoyABAMqOtXazpKmdnZ3MdNjHmjVrTo/H45/y3r/Q3d39s1QqNRB1JgCoNnzjAQCUHWvtVuec3b1793RJb0Wdpxxks9kPO+eaJN3b1NTEMt4AMEwYQQIAlJ1EIrHLe98bBMGon2aXTqeDbDbb4r1vcM79oqmp6W5RjgBg2DCCBAAoS9bazc65UV2Q1q9fH9+6devHjDEnS7q1qanplagzAUC1oyABAMqS936LtXbUFqTOzs7x27Ztu1HSmFgs9v1FixZtjzoTAIwGFCQAQFny3m8xxpwbdY4orFy58vj+/v4bvffb6urqflxfX98XdSYAGC24BwkAUJYGBwc3e+8n5PP5cVFnGUmZTObUurq6P3LOvVpXV3cr5QgARhYjSACAsjRx4sQt/f39rr+/f6ak30WdZyRkMpl51tolzrkHk8nkr8ViDAAw4hhBAgCUpfr6+oKkHbW1tTOjzjICTCaTucpau6RQKGSampruEuUIACLBCBIAoJxtCcOwqgtSZ2dnrL+/v9V7f4akf1uyZMmLUWcCgNGMggQAKFvGmLestWdEnWO4pNPpSf39/TdKihljljc2Nu6IOhMAjHZMsQMAlLMtkmZ6703UQUotn8/PHD9+/E2SCt3d3bdQjgCgPDCCBAAoW977Ld77mo6OjsmSdkadp1Ta29tPcc7dYIx5qba29lepVKoQdSYAQBEFCQBQthKJxM58Pj8Qj8dnqUoK0po1axY455Le+3ubmpruEYsxAEBZYYodAKBsGWO8igs1zIo6SynkcrkrhspRtqmp6W5RjgCg7DCCBAAod1uNMRW9kl06nQ4mTpy4RNI53vufNTU1PRd1JgDA/lGQAABlzTm32RgzP+ocRyufz49zzn1C0gRjzPcbGxu3RJ0JAHBgTLEDAJS1QqGw2Rgzbfny5fGosxypzs7Oyc65T1tra4IgWJFIJChHAFDmKEgAgLJWW1u7xXtvTz755OlRZzkSmUzmAwMDA583xvSEYfjDxYsXd0WdCQBwaBQkAEBZSyQSu7z3vZIq5j6kjo6Oudbam7z3jz/00EO3JpPJ/qgzAQAOD/cgAQDKnrV2s7W27Fey896bbDbbaK1dUCgUMkuWLNkQdSYAwJGhIAEAyp61drNzrqwLUjqdDvL5/BJr7TnGmJ8vWbLk2agzAQCOHAUJAFD2CoXCVmvteVHnOJAVK1bUjR8/PiVphqQViURiU9SZAABHh3uQAABlz1r7lvd+Qj6fHxd1lve68847p82ePfuLQRDU9ff3f6+xsZFyBAAVjIIEACh7tbW1W40xTmW2UMPatWtPKhQKn5O0taur65bW1taeqDMBAI4NBQkAUPbq6+sLzrmdKqOC1NHRcUYYhn8g6bmenp50KpUaiDoTAODYcQ8SAKAiWGs3q0wKUi6Xu0hSo3PunmQyeU/UeQAApUNBAgBUBOfcZmvtGVFmSKfTwYQJE5qdc+fF4/GfNzY2PhNlHgBA6THFDgBQEZxzWyTN8N6bKK6/YsWKuokTJ35yqKT96+LFiylHAFCFKEgAgIoQi8U2e+9r8vn8FElKp9Pj8/n8iEy56+zsHD9r1qw/8t5P6e/vvyWZTL4xEtcFAIy8SH4LBwDA4VqxYkXd8ccfP8M5N9sY8yfGmGe89857P1ZSYd26dd9etmyZG67r5/P5md77T3rve6y1P00kEruG61oAgOhRkAAAZSufz893zi2RVC+pzxgzw3u/VdJLko6TlG5qavrpcF0/m82eaYz5mDHmyQcffDAznEUMAFAeWKQBAFC2wjB8QdIuY8wmSc9578+UFLPWbvXeny5p2O4D6ujouNgY0+CcW5tMJtcN13UAAOWFe5AAAGUrmUx2x+PxtDHmaUnTVSxL47z3E4wxD9TW1r5U6mu2tbXZTCbTZK1dLOl2yhEAjC4UJABAWVu8ePFrzrl7jTFnWmv7vfdjJE323m+vr6/vLeW1Ojs7YwsXLlwaBMEFsVjstsbGxt+W8vwAgPJHQQIAlL2HH374Pufc7d77GZKMMWaq937z0Z4vl8udk81mb25vbx+791g+nx/X19f36TAMPyDph4sXL365FNkBAJWFggQAKHvLli1z8Xg8P1SQ5JybImnr0Z7PGLPAGPOJmpqaGzo7O2O5XO4459zNksLe3t7liURiS6myAwAqCwUJAFARhkZ0bpFU472vs9YOHM152tvbpzvn5kj6raQb9uzZ8yXn3GclvVJXV/eTVCq1p2ShAQAVh1XsAAAVIwiCJ5xztZJMoVA4qu9h8Xh8oTHmQu/9Ru/9eGvtn0v634lE4lfGGF/axACASsMIEgCgYrz99tvPee/XSZoQBEFwpM9Pp9OTvPfnee/fkDTHWntSGIYbJDXm8/nzSx4YAFBxKEgAgIoxNP3tt5LixphxR/r8CRMmXG2MuVTSTEkfcM49aa19xnv/kvf+mo6OjpNLnRkAUFkoSACAimKMWSfJO+emHcnzcrncad77+ZJqJU2Q9KwxZpK19kJJpxpjbDweP3MYIgMAKoiJOgAAAEfgg7W1tdedfvrp//H111/3XV1dcUldkl6T9KCkrKQHJL3rXqJ8Pj9T0h977xu99wPGmF1D+ymlvffPFgqFF/v6+t5KpVJHtfADAKB6UJAAAJXgOEl/J+kzkybEey696MS3jp9dt3nKJG3u3V2o+f2buyc//tSOU994c/cJktZL+qqKRUmdnZ3jBwYGvu6cu0HSHmPMSu/9+oGBgcdbW1vfjO4lAQDKEQUJAFDu6iWlj589duDmPzq98/N/ePqLB3rgwxu2Tvn7//mbj258cud5zvm/TafT3x43btxfBUHwaUm7vPf/T11d3er6+vq3Ry4+AKCSUJAAAOXsekm3NdYf//Dyf7z017Hg8Jbh/sGtL5z67X986vpTPnjGs9/5zncmW2s7nHP/nEwmnx7mvACACnfES6QCADBCPiyp/eMtc+797v+4+F5rD/93eheeP23nOWdM/d2P0082Pf30sw9ceeWVn08mk28NX1QAQLVgBGl0+IqkRkmfkLQr4iwAcDispI1zz5tqO2676pdHe5J/+O7TZ3/nn59eKul8SYweAQAOiWW+AQDl6BPxuP3Qd/7z/PyxnORrXz776dPmTHhV0t+WKBcAoMpRkEaHn0j6oqQ9UQcBgMP0uUsXzNh41mmTeo/1RF/6zBn3SmqRNP3YYwEAqh0FaXTokrRJkos6CAAchqmSPtK8+PhnSnGyj18757UxY4IBSUtKcT4AQHWLRR0AI2J/9yDNlvQHkk6TNFNSt6QXJKWH3u/73Csk/aGkL0i6XMUNGJ+R9ENJ2yV9VtI8SVOGnvs9Sa8M4+sBUN3OMUbBdUtOfr0UJ4sFxs85cfzrzzzfNbcU5wMAVDcK0ug0R9J3VPz73yjpt5JmSbpYxRuZvyJp2z6PDyR9Q8UCtFbSGUOPPUlSr6Q6SesknSJprqRvSvqSpMKwvxIA1ei4MXWx3ePGxMJSnXD61JpuSceX6nwAgOpFQRqdGiTVSPq2pIf2Od6i4ijRBZLu2ud4nYrT874mKVRx9cPvSDpdxZGkP5c0MHT8byRdqOII1RvD+BoAVK94EJR2SnAQWCcpXspzAgCqE/cgjU4PSvoHSY+85/jLQ+/H7+c5aRXLkVScYvfU0Me/ULEc7T3+m6GPJ5YkKYDRaNOu3eG4QuhLthXF9p0DEyS9WarzAQCqFwVpdHpKUqeKhWeCitPqUpK+fJDnbHrP54OHOA4AR+sl57y9+8G3ZpTqhG9u2jVL0kulOh8AoHoxxW50miTp0yourDBdUp+kFyW9puJ9Rfvjj/A4ABytVyX95o7Vr5+56Irjthzrye57eMu07TsHpkladezRAADVjoI0+hhJfy3pbEkZSfepuLu8k3SmiqvUAUDUbvv1/Zv+sqt38KFJ4+PHtODLP33/mYslrde7V+gEAGC/mGI3+kxRsRw9JOm7Kk6323sz9JioQgHAe/yvru7B3d/4+42XHMtJ7rxv08wHHtk2X8VfDAEAcEgUpNFn74IKY1QcTdprvIr3IUn8dwEgerskfXVl5vX67//4hdOO5gS/e6V37J//9aOfcM7/XMUtCgAAOCSm2I0+vSrufTRPxd+oPitpmqSPqHgfkiQtlrRZxdXuACAqP3POX/h3//Cbr3b3Dt7xtS+f/fThPvHhDVun3PwXD9+48+2B1yR9bhgzAgCqDCMFo9N/l5RTcR+jG1Tc4PUHkpZJapc0VdKCyNIBwJCPf/zjfz112ox/+YfvPn196vP3NP/uld6xB3t8f3/BfuPbGxd88ub7v7B1W99vJS1ScTQKAIDDUrI9JlDWviKpUdInxA8KACpINptNGGMuaG5ufioMw/9WU2NPO/esyc9fdtHMFy44d8rWs06f1L1py566p367c9r9j2w55bEnd5y18+0Bq+Ivgv6b2HoAAHCEmGI3OkyPOgAAHKnVq1efa4y5WNJtYRi+KCk/MOCWPvbkjo8/9uSOJr17U2snaZ2kv5d0i6TtIx4YAFAVGEGqbnMkXafi/UU9Ku59xL5FAMreypUrZ9XW1n7OGHN/IpG49wAPm6TiPZS7JW1VcfNrAACOCSNI1e0kFfc1ekXFe4woRwDKXjqdHlNTU3OD9/7lxsbG+w7y0K6hNwAASoaCVN3uHXoDgIrgvTf5fP46Sc459yvxix0AwAgLog4AAMBeCxcuvErSWbW1tSsaGhpYVAYAMOJY5hsAUBYymczZ3vvLnXO/rK+vfzvqPACA0YmCBACIXCaTmWGMaTXG3J1MJl+KOg8AYPSiIAEAIrV8+fK4MWap9/61hoaGgy3KAADAsKMgAQAiddJJJ10jqba3t/d2YwyLMgAAIkVBAgBEJpvNXi7pzMHBwdtSqdSeqPMAAEBBAgBEYtWqVacbY66y1v6qpaVlW9R5AACQKEgYRsuXL493dnay1xaA98nlclPj8fhS59y6RCLxbNR5AADYi4KEYTNnzpxFfX19n16+fHk86iwAykdnZ2fMOXe9c25rb2/vXVHnAQBgXxQkDKd7jTHjTzrppGu99ybqMADKQ39/f7O1dsLg4ODPU6lUGHUeAAD2RUHCsEkkErucc7dJOjWfz18ddR4A0ctkMpcYY86VdFtra2tP1HkAAHgvChKGVTKZ3BqLxW7z3l+Sy+UuijoPgOhks9k51toG7/3qxsbGTVHnAQBgfyhIGHaLFy9+zXvfISmRyWROjToPgJGXyWQmWmuvd85tbGxsfCLqPAAAHAgFCSMimUxu9N4/GATBx/P5/Myo8wAYOZ2dnTFrbUrSjt7e3kzUeQAAOBgKEkZMU1PTr8MwfNZ7/4dr166dFHUeACNjz549iyVNrampuZ1FGQAA5Y6ChJHke3t7V0naUigU/qCzs7Mu6kAAhlc2mz3fWvth59wv6+vr3446DwAAh0JBwohKpVLhwMDAL733tr+/f2lbWxv/DQJVatWqVSdIanHOrUkmky9FnQcAgMPBD6cYcS0tLbuttbd670+49NJLm6LOA6D08vn8uFgslvLeP5VMJtdFnQcAgMNFQUIkGhsbdwwMDNwq6YJsNntl1HkAlE5bW5v13n/MWrtrzJgxq6POAwDAkaAgITKtra1vhmH4C2PMRzKZzLyo8wAojUsuuaTeez9r9+7d6fr6+kLUeQAAOBIUJESqubn5ee/9r40xzdlsdk7UeQAcm46OjjOMMZcFQXDHddddx6IMAICKQ0FC5Jqamu631j5hjLmhvb19etR5ABydXC431Vq71Bhzd0NDwwtR5wEA4GhQkFAWGhoaVjvnXonH43+UyWQmRp0HwJFZsWJFnXPuU5JeSiQS90WdBwCAo0VBQlkwxvjXX3/9dkldxpgb0+l0TdSZABw2M3v27GuNMWFPT89KST7qQAAAHC0KEsrGzTffPDgwMJA2xoyZMGFCqyQTdSYAh5bL5S6XNGfPnj0/S6VSA1HnAQDgWFCQUFZaW1t7nHO3Sjo1l8tdHXUeAAe3atWq0yXVO+duX7p06fao8wAAcKwoSCg7yWRyaywWu817f0k+n7846jwA9i+dTk+KxWLXSnqwubn5+ajzAABQChQklKXFixe/FgTBau99Qy6XOy3qPADebf369fHx48d/0hjz+0QicVfUeQAAKBUKEspWQ0PD45Lu995/vKOjY3bUeQC8Y9u2bS3GmHhtbe3txhgWZQAAVA0KEspaY2Njp6RngyD4o1wuNzXqPACkXC53kaQzBwYGflZfX98XdR4AAEqJgoSyV1dXt8p7v905d0Mmk6mNOg8wmnV0dJwsKRGG4arW1tbNUecBAKDUKEgoe/X19QVr7U+NMXFjzMfb2tr47xaIQD6fHxeLxZaGYbixubn5N1HnAQBgOPCDJipCIpHYtWfPnluNMcdfeumlTVHnAUabdDodOOc+4Zzb+cgjj2SizgMAwHChIKFiLF26dLsx5t+89/NY/hsYWRMnTmyUNKmnp+cXy5Ytc1HnAQBguFCQUFESicTrkn7uvU9ks9nzo84DjAaZTGae936e9z6dSqV6o84DAMBwoiCh4jQ1NT0nqdN7f82qVatOiDoPUM3uvPPOWdbaJmPMmmQy+UbUeQAAGG4UJFSkxsbG+4IgeDIIght/9atfTY46D1CN0un0mMHBwRu89880NjY+EnUeAABGAgUJFauhoWG1MeaNurq6P8rn8+OizgNUk7a2NjthwoSUMWZPXV3dqqjzAAAwUihIqFjGGO+9/5W1djAMw4+l0+kg6kxAtbjssssWSZrZ3d2drq+vL0SdBwCAkUJBQkVLJpP91trbrLUzJk2adI0kE3UmoNJlMpmznXOXBEFwRyqV6oo6DwAAI4mChIq3ePHiLufcj7z3Z+ZyuaujzgNUskwmM8MY0+q9v7ehoeGFqPMAADDSKEioCslkcqukX0q6NJPJzIs6D1CJ2tvbx0r6lDHmpaampnuizgMAQBQoSKgajY2NLxYKhYy1dkkmkzk16jxAJWlra7M1NTUfM8YMdnd33yHJR50JAIAoUJBQVZYsWbJB0v2SPpHJZD4QdR6gUlx00UWXSzpxYGDgF6lUaiDqPAAARIWChKrT2Nh4tzHmGWvtDeyRBBzamjVrTg+C4EpjTHtra+vmqPMAABAlChKqkZ8xY8YqSd1jxoz5RCaTqY06EFCu8vn8zDAMrzfG3J9IJJ6KOg8AAFGjIKEqLViwYHBgYOBW51zcGHMjeyQB75dOp2ucc9d773//4IMP3h1xHAAAygIFCVWrpaVlt3PuJ5JmTJw4ccne4/l8/sRsNvsf8/n8WRHGAyI3fvz4FmNMTaFQ+MWyZctc1HkAACgHbKqJqrdq1aoTgiD4jLX2Xufc9iAIrnPOXeW9/x/JZDIbdT4gCtls9nLv/Udramq+v2jRIu47AgBgCCNIqHrXXHPN7yX9ynv/6SAIvuK9/6Ck1621rHKHUSmXy51mjLnKe99OOQIA4N0oSKh66XQ6CILgDO/9XOfchc65bkk7vPfH5/P5cVHnA0ZSJpOZ6Jy71hizsbm5+TdR5wEAoNxQkFDV0ul0zaRJkz7hvf9jY8y93vuXjTHnSipIujwMwzOizgiMlPXr18ettZ+01m7v6urKRJ0HAIByREFCVZs4ceJc59wXvPd7jDE11toXvfddxpjzjDHbjTEXRJ0RGClbt269xns/tru7++epVCqMOg8AAOWIgoSqlkgkHo3FYp+VdIsxZpP3/iJr7U5JoaRJxphT2tvbp0ccExh2q1evnm+MOcc598tUKtUbdR4AAMpVLOoAwHAyxnhJL0t6ub29PV9bW/tQGIaXWms/4JwbKylhrX1Z0o8k+UjDAsNkaCXHJmPMmubm5lejzgMAQDljmW+MOplMZqIx5mpJXzPGjFXx/4ObGhsbn4g4GlByK1eunBCPx28OguC5RCKxKuo8AACUOwoSRq01a9YscM79gff+GmNM+8DAwDdaWlp2D315hqQrJE1TcTreW5LulrR7/2cDyk86nQ4mTJjwGWut7+rq+lfuOwIA4NCYYodRq6GhYX17e/uWeDxuvffXBkHwoqRHJH1LUlM8Zt3YsbHd3nuza3dhXBj6Pkm3Svp7Sa9FmR04HJMnT77aOTe5r6/ve5QjAAAODyNIGPXy+fzMMAz/9tZbb73+1ltvnXzOWVOfv+lTZ61b2vrB12trY06Sduzoi//4Z8+f9uOfPnfFprd2jZd0o6SOaJMDB7Z69eoPxWKxjwdB8JPFixe/HHUeAAAqBQUJkDR27NhvFQoDbV//00s7/+SLp95/sMfe/B/uvnJ17pWFkpok3TUyCYHDl8vljjPGfDYMw7uSyeS6qPMAAFBJKEiAtFDSPX/5tQt/+ac3n//s4Tzhc3/666tya147W9KZkrYPazrgCLS3t4+NxWJfNMa82tTU9Kuo8wAAUGnYBwmQ/vuFF8x45nDLkSQt/8ePdk6fOsZL+sYw5gKOSFtbm43FYtcbYwZ6enqYAgoAwFGgIGG0O03Swj+9+bwHjuRJsVjgr73mlIckfUpSMCzJgCN0ySWXfMQYc4L3/uepVGog6jwAAFQiChKqSj6fPyWTycw4gqdcNWliTVfD1SdtPtJr3fjx056XNFPSuUf6XKDUOjo6zjDGfMR7vzKZTG6NOg8AAJWKZb5RVZxzSWPMjGw2+7qkpwcHB19oaWnZdpCnnDB1at3Oo7nWWadP7Y3H7eDgoPuAJDaZRWQymcwMa+313vt7k8nk01HnAQCgklGQUFWMMTuNMVdKmuOcWxqPx8dls9mcMWaz9367994PPa7OGDP+W9/61sXdb/9uzNFfT16MxCJCmUym1lp7g6QXm5qa7ok6DwAAlY6ChKrivd8uyXrvX/He/z4Igsne+3nOuSmSxhpjaiVNlFTrvfdnn3327o7VT086mmu9+FLXuIEBVyM2jUV0jDHmOl+0UpKPOhAAAJWOgoSK1tnZGRsYGJhqrZ0WhuF0Y8x5YRieYa2dKCnunJOkQUneDJG0RtJzQRA8/vzzz/dv3db7yL33/376Ry4/4WBT8d7np7984TRJW7773e9u+vKXv1zy1wYcSkdHx0XGmA/GYrEfLFq0qD/qPAAAVAP2QULZa2trsxdccMGU2traad776caYqcaYqZKmGmMmqfhb9N2SdqhY+m/23j8sabcxxks62Xv/G2vts5J+29XV9VIqldqzzyUe/PCFM8fc8W/JlYebqVAIzYev/MWXCuGY1bfeeuvr1trXBgcHHxk3btyz9fX1hRK+fGC/1q5de1IYhp82xvwqkUg8FXUeAACqBQUJZSGdTgd1dXVT4vH4NO/9FGPMVO/9FGvtFElTvPeBpIIx5m3v/TZjzDZr7bZCobBtzJgx2+rr6/uGzjNpwoQJX5U01ns/PQiCk733P7HW5hsaGt48wOWvNMb8+i+/Nu9nf/LF8587nLz7bhTb0dERD4Jgvvf+fGNMKOlJY8xjiURiSyn+bID3Wr169ZRYLPYF7/2Gpqamu6LOAwBANaEgYUS1t7ePHTNmzPQwDKc756Zba6cPjQpN9t7boYKx03u/zTm33RizzRiz03u/I5lM9ujQ91iYTCbz18aYyyXd65xb29zcvOEwnveNeMy2/dmXz7/ja38y96CrgN38H+6+cnXulYWSmiT9+w+n6XS6ZtKkSec55xZIOs4Y82oYhut37dr1dCqVCg/5hwMchuXLl8dPOumkz1prexsaGm4bGiUFAAAlQkEqndskvSzpm1EHiVo6nR4zZsyYqbFYbJoxZpqkqZKmSJrqvR9rrfWSuiXtKBQK22Ox2NahxRV2PPTQQ28vW7bMHcv1s9nszZKCeDz+s0WLFm0/gqeT+J9nAAAgAElEQVT+X5K+fc5ZU5+/6VNnrVva+sHXa2tjTpJ27OiL//hnz5/2458+d8Wmt3ZNkPQJSR0HOlEmk/mAtXa+pHOMMQPOuY19fX0brrvuureP5bUB2Wz2OmPMSd3d3d97z1RRAABQAhSk0hmugvRhSd+S9A+SOg/j+IhIp9M148aNmxoEwd4CNNUYM805N03SOGOM896/reJ9QTu89zv2jgSNGTNm53Dep5NOp8fMmDFj8CivsffPtSket27K5NqdhYIPurr7J4eh71Px7/nvdJgr161YsaJu9uzZ5xlj5kua6b1/IQiC9YsXL36R3/zjSOXz+flhGDZ673/Q3Nz8VtR5AACoRhSk0qm6gpROp4Px48dPdc5Nr6mpmRqG4TQNFSHv/YShh3Vba7dL2hGG4Y4gCLaHYbi9t7d3R4VPK5sh6QpJ0ySFkjar+Oe8+2hPmMlkPhAEwQLn3LnGmB7v/QZr7cZEIrGrNJFRzVauXHl8TU3NZ40xmcbGxseizgMAQLVimW9IKm42aYw533s/XUMlaO99QUEQFJxz26y125xzrxQKhfXGmG2zZs3avmDBgsGosw+TrZJuL+UJk8nkG5LeSKfT+QkTJsz13i9wzl2Zy+WeGbrZ/lWxjw32Y+XKlRPi8fgnJT1GOQIAYHgxglQ6e0eQlkv6vKSzVRxteEHSj/TuKVnfknSepI+/5xyBpDsk3S3pO5KWSbrwPY/5lKSvH+B49z7n+ZikiyTNkdQr6QlJv5D0+j7P+fOhx3xq9uzZX6upqblx/vz5a7/4xS/eue+UuMNcHAFHzqxdu/aUMAwXSDpLxamIGwYHB59oaWk56pEqVJd0Oh1MmDDhM8YY193d/aMKH5kFAKDsUZBK5zYVC8okSX2SnpU0S9LpkgYktUnau1fJ4RakuSpOpWuRlJf0jKT7VCxf+zs+ICku6W8lnTN0/PeSZg5db2Do2ntXadtbkNpVLFg7JP1Q0j3H+GeBI9TZ2Vk3MDBwjnPuEknTvfe/s9ZueOihh5451kUrUNlyuVyj9/7snp6e76VSqd6o8wAAUO2YYldaJ0jaKOnbKpYkSbpaxSLyhaH3RzIS87iKhadF0m/1zr1GBzouSUlJH5L0nyU9us/x04dy/ZmkL++TY7yKo03/ScVChQgM7eO0oa2tbePChQvPcM4t8N5ff/HFF+9YvXr1Bufc44wqjT4dHR1zJX3YWnsL5QgAgJFBQSotL+lf9E45kor75HxExSlxp6g4DW84XaviSNWLKi6tvdc2SY8MZZkhae8mpkbST0Q5KgtDo0XPSno2l8tNdc7ND4LgsiAIrspms0875zY0Nze/GnVODL9cLnecMabZe59PJBKvH/oZAACgFChIpbVZ0pv7Ob5exYJ0nIa3II2RNH3o7UcHedwUvVOQpHem3KGMNDY27pC0Np1O/3rSpEkfcs4tiMVin8lms9ucc+u3bNnyxE033dR3yBOh4qxYsaLOOXe9pOeSyeQjUecBAGA0oSCV1s4DHN+7WenkQzz/WO8JmzH0/lEdZBNTvb/EbT7G62IYDd2U/5Skp9rb26fH4/H5QRBcedxxx12dyWR+471/lD1xqkdbW5udNWtWKgiC/pqamjuizgMAwGhDQSqtKYc4vuUAX99rwiG+fih7C1q/pA1H8DxWxaoQLS0t2yTlOzs77+rr6zvbWjvfe/+lfD7/WqFQeHTs2LHPDOcmvBh+l1xySb0xZvbu3bu/l0gk+LsEAGCEUZBKa5ak4/X+EZr5Q+/f2OdYTMURo30XbTjtGK/fM/Q2V1KNiqvW7WUk/ZOkWhWXIWfZ7go2VIKelPRkJpOZEYbhgiAIkv39/U3ZbPYJ7/1jyWRya9Q5cWRyudw5ki4LguAn11133dtR5wEAYDSyUQeoMkbSl1QsIXtdreKS3OslbRo61qtiQTp/n8eNU3Gp7QM5UJl97/EOFVem+5re/fd7raQTJT0gylFVSSaTW5PJZHb69On/4Jxb670/2RjzlUwm89lcLndBZ2cnvwipAPl8fqakVufcPYsXLx7uxVwAAMAB8INTaT2j4v5D/zL08SxJZ0jqknTLPo97UFK9pP9bxSW6B1Xcj2jr0Nu+9o4Ctai4yENaxVXyDnT8l5IukXSZpB+ouAz4TBWX/n5D0r+V4HWiDC1YsGBQxWXmN65ateqEeDy+wDm3pL+/P7FmzZrHe3t7NyxdunT7oc6DkbdixYq6MAxvkPRSMpm8N+o8AACMZowgldbLkv5K0lsqFp7Jku5Vcf+jfZdmXifpfw497ipJl0t6WNIySe+95+CZoa+dIKlRxf2PDna8T9LXJf1CxWJ2iYqb166U9BeS9pTihaK8XXPNNb9vbGxcuXnz5u947zvDMDxl3Lhxf5LL5f5w9erVH2pra+P//fJhZs+efY2koFAorBIjvAAAROpYV00DUCGG9tWZ770/3xizp1AobNi1a9djbEA6ctrb26fX1dXVNDQ0/Pt9irlc7grv/eWDg4P/e2gRDgAAECEKEjDKrFixom7mzJkXBEHwYUlTvPfPxmKxDYsXL/6dGL0YVrlc7mOSzjHG5BOJxMOrVq06PR6P31goFH6+ZMkSNmsGAKAMUJCA0cu0t7fPicVi840xZ1lruyRt6O/vf7ylpWV31OGqUTabvdkY0+i9n+ScWxGLxT7gvX+0sbHxzqizAQCAIgoSAOXz+XHGmLlhGM6XNFHS07FYbP3ixYtfizpbtVi+fHn85JNP/kvv/XTvvTPGNFhr1+3evfvrLOkNAED5oCAB+Hfee5PNZj8YBMEC7/2Z3vttxpj1mzZtevKmm27qizpfJevo6Dg5CIJbvPf3SzrVGDPDe79H0n2Sft7U1PRKtAkBAIBEQQJwAJlMZqIx5kJJFxpj6rz3TwVBsH7fBQZw+HK53CLv/Z+puB/a6ZI2GmN6VdwPbWMYhj9tbm5+PtKQAACAggTg4Nra2uzChQvPcM4tkHSq936TtXZ9d3f3U6lUauCQJ4AkKZvN/pmky4wxMyQ9K0lDm/qu894/a4y5q7GxcUe0KQEAAAUJwGFbvXr1lCAILrTWznPOxZ1zv5G0vrm5+a2os5WzbDY7R9KPVdznbJukuLX2Ue/9hv7+/gdaW1t7Ig0IAAD+HQUJwBFLp9PB+PHjz7TWzvfef9AY86akDYwqvZ/33qxdu/aLg4ODnzXGDBhjCpKWh2GYX7Jkyc6o8wEAgHejIAE4JrlcbqqK9ynNdc7FjTFPhmG4gVGlokwmM89au8J7L+99exAEtyUSiWejzgUAAPaPggSgJLz35s477zylUCjMN8Z8SNIO7/3jPT09G1Kp1J6o80Uhk8nUWmv/q/e+1RiTDsPwu83Nza9GnQsAABwYBQlAyeXz+ZlDizpcYK31zrknrbXrE4nElqizjaR8Pn+D9/6/GGNWS/q70fb6AQCoRBQkAMMmnU7XjBs37vyhfZVmW2tfc86tr6ure7q+vr4Qdb7h1NnZObmvr+9WY8w2Y8x/pBwBAFAZKEgARkQmk/mApAVBEJzjvR+01j7e29u7YenSpdujzjYcOjs76wYHB//47bffvi2VSnE/FgAAFYKCBGBEpdPpMePHj7/AGDPfWjvde/+yMebRBx988Plly5a59z5+aLre5wYHB29taWl5LYrMAABg9KAgAYjMypUrj4/H4/OttecZYwa894/X1taur6+vf3vvY7LZbNIY8xeSOrq7u7+fSqW6IowMAACqHAUJQOTa29vH1tbWzpU033s/2RjzjHNu/QsvvLDpzDPP/Kpz7hRJsySt7enp+eFoXRUPAAAMPwoSgHJiVq9efUoQBPONMWc558YbY+qttXc650JjzFzv/eqenp5bU6lUb9RhAQBA9aEgAShLnZ2d4/v6+tokXSmpxxizxXu/xVo7xzl3ZxiGP16yZMnOiGMCAIAqQ0ECUJZWrVp1Qjwe/4mkR7z3Y4wxx3vvp0nqkzRGUqcx5te1tbUPVfuS4QAAYOTYqAMAwP7E4/EFxpg93vtJxpgxzrlua+0bkkJjTNx73yrpf/X19X0zm82eGXVeAABQHRhBAlCWcrncnxtjJoVhuMsYs8daO7j3a865qZKWGmPGShonaVDSTyT978bGxk0RRQYAAFUgFnUAANif7u7uf5YUplKpcD9fNvl8/oeFQuEkSecaYy41xnxa0omSvjCiQQEAAACg3Nx+++3TOjs7Z0edAwAAAABGwlckrVJxSl0pfX3ovHUlPu+x+qaKufZ6b85/kfT/jXQoAACqHYs0AAAAAMAQ7kECgPL0z5J+EHUIAABGGwoSAJSnHVEHAABgNKIgARhJfy/pfEl/IKlr6FidpJ+pOOX3m5Ke2Ofx/yJplqQb9zlWI+l6SfWSJkraJOkOSWvfc61xkv5Q0jmSZkv6vaTHJd0maeAQOQNJH5N0kaQ5knqHcv1C0uuH8Tq/IumKoet/QdLlkrykZyT9UNJ2SZ+VNE/SFEkvSPqepFf2Ocd/GjrHNYdxvb3Ok/Q3krZK+ktJbx/BcwEAgLgHCcDI2jj0/tx9jp2hd/4tOmef45MlnSDpt5L69jn+F5KulLROUqeKBerPJF26z2OmqLiAQbOkUNJDkuIqlp5/lDT2IBnjKha5P5TkJN2nYrmqH3ru2Yd6kUMCSd+QdKaK5e01SRerWGD+i4plZp2kZ4c+/qaO7ZdWZ0n6lqSdQ9elHAEAcBQYQQIwkh6T9GkVC8EDQ8fOVnF0pVfvLkjn7POcfU1VsRD1DH1+j4qF5lIVi5BUHHGaKelfJf1y6PxG0k2SrpN0rYojSfuTlPQhSf9Z0qP7HD9d0reHrv3loXMeTJ2KBetrKpY0I+k7Q+d5RtKfqziSZVQsTReqONL1xiHOuz8fHDpHr6S/VnGECgAAHAVGkACMpN+pOLXuvH2OnSPpJRWnsJ2ld35xc6CC9FO9U44k6TcqFo1JQ5/HJDWoOBVubznS0PufqDiykjxIxmslPSXpRRVHova+bZP0iIqjWjMO+irfkVaxHO29/lNDH/9C70zz80OvQSpOGTxSJ0n6u6GPvylpy1GcAwAADGEECcBI8ioWnnoVC02viqVojaQ3VbxX5zQVp52dq+JCBa++5xwv7Oecg/t8PlPF6W1P6P2jPAMqjt5cquI0u93v+foYSdOH3n50kNcxRYdXRDa95/PBQxw/UpNULEcTVHyt5baXEwAAFYeCBGCkbVSxIJ2rYlGoU7G07F384FwVp5nNkXSX3l9yug9x/mlD7w90D87OoffTVbwvaF97R4YeldRxkGu8eYgMex1oGt6hpucdrqkqFrV/UnFhiC+puLhDqc4PAMCoQ0ECMNIeH3p/noo/4EvS0yoWlx4V70l6VcV7czbs5/mH+uF/7/03kw/w9b3Hd+7na3uP9R/g2uWmV8VpdZskLVBxEYirVCyWAADgKHAPEoCRtlPFe5HOU7EMvaXiVLq99+icreJS4F7vXvL7cG1R8b6f81QsWfuqUXFKX4/efR/TXnuPzx167L6M3tm89b3njcpWvTNd7/sqTtW7ScUlzgEAwFGgIAGIwmMqLi4wV8XRo72eUvGH+6slPa/9l5hDKai4rPbJKi64sJdRcenuqZKyB3l+h6TxKq4+t++/kddKOlHF1ffKcQrbWyouSjFJ7943CgAAHAGm2AGIwkYV9yQar/cXJKm46MCxTHH7NxWXzf6siputvqHiUtgn653V7Q7kl5IukXSZiqNFv1Vx4YcPDZ3n344h13D7hYpT7K5RsSS+d4ELAABwCIwgAYjC0yre57P3471+p+J9NdL7l/c+EjtU3K8oo+JUuYUqTru7XdJX9f7V6/bVJ+nrKpaNLhXL0iRJK1XcpHbPMeQabv0qTrWzKi7YUC5TAQEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI5dLperz+VyU6POAQAAjp6NOgAARGX58uXxXC5X39bWdsz/Fnrvjff+q8aYvyrF+QAAQDT4Jg5g1DrxxBOXeu9/eOWVVx5/rOcyxnhjzD3Ouesvvvji5lLkAwAAI4+CBGBUWrt27SRrbYsx5pG77777zVKcc2BgICepYIxJ5fP5U0pxTgAAMLIoSABGpcHBwUbv/ULv/cZly5a5Upzz+OOPf95au1rSpZJu6OzsrCvFeQEAwMihIAEYdTo6OmZLutoYUwjDcGOpzrtgwYLBMAwfMMZ0SfpYf39/C/cjAQBQWfjGDWC0MdbaZkkLJPUEQfBKKU8eBMFT3vuBMAwHJX350ksvvb6zszNWymsAAIDhQ0ECMKrk8/mLJN1kjClIWltXV/f7Up6/q6vrZUl5a22vc26nMeZz/f39NzLdDgCAykBBAjBq5PP5s7z3CUld1toe7/22+vr63lJeI5VKDUja5b3fYoyZ4px7znv/if7+/ptXrlx5zKvlAQCA4UVBAjAqdHR0nOGcu957/0FjTE0YhjskhcNxLWPM2977AUldxphZkh6TdGltbe3ncrlcvSQzHNcFAADHjoIEoOrlcrlzgiC4QdIZ3vs93nsjaacxZlgKknOu31obGGN+572f6b0fP3Tdeknnp9Pp+HBcFwAAHDsKEoCqlsvlrvDe//+S5kh62Vo7W9IWa23BGDMsRcUYM+i9t977XmNMYK29yhjzlDHmL2tra/9paBoeAAAoQ6ysBKCqOeemWWs7vfcXS6qRNE3Seu99IGmMitPdfCmvaa213vvJki723t8vaWZ/f3/62muvfb2U1wEAAKXHCBKAqpZMJu/o7+//R2PMf7XW7vTez5J0oqRx3vtg5cqV40t9Te+9lXS/pL/t6en5G2PMr+vq6q4o9XUAAEDpUZAAVL3W1taehx566IEwDF8Nw/CvrLW3GmNelnTl2LFjp5T6ev39/Xds2rTp/21sbLwvlUoN9Pf33+W9Pz2fz59Y6msBAIDSoiABGBU+/OEPz7HWjgvDcG0ikbjDOfcd59yf7ty5861SX6u1tbXnpptu6tvn882Sng7DsL7U1wIAAKXFUrMARoV8Pn99GIZBMpn8WRTXv/POO6cVCoWveO9/1NTU9EoUGQAAwKExggSg6rW3t4/13n8oFos9FlWGRYsWbffePyXpo1FlAAAAh0ZBAlD1amtrz/Pe9y5evPjFKHP09fX92hhzUiaTOTXKHAAA4MAoSABGgwu9908YY0q6nPeRuu66694Ow3CjpKvFFGcAAMoSBQlAVVu5cuXxzrmZzrmNUWeRpEKhcE8QBDOy2ewZUWcBAADvR0ECUNXi8fgFkl5dsmTJzqizSMUV7rz3G6y1V3nvGUUCAKDMUJAAVK3169fHgyC4wDm3Ieos++rv739A0tQ777zzrKizAACAd6MgAahaW7du/ZD33o8dO/aZqLPsq7W1tadQKDwyODi4qK2tjX+HAQAoI3xjBlC1rLXzJD1VX19fiDrLe4Vh+IAxZvzFF198QdRZAADAOyhIAKpSLpebKmmOtbYsFmd4r5aWlt3GmHXW2o92dnbGos4DAACKKEgAqpIxZq73fnNDQ8ObUWc5kO7u7nXGmLrdu3fPjToLAAAooiABqDree+Ocu6BQKDwZdZaDSaVSe5xz66y1VzCKBABAeaAgAag62Wz2g8aY8fF4/ImosxxKT0/Pg5JiAwMD86POAgAAKEgAqpC1dr6k5xKJxK6osxxKKpUasNY+EIbhFevXr49HnQcAgNGOggSgquTz+XGSzhwcHCzLxRn2p6ura70xxmzevPnCqLMAADDaUZAAVBXn3Pne+94lS5a8GHWWw5VKpQacc/cGQfDRTCZTG3UeAABGMwoSgGozz3v/hDHGRx3kSOzatWu9pH5r7UVRZwEAYDSjIAGoGitXrjzeWjujpqam7BdneK9UKhV67x+UdCmjSAAARIeCBKBq1NXVXeice2XRokXbo85yNHp6eh5zzg0EQXBx1FkAABitKEgAqsL69evjzrnznHOPRZ3laKVSqdBa2+m9v3xosQkAADDCKEgAqsK2bdvONsa4sWPHPhN1lmPx0EMP/cZ731UoFBZGnQUAgNGIggSgKnjv5xljnqyvry9EneVYLFu2zA2taPdhRpEAABh5FCQAFW/16tVTJJ3c19dXcYsz7E9zc/NT3vvtYRh+JOosAACMNhQkABUvCIILjTGbW1tb34w6S4l4SXcbY+an0+lJUYcBAGA0oSABqGhtbW3Wez/POfd41FlKqamp6Xlr7ZYJEyZcFnUWAABGEwoSgIp20UUXnWaMqSsUCk9GnaXEfBiGdxlj5g9NIQQAACOAggSgogVBMNcY83xLS8vuqLOUWjKZfMl7/7q19qNRZwEAYLSgIAGoWEOrvJ05ODi4Meosw8Vae4+19vxcLjc16iwAAIwGFCQAFSsMw7mSepYsWfJi1FmGSyKR+J0x5lXv/ZVRZwEAYDSgIAGoVEbShd77x40xPuoww8kYs1bSeblc7rioswAAUO0oSAAqUiaTOSEIgqlhGFbF3kcH09DQ8KYx5gVJ9VFnAQCg2lGQAFSkIAjmee9/t2TJkp1RZxkJg4OD9xhjTl+1atUJUWcBAKCaUZAAVJxMJlPrvT9P0oaos4yUa6655vfOuedisRijSAAADCMKEoBKdLb3frC7u/vZqIOMJO/9XcaYD2az2TlRZwEAoFpRkABUHGPMBdbap1KpVBh1lpGUTCa3eu+fstZeFXUWAACqFQUJQEXJ5XJTrbUnG2OqfnGG/THG3O29PyGTyZwadRYAAKoRBQlARfHez5O0paGh4c2os0ShsbFxh6QnJX006iwAAFQjChKAipFOpwNr7Tzv/ahZnGF/giC42xhzfEdHxxlRZwEAoNpQkABUjEmTJp0uqba2tvbJqLNEafHixV3GmI2xWOxq772JOg8AANWEggSgYjjn5nnvn62vr++LOkvU+vv775U0NZvNfijqLAAAVBMKEoCKkE6nxxtjTnPOPR51lnLQ2traMzTV8Oq2tjb+LQcAoET4pgqgIkyYMGGu974rmUy+HHWWcuGce9AYM2nhwoVnR50FAIBqQUECUAnM0Op1T0ryUYcpF8lksts597Bz7ipGkQAAKA2+oQIoe+3t7Sdaa6d47x+LOku5CcPwAUnjLr744guizgIAQDWgIAEoe7W1tRc4515JJpPdUWcpNy0tLbslPWSt/Wg6nQ6izgMAQKWjIAEoa5lMptZ7f55z7tGos5Srt956a52kmsmTJ8+LOgsAAJWOggSg3J3tnBt49NFHn4s6SLm66aab+owx65xzlzOKBADAsaEgAShrQRDMi8ViTy5btsxFnaWchWH4sKSaiRMnzo86CwAAlYyCBKBs3X777dMknbhnz54nos5S7pLJZL/3/j7v/UczmUxt1HkAAKhUFCQAZWvMmDFzvfdvtra2bo46SyWoq6t7VFJorb0o6iwAAFQqChKAspROpwNr7bxCocDS3oepvr6+YIy533t/WTqdHhN1HgAAKhEFCUBZmjRp0une+5rdu3f/JuoslaS2tvYxSQPjx49nFAkAgP/T3p3HR1Xeexz/PmdmkpCwY9gVUFxBEQVZXKOSzIQQiku0davdbGtve7vf9t6WS1vt4m29XW9xqUtdU5USSGYmauMKqIiCIloRN7ggYIAEyTrnuX+c4RJjAtnIySSf9+uV15AzZ875TrB0fvk9SwdQIAHokVzXneq67mtFRUX1fmdJJcku0hOO45wZj8ez/M4DAECqoUAC0OMsXbp0gDHmWGvtS35nSUUrV658WVKVtXaW31kAAEg1xu8AANBcPB6f7brutEgk8ntJ1u88qSgWi02x1uY3NDT8trCwcF8rp6VJOlrSCZImSMqS1D/53D5JH0l6R9I/Jb0pqfawhgYAoAcI+h0AAJoxiUTidGPMGlEcddjKlStfmTlz5lnp6elnSnq0yVMTJV0i6XxJZ0rKDARMYujQjMr0tEB9elqgTpLqGhJp9XVuWuWu2iGNjW5IUr2kVZL+IelhSa926xsCAKCb0EEC0KOUlpaOCwQC16Snp/8mJydnr995UllZWdlJkha88sorv/v+978/T9J1ks4+Ymi/D088Ycjb004f8e7sGaO3Tjt9+K5gMNBqMbrmpe2DVz2/dcSqF7aO3/D6rvHbPvhopKTVkm6TdKekuu54PwAAdAcKJAA9SjQaXWCM6RcOh+/zO0svEJgyZcrCdevWfSYUcsbOPGPUy1dcdvza/PCErZ256FPPbj7i7ns2THn62S1T99U07pF0k6Q/yesyAQCQ0iiQAPQYFRUVGfX19d9uaGh4pKCgYIPfeVLcdEl/ykgPTM6bM37lD787/fnRo/t36Ryi3btrQzfe9MJpy8o2nbV3b8MWSddLerwr7wEAQHdjFTsAPUZdXd1J1tqGffv2/dPvLCksIOknklZMnzYiWL78ot//4eacp7q6OJKkwYMzGn51w9nPPRG75A85547d5TimXF4nKaOr7wUAQHehgwSgx4jFYl+QtDkcDsf8zpKijpD0UGa/4LTvfXvaks9fM/mt7rz50uUbx/zHf664ZPee+nckLZC3Ah4AACmFDhKAHqG0tHSkMWZMfX39ar+zpKijJD09dkz/iY8Uz1vc3cWRJM0vmLglXnLR4uMmDu4v6VlJJ3d3BgAAOosCCUCP4DjOFNd1NxcWFu70O0sKGi/pmYnHDE5b9vD8uyedMKzaryCjR/evLfv7p+4/berwnZKelDTVrywAAHQEBRIA31VUVASNMVOMMS/5nSUFjZD06LETB9cvf3j+fUcM6+f7SnLp6UF3aXHhkumnjXhXUlTSsX5nAgCgrSiQAPiupqbmOGttoKqqis1H2ydNUsnYMf0zHrq34MGsrFDC70BN3X93ZNkJxw/dI6lM0kC/8wAA0BYUSAB85zjOqcaY14qKikO/ojwAACAASURBVHzvfqSY3wwamHb8g3/Nv2/o0IwGv8M0l54edB96YO7fRgzPHCDpXrEwEAAgBVAgAfBVRUVFf0kTE4nEy35nSTEXOI75yr99d/rfjzpyYI3fYVozqH96408Xzvp7IGAikq7yOw8AAIdCgQTAV3V1dVOttR/OnTv3Xb+zpJAMSYvPO3vMc1defuI7foc5lEjuhG2FBUc/IelmSdk+xwEA4KAokAD4yUg6LRAI0D1qn3/J7Bcc+fOfnPWU30Ha6ueLzlo5dEiGlfQffmcBAOBgKJAA+CYajY6TNHD37t1r/c6SQjIlfWfB/GOfGj26f63fYdoqKyuU+Nw1kx6X9CVJI/3OAwBAayiQAPjpNGvtxqKior1+B0khX8jMDGX94LvT1vgdpL2uv+7U14dnZ1ZJ+pbfWQAAaA0FEgBfFBcX93Mc50THcdj7qH2+dP65Y18YNDC90e8g7RUMGrug8JiVkq6VFPI7DwAALaFAAuCLQYMGneS6bv2ePXve9DtLCplujE768hdOTtk5W1/90pT1oZAzUFKB31kAAGgJBRIAv5xurV1bVFTUozY37eEuGjtm4OYppwzf43eQjho6NKPhpBOHvinpIr+zAADQEgokAN2utLR0pKRRjY2NKTePxmdzTjs1e6PfITpr5hmjNkrKFRvHAgB6IAokAN0uGAxOdV13c2Fh4U6/s6SQQZJOPXPW6JTfLyqcO+EdScMlneBzFAAAPoECCUC3qqioCFprT7HW0j1qn8mSnEjuuP/1O0hnTZs6fHe/fsF9kqb4nQUAgOYokAB0q7q6uuMlBSS95neWFHPCoIFpVYMHZzT4HaQrDD8ic6foIAEAeiAKJADdylo71RjzWn5+fp3fWVLMhGHD+lX6HaKrDM/uVylpgt85AABojgIJQLdZsmTJYEnH1NXVMbyu/QZlZQZr/A7RVQYMCNVIGux3DgAAmqNAAtBtMjIyTpW0s7Cw8D2/s6SgARnpwXq/Q3SVjH6hekkD/M4BAEBzFEgAuosxxkwxxqzzO0iKCgQCjut3iK4SDBhXUtDvHAAANEeBBKBbRKPRcZIG1dXVvex3lhS1t6auMc3vEF3lo32N6ZKq/c4BAEBz/PYOQFc7Q9LVki6UdIQkK2n75Zdf/vQll1yy8vbbb+dDccfsratJpPsdoqvU1DakiQIJANADUSAB6CrZkm43RgXHjBv49mmnDntr1PDMV125ZvP/7hu05uWdkdtvv/2L8gqnr0ja62/clPPeh7tqes2iBjt21AyRxFw0AECPQ4EEoCscJali1IjMfj/9wWm3RC4Yu62Fc565/+9vHXXjr9fOr9xd/7SkCyT1mmWru8Eblbtqh9bVNTrp6cGUn4u0fce+YZLe8DsHAADNMQcJQGdlSFpy9PiBpvzhvDtaKY4kSZ/+1DHvRYvzbh01InOEpAfEv0Ht8XoiYQPPvbBtqN9BOmvrBx+lV1XVD5L0ut9ZAABojg8nADrrG/0zg8fd++dz/jZ0UHrDoU4eOyqr9tbfnvVgKOScK+mKbsjXW7wn6e3Yo++m/OaqS/6+cby12ifpBb+zAADQHAUSgM4ISvrO/Pxxzxw1pn+bNzGdOmnongvOGvW8pH87fNF6pSdeXrdjvN8hOmvVC1vHS1ohqdfs6wQA6D0okAB0xmxjzLCvXHviK+194TWXH7tW0kmSju36WL3WstffqDx254c1Kbvcd2OjNS++tP1EScv8zgIAQEsokAB0xglDBqdXTjiq/772vvCc2SN3pqU5dZJOPAy5eqtlDQ1u5Z9vXXeS30E66t4HNkyoqqrPkHSv31kAAGgJBRKAzhiWlRlo89C65jL7BffJ2ysJbdMoqbg09vY0v4N01IMPvXG6pKhYwRAA0ENRIAHojJ0ffdSQ2dEX79vXmCVpRxfm6Qtu2rxlb/atd7460e8g7VX++LsjXn3twxMl/dTvLAAAtIYCCUBnvLprT/2QjW/vyWrvCx97esvw+gY3TVK75y/1ce9LevAvd716rt9B2uvm368511r9Q9Iav7MAANAaCiQAnfGctdp2y93/nNzeF95bvOkUSS9LeqfLU/V+39+8ee+A//zZytP8DtJWi/+y7thX1384UdI3/M4CAMDBUCAB6AxX0n89UvruORvfansXacXq7UP/8czWGZJuOHzRerWtkn5xf/Eb52/ctLvd3bvuVllZG/rT4lfyJC2WtN7vPAAAHIzxOwCAlJch6ckjx2SNfviO8+8dMyqr9mAnb3izavA11z/7xeqP7Mqqqqo5kmz3xOx1QpKePmbCoOzHyi65Jxg0Pfbn+KmikgUvvrQ9U9Lpkqr8zgMAwMHQQQLQWbWSCj/cZRsXXPPUN/5avHFCayf+4bbXjl9wzaOfC4QG7bvtttsaY7FY/sKFC/l3qGMaJF3+1tt7hnz5Xx473+8wrfnBj54948WXth8v6TJRHAEAUgAdJACdFo/HZ+zevftnN95445Fr1649dtSIzA+mTB668ZhxA3YmXNd56+3q7JfXVx67Y2ftIEmLf/vb395y3HHH/UzSJEm/N8bcEw6HWfa5Y3IlLft00fEVv7rh7FV+h2nq5t+9eNLNf3j5EmvttZLu8jsPAABtQYEEoFOi0ej0QCDwHdd1pwYCgflz5sxxJV0l74P7kfKG0L0tqUzSPZLelaSSkpLctLS031prZYwpbmxsfKCgoGCDX+8jxRU5jrn/ystPiN2w6MwX/A4jSX++7c3Zv/z1MxcMGDDwpl27dv2b33kAAGgrCiQAHVZaWnpyMBi8PpFIzDHG/DESidysNs4pWrhwoXPGGWd8LhAIXG+tbTDG1En6S0NDwzPz5s3b2Nbr4P99WtKd8+ZOWPGn/77gST+D/PinK0+/657X8gsK5m366le/+qK19sZIJLLOz0wAALQVBRKADiktLT01EAhcLiksKeG6blF+fv5b7bnG0qVLB6SlpV0vqUBSvbV2t+M4g40xy1zXXWeMeS0vL2+bMT13AYIeJs8Y89D0aeM++u1NM+4aO2ZATXfefE9VXfALX30ssuq5rZMkXReNRndZa282xtRK+lF2dvayadOmNXRnJgAA2osCCUC7xOPxLNd18yV9VVLIcZyA67qLI5HInR25XiwWmyTp94lEoioQCGRaazcYYzKttcMlDZJUWl9ff/f8+fM/6Lp30TsVFxeP3Lp16y2/+MUvzqnZV2m//rWppV/87OSN3XHvh5a8eeTPb3qhYPuOfdXyFmR4sbi4ODBo0KCvS/qK67r7JP1PIpEoLigo2NUdmQAA6AgKJABtkvywO8N13XMdx5nlum6DpIHGmDRjzGfz8vLe7sh1rbUmHo9fL+kMa20/Y8wga+1ax3EaXNedaowprqqqureoqKi+a99R7xKPx4+01v7QWptXWVn5zSuuuGKmpG9NOmnYph9+d/rj55w1dufhuO9L67YP+ukNz523+qXtU6y1t0j6npqsVrd8+fIhgUDgW8aYiLXWdRynxFq7Oj09/ZmcnJy9hyMTAACdQYEE4KAqKiqCDQ0NM6y1s6y1udbad621jY7jHG+t3WKMeWXlypX/tWjRIrej94jFYqdZa38paZW19kRjzGB5c5AeqK6u/ivF0cEtW7ZsTCgU+rq1tkjSA9nZ2T9ODmU7UdJvjFHepJOGvf7Zq05addnFx7/XFfeMlr898tY7Xp3x4prtp7iuXSXpm5Keb+nceDw+2XXdPxtjXNd1qx3Hca21z0namEgk1mZlZb2Zk5PT2BW5AADoLAokAAdVUlJyVCgUutYYM0PSGmttQNI0SVuMMf2stQ9GIpFHOnOP1atXh3bs2PEdY8yYRCJR7ThOrqSN1tpv5+fnb+6K99FbRaPR8dbaqx3HuTi5IuC14XB4TbPTpkv6oaR5Q4Zk7Jl1xqhXzztn7Kb8/PFbBvVPb1NhUlfX6JRG3xld8dT7E1as2jp5+4592ZL+IenG5ONBxWKxq6y1lyb/m9kpaYekUZKGGWMek7Q2HA6Xtue9AwBwOFAgATiksrKysY7jXOy6bp4xxjHG1Fhr1xpjZhhj/iMvL+/Fzt4jFotdLOkz1tr0QCCwzHXdXdbacaFQ6O4LL7yQ+UfNLF68ODR+/PjzXNedbYzJtdaGJN2zatWqPxykmzda0tWSLpV0aiBgbPYRmTtGjsjcOXZM/51ZmcH6zKy0eknaV9MYqtnXkLZl696h27Z9lL19R012Q4MbkrRe0sPy9jXa1Na8ZWVlxziOc5vjOK+6rntCskh6Q1K2pEmu694wd+7cRzv+EwEAoGtQIAFok+TiDP8uqUjS05I2G2POcl33qq7o8kSj0VMk/c4Y80BVVdVd69evr5s9e/ZFksYnEom78vPzd3T2Hr2BtdaUlpZOchwnR9I8Y8xeeYtZhILB4HVz5sxp615SQyWdK2mypBMkTZCUJal/8vl9kj6S9I6kf8orjJ6UtK2j2ePx+FckzUgkEruNMSdLqpY00Fr7i/z8/PKOXhcAgK4U9DsAgJQxXFKd4zj/bq09zlo7zVr75N69ez/siotv27btnyNHjvxVfX39E0VFRTWStHDhwkdmzJhxUSAQuCYej9+dl5e3vSvulYqKi4sDmZmZp8ZisZmhUOgi13W3WGtfN8ZMlrRd0ku7du36ZzsuWSlpSfKru6y11hYaY94xxnwgaabrujdTHAEAehI6SAAO6Y477sgYNWrUddbatyORSMmyZcvGBIPBix3HyczLy/vF4bz3woULnVmzZi2w1h7tOM5dfbVIikajx0taLK+rsy65Itzp1tpKY0xAUnE4HH7Y35QHF4vFhlprvy5pvDFmnKRbk3+vT+Xl5T3ldz4AACTJ8TsAgJ5v5MiR86y1jdXV1TFJmjdv3hZr7WJr7V2H+96LFi1yV65cucQYs8l13WtKS0tHHu579kSRSOQNa+03rbUrJE2UdLK1ts4Y86akQa7rtnk+kF/C4XClMWaXpPHW2h+Fw+H7XNddaq09LxqNTvc7HwAAEh0kAIcQi8XOcF13jqRbfJ4HZKLRaMRxnFMk3ZuXl/e+j1l8E4/Hh1trv2+tLZD0mOM4e1zXnV1fX39ZKmymG4vFTkskEs7cuXNXNzk2RdL8xsbG0oKCgk4v+AEAQGfQQQLQqtLS0pHW2lzXdWM9YJEEGw6Ho5LWu657xbJly8b4nMcXjuNkWGurJf1R0omu6x5jjPnHmjVr/P77aZNwOLymaXGUPLZW0sPBYHBuLBY7w6doAABIokAC0IqysrL0YDB4qTFmQ0/5rb4xxubl5S0zxqwOhULXRKPR8X5n6k7xeDyrsbHxEknPRyKR31lrv22tXWet3d2ZjXp7gnA4vL6xsbFUUjjZUQIAwBcUSABa5DjOhZKCVVVVZX5naS4cDj+eSCTWSbq8rKxsrN95uoO11riuu8BxnL0ZGRnlkpSfn/9SIBC4VVLc53hdoqCg4EVrbVTS/FgsNsnvPACAvok5SAA+IR6PT7bWLmhoaPjLvHnztvidpxUmFovlW2tPaWhouLewsPA9vwMdTrFY7GxjzJnW2lvC4XCl33kOp2g0OssYc6G1tjgSibzhdx4AQN9CBwnAxyxZsmSw67oFxpgne3BxJHlzksqMMS+FQqErHn300aP8DnS4lJSUHCXpvEQiUdLbiyNJikQiK40xT0q6tLy8/Fi/8wAA+hYKJAD/b+HChU56evpFkrbm5uY+7XeeNrDhcDgmaU1jY2OvLJLi8XhWKBS6JJFIvJSfn/+a33m6S15e3lPGmGcTicRlsVhsot95AAB9BwUSgP83c+bM2YFAYHhGRsZSY4z1O09bRSKRuKQ1iUTiypKSkgl+5+kq1lpjrb3IcZyazZs394p5Ru0RDocrHMdZYYy5rLS0dJzfeQAAfQMFEgBJUklJyQRjzPkNDQ1Lc3Jydvudp70ikUjcWrsiLS3tyuXLl5/od56uUFZWdrakIxOJxEPXXXddg995/JCXl/cPa+2LjuN8pq8syAEA8BcFEgCVlJRkhkKhBZJeLCgo2OB3no6KRCJPJBKJVYFA4OJoNHq833k6IxqNjg8EAudJKu0Be1D5KhwOx40xrxljruqr+18BALoPBRIApaen50uqq6qqetTvLJ01d+7cRyWtNMYUxePxE/zO0xHxeDzLGHOxpLXJTVT7OhsOh0scx3klGAx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"</svg>\n"
],
"text/plain": [
"Plot with title “Graph for 5 rules”"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
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}
],
"source": [
"plot(rules.all[1:5],method=\"graph\",interactive = F)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
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"language_info": {
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