src/PricePredictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Gas Price Data Exploration\n",
    "===\n",
    "\n",
    "I compare a variety of machine learning regression algorithms to determine which will perform the best with the gas price data from the EIA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing packages into '/home/nbcommon/R'\n",
      "(as 'lib' is unspecified)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The downloaded source packages are in\n",
      "\t'/tmp/RtmpLp6kCM/downloaded_packages'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing packages into '/home/nbcommon/R'\n",
      "(as 'lib' is unspecified)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The downloaded source packages are in\n",
      "\t'/tmp/RtmpLp6kCM/downloaded_packages'\n"
     ]
    }
   ],
   "source": [
    "#Load libraries and tools\n",
    "library(\"devtools\")\n",
    "library(\"AzureML\")\n",
    "library(\"httr\")\n",
    "library(\"RCurl\")\n",
    "library(\"jsonlite\")\n",
    "library(\"XML\")\n",
    "install.packages(c(\"caret\",\"corrplot\"))\n",
    "library(\"caret\")\n",
    "library(\"corrplot\")\n",
    "install.packages(c(\"e1071\",\"cowplot\",\"gbm\"))\n",
    "library(e1071)\n",
    "library(ggplot2)\n",
    "library(cowplot)\n",
    "library(plyr)\n",
    "library(gbm)\n",
    "library(reshape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "I use a helper function to load in data from the Azure Blob Storage location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#This function simplifies calls to the Azure Blob Storage containers to read and put data\n",
    "azureBlobCall <- function(url, verb, key, requestBody=NULL, headers=NULL, ifMatch=\"\", md5=\"\", ...) { \n",
    "  urlcomponents <- httr::parse_url(url)\n",
    "  account <- gsub(\".blob.core.windows.net\", \"\", urlcomponents$hostname, fixed = TRUE)\n",
    "  container <- urlcomponents$path\n",
    "\n",
    "  # get timestamp in us locale\n",
    "  lct <- Sys.getlocale(\"LC_TIME\"); Sys.setlocale(\"LC_TIME\", \"us\")\n",
    "  `x-ms-date` <- format(Sys.time(),\"%a, %d %b %Y %H:%M:%S %Z\", tz=\"GMT\")\n",
    "  Sys.setlocale(\"LC_TIME\", lct)\n",
    "\n",
    "  # if requestBody exist get content length in bytes and content type\n",
    "  `Content-Length` <- \"\"; `Content-Type` <- \"\"\n",
    "  if(!is.null(requestBody)) {\n",
    "    if(class(requestBody) == \"form_file\") {\n",
    "      `Content-Length` <- (file.info(requestBody$path))$size\n",
    "      `Content-Type` <- requestBody$type \n",
    "    } else {\n",
    "      requestBody <- enc2utf8(as.character(requestBody))\n",
    "      `Content-Length` <- nchar(requestBody, \"bytes\")\n",
    "      `Content-Type` <- \"text/plain; charset=UTF-8\" \n",
    "    }\n",
    "  } \n",
    "\n",
    "  # combine timestamp and version headers with any input headers, order and create the CanonicalizedHeaders\n",
    "  headers <- setNames(c(`x-ms-date`, \"2015-04-05\",  unlist(headers)), \n",
    "                      c(\"x-ms-date\", \"x-ms-version\", unclass(names(unlist(headers)))))\n",
    "  headers <- headers[order(names(headers))]\n",
    "  CanonicalizedHeaders <- paste(names(headers), headers, sep=\":\", collapse = \"\\n\")\n",
    "\n",
    "  # create CanonicalizedResource headers and add any queries to it\n",
    "  if(!is.null(urlcomponents$query)) {\n",
    "    components <- setNames(unlist(urlcomponents$query), unclass(names(unlist(urlcomponents$query))))\n",
    "    componentstring <- paste0(\"\\n\", paste(names(components[order(names(components))]),\n",
    "                                          components[order(names(components))], sep=\":\", collapse = \"\\n\"))\n",
    "  } else componentstring <- \"\"\n",
    "  CanonicalizedResource <- paste0(\"/\",account,\"/\",container, componentstring)\n",
    "\n",
    "  # create the authorizationtoken\n",
    "  signaturestring <- paste0(verb, \"\\n\\n\\n\", `Content-Length`, \"\\n\", md5, \"\\n\", `Content-Type`, \"\\n\\n\\n\", \n",
    "                            ifMatch, \"\\n\\n\\n\\n\", CanonicalizedHeaders, \"\\n\", CanonicalizedResource)\n",
    "\n",
    "  requestspecificencodedkey <- RCurl::base64(\n",
    "    digest::hmac(key=RCurl::base64Decode(key, mode=\"raw\"),\n",
    "                 object=enc2utf8(signaturestring),\n",
    "                 algo= \"sha256\", raw=TRUE)\n",
    "  )\n",
    "\n",
    "  authorizationtoken <- paste0(\"SharedKey \", account, \":\", requestspecificencodedkey)\n",
    "\n",
    "  # make the call\n",
    "  headers_final <- add_headers(Authorization=authorizationtoken, headers, `Content-Type` = `Content-Type`)\n",
    "  call <- httr::VERB(verb=verb, url=url, config=headers_final, body=requestBody, verbose())\n",
    "\n",
    "  #print(\"signaturestring\");print(signaturestring); \n",
    "  #print(headers_final); print(call)\n",
    "  return(content(call,type=\"text/csv\",header=FALSE))\n",
    "} \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We first load the training US EIA data. Then we add column labels and adjust the metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In Sys.setlocale(\"LC_TIME\", \"us\"): OS reports request to set locale to \"us\" cannot be honoredWarning message:\n",
      "In Sys.setlocale(\"LC_TIME\", \"us\"): OS reports request to set locale to \"us\" cannot be honored"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t1081 obs. of  9 variables:\n",
      " $ Date       : Date, format: \"1994-11-23\" \"1994-12-02\" ...\n",
      " $ Week       : int  47 48 49 50 51 52 1 2 3 4 ...\n",
      " $ CrudePrice : num  17.7 17.8 17 16.9 17.1 ...\n",
      " $ GasStock   : int  168748 173223 172344 171935 168641 170102 172617 174547 175522 177741 ...\n",
      " $ DaySupply  : num  27.7 28.3 27.2 27.3 26.3 26.2 27.5 28.1 29.3 30.5 ...\n",
      " $ GasImports : int  170 176 368 248 285 284 110 274 178 186 ...\n",
      " $ RefinerPerc: num  95.3 95 91.8 92.9 92.8 93.9 95.3 92.7 90.4 88.8 ...\n",
      " $ OilExports : int  101 98 98 98 98 99 100 100 100 101 ...\n",
      " $ OilImports : int  7161 6224 7030 7222 6566 7334 7136 6442 6521 6465 ...\n",
      "'data.frame':\t1081 obs. of  3 variables:\n",
      " $ Date    : Date, format: \"1994-11-23\" \"1994-12-02\" ...\n",
      " $ GasDate : Date, format: \"1994-11-28\" \"1994-12-05\" ...\n",
      " $ GasPrice: num  1.18 1.14 1.12 1.1 1.09 ...\n"
     ]
    }
   ],
   "source": [
    "key <- \"<private access key>\"\n",
    "trainfeatures<-azureBlobCall(\"https://blobstorageaccount.blob.core.windows.net/blobstoragecontainer/old_trainfeatures.txt/part-r-00000\", \n",
    "                       \"GET\", key)\n",
    "names(trainfeatures)<-c(\"Date\",\"Week\",\"CrudePrice\",\"GasStock\",\"DaySupply\",\"GasImports\",\"RefinerPerc\",\"OilExports\",\"OilImports\")\n",
    "trainfeatures$Date <- as.Date(trainfeatures$Date)\n",
    "\n",
    "trainvalues<-azureBlobCall(\"https://blobstorageaccount.blob.core.windows.net/blobstoragecontainer/old_trainvalues.txt/part-r-00000\", \n",
    "                       \"GET\", key)\n",
    "names(trainvalues)<-c(\"Date\",\"GasDate\",\"GasPrice\")\n",
    "trainvalues$Date <- as.Date(trainvalues$Date)\n",
    "trainvalues$GasDate <- as.Date(trainvalues$GasDate)\n",
    "\n",
    "str(trainfeatures)\n",
    "str(trainvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we load the final test data to see how our models do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In Sys.setlocale(\"LC_TIME\", \"us\"): OS reports request to set locale to \"us\" cannot be honoredWarning message:\n",
      "In Sys.setlocale(\"LC_TIME\", \"us\"): OS reports request to set locale to \"us\" cannot be honored"
     ]
    }
   ],
   "source": [
    "testfeatures<-azureBlobCall(\"https://blobstorageaccount.blob.core.windows.net/blobstoragecontainer/old_testfeatures.txt/part-r-00000\", \n",
    "                       \"GET\", key)\n",
    "names(testfeatures)<-c(\"Date\",\"Week\",\"CrudePrice\",\"GasStock\",\"DaySupply\",\"GasImports\",\"RefinerPerc\",\"OilExports\",\"OilImports\")\n",
    "testfeatures$Date <- as.Date(testfeatures$Date)\n",
    "\n",
    "testvalues<-azureBlobCall(\"https://blobstorageaccount.blob.core.windows.net/blobstoragecontainer/old_testvalues.txt/part-r-00000\", \n",
    "                       \"GET\", key)\n",
    "names(testvalues)<-c(\"Date\",\"GasDate\",\"GasPrice\")\n",
    "testvalues$Date <- as.Date(testvalues$Date)\n",
    "testvalues$GasDate <- as.Date(testvalues$GasDate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "\n",
    "Now we join the two data frames together to do predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Date</th><th scope=col>Week</th><th scope=col>CrudePrice</th><th scope=col>GasStock</th><th scope=col>DaySupply</th><th scope=col>GasImports</th><th scope=col>RefinerPerc</th><th scope=col>OilExports</th><th scope=col>OilImports</th><th scope=col>GasDate</th><th scope=col>GasPrice</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1994-11-23</td><td>47</td><td>17.72667</td><td>168748</td><td>27.7</td><td>170</td><td>95.3</td><td>101</td><td>7161</td><td>1994-11-28</td><td>1.175</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1994-12-02</td><td>48</td><td>17.78</td><td>173223</td><td>28.3</td><td>176</td><td>95</td><td>98</td><td>6224</td><td>1994-12-05</td><td>1.143</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1994-12-09</td><td>49</td><td>16.986</td><td>172344</td><td>27.2</td><td>368</td><td>91.8</td><td>98</td><td>7030</td><td>1994-12-12</td><td>1.118</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1994-12-16</td><td>50</td><td>16.86</td><td>171935</td><td>27.3</td><td>248</td><td>92.9</td><td>98</td><td>7222</td><td>1994-12-19</td><td>1.099</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1994-12-23</td><td>51</td><td>17.104</td><td>168641</td><td>26.3</td><td>285</td><td>92.8</td><td>98</td><td>6566</td><td>1994-12-26</td><td>1.088</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1994-12-30</td><td>52</td><td>17.7075</td><td>170102</td><td>26.2</td><td>284</td><td>93.9</td><td>99</td><td>7334</td><td>1995-01-02</td><td>1.104</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Date & Week & CrudePrice & GasStock & DaySupply & GasImports & RefinerPerc & OilExports & OilImports & GasDate & GasPrice\\\\\n",
       "\\hline\n",
       "\t1 & 1994-11-23 & 47 & 17.72667 & 168748 & 27.7 & 170 & 95.3 & 101 & 7161 & 1994-11-28 & 1.175\\\\\n",
       "\t2 & 1994-12-02 & 48 & 17.78 & 173223 & 28.3 & 176 & 95 & 98 & 6224 & 1994-12-05 & 1.143\\\\\n",
       "\t3 & 1994-12-09 & 49 & 16.986 & 172344 & 27.2 & 368 & 91.8 & 98 & 7030 & 1994-12-12 & 1.118\\\\\n",
       "\t4 & 1994-12-16 & 50 & 16.86 & 171935 & 27.3 & 248 & 92.9 & 98 & 7222 & 1994-12-19 & 1.099\\\\\n",
       "\t5 & 1994-12-23 & 51 & 17.104 & 168641 & 26.3 & 285 & 92.8 & 98 & 6566 & 1994-12-26 & 1.088\\\\\n",
       "\t6 & 1994-12-30 & 52 & 17.7075 & 170102 & 26.2 & 284 & 93.9 & 99 & 7334 & 1995-01-02 & 1.104\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "        Date Week CrudePrice GasStock DaySupply GasImports RefinerPerc\n",
       "1 1994-11-23   47   17.72667   168748      27.7        170        95.3\n",
       "2 1994-12-02   48   17.78000   173223      28.3        176        95.0\n",
       "3 1994-12-09   49   16.98600   172344      27.2        368        91.8\n",
       "4 1994-12-16   50   16.86000   171935      27.3        248        92.9\n",
       "5 1994-12-23   51   17.10400   168641      26.3        285        92.8\n",
       "6 1994-12-30   52   17.70750   170102      26.2        284        93.9\n",
       "  OilExports OilImports    GasDate GasPrice\n",
       "1        101       7161 1994-11-28    1.175\n",
       "2         98       6224 1994-12-05    1.143\n",
       "3         98       7030 1994-12-12    1.118\n",
       "4         98       7222 1994-12-19    1.099\n",
       "5         98       6566 1994-12-26    1.088\n",
       "6         99       7334 1995-01-02    1.104"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Join the two data frames together\n",
    "traindata<- join(trainfeatures,trainvalues,by=\"Date\",type=\"left\")\n",
    "testdata<- join(testfeatures,testvalues,by=\"Date\",type=\"left\")\n",
    "\n",
    "head(traindata)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Visualizing the data\n",
    "\n",
    "The next step is to visualize the data. Because we have a time series, we'll look at all of the training features together. Non-random values may indicate that the dataset will be helpful in predicting the final gas price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfmd <- subset(traindata,select=-c(Date,GasDate))\n",
    "df<-melt(dfmd, id.vars='GasPrice')\n",
    "#ggplot(df, aes(price,y=value,color=variable))+geom_point()\n",
    "ggplot(df,aes(GasPrice,y=value))+geom_point()+facet_grid(variable ~. ,scales=\"free_y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there are some strong correlations here. We look more specifically to see if any of the datasets are strongly correlated with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlationMatrix <- cor(dfmd)\n",
    "corrplot(correlationMatrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It looks like there are some strong correlations and a couple of strong anti-correlations. We'll keep all the datasets for now and see how the predictions look.\n",
    "\n",
    "\n",
    "# Training the Model\n",
    "\n",
    "We'll try a couple of different models to see how well they perform at predicting the gas prices.\n",
    "\n",
    "## Models - Linear Regression\n",
    "\n",
    "Start with a simple linear regression to see how it does.\n",
    "\n",
    "We split the training data into two parts: we'll train on 80% of the data and use the remaining 20% to validate our training model.\n",
    "\n",
    "We will also plot the training values, the in-sample testing values, and how well the model is doing predictions in-sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = GasPrice ~ . - Date - GasDate, data = trainsubdata)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.46256 -0.08965 -0.01795  0.06537  0.65125 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  1.489e+00  2.007e-01   7.420 2.81e-13 ***\n",
       "Week        -3.637e-03  4.192e-04  -8.675  < 2e-16 ***\n",
       "CrudePrice   2.338e-02  3.899e-04  59.971  < 2e-16 ***\n",
       "GasStock    -4.662e-06  2.673e-07 -17.440  < 2e-16 ***\n",
       "DaySupply   -2.804e-02  4.164e-03  -6.733 3.04e-11 ***\n",
       "GasImports   4.490e-05  2.616e-05   1.716   0.0865 .  \n",
       "RefinerPerc  9.718e-03  1.497e-03   6.491 1.45e-10 ***\n",
       "OilExports  -3.080e-04  7.372e-05  -4.179 3.23e-05 ***\n",
       "OilImports  -1.248e-05  7.137e-06  -1.749   0.0806 .  \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.1457 on 856 degrees of freedom\n",
       "Multiple R-squared:  0.9758,\tAdjusted R-squared:  0.9755 \n",
       "F-statistic:  4307 on 8 and 856 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"RMS Deviation: $ 0.15\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In loop_apply(n, do.ply): Removed 172 rows containing missing values (stat_smooth).Warning message:\n",
      "In loop_apply(n, do.ply): Removed 172 rows containing missing values (geom_point)."
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#split the training data into a train and !train (test) sets\n",
    "train <- createDataPartition(traindata$GasPrice,p=0.8,times=1,list=FALSE)\n",
    "\n",
    "#Train the model\n",
    "trainsubdata <-traindata[train,]\n",
    "lm_model <- lm( GasPrice~.-Date-GasDate, data=trainsubdata)\n",
    "\n",
    "#Look at the model outputs\n",
    "summary(lm_model)\n",
    "\n",
    "#Predict the values for our test data\n",
    "testsubdata<-traindata[-train,]\n",
    "lm_predictedY <- predict(lm_model, testsubdata)\n",
    "\n",
    "#Sore the data as a mean squared difference\n",
    "lm_score<-sqrt(mean((testsubdata$GasPrice-lm_predictedY)^2))\n",
    "print(paste(\"RMS Deviation: $\",sprintf(\"%.2f\",lm_score)))\n",
    "\n",
    "\n",
    "#Plot the model preditction to see how it looks\n",
    "predData<-data.frame(testsubdata$Date,lm_predictedY)\n",
    "colnames(predData)<-c(\"Date\",\"GasPrice\")\n",
    "\n",
    "p1<-ggplot(trainsubdata,aes(Date,GasPrice))+geom_line(aes(color=\"Training Data\"))+\n",
    "  geom_point(data=testsubdata,aes(color=\"Test Data\"))+\n",
    "  labs(color=\"Input Data\")+\n",
    "  geom_line(data=predData,aes(color=\"Predicted Data\"))+\n",
    "  theme(legend.position=c(.3, .75),panel.border = element_rect(fill=NA,color=\"black\", size=0.5, \n",
    "                                    linetype=\"solid\"))+ \n",
    "  coord_fixed(ratio=2000)+xlab(\"Date\")+ylab(\"Gas Price ($)\")\n",
    "  \n",
    "predVact<-data.frame(testsubdata$GasPrice,predData$GasPrice)\n",
    "colnames(predVact)<-c(\"pp\",\"ap\")\n",
    "p2<-ggplot(predVact,aes(x=pp,y=ap))+geom_point()+ ylim(1.5,2.5)+ xlim(1.5,2.5)+\n",
    "    geom_smooth(method=lm,   # Add linear regression line\n",
    "                se=TRUE)+theme_bw()+    # Don't add shaded confidence region\n",
    "    xlab(\"Predicted Price ($)\") + ylab(\"Actual Price ($)\")+ coord_fixed()+\n",
    "    ggtitle(paste(\"RMS Deviation: $\",sprintf(\"%.2f\",lm_score)))\n",
    "\n",
    "plot_grid(p1,p2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using the RMS deviation as our metric for model performance. The smaller the deviation, the closer the model predicts the data.\n",
    "\n",
    "### Out-of-sample Performance\n",
    "\n",
    "We'll now train the model on all of the training data and use that model to predict the test data- the gas prices for the last couple of months in the dataset. We'll evaluate the model and plot the residuals and the RMS deviation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"RMS Deviation: $0.09\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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//bSsrCwhIaG6urq8vPzAgQPm5uY5OTlhYWFqLtSg0tJSQoi1tXWTa3jw4AF94OPjo/KSQCBo3749IeThw4d6qIQQIpPJ6IO0tLQGC+sTgh0AADSFQqHIycmJi4ujHUKOjo71RTRq1qxZhJA9e/bQpzKZbP/+/RwOp76zvL29aWbat2+fs7PzlClTNm3adOnSJXprTLtoB9Vff/1Fn1ZXV69YsYIQsnfv3jVr1tCxX8bGxhMnTjx8+DCPx9u+fTszSOCdd97h8/l//vnnlStXlOssLy+nw0Ppu1Bj1apVK1eu7NKlC+0e4/P5/v7+J06csLe3P3/+vCYhoz5CoTAqKooQEh4evnz5ctox6ebmdujQoX79+slksv/7v/+rfVZpaenJkyfHjh3L5/MFAsHkyZM/++wzQgjtgGyyDh06EELo7Omm1cB0tjk7O9d+lR5kymilEnr3ubKy8sWLFyolmZ9Lg1fUMwQ7AABohFOnTtEF4bhcrrOz87vvvpuVldWzZ8/k5GRzc3M1J06ePNnIyOjQoUM0mZ05cyYnJycgIEDNyK3Y2Ng1a9aYmJiUlJQcPHhw+fLl/v7+lpaW48ePP3/+vBbfFL2VLBKJqqurCSEnT57My8vr2LHjxIkTVUr27t27a9euNTU1TNejjY3NyJEjyd/9c4zDhw+LxeK2bdsOHjy4CU2ytrb28/MjhKjkxUZJTEwUi8Xm5uZLlixRPm5gYPDpp58SQpKSkuhdUWVTp0718vJSPkLX+ygsLMzLy2MO+vj4ZGZmZmZmDh06VJPGLFu2jBDy448/9uzZc+PGjYSQsrKyRr2dyspKQgiPx2PWHFEmEAgIIRUVFVqsZPDgwbS3ld6TZUgkkujoaPq4vLy8Ue9C1xDsAACgWSwtLTdu3Kgy3bU2a2vrcePGlZWVHTlyhChNm1BziqGh4VdffZWTk7N379558+b16tWLz+dLJJKEhIShQ4fSaKILly9fJoQUFxcPqQvdLyEzM5MpT/vkDh48yNyeI4Ts37+fEDJlyhRmmJoat2/fnjdvnre3t5mZGbOWMv2gXr582eQ3Qtcx7t+/v4mJicpLQ4cO5XA4CqWhcgzaf6msbdu2dLhhSUkJc5DP57u4uLi4uNC1Sxo0d+7cyMhIU1PTO3fubNiwgRDi4+PTtWvX8PBwepe2FbK3t6cjIL/99tu1a9dmZ2fX1NTcunVr3Lhxz549o9FQk5+vPrWu1gAAQCvHLAhXXFx89erV8ePHl5aWjhs3joYh9ehd1z179pSXlx8+fNjY2HjSpEkNnmVpaTl9+vSYmJgbN26UlJQcOHCA9ietX7+eRp/mo2MEBQIBzSj05lphYWFKXegyKyKRiDl9woQJpqameXl5586do0cKCwvp4ikN3oclhPzwww99+vTZvn17enp627ZtBwwYQGdF0ImZtIepaQoKCgghbdu2rf2Subk5nYdByyirfY/S0NCQxpeampomN4YQsmLFiszMzJ07dzKrRqelpa1du7Zr166ajFSjt5JlMlmdzaA/ETMzM+1WEhERMXr0aIVCER4e7uLiYmho2KtXrzNnzixcuJAOyNNwPq/eINgBAEAj0AXhjI2NraysfH19Dx8+HBgYKJFI5syZI5VK1Z87ZswYW1vbU6dOffvtt2KxeMKECXQyrObokK+UlBR68zQmJqbp70TJnTt3CCEeHh70qVwuJ4Qws03rtHbtWuVW0dVMaC8dIeSXX36RSqWdOnXq3bu3+ku/ePFi8eLFMpls1apVQqHw8ePHly9fprMiAgMDCSFNHpHGaOyoRGYusC5YW1uHhITQDW1v3Ljx3Xff2dvbZ2VlTZ06tcFzmcRZZy8mPUiXy9ZiJUZGRgkJCXv37h01apSrq6uTk9OwYcMOHjwYHR1NB9517NixwZbrE4IdAAA0Hd2FicfjPXny5Mcff1RfmM/nT506VSqV0sH46idbqNGmTRsaejTcLla94uLiixcvEkICAgLoEdpV1qjZjrRnLj4+no7So+PtlNdVrs+RI0ckEgkddqYySLE5N2EpOu2jzv1wy8vL6RA3TZYF1hEHB4cFCxYkJCQQQu7fv8/MV60Ps79c7ZJisfjPP/8khDQ4JKAJlXC53OnTp584cSIjI+Ply5dnz5595513rl69Sn/WdH+U1gPBDgAAmsXLy2vKlCmEkA0bNigPMqsTHVRXU1Pj4OBA5xw0DV0iuM7x7421ceNGegOOuW06YMAAQsjt27efP3+uYSVvvvmmra1taWlpYmJiZmYmTYqa3IelI/ZqL71RXl7+xx9/1C7P4/GIxt14vXr1IoTcuXOn9gyJ5ORkhULB4XB69uypSVW6069fP3pTODc3V31JR0dHGrlOnz6t8tK5c+dqamq4XO6QIUP0UAn5u7fY39+f7pjXeiDYAQBAc9H9NJ89e/bLL7+oL9mvX7/PPvtsxYoVmzZtYvZvqNP9+/fpCsa1vXz58vjx46SuYf6N9e2333799deEkJEjR/r7+9ODY8aMsbe3l8vlS5cupbdlVdTOSXw+n+6QERcXR5e37dOnjyY36ejN6Nq9g+vXr69zdB3NQMqTGNQYO3asiYlJZWXlN998o3xcKpWGh4cTQgIDA5u8sJxUKs3Nzc3NzZVIJJqUr28LuMzMTNp32OBdVPJ3Vv7pp5+U96RRKBT0hzhs2DDlbSfovsC5ubkqObhRldTpt99+o52yH3/8cYNt1jddrn6sP9h5AoCVsPNEq6J+Cwfa/dajR4/ap0yZMqXBymvvPBEXF8fhcIYNGxYTE3Pv3r3y8nKRSJSenr5161ZmW/CUlBQNm83M+RCJRPn5+bdu3dq2bRuzBnKHDh0KCgqUz2K2OBs5cuS1a9fohhnV1dVXrlxZvny5jY1N7QtduHCBEGJsbEw7hKKiomqXqb05BLNE8IoVK8rLyxUKhVAopHGBjiNcvXq1cg00Qw8YMKC4uLjByhV/jwWkCxRXVlYqFAq6QDGpa8N7GpTj4uJqt5z2FN67d4850tidJ6ytrb/44ovs7GyF0s4TaWlpffr0IYR06tRJk0pKS0vpPmC9e/e+deuWXC7Pzs5+77336Nu5cuWKcuFNmzbRFtIPtmmVHDt2bP78+QkJCQ8fPszOzr548eKHH35I/yYJCQnRpM16hmAHAK0Xgl2roj7YnT17tvbXfHOC3eHDh9X0ShgaGm7btk3zZteHy+XOnDmzdkhSKBRbt25l+hSNjY1tbGxouCGEGBgY1C4vl8uZu3JcLldlAy6qzuzFrPnC5XLt7Ozo/NPZs2fTqKES7O7du0dbxePxnJ2d27VrN2zYMDWVSyQSZrNdAwMDpn9OzZZiOgp2zKfn6elJt/pgljA0NTW9dOmSJpUoFIqrV68yc26YHxAd66lSsr5g16hKmCW1VdDZQhq2WZ9wKxYAALRg2LBhtOuFLlHWfBMmTHj69Ol//vOf8ePHe3h4GBsb01zSt2/fVatWpaWlLVy4sAnV0ko8PT0nTpy4YcOGv/76a/fu3XWuWPHRRx89fPhwyZIl3t7ePB6vrKzMzs5u6NCh69atq3M3CA6Hw0ztHDJkiCY3FqnY2NhNmzZ17drVwMBAKpX2798/NjaW2SpXRdeuXU+ePDlixAhLS8vc3NwXL15kZ2erqZzP5x84cCAuLm748OEWFhYVFRVt27adMWPGjRs3QkJCNGyhVmRkZERFRY0ePbqqqooOH8zJyXF3d3///fdv3bpFV2PWhK+v77179xYsWNCuXTsul+vg4PD2229fuHDhww8/1LwxmlcyePDgTz/91NfX197e3tDQ0MXFZcqUKUlJSbt27WKiaqvCUTR7HnVrUFRUZGdn5+jo2ODQSwB4hdD+jzrn9AHAq4t+a2dmZrq4uLR0W9im1fXYpaenCwQCuug2nSsEAAAAbKLTpfJec60r2CkUipCQELFY3NINAQAAAF1BsNOd1hXstm7devHixbfffrulGwIAAAC6YmhoGBwcLBAIWrohLNSKgt1ff/318ccf+/j4tMZVYQAAAEBLTE1NDx06RNdzAe1Stzikns2dO1csFu/YsUMrK4kDAABVXl7OLJamnkwm43A4dLkNnaLz9vRzP04qlapfCVlb9Pmmampq9PNdqbc3pVAo5HK5fuaZKhQK/fyYZDIZl8vV8FpGRkbMpnbN0VqC3fbt25OSkhYtWtS/f//bt2+3dHMAANijsrLy+fPndOUw9aqrq7lcrh4SA13vV/PvvOaoqKgwNTXVw4Xodmr6iSYVFRVmZma6vgoNW/rJ+lKptKamxsTERNcXom9KP797YrGYz+dr8ndFYmIin89nT7DLzs4ODQ11dXX96quvWrotAAAsZG5uXns30tpEIhGXyzU2NtZ1e2QymVwuNzAw0MOXq1AotLa21sOF6HK1+ulIEwqFeriPqVAopFIpl8vVQ1qVSCTV1dXm5ua6vpBcLpfJZDweTw9ptby83MjIyNDQsMGStTeubbJWEezmz59fWlq6b98+Df/+uHPnDt3kjqHhRnUAAAAALNbywW7Pnj2JiYlTpkwZO3ashqfk5uY2uM80AAAAwOumhYNdXl7ev/71Lxsbm82bN2t+1qBBg549e6Z8pKSkpHfv3tpuHQAAAMCrpIWD3dKlS4VCYWxsrIODg+ZnmZiYeHh4KB8pKirSdtMAAAAAXjEtvI7d9evXCSFr1qxpoyQwMJC+2qtXrzZt2qgMpwMAAACAOrX8GDtCSF5eXp3HCwoKCCHl5eX6bQ4AAADAK6mFe+yePn2qqOXWrVv01czMTIVCsWHDhpZtJABA0yQnJ8+ZM6djx44CgcDT0/Ojjz7Kz8+vr3BSUhLnn+zs7JQL5Ofnz5w508bGxszMbNSoUQ8fPtT9OwCAV0yr6LEDAGClsLCw0tLSWbNmubm5PXjwYOvWrSdOnLhz546axbqioqJcXV3pYyMjI+Z4TU3Nm2++mZ+fHxkZaWZmFh4ePmTIkLt37zo6Our8bQDAqwPBDgBAV2JiYjp16sQ87d69+/Tp03/55ZeQkJD6ThkxYkTXrl1rH9+/f/+dO3fOnTs3dOhQQoifn5+np2dERERkZKQuWg4Ar6gWvhULAMBiyqmOEDJkyBBCSHZ2tvqzRCIR3aBT2dGjR9u2bUtTHSHExcVl+PDhhw8f1lpbAYAVWmOPXY8ePWr/owYA8Kq7dOkSIaRbt25qyvj7+5eWlgoEgpEjR0ZERHh6etLjaWlpKnuCde3a9eTJk1VVVco7gBUVFTHDlBnYmwfg9dEag11rcPLkydGjR9PHV65c6d+/f0u15K+//mrfvj19nJ6e3qFDB/XllVvO4XD4fL65uXmbNm26des2adKkiRMnNm3Lv8Y2AwBUCIXC0NDQnj17jh8/vs4CFhYWixYtCggIMDU1vX79elRUlJ+f3+3bt52cnOjpPXr0UC5vbW2tUCiKi4tpAer69eujRo1SqdnHxyc0NFST9T7lcjkhpLKysrHvrrHoX+962L+VEFJTUyMUCvVwIT2/Kf0s4Kq3NyWXy+VyuR7+CGF6jvTwpmQyWVVVlSab0srlcm3tXYtgx2YKhUIikRQVFRUVFaWlpf38888+Pj6HDh3q3LlzSzcN4PUiFouDgoIqKyuTkpLq++OqX79+/fr1o4/Hjh07dOjQwYMHf/PNN41aGcDLy6t2eboNua2tbYOni0QiLper3AWoIzKZTC6XGxgY6OHLVSgUWltb6+FCUqlUoVDw+XxdX4gQIhQKbWxsdH0VhUIhlUq5XG7TegQaRSKRVFdXq5lXpC1yuVwmk/F4PG0FKTXKy8vp/30NltRiYxDs2Kxz584mJiYFBQVZWVn0SFpaWkBAwLVr19zd3Vu0aQCvkerq6rfffvv27dvJycma93YPGjTIzc3t2rVr9Km1tXVJSYlygeLiYg6HY2VlpXywffv2q1evVqkqNzc3OTm5qc0HgFcJJk80QnR0NF1cqmvXrsXFxR9++GGbNm2MjY379et3/vz5OosVFBS8//77Dm306X4AACAASURBVA4OAoHA398/NTWVKbZy5UpajNlpQ/kgvZkyZMgQ5gYoIaRjx4701c8//1yTBsfGxt68eTMzM/Pp06dvv/02PVhYWPjhhx8yZZ4+fTp37tw+ffo4OTkZGxvT1bZCQkKUl8hS3wxNagB4bUkkkkmTJl26dOnEiRM9e/Zs1LlSqZTpZ/Lx8UlLS1N+9f79+x4eHiYmJlprKwC8+hDsmqK0tNTPz++7777Ly8urrq6+du3aqFGjHj9+XGexXbt2FRQUiMXiS5cuBQYG1v67WQ83CDw9PQ8dOjR48GD69MSJE0+fPqWPHz16tGPHjhs3buTm5lZXV4vF4ufPn8fGxvbu3Ztu+Nag5tcAwFZSqXTq1Klnz55NSEgYMGBAg4WVnyYmJr58+ZIZ4PvWW29lZWWlpKTQp1lZWWfPnp0wYYIumg0Ary7cim2KrKwsDoczbty4mpqaU6dOEUKqq6u///77TZs2qRQzMTFZsGABIeSnn34Si8USieSDDz548uSJ8ngFNXfWFy5cOHDgwPXr19Ona9asoaNk/Pz8GttmLpe7fPly5lshJSWF3hLicDi9e/eeMGFCu3btrKyssrOzv/3227S0NLFYvGbNmqSkpAaboUkNAK+nBQsWxMfHz5s3r6Cg4NChQ/Rghw4d6DSI06dPjxkzZvfu3e+++y4hZNy4cc7Ozt27dzc3N79x48aOHTtcXV2XLl1Kz5o+fXpUVNS777775ZdfmpqahoeHW1parly5sqXeGgC0Tgh2TRQdHb1kyRJCSFBQUHx8PCHkxo0btYvFxcXRP6kDAwMnTZpECHn+/HlKSsqwYcOYMmp67KZMmeLr68skqvfff78501GVF1l4+fIlfTB69OixY8fSxxKJRCKRdOrUafjw4YSQ1NTUmpoaPp+vvhma1NDkNgO80q5evUoI+eGHH3744Qfm4EcffbR161by9yBuOhGVEDJixIi4uLj4+PiKigonJ6fZs2eHhYXZ29vTV/l8/pkzZ5YvX758+XKJROLv7x8XF6c8HxYAgCDYNQ3TD0cI6dmzJw12KuOaCSGWlpZvvfUWfRwUFGRqakoXEbh586ZysGtx33777d69e+/evSsSiZSPSySSwsJCTb45ml8DACvdv39fzaujRo1SXrNzxYoVK1asUFPe0dFx3759WmscALARgl1TtG3blpm9zKwLwPzZzXBwcGB64zgcjr29PQ12paWlysWU/2Wvrq7WUZsJIffu3WMeOzs70wcLFy5U7ktQoUl7ml8DAAAAaAUmTzSF8s7cam6k5ufnM6FNoVAUFBTQx5aWlkRpaJ1YLGZOef78udZbS8nl8qioKOYpnUghFAp37NhBj6xataqoqEihUNRetl6N5tcAAAAA2oJgp0OlpaVHjx6lj3/77TdmMfdevXqRv+MdIeTRo0f0pbS0tNOnT6tUojxATTkCNsrz588nTZrEzJwYPXo0HST38uVLpqPxgw8+oMtdnjx5snYN9TVD8xoAAABA13ArVremTZs2Y8YMhUKxd+9eesTd3Z32lvXu3ZseKSgo6N27t4+PT1JSksp6B4QQe3t7AwMDevxf//rXm2++yePxpk+frsnAtTlz5piYmBQWFmZmZjIH7ezstm3bRh+3b9+ey+XSZLZs2bKQkJBbt259/fXXtauqrxma1wAAAAC6hmCnQ+7u7mZmZtu3b2eO8Pn87du307VOAgMDu3btSsdWP378+PHjx3QR44sXLypXYmhoOHr06GPHjhFCzp07d+7cOUKIv7+/JsHu0aNHKkfolmLMthOmpqYLFiygOS8xMTExMZEQMnLkSLqGiybN6N+/v4Y1AAAAgK7hVqwOmZqapqSkhISE2NvbGxsb+/n5nTlzhtlnwsDA4OTJk8HBwebm5mZmZiNHjrx8+bKvr2/terZv3z516lRbW9smLGXM5/Otra29vb3feeedgwcP3rlzR2Wj2E2bNoWFhXl6ehoZGXXs2DEiIkJ5KJ4mzdC8BgAAANApjvKUzFdXUVGRnZ2do6Njbm5uS7eFREdHL1u2jBDi4+OjfrEDAFDPzc2NEJKRkdHSDXm10b1ip02b1mBJkUjE5XKZyf66QxfwMzAw0MPWO0Kh0NraWg8XkkqlCoVCPyt3CoVCOqZZpxQKhVQq5XK5yovq64hEIqmurjY3N9f1hej6kTweT83uANpSXl5uZGTELKOhBt3ggIaHZkKPHQAAAABLINgBAAAAsASCHQAAAABLINhp37/+9S+FQqFQKDDADgAAoGVx/qmlm6NzCHYAAADwutDDRJCWhWAHAAAA7PQ6dNGpQLADAAAAYAkEOwAAAGAndizW2ygIdgAAAPC6kMlkLd0E3UKwAwAAANai61QoP2A3BDt2Wrt2LYfDefToEX168eJFDocTHR3dss0AAABoEcqR7vHjxy3YEl1DsNOT+/fvK6+jY2Ji0rlz5zVr1pSVlbV001QlJSVxOJytW7fq4VrKHwuXy7WwsPD09AwKCtq5c6dIJGpUVfpsNgAAQOtk0NINeL14eHiMGDGCECIUClNSUjZu3Hjy5Mnff//dyMhIp9d94403UlNTPTw8dHqVJmM+FpFIlJ2dfebMmfj4+HXr1v38888DBgxo6dYBAAB7sP4mEoKdXvXs2fP777+njysqKgICAm7fvr1///45c+bo9LoWFhb+/v46vURzKH8shJCKioqvv/76yy+/HDNmzPXr1z09PVuwbQAAwBoPHz5s6SboHG7FNkX16iXVq5c0sxIzM7O5c+cSQq5fv04ISUhI4HA4P/7446+//tq3b18TE5NJkyYxhfft2+fn52dubi4QCPr167d3717lqioqKpYuXdqmTRuBQODn53fu3DmVa9U5xi4uLm7QoEGWlpampqY+Pj6rV68Wi8Wff/457TxbvHgxvUPq7u6urWZo/smsW7cuNDS0pKQkLCyMHhSLxZGRkUOGDHFycjI0NHR3d1+0aJFQKKSv1tds9WcBAMDr43VIdQQ9do2lnOfoY6ONm7VY/2+//Xb8+PFhw4bNnDnTwcGBHly2bFl0dHSfPn3mzp3L4XBOnz49c+bM9PR0Gnrkcvn48ePPnz8/bNiwQYMGpaenjxkzpsH+uSVLlmzZssXV1fW9994zNTV98uTJli1bFi9ePHHiRJFIFBERMWPGjOHDhxNCzMzMdNcMNVavXr1p06b4+HiZTMbj8fLy8tauXTts2LDJkycLBILbt29v27btwoUL165dMzIyqq/Z6s9qctsAAABaJwS7+pWVEVlNw8WKi1SPmJgSY+MGz6uoqNi+fTshpHfv3szBhISExMTE0aNHM0fOnDkTHR0dFhb22Wef0SMymSw4ODg8PHzOnDnu7u579uw5f/783Llzf/jhB1pgxIgRs2fPVnPp48ePb9myZeDAgadPnxYIBP99H8XFAoHAxcWlsLAwIiLC19dXuRJdNEM9Gxubbt263bx58/Hjx97e3k5OTjk5OdbW1kyB2NjYkJCQuLi42bNn9+jRo85mqz+ryW0DAIBXy2vSXUcQ7NSQ/OdLRVVVg8WqN4SpHOH6vMGf9UGdhW/durVgwQJCSHFx8fnz5/Pz87t27fruu+8yBd566y3lVEcIiYmJMTMzCwkJyc3NZQ5OnTr1yJEjSUlJH3zwQVxcHI/HY/IWIWTWrFnr169/8uRJfW2m2Wvz5s1MqiOEKKef2nTRjAY5OTkRQgoKCgghRkZGtI9NKpWWlJRIpdLAwEADA4Pz58+riWhNOwsAAFohZuPXxi5H9/qkOoJgp46tPeefvXGKuhbg4ChlI4prZ19flc+fP4+JiSGEGBkZtWvX7r333vvkk0+Mlbr3fH19VU75448/KioqXF1da9eWk5NDCLl7966Li4uLi8v/msTh9O/fX02iun79urW1da9eveorUJsumtFYiYmJ4eHh165dk0qlKlfX+lkAANB6MJGOeaom26kUfvDgga6a1Soh2NXLcElo7YO150wY/nuD5nUGBwcfOnRITYE2bdqoHCkuLnZxcaE3bVV06NCBEFJWVtalSxeVl5jxeXUqLS1t166dRi3WZTMaROOXnZ0dIeT48ePjx4/38vJav369p6cn7WsMCgqqrq5WU0PTzgIAgFeUSqp7DSHYNYt2Z06Qun4jzc3N8/Pz6Q3EOk+xsLCgNyuV5efnq7mKpaXly5cvG9UwXTRDPaFQeO/ePTMzs86dOxNCNm3aZGRklJqaam//3w7R8vJysVisvpKmnQXAMgqFQi6X19Q0PGhYJpMpFApNSmqFcj+67tD3rrfve/18evr8McnlcrlcruurSKVSmUym+ZvicDgSiaTBYgEBAfPnz9+2bZvKcZlMpodNY+VyuVQq1eR3T6FQaOtXFMGucbSe5BrUt2/fo0ePHj16NCgoqM4Cb7zxRlJSUlZWFnMbVKFQXL16VU2dffr0OXLkyM2bN+u8G8vj8UitbZJ10Qz1Nm7cWFNTM2XKFNqezMxMd3d3Jp8RQlJTUxtsdoNnAbwm6DJAmhQjeunzoPfR9BO2mO1tdH0hfb4p/Vyolf+YGiwcEBBQZ0ktpqgGNep/Pa1AsGvtFi5cePTo0dDQ0AEDBtDJBFRiYuLAgQOtrKymTZt26tSpdevWMdNRd+/erX5k27x5844cObJkyRLlWbFFRUVmZmZGRka2traEkKysLF03oz4VFRURERERERGWlpb//ve/6cF27dqlpKRkZGS4ubnRMp988onyWXU2u8GzAF4H9Kulvu52ZVwul8vlalKymWQymVwu5/F4evh+pe9dDxeSSqUKhUIPnx75+03p+ioKhYJ2ONG/nHVKLpc36nevwfkTTKojhHC5/1u19+YzM2EFz9ubq3xQR7hcLo/H08+vBAPBrrUbNWpUaGhoREREly5dgoKCnJ2d8/LykpOTnz17lpmZaWVlNXPmzNjY2O3bt6enpwcEBKSnp8fHxwcGBiYlJdVX55gxYxYtWrR161YvL6+JEycKBIKnT5+ePHnyyZMnLi4u3t7eDg4OMTEx1dXVDg4OVlZWixYt0kUzGMxk4aqqqqysrN9//72iosLFxSUuLo4O4COELFy48PTp076+vtOmTeNyuYcOHerQoYOFhQVTSZ3NbvAsAABo/VT62BqV6uicRerPPONtJ9vK5SQ4kDjbab2ZrYOCFQoLCwkhjo6OLd2Qet27d48QEhwcXF+BY8eOEUJiY2PrfPXIkSOBgYFWVlZ0Ou3YsWN37dolkUjoq+Xl5UuWLHFwcDAxMenfv/+5c+c+/fRTQsjDhw9pAXr/cdOmTcp17t69u3///gKBwMzMzMfHZ82aNSKRiL504cKF/v37m5iYEELatWunrWbU97EwzMzM3N3dJ0yYsH379oqKCpXC+/bt69atm7Gxcdu2bZctW1ZRUWFpaTl48GCmQJ3NbvAsaM1cXV1dXV1buhWvvJycnP3792tSsrKyUiwW67o9CoVCKpVKJBK5XK6HaxUVFennQjU1Ncy/h7pWVFSkh6vI5XKJREJ7InWturq6rKxMW7UF/O2Bkot/pA9fVtNnvmJXfAYdTqprZWVl1dXVmpSMioqKiorSykXVTRh+hRQVFdnZ2Tk6OiqvsgYArzp6Dz0jI6OlG/Jqy83NTU5OnjZtWoMlRSIRl8s11mCJ9Wait2L1c4dUKBRaW1vr7VYsn8/X9YUIIUKh0MbGRtdXUSgUUqmU3k9sfm3q1yiRSCTV1dXm5ubNv1Cdq9ZV1XC/ONguo8A4eEDB+D553t7eergVW15ebmRkZGho2GDJTZs2EUKWLVvW/IviViwAAADoEJOq6QOd9ijVmerkCvLdCeeMAuMA79K3+hawokerXjqPqwAAAPDaqt1X2tjeU81ny9a3w0RcquPN5+berqKQ4TmsX+cOwQ4AAABaI+VI1+Sb6WfuWJ+8aeNsI1kyLsuAx+rOOkJI027FZmVlpaSkPH36ND8/v7y83NbW1sHBoWfPngMHDtTK3XEAAAB4zdXZ1afmNm6d3XV3/zLde97RwkS6YkKGqZHOVyRuDRoR7HJycnbt2qVmcTIDA4OBAwfOnTt30qRJdOd1AAAAAG2pvdQwfVBnqssoNN6S6MI3UKwKynSw1NNGHS1Oo1uxGRkZs2bNcnNzW7t2rZolZ6VSaUpKyowZM1xcXCIjI7EdJwAAwGuudh+bFidP0JxXZ6orExt8c8ylWsoNGZ7Tzr5KW1ds/RoOdh9//HGnTp327Nmj+aZ+hYWFoaGhnTt3Pn78ePOaBwAAAK82lYXWND+ryVeUSDmbjrrkl/In+hb6dS5rcj2vooZvxW7YsEH5qZOTk6+vr6+vr5ubm62trUAgKC4uLioqSktL+/3332/cuMHssP7XX3/t2rVrzJgxOmk4AAAAvPZqd9fJ5WRzgsvTHJNh3YqD+he0SKtakKZj7BwdHadOnTpt2jRfX181xUQi0bFjx37++efExMSamtflfjYAAABonUqnXe3pFMpbhzH2XXC885dZV7fKWUPzdNi41qrhW7Ht27f/7rvvXrx4ER0drT7VEUIEAsGUKVPi4+OfPn26ePFiZoP5VuX4HfLdOf1drqSkhNOQyMjIplWelJTE4XC2bt2qvtj9+/eZa3G5XAsLC09Pz6CgoJ07d4pEIl1cEQAAoFEsLCyUvxlrF1DJeQEBAcr7wFLn7lmdvm3jZC1ZNCabx637Zm6nTp201eZWqOEeuydPnhgYNHpVFDc3t82bN2s+LE+fbrwgfxaQBYToZ5FCQ0PD6dOnM08LCwtPnTrl4eExYMAA5qC3t7ceWuLh4TFixAhCiEgkys7OPnPmTHx8/Lp1637++WflxgAAAOhZ7cU06lzcRKFQ0MxXZ6pLyzTdndzG3ES2cmKmqXHdi5t07txZJmPzuicNJ7YmpDqtnKs7NVJCCFHICUcvyzMLBIK9e/cyT69evXrq1KmAgIAff/xRH5dX0rNnz++//555WlFR8fXXX3/55Zdjxoy5fv26p6enntsDAADQWDTt1R5al1NsuCWhLZdDlo7LcrCUtETTWoXXcecJiYwQQuTNmG3NOTOAc0abXVz79u3z8/MzNzcXCAT9+vVTDoIKhWL79u19+vSxsrIyNTVt3779jBkzXrx4QQj5/PPPaQ/c4sWLace1u7u75hc1MzNbt25daGhoSUlJWFgYPSgWiyMjI4cMGeLk5GRoaOju7r5o0SKhUEhfre+K6s8CAADQotqprkzEizzsWlnNmzM8p1PbeocYdenSRcdNa3nN7VGTyWRHjhxJTU0tLS11dnaeOHFinz59tNIy3alpRrBTznP0sWLElWa2Z9myZdHR0X369Jk7dy6Hwzl9+vTMmTPT09Np2Pryyy8/++yzbt26zZ8/38jIKCMj49SpU7Nnz27Xrt3EiRNFIlFERMSMGTOGDx9OCDEzM2vs1VevXr1p06b4+HiZTMbj8fLy8tauXTts2LDJkycLBILbt29v27btwoUL165dMzIyqu+K6s9q5ucDAADAqJ3qaqSc6ATX/FLDt/oWBniX1nfi65DqSKOC3dKlS0tLSw0NDb/77jsej0cIqaysfPPNNy9fvsyUCQ8P/+STT8LDw7XfUu3RMNjlVBVVyRvuy/1TlKNyxNbQ0sJA01kjZ86ciY6ODgsL++yzz+gRmUwWHBwcHh4+Z84cd3f33bt3e3l53bhxg8/nMwXo4s89evQoLCyMiIjw9fWdPXu2hldUYWNj061bt5s3bz5+/Njb29vJySknJ8fa2popEBsbGxISEhcXN3v27PquqP6spjUMoLWRyWR379598eJFYWGhiYmJg4NDjx497O3tW7pdAGxQXV2t0hFQ5zp2tVOdQkF+OO2c/tKkf6eySX71Lm7ymqQ6onmwy8vL27x5MyHkrbfeoqmOELJ27VrlVEetX79+0KBBI0eO1GIrtYuOsWsw2HW5OrW0pqLB2jwuBakcmegwOL77hjoL1xYTE2NmZhYSEpKbm8scnDp16pEjR5KSkj744AMOh8Pj8bjc/9005/F42p1u7OTkRAgpKCgghBgZGdH/taRSaUlJiVQqDQwMNDAwOH/+vJqI1rSzAF4Jcrn85MmTO3bsOHv2bFnZP1Y65XA4Pj4+kydP/uCDD+j/RwDQZGVlZRYWFvSx5qsT/3LZ4eoTi47O4nkjXtY1lZaQ1ynVEc3H2P3xxx/0wbRp0+gDsVi8fft2poDyzOTaE1VaFQ177DxN2toYWCj/V2cxlTI2BhYdBa6aN+aPP/6oqKhwdXV1UkI/5JycHEJIcHDww4cPu3XrFhYWdv78eT1s1JaYmOjn52diYmJvb+/k5OTm5iaVSmljtH4WQCt34MABLy+vsWPHxsfHq6Q6QohCobh///5nn33m5uYWEhLy8uXLFmkkAGuo352idnfdhTSrY9ds7Sxq/jUui29Q91mvVaojmvfYMVvE9u/fnz44f/58ZWUlfbxgwYLo6Ohnz54FBAQIhUImBbZCMvl/I51c3kDJG74/1j5Ye85E0dBTzWlPcXGxi4uLckRmdOjQgRDy5Zdf2tnZbd++/fPPPyeEWFhYfPTRR+vWrdPijGMav+zs7Aghx48fHz9+vJeX1/r16z09PWnXYFBQkPpA2bSzAFq5AQMGXL16VeUgn8+3trYWi8Xl5eXMQalUGhsbe/DgwT179rz99tv6bSbAa6F2qnuUJYg918bEUL78rUwLQd3Lq71uqY5oEuxKSkoIIenp6fSpQCCgR1JTU5ky8+bNE4vFzs7OQ4cO/fXXXwsKCmgZQoiVlZX2W90MNX8vXqOVPYibP3PC3Nw8Pz+f3risswCPx1uxYsWKFSsyMjKSkpK2bt361VdfWVlZrVq1qpmXpoRC4b1798zMzDp37kwI2bRpk5GRUWpqKjNyqLy8nNkmrj5NOwuglWNSXceOHd95552BAwf269eP/glECKmpqXnw4AFdwOj48ePV1dWVlZV37txBsAPQutqpLq/E8JsEF7mcLBqf5WpXdyfCa5jqiCbBTnlEPCHE0dGxdplevXopP5VIJMxZ5eXlTZiqqTuSvzN902bFNj/Jqejbt+/Ro0ePHj0aFKQ6Vk8FvdczefJkOzu748eP02BHxzs2Z63FjRs31tTUTJkyhVaVmZnp7u6uPB5cOcHXd8UGzwJ4FXG53ODg4JUrV/br16/2q3w+v3v37t27d58/f35JScmePXsiIiL030gA1qud6iqqeJFHXCuqeCHDc95wr6zzrNcz1RFNgl3v3r0JIdnZ2XR0P31KCLl79y7dDdbKyopZ25bOF1Muxsy0aCWYHjuZNnrsmm/hwoVHjx4NDQ0dMGCA8uDrxMTEgQMHWllZXbp0aeDAgczx0tJSqVRqYmJCn9ra2hJCsrKymnDpioqKiIiIiIgIS0vLf//73/Rgu3btUlJSMjIy3NzcaJlPPvlE+aw6r9jgWQCvorS0NNqT3SArK6vFixfPnz+/af8zAoDmqmu4EfFuucWGE/oVDu1WUmcZNamOiSWaz894tTQc7K5fv04I+eKLL+h6HPv37/fy8iooKGBSyOzZszdt2kQfT58+ff/+/SYmJvSsVki7t2Kbb9SoUaGhoREREV26dAkKCnJ2ds7Ly0tOTn727FlmZqaVldXgwYO9vLz69u3r6upaXFz822+/KRSKxYsX09O9vb0dHBxiYmKqq6sdHBysrKwWLVpU37Vu3bq1YMECQkhVVVVWVtbvv/9eUVHh4uISFxdHx/MRQhYuXHj69GlfX99p06ZxudxDhw516NCBmaZU3xUbPAvgVaRhqmMYGhp6eHjoqDEAr6eHDx/Onz+fmZQpV5CY087P84z9OpcFD6h7cZMuXbooT+hUDnDKx+lj9sU7TQfgM/9aBQUFTZs27fDhw8zNuMGDBzPF6I4IrXlhJybYNWfnCe36+uuv/f39t2zZEh8fLxaL27Rp07Vr108//ZTe9d6wYcOZM2fOnj1bUFBgb2/ft2/f1atXM314BgYGhw4dWrVq1Y4dO8Ricbt27dQEu+fPn9P/N8zMzOzs7IYPHz5u3Lhp06aZmpoyZSZOnLhv374NGzZ89913tra2kydP/uKLL9q2bcsUqPOKDZ4FAADQWIMGDaIP5s+fTwiJiYn57Yr9tXRzd4eqkOE5dS5uopLqiNKes5z6VkNhlzp22K1TXl6eq6srvfeqzNra+uXLl8bGxoSQqqoqGxsbsVg8evTo48ePa7+x9SsqKrKzs3N0dFReDa5OzwvIl0cJISQ8mDi1rnkdAKCK3tzPyMho6YaounTp0uXLl4VCYY8ePYKDg1vnvtiM3Nzc5ORkZrEqNUQiEZfLpf+k65RMJpPL5QYGBnr4rhUKhdbW1nq4kFQqVSgUzGLyOiUUCm1sbHR9FYVCIZVKuVyujkZVqfxQysrKzM3NmadMqmPILEdK2n5qZVoTNvUva7M6psHSO7B1/qwVCkV9x5vQcg2Vl5cbGRkZGho2WJLe+Vy2bFnzL6rpOnaOjo4rV66sffyzzz5j/glISEigEyEDAwOb3zIdaebkCQB43Rw4cGDixImzZs1ijixcuNDf33/VqlUbNmyYOnVqQECASFT33pTJyclz5szp2LGjQCDw9PT86KOP8vPz67uQ+sJJSUmcf2Lm5wKwg4WFBf3dJnVNmJALukmcVhnx5SsnZKlJddCIvzLDw8PNzMwiIyOLi4sJIVZWVqtXr166dClT4JtvvqEPxowZo91WalErvBULAK3ZwYMHjxw5MmPGDPr01KlT33//vXKBq1evRkZGMrsCKgsLCystLZ01a5abm9uDBw+2bt164sSJO3fuKHdLNKpwVFSUq+t/l0DHRszwSmtUH6rCsG21S7iCcBePyXazr6pdQJNUV1+nHcs0IthxOJxPPvlk9erVGRkZCoWiXbt2yn2zCoWCrrLL4XA6deqk/ZZqCYIdADTK7du3CSGjep/J0wAAIABJREFURo2iT/fu3UsfdOzYceTIkfHx8dnZ2b/++mudwS4mJkb538Pu3btPnz79l19+CQkJaVrhESNGdO3aVRtvC6D1GjRo0D+2sOJZVLlsVPCsDHOj3nAfXbt8g6mOud+qku3YN3OCNCrYUTwer3379rWPczicxs4gaxH/C3YN7TwBACCXyzMzMwkhzL97ly5dog8OHjzYo0eP4cOHv/3220+fPq3zdJW/cocMGUIIyc7Obk5hkUhkYmLyOnQ8ALvV138WEBBAH8TExMyfP59w+FUuXyqM3PjCAzs31JvqmKqYHcmUjyiXl8lkMplMZRN2NmnVA351oQZj7ABAM/7+/jKZjE4aW7x4MV0/8s8//6Svfvjhh1wut6KighAiEon8/f0JIR07doyNja2vQhoKu3XrpsnV6yzs7+9fWloqEAhGjhwZERHBrCEKwA401THddd9/HxNz2vnSQ8u+HcoXjX2jdvnasyWYRUxY2Runidcv2OFWLABopry8XP53375IJJLJZMz+sBYWFnTCBLN1XmlpKYfDqW8WBSFEKBSGhob27Nlz/PjxDV66dmELC4tFixYFBASYmppev349KirKz8/v9u3bygubE0KePHmya9culdpMTU2ZtSoBWo8GVyGJ/93u0kNLd4eq+SNfcmuVUjMH9nXWcLBbsWLFypUrVf7t0MSlS5du3rzJLKXbSkha2QLFANBq3blzhxBiamoqEokiIyPHjh0bERFBd/ObPn36tm3bCCEXL14MCAiwtra+d++emqrEYnFQUFBlZWVSUlKDK0fUWbhfv37MzmZjx44dOnTo4MGDv/nmmw0bNiif++eff27cuFGlQh8fn1WrVtF5b+rRBUr1sNEz7VDRz70wiUTCbF+uU/p8UzU1NZr8QJtPLpczM1V1RCgU0gs9e/aM/sLTP5CuP7M5/Lu9lWnNwhHpshqJ6J/rrXl4eKj5BNS8RH9Mun5TlFQqlUgkmvxKyOVybf3mNBzsoqKivv3229mzZ4eEhNS5YaIKsVh87Nixbdu2paSkBAcHt7Zghx47AGiUTp063bp1a+bMmX5+fmfOnKEHhw4dSh+kp6cTQtT/6VtdXf3222/fvn07OTm5wZ4zDQsPGjTIzc3t2rVrKscHDBhQe+MfkUiUlZWlsvF3ndi6jp2VlRX71rHT5AfaTLpex07ZvXv3+Hw+87v3LNfkp5R2RgbyFROynO0NVOJKg7Ml1Hw4crlcb2PsNF/HTouN0aii6urqmJgYX1/fjh07hoaGHjp0KCMjQ640+6CqqurWrVsxMTEzZsxwdHScMmVKSkqKtpqoXawJdhcvXuRwONHR0To9BQCmT59OCCkuLk5MTJRIJIQQR0fHcePG0VeTk5OJ2q8ZiUQyadKkS5cunThxomfPnuqv1ajCUqm0dlixsLDoXUvHjh0bepcALUll1bqCMn7UURepnPPh6Gx3B9XFTVT+d3ttx9LVp+Fgp/wvwtOnTyMjI99555127doZGBjY2Ni4uLiYmpqamJj06tVrwYIF+/btY8agEEK8vLwarD81NXXNmjVDhgxxd3cXCAQCgcDLy2vu3Ll3795t2ltS73+TJ/Q1K7akpITTkMjISD21Ri/u37/PvDUul2thYeHp6RkUFLRz5041I5DqRBdl3bp1q46aCtCgJUuWTJw4kXlqY2OzZ88eOpGirKzsyJEjhJCRI0fWea5UKp06derZs2cTEhIGDBig/kLqC0ul/1iRNTEx8eXLl/3792/COwJoVVRSXZmIt+HXdmUig/eH5/T0qFApXOcfUSrZ7jWPeg3fik1LS9u2bdsXX3xRVFSkfFyhUBQXF9d3G7tv376RkZG19wOpbePGjYmJifSxsbFxVVVVenp6enp6bGzsli1bFi5cqMG7aAT999gZGhrSv/ipwsLCU6dOeXh4KP/D7e3t3dhq33jjjdTU1EbtON6EU5rDw8NjxIgRhBCRSJSdnX3mzJn4+Ph169b9/PPPDX7DAbQefD4/Pj7+8uXLd+/edXR0DAgIYLZ8KCgoCA8PJ4QoJz9lCxYsiI+PnzdvXkFBwaFDh+jBDh069OjRgxBy+vTpMWPG7N69+913322w8Lhx45ydnbt3725ubn7jxo0dO3a4uroqLxEPwAI1Us43CS75pfwxvYWDfBoxMvI1D3P/oNBMaWnpN9980+BdbS6XO378+OPHj8vlcg1rjoiIiI6OvnnzZklJiUKhqKqqunDhgp+fHyGEx+Pdv39fk0oKCwsJIY6Ojg2WjE1VzNmhmLNDceuFhg3UsitXrhBC3nvvvZa5vF7QUeTBwcHKB8vLy//v//6Pw+FYWVk9ffpUw6rokKYtW7booJnwCnB1dXV1dW3pVjSdj49P7X8nP/roI/rqiRMnCCF79uzRpHBkZGTv3r2trKwMDAxcXV3nzZuXk5OjYTNycnL279+vScnKykqxWNzId9kUdFC55t8UzVFUVKSfC9XU1EgkEj1cSKFQFBUV6eEqcrlcIpHQsYM68uBvd+/evXHj5oeRpb3nKeZtLLuf9qA2rVxRJpNJJBKZTKaV2tQrKyurrq7WpGRUVFRUVJRWLqrpYD0LC4slS5Y8ePDg999/37Bhw9ixY728vKytrfl8vqOj4xtvvDFr1qydO3e+ePHi6NGjo0eP1nyY6sqVK5cuXdqzZ09LS0tCiJGRUUBAwIkTJ6ysrGQyWXx8vIb1aEgrPXYhO0nITq00hyQkJHA4nB9//PHXX3/t27eviYnJpEmTCCFisTgyMnLIkCFOTk6Ghobu7u6LFi2iU4colQFzTD0nT57s16+fiYmJs7PzqlWrlG/fNOEUQkh5efnixYsdHR0FAoGfn19ycvLatWs5HM6jR48a9U7NzMzWrVsXGhpaUlISFhZGD6p/m59//jnt81u8eDG9sevu7q7JhwPQStT5pykztGDUqFEKhYLZrEx94RUrVly/fr24uLimpiYjIyMmJqZNmzYt864AtETlJuzxW46/P7Fwsa1Ws7gJNKjR69jRKferV6/WRWsYFhYWXbp0uXLlSmVlpXZrljRvjJ1ynqOPd73f7DYR8ttvvx0/fnzYsGEzZ850cHAghOTl5a1du3bYsGGTJ08WCAS3b9/etm3bhQsXrl27pmaDyISEhGPHjo0ePbpPnz6nT5+OiIiQSqVRUVFqLq3+FJlMNmbMmIsXLwYGBvr7+z979mzMmDEDBw5s8jtdvXr1pk2b4uPj6aQk9W9z4sSJIpEoIiJixowZw4cPJ4SYmZk1+cMBaKxjx45psuYcIy8v78WLF5qsHgAAKqnuxjPLI9ecLQXSFRMyTQxVv6GR6jTXShcoLi0tpT/yPn36aLdmzXvsSkT/m2mhRkGZ6hFzY2Lc8NTmf0hISEhMTBw9+n+bpTg5OeXk5ChP2I6NjQ0JCYmLi5s9e3Z99Rw5cuT8+fM0eJWXl3t7e//www9fffWVmrij/pTdu3dfvHhxwYIF3333HS0/cuRIpoOhCWxsbLp163bz5s3Hjx97e3urf5s9evQoLCyMiIjw9fVVftdN+3AAGuutt97q3r378uXLg4KC6B8V/8/efYc1dfVxAP/dmwQIe29wgAIqgooLxb33QOtorfraWltbW221y7a2ta2vVrretli1au2wbq0LUKwTKg5AEVBR2bI3ZNx73z8ujZckhCRkAb/P06dPOPdwcwJIvpzZnNTU1J9++mn79u1r167FYIcQl9KjveRS3cMnFttivc349OrpOc62TTesw1SnIZMLdqWlpTdv3ly/fn1FRcWYMWNmzZql2/tL1Q527x2EenHLN1y3X76kb2dYOVqzVk2bNo2b6gDA3NycjVZSqbSiokIqlY4ZM4bP558/f15Fdlm4cKGsO83GxiYyMvKrr77KzMxUcYSR6k/5/fffeTze+vXrZfUXLFjwySefZGRkaPYKOdgdv4qLi7V+mdp9FkJaSE5Ofv7551esWDFx4sQhQ4b079/f09PTwcGhoaGhtLQ0PT09MTHxzJkzqncnRqhjkpuURRAEo2yJQ1k1P+q4j5QiV4zP6uomkruKqU5TphLsTp8+LddftXnz5lWrVimdq5eWlvb1119zSxoa5Pe5aY5Y7WDnagsl1U1KauV/3gAArBT6wlxt1GzLUwMHDlQsPHHixMaNG69du8ad9FZQUKDiPn379uV+6OXlBQCqt1xX/Smpqane3t6enp6yCgRBDBw4sDXBTo4WL1Prz0JIIwsXLmTXHNTV1R08ePDgwYOq6/v4+IwYMcIgTUOoDeN219WJeJsO+5bX8JeMygvtXAnQZHNsTHVaMJVgJxQKO3XqxDDMkydPRCJRQUHBoUOHRo4c2a9fP8XKOTk527Zt0+6JZKOrLa6M/nC6kkLFNRPfaj8s+ZTiJOiTJ09OnTq1e/fun332mZ+fn6WlJQDMmjVLJFKWLv9la2vL/ZDP58O/ZwRp9ynV1dXe3t5yn8LOAtQaG7/YDSO0e5nafRZCmtq7d++qVavee+892YETzXF2dn799ddXr17N7m+HEFKKIIi0tDTZhzRDfHfSK7/MfFxo2bAeZU1X7mGq05KpBLvhw4c/evQIABiGuXv37scff7xv376IiIiEhITevXvLVVY8M6eyspKdXN8i3e5jp5OVE6DsDOOoqChzc/OLFy+6uLiwJdXV1QY4wFGOjY0NO2bKVVRUpPUNy8rKUlNTra2tAwMDQduXaSJfHNQR9O/fPyYmJiMjY/v27TExMampqdyxJDs7u4iIiLlz586dOxcX7iDUooiICO6Hv19wTX1s1cu3duHwIrppFwSmOq2ZSrCTIQiiR48ef/zxR319/bFjx9avX89u7M7FnpnDLZHbPFmFp8FOq1WxukpyLcrJyencubMsuADAxYsXDfTcHMHBwefOncvLy2OHaAGAYZjExEStb7hp0yaJRPLMM8+wJw+2+DLZanKdjibyxUEdR0BAwObNmzdv3lxZWZmdnV1SUiIUCl1dXTt37myYE98RagciIiKio6NlH8YmO5y+6ejlKHp1ch5JMNz3ZEx1rWG6v5LmzJkDAIpHXLeS+nPsjKtTp04PHz7Mzs5mP6ypqXn33XcN34xnnnmGoqhPP/1UVvLrr79qN8Gupqbmww8/3Lx5s52d3YcffsgWtvgynZycACA3N5dbaCJfHNQB2dnZBQcHjxw5ctCgQV27dsVUh5AK3O5tuVR3M8t679/utpbSNdNzLM2b/OmOqa6VTK7HToadL6V6fpgWnp4Va9rBbsWKFTExMQMHDpw/fz5JkgcOHPD395ebD2cAixcv3rlz548//piZmTlkyJAHDx4cOnRozJgxcXFxLb6l3bx586WXXgKAhoaG3NzcxMTEmpoab2/v33//3d/fn63T4svs0aOHq6trdHS0SCRydXW1t7dfuXKliXxxEEIIqSbLdtwFE9nFFt+f8uKT9JrpuS52TTY3wVTXeib65yZN03v27AGFNZutZ/izYrUzY8aMX3/91cXF5Ycffvjjjz9mzZp19OhR9c/z0BUej3fq1KmXX345NTV1y5YtWVlZJ0+eZGNZi0EqKysrOjo6Ojr64MGDDx48GD169E8//ZSenj506FBZnRZfJp/PP3DgQM+ePbdv375+/fotW7ao81kIIYRMBzfVldfwtxz1FkvJlybkd3VrMjda9jc/i/iXgVrZXijfVMZgkpKSPvzwwyVLlowYMYJdJikSif75559PPvmEXYYWGxs7ZsyYFu9TWlrq7Ozs5uZWWFioohoDsGwHsC/4mYEwvpdOXkSHM3To0NTU1IqKCvz3hvTN19cXAGTD7kg7hYWF8fHx8+fPb7FmXV0dSZIWFhYt1mwliqJomubz+Qb4NVJWVubg4GCAJ2LPVBUIBPp+IgAoKytzdHTU97MwDCOVSkmSZKc7a6fp5ibkx/s655WZPzv8yfg+Tc6B9PPzE4lENjaNG4bJfb90mFVommaPPjLAVIrq6mpzc3Mzs5YPLYiKigKAN954o/VPqv1QbG5u7qFDh1JSUsrKyiIjI5955hl2+SSPx+POaldNKpWePHny5MmTACAUCs3NzauqqmiaBgBzc/OvvvpKnVSnPikFsh8NowbatqS4uJj7DT1w4MDly5cXLFiAqQ4hhJBq3FRHM8T/TnrllZmPDSmXS3VBQUFi8dMjARTfX5rb3Bgp0ibYMQyzYcOGzz//XPZt6NGjB03TYWFheXl5JEnm5+e7ubmpc6vevXvv3bs3Jibm+vXrBQUFVVVVtra2/v7+o0aNevHFF/38/LRongoSzoQ97VbFdkALFiwQiURhYWFCoTA5OfnEiROOjo6ffPKJsduFEELIpMmdG7bvkkvKY+sePnULhz8xVpM6Am2C3Zo1a9g+Qy6BQLBixYr333+fpukTJ04sXbpUnVtZWlouXLhw4cKFWjRDC+zKCTMeiCmgMPqrZ+7cuTt37tyzZ09lZaWzs/PChQs/+uijrl27GrtdCCGE2oxzqQ4nrzt5OIhfm5LLI5u8AeOCCd3SeIA5ISFBMdWx5s6dyz64cOFCqxqlN2yPnZkAwOQXT5iOF1544erVqyUlJRKJpKCgYO/evXLzWxFCCCE53O66lEfWu8+52VpSb87IsVJjcxMcdW0NjYPd999/zz6wtbVlN7OQ6datGzsCm5KSopPG6Ry7iZ0FHwCDHUIIIaQf3FSXU2L+7UkvPg9en5rjaifmVlPRVyeX7TDqqU/joVh2i3+BQHDp0qXg4OAff/yRe7Vbt25PnjxhDwczQWyPnbkAAIDBOXYIIQ2JxeLjx48nJSVVVFR8+umnZmZm7I6bTk5OuJwIIRY31ZXVCP572FcsJVdPy+nm0WRzkxZHYDHMaUfjYMee4B4WFhYcHKx4lT0tsbq6uvUt04fGOXbYY4cQ0tzt27dnzJjx4MED9sN169bFx8ez84mvXLkyePBgo7YOIZPATXUNEvLLo94Vtfz5EUUhnWu41dSZV6e/HU/aN42HYlXv+8L21VlaWmrdIL1qHIrFOXYIIQ2VlJSMGzdOlupYzz33HDv/5NSpU0ZqF0ImpMnmJjR8d8Iru9hidO/ySf2anOeuRapD6tM42LG/xa5fv/748WO5S3FxcexvPQ8PD500Tuek7FAs9tiprbASrj0EKQ5bow4vKiqKHa/g4vP5s2bNAoDz588boU0ImbADV12TH1kHetU923RzE63XwGLUU5PGwW7QoEEAIBaLR40a9csvv7CFeXl5W7duZX/ByeqYIAkGO02IpbD1DPxwDtb9CXF3QCxt+VMQaq+OHTsGAMHBwVlZWdzyAQMGAEBubq5xmoWQyeB218Wn2h+/5uRuL141NZfPe/p2izubGIDGc+yeffbZP/74AwCysrIWLVrEFu7atYtb57nnntNF23SPu3gCg12LjtyAkmro4gJ5ZfBbAvyVDGN6QHd3cLUFexMdbEdIX9jhiDVr1nTp0oVbzh7rpPowQ4TaPW6qS31steucu42QenNGjrXF081NMNUZhsbBbtKkSePHjz9z5kxzFaZMmTJ69OjWtUpf2D6nxh47HF5UKbsUYu6AkzWsnQgiKcTchnN34dD1xqtmfHCxAR9H6OoCXV3B1wn4ej9zDyFjoigKAKysrOTKc3JyoKXJxwi1b9xUl1dq/t1Jbx4PXp+a42b/dHOT1qc6XDyhJo2DHUEQv//++/Tp09l9T+RERETIxmdNUJPtTvAnpHk0A7svA03DwsFgLgBzAUT2h4m94cZjKKiAgsrG/+eVQ8IDAAA+D3wcwdsBPB3A2wF8HMFWaOzXgJBOeXh4PH78+K+//oqMjJQV1tTU/PDDDwDg5eVlvKYhZExNNjep5m867NsgJldOzu3uWa/is1rExjjZvDpMderT5kgxBweH+Pj4vXv3/vrrrzdv3qyoqLC3tw8NDV24cOFzzz3H4/F03kpdwTl2ajqbBg+LoV9nCPV9WmhlDhHdn37IMFBQCVlF8KAYsooguxQeFjde4pMwsx9MCAac6orajaFDhz5+/Hj37t01NY27NmzatCk2NpYdoh06dKhRW4eQcXBTnUhCRh33Ka/hzx1S1N+/ya5nWnfXYZ7TgjbBDgB4PN7zzz///PPP67Y1+ibhDsXiT0szSmvg0HUQCmCBym25CAI87cHTHoZ2BwCgaCishPwKyC2Di5mw/xrczoNlw8EBZ+OhduHll1/+7bffGIY5ePAgWyLbnp0kyZdfftl4TUPI+BgGtsd5PCqyCA+smhKm8eYmSIc61rwQXDyhGkVDRiHsuAAiCcwK0yyT8UjwcoD+XWBmP/h4JoT4wt18+OAQXH+kr9YiZEjh4eHvv/++0ksffvhhv379DNwehIyO21134KpLQoZtd8/6ZWPyuWM1mOoMT+Meu6KiIvYoWAsLC7nRh4sXL7Kn6/Tu3dvV1VVXTdQhHIpVqrQGUnLgTh6k5UODBACgmxuMasU/RmsLeG0sxN2B/dfgf2dh7gCYoOSYEoTamI8//jgoKGjTpk3JyclsSe/evd9555158+YZt2FqUmdUi/mXAdoDBhxoM+SIXnt6UbKnUHwubqp74e3jIo91rnbi1yZn83m0rG5QUJD6jTTYzx77FAZ7LkP+g2JpHOy+++67Tz75BADefPNNuWB37NixLVu2AMAHH3ywYcMGXTVRh8TcYNexV8UyDNx7Aik5kJwDeeWNhV4O0MsLenlDgEdrp8cRAGN7QoA7fB0D+6+BkzX079LyZyFk4ubPnz9//vyqqqry8nJHR0cbGxtjt0gtDMPQNC2VtrwXJU3TAKBOTZ1g1xrrG/vaDba9rWG+egzDGOzbRNM03fQtMyMjg33w8ssvU1b9RD6bCaqq+sYK60XvyCoGBARo1EKKotT8KdUJxRelDwzDUBSlzotiGEZXP6IaB7uzZ8+yD1544QW5S8uWLWODXVxcnGkGOxyKZVXVw/fnILMQAIDPg2Bv6NMJevuAo/xODq3l6wSrxsEXJ2D73+BgCf5uOr4/QkZha2tra2tr7FZohiAIdfZkIQhCzZqtxHZjGGabGPYVGSDYGfJFgUE22WFfEftTIStMT09nP1yxYgVt5iv2+pgAxiz3PUKc8/LLL7PrxAMDAzV9LvZ7ZKwXpSfsK1Lzn56unlTjYMfuum5lZdW9e3e5SwEBAZaWlnV1dQ8fPtRN63QNh2IB4HEpfBcHpTXQwxOGB0Kwd+PhuXri6wQrRsHXsfBNHLw3Fdza2LshQo0uXLgQExMDAC+++KKvb+Ny8ezs7G3btgHAuHHjhg0bZsz2qcS+h6mzZQH7JmSAzQ0oimIzkGHeXHk8ngGeiO2YMczWEGp+Q1uJ7RfkPtfdu3dlMYXhOYh8tzA8a/O8D3l1jfMTSJLUbl4dRVGG+dmjaZp9LgOESDbYGXi3EI2DXWlpKXACL/eSbBS5pKREJ43TOXZVrEUH7rFLeAC7LoKEhhl9YWofMMzIRLA3LAqHXZcg6gy8PxWsLVqoL6WBRxqobQipKSoq6siRI/7+/tzhCG9v7/3792dmZqanp5tysENIJ7jz6sRSUuT7X0bgLijewas6LyvH1RJGp3FctbCwAIC6ujp29yaue/fu1dXVAYBQaKK707I9dmYddY7dn//AtvMg4MOqsTDNUKmONSwApoRAURV8E9fCmbMiCazbB9/EQocM3sh0Xb9+HQDGjRvH/eObJMlRo0bJriLUQTAM7Ixzpy0C+NXxgpI9xm4OakLjYCfbYP2dd97hTjykaXrdunVydQyMaimrdeQ5dqdT4XQqeDnA+mnQ28cIDZgZBgP94P4T+Dau8RuhVHw6lNdBcjZczjRg4xBqSVFREQC4u7vLlXt4eACeFYs6AG533eFEl8vpdv4e9Wb5nwHnz/ALFy4Yo2moCY2HYgcPHpyeng4ABw4cCA8PX7x4sa+vb3Z29s8///zPP//I6ui4mepRERdYYikQBJjxADrYkWLJ2bD/GthZwuoJRtsxmAD4zzCoF0NKDvxwDl4ZDTyFPyskFJxJBaEAKAb+SIRe3mCP+xsj08B21GVmyv/BwS4PNOUTdxBqPW6qu5hmdyTR2dVO8sa0XNtnvgGA5cuXA8DFixfZCVp4XIRxadxjt2jRItnjxMTEFStWTJ48ecWKFbJUBwCLFy/WSeM0JaFa6LSTUGDGA3a6ZMfpsXtUAj+cAzMevDHOyOdA8ElYOQZ6+8CtbPgmFqQKQfx8OlTWw5ieMHcA1Ilh+984IItMhbe3NwDs27cvKSlJVpiYmLh//37ZVYTaJbY3h5WWY7nzrIelGbVmerat8OnEGu7x8QbbWQYppXGwGzFixPTp01VUmD17dkRERCuapD2Ggbv5qipIKBDwgCQAOkywqxHBj/EgoeD5oeDrZOzWAPBJeGkkdHWB1FzYdalJvylFQ8xtMOPD6B4wMhD83SAtHxLlZ3IiZBzs2giJRBIeHj5nzpy1a9fOmTNn6NChEolEdhWh9ke2ZR0A5JeZf/OXN8PAa1PyPB3FsnJuqmNhtjMibdb67t69e8SIEUovjR49+ueff25Vi1onQWUO6GjBrlYE/4uDoiqYGAKD/Izdmn9ZCOCN8eDtCFfuN8l2V+9DaQ0MDwBbIRAELI0AAQ9+uwpV9UZtLkIAAPDKK6+wmyNIJJIDBw5s3rz5wIEDsu0t8KxY1C5xR2Cr63lfHvWpFfGeH/mkh08tACxfvnz58uU9evQwXgOREtoEOzs7u7Nnz+7evXvs2LEuLi58Pt/FxWXcuHG//PJLTEyMEbdiJwi48bjxeAmlJBQI+AAAJNH+V8XeeAzvH4KMQgj1hdkmdo6llTm8NRG8HeBiJuy5DAwAzcDJFOCTTw8fc7eD6X2hRgS/XjVqWxECAIDQ0NCNGzcqvfTpp5+GhoYauD0I6Rs31Ukp4uu/vIsqBRP6lo0MLgfOvDo170b8Sx9NRVwaL55gkSS5aNEO4cKEAAAgAElEQVQi7nw7UyDgQYMEbj2GAV2VV5BIwcYCgA127bfHrqoefr0K1x6CgAeRYTAhuLXng+mDjQW8ORE2n4K/M4AgIMADCitheAA4cE6/mNALkh7CtYcwowI87I3XVoQAAODtt9/29/ffuHHjrVu32JLQ0ND33nsvMjLSuA1DSOe4qY5hYOdZj4w8yzD/6vkRT2TlqlMdQRCyVRTcPMctR/qgZbAzTexy14QHzQY7MQUCHkC7DnbpBfD9WagRQTc3WBIB7nbGblDzbIXw1kT470k4nw5X7gFJwsTeTSqQJIzrBdvOw+X7EBlmpFYixBEZGRkZGVldXV1WVtaGzopFSCPcVAcAR/9xuZhm18WtfsWEfFKTbgI2wyn20mG206uWg11DQwP7wMzMjCRJ2YeqsfsYGxhJgqc93M6FWhFYmctfpRmg6MZgR5DtM9hV1UN0PDRIYcFgGB1kih11cmyFsHYSbDoJBRUwyA9cFQ4c69sZhGZw9R7M6gca/UJBSH9sbGww0qEOIv624+FEVzd78dqZOWb8p3OY1B+ERQbWcrCTHSNx+PDhGTNmqHmqhLHC+CA/OHQdbmXDkG7yl7jHTpBEO9zHjgHYeREq62HeQBjTdiazstnubBoMD1By1YwHA7vC+XS4kwfBuKEEMqzbt28DgFAo9PPzYx+r0KtXL4M0CiH94nbX3c622vu3h5U5tXpajrXF0wnsQUFBSrvikCloV0OxABDWBQ5dh5uPlQU7KQA8HYpt8ZiKNicmFVJyINQXxra19xc7IcxqfnlHRHc4nw4XMjDYIUMLDg4GgH79+iUlJbGPVcChJdQOcFNdQbnZ/0550wwsH99kcxPZabDsz7yKeIf/KIxCm1WxpszdDtzt4E6eklMo2JL2OscuuxQOXgc7S1gaYdBDYA2giwt4O0JyNtSoNQsAIYSQcgSH4lVuqqtt4G096lPbwFsQURjSuVpWLkt1Mi2mN8UKGPj0qr0FOwAI8QGRFNIL5MvF7TfYiSTw/TmgKFg2DKyNMLlR78L9QUpDQpax24EQQm2WXJiT+5Cb6iia+OaEd2GF2bjQsrEhpbJyxVTHUhrduIVMU1q/BKSOlodiq6sbozo7u072ockK9oEztyElR37krrHHrj3Osdv3DxRVwage0NPL2E3Rj0F+cOAaXL3fluYOonbg8OHDAODg4CB7jFB7IludKrcMdudZ97Qcyz5dqxdEFKp5K+6UO4xuxtVysLO2tlbxoQnq7g5CASRnw8LBTcqbzLEj288GxRcz4Xw6eNrD3AHGbore2FtCTy9IzYW8cvByMHZrUIcxY8YMpY8Rak/kUt2xa84X7th3dm14ZWI+972yue46GcxzJkLjxRPfffcd25XatWvXKVOm6KNNrcQnIdgH/smCnDLwcXxa3rgqlt3upL0Mxd57Ansug7U5vD6+8aW1V8MCIDUXLmbCvIHGbgrqkMLCwhiG4fF48fHxVlZWLX8CQiaPYRi5VHfhjt3+yy6uduK1M7PNBbQsqrWY6pDp0HiO3auvvvraa6+tWrWKezCwqQnxAQC4ld2ksP0tnqiog+/PAsPA8pHgbOodqa0V4gs2FpDwoB0uZ0Ztwp07d27cuCESiTDVoXZDLtXdybbaedbD0pxaPT3XRvh0BWJgYKDBm4a0p3Gwc3JyYh/4+ZnMqfIKenkDSUBKTpNCbrDjtf0NimkGtv8NlfUwObTdTq3j4pMw0A86OeHaWGQc7Hubmju0I2RcRFNsodxQaVpaGvfDokqz7056MQzx0vgCL0eRrDwgoMkWo3jeq+nTONgNGTKEfZCTk6O6phHZWEBXV8gqhqr6p4VyiyfaerA7dB3S8qG3D8zoY+ymGMqCQfDGeLCzNHY7UIf0+uuvA8D9+/dv3Lhh7LYg9JRihlN6hBf7oLmlqXUi3pdHfWoaePMinvTpqnxzE26kw3hnyjQOdu+++y6fzweAn3/+WSwWt1jfWEJ8gGEgNfdpiZizeKKtz7HLKIRTyeBmCy+OaAPnhiHUDvTr1+/ZZ5+laXrixIlffvnlxYsXbzdl7AYiBKByu2Au7iAszRDfnvDKLzMbE1I+sW+Z3pqGDETjxRMhISF79uz5z3/+c/PmzZEjR7733nvBwcEuLi5y1YxyVixXn05wMKnJERTyc+za7FQtCQU/XwSCgOUjwdLM2K1BqGOQnTxRVFT05ptvKlbAJYHIYFrZWyY3tW73Obfb2VahXWqeG95kcxP23DCdPCMyJI2DHfes2CtXrkyePFlpNaP/jvO0B2drSMsHKQV8HoCRzoqVUlBYBYUVUFgJ+RXgYAVTQkEoaNU9z6RCURUMD4DOzjpqZVtDxDbZyYYZe9VYLUEIIQPTbao7fs3pXKqDn3v9q5NzSc4YHi6Dbbva21mxXCG+cDYNMgob1xbInRVrgKHYkhr44BA0SJoUXrkHkf0hvFuzB3/RDBDQ7ABrURX8dQtshRDZX7eNbTPkUh1bgtkOIdQRqJPquHsFy0rYB3Kp7mqG7f4rrk42kjem5Zrxn74pKj03DDvt2op2Hex84Gwa3Hz8b7DjDsUaZFVszG1okEC/zuDvBp724GYLSY/g+E3YcQHOp8PMfiCWQmElFFRAYSXUi6FODHViaJCAjyO8PBrcbJXcc+9VEFOwaABYmeu9/QghGTx5ArUhbJKTHSzBkkt19wuE22M9hWb0WzNy7CylsnI1++qMPi6HmtOeg12gJ9gJ4eoDmNMfzAXyc+wAgGYaH+hDrQguZoKdJSwf0TgWDACTesPArvBHIlx/BFtOPa1MkmBlBpZm4GEPJAEPiuDjo7BsGPTp1OSe/2TB7VwI8oTB/vpqNkJIKTx5Apk+ubClItWV1fC/OeElpYiXJuR5OT3d3ERFqsMk11ZoHOxM/6xYGT4JIwLh6E24ch9GBoHYsMHu73QQSWByyNNUx3KyhldGw+1cuPEYnK3B3R487MDVFnicyQ0XM2HvFfguDiaGQIgP1ImgVgy1IjiZAnwePBfe7DAuUp9sSBeHcZH+xMfH79mz59KlS3l5eR4eHhMmTPjwww9dXV2bq19UVLRmzZoTJ06IxeKhQ4dGRUVx32hVX0UdgYphVhXkUl2DmPzyqE95jWBeRFF//zbzno7UpHGwM/2zYrlGBMJfyXA2DUYEye9jB6DH0VgpDXFpYM6Hkc3s193LG3p5N/vpEd3B1wn+dxZOJsPJ5CaXZvQFdztdNrXNYcZeVTrNjltB9R3kPh2n6CHVaJreuXPn/v37CwoKPDw85syZs3TpUpJUa6+oDRs2VFZWLlq0yNfXNy0t7bvvvjt16lRycrKNjY1iZYlEMm7cuKKioi1btlhbW2/cuHHEiBEpKSlubm4tXkUdBzfbaZHqaIb49qRXdrHFyODyyf1KuZfw74T2QeNgV1tbW1paam9vb2urbAqYibGzhLDOkJgF6fmNiydkZ8UCAE0D6Od81cQHUFEHY3poPxOukxN8OB3i7oCUBqEZWJqBhQCszDvEIRMtYnMYG8gUQ54WMNshFebNm7d//372cWpqakxMzLlz53777Td1Pjc6Opq7cX9ISMjChQv379+/dOlSxcq//fZbcnLyuXPnRo4cCQDh4eF+fn6bN2/esmVLi1dRh8IwTFlZmaOjY4s15VIdAOw+55byyLp355rFo55wyzHVtRsabFB8/PjxAQMG2NjYdOrUyc7OrkePHjt27NBfy3RlbC8AgNg7SubY6WnCAMPAiWQgSRgf3Kr7WJnD9L4wOwwm9YYRgTDID4K99Th23OY0F8Wai3pE7GD2P302CmmJSrjEFOQZuxVKHDhwQJbqZH7//Xc111LIHcc0YsQIAMjLU/5Kjx075uXlxeY2APD29h49evSRI0fUuYqQIsVUd/K647lUB1/nhlcn5ZGEqmWwqO1St8fum2++WbVqFbfk7t27y5YtS05O/uabb/TQMJ3p6gK+TpCSA54OAJxVsQBA6SfYpeVDYSWEdQGntjRq3QY1NEj/juvaYJ5lKZK7osPOPGQAVOIV6ZH9hKu72Rtvm9pRKrt371ZavmvXrpkzZ2p6t8uXLwNnr2M5d+7c6dmzJ7ekV69ep0+fbmhosLCwUH1V05agdk+W6pYvX974YN2v+y65OVhL10zPsTB7ukc/prp2Rq1gl5mZqXSndQD49ttvp06dOnbsWJ22SsfG9YLtf0NuGUDTOXZ66rE7mQIAMKm3Xm6OZKhbSdS5mNsQfM6partX8QnnCgn59DuqUarDcViDoR8/JMwtCHePpyW3kqSH9xGWVoJnl5paqgOA69evsw9++umnGTNmHDx48KWXXuKWq6+srOytt97q06fP1KlTm6sQGhrKLXFwcGAYpry83MPDQ/VVWeHVq1dfffVVuTv7+PhERkaKRPJ/AimSSqWGOQaUpmmapimKMsxziUQiAzwRRVEMw9AGOdeIfVHNXc3MzGQfvPLKK431zf2ijjgBiF6dmGMrbJA+3d4EVNyHYRiKokiSVHNSaWtIpVKpVKrOT2krsS+Kx+MZ5kdCLBarMxVShzsFqhXsoqOjJRJJc1e/+eYbEw92YV3gjwSoEQHIrYrVw7++7FJIz4dubh33WAiD4YUNAnOLK6e+H1NqN6bULtWmbkbovTxzbc4vxhWyhsHkZkt+/BpomvTvzhs6ggzsSd+9Ldm3FywsBMteJlxNcRFASUkJAISFhS1btgwAli9fvm3bths3bhQXF2t0n/r6+lmzZtXW1sbFxfF4+pnb++8TZWVlyRUKBAL2zazFT6dpmiAIdWq2Ek3Tsr3W9P1c7Gs3TIJU8+vceqqfiA2XsojP8J1FPpuAFJrlftTJZRb3va9bt24q7sPmVIZhuNHEyspK9ri2trYVL6IJNhYb4KsnC9+G/ANG30/EpVawO3/+vOzxc889N3DgwLt3727bto1NexcuXDDxPanNeDAsAE6mAI9sjHR6WhVbWgPfxgEDMDlEx3dGSvD5vD5hEX12Mnk50vNxwSk3L14LmhlyL9mmTp3PlmU4bt8erqLQI5qWHPwDaJpw96DvZ9L3MwknZ6ayAvh8wZKXCM/mV4kbFftbztfXV1bSuXPnGzduiMUa/AkhEolmzpx569at+Ph4f/9md6F0cHCoqKjglpSXlxMEYW9v3+JVmVGjRpWVyZ/jXlhYGB8fb2lpqU5rSZI0wNguRVE0TfP5fAO8dzQ0NFhaWhrgiaRSKcMwAkHrTo1UD/uilF66e/eumdnTc8QZUijy+YIRuAqKovnV8WZm82SXWhyEZRhGKpWSJCn7a0Tuy2hlZaWr/e3EYrFIJFLzp7Q12KTF4/EM0A1JUZS5uTn329EcHf58qhXsHjx4wD5Ys2aNbAVWcHAwOyRRVVVVXFysYmcmUzAyCE6nNnbXwb9z7HQb7MprYfMpKK2BWWHQ20eXd0aqEV4+goVLKC9vj9PH45ICFwY/iHGuVPdz8YAyQ6EunWfyc8nefQQLlzC52dTVi9StGwAgWPQC2amLsVvXAu7vXE1//4rF4sjIyMuXL8fExPTp00dFzZ49e167do1bcvv27a5du7IndKu+ihBLYcEEKfb8gLbozq/4S1D6K/cCTq1rr9QKdrJNiWfNmiUrnD17NhvsAKCystLEg52TNUzoDXX/jt3rvMeusg42n4KiKpgSClOwu84YeCPGLsj9+qc7XQ4ld3s98PF2L1UjZRjdDIwpK5XGnAShJX/abAAgvH35cxbyJ89kxCLC3sHYrWvZzZs3Zb/ubt68yT6QlbB+/PFHxU+USqXz5s07e/bsqVOnBg9uYd7ntGnTDh8+/Pfffw8fPhwAcnNzz549u3LlSnWuIgQKqS46OnrJB6mUzRBebZJZ4VbuJe1SnSkPzSEZtYKdbCood9ccZ+enk8gMPH6snciwp491G+zK62DTCSiqgikhMKufbu6JtLDv2b+G7h+3P9n/u7udl+W6ft2p8LcFR4lzQ0FZkmOqq5jcbIIBRu3fVDgVT2vSI3+CRLzCP/PnhPFsCTP2KlhaEpaWoNBvaoJf3qysrOjoaLlCuRKlwe6ll146fPjwiy++WFxcfODAAbbQ39+fXQYRExMzadKkPXv2LFiwAAAWLly4devWBQsWfPrpp1ZWVhs3brSzs5MtXFN9FSHFzU3O3HSUOs4lG+6b574PjBQUfmg1ZeLTrhCrPZ8Vq4IOF08wDHx/FoqqYEQgzAxruT7Sq0tzYphRJdJTx0LvpPx8u6t40wbpkE944cPl6zGMZNc2Jjdb1GOpu8ueMoGUe1HFilpHCb9MIMXhWo3Qt67TGXcvOFTv8nzajSr7Grbv0fCEhAQA2LZt27Zt22SFr7zyynfffQf/zvWR/eUsEAhiY2NXr169evVq9tCw33//XbbiVfVV1MEpprobWTa/XXBztJZ+tIxxsI7iXsJB2PZNs2C3detWpVtdK5Z/8cUXrWqXnumwx+7sXXhQBH074RGupoJwchY8u5QpK6UunaeuJUhPHqML8gXzFnHrUEmJTG42CAR0WmqB3cjR3a5esq9RfVtritx2p8usIseZofdOOVe0p/Chc3TqLerKRRDwwdyCMLeg01IbSOaVoEdynaMdYaPB27dvq7g6YcIEuYnnbm5uv/76a3P1VV9FHZZiqssrNY8+48njMa9MynWwbvKHaytTnVynna5WTiAd0izY/fTTT2qWm3iwI3QU7Iqr4cA1sLaAxUNNcAeuDo1wdOJPm80fO1H80//om0l0z95k8L97gDU0UKePA19gtmoddS2BunA27kZP3qhxHpLPS5p23cl0q7P4M8U/qEYIAF9ndLrgUF3LawPTD4yDYaSnjjOlTeY4fuafd8+ywVgtag3Ft0yETIrij2hlHX/LUZ96MfnS+LzunvXcSzrpq8MwZ+I66lAsCaCLDYr3XgGxFBYMAmvc+N00CS0Fzzwn/maz9PA+QeeuhI0tAEjPnmZqqnmjxxMurvxJ00j/7tJ9v1Bxp3OhT4G5JNmmNsW6/o51XaZVQ6alqJZHTSm233Gnq52Ut92ruIFHr8x2W5/l+Xa3HGO/NhNF30tnSot5ffrz5ywAsYiprweALdeVb8lr+gIDA43dBISapZjqRBJy61GfkipBZHhxeGAV9xKOwHYQHTXY6aLHLjELUnMh0AMiAlqujIyFcHPnT5gi/euw9OAfgsUvMiVF1OW/CXsH/ojGXbXJ7oGCN96mrl48eXNf7xrLCSX2E0qebgyWZy72FJuJCOalHg93eZZY0uTUIvuV2W5rZ2xt5gk7OurqJQAgBw8FHg+EloTQEgCosVfUH3jFMW6E1KGY6mgavj/lmfXEYliPiukDSozSKmR0agU7Pz8/fbfDwFof7OrFsC8ReCTMH4RT60wdb+gIOi2VvnubupZA304GiuJPmg6cHSMJaxv+2EnTxk4iYgc7S/i9qyx71gp71Ap71ggDa4U5FuIFwfeTbGsBoI6kVwVmH7nV7crPHw7vn0axZ9NhEPkXU1FOp98hPL0Ut6ZT8/Re/GIipI6srCzuCRCs3y663ciyCfSqWzK6UO4Sdtd1HGoFu/v37+u7HQYmFAAA3HsCXV20vMOBJKiog4m9wUfJYhJkYgiCP/dZcdTn0iP7QSohu/iRvZXvE8uGj3NOVeecqmQJw7XpOgkidvABt7LIJ47Lc9y+930C7WsVZwukUuCr+qVBJV4GmuYNilB6VelpH3KXEEItUjr182yKw5mbjq524lVTc/m8Jv0WmOo6FL2fp2GaRgaBtQUcuQ7F1dp8elkt/J0OzjYwXdU28siEEA6O/KmzQCoBkuRPj1Sx1IUZe5X9j1siV+fNgJxKPvVRlpdnQ8sHxbQbTHmZeON6yY4fmMpmDvagKPqfq2Ah5PV5uvEPETuY/a/JrZp+STHVIaQ+panuRpb1nnh3W6F03axsa4smS7sw1XU0HTTY2QphaQSIpLDtvDYDsn9nAM3AtFAw66BzFNskXv/BvGGj+BOnER5erbxVoZl4vX+urZR3OLmbb31HyXbSk0eZulo686446nM6+YZiBfp2MlNTzevXXzbMLXcOL7eyLEBjqkNIfUpTXX6Z+bYzniTJrJyc52on4V7CVNcBddBgBwChvtC/CzwognNpmn0izcClDBAKoH9X/bQM6Q1/8gzesFE6udV27+K9HiUh1ZZX/uk5rNyGe4mpKKdTb9HZj5naWp08lymgH2XRqbcID0+241Py2y7Jb7ugvo5bh7p6EQhCNg6rdOdhAzUXofZIaaqrquNvOepTJ+YtG1MQ5F2nWAF1NB26x+m5cMgohANJ0NsHXG3V/azbuVBeByMCwbxDf/E6LnYeHg3Msp4Pb9jUbbrvE3OzB+V1gdd/EHU7mb6eSD+493QrHaGQcHLhT59D+nYyaqtbh2Gkxw8Bw/Cnzib9upHdgyR/7KGTb4gfPeANHUn2DCacXJjCAvrhA9KvG5k8w9jNRagdUprqxFLiy6PexZWCmYOKhwTJz5HA7rqOqUNnE2sLmDcQtp2HXZfgrUnqLm69kAEAENFdny1Dpk12Ftb//nOEfpQl3btTeuyA9ORRkEoAgPTtTHQPhOpqpqKMKStj8nKo08fIF181dqu1R924xuRmk71CSL9uAEC4upm9slp69gx1PlZ64gicOEK4ebDDr/OsThu7sQi1Q0pTHcNA9BnPrCfCoUGVMwfKb26Cqa7D6tDBDgAG+cHV+5CaC5czYagaWa2qHpJzwMsBumi7nBa1G2y8Izt3Fbz2lvT3PUxFGRnSl9dvAOHixq0m2fYd/SCTKcgnPDyN1FKNMTmPgSAIZ1ewsACxmDp9HPh8/mROVxyPxx83iTdgMH33Nn33Nv3gPkglhK3dcedy47Va91QfCCanV69e+msJ6siaO/7kj0tu/9yz7elbu2xsgdx6MEx1HVlHD3YAsGgIvH8Ifk+EIE9wsm6h8uV7QNEwDHckRhxk4gRonHB5mnGRXwrAGzqcfpBJXT7Pj1xg8KZpg8nPFX8fBTQNAIStHQiFTFUlb8RYwtFJriZh78AbHMEbHAFiMf3wAWFnJ0mNU3XntrZOIjg4WP3KeM4S0ofmUt3faS4nrzt6O4lWTcnlkbi5CXqq4y6ekHGyhmcHQ70YoltaIcswEJ8OZnwY0s1QjUMmT25BgOL6ADKoF+HoRN28ztTWGLBd2pOeOQE0zevTn+weCDweU/SEsLPnjxqn6nPMzMiAIMJdeZckrn5FSLfScy33J/hamVOvTckVmtHGbg4yLdhjBwAwpBuk5MC1h3A6BSaFNFstvQBKqmGQH1h2lA0ukC4QBC98mPSvw3TCZd7o8cZuTQvoxw/p9DtkFz/+vOcai6QSAAC+QK6mLMLK7fmness6hJAi4t+RVLl+X6XddQXlZl/95c0w8NqUXA8HsdxV7K5DGOwaPRcOmYVw+Ab08ILOzsrrXMgEAByHRU8p3b9D8SAK3oBwadxp6soF3vBRignJpFAnjwJBNJlOp6zBqjeoY0vaQaQ7fPiwsZuA2j+CMz+OfczGu2Y2N+FtOeJb28B7fvijHj71clcx1SHQSbCrqqqKioq6dOkSAAwfPnz16tWWlpatv62BWVvAs+Hwv7Ow6xK8Pw34CmPUtSK4+QicbSDA3RjtQyapufNPFQslYW9Rl85TyTd5/QYYpGnaoDPu0o+yyKBehI/85izqb0HXng5YmzEDt25B+kU0cwpOc5ubfHXcp6hSMGNgyeDuJQBNzorFVIdYGs+x+/XXX52dnZ2dndetWwcAFEWNGzfuo48+iouLi4uLW79+/dixY6VSqR6aqnf9OsOQbpBdCr9chqqmfwjdyoaPj4KYgmHdVRxGhVCzAuhvaGCoS+eN3ZDmMYz0zF9AEPwJU4zdFIQ6NKWpjmbgx9Oe9wqEgwKqZg0qlruKqQ7JaBzsrl69WlpaWlpaOmHCBAA4fvx4YmIit8KVK1f27t2r/g2zs7OjoqImT57s6elpZmZmb28/aNCgL774orpaq2NcW2fBYHC1hYuZ8OY++OlveFAEhZUQdQa+iYXyWpgcAhM0WCSHOgQ1e6eyLEWnnCuZ/NyI/WNN8wAG+k4Kk5dDBocqroEwzQYj1C5FREQoLd9/2fXafduubvUvjM3H/gWkgsZDsTdv3gQACwuLIUOGAMBff/2lWOfAgQOLFy9W524ZGRlBQUGy6aIWFhaVlZWJiYmJiYnR0dGxsbH+/v6atrA1hAL4aAZcugdn78DV+3D1PhAEMAz08ISFg8HD3pBtQW2GLNupDkA/+jyZXGK/+pHn5ZBMufFKpQsRDIphpHGngCD4YyYYpwFtQUVFRVRU1OnTp3Nzc0UikdzVkhL5HWIRahHDMNzR2OZS3cU0u7+SnJxsJG9MyzXjy2/fgN11iEvjYJeTkwMAAQEBZmZmAJCUlMSWb9++3dXVdfr06QzDpKSkqHk3iUQiEAjmzJkzf/78oUOH2tnZVVZW/vnnn2vXrn306NGsWbNu3bpFkgbdk8VCAGN6wOggSMmB2DtQUgMz+8FAPBYWtVqcY1WCXc2kErsv7vms656jYuEFN/YxeTlMVSUZpN/Nb+nUW0xBPq9Pf8LNQ649en3eNqS6unrw4MHp6enGbghqb2TZjk110dHRchUy8y1/PushNKPXTM+xt5Kf6YSpDsnRONiVlZUBgJubGwAwDHPv3j0AsLS0XLJkCUmSoaGhN2/eLC6WH/5vjre39/379318fGQldnZ2L7zwgrOz86xZs1JTUy9fvtzcXzB6RRAQ4gshvoZ/ZtSGNbeWovEqAXNC7p1NClqV7V4hoD7vki+75CYSTCu23+deVsWn2Duw/6d77hf/+A1QUrO3PyJs7fTXcurKBSAIXtPN6lS9lqY9i9ya7WblhJyvv/4aUx3SE4ZhmtuIuLDCLOqYN83Aa1NyfZzl+4kx1SFFGneGsQMQEokEAPLz8+vq6gDA34UPenAAACAASURBVN+f7VdzdXUFAJpWd79Ee3t7bqqTmTZtmlAoBIDMzExNW4iQET3taVO2JW+xmXRS34wcC/GHD7xW5LgCgL2E9/F977Qrwd+mdz5zPdBZ8vRvLS+R2aPoT0EsAoqiEi7rsc1PCuiHD8iu/oSrW8u1lUU32RbE7TXVAcDx48cBgMfjzZw5ky1ZtGgR++srIiJi+fLlxmwcauOaS3XV9bwtR3xqGnhLRhf28q01cKtQG6VxsLO1tQWAW7duVVdXnz9/ni0MCGjc243NeU5O8kcPac3GxkZXt0LIMLj5RjHu5FqIJ/XNKDaTfpnh+2Nal7uXQ9Y+8qjjMWcdq/pUW567FuTTYAYANhTv0K1ung1mvJFjwUJIJ14GvS02Z1Mjb9BQdSq34+imGvtH5iuvvLJnzx62ZPfu3Xfv3u3fv39qauqaNWuM2jrUhjWX6iQU8fVf3k8qzKb1Lxnes0KxAnbXIaU0Dnbdu3cHgPLycn9//2XLlrGFffv2ZR9kZWUBgIuLSyubdeTIkfr6eoFAMGLEiFbeCiFTwO3TumfZMKVPRpWAXpzvzBDM+/65gUNSpvTJ+Nb3Sfc6i3NJQUE1wl9Su4ZUW+72LBHwPvvW9RFTU73wt5l6aZlYRN24RtjYkj17swVE7GD2P6WvQi9taAvYdfpDhgyRTXWnKMrKyurVV1+tqKh4//33jdo61FY1l+poBn445ZmRZzkkqDIyXMnspq5dNZv6TfxLm1aiNkXjOXYTJ05MSEgAgKKiIlnh1KlTAaCgoCAvLw8Aevbs2Zo2lZWVvfHGGwCwYsUKdmxXzsOHD//8809uCdtTiFBbkWxTN7lPRr8qq989Sqt5FFv4VvfsYjPJhgfe//zTU0ATcU6VKwMfAUC0d9Er2a7s0K3OUTevQ0M9OWQY8HiAqyWaJxQKa2pqLCwsLCws2JIHDx50796d3bbz3LlzRm0dapOaS3UAcOiqy7X7tj186l4YW6AYxoKCgtj57mpq7nAL1C5pHOxWrly5Y8eO7OxsWcnChQvZJMfOQQGAgQMHat0gsVg8e/bsnJycnj17fv7550rrZGZmvv3221o/BULGxfZ7EbGDr9vWQtNuMCJ2cLFA+l1651SbugXBDyQkAwD3LRtinarGl9oNODD2n8hY3TaGSrgEJMkbEA6arJbogBwcHGpqaoqLi3k8npOTU2lp6ZIlS2bPnv31118D/m2JNKci1V26a3f0H2d3e/Grk3N5ZGs3N8Feuo5G42Dn6OiYkJCwadOm69evm5ubT5gwYdWqVewlsVjMziAeP17Lk84lEskzzzxz/vx5X1/fEydONHc0WVhYWGxsk7e3qqqq2bNna/ekCBmF0qjEFjKlxWGW1lWXRsnKf/ApGl9q97KuO+3o7EdMfi7ZI5iwd9ConR1Q586dc3JyHjx4AAB9+/aNjY29cuXKlStX2KvsHBWE1KQi1d3LF+486yE0o1dNzbW2oAzZKtQ+aHNWrIeHx1dffaVYvnLlytY0RSqVzp8//8iRI97e3ufOnevUSf60ShknJ6cxY8ZwS0pLS1vz1AiZFMJJfpbqGaeKDMuG+UUuTGUFYaeznbKpKxcAgBduhB2F2pzevXtfvHjx8OHDn3322dKlS+X+tnz99deN1TA1MQyjzn4FDMOoWbP17QEAmqYN0J/EviKDdVy1+NVTsW9OUaVZ1HEfioJVU3K9HBsUx0sDAwPZ+7fy26Tm57LfJsP8SNA0bcifPYM9F03TBngiLm2CnT6wfXWHDx/28vKKj4/38/MzdosQMjLurngMAdHeRVszfanEK/xxk3Rz/5pqOuUm4exK+gcoPiOS88EHH7DLxWianjdvXnp6+ubNm+vq6oRC4Zo1axYtWmTsBqrCxjWKarn7h30HUqemThjmDY997QYLdqq/ehkZGc1dqm3gbT7iU13PWzwqv5dPleLXJiAggHtzNb9NYrGYPVBA/UbKUfOHp5XYANT+fvbUfFFyZ5C0hkkEO7FYPHfu3KNHj3p6esbHxxv4GDGETFaTYdD6etFn6+l/rsDo8exCh1aikxKAonj9BwHnt4lctsNxWBlXV1fuWq6PPvronXfeefLkiaurq2w5hclil0Py1PixIUmSJEl1arYS2z1DkqQB8hb72g3wROyLUvHVS09Pb+4sJYom/nfa90mF+aR+paOCKxT3rAgMDJQrUf/bRFGUrLKmkY7t7DTAEVAURRnmZ499UQb72VPzRemwMVoGu4SEhB07diQlJZWWlorFYsUKhYWFat5KLBZHRkYeP36cTXXdunXTrkkItW/EpVHfuHZ6Mdf1k23zPvDPbSFySaXAV/Wvm76dIo05CRZCsv8guUsY5tRkbm7u69tmTqdR872ZjYAGeBdnOxEN+eZqmGAHyvIWO/Z39+5dFW3Yc949Lceqb9eaZ4YWKa0m903R9Nuk3TJYQwY79ntkgCdiv02GeS72WQx8Mqo2we7zzz9/9913dfL0cqkOJyAjpBTbi/aRX964Uru1jzwyrRq458kCAEgkdM4jJvsRnf2YyX7EiBrMXltLuChfbEHfvS357WcgScGi/xBW1oZ5CW1di7PolM48RoilYrUEAJy87hSf6uDr3LBiYh6pLPvhXsRIfRoHu2vXrr333nu6evrY2Fh2k5Ty8vLw8HDFCmvXrl27dq2ung6hNq1MIJ0Veu/8taDv0zo9EjbIyum7t6UH/2Cqqxo/FlqCWCw9fUzw3DLFm9AZdyV7dwJBCBYtI/3wTyl1sduaqIDBDjVn2LBh0dHRzV29ds9m3yVXR2vpmuk5FgIlE78w1SGNaBzsfvrpJx1ubCi7VX19fX19vWIF3B0KIa40q/pFvbIOJnf7M7kbM7KYsLKSHjtI3bgGPB5v0FCyix/h25lwcBR/9yV9O4V+lEV2brI9PX0/U/LLdmAYwaJlZPfGdwu5BRM4FIuQDkVEqFp1fr9A+OMZLzM+vWZ6jqONkmMDMdUhTWkc7K5ebfyl/+qrr1pYWGzevBkALl++nJSU9PHHH5eXl2/evHnYsGFq3m3KlCm4/zVCGjntXPF2t+zNmb6SnT+AWMxUVxFePoK5Cwl3T1kd/uTpkuhvqZNHyRWvy9ZGMAV5kj0/AU0LFi4hAxuPh1FcBis/yIsAAMDOzo77oUgkamho7DS1srLiq5zRiDosNtU1111XVCmIOuYtpWD19DxflwaldRDSlMYT+tgzJ5ycnL766ivZWXXh4eGvvfZaXFwcj8dbv3693G9AhFArySWtb32f7PQqZkpLRDUVG/zyzFau4aY6ACC7diMDe9KPH9J3UhrvUFUp+TkaxGLBM8/JjoVF6qtoqr6+Pjs7+8UXXwSA/v37c49YRIiluq+uTsT78qhPVT1/0cgnIZ1rlNbB7jqkBY3/yqytrQWAoKAg7iIjiUQiEAhCQ0P79Onzzz//fPLJJ3v27NFxSxHq2GQHkbE7kqwKeJxsU3fRoTrNqv7zs0MU+9j4E6eJM+9KTx03C+oFlFTyczRTWcEfP4UM6WuM5rdDPj4+0dHRSUlJ58+fj4qKWrdunbFbhEyCbA2s4iX2cCYAAILf4PNf2sp8Yt+y0b3Lld4HUx3SjsY9dkKhEADYw75kuzdlZWWxD9gf6LNnz+qsgQghDtk+cxKSifYuSrNqnJlKxA5m/5PVJNw9eAPCmZIi6vLfkl92MPm5vEFDeaPGGafd7Rd7Rs7OnTuN3RBkQlSvgQUAkfta2iqMVxk3P+KJ0gqY6pDWNA529vb2AFBcXAwALi6NBx+tW7cuLS1tx44d165dA4CSkhKdNhIhpC5utuONGg8CM+mpY3RmOtnFjz9NyXnKil19OMFOqZKSktLS0vLycnYotry8PD8//9ChQ2fOnAGAR48eGbuByFQ0l+pk3XVS50WU/QSy/o554SalG9thqkOtofFQrLu7e25uLrv/cM+ejfOvjx49evToUVkdJycnXbUPIaQ1ws6OFzGCOhdDOLnwFy3jnlchd7zE07PLMNU1Q/Z3rBZXUceRkZGh+pgByiZC7LyUkJaY534AtMhgDUMdh8Y9dr179waAgoKCjIyMTp06ybId17hxONyDkL60mL24oY0/cix/7ETBspcJSyulFeDfeXvsf7ptascxZ84cYzcBGZ+Kc2ABIDo6mrYIFHmuJ0BsnvM2IS1WWg2761AraRzshgwZ0qlTp06dOsXHxwPAZ599Jnf4iZOT04YNG3TWQISQAtUJrMlVM3PemImEYwud6IqbniD1zZgx47PPPjN2K5CRtTivrrhK0ODzBRBmgrxPyYZMuavLly9fvnx5jx499NZA1FFoPBS7dOnSpUuXyj6cNm3a4cOHN27cmJqaamZmNmbMmE2bNrGziRFC+iNLb5jJDCMqKkquhCRJe3v7gQMHBgQEGKVJyHS0mOoaxGTUMR/gmwuKtvGrL0DTze3Y6XcXL14EAIIgcHtX1Bo62FRz+vTp06dPb/19EEJa4M6QY6mYMIcpUGstnhWLUHNoGv53yiunxHxYj4oXXo8AaLK/3dM9UP6F2Q61Bu6WjlCb11zvHfcMCdWRDmfXtSg3NxcA7O3tra2tjd0WZFpa7K779YLbrYfWPX1ql44pbK4O212HUOtpH+x27Nih9HTXAQMGDBgwoBVNQgjpjDpddJjq1OHj4wMAmzdvfvPNN7nle/fu/eqrrwAgKSnJOC1DRtViqjtx3SnmlqOvS8PrU3N5pPJ+OEx1SIfUDXZTpkxhtzj57LPP2EWv69atKy0tVazZt2/f69ev67CJCCF9wDynE4WFhfgbr8NqMdXdyLL+87KrnaX0jam5Fma00jqY6pBuqRXsrly5cuLECQCws7NTffgdANy4cSMlJYXdFQUhhNq3yspKYzcBGUeLqS672OKHU158kl4zPdfZVtJcNYZh5DaXwAl2qDXUCnZsqgOACRMmsEeKqXby5EkMdgihduCjjz7ifhgTE1NT8/S89rq6ul27dgEAn4/zlTuWFlNdabXgv4d9xBS5ZlpOFzcl05ZY7K51mOSQDqn1yyghIYF9MGzYMMWro0ePZh88fPiQPTT26lUc4kHICNjRVZxXp0Nyu3LGxsbGxsYqVvP29jZUi1AbUCciNx/xqazjLxz+pHfnmuaq4V7ESB/UCnb37t1jHwQGBipejYuLYx/s379/7ty5oMafMggh/VER7zDP6Qn7qw8hAKAZ+CnWM6/UfFjPigl9ypqrhqkO6YlaJ0+UlJSwDxwcHFRU69q1K/ugqKiolc1CCLWSYobDVKdzfD6/c+fOa9eu/fjjj43dFmQq/rjolnTfpodP3ZJRzW5ugqkO6Y9aPXZisVjuAQB8+eWXctudkGRjTKytrdVR8xBC2sMk13qyyU/s9HbF7U4Q4opLcTx1w9HTUfTalFw+D2fOISNQK9jZ2dmVlZUBwP379wcOHMgWPv/883LVHj16xD7ADTwRQu0MO5kYz0tEKtx6aPPrBQ9bofTN6TlW5lRz1bC7DumVWsHO3d2dDXZHjhxZuHBhc9WOHTvGPnBxcdFJ4xBCyETIJhMjpNSjIosfTnvzSXh9aq6LXbObm8hSHXeLE1wVi3RIrWA3YMCAtLQ0ADh06NC5c+dGjRqlWOfy5ct79+5lH4eFhemwiQghZHQXLlyIiYkBgBdffNHX15ctzM7O3rZtGwCMGzdO6aYBqIMor+FvPeYtkpIvjM3t5tnC5ibQNNUBHg6LdEqtYDd+/Hh2ryaapqdOnfrf//532bJl5ubm7FWRSLRr164333xTKpWyJezRFAgh1G5ERUUdOXLE39+fuwGKt7f3/v37MzMz09PTMdh1WGIpEXXcu7xGMH1AcXhABQCPLV++fLlczQsXLoBCqkNIt9QKdrNnz/b09MzPzweAurq6lStXvvXWWyEhIXZ2dpWVlSkpKXV1dbLKrq6u8+bN01d7EULIGNhzw8aNG8fj8WSFJEmOGjUqMzMTTxXrsGgGfjzt+fCJMDywcuZAVTtC4NFhyDDU2u5EIBB8++233JL6+vqEhIQzZ84kJCRwUx0AREVFWVhY6LKNCCFkbOwuTu7u7nLlHh4eAMAepa0oPz//tddeGzx4sFAoJAhCtsJMqRkzZhAKBgwYwF6Ni4uTu+Ts7Nz614Va6c9Lrtfu2wZ61S0bW8DtiZPrrsNUhwxG3WNwZs2a9cEHH7S4V9O6desWLFjQ6lYhhJBpYTvqMjMz5cozMjJkVxVlZWXt27evf//+gwcPjo+PV/0U69ate/bZZ2UfPnjw4O233540aRK3ztatW318fNjHsvkwyFjiUx1OXHfycBC/PjVXwGNoWnk1uVSneDgsQjqkwfmGGzZs6Nq169q1a5XuP+zi4vL555//5z//0V3bEELIVHh7e2dmZu7bt2/VqlWy9WGJiYn79++H5o8UCw8Pf/LkCQB89dVXLQa7wYObHBby/vvvEwQht7HU2LFje/XqpfWrQDp0O9tq1zk3W0vqzRk5Vhbym5tER0crzrGT4WY7XDaBdEuzg6uff/752bNnHzp0KDY29tGjRxUVFfb29p07dx4zZsysWbNsbGz01EqEEDKuYcOGZWZmSiSS8PDw6dOnd+nS5eHDh0eOHGEXjTW3ckK2bbumaJr+5ZdfIiIiunTpIneprq6OHdjV7s6oldiv/JCxS6D7dgGfeXN6tqudWEX95gZhMc8hPdEs2AGAtbX1okWLFi1apI/WIISQaXrllVd27txJ07REIjlw4AD3Eo/He/nll3X7dPHx8dnZ2R999JFc+dChQysrKy0tLcePH79582Y/Pz+5ChKJpKZG/tT56upq3Tavw2JTnZmVt8jnv4wYXpuS38WtobnK0dHRuBcxMjyNgx1CCHVAoaGhGzdufOeddxQvffrpp6Ghobp9ul27dllZWc2ZM0dWYmtru3LlyoiICCsrq6SkpK1bt4aHh9+6dYtdvSFz7ty5CRMmyN2tZ8+ea9euraioaPF5pVIpQRANDc2GFV1hGIZhGK17NDUikUgqKytbf5/G09IJssvoPxi+s6D45+1bfu4ZFSWrwHbCyTpT/fz81Pmaa0cikejv5lw0TbOLdQzwRBRFUVSzJ3boCvuzZ5gXJZFIxGJxc3NwuWia1tU/Bwx2CCGklrffftvf33/jxo23bt1iS0JDQ997773IyEjdPlF1dfWhQ4dmz57NPZ5xwIABshWykydPHjly5PDhw7/++usvvviC+7nu7u7cOMhycXHh8/nqzJapr68nSdIAyzIoimIYhsfjGeDNlaIoa2trnT0RQxfd/treokJQsgsAuLtA0DQNnPF3vU5PoijKANOfGIahKIogCHWiSStJJBKRSGSAI0lpmmZTlAH+rqitrTUzMxMIBC3W1GFjMNghhJC6IiMjIyMjq6ury8rKHB0dZe+sKSkpvXv31tWz7N+/v66ubvHixSrqDBs2zNfX99q1a3LlISEhf/75p1xhYWFhfHy8Ou/N7LudAd7FAYCmacMEOzaX6PCJyrP2m3k1bnAj934s64bU9yCsYcKWrGPVAM9FUZRhnoj9SeDxeAYIdgRBGOwflIwhusERQqg9sbGx6dSpk42NTUVFxQ8//BAWFhYSEqLD++/atatTp04jR45UXY0dNtXh8yJdwal1yIiwxw4hhDTDMMz58+d37Nhx8OBBnU9Hy8rKunTp0nvvvScX2qRSKZ//9Df2iRMn8vPzlyxZottnRyoo7j8XHR2tWA1THTIuDHYIIaSu3NzcXbt27dy58+HDh+rUZxjm4MGDAJCcnAwAp06dcnFx8fDwGDJkCADExMRMmjRpz5493H3dd+/ezTCM4jjslClTPD09Q0JCbGxsrl+/vn37dh8fn1WrVunqpSF1yPYouXv3rnFbglBzMNghhFALxGLxsWPHduzYERMTQyscL2BnZ9fciTsURXGXMrC7okyePPmvv/6Cf5cBcm/IMMyePXuGDh2quI/J2LFjf//998OHD9fU1Hh4eCxevHjDhg0uLi46eYFIVwICAozdBNTRYbBDCKFmpaam7ty5c+/evSUlJUorjBo16vjx45aWlkqv8vl8FfvQTpgwQe4qQRDN9QWuWbNmzZo1ajccGUFAQABuO4yMDoMdQgg1S+la17CwsAULFqxevRoAXFxcmkt1qEMJCgpijyFByLgw2CGEkFoCAgLmz5+/YMGCbt26AQAb7BACXDCBTAkGO4QQallAQMCGDRumTZsmFAqN3RaEEGoW7mOHEEIty8jImDdvnpub26JFi86cOWOAg49QW4HddcikYLBDCKFmTZs2jbt7XHV19S+//DJhwgRPT08jtgqZDkx1yNRgsEMIoWYdPXo0Jyfniy++6N69O7e8qKiIfRAfH//5558XFhYao3XIyDDVIROEwQ4hhFRxd3dft25dRkbGhQsXnn/+ebk1sEVFRe+++66Pj4+xmocQQlwY7BBCSC0RERG7du0qLCyMjo4eOHDg/9u784Cq6vz/45/Lvi8qgokbkLuAY2K45oJD7gqkuZRtamk25ZhNWqahaTiW5Uw2ZTmZue+ZuWU2phZabowyuWDufBEFFJC7nN8f59eZOyzXi8I9l0/Px19n+XDO+3POvfDirNazeM7F7xCH6+CcCHYAUAn+/v5jxow5cODA8ePHX3zxxTp16uhdEXRAqoPTItgBwN1o1arV/PnzL168uHr16sTERL3LAQAhCHYAcC88PDySk5O3bt2qdyEAIATBDgAAQBoEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJCEm94FAACql6IoiqKYTKY7trRYLHa2vPeShBBms7m6V6Suy2QyGQwGB6xICOGArSd+65QD1uKwdZnNZovF4pitJ4SwWCwWi8UBazGbzQ7rlIpgBwDyU7OdPc0MBoM9LauEY1ZkZ9+rcHWs6O7W4sg95cgV2f/Vq5I1EuwAQHIGg8HFxcXd3f2OLY1Go50t75F6eMbNzc0BB9LUHjlgRSaTSVEUB2w9IYTBYHDAitRjdS4uLq6urg5Yl9lsdkCn1KNorq6uLi7VfjWai4uLm5ubPZ2qws8n19gBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJLQ/wHFV65c2blzZ3p6+sGDBw8fPlxYWBgYGHjjxg296wIAAKhh9A92K1asePHFF/WuAgAAoMbTP9gFBAT07NnzgQceeOCBB3799ddJkybpXREAAECNpH+we/LJJ5988kl1eMWKFfoWAwAAUHNx8wQAAIAkCHYAAACSINgBAABIQv9r7O7ChQsXvvrqK+spt27d0qsYAAAAJ1Ejg11GRsbYsWP1rgIAAMC51MhgFxMTs2rVKuspBQUFTz31lF71AAAAOIMaGezCwsJSUlKsp1y7do1gBwAAfue4eQIAAEASBDsAAABJEOwAAAAkQbADAACQhP43T1y+fLlNmzbqcElJiRAiPz+/Tp066pQ+ffp89tlnuhUHAABQc+gf7Mxm87Vr16ynKIqiTcnPz9ejKAAAgJpH/2AXHh6uKIreVQAAANR4XGMHAAAgCYIdAACAJAh2AFBdLl26NHHixPj4eG9vb4PBkJWVZaPxzp07Df9Lu41MlZ2dPWrUqFq1avn5+SUmJp44caJ6qwdQA+l/jR0AyOrMmTMrV65s3759fHz87t277fmR+fPnN2jQQB329PTUphuNxt69e2dnZ8+bN8/Pz2/WrFkPPfTQ0aNHQ0NDq6V0ADUTwQ4AqkvHjh2vXr0qhHj33XftDHYJCQmtW7cuO/2LL744cuTIN9980717d3XJkZGRaWlp8+bNq9qaAdRonIoFgOri4nI3v2MLCwvLPitg06ZN9evXV1OdECI8PLxnz54bNmy41xIByIUjdgDgRDp37pyXl+fj4/PHP/4xLS0tMjJSnZ6RkdGqVSvrlq1bt/7666+Li4u9vLy0iRcuXPjqq69KLdPV1dXHx6e6KwfgDAh2AOAUAgICJkyY0KVLF19f34MHD86fP79jx46HDx+uV6+eECI3Nzc2Nta6fXBwsKIo169fVxuoMjIyxo4dW2rJrVq1mjx5cqlHwZfLYrEIIW7dulUF/bFJPSRpMBiqe0VCCKPRmJub64AVObhT9uzQe+ewTlksFovFor6Aqlpph8Md0Cmz2VxcXGzPkXuLxXJ3B/jLItgBgFOIi4uLi4tTh/v27du9e/du3botWLBgzpw59i+kVatWH374YamJrq6uHh4etWvXvuOPFxYWuri4WB8CrCZms9lisbi5uTngj2tubm5wcLADVmQymRRFcXd3r+4VCSFyc3Nr1apV3WtRFMVkMrm4uLi6ulb3ukpKSm7fvu3v71/dK7JYLGaz2dXVtaqClA0FBQWenp4eHh53bFmFxRDsAMAZde3atWHDhunp6epocHDwjRs3rBtcv37dYDAEBQVZTwwPDx8zZkypRV25csXOWzcA1HTcPAEATspkMmnHmVq1apWRkWE99/jx4xEREd7e3nqUBsBJSXLEzmAw+Pr6Xr161TEXNwBwGO2hbtIzmUxubv/9nbxly5ZLly498cQT6uiAAQPWr1+/Z8+ebt26CSEuXLiwa9euCRMm6FMrAGclSbCrVavWunXrBg8ebE9ji8VSXFzs5uZmz2lvHRUVFQkhnP/f8cLCQldXV+snqTqnkpISk8nk5eXlgOsq7oWiKEVFRc6/SdXvkbu7e3VfUTRs2LBqXX61UhRl7dq1QogjR44IIbZu3RoSElKvXr1OnToJIbZv396nT5/PPvts+PDhQoh+/frdd999MTEx/v7+hw4d+vjjjxs0aPDCCy+oixoxYsT8+fOHDx+emprq6+s7a9aswMDAicOByQAAIABJREFUP//5z/p1DoAzkiTYCSF69+5t551cJ0+ebNGixWOPPbZ48eLqrupe1KtXz8fH5/Tp03oXYsutW7f8/Px69Oixfft2vWu5gxdffPHdd9/ds2ePdn26c8rOzg4NDe3fv//69ev1rsWW/fv3d+zYceLEiTwg1waz2ZySkqKNPvfcc0KIvn37fvnll+K3i7jVG1GFEAkJCcuXL1+/fv3Nmzfr1as3evToGTNmhISEqHPd3d137Njx0ksvvfTSSyUlJZ07d16+fLn1/bAAIGQKdgDgbNzc3Mo+aliTmJhoPXfSpEmTJk2ysbTQ0NBly5ZVZX0ApOPU56QAAABgP4IdAACAJAh2AAAAkiDYAQAASOL3ePNE8+bNbVzO7DwuX76sdwl35uvrWyM2phDinXfeeeedd/Su4s7q1q1bIzZpfHx8jagTAH5Xfo/BDgB+b4qLiy9dumRPMxcXFwc841N947urq6sDniqfn59fVFTkmDe+K4pi/ZTp6pOfn19cXFzda1EUxWw2u7i4OODxn0aj0Wg0FhQUVPeK1M+eYzpVWFho58M+1dfXVslKCXYAIL9z58794x//uGOzzMxMV1fXqKgoB5TkGEVFRWfPnq1Vq1ZYWJjetVSZnJyc7Ozshg0b+vn56V1LlcnKyiosLGzRooU0b5BSFOXEiRO+vr6NGjWyp32p9z7fNYIdAEjO19e3c+fO9rScP39+UFDQ6NGjq7kix8nMzHz77bcHDx6cnJysdy1V5osvvli9enVqamqHDh30rqXKrFu37tixY19//XVVHbjSXUlJycyZM2NjY0eMGGFP+/vuu69K1kuwwx2EhYV9++23zZs317sQAHfJ39+/V69e9rR0d3f39fW1s3GNEBgYKIRo2LChTJ364YcfhBCxsbEydSo4OFgI0bNnT8ecy3YA9Vx5rVq1HLybat5dsVFRUerbeO5CYWHhM88806hRI19f344dO6rfDSFEbm7ugAEDfHx8oqKitmzZok5MS0uLjo52dXWdM2eOtoRevXoZfvPggw/aWFfLli2jo6Pvrk4Hl1rZTerI2lSLFi1q0aKFp6dnw4YNp02bZjQaK2pZp06dU6dO6Vizvp9PIcT777/fpEkTLy+v6Ojoq1ev2lid7h/RSlULALBHzQt296KoqCgwMHDTpk2//vpr7969+/btW1RUJISYMGGCu7v7lStX5s2bN2zYMPV21EaNGs2dOzchIaHUQpYvX15QUFBQULB79+6KVnTo0KHr169fvHjx6NGjdtZmMpl0KfUuOLi2uXPnTp8+PS0tLScnZ82aNRs3bnzyySedvOa7c+9FLl269N133/3nP/95+fLlRYsWeXt7V7QuZ/iI2l8tAMBeSk0TGRm5efNmRVFefvnl+vXrBwQEdO7cOSMjQ50bGBiYlpbWvn37iIiIadOm2ViOyWRyc3M7fvx4YWGhh4fHsWPH1Oldu3Z97733tGZJSUlvvfWWNtqzZ8/169ffscg//elPzz777BNPPDF58mRtYmBg4Jtvvvnggw82b948NTVVmzhnzpw2bdokJCToUqry2yZVD4MVFBSoE//4xz8uX75cUZTQ0NATJ07oVVtBQYGvr+/q1au1KSdPnnR1dT1y5IiiKOfOnRswYEDt2rVDQkJmzZr1zDPPuLi41K9fv1GjRrt27dKlZn0/n/fff//GjRvvWKTiHB9R+6uFw9SqVUt9IJQ0fvzxRyHECy+8oHchVSk1NVUI8eWXX+pdSFXq2rWrEMJoNOpdSJVR/9ft0aOHg9dbg4/YxcbGHjlyJCcnp3v37taHcP7zn//88MMPP//88yeffGLjaER6erqXl1dERMSpU6fMZnOrVq3U6dHR0RkZGTbW+8ILLzRq1GjAgAHHjx8vt4HZbF6+fHlSUlJycvIXX3xhsVi0WSdPnty3b196evqqVau0s1RHjx79+eeft23b5vhS711115aenl5cXDxo0CBtSrNmzVq3br17926z2dy/f/+WLVueP3/+7NmzvXr1+sc//hEcHPztt99mZWX16NFDr5pVjv985uXl/fLLLydPnqxbt26jRo3eeuutihbuDB9R+6sFANivBge7Rx99tHbt2u7u7lOnTv3pp59KSkrU6RMmTDAYDAEBAfHx8RX94czPz3/qqafmzJnj7e198+ZNPz8/7f7qgICAmzdvVrTSP/3pT5s2bdq6dWuTJk169ep1/fr1sm127NhhMpm6devWq1evW7duWZ+2U2vz8/N76qmnVq5cqS3TxsOcqrXUe+SA2q5du1anTp1S19KGhYXl5OQcOnQoOzs7NTXV29vb19c3Li7OSWpWOf7zefHiRSHEnj17MjMzt2zZ8t57761du7bcls7wEbW/WjhSt27d4uPj9a6iKgUEBPTq1atp06Z6F1KV1N9CderU0buQqtSuXTv1Uma9C6kyLi4uvXr1+sMf/uDo9Tp4fVVo8eLFbdu2bdCgQbNmzUwmk/aHtlatWuqAl5dXuX9UioqK+vXr9/DDD48fP14I4efnd/PmTeW3Z+jn5+fbeDJQv379YmJiWrZsuWDBAn9//++++65sm6VLlw4aNMjNzc3Dw6N///5Lly7VZtWtW1cdCA0N1V4sYePpStVd6r1wTG21a9fOyckpdXXXlStX6tSpc/78+UaNGlXqxnhHbk/Hfz7Va9SmTJkSHBzcunXrUaNGff311+W2dIaPqP3VwpHWrVv3ySef6F1FVWrWrNmOHTuee+45vQupSsOHD9+xY4dMzzoRQsyfP3/Hjh3SPOtECOHh4bFjx460tDQHr7emBrsTJ068+uqrq1atOn/+/C+//OLi4qLY93ajkpKSwYMHN2vWbN68eeqUqKgoV1dX7YTRsWPHtBNJtrm5uZUKHEKImzdvbtiwYdWqVWFhYWFhYRs2bFi3bp16ol0IkZWVpQ6cPXu2Xr166nBF/6BUd6ll23h6et6+fVsdvXbtmo3GDqutffv2Xl5eGzZs0KZkZmYeP368e/fuDRo0+PXXX61PIwohbDxJ3JHbU5fPZ8OGDQMCAu64Cif5iNpZLQCgUmpqsMvPz/f392/SpIkQYtGiRWaz2Z6fMpvNw4YN8/b2XrBgQXFxcXFxscVi8fb2HjJkyIwZMwoKCjZt2vTTTz+pz7E0mUxqA22goKBg1apVV65cyc7Onjlz5uXLl8s+83PdunVBQUGZmZmHDx8+fPhwZmamn5+flkvmzp1bUFCQlZX1wQcfPPLII/qWWlZ0dPTOnTuFEPv27fvpp5+coTY/P7/XXntt/PjxW7ZsKSgoSE9PT05OHjZsWHR0dLt27WrXrv36668XFRXdunUrPT1dCFG3bt0zZ87ovj11+Xy6urqOHDkyLS0tPz8/MzNz2bJlffv2LbsWJ/mI2lktAKByHHyzxr2LjIxUbwWaMGFCixYtunTpMmPGDF9f38uXLyuKEhgYeP78ebXliBEjPvjgA+uf/eWXX0p1f/fu3Yqi5OTk9OvXT736W72lUVGUSZMmWbdcvnx5Xl5ep06dAgIC/P39O3fuvHfv3rLlJSQklLrbcdq0aX369FFre++99xo1ahQSEjJjxgx1rnXBDi617Cbdt29f69atY2NjH3/88W7dulV0V6wja1P9/e9/b9asmbu7e3h4+KuvvlpSUqJOP3v2bN++fYODg+vWrTt79mxFUT7//PN69eoFBgZ+/fXXutSs4+dTUZSbN2+OGDHC19c3PDz87bffLrdCJ/mI2lktAKBSDIp9Z4icR0hIyObNm+15qq2zCQoKOn78eHh4uN6FlFZzN6kTqtEb02k/ogAAO9WkU7ElJSUbN25UXxKsdy2SYJNWITYmnNClS5cmTpwYHx/v7e1tMBi0ayjLtXPnTsP/KnXfZXZ29qhRo2rVquXn55eYmHjixInqrb4ClerUoEGDDGVoN9HfscsOs3v37ieeeOL+++/38fGJjIwcP358dna2jfa294WT7KlKdcp2YyfZU5XqkV5fqJr0Rrbx48dv27Zt0aJF6rv/cO/YpFWIjQkndObMmZUrV7Zv3z4+Pt7ON6bMnz+/QYMG6rCnp6c23Wg09u7dOzs7e968eX5+frNmzXrooYeOHj0aGhpaLaVXrFKdmjJlysiRI7XR06dPv/LKK3369LFuU1GXHWnGjBl5eXmPPfZYw4YN//3vfy9cuHDr1q1Hjhzx9/cv29j2vnCePVWpTtnTWPc9VakeqXT4Qul9LhgAUF3MZrM68M477wghzp49a6Pxjh07hBDai0NKWbJkiRDim2++UUfPnz/v4eExadKkKq3XLpXqVClTp041GAxnzpxRR2132ZFOnjxpPbps2TIhxOLFi8ttbHtfOM+eqlSnbDd2kj1VqR7p9YWqSadiAQCVYuMBQDYUFhYqZS6/3rRpU/369bt3766OhoeH9+zZ0/pRRA5zd50SQlgslqVLl3bp0kW9Y91auV12pGbNmlmPPvTQQ+K353iXZXtfOM+eqlSn7Gys756qVI80Dv5CEewAAP/VuXNnX19fPz+/IUOGnD59WpuekZFR6qmErVu3PnPmTHFxscNrvEu7d+/+9ddfR48eXWp6RV3W0ffffy+EaNOmTblzbe8Lp91TtjtlT2Nn21P29MjxX6iadI0dAKD6BAQETJgwoUuXLr6+vgcPHpw/f37Hjh0PHz6sPqo6Nzc3NjbWun1wcLCiKNevX9eeZe3klixZ4uvrm5KSok2x3WW95ObmTp48uW3btv3796+ogY194Zx76o6dst3YCffUHXuk1xeKYAcAEEKIuLg47XbRvn37du/evVu3bgsWLJgzZ46+hVWJgoKCdevWJSUlWb/mzgm7XFRUNGTIkFu3bu3cuVOa92tVqlPlNna2PWVPj/SqmWAHAChH165dGzZsqL7TRQgRHBx848YN6wbXr183GAxBQUF6VFdpq1evLiwsLHse1lqpLjve7du3Bw8efPjw4d27d0dFRVXUzPa+cLY9ZWenKtVY3z1VqR5pHPaF4ho7AED5TCaT9qbgVq1aaW8BVh0/fjwiIsLb21uP0iptyZIljRo10q5Vr4h1lx2spKQkOTn5+++/37p1a9u2bW20tL0vnGpP2d+pyjbWa09VqshSHPOFItgBAIQQwmQyWY9u2bLl0qVL2mtUBgwYcOHChT179qijFy5c2LVr18CBAx1d5V05c+bM3r17R40aVSoK2O6yI5lMpmHDhu3atevLL7+Mj4+33dj2vnCePVWpTtlu7CR7qrI9sh512Beq5r1SDABgJ0VR1q5dK4TYsmXLkiVL/v73v4eEhNSrV69Tp05CiO3bt/fp0+ezzz4bPny4ECIxMfG+++6LiYnx9/c/dOjQxx9/HBoaeujQoZCQECGE0Whs167dtWvXUlNTfX19Z82adfny5SNHjjj+6vVKdUo1ffr0mTNnnjp1KjIy0npRtrvsSE8//fTixYvHjBmTkJCgTYyKilKvry/VKdv7wnn2VKU6Zbuxk+ypSvVIty/UvT8KD7Ch1K9RNzc3Hx+fsLCwmJiYpKSkd955Jycnp0pWtHLlyum/SU9Pr5JlAjWd0Wgs+2u/b9++6tytW7cKIZYuXaqOzps3r127dkFBQW5ubg0aNBgzZszly5etl3blypXhw4cHBQX5+Pj07t07IyPD0f1RFKWSnVIUxWKxNG7cuHPnzmUXdccuO0ypJ1+oxo8fr84t2ynb+8JJ9lSlOmW7sZPsqUr1SK8vFEfsUL2ioqJsP23Iy8vr+eefnz17tpvbPd3Kk5ycrP4TL4T44IMPxo0bdy9LAwCgJuIaO+isuLg4LS2te/fuhYWFetcCAEDNxuNO4DjJycmPP/747du3L1++/N13323YsEE7pbJ3797HH3989erV+lYIAECNxhE7OE5kZGS/fv2SkpImTJiwatWqY8eONW3aVJu7Zs2ab775Rhv95JNPxowZEx8f37hx46CgIHd396CgoJiYmHHjxh06dMh6sYMGDTIYDNp5WCHEs88+a/jN008/rU1Xr4F49NFHIyIifH19vb29IyIiRo8ereNjqwAAqEIEO+imWbNmmzdvdnd316b87W9/04Znzpz50UcfHThw4Ny5c3l5eSaTKS8v7+jRox9++GFcXNzbb79d2dVdv3794Ycf7tOnz4oVK86ePVtYWFhcXHz27Nl//vOfcXFxL7/8MtebAgBqOoId9NS0adMBAwZoo9ZH7FQhISHR0dEJCQmdOnWqXbu2OtFisUyZMuWHH35QR4ODg0NDQz09PbWfCggICP1NYGCgEMJsNg8cOHDbtm1qAw8Pjw4dOjz44INarExLS0tNTa2eXgJwdo0bN1aP8Y8cOVKdsmHDBu3A//Hjx/Utz1rZUqub7pti5MiR6to3b95cboOYmJjOnTuXO+vkyZOurq4Gg6Fjx47VWaMTIdhBZx06dNCGb9y4kZeXpw5PmTLl+PHj2dnZR44c2b59+969e69evfr8889rjT/++GN14NNPP71y5Uq/fv20WXPnzr3ym7/+9a9CiM8///xf//qXOrdx48YnT548cODA/v3709PT/f391emzZs26cOFCdfYVwH+dPHnSUIanp2d4ePjAgQPXr1+vd4F3T+vOG2+8oVcN0mzegwcPfvHFF0KImJiY/v37l9vGYrGYzWaLxVJ2VvPmzZOSkoQQ+/fvX7VqVbWW6iS4eQI6K/VevLy8PPUY27PPPiuEOHfu3L///e+cnJzCwkJFUby8vLSWBw8etH8ty5Yt04ZnzpzZpEkTdTgmJmb48OEffvihEOL27dvr1q2bOHHiPfQGwD0pKSm5ePHixYsXN23a9Oijj37++ecuLvocgAgODm7Xrp06XFNem3ZHd7d59d0UU6dOVa+Teemll6ynG43G999///PPP8/MzFQfquDl5dWwYcPY2NhHHnnkkUce0VpOmjRJvTNv6tSpKSkper0yzmEIdtDZ9evXrUfVVCeE2Lx586uvvmrjsP+1a9fsX8vPP/+sDT/22GOPPfZYuc2OHj1q/zIBVJWIiIi2bdsqinLu3Dnt7qjly5f36NHD+v4nR+rWrVul/nt0Zve4eXXcFP/5z3927NghhPDx8RkyZIj1rEceeWTDhg3WU4xG4+nTp0+fPm0ymayDXYcOHaKiok6dOnXq1Klt27YlJiY6pni9cCoWOjtw4IA2HBwcrAa7jRs3Dhw40PbFHCUlJfav5caNG/Y0y83NtX+ZAKpKQkLCmjVr1q5de/DgwY8++kibrj0CqdRlXsuWLWvXrp23t/cDDzygNd6/f/+oUaOaNGni5eXl5+cXHR09bdq0sv8BlpSUpKamRkVFeXp6NmnSZOrUqUVFRWVLsnFh2eHDh8eMGdO8eXM/Pz9vb+8mTZqkpKR89913QohevXpZHxCaMWOGthDrKz2qttQ7usfNe3eborKdLdfixYvVw3UPP/ywn5+f9TLVVBcUFLRp06bmzZu3a9fup59+WrZs2bBhw7QLbDRazrPuvqw4Ygc9ZWZmWl8M26NHD3Vg1qxZ2j2qAwcOnDNnTmRkpLu7+759+9TXQVZWUFBQTk6OOjx69Oj69euX26xly5Z3sXAAVWjQoEHPPPOMOnz58uWyDd5+++2lS5eqw9ovitdffz01NVUbvX379rFjx44dO/bpp59u27atdevW6nSLxTJkyJAtW7aoo1lZWbNnz96zZ8/t27ftLG/OnDnTpk0zm83alKysrKysrGbNmnXt2tWeJTis1HLd3eYtlz2bws7OVkS7463Ub/4jR46oAx07duzfv/9rr73m5eXVtm3btm3bWr8jWKP9+K5du8xms6urq+311mgEO+gmMzNzwIABJpNJm/Lcc8+pA8eOHdMmzp49u3nz5uqwjdMB1leKWP+iUcXGxu7cuVMdbtOmTalrNVRGo1Gvq3kAaKzPr5X7QvSlS5e6uLhERkZ6eHio39kVK1a8+eab6tyRI0cOGDDg5s2bf/3rXzMyMi5dujR48OCMjAwPDw8hxEcffaRFpQYNGgwePDgnJ2flypVlf2mUa82aNX/5y1/UYXd395SUlGbNml28eFH79TJ27NjExMTJkyerowkJCb1791aH1euJHVZqRe5i85brjpuiUp0tl/qIK3W4ffv21rPq1q2rDuzcuXPevHkFBQXWx/PKiouLs15m27ZtbTSu8arkjbNARSIjI7UPW3Jy8ubNm9euXbtw4cKUlBTrJ9gJIVJSUrSfCggI0KZ//PHH6sSff/5Ze+KJECI0NNR6Rdr/oEKIoUOHGo1G67mffvqpNjcgIGD37t3Wc0+fPj179uzw8HC93v8N/A6dOHFC+1ZGREQkJSUNGTLE+tSqEOKjjz5SG1vfxRkSEpKenq5Ov3nzpqIoMTEx6qwhQ4Zoy7d+S/WaNWvUibGxseqUwMDAq1evqhO1W+yFECNGjCi7xmPHjpX6cTc3twMHDmjrMplMWVlZ2qj2g9OnTy/V6+ootbo3711vCvs7Wy7ri55Pnz5tPSs7Ozs4OFj8ryZNmowaNWr79u3lLk37i7N+/XobK5UAwQ7VyzrY2dCtW7dbt25pPzV48GBtlsFgiIuL69Chg7u7u/W/j6WC3cKFC60X6OXlpT7H7rvvvlMUxWQydenSxbpBVFRUz549u3Tpct9992kTCXaAw1gnj3KlpKSYzWa1sXW2SEtLs16O9owkG1555RVFUW7fvq39Dhk5cqS2BKPR6Ob2/89f2Qh2+fn52pTk5GQbXdOalQp21VRqtW7eu94U9ne2ItZPNs3NzS01d+vWrSEhIeUudvz48WWXph3k0+KsrDjxBJ15e3v/5S9/2blzp4+PjzZx7ty52sE5RVF+/PHHH374wc3NzcYLJ4YNG2b9JS8uLr569erVq1fV61FcXV03bdrUt29frcGpU6d27dr1r3/969KlS+oUV1dXuS+8AJyfu7t7vXr1+vXrt3r16lWrVpV7KtD64ZeizJ315VJvn8rPz9cedRYWFqbNdXNzq1Onzh0XYn1zlfbIpEpxWKkVuYvNWy57NoX9na2I9aNViouLS81NTEzMyspasWLFuHHjrE/yCCH+9re/7du3r1R77b4TaR5eUxGusYNDubi4eHh4BAYGhoWF3X///V26dBk5cmStWrVKNbv//vsPHjw4ffr0bdu25ebm1qlTp3v37q+//npBQUFFS65du/bevXunT5++Z8+e7OzsspehBAUFffnllzt37ly6dOmBAwcuXbpUVFTk5+fXsGHDP/zhD4mJib179y5bCQAHGDt27KJFi+xsXOocnPXXdty4cVOmTCn7I+ptkgEBAS4uLmpgunLlijbXZDLZc5Om9YrOnj1rZ7W6lFrKvWzectmzKezvbEW0Y2xCiGvXrpW9ItDHx2fo0KFDhw7dv3+/j4/PwoULhw4deurUKSHE999/b/2qCaPRqP35sF6snPQ+ZAgA+D2yPlc4duxY243LvcxLEx0drc6KjIwsKCiwnmU0Gj/99NP8/Hx11PrCtezsbHXiXVxj5+7url2IpiiK2Ww+d+6cNqqdLZ08ebIDSi1XFW7eu94U9ne2XGazWXuy6erVq61nnT592vpnY2JiOnXqpCjKhAkT1PalTvIePnxYnW4wGLSNKSuO2AEAarYpU6aMGDFCCHH69On4+Phx48Y1aNAgNzc3PT193bp16isH1YNDY8aMUe++z8vLa9eu3eDBg69du7ZixQo7V6S+ukAIYTQaO3bsOHTo0KZNm169enXXrl1JSUna+6bDw8OzsrKEEEuWLPH09FTPUaivdnVYqdXNnk1hf2fL5eLi0rlzZ/XW4B9//DE5OVmb9dVXX02dOjUpKal79+4RERFFRUUGg2Hjxo1aBtXu21Bp7xZv2bJlRVfmyUPvZAkA+D2qwkNKiqJMmzbNxqui/u///k9tZjaby754IDY2Vvtjb/uInaIob731VrmXpqlvvlJpjzvRtGvXrvpKre7Ney+bws7OVmTJkiVqy+joaOvp77//fkXLFELExcWVejCC9taKN954w/YaJcDNEwCAGu/NN988cODA6NGjo6KivL29vb29Gzdu3KVLl2nTpn3//ffazVguLi4bNmyYMWNGkyZN3N3dGzRo8NJLL+3Zs8f65i3bXnnllUOHDj399NNNmzb18fHx9PQMDw8fOHBgQkKCdTEvv/xy48aNtXOyupRa3ezcFPZ0tiJDhw5V2xw9etT6pRdDhw794IMPBg4c2LRpU+10rbe3d5s2bV5//fVdu3ZZb/m8vDz1sJ+bm9uYMWOqcAs4J4Ni87nSAAAAeklNTX3ttdeEEBMnTlywYEG5bdq0aRMYGLh3795y5y5cuPD5558XQowePdr6maayItgBAAAnVVRU1LRp0wsXLnh7e589ezY0NLRsm+jo6ICAgHKDndFojIyMPH/+vI+Pzy+//GL94FJZcfMEAABwUt7e3kuXLv3222+FEOfOnSs32LVo0aKiM9Tnz59/8sknhRDR0dG/h1QnOGIHAAAgDW6eAAAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJDvRakmAAAEG0lEQVQEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACTx/wB0o4LzxiIa5QAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "finalmodel <- lm(GasPrice~.-Date-GasDate, data=traindata)\n",
    "\n",
    "finalpredictedPrice <- predict(finalmodel, testdata)\n",
    "\n",
    "finalpredtestData<-data.frame(testdata$Date,finalpredictedPrice)\n",
    "colnames(finalpredtestData) <-c(\"Date\",\"GasPrice\")\n",
    "\n",
    "p1<-ggplot(traindata,aes(Date,GasPrice))+geom_line(aes(color=\"Training Data\"))+\n",
    "  geom_point(data=testdata,aes(color=\"Test Data\"))+\n",
    "  labs(color=\"Input Data\")+\n",
    "  geom_line(data=finalpredtestData,aes(color=\"Predicted Data\"))+\n",
    "  theme(legend.position=c(.3, .75),panel.border = element_rect(fill=NA,color=\"black\", size=0.5, \n",
    "                                    linetype=\"solid\"),axis.text.x = element_text(size=7))+ \n",
    "  coord_fixed(ratio=150)+xlab(\"Date\")+ylab(\"Gas Price ($)\")+ \n",
    "  scale_x_date(limits=as.Date(c('2015-01-01','2016-05-01'),breaks=date_breaks(width=\"6 months\")))\n",
    "\n",
    "finalscore<-sqrt(mean((testdata$GasPrice-finalpredictedPrice)^2))\n",
    "print(paste0(\"RMS Deviation: $\",sprintf(\"%.2f\",finalscore)))\n",
    "\n",
    "predVact<-data.frame(finalpredictedPrice,testdata$GasPrice)\n",
    "colnames(predVact)<-c(\"pp\",\"ap\")\n",
    "\n",
    "p2<-ggplot(predVact,aes(x=pp,y=ap))+geom_point()+ ylim(1.5,2.5)+ xlim(1.5,2.5)+\n",
    "    geom_smooth(method=lm,   # Add linear regression line\n",
    "                se=TRUE)+theme_bw()+    # Don't add shaded confidence region\n",
    "    xlab(\"Predicted Price ($)\") + ylab(\"Actual Price ($)\")+ coord_fixed()+\n",
    "    ggtitle(paste(\"RMS Deviation: $\",sprintf(\"%.2f\",finalscore)))\n",
    "\n",
    "plot_grid(p1,p2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Our final out-of-sample performance for the linear model is reasonable. This validates our techinque of training the model on one set of the time series data and then using that to predict the future gas prices.\n",
    "\n",
    "\n",
    "## Models - Support Vector Machine\n",
    "\n",
    "We'll try a non-linear SVM to see if we get better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "svm(formula = GasPrice ~ . - Date - GasDate, data = trainsubdata, \n",
       "    kernel = \"radial\")\n",
       "\n",
       "\n",
       "Parameters:\n",
       "   SVM-Type:  eps-regression \n",
       " SVM-Kernel:  radial \n",
       "       cost:  1 \n",
       "      gamma:  0.125 \n",
       "    epsilon:  0.1 \n",
       "\n",
       "\n",
       "Number of Support Vectors:  275\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"MS Deviation: 0.0124255421669924\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In loop_apply(n, do.ply): Removed 169 rows containing missing values (stat_smooth).Warning message:\n",
      "In loop_apply(n, do.ply): Removed 169 rows containing missing values (geom_point)."
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_model <- svm( GasPrice~.-Date-GasDate, data=trainsubdata, kernel='radial')\n",
    "\n",
    "#Look at the model outputs\n",
    "summary(svm_model)\n",
    "\n",
    "#Predict the values for our test data\n",
    "svm_predictedY <- predict(svm_model, testsubdata)\n",
    "\n",
    "#Sore the data as a mean squared difference\n",
    "svm_score<-mean((testsubdata$GasPrice-svm_predictedY)^2)\n",
    "print(paste(\"MS Deviation:\",as.character(svm_score)))\n",
    "\n",
    "#Plot the model preditction to see how it looks\n",
    "predData<-data.frame(testsubdata$Date,svm_predictedY)\n",
    "colnames(predData)<-c(\"Date\",\"GasPrice\")\n",
    "\n",
    "p1<-ggplot(trainsubdata,aes(Date,GasPrice))+geom_line(aes(color=\"Training Data\"))+\n",
    "  geom_point(data=testsubdata,aes(color=\"Test Data\"))+\n",
    "  labs(color=\"Input Data\")+\n",
    "  geom_line(data=predData,aes(color=\"Predicted Data\"))+\n",
    "  theme(legend.position=c(.3, .75),panel.border = element_rect(fill=NA,color=\"black\", size=0.5, \n",
    "                                    linetype=\"solid\"))+ \n",
    "  coord_fixed(ratio=2000)+xlab(\"Date\")+ylab(\"Gas Price ($)\")\n",
    "  \n",
    "predVact<-data.frame(testsubdata$GasPrice,predData$GasPrice)\n",
    "colnames(predVact)<-c(\"pp\",\"ap\")\n",
    "p2<-ggplot(predVact,aes(x=pp,y=ap))+geom_point()+ ylim(1.5,2.5)+ xlim(1.5,2.5)+\n",
    "    geom_smooth(method=lm,   # Add linear regression line\n",
    "                se=TRUE)+theme_bw()+    # Don't add shaded confidence region\n",
    "    xlab(\"Predicted Price ($)\") + ylab(\"Actual Price ($)\")+ coord_fixed()+\n",
    "    ggtitle(paste(\"RMS Deviation: $\",sprintf(\"%.2f\",svm_score)))\n",
    "\n",
    "plot_grid(p1,p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The in-sample performance is significantly better than the linear model. We look at the out-of-sample performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"RMS Deviation: $0.26\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In loop_apply(n, do.ply): Removed 11 rows containing missing values (stat_smooth).Warning message:\n",
      "In loop_apply(n, do.ply): Removed 11 rows containing missing values (geom_point)."
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "finalmodel <- svm(GasPrice~.-Date-GasDate, data=traindata, kernel='radial')\n",
    "\n",
    "finalpredictedPrice <- predict(finalmodel, testdata)\n",
    "\n",
    "finalpredtestData<-data.frame(testdata$Date,finalpredictedPrice)\n",
    "colnames(finalpredtestData) <-c(\"Date\",\"GasPrice\")\n",
    "\n",
    "\n",
    "p1<-ggplot(traindata,aes(Date,GasPrice))+geom_line(aes(color=\"Training Data\"))+\n",
    "  geom_point(data=testdata,aes(color=\"Test Data\"))+\n",
    "  labs(color=\"Input Data\")+\n",
    "  geom_line(data=finalpredtestData,aes(color=\"Predicted Data\"))+\n",
    "  theme(legend.position=c(.3, .75),panel.border = element_rect(fill=NA,color=\"black\", size=0.5, \n",
    "                                    linetype=\"solid\"),axis.text.x = element_text(size=7))+ \n",
    "  coord_fixed(ratio=150)+xlab(\"Date\")+ylab(\"Gas Price ($)\")+ \n",
    "  scale_x_date(limits=as.Date(c('2015-01-01','2016-05-01'),breaks=date_breaks(width=\"6 months\")))\n",
    "\n",
    "finalscore<-sqrt(mean((testdata$GasPrice-finalpredictedPrice)^2))\n",
    "print(paste0(\"RMS Deviation: $\",sprintf(\"%.2f\",finalscore)))\n",
    "\n",
    "predVact<-data.frame(finalpredictedPrice,testdata$GasPrice)\n",
    "colnames(predVact)<-c(\"pp\",\"ap\")\n",
    "\n",
    "p2<-ggplot(predVact,aes(x=pp,y=ap))+geom_point()+ ylim(1.5,2.5)+ xlim(1.5,2.5)+\n",
    "    geom_smooth(method=lm,   # Add linear regression line\n",
    "                se=TRUE)+theme_bw()+    # Don't add shaded confidence region\n",
    "    xlab(\"Predicted Price ($)\") + ylab(\"Actual Price ($)\")+ coord_fixed()+\n",
    "    ggtitle(paste(\"RMS Deviation: $\",sprintf(\"%.2f\",finalscore)))\n",
    "\n",
    "plot_grid(p1,p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The out-of-sample performance looks like it has an unaccounted for bias.\n",
    "\n",
    "## Models - Gradient Boosted Regression Tree\n",
    "\n",
    "Finally we try the GBM to see how it performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>var</th><th scope=col>rel.inf</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CrudePrice</th><td>CrudePrice</td><td>83.93061</td></tr>\n",
       "\t<tr><th scope=row>GasStock</th><td>GasStock</td><td>14.24023</td></tr>\n",
       "\t<tr><th scope=row>Week</th><td>Week</td><td>0.7284642</td></tr>\n",
       "\t<tr><th scope=row>DaySupply</th><td>DaySupply</td><td>0.3851761</td></tr>\n",
       "\t<tr><th scope=row>OilExports</th><td>OilExports</td><td>0.3021899</td></tr>\n",
       "\t<tr><th scope=row>GasImports</th><td>GasImports</td><td>0.1754622</td></tr>\n",
       "\t<tr><th scope=row>RefinerPerc</th><td>RefinerPerc</td><td>0.1513438</td></tr>\n",
       "\t<tr><th scope=row>OilImports</th><td>OilImports</td><td>0.08651777</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & var & rel.inf\\\\\n",
       "\\hline\n",
       "\tCrudePrice & CrudePrice & 83.93061\\\\\n",
       "\tGasStock & GasStock & 14.24023\\\\\n",
       "\tWeek & Week & 0.7284642\\\\\n",
       "\tDaySupply & DaySupply & 0.3851761\\\\\n",
       "\tOilExports & OilExports & 0.3021899\\\\\n",
       "\tGasImports & GasImports & 0.1754622\\\\\n",
       "\tRefinerPerc & RefinerPerc & 0.1513438\\\\\n",
       "\tOilImports & OilImports & 0.08651777\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                    var     rel.inf\n",
       "CrudePrice   CrudePrice 83.93061355\n",
       "GasStock       GasStock 14.24023240\n",
       "Week               Week  0.72846420\n",
       "DaySupply     DaySupply  0.38517613\n",
       "OilExports   OilExports  0.30218987\n",
       "GasImports   GasImports  0.17546223\n",
       "RefinerPerc RefinerPerc  0.15134385\n",
       "OilImports   OilImports  0.08651777"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"MS Deviation: 0.0109074694752203\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In loop_apply(n, do.ply): Removed 166 rows containing missing values (stat_smooth).Warning message:\n",
      "In loop_apply(n, do.ply): Removed 166 rows containing missing values (geom_point)."
     ]
    },
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iIpo9e/Ybb7whdExGRUVt2rSpU6dOLMv+3//9X9m7CgsLk5OTH3nkEblcrtFonnjiiXfeeYeIvvvuu0q3hIgaN25MRMLq6crV4OgyjIiIKPuqcLFUt6KHKqnJEOwAAOAe/Prrr8IAnEQiiYiIePLJJ9PT09u1a7dnzx4fHx8XNz7xxBNKpXLTpk1CMtuxY8eNGzd69OhRv379u92SlJQ0ffp0tVpdUFDwzTffvPHGG927d/fz8xs0aFDVDpYJcwSNRqPFYiGi5OTkrKysJk2aJCYmlirZoUOH2NhYm83m6HoMDAzs168f3eqfc9iyZYvJZKpbt+5DDz1UiSYFBAR07dqViErlxXuybds2k8nk4+MzceJE5+symWzGjBlEtHPnzrIj3SNGjGjatKnzlaFDhxJRbm6u8+SzmJiYtLS0tLS0Xr16udOY119/nYg+//zzdu3azZ8/n4iKioru6e2UlJQQkVQqlcvlZV/VaDREVFxc7IVKajIEOwAAuC9+fn7z589v0aKF62IBAQEDBw4sKioSVhK4WDbhoFAo5s6de+PGjXXr1r344ovt27eXy+VWq3Xr1q29evUSookn/P7770SUn5/fszzCeQlpaWmO8kKf3DfffMOyrOOiMOtu+PDhQj+ca8ePH3/xxRdbtmyp0+kcqyKEL1RGRkal34iwj3GXLl3UanWpl3r16sUwDM/zx44dK/WS0H/prG7dusKUx4KCAsdFuVweGRkZGRnpYm6ls7Fjxy5cuFCr1Z44cWLevHlEFBMTExsbO3v2bGGUFqoEgh0AANwDx4Zw+fn5hw4dGjRoUGFh4cCBA4Uw5JpjbazBYNiyZYtKpRo2bFiFd/n5+T311FMrV67866+/CgoKvv76a6E/ac6cOY7VpvdJmCOo0WiEjCKMxOXm5u4rT15eHhEZjUbH7YMHD9ZqtVlZWbt37xau5Obm7ty5k9wYhyWiVatWdezYcfXq1RcvXqxbt25cXJywKiIsLIxu9TBVjrA5XN26dcu+5OPjI6zDKLuBXNkxSoVCIcRTm81W6cYQ0eTJk9PS0j777DPHZntnzpyZOXNmbGzsmTNnKrxdGEpmWbbcZgj/IjqdzguV1GQIdgAAcA8kEolKpRK2/+jcufOWLVvi4+OtVuvo0aOd9wEp14ABA4KCgn799dcVK1aYTKbBgwcL29e5T5jytW/fPmHwdOXKlZV/J05OnDhBRMJme0TEcRwRjRo1ysVuYTNnznRu1eDBg8lpbey3335rt9ubNWvWoUMH14++du3aq6++yrLs1KlT9Xr9+fPnf//9d2FVRHx8PBFVekaaw73OShQ65zwkICBgzJgxwp4sf/311yeffBISEpKenj5ixIgK73UkznJ7MYWLwlZ5nq6kJkOwAwCAyhNOYZJKpRcuXPj8889dF5bL5SNGjLDb7cJkfNeLLVyoU6eOEHrcPC7Wtfz8/N9++42IhI1LiEjoKnOnD8lB6JnbvHmzMEtPmG83cuTICm/84YcfrFarMO2s1CTF+xmEFQjLPso9D9dgMAhT3IQy1SI0NHT8+PHCnnanT592rFe9G8f5cmVLmkymf/75h4gqnBJQJZXUZAh2AABwX5o2bTp8+HAimjdvnvMks3IJk+psNltoaKiw5qByhPPEyp3/fq/mz58vDMA5hk3j4uKI6Pjx43c7JK2svn37BgUFFRYWbtu2LS0tTUiK7ozDCjP2ym69YTAYHGdvOBP2y3WzG699+/ZEdOLEibIrJPbs2cPzPMMw7dq1c6cqz+nUqZMwKJyZmem6ZFhYmBC5tm/fXuql3bt322w2iUTSs2dPL1RSkyHYAQDA/RIOb7h8+fK3337rumSnTp3eeeedyZMnL1682PVhr6dPnz558mS5L2VkZPz8889U3jT/e7VixYoPP/yQiPr16yecKEVEAwYMCAkJ4Thu0qRJwrBsKWVzklwuF07I2LBhw8aNG3me79ixY5MmTSpsgDAYXbZ3cM6cOeXOrhMykPMiBhceeeQRtVpdUlLy3//+1/m63W6fPXs2EcXHx1d6Yzm73Z6ZmZmZmWm1Wt0pL8xNLCstLU3oO3RnAFTIyl988YVjuxki4nle+Efs3bu30NsqEM4FzszMLJWD76mSWgfBDgAA7lebNm2E7jdhGwvXZs2atXDhwgp7s06fPt22bds+ffqsWrXq9OnTxcXFJpPp0qVLK1as6NKli5BsJkyY4GYLOY4zm81ms9lkMuXk5Bw/fvyTTz7p3LnzhAkTeJ5v3LjxunXrHIU1Gs2SJUuIaOvWrQMGDDhy5IiQDKxW66FDhyZPnixsyVaK8I62bt2alJRE7nXXEVFCQgIRHTt27M033xR22cjPz3/77bfnzZsnzCMsRRhJ3Lx5szvZLjAwUNiQ74MPPli0aJHQMZmamjps2LA///xTKpUKp6ZWzunTp8PDw8PDwx1LRlxr0qTJBx98UGp8+ezZs0OGDCGiZs2auTMAOnHixDp16uTn5w8YMOD48eM8z2dkZIwePXr//v1l386qVauEFpaKyPdUCRHl3uL4mhcUFDgu3v8kyCrmoTNoAQBAZISVjP369Sv31V27dgm/Vn755ZdStwwfPrzCyoWOq7Vr1zqubNmyxcUvL4VC8fHHH7vf7LuRSCSjRo3Kz88ve+Py5csdfYoqlSowMNBxbKhMJitbnuO4qKgoR7UZGRlly/Tp04eIli1b5nzRseeLRCIJDg4W1p8+99xzzz77LBFNmzbNufCpU6eEVkml0oiIiPr16/fu3dtF5Var1XHYrkwmc/TPSaXSzz77rFTzhB7QDRs2lG258N5PnTrluOLYJ8X5X9wFx1cvOjpaOOrDsYWhVqs9ePCgO5XwPH/o0CHHmhvHP5Aw17NUycWLFwuvGgyGSldS4ULgcr95qhF67AAAoAr07t27Y8eORCRsUXb/Bg8efOnSpY8++mjQoEGNGjVSqVRCLnnggQemTp165syZl156qRLVCpVER0cnJibOmzfv6tWrX375Zbkn0L/yyit///33xIkTW7ZsKZVKi4qKgoODe/Xq9f7775d7GgTDMI6lnT179nR/ZWVSUtLixYtjY2NlMpndbu/SpUtSUpLQ7VdWbGxscnJyQkKCn59fZmbmtWvXrl+/7qJyuVz+9ddfb9iwoU+fPr6+vsXFxXXr1n366af/+uuvMWPGuNnCKpGamrpo0aKHH37YbDYL0wdv3LjRoEGD559//tixY8JuzO7o3LnzqVOnxo8fX79+fYlEEhoa+thjj+3fv//ll192vzFVUknNxPA1rQsRAAAARC0vLy84ODgtLS0yMrK62yI26LEDAAAAr/LoVnn/cgh2AAAA4FUIdp6DYAcAAABepVAohg4dqtFoqrshIiSSOXZms/mTTz65evVq2T0eAaBW0+l0bm4bAQAAIgl2wjRMnU7nfHgfANR2c+fO9fX1LfdAJHCfwWD4448/3CnJsizDMMJ2Gx4l/Orxznic3W53vRNyVfHmm7LZbFVy6kaFvPameJ7nOM6xJYqnn+WdfyaWZSUSiZvPUiqVjkPt7oc3vte9RqvVTps2rbpbAQBVZsWKFdXdBDEoKSm5cuWKsHOYaxaLRSKReCExcBzH87z7v/PuR3FxsVar9cKDhOPUvBNNiouLdTqdp58ihC3vZH273W6z2dRqtacfJLwp73zvmUwmuVzuzt8V27Ztk8vlCHYAAOAWHx8fd2aqGI1GiUSiUqk83R6WZTmOk8lkXvjlqtfrAwICvPAgu93O87x3OtL0en2551JULZ7n7Xa7RCLxQlq1Wq0Wi8XHx8fTD+I4jmVZqVTqhbRqMBiUSqVCoaiwZNmDaysNiycAAAAARALBDgAAAEAkEOwAAAAARALBDgAAAEAkEOwAAAAARALBDgAAAEAkEOwAKonZEef8v+puDtREe/bsGT16dJMmTTQaTXR09CuvvJKdnX23wjt37mTuFBwc7FwgOzt71KhRgYGBOp2uf//+f//9t+ffAQDUMtjHDqAyyiY5Zkccn5BSLY2BGmvWrFmFhYXPPPNMVFTU2bNnly9f/ssvv5w4ccLFZl2LFi2qV6+e8LFSqXRct9lsffv2zc7OXrhwoU6nmz17ds+ePU+ePBkWFubxtwEAtQeCHQCAp6xcubJZs2aOT9u0afPUU099++23Y8aMudstCQkJsbGxZa9/9dVXJ06c2L17d69evYioa9eu0dHRCxYsWLhwoSdaDgC1VI0bir148aJGoxGGIdLT06u7OQAAleec6oioZ8+eRHT9+nXXdxmNxrKneP/4449169YVUh0RRUZG9unTZ8uWLVXWVgAxckxsqO6GeE/N6rHjeX7MmDEmk6m6GwIAUPUOHjxIRK1atXJRpnv37oWFhRqNpl+/fgsWLIiOjhaunzlzptSZYLGxscnJyWaz2fkEsLy8vGPHjpWq02q1Vs0bAKhVnPMcwzBl/14SpZoV7JYvX/7bb7899thjmzdvru62AABUJb1eP2XKlHbt2g0aNKjcAr6+vhMmTOjRo4dWqz1y5MiiRYu6du16/Pjx8PBw4fa2bds6lw8ICOB5Pj8/XyggOHLkSP/+/UvVHBMTM2XKlLy8vAobyXEcEZWUlNzru7tXwq9Y7/Sj2Gw2vV7vhQd5+U258w96/7z2pjiO4ziuCv8IKbX2iIgYhsnNzXXEOy+8KZZlzWazO4fSchxXVWfX1qBgd/Xq1bfeeismJuatt95CsIOaDGtg4V6ZTKYhQ4aUlJTs3Lnzbuepd+rUqVOnTsLHjzzySK9evR566KH//ve/8+bNc/9BTZs2LVteOIY8KCiowtuNRqNEInHuAvQQlmU5jpPJZF745arX6wMCArzwILvdzvO8XC739IOISK/XBwYGevopPM/b7XaJRHK3b9oqZLVaLRaLi3VFVSIoKIjjOJZlpVJpVQUpFwwGg/DTV2HJKmxMDQp2Y8eONZlMn376qXd+KgAAvMNisTz22GPHjx/fs2dP48aN3bzrwQcfjIqKOnz4sPBpQEBAQUGBc4H8/HyGYfz9/Z0vNmzYcNq0aaWqyszM3LNnT2WbDwC1SU1ZPLF69eqdO3e+/PLLXbp0qe62AABUGavVOmzYsIMHD/7yyy/t2rW7p3vtdrujnykmJubMmTPOr54+fbpRo0ZqtbrK2gogIl6YUVcz95KsEcHu+vXrU6ZMqVev3ty5c6u7LQAVK3e/OmxiB2XZ7fYRI0bs2rVr69atcXEVjODb7XbnT7dt25aRkeH4W/fRRx9NT0/ft2+f8Gl6evquXbsGDx7siWYDiEOpbFe1Ua9mpjqqIUOx48aNKywsXL9+vU6nc6f8kSNH3nrrLecrNpvNM00DKEfZOXZIdVCu8ePHb968+cUXX8zJydm0aZNwsXHjxsIyiO3btw8YMODLL7988skniWjgwIERERFt2rTx8fH566+/Pv3003r16k2aNEm466mnnlq0aNGTTz75wQcfaLXa2bNn+/n5vfnmm9X11gBqBQ/129XYVEc1IditXbt227Ztw4cPf+SRR9y8JS8vb+fOnR5tFQDA/Tt06BARrVq1atWqVY6Lr7zyyvLly4lImMQtLEQlooSEhA0bNmzevLm4uDg8PPy5556bNWtWSEiI8KpcLt+xY8cbb7zxxhtvWK3W7t27b9iwwXk9LAB4R01OdVTtwS4rK+u1114LDAxcunSp+3f169evVAbPy8sru7AZwBOwJBbcd/r0aRev9u/f3/k/ZZMnT548ebKL8mFhYevXr6+yxgHAvavhqY6qfY7dpEmT9Hr9Rx99FBoaWr0tKSU5OdmxXbXwN3d1uXr1qqMlly5dqrC8c8slEolSqQwODo6NjR05cuR3333Hsqx3miFiNWTUNcdAF7OquxEAAP8mNT/VUbUHuyNHjhDR9OnT6ziJj48XXm3fvn2dOnVmz55drW2sxXiet1qteXl5Z86c2bhx47Bhw9q0aXPu3LnqbpcI3RpM8xI7R7O20NytdDnbq88FAPjXqhWpjlugjzoAACAASURBVKp9KFaQlVV+z0NOTg4RGQwG7zZHPJo3b65Wq3Nychyn7p45c6ZHjx6HDx9u0KBBtTatduMTUpwHZEenphhtpFN6rwFGCxmtREQGcwUlbxTQyXTqG0P/ppMSAQCqmJupzmonhkherdmqmnvsLl26xJfhOOgwLS2N5/l72nXdo5YsWSKMRcbGxubn57/88st16tRRqVSdOnXau3dvucVycnKef/750NBQjUbTvXv3AwcOOIq9+eabQjFHD6XzReFQoJ49ezZs2NDxapMmTYRX33vvPXcanJSUdPTo0bS0tEuXLj322GPCxdzc3JdfftlR5tKlS2PHju3YsWN4eLhKpdJoNNHR0WPGjHH+JnbdDHdqEJ/zrVJGp6Z853NgpTyFiK7nU6o3Tve5yXJrWwxbRUPrXx2ir/+gU+mebhEAgGi5+evMaKZXltDrK8harRt11Igeu1qnsLCwa9eujjHNw4cP9+/f/8SJE82aNStbzDEj7eDBg/Hx8cnJyb169XIu5oWDbqKjozdt2tS7d29hE6xffvnl0qVLwg74586d+/TTT50LX7ly5cqVKxs3bty/f3/Hjh0rrPz+a6iN7BwpeUvfn9/7p+4Dh2RDVuwiG0ufPOOlp5tv/VejwmCXpiciupJDret5tkkAAKJUKtWNGzdO+GDlypXO140Wen0FHbtIXWPJ4zsju4RgVxnp6ekMwwwcONBms/36669EZLFY/ve//y1evLhUMbVaPX78eCL64osvTCaT1Wp94YUXLly44HzunosT4l566aVu3brNmTNH+HT69OnCaY9du3a91zZLJJI33njDsbvpvn37hGDHMEyHDh0GDx5cv359f3//69evr1ix4syZMyaTafr06cK2Mq6b4U4N4sNyVNeSIbeUBBWlUSAVm4mI7BzJvNIJbr3VY2d3GezsLBlMREQma+nrMo8f/AgAICqOSOf41JHtCkvolSV0LpUGdKb3niPPH0LrCoJdJS1ZsmTixIlENGTIkM2bNxPRX3/9VbbYhg0bhK3h4+Pjhw0bRkRXrlzZt29f7969HWVc9NgNHz68c+fOjkT1/PPPu3/QZFmtWrVyfJyRkSF88PDDDzt2ELRarVartVmzZn369CGiAwcO2Gw2uVzuuhnu1FDpNtdYdpbCLZlEpLbcngNqY70U7NzssSs03fzD0eQ0LpBbTDM20ROdqE9LTzUPAEAcXA/Cjhs3bv/+/UVGmriUzqVSfAd659lqTnVU7XPsytW2bVthsl1kZGR1t6V8jn44InIc/ljqfG4i8vPze/TRR4WPhwwZotVqhY+PHj3qlWa6a8WKFXFxcVqtVqlU+vj4CJmMiKxWa25urndqqHXsHEVYbxCRwuwU7Ox3v6FKWeyUFBVHFfXYBafEJUXFJUXFOffYXdeTjaXr+R5uIgBALefO1LqCYnppEZ25SgM605wXasRgCHrsKqNu3boKhUL4WKVSCR9wZXa8CA0NdfTGMQwTEhJSUlJCRIWFhc7FnHcotVgsHmozEZ06dcrxcUREhPDBSy+95LwnfinutOf+a6iNWO5mj53MZlLwViujIDdmvFUJx4LcpKi4pEziW5W/r57zut0JXNwrdLNYgYmIbi6qBQCAcrmT6nhZyPMf0rUsGtGbJj9RUzYfqIk9djWfUnl7ZwsXA6nZ2dmO0MbzvLB7CxH5+fmR09Q6k8nkuOXKlStV3loBx3GLFi1yfPrQQw8RkV6vd6x7mDp1al5envOqZHfcfw21lJ2lOrab2/To7MXCB14IdmXPvSj3JAwXxY5dI0KwAwC4O7dSnSJC12HjtSx6Kr4GpTpCsPOowsLCH3/8Ufj4+++/F7rriKh9+/Z0K94R0blz54SXzpw5s3379lKVOE9Qc46A9+TKlSvDhg1zrJx4+OGHhUlyGRkZjo7GF154ITAwkIiSk5PL1nC3Zrhfg8jwNqu//ebgux9bJHzgnR67+1RgJCIyirMjFQDgft0t1Tkvg+XkdTVt1+YUyccOpNcfr0GpjjAU62kjR458+umneZ5ft26dcKVBgwZCb1mHDh2EKzk5OR06dIiJidm5c6fdXnqWVkhIiEwmE66/9tprffv2lUqlTz31lDuHf48ePVqtVufm5qalpTkuBgcHf/zxx8LHDRs2lEgkQjJ7/fXXx4wZc+zYsQ8//LBsVXdrhvs1iIzUkM/c6o71sd+cZud6xlsNISy8KLrzbwShP6+GnJYGAFBdXPfVCdnuul45d1NUnkE2vHv2uEE160BUQrDzqAYNGuh0utWrVzuuyOXy1atXC3udxMfHx8bGCmeEnz9//vz588Imxr/99ptzJQqF4uGHH/7pp5+IaPfu3bt37yai7t27uxPsyp4eFhMTs2nTJsexE1qtdvz48ULO27Zt27Zt24ioX79+wh4u7jSjS5cubtYgMoqCPCKy6QLkxfmn/WYm1c0moqTjRNURj5gdcS4eKiGmtUF93McofGqxERHpS4jlSCq5Y9AW8Q4A/s3cGYFNy1XO+z6qyCgb0SP7kQ55RDUu2GEo1oO0Wu2+ffvGjBkTEhKiUqm6du26Y8cOxzkTMpksOTl56NChPj4+Op2uX79+v//+e+fOncvWs3r16hEjRgQFBVViK2O5XB4QENCyZcvHH3/8m2++OXHiRPPmzZ0LLF68eNasWdHR0UqlskmTJgsWLHCeiudOM9yvQUzkhjwiMoU3JKIw6x1/IJU76a2quJm6nIuNTw099EdM/1x/oWFmOxERx9P5TM80EQCgFnIn1aXmquZ9X99gkj3XO/ORDl48buheMM5LMmuvvLy84ODgsLCwzMzq/2W1ZMmS119/nYhiYmKEDjkQn7Ofbo6+uKeg+yD/335aUj9zepM051c92unF7IiT8hRuUaSr7lgBUfahQpLbdaR5twKfzyNyx7f8h4jGpKYIP/PDOtIj2eVk0BrVYxcVFUVEqamp1d2Q2i0zM3PPnj0jR46ssKTRaJRIJI7F/p7DsizHcTKZzAtH7+j1+oCAAC88yG638zzvnZ079Xq9MKfZo3iet9vtEonEeVN9D7FarRaLxcfHx9MP4jiOZVmpVOp8OoA7qe5ypvrDzfXMVumzvW/0bnVzjnWLFi1c3GIwGJRKpWMbDReEAw6E8HCf0GMHUBmqkjwiojoRRKRlvb1z0f9dqXvm99ZRpor/YxFklXcp9CGih/P8GJ6IiCfyVRMR5RTXrAwHAFAt3El1F2+oP9wcZbZKx8TfTnU1E4IdQGWoi/PsjEwaEkpEOrtXf474hJT6JqWSY+L1fs4Xyy3cP9dXypOV4cIs8vaGm1tkR/gTEZVgYSwA/Ou5k+rOpmnnfx9ltjIv9b/+UEyNTnWEYAdQOTpjXp48UKlTEpGGu+PnyAvdYDHFPYmoR76PkmNKPf12M4iIaGBeABEti8omoodz/YWXArVE972G16NTCQEAvMCdVHc6VfvRD5F2lnllQEaXZkVeaNV9QrCreq+99ppwJBom2IkVbyxR2M258iClVklErS1xo1NTiGhHRIoXUh3Hk5K1EFHPfN+9h1tePfrouOspZY8Is9pp7LUD8bm+qWrrRw1usAw9nOv3V9MUIgrUEd3adY9PSEntkDI6NWV0asrplm61n9kRJ6Q6xwcAALWOO6nu2BWfRT/U44l5bVB6pya1INURgh1AZejziChXHixXKohh1GQhotGpKd7ZoNjGkpIzE1G4Rd7OoNEUZVlZulZmeZbZRk2Ml3xYqX/TeL3cXhQc3cGgK8gqoltDsY4eO/bWYXiVG5xFvAOAWqfsdmBlHbnks3RbXYah1walt21Y7IVWVQnsYwdwz3h9HhHlK4IYCUNqtdp+c4s4rwU7FX87gsk4u4KzFZlKL8Sz2CjUnkNEXL0Goy+kpIfuDMi5LL98lqhLhD8xDNlv5Tk7S0lRcUSUdJnoMvEJKc5BrVQf3t0ynOu99AAAao7z5887L4kt1+/n/FZuj1BIucmD05pHGr3TsCqBHjuAe8brc4moQBlIRIxGp/HiWbHCU1SchWdu//CqeNOpdLpx54xes420bAkRqX01RHTBL4aIMjJWJEXFqeQkk9xubZOTd2S1UtENvXEAICbu9NUdOOu3cnuEQsa9mVjLUh0h2AFUAl9USERFqkAiYrRaudXIEE90uw/Mo2x2UnKWIm2IXeMrXNGwpr8z6KtDdxSz2knLGolI469liJ7SDLmmsvTJ81VyTNgfcavqxlVu8QS65QCg9nJnXt3+M/6f7ohQK9i3hl5rVreWpTpCsAOoDLOZiGxyNRGRRks8362ukYg4rwQ7q51X8ha7XH3j6akpvl2ISM0aiehKzh3F7NzNYCfVabRKIqJfggt8WGnXAh9HARfGp4e2NWg80X4AgGrhTqrbcSLw053hGiU7fUhqozCzF1pV5RDsAO4ZbzETEatQERGj1RFR30bFROSdY1zsZpuE51i5Su7vm6sIJKKDwS8lRcV9XCeOc2qAjSUNayQiRq31URMRHfEzElG0SSkUmO9/1zHWembFknP1Z1yJqLAxkWaFVAyH1wCAyLmT6pKPBa7dG+ajZt8amtogtFamOkKwE6uZM2cyDOOYSfDbb78xDLNkyZLqbYZ4WCxExMmURERaLRFJTSVExHon2JnMRMTKlSo57Qn8HxH5224efSHdeTur2VjScSU8w5BarVMSEWXLbUQUZrm5zGJSzl0HVSPMciKqa67gZIt6ZsXZg63mXKz3hcYb+7wAAFSOO6luyx/B6/eF+WvtM4ZdjQqpramOEOy85vTp04wTtVrdvHnz6dOnFxXVuH1xdu7cyTDM8uXLvfAs5y+LRCLx9fWNjo4eMmTIZ599ZjTe28wGbzabN5s5kpBcQUSMRktEMksJEXFe6rGzEBGvUPqoqEDOEpGvvZwzzWwsadgSu0JNDCMMxeYobUQUar0Z7Kz2myUPNiydycKtCiJqZFYF22RUZv0En5DyEZdCRN0KfBS8pGe+77NG7HgCADWUO6lu219B36WE+GrsUx9LjQi0VlieiFq0aOH6oNjqgu1OvKpRo0YJCQlEpNfr9+3bN3/+/OTk5D/++EOpVHr0ua1btz5w4ECjRo08+pRKc3xZjEbj9evXd+zYsXnz5vfff3/jxo1xcTUyLljMZolKJqSpmz12xeStOXacSQh2Ku2+uEfk/kTkby/nB9nOkpY1shotEWmVNPpiyq+hDxFRiPVmYUewY8s0O8wqJyJ/m3TStTr/1zi9bOXFZhqrT2lZMoKIYorVOlZSLOWw40mNxfM8x3E2m63CkizL8jzvTskqYbfbKy5034T3zjCMF55FRN756nnzn4njOM7z/3Wz2+0sy1b5mzp//ny5153f0Y+HQ78/FBqgs09N/Cfc38K6sbCsWbNm7jSV4zi73e7O9x7P81X1LYpg51Xt2rX73//+J3xcXFzco0eP48ePf/XVV6NHj/boc319fbt37+7RR9wP5y8LERUXF3/44YcffPDBgAEDjhw5Eh0dXY1tK5fdaDZLVcI2v8IcO4nJez12rNlMRLxCySekdP+2LxH5ldtjZ+c1rNGkCiIijYKI6OADu/mDb7QveYBPWPfhz3SOJxtLcik9eC2OiEZlBLcoUe8OLNobWFTn1nDtgwW+VN5K2GILaZXUKUNLRDKeaV+k3R9g8Ng7hiog9Iu7U8zx/x7F87x3HkS33rvI3pR3HlTb/5nOnz9fboWOFMXztHZf+K6TgWH+1mmPXQ3ysRFV3IBmzZq534Z7+tGrEhiKvWeWaRMd/7ufenQ63dixY4noyJEjRLR161aGYT7//PPvvvvugQceUKvVw4YNcxRev359165dfXx8NBpNp06d1q1b51xVcXHxpEmT6tSpo9Founbtunv37lLPKneO3YYNGx588EE/Pz+tVhsTEzNt2jSTyfTee+8JnWevvvqq8O3YoEGDqmqG+1+Z999/f8qUKQUFBbNmzRIumkymhQsX9uzZMzw8XKFQNGjQYMKECXq9Xnj1bs12fVflWS1miVIIdqTREpHE7L1gx5stRMQolMyOuEKZnYj8beX8hcZbzBLiOLWGbgW7ElZqkqp1dgMRKWRETp12RPRqatgb1+psPdb0seyAcIuCiIpkbLtC9efy/aVqtthIX0JhGlvrYg3PEBF1LtQJL2FAtmYSfihkbpBIJFKp1J2S90nYHtY7z3LzvVfJg4jIa8/yzoOE7x8vPEgqlUokkiqs8OLFi5LyOP56YRjJN7+H7zoZGOpnmzYkNcSPLbd8KTExMe63wf0fqCr8eUeP3b0pFeYs0yYq5y+twvq///77n3/+uXfv3qNGjQoNDRUuvv7660uWLOnYsePYsWMZhtm+ffuoUaMuXrwohB6O4wYNGrR3797evXs/+OCDFy9eHDBgQIX9cxMnTly2bFm9evWeffZZrVZ74cKFZcuWvfrqq4mJiUajccGCBU8//XSfPn2ISKfTea4ZLkybNm3x4sWbN29mWVYqlWZlZc2cObN3795PPPGERqM5fvz4xx9/vH///sOHDyuVyrs12/VdlWwZz0ttFrNSJZcSETHCUKyxhLw1FMuaLUT0keFjCqACGUtOPXYnmjt1rRmNRMQptUSkURIRGa2klweFmzOIZZUyaVJU3Ef2FO2tL0OY7WYvXUKeX4tiNc/Q5tD8ZzOC12Q/8twOg3OnXUYB8Sz/2PVv5RyTHFzQP9e/U6HW0+8aAKAK8Tyt21dnx4mAOgHWt4ZcC/Rxa1ZAzZxUVwqC3V2xv27l8u84WZ07drhsMdvGtaWuSGNbS2LbVFh/cXHx6tWriahDhw6Oi1u3bt22bdvDDz/suLJjx44lS5bMmjXrnXfeudkwlh06dOjs2bNHjx7doEGDtWvX7t27d+zYsatWrRIKJCQkPPfccy4e/fPPPy9btqxbt27bt2/XaG7uVZafn6/RaCIjI3NzcxcsWNC5c2fnSjzRDNcCAwNbtWp19OjR8+fPt2zZMjw8/MaNGwEBAY4CSUlJY8aM2bBhw3PPPde2bdtym+36rkq2zGolnjdLVHLhp0ejI6JfrycntV2WZKfxOzy+hW9WjrklkUHGElGRjKVbc+xGp6ZYGzuVMxmJiLQaIlLLiYhMVrquqBtpSmvzY9+TOiMRBf4eR0TfBqQMz+8afGtRxTMZwUR0VW3ZHlTwbEZwtwKfUsOs+hJqYrrUIP0QEX0bqm9bpHH02GGOHQDUBK4XTHA8s2p75KHzvg3DTNOGpGmVbm3XXitSHVUu2KWnp+/bt+/SpUvZ2dkGgyEoKCg0NLRdu3bdunXz8fGp8iZWF/b3/by54gXP5aQ9q+Vuwe7YsWPjx48novz8/L1792ZnZ8fGxj755JOOAo8++qhzqiOilStX6nS6MWPGZGZmOi6OGDHihx9+2Llz5wsvvLBhwwapVOrIW0T0zDPPzJkz58KFC3drs5C9li5d6kh1ROScfsryRDMqFB4eTkQ5OTlEpFQqhT42u91eUFBgt9vj4+NlMtnevXtdRLTK3eWa8F1hkt7qsdNoOOKD7bePavXoGgKOI32ehYiKpSwRGaQsy5C/XUJESVFxL7C3n8sWlxCRRKMlIl81EVGhkQqVdTsTtSnSCMFO8Hh+3MR/tkv5Gf+ozblytoFZGWKVndWaDwaUEFG3Al2pNpxIo25FNx/0p3/Jn37GR3P8G5iUV9UWAgCobq5THcsxnyRHHr7k2zjcNCUxTSOuVEf3FOxu3LixZs2aL7/88m6/qmUyWbdu3caOHTts2DBPL/P0AmnXB93psZO0e6D0jbGt71bnlStXVq5cSURKpbJ+/frPPvvs22+/rVKpHAU6d+5c6pY///yzuLi4Xr16ZWu7ceMGEZ08eTIyMjIyMtJxnWGYLl26uEhUR44cCQgIaN++/d0KlOWJZtyrbdu2zZ49+/Dhw84r6YSnV/ldLrTYm3iCWp322THuyio+OoXZ2fW6om2gtZzlC55QbCElZyEig4wjIp6hQpndz3arx87538doJCKZTkNEQToioswiylHWJaI2xZpS/cy7QwbT5dg9gYaXW1xtb9DuOtJ8b2BhpsJ6WW3pXKCT8XfM6s3JMj5RfIKImIDAS2rzYb/iR3P8HyjSXlVbsDAWAKqX61RnszP/3Rp54qquRWTJG4PTVXK3JtDUolRHbga71NTUmTNnbtiwwfXSdLvdvm/fvn379r322mvTpk179dVXa3W8k/YbWPp39YhRZRdMyEeMcr/OoUOHbtq0yUWBOnXqlLqSn58fGRkpDNqW0rhxYyIqKioq+z3nmJ9XrsLCwvr167vVYk82o0JC/AoODiain3/+edCgQU2bNp0zZ050dLTQ1zhkyBCLxVUvUeXucoHZEfeAXUu3xkCFtQJ5Mja4vOULnlBsISVnJiKD9OYPY6HM7sfe/Fa1Of3luck8qwfVl/tqiChYRwxRup5ylXV5htqUOSss1CInoiyFjYiO+pQE9PpLuH4wwPBMRnDr4jvKXzWMlHP1Zf0G6mzv8Qyl+BuI6MF832/D7ntVCgD8CzivAOWr9MQe16nOamf+u7XeyavalvWK33g0XSmv+NG1K9IJKv5t9NZbby1ZssTsxqCkQ25u7pQpU1asWLFixYoBAwbcR/NqHOX8pc7ZrmpXTlB5C559fHyys7OFAcRyb/H19RUGK51lZ2e7eIqfn19GRsY9NcwTzXBNr9efOnVKp9M1b96ciBYvXqxUKg8cOBASEiIUMBgMJpPJdSWVu8s1H5uUbgU7Qa7C1sSkkvGMnfH4sthiM6k5MxGl9FgrOTWEiPJlbESJ8lizlKWpN1e5CnHzbVsEEQ279E5yQQGfkLImKo6ISEevXG/dxqBheHLuhhNWTmQpS2/LdMDf8ExG8J8hM4RPhZr/So+1SPgI83/Mcp6IUvyKs5S2YZmBbza9ZpHgfDEAcKXUrzmGYaoq27lOdWar5KMf6p27rmnXyPBy/1SFjKlwZ5PamOrIne1O5s2b55zqwsPDExMT586du379+uTk5P379//www9r1qyZPHly9+7d1Wq1o+TVq1fXrFnjkVZXK+X8pY7/eeFxDzzwgNVq/fHHH+9WoHXr1unp6enpt3eR5Xn+0KFDLurs2LFjfn7+0aNHy31VKpUSEXvnFo2eaIZr8+fPt9lsiYmJQnvS0tIaNGjgyGdEdODAgQqbXeFdleDDSomoWHq7Az9Pbmd4CrB5YzTWaicFZyUiUir5hBQ+IaVjaDslxygZGzltX6JjJe2KNESkl9vpzl1ITvoafe3S+ubbvenzbSnCcRQ58tLB7pB/MRHZr/7jqKStQRNTrE4OLhBqJiKWoe/C9AF2aUKen0feMwBARVynOqNFsmBL1Lnrmo6NDa8OSJe7ccR1LU115P4+dmFhYZMmTTp06FBGRsbmzZunT5/+5JNP9uvXr0ePHo8++ujo0aMXLlx44MCB3NzcjRs3JiYmyuXyiisFN7z00ktENGXKlFLTwrZt21ZQUEBEI0eOZFn2/fffd7zkYh6k4MUXXySiiRMnOh/blZeXJwxQBgUFEZFzRPNQM+6muLj43XffXbBggZ+f37vvvitcrF+//j///JOamuoo8/bbbzvfVW6zK7yrEnR2Cd3ZY5fYYAARCatKhbB1n49wwc6RijMTETnmOajVRKSym4jIZKUGP/Ukov+7XHdQTgAR5ctLT58Qlk20MWhGp6bsi0rhE1JY7tZQbJkeu4sas0Hqw/9zmW79SZ2YHUBEpUZdN9TJI6KRmcFV90YBQIQ8tNex61RXbJbO/a7+hQx1t+aFrw5Il4k61ZE7Q7ENGzacOnXq6NGj3Zkwp9Fohg8fPnz48NTU1IULFwq/8uF+9O/ff8qUKQsWLGjRosWQIUMiIiKysrL27Nlz+fLltLQ0f3//UaNGJSUlrV69+uLFiz169Lh48eLmzZvj4+N37tx5tzoHDBgwYcKE5cuXN23aNDExUaPRXLp0KTk5+cKFC5GRkS1btgwNDV25cqXFYgkNDfX3958wYYInmuHgWCxsNpvT09P/+OOP4uLiyMjIDRs2CBP4iOill17avn17586dR44cKZFINm3a1LhxY19fX0cl5Ta7wrsqwfdmj93NwMQnpNh/+ZGInr6+WtLQ44dk2FlScRYiYpQ319wwQrDjTES+N478fT699ZyGGQ8U3dxYTi8rveDrhE8JEbUu1qTLbvbw2Tl6KP9Roj+zFeVMot0RdG1IdiCfd3OcPTE7wCzhfw0uFD7lE1KYHXGHfUsuasyDc0L5Hquq+h0DALjiOtUVGaXzN0el5qgejCl8vk+GREIVDvzW6lRH7gS7CxcuVGJP5KioqKVLl3rnHEDR+/DDD7t3775s2bLNmzebTKY6derExsbOmDEjLCyMiCQSydatW2fMmLFx48Y//vijTZs2v/zyy65du1wnqmXLlnXq1Onjjz9OSkqSSCT169efNGmS0Oklk8k2bdo0derUTz/91GQy1a9ff8KECR5qhsCxWFin0wUHB/fp02fgwIEjR47Uam9ve5uYmLh+/fp58+Z98sknQUFBTzzxxH/+85+6des6CpTb7Arvuld8Qgq7Z4f9/E9FwqLUhBQiYjRaItLZS4o9P8HMzpEvb+GkMpLeGvlVqYlIaTMlRcUlnW5EFNQn369FsYqIOOILyoyunvAxEVEbg+Y/beKSMogy6F0uxZczENH5vj8zv/UuVf6MzjQkm/i8XCJqZlQ1L1H/HFxokN7Oi3xCyk/H6ZwmuYnxZ/bUcekDOHwCAMpXhSeiClynuvxi2bzv62foFQlt8kf1zHTnybU91RFRlU1arF55eXnBwcFhYWHOu6wBeIL9l5/YvTs+qjfplTHRfhoiIvbIIfu3X31VZ8T16K4zH/Xs0w9coPprPwiSlGhnzRWusDuT7Tt+frTthX2BRan72/nZpWYJr+IYIsqXseE9j95Mn07TKaBHzwAAIABJREFU7K7vb2uU8E26n3BcufTbY5H2TOUHHzE7u5Z64uenPhyR9a1sxDPSdh2nrR7y/qXIcS3/+SIil5y2IzZa6eNNeRNOvi9p0Fgx/tUqfL9RUVFE5BhMh8rJzMzcs2fPyJEjKyxpNBolEonzHkwewrIsx3GyW8dweZRerw8ICPDCg+x2O8/z3pmJpNfrAwMDPf0UnuftdrtwLlYVVlvuqlir1WqxWO5pN1zXqU5fLJ/7XVRmvqJfO/1TD2Y5nsnzPMdxjrPFnFV5qjMYDEqlUqFQVFhy8eLFRPT666/f/0NxVizAPbKYicjiOHmCiNHqiMiHLfHCWbHCUCwrd/q9q1YTkT8r6633E84WE1IdESlVYY7s9aX29sy/Ez6memZF4K0tWpQcE2LNYfwDiGHKThA0SjREN3fF+4+5K89ItgYX5sTdMZVQoyBVaFCqsh5/9RKxbu32CQD/WryTSlfiOtVlFyr+8039zHzF4E65Tz+U9S/pqxPc7+ZbLMv+8MMPBw4cKCwsjIiISExM7NixY5W0DKCGspiJyCS5efIEEZFGS0RazivBjiMlZ+YUQbcvqdRE5GuXxOf5ElGmwlrHevOvQ159e/85hZRGp6YQUVJU3AldSe8839bFmr0BRUTUN89fyVkkzW7+R805se0/T3+e+5uIyFTCF+Tz19Nu+Dd+NHOJqkyXhEZJhTI/MqfyZjOjxdGxAOBBjlQ3btw4YSaPs8wCxdxNUfpi+fDu2QM75rlToWhSHd1TsJs0aVJhYaFCofjkk0+EXtmSkpK+ffv+/vvvjjKzZ89+++23Z8+eXfUtBagZhCPFLBKl7FawE3KMD1vMubWH+X1h7bySs5rlt1cyCYsnQqzygTn+WUrbDyEF49JDiahYyv0kPTb61lEQCqefdcc0OyHYDc0KJCJJ63JOIlHJ6euIV19Nb7nk/OevqbXE8xeC2gjXS9EqyCxREhFZzIRgBwAeI6S6cePGCZ8KHzjiXVquct73UUVGmZupTkyRTuDuUGxWVtbSpUu/+OKLrKwsx1j7zJkznVOdYM6cOb/++mtVthGgJuENRSwjtco1jq59RqMjIi1bwnq+x463WBniOUXpodj3zF0CbbIfQwrSVFYiKpCz9R48Njbm9v5zQrDTKcneJ+Wkj7DjiZqI1KzkkRy/IoWfpH7Dso/rfDlOL7MTUaBNuuvAGmKYs36t5FKSlfkvh0ZBFomKiKiyp3oAwL8N48TNW0qlOgfhSmquat739Q0m2bO9Mqsl1VXiHVU5d4Pdn3/+KXzgmH5rMpmcD5hyfg9l+0UBxCNfb1D4y+ROPztqNUkkPmyxF1YicVYzEfEKpx47lYaI+OtpRLQpTJ+ushLRSZ3RJOWcT8JQyoiIIgNJKqEzj+40Sri2RVoiGpDr58NKzwR1oDL/GRISYb7CTkTNStTdC3wP+xpyJAFlu+uISKMkE6MiIt5yD6fUAMC/Vqno486Waq7n1V3OVM/+NqrYJB3dOzO+Tb6LkgJPpDoXn3qNu8HOsdNsly5dhA/27t1bUlIifDx+/HiTyXTmzBlhkY4jBQKIjd3OlxTny/zVzuGGYRiNVmM3emGOncR2xyZ2RDd77IgoT2E76G9IVVmJ6ISPsdSNSjmRYwhVIjmrMzU1qlQcMzg7gIim111+tycWyjiWoQ5FWilPP4UUmKzljMOS0GMnVRChxw4APMJ1quPUzRdsqWeySkf1zOzVquJUJxxWWYWqsYuulIrn2AmbDF+8eFH4VKPRCFecj2Z68cUXTSZTREREr169vvvuu5ycHMfWxP7+/lXfaoBqwhcWEM/nygK0pf621Gg0piLW83PsJFYLEfFK5x67myHvp+BCO8Mf8i9+IeafXYGFpW5USIluBTtmR9wnPg06FmnbGrQDcgPSVNY//Uru9kSO+EKZXVhC+0Nofq8C8lWXUyzYhy4zwlCsaHvsWJY9efLktWvXcnNz1Wp1aGho27ZtnQ+sAwA3lRuDfH1977ZO1nWqY9Ux1qiFjEX6fPyNh2IqPhmhWbNmbrazNqo42AUEBDh/KuxGW0r79ndMu7ZarY67DAaDTqe7jxYC1CB8gZ6IcmWBpYIdo9Wps7MYjiXy7ImxEquZiMhpKJaUKpJIiOO+C8sjIo74deG5d7Q5IYVK9dgRpSutRPT89RAdK1kZqedd/qmpl9sDbbIzOtN5jblbLqnL25Kp6cm4Z0OCH89p+Pyxdz7PyvXouWpexnFccnLyp59+umvXrqKiIueXGIaJiYl54oknXnjhhfDw8OpqIUCtU+5OxaV+vhzKprqVK1c6ptmx2o6WyDlSmfKVAdcfaFx+Dc6aN2/OinpXpoqDXYcOHYjo+vXrwt6/wqdEdPLkSZvNRkT+/v7R0TdPUhL+lnUuVrW7GgJUL76ggIgKZP6le+y0WiIKM14nivJoA5iyQ7EMw6jUPM/tCywu3VqndCUsnhCCHZ+Q8lLSYCIamhlIRFtCyx+2EI4LI6J8GUtEW0MKnk9L4cpbEisUK5ayRKSzS4lo8a80sC01KefPwP9n77zDoyjXNn5P2ZrNppEEQggh9BKKhBIg0ouIiAFRpAgcFRERj6AcPVhALBg+QOUAUQTEggqIIEWSIEgLJTQp0ksSSkhPNtky7ftjkmWzPT0b5ndxce2+097Z7M7c81QP4+eff/7vf/977do1u0sFQTh37ty5c+cWLFgwceLEhQsXhoSE1PAMJSTqN45sdWI0//lUryXbQimeeOWxO+6ourZt2/LVU7+gyptqVBjXMXYpKSkpKSmvvPKK+PbHH39MSUnZtWuX+aOZPHlySilDhgwBoFKpzCMqlT23zcNEXl4e4YrFixdXbOdJSUkEQSxf7jBASuTcuXPmY5EkqdVqmzdvHhsb+8033xQXWwdjVckR6y15uQByaD/vspX5xcTYkekbq/v4NGMAAGUZXUk/NVb2zEQT4exq1awBoluga3jJ2ywZC0DNk6kqU4rWWhGaEQYnbwtMVnPdAARr/k8MIrQWtaXoaB6AN0cBeIOM/jvNzXOqu0RHRz/77LNWqk4mkwUFBVnVx2dZdu3ata1atdqyZUvNzlFCwlOx8roa7YXnOvfA/n1T83/bmggC8dqI291buqXqyjvJcmF5RpUsv1wZ3K1jFxERIb6IjY0dN27cb7/9ZrZk9u3b17zarVu3AEhBJ5bI5fLx48eb32ZlZe3evTsiIiI6+kGLp3bt2tXATCIiIgYPHgyguLj49u3biYmJW7ZsWbBgwU8//WQ5GQknCPm5AHJpvxZWDywaDQA5Z6ruCYjJE2VcsQDZsQsApFuvTJQWsQOgkuPFB79U/By9lDm7HECCf/7ktOSRXRweMUCDfb6PpipCjxmagACA5kH21yywsNgBqAfNCo8cOSK+aNmy5dNPP927d+/u3bs3aNBAHGQY5sKFC0eOHNm9e/fOnTuNRmNRUdGZM2eeeuqp2puyhIQnYSl9TCbr66dzVXfoos9XCSFymp/9ZFqbxq4tFDVTr64utGl1V9gNGjRIJpMxDHP+/Pl58+aZx/38/IYNGya+NhgMJ0+eBNC+ffsqn6jnolarv//+e/Nb8TYQExOzbt26Gp5Jly5dVq1aZX6r0+k+++yzhQsXDh8+PCUlxexPl3DCrmu7hsBnefjYvCxOwANHJ9Wlm3HvXlKo9uwJijECIBWVbeUpmhgBCMJcABGOn8UaaHDOq905r5JnjyEdENPK/po6SrTYkQCmpCbD8/OmSJIcPXr0nDlzunfvbrtUJpN16tSpU6dO06ZNy8vL++677+Li4mp+khIS9RLnqu7ABZ/VSSEKmp8zKq1ViAtVV/9KEDvH3XInwcHBc+bMsR1/7733zO2it2/frtfrAQwaNKiq5lcHIRKjzf+qap8//PBDr169vL291Wp19+7dLYWgIAhff/11VFSUr6+vl5dXs2bNJkyYIFpGP/jgA9ECN3PmTNHNGh4e7v5BNRrNggUL3nzzzby8vPnz54uDer1+8eLF/fr1a9SokVwuDw8Pf/XVV3NycsSljo7ofKt6A5EYHaaXF9BcnoxDaWBZyaKgYIaWE6h2YUezBgCEqtI92r28AAgEeVHVukMoIkMdrhho4XL0USE2CjKbuFnRLlhIcwC865HF7vz587/88otdVWeFr6/vzJkzr169OmnSpBqYmISEp+O8hK9zVZd0xu/rxBCVnPvP6FuSqrOlHC3FPvroI41Gs3jx4tzcXAC+vr5z586dNWuWeYXPP/9cfDF8+PCqnWXdwUrMWbq6Ksy///3vZcuWRUVFvfjiiwRBJCQkTJw48cqVK6LYWrhw4XvvvRcZGTlt2jSFQpGamrp79+7Jkyc3bdp01KhRxcXFcXFxEyZMGDhwIIAKJCDPnTt36dKlW7Zs4TiOoqiMjIx58+YNGDBg7NixarX69OnTK1as2L9///HjxxUKhaMjOt+qkp9PnaKxQS62dhCx/ALwoGrMYkfIK/upEl4akGSmJrSYUjUPtC1O/ABfNWQUGA4Agn1KyqbYIgxOXr2rCHh7rE+/Hu1/iEtFHfBIVJbyVrqSy+XmqBUJCQlHmCWd+MLKfelc1SWc9v/+r2CNkpsbm9o00EVxpYdQ1aFcwo4giHfeeWfu3LmpqamCIDRt2tQy41U0LImr1Y8KMfOuxt/S37Uc+f6enVZpE89+YDXyVHC/2KB+bh4lMTFx2bJl8+fPf++998QRjuNGjx790UcfTZkyJTw8fP369a1atTpx4oRMJjOvIAaZdu7cOSsrKy4urkePHpMnT3b/1Czx9/ePjIw8efLkpUuX2rVr16hRo7t371rWuFm7du3UqVM3bNgwefJkR0d0vlXFJlYH8WMpb46yFHaWCARBVL+WoVkjAEppR9iZk1gtRxzuiKJkU14+eNEHmfB12tmVIBCsRXouAGic6klapQAgGIyer+gkJCSqC+epo85V3R8n/X88EOyjZv8zOrWxv4ta6A+nqkO5hJ0IRVHNmtnpKUkQRJXXca5dlqdvymccZguasVV7Ot7gvrCLj4/XaDRTp04Vq8mIPPvss1u3bk1KSnrhhRcIgqAoiiQfOM0pilKr1W7u3x3E+luZmZkAFAqFaGNjWTYvL49l2UGDBtE0vW/fPicSrWJb1UFuZKJAj04OKpaE6RUAUlWOhB1JVk8WvSViViyhsq+wLLUdP8iFLZls1ca7GJpCdHZVoaWRb4mwcy4BaTnNEpS5pVj9lneHDh06fPhwTk5O586dR48eTdPlvpZKSEiIEAQhGu2uXr1qDu6yZcuRBr8eCfTTsP+JvRXi7yJT7aFVdaiAsHt4eDV0jDsWuwkNh1qNPBXcz/2jHDt2TKfTNWnSxHbR3bt3AYwePfrTTz+NjIx85pln+vbtGx0dXd3OzR07dnz00UfHjx9nWdZqMlW+VV3jxyO4mYUvJkBlr2tWqFEOIF3x4DHR0iQmgCAFThCcuTUrj4wTLXYOr33swORp3yLI261pjOiMEZ1drFOiFMMAYKzGmVhUyGAklXJz54n6oux+/vnnDRs2aLXa9evXiyPTp0+3zEPq2bPnnj177D5u7d27d/369QcPHrx9+3ajRo2GDRv2/vvvBwXZzyt2vnJSUpIY4WomICBArBsqIVHv+flg0PaUAD8N887o1IZ+kqpzhiTsHLKwxTSrke8iP7BNmPgu8oPKHCU3Nzc0NFT0YlvRokULAAsXLmzQoMHXX3/9wQcfANBqtTNmzFiwYEEVWghE+SUWcdi5c+cTTzzRqlWrjz/+uHnz5uK9KjY21m6FITMV26oOYmDA8UjNRuuGdpZuDX+HPb1R7NkAG0enQJIkBE4AXW3Cjr953bf4Phy4YkUoEqMegW8VlY+0+sI/di9aiHSo7RQ0DKRCYTKKHun6ouvwyy+/bN26dcKECeLb3bt3W6o6AEeOHFm8eLE5msKS+fPn5+fnT5o0KSws7MKFC8uXL9+1a9eZM2esyuC5v/KSJUvMz4H1LIBVot7jyAkrmusuXbrkaMOfDgTtOBEQ5GN6e3RqAy3j/CgPuaqDO8Ju9uzZc+bMqUC3nEOHDp08eXLmzJkVmlgdxSqMqfKZE97e3vfv3xcdl3ZXoChq9uzZs2fPTk1NTUpKWr58+SeffOLr6/vWW29V8tAiOTk5Z8+e1Wg0oid96dKlCoXiwIED5mKEhYWFYrKzEyq2VR1ETBHQOYjHFdtOdChcmhDScrBNSR9BbOxVnXKGXb/aV68zkTIN7SyffUSnapyDk4QhhQwGUulryhbf1oPkCZHTp08DMNd1Miett2zZcujQoVu2bLl9+/bmzZvtCrv4+HjLmONOnTqNHz9+48aNU6dOrdjKgwcP7tChQ1WcloREjVKx0DpBwLd7G+752y/Y1/TOmFR/jaTqXOO63MmSJUuaNWv28ssvHzt2zJ096vX6X375pV+/fn369Pnrr78qPcM6hzA42fyv8nvr1q2byWTatm2byzXDwsKmTp26f/9+hUKxc+dOcVDMX6lM27tFixYxDDNq1ChxV2lpaeHh4ZYlpg8cOGC5vt0jutzKUzCxAKB3YOYXG8Vm0/52HbUCQRDgqy8vVsjNEYp0AIyksm70rbFGScNIKCjWVH80HcDzfFpaGgBzbPGhQ4fEF7/88suXX34pdmG5evWq3c2tMsn69esH4Pbt25VZubi4uC4UQZXwaGq4+ZXdwwmlOFJ1vIB1fzba87df4wDju2NvOVd1bdu2lVSdiFvuPKPRGB8fHx8f36JFi1GjRvXo0aN79+6hoaHmiH6DwfDPP/8cO3bswIED27ZtKywsrM451yumT5++bdu2N998Mzo62tIsumPHjt69e/v6+h46dKh3797m8fz8fJZlzY3aAgICAKSn2/QccAOdThcXFxcXF+fj4/P++++Lg02bNv3rr79SU1PDwsLEdd555x3Lrewe0eVWnoJosdM7unrk5QoEkU/7quR2FvIkSQo8V203XOF2yWduJOuoA0602EEQCJMJUNQD6dGnTx+O48Sm2DNnzhR/dzdu3BCXvvLKKyRJ6nQ6AMXFxX369AHQsmXLtWvXOtqhKAojIyPdObrdlfv06ZOfn69Wq4cOHRoXFyfVFZcoL85LjdQ8TlTdmqRGf533DQs0zo1N1apYu6uJSJLOEtfCrmXLlleuXBFfX7161dzVlCAIX19ftVqdm5vrqN9oq1YOStRLlDJs2LA333wzLi6ubdu2sbGxISEhGRkZe/fuvXbtWlpamq+vb9++fVu1atWtW7cmTZrk5ub++uuvgiCYHdzt2rULCgqKj483Go1BQUG+vr6vvvqqo2OdOnXq5ZdfBmAwGNLT048eParT6UJDQzds2CDG8wGYPn16QkJCjx49xo0bR5Lkpk2bWrRoodVqzTuxe0SXW3kKjHOLXX6eSeHNEpTGfuoCSQpC9blihawM8cVtRWrj6jqIG9NwbKhWyqAnFQAIxgAo6kGQXWFhobkpdnFxMcdx5qdWrVYrXvfMIQf5+fkEQThpvpyTk/Pmm2926dLliSeecHlo25W1Wu2rr74aExPj5eWVkpKyZMmSXr16nT592ipO5vLly2vWrLHam5eXl/k3LvEwY2s5M2ek1gqOVR2x8o+QI5e0zYL1c2PTvBTOvFKSqrPCtbA7f/78ihUrPvzww+zsbMtxQRByc3PFYsW2dOvWbfHixY8++mjVTLNe89lnn/Xp0+fLL7/csmWLXq9v2LBhhw4d/vvf/wYHBwP49NNPExMT9+zZk5mZGRgY2K1bt7lz55pteDRNb9q06a233lq9erVer2/atKkTYXf9+vX4+HgAGo2mQYMGAwcOHDFixLhx47y8HhSxGDVq1A8//PDpp5+uXLkyICBg7NixH374YePGD4SE3SO63MojEEotdga7FjueF/Lz8r2bAGhoT7IKJEmC56rNFbvk/OrXEAxAR3FioGeVBAM4RzyEOa7U+REVNPJIJQDSZES9SJ44c+YMAC8vr+Li4sWLFz/++ONxcXFieOv48eNXrFgB4ODBgzExMX5+fmfPnnWyK71eHxsbW1RUlJSUZFkB1P2Vu3fvbu6B8fjjj/fv379v376ff/75p59+arntjRs3Fi1aZLXD9u3bv/XWW44u15aIURY1ECAr+uAsCzlVHyaTKS8vrwYOVJMnxTCMO39Qd3C+H57nnXeJcIecnBx/f3+rwRMnTli+FQSB53me5zme+ObPZieuayOCi2YOu0JwnOPHJURERJT3cxD/TJU/KXdgWdZkMrnzleB5vqq+Oa6FnUwmmzVr1pQpU9atW7dq1SrnxQNJknz88cenT58+bNiwGnbhewo9e/a0fTwaOXLkyJEj7a4/Z84cu83czMTExCQnu7jBd+jQwf1nsueee+65556zHLG6Jto9osut6j4sV6JFTGVN/n+ngSDQQZMPns+R+ZMkfOyVERRAkALPVI+cIRKj15pKWhqInbtqEjcVpFIGPaUEQDIG1AthJ9K6detTp05NnDixV69eiYmJ4mD//v3FF6JDw3l6mdFofOqpp06fPr13716XljM3V3700UfDwsKOHz9uNR4dHZ2SkmI1WFxcnJ6ebllC3BHFxcUkSTqpJVZVcBzH8zxN0zVwp8jJyfH19a2Zu7ggCOZi8tVKTk6OO39Qd3CyH0EQWJYlSdLl04hLRC1lfnvhwgWrFViWZVmWolUrd4SevqFpGaKf82S62mnqd8VsdTzPi52WakCCFxYWKhQKudxe+E5ZqnAy7pbM0Gq1r7322muvvXbs2LG9e/ceOHDgypUrmZmZOp3O398/ODi4c+fOffv2HTJkSGio466TEhJ1GPmf0WK1trVFmIAHUmbdQQBYHJ0LIFfmp3D0oyFJghB4AZmF2PU3RkfBq0pj4RowJXeLQqrayyBXDJUcBtEVazIC9UfZjR8//tSpU7m5uTt27BBHgoODR4wYIb7eu3cvnN5gTCbTmDFjDh06lJCQ0KVLF+fHKtfKLMvaihWtVtu1a1erwXv37lUsEldCogox2xccWYgYjvxyV+jftzTtmxT9e2S6QubsWid5YB1R7lpoojtg7ty51TEbCYnawkkXYCMDPYPnDsz8Fs03e69T0KPs7kEgSEIQeE44eYvYdxEdQvFI06qcWwNTya9VV+MWOzdRyUsSO0hTvbLYvfbaawcPHvztt9/Et/7+/t99952YSFFQULB161YAQ4daFyoXYVn22Wef3bNnz65du6KjratglmtllmUtiyLt2LHjzp07U6ZMqfB5STyEWJnNUBvJE45UnYEhl+1odvmOJrJp0etPpMtpSdVVEKlAsYSEtaqzwsRhbVj0vOshAG6ojTsaRC+BPdckQQDgOZ4XKFRDFbcGTKmwo3jUSIBdeVHJYSQeWOxqO9muypDJZFu2bDl8+PDff/8dHBwcExMjVvMGkJmZ+dFHHwEYNcq+3H/55Ze3bNny0ksvZWZmbtq0SRxs0aJF586dASQkJAwfPnz9+vViGIPzlUeMGBESEtKpUydvb+8TJ06sXr26SZMms2bNquazl6hv1G4arENVZyKX/h52+Y7XIxGFMx+/TVPOJimpOufUvrA7cODAjh07jhw5cvPmzfv37wMIDQ3t27fvzJkzO3bsWNuzk3jY4XisDo0GEFXgBSDFuwgOivQKBAmA56rrshloMrti66jFTk7BRCsBUIyDEs+eTK9evXr16mU12Lx5cyfpSgCOHDkC4Kuvvvrqq6/MgzNmzBCr34mxPubEW+crDx48eMOGDVu2bNHpdI0aNZo8efL8+fMti0dKSNRxHKm6IgP12Zaw6xnKR5rlzXz8nqWqmzatpAWUmPkHSdW5Qe0Lu0WLFpkjV5RKpcFguHLlypUrV9auXfvll19Onz69dqcnUfcxcdh+Cv3bwc9eTkOFYTnQFEylIioqX3NHacpQOK6QSZIAeIGvjgg4DUcq+RIHSp11xQIQ5PUqK7bynDt3zsnSYcOGWT4HOF9Z7EBTZTOTkKhZHKk6vYlcsq3J9QzlIxEFLw68SVMPYpPNqk58feDAgVqvuucR1ERWtnP69eu3bNmykydP5uXl6fV6g8Gwf//+Xr16cRw3c+bM8+fP1/YEJeo6F+9g+xkkX6n4Huy6Ne/kAaWV7ZoY5A0Y+pTGcc59qcVOYPnqUDQBjAxAlowFkK500VSnNpHXq+SJ33//vVzrZ2RkuNmhR0LiocKhrc5IfbI57PIdVY9WBa8MS7VrqxPx0G5GtULtC7s5c+bMmjWrS5cuPj4+ABQKRUxMzK5du3x9fTmO27JlS21PUKKuI9YENlXOjGWl7aakJhtZoLT0SZ88bwDJvjpnuxBj7Hi+wo+U3Mnj/Lm/7c4t0EQD+Llhdss+ZzYGZdfBADsRUqlAPSp3MnLkyM6dO69fv15sL+GEs2fPvvbaa82aNTO3+5OQkBBxpOpydfSCn8NvZKgGd8qd8dhtinR4zZBUXbmofVesXbRabdu2bZOTk4uKimp7LhJ1HVHYMW4Iuz/OokUQWgTbX3qqdXKXSyVZFGvDoudwyQBMHKakJj9aMB7AnoB8caldXWWOsauwK5b7/VeoveQd7ISW7mv5BY6t6i6fmvqE/ezLOgIp1j8z1p/kiTNnzjz//PPTp09/7LHHevfu3a1bt5CQED8/P4PBkJ2dffHixaNHj+7evdt5dWIJiYcWR6quoJj+bEvYnRx53/Z5E/rdc1JkUFJ15aX2LXZ2yc/PF78NUVFRtT2XOsrBgwcJgli2bFm1buIRiAKCdSXscovwyzFsP+NwBbOqE2l/PlrcLQFhZGZYAaXtkpM0JTV5rcqBtUyMseMr7ooVjAYY7acdsIU6AKxSU8Fd1xSEUgnxROoF48ePF2tDFBcXb968+Y033oiJiWnevLm/v39ISEhkZOTTTz+9ePFis6pr0qRJv379anPGEhJ1CUeqLkdHL9zYND1YIegrAAAgAElEQVRbMahT7r8G3SXtqToxW0JSdRWgzgm77OzspKSkYcOG5eXlDRo0KDY2trZnVFny8vIIV5g78NYPzp07Zz41kiS1Wm3z5s1jY2O/+eYbJ5007ZKUlEQQhJgV6AjBDYsdkRjtfzh6bVj0v4lo58VNrLZieYQZ0724ot+Dbqxp2guAkwLFqIzFjufBcYLRaH+hTgeAVdV1YSdTKQA4kqcex/fff3/06NHBgwe7XLNBgwYLFy68dOmSJOwkJEQcqbp7efIFP4ffzZXH9sx6vr8zW93+/fst30qZE25SV1yxf/zxx2OPPWZ+26hRo7i4uFmzZtltAsMwjFXIS13uXiWXy8ePH29+m5WVtXv37oiICMsCpO3atSvvbjt27HjgwIGIiIhq3aQyREREiHfE4uLi27dvJyYmbtmyZcGCBT/99JPLSq3lQvyts471lK2Ss1uvxC4sBz09CwhNDMgDsDYsem0hxtmrYyegcjF2JhMAMCYIAmy+9oKHCDuFl0IgCKIeZcV269YtISHh0qVLq1evTkhIOHv2rOXdxcfHJyYmZuzYsWPHjlU47X0kIfFQ4UjVpWcrPt0cll9MP9Pn/oiobLvriIhlTSQxVwHqirBTqVRNmzYVBCEjI8NoNN69e/fXX3/t37+/bW8cAH/++eewYcNqfpIVQ61Wf//99+a3R44c2b17d0xMzLp16yqzW61W26dPn+repDJ06dJl1apV5rc6ne6zzz5buHDh8OHDU1JSmjdvXlUH4t1zxVaM1mejk7Lb8BAS/QtdrEqRAASugq5YgTEBgCCAYWDbWLBIB4Cv88LOS0WYICfEzhP16ILcunXruLi4uLi4/Pz81NTUrKwslUoVFBQUHh5eMx3fJSQ8CEeqLjVTsejXsEIDPS7m/vCurlWdRMWo+CUpPT39iy++eOGFF2JjY3/88UeO4+7du3fv3r3MzMwK7K1v3743b968deuWXq8/f/78M888k5ycHBMT8/ffdpIEAwICBpWlb9++FT6R8jL1mwf/Ks/27dsJgli3bt3mzZu7deumUqnGjBkDQK/XL168uF+/fo0aNZLL5eHh4a+++mpOTo55Q6uAOfN+/vjjj+7du6tUqpCQkLfeeotl2cpsAqCwsHDmzJnBwcFqtbpXr1579+6dN28eQRAXL14s15lqNJoFCxa8+eabeXl58+fPFwedn+YHH3wg2vxmzpwpOnbDw8Nttxr1aPiRH17Nz8txcORK4cNSPfM1p72Ls+UuiowQIAEIfEVdsaLFDhBMdryxRbk6AIJXXRd2GgWMpIJm7DuU6wE+Pj6RkZH9+/fv2bNnRESEpOokJKxwpOquZ6g+3tRUZ6AnD7gnqbpqpSIWO0EQ5s+f/8knn5hKb0Xt2rXjeT4qKur27dskSd65cyc42EHmoSsIgmjXrt1PP/2k1+u3bdv27rvviq0YLYmKikpMTLQcyc7ONjf5qVasxNzUb7DmX1Ww219//XXnzp0DBgyYOHFiUFAQgIyMjHnz5g0YMGDs2LFqtfr06dMrVqzYv3//8ePHnXh8tm/f/vvvvz/22GNRUVEJCQlxcXEsyy5ZssTJoZ1vwnHc8OHDDx48OGjQoD59+ly7dm348OG9e/eu8JnOnTt36dKlW7Zs4TiOoijnpzlq1Kji4uK4uLgJEyYMHDgQgEajsf1w9h0+fWTfirhr+8f3OR7SoLLuMF+GOnm0w1kv/cw2N1NVpj553rRA7AkocL0lSQAQuBJXbLntVUzJr8ms8CwRiop4gmwXUaUlmKsBLwX0pFLLFqN+WewkJCTcwZGqu3JXtfi3MIOJnDLwbr8OzkKnJFVXeSoi7GbPnr106VKrQZlMNn369Hnz5vE8v2PHjqlTp1ZyZk8//fS2bduOHz9eyf1UmC0nkFXW+ZZ8zc5qX++zHnmkGbqWs/v79u3bd+zYYRVlePfuXT8/P/PI2rVrp06dumHDhsmTJzvaz9atW/ft2ycKr8LCwnbt2n311VeffPKJEy3ofJP169cfPHjw5ZdfXrlypbj+0KFDJ0yYUL7Ts8Df3z8yMvLkyZOXLl1q166d89Ps3LlzVlZWXFxcjx49LM/aaqvDVzH347UH105d8PmGVR+W+XAc5Uk4CbAblu0bYpCHGORfXgx/ssvlr5ipwIHE0kInziBJAIJQwRg7wazn7OVPyAw6PaluFuQ4zLhu4KWAgVQEmHJreyISEhI1jSNVdyHNa8m2UIYlXh52O7q1w4fk1q1bGx1kj0mUi3L7EY4cOWKr6kTGjh0rvrDKZKkY4h+Y42qte1LSBSRfK/PPLlbrJF9D8tVyH2vkyJGWqg6AQqEQhQvLsllZWffu3Rs0aBBN0/v27XOyn/Hjx5vNad7e3mPGjCkqKrp8+XKFN9mwYQNFUe+++655/eeee65169blPkMLGjVqBEB02VfsNK22yr5/L6TtIJKkz6WU2cqs6hQ8AWBKarL4f2ZP+6puJZ0MIEJfIoLFF5q0i0ZCccT3QT1Fh6JQrGPH8hUzVA08UlJmvdeh5231qJrVFcnquh8WgJcCRlJJCwwlcJLBTkLi4cGRqjuX6vV/W0NZjpgx/I4TVScZ6qqQclvsVqxYIb7QarXPPfecZXR8y5Ytg4ODMzIy7AbGlQue59evXw/gkUceqeSuKsygdm5Z7KJtcgAeaVbuY/Xo0cN2cMeOHR999NHx48ctg97u3r3rZD9WH1fjxo3hKmXY+SZnz54NDQ0NCQkxr0AQRI8ePS5duuRkn+WiAqfpaKvcrAdbmbXRksthz94NWNjszoqwaFHbGVjYxcAABIKNMgAsITQ2yPlHfjclLfjbq0NSeNyGIzAwGN7J8ZzIkhi7ilns1FyJNU7DUrDK2+U4Jae/p25ckf3WLGo57pEKAEreKAh13XEsISFRJThSdSevey/f0RgEXn8ivXMzh+1bJFVXtZRb2InVAmUy2cGDByMjIy2FHYCWLVtmZGTcvHnTzb2lpKS8//77U6ZM6devnxgkZzQajx079uGHH4pmv1psev2UTT7ui/3sJEy82K8KjtWwYUOrkZ07dz7xxBOtWrX6+OOPmzdvrlarAcTGxjq3VGu1Wsu3NE3DldXT+SaFhYWhoaFWm4hRgBVGFG3in7tip2m11fUcdeJ57FkRy9ikHbQuVr6SGgxgyeWwY746mgTLw+ggC8LAYMq95MbGSQDOafSdC9XvbJ72ARpf0LRtWWratltIswSCACBUqNwJkRj9NOcvvvbirO3oYq0TvSdY7FRyGEglAAVvACRhJyFR/3Gk6o5f9V6xqzFJYNYT6R2bSqqu5ii3sBPvylFRUZGRkbZLxcCswkJXhSFKYVl2586dYndFlUqlUCgKCgp4nhd3tWzZskGDBpV3htXKmn+V0XZVkjkBwLZc39KlSxUKxYEDBwIDA8WRwsJCvV5fNcdzG29vb9s05/v371d4hzk5OWfPntVoNG3atEFFT9Nqq78u4c2g7limv4JTVms2K1YCKKQ4b45qbJA31DDpBTKjA4udkQFN4ZFC9X05c0Kr61yofv5OAIAL6jZtSYh/IWfCjiwpdyIQQPmruKlK9ZwXR1kvK9YBMHiCsFPSMJotdrU9GQkJierGkao79I/PV4khcpqfPTKtTajDuvSSqqsOyi3snKf3i7Y60e7iDh07dvz+++8TEhJOnDhx9+7dgoICrVbbokWLAQMGvPTSS1VY6qwKqSox55y0tLTw8HCz3EEtdVaJjIz8888/b9++LbpoAQiCcPTo0QrvcNGiRQzDPPPMMxRFwY3TFFezMjpabdUvNRpnS54lRA+sMDhZGJxMJEYHmmgA57z10XmaLy82bWB6Iz7kJSNj55mESIyGFxrT8i//6bSjQd4dBQOgmV55Q2XIlAXKS38ozoQdJbYUE4QKVcAwG+o0Nha7fSm6XoBB4QHCjiTB0AqIFjtJ2UlI1Gscqbr9F3y/SWqkoPk5o9JahdhRddOmTYPF1V6qQly1lPsWJNYxOXHixK1bt6wWJSUlXbt2DaXR8e6gVqvHjx//7bffnjt3Ljs7m2GY3Nzc48ePL1q0qG6quhqjadOmN27cSE1NFd/qdLp33nmn5qfxzDPPcBy3cOFC88gPP/xQsQA7nU73/vvvx8XF+fj4vP/+++Kgy9MMCAgAkJ6ebjlotRX0HNakwh7BJhmAY1odgCCTjATBUwttLXbmgLzueV4AjvsU5chKVkoMKACglkM02bl2xZY/K1Y8upp3aLHLvKcDYJJ7gLADIFA0ADkY6VItIVFnsexpWbE9OFJ1iaf9Vic2Usu5t0ffckfVwZ7PSqIylNti17Nnz5s3b5pMpgEDBnzwwQfi4O3bt5csWWJ+27Nnz6qb4UPK9OnTExISevToMW7cOJIkN23a1KJFC6t4uBpg8uTJa9asWbVq1eXLl3v37n3t2rVff/110KBBSUlJLkuznjp16uWXXwZgMBjS09OPHj2q0+lCQ0M3bNjQokULcR2Xp9muXbugoKD4+Hij0RgUFOTr6/vqq69abrU07SccyEGIEmobDyYQZJIB2Byc+1tQbpacPXO4Q6dCtVWMnWUKalSBBsAxra6hqaT3Q2JAQQM91KXlYly6YnmOr5jFTlWq58ymOzF/QgCYQh0ARukZwg6UDICMZ+vlQ7jJZPr9999TUlLy8vIWLlwol8vFeNCAgADp5iThKVh9VwmCKK/NzJGq++OU/4/7gzVKbm5satNAhz2ja8UB9fBQ7luQuYbZ9evXJ02aJL5et27d7NmzzaF1EydOrKr5PbSMGjXqhx9+CAwMXLly5U8//RQbG7t169aav3NQFLVr165XXnnl7Nmzixcvvn79+s6dO0VZ5lJlXr9+PT4+Pj4+fvPmzdeuXRs4cODXX3998eJFy7ZmLk+TpulNmza1b99+9erV77777uLFi622wr5s9PbH/Naw99mIwu6+nEn21V1RG26oTB11aoNjW1L3Ai8ewgmfonwZC4AhhX3+BQC85KUxdo5/MYS4rPzJE8LgZBlPNDWUSEkrV2yBHmpWB8DkKcJOJgMgE1w06vBEzp07165duzFjxnz66aerVq0qLCzctGlTYGBgYGDgkSNHant2EhIVp1w3F0eqbuuxBj/8Fezrxc57+lZ1qLpKmhgfHsptsRs+fPjQoUN3797taIURI0aITQIk7NKzZ0/LZ6MRI0Y4elR67rnnnnvuOcsRy8Ilffr0cbmf119//fXXX6/MJgC0Wu3//ve///3vf+aRd999V6vVOmku0qFDB/ef/5yfJoCYmJjkZOvScZZblZjctnSz3XmwiQaQKS/xq57WFo/O8KPzsgE7fUpogXikQH1JY8inuTyaA3BUqyukuLVh0SsVJRNwdkkhxOSJioSL/OdmyMQ7JVMSy52gtGCe78Ho5cpwIDAkxAOEHZEYPV0VNBBND/u/8xOXMx0OC0F7HFlZWUOGDLEqxDNx4sS33347IyNj165d0dH2q2FLSNQnrFSd6FcFYAqcxjYY76dh3h6d2sjPTvscVC5VwlLPVcDE+FBRbosdQRAbNmyIiYmxuzQmJua7776r9Kwk6hBWWbGbNm06dOjQiBEj6s5j0+5GZdSDufybMDg5yCTTUXwRVZJ78bemCIAqK912/aHZPpcPdfTiqNOaYgAX1YYLXvq1ISXnrqBLJJ0TVyxJlpY7Kef8icTo0FJzHUpdseKsRM0awNIAnsp91lEjjTqCOD0DKQBQ8CTKurnziq0LQ3oWS5cutS2vSNN0bGwsAOcltSUk6geObHWmoJfZBuMJ0x396fEuVZ2tJnOp0mxvN3XnBlQHqUhLMT8/v717937//fc//PDDqVOn8vLyfH19O3fuPH78+IkTJ4ppjBL1hueee85oNEZFRalUqjNnzuzYscPf3//DDz+s7Xk9QBBKukp4K/H5+DKLgo2y+4oHPsHT2mIAXrnpQGernXTP14QY5AD+0egBZMuZR6LP2R6LchVjhwq1FFNbuF/F5AlLSRRopAUC2fJa68JSLgwUD0DJWz80Lk9CThGWjKuNOVUF27ZtAxAZGbl169aIiAjzePfu3VeuXGmV3yMhUadwKYPcMYDZqrpp06YBhKnh66zfUyRzW5H6OsFk2N3WylYnHk4yvFUTFRF2ACiKev75559//vmqnY1EHWTs2LFr1qxZv359fn5+gwYNxo8f/8EHH1je2God84WBsVI+PB/Ayq6qH4R6fNP7F5yap827bbmWKKHMKQvnNc6q6LmMsRO48pvsymbCqngSwIBs7ZzURs+3v5YpZxswdD7NmQi+3PutDYwlFjvrG0m+HnnFKNBDq6qNaVUaMeV/9uzZzZqV6S3j7+8P4N69e7UzLQkJV9hVdYIgmMcrpuoAgCBNwW+wfiMJ4w1F6r8JNsfuto48sJKqqyYqKOwkHh5efPHFF198sbZn4RiW7Zy4KpXofkTb3UrYCUU6QhCUfK/LHde0DAaAe/kwEXKV3k6LerO0uuDlMOYXzmPsRFdsOS9VoqwUEyYYUpDxhJoj1Dy56mKzML18UI7PhobZgQydJfOYXAQDycGexU5MRj6bjt4ta35SVYBYTNHLy8tqPC0tDa4KfEpI1EHcv1hdvHjR9hsuCOj1zJakM36EMVWZNqe8qk6i+ij3xej+/ftJSUlJSUkHDx60WnTgwAFxUWU6E0hIlAshP88v4/KjeQcBcDzKWMt0hQAKKS1d+jWnKRhJBcXaEUleHAGgiOJuqex3M3PZeaLEYlehlmJqjmQJYWDXiyaCV3Hk29dDwvRyAN0KvCgBfiY6S+agXUZdQowLNBAPYuwetLstbdG7v8qaDNc0YnnO7du3Ww7qdLqVK1eitMmyhET9wJx/SpKk3WxIXsA3SY2SzviRxhvKWzMJxrpBkUhVqboKhOVVCZWv9lcrlNtit3z5cjG+as6cOZZ1KwBs27ZNrEbx3nvvzZ8/v6qmKCHhDJYF0MSYJhdMJkLOcFCUfqmFwkIABZRGVjoio1BEylSsHemm5igAx7VFvNOGCS7r2IEvccWW67LjxZNFFH/MR1dMC+EGxaxbwXcVTKCJjsr38mNpEkSWnEVZnVQ3EQYnH0i8Dizrljvpc4w0j3M8WA4ArmSg2AS13OEe6ix9+vS5devWt99+q9OVdL1ctGhRYmKi6KK1uhhKSNQnpk+fHh8fb37L8cT/doYcv6pt0Uj/5ihGrfjU7lZVa6ureb9t5av91Rblttjt2bNHfGHrnnvhhRfEF0lJSZWcloSEu7AMAFrgmhlTUTbMTiix2HmbLXa+B6Mz5LdZPtc2vVR0xX7R1E6kVImccp0VKwq7ivzyNSxVRHMADATnz9BygZzT6tZ5jb5joVdjgxzAk+GP1X1VJ0LLadjUsTNYvMstquEZVQ2vvPKKeKHfvHmzOLJq1SpR1ZEk+corr9Tm5CQkHFNec5eloLGtgMHxxIpdIcevaiOC9XNGpakV9pO66qUH1lPsduUWdtevXwfg5eXVqlUrq0WtW7cWu8TeuHGjSiYnIeESgS3xUbY2XIdV/oROtNh5yyigNJqtiOLFFFSzthMby4qu2ISAAvGt5T/LwzntFUtANNi5lHYcd+ijf73+zVPmOXhxZBHJAyimBQAX1G03B+ee0BYpeeLZe/4A5uVsdP1Z1A1oRUmBYsuPId8iIyXPYUPwOk2vXr3mzZtnd9H777/ftWvXGp6PhIT7CIJgFnPum51sVR3DEV9sb3zsirZViP4/o1O9HiZV50GU2xWbnZ2N0m+JlXo1f12ysrKqZHISEq7hSoRdhP46fEv8fSJCYQGAAlorox7IOD3F0wKh4AkxedNMB6ItQ2ScaHvY0XHc7TzhKiuWSIzuoFOlFHTQUdznYSUGQjVHpSpNAHJpliE0PwWPAdYf1xZNvR34QnoQgL/8C8UOY852XTeQlVjsyrQU++cOAIT44k4edPaDGD2ABQsWtG3bdtGiRWfOnBFHOnbs+Pbbbz/77LO1OzE3ceeOLpRSA/NBDfrXatKJVmdPiud597eyUnWCIDAs8fmO0L9vato0LnrjyTSlzP5DbNu2bSv2CdTYd088RMWOVd5NavgHJVJuYadUKhmGKS4uvnbtmrnjp8iVK1eKi4sBqFSeWc9AwhNhSoRdmP4GAYHlLB42Sl2xMovSimKxYi+OMpJl0hFIxmQi5UqZi6M5S54gSurYuZxy6yIlgECm5GC0QCh5woePFAavzml+d9kfpkxZIIATPkUAvDkqn+ZStB7jv6QVNAC6rCv22n0A+EgbDS3WXgOuAZ4QMmjLuHHjxo0bV1BQkJub6+/v7+3tXdszcgtBEHieZ1nXKTjivd+dNasEMde4uhHPvcb8aDXz6QmCUE0HMplMtgkTRQYs/b3p5TvqRyIKXhmWRlMCb3Opa926NSpx+hzHufktrRJ4nudtz8EpJpOpvNMTBIHjOHe2sjWWVZhyC7vGjRtfvHgRwNtvv/3zzz+bU6B5np87d655nSqZnISEa0otdipWH2K8y3Ah5iUlMXa0hi4j7HgAao7MKavhKM6oJ5wJO5edJwhK7Dwh2LXY3c5FThEiQwGgdbEKpX1sUVpC74rqZBvAP6JRcRCQDwAXvPTFJK/myf1+BSzhGUG7AGRK0RVbxmKXU4S1YdZxjZ5ig7RFq9W67JVc1xAzHN1Zzc01K4loxqiZMjHiGdWAsKvJk0K1Fdm5ePHiypUrp0+fLr5dsWJFsZH+bEvTG/dVUS0KXhmWTpGATXPuNm3aVPK44t+oxr57LnNdWZaladrybQWOJZ6Rmz+9CuzfLuUWdtHR0aKw27RpU69evSZPnhwWFpaamrp27dpjx46Z16mq+UlIuIBhANyTBTdkMpobrrN8GWHHkjIDpaIpCIOTRW+sXhR2NlXWKNZkIrWVstiV7TxhJcTWHEBaNlY9DwAti5QAAkwUJYAj4MVSAIooXpzhmuDkIiMAsIRwWlvUK8/7T/8CV59CHYKWl8TYWQ4W6AEPzIS1ZP/+/QkJCQBeeumlsLAwcTA1NfWrr74CMGTIkEcffbQ25+cU8R7mTlsg8SZUAw2EOI4TNVAN6C3x3GvgQOK9v2baL7n5By0v//zzj6hCxDRYQRDyi4jFW8NvZSr7tM1/cfAdkiRsVV2VBNVxHFcz3z2e58VjudRblXehisKuhjtylVvYTZo0ae3ateLro0ePHj161HadyZMnV3JaEhJuIiZPXFK3apif0Vx/neUsqk4UFhbJvGVUyUVI1HZFpOiKJa1sRTRnMsoVvq5+EM5i7CgSwHfGzzpgq9UihsOtLPAC9AxQ6oolQQQwsvtyJqpQDeCaqqQw8vN9MIHH51QykRj9p39htwLNHw0K4DmOS0pGA5DxZYRdvh7wqaUJVRFLly797bffWrRoYVnLKTQ0dOPGjZcvX7548WJdFnYSEu5g214ir4he9GvT2zmKR9vl/WvQXbsXQClVoq5Rbptnv379nnzySScrjB492jaVRkKiuuBYAKnKJgylCDOkPciKFQShSKejveUWT0rC4ORIdjyAlC5fld0JR/KciZQrKmqxIxKjZ9z4AADFEzMFa4t1vr6kCkqxCcKgw62KS4JQg40yAE9k+gHY2SDffAgxKJAblPzu1I0N+566PnKvp6g6AJSc4gmSFljzo64glCl34qGcOHECwJAhQywfvkmSHDBggHmphITnYqvqcnX0J5ub3s5RDOqU88JgSdV5DBVxZn/77bf9+vWzu2jgwIFme56ERE3AsAAYUm6SKeW8yZwVKxQXgecLKG95WSMcT8sAgDGVGTUZARgJudyxvVxUdHadOaILlQMPQNyBVTxZYWmxjwI9TNl5Go4UCABoW6yiBDyW6ZslY4/6FFrtliQglxG6YdYtXuo4MgoMQdMCY3Zi6BkIApYK1trUg9QqALGhTsOGDa3GxY4UUq9YCY/GVtXlFNIfb256N1cxtHP2hEfv2r30SaqublIRYefj47Nnz55vv/128ODBgYGBNE0HBgYOGTLku+++S0hI8JQ0MYl6AscCYEDzpEwmsKw5yalQzJywFnYcrQAAUxlhJ5hMABhK4TLC1UmMnZiPSwl21igoFXafJ2L4nmcBJPkXAHg807dnvncDht4ZmMd5RuVL19AkWEIm4x8EGhebANjpNmFbJrouIxrqLl++bDV+6dIl1FRYlYREdWCr6u7nyz7cGH4vVz6ye9a4GEnVeRjljrETIUly0qRJkyZNqtrZSEiUG4YBwJI0T9EyFJldsWJKbF7ZWicAOJkcAG80lZFwJhMAlnTqiHWVFVsi7OwtMpSKHJ0BraEC8FNwVpsi5YhMH7H8yu+BeSXT9igjll1oCkaSlllY7IqNADCN8SQZZ0toaOjly5d//vnnWbNmRUVFiYNHjx7duHGjuLRWZychUUFsVV1GnvyTzWHZhbLYnlmjety3WxJEUnV1mRrKypaQqCYE0WJH0jxJy3j2QYFiXQGAfMLGFUvJAXAmO67YEmOeU+wKO1GNcQQPS4udRTYVY5Em/2TGeACXNYZvQ7K8OGrKnUA9xe8JyEe9UHUAaAoMZDLhwTl7aKsJK8TcCIZhevXq9fTTT7/11ltPP/10nz59GIYxL5WQ8CxsVd3tbMWHvzTNLpQ90+f+Uz0z7W4lqbo6jmuLncFQkqwnl8tJkjS/dY5SqazUvCQk3IRhAMjktFxB03jgihV0OgC2MXaQywFwBjuuWJZ2VpDDZecJS1fslNRkRDxYZLIowspTOwDtJbUho1HmO9cbkQKxx7+gmCxfncy6jIwEQ8g00JmVbY7HFFd2xowZM9asWcPzPMMwmzZtslxEUZTUK1bC4xBV3bRp08S38fHxaVmKTzeHFejpcTH3h3fNtruVpOrqPq4tdqpStm3bZvnWOdU/cwkJAPjy5gYAPwa8dJI5SwgCV+qLFQoLARTYxNjxtByAYCzb1spkMpEZZ+oAACAASURBVC9yjiNXrDA4+dfIZQAeF16IY60Nb5YWu9bFyrsKpoDmUlWmPQGFAH4NynV5XA+CosAQtIx/4Iq1bBTruXTu3Pmjjz6yu2jhwoWdO3eu4flISFQGK1UH4MVZS//7bWChgZ4y8J6k6jwayRUr4cEQidEKjgBgIHkDKQDgjKV1NXSFAAoorVWiqyBa7Ixlym8IJiMA3qkrtsRi56zzBAkA9tonmi12mxr3aWSUX1KXKJ3X29yc0fbWT43sX0M9FJoER8pogSNKvdFlzaMezH/+85+NGzdaarjOnTtv3LjxP//5Ty3OSkKivNiqOl7V3th0iUB6TRlwd0CknUfNtm3bSqrOU6hg8oSERK0j5lQqeQqAgeKNJA+AK+36IhQWwF5WLGQKlCo5M3qdSQZQSjcsdo4fhUhKFH12lJ2p1GLXpkhJCLjoVSLsrqmM1xrfL5lwvQiwE2FJGgDFMWK7Cb2DInaeeMpjxowZM2ZMYWFhTk6OB/WKlZAwYxtXx6s6GMPiBEIpv7Owf+TTtptUvl2YRE0iWewkPBs5TwAwEoJBFHalXk9BVyhQdDGhsq45LJejNKjOjE5nAqD0qmDyhEhJSzHOTksxs7D7162PAVxWG6229USJ4wSWkAEgS9v46k0AkNOr/pyjt7d306ZNJVUn4XHYqjrOq6ux6RKBUMrvfEgXJNlu0rp16xqZmkSV4dpiV1hYUjdVjJwzv5WQqAsoRWFH8iaSB8CbNZSukFN5CQRhXXNYJsbYlRF2n91a8glC/X0rVe6EJEkOgMDbumLNRViCmfsALns9yECqZ5JOhKVkACixXawgDDjxtZZoqZT3FwYn38rG/N8woC0m9KrlSbrDuXPnAKhUqubNm4uvndChQ4camZSERAWxUnXx8fEv/nulKfRjATL5nY/pgj9tN2nbti3LsrbjEnUZ18JOo9E4eSshUVuIvV8VAgnARApGUgAgMCViQtAVFnqHAFCU/Y4TcjnwoPOE6M+dx4UA+Hf2fxIS850rLZcxdoRgp220sfTC2JC5D6BT/oYmRX6D22NcT7fO1OPgSlyxLAAYDeFZ57xkGUq6PwBfFeA56RSRkZEAunbtmpKSIr52QuX7hUtIVB+2trrzqV5882UCwyvuLKAK9sbHx1ut0LZtW+lb7YmUO8Zu+fLlgiAIghARETFixIjqmJOEhJsIg5MTT0yBRfLE/OIJoxKLhd57wHF6uTcATdnCO4RSDoAo64pVcxQAHe2s5ojL5AmSJAAQvB2LnWhGJEk0NN1jKEUu5QvAOvivHsGTMgA0zwAQWBZAIJOJokJovL1VIIl6UtlOQsJTsFV1x696r9jVmCTw5lN3OoY/CzxrtYKUKuG5lDvGbubMma+99tqsWbPERjoSErVLjKajQBAmQhCTJ+QcAaBtwggAorCLLNsRgKJlPEESbJkoNy+OBFBMcXDV58pua50SxBg7wU7yhGix81XwgaZMvU+QQBAAZPW3B1WJxY5niMToZnufEAdH/P40AJKAtxL5krCTkKgpbFXdiWslqm7WiPSO4XbqTEqqzqMpt9EgICAgOzsbQPPmzathPhIS5YNnWYbgBQKiK1b0zAYwNIAi2huArOx3XEaDgQymMomapcLOqcVOjLFz8ihElLhibU12osWuCZlNCxznHyQO1mOLHUfLABAcC0DFlXxkUfkaIjFaGJz8WEd4indny5YtAPz8/MyvJSQ8C1tVd+SydtUfITQl/Htkevsmkqqrh5T73tK7d2+xUnFaWlo1zEdCopywjGirMxE8ACVHAvBjKAAGmRoAXVaK0SRMpFzBlLHYqTkSgI7k4SqbwYkrtmSZYEcdGhnQFEKZewDIoGBFHows6PprseNJGsB3spcBqPiSj6xnQUl47hDPyTEYNWqU3dcSEh6Brao7+I/P14khcpqf82Ra68Z2LOeSqqsHlNsV+84779A0DWDt2rUmU30pPCrhubCsiRQAGCgepRY7H4YCUEzaE3YUjKScKE2eEGWchqdQ6op1DuXKFUvYc8WaOChoBBozAFBBwd5KAPCuv133BIpGqchWPrDYeZFw8vHVdaKiorp27dq9e/eionrRIk2ivmOr6v445f9VQoiXgpv39C1J1dVjyi3sOnXqtH79epVKderUqf79++/cuTMtLc1gQ3XMVULCDixL8YEAjIQAQMETAHw5GoCBUgKgyhrGZBSMpILkHjyTCIOT2xgfAZA79C+XxUecxdiVuGLtZE+YWMgphHKZALybBPtrAMBP7c7peSQCJQOg4ImOOvWkuw3EQS1LtSryYDF7/vz5kydPGo1GLy+v2p6LhIQLbFXd1mMNfvgr2NeLnff0raaBdu7RkqqrN5TbFWvZB/bw4cOPP/643dWkHGmJmoFgGZZU/+STvIccCUDOk8LgZC7pD/bSziJ7FjsZBYaUkSYTBMEs0+SsgSMoaw1odSAAzmPsRIudnaRYsBzkNJrwGTxByIMDG/rgyj00qL/VbTlKBkDJE+9dazwi0xdAloxtwNDnQj+p7alVnDZt2pw+fVp6apWo+9iqup8PBm1PCfDTMG+PTm3kZ+1qkyRdPUPqPCHh4XAsQ8hkJFZGvgMgtvhtAIJBD6CYUpGkdVQcTcIIBSEIMDcfy8kO1t+55xVqvWd7OIuxo2kAlMDYSjuWB01ByMwg/Pwhk42JwryR8K+/dh+BpgEoeFLMYgGw368AgJB2qzanVTlef/11AFevXj158mRtz0VCwiG2qu6ng0HbUwKCfJh3x96SVN3DQP1NzJN4OCBYlpXTNAWCkIlvAUBfDKCIUNE2Ty4yGkVkSVcxQiYDwB09TEA4Gdw7wsWRAOcFiuUKAHLWWNJSzELesRy0XKFQVEQ2CQegUVpX16tniK5YFU+KWSwADvvqHs/yU966UavzqhRdu3adMGHC999//9hjj7311lvdu3cXs2XNSJ0nJGodK1UnCFi/r2HSGb9gX9Pbo1MDvK3bNkuqrl4iCTsJT4bnCZ5jCZqmAJJGaX9SQa8HoCPVtsKOJmEkzc0nvMBxfMqRYkp1LbirOwd0ZrGTywHIeJNtGALDI8iUAYAICnbrvDwcgqYBKHnSv9Rilyfj/tYUd8u4C5MRctc9eesg5s4T9+/fnzNnju0KUvyJRO1ipep4Ad/+2ejPs76NA4xvj071UVt3BpNUXX2l3MJO6hUrUYfgOAAMQdNkiSdUrJ22786hR+E933fIjOzDVlvIKDCisDOZAPAXzgq6whSfPpTcaaNYNzpPgCRZkqZ5k9XtnRfA8wgy3gdABAaV4+w8F5kMgJIjfEstdgaS7xk5nDv0F5+eSka0rNXJSUjUQ2xUHRG/u9Hhiz5hgca5salalaTqHiLKLeykXrESdQiWAVBisaNlAMCxRGL0UbZ9EcUxpLAsMHpZYpnSdGK5EwAwGQFwx5IB7PftE+jGT4FwnhULMIRczhutDDcsDwABelHYPRwWOxkNoH/+JLlQ0lb850c+hcnIHYKQeguSsJOQqFJsVd3KP0KOXNJGBOvfik3zUlgXcpJUXf2m3MKuqKgoOzvb19dXq9VWx4QkJNxH7EPKEDIZVWKxO++1DoAfS+fL7Belk1EwQQFAMJmQm8NfuciFhN1WhIS6MNiBIMR6Js5gKbmcZ6yFHQcA/sUPlStWBkBjKngwJJMRwY0A8Gk3PbQws9R5QqJuYqXqGI5YviP05HVNi0b6N0elqSVV9/BRDmH3+++/f/jhhykpKWIoSdu2bWfPnv2vf/2r2uYmIeEKlgXAErSMKonrUggEAB+Guq2wXz1bJYeJkgEAYyo4mKwUhOLIXrgBuRtyw1l1YgAAQ8kVJp2VK1YUdn76DELtRXg9FAZvUi4DoGXyHwzJ5ERAA0LjLdy6WVuzqiRS5wmJOoi1qmOJz7eHnrmpadek+I2RaQqZdSMcSdU9DLhb7uSLL74YOXLk8ePHzQHC//zzzwsvvPDaa69V29wkJJxBJEa32T8awEXv3dHXo1Ha7YAE4c2Sjix2KjlMhAIADAbuxDEjKf+T7gJA4cYzjrMAOwAAS8rlfEm5E7O8YzjQAuutz3lYAuxKXbFa7oHFTpTdRGgTobBAyM+rtZnVOHv37p0yZUrLli3VanXz5s1nzJhx//59J+vfv39/4sSJ/v7+Go1m2LBhVrdt50slPA6ilIptbvUFMDBk3G9hZ25qOoQVzX5SUnUPL24Ju8uXL9vNAgPw5ZdfJiYmVumUJCScQSRGi/8AKHkSgIEUALQ+Nh5AB90IH5YkQeTR1sHCInIKDCUHYDp9ykufe9w76lqBCoC8SoQdJZcJjMCWuZ6yPIJN9wmBJ4Iauj5GvYCUyVBW2EEmB0CGNQMgeFrRE57nV69ePXTo0I4dOw4dOnT16tU8b6cjsF3mz59/+vTpSZMmrVy5csyYMevWrevZs6ejFDSGYYYMGbJnz57FixevWbPm7t27/fr1y8jIcGephMdhqecqIO+uX79u+dZgIhf/1uSfdHWXiMI3nkyT05Kqe3hxyxUbHx/PMNb1b8x88cUXgwcPrropSUg4RNRzZsQGYkaSB2AkeAAUz/owNIA8usRiZ6dLmEwOAP+cBXBY26NID7hhsSOct50AALC0AgDBmoAHdeoMDIIZMXMi0MX29QW1mgKg5vQPhmQyAESTMADc4f1ERAtC4zGdN5599tmNGzeKr8+ePZuQkPDnn3/++OOP7mwbHx/funVr89tOnTqNHz9+48aNU6dOtV35xx9/PHPmzJ9//tm/f38AvXr1at68eVxc3OLFi10ulfAsKmylEze8cOGC5WCxkfy/rWGX76i6tSh85bHbNGVdeUdSdQ8Vblns9u3bZ349ceLE5cuXz5gxQyYriTbfv3+/VMBJogawUnUAAkwyALk0C6CY5gFkKPf4shSAfJoFsKuhnd6vpEIBgBD424qQG6pmecWAGxY7krTuTmYLS4tFksuE9+lNaGjMwEOTEgugWwubT1MmA0A2aQqC4G9c48+cqoVpVYhNmzaZVZ2ZDRs2uJlLYanqAPTr1w/A7du37a68bdu2xo0bi7oNQGho6MCBA3/77Td3lkrUe8xWvZiYmGnTpr3xxhviuM5AfbK56eU7qt5t8l8dni6pOgm3hN21a9fEF7Nnz16/fv2MGTOWL1/+5ZdfioMFBQWZmZnVNUEJCceEGeQAbqlMALJp1kgKjQ1yX5YGkCvjADx2z1oLAmC8fMQXB3x6AzCxAKBwlRU7IRpTYlysw5MKABRTRtgZGAQzYkrsw+WKtUTMk4VKLeYFC0UeUw7z22+/tTu+bt26Cuzt0KFDsKh1bMX58+fbt29vOdKhQ4fr16+LDWqdL5V4SIiJKXMZKiimPtkcdvO+8tH2+S8NuWPrVZBU3UOIW65Yc0RIbGyseXD06NEvv/yy+Do/Pz8o6GEJDJeoOzQ2ygGkK00A+CHJFw+/FGqU+zAUgAKbEBMz73tPGo9OeopP0T7oNqGSuzhW2xDX82FpOQCKM1n+sPQmBJvu8yRF+Ae43kX9gLbJMZaVfCJkeHMu4x6Kimp6ShXlxIkT4ouvv/561KhRmzdvFq975nH3ycnJefPNN7t06fLEE084WqFz586WI35+foIg5ObmNmrUyPlS82BycvLMmTOt9tykSZMxY8YYjUaXk2RZtjLh/O7D8zzP8xzH1cyxjEZjDRyI4zhBENwJwTQYDEqldWNBl38gK1X30oz/+nbfcDdXMTAye/yjd3gelkdu1aqVO/t0jiAIHMeRJEm6jESpNCzLsixbyQm7g3hSFEXVzFfCZDK549UUBKGq5uOWsDN/Tf39/c2DDRo0ML/mOPsZiBISVYgwONnKGxumlwN4r/H3e3sHAkhXMn3ylBPuBgBwlDxBJEZTCpzyLt4TkL88fOC/0pPFb7eXK2HnDpxMDkAuWLlihWBThknbQFX9V8Y6Ar23bxG6PXhPEGLOMgBqwBD+Tjqh9amdmZWfrKwsAFFRUS+88AKAadOmffXVVydPniyvm0Kv18fGxhYVFSUlJVFUNdby0+v1VmH1AGQymXgzc7k5z/MEQdTAJZ3nefFuVwM3V/Hca0ZBuvk521JUVOR8QytVJ9CBhqbL7uYqBnfKeqbXbUEo05+6ZcuWVfJHFHWqIAg1EHAlyuIa+O6ZxXdNPsBU94EskXrFSngSVtqun6G7gOvdO5f0Yk9XmAgBT2T6oVTY3etRJsZO3JYjEN3jvDjyTWj0lNRkAF5V0b+Uo+QAFPwDYcdfPN/x951K3pjv/7AE2BGJ0SDAEgItlF40aZm5ZQfh6yd/dXatTa78iHljYWFh5pHw8PCTJ0+aTPYLJdrFaDQ+9dRTp0+f3rt3b4sWLRyt5ufnl5dXphZMbm4uQRC+vr4ul5oZMGBATk6O1Z7v3bu3d+9etVrtzmxJkrQ1JlU5HMfxPE/TdA3cXA0Gg1qtroEDsSwrCILMJhTBLuWSSlaVTURVJ8ibDO6UO7FfJkGUeTCtQverIAgsy5IkWa1PIyImk8loNLr5La0MotKiKKoGzJAcxykUCrncteWgCr+f5RN2S5YssTTaORn/9NNPKzUvCQkHpEUlL9oBjsd/n4Di4AeFlLePkgZAJEYvUIaaV8uTcQAC3c68rBJhJ4gWO/6BK4G//I93ThoAk+9DIezMsttI8jRXcicg3LvP1WWsKlOUa1uTyTRmzJhDhw4lJCR06dLFyZrt27c/fvy45ci5c+ciIiJUKpXLpRL1GFHVxcfHT5s2DQAva2gMWybIQ4Z3zXm2T4bV91EKqpMon7D7+uuv3RyXhJ1ENXH9PoqMAHDkCj/cmJembNKg9ElSDLYTEQsUU0nRdsqd2KNKhB1PywEohP9n78zjm6jW//+Zmexp0n3fkAIt+ypYoKAgiqDs1w0F8SpuuIALbl9/bqDcKqD3ehUXXMCFi7IIilqgCEhBQJC9Ze2+p0uapFlm5vfHtCEkaUnaNGnS837xx/TMkznPyYTMJ88553lscgOZm2aELV0mYidgoqC0/iH2+5mBI0eOWJcUHznStKXX2iLw0UcfOb7QYrHceeedO3bs2LZtW3q6k608tkyZMmXjxo2///772LFjARQVFe3YsWPBggWunCX4L3Y/FewiebaxulWrVpXXSt76IalRK75lcOldGfa5vomqI4BMxRL8Dn2zeKuvqKN5zqIOs34pFtoIu1rGAqdJ7JxBU1B4Yo0dJ5YAENtE7ISiZ+iCwo7hYGlW3CJPvLk+5cKFC6tWrbJrtGtxKuwefvjhjRs3zp8/v7Ky8vvvvxcae/ToIWyD+O233yZNmvTVV1/dfffdAGbPnr18+fK77777zTffVCqVS5YsCQ4OtiaHb/0sIWCgKMq2wpPtqYJK6bINSdpG0V0Z5WPTimHz6wlE1RGa6SqruQkBQ0OzarJoagAYlaHWU4Wyy4qq1llJsZZ0nlIKzyxvEEsBSHkT0LSQmbeYAfAUxYV3CWFnfYeNtE3Uwf+nYtvM/v37AXz88cf/sOHTTz8Vzgprfay708RicVZW1vXXX79o0aJ58+bFxMTs2rXLuuO19bMEP6WVmX07VVdYJV22ManeILpjdMWkofbLKImqI1hxKWKXkpLS0X4QCC6ibxZvdF0NAHPQZWFX3Byx4ynUiSxOZZzd9ovPZDl7AZWHVorzYjHsdsVaLAC0tIqSd/hq9E6C8A4LhUAaaV7GUV1Z2J04caKVsxMnTrSbd4uOjv76669bsm/9LMHvcF3V5VfKlm1Iamhk5txQPmEgUXWE1nBJ2J07d66j/SAQXETXLOxUjRoArPryrp0aEatlWBXL1DMsS4HKcr7Azrbx2/0AEB7kGd94iRSAhLMXdqWSmKCuFBznJ+Tk730bKGkUhctMGr/ePGH3fCUQvIDdp+5UoWL5j4lmC/XIxOL01Ho7Y6LqCHaQNXYEP0NvQl/daXNoVIilFgAVfEWuh2KZKU0nL5O0WNrYDpkE8JywE6rQSh2EXbkkKrgrCTsAFloEQCcKCjFpIPJjYZeWluZrFwhdC7t8dScLlMt/TGA56tFbSkb0IqqOcHWIsCP4GaxO90TJR/oyi0bMApJbtbOrsNt6Nl9mStXL7xroaow5XAkAg5KuZucadFO6Exthx1pAUfuCr+vTxYQdR4sA6CVq6Lv0GjsCwS1OnTpl++dfF4L+81MCgCdvLRrcvcHOmKg6glOIsCP4GaKGOornlSyjZBk9zVVLzLZTrs+mFnyol51SGuDaltiMXugRjbiQqxq6hsQ+YsebjGZaki9Lojs8N2ongxEBMEhUABF2BIJz7KpI2cXqHnhmrTH+VbEYT91aNKCbvarr3r27N1wk+CFE2BH8DJG+HsCn8ZURZlGt6PLWV0HGUVnpeYpGuJzohKI8p+oASioBIIFNuhO9ziRRwFn11ACGykrfLOqVhOC1IesyuaF0YrKvPSIQOilOM5s89NBDrGqsMf41ijczF5754K0jdrl1evfu7VhfhEAQ6GLzQwQ/h+fxp+IZAMdV+jsHnHu4z0Wh3brRlZ+QI/zziXu0VAqbiB2VlW7Q1p4VFX+elB61/yrJaQMG4V4I6U7yZSbpoJ+Z0df72CcCoXNjt1uCDb7ZmPAaeJOk4Flad8TOmMzAElrH98KuoKBgxYoVkydPjouLk0gkISEh11133dtvv63Van3tGqHTYTBjcsVCABXiK7ZH+ErJ2SFs/xTW2I2+lK7gaBlH1YibchTbplkJeIw0B0AYe5caOIHgLnaqbvepEGPcC2ANssJnGcMxoVEoJgai6ggu4OOp2Nzc3N69e1tj0TKZrK6u7sCBAwcOHFi1alVWVlYrBbMJXRC9CWpWC6BcavG1L04QiWkzJbZungg1iwBonKVKDnjMNAegTtQVx04guI6dqss6Grrm9xiKrZcWPE035tqeIpKO4CI+jtiZzWaxWDx79uytW7fW1tYaDIba2tqPP/44JCTk0qVLM2bMsOZkJ3QF6gwobHXdiMEEtUULYO+4pjStPpx4dUREw0RLrAmKw0yCsOuMGrSjyQqv3x2qPacwXt2UQOiq2Km6X4+Erfk9JkjGygoW2qm63bt3g0BwDR9H7BISEs6dO5eYmGhtCQ4OfvDBByMiImbMmHH8+PE//vjDbqMQIYBZvRtnSvGfeyFuYauBwQQ1Vw+AUqk7j56zwtAw0RIp36Rmwi0iANViV5PqBRLfxlR/G1MtHHfCO0Ug+Bw7VffjnxHr90WGKC0vzCyIC3vGOvcKouoIbuJjYRcSEhIS4mRT4pQpU+RyucFgyMvLI8Ku61Cjh5lFWR0Sw5wb6E0IttSzIgkkUu+65hIMDSMkKrZpeWiYWQSgpotNxdoVbfNrVdd6QTA7+vXr13GeEAIMO1W3YX/Exv2RYUGW52fmx4aaAFi3wZIZWIK7eEDY1dfXr1ixYu/evQDGjh27aNEihULR/ssKqFQqT12K0PkRyoWV1l4WdtUNuFiJIcmgaQAwmJBo0ZoVap+52CoMDSMtibA0RezCzAyA6uapWL+WOG4RMCPt37+/68Z2VV8JBKc4Fqlbtzdq66HwyGDzCzPzI9VXBPiJqiO0AbeF3ddff/3kk08C+Oc//7ls2TKWZW+66aYDBw4IZ7dv375t27bff/9dJGqXZNy0aZPBYBCLxddff317rkPwL3SNAFBc2/Tn5cDPOQDgJ+Q0NrJKTqdXRPrCu6sjoqGnJSKeZXh2T3LOlksz0LwzNGC0DoFAaDOOqu7bPVE/Hw6PCja9OKsgXEVUHcEDuC2/cnJyqqurAUycOBHAli1brKpOYN++fWvXrr3vvvva7JNGo1m4cCGARx55JCoqytGgqKjo559/tm3R6XRt7o7QSTCzMLEAUK9vavnk1DUTq0L+F12deU1ZmcREZaX/qv6V4nlO2UnjuELEDoCENwHyhao5wM7nwj/YOoHkiCcQujp2qo7n8cXOmJ3HQ2NCTC/MKggLIqqO4BncFnZHjhwBIJPJRo0aBWDr1q2ONt9//32bhZ3JZJo5c2ZhYWHfvn3feustpzYnT560XVhKCAyMzZtHDWYAkP06sqx8iJylHyuM7maQzhx0FgCtrQeAoM47FWuipQAknCkjf9yq8mvmImK+4Z9nkO1r1whtYePGjb52gRAg2Kk6jscXO2Ozj4ckhBtfmFmgVlyxd56oOkJ7cFvYFRYWAkhNTZVIJAAOHToktH/66adRUVFTp07lef7YsWNt88ZsNt9xxx27du1KSkr66aefWlqr17dvX7v6KjqdbtGiRW3rlNBJMJoRbtZUi8MMJgAYXhckZ+kv46r66GS3VAWnGKTn5UaqQQuA6qwrLxkaJkoMYEvsVDSvsdOILLbVbAl+xLRp03ztAiEQcFB11Kpf4/adUSdFGhfPKFDLiaojeBK3hZ1Qny46OhoAz/Nnz54FoFAo5s2bR9P0oEGDjhw5UllZ2QZXLBbLXXfdtWnTpoSEhJ07dyYnt1hfMiEhYf78+bYt1dXVRNj5NVRW+oha5a5LfR5Lu7SIrlyInLE1agC/hdf+pRZfWxeUUaM6Lzcyhk4v7GgpACXLAAgzi3iqy+2KJRAItjiqug+3xe3PU3ePNjw3o1ApveL7gag6QvtxO0Gx0WgEYDabAZSUlOj1egA9evSgaRqAsCSuDVmFzWbz7bff/sMPP8THx2dnZ6ekpLh7BYK/M6pORfGYoAkGQGWlj9eoOfDZYVpBGAlS6eeGtwHcV7rUt662hIiGkZIAULA0gHCzqE7EWiiyWZJA6KLYqTozS723JWF/nrpnrGHxjAKi6ggdgdsRO7VardFojh49qtVqd+3aJTSmpqYKB4LOCw8Pd+uaJpPp9ttv37x5c1xcXHZ2Nikj1tUQdr/2aZADGF6rBKDg6KF1ihNBBo3YYqA5AHKWBhBpEgOoEJs65+QmQ8MkCDsLDSDEzNSKumLZiUCltrZ2xYoVrSzqSgAAIABJREFUv/zyS1FRkfAT15aqqiqfeEXotNipOgtLfbgt7q8LQd2jDU9PK1RIr4iAEFVH8BRuC7tevXrt37+/pqamR48e9fX1QuOQIUOEgwsXLgCIjHQjG4XJZJo1a9aWLVsEVdezZ093XSIEBv0bFADijZKERkmfBrmEp3eE1wHQMywAGUcDiDaJAZTJOqlaEipPAFByDAC1RVQubfS1UwTPoNVq09PTz5w542tHCP6BnaozWaj3tyb8fSmoV5zhmWkFcglRdYSOwu2p2FtuuUU4qKioaGxsemjddtttAEpLS4uLiwH07dvXxavZqbpevXq56w8hMBBzVJpOLhxfVxckLLD7PbQBgIHm0Ty5GWMUAajorEW6rOlOlCwl5igFR2sZssAuQHjvvfeIqiO4iJ2q0zUyb65P/vtS0JDuDc/PzCeqjtChuC3sFixYkJSUZNsye/ZsQclt2bJFaBkxYoSLV8vKyhJeVVNTM3LkyAgH/vWvf7nrIcHv4CfkpBhkUo4qkpkADKtXjqlRWSh+X4gWQCPDAZBzNIAos7hexBoYtxdxegcR0zQVK2cZFcsAqBd1UlcJ7iJ8UzEMM336dKFlzpw5Qp3rjIwMkoCJYMVO1emNTOamxIvl8gHddI9NKhIzVyy6JaqO4HHcnooNCwvbv3//smXLDh8+LJVKJ06cKBSiAGAymYRvt5tvvtnFq1mL8BgMBoPB4GggLNojBDwnu79rzvn8q9iq5y/G3lgd3Fsn/0vdUC9iAeht1thFG8UVEjM6ayEHEQ1WJETs6GZhRyJ2AUJeXh6Axx57bMmSJUKpwy+//FKn091www3Hjx//7LPPfO0goVOQm5vLMIz1z4ZGZtmGpEsVssHdGx6fTFQdwRu0pfBXbGzsypUrHdsXLFjg7qVuvfVWUmCRAIArLQawL6ThpMrQX6sAsD2sHsCbjTkvjtWYcl6Vc/SD+XtCLYsiElL5CR/72N2W4cVNwi7YIgg7Uk8sQNBqtQBGjRpFUZTQwrKsUql8/PHH58yZ8/LLL69bt86nDhJ8T25uru2fmgbR2z8kl9ZIMvrUPXBjCX3lDBlRdYQOwu2pWAKhI+BLSwAcD9IfUDdVh/s9TAsgPAjR+6YCkLP0tujxFA+oOmnZCQFeIgVwXe39dxR/DOCRHrOJqgsM5HI5AJlMJpPJhJbz588DsFgsAHbu3OlD3widAbsZ2Mo68Zv/61ZaIxnTt/aBCUTVEbwHEXaETgFfXlotMZdLzQeDGwAYaf6AugHAI5b0RoYHIOfoKLMYABXUSbMTC9BiCYBzyjV7wxcAoKRSX3tE8AyhoaEAKisrGYYRMjrNmzdv+fLlr776KsiikS6PnaqrqJMs+T65sl580yDNAzeW0tQVxkTVETqUNgq7/fv3P/jgg4MHD05KSopxhme9JAQ4PM/X1lDKngAOBOsAHAhusO6QEPLYKVgq2igG8Er5t75z9Or8WzUPgJKl1RYawOOXPvC1RwTP0K1bNzRH6YQET/v27Xv66acLCgoAkB39XRk7VVeskb7xv+RqrfiWIZp7xpZTRNURvEtb1ti99dZbL774osddIXRZeL0eHNcgCppXkPNFYvqzvQr2B+uEGUwqK91C8SaKk3F0lEkEoEzSSXOdAKCy0q9hZADmlUTOqAgDUE/SnQQKAwYM2LNnz8aNG5cuXXr//fdnZWXZnn3qqad85ZiL8DzvSk0gnuddtGy/PwA4jqPshE/H9NVxHdklwSnWyJZtTKrXi6aPqJx+XSUA22XkaWlpnnpvvXmbvNMXx3GBNyihFy90ZIvbwu7gwYMvvfRSR7hC6LroGgDU0UEUBcuNObRDHNnA8AqOjjGKAXw6opPWExPQMSwAikeImeHAn1Xa1ycg+CmvvPLKAw88AIDjuDvvvPPMmTOZmZl6vV4ulz/99NNz5szxtYOtIcg1lr36zwzhCeSKpUfwzgNPGHtHCDu73RIFVbJ3NnWrN4huH1U+aUil3eBSU1M9+8Z67Ta5+OFpJ4IACrzPnouD4nneUx9Rt4XdJ598QvaxEjwLr9MCKDEHRathp+r4CTlUVrqBZuUsPV4oIxsS6hMnXUTfHKL7Orb6qbR8kqA4YIiKihJqYQu8+uqrL7zwQnl5eVRUlHU7RaeFoiiKomzTcLQETdM0Tbti2U6E8AxN016I2Alj93hHZ86coW2+sC6UyzM3JulNzJ2jyycNrbZd6ZSWlubZrgW8cJuswU7a8Qe3p2FZ1jufPWFQXvvsuTgoDzrjtrDLyWna4vf444/LZLLMzEwAf/zxx6FDh15//fWamprMzMwxY8Z4yj9Cl0CnA7Ar/ItR3aY4nuQn5OgOvsbUa7oZpHT3HlRsvNf9cxV+Qg6TNZKnQPFopFmi6gIbqVRql629M+Pis1mQgF54igtBRG8+XD3b0enTp20veLZU/s6mpEYTPef6khsH1gGXT3XQojqv3SavCTvhHnmhIyFW552+hF680JEtbgs7YaVweHj4ypUrP/64KZ3YyJEjR44cOWbMmOHDh//f//3f0aNHPewmIXChstIfLIr8N7pViE2zatKR5STrm1gu4et4jqIl0273iZOus1q+r5FeIGfpRpoHyWAXQFx1FZ3T7J6EzoCwi1nAIzNOdrslThUqlv+YaLbQ940rGdNHA1wOz5CtEgTv47aw0+l0AHr37m37A8hsNovF4kGDBg0ePPjPP/984403vvrqKw97SvB/qKx04cBO7kSYxQCqJS3Ht0RiAKKhw6nozr7hWsyAgwrQ3Si7m58w1dfuEDzGe++917oBEXadE7tAHUVR7dR2dqruZIFy+Y8JFpZ6eGLxiJ61ttcmqo7gE9wODwpZOhUKBQDrypILFy4IB8J/mB07dnjMQYLfwh46wP5++ZNgVXW2x8JBhEkEoKrl7a5USCgUCtHEWzvKV88hFoGlGACUqC1bzgkEQkfTnjlZO1V35ILq3c2JLEc9NqkkPbX+kUceefTRR4VTRNURfIXbz56QkJCGhobKykoAkZGRQuPixYuXLl2ak5Nz8OBBAFVVVZ71kuB3UFnpp/YNiDKKI0wvCxsgHA2scTshYlcpsbR0NfGdc2AxQ67oOIc9hZhpEnY0EXaBRXBwsO2fRqOxsbFROFYqlSJyu7sAdqru8HnVf36Opyg8dWvRh2/PtlYLfuihh/bs2UN2GRJ8hdsROyH5cFlZGYC+ffsKjZs3b+7bt6+QCwBXLmgg+BwqK13459UeecQ1SiRc059C+2cnu99XEmFrKWi7SLMIQJW4RWEHsdgvVB0ACQMWNABKIva1LwRPUnslBoOhoKBg/vz5AK699tqKigpfO0hwlTZIrtOnT9upuoPn1P/+KZ6m8NRtRYOuabA9tWfPnva6SCC0A7eF3YABAwCUlpbm5uYmJydbtZ0tN910kwdcI3gCuwlQr8m7MLNYxlFi0FTzV2iImZldGv5IYbSdJT8hJ8Io0tOckAHO33cbTK5IrxEXA/hA+76vfSF0LImJiatWrRoyZMiuXbtWrFjha3cIHYWdpAOw93Twf36OF4v4Z6cXDEh2ruq8sNuXQHCK28Ju1KhRycnJycnJ2dnZAJYuXWr38Q0PD3/ttdc85iChHbgl4xrN+OU46gye6TrOKAZA8RA3f8ZULAMgTSenccUHxsIhuTHWKIqoSs/xd1UnvOFmigdgZHhvRkkJviI5ORnA6tWrfe0IwTl28Tl3w3WOqm73qZBPsuKkIu7ZaQVp8VfUCCaxOkJnwO11Iffff//9999v/XPKlCkbN25csmTJ8ePHJRLJjTfeuGzZMuGbjuBf/Hocm49g5ym89Q8w7ci5I6iZeKNE+FPCwcSAn5DTf+M4AFKOCjMzVWKLVcNV1vHBbEOtMj48qL1D8C1WGWemeQCNFEliF1BUVVXZpkPjed5gMOzfv//XX38FcOnSJR/7R2iZ6urq0NBQd0NoFEVlZGRY/1y1ahWAPaeCP9seKxVxz0wr7BV3xe9gO1VH1tgRfIUHFvxOnTp16lSS1sHvqW8EgKoGVGoRE3w166shROwASDgaDEdlpbODvzbvXwlgQdH6/3ugKWsJlZUeYmbK+CGHRMed5Cb2T4SInYkhX+sBhXWvWBvOEvwOO1UH4KGHHrpjwbovd8bKJexzMwq7R1+h6nbv3m0rHFtSdXbikog/Qkfg1WzIBC/j1symsTnZSKW27T1ao1ZxzRE7Mdf0RXZLTlMWgB0hczT6y8bCltgqicXfJy6t7zZLA4CR9mrVZ4Jv+cc//uFrFwiexE7VATBHzPl8R2yw0vLKHfl2qk7IbMLzvNlsNplMLqo6py0EQvtpe8Tus88+MxicLMgaPnz48OHD2+ESwZPYpRppReo1Ngu7ek8ss4u3Ruz4pm8uNduUjT3aJI7Msc11IgJQ2XISO7/DDA5AI8XjyqwuhEBl2rRpS5cu9bUXBI/huK7OHHanOfIBtcLy7LSCuDCj7SmSr47Q2XBV2N16661CipOlS5cKm14XL15cXV3taDlkyJDDhw970EWCB2lFZ1iFXUNj269v1ZGX19hZhZ2lSdjFCvsqmuVmpEmM5lwngSGDhDV2RjIVG1g47nulaTokJGTEiBGpqak+cYnQEThRdRFzzZH/pCxVL82qiwsz2Z4iqo7QCXFJ2O3bt++nn34CEBwc7BigtuOvv/46duyYkBWF4HPs5jcfKoo68vqDgx58jYqNs23nOFTUNx3rrvg56jaCtou1RuzYK3bFAog2XZHgLdzMoLnsRACoOliFHZmKDSyuWiuWEABYVd2qVaseeughgDLFPGUJnU6bi5c/bAhXXTGx4BFV1/4SZwSCHS6tsRNUHYCJEycKJcVa5+eff26XU4QOY1JlSB+d3PTRSu7sGdt2jQ5VDVBKAaChfcIOgJylr9E3lZuLNjfJONXliJ3E1tg2YhcYmMEDMNE8AkWqEgAUFRUVFRU1NDRc3ZTgn9jF6j78aNV1t2+2hE6PDzO+/5i+/arO9bV3BEJ7cEnY7d+/XzgYM2aM49nxzXTv3l1oyckhD7NOyjUGqZHmTUa9fvUH7KED1nZtIwD8Jzr986T0ufr25jGeVhkaxNIsBQBTK0KFRutUbLRJ9MylWDb+Y35CzisNOUKh2M2j/hsAGkgYgkVId0JzATAigpXExMTExMSPPvrIrn3t2rXDhg0bNmyYT7wieAo7Vcfx1Kpf43YeD0mMML44qyBYccUvzzbH6oi2I3gBl4Td2bNnhYO0tDTHs9ubefvtt4UWxzUKhM4ADSq5UXpKaZgy5KyO4czff81u3waeB6BtxOdJV4i59mi7D2puAvD/UoosFD+8Tik0WoXdQK3yzXMJ5//3AYBaPa7X3QaACvLzLHbN8BNypseMA3AiY52vfSF4g7KyssOHD5OFxX6No6r78Je4fWeCkyIan5+Rr/aQqhMgE6+EjsYlYVdVVSUchIaGtmJmjdiRsomdB9ugUXyjWMpRF+WNu0Lrxw89XSQ1WbK2Wb7/BiyrdbZhom0lyPiqSkXJ+fPy7u90Kz2q0g9oUDxzKTbEzASxNAA9zUWaRACSDJJ+G8c1NCKYbwBAKQNE2AGgu6VQ8YmUOsTXjhC8QV1dna9dILQLO1VnZqn3tiTsz1WnxBhe+keBWnFFpnGyW4LQ+XFp84TJZLI7APDuu+/apTuh6SaZqNPpPOQeoV3YybJrDFIAFxRGACeDDGOuPXXp/K3soQN8Xd2k+G9a+iy4u1OVPZgDnt+rTv896akDuXcMq1e+eS6Bo3gh3ck5ReOABoVgOakqJLNb+mulM8AwkF197aa/wIwaw4xysmiB4I+8+uqrtn/+9ttvtsvs9Hr9F198AUAk8kCyd4L3sVd1Fuq9rQl/XwrqGWt4ZlqBQnrFFihPqTqe58ncK6HjcOnLKDg4WKPRADh37tyIESOExrlz59qZWYvqBAXKtJr/wnFgdlxWdbdWhuwPaRCE3UV5kzovuXU3jEbz16u53NPnq24d1G9bsdTk9GpuaDuW5Q7/aZHI/1IPfqgg/faQ8McKAeBGTbDCQnPgbYXdLVXB7ySXqk21lLrdlS4IhI7BrvJ1VlZWVlaWo1lCQoK3PCJ4DDtVZ7JQ721NPHZJ2SdRt2hKkVTcIapOwE7bkflZggdxaSo2JqapANSmTZtaMfvxxx+FA1Jdx+eU118+futswvd/9zy2r/+kqhAA+bKmXa/nKwCpVHzfQ8zwdFVNye4/e/fXKtrZL3f6JK+tL08euip5LIBTyqYK2eOq1YO1Sq2IK5U27Sw7EWQYUae6ri5IatRSiaS4MMG/uf32233tAsE97FRdo4nO3Jh07JKyX5Lu6akdq+oEeBs8fnFCV8YlYWetJLFhw4adO3c6tfnjjz/Wrl0rHJMNYj6ntHnZz5ga1cL8WI3YEmYWCRtUi2QmAPMKcr4R9jrTtGjmXad6T44zSXccThtXrW5Pv+zBfQAudWsKFhrpy19YUo46rTSUSc0AGhjuh2gNw2PJuQQAdErP9nRKIPgKkUjUrVu355577vXXX/e1LwRXOX36tJ2qM5jozE1JZ4oVg7trF00tlIg6XNURCB2HS8Lu5ptvFg44jrvttts++OADo/FyrjOj0bhq1aqJEydaLE1bh4TSFAQfMjSvSVolG6QAXulRtC9EC4ClUCgzAfg8KT2/CpbmZcFHU27+MvoeFS/Z/HevO8vC7K7m4jwsX1fL5Z2h4uLLlIlCi5lqEnaH1LqDat1dA84JEbtqsXlbRC2AkbUqAHR3IuwInRS7mEpmZqZtoMVsNl+8eHHZsmVSqdS3fhJcxDFpQ0Mj89YPyXkl8mE9tE9MLhZfWTOGqDqC3+HSGruZM2fGxcWVlJQA0Ov1CxYsePbZZwcOHBgcHFxXV3fs2DG9Xm81joqKuvPOOzvKX4KbRJib0v++0KPwweKo76NrdEyTmuN4fPI7avUIUQDAQfW1d94UzH793pKzid/FaNrQF3doPziOGT5Sb8K8gpzPk9LNzRG7Jd1LBCVXJjEDqJRY/g7SF0tN8UYJp1BRkVGeGCuB0IGMHz8eQHIyWTbgxziquno9s2xjUkGlbFRa3fybSugrYx1E1RH8EZeEnVgs/ve//z1z5kxri8FgsGYttmPFihUymcwz3hHahO1mWGvBrgMhugMhF+0sD14EAIZGagwASHv1ylHpr6sNkvC0iXKvIlZhFRe8d59UImEGX2vY29RovYix+aBUagJQKTE/WZWzNfLOh4qi1qvz55ANYoROz/bt233tQpfGbhtpG9alOaq6mgbR2xuSSzSSjD51D9xIVB0hQHBpKhbAjBkzXnnllauaLV68+O67726fSwRPEm4WAdCI2VZsWA5ldaBpSMVIUo2lQcU0Xlb8Ls7DHt2VJ9XX6lIGQiazpjtmm7+Kjc2zGxcVxjxF423D566MTN8UVQPg1/C6dha6IBC8wO7du19++eWXX365oKDA2lhQUCA07t6924e+BTyOyUHcTRfiqOo0WtHSH5JLNJIxfWsfmEBUHSFwcFXYAXjttde++OKLqCjns2aRkZGffvqptfgEwYdYpVhcoyS9NghAtci+EuuBlCvkmkYHhRgUYFaGAPic+vjXw6nPXYp9sd6JqnPUYTwQfz4HwA5l+h9ncaECL2tzAFinYk3Ni+30NDdg5HGR6WUA2WH1kTf89U1sldNrEgidihUrVixZsmTdunXx8fHWxoSEhPXr1y9ZsuT999/3oW+E1rlw4YJdS0Wd5I313cpqJBMGah64sZS+UiUSVUfwa9xLqjl37tyZM2du2LAhKyvr0qVLtbW1ISEh3bp1u/HGG2fMmKFSqTrIS0LbuLM8LE0n5yloxPbCLqyp0BfUctQbAEAuAQA2KARAaOHJPjXqsTXq93uXArEAanRQSiHNTgeQYpAyWSM58FYFWVLckFpzrFwc9aclhToCjsfgZEBzeSrWRF+e2OUn5FhlnJZpLZRIIHQehKJhN910E8Mw1kaapseNG5eXl0dKinVaHGN1ZbWSt75P0jSIJw7W3D2mnHJH1VkjhSRHCaHT4kbETiAoKGjOnDlr1qzZs2fP8ePH9+zZs2bNmrlz5xJV13ngck8LGelUFgZAPcOabdKO8BNy+Ak54c23a0DTBlao5QDAq0MARFXlCo1KgwaAhcWL3zepurE16pN/DLinJBw2YTb+r4Mint0Xcp1GT9XpAUAlAwBz8+fLmvfErSIWBELnQaiUaE3qaSU2NhZAWVmZ01eVlJQ88cQT6enpcrmcoihrFnenTJs2jXLAmm1q+/btdqciIiLaP67AxlHVFVVL3/hfsqZBPG1E1eyxbVR1wjGpHkHonJAyOAGHxWz+8uP/SVL6jDyuYhkA1ZKmcJ2tqApVIFSBGj1SY3CsEPWGpr2xUIcCCNc3PaWUjXUATCyMzSG/awwSALFGsfDnt/uR3gMhJ3JYitkfPIIHzCwA0FRTZI6lwPBoYFjb3m2DdgSCXyAE6vLy8uzac3NzrWcduXDhwrp166699tr09PTs7OzWu1i8ePE999xj/fP8+fPPP//8pEmTbG2WL1+emNj0a6zr5FhxrMHlSsDMUdUVVMmW/ZBYbxDdMbri1mHVdmddV3UEQmeGCLtAg68oB8teY5CNrVULEbtqsdmp5fAU/Hoc4UEYk4qtRxEXAgDSyBAAVPOXZpC5juVg+xUq7MaQck2xuLu16aOzg7bX9j6sGqwOV9XXNJkxNADwE3KMu56GxVw44Rd7P6/UdiSSR+jkJCQk5OXlrVu37sknn7TmYD9w4MD69evRckmxkSNHlpeXA1i5cuVVhV16+hW/dl5++WWKouyKN06YMKFfv35tHoX/Iig5iqIcJZ0guezaHVXd+TJ55sZEnZG5c3TFZDdVHYHgRxBhF2hw5U3BtrtLwxUsDUAjcr6Obdpg9I5Faiy6R6J7JPrFA0BQmIoDTaNpSVyIpa7RDNvvy1CzCICMuzyJP6ckEsB+9Yge0ShqFnaXFyMzNFgKEiehBSLmCH7EmDFj8vLyzGbzyJEjp06des0111y8eHHTpk1CYvYxY8Y4fRVNu73cRYDjuDVr1mRkZFxzzTV2p/R6vTCx27Yr+zV26s1ubtRq4KjqLpbL3tmUqDMy/xhZSVQdIbBp45cOodPCl5UC4EHdVREfYxQDKJaZ4ExFScUYkAgKkIgwKAkiBgDiDo8qljUCuCQ3olnYCcruDUMOmiN28mZhp7YwM8tDi2SmhsS0FJsN04z1k8UwEEvQ1scbgdBJeOyxxwSVZjabv//++8zMzO+//15QdQzDPProo57tLjs7u6Cg4L777rNrHz16tFKpDAoKmjFjxvnz5x1faDabaxzQarWeda8z46jqLpQrl21M1hmZuzIqpgyvsjvroqojuyUI/gKJ2AUUVFb6hhO9JiH4u5iqu8rCR9epQeHh+z9+JMilrS3C3Gix1JTYKNkXoo01Slj6YKP5IYkIADgeAMKaInZNP5RvLw9TsszK5PLXp9NnSi9fyhqxo2gGMrHHRkgg+IhBgwYtWbLkhRdecDz15ptvDho0yLPdffHFF0ql8h//+Ie1Ra1WL1iwICMjQ6lUHjp0aPny5SNHjjx69Kiwe8PKzp07J06caHe1vn37Pvfcc7W1tVft12KxUBTV2NjokVG0glCTrc0RTQChoaGOjY7bk08Xq//7a4qFpe67/uJ1PTU2ZZIAICUlxZW3RaCmpsa205qaGrvXchyHdoRp3cJsNrvueXvgOM47O0U4jmNZlmU7PFWC8NnzzqDMZrPJZGppDa4tHMd56pNDhF2g0Ucn04gt73YrvassHDwPsYRyTdVZKZKZUIdcRWOp1BRnlBgtTWvsLBz4CTkXDr0PnJOxTZ+/ecWRHPgvYytfByJt+rF+PulBQ0m4jhAYPP/88z169FiyZMnRo0eFlkGDBr300kuzZs3ybEdarXbDhg0zZ84MCgqyNg4fPty6Q3by5Mk33HDD2LFj33vvPbvsoTExMbZyUCAyMlIkErmSu8BgMNA07YVtGSzL8jzPMIynHq4ZGRkA7OoenSxUfvhbkpmlHpxQMipND1xxNi0tzd1erCXRnSIMSiTyxoOVZVkvJKPgeZ5lWYqiXJEm7cRsNhuNRtvPfAfBcZygorwgwXU6nUQiEYuvHt3woDNE2AUOVFZ6EEsnN0r3hmhPBBmOqPSDtQrK/S/oIpkJwBllY6nUPKJWcsnI8kEMAJYDALFJB0DKUQAGNCiG1iu3h9flT/kdQFgQukXgUhUAMM3f1aLbZnhmeARCJ2DWrFmzZs3SarUajSYsLMz6ZD127NiAAQM81cv69ev1er3jPKwtY8aMSUpKOnjwoF37wIED//e//9k1lpWVZWdnu/JsFp52XniKA+A4rj3CznarrKDqcOXT8Vh+0HtbEjge88dfGNXbSFFXPDg7Yl2dEAryzrvnHbFlDax6oS+WZb3TkfCxYRjGC8KOoiiv/YeyQkIpbkNlpQv/fO2IE3rr5BSPU0oDgLVxVQAgdbtu7zex1WviqnaG1xdLTTSoRk29sMaO4wBAbtIBkHEMgHnFkQA+j6sUXkgBr0xFtwgAJEhHCGRUKlVycrJKpaqtrf3www+HDRs2cOBAD17/iy++SE5OvuGGG1o3E6ZNPdiv3yGse7OqulWrVllPHbmgWvljAg88dVvR4Gtq7F5IdksQAhgSsXMPWz1HZaV3nn2dgmN9GuQATgYZAKyLqX4rL0Eql7t+ESEFybEg/YN9LgIolZoBmDW1PEIBsBx4QM7qAUzSxXIDlpn+WEkp6e/v2nKFJxRgE7EjEAIPnud37dr12Wef/fDDDx5fjnbhwoW9e/e+9NJLdqLNYrHYzvH99NNPJSUl8+bN82zvnsJrFRpOnTrl2LjnVPCn2+OkIu6ZaYW94vQ63RVniaojBDZE2LmBY5RO0HZ8bQ0VHCIomsvFGHxIUCo2AAAgAElEQVSh+fo3KNAs7KrEFuWDT1JyhVtXENwWxlVs2IGCzXx9nfDNzHIwNBhFvMXMSMQGvenDlTDo6YwbcGWQWfg6JxE7QkBSVFT0xRdfrF69+uLFi67Y8zz/ww8/APj7778BbNu2LTIyMjY2dtSoUQB+++23SZMmffXVV3fffbf1JV9++SXP847zsLfeemtcXNzAgQNVKtXhw4c//fTTxMTEJ5980lND8yB2WUg6Tts5boAFsPtkyGfbY+VS9rnpBd2j7WU3UXWEgIcIu/bCV1bolr+xPLns/6UU2bYLCs878k7oK9Ikuqc0ol7EHlM17fuiU3q27YKC26LQYACorxe+lVke+hqdCiiK6pcSyXPHjgBgBg21d0aI2BFhRwggTCbTjz/++Nlnn/3222/CzkdbgoODbZWZLSzL2m5lELKiTJ48eevWrWjeBmh7QZ7nv/rqq9GjR6ekpNhdasKECd9+++3GjRsbGhpiY2Pvu+++1157LTIy0iMD9CCOs8MdpO2cqrqsv8PW7IpWStnFMwq6RRFVR+iKEGHXXp7f9PDrXMLtZaF2ws4Wu1CfZ9We9eKvnU8IMTPP9yzUMp7ZLi4NCwZAN1yO2Jm1OgCsTCmeeZuptAQ8R8Un2r1KSHRCk6lYQkBw/Pjx1atXr127tqrKPv+ZwLhx47Zs2aJQOA+Ni0SiVjTNxIkTHTPuthQLfPrpp59++mmXHQ9wnKq6X/4K+2ZPtErOLp5ekBRJVB2hi0KE3WXaFmObWBUM4BqDbGJVyC8Rtf0a5LUiVthYChvVFWsUbzraiwV/UWG6v3jK6vt+BKAzQumJrAIinppcGTK3JPKM0vBBYrnQ2H75qIgI5gGJvilVEsvBrNUDYOVKyGSShc+D59HC2m0SsSMEBk73ug4bNuzuu+9etGgRgMjIyJZUHaGDcKrqNh2I+CEnMjTI8vyM/Lgwk91ZouoIXQfy+AWaN7o6HtvhKJXCzKIRdaoimclC8Stzk168GPfn/r65fwycWxJhZzm7NGKgVjFEq5xZHvrB6WTVL6N2nMLCb1BR3y7P2fz8VaeuKdg9aN2xHjSwqFeBmeadutoGJKEhAGSGpojdh7Hpz5xdBOATy8dUVjoYBs7SNVEkYkcIUFJTU1999dW8vLyDBw8uXLjQ1+50Ujp6t4RTVbdub9QPOZGhQeYXZzpRdd27d+9QlwiETgURdk62RLTC9vgcAKEW5voa9ZMFMe+fSWZ4fBFX9WFiRTeD9JXz8SUys55m3zyXGMRe8d7eUR5mpvmkMUe/jKuiQfXUy24sTrdw0Oha6MkFmKyRxZ+9PbckwkRxqxIqbhx6Zmd4+3SiHRJJnYhtZP+O3J/+eVI6gAiTCECVxIKW3zearLEjBCKpqamvvfbac88917NnG5eudh3stF1LUk8ikdA07VYBAKeq7ru9UVsPhUcFm//v9vyYUBKrI3R1yOPXPWr0+PT48uJdg385nLosL3FWeRhP4afImtdTii/JjQdCdKOuPfWfpPJIk+iJghjrq7rrpf21il2h2gqJ+WSQHkBPvRzA50npptbSmLcGlZU+QCuPNoo3RGm6jzn2ZFr+HyFN5SA9tYaPykovlZpijRJrS0+DDMBF2dXzO5CIHSHAyM3NvfPOO6Ojo+fMmfPrr796ofCRX8Pb4NTArjyDK9rOUdXxPL7MjvnpUHh0iOnFWfmRarOdAVF1hC4IEXaXkXJX/2ZprKiZWbVRz/DvdCud0//C6OGnhow4cUSl1zJs2qhjY4edKpea30sqqxGxT+bHRJmaqohMrQwDsDlKA+Cswgigl65pbZ25HU+H8Ro1gG0RdRwuf3V6UNUBKJGZgy2MNfqYppMDyFW2JuxIxI4QSEyZMsVWgmi12jVr1kycODEuLs6HXnVBHFUdx+PznTHb/w6NDzf+3+354Sqi6ggEgAg7KykGacHuwc9cim3dbMix/8nZRvWtd7zco+h/0dWH1LrTQQZbg3kFObVi9r3ksmALc3bvwA1He80piZhZHsqB3xJRCyBPaQDQQ9+UN3jY2bZUsBBU13hNMIAdYZenXz2eXaVUYgZgDdql6WTlUnOtuDU1StbYEQKJzZs3FxYWvv3227169bJtr6ioEA6ys7PfeuutsrIyX3gXULQUtDt9+rRTVbd6e2z28dDECOOLMwuCFfZzH0TVEbosRNg1iaE3ziYEW5gXL8R1M0jRwhYK46FDKTUni9Qp4vTRTiVUxXU5AOYV5CzvVvZaSnGe0jCpKvjjU9cMq1ceCNGVS80A8mUmI80P0rpREMKpz3KWHlmjOh1kKGnegdsROfNKpCYAsUYxALWFiTVKzigMrnRHInaEgCEmJmbx4sW5ubm7d++eO3eu3R7YioqKF198MTHRPu8PwV2cTto6XVTH8dRHv8T/fjLkmmjDS//IVxNVRyDYQNKdAIC513eWHcsNIoXCons/N3nKoDyh3bZoGK9r4H/6wUyLjwy9K4Wi4EzZ1DUH77Tj/5AwANDjx+unVYSOrVF/Gt/0+95C8b+F195WGTq6NmhvSEObfR5ZFyTjKNtwXUcgCLs4owTAdbVBFI8zysZLQ3KSw1t8SVMeOyLsCAFHRkZGRkbGv//972+//Xb16tUHDhywnrJY2rpaltAyTlUdy1Ef/hJ3IE+dEmN4bnqhQmo/gUBUHaGL0xUfvwYTNv2F3bmXW3SbNlE8P2Xg4eyw+puqgm8vdyJbLJu/p/S6rWGTJNFRLV1ZIgIAChA3F9k6N2XXu8llUwbl/RhZazV7P6kcwGOFMU4ucTWEUCKVlT6++op5WI+H64QLnpcbAfTUyyQ8vexcIge+jnpLJm7VQ1IrlhDQqFSq+fPn79+//8SJEwsXLoyIsM9tRHAFi8ViMjXPNjjbY+FU1ZlZ6r2tCQfy1D3jDM9NLyCqjkBwpCsKOxGDLUcxtqC5qGt5qazk/Allnz2h2sfT8htpPjMvMcTcJM0EFcWdPsH9/VelOmlH6A3xoS1eWYjSiRjYqhp+Qo6d6toTqj2q0k+pCLVO+7roua3lmBqVmeb3hNajIwuXnQgyAOjXIF90KaZ3g/zz+MpXUu+OOXB1h0nEjhDw9O3bd/ny5cXFxevXr584caKv3fFLOI5zfQbWZKFWbkk4ciGoT6Ju8fQChdS+thtRdQQCuqawk+xMX52YjmbRds+v8wGsif8VwDlF47+uKYk2ivcd7Pv8xbh4owRAsIWxbFwHhvkk/O5QFd0vvsUrMzQYuiluZ4cg76wi7z9J5QyPRwujhbOuaDurzejaoLE16l562Xl5YwNj/9XmWUpkJo3YMqpGtfhSbIXE/HLPIjtnHBGilRKmpfMEQkAhkUhmzZq1bds2XzsSODhVdY1m+p1NSccuBfVL0j09tUgqJqqOQHBOlxN2doqkX4P89vJQAH+r9ELLO93Kvo2pjm+UvHo+/ti+fj31srfOJvJ1ddnRNxWK4/onQNrqRKREBJELmmZ9dHWFxHxfSYTactnaOs3aygsnVKt/O5T26+FUtYWxph1xK8eyu2yIqokwi+Qs/XKPohrR1bOzTB+KBTdCLrmqIYFAINjjXNWZ6Hc2JZ4uUgzurl00tVAiIqqOQGiRLifsrAyvCzr5x4BD+/tNrAqpFbNHVE0lIEwUN6/fhWsyjr7ZvUTJMtkHe88riTwRZJja+00ASS1vGhAIlkMtu3rvRpr/OKFSbWHuLW1aoGMrzloRaveWRNCgfoiu2RFW/1yvwqv31G6e6J1/VtHIgf/ZZplgK8SFYEhyRztFIBACEKeqzmCiMzcl5RYrru2hfWJysZixn7olqo5AsKWLCrtgC/PVie7dGqUbo2se6nOxf/qxuuZYlDBVqhFb3uxevCqhwkhz5WLT/L4XTRT3eVJ6K/OwAs9OwqKbXfLhs/gKC8XPK44U/ow0icZr1B+e7ibM/7ak7XroZSaKm9Pv3OQhufkyo63PHQQH/q4B524feK5KTPb9EQiEjqIlVffOpsS8Evng7tpHbikWEVVHIFwN36c7KSsr2759+8GDBw8dOnT06FG9Xh8cHFxb61JwqM2IOPqk0vB1bPUb3Ysdz/ITcgRd9WRa/pNp+banIlRXuXKo4ioG1uuXSs3bImpvqwzNzEvaFVa/+GLs8LogAP20ihuHnTbSvG2yFSspBtkluYn1yp5T6/twIshw4so8zB0qJQkEQlfDqarTGZl/bUi8UC4f3rP+0VtKGPrqqs42y3FL1cwIhMDG98Luu+++W7hwode6E8RKtcQ8c9BZGj7OyfFhQsWkqtDHC6IfL4gGUCYxnVMYR9eqlucmPdY7P9TCqH8ZrWVYQUXxE3L4Bq1p+0t/2GS/84nAIqqOQCB4CqeSDoDWwCzbkJRfKRuZVvfQzaU05Z6qE/4k2o7QBfG9sFOr1ePHjx82bNiwYcMKCgqefvrpju5R0CVUVrptiVXbU62/0IPsDK9PHHNkeF3Q8DrlYK3ih+iaDdGavQf6/LM4akSdql+DvFJiGZh+3Bq646urAISLpvITZnjWEwKBQPA+p0+fdlpJTKMVvb0hubRGctMgzT1jyx1NyAwsgdASvhd2999///333y8cf/fdd17r1yrv7FpcOeuR3oWLa8SWXyJqf4m4PPV8b//zew/26dcgZylEmkTPXIp9sWehoO24ykoADUGRHvTEXUi4jkAgeITc3FzaWcbLmgbRso3JpTWS8QNq2qnqSNCO0AXxvbDzLV4O0Tle3HGTxMkgw1Op+R+e6ra4Z+HC/JhHiqL+nVRWKjUDMFdU0IBe5T1hZxWgtj4TCARCOzlz5ozT9qp68Vs/JFXUSSYO1tw9pr2xOqLqCF2Qri7sfA4/IeePsxh96Qp592Vc1ffRNTqGNTD8B6eTX7gYt7R7KQBLZZUEMAd7NWJHxByBQPAsLa2rK66WvvVDUp1edMfoiluHVTsakBlYAuGqEGHne0b1hKFbjlCA1Roe0zEsgK9iKxddinmwOGp+UZTp5OsivcFCiejQlouaEQgEQuemJVVXUCldtiGp3iC6M6Ni8tC2qDohPics2iOxOkKXxS+F3d9//71kyRLbFmsxaT9F1lzNwm7q00zzb6QUfXEipVBmCqstV7JMqTROJe+i2QcJBIK/05Kqu1Au/9eGRL2JuW9c2fgBNY4GrsfqiKQjdHH8UtiVlZWtX7/e1150FHbabl20pkbM7g9uMFH86NqgoTUvT5L70DsCgUBoIy2purOl8nc2JTWa6PvGlY7r7ySJKZmBJRBcxy+F3bhx4zQajW2LRqPp0aOHr/zxOLb7KngKv4bXCe1Z4fUJuiil1Je+EQgEQhtoSdWdKlQu/zHBbKHvv7F0bF+i6giE9uKXwk4sFodeuc6M4+xrQgcqnyelf54HPplsaCAQCJ0Oa1I6u/nQllTdiQLlyi0JFpZ6ZGLxdan1jgZE1REI7uKXwo7gtNoYgUAg+IqWqj60JOkAHLmo/u+2RI7HY5NKhvckqo5A8AxkGX7nhUg3AoHg17Si6g6dV3+wLREUnrqtqBVV57QuBYFAaAUSsSMQCIQAh+d5nuctFstVLTmOc9HyqowZM+bDDz90emrfmZCPs+JENP/E5MIByQ2OS2lSU1Otkk44MJvNbfZEGJEXNKIQpPTIu+dKX17oSBiRd/piWZbjOO+8ewA4jvPCIi6O41iW9dqgBIiw69TY7ZAlEAiEtiFoO1fMPFKGKyMjAy1kHtlzOnT1jjipiF94W35qvM5pV2Kx2LGlzWmtXBy7p/BaXwHWEd+MF/qCd9891//reaRH3wu70tLS/v37C8fC/9v6+vqIiAihZdKkSV999ZXPnOsEtFR5jEAgEFyEoiiaph3VkiNms9lFy1YQVN2qVascT/3z+V9NMU+CM/AXn0uNf5imaceHWUtL69rslTAiL0TsLBYLz/PtfPdchKIoL3QkxOpommYYxgt9sSzrhUEJUTSGYZyWKvYsNE2LRCJXBuXBz6fv19ixLFvdjFarBcDzvLWlvt7J2osuiN16O7L8jkAgdCqsMQlB1Tnl/he2m2KeBKuVFTzFGI49+uijjjZkwwSB0E58H7FLSEggicJdgYg5AoHQmeF5vpXdEpsORJijH6fYemnBIroxT2h8+OGHbQN7RNURCO3H98KOQCAQCAFAK6pu3d6orYfCKbZWVvA01XjWqU3rqo78/icQXMT3U7EEAoFA6PxQV2J3thVVt3F/xNZD4So5K3VN1ZEUJwRCeyDCjkAgEAhXoXWx1YqqW78vasP+SLXC8uKsfNpB1X300UcgM7AEgkchU7EEAoFAcBshK0orko7n8e2e6G1/hYUGmV+YWRAbahKW0z300EOCwX//+18QVUcgeBoi7AgEAoHQFlpRdRyP1dtjfz8ZkhBuXDyjIER5OUGrIO+ETMikvASB4HHIVCyBQCAQ3ObUqVMtnbKquvgw43PTr1B1tqSmpsKZqnPcJ0F2ThAIrkMidgQCgUC4CnZp8VtJVsdx+Dgr7o/TwUmRjc/PKFDJWadmaWlpLRV0EiZ5rd0RVUcguEVACbu6urp7773X114QCASPUV1dHR4e7msvCICNwGplBtZsod7/KeHoxaA+ifqFUwplYufSrXfv3izrXPA5dkcgENwiQISdXC6fOnXq5s2b165d62tfCASCJ4mJifG1CwQnWPdAWDMMmyzUe1sSjuUH9U3ULZpaJBG1qOpavzKRdARCewgQYadQKDIzM1UqlesvycnJOX/+/JQpU9Rqdcc55n1++eWX6urq2bNn+9oRD7NhwwaKoqZPn+5rRzyJ2Wxet25dXFzcuHHjfO2LJ6murt62bVtaWtqwYcPaf7XHHnus/RcheBarqrMev/efT97dnHimSNE/WffUbW6oOg/WPicQCAgYYQegZ8+ea9ascd1+3rx558+fz8zM7NWrV8d55X3S09M1Go1bb4VfkJ2dLRKJAmxcWq123bp1/fv3D7BxHTx4cNu2bTfffPPKlSt97QvBKzDKzI2JeSWKoSnaBZOKRYzzkFtLsToSoiMQPAjZFUsgEAgEN7AN1wEAo25MXJFXori2R1tUHYFA8CyBE7EjEAgEgpfhGbUxaTkn6zWqd938CSV0C7ECF1WdMCdLAngEQnsgETsCgUAguIF1twTPhDQmreBkvUR129qp6mzrzzqtRUsgEFyECDsCgUAguMeqVat4UYSx2394WU9xzcbVr3RrZ6yOQCB4iq47FTt27FiJRBIcHOxrRzzM1KlTBw4c6GsvPM/s2bMZhvG1Fx5GLBbPnz+/T58+vnbEw0RGRs6fPz89Pd3XjhA8gF3wTJgn1WhFwcO/K6uR3DxYM3tMWkvxtfaoOiFNcZtfTiB0Wcj/HAKBQAhwysrKsrOz77rrrqta6vV6mqZlMpnwp9N6X7/vP/f2D8mV9eJbhmjuyihvm6pjWZbjOJFIJHThdO7V6ePJ1tLF55dGowkNDfXC9K7FYuF5XiwWd3RHADQaTVhYWEf3wvO8xWKhadoLv6tNJpPRaHQrbVnb4DiOZVmGYeiW4syeQ6vVSqVSiURyVcsVK1YAWLhwYfs77boROwKBQCC4iyy459gHT1VrxTOuq5p+XWVLZu2fgb2qqgOJ6hEIziDCjkAgEAguIQ/t23Py9mqt+I7RFbcOq27JzFHVOZ3PtWtpQygORNsRCA6QzRMEAoFAuDrysP49b90hVsSIKz5sj6pz2gKAt6H93hIIXRa/EXZlZWVr16598sknR40apVQqKYoKCQlpxV6v12dmZl577bVqtVqhUPTp0+fFF1+sq6trp/Gbb75JtcAzzzzThnEVFBSsWLFi8uTJcXFxEokkJCTkuuuue/vtt7VabUsvOXz48B133BEbGyuVSpOTkx988MH8/HzvG/vFuPzofrn7CXf9yn40Lo/fL4KnUEZem3rb72J5VMHeR8XV39qVFBOAL/bAEhVIINjD+wnCukJbgoODWzIuKyvr37+/YBYWFpaUlCQcJyUlXbp0qT3Gb7zxBgCpVBruwCuvvOLuoM6cOWP7y9W6YBlAt27dzp496/iSb775RiQSAVCpVKmpqcJLVCrV/v37vWnsL+Pyo/vl1ifcrSv70bg8e78IVkpLS7/55htXLHU6ncFgsGsUK2IGzqkYOp+L7r8QQEZGRkZGxqlTpzKuBC08UOAMi8ViMpk4jnNrII7XceVV1dXV7nbUNsxms8lk8kJHPM9XV1d7oReO40wmk7AppKMxGo319fVe6IhlWZPJxLKsF/qqr683Go2uWC5fvnz58uUe6dRvhN1nn302fvz4xYsXr1+//t1332398TB+/HgAcXFxO3fuFFry8/PHjBkDYMiQIXa30y1j4cEzd+5cjwzq+PHjEolk9uzZW7dura2t5Xm+trb2448/FkIa/fv3t+s9Ly9P2FyzaNEi4ctXo9FMmzYNQGxsbENDg3eM/WhcfnS/3PqE+9H9cmtcnr1fBCvtFHYA4q9dEtnnEUFLZTijFaXlQWFndzUXX0KEXZshwq6dEGHnKt9++20rj4dDhw4J/+d//vln2/aqqqrQ0FAA69ata5sx7+kHT01NTUFBgWP7hg0bBK92795t237vvfcCGDNmjG2jTqeLj48HkJmZ6R1jPxqXH90vW1r/hLfnyk7pPOMiwq6DaL+ws6UVVdeS2LK7As/zbRZ2bYAIuzZDhF078Ymw85s1dq6zZ88eAKGhobfccotte3h4+M033wzgm2++aZuxxwkJCUlMTHRsnzJlilwuB5CXl2dtNBqNmzZtAvDoo4/aGisUivvuuw/Ad9995wVjPxqXx+m4cbmFH90vQldA+BZtHd5G2/FkVRyB0JEEoLCrrq4G4PRBJayf++OPP9pmbGXv3r0jRoyIj4/v1avXtGnT1q5da7FYPOT+Fdimajxx4oSwkn3s2LF2Ztdffz2AI0eONDY2drSxR/DOuKx0/vvlFn50v9qG1+4Xof3YqbpWRJs1nNDxThEIXZoAFHbCwqCioiLHU4WFhQCqqqoqKyvbYGzl/Pnzf/75Z0lJydmzZzdv3nzvvfeOHj26vLzcU0PYtGmTwWAQi8XCk1IgNzcXQFBQUExMjJ19jx49AHAcd/bs2Y429qNxWen898st/Oh+tY2Ovl/epKSk5IknnkhPT5fL5RRFXbp0qRXj7du32+0FjoiIsDWoqKi49957w8LCgoKCJk6cePr06Y71vpmW1Nju3bu94wCBQHCdABR21113HQCNRrN9+3bb9pqamt9++8163AZjAPHx8a+//npOTk5ZWZnRaDxz5swLL7wgEokOHDgwffp0j/wY1Wg0QlGRRx55JCoqyrYdQHh4uONLrIVlrK52nHGb8f644D/3y90eO+jKdr14eVzwyv3yMhcuXFi3bl14eLjrlXOXL1++vpnPP//c2m42m2+66aYdO3a88847q1evLi0tvf76672mdx3ffEHV2bV7oWwXgUBonQCsPDFq1KihQ4cePnz4/vvv//bbb0eNGgWgpKRk7ty5wsQrAL1e3wZjAPPmzbPtKzU1denSpUOHDp01a1ZOTs6mTZumT5/eHudNJtPMmTMLCwv79u371ltv2Z4yGAwAnJacs+ansLraccZtwyfjgv/cL7fwo/vlLh19v7zPyJEjBe21cuXK7OxsV14yYcKEfv36ObZ/8803f//9986dO2+44QbhyikpKZmZme+8845nfW4Jq4Y7ffq0NV+d0+TD/ijBCYSAIQAjdgC+++67xMTEwsLC0aNHx8TEpKSkJCQkbN++/cEHHxQM1Gp124ydMnPmzJEjRwLYsmVLe9w2m8133HHHrl27kpKSfvrpJ4VCYXtWWMZuMpkcX2hd0mR9SccZtwFfjaslOuH9cgs/ul8ewVP3yye0rcq4Xq931EY//vhjfHy8oOoAJCQkjB8/XtjI4mW8n4WYQCC4TgBG7AD06NHjyJEj77777ubNmy9cuKDX68eOHfvss8+KRKJPPvmEpmnbqSW3jFsiPT193759Fy5caLPPFovlrrvu2rRpU0JCws6dO5OTk+0MhPQr1jiiLcLsmNWmQ43dxYfjaoXOdr/cwo/ul6do//3yI0aPHl1XV6dQKG6++ebMzMyUlBSh/eTJk3379rW17Nev3y+//NLY2GibU7qoqOjnn3+2uybDMJ5V2wQCodMSmMIOQHh4+NKlS5cuXWrb+PrrrwPo3bt3UFBQm42dIvwub/MEhBAj2bhxY3x8fHZ2tvWr3Ja0tDQADQ0NZWVlduvWz507J/jQs2fPjjb2o3G1Qme7X27hR/fLU7TzfvkLarV6wYIFGRkZSqXy0KFDy5cvHzly5NGjR2NjYwFoNJpBgwbZ2oeGhvI8X1NTIxgInDx50rbel0Dfvn2fffZZp3LcDo7jAOh0Ohd9rqqqstvhgRZ0vx3C3fTOmjyz2Wz91dGheHlQrrzP7cdrg+I4Tkib19EdWb9JvDAolmUbGxtdidxzHNe2AL8jASvsHOE4TkhKJ6TI96AxACHRsbUcmVuYTKbbb7998+bNcXFx2dnZwtZCR/r166dSqbRa7e+//37HHXfYntq1axeAQYMGWX+4d5yxH42rFTrb/XILP7pfnqI998uPGD58+PDhw4XjyZMn33DDDWPHjn3vvffefvtt1y/St2/fVatW2TUyDCORSJxuebFDr9fTNN2eO+ii/mZZluM4kUjkhYerRqMJDQ31QkdCIl+xWNzRHQHQaDTW7UodB8/zFouFpmmGYTq6L5PJZDQabZMrdRAcx7EsyzCMp4RUK2i1WqlU6nRRsh2edMYjaY69zFXz1zslMzMTgEKhKCwsbJtxS7nLrTtq165d65ZLPM8bjcbbbrsNQFxcXG5ubuvG99xzD1rO+L9s2TLvGPvLuPzrftly1U+4H90vW1ofV0fcr86DUDP34sWLbr0qKSlp3LhxwnGvXr1uvvlm27PPPvssRVHCgirMn7YAAB5USURBVLzWaWfliY6AVJ5oJ6TyRJsJ+MoTgSnstm7d+sEHHxQUFAh37uLFi4sWLRJ+rn3wwQdtNj506NCNN9743XfflZaWCi1VVVUrV65UKpUA+vfvbzab3RqIW09Tnudzc3OF34LWGp01NTXCPsGYmBitVusdY38Zl3/dL1uu+gn3o/vl+rg8fr86FW0TdnFxcePHjxeOp0+fnpCQYHv2lltuSUlJceU6RNgRYdc2iLBrJ0TYtUZJSUl4M0KolqIoa8u9995rayx8hwIQi8XWJcMMwziNJbhufPDgQWukUy6X2+6W7du3r7tf2TzPb9261Xq1cGc4+rBmzRohJK5SqdLS0oQZk6CgoH379jlev+OM/397dxoWxZWwfbxYlUUWlUEdUEBEkwyLEjUqxhjRwSUSBCSjYMzELTr6Qa8EvTAxGjQzccnMNRoncQmGIS64L3HfEhxxxIzihkZAoyKioqADSAP9fqj3qaeeBtpuhO7m+P99quV01Tl1aPu2llPNol3Nq7+M+gs39og1i3Y1en9ZFEOCnU5ylfslKSlJnpXHtDt27Jg8e/PmTXt7+5kzZxqyd4Idwa5hCHbPiWCnj/weiPpERkaqC1+4cGHq1KmBgYGurq4tW7b08/ObOHFidnZ2nVs2vPCjR4+WLFkSGRnZuXNnFxcXOzs7Dw+PQYMGrVy5smH/FD5z+IZ58+bV/tTp06djYmI8PT3t7e29vLzef//9vLy8+nbRdIUtv13Nq7+M+gs3asvNpV2N3l+WoKamRh5qWH6R7ldffZWenp6RkSGv3b9/v42NTVpamjz7+9///r333vvrX/+6Zs2aqVOn2tvbe3t7FxUVyWsrKysDAwM7dOiwdu3ajRs3BgUFeXh4FBQUGFINgh3BrmEIds/JLMGOkSQBoKlUVVXVvpV++PDh8tm4ffv2DR06NDU1Vb5tcenSpevXr8/NzX3y5En79u2HDh06f/589aPHd+/enTlz5g8//FBZWRkWFvbll1++/PLLhlSjsLDw6NGjf/jDH55Z8vkfnjAQD088Jx6eaDDLfHhCPqkvv+/nOb1AT8UCgInZ2trq+c9zRESEeu2sWbNmzZqlZ2uenp5paWmNWT8AwhHzzRMAAAAvIIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAieigUA8VVUVBQUFBhSzNra2pDRGZ6T/MZ3GxsbE4xCUlpaWl5ebpo3vmu1WltbU/ywlpaWVlRUNPVetFptdXW1tbW1CUYG0Wg0Go3m8ePHTb0j+W/PNI0qKyuzs7MzZAQceQSWRtkpwQ4AxHfjxo1vvvnmmcWuXLliY2Pj7+9vgiqZRnl5eX5+fuvWrdUjAjZ39+/fLyoq6tixo7Ozs7nr0miuX79eVlb20ksvmSCCm4ZWq718+bKTk1OnTp0MKe/m5tYo+yXYAYDgnJycwsLCDCm5bNkyNzc3+T0ZYrhy5coXX3wRFRUVExNj7ro0mu+//z49PT05Obl3797mrkuj2bp16/nz5/ft22eCwZBNo7KycsGCBSEhIWPHjjWkfIcOHRplvwQ7ABBcq1atwsPDDSlpZ2fn5ORkYOFmwdXVVZKkjh07itSoU6dOSZIUEhIiUqPc3d0lSRo0aJBprmWbgHytvHXr1ibuJh6eAAAAEIQguRgWy9/fPzc3V5m1tbW1t7d3cXHx9PT09/cPCwtLSEho06bN8+9o06ZNly5dkqdHjBjx6quvPv82AQBoXgh2MKmqqqqqqqqysrLCwsJz585t2bJlzpw506dPX7Ro0XOeft+0adOWLVvk6Xbt2hHsAAAvIC7FwswqKioWL148cODAsrIyc9cFAIDmjTN2MJ2YmJh333336dOnd+7c+fHHH7dv367RaORVGRkZ7777bnp6unlrCLzgBgwY0FhjLlgIFxeX8PDwgIAAc1ekMfn6+oaHh7dt29bcFWlMoaGh9vb2wox1IkmStbV1eHh4SEiIifdrpdVqTbxLvFDU99glJib++c9/VlZduXJl5MiRV69eVZYcPnz4zTfflKfXrl2bmZl5/vz5O3fuPHr06L///a88GlCfPn0mTpwYGhqqfOrtt9/esWNHfRV4//33V69eLU9rtdp9+/Z99913p06dunv3bk1NTfv27V9//fVp06b17NmzEVsNAIBZcCkWZtO1a9ddu3aph+ResWKFMr1gwYJVq1ZlZmbeuHGjpKSkqqqqpKQkOzv766+/7tWr1xdffGHs7h4+fDh06NBhw4Zt2LAhPz+/rKysoqIiPz9/3bp1vXr1+uijj/hPDgCguSPYwZwCAgJGjhypzB45ckSngIeHR1BQ0ODBg/v166c8PFtTU5OYmCiP5CRJkru7u6enZ4sWLZRPyU/dyuRRrKqrqyMjI/fv3y8XsLe3792792uvvabEysWLFycnJzdNKwEAMBGCHcxMPXL6o0ePSkpK5OnExMQLFy4UFRWdO3fuwIEDGRkZd+/enT59ulJYucD67bffFhYWjhgxQln1l7/8pfB/LF26VJKkf/7znz/99JO81sfHJycnJzMz8+TJk6dPn27VqpW8fOHChbdu3WrKtgIA0LR4eAJmpnOndklJiXyO7YMPPpAk6caNG5cuXbp//35ZWZlWq23ZsqVSMisry/C9pKWlKdMLFizw9fWVp4ODg8eMGfP1119LkvT06dOtW7fOmDHjOVoDAIA5ccYOZvbw4UP1rJzqJEnatWtXYGCgj4/PsGHDxo0bN2XKlA8++GDx4sVKyQcPHhi+l//85z/K9Lhx46xU5FQny87ObmAzAItUUFAwY8aMPn36ODg4WFlZXb9+XU/hQ4cOWf1fOs9dFhUVJSQktG7d2tnZOSIi4vLly01b+3oY1ai3337bqpZevXrJa5/ZZJM5evToe++916VLF0dHx86dO0+bNq2oqEhPef19YSE9ZVSj9Be2kJ4yqkXm+kJxxg5mlpmZqUy7u7vLwW7Hjh1RUVH6n2aorKw0fC+PHj0ypFhxcbHh2wQsX15e3saNG3v27NmnT5+jR48a8pFly5Z5e3vL0+pbVzUazZAhQ4qKipYsWeLs7Lxw4cI33ngjOzvb09OzSapeP6MalZiYGB8fr8zm5ubOnj172LBh6jL1NdmU5s+fX1JSMm7cuI4dO166dGn58uV79+49d+6ccq+Imv6+sJyeMqpRhhQ2e08Z1SKZGb5QWqApde7cWfljS0xM1Fmbk5OjfuFEdHS0vFw9+EhkZOTly5crKyu1Wu2JEyeU5Z6enupNRUdHK6tWrlypsyP1/5PGjx+fVI+0tLQmOxKAGVRXV8sTX375pSRJ+fn5egofPHhQkqTz58/XuTYlJUWSpCNHjsizN2/etLe3nzVrVqPW1yBGNUpHUlKSlZVVXl6ePKu/yaaUk5OjnpXvHlmzZk2dhfX3heX0lFGN0l/YQnrKqBaZ6wvFGTuYjTyOXVVVlbJk6tSp8sT58+eVhYsWLerWrZs8ree+Omvr/72voLq6WmdtSEjIoUOH5OnAwMCZM2fW3oJGo1FvBBBAw/6ky8rK5Kuc6oU7d+787W9/O3DgQHnWy8tr0KBB27dvX7JkSSNU1BgN/p7W1NSkpqb2799fuctWUWeTTalr167q2TfeeEOSpNu3b9dZWH9fWE5PGdUoAwubt6eMapHCxF8ofsZgOrm5ubt37966deuKFStGjx4dGBioHp04NjZWGZ3Y3t5eWX7y5El54uzZswsWLKhv4+qHMH766Sd1XpQkaezYscr0/Pnzjx07pl6bl5f3+eef+/n53bt3rwHtAkQSFhbm5OTk7Ow8atQoZXRxSZIuXrz4yiuvqEv+7ne/y8vLq6ioMHkdG+jo0aO//vrr+PHjdZbX12Qzkq9OBAYG1rlWf19YbE/pb5QhhS2tpwxpkem/UJyxg+ls3rx58+bNda4aMGCAfF5aNmjQoG3btsnTEydO/Oabb6ysrH7++efap+IUwcHByvTGjRt37Ngh366Xnp7ev3//hISEtWvXyiOelJaWDhw40N/fv1OnTpWVlbm5uQUFBY3RPqB5c3Fx+dOf/tS/f38nJ6esrKxly5b17dv37Nmz7du3lySpuLhY5+VI7u7uWq324cOHcgHLl5KS4uTkFBsbqyzR32RzKS4u/vDDD7t37/7WW2/VV0BPX1hmTz2zUfoLW2BPPbNFZvtCPf/VXEAP9T12dXJwcJgzZ45Go1F/6urVq8pwxOqS6nPUOvfY3b9/38PDo/b2Dx48KBd4+PDh8OHD9dTExsamqKjIdIcGMKEG3I52/PhxSXVrrIeHR1xcnLrAokWLJEkqKChoxHoaxahGlZaWOjo6JiQk6Cmj02SzKCsrGzBgQNu2bX/55Zf6yujvCwvsKUMaZVRhs/eUUS2SmewLxRk7mJS1tbW9vb2rq2u7du26dOnSv3//+Pj41q1b6xTr0qVLVlbWvHnz9u/fX1xc3LZt24EDB37yySePHz+ub8tt2rTJyMiYN2/e8ePHi4qKap/bc3Nz271796FDh1JTUzMzMwsKCsrLy52dnTt27NijR4+IiIghQ4bUrgnwwnr99dc7dux4+vRpedbd3V3n6fKHDx9aWVnpDEVpsdLT08vKympfh1XTabLpPX36NCoq6uzZs0ePHvX396+vmP6+sLSeMrBRRhU2b08Z1SKFyb5QBDs0rWvXrjXsgz4+PuvWrau9XFv/GCgBAQHr16/Xv9nw8PDw8PCGVQl40VRVVSl3fL/yyis6v6MXLlzw8/NzcHAwR9WMlpKS0qlTJ+Ve9fqom2xilZWVMTExJ06cOHDgQPfu3fWU1N8XFtVThjfK2MLm6imjKqnDNF8oHp4AAEiSJOk8crRnz56CgoLXXntNnh05cuStW7fky0mSJN26devw4cORkZGmrmWD5OXlZWRkJCQk6EQB/U02paqqqnfeeefw4cO7d+/u06eP/sL6+8JyesqoRukvbCE9ZWyL1LMm+0JZ6Tn/AQBo1rRa7ZYtWyRJ2rNnT0pKyldffeXh4dG+fft+/fpJknTgwIFhw4Z99913Y8aMkSQpIiKiQ4cOwcHBrVq1OnPmzOrVqz09Pc+cOSPfvarRaEJDQx88eJCcnOzk5LRw4cI7d+6cO3fO9HevG9Uo2bx58xYsWHDt2jWdu371N9mUJkyYsGbNmkmTJg0ePFhZ6O/vL99fr9Mo/X1hOT1lVKP0F7aQnjKqRWb7Qj3nPXoAAIul0Whq/7M/fPhwee3evXslSUpNTZVnlyxZEhoa6ubmZmtr6+3tPWnSpDt37qi3VlhYOGbMGDc3N0dHxyFDhly8eNHU7dFqtUY2SqvV1tTU+Pj4hIWF1d7UM5tsMjojX8imTZsmr63dKP19YSE9ZVSj9Be2kJ4yqkXm+kJxxg4AAEAQ3GMHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwB40fn4+FhZWVlZWcXHx8tLtm/fbvU/Lly4YN7qqdWualMz+6GIj4+X975r1646CwQHB4eFhdW5Kicnx8bGxsrKqm/fvk1ZRwtCsAMAmEFOTo5VLS1atPDy8oqMjNy2bZu5K9hwSnM+/fRTc9VBmMOblZX1/fffS5IUHBz81ltv1Vmmpqamurq6pqam9qpu3bpFR0dLknTy5MlNmzY1aVUtBMEOAGApKisrb9++vXPnzlGjRo0ZM6bOn2rTcHd3D/0fzeW1ac/UsMNr3kORlJQkj8s2c+ZM9XKNRrNs2bIePXo4OTlduHAhMzOzZcuW/v7+MTExOgFu1qxZOpsSG++KBQCYmZ+fX/fu3bVa7Y0bN86cOSMvXL9+/ZtvvjlhwgSzVGnAgAFZWVlm2XWje87Da8ZDcfXq1YMHD0qS5OjoOGrUKPWq0aNHb9++Xb1Eo9Hk5ubm5uZWVVWNHj1aWd67d29/f/9r165du3Zt//79ERERpqm8uXDGDgBgZoMHD968efOWLVuysrJWrVqlLE9PT5cndG7zSktLk88evfrqq0rhkydPJiQk+Pr6tmzZ0tnZOSgoaO7cuQ8ePNDZV2VlZXJysr+/f4sWLXx9fZOSksrLy2tXSc+NZWfPnp00aVK3bt2cnZ0dHBx8fX1jY2N//PFHSZLCw8PVr6OdP3++spFbt241UVWf6TkPb8MOhbGNrdOaNWvkc2xDhw51dnZWb1NOdW5ubjt37uzWrVtoaOjPP/+clpb2zjvvtGrVSmc7Ss5TN19YjfL+CgAAjHL58mXll2jy5MnK8nv37inLAwMD5YXqe8ISEhKU6R49esgFPv74Y3WiUnTo0OH8+fPKxqurq4cPH65Tpl+/fu3atZOnx44dW3uP6i18/vnnNjY2tXckX+YbNGhQfb+2N2/ebKKqNvXhbdihMKqx9QkODpbLL1u2TL185cqV8vJhw4bJxfr166dnO3v27JHLu7q6VlVVPXO/zRqXYgEAFkR9fa3OF6KnpqZaW1t37tzZ3t7e2tpakqQNGzZ89tln8tr4+PiRI0c+efJk6dKlFy9eLCgoiIqKunjxor29vSRJq1atUn7jvb29o6Ki7t+/v3HjxurqakPqtnnz5jlz5sjTdnZ2sbGxXbt2vX379qFDh+SFkydPjoiI+PDDD+XZwYMHDxkyRJ52c3MzZVXr04DDW6dnHgqjGlunkpKS7Oxsebpnz57qVb/5zW/kiUOHDi1ZsuTx48fq83m19erVS73N7t276ync7Jk7WQIAXkTqU0p+fn7R0dGjRo1SX1qVJGnVqlVyYfVJIw8Pj9OnT8vLnzx5olWd1xk1apSy/dzcXOUjmzdvlheGhITIS1xdXe/evSsvXL16tVJS/xk75eO2traZmZnKvqqqqq5fv67MKh+cN2+eTquboqpNfXgbfCgMb2ydlFQnSVJubq56VVFRkbu7u/R/+fr6JiQkHDhwoM6t2dnZycW2bdumZ6cCINgBAMxAnTzqFBsbW11dLRdWZ4vFixert1NSUqJ/O5IkzZ49W6vVPn36VDkFFR8fr2xBo9HY2v7/61d6gl1paamyJCYmRk/TlGI6wa6Jqtqkh7fBh8LwxtbnyJEjSsni4mKdtXv37vXw8Khzs9OmTau9NeUknxJnRcXDEwAAS2FnZ9e+ffsRI0akp6dv2rSpzkuBvXv3Vs8+fPjwmZt99OiRJEmlpaXKAB/KnWqSJNna2rZt2/aZGykuLlamfX19n1m+NpNVtT4NOLx1MuRQGN7Y+qiHVqmoqNBZGxERcf369Q0bNkyZMsXFxUW9asWKFf/61790yivPnQgzeE19uMcOAGBmkydP/sc//mFgYZ1rcK1bt1amp0yZkpiYWPsj8mOSLi4u1tbWcmAqLCxU1lZVVRnykKZ6R/n5+QbW1ixV1fE8h7dOhhwKwxtbH+UcmyRJDx48qH1HoKOjY1xcXFxc3MmTJx0dHZcvXx4XF3ft2jVJkk6cOKF+1YRGo3n8+HHtzQqJM3YAgGasVatWQUFB8vTBgwfbtm3ro+Ll5XXs2DH5Dn17e3ul5K5du5TnQ9etW6fRaAzZkXJj2Y4dO9RDu9XU1Pz666/KrHK1tKyszCxVbWqGHArDG1sfHx8fV1dXeTonJ0e9Ki8vTwlqMmtr6x49eihj1KnPKUqSdOnSJXnCyspKqbmoOGMHAGjeEhMTx44dK0lSbm5unz59pkyZ4u3tXVxcfPr06a1btxYWFo4YMUI+OTRp0qSpU6dKklRSUhIaGhoVFfXgwYMNGzYYuKOkpKTY2FhJkjQaTd++fePi4gICAu7evXv48OHo6Ojk5GS5mJeX1/Xr1yVJSklJadGihaura7t27eRXu5qsqk3NkENheGPrZG1tHRYWJj8a/O9//zsmJkZZ9cMPPyQlJUVHRw8cONDPz6+8vNzKymrHjh3K7YDKcxuyU6dOyRMvv/xyfXfmicPcN/kBAF5E9Q20Vqf6hlJTzJ07t87x0mT37t2Ti1VXV9d+8UBISIjyY2/IOHZ13pqmHrxNGe5EERoa2nRVberD+zyHwsDG1iclJUUuGRQUpF7+97//vb5tSpLUq1cvjUajLq+8teLTTz/Vv0cBcCkWANDsffbZZ5mZmePHj/f393dwcHBwcPDx8enfv//cuXNPnDjRpk0buZi1tfX27dvnz5/v6+trZ2fn7e09c+bM48ePOzo6Grij2bNnnzlzZsKECQEBAY6Oji1atPDy8oqMjBw8eLC6Mh999JGPj49yTdYsVW1qBh4KQxpbn7i4OLlMdna2+qUXcXFxK1eujIyMDAgIUC7XOjg4BAYGfvLJJ4cPH1Yf+ZKSEvm0n62t7aRJkxrxCFgmK+0L8EJcAADQHCUnJ3/88ceSJM2YMeNvf/tbnWUCAwNdXV0zMjLqXLt8+fLp06dLkjR+/Phvv/226apqIQh2AADAQpWXlwcEBNy6dcvBwSE/P9/T07N2maCgIBcXlzqDnUaj6dy5882bNx0dHX/55ZcOHTo0fZXNjIcnAACAhXJwcEhNTT127JgkSTdu3Kgz2L300kv1XaG+efPmH//4R0mSgoKCXoRUJ3HGDgAAQBg8PAEAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAI4v8B9WCm7bk/DU4AAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- gbm( GasPrice~.-Date-GasDate, data=trainsubdata, distribution=\"gaussian\", interaction.depth=3,\n",
    "            n.minobsinnode=10, n.trees=150,shrinkage=0.1)\n",
    "\n",
    "summary(model)\n",
    "\n",
    "predictedY <- predict(model, testsubdata, n.trees=model$n.trees)\n",
    "\n",
    "score<-mean((testsubdata$GasPrice-predictedY)^2)\n",
    "print(paste(\"MS Deviation:\",as.character(score)))\n",
    "\n",
    "predData<-data.frame(testsubdata$Date,predictedY)\n",
    "colnames(predData)<-c(\"Date\",\"GasPrice\")\n",
    "\n",
    "p1<-ggplot(trainsubdata,aes(Date,GasPrice))+geom_line(aes(color=\"Training Data\"))+\n",
    "  geom_point(data=testsubdata,aes(color=\"Test Data\"))+\n",
    "  labs(color=\"Input Data\")+\n",
    "  geom_line(data=predData,aes(color=\"Predicted Data\"))+\n",
    "  theme(legend.position=c(.3, .75),panel.border = element_rect(fill=NA,color=\"black\", size=0.5, \n",
    "                                    linetype=\"solid\"))+ \n",
    "  coord_fixed(ratio=2000)+xlab(\"Date\")+ylab(\"Gas Price ($)\")\n",
    "  \n",
    "predVact<-data.frame(testsubdata$GasPrice,predData$GasPrice)\n",
    "colnames(predVact)<-c(\"pp\",\"ap\")\n",
    "p2<-ggplot(predVact,aes(x=pp,y=ap))+geom_point()+ ylim(1.5,2.5)+ xlim(1.5,2.5)+\n",
    "    geom_smooth(method=lm,   # Add linear regression line\n",
    "                se=TRUE)+theme_bw()+    # Don't add shaded confidence region\n",
    "    xlab(\"Predicted Price ($)\") + ylab(\"Actual Price ($)\")+ coord_fixed()+\n",
    "    ggtitle(paste(\"RMS Deviation: $\",sprintf(\"%.2f\",score)))\n",
    "\n",
    "plot_grid(p1,p2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We see that the model predictions work fairly well. The most important variable is the crude oil spot price. However, there are a handful of other variables that also play a role in predicting the gas price. \n",
    "\n",
    "## GBM Model Optimization\n",
    "\n",
    "Our next step is to optimize the model by tuning the hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7387            -nan     0.1000    0.1259\n",
      "     2        0.6340            -nan     0.1000    0.1043\n",
      "     3        0.5459            -nan     0.1000    0.0875\n",
      "     4        0.4731            -nan     0.1000    0.0732\n",
      "     5        0.4132            -nan     0.1000    0.0610\n",
      "     6        0.3614            -nan     0.1000    0.0528\n",
      "     7        0.3158            -nan     0.1000    0.0434\n",
      "     8        0.2772            -nan     0.1000    0.0390\n",
      "     9        0.2426            -nan     0.1000    0.0348\n",
      "    10        0.2127            -nan     0.1000    0.0282\n",
      "    20        0.0770            -nan     0.1000    0.0070\n",
      "    40        0.0274            -nan     0.1000    0.0009\n",
      "    60        0.0180            -nan     0.1000    0.0002\n",
      "    80        0.0143            -nan     0.1000    0.0001\n",
      "   100        0.0124            -nan     0.1000    0.0000\n",
      "   120        0.0115            -nan     0.1000    0.0000\n",
      "   140        0.0107            -nan     0.1000    0.0000\n",
      "   150        0.0104            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7204            -nan     0.1000    0.1417\n",
      "     2        0.5997            -nan     0.1000    0.1251\n",
      "     3        0.5019            -nan     0.1000    0.0952\n",
      "     4        0.4212            -nan     0.1000    0.0811\n",
      "     5        0.3533            -nan     0.1000    0.0697\n",
      "     6        0.2998            -nan     0.1000    0.0540\n",
      "     7        0.2538            -nan     0.1000    0.0457\n",
      "     8        0.2136            -nan     0.1000    0.0400\n",
      "     9        0.1822            -nan     0.1000    0.0314\n",
      "    10        0.1566            -nan     0.1000    0.0266\n",
      "    20        0.0466            -nan     0.1000    0.0044\n",
      "    40        0.0160            -nan     0.1000    0.0005\n",
      "    60        0.0109            -nan     0.1000    0.0001\n",
      "    80        0.0089            -nan     0.1000    0.0000\n",
      "   100        0.0076            -nan     0.1000   -0.0000\n",
      "   120        0.0067            -nan     0.1000    0.0000\n",
      "   140        0.0060            -nan     0.1000    0.0000\n",
      "   150        0.0058            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7103            -nan     0.1000    0.1533\n",
      "     2        0.5881            -nan     0.1000    0.1257\n",
      "     3        0.4857            -nan     0.1000    0.1051\n",
      "     4        0.4032            -nan     0.1000    0.0822\n",
      "     5        0.3363            -nan     0.1000    0.0676\n",
      "     6        0.2804            -nan     0.1000    0.0566\n",
      "     7        0.2356            -nan     0.1000    0.0450\n",
      "     8        0.1973            -nan     0.1000    0.0362\n",
      "     9        0.1668            -nan     0.1000    0.0305\n",
      "    10        0.1411            -nan     0.1000    0.0242\n",
      "    20        0.0388            -nan     0.1000    0.0042\n",
      "    40        0.0118            -nan     0.1000    0.0003\n",
      "    60        0.0081            -nan     0.1000    0.0001\n",
      "    80        0.0065            -nan     0.1000    0.0000\n",
      "   100        0.0054            -nan     0.1000    0.0000\n",
      "   120        0.0047            -nan     0.1000   -0.0000\n",
      "   140        0.0042            -nan     0.1000    0.0000\n",
      "   150        0.0040            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7368            -nan     0.1000    0.1282\n",
      "     2        0.6329            -nan     0.1000    0.1049\n",
      "     3        0.5425            -nan     0.1000    0.0881\n",
      "     4        0.4691            -nan     0.1000    0.0745\n",
      "     5        0.4068            -nan     0.1000    0.0638\n",
      "     6        0.3543            -nan     0.1000    0.0518\n",
      "     7        0.3092            -nan     0.1000    0.0450\n",
      "     8        0.2717            -nan     0.1000    0.0380\n",
      "     9        0.2394            -nan     0.1000    0.0321\n",
      "    10        0.2122            -nan     0.1000    0.0277\n",
      "    20        0.0772            -nan     0.1000    0.0068\n",
      "    40        0.0269            -nan     0.1000    0.0007\n",
      "    60        0.0180            -nan     0.1000    0.0002\n",
      "    80        0.0146            -nan     0.1000    0.0001\n",
      "   100        0.0130            -nan     0.1000    0.0000\n",
      "   120        0.0119            -nan     0.1000   -0.0000\n",
      "   140        0.0113            -nan     0.1000   -0.0000\n",
      "   150        0.0110            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7160            -nan     0.1000    0.1422\n",
      "     2        0.5977            -nan     0.1000    0.1169\n",
      "     3        0.4978            -nan     0.1000    0.0957\n",
      "     4        0.4159            -nan     0.1000    0.0797\n",
      "     5        0.3479            -nan     0.1000    0.0656\n",
      "     6        0.2940            -nan     0.1000    0.0536\n",
      "     7        0.2485            -nan     0.1000    0.0445\n",
      "     8        0.2112            -nan     0.1000    0.0347\n",
      "     9        0.1789            -nan     0.1000    0.0312\n",
      "    10        0.1531            -nan     0.1000    0.0266\n",
      "    20        0.0454            -nan     0.1000    0.0044\n",
      "    40        0.0162            -nan     0.1000    0.0004\n",
      "    60        0.0112            -nan     0.1000    0.0001\n",
      "    80        0.0093            -nan     0.1000    0.0000\n",
      "   100        0.0082            -nan     0.1000    0.0000\n",
      "   120        0.0072            -nan     0.1000    0.0000\n",
      "   140        0.0066            -nan     0.1000    0.0000\n",
      "   150        0.0064            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7072            -nan     0.1000    0.1550\n",
      "     2        0.5828            -nan     0.1000    0.1238\n",
      "     3        0.4830            -nan     0.1000    0.0976\n",
      "     4        0.4018            -nan     0.1000    0.0825\n",
      "     5        0.3341            -nan     0.1000    0.0675\n",
      "     6        0.2783            -nan     0.1000    0.0553\n",
      "     7        0.2334            -nan     0.1000    0.0451\n",
      "     8        0.1960            -nan     0.1000    0.0363\n",
      "     9        0.1658            -nan     0.1000    0.0285\n",
      "    10        0.1407            -nan     0.1000    0.0239\n",
      "    20        0.0382            -nan     0.1000    0.0038\n",
      "    40        0.0122            -nan     0.1000    0.0002\n",
      "    60        0.0086            -nan     0.1000    0.0000\n",
      "    80        0.0071            -nan     0.1000    0.0000\n",
      "   100        0.0060            -nan     0.1000   -0.0000\n",
      "   120        0.0054            -nan     0.1000   -0.0000\n",
      "   140        0.0049            -nan     0.1000   -0.0000\n",
      "   150        0.0046            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7389            -nan     0.1000    0.1323\n",
      "     2        0.6324            -nan     0.1000    0.1069\n",
      "     3        0.5486            -nan     0.1000    0.0884\n",
      "     4        0.4765            -nan     0.1000    0.0740\n",
      "     5        0.4119            -nan     0.1000    0.0630\n",
      "     6        0.3600            -nan     0.1000    0.0529\n",
      "     7        0.3141            -nan     0.1000    0.0464\n",
      "     8        0.2763            -nan     0.1000    0.0381\n",
      "     9        0.2424            -nan     0.1000    0.0334\n",
      "    10        0.2139            -nan     0.1000    0.0275\n",
      "    20        0.0759            -nan     0.1000    0.0064\n",
      "    40        0.0262            -nan     0.1000    0.0007\n",
      "    60        0.0172            -nan     0.1000    0.0002\n",
      "    80        0.0138            -nan     0.1000    0.0001\n",
      "   100        0.0121            -nan     0.1000    0.0000\n",
      "   120        0.0112            -nan     0.1000    0.0000\n",
      "   140        0.0105            -nan     0.1000    0.0000\n",
      "   150        0.0101            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7266            -nan     0.1000    0.1461\n",
      "     2        0.6093            -nan     0.1000    0.1176\n",
      "     3        0.5065            -nan     0.1000    0.1015\n",
      "     4        0.4269            -nan     0.1000    0.0756\n",
      "     5        0.3593            -nan     0.1000    0.0665\n",
      "     6        0.3006            -nan     0.1000    0.0614\n",
      "     7        0.2541            -nan     0.1000    0.0459\n",
      "     8        0.2149            -nan     0.1000    0.0392\n",
      "     9        0.1821            -nan     0.1000    0.0316\n",
      "    10        0.1562            -nan     0.1000    0.0259\n",
      "    20        0.0458            -nan     0.1000    0.0052\n",
      "    40        0.0153            -nan     0.1000    0.0003\n",
      "    60        0.0104            -nan     0.1000    0.0001\n",
      "    80        0.0086            -nan     0.1000    0.0000\n",
      "   100        0.0075            -nan     0.1000   -0.0000\n",
      "   120        0.0068            -nan     0.1000    0.0000\n",
      "   140        0.0062            -nan     0.1000    0.0000\n",
      "   150        0.0060            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7180            -nan     0.1000    0.1532\n",
      "     2        0.5921            -nan     0.1000    0.1254\n",
      "     3        0.4905            -nan     0.1000    0.1015\n",
      "     4        0.4065            -nan     0.1000    0.0824\n",
      "     5        0.3381            -nan     0.1000    0.0709\n",
      "     6        0.2813            -nan     0.1000    0.0566\n",
      "     7        0.2355            -nan     0.1000    0.0445\n",
      "     8        0.1969            -nan     0.1000    0.0368\n",
      "     9        0.1656            -nan     0.1000    0.0304\n",
      "    10        0.1406            -nan     0.1000    0.0249\n",
      "    20        0.0361            -nan     0.1000    0.0043\n",
      "    40        0.0112            -nan     0.1000    0.0003\n",
      "    60        0.0078            -nan     0.1000    0.0000\n",
      "    80        0.0064            -nan     0.1000   -0.0000\n",
      "   100        0.0055            -nan     0.1000    0.0000\n",
      "   120        0.0049            -nan     0.1000   -0.0000\n",
      "   140        0.0045            -nan     0.1000   -0.0000\n",
      "   150        0.0043            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7138            -nan     0.1000    0.1218\n",
      "     2        0.6126            -nan     0.1000    0.1026\n",
      "     3        0.5279            -nan     0.1000    0.0851\n",
      "     4        0.4589            -nan     0.1000    0.0704\n",
      "     5        0.4001            -nan     0.1000    0.0580\n",
      "     6        0.3468            -nan     0.1000    0.0513\n",
      "     7        0.3035            -nan     0.1000    0.0435\n",
      "     8        0.2655            -nan     0.1000    0.0367\n",
      "     9        0.2332            -nan     0.1000    0.0318\n",
      "    10        0.2056            -nan     0.1000    0.0267\n",
      "    20        0.0744            -nan     0.1000    0.0071\n",
      "    40        0.0257            -nan     0.1000    0.0006\n",
      "    60        0.0176            -nan     0.1000    0.0003\n",
      "    80        0.0141            -nan     0.1000    0.0001\n",
      "   100        0.0123            -nan     0.1000    0.0000\n",
      "   120        0.0114            -nan     0.1000    0.0000\n",
      "   140        0.0107            -nan     0.1000   -0.0000\n",
      "   150        0.0105            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.6972            -nan     0.1000    0.1390\n",
      "     2        0.5793            -nan     0.1000    0.1145\n",
      "     3        0.4856            -nan     0.1000    0.0972\n",
      "     4        0.4094            -nan     0.1000    0.0740\n",
      "     5        0.3427            -nan     0.1000    0.0631\n",
      "     6        0.2893            -nan     0.1000    0.0531\n",
      "     7        0.2450            -nan     0.1000    0.0435\n",
      "     8        0.2092            -nan     0.1000    0.0357\n",
      "     9        0.1775            -nan     0.1000    0.0311\n",
      "    10        0.1513            -nan     0.1000    0.0263\n",
      "    20        0.0447            -nan     0.1000    0.0039\n",
      "    40        0.0161            -nan     0.1000    0.0003\n",
      "    60        0.0111            -nan     0.1000    0.0001\n",
      "    80        0.0089            -nan     0.1000   -0.0000\n",
      "   100        0.0079            -nan     0.1000   -0.0000\n",
      "   120        0.0071            -nan     0.1000   -0.0000\n",
      "   140        0.0065            -nan     0.1000   -0.0000\n",
      "   150        0.0062            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.6864            -nan     0.1000    0.1420\n",
      "     2        0.5677            -nan     0.1000    0.1188\n",
      "     3        0.4709            -nan     0.1000    0.0957\n",
      "     4        0.3901            -nan     0.1000    0.0791\n",
      "     5        0.3243            -nan     0.1000    0.0648\n",
      "     6        0.2700            -nan     0.1000    0.0558\n",
      "     7        0.2266            -nan     0.1000    0.0434\n",
      "     8        0.1908            -nan     0.1000    0.0342\n",
      "     9        0.1610            -nan     0.1000    0.0307\n",
      "    10        0.1373            -nan     0.1000    0.0239\n",
      "    20        0.0359            -nan     0.1000    0.0038\n",
      "    40        0.0116            -nan     0.1000    0.0003\n",
      "    60        0.0079            -nan     0.1000    0.0000\n",
      "    80        0.0064            -nan     0.1000    0.0000\n",
      "   100        0.0055            -nan     0.1000    0.0000\n",
      "   120        0.0049            -nan     0.1000   -0.0000\n",
      "   140        0.0045            -nan     0.1000   -0.0000\n",
      "   150        0.0042            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7449            -nan     0.1000    0.1312\n",
      "     2        0.6379            -nan     0.1000    0.1073\n",
      "     3        0.5483            -nan     0.1000    0.0901\n",
      "     4        0.4728            -nan     0.1000    0.0749\n",
      "     5        0.4079            -nan     0.1000    0.0626\n",
      "     6        0.3554            -nan     0.1000    0.0513\n",
      "     7        0.3095            -nan     0.1000    0.0466\n",
      "     8        0.2730            -nan     0.1000    0.0377\n",
      "     9        0.2393            -nan     0.1000    0.0311\n",
      "    10        0.2108            -nan     0.1000    0.0282\n",
      "    20        0.0760            -nan     0.1000    0.0061\n",
      "    40        0.0270            -nan     0.1000    0.0008\n",
      "    60        0.0185            -nan     0.1000    0.0003\n",
      "    80        0.0151            -nan     0.1000    0.0000\n",
      "   100        0.0132            -nan     0.1000    0.0000\n",
      "   120        0.0122            -nan     0.1000    0.0000\n",
      "   140        0.0116            -nan     0.1000    0.0000\n",
      "   150        0.0112            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7295            -nan     0.1000    0.1488\n",
      "     2        0.6091            -nan     0.1000    0.1228\n",
      "     3        0.5109            -nan     0.1000    0.0945\n",
      "     4        0.4266            -nan     0.1000    0.0877\n",
      "     5        0.3589            -nan     0.1000    0.0659\n",
      "     6        0.3004            -nan     0.1000    0.0566\n",
      "     7        0.2536            -nan     0.1000    0.0486\n",
      "     8        0.2141            -nan     0.1000    0.0406\n",
      "     9        0.1813            -nan     0.1000    0.0326\n",
      "    10        0.1541            -nan     0.1000    0.0272\n",
      "    20        0.0455            -nan     0.1000    0.0043\n",
      "    40        0.0166            -nan     0.1000    0.0003\n",
      "    60        0.0114            -nan     0.1000    0.0001\n",
      "    80        0.0094            -nan     0.1000   -0.0000\n",
      "   100        0.0082            -nan     0.1000   -0.0000\n",
      "   120        0.0073            -nan     0.1000    0.0001\n",
      "   140        0.0066            -nan     0.1000   -0.0000\n",
      "   150        0.0063            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7206            -nan     0.1000    0.1584\n",
      "     2        0.5945            -nan     0.1000    0.1288\n",
      "     3        0.4904            -nan     0.1000    0.0992\n",
      "     4        0.4060            -nan     0.1000    0.0860\n",
      "     5        0.3370            -nan     0.1000    0.0725\n",
      "     6        0.2803            -nan     0.1000    0.0604\n",
      "     7        0.2347            -nan     0.1000    0.0436\n",
      "     8        0.1963            -nan     0.1000    0.0371\n",
      "     9        0.1656            -nan     0.1000    0.0306\n",
      "    10        0.1406            -nan     0.1000    0.0236\n",
      "    20        0.0372            -nan     0.1000    0.0037\n",
      "    40        0.0120            -nan     0.1000    0.0004\n",
      "    60        0.0081            -nan     0.1000    0.0001\n",
      "    80        0.0066            -nan     0.1000   -0.0000\n",
      "   100        0.0056            -nan     0.1000   -0.0000\n",
      "   120        0.0050            -nan     0.1000   -0.0000\n",
      "   140        0.0046            -nan     0.1000    0.0000\n",
      "   150        0.0044            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7278            -nan     0.1000    0.1268\n",
      "     2        0.6229            -nan     0.1000    0.1044\n",
      "     3        0.5366            -nan     0.1000    0.0881\n",
      "     4        0.4650            -nan     0.1000    0.0734\n",
      "     5        0.4036            -nan     0.1000    0.0605\n",
      "     6        0.3511            -nan     0.1000    0.0523\n",
      "     7        0.3071            -nan     0.1000    0.0450\n",
      "     8        0.2694            -nan     0.1000    0.0377\n",
      "     9        0.2371            -nan     0.1000    0.0314\n",
      "    10        0.2094            -nan     0.1000    0.0278\n",
      "    20        0.0749            -nan     0.1000    0.0062\n",
      "    40        0.0269            -nan     0.1000    0.0005\n",
      "    60        0.0181            -nan     0.1000    0.0002\n",
      "    80        0.0145            -nan     0.1000    0.0001\n",
      "   100        0.0128            -nan     0.1000    0.0000\n",
      "   120        0.0117            -nan     0.1000   -0.0000\n",
      "   140        0.0111            -nan     0.1000    0.0000\n",
      "   150        0.0108            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7134            -nan     0.1000    0.1434\n",
      "     2        0.5907            -nan     0.1000    0.1202\n",
      "     3        0.4941            -nan     0.1000    0.0945\n",
      "     4        0.4134            -nan     0.1000    0.0834\n",
      "     5        0.3452            -nan     0.1000    0.0700\n",
      "     6        0.2905            -nan     0.1000    0.0529\n",
      "     7        0.2435            -nan     0.1000    0.0455\n",
      "     8        0.2066            -nan     0.1000    0.0376\n",
      "     9        0.1771            -nan     0.1000    0.0295\n",
      "    10        0.1518            -nan     0.1000    0.0251\n",
      "    20        0.0454            -nan     0.1000    0.0041\n",
      "    40        0.0160            -nan     0.1000    0.0005\n",
      "    60        0.0112            -nan     0.1000    0.0001\n",
      "    80        0.0095            -nan     0.1000    0.0000\n",
      "   100        0.0083            -nan     0.1000    0.0001\n",
      "   120        0.0074            -nan     0.1000    0.0000\n",
      "   140        0.0069            -nan     0.1000   -0.0000\n",
      "   150        0.0067            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7041            -nan     0.1000    0.1541\n",
      "     2        0.5820            -nan     0.1000    0.1245\n",
      "     3        0.4807            -nan     0.1000    0.1027\n",
      "     4        0.3969            -nan     0.1000    0.0829\n",
      "     5        0.3294            -nan     0.1000    0.0692\n",
      "     6        0.2743            -nan     0.1000    0.0553\n",
      "     7        0.2299            -nan     0.1000    0.0462\n",
      "     8        0.1941            -nan     0.1000    0.0374\n",
      "     9        0.1645            -nan     0.1000    0.0298\n",
      "    10        0.1397            -nan     0.1000    0.0249\n",
      "    20        0.0370            -nan     0.1000    0.0035\n",
      "    40        0.0119            -nan     0.1000    0.0003\n",
      "    60        0.0085            -nan     0.1000    0.0001\n",
      "    80        0.0070            -nan     0.1000    0.0000\n",
      "   100        0.0060            -nan     0.1000   -0.0000\n",
      "   120        0.0053            -nan     0.1000   -0.0000\n",
      "   140        0.0048            -nan     0.1000   -0.0000\n",
      "   150        0.0046            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7483            -nan     0.1000    0.1317\n",
      "     2        0.6411            -nan     0.1000    0.1090\n",
      "     3        0.5491            -nan     0.1000    0.0922\n",
      "     4        0.4741            -nan     0.1000    0.0748\n",
      "     5        0.4101            -nan     0.1000    0.0630\n",
      "     6        0.3594            -nan     0.1000    0.0510\n",
      "     7        0.3115            -nan     0.1000    0.0484\n",
      "     8        0.2727            -nan     0.1000    0.0377\n",
      "     9        0.2402            -nan     0.1000    0.0333\n",
      "    10        0.2130            -nan     0.1000    0.0284\n",
      "    20        0.0760            -nan     0.1000    0.0068\n",
      "    40        0.0283            -nan     0.1000    0.0008\n",
      "    60        0.0192            -nan     0.1000    0.0002\n",
      "    80        0.0154            -nan     0.1000    0.0001\n",
      "   100        0.0134            -nan     0.1000    0.0000\n",
      "   120        0.0123            -nan     0.1000    0.0000\n",
      "   140        0.0115            -nan     0.1000    0.0000\n",
      "   150        0.0111            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7371            -nan     0.1000    0.1460\n",
      "     2        0.6102            -nan     0.1000    0.1250\n",
      "     3        0.5070            -nan     0.1000    0.0987\n",
      "     4        0.4259            -nan     0.1000    0.0778\n",
      "     5        0.3569            -nan     0.1000    0.0704\n",
      "     6        0.3011            -nan     0.1000    0.0581\n",
      "     7        0.2566            -nan     0.1000    0.0437\n",
      "     8        0.2195            -nan     0.1000    0.0367\n",
      "     9        0.1860            -nan     0.1000    0.0327\n",
      "    10        0.1579            -nan     0.1000    0.0275\n",
      "    20        0.0467            -nan     0.1000    0.0039\n",
      "    40        0.0166            -nan     0.1000    0.0004\n",
      "    60        0.0113            -nan     0.1000    0.0001\n",
      "    80        0.0092            -nan     0.1000   -0.0000\n",
      "   100        0.0078            -nan     0.1000    0.0000\n",
      "   120        0.0068            -nan     0.1000    0.0000\n",
      "   140        0.0062            -nan     0.1000    0.0000\n",
      "   150        0.0059            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7258            -nan     0.1000    0.1550\n",
      "     2        0.5994            -nan     0.1000    0.1310\n",
      "     3        0.4960            -nan     0.1000    0.0987\n",
      "     4        0.4106            -nan     0.1000    0.0829\n",
      "     5        0.3420            -nan     0.1000    0.0680\n",
      "     6        0.2856            -nan     0.1000    0.0566\n",
      "     7        0.2383            -nan     0.1000    0.0458\n",
      "     8        0.2008            -nan     0.1000    0.0372\n",
      "     9        0.1699            -nan     0.1000    0.0319\n",
      "    10        0.1444            -nan     0.1000    0.0252\n",
      "    20        0.0389            -nan     0.1000    0.0043\n",
      "    40        0.0123            -nan     0.1000    0.0003\n",
      "    60        0.0083            -nan     0.1000    0.0000\n",
      "    80        0.0064            -nan     0.1000    0.0001\n",
      "   100        0.0054            -nan     0.1000    0.0000\n",
      "   120        0.0047            -nan     0.1000    0.0000\n",
      "   140        0.0042            -nan     0.1000   -0.0000\n",
      "   150        0.0039            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7522            -nan     0.1000    0.1328\n",
      "     2        0.6431            -nan     0.1000    0.1090\n",
      "     3        0.5542            -nan     0.1000    0.0894\n",
      "     4        0.4791            -nan     0.1000    0.0762\n",
      "     5        0.4160            -nan     0.1000    0.0632\n",
      "     6        0.3661            -nan     0.1000    0.0528\n",
      "     7        0.3220            -nan     0.1000    0.0451\n",
      "     8        0.2797            -nan     0.1000    0.0408\n",
      "     9        0.2471            -nan     0.1000    0.0321\n",
      "    10        0.2178            -nan     0.1000    0.0294\n",
      "    20        0.0792            -nan     0.1000    0.0066\n",
      "    40        0.0278            -nan     0.1000    0.0007\n",
      "    60        0.0187            -nan     0.1000    0.0001\n",
      "    80        0.0150            -nan     0.1000    0.0001\n",
      "   100        0.0132            -nan     0.1000    0.0000\n",
      "   120        0.0121            -nan     0.1000    0.0000\n",
      "   140        0.0113            -nan     0.1000   -0.0000\n",
      "   150        0.0110            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7295            -nan     0.1000    0.1498\n",
      "     2        0.6087            -nan     0.1000    0.1148\n",
      "     3        0.5085            -nan     0.1000    0.0985\n",
      "     4        0.4291            -nan     0.1000    0.0814\n",
      "     5        0.3619            -nan     0.1000    0.0656\n",
      "     6        0.3039            -nan     0.1000    0.0572\n",
      "     7        0.2602            -nan     0.1000    0.0446\n",
      "     8        0.2188            -nan     0.1000    0.0420\n",
      "     9        0.1877            -nan     0.1000    0.0318\n",
      "    10        0.1608            -nan     0.1000    0.0272\n",
      "    20        0.0483            -nan     0.1000    0.0047\n",
      "    40        0.0162            -nan     0.1000    0.0003\n",
      "    60        0.0109            -nan     0.1000    0.0001\n",
      "    80        0.0092            -nan     0.1000    0.0000\n",
      "   100        0.0081            -nan     0.1000    0.0000\n",
      "   120        0.0072            -nan     0.1000   -0.0000\n",
      "   140        0.0066            -nan     0.1000   -0.0000\n",
      "   150        0.0063            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7265            -nan     0.1000    0.1541\n",
      "     2        0.6004            -nan     0.1000    0.1315\n",
      "     3        0.4968            -nan     0.1000    0.1036\n",
      "     4        0.4113            -nan     0.1000    0.0863\n",
      "     5        0.3431            -nan     0.1000    0.0659\n",
      "     6        0.2854            -nan     0.1000    0.0560\n",
      "     7        0.2388            -nan     0.1000    0.0481\n",
      "     8        0.2007            -nan     0.1000    0.0387\n",
      "     9        0.1688            -nan     0.1000    0.0307\n",
      "    10        0.1427            -nan     0.1000    0.0264\n",
      "    20        0.0388            -nan     0.1000    0.0039\n",
      "    40        0.0118            -nan     0.1000    0.0002\n",
      "    60        0.0082            -nan     0.1000    0.0000\n",
      "    80        0.0068            -nan     0.1000    0.0000\n",
      "   100        0.0058            -nan     0.1000   -0.0000\n",
      "   120        0.0052            -nan     0.1000    0.0000\n",
      "   140        0.0046            -nan     0.1000   -0.0000\n",
      "   150        0.0044            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7433            -nan     0.1000    0.1289\n",
      "     2        0.6355            -nan     0.1000    0.1059\n",
      "     3        0.5481            -nan     0.1000    0.0881\n",
      "     4        0.4738            -nan     0.1000    0.0742\n",
      "     5        0.4109            -nan     0.1000    0.0612\n",
      "     6        0.3587            -nan     0.1000    0.0519\n",
      "     7        0.3127            -nan     0.1000    0.0450\n",
      "     8        0.2734            -nan     0.1000    0.0386\n",
      "     9        0.2409            -nan     0.1000    0.0315\n",
      "    10        0.2126            -nan     0.1000    0.0273\n",
      "    20        0.0776            -nan     0.1000    0.0071\n",
      "    40        0.0277            -nan     0.1000    0.0005\n",
      "    60        0.0187            -nan     0.1000    0.0003\n",
      "    80        0.0148            -nan     0.1000    0.0001\n",
      "   100        0.0128            -nan     0.1000    0.0000\n",
      "   120        0.0118            -nan     0.1000    0.0000\n",
      "   140        0.0110            -nan     0.1000   -0.0000\n",
      "   150        0.0107            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7241            -nan     0.1000    0.1482\n",
      "     2        0.6058            -nan     0.1000    0.1180\n",
      "     3        0.5112            -nan     0.1000    0.0936\n",
      "     4        0.4298            -nan     0.1000    0.0794\n",
      "     5        0.3614            -nan     0.1000    0.0675\n",
      "     6        0.3023            -nan     0.1000    0.0574\n",
      "     7        0.2551            -nan     0.1000    0.0463\n",
      "     8        0.2181            -nan     0.1000    0.0363\n",
      "     9        0.1860            -nan     0.1000    0.0330\n",
      "    10        0.1593            -nan     0.1000    0.0256\n",
      "    20        0.0469            -nan     0.1000    0.0044\n",
      "    40        0.0170            -nan     0.1000    0.0003\n",
      "    60        0.0116            -nan     0.1000    0.0001\n",
      "    80        0.0096            -nan     0.1000    0.0000\n",
      "   100        0.0085            -nan     0.1000    0.0000\n",
      "   120        0.0076            -nan     0.1000    0.0000\n",
      "   140        0.0069            -nan     0.1000   -0.0000\n",
      "   150        0.0067            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7169            -nan     0.1000    0.1536\n",
      "     2        0.5912            -nan     0.1000    0.1233\n",
      "     3        0.4878            -nan     0.1000    0.1025\n",
      "     4        0.4043            -nan     0.1000    0.0832\n",
      "     5        0.3364            -nan     0.1000    0.0657\n",
      "     6        0.2809            -nan     0.1000    0.0552\n",
      "     7        0.2352            -nan     0.1000    0.0455\n",
      "     8        0.1975            -nan     0.1000    0.0385\n",
      "     9        0.1667            -nan     0.1000    0.0312\n",
      "    10        0.1415            -nan     0.1000    0.0240\n",
      "    20        0.0373            -nan     0.1000    0.0038\n",
      "    40        0.0120            -nan     0.1000    0.0003\n",
      "    60        0.0082            -nan     0.1000    0.0001\n",
      "    80        0.0069            -nan     0.1000    0.0000\n",
      "   100        0.0059            -nan     0.1000    0.0000\n",
      "   120        0.0052            -nan     0.1000   -0.0000\n",
      "   140        0.0047            -nan     0.1000    0.0000\n",
      "   150        0.0045            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7499            -nan     0.1000    0.1243\n",
      "     2        0.6430            -nan     0.1000    0.1094\n",
      "     3        0.5543            -nan     0.1000    0.0883\n",
      "     4        0.4773            -nan     0.1000    0.0738\n",
      "     5        0.4133            -nan     0.1000    0.0637\n",
      "     6        0.3609            -nan     0.1000    0.0504\n",
      "     7        0.3139            -nan     0.1000    0.0462\n",
      "     8        0.2760            -nan     0.1000    0.0375\n",
      "     9        0.2422            -nan     0.1000    0.0332\n",
      "    10        0.2135            -nan     0.1000    0.0279\n",
      "    20        0.0765            -nan     0.1000    0.0067\n",
      "    40        0.0272            -nan     0.1000    0.0007\n",
      "    60        0.0185            -nan     0.1000    0.0002\n",
      "    80        0.0147            -nan     0.1000    0.0001\n",
      "   100        0.0128            -nan     0.1000    0.0000\n",
      "   120        0.0117            -nan     0.1000    0.0000\n",
      "   140        0.0110            -nan     0.1000    0.0000\n",
      "   150        0.0106            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7304            -nan     0.1000    0.1429\n",
      "     2        0.6139            -nan     0.1000    0.1161\n",
      "     3        0.5147            -nan     0.1000    0.0980\n",
      "     4        0.4325            -nan     0.1000    0.0847\n",
      "     5        0.3646            -nan     0.1000    0.0695\n",
      "     6        0.3082            -nan     0.1000    0.0548\n",
      "     7        0.2589            -nan     0.1000    0.0502\n",
      "     8        0.2202            -nan     0.1000    0.0375\n",
      "     9        0.1894            -nan     0.1000    0.0302\n",
      "    10        0.1625            -nan     0.1000    0.0268\n",
      "    20        0.0458            -nan     0.1000    0.0041\n",
      "    40        0.0162            -nan     0.1000    0.0004\n",
      "    60        0.0104            -nan     0.1000    0.0001\n",
      "    80        0.0082            -nan     0.1000    0.0000\n",
      "   100        0.0069            -nan     0.1000    0.0000\n",
      "   120        0.0062            -nan     0.1000    0.0000\n",
      "   140        0.0056            -nan     0.1000   -0.0000\n",
      "   150        0.0054            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7230            -nan     0.1000    0.1595\n",
      "     2        0.5967            -nan     0.1000    0.1294\n",
      "     3        0.4953            -nan     0.1000    0.1070\n",
      "     4        0.4128            -nan     0.1000    0.0883\n",
      "     5        0.3413            -nan     0.1000    0.0693\n",
      "     6        0.2847            -nan     0.1000    0.0560\n",
      "     7        0.2383            -nan     0.1000    0.0448\n",
      "     8        0.1993            -nan     0.1000    0.0384\n",
      "     9        0.1683            -nan     0.1000    0.0308\n",
      "    10        0.1429            -nan     0.1000    0.0254\n",
      "    20        0.0371            -nan     0.1000    0.0041\n",
      "    40        0.0112            -nan     0.1000    0.0003\n",
      "    60        0.0076            -nan     0.1000    0.0001\n",
      "    80        0.0059            -nan     0.1000    0.0000\n",
      "   100        0.0049            -nan     0.1000    0.0000\n",
      "   120        0.0044            -nan     0.1000    0.0000\n",
      "   140        0.0039            -nan     0.1000    0.0000\n",
      "   150        0.0037            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7528            -nan     0.1000    0.1347\n",
      "     2        0.6399            -nan     0.1000    0.1095\n",
      "     3        0.5476            -nan     0.1000    0.0924\n",
      "     4        0.4721            -nan     0.1000    0.0761\n",
      "     5        0.4098            -nan     0.1000    0.0621\n",
      "     6        0.3560            -nan     0.1000    0.0517\n",
      "     7        0.3114            -nan     0.1000    0.0436\n",
      "     8        0.2706            -nan     0.1000    0.0397\n",
      "     9        0.2360            -nan     0.1000    0.0325\n",
      "    10        0.2081            -nan     0.1000    0.0286\n",
      "    20        0.0721            -nan     0.1000    0.0063\n",
      "    40        0.0259            -nan     0.1000    0.0007\n",
      "    60        0.0176            -nan     0.1000    0.0001\n",
      "    80        0.0142            -nan     0.1000    0.0001\n",
      "   100        0.0125            -nan     0.1000    0.0000\n",
      "   120        0.0115            -nan     0.1000    0.0000\n",
      "   140        0.0109            -nan     0.1000    0.0000\n",
      "   150        0.0106            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7383            -nan     0.1000    0.1533\n",
      "     2        0.6155            -nan     0.1000    0.1249\n",
      "     3        0.5133            -nan     0.1000    0.1052\n",
      "     4        0.4307            -nan     0.1000    0.0821\n",
      "     5        0.3615            -nan     0.1000    0.0683\n",
      "     6        0.3052            -nan     0.1000    0.0580\n",
      "     7        0.2585            -nan     0.1000    0.0445\n",
      "     8        0.2203            -nan     0.1000    0.0384\n",
      "     9        0.1879            -nan     0.1000    0.0325\n",
      "    10        0.1588            -nan     0.1000    0.0299\n",
      "    20        0.0456            -nan     0.1000    0.0045\n",
      "    40        0.0159            -nan     0.1000    0.0003\n",
      "    60        0.0107            -nan     0.1000    0.0001\n",
      "    80        0.0087            -nan     0.1000    0.0000\n",
      "   100        0.0075            -nan     0.1000    0.0000\n",
      "   120        0.0067            -nan     0.1000    0.0000\n",
      "   140        0.0060            -nan     0.1000    0.0000\n",
      "   150        0.0057            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7323            -nan     0.1000    0.1588\n",
      "     2        0.6033            -nan     0.1000    0.1268\n",
      "     3        0.4961            -nan     0.1000    0.1037\n",
      "     4        0.4107            -nan     0.1000    0.0823\n",
      "     5        0.3397            -nan     0.1000    0.0695\n",
      "     6        0.2817            -nan     0.1000    0.0573\n",
      "     7        0.2340            -nan     0.1000    0.0454\n",
      "     8        0.1965            -nan     0.1000    0.0384\n",
      "     9        0.1658            -nan     0.1000    0.0304\n",
      "    10        0.1396            -nan     0.1000    0.0258\n",
      "    20        0.0361            -nan     0.1000    0.0039\n",
      "    40        0.0112            -nan     0.1000    0.0002\n",
      "    60        0.0074            -nan     0.1000   -0.0000\n",
      "    80        0.0060            -nan     0.1000    0.0000\n",
      "   100        0.0051            -nan     0.1000    0.0000\n",
      "   120        0.0044            -nan     0.1000    0.0000\n",
      "   140        0.0040            -nan     0.1000   -0.0000\n",
      "   150        0.0038            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7317            -nan     0.1000    0.1296\n",
      "     2        0.6270            -nan     0.1000    0.1070\n",
      "     3        0.5377            -nan     0.1000    0.0894\n",
      "     4        0.4649            -nan     0.1000    0.0705\n",
      "     5        0.4033            -nan     0.1000    0.0620\n",
      "     6        0.3497            -nan     0.1000    0.0515\n",
      "     7        0.3063            -nan     0.1000    0.0453\n",
      "     8        0.2690            -nan     0.1000    0.0377\n",
      "     9        0.2363            -nan     0.1000    0.0337\n",
      "    10        0.2083            -nan     0.1000    0.0279\n",
      "    20        0.0748            -nan     0.1000    0.0065\n",
      "    40        0.0253            -nan     0.1000    0.0007\n",
      "    60        0.0169            -nan     0.1000    0.0001\n",
      "    80        0.0137            -nan     0.1000    0.0001\n",
      "   100        0.0123            -nan     0.1000   -0.0000\n",
      "   120        0.0114            -nan     0.1000    0.0000\n",
      "   140        0.0107            -nan     0.1000    0.0000\n",
      "   150        0.0103            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7175            -nan     0.1000    0.1393\n",
      "     2        0.5952            -nan     0.1000    0.1159\n",
      "     3        0.4985            -nan     0.1000    0.0934\n",
      "     4        0.4183            -nan     0.1000    0.0808\n",
      "     5        0.3512            -nan     0.1000    0.0689\n",
      "     6        0.2981            -nan     0.1000    0.0524\n",
      "     7        0.2520            -nan     0.1000    0.0436\n",
      "     8        0.2132            -nan     0.1000    0.0398\n",
      "     9        0.1813            -nan     0.1000    0.0322\n",
      "    10        0.1546            -nan     0.1000    0.0272\n",
      "    20        0.0437            -nan     0.1000    0.0046\n",
      "    40        0.0150            -nan     0.1000    0.0003\n",
      "    60        0.0105            -nan     0.1000    0.0001\n",
      "    80        0.0086            -nan     0.1000    0.0000\n",
      "   100        0.0076            -nan     0.1000    0.0000\n",
      "   120        0.0068            -nan     0.1000    0.0000\n",
      "   140        0.0062            -nan     0.1000   -0.0000\n",
      "   150        0.0060            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7053            -nan     0.1000    0.1527\n",
      "     2        0.5814            -nan     0.1000    0.1239\n",
      "     3        0.4804            -nan     0.1000    0.0972\n",
      "     4        0.3974            -nan     0.1000    0.0799\n",
      "     5        0.3303            -nan     0.1000    0.0691\n",
      "     6        0.2743            -nan     0.1000    0.0540\n",
      "     7        0.2285            -nan     0.1000    0.0471\n",
      "     8        0.1912            -nan     0.1000    0.0369\n",
      "     9        0.1605            -nan     0.1000    0.0291\n",
      "    10        0.1365            -nan     0.1000    0.0257\n",
      "    20        0.0354            -nan     0.1000    0.0037\n",
      "    40        0.0116            -nan     0.1000    0.0003\n",
      "    60        0.0082            -nan     0.1000   -0.0000\n",
      "    80        0.0064            -nan     0.1000    0.0000\n",
      "   100        0.0055            -nan     0.1000    0.0000\n",
      "   120        0.0048            -nan     0.1000   -0.0000\n",
      "   140        0.0044            -nan     0.1000   -0.0000\n",
      "   150        0.0042            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7207            -nan     0.1000    0.1260\n",
      "     2        0.6188            -nan     0.1000    0.1068\n",
      "     3        0.5330            -nan     0.1000    0.0840\n",
      "     4        0.4592            -nan     0.1000    0.0734\n",
      "     5        0.3988            -nan     0.1000    0.0608\n",
      "     6        0.3481            -nan     0.1000    0.0489\n",
      "     7        0.3046            -nan     0.1000    0.0416\n",
      "     8        0.2675            -nan     0.1000    0.0361\n",
      "     9        0.2381            -nan     0.1000    0.0312\n",
      "    10        0.2088            -nan     0.1000    0.0271\n",
      "    20        0.0746            -nan     0.1000    0.0077\n",
      "    40        0.0262            -nan     0.1000    0.0007\n",
      "    60        0.0176            -nan     0.1000    0.0003\n",
      "    80        0.0141            -nan     0.1000    0.0001\n",
      "   100        0.0125            -nan     0.1000    0.0000\n",
      "   120        0.0115            -nan     0.1000    0.0000\n",
      "   140        0.0108            -nan     0.1000   -0.0000\n",
      "   150        0.0105            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7040            -nan     0.1000    0.1376\n",
      "     2        0.5872            -nan     0.1000    0.1178\n",
      "     3        0.4921            -nan     0.1000    0.0935\n",
      "     4        0.4118            -nan     0.1000    0.0829\n",
      "     5        0.3466            -nan     0.1000    0.0702\n",
      "     6        0.2926            -nan     0.1000    0.0568\n",
      "     7        0.2469            -nan     0.1000    0.0481\n",
      "     8        0.2103            -nan     0.1000    0.0360\n",
      "     9        0.1798            -nan     0.1000    0.0316\n",
      "    10        0.1544            -nan     0.1000    0.0258\n",
      "    20        0.0445            -nan     0.1000    0.0049\n",
      "    40        0.0150            -nan     0.1000    0.0004\n",
      "    60        0.0101            -nan     0.1000    0.0001\n",
      "    80        0.0083            -nan     0.1000    0.0000\n",
      "   100        0.0073            -nan     0.1000   -0.0000\n",
      "   120        0.0067            -nan     0.1000    0.0000\n",
      "   140        0.0062            -nan     0.1000    0.0000\n",
      "   150        0.0059            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.6919            -nan     0.1000    0.1456\n",
      "     2        0.5689            -nan     0.1000    0.1239\n",
      "     3        0.4678            -nan     0.1000    0.0959\n",
      "     4        0.3904            -nan     0.1000    0.0822\n",
      "     5        0.3237            -nan     0.1000    0.0629\n",
      "     6        0.2695            -nan     0.1000    0.0556\n",
      "     7        0.2246            -nan     0.1000    0.0447\n",
      "     8        0.1886            -nan     0.1000    0.0356\n",
      "     9        0.1587            -nan     0.1000    0.0296\n",
      "    10        0.1351            -nan     0.1000    0.0236\n",
      "    20        0.0352            -nan     0.1000    0.0036\n",
      "    40        0.0114            -nan     0.1000    0.0000\n",
      "    60        0.0076            -nan     0.1000    0.0001\n",
      "    80        0.0062            -nan     0.1000    0.0000\n",
      "   100        0.0055            -nan     0.1000   -0.0000\n",
      "   120        0.0048            -nan     0.1000   -0.0000\n",
      "   140        0.0043            -nan     0.1000    0.0000\n",
      "   150        0.0042            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7673            -nan     0.1000    0.1336\n",
      "     2        0.6599            -nan     0.1000    0.1108\n",
      "     3        0.5690            -nan     0.1000    0.0932\n",
      "     4        0.4916            -nan     0.1000    0.0780\n",
      "     5        0.4268            -nan     0.1000    0.0650\n",
      "     6        0.3721            -nan     0.1000    0.0568\n",
      "     7        0.3244            -nan     0.1000    0.0452\n",
      "     8        0.2828            -nan     0.1000    0.0431\n",
      "     9        0.2486            -nan     0.1000    0.0344\n",
      "    10        0.2187            -nan     0.1000    0.0301\n",
      "    20        0.0759            -nan     0.1000    0.0067\n",
      "    40        0.0265            -nan     0.1000    0.0009\n",
      "    60        0.0174            -nan     0.1000    0.0001\n",
      "    80        0.0141            -nan     0.1000    0.0001\n",
      "   100        0.0124            -nan     0.1000    0.0000\n",
      "   120        0.0115            -nan     0.1000    0.0000\n",
      "   140        0.0108            -nan     0.1000   -0.0000\n",
      "   150        0.0105            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7504            -nan     0.1000    0.1500\n",
      "     2        0.6287            -nan     0.1000    0.1208\n",
      "     3        0.5261            -nan     0.1000    0.1040\n",
      "     4        0.4381            -nan     0.1000    0.0879\n",
      "     5        0.3681            -nan     0.1000    0.0705\n",
      "     6        0.3099            -nan     0.1000    0.0542\n",
      "     7        0.2616            -nan     0.1000    0.0497\n",
      "     8        0.2237            -nan     0.1000    0.0384\n",
      "     9        0.1901            -nan     0.1000    0.0332\n",
      "    10        0.1632            -nan     0.1000    0.0280\n",
      "    20        0.0471            -nan     0.1000    0.0050\n",
      "    40        0.0164            -nan     0.1000    0.0003\n",
      "    60        0.0112            -nan     0.1000    0.0001\n",
      "    80        0.0093            -nan     0.1000    0.0001\n",
      "   100        0.0079            -nan     0.1000    0.0000\n",
      "   120        0.0069            -nan     0.1000   -0.0000\n",
      "   140        0.0062            -nan     0.1000    0.0001\n",
      "   150        0.0059            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7391            -nan     0.1000    0.1598\n",
      "     2        0.6079            -nan     0.1000    0.1266\n",
      "     3        0.5013            -nan     0.1000    0.1057\n",
      "     4        0.4152            -nan     0.1000    0.0828\n",
      "     5        0.3447            -nan     0.1000    0.0682\n",
      "     6        0.2878            -nan     0.1000    0.0569\n",
      "     7        0.2395            -nan     0.1000    0.0482\n",
      "     8        0.2007            -nan     0.1000    0.0394\n",
      "     9        0.1689            -nan     0.1000    0.0313\n",
      "    10        0.1433            -nan     0.1000    0.0254\n",
      "    20        0.0365            -nan     0.1000    0.0042\n",
      "    40        0.0117            -nan     0.1000    0.0004\n",
      "    60        0.0081            -nan     0.1000    0.0001\n",
      "    80        0.0063            -nan     0.1000   -0.0000\n",
      "   100        0.0054            -nan     0.1000   -0.0000\n",
      "   120        0.0047            -nan     0.1000   -0.0000\n",
      "   140        0.0042            -nan     0.1000    0.0000\n",
      "   150        0.0040            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7497            -nan     0.1000    0.1319\n",
      "     2        0.6379            -nan     0.1000    0.1094\n",
      "     3        0.5490            -nan     0.1000    0.0883\n",
      "     4        0.4741            -nan     0.1000    0.0754\n",
      "     5        0.4109            -nan     0.1000    0.0640\n",
      "     6        0.3583            -nan     0.1000    0.0530\n",
      "     7        0.3124            -nan     0.1000    0.0483\n",
      "     8        0.2743            -nan     0.1000    0.0387\n",
      "     9        0.2401            -nan     0.1000    0.0336\n",
      "    10        0.2127            -nan     0.1000    0.0285\n",
      "    20        0.0747            -nan     0.1000    0.0073\n",
      "    40        0.0249            -nan     0.1000    0.0007\n",
      "    60        0.0167            -nan     0.1000    0.0002\n",
      "    80        0.0137            -nan     0.1000    0.0001\n",
      "   100        0.0122            -nan     0.1000   -0.0000\n",
      "   120        0.0112            -nan     0.1000    0.0000\n",
      "   140        0.0107            -nan     0.1000    0.0000\n",
      "   150        0.0104            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7307            -nan     0.1000    0.1439\n",
      "     2        0.6089            -nan     0.1000    0.1214\n",
      "     3        0.5124            -nan     0.1000    0.0942\n",
      "     4        0.4304            -nan     0.1000    0.0794\n",
      "     5        0.3617            -nan     0.1000    0.0708\n",
      "     6        0.3056            -nan     0.1000    0.0553\n",
      "     7        0.2578            -nan     0.1000    0.0475\n",
      "     8        0.2184            -nan     0.1000    0.0390\n",
      "     9        0.1862            -nan     0.1000    0.0322\n",
      "    10        0.1612            -nan     0.1000    0.0256\n",
      "    20        0.0454            -nan     0.1000    0.0046\n",
      "    40        0.0155            -nan     0.1000    0.0003\n",
      "    60        0.0109            -nan     0.1000    0.0001\n",
      "    80        0.0093            -nan     0.1000   -0.0001\n",
      "   100        0.0083            -nan     0.1000   -0.0000\n",
      "   120        0.0074            -nan     0.1000   -0.0000\n",
      "   140        0.0069            -nan     0.1000   -0.0000\n",
      "   150        0.0065            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7242            -nan     0.1000    0.1602\n",
      "     2        0.5974            -nan     0.1000    0.1226\n",
      "     3        0.4928            -nan     0.1000    0.1047\n",
      "     4        0.4088            -nan     0.1000    0.0847\n",
      "     5        0.3403            -nan     0.1000    0.0690\n",
      "     6        0.2826            -nan     0.1000    0.0587\n",
      "     7        0.2365            -nan     0.1000    0.0462\n",
      "     8        0.1977            -nan     0.1000    0.0395\n",
      "     9        0.1672            -nan     0.1000    0.0307\n",
      "    10        0.1412            -nan     0.1000    0.0255\n",
      "    20        0.0365            -nan     0.1000    0.0038\n",
      "    40        0.0117            -nan     0.1000    0.0002\n",
      "    60        0.0081            -nan     0.1000    0.0000\n",
      "    80        0.0066            -nan     0.1000    0.0000\n",
      "   100        0.0057            -nan     0.1000    0.0000\n",
      "   120        0.0051            -nan     0.1000   -0.0000\n",
      "   140        0.0046            -nan     0.1000   -0.0000\n",
      "   150        0.0044            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7501            -nan     0.1000    0.1350\n",
      "     2        0.6385            -nan     0.1000    0.1116\n",
      "     3        0.5461            -nan     0.1000    0.0930\n",
      "     4        0.4717            -nan     0.1000    0.0760\n",
      "     5        0.4090            -nan     0.1000    0.0625\n",
      "     6        0.3552            -nan     0.1000    0.0516\n",
      "     7        0.3092            -nan     0.1000    0.0464\n",
      "     8        0.2716            -nan     0.1000    0.0389\n",
      "     9        0.2387            -nan     0.1000    0.0316\n",
      "    10        0.2099            -nan     0.1000    0.0286\n",
      "    20        0.0754            -nan     0.1000    0.0062\n",
      "    40        0.0275            -nan     0.1000    0.0005\n",
      "    60        0.0186            -nan     0.1000    0.0003\n",
      "    80        0.0149            -nan     0.1000    0.0001\n",
      "   100        0.0131            -nan     0.1000    0.0000\n",
      "   120        0.0120            -nan     0.1000    0.0000\n",
      "   140        0.0112            -nan     0.1000    0.0000\n",
      "   150        0.0109            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7367            -nan     0.1000    0.1485\n",
      "     2        0.6128            -nan     0.1000    0.1212\n",
      "     3        0.5118            -nan     0.1000    0.0983\n",
      "     4        0.4291            -nan     0.1000    0.0864\n",
      "     5        0.3595            -nan     0.1000    0.0687\n",
      "     6        0.3026            -nan     0.1000    0.0558\n",
      "     7        0.2570            -nan     0.1000    0.0439\n",
      "     8        0.2185            -nan     0.1000    0.0394\n",
      "     9        0.1847            -nan     0.1000    0.0323\n",
      "    10        0.1601            -nan     0.1000    0.0259\n",
      "    20        0.0470            -nan     0.1000    0.0047\n",
      "    40        0.0171            -nan     0.1000    0.0005\n",
      "    60        0.0117            -nan     0.1000    0.0001\n",
      "    80        0.0096            -nan     0.1000    0.0000\n",
      "   100        0.0084            -nan     0.1000    0.0000\n",
      "   120        0.0075            -nan     0.1000    0.0000\n",
      "   140        0.0070            -nan     0.1000   -0.0000\n",
      "   150        0.0068            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7284            -nan     0.1000    0.1523\n",
      "     2        0.6000            -nan     0.1000    0.1338\n",
      "     3        0.4973            -nan     0.1000    0.1029\n",
      "     4        0.4117            -nan     0.1000    0.0866\n",
      "     5        0.3423            -nan     0.1000    0.0677\n",
      "     6        0.2843            -nan     0.1000    0.0575\n",
      "     7        0.2371            -nan     0.1000    0.0441\n",
      "     8        0.1997            -nan     0.1000    0.0358\n",
      "     9        0.1684            -nan     0.1000    0.0326\n",
      "    10        0.1429            -nan     0.1000    0.0254\n",
      "    20        0.0379            -nan     0.1000    0.0037\n",
      "    40        0.0129            -nan     0.1000    0.0003\n",
      "    60        0.0089            -nan     0.1000    0.0000\n",
      "    80        0.0072            -nan     0.1000    0.0000\n",
      "   100        0.0059            -nan     0.1000    0.0000\n",
      "   120        0.0053            -nan     0.1000    0.0000\n",
      "   140        0.0048            -nan     0.1000   -0.0000\n",
      "   150        0.0046            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7790            -nan     0.1000    0.1370\n",
      "     2        0.6657            -nan     0.1000    0.1137\n",
      "     3        0.5707            -nan     0.1000    0.0946\n",
      "     4        0.4926            -nan     0.1000    0.0782\n",
      "     5        0.4277            -nan     0.1000    0.0682\n",
      "     6        0.3698            -nan     0.1000    0.0557\n",
      "     7        0.3225            -nan     0.1000    0.0462\n",
      "     8        0.2817            -nan     0.1000    0.0404\n",
      "     9        0.2463            -nan     0.1000    0.0318\n",
      "    10        0.2167            -nan     0.1000    0.0287\n",
      "    20        0.0774            -nan     0.1000    0.0063\n",
      "    40        0.0276            -nan     0.1000    0.0007\n",
      "    60        0.0178            -nan     0.1000    0.0002\n",
      "    80        0.0142            -nan     0.1000    0.0001\n",
      "   100        0.0123            -nan     0.1000    0.0000\n",
      "   120        0.0113            -nan     0.1000    0.0000\n",
      "   140        0.0105            -nan     0.1000   -0.0000\n",
      "   150        0.0102            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7630            -nan     0.1000    0.1504\n",
      "     2        0.6336            -nan     0.1000    0.1274\n",
      "     3        0.5305            -nan     0.1000    0.1022\n",
      "     4        0.4459            -nan     0.1000    0.0858\n",
      "     5        0.3735            -nan     0.1000    0.0746\n",
      "     6        0.3158            -nan     0.1000    0.0573\n",
      "     7        0.2663            -nan     0.1000    0.0481\n",
      "     8        0.2254            -nan     0.1000    0.0406\n",
      "     9        0.1938            -nan     0.1000    0.0307\n",
      "    10        0.1667            -nan     0.1000    0.0277\n",
      "    20        0.0483            -nan     0.1000    0.0052\n",
      "    40        0.0166            -nan     0.1000    0.0006\n",
      "    60        0.0113            -nan     0.1000    0.0001\n",
      "    80        0.0091            -nan     0.1000   -0.0000\n",
      "   100        0.0076            -nan     0.1000    0.0000\n",
      "   120        0.0069            -nan     0.1000    0.0000\n",
      "   140        0.0063            -nan     0.1000   -0.0000\n",
      "   150        0.0061            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7523            -nan     0.1000    0.1626\n",
      "     2        0.6192            -nan     0.1000    0.1303\n",
      "     3        0.5104            -nan     0.1000    0.1028\n",
      "     4        0.4238            -nan     0.1000    0.0861\n",
      "     5        0.3526            -nan     0.1000    0.0697\n",
      "     6        0.2930            -nan     0.1000    0.0575\n",
      "     7        0.2451            -nan     0.1000    0.0481\n",
      "     8        0.2046            -nan     0.1000    0.0389\n",
      "     9        0.1724            -nan     0.1000    0.0332\n",
      "    10        0.1456            -nan     0.1000    0.0251\n",
      "    20        0.0380            -nan     0.1000    0.0048\n",
      "    40        0.0108            -nan     0.1000    0.0003\n",
      "    60        0.0072            -nan     0.1000    0.0001\n",
      "    80        0.0058            -nan     0.1000   -0.0000\n",
      "   100        0.0051            -nan     0.1000    0.0000\n",
      "   120        0.0044            -nan     0.1000   -0.0000\n",
      "   140        0.0040            -nan     0.1000    0.0000\n",
      "   150        0.0038            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7636            -nan     0.1000    0.1361\n",
      "     2        0.6518            -nan     0.1000    0.1103\n",
      "     3        0.5592            -nan     0.1000    0.0909\n",
      "     4        0.4826            -nan     0.1000    0.0749\n",
      "     5        0.4170            -nan     0.1000    0.0639\n",
      "     6        0.3642            -nan     0.1000    0.0490\n",
      "     7        0.3184            -nan     0.1000    0.0466\n",
      "     8        0.2792            -nan     0.1000    0.0374\n",
      "     9        0.2457            -nan     0.1000    0.0342\n",
      "    10        0.2170            -nan     0.1000    0.0284\n",
      "    20        0.0773            -nan     0.1000    0.0071\n",
      "    40        0.0280            -nan     0.1000    0.0007\n",
      "    60        0.0186            -nan     0.1000    0.0002\n",
      "    80        0.0149            -nan     0.1000    0.0000\n",
      "   100        0.0131            -nan     0.1000    0.0001\n",
      "   120        0.0120            -nan     0.1000    0.0000\n",
      "   140        0.0112            -nan     0.1000   -0.0000\n",
      "   150        0.0109            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7471            -nan     0.1000    0.1451\n",
      "     2        0.6236            -nan     0.1000    0.1246\n",
      "     3        0.5245            -nan     0.1000    0.0989\n",
      "     4        0.4437            -nan     0.1000    0.0747\n",
      "     5        0.3704            -nan     0.1000    0.0761\n",
      "     6        0.3138            -nan     0.1000    0.0577\n",
      "     7        0.2657            -nan     0.1000    0.0467\n",
      "     8        0.2260            -nan     0.1000    0.0376\n",
      "     9        0.1932            -nan     0.1000    0.0332\n",
      "    10        0.1664            -nan     0.1000    0.0266\n",
      "    20        0.0486            -nan     0.1000    0.0049\n",
      "    40        0.0171            -nan     0.1000    0.0005\n",
      "    60        0.0112            -nan     0.1000    0.0001\n",
      "    80        0.0092            -nan     0.1000    0.0000\n",
      "   100        0.0080            -nan     0.1000    0.0000\n",
      "   120        0.0072            -nan     0.1000    0.0000\n",
      "   140        0.0065            -nan     0.1000    0.0000\n",
      "   150        0.0063            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7349            -nan     0.1000    0.1561\n",
      "     2        0.6053            -nan     0.1000    0.1236\n",
      "     3        0.5002            -nan     0.1000    0.1069\n",
      "     4        0.4128            -nan     0.1000    0.0872\n",
      "     5        0.3424            -nan     0.1000    0.0658\n",
      "     6        0.2852            -nan     0.1000    0.0542\n",
      "     7        0.2397            -nan     0.1000    0.0460\n",
      "     8        0.2018            -nan     0.1000    0.0370\n",
      "     9        0.1707            -nan     0.1000    0.0300\n",
      "    10        0.1451            -nan     0.1000    0.0244\n",
      "    20        0.0380            -nan     0.1000    0.0041\n",
      "    40        0.0121            -nan     0.1000    0.0002\n",
      "    60        0.0084            -nan     0.1000   -0.0000\n",
      "    80        0.0068            -nan     0.1000    0.0000\n",
      "   100        0.0059            -nan     0.1000    0.0000\n",
      "   120        0.0052            -nan     0.1000    0.0000\n",
      "   140        0.0047            -nan     0.1000    0.0000\n",
      "   150        0.0045            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7300            -nan     0.1000    0.1281\n",
      "     2        0.6264            -nan     0.1000    0.1031\n",
      "     3        0.5393            -nan     0.1000    0.0868\n",
      "     4        0.4668            -nan     0.1000    0.0728\n",
      "     5        0.4051            -nan     0.1000    0.0588\n",
      "     6        0.3562            -nan     0.1000    0.0511\n",
      "     7        0.3125            -nan     0.1000    0.0452\n",
      "     8        0.2756            -nan     0.1000    0.0376\n",
      "     9        0.2423            -nan     0.1000    0.0346\n",
      "    10        0.2150            -nan     0.1000    0.0284\n",
      "    20        0.0760            -nan     0.1000    0.0064\n",
      "    40        0.0281            -nan     0.1000    0.0009\n",
      "    60        0.0189            -nan     0.1000    0.0003\n",
      "    80        0.0153            -nan     0.1000    0.0001\n",
      "   100        0.0135            -nan     0.1000    0.0000\n",
      "   120        0.0124            -nan     0.1000   -0.0000\n",
      "   140        0.0116            -nan     0.1000    0.0000\n",
      "   150        0.0113            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7205            -nan     0.1000    0.1412\n",
      "     2        0.5999            -nan     0.1000    0.1181\n",
      "     3        0.5013            -nan     0.1000    0.1003\n",
      "     4        0.4221            -nan     0.1000    0.0775\n",
      "     5        0.3532            -nan     0.1000    0.0703\n",
      "     6        0.2985            -nan     0.1000    0.0512\n",
      "     7        0.2533            -nan     0.1000    0.0469\n",
      "     8        0.2172            -nan     0.1000    0.0342\n",
      "     9        0.1874            -nan     0.1000    0.0288\n",
      "    10        0.1606            -nan     0.1000    0.0273\n",
      "    20        0.0464            -nan     0.1000    0.0045\n",
      "    40        0.0168            -nan     0.1000    0.0004\n",
      "    60        0.0117            -nan     0.1000    0.0001\n",
      "    80        0.0095            -nan     0.1000    0.0000\n",
      "   100        0.0082            -nan     0.1000    0.0000\n",
      "   120        0.0074            -nan     0.1000    0.0000\n",
      "   140        0.0067            -nan     0.1000   -0.0000\n",
      "   150        0.0064            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7078            -nan     0.1000    0.1504\n",
      "     2        0.5829            -nan     0.1000    0.1336\n",
      "     3        0.4833            -nan     0.1000    0.1004\n",
      "     4        0.3995            -nan     0.1000    0.0865\n",
      "     5        0.3325            -nan     0.1000    0.0652\n",
      "     6        0.2774            -nan     0.1000    0.0540\n",
      "     7        0.2326            -nan     0.1000    0.0447\n",
      "     8        0.1963            -nan     0.1000    0.0362\n",
      "     9        0.1650            -nan     0.1000    0.0320\n",
      "    10        0.1393            -nan     0.1000    0.0258\n",
      "    20        0.0379            -nan     0.1000    0.0035\n",
      "    40        0.0120            -nan     0.1000    0.0002\n",
      "    60        0.0082            -nan     0.1000    0.0001\n",
      "    80        0.0067            -nan     0.1000    0.0001\n",
      "   100        0.0057            -nan     0.1000   -0.0000\n",
      "   120        0.0050            -nan     0.1000    0.0000\n",
      "   140        0.0045            -nan     0.1000   -0.0000\n",
      "   150        0.0044            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7353            -nan     0.1000    0.1318\n",
      "     2        0.6263            -nan     0.1000    0.1069\n",
      "     3        0.5389            -nan     0.1000    0.0877\n",
      "     4        0.4652            -nan     0.1000    0.0718\n",
      "     5        0.4055            -nan     0.1000    0.0618\n",
      "     6        0.3546            -nan     0.1000    0.0513\n",
      "     7        0.3079            -nan     0.1000    0.0458\n",
      "     8        0.2704            -nan     0.1000    0.0373\n",
      "     9        0.2373            -nan     0.1000    0.0331\n",
      "    10        0.2103            -nan     0.1000    0.0274\n",
      "    20        0.0759            -nan     0.1000    0.0068\n",
      "    40        0.0270            -nan     0.1000    0.0005\n",
      "    60        0.0185            -nan     0.1000    0.0002\n",
      "    80        0.0151            -nan     0.1000    0.0001\n",
      "   100        0.0134            -nan     0.1000    0.0000\n",
      "   120        0.0124            -nan     0.1000   -0.0000\n",
      "   140        0.0117            -nan     0.1000    0.0000\n",
      "   150        0.0114            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7223            -nan     0.1000    0.1452\n",
      "     2        0.6007            -nan     0.1000    0.1236\n",
      "     3        0.5035            -nan     0.1000    0.1019\n",
      "     4        0.4225            -nan     0.1000    0.0797\n",
      "     5        0.3547            -nan     0.1000    0.0668\n",
      "     6        0.2970            -nan     0.1000    0.0553\n",
      "     7        0.2497            -nan     0.1000    0.0469\n",
      "     8        0.2124            -nan     0.1000    0.0371\n",
      "     9        0.1823            -nan     0.1000    0.0306\n",
      "    10        0.1558            -nan     0.1000    0.0273\n",
      "    20        0.0454            -nan     0.1000    0.0046\n",
      "    40        0.0163            -nan     0.1000    0.0005\n",
      "    60        0.0117            -nan     0.1000    0.0000\n",
      "    80        0.0098            -nan     0.1000    0.0000\n",
      "   100        0.0086            -nan     0.1000   -0.0000\n",
      "   120        0.0079            -nan     0.1000   -0.0000\n",
      "   140        0.0073            -nan     0.1000    0.0000\n",
      "   150        0.0070            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7122            -nan     0.1000    0.1558\n",
      "     2        0.5879            -nan     0.1000    0.1283\n",
      "     3        0.4847            -nan     0.1000    0.1018\n",
      "     4        0.4022            -nan     0.1000    0.0837\n",
      "     5        0.3355            -nan     0.1000    0.0695\n",
      "     6        0.2804            -nan     0.1000    0.0547\n",
      "     7        0.2344            -nan     0.1000    0.0453\n",
      "     8        0.1966            -nan     0.1000    0.0375\n",
      "     9        0.1651            -nan     0.1000    0.0307\n",
      "    10        0.1404            -nan     0.1000    0.0241\n",
      "    20        0.0375            -nan     0.1000    0.0038\n",
      "    40        0.0126            -nan     0.1000    0.0003\n",
      "    60        0.0090            -nan     0.1000    0.0001\n",
      "    80        0.0074            -nan     0.1000    0.0000\n",
      "   100        0.0065            -nan     0.1000   -0.0000\n",
      "   120        0.0059            -nan     0.1000   -0.0000\n",
      "   140        0.0053            -nan     0.1000    0.0000\n",
      "   150        0.0050            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7511            -nan     0.1000    0.1311\n",
      "     2        0.6432            -nan     0.1000    0.1070\n",
      "     3        0.5564            -nan     0.1000    0.0887\n",
      "     4        0.4812            -nan     0.1000    0.0745\n",
      "     5        0.4196            -nan     0.1000    0.0628\n",
      "     6        0.3623            -nan     0.1000    0.0535\n",
      "     7        0.3150            -nan     0.1000    0.0459\n",
      "     8        0.2779            -nan     0.1000    0.0371\n",
      "     9        0.2451            -nan     0.1000    0.0338\n",
      "    10        0.2163            -nan     0.1000    0.0281\n",
      "    20        0.0788            -nan     0.1000    0.0071\n",
      "    40        0.0281            -nan     0.1000    0.0008\n",
      "    60        0.0187            -nan     0.1000    0.0001\n",
      "    80        0.0151            -nan     0.1000    0.0001\n",
      "   100        0.0133            -nan     0.1000    0.0001\n",
      "   120        0.0123            -nan     0.1000    0.0000\n",
      "   140        0.0116            -nan     0.1000    0.0000\n",
      "   150        0.0113            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7388            -nan     0.1000    0.1456\n",
      "     2        0.6144            -nan     0.1000    0.1188\n",
      "     3        0.5160            -nan     0.1000    0.0967\n",
      "     4        0.4302            -nan     0.1000    0.0822\n",
      "     5        0.3644            -nan     0.1000    0.0650\n",
      "     6        0.3071            -nan     0.1000    0.0566\n",
      "     7        0.2601            -nan     0.1000    0.0473\n",
      "     8        0.2211            -nan     0.1000    0.0395\n",
      "     9        0.1900            -nan     0.1000    0.0314\n",
      "    10        0.1642            -nan     0.1000    0.0263\n",
      "    20        0.0487            -nan     0.1000    0.0049\n",
      "    40        0.0163            -nan     0.1000    0.0004\n",
      "    60        0.0110            -nan     0.1000    0.0001\n",
      "    80        0.0091            -nan     0.1000    0.0000\n",
      "   100        0.0081            -nan     0.1000    0.0000\n",
      "   120        0.0073            -nan     0.1000   -0.0000\n",
      "   140        0.0068            -nan     0.1000   -0.0000\n",
      "   150        0.0067            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7293            -nan     0.1000    0.1622\n",
      "     2        0.6018            -nan     0.1000    0.1290\n",
      "     3        0.4985            -nan     0.1000    0.1048\n",
      "     4        0.4134            -nan     0.1000    0.0860\n",
      "     5        0.3436            -nan     0.1000    0.0686\n",
      "     6        0.2863            -nan     0.1000    0.0547\n",
      "     7        0.2398            -nan     0.1000    0.0448\n",
      "     8        0.2024            -nan     0.1000    0.0362\n",
      "     9        0.1708            -nan     0.1000    0.0321\n",
      "    10        0.1441            -nan     0.1000    0.0268\n",
      "    20        0.0383            -nan     0.1000    0.0040\n",
      "    40        0.0119            -nan     0.1000    0.0003\n",
      "    60        0.0086            -nan     0.1000    0.0001\n",
      "    80        0.0070            -nan     0.1000    0.0001\n",
      "   100        0.0062            -nan     0.1000    0.0000\n",
      "   120        0.0055            -nan     0.1000    0.0000\n",
      "   140        0.0050            -nan     0.1000   -0.0000\n",
      "   150        0.0048            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7520            -nan     0.1000    0.1325\n",
      "     2        0.6423            -nan     0.1000    0.1097\n",
      "     3        0.5537            -nan     0.1000    0.0894\n",
      "     4        0.4774            -nan     0.1000    0.0746\n",
      "     5        0.4120            -nan     0.1000    0.0624\n",
      "     6        0.3600            -nan     0.1000    0.0514\n",
      "     7        0.3127            -nan     0.1000    0.0470\n",
      "     8        0.2747            -nan     0.1000    0.0381\n",
      "     9        0.2402            -nan     0.1000    0.0322\n",
      "    10        0.2122            -nan     0.1000    0.0272\n",
      "    20        0.0784            -nan     0.1000    0.0066\n",
      "    40        0.0280            -nan     0.1000    0.0007\n",
      "    60        0.0192            -nan     0.1000    0.0002\n",
      "    80        0.0156            -nan     0.1000    0.0001\n",
      "   100        0.0136            -nan     0.1000    0.0000\n",
      "   120        0.0124            -nan     0.1000    0.0000\n",
      "   140        0.0116            -nan     0.1000    0.0000\n",
      "   150        0.0112            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7346            -nan     0.1000    0.1482\n",
      "     2        0.6132            -nan     0.1000    0.1215\n",
      "     3        0.5087            -nan     0.1000    0.1011\n",
      "     4        0.4291            -nan     0.1000    0.0810\n",
      "     5        0.3628            -nan     0.1000    0.0678\n",
      "     6        0.3069            -nan     0.1000    0.0569\n",
      "     7        0.2590            -nan     0.1000    0.0473\n",
      "     8        0.2190            -nan     0.1000    0.0382\n",
      "     9        0.1866            -nan     0.1000    0.0332\n",
      "    10        0.1600            -nan     0.1000    0.0262\n",
      "    20        0.0480            -nan     0.1000    0.0042\n",
      "    40        0.0165            -nan     0.1000    0.0004\n",
      "    60        0.0113            -nan     0.1000    0.0001\n",
      "    80        0.0093            -nan     0.1000    0.0000\n",
      "   100        0.0082            -nan     0.1000   -0.0000\n",
      "   120        0.0072            -nan     0.1000    0.0000\n",
      "   140        0.0066            -nan     0.1000    0.0000\n",
      "   150        0.0063            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7265            -nan     0.1000    0.1568\n",
      "     2        0.5985            -nan     0.1000    0.1250\n",
      "     3        0.4947            -nan     0.1000    0.1030\n",
      "     4        0.4103            -nan     0.1000    0.0862\n",
      "     5        0.3419            -nan     0.1000    0.0681\n",
      "     6        0.2868            -nan     0.1000    0.0540\n",
      "     7        0.2393            -nan     0.1000    0.0475\n",
      "     8        0.2013            -nan     0.1000    0.0378\n",
      "     9        0.1701            -nan     0.1000    0.0309\n",
      "    10        0.1434            -nan     0.1000    0.0255\n",
      "    20        0.0386            -nan     0.1000    0.0038\n",
      "    40        0.0124            -nan     0.1000    0.0004\n",
      "    60        0.0084            -nan     0.1000    0.0001\n",
      "    80        0.0067            -nan     0.1000   -0.0000\n",
      "   100        0.0059            -nan     0.1000    0.0000\n",
      "   120        0.0052            -nan     0.1000    0.0000\n",
      "   140        0.0047            -nan     0.1000    0.0000\n",
      "   150        0.0046            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7274            -nan     0.1000    0.1266\n",
      "     2        0.6227            -nan     0.1000    0.1076\n",
      "     3        0.5348            -nan     0.1000    0.0854\n",
      "     4        0.4634            -nan     0.1000    0.0726\n",
      "     5        0.4024            -nan     0.1000    0.0617\n",
      "     6        0.3516            -nan     0.1000    0.0504\n",
      "     7        0.3072            -nan     0.1000    0.0441\n",
      "     8        0.2690            -nan     0.1000    0.0378\n",
      "     9        0.2373            -nan     0.1000    0.0310\n",
      "    10        0.2094            -nan     0.1000    0.0277\n",
      "    20        0.0766            -nan     0.1000    0.0063\n",
      "    40        0.0295            -nan     0.1000    0.0006\n",
      "    60        0.0205            -nan     0.1000    0.0002\n",
      "    80        0.0169            -nan     0.1000    0.0001\n",
      "   100        0.0149            -nan     0.1000    0.0000\n",
      "   120        0.0137            -nan     0.1000   -0.0000\n",
      "   140        0.0129            -nan     0.1000    0.0000\n",
      "   150        0.0124            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7064            -nan     0.1000    0.1458\n",
      "     2        0.5905            -nan     0.1000    0.1192\n",
      "     3        0.4988            -nan     0.1000    0.0949\n",
      "     4        0.4183            -nan     0.1000    0.0823\n",
      "     5        0.3529            -nan     0.1000    0.0646\n",
      "     6        0.2981            -nan     0.1000    0.0548\n",
      "     7        0.2506            -nan     0.1000    0.0476\n",
      "     8        0.2128            -nan     0.1000    0.0387\n",
      "     9        0.1830            -nan     0.1000    0.0299\n",
      "    10        0.1571            -nan     0.1000    0.0255\n",
      "    20        0.0480            -nan     0.1000    0.0041\n",
      "    40        0.0178            -nan     0.1000    0.0004\n",
      "    60        0.0125            -nan     0.1000    0.0001\n",
      "    80        0.0103            -nan     0.1000    0.0000\n",
      "   100        0.0090            -nan     0.1000    0.0001\n",
      "   120        0.0081            -nan     0.1000   -0.0000\n",
      "   140        0.0074            -nan     0.1000    0.0000\n",
      "   150        0.0071            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7016            -nan     0.1000    0.1516\n",
      "     2        0.5775            -nan     0.1000    0.1247\n",
      "     3        0.4778            -nan     0.1000    0.1006\n",
      "     4        0.3985            -nan     0.1000    0.0806\n",
      "     5        0.3312            -nan     0.1000    0.0666\n",
      "     6        0.2765            -nan     0.1000    0.0565\n",
      "     7        0.2330            -nan     0.1000    0.0455\n",
      "     8        0.1953            -nan     0.1000    0.0366\n",
      "     9        0.1647            -nan     0.1000    0.0311\n",
      "    10        0.1396            -nan     0.1000    0.0246\n",
      "    20        0.0396            -nan     0.1000    0.0036\n",
      "    40        0.0130            -nan     0.1000    0.0003\n",
      "    60        0.0089            -nan     0.1000    0.0001\n",
      "    80        0.0072            -nan     0.1000    0.0000\n",
      "   100        0.0061            -nan     0.1000    0.0000\n",
      "   120        0.0053            -nan     0.1000    0.0000\n",
      "   140        0.0048            -nan     0.1000   -0.0000\n",
      "   150        0.0045            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7439            -nan     0.1000    0.1300\n",
      "     2        0.6375            -nan     0.1000    0.1064\n",
      "     3        0.5493            -nan     0.1000    0.0878\n",
      "     4        0.4747            -nan     0.1000    0.0742\n",
      "     5        0.4118            -nan     0.1000    0.0630\n",
      "     6        0.3600            -nan     0.1000    0.0518\n",
      "     7        0.3115            -nan     0.1000    0.0465\n",
      "     8        0.2735            -nan     0.1000    0.0374\n",
      "     9        0.2404            -nan     0.1000    0.0336\n",
      "    10        0.2125            -nan     0.1000    0.0275\n",
      "    20        0.0756            -nan     0.1000    0.0067\n",
      "    40        0.0272            -nan     0.1000    0.0007\n",
      "    60        0.0180            -nan     0.1000    0.0002\n",
      "    80        0.0146            -nan     0.1000    0.0000\n",
      "   100        0.0126            -nan     0.1000    0.0000\n",
      "   120        0.0115            -nan     0.1000   -0.0000\n",
      "   140        0.0107            -nan     0.1000    0.0000\n",
      "   150        0.0104            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7249            -nan     0.1000    0.1555\n",
      "     2        0.6050            -nan     0.1000    0.1185\n",
      "     3        0.5078            -nan     0.1000    0.0944\n",
      "     4        0.4236            -nan     0.1000    0.0837\n",
      "     5        0.3595            -nan     0.1000    0.0644\n",
      "     6        0.3052            -nan     0.1000    0.0549\n",
      "     7        0.2582            -nan     0.1000    0.0454\n",
      "     8        0.2186            -nan     0.1000    0.0408\n",
      "     9        0.1865            -nan     0.1000    0.0311\n",
      "    10        0.1616            -nan     0.1000    0.0250\n",
      "    20        0.0468            -nan     0.1000    0.0049\n",
      "    40        0.0161            -nan     0.1000    0.0005\n",
      "    60        0.0108            -nan     0.1000    0.0001\n",
      "    80        0.0088            -nan     0.1000    0.0000\n",
      "   100        0.0074            -nan     0.1000   -0.0000\n",
      "   120        0.0066            -nan     0.1000    0.0000\n",
      "   140        0.0060            -nan     0.1000    0.0000\n",
      "   150        0.0058            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7178            -nan     0.1000    0.1564\n",
      "     2        0.5890            -nan     0.1000    0.1259\n",
      "     3        0.4855            -nan     0.1000    0.0993\n",
      "     4        0.4028            -nan     0.1000    0.0849\n",
      "     5        0.3360            -nan     0.1000    0.0671\n",
      "     6        0.2794            -nan     0.1000    0.0546\n",
      "     7        0.2330            -nan     0.1000    0.0443\n",
      "     8        0.1955            -nan     0.1000    0.0354\n",
      "     9        0.1644            -nan     0.1000    0.0302\n",
      "    10        0.1393            -nan     0.1000    0.0249\n",
      "    20        0.0370            -nan     0.1000    0.0038\n",
      "    40        0.0120            -nan     0.1000    0.0003\n",
      "    60        0.0083            -nan     0.1000    0.0000\n",
      "    80        0.0067            -nan     0.1000    0.0000\n",
      "   100        0.0058            -nan     0.1000   -0.0000\n",
      "   120        0.0051            -nan     0.1000    0.0000\n",
      "   140        0.0047            -nan     0.1000   -0.0000\n",
      "   150        0.0045            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7318            -nan     0.1000    0.1300\n",
      "     2        0.6254            -nan     0.1000    0.1043\n",
      "     3        0.5381            -nan     0.1000    0.0866\n",
      "     4        0.4658            -nan     0.1000    0.0729\n",
      "     5        0.4062            -nan     0.1000    0.0585\n",
      "     6        0.3530            -nan     0.1000    0.0516\n",
      "     7        0.3089            -nan     0.1000    0.0447\n",
      "     8        0.2708            -nan     0.1000    0.0357\n",
      "     9        0.2393            -nan     0.1000    0.0325\n",
      "    10        0.2107            -nan     0.1000    0.0283\n",
      "    20        0.0770            -nan     0.1000    0.0062\n",
      "    40        0.0282            -nan     0.1000    0.0003\n",
      "    60        0.0188            -nan     0.1000    0.0002\n",
      "    80        0.0149            -nan     0.1000    0.0001\n",
      "   100        0.0129            -nan     0.1000    0.0001\n",
      "   120        0.0118            -nan     0.1000    0.0000\n",
      "   140        0.0110            -nan     0.1000    0.0000\n",
      "   150        0.0107            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7217            -nan     0.1000    0.1419\n",
      "     2        0.5987            -nan     0.1000    0.1173\n",
      "     3        0.5010            -nan     0.1000    0.0989\n",
      "     4        0.4201            -nan     0.1000    0.0827\n",
      "     5        0.3551            -nan     0.1000    0.0651\n",
      "     6        0.2965            -nan     0.1000    0.0588\n",
      "     7        0.2504            -nan     0.1000    0.0458\n",
      "     8        0.2112            -nan     0.1000    0.0389\n",
      "     9        0.1812            -nan     0.1000    0.0287\n",
      "    10        0.1545            -nan     0.1000    0.0260\n",
      "    20        0.0466            -nan     0.1000    0.0047\n",
      "    40        0.0165            -nan     0.1000    0.0004\n",
      "    60        0.0108            -nan     0.1000    0.0001\n",
      "    80        0.0086            -nan     0.1000    0.0001\n",
      "   100        0.0073            -nan     0.1000    0.0000\n",
      "   120        0.0065            -nan     0.1000    0.0000\n",
      "   140        0.0058            -nan     0.1000    0.0000\n",
      "   150        0.0056            -nan     0.1000    0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7090            -nan     0.1000    0.1578\n",
      "     2        0.5831            -nan     0.1000    0.1245\n",
      "     3        0.4841            -nan     0.1000    0.0997\n",
      "     4        0.3999            -nan     0.1000    0.0840\n",
      "     5        0.3340            -nan     0.1000    0.0677\n",
      "     6        0.2778            -nan     0.1000    0.0527\n",
      "     7        0.2316            -nan     0.1000    0.0452\n",
      "     8        0.1946            -nan     0.1000    0.0364\n",
      "     9        0.1649            -nan     0.1000    0.0288\n",
      "    10        0.1409            -nan     0.1000    0.0243\n",
      "    20        0.0379            -nan     0.1000    0.0042\n",
      "    40        0.0121            -nan     0.1000    0.0003\n",
      "    60        0.0080            -nan     0.1000    0.0001\n",
      "    80        0.0063            -nan     0.1000    0.0000\n",
      "   100        0.0054            -nan     0.1000   -0.0000\n",
      "   120        0.0047            -nan     0.1000   -0.0000\n",
      "   140        0.0043            -nan     0.1000    0.0000\n",
      "   150        0.0041            -nan     0.1000   -0.0000\n",
      "\n",
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        0.7158            -nan     0.1000    0.1574\n",
      "     2        0.5920            -nan     0.1000    0.1300\n",
      "     3        0.4905            -nan     0.1000    0.1020\n",
      "     4        0.4051            -nan     0.1000    0.0843\n",
      "     5        0.3373            -nan     0.1000    0.0683\n",
      "     6        0.2801            -nan     0.1000    0.0539\n",
      "     7        0.2351            -nan     0.1000    0.0434\n",
      "     8        0.1969            -nan     0.1000    0.0381\n",
      "     9        0.1659            -nan     0.1000    0.0301\n",
      "    10        0.1408            -nan     0.1000    0.0228\n",
      "    20        0.0392            -nan     0.1000    0.0038\n",
      "    40        0.0129            -nan     0.1000    0.0004\n",
      "    60        0.0091            -nan     0.1000    0.0001\n",
      "    80        0.0076            -nan     0.1000    0.0000\n",
      "   100        0.0067            -nan     0.1000   -0.0000\n",
      "   120        0.0060            -nan     0.1000   -0.0000\n",
      "   140        0.0055            -nan     0.1000   -0.0000\n",
      "   150        0.0053            -nan     0.1000   -0.0000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Stochastic Gradient Boosting \n",
       "\n",
       "1081 samples\n",
       "  10 predictor\n",
       "\n",
       "No pre-processing\n",
       "Resampling: Bootstrapped (25 reps) \n",
       "\n",
       "Summary of sample sizes: 1081, 1081, 1081, 1081, 1081, 1081, ... \n",
       "\n",
       "Resampling results across tuning parameters:\n",
       "\n",
       "  interaction.depth  n.trees  RMSE       Rsquared   RMSE SD      Rsquared SD\n",
       "  1                   50      0.1592594  0.9731727  0.008263015  0.002277426\n",
       "  1                  100      0.1301041  0.9807766  0.006664186  0.001822492\n",
       "  1                  150      0.1223538  0.9829203  0.006466226  0.001692656\n",
       "  2                   50      0.1299669  0.9811437  0.006512066  0.001698583\n",
       "  2                  100      0.1099042  0.9862339  0.005927433  0.001390441\n",
       "  2                  150      0.1028355  0.9879491  0.005722198  0.001207215\n",
       "  3                   50      0.1169804  0.9845376  0.006200813  0.001456637\n",
       "  3                  100      0.1006655  0.9884174  0.005811831  0.001184869\n",
       "  3                  150      0.0957689  0.9895125  0.005633025  0.001071457\n",
       "\n",
       "Tuning parameter 'shrinkage' was held constant at a value of 0.1\n",
       "\n",
       "Tuning parameter 'n.minobsinnode' was held constant at a value of 10\n",
       "RMSE was used to select the optimal model using  the smallest value.\n",
       "The final values used for the model were n.trees = 150, interaction.depth =\n",
       " 3, shrinkage = 0.1 and n.minobsinnode = 10. "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#run the train function to optimize the hyperparameters\n",
    "gbmFit1 <- train(GasPrice~.-Date-GasDate, data=traindata, method=\"gbm\");\n",
    "gbmFit1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now that we have the model optimized, we apply the optimized hyperparameters to the entire training model set, then use that to make predicitions on the final set of data. The predictions are shown below. We found that the SVM has an offset that just doesn't work. The linear model does better, but the Gradient Boosted descision tree does the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"RMS Deviation: $0.07\"\n"
     ]
    },
    {
     "data": {
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "finalmodel <- gbm(GasPrice~.-Date-GasDate, data=traindata, distribution=\"gaussian\", interaction.depth=4,\n",
    "            n.minobsinnode=10, n.trees=200,shrinkage=0.05)\n",
    "\n",
    "\n",
    "finalpredictedPrice <- predict(finalmodel, testdata, n.trees=finalmodel$n.trees)\n",
    "\n",
    "finalpredtestData<-data.frame(testdata$Date,finalpredictedPrice)\n",
    "colnames(finalpredtestData) <-c(\"Date\",\"GasPrice\")\n",
    "\n",
    "\n",
    "p1<-ggplot(traindata,aes(Date,GasPrice))+geom_line(aes(color=\"Training Data\"))+\n",
    "  geom_point(data=testdata,aes(color=\"Test Data\"))+\n",
    "  labs(color=\"Input Data\")+\n",
    "  geom_line(data=finalpredtestData,aes(color=\"Predicted Data\"))+\n",
    "  theme(legend.position=c(.3, .75),panel.border = element_rect(fill=NA,color=\"black\", size=0.5, \n",
    "                                    linetype=\"solid\"),axis.text.x = element_text(size=7))+ \n",
    "  coord_fixed(ratio=150)+xlab(\"Date\")+ylab(\"Gas Price ($)\")+ \n",
    "  scale_x_date(limits=as.Date(c('2015-01-01','2016-05-01'),breaks=date_breaks(width=\"6 months\")))\n",
    "\n",
    "finalscore<-sqrt(mean((testdata$GasPrice-finalpredictedPrice)^2))\n",
    "print(paste0(\"RMS Deviation: $\",sprintf(\"%.2f\",finalscore)))\n",
    "\n",
    "predVact<-data.frame(finalpredictedPrice,testdata$GasPrice)\n",
    "colnames(predVact)<-c(\"pp\",\"ap\")\n",
    "\n",
    "p2<-ggplot(predVact,aes(x=pp,y=ap))+geom_point()+ ylim(1.5,2.5)+ xlim(1.5,2.5)+\n",
    "    geom_smooth(method=lm,   # Add linear regression line\n",
    "                se=TRUE)+theme_bw()+    # Don't add shaded confidence region\n",
    "    xlab(\"Predicted Price ($)\") + ylab(\"Actual Price ($)\")+ coord_fixed()+\n",
    "    ggtitle(paste(\"RMS Deviation: $\",sprintf(\"%.2f\",finalscore)))\n",
    "\n",
    "plot_grid(p1,p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "Final Notes\n",
    "===\n",
    "So the model works fairly well now. I am off by an average of around $0.11 on the predicted price of gas. The model follows the larger trends and swings in the data."
   ]
  }
 ],
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   "mimetype": "text/x-r-source",
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