05a-rnns-answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sequential data modelling\n",
    "We have added a `?` in the exercises where you need to fill in.\n",
    "\n",
    "In this notebook we will use RNNs to predict future temperature using the Jena weather dataset. We will start by using a SimpleRNN and then see whether we can improve our predictions using a GRU.\n",
    "\n",
    "Before the modelling step we need to do our usual preprocessing. This time you will see a few things you have not seen before.\n",
    "\n",
    "1. We will resample the data. The data consists of measurements every 10 minutes, we will instead select a datapoint every hour using the `seq()` function to select our data. We do this to make our training a little faster and have a somewhat meaningful problem.\n",
    "1. We will see a different way to create sequences from a dataset. This approach is more dynamic than the reshape approach as it can produce more sequences than the reshape approach. Hopefully, it is also more intuitive.\n",
    "1. We will not split our training data into a validation set, as we will leave that splitting up to Keras when we try to fit our data. Thus, we only split into train/test.\n",
    "\n",
    "In short;\n",
    "1. Load data and some cleaning.\n",
    "1. Sub-sample data for every hour.\n",
    "1. Create sequences.\n",
    "1. Split data.\n",
    "1. Scale data.\n",
    "1. Baseline SimpleRNN\n",
    "1. GRU RNN\n",
    "1. LSTM RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(keras)\n",
    "library(ggplot2)\n",
    "source(\"05-helpers.R\")\n",
    "\n",
    "use_multi_cpu(2L)\n",
    "# This specifies the number of threads TensorFlow will use. You can change this number, \n",
    "# but it depends on the model, what the optimal number of threads might be.\n",
    "# In my experiments 2 threads performed quite well for simpleRNNs.\n",
    "# Using more than 2 threads increased communication overhead between CPUs and decreased the training speed.\n",
    "# To change this value simply set the value which you want and then -\n",
    "# click \"Kernel\" -> \"Shutdown\" and then \"Kernel\" -> \"Restart\".\n",
    "# If you do not shutdown the kernel, the change will not take effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load and clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  `Date Time` = col_character(),\n",
      "  `p (mbar)` = col_double(),\n",
      "  `T (degC)` = col_double(),\n",
      "  `Tpot (K)` = col_double(),\n",
      "  `Tdew (degC)` = col_double(),\n",
      "  `rh (%)` = col_double(),\n",
      "  `VPmax (mbar)` = col_double(),\n",
      "  `VPact (mbar)` = col_double(),\n",
      "  `VPdef (mbar)` = col_double(),\n",
      "  `sh (g/kg)` = col_double(),\n",
      "  `H2OC (mmol/mol)` = col_double(),\n",
      "  `rho (g/m**3)` = col_double(),\n",
      "  `wv (m/s)` = col_double(),\n",
      "  `max. wv (m/s)` = col_double(),\n",
      "  `wd (deg)` = col_double()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "raw_data <- load_jena_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 1\n",
    "Remove the `Date Time` feature from `raw_data` and store the result in a variable called `data`. Verify your work using the `dim()` function. The data should have dimension `(420551, 14)`.\n",
    "\n",
    "**Hint** `Date Time` is the first column in `raw_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>420551</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 420551\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 420551\n",
       "2. 14\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 420551     14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- raw_data[,-1]\n",
    "dim(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Sub-sampling\n",
    "Let us now sub-sample our data by selecting every 6th data point. This will leave us with a data point for every hour instead of every 10 minutes.\n",
    "\n",
    "To do this we will use slicing in conjunction with the [`seq()`](https://www.rdocumentation.org/packages/base/versions/3.5.3/topics/seq) to specify the indices we want to select. To see how it works, run the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>3</li>\n",
       "\t<li>5</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 3\n",
       "\\item 5\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 3\n",
       "3. 5\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1 3 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2</li>\n",
       "\t<li>4</li>\n",
       "\t<li>6</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2\n",
       "\\item 4\n",
       "\\item 6\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2\n",
       "2. 4\n",
       "3. 6\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2 4 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vec = c(1,2,3,4,5,6)\n",
    "odds = vec[seq(1, length(vec), 2)]\n",
    "odds\n",
    "evens = vec[seq(2, length(vec), 2)]\n",
    "evens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2\n",
    "Select every 6th datapoint from the dataset using the `seq()` function. Save the resampled data in a variable called `data_hourly`. Verify your work using the `dim()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>420551</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 420551\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 420551\n",
       "2. 14\n",
       "\n",
       "\n"
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      "text/plain": [
       "[1] 420551     14"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>70092</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 70092\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 70092\n",
       "2. 14\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 70092    14"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(data)\n",
    "data_hourly = data[seq(1, nrow(data), 6),]\n",
    "dim(data_hourly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like last week we want to predict the temperature, `T (degC)`. Let us plot that feature and see how the temperature varies over those years using the resampled dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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UGqgjchQUpLtkTbYFKGDKk5hZTnak4Z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KfVYf10hGJITwIST76EdJvTGeWugwJIwUaK35PNK0CSD5AA6bNGm/MeYIL0FCDx5ENI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\nYgiQEGIIkBBiCJAQYuj/ATqrl6ni49KzAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data_hourly, aes(x = 1:nrow(data_hourly),y=`T (degC)`)) + geom_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Creating sequences\n",
    "Now we will create sequences from the data using a function. The function accepts a number of parameters and returns a list containing two objects, the sequcences (`x`) and the targets (`y`).\n",
    "\n",
    "Let us briefly explain what the parameters do. First you specify the `data` (the function expects 2D tensor / matrix) which is used to generate the sequences. The `sequence_length` is the length of the sequence to generate, each sequence will have that length. The `target_shift` is the number of time-steps between the end of each sequence until the target, the time-step we want to predict. The `step_shift` is the number of time-steps between the start of each sequence. The function will try to use as many datapoints as possible given the parameters to create the sequences.\n",
    "\n",
    "Run the cell below to define the function and continue reading below. There is no need to read through the function and try to understand it. We just need to know how to use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_sequences_x_y <- function(data, sequence_length, target_shift, step_shift) {\n",
    "    data <- as.matrix(data)\n",
    "    start_index = 1\n",
    "    end_index = dim(data)[1]\n",
    "    # We assume that the input is in legal ranges\n",
    "    elements = end_index - start_index + 1\n",
    "    # our targets are a single data point\n",
    "    target_len = 1\n",
    "    single_sequence_length = sequence_length + target_shift + target_len\n",
    "    number_of_sequences = floor((elements - single_sequence_length)/step_shift) + 1\n",
    "    \n",
    "    # Initialise variables we need in the loop\n",
    "    # We store the index which we should start with in each loop in current_start_index\n",
    "    current_start_index = start_index\n",
    "    sequence_x <- array(0, dim = c(number_of_sequences, sequence_length, dim(data)[2]))\n",
    "    sequence_y <- array(0, dim = c(number_of_sequences, dim(data)[2]))\n",
    "    for (sequence_index in 1:number_of_sequences) {\n",
    "        # We get the current sequence data\n",
    "        sequence_x[sequence_index,,] <- data[current_start_index:(current_start_index+sequence_length-1),]\n",
    "        sequence_y[sequence_index,] <- data[(current_start_index+sequence_length+target_shift):(current_start_index+sequence_length+target_shift+target_len-1),]\n",
    "        # We update our next start\n",
    "        current_start_index <- current_start_index + step_shift\n",
    "    }\n",
    "    list(x = sequence_x, y = sequence_y)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3.1\n",
    "To get a clearer idea of how this function works. Take the first 7 elements from `data_hourly` and pass it as `data` to the function. Set the `sequence_length` to 3, `target_shift` to 1 and `step_shift` to 1. The function returns a list which has two objects, `x` and `y`. Check the dimensions of `x` and `y` and see if they make sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_test_data <- create_sequences_x_y(data = ?, sequence_length = ?, target_shift = ?, step_shift = ?)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "\\begin{enumerate*}\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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       "1. 3\n",
       "2. 3\n",
       "3. 14\n",
       "\n",
       "\n"
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       "[1]  3  3 14"
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       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>996.52 </td><td>-8.02  </td><td>265.40 </td><td>-8.9   </td><td>93.3   </td><td>3.33   </td><td>3.11   </td><td>0.22   </td><td>1.94   </td><td>3.12   </td><td>1307.75</td><td>1.03   </td><td>1.75   </td><td>152.3  </td></tr>\n",
       "\t<tr><td>996.50 </td><td>-7.62  </td><td>265.81 </td><td>-8.3   </td><td>94.8   </td><td>3.44   </td><td>3.26   </td><td>0.18   </td><td>2.04   </td><td>3.27   </td><td>1305.68</td><td>0.18   </td><td>0.63   </td><td>166.5  </td></tr>\n",
       "\t<tr><td>996.63 </td><td>-8.85  </td><td>264.57 </td><td>-9.7   </td><td>93.5   </td><td>3.12   </td><td>2.92   </td><td>0.20   </td><td>1.82   </td><td>2.93   </td><td>1312.11</td><td>0.16   </td><td>0.50   </td><td>158.3  </td></tr>\n",
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       "\\begin{tabular}{llllllllllllll}\n",
       "\t 996.52  & -8.02   & 265.40  & -8.9    & 93.3    & 3.33    & 3.11    & 0.22    & 1.94    & 3.12    & 1307.75 & 1.03    & 1.75    & 152.3  \\\\\n",
       "\t 996.50  & -7.62   & 265.81  & -8.3    & 94.8    & 3.44    & 3.26    & 0.18    & 2.04    & 3.27    & 1305.68 & 0.18    & 0.63    & 166.5  \\\\\n",
       "\t 996.63  & -8.85   & 264.57  & -9.7    & 93.5    & 3.12    & 2.92    & 0.20    & 1.82    & 2.93    & 1312.11 & 0.16    & 0.50    & 158.3  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 996.52  | -8.02   | 265.40  | -8.9    | 93.3    | 3.33    | 3.11    | 0.22    | 1.94    | 3.12    | 1307.75 | 1.03    | 1.75    | 152.3   |\n",
       "| 996.50  | -7.62   | 265.81  | -8.3    | 94.8    | 3.44    | 3.26    | 0.18    | 2.04    | 3.27    | 1305.68 | 0.18    | 0.63    | 166.5   |\n",
       "| 996.63  | -8.85   | 264.57  | -9.7    | 93.5    | 3.12    | 2.92    | 0.20    | 1.82    | 2.93    | 1312.11 | 0.16    | 0.50    | 158.3   |\n",
       "\n"
      ],
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       "     [,1]   [,2]  [,3]   [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]   [,12]\n",
       "[1,] 996.52 -8.02 265.40 -8.9 93.3 3.33 3.11 0.22 1.94 3.12  1307.75 1.03 \n",
       "[2,] 996.50 -7.62 265.81 -8.3 94.8 3.44 3.26 0.18 2.04 3.27  1305.68 0.18 \n",
       "[3,] 996.63 -8.85 264.57 -9.7 93.5 3.12 2.92 0.20 1.82 2.93  1312.11 0.16 \n",
       "     [,13] [,14]\n",
       "[1,] 1.75  152.3\n",
       "[2,] 0.63  166.5\n",
       "[3,] 0.50  158.3"
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       "\t<tr><td>996.50 </td><td>-7.62  </td><td>265.81 </td><td>-8.30  </td><td>94.8   </td><td>3.44   </td><td>3.26   </td><td>0.18   </td><td>2.04   </td><td>3.27   </td><td>1305.68</td><td>0.18   </td><td>0.63   </td><td>166.5  </td></tr>\n",
       "\t<tr><td>996.63 </td><td>-8.85  </td><td>264.57 </td><td>-9.70  </td><td>93.5   </td><td>3.12   </td><td>2.92   </td><td>0.20   </td><td>1.82   </td><td>2.93   </td><td>1312.11</td><td>0.16   </td><td>0.50   </td><td>158.3  </td></tr>\n",
       "\t<tr><td>996.87 </td><td>-8.84  </td><td>264.56 </td><td>-9.69  </td><td>93.5   </td><td>3.13   </td><td>2.92   </td><td>0.20   </td><td>1.83   </td><td>2.93   </td><td>1312.37</td><td>0.07   </td><td>0.25   </td><td>129.3  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
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       "\\begin{tabular}{llllllllllllll}\n",
       "\t 996.50  & -7.62   & 265.81  & -8.30   & 94.8    & 3.44    & 3.26    & 0.18    & 2.04    & 3.27    & 1305.68 & 0.18    & 0.63    & 166.5  \\\\\n",
       "\t 996.63  & -8.85   & 264.57  & -9.70   & 93.5    & 3.12    & 2.92    & 0.20    & 1.82    & 2.93    & 1312.11 & 0.16    & 0.50    & 158.3  \\\\\n",
       "\t 996.87  & -8.84   & 264.56  & -9.69   & 93.5    & 3.13    & 2.92    & 0.20    & 1.83    & 2.93    & 1312.37 & 0.07    & 0.25    & 129.3  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 996.50  | -7.62   | 265.81  | -8.30   | 94.8    | 3.44    | 3.26    | 0.18    | 2.04    | 3.27    | 1305.68 | 0.18    | 0.63    | 166.5   |\n",
       "| 996.63  | -8.85   | 264.57  | -9.70   | 93.5    | 3.12    | 2.92    | 0.20    | 1.82    | 2.93    | 1312.11 | 0.16    | 0.50    | 158.3   |\n",
       "| 996.87  | -8.84   | 264.56  | -9.69   | 93.5    | 3.13    | 2.92    | 0.20    | 1.83    | 2.93    | 1312.37 | 0.07    | 0.25    | 129.3   |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1]   [,2]  [,3]   [,4]  [,5] [,6] [,7] [,8] [,9] [,10] [,11]   [,12]\n",
       "[1,] 996.50 -7.62 265.81 -8.30 94.8 3.44 3.26 0.18 2.04 3.27  1305.68 0.18 \n",
       "[2,] 996.63 -8.85 264.57 -9.70 93.5 3.12 2.92 0.20 1.82 2.93  1312.11 0.16 \n",
       "[3,] 996.87 -8.84 264.56 -9.69 93.5 3.13 2.92 0.20 1.83 2.93  1312.37 0.07 \n",
       "     [,13] [,14]\n",
       "[1,] 0.63  166.5\n",
       "[2,] 0.50  158.3\n",
       "[3,] 0.25  129.3"
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       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>996.63 </td><td>-8.85  </td><td>264.57 </td><td> -9.70 </td><td>93.5   </td><td>3.12   </td><td>2.92   </td><td>0.20   </td><td>1.82   </td><td>2.93   </td><td>1312.11</td><td>0.16   </td><td>0.50   </td><td>158.3  </td></tr>\n",
       "\t<tr><td>996.87 </td><td>-8.84  </td><td>264.56 </td><td> -9.69 </td><td>93.5   </td><td>3.13   </td><td>2.92   </td><td>0.20   </td><td>1.83   </td><td>2.93   </td><td>1312.37</td><td>0.07   </td><td>0.25   </td><td>129.3  </td></tr>\n",
       "\t<tr><td>997.05 </td><td>-9.23  </td><td>264.15 </td><td>-10.25 </td><td>92.2   </td><td>3.03   </td><td>2.79   </td><td>0.24   </td><td>1.74   </td><td>2.80   </td><td>1314.62</td><td>0.10   </td><td>0.38   </td><td>203.9  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
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       "\\begin{tabular}{llllllllllllll}\n",
       "\t 996.63  & -8.85   & 264.57  &  -9.70  & 93.5    & 3.12    & 2.92    & 0.20    & 1.82    & 2.93    & 1312.11 & 0.16    & 0.50    & 158.3  \\\\\n",
       "\t 996.87  & -8.84   & 264.56  &  -9.69  & 93.5    & 3.13    & 2.92    & 0.20    & 1.83    & 2.93    & 1312.37 & 0.07    & 0.25    & 129.3  \\\\\n",
       "\t 997.05  & -9.23   & 264.15  & -10.25  & 92.2    & 3.03    & 2.79    & 0.24    & 1.74    & 2.80    & 1314.62 & 0.10    & 0.38    & 203.9  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 996.63  | -8.85   | 264.57  |  -9.70  | 93.5    | 3.12    | 2.92    | 0.20    | 1.82    | 2.93    | 1312.11 | 0.16    | 0.50    | 158.3   |\n",
       "| 996.87  | -8.84   | 264.56  |  -9.69  | 93.5    | 3.13    | 2.92    | 0.20    | 1.83    | 2.93    | 1312.37 | 0.07    | 0.25    | 129.3   |\n",
       "| 997.05  | -9.23   | 264.15  | -10.25  | 92.2    | 3.03    | 2.79    | 0.24    | 1.74    | 2.80    | 1314.62 | 0.10    | 0.38    | 203.9   |\n",
       "\n"
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      "text/plain": [
       "     [,1]   [,2]  [,3]   [,4]   [,5] [,6] [,7] [,8] [,9] [,10] [,11]   [,12]\n",
       "[1,] 996.63 -8.85 264.57  -9.70 93.5 3.12 2.92 0.20 1.82 2.93  1312.11 0.16 \n",
       "[2,] 996.87 -8.84 264.56  -9.69 93.5 3.13 2.92 0.20 1.83 2.93  1312.37 0.07 \n",
       "[3,] 997.05 -9.23 264.15 -10.25 92.2 3.03 2.79 0.24 1.74 2.80  1314.62 0.10 \n",
       "     [,13] [,14]\n",
       "[1,] 0.50  158.3\n",
       "[2,] 0.25  129.3\n",
       "[3,] 0.38  203.9"
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       "<ol class=list-inline>\n",
       "\t<li>3</li>\n",
       "\t<li>14</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 3\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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       "1. 3\n",
       "2. 14\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  3 14"
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    {
     "data": {
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       "<ol class=list-inline>\n",
       "\t<li>997.05</li>\n",
       "\t<li>-9.23</li>\n",
       "\t<li>264.15</li>\n",
       "\t<li>-10.25</li>\n",
       "\t<li>92.2</li>\n",
       "\t<li>3.03</li>\n",
       "\t<li>2.79</li>\n",
       "\t<li>0.24</li>\n",
       "\t<li>1.74</li>\n",
       "\t<li>2.8</li>\n",
       "\t<li>1314.62</li>\n",
       "\t<li>0.1</li>\n",
       "\t<li>0.38</li>\n",
       "\t<li>203.9</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 997.05\n",
       "\\item -9.23\n",
       "\\item 264.15\n",
       "\\item -10.25\n",
       "\\item 92.2\n",
       "\\item 3.03\n",
       "\\item 2.79\n",
       "\\item 0.24\n",
       "\\item 1.74\n",
       "\\item 2.8\n",
       "\\item 1314.62\n",
       "\\item 0.1\n",
       "\\item 0.38\n",
       "\\item 203.9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 997.05\n",
       "2. -9.23\n",
       "3. 264.15\n",
       "4. -10.25\n",
       "5. 92.2\n",
       "6. 3.03\n",
       "7. 2.79\n",
       "8. 0.24\n",
       "9. 1.74\n",
       "10. 2.8\n",
       "11. 1314.62\n",
       "12. 0.1\n",
       "13. 0.38\n",
       "14. 203.9\n",
       "\n",
       "\n"
      ],
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       " [1]  997.05   -9.23  264.15  -10.25   92.20    3.03    2.79    0.24    1.74\n",
       "[10]    2.80 1314.62    0.10    0.38  203.90"
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     "data": {
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       "<ol class=list-inline>\n",
       "\t<li>997.43</li>\n",
       "\t<li>-9.67</li>\n",
       "\t<li>263.68</li>\n",
       "\t<li>-10.63</li>\n",
       "\t<li>92.6</li>\n",
       "\t<li>2.93</li>\n",
       "\t<li>2.71</li>\n",
       "\t<li>0.22</li>\n",
       "\t<li>1.69</li>\n",
       "\t<li>2.72</li>\n",
       "\t<li>1317.35</li>\n",
       "\t<li>0.36</li>\n",
       "\t<li>0.75</li>\n",
       "\t<li>132.5</li>\n",
       "</ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 997.43\n",
       "\\item -9.67\n",
       "\\item 263.68\n",
       "\\item -10.63\n",
       "\\item 92.6\n",
       "\\item 2.93\n",
       "\\item 2.71\n",
       "\\item 0.22\n",
       "\\item 1.69\n",
       "\\item 2.72\n",
       "\\item 1317.35\n",
       "\\item 0.36\n",
       "\\item 0.75\n",
       "\\item 132.5\n",
       "\\end{enumerate*}\n"
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       "1. 997.43\n",
       "2. -9.67\n",
       "3. 263.68\n",
       "4. -10.63\n",
       "5. 92.6\n",
       "6. 2.93\n",
       "7. 2.71\n",
       "8. 0.22\n",
       "9. 1.69\n",
       "10. 2.72\n",
       "11. 1317.35\n",
       "12. 0.36\n",
       "13. 0.75\n",
       "14. 132.5\n",
       "\n",
       "\n"
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       " [1]  997.43   -9.67  263.68  -10.63   92.60    2.93    2.71    0.22    1.69\n",
       "[10]    2.72 1317.35    0.36    0.75  132.50"
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       "<ol class=list-inline>\n",
       "\t<li>997.81</li>\n",
       "\t<li>-9.59</li>\n",
       "\t<li>263.74</li>\n",
       "\t<li>-10.47</li>\n",
       "\t<li>93.2</li>\n",
       "\t<li>2.95</li>\n",
       "\t<li>2.74</li>\n",
       "\t<li>0.2</li>\n",
       "\t<li>1.71</li>\n",
       "\t<li>2.75</li>\n",
       "\t<li>1317.44</li>\n",
       "\t<li>0.24</li>\n",
       "\t<li>0.75</li>\n",
       "\t<li>161.6</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 997.81\n",
       "\\item -9.59\n",
       "\\item 263.74\n",
       "\\item -10.47\n",
       "\\item 93.2\n",
       "\\item 2.95\n",
       "\\item 2.74\n",
       "\\item 0.2\n",
       "\\item 1.71\n",
       "\\item 2.75\n",
       "\\item 1317.44\n",
       "\\item 0.24\n",
       "\\item 0.75\n",
       "\\item 161.6\n",
       "\\end{enumerate*}\n"
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       "1. 997.81\n",
       "2. -9.59\n",
       "3. 263.74\n",
       "4. -10.47\n",
       "5. 93.2\n",
       "6. 2.95\n",
       "7. 2.74\n",
       "8. 0.2\n",
       "9. 1.71\n",
       "10. 2.75\n",
       "11. 1317.44\n",
       "12. 0.24\n",
       "13. 0.75\n",
       "14. 161.6\n",
       "\n",
       "\n"
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       " [1]  997.81   -9.59  263.74  -10.47   93.20    2.95    2.74    0.20    1.71\n",
       "[10]    2.75 1317.44    0.24    0.75  161.60"
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     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>p (mbar)</th><th scope=col>T (degC)</th><th scope=col>Tpot (K)</th><th scope=col>Tdew (degC)</th><th scope=col>rh (%)</th><th scope=col>VPmax (mbar)</th><th scope=col>VPact (mbar)</th><th scope=col>VPdef (mbar)</th><th scope=col>sh (g/kg)</th><th scope=col>H2OC (mmol/mol)</th><th scope=col>rho (g/m**3)</th><th scope=col>wv (m/s)</th><th scope=col>max. wv (m/s)</th><th scope=col>wd (deg)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>997.43 </td><td>-9.67  </td><td>263.68 </td><td>-10.63 </td><td>92.6   </td><td>2.93   </td><td>2.71   </td><td>0.22   </td><td>1.69   </td><td>2.72   </td><td>1317.35</td><td>0.36   </td><td>0.75   </td><td>132.5  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       " p (mbar) & T (degC) & Tpot (K) & Tdew (degC) & rh (\\%) & VPmax (mbar) & VPact (mbar) & VPdef (mbar) & sh (g/kg) & H2OC (mmol/mol) & rho (g/m**3) & wv (m/s) & max. wv (m/s) & wd (deg)\\\\\n",
       "\\hline\n",
       "\t 997.43  & -9.67   & 263.68  & -10.63  & 92.6    & 2.93    & 2.71    & 0.22    & 1.69    & 2.72    & 1317.35 & 0.36    & 0.75    & 132.5  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| p (mbar) | T (degC) | Tpot (K) | Tdew (degC) | rh (%) | VPmax (mbar) | VPact (mbar) | VPdef (mbar) | sh (g/kg) | H2OC (mmol/mol) | rho (g/m**3) | wv (m/s) | max. wv (m/s) | wd (deg) |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 997.43  | -9.67   | 263.68  | -10.63  | 92.6    | 2.93    | 2.71    | 0.22    | 1.69    | 2.72    | 1317.35 | 0.36    | 0.75    | 132.5   |\n",
       "\n"
      ],
      "text/plain": [
       "  p (mbar) T (degC) Tpot (K) Tdew (degC) rh (%) VPmax (mbar) VPact (mbar)\n",
       "1 997.43   -9.67    263.68   -10.63      92.6   2.93         2.71        \n",
       "  VPdef (mbar) sh (g/kg) H2OC (mmol/mol) rho (g/m**3) wv (m/s) max. wv (m/s)\n",
       "1 0.22         1.69      2.72            1317.35      0.36     0.75         \n",
       "  wd (deg)\n",
       "1 132.5   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>p (mbar)</th><th scope=col>T (degC)</th><th scope=col>Tpot (K)</th><th scope=col>Tdew (degC)</th><th scope=col>rh (%)</th><th scope=col>VPmax (mbar)</th><th scope=col>VPact (mbar)</th><th scope=col>VPdef (mbar)</th><th scope=col>sh (g/kg)</th><th scope=col>H2OC (mmol/mol)</th><th scope=col>rho (g/m**3)</th><th scope=col>wv (m/s)</th><th scope=col>max. wv (m/s)</th><th scope=col>wd (deg)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>997.81 </td><td>-9.59  </td><td>263.74 </td><td>-10.47 </td><td>93.2   </td><td>2.95   </td><td>2.74   </td><td>0.2    </td><td>1.71   </td><td>2.75   </td><td>1317.44</td><td>0.24   </td><td>0.75   </td><td>161.6  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       " p (mbar) & T (degC) & Tpot (K) & Tdew (degC) & rh (\\%) & VPmax (mbar) & VPact (mbar) & VPdef (mbar) & sh (g/kg) & H2OC (mmol/mol) & rho (g/m**3) & wv (m/s) & max. wv (m/s) & wd (deg)\\\\\n",
       "\\hline\n",
       "\t 997.81  & -9.59   & 263.74  & -10.47  & 93.2    & 2.95    & 2.74    & 0.2     & 1.71    & 2.75    & 1317.44 & 0.24    & 0.75    & 161.6  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| p (mbar) | T (degC) | Tpot (K) | Tdew (degC) | rh (%) | VPmax (mbar) | VPact (mbar) | VPdef (mbar) | sh (g/kg) | H2OC (mmol/mol) | rho (g/m**3) | wv (m/s) | max. wv (m/s) | wd (deg) |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 997.81  | -9.59   | 263.74  | -10.47  | 93.2    | 2.95    | 2.74    | 0.2     | 1.71    | 2.75    | 1317.44 | 0.24    | 0.75    | 161.6   |\n",
       "\n"
      ],
      "text/plain": [
       "  p (mbar) T (degC) Tpot (K) Tdew (degC) rh (%) VPmax (mbar) VPact (mbar)\n",
       "1 997.81   -9.59    263.74   -10.47      93.2   2.95         2.74        \n",
       "  VPdef (mbar) sh (g/kg) H2OC (mmol/mol) rho (g/m**3) wv (m/s) max. wv (m/s)\n",
       "1 0.2          1.71      2.75            1317.44      0.24     0.75         \n",
       "  wd (deg)\n",
       "1 161.6   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_test_data <- create_sequences_x_y(data = data_hourly[1:7,], sequence_length = 3, target_shift = 1, step_shift = 1)\n",
    "dim(sequence_test_data$x)\n",
    "sequence_test_data$x[1,,]\n",
    "sequence_test_data$x[2,,]\n",
    "sequence_test_data$x[3,,]\n",
    "dim(sequence_test_data$y)\n",
    "sequence_test_data$y[1,]\n",
    "sequence_test_data$y[2,]\n",
    "sequence_test_data$y[3,]\n",
    "data_hourly[6,]\n",
    "data_hourly[7,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3.2\n",
    "Copy your solution to exercise 3.1 and try using the first 8 (instead of 7) elements of `data_hourly` as `data`. What are the dimensions now? Does it make sense that a new sequence was added? What if we increase the `step_shift` to 2?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>4</li>\n",
       "\t<li>3</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 4\n",
       "\\item 3\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 4\n",
       "2. 3\n",
       "3. 14\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  4  3 14"
      ]
     },
     "metadata": {},
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    {
     "data": {
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       "<ol class=list-inline>\n",
       "\t<li>4</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 4\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 4\n",
       "2. 14\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  4 14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_test_data <- create_sequences_x_y(data = data_hourly[1:8,], sequence_length = 3, target_shift = 1, step_shift = 1)\n",
    "dim(sequence_test_data$x)\n",
    "dim(sequence_test_data$y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3.3\n",
    "Now input all `data_hourly` as `data` and set the `sequence_length` to 96 (3 days), `target_shift` to 23 (23 hours) and `step_shift` to 24 (1 day). Store the `x` part of the output as `x_hourly` and `y` part as `y_hourly`. Check the dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_data <- create_sequences_x_y(data = ?, sequence_length = ?, target_shift = ?, step_shift = ?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2916</li>\n",
       "\t<li>96</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 2916\n",
       "\\item 96\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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       "1. 2916\n",
       "2. 96\n",
       "3. 14\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2916   96   14"
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     "metadata": {},
     "output_type": "display_data"
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    {
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       "<ol class=list-inline>\n",
       "\t<li>2916</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 2916\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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       "1. 2916\n",
       "2. 14\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2916   14"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_data <- create_sequences_x_y(data = data_hourly, sequence_length = 96, target_shift = 23, step_shift = 24)\n",
    "x_hourly <- sequence_data$x\n",
    "y_hourly <- sequence_data$y\n",
    "dim(x_hourly)\n",
    "dim(y_hourly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3.4\n",
    "Notice that `y_hourly`, our targets, have 14 features in them. Since we only want to predict the temperature, we should remove all other features from `y_hourly` and save it as `y_hourly_temp`. Put differently, only keep the temperature feature. Verify the dimensions as always."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hourly_temp <- ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "2916"
      ],
      "text/latex": [
       "2916"
      ],
      "text/markdown": [
       "2916"
      ],
      "text/plain": [
       "[1] 2916"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hourly_temp <- y_hourly[,2]\n",
    "length(y_hourly_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Splitting\n",
    "Now we split our data to train/test. Unlike usually, we will not split our data to a validation set. We will let Keras handle that for us in the `fit()` function.\n",
    "\n",
    "Like above, we have provided a function which splits the data for you. The function accepts three parameters `x_data` and `y_data`, the data it is supposed to split and a `fraction`, the fraction of the data that should be used as test data. The function will select the last elements from the to create the test set. The function assumes that the number of rows in `x_data` and `y_data` is the same.\n",
    "\n",
    "The function returns a list containing `x_train`, `y_train`, `x_test` and `y_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_dataset <- function(x_data, y_data, fraction = 0.2) {\n",
    "    train_start_index <- 1\n",
    "    train_end_index <- train_start_index + floor((1-fraction) * dim(x_data)[1]) - 1\n",
    "    test_start_index <- train_end_index + 1\n",
    "    test_end_index <- dim(x_data)[1]\n",
    "    \n",
    "    x_train <- x_data[train_start_index:train_end_index,,]\n",
    "    x_test <- x_data[test_start_index:test_end_index,,]\n",
    "    \n",
    "    y_train <- y_data[train_start_index:train_end_index]\n",
    "    y_test <- y_data[test_start_index:test_end_index]\n",
    "    \n",
    "    list(x_train = x_train,\n",
    "        y_train = y_train,\n",
    "        x_test = x_test,\n",
    "        y_test = y_test)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4 \n",
    "Use the function to split `x_hourly` and `y_hourly_temp`, using `fraction = 0.2`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_data <- ?\n",
    "\n",
    "x_train = split_data$x_train\n",
    "y_train = split_data$y_train\n",
    "x_test = split_data$x_test\n",
    "y_test = split_data$y_test\n",
    "dim(x_train)\n",
    "length(y_train)\n",
    "dim(x_test)\n",
    "length(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2332</li>\n",
       "\t<li>96</li>\n",
       "\t<li>14</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 2332\n",
       "\\item 96\n",
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       "1. 2332\n",
       "2. 96\n",
       "3. 14\n",
       "\n",
       "\n"
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       "\\begin{enumerate*}\n",
       "\\item 584\n",
       "\\item 96\n",
       "\\item 14\n",
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       "1. 584\n",
       "2. 96\n",
       "3. 14\n",
       "\n",
       "\n"
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      "text/plain": [
       "[1] 584  96  14"
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       "584"
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       "[1] 584"
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     "metadata": {},
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   ],
   "source": [
    "split_data <- split_dataset(x_data = x_hourly, y_data = y_hourly_temp, fraction = 0.2)\n",
    "x_train = split_data$x_train\n",
    "y_train = split_data$y_train\n",
    "x_test = split_data$x_test\n",
    "y_test = split_data$y_test\n",
    "dim(x_train)\n",
    "length(y_train)\n",
    "dim(x_test)\n",
    "length(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Scaling\n",
    "To help our network train faster, we need to scale our data. As in the previous weeks, we use the training set to scale the training and test sets. To scale a dataset we first needed to compute the mean and standard deviation, using only the training set. We use the [`apply()`](https://www.rdocumentation.org/packages/base/versions/3.5.3/topics/apply) function for this.\n",
    "\n",
    "## Exercise 5\n",
    "Fill in the apply function below to compute the mean and standard deviation of the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We compute the mean and sd for each feature independently.\n",
    "mean <- apply(?, ?, mean)\n",
    "std <- apply(?, ?, sd)\n",
    "\n",
    "mean[2] # The mean temperature\n",
    "mean[1] # The mean air pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "[1] 8.991606"
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     "data": {
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       "[1] 988.8198"
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   ],
   "source": [
    "# We compute the mean and sd for each feature independently.\n",
    "mean <- apply(x_train, 3, mean)\n",
    "std <- apply(x_train, 3, sd)\n",
    "\n",
    "mean[2] # The mean temperature\n",
    "mean[1] # The mean air pressure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like last week, we cannot use the `scale()` function since it only works with (at most) 2D tensors, so we need to do this manually. Since we already split up the dataset into train/test, we need to scale both of them. We store the result in `x_train_scaled` and `x_test_scaled`. We provide the code for this, but we recommend reading through it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an empty 3D tensor with same dimensions as feature_matrix_reshaped.\n",
    "x_train_scaled <- array(0, dim = dim(x_train))\n",
    "x_test_scaled <- array(0, dim = dim(x_test))\n",
    "\n",
    "# For each feature we subtract the mean and divide by the std.\n",
    "for (j in 1:dim(x_train)[3]) {\n",
    "    x_train_scaled[,,j] <- (x_train[,,j] - mean[j]) / std[j]\n",
    "    x_test_scaled[,,j] <- (x_test[,,j] - mean[j]) / std[j]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see if this all makes sense and check the first temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "-8.02"
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       "-8.02"
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      "text/markdown": [
       "-8.02"
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      "text/plain": [
       "[1] -8.02"
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       "[1] -2.015909"
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     "metadata": {},
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   ],
   "source": [
    "x_train[1,1,2]\n",
    "x_train_scaled[1,1,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. The models\n",
    "Last week we used a dense network to serve as our baseline, this week we will use a SimpleRNN and then see if a GRU or an LSTM helps improve our predictions.\n",
    "\n",
    "Notice that instead of providing validation data, we simply specify the fraction of training data to use for validation using the `validation_split = 0.2` parameter in the `fit()` function. According to the Keras documentation the [`fit()`](https://keras.io/models/sequential/) function will use the last elements of the training data (similar to what we have done for the test set). This is what we want, since we do not want to validate our data from the past. Also notice that our test data is even further ahead in the future.\n",
    "\n",
    "## Exercise 6.1\n",
    "- Create a network with a `simple_rnn_layer` with 16 units.\n",
    "- After the simple RNN, create a regression layer.\n",
    "- Use mae loss.\n",
    "The `simple_rnn_layer` will need the `input_shape` parameter since it is the first layer in our network. We have shaped our input so that it has a sequence length of 96 and 14 features. You need to provide these values as the `input_shape`.\n",
    "\n",
    "**Hint** To speed up training it is helpful to remove the `callbacks=list(Progress$new())` line from the `fit` function. The side-effect is that you have less idea of how far in the training process you are, but you can be sure that it is about 1/5 faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in parse(text = x, srcfile = src): <text>:9:1: unexpected ')'\n8:     loss = ?\n9: )\n   ^\n",
     "output_type": "error",
     "traceback": [
      "Error in parse(text = x, srcfile = src): <text>:9:1: unexpected ')'\n8:     loss = ?\n9: )\n   ^\nTraceback:\n"
     ]
    }
   ],
   "source": [
    "model <- keras_model_sequential() %>%\n",
    "    ?\n",
    "\n",
    "cat(summary(model))\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_adam(lr = 0.001),\n",
    "    loss = ?\n",
    ")\n",
    "\n",
    "history <- model %>% fit(\n",
    "    x = x_train_scaled,\n",
    "    y = y_train,\n",
    "    validation_split = 0.2,\n",
    "    epochs = 40,\n",
    "    batch_size = 16,\n",
    "    callbacks=list(Progress$new())\n",
    ")\n",
    "\n",
    "plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "simple_rnn_6 (SimpleRNN)            (None, 16)                      496         \n",
      "________________________________________________________________________________\n",
      "dense_9 (Dense)                     (None, 1)                       17          \n",
      "================================================================================\n",
      "Total params: 513\n",
      "Trainable params: 513\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 60 - loss: 2.099205, acc.: , val loss: 4.06275815677847, val acc.:   \r"
     ]
    },
    {
     "data": {
      "image/png": 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mRkaC+QJOm4445Lvr5r166RSKS5n0VR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\nvF9eKVwub5t/jzPz+JgB43NgjM+BaecRmQfjc2CWHx+TPKONZtH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cLq39TkVVSTRmbD0AAEB3BDt9xHPzVK9PayuFZgx2QojLM9O1Rp2q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fQ/PmkJwMWzY2rGFX5nIeEpJY5eLkmZy8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\nDgAAAABAQ/wfuAzW1xn1gpQAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- keras_model_sequential() %>%\n",
    "    layer_simple_rnn(units = 16, input_shape = c(96, 14)) %>%\n",
    "    layer_dense(units = 1)\n",
    "\n",
    "cat(summary(model))\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_adam(lr = 0.001),\n",
    "    loss = \"mae\"\n",
    ")\n",
    "\n",
    "history <- model %>% fit(\n",
    "    x = x_train_scaled,\n",
    "    y = y_train,\n",
    "    validation_split = 0.2,\n",
    "    epochs = 60,\n",
    "    batch_size = 16,\n",
    "    callbacks=list(Progress$new())\n",
    ")\n",
    "\n",
    "plot(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should see an average validation loss of about 4, we also see that the model seems to have started overfitting to the data. Usually, when we start overfitting to our data, we would not try a more expressive model as we would expect to overfit the data even more. Against our intution, let us try to use a GRU layer instead of a simple RNN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 6.2\n",
    "Let us try a GRU layer instead of the SimpleRNN above. Copy the model above and only change the `layer_simple_rnn` to `layer_gru`. Notice that the GRU takes even longer to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "gru_2 (GRU)                         (None, 16)                      1488        \n",
      "________________________________________________________________________________\n",
      "dense_10 (Dense)                    (None, 1)                       17          \n",
      "================================================================================\n",
      "Total params: 1,505\n",
      "Trainable params: 1,505\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 60 - loss: 1.921874, acc.: , val loss: 3.57535881148671, val acc.:   \r"
     ]
    },
    {
     "data": {
      "image/png": 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NxIEofDYbFYqqqqAoGA3rXE2O12WZYr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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- keras_model_sequential() %>%\n",
    "    layer_gru(units = 16, input_shape = c(96, 14)) %>%\n",
    "    layer_dense(units = 1)\n",
    "\n",
    "cat(summary(model))\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_adam(lr = 0.001),\n",
    "    loss = \"mae\"\n",
    ")\n",
    "\n",
    "history <- model %>% fit(\n",
    "    x = x_train_scaled,\n",
    "    y = y_train,\n",
    "    validation_split = 0.2,\n",
    "    epochs = 60,\n",
    "    batch_size = 16,\n",
    "    callbacks=list(Progress$new())\n",
    ")\n",
    "\n",
    "plot(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! We see that the GRU actually outperforms the SimpleRNN and does not start overfitting the data. This seems to indicate that there were some long term dependenices which the SimpleRNN was not able to account for and it started overfitting to latest datapoints in the sequence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now compare evaluate our GRU against the test set by running the cell below. We will see that the test loss is similar to our validation loss, which is very good, it means that we have not methodologically overfit to the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>loss:</strong> 3.50417569238846"
      ],
      "text/latex": [
       "\\textbf{loss:} 3.50417569238846"
      ],
      "text/markdown": [
       "**loss:** 3.50417569238846"
      ],
      "text/plain": [
       "    loss \n",
       "3.504176 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model %>% evaluate(x_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 6.3\n",
    "The test loss can be a bit abstract for us, so let us also manually compare the predictions of the model on the test set against the test labels. To get the predictions we use the function `predict()` and pass in the model as first parameter and then the `x_test_scaled` data. The function will output a predictions for each sequence in the test data. Store these predictions in a variable called `predictions`. Then combine the `predictions` column-wise with `y_test` using the function `cbind()`. Then print out the first 10 rows (using `head()`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions <- model %>% predict(?)\n",
    "compare <- cbind(?, ?)\n",
    "head(?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col></th><th scope=col>y_test</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>13.441838</td><td>14.43    </td></tr>\n",
       "\t<tr><td>13.331948</td><td>11.55    </td></tr>\n",
       "\t<tr><td> 8.555273</td><td>10.81    </td></tr>\n",
       "\t<tr><td> 6.192822</td><td>13.77    </td></tr>\n",
       "\t<tr><td> 8.382848</td><td>15.76    </td></tr>\n",
       "\t<tr><td>11.493831</td><td>12.38    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "  & y\\_test\\\\\n",
       "\\hline\n",
       "\t 13.441838 & 14.43    \\\\\n",
       "\t 13.331948 & 11.55    \\\\\n",
       "\t  8.555273 & 10.81    \\\\\n",
       "\t  6.192822 & 13.77    \\\\\n",
       "\t  8.382848 & 15.76    \\\\\n",
       "\t 11.493831 & 12.38    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "|  | y_test |\n",
       "|---|---|\n",
       "| 13.441838 | 14.43     |\n",
       "| 13.331948 | 11.55     |\n",
       "|  8.555273 | 10.81     |\n",
       "|  6.192822 | 13.77     |\n",
       "|  8.382848 | 15.76     |\n",
       "| 11.493831 | 12.38     |\n",
       "\n"
      ],
      "text/plain": [
       "               y_test\n",
       "[1,] 13.441838 14.43 \n",
       "[2,] 13.331948 11.55 \n",
       "[3,]  8.555273 10.81 \n",
       "[4,]  6.192822 13.77 \n",
       "[5,]  8.382848 15.76 \n",
       "[6,] 11.493831 12.38 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions <- model %>% predict(x_test_scaled)\n",
    "compare <- cbind(predictions, y_test)\n",
    "head(compare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that \"on average\" we are about 3 degrees off, that is what the MAE loss of 3 means."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we saw that the GRU did improve our predictions and it was very easy to adjust our original model to use a GRU instead. In the bonus exercise below, we suggest that you also try an LSTM, they tend to perform even better.\n",
    "\n",
    "In this notebook we also saw three new things. First, we resampled the data so that we had a datapoint every hour using the `seq()` function. Second, we saw a different way to create sequences from a dataset. We created a helpful function for this which we can hopefully reuse in later notebooks. Third, we did not split our data into a validation set as we left it to Keras, which managed it for us in the `fit()` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus execrise\n",
    "Copy the model from above and try using an LSTM instead of a GRU. Can you improve the performance. Does adding a dense layer after the LSTM help?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "lstm_3 (LSTM)                       (None, 32)                      6016        \n",
      "________________________________________________________________________________\n",
      "dense_14 (Dense)                    (None, 16)                      528         \n",
      "________________________________________________________________________________\n",
      "dense_15 (Dense)                    (None, 1)                       17          \n",
      "================================================================================\n",
      "Total params: 6,561\n",
      "Trainable params: 6,561\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 60 - loss: 1.698695, acc.: , val loss: 3.75901296051973, val acc.:   \r"
     ]
    },
    {
     "data": {
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rc5fPiwoijqFQUAAN6i0WD36aefhoeH\nmzP/NgKAx8fzPIliliank+d5hmFUqggAALxIi0Ox+/btO3/+/OjRo3fu3HnfU06nMzU1Nf2h\noig5+P3kfoumfrEx96hdiEf6l0hTJWmtnvs+0Ij76mnRosWK0WMoog8VLkJEpCi0dnXLli3z\ns2wNfolQ0kNo81rTZklqF5KFpr5EoCLNBbvk5ORPP/10zJgxJpPp389u2bJlypQp6Q8jIyOr\nVq2asxNpbY06o9Godgn3s1gsFotF7Sqy0NpXSRCEQoUKqV1FFvfVM2DAgK1bt64aMpCea0cW\nC+3dXTwudsnhw/lZtta+RBqc4+Hn56d2Cffz8fFRu4QsjEaj1i5/rX1jkxdKcjqdeXtAyAea\nC3Zffvll7dq1a9as+Z/PFi5cuF69eukPBUHIwbedexxKU9+vHMfJsqydaU8sy3IcJ0mSLMtq\n1+LBMAzLspIkqV1IBoPBIMuypkried7lct3XuHz58nZr1mzcuDH5xtXaXbuMHDkyICAgf77/\nca09kgavNZZliUg79TAMw/N8gbjWVOSla83lchkMhrw9JnibtoLdsWPHjh49umjRoge9ICws\nLCwsLP1hfHx8QkLC457FarUKgpCYmKidH+4Wi8XhcGjn95/JZLJYLHa73eFwqF2LhyAIgiAk\nJyerXYgHy7I2m83lciUmJqpdSwabzfafV8Rzzz333HPPpT/MwVWTMwEBATzP59vpssNqtdrt\ndu38SjabzWazOSUlRbxvKqR6TCYTy7J2u13tQjw4jgsMDBRFUTuXP8Mw/v7+mvrGDgwMZBjG\nGyVpre8WHklbwW7btm0pKSnDhg1zP0xOTp43b17NmjXffPNNdQuD3IuKilq4cGFUVFSRIkW6\ndOnSqVMntSsCAADQG20Fu2HDhg0YMCD94ZgxY/r27Zu5iw4KqP3793ft2lVsGk7NWtCd25vH\njD106ND06dPVrgsAAEBXtBXs/Pz8Mk8iZhjGz8/ParWqWBLknqIoI0eOFPv2p4i+nqbmLZcM\ne7lLly61a9dWtTQAAABd0Vawu09kZKTaJUAeuHr16qVLl6jjCxlNZctRjaf/+OMPBDsAAIA8\npOlgB7rCZF0Nm2W0c/PKv8myvG7dut27dzudzrCwsIiIiMy3hh0/fvz9998/deqUv79/69at\nR48e7evrq2K1Bc7Nmzfnz59//Phxi8XSunXr/v378zx+FgEA5AGN7jwBBZQkSRcvXoyOjs4c\n2kqWLFmqVCnamGlz0qvRdPy4ZrfllWW5V69eI955Z6VLXscbX5/30XPPPZe+MvaRI0fatWu3\nxcf32sjRpzt3+2jjpp49e2pqIQaNu3z5cuPGjb84+dehps13PFX5zQULIiIitLO4BgBAgYZg\nB3nmhx9+CA0NrRcWVrt27bCwsN9//93dzjDM/Pnz6cvP6YPZtH0rfRNJo0YM6N2rbt266hb8\nIEuXLv3tr9O07Gt6ZSQNHU7Lvj6R5pgzZ4772QkTJji69aDXxlJYA3q2Nc1ftP/ChTVr1qhb\ncwHyxhtvJIQ1oFnvU/uO1DOCPv3it+Mn1q5d635WUZRVq1aFh4eXKVOmUaNGS5Ys0c4yQAAA\n2odgB3ljz549Q1599XbfAbR5G23acrFl6759+547d879bJMmTXZu39bFaKj6w7rm56MWTXt3\n9uzZ6hb8ENu2baMOHcnq73ksCNTtxa1btxKR0+k8ceIEPdsq49VmX2rQ6PDhw2pUWiDt3r07\ny4RLqz81a7579273o0WLFo2c+PZfLVrZ53x47oWuby9YOGnSJHUKBQAogDCvBfLG3LlzqXsP\natfB8ziij/3SxUWLFi1YsMDdUK1atSVLlqhW3+NwOBxkyromp485LS2NiFiW5Xneed+6zQ6H\nIGhucyHNUhSF7tvRkvU8jIuLmzVrFs2cQ7XrEBFVq0FVq33xcv9+/fpVrlw53ysFACh40GMH\neePixYtUtXqWpuo1Lly4oFI5uVKrVi36fQdlnvX12/ZnnnmGiDiOCw8PpzWrKH0S4Y3rtOv3\nZ599Vo1KC6SGDRtmmXCZlEQ7d7gnXJ4+fVo0Gj2pzq1MWSpb7tixY/leJgBAgYQeO8gbQUFB\n0bduZmm6ebNw4cJ5cvCdO3fu2rUrNTU1NDS0e/fu3r6D8rXXXvvxxx+vvjWBXuhMPE/btvgf\nPfzOjh3uZ+fMmdO6des7rw6jsAaUlEi/bO7TtUvLli29WpKezJ49u2XLlklJidSwMaWk0E/f\nN6tW9cUXXyQik8lEopNcLsr8X2y3m81m1coFAChQ0GMHeSMiIoK+Xk6XL3ke/3WSfvwuIiIi\n90ceP3589z59Fp6N+uJOzKj3ZrZq1SolJSX3h32IgICAzZs39wopW/LjBUXen9XRZNyyZUup\nUqXcz5YsWXLv3r0TOrTvFHunj4/xq0WL5s6d69V6dKZcuXK7du3qVz4k9JdNjY8fnTp48Lff\nfuveeL5GjRolCtlozaqMV/+63S8pUbM3UAMAaI2m1xJ7pPj4+Bxs5m21WgVBiImJ0c7nbrFY\nHA6Hdu7+M5lMFoslKSnJcd9ksgdTFOWNN95Y+vXXVKEiSZJw6eL48ePHjBmTy0o2bdrU/9VX\n6dMvKLgkEZHDQeNeGxhWT/V7L1iWtdlsoigmJiaqW0lmNpstNjZW7SoyBAQE8Dx/9+7d7L9l\nz549vXv3tletRpWq0JVLwoH9ixYt6ty5c16VZLVa7XZ7Dn5ueInZbDabzYmJiaIoql2Lh8lk\nYlnWbrerXYgHx3GBgYFpaWnJyclq1+LBMIy/v398fLzahWQIDAxkGMYbl39QUFCeHxO8CkOx\nkDcYhpk9e3b//v0PHTpkMBgaNGhQtmzZ3B928+bN1KatJ9URkdFIEX03fvSB6sEOvKRRo0Z7\n9+79+uuvL168WLJWzZ6zZ1WsWFHtogAACgwEO3g8qampx44dS0xMrFatWsmSJe97tkqVKlWq\nVMnD09ntdipUJEuTxaKdvgTwhuDg4DfeeEPtKgAACiTMsYPH8Ntvv4WFhXXs3fulceNr1anz\nv//9z9s7LlSvXp327aHMZ9m9KzQ01KsnBQAAKKDQYwfZFR0d/fLLLydF9KXuPYhl6eKFZRMn\nFCtWbOzYsd476dChQ1etWnXpnTepe08ym+n3Hcaffnh30ybvnREAAKDgQo8dZNe3336bVKkK\n9ehFLEtEVC6ERozy9prDFovlp59+6hZcInDaZJ8xIxtevvjDDz88/fTTXj0pAABAAYUeO8iu\n69evU5kyWZrKlouNjU1LSzOZTN47b4kSJRYvXiwIgsFg8PZCJwAAAAUaeuwgu0qWLEn37SRx\n4XxQUJBXU11mzH37UAEAAEBWCHaQXb179w64eJ5WLPfcyhB1jhbNHz58uNp1ARRUJ0+e/N//\n/te9e/d333332rVrapcDAHqAYAfZFRwcvHTp0lJbf6H2bahbJ/6VIUM7d3711VfVrgugQFqx\nYkWL1m0+/vvcOrPfh7t2N2zY8MCBA2oXBbmSnJw8Y8aMVq1aNW3adMyYMQjroArMsYPH0KRJ\nk3379v3111/x8fHVqlUrWrSo2hVB3rDb7TzPC4KgdiFedPr06Q8//PDvv/+2Wq3t27cfPHiw\nip/v9evX33rrLZo6nRp6dkuzr1g+fPjwgwcPensrZPASURQ7dOhwSnRSxxfI5HNmx6+bmjf/\n7bff/r3eJ4BXoccOHo/RaKxdu3aLFi2Q6vRh586dzZo1K1u2bJkyZbp27XrmzBm1K/KKI0eO\ntGrVar1M53r3Ody42ZTPvxg4cKCKmwru2rUrtXiJ9FRHRPRir+gbN/Ph63/q1Knp06ePGjXq\nk08+SUhI8PbpnhyfffbZqaRkWvAJtetALZ+l6TPjataeNGmS2nXBEwfBDuDJdeTIkZdeeul0\nw8bKilWuz5ft8vPv0qXL7du31a7rYbZv3z5q1KjevXvPmDEj+6WOGzdO7NGLxv2PmjWnTp1p\n0eItBw5sUm9BRFEUyWjM0mQwEM+lpaV59bxLly5t06bN/KPHVjqlyd98W79+/X/++cerZ3xy\nHDhwgFo8m+W/te3z+/fvV68ieEIh2GXXd999Fx4eHhwc/Mwzz8yZM8fbP38B8sGMGTMcL3Sh\niL5UIpjKlqPxE+6WLbdo0SK163qgd999t9fAgSvTxG1lyn30x+4GDRpkp4srJSXlr7/+ojZt\nM5oCAqlefRV/6daqVYv+iaKr0RlN+/b4MEzVqlW9d9Lz589PmjRJnD6T3p1BI0bRp1/crd9w\nxIgR3jvjE4XjOJJcWZokieM4lcqBJxeCXbYsX7582LjxfzV/Vpy/6MqAl99fsxY3DYAO/P33\n31SnXpamOvX+/vtvlcp5hCNHjiz87DNa9CmNGkMRfemDjxKfbTNq1KhHvpFlWYZhyHX/L10V\nZ7PVqFGjb69eNH4Mbd5IJ47Tqm/ovelTp0719fX13km3b9/uqFSZ6oZ5HjMM9R909OjRGzdu\neO+kT46mTZvS1i2UnOx5rCj0w3fh4eFq1gRPJMzSfbS0tLTJkyfThLeoSTMiospVqVr1n/pF\nDNy7t2HDhmpXB5BzVqv1Tnxclqb4OKvVqlI5j7Bjxw6q/QyFlM9o6tHzWI+uMTExhQoVesgb\nfXx86tWrd+D7dTRmvKfpxnXatyd82BBv1vsIs2bNeuqpp1auXHn9+vXy5cu/snBBhw4dvHrG\n1NRUui84WixEZLfbvXreJ0S/fv02bNiwe/AAer49GY30+47ghPipX0eqXRc8cRDsHu3ChQsp\nqanUqElGU+EiVLnqiRMnEOygQOvSpcv7kV9R3TDy9yciunyJNqzv/LFGh2JFUSQh67w0QSAi\np9P5yPfOmzevTZs2STdvUN0wiouljRte6tJF3d4Ug8EwdOjQMWPGmM3mxMREURS9fcYaNWrQ\nggUUH0cBgZ6m3bsCAwNLly7t7VM/CTiOW7NmTWRk5M6dO1NTU+s+33b48OGa/TMJdAzB7tF8\nfHxIUSjVTr6WjFZ7io+Pj3pFAeSBMWPGHD58eEefXvRMHRJFOnxw2MCB7dq1U7uu/1a3bl36\n4guKiaH0/rltW0qXLp2dG7QrVqy4d+/exYsXnzl9MiAg4PkPP+jUqZN3y9WeFi1atKpff9v4\nMdR/EBUtSsf+pK+Wvjt7lsFgULu0gmT37t1r166NiYkpU6bMkCFDymTaaNFgMAwaNGjQoEEq\nlgeAYPdoZcuWrVK58pnly+iVkZ6mvXt8rlxu0aKFqnUB5JbBYFizZs2vv/564MABHx+fZpPf\nqV27ttpFPVCrVq2eb9Jk8+gR1DOCgoLoyBH68bsPvvkmm3vNFStWbOrUqVar1W63u+6bb/dk\nYBjm888/nzt37nefLrp9+3alSpXGLFzQsWNHtesqSBYuXPju7Nn03PNUtjydPrW8UaN169bV\nr19f7boAMjAqruSUe/Hx8Tn4AW21WgVBiImJyf7nfurUqc6dO8cHl6Rq1enGdeHA/jlz5kRE\nRDzuqR/EYrE4HI7sjCjlD5PJZLFYkpKSHA6H2rV4CIIgCEJy+sRktbEsa7PZRFFMTExUu5YM\nNpstNjZW7SoyBAQE8Dx/9+7dvDqgKIqLFy9ev379nTt3qlatOm7cuLp16z7WEbQW7Mxmc74N\nxWaTyWRiWVY7E+84jgsMDExLS1P98j9//nzTpk3F9+dR6NOepm8iS235+fDhwyzr3TsRJUk6\nd+5cbGxsxYoVixQp8u8XBAYGMgzjjcs/KCgoz48JXoUeu2ypXr36gQMHIiMjo6Kiiteu1XXG\n9CpVqqhdFMATRxCE11577bXXXlO7EHgS7dq1SyxfISPVEVG3F6O//CIqKqpSpUreO+/JkydH\njBhx5tw5slgoKal/377vvfceBtDhQRDssstms40ePVrtKgAAQB2SJJEh6zZ0HE8s49UO4ISE\nhL59+16tU4/mLiSTiS6c/2ry235+M7GnBTwI1rEDAAB4tDp16tDfpyn6SkbTr9sCrdaKFSt6\n76Q//fTTVd5AI0eTyUREFFKexk/4/PPPtTNPBrQGPXYAAACPVrNmzf69e3819jXq1ZtKBNOp\nk7RuzZxFiwRBePSbcyo6OppCylPmOXwVKqSlpd2+fbtUqVLeOy8UXAh2cL9jx2aE+kAAACAA\nSURBVI598803169fL1WqVN++fb26xxGA9rlcruXLl//yyy8JCQk1atQYPXo0fqE+sWbNmlWt\nWrXVq1ffunWrQoUKI1eubNKkyaPflgvFihWjTZtJUSj97u8rVwRBwD0N8CAYioUsVq9e3art\n81/durO1wlNfXrnaqlWrjRs3ql0UgGoURRk4cOAbc+ftrFTlz1bPRV660qRJk7Nnz6pdF6iD\n47j+/fv/8ssvFy5cWLNmjbdTHRG98MILQXGx9NVSkiQiotu3aN4HERERWEgVHgQ9dpAhNjZ2\nwoQJ9M5kahrubhGfqTN69Ojw8HCLxfLQtwLo08aNG3/et4++jCSbjYiobbuU+XPHjx+/YcMG\ntUuDJ0KhQoWWLl06YsSI6B++I5uNrl3t0Lbt1KlT1a4LtAvBDjIcOHAgxdc3PdUREbV+LuGT\nRUeOHGnWrJlqZQGoZ8+ePdSoiSfVuXXodHDwAFEUvTqzCiBdgwYN9u3bd+jQobt371apUsWr\nS6uADiDYQYb/uJmfYcjAS+4hAIAn03+tZJ7N7S4A8oTRaGzcuLHaVUDBgDl2kKFWrVp06yad\nOpnRdPiQkJRUs2ZN9YoCUFOjRo1ozx+UeUH/9T+GhYVheVgA0Cb02EGG4ODgN8aPn/XWBOoV\nQWXL0T9RtHrlpEmTbJnHoQCeJO3bt39+3brNw1+mF7pSQADt2+t79PAHW7eqXRcAwH9DsIMs\nxo4dGxISsnz58is/bwwJCRn4ycfPP/+82kUBqIZhmC+//PLrr7/++eefExISQkNDR82fh+VO\nAECzEOwgC4ZhOnfu3KtXL4vFkpSUhMXNAXieHzBgwIABA9QuBADg0TDHDgAAAEAnEOwAAAAA\ndAJDsQAAAAXY77//fuTIEVmWa9as+eyzz6pdDqgMwQ4AHsalKMmyLCpKqqykyrJDUVJk2ako\nqbIikkJEdlkRZZmI7Irng3TitRuyLBuNxjxc9c3MskLWozEM489lDD5wRJZ7O6anv1hgGDPL\nElGw05UmSQZFMWIhOij4FEUZPnz4dz//TPXqE8vSxx+3bdJk2bJlHMepXRqoBsEOQP/c4SxB\nkpNlOVmWkyQ5RZYTJMn9MEV2PyulyHKarCTJcoospylKsiQny7Lrv5bnfUzJefA5eAHPMBaW\nNTKMiWXMDCOwrJVjeYbxY1gLx5pZ1syyVpYxs5yZZfw5zo9lAjjOn+OsHOvHsgZEQ1DbihUr\nvtuxg5atoCJFiIjiYn9+ZeiSJUteeeUVtUsD1SDYARQMdlkWFSVBkp2KkiLLiZKcqsh2WUmU\npBRZsStyiiynxcTH2FOTZTlFlpPTo5skp+VBONMhl6LE52JXFTPL+nNsIMfZOC6I5wrxfCDH\n2ji+EM8GGwwlDIaiHCewBSn83bp1a+bMmbt375Yk6Zlnnpk4cWK5cuXULgoe5qeffqJuPTyp\njogCbdSz948//ohg9yRDsAPwCnfwSpOVBElyKIpdllNkWVQoUZIcCqXKUpqiOBSyS5LoDm2y\nkqrIokKiIttlRVKUJFkmogRJFhXFnnWIE7TALst2Wb7hdD3oBQxREZ4vYeBLGAwlDFwZQSgr\nGMoJQlnBIGivty8hIaFt27bRxUrQ4GFkEK5u27KzVasdO3Zg0T4tS0pKIqs1S5N/QFJSkkrl\ngCYg2AE8TKqsJMpSkiQnSnKSLCXIcqIkJ0lyiqK44hITnM7b9lT3gGaqLCfKcoosp8pKSkHL\nYWaW9WUZM8sGcJyJYXxY1sqyZpbxYVk/jjMxjJEhX5Y1MIwfx3EM+bMsEfmwrHumWvoHDENW\nlh04cOAf5Z+iLt0yTnDyWOAHcw4ePJjjCtNkxfFf/Y5JsixlahcVssvSvacUmRQiEmVPMk6S\nZYPRmOwQUySJiOIlSVKUJEl2kmKXFbssiQq5/yvtspIgSbns51SIbrlct1yuP1PTMrezRCUE\nQ1mDoZxgqOxnqeFvLSlLxYl4VdPeggULoq0BNOt9cs9QrBeW8O7kd9999/PPP1exKniQBEm6\nIDp9OnQiyvpts39v1apVVSoKNAHBDp4sLkVJkOV4lxQvSQmynCDJ8ZKUIMkJkpTg/kDO+Dgx\nb2aY5R8Ty5oZxo9jrSxr4VhflrWwrJXj3A8tLGthWT+O9WdZC8v6sqwvy/pxrJXl8nbAsJiv\nL12NztJ07XoRH5+A3EzozqO54Far1W63u1wP7GbLLFVWUhU56d7cxARJSpTkRElKlJVEWUqQ\n5FhJinVJcZIU45LiJMmZve8Wmeiq6LwqOnenEMUlEF0jIoFlQgShgmCoaBQqGY1PmYwVjYIp\nH6Pe0aNHKbw5sZnWwGrx7JHPPsm3AuBB4iXpkui8KDoviuJFh3hBdF4QxbsuiYgovKXnRYpC\nly7Rls2+u3e9uX27itWC6hDsQA9ERYmTpFiXFC/JcZIrXpJjJffHUpzLFe9OcrIc55KStdeX\n5sexHDF+LMMyjIVleYYxMYyJZQ0MmRlWYBkzy7qn9vuzjIHxdK35sIw7tPkwjC/H+bGMhWWL\nFCoUm3m7epX07Nlzbe/eFN6cQmsSEd24Tsu+6D1ihNp1PTYflvEhzvbQPHro0KFJkyZFHTvG\nsmyT5i1emTjRv0zZ607ndafrqtN5w+m84ZKuOp13XJL00NgnysrfaY6/0zL2emGJSguGSkZj\nJZPxKcFQ2WSsaBTMrLcWHxUEgRxZehbJkWYymbx0ujyUmprq4+OjdhV5QFKU607XZafziui8\nJIpXnK5LovOiKMa6sjETlGH4t994plTJd7//vkKFCt4vFrQLwQ40zd0dEidJSamORKIbdnvc\nvRZ3Z0m8LMe6JLWmoPlynC/DeLrBONaHYX1Yxspxvizjw7C+LGPlOKN7lJNhBYb8Oc7IMD4s\n68uyPEPuGKdK5V7VtGnTSRMmzHp9rFguhASBzv7ds0uXYcOGqV1X3jtz5kzXrl1TO3el4SNJ\nkv746Ye/Xui0c+fOusWL3/dKUVauOJ2XROclp/OCw3FRdF4SnVecTlF+YNqTiS6Jzkuic0uS\n57Zihqikga9oNLpDXiWjsaLRkKt+0ExatWr120fzqeML5GclInI6ad2aVq1a5cnBvSEtLe3D\nDz+MjIyMjY0tWrTo0KFDhw8fzvN58Etty5Yt8+fPP3/+fNGiRbt16zZs2DBBEHJ/2HQuRbnh\ndF11uq6IYrTTddXpdH981enKZqfvffw5roLJ+NnJk6XlnN8MBLrBKAVqpOk+8fHx2RxSycxq\ntQqCEBMTo53P3WKxOBwOp9OpdiEeJpPJq3vFpspKrLtfzSXFyVKMS4qX5FiXy53SPLnNJcXl\nepLTY/FlWX+O9ec4P5YN4Dh/jrVynB/LuB9aOc49amlhGQvH+fNc2aDCLqeYmJiYjzU+gs1m\n00KPndvly5ePHj3qdDorV64cGhqqdjkejzUU+0h9+vT5hRdo3P8ymt54fWC1KrNnz87O2yVF\nuc3xlxTlWGzcWXvqOYfjH9EZl53umUyCeK6CIJQ3CuUFobzRUMFoLGsw5OBuXEmSevXqtePE\nCXq2NfE8/b6jmr//5s2bzWbz4x4qb3EcFxgYmJaWlpycZd2cESNGrDlwkIYMpzJlKOocLf74\ntV4933777Vyebs2aNSPGjaOIPlQ9lG7dpMivujVssHjx4syvYRjG398/Pj7+IceRFOWOS7ru\ndN1wOa85XdecrhtO5zWn67rLddPpyvEcDxPDlBMM5U3GEEEoLxjKG4UKRqEQxwUGBjIM443L\nPygoKM+PCV6FYKcJ+gh2TkWJk6Q4lxQny+n9avGSHONyxUlSnHtgVJLiXFK+rb5hYphAngvg\nuACODeR4f5YJ5PlAjrOyTADPBXCcP8sGcFwAz/k/ZucZy7I2m00UEeweJiAggOf5u3fvql1I\nhrwNdnXq1Lk8eBg1apLR9OP3Yfv3bNy4MZtHMJvNZrM5MTFRFEV3S4wknU1znHOIZx3iWYfj\nbJp4+zGr5RimBM+VFoRSBr6MIJQW+NKCUNpgKMbzD897siyvXbt27969oijWqVOnT58+edtT\nlTP/GexOnTrVvFVr+vpbKnavc/Ts3+wrQ06cOFG0aNEcn0sUxWrVqsWPGEXPtvY03bpJ/V/a\nsHZt/fr101/mDna34uLuuly3nNJdyXXHJd1wum65XO5/b7pcjxx8f6QAjistGMoaDCFG9/3U\nhnKCUNzw312SCHaQDkOx8AgKkSeuecKZp6fNPWUtTpZiXVKsyxUnyfk2fU1gmUCWC+S5QI4N\n5PhAjg3kuECOtfF8AMvaeC6Q5wNYNpDn8nPuOTyBLBYLJSRkaUpMsN63/MRjKsRxDX3NDX0z\n+sniXNI5UXSnvXOiGOUQr4oP+yNQUpRopyv6X+uwGBgmiOeK8nwRng/iueI8H8RzQTwf4L7P\nhuN8WaZNt24RffsKHGe323PzWXjb6dOnqUyZjFRHRJUqy1b/v//+OzfB7uLFi/EJCRTePKOp\naDHqO+Dj23d/vXUnVpJjXFKMy3VXku5IUsJj9q0+iJFhSgmGUoKhjMFQ2mAoaxRKG/gyQp4N\nssOTBsHuyRXvDmouKU6W4u4FNfd4aKJCCYpyx+FwR7p8K8nKsTaOs/Fcem6zcXwAx9p4rojR\nVMzHZHQ4bDzn67X54/nj2rVriYmJISEhRqNR7VogV1544YW/vlpOjZqQvz8R0c0b9MN3naZP\nz9uzBPJcGO8TZs64PyBZlqMc4lmHeN4hRjnE86J4wSGKj+ofcirKDafrIQvvpTOxrIlhfFhG\nYBj3xwJDZpZzb9fGMOTPcURkZVmWYXxZhicysqwPw6Rv7+bLsjwRQ55XCixrZhgiMrGMZ2Wc\ne0/ljJ+fH93XWe50Uqrdz89PVihRlojIqZBdlh2KZyu8VFlOUxSHoiRIcoos22U5RVbcG664\nV7eJl6RYiaGNvxBvyHLkXhG/EP1yJ7edYYU4zr2oYSnBEGzgSxkMJQ18ScFQNC/mBQKkw/eT\n3iTLcpwkx7lcMZ6s5pm4Fp9peNT9Qb7dbuAeDw3kuPR+NVvWDrYAjnO3cA/uYBMEQRCEZOXx\nqna5XDt27Lhw4ULx4sVbtGhhsVhy/dnkypkzZ0aPHn306FHiOB9BeO2118aOHZuH+6hCPnv1\n1Vf37dv3W59eVC+MJIn27+3VuXOPHj28fV4Ly9byMdXyybhlVVKUq07XBdH5j0M8L4qXHGK0\n03lZdP7n4n+PlCbLaUTx+fU3XfpOvunS8x8RMQzDsqyiKHKmMQG5XEXmy68UUaT0wWKGoV9+\nbUNEf53NVTVm3xy/VWCZIjxfnOeK8IbiPFfUYChh4IINhuIGPthgwPbEkD8Q7AoGp6K4by+I\nk2X3uGesS4qVpNj0oHYvt+Xspqoc4Bnm3hgoF8hzgSwb6NlSibPxnuHRAI6zqTceGh0dHRER\ncebGTSoXQjeuFyPlyy+/rFevnirFEFFCQkJERER0aE364V3y80v98+ismdMsFsvQoUPVKgly\nief5VatWbd269eDBgyzLho8e1ahRo3w4r8vlWrt27ZEjR3x9fZs3bx4eHs4xTBnBUEYwNLdk\nudfhlst1RXReEZ3RTtdNl+um03XH5brlct12uVIffENuPhMVRbxvb7fsZEq/rEPe3u/3MjJM\nIZ6zcVwxg6G42cdfkooa+EIcV5jnihoMhTkuiMfgKagPN0+ozKEosS4pTRBupaXdSnPEeKLb\nvdtCZSnWJcVIUlJ+9a+xRAEcV8jABwmClSF/hg3k2ECec09Zc4+QFuL5AJb14/J1PNTTY5f8\nGNvJd+jQYb/Rh96cSEYTyTItX1p825Y9e/b4+fnlvp4c3Dzx+eefv/XlUvpsacYasLt22ubP\nPXPmDJtHg8u4eeKR8vbmidz7980Tj5SSktK+fftTd2MorD6lpdGunf1efPGDDz6472UOh+Ph\nY/3JsnzT6YqRpHs7C8spspwiy2kMkygrdlG0y4qLKEWW3RvcKUQJkkRESbIsK5Qiy/n2Z6T3\nCCxjZlh/jrWyrD/H+XOcP8caHGn//PlnwrVrQUZjq3r1wipXKsRxNp5L71bMzl2xj2vfvn1L\nliy5cOFCiRIl+vTp065du8d6O26egHTosfMWp6K4F1qLlaQYSbrr9CS2zI0x+bgAmz/H2TwR\njbPxXADL2ng+0PMBF3BvTpt7uq63lzvJBxcuXNh/4CD9sIGMJiIilqV+A29s+WXHjh0dO3Z0\nvyYuLm7Xrl23bt2qXLlykyZNvD0kevHiRapcJcvK/lWrxcbGxsfH22y2XB5cluXjx48nJiYG\nBQVVq1Ytl0d70siy/M033yxbtuzq1aulS5d++eWXe/Toodkh8mnTpp2SFfpqBblzW9/+y4e9\nHB4e3r59eyKSZXnp0qWffPJJdHS0zWaLiIgYP378fy5ZYmHZCkbh30vZmkwmlmWzefNEmqKk\nybJElCzJRJQoy7KipCqKw73TsSwTkfPeJnvp7RIx6fdaJUlZOuscipKa9e9YhmEEQZAk6T/j\nOMeQ3725egaGSZ+A688yRGTmOIHIzLICy7in/VlY1sxxZobx51j3Lnn//YmFlM3Op59X1q5d\n+8ro0dSpM3XpfiY6+tcRr7559uzYsWPzswbQDQS7nEiU5DsuV6x7IQ9Zvut03ZWkWJcrVpJj\nJc89U/nTx+briWVsIffoJ88Hsuy9uMYHcGwg545xebxnlPbFxMSQyeiZ0u7GslS06J07d9yP\nfvvtt+HDh8eafKhwYbpwvm7lyitWrMh9wHqIwoUL05GjWZpu3DCZTLnvQTx//vyQIUNOnD1L\nhYvQzZtNGtRfsmRJ4cKFc3nYJ8esWbPmfbWc+vanciFx56NGvvPO9evXNftrddOmTTRyDKX3\nxpUIpjZtN23a5A52CxYsmPHJYhr2ClWpGnvt6sLPl1y8eHHZsmVeKsbEMCaOI6JCOb0T4syZ\nM5MnTz5w4ADDMA0aNJgyZUqlSpXSnxVF8fvvv7948WJgYGDLli0rVqyYN3Vrid1unzBhAv3v\nLWr5rKepXtiHY0d16dKlbNmyalYGBROC3f0cinLJIcZKcowk3XY63ektVpLvSK4YlxTjkmLz\nZR6bjecCPbcUcLZ794faONbGcYE8Z+P5QJa18Zyg1U4F1ZUrV451OOTz/1D5e10SKcl04bx7\ns53bt28PGzYsrtdL1O1FYhhKST40+e1x48Z57/cfEXXp0mX+/PkpG9dT+45ERLGxtGh+7969\nDQbDo976MKIoDhw48HSJkjTrQ/LxoYSEP6ZPHTFixJo1a/Kmbr2Ljo6eN38+fbyEKlchInq6\nJj1V+f0xIyMiInKzcIb32O12smSd4O/nZ791g4gSExPff/99mjmHatchIipVmio+tfGlngcO\nHAgLC1Oj2EeIjo5u3759YtNw+uAjUpTtG9cf6dBhx44dwcHBRHTr1q2OHTteSE2l0JoUEzNt\n2rQZM2b0799f7arz2KlTp5JcLmreIqOpWnWxZKkDBw4g2EEOINjd75xDbPHPJS8dnCEK5LhC\nvCeu2TjOxnGFeK642exPZCUl8N5iH09aB1ueCwoKGjhw4BfvTqbX36AqVelqNC38qGFoaOPG\njYlo06ZNcYWLUPd7dy/6Wmj0uM39ImJjY73XaVemTJlFixaNGTMmfuU3ZLPRP1EtGzWaMmVK\nLg+7d+/e01ev0bxFntsD/f3prbd3dHshKipKl90bee748eNUrLgn1blVr+EKtB0/frx169YP\nfp9qatSosWf3bs8+vEQkSbR3T2jnF4jon3/+ERnGk+rcCgXRU5VOnTqlzWA3a9asxNrPZGzd\nUbVa3DtvzpkzZ/78+UQ0duzYC8WDacq77vVHxIMH3n77rQYNGmTu0tMBhmHo350FspxXU2/h\nSYNgd78cDyhYWLYQzwXxvI3zDIzaOK4wzwd6HnKFHpzY8nnnCVEU161bd/LkyUKFCrVt21av\nU7KmTJnCcdzSsaPcX9gOHTrMnj2b4zhyD9QWKZLl1UWLybLs1WBHRO3bt2/YsOHu3btjY2Or\nV69ep06dR7/nUW7cuEHFilHmHQICbWT1v379OoJddhiNRnKkZWlSFHI4TCbTA96hsqlTp7Zr\n187Bc9SsOdnttG51WUfakCFDyL1gsugku50yT6pLSMiTG4a84eTJk9Sle5amRk1O/byRiFJS\nUrZv305LIzNWlasX5qjx9ObNm3UW7GrUqBHo4xO35Wdqe++GiePHhOvXMu91AZB9CHb3K/Sv\n+9VZokI8V4jnbRxn49giBoM7utk4Nojjgu6FtoePisqyLEkSm48ridvt9r///ltRlCpVqmSe\nOn3nzp327dtfSHPQ0zXp9Jl58+a9/fbbw4cPz7fC8o3RaJw+ffo777xz8eLF4ODgzL/bypUr\nR8u+osyLYJ06YTKZ3ANAXmWz2dLv3sgTwcHBdP0apaVRehC5e4cSE0qVKpWHZyko4uPjjx8/\nrihKaGhoNjN6vXr1/F2uhF8203PPe5o2rrfxXJ7Ebm94+umnf/jhh+nTpx8d/Z2Pj0/Lli3f\nWfyJ+9u7YsWKVSpXOvPlZ/Tqa+T+ifTLZmtcbPPmzR9xUJX4+vrSfbe6JyX5+voSUXJysizL\nWabJElFAQFJSUj4WmB9MJtPcuXMHDB5Cp/+iSpXpyhXa8OPEiROfzEsYcg/B7n5GhpkXXCzQ\nE90496pFuRkXPX/+/KRJk/744w+Xy1WzZs3Jkyfnw5jImjVr3nnnndgUO7FMgNE4derU3r17\nu58aP378haLF6N0ZntGN48cm/W9s48aNa9So4e2qVGE0GitXrnxfY/v27asuWHB6+lR65VUq\nXIROHKM5s0aOHOnj4/OfB9Gyhg0b1qpY8c+Z02n8BPLzo7t3aPrU51q3DgkJUbu0/LZ8+fIp\nU6Yk8zyxrDktbeLEie5+rIfz9/f/6KOPhg4dKu7bS+VC6HyU8dDBhUuX/uedpBpRt27dn376\nSVGU+27dZRhmyZIlXbt2vXP8GFWpSteums6cnr94sbp30siyvHLlyvXr1yckJFStWnXUqFHp\nU8fatWt3+LPPqUVLsvoTEcXH03dr248aSUSFCxcOCgq6e2A/tWnrOVBaGp04VrVrZ1U+C69q\n3779tp83f/bZZxd//61EiRJ9vv46PDxc7aKgoMI6dt4VExPTrFmzW9VDqVt3Mgi04zfTd2s2\nb958X4rK26HYAwcOtO/ShcZPoJatiGHo9x00a8b3K1c2adLE4XCUKVNGWvJlxi0FRDTxjbH1\n67355pvpDRpc7iQH69g93JUrV8aNG7dz505iGKMgDB8+fMKECXy21zjNwTp23nPlypVXXnnl\nwOHDFGijmJg2rVstWLDAq2PK2ZSf69jt3r27c48e9M5UatyEiOjgAZo88Zsvv7xvntyD1rGL\niopasWLF5cuXy5Ur16dPn3yLxTlYx+6REhMT16xZc/78+WLFinXp0uVxO34ea7mT7Bg+fPi6\nHTuoc1cKtNHhQz77927evLl69epE5HK5IiIifjtyhBo3JUWh3X+0aVA/MjLSPb1s9erVr054\ng4YOp2fqUEwMLfviGZbZsGFDLu83yj1vrGOXS1jHDtIh2HnXlClTPt6zl+YuoPQ/rBfOD4+9\ns3bt2swvy9tg179//00+vjRiVEbT55+2vHVj1apV8fHxFStWpNXfZ5lhNmfmkFLBM2bMSG94\nEoKdW2xs7K1bt8qXLy9knqOWDZoKdkSkKMq5c+cSEhKKFCmSb3fS7dq1a+3ate4v4LBhw8qU\nKXPfC/Iz2PXr12+znz8NG5HR9NXSZpfOr1u3LvPLdLBAsbflLNg5nc7ff//90qVLpUuXDg8P\nT7+gfvvttx4DB9HS5VTk3i3GSz55+sxf27dvdz9SFGXTpk179+5lGKZx48Zt27bNfNjVq1fP\nnTv3woULFoulffv2kydP1kLOQLADLcNQrHedPn2awhpQ5uGSBg1PzZ7x4HfkgejoaHq+Q5am\nik9dOXSAiPz9/YODg6/t30sdX/A85UijP49Wb9vGqyVpls1m00LPVu4xDFOpUqX83Hnio48+\nmvHBh/R8O6pSbcfJk183avTdd9+peOvl9evXqfXTWZrKlbu6+3eVyslw586dqKioIkWKhISE\n6PU+x/Pnz/ft2/fc7TtUujRdjS7n7798+fIqVaoQ0e7duymsfkaqI6IOnY6v+jYxMdFqtRIR\nwzDt27d3L8L3bz169Ojdu7cgCCzLpqSk5MtnA1CwIdh5l6+vLyVl7dFJSvT2HWrFihU7EX0l\nS9OVyyVKlCAihmGmTZs28JVXSBSpbj2Kj6flS2sWK9q1a1evlgQ6ExUVNWPOHJq3gKrVICLq\n3tPxTeQrr7xy6NAhtbJLcHDwsYsXszRduKDu9HNRFN98883Ir1eQr5ns9pqhoQsXLvz3jM+C\nTpKkwYMHnysbQgs/JUEgl/PiogWDBg3auXOnp9/uvknKj7/6pq+vb1pa2qNfBwBE+vzzUTs6\nduxIG9bT9Wuex/YUWhHZqVMnr5500KBBtG4tHT7oeXzsT1r17aBBg9yPOnTosPSTTypt38IO\n7Gud9FavypVWrlz5uAOR8ITbtWsXPVXZk+rcur54JTr6/PnzapU0ZMgQ+ul7+n2H5/H+vbR6\n5bBhw9Sqh4imTZsWufN3+mIZbfiFNvx8rETJvn376q/b6cSJEyfPnaMx4z33mPMGenVU1M2b\n+/fvJ6JGjRrRgf10+3bGGzb8FBoa6u6uA4A8hx477+rcufOuXbtWDOpHDRuT0Uj79zWoXOn1\n11/36klbtGgx7e2J702amBoQQMSY4mJff/31zDNXOnTo0KFDh0fuEQ7wIJIk0X0LA/EcMYyK\nc9caNmw4b/bsyZMnJ374PrGMr8v19pTJLVu2VKseu92+dOlSmruAQsoTEZl9aezrF/tFbNiw\noWfPnmpV5Q13794l/wDKvOwfb6Cgwu7t+1q2bNmldevvRwylzl2pUCE6UZapiAAAIABJREFU\ndMC0Z/e8TZtUKxdA7xDsvG7evHldu3bduXOnKIp1u3dt165dPoxVDRs2rGvXrkeOHJFl+Zln\nnvnPnZGQ6iDH6tatS1OnUvQVKlXa07Rtqy0gwL1jm1peeumljh07njhxQpbl0NDQgIAAFYu5\nefOmKIpUIdMa0RxHIeWvXLny4DcVSGXLlqW7d+j2rYyJdPHxdO1q+p3Fn3zySeNvv92wYUPM\ngZjq1au/9vvvqq/F43K51q1bd/jwYaPR2KxZM21uMQKQMwh2+aFx48bunazyU+HChZ977rl8\nPik8IWrVqtWvV6/lY1+jXr2pWAk6eYK+X/vBp5+qvg6F1WrN/2vtPwUFBXEcJ12NzlhaSFHo\nanTx4t6diZH/Klas2LF9+/WT36bX36CQ8nT5Es19/9mmTWvW9Gx6xnFcnz59+vTpo26d6dLS\n0jp27PjntevUoCGJ4mdDhnZr+9zixYvVrgsgbyDYAUBOzJ49u0aNGqtXr7558+ZTTz01cs2a\nRo0aqV2Uhlit1u7du6/6YA5Nf48KBZEk0VdLi9pT2rVr9+g3FzTz5s0zT5y4+uX+Cssysty5\nc+eZM2cyj3+TRJ5zOp0Oh8NisWRunDVr1p/JKbT8G8/w8YBB64YMar5mzYsvvqhOlQB5CsEO\nAHKC47h+/fr169dP7UK0a+bMmTFDhmzr1Z1Klaa7d0v5Wz9dulQfy+vcx2q1Lly4cMaMGVeu\nXClZsqS6g+Buly5dmjhx4o4dO5xOZ4UKFSZOnJi+osrmzZup74CMSYGFi1D7jps3b0awA31A\nsAMA8AqLxfLtt9/+9ddfZ86cKVKkSFhYmL4ntlqtVvd+EqpLSkrq1q3b5XIh9PES8vP7Z++e\nAcOGr1ru476Zxm63k9k3yxssFvs1vc19hCcWgp2m7d69e8mSJe497Pv27avLQRwAfatWrVq1\natXUruLJsmzZssuCkd6eQhxHRNSlG4ni1KlT3cGuRo0at3bvonr3FtOWZdq9K7RlC/XqBchL\nWMdOu1atWtW5Z89f/APPdu/1W8ky/V99dd68eWoXBQCgdWfPnqXaz3hSnVu9sLNnz8qyTEST\nJ0/22b6VFs6nM6fpz6M05e2S8XGvvvqqauUC5Cn02GlUcnLyG2+8QW+8TeHNPU316r0/ZlTX\nrl1Lly790LcCADzRrFYrRV/L0hQX5+fn515qqnLlyuvXr58+ffrh18cIghAeHv7O/I+0MC8Q\nIE8g2GnUyZMnU4ihZuEZTdVqOEsEHzx4EMEuM5fL9cMPP/z5559ms7lFixYNGzZUuyIAUNkL\nL7zwRdeu1KETVatORGS305efdenSJf0FNWvWXLdunWr1AXgTgp1GsSxLikyKkmVfRUXR6ybi\nOZOSktKpU6fjN25Qvfpkt8//dMngvn3ee+89tesCADWFhYW9NW7ce6NHUu3a5OdHfx6tExIy\nefJktesCyA8IdhoVGhoaIAjx27ZQm3tbgR09LNy8Ub9+fVXr0pbp06cfF50U+S0ZTURE0Vc+\nHza4adOmWJkZIM/Jsrx169aTJ0/6+/u3bNmyfPnyalf0MGPGjGnTps327duTkpJq9nmpbdu2\nWvirWJKky5cv+/n5FS5cWO1aQLcQ7DTKx8fnww8/HDR0GJ06SU9VosuXaOP6KVOmlChRQu3S\nNGTDhg302jhPqiOiUqXpubYbN25EsAPIW8nJyS+++OKhqCiqEUpJSVOnTp0yZcrgwYPVruth\nqlatWrVqVbWryBAZGTl9+vS4uDgievrpp+fOnRsaGqp2UaBDCHba1bFjx+1lynz22WcX/9hZ\nsmTJvqtWaWSvJO1ISUkhP78sTX7W5Lu3VCpHn65fv37hwoWiRYuWL19eC30eoIpJkyYdsqfS\nN6vdK8CJRw9Pfev/7N15XBT1/wfw9+zszh6s3KIgCIioqECAihKaB+KdkkeaRz8ts7RvWWnl\n18xM7TI1s8wsyyvvoywtUzzyRLzvAxVFyAvl3Ht3fn8MLnh8FWF3Z1lezz96fGZ29jOvAXZ9\n95mZz7zXvHlz63PD4NHWr1//zoQJ9M679HQiaTRHlyzu37//9u3b/fz8xI4GrgZf004tOjr6\n22+/3bhx47x581DVPahJkya0Z1fpssVC+/ZERkaKl8ilaDSaUaNGRT/1VMrQoQmJiV26dMnI\nyBA7FIiA5/k1a9bQ8FdL5/WNbaZv9fS6detEzVWVfPHFF/TSK9Q+ieQK8vKm19+4GVDnp59+\nEjsXuCCM2IFTWLdu3bJly3JycurXrz9y5MgWLVqU512TJk3q2bOnXiaj1s+QVksrlwXrtK+8\n8oq901YTH3zwwcqDh2jRUgoMIk3xoVkzX3zxxdTUVIX1WUxQPRiNRo1GQ15e96z18cnLyxMp\nUdVz8eJFiixz4pVhqGnUhQsXxEsELgsjdiC+qVOnvvLOmG0hYWf7DtigdOuWkrJ+/fryvDEu\nLm7lypXNT5/kRo2oMf69nj7e69atq3HfyVlXcfbs2ZEjR7Zr165v376//PKLMNWq/dy5c2fp\n0qX03wkUGEREpHKjse+dy8vbtGmTXfcLTojjuJCQEDp0sHSV2UyHDzVq1Ei8UFWMt7c33bj3\nKpEb1319fUWKA64MI3YgsrNnz371zbc053sKb0BE1CGJ6oePGTOmU6dO5XmwZkJCwsaNG41G\no0wms3tW8aSnp/fq1cvQrgP1SKFbN7dPnrJv377Zs2fbb485OTlmC0+h9UpXSWUUEnrlCh6p\nWR19+OGHw0aNIpmMWiZQYQEt/DmUtwwePFjsXFXGgAEDvpo3lxo2It+aRERpe+X/bO/3++9i\n5wIXhMIORLZ//36qX7+kqhMkJd+Z/sXp06fLf122a1d1RDR69GjDC4PoxWEly+06LH/5xb59\n+7Zp08ZOe6xVqxZDPJ99tWTEjogsFrp6FfdlV089evT4Vqv95JNPsr/8nGXZdu3afbpypVqt\nFjtXlTF27Nhz585tHDSAIiKouFiRdWXylCkxMTFi5wIXhMIORCaRSMhsvmcVzxNPuAHT6tat\nW+fOnaNPvihd5e9Pcc13795tv8LO19e3V69e6z7/hCZNJW9vMhpp3ndBrCQ5OdlOewQn169f\nv379+uXm5qrV6vKMpkNZHMctXLjwwIEDR44ccXNza9OmTZ06dcQOBa4JhR2IrFWrVjR2LJ04\nTk3v3s264feaXp4RERGi5nIijPD0kfsvqrP7Y0imTZuW/8orW5/vTYGBdOtmWK1a3//8s6te\nwgjl5OPjI3aEKqxZs2bNmjUTOwW4OBR2ILJ69eqNf/fdqe++TT16UmAQnTrJbd0ya8EClz+7\n+lBHjx5NS0uTSCStWrVq0qSJsNLHxyciIuL0+t/opbvzwWZfpQPprce8Y9cwHh4eK1asOHXq\n1NmzZ2vXrh0XF8dxnF33CGUdOHBg27ZtGo0mJiame/fuGMMGgPJAYQfiGz16dFRU1NKlS3N2\nbg8LC3tt82anmi/eMXieHzt27MJly6hJJPEW+vDDEcOGTZkyRXj1q6++evbZZ/U52RQTS7m3\n6Ne1Q/r2TUhIcEAwZ5u+v5qYNGnSN/PmUYt4Uqpo6dLmc+euWbNGqVSKnQsAnB0KO3AK7du3\nb9++vdgpxPTLL78s/G09zV9YcrNC5qXv3xgZExPTu3dvIoqNjf3nn3+++eab0zu2+vj49Pzk\nkz59+oicGOwmNTX1m/nz6bsfqF4YEZFGk/7Wf6ZMmTJ16lSxowGAs0NhB+AUli9fTi8MLL0F\nNSSU+vZfsWKFUNgRUb169WbMmCFaPnCg3377jTp1KanqiEiloheH/fb1DBR2APBYuGgDwCnk\n5eWVTHBlVbNmbm6uSHFATIWFheTucc8qD4+CggKR4gBAVYLCDsAphIWF0dEj96w6eiQ8PFyk\nOCCmxo0bU3raPfdBp+1t2rSpeIkAoMpAYQfgFMaMGSP/ayMt/4Xy8ynvDi1aoNyx7a233hI7\nF4jglVdeCSoqpMkf0dkzlHWFfllEK5ZNnDhR7FwAUAWgsANwCpGRkYsWLaq3eRP16kYpPRru\n2bl06dKGDRuKnQtE4OHhsXbt2i5KufzNUezQwU337Vm5ZEl8fLzYuQCgCsDNEwDOon379mlp\naTdu3GAYpmbNmo9/A7iukJCQRYsWmc1mk8mExzwAQPmhsANwLn5+fmJHAGfBsizLsmKnAICq\nBKdiAQAAAFwECjsAAAAAF8HwPC92hoozGAwVeH4iy7IMw5hMJntEqhiJRMLzvPP8LiQSiUQi\nMZvNzhOJYRiGYSxlJ4AQm1Qq5XnebDbft16v12/atCkzMzM4OLhTp04KhcKRkZzqD9sJP2ss\ny1osFuf5w3bCz5rwpeo8nzWGYYTfmvNEIiKWZR/87IvITp81k8nkyG8wsImqfY2dVqutwN+x\nu7u7TCYrKChwnm9StVqt1+uNRqPYQUooFAo3NzetVqvX68XOUoLjOJlMVlxcLHaQEhKJxMvL\ny2g0FhYWll1/7ty5QYMGXczPp7rBlJUV7KZatGhRkyZNHJPKy8srPz/fMfsqDw8PD6lU6lSR\natSoUbHvDTtRKpUqlUqj0RgMBrGzlJDL5SzLajQasYOUYFnW09PTaDQWFRWJnaUEwzAeHh5O\n9Yft6ekpkUjsEQmFXZVTtQu7io1yCW9xkhGys2fPfv7558ePH69Ro0ZSUtLo0aNVKpXYoZzr\nRySwRhI7SAlrkrKRzGbzyy+/fLFhBL09hqQyMpkuf/v1Sy+9tGPHDo7jHBzMeThbJKf6wxY4\nWySnyuOEX0fkfHnIKSOBKHCNnZhOnz6dlJT0u8mS+dKI492enfn7H3369HGesQR4UkeOHDl1\n4SK9+TZJZUREUimN+k/GtWtpaWliRwMAgGoBhZ2Y3nvvPV3XHvTuOEpsTZ260Kxv069e/eWX\nX8TOBRWUm5tLnp5UdtYxqYx8fG/duiVeKAAAqEZQ2ImG5/mDBw9Sx+TSVSoVJbZJT08v59vX\nrVs3dOjQHj16vPfee1lZWfYKCuUWGhpKN2/Q9Wulq27fppzs+vXrixcKAACqERR2ohFu9aL7\nbpgwGKTScl34+N57770yZuwfnj77WrT66XzG008/ffz4cbsEhXILDw9P6dmTPhxP58+R2UwX\nMujD/3ZJSsLj2wEAwDFQ2Impffv2tHolWS93vXWTtm/t0KHDY9+4d+/en5cto2/n0kvDKaU3\nffyJttdzb7zxhn3jQjlMnz59UIvmkldfpqRnmOFDn2/a5Ouvv2YYRuxcAADOq3///mq1WuwU\nLqJq3xVb1X366afpSUk3/vMaJSRSUSH9uSGlffsePXo89o3//PMPNWtOgUGlq3r1PrHsl9u3\nb3t7e9sxMTxOjRo1Zs6cOXXq1CtXrgQGBuKrCgDAho4cORITE4P7fx8BhZ2Y/P39d+/ePW/e\nvBMnTqhUquTPP09JSSnPGy0WC0nufYIkKyEip5owszpTqVSNGjUSOwUAgKvZuXOn2BGcHU7F\niszT0/Pdd99du3bt/Pnzn3vuuXKes2vZsiUdPEA3rpeu2rghPDy8Zs2a9goKAABgCzzPf/zx\nx0FBQQqFIjIycvXq1fdtsHz58hYtWqhUKnd392bNmi1fvlxY37lzZ+GiI4ZhmjVr9uiNqy0U\ndlVSu3btnuuUTP95jVatoK1b6MvPuSULZ86cKXYuAACAx5g2bdrEiROfeeaZP/74Y8KECZMm\nTTpy5Ij11RUrVgwYMCAwMHDVqlXLli2rWbPmgAEDNmzYQESzZ8/u2bMnEaWnpy9evPjRG1db\nOBVbVc2ZMyd+4cL169ffvHmzSZMmb23ZEhERIXYoAACAR+F5ftasWU2bNl2yZImwpnXr1sHB\nwdbH81y8eLF9+/bLly8X1rRu3drHx2fZsmXdunULDw/39fUlIutw3SM2FuHYnAMKu6qKZdlh\nw4YNGzZM7CAAAADllZWVlZOT06dPH+saf3//Zs2aHTt2TFgcN27cuHHjrK+6u7vXrl37ypUr\nD+3tiTauJnAqFgAAABzk2rVrRHTfFeEBAQHWdkFBwYcffhgZGenh4SGVSqVS6dWrVy0Wy0N7\ne6KNqwmM2AEAAICDPHSmkrJTOvTo0WP37t3vvfde586dPT09GYbp1KnT/+rtiTauJlDYAQAA\ngIMIY3XCuJ1VZmam0MjIyPjnn3+GDx8+depUYY3JZLp9+3ZoaOiDXT3RxtUHTsUCAACAg4SE\nhPj6+v7111/WE6bnzp07evSo0DYajUQUGBho3f67777T6XTWIT1hUjCTyVSejasnjNgBAACA\ng0gkktdee23y5Ml9+/YdOHDgjRs3Pvvss9jY2DNnzhBR/fr1g4KC5s2b99RTT/n4+Kxbt+7g\nwYNt27Y9ePDgtm3bWrRoIVyN98knnzRp0uTZZ5999MZubm5iH64IMGIHAAAAjjNx4sT3339/\nz549AwYMmDNnzldffdWqVSth+E0mk61du7Zu3boDBgzo3bt3UVHRb7/99s4778jl8t69e2dn\nZw8fPjwmJmbKlCnjx49/7MZiH6g4mCr9wLW8vDxhPPaJuLu7cxyXm5vrPMeuVqv1er3wZ+0M\nFAqFWq0uLCzU6/ViZynBcRzHcUVFRWIHKSGRSLy9vQ0GQ0FBgdhZSnl7e9++fVvsFKU8PT2l\nUumtW7fEDlLK3d1do9FU4HvDTlQqlUqlKigoMBgMYmcpoVAoJBKJRqMRO0gJlmW9vLx0Op3z\nfPwZhvHw8MjLyxM7SCkvLy+GYezx8RfmjYMqBCN2AAAAAC4ChR0AAACAi0BhBwAAAOAiUNgB\nAAAAuAgUdgAAAAAuAoUdAAAAgItAYQcAAADgIlDYAQAAALgIFHYAAAAALgKFHQAAAICLkNqk\nF41Gk5+f7+/vT0RarXbFihW5ubkpKSn16tWzSf8AAAAA8Fg2GLE7c+ZMaGjowoULichkMrVp\n02bo0KFjxoyJjY09fPhw5fsHAAAAl8HbmtgH5FxsMGI3fvz4WrVq9e3bl4iWL19+4MCBOXPm\nJCcnDxky5JNPPlm1alXldwEAAACuoaioyLYdqtVqhmFs22fVZYPCbteuXTNnzgwLCyOitWvX\nNm3a9LXXXiOiUaNGvf/++5Xvv0rYtm3bqlWrbty4ER4ePmLEiJCQELETAQAAQLVjg1OxeXl5\nwtV1ZrN5+/btXbt2FdbXrFnz+vXrle/f+U2fPr3fkCGrLLSjafSP5zISExPT0tLEDgUAAADV\njg1G7GrVqnXx4sV27dpt3br1zp07nTt3FtZnZWX5+PhUvn8nd+bMmc+mT6dZ31JEYyKiPv30\nSxaOGjVq//79EgluOgYAAADHsUFhl5yc/MEHH2RkZCxbtiwsLKxNmzZEdOPGjVmzZj399NOV\n79/J7dq1ixpFlFR1gt79Lv/046VLl4TT0wAAAACOYYPCbvLkySdPnvzss898fX1///13lmWJ\n6I033rh8+fLixYsr37+TM5lMJL33xyiVEjEmk0mkRAAAAFBN2eBcob+//969e/Pz83Nyclq2\nbCmsHDNmzNmzZ6Oioirfv5Nr0aIFnTpJV7NKV23609fHG8N1AAAA4GC2uQhMo9EUFxfLZDIi\n0mq1CxYs2LFjR3FxsU06d3KxsbEv9u9Po/9Dq1fS7p303Tc0+6svv/xSKrXN5M8AAADgSCaT\niWGYLVu2VHgDEWGCYhv44osvpv93XPyBtOAf5ibn3f59zZpu3bqJHQoAAACIiLZu3XrgwIHy\nb8+y7LZt2+Li4iq8gYgwQbENSCSSIUOGDBkyROwgAAAAVZ7FYtm/f39mZmZQUFB8fHzlz4DN\nmDGje/fuzZo1K+f2DMO0bdu2MhuIyAYjdrt27Xr//ffvm6A4LCxs1KhRmM4NAAAAyu/q1avt\n27fv3b377PH/7d+rZ5s2bc6fP1+ZDtu3b79x48bRo0fHxcWZzWaGYX788cfQ0NChQ4cS0YkT\nJ5KTk729vT09PTt16pSRkUFlzrRaLBaGYZYtW9apU6fGjRsHBwdbz08+egMiOnr0aHR0tFKp\njIuL27ZtG8Mwx44dq/SP5/FsMGJ33wTFw4cPF9ZXnwmKAQAA4IlITp+ghz3mddGUKf3clSNe\nf0kplRpM5p+OnVzy/tiPPvpImHPjPpaGjelh68vaunVrSEjI+++//+qrrxIRy7Lff//9mjVr\nwsPDiahPnz7x8fFZWVlms3nYsGEvvvji7t27S0NKJCzLTp8+fePGjX5+fvPnzx85cmSfPn3k\ncvmjN1AqlT169GjTps3OnTszMzOHDRsmbFzRn9YTwATFAAAA4GjSdSvIYnlw/UeN61vbnJR9\nNTaKiOi3h1/WZRz7If+4wu5BvXr1io2NFdp79+6Vy+UqlYqIXnjhhf79+/MPlJuDBw/28/Mj\nog4dOmg0mszMzIYNGz56g/z8/KysrMmTJ7u7u0dFRY0cOfKll1560pwVgwmKAQAAoBqpX7+0\ndjx8+PCUKVNOnTpFRHq93mg0ms3m+7avW7eu0FAoFESk1Wofu8GVK1dYlrU+ON6Rt1nYYFRw\n8uTJISEhn332WXFx8ZIlS8pOUDxhwoTK9w8AAABgK9YTqRkZGV27du3YsWNmZua1a9cWLFjw\n0O0Zhnl0hw9uwPO8VCq1rn/oeWQ7scGInTBBcUFBgVKpFKayI6IxY8bMmjWrVq1ale8fAAAA\nXIxp2GsPXT9u3LjC8+cmJsb7q91uabVTd+831PKfOXPmQy9Q4+9WHRVz4MABk8k0ZswYoXrZ\nt29fZXory9/fX6/X5+TkBAQEENHBgwdt1fNj2WwSXaPRuHPnzpycHIlEEhgYmJCQUKNGDVt1\nDgAAAK7EUjvgoevfmT7zzTffDJ3zk5dCfkenT05O/mbWbKpd+yGX45WbSqXKyMjIy8vz9PQs\nuz4kJMRsNu/bt69FixZr167ds2cPEVmrscpISEjw9fWdOnXql19+eenSpe+//76SHZafDQo7\ni8Xy7rvvfv3110aj0brSzc1t4sSJY8eOrXz/AAAAUE14e3svXrw4Ozs7MzOzbt26QUFBle9z\nxIgR48aNW7FiRVZWVtn1LVu2HDt2bM+ePRmGSUlJ+fXXXzt27BgdHZ2enl7JPXIct3r16tdf\nf71mzZoxMTGTJ09OSkpyzF2xzIN3fzypadOmvfvuuykpKd27d/f397dYLNnZ2WvXrt20adPC\nhQvtOm1vXl6eyWR60ne5u7tzHJebm1v5Y7cVtVotXLMpdpASCoVCrVYXFhbq9Xqxs5TgOI7j\nuKKiIrGDlJBIJN7e3gaDoaCgQOwspby9vW/fvi12ilKenp5SqfTWrVtiBynl7u6u0Wgq8L1h\nJyqVSqVSFRQUGAwGsbOUUCgUEolEo9GIHaQEy7JeXl46nc55Pv4Mw3h4eOTl5YkdpJSXlxfD\nMPb4+Pv6+tq8z8LCQtt2qFarH3sZnOOZTCaLxcJxHBHt3bs3ISEhPz/f3d3d3vu1wYjdzz//\n/Pbbb0+fPr3syldeeWXEiBGzZs3C8xgAAACgWuF5PiIiIjExcebMmVqtdtKkSW3atHFAVUc2\nuSv24sWLD300as+ePU+fPl35/gEAAACqEIZh1qxZc+XKlaCgoKioKDc3tyVLljhm1zYYsZNK\npQ8dtDcajY68vxcAAADASURFRaWmpjp+vzYYsYuJiZkxY8Z9F4jodLo5c+aU/4G7AAAAAFBJ\nNhixGzduXPfu3cPDw7t27VqnTh2e57OysjZs2HDt2rVNmzZVvn8AAAAAKA8bFHZdu3Zdu3bt\nuHHj5s6da10ZGRn5ww8/JCUlVb5/AAAAACgP20xQ3KtXr169euXk5GRnZzMMExQUhGdOAAAA\nADiYzZ48QUQBAQGVn6wZAAAAXJhj5umttipe2DVq1Kg8m505c6bCuwAAAAAX4+bmJnYEV1bx\nws4es1EDAACAazObzbbtEHOrlVXxwm7Xrl02zAEAAADVgc0fWOecjxQTC85zAwAAALgIFHYA\nAAAALgKFHQAAAICLQGEHAAAA4CJQ2AEAAAC4CBR2AAAAAC4ChR0AAAAAEZHJZGIY5q+//hIa\nW7ZsuW+DzMxMhmFOnDhR/t4e7MSuUNgBAAAA3INl2W3btsXFxVXgvVu3bj1w4EAlO6kwWz4r\nFgAAAKCSbt++vWLFikuXLtWtW7dv3761atVyfAaGYdq2bVux986YMaN79+7NmjWrTCcVhhE7\nAAAAcBbp6ekxMTHvzf9p7uWs//6yNDY2dvv27ZXpsGXLlqNGjbIubt++nWXZ7OzsEydOJCcn\ne3t7e3p6durUKSMjo+y7yp5FPXLkSHx8vJubW1RU1N69e63bPLSH9u3bb9y4cfTo0XFxcWU7\nuX79+oABAwICAlQq1dNPP717924islgsDMMsW7asU6dOjRs3Dg4OXrhwYWUOljBiBwAAAI43\nKfua5YGVZrN5wZ9/F3w0haKihTWFZ04P2PbP/wXX4zjuwU7+6+8nlzxmiOqFF1747LPPZs+e\nLZFIiGjlypXt2rWrU6dOhw4d4uPjs7KyzGbzsGHDXnzxRaHYuo/FYklJSWnTps3WrVtzc3Nf\nfPFF60t9+vR5sIetW7eGhIS8//77r776qslksm7cs2dPT0/PI0eOqNXqCRMmdO3a9cKFC76+\nvizLTp8+fePGjX5+fvPnzx85cmSfPn3c3NzK8zN8KBR2AAAA4Gizrt808fxDXujW457FRhHF\njSK+vZ330E7G1PaTP25Hzz///Ntvv7179+7WrVubzeY1a9Z8/vnnRLR37165XK5SqYjohRde\n6N+/P/+wPPv27cvMzExNTXVzc3Nzc3vzzTetI4gP7eGhT609fPhwWlraqVOn/Pz8iGjKlCnf\nf//9n3/+OXjwYCIaPHiwsL5Dhw4ajSYzM7NJkyaPO6z/CYUdAAAAuKxatWq1b99+9erVrVu3\n3r59e2FhYe/evYno8OHDU6ZMOXXqFBHp9Xqj0Wg2mx98e1ZWFsP+y0aMAAAgAElEQVQwwcHB\nwmJ4eLj1pYf2IJU+pLK6cOGCRCJp1KiRsKhUKoODgzMzM4XFunXrCg2FQkFEWq22MseLa+wA\nAADAlb3wwgtr167leX7FihU9e/asUaNGRkZG165dO3bsmJmZee3atQULFvyv9+r1eiKyjsNZ\nz66Wv4eHslgsBoNBaD90kK/CMGIHAAAAjnY7NvKh68ePHz971Wp6dSSFN6BLF+n774a0a/vN\nN99UZl/PPffca6+9tnfv3rVr1y5atIiIDhw4YDKZxowZI5PJiGjfvn3/672BgYE8z1++fDk0\nNJSITp8+Lawvfw9EFB4ebrFYTp06JZxjLS4uvnz5ctnBPxvCiB0AAAA4i48++mjiiFdqffcN\nDXze96vp7w984csvv6xkn+7u7t26dfvwww8lEklycjIRhYSEmM3mffv26fX6ZcuW7dmzh4hy\ncnIefG+rVq18fHwmTZp0586dc+fOffvtt8L6R/SgUqkyMjLy8kqvC4yOjk5ISBg7dmxubm5R\nUdG7775bo0aNXr16VfK4HgqFHQAAADgLmUz2zjvvnD9//vr16xcvXvzvf/8rXHlWSQMHDkxN\nTe3fv79wDVzLli3Hjh3bs2fPgICA1NTUX3/9NS4uLjo62nrdm5VSqdywYcPx48cDAgL69Okz\nfvx4IrJYLI/oYcSIEXPmzImMvGdIctmyZRzHNW7cODQ0NDMzc+fOne7u7pU/rgcxD70HpKrI\ny8srey9xObm7u3Mcl5ub6zzHrlarhesuxQ5SQqFQqNXqwsJC4doCZ8BxHMdxRUVFYgcpIZFI\nvL29DQZDQUGB2FlKeXt73759W+wUpTw9PaVS6a1bt8QOUsrd3V2j0VTge8NOVCqVSqUqKCiw\nXm0jOoVCIZFINBqN2EFKsCzr5eWl0+mc5+PPMIyHh0fZ8RjReXl5MQxjj4+/r6+vzfssLCy0\nbYdqtdq2l6lVaRixAwAAAHARKOwAAAAAXAQKOwAAAAAXgcIOAAAAwEWgsAMAAABwESjsAAAA\nAFwECjsAAAAAF4FHigEAAIDjSKVS284ji0nsykJhBwAAAI6jVCrFjuDKcCoWAAAAwEWgsAMA\nAABwESjsAAAAAFwECjsAAAAAF4HCDgAAAMBFoLADAAAAcBEo7AAAAABcBAo7AAAAABeBwg4A\nAADARaCwAwAAAHARKOwAAAAAXAQKOwAAAAAXgcIOAAAAwEWgsAMAAABwESjsHsZikdzJFTsE\nAAAAwJORih3AuTB6nezEUe5gGvF80fD/kASFLwAAAFQZTlfYZWVlLVy48PTp0zzPh4aGDh48\nuFGjRg7bO7dvN7d/t9CWZZw1Nohw2K4BAAAAKsm5RqRMJtOECRPc3Ny++OKL6dOn16xZc9Kk\nSVqt1mEBDE/FWUfpZAfTHLZfAAAAgMpzrsKuuLi4Z8+er776ap06dfz9/fv27VtcXPzvv/86\nLADv4Wmq30Bos1evsNcdt2sAAACASnKuws7DwyMlJUWpVBJRYWHh+vXrAwMDg4KCHJnBENPC\n2pYdTnfkrgEAAAAqw+musSMii8XSt29fo9HYtGnTyZMny2Qy60sXLlzYtWuXdTEpKcnT0/NJ\n+2dZloiUSiXP8w95uWEEX6s2c/0aEclOn2CSu5GbugJH8USkUqn1v85A+JlzHCdxmttHpFIp\ny7JC0e8MGIYhIqeKREQMwzhVHuHvx6kisSwrl8vLfquIS/jUcxwnfC85A5lM5lR/SMJfkVQq\ndZ5IDMNIJBLnyUNEDMPY47dmsVhs2yE4APPw4kZsV69evXPnzoYNGy5dujR9+nS1uqS0+uOP\nPz766CPrZosWLWrcuLHN927ev8e0ZrnQlnbqzrZPtvkuAAAAnJzRaHSe/wuCcnLSwk5gsVhe\neOGFwYMHd+vWTViTk5Nz6tQp6wZNmjSx1nzlp1QqpVJpUVHR/zx2o5H7+gvSaoiIargbXh9D\ndv6faYVCYTQazWazXfdSfjKZTKFQ6HQ6o9EodpYSUqlUKpXqdDqxg5RgGEatVptMJkfe3PNY\narW6qKhI7BSlVCoVy7KFhYViBymlVCr1er3zjENwHCeXy7VarclkEjtLCWHEzmAwiB2khEQi\ncXNzMxqNzvPxJyKVSqXRaMROUcrNzY1hGJt//C0Wi4eHh237BHtzlnN/gsOHD3/33XezZ8+W\ny+VExDDMfWcnAwICAgICrIt5eXl6vf5J9yJ0rtfrH1XURsVwabuJiAoLLCePGRvaflywLJlM\nZjQanaeKEs4zGo3GCvx47YTneYZhnCePcHrIYrE4TyQicnNzc6o8wokhp4okl8uNRqPzVFHC\nGVij0eg8hZRwntF5fmssy7q5uZnNZueJJJz0dJ48RKRSqcjJPmsgFme5gkoQHh6u0+m++uqr\nrKysa9eu/fjjjzqdLi4uzvFJDE81w7wnAAAAULU4V2GnVqsnT56s1WrfeeedN99889y5cxMm\nTKhdu7bjk/DuHsb6DYU2m53FXstxfAYAAACAJ+Jcp2KJKDg4uOztESIyxraQnTsttGWH081d\neoqbBwAAAODRnGvEzqmYg4LNNWsJbenpE4ymWNw8AAAAAI+Gwu5RjHElkxUzZjN37LC4YQAA\nAAAeDYXdo5gaR/EqN6EtO5xOTjMdCQAAAMCDUNg9Cs+yxqgYoc0UFcrOnxE3DwAAAMAjoLB7\nDEN0HOY9AQAAgCoBhd1j3DPvSc5VSU62uHkAAAAA/hcUdo9nbNbS2uYO7hMxCQAAAMAjoLB7\nPHOdIEtAHaEtO3dakp8nbh4AAACAh0JhVy6GuLuDdhaL7HC6qFkAAAAAHg6FXbkYG0Tw7h5C\nW3bsEKPXiZsHAAAA4EEo7MpHIjHENBeajF4vO3FU3DgAAAAAD0JhV17G6DheLhfasgP7yGIR\nNw8AAADAfVDYlRcvlxubPiW0JQX5soyz4uYBAAAAuA8KuydgbNaydLLiA5j3BAAAAJwLCrsn\nYHH3MFknK87OwmTFAAAA4FRQ2D0ZAyYrBgAAAGeFwu7J3DdZMVOQL24eAAAAACsUdk+s7GTF\n3KH9omYBAAAAKIXC7ok9MFmxXtw8AAAAAAIUdk/u/smKj4gbBwAAAECAwq4ijFGxvIwT2rKD\naZisGAAAAJwBCruK4BUKY+TdyYrz82Tnz4ibBwAAAIBQ2FXYPZMV798jbhgAAAAAQmFXYRYP\nT2ODCKHNXsthr2SKGgcAAAAAhV0lGFsmEsMIbQ6DdgAAACA2FHYVZ65Zy1w3RGhLL2WwN66L\nGgcAAACqOxR2lWJokWBtcwf2ipgEAAAAAIVdpZhCwsx+tYW29MxJJj9P3DwAAABQnaGwqyxD\n81YlLbMZTxgDAAAAEaGwqyxToya8h6fQlh09SFqNuHkAAACg2kJhV2kSiSEuXmgyRqP86CFx\n4wAAAEC1hcLOBozRsbxSKbRlB9MYk0ncPAAAAFA9obCzAV4qMzzVXGgzmmLpyaPi5gEAAIDq\nCYWdbRhjm/NSqdDm0naTxSJuHgAAAKiGUNjZBq9yMzV9SmhL8vNkGWfFzQMAAADVEAo7mzG0\nSCBJyc9TlrZb3DAAAABQDaGwsxmLh6exfkOhzV7LYbMui5sHAAAAqhsUdrZkjH/a2pan7RIx\nCQAAAFRDKOxsyVw7wFw3RGizly6w13JEjQMAAADVCwo7G9PHJ1rbHAbtAAAAwIFQ2NmYOaSe\nOSBQaEvPn5XcvCFuHgAAAKg+UNjZnsF6pR3Py/fvETULAAAAVCMo7GzPFNbA7OsntKVnTkju\n3BY3DwAAAFQTKOzsgGGMLe9eaWexcOl7RU0DAAAA1QUKO7swNmxs8fIW2tITR5jCQnHzAAAA\nQHWAws4+JBJDi5Ir7RizmTuAQTsAAACwOxR29mJsEsV7eApt2ZGDjFYjbh4AAABweSjs7IZl\nDXHxQpMxGWUH08SNAwAAAC4PhZ0dGaNjeZWb0OYOpTM6nbh5AAAAwLWhsLMjXiorHbTT62RH\nDoibBwAAAFwbCjv7MsY05xUKoS0/sI8xGsXNAwAAAC4MhZ198XK5IaZ5yYJWIzt2SNQ4AAAA\n4MpQ2NmdMa4lL5MJbS59D2M2iZsHAAAAXBUKO7vjlUrjU82ENlNYKDt6WNw8AAAA4KpQ2DmC\noUUCL707aLdvJ2PCoB0AAADYHgo7R+BVbsaYu4N2xUWyYxi0AwAAANtDYecghvhEXsYJbQza\nAQAAgD2gsHMQXqk0PhUntDFoBwAAAPaAws5xMGgHAAAAdoXCznEwaAcAAAB2hcLOoTBoBwAA\nAPaDws6hMGgHAAAA9oPCztEwaAcAAAB2gsLO0TBoBwAAAHaCwk4EGLQDAAAAe0BhJwIM2gEA\nAIA9oLATh6FFAi+7+/TYtF1kNIqbBwAAAFwACjtx8Co3Y0xzoc0UFTL794ibBwAAAFwACjvR\nGFok8FzJlXa0cxuj14saBwAAAKo8FHai4ZUqQ7OWJQtaDYtBOwAAAKgcFHZiMjZP4JUqoc3u\n28VoisXNAwAAAFUaCjsx8RxnaNFKaDNGA5e2W9w8AAAAUKWhsBOZMS6er+EutGWHDzAF+eLm\nAQAAgKoLhZ3IeFaqb5kotBmzSb5vp7h5AAAAoOpCYSc+Y2QMefsIbdnxI5LbueLmAQAAgCoK\nhZ0TYFm+bceStsXC7flH1DQAAABQVaGwcw5RMRa/2kJTduYEe+O6uHEAAACgKkJh5xwYxtym\nfUmb57ld20RNAwAAAFUSCjtnYW4QYQkIFNrSC+fYnKvi5gEAAIAqB4Vdeel0uuXLl0+dOvXH\nH3/8999/7bKLp9ta2/IdW+yxCwAAAHBhKOzKJTMzMyEh4asPP7j298ZfZ89q1arVxo0bbb4X\nc0g9c91Qoc1evSK9lGHzXQAAAIALk4odoGoYOXJka3e3ec8/K5VIiOjnYyf/85//NGvWzM/P\nz7Y70rVu77b0J+J5IpLv2GIKrkcSFN8AAABQLigaHi8rKys9PX1a+0Tp3RpraFSTYDm3efNm\nm+/LElDHFN5IaEtu3pCdPGrzXQAAAICrQmH3ePn5+SzDeCkUZVf6uSnz8+3y+C99247EskJb\nvms7YzTaYy8AAADgelDYPV5oaKiU47ZfKb1N9bZWd+jajYiICHvszuLhaYiOFdpMUSF3YK89\n9gIAAACuB4Xd47m5uY0ZM2bYhs2rz5z/t6h4z9WclDW/P9Uq4ZlnnrHTHg0JbXl5yQChLG0P\nU1xkpx0BAACAK8HNE+XyxhtvcBw3evbsW7duKZXKvn37TpgwQWK32xp4pdIQnyD/ZysRMUaD\nfM8/uo5d7bQvAAAAcBko7MpFIpGMHDly5MiRt2/f9vT0tF9JZ2Vs1pI7cpApyCci2bFDhphm\nFl8b34ELAAAALganYp+Mt7e3A6o6IuJZael8xRaLfCceMgYAAACPgcLOeZmaRJlr+QttacZZ\n9vIlcfMAAACAk0Nh58QYRt+2o3VJvn2zMHExAAAAwEOhsHNq5rohptAwoc3euCY7c1LcPAAA\nAODMUNg5O327TtaninH/pDJms7h5AAAAwGmhsHN2Fh9fY5MooS0pyJcd2i9uHgAAAHBaKOyq\nAH1iO14qE9rc3n8YTbG4eQAAAMA5obCrAnh1DWP800Kb0evlO1LFzQMAAADOCYVd1WBokWBx\n9xDaspNH2X+zxc0DAAAATgiFXdXAS6WGdsl3F3j5lj8x9QkAAADcB4VdlWFsEGG2Tn1yLUd6\n8pi4eQAAAMDZoLCrSnRtk61Tnyh2bGH0enHzAAAAgFNBYVeVWHxrGqPjhDajKebSdombBwAA\nAJwKCrsqRp/YllcqhTZ3YJ/kTq64eQAAAMB5oLCrYniFUv9025IFs1m+bbOYaQAAAMCZoLCr\neozRceaatYS29MI56aUMcfMAAACAk5CKHaBSWJatwLsYhiEiqVTKO82MIRKJhGXZ8ucxde7B\nLpkvzHii2LpJ91J9ktryVyn8YFmWldq028pgWZZhGOfJI5FIiMipIgmcKo/1syZ2kFIMw1Ts\ne8NOhD8kp/qsSSQSiUTiPHmE35dTRWIYxtk++3aK5Dz/SkL5OdHfZQVwHFeBdwlfEwqFwtZx\nKo5lWblcLpPJyvuG8IbUJIpOHCUi5nau8vhhSmhj2zxExHGc8/wTKPxjo7x7faHohJKFZVnn\niUREDMM4VR6hanGqSMJnzXn+ubJ+1pynShD+J0r4C3cGzvlZc6qvI7r7U7J5JLPZbNsOwQGc\n5aukYrRarclketJ3ubu7cxxXVFTkPF/uarVar9cbjcbyv4VJeMbtzEnGZCIifuum4pAwvoa7\nrfIoFAq1Wq3VavVOM6MKx3HCb03sICUkEom3t7fJZCosLBQ7Sylvb2+nyuPp6SmVSp0qkru7\nu0ajqcD3hp2oVCqpVKrVag0Gg9hZSigUColEotFoxA5SgmVZjuOMRqPzfPwZhvHw8HCqP2wv\nLy+GYewRSaVS2bxPsCtcY1dV8R6epQ+QNRjkWzeJmwcAAABEh8KuCjPEJ1q8fYS27Nxp6YVz\n4uYBAAAAcaGwq8J4ltUld6O7l8IotvzJPMnJXAAAAHAxKOyqNnNQiLFRU6HNFORze3eKmwcA\nAABEhMKuytO3T+YVd59Fkb6HvXld3DwAAAAgFhR2VR6vctO3bleyYLHIN28kp7nbFwAAABwJ\nhZ0rMEbHWeoECW02O0t24qi4eQAAAEAUKOxcAsPokrvR3cmE5Ts2M1pnmYMKAAAAHAaFnQ0Y\nDIZZs2YlJiY2aNCga9eumzaJMKWc2dfPENNcaDNarXz7FsdnAAAAAHGhsLOBN954Y9m3s8eG\nBa3s3K67UvrqsKErV650fAxD63YWdw+hLTt5lL1yyfEZAAAAQEQo7Cpr3759f61f/+fzKYOa\nRrQOqvNey2azk9uNHz/e8Q8I4qUyfbtOdxd4xZY/GbOzPDcJAAAAHACFXWUdPny4RUDtuu41\nrGt6N6xfkJ9/4cIFx4cxNWhkCmsgtCW5t7jd/zg+AwAAAIgFhV1lcRxXfO/zHrQms4XnlUql\nKHl0HTrzHCe0ufQ97LUcUWIAAACA46Gwq6x27dodzb2z7XKWdc20fQcaNmwYHBwsSh7ew1P/\nTFLJgsWi2PgrTsgCAABUE1KxA1R59erVmzhxYq8JE3o3Cg/1cN+dnXPwdv6vv/7K3H2Eq+MZ\no+Nk58+ymReISJJ7S7Z3pyGx3WPfBQAAAFUdRuxsYPjw4es3bnRLaH3Mp1Zcn+f37dsXFRUl\nZiCG0XXqzstKTsjK03bjhCwAAEB1gBE724iNjY2NjRU7RSmLu4ehTQd56p9ERBaL/K/1msHD\nrTMYAwAAgEvCiJ3LMsQ0MweVXOfH3rzB7dslbh4AAACwNxR2rothdF16lp6Q3beTvf6vuIkA\nAADArlDYuTKLh6chse3dBYv8z/VkNosZCAAAAOwJhZ2LM8TFmwPrCm325nX5/t3i5gEAAAD7\nQWHn6hhG16kHLy25S4bbu5O9eV3cRAAAAGAnKOxcn8Xbp3QeO7NZ8cdaxoQpiwEAAFwQCrtq\nwRAXb64TJLQlt27Kt28WNw8AAADYAwq76kEi0XVL4eVyYUl25IA046y4iQAAAMDmUNhVFxYP\nT11Sl5IFnlf89TtTXCRqIgAAALAxFHbViKlxlLFxpNBmtBrln78Rz4sbCQAAAGwIhV31ok/q\nYnH3ENrspQvcof3i5gEAAAAbQmFXvfByha77cyQp+b1zO7Zg9hMAAACXgcKu2jHXCdK3TBTa\nDGY/AQAAcCEo7KojQ6s2ljKzn3A7MPsJAACAK0BhVy1JJNoys59whw9IL5wTNxEAAABUHgq7\nasri4anrUGb2k42/SfLzRE0EAAAAlYXCzu5u3rz59ttvx8XFRUVFDR069Pz582InKmFqEmWM\nuDv7iU6r+G0VY8bFdgAAAFUYCjv7Kioq6tatW/Y/27+Mi/yhdQvvyxeSk5MvXbokdq4S+k7d\nLL41hTZ7/V/5lr/EzQMAAACVgcLOvr799lsfnWZ932efDa+XFFJ3XpeklJCgiRMnip2rBC/j\ntD37lT5q7Ngh2Ykj4kYCAACACkNhZ1+HDh3q1SBMKin9OfeNCD948KCIke5j8fbRJ3e3Lsr/\n3sjeuCZiHgAAAKgwFHb2JZfLtcZ7LlzTGE3yuyNkTsLYqIkxprnQZswmxW+rGb1O3EgAAABQ\nASjs7Ktjx44/Hzt1S6MVFg1m81fphzt16iRuqgfp2iWbrTPb5d2W/LoKj5EFAACocqRiB3Bx\nAwcO3LRpU+zPS/tHNFBIpb+dv8DUrDV+/Hixcz2AZbXdn3NbNI/RaolIcu60eec2ujuMBwAA\nAFUCRuzsSyKRLF68+OPpM27Ub3SuduD/jXk3NTVVrVaLnesheHcPXffniGGERdOf65krmaIm\nAgAAgCeDETu7Yximd+/evXv3FjvI45lCwvSt2sj37CAislhka5bpB73Eu3uInQsAAADKBSN2\ncA9DQhtTaP2SheIi5drljNEgaiIAAAAoLxR2cC+G0XbrxXv5CEvszeuKjb/iRgoAAIAqAYUd\nPECpMg8YQkqlsCQ9d4bbtU3cRAAAAFAeKOzgIXifmrIXhtLdeZXlabulp46LGwkAAAAeC4Ud\nPJykQSNzh84lCzyv+Ot3Sc5VURMBAADAY6Cwg//JHP+0MTpWaDNmk+rXlUxBvriRAAAA4BFQ\n2Dm7K1eu7Nix4+LFi6LsXdehi7luiNBmiouU61YwRqMoSQAAAOCxUNiJ7OTJk4MHDw4LC4uP\nj580aVJhYaH1pdu3bw8ZMiQuLm7ki0NatWzZq1evq1cdfjKUZbXP9rF4eJYs3bgmx02yAAAA\nzgqFnZiOHz/euXPnoGtXv0toNr5BSNqaVb179zbeHRIbPXp0/vGjGa8NvTLqpauvv+xz49/h\nw4ebTCYHh+SVKm1Kf57jhEXZudPybX87OAMAAACUBwo7MY0bN25EZMTs5Had6gX3i2jwV/+U\n/EsXFy9eTEQXL178+6+/ljzbKbCGmoi8lYqfuiWfP35s3759js9pqelX9mlj3ME0bv8ex8cA\nAACAR0NhJxqe5w8fPtwvooF1jZtM1iO83sGDB4koKyvLW6kIKPNUWTUnC/PyzMrKEiErkSms\nga5jV+ui/J9U6YmjoiQBAACA/wWFnWgYhpFKpbp7T61qTSaO44jI39//jk5/S6O1vqQ3mzPz\nC/z9/R0d9C5jdJyhZWLJAs8r//5DmnlBrDAAAADwIBR2YkpKSpq5/5Dl7r0IWQWFq0+fT0pK\nIqIGDRokJCa+vHHLbZ2OiIqNxlGbttUKCU1ISBAxsD6xnbHpUyULZrPit1Xs9X9FzAMAAABl\nScUOUK19+umnHTt2TFy8smtYSL7e8MuJMx179uzWrZvw6rfffjt8+PCwOT+HerpnFRTVDQ//\n6acfubs3MYiDYXTJ3ZjiIumlDCJiDAblql80A4dZvLzFTAUAAABEhMJOXH5+frt27Zo/f/6x\n48fVavWXI9/o0aOH9dXatWuvX7/+2LFjFy5cCAoKio2NZVlWxLQlWFbXq69q5RJJdhYRMVqN\ncvUvmoHDeJWb2MkAAACqOxR2IqtRo8bo0aPVarVerzc+MPcvwzDR0dHR0dGiZPtfeKlM06uf\n6pefJXm3iUiSd0e5drn2+cG8TNTRRAAAgGoP19hBRfAqN23fgdZROvbfbOWa5YzD59gDAACA\nslDYQQVZPL20fV6wjtKxWZnKdSsYM2o7AAAA0aCwg4oz1/LXpjzPsyUn9NnMC4r1a8hsFjcV\nAABAtYXCrmq7c+fO+fPnH7w4z2HMwaHalOf5u3d1SDPOKv9YSxaLWHkAAACqMxR2VdWlS5ee\ne+65Bg0aJCQkhIWFzZw5k787H56DmUPDdD16k6Tkb0l67rTyr99JpDAAAADVGe6KrZI0Gs3A\ngQOfYunsiBf91W7bLl8d8fUsjuNGjRolSh5TeCNdj96K39cIY3XSk0cVEkbXqYf18bIAAADg\nABixq5LWrVvH37o5v1vHYA93jmU71Qv+JrndjBkzrOdks7KyXn311aioqMjIyOHDh1++fNne\nkYwNIrRlKjnZ8SPyrZvsvVMAAAAoC4VdlXTx4sVm/n5cmfmKEwL9CwoKbt68SUS3bt3q3Lmz\n+fiR7xOb/9gmnjt7Kjk5+dq1a/ZOZWoarevQ2VrbcYf2y7dvxjlZAAAAh0FhVyX5+PhkFRSV\nXZNVUCiVSj09PYlo2rRpUSrFsl5dk0ODk0LqLurRKcHb45NPPrmvE61Wa7H1XQ7GmOb6Z5Ks\ni1z6XkXqX6jtAAAAHAOFXZXUo0ePo3kFPx09KSze0enf3vJPSkqKSqUiosOHD/duVL/s1W29\nG4UfOXLEurh69eoWLVrUrVs3JCTktddeu379ug2zGZq30rdub12UHU5X/P0HajsAAAAHwM0T\nVVJQUNA333zz5ptvfn3gSG216si1mw2feuqzzz4TXpXL5cXGeyYKLjYY5XK50F63bt27b745\n9ZmEDkmtb2m1k3elDRgw4K+//uI4mz0QzNAykVhWvn2zsCg7dpgMBl23FOudswAAAGAP+Ie2\nqurWrVtaWtroqZ90eHnET8uW/f777+7u7sJLycnJcw8dy9cbhMUig/Hbg0c7depERDzPT5w4\n8fN2ia/ERIZ5ecQH1F71XLeirCurVq2ybTxD81a6jl1L76U4c1L560oGcxcDAADYE0bsqjAf\nH5/evXs/uP61115LTU2Nmf9L34hwCcOsPnO+dsNGb7zxBhHl5+f/+++/nXp1tm6slErbBNU5\ndeqUzeMZn2pGEtZ6HlZ64Zzi1xW6Xv2sT6oAAAAA28KInQuSSqVr1qwZ9+mn18Ma5oSGj5k8\nZf369cKZVpVKJZVKb2m1Zbe/pdVaR/tsyxgVU/YMrPRihnLVUsZosMe+AAAAAGMnroll2f79\n+/fv3/++9RzHde7c+cN/9q5M6SZnWSLadjlrS1bO2O7dreIeYVkAACAASURBVNtcvHhxxYoV\n165dq1OnzoABA4KCgiqTxBjRlCQSxYZ1wjNk2axM5eql2pTneYWyMt0CAADAgzBiV+1Mmzbt\nklQe+cPilzdueXbV+p7rNk6cOLFJkybCqxs3bmzTps2Z39bWuXj26OqVCQkJ27ZtK/v2Gzdu\n/PrrrwsWLDh06FA592hs2Fj7bB/r82TZq1dUyxcyhYU2PCgAAAAgjNhVQ76+vtu3b1+7du3J\nkyeDvb0ndO7cqFEj4aX8/Pw333xzVrvE/4tqLKz55uDRUaNGHTx4UKlUEtHq1avHjh0bwMm8\nlYqJN24+k9Txxx9/LM/ttKb6DXUp/RW/rmBMJiKS3LzhtvQnTZ+BFh9fux0oAABAtYPCrjqS\nyWTPP//8g+vT0tKUJqO1qiOikbFRU3alHTx4MDEx8ezZs2+99dbcpGf6N25ARP8WFfdY9duU\nKVM+/vjj8uzUFBqmef5F1dqljFZLRExBvmrpz9rn+pvrVOpULwAAAFjhVCyU0ul0ak5Wdo2E\nYVQymU6nI6LVq1d3DAoQqjoi8le7fdYuccmSJXyZyYfT0tJmz549d+7cEydOPNi/JaCOZsBQ\n3t1DWGR0WuWKxbJzp+11PAAAANUMCjsoFRUVdfFO/tEbN61r9mX/e0Oni4yMJKKbN2/Wda9R\ndvsQD/fCwkK9Xk9EFotlxIgRz6ek7F28YOuP8zonJU2aNOnBXVh8fIsHDjPXrCUsMmaT4vc1\nsmPlvVwPAAAAHgGnYqFUSEjIqyNHpixcOCGxRWNfn2M3bk3enfb222/XqlWLiEJDQ//elsoT\nWR9WlpZ9LSAgQKFQENG8efP2b/77yEsDg9xrENGJm7kdfpgXFxfXvcz9tgJeXUPbf4hy7XI2\nO4uIyGJR/L1BotHoWyY67lABAABcEUbs4B4ffPDBf8aPn5WZ02XthrnZN8ZNmfrOO+8ILw0a\nNOiymX9ry47bWp3RYtl4IfPdbbvGjBkjvLpy5cpxCc2D7g7pNa3p81ps9MqVK8t2rtFoTp06\ndfPmTV6h1PYbbKrfsOQFnud2br00/dPx778/b968/Px8Bx0tAACAa0FhB/eQSqUvv/xyenp6\ncXHx3r17Bw0aJLk7vbCPj8+yZcv2mChg9g9eM74b/NfW1999d/DgwcKreXl5td3cynblr3a7\nffu20DaZTJMmTQoPD+/Qrl3jxo2fe+65zOxsbc++xug46/ZRFuNbrDF13nfx8fEnT550yOEC\nAAC4FJyKhScQGRmZmpqanZ1dUFAQFhZWdqKT+vXr77hytVv9UOuaHVeuNoxPENpffPHFH4sX\n/dmnR8s6/teLi8em7hw8ePDff/9Nyd2KGInnkXTh9G6op8fG51OmpR0YMWLEzp07GYYhIrPZ\nvGzZsu3bt+t0uujo6BEjRtjpORkAAABVHUbs4InVqVMnIiLivunr3nvvve+Pn/4q/fCNYs3V\nwqIPduzZnHNdeECtVqudM2fOD12Tng4MYBkmQK3+qXtycfbVDRs2ENG6vKL3tu4s29WY+Lgw\nk+Hs2bNExPP84MGDp02Y0ORmThtd0fYli1q3bn3r1q2y26enp//www+LFy++fPmy3Q8eAADA\niaGwA9uIi4tbsGDBvMs5db+dX/+7nzfrTCtWrAgODiainJwcvV7f3L+2dWM5y8bU8rt48SIR\nFRUVpf97vWxXDDErU7p6nD1JRMuXLz+xd0/60P4fJrZ8Jz52x6C+0Urugw8+ELY0mUxDhw7t\n27Pn39/PWTbt84SEhLlz5zrumAEAAJwMTsWCzSQlJSUlJeXm5rIs6+npaV3v4+MjkUgu5xc0\n9PGyrszML2jt50dETZs2/ejGzezCojo11NZXpRJJg3MnDdtq7Ni2bUDjhj7KkgfLMkSj4qKH\nbCl5ytmMGTPO7N51fPigALWaiLZfvtpr0kcxMTHx8fEOOF4AAABngxE7sDEfH5+yVR0ReXp6\ndu/e/Y3N2+/o9ETEE01PO5RlMnft2pWImjdvntSl67Or1m/IuHT+dt7VgiLrG7kD+8bX8fVS\nKsr2JmdZs9kstJcvXz6pTSuhqiOitsGBAxo3Wr58uV0PEAAAwGlhxA4c4csvvxw0aFDDuQsi\n/XyvFhZpOPm8efNq1qwpvDp79uwZM2a8unTpzZs364WELHvxhWhtofBSY5XCq0kjncmkkJb8\nrS4+cbpFixZCOzc3N7DMOB8RBbqrD969FVdw4cKFCxcuBAQENG7c2HqHLwAAgEtCYQeO4OXl\n9ccff+zdu/fMmTN+fn7PPPNMjRqlD7FQqVQffPDBBx98YDAYhHsydEcOKLZuIrOZiPzVbnqz\nec/Vf69rNL+ezdiQlbN1/kLhjWFhYbuycuIDSq/e252V07RnyXnYvLy8119/fcvff/ur3W5o\ntI0jI+fOnRsWFua4wwYAAHAsFHbgIAzDJCQkJCQkPGIb6522xqeaWWoHKH9bxRTkE5GcZRMC\n/VdduiKNjt226JeQkBBhs/fff3/4/71Yy03Zs0FYscH4ZdrBI4XFs4YPF159++23C04cO//a\n/wWo1YUGw5ubdwwdOnTz5s1yudy6xzt37ty4cSM0NPS+m3yLi4tnz569c+dOk8kUHx8/evRo\nb29v2/0wAAAA7AJnpsBJmWsHFA8Zbq4bYl3TN7TuvFaxob4+1jXJyclfzPxqXPrRml99HzLn\np50mWrFiRe3atYkoOzv7j99/X9A9WbgCrwbHfde5fe6Vy9u3bxfem5WV1a9fvwYNGiQmJtar\nV+/TTz+1Xrqn0+m6dOmydcmiQR6q4X6epzb83rZt29v3nuEFAABwQhixA+fFK1WaPgMV2zfL\nDu0X1rDZWW6LftB16m4KayCsef755/v163flyhW1Wu3jU1rz5eTkuHGyYI/SqYzlLBvu5Xn1\n6lUi0uv1gwYNCjfqT78yJNC9xq6s7BE//iCVSseOHUtE33zzjfTWze1D+slZloj+L6pJj5W/\nffzxx1999ZXDjh0AAKACMGIHzo1ldR06a7v0JJlMWMEUFynXLlf+tlI4S0tEDMMEBweXreqI\nyN/fv9hgvFJQaF2jN5sz7uQFBgYS0YYNGwqvZi3s0SnU00MmkbQLDvq+S4evv/5aq9US0d69\newc2bSRUdUTEEA2NbrJnzx4HHC4AAEBloLCDKsDUNNr44gjyKr3KTXrujNv8OdzuHXT3/Ol9\nAgMDu3XvPvSPv/8tKiaiIoNx1KZtXkF127ZtS0QXL16MqVVTIWWt2ycEBuh0uuzsbGGR5/my\nvfE8LzzfDAAAwJmhsIOqwVKrNj/iDWNEpHUNYzLK9+xQLf6Bzbn60LfMmDFDFdEkfO6CRt8v\nDPzmh2MS2U8//STcOeHj43O1sKjsxlfyCyUSiXCHROvWrRefOK01mUp2zfM/Hj3RunVrex0b\nAACAjTD3jUxULXl5eaa7//qWn7u7O8dxubm5znPsarVar9cbjUaxg5RQKBRqtbqwsFCv14ud\npQTHcRzHFRUVsZcvKTZvlNzJLX2NYYyRT+kT2/Fu6gffeP78+YyMjICAgMjISOs8dtevX09M\nTBwfG/l6s6cYony94YXfNiqaRC1YsICIDAZDly5dzDnZ/xfVmGPZZafOZkukqampXl6lT84Q\nqkCDwVBQUGDXA38i3t7eTnWTh6enp1Qqve/ZvuJyd3fXaDQV+N6wE5VKpVKpCgoKDAaD2FlK\nKBQKiUSi0WjEDlKCZVkvLy+dTldUVPT4rR2CYRgPD4+8vDyxg5Ty8vJiGMYeH39fX1+b9wl2\nhcLOKaCweyxrYUdEZDZzRw5wO7cyZX9iEokptL4htoU5OJTKcdo0NTV15MiR3rwl0F197Pqt\n0CZNli5dap3TRKfTzZ07d+fOnUajMT4+/vXXX/fw8Cj79kOHDl24cMHd3T0mJsbPz8+GR1oZ\nKOweC4XdY6GweywUduDMUNg5BRR2j3VPYUdERJK82/LNf0ozL9y3pdnXzxjb3NQ4kpdx9Eh3\n7tzZsWPHtWvXIiIi2rRpU86r6PR6/UsvvbRza2qUX807Ol223jht2rQ+ffpYN0hNTf3hhx+u\nXLkSGBg4bNiwzp07l/soKwuF3WOhsHssFHaPhcIOnBmmO4GqyuLpre07UHb2lHz7ZusdskTE\n3rrB/r2B35FqbBJljGhqqR1A/+NJYl5eXr169XrS/U6dOjX70IGTw4f4q92IaMXpcyPeeisy\nMrJhw4ZENH/+/MkTJrweFz3s/9u788CYrv5/4J97Z8nMJDPZZzLZEUkQxFo0aImm9lCqammt\nsXy7KY0uVFuqpR5U67Fr0RT1s9VStRUl+sS+CyEi62SRzCSZ9c79/TE6DUJCkTF5v/5yz733\nzDlHJt7uvefchmEX8gvHjxgx4aOPxo8f/6i9BAAAeAgIdvBsM0c0NIdFiK5eFp05IUi/Zi9n\njAbxif+JT/yPF4msAUGW4DpcaF1O6Vedu7QP9tNPP/38cidbqiOiAQ3Cf7mYumHDho8//rio\nqOjTTz9d1/vll+uGEtErEWEdgwN7fPll37591Wp1Nes3GAwsy971JgwAAIDqQLCDZ59AYI5o\naI5oKMjLEZ08Jrx4lqlwo40xmwXp1wTp1+jgXl4q5YJCrCp/q7sH5+7Be3jyMteH+iij0ajV\naoMV8oqFoe4KjUZDRKdOnfIQCmypzqZDcECATHr8+PEePXpUWXlKSsonn3xy6tQphmFatGgx\nY8aM6Ojoh2oeAADUcgh24Dw4lZp7uSfTMVZ07pTo1DG2+NZdBzB6vTD1EqVespfwQhHv4Wl1\n9+Bd3XixmAQCErvwDMNLpMSyJBbzZjNze6k8njEYxETTX+pksv6zeB5P9FqjCJVaKVu19AWd\n7tDg/nd96NE3XpOmnhEtusKzAiLiRWISCnlXN16u4F3drAp3q6sbL5dfydP069dvXOMGPwx/\nnedp+elzffv23b9/f0hISMXaDAaDRCJ5nKMGAABOBJMnHAImT1Tp3skTVeB5gSZXkJEuyLgu\nyMxgHObJ9AcoNhg9JC72zcFbfxNGt1iwYIFtc/369XPmzLl+/bqbm1uvXr2mTp1qn8Nrh8kT\nVcLkiSph8kSVMHkCHBmu2IGTYhhOpeZUamrVlqxWNidLdPOG4GY6m5XJmB3lX9C7VEx1RLS4\na+e/ikrEf/3J+QVsOJL84cRJ0zu26/hiu7yy8s8P7h88OHXr1q1C4e2vsNlsPnr0aElJiVKp\nbNWqFd6TAQBQOyHYQS3AstaAIGNAELWJIZ5ndFpWW8wUF7MlxYKSYqbkFqstIX0585BXTK0i\nESMQ8i4uJBLxAiGJxSQQ8Axzq1x//fr1Aq1W6uoaERHh6+tLZjNxXFZWVvr58zFB/sL7zNK9\ni6tI1EnlQwf3EdEQovixwxQuYiKK8Pbc7NezybKftm7d2rdvXyK6ePHiiBEjCm5mBCsU14pL\nwhs3XrlyZfWnawAAgNNwuGBXVFS0YsWK06dPm0ymunXrDhs2LDw8vKYbBU6EYXiFO6dwp8CQ\nSnZaLGQxk8nE8Dxj0N8uFQjsS+JZxWKGYUgoZMQu93vzhISoQYVNw99/mDdlSun5Sy+EBNp3\nZWp1/Tdt3/ZzkqvFfPbPQ8arqe0C1GxlF9sYIluqs3ETi/cNekWTel50OsTg5TN29Oj2MvF/\nxo2QCAUlRtOI7b+PGTNmy5YtDzk0AADwzHO4YDd9+nSxWPzZZ59JpdKkpKTPP/982bJleFoc\nng5eKCShkCRSnojcPSo/hoiIHuFOp4uLS/6dFwV1ZvPp/EIuLMIslYY3bTFq1Kg+85cMjoqs\n4+EuYJjYqIbhctf7PR0Y6q4IJZ5+3y4hSu7eSciytkTo7iJe2jU26Pvlly5dioyMfPhmAgDA\nM8yxgp1Op/P19R08eHBQUBARDR069MCBAzdv3qxfv35NNw3g34qNjR2w8Ptz+YVRvt5ExBPN\nPnq8ffv2UqmUiFiWXb58+cGDB5OTk/MZJiYmxr9du1KeZwsL2NzsvT+ubCBi63lWnjXFAkHF\nTU+p5K83X1McPSjS6zilH6/y44WiR2uzTqdbt27dlStX/Pz8evfuXbdu3UerBwAAng7HCnZy\nufzDDz+0bxYWFrIsiyk54BzatGkzbHRCxyVLBjaKUMlku9Mzrpksu1b9VPGYDh06dOjQ4Z9t\nhrH6+Fp9fMMnJHbr2VNapnstMtxTKnEVi19uEuXDc2S1VvpZjXy8KT+X9uwkImLZcplbWpk+\n3WSW1g1r3Suekcmq0+DU1NQ+ffqoeOtzAX4pJdo5c+bMnTu3f/9/1nMxmUwpKSkajaZ+/fpR\nUVEPPyQAAPCYOe5yJzqdbvLkya1atXrzzTfthfv27Zs/f759c/bs2fXq1XvYmlmWZRiG47iq\nD31aWJbled5x/i4YhmFZ1mq1OlSTGIax3ifH1AiBQMDz/MM2affu3Zs2bSooKIiOjh47dqyn\np2c1TzQYDElJSWfPnlUqlfHx8Q0aNCCzmc/JsmZm7FqxLEIiDrlz2eQH4D082cBgRh3A+AeS\nfwAjV1R6WOvWrZtxpgUvvWC7yfv/Ll8dvefg2bNng4ODiejEiRODBg3Kz8jwl7um3Srp2Lnz\nTz/9dFd3/s13rbi42MOj8iuU/wa+a1VytO+abYge4bv2RNn+1mq6Ff8QCARE9Nj/XbNYLC4u\nLlUfB47EQYNdZmbmF198ER0dPWbMmIoLN/z+++9fffWVfXPhwoW2F3Q+FFuFjtlxx8EwDvqz\n4Tgc5wcpKytr5MiRh/btbeOvjvT27Nu2Tbs6IWxhPlXvtzwjVzABgYx/IKMOYAKCGC9vIkpP\nT69bt272W6O8pP884dpu1bpRn36WkJCg1WqbNm3a08fjyxeeFwsEmrLygVt2BrSLWbt27R01\nP/wQmc3mWbNmzZs3r7CwUKFQjBo1atq0aa6uD/eCkAdwwB9sB2ySo3Gc75qdo/2tPaEhMpvN\neL3hM8exfjRtTp8+PWvWrIEDB1b5FiYsUPyEOMMCxU8Yy7L3mxVbUzIzM7VarY+Pj1KpJCKG\ns7D5mi2LFgYU5ncIDqh+PbxEYlWqNULRRwu+W97tJbbCPJEe67fEvDl8/PjxGzdu/Crxg3Oj\nhgj+/n9X2q2SqGWrz58/7+vraz/+ERYo/uyzz7au+nF25/Yt/FSXC28l7j9UP6bDkiVLql/D\ng2GB4iphgeIqYYFicGTVWk/rabpw4cLXX389YcKE6rxbEwDsAgMDY2JibKmOiHiBkPPz36Ep\n+u1a+j8H8VRQrv8zM5t3ue9Mc8ZgEGRcV19LXdn9jlSnM5pKzabGjRoRUW5ubj0Pd0GFq+n1\nPN0FDJObm/tvuqDRaBYuXLg2vlvv+vUC5W6dQ4M29+u1Y+vWU6dO/ZtqAQBqD8eaPGEymebN\nm9erV6+QkBD7//Ld3Nyw3AnAo4mMjPz14B8Wq/X2qsgMrTxzYW1hyR/z/suWFAvyclhNLpuX\nI8jLZcrLHlyV3EX8x6D+/MkjfNa1PiIq8vUyWzkRe3tC7rn8Qp5hAgLuuDRYUlLCcRxbvQWZ\niSg1NdXDRdzcT2kvCZS7NfTxunjxYnR0dPV7DQBQazlWsLt48WJubm5SUlJSUpK9MCEhoXv3\n7jXYKoBn17Bhw1avXj1k62+T2rT0kLj8euXal0f+92NSEjGM1cPT6uGZ5+E959ffjh07FqhQ\n9G3bum+b51xuFQrychhtSaUVMhYLk50ZTvT1i+2JiCeeIabYYFx/8cqEoUO8/p48cerUqcmT\nJx8/fpyIQkJCvvjii65du1bZWoVCUWYy6y0W6d+vSuOJ8sv1CkXl0zsAAOAujviMXfXhGbsn\nBM/YVckBn7EjIi8vr3sfssnIyJg6deru3bvNZnNERMTHH3/88ssv23bl5OS8+OKLLd3d+kXW\nN1q4ZafPWVXqnTt3isVi0pcL8nIEmlw2L5fNyRZoi6ka3xfeRWL1UxfL3SfOnR8qdXmzcUOp\nUPjLpSsfHzi8fuOmtm3bPvh0juPat28fp5DN6tTeNhV3XsrJOecuHz169HFlOzxjVyU8Y1cl\nPGMHjgzBziEg2FUJwa46Kg12NhaLxWw22xZDtktISODOnl7Xp5ttU2+xtPtxXf/x//fWW2/d\ndTpjNLB5uYK8HDYvR5CbwxYXVSfn2a08c2G9zrB+82bbZnFx8eLFi0+fPu3q6vrSSy/169fP\nPvn93LlzAwYM8LFamqmUl4tupZbply9f/sILL1T/sx4Mwa5KCHZVQrADR+ZYt2IB4AkRCoVC\n4d3f9+Tk5O/btbRvSoXCAQ3D/zxy5N5gx7tIuOBQLjjUtskYjawmR5Cbw+ZmC3Kz2eJbD/70\nYU0aDrFamR8WW/0DC2Vub3wwmfTl3eqFluiKpk2auGvXrmXLltmOjIqKOnr06KZNm9LS0l4J\nCoqPj3+of1esVmtGRgbP88HBwYI7X8gBAFAbINgB1F4sy1rvvPBm5ak6cx14FxcuKJQLCrVt\nMgY9m5MtzMthc7MFOVlMqe7eU4QsS/l5gvw8P6J9ff553u691s07rv5ly5YtvXv3tpXI5fKh\nQ4c+Qnd2796dmJiYefMmEfmp1V9++SUm1wNAbeNwy50AwFPTvn37JSfP2pNdqcm85tzFjh07\nPmw9vETK1alnbBOjj3+1dOx7uxq3euPXXbml1bqX5yWRnBk1JPbCCenOLaJTxwT5efd7TxoR\nmUym+fPnd+7cOTo6euDAgSkpKfZd586dGz58+Fv1QzXvJhS8N+bDqIgxY8ZUPAAAoDbAFTuA\n2mvatGmdOnXq9NOGfpH19RbLD2cu+EZEDh8+/F9W2+7lrgdPnQ6fN69tgFomEhbq9QM7dRrx\nUqwwL5styL/34TyGyE8ooHOnhedOExEvElv9/C0BgVb/QE4dwMv+ee3EqFGjUpOPTGjdXF0/\n5I+MzD49e65Zu9b2BN633377ekS9t1veXhVldLPGV28Vz5s376effiIAgFoDwQ6g9vL29j5w\n4MD333+/6dgxkcjljQnvDx8+/N5H8R7B5MmTe/fufezYMaPR2KhRo7Zt2xqJjESM0Tht9MhO\nUlG3eqH3O5cxmwQ30wU3022bVg9Pq3+QxT/gyM2slD/2p7w50M/NlYi61gv1d3OdMGHC8ePH\nGYa5fv36y/5+Fet5LkC94/yVf98XAIBnCIIdQK3m4eHx8ccfP4maGzRo0LZt27teKca7uLw6\n+aO4uLh+9UJ71q9r4rgTuZrmYfXiW7cU5OUwlb3cli2+xRbfEl4404no6thhLhWmRLzeKDJx\n/595eXl+fn4+Pj4ZJXfMUM4o0WJCHwDUNgh2APBUNWjQYO/evbNmzZp04qRCoejSpUv7d97R\ny2SMxcLmZguyMwU5mYKsTKaskrUtXO6c6Oopkazs/pJ32mUBS0MGDfq/hIRuYXVa+CmJ6Fx+\n4ey/jn8++5un1CsAAMeAYAcAT1v9+vWXLl16VyEvFHKBwVxgsG2T0ZYIszPZ7ExhThabl0OV\nXcwTsszARhF05AAdOfCqSNT6nbEbjx1ff/HyhYKiAxlZI0aPHjBgwBPvDACAI0GwAwBHxCvc\nzQp3imxkJCKrVZCfJ8jMOL97l3epNsS9krdQMGZzHYbeb9XMtml2kVBoXevxvzg/f87Pn7Cm\nHQDUDgh2AODwWJZTqTmVun6L5/bs2TNv268qo/75kKBWfkqXooJKn8wTGQ10+QJdvkB3TrPl\nwyOJwTJPAOC0EOwA4FkSGxsbGxtr+7OZyMJxTG62MCeLzbopzMlkdJWsjXzXNFuJpzenDrD4\nB1j9AzlfFVVjQeYHKyoqOnv2rFAobNKkiVwu/5e1AQD8Gwh2APAM4wUCPiDIFBBELduQ7cm8\n2yHvvk/mMbcKhbcKhRfOEBEvFFn91Jx/IKcOvGa2pGnyQ0ND69SpU/0GLFy48KuvvnJliLNa\neYl0xowZr7766uPqHQDAw0KwAwDncfvJvIiGRiKGs7B5uYLszAddzLOYBZkZgswMImpE5Kkr\nPZKVc9RV3j1hrHtkQ76qJ/O2bds2e8b0DfHdO4cGEdG6i6mjJ0yoU6dOq1atnkTvAACqhGAH\nAM6JFwg5/0DOP5BatikrK3vrzTekeTk9wupGeHtGeHlKhJWENn+5W7/I+kREOzbRb1usXt5c\nQJDFP8jqp7Z6+xLD3HX84sWLP2jT0pbqiGhAg/AjmdlLly5FsAOAmoJgBwDO75NPPsm9fHlL\nv55eUgkRbbh0ZcnZiys/n6Y0Gaw3rrsY9JWcY7WyBflsQb7o9Aki4iRSnbunS736fECQVe3P\nu0iIKCsrq1GrphVPivL1WZWZ+TS6BABQGQQ7AHByFovll19+2fV3qiOifpH1V529uPjsxUmT\nJm3ZsuXHzz7dPbDvgysRGPQeBj3lZRMRMYzV24fzCxjbMrpQf0coPF9QGBhS78n0AwCgagh2\nAODkysrKjEZjoOKO+apBCnlRURERqdXqk3maUpPZTSyy7dKZTJP2HZo2crjSUM4WFhDP310j\nz9su5k2oH0pEPJHtHm1Kdu5fWdkzvphZ4UD+8OHDly9fViqVHTp0cHd3f2K9BAAgIsJ6TgDg\n5BQKhY+Pz+HMbHuJxWo9mpVTr149ImrRokV4VOOEnXu0JhMR3TIYh2/ffVEql702tGzY2Lku\n7h/sP3RvtKvI/uRdK3+/5DcGvvi/g9L/97NL8kH92VMD43u/+dqAbd9/+/l777Zp02b//v1P\nrJcAAES4YgcATo9hmMTExAlTp0qFwi51gvPL9Z8eSi5zk9teOCYQCJYuXTpq1KjQ75cHKeQZ\nJbrGzZsvWbyYYRgiunA9Xc7xFWdNaMr0m1KvDn8pVlSYT1ZrJR+nLxdeu0LXriiJtsW0tD7f\nQsAwPNHaC5ffGjdu34EDSqXyaXUdAGodBDsAcH5vvvlmeXn5iG++0el0RPT888//vOwb+2LC\nwcHBO3fuPHPmzI0bN0JDQ5s0acL8HeW8vb2vaLUV9HJenwAAIABJREFUqzpXUPDJ0eMDf1pv\n5Dg2L0eQk8XmZAlystiS4ns/lyESMIztDwMbRrwSUb8oaaVLk2ir2p9TB1o9PJ9stwGg9kGw\nA4BaYdy4cQkJCYWFhVKp9N73Q7AsGx0dHR0dfVd5//79Oy9atPZC6msNw4kot7Qscd+fgwYN\nYhiGFwq5gCAu4PZaJ4xebw95gtwsRl/JTFuxgPUzG+n4X7ZNzsXlmslyuVSv9/Zt06+/h9r/\nMfcZAGofBDsAqC0EAkFYWFh5ebnFYqnmKeHh4XPnzv2/SZO+Sk7xkkpO5WliXuz0ySef3Hsk\nL5Va6oZR3TDbpjk3Z/KQ1z9t1yrAze2+7TEa6xPVdxWToYRfs8wgcxPVqWvx87eqAzilH1W1\nPDIAwL0Q7AAAHqRfv34dOnQ4fPhwUVHRlKZNW7ZsWZ2zRH5q385xnZYtXdYttl2gf2G54be0\ndB9vz7hGDQTFt+49niGSlpfS+TPC82fI9qo0ldri58+pA6zqAKun12PuFQA4KQQ7AIAqKJXK\nPn36POxZEydONJvN3Rct4sxmjuc7dOjwn+mJ5SEhjF5/eP3P6bt2JDRrcr9zGY5jsjPF2X+v\ndSyV2UMepw7gpdJH7gsAODcEOwCAJ0IoFE6ZMmXSpEnXrl1TKpU+Pj62cl4qvUKCX85ftgc7\n20p4J3Lzov3VbGUzbUlfLrx+VXj9qm3L6uHJqQOtan+Lnz/vp+YF+E0OALfh1wEAwBMkkUga\nNmx4V2Hjxo2naApulGhD3BVExBDtSc/ou3nnpfPn3cvLhLnZtnkYbHFRJcsjE7HFt9jiW3Tx\nrAsRCQScj9J2JY+7zzttAaD2QLADAHjamjdv3qNPnx6/bJnWvm2Yp/v/svM++/Poxx9/LPfw\nsHp4mPwDiFrdvHnz2MGDCl1JM1/vALIKcrIYfXkldXGcIC9HkJcjOnWMiHix2Kry59T+VnWA\nRaXm3T2eWqc4jtNoNL6+vkIh/mUBqDH4+gEA1IC5c+cuWLDgo7Vrs7Ky6tWrN2XmVwMHDrTv\nXbZs2bRp0xp4KFxFohO5mrgePf773/+Ky0oF2Zn6q6lpB/Y39vFyqWzaLGMyCW6mC26m2zZ5\nmSvn529V+3N+AZzan5fKiEir1ebn5wcHB4tEosfSF4PB8NVXXy1fvtxgMLi4uAwZMuTjjz92\nqzAd+Ny5czt37iwqKoqKiurfv79YLH4snwsA92L4B78rx7EVFxdXf9kCO4VCIRaLCwsLHafv\nbm5uRqPRbDbXdENuk0gkbm5uOp3OaDTWdFtuE4vFYrG4tLS0phtyG8uyXl5eJpNJe+fqtTXL\ny8vL9v5TB+Hh4SEUCgsKCmq6If9QKBQPtdzJkyaTyWQymVarNZlM9sKUlJS+vXpu6NsjNjSY\niDJ1pXFrN/YbPWbixIlENHz4cFHqxaTeXW0Hm63W9BJtmKdHde6/ml3d/peVs+P0meO5movF\n2iGjRk+cOFFwZ0CUSCQsy5aXV3Z18D7ee++9Yzt3zIvt2FjpfbGg6P29h+o+H7NkyRLb3oUL\nF874/POX6gT7ymSHbmZxHp7btm2r/us3BAKBp6enwWBwnK8/wzDu7u7FxZUsSV1TPD09GYZ5\nEl9/+7Oh8KxAsHMICHZVQrCrDgS7Kj0TwW7ixIl0ImVx1872kg2Xrkw+ce7kyZNEFBERsaZL\nh04hQfa9nx5Mvq4KWPTpJ4KcLDYnW3/lsoKvbAbGPa4Vl+g8vCI7xXJ+/laliheJ09LSUlJS\nOI5r2rRpVFRUdSq5ceNG61atTo8YXN/r9m3fTF1pwyWrdu3ZExUVdfbs2W4vdfm1f+/2QQFE\nZLZaB27eYQmLWL16dXUqJwS76kGwAzvcigUAcCyFhYXRijuWNQ5xlz84H+utVi4olAsKnT17\n9pqFyxfEvficv6rMbM4tLW+o8pXfZ63juh7uRBzt20VExLIaK3/xwuXysrKrxSWfXUzt0f/V\nOXPm2N+uduTIkZkzZ549e9bDwyMuLi4xMdHLy4uIUlNT/d1c7amOiALlbmGe7qmpqVFRUTt2\n7IgNDbalOiISseyn7du0WbXeYDBIJJJHHyMAuA+2phsAAAB3qFu3bnJWTsWSw5k59erVs/25\nXbt2y0+ds+/SmUzrLqbGxMQQUUlJydy5c1f1fKl7vVAfqTREoajjrqizYOmhFu0MPfomM6IL\nBYX3/VSrVUn8wIbh77RqtqDLC9lvj57owmctmCM+kSLIuvnXoUMDXunbkSzb4rsuaNP80u+/\nDRgwwHaV0cPD45bBaOQ4e00cz+eV6T09PYmotLTU884A5y2VcBz3ULd6AaD6EOwAABzLyJEj\nT2rLEvf/WVCuLzdbfr5wefrhvz744APb3unTp+/PL+q9YevKM+e/O346ZvV6VWSDoUOHElFa\nWpqQ52P+vjxGREpXWVOl77Fr6eYGUcd81CO27674QUbOYrBwVBmGKMLLs4Gx3GXvTlnSyheT\n910ZM+yzDm3bBqi71Qvd/mpvbfr1pKQkImrWrJk6JOSjPw5zPE9EPNHnh47KfHzatm1LRI0a\nNTqQkVlW4TmT7VfTAwICbLEPAB47BDsAAMeiVquTkpL2lJsCv1vmNfe/7x85NmP2N926dbPt\nDQgIOHjwYHDnl5bk3dpstA4Y/9bGjRttK4zI5XIjx5UYTRVrK9Dr3d3diahbt27Xjebvjp+2\nletMpn4btw+5lF7+RoLh5Z5bc/I1Zfe9iiZgGF/ZP6+7kAqF+wf1a5VxVXzsqEtO1sr//ndj\ntqbpsjUDNu2IXr5mRdqNJUuW2O60vvLKK971wuI3/HogI/NSYdGCY6c+/OPP6dOnMxUW27t4\n8eKPP/64atWq1NTUxzaIALUVJk84BEyeqBImT1QHJk9U6ZmYPGGXl5dXVlYWEhIiuM9Dcnfh\neb5z586tWOuCl14UMAwRLT11bmrKqeTkZG9vbyLas2fPuHHjlCwFyt1O5RUERUSsXbvWtuvd\nd981n0hZ1TPOXluWrlTp5iqqznLHDMO5e6SbLFfL9BalX4sePV19fO07CwoKvvjii+3bt+t0\nuvDw8MTExB49etj3TpkyZcXSpdEqXyI6nZefMH78lClTKtaNyRPVgckTYIdg5xAQ7KqEYFcd\nCHZVeraC3SNITU3t37+/3Khv6ae6eqv4XIlu8eLFcXH/xLWioqL9+/drNJqIiIgXXniBZW/f\nt7l69WpsbOyEZlFjmzcRsey6C6kT9x3c8P82tgury+bmHFybVJfnwryqu9yx1d2DU6l5lZpT\nqTmVHy9zJSKTyXTXCnbr1q2bOnHi7wP7Rvl6E9FpTX7cz5tmf/ddfHy8/RgEu+pAsAM7BDuH\ngGBXJQS76kCwq5LTBzsiKi8v37hx49WrV/39/Xv16uXn51fNE//8889JkyZdvXqViNRq9YwZ\nM3r27GnblZ+fHxcX52nUD2ncwF3sYrFaO0c1DBaLGK5aI8nLFZxKbVX5cSo1p/Tj5QpbeXx8\nfDchvf9cc/uRM5NTDggkv/zyi70Ewa46EOzADsudAAA4FZlMNnjw4Ec4MSYmJjk5ubCw0GKx\nKJXKio/B+fr6Hj58ePXq1cfOnFEoFF27dvVq377UahUU5rN5OYK8XDY3m9XkMZbK/3fK6LRC\nnZauXrZt8lIZp1JbVeoWLsJID3nFI4Pl8oKMnMrqAIBqQbADAIB/BAQEVPrmCalUOnr06DuK\nWJbzVXG+KrNtJWOrlS0qYPNybe+uZfPzmPtc8mf05cL0NEpPm9WyyR07eLqh1YaHhT22zgDU\nPgh2AADwOLCs1Udp9VFaGjUhIuJ5trhIkJfL5uUKNDlsXg6j11dRA0MftWttZQX86mW379v6\n+jFq9VNoO4DTQLADAIAngGGsnt5WT2+KbGQrYLUlbG42q7Fd0stlyip/Zo61cpSbLcjNFhER\nkZXoYmnZtXKjOCS0Wdfu5B9AUtnT6gPAswfBDgAAngarwt2qcKfwBrZNpqxUkJcr0OQyeTl8\n1k3x/XIeUV0317purqTX0safiYhXuHO+Kk7pV+om33Xm3KU8TZ06dbp37y6VSiutAaBWQbAD\nAIAawLu6WeqGWerefqLOZDCwmlyBJleQl8tqctmiArJaKz2R0ZYItSXCtFQXoteJyszmC0f2\n7/zt1w6vvqZqGs35KKl6y/4BOCUEOwAAqHm8RMIFh3LBobbNory8wV06//pqbw8Xlwef6CoS\ntVKrWqlVdDqFTqeQQMB5efNKP85XZfT0LpW7y1WqJ956AIeBYAcAAA6HEYtTcvK0RlPFYLfg\n2KnoFi3a1QnhMjPE91uMkOME+RrK1wiJXIgURHl6Peej8olqYlWqOKXK6uFFLF6nCU4LwQ4A\nAByOp6dn06ZNFx4//dWLMbaS1KJb0w4dXfveB+Vt2qxbt27LnNm/9u9VnapUUimVaemvP22b\nvFBo8fK5VFxyPDMnh6fgVq17vjqAufPNaZcuXbp69apKpYqOjhaJRI+3awBPFIIdAAA4ovnz\n53fr1u3KreLOoUHZurLlp8+9NnRomzZtiKh+/fqHbmbmlpb5ubnaDr5UWPTW7gNrvpnlZTQU\nnDvjbTGLBZVflmMsFpEmtzFRY6UHEVFGav7MTz0iGliVKl7pVyKRJXz08d49e0LcFZqyclVI\nyOLFixs3bvy0Og3wbyHYAQCAI2rUqNHhw4cXLVq09fJlr4CQOQnj7a84a968eee4l1/ZuO2b\nzh0ivb1O5mne3X2gfZ++she7GIgSf/0t6EbajI7t7FVZrFbh/W+/+oqEdO0KXbtCRBKi9Y3r\nU+MwsUBgsVqXnjo/YUzCpl2/u7m5/fseZWVlnT9/XiaTNWvWzNXV9d9XCHAvBDsAAHBQgYGB\nM2fOrPRdsd9+++0XX3wRl5RkMplsb8WYNGmSbZdCocgtLat48IGMrHf//OuvzZsE+bm7Vv3Y\n0U/p7iK+34faL/UJWXZs88ZjmzfWL/lWHBTC+So5HyXvq+R9H3o2Bs/zU6ZMWbFihb9MqjOZ\nBG7yOXPmdO3a9WHrAagSgh0AADx7FArF7NmzZ86cqdFoVCqVoMISJ3369Hnlh5Ujo6PaBqiJ\nqMRomnYouUO3Hlydelydem+PGLM4pnVc3RAiIp6IoRtanYdM5i687yIpUs5C6WmC9DTbJs8w\nJRIp5+0rq1OP8/G1+qqsCndiGCI6dOjQH3/8YTQaW7Ro0bt3b/bvy4SLFy/e+tOaQ6+/Eq3y\n5YmWnjw7ZsyYvXv3huH9afC4IdgBAMCzSigU+vv731XYunXriZM/jJs5s32Qv6dEciAjs05U\n46lTp9r2tmjRYt3F1NvBjiGL1fr65h2dhryROG5c+fW0+R9OntS6mYdE8oAPZXjeTV9OmTco\n84athBeLeR/l4avXDv0vxctFXKg3TP1pzdKlSzdu3CiRSIhoyZIlM1+IiVb5EhFDNLpZ473p\nN1evXv3ZZ5897iGB2g7BDgAAnM0777wTFxe3Z88enU73n2bN4uLi7PNep02b9uKLL47Yvvu1\nhhF6i+W/J87ckrmNHz+ed3WVRjXNC4t8ed3mpN5d63q4l5stP5+/xEtlQ16KFd8qZLQl9/s4\nxmRisjPby8TtX3jeVjLrxZhjuZpLc2e16t6D81GVFhZGeHtWPKWBj9fFrKzq9KW0tPSbb77Z\nsmWLRqNp1KjR+++/HxcX96gDA84PwQ4AAJxQZGRkZGTkveWhoaG///77zJkzEw6niESiF1/s\nvDAx0T6VYcaMGRP0+kZLVvm5uRaU6xs2bvz991+YIiJMRLs2b94w+6ufencV3Lk2SqUYhmml\nVhER7dpGRNn/N6LMbK54wMXCoqAWz1VZj9VqHTZsWPHF8/9p11rtJjuQkZUwbNiCxYvt80gA\n7oJgBwAAtUv9+vVXrFhR6S6ZTLZo0aJPPvnkypUrfn5+ERER9ufkruXkaI2mu1Ldq5u2z/5g\nkr+AyTp5IlgiZu+T+RgitzvXw/up18ucuwe75RfeV2n1UXI+vpWunLxz587z//vr9IjBXlIJ\nEbVS+3lJJJMnT+7evTuLZZahMgh2AAAAdwgMDAwMDLyrMCAg4Mqt4oorp1wpKv716vWvnu9o\n8PVddObS8Z9/+X1gH/s93yKDwVMiud/FPSHLCnUlpCuh1Iu2Eo7opsFUJBAqo5p4N2ho9VVa\nFR5nzpxpHxRgS3U28RH1xu3al5OTExAQ8Fg7DU4CwQ4AAKBqsbGxM3x839l9YHan9jKRMEtX\nOnLH7r59+/r6+hLRuHHjXli/fsivu95qGS0RCjZeujr/1LldW7dGqXwFBRo2XyMozGc0eay+\n/H71C4hCJeJQIrp8ji6fIyJeKHxLxF+OvmN5ZK3RREQymeyJdhaeXQh2AAAAVZPJZD/88ENC\nQkLgd0tVrq43tboucXGzZs2y7fX09NyyZcu0adO6bdphNpujo6M3bNjQsFkzK5HV/5+Lf4y+\nnM3PExQUsAV5bEE+W5jPGAz3+0TGYlETqUPuuHbIEP365uuq1AtWH1+rjzJVkz/ts8+Sk5Ot\nVmvr1q2nTZvWoEGDJzQC8ExgeJ6v6TY8uuLiYsv93gN9fwqFQiwWFxYWOk7f3dzcjEaj+c5H\na2uQRCJxc3PT6XRGo7Gm23KbWCwWi8V3rVBag1iW9fLyMplMWq22ptvyDy8vr6KioppuxT88\nPDyEQmFBQUFNN+QfCoWivLz8EX5vPCEymUwmk2m1WpPJVNNtuU0ikbAsW15+3wtLT5lAIKh0\ngeKaYrFYzp07V1JSEhAQUOkqdDzPWyyW6r9h9vyRw7Pff299n+7VmZNxL72F05SVu4qE2aVl\nS06dXX8tY9++fSEhIY9QVaV8fHweV1XwdODRSwAAgOoSCoXNmjXr3bv3/dYWZhim+qmOiHSs\n4M+bWXeluvf3Hvyfh4+lUVNOpeaFD6pNKhSEuMt9ZNImSp/vXnrx5QC/L7/8svqfDs4Ht2IB\nAABqTIMGDQzE7Lp24/aayUQXCoqWnDw7cP73+ogIIiKeZ7UlbIGGLdAICgqYwny2MJ+5z1Xn\nnvXrfn727FNrPDggBDsAAIAa4+7uPnXq1Dc+/zyxbctWatXloltfHkkZNnJkhC3VERHDWN09\nrO4eVC/8dgnPsyXFbIEmaf7cdjJJAx8ve20lBqN9TT6onXArFgAAoCaNGjVq9nffbdAZ+u3c\n99+s/Lc+/uTzzz9/0AkMY/XwtIRFaJu27PnLlvxyva242Gicf+xk9+7dn0ajwVHhih0AAEBN\nYhgmPj4+Pj7+YU8cPnz4gQMHmi5b0zu8Hsswv1691qBlq/Hjxz+JRsKzAsEOAADgmcSy7OrV\nq3fs2HHs2DGO42a+/V7Pnj2ZR5pdC04DwQ4AAOAZ1q1bt0GDBjEM41CrHUFNwTN2AAAAAE4C\nwQ4AAADASSDYAQAAADgJBDsAAAAAJ4FgBwAAAOAkEOwAAAAAnASCHQAAAICTQLADAAAAcBII\ndgAAAABOAsEOAAAAwEkg2AEAAAA4CQQ7AAAAACeBYAcAAADgJBDsAAAAAJwEgh0AAACAk0Cw\nAwAAAHASCHYAAAAATgLBDgAAAMBJINgBAAAAOAkEOwAAAAAngWAHAAAA4CQQ7AAAAACcBIId\nAAAAgJNAsAMAAABwEgh2AAAAAE4CwQ4AAADASSDYAQAAADgJBDsAAAAAJ4FgBwAAAOAkEOwA\nAAAAnATD83xNt+FpS0xMTElJ2bp1q5ubW023xUFt3bp13rx5kydPfumll2q6LQ6qqKioX79+\nMTExn3/+eU23xXGNGzfu8uXLe/furemGOK6kpKRly5ZNnz69Xbt2Nd0WB5Wenj58+PCuXbtO\nmjSpptviuAYPHlxWVrZp06aabgjUvNp4xU6v12u12lqYaKvPZDJptVqTyVTTDXFcVqtVq9Xq\n9fqabohDKysr0+l0Nd0Kh2YwGLRarcViqemGOC7bd81gMNR0QxwavmtgVxuDHQAAAIBTQrAD\nAAAAcBLCmm5ADYiOjpbJZEJhbex7NQUGBsbGxvr7+9d0QxyXWCyOjY1t1KhRTTfEobVu3Ro/\nRQ9Wp06d2NhYHx+fmm6I43J1dY2NjW3YsGFNN8ShtWvXDk+GgE1tnDwBAAAA4JRwKxYAAADA\nSSDYAQAAADiJ2vWcWWlp6ZIlS86cOWM2myMiIsaMGaNUKmu6UQ4hKytr7ty5V69e3bx5s70Q\nw1VRUVHRihUrTp8+bTKZ6tatO2zYsPDwcMIoVXDz5s0ff/zx4sWLPM/XqVNnyJAhkZGRhCGq\nzN69e+fPn//RRx+1adOGMER3evvtt9PT0+2bEolk/fr1hFG6044dOzZt2lRYWBgQEDB06NBW\nrVoRhgiIqLY9Yzd9+vTS0tKEhAQXF5ekpKT09PRvv/2WZWv7ZctDhw4tW7asWbNmf/zxR8Vg\nh+GqaMKECWKxePTo0VKpNCkp6eTJk8uWLZNIJBglG4vFMnLkyKZNm7766qssy65bt+6vv/5a\nsWKFVCrFEN2luLj47bffLi8vnzhxoi3YYYgqGj58eN++fW0jQ0Qsy3p5eRFGqYK9e/euWrXq\nrbfeCg4OTk5O3r59+7x582QyGYYIiIj4WiM/P79Xr15paWm2TZ1OFx8ff+rUqZptlSPYu3ev\nRqNJTk7u3bu3vRDDVZFWq/3yyy8zMjJsmxqNpmfPnqmpqRglu+Li4o0bN5aXl9s2MzMze/bs\nmZaWhiG618yZM5cvXz5kyJDk5GQe37V79OvXLyUl5a5CjFJFo0aN2rt3712FGCKwqUVB/sqV\nKyKRqE6dOrZNNze3wMDAy5cv12yrHEGnTp18fX3vKsRwVSSXyz/88MOgoCDbZmFhIcuyPj4+\nGCU7d3f3Pn36SKVSItLpdFu3bg0MDAwKCsIQ3SU5OTktLe3111+3l2CIKjKbzUajMTk5+d13\n3x0xYsTMmTOzsrIIo1RBYWFhbm4uEb399tv9+/efOHHipUuXCEMEf6tFwU6r1crlcoZh7CXu\n7u4lJSU12CRHhuG6H51Ot2DBgvj4eE9PT4zSXaxW6yuvvDJo0KCbN29+8cUXIpEIQ1RRaWnp\nokWLxo8fL5FI7IUYoorKy8s9PDwsFsu4ceMSExNNJtOHH35YVlaGUbIrLCwkoj179nzwwQcr\nVqyIiIj47LPPSkpKMERgU4uCHRFV/ImHKmG47pWZmTlx4sSoqKg33njDVoJRqohl2fnz58+Y\nMUOhUHz00UelpaWEIapg+fLlzZs3j46OvqscQ2Tn7u6+atWq9957Lzw8PDw8/IMPPjAYDEeO\nHCGM0p0GDBgQGBgol8uHDx/OMMyxY8cIQwREVKuCnYeHh1ar5StMFikpKfH09KzBJjkyDNe9\nTp8+nZiY2LNnz7Fjx9p+gWKU7hUYGNi4ceMPPvigpKTkwIEDGCK7U6dOnThxYvjw4XeVY4ge\nQCqV+vr6FhQUYJTsbFNJXF1dbZsCgcDLy+vWrVsYIrCpRcGufv36ZrM5LS3NtqnVam/evNmg\nQYOabZXDwnDd5cKFC19//fWECRN69OhhL8Qo2Z08eXL06NFGo9G2yTCM7a19GCK73bt3l5WV\njRkzZtCgQYMGDSopKZk7d+7MmTMxRBXduHHju+++s1gstk2DwZCfn+/n54dRsvPy8vL09LQ9\nV0dEJpMpPz9fpVJhiMBGMG3atJpuw1MilUpv3Lixf//+iIiI8vLyhQsXurq6Dho0CNeub926\nVVZWduPGjZSUlNjY2PLycpZl5XI5hsvOZDJNnTr15Zdfbt68efnfMEoVyeXyLVu2XLt2LSQk\nRK/Xr1279vLlyyNHjvT19cUQ2TRp0qRrBX/88cewYcP69Onj4eGBIbITCASLFi3KysoKDQ0t\nKSlZvHhxaWnp2LFj8V2zYxiG47gNGzbUrVtXKBT+8MMPGo0mISEBQwQ2tWsdu/Ly8iVLlpw8\neZLjuEaNGo0ZMwaXqYlo5MiRGo3mrpJevXphuOxOnz49ZcqUuwoTEhK6d++OUbK7cePGypUr\nL1y4wDBMcHDw4MGDmzZtSvje3cfQoUPHjRtnW60NQ1TRtWvXVq5caZvjGRERMWrUKJVKRRil\nCqxW65o1a/bs2VNaWhoRETFu3DjbnH0MEVBtC3YAAAAATqwWPWMHAAAA4NwQ7AAAAACcBIId\nAAAAgJNAsAMAAABwEgh2AAAAAE4CwQ4AAADASSDYAQAAADgJBDsAAAAAJ4FgBwAAAOAkEOwA\nwEHFxsaGhobWdCsAAJ4lCHYAAAAATgLBDgAAAMBJINgBQLUcOHCgS5cuCoVCJpM1b958xYoV\n9l0tWrRo27btvn37WrduLZPJvLy8hg8fXlJSYj9g586dHTp0kMvlUqk0KirqP//5D8/z9r27\nd+/u2LGjXC738/N79dVXr169at8lFAqvX7/etWtXuVwul8sHDBhQVFT0dPoLAPAsQrADgKrt\n3bu3c+fOJpMpKSlpy5Ytzz333IgRI+bMmWPb6+LikpaWlpiYOG/evIyMjG+//XbNmjXDhg2z\n7d28eXP37t1dXV3XrFmzbdu2uLi4999/PzEx0bZ39+7dcXFxEolk0aJFX3755fHjxzt06JCb\nm2vby3Fcnz59OnTosGbNmjFjxvzyyy/vv//+0+8+AMAzgwcAqEqzZs3CwsLKysrsJb169ZLL\n5Xq9nuf5559/nogOHjxo3ztixAgiysjI4Hk+MjIyODjYaDTa98bHx4tEooKCAp7nW7ZsWadO\nHbPZbNv1119/icXi+fPn8zzfuXNnItq4caP9xHbt2imVyifbVQCAZxmu2AFAFTQazcmTJ7t3\n786yrOFv3bp10+l0Z8+etR3j6uoaExNjP6VDhw5EdO7cuezs7EuXLnXr1k0sFtv39uzZ02w2\nHz16tLCw8NixY127dhUKhbZdrVu3NhqNb7/9tm1TIpHEx8fbTwwLCysoKHjS/QUAeHYh2AFA\nFbKzs4lo/vz50grGjBlDRJmZmbZjVCoVwzB7FB19AAACiElEQVT2U7y9vYkoLy8vKyuLiAIC\nAipWqFarbdXm5OQQkVKpvN9H31WtSCSyWq2Pr2cAAM5GWNMNAIBnw/Dhw0eNGnVXYVhYWKUH\nWywWImJZ1hbL7kpjPM/b9rIse+9eAAB4ZAh2AFCF4OBgIuI4rk2bNvc7Jicnh+M4gUBg28zL\nyyMilUoVGBhIRLbrdna2zcDAwKCgICK6efNmxb03btyQyWS+vr6PuRsAALUAbsUCQBW8vLxa\nt269efPm4uJie+GqVas++eQT25U5ItLr9b///rt9786dO11cXFq3bu3n5xcVFbVt2zaDwWDf\nu3HjRplM1rZtW7lc3rhx423btul0OtuuS5cuhYaGLly48Kn0DADA2eCKHQBUbdasWV26dOnY\nseP777/v5+d36NChr7/+etCgQfZJD0FBQe++++6NGzfCwsJ27dq1efPmoUOHenp6EtHXX3/d\ns2fP3r17jx8/XiwWb9269bfffps5c6ZCoSCimTNn9urVq0uXLu+8805paek333yjVCoTEhJq\nsrcAAM+ump6WCwDPhkOHDnXp0kUul4tEovDw8FmzZtnXKHn++ecjIyOPHTvWoUMHmUzm6ek5\natQonU5nP/f333+PiYlxdXV1cXFp1qzZihUrKta8ffv2Nm3ayGQypVLZp0+f1NRUW3nnzp1D\nQkIqHmlbReXJ9hMA4FnG8BXWfwcAeAQxMTEFBQWXLl2q6YYAANR2eMYOAAAAwEkg2AEAAAA4\nCQQ7AAAAACeBZ+wAAAAAnASu2AEAAAA4CQQ7AAAAACeBYAcAAADgJBDsAAAAAJwEgh0AAACA\nk0CwAwAAAHASCHYAAAAATgLBDgAAAMBJINgBAAAAOIn/D2sHX5/Xj1U3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- keras_model_sequential() %>%\n",
    "    layer_lstm(units = 32, input_shape = c(96, 14)) %>%\n",
    "    layer_dense(units = 16, activation = 'relu') %>%\n",
    "    layer_dense(units = 1)\n",
    "\n",
    "cat(summary(model))\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_adam(lr = 0.001),\n",
    "    loss = \"mae\"\n",
    ")\n",
    "\n",
    "history <- model %>% fit(\n",
    "    x = x_train_scaled,\n",
    "    y = y_train,\n",
    "    validation_split = 0.2,\n",
    "    epochs = 60,\n",
    "    batch_size = 16,\n",
    "    callbacks=list(Progress$new())\n",
    ")\n",
    "\n",
    "plot(history)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}