{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "collapsed_sections": [
        "tyILQVV21UID"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# CS490/590 DL: Tutorial for RNNs / LSTMs\n",
        "\n",
        "1.   List item\n",
        "2.   List item\n",
        "\n",
        "This notebook provides an introduction to Recurrent Neural Networks.\n",
        "\n",
        "Covered are:\n",
        "* Implementations of RNNs and LSTMs\n",
        "* Applications of RNNs\n",
        "* Common challenges with RNNs and solutions\n",
        "* Extensions of RNN architectures\n",
        "* Examples of real world applications\n",
        "\n",
        "Originally authored by Ian Shi."
      ],
      "metadata": {
        "id": "XG2qIXj66Ewn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Notebook Utils\n",
        "Run these code blocks before starting.\n"
      ],
      "metadata": {
        "id": "RSSZ90T2OdkZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.integrate import odeint\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "np.random.seed(413)\n",
        "torch.manual_seed(413)\n",
        "\n",
        "def plot_extrap(ds, tps):\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    plt.plot(tps[0], ds[0]['obs'], label='Train Region')\n",
        "    plt.plot(tps[1], ds[0]['pred'], label='Unseen Extrapolation Region')\n",
        "\n",
        "    plt.axvline(tps[0][-1])\n",
        "    plt.title(\"Extrapolation Task\")\n",
        "    plt.xlabel('t')\n",
        "    plt.legend(loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "def lv(x, t, params):\n",
        "    dx = params[0] * x[0] - params[1] * x[0] * x[1]\n",
        "    dy = params[2] * x[0] * x[1] - params[3] * x[1]\n",
        "    return dx, dy\n",
        "\n",
        "def lv_solve(tp, initial, params):\n",
        "    return odeint(lv, initial, tp, args=((params,)))\n",
        "\n",
        "def generate_lv_dataset(n, tp, i_range, p_range, noise_var=0.25):\n",
        "    dataset = []\n",
        "\n",
        "    for _ in range(n):\n",
        "        alpha = np.random.uniform(p_range[0][0], p_range[0][1])\n",
        "        beta = np.random.uniform(p_range[1][0], p_range[1][1])\n",
        "        delta = np.random.uniform(p_range[2][0], p_range[2][1])\n",
        "        gamma = np.random.uniform(p_range[3][0], p_range[3][1])\n",
        "\n",
        "        x = np.random.uniform(i_range[0][0], i_range[0][1])\n",
        "        y = np.random.uniform(i_range[1][0], i_range[1][1])\n",
        "\n",
        "        params = (alpha, beta, delta, gamma)\n",
        "        traj = lv_solve(tp, (x, y), params)\n",
        "        \n",
        "        traj += np.random.randn(*traj.shape) * noise_var\n",
        "\n",
        "        dataset.append(traj)\n",
        "    \n",
        "    return dataset\n",
        "\n",
        "def visualize_lv(lv_tp, lv_data):\n",
        "    fig, ax = plt.subplots(2, 2, figsize=(20, 6))\n",
        "\n",
        "    for i in range(4):\n",
        "        r = i // 2\n",
        "        c = i % 2\n",
        "        ax[r][c].plot(lv_tp, lv_data[i][:, 0], label=\"prey\")\n",
        "        ax[r][c].plot(lv_tp, lv_data[i][:, 1], label=\"predator\")\n",
        "\n",
        "        ax[r][c].scatter(lv_tp, lv_data[i][:, 0])\n",
        "        ax[r][c].scatter(lv_tp, lv_data[i][:, 1])\n",
        "        ax[r][c].legend(loc = \"upper left\")\n",
        "    plt.show()\n",
        "\n",
        "IRANGE = [[2, 4], [0.5, 1]]\n",
        "PRANGE = [[0.35, 0.5], [0.15, 0.25], [0.05, 0.1], [0.25, 0.55]]\n"
      ],
      "metadata": {
        "id": "lhAgTgWmOcgM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Code Examples"
      ],
      "metadata": {
        "id": "5-gyskrQa4QV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RNN Implementation\n"
      ],
      "metadata": {
        "id": "Nd9UwY6U9avl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's see how we can implement the RNN from scratch in PyTorch.  \n",
        "\n",
        "Recall the equations (with biases omitted) corresponding to a single RNN update: \n",
        "\n",
        "$$\\begin{align}h_t &= W_{ih} x_t +W_{hh} h_{t-1}\\\\\n",
        "a_t &= tanh(h_t)\\\\\n",
        "o_t &= softmax(W_{ho} a_t) \\end{align}$$\n",
        "\n",
        "The softmax convert outputs to probabilities, which can correspond to class labels / tokens, and is needed in most NLP tasks.  \n",
        "We won't always need it though, so its set as an option. Linear layers can be used to represent all other operations."
      ],
      "metadata": {
        "id": "u3NkNQ9NW4ID"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class OurRNNCell(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim=None, softmax_out=False):\n",
        "        super().__init__()\n",
        "        if output_dim is None:\n",
        "            output_dim = input_dim\n",
        "\n",
        "        # Input processing\n",
        "        self.in_net = nn.Linear(input_dim, hidden_dim)\n",
        "        self.hid_net = nn.Linear(hidden_dim, hidden_dim)\n",
        "\n",
        "        # Output processing\n",
        "        self.activation = nn.Tanh()\n",
        "        self.out_net = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "        # Apply softmax to get categorical output (example, word embeddings)\n",
        "        # Since our later tasks output scalars, ignore.\n",
        "        self.softmax = nn.Softmax()\n",
        "        self.softmax_out = softmax_out\n",
        "\n",
        "    def forward(self, x, prev_h):\n",
        "        \"\"\"\n",
        "            X has shape B x 1 x D\n",
        "            H has shape B x 1 x H\n",
        "\n",
        "            (second dimension is flattened)\n",
        "        \"\"\"\n",
        "        new_h = self.activation(self.in_net(x) + self.hid_net(prev_h))\n",
        "\n",
        "        out = self.out_net(new_h)\n",
        "\n",
        "        if self.softmax_out:\n",
        "            out = self.softmax(out)\n",
        "\n",
        "        return out, new_h"
      ],
      "metadata": {
        "id": "08ohJYNS51vZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The RNN sequentially processes data using the same RNNCell. At each time step, the hidden state and outputs are updates as:\n",
        "\n",
        "$$o_t, h_t = RNNCell(x_t, h_{t-1})$$\n",
        "\n",
        "This process starts at $t=1$, where $h_0$ is randomly initialized, and continues for the length of the sequence.  \n",
        "The variable $o_t$ is the output for that time step. A visual representation of this process is shown below:"
      ],
      "metadata": {
        "id": "g2jy6uP1YZRM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAtIAAAEECAYAAAAF5dMFAAAgAElEQVR4nOzdd1QU1/vH8fcsvSqgYu+9YcMuGjG22LvGbowtGntJMfYYWzQ21Bg1xti7gi12A4oVrFGxFxRRell25/cHP/cbokmUILPg8zrHc2R2dvczu7Ozz965c6+iqkkqQgghhBBCiLei0zqAEEIIIYQQGZEU0kIIIYQQQqSCFNJCCCGEEEKkghTSQgghhBBCpIIU0kIIIYQQQqSCFNJCCCGEEEKkghTSQgghhBBCpIIU0kIIIYQQQqSCFNJCCCGEEEKkghTSQgghhBBCpIIU0kIIIYQQQqSCFNJCCCGEEEKkghTSQgghhBBCpIIU0kIIIYQQQqSCFNJCCCGEEEKkghTSQgghhBBCpIKl1gGEeBsPHz7Cx2cJ27Zt59q1ayQmJmodyazpdDry5s2Lt3d9Bgzoj6dnFa0jpVpSUhI7duxkxYqV+PsH8OzZM60jmT0nJycqVqxAly6d6datK/b29lpHSrVbt26zYMFCfH39uHnzJnq9XutIZs3CwoICBQrQuHEjBg4cQJkypbWO9F64du0PFi5chJ/fHm7fvk1SUpLWkcyapaUlhQsXpmnTJgwaNJCiRYtoHemtKaqapGodQoh/o6oqW7duY/Dgz/HyqkOPHt3x8PDA1tZG62hmzWAwEBJyi61bt7FixUp69erJxInfYG1trXW0t/LkyRN69epDSMgtBg0aiLd3fdzdc6AoitbRzNqLFxGcOHGCxYt9iI2NY/XqVZQtW0brWG/FaDSyYsVKvvjiK1q0aE6XLp0pXbpUhtuH05ter+f69RusX7+B9es3MGLEMEaMGI6FhYXW0TIlg8HAggWLmDbtW9q1a0vnzp0oXrwYVlZWWkcza4mJiVy9eo21a9exZctWJk78hn79PkWnyzgdJqSQFhnC7t2+9OzZm1WrVtC4caMM9SEzFyEht2jdui3e3vWZNWtGhnkNY2Ji+PDDRhQuXJjFixfh5OSodaQMJzExkZkzZ+Pjs4Tffz9Ovnx5tY70xlasWMlXX41nw4Z11KxZQ348pcKFC0G0a9eBPn16M2bMKHkN34G5c+cxb958Nm3aQOXKlbSOkyGdPHmK9u07Mm7cGAYM6K91nDcmhbQwe+Hh4Xh4VGL+/Hm0atVS6zgZ2v37D6hatTqrV6/C27u+1nHeyPjxEzhx4gR+frulFfI/MBqNDB8+klu3brF16+YM8UPqzp27VKtWg40b11OnTm2t42Roly5dxsurHkeOHMpwZyXM3dWr16hd24v9+/dSsWIFreNkaCdPnqJ585b8/vvxDNPNQwppYfZ++GEBu3fvZu9eP62jZArz5s3Hz8+PPXt8tY7yr168eEGZMuXZuXM7lSpV1DpOhhcREUmxYiXYv38vHh7ltY7zr8aN+5JHjx6xcuVPWkfJFMaO/YLQ0FBWrFiudZRMZcCAQdjY2DB37hyto2QK/fsPxM7Oju+/n611lDdi/k0S4r3n6+tL586dtI6RabRq1ZLz5y8QHh6udZR/FRh4Gjc3NypU8NA6SqaQJYszjRs3Yu/evVpHeSN+fnvks5+GOnfuxG+/HSQ+Pl7rKJlGUlIS+/btl/00DXXu3Alf34zTcCaFtDB7ISG3KFmypNYxMo3cuXOhKAphYeY/6kVISAglShTPEN0QMoqSJUsSEnJL6xhv5ObNm/LZT0MlS5bg4cOHUkinoaSkJO7evSv7aRoqWbIEISEhWsd4Y/LtJMyeXq/HxkZG50grL68iNxgMGif5d0lJBukXncZsbKwzzJBcSUlJ8v6nIRsbGwwGA6oqPTrTUvJ+KqNzpBUbG5sMc4wCKaSFEEIIIYRIFSmkhRBCCCGESAUppIUQQgghhEgFKaSFEEIIIYRIBSmkhRBCCCGESAUppIUQQgghhEgFS60DvClVVQkPDyciIhI3NzecnZ1QFEXrWEIIIYQQ4j1l9oW0qqrcvn2HkSNHkZCQQPHixQkODqZcuXJMmjQBR0fHt3484J0U4e/ysYUQQgghhHkx+64dN27cpF69+rRo0ZydO7czZ84sdu7cTmRkJB991JyoqOi3erybN0PYuHHTO8m6adNm7ty5+04eWwghhBBCmBezLqT1ej29e/ehTJnSdO/ezdTSa2try5w5s/njj+tMnjzlrWZpOnv2LLduvZvpcY8cOUp09NsV9kIIITIGVVWJiIjk6NFj7Nixk5CQWzJLoBDvObMupE+c+J3ff/enQ4f2r3SXcHJyxNu7Pj/+uJywsDBUVUVVVYxGo+nA9nLZy78NBgO//LLmlef583p/vc/rbn/dY7948QI/vz3v5HUQ5uXll+mhQ4dZv34D586dzxDTbYu0kZSUxOXLV9i0aTN+fnt4/vy5FFPvAYPBwLJlP1K1anWWLfuRffv2U6tWHebN+0Hef6E5VVUJDQ3F19ePTZs2c/XqNYxGo9ax3gtmXUgfOnQYo9GIh4fHK7cpioKHR3meP3/OqVOBHD58hA8/bISnZzWePHnCtWt/0KTJR1Sq5MmxY8eJi4uje/ee7Nq1m19/XUfLlq1ZuXIVDx8+onnzllSvXpOFCxczevRY2rfvyKef9ufhw0cAzJ07jw8+8KZZsxYA7Nixk/r1G1C1anWePn3KpUuXadLkI0JCQhg8eAgtW7bm4sVL6fpaifSRlJTE/PkLqVWrDn5+e7h79y7t23dkwIBBJCQkaB1PvEOqqpo+62PGjOXBg4f8/PNqKleuyrlz56WYysSMRiNTpkxjxIhRLF3qw88/r2TBgh+YNGkCI0eO5uzZc6Z1/9oQI8S7Fh4ezpAhQ2nYsAmBgae5cuUqH37YiM8/HyaNPOnArC82vHs3ub+xra3ta2/Pli0bAHfu3KFr16506dKZPn36kpRkIF++vEyePInq1WsSExODtbU1U6dO4cKFIJo3b0bfvp/g6OiAg4MD48aNpWHDxuzfv5/Vq3/Gxsaa9u070qFDRw4ePECnTp24evUa+/cfAMDLqw537txl7NhxGAxGChTIz8yZM/D2/pBx48ZSokQJsmfPlj4vkkg3BoOBceO+xMdnCUePHqZixQoA1K5dm1q16uDlVYeuXT82rS8Xn2Yu589foHHjprRt24YFC35Ap9MxaNAAGjRoSL9+/Tlx4hjW1taAvPeZza5du5k8eQqjR4+ibl0v0/Lq1atjMBjYsGEjlStXIiwsjLlzf+D+/ftUrFiRfv36/u33lxBp4cmTJzRt2ozo6BgOHjxA7ty5AChRojgdO3amdu1adOzYAYD4+HgePXoMgJWVFXny5DYdo548eUJMTCyQfNzKly8vFhYWGmxRxmPWLdJ2dnYAxMXFvfb2yMgoAOzt7XF2dqJSpUqmncLe3p5KlSqiKAqqqmJhYUGBAvmxtLTEycmRAgXy4+bmhq2tLVWreuLk5ETr1q1wdnbCxsaGL74Yh79/AAcPHiJXrpyUKVPG9LxZs2Y1FVEAjo6O5M6dG4DcuXNToEB+7O3t38lrIrSzefMW5sz5nrFjx1Chwv/OkhQqVBBFUdiyZatpmMbz5y8wYMAgjh8/oWFikVYiI6Po2bM3tra2TJ/+LTpd8qHT0tKSAgUKcObMWe7evUd0dDSTJ0+lY8fOeHnVw8dnCXq9XuP04r8wGo2MHz8Ba2trPv20b4rbIiMjAXj06BExMTG0bt2Wpk2bMGfOLH799Vd69uwtp9fFO2M0Ghk8+HOCgoJZtmyJqYgGTDXLpk2bTT/sHzx4yPz5C6hWrQalSpXh1q3bpvX37z9A27btaNq0GT/8MF/OsL4Fsy6ky5cvB8C9e/deuS15WLzbKIpC+fLlgdS3/uh0OnQ6XYr7FylSGHt7e65evQqAlVXKxvu/PpeczsvcEhISmDJlGpaWlvTo0T3F+//4cShGo5G4uDiSkpKYMmUaq1f/wsqVq+RglEmsX7+eoKAgOnfuhLOzk2m5qqo8evQIVVVJSEjg66+/oUaN6qxb9yuTJ09i+PCR/Pjjcg2Ti//q6tVrBAUFUa5cWfLnz5fituDgYADy5s1LaOgTTp0K5PTpM7i6ulKxYkX27dv/1iNLCfGmzp07z+bNW6hQwYOaNWukuO3lGf34+HjTsiJFCjNq1Ei8vOqgqipLliw13fbxx11o1aoVq1atYM6cWdIY+BbMupBu2LAhdnZ27Nu3/5XbjEYjR48epXz58pQuXeq19/+n06uJiYksXbrsb587JiY2RUv4vxXpiqKY1nn06DFbtmz9x/VFxhIScovLly9TqlQp8uTJneK2U6dOAVC6dGmsrKyYM2cW48d/jaWlWfecEm9h925fALy966dYHhsbS1BQME5OTuTOnYsDB35j8uQp6HQ66tb1oly5sqxdu06LyCKNXL58GVVV8fDwMJ2JgOTvl99+OwhA48aNyJ8/Hz//vJJ27dqiqioPHjygQIECODo6aBVdZHL79x/AYDDg7e39SjeMl/tmqVKlUtQv/v7+tGnTmjZtWvPjj8uJiEg+q6KqKleuXDE1YIo3Z9aFdKFCBenTpzfr1q3n/v0HKW47cuQo586dZ+LEb17pg/byVOrt23dSjOIByQWvwZC87OrVaynul5iYaPr/wYMHURSFevXqvTbbX4fQe3mATUpKIioqynShosgcQkNDMRgMVKjgkeKgZDQa2bZtO9bW1vTs2UPDhOJdun//Pra2tpQsWTLFcn//AEJDQ+ncuRNZs2blq6++YNSokabbrayspGtHBufklHwGokiRwimW3717j/37D1C3rhe1a9fC0tKSDh3aY21txebNWzhz5izz5n0v/UzFOxMWFgbwSvGbkJCAr68ftra2DBjQ37RcVVWOHTtO7dq1GTJkMJGRkWzcuBGA58+f4+DgIH36U8Gsm8wURWHGjOno9XpatWrN3LnfU7x4MQ4dOsyoUWPw8VlEixbNTeuXLFmCEiVKsH//fjw9PVm5chUODg7MmfM9OXLkoHLlStSpU5tdu3ZRvnw5ChdOeWBcuXIVhQoVIjExkQkTJjF9+jQ8PJK7jdSrV48pU6YREHCSxMREtm3bTlxcHLNnz2H48GHkzp2LsmXLsG7dehwdHWnZskW6vlbi3SpcuDC2trY4OzunWH7w4CH27dvPmDGjKVu2zCv3k+4+mUPp0qX544/r2Nn970smLi6OCRMmkidPHiZPnoiiKKaLeiD5LEZQUDCLFi3QIrJII56eVciRI0eKxpzExEQ++2ww9vb2LF26JEVL9fbtOzh1KpAaNaqTJ08eLSKL98TLH3dZsmRJsXz9+g1cu3YNH59FFCxYwLTcaDTy8OFDcuXKSZ48ualXry4//LCA7t27ce7ceSpWrJiu+TMLsy6kIfmCw4UL53PkyFHWrPmVhIQEChYsyN69fpQsWSJF66CNjQ379u1h+/YdBAQEMHbsaPLkyY2joyM2NjYoisL06dPYuHETDx8+pE+fXqb7qqpK164f8/DhQ54+fcr69WupVq2q6fFLlCjO9u1bOXLkCLly5WL27FlUrlyZ7NmzYWGhw9ramh07trNly1aKFCn82qJKZFx58+ahX79P2bZtO0OGDCZXrpwcOnSYwYM/Z9iwoYwf/5WM0JCJjRw5gl27drNq1c8MGNCfJ0+e8tVXXxMdHYOv7y6yZ8+eYv2EhAQ++2wwAwb0p3PnThqlFmnB1dWVuXPnMGTIUAoVKkjOnDlZtuxHYmJiOHLkEMWKFTWtqygKffr0pnfvXowaNYYPP2zE778fI2fOnBpugcisOnXqyA8/LGDRosVUreqJjY0ta9as4euvv2Hq1Cn07t0rxffSo0ePyZkzJ5aWliiKwqBBA2ndui0HDvzGyZOnaNeurYZbk3GZfSENYGFhQf36H1C//gf/um6+fHn57LOBpr//fJoVkkfz6NGje4plCQkJJCUlYWdnR/fu3V77uIqiULlyJSpXrmRaNm7cmBTr5MmTm8GDB/1rRpHx6HQ6pk+fRr58eenVqzdWVlbkypWLn39eSe3ataSIzuTKlSvLnj2+TJv2LTt37sLGxoYmTRqzcOECsmRJeZbCaDQyYcIkGjTwZujQz0lMTDSNQCQypk6dOlKsWDH27NnLtWt/MHLkCLy966c4Df7kyRP8/QP46KOmWFpa4ulZhdmz57Bv3/6//V4R4r9wcXHht9/2M3XqNNq2bY+trS0FChRgzx5f06hlf3bw4EE++KCeaXmTJo0pXrw4Pj5LsLe3T/GjULy5DFFIv0txcXH4+e0hLi6OwMDT1K5dm6JFi2gdS5ghW1tbRowYzvDhw1BVNcUFpiJzUxSFqlU92bp18z++96qqsmiRD6VKlaRbt64YDAY++2wIy5f//YXNwvwpikKVKpWpUqXy366zaJEPkyZNxt//BNWqVTVdc/NyvgMh3oXcuXOxcOF80zCLf+5m9GeqqnL06DGmTZtiWmZjY0P//p8yZsw4OnfuhI2NTbpkzmzM+mLD9BAXF8e1a38wadIEcufOzf3797WOJMycoiivDJf40stp6mNjYzEajURGRmIwGKSvdCbxb+/9zz+vZvnynzh+/ASffPIpHTp0kpnF3hOVKlWkePHiFClSGFVV2bZtOxUqVEgxgYsQ78rLYXxfx2AwcOnSZS5evIiNjW2K76OePXvg7OyMl1cdaRhKpfe+RdrV1ZUvvhirdQyRiYwfP4H79+/TqFFDNm/ews6du5g7dw5Zs2bVOpp4h1RVZd++/Tg42BMcHIxOp8PKyoomTRprHU2kg2bNPuLx48d8+mnyKAkuLi7s2rUDBwcZ/k5oa9Wqn9m7dx85c+bkk0/64uOzCDc3NyB5P/3ii7HUr1//Xx5F/J33vpAWIi0pisKUKZO0jiE0oNPpWLNmtdYxhEZ0Oh2fftqXvn0/wWAwYGFhIS18wiz07t2L3r17/e3tw4cPS8c0mY8U0kIIIUQaURRFJmMS4j3y3veRFkIIIYQQIjWkkBZCCCGEECIVpJAWQgghhBAiFaSQFhmCDB+XdjLaa5nR8pq7jPZ6ZrS85kxey3dHXtu0k9FeSymkhdlzcnIiIiJC6xiZRmxsLKqqZojB9x0dHYiMjNQ6RqYSERGJo6Oj1jHeiKOjo7z/aSgiIgI7OzssLORiyLSiKAr29vayn6ahjHSMAimkRQbg4VEef/8ArWNkGlevXsPGxoZcuXJqHeVflStXjqCgYOLi4rSOkmn4+/tToYKH1jHeSIUKHvj7+2sdI9MICDhJsWLFsLeXKevTipWVFWXLlpHvqDTk7++Ph0d5rWO8MSmkhdnr1KkjP/20gtjYWK2jZHiqqrJ4sQ/NmzfDzs78v0zLli2Dq6srW7Zs1TpKphAUFMyFC0E0btxI6yhvpFOnjixZstQ03bZIPYPBwMKFi+jQob0Mz5eGdDodHTq0Z/FiH5KSkrSOk+Hp9Xp8fJbQuXNnraO8MSmkhdlr2PBDChUqyLhxX8p0y/+Bqqrs3LkLX18/Ro0aqXWcN2Jtbc3Eid8wduwX3Lp1W+s4GVpkZBSfftqPIUMGkzOn+Z+NAOjcuRN6vZ6ZM2dnuH6T5kRVVVauXEVw8EUGDRqgdZxMp2/fvty5c5clS5bJfvofqKrKDz/MJzz8OT16dNM6zhuzmDBh/AStQwjxTywsLKhfvz5ffvkVFy9epEaN6jLt7ltKTExk2bIfGTJkKCtWLMfTs4rWkd5Y8eLFeP78OcOHj8DDozwFChSQGePe0sWLl+jQoSPu7u788MNcLCwstI70RqysrKhduzaffz6MR48eUb169QzRt9+cxMbGMnPmbKZO/ZaNG9dTrFgxrSNlOjY2NlSvXo3+/QcSHR1N1aqeWFtbax0rQ4mOjmbChEksXryEzZs3kj9/fq0jvTFFVZPk55PIEEJDQxk8+HOOHj1G48aNKF++HLa2tlrHMmsGg5GQkBB8ff1wcLBn8eJFVK3qmeEKUYPBwOrVvzB+/ATy58+Ht7c3OXLkIINtRrp78SKC33//ncDA0/Tv349x48ZkyM/M7dt3GDhwEEFBwTRt2oTSpUtJofIvkpIMXL9+nV27dpMvX14WLVpI2bJltI6VqV29eo2BAwdx48ZNmjdvRvHixbGykm40/yQxMZGrV6+xe7cvJUuWYPHiRRQtWkTrWG9FCmmRoRiNRoKDL+Lr68e1a9dISEjQOpLJs2fPePjwEeXKldU6iomFhQV58+alfv0PqFvXK8O35j1//hw/vz34+wfw7NkzszqNevz4CapXr2ZW/U+dnZ2pWLECzZo1I2/ePFrH+U8MBgNnzpzFz28PN2/eRK/Xax3J5P79+8TFxZlVa6+lpSUFChSgUaOGVK9eDSsrK60jvReSkpI4efIUe/fu4/bt22a1n966dQudTkeBAgW0jmJiZWVFoUKFaNy4EZ6eVczq+PmmpJAWIo2MGjWG/fsPEBgYIF9a75mQkFuUL1+B9evX8tFHTbWOI9KRqqp0796T0NBQ/Px2Z5huM+L9YjQaadWqDTY2NqxfvxadTi6RSyvySgqRBh48eMjSpcu4cOGCjDDxHpoxYyYxMTFMnTpNLoh9z1y9eo21a9fx228HOXbsuNZxhHitU6cC2b3bl61btxEUFKx1nExFCmkh0sD33881Dcg/deq3MgzSe+T27TusWLESSB6n189vj8aJRHpRVZVp077FYDBgNBqZOnUaRqNR61hCpPDnfdNgMDBlylTZT9OQFNJC/EePHz9m6dJlpr+Dg4PZunWbholEelFVle++m2Ea51hVVSmm3iOXL19h48ZNpr8PHTrM0aPHNEwkxKtOnjzFnj17TX/v2LGT8+cvaJgoc5FCWoj/QFVV5syZS1RUVIrl3347XSaReA/cvn2Hn39enWJZQMBJ9u7dp1EikV5e/oj68wXPBoOBqVOnmdVFsOL9pqoq3347PcVZUr1ez7ffTpf9NI1IIS3Ef/D4cSg+PkteWX7u3Hm2b9+hQSKRnmbMmPnaGTenTJkq3XsyucuXr7Bhw8ZXlktfaWFOTp48ha+v3yvLt2/fwblz5zVIlPlIIS3EfzBnzvem1uisWbPi6OhoGr7n22+nm9XwfCJt3bwZwurVvwDJw8zpdDpcXFwA+P13f/bvP6BlPPGOTZ/+nenznTVrVtM+oKqq9EEVZuPlBdA6nQ5nZ2eyZMmCoijo9XqmTftW63iZghTSQqTSgwcPWb78Jzw8PNiwYR1Tp06mQQNv/P1P0LJlCy5evMSOHTu1jinekdmz52BhYcHnnw/h4sUgnJycOHBgH9999y25cuVi2rRvzWoMWZF2Ll++wubNW6hZswa7du3g88+H0KVLZw4ePMCHHzbg0KHDnDjxu9YxxXsuMPA0e/bs5YMP6rFv3x569+5F//798PPbTZ06tdm1a7f0lU4DUkgLkUqHDx9m8eKFnDrlT/v27bCxsUGn01GlSmW2bt3MsWNHuHLlqgyHlgmFhobi5uZGcPAF5s6dQ758eVEUBRcXF0aPHsWlS0E0b96M4OCLWkcV78CRI0fYuHE9R44c4qOPmmJpaYmFhQV163qxZ48v+/fvJSAgQFqlhWZUVeXYsWOm/dHbuz4WFhZYWlrSqFFDDh36je3bt3L06DHpK/0fyYQsQqSR5ct/wtfXj82bX+03KTI/F5dsnD17mkKFCmodRaSzKVOm8fjxYxYs+EHrKEL8rZEjR2Nra8uUKZO0jpKpSIu0EEIIIYQQqSCFtBBCCCGEEKkghbQQQgghhBCpYKl1ACHeF/Hx8ezbtz/FxYeNGzfi5s0Q/vjjDxRFwWAwkDNnTmrXrsXDh48ICAhAURQAPvigHlmzZtUqvviPzp+/wK1bt0x/FylShKJFi7Bv335UVUVVVYxGIx9++CHOzk7s33+A6OhoFEUhb968eHpW0TC9+C+io6NfGQ6xVauWnDlzlrt375o++wULFqRKlcqEhNzi/PnzKIqCoig0bPgh9vb2GqUX74vnz59z+PAR099WVlZ89FFTAgJO8ujRIxRFISkpieLFi+PhUZ5r1/7g0qVLKIqCTqejSZPGWFtba7gF2pAWaSHSSUJCInfv3qN79558/fU3PH36FICYmBj8/PbQpk07Dh48ZLqCOjY2lp07dzFy5Ghu3ryJjY2NlvHFf/Ts2TOWLl1Gu3YdOHUqEL1ej8Fg5PHjx/TrN4Bx474kIiICS0sLVFXl3r17DBo0mN27fbWOLv6jmJgYbty4QceOnZkxYybh4eGoqkpUVBTr1q2nTZt2nD59xrR+dHQ0a9b8yvjxE3j06JFpbHoh3qXo6BguX75CmzbtmD9/AS9evAAgMjKSn35aQfv2Hbl48ZKpcScyMpIff1zOlClTCQ8PR6d7P0vK93OrhdBAlizO9O3bh+zZs1OkSGH69v0EOzs7qlWrSosWzQFo3rwZderUBqBIkcK4u7uzadMGRo4cgZ2dnZbxxX/k7V2fRo0aAtC9ezcqV66Ek5Mjn3zSh2zZspEvX1569+6Fg4MDOp2OGjVq0KVLZ5Yu9ZHW6AzO3d2dAQP6Y2dnR/ny5enTpzc6nY4PPqhHgwbeAHTo0J4qVSoDULZsGbJly8bu3TsZMKD/e9nKJ9Jfvnx5GTRoADqdDk9PT7p2/RhFUWjUqCFeXnVQFIWPP+5C+fLlAKhQwYOcOXPi57ebXr16vrc/+KSQFiIdWVlZ4eTkZPqlD2AwGEwz5L2cJRHg+vUbKIpChQoe6Z5TvBsuLi4YjcYU7/Nvvx3k3r17REdHm85GGI1GFi/2YcSI4e9tK09mY2dnh52dXYrPfkJCAmvXrgOSW6FfCgw8TeHChciXL2+65xTvN0dHR6ysrIiIiDAti4mJYePGTRgMBuLj403LDx06TNWqnuTIkUOLqGZDjtBCpCNFUXB3z8GLFxGmyRr8/PZQuHBhFEXh2bNnQPJg+kuWLGXo0CGm02gi48uZMyeQ3BcRIC4ujtWrf6Fevbo8f/7C1H/+zJmzFC1alFy5cmqWVaQtnU5HjqsNQrsAACAASURBVBw5CA8PNy375Zc1VKtWDcD02dfr9axe/QuDBg2Uz75IdxYWFin2U1VV8fFZQv369VFV1bSfJiQksH37Dnr06K5lXLMghbQQ6UhRFLJly8bz588xGo3ExsayZs2vDBv2OYqi8ORJcr/pw4ePUKhQIdMv/ZcHsKdPn8osVBlYtmzZgP8VTStXrqJt2zbkzp2b58+fo9frSUxMZPFiHz799BNTIRUXF8fjx49TtAaJjCX5s+9mKlDCw8M5fPgI3bt3A+DJkycAbNmyFS+vOjg6OgLJZyfCwsJ49uyZfPbFO6coCm5ubqZj1JMnTwgOvkjr1q1MfwOsWvUzLVu2MHU5NBgMPH361NRI8D6RQlqIdObm5kZMTAxGo5GVK1fRs2cPsmXLhrW1NU+ePCE+Pp5ffllD3759TIVUTEwMQ4cOZ9SoMRqnF/+Fq6sriqIQFRVFWFgY586dp3nzZri5uREREYFen8SGDRtp0aJ5ij7xvr5+VK1ag2fPwv/h0YW5c3NzIyoquQvP/PkLGTZsKNmyuWFhYcGTJ0958eIFhw4dNhUtkFy49O3bjxkzZmmYXLxPsmVzIyoqCqPRyMyZs/nyyy9wc3NDp9Px9GkYT58+5cKFILy965vuc+/efXr06IWPz1INk2tDCmkh0ln27NmJiYkhNDT5l36DBt4oioK9vT3h4eH8+uta2rRpnWKUDgcHB4oXL07Vqp5yujcDc3Cwx87OjsjISBYsWMSgQQOxtLTEwcGBxMRE7t27x/HjJ2jevFmK+70c+tDd/f3ui5jRZcuWjaioKG7fvkNERAQVKnhgaWmJjY0N4eHhLFmylF69emJlZWW6j7u7O7ly5aJ69Wry2RfpInk/jSY4+CL29vYUK1YUa2trLC0tefbsGfPmzeezzwZhYWFhuk+BAvlxcXGhevVqGibXhhTSQqQzFxcXEhIS+O67GQwZMhgLCwsURcHV1ZWQkBBOnQqkSZPGKe6jqiqHDx+mUKFCrF27znR6DSApKYkTJ35n585dREVF//XphBmxsbHF3t6eM2fOEhcXZ7r63c3NFYCpU6fRv3+/FF9QAAEBJylfvhz79u3n6NFjKU7x37t3n+3bd3Dp0uX02xCRKi4uLkRHRzN16jTGjBmFTqfD2toaR0dHgoKCePLkKVWreqa4j8FgIDAwEDc3N379dS0REZGm2xITEzl8+Ah+fnuIi4tL780RmZSLiwsvXrzghx/mM3ToECC5EcDW1paTJ08CULJkiRT30ev1BAdfxMrKirVr16XYH+Pj4zlw4Df27dtPYmJi+m1IOpFCWoh05uSU3PfR3d3ddDBKLqRdCA6+yMCBA14ZqSEuLo6goGCeP39O9uzZ6du3HwAvXrygY8fOREZGkjdvXjp27JRiwhdhXqytrbCxsSEg4CRjxowytTC6ubkBkCVLFjw8yr9yvyNHjhIZGUnx4sWZNGkyV65cBWD58p+YMWMmnp6eTJ48hZMnT6Xfxoi35uTkRExMDFWqVMbd3R0Aa2trnJycOHPmLMOGDX2l1fnhw0fcu3efiIgIFEVh9Ojk7l2PHz+mdeu2WFtbY2FhQb9+A9J9e0Tm5OTkxNOnT/H2ro+ra/KPfHv75EL6zJmzDB/+6n56/foNwsPDiYmJISIigokTJwNw69Zt2rRpZ+q+NmLEqHTfnndNCmkh0pmrqyu5cuViyJDPUhyMXFxcGDhwAOXKlX3lPqdOBeLlVYfOnZML5ZcHt5kzZ1O8eHEaN25EsWJFuXAhSFqmzNjL1sfRo0fi4uJiWu7i4oK7uzvffDP+lS8og8HAsWPHmDVrJoULFyI6OhonJyfu33/AlCnTmDRpArlz5yJHjhycPXs2vTdJvAU3N1dKlSpFz549TMssLS1xcnLiq6++IE+e3K/c58SJE7Rt24ZmzT4iMTERV1dXVFXlyy+/plGjRtSoUZ1ixYoREHBSLkYUacLV1QVPzyq0a9fWtMzGxgZHR0emTJlk+v75s6NHj9KzZw8aNvyQ+Ph4XFyyoqoqo0aNplOnjlSsWIGiRYty8mTm20+lkBYinVWsWJE1a1aTJUuWFMuHDBnMl1+Oe20/yAMHfjNN1LJr124aNPDm/v0HHD9+nIYNP0RRFPz9AyhZsoRM3GLGdDodkydPpFevnine57Jly7BmzWpy5nR/5T5PnjwlLOwZRYoU5vr1G1hYWGBpacnJkycpU6Y0WbNmRa/X4+/vT5UqMnGLOfPy8mLlyp+wtbU1LVMUha+//vJvh7t73Wf/3r37BAScNF1fcfz4cSpXrpRu2yEyt0aNGrFkiU+KiYB0Oh3fffetaZSZvzpw4Ddq166Fqqr4+vrh7e3N7dt3OH/+Al5eXgAcP34iUx6j3s9paITQUL58eV870ULTpk1eu77RaOTkyZPMmPEdAAEBATRv3ozDhw9TuHBh9Ho9ERGR+PgsYfbsWa/0rxXmQ1EU2rdv98rynDlzmsaY/quzZ89StaonFhYWHDt2DG9vbxYuXETbtm2A5BbrdevW4+XlZZoZT5inv/YrheR9ok2b1q9d32AwcP78eb7++itUVSU4+CIxMTFcvnyZAgXyo9frCQ0NZePGTXz//Ry5GFGkiddNAqYoSooW6j8zGAxcunSZKlUqExMTy4MHD7lz5w4JCQnkz58PvV7PvXv32bt3L0uXLsl0+6kU0kKYOVVV6dChvenCtC+//IKwsDA+/rgLbdu2YeXKVdy5c4e5c78nb948GqcVaS1btmwMGTIYgCZNmrBx40ZatmxJwYIFGDt2DAsXLqZ48WLMmjUj031Bve8MBgP9+/cjX768ppZrVVVp1KghtWrV4qefVuDo6MDy5ctMY5QLkd70ej0jRw43dTsaOXI4Dg4O1KxZgw0b1vHLL2twdnZmzZpfcHZ20jpumlNUNSlzdVYRQiPLl/+Er68fmzdv1DqK0ICLSzbOnj1NoUIFtY4i0tmUKdN4/PgxCxb8oHUUIf7WyJGjsbW1ZcqUSVpHyVSkj7QQQgghhBCpIIW0EEIIIYQQqSCFtBBCCCGEEKkghbQQQgghhBCpIIW0EEIIIYQQqSCFtBBCCCGEEKkghbQQQgghhBCpIIW0EEIIIYQQqSCFtBBCCCGEEKkghbQQQgghhBCpIIW0EEIIIYQQqSCFtBBCCCGEEKkghbQQQgghhBCpoKhqkqp1CCHeVEJCAtu372D79h1cu3aNhIRErSOZPH/+nKioKPLnz691FBOdTke+fHmpX/8DunXrSvbs2bWOlGqqqnL16jV+/nk1AQEBhIU90zpSCpcvX6Zo0aJYW1trHcXEycmRihUr0rlzJ2rWrIFOl3HbTmJjY9mwYSO7d/ty8+ZN9PokrSOZPHnyhKSkJHLnzq11FBNLS0sKFMhP48aN6Ny5M1myOGsd6b0QFRXNunXr8PX14/btOyQlmc9++vjxYxRFwd3dXesoJpaWlhQuXIimTZvQsWMHHB0dtY701qSQFhnG+fMX6NGjFzqdjo8/7kz58uWxtbVFURSto5mtpKQkbt26xbZt2wkIOMmcObP4+OMuGe41S0hI4Msvv2bFipW0a9eWBg28cXd3z3DbkZ5UVSUiIoITJ35n9epfqFGjOosXL8yQP6aOHTtOr159cHfPQadOnShVqiQ2NjZaxzJrer2e69evs3HjJq5d+4NFixbQvHkzrWNlanv37uPTT/tTqFBBOnbsQPHixc3qh7U5SkxM5OrVa6xfv5579+6zfPky6tf/QOtYb0UKaZEhnD17jkaNmvDFF2MZNGigHJzektFo5Nix43Tp0pWxY8cwePAgrSO9Mb1eT9eu3bl//z5r1vxCgQL5pYB+S+Hh4QwdOpygoGCOHDmUoVon9+8/QJcuXZk58zu6deuKhYWF1pEyFKPRyI4dO+nXbwDz5n1Pp04dtY6UKW3duo1+/QawcOF82rZtk6HP/mjBYDCwbt16hg4dzsqVP/HRR021jvTGpJAWZi82NpYaNWrTu3cvhgz5TIqo/+Ds2XM0afIRe/f6UaGCh9Zx3oiPzxIWLfLhyJGDuLi4aB0nw0pMTKRLl65ky5aNxYsXZojP0bNnz6hatQaTJ0+kS5fOWsfJ0PbvP8DHH3fj7NnT5M2bR+s4mcqjR4+pUqUqS5Ysplmzj7SOk6Ft3ryFYcNGcPr0SXLkyKF1nDcihbQwe7/8sobvv5/LyZP+WFpaah0nw/vii6+4ffs2v/76y9+uc/z4Ca5fv24qtqpVq0bWrFnZs2ePaZ2sWbPSqlVLQkND8fNLXm5hYUGbNq3TrJ9bbGws5ctXZNGiBTRs+GGaPOb77N69+3h4VOT06VMULlzoteuEh4ezY8dO099OTk60bt2K3bt9CQsLQ1EUVFWlQYMG5M2bh127dpuWly5dmqpVPdMs78yZszh8+Ag7dmyTluj/SFVV+vTpi4uLC7Nnz0zX5w4KCubMmTOm40mpUqUoXbo0W7ZswWg0oigKlpaWdOrUkYSEBDZt2ozRaASgSZPG5MyZM13zvq0vv/yaW7du8csvP0tL9H9kNBpp374jFSpU4Ouvv9Q6zhuRd1yYvS1bttKrVy8potNI9+7dOHz4CFFR0X+7jpWVFVu2bKV370+Ij4/H0dERRYG4uDh69/4EP789uLq6AmA0qgQGnmbevPmoqpqm3W7OnTuPoigZrs+cucqXLy/16tXF19fvH9eLjY3l00/7s3XrNpydnVEUBRsbG5YuXcbQocOxtrbGwcEeVVUxGAx89dV4rl37A2fntO0ysnXrNnr37iVFdBpQFIU+fXqzc+cuEhPT9yJtS0tL/P0D6NWrD3fu3MXZ2RlVVbGwsODzz4fx008rcHV1RafTYTAYuXv3HhMmTCImJhZ7e4d0zfq2DAYD27fvoE+f3lJEpwGdTkfv3r3YsmWr1lHemLzrwuxduXKVihUraB0j0yhUqCBGo5HQ0NC/XadatarUqlULRVHo1Kkj+fLlJWfOnLRt2waA6tWrUbeuF4qikDt3LrJkycLWrZvp0aN7mhbSV65coWzZMvIjKg1VqFCBK1eu/O3trq6udOjQHjs7OypWrMiHHzZAURQ+/LABRYsWxdHRkU6dOpoKnxIlSvDpp32ZNm0KJUuWSNOsly9foUIF+eynlQoVPLh9+zZxcfHp+rylS5eiUaOGADRv3oxSpUri7OxE27ZtsLOzo0yZMjRt2gSdToezsxMuLi6sWbOaQYMG4OzslK5Z39bLizozSle5jKBixYr/eIwyN1JIC7MXHx+Pvb291jEyDWtraxRFQa/X/+N6rq4u/z/yQySQfGp44cLF2NnZERr6xLTejRs3sbe3p2DBAmmeNSEhUd77NGZvb0dCQsI/ruPg4ICNjQ0vXrwwLbt16zZHjhwlLCzMdNrdaDSybNmP7+zahYSEBHn/05C9vT16vR6j0ZDuz/3yDNaf96n16zcQFxeX4kf9s2fPCAkJoWbNGhmiHz8kX38g+2nasbOz/ddjlDmRQlqI98ybfjnlzJkTVVVNX3x37twlMjISV1dXnj9/DiQXUgsXLmLQoAEZ5kvvffcm75ONjQ0uLi6m91lVVRYtWkyLFs1JSEgwtWgGBp6mSJEichFoBqHlZzR79uzodDrTPhUdHc3x4ycoU6Z0iv1s+fKf6N27l3STeI9ltO8S2VOFEK+VLVs2IHmiGVVVmTfvB4YMGYy9vT2Rkcmt1KdOBVKoUCFTIWU0Gnn48BEPHz5K8ViqqvLo0WPu33+QvhshUkVRFNzcXHn2LHnSm/PnL5A1a1ZKly4FQGRkJElJSaxa9TM9enQz3S8qKpqQkFuv9MGNiYnh5s0Q4uPTt0uBMB9Zs2bFysqKZ8+eoaoqixf70KdPb7JkyWI6nty9e48XLyIoU6Y0kHzcePLkCXfv3kvxWKqqEhYWxu3bd1BVGS9BaEsKaSHEa708FRsZGcm5c+dxdXWlYMECZM2ahadPn5KYmMjKlavo06eXqQUhLCyMfv36s2DBwhSPFR4ezmefDWbOnO/TfTvE21MUBVdXV6KiokxFT//+n5r2iadPn7Jr127q1vVKMULLli1bqFWrDgbD/7oOqKqKr68ftWrVITY2Nt23RZgHJycnrKysiIqK4unTp4SE3KJataq4urry9GkYqqqyYMFCBg7839mtuLg4vvpqPCNGjEzxWImJiUyYMInBg4dosSlCpCBX8AghXsvBIflq+YiICPbvP8CkSRP+v6XSjdDQUHbu3IW3d33TepB8+jZHjhzUqVM7xWO5ubmRJ08eatWqma7bIFLPwcGB+/cfcPDgIWrUqI6bm5vpLMXDhw/x89vD/PnzUtzH09OTsmXLYGtra1qmKAqVK1emWLGiZM2aNV23QZgPW1sbdDodERGRzJ+/kFGjRqLT6ciWLRvPnz/n9Okz5MiRnTx5/jfNur29PUWKFMbOLmX/YxsbG0qUKEGePHkyXDcAkflIi7QQ4rXs7GyxsrJi69ZteHpWMXXfyJYtG+Hhzzl8+AitWrVMcZ+kpCQCAk5ia2vLypWriImJAZJbJU+cOIG9vT3Ll/9EZGRUum+PeDtZs2YlNDSU7dt30KVLZxRFMU0vvmTJUnr27PHKCC1HjhyhVKlSrF+/gTNnzppOux8/fpxSpUqxefMWfv/dP8Odjo+IiOT69RsEBJzEz2+P6WJL8easrKxwcHAgMDAQOzs70zjm2bNnJz4+nhUrVjJgQP8UhbGqqhw5cpTs2bOxfPlPhIWF/Wn5EfLkyc1PP63gwYOHmmyTECCFtBBvLDIyinPnzrNnz142b96S6ft72trakjVrVh48eGCaVvjlKf979+7RqlVLrKysUtwnuX/0Q3Q6HXq9nlmz5gAQGhrK7dt3UBQFW1tbJk+eku7b81+oqsr16zc4evQYW7du4+LFS1pHeufc3d0JDQ2lefNm2NjYAP/r7qMoymsnXjl8+AgJCQnUrVuX/v0HEB0dg6qqpuU1atTg88+HZrgfUgsWLKRyZU9q1KjFwIGfaR0nQ1IUBXd3d4KDLzJgQD/TcldXV5KSkqhYscIrEznFxsZy/vwFVFWlYMGCDB06HFVViYuL49SpQFRVpUSJEgwZ8nl6b47ZePToMQEBJ9m1azfbt++QH3kakEJaiDe0ZcsW2rXrQJMmHzFw4Gfo9UlaR3qnrKyscHR0ZOLECSlaHl1dXWnfvh316tV95T7Hjx+nd+9e1K3rRVxcvKnw8vcPoF27tjRq1JD4+Hjc3NzSbTvSQmRkFN26dadp02a0adOOY8eOaR3pncuaNQsNGnjToIG3aZmTkyNOTk5Mn/7tK5OkJCQkcPXqNb79dirOzk4oioK1tRUGg4HTp88we/ZMXFyyoqoq9vZ26b05/8m4cWM4ceIYiqJQr15d6U6QCoqikCWLM2PHjkkxyouLS1Y8PavQo0f3V+5z8eIlPD2r0LlzJ/R6vel4EhJyi2LFitK9ezcMBsN73WVowoSJtGjRiubNWzJv3g9ax3kvmX0hraqq6V9qbk+rDHq9nqioaNOvvbi4uHf2fMI89ejRnePHj2JhYUG1alVxdDTvGbf+K0tLSxYunP/K1NytWrVkzpzZr51t7vff/alTpzaqqrJz506qV6/GgwcP8fcPwMurDgDbtm3Hy6sOd+7cTZftSAvOzk6cOHGM9u3bAVC/fn2NE717jRo1YvHiRSmKRgcHB7Zs2fTaiVfu3LmLq6sLLi4uHDp0GA8PD+7du8/Tp2HY2iYPpxcQcJISJUpkuNEWdDodFy9eRFVVPvignhTSqfTVV1/Su3fPFMtq1qzJypUrXjuRk7+/v+m6iq1bt+HtXZ9bt27j7+9PnTp1UBSFLVu24u1dn5s3Q9JlG8yNj88ivv9+NgDe3t6yb2ogTQvpy5ev0L17Tzp16kKHDp3o0aMXq1f/wqVLl1Ms7969Jxs2bERVVTZt2sz58xde+3gXLgTRqFETKlXy5MKFoNeuM2XKNGrWrE3//gPTclNMXo5r2aFDJ0aNGs3HH3djx46dDBo0+J08nzBfiqJw+vRpDAbDe3HAsrCwoEmTxq+M5+rhUZ7cuXO9sr7BYCAoKIiqVauSmJhIVFQUV65cITo6msDAQOrUqUN8fDxhYWHcvXuX6Oi/n6Lc3CiKgtFo5NSpQAoWLEiRIoW1jvTOlSlT+pXttLKyokGD1+/7gYGBptkwAwNPU7NmDXbv9uXkyZPUrl0bnU5HYOBpateuxa5du9JrM9LMsWPHsbS0pEaNGlpHSTdp3UjVoIH3KxOXFCxYwDSs4l8FBJykXr16GI1GgoKCsLa25u7duwQEnKRuXS9UVeXSpUs4ODhw/fr1NMv5Ukb4sacoCv7+AXK2RENpWkgXLVqEWbNmEBMTw/79B/jmm/G0adOa4sWLMXPmd0RERLBnz16++246zZs3IzIyin79BjBy5KjXPl7JkiWYNGkiFy9e/NtZbj7/fAiFCxfm7t1307p16NBh5s79gZUrV+Djs4jFixcyd+48Hjz433i4qqoye/act/7QBQdfZPv2HWkdWbxDhw4dBqB+/Q80TmJ+VFVl2LCh5MqVE2tra8aMGU2JEiUoVqwogwYNJE+e3Nja2jJu3FgKFizIH3/8wZYtW1MMlWbOHjx4yPXr1/HyqvPa1vj3XfHixenduxcA3bt3w9ramk8+6U3+/Pnp378fiqLQuXMnHBwc6NKlMxMnTubx48cap34zRqORY8eOkzdvXgoXLoSqqu9FX1Sj0cj06TO4fFmb6Zo7deqIh0d5dDodkyZNxNraGi+vOrRo0Zxq1aqiKApfffUltra2pinI09KTJ0+YMmWaaTx1c2QwGDhy5CguLi5UqlRR6zjvpTQtpK2trcmRIwfOzs5YWVmRPXt2HBwcsLKywt3dHUdHRywsLHB3z4GdnR3Ozk7s2rWDJUt8Xvt4NjY2FChQAEvLvx+lz9nZiaJFi6blZpioqsqqVT9Tq1ZNsmRxBpKvZJ85c0aKVrrnz5/j6+v31o9/9uxZbty4kWZ5xbuVlJTE0aNHyZMnD8WLF+P+/QcEBQUTHh6eIVou3jVLS0vatGmNoigoikLr1q2oVq0qOp2Odu3aYmFhgaIotGjRnOrVq1G7di369RtAlSrV2Lx5C0lJ5t3n/Pjx4+j1ery96xMbG0tw8EVu3gx5LwqqN+HpWcU0EkORIoX5+OMuODg4UKlSRYoXLwZAgQL56d69G+7u7kRFRVGsWElGjRpj9qMuPHz4iBs3blCzZg18fJZSp05dKlXypFOnLjx58kTreO+MhYUFhQsXwsOjIh9/3I3g4Ivp+vytWrXExsYGRVH48MMGeHvXR6fT0bJlC1PLdt26XjRq1PCdtMTmyJGD27dvU7RoCcaPn2CW7/X9+w+4ceMG1atXw8bGhps3QwgKCs5QZ/wyOs37SFesWIGcOd1TLFNVlfj4+H+ca91oNBIdndxn+XUfIKPRSFhYGC9evDAVOaqqkpCQQHR0tOnK35iYmH8sgvR6PQcPHiIsLMy0XtmyZShfvrzpMX/8cTlxcXGvnAZ7meHRo8fExsamuC0uLo4VK1a+to+3qqpER0cTGhpq9sXF++Tp0zAuX75CiRLF6dKlK/37D2DixEmULl2OnTsz3qlqrWXPnp0BA/pz/vx52rfvSPXqNdm6ddsrs+KZiyNHjmJpacnly1do2rQZ06d/R5MmH9GpUxezzWzOhg0bik6nY9as2ZQpU47Ro8eabUEdGBhIfHw8R48e4+nTp2zfvpVt27awe7cv06ZN1zreO9W2bRtKlCjBr7+uxdOzGl26dOXixUvvReOBoiiMHTuGmJgYJk+eQunS5fj662/MqqA+efIk8fHx5M6dm48+as6oUaMZMWIkpUuX48iRo1rHey+8s0JaVVWioqKIjPzfP71en2Kdb7/9jtKly9GzZ2/TsqioaAYP/pxevfowadIU5s6dl6LQVFWVY8eO06xZCyZNmsKYMeM4d+5cig/1vXv36dq1OwsWLKJfvwEMGzaChIQEfHyWUKZMeerVq4+PzxK++OIr2rRpx9dff/Pa08uKotCxYwdu3ryJh0clunbtztKly4iMjGT69GmoqsqECZOYNWsOt2/foX37jnz2WfJMS0lJSXzyyaeMGDGKZct+pHHjpuze7Yuqqjx79ox27ToQEHCSX375lfbtO7Ju3XoguXCfN+8Hhg4dzrx582nYsDE3btxM8/dHvL1Tp04RFxdHUFAwgwd/xs6d21m/fi1ZsmRh5sxZr/1iedcXwmZ0gwYNIFu2bKiqypkzZ2nbtj21a3uxdeu2V44XWkpKSuLYseMYjUYiIyPZvXsna9asZtiwz9m4cRPnzp1/5T7y3v+zvHnz0KdP8rE/IiKCmTNnUbZseUaPHvvKFPNaO3o0eZSWnj178M03X+Pm5kb27Nmwt7d/pW+uwWDg3LnzbNu2nfv3H2T4fcDS0pIvvhgLJI/MsnbtOqpUqUq3bj24dOlyht++f1O0aBG6dv0YgGfPnjFlylTKlCnPN99M5OnTpxqnSx5yEuDcuXPMnfs9mzdvZO3aNURGRprqpz8zGAymmuzlOP8vJSQkEBcXZ/onZ9vezDub2TA2NpbRo8ek6Jbx14sKx40bw7Nnz7h0KXlMVr1eT5cuH5MzZ05Wr16FpaUl+/cfYMaMmaZWZ3//AJo1a8H+/XupWtWTxMRE2rZtb7o9KSmJbt264+XlxYQJ44mJifn/4rmuqZ/e4MGf4+Liwvffz+bUqUDq1atP376fUKBA/le2o0WL5sybN5fp07/j11/X8uuva5kwYRJbt26mWrWqTJgwHlVVOXDgABs3rjfliImJZdeu3fTv34/x47+iTJnS9OjRi2vXLuPm5sbOndvJn78QXbt2YcSI4abn+/HH5Sxa5ENQ0DlsbW0ZMmQoY8aMZdOmDXIRgcYOHTqMoigsXDjfNPSb0WgkNjb2tUVf70ST1wAAIABJREFUdHQ0K1f+TPXq1ahSpXJ6x/1Xc+Z8bxbD0GXPnj3FRAuBgadp06Ydnp5VKFiw4CtjVWvh9u073L59m1q1ajJ37hzTCAMvT5/+uUU6KSmJJUuWcfToURwdHenRo7tpxBJzERh4mrFjv9A6BuHh4Sn+fvHiBTNnzmLp0mX06/epWfSffzkOdt68eRk7drTpOHz//gPCw8PJly+faV2DwcC4cV/i7OxM/vz5qFevPiNGDGPAgP5axX+tiRMnp5h98t8kJiZiY2NjOkuckJDAmjW/snHjJlq1akmtWrX47LOBr1yYbI6WLl1GSMitt7rPX0fpCgsLY9KkySxcuIi+fT9Jy3hvxWg0cvjwEbJkycKvv64xdaFKSkpCr9e/9nvp2rU/mDx5CgcPHsLS0pKgoHOm74Gff17NkiVLefEigjp1ajNr1gyz+I4wd+9sr3dwcGDx4kWsXPmT6Z+nZ5UU6ySPK5nF9Pcff1zHz28PHTt2MBXgf+3/vGTJUvLnz296LGtra4oUKWK6PSgomOPHT2A0Glm3bj07d+7C1dWFo0ePmZ7P0dHRNKSXu7s7Sf/H3n0HRk3+cRx/567XvaEgUCgFGS17Csjee6uAqIDsKYiKIEPZe09ZirKnIEORvYc/Ni2z7FEopS2lvfYuvz9qT86CllKaXvm+/oHm1ieXXPLNkydP4uMJDw9/7nwkFN49uHbtCidP/o+JE8ejqiqdO3fFaDRaNqqJ/UIT//bwcCc4+By9e/fk8OEj3Llzl/DwcB4/jrAqiJ99ndlsZt68+eTKlZP16zewfPkKnJyc2LVrt5w61lh8fDw7duwkR44ctGjR3DL96tUQbt68SalSfxfKqqry0Uef0K5dB/r3/+KF65ZIYAsHiHv37iU2NpauXbtYiujEs2P29vYUKVLYMm3YsO/InDkTP/64mAoVylOzZm3p+mPD7t69x+nTpyldupTViBN79uwhPj7e6iDp8uUrTJ48hbffzsvHH39Eo0YNGTRo8L92UxQipUJCrnHhwgVq1qxBvnx/10pBQcFER0dTunTpJK8JDAxgypRJFC5ciNDQUObNm29pte7UqSMdOrRn3LgxLFq0QIroZHptLdIpcevWLUwmk2XQ9ee5ffs2mTNneuHONzQ0FJPJhJeXp2UlGDt2DNmzZ7c8x8nJKVlH46qqMmrUGAYM+BI7OzuKFi1CkSKF8fX15eOP2/HgwcMkw4A9efLEsqOdPn0mGzZs4P333yNHjhyW93ze50RFPcHe3sCtW7fIlSunJXvNmjWoW7dOumiVe5Ndv36D4OBgmjVrajViw8aNGwGoXbuW1Tq5ePFCHj16xNtvJx1vN73o168vAQEFNc1w7949FixYaPlbURRKly7F118PoGHDBsybN58DBw5omDDB779vx97enrJly1qmhYWFsW/ffsqWLWNpEIiLi2PFipXcvHmT999/j08++Zhp02YwY8ZMGjSon25a7MqUKc2YMaO0jkG/fv2t/vb09KRTp4589lkfsmfPxrRp0zVK9reDBw9iMpmoUKG85TeeME76JpydnalS5e8bE2XJ4kPTpk0srdSPHz/G2dk53Y3yMnToYKubovyXZcuWWx0MODg40LJlC77+egCBgQEpPhhO3B/+8/Uvmv5fjyVH586dXvo1HTpYtzpnzpyZ7t270aNHN9zd3RkzZmyKsryqXbt2/TUca3Wr72PNmrXodDrq16/33O/p0KHDfPhhGxwcHJgzZy59+vTC2dnZ0sVu4sTxaTkbNi99bNX/kjNnTuzs7Hj06NELn5M9e/YkF+49q2DBgjg5OZEjRw5q1apJrVo1qVq1SpKxK5Nr9+7dXLjwdx84RVEoVqwYrq6u2Nv/Xdwm5pk2bQZ37txlzZq1jB07jh9//IGvvvqSQoUKoaoq0dHRbN/+h9UG2WQyMWDA1xgMBgoVCiQmJoaaNWtY8vv7+6cou0g9R44cwWg0WrU+mUwm1q/fgKenJ9WqVePatetcu3YdRVEsI1SIfzdr1hwePHiAoiiUKlWSNWtWsW/fHpo1a5puDh5NJhP79u3Hz8/PqvvXzp27ePToES1aNMdsNrN//wHs7Oxo0aI5FSokjDWs1+t5662sSbowiISuEYkHUe7u7nzxRX/OnDnFuHFjnjtOuRZUVeXQocMAVK5c2TI9LCyMPXv2UqFCebJnz8b06TM5duw4np6erFy5nPLly3Hp0mW2bt1G3759/nXkqfQuPj6ekSNHAwkFdOvWrTh27AhLlvxAoUKBr1REf/nlAD7//Isk+/NTp05TrlwFq31v4ms6d+7Kd9+NSLO+2ZcuXeann34GIFOmTHzzzSDOnj3Ft98OJUuWLGmS4XlUVWXPnr3o9XqqVft7ONbo6GjWr99Avnz5KFWqJJcuXebGjZtWr929ew9Vq1bls8/6cP36ddatWw8kdOExGo24u7un6bzYulQtpFVVJT4+nvj4+L/uBmjEbDZbpsfExGA2m4mNjcVsNmMymTAajcTGxmIymXj77bxUrPguq1evsVyos23bNiChk7/ZbKZz505cunSZ8+eDUFWViIhIDh48yP379zEajeTKlZNu3boya9ZsHj+OQFVV1q/fwO+//275zIQ8JkuWxNE8XtSxvm/ffpb3UlWVTZs2UadObUvLuYeHO2FhjzAajTx8+JBMmby5fz8Ug8FApkzeqKrKkSNHUFWV27dvW66k9fHxISIiktjYWJydnVEUhaFDh3Do0GGOH/8TVVW5d+8eI0dq33L0pksskp5tfQoJucaxY8dp1Kghbm6uDB06jJCQEA1T2pbQ0FBmzZpN8eLFWbVqBYcOHaBZs6bPvcOZlq5eDeH27dtUq1bVUtyrqsqaNWuxt7enefPmnDx5ikmTJqMoCmPGjKJz504oikJoaCjHj/9Js2bN5MDqHyZPnoLZbKZ//885d+4M48aNIUeO7P/9wjSkqirHjh0ja9asFCoUaJm+Z89eHj9+TLNmTTEajSxevBh//9yW14waNYYmTZrx7rsV0l3/6Je1Zs1agoODadOmNUePHmbp0p8oXLjQK6/PqqqyefMWtm7dlqQoPnnyJEeOHOXcuXNW081mM7/+upktW15+uNmUZhwzZizOzs58880gzp07zfDh32paQCeKj49n//4D+Pr6Wh3gHzx4iFu3btG4cSN0Oh0DBnzNnTt/X7yrqio3btwgZ05fqlevRsmSJZg+fQZxcXF/3Xo9X7o5c2YrUvUw+dSp03z77XdERkZSrFhR2rf/lDp1alOuXDlGjBhJTEwMZcsmjL3ZuHEjbt68xZEjR7Czs6NXrz5Mnz6VDRvWM2TIUDp16oKvry8Gg+GvizwGYjQaadKkMevXr6V//y8oVqwYBoOBwMBA9uzZS8uW77NmzSpGjx7J6NFjadKkKYUKFcLfPzeffdaHxYt/YOXKVRQqFEjr1m345JOPWbz4B6pUqcyQIUPp3bsX9evXs5onDw8P6tatQ8uW7+Hu7k5cXByxsbH89NOPlpWtY8eOHD16jI4dO1OrVk1cXFzo2LEDly5dokOHjhQoUICiRYvQs2cPFi5cxJgxo1EUhUmTJjBs2Lfcu3ePPn16oygKVapUZtWqFXz99UB8fX1xcnJiyJDBsmJryGw2c/jwYbJnz265mAMgJCSE2NhYatWqyaFDhwkLC6N8+XIaJrUt+/cfYO7c2TRp0jjdnfp+1tGjRzGZTJYLTCFhZ3ThwgUCAgLIksWHzz/vT+/evayKC5PJxMCB31C1ahX69+8nhfQzIiIicXNz4+LFIN566y2t47xQdHQ0J06cpEyZ0ri4uFimJ45t7Ovry7Bh39GmTWtLw4pOp+Obbwby5Zf9adv2Y5o3b8mGDetwcHDQajZSzGQyceXKVU6e/N8L7z6YUjqdjqVLf0JRlCT7t/fea4mbmxsNGtS3mq7X69m06RccHR3T5Pd0//59cufOzeXLF9Jdf+Fr165z6dIlPvqorVVX1XPnzqOqKnXq1Ob337ej1+utbtRy8+YtcubMaWkU6NOnN+3bf8rhw0c4fvxPqlWrmubzYusUVY1PtfMjqXGqRVGUv1qz4yynx+Pi4rCzs0On01keT2zlNhgMlhbwxBUj8TmJrc92dnaWacn5/GfnJzw8HE9PT+Lj4y1dTry9vZOcqkvMk/hZz04DLM9XVdWy0Ui8O1ZixmffK/E7sLe3f+N3wP7+b7N27WpKlCiuyecbjUYCA4vQpEljJkwYZ1keUVFRtGjxHlFRUfj4+DB79iyyZfu7KHj48CF58+Zn9eqV1KxZQ5PsL5Itmy87dmzXtI+0qqr/uW7PnDmbAwcO8PPPS9IoVVITJ05i2rQZHDy436rLwY8/LmH48JHkzu3Hxx9/RNu2H1r99mfNmsPZs2eZMmWS1bZJaxMnTuL8+SDmz5+nWYbk9nN1cnLlypVLVr+rtBQREUmrVq3p1asn9erVtUyPiYlhyJBhnDhxgvr169GrV0/0ej2xsbHcu3efnDl9URSFZcuW06ZNWzZsWEfjxo00mYd/UhQ7wsJCk9VH+lX7I9u65GyjYmJicHJyJTo6EicnpzRKljDsXbNmLViy5AcaNmxgmX7t2nWaNm2Ou7sbWbNmZe7c2VbLevXqNSiKYrlo/smTJ7z9dgEqVaqInZ0d338/1+qgUQvh4eF4eWVGVW3jPhqp2iKdWj82RVGsTu/+80g+cZSLxOc8ry+loihJit2XzacoimUFNBgM/3o6R1GUJDleNO3Z/z+vJS5x/myxBSMjsre35/jxozg5WbeCuLq6smnTL3/96L1suh+kFmxl59y7dy8+/bSD1QhDAB991JbGjRuh19vh6upiVUSvWbOWsLAwpk6djE6nY/DgoXz33TCbmefXzVa+B3d3N379dWOS6Y6Ojowdm9Bv+Nl5GTlyNBMmTOT48aMEBBS0PBYZGZk2gVOZrSyn1yU9z3+VKpW5fPlCkgMiP79cHDy4j8jISLy9va1qDFVV2bdvP4MGfW2Z5uLiQocO7Zk+fQZNmzZJ8fVkbzLpLyBEMnh4uD+3767BYMDHx+e5ZygSzzYk/pvRb1yQURkMBjw9PZPsVBVFwdPTEzc3V6sieufOXUyYMBFXVxdmzpzFiBGjuHDhQrreKYsXe3ZY0/+a7uHhTs6cOfHxSbjJ0OHDR3Bzc6NixYppFVe8IRRFwdvb+7nrpqOjIz4+PkmK6PDwcM6ePYuHh4fV/qhz506oqkrJkiVkO5UC0oQmxGvQt+/nXL9+nWzZsjF27DiWLl3G2LGjyZo1q9bRxGsUExPDV18l3JlvwoRJluldunSWHdQboHv3bty4cZPPPkvoE3/mzFnWrFn13Jt9CZGW5s6dx88/L+XBg4e0bv0h8+fPs7Rm58qVk3btPqF69eoap7RNUkgL8RqMGzfG6gIaVVXT9QV1InU4Ojqyb9+eJNNl2b8ZnJycmDRpApGRUcTGxuDp6ZluhnEUb7ZOnTrSseOnQMIF9M+ul4qiMGXKJBnUIIWkkBbiNUhvQ7iJtCHXNgidToeHhzsgY/GK9OO/DublYD/l5PBDCCGEEEKIFJBCWgghhBBCiBSQQloIIYQQQogUkEJaiDeMDMP3ZpPl/+aSZS9sga2tp1JIi3TP0dGR6OhorWNkGEaj0epOoOmZg4O9LPtUFh391GYuiHRwcJDln4qio6MxGAzodHJhWWqyt5ftVGp6+jTGZrZRIIW0sAEBAQX53/9OaB0jw7h6NQSdTmcTY1oHBARw5sxZ4uNt41axtuDEiRMEBARoHSNZAgMDOHFCfvup5cSJk+TOnRsnJ0eto2QYBoOBfPnyceLESa2jZBj/+9//bGYbBVJICxvQvHkzFi1aJMVUKvnxxyVUrVoFNzdXraP8pxIliqOqKjt27NQ6SoZw48ZNdu3aTf369bSOkizNmjVl4cJFmEwmraPYPFVVWbBgIY0aNZThOVORXq+nSZPGLFiwELPZrHUcm2c2m1m4cBHNmzfTOkqySSEt0r3mzZsRH29i5szZNtd3Kr3588//sWDBQr788gutoySLs7Mz/fv3o3//L3n06JHWcWya0Wikb99+vP/+e/j759Y6TrJ06NCeoKBgVqxYqXUUm7d9+x9s2vQrfft+pnWUDKdnzx7s3r2HzZu3aB3F5q1bt56jR4/RpUsnraMkm37YsCHDtA4hxL8xGAy8805ZOnfugsFgoFSpkjJ4/Esym83s2bOXli3fZ+DAr2nSpJHWkZKtWLFi7N27lzlz5lKjRg08PDzkdtsvKSwsjO7de3L+fBA///wTjo62cWrf2dmZwMAAOnXqQpYsPhQpUkTuvvaSzGYzGzb8QocOHZk2bQoVKpTXOlKG4+bmSp48/nTq1IXcuXMTEFBQtlEvyWQysWzZcnr27M38+fMoUaK41pGSTVHVeGniEzbhxImTfPJJe3Q6HR9+2JqiRYvi6OgoG6x/ER8fz9WrV1m/fgOHDh1m0qQJfPhhG5v7zmJjYxk0aDCLFi2mZcsW1KxZg6xZs9rcfKQlVVV5/Pgx+/cfYMmSnyhfvhyzZ8/Ex8dH62gvbe/efbRv/ylZs2ahVatWBAQUtKmLkbQQFxfHxYsXWbVqNcHBF5g1awaNGjXUOlaGtm3bb3Tu3BV//9x88MH75M+fX7rR/Aej0fjXWacV3LhxkwULvqd69Wpax3opUkgLmxIbG8uGDb+wYcMvBAcHExtr1DqSRUxMDJGRkemqUNHpdOTM6Uv16tX46KO26Srby1JVlaCgYH78cQmHDh3iwYOHWkeycvPmTbJnz56uWkzd3FwpUaIErVu3okKF8ukq28uKjo5m5cpV/PrrZi5fvkxcXPq5ZiIyMpL4+Hi8vLy0jmJhZ2eHn18u6tatQ+vWrf+6bbl43SIjo1i+fDmbN28hJORaurq2Jzw8HJ1Oh7t7+lkX7OzsyJPHn/r16/HBB+/j6pr+r935JymkhUgl48aNZ+vWbWzf/ptNFyzi5d2/f5+iRUuwbt0aypcvp3UckYZUVeXzz7/g7t27/PzzEjlLItIlVVX59NNOODo6MnPmdFlPU5EU0kKkgvDwcAoVKsrdu3f5/fdtNndqSryaoUO/5bvvhtOoUUM2bFgnO6k3yO3bd8ifvyBxcXEcPXqYokWLaB1JiCSCgy9QvHhJFEXh3Lkz5M7tp3WkDEOazYRIBd9/P5/bt29jNpsZMWKkjC7yBgkLC2Pq1GkAbNmylSNHjmqcSKSliRMn8eTJE4xGI2PGjNU6jhDPNXbsOGJiYnj69Cnjxo3XOk6GIi3SQryiyMgo8uUrwL179yzTdu78g6pVq2iYSqSVIUOGMXz4CMvfDRs2YOPGDRomEmnlxo2bFC5clIiICCDhDnfSKi3Sm6CgYEqWLM3Tp08BcHFx4dSpE+TJ469xsoxBWqSFeEVz5syxKqIBRowYKTeReAOEhoYya9Zsq2mbN2/h6NFjGiUSaWnKlKmWIhqQVmmRLo0fP8FSRAM8efKECRMmapgoY5EWaSFeQUREJAULBnLnzh2r6YqisGPHdmmVzuCGDfuOb7/9Lsn0Jk0as3btarnoNAO7ceMmRYoU4/Hjx1bTDQYD//vfcQoVCtQomRB/u3DhIsWLl7QqpAFcXV05deqEzdycKT2TrbwQr2DevHlJimhIuEJ65MhR0iqdgYWGhjJz5qznPrZx4yb+/PN/aZxIpKWpU6clKaIhYfzm0aPHyHUSIl0YN258kiIaICoqSlqlU4m0SAuRQuHh4RQuXIynT5/Ss2cPXF1d2Lt3H++//x6jRo0hODiY7dt/o1q1qlpHFa/B0KHfMmLESGrUqM6QIYNp2LAxy5cvZenSZaxYsZJ69epKq3QGdevWbQICCuHm5kafPr0IC3vEgwcPKFWqJBMmTOL69eucOPGntEoLTV2+fIWAgEJkz56dfv36EhwcjL29PTlz+jJ58lQePnzI+fNn8fPLpXVUmyZbeCFSaMOGX2jfvh3nz59h2LAheHt7YzAYaNv2Q/788yiLFi1gw4ZfMJvNWkcVqSwsLIzLly+zc+cfbNnyKxUrvouiKBQoUIAffljEn38ew8vLizNnzmodVbwGK1eu5JtvBnLmzEm+/PILXF1dcXR0pFu3rpw69T+mTp3MihUrpVVaaEZVVZYuXcbEiRM4ffokvXv3xMnJCRcXF/r168uZM6cYOXI4y5cvl/X0FUmLtBApFBMTg6Ojo+XvBQsWsnnzFtasWWWZZjQasbOzk1bJDMZoNKLX69Hr9ZZpXl6Z+fPPY5Y+h6qqYjQa5VbWGdA/f/sjRozi7t27zJgxzTItNjYWe3t7GVNcaOJ525/+/b/E0dGRESP+vq4jJiYGBwcHWU9fgZ3WAYSwVc/uSF/E3t4+DZKItJac5aooihTRGVRyfvuy7IWWkrv9Sc66LP6dNJMJIYQQQgiRAlJICyGEEEIIkQLStUOINGQ2mwkJuWYZFk9RFPz8chEeHk54+N9DaXl5eZI5c2YePnzIw4dhltN0OXP6Sl82G/bw4UPCwh5Z/vby8sTDw4OrV0Msy1Wn0+HvnxtVVbl6NcRyIVD27NlwcXHRJLd4dfHx8YSEXLMsz8TlfO/efaKioizP8/HJjIeHB6GhoYSHP0ZRFJydncmRI7tW0cUbJC4ujpCQa5a/9Xo9/v65uX37DtHR0ZbpWbNmxc3Nlbt37xEZGYmiKLi6upIt21taxNaUtEgLkYaio6OZNm06RYoUo3r1msydOw+j0cjOnbv4+ONPKFAggMGDh3D8+J8A7Nixk8aNm1KzZm2WLPlJrq62cbt27aZ16zbkz1+QgQMHcerUacLCwpg+fQb58xekUaMmLFy4CJPJxKNHj/jmm8EUKlSEAQO+5ubNW1rHF68gLCyMsWPHERhYmPr1G/LDDz9iMpn49ddfadnyfQICCjFq1GjOnTsPwC+/bKRmzdo0atSEdevWa5xevCnu3r3H8OEjKFgwkKZNm7N06TIA1q5dR6NGTShUqAjjx0/g8uXLAKxcuYoqVarRosV7bNmyRcvompFRO4RIJc8bteN54uPjKVy4GIUKBVo9d/bsOfTq1YcLF4LIk8cfAJPJRI8evRg6dMgbeaRvS/45aseLzJkzl27denD58kWr5ezllZn27dsxdepky3O3b/+DM2fO8NlnfV5rdvFqnjdqx/NERUWRN29+WrZswcyZ0y3Thw79ljFjxvLw4X1cXV2BhO1Ex46dmTJlEp6enq81v3gzPG/Ujud59OgRuXL507t3L0aOHG6Z3qdPXxYtWsyjRw8sIxY9ffqULl26MWfOLJydnV9r/vRKWqSFSGM6nQ4vL0/Cw8MtLcwmk4m1a9dhMpmIiIiwPPePP3ZQokRxKaIzkMyZMwNY3RVv06ZfiYmJISwszDItLi6OpUuX0alTxzTPKF6PxHF8n1320dHR/PrrrxiNRqsuHsuXr6Bx40ZSRIs05+rqir29vdV6+vhxBL/99jvR0dFWd0r84Ycf+eijtm9sEQ1SSAuR5hRFwc3NzWqnuXHjJvz8/AAsxZTRaGTVqtW0bfuhJjnF6+Hm5gZAZGQkkFBIrV69hqJFi/Dw4UNUVUVVVVauXEXjxo2kX3QGotPpcHV1tSx7gJ9/XkqRIkUALP3no6Ki2L17D40bN9Ikp3iz6fV6XFxcrNbT+fPnU7ZsGVRV5dGjcAAePHjA6dNnqFGjulZR0wUppIXQgKenJ9HRT1FVldjYWFauXEW3bl1RFIXQ0FBUVWXFipU0atTwuYVUYrFlNpul37SNSWxhjI6Ottx97L33WuLj48ODBw8sj+3YsZOGDRs89z0Sl73cNdP2eHp6WC7aCg8P58CBg7Rq9QGQUJioqsr338+nfft22NklHQ9AfvvidVMU5a99VMJ6eu/ePYKDL9CgQX1UVbUc8M+ePZcuXTo/94Zjb9J6KoW0EGlMURQyZcpEZGQkRqORJUt+ok2b1nh4eGBnZ0dkZCRPnjxh37791K9fz+q1v/66maCgYADmzfueEiVKExER+byPEelUpkyZgISiKSIikn379tOgQf2/zlI8QVVV5syZS9euXawKqdDQUH744UdUVeXChYvUrVuf77+fr9VsiBRI/O0/ePAQgDlz5tGlS2fLwVVkZCR37tzlxo2blC9fzvI6VVVZvnwFt27dBmDUqDG88075DF+gCG0oioK3t5flwG7q1On06dMbDw8PVFUlKiqKK1euEhMTQ5EihS2vU1WVH39cQmhoKAADBgykVq06GX49lUJaCA1kzpyZiIgI7t8P5dix49SvXw9HR0d0Oh2RkZFMmzaDLl06J2mRevDgAe7u7iiKQo0aNfDy8sTNzVWjuRApkSmTt+XMw7x58+jZswc6nQ5nZ2eio6O5du06N2/eonTpUlavi4p6YrlTWf78+XBzc6No0aJazIJ4BZkzZyY0NJRbt25z//593nmnrOWsU0REBNOmTad7925Ww1wmnE5/hIuLM4qiUK1aVfz8/J7bEihEakhYTx9YhuAMDAyw9INO2EdNp0eP7knW04iISJydE9bTKlUq4+/vn+HX04w9d0KkU97eXjx58oRZs2bTtWuXv/pOumAwGDh79hxPnz6lRIniluerqsr9+/epVauW5cLDffv2UbJkSS5dusz9+/e1mhXxkgwGA66urpw9e46wsEeUKlUSRVHw8vIiOjqamTNn8dlnfax2UHFxccTGxvLeey1RFIXY2FhOnTqNn58fZ86clS4eNsTb25tHjx4xa9Zsy3JObJHev/8APj6ZyZs3j+X5qqpy585dWrRojoeHBwC7d++hTJnSnDt3nkePHj33c4R4FZkyZbIMzdm/fz/LNgrg99+3U7BgAauL4FVV5ebNW7Ru/YHlwHD37j2ULl2Ks2fPZegzp1JIC6EBd3d3jEYjMTExFCuW0Kro6uqKg4MD27f/QefOnawKqZCQayxhfKVbAAAgAElEQVRbtoJWrVoTFxeHqqrs2rWbGzduEBQUxAcftMZoNGo1O+Il2NnZ4ezszKZNv1q16CSepcicOTN+frksz1dVlblzv2fcuPFs2bIVgEuXLvPo0SN27NjB99/PZ9Wq1ZrMi3h5bm5uREdH4+bmRs6cvkDCgbWiKOzevYf27dtZ/fZPnDjJmjVr+eST9kDC+rB7924uXrzEpUuXaN36Q+Lj4zWZF5Fxubm5cffuXfLnz2fpjubtnXA2bd++fXz88UdW6+mBAwdZvXo13bv3BP5eT4OCgrlw4QJt236UYQ/4pZAWQgOenp5kyZKFr7760rIx0uv1eHh40K7dJ5YdbKKQkBDeeacsjo6O2NnZER8fz+HDRxgzZjS1atXk4cOw532MSIf0ej1ubm706/eZ1d3qvLwS1onOna2Hu4uOjqZIkcLcvn2b7NkTnr9r127atGnNhx+2IVu2bNIqaUO8vLzInTs3vXr1sPz27ezscHNzo2fP7nh7e1s9PywsjLffzmuZHhkZxeXLVxg1agSVK1ey9EcVIjV5enpSpEgR2rX7xDLN0dEBZ2dn+vXrm+Qi+OjoaLJnz24Z3jMsLIwHDx4ybNgQ3n23Avfu3UvT/GlJCmkhNBAYGMiUKZN4662sVtN79OhGv359kzy/atUqrFy5irZtPyQ6Oprbt+/g4uJM7tx+nD59hoIFCxAba8zwF3VkBHq9no4dO9CzZw+rFp2SJUsyb94cy+nTRC4uLvj5+REeHk7evHkxm83s27fPMjTarl27qFKlitXte0X6VapUSWbOnG5ViOj1er7+egDt27dL8vzq1auxdOkyPv74I548eUJQUBD58+fDx8eHvXv38e677/L0aUxazoJ4A5QvX46pUyfj5ORkmebs7MyQId/w/vvvJXl+zZo1WLp0GW3atCY6Oprjx/+kbNkyeHh48Ntvv1O7dm2ePMmY2ygppIXQQEBAQVq3bmVVSCmKQr9+fXF3d0vyfJPJxM6du8iXLx/bt//B7t27qVKlCjqdjo0bN1GzZg3mzZuXlrMgUkin0/HVV18muYFBuXLvULduHat1ItHGjZto0aI58+bN48mTaM6ePUfp0qW4c+cu4eHhBAcHc/VqSFrNgngF5cq9k2Q0Hr1ez4ABX2Jvb5/k+RERkQQFBWFvb8+xY8fZtWsX1apVBWDDhl+oWrUKixYtTpPs4s1Ro0Z1qlatYjXN0dGRL7/84rkXD96/f5/Q0FDCw8M5d+48u3btpnr1akDCelqpUkWWLFmSJtnTmhTSQtgAvV5Pu3afcOHCBerXr4e9vT2tWn1guYI/LOwRLVu2fG4RJmxfpUoVMZnM1K1bF5Mpnvfffw8PDw8yZfKmZs2a6HQ6AgMDtI4pXgMXF2fee68l9+7do3LlSnh4eNC4cWMA6tevx7Vr12jV6n2NU4o3nZeXFw0bNiQmJoZSpUri45OZunXrAtCkSWOCgy/Qpk1rjVO+Hoqqxsu5YCFSwYIFC9m8eQtr1qzSOorQgJdXZv788xj+/rm1jiLS2IgRo7h79y4zZkzTOooQL9S//5c4OjoyYsR3WkfJUKRFWgghhBBCiBSQQloIIYQQQogUkEJaCCGEEEKIFJBCWgghhBBCiBSQQloIIYQQQogUkEJaCCGEEEKIFJBCWgghhBBCiBSQQloIIYQQQogUkEJaCCGEEEKIFJBCWgghhBBCiBSQQloIIYQQQogUkEJaCCGEEEKIFJBCWgghhBBCiBRQVDVe1TqEEMllNps5ffoMmzdvITg4mNjYWK0jWVy5coXbt+9QseK7Wkex0Ov1+Pr6Ur16NapUqYyDg4PWkV7Jo0eP2LJlKwcPHuLhw4eoavrZfK1Zs5a6devg4uKidRQLd3d3SpQoTsOGDfH1zaF1nFdiMpk4fvxPtmzZyuXLl4mLi9M6ksXZs+eIiYmhVKmSWkexsLOzw8/Pjzp1alOu3DsYDAatI70R4uPjOXz4CNu2/UZISEi6Wk9PnDiJTqejaNEiWkexMBgM+Pv7U7duHcqUKY2dnZ3WkV6aFNLCZty7d49evfqwZ89e6tatQ9GiRXB0dNQ6VrpmMpm5cuUKmzdvwcXFmdmzZ1G2bBkURdE62ksxmUwsWfITQ4YMI1eunNSoUYMsWbJgY7OR5sLDH3PgwAGOHj1G165d+Prrr2zyNxMSco3u3Xtw6tRp6tevR2BgAPb29lrHStfi401cvHiRTZt+JWdOX2bNmknhwoW0jpWhBQUF0717Dy5dukyjRg3Jnz8/BoPtFYZpyWg0EhQUzK+/bqZgwQLMnj2Lt9/Oq3WslyJLWNiEmzdvUbVqdapWrcLp0yfw8fHROpJNGTt2NAsWLKRevQYsWfIDDRrU1zpSspnNZoYO/ZYlS35i8eKFVK1aBZ1OeqW9jDNnztKpU2dOnDjB6tUrbap18uzZc9SpU49WrT5g+fJluLu7aR3JpowdO5rJk6dSvXpN1q9fS4UK5bWOlCEdPXqMRo2a0KVLZzZu3JCuzkzZgqioKEaOHE3lylXZvHkTxYsX0zpSskmLtEj34uLiqF+/IYGBgUyaNAG9Xq91JJukqiobN26ia9fuHDy4Hz+/XFpHSpYNG36hZ8/e7NmzC3//3FrHsVkREZHUrl2HBg0aMHjwIK3jJEt0dDSVKlWhefPmDBw4wObOpKQXqqqycOEihg8fyYkTx/H09NQ6UoYSERFJmTLv0Lt3L7p37yrraQqpqsqkSZNZvPhHDh7ch6urq9aRkkWadUS699tvv3P1agijR4+UIvoVKIpCo0YNqV+/HuPHT9A6TrIYjUaGDv2WMWNGSRH9itzd3Zg3by7Tpk3n7t27WsdJlmXLlmMwGPjii8+lOHkFiqLQrt0nFClSmJkzZ2sdJ8P5/vvv8fPLRZcunWQ9fQWKotC7dy+8vb344YclWsdJNimkRbq3fPkKOnRoj7Ozs9ZRbJ6iKHTr1pWNGzfx9OlTreP8pzNnzhIWFkbz5s20jpIhFC1ahGLFirJ16zatoyTL8uUr6NKls/SHTgV6vZ4ePbqzcuUq4uPjtY6TYZjNZlauXEW3bl1t8kK59MZgMNC1axeWLVumdZRkk0JapHsnT56ifPlyWsfIMAoWLEBsbCx37qT/VsnTp09TtGgRnJyctI6SYZQvX54TJ05qHSNZTpw4Sfny0qc3tZQr9w4XL14kOjr9H0Tbiri4OM6cOSv7qFRUvnx5Tp48pXWMZJNCWqR7kZGReHh4aB0jw3B2dkZRlHQ1dOCLREU9wd3dXesYGYqHhztRUVFax0iWqKgoWf6pyMPDg6dPn2IySYt0alFVlejoaFlPU5EtbaNACmlhI6TfWeqxte/S1vKmd7b2fdpa3vRMvsvXR77b1GNr36UU0kIIIYQQQqSAFNJCCCGEEEKkgBTSQgghhBBCpIAU0kIIIYQQQqSAFNJCCCGEEEKkgBTSQgghhBBCpECqFdKqqqKqamq9nRBCCCGEEOnaK93PUlVVzp8PYtas2dy5cwc7OzsyZ85M9+7dCAwMQFEUVFVl3rzvOXLkKDlz5mTYsCGplf21i46OZsCAgYSHh9OpU0cqVapo9biqqsyePYcHDx4mea2LizN16tQhMDAAnU4a/oUQQgghMpoUV3iqqrJ48Q/Uq9eAevXqsnz5Un766UeqVq1C/foNWbNmraWFulKlSty5c4c9e/akWvC0YG9vT/PmzVizZi03b9587nMqV66MoigMHToMg8FAgwb1qVGjOi4uLrRu/SF9+36O0Wh8qc/9+eelmM3m1JgFIYQQqcBsNhMdHY3JZAIS9oExMTGWv4UQb6YUt0gfOXKUHj16MWvWDBo0qG+Z3rJlC8LDw/n0007ky5ePYsWKEhgYQNGiRTly5EiqhE4rdnZ2VKlSmSxZsjz3cUVRKFy4EFeuXAGgUKFASpUqCcC771agcePGlC//LoqiMHnyxGTdrcdsNjN79hxat26VejMiUkVi96W4uDjs7OwsZxqMRiN6vR69Xm9zd2QSyZe47AEMBgOKomAymYiPj8fe3l6WfQalqirHj//JwIGDCA19QPbs2Zg1aybLli1n3bp1ODo6MmvWTAoVCtQ6qngDqapqtR3S6XSoqir7pTSUohZpVVUZPnwEzs7OtGzZwuoxRVFo3boVZrOZESNGvlS/6cRC5Xn9rV80/VUk9z1T2jUje/ZsdOz4KTNmzCQ4+ILV55rN5ud+7okTJzl/PuiFeV/0OvH6HT16jHr1GlChQkUqV67KoUOHmTBhIpUrV6VcuQr8/vt2rSOK1+Tp06d8+eUAKleuSvHipRg69FtOnjxFnTr1qFSpCp9+2umlzzwJ23Dx4iW6devOhAnjOXLkICaTiQoVKnL+/Hlmz57FnTt3GTlylGyXRZpTVZXvv59PlSrVKFu2PHXq1CM4+AKff/4F1avXpHz5d9mzZ6/WMTO8FLVIR0U9Ye/efRQqFIiLi0uSx11dXSlevBg7d+4iIiISDw93ACIjIxk/fgK3b98hNDSUtm0/pE6d2gAcOnSYZcuW4+7uzq1bt3BxcWH69KmYzWbWrVvP1q3bMJvNODs7M3z4t1y9GsLXXw/kyZMntG37Ib/+upnChQvTvHkzZs+eg8lkQq/XM3HieLZt+43Nm7egKAqDBg0kb948rFq1mh07dmIymXBzc+O7777F3d0NVVXZuHETq1evIWvWrLz9dl7i4+NTvJEsW7YMJpOJbdt+o2DBApw7d54hQ4bi7+/PlStXKFWqFF988TkGg4G1a9fx5ZcDiIiIoFu3HtjZ2TF27GhcXFz44Ycf2bx5C9myZeP69ev07fsZlStXSlEm8fLOnw+ib99+/PzzT/j65qBXrz7UqVOPli1bMG/eXOrVa8DIkaOoVaumHP1nMGazmW7delCu3DuMGjWCy5evUKhQEZYs+YmNGzcwZcpUFixYSNeuXShbtozWcUUqMpvNDBjwNdOmTaVo0SKoqoqbmxv3799n6NAhLFnyExcvXqRx40aW15hMJk6cOMm9e/cICAggd24/2SaIVJd4/dnhw0fYunUL9vYGKlasTJky79C//+eMHTuGpk2bM2nSZCpXrmRZB/9Zyzy7bqqqKutqCqSokL5//z5Pnz7F2dn5hV+6k5MT0dHRREb+XUjfuXOXqlWrUrp0KUJCrlG1anXGjh1Ny5YtaNeuA9u3/0bOnL4EB19gwICvAVi+fAXDh4/k4MF9uLu788EHrRk+fCSjR4/k88/70bhxU8qWLUuVKpVZunQZAwd+TY8e3alevSaDBw/C3d2datWqMm7ceCZOnICfXy7mzJnH3Lnz2L9/L87OTtSuXZcpU6YyePAgpk6dxvz5C9m8eRO+vjnYt28/9+/fT9b38rxi28PDA4DLly8DsGzZcrZs2Upo6F3i4uIpW7Yc7u7u9OzZnRo1atCzZ3cGDRrMsGFD0ekUnJycMBqNjBw5muLFizFlyiT27NlL06bNOX36JDlyZH/p5SdejqqqDBr0DRMnTiB3bj8gYWf59OlT+vb9jJMnTxIWFkalStYHNnFxcezYsZO8efPy9tt5tYguUsGuXbsxm8107tzJctpUVVVq1qxB3rx52LlzF3nz5iVXrpxAwvqye/ceduzYiZeXJ82aNbOsN8K2xMXFUapUKd55pywA8fHxnD59hnz58pEzpy+dO3eiQIH8NGzYAEVRiIuLo1+//vj65sDDw4MePXoxePAg2rdvJwWKSFVhYWHMnj2X/fv34OLigtlsRq/Xo9Pp6NGjGz/+uISoqCiqVq1i9bqjR48xduw4QkNDcXZ2Zv36tTg6OgIwbdoMfvnlF0wmE1myZGHmzOn4+PhoMXs2JUV9FlxcnNHr9URHR7+wpTYsLAxFUdDr9UDCziV//nyULl0KRVHw989NnTq1mTJlqqXLQp8+n7F69RocHR355ptBqKrK6NFjKVq0CLdv3yEoKJgiRYqwefMWDAYDBQsWxGw2U6dObfr06c3u3btwc3OlZMkS1K9fjx07dqIoClFRT6hevTrVqlXFYDAwdeo0ypQpzfXr1wkKCqZMmTKsXr2Gp0+fMmrUGN5//z1y5cqJTqejYsV3//WA4VnPe05in0onJycAPv+8L1u3bkav1/PkyRNy5MjOoUOHAPDwcMfNzQ2dTkfWrFnImjUrer0ee3t7NmxYx/jx43j69CkeHh7ExsZy/vz5lCw+8ZJUVaVbt66UKVMaSGilOnDgILly5aJAgfy0avUB58+fZejQwSiKgtls5tNPO9GoURMaNWpCSEiIxnMgXkW2bNkYPvw7SxevQ4cOWQppR0dHDhzYy5EjB8maNSuqqjJnzlyOHj1K8+bNuHDhIqVKleH48T81nguREg4ODgwa9LVl2d+6dZuLFy9Svnw5DAYD2bK9RatWH+Dq6gpAUFAw8+cvoGDBgnTt2oXKlSvx5ZcDpNuPeC3mz5+Hs7MzAKGhoZw4cZLSpUvh5eVF9+7dOH/+LL1797KqTcqUKc2MGdN5/DiC3377nQ0bfrE81rNnd2rWrEnTpk348cfFZM6cOc3nyRalqJDOkiUL/v7+XLt2ndjY2CSPx8bGcuXKVfz8/PDxSVgQzysy3dzcuHjxEiaTiXnz5hASEsIHH7SmYMFADh8+TExMDLdu3eLx48ds27aNbdu24ebmSp8+vazex8/PD4PBgJubq+Wz2rRpzR9/7OD27TusXLmKFi2aAwl9HW/fvs3du3ct75kliw+9evUgPPwxoaGhZM+eLSVfy3PdvXsXgHLl3gESLkxbtmw5tWvXZf78BYSHhyer20hoaChdu3ajXbsOlm4uERERqZZTvJhOp6NWrZqWg8IbN24SHBxM5cqVsLe3x87ODn//3BgMBoC/Li6dxLx5c2XowwwgIKAgfn65gISDqt9/346DgwMVK1ZEURSyZs2Kt7c3iqIQExPD5MlTefo0hmLFijJ58kS8vb0ZPnyE9KHNAA4dOoTZbE4yFGqi7NmzUb16NXx9fYGE9cVgMFi2HUKklkyZMlkaJgH27t1HbGwsNWrUQKfT4eDggL9/7iTrnqIoHDt2jHbtPqZ48WJMnz7DMvKMTqcjKCiI1q1b4ejoKGdRkilFXTt0Oh2dO3fk88+/YO/efdSqVdPq8X379hMeHs6wYUOxs3vxRxiNRkur67179zhy5BA3b95i8eIfGD58JB06tCdr1qzkyJGDfv36Wl4XGRllGaMasJyWeFatWjXJli0b8+cv4PLly/Tv3w9IaBnOkiULOXPmtHrPiIhIy0bveUPPpWQnaDab2bDhF/Lnz0/dunUwGo20bPk+3t7ebN26GScnJ44ePQokXGRYuHAhqxX3l1824uHhgY+PD3Xr1mfmzOm0a/cJ4eHhlh3zwYOHKF++3EtnEy8ncfkrisL+/fuJj4+nevVqlscfP044qPHwcEdRFNzd3eRAJ4NI7MqReOr+wIGDBAYG8NZbWS2P37p1mxw5smNnZ0dAQEHLjsnBwYGcOX25fv265dSrsB2J1+hkzpyZypUrsX//AQDKlft7mzt//gKKFy9O6dKl8Pb2ZtOmhBa+u3fvsn37H3z2WW9Z7i8wd+48zGYzXbt2sdr3Xbt2ne++G87IkcN56623LNNVVWXixElkzZqVjz5qq0XkdMVsNqMoCoqi8NtvvwNY7ZcePXqEwWCwnDFJtGfPXtq1+wR3d3c6duzM4cNHqFChPHFxccTExEh3jpeU4uayTp06UqFCeQYN+obIyCjL9IcPH/LNN4Np2LABnTp9avWae/fuExERCcCTJ0/YtWs37dt/gtlsZtiw73j8+DG5c/vx2We9LcOL9e7dk61bt3HnTkLL7uPHEQwZMtSqsI2JiUlS6Do4OPDxxx8xYcJEKlZ819Ja+Ox7Pnz40JJ5yJChuLm50rRpE3bv3oPJZEJVVa5eDeHJkyc8ePDwhSOJJBbez/794MEDvvtuBLt37+Gnn37E0dERo9HIhQsJpwUT+5DfvHmL+Ph4Zs2aTWxsLK6urphMJmJiYggJuUamTJm4cuUKMTExVK1aFZ1OR0jINYxGI0ajkenTZ6R0EYpkioiIpGbN2jRp0gyz2cyOHTsBKFmypOU5gwZ9w969cnV0RpN4QU/+/AEcOHCQixcvcf36dYoXL24pji5evMSnn3bEbDb/ddHwaks3n8jIKC5cuEi1atXk7IQN2rlzF++99wFffTWA2NhYduzYibOzM3nz5gEStg0LFy4iTx5/AEsDz6hRY6hXryE1a9agT5/e0rL3HGazmUmTpjB58tQk+9Y//viDhQsXsW/f/iSvGTduAtOmTX/jz/AcPHiIwMAiTJgwkZiYGHbu3IWbmxvFihUFsHQxPH36jNXrVFXl2rVr5M2bh5YtW5IlSxamTZuO2WwmOPgCfn5ycezLSvE40i4uLmzatJFvvhlM7dp1aNGiOWazmdWr11ClShW++26YpV8wJIy72qFDO6ZOnYazsxM7duykefNm9OnTGwBnZyd69epDoUKFCAoKYsSI7zAYDHTq1JHw8Mc0aNCQ6tWrEx4eTs+ePThy5CiDBw8hc+bM9OjRk5o1a/L1119ZPk9RFD755GPmzJlLgwYNrLJ3796Nx48jqF+/IZUrV+bBgwd89dWX6HQ65s2bQ48evWjXrgOFChUiKioKFxcXRo0ajV6vp0ePblbv9emnnTh06DCOjo4MHjzUUtg+fvyYEiVKsHv3TstG1sXFhWHDhjB16nSePIkmLCyMTz75iMmTp9KmTWscHR2pXbsW77xTls6du5IjRw4KFMhPrly5aNXqA9q370C1atWIioqiVasPmDZtuuX7E6/PsWPH2LFjJ7Vr1+L27Tts2PALTk5OZM6cCVVV2bdvP9evX09yZkbYvsThpW7fvo1er2fOnLkA5MqVC1VViY6O5uuvBzJo0EBLYf3sdSHTpk0na9YsDB06RHZONiguLg4HBwd69+7FzJmz0Ov1mM1mbt68haOjIz169KRbt654e3tbXqPT6Rg4cACffdabli3fp1OnLixcON/SmCMS6HQ6xo4dbfn/sxo1asi33w6jVq1aSV4zY8Y03Nzc3vjf008//cyFCxeIi4tn167dXL16ldy5c2NnZ4fZbObnn5ei1+stF8omun37DlmyZMHBwQEHBwc6dGjP5MlTCAoKZteu3VSvXu2N/25flqKq8a90WKeqKvfu3ePUqdM4OztTpEgRyygdz0o8BQEJLcCurq44ODhYjuAjIiJxdnYiNPQBnp4elg70iZ9hNBp59OgRPj4+6PV6q/GfE9/3nwtfVVUePHhA5syZn/tYbGws4eHhlvd89rHHjx+j19vh6urCjRs38fT0xNXVxeoH/29jUCeebnne92U0GgkNfUDWrFmws7MjJibGqj+S2Wzm4cOHeHt7W+2UHz9+TExMjOWipri4uDfiRhD+/m+zdu1qSpQorsnnh4eHU7FiFUqUKM6lS5fo06c3Y8eOx8vLE19fX+7evcvixYuS9K2/efMWb7+dn02bfqFmzRqaZH+RbNl82bFjOwEBBbWO8q9mzpzNgQMH+PnnJZp8vqqqjBs3nhUrVlKgQAH0ej2FCxdm+vQZNGrUkFOnTtGlS2c+/vijJL/DzZu3MHPmLBYtWvDCmzppYeLESZw/H8T8+fO0jvKfnJxcuXLlEtmyvfXfT34NjEYjffr05dixY5QuXZoRI75j/PiJbNv2G1my+NCpU0datGhute2OjY21NCItXLiITz/txKZNv1jduExLimJHWFgoXl5eWkfJEGJiYnByciU6OtKq8fB127JlKx07dqZBg/pcuHCBAQO+omvX7pQuXQqz2YxOp2POnFlJLhhcvnwFdnZ2lnuA3Lhxk4IFA+nevRt37txh+vSpmq8b4eHheHllRlXjNc2RXClukU6kKApvvfWWVT+m53m2AP3nglUUxVJ8P+9CP0VRcHBwsPqMFxWq/3zdi/r6KIqCo6Pjc3MrioKnp6fl78RhrZ73vJctYhPnxdc3h2XaP398Op0uSe5/Zkp8H/H6eXp6cuDAPi5evEjOnL74+PjQtGkTLly4iJOTE/7+uf/1WgBhuxRF4Ysv+tOsWTOMRiMBAQXR6XS0bfsh9+/fJ08efzw8PJKMxbpnz17Wr9/AqlUrcHR0ZObM2XTr1kW6d9gYe3t7Zs2aYSlMFEVh9OiRjBw53LIsn13248dPYPz4iRw5cog8efwt91l48OCBJvlFxlW3bh0OHz7I3bt3KViwAC4uLpw9e4rg4AtkypQJX98cSfrmq6rKrl27GTp0iGWar28O2rb9kB9++JHq1atZ1RkieWTvL0QyuLu7WW7/DgkXuBYtWkTDRCKt6HQ68ufPZzXN1zeH1cHws44cOcqIEQnj3B88eIjw8HD2799P9+5d0yKuSGXPDuP6vL+fFRtrxMnJCReXhDOqp0+fwdXVlXfffTdNsoo3h6IoSbZDLi4ulCxZ4oWvMRqN3LlzxzKaWuL7dO/ejUWLFlOkSJEMf4b7dZBCWohUpqoqw4eP5NKlS+j1esaMGcumTb8yatQIqy5LIuN5+vQpn37aieDgYHbu3GU5a/XPsVxFxtStWxeOHj3K5MlTcXV1ZcWKlSxatMBycaIQWvn++/n89NPPnDlzlu7dezJx4gTLkMFFihSmWrWqViN+iOSTQlqI16BLl07Y29szdepkIOGOaNIVJ+Ozt7dn06aEO4MldgcwGAy4u7tpHU2kAR8fH9atW8O5c+d5/PgxPXp007y/qRAA77//vqVfdFxcHK6uLpbHdDodK1YsSzJMnkgeKaSFSGWJN+kQbx69Xi+3A3/D2dnZSbcvke48bxCIZ0nf6JSTK1+EEEIIIYRIASmkhRBCCCGESAEppIUQQgghhEgBKaSFEEIIIYRIASmkRbpnMBiIjY3VOkaGERcXB/DCsXDTEzs7PUajUesYGUpsrNFmbiBkZ1QQLFgAAARzSURBVGcnyz8VxcbGotfrZSjGVJawnsZpHSPDiI2NtZltFEghLWxAnjz+BAUFaR0jw7h9+w6qqpI5cyato/ynPHnyEBx8AbPZrHWUDCMoKIg8efy1jpEsefPmld9+KgoKCiZ79uw4OjpqHSXDsLOzI1euXLKepqKgoGDy5LGdsdelkBbpXv369Vm2bLnWMTKM9es3ULx4Mby9vbWO8p/KlCnNw4cPOXHipNZRMoTHjyPYunUbderU0TpKstSrV1d++6lo2bLl1KhRXQrpVGRnZ0ft2rVkPU1Fy5Ytp379elrHSDZFVeNVrUMI8W/CwsIoVqwk06dPpWnTJlrHsWk3b96ibNlyLFnyAzVqVNc6TrIMGTKM/fv3s2XLr9jb22sdx2aZzWb69evP1atXWbduDTpd+m9HuXbtOu+8U55Vq1ZQqVJFrePYtLNnz1G5clV2795J4cKFtI6ToQQFBVOxYmV+/30bJUoU1zqOTTt8+AiNGjXhwIF9vP12Xq3jJEv635KKN563tzdz5syiU6cubN68RU7zp9CVK1dp0KARrVp9QLVqVbWOk2xfffUFT58+pUOHjkRGRmkdxyYZjUZGjx7LmjVrmTFjuk0U0QB+frkYPXokrVq1Yf/+A6iqtPukxMmTp2jatDlffNGfQoUCtY6T4RQsWIBvvhlI8+YtOX78T63j2KzDh4/w3nsf8O23Q22miAZpkRY2QlVV1q1bT69efahcuRKffPIxxYoVw9FRbrv9b0wmE1euXGXduvUsWrSY9u3b8e23Q22uZff+/fu0b/8pV65cpUeP7tSoUZ2sWbPIRVP/ITz8Mfv372f27DlERz9lyZIfbK410mw2s2jRYgYO/IbGjRvRpk1rAgMDbG4dTmtxcXFcvHiJFStWsmLFSj7/vC+ff97PJi4ytkUmk4kZM2YxatRoWrZsQevWrcifPx8Gg0HraOma0WgkKCiYZcuWs3btOr79dihdunS2mYN9kEJa2Jjbt+8wZ85c1q/fQHBwsFzR/x90Oh2+vr7UqFGdbt26UqZMaa0jpVh8fDy//LKRRYsWc/DgIR4+fKh1pHTPzc2NEiWK06ZNaz76qC3Ozs5aR0qxq1dDmDFjJps3b+Hy5cuW0WfE8+n1evz8/Khbtw7du3eTlug0Ehx8gZkzZ7Fly1ZCQkKIj4/XOlK6ZmdnR548eahfvx49enS3qZboRFJICyGEEEIIkQK203YuhBBCCCFEOiKFtBBCCCGEECkghbQQQgghhBApIIW0EEIIIYQQKSCFtBBCCCGEECkghbQQQgghhBApIIW0EEIIIYQQKSCFtBBCCCGEECkghbQQQgghhBApIIW0EEIIIYQQKSCFtBBCCCGEECkghbQQQgghhBApIIW0EEIIIYQQKSCFtBBCCCGEECnw/3brWAAAAABgkL/1HHYXRSINAACDSAMAwCDSAAAwiDQAAAwiDQAAg0gDAMAg0gAAMIg0AAAMIg0AAINIAwDAINIAADCINAAADCINAACDSAMAwBANWgi/cU7EIAAAAABJRU5ErkJggg==)"
      ],
      "metadata": {
        "id": "7qncjq2NU5tr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class OurRNN(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim):\n",
        "        super().__init__()\n",
        "        self.hid_dim = hidden_dim\n",
        "        self.in_dim = input_dim\n",
        "\n",
        "        self.rnn_cell = OurRNNCell(input_dim, hidden_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "            X has shape B x T x D where\n",
        "                B = Batch size\n",
        "                T = Length of sequence\n",
        "                D = Number of features\n",
        "        \"\"\"\n",
        "        h = self.init_hidden(x.shape[0]).to(x.device)\n",
        "        \n",
        "        # Tracks output of whole sequence\n",
        "        out_arr = []\n",
        "    \n",
        "        # Combine input at time t with previous hidden state\n",
        "        for t in range(x.shape[1]):\n",
        "            out, h = self.rnn_cell(x[:, t, :], h)\n",
        "            out_arr.append(out)\n",
        "\n",
        "        return out_arr, h\n",
        "\n",
        "    def init_hidden(self, batch_size):\n",
        "        \"\"\"\n",
        "        Hidden state must be assigned initial state. Usually non-informative,\n",
        "        so set to zeros or random floats.\n",
        "        \"\"\"\n",
        "        return torch.randn((batch_size, self.hid_dim))"
      ],
      "metadata": {
        "id": "k_XLobEnUkHM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The equivalent PyTorch implementation is available at: https://pytorch.org/docs/stable/generated/torch.nn.RNN.html.  \n",
        "For simplicity, we'll use the out-of-box PyTorch implementation. Several options, such as \"n_layers\" and \"bidirectional\", will be explored later."
      ],
      "metadata": {
        "id": "B1dgHDzsimp4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LSTM Implementation"
      ],
      "metadata": {
        "id": "0k7YASbAXZzc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The LSTM addresses the vanishing gradient problem through the inclusion of a cell state. This cell state can only be updated through several gates, meaning it can more easily preserve long-term information.\n",
        "\n",
        "Each LSTM update is represented by the below equations (biases omitted):\n",
        "\n",
        "$$ \\begin{align}\n",
        "f_t &= \\sigma(W_{fi} x_t + W_{fh} h_{t-1}) \\hspace{1em} [\\text{Forget gate}] \\\\\n",
        "i_t &= \\sigma(W_{ii} x_t + W_{ih} h_{t-1}) \\hspace{1em} [\\text{Input gate}] \\\\\n",
        "g_t &= \\sigma(W_{gi} x_t + W_{gh} h_{t-1}) \\hspace{1em} [\\text{Update gate}] \\\\\n",
        "o_t &= \\sigma(W_{oi} x_t + W_{oh} h_{t-1}) \\hspace{1em} [\\text{Output gate}] \\\\\n",
        "c_t &= c_t \\odot f_t + i_t \\odot g_t \\\\\n",
        "h_t &= tanh(c_t) \\odot o_t\n",
        "\\end{align}\n",
        "$$\n",
        "\n",
        "The key thing to remember is that the sigmoid activation squashes outputs between 0 and 1. Thus, when multiplied element-wise to the hidden or cell state, it represents how much of each dimension to keep / throw away.\n",
        "\n",
        "\n",
        "A graphical representation, taken from https://colah.github.io/posts/2015-08-Understanding-LSTMs/, is shown below:"
      ],
      "metadata": {
        "id": "HAbOegENn-Qd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "F8JsUxq5ppM8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class OurLSTMCell(nn.Module):\n",
        "    def __init__(self, in_dim, hid_dim, out_dim):\n",
        "        super().__init__()\n",
        "        self.forget_gate_i = nn.Linear(in_dim, hid_dim)\n",
        "        self.forget_gat_h = nn.Linear(hid_dim, hid_dim)\n",
        "\n",
        "        self.input_gate_i = nn.Linear(in_dim, hid_dim)\n",
        "        self.input_gate_h = nn.Linear(hid_dim, hid_dim)\n",
        "\n",
        "        self.update_gate_i = nn.Linear(in_dim, hid_dim)\n",
        "        self.update_gate_h = nn.Linear(hid_dim, hid_dim)\n",
        "\n",
        "        self.out_gate_i = nn.Linear(in_dim, hid_dim)\n",
        "        self.out_gate_h = nn.Linear(hid_dim, hid_dim)\n",
        "\n",
        "        self.sig = nn.Sigmoid()\n",
        "        self.tanh = nn.Tanh()\n",
        "\n",
        "    def forward(self, x, hid_state, cell_state):\n",
        "        # Forget unwanted cell states\n",
        "        fg = self.sig(self.forget_gate_i(x) + self.forget_gate_h(hid_state))\n",
        "        cell_state = cell_state * fg\n",
        "\n",
        "        # Compute which cell states to update\n",
        "        ig = self.sig(self.input_gate_i(x) + self.input_gate_h(hid_state))\n",
        "\n",
        "        # Compute update values\n",
        "        ug = self.tanh(self.update_gate_i(x) + self.update_gate_h(hid_state))\n",
        "\n",
        "        # Perform cell state update\n",
        "        update = ig * ug\n",
        "        cell_state = cell_state + update\n",
        "\n",
        "        # Compute output from cell state\n",
        "        og = self.sig(self.out_gate_i(x) + self.out_gate(hid_state))\n",
        "\n",
        "        hid_state = og * self.tanh(cell_state)\n",
        "\n",
        "        return hid_state, cell_state"
      ],
      "metadata": {
        "id": "PiHgf-YsYUX4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The LSTM is pretty much identical to the RNN in terms of its iteration.  \n",
        "The only difference is that the cell state much be initialized in addition to the hidden state."
      ],
      "metadata": {
        "id": "QNX4-Y9Wj1yF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class OurLSTM(nn.Module):\n",
        "    def __init__(self, in_dim, hid_dim, out_dim):\n",
        "        super().__init__()\n",
        "        self.in_dim = in_dim\n",
        "        self.hid_dim = hid_dim\n",
        "        self.out_dim = out_dim\n",
        "\n",
        "        self.LSTMCell = OurLSTMCell(in_dim, hid_dim, out_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "            X has shape B x T x D where\n",
        "                B = Batch size\n",
        "                T = Length of sequence\n",
        "                D = Number of features\n",
        "        \"\"\"\n",
        "        h = self.init_hidden(x.shape[0]).to(x.device)\n",
        "        c = self.init_cell(x.shape[0]).to(x.device)\n",
        "\n",
        "        out_arr = []\n",
        "\n",
        "        for t in range(x.shape[1]):\n",
        "            h, c = self.LSTMCell(x[:, t, :], h, c)\n",
        "            out_arr.append(h)\n",
        "        \n",
        "        return out_arr, h, c\n",
        "\n",
        "    def init_hidden(self, batch_size):\n",
        "        return torch.randn((batch_size, self.hid_dim))\n",
        "\n",
        "    def init_cell(self, batch_size):\n",
        "        return torch.randn((batch_size, self.hid_dim))"
      ],
      "metadata": {
        "id": "QL1UyjGHjxDy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The equivalent PyTorch implementation is available at: https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html. \n",
        "\n",
        "The GRU similarly contains several gates, but combines the input and forget gates, and merges the hidden and cell state. The main benefit of the GRU is memory efficiency, at about the same performance. In practice, due to the interchangeability of all three approaches, it's easy to apply them all and chose the one that works.\n",
        "\n",
        "The GRU PyTorch implementation is available at: https://pytorch.org/docs/stable/generated/torch.nn.GRU.html"
      ],
      "metadata": {
        "id": "SVa1I48sqheZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applications of RNNs\n",
        "There are several broad tasks you may wish to perform on sequential data:\n",
        "\n",
        "\n",
        "*   **Prediction**: Extrapolation / interpolate from an observed sequence.\n",
        "*   **Classification**: Assign a class label to whole sequences.\n",
        "*   **Translation**: Convert sequence in one space to another.\n",
        "\n",
        "We will investigate how RNNs can be applied to each of these contexts. \n"
      ],
      "metadata": {
        "id": "A4_nb_E_DoWv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aside: In previous years, tutorials were on NLP tasks, which are likely more relevant and interesting. However, training never converged during the tutorial time, which was quite unsatistfying. Since PA3 and the majority of CSC413 focuses on NLP, we'll mainly focus on non-NLP applications of RNNs. This year I've implemented some toy examples, but each section contains examples of real world applications where RNNs have been applied. "
      ],
      "metadata": {
        "id": "ZjwZthn_Ilvv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RNNs for Sequence Prediction & Forecasting\n"
      ],
      "metadata": {
        "id": "yoaYYfCF9VQX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Background"
      ],
      "metadata": {
        "id": "QYtcBj7QcpNk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The RNN can be applied to predict the next observations in a sequence of data. \n",
        "\n",
        "\n",
        "Given an observed data sequence up to time $t$, denoted $x_{1:t}$, we can apply the RNN for prediction of the data values $x_{t:T}$ where $T>t$. Typically, values in this prediction region are unseen during training. This task is visually shown below:\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "mUuvrhDpZuyN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "pqDC80Zxdjgt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We apply the RNN in a manner known as autoregressive prediction for this task.\n",
        "When input data is available, we update the RNN normally:\n",
        "\n",
        "$$o_t, h_t = \\text{RNNCell}(x_t, h_{t-1})$$\n",
        "\n",
        "In the prediction region, where input data is unavailable, the RNN update is performed using the previous output:\n",
        "\n",
        "$$o_t, h_t = \\text{RNNCell}(o_{t-1}, h_{t-1})$$\n",
        "\n",
        "This is visually shown below:"
      ],
      "metadata": {
        "id": "gmsJ1epBdhi9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "rAKSfMXdW8Zu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Toy Data: Lotka-Voltera Model"
      ],
      "metadata": {
        "id": "YUOz7MQqW8NT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We consider a simple sequence prediction example.\n",
        "\n",
        "**Scenario:** Imagine you are an ecologist, tracking the populations of lynx and rabbits, which have a predator-prey relationship. You take measurements of both the predator and prey throughout the year, and discover a cyclical nature in their population levels. To avoid having to repeatedly trek into the wilderness, you decide to build a predictive model using an RNN. \n",
        "\n",
        "As as matter of fact, given observations from different eco-systems, could we forecast them all? Let's see how it might be possible!\n",
        "\n",
        "(Aside: using an RNN is overkill here, this is just a demo.)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "CElxH7kHaTwt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Background\n",
        "\n",
        "This part describes how we generate the synthetic dataset. Skip if not interested.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "tyILQVV21UID"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use synthetic data to mimic actual observations. The Lotka-Voltera equations describe an overly simplified model of predator and prey in an eco-system over time. They are described by the following ODEs:\n",
        "\n",
        "$$ \\begin{align}\n",
        "\\frac{dx}{dt} &= \\alpha x - \\beta x y \\hspace{1em} [\\text{prey}] \\\\\n",
        "\\frac{dy}{dt} &= \\delta x y - \\gamma y \\hspace{1em} [\\text{predator}]\n",
        "\\end{align}\n",
        "$$\n",
        "\n",
        "The parameters $\\alpha, \\beta, \\delta, \\gamma$ represent the rate of predation, reproduction, etc., and affect the shape and frequency of these curves. To generate synthetic data, we will randomly sample the parameters, and then solve for the equations."
      ],
      "metadata": {
        "id": "k3teTQu31lN5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Generation\n",
        "\n",
        "Commonly, synthetic datasets are used to demonstrate the properties of a particular method, such as the RNN, before demonstrating their applicability on real data. We generate a synthetic dataset using the Lotka-Volterra model for predator-prey interaction, contained in the utility code. Random noise is added to increase the difficulty of the problem.\n",
        "\n",
        "We treat the two populations as a single 2D input sequence. For different eco-systems, the frequency and amplitude of the trajectories vary."
      ],
      "metadata": {
        "id": "FcXKroAxu9qP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lv_tp = np.linspace(0, 50, 50)\n",
        "lv_data = generate_lv_dataset(1500, lv_tp, IRANGE, PRANGE, noise_var=0.25)\n",
        "\n",
        "visualize_lv(lv_tp, lv_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "Vzs_YBSMHz5d",
        "outputId": "fc8c099c-ffed-4848-cecd-d9e222407752"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To demonstrate the extrapolation ability of RNNs, we will divide the dataset into an encoding, validation prediction, and test prediction region:\n",
        "\n",
        "\n",
        "*   Each trajectory is split at 50% and 75% of the datapoints. \n",
        " * **Encoding region**: First 50% of data.\n",
        " * **Validation Prediction Region**: Data between 50% and 75%.\n",
        " * **Test Prediction Region**: Data after 75%, held out during training.\n",
        "\n",
        "*   The trajectories in the dataset are split into training/validation/test sets.\n",
        "\n"
      ],
      "metadata": {
        "id": "gZqFdwZLMHdU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 5))\n",
        "\n",
        "val_split = int(0.5*len(lv_tp))\n",
        "test_split = int(0.75*len(lv_tp))\n",
        "\n",
        "plt.plot(lv_tp[:val_split], lv_data[0][:val_split])\n",
        "plt.plot(lv_tp[val_split:], lv_data[0][val_split:], ls='--')\n",
        "\n",
        "plt.scatter(lv_tp[:test_split], lv_data[0][:test_split, 0])\n",
        "plt.scatter(lv_tp[:test_split], lv_data[0][:test_split, 1])\n",
        "\n",
        "plt.axvline(lv_tp[val_split])\n",
        "plt.axvline(lv_tp[test_split])\n",
        "\n",
        "plt.axvspan(lv_tp[val_split], lv_tp[test_split], color='yellow', alpha=0.2)\n",
        "plt.axvspan(lv_tp[test_split], lv_tp[-1], color='red', alpha=0.2)\n",
        "\n",
        "d_max = max(lv_data[0][:, 0]) + 1.5\n",
        "plt.text(9, d_max, \"Encoding Region\", fontsize=18)\n",
        "plt.text(31.5, d_max, \"Validation\\nPrediction Region\", fontsize=18, ha='center')\n",
        "plt.text(44, d_max, \"Test\\nPrediction Region\", fontsize=18, ha='center')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "-3AADx20NIS4",
        "outputId": "7c2ed22a-7797-45ba-c649-66b151625bd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's preprare the data for PyTorch. We'll also implement the split process here."
      ],
      "metadata": {
        "id": "uduIC10FRIE2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "class LVData(Dataset):\n",
        "    def __init__(self, data, tp):\n",
        "        self.data = data\n",
        "        self.tp = tp\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        traj = self.data[idx]\n",
        "\n",
        "        # Individual trajectories are split into train/val/test regions\n",
        "        val_split = int(0.5 * len(traj))\n",
        "        test_split = int(0.75 * len(traj))\n",
        "\n",
        "        train_traj = traj[:val_split]\n",
        "        val_traj = traj[val_split:test_split]\n",
        "        test_traj = traj[test_split:]\n",
        "\n",
        "        train_tp = self.tp[:val_split]\n",
        "        val_tp = self.tp[val_split:test_split]\n",
        "        test_tp = self.tp[test_split:]\n",
        "\n",
        "        return train_traj, val_traj, test_traj, train_tp, val_tp, test_tp\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "lv_data_tensor = torch.Tensor(np.array(lv_data)).to(device)\n",
        "lv_tp_tensor = torch.Tensor(np.array(lv_tp)).to(device)\n",
        "\n",
        "# Splitting data into training / validation / test trajectories\n",
        "lv_data_tv, lv_data_test = train_test_split(lv_data_tensor, test_size=0.1)\n",
        "lv_data_train, lv_data_val = train_test_split(lv_data_tv, test_size=0.1)\n",
        "\n",
        "# Create PyTorch data structures\n",
        "lv_train_set = LVData(lv_data_train, lv_tp_tensor)\n",
        "lv_val_set = LVData(lv_data_val, lv_tp_tensor)\n",
        "lv_test_set = LVData(lv_data_test, lv_tp_tensor)\n",
        "\n",
        "lv_train_loader = DataLoader(lv_train_set, batch_size=32, shuffle=True)\n",
        "lv_val_loader = DataLoader(lv_val_set, batch_size=256, shuffle=False)\n",
        "lv_test_loader = DataLoader(lv_test_set, batch_size=256, shuffle=False)"
      ],
      "metadata": {
        "id": "TgLbU_MfNQ1O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Model"
      ],
      "metadata": {
        "id": "o50GJpECkafF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For simplicity, we'll use the standard PyTorch LSTM to power our predictions.  \n",
        "We need to implement the autoregressive prediction though, and will use a wrapper model to do so."
      ],
      "metadata": {
        "id": "7i6tA5vHWgiK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LSTMPredictor(nn.Module):\n",
        "    def __init__(self, in_dim, hid_dim, out_dim=None):\n",
        "        super().__init__()\n",
        "        if out_dim is None:\n",
        "            out_dim = in_dim\n",
        "\n",
        "        # Use to map hidden to output space\n",
        "        self.out_net = nn.Linear(hid_dim, out_dim)\n",
        "\n",
        "        self.lstm = nn.LSTM(in_dim, hid_dim, batch_first=True)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # The LSTM returns the output for each timesteps in a (B x T x H) array\n",
        "        # The other output is a tuple of the final hidden and cell states.\n",
        "        # For the forward function, we will just pass the values through.\n",
        "        out, state = self.lstm(x)\n",
        "        return out, state\n",
        "\n",
        "    def predict(self, x, K):\n",
        "        \"\"\" \n",
        "        This method implements autoregressive prediction.\n",
        "\n",
        "        K: the number of steps we want to predict.     \n",
        "        \"\"\"\n",
        "        # First, we want to get the hidden representation of observed data\n",
        "        out, state = self.lstm(x)\n",
        "\n",
        "        predictions = []\n",
        "\n",
        "        \"\"\"\n",
        "        To predict unseen data, we use the last LSTM output as the input for \n",
        "        the next timestep. Recall, `out` is (B x T x H), meaning we need to\n",
        "        use a linear layer to map to the output dimension. This must be \n",
        "        repeated for all outputs to get our predictions.\n",
        "        \"\"\"\n",
        "        last_output = out[:, -1:, :]\n",
        "        next_input = self.out_net(last_output)\n",
        "        for _ in range(K):\n",
        "            # We want to persist the hidden/cell states for the next LSTM cell\n",
        "            raw_out, state = self.lstm(next_input, state)\n",
        "\n",
        "            pred = self.out_net(raw_out)\n",
        "\n",
        "            predictions.append(pred)\n",
        "            next_input = pred\n",
        "\n",
        "        return torch.cat(predictions, axis=1)\n"
      ],
      "metadata": {
        "id": "BNI6neUtXD7J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Training\n",
        "Now we train on the data. We want to minimize the mean squared error (MSE) between predicted and actual populations.  \n",
        "Also note that adaptive optimizers, like Adam, are believed to work better on recurrent architectures."
      ],
      "metadata": {
        "id": "k-EoNZa9e4iC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.optim import Adam\n",
        "\n",
        "lstm = LSTMPredictor(2, 100).to(device)\n",
        "opt = Adam(lstm.parameters(), lr=3e-4)\n",
        "\n",
        "# Main training loop\n",
        "for epoch in range(41):\n",
        "    t_loss_sum = 0\n",
        "    for batch in lv_train_loader:\n",
        "        opt.zero_grad()\n",
        "        train_region, val_region, _, train_tp, val_tp, _ = batch\n",
        "\n",
        "        # Fit model on training region, predict in validation region\n",
        "        n_steps_pred = len(val_region[0])\n",
        "        pred = lstm.predict(train_region, n_steps_pred)\n",
        "\n",
        "        # Take a squared loss between predicted vs actual\n",
        "        train_loss = nn.MSELoss()(pred, val_region)\n",
        "\n",
        "        train_loss.backward()\n",
        "        opt.step()\n",
        "\n",
        "        t_loss_sum += train_loss.item()\n",
        "\n",
        "    # Evaluate validation loss at specific intervals\n",
        "    if epoch % 10 == 0:\n",
        "        # Make sure to not compute gradients when computing validation loss\n",
        "        with torch.no_grad():\n",
        "            v_loss_sum = 0\n",
        "            for v_batch in lv_val_loader:\n",
        "                train_region, val_region, _, train_tp, val_tp, _ = v_batch\n",
        "\n",
        "                n_steps_pred = len(val_region[0])\n",
        "                pred = lstm.predict(train_region, n_steps_pred)\n",
        "\n",
        "                val_loss = nn.MSELoss()(pred, val_region)\n",
        "                v_loss_sum += val_loss.item()\n",
        "\n",
        "        avg_t_loss = t_loss_sum / len(lv_train_loader)\n",
        "        avg_v_loss = v_loss_sum / len(lv_val_loader)\n",
        "        out_msg = \"Epoch {}: Train Loss {}, Val Loss {}\"\n",
        "        print(out_msg.format(epoch, avg_t_loss, avg_v_loss))\n",
        "\n",
        "with torch.no_grad():\n",
        "    plt.plot(train_tp[0], train_region[0])\n",
        "    plt.scatter(val_tp[0], val_region[0][:, 0])\n",
        "    plt.scatter(val_tp[0], val_region[0][:, 1])\n",
        "    plt.plot(val_tp[0], pred[0], ls='--')\n",
        "    plt.axvline(train_tp[0, -1])\n",
        "    d_max = max(torch.max(val_region[0]), torch.max(train_region[0]))\n",
        "    d_max = max(d_max, torch.max(pred[0]))\n",
        "\n",
        "    plt.text(9, d_max-1, \"Encoding Region\")\n",
        "    plt.text(29, d_max-1, \"Val Region\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "Vzbgf7c5e2xB",
        "outputId": "489eca02-b66d-42ba-9f5a-988b05993692"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: Train Loss 22.317495019812334, Val Loss 20.486251831054688\n",
            "Epoch 10: Train Loss 3.326574771027816, Val Loss 2.8611369132995605\n",
            "Epoch 20: Train Loss 1.0462459168936078, Val Loss 0.9404670000076294\n",
            "Epoch 30: Train Loss 0.5454302579164505, Val Loss 0.5063781142234802\n",
            "Epoch 40: Train Loss 0.37089532692181437, Val Loss 0.32455384731292725\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2xH0wDzD29ep4F8y2iAuMI8LXNhsUiX2RAn4Bm9JLSO0VhreH9X9xABIi/CmpaaS6Qc7KlDRPg97IwZwKUm1UPp9fk8+7B96VThQ3QAp4E/Iq6jlVXGtV++CF9IpQC4NOy124pAX2Z1egNwqrFvA0payxjLf3vy294G6AFPAmbE4/Wz5vQwGPD/cHIKtU5sElzbMrswxFgZHxthHwuADpBXcXpIA3YfPJEiICvBkQFWize8SHyx24pHV2ZpUxICrQ6jZWM8HewQR6BpJTLZ0oro4U8LOYTIIt6SWM7xtu08ZBfl4eRAV5kymdKJJmMBhN7D1dbnX/94XEBsbKHbgbIAX8LMcKqimt1TG+X+fGp3UEaSWUtMSR/CpqdUar9j9pjtiAWArqZEGZqyN94GfZmqHmv8f1Dbf5vRLC/fnpWJHN7yNxPXZmqg2sbHWAaeYv4/6Cn6d1Om1KHIfcgZ9l+6kyEsL9iA62/ZRuaSWUtMSurDJ6hvnRPdi2hTYBXgFoFPnr7+rI/4Oo+e9dWWWM7mX73TdIK6GkeYQQ7M6yff4bILs6m/lb53Oy/KTN7yWxHVLAgaMFVVTW67mkj33mDkoroaQ5MoprKa3VkWqD/icXYjAZ+OrkVxwrO2bze0lshxRw1PQJYLcduNlKKA8yJU3ZZR7gYIcdeExADIBsauXiSAEHtp8qJT7cj5gQ2+e/4VcroeyJImnKzswyIgK86BXhb/N7eWu9ifSNlG1lXZwuL+Amk2BnZhmjbWzbuhBpJZQ0RQjBtoxSRve2bR1CU+IC46QX3MWxSMAVRXlCUZQ0RVEOK4ryuaIoLtej8lhBtZr/7m2f9IkZ2ZVQ0pSM4loKqhqsOoe1LXoG9cQkTHa7n8T6dNoHrihKLPAYkCiEqFcUZRlwC/ChlWKzC9tPlQIw2t4C3sRKGOhjm5Jpietwrg6hj/0E/MWxL9ptty+xDZamUDwAX0VRPAA/IM/ykOzL9lOl9AzzI9ZO+W8z0kooacqW9BLiQn3pGW6/4hop3q5PpwVcCJELvAacAfKBSiHE2guvUxTlPkVRdiuKsru4uLjzkdoAk0mwM8v++W+QVkLJrxhNav7bnukTgOyqbB748QH2Fe2z630l1qPTAq4oSigwA+gFxAD+iqLcfuF1QohFQoiRQoiR3brZvs9IRzheWE1Fnf3z3yCthJJfOZxbSVWDgbF2FnBPrSdbcrfIYh4XxpIUyhVAphCiWAihB74GxlonLPvwa/7b/jtwaSWUmNlyNv89to99NxLdfLvhqfGUXnAXxhIBPwNcoiiKn6Im0yYDLjXiY/upUuJCfYkLdUxTH2kllICa/x7YPZCIAG+73ler0RITECO94C6MJTnwHcCXwF7g0Nm1FlkpLptj9n87In1ipleEv8yBd3Ea9EZ2Z5Uzzs7pEzNxAdIL7spY1E5WCDEPmGelWOzKiaJqyh2U/zYTH+5PSY1OWgm7MHtPl9NoMNmljXFzDI4YLHPgLkyX7Qe+PeNs/tsBDhQzTa2ESbHBDotD4jg2p5fgoVFItVMfngt5NOVRh9xXYh26bCn99lNlxIb40iPMcU3tpZVQsiWjlGE9Qgjw7rJ7KYkFdEkBN/u/HZk+AWkl7OpU1us5lFNhd/tgU7Krsrl+xfVszNnosBgknadLCvjJohrKanVc4gD7YFOklbBrs/1UKSYB4+xsH2xKkHcQ6RXpZFZmOiwGSefpkgJu9n87egcO0krYldmaXoKvp5aUnrYf4NASQV5BBHoGklMtveCuSJcU8B2ZpQ7Pf5uRVsKuy5aMUlJ7heHl4bhfQ0VRiA2MlVZCF6XLCbgQgu2nyhxSfdkcTa2Ekq5DQWUD6UU1DrMPNkV6wV2XLifgv+a/Hf+LA7IrYVflXPtYBx5gmkmNTiUlMsXRYUg6QZfzLpnz32OcRMCbWgmlF7zrsCW9lDB/LwZ1D3J0KMweONvRIUg6SZfbge84VUZMsA9xofbt/90S0krY9RBCsCW9hDG9w9FonKMntxBCTudxQbqUgKv571IusePcwbYwWwkzS2QKpatwqkQdnzbWCfLfADnVOYz9fCzfn/re0aFIOkiXEvD0ohpKnSj/bSYh3J/T0onSZdiarua/7T3AoSW6+XWjRl8jDzJdkC4l4M7k/26KtBJ2LTanlxAb4ktPJ7CxAnhrvYn0jZRecBekawl4ZhnRwT70CHOO/LcZaSXsOpjHp43r6zxpPEB6wV2ULiPgQgh2OFn+24y0EnYd0vLU8WnOYB9sSlxAnJzM44J0GRthRnENJTWO73/SHGYrYWaJtBK6O1vS1TTe2D7OJeCX97ic7v7dEUI43QZH0jJdRsC3nSoDnC//DeohJiAPMrsAW9JLGBAVSLdA+45Pa4spCVOYkjDF0WFIOkiXEfAdp0rpHuTjNAdHTfH10tI9yEdaCd2cBr2RXVll3DY63tGhNEuNrobvDxbw5o9nyKuoJybElzlTBzAzJdbRoUlaoMsI+L4zFYxMCHXat4fx4X5yB+7m7D3j2PFprVFQW8CVX16JsegG6ipSAcitqOfprw8BSBF3UrrEIWZZrY7cinqS45w3vyythO7PlvQStBqFVAeO8WuJbr7dQGgxaErPe7xeb+TVNccdFJWkLbqEgKflVQKQFOO8Ai6thO7PlnR1fJozDrDWarSYdKFovMou+l5eRb0DIpK0hy4h4IdzqwAY7MQCLq2E7k1Vg56DORUOnb7TFp5EoPEsv+jxmBDnqpuQ/ErXEPC8SnqE+RLs53w7HzNNrYQS92PD8WJMAi7t383RobTI8OjeF+3AfT21zJk6wEERSdqiSxxipuVWOnX6BKSV0N1Zd6SQcH8vhjtwfFpbPDDyN0QdTmBDkTf5FY3SheICuL2AVzXoySqt46aRPRwdSqtIK6H7ojOYWH+siKuHdEfrJO1jmyM1OpXU6FS40tGRSNqL26dQjuSZ89+Ob5zfFtJK6J7syCylutHAlMTujg6lVQwmA+nl6ZTUlzg6FEk7cXsBP5yrOlCc+QDTjLQSuidr0wrx9dQyvp9zlc9fSK2+lutXXi/7grsQbi/gaXlVRAV5O13pcnNIK6H7IYRg3ZFCLusfgY+n1tHhtEqQVxABngGyK6ELYZGAK4oSoijKl4qiHFMU5aiiKGOsFZi1OOwCB5hmpJXQ/TiUW0lBVYPTp08AFEUhLjBO9gV3ISzdgb8B/CCEGAgMBY5aHpL1qNcZySiuYbCLdPhrl5VQVweHv4L6CjtFJbGEtWmFaDUKkwZGOjqUdhEbIPuCuxKdFnBFUYKBy4DFAEIInRDCqVTlaEEVJgFJLnCACW1YCesrYOOrsGAIfHkPrHrSztFJOsPaIwWMSggl1N/L0aG0C7OACyEcHYqkHVhiI+wFFANLFEUZCuwBHhdCnKc+iqLcB9wH0LNnTwtu13HSzh5gukqP7WathNWFsH0h7FoMumroNwV8w+DgUki9D3qkOi5gSatkldRyorCG56clOjqUdjO9z3RGdR+FSZjQKs6ds5dYJuAewHDgUSHEDkVR3gDmAs81vUgIsQhYBDBy5Ei7/lk/nFtFmL8X0cE+9rytRcSH+6lOlPIs2PIm7PsETHpInAnjn4DoZGisgVO/wA9z4Xc/gsbtz6JdknVHCgG4MjHKwZG0nwFhAxgQdnHlpaG8nLpdu6jbtRvFy5OoOXMcEJ3kQiwR8BwgRwix4+y/v0QVcKfhcF4lg2OCnLaFbHMMCW4k5fg/4c0toNHC0Nkw7nEI7/PrRd4BcMU8WP4gHPoCht7suIAlLbLuSCGDooPo4YQ96FtCb9Szs2AnsQGxJAQnULrkQyq//prGkycBUHx8CJw0EQBTYyOKoqB4uUZ6yB3p9NZNCFEAZCuKYv5zPRk4YpWorECjwciJwmqXSZ+YuaH6Y64wbaV++L3w+AGY/ub54m0m+RaIHgY/zged9I47GyU1jew+XeZSu29Qi3nSHruXtemrADBVV+MRGUm3J54g/rPPGPDBHGJ7rMX0bAinJw+j6OkHHBxx18bS996PAp8qinIQGAb81fKQrMPJwhr0RuEyFkIzvap2s9GUzJ6BcyAopuULNRq46hWozlNTLRKn4uejRZgETHExARf7D3NpmkC3aw8A3R57lJ6L3yfi/vvw8ziJsvr/oDIbjYfAN7SGsu+3Uf3Biw6OuutikYALIfYLIUYKIZKFEDOFEBf3onQQh88dYLqGAwWAylx8q7PYbkrkaH5V29fHj4HB18OWN6BSenedibVHCokN8XWJFg5NKfvoY+r9PNjWrfLib/70Iuh/7Q0eOawK71AdeW98jj4vz45RSsy47enX4bxKAn08nHIGZotkbQLghG8KR9oj4ABXvgjCpKZSJE5Bnc7AppPFXJkY5VLnL7qcXKp/+om8SYkcq8vEYDKcf8EFmwSNFuLGloPRRO7//RGhlxXE9sZ9BTy3yuUOMMncBL6heMYktW8HDhDSE8Y+qh5mZu+0bXySdrHpZAmNBpPLpU/KP/8MFAXtjdPQmXScqTpz/gXBcRc9xyvQSPQELaa6OgxlTvMGvMvglgJuMJo4ml/lcvlvsjZCwngGxoSQXlRDo8HYvueNfwICouCHp8Fksm2MkjZZm1ZIkI8Ho5xw9mVrmGpqCRqTyJjtf+eL3AJ6LJkOB5f9esHk58Hzguk8nr4E3T+fXl99iWeUa1SbuhNuKeAZxbU0Gkyu5UApz4KKM9BrAokxQRhMgpOFNe17rncATJ4HubvVnbjEYRiMJn46VsjkQVF4al3r1yv6N4nEJGwkpCKbgTodnpXZ8O1jv4p48iy47k0I7gEo6ufr3oTkWSienhhrasmfPx99QYFD/zu6Em450MElDzAz1fw3CZcySFHjPppf1f4/QkNnw85Fai580DTw8rdNnJJW2X26nIo6vUulT4QQ6DKz8P7pRRSDeki53s+XEq2Gm6pr1cPL5Fnqxcmzfv36AowlxVSt/JbGk+nE//dDFA+3lBenwrW2CO3kcF4lvp5aekUEODqU9pO5EfwjodsAEsL98fHUcDS/uv3Pl7ZCp2BtWiFeHhouc+LZlxdSu3kLp665hppjRece+8Hfj0UhZzcP7XQ4eSUk0P2FF6jfs4fiO5Jhfgj8K+n8NIzEqrilgKflVpEYE+TU46vOQwjVgdLrUvUQSaMwsHsQR/KbsXK1hrQVOhQhBGuPFDC+bwT+3q6z+yz7+CO0ERH49fv1j05/nY4CDw8qNUqzh5ctERxfR0jfRkr3magt8IQL0zASq+J2Am4yCdLyKl2mAyEApelQnQ8Jl557aFB0EEfzqzveFe6KF6St0EEcK6gmp7zepaovG09lUrtxE6G33IJmyrxzh5T9daolMN03UD28bC8/vUjUsFK8Ao0UHQhCCFTv+E+y2McWuJ2AZ5XWUqszukwPcEBNnwD0uuzcQ4nRgVTW68mvbOjYWqHxMPYR9TAzb78Vg5S0xdq0QhQFJg9yHTdG+SefoHh6EnrLzecdUvbXqR7wE8NvaTHn3SyVOWg8IGZMOXGXlXHOxSvfEdoEtxPww2eHGLuUhTBrEwTFQljvcw8lnn0HYR7K3CHGPa62nJW7cLuy7mgBw3uGEhnoGt0vhV5P1Zo1BF1zDR4RZ+d1Js+CJw4T+WwJwd7BZAd1MJd/Nt3iG6bH09eEECCMdCgNI2k/bifgabmVeGk19ItykQNMIVQHSoKa/zYzoPuvTpQO4xMMl82BU+sh42drRSpphdyKeg7nVrlU+kTx9KTPqu/p9n9PXPw9RWH1Dav506g/dWzRJl5xYYIz68MpSgvrWBpG0m7cTsAP51UyoHug63hwi45CXcl56ROAAG8P4sP9OFrQCQEHGPU7CO6p7sJlcY/NWZemep9dxT4ohEAIgTY4GM+o5mMO9Ars+MJN0jCKRsEz1IfyE/7oQp1uXK5b4CIq1z6EEKTlVbmY/9uc/770om8lRgd1LoUC4OENk56F/AOQ9rUFAUraw6rDBfSNDKB3N9d451fz009k3XgT+vz8Fq85XnacJzc82fEZmWfTMMyvoNuiX8DTi6J/vm5ZwJJmcSsBz62op6JOz2BXy3+HJqg9TS5gUHQQp8vqqGk0XPy89jDkJogaAj+/BPR5bMQAACAASURBVAadZXFKWiS/sp5dWWVcl9xK+18no+yjjzGUl+HRreUct96kZ03WGo6Wdn5WuWdUJOG/+x3VP/xA3d69nV5H0jxuJeCHc88eYLqKA8VkhKzN59kHmzIoWrVhHe9sGkWjgSvmq2X6ez7sZJCStvjuQD5CwPRhriHgDcePU7dzJ2G33tpqtWSfkD4oKJwoP2HR/cLvuRuPyEhKF39g0TqSi3GdaoN2kJZXebYIphO5O0dQcAgaKi7Kf5s550TJr2ZEfCcbI/WdrP6B2PB3GDYbvF3kZ+NCfHswjyGxwfSKcPL2BQeXwU8vUra2GsXDj5DE1n/9fT18iQ+Kt1jANX5+9HjvXbwSEixaR3IxbrYDr6RfZAA+ni4yTTvr1/4nzRET7EOQj0fnnChmFEUt7qkrga1vdX4dSbNkltRyMKeS6UOdfPd9cBl8+xiGwlyqTvsSnFCLdv3cNisk+4X242T5SYtv7zNoEBpfX0w6HSadTOdZC/cS8Lwq18p/Z26E8H4QFN3stxVFYZAlB5lm4kZA4gzY+m+oKWr7ekm7+fZAHooC04Y2///QaTg7TUfjaaL7iErC+te0q0IyKSKJQK/Ai4c7dAJjZSWnpl1H2Yf/tXgtiYrbCHhRVQPF1Y2u40Ax6uH01hbTJ2YSY4I4XlCN0dTBkvoLmfQ8GBpgwz8sW0dyDiEEKw/kMSohjOhg37af4EjOVkJqtBDSpw7vION5j7fEPUn3sHTaUjw0lmdbtcHBePfpQ+l772EoLbV4PYkbCfjhPHMLWRfZgeftB11Ns/bBpgyKDqJeb+R0qYWT5yP6wog7Yc8SKM2wbC0JAEfzq0kvquE6Z0+fQMuVkHaukIycMwdTYyPF//63Xe/rrriPgOdWoSiq4LkEWWf93y3kv80kRpsPMi1MowBMeAq0XvDzXyxfS8K3B/PQahSuSeru6FDapoVpOu2pkHzox4dYsGeBVcLw7t2L0FtuoWLZFzSetDy33tVxIwGvpFeEPwGu0sYzcxNEDgb/iFYv6xsZgIdGsewg00xgdxjzsFrYkys9uZYghODbA3mM7xtBeIC3o8Npm1am6bRFta6afUX7rBZKxMMPofH3p+LLL622ZlfFbQQ8Lc+FZmAaGuHM9jbTJwA+nlr6dAvo2HCH1hj7mGx0ZQX2nqkgp7ze+d0nTWlSIckTh9vdZbB/aH9Olp/seGvjFvDIXkfC1bVENvxdDnywELcQ8LJaHbkV9a5zgJm7Bwz1bR5gmhkUHWi5E8WMTxBM+BNkboB9n1pnzS7Itwfy8PbQMGWwa/Q+sYT+of2p1ldTUGuFWZdn7YzemhwURWAoykGskAMfOovLC3ijwciL36YBkNIz1MHRtJPMjYAC8WPbdXliTBAFVQ2U11rJPzvyd2rufcXDsO8T66zZhTAYTXx3MJ9JAyMJ9PF0dDg2p39YfwCLC3qAc3ZGAF2Nlozvoqg4rsiBD53EpQW8ok7HHYt3snx/Hk9O6c/IeFcR8E0QnQy+7YvXfDBrlTw4gIcX3LoM+kxURXz3Euus20XYkVlGSU2ja6VPLKBfSD8ui7sMP08/yxdrYlv09DfiE6Kn+FAgxuIONsySAC4s4GdK67jhna3sP1PBG7cM45FJ/VAUF5iBqa+HnJ3tTp/ArwJuFSeKGS8/uOVz6DcVvvsD7FhkvbXdnJX78wjw9mDiQNeZvGMJAV4BvD35bUZ1H2X5Yk1si4oCkSlVGBs1lKQ7eSGUk+KSAr73TDnXL9xCWa2OT34/mhnDYh0dUvvJ3gFGHSS0X8AjAryJDPS2roADePrAzZ/AwGmweo4stW8HjQYjqw/nMyUxynVaNliJekO95YtcYGf0DdMT3EdHWZoG3Zkzlq/fxbBYwBVF0SqKsk9RlO+sEVBbrD6Uz+xF2wnw8eDrB8eS2quTTZ4cReYmULTqBPkOYB5ybHU8vOCmDyFxJqx9BjbJvs2tsfFECVUNBq5zkc6D1uLDwx8y9rOxNBobLVuoGTtjtz89g+LtTfVPcnpUR7GGafpx4ChgUwuIEIL3N2Xy19VHSekRwn9+O9I1/LcXkrkRYod3uCvgoOggtmacQmcw4eVh5TdOWk/4zWL1808vqGX+E/503og3icrKA3mE+nkyvm/r/n13IyYgBoMwkFGRQWJ4omWLJc86z8LoCfRZfR2eUV0jJWVNLFICRVHigGuB960TTvMYjCaeW3GYl1cd5eqk7nx27yWuKd715aqFsAP5bzOJMUHojYL0ohobBAZoPeD692DYbfDLX9VqTSv5ft2FOp2BH48Ucs2QaNcZ2Wcl+oX2A6zkRGkGs3jrsrIQRqNN7uGOWPoqXAD8CWhx6KKiKPcpirJbUZTdxcXFHb6BEIKHP9vLJ9vPcP+E3rw1e7jr5h6Pr1ZHdA+8tsNPTYxWd+xWc6I0h0YL09+C4XfCptfgl1dsdy8X5MejRdTrjV3GfdKUnoE98dZ620zAAeoPp5Fx7TQql6+w2T3cjU4LuKIo04AiIcSe1q4TQiwSQowUQozs1sr4plbuw9VJ0fxlZhJPXz0IjcaF39YfWQlBcRAzvMNPTQj3x9tDY/2DzAvRaGDaAki6ETa/DtWFtr2fC7Fyfx7dg3wYleBi5y5WQKvR0jekr00F3GdwIr5JSRQt+BemWgubt3URLNmBjwOmK4qSBSwFJimKYpOqkJkpsdx+SbwtlrYfjdWQ8TMMuq5TuWUPrYaB3QNtuwM3o9HAxD+rufCd0l4IUFmnZ8OJIqYlR7v2JsICbh10K9P7TLfZ+oqiEPX0XIzFJZS8b9OsrNvQaQEXQjwthIgTQiQAtwA/CyFut1pk7sbJtWBshMTO/wKoTpQqq/WkaJXwPjBoGux6H3RyN/RDWj56o3CZuZe2YHqf6TYVcADfYcMIuvZayj5Ygj4vz6b3cge61kmMIzmyEvwjocfoTi8xKDqI8jo9BVUNVgysFcY+ps7slD1TWHkgj4RwP4a4Sr95G2ASJk5XnaakvsSm94n84/+heHlRt896HRDdFasIuBDiFyHENGus5Zbo6+HkOvXwUtP5A1jzkGO7pFEAeqSqf3C2vQWmrusMKKlpZFtGKdcNjXGNal8bUdVYxbRvpvFdhm1LPjxjYui7fj3B13b8sL+rIXfg9iDjZ9DXWpQ+ARjYXXWiWK0zYXsY+yhUnIaj39rvnk7GT0cLMQm4Oqlrl3uH+IQQ5BnBm5s20mvu94x75WeW77NNDxNtgD8Atdu2IfR6m9zDHZACbg+OrASfkDan77RFoI8nPcP8bFOR2RIDroGw3rD1zS7rC1+bVkiPMF8GRXes+MrdWL4vl4qKCBo0OQggt6Kep78+ZBsRP7iM+qcHc+bueyj9/VDZbrYFpIDbGoMOTqxWhVBreevRQdF2cqKY0WjVKT65e9QhFF2MmkYDm9JLmJLYvUunTwBeXXMcfX0UGu8iQE2p1euNvLrmuHVvdLZnuK93DoFx9ZTsNqH77A9SxJtBCritydoIDZUWp0/MDIoOIrO0ljqdwSrrtYuht6pTfLZ2vUG0G08UozOYmJLo/oMb2iKvoh5TYzSKYkTjXXze41alSc/wqOGVKBpBwTYfxI8vWPc+boAUcFtzZCV4BUDviVZZLjE6CCHgWIEd0yhefpB6LxxfBSVdaxDt2rQCwvy9GOEqveZtSEyIL8baPtRn34FJH3Le41alac9wPxORyVXUFnpTdaDMuvdxA6SA2xKTEY59D/2mqK1brYC5N/jB7AqrrNduRt2rTrTf9rZ97+tA9EYTPx0rYvLASDy6WO+T5pgzdQA+mhAMNYPBpL6efT21zJk6wLo3atIzHCCkbx0BMQ0QIP+IXoh8VdqSM9ugrsRq6ROAuFBf+kYGsOqQFeYTdoSAbjBsNhz4HGo63tPGFdl+qpTqBgNTB3d3dChOwcyUWP52wxC6dyvGIyCN2BBf/nbDEGamWLkf/wU9wxUFekyuJ/j++da9jxsgBdyWHFkJHj7Q90qrLakoCtenxLIzq4zssjqrrdsuxjwChga1OrMLsDatEF9PLeP7da3Wsa0xMyWWSakniB+whi1zJ1lfvKHZnuFc9yYi6UbKPv2U2m3brH9PF0UKuK0wmVTvdJ/J4B1g1aXN3fBW7LfzHMGIfqqbZtd/QGfnPx52xmQSrDtSyIT+3Vy3+6WNGBA2gKK6IgprbdjoLHkWPHEY5leon5NnIfR6yj/6mPzn52Gqt/LBqYsiBdxW5O2F6jyrpk/M9AjzIzUhjG/25dqnL0pTxj4KdaVqKsWNOZRbSUFVA1MGS/fJhVwWp/az//HMj3a9r8bbm+4vvog+O5uShQvtem9nRQq4rTiyAjQe0P8qmyw/MyWWjOJaDufa0RMO0HMMxI5QDzPduLx+TVoBWo3C5IFSwC+kV3Av+oX2Y23WWrvf2390KsE3/obSD5bQcOyY3e/vbEgBtwVCwNGV0GsC+Ia0fX0nuHZINF5aDd/YqJS5RRRFzYWXZagDKtyUtUcKuaR3GMF+lhdfuSNT46dyvPw41To72lnPEvXkk2gDfMi/93rEvBD4V1KXLfKRAm4LCg9DeZZN0idmgv08mTiwGysP5GEwtjgQyTYMmg4hPd22sCejuIb0ohqmJEr3SUvcOuhW1s9aT6CX/dsLaM+sJXp4IZGDS1AUAZXZ8O1jXVLEpYDbgiMrQdHAANt2U7s+JY6Smka2ZJTa9D4XofWASx6G7O2w5hlotNGcTgex7oh6OHelK1ZfmuxToRvoFYivh5ULeNrLTy8SGFWFf3cdcDaTp69XKzi7GFLAbcHRldBzrOqdtiETB3YjyMfDZh3hWmXk3erszG1vwduj1YIlN2FNWgHJccHWrzC0B0VH1Q87sLdwLzeuvJGCWjvXJDSp1Cw95k/W2m6Y9Mp5j3cVpIBbm+ITUHzMpukTM94eWq5NjuGHwwXUNtqxNwqAhzdMfxPuWQM+wbD0VvjsFqg4Y984rExRVQP7zlS4Zu+TnN1q3x0f+wydCPUJ5Xj5cX48bV83StNKTZ9QPY2VHhTsCUYEXeBJP7hMzY/Pd988uRRwa3N0pfp5oH3mW1yfEku93njubb/d6XkJ3L8BpvwFMjequ/HN/1Lnabog646qP8cprlh9uflfqvMp0D5/fHoF96J/aH/WZK2xy/3O0aRS0z9KR0RSNZVZflRqrvv1mrMdDanMBtw3Ty4F3NocXQmxIyHYBhVqzTAyPpTYEF/7u1GaovVU/eEP74A+k+DH+fDueMja4riYOsmatEISwv3oF2nd4iubU3wcjn0HQTGg2K/waGrCVPYX77dvGuWCSs2IMSH4D+lNwZIfaDh+Qr2mSUfDc7hhnlwKuDUpPw35B+ySPjGj0SjMTIlh08liiqsb7XbfZgnpAbd8CrOXqpWaH16jirmLUNWgZ1tGCVMGu2Dv7y1vgIcvBNl3atCU+CkArDu9zq73bVqpqfwxjZh3/otHeDiNJ872Jm8pH+5meXIp4Nbk0Bfq50HXtX6dlZk5LBaTgG8POMkU7wFXw8PbYfhv1bf1LtI75ZfjxeiNgqmuVn1ZmQMH/wcj7gSNfX3rCcEJzB44m17Bvex63wvxiIig9+pVBF939nfvgo6G52jpcRdFCri10DfAjvfU3idhve16635RgSTFBrHc3r1RWsPLH6YtUCtRV/0JMtY7OqI2WZtWQESAN8N6uFjb0q1vqZ/HPOKQ2/959J8ZHzveIfduisbbG4CqNWup0E4/r6MhoP578vMOiMx2SAG3Fgc+g9oiGP8Hh9x+5rBYDuZUkl7kRJ5sjRZ+8z50GwBf3OnUwyAaDUZ+OV7MlYmRaDUulD6pLYW9/4Uhs9QUloMoqC3gaKl97IutIYSgYtkyCt7/noYhf76ooyHJsxwdolWRAm4NTEbY8ibEDLd4cHFnmT40Bo3igA6FbeEdqObENZ7w2c1Q55xTVbZmlFLTaHC96sudi0BfB+Med2gYj/78KC/veBlQhx+Pe+Vnm0+ubw5FUYj5x9/RhoSQ+/YqjPfuOK+jobshBdwaHFkB5Znq7ttBh1+RQT6M6xvhmA6FbREarx5uVmarO3EntBiuTSvE30vL2L7hjg6l/TTWwM731IrfyIEODWVK/BQOFB/gvzv28/TXh8itqLf95PoW8AgPJ/afr6HLzqbg+eed7/fBikgBtxQhYMsCCO9rN+93S1yfEktOeT17Tpc7NI5m6XmJ+hY2cyOsmqP+3JwEc+/vywdG4u3hQr2/9/4X6sth/BOOjoQpCaob5d87v6Jef36XSptMrm8Dv1Gj6PbYY1StWkX97t12vbc9kQJuKad+Ua2DYx9Tc74OZOrg7vh6ah3rCW+NYbNVsdmzRH3r7yTsOVNOSU2ja1VfGnTq4WX8eOgxytHREB8Uz8CwgdR57G32+1afXN8Owu+7l7h338FvlON/PrbCw9EBuDyb/wUB3WHoLY6OBH9vD6YMjuK7g/nMu24wXh5O+Pd50vPqYeYPcyGsD/S7wq63F0KQXVbPnjNl7Dldzt7TFRwrqMLHU8PEgZF2jcUiDi1TB4bMcJ6OkFPip3Cs9C0UbQ3CeH4hlCP6yigaDYGXXw5A/eE0jKUlBEyY0P4FDi5TC38qc1T74eTnnS6P3mkBVxSlB/AREAUIYJEQ4g1rBeYS5O2DzA1wxQtqbxAnYGZKLCv257H+eJFzDuPVaOD69+CDq+DLu+H3P6ouFRtyorCa9ceKVME+U05JjdrFzt9LS0rPUB6Z2Jcpg7sT5OMivb9NJti8ALoPUW2rTsJN/W/Cp+ES/pqRS73x1zSKTSbXd5Cif75G/d599PxgMX4jRrT9BHMpvrma01yKD04l4pbswA3AH4UQexVFCQT2KIqyTghxxEqxOT+bF4B3kNqZz0m4tG8EEQFefLM31zkFHNQZobM/h/9Mgs9mwb3rwS/MJrc6lFPJDe9sQW8UJIT7cVn/bgzvGcqI+FD6RwW6lmXQzLHvoPQk3PiBww7NmyPEJ4Q7UkMI9Azj1TXHyauoJybElzlTB9hm+HEHiH39dU7fehvZDzxI/Ccf4zNgQOs77NZK8d1BwIUQ+UD+2a+rFUU5CsQCXUPASzPUvifjHrdb97f24KHV8JsRcSzaeIr92RUM62GbiUAWE9IDbvkMllwNKx5Wv7ayGNXpDDy+dB/h/t588/BYooNdsD3shQihpu1Ce8GgGY6O5iKOlx1ndcnrfPnIfKID7FvW3xoeoaH0XPw+WbNvJfv39xI/7za8dsxreYftIqX4VkmSKoqSAKQAO5r53n2KouxWFGV3cXGxNW7nHGx9U/U2j37Q0ZFcxCMT+xIZ6M3TXx9Cb+9pPR2hxyiY8hIcXwXb37H68i9+e4TM0lpev3moe4g3qC6evL0w7jF1sIaT4evhy9a8raw9bf95mW3hGRNDz8XvY9LpKFv4RuvNrlykFN9iAVcUJQD4CviDEOKiCbtCiEVCiJFCiJHdutl2wIHdqC6E/Z+rrgo7te7sCIE+nrwwPYmj+VUs3pzp6HBaZ/QDqv1y3fOQs8dqy64+lM/SXdk8MKEPY/tEWG1dh7P5XxAQBUNvdXQkzdIzqCeDwga1KuCOLPTx7tuXhM8/Iyqphb5B5h12k5a152iuFN/BPcctEnBFUTxRxftTIcTX1gnJBdjxDhh1qnXQSbkqqTtTEqNY8OMJzpTWOTqcllEUmPGW2kXvi7tUX7OF5FXUM/frQwyNC+b/ruxveYzOwpntcGo9XPIQePo4OpoWmZIwhYPFB8mvyb/oe8v35Tq80Me7d2+U0DgMDRoK9gSpI9nMmHfYF7SsbbYU/+AyDCsePa/nuGHFo3YV8U4LuKL221wMHBVCvG69kJychkrYtVhtGRvex9HRtMoLMwbjodHwzPJDzl2N5hsKN34I1fmw4hGLinyMJsH/LduP3mjijVtS8NQ6oZWyM+gbYOWjEBQHo37n6Ghaxdxi9ttT3170vVfXHHeKQh8mP09dWQDlJwPI3RKmjhK9cIfdpGVtc6X4daufx8PYcN5jHsYG6lbbr2GWJa/uccAdwCRFUfaf/bjGSnE5L7uXQGMVjHNM06qOEB2sOgA2nSxhxX4naTXbEnEj4MoXVIfFjnc7vcy7GzLYfqqM+dMHkxDhb8UAHcyGV6DkBEx/Q+0v48T0DOrJ9X2vp2dQz4u+11JBj90LfZJnEfTIP4kar1CT582ZTTEYJvytQw4Tn/rmh1g0ffybvTkMX3g//V553ibpok4LuBBisxBCEUIkCyGGnf1YZc3gnA5Do3rY1msCxA53dDTt4vZL4hnWI4QXvztCea3O0eG0ziUPwYBrYO1zkNvxfPiB7Ar+te4E1yZHc9MI5zpssojcvWqztJTboa99C586y4vjXuSqhKsuerylgh6HDJBOnkXY+0eIfeNNGsq9OP3S/9DltN9lkmdqvm+O+fFv9ubw7Ma/offfiuJZYZN0kZu8v7QTB5ZCTYHDWsZ2Bq1G4W83DKGqXs9fVzm+3WerKArMeBsCzfnwinY/taZRtQxGBnrz15lDXG+iTksYdGpayb8bTHnZ0dF0CIPJwEdpH7E1d+u5x+ZMHYCv5/ktJxxd6BM0dQo9P1yCNiQEjZ9fu5/3vtft1Amv8x6rE16873U7AK+uPYzwyUBXNgZdsZpWsna6SAp4eylMg19ege7J0Huio6PpEIOig7j3st58sSeHrekljg6ndfzC4KYlUJWn+sNbyocLAcUn1Gk/W//NS8v3c6asjgW3pBDs5yIVle1h0z+hKA2uWwC+TurpbwGTMPHFiS94aftLNBjUXPHMlFj+dsMQYkN8UYDYEF/+dsMQmxT6dMTt4jd8OPFLP8cjLAyh01HXjgZYw669j+fFfeSYIjAJhRxTBM+L+xh27X0IIcivMFJ3+j4aC68Dft1QWDNd5HxGUmfkyAr45kE19zj9305V/dZeHp/cj1WH8nlm+WFWP34pPp5O3HUvbqTanmDtM+qUo0seUB8vz4LMTaoXOnOj+m7oLGONY4m8/HVSe9mmotMhFByCTa+pwxoGXO3oaDqMl9aLZy95lt+v/T2LDy/m4WEPA6qItyXYy/flWlTNaXa7mA9MzekL8/2bw/yureT99yl56226P/csobNnt3gPdZ2HuHnN5PPi1ATt4eGfXiY6ZDp5FRe32LBmukjuwFvDZIKfX4Zlv4XIQXDfLxAzzNFRdQofTy0vzxxCZkktb69Pd3Q4bTPmYeh/Nax9Fr6+HxYMgTeGwspHVCtdwjiYtoD8O7eygFuZod3KH/TvO1WbWosw6mH5Q6pD5+q/OzqaTjM6ejTX9LqGxYcWk1WZ1a7nWMNqaInbJfyuuwi49FIKXniRon++jjC1XAw3MyWWLXMnkfnKtWyZO4nA8GM8v+V5dEYdT1xp+3SRFPCWaKiCpbfCxn/AsNvhru/tPvHb2ozvF8ENKbG880sGxwuqHR1O6ygKzFwIwbFw4gc1dXX1q/DQdnhS7QNSlXQ7d68oZbGYQWXKA2h3v6+mudyBrW9CwUG49p826xNjL+aMmoOP1oe/7vhru663htXQEreLxs+PuLffIuTmmyn9z3/Ie2oupoaGNp+3PX87czbMYXD4YN6Y9AY3jehl83SRTKE0R0k6LJ2t9ju5+lVIvdcl0ybN8cy1g1h/vIinvz7Ilw+MRePMzZz8wuCR3aBoLuq1rjeaePjTvaQX1fDh3akE950Koka12/mG/pp2cUWKjql/iBJnqB8uToRvBM+PfZ5I3/a167WG1TAmxJfcZq5vb/pC8fCg+/x5eMbEUPreexgeexSvHi3PHD1QfIDHfn6MhOAEFl6xEH9P1cLannSRJcgd+IWcWKt2yasrhd+ugNH3uY14A4QHePPstYnsPVPBvJVpmExOnnLQel4k3kIInvnmEJtOlvDXG4Ywvl+E+v/oujfUsvwfnrJ7SbPVMBnVw1uvALjmNUdHYzWuSriK4VGq9batojJrWA2t4XZRFIWI+++jz5of8OrRQx2Y/M1yTLqL7bjeWm8GhA5g0ZWLCPZu0tzOxqX2UsDNCAGbXlfbm4b2VPPdvRwzoNjW3DA8lvsu683H20/z+P/2ozM4ccOrZnjr53SW7c7hsUl9mTWyya5I6wG/WawOlv7mATixxnFBdpbtCyF3N1z9DwhwoQET7UAIwd93/p1/7PpHq9dZQ3yt6XbxONvDqX7vXvKffpqsG2+i4ahqyT1UrFY5DwwbyEdXf0SEb5O+O+ae4k1K7fn2MauKuEyhgOo3XvGwWgWY9BuY/hZ4td8P6mooisKfrxlEmL8Xr6w+RmW9nndvH46fl/O/HL7Zl8M/153ghpRYnmiuz4mnj9qa9r/XqYfPd3wD8WPtH2hnKM2An/+iFjMNudHR0VgdRVHQm/R8ceILrutzHYnhic1eZxbZ1lwo7XGpWDt94TdiBHHvvkP+c8+RedMsTs4YyvN99vPihL8yvc/0i2sP7NBTXLFnj4yRI0eK3bYYMFqaoZZfD7ut4y6R/IPqL3pltmpdG/OwW6VM2mLZrmzmfn2Q5LgQltw1ilB/r7af5CC2ZZTy2w92MCI+lI/uGd36yLjaEnXqT00R3P29Or3GWakrU/vr7HxPbZL20I5OH5jf/N42AP53/xhrRmg1qnRVTP9mOjEBMXx89cdoOzFH9kKLIKg7dFv5yS+kqOAUm/74WxL3lJI3NIbxn63CW9vMRK75IajDyi5EUfurdABFUfYIIUZe+Ljrp1COfQ+LLleH5C66XG34U9OOvuNCwJ4P4f0r1BL5u76HsY90KfEGmDWqB+/cPoIj+VXc9N428ivtP3y2PaQXVXP/x7uJD/fnvdtHtj3v0z9C3X17B8DHN0ChE84ZKTsF3z8JryfC+r9A9DA1Zhd3O7VGkFcQT456kkMlh/jq5FedWsNeDbGaKwTaU7iHmzf+jleubiT/z3cw+uF5QeeGCAAADUZJREFUeGu9MdXVYay+wNllh57irivgRgOsm6da/cJ6wwOb1d3z/s/g3yNg29uql7Y5dLVqjvTbx1U/8QOboOcl9o3fiZg6uDv/vTuVgsoGbnxnGxnFNY4O6TyKqxu5a8kuvDy0LLlrVPsrLUN6wB3LQZjgvUvVHiuNTmCfzN4J/7sD3hwOe/8LQ36j2iNv/xJi2zGv0cW5tte1jO4+mg8Of4DBZOjw8+3REKslL/r643kEegXy6bWfMum3fybgsssAKPvkU9InX0HxwoUYa87+/rS3p7gFuGYKpaYIvrwHsjbBiLvhqld+7Y9cfALWPA3pP0JEf/V7fZsMfi0+oaZMio/B5U/DZU9e5HLoqhzOreSuJTsxCfjw7lEkx1lWul3TaEAIQaAFw4LrdAZuWbSdk4U1/O/+SzoXU00x/DQf9n0CAd3VKUBDbrLOuy2TEYqOQs4uqMoFrTd4eJ39fPZD66V+1tXC7g8gewf4hKhtYVPvg0DrzS519hSKmcLaQorri0mKSEII0aHeNeNe+blZi2BsiC9b5k6ySnzn3UPTgIf/SQzV6qHohj9dhofm/POihqNHKf73W9T8/DOa4GDC776L0NtvR3tqlVUm27eUQnE9AT+zHZbdCQ0VMO1fMKyZySRCqA6ENU+rb1MHXANTX1a7uq18TBX737wPfazzP9udyCyp5Y7FOyiv1fGf345kbN/OTbMprWlkxttbKKxqYEyfCKYkRnFlYhRRQW0PIhBCkFFcy9aMEr7Zl8uB7Areu2MkVyZaOP0oZzesehLy9kHPMarTIzq5Y2vUFKsukeydqmjn7QNdB96xhMSr7xSH3aamd6yMqwh4UxbuX4jBZODRlEfbJeT2yIH3mvs9AhMegUfwjlyN4lFJbcYcMAST+cq1LT6v/nAaJW+/Tc369QRcMZkeb71llXhcX8CFUFu5rntOnY5x88dtH0yZ279ufBUMDWAyQI9L1GZJQTGdi6MLUFjVwG8X7+RMWR1L77uEoR0cjKwzmLh98Q4OZFdwy6gebDhRTNbZqUBDe4QwJTGKqYOj6Bv5a1/rgsoGtqSXsCWjhC3pJRRWNQLqruoPV/TjppEtF1F0CJMJ9n0MP72gTv8Z+TuY+OeLqx3ry9XD8dIMKMuAkpPqLMryLPX7Gg/19Rc36uzHSHXQsMmgvu4MjWA0f9apn4UJopJsOsvS1QRcCMGL21/kyxNfcnWvq/nLuL/gpW37IN3SXimtrWE0GRnz5gJqfH9A61OASRdOQ95NGOsT2r3Lrz90GMXTA5+BA9EXFFC7fTshM2d2KL6muLaAN1arh5Np36iFGjPe7lhntuoCtbLNLxwun6sWh0hapai6gRsWbqVeZ+Trh8YSH97+4QjPfHOIT3ec4Y1bhjFjWCxCCNKLalh7pJC1aQUcyKkEoHeEP8N6hnAgu4KM4loAQv08Gds3gnF9IhjXN5yeYX62aQ1bXw7r/6p2M/QJUXtt1xSpYl2aAfVlTS5W1Hx69LBfBTt6qFNaTV1NwEEV8Q8Of8CCvQsYHjmcNya+QYiPbTsvtraLHz/Qmyu/mIq+MYSG4okYqpIBbad3+WWffopHZCRBV17Z6XhdV8CFgA+nwZmtcMV8dQ5lF3OKOIqM4hpufGcrwb6efPXgWMIDmrFKXcAn20/z7PLDPDChD3OvHtjsNfmV9fx4pJC1Rwo5nFvJ0B4hjOsTwdi+4QzqHmTf8v6CQ7D6KTi9BYJi1QPx8D4Q3hfC+qhfhyaoOWwXwBUF3MwPmT/wzOZn6BnUk2XXLcNTY7uN1vl5dCMewXvx8D9FWN2dbJ07mWNlxziS5cc/16ZbtMu3Fi0JuPNXbigKTHxaFXI3rYx0Vvp0C+D9O0dy63928Lv/7ubzey/B16vlA9/tp0qZvzKNSQMjW62aiw725Y4xCdwxJsEGUXeQ7kPg7lXq4AQP5/XAdwWu6nUVUf5R5FTn2FS8AfIqatD45OHhn45nyE40XuUY62PJL1DfeQ0MG8jAMLhh+MVj4ZwJ5xdwgITxjo6gyzIiPow3bknhwU/38Ojne3n39hF4NDMoOLusjoc+3Ut8uB8LbhmG1pmbZDWHFG+nICUyhZTIFADWnV7HhuwNpEanMipqFNEBnffHm4SJ9Ip0Qr1D6ebXjW5RGdSHfQCAsa4ndQUzMNYOIDbE+dJireEaAi5xKFcldeeF6YN5fkUa81am8ZeZSeflpWsbDdz70W4MRhPv3zmKIAtsgxIJqHnxTTmbWJ+9nhUZKwCIDYhlQtwEnh79dIvP0Zv0eGm9MJgMbMjeQGFdIXuL9rKrYBdlDWU8MeKJ/2/vfmOrLM84jn9/rUBR1NLQKogKjK1Kmq0ia0QQ0cX5ZyZiJEamzugS3YKJf7ZMNBoQwws1qDExEowMXzi10f3xhfFPlIivlOpAYYooumwMWhZLZYnWQK+9eO7ioZyWegq97xuvT3Jynt49T/jlSs/Fc+7nOffDDU03cNs5l3Df69189eUkbG9xMj32rd0q4Q3cDcqvZk7iP7u+ZsWbnzKhdjQLz5sKQE+P8bvWDXzcvpvV17cw+Ui6E7yLRhJLZy1lydlL2NK5hbb2NtbtWEfXN137XrPw9YV07+mms7uTzq876ezuZN7UeSyeuZgqVXH7m7fTYz00HN3ArAmzaBnfwszxxbmBBTMaGV197ZCvZInNG7gbtD9c2MiOrq948JXNnHhcDVecOZFH39jCy5t2cPcvTmfOj+pjR3RHmCpV0VjXSGNdI1effvW+8b09e6mprqGru4sJYybQNK6JsaPG0tzQvG+/1ktbqR1VS8PRDWWvZDrca3UPB2/gbtCqqsQD839Cx+5u7njhfTa372bl2q1cMX0iv549OXY89z1SXVXN8rnLB3xNY11e0yGVyHctFBfFyKOqWHHtmUxtGMPKtVs545Rall3edHiu1XbODciPwN13dlzNCFZf38ITb23lpnOnpH2He+eOYN7AXUVOPL6Gey4tvyC/c254+BSKc85lyhu4c85lakgNXNJFkjZL+kTSokMVyjnn3MFV3MAlVQOPARcD04AFknxS1DnnhslQjsBbgE/MbKuZfQM8C1x2aGI555w7mKE08JOAf5X8/O8wth9JN0pqk9S2c+cgbjbsnHNuUA77SUwzW2lmM8xsRn29f9XaOecOlaE08G1A6X2uJoYx55xzw6DiO/JIOgr4GPgZReNeB/zSzDYNsM9O4J8V/YMwDvhvhfsOlxwyQh45c8gIeeTMISPkkTNWxlPN7IApjIq/iWlmeyTdDLwCVAOrBmreYZ+K51AktZW7pVBKcsgIeeTMISPkkTOHjJBHztQyDumr9Gb2EvDSIcrinHPuO/BvYjrnXKZyauArYwcYhBwyQh45c8gIeeTMISPkkTOpjBWfxHTOORdXTkfgzjnnSngDd865TGXRwHNY9VDS55I+kLReUlvsPL0krZLUIWljyVidpNckbQnPYxPMuETStlDP9ZIuiZzxZElrJP1D0iZJt4Tx1GrZX85k6impRtI7kjaEjPeG8cmS3g7v8+ckjUww42pJn5XUsTlWRgDMLOkHxTXmnwJTgJHABmBa7Fxlcn4OjIudo0yuOcB0YGPJ2APAorC9CLg/wYxLgN/Hrl9JnvHA9LB9LMWX2KYlWMv+ciZTT0DAmLA9AngbOAtoBa4K4yuA3yaYcTUwP3YNex85HIH7qodDYGZrgS/6DF8GPBW2nwLmDWuoPvrJmBQz225m74Xt3cCHFIu3pVbL/nImwwr/Cz+OCA8DzgeeD+NRazlAxqTk0MAHtephAgx4VdK7km6MHeYgTjCz7WF7B3BCzDADuFnS+2GKJerURClJk4AzKI7Kkq1ln5yQUD0lVUtaD3QAr1F8yt5lZnvCS6K/z/tmNLPeOi4LdXxY0qiIEbNo4LmYbWbTKW5wsVDSnNiBBsOKz4jJHVkAjwM/AJqB7cDyuHEKksYALwC3mtmXpb9LqZZlciZVTzPba2bNFIvgtQCnxcxTTt+MkpqAOymy/hSoA+6IGDGLBp7Fqodmti08dwB/ofijTFW7pPEA4bkjcp4DmFl7eAP1AE+QQD0ljaBoik+b2Z/DcHK1LJczxXoCmNkuYA0wE6gNi+RBQu/zkowXhSkqM7Nu4I9ErmMODXwd8MNwhnokcBXwYuRM+5F0jKRje7eBnwMbB94rqheB68L2dcDfImYpq7cpBpcTuZ6SBDwJfGhmD5X8Kqla9pczpXpKqpdUG7ZHAxdQzNWvAeaHl0WtZT8ZPyr5z1oUc/Rx/y7DWdakhUueHuHbVQ+XRY60H0lTKI66oVgg7E+pZJT0DDCXYhnMdmAx8FeKM/6nUCzve6WZRTuJ2E/GuRQf943iCp+bSuaah52k2cBbwAdATxi+i2J+OaVa9pdzAYnUU9KPKU5SVlMcRLaa2dLwPnqWYmri78A14Ug3pYxvAPUUV6msB35TcrJz+HPm0MCdc84dKIcpFOecc2V4A3fOuUx5A3fOuUx5A3fOuUx5A3fOuUx5A3fOuUx5A3fOuUz9H62R0Zxaw1ydAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nice! It looks like our RNN can perform extrapolation quite well. We confirm the generalization by evaluating loss on both the test region, and the trajectories in the test set.\n",
        "\n",
        "You can make arbitrarily long autoregressive predictions. To get predictions in the test region, we just continue the autoregressive process. However, predictions typically become less accurate as the length of the prediction increases."
      ],
      "metadata": {
        "id": "5Qfw17psS5G-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Results"
      ],
      "metadata": {
        "id": "_MyQZXKIb_NI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss_sum = 0\n",
        "\n",
        "for batch in lv_test_loader:\n",
        "    opt.zero_grad()\n",
        "    train_region, val_region, test_region, train_tp, val_tp, test_tp = batch\n",
        "\n",
        "    val_len = len(val_region[0])\n",
        "    test_len = len(test_region[0])\n",
        "\n",
        "    # Can set arbitrarily long sequence\n",
        "    n_steps_pred = val_len + test_len\n",
        "    pred = lstm.predict(train_region, n_steps_pred)\n",
        "\n",
        "    val_pred = pred[:, :val_len]\n",
        "    test_pred = pred[:, val_len:val_len+test_len]\n",
        "    test_loss = nn.MSELoss()(test_pred, test_region)\n",
        "\n",
        "    test_loss_sum += test_loss\n",
        "\n",
        "print(\"Test Loss: {}\".format(test_loss_sum / len(lv_test_loader)))\n",
        "\n",
        "# Plotting result\n",
        "with torch.no_grad():\n",
        "    ground_truth = torch.cat([val_region[0], test_region[0]], axis=0)\n",
        "    cat_tp = torch.cat([val_tp[0], test_tp[0]])\n",
        "\n",
        "    cat_pred = torch.cat([val_pred[0], test_pred[0]])\n",
        "\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    plt.plot(train_tp[0], train_region[0])\n",
        "    plt.scatter(cat_tp, ground_truth[:, 0])\n",
        "    plt.scatter(cat_tp, ground_truth[:, 1])\n",
        "\n",
        "    plt.plot(cat_tp, cat_pred, ls='--')\n",
        "    plt.axvline(train_tp[0][-1])\n",
        "    plt.axvline(val_tp[0][-1])\n",
        "\n",
        "    d_max = max(torch.max(cat_pred), torch.max(ground_truth))\n",
        "    plt.text(9, d_max-1, \"Encoding Region\")\n",
        "    plt.text(29, d_max-1, \"Val Region\")\n",
        "    plt.text(42, d_max-1, \"Test Region\")\n",
        "    plt.title(\"Dots show ground truth. Dashed line shows model prediction\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "rq49k1NIS4y9",
        "outputId": "f477658e-95aa-4399-c190-1bce118365e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 3.621645927429199\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scheduled Sampling"
      ],
      "metadata": {
        "id": "MehMgRnbi4a_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "While our toy problem is quite simple, autoregressive prediction can often be difficult to train. In particular, reusing outputs as inputs can cause errors to accumulate as the length of prediction increases. \n",
        "\n",
        "One common enhancement is to apply Scheduled Sampling (https://arxiv.org/pdf/1506.03099.pdf). During training, we randomly allow the model to use the true input, thus correcting any accumulated errors. In practice, this allows faster convergence and better performance.  \n"
      ],
      "metadata": {
        "id": "tcM2dxRzJUk7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![scheduled sampling.drawio 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ZH79NNP4+GHH66dR6i1r+0x1ApZmROp5TRp0iTFi8KI8xIdDmiaI4F6CIR73smHnwggveeddvqJkeeEVhhu2rRJd+50uDmGoenV+rz55pvK83bw4MGKGDPz7Atto7Yuc+bMqX2+C0b1ea99duv1iKrTkUTYhntfMDBJIFoCFIbRkosyn5EeQ+2ikNAHhfqgUs2rQkj7czXPmDFj6nyp1icM5ataHoDaNPJA0s6HbOjhGCrOQuukDoGrdRfh+Morr+Cuu+5SRJ28KMIJQxkC174wtA9XEYZ69bd6HmWULmc2EohZAtE87yJ9TmiFkwgtvXnN4YRhaHo1nYyiiOBSF76ZefZph6rVQFCnCL3//vu1H7XhhKH8XCv+tHURYaj3vtAb8o7ZIGTDIyZAYRgxMnMZonlQOtFjqCesIu0xVIWh/K32FKhD2tqeTy3B0B6+aHsMKQzNxSVzk4AdBMw874w+J+zsMYxUGOo9+7RTgEJ3gAh9LkbTY0hhaEfkxnaZFIYO+9/Mg1I7HKudtyK9aeHmGGrzaOfvSbO1Q8nhhJV2/oxVcwxlJaJ2nsyMGTNqv/KjnWNIYehwINMcCRggEOnzLjS9keeEVhhKD5p2lEN9fl199dVRzTGMRhiqPX9qz2XoHEN5jmqf12p9o51jSGFoIBCZJCICFIYR4TKfONycG3VooaGhgXCrj42uSk5JScGoUaOUhhgRhtpVyRMmTMDy5csNr0pW86qTprVDKtr5h9pVyQ888ADKysqUIZxIViVTGJqPTZZAAlYTiFQYin0jzwlZbaw+y0Ln4OmtMlbFmZQfyarkaIVh6LNP+9wPt9OEPBPlz6FDhyJalUxhaHXUsjwKQ8aAYQKhk6gNZ4wgYUMvkgiKYlISIAES8A0Bvfnivqk8KxooAhSGgXKntY3R9uRJyWZPMglXu9AVzeqKRWtbw9JIgARIwFsEQhemhNsr0Vu1Zm2CToDCMOgeZvtIgARIgARIgARIwCABCkODoJiMBEiABEiABEiABIJOgMIw6B5m+0iABEiABEiABEjAIAEKQ4OgmIwESIAESIAESIAEgk6AwjDoHmb7SIAESIAESIAESMAgAQpDg6CYjARIgARIgARIgASCToDCMOgeZvtIgARIgARIgARIwCABCkODoJiMBEiABEiABEiABIJOgMIw6B5m+0iABEiABEiABEjAIAEKQ4OgmIwESMBZAldffTXef/99Z43SGgmQAAnEOAEKwxgPADafBLxKoFGjRqipqfFq9VgvEiABEggkAQrDQLqVjSIB/xNgj6H/fcgWkAAJ+I8AhaH/fMYakwAJkAAJkAAJkIAtBCgMbcHKQkmABEiABEiABEjAfwQoDP3nM9aYBGKCwOOPP45HH300JtrKRpIACZCAVwhQGHrFE6wHCZBAHQJcfMKAIAESIAHnCVAYOs+cFkmABAwQoDA0AIlJSIAESMBiAhSGFgNlcSRAAtYQ4FCyNRxZCgmQAAlEQoDCMBJaTEsCJEACJEACJEACASZAYRhg57JpJEACJEACJEACJBAJAQrDSGgxLQmQgGMEOJTsGGoaIgESIIFaAhSGDAYSIAFPEuDiE0+6hZUiARIIOAEKw4A7mM0jAb8SoDD0q+dYbxIgAT8ToDD0s/dYdxIIMAEOJQfYuWwaCZCAZwlQGHrWNawYCZAACZAACZAACThLgMLQWd60RgIkQAIkQAIkQAKeJUBh6FnXsGIkENsEOJQc2/5n60mABNwhQGHoDndaJQESaIAAF58wREiABEjAeQIUhs4zp0USIAEDBCgMDUBiEhIgARKwmACFocVAWRwJkIA1BII+lLxn6RcoXrcWx8vK0LZvX6T37o1GjeKw5cMZqKmpQVJmJjoM+xkSUlKsARoDpexZthQH1qxBxaEytO0TwhTCNAuZw4ajSTKZWh0Ou5d9gQNr1+L4wRD2H6nxTPZWM7erPApDu8iyXBIgARIIIXCksBBNkxJRXVwMeZHWVFcjJaM9mqWmolF8vJL6SHExSvPzcaS0FFlDhyIpuyviKGTCxlJdpkt/ZJoRwrRIw3Q4krKzydSCu/Mn8VxTg5R2YdiXlSJrCNlbgN32IigMbUdMAyRAArFO4MDmjSiYNw8JCU3QeeDpEeOoqDyOHcuXo9OFFyOla7eI8wcxw4GNG1CwYL4ppjuXL0fHCy5GSjcyjSRGSn6M5yZNmqDz6dHF886vl6PjRYznSLg7lZbC0CnStEMCJBARgaAMJZfv3o2NkychtW07dDzrrIgYaBMXrFyOA7t2of2wEegwdFjU5QQho2VMV63AgYICtB8+Ah2GxDZTo3FRvmc3Nrw+EaltM9DJTDyvWI6SXbuQ8bNz0WHIUKPmmc4BAhSGDkCmCRIggcgJBGXxSeWO7Ti09Xsktm4dOYSQHGUFBUBiItJHnGe6LD8XQKbueY/s3WPvlGUKQ6dI0w4JkEBEBPwuDKVXK6HiGKqKiyJqt5HE8a3T0LhzFyNJA5WGTN1zp/QUJhxjPLvnAecsUxg6x5qWSIAEIiDg56Hk4rVrsW3GB8gePARJFvQU6mHL/24d0s8eFDNzDmUF97bpDjA9ZxBSsjnnUBtzRevWYrsT7GMoniN4FDqelMLQceQ0SAIkEHQC3018BYlNGiMrioUmRtnkr/gaRyur0GvMXUaz+DrdD0ybIGvgQNvaEWtMjYJcP/FlNFPYR77QxKgNhf3xKvS6Izbi2SgXN9JRGLpBnTZJgAQCS0C28Mj7aDo6n36G7W3cPHcuMkeORNqpfWy35aYBYZo/cwY62ShM1PZtnjsHmSMvCTxTo/4sL9yD/A9noPMZTsTzHGRefAnSegc7no2ydysdhaFb5GmXBEigXgJ+HUquPliG41u/d8S7R8tKkdx/QOD35CNTR8JJ14iT7I+VlaJFDMSze940ZpnC0BgnpiIBEnCYgF8Xn8iqTTsWnITDHwsLUcjU4ZtPY47s3WPvlmUKQ7fI0y4JkEC9BPwoDPd8uQzVBflI69rVOe/Gx+N4q9Zo0bGTczYdtFT41TJU5echzcmNvQPO1Kj7FPYFeUhzcjEO2Rt1j23pKAxtQ8uCSYAEzBDw41CybGSd0rIl2nTvYabpEeXdv2ULykoO4KTbx0SUzy+JN8jm4GTqirvI3hXsrhulMHTdBawACZBAUAis/usz6Ny3P5LatHGsSYdkscu6Nej7wEOO2XTS0A9M+yGpTbpjZn9guhZ9HxjrmE0vGlr9l2fQuZ8b7IMbz170c2idKAz94CXWkQRIwBcE8mfNRJv27dEoPt6x+lZVVeL7BQvQ58GHHbPppCFXmFZW4vuF89HnwXFONtVztlxhH/B49pyTdSpEYegHL7GOJBCDBPw4lFy5Zxeqdu923Fv5G9Yj+7obIfMyg3a5xnTjBmRfe0MgmRqNEbfYF2zYgBOui232Rn1kRzoKQzuoskwSIAHTBPy4+KSqcA8qdxWYbnskBVRWHsemOXNw2vjHIsnmm7RuMd08Zw76B5SpUee7w74Sm+Z8Gth4NsrezXQUhm7Sp20SIIGwBPwoDDdOfg3tu3VDok3H4OnBOlxYiB2rv0W/scEc9pQFPe27negC02/Qb+wjMX2Hkn1sup/CMDb9zlaTgOcJ+HEoecPrk5Cc3AJtT+7lGN9jZWUoRyNkDB3mmE0nDW14fSKSk5NdYApkDB3uZFM9Z4vx7DmXOFIhCkNHMNMICZBALBDY9vFHOLotF9lDhjrX3Ph4NO3d13J7Bw8eVASZ21eQmOqx9Apnvbptm/URjuXm4oQAxLPbcewn+xSGfvIW60oCJOBpAkf2FqJi+zY0a9LEsXqaPflEemYfe6zu/MRhw37ofayoqMCiRYuQkJDgWHtCDR3ZV4iKbf5iqgfL65z16qzEs7BP8E88uxaoATJMYRggZ7IpJBAkAn4cShb+Tp4tu3/LZlQ1b45Ol4yy3PV79uzB+PHjsWnTJsyfPx9Nmza13IbRAoPCVK+9XuKsV78gszcaf7GWjsIw1jzO9pKATwj4cfGJirZ8zbeIr662nbSs3mw3ZCgyzhlsm63bb78dLVq0wPPPP2+bDSMFO8V085xP0dZmpnrt9Qpnvbo5yn7wUGQMsi+ejcRarKehMIz1CGD7ScCjBPwsDGV1crPG8ejQ/zTb6BblbMWBffvQ67d32mZDCi4qKkL79u2Rl5eHdu3a2WqrvsJ/YNoYHfr3t60OTjHVa4BXOOvVTVYny/SIDv1sZr93H3qNsTeebQueABVMYRggZ7IpJBAkAn4dShYflOZsxdZ3/oVOAwYgJTPTFrfEZ7RHVWISmrZsaUv52kKvuOIKjBo1CjfddJPttsIZEKY57/wTHQcOREoH/zPVa6cXOOvVqzRXE88BZe9aYHvQMIWhB53CKpEACfifQMGihYgrP4zWHTpY2piqimNIyOiAxp27WFpufYW9+uqr2LBhAyZNmuSYTT1DBYsXIu5wMJjqtc8rnPXZL0Dc4fJAxLOrQewD4xSGPnASq0gCJOBfApU7tqOquMiSBhTn5GCfbIcz+jq06NjJkjKNFLJ06VKMGzcOixcvNpLc9jSWMs3Nwb6cXGRfez1adOxoe93rM+A1znp1tZR9bTy7z95Vx3vMOIWhxxzC6pAACfxAwM9DyaE+3PbeFFSWHECnM8+K2r27V3+L/Tt2IGPwUGSNODfqcqLJKCtnzznnHOTk5EST3ZY8ljBdsxpFO3ag3aAhjjPVg+JFznr13Pbeu6gqKUFHU/Es7LejnQvxbEtABqhQCsMAOZNNIYEgEfDz4pNQP5Tm5iBv9izUlB9G1oCBSEpLi8hVccnJKNm3Hwnt0pGafWJEea1IXF1djVatWqG0tNSK4iwpQ5junD0LOHwYWQOjY3pg3340TU9HajfnmepB8CJnvXqW5gj7mag5cgQdzcRz27ZI7drNknhgIdYRoDC0jiVLIgESsJBAkIShimXfqpVI694d1cXFqCotwf6NG9GsZSoSW6chvnFjJVnlsWPYt3EjjpSVoiYuDj1/eQviklMsJBtdUY0bN0ZlZWV0mW3Mpcs0NRWJaf9jWiVMN21EeUkJauLjPcNUD4tXOevVde+Kr9Gm50kh8dwSia1aIf7HTd7rsJd4vvlWT8SzjSHp+6IpDH3vQjaABKwjsGLFCnz88cdYtmwZtmzZgn379uGhhx76yckY1lmsvyTZVDk9PR3du3fH2WefjcsuuwwDBw50yrytdo7u34+c99/D8cOHUHP8OE695Vdo1CgO5QcOYMf8eUju3AWtTj4ZKR7pUfGDUFeZVh4+jOqKCpx66/+Y7pw/D827dEGrk05Caldv9BDqBZgfOOvV+2jRfuRMm4qqw4dQdbyiTjzvnD8fzU/ojNY9vRPPtt7cPi+cwtDnDmT1ScAKAuvXr8fDDz+MdevWYfTo0Rg+fDh69eqFjIwMxMXFWWEiqjJkaE3mXUn95Gi2qVOn4tRTT8XTTz+t1I+XcwT8KlicI2SNJXK2hiNLiZ4AhWH07JiTBAJB4JNPPsFVV12FJ598Evfdd5/n2zRhwgQ88sgj+OCDDzBy5EjP1zcoFaRgccaT5OwMZ1oJT4DCkNFBAjFMQHriZGjWiyKrvlXJqpiVoW/2HDoTwBQs5OwMAVpxmwCFodseoH0ScJGAnGYxdOhQT/YUNiREpOfw888/x8yZM10kGDumG/JH7JCwt6XkbC9flt4wAQrDhhkxBQkEkoD0tl1zzTXIzc31ZPuMvCCzs7Mxbdq0wCxI8aQjfqyUEX94uf5+qRs5+8VTwa0nhWFwfcuWkUC9BMaPH4+qqiplIYcXLyMbXMuCmfj4eDzxxBNebEKg6kTB4ow7ydkZzrQSngCFIaODBGKUwIgRIzB27Ficd955viUwb948PPvss1iwYIFv2+CXilOwOOMpcnaGM61QGDIGSIAEQgh07NgRX3/9NTp06OBbNrt27cIZZ5yBvLw837bBLxWnYHHGU+TsDGdaoTBkDJAACYQQaNasGcrLy13dp7A+pxgZSpZ9DpOSknD06FH612YCFCw2A/6xeHJ2hjOtUBgyBkiABDQEKioqIKeK1NTUeJaL0Rek0XSebahPKkbOzjiKnJ3hTCsUhowBEiCBEAJefwEZrZ/RdF4OADnV5fXXX1dWWHv18hvnWbNm4d133/U0Uz1f+42zXhsWLlyIyZMn+469V+89p+vFxSdOE6c9EvAIAa+/gIwMJQtKr7fDiLsvuugiLF68GFOmTMEVV1xhJIvjafzGWY51/PLLL/HRRx/hwgsvdJxXtAb9xlmvnWo8//vf/8bll18eLQrmc4kAhaFL4GmWBNwmEIQXUBCE4dKlSxXhcujQIZxyyinKedVevPwUL/Pnz8eVV16J0tJSnHbaaVi5cqUXkerWyU+c9Rrgl3j2TUC4UFEKQxeg0yQJeIGA319AKkO/t0N6V+bMmaM0JyUlBf/4xz882WvoJ87SWyg9sHI1b95cOfJROPvh8hPncL2F2nh+++232Wvoh8DT1JHC0GcOY3VJwCoCXn8BxcJQsrZ3RfWrV3sNvR4vKj9tb6H6Mz/1GvqFc0O9hV6PZ6ueo0Esh8IwiF5lm0jAAAGvv4CM1s9oOgNIHE+i7S1UjXu119AvnLW9hSpTP/Ua+oVzQ72FXo9nx292HxmkMPSRs1hVErCSgNdfQEbrZzSdleysKEt6C88991xl2yARg2VlZco8Q2mPnAG9efNmK8xYVoYfOEtv4ahRo9C4cWOkpqYqPEtKSpRjE0866SSsXbvWMh52FeQHzuF6C/0Uz3b5LwjlUhgGwYtsAwlEQcDrL6CgDyXffPPN+PTTT/Hiiy/ixBNPxO23367MNXzmmWfw2muvKfPiRo4cGYVn7cni9XiRVl977bX473//i5deegnt2rXDuHHj8P777yvngb/11luYPn06LrjgAnsAWVSqHzjrNdVv8WyRuwJZDIVhIN3KRpFAwwT8+gIKbVkQ2rFq1SpFGKqrZ/fv3482bdo07EQHU/iN85IlSxRhKEJRrj179iAjI8NBYtGZ8htnvVb6IZ6j805s5KIwjA0/s5Uk8BMCRnvkvI4uiC9SLzL3G+dQYehFpnp18htnI8LQL+xZzx8IUBgyEkiABDxJwKhw5YvUGff5jTOFoTNxQWHoHme7LFMY2kWW5ZIACZgiYFSIGE1nqjI2Zw4derPZXFTF+40zhWFUbrYkkx/i2ZKGBrQQCsOAOpbNIgG/EzAqRIym8zIPP7xI/caZwtC9iPdDPLtHx/uWKQy97yPWkARikgCHkr3ldgpDZ/zhN84cSnYmLpy0QmHoJG3aIgESsJwAX6SWI9Ut0G+c2WPoTFxQGLrH2S7LFIZ2kWW5JEACjhDwm2Dx64vUb5wpDB25/XSNcCjZPfZWWKYwtIIiyyABErCcAIeSLUdqqkAKQ1P4DGf2G2e/fugYdkgMJqQwjEGns8kk4AcCRl+QRtN5uc1+6GHxG2f2GLoX8X6IZ/foeN8yhaH3fcQakkBMEjAqRIym8zJEP7xI/caZwtC9iPdDPLtHx/uWKQy97yPWkARikkAsDSXv2rULs2fPxm233eZZX/tNGO7YsQMLFy6EnOHrp8tvnPXY+iGe/RQTTteVwtBp4rRHAiRgKYEgvEgtBWJTYeRsE9iQYsnZGc60Ep4AhSGjgwRIwNcE+CJ1xn3kTM7OEKAVtwlQGLrtAdonARLQJRBLQ8l+CAEKQ2e8RM7OcKYV9hgyBkiABHxGwOgL0mg6nzXfc9UlZ2dcQs7OcKYVCkPGAAmQgM8IGH1BGk3ns+Z7rrrk7IxLyNkZzrRCYcgYIAES8BkBDiV7y2EULM74g5yd4UwrFIaMARIggYAS4IvUGceSMzk7Q4BW3CbAxSdue4D2SYAETBGgYDGFz3BmcjaMylRCcjaFj5ktIEBhaAFEFkECJGA9AQ4lW8/UTIkULGboGc9LzsZZMaU9BBoBqLGn6IZLralxzXTDlTOYQm5ity7yM0ee/LzNz+gL0mg6c601ljvIzwO3OAeZqV5UucU5XF2MRb71qYLwfLaeijMlKsLQDQd4KfjNoHarHW7ZNcPKSw9B8jPnSSf4GbVhNJ25FhvL7VZdnLDrhA0+IwC3OJO9sXs8FlJRGJr0sls3sVt2TeL6SXa32uGWXfIzTsCPQ8luxZUTdp2wQXFCYSgx4FasGX86BTslhaFJ/7oVwG7ZNYmLwtBigG7FgVt2vSQcvFQXJ/zhhI1YY+ql9nqpLm7FmsWPZ98WR2Fo0nVuBbBbdk3iojC0GKBbceCWXS+9vLxUFyf84YSNWGPqpfZ6qS5uxZrFj2ffFkdhaNJ1bgWwW3ZN4qIwtBigW3HghF0OJRsPFif84YQNihNvDaPGms+N33HBTklhaNK/vHHMASQ/8gtHwGhsGE1njrSx3G7VxQm7TtigMKQwlBhwK9aM3eXBT0VhaNLHbgWwW3ZN4mKPocUA3YoDJ+watWE0ncXodYtzqy5O2HXCBoWht0RRrPnciWeEH2xQGJr0Em8ccwDJj/zCEeBQsvHYcOI+csIGhSGFIXsMjd/3dqWkMDRJNtYeliZxscfQYoCMP75InXqRxlqsxVp7Kcotfjj7uDgKQ5PO48PDHEDyIz9zBCgMvSoM//rXv+K6665DZmamKRfH2jPCrfZSGJoK00Bljglh+Pzzz+O2225DcnKy5c5z6yZ20u7NN9+MX/3qVzjnnHPILwoC5BcFNAB2DSX/5S9/wU033YR27dpFV7F6cjl5X2qr4YTdSG2kpKTg4MGDuOuuuzBu3LioeUdq1yqnBsWumXgPCgMrYuLpp5/G6NGjkZ2dbUVxni4jJoRhYmIiqqqqcP/99+Phhx+2VCDGwo2TkZGB0tJSDBs2DI888oilApH8zD0fgszPaNuMplNJt2jRAuXl5bjnnnuU50F6ero5J2hyR1oXqww7YTdSG2+88YbCWI5cPX78OO6++25FILZp0yaiZkdqN6LCY0Dkm4n3WGNfX+ykpaWhpKRE6WSSOM7KyrIq1DxXTkwIw5deegljx45FdXW1sgz+3nvvtUwgxsKNM3XqVPzmN79RxKE8ZAYNGmSZQCQ/c8+EIPMz2jaj6VTSr7/+uvIMEMFSWVmpiBd50Ldu3dqcM1zcZiNSBtqG7tq1C4WFhYiLi1Oej/J36L/l/926dcO2bdvC/l6bV80vefbu3auYa9q0qfKB/rvf/U7h3bJlS0O8zbTNkIEwiYJi10y8B4WBmThQ87711lu44447lOfGsWPHlJ5w6Shp27atFcV7qoyYEIZCXNR+cXGxAj8hIcEygejHG2fVqlURB+Hll1+OvLy82nyqQPzDH/6A4cOHR1yemoH8Yo+fCJE9e/bUihCJAVVUaP89ceJE5eGr9zs1nfwtX+67d+9uMJ0qViRPly5dsG/fvlrBIh+NqkA0Klj0gt6P8fzUU09h+vTpyoezvPTkb/WP9v85OTkKN73fhcsnPbMiBrWXCER5cIQ0FgAAIABJREFUnrz33nuGnht+Yrpjxw7lOamNNa3IDo11vXQ9e/bE999/Hzae9fJo7xG930cb735irwaTsMvNzVV8EB8fb+hvo2l79eqlfESpHzryYSkfOiIQzTw3DN0IDibypTCUuUePPfaYaUxNmjTBZZddhv/85z9Rl+XHG0f4zZo1K6I2b926Vekx1F7NmjVT+E2bNi2isrSJyS/2+D355JP48MMPa0WIiAr1jypOVKFh5OciNGW6QyR55ItfbJkRLEERhkZv3kjvVXlpygd5WVmZYqJ58+aK2JHRG+mxTUpKMmQ6UruGCjWQKBq7Ettz586tE4t6MV3fzzZv3qz0zoaL50jLk3KOHj0aVbxHw8AA2gaTmLH7zDPP4PPPP1faKx8l6oeM+m+jf2vzq3nkHSjTIqx+bjQIxOEEvhSG0TCSeS1FRUVK1ljvMYyU3/79+5UXr/rlL5PKO3XqpCwOuOKKKyItrk56Mw8AM4adtEt+ZjzVcN5IfSlxLIJF/dARwSLXQw89FJFgoTCs3zfPPvss/vSnP9X22Kh8pccwkitS/0ZSdn1pg2LXTLwHhYEVMSHiWoaNZUGV+qEjPY3yoSO9hkY/dKyoi91lxIQwlDmG8lCSG0QCnXMMIwsrWbTzyiuvQBbxWCUI1RrEwoOH/CKLNzW1nauSZcRBnUunChbpATd7BTmeI2mb9MjKLhDCdPz48cozt3HjxlHhjcRuVAbCZAqKXVmVHG28B4WBFXEhvcGyMlmYyMeNPDdEEMrIY7TXLbfcgh49euDBBx+sU4S8M6QDS3o/tZfUoaCgAK+99lq0Jg3liwlhyFXJhmJBN5H0dokY7NixoxKkZnsIQ40E/cFDftHHntHYMJpOalJRUaEIFnnoqoLFzIM9luI5Es7C5dVXX8Wdd94ZfQD8mDNSu6YNBsiu2XiPNfbhYkd6CVu1agUZLXv00UeV+chWXP3791eEYeh82xEjRiiCc86cOXXMXHnllcoc1q+//toK82HLiAlhyH0MzcXQJ598gpEjR5orJOBf5fXBIb/oQsfoS8loOrUWMoIggkUmplt9RVoXq+w7YdcJG3o8aNdclJiJ91hjXx9p6aX77W9/a84ZPskdE8LQTl/wxjFHl/zILxwBu4aSzRGvP3eQ4znIbaMg1Y/rWPO5nc8GP5VNYWjSW7xxzAEkP/IzR4BH4gk/J+4jJ2xQoDnjS6P3XKz53CiXoKejMDTpYd445gCSH/mZI8AXKYWh2Qhib1k4gnw+2xNbXi+VwtCkh3jjmANIfuTHoWRzMUBhaJ4feyopju2JIn+WSmFo0m8UNuYAkh/5me2tcCuGYk1MuMWZds09I8zkjjX2ZlgFKS+FoUlv8sYxB5D8yI/C0FwMsMfQPL9YE/lGifH5bJRUsNJRGJr0J28ccwDJj/w4lGwuBigMzfOjMORQsj1R5M9SKQxN+o3CxhxA8iM/cwS4+ITC0GwEURSZ7bW32gNuvResbodfy6MwNOk5twLYLbsmcf0ku1vtcMsu+VlNgMKQwtD6mHKKqZd6Kr1Ul6A8n+2JTPtLpTA0yditAHbLrklcFIYWA3QrDpywyw2ujQeLE/5wwgbFCT903BTlxu+4YKdUhKFbTaypcc20ZU2Wh6VbF/mZI09+3uZnVIgYTWeutcZyB/l54BbnIDP1khAOVxdjkW99qiA8n62n4kyJjWpI3xnStEICJBARAaNCxGi6iIwzsWd692PNFYznWPO499pLYeg9n7BGJEACAPw4lBxkx1GwOONdcnaGM62EJ0BhyOggARLwNQG+SJ1xHzmTszMEaMVtAhSGbnuA9kmABEwRoGAxhc9wZnI2jMpUQnI2hY+ZLSBAYWgBRBZBAiRgPQEOJVvP1EyJFCxm6BnPS87GWTGlPQQoDO3hylJJgARMEjD6gjSazmR1Yj47OTsTAuTsDGdaCU+AwpDRQQIk4EkCRl+QRtN5spE+qhQ5O+MscnaGM61QGDIGSIAEfEaAQ8nechgFizP+IGdnONMKhSFjgARIIKAE+CJ1xrHkTM7OEKAVtwlwKNltD9A+CZCAKQIULKbwGc5MzoZRmUpIzqbwMbMFBKIWhosWLcLrr7+OadOmWVCN2CuC/Mz5nPzM8Tt48CBuvvlm/OMf/0BycrK5wmzKHUtDyX7wh98Ey5w5c/DWW2/h/ffftylC7SnWb5z1KHzxxRd45ZVXqA/sCRHbS41aGF500UVYvHgxpkyZgiuuuML2igbNAPmZ8yj5meP30EMP4fnnn8f999+PZ555xlxhNuU2+oI0ms6malpSbJD8YQkQCwo5//zzsWTJEkydOhWjRo2yoERnighCPF922WWYN2+e8uE5evRoZ8DRimUEohKGS5cuxYUXXohDhw7hlFNOwbp16yyrUCwURH7mvEx+5vhJ71R6ejqOHTuGpk2bYt++fZ7sNTT6gjSazhw1+3IHzR/2kTJe8pdffgkRhn58R/k9nlevXo2zzjoLR48exYknnogtW7YYdxxTeoJAVMJQemukm16ulJQU5auAvYbG/Ul+xlnppSQ/c/ykd2rChAmoqKhAQkIC7rvvPk/2GsbKULJf/OEnwXLJJZfgk08+UW6U1NRUvPHGG7j66qvN3TgO5fYTZz0kV155JaZPn678qlWrVnjhhRdw0003OUSPZqwgELEw1PbWqBVgr6FxV5CfcVZ6KcnPHD9t75Rakpd7DY201s8vUj/5wy+cv/nmGwwaNAhHjhypDZ8ePXpg06ZNRsLJ9TR+4awHauPGjejbt6/y0aleHTt2xM6dO13nygoYJxCxMNT21qhm2GtoHDj5GWfVUG8h4y9yltreKTW3l3sNjbTQzy9SP/nDL5x//vOfY8aMGXVCp2XLlnj55Zdx4403GgkpV9P4hbMeJFnQ9vbbbyu/atKkCY4fP47ExESl1/D22293lSuNGycQkTCU3ppzzz1XmZckYrCsrEyZwyGBnJ2djc2bNxu3HIMpyc+c08nPHD/pnUpLS0N8fDySkpJQXFys/L+8vByVlZUoKiry1FzDoA8l+80ffhAs69evR79+/dCsWTNlCPnAgQOorq5GTU0N2rRpg7y8PHM3kQO5/cBZD0Nubi66d++uCELRBxLf8qyRZ4s8b+T5wssfBCIShvI18Omnn+LFF19UJpXKF4DMNZRVja+99ho++OADjBw50h8td6GW5GcOOvmZ4yerkB955BE8++yzuOeee5QPOnlhvvTSSxg7diyefPJJZZWyVy6jL0ij6bzSLrUeQfWHm5xvuOEGzJw5U+mhGjZsGGSERhZDSIw/8cQT+Oijj3DBBRe4WcUGbfs1nseMGYN//etfeO655yCrkgcOHIht27YpemH8+PHKvEOZ+8nL+wQiEoba5qxatUoRhitXrlR+vH//fuWLjJcxAuRnjFO4VORnjp/k9voLyGj9jKYzT8zeErzeDq/XT/WOuqhKRMmIESMgPVlySc9V48aN7XWiBaX7hbNeU6V3Ni4uDoWFhejTpw/27NljAREW4TQBy4Sh0xX3u71QYeP39jhdf/IzT9zrL6CgDyWHetDr/vB6/UJ5yoKHwYMHY8eOHeZvFgdL8BtnPTTSUXTSSScpW2Hx8h8BCkOXfEZhYw48+Znj54ceQ6MtDMKL1A/+8BvngoICnHHGGcjPzzcaSp5I5zfOetBkbmfXrl2Vecy8/EeAwtAln1HYmANPfub4+UGIGG1hEF6kfvCH3zjLMKYsRNm9e7fRUPJEOr9x1oMmC1Nlm5rS0lJPMGUlIiNAYRgZL8tSU9iYQ0l+5vj5QYhwKNm8j60swW+CRYYxe/Xqhb1791qJwfay/MZZD8jhw4fRrl07ZdcSXv4jQGHoks8obMyBJz9z/PwgDI2+II2mM0/M3hK83g6v1y/UOzKMKbtn+G2bFL9x1rsr5Dg8OfVEu8m4vXcPS7eSAIWhlTQjKIvCJgJYOknJzxw/CkPz/KwuweuCwOv1C/WHDGN27twZJSUlVrvK1vL8xlkPhmxs3bx58zonoNgKjYVbSoDC0FKcxgujsDHOSi8l+Znj5wdhyKFk8z62sgS/CRYZxmzfvr2y0bKfLr9x1mMr29bIRtdVVVV+Qs+6/kiAwtClUKCwMQee/Mzx84MwNNrCILxI/eAPv3GWYUz1ZB+jseSFdH7jHI6Z7Gcoe0fK37z8RYDC0CV/UdiYA09+5vj5QYgYbWFQXqReb4fX6xcaL7LRdXJyMo4dO2Y0lDyRzm+cw0GTHkMR537YVNwTjvdQJSgMXXIGhY058ORnjp8fhCGHks372MoS/CZYZBizadOmSq+Vny6/cQ7HVs6rlnme4gNe/iJgShjOnj0bjz76qL9a7JHairAhv+idQX7Rs1NzGhVe5i1FV4LRF6TRdNHVwrlcXm+H1+un5ymvx7henf3IWa8djz32GMaNG6fMNeTlLwJRC0N/NZO1JQES8BsBoy9Io+m83n6vt8Pr9fO6f43Wj5yNkmI6uwhQGNpFluWSAAmYImC0tycoL1Kvt8Pr9TMVbB7KTM4eckaMVoXCMEYdz2aTQFAIBOVF6vV2eL1+jOegEGA73CZAYei2B2ifBEjAFIGgCBavt8Pr9TMVRB7KTM4eckaMVoXCMEYdz2aTgNcJcCjZWx6iYHHGH+TsDGdaCU+AwpDRQQIk4EkCRl+QRtN5spGaSnm9HV6vn9f9a7R+5GyUFNPZRYDC0C6yLJcESMAUAaMvSKPpTFXGgcxeb4fX6+eAixwxQc6OYKaReghQGDI8SIAEPEmAQ8necgsFizP+IGdnONMKh5IZAyRAAgElEJQXqdfb4fX6BSW8yTkonvRvO9hj6F/fseYkQAIAgvIi9Xo7vF4/uRl2L/0CB9auwfFDB9G2T1+k9+6NRo3isOWjGaiprkFSVhY6DB2OhJQUz947fuAcCi9/3yFs2FaMrQWlKCw+jLLDFaisqkbj+DikNE9Au9bN0S0zFSef0BpZ6S08y54V+4EAhSEjgQRIwJMEOJTsLbd4VbAc2VuIpomJqC4uxu5lX6Cmqhop7dujWWoqGsXHKxCPFBejND8fR0pLkTV0GJKysxGX7E1x6FXOetG4avNeLP62AKWHjqFLZho6pKcirWUSWiQ1VUShiMND5cdQVFKOXftKsb2gCKktmmJYv0yc1qOttwKctaklQGHIYCABEvAkAaMvSKPpPNlITaW83g6v1e/AhvUoWLgATROaoNPA0yN2b0XlcexYvhydLrwYKV27RZzfrgxe46zXTukhnL44B0ePV6NP90x07ZhmGEdOXhHWbClAsyZx+PmwruxBNEzOuYQUhs6xpiUSIIEICBh9QRpNF4FpV5J6vR1eql/57l3YOPk1pLZrh45nnhW1vwpWrcSBgny0Hz4CHYYMi7ocKzN6ibNeu75avwdTPtuEYQO6onf3DlE3fe2WXVi8MgfXn98TZ/bKiLocZrSeAIWh9UxZIgmQgAUEOJRsAUQLi/CSYKncsR2Htn6PxNatTbewrKAASExE+ojzTJdlRQFe4hzankXf5EP+nH92T7RLSzbd3MKig/hs2SYM75+l/OHlDQKGhSEnl5pzGPmRnzkC5nIHOf68/CKNxGteb4cX6le+ezcSKo6hqrgoErSG0sa3TkPjzl0MpbUzkRc4h+sp/L8vt+PSYaegZXKiZQhKDh7BrMXf4eKzurDn0DKq5gpqUBhycqk5wORHfuYImMsdC/Hn1RdppJ7zejvcrl/x2rXYNuMDZA8egiQLegr1/JO3bh3aDhqElGz35hy6zVmPi3xY/vndlbjmgr6W9BSG2pCew2lzV+PBGwZwzmGkDw4b0ocVhpxcao42+ZGfOQLmcgch/jiUbC4GrM7ttmD5buIrSGzSBFkDB1rdtNry8ld8jSPHq3DKHXfZZqOhgt3mrFe/lz5Yg6yM1qbmFDbUbplzmL+nGPdc1aehpPy9zQR0hSEnl5qjTn7kZ46AudxBiT+jL0ij6cxRtT+319vhZv2OFBYif+aMqFYfR+q5zXPnInPkSKSd6o5AcZOzHisZdZi/qgBXjOgdKcqI089YsBbnnsatbCIGZ3GGnwhDTi41R5j8yM8cAXO5gxR/Rl+QRtOZI2t/bq+3w836VR8sw/Gt39vvBABHy0qR3H+Aa/scuslZD/DzU79Fr26RbUkTraNkK5v1Wwtw/+h+0RbBfBYQqCMMpaeBk0ujp0p+0bOTnORHfloCRoeSjaYzR9f+3F5vh5uCRVYh27HgJJxX3VyI4ibnUB4yJeWNmd/hxkvtG74PtfnOrBW4bdQpnGto/yMnrIVaYcjJpea8QH7kZ46AudyxHH/NmjVDeXk54uLizEF0MXd1dTWSkpJw9OhRF2tRv2m3BEvhV8tQlZ+HNCc3oY6Px/FWaWjRsaPj/nCLs15DP1u+E/lFxzC4f7ZjHJZ8k4ustKY4//ROjtmkoboEaoUhJ5eaCw3yIz9zBMzljuX469ixI77++mt06BD9Zrvm6JvPvWvXLpxxxhnIy8szX5hNJbglWDZMnoSUli2R3r2HTS37abH7t2xB2YEDOOk3YxyzqRpyi7NeQyd9uA7ZHdtFdLKJWWAynJybV4gxl59qtijmj5KAIgw5uTRKej9mIz/yM0fAXO6gxp/RodURI0Zg7NixOO88b2xQHI03582bh2effRYLFiyIJrsjedwSLKv/+gw69+2PpDZtHGmnGDlUWIi8tWvQ9w8POWbTi8Lw0Te/wiVDrd23sCGgsq/h7M+/w+O3ntlQUv7eJgKKMOTkUnN0yY/8zBEwlzuo8WdUiIwfPx5VVVV4+umnzYF0MffDDz+M+Ph4PPHEEy7Won7TRv1hdQPyZ81Em/bt0Sg+3uqiw5ZXVVmJ7xcuQJ8HH3bMpheF4b0v/xe3XXkWGsc7N02jsqoab/znS7xw9xDH2dPgDwQa5e09WMPJpdGHAyfnRs9OcpIf+YUjYFSIrFixAtdccw1yc3PNwXQxd3Z2NqZNm4aBNu7RZ7Z5Rv1h1k5o/so9u1C1e7fVxTZYXv7GDci+9gZIu5283OKs18a7XliMu68b7GTzFVsv/3sJXrnXG2dXO954DxhsNPfrHTWcXBq9Jzg5N3p2kpP8yC8cAaNDyZJ/1KhRGDp0KO677z5zQF3IPWHCBHz++eeYOXOmC9aNm3RLsFQV7kHlrgLjFbUgZWXlcWyeMwf9xz9mQWmRFeEWZ71asscwMt8FJXWjiTPW1nByafTu5OTc6NlJTvIjP3MEfsi9fv16pbftgw8+wMiRI60o0pEyPvnkE1x11VWQXs9evXo5YjNaI24Jlo2TX0P7bt2QaNMxeHo8DhcWYsfqb9Fv7LhocUWdzy3OehXmHMOo3ejrjI3++Pcvazi5NHof8saJnp3kJD/yM0fgf7lVkfXkk0/6oudQegofeeQR34hZtwTLhtcnITm5Bdqe7JxwPlZWhnI0QsZQ54cz3eKsdx/yw92qp5O/ymn0u5c+r+Hk0uidxq726NlJTvIjv3AEIhlKVsuQnkNZyLFu3TqMHj0aw4cPV3riMjIyXN3nUPYp3LNnj9KzuWjRIkydOhWnnnqqsmDG6z2FKlu3BMu2WR/haG4usocMNXezRJI7Ph5Ne/eNJIdlad3irNcATvWxzK2+KqjRnRMW1XByafQ+4+Tc6NlJTvIjv3AEzLwgZWj2448/xrJly7Blyxbs27cPDz30EB57zPk5Y9K+pk2bIj09Hd27d8fZZ5+Nyy67zNMLTfR8YsYfZqL8yL5CVGzbhmZNmpgpJqK8PPnkB1xcHBhR2AQmMXsMTbqSPV7mAJIf+dkhDM1RtTZ3RUUFEhISrC3UhdLcEobSVCfPSpbNrauSktDp0lEuUIayCrqmpsYV23pGg7odlmcAe7AinGNo0imcI2cOIPmRXzgC0Qwlm6PJ3PURcFuwlK/5FvHV1bY7adOcT9Fu6FBknO38Ni3SOLc5hwIO6gb6tgeSjw1wVbJJ53FyrjmA5Ed+5ggwt1ME3BYscjReYpMm6NCvv21NLsrZigP79qPXb++wzUZDBbvNWa9+sXzkZkP+CuLvuY+hSa9ycq45gORHfuYIMLdTBNwWLKU5W7H1nX+i04ABSMnMsqXZ8RntUZWYhKYtW9pSvpFC3easV0eZa/jnd1fimgv6ol1aspFmRJSmsOggps1djQdvGICs9BYR5WVi6wnw5BOTTDk51xxA8iO/cAQ4lGwuNqzO7QXBUrB4IeIOH0brDh0sbV5VxTEkZHRA485dLC03msK8wFmv3l+t34P/+3I7Lh1m7dnJcjbyrMXf4eKzuuDMXhnRIGMeiwnwrGQLgHJyrjmI5Ed+egS8+oI05y3/5vaSPyp3bEdVcZElMItzcrBPtsO59jq06NjJkjLNFOIlzqHtWPRNPuTP+Wf3tKTnUHoKP1u2CcP7Zyl/eHmDgCIMObnUnDPIj/zMETCXO6jx5+UXpDmP+TO31/yx7b0pqCw5gE5nnhU10N2rV6No5w60GzQEWSPOjbocKzN6jXNo26TncMpnmzBsQFf07h59z+3aLbuweGUOrj+/J3sKrQwgC8pShKGUw8ml5miSH/mZI2AudxDjj0PJ5mLC6txeEyyluTnI+2QWqg4fRqeBpyMpwiPz4pKTlYUmTdumI7XriVbjiro8r3HWa4hMAZq+OAdHj1ejT/dMdO2YZri9OXlFWLOlAM2axOHnw7pyTqFhcs4lrBWGnFxqDjr5kZ85AuZyM/7M8WPuhgl4VbDs+3YV0rqdiOriYlSVlmD/pg1oltISiWlpiG/cWGlY5bFj2L9pI8pLSoC4OPS4+VbEJac03GgXUniVsx4KGa1Y/G0BSg8dQ5fMNHRIT0VayyS0SGqKxvFxqKyqxqHyYygqKceufaXYXlCE1BZNMaxfJk7r0dYFujRphECtMJTEnFxqBFn4NORHfuYImMvN+DPHj7nrJ+AHwXJ0/37kTHsPVeXlqKqowKm33opGjeJQfuAAdsyfhxadu6DVSSchtZt3eghDqfuBc2id5cN0w7ZibC0oRWHxYZQdrlBEoYjDlOYJaNe6ObplpuLkE1qzh9AHD5o6wlDqy8ml5rxGfuRnjoC53EGKPw4lm4sFq3P7UbBYzcCJ8sjZCcq0UR+BnwhDteeQk0ujDxxOzo2eHePPHLsg8eML0nwsWFkC/WElzfBlkbMznGmlnhhUF5/odQ1zcmn0ocPJudGzk5zkR358QZqLAatz0x9WE9Uvj5yd4UwrUQhDNQsnl5oLH/IjP3MEzOX2c/xxKNmc763OTcFiNVEKQ2eI0kqkBHSHkvUK4eTSSNHWTU9+5GeOgLncjD9z/JgboDB0JgrI2RnOtGKix5DwSIAESIAESICCxZkYIGdnONMKhSFjgARIwGcEOJTsLYdRsDjjD3J2hjOtUBgyBkiABHxGgC9IbznMSX/IgVzyR2yql/p/7c+iJVRdXY1zzz0XCxYsqGNDfv75559j6NChiIuLw69//WuMGTMG/fr1i9ZUxPmc5KxXOSfYjxgxAgsXLvQc+4idFdAMhucYBrT9bBYJkIBHCbj9gvQoFteq5ZQ/cnNzcckll6Bp06ZYvXo1WrdujTZt2iAlJQWXXnopBg0ahJ/97GcKh6qqKkyePFkRb0YvyTN69GhFGN5+++212VasWIFrr70Wl19+OQoLC/H222/j8OHD6NWrF7Zv364IRScupzjrtSXW2TvhXz/YoDD0g5dYRxKIQQIcSvaW050ULPv370d6ejpmzpyJyy67TOk9lN68jh074qqrrsJLL72kwHnssceUHr5FixYZhvXoo48qeRYvXlyb59ixYzjrrLPw5ptvom/fvkoP4apVqxAfH49f/vKXaN++PZ5++uk6PVyGDUaY0EnOelWLZfYRuiqwySkMA+taNowESIAErCPgpGCZNm2a0qt34MABtGzZUmmEiEMRhhdeeCH+9re/1Yq00CHn+los4jI7Oxuvvvqq0iuplvv8888rw8qffPKJ8jOx8/HHH+O0006D9KJ17dpV6T1MSkqyDmiYkpzkrFeFWGZvu3N9YoDC0CeOYjVJgARIwE0CTgoWmdu3du1afPXVV7UC8J133sEvfvELlJWVITk5WRFsItZOPfVUBYsMEU+ZMkUZbpZhZxGM3377rSLqZDharpycHAwYMADSKya9gXLt3r0bZ599Nn73u9/hnnvuUXom5XebNm1Cjx49lHKysrIU4dizZ0/bXeAkZ73GxDJ7253rEwMUhj5xFKtJAiRAAm4ScEqwiBDr1q0brr/+ehQVFWHv3r34/vvvMWTIELzwwguKaJs6daoyH1CuL774Auecc44iIGWYWATksGHDlOFgEX35+fn4xz/+ocwRnDNnDiZMmIC5c+fWCs733nsP1113nfKzQ4cOKWVKb+XBgweVeY5yybzGP/7xjzj//PNtd4FTnPUaEuvsbXeuTwxQGPrEUawmCZAACbhJwCnBIgs/MjIy8O9//xupqalKk/fs2YMnnnhC6TEU8Sc9eRs2bFCGhf/5z39i+PDhypCzzDW8//778dBDDykiTnoQZWj0o48+UoThpEmTsHz5cmVhiXpJubJC9pFHHlHSSBkffvgh1qxZU7vgROzKSuVbbrnF9nmGTnHWi6VYZ+/m/eUl2xSGXvIG60ICJEACHiXglGARkSbbmXz99dc4/fTTa2ncd999isjLy8tTBNtrr72Gv//974rQU4eFn332WWUIWHoIpb4yPNy2bVtFKKo9ilKgLGySS3rIpHdQhp5l3qIMI8tQ8xlnnKGISHVrHBGjcsliFyu2y6nPxU5x1qtDrLP36K3neLUoDB1HToMkQAIk4D8CTgkWWRjy5JNPoqCgoFbwCa3BgwejoqICX375pSLOpJdQVizLAhHpzRNRJ9vYyOriZ555Rvm//O65555TxKHMRZw4caIyb1GGm1VhKHlkmFrE4rJly3A+iQhhAAAftklEQVTRRRcp29U0a9as1kk33XQTunTpUiso7fSeU5z12mA3+9mzZ+PTTz/1LHs7/eqnsikM/eQt1pUESIAEXCLghGCRHjwRewkJCUrvoPQEylw/6RmU+YUyBCy9ibLQpHHjxti5c6cyl1DmIcolQ9DSWzhy5EhlsYhsbbN161ZlvqKsOJb5hjI3UeYlqj1/0st45MgRRfQ98MADiriUlc/ankERnlLmH/7wB9vpO8FZrxF2s9+yZYsyF1QW/niVve3O9YkBCkOfOIrVJAESIAE3CdgtWN566y3861//qj15RHr8RKx89913GDdunDK/UHr+5FJXDsu8wunTpytiTtJdeeWVytxDGWpWt5l56qmnsHnzZmUuogjKE044oc6G1ZWVlWjSpIliQzazvuaaa+psZi11kDwvv/yyIlrtvuzmrFd/p9iLmBcfqJuFe4293b71S/kUhn7xFOtJAiRAAi4SsFuwiNhTL/m3Kh7UfQpDTx6RNLKKWOYHqpdscdO7d+865RQXFysnp6iCUnr+ZFsa6RXU2pMhZhmGDp1DKD2NMldRtr5p0aKF7R6wm7NeA5xiL5uHP/jgg0oPrhfZ2+5cnxigMPSJo1hNEiABEnCTQFBOohHxKEIv9JzkcGxl6xzZ208EjRNXUDjrsVJPVREhamQRj9PsnfCvH2xQGPrBS6wjCZAACbhMQOb7ydCfkRe6y1Wt17z0QMpClLFjx+KGG26oN62cwyxzHefNm1dnIYyd7QsKZz1Gwl6G/mUfyv/85z+eY2+nX/1UNoWhn7zFupIACZCASwTkODgZltWu1nWpKqbNyjw32b9Qtp+p75Lfy8bWocPYpitQTwFB4qzXTC+zt9OvfiqbwtBP3mJdSYAESMAlArLwY/369UhPT3epBrFhlpxjw89ebiWFoZe9w7qRAAmQgEcIyDnBM2fOVE4d4WUfAXK2jy1LNkaAwtAYJ6YiARIggZgmIJtAy8bT8jcv+wiQs31sWbIxAhSGxjgxFQmQAAnENIHrrrtO2eRZu9VITAOxqfHkbBNYFmuYAIWhYVRMSAIkQAKxS+CRRx5RTiSRxRi87CNAzvaxZcnGCFAYGuPEVCRAAiQQ0wTeffdd5Vi59957L6Y52N14crabMMtviACFYUOE+HsSIAESIAGsWbNGOWdYjpzjZR8BcraPLUs2RoDC0BgnpiIBEiCBmCeQnJyMHTt2oHXr1jHPwk4Acszf9u3bydlOyCw7LAEKQwYHCZAACZCAIQIXX3yxcjzc5Zdfbii92URypvGBAwfwr3/9y2xRvsrvNOdQOLt27cLZZ5+NK6+8Es8995zj7H7/+98rJ6MsW7YMHTp0cNx+rBukMIz1CGD7SYAESMAggeeffx45OTmYNGmSwRzRJ5M9E6+66io0adIEhw8fjr6gBnKKCLn55pvRq1cv22xEWrCTnEPrNnv2bNx44404dOgQZCHMo48+Gmn1TaeX86LlZBrpoX7nnXdwySWXmC6TBRgnQGFonBVTkgAJkEBME5D5hRdeeCF27txpK4evvvoKgwcPVs5mbtGiBb799lt069bNcpvSjhNPPBFpaWlYsmQJunbtarmNaAp0inNo3R566CG89NJLOHLkiPIrORLQLWGoHleYmJgI6Tl+5plnokHJPFEQoDCMAhqzkAAJkECsEjjjjDOUja7PO+88WxDI3LoBAwagqKhIKb9ly5ZKD6UsfLH6WrRokTIsXlpaioyMDEUc2iFAo6m33Zy1ddq9e7fCVwR4WVlZ7a+8IAylMjLnsl+/fsqK+Pbt20eDk3kiIEBhGAEsJiUBEiCBWCcgPUorV65UhvisvqSnSgTRunXr6hR93333QYZXrb7+/ve/495771WGTeVq164d/vvf/6J79+5Wm4q4PDs5ayvz1FNPKb1xFRUVOH78eJ16ekUYSqVkD02ZViC9muPGjYuYJzMYJ0BhaJwVU5IACZBAzBOQHiURUJs2bULnzp0t5XHmmWdi1apViI+PV8qtqalRBEvv3r2V7XKsvkRkPPvss3WKlbZ9/vnnrp8JbSdnbYPfeOMNvPDCC2jVqpXCWjgL/2PHjrk+lNy0aVNUVVWhT58+ijCUhUjykSALoHjZR4DC0D62LJkESIAEAkngN7/5DZo3b25pL15+fr4y32/QoEE499xz8corr0Dml8k8wOrq6p/0ZlkB9vzzz8e8efOUopo1a4bGjRsrvYcydCnDlrI62M3LDs71tef+++9XFvpMmzYNJSUlrgtDmUZwzTXXWB5rbvrUD7YpDP3gJdaRBEiABDxEQLYzycrKUkSb/G31JXslypByXl6esthl6dKlytYl/fv3t9SUDBl///33ygIXGUYVMZqbm4sPP/xQGc6WoUs3L7s5a9tWUFCg+FIEusw1vOKKK3D33Xe7tl2NDKXPmDFD8blar8zMTDfdETO2KQxjxtVsKAmQAAlYR0C2eZGevAkTJlhX6I8lvf3225g7d27t8XsjR46EDHlaLQxE+Mlw5YIFC5QzoG+77Tb8/Oc/t7w9Zgq0k7O2XtJbKJc6l1OE4ahRo3DTTTeZqX5UeWXfyo8++kgRhnKF1i2qQpnJMAEKQ8OomJAESIAESEAloPZmSa+e1YJNxIhssHz77bfbBnzPnj3KimSZXydzG19//XVlVfKUKVNssxlNwXZyVuuj7S202pfRtDk0j9frZ0UbvVQGhaGXvMG6kAAJkICPCIT2Zkkv0//7f/8Pv/jFL0y1Qha1yNw/J1cH7927F126dFG2a5G5hl667OKsttEPPXJ+qKOXYsZMXSgMzdBjXhIgARKIYQLSmyU9TDIv7ZtvvlHmpclmxGaOUVu/fr0yhLl161bHycpik1/+8pe4+uqrHbddn0E7OPult9Bv9fRU4ERZGQrDKMExGwmQAAmQwA/zv6xcyfrqq68qW6b87W9/cxyv2Fy4cGHt3EbHK1CPQas5+6m30I919VLsRFoXCsNIiTE9CZAACcQ4ARFQsuikdevWyt53q1evtmzvO1n8IT2P119/veOU5bSVDh06KMPJsijF7ctOztI2v83d81t93Y6faO1TGEZLjvlIgARIIEYJ2HlahpxbLFvFiEBz47r00ktx3XXX2XIEX6TtsZOz1MWP8/b8WOdI/e52egpDtz1A+yRAAiTgQwJ2nK/71VdfQTZ1lh5It6633noLc+bMwfvvv+9WFerYtYOzH3sLVSjsNbQ/LCkM7WdMCyRAAiQQWAJyrJxsRiznHMtl5nxdOZ6usLBQ2ULGrUuOXUtPT1eGk5OSktyqxk/sWsnZr72FKhT2GtoblhSG9vJl6SRAAiQQeAKzZ8/GjTfeiIMHD2L8+PF49NFHo2rzBRdcgDFjxiirkt28xP5VV12FG264wc1q/MS2VZz93uvm9/p7Kqh0KkNh6HUPsX4kQAIk4AMCsqWKbEp95ZVXRrVdTVVVlXI28v79+5Wzit28/vnPf+Ljjz/G9OnT3ayGrm2znP3eW8heQ/tD0jVh2KhRI/tbF8ZCTU2Na7aDaDh/3yFs2FaMrQWlKCw+jLLDFaisqkbj+DikNE9Au9bN0S0zFSef0BpZ6S2CiCDwbdqzdAmK163F8YMH0bZPX6T37o1GjeKw5cPpkNspKSsTmcNHoEmL5MCzYAPtISD7Fk6cONHVYWS1ZTKMLMPZ0fZ82kPIulJluP/Xv/615SfWWFfDhkuSXkNZtS1t4WUtAVeFoRsCTQSpG3atdZs3Slu1eS8Wf1uA0kPH0CUzDR3SU5HWMgktkpoqolDE4aHyYygqKceufaXYXlCE1BZNMaxfJk7r0dYbjWAtwhI4UliIpkmJqC4uxu5lXwDV1UjOaI9mqaloFB+v5DtSXIzS/DwcLS1B5tCfISk7G3HJ7vb20KUkQAIkQALRE6AwjJ5dzOaUHsLpi3Nw9Hg1+nTPRNeOaYZZ5OQVYc2WAjRrEoefD+vKHkTD5JxLeGDjRuxaMA9NmjRB59NPj9hwRWUldnz9NTpdeDFSunWLOD8zkAAJkAAJuEeAwtAi9rIxqmzIOnfu3DolyiHwMiQhc2c2b96Ma665Bg888ECdzVufeOIJZGdnKz+Tfy9dulQ5yF3285JL8kl3uZwIoP7MompHXMxX6/dgymebMGxAV/TuHv0+Y2u37MLilTm4/vyeOLNXRsT1iCaDyl9OVZCrT58+mDZtGnr06BFNcabyqPEiE/XPOeccU2VZmbl8925snDwJKe3aodOZZ0VddMGKFTiwuwAZw3+GzCHDoy4nmoxy/wwaNAh/+tOflIUQ6qUyl/9r76+GbBj1ldh95513au/3hsrl70mABEjAiwQoDC3yit7LQ7ZvuPfeezF48GBF9KnCJCMjo86LKVQY/vGPf8S7775bKx69IgwXfZMP+XP+2T3RLs38XLLCooP4bNkmDO+fpfyx81LFwhdffFErxORnwj4SkWBVHY2KDavsGS2ncsd2HNr6PRJbtzaaJWy6soICoFki0s89z3RZkRQgfhVRKKdyaD+mwv28obKN+orCsCGS/D0JkIAfCFAYWuSlcC8PER25ublKz4Uq8FJTU5VJv2pvRqgwlC0fpFfr5ZdfVnqzvCAMpafw/77cjkuHnYKWyYkWUQNKDh7BrMXf4eKzutjWcxgq0NXKqz+XbTb69++viPiSkhKlF1EEpPqzyZMnK1lUURnqD60g+OabbxQxItfUqVPr9Eqq9qQ86Unevn27EgNe6DEs370LCRUVqCoussy3akHxrdPQuHMXy8sNV6DqD1nZKtuOqHzlPmvXrh0WLVpUKxjVDwYpS9u7X5+vtD3Psr2K+mFBYeiYi2mIBEjARgIUhhbBNdpjKEPCjz/+uLLaTf4twi9UGMqwslyhgtKtoWSZU/jnd1fimgv6WtJTGIpceg6nzV2NB28YYMucQ3mR33333bVCW8/lqhAIFeyy8k2mAojgu+OOOxTRKJd2aD9UGMowplZYqmWKn7XlqencFoay2njb9A+QPXgIkizoKdTjm79uHdLPGYSUrvbPOVT9IcJeNksW8S3357hx45S99uQ+kj+yLYpM7ZCVsOpHQEO+6tmzp9KTrwr6UJ9yKNmiByqLIQEScI0AhaFF6MPNMdTOc9L2NMmRS0uWLFFEx3PPPVdnjqEIwwsvvLD2BdSmTRtX5xi+9MEaZGW0NjWnsCHMMucwf08x7rmqT0NJI/59aA9fqK9k2P6KK66oM+yv7U0U4abtdRwwYEC9wlA7PK32GP/+979Xyhdhoi1P/X/EjbIww3cTX0FiQhNkDRhoYal1i8pf8TWOHq9Erzvuts2GWrAqDOVotT//+c+KCNy0aRMWLlyIq6++utZ38jOtkFOnFrz55pvKx5qer8SG1r/a2Aotz/aG0gAJkAAJ2ECAwtAiqKE9hnq9VNqXiBy1pAoFeWFpF5+o/9Z7wTm9+ES2pJm/qgBXjOhtEanwxcxYsBbnnmb9Vjb19RiqvbWqMFTFgF4PsJq2IWGoFRuqMJTTHLQ9TaHC03a4YQzIljT5M2eg08DIVx9HWufNc+egw8UXo03vfpFmjSi9et889dRTijC89dZblXNv5b7S+k77caYuDpOeYPmYu+uuu2p7BbW+kopIT6/2UhcxSQ8kewwjchUTkwAJeJAAhaFFTtETEiIK5EWhzkHSm5smYqNLly61C1S0w8rqC0nmSuXl5bmyKvn5qd+iV7fItqSJFqlsZbN+awHuH22tcAg3x1Dt/RHBECoMI+kxFP+qvb8y5KwnDL3aY1h9sAzHt34frcsiynesrAwt+p9m+z6H2qH9GTNmYO/evVi+fHntRrjqNIBoewzDiT/OMYwoHJiYBEjAowQoDC1yTCRzDLVzBUUIalcha4WhVC3cSmaLql1vMTK38I2Z3+HGS+0bYgytwDuzVuC2UadYPtcw3Kpk6f3RDiVrh3ZD549p5xiGzk2TdqhzEfWEocxJCxWQXphjKKuQ7VhwEi6wnFiIojfnU11YsnPnztqh5PrmGIbzVegcQ+3HH4eSI38q7Vm2FAfWrkHFwVK07dPvJyfqJGZlIWvYcDSxedP0IJ3eJJvRC1PdU4oAJGVmosOQ4UhITY3cYR7LESS/eQkthaFF3gi3KlleUuEWLYhpNZ8IEnUfQ3UoWa1aaM+jRVVusJjPlu9EftExDO7/w2IYJ64l3+QiK60pzj+9k+XmQucWavcx1Bva1a5Mlcpot7oRn9xwww3KqmMZJpaewoaEYehK19BVs5Y3uIECC7/6CpX5O9DGgQUhtVWJj8fxVmlo0bGjbc3VCsPy8nLlvlLvL71ee3VouL5VyVpfaVcla2OIPYbGXFr3RJ2lqKmuRkpGhu6JOkdKS5E1dLhtJ+oE5fSm0FOKaqqqkdI+9JSiIpTmF+BoaSkyhw6zjamxKDCXKih+M0fBvtwUhvax9X3Jkz5ch+yO7SI62cRso2U4OTevEGMuP9VsUczfAAHZyDq5ZUukd3dug+/9W7agtOQATr59DP0TYwQObNyAggXzkZDQBJ2jmNNaUXkcO79ejo4XXWx6dXtQTm86sHkjCubNM8V0x/IV6HTRRUjJtn/HALMhHxS/meVgd34KQ7sJ+7j8R9/8CpcMtXbfwoZwyL6Gsz//Do/femZDSfl7kwTW/OUZdOrXD0lt0k2WZDz7IVnssnYN+vzhIeOZmNL3BKw9UWcX2g8fgQ5DhkbFxc+nN2kbrDJNbdsOHc8ycUrRyhUo2bUb7X/2M7QfHB3TqBwRYaag+C3CZruSnMLQFez+MHrvy//FbVeehcbxcY5VuLKqGm/850u8cPcQx2zGqqH8WTPRpn17NIqPdwxBVVUVtsyfj75jH3bMJg25T8DqE3VqmiWibRQn6vj59KZQL1rKdNePpxSNcPaUIqORGSS/GW2zm+koDN2k73Hbd72wGHdfN9jxWr787yV45d5hjtuNNYOVe3ahavdux5tdsGEDTrjuBjRq1Mhx2zToLAHp1UqoOGbLAqdIFzL5+fSmuj2FwTmlyEg0BsVvRtrqlTQUhl7xhAfrwR5DDzrFwipVFe5BpfQUOHhVVh7HpjlzcNr4xxy0SlNuECheuxbbZth7ok7eunVoO2hQg/Pj/H56k+q/4u/WYtt/7GX6wylFg5HStasbYVPHZlD85jrICCtAYRghsFhKzjmGwfb2xsmvoX23rkhsneZYQw8XFmLH6m/Rb+w4x2zSkDsElBN1mjRGVhQLTYzWWE7UOXK8CqfccVe9Wfx+epPauPUTX0GzhMbIGmDfhvT5K5bj6PEq9LrjTqNusC1dUPxmGyCbCqYwtAmsFKtuaaKa0G53Es6sl47YCvqqZHVvQz1fGPGVmk/rM9kbT3uOso3hZbpoEYYtmjdH2169TJdltADZ5LocjZAxNPKpAuqen1pbF1xwQe0G8kbrEJpOPQpP7lcjexFq9xrlFjX61GX7lLyPpqPz6WdE6xbD+eREncyLL0Fab/3jNINwepPAcJpp1qWj0LrXKYb9YHXCoPjNai5OlEdhaBPl0L0Hw+1zGGreS8IwaPsY1udqednLJRtRR3r5VRhum/URjubmIjvK1Z2RclLSx8ejae++UWXV85F65GA0flMroRWGRo6cDN2EPqrGBDyTkyfqHC0rRXL/AWFP1AnC6U0SLo4yLS1F8mnhmToRvkHxmxOsrLZBYWg1Uc2m1fKyOuecc2otaHsX5IdyVvLkyZOV36s9VF4ShkE6+aQhN4eKDvV8ZckniyQef/xxvPjii7XHEqq+FB/LWbxz586F9F6p6U4//XTcd999ygbY06ZNQ48ezu0V2FBb1d8f2VuIim3b0CyhidEsptNFumBAa1BPGKp+ePrpp/Hwww+jpKRE4S33U//+/XXvMSlTu0m1nI0s5ej1GGp7KaXM7du3KxubyyUn5shxlupJNw3d06mpqT+5300D9WgBXjlRJ0jPMK8wdSLkguQ3J3hZbYPC0GqigPKSkReKekayngntSy7c6ShGhrVsqH6dImPlq01PGKrH3om41zsxQxUEesesPfDAA7Un2RQUFCinoiQmJtrtrojLd7IXYt+Wzahu3hydLhkVcT0lg54wVH+mnkWdmZlZ2+sb7h5r06ZNndNQJJ2eMJRzlnNzc5XytP6fNGkS1NOJtB97zz33HFRfy0k4oUco+iUmonKOJlPhV8tQlZ+HNA+cqBOUUY89Xy5DdUE+0pxcEOLAKUXhYi0ofjN7L7mVn8LQBvINzTsKHVbWHscmLy11jpoXhGGszPMI12P48ssvK719kQjDu+++G2q+hmLBhvCLuMjyNd8ivro64nyRZtg85//QdsgwZJwT3RZIenMM1WPs1N66wYMHK6LP6D0mQ8d6vfRqD6T27Gy1vXpzDEPTq/e01GfAgAHwW0xE6ltt+g2vT0Jqq5Zo4+CJOvLRUXbgAE7+zR11qh6UedJySlFKS2eZyilFwvSk3zh/SlFQ/GbmPnIzL4WhDfQb6jEMPbNXrYIMTclLxEvCUOoWCyvD9IShdhFJJMJQm88PwnDD5NeU1aMd+vW34W74ociinK04sG8fev02+pWO9c0DDT3rur57TIZ/tT36esJQnSIQOh1E7bkM7THUS68KSO09LULUDzFhJhDcO1FnLfr8YWydqgdlZ4XVf3kGnV04pShv3Rr0fcD5U4qC4jcz95GbeSkMbaAfbqGJvIDefPNN3HPPPZChLxEQoXPPvDTHUEUTC3tJRSoMZZrAkiVLlCHi0KFkvwnD0pyt2PrOP9FpwACkZGbZcEcA8RntUZWYhKYtW0ZdfqTC8M4772zwHnOqx9BvMRG1kwAUzP4YaRkZzp6oU1mJ7xcsQJ+QE3WCsherK6cUCdOF89HnQee3lgqK38zcR27mpTC0iX64VckyNCVDXdqXnDoRfuLEifDaULKKJ+i7zxsRhuqcQ3VRg7AJgjCUdhQsXoS4w4fQukMHS++IqmPHkNC+Axp37mK63EiEodqzJ3+rcwRD/acOO4ebYyhzBrX51eHg999/P+I5hrEkDN06USd/4wZkX1v3RJ2gnN7kJaamb2QDBQTFbwaa6skkFIY2uiV0H0MZKhZRKJc69KWuSlZ/58UeQxVRkM+rbEgYCgPVn7LSeMyYMZAFBiIMy8vLa/0abvWyVxefhIa/lSsfi3NysC83B9nXXo8WHTuZvtMiFYbh7jGpiHZV8oQJE5T/P/XUU3X2MZR0ejsHqHEQ6arkV199FbEwlOzWiTqb58xB/5ATdYLS8+QlpqZvZAMFBMVvBprqySQUhp50i3crJT2HUz7bhGEDuqJ39+h7l9Zu2YXFK3Nw/fk9cWavDO82OAZrtu29KagsOYBOZ54Vdet3r/4W+3dsR8aQocj62XlRl8OM/iPg3ok636Df2EfqAAvKXDUvMXUiIoPiNydY2WGDwtAOqgEvU+YcTl+cg6PHq9Gneya6djR+pFpOXhHWbClAsyZx+PmwrshKbxFwWv5rXmluDnbO/hg15eXoOGAgktKM+1daG5ecjJL9+5HQNh2p2Sf6DwBrbIqArEpOTm6Btie7f6JOUFa3KqcUtWjuCaamgsNg5qD4zWBzPZeMwtBzLvFPhWQrm8XfFqD00DF0yUxDh/RUpLVMQoukpmgcH4fKqmocKj+GopJy7NpXiu0FRUht0RTD+mXitB5t/dPQGK3pvpUrkdajO6qLi1FVWoL9GzeiWWoqEtukIT6+sUJF5hDu27QR5SUlqImPR89f3hL2BIoYxRhzzd728Uwc3SYn6gxxru1hTtQJyn5422Z+hKPbt3mCqRNODYrfnGBlhw1XhaEdDTJSZk1NjZFkTGOQgPQgbthWjK0FpSgsPoyywxWKKBRxmNI8Ae1aN0e3zFScfEJr9hAaZOq1ZEeL9iNn6nuoLD+E6uOVOPWWW9GoURzKDxzAzvnz0LxLF7TqeRJSu7GH0Gu+c7o+XjpRJygnaMg5yRXb/XNKkdmYC4rfzHJwK79rwtCtBtMuCZAACZCAvQScPFFn/5bNqEpqjk6X6p+oE5TTm7zE1N7o+aH0oPjNCVZW26AwtJooyyMBEiABEoBXTtQJ0ulNzjH9FG2HDI36lCIrwj9IfrOCh5NlUBg6SZu2SIAESCBGCDhyos7WH0/UGVP/iTpBOb1pw+RJSGzSxPOnFFkV4kHxm1U8nCqHwtAp0rRDAiRAAjFEoCxnK76XE3UGDkRKh0xbWi4n6lQnNUdCamq95Qfl9KYfTil6G50GDPT0KUVWOTsofrOKh1PlUBg6RZp2SIAESCDGCBQsWoi48sOeOFEnKKc32XZKUcUxJGRYc0qRlWEeFL9ZycTusigM7SbM8kmABEggxglYfaLO/m05OGF05CfqBOn0JquZWnlKkdXhHiS/Wc3GjvIoDO2gyjJJgARIgATqEFBO1DlQjE5nnR01md2rV/94os6QqE/UCdLpTVYw3bX6GxTtyEPGkMFRM43aoRFkDJLfImi2K0kpDF3BTqMkQAIkEFsElBN1Zn2MmqPl6Nh/AJLatIkIgHKizr4fT9Tpam6/zKCc3lSauxU7Z8/64ZSi06Jkur8ICenpSO3aLSJ/uJE4KH5zg10kNikMI6HFtCRAAiRAAqYI7Fu5Amk9emhO1NmAZqktkZiWhvjGzp6oE5TTm/atWIG0nlqmG9GsZSoSW/+PaaWcUrRxI46UlqImLg49b/bvKUVB8ZupG8nGzBSGNsJl0SRAAiRAAuEJ/O9EncOoPl6BU2/5leZEnflo0aUzWjpwok6QTm86un8/cqbJKUWHUV1RgVNv/R/THfPnIblzF7Q8+WRf9BA2dO8EyW8NtdXJ31MYOkmbtkiABEiABEiABEjAwwT+P1DOaXPEwQz6AAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "iNLgOdfPvY36"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A simplified version is shown below. Typically, the probability of using ground truth data is lowered as training is progressed, forcing the model to learn on its own. **Always make sure to disable replacement  when evaluating model performance**."
      ],
      "metadata": {
        "id": "Mco9XE4fvahv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LSTMScheduledSampler(LSTMPredictor):\n",
        "    def __init__(self, in_dim, hid_dim, out_dim=None):\n",
        "        super().__init__(in_dim, hid_dim, out_dim=None)\n",
        "\n",
        "    def predict(self, x, ground_truth, K, threshold):\n",
        "        \"\"\" \n",
        "        Autoregressive prediction with scheduled sampling.\n",
        "\n",
        "        One cavet is that you need to have data for the prediction region.\n",
        "        (Which you should, for validation purposes)\n",
        "\n",
        "        Threshold is the probability we replace the model output with ground \n",
        "        truth. Set to 0 when evaluating.\n",
        "        \"\"\"\n",
        "        out, state = self.lstm(x)\n",
        "\n",
        "        predictions = []\n",
        "\n",
        "        last_output = out[:, -1:, :]\n",
        "        next_input = self.out_net(last_output)\n",
        "\n",
        "        for i in range(K):\n",
        "            # Randomly replace model output with ground truth\n",
        "            if np.random.uniform(0, 1) < threshold:\n",
        "                next_input = ground_truth[:, i, :].unsqueeze(1)\n",
        "\n",
        "            raw_out, state = self.lstm(next_input, state)\n",
        "\n",
        "            pred = self.out_net(raw_out)\n",
        "\n",
        "            predictions.append(pred)\n",
        "            next_input = pred\n",
        "\n",
        "        return torch.cat(predictions, axis=1)\n"
      ],
      "metadata": {
        "id": "UCIjA2zfkTuB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Missing / Irregularly Sampled Data"
      ],
      "metadata": {
        "id": "6UPngmIUi1f4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One problem you may encounter when dealing with sequential data in the wild is that of **irregular sampling**. This is when the time that elapses between observations is not equal, i.e., your data is not evenly spaced. Alternatively, you can view the problem as **missing** certain observations. While this isn't an issue in NLP, other forms of sequential data often feature this problem.\n",
        "\n",
        "An example of irregularly sampled data is shown below."
      ],
      "metadata": {
        "id": "d_o8sEEZWtpT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ir_lv_tp = np.linspace(0, 50, 100)\n",
        "\n",
        "# Add noise to time to simulate irregular sampling\n",
        "ir_lv_tp = np.sort(ir_lv_tp + np.random.rand(*ir_lv_tp.shape)*3)\n",
        "ir_lv_data = generate_lv_dataset(10, ir_lv_tp, IRANGE, PRANGE, noise_var=0)\n",
        "\n",
        "visualize_lv(ir_lv_tp, ir_lv_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "Yi2jpMZ-Ynsx",
        "outputId": "448e82ee-8889-4658-ce02-5c40851d1beb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Irregularly sampled data appears frequently in real datasets. For example, in a medical setting, measurements of patient vitals may increase in frequency when status deteriorates, and vice versa. \n",
        "\n",
        "Several strategies exist to handle irregularly sampled data:\n",
        "\n",
        "\n",
        "*   **Interpolation** of input data on regular interval (https://docs.scipy.org/doc/scipy/reference/interpolate.html)\n",
        "*   Incorporating **time delta** between observations as input feature into RNN\n",
        "*   Sophisticated methods, for example **GRU-D** (https://www.nature.com/articles/s41598-018-24271-9), which learns a decay function between observations. \n",
        "\n"
      ],
      "metadata": {
        "id": "9hlNiotxqL1g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Real World Applications\n",
        "\n",
        "The LSTM is ubiquitous in sequence prediction, and was in-fact the dominant method until 2017. For NLP sequence prediction tasks, the Transformer / Attention architecture has made the LSTM somewhat obsolete, and you will learn about them in the upcoming lecture and PA3.\n",
        "\n",
        "For many non-NLP domains, the LSTM is still the go-to deep neural method. Some examples include:\n",
        " \n",
        " * Epidemiology, COVID caseload forecasting:  \n",
        " (https://www.sciencedirect.com/science/article/pii/S0960077920302642)\n",
        " * Financial time series forecasting:  \n",
        " (https://doi.org/10.1016/j.physa.2018.11.061), (https://ieeexplore.ieee.org/document/7364089)\n",
        " * Enviromental forecasting:  \n",
        " (https://www.mdpi.com/2073-4441/11/7/1387), (https://doi.org/10.1016/j.apr.2020.05.015)"
      ],
      "metadata": {
        "id": "1oJHIGWEhCVX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RNNs for Sequence Classification"
      ],
      "metadata": {
        "id": "vcth9MxGXwB1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sequence classification is another important task RNNs can be applied to.  \n",
        "In this task, we are given an observed sequence, and asked to assign a class label to the sequence.\n",
        "\n",
        "When applying RNNs for this task, we first encode the observed data sequence into a hidden representation. Typically, the last hidden state is used as the representation of the entire sequence. Another neural network is used to map from the last hidden state to class labels. Alternatively, you could also use other machine learning methods. The process is visualized below:"
      ],
      "metadata": {
        "id": "iBWl-scT9u_O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "epv0cHMwINp_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Toy Data: Handwriting Classification"
      ],
      "metadata": {
        "id": "afPNrU3jlL-g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use the UCI Character Trajectories dataset as our toy example.\n",
        "\n",
        "The dataset can be found here: https://archive.ics.uci.edu/ml/datasets/Character+Trajectories.  \n",
        "As a side note, the UCI repository contains many benchmark datasets, which can be a useful way to evaluate your research.\n",
        "\n",
        "First, lets load the data!"
      ],
      "metadata": {
        "id": "DFB4saVz9pXs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib.request\n",
        "from scipy.io import loadmat\n",
        "\n",
        "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/character-trajectories/mixoutALL_shifted.mat\"\n",
        "fpath, _ =  urllib.request.urlretrieve(url, 'chartraj.mat')\n",
        "\n",
        "ct_raw_data = loadmat(fpath)"
      ],
      "metadata": {
        "id": "9D1GW0uZVQOO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Exploration / Processing"
      ],
      "metadata": {
        "id": "HJOehPnywzoY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The UCI Character Trajectories data set contains the trajectories of an electronic pen as it writes one of 20 alphabetical letters. There are ~150 trajectories for each letter, and 2585 characters in total.\n",
        "\n",
        "Each trajectory is 3 dimensional, corresponding to the x-position, y-position, and pen-tip force while writing the character. Each trajectory is evenly sampled, and has approximately 100 observations.\n",
        "\n",
        "\n",
        "We first load the labels, and establish a character-index (and reverse) mapping."
      ],
      "metadata": {
        "id": "mXcnrasALZSX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chars = ct_raw_data['consts'][0][0][3][0]\n",
        "cid_map = {c[0]:i for i, c in enumerate(chars)}\n",
        "rcid_map = {i:c[0] for i, c in enumerate(chars)}\n",
        "\n",
        "ct_labels = ct_raw_data['consts'][0][0][4][0] - 1\n",
        "\n",
        "print(\"Dataset contains letters: [{}]\".format(\"\".join([c[0] for c in chars])))\n",
        "print(\"Dataset contains {} datapoints.\".format(len(ct_labels)))\n",
        "\n",
        "count = {rcid_map[i]:0 for i in range(20)}\n",
        "\n",
        "for l in ct_labels:\n",
        "    count[rcid_map[l]] += 1\n",
        "print(\"Individual character counts are: {}\".format(count))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8_JAA_dKqzh",
        "outputId": "1f68321e-ceef-4cd3-da70-ae2c1ececeb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset contains letters: [abcdeghlmnopqrsuvwyz]\n",
            "Dataset contains 2858 datapoints.\n",
            "Individual character counts are: {'a': 171, 'b': 141, 'c': 142, 'd': 157, 'e': 186, 'g': 138, 'h': 127, 'l': 174, 'm': 125, 'n': 130, 'o': 141, 'p': 131, 'q': 124, 'r': 119, 's': 133, 'u': 131, 'v': 155, 'w': 125, 'y': 137, 'z': 171}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we load the data, remove leading/trailing zeros, and visualize.  \n",
        "We'll subsample the trajectories for faster training."
      ],
      "metadata": {
        "id": "Cv5zdS5hMCdB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ct_data = ct_raw_data['mixout'][0]\n",
        "ct_data = [d.T for d in ct_data]\n",
        "# Strip zeros\n",
        "ct_data = [d[~np.all(d==0, axis=1), :] for d in ct_data]\n",
        "# Take only one third of dataset\n",
        "ct_data = [d[::3] for d in ct_data]"
      ],
      "metadata": {
        "id": "u10ieXBUND9H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax = plt.subplots(2, 2, figsize=(20, 8))\n",
        "ax[0][0].plot(ct_data[0])\n",
        "ax[0][1].plot(ct_data[400])\n",
        "ax[1][0].plot(ct_data[800])\n",
        "ax[1][1].plot(ct_data[1200])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "tEACk3XgTspO",
        "outputId": "4dd2b05b-3c59-473b-8de7-ccecdad8c2b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Trajectories of Uneven Length\n",
        "But wait, a problem has arised... The trajectories are different length! How can we efficiently process them in a mini-batch, when tensors must be of a fixed length? To address this problem, PyTorch has a built in method to efficiently handle data with different lengths. The documentation is shown below:\n",
        "\n",
        "*   https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pad_sequence.html\n",
        "*   https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pack_padded_sequence.html\n",
        "*   https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pad_packed_sequence.html\n",
        "\n",
        "These functions first pad the uneven sequences with zeros at the end, such that they are of equal length. At train and test time, we can use PyTorch to convert these sequences into a data structure for efficient computation, and then revert it back."
      ],
      "metadata": {
        "id": "bGt-LmAzNFFr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "class CharTrajDS(Dataset):\n",
        "    def __init__(self, data, labels):\n",
        "        self.labels = labels\n",
        "\n",
        "        # Use PyTorch built-in to pad zeros.\n",
        "        t_data = [torch.Tensor(d) for d in data]\n",
        "        self.data = pad_sequence(t_data, batch_first=True).to(device)\n",
        "\n",
        "        # Need to persist original lengths\n",
        "        self.lens = [len(d) for d in data]\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.data[idx], self.labels[idx], self.lens[idx]\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "ct_tv_data, ct_test_data, ct_tv_lab, ct_test_lab = train_test_split(ct_data, ct_labels, test_size=0.1)\n",
        "ct_train_data, ct_val_data, ct_train_lab, ct_val_lab = train_test_split(ct_tv_data, ct_tv_lab, test_size=0.1)\n",
        "\n",
        "ct_train_ds = CharTrajDS(ct_train_data, ct_train_lab)\n",
        "ct_val_ds = CharTrajDS(ct_val_data, ct_val_lab)\n",
        "ct_test_ds = CharTrajDS(ct_test_data, ct_test_lab)\n",
        "\n",
        "ct_train_loader = DataLoader(ct_train_ds, batch_size=64, shuffle=True)\n",
        "ct_val_loader = DataLoader(ct_val_ds, batch_size=256, shuffle=False)\n",
        "ct_test_loader = DataLoader(ct_test_ds, batch_size=256, shuffle=False)"
      ],
      "metadata": {
        "id": "n7uZ0Gg8VrFP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Model "
      ],
      "metadata": {
        "id": "IYdnEyA7lPpJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pretty standard. We use a simple 1 layer neural network classifier on the last hidden output. The only tricky part is that we must retrieve the last non-padded output from the array of outputs."
      ],
      "metadata": {
        "id": "bPWnEqXP7Mq4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
        "\n",
        "class LSTMClassifier(nn.Module):\n",
        "    def __init__(self, in_dim, hid_dim, out_dim, lstm_model=None):\n",
        "        super().__init__()\n",
        "\n",
        "        if lstm_model is None:\n",
        "            self.lstm = nn.LSTM(in_dim, hid_dim)\n",
        "        else:\n",
        "            self.lstm = lstm_model\n",
        "\n",
        "        self.classifier = nn.Sequential(nn.Linear(hid_dim, out_dim))\n",
        "\n",
        "    def forward(self, x, lens):\n",
        "        # Pack the input for processing\n",
        "        packed_in = pack_padded_sequence(x, lens, batch_first=True, \n",
        "                                         enforce_sorted=False)\n",
        "        out, (h, c) = self.lstm(packed_in)\n",
        "        \n",
        "        # Convert back from PyTorch data structure\n",
        "        out, last_ind = pad_packed_sequence(out, batch_first=True)\n",
        "        \n",
        "        # We must reselect the last non-padded output\n",
        "        class_in = out[torch.arange(out.shape[0]), last_ind-1, :]\n",
        "\n",
        "        pred = self.classifier(class_in)\n",
        "        return pred"
      ],
      "metadata": {
        "id": "DQMP-hWYcvdv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Training"
      ],
      "metadata": {
        "id": "qkVrVn-eGmix"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As standard, we use a cross-entropy loss when performing multiclass classification."
      ],
      "metadata": {
        "id": "xu7z_qdp8Skq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_loop(model, opt, t_loader, v_loader, epochs):\n",
        "    for epoch in range(epochs):\n",
        "        t_loss_sum = 0\n",
        "        v_loss_sum = 0\n",
        "        for batch in t_loader:\n",
        "            opt.zero_grad()\n",
        "\n",
        "            data, label, lens = batch\n",
        "            pred = model(data, lens)\n",
        "\n",
        "            loss = nn.CrossEntropyLoss()(pred, label)\n",
        "            loss.backward()\n",
        "            opt.step()\n",
        "            t_loss_sum += loss.item()\n",
        "        \n",
        "        with torch.no_grad():\n",
        "            for batch in v_loader:\n",
        "                data, label, lens = batch\n",
        "                pred = model(data, lens)\n",
        "\n",
        "                loss = nn.CrossEntropyLoss()(pred, label)\n",
        "                v_loss_sum += loss.item()\n",
        "        \n",
        "        if epoch % 5 == 0:\n",
        "            out = \"Epoch {}: Train Loss {}, Val Loss {}\"\n",
        "            avg_t_loss = t_loss_sum / len(t_loader)\n",
        "            avg_v_loss = v_loss_sum / len(v_loader)\n",
        "            print(out.format(epoch, avg_t_loss, avg_v_loss))"
      ],
      "metadata": {
        "id": "uwNloq8q6qYX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.optim import Adam\n",
        "\n",
        "model = LSTMClassifier(3, 50, 20).to(device)\n",
        "opt = Adam(model.parameters(), lr=1e-3)\n",
        "\n",
        "train_loop(model, opt, ct_train_loader, ct_val_loader, 21)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebXDLKgMgtm0",
        "outputId": "0259e3b7-4034-4f44-9b1c-694a8cf7e6eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: Train Loss 2.957561782888464, Val Loss 2.7994810342788696\n",
            "Epoch 5: Train Loss 0.9530275351292378, Val Loss 0.7569835186004639\n",
            "Epoch 10: Train Loss 0.4503691494464874, Val Loss 0.27457088977098465\n",
            "Epoch 15: Train Loss 0.25033595392832886, Val Loss 0.15031218901276588\n",
            "Epoch 20: Train Loss 0.17828103416674845, Val Loss 0.11894179042428732\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Results"
      ],
      "metadata": {
        "id": "260SNkeQGnhb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "def evaluate_model(model, t_loader):\n",
        "    with torch.no_grad():\n",
        "        test_loss_sum = 0\n",
        "        test_correct = 0\n",
        "        all_pred = []\n",
        "        all_lab = []\n",
        "        for batch in t_loader:\n",
        "            data, label, lens = batch\n",
        "            \n",
        "            pred = model(data, lens)\n",
        "\n",
        "            loss = nn.CrossEntropyLoss()(pred, label)\n",
        "            test_loss_sum += loss.item()\n",
        "\n",
        "            pred_lab = pred.argmax(1)\n",
        "            correct = sum(pred_lab == label)\n",
        "            test_correct += correct\n",
        "\n",
        "            all_pred += list(pred_lab)\n",
        "            all_lab += list(label)\n",
        "        \n",
        "        print(\"Test Loss: {}\".format(test_loss_sum / len(t_loader)))\n",
        "        print(\"Test Acc: {}\".format(test_correct / len(ct_test_data)))\n",
        "        f1 = f1_score(all_lab, all_pred, average='macro')\n",
        "        print(\"F1 Score: {}\".format(f1))"
      ],
      "metadata": {
        "id": "T0OeZypws_2T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, ct_test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8l38ZTTZL1yC",
        "outputId": "fc4e9e74-7822-4914-c779-2bb45d9757c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.3226592540740967\n",
            "Test Acc: 0.9195803999900818\n",
            "F1 Score: 0.9079146989812212\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gradient Clipping"
      ],
      "metadata": {
        "id": "SW_UCrsNGCzx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you may have seen in the lecture notes (https://csc413-uoft.github.io/2021/assets/readings/L07b.pdf), gradient clipping is a method to prevent exploding gradients. This is done by capping the magnitude of the gradient. While this means your gradient becomes biased, it is sometimes worth the tradeoff of an exploding gradient. \n",
        "\n",
        "PyTorch provides a built-in method to do this:  \n",
        "https://pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html\n",
        "\n",
        "An example code snippet is provided below."
      ],
      "metadata": {
        "id": "nndd9cgkFAJ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Obtain model predictions as usual\n",
        "batch = next(iter(ct_train_loader))\n",
        "data, label, lens = batch\n",
        "pred = model(data, lens)\n",
        "\n",
        "# 2: Compute gradients\n",
        "loss = nn.CrossEntropyLoss()(pred, label)\n",
        "loss.backward()\n",
        "\n",
        "# We can inspect some weights to check the gradients\n",
        "print(\"Unclipped Gradients:\\n\", model.classifier[0].weight.grad[0])\n",
        "\n",
        "# 3: Perform gradient clipping (set norm to be small for demonstration)\n",
        "torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)\n",
        "\n",
        "# The gradients are now clipped\n",
        "print(\"Clipped Gradients:\\n\",model.classifier[0].weight.grad[0])\n",
        "\n",
        "# 4: Update weights as usual\n",
        "#opt.step()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8Gf7zZyID1D",
        "outputId": "a8f6bb1e-a19e-4d02-d786-94cffb4da0c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unclipped Gradients:\n",
            " tensor([ 0.0053,  0.0023, -0.0028, -0.0017,  0.0035, -0.0018, -0.0025, -0.0067,\n",
            "        -0.0064,  0.0025, -0.0027, -0.0040, -0.0027, -0.0019,  0.0057,  0.0014,\n",
            "         0.0032, -0.0001,  0.0016, -0.0022, -0.0011, -0.0035,  0.0028,  0.0011,\n",
            "        -0.0065,  0.0007, -0.0005, -0.0013,  0.0016, -0.0037, -0.0016,  0.0015,\n",
            "         0.0033, -0.0013, -0.0003, -0.0006,  0.0028,  0.0029, -0.0003,  0.0064,\n",
            "        -0.0028, -0.0006,  0.0018,  0.0044,  0.0018, -0.0054, -0.0016, -0.0018,\n",
            "        -0.0015,  0.0017])\n",
            "Clipped Gradients:\n",
            " tensor([ 1.6998e-04,  7.2170e-05, -8.9307e-05, -5.3640e-05,  1.1142e-04,\n",
            "        -5.7094e-05, -7.8913e-05, -2.1524e-04, -2.0608e-04,  7.9146e-05,\n",
            "        -8.5382e-05, -1.2937e-04, -8.6120e-05, -6.2365e-05,  1.8242e-04,\n",
            "         4.4227e-05,  1.0153e-04, -4.7061e-06,  5.2048e-05, -7.0667e-05,\n",
            "        -3.4231e-05, -1.1120e-04,  8.9635e-05,  3.4033e-05, -2.0938e-04,\n",
            "         2.2007e-05, -1.5517e-05, -4.0708e-05,  5.1722e-05, -1.1769e-04,\n",
            "        -5.2406e-05,  4.7360e-05,  1.0460e-04, -4.2768e-05, -1.0889e-05,\n",
            "        -2.0400e-05,  8.8409e-05,  9.3799e-05, -1.0574e-05,  2.0391e-04,\n",
            "        -8.9459e-05, -1.9842e-05,  5.9005e-05,  1.4154e-04,  5.8944e-05,\n",
            "        -1.7456e-04, -5.2466e-05, -5.9188e-05, -4.7700e-05,  5.3610e-05])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Stacked RNNs\n"
      ],
      "metadata": {
        "id": "hKafwoHMwqPL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One way to increase the capacity/representational power of an RNN is by stacking layers, in an architecture known as a Stacked RNN. \n",
        "\n",
        "In this configuration, the outputs from the first layer of the RNN is used as input for a second layer of RNN. This continues for however many layers are specified. This is visualized below. Just like CNN, it is believed that deeper layers of the RNN will learn higher level representations of sequential features.\n"
      ],
      "metadata": {
        "id": "maGGoLc3Kul-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "QSczWZ0rwvTN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "PyTorch provides a very simple option to increase the number of layers in its default RNN/LSTM implementations."
      ],
      "metadata": {
        "id": "Btk4NRB-LnOl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "st_lstm = nn.LSTM(3, 35, num_layers=2)\n",
        "st_lstm_classifier = LSTMClassifier(3, 35, 20, lstm_model=st_lstm).to(device)\n",
        "\n",
        "st_opt = Adam(st_lstm_classifier.parameters(), lr=1e-3)\n",
        "\n",
        "train_loop(st_lstm_classifier, st_opt, ct_train_loader, ct_val_loader, 21)\n",
        "evaluate_model(st_lstm_classifier, ct_test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EwN80I0Lpk_",
        "outputId": "264317c0-1419-453e-9a33-13162066e780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: Train Loss 2.985769200969387, Val Loss 2.9075199365615845\n",
            "Epoch 5: Train Loss 0.9549692160374409, Val Loss 0.773076593875885\n",
            "Epoch 10: Train Loss 0.2996649407857173, Val Loss 0.22395890951156616\n",
            "Epoch 15: Train Loss 0.14644804919088208, Val Loss 0.09769528731703758\n",
            "Epoch 20: Train Loss 0.09154126833419542, Val Loss 0.05889343470335007\n",
            "Test Loss: 0.15962520614266396\n",
            "Test Acc: 0.9860140085220337\n",
            "F1 Score: 0.9841151742993848\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It looks like the test performance increases a little bit (depending on the random seed). As usual, you should always trade off model complexity and the potential for overfitting. "
      ],
      "metadata": {
        "id": "wMIJFKP3SZkb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### RNN Dropout\n"
      ],
      "metadata": {
        "id": "0e11teoOVi-z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropout is commonly used to prevent overfitting and regularize neural networks. Could we do this for RNNs too? \n",
        "\n",
        "For Stacked RNNs, dropout is available out-of-the-box in PyTorch between RNN layers, where each RNN output has a specified probability of being dropped out. As always, remember to switch the model to eval mode when evaluating test performance, when applying dropout."
      ],
      "metadata": {
        "id": "7uQb-xfYVmRx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "do_lstm = nn.LSTM(3, 35, num_layers=2, dropout=0.5)\n",
        "do_lstm_classifier = LSTMClassifier(3, 35, 20, lstm_model=do_lstm).to(device)\n",
        "\n",
        "st_opt = Adam(do_lstm_classifier.parameters(), lr=1e-3)\n",
        "\n",
        "train_loop(do_lstm_classifier, st_opt, ct_train_loader, ct_val_loader, 21)\n",
        "do_lstm_classifier.eval()\n",
        "evaluate_model(do_lstm_classifier, ct_test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vWlTFxHWGht",
        "outputId": "d899b987-a0b8-4e75-8dac-b4e692f41040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: Train Loss 2.9878439001134924, Val Loss 2.966567039489746\n",
            "Epoch 5: Train Loss 1.35171352528237, Val Loss 1.273581624031067\n",
            "Epoch 10: Train Loss 0.610712515341269, Val Loss 0.4882289469242096\n",
            "Epoch 15: Train Loss 0.3239069496457641, Val Loss 0.2535933405160904\n",
            "Epoch 20: Train Loss 0.2530553610743703, Val Loss 0.1803867407143116\n",
            "Test Loss: 0.2938692569732666\n",
            "Test Acc: 0.9510489702224731\n",
            "F1 Score: 0.9510413701641323\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Bi-Directional RNNs"
      ],
      "metadata": {
        "id": "LApOqh55GIsO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bi-directional RNNs are another innovation for RNNs, and are particularly useful for NLP tasks. Consider the following task where you want to interpolate a sentence (fill in the blank).\n",
        "\n",
        "**\"The _____ is a flightless bird that lives in Antarctica\"**\n",
        "\n",
        "\n",
        "If we applied the RNN normally, from left to right, the model would not have the context to accurately predict the blank.  \n",
        "Working from right to left however, enough context is available.\n",
        "\n",
        "The Bi-Directional RNN simply runs the two separate RNNs from either direction of the sequence. Then, the output from each RNN direction is concatenated prior to output. This is shown visually below, again from Chris Olah's blog: http://colah.github.io/posts/2015-09-NN-Types-FP/\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "FAlQ1AJtV7DL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "vJsfcqiXUOs1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PyTorch again allows easy application of the Bi-Directional RNN as a built-in option: https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html.\n",
        "\n",
        "\n",
        "We demonstrate a bi-directional LSTM below, but its unlikely to perform better since no backwards context is required for our task."
      ],
      "metadata": {
        "id": "6iOC5fODUUDp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bi_lstm = nn.LSTM(3, 35, bidirectional=True)\n",
        "bi_lstm_classifier = LSTMClassifier(3, 70, 20, lstm_model=bi_lstm).to(device)\n",
        "\n",
        "bi_opt = Adam(bi_lstm_classifier.parameters(), lr=1e-3)\n",
        "\n",
        "train_loop(bi_lstm_classifier, bi_opt, ct_train_loader, ct_val_loader, 21)\n",
        "evaluate_model(bi_lstm_classifier, ct_test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pm5Apa-sUTo8",
        "outputId": "765c80b6-9179-4d48-c8bc-6010888a3d79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: Train Loss 2.9610093606484904, Val Loss 2.8003278970718384\n",
            "Epoch 5: Train Loss 1.573724076554582, Val Loss 1.264131784439087\n",
            "Epoch 10: Train Loss 0.8212869763374329, Val Loss 0.5501769334077835\n",
            "Epoch 15: Train Loss 0.5236696979484042, Val Loss 0.330549493432045\n",
            "Epoch 20: Train Loss 0.3438040497335228, Val Loss 0.2192244715988636\n",
            "Test Loss: 0.3458893299102783\n",
            "Test Acc: 0.9615384340286255\n",
            "F1 Score: 0.9568141219943875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Real World Applications\n",
        "\n",
        "Again many real-world problems can be framed as a sequence classification problem.\n",
        "\n",
        "For example, in the domains of: \n",
        "\n",
        "*   NLP: \n",
        " *  Sentiment analysis: Classifying language as positive/negative towards a subject (https://www.tensorflow.org/text/tutorials/text_classification_rnn).\n",
        " *  Hate speech detection: Classifying language as hate speech  \n",
        " (https://ieeexplore.ieee.org/document/8712104)\n",
        "* Spoken Language Processing:\n",
        " * Speaker Identification: Identifying different speakers in audio data.  \n",
        " (https://arxiv.org/abs/1710.10468)\n",
        "* Health:\n",
        " * Human Activity Recognition from accelerometers:  \n",
        "   (https://arxiv.org/pdf/1611.03607.pdf)\n",
        " * Sleep stage classification:  \n",
        " (https://www.frontiersin.org/articles/10.3389/fncom.2018.00085/full)\n",
        " * Prediction of ICU Mortality:  \n",
        " (https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC6934094/)\n",
        "\n",
        "*   Computational Biology:\n",
        " *  Classification of DNA sequences  \n",
        " (https://www.nature.com/articles/s41598-018-33321-1)\n",
        "\n"
      ],
      "metadata": {
        "id": "ntXs0RkElRZg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RNNs for Translation: Seq2Seq"
      ],
      "metadata": {
        "id": "-gRcZgqDXy1c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "RNNs can be applied to the NLP task of language translation in a manner very similar to autoregressive prediction.  \n",
        "Instead of outputting real values as before, the RNN decoder can simply be made to output word vectors. Thus, you convert one sequence to another, which is why this task is sometimes known as Seq2Seq.\n",
        "\n",
        "A code example is shown in the tutorial below:"
      ],
      "metadata": {
        "id": "p3C9jx6IF1pD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "See: https://colab.research.google.com/github/csc413-uoft/2021/blob/master/assets/tutorials/tut07_rnn.ipynb"
      ],
      "metadata": {
        "id": "_7ic03p7wU3a"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GB1SLqBFXOvF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}