Plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates some of ConX's functions for visualizing the behavior of networks. First, let's create a simple XOR network to explore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "ConX, version 3.6.1\n"
     ]
    }
   ],
   "source": [
    "import conx as cx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Dataset**: Dataset for XOR 2-3-1 Network\n",
       "\n",
       "**Information**:\n",
       "   * name    : None\n",
       "   * length  : 4\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : (2,)\n",
       "   * range  : (0.0, 1.0)\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : (1,)\n",
       "   * range  : (0.0, 1.0)\n",
       "\n"
      ],
      "text/plain": [
       "**Dataset**: Dataset for XOR 2-3-1 Network\n",
       "\n",
       "**Information**:\n",
       "   * name    : None\n",
       "   * length  : 4\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : (2,)\n",
       "   * range  : (0.0, 1.0)\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : (1,)\n",
       "   * range  : (0.0, 1.0)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = cx.Network(\"XOR 2-3-1 Network\")\n",
    "net.add(cx.Layer(\"input\", shape=2))\n",
    "net.add(cx.Layer(\"hidden\", shape=3, activation='sigmoid'))\n",
    "net.add(cx.Layer(\"output\", shape=1, activation='sigmoid'))\n",
    "net.connect()\n",
    "\n",
    "XOR = [\n",
    "    ([0, 0], [0], \"1\"),\n",
    "    ([0, 1], [1], \"2\"),\n",
    "    ([1, 0], [1], \"3\"),\n",
    "    ([1, 1], [0], \"4\")\n",
    "]\n",
    "net.dataset.load(XOR)\n",
    "net.dataset.info()\n",
    "\n",
    "net.compile(loss='mean_squared_error', optimizer='sgd', lr=0.3, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "require(['base/js/namespace'], function(Jupyter) {\n",
       "    Jupyter.notebook.kernel.comm_manager.register_target('conx_svg_control', function(comm, msg) {\n",
       "        comm.on_msg(function(msg) {\n",
       "            var data = msg[\"content\"][\"data\"];\n",
       "            var images = document.getElementsByClassName(data[\"class\"]);\n",
       "            for (var i = 0; i < images.length; i++) {\n",
       "                if (data[\"href\"]) {\n",
       "                    images[i].setAttributeNS(null, \"href\", data[\"href\"]);\n",
       "                }\n",
       "                if (data[\"src\"]) {\n",
       "                    images[i].setAttributeNS(null, \"src\", data[\"src\"]);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<svg id='XOR 2-3-1 Network' xmlns='http://www.w3.org/2000/svg' image-rendering=\"pixelated\" width=\"400.0px\" height=\"346.66666666666663px\">\n",
       " <g >\n",
       "  <svg viewBox=\"0 0 300 260\" width=\"400.0px\" height=\"346.66666666666663px\">\n",
       "    <defs>\n",
       "        <marker id=\"arrow\" markerWidth=\"10\" markerHeight=\"10\" refX=\"9\" refY=\"3\" orient=\"auto\" markerUnits=\"strokeWidth\">\n",
       "          <path d=\"M0,0 L0,6 L9,3 z\" fill=\"black\" />\n",
       "        </marker>\n",
       "    </defs><rect x=\"124.0\" y=\"24\" width=\"52\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-static-XOR 2-3-1 Network-582754_output_1\" class=\"picture-static-XOR 2-3-1 Network-582754_output\" x=\"125.0\" y=\"25\" height=\"50\" width=\"50\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: output (output)\n",
       " output range: (0, 1)\n",
       " shape = (1,)\n",
       " Keras class = Dense\n",
       " activation = sigmoid</title></image><text x=\"180.0\" y=\"52.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >output</text><line x1=\"150.0\" y1=\"104\" x2=\"150.0\" y2=\"77\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from hidden to output\n",
       " output/kernel:0 has shape (3, 1)\n",
       " output/bias:0 has shape (1,)</title></line><rect x=\"74.0\" y=\"104\" width=\"152\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-static-XOR 2-3-1 Network-582754_hidden_2\" class=\"picture-static-XOR 2-3-1 Network-582754_hidden\" x=\"75.0\" y=\"105\" height=\"50\" width=\"150\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,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\"><title>Layer: hidden (hidden)\n",
       " output range: (0, 1)\n",
       " shape = (3,)\n",
       " Keras class = Dense\n",
       " activation = sigmoid</title></image><text x=\"230.0\" y=\"132.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >hidden</text><line x1=\"150.0\" y1=\"184\" x2=\"150.0\" y2=\"157\" stroke=\"black\" stroke-width=\"2\" marker-end=\"url(#arrow)\"><title>Weights from input to hidden\n",
       " hidden/kernel:0 has shape (2, 3)\n",
       " hidden/bias:0 has shape (3,)</title></line><rect x=\"99.0\" y=\"184\" width=\"102\" height=\"52\" style=\"fill:none;stroke:black;stroke-width:2\"/><image id=\"picture-static-XOR 2-3-1 Network-582754_input_3\" class=\"picture-static-XOR 2-3-1 Network-582754_input\" x=\"100.0\" y=\"185\" height=\"50\" width=\"100\" preserveAspectRatio=\"none\" href=\"data:image/gif;base64,R0lGODdhfAA+AIcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAAAfAA+AEAIjgABCBxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3rMyAAOw==\"><title>Layer: input (input)\n",
       " output range: (0.0, 1.0)\n",
       " shape = (2,)\n",
       " Keras class = Input</title></image><text x=\"205.0\" y=\"212.0\" font-family=\"monospace\" font-size=\"12\" text-anchor=\"start\" fill=\"black\" alignment-baseline=\"central\" >input</text><text x=\"150.0\" y=\"12.5\" font-family=\"monospace\" font-size=\"15\" text-anchor=\"middle\" fill=\"black\" alignment-baseline=\"central\" >XOR 2-3-1 Network</text></svg></g></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.picture()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc46afbcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cx.view(net.get_weights_as_image(\"hidden\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc35eab978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot_layer_weights('hidden', colormap=\"RdBu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "========================================================\n",
      "       |  Training |  Training \n",
      "Epochs |     Error |  Accuracy \n",
      "------ | --------- | --------- \n",
      "#  533 |   0.00652 |   1.00000 \n"
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    "net.train(epochs=1000, accuracy=1, report_rate=25, record=True)"
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   "execution_count": 7,
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11682b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot('loss', ymin=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot_activation_map\n",
    "\n",
    "This plotting function allows us to see the activation of a specific unit in a specific layer, as a function of the activations of two other units from an earlier layer. In this example, we show the behavior of the single output unit as the two input units are varied across the range 0.0 to 1.0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c5db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot_activation_map('input', (0,1), 'output', 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can verify the above output activation map by running different input vectors through the network manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06735210865736008\n",
      "0.09960916638374329\n",
      "0.9248424768447876\n",
      "0.8984166383743286\n"
     ]
    }
   ],
   "source": [
    "print(net.propagate([0,0])[0])\n",
    "print(net.propagate([1,1])[0])\n",
    "print(net.propagate([0.5, 0.5])[0])\n",
    "print(net.propagate([0, 0.6])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ppcSguAY4UtLhJMFwCXBqbptvAUuBv5M0l+Ry+vZehY5NUDSzdigrKEbEdkmnA1eQ5AsviohbJJ0LrI2IVem6/yJpHbAD+EBE/LJXuT2rJum4AnXbFhE3FToLM5v2yrzNLyJWA6tzyz6ceR/AmemrkH5V+xeSJqp6bHM4sKDoAc1sehuw97l2/YLimoh4Za8NJF1VYn2GphkzmL333k8teOSR0VUm48majtPUiWqhnMHbbRng7Ylq+2v1hBD9AmLRbczMOlodFHuR9PyI+LcyK2Nm429sgyJwJXBoWRUxs+mjtUFR0ucmWwU8s/zqTMEee8Dcubs+zt62bfdtnpyY4cvneorkfgbND40qn9SUiWqhmnGKbckx1nmctmh7S/E04E+BLV3WLS2/OmY27to+yewa4OaIuDa/QtI5ldTIzMZa21uKb2SSUSURcXj51TGz6aC1QTEiflVXRcxsemh1S1HSiohYPtVtajF7Nsyf33uT++6b8HmfJ8sfWt3kJHpbJpFoyuzdw5Yz6HGa/DtThVYHReD1knpFDgG/W2J9zGzMtb2j5QMFyvh+GRUxs+mjtS3FiPgKgKT3RcRns+u6LStK0iLgsyTT/VwYEefl1i8DPslTE0Z+PiIuHOZYZtYsbb987ng7SRDLWtZlWV9FnsCV+kZEnF644Fmz4MADB6pLHTnGJsvnEEf1vNumTlRb5DhlmG6TSLQ6KEpaSjKT7eGSVmVW7QsM2zNd5AlcZjamWh0UgWuB+4C5wKcyyx8BbhzymEWewAXwBkkvB34BvD8i7slvIGk5sBzg0L2a8DACM+un1UExIu4C7gJeXE91dvkOsDIitkh6F/AVYLcpytJHHa4AWLj//lFvFc1sKBHM2L511LWYVKF4LekRoBN0ZgOzgMci4ulDHLPvE7hyz1C4EPhE31Jnzeo7TpHtvUfiTfccY15d4xbzpvtEtXUeZyQi+v5fHKVCQTEi9u28lySSHOCJQx6z7xO4JB0UEZ0ItRj4+ZDHMrOmaXhQHLjDMRLfAl4zzAEjYjvQeQLXz4HLOk/gkrQ43ewMSbdI+hlwBklPt5mNg05QLPIagaKXz/8t83EGsJApPH6kwBO4zgbOHrZ8M2u4BrcUi/YB/dfM++3AnSSX0M1R4N7ngYvM5RgB9mponrGunFNT8oyeqLbFGn75XDSneFrVFTGzaWLnzt1mwW+SQjlFSUdI+o6kTZIekPRtSUdUXTkzG0MNzykW7Wj5OnAZcBAwD/gmsLKqSpnZmBuDoLhXRFwSEdvT19eABk/+Y2aN1fCWYtGOlu9KOgu4lGQQ95uB1ZL2h4bM0D17Nhx8cOWHmZPvfBkiN9LqJHlOWzpeimxTxsQMnqi2gHHoaAHelP77rtzyJSRB0vlFMyum4R0tRXuf/ZAqMyvPGLQUkfQSYEF2n4j4agV1MrNxNg6Xz5IuAZ4D3ADsSBcH0JygOGsWHHJI/+1KVkaOsam65an6/cI0NcdYZJs2TyLRqolqxyEoktzWd3REeHouM5uaMQmKNwMHkkw4a2Y2vDEJinOBdZKuB7Z0FkbE4sl3MTPrIqLRaaaiQfGcKitRij33hAULRl2Lsc4xAuTnSy7yCzSKNkFV4xTbkmOs8zgDK7Gl2O/JoJnt3gBcDrwoItb2KrPokJx/GbCuZmbdlRQUiz4ZVNK+wPuAHxcpt+dtfpJ+kP77iKRfZ16PSPr1MCdiZtNcebf57XoyaERsJbnjrtuUhn8FfJyCc8D2e3DVy9J/9+21nZlZYYO1FOdKyl7urkgfWAcFngwq6TjgkIj4v5I+UOSADX7QoJmNreJBcXNELBzmEJJmAJ9mwMeZjE9QnDWLnQfO2/Vx4IfPAGzbNvFzCXmP3TpeoJbOl7qS6m3peIF6JpEoo+Nl2HIGPc7IOl7Ku/e535NB9wVeAFydPG+PA4FVkhb36mwZn6BoZu1QXu9zzyeDRsTDJMMJAZB0NfBnpfQ+m5mVpqSgGBHbJXWeDDoTuKjzZFBgbUSsGqZcB0Uzq19J4xT7PRk0t/ykImWOTVDcsgXuvPOpzwsWzNttm6HyjP0M8eWO0wDvfF6q209jOk0iUVZ+cBSTSNSWYxyT2/zMzMoxDpPMmpmVxi1FM7McB8XqbdsGGzb03iafZ5zRkC9mnHKM3eR/ym3JMUI54xTbMolEbRPVuqVoZpbhoGhmluGgaGaWMSaTzDbe1q1wzz29t9kjd7bz5x864XOhcYw7dvTfZlC5v5pzNm3afZsG/xINKt9GyP/cu/1SNiXPON0nqi2FW4pmZhkOimZmGQ6KZmYZDopmZhnuaKnH1q2wcWP/7XoZquOlJnPuv3/igvyEuGOkyKQSbel4KbJNGYOmaxt4XQa3FM3MMhwUzcwyHBTNzDIcFOtRZEKIQeVzjNCcPON0yjFC/0klmppjLLJNmyeRGJqDoplZypPMmpll+PLZzCzDQbEeW7eWn1PsZmRjGfv8EhXJMTYqpzRFbckxwvSbRKKQBgfFSv9PS1ok6VZJ6yWd1WX9mZLWSbpR0j9LOiyzboekG9LXUM9vNbMG6rQUi7xGoLKWoqSZwAXAq4ENwBpJqyJiXWaznwILI+JxSe8GPgG8OV33REQcU1X9zGxEpvHl8/HA+oi4HUDSpcApwK6gGBHfy2x/HfDWCutjZk0wjXufDway075uAE7osf07gO9mPs+RtJYkPXReRHwrv4Ok5cBygNmzDyX//Kepyk9K282h8+f33mBEfxF3yzFC37GMbc45Fnk4VlPyjFXcL926HOM0bSkWJumtwELgFZnFh0XERklHAFdJuikibsvuFxErgBUA++yzMGqrsJkNbxpfPm8EDsl8np8um0DSycAHgVdExJbO8ojYmP57u6SrgWOB2/L7m1n7RDS3DVNl7/Ma4EhJh0uaDSwBJvQiSzoW+BKwOCIeyCzfT9Ke6fu5wEvJ5CLNrN12FnyNQmUtxYjYLul04ApgJnBRRNwi6VxgbUSsAj4J7AN8UxLA3RGxGDgK+JKknSSB+7xcr7WZtVQwuvxuEZXmFCNiNbA6t+zDmfcnT7LftcALBzlWGZPMDmdiY7tvx8sITadJJNo8UW2Rbdo8UW1UVG5ZGtHRYmbTSwUPCi6Ng6KZ1a653SwOimZWM18+12T7dug2Xrl+7c0x7hzjHCOM9yQSdU1UW5aygqKkRcBnSTpzL4yI83LrzwTeSfL1bgL+KCLu6lVmUyaSNrNpotP7XOTVS2Z+hdcCRwNLJR2d26wzv8J/Bi4nmV+hJwdFM6tV5/K5hHGKu+ZXiIitQGd+haeOFfG9iHg8/XgdyU0kPTkomlntBgiKcyWtzbyWZ4rpNr/CwT0Om59foauxySlG7GTbtsd2fd60ae/dtpk5s84adez+d6eUPOOO3KCGEvKBe1U0jrGOpPowxxinSSTqmqi2DMFAQ3I2R8TCqR5zkvkVuhqboGhm7VHSkJwpza8wGV8+m1ntSsopDj2/Qi9uKZpZrcq693mK8ytMykHRzGpXVj5z2PkVehmjoLgD+PWuT91mO7///t07X8o2a1aRrfoM8G7IBJy7dbyAJ5GooyJdNHX27mH4jhYzsxwHRTOz1IBDcmrnoGhmtXNQrMXEnGI3+TzjMDnGIk/4G7zMidmdeQsWlH+QbobIXVY1wDurSZdWVUwiUWTgdRlljGKi2iKcUzQzy3FQNDNLuaVoZpbjoFiL/jnFvDJyjNXYPbszcJ6xprGOdeQYm6SMHGORfQbNM9Y1UW1ZHBTNzFLT+hGnZmZ5zimameU4KNZiJ/BY3616qSLH2G1c43BjHUc0lnFAZdwv3eT/MHllTFQ7zD5Fcn1NHadYZbllGKOgaGZt4MtnM7McB0Uzs1QATR645aBoZrUr6RktlRijoDj1jpa8UU1UW0xDO166DBrfa/PmiQtGNMA7f8lWxSVcWRPV9ttnmEklPMlsMWMUFM2sLRwUzcxSbimameX4Nr9a7ASeGHCfmQMfpS2TSMzLPwyrmxE9IKspOca61DGJxDCTOZQx2e0w3FI0M8vx4wjMzFKBh+SYmU3gy+dalD9OsYim5Bh3n2Ri9m7bFMozjsBuk0hEk9sRU1fGJBJljEvst33R4wzKOUUzsxz3PpuZpdxSNDPLcVCsxU6gy83KNXvyyYljH++/f86IatLNxDzjyHKMfcZHdp2odozzjMM8uGqYsY5l3C9dhsBDcszMJmjynzgHRTOrnS+fzcxSTX/EaV23O04gaZGkWyWtl3RWl/V7SvpGuv7HkhbUX0szq0Kn97nIaxRqbylKmglcALwa2ACskbQqItZlNnsH8GBEPFfSEuDjwJt7lxzA1krqPBVPPrnfbsv6db7MmlVVbfIa0vFSwHQb4J1XxRMBRzUhRLdjN8koWorHA+sj4vaI2ApcCpyS2+YU4Cvp+8uBV0lSjXU0swqV1VKs4qpzFEHxYOCezOcN6bKu20TEduBh4Fn5giQtl7RW0lp4tKLqmlmZyrp8zlx1vhY4Glgq6ejcZruuOoHzSa46expJTrEsEbEiIhZGxELYZ9TVMbMCOh0tRV59VHLVOYre543AIZnP89Nl3bbZIGkP4BnAL3sXe89mOOOu9MNcYHOvrUcpP4nEnXf2/pxq9DkNyefUHp3zOmyqBd0HV5yTlFfEnORKcJcVEbEifd/tqvOE3P4Trjolda46J/2ORhEU1wBHSjqcJPgtAU7NbbMKeDvwI+CNwFURvTPrEXFA572ktUnrcXz4nNphHM8Jyj2viFhURjlVqf3yOc0Rng5cAfwcuCwibpF0rqTF6WZ/CzxL0nrgTGC3BKqZTXuDXHVS9KpzJIO3I2I1sDq37MOZ908Cf1B3vcysVSq56hzXO1pW9N+kdXxO7TCO5wQNPK80R9i56pwJXNS56gTWRsQqkqvOS9Krzl+RBM6e1CdomplNK60ekmNmVjYHRTOzjNYGxXGZVKLAeZwpaZ2kGyX9s6TDMut2SLohfa2qt+bFFTjHZZI2Zc7lnaOoZz8FzuP8zDn8QtJDmXWN/64kXSTpAUk3T7Jekj6Xnv+Nko6ru461iIjWvUiSqrcBR5DMavAz4OjcNn8CfDF9vwT4xqjrPeR5/C6wV/r+3dnzAB4d9TmUdI7LgM+Puq5TPY/c9u8lSfy36bt6OXAccPMk618HfBcQcCLw41HXuYpXW1uK4zKpRN/ziIjvRcTj6cfrSMZitUmR76oNBj2PpcDKWmpWkoi4hqSHdjKnAF+NxHXAMyUdVE/t6tPWoFjapBIjVuQ8st5B8pe6Y046IcZ1kl5fRQVLUPQc35Bekl0u6ZAu60et8HeVpjgOB67KLG7Dd9XPoL+vrTSu4xTHjqS3AguBV2QWHxYRGyUdAVwl6aaIuG00NZyS7wArI2KLpHeRtPBfOeI6TcUS4PKIyD6faVy+q7HX1pZiJbf3jECR80DSycAHgcURsaWzPCI2pv/eDlwNHFtlZYfU9xwj4peZ87oQ+O2a6jaIQt9Vagm5S+eWfFf9DPIzaK22BsVdt/dImk3yS5jv0evc3gMFb+8Zgb7nIelY4EskAfGBzPL9JO2Zvp8LvBTIzl7eFEXOMZuXWkxyT3zTFPmdQ9Lzgf1IbivrLGvLd9XPKuBtaS/0icDDEXHfqCtVtlZePkdFt/fUreB5fJJksshvpv1Ed0fEYuAo4EuSdpL8cTsvJj7SoREKnuMZ6WQg20m+q2Ujq/AkCp4HJL9nl+b+ALfiu5K0EjgJmCtpA/CXwCyAiPgiyXwFrwPWA48Dp42mptXybX5mZhltvXw2M6uEg6KZWYaDoplZhoOimVmGg6KZWYaDog1E0rUVlLlA0qmZz51Zcy7MLDs7nZ3lVkmvSZc9LZ11Zms6/s9syhwUbSAR8ZIKil3A7s/W+EZEvBNAyQPOlwD/CVgE/I2kmRHxREQcA9xbQZ1smnJQtIFIejT99yRJV6cTOPybpL/vzEIk6U5Jn5B0k6TrJT03XX6xpDfmywLOA34nbfW9v8thTyEZEL0lIu4gGTx8fJXnadOXg6JNxbHA/wCOJpln8KWZdQ9HxAuBzwOf6VPOWcD3I+KYiDi/y/ppMTuLNYODok3F9RGxISJ2AjeQXAZ3rMz8++K6K2Y2LAejia5IAAAA0klEQVRFm4otmfc7mHgvfXR5v530d07SDJIZrIuYFrOzWDM4KFpV3pz5tzNjzJ08NS3YYtLJBoBHgH17lLUKWKLkuTuHA0cC15daW7NUK2fJsVbYT9KNJK3JpemyLwPflvQz4J+Ax9LlNwI70uUXAw9mC0pno7mMZLqt7cB7chO4mpXGs+RY6STdCSyMiM1D7r8s3f/0Oo5nluXLZ2uiJ4DXZgdvd9MZvE1yGb6zlprZ2HNL0cwswy1FM7MMB0UzswwHRTOzDAdFM7MMB0Uzs4z/D7NsEA4VDD//AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117f0f470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Activation of hidden[2] as a function of input[0] and input[1]\n",
      "rows: input[1] decreasing from 1.00 to 0.00\n",
      "cols: input[0] increasing from 0.00 to 1.00\n",
      "\n",
      "0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n",
      "0.04 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n",
      "0.05 0.04 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n",
      "0.06 0.05 0.04 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n",
      "0.09 0.06 0.05 0.04 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n",
      "0.11 0.09 0.06 0.05 0.04 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n",
      "0.15 0.11 0.08 0.06 0.05 0.04 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n",
      "0.19 0.15 0.11 0.08 0.06 0.05 0.04 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n",
      "0.24 0.19 0.15 0.11 0.08 0.06 0.05 0.03 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 \n",
      "0.30 0.24 0.19 0.15 0.11 0.08 0.06 0.05 0.03 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 \n",
      "0.37 0.30 0.24 0.19 0.14 0.11 0.08 0.06 0.05 0.03 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 \n",
      "0.44 0.37 0.30 0.24 0.19 0.14 0.11 0.08 0.06 0.05 0.03 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 \n",
      "0.52 0.44 0.37 0.30 0.24 0.19 0.14 0.11 0.08 0.06 0.05 0.03 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00 \n",
      "0.59 0.52 0.44 0.37 0.30 0.24 0.19 0.14 0.11 0.08 0.06 0.05 0.03 0.03 0.02 0.01 0.01 0.01 0.01 0.00 \n",
      "0.67 0.59 0.52 0.44 0.37 0.30 0.24 0.18 0.14 0.11 0.08 0.06 0.05 0.03 0.03 0.02 0.01 0.01 0.01 0.01 \n",
      "0.73 0.67 0.59 0.52 0.44 0.36 0.30 0.24 0.18 0.14 0.11 0.08 0.06 0.05 0.03 0.03 0.02 0.01 0.01 0.01 \n",
      "0.79 0.73 0.66 0.59 0.52 0.44 0.36 0.30 0.23 0.18 0.14 0.11 0.08 0.06 0.05 0.03 0.02 0.02 0.01 0.01 \n",
      "0.83 0.79 0.73 0.66 0.59 0.51 0.44 0.36 0.29 0.23 0.18 0.14 0.11 0.08 0.06 0.05 0.03 0.02 0.02 0.01 \n",
      "0.87 0.83 0.78 0.73 0.66 0.59 0.51 0.44 0.36 0.29 0.23 0.18 0.14 0.11 0.08 0.06 0.05 0.03 0.02 0.02 \n",
      "0.90 0.87 0.83 0.78 0.73 0.66 0.59 0.51 0.43 0.36 0.29 0.23 0.18 0.14 0.11 0.08 0.06 0.04 0.03 0.02 \n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# map of hidden[2] activation as a function of the inputs\n",
    "net.plot_activation_map('input', (0,1), 'hidden', 2, show_values=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169ab630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# map of output activation as a function of hidden units 1,2\n",
    "net.plot_activation_map('hidden', (1,2), 'output', 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the network actually solve the problem? We can look at the intermediary values at the hidden layer by plotting each of the 4 hidden units in this manner:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d90518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot_activation_map('input', (0,1), 'hidden', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d380fbb8e63142729b50985ec732133e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>SequenceViewer</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "SequenceViewer(children=(VBox(children=(HBox(children=(IntSlider(value=0, continuous_update=False, description='XOR 2-3-1 Network Playback:', layout=Layout(width='100%'), max=533, style=SliderStyle(description_width='initial')), Label(value='of 534', layout=Layout(width='100px'))), layout=Layout(height='40px')), HBox(children=(Button(icon='fast-backward', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='backward', layout=Layout(width='100%'), style=ButtonStyle()), IntText(value=0, layout=Layout(width='100%')), Button(icon='forward', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='fast-forward', layout=Layout(width='100%'), style=ButtonStyle()), Button(description='Play', icon='play', layout=Layout(width='100%'), style=ButtonStyle())), layout=Layout(height='50px', width='100%'))), layout=Layout(width='100%')), Output()))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=432x288 at 0x1168C7E10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.playback(lambda net,epoch: net.plot_activation_map(title=\"Epoch %s\" % epoch, format='image'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Additional Hidden Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = cx.Network(\"XOR 2-3-2-1 Network\")\n",
    "net.add(cx.Layer(\"input\", shape=2))\n",
    "net.add(cx.Layer(\"hidden\", shape=3, activation='sigmoid'))\n",
    "net.add(cx.Layer(\"hidden2\", shape=2, activation='sigmoid'))\n",
    "net.add(cx.Layer(\"output\", shape=1, activation='sigmoid'))\n",
    "net.connect()\n",
    "\n",
    "XOR = [\n",
    "    ([0, 0], [0], \"1\"),\n",
    "    ([0, 1], [1], \"2\"),\n",
    "    ([1, 0], [1], \"3\"),\n",
    "    ([1, 1], [0], \"4\")\n",
    "]\n",
    "net.compile(loss='mean_squared_error', optimizer='sgd', lr=0.3, momentum=0.9)\n",
    "net.dataset.load(XOR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"288pt\" version=\"1.1\" viewBox=\"0 0 720 288\" width=\"720pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       " <defs>\n",
       "  <style type=\"text/css\">\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\n",
       "  </style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 288  L 720 288  L 720 0  L 0 0  z \" style=\"fill:#ffffff;\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
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       "   </g>\n",
       "   <g id=\"matplotlib.axis_1\">\n",
       "    <g id=\"xtick_1\">\n",
       "     <g id=\"line2d_1\">\n",
       "      <defs>\n",
       "       <path d=\"M 0 0  L 0 3.5  \" id=\"mc28cca78ad\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n",
       "      </defs>\n",
       "      <g>\n",
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       "      </g>\n",
       "     </g>\n",
       "     <g id=\"text_1\">\n",
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       "      <defs>\n",
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     "text": [
      "========================================================\n",
      "       |  Training |  Training \n",
      "Epochs |     Error |  Accuracy \n",
      "------ | --------- | --------- \n",
      "#  886 |   0.00721 |   1.00000 \n"
     ]
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   "source": [
    "net.reset()\n",
    "net.train(epochs=2000, accuracy=1, report_rate=25)"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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+5+OOurWO2BT+SHaibFatWqVNmzZp3bp1am1t1fLly5XJZPS+970v12bOnDm64447dMstt0iSTjvtNF1xxRWaNWuWZsyYoSeffFK//e1vddttt1XrMACghwm/+lNemUym5I3b99hjD2vfsWPHlmNZAOoYsSkAdEfEpvBHshNl87nPfU6//vWvc49HjBihwYMHa9WqVblty5Yt07333pt7vPvuu+vnP/+5PvOZz+jrX/+6Nm3apMsuu0yTJ0+u6NoBoOcK740EAPWF2BQAuiNiU/gj2Ymy+dWvflWyzYwZMzRjxoxO2z7xiU/oox/9qFasWKHBgwdrxx13LNcSAQBF4n56TvkNALWN2BQAuiNiU/gj2Yma1KtXr9zNgQEAlVSZrwoBQHdCbAoA1UJsCn8kOwEAQB4CSgAAANQKYlP4I9mJumWqiG3aHspkzH1M7W3VtTMZe4Vznz6l5rVVDbf18dku2avQ+1Sgd43vqhwf9RyZnq+4fWzz2vqEj8u51rSyUoelCripirqp2rfkX+G8o8NevT3cH6VP2MbWPk7l+DhrNVVFN63VNa+tGrurj2kOUzV21/oLnztbFfvE8VUhAEia6Y5zpSpA+76quqqi+7C1T+pVPsnxfcaK+w7qM5brGJJ8BzdVSq+EJM9rLbE9b/VwbPWB2BT+SHaibhUml8JkkivBZ+pjS46FP1Ha+/ZxzWtKOMZZa6lkp61P1HFsY8Tp43OO4vaxrdU2jq1PUmtVVubEVhAks922z9Xep0+YpLMlNSuxVtM8pm2+85bqk8S8UfaXTVqSJfsPAIjNFgrYkp5JJDvjvIuY+iSdMkhqDt9xfM+HTxKs1PqTekevVEK1VuathHo+tvpAbAp/JDsBAECejOKFB3x6DgAAgKQRm8IfyU4AAJCH+yIBAACgVhCbwh/JTgAAkIeAEgAAALWC2BT++IsBAAAAAAAAUBe4shN1yVZcJ9xnYiu+41v53Da+a3vUeV2FbpJca7gvSjEi33mTWqtr/b59fM9r2DaptaZSxbc+T5mqd0v2KuC2quu+1dhd7X36hG1shYUqsVbTPFG3lZrD1SfqHFHOdf7+ihUsinsTeO6LBAAuha+SKcv2/P1RX/WCffekAAAgAElEQVRtbeMUzfGZN44kxy/3WpOct1prBbo/YlP4I9mJupUpeD0Mk02F20PptLmPqb2tKrorwWdjmzdONXbbWjMZ/2rspnkaG/3OkWneUn2inlfTeetKH1cC1va3lNRaUwqK34oDR/Kyra14e3u7ORFWiWRnR0dxAjZs45PU9F2rad7C/VHmSDrZGfW8+iY7XW0TxVeFACBpKRX/U71UslMqTo4Fhm3hdt9XblviLWtYk23elKFtqT62tbre5WzzlHutPuspNUacPj7ryf+dr9zRg+u56e5cfzP1cHzdB7Ep/JHsBAAAeQgoAQAAUCuITeGPZCcAAMiTUbzwgK8KAQAAIGnEpvBHshMAAOTJKN59kQAAAICkEZvCH8lO9BiuAjG2/T5FhUrNEadP1DFc45R7rb7zxukT57zFWavv3NHbl7gbU5D7n7xtlmI0vtvDfbbtpuJBriI4rvlNj5M8hijzRlmXzzEntdYkz3XZxf2qEJ+eA0C11EPRHwAwIzaFP5KdqEumAjiuCtrS9vamwjK2itumgjO2gjy2NdnmsI3vKnRjK2oUZ63ptNRgeHUwnSPfecN9XV1rJuMu+uPTJ53eXnwpaoX48HHUtaZT24sQWWU7ohffyWa3FyMybbcVKLIV8enwmLdUH1PxHcm/mJLvWl1Fe6Ien63CvWuOIDA/D7Y+tgJFrnNtKlBUsWrs3BcJAJJWGGakLdvz+RQoMm1PW7a75jWNlVVyBVlSlrFca5Vln28qo1QBId8+UcdwjZPEvC6m8SsRTcQ5F91Jtc5rz0RsCn8kO1G3fK8qtO1Lsr1pv+mKQtdVka55y71W07Y485aau5pXd0Ydx3eOsn+uWJUrACOo1XV1VakrMrs1AkoAKAffWCCJ9oFjHNs+03ZXVWpfPvPm7zPxWVM1E522scqd6KzUHLU0b6XU+/HVFmJT+CPZCQAA8vBVIQAAANQKYlP4I9kJAADypMSn5wAAAKgNxKbwR7ITAADk4atCAAAAqBXEpvBHshN1y1ZUJokCRa7CPrbxXQWKTMWUbAV8bOPbivu45nUdR9Q+vvOGfZIoUGQ7F759fIspeRcoUlbqSKCQjmQudBNur1SBosL5TX3CNqaxTPNGWWuUeUvNE6dAkW2frTiSrRhRtylQ1KB44QFfFQIAG9N1SaUK/pgK9rj62MIi33cO06u57boqW2Ek11hJtM2fP4mxfI8hzrux7zjVKFyU9By+c9fD/S7rvTBTdRCbwh/JTtQlU4XwMAHlSkZGrTTuM2/hflOyMOq8Yf+oVcNdfVzJTlcfn4Swba2+xxfnufHp47vWsF3UtSqr0gVtCvebtiW53XfekCnhZtoWPk5qrVHnLTWe7zHb5kjivJY616X2l01ahAcAkDxb2GJLYNqKAtna25KRvkxz2iqlB5JsH9uVuyBTOH8S4/iOldR59Z23K/NXcvy4c3f3ZGA1z2t9IzaFP/5iAABAHm4CDwAAgFpBbAp/3PgAAAAAAAAAQF3gyk4AAJAnLT4JBwAAQG0gNoU/kp0AACAPFS8BAABQK4hN4Y9kJwAAyJOSlInZDwAAAEgSsSn8kexEj2GrPO7aX6pauc8cPn1853WpVB+fMZI4r5V4PkuNY95XXGcxFafqdq1WY3fNY3pczrXaxnf1iXvMXV1rEue6YtXZ+fQcALqblPwrPcfpAwCVR2wKfyQ7UbcyBR/+pNOdf5vaF+5Lp83tU6ni8W1j5Pcx7fOZN52WGiz/r3X1MW1vaLAn+NJp8/E1NPit1TRGnD6ZTPFaMxn7+L59fNcaPjaOZfpnQ5CVOjrsi+3oKE5idXRI7e2GsQLzWKYxJCnrmNtn3vzxTH0K58hm/7XPZ622uaPOW2oeUx/bOQ3bh8fSlT7ZrHmba/2F+03noCwIKAEgaabrklIFv00KX/Vtd64LDG313py2dw5bn7SjTyHX2rOOcUz9XPOm3hsvSh/X2uN8lm8az/YcxGFbU5zjSCpKSPL4usO8lVLvx1c+xKbwR7ITdcv3qkLbviTb+/TxnTdOn+6+1iSv7nTt816re1nRuK4aLKdS41ck2VZD81ZT1Y45o3jhAV8VAgCXwlfJUslO31dVWyLFNU6cPlHb2q4cdSXqfPe5tked18UnWetqb5NUojPpiKHcydRam7dS6v34yofYFP5IdgIAgDwZxbsvEgAAAJA0YlP4I9kJAADyxP2qEJ+eAwAAIGnEpvBHshMAAOThvkgAAACoFcSm8EeyE3XJVAyoVIEi070XfQsUpdPu+z7aiuaY1mpbT9yiP6Z5XQWKkiimVO4CRbbn0reP71rDiuvGYkRxCum0tRUXr7EVxrEVtUmqQJGpkE7h/nIWKLLNXakCRab2lShQZKv4nt+nYvfwJKAEgHLwfWU1FRByvENbx3e9e8TpE1XKc/w412D59Cl1TEmM5VNsqpRavJ9nLRUuquT85VZ4fPVwTMkiNoU/kp2oW8UJKvN233HyxytMptmSeGF7UxLTlJi1VW4P57QV3rH1iTpvqXkqsdauzluJtaYs/y1pe0KqMCll2pbPlhyzJcCS2B53rVH7hI+7w1p9k6xJrbUwmVlq7lLHm5i0uC8SACQviWSnZE94msYv9a4Rp09UturqcRKFNr59kpo7TrLW57z6jlWJZGC1Eo5JntdaRNGiKIhN4Y9kJwAAyMN9kQAAAFAriE3hj2QnAADIk1G88ICAEgAAAEkjNoU/kp0AACBPRtwXCQAAALWB2BT++IsBAAAAAAAAUBe4shN1y1Q8yLQ9f7+pT5wCO7b1RO1jG8dVAMl3ra7K8a5q6VHPUZy1JjFvyFR53VaB3netuYdB1lAm1VDQplSFc1OhGlvxGttYtkI6vsV34lRjt1Uaz/9dy2vNL/wTtYiQq7iQ7XzYzpFNVauxcxN4AEhSSsVfqEwV/Db1iTJOKKmiOb7jx6ky7jtWnD5daVuOsZKcvzvMWyn1fnyQiE0RB8lO1KVUantiq3CbVLw9ZEp4pdPu9oVJsEzG3t7VJ+q8tsrq+fui9ilVOd6UGDSty3Suo6zVp4/vcyNJDQ1lXGuYdOqImMhLKilXartvArGjo7iPqTJ8nLW6kp2meUutNanz2tGx/Sefb+IyTp+kkp0Vq8bOFz8AIGmF4USpZKdkrrptah/n3cGnj+0dK5D9HcNWTd7WJyt3ojVqH995XX1s59tnXtdYpfr4jJP/uxzz+qxHsv/NJK3cx1cNrg81KnVeawuxKfyR7AQAAHmoeAkAAIBaQWwKfyQ7AQBAnpT49BwAAAC1gdgU/kh2AgCAPHx6DgAAgFpBbAp/JDvRY7iK2dj2u4oN+bR39fERZwzXWpM4Pt8x4vTxPde+fdxrddzxx3T/RNN9FUvda9GnT1LbS/Xp6lptBX+irKkr80bZ7zNOUuc1zrku3F/RAkV8eg4AKI3iMADKj9gU/kh2oscoVaDIVJDHVnDIVgzIVGwov49prIYGc/EdW1VyV9Vw0/i27Y2N7gI/pn22Ykq2tfoUg3L18Xlukl7r9huEFyau3rs1eGGhm3CbqShOe7t9saaiQK5CRFHnDdvb5o5awKfUPKY+4dpNc7uKKdnmjjpvlD6mauyueW0Finz6mI7NVeQoHCe/T9i27EnPBkmNMfrx6TkA2KRU/MraUfDbJIkCRa4K51H7pB3zximwY3r3SzvGitMn6hilxKlyH3Uc11hx3u19/jZ8nv84yj1+3Pm7e3K+2ue1OohN4Y9kJ3qcUldflrO9T5848/r0iXKVaLnWGqdPua/udO3r8tukz9V7ldTT5q2mpI6ZauwA0C2ZkpSlqrGbtruqNNva2wQx+kSd18U2r2173D5daZvfxySpZGdSCTnfBFip9Zcz2VmtRGe9RJ/1msR1IzaFP5KdAAAgDwElAAAAagWxKfyR7AQAAHniVrz0v85lxYoVuuqqq7Rs2TINHz5cX/3qV7XHHns4+2SzWd1666267777tGnTJu2777666KKL9P/+3/+LsWYAAADUNmJT+CM9DgAA8qRj/vjZsGGDxo0bp5UrV+rcc8/Vm2++qcMPP1zr16939rvqqqv0pS99SUceeaQ+85nPaPHixRo7dqw2bNjgvQYAAADUOmJT+CPZCQAA8lQmoLzxxhu1detW3X333Tr99NN11113KQgC3XDDDc5+d999t2bMmKEZM2boE5/4hO644w698cYbevLJJ73XAAAAgFpHbAp/fI0ddauw2ExYhdtWhCadNvdxtY8yRv56ovaxjVOqGrutj6kYUa2utavzxlprKrDf7D8wVA4Pq2LbqqUXti9Vddu031WNPeq8hdtN6zIdW6kK4VEqnOdXDo8yb6m5TcdXaq1R+7ieH9scrj62IkK2582mcO6KFXZKS8rE6Of3VaHHHntMxx9/vHr16iVJampq0vHHH68FCxbo3//93639hg4dqrfffjv3+J133pEkDRkyJMaaAaByCl8lw5DH9upueodIskCRbW6fAiiu9bj6+GyP26crbbvSpxJjlXveaq0VMCM2hT+u7AQAAAAAAABQF7iyE3UplZIyBR/+hFcMFm7P3194VaHtSsNwfNOVg7bxw35R+pjW75q3VB/bVY6utUbt4ztv3LWanptS64+8VkkpWa7utF11mf+7cJ/pakLXVXmmqweTvLIzyasl29ujrdX3HJVjrVGvQk36yk4T2xWuvld2VuTqznhf/clmOzR//nyl8/7POmrUKE2cONHYfuXKlTrmmGM6bRs2bJief/555zzXXnutpk6dqk9+8pMaNmyY/vjHP+p73/uePvCBD3ivGQAqqTAESRX8LhSo+Oo625WUrncH2z6fdxTbu1Ug+/VWrj6md5ms3Ociah/TeXONEaeP71W3YZ+oa3WNZetjYxqr1Bhx+nRlPSGPyCrWvPm/KzVvpVTrvFYGsSn8kewEAAB5GhTnnzNBIK1du1bLly/PbRs2bJi1/aZNm3JfEwo1Nzdr48aNznlWrlypt956S3vttZdSqZTa29u1ZMkStbe3q6GBsAYAAKC+EJvCH2ceAADkiffpeSbToMmTJ+vyyy+P1H633XbT2rVrO21bvXq18/5G2WxWZ5xxhr70pS/p0ksvlbQ9iB0xYoQOO+wwffrTn/ZeNwAAAGoZsSn8kewEAAB54t4E3s9ee+2lZ599ttO25557TnvttZe1z4YNG/TPf/5To0ePzm3baaedNHz4cL388stlWysAAACqhdgU/ihQhB7DdL/Mwv2+4/mMYZvfZxzXMfj26e5rLfV8efVJSVLwr3si8lO/P1L119DV9ZddOuaP34vo6aefrkceeURPPfWUJOmZZ57Rww8/rNNPPz3X5rrrrtOcOXM0Z84cSdKAAQO0xx576O6771bw3vl44YUXtGjRIo0aNSr2EQNALfIMTasizhq7w3EBqCXEpvDHlZ2oW4WFaMLHtqI2psI/DQ3bfwrZCt3YChqFfUxjZTLFfcJCPabxfQoshdujzps/v2keUx/XvHHWapu38LnJZNwFinzWmgqC7YVrTFzFd0x92tqK25uK4pTa7ypEZJq3vd2cDHMdm62Aj629a62FfcJxTWOZ5rWNU2qt7e32tZqKGpmOr9Q5shUosvUxPRe+85r6VCzhGe+rQr6OPvpoXXTRRRo3bpw+8IEP6IUXXtCFF17Y6cbwP/3pTzVhwgRJ0gknnCBJuvHGG3XGGWdo3rx52m233bRw4UJNmTJFp5xyStnXDABxpSQ1FmzrKPhtUviq32Fp73p3sO3z6WNLGwSOcXz7pB1jpWQusuLqE3WMOH1M85Zah2+S1zWeq3hRVK71uJ6Hrs7r4lt8KylxzkV3Uq3zmhxiU/gj2Ym6FOeqQts+n/ZR5o0yR9yrUJM8Np8+1Vprold3JqWiV+BFUIm12JKs1Zi30muIqpbWUlJlAkpJuuaaa/SZz3xGS5cu1d57762RI0d22j937lw1Nzd32nbsscfq1Vdf1XPPPacNGzZon3320Z577lmR9QJAVxSGIKWqsZu2uyou29r7rClKn662t/UJHGPZ9rn6dKVt3HnjJCht8/qM4xtllFqLabxyJzorNUctzVsp3f/4iE3hj2QnAADIE/e+SPE+SRg5cmRRIBmyVczs27evDj300FjzAQAAoDshNoU/kp0AACBPRtxRDQAAALWB2BT+SHYCAIA8cb8mRBAKAACApBGbwh/JTtStwkI0YTEbW1GedLr4fo7WgjaOAkW2ojmuPqZ5TW1tY9jGcY3VnQoUNTZGn9e11lQqUMp0d5quFCgyFaKJWmwoTp+wiE/hvKZiPPntbfPGKVBkKrTjU8QpqQJFpc5rEgWKbPfaDPvYjsO01q4WKLI9x4mrzD2RAKAnScn/S5iu4j9R27rG8OnjKuxje9eI0yfpe0+WS5x5fYv++I5jGytO5JDE/UgrcS/PpOfxmbv73O/SrvDYaveYiE3hj2Qn6papwrlpe377qMnOcDyf9j59XElWW/EiWwV3Wx9be9daTX18543Tx5QEjbJ+4xzG1o6iQqaCQ+FjWzLS1N6VpLLNYSv8Y5s36thx5s2fJ0qiNXxsOwaf7XHXGrVP3KJStnNuq97e1fVXrMiRrX4uAKArfJOdWUVPPmRlTgcklewM5zBxpSF8+iSVlKuEpJKdcddfOFbSyUCfBF81KrSXYx7fuWs3MRhN90riEpvCH8lOxPLUU09Jkq688kq9/vrr2n///XXVVVdZq44FQaDTTjvNOt6VV16p/fbbTytWrNAll1zSaV8mk9Htt9+e2NoBAC5p1WqoCwA2xKYAUK+ITeGPZCe8vfrqqzrmmGMkSdOnT9dFF12kWbNmafz48XrhhRfUp0+foj6pVEqjR48u2v7oo4/q4Ycf1g033CBJWr9+ve666y595zvfUUPD9j/PtOvyPQBAwuJ+cs4n7gCqg9gUAOoZsSn88U4NAAAAAAAAoC5wZSe8XXfddWppaZEkXXvttZKksWPHasiQIbr99tt17rnnGvtddtllRdvmzp2rT37yk+rfv3+n7Zdccol69eqV8MoBAKXxVSEA3QuxKQDUM2JT+OPKTnj7y1/+ookTJ2rixIm5bb1799ZRRx2lJ598MvI4r776qubPn28MQOfOnau7775br7zySiJrBgBElYn5w1eFAFQHsSkA1DNiU/jjyk54W7lypXbbbbei7UOGDNGKFSsij3PjjTeqpaVFY8eO7bS9X79+uuaaa9Ta2qpnn31W06ZN009/+lOlTGW9Jc2fP19z5szJPX7ppZfU3r61qJ2leyyuKuO+fZKYN06fOGv1Gce1Vt8+vuuXpJTh079UYKkvGqcKeP5vU5+o47vmiFOx3Ld9nMrktnlMj7t7NfY4aw33d2XeKPvLJJXKJPp6CQDl1h1i021ZW11yu5SiX8tUzarkvu9UleoDoD4QmyIOkp3wtnXrVjU1NRVtb2pq0pYtW0r2z74X7P3sZz/Tv/3bv3UKFFtaWvTWW2+pd+/ekqTHH39cxxxzjMaNG6ezzz7ba52ZTOfH4b3kbfeUb2goTqA1NBSPI21vZxonk7GPn0qZx2poKO6TTpvHsW0P95nGt22Ps1ZTH9daTWPE6eMzbyiVCooD/yArmf6xkc1KHR3mgbKGPuFj01gdHeb2rn/k+PSxbe/oMCfGXMdm6tPRYW/vs9bwsWmsOGttb4+31ih9gsDvHIVrtT2nScxr6pPNViT56fr/LgDUou4Qm6YkNRZsSxf8Nil81e9476dUu/w5bPtc6dfCPimZE6qWj5Fzc/v0yTjGsvXx+XJrSu5j9ukT50u1pj6uMeLkdkzj2Z6DOGxrijpvV+c3SfL4usO85Vap580HsSniINkJb7vttpvWrFlTtH3NmjXGT9ULzZ07V5L0z3/+U5/+9Kc77QsDydBhhx2mCRMm6I9//KM1oDzyyCN15JFH5h7/8pe/1Le+9dOixGX4uNQVjVG2xW3v0yfOvN1lrXH6eM8rvrhQ96pw1WNPkMkQUALoXrpDbPp/CxYUxSWpgt+FTNtd8Y2tvY1trCTmLcWWKCq1Xt8+cduW6pP0WIXijG3jk6B08R0nqXlLqdQ8tTJvOUX5u6vG8RGbIg6SnfC29957a+HChUXbn376aU2ZMqVk/xtvvFGSdOKJJ2ro0KEl27e2tnJDeACokLgBJV8vAlAtxKYAUL+ITREHyU54+/SnP63TTz9dkrRw4UKNHj06d8P2M844I9fu3//93zVhwgQdf/zxuW2rV6/W/fffL0m68847i8Z+/PHHNXz4cO22224KgkC33nqrHnzwQf36178u81EBAKT4XxUioARQLcSmAFC/iE0RB8lOeJs0aZJmzJghSTr00EM1bNgwvfnmm/rxj3+skSNH5trdeuut2nXXXTsFlLfccosGDBggafun54Xuv/9+XXXVVdpll13U2tqqVCqlmTNnatKkSWU+KgCAxFeFAHQ/xKYAUL+ITREHyU7Ecs0110iSLrroIr3xxhtqaWnJBYqh1157TY2NnW/Ffv755+uzn/2sJBXtk6T//M//1MUXX6y///3vam5u1vDhw2N/TaiweE34Aml7ocxkij/9Saft94o0tS9V9CdqUaNSBXxMa0qi6E/+Wrt9gaIgK3UU3FXGVfTHVaDIVFhGshffSapAkWl8W1GbJAsUtbf7rzXJAkW2fYXbXcdmm8f0PJQqUBSnTxIFimJU7k0CASWA7qg7xKa+L62BohfUsRXwcRUQslU4d/Xxaetauy2Ec80bp0+5JHWfUptSx5TEWEkWovEt1lOJ+10WzlGpv5NaLPCTpFTBf1eiTgKxKeIg2YkuGTJkiIYMGWLc16dPn6Jtffv2LTnmzjvvrJ133rlL6zIlFsMEoU8y0pZQC9uaiiC5K4RH62MbxzZvqT5R5016rbaiSUmstdT61REUJ5wCwzbXdsmeZAv7RRnLNb5tv63ytm17nGPzmde133bM+b+jrtWW4LMlHH3Pq6utbV6f58E2b5y/iyppaJAM/94via8KAagFNRubyj/ZmVX0JEJW5mSgKxkZWNaUdfTxGT8cy8S2Vpc4fcql3MlOKV6SMuo4pcbwOa9JFS7yndelWgnHJM9rLapWqEdsijhIdgIAgBwCSgAAANQKYlPEQbITAADk8FUhAAAA1ApiU8RBshMAAOTEDSj59BwAAABJIzZFHCQ7UbdMxYZM2/P3m+4VGeeekza2ojw+hZFs96m0vQnY+tjm8O1jG8dVQMi3j3HeVIm73pju/+gqUOS6V6StQFHUsUoVKDLtt92/0rbddW9J17GZ7iPpWmvU8xo+9l2rz71Ho5zXKH1c4/g+Dz7zljrXhX0qdI9PV3ExAEB8hSFOUPDb1L5wn+semaawq1SBI1MoaJpXlm22McLtPvdzjHO/Q9/x4xTeKfdYXW1bjrGSmL9a89ayej++ciE2RRyu8h4AAAAAAAAA0G1wZSfqlqmyumT/VMh0VWEms/2GyLb2hVcbZjLuau+ZjPnKyMI1xanGnsnYb9xsuyLT9QlZ1D7hcfms1bePba1p12ejPlfwma7eDLW3l//Kzo4Ocx+fKzWTurIzSjX2cl7Z2dWrJeP0cT3/tu1JrLXUlZ2Ffxeutgniq0IAkLyUiv/hlSr4Xch0FaftaknbO0Spauw2hfss74bOMWxrjXNlm08f17mwXeljO0+2PlkVH1uUyvRR+/iu1fW3FGetpueuVJ8oY5Qax3U1sE3U6KjUOaqEJM5rT0RsijhIdgIAgBy+KgQAAIBaQWyKOEh2AgCAnMZGqanJvx+fngMAACBpxKaIg2QnAADIaWiw377DhYASAAAASSM2RRwkO1GX4ryw2e4taWtb7jl85/Vdq2tenz5xzpFrfON2w51sUkGJO9yYKlf7VPou3G96HHWsKFW0bWNFXWucY4uz1qh9fM9R3LW6+KzV5/lPaq2+fxcVqsYe975IAAC3whCnVAjoW727q2NUW7nX2p3OBYB/ITZFHCQ7UbcKP/0JH7sKFEUtjGMrsNPQULpAkWleUzElW1vb+m1vArZ5oxRTitLHtP5Sa3V9Omc+hqA4iA9iFv2JWlQofxxbgSJT8RqfeeOu1TavreiPrchOW1vxHKa1xFlr+Ng0d3u7PfnnKhTkm+z0Kabkev59z6vPWtvbzdvDPvnrqkCiU4p/XyQ+PQcAu5Skwm9htuftsyl85U/LXjAn6hhx+qRizOtboCipYkppx7y+76QpmY8xHWMsnz62eUsxjR/n7dlVQMinvU1S49jGSmo9ccaKI6lCXvWK2BRxkOxE3UriCshSV3DGuVIzyhxx5i01dxJrjTNOEmuNU5kREVToSsHIamkttaBK54NPzwEgeaZYplQ1dtN2W0zk2u6zpqTmjbKvUOBo71qXD9/2tjWVWqvPWF1tG7a3SSrZWe5EZ5yxXOP5Vo7v6hhxkegsjdgUcZDsBAAAOQSUAAAAqBXEpoiDZCcAAMjhq0IAAACoFcSmiINkJwAAyIn76TkBJQAAAJJGbIo4SHYCAICcxkapqbCKRgSuulYAAABAHMSmiINkJ+qSqcBPWDE8iQJFqZS9ArlrfFMf21pN49gqn7v6JLlWW8X6cq9VCqRswa26S1U496m67RrLtC8sHBN1rEqsNZu1Vw33Obak1ho+No1VWGW81PZwnMLjS3KtPvPG6WObN2xnq05fWI29zEWLUimpoWH7j6+2tuTXAwD1pDDECR/bLljKqrgit6uiuCmEKlUBPGqFdVuxIVt1+JBPVe9UifY+BYpsBXZ8i+PY1uRaq41PH9/xw+NKKkoo51orNRbqA7Ep4iLZibpVeKl7mESzJeAymeJ9rkvmbUlK1yX2piSfaY5UyjxOmAy0JRdtc9uSlL5rNfWpxFpTHQklujo6qpPstM2bvz/q8cVJdnZ0dH1e135XstM0t209SSQQS+03PRfVTHb6rL9C1YMBb0gAACAASURBVNnj3hepvT35tQBAvUip+B9eYbhjSxYGKk482hJ2tncTV3LUp49tXtc7k+Xd3tnHti+QPSlc2Md1XK7Esq2PLSFceD5c59q3j21eW5/wcVeT1/l9fCrHR523FNO6Sp1Xn3Hyf8cdJ1TOiwarNW+tIjZFHCQ7AQBADhUvAQAAUCuITRGH61sHAAAAAAAAANBtcGUnAADI4dNzAAAA1ApiU8RBshMAAOTEvS+SreAZAAAAEBexKeIg2Qm8x1Yd3NbWp71vH9954/SpzbUG5ptxByouzlKqMrVtv62Pa7tpblsf33lt+13jJLHdd17Xft9zZNuexLxR9vv+XfieV59z5LP+ChUo4tNzACiPwhin1L/DTUVKbH1c230qopfqUy1JrKncFdQBlAexKeIg2Ym6ZKoQ3tDQ+XehTMZcfdxUvT2VMo9jquie38e0z9THNq/rUy3bm4CtWnqptUbtk+RaJSllCimznhW0pe1VtwurfgeBvZK5q2K5aZxwX1fmze8T9fhsazVVVneN4zuvb5/wsWks21pdybw41cyjPhe+Fevj9DG1DwL7GGEfUzX2Mic9+fQcAJKXktRUsK09b59N4St+u6W9q0KzbzV2U5+spb1rjJT8q3S7qrGb9mUM212V423jpz37pB1j2fj0Scmv6na4dlMf07yl1mE7Fz7tXccbJ+lsk8RYcSKrpMZJYt5Kzl8NxKaIg2Qn6pbvVYWmfaWucPS9YtKnj8864/ap2bW6lxVNnKsru/O8KFZLz0UtraWExkapqfBf5ACALrNdpelztaYtkefa7lpP1D6+88bpE+VK10KBYXupNfmKOm8pPn18xw8jCp+1+iYvbX1cbZNICJY6Dz4J5K6OEWW8ckd31Zq3mohNEQfJTgAAkNPQYL8C3oVPzwEAAJA0YlPEQbITAADkxL0vEgElAAAAkkZsijhIdgIAgJy490UCAAAAkkZsijhIdqJumYoN5f8uZCpQ5Fv0J51236PSNkfhmjIZ86X6tnlt47j6VLNAUTodmAsRBdpejKhQnEI6PgVtkixQFLeQThIFikztXUVwkixQ5FvEyXYPyyTXGvW5iFOgyHW/TdNafQsjmdaazVbkI2oqXgJA8lKplNKNjZ22Zd57D0hZ3ssCmQvO2AoUmd6ZbGNI9gIypj6VqPbuKiBUal/h+KYQ1zWGqxiQJVxO/N6T5RJnXp/n1Pf5jzOvSznvC5rkvTzjjpfE3N39fp7EpoiDZCfqlikpZ9oeMlVLt1VQD/dFrd6eP5apCJJprdZq5Y6iST5rdbX36WMbJ+xvXKtsb8aBPbFUuN20LW4f11i2cfJ/18Jas1m/7Umu1TRH+A83n2OI8zwkcV595w23+8xrOke+6w+Cmk528lUhAHBIpZQufHF97zXeluzMKHrCKPVe+0JZxxhZzz62BKXrLcOnjy1hG/axjVXIlcR1JX1cSeEo85bS3ZOdvkm8aiY7fcdKKkGZVDGlOOq5SjuxKeIg2QkAAHJsV5aXQkAJAACApBGbIg6SnQAAIKexUWpqqvYqAAAAAGJTxEOyEwAA5FTyq0Kvvfaavv3tb2vZsmUaMWKErrjiCu25554l+23YsEE/+MEP9Mgjj6ihoUEHHXSQZs6c6b8AAAAA1DRiU8ThuGMfAAAAAAAAAHQfXNmJumQrrpP/u5CpeJCtunqpojy2Ndmqlnd13jh9SlWOj9rHVbHeWjBJhsrUkrlitRSvwrlpv61iuWss076waEw51+qqxu5z7mzbk1yraY5ShXeqtVbfcWwFnlxrtZ0Pn3Nk6lOqoFFCKlXxcv369Ro3bpxGjx6tGTNm6Pbbb9cRRxyh559/XgMGDLD227hxo4444gg1NTVp+vTp6t27tx555BEFQaAUN2cCUKtSqeLvYb73mpWyvLanOzqKio64iuzYiv74Vho3FfhxFQhKotq7q72rj09xGnuBTPc+n+I7rndpV+X6rrTNX49trXGqk1ejqE215q0Uzms8xKaIg2RnnQuCQH//+9+9+jQ1NWno0KFlWlHlFN7EOHyBtL1Q2pKOPu1LvRBHncN33jh9XO19+mQy2++j4rNWZRNIdtoSl/n7C//x0NEhtbdHn9c2d/i4oyPavEknO23zRh07zrz585j62CrW+6zJ93mI8zdQiWSnrTp9N0l2lnptsPGN5W644Qa1t7frzjvvVFNTk0466SSNGDFCN9xwgy655BJrv+9+97vasmWL/vKXv6hXr16SpHPOOcd/wQCqosfGpum01Ldv502trdv/w/baHgQKCt47bEk527uhKxnp6lO4Ite8tnemOH1s2336uI7L9vZmW6vPu64rqRzuj5q8DmT/CqapT/jY1Mdn3jh9fP8mXYnlOGuNOkdQ8DvuOKFSz3fU8eIcX3eZNynEpoiDZGed27Bhg/baay+vPu9///u1aNGiMq0IAFDLKvXp+RNPPKHjjz9eTe9d6dTY2Kjjjz9eCxYscAaUv/rVr3Teeefpn//8p1555RWNHDky0r2UANQGYlMAgA9iU8RBsrMH2GOPPbRw4cJIbVetWqUpU6aUeUUAgFoVN6DMZjs0f/58pfPuXzFq1ChNnDjR2H7lypU65phjOm0bOnSonnvuOcccWb366qv605/+pO9973saMWKE/va3v2natGn64Q9/6L9oAFVBbAoAiIrYFHGQ7KxzTU1NOvXUU7XLLrtEat/Q0KBJkyaVeVUAgFoV96tCUqC1a9dq+fLluS3Dhg2ztt68eXPuqz6h5uZmbdy40dln69atWrx4sRYvXqyBAwfq2Wef1SGHHKIJEyZo8uTJcRYOoIKITQEAPohNEQfJzjrX3Nys73//+5HbDxgwQN/97nfLuCIAQC3LZIrveRxFQ0ODJk+erMsvvzxS+1133VVr167ttG316tUaMmSItc8OO+ygfv366ZOf/KQGDhwoafsn9IceeqgeffRRAkqgGyA2BQD4IDZFHCQ7AQBATlNTccHgKHxvAj98+HD97W9/67Tt2Wef1fDhw539RowYoaCgmAfVLgEAAOoTsSniINkJSVJra6v2339/LVy4UAMGDKj2crosldp+uXu+8LHtNSedLu6TyRRvs43vah/2MV1+b+rjmtd2Cb9vn7KvVYHSthp/toriQWCvNF643VUNO5zDVL3bZ96wj6mCezhHlHnjVjiPOn643VTN1XauJf/2tj6m8xe2sz2ftmrsruchib8B2zbXvK5jNu2L+ny6jjccJ39/Nmv/P22CKnUT+E996lM69thj9ec//1ljxozR008/rYcfflhz587NtfnBD36glpYWSdLHPvYxSdKZZ56p//u//9Nll12mXXbZRX/961/15JNP6itf+Ur5Fw2gYuotNjVVY8+92FqC05SkVMH7RKajQynDe5+pirVUuhq7rRJ41ErTacsYsqwn3G6b1zWWbx9TW1eFcNM7rGt8U8X6Uu/S1ah+HSfd4tPH9/l3nYM4a41Tqb2rYyQ5jmmsSv2dVGteX8SmiINkJyRt/+Rh+fLl6nD9w7ubsSU7bbkCU7LTltQM9xXGpq72Pn18563VtUq2oCWwJ5BsCbjC7bbknmss2/i27bZ5wsdRx3KNn9Ra4xybaz2+7bt6jlzbpfL+Dbjmdf1N2hKttr+ZpP4uyizufZF8P7w+8sgj9eUvf1lHHnmk3v/+9+vFF1/UxRdfrPHjx+fa3HTTTZowYYKkfwWUX/jCF/T4449r5MiRamlp0aJFi3TxxRdbbzYPoHuqu9g0nZZ69+68LXwfsRxjetu24o1BYEx22pKBGbmTjqaXe1vi1CRrGSNkS1Da5nUlFn37mMawtbUlbW19TNtciV9bn0ood7JTMh+bawzfBKnv3L5jJ5XsjDNWUuP4qta8cRCbIg6SnT3AddddpwULFjjb1E0gCQDokkp9ei5JV111lc477zwtXbpUI0aMKPqa0IMPPqjeBcmBpqYm/epXv9KiRYu0atUq7bvvvho6dGhlFgwgEcSmAICoiE0RB8nOHuDpp5/WokWLdPjhh1vbtNu+pgsA6FHiV7yMZ6+99tJee+1l3Lfrrrta++2///7af//9y7UsAGVEbAoAiIrYFHGQ7OwB9ttvP7355pu6/vrrrW22bNmim266qYKrAgDUorifnnMPdgBREZsCAKIiNkUcJDt7gDFjxpQMFtPptI499lg1NjZWaFXlZ7rfpGl7KJ0u3mfalr8vyhj580ftYxvHdX9M3z5JrTWVMt/ZJRUEUuC4p6GtQE3U7UkVpyk1lmtfUgWKfPrEKVDkc2/JUmuN+lyE4/ocg+/zkNTfgOs+mLbzGt5P07Yv6rw+689mK/KxdkODFOetgIASQFQ9MjZNpYrLCYdf1bd9ZX/bNuPN0lOG9550EChteE+Je8/OwhlK3e/SxHW/S9O7n+uel3H6RB0jf3+hlMznL8niO7Z5fe6dGI6dxFi+feKMn6Qk5q/2McCN2BRxkOzsAcaPH68lS5Y42/Tq1Uvz5s2r0IrKz1RNPHzcYPmrNxUoMm3LH9+UpHTlImyJzcI+rnFsSUrfPomtNSVL1fUSSUKfZFecpFxSyU5XgaJyJjttFcvLney0VazP3x9ljqSTneX8GyiV7Iyz1nIkO+MUm4qhsVHi26MAyqknxqZKp6U+fYq3SfaALJuV2to6d2ltNb4XBNns9uRo4XbZkzilCgLlsyX9XMlDV5+o88bpk7WsyzWG7ThshZFs8/pyzeuTRA4fm/qYCk65/i58+9jWajs229+FrY+rfdgnSk4rKPgdZY5S5ynqOKGofyO+5ygppdbvez6SQmyKOBw1lAEAAAAAAACg++DKTgAAkBP3JvB8VQgAAABJIzZFHFzZWefa29v18MMPR26/detWPfroo+VbEACgpoU3gff9AYAoiE0BAD6ITREHyc46t2nTJk2fPj1y+9WrV+uCCy4o44oAALUsbkDJp+cAoiA2BQD4IDZFHHyNvQdYtWqVJkyYEKnt1q1by7ya6in1Yuf7YmirfO5qH7WPq0q6a19X543TJyVHRWpbMRVXFeuo20sVa/Hp47vW8HE512ob32feUnP7zmvbn8Q5irPWpP4Goozjs9aurKXUvBXAV4UAlFuPjU1NlTBN2/P3R62eKRlfiFNBYC0o4qo0HrVAUZyX/kr1SWrearwbUx0c+BdiU8RBsrPO9e7dW1dccYVXn0GDBpVpNZVliydtL5SmT38aGszV203V3sMxXPFn1D62S+9tY8Tpk9RaU0FgrtBt2y7ZK34HgbnUnqkyealK3KY+vvPa+oRJKNNYcddqmsNWydw2r60au+t5MFVjL7XWKBXcfc9R2Mf1PCTxN2Criu46R7a12vrYkqOmc2QbwzZOBfDVHwDl1GNj00xG2nHHztuamrb/NgWboYJq7GpqMr9Id3QoY3rf2LZNgeX9xFXVuXBfWn4Vt+P0STn2uRK2hX1clbVtOt77MfUx9csYtpeqZG1aQ9rSJ04VcMl8/tKeY/n2sT1vrjFcz2fUtnH7JNG+VJ+k5o46Rtyx4kgV/Hcl8onEpoiDZGed69Wrly6//PJqL6MqChOX4eNSVzRG2Ra3vU8f33nj9ElkrUm+q/pcyZikal9dF+fqynpU7ecBkggoAZRXj41NTZ8kR7my09TH8CKdCgLzlZ2plPU91HW1ZuF2n7Zd7WMTtU+c8X37BJ7zuuZOon34DEdda5TxovaxtXVtt4mTWIzaJ06SMOnEYhKJ0ySTr75Mf/OVQGyKOEh29kBvvvmmli1bpi1bthTtO/LII9XY2FiFVQEAakFDg/uCWRu+KgQgLmJTAIANsSniINnZgwRBoPPPP1833nijmpqa1NzcXNRm6dKlGjhwYBVWBwCoBQ0N8S6kJaAE4IvYFABQCrEp4iDZ2YM88sgjuvvuu/Xkk0/qkEMOqfZyAAA1KJOJ9+k5APgiNgUAlEJsijhIdvYgr7/+uo444giCSQCAVdyAkk/PAfgiNgUAlEJsijhIdvYgY8eO1X/+539q69at6tWrV7WXU1aplH+BonS6eJ9pWziG6V7ytvZhH1PF9yTmrdhaU8XfH0jZqobbtkv2KtpB8K+fUmOVqhpumsN3XtvcYTtbtXTfquG2OaKOH263VWN3PQ+FfUqtNWof33MU9vF5HqKc1yjjxP1b7ep5dY1h6lOhIk2W2hcAkLieFJsqlfpX9fVQR0fn34W2bSsO4jo6zO0zGWnr1uJps1nr+OkgUNrwPpRRceERV3Vw21uGq48pnC1Vvdun4rdvwRnXOFHnjVugyDRvqbFsxXdsY8Wp+O1Tjd1n3jjrKbdynyN0DbEp4iDZ2YP0799fo0eP1vjx43XWWWdpl112KWpz0kknqakwEOumGgr+usMXSNsLpS3pmFT7VEpqbDT3KYxjfeet1FpTCooDryBGMsgWqPsk+Do6pPZ28/jh/sLkkO+8trmTTnb6JB2TTHbazlG5k522OXyfh1Ln1cSWNLX9Q9N0jqKsNYlkZ+F5sj3HCctkuC8SgMroUbFpOi316VO8TbIHZNms1NZW3MfUvq3N+F6Wzmat8VKQzW5PqBZuV/REnutd2LePKzmadewr3G55Ry9Z0du2VlM/0zGUSlCa1uA6F7bcjqlPOK4piZw1rMt1rn37BB7zutqHfQqVOq9R+wQFv6PM4TpPtjniJE3LNU6ou38DnNgUcZDs7EFef/11PfHEE5Kk733ve8Y2xx13XH0ElACAWPj0HEClEJsCAEohNkUcJDt7kAMPPFDLli2r9jIAADWMYBJApRCbAgBKITZFHLYryAEAAAAAAACgW+HKzh6oo6NDf//735XNZrX33nuXbZ729nZde+21uu+++9Tc3Kxp06ZpypQpzj4rV67Ut771rU7b0um0fvSjH5VtnQCAf2losBdCc+G+SADiIjYFANgQmyIOruzsYebNm6cRI0ZoxIgR+uIXv5jb/sQTT+iQQw5JdK5LL71U//3f/61zzjlHxx9/vM4++2zddtttzj5r1qzRT37yE+2xxx4aPnx47icJpgrtSY/lGt+nj6tKumtfV+f9175AqdytsfN+AnWumB33RwmN013m7U4/lThHtfY8uNZTzbUWzl0hjY3xfggoAcTRo2LTsCpluX4ymWjb8n/CQPH/s3fm8XJUZd7/VXXf2/cmJCGBLCSBQBJZojiyDiCGbZKAopAIKriMAyL6qiOfeRWNCIMBFOUDirjHcWZcR1zYHJUdIhKiQBDhZTFBkpiwBEMSQnLv7e6q949OdbqrnudUnXOr+26/7+dzjX3Oec7z1Knm1nOfqjq/hh/p17ln+NGwtXFRMm81Q8VvFnEkQoYqzE2JC3yycwTx4osv4qyzzsLVV1+NffbZp2kj+KOOOgpr167FunXrsPfee/fb10svvYTrrrsOP//5z3HaaacBqCWLS5Yswbvf/e5U+0984hMolUrO/j0vubdHdDdIuytULCZ/IRaL8h4h0vzArnxSQttYuVBI+i0U7Py62Jhi9TzsLHbGCCwVzk0K15oat2Qjjdf8NvZLsdr41fxE82aN1aT2bbLRVNc1v1JBzKDCKqp7u65rf9cosrH5zqSpsWuxxtcjDM2K9S6xZl0j7dxIcbWp6Bn97UsIIa1mJOWmKBSAceOa2yLhpaLhT7K4Gntnpzy+Uqn98o5fJwoF/VpTqaCwZUuyva8PYWweH7ritoaLjXaV02w8oc9z8GtS45aoCPP5yhym+VxUwCU/Yezf+Hjb7MHGRjoHpjm08ZpNWhw25y2v8SYb03fA5jzY+rWdq3230fsPc1PiAp/sHEHcf//9OOqoo/DP//zP6OrqaurzPA8zZszAunXrcvH1xz/+EQCwYMGCettb3vIWPP3009i4cSM2btxotF++fDluv/12bNiwwTkG4Ua18Uey0ebRfJj82tjY+s071sjM5u49MZBWoGrjU3uDwu9AMpiOeZB+L6IbJ7Y/TEIJIbaMqNw0uvvc+JP1aU0bG4tf3J7vw/M8+QfmJzMHyw9GmN92xWpjo421bU+z0TDNlWWcaX5bmyxrlYU85sgy31CBuSlxgU92jiCKxSKCnU8JebH/8sMwxHPPPYdJkybl4mvDhg3YY489mhLXqVOn1vsAYOLEiaJtqVTCRz7yEfT09GDt2rX4+Mc/3nSnP86KFStwzz331D8/+uijKJf7cjgKQggZeRQKg6smTAgZvoyk3LTP9CQ/IYQQFeamxAUWO0cQRxxxBH73u9/hrrvuwujRowGg/orKlVdeCc/zsN9+++Xia/v27eiMXs3ZSZRcbtu2TbWbOXMmnn/+eey+++4AgFtvvRVvfetbcfjhh+Nd73qXaLN582Y888wz9c8vvvgiwtD0ogohhBAN33fLJnn3nBBiy0jKTQP+pU4IIU4wNyUusNg5gpg8eTK++tWv4s1vfjMmT56MV199FTNmzABQS8puuukmFLQNIS2ZMmUKNm/e3NS2adMmAMBee+2l2kWJbsSCBQswf/583H777WpCuWDBgqZXkq6//npcddVS19AJIWREI+7XSwghLWAk5aZLP/c519AJIWREw9yUuMBi5wjjX/7lX3DiiSfizjvvxLp16+p3yxcsWIDJkyfn5me//fbDK6+8gtWrV2PWrFkAgEceeQRdXV31V4ayEoZh4tWmNDwvKb4T5comgR9JKEgS8WmHQJGN37xj9YCaGFGcrMIrwC4BF+lJBltRI1eBoqw2eQsUSeNNQjqS0E6eAkWa73YJFOUlFJWHQJFkY/KrPYljK1BkOgca8Vil89UCvErFzQ+fWiKEODBSclP4PhArnKaqZ4ZhUqBISnKBmgiRljcYBIqka5m3bVvtWtBItQpfmD+AvVCLJlwTwk64RrMx7adoG6smFFQU/JriN81lOzZNFEqbz4Y8HohzmcPFJus5zVOESLPJW0wp6xwu80hzDdZMjrkpcYHFzhHIjBkzcM4557TUx6GHHooDDjgAX/rSl/Dtb38bvb29uPbaa7Fo0aKmvZLuu+8+/Pa3v8Xll18OAFi5ciVmzpyJcTvVKm+66Sbcdttt+N73vmcdQzwPjHJSkwK5ZGNULPfsx2exsfWbd6y14pxwcZDUoDWFaNsim2kuG78mH7Z+Tb4b/3X1m+ajv2tkarf1a2sTnXvtD6/BFKuryrlNrLZzSPO0K2HT/mBOgwklIcSREZGbeh4QE2Gq/97Ufuf29iYTOO0aWi7vUndvpLPTfCc+HhMAv6cn4cMLQ4RCnB4A7dlbrWAXKDYF6IUczSZQbLTCqOk5Yc1Ga4/7Tbty5lXsNPmSznQeRTAXBrLYaZonj/Emm7yLqlnnyGueQZnNMTclDrDYOczp6enBpZdemnn8JZdcglGjRvXbr+/7+MEPfoBTTz0Vd955J1555RVMnjwZV199ddO4hx9+GF/96lfrCeUPfvADfP3rX8f06dPR29uLl156CZ/85Cfxnve8p98xEUIIyYD25C0hhOQAc1NCCCFWMDclDrDYOcwJgqBpg/T169dj+fLlOOywwzBp0iSsXbsWAPDYY49h3rx5qJpeX7XkiCOOwLPPPos//OEPKJVKOOKIIxL7Lp177rk488wz65+vueYafPSjH8XatWvR1dWFAw44AOPHj88tJkIIISkwoSSEtBDmpoQQQqxgbkocYLFzmDNq1Chcf/31AIAtW7bg6KOPxgMPPIAjjzyyadzNN9+Myy+/PLEJe3/p7u7Gcccdp/aPHj064XPmzJmYOXNmrnEQQgjJiLYvbBp8VYgQkgHmpoQQQqxgbkocYLFzBHHbbbdhzpw5iWQSAN72trfh0ksvxSOPPIJDDz10AKLLn/gWR9F2Rdp+8r4v29jsj6mNj/qy2tj6dbHxPP2Xv6cJ6mj7UWpiM6Z9JDUbza+tOI00l61fzY9pjy0bvyYfJoEim30wbfdOTVvXrDaN+5pKe2Tangeb75FLrLb7y+axrlnWurHftFdsnqSJVGkwoSSEWDKiclPfB0ql5rbod7z2O7erS07ipN+3hUJtj894X1+fOXmU9vmU/CpzeGGIghK/tu+kB3nPSdOenZqNJFxk2mczDwGkKB6pzbSfoq2Nqd1mv0XXvSWzXtW1sbbttn77Y0OGGMxNiQOaPAkhhBBCCCGEEEIIIYQMKfhk5whi9OjRePrpp1GpVFAsNp/6V155BevWrcv9VaGBQnqaMfpcUGQYpScvfd88XnoiUxsfxSTZ9NevU6wIDXc7LJ7s1O602T4tabKRxqfd4ZP6bP1qNqYnO12fluzvk53V6sA92Smdi7Q1sj0PEmnjpTWR9vxpx5Odkl+XJzvbcYfa9e45IYRYMpJyU/g+MHZsc1t0zNqTl0FQU1lvpKNjl10j5bKe+8TniNAUjoOg1teA19MDT0g0w2oVHdu3i9NrTyAGsFM+t7Wx9etqk9Wvi03aU5dxG9vsIC3bklTuTU/MSt9gaQ7TeFu/tjZe7N/4HBKmc6o98Wv6DmS1STtmCdtYbecA3OLKBeamxAEWO0cQxx13HKrVKubPn49zzjkHkydPxpo1awAAX//61/H6178e+++//wBHSQghZEBx3ReJEEIsYW5KCCEkFeamxAEWO0cQo0ePxh133IElS5bg4osvxgsvvIDp06cDAM444wx86lOfgqdtOEkIIWRkUC4nnujJBPdFIoRYwtyUEEJIKsxNiQMsdo4w9tprL3zzm98c6DAIIYQMVioV/ZVHE0woCSEOMDclhBBihLkpcYDFzmFOGIbYvHlz5vG7774776ATQshIhvsiEUJaCHNTQgghVjA3JQ6w2DnM2bp1KyZMmJB5/EsvvYQ99tijhRERQggZ1Ljui8S754SQDDA3JYQQYgVzU+IAi53DnDFjxmD16tX1z5/+9KcBAB/60IcwadIkrF27FgBw9dVX45BDDsHuu+8+IHG2grhgZPRZE7wsFJKK5YWCPN7zoDO83AAAIABJREFUamKYWcdHfiW1dFu/muK6bhOiWEj+ovfCAKgqFwDtgiKpW5uUtbWLkknBXXpFwVXJ2kY53ibWaN6sat9psdrYROdGOg+aarjp2LKukcnGpFivfY9cvjNSrKb9e6Q1kdbI1q82T6NNFsX3tAQsfu60c5w3vHtOCGkhIzY39X1gt92SbY3/xgkCoK+vuU1L+CJldekaHZ8jbiP5jV9ffV/16yk5TqFSgSdcp03K6lq25DvYZJ0D0BWoPYNf2yuyjY3t/GHsX60/woOuiK7Z2OLyPHYrbdIU7vOwSVszGxuXmPLw6zJXy2FuShxgsXOY4/s+Zs6cCQB44IEH8Oyzz2LFihX114Fe97rXAQBOOukkzJ49G//3//5f7LXXXgMWb57E87GooKjlk54n25iKjvEipZYHRjbRj6vfxnmyxA/sTGYkm2qoF05CpU9qtxmb1qcV5rSilanw08pYo6Re+wOh1bFqfrPO7eLX1ib6bLPeLufBFKupwCzNn9Wvabxmk7XwauOnVfDuOSGkhYzY3NT3ga6u5jZTPgEAPT3J5E67Qef7QKmUbO/t1ZPKchno7Ey2d3YmbZSbsJ7vq8VUPwjEwogHQLp3X4BeSAkcbLLOEaEVNSUbrWBrYqCKnVKbqfCr2dgy2IqdEdKxmebIsxiZ1cYlHhN5FU7j87St8MnclDiQdkOHDCOeeeYZTJs2Tdz3qFQqYcKECVizZs0AREYIIWTQEN09t/0hhBBLmJsSQghJhbkpcYDFzhHE4Ycfjttvvx233HJLvS0IAgRBgO985zv461//Wr+bTgghZITChJIQ0iaYmxJCCEmFuSlxgK+xjyD2339/XHXVVXjnO9+JCRMmYPLkyVi3bh0AoFKp4Ic//CF2i+8lRAghZGRRqch756bBV4UIIZYwNyWEEJIKc1PiAIudI4wPf/jDWLhwIZYtW4YXXngB06dPBwCceOKJGDdu3ABHlx/SvpbR1kPSfpdRv2Qjjdf25tTG29po85j28lRtACAMkpuymPY7NInj2AgUaXtQmebvr992xNq4H6W0D6OLmJKNQJHNXpsm361c12iMtt4Saechy1rH+7PEartGab41MaIsbfH+gYAJJSGkjYyU3BSel9xTM7oGaE8gdXXJe2dK4wsFee/MUsmsztnbm/z93deXec9O+L687yegXue8MERBOAbT/pseZCEiyca036X2WqNmEyp+TYJJGtr+paZj1tptbFq5t2Q0No/2tD6NrDbRMQ31bMVljYY8zE2JAyx2jkCmTJmCd7zjHQMdRsuJq5ZHOZumZi4VIzUF9ajoaCtQlNVG8xv1aQVY0SYMgWrGAmJjn5TQZlUfj/zmVey08ds4Vx7FTpMae9ZjyLvYmbUgnObbpdiZ9VyY1siEbaxpAkUDUey0KQgPxmKn66s/TCgJIY6MiNy0UADGjEm2AWY19vgf+MWiPL5c1kUeBUX0VJu4X4Mau3Yt83xf3I81rFbRsX17sh1yYRHQBYGkIqVWDDRlJCYb7eoWb89y5cwaa1ohUirw2vi1zM6MNiHkInIA/di0orNkkybWlNUm+qzFKmEqLEs2pvE2NqZjNn1nbDIxW78DCnNT4gCLncOcnp4eXHTRRXjrW9+KmTNn4tprrzWOv+yyyzBq1Kg2RUcIIWTQwX2OCCEthLkpIYQQK5ibEgcoUEQIIYQQQgghhBBCCBkW8MnOYU4Yhnj++eexbds29PX14fnnnzeOD2xfOSWEEDK80PaDS4OvChFCMsDclBBCiBXMTYkDLHYOc7q7u/GjH/2o/rnx/xNCCCEJ+KoQIaSFMDclhBBiBXNT4gBfYycjBkmhPd6fpc00V9r8WW1s/db6oi2l4z/YpUrdip9Wzz/Qfgfq+IbSD9couR5xWjl33kQJpe2PQ3zPPPMM3v/+9+OYY47B+9//fqxevdrK/oYbbsD555+PBx980No3IYS0nUJB/onEf+I/pr7+jE2zc53L9idKbHf+RIIptj9xbMam4WIz0uAakZbD3JQ4wCc7Rxg/+9nPcOWVV2LNmjXia0GrVq3ChAkTBiCyfPG8pDJ59FlTOS8UkoXEKAeNoymfS3O42Gh+peOqz+8BnqSfFyqP/ZsuAJpyp6Qyrd1pC0P9Dpxmk4ffxv68YtWUxrV1tVVj12xsYtXOp+m1D22NNNXWaL4s65q2RraxVir5fAc0NXabNQJqbdo6aeOlNTLFH481TX0+L8ploK+v5W42b96MuXPn4qijjsKFF16IH//4x5g7dy4ee+wxjB8/PtX+b3/7Gy644AJs2LABJ554Ig4//PCWx0wIyZ+RkpuiWATix9HVVfu3VJJtPC/5+7izE+joSI6N1NPj1wnPSyqrR1QqtX7JRvJbFP50NOUMxaJq4wnXs2JPj3pdNG1mEO/zYae47WLjA4msW5sjwhPmGyg19izFX+n4NButXYpLWodGH7aZTlabKEbJt2kOrd32vNna2Kxpf2yyzmE7T64wNyUOsNg5gnjyySfxnve8BxdddBHmzp2LMWPGJMaMHTt2ACJrDaanI21s8hxvY2PrF2jDndV2PVk2WPwOJKYnBAfCb7v82zCQsZh8D6Y1ciGt4K1hedzf/e53EYYhfvKTn6CjowOnnnoqZs2ahaVLl+LCCy9Mtf/IRz6Cz3zmM7jgggvsYyWEDApGWm7qdCc+3qfdPQ+CWl+c6ClKCa1P8mEaazm/p7XLs9T7TE9xuo51tbGdX7NLi0lrt7FxidXmb4rQwq9pfFqfrX9pnBaXNkfehcWsNi4FxzyKlDbr2FaYmxIHWOwcQTz00EM44YQTcMkllwx0KIQQQgYrrvsiWSaUy5cvx/z589Gx8wmlYrGI+fPn4/7770+1/f73v4+NGzfivPPOY0JJyBCGuSkhhJBUmJsSB1jsHEHMnDkTmzdvHugwCCGEDGYcE8pKtYpbb70VW7ZsqbcdeeSRWLRokTh+w4YNOOigg5rapk2bhj/96U9GPxs3bsRnPvMZ3HbbbfC1p4kIIUMC5qaEEEJSYW5KHGCxc5gThmE9iTzwwANxwAEH4LOf/SzOPfdc7L777onxu+++O7y097AJIYQMX0x7mJqwvHu+fft2lGL71HV1deHVV1812n34wx/Ghz70IcyZM8c6RELIwMPclBBCiBXMTYkDLHYOc7Zu3Spu6n7FFVeI41966SXssccerQ6LEELIYMXx7nmxUMCCBQuwePHiTOOnTJmCl19+uant73//O/baay/V5q677sLdd9+Nc889F3fccQcAIAgC/PnPf8b++++PQw45xDpuQkh7YW5KCCHECuamxAEWO4c5Y8aMwerVqzOPl+6oD1XiDwFET5Sb1NIlG2m8aY920/xZbbR5PE/fA95DKCt+a0rgJmXnqC/eL7Vp84dKPFli6o/f+FytiDWaN+t6Z4lV8mETq3Y+Tb4lG9NaRP1Z1jVtjWxjTYsrq40pVgkt1mgdTH1Z/Np8LzR/eeN699ySmTNnYuXKlU1tjzzyCGbNmqXa9Pb2Yr/99sPFF19cb6tUKvjJT36CzZs342tf+1rL4iWE5MOIzU19P6m6Hv2u1YQ3urqSiZ/2O9r3a+Pj14lSSU8eCwVZqd3Gb6GgqyRr13Xfr6m7x/DKZYRKIu2FIQqC/wJkZXLtaqlIQaloyuG2it6mflvBmYEUKNLU1bV2DdMx22Y6WW2ieGyPYbDJTrZyjQYtzE2JAyx2DnN838fMmTMHOoy243myiCSgC15KxUhN8DIqOkpFSrUYaWGj+Y36xDwwyLnYmdUm72Jnf/262JguoNVq64udkg/bgmBexU7T98Jkk1ex03Qe4qQV/rKei7Tjdfl+t6rY2Q7atAn8WWedhZNOOgn3338/jjnmGKxYsQL33nsvLrroovqYq666CgcccAAA4G1vextOOeUUnHLKKU3zdHd34/Of/zze+c532sdMCGk7IzU3he8DcbX5NDX2IEgWI4tFOdksl/VrrlTQBGpFVimplGwKBd2vds3QEuNKRYzVCwJ4SuE3rFbRsX17sh3JwqNWyDPdMvV3/kg2WoFSUtBOK/DFbbSrbZqit1TgNWEbq2SjrV8Iee2yzhv3YVO8trGJPmvnWVNj145NWo+0dc1qYzpmzYetTdq6Zpkny/coF5ibEgdY7CSEEELILvr69Kd0TFgmlHPnzsXixYtxwgkn4IADDsBTTz2FCy+8EMcff3x9zPe//33Mnz8fQC2hJIQQQgghIwzmpsQBFjsJIYQQsotqVX+lMmeWLFmC8847D0899RQOOOAA7L333k39d999d2Kj+DjPPfccRo0a1cowCSGEEELIQMHclDjAYichhBBCdtEmxcuIvffeO5FIRuy5556p9sNmPz9CCCGEEJKEuSlxwGZ7DUIIIYQQQgghhBBCCBm08MlOMmLQVNJN/ZqN59mNt7XR/Ya1TaCFm1SejTK0qd3WptXz2/p1tUmbS/qcV6ymubLM79LuGqtmYxqfR6zSXHl9B2z9ptnYrJFN/O1SY3fdBJ4QQogBL6k0vlO8xzOppcd/H2uiP4WCLHQkzdHY5/vJa4vkQxMoivxK1yfNRjsGk9JnGIqxemGYEEjRRFNchFRMc7mIu7RJarCtDNfjIoMI5qbEARY7ybBEUmNPE7wsFJJFRi1v1NTSpTlcbDS/CEN4VWW/Elul8UrFXICRbKrVpI128dHmiGykfVfy8Jtmo/nVVLElP9G8Wqx5qbHbnE/peE3jAfk74LKupkKtbaym85BF4Txuk1WN3fRd1dTYTTZZ18i01kNNjZ0QQohK6Psod45uait4tT/FCh0dspHnJUU5OjtrP3HKZTl59Dygt1eev1zWry3xfKmzs6YEr42T5untlWM17b2n9VUqtRv7MYo9PYlrlklZXcPFJt5Xha6uHhE/Ahe/A6XGbqsC7ivttn7T0PxofqW1NcWqnQvJJi0Obf2yjk3zYWtjE8+AwtyUOMBiJxm2mJ7KtLHJc7yNjdGv2UX/aXVRxeUpt1b6JbvgGpE274tECCEjBq+5rBVGTzFqd+KlO+XaHfEgsLtDH9loT2tKT3tqfrWnMW1tDE92ekqf9tSlTbuLjdSeVkBsdawmHzZzuNhoRTObdm2eNEzzxcdpPlxiNdloZC1Gmo7Htjiq2QyZQifA3JQ4wWInIYQQQnbhevecCSUhhBBCCMkb5qbEARY7CSGEELILvipECCGEEEIGC8xNiQMsdhJCCCFkF+Vyco+4LPDuOSGEEEIIyRvmpsQBFjvJsEUSkZTaIzo6kn0mschWChT5fghP2jXFJGiSJmgjibtoFwAbERztTlsQ1C5MEproSx5+TTbaGqWJzeQhUJQWazsEirJ8B7LEmmVdo89Z1yiKz0b0pxUCRTYq6v2NNS0BC4JmoQbtHOeNJuSVBhNKQghRqVaBV15pbivtFPzp6pSTR390FX4llktpyWm5LF/XKxU9H+vrk69lnpe8DmjK6pWKnlPa2oShHmtkEw81DBOxFioVeMJ1LID9foeacE1RaM8ivJP1SmkS3THttSjZ2Ph1sTGNzUNsyCTWBOSjZeAyh+v+olnmylOESLPJa+/PtsDclDjAYicZtsRzq6igaLOHuufJ4z1v10+W8bY2+gbgobn4oik+a0UzDdNcUmJqqzStJcV5+HWN1WZdo89Z5zIVlrV+21htz3/kN+s8pn7bNdLWw7ROrfwOmIqmafP3x29aAuZikwd8VYgQQnInDJOi6L5fSzw7FQEhv7MMFGN92jWrUAB6epLtfX2yinpks2NHsr1SSRYWtJuh5TJQKsnzaza+L9v09pqfGhCU3b2+voSNHwSqwI4i1aQW7ALFRiqcphUITcXWrH4bfUmfB1Ox0yVj0f4GSlOn7y8DWezMQ0Hd1ca2YG/y0VKYmxIHWOwkhBBCyC6YUBJCCCGEkMECc1PiAIudhBBCCNlF2lYGGnxViBBCCCGE5A1zU+IAi52EEEII2QXvnhNCCCGEkMECc1PiAIudZFgi7Y0Z7Ytp2gpIspHGm4SLTPNntfFg2GfTtP+iZmO732HaXP3xG/m2OT4bvy42aftaasIyWY/BNL/Wb1qLrGsUb89rH8kssUafbdY7bX/MrGtkmk+ap3Edsv53kvbfYlaBIpvvRbvuTkt7tWWBd88JIUQlCOTtMQF9v/duv4hCobnT6+iEX1L2wezqSv4uLpXMG9Z3dyfbJVEj7cmqQqG212aaSGLcRlJWNsWq2XR2Jm2U66sXhigoBRNt30kP8n6RkpCOD/M+m4Eyl+TX3/kjIcUafZbm1/YvNe3PaGujjdcw7S0p7SOZFmuWDCSaQzvPNu02fl1sXOYe1jA3JQ6w2EmGLfG93qM8SNkDXhUoksZHokJScdQkUJTZJsi52GlTHLO1sfWbV6xZlLjjNiYV8LyKnZoCeJpAUSuLnTZFubSkIGus0WdNjV0irzUyIf3hlXb+B6rYGZ8nTZApL8pl+Q/KNJhQEkKISrUKbNnS3Bb9qtX+hvfHl1DqiLV5PvwO4U+4SI09ThDoCueaTdTXiKasXi7r13VbmyDQF8PCxvN9eMLTB2G1io7t28XpTcI4WUV/POgFysgmvtras2qmK6oUazSvZmdbQLSxMcVqK1wUCD5Cxa9pTlNBWDpHkt/IRjunWqy2xyeR5lfCdE61Irht/HGbPMShMsHclDjAYichhBBCduG6LxIhhBBCCCF5w9yUOGC6+UQIIYQQQgghhBBCCCFDBj7ZSQghhJAaYei+CTxfFSKEEEIIIXnC3JQ4wmInIYQQQnbh+qqQps5GCCGEEEKIK8xNiQMsdhJCCCFkF653z4tMKQghhBBCSM4wNyUO8OwTQgghZBeuCWWhkH8shBBCCCFkZMPclDjAYicZlnge4Mfkt6LfdfH2xv54n+/L431f/t1ZKOhPy2s2vhfCQ2w/Ee1RfdMj/FpftM9JnGpV38dEm0uyqVaBSiW732j+IGiN38a54ja2axT5ifdF82rnKH5s2vE2+shqY1ojaXzasUlrlEes0WftGKTvXlqsLuua5fhs1yiy0cj6HWj0K81nOrZW4vqqEPdFIoQQlSAAXnmluS1KY7Rf974PlErNbV0dRXSXksmm53fA3y1I5pTVKlAuyw7KZfmaLNlIiXJ0ENp13dYmDPVYbWy0BL5SgacsdqFSgSfklQEQX9Ga2519NhSFuXzIisG2c0dzaLHaXqFtbLSxtu0A4Al9aWvhO9hIfm3Jy8a0FjbtpvOVh980Hy2FuSlxgMVOMmyJ5zhREVIrdkoFUqktao9+sow32iAULiqhXmAxFSi1gontXFqf1G7r13Z+W7+N/f31q/VFn7POZZrf1kZrz+s85BWr7Rql+W51rC7f1f76TSvWDlSCVqmYbyQQQgixJgiA3t7mtihn1N607OlJXiZ8v4CScPfc8yrwu0qJdvT26k83FQo1J3H6+pI2WrGhXE5WZF1tenvNTw1ktVGur57v145Nmj4IxCKPB0BavQLsi11a4VRCKvqlzQ3Isdr4jRioYmcAeV1NxUvPwUaaw5Y8beLrYZrbpRiZh980Hy2FuSlxgMVOQgghhOyiXFb/GDTCu+eEEEIIISRvmJsSB1jsJIQQQsguXPdFIoQQQgghJG+YmxIHWOwkhBBCyC64LxIhhBBCCBksMDclDrDYSYYt8e17on2RTFsBSTbSeJNwkTq/J+3NCaAq7KSjic2Y9vgLQ32fShuRGJMfbWN4G79p8/fXb2N/1j0SbWON5s16DGl7M9rY2B5D2rHZ7iOZNda0c5PHecgSq7aXqI1fW5us4xu/o1ls2pWwud49Z0JJCCEqYZjcHjPKGbUtNTs7k5eOQkHe49OHj1GFjsSme35nqbZXpYTvA11dyd/fpVIy0dWuDYVCbd9Mbd9wG5uuLrOSqPQaqxSrdo32/dqiSig2XhiiIByDtGdn2j6XkpCOyUZZCWsbyS+Utght/1Ib0RyXfSelvUpNfl1spOPW9kg17Z1qu6+qq03WOfKYe9DC3JQ4wGInGZZ4XjJxjPIgLaGU1NKleaJ2rTiqChRBESOqVvqvxB31aYlmK4udtn7T5u+v3zSbvIpsQHbl+LyLnZrfrHM39uURq1ScTzs37Sh2audCm19rty12avPEz1HaHKZCeyvhq0KEEJI71aquxq79apdqc9qv6I4OH50TupNCmKMDeIHyOz1SY48TBLIau5QUl8v6NUNTY9dsJL9ZbOLCJYWC/PRBRci5d+L5PjzBJqxW0bF9e7IdSQGctKKcVKR0EdeRbEzjJb9ZrvJZYzVlJrZFVqk/VPza2pj8BoqPEHrRWbJJK3hntTHNYzoPplizzJMWf9wm7TufG8xNiQMsdhJCCCFkF66vChFCCCGEEJI3zE2JAyx2EkIIIWQX5bL+ZI0JvipECCGEEELyhrkpcUB70pkQQgghhBBCCCGEEEKGFHyykxBCCCG7qFR495wQQgghhAwOmJsSB1jsJMOWxAbtKbsnS/2ajefZja91AkAo7EAtqIZLbab2xv6sNqa5bGxaPb+t33bFGvVlaW9nrFljtPWbJS7pc17fSZtYXGLN67uaJS5TLE1dzfO0LV3jJvCEENIS4r9aTXqHUXu8T/sV7fu19oR4ZuhDk6/xvJo6pxe/FhUKSSeaCmckXCRdzzSBIs1G8ptmI8WlxWpSEtX6wrDWHvPrhUnR0Syq4VnaTO02czf2seRDhjTMTYkDLHaSYYnnJdUro/xFy3GkfMyUK0mClJr4I1BLilQVbRslbheF86yq4Wk2kmqnFL9pDpdYK5VkrGkXPen4NBvt3EQ2cZXPaF7pGGzOp4uN1q6dT1tV9CyxZlEVj/xm/c5HNtp50I4tfm4ayao2b/qumtbVxsb2eHf2S+qcLcd1E3jePSeEEJVqFdi6tbktelBJe2ApDJP5bF+ffOkr7vyrLp6HjhldQkdHSZzf96voHCt0eF7NUSOdnbucNGKSlLe1kfxmsYkvYLEIdHToc0gUi2qsiWIwgGJPT+Ja6cO8R5xUdKzAXo1d8mMaD8FvWqy+YFOFrOJuuvpLfWnZQrw/gPn4ChltbBXdIxvNt7RGaWS1MflNU0vP6lebx7ROA5bpMTclDrDYSZx45plnAABXX301nn32WRx88MG48MILMWHCBHF8GIa488478atf/apuO3HiRCxcuBCnnnpqfdzzzz+Pr3zlK022vu/j85//vHWMpqcybWzyHD/ksX3yrpW0K47BcryDBduCaitJ88tz5wbvnhNChiBDITeV7hdL7Y3jbe6FSk92BvARKrlpiEC+ey/d1Y+erJQcm56WtLHRxqfZSE8ruMQq9HlauzCF65OdtnNJfV7sX1e/rjY2T62a+lxiDYV+ySZs6MsajzR3lj6NrDZpfiVs10iaK2tsbYe5KXGAAkXEmhdeeAHHHHMMjjnmGLzwwgs444wz8Pvf/x4nnXQSysqt6a1bt+KMM87Ajh07MH/+fMyfPx/jx4/HmWeeiauuuqo+buPGjfjiF7+IKn+ZEULIwBD9xWz7w+IyIWSAYG5KCCHDGOamxAE+2Ums+epXv4rx48cDAH7605+iUChg0aJFmDZtGn7+85/jrLPOStjstttuWLduHcaMGdPUPnbsWFx33XX45Cc/2dR++eWXo1SSX7khhBDSQnj3nBAyxGBuSgghwxjmpsQBFjuJNb/73e/qr/cUdr4iMm7cOBx33HFYtmyZmFAWCoVEMgkA48ePF++4P/7446hUKjjggAMwbty4nI+AEEKIChNKQsgQg7kpIYQMY5ibEgdY7CTWrF+/HtOnT0+0T58+HRs2bMg8z/bt2/HNb34zkYD6vo+3ve1t6OnpwbZt23DRRRfh4osvVudZsWIF7rnnnvrnRx99FOVyn7h9T+O/cfIQKPK9EJ62k4mtQJGtkE4rBIokgR/bzaNcYpXEXaS2dggUSbHaiu+4ChTZxOoqpGMba9YNxPIWKNK+F2mxZrGxFXHKEqvkw8YvansiBbHPbaFS0dUyTPBVIULIADEUctPe3j688krzOJO2T81vUmenUpEvS8VibXx8z05JtLNu4/vwursS7R2jqvCrseuAlhRH4pXSQWg20UFI12iTQFFWG00FXpsji00MTxBJLFQq8AwiSPHren0uoS2A3XU/ilwTBEoTMJJssvrXxtq22/qN2zXiIblfXzRGWgtNOMi0di4yDXlIO9ju5WnjN6t4ku28/Ya5KXGAxU5izbZt29Dd3Z1oHzVqFLbGZSYFoj2Pzj33XBQKBVx22WX1vn333Rfr1q3D1KlTAQDXX389zj77bBx88ME4/fTTxfk2b95c31geAF588UUEQZDIV6LkTyt2el6yT2qL2qOfpnaEhl/6iriPJPqjJWJauzaPS3vUpxUj4zau82s2ml+b+bX+vGKNPmedyyVW2+9AnseWx7rartFgiDXrHGm+tT/GbPwOJEwoCSFDjKGSm/b2No+LckxJBBwAenqSKZDvy+OrVaC3N5mb9vbqv56rRR9dozoTNsXOMhDG7uprN/8KhVqgElpeVy4D0pYAvb16om5jo90w9n15jjQboVrs9fUlFtsPAmPxp4DsxSFv5/isRKss2QQWfhttsl7VA8hCIO0qdkrjTerj0nhNBMkUz2ArdgL5xJpH0TR3mJsSB1jsJNZMnjwZL7/8cqJ906ZN2GuvvYy2YRjigx/8IADgoYcewrJlyzB69Oh6/5gxY5peKXrHO96B//zP/8Svf/1rNaFcsGABFixYUP98/fXX4xvfWGp1TIQQQnZSrSLkq0KEkCHEUMhNlyxhbkoIIU4wNyUOsNhJrNlvv/3w2GOPJdofe+yxpsQuThiG+NjHPoY77rgDALBs2TJMmTIl1V9HR4eqpEkIISRfeA+cEDLUYG5KCCHDF+amxAXlPQFCdM4880zcfPPNuPnmm+uv6CxbtgyPPPLYePeMAAAgAElEQVQIzjzzzPq4a665BsuXL69//tSnPoWbb74Z99xzD+655x7MmDEjMfcTTzyBnoZXYe6++27ceuutOPbYY1t4RIQQQiKi/alsfwghZKBgbkoIIcMX5qbEBT7ZSaw5++yz8atf/QoAcNhhh+Hggw/Ggw8+iIsvvhiHHHJIfdyVV16JT3ziEzj66KOxdu1aXHXVVeju7sZhhx2WmHPdunUYPXo0vvGNb+D73/8+Zs2ahZ6eHvzlL3/BBz/4Qbz//e9v1+ERQsiIhskhIWSowdyUEEKGL8xNiQssdhJrfN/H//zP/wAAHnzwQTz77LN4/etfj/33379p3H333Yc99tgDADB16lSsXr1anTPaVP4rX/kKzjnnHKxZswbd3d147WtfK6pruhDffD1Lv2bjeYoQUVj/H6HPQUBImyev+W3nkvznLVDUX7+m/qEkUKTN7zJHnqI/NvM0/mszl60P27hadcxpfbG2MMXvQL2y4yIOENkRQshAMFRy0/iWc9FnbSs6STNH09HxvFp7PHetVvX5NZsg9OHFygue78OTxIN8vyZSJF3PNDX2QkG2KRT0YG1sNGV1LR5Tn0277xv/4PDC5N8P2mhNMEezMf2Z48H+Gm1jM2CiNWTEwNyUuMDX2AkhhBBCCCGEEEIIIcMCPtlJ+sXhhx+Oww8/XOxrvJteLBYxc+bM1PkKhQIOOeSQpleOXPC82k3dRqKbr9oNXekmsHYz1/MAT7pXFFT1J7W0W/HSLfcwrI3POkfUZ2NTTYlVmqtaTbbb+k2z0fzGYzU9qmBrE4ZApaLPI52fqE8an3WN0nxINrbnLAz1ddJiNa2r9sSizRqZnojUfFcqSTvTmkb9Wc6Fya/piU/tOxMEcqxCvIPxjrPr3XNCCBkMDNbctFoFtmxpbuvrq/2rXU7CEOjsbG4rl2s/cTo6av/GHyyU5ogoFmWbYLcSOjtKzWO7iuiInDRSqdQmkK6XnZ27nMRtouAa8bxdi9IfmzS/EsXirkXMahPvKxbhxf8Aaezu6UnkGxXIT0aaMhzJJu013/jZ8ZH+1FPcprrzJ21chKfElZZjxPtNT7lqNtKejbZzuNqY8DPaaGtnmsNkYzpHWecwzdNqmJsSF1jsJMMW/RV0OxuxLfZvv+jv68uk/6S9Yj4Q/gfqO5Dl1XBiJusaDdK1rMJtb6TBeTSEEDI4kO5hRp+1e3fSfTLpPmVje6JwabjfGvUlCqTwEcbbfB+AUMgLAvOTBFLxT7OJXonXgs1qo81jitXWRngiwjO9Jg/99XObdq3P9LeJ7fwuNq1szyvWsKEvazyhoc/l70DTfFnHaX1pNnFs5zDN1WqYmxIXWOwkhBBCSB0qWBJCCCGEkMECc1PiAoudhBBCCKnDV4UIIYQQQshggbkpcYHFTkIIIYTUYUJJCCGEEEIGC8xNiQssdpJhiyQ2JLVHWAkUATUxojhpoj+SCEpWERXTHC422qZPgC7YkqdAkSZck1VMyVWgyCQqkzXWvAWKbL4DNufGND6yySqM1BhXlnORtkbSfyfRWmt9GUV/EjFIPuLjTGtku6eqsIZhGCIUbNI2gW+0aNfrO+18VWjVqlX43Oc+hyeffBIHHnggLrnkErzmNa8x2txwww24+eab8fTTT2PcuHF485vfjPPPP18WziCEkEFCEACvvNLcFqUk2qXM85LiQpJeH1DT15G2nJTmaLTx/eSenZJNV0cRYWdyNz+/owPF3QJZuFNLpMtlPZ+Q1JeA2oFruUBcoEhK6k1zuNh4XjJWbY7IRBDELFQq8ITcNIC5uKMJFEk2Ieyv6zY2WiGqqMxhKly5FLWy2kRjpJg00R/TOkg2aWuWh96Dyxza/pz9nT+LeFQeMDclLrDYSYYlnpfMNaJETstBNBtxfBgCgVIAMSlNa0WTeLvNWFcbLdkz9eUVqzZ/Hn5N/Tbzp/lu/LcVsdqua17nwfS9sInVdo2idlPROes8adicH9N/PxrSGgpzuNyhbsddbde757Y2L7/8Mo477jgce+yxuPTSS/HDH/4Qc+fOxeOPP44JEyaodh//+Mfxzne+E/PmzcPGjRuxZMkSPProo/jOd77jEDUhhLSHIAB6e5vbohxTEg4HauPjlw/fl8dXq0BPT7JwKc3RaNPbm83G9wvo8JPV1NCroNhVSrQD0G9KFgq1YOP09ekCReUyUBL89PYmk3Xtpro2h8nG92WbSiUZq+lGPwCvry+x2H4QqII5uq67PB6KTQD7gpSNTQBZ2T1U5tDGRzatLnZKY7WinWluzcZUlBtMxU4geWy2c7ej0AkwNyVusNhJCCGEkDouT4C48N3vfhe+7+NHP/oRisUiFixYgNmzZ2Pp0qX41Kc+pdr95S9/QanhD8/x48fjgx/8IL72ta+hU3t8iRBCCCGEDEmYmxIXWOwkhBBCSJ3qzp9W88ADD2DevHko7nxEqVgsYv78+bj//vuNdqXYEzbFYhG+78M3vDpICCGEEEKGJsxNiQssdhJCCCGkjuu+SJVqFbfeeiu2bNlSbzvyyCOxaNEicfz69esxZ86cprapU6fikUceyeyzp6cHX/rSl/De9763npgSQgghhJDhA3NT4gJXnwxb4vsPRTdW4u2N/QkbT97pw9P2FkwTKNKEfGxEf0x7GtrYmPZH1PryilWb3+TXVpxG2zfRZk9IzU80b3/XKD5fllhtzo1pvCkW23V1WSPTnq1Z9/l0+Q5osZrOvymejHuGSnsNpe0/5Lo/UX9xfVXINtYdO3YkXu0plUp49dVXM9kHQYAPfOADqFQq+NKXvmTpnRBC2ksYJnV0otxU26ayszN5efI8eXyxKG8tWSrpl7jIJp7/2vgteD4KxQ5x7z6/owS/W7g6FIu1PTvj19BSSd9c3/eBrq6kTVdX9j07CwV9E1PNpliUVaH6+pJ+08QzJRslB/HCEAVlLlN+oO3ZGfeQlmNI4jsmG+msmYR9tOfdJJs0ERxtz1PNRhtvs5enZpMl1iz5kmmcKVZbm6ECc1PiAoudZNgST8iiRE5LKH1f7vM1bT4pc4wUGyVMquhZiziuxU7Nr0ZehVmTGruWaLay2GkqXLWy2KkppcfnyxJTnsVO27WI/LSy2GlTdGxHsdOkxm5TREb/i53tSlJd754XCwUsmDcPixcvzjR+ypQpePnll5vaNm3ahKlTp6bahmGI888/HytWrMC9996LcePGOURMCCHtIwiA7dub26I0Sbuc+H5SFV27LEVq7NLNfpMae1Yb3a+P4rhu8WGCYncRnd1CBbavL6FKDqC2EJoae6TgnsVGU0Uvl/Xc1KTGLiH51f6YMNh4vg9PWLywWkVH/AsT2UBXY5eQCkVpRTkpR7EV5NHyHNsbvVkUv7PYpOVRWqwmoSgpt7ONVbshbioIa2JKNjYuN+Lj69ougSLmpsQFFjsJIYQQUqddRdVZs2bhoYceampbuXIlZs+ebbQLwxAf+9jHcNttt+Hee+/NlIASQgghhJChCXNT4gJ3TCWEEEJInegJENsfW84++2z8/ve/x3333QcAWL58OZYtW4azzz67PuYLX/gCbrzxRtx44431tk9/+tO46aabcPfdd2Pfffd18EwIIYQQQoYKzE2JCyx2EkIIIYQQQgghhBBChgV8jZ0QQgghdVyFkWxtjj32WFxyySU48cQTMWvWLKxevRqf+cxnMHfu3PqYH//4x5g/fz4A4PTTT68rXAK1V40aWbVqVaKNEEIIIYQMbZibEhdY7CSEEEJIHddXf1y45JJL8IEPfACrVq3C7NmzE3scLVu2rEkVs1QqYdOmTeJc3AieEEIIIWT4wdyUuMBiJyGEEELqBAAUrVojrpvHT506Vd3Iffz48U2fPc9LtBFCCCGEkOELc1PiAoudZFjieYAf25E2+hxvr/d7Ibz4r8QgBELhPlIQAFXhV261Krc32klt8XapzeQ3zUZqr1SAULkEmI7PJlapPc1G8xuPNW2tpVg1mzDU55JsoliyfgfCUF8LoNaX9fi0WKXjjY+P+5D8mr5jkR/JRvIbjbeJVcPmvxOXWG3WKG4T7wqCRHKlHZnpLnX8lR3XV3hsaefdc0IIGSkEAbB1a3Nbd3ftX+3y5/tAR0dzW6UiX8qKxdp4z2tu9zyg4SGkhE2hkGyXbGz9AkBXZwFhKdnhF30Ux41DoicIgL4+OdhyWc+XyuXmtkJBTvijg5AWPM0mjo1fx1g9JccpVCrwKpWmtmik9FUqCu3C6WoiEoTJgq/41eYw5TNFwSZLrFnyozD2b5Y5TOsgHXfamrnYxElbj7xsJNqlih6HuSlxgcVOMmyJ5w1REqblIB5C4UIQ6oUOqV1LoEw2UrvNWFcbU6FosMVq4zdCK+TlEWv0Oet3wPS9MPno7xpFvk1F56zzZO2Xxkt+tWOw8ZtnrDZrlDL3QCWCedGuoiohhIwkggDo7W1ui3LSeEEzorc3eRny/VqBMU61WhsfLzr29uqXwmoV6OnJZmPrt2bjo6OUTLxD30MxqvQ2smOHnqgXCrVg4/T1JSu22o3bchkoleT5TTZStbhUyu7XwcbzfbXw6wdB4m+W6LNQu0aA5HU97VofIHuBLDD4leYIoKskh4qNKdZWFzu1uT3ox6fhYiPNYUtexU5gYHJE5qbEBRY7CSGEEFKHd88JIYQQQshggbkpcYHFTkIIIYTUYUJJCCGEEEIGC8xNiQssdhJCCCGkCZdXhfh6ESGEEEIIaQXMTYktLHaSYUt876BoCyBpT6EaYU2QqBFb8Z0sQjRZ5spb9Md2Lu04BjJWaR9M01pLx6Adl+m8SX6iWLLO5SJQZPsdM43X9hiV9p1MW9essUafXdZbQtvjM89YNb+C79Byz05pr6HBmoBxXyRCCMmfMExuwRjlptJemFG7tHemJCpULNa2hIznuZ2d+mUusolTKiVtPM/Or8mm4HsodBSSuXqpC75kEDnq6UkuSKmU3OdTE3ksFPRNTLX9NjWbvr7sfl1sCgVdrEnIf7yd8RWEuaSn4nzI+2yabEz5gbQHpyZcpI2P/NrMo9loe2NCaTftaWmaJx6Xya+tjWmvUKlPa0/rcxnXbpibEhdY7CTDEim5ihIqLYfyqhYFJ5uiXESrC4iawnWri522fl1i0tQ301TDs86fZ7HTpTCbR7HTpHBuOrZWFzs1NXaJtOKl7fg8ip3af9NhqCZdQ73YKf2BQwghpH8EAbB9e3NbdDnULkO+XxMDj9tI4yNldelmf5oaexabSkW+HGpzRLHKNj6K40qJAk9pdAj1CtTXl1wMoLYYksK5lPCXy3oOoqmiazaSX60S7WJjiNXzfXixBQ93ju2If8kAUVHcg15wNNnYiOtoBSpTjiHZpBUQs9qk5V1a/mZjowks2dq4iEeFMBeRJR9SLGnfi0abtHOTF8xNiQssdhJCCCGkTnXnjy2DtXhLCCGEEEKGLsxNiQssdhJCCCGkjuG5GkIIIYQQQtoKc1PiAoudhBBCCKnDfZEIIYQQQshggbkpccG0JQMhhBBCCCGEEEIIIYQMGfhkJxkxeF7K/aAQ2dSqXdptbVzmj/qztucVq61f1/lt/Gr9eZ03Td3cFEfaecvrO6DN31+/tja25ybNdytjzRKvNL1Fn3ZHOm2OgbiLzU3gCSGkNWj6eCbtybhGjabP6Hm19rhQkEnP0cbG1q+LTeB7CeGdXTY1IZ9Eb6GQdOL7sthQJFwkXfM1gSLNxsavqV+ziYSLpFglm2ic7ydsvDBMrFuaqIwkPGOjbh61S3mMrVp5GoNVQZzkB3NT4gKLnWTYklBjr/+rXA4DQd7SpA4uKUJqEplALfHQlKn76zeaX7PJ6jdLrPF2W79Rn22skhq7SUVbs5Hm145Xs4nmlc6FVkhLU47vb6ymIqHp2FxU7rMonJuKhyal+XbEmvU7nIJ2hNJMmuKlTbGzXYk8XxUihJD8qVaB3t7mtkiYW7uUhSHQ0dHc1tVVEyaPU9z5V128VhiGZjV2yaZa3dUXMWqU7DeKT6pRagLqmt9gTCc6O+QrkF/sQOcEwYnnJRe2szN5AMCuYKQcxdbG85ILos3RSFyN3eTX87LHujNOTxhf7OlJfMkqsFcN12zS1NVtxks2WcRpstik5Ta2N6Wl/rSiXCGjje25iWw0375iYzPHQMLclLjAYicZtig3hlMvHv3C4emwIeU7pyfihjwuT9eONAbrGvH8pOJ695wrSwghZmyf7JTuDWr3eqOxiQKicn/RZFOtJh8c1O4v+r48h8m35jcMPSSfQdzZ54XJJxmiAOLt0dOYkmPtyUtpHpONND7Lk51ZbUxzCX1e9FmwkVZUenLT1cbUrs1t85RonrGaxtvM7+IXqOVK0hOz8TZpXFo8JhtTn8u4tFjyhrkpcYHFTkIIIYTUCZD+BAUhhBBCCCHtgLkpcYHFTkIIIYTU4d1zQgghhBAyWGBuSlxgsZMQQgghdbgvEiGEEEIIGSwwNyUusNhJhiW1vU/iGyNFCtoGyUtpYyRNhMa0CZFEXgJFNhs7Re15CRRJPmz95hWriziNq0CRJgqlHUMeQjppsUrCO9J+lKbzoM1jQvsOmM51ljkAe4GitFil7430367BbxgEuSRXIeQ70mmb+scFitqR6GmxEkIIcScIgO3bm9uiS4+2nbTnJQWKpMshUNOr8f3kPpieZxYokmyApI126e7oqG1DKc1RLss2TrEWfHjdnYlNAjtGj4HfHcvTOjpk0Z8ooKwK50AtB5RsgiApUKQpukeEYVKgyPPMfiUkmyhXFWy8MEzksoVKBZ6W30bhxueBLlCkiftIR2ASy3HJQbLmR2Hs3yxzmOZ2zcukdY1/A0zroK2fyaZVe2um7VGaF8xNiQssdpJhirS9eVTsNKhW2xScNLEem/m19lbPP9Ri1ZLMNBGcPGKV/ESf+7tGLjamQnHWubPGldUmjzUa6FiVuU0Jbn8ZrHeotT9aCCGEuBOGydpYVOyTdHGAWs1OEgGXxlertUJhvIDY26tfPiOF+LhNqZQcq9XkqlVgxw652On7yWKtya8x1qKHru5iIr8vlkoAYk6063pfX7Li3Ggj5VLlsrwgvb3JBTHd/I1sJAUpKdZyWa/8SjZREVWw8fr6En79IDAWqaRcQCvueaipjEtzaMVR5Suv2pjImrdEZ1cqR7er2CmNl4qXprm1AmOazVCGuSlxgcVOQgghhNRxvXvOJJQQQgghhOQNc1PiAoudhBBCCKnj+loWIYQQQgghecPclLjAYichhBBC6rgqXhJCCCGEEJI3zE2JCyx2EkIIIaQOE0pCCCGEEDJYYG5KXGCxkwxfbARTonZNNMVm7FAR/RkOsbZDoMjEQAgUaX5t4sk7Vsl32n9vpvmz+s3pvIUp87iIFEkbzdsSf2WnXa/vBAAM8goqfL2IEELSaP5zPQxrsiFBIMuHSJqEmk6h58ntmtZmZFOtJjVzJBtNe0ebw+Q7sskj1sD34MUaPc+HJ6kpFQq6GpSmxh7ZxPOEQiEZbJoau2Sj+fV92a9mE32WbLTx0knbiReGmdXYTe2aoJHqV7ExkdVmqIv0jGSYmxIXWOwkwxcpS2v8Vxof75PagFoSoWVpJlXsrDZaRqnNEdloKt0usVYq2WO18ZtmI/mVVCdNGXHUn9UmbV2l8dF8/fGbZiOthaYWKs0BmM+DZKOdm0ab+HxSTKY1MhU084zVYj20ZEib3bR3UJ53ngciSeO+SIQQ0goCAD1NLX19tT/FKhVZdTsMk2rm5bJ8qYxqeFL9SlJEB4BiUbYJw6Sod29v7SfrHFGskUh43KZSkf1KwucmP8GYTnR2NF+1iiUfHZ3CQUfBSPlBqbTLSSNRLha38byaunsjnZ3yHC420QJJsUo2UZySjeclT0SxCE8r/AIo9PSgEPui+ZCVzE15j9SXlifFj1jza7KpIlkcM+U2nhJXWj6UtSBsspGeWrSdw9VGmsMmj21XAZm5KXEh7fcGIYQQQgghhBBCCCGEDAn4ZCchgxHb15eHm//B4ncgGazHbLudwFBgqMefM9wXiRBCWoV8vdFeUnB5jT3x5KPhJYioL4uN9kKQNkdkY/Pqu+mlI81PGHoIY8+Xhb4PQHhqMQj019i1V9yDwPyaeVpbFhttfu2VeNNr7Bnn8kzzw/519Tzabf262rSyPa9YQ4d4XGxs5hhImJsSF1jsJIQQQkgd11eFWDImhBBCCCF5w9yUuMBiJyGEEELqhODdc0IIIYQQMjhgbkpcYLGTEEIIIXUC8E44IYQQQggZHDA3JS6w2EmGJ5KqcxY19riNrXq3qxp7FnXrKHaTWrXUZ1JjNymEmzZmSmtLi9Vko7W3Q41d249SOuZovv74NdnYKpab4tewOZ+m+aSYbBXrI9+2auxp3+GM65qn6rpko81juks9UEkd754TQkgrCAHE5cyDhr4kvb0FlMvN+ypWKh6q1eSuesVibQvG+J6WnmdWY9ds4mrslYp8CS0U5DkiGyml0PwWCkBPT3I8UDuGrLF2dRYQlpIB+YUCirtV5T0JtT0sy2U9D40rnEeLoREESTV2zUZTgddijcZLCy7F2tFhVI73gISNX6ns8tPYDvkbrOUTpsKVSw6S1SaM/dtIUZnDlAe6vFqd1cZ0TLbrrdnkketlUZ/PA+amxAUWO8nwRStyaMUfKZGR2qI5bNpNfXn4jWy0Ql5Wv2k2Unuea5GHXxebtN37bb5LecZqGp9ljizz2MZqM49LrDbfGdN3OOrvJ6YE19bGdVy74SbwhBDSCkIA8UJRVCaQC2SVilxK8ITKYrUKlErJYmBHh36prFZrhcK4TW9v0sb35dpYoVAbLxU7NZtqVbbZsUO/h1mpAKNGZY3VR0cpuaah76HY1SU70HLBvj5g+3a5PS42lHYTtrc3WaTUbCqVZIEyQroRH42NV36B2hdDEk4y5FBeb7wwD/hBIBa2AohyUChAF+TRZJwCxcZEVptoxaT/2kJljkAZH9m0stipjdMKjC42/c332iVmxNyUuMBiJyGEEELqVJH8czwLg7V4SwghhBBChi7MTYkLhufsCSGEEDLSYGJICCGEEEIGC8xNiQt8spMQQgghdbgvEiGEEEIIGSwwNyUusNhJhi+aqIxJQCjepwkFmQSEbOa39Zu2t6RNrFp743xZfNiKDbnYaPuapsVvuw+mrUBRq2O1/e5ljd8UVxaBoizf1ygW21g1bNaosT9mE0pt+gxGbPbzlPZRStu3Kd7frrvaLntQRXaEEEI0QgDxPRi92L9xki/hVau+uJVjEHiobbPYPJdpz85CIamXA9S2loxfXj1P3vaxWJT3/QRq21NqNtL+op2dZh3R7dvlPUnjW15qsRZ8D4WOghxrsRN+Sdg70/drm4XGF7FcTm5ImrZnZ1dX9j07o81QJSThy2jerq5krH19sl9TDlUqycJJgo0XhvCFY3DdszPuIS0vkcR3TDbSq60mYR/tVVjJJk2wR1sPrV2bQzo2k2/JxrT352CDuSlxgcVOMjyxLVBF7VmLVHkWO2385lnsTBMoamWxU1OtH8hip21BULNxiVVTGrcRU8qr2Kmdm8b+LPO0otgpnQeHAqmUELswEMXOdiRtvHtOCCGtIAQQryyGsX/jeIj/uVatFlCtJuXVfT+E5/mJQp7vy5o1gK6k7vtJBXcpVQFq9b5CQS52aimFZmOKVVNwB7IrxxeLPorjSmKBp9jpo7NT+NM4UmOP43luauxZbcplvXBaKCSrudG8Wt4V96upzzfaxJTXvUJBLo5Vq/AEESctbwmU9sgmfgRpBUTJj2Rjym20WG3yNM1v2pySTVq+p8WqFZElm9DRb+OYdgkUMTclLrDYSQghhJA6pj9CCCGEEEIIaSfMTYkLLHYSQgghpE6AmuolIYQQQgghAw1zU+ICi52EEEIIqSPtl5UF3nEnhBBCCCF5w9yUuGDYqIMQQgghhBBCCCGEEEKGDnyykwxfNHEakyiPZKMJqdi029q0ev6BjjXqz9pu4zfvWLV5bGNNI6tgT7vOs02stufZ9TuTdf56V3I+1w3obcZrc+Sx8X07cL17TgghxIQksRHE/s1iI2lP7xwd7rz2NXoIgGpVlhHxvFp/XPRHEgjXRMM9r9YuCQeZNBUlG0lkPB5rHMlGm8cUq+8BgZ/s8LyapLwXzzcKhaQTSTgoi40kFBTNJeU5krhQ5FebS1JvNwkUSf2aTRjW2mOxJtYsaodZNTzelyaCo6mTS+P6O0d/bVrJYIsnT5ibEhdY7CTDl/hFPVIU1LIoSbpRy5bCUG43KVmHYULVULVJyw4lTLFm9dtok/X4TLFKfqP5bY5PkgA1ZcSajTa/dryRjabGnjXWtAKercp9Vr+Rb5vvjEm9XfNjKpjmFaumTG/4DsT/4APkAqKpqGj689MWG79Sf7uKn0woCSGkFYRIqrGnFTuBpL5yB6SrQRB42L5dKuf4KCp/8UXt8eJfGCbV2CuVpKC3aQ4A6OuTL9NRDU/ya1Jj1/zEY+3rk2ONxklzdHcVEY5KFir9Qgc6xwoBeV7NUdyBdgCRTW9vc1tnJ8QTFOXQUv7T2ZksqjYutGQTXxDNrxRDRLGYXOyd46TCZkdPj/gFSMsx4v0V2Cuc+0i+wprmV8qxbG2qSN9bMm6Tpxq7ZlcQ+qR8L22dXWzygLkpcYHFTjLysH1yrNV+Wx3PQPk1MdLOwWDF5alOMiLIo5hLCCEkjnTbS2qPCJAs2egljdpNvubSg3bvtLFPerIzbqPdI9fmMPk2PVFqG2u1Ktf9pCKr7xtiDT2EQtkm9EL5aU3fT7anPdkp2UhtQC1Q7clLySbK37QnO+ML6/pkpxCrp8XqeeJie6G00jv7ID/ZmfZUZtZ5TOOzxmPr19XGxm9aX/I3g15kTZvfi31uF8xNiS0sdhJCCCGkzkC9Pk8IIYQQQuuDvj4AACAASURBVEgc5qbEBRY7CSGEEFKHrwoRQgghhJDBAnNT4gKLnaSl/OxnP8OvfvUrdHV14X3vex/e+MY3ptrs2LED1113HR555BHss88++OhHP4rp06e3IVpCCCG8e04IGc4wNyWEkKEFc1PigmGjDkL6xxe+8AWcd955eM1rXoPRo0fjhBNOwG9/+1ujTRiGePOb34wf/vCHOPzww/H444/jyCOPxAsvvNCmqAkhZGQTYNcG+zY/hBAy2GFuSgghQw/mpsQFPtlJWsLWrVtx2WWX4dvf/jbe+973Aqgli4sXL8bJJ5+s2v3617/Gfffdh2effRbTpk3Dv/7rv+LQQw/Fl7/8ZVx55ZXZA5AUpaPPpp3PJfVuabxJTdykcB79ZInVdn5TrFn9pvXZqoZLfm3nb5wry1iTjebXtBYmNfY8Y826riaRJdtjs5mnMa4sx2cSOnKJ1fY4IjPhs40quqndxmYo3Y12fVVoKB0jIWTkMeC5KUIk//w2SaYAsoayJmfi7xzf3FepAGEozx8E3k5x8Ob+YjEp8ON5so5OtQr09MiiP5qNJurd2alf6otFoFRK+pFsfF8WGq9WgR077GIt+B4KHYWEjd9Zgh8X5QkCWRkportbFhaScqZisabcLvVJfqJ5y+WkTamUFBDSVJwi+vqSNibFqa6upF/NRxCgEFeHjw4DsmiOKccIhH6TjfS0l285PvJrM49m0w6BIk+IS/Nrit821rxgbkpcYLGTtIQVK1agr68Pp59+er3t7W9/O77yla9g06ZNAIAJEyYk7O6++24cffTRmDZtGgCgWCzitNNOw2233WYXQBgmL67R51YWOzWpSpONbQFRm19LKFwLs62M1dbGpYBYrco2WtLYymKn6XthitWm2Gn7XdXmakex03SetcRb+24byFrYbEexcyjRzn2RnnrqKVxyySVYvXo1Zs6ciSVLluDAAw9Mtbvllltw7bXXYuvWrXjTm96ESy+9FGPGjGlDxISQocqA56YIAcQLPEFDn4SHWvknPo803t/501x2KJeBcllWCPf9EJ7ni8W/eDFSSlWAWo3N9+UCoskmDJM2vl8rXkoUizW7LDZa2lUs6rHWisKSjY/iuFKimFPqDpG4WkaLoREEtRPSSEeHXPnt66sFJSH5icaKUvOC3zTleMlGU3DXFs/zxGPwKxX4yrGFYZjIQdIUzoFshTxTbqP9V2VrkxZrVpu0XNLlxrqU/7r4bbRJ/sZpDcxNiQssdpKWsH79eowfP77pP/B99tkHALBhwwYAckK5fv16zJgxo6ltn332wfr161Vff/rTn7BixYr65wcffBBl5W4hIYQQM+3aF2nTpk04/vjjceKJJ+KKK67Aj370Ixx//PF47LHHsOeee6p2d955JxYtWoQrrrgCc+bMwSWXXIInn3wS//u//9uGqAkhQ5WBzk3DUClcEUIIMcLclLjAYidpCVu3bsWoUaOa2kaPHg0A2Lx5s2q3ZcsW7L333gm7LVu2qDbPPvss7rjjjvrndevWoZDyxBchhBAZ4VmVzHY2LF26FB0dHfjv//5vFItF/NM//RNmz56NpUuXYvHixardFVdcgfe+97248MILAQCzZs3CnDlz8PDDD+PQQw91iJwQMhIY6Nw0DLmDHCGEuMDclLjAYidpCZMmTUokgS+//DIAYPLkyUa7eML58ssvG21OO+00nHbaafXP119/PZZ+4xsuYRNCyIhH2vuqFaxYsQLz5s1DcefGaoVCAfPnz8cDDzyQavfhD3+4/vmggw7CjBkz8MADDzChJISoDHRu+n/+z7UuYRNCyIiHuSlxgcVO0hL22WcfbN26FevXr6/vcfTEE0+gWCzWP0vMmDEDv/zlL5vannjiicTrQybWrl2LVX/9Kz79+c83d6QJFNnsX6htJq7tCWlro40F9D0NNd8DFWvaXpE2x2e7z6bmw1bcJ22erN8N0/FG82hrmGV+wLzJvO2xpa1rlrZozyhpb6fOTvPeozZ+DeuqvfJikyy57MuZ116e8fF/6uvDdmmTsxypBgF+W6mgb9w4a9t1zzyDjbfe2lRMOPLII7Fo0SJx/IYNG/Da1762qW2vvfbCww8/rPrYvHkztm/fjr322ithF72GSgghEgOdm/b2rsW4cV+P9fixf+NIeyr6Sru0v2c0h3zd8DygWEz2eV7yctzbq2vTbN8uTo9iURYK8n1doEjb8lLbz1OyMfktlfRYpZgiv/FLbwFVePErdaWS3OeykXJZ1hSQbIJAFyiSbKJx0lx9fdn9NtrET7hBcEicS1OKMuxtKhW10vZrlPI9k43Uro01PVFouw+85sc2X7WJJ2tcWeZvnCvir319KDM3ZW46SGGxk7SEI488EtOmTcM3v/lNXH755QiCAN/61rdw8sknN71C9Kc//QkPPPAAzj//fADAwoULcdlll2HZsmWYO3cuXnzxRfziF7/ARRddlNn3vvvuiwPnzAG6uvD7++/H9u3bMe+f/indMI9X313myOuVe9t5hPEPrlwJAHj+xRdx6oIFrfHrwkCtUYZ5/vzEE3j6mWfw9re8JZf5BnSOPOdR5nv6r3/Fg48+irMbnnhpFVk2tG8nLiu79m9/w13Ll+P9Z56J6S+8gHG7766rN/STPffcExd/9rPYsnEjQofvwes6OrB127bM43fs2IGO2F+WpVIJ27W/mnfaAEjYdXV1Ge0IIWSgc9Mjj3wtDjhgAh58cDl27NiON73pJLhdpVy0mC09KNO0oz3et3LlgwCA5557HgsWnJrJJi/fZhtJur2gV1MB8wMC2nibeXby5//3//D0qlV4+9veps+TdiPeJaasYw1IJdAC8nmqT5vjL888g4f+/Ge8S8lN8/5Lp5V/ObXj6ce1f/sb7l2+HO8980x07cxNfeamzE0HIQa5OEIIIYQQQgghhBBCCBk68MlO0hKKxSKWLl2KM844A7///e+xefNmvPjii7jnnnuaxt1777347Gc/W797/oY3vAGLFy/GW97yFhx33HFYuXIlXve61+G8887L7PuMM87AGWecAQBYsmQJNm7ciCu/+MXcjm04c+21tf2kHnroIVx5zTUDHM3Q4L/+679www034MqvfnWgQxkS3HDDDdh07bW4kvvqZuKuu+7Ck+vW4YttWK9isYh/v/TSlvuJmDJlSn2/vIhNmzYlXgNqZOLEiSgUCgm7v//970Y7QggZbLnpddddlduxDWcac9Mvf/nKAY5maFDPTb/85YEOZUhwww034OVrr8WXmJtm4q677sKqdetwNXNTAMxNBzMsdpKWcfLJJ+Opp57CnXfeie7ubixYsABjx45tGvOOd7wDRx99dFPbFVdcgbe//e1YuXIlLrjgApxwwgkoFKQ9iAghhAxVZs+ejYceeqip7eGHH8ZrXvMa1aZYLGLGjBl4+OGHcfLJJwMAtm3bhqeffhqzZ89uabyEkKEPc1NCCCEazE2HFyx2kpYybdo0vO9971P7p0yZgilTpiTaDz30UCqXEULIMObd73435s6di3vvvRfHHXcc7rvvPixbtgyf+9zn6mMuv/xyHHTQQQCAt7/97XW7pUuX4rzzzsPEiRNx9dVXY+zYsZg/f/6AHAchZGjB3JQQQogEc9PhBYudZFgzZ84cbN26daDDGDJEd634tEJ29t13X7zxjW8c6DCGDNOmTcPxxx8/0GEMGSZNmoR58+YNdBgt4ZhjjsGSJUswb948zJgxA2vWrMG///u/49hjj62P+elPf1pPFKOEcvHixXjwwQex7777YuLEidi6dSuuv/56jB49ekCOgxBCbGBuagdzU3uYm9rB3NQO5qbMTYcKnouqVQsYFEEQQgghpL08//zzeOaZZzBz5szE01Rbt26tq1t2d3c39T399NN46aWX8IY3vKFJSZm0hHwknocWzE0JIYSQEQhz0yFBam7KYichhBBCCDHBYichhBBCCBkspOamfjuiIIQQQgghhBBCCCGEkFbDYichhBBCCCGEEEIIIWRYQIEiMuR5+OGH8eCDD2Ly5Mk45ZRT0NnZmWpTqVRw6623Yv369Tj44INx9NFHtyHS9hOGIe666y6sXr0as2fPxgknnADP05/4rlQqAID7778fq1evxpgxYzB37lxMmjSpPqa3txe/+93vErbRxt7F4vD6tfLkk0/ivvvuw9ixY/GWt7wldaPpv//971i5cmVTW1dXV9PG1sOJBx54AI8++iimTZuGBQsWpJ7/FStW4JVXXkm077///thnn30AAHfeeSeA2ve3kaOOOgq77bZbTpEPDjZv3gwAePTRR9Hd3Y0jjjgik90f/vAHPPLIIwCAqVOnYsGCBfX9gwghhAwszE11mJv2H+amZpib9g/mpmTYEIbhYPghxIklS5aEu+22W3j66aeHM2fODA8//PDwlVdeMdr09vaGJ5xwQjh9+vRw4cKF4bhx48ILLrigTRG3jyAIwjPPPDOcNGlSuGjRonDixInhu971rjAIAtVm0qRJ4aRJk8LJkyeH8+bNCw8++OBwt912C2+55Zb6mA0bNoQAwn322SecOXNm/WfLli3hli1b2nFobeN73/te2N3dHZ566qnh6173unDWrFnhc889Z7T5zW9+ExYKhaa1OfbYY9sUcXv5t3/7t3DcuHHhwoULw7333js8/vjjw56eHqPNqaee2rQ206dPDwGEP/7xj+tjOjo6wo6OjnDq1KlNY5944olWH1JbOf7440PU9gUMfd8PjzrqqEx2n/70p8OxY8eGCxcuDBcuXBjOmDEjPPbYY8Pt27e3OGIyghnoPJG5KRkyMDfVYW7af5ibmmFu2j+Ym5IhRGouN9CJJBNK4sxTTz0VFgqF8Ne//nUYhmG4ZcuWcObMmeFll11mtPva174WTpw4MXz++efDMAzDP/zhD6HneeGKFStaHnM7+cUvfhF2d3eHq1atCsMwDP/yl7+E3d3d4U033aTa/PKXvwx/+ctfhpVKpd720Y9+NJw2bVr9c5RQ/u1vf2td8IOATZs2hbvttlv4rW99KwzDMOzr6wv/8R//MTzvvPOMdr/5zW/CPffcsx0hDih//OMfQ8/zwuXLl4dhGIYvvvhiOHny5PC6666zmmf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ebab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#net.plot_activation_map('hidden', (0,1), 'hidden2', 1)\n",
    "cx.view([net.plot_activation_map('hidden', (0,1), 'hidden2', i, format='svg') for i in range(2)], scale=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting training error (loss) and training accuracy (acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117654278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot(\"loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1181890b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot(\"acc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1185394e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot([\"loss\", \"acc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117654320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot(\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Your Own Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 21,
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   ],
   "source": [
    "data = [\"Type 1\", [(0, 1), (1, 2), (2, .5)]]\n",
    "cx.scatter(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\"My Data\", [1, 2, 6, 3, 4, 1]]\n",
    "symbols = {\"My Data\": \"rx\"}\n",
    "cx.plot(data, symbols=symbols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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