jupyter_notebooks/LittlesLaw.ipynb

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   "source": [
    "# Extended results and code explanation supporting paper *Message-Passing Neural Networks Learn Little's Law* by Krzysztof Rusek and Piotr Chołda\n",
    "\n",
    "This *jupyter notebook* provides detailed results related to application and implementation of Neural Message-Passing for performance evaluation in Jackson networks of queues. Some of the presented ideas may be not clear until the paper is read.\n",
    "\n",
    "\n",
    "First, the obtained results are given. At the end of the notebook, the full code of the implementation is discussed as well.\n",
    "\n",
    "**If you decide to apply the concepts presented or base on the provided code, please do refer our paper: Krzysztof Rusek and Piotr Chołda, ''Message-Passing Neural Networks Learn Little's Law'', IEEE Communications Letters, 2018, *accepted for publication*.**"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import datetime\n",
    "import argparse\n",
    "import os\n",
    "\n",
    "import graph_nn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import IPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "acxZZQOITAJf"
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   "source": [
    "args = graph_nn.args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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    "colab": {},
    "colab_type": "code",
    "id": "T-aIHxI4TAJn"
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   "source": [
    "def make_set():\n",
    "    ds = tf.data.TFRecordDataset([args.eval])\n",
    "    ds = ds.batch(args.batch_size)\n",
    "    serialized_batch = ds.make_one_shot_iterator().get_next()\n",
    "    return serialized_batch\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JNhaN3XKTAJw"
   },
   "source": [
    "## Confidence intervals\n",
    "\n",
    "The confidence intervals for the test statistics are computed using the method similar to the bootstrap technique. According to the bootstrap approach, the distribution in the population is approximated by a random sampling with replacement from some set. Since we have the full generative model for the networks, we can estimate the confidence interval using the Monte Carlo method just by sampling from the whole population (instead of using the bootstrap technique). Then, the confidence intervals can be estimated as quantiles of the samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P5I8Wxs-TAJx"
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   "outputs": [],
   "source": [
    "def bootstrap(ckpt_='model.ckpt-197400', nboot=9, nval=3):\n",
    "    graph_nn.REUSE = None\n",
    "    g=tf.Graph()\n",
    "\n",
    "    with g.as_default():\n",
    "        global_step = tf.train.get_or_create_global_step()\n",
    "        with tf.variable_scope('model'):\n",
    "            serialized_batch = make_set()\n",
    "            batch, labels = graph_nn.make_batch(serialized_batch)\n",
    "            n_batch = tf.layers.batch_normalization(batch) \n",
    "            predictions = graph_nn.inference(n_batch)\n",
    "\n",
    "        loss= tf.losses.mean_squared_error(labels,predictions)        \n",
    "        \n",
    "        saver = tf.train.Saver(tf.trainable_variables() + [global_step])\n",
    "\n",
    "    with tf.Session(graph=g) as ses:\n",
    "        ses.run(tf.local_variables_initializer())\n",
    "        ses.run(tf.global_variables_initializer())\n",
    "\n",
    "        ckpt=tf.train.latest_checkpoint(args.log_dir)\n",
    "        ckpt = os.path.join(args.log_dir,ckpt_ )\n",
    "        print(ckpt)\n",
    "        if ckpt:\n",
    "            tf.logging.info(\"Loading checkpint: %s\" % (ckpt))\n",
    "            saver.restore(ses, ckpt)\n",
    "        \n",
    "        \n",
    "        for boot_index in range(nboot):\n",
    "            label_py=[]\n",
    "            predictions_py=[]\n",
    "\n",
    "            for i in range(nval):\n",
    "                val_label_py, val_predictions_py, step = ses.run( [labels,\n",
    "                                                                   predictions,\n",
    "                                                                   global_step]\n",
    "                                                                )\n",
    "                label_py.append(val_label_py)\n",
    "                predictions_py.append(val_predictions_py)\n",
    "\n",
    "            label_py = np.concatenate(label_py,axis=0)\n",
    "            predictions_py = np.concatenate(predictions_py,axis=0)\n",
    "            yield [\n",
    "                np.mean((label_py-predictions_py)**2),\n",
    "                graph_nn.fitquality(label_py,predictions_py), \n",
    "                np.corrcoef(label_py,predictions_py, rowvar=False)[0,1]\n",
    "            ],label_py,predictions_py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B7DgHpeaTAJ2"
   },
   "source": [
    "We use 99 bootstrap samples of 32 batches (with a single batch size equal to 64 samples)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G_EdZakQTAJ4",
    "outputId": "2c03593a-081e-4587-884c-6effca643dda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "202752"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "32*64*99"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z9Y2fbcATAJ_"
   },
   "source": [
    "## Evaluation samples\n",
    "\n",
    "All the evaluation sets are generated using commands given below. The details concerning generation of the code are provided in further parts of this notebook.\n",
    "\n",
    "```bash\n",
    "python3 samples.py -o eval.tfrecords -N 202753 -n 40 --rmax 0.9 -g ba\n",
    "python3 samples.py -o eval_er.tfrecords -N 202753 -n 40 --rmax 0.9 -g er\n",
    "python3 samples.py -o eval_er60.tfrecords -N 202753 -n 60 --rmax 0.9 -g er\n",
    "\n",
    "python3 samples.py -o eval_snd_2038.tfrecords -N 202753  --rmax 0.9 -g snd \\\n",
    "\t--sndlib sndlib/sndlib-networks-xml/cost266.graphml \\\n",
    "\t--sndlib sndlib/sndlib-networks-xml/france.graphml \\\n",
    "\t--sndlib sndlib/sndlib-networks-xml/geant.graphml \\\n",
    "\t--sndlib sndlib/sndlib-networks-xml/india35.graphml \\\n",
    "\t--sndlib sndlib/sndlib-networks-xml/janos-us.graphml \\\n",
    "\t--sndlib sndlib/sndlib-networks-xml/nobel-eu.graphml \\\n",
    "\t--sndlib sndlib/sndlib-networks-xml/norway.graphml \\\n",
    "\t--sndlib sndlib/sndlib-networks-xml/sun.graphml \\\n",
    "\t--sndlib sndlib/sndlib-networks-xml/ta1.graphml \n",
    "\n",
    "\n",
    "\n",
    "python3 samples.py -o eval_snd_janos-us.tfrecords -N 202753 -n 40 --rmax 0.9 -g snd \\\n",
    "    --sndlib sndlib/sndlib-networks-xml/janos-us.graphml\n",
    "\n",
    "python3 samples.py -o eval_snd_janos-us-ca.tfrecords -N 202753 -n 40 --rmax 0.9 -g snd \\\n",
    "    --sndlib sndlib/sndlib-networks-xml/janos-us-ca.graphml\n",
    "\n",
    "python3 samples.py -o eval_snd_cost266.tfrecords -N 202753 -n 40 --rmax 0.9 -g snd \\\n",
    "    --sndlib sndlib/sndlib-networks-xml/cost266.graphml\n",
    "\n",
    "python3 samples.py -o eval_snd_germany50.tfrecords -N 202753 -n 40 --rmax 0.9 -g snd \\\n",
    "    --sndlib sndlib/sndlib-networks-xml/germany50.graphml\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zIRTpAoOTAKA"
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   "source": [
    "\n",
    "## Evaluation results\n",
    "\n",
    "Below, the results are organised into sections labelled as **X**/**Y**, where __X__ describes the training set, and __Y__ describes the test set. Both **X**  and  **Y** are represented with abbreviations BA or ER (the Barabasi-Albert or Erdos-Renyi models, respectively), or a network name retrieved from the SNDLib library. For example, the section BA/ER shows the results of a model trained with Barabasi-Albert random networks, and tested on a test set drawn from the Erdos-Renyi network model.\n",
    "\n",
    "Our best checkpoints (snapshots of the weights) based on the evaluation results are as follows (respectively for the both types of random network models):\n",
    "- BA 197400\n",
    "- ER 199700\n",
    "\n",
    "All the snapshots are stored locally in subfolders of `log/`. They are not published within this notebook because of their large size. \n",
    "\n",
    "\n",
    "Each section with result analysis contains the confidence interval (95%) for mean squared error ($\\mathit{MSE}$, denoted as `mse`), $\\mathcal{R}^2$ (`R**2`), and Person correlation $\\rho$ (`rho`) presented in a table form. The quality of the output prediction is visualized in the form of a regression plot comparing the 'true labels' (normalized delays) and the model predictions. The analysis is concluded with a histogram of residuals (i.e., model errors).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "source": [
    "def print_report(bootstraps_samples):\n",
    "    ci=np.percentile(bootstraps_samples,[2.5, 97.5], axis=0)\n",
    "    m = np.mean(bootstraps_samples, axis=0)\n",
    "    stat=np.stack([ci[0],m,ci[1]],axis=0)\n",
    "    with pd.option_context('display.precision', 4):\n",
    "        df = pd.DataFrame(stat,\n",
    "                          columns=['mse','R**2','rho'], \n",
    "                          index=['ci_lo','mean','ci_hi'])\n",
    "        IPython.display.display_html(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RMTnjMjLTAKG"
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   "source": [
    "def print_regplots(label_py,predictions_py):\n",
    "        plt.figure()\n",
    "        plt.scatter(label_py,predictions_py, \n",
    "                    color=\"gray\", \n",
    "                    facecolors='none',\n",
    "                    s=1)\n",
    "        l = graph_nn.line_1(label_py, label_py)\n",
    "        l[0].set_color('k')\n",
    "        l[0].set_linewidth(1)\n",
    "        plt.grid(True,color='lightgray')\n",
    "        plt.xlabel('True label')\n",
    "        plt.ylabel('Predicted label')\n",
    "        plt.title('Evaluation' )\n",
    "        plt.tight_layout(True)\n",
    "\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "\n",
    "        plt.figure()\n",
    "        plt.hist(label_py-predictions_py,50)\n",
    "        plt.title('Histogram of residuals' )\n",
    "        fig_path = 'rez_hist.pdf'\n",
    "        plt.show()\n",
    "        plt.close()\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tMZPT8zzTAKM"
   },
   "source": [
    "## BA/BA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7jsJIcZQTAKP",
    "outputId": "ab192e54-92b8-4d79-e90a-a4dd617733f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Loading checkpint: log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Restoring parameters from log/ba16/model.ckpt-197400\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.9923</td>\n",
       "      <td>0.9972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.9929</td>\n",
       "      <td>0.9974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.9934</td>\n",
       "      <td>0.9976</td>\n",
       "    </tr>\n",
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lnDqFhYVUVFTg5eXFiRMncHJyYtmyZXTs2JGkpCT69etX7/js7GxOnDhBdXW1VDZvRezZozIBv9Ba71ZKeQG7lFJrtdaH7BiTEMIOiouLycnJISoq6or3go4ePcrSpUuZMWMGAQEB+Pn54eDggLOzM46OjoSGhvLhhx+SlpbGu+++y6xZs654nlmzZmE0GmWIr5Wx2wO/WuuzWuvdta9LgcNAV3vFI4RoWmazmQ0bNnDixInL9iUkJLBs2TJycnLYv38/y5Ytw2g02vZrrdFac+7cOT788EPWr18PgKurKz/++CNz586lqqqKP/3pT8yePRullK1Kxblz52zncXR0lCTVCimttb1jQCkVAiQBfbXWFy7Z9wzwDEBQUNDgxMTEZo3NaDTi4uLSrNe0l/bSVmmnfZhMJvLz83FxccHX1xeLxYKDgwNgLW9kNBrx8vKiuLiYqqoq/P39cXJyqtczMpvNlJSUALB9+3befvttunbtyu9+9zs6deqEs7MzTk5OALbzODs74+/v3/wNbgIt7Xd6syIiInZprYdc6zi7T6ZQSnkC3wIvXJqkALTWi4BFAEOGDNHh4eHNGl9mZibNfU17aS9tlXbaR12vqHPnznz//fecPn2aX/7yl5f1cI4cOYKTkxPffPMNLi4uvPLKK5w6dYotW7Ywbtw4nJycmDZtGqdPn2by5Mm8+OKLDB48uN45iouL+fLLL/Hz8+Pee++le/fuzdnUJtPSfqfNxa6JSinlhDVJfam1/s6esQghmpZSiqCgIFxcXAgNDcXFxQUnJye2bt1KeHg4/v7+bN26lTVr1uDr64urqyvV1dVs3ryZnTt3UlhYyE8//URCQgL9+/dn/vz5PP300xw7duyya/n6+vLMM8/g7e0tQ31tgD1n/SngE+Cw1vr/2SsOIUTzqKys5L333qNbt25UV1eTm5tLZmYmq1evJicnh3HjxrFmzRq8vLwYO3YsvXv3Zt++faxcuZJjx46xZs0aYmNj2b17N05OTnTo0KHBh3C7dOnSjK0TTcmeParbgUeAA0qpvbXbXtdaX172WAjR6jk7OxMVFUXnzp1xc3OjoKCAXr16cdtttxEdHY23tzfTpk3D398fd3d3vv32Wzp16sSyZcsoKSnho48+oqqqCovFQteuMu+qPbFbotJapwBSk0SINuj48eMYjUYiIyNt2xwcHC6rPH7u3Dm2bt1KSUkJM2fOpH///gCsXLmSDz/8kN27d/PGG28wZcoU8vLy2LRpE/7+/oSFhTVre4R92X0yhRCi7fnuu+8oLy/nN7/5jW1m35XULQHfu3dvwDrh4ssvv+TVV18lKCiI119/nUceeYSPP/4Yi8XC008/TadOnZqrGaKFkEQlhLjl7rnnHoxGI0opioqKbGWPMjIy8PDwsA3dOTs7c9999wGwa9cunn32Wc6ePctdd93FlClTSEtLIysri2nTpgHIkvDtlCQqIcRNy8/PZ+XKlYwePZrQ0FB69epFRUUF//znPzl37hwPPfQQ3bp1Y8mSJfj5+bFgwQI2b97MmTNnGDduHH/84x/5+OOPGTFiBI888gjnz5/n5MmTPPXUU3Tp0uWqy3s0pKqqiuLiYplU0QbIUvRCiEZLSUlhy5Yt9bYdP36cH3/8kaysLLKzszGbzVgsFrKzszl37hyenp54eXmRkZGBl5cXERERaK3Zs2cPS5YsoXfv3ly4cIEvvviCkSNH0q1bN8A6a2/FihVkZWXdUKwrVqxg0aJF9SpTiNbpqj0qpdQBrMViL9sFaK11TJNFJYRocbTWbNq0CUdHR8xmM2fOnGHKlCns3LmT7OxsJk6cyODBg3n33Xfx8fEhOjqamJgYjEYja9as4fjx44B1uO/HH3/kzTffxGKxMGvWLMaPH8/UqVO555572L17NwcOHMDT05OjR49SWFhIaGjodccbFRWFwWDA19f3Vv8oRDNraOhvSrNFIYRo0SorK/n666+Ji4ujX79+xMfH23o6R44cwdXVleTkZI4cOYLRaKSmpoY1a9bYPh8cHExUVBRBQUH8z//8DykpKcyZM4ennnqKjRs3cuTIEduxAwcOJCwsjA4dOjBmzBg8PT1vKObo6Giio6NvruGiRbhqotJa2/rbSqkeQITWep1Syq2hzwkh2p6KigpOnjyJp6cnnTp1Ys6cOSxZsoQjR44QGhpKeXk5Li4unD59GpPJRJcuXcjLywPgoYcewsvLi08//ZQnn3ySqKgo2+y9AQMG0KdPH7TWtod36xZKBFkjSlhdM+EopZ7GWhTWD+gJBAN/B8Y1bWhCiJbC39+fl156CTc3N8C6AGGHDh1wcHAgOzubmpoafHx8MJlMREdHExgYiFIKNzc3duzYwWuvvQbAY489RseOHXFycsJkMvHNN99w9uxZZs2aZTu3EJdqTM9oHhALbAPQWh9VSsmDDEK0M15eXly82kJQUBD79+/HbDbj7e1t21dQUMDBgwcpLS3lu+++48SJE7zyyivU1NTg6+vL+fPncXV1pbS0lJKSEluVcyGupjGJyqi1rr6oW+7IlSdZCCHaIK013377LQaDgYMHD9KrVy9mzpzJsGHD6NKlC4mJiVRWVnL27FnAulx8amoqKSkpjBo1irfeeguz2UxaWhrnz58nJCSEkydPMmfOHMLDwzGZTDg7O3P8+HGWLFnCvffeS9++fe3catGSNCZRJSqlXgfclFJ3As8BK5o2LCGEvdXU1PD555+jlOLs2bN4eXlhsVhIT09n3bp15OfnExwcTFZWFmazGbDO6Pvb3/6Gm5sbv/jFLzAYDGzZsoXg4GDKy8sJDQ0lJiaGyspKfH19MRgMtiXhtdaYzWZawhp5omVpTKJ6DZgLHACeBeKBj5syKCHErVdZWQlwxXtBeXl5uLq64u3tDUB5eTnZ2dlkZ2cD1pl4Hh4epKSkANiepTp69CgAJSUlrF69mrNnz/Lqq6/y0EMP4e7uzscfW/+rmDBhAp988gnOzs4MGDCAAQMGXBZDz549+e1vf9tgRXTRPl0zUWmtLUqpz7Deo9LAES1/8gjR6nzwwQdorXn55Zfrba+srOSjjz5CKcULL7yAt7c3K1assE0Z9/T0JDc3l5ycHNtnAgICqKmpobCwkC1btrB161aGDh3KtGnT8PLyws3NDaPRaEuOn332GT/72c/w9fXFZDLh6Hjl/3okSYkruWZlCqXUZOAY8FfgfSBTKTWpqQMTQtxaAQEBdOzY8bLtLi4uBAYG4uDgwN///ndycnLo37+/7UHZ++67r14hWIPBQEFBAceOHePDDz/k3LlzvPXWW9x9993069eP/Px8lixZQpcuXWw9NHd3dzw9PVm8eDHvvPMOJpOpeRot2oTGDP29A4zRWmcCKKV6AiuBVU0ZmBDi5lgsFt59910iIyOZOHEip0+fvmLNPJPJhKurKz169ODYsWN8+umnhIeHM3r0aADWrl1LTEwM3t7eXLhwgYKCAhISEigoKGDSpEn8/ve/5+DBgxw+fNj28O/tt98OwNChQzl//jyTJ0+2PR9lNpul5ySuS2MSVWldkqp1HChtoniEELdQVVUVNTU1AMyePZvz589TU1ODwWDAwcGByspKkpOTycrKIjg4GACz2cyRI0c4cuQIt912G+fOnePcuXOEhoYSHx/P5s2bmThxItHR0bi5ubF161ZCQkIYOnQoycnJDB48mIEDBwIwYsSIevHUVUEX4no0VOtveu3LnUqpeOBrrPeoHgB2NENsQoibYDAYGDp0KL169QKsS2zs3LmT9evX4+fnx+zZs1m0aBHl5eWANamsWbOGoqIiAAYMGMCYMWMoLCzk8OHDLFy4kO7du3Po0CE6duxIfHw8p06dorq6mnXr1uHr60tAQAC+vr78+OOP3HbbbbJ2lLglGupRTb3odS4wuvZ1PiCPkAvRwlVXV7N582aKioro3r07vXv3JjMzk+rqary8vEhKSrIlKYD169dTXFxse19cXMzixYv5wx/+QHl5OdOmTaNPnz6EhIQAEBgYSNeuXYmLi+PYsWN4eXkRGBjI7t272bNnDx06dJBEJW6Jhmr9PdGcgQghbi1nZ2fuv/9+goODMZvNhIWFoZTCZDJRUFBARkYGDg4O+Pj4UFFRQX5+vu2zVVVVLFq0iLS0NEaOHMmwYcNwdXVl5MiRgPWZp40bN+Lq6kpcXBzh4eG2z9Y9J1U3/CfEzWpMrT9XrM9RRQOuddu11k82YVxCiBtQVVWFo6Ojbfp3eHg4SUlJbN26FUdHR2bPnk1aWhq7d+8GrPejCgsLAevUcIvFwv79+1m3bh09e/bk2WefpUOHDpjNZiorK8nIyCAuLg6lFOPGjWP9+vWkpqYyfPhwWwwZGRmsW7eOyspKxo8f3/w/BNHmNGbhxP8DOgMTgESsRWllMoUQLUxlZSVvvfUWixcvxmKxANZFDesezq2urmbFihW2JHUxR0dHLly4wL/+9S+2bt3KnDlzmDZtGuHh4bi7u9uOy8/Pt30+IiKC7t27X7Y8fNeuXYmMjLTdGxPiZjVm1l+41voBpdS9WuvPlFKLgeSmDkwI0TgWi4XExEQ6depE9+7d8fb25j//8z8ZO3YsmzZtArCVJfL19eXChQu2kkdgXcJj48aNHDp0iLvvvptBgwahlCIqKorg4GB69+5NYWEh1dXVfPHFF7YhwsDAQJ544vI7BB06dGDmzJlN33DRbjQmUdXUfj+vlOoLnAPkDqkQLURpaSlJSUl06dKFsLAwXFxcbPvqelZ16z35+fnZVtqtG+Zbu3YtUVFRzJs3z/Zg7uOPP84777xDdnY2AwcOtPWqXn75ZVmOQzS7xiSqRUopX+C3wI+AJ/AfTRqVEOKKtNYcOHCAwMBAAgMDAWsP5qGHHiIlJYXNmzcD/y5FZDAY6iWrnTt3AtYK5z/99BMdO3bk17/+NQMGDMDBwYHY2FgsFgvbtm1j6tSply0Bf/EwoBDNpTG1/uoK0CYCYU0bjhDiasxmM8eOHeP7778nJCSExx57zPZc1Jw5c6iurgbqJ6e673XKyspISUkhMzOTkSNH8uyzzzJhwgTb/sLCQt5//30AoqKiGDRoUDO1Toira+iB35ca+qDW+v/d7MWVUp8CU4A8rbUsQCNEA+pm2A0aNIiYmBgsFgsJCQmYzWZKS0sJDg7m7NmztuR0cZkis9nMjh07SE1NZe7cuaxYsYKMjAyio6PrXcPBwQEvLy+6d+8uM/ZEi9FQj8qrGa7/v1gL3X7eDNcSolXr0aOHrYJ5VlYWgYGBuLu7U1payvLly6mpqSEgIICCggIcHBxsdf1OnDjBqlWr8Pb25uGHH2bq1KksXbqUwMBANm/ezOzZs+nWrRsAPj4+vPRSg3+jCtHsGnrg9/dNfXGtdZJSKqSpryNEW9C7d2+Sk5PZvXs3Sik2btxo6zXV1fPz9PSkuLgYs9lMXl4ey5Yt4/Tp00yYMIHIyEiUUiQmJgLg6upKRUWFVDIXLV5jJlMIIVqIuuXeHRwccHFxoby8HKWUbfr5yZMnMZlMpKam8u677zJ48GDuuecefH19MRqNmEwmevTogaenJ9OnW8t5XqmiuhAtibL3Goi1PaqfrnaPSin1DPAMQFBQ0OC6vwabi9ForDfdty1rL21tTe3UWlNcXIyLiwseHh7k5ubWuwdV9+/XwcEBsK68+5e//IUePXrw6quvEhERQUlJCdXV1RgMBgwGAyaTCaUUnTt3tlu7bqXW9Pu8WW2trREREbu01kOudVyL71FprRcBiwCGDBmiL64p1hwyMzNp7mvaS3tpa0tpp9lsJjU1lZ49e9KlSxfb9oMHD7Jy5UpmzpxJdnY269evB6zLwefn55OdnV1vZh9AUVERq1evJi8vj0mTJjFgwAC6du1Knz59AOuzVo6Ojri5uZGZmYnBYCAsrG1M4m0pv8/m0J7aejG7zvoToj07c+YM69ev58yZM8yaNcu2/eDBg1RWVrJ37172799v275nzx7b67okVVchffv27cTFxfHII4/g6+tLfn5+vUTm5fXvuVHt8T860bo1ZtZfb2Ao1od9wbr8x/ZbcXGl1BLgDiBAKZUNvKG1/uRWnFuIli44OJipU6fSo0djtgkSAAAgAElEQVSPetvd3NxwcHCgoKCAqw3Na61JT08nISGB8PBwfv/739OlSxceeughkpOTbZXNhWgLrjnrTymVBAzSWpfWvv8d1qXob5rWes6tOI8QrUV+fj4pKSn069ePr7/+mq5du7J+/XqmTp1Kz549ee+996ioqACsPa4rKSgoID4+nrKyMv7xj39w99131+sxjRgxgn79+tmqogvR2jXmHlUgUH3R++rabUKI65SRkcH+/fvp0KEDYJ2lB7Bu3TqWLl162fEXT5gwGo0kJiaSlpbGmDFj6N+/P+7u7lRWVtZLVAaDAV9fX0lUos1oTKL6HNiulPq+9v004LOmC0mItmvYsGEEBgYSGhrK6NGj+eijjzAajfXWhLp4uM/Hx4eioiIOHDjA2rVrCQsL45133qGqqoqePXuyfft23N3dGTdunL2aJESTa0ytvz8qpVYBI2s3PaG13tPQZ4QQ9ZlMJhwdHTl58iSurq58/fXXREZGYjAYKCsrsx136T2pw4cPk5CQQFVVFS+99BJ9+/YlJCSElStXMnz4cO655x4SExMxm83cddddzd0sIZpFY6enuwMXtNb/Ukp1VEqFaq1PNGVgQrQF1dXVLF++nEOHDhEWFmZbYgOsZZCMRuMVP1dZWcnGjRs5ePAgd955JzExMURGRnL//fcD1oKx7u7ulJWVER8fT1VVVbO0Rwh7aMxS9G8AQ7DO/vsX4AR8AdzetKEJ0fodOHCAQ4cO4eDgYCtzBNYhvislKYvFwp49e9i4cSN9+/bl97//va3H5enpaTuubrkNT09PfvWrX9UrQCtEW9OYHtV9wEBgN4DWOkcp1RwFa4Vo9aKiokhPTyczM5Nz587h6upKdHQ0+/fvr5e4ALKzs4mPj8fFxYUHH3yQoKAg21Cgi4sLEydOvOI1pASSaOsak6iqtdZaKaUBlFIeTRyTEK2S1pqioiL8/PxQSlFVVYXJZGLSpEl88sknVFRUUFNTQ3Fxcb0kVVZWxoYNG8jMzGTs2LEMHjzYtlS8UopXX31VnokS7VpjEtXXSql/AD5KqaeBJ4GPr/EZIdqdXbt2sXLlSqKjo+nUqRMpKSnU1NTg7OxsW9QQsN2nMpvN7Ny5k8TERAYMGMBzzz2Hq6srnp6ejB49mo4dO+Ls7CxJSrR7jZn197ZS6k7gAtb7VP+htV7b5JEJ0YpYLBaOHTsGWEsgHTx40Lbv0vtHSilOnDhBfHw8Hh4ePP7448ybN4+AgADy8vLw8PDAzc2tzRSNFeJmNWYyxZta61eBtVfYJkS7lpaWRkJCAnFxcaSnp+Pi4nLZJAmTyYS7uzsVFRVcuHCB9evXc+LECSZPnkxcXBx33HEHUVFRAHTq1InvvvuOAwcO8PTTTxMUFGSPZgnRojRm6O9O4NKkNOkK24RoN7TW7Nmzh/3791NeXs62bdsArjiTz2w2U15ezubNm9m8eTNDhgzhnXfeYdiwYfTr1++y4/v06YPZbMbPz6/J2yFEa9BQ9fSfA88BPZVS+y/a5QVsaerAhGjJtmzZwrp162zTxEtLS696bGZmJomJibi6uvLUU0/h7+/PnXfeiY+PD++99x7l5eUMGjSISZMmAdaZgnU9LCFEwz2qxcAq4M/AaxdtL9VaFzVpVEK0UCkpKezcudNWOLaysvKqxxYXF7N69Wpyc3P5/PPP6dq1KykpKRQVFbF3715uv/12SkpKAOt6UkKIK7vqAxha6xKt9UngL0CR1jpLa50FmJRSw5orQCFairNnz3L8+HFKSkpsEySutAxHTU0NmzZtYtGiRXTp0sU2my85OZmioiJGjBjBmDFjCAgI4MEHHwQgNDS0WdsiRGvSmHtUHwGDLnpfdoVtQrRqRqORhIQEIiMj6dWrl217eno6GRkZ9O7dm6VLl9oS08XTzetorTly5AgJCQkEBQXx7LPP4uPjg7e3N5s3b8bX15e4uDjGjh1rS3QRERG89tprbWp5cSFutcYkKqUv+rNRa21RSrX4JeyFuB4FBQXs3buXmpoaW6KqqqqyLb1x8eq6V/v8qlWrKCkpYerUqURERODs7ExVVRUjRozA19cXX19f/P39bZ/Zu3cvx48fZ+rUqU3XMCHagMYknONKqYVYe1FgnWBxvIHjhWh1unbtyhNPPEFAQIBt29atW22vAwMDyc3Ntb03GAxYLBaMRiNJSUns3r2bkSNHEhsbi6OjI3FxcWzevBmlFEOGDLliLb6DBw+SmZnJHXfcITP8hGhAYxLVz4C/Ar8BNLAeeKYpgxKiue3Zs4dNmzbx0EMP2Z552rFjh21/fn5+vePNZjNpaWmsXbuWkJAQnnvuOdvihR06dCAuLo7Q0FA6dOhw1YKx06dP58KFC5KkhLiGxlSmyANmN0MsQthNeXk5Fy5c4OzZswQEBLBo0SLbzD6wVp6ok5uba1taY8aMGfTo0YN+/fpx9OhRIiIiCA4O5u233yYgIIDIyEhiYmLq9dTquLm54ebm1iztE6I1a+g5qle01v+jlPob1p5UPVrrhU0amRDNZO3atRw8eBBPT09++OEH20KFl6qsrGTTpk0cOHCAO+64g7i4OFvx2IkTJzJ9+nQACgsL8fPzo6CggOTkZDIzM3nmGRmEEOJGNdSjOlz7fWdzBCJEczt37hzx8fEYDAbb80yOjo6XJSmLxcLevXtZv349ffr0Yd68eXh4eGA2m/H29iY6OpqysjLbw7/+/v4sWLCAnJwcDh06RNeuXZu9bUK0JVdNVFrrFbXfP2u+cIRoPllZWZw+fRpnZ2fbNpPJVO+YM2fOEB8fD8DDDz9Mt27dMJlMODg42HpTqamp1NTUMHny5HqfDQoKklp9QtwCDQ39reAKQ351tNb3NElEQjSRQ4cOYTQa8fT0ZNmyZRw8eBAPDw/Ky8svO7a8vJz169eTkZHBuHHj6N+/PwaDAZPJRHBwMNnZ2QwcOJBRo0aRlZVFWFiYHVokRPvQ0NDf27XfpwOdsS4/DzAHyL3iJ4RooWpqavjmm28AuPPOO23LcFxaRNZsNrNr1y42bdpEv379mDdvnm3Cg6OjIyaTidDQUMLCwrjttttwc3PDx8eneRsjRDvT0NBfIoBS6h2t9ZCLdq1QSsl9K9GqODk5ERUVxfHjx21DdlB/qC8rK4v4+Hjc3Nx47LHHCAwMBKB79+7MmjWLvXv3sm/fPoYOHWqbii6EaHqNeY7KQykVprU+DqCUCgVkOXrRahw7doyTJ09y6NChK+6/cOECa9euJSsri7vuuovo6Oh6zz6NGjUKd3d3hg8fzvDhw5srbCFErcYkqheBTUqp44ACegDP3oqLK6UmYi166wB8rLX+71txXiEA8vLySElJIT09nZqamsv2Ozg4kJSUREpKCoMHD2bevHlXrLnn6+tLcnIyW7Zs4Y477mDYMKnJLERzaswDvwlKqQigT+2mdK315avDXSellAPwAdaFGbOBHUqpH7XWV/6zV4hG0lrz1VdfkZGRAVjLHV3q+PHjxMfH4+Pjw9y5c20P5I4ZM4aIiAiKi4vZvn07AwYMwNfXlw0bNgANL+shhGgajVmK3h14CeihtX5aKRWhlOqttf7pJq8dC2ReNKT4FXAvIIlK3JTCwkJbkoL6VSXOnz/Pb3/7W/bt28fEiRPp3bs3BoMBFxcXXF1d2bhxIyEhIZctXvjoo48CshyHEPagrrSeTr0DlFoK7AIe1Vr3rU1cW7TWA27qwkrdD0zUWj9V+/4RYJjWev4lxz1DbW3BoKCgwYmJiTdz2etmNBrbzRIMrbmtVVVVlJSU4OXlRUVFxWVDfUajka+++opvvvmGBx98kBkzZtja6u3tjYeHByaTierqatuDu61da/59Xo/20k5oe22NiIjYdclkvStqzD2qnlrrWUqpOQBa6wp1tSqbTUBrvQhYBDBkyBAdHh7eXJcGrMuIN/c17aW1tXX79u2sWrWK8ePHc/bsWduU84vVrRG1evVqOnfuzBNPPMGMGTNsQ3kAffr0YdasWc0ZerNobb/PG9Ve2gntq60Xa0yiqlZKuVH78K9Sqidw0/eogDNAt4veB9duE6JRHBwccHJyYsOGDfWG9+oUFhayatUqiouLmTx58mX/wD08PPDw8KBLly7NFbIQ4gY0JlG9ASQA3ZRSXwK3A4/fgmvvACJqp7ufwVqh/cFbcF7RTvTo0eOKs/mqq6tJSkpi165djBgxgtmzZ+Pq6oqzszMjR47Ezc2Nhx9+mOrqavr06XPVZTiEEC1Dg4mqdogvHWt1ituwTk9/XmtdcLMX1lqblFLzgdVYp6d/qrW+fOxGiIucP3+eI0eOcPDgQbKzs+vt01pz8OBB1qxZQ48ePXjuuefw8fHBbDZjMpm4//77CQkJ4fTp02zbto2jR48yd+5cgoOD7dQaIURjNJiotNZaKRWvte4HrLzVF9daxwPxt/q8om2prKykqqqKwsJCfvjhhyvW5svLyyM+Pp7KykqmT59OSEgIYB0ejIiIID09naKiIr766ivuvvtujh49SkxMDJ07d27m1gghrldjhv52K6WGaq13XPtQIW69L7/8kjNnztSrWF6nqqqKTZs2sX//fkaPHs2QIUMICwsjKysLgNjYWMaOHYvZbCY7OxtfX18cHBzw9/dnxIgRODo25p+AEMKeGvOvdBjwsFLqJFCOdfhPa61jmjIwIcBai69Xr15cuHCB0tJS23aLxcL+/ftZt24dvXr1sq0RBZCTkwOAq6srAQEBKKVwdHQkJCSEhQsXkpmZyfz58694PSFEy9OYRDWhyaMQ4gpOnDjBt99+e9lQX05ODvHx8VgsFmbPnn3ZPaa4uDiio6Pp1KlTc4YrhGgiDa1H5Qr8DAgHDgCfaK1NVzteiBuhtWbLli106dLFtqZT3TNRu3fvrleyqKKigvXr15Oens64ceMYMGBAvfJIvXv3ZtiwYVI9Qog2pqEe1WdADZAMTAKigOebIyjRfpw/f55169YRHBxMQEAARUVFfP7551xcMcVisbBr1y42btxI3759mT9/vm2NqP79+5OXl0eHDh24++67ZfkNIdqghhJVVO1sP5RSnwDbmyck0dYlJSVRU1PDuHHjKCgoYNCgQQwaNIjPP/+cwsLCes81nTp1ivj4eFxcXHj00Ufp3LkzXl5eREREsH//foYOHUrXrl3t2BohRFNrKFHZnqSsfeapGcIR7cH27duprKxk4MCBLF68GIAjR47Y7kVprSktLWXdunUcP36cu+66i759+3LPPfdQU1NDQkICrq6u/PrXv7ZnM4QQzaShRNVfKXWh9rUC3Grf1836827y6ESbNHfuXM6ePYujoyPOzs5UV1fbkpTZbGbbtm0kJyczaNAg5s+fbyvCOWjQIMrLyykuLmbAgJuqiSyEaEUaWoreoTkDEe1HVlYWy5cvv2x73RpRHTp04Mknn6Rjx44AKKVsK+t6eHgwceLEZo1XCGFf8rSjaDIWi4UNGzYQGBhIv379MJvNpKamsn79+nrHnT9/njVr1pCTk8OECRPo06cPnTt3Jj8/H4vFQs+ePRk/frydWiGEsDdJVKLJlJeXs3nzZtzc3PD29ub777+npKTEtr+mpobU1FRSU1OJjY3lvvvuw8nJCaUUubm5ODg44ODgILX4hGjnJFGJW85sNtu+evXqRUZGBosXL6a6utp2TEZGBqtWraJTp048/fTT+Pn5AeDp6Ym3tzc5OTmYzWaCg4MZPXq0vZoihGgBJFGJW+7//u//OH36NF26dOHMmTMYDAZbkiosLCQhIYHCwkLuvvtuIiMj6dy5M05OTpw4cYK+ffsyatQoDhw4QHFxsSQpIYQkKnHrubi4YLFYOHPGug6mxWKhurqa5ORkdu7cye23386sWbNwcXHBwcGBxx57DIvFwp///Gf27NnDHXfcQWxsrJ1bIYRoKQzXPkSI6zN58mTb67o1ot5//32Ki4v5+c9/bqtabjabGTt2rG2aenh4OEajsd59LCGEkB6VuGkpKSns2rWLyMhIqqurOXnyJGBdI2rVqlWUl5dz33330atXL9uKvOHh4fTo0YOYmH8X4Z8xYwYlJSVSTFYIUY8kKnFTtNZUVFRw/vx5UlNTAesaUYmJiezbt49Ro0YxdOhQHBwcqKmpoU+fPuTl5TF9+nRbvb46rq6uuLq62qMZQogWTBKVuCFaa/bv38/y5cvRWmMwGDCZTLY1oiIiInjuuefw9PQEwN/fn7CwMCZOnFiv4rkQQlyLJCpxXY4ePcrq1asJCwtjx45/L/p85swZ4uPjMZvNtjWiXF1defLJJ3F3d2fx4sXs2LGDkSNHSoVzIcR1kUQlrktGRgaFhYUUFxcD1jWiNmzYwOHDhxk7diwDBw7E2dkZk8lE9+7dbWWQHnjgAUpLSyVJCSGumyQqcU2FhYXs3bsXHx8fdu7cCViXiN+9ezcbN24kKiqKefPm4e7uDsDLL79MWloaISEhtnP4+Pjg4+Njj/CFEK2cJCpxTV9//TV5eXm2e0unT58mPj4eR0dHHn74Ybp06WI71sXFBWdnZwYNGsTSpUspLS3lySeflPtSQogbJolKNOjo0aO21xcuXGDdunUcO3aM8ePHExMTY1vk8NFHH2Xnzp0MGzbMdnx5eTkXLly47JxCCHE9JFGJeo4dO8bBgwc5deoUhYWFgLV23/bt20lKSmLgwIHMmzcPPz8/nJ2dcXV1ZfDgwVRUVHDo0CEiIiLo3r07AE888YRtRqAQQtwoSVTtnNlsZt++ffTs2ZPq6mq++eabevtPnDhBfHw8Xl5e9daIqqys5OWXX7Ydd/LkSXx9fetNllBKIStDCyFull0SlVLqAeB3QCQQq7XeaY84hLUHtWLFCjw8PGyLEwKUlJSwZs0asrOzmTBhAtHR0WitcXJyIiQkpF5FCYCQkBAWLlzY3OELIdoBe/Wo0oDpwD/sdH1RKzg4GAcHB8rLyzGbzZhMJlJTU9myZQtDhw7l3nvvxdnZmX79+uHv709QUBDh4eH2DlsI0Y7YJVFprQ8DMixkJ0ajkS1btlBRUcGBAwcwm80ApKam8uGHH9KxY8d6a0QNGDCA8ePH4+HhYc+whRDtlNyjamdycnL4/PPPMRqNtm1FRUUkJCRQXl7OxIkT6dWrFy4uLjg5OaG15o477pAkJYSwG6W1bpoTK7UO6HyFXb/WWi+vPWYT8MuG7lEppZ4BngEICgoanJiY2ATRXp3RaMTFxaVZr9lUysvLKS0tpe53XlVVxZdffsn333/P7NmzmTt3ri2BKaXQWuPu7k6HDh3sGfYt15Z+pw2RdrY9ba2tERERu7TWQ651XJP1qLTW42/ReRYBiwCGDBmim/v+SGZmZpu4J7Np0yaSkpLQWqO15vDhw6xevZrg4GDmzp1Lhw4dMBqNbNy4kW7dujF+/HhSUlKYNGlSm6so0VZ+p9ci7Wx72lNbLyZDf22Q1pq0tDQ6depEVlYW27dvtz0TlZ+fz6pVqygtLWXatGmEhobaPmcwGPD29sbd3Z1u3boxZ84cezVBCCFs7DU9/T7gb0BHYKVSaq/WeoI9YmmLCgoK+O677/Dy8qK0tBSwDvMlJSWxZ88eRo0aRWxsLA4ODsTExNC/f382bNhAQEAAL7zwgp2jF0KI+uw16+974Ht7XLs98Pf3x9/fn8LCQrTWHDhwgLVr1xIWFsZzzz2Hl5eX7R6Up6cnYWFhhIWFkZmZae/QhRDiMjL01waUlZVRXFxMx44dWbp0KWazmcLCQs6ePcuqVauoqalh5syZdOvWDQBHR0cmT57Mzp076devn52jF0KIhkmiaqW01pSVleHl5cUPP/zAsWPHbEN9FRUVbNy4kUOHDjFmzBgGDRpEYGAgRUVFdO7cmYcffhhXV1cGDBhg72YIIcQ1SaJqpepm8fXq1YvKykrAWvZoz549bNiwgcjISNsaUQaDgaeeegqtNQ4ODjg6yq9dCNF6yP9YrVDds06urq5kZGQAkJ2dTXx8PAaDgYceeoigoCAA/Pz8MJvNKKVwdna2W8xCCHGjJFG1MhaLhffeew+TyYTJZKKsrIz169dz9OhR2xpRBoOB2267jbKyMtLS0oiOjsbJycneoQshxA2RRNWK7N27l927d1NVVYXZbGbHjh0kJSXRv39/5s+fj6urK+7u7kyfPp2wsDCMRiOnTp3i4MGDTJgwod4SHEII0VpIomqB6obqDAYDVVVVfPLJJ5w/fx6TyQRY136Kj4/Hw8ODxx9/nE6dOuHl5YXBYGDOnDkEBgYC1qHB2267jVOnTuHm5mbPJgkhxA2TRNXCaK15//33UUrh4+ODg4MDBQUFgHWyxNq1azl16hQTJkwgKioKT09PvL29mTlz5hVLHcXFxREXF9fczRBCiFtGElUL88knn1BSUgJAcXExACaTia1bt7J582aGDBnCPffcY5sYcfvtt0siEkK0aZKoWpjc3FwurmifmZnJqlWr8PPz46mnnqJbt25UVFTg6elJWVmZTJIQQrR5kqhakLoVdsHam1q9ejW5ublMnDiR3r17A1BRUUFMTAzDhw/H1dUVb29ve4YshBBNThKVne3bt4+dO3fStWtX9uzZQ01NDSkpKWzfvp24uDhmzJiBs7Mzzs7ORERE4OLiwp133tmm1qQRQoiGSKKyg8TERHJzc/Hw8GDnTuuakadPnyY9PZ3Vq1cTFBTEs88+i4+PD3379mX69OkopewctRBC2IckKjtITU2ttxR8QUEBq1atoqSkhHvvvZeYmBiCg4MZMmQIoaGhkqSEEO2aJKpmYrFYKC4uJj09HRcXF4xGI0ajkaSkJHbv3s3IkSMZNmwY/v7+PP/88/YOVwghWgxJVM1Aa80HH3xAUVERBoMBs9l82RpRAwcOZObMmTKLTwghLiGJqhlUVlZSVFQEQE5ODqtWrcJoNDJv3jyGDx9OVFQUPXv2tHOUQgjRMkmiagLHjx8nOTmZSZMmsWTJEi5cuEBlZSUbN24kLS2NMWPGMHjwYGJjY5k8ebK9wxVCiBZNEtUtVlZWxtGjRzl58iTnzp3j/Pnz7N69mw0bNtC7d2/mzZuHh4cHHh4eTJo0yd7hCiFEiyeJ6hb7/PPPyc/PZ8aMGSxbtoyPP/4YpRRPPvkkgYGBODg4MGrUKHr16oXBYLB3uEII0eJJorpFampqOHbsGEVFRdTU1PDYY4+RkZHBuHHj6N+/Py4uLrz++uv2DlMIIVodSVS3wNatW1m9ejUWi4UdO3aQmJhITEwMzz//PB4eHlRXVzN48GB7hymEEK2SJKqbUFpayvLlyzlz5gxZWVnEx8fj5ubGY489Rv/+/Xn22WfJzs5mx44dxMbG2jtcIYRolSRR3YSkpCT27NnD2rVrycrKYsaMGbz00ksopQgKCgIgODiY4OBgO0cqhBCtl9zNb4SysjJ27NjBqlWr+OKLLzCbzVRXV7N8+XI++ugj4uLiiI+Pp0ePHpw8eZLo6Gh8fX3tHbYQQrQJ0qNqhLpFCw0GAxaLhV/84hckJCTg5+fH3LlzefDBBxk6dCguLi5ERETYO1whhGhT7JKolFJvAVOBauAY8ITW+rw9YmmMmpoa+vbtS3p6OkuXLiUnJ4d//vOfjBw5ko0bNxIaGoqjoyNDhgyxd6hCCNHm2KtHtRb4ldbapJR6E/gV8KqdYmlQaWkpmzdvJi0tjeTkZJ5//nlefPFFvLy8AJg1a5adIxRCiLbNLolKa73mordbgfvtEUdDKisr2bRpE3v37uWDDz6gb9++7Nq1i5CQEHuHJoQQ7YrSWts3AKVWAEu11l9cZf8zwDMAQUFBgxMTE5slrsOHD/PHP/6R3NxcFi5cyOTJk9t8JQmj0dguVg6WdrYt7aWd0PbaGhERsUtrfc17Jk3Wo1JKrQM6X2HXr7XWy2uP+TVgAr682nm01ouARQBDhgzR4eHhTRDtv5WVlfHHP/6Rv//97wwdOpQ//elPBAYG0qtXrya9bkuQmZlJU/98WwJpZ9vSXtoJ7autF2uyRKW1Ht/QfqXU48AUYJy2d7cO65pRS5cu5eWXX2b06NHs27fPtlx8VFSUvcMTQoh2y16z/iYCrwCjtdYV9ojhYgcOHGDBggUUFxezePFiRo4cCUD37t3JzMy0c3RCCNG+2eumy/uAF7BWKbVXKfV3ewRx/vx5XnjhBcaOHcsDDzzArl27bElKCCFEy2CvWX92HWS1WCx89tlnvP7660yZMoVDhw7RsWNHe4YkhBDiKtpdZYqdO3cyf/58tNb8+OOPDB061N4hCSGEaEDbnm99iT//+c9MmTKFZ555htTUVElSQgjRCrSrHtW0adP42c9+JgVjhRCiFWlXiSoyMtLeIQghhLhO7WroTwghROsjiUoIIUSLJolKCCFEiyaJSgghRIsmiUoIIUSLJolKCCFEiyaJSgghRIsmiUoIIUSLZvcVfq+HUiofyGrmywYABc18TXtpL22VdrYt7aWd0Pba2kNrfc2K4K0qUdmD+v/t3VuopWUdx/HvF1OcKJrQMNHpQA7JMNR00TBl3dgUU0QygYcuUulwNYJBYIUFCXVhRkSUhJREMBlBHmIixxkYEjxvbDs5jubhxpFEKKUsSab5d7HesaXo7LXH9e7n3Wv9PrBhvft517N/D3u/+7/fw34eXZhkqeRZMC9jzThny7yME+ZrrONy6S8iIgYthSoiIgYthWpp17UOsILmZawZ52yZl3HCfI31JblHFRERg5YzqoiIGLQUqgmo16gPq/vVm9S1rTP1QT1fPaAeUWfuySJ1m/qI+pj69dZ5+qJerz6jPtg6S5/Udeo+9aHu5/by1pn6oJ6s3qs+0I3zqtaZVloK1WT2ABur6n3AX4BvNM7TlweBzwK3tw4ybeoJwE+ATwIbgM+pG9qm6s0vgG2tQ6yAw8BXq2oDsAXYMaPf0/8A51bV++YF3BkAAAO0SURBVIFNwDZ1S+NMKyqFagJVdVtVHe427wbObJmnL1V1sKoeaZ2jJ5uBx6rqiap6Efg1cF7jTL2oqtuBv7fO0beq+mtV3d+9/idwEDijbarpq5Hnu80Tu4+5ergghWr5vgD8oXWIWLYzgCfHtg8xg7/U5pX6LuADwD1tk/RDPUFdBJ4B9lTVTI7ztbyhdYChUPcCb3+Vpiur6pZunysZXW7YuZLZpmmScUasJuqbgN8CX6mqf7TO04eq+i+wqbs/fpO6sapm+h7kuBSqTlVtPVa7einwaeBjtYqf6V9qnDPsKWDd2PaZ3ediFVNPZFSkdlbVja3z9K2qnlP3MboHOTeFKpf+JqBuA64APlNV/26dJ47LfcB69d3qScBFwO8aZ4rXQRX4OXCwqn7QOk9f1LcdfdJYXQN8HHi4baqVlUI1mR8Dbwb2qIvqT1sH6oO6XT0EfAj4vbq7daZp6R6GuQzYzeim+2+q6kDbVP1QbwDuAt6rHlK/2DpTT84BPg+c2x2Xi+qnWofqwenAPnU/oz+49lTVrsaZVlRmpoiIiEHLGVVERAxaClVERAxaClVERAxaClVERAxaClVERAxaClXEMqinjD0K/bT61Nj2SVP8OlvVm5fY50vqD5fZ76FZnf0/ZldmpohYhqr6G6MZrFG/DTxfVd8f36f7R1Sr6sjKJ4yYPTmjipgC9axuXaSdwAFgnfrcWPtF6s+616epN6oL3TpDx1yyQd2i3qX+Sb1DXT/W/E71j+qj6jfH3nNJ1/eieq2aYz1WrZxRRUzP2cDFVbWgHuvY+hHwvaq6u5v1exew8Rj7HwQ+WlWHu+m8vgNc2LVt7t77InCfuovRxMnbgQ9377mO0ZRRvzr+oUW0k0IVMT2PV9XCBPttZTS90dHtt6prquqF19h/LfBL9T2v0ra7qp4F6O5pfYTRcf1BYKH7Gmt4+RInEatKClXE9Pxr7PURwLHtk8deC2zuFnCcxHcZFaRr1bOAW8faXjkHWnX9X19V35qw/4hBy3XriB50D1I8q67v7g9tH2veC+w4uqFuWqK7t/D/JUkufUXbJ9S16hsZrVh8R9f/BeqpXf+nqO847sFENJZCFdGfrzGarf1ORisKH7UDOEfdrz4EfHmJfq4GrlHv5+VnaTCaTfsW4AHghqparKo/A1cBe7sZt28DTnvdo4loJLOnR0TEoOWMKiIiBi2FKiIiBi2FKiIiBi2FKiIiBi2FKiIiBi2FKiIiBi2FKiIiBi2FKiIiBu1/kB0njz0DwhgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval.tfrecords'\n",
    "args.log_dir = 'log/ba16'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=55.3\n",
    "args.W_scale = 22.0 \n",
    "args.mu_shift = 0.34\n",
    "args.mu_scale = 0.27 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-197400', \n",
    "                                           nboot=99, \n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "63cvcd7ITAKW"
   },
   "source": [
    "## BA/ER\n",
    "\n",
    "With this exemplary results, you can note that despite there is a non-negligible bias, the correlation between true labels and model predictions is really high. Therefore, such a model can be tuned to a particular network using a small number of additional labels. This property is useful for the following procedure of transfer learning:\n",
    "\n",
    "1. Given a trained network, compute the prediction.\n",
    "1. Collect a few delays in a particular network under study.\n",
    "1. Prepare a simple linear model that corrects predictions obtained form an MPNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vWtpJv0vTAKX",
    "outputId": "ffdf03d3-6363-4bee-fdcb-4c1e5fa3635c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Loading checkpint: log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Restoring parameters from log/ba16/model.ckpt-197400\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>11.3472</td>\n",
       "      <td>-21.9837</td>\n",
       "      <td>0.8494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.5073</td>\n",
       "      <td>-20.2154</td>\n",
       "      <td>0.8631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>11.6236</td>\n",
       "      <td>-18.5622</td>\n",
       "      <td>0.8748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_er.tfrecords'\n",
    "args.log_dir = 'log/ba16'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=55.3\n",
    "args.W_scale = 22.0 \n",
    "args.mu_shift = 0.34\n",
    "args.mu_scale = 0.27\n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-197400',\n",
    "                                           nboot=99, \n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iD58yoA0TAKa"
   },
   "source": [
    "The result is likely to be biased because the mean and standard deviation of the delay in this network are not the same as for training samples for BA graphs. We can check this hypothesis by using the true values of mean and standard deviation obtained from Erdos-Renyi networks. The predictions are better, but still biased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "utZUUnubTAKc",
    "outputId": "f8dc824a-be65-4456-8a33-31ad9010cb48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Loading checkpint: log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Restoring parameters from log/ba16/model.ckpt-197400\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>8.7502</td>\n",
       "      <td>-8.5974</td>\n",
       "      <td>0.7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.0614</td>\n",
       "      <td>-7.8326</td>\n",
       "      <td>0.7191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>9.2678</td>\n",
       "      <td>-7.0411</td>\n",
       "      <td>0.7364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_er.tfrecords'\n",
    "args.log_dir = 'log/ba16'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=69.3\n",
    "args.W_scale = 15.95 \n",
    "args.mu_shift = 0.199\n",
    "args.mu_scale = 0.12 \n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-197400',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w1CD8PG0TAKe"
   },
   "source": [
    "## ER/ER\n",
    "\n",
    "In general, the model trained with use of Erdos-Renyi networks generalizes far better in comparison to the models trained with Barabasi-Albert networks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "px1iuX6yTAKf",
    "outputId": "1ece42b3-58fc-49a6-c918-cac4bb10e03e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Loading checkpint: log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Restoring parameters from log/er3/model.ckpt-199700\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>0.0176</td>\n",
       "      <td>0.9802</td>\n",
       "      <td>0.9937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0188</td>\n",
       "      <td>0.9817</td>\n",
       "      <td>0.9943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>0.0204</td>\n",
       "      <td>0.9831</td>\n",
       "      <td>0.9948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_er.tfrecords'\n",
    "args.log_dir = 'log/er3'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=69.3\n",
    "args.W_scale = 15.95 \n",
    "args.mu_shift = 0.199\n",
    "args.mu_scale = 0.12 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-199700',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JyERIaEFTAKl"
   },
   "source": [
    "## ER/BA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C4IP-K7xTAKm",
    "outputId": "568540a3-c040-48be-b6fc-7bb8b5b5b58f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Loading checkpint: log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Restoring parameters from log/er3/model.ckpt-199700\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.9328</td>\n",
       "      <td>0.9745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.1157</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>0.9769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>0.1251</td>\n",
       "      <td>0.9418</td>\n",
       "      <td>0.9786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval.tfrecords'\n",
    "args.log_dir = 'log/er3'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=69.3\n",
    "args.W_scale = 15.95 \n",
    "args.mu_shift = 0.199\n",
    "args.mu_scale = 0.12 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-199700',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YhRG9sa1TAKq"
   },
   "source": [
    "## ER/ER 60\n",
    "\n",
    "The model based on the Erdos-Renyi networks generalizes to a larger network size, never present in the training sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aoJM9h10TAKs",
    "outputId": "3599b0ff-816b-46b8-9279-61924e45f8ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Loading checkpint: log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Restoring parameters from log/er3/model.ckpt-199700\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>0.0951</td>\n",
       "      <td>0.9067</td>\n",
       "      <td>0.9636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.1146</td>\n",
       "      <td>0.9244</td>\n",
       "      <td>0.9715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.9369</td>\n",
       "      <td>0.9773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_er60.tfrecords'\n",
    "args.log_dir = 'log/er3'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=69.3\n",
    "args.W_scale = 15.95 \n",
    "args.mu_shift = 0.199\n",
    "args.mu_scale = 0.12 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-199700',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_IhWlwmaTAKw"
   },
   "source": [
    "## ER/ SNDLib $n\\in (20,38)$\n",
    "\n",
    "Here, we evaluate the model with the tests using some real network topologies retrieved from SNDLib. The node range $n\\in (20,38)$ is most common in the whole training set (the details are provided below in the notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BH6fOQr_TAKx",
    "outputId": "b0f48931-fc67-4802-a49f-4aebac160d24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Loading checkpint: log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Restoring parameters from log/er3/model.ckpt-199700\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>0.0671</td>\n",
       "      <td>0.9066</td>\n",
       "      <td>0.9752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0725</td>\n",
       "      <td>0.9142</td>\n",
       "      <td>0.9776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>0.0777</td>\n",
       "      <td>0.9219</td>\n",
       "      <td>0.9803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oQKUoyh2jvLycP/7xj6xatYpRo0bx+9//vtUglZyczL59+7jvvvvw9fVly5YtlJeXExUVha2tbbvfd9dddzFp0iRtbVVrnJ2defzxx3FxcWlxXErJ7t27cXNzu+4AebtRXX+Kotz2mveIioyMpLa2lr1799KvXz8aGhq0a3Jzc/nhhx8wmUxUVVVRVlbG2rVrWblyJdOnT2fGjBktglRqairV1dU0p6HbtGkTf/3rX6mtrW3x7ri4OGJiYtosX8+ePenWrVuLYw0NDSQkJLBnz54brf4tT7WoFEW5rSUnJzN37lzKysr44IMPmD59OgC/+93vtHEjgMTERM6cOUPPnj0ZPXo0Q4cOZc+ePbi6urY6RX337t3069eP+vr6Tsk+0Tzh4mZmtrBWKlApinJbKi4u5ne/+x3ffPMNDz/8MF5eXgQHB5OZmUmPHj2uyGh+3333ERUVhb+/P0II7OzsGDdu3FWf/8gjj1BUVKQFkqlTpzJ16tQOrUNgYGCHPu9Wpbr+FEW5rZhMJv75z38SFRWFg4MDJ0+e5IEHHkCn05Gbm8vy5cs5dOjQFfd169aN/v37tzsTure393WNVyk/n2pRKYpy29i7dy8LFy7Ezc2N77//nr59+1JXV8fIkSPp0aMHX3zxBT4+PnTr1o23336buLg4RowYAVgmL0gpW3QHKtZBBSpFUW45ZrOZtLQ0goODcXFxIS8vj9/85jfs3LmTt956i0cffRQhBI2Njbz99tv4+voSFRWFXq8nJiYGNzc3amtrtckUSUlJHD16lPz8fBYvXoyrq2sX11C5nApUiqLcMs6cOaPl0NuwYQPe3t4UFxfz3nvv8fTTT5Oeno6zszNg2SZ+27Zt+Pj4cNddd5GcnKxtLti/f/8WkykSEhKora3F1dVV257DbDZjb2/frnLV1NTg6OhoFRso3o5UoFIU5Zbx5Zdf0tTUxK9//WsaGhr4wx/+gL+/P/v27aN3794UFhbS2NiIh4cH5eXlpKSk0KtXL+rr6ykvL0en05GSkkJAQAADBgwAYNeuXXh7ezN27FiqqqpYtmwZ7u7uVFZW8sorr1wx6y4nJ4edO3cyYcIEAM6dO8eKFStUgtlOpAKVoihWr6mpCRsbG+Li4jh8+DDDhg2juLiYadOmERgYSFpaGrt37yY3Nxd3d3deeOEFsrOzAQgICCAxMRE3NzcqKipwdXXFz89Pe3ZmZibZ2dl4e3uza9cuADw9PXFzc2t1oW5OTg6ZmZnk5eXh7OyMi4sLvr6+V6yDUjqOClSKoli1EydO8OWXX+Lk5MSuXbvYtWsXgwcPZvXq1eTn55OQkMDZs2epqakBICgoiOLiYk6dOoUQgoyMDMLCwqiurqaiooLKyko2bNjAggULAHjsscdoaGjA3t6e6OhonJycmDp16lVn9MXFxdGzZ098fHw4e/Ys3t7eV6RFUjqWClSKolg1e3t7MjIy2Lx5M/3792f+/PmMGjWK8PBwQkJCaGxsxMnJibKyMs6ePUtMTAw//PADp06dws7Ojvr6eubNm8eWLVvIz89HStki27mtra0WlKKjo4mOjm6zPDqdDl9f306ts9KSClSKolitjIwMXnzxRTIyMli1ahWTJk2isrISJycnAAwGA+PGjeODDz6gsLCQX/3qVzg7O5ORkQHAiBEjGDJkCAC9e/emqqqKurq6a6Y0upra2lr0ej12dnYdU0GlXdSCAUVRulRVVRVJSUlkZWWxceNGqqurqamp4ZVXXiEmJoYBAwZw7NgxJk2aBICrq+sVWSUmTpzIoEGDMBgMAIwcOZLZs2czZMgQ7dpNmzaRnp7O7NmzaWpquu5ymkwmlixZwieffHKDNVaul2pRKYrSpfbv38/+/fvx8fGhoKCAc+fO8fbbbxMREcHTTz/NsGHDOHz4MAEBAaxevZohQ4YwYsQIpJScOXMGHx8fysrKSEpKoqKigpkzZ2JjY0NYWFiL9zzwwAPU1dWRkpLC9u3bGTt2LIMGDbpq66g52WzzlHOdTkfv3r2vyHKudD4VqBRF6VKDBg2itraWHTt2sHPnTmxtbZkxYwbu7u706tWLpKQkzGYzffr0ob6+nsbGRsCyKeGaNWswGAwsWrQIV1dXTp8+zfr16/H19aVnz578+OOPDBw4kODgYEJCQgA4deqUlrmioqKCyZMnt1quDz74AJ1Op02UEEIwY8aMm/OlKC2oQKUoSpfS6XQkJCSwatUqXnnlFV577TXS0tIoKSlpsWVGWloakyZNIi4uDgBfX188PDzw9PTExcWFRx55hC+++IITJ05w4sQJcnNzOX36NA4ODgQHB2vPCQ8Px9vbm9WrV+Pt7X3Vctnb22sLghMTE3FwcNDerdxcXRqohBCfAlOAQinl3V1ZFkVRbi6z2czKlSt57bXXuP/++zl9+jQ+Pj5UV1cTGRlJfHw8xcXFmM1mXF1dqaysZP/+/RQWFjJlyhQcHBxYvHix9ry77rqLefPmkZaWxo4dOwgICGDo0KF07979ineXlZVRUlJCXl7eVcs3d+5cwDI2lZCQgIuLiwpUXeSqgUoIkQrI1k4BUkrZrwPevwx4H1jeAc9SFOUWsX37dt544w3q6urYsGGDNjMvPT2dtWvXEhMTQ0pKCr6+vsTFxTFy5EhOnjzJ5s2bSU1NZcqUKa0+183NDR8fH3Q6HdnZ2VrC2Z8KCQnh0UcfJSAg4Jpl1ev1zJ8/X5uoodx8bbWoWv+T0IGklLuFEMGd/R5FUaxDSUkJb7zxBt9++y2TJ08mIiICHx8f7bzBYMDHx4e0tDQAbGxsSEpKIi4uTmsZNefyu5ru3buj1+txdHS86jVCCCIiItpd7tZaZcrNI5pntrR5kRBBQJiU8jshhANgI6Ws6pACWALVt1fr+hNCzAfmA/j7+w9MTEzsiNd2OKPReFusrbhd6gGqLtbEZDKxdu1a3nvvPSZMmMBTTz2Fvb09JpMJHx8f9Ho9dXV1lJeX4+bmRk1NjbZ5YW1tLR4eHtja2tLU1IROp7uurTjKy8sBcHd37/B63eq/L826qh5hYWGHpJSx17rummNUQoinsQQKT6AXEAD8Cxh7o4VsDynlh8CHALGxsTI0NPRmvPa6ZWRkYK1lux63Sz1A1cVafP3117z44ovY2dkxa9Ys5syZg8Fg4JtvvtECzpAhQwgODiYpKYlevXpx7NgxxowZQ15eHklJSXh4eLQYjwJLMlhnZ+cWLbKfklLy1ltvodfrefnllzu8brfy78vlrL0e7ZlMsQCIAw4CSCnPCCGu/idDUZQ7QlNTE+fOnSMkJISmpiYaGxtbrDHKz8/ntddeY/PmzYwcOZJBgwZhNBoxGo0cP34cOzs7jEYjQghOnz5NSUkJVVVVHDlyBL1eT0ZGBufPnycwMPCKbrra2lpWrFiBt7c3zz//PADr1q0jPz+f559/noKCArKyshgyZAiLFi26qd+L0vHaE6iMUsqG5kVvQggbWp9koSjKHeTQoUNs27aN8ePHc/z4cS0wVVdX89///d+sXbuWuXPncubMGQ4cOMCJEye0NVAXL17EZDIBlvGi+vp6oqOjqa+vJzIyksGDB3PhwgU+++wzdDodQ4cObfFuBwcHxowZg6enp3bMbDZrz0xISCAjI4Pg4GD8/f1v0jeidJb2BKpEIcRvAQchxHjgeWBTR7xcCLEGGA10E0LkAH+QUqr8JIpyCwgLCyM/P5/w8HDq6+vx8PBgxYoV/OlPf0Kv1/P6669TV1fH0aNHOXjwoHafwWDAycmJyspKLbFs//79sbW1JSIigjNnzlBbW0uPHj2YPXs2Xl5e2j5R0dHRjBw5EiHEFTP6Zs6ciZQSIQTjx48nMjJSTYK4TbQnUL0GPAWkAs8AW4CPO+LlUspZHfEcRVFuPldXV4KCgsjLy0NKyccff8zBgwdZsGABcXFxHDlyBLDM9Js8eTLbt29Hr9cjpcRoNPLSSy/h4uLCd999R1JSEv379yc/P581a9bQv39/HnjgAS0NUmlpKaWlpVRXVwNw8OBBbG1ttc0PmzX3/Pj4+LQ6dlVdXY3BYLgtJkDcSa4ZqKSUZiHE51jGqCRwSrZnqqCiKLe1TZs2kZKSwv79+9m3bx+LFy/mueeeIzY2lvz8fC1QpaamEhkZyaBBg8jOzsbNzY2wsDBMJhMNDQ3s27cPIQS7du1i4cKFxMbG0rdvXy1Lul6vx9PTk9/97nfo9XrMZjPbtm3D0dHxikDVFqPRyN/+9je6d+/O008/3Vlfi9IJ2jPrbzKWWX5nsSz27SmEeEZKubWzC6coinUyGo2sX7+ebdu24e3tzccff8zs2bP54osvWL9+PRUVFTg5OWnTzNetW0fPnj3JycmhsrKS48ePYzKZtLVOer2epqYmrfV18eJFlixZQmxsrJaLr3m3XSEEc+fOvSKD+rVUVFRgZ2fXKdPUlc7Vnq6/t4F7pZQZAEKIXsBmQAUqRbkDnT17lsWLF5OcnMy0adMIDg5m5MiRmM1mioqKqK2tpU+fPhQVFdHQ0MDAgQPJysqiZ8+eZGZmYjabAcuU6MbGRm2L+GHDhmmJY11cXAgMDMTBwYE///nPTJgwgdjY/yy3CQwMvO5yV1ZWUl9f32Z+P8U6tSdQVTUHqUvOAR2y2FdRFOtXWFjId999R2hoKF9++SWfffYZc+bMISYmBg8PD/r27Yu/vz+pqamUlpYClll9wcHBODk5MX78eP7v//6P/Px8hBCYTCZiYmIIDAzEw8MDIQQHDhxg0KBB2hiTi4sLM2bMYNmyZVpgA8u6KLPZfN2tKYDQ0FBefPFFXF1dO+aLUW6atnL9PXTpx2QhxBZgHZYxqhlA0k0om6IoXcxkMpGdnc3XX39NfHw8ISEhHDlyBBsbG5YuXUpVVRVjxoxBp9MRFBREWFgY586do7y8nLi4OPbu3UthYSEDBw7k4sWL9O7dGyklsbGx+Pr6YjKZMBgM9OjR44p319XVUVpaSmxsLLm5uaSnp+Pp6cmhQ4dYuHAhHh4e110fNze3jvhalJusrRbV1Mt+LgBGXfq5CHDotBIpitKlmne/zc/P580332Tv3r1cvHiR6dOnExERoSVyfeCBB2hoaECv11NTU8Pu3bsZNWoU9vb25Obmkp2dzfHjx/H29qa0tJTs7GwefPBBSkpK6N69O2vXruXMmTO8/PLLreblS09Px87OjsGDB7NhwwZKS0vx9/fHxcXlZ7WolFvXVQOVlPLJm1kQRVG6npSSd999FyklZ8+eZfny5YwcOZJ58+ZRXFzcIiFs//79qaysZPny5eTn51NXV4etrS3l5eWUlpbi5+fHY489RlBQECaTierqajw8PCgpKQEsufe8vb21SRLNjEYjKSkp1NXVaQt4586di9lsxtbWljFjxty8L0SxCu2Z9WePZR1VH8C++biUcm4nlktRlJto3759nDx5kokTJ3Ls2DE2bdrEAw88wB/+8Afq6uq09Uvl5eVkZWWxY8cOJkyYwO7du8nMzARg5MiR3HPPPRQUFJCWlsann37KgAEDyM3N5fDhwzz5pOXfvkajka1btzJ+/PgrghRYNkiMj49n2LBh/Pa3v9XGrZQ7V3tSEK8A/IAJQCKWpLRqMoWi3OLq6+tpamoiPz+fHTt2kJSUxKhRo9ixYwe/+MUvCA8Pp3///tq1BoOB0NBQtm/fTm5uLgUFBWRk/GeeVW5uLkuXLsXX15ejR48ihMDb25ucnBzKy8tZv349X3zxBVVVVfz4449ay+qnoqKiGDt2LLGxseTk5Ghdkcqdqz2z/kKllDOEEA9IKT8XQqwG9nR2wRRF6TyZmZmsXLmSgIAA7Ozs2Lx5M+np6SxevBg3Nzdqa2sxGo1kZ2fTv39/jhw5QmNjI4MGDWLlypX06tWLHTt2aM/T6XSUlpZSWVlJfHy8NpU8ODiY+Ph4HB0dyc7OBiyplx5//HF8fX1bLZu9vT3Dhw8nNTWVDRs2MGrUKEaPHt3p34livdrTomq89N9yIcTdgBugsqcryi1KSsmKFStoamoiISGB+fPnA/DOO+/g6uqK0Whk3rx5BAUFUVxcrGWYAKiqqsLf35+AgABMJhO2trY4OztjNpuprq7GbDZz+PBwa1t6AAAgAElEQVRhevfuzbhx4/Dx8WHEiBH07dtX20fKaDTSs2dPrSzNiW5MJhObNm3i6NGjAAQEBBAREUFwcDDnzp1rMU1dubO0p0X1oRDCA/hv4BvAGfh9p5ZKUZRO0djYSElJCdnZ2WzevJlu3boxZ84c/Pz88Pb2RkpJTk4Oa9asoVu3btTX1wP/SSR7+vRpLl68iKOjoza1vHn8qjkzuhCCZcuWMWbMGEaMGKFNfpgwYQIbN25skWfv66+/5tSpU7zwwgvaJIqSkhKio6Px8PDg0UcfZefOnezZs4cHH3yQfv363eRvTLEG7cn115yANhEI6dziKIrSmdasWcOSJUvIzMxkzJgxDB06lMbGRhoaGmhoaODixYsA1NTUtJgC3rdvX1JSUrCzs8PR0ZGioiL+67/+i4aGBvLy8sjOzubs2bM8+OCDeHh48MUXX7Bz506ioqLw8vICLAFs+vTpLca1DAYDDg4OCCFwd3fn6aefvmJBbkREBOXl5QQFBd2Eb0ixRm0t+G1zO0wp5d86vjiKovxcVVVV2NvbYzAYMJlMZGVl4e/vT3JyMgcPHsRoNPLWW28xZMgQJk6ciL29PTU1NdoaJoPBgE6nw9bWFiklAQEBnDhxAoBjx44Blq66wMBATp8+TXx8PPPnz2f79u2UlpYSERFBUFAQbm5ujBs3jqSkJFJSUhg3btxVZ+5NmTKlxefW9o7y9/fnoYceuuK4yWRi165d2kJj5fbVVovKpY1ziqJYkYqKCt555x3Cw8OZOXMmKSkpbNmyBQ8PD1JSUti6dSuhoaH88MMP/PjjjzQ0NFBXV0d5eTm1tbUEBgaSnZ1NU1MTJpMJKSU9evRg9OjRrFq1ioqKCnx8fJg0aRI9evRg3bp1WmaImJgYSkpKmDp1KiaTCbPZjI+PD3l5eeTl5TF8+HAcHG48R4DRaGTv3r3cfffd+Pj4UFpayt69e8nLy1OB6jbX1oLf/7mZBVEU5eezt7cnJCRES0UUEhKC2Wzm888/59y5c0yePJmlS5fi4ODAl19+CfynSw3QZuQ99NBD6HQ6tm/fzrZt23jiiSd44oknSE1NZdiwYeh0lvlXM2fOBCytuISEBJqamhgwYAArVqzA19eXoKAgbG1tiY2N7ZAgBZaZinv27KG2tpYpU6bg7e3NL3/5S7p169Yhz1esV3smUyiKYuXs7Ox47LHHaGhowGg08uGHH/LWW28xbNgw9uzZo40Dffvtt9o9VVVVeHl5abP1HnroISIiItixYwe1tbUALFu2jICAAPz8/Ni7dy/Dhw9v0Y138OBBmpqatO0zunXrRllZGTk5OSxatAhPT09KSkpYs2YNQ4cOJSYm5mfXMSwsjGnTphEaGqodu/xn5falApWi3OIqKyvJz8/nhx9+YOfOnezcuRNXV1deeOEF7OzsqKuro76+ngsXLnDo0CEAnJycyM3NBSz7PDU1NeHm5obZbObYsWNa6iKAnJwccnJyAMs41pAhQ7Rz0dHRGI1GRowYgYuLC/Pnz+fixYsUFRVpXYN1dXWUlJRQUVHRotxpaWmkpKQwffp0XFyuPdKg1+uva6NE5fahApWi3OK2bNnC/v372b59O4WFhdpi2h49enD27Fnef/99bG1taWhoAMDR0VH7GeCee+7BxcWF9PR0Tp8+TXV1tbZHFICtrS12dnYIIejdu3eLd3t7e2sbGzbr3r078fHxnDhxglmzZhEQEMBrr72Gra1ti+vOnz+vZVpvT6BS7lxq1p+i3MISExP58ssv2bFjB/fddx8TJ07EwcGBjIwMmpqatMW0zYHJ29ub4uJi7bijoyNjxoxh9erVnDlzRtv9tqKigl/96ldkZWXh5+enTTH/qbKyMrZv386wYcNabGZYXl7eYmyqee1UUVGRdmzChAkMGTLkqs9WlGbtmfUXDgzCstgXLNt//NiZhVIU5UrV1dVUV1fj5+eHlJKvv/6aefPm0b17d5KTk1m3bh25ublaBoisrKwrnlFUVISzszO2traUlpYSHR0NwMSJE4mOjsZgMLB+/XoaGxupqamhT58+VzxjxYoVVFdXEx4eTrdu3Th16hS+vr5aoBJCsHjx4ivuO3v2LCtXrmTqVMsOQjY2NipIKe1yzVl/QojdQIyUsurS5z9i2Yr+hgkhJgLvAnrgYynlmx3xXEW5HX355ZdcuHCByZMn8/rrr3Pu3DkeeeQRYmNj+eKLL7RWko+PD/n5+YAlB9+gQYMwmUzU1dWRlpZGbW0t1dXV9O3bl2HDhiGlxNPTE09PTwoLC7UME59++ik6nQ4XFxfmzZundd1VV1dTVlbGnj17ePbZZ3nqqafw8/NrUdbm2YGX8/LyIiQkpEVmCkVpj/aMUfkCDZd9brh07IYIIfTAP4DxQA6QJIT4Rkp54kafrSi3o6amJvbs2cP777/P3Llzueeee9Dr9dpEh2aXT1owm80EBQWxbt06vLy8GDp0KBcuXCAnJ4fi4mI2bNjAuXPn8PLyolevXmRlZdGjRw/q6uq0LsL6+npqamq0QDV16lQ2b95MSEgIPj4+7d6Gw93dnTlz5rTITKEo7dGepLTLgR+FEH+81Jo6CHzeAe+OAzKklOeklA3AF8ADHfBcRbFaR48e5V//+helpaVXnDObzVRVXbmDTl1dHRs2bODVV1+lpKSEpUuX8vvf//6q65Pq6urw9vbWPu/duxeAkpISUlJS6NWrFw4ODjQ0NHDu3DntXF5enrYTr4ODA1JKoqOjWbBgQYtt3wsKCsjPz2ffvn1cuHDhur8Ds9l8RXBVlLZcM1BJKf8MPAmUXfr1pJTy/+2Ad98FZF/2OefSMUW5bRUXF1NQUEBNTc0V5zZv3szf/vY3EhISSE5ORkrJjh07iIqK4uOPP+bZZ59l+vTpnD9/nlOnTjF79uyrvqc5USxYJjyAZfZefX09Qgh+/etfM3fu3BYpjKKionj22WdxcHDAxsaG8PBw+vXrd8WC2piYGCZOnEhQUFCLANZeZWVlfPLJJ1dMV1eUqxHN/dptXiTEcCBMSvmZEMIbcJZSZt7Qi4X4BTBRSjnv0uc5wGAp5cKfXDcfmA/g7+8/MDEx8UZe22mMRuNt0fd+u9QDrLcuZrO51TGcwsJCTCYTOp2OiooKli9fTnx8PHPnzmXWrFnaIlywrClqztXXGiGENmal0+lwdXWlpqaGxsZGHBwcMJvNuLq6YmNjoyWkdXJyor6+XstWYWtri5eXF1VVVej1ei0n4I2qq6ujoaEBNze3DnleV7LWP2PXq6vqERYWdkhKGXut69qzFf0fgFgss/8+AwzASmDYDZYxFwi87HPApWMtSCk/BD4EiI2Nlda6Ej0jI+O2WCV/u9QDrL8ux44d48CBA8yYMQMPDw+MRiMXLlzg2LFjvPXWW0RERPD000/j6OhIbW0tO3fu1O61t7envr4ef39/LeO5jY0NdnZ21NbW8tJLL3Hq1CkSExOJi4tj//795OXlAZYgZzKZCAgIICAgAE9PT2JjYxFC0NDQwKFDh8jNzSU0NJTAwEDefPNNvLy8WLhwYav1uF4ZGRn07du3Q57V1az9z1h7WXs92jOZ4kFgAJACIKW8KIToiNV5SUCYEKInlgA1E7h6X4ai3GYKCgrIy8vTuvI2bdrEsmXLuOuuu3j55ZdbLMr9qeZ9opqDFFgmWzQ1NeHn54e9vT2bN1sm5zYHuEmTJmE2m3F2diYrK4tevXqxdu1abGxsSEpK4rnnnsPW1pa4uDjy8vK46667EEIwb9487O3tr1mfyspKnJycWmwPoigdoT2BqkFKKYUQEkAI4dQRL5ZSNgkhFgLbsUxP/1RKmdYRz1YUa5eZmUl6ejqTJk1i7dq1xMfHc+bMGcaNG8eoUaO08RudToeDg0OLiRM6na7N3W579+6NjY0NQ4cO5fTp09TW1jJ48GC2bt2Kn58fzzzzDHfffTcAzzzzTIv8fwAHDhzgu+++Y9q0aQwYMIC77rr20HF+fj5Lly4lJiZGWyelKB2lPYFqnRBiKeAuhHgamAt8fI172kVKuQXY0hHPUpRbQfMWGmfPnqWoqIjly5fz2WefMWjQIBYuXIi9vf0V08tramqoq6sDLGNPrQUpg8GAn58f2dnZnDlzhnvvvZfx48czfvz4Fs/x8fFpcZ+fnx/z5s1rcaxnz5707t2bgICAdtfL2dmZwMDA67pHUdqrPTv8viWEGA9UYhmn+r2Uckenl0xRbhENDQ3s2LGD8PDwNvv5v/rqK44dO0afPn3YvHkzu3btQqfT8cQTT9C9e3d69+7NyZMnr7iveUPD5rGln/L29qaoqAgfHx+ys7OprKzk/Pnz+Pv7YzQaWb9+PaGhoUyaNEm7p7a2lvXr1+Pr68uECRNaPM/f359Zs2Zd8Z7S0lJycnLo27fvFWunnJ2dmTt37jW/K0X5OdozmeIvUsrfADtaOaYod7yioiKSk5OpqalpNVA1NjbS1NRERkYGlZWVLFmyhPT0dMaOHUtUVBRCCEwmU6tBCtCmi7/wwgvs3buXY8eOUVdXR9++fUlNTcVoNOLi4qLN2qqpqeHzzz/H0dERGxsbKisrqaqqwsnJiSNHjjBjxgy++uorMjMztVmDhYWFfP/994wePZru3bu3Wo4dO3aQnp6Oh4dHi7x+itLZ2tP1Nx74aVCa1MoxRbmj7Nq1ix9//JGnn36aOXPm4OPjQ2VlJdXV1S22VF+2bBnZ2dns37+fvXv3Ehsby5tvvtliIkRbKisraWhoYMmSJVy+nCQ1NZVp06Zx5swZsrKycHV1xd7entDQUJydnamoqMDGxoasrCzKysr48ccftTVckZGRGAwGpk2bBsCFCxc4ffo0wcHBVw1Uw4YNw8/Pr9Xt4hWlM7WVPf054HmglxDi2GWnXIB9nV0wRbF2UkpMJhNCCEJCQqioqOCTTz6hqqqK5557jrNnzxIWFsbZs2f5+OOP8fT0ZN68eXh5ebUapJpny5lMJm2PKEALDFJKvLy8kFJSV1eHi4sLgYGBREdHU11djaurK4MHD77iuZmZmaxcuZKCggLGjh2Ln58ffn5+DBo0SLsmJibmmkGoeTq7otxsbbWoVgNbgf8DXrvseJWU8sr8L4pyB2hqasJsNmNra8uYMWMYM2aMdi4xMZGqqio8PT05fPgwW7duZfv27RQVFXHfffcRHh7e5rObF+aWlJRoQUoIwcWLF4mIiMDV1ZUJEyYQGhrKsmXLuHDhAlJK9u/fz3fffcfMmTNbfYetrS1msxkhxFUX7dbX1+Pi4tLqQmRF6WptZU+vACqEEO8CpZdlT3cVQgyWUh68WYVUFGvxySefUFZWxquvvoper6ehoUHLJHH48GHAsj7q4MGDbNiwgSFDhvDwww9jMBiueFbztu3NEyQaGxsxGAxaVglbW1saGxu1iQuVlZWsXr2aBQsWcOHCBWxsbPj+++/x8vLCz8/vqpke/P39mTFjBv7+/tp+U5c7f/48X375JbW1tbz++utXbHCoKF2tPWNUHwAxl32ubuWYotwRunfvjpOTE5WVlbi6uvK3v/0NDw8PHnnkEaSUnDp1im3btnHXXXfxySefEBISQkJCgrZA93LFxcXaz0IIbGxsCAgIID8/H3t7e37zm98QHx/P/v370el0hISEEB4ejpeXF1OnTkWn07Fx40aioqJ45plnaGxsJDU1ld69e7dIhyOEIDIy8qp1SkpKora2ltDQ0FYDqqJ0tfYEKiEvG8GVUpqFEGoLe+WONG3aNL766ivee+89nnjiCXx9fSkrK+Pdd99l5cqVVFRUMHXqVHr16kVmZianT5++6rOa8++BJZg0NjZy/vx5DAYDUkq+/fZbpkyZQnR0NBUVFQwb9p+sZTExln8n+vn5aa2kQ4cOsX37dsaOHcvw4cO1a00mE3//+9/x9PTk8ccfv6IcEydOpF+/fvTu3bvdW3Yoys3UnoBzTgixGEsrCiwTLM51XpEUxfqYzWY2bNiAt7c3J05Ytkz7/vvvycnJIT4+npSUFEaMGEFcXBw2Npa/Vs0bEDa7PKOEg4MDzs7OVFZW4ujoyIABA9i7d6/WyjIYDFRWVrJr1y5GjRrVYvuP2tpa9Ho9dnZ2LTYsjIyMpLy8/IpdeYUQ2NnZXbW15OLics3xM0XpSu0JVM8C7wFvABL4nkvZzBXlTtHQ0EBaWhpeXl6YzWaklGzZsoUdO3YQHBzM888/j4tL6ykwm5PKXp5RwmQyaTP/fvGLX+Dg4KDtG2VjY0N0dDSpqamcOXOGpqYm3N3daWpqQgjBO++8g6ur6xVJYt3c3Jg4ceIV79fpdDz33HMtjjU1NaHX61ULSrkltCczRSGWhLGKcsfJz89n5cqV2v/QS0pKsLOz45NPPqG+vp6HH36YoKCgNp9x9913U15eTkBAAHv27KGxsVFrben1eoKDg/nrX/+qXT9t2jRyc3MxGo2Eh4eTm5urZUt3cnKid+/eN7TlRl5eHh9++CEjR47k3nvv/dnPUZSbpa11VL+WUv5/Qoi/Y2lJtSClXNypJVOUmyQ3N5cDBw7Qr18/fH19cXV1BSwb/C1dulS7zmg0snPnTlJTUxk9ejSxsbHtms596NAhTCYTPXv25JFHHqGoqIjz589r41cFBQXahobdunXj7rvvpk+fPqSlpXHq1CkiIyPx8fHB2dkZsLTArsfGjRuprKzkscceQwiBra0tbm5u2vOklBw4cAB/f/9rBl1F6Qpt/S1rzueSDBxq5Zei3BZOnTrF8ePHWb16NWvWrNGOZ2dbNqC2sbEhNTWV9957j6amJhYsWEBcXFy71xw1Tz/fs2cPoaGh3HPPPcyaNYtevXphMplYtWoVvr6+DBkyhPnzLb3qOp2O0aNH4+LiQlhYGHq9nqysLNauXXvNnXGbNyZsVlBQwMWLF7WuRy8vL1588UVtwW9JSQnx8fEt9rtSFGvS1jqqTZf++/nNK46idK7c3FySk5MZO3as1qIYMWIEOp2Oc+fO0a9fP8xmM/Hx8QQEBFBWVsZXX32FyWRi9uzZ7dryojV6vZ4BAwa0OPboo4+ybds2UlJSsLOzw9PTk7/85S+Eh4drraaqqirKyspwcXHh9OnTpKen069fv6uumWpsbOTtt9/Gx8eHKVOm4OjoyNy5c5FSXnWfKC8vLx588MErMqsrirVoq+tvE610+TWTUk7rlBIpSidKS0vjyJEjhIaGarPjDAYDaWlpFBcXM27cOJKTk9m5cyeJiYmcOHGCsWPHEh0dfdUWlLOzM+Hh4Rw6dGVHg6enJ6WlpZhMJnJzLRtY19XVYW9vj8FgYOrUqQwaNIhvv/2WzZs34+DgoI0/RUVFUVNTQ3R0NEVFRdx7771ERka2GSz1er2W6++jjz7S9p9qixCCfv36tev7U5Su0NZkircu/fchwA/L9vMAs4CCziyUonSWUaNGERISQkhICNu2baO0tLRF5ohly5aRnJzM999/T79+/ViwYEGLTQtbU11dTVFRUavn7OzsWLRoEUuXLqWwsJB//OMfFBcXM3jwYG2GXl1dHbm5uXh7ezNjxgy8vb0ByxT2kSNHApYM7c0Lgtui0+mYOXMmUkoMBoNqJSm3hba6/hIBhBBvSyljLzu1SQiR3OklU5ROYGtrq02WOHPmDOXl5Xz99dfk5eWRlZXFli1bsLe357/+67/w9fVt1zN1Oh0XLlwgJCSE4uJiKisrtXN5eXkcOXJEW+9UUlKCwWCgW7du1NTUUFhYSFBQEI8//jh+fn7XDIrtJYS4Yp8pRblVtWcdlZMQIkRKeQ5ACNET6JDt6BXlZjp+/DjJyclkZWVx7733YjKZMJvN/Pjjj+zYsYOsrCzuu+8++vTpc831RT179iQzMxNAm6Rw7tw5YmNjSU5ObrG49+TJkyxYsEC7tnm8aP369aSlpfHUU0/Rs2fPTqy5otza2hOoXgIShBDnAAEEAW13eiuKFSksLOTgwYMcOXIEs9mMXq/n/PnzVFdXs2/fPvbs2cPAgQNZsGBBixx5bcnPz+eBBx5g48aNgKWLr7GxkfT0dO2zu7s7RqOR+++/X7svMTGR3bt389xzz9G/f3/s7OxU95yiXEN7FvxuE0KEARGXDqVLKY2dWyxFuTFSSsxmM8nJyZSXl5OSkqKdM5lMfPfdd2zduhV3d3eeeuopunXr1ubzmjOad+vWDTs7O4qLi6murtbOu7i4aMdsbW1paGjAzc2NRx99VHvnP/7xD2pra7Gzs9MmPbS1db2iKBbXXAgihHAEXgUWSimPAj2EEFM6vWSKcp2klEgpqaqq4s0336SkpITNmzdr66EAysvLWbt2LZs2bWLcuHH88pe/vGqQuny7i+buuieffJK4uDiamppajCeZTCbtc1xcHGazmfT0dC1remJiImVlZRiNRjw9PfHy8uqMr6CF8vJytm3bRnl5eae/S1E6U3tWLH4GNAD3XPqcC/w/N/JSIcQMIUSaEMIshIi99h2Kcm2ffvop77//PmCZcq7X63F2diY/P5/GxkYSExNZunQpvr6+LFiwgIiIiFbHopycnBg/fry2aLb5GpPJhL29PcnJyZhMJtzd3XnssccIDg5m0qRJ1NXVERYWxsiRI5kyZQojR47UuhKbk8g+9thjzJ49G4CsrKx2b0f/c5w4cYKDBw9qSXQV5VbVnjGqXlLKR4UQswCklLXixjNZHscy7X3ptS5UlNZUVFRw4sQJBg4cqLV8jEYjlZWVLF++nIcffpiLFy9SVVXFqVOn2L59O35+fsyfPx8PD482n11fX09VVRWxsbEcPXoUX19fSktLCQ0N1SZJNHf/FRcXc/78eQYOHMjrr7+ubXzYvA1Hs1GjRjFw4EAcHBzQ6/WYTCaWLVuGk5MTr7zySqd8R7GxsSozunJbaE+gahBCOHBp8a8QohdwQ2NUUsqTl551I49R7kBms5nvv/+e4uJiTp8+jaOjI9HR0ZSWlqLT6WhqaqK4uJjly5cTFhbGqlWrKCsrY/Lkye0eDzKZTBw6dEjLVl5aWkr//v2JjIwkISEBs9mM0Whk27ZtTJkyhQcffJDIyMirZn4Ayzqof/7zn/Tv358HHngAvV7PxIkT2z154+ewtbWlb9++nfZ8RblZ2hOo/gBsAwKFEKuAYcATnVkoRbma4uJi9u3bh6OjI87Ozlrw+fHHHykosKxDb+7mW7JkCXFxccycOVPbIwrQMpH/lBACvV6vbYExYMAAdDodBw8eJCMjg3379uHu7k55eTkODg4EBwdz9913U1tbS3l5eZvjTo6OjgQGBuLv768dGzx4cEd9LYpyWxOXbd575UlLkycAqAWGYJmefkBKWXzVm/5z73dYMlr81O+klBsvXZMAvCKlvOoCYiHEfC7tf+Xv7z8wMTHxWq/uEkajsVP/dXyzWHM9pJTk5+djMBi0NVAuLi5IKTGZTNTW1rJz504++OADoqOjeeWVV1pdQOvq6tpiUe7lmmf3NS/KbWhooKSkBHt7e8AyfmU0GqmursbV1RWDwUBJSQlg2aa+s1jz78v1UnWxPl1Vj7CwsEM/SSjRqjZbVFJKKYTYIqXsC2y+ngJIKcddz/VtPOdD4EOA2NhYaa3TeTMyMm6LqcbWUI/CwkLc3d1bzLqD/7SUPD09SU1NbXGuoKCArVu3UldXx/33309wcDAODg6tZgQfPXo0Ukp++o+egIAARo8ezZkzZxg3bhwZGRl88803uLu7M3bsWIQQHDp0iJKSEoYPH05kZCTp6ens3LmT0NBQRowYccW7du7cyQ8//MDzzz9/zSnwbbGG35eOoupifay9Hu3p+ksRQgySUiZ1emmUO97Fixf56KOP6NevHw8++GCLcwaDgTFjxrBixQrtWH19PQkJCRw7doxRo0YRGxurjRX9dAx08uTJxMfHk5CQoAXBcePGcfjwYUpKShg6dCiurq44Ojry+eefYzAYtH9p1tfXs2HDBtzc3DCZTERFRWFjY0Pfvn0JDQ29auojW1tbnJyc2r0liKIoV2pPoBoMPCaEOA/UYOn+k1LKn51uWQjxIPB3wBvYLIQ4IqVUickUPDw8CAgIoLy8nNraWi2T+IULF1i/fj1VVVUIITCZTBw7dozvvvuO3r17s2DBApycrszsZTAYtN104+PjtZ89PDwoKCggMDCQwsJCSkpKOHfuHLt27dISzA4YMIBf/vKX9OjRg8rKStzc3AgKCmLKlCktxrzays83fPhwhg8f3mHfj6LcidoTqDo8gEgpvwK+6ujnKreukydP4ujoSFBQEN7e3hw+fJj8/HwyMzM5fvw4lZWVWu683NxctmzZgtlsZubMmVfNKC6lpLGxkR49enDhwgUaGxu1MSh3d3diYmKwsbHB3t4eOzs7kpMtQ6V6vZ5nn30WDw8PrXXW2NhIRUUFx44dIyAgQNt0UFGUztfWflT2wLNAKJAKfCKlbLpZBVPuHA0NDaxbtw53d3eefPJJTp06Rd++famvryc5OVmboVdbW8vu3btJTU1l7Nix9O/f/5pdah4eHri4uGifXV1daWho4NSpUzQ2NrJ169YW19vZ2eHi4tIiSIFlosQjjzxCeno6YWFhLe45ceIEP/zwAzNmzLjmGi1FUa5fW3/LPwdisQSpScDbN6VEyh3H1taWadOmcf/991NbW0tdXR3l5eXs37+f+vp6oqOjOXToEO+//z5ms5mFCxcSExPTrnGf5l15m3fEraiooKGhQdsGo3nvJ4PBwIABAxg4cCADBw5Er9ezfft2Pv30U627MDIykv+/vXuPqrrMHj/+3txBEFQQZRRRg0JIQFFTNLxrZlqaWWtmSjMbHW8tV9pYTdM0TY3Tb1rWtBr7jtWqGStvad4qswt5QUcwkYuKd0ERVC6KwOHi8/sDOGEqAoLnoPu1lmtxOJ/zOfs5Ctvn+TyfvR966CF8fLmgrNwAAB65SURBVHwue4+kpCSysrLYtm1bI38ySimofemvW9VuP0TkfeB/NyckdTuKjIzk/Pnz1np9Fy9eBCqvTS1evBh3d3drzybgsjYaNXl7e1NQUGB9vHnzZs6cOYPFYrHeP+Xh4cHUqVPJzc3l/vvvp0OHDjg6OlJcXMzf//53XFxcuOeeezhz5gynTp2ioqLC2ljxagYOHEhhYSHh4eHW76Wnp5OVlWVtc6+UarjaElVZ9RfGmHKtIqEaw9GjR/nvf//LmDFjiIiIACp3+v30008kJCTg4uJC27ZtOXz4MJs3b+bIkSMMHz6c8PDwy3bxXS1JQeWMqfo6FFQ2R6w+1tnZmWeffRZnZ2ecnZ1ZtGgRDg4OvPDCC3z33XckJSUBWFu933vvvZw5c4ajR48SGhp6zTG1b9+eqVOnXva9LVu2kJmZSURExBUzMKVU/dSWqCJEpPquSAHcqx5X7/pr2eTRqVvOsWPHMMaQnZ3NwYMHSUxMtNbkExGKi4v54Ycf2LJlCz169OCZZ56ptTTRLwUFBXHs2DHra4KCgvDx8aFjx46EhYVRXl5OVlYWgYGBjB492pr8iouLuXjxItOnT7dWmKiuHfjll1/i7+9P69atAcjMzGTp0qWMGDGCyMjIq8YxduxY8vLyNEkp1Qhqa0Vf998OStXRzp07McawY8cO4uPjgZ/baRw+fJiNGzfi7e3Nk08+iZ+fH4GBgWRkZFBbBZWay4B33nknwcHBhIeHc/r06Stuwl27di0pKSlMmTKFqKgo6/dHjRrFyJEjcXR0xGKx4ODgQHBwMH379iU+Pp6LFy9aE1VFRQUlJSWUl197b5Gvr+8N3eCrlPpZXbanK9VoxowZQ1JSEsYYDh48iKurK9nZ2WzatIlTp04xYsSIy9pvnDhxwvramkt6NdVcBkxKSuJ3v6tsQJ2Tk3PZcevWrSMzM5Pu3btbN1EA5ObmUlZWhr+/PydOnODDDz9k8ODBDBgwgGHDhhETE3PZPVqdOnXipZde0qLKSt0kepVX3TTFxcWsWLGC9PR0zp8/j7+/v7VHlJ+fHzNmzCA0NPSaCaBmknJ0dCQsLMy6xNey5c8r0Vu3bmXlypVXvL6wsNBaYqlmXbP//Oc/LF68mPLyclxdXXF3d7d27xWRq95IXJ8kVVpaar2JWClVfzqjUk2qoqKCY8eO0alTJ06cOIGjoyNubm5s3bqVjRs30rZtW6ZPn463t/c1l/c8PDwoKiqyPo6MjOS+++4jLy+PgwcPUlFRgb+/P76+vhw5coScnBwuXbpEly5dWLhwIZMmTcLf358RI0ZQUlJyRfHNe+65h8LCQhwdHfHw8KC4uNg6k1u/fj1OTk6MHDmywZ/Bxo0bSUpKYurUqZdVT1dK1Y0mKtUoysrKOHfuHO3atePChQt8/fXXhIWFkZ+fz6ZNm/D29iYmJoacnBy++uorzp07x6hRo4iIiLgsCV1Nt27dOH36NOXl5ZSUlJCbm8vixYtxdna23hPl4uJCVlYWUNkKvqysDAcHBywWi3VpcOnSpeTm5rJgwYLLCt7WbLfh5eXFb37zG1q2bIkxhuTkZFxcXG4oUd1xxx0UFxfrxgqlGkgTlWoUX331Fbt372bSpElYLBZSU1NJTU0FKmvhnTlzhvnz57Nz505iYmKYOnUqRUVFBAUF4ebmxu7du6963gkTJlBWVsavfvUrAgMDWb16NRaLBRGhX79+nDhxgszMTFJTU611Afv27YuIsG/fPsaNG0f79u1ZuXIlFouFvn371npPVHVr+OprWLNmzbrha1Hh4eGX3WOllKofTVSqUYSEhHDx4kV8fX3x8PBgzJgxfPvtt0DlNu933nmHwMBApk+fTsuWLa2zqLS0tKsmgtjYWPz9/QkNDeW1116jrKzMejOvo6MjISEhHD9+nOjoaLp3705ubi4hISFUVFTQsmVLMjIysFgsnDlzhvDwcOvMa9CgQbUmnhUrVpCfn2+ddXl6ejbNB6aUqjNNVKpR3Hnnndx5551A5TLghg0byM3NZe3atZSVlfHqq68iIpSXl19WOQK46rWp7t274+Pjw+eff054eDh5eXlkZmYyadIk1qxZg8ViYd++fbi6ujJixAgCAwNJT0+3Vq7o2LEjRUVF9O3bF4DHHnsMuP4miKFDh1JYWHhFLyyllO1oolI3rKCggJUrV9K3b1+6detGamoqmzZtIjExkXvvvZdevXrRr18/1q9fD0DXrl3JzMzEYrHg7u5OcXHxZefz9/fn008/JTIykuTkZNq3b8+jjz5KWloaDg4OTJgwgYyMDHr37k1QUBAAWVlZfPbZZ4SEhDBx4kRrW/nqdhx1Xb4LCwtrvA9GKdUoNFGpOjPGkJaWhsViYevWrbRu3ZoOHTqQm5tLZmYm3333Hd988w0vv/wynTt3ZubMmdbrRgkJCdYbc4cNG0Z+fj6ff/45xcXFODo6UlFRAUCXLl04duwYly5d4scff2TWrFm4u7uzdetWtm/fDvxcz++3v/2tdQdf27Zt6devHwEBAbz++utERUUREhJimw9KKdWoNFGpOsvOzmblypX4+fmRl5dHXl4eR44cwRhDeXk5//znPyksLOThhx++okfU2bNncXd3JyQkBA8PD9avX09paSleXl707NnT2uuppKTEukuvtLSUc+fO4evri7e3N/379+f06dP4+vri5eVFp06dANi/fz9Hjhxh5MiRFBUV4e7ufsWGia1bt5Kfn8/999+vN+oq1cxoolJ15ufnR2xsLDk5ObRp04bc3FyOHTtGfHw8u3fvZvDgwURFReHm5oazszMlJSU4ODjg6emJiDBw4ECOHz/Ohg0byMzMpGfPnpcljjFjxvD2229bK1C0aNGCTp068fnnn3PgwAGmTJlCTk4OO3bsYMaMGdabfb/44gtKSkpo3749UVFRzJ07F4BDhw5ZY09KSuLs2bMMHz78iutPp06dIj4+niFDhugWcqXskCYqdV0lJSWsXLmSu+66i5iYGF577TUcHR3ZuXMncXFxjBw5kvnz51s3RZSWltKqVSueffZZvv32W7Zu3QpUXkdKTEzEzc0NgOTkZDw9PRkwYIA16Tz11FPs27ePoqIioqOjcXZ2xsnJieDgYNq1a0e3bt1wcXG5rBLFsGHDSE1N5a677rrmGJ544glKS0uvukniwIEDpKSkcMcdd2iiUsoOaaJSV4iLi6OwsJBRo0YhIly8eJHDhw/j6upKQEAAfn5+/O1vfwMqGxPefffdBAYGcvToUfLy8oDK61n/+Mc/CA4OJioqij59+tC2bVvS0tIoKCjg7rvvJjk5mbi4ODw9PTl16hQdOnSgR48efPfddxQVFRETE0NJSQmpqam0bdsWJycnIiIirO1BqvXo0YMePXrUOqbatpn379+foKAg61KiUsq+aKJSV0hKSiI/P58RI0bg5OREmzZtmDNnDsePH2fcuHGkpaUxdOhQunfvjohw9uxZzp49C/xcONbHx4ecnBxOnDjBzJkzreeeMmUKpaWltGnThoiICNasWcOGDRuAymtNPXr0sN7k6+DggLu7O7/73e+ss7Cm4OzsTOfOnZvs/EqpG6OJSl1h8uTJlJeXW7d2b968mffff5+1a9cSGRnJRx99xN69e4HKkkPVM6Hdu3fj4eFBaWkpY8eOJTU1la5du1JaWoqzszOrVq0iLy+PKVOmABAYGEhxcbE1uVUni+ot59Wq741SSt2ebJKoROQN4AGgFDgMTDbG5NsiFnUlLy8v69dr1qxh2rRptGjRgsmTJxMUFMSBAwfw9fWlrKyMcePGERgYCFTWtDt37hxOTk44OjpSUFDA2rVrSU9PJzY2lsLCQgoKCjDGWLebT548mbS0NA4fPszo0aOvGo8xhu+//x5fX1+6d+9+Uz4DpZT9sNWM6htgQVWL+4XAAuA5G8WiriIzM5Nnn32WH3/8kdjYWEJDQ+nevTvJyclAZVX0vn37snHjRp544glrPb93330XFxcXRo8eTUpKCjk5Ofj4+NCyZUueeOIJa3ffxMRESkpKyMvLo2fPngwbNuyasRQXF7Nlyxb8/f01USl1G7JJPypjzCZjTHV71B1Ah9qOV02ntLSU/fv3W2+4tVgs/PnPfyYiIoKQkBDS09Pp1auXtb5edZv28vJyMjMzycnJobi4mCVLlvDee+8B0KJFC+vNvCLClClT6NGjByKCg4MDp06dIj8/n8GDB7Nq1SqWLVtWa4weHh5MnjyZRx55pGk/DKWUXbKHa1RPArX/plJNZvv27cTFxfHAAw+QkZHB7Nmz8fDwYPr06dY28AEBARw8eJBVq1bh6upK69at8fLyIjY2lokTJ1JWVsbJkycBaN26NaNHj+bjjz9GRKy79WoKCwujrKyMnj17YrFYrMmvNtXLi0qp249cq1ndDZ9YZDNwtavgLxhjvqg65gUgGhhnrhGIiDwNPA0QEBDQMy4urknivVEWi+WKhnz2Li8vD2MMWVlZvPXWWxw8eJD58+cTHR2Nk5MTJSUleHp64urqSn5+vrWobE2enp54eXlRUVGBxWLhwoULuLi4WHf+OThcOWm/cOEChYWF+Pj44O7u3mTja45/J9eiY7FPt8pYbDWO4ODgRGNM9PWOa7JEdd03FpkE/A4YYoypvXNelejoaJOQkNCkcTXUoUOHuOOOO2wdRp0ZY3j99deJi4sjISGBadOmERAQQFRUFO3ataNLly4UFBTg5eWFg4MD5eXlnDt3juzsbFavXm09T6tWrZg9ezZlZWUsX76cQ4cO4enpSVFREb179+bo0aNMnDiRVq1aWV+TnZ3Nrl27GDhwYJ3baBhjOHDgAIGBgdb6gdfT3P5OaqNjsU+3ylhsNQ4RqVOissk1KhEZCcwHxtQ1SanGY4xh9erVvPPOO+Tk5PDkk0/i7u7O2bNncXFxoUuXLta+TtUzonXr1rF48WLWrVvHuHHjAOjQoQPjx48H4Ny5c9Z/7NOnT2fWrFkUFBSQnZ3N22+/fVlrD39/f0aPHn3NJJWens6bb77J0aNHrd87dOgQy5Yts/a4UkrdPmx1jeodwBX4pqrO2w5jzDQbxXLLO3HiBMuXL+e+++7DwcGB2bNnk5WVxXPPPUd+fj6tWrWyJpLS0lK2b9/O5s2bCQ8Ptyal6tJCrq6uhIeH065dO1q2bImrqytbtmwhMTGRyMhIhgwZgpubG4sXL8bT05OBAwdy5MiRei0rlJSUcOHCBUpKSqzf69ixI7169dJdf0rdhmySqIwxzX+u3IxUVyFfuHAhGzdu5MUXX2TGjBmcP3+e5cuX06pVK2vpIx8fH3766SeMMTg5OVmrTgwcOBAnJyfy8/MpLy/Hz8+P7OxscnJyiI+Pp7i4mJSUFB544AFEhNatW+Pu7k5sbCyxsbH1ird79+5069btsk0Ybm5ujBo1qlE/F6VU82APu/5UEzLGsHPnTj7++GOGDRtGSkoK7dq1o6Kigh07dpCTk4PFYgHAyckJJycnJkyYwIULF2jTpg0ffPABGRkZ3H///Rw8eJCMjAzatGlDv379+PLLLzl+/Di//vWv+eGHHzDGICKICJMmTapzjBaLhZMnT9K5c2drJfVf7hRUSt2+9LfBLWzPnj3MmjWLoqIiawfeavHx8SQkJODr64uzszPnz59n2rRp5OXl4eLigpeXF9nZ2fTp04eMjAw2btxI165dAayVywcNGsTJkyfp2rXrDV2I/f7779m5cycTJ06stQK6Uur2pInqFpSbm8tLL73EihUreOWVV3jqqaesbTSqBQUFERISwrBhw9i9ezeOjo44OztjjOHo0aN88sknlJeXM2XKFMaMGcNXX32Fv78/jz32mHWDxbFjxy67jtRQYWFhlJSUXNFsUSmlQBPVLcNisVBaWspHH33Eq6++aq1yfq2baTt06MBjjz0GwPDhwzHG8MYbb9C/f39atWpFeXk5np6e1nbzUVFRV5wjMTGRwsJCdu3axYgRI+jVq1eDYu/YsSMdO3Zs0GuVUrc+TVS3iD/+8Y8sXboUBwcHxo8fz9SpU2ndujUJCQn4+Pjg5OTEZ599Rv/+/enfvz9QuaW8erfe6tWr8fb2xtXVldDQUFq0aEFhYWGt7TWmTJnCqVOnWLVq1c0aplLqNqSJqpnLyclhwYIFrFmzhkGDBnH33XcDlUVji4uL2bBhw2W7+i5dumR97d69e4mPj8cYw+nTp4mIiMDb2xuASZMmUV5eftXKEtW8vb3x9vbmxRdfbMIRKqVud5qomqny8nLeffdd/vKXv/D4449z5MgRa3uO6p13ABMmTMDFxcW6DT06Opo33niD0NBQhg4dyq5du9i5cyfz5s3Dzc2NI0eOAODr62uzsSmlVE2aqJqhuLg4Zs2ahZ+fHz/88ANhYWHW54wx7Nu3j7Zt2+Lr60u3bt0AeP755wEoKvq5EIibmxt9+vShpKQEd3d3a3JTSil7oomqGcnMzGTevHls27aNN998k/Hjx1+RXM6ePcuKFSuslSNGjhxprbN38uRJvL29mTdvnvX4a92Me/78eZYsWUJUVBSDBg1qukEppdR12KTWn6ofi8XCwoULiYiIoGvXruzbt4+HH374iiRljGH79u0EBATg5+dHeno6WVlZQGWl9CVLltR548OlS5coKiqy3gyslFK2ojMqO/f1118ze/ZsgoOD2blz5zVvrK2oqGDbtm3s2bMHgMcff5x77rmH9u3bA5U36fbs2bPOfZ18fHx4/vnndTlQKWVzmqjs1NGjR5k7dy7JycksWrSI0aNHX/PYs2fPEhcXR0pKCn5+fvTu3RsXFxe2bt2Ki4sLDz74II6OjrWeo6bU1FQ6d+5c53YaSinVlHTpz84UFxfz8ssvEx0dTXR0NCkpKddNMPHx8aSkpHDXXXfx8MMPEx0djTGGY8eOkZmZWa/3LykpYeXKldhrg0ql1O1HZ1R2whjDmjVrmDt3LtHR0fz00091XqYbMGAA7dq1IyoqylrM1cHBgTlz5tR76c7V1ZU+ffoQGRlZ7zEopVRT0ERlBw4cOMDs2bPJyMhgyZIlDBkypF6v9/HxuWr5oqv1gLJYLCQnJxMWFnbVNvAiwsiRI+v1/kop1ZR06c+GLly4wHPPPUdMTAwjRowgKSmp3kmqvpKSktiwYQP/+9//mvR9lFKqseiMygaMMXz66afMnz+fIUOGkJycbN2d19TCwsK4ePGiLu0ppZoNTVQ32d69e5k1axbnz59n2bJlxMTEXPU4Ywz/+te/8PDwqFcTwutp0aKF3sCrlGpWdOnvJsnLy2P27NkMHTqURx99lISEhGsmKaWUUj/TGVUTu3TpEh9++CEvvPACY8eOJS0trU4FX0WE3//+9zchQqWUsm+aqJrQrl27mDlzJg4ODmzYsIGePXvaOiSllGp2bLL0JyJ/EZG9IrJHRDaJSIAt4mgqZ86cYerUqYwZM4bp06ezbds2TVJKKdVAtrpG9YYxprsxJhJYD7xkozgaVXl5Oe+88w7dunXD09OT/fv3M2nSpFqbDyqllKqdTZb+jDHnazxsARhbxNGYdu3axfjx42ndujXff/894eHhtg5JKaVuCTa7RiUifwUeBwqAZrtf+tSpU8yfP59vv/2WRYsW8cgjj2jFcaWUakRiTNNMZkRkM9DuKk+9YIz5osZxCwA3Y8yfrnGep4GnAQICAnraS7HU0tJSPvroI/79738zceJEnnzySWuDwubMYrFctfRSc6RjsU86Fvtjq3EEBwcnGmOir3dckyWquhKRQGCjMea6a2XR0dEmISHhJkRVu02bNjF79mw6d+7MW2+9RUhICIcOHbpmr6jm5FYZB+hY7JWOxf7YahwiUqdEZZOlPxEJNsYcrHo4Fthvizjq69ixY8ydO5c9e/awaNEiHnjgAV3mU0qpJmar7Wh/E5EUEdkLDAfm2CiOOikuLuaVV16hZ8+eREVFkZqaypgxYzRJKaXUTWCrXX/jbfG+9WWMYd26dTzzzDNERUWRmJhIUFDQTXv/kpISSktLadmy5U17T6WUsjdameIa0tPTmTNnDkePHuW9995j2LBhNz2Gjz/+mKysLP7whz/cEhdslVKqIfRO1F8oLCxkwYIF9OvXjyFDhrB3716bJCmAkJAQwsPDcXZ2tsn7K6WUPdAZVRVjDMuWLWPevHnExsayd+9eAgJsW9lp4MCBNn1/pZSyB5qogOTkZGbNmkVeXh6ffPIJAwYMsHVISimlqtzWS3/5+fk888wzDB48mAkTJpCYmHjNJGWMYd++fRQUFNzkKJVS6vZ2Wyaq6h5RoaGhXLx4kbS0NGbMmIGT07UnmJmZmSxfvpyvv/76JkaqlFLqtlv6S0hIYObMmRhjWLt2Lb169arT69q1a0dMTAzBwcFNHKFSSqmabqsZ1euvv87o0aN5+umniY+Pr3OSAnB2dmbo0KF06tSpCSNUSin1S7fVjOrBBx9k2rRpt0TxWKWUul3cVokqNDTU1iEopZSqp9tq6U8ppVTzo4lKKaWUXdNEpZRSyq5polJKKWXXNFEppZSya5qolFJK2TVNVEoppeyaJiqllFJ2TYwxto6hzkTkDHDc1nFcgy9w1tZBNIJbZRygY7FXOhb7Y6txdDLG+F3voGaVqOyZiCQYY6JtHceNulXGAToWe6VjsT/2Pg5d+lNKKWXXNFEppZSya5qoGs//2TqARnKrjAN0LPZKx2J/7Hoceo1KKaWUXdMZlVJKKbumiaqRiMhfRGSviOwRkU0iEmDrmBpKRN4Qkf1V41ktIj62jqmhRGSCiKSKyCURsdtdTbURkZEickBEDonIH2wdT0OJyAcikiMiKbaO5UaISEcR+V5E0qr+bc2xdUwNJSJuIvI/EUmqGsufbR3T1ejSXyMRkZbGmPNVX88Guhljptk4rAYRkeHAd8aYchFZCGCMec7GYTWIiIQCl4D3gGeNMQk2DqleRMQRSAeGAZnALuAxY0yaTQNrABG5FygEPjbGhNs6noYSkfZAe2PMbhHxAhKBB5vp34kALYwxhSLiDGwF5hhjdtg4tMvojKqRVCepKi2AZvs/AGPMJmNMedXDHUAHW8ZzI4wx+4wxB2wdxw3oDRwyxhwxxpQCnwFjbRxTgxhjfgRybR3HjTLGZBljdld9fQHYB/zKtlE1jKlUWPXQueqP3f3u0kTViETkryKSAfwaeMnW8TSSJ4EvbR3EbexXQEaNx5k001+KtyIRCQKigJ22jaThRMRRRPYAOcA3xhi7G4smqnoQkc0iknKVP2MBjDEvGGM6AkuBmbaNtnbXG0vVMS8A5VSOx27VZSxKNTYR8QRWAc/8YkWlWTHGVBhjIqlcOektIna3LOtk6wCaE2PM0DoeuhTYCPypCcO5Idcbi4hMAkYDQ4ydX8isx99Lc3QS6FjjcYeq7ykbqrqeswpYaoz53NbxNAZjTL6IfA+MBOxqw4vOqBqJiATXeDgW2G+rWG6UiIwE5gNjjDFFto7nNrcLCBaRziLiAjwKrLVxTLe1qg0I7wP7jDFv2jqeGyEiftW7ekXEncpNO3b3u0t3/TUSEVkF3EnlDrPjwDRjTLP8n6+IHAJcgXNV39rRjHcwPgT8E/AD8oE9xpgRto2qfkRkFLAIcAQ+MMb81cYhNYiIfAoMpLJSdzbwJ2PM+zYNqgFEpD+wBUim8ucd4HljzEbbRdUwItId+IjKf1sOwHJjzCu2jepKmqiUUkrZNV36U0opZdc0USmllLJrmqiUUkrZNU1USiml7JomKqWUUnZNE5VS9SAibaoq5O8RkdMicrLGY5dGfJ+hIrLmOsc8JSKL6nnezOZcDV/dnrQyhVL1YIw5B0QCiMjLQKEx5v/VPKbqhlAxxly68gxKqfrSGZVSjUBE7qjqT7QUSAU6ikh+jecfFZElVV/7i8jnIpJQ1Qvonuuc+x4RiReRn0Rk2y+qoHQSkTgROSgiL9Z4zRNV594jIu+KiP6sq2ZLZ1RKNZ67gMeNMQkiUtvP1tvA340xO6qqb68HaisEug8YUNUfbCTwKjCx6rneVa8tBXaJyHoqCwk/BPSres3/UVl66ZOGD00p29FEpVTjOVzHxoxDgTsrVwgBaCUi7saY4msc7wN8LCJdr/Lc18aYPICqa1r9qfy57gUkVL2HO5e3ClGqWdFEpVTjuVjj60uA1HjsVuNrAXpXNUKsi79SmZDeFZE7gK9qPPfLGmim6vwfGGP+WMfzK2XXdN1aqSZQtZEiT0SCq64PPVTj6c3AjOoHIhJ5ndN583Nrj0m/eG64iPiIiAeVVfu3VZ3/ERHxrTp/GxEJbPBglLIxTVRKNZ3ngK+B7VR25q02A4gRkb0ikgZMvc55FgJviMhuLp+lQWUbkC+AJOBTY8weY0wy8Gdgs4jsBTYB/jc8GqVsRKunK6WUsms6o1JKKWXXNFEppZSya5qolFJK2TVNVEoppeyaJiqllFJ2TROVUkopu6aJSimllF3TRKWUUsqu/X/gcXrPu2e8TQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_snd_2038.tfrecords'\n",
    "args.log_dir = 'log/er3'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=69.3\n",
    "args.W_scale = 15.95 \n",
    "args.mu_shift = 0.199\n",
    "args.mu_scale = 0.12 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-199700',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R-MFkQlzTAK2"
   },
   "source": [
    "## BA/SNDLib $n\\in (20,38)$\n",
    "\n",
    "The Barabasi-Albert networks are not useful for training if we would like to obtain high quality predictions for type of networks. However, we have noticed that BA networks provide a very interesting property. When you look at the clusters in the regression plots given below, you can note that each of five clusters is separately characterized with a large level of correlation (with a true label), although the overall correlation value is small. You can see that the 'within-cluster variance' corresponds to the variance of parameters for one particular topology; on the other hand, the 'between-cluster variance' represents the variance between topologies (__BA/germany50__ supports this claim).\n",
    "\n",
    "Given the general model and a particular network topology, one is interested in obtaining an accurate model for this topology. Such the model can be constructed by using a small number of training samples. Since the 'within-network correlation' is high, a simple linear model is sufficient to exercise transfer learning of the topology representation from the general training set. In other words, the model can be inaccurate for some topologies. However, change of other parameters (such as traffic) results in the error at a similar level, so it can be easily corrected.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zLPdQoaSTAK3",
    "outputId": "b2c24e83-4c13-4dc7-e22a-2bde6aa305d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Loading checkpint: log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Restoring parameters from log/ba16/model.ckpt-197400\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>1.4253</td>\n",
       "      <td>-2.6267</td>\n",
       "      <td>0.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.4945</td>\n",
       "      <td>-2.3634</td>\n",
       "      <td>0.5122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>1.5628</td>\n",
       "      <td>-2.1410</td>\n",
       "      <td>0.5519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_snd_2038.tfrecords'\n",
    "args.log_dir = 'log/ba16'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=55.3\n",
    "args.W_scale = 22.0 \n",
    "args.mu_shift = 0.34\n",
    "args.mu_scale = 0.27 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-197400',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "avyPt7BuTAK7"
   },
   "source": [
    "## ER/janos_us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IsYm2setTAK7",
    "outputId": "7f72902e-3b85-4ec1-922a-ac8ccdf27ef4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Loading checkpint: log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Restoring parameters from log/er3/model.ckpt-199700\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>0.0192</td>\n",
       "      <td>0.9434</td>\n",
       "      <td>0.9884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.9468</td>\n",
       "      <td>0.9893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>0.0222</td>\n",
       "      <td>0.9499</td>\n",
       "      <td>0.9900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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163fJZ76+vjz33HPV7ldSUsKxY8coLi6+rst1KKXq9AyXKCMP/AohmoTi4mJeeeUVQkJC6NOnDzExMdx99911Sh4uLi6MGTMGX1/fKu+bTKZLtq38cK+LiwszZ85k+vTp134ClQQFBfHiiy9Kb6qOJFEJIRq9NWvW0KtXL6Kioti/fz9//etfrcN0Fy5c4MyZM7W28cUXX/Duu+9iNpsBiI2N5dVXXyUyMtK6zfr163nttdfIzs4GICoqiuXLl3Po0KF6OCtRV5KohBCNVkJCAnfddRfPPvssH3/8MT///DOdOnWqss2//vUvvvrqK4qLiy/bjtFoxGg00rJlS+tDvxVLpm/ZssW6nYODAwaDgXXr1lFaWkr79u3p2rUrAQEBQNn091OnTl3fkxS1kkQlhGh0CgoKePHFFxk6dCi33nor0dHRjBkz5pLttNa4ubnRsWNHa+KpTl5eHjk5Odjb25OSkoLFYiEwMJChQ4dWKak0atQo/P39iY+Pp6CggDZt2jBr1iz8/Pwwm81s2rSpSmITDUMmUwghGg2tNf/+97955plnGD58OIcOHcLf3/+y25tMJhITE2nVqlWN96oqKlOkpaWxbNkyAGbPnm2t21fZvffeS35+Pt7e3qxZs4bU1FQeeughDAYD06ZNu+Q+l6h/kqiEEI3C0aNHWbRoERkZGXz33XeMGDHC+ll2djbp6elVShhprUlPT2fBggWXPBsFcPz4cby9vfH19cXe3p5p06aRmZlJXFwc6enp1iFAs9lMRkYGrVu3BsDZ2RlnZ+cqx624r9WrV696O39xeZKohBA2lZOTw//+7//y7bff8re//Y158+ZVKf4KZZMpTpw4gYeHB/fddx/t27fn1KlTfPvtt4SGhjJhwoQq2+fm5rJ8+XI8PT1ZsmQJFy5coHv37tjb29OpUyfy8vKsxWT/+OMPduzYwcyZMy95mHfmzJlYLJZL4hENS+5RCSFswmKx8M0339CjRw9yc3M5evQoCxcurDYphIaG4uXlRV5eHgUFBURGRlJUVET37t0vSS7nz59nxYoVeHh4kJuby/79+/nkk09Yv349AGFhYfz0008UFRUB0KlTJwIDA6sd0ru4Yrqwjcv+F1BKHQYuXS0MFKC11n3rLSohRLMWFRXFwoULMZlM/PLLLwwePLja7TZv3ozBYKB///7k5OTg7+9P27Zt+eGHH/Dz8+Oxxx67ZJ+cnBzS0tIYPHgwLVq0oFu3bhw/ftw6bDhu3DjOnTvH6tWrGTBgAF27dq0ypCgan5r+VLirwaIQQtwQMjIy+Mtf/sKqVat49dVXeeihhygtLeX999+nc+fOTJo0ybqt1prdu3fj4ODAyJEjmTNnDl5eXnh5eTF16lR8fHyqtG2xWEhISCAgIICnn34ad3d34uLiKCkpYebMmdbtunbtytGjR4mNjaW4uJidO3fSr1+/aqtWiMbhskN/WuszFV/lbwWV/3weyGyQ6IQQzYLZbOaTTz6hZ8+e2NvbExsbyyOPPIKdnR1aa4qLi0lPT2fnzp3WyhBKKebPn89jjz3Gnj17+P77763Ddb1798bPz6/KMeLi4lixYgVbtmzBw8OD7OxsfvzxR3799dcq22mt8fT0xNPTk379+nHq1Ck2btxIYmJiw1wMccVqvUellHoM+DewtPyt9sAv9RmUEKL52LlzJ6GhoSxfvpwNGzbwj3/8A29vbwoLC4GyWXbPP/88eXl5bNy4kZKSElJSUti+fTseHh7W2XkWi4UdO3ZY96tQUlJCdHQ07dq1o3///rRo0YLc3Fy8vb0ZNWoUI0eOrLL9wYMH2bp1K0OGDKFfv36MHj2agoICUlNTG+R6iCtXl7uEC4DBQCSA1vq4Uqp1vUYlhGjy0tLSeOGFF9i4cSNvvfUWM2bMsD7rdODAAVavXs3UqVPp3bs3SimmT59OYWEhTk5ObNu2jbi4ONLS0jh69Cg9evSgV69eHD58mJiYGBYvXkyLFi0A2Lt3L5s2bcLHx4cJEybw3XffcfLkSWbMmMHw4cMviat9+/YEBQVZK1zcfPPNBAcH07Jlywa7NuLK1GXWn1FrXVLxQillT/WTLIQQgtLSUt5991369OlD69atiYuLY/jw4aSlpQFlPSM7OztatWqFp6cnRUVFvPPOO0RGRtKpUydiYmIYNmwYEydOtA7vXbhwgbFjx9K7d2+cnJz47LPPOHXqFG+88QY5OTn4+vqSmZmJg4MDQ4YMYeDAgZw7d65KgdkKvr6+zJw509q2UqrWB4aFbdUlUUUopf4bcFFK3QH8BKyu37CEEE1ReHg4/fv3Z926dWzbto0333wTV1dXvvjiC77//nsAIiIiCAsL44477sDHx4ePPvoIo9EIlK0b9dNPP3Hw4EEGDBhgrd8XGhqKq6srU6dOZejQoRQXF2OxWLBYLDg6OjJ37lzr2lTjxo3j7NmzfPLJJxw/ftxm10JcP3UZ+nsBeAQ4DDwOrAWW1WdQQojGLyYmhtLSUvr160diYiLPPPMMe/bs4d1332Xy5MnWHorBYGDMmDHWag8VCxKePHkSR0dHjEYj/fv3Z+zYsZhMJoYMGUKfPn0AGDp0KF5eXphMJl5++WVCQkLo2LEjWmuKiop44YUXrMepGAoE6Ny5M2lpadZqE6Jpq7VHpbW2AF8D/wf8L/C1rq4/LYS4oaxZs4Z///vfvPrqq/Tv35+ePXsSExPDlClTLhlGGzZsGAEBAWRnZ1NYWEh+fj6RkZGsX78ek8lEUlISb7zxBmazGR8fH8LCwsjOzsbV1ZWBAwdak9yBAwdwcXHB09MT4LLDdV26dGH27NnWGn+iaau1R6WUuhP4BDhB2cO+nZVSj2ut19V3cEKIxsvX15d//vOf1qE+FxcXa829tWvXcuzYMR599FE8PDwwm818+OGHGAwGnnvuOTw8PDh+/Dhnz54FqHI/af/+/Vy4cIGVK1fyyCOPANCvXz8uXLhAYWEh3bp1IyIigpUrV9K1a1drEhPNV13uUb0D3Ka1Hqm1vhW4DXi3fsMSQjRWJ06cYNKkSbz++ut8/PHH/PLLL0RHR/Pzzz+TkZEBlE2oyM/P5+9//zvh4eHWCRRms5nff/+dvn37YjKZyM3NBcqeberduzeurq7cd9991uU4Kg/eREdHc+DAAcxmM927d6d37944Ojra5BqIhlWXRJWntU6o9PokkFdP8QghGqnCwkL+53/+hyFDhnDzzTdz+PBhbr/9djIzM0lOTsbX1xcPDw8KCwsZN24cQ4cOBWD37t18/fXXjBgxAjs7O9q2bYvZbGbw4MEEBwcDZQ/w9u/fn6VLl2I0GpkyZQq9e/dm//791uPPmTOHefPmYTAYGDFiBFOnTrU+YyWat5pq/d1T/uM+pdRa4EfKpqXfC+xtgNiEEI2A1pqVK1fyzDPPcNNNN3Hw4EEKCwvZvHkze/fuZerUqbRt25a0tDR+/vlnTp48ScuWLZk3bx633nor3377LW5ubiQnJ2OxWAgICGDr1q1s3boVKFtVd+jQoSQnJ3Pu3DlOnDhBeHg4AMnJydaFDeU5pxtXTfeoJlb6+Rxwa/nPF4BLF38RQjQ7r7/+Ol999RUODg58/fXXjBw5kuzsbD7//HMMBgNQVjjW0dERf39/3N3dcXR0JCsri9zcXFatWkWbNm0wGAycOHGCTp068emnn+Lj40NAQABGo5HbbrsNf39//Pz86NGjBw4ODly4cIF27dpZe1zixnbZRKW1fqghAxFCNB65ubm8/PLLfPzxx4wYMYJnn30WpRQ7duzg+PHjODs74+fnx+nTp8nOzgagXbt2REVFoZRCa01CQgKJiYmkpqZan5O68847OXPmDJmZmTzwwAPs27ePvXv30qVLF+zt7fHw8ADgnnvuuWxs4sZTl1l/zpQ9R9ULsE6v0Vo/fK0HV0qNA94HDMAyrfX/u9Y2hRBXR2tNQUEB33zzDS+99BJDhw7lf/7nf+jWrRuHDh0iL6/qrenAwEDGjx/PsWPHOHHiBKdOnaJ///60adOG8PBwMjIyWLJkCX//+99xdHTE1dWVgwcP0r17d2t5pMTERM6ePUtpaams+yQuqy53Ir8F2gJjgQjKitJe82QKpZQB+AgYD/QEZiilel5ru0KIq/PDDz/Qs2dP3njjDSZNmsSwYcMwGo2cPn2avLw8hgwZApQtJujl5cXhw4f56KOP2LRpE0lJSQAUFRXRpUsXSktLiYmJITc3l3vuuYcHHngAo9FIUVERU6dOZcaMGfzwww94eXkxd+5cawUKIapTlz9humqt71VK3a21/loptRzYdh2OPRhI0FqfBFBK/QDcDcRch7aFEHWUnZ3NE088wfLlyxk3bhzPPPMMycnJHDp0iJCQEAYPHsyOHTuIjIwEymr15eTkkJOTY22jtLQUg8HAuXPn+OWXX/Dx8cHf359ly5bh4eHB008/bR0+VErh7++Pv78/7dq1sy7f8eKLL0q9PVGtuvSoSsu/ZyulegNewPWoS+IPVF4AJqn8PSFEAzCbzXz66aeMGzeOwsJCHn/8ce6++25CQ0Ot0767du1K27ZtGTp0KA4ODlX2v3iozmw2k5+fT0pKCnfccQdTpkzB0dGRvLw8TCYTdnZ21kTk5ubGo48+ytChQxk4cCCDBw+WJCUuS9VWDUkp9SiwEugLfAm4A3/VWn9yTQdWahowTmv9aPnrPwFDtNYLL9puLjAXwM/Pb2BERMS1HLbZMBqNODk52TqMRk2u0eUdOHCAl19+GUdHR/77v/+bfv36UVhYSEFBAUopSkvL/j6tmBjh7u5Ofn7+Je04OTnh4uJCSUkJhYWFeHl54eTkhMFgQGtt3cfDwwOj0YjWuslWkpDfp9pd6TUKCgrar7UeVNt2tQ79aa0rCtBGAIF1jqB2yUCHSq/bl7938fE/BT4FGDRokO7atet1DKHpSkhIQK5FzeQalTGZTHz33Xe0a9eOoKAg/u///o8NGzbw3HPPkZ2dTbt27cjLy7tkJdya+Pn5kZKSAsDYsWMZOnQoZrOZP/74A3t7e/bt20dJSQmlpaUsWbIEb29v/v73v5OXl8eLL77YJCdOyO9T7errGtX0wO/TNe2otf77NR57LxCklOpMWYK6H5h5jW0KIS5iMpk4c+YMYWFhbNiwgZCQEF566SWmT5/Ohx9+iNlsvmySMhgMmM1m62sHBwemTp1KUFAQUVFRHDhwgNjYWEJCQqwr8Hp7e1NQUACUPaTr5uYGwKRJkyguLm6SSUrYVk2/MR71eWCttUkptRBYT9n09C+01kfr85hC3Ih2797NTz/9hNFo5KGHHsLX15eUlBTWrFnDsGHDqmzr4uJCUVGR9XXlJAVlq+OmpaXh7u7OgAEDiIiIID8/n/fff5/Ro0fz6KOP4urqSl5eHl9++SU5OTk4ODiwZs0akpKSrEVmhbgSNT3w+7/1fXCt9VrK1rcSQlxnv//+O++88w779+/n3nvvpW/fvqSnp+Pj40NmZibHjx/n+PHjjBo1CgAvLy/Gjx9PVFQUeXl5lJSUoLUmMzMTKJtY4e3tzZYtW9iyZQvDhg1j8uTJ7N69m4SEBPLy8hg4cCBQlvBatmxJQEAAAHl5eWRlZWE2my+ZlCFEbaQPLkQzYzQaeeedd3j11VcZNGgQ8+fPx83NjZtvvpmwsDAyMzNxcnKyVouoMHv2bMLDw4mPj7dOoqgsISGBtm3bWl/v2rWLbt26MWvWLEpKSqwJqLCwkIyMDBYu/M+8qPvvvx+LxWItuyTElZBEJUQzsnz5chYvXkzLli1ZtmwZ+fn5ZGVlUVRURFhYmHU7s9mMvb09JpPJ+p6DgwOjR4+msLAQpRRms5nExMQq7QcGBtKhQwf27t2Lt7e3dWHCysttrF69mri4OObOnUu7du2AstmDkqTE1ZJEJUQTsnHjRs6ePcvs2bNRSlkTxMmTJ3nqqac4dOgQY8eOpVu3bqSmppKXl1ftdHCTyXTJpIalS5cyb9487r//fn7//Xf69OmDs7Mz0dHRuLu7Ex4eTsuWLQkJCWHgwIH4+vpesszGb7/9xvHjx+nVq5dUOxfXjS1n/QkhrlBqaipJSUmsWLGCM2fO8Mgjj/DZZ5/x4YeRbfHiAAAgAElEQVQf8vTTTzNv3jy01uzdu9faW+rZsydRUVFV2jEYDNaHcCsUFRVRVFREVlYWhw4dAmDy5Mn4+/uTnJzM2bNnad++PUop2rRpU218Dg4OuLq6Mm7cOFnUUFw3dZn11x0IBSrmr04E9tRnUEKI6g0fPpxTp06Rl5dHbGwsffr0YfDgwRw8eBBPT0/ee+893N3dAayz944ePYqHhwcODg7WiREVs/lcXFysCcXBwYE2bdqgtebBBx+kdevW5OTk8OOPP+Ls7MysWbNqXahwzJgxjBkzpr5OX9ygap31p5TaCgzQWueVv34J+K1BohNCVJGSksKFCxdYsWIFmZmZTJo0ieDgYDp0KHt2/oEHHiA6OpqDBw9a9zEajRiNRvr27WtNVH5+fmRlZVFQUICzszPDhg0jOTmZX375hcmTJ9OxY0cA9u7dS0pKinUZeVlRV9hCXe5RtQFKKr0uKX9PCFEPiouLiY+PZ//+/UyYMIE2bdpw9uxZNmzYwPbt21m5ciUjRozgvvvuw8HBATs7O1555RVat27N1KlTiY6Orrbdyu+npKRgMBgIDQ3F1dWVXbt2oZS6ZCmP0NBQnJ2d6d69u0wrFzZTl0T1DbBHKbWq/PVk4Ov6C0mIG9tPP/3EyZMnAYiMjKRv3758/vnnvP/++9x2220cP36csLAw0tLSsFgs1iUyUlNT2blzJxaL5ZI2AwMDrW3efPPN7Nq1C7PZzNGjR+nWrRvDhw/H0dGR0NDQKvu5uLgwePDgqz4XrTVbt26lTZs2slqvuGq19uO11q8CDwFZ5V8Paa1fq+/AhLhRde/enR49enD33Xezbt06br31Vr744gv++c9/8vPPP1NSUsLIkSOr3be63pRSypqkAJKSkqxTxWfPng1AUFAQmzdvZteuXdf1XPLy8tiyZQtbt269ru2KG0tdp6e7Arla6y+VUr5Kqc5a61P1GZgQN5ro6GjMZjM9e/Zk1apVvPPOOxw9epTbbruNAQMGkJaWxttvv13jIoOVn4tycXHBZDJZK6FX1O1LTEzEYrEwb9482rRpQ0JCgrWSRIsWLa7rOXl6ejJ79my8vLyua7vixlKXpej/BgyibPbfl4AD8B1wc/2GJkTzpLW2Tg3PycnB3t6eHTt2sGfPHiwWC/n5+SxdupQePXqwYMEC3N3dsVgs1kKvULbKrsVioVevXhw9WrVEZkXVico1+1xdXSksLMTFxQWLxYLRaKxyz6lVq1YsXLgQs9nM6tWrad++PSEhIdflfLt06XJd2hE3rrr0qKYAIUAUgNY6RSlVrwVrhWjOvvnmG06fPk3fvn2Jjo5myJAh7Nmzh6SkJNauXYu9vT2zZs3Cz88PoNp7ThaLBX9/f44ePXrJWlFGo9HaezIYDPTq1QtfX1/s7e0JDAzk448/xt/fHx8fn0vazc/PJyoqivT09OuWqIS4VnVJVCVaa62U0gBKKbd6jkmIZishIYHTp08DcOLECTw8PDhy5Ah79uxh9+7djBgxgr59+9Y6DdzJyYnk5LLl2yonqYoafRXJzWKxWO9btWzZksGDBzN69GjrdPaLeXl5MXfuXDw8PLBYLMTGxtK5c2dcXV2v9dSFuGp1SVQ/KqWWAt5KqceAh4FltewjhKhGTEwMULaMu9FoZMeOHURERDBmzBiee+65KoViHRwcrPeXLlZ5u8prRk2aNImwsDDrc08Ac+bMQWuNq6srdnZ23HLLLTXGWFGfLzY2ln//+9+EhoYyYcKEqz9pIa5RXVb4fVspdQeQS9l9qr9qrTfWe2RCNEEFBQVERUUREhJirRBR2YgRIygsLCQtLY0333wTZ2dnHnzwQdq0aXNJNfPLJSmgynCf2Wymf//+2NnZ0b9/f0pLSzEYDKxevRo7Ozs6duyIUuqKz6VTp04MGTKEfv36XfG+QlxPdZlM8YbW+s/AxmreE0JUEh0dTXh4OFBW7igtLY3o6GiGDx/OTz/9RFxcHGvWrOHMmTOMHTuWnj17VrukxuUEBARw9uzZKhMh+vTpw6RJk6zJqOJZqMjISAoKCtBaX1WicnFxYdy4cVe8nxDXW12G/u4ALk5K46t5T4gbWklJiTXx9O3blzVr1nDgwAEsFguZmZl8++237Nixg0GDBjFx4sSrKtqanJxMz549iYmJYebMmXTp0sV6P+vihPT444+jtZayR6LJq6l6+nzgCaCLUqryU4QewM76DkyIxig3N5eYmBgGDBhQJdEUFxfz9ttvY29vz5IlS4iLi7OuwRQfH88//vEPfHx8ePTRR69o+YuK+0+9e/cmIyODrKwsxowZw0033YSfn581MUVERLB9+3Yef/xxWrVqBSAJSjQbNfWolgPrgNeBFyq9n6e1zqzXqIRopHbv3m2t3hAbG0vHjh3Zu3cvHTp0sC6zHhkZSUREBHZ2dvzwww+kpqYybtw4unfvXmv7Fc9HVbjzzjvZtGkTR44csX527NixS8oa2dnZYTAYrmqIT4jGrqbq6TlAjlLqfSCzUvV0T6XUEK11ZEMFKURjMXjwYFxcXPD09OTs2bM4OztTXFxMYWEhUDabr3Pnzrzzzjts3ryZoUOHMmXKlDoXdK2cpAYMGEBISAilpaVkZ2eTkZHByZMnq51aPnz4cIYPH359TlKIRqYu96g+BgZUep1fzXtC3BC8vb0ZOnQoH330EQDjx4/n3nvvRSnFm2++yb59+3jllVfw8/Pj8ccfty7VfjWioqIYN27cNRWFFaI5qEuiUrrSlCSttUUpJUvYixvWqVOnyMnJoUWLFhQXF/PZZ5+RmJjIunXryM7OZuLEiXUqG9SyZUsyMjKqnfXn6emJv7//JcvFC3Ejqsvd1pNKqcVKKYfyryXAyVr3EqKZ0VqzY8cOPDw88PHxIScnhw8++ICwsDCWLVtGYGAg8+bNq1OScnBwICMjAycnJ7TW9OnThwceeMBa1bxDhw4UFBTw/vvvV1tCSYgbSV3+XJsHfAD8BdDAZmBufQYlREPTWrN9+3a8vLzo27dvtdtER0ezadMmXFxcKC0t5dChQ2zcuJFOnTrxxBNP4OFR9xKYpaWltGnThnPnzgHg7++PyWTCbDbTqVMn7rjjDn777TcpXSQEdatMcR64vwFiEcJmSkpKCA8Px9PTkx49elSZ/KC1JjIykiNHjqCU4vTp06xdu5bi4mKmTp1qXba9NhUTLypUJClnZ2d69eqFq6sr99xzD506dcLDw4OZM2de35MUoomq6Tmq57XWbyql/kFZT6oKrfXieo1MiAbk5OTEmDFj2LBhAxs3bmTChAnWJTdycnJYv349RUVFbNmyhcOHDzNy5EgGDRpEYGCgtchsTZRSjB8/nqioKM6cOUPXrl1JTEzEbDbzpz/9yVpuKSgoiJycnCvqnQnR3NXUo4ot/76vIQIRwhb27dtHaWkpw4YNo0+fPsTGxpKZmYnRaCQiIoJdu3bh6elJVFQUmzdvJjg4mAULFuDmVraIQF2SVIsWLcjKyuKXX35hzpw57Nmzh86dOzNr1qwq2+Xk5LBy5UoSExOZP38+rVu3ro9TFqLJqek5qtXl379uuHCEaFjh4eEUFRUxdOhQa6/mxIkTfP311+Tm5pKcnMxnn30GwMyZM/H3969z205OTvj7+2MwGHB1daWkpIRWrVoRExPDqVOnGDRoUJXt165dS2JiIoGBgbIirhCV1DT0t5pqhvwqaK0n1UtEQjQQrTU333wznp6emM1mjh8/TmJiIlC2btTmzZuJj49n9OjR9OvX77Iliezt7assAQ9YJ0qcPPmfCbLTp0/Hzc2Nhx9+2Dq7r7IhQ4bQqlUrbrvtNpmWLkQlNf1reLv8+z1AW8qWnweYAZyrz6CEqE8lJSUYjUbOnz/Ppk2bgLLKDtu2bcNsNrN//362bNlCnz59WLBgAS4uLjW2d3GSsre35/HHHyc+Pp6wsDCKioro1q0bgYGBAJddtDAwMNC6jRDiP2oa+osAUEq9o7WuPEaxWikl961Ek2GxWDhz5gwdOnTA3t6eb7/9lqSkpCrbHDp0iDNnzrB27VqcnZ2ZM2cObdq0uarjmUwmTpw4QX5+Ps8++yx5eXkylCfENajL+IKbUipQa30SQCnVGZDl6EWTER0dTVhYGCNHjuTWW28lIyOjyue5ubmsXLmSM2fOMGbMGHr16oVSCkdHR0pKSmptv2K7isUMnZyc+P777wHw8/OzrpgrhLg6dalM8RSwRSm1RSkVAfwBPFm/YQlxfRQXF9OxY0cCAwPJyclh9erVFBUV4e7uTpcuXdixYwcff/wxXl5eLFiwgN69e1srkNclSY0fP966om5AQABQtky8o6MjN91001X3yoQQ/1GXB35/V0oFAcHlb8VprY017SOELf36668kJSUxdOhQ3njjDQYOHGid1FAxiaGwsJCnnnoKb29vHnnkEesaTtXx8fEhM/M/K9t4eHjg7++Pj48PQUFBbNy4EZPJRJ8+fcjNzcXR0ZExY8ZIkhLiOqnLUvSuwNNAR631Y0qpIKVUd631mvoPT4jamUymKrPkcnJyyMrKsr4+evQorq6uFBYWkpGRwfr166usEXXxGk4V21aonKS8vb2xs7MjJSWFuLg4fH19eeGFF8jLy8Pb25vg4GCEENdXXYb+vgRKgGHlr5OBV67loEqpe5VSR5VSFqXUoNr3EKJ6hw4d4tVXX+XYsWPW9+zs7DCZTNahO5PJRE5ODhERESxdupQ2bdqwYMECgoODq11osHKSulh2djaZmZnk5ubi4eFBWFgYMTEx17SchxCiZnWZTNFFaz1dKTUDQGtdqK59GdEjlE17X3qN7YgbnJOTE+7u7jg6OmKxWLCzs2PQoEHWmnpKKY4cOcL69etp27Ytc+fOpUWLFld1rA4dOuDg4ED//v05c+YM7du359ixY7Rv3/56npIQ4iJ1SVQlSikXyh/+VUp1Aa7pHpXWOra8rWtpRtzgcnJySE9PJyAggJUrV2IymZg/fz7R0dEkJSXh4uLCt99+S1ZWFnfeeSddu3atc9sVy747ODhQWloKwE033cTp06dp0aIFffr0AaB///71cm5CiP+oS6L6G/A70EEp9T1wM/BgfQYlxOVULK/Rtm1bPv/8c+A/08OdnJx47733KCkpYevWrbz77rsMHjyY+++//4oqPSil6NmzJ71798ZkMuHs7Ex0dDTbt28nOTmZkpIS6UUJ0YDUxSuLVvmwrMvTHigEhgIK2K21Tq+1YaU2UVbR4mIvaq3DyrfZAjyrtb7sA8RKqbmUr3/l5+c3MCIiorZD3xCMRiNOTk62DqPBFRYWkpOTg5ubGyUlJVgsFrTW1u/h4eF8/PHH9OvXj2effbbGqhLVraxbnbZt25KdnU1xcTGenp64uLhctpxSU3Sj/i5dKblOtbvSaxQUFLT/ooIS1arxz0yttVZKrdVa9wF+q/PRy/a9/Uq2r6GdT4FPAQYNGqSvZPimOUtISLiioazmoqioiK1bt1oXOqwYljt37hzr1q2jqKiIiRMn0qFDB1xcXAgPD6+2nYqhPSgredS3b19OnTpFfn4+Dg4OFBYW4u7uTnBwMMOHD6ekpIS8vDxatmzZYOfaUG7U36UrJdepdvV1jeoyHhKllArVWu+97kcX4iL79u2joKCAESNGVHsP08XFhaKiInbv3o2joyPFxcVs2bKF6Ohobr31VgYNGnRJwdeKntPAgQOJi4sDynpmvr6+eHh4cM899/DTTz+RlZXFU089haurK8XFxdZq6lA2vNgck5QQTUFdEtUQYLZS6jRQQNnwn9ZaV79edx0opaYA/wB8gd+UUge11mOvtj3RfGzfvp2cnByGDRuGo6Njlc/MZjPLli2jtLSUXr16sW7dOn788Ue6detWZY2oi1UM70VHR+Pp6Ymvry9xcXEMHDiQIUOGcObMGUaOHImLiwuenp4AVZKUEMK26pKornsC0VqvAlZd73ZF0zd79myOHDnC66+/zqxZs4iJicHNzY1Dhw5Zq56npKTwxhtvYLFYuP/++y+Z2HDxvacuXbpw4sQJSktLycjIIDMzE0dHRwYOHIjRaOSrr77Cx8eHRYsWNfTpCiHqoKb1qJyBeUBX4DDwudbadLnthbgeWrVqRatWrbC3t2fLli0kJycDZfeU8vPz2bx5M3FxcYwePZr+/ftXO6nh4gkSKSkpODs74+LiQlZWFh4eHlgsFhISEujevTujRo2qtrp5xT2s5jRxQoimqKZ/gV8DgyhLUuOBdxokInHD69GjBwDJyckEBARgsVjYt28fH374IQaDgYULFzJgwIDLJpCL7235+Pjg6OhIVlYWDg4O5Obmkp+fT3h4OEophg8fTt++l45kf/7557z//vvWhCWEsI2ahv56ls/2Qyn1ObCnYUISN6rz58/j6elJVFSUdTHCxMREvvvuO8xmMw888ABt21b3xMN/ODk5cdddd+Ht7c2pU6fw8vJi+PDh/PDDD7Ro0YKsrCxGjhyJq6trrc9CeXh4YDAY5MF0IWyspkRVWvGD1tok/1hFfcjPz7eWO/r444+ts/ry8vLYvHkzJ06cYOzYsdY1oi7HYDBgNpsxGo2kpqZah/8MBgNubm60aNGCUaNG4enpSYcOHeqUfO6///7rc5JCiGtS09BfP6VUbvlXHtC34melVG5DBSiaty+//JKPPvqI8+fPA2WJa+fOnfzzn//Ezc2NhQsXVlkjqjqurq5VelodOnQgMDAQV1dXpk2bRvv27Vm8eDEmk4nvv//+ktV9hRCNW01L0Rsu95kQV0trzZEjR2jbti2+vr7WKehhYWGcPHmStWvX4uXlxcMPP4yvr2+d2iwsLMTf3x+A7t27W5fu8PLyqrK6rsViobS0VO45CdHE1L0AmhDXICUlBQcHBywWCz///DN2dna0atWK8+fPk52dzYYNG0hJSWHs2LGXXX4DynpPRqPRWjbJ1dWVTp06MWHCBLKysvD397/svgMGDCAkJETuOQnRxEiiEvWqqKiIzMxMli1bhqenJ6NGjQLKejfJycns2rWLXbt2MXjwYKZMmYKDg0ON7XXq1ImYmBgAhgwZQuvWrQkPDyc3N7dOhWIlSQnR9EiiEvXqm2++IS0tDSgrf/THH38AEB8fz7p162jdujWPPfYYPj4+l22jW7duxMfHM2TIECIjI63vp6am4uXlRUFBARcuXKgyzCeEaD4kUYl6FRwcjJ2dHZmZmZw7d46MjAx+//13MjIymDBhAkFBQTXu7+DgQHx8PN7e3vTt25ejR4+Sn5+Ph4cHd999Nw4ODmzYsIFdu3ZV+yyUEKLpk0QlrpnWGrPZXO2aTy4uLqSkpACwefNm9u3bx80338z06dMv2b5iirmbmxtOTk7k5+cTGhpKUVERUVFRpKSk8Mwzz5CXl4erqysGgwGtNaNHj67zxAshRNMjiUpcs59//pm4uDiefPJJXFxc2LZtG+fOncPLy4tdu3YRExPD+vXrCQgIYP78+dbCr5W1bNkSLy8vTp48SUFBAQUFBQAcPXqU6dOnU1hYSKdOnYCyB3ErKKW45ZZbGuQ8hRC2IYlKXJPk5GQSEhJwcnJi9+7duLq6smXLFqCs0sS6desoKChgypQpdO7c+bLt3H777aSmpnLy5EmcnZ3x8/OjQ4cOtG3blrZt2zJ9+vQGOiMhRGMjiUpcNZPJxL59+yguLkYpxfbt23FycqK4uJiIiAgOHTrEiBEjCA0NvWSNqApKKR5++GFKS0vZunUroaGhTJgwoYHPRAjRmEmiElft7NmzHDx4kO7du3P+/HkyMjLYu3cvGzZsICgoiCeeeKLWdZ1atmxJ69atMRqNdO3alS5dujRQ9EKIpkISlbhiWms2b95MSUkJjo6OxMfHk5KSwtq1azGbzZesEVV52ffKfH19eeKJJ0hPT+fkyZPMmDFDltQQQlxCEpW4YqtWreLw4cM4ODiQk5NDeHg4sbGxjBo1ipCQkEuSjcViITg4mDNnzlBUVGStSjFo0CAAtmzZwtGjR/H19a3xPpYQ4sYkiUrUWU5ODsePHyclJQWLxcK5c+f47rvv6NKlCwsWLMDV1RU3NzfrjL0KSilcXFwoLS219q769+9PaGgoACNGjMDPz4+AgABbnJYQopGTRCVqVFJSQnx8PF26dGHbtm3s37+fgIAA3nzzTVxcXHjooYeqTBdv164dZ8+excPDg4yMDAC8vLw4cOAAffr0IT09HbPZTL9+/az7tG7dmtatWzf4uQkhmgZJVKJG+/btY+PGjUBZlfK9e/dy6NAhbr31Vvr27Vuldp7BYCAhIQEoS3AjR47E29ubwMBA68zAtWvXcurUqUuWixdCiMuRRCUuq7i4mDZt2mBnZ8eOHTvYunUrISEhzJ07F2dnZ1xdXSksLATA3d0dV1dXzp8/j729PbNmzaJNmzbWtnbs2EFkZCRBQUEEBATg7Oxsq9MSQjQxkqhEFUajkdjYWFq0aMG6devYvXs369atw93d/ZI1oiqS1IABA+jRowe5ubls27aN4cOHV0lSUFb1/MKFCxQUFHD+/HksFstln60SQojKJFGJKvbt28emTZswmUysWrWKpKQkxo4dS48ePazDfBVLcZSWlgIQGxtLVFQUs2fPZsmSJdW2GxwcTHBwMCaTCYvFUutyHkIIUUESlbBKS0tjz549HDhwgA0bNjB48GAmT55cJal4e3uTnZ1dZb+KKed1eQaqusK1QghRE3m68gZ2/Phx9uzZY53Y8Ouvv/LKK68QFxfHY489xm233YaDgwPTpk2jRYsWGAwGhgwZgp2dHZ6enrRv395aecJiseDi4mLL0xFCNFPy5+0NbMOGDaSnpxMZGcmqVatITExkxowZVe5DKaXo2rWrterE+vXrgbIp54mJiYSGhnLkyBEMBoNMMRdC1AtJVDeY0tJSTp06RXx8PAUFBYSHh7N3715uueUWFi5ceMmwnpeXF05OTjg7O1NcXExISAgtWrQgODiY+Ph4QkNDGT9+PFprKX8khKgXkqhuMLt27SI8PJy4uDh+//132rdvz9/+9jeKi4sxmUxVtnVyciI7Oxuj0UhwcDA7d+6kQ4cO1od1hw0bZt228vNUQghxPUmiasbOnDlDamoqQ4YMsSaS5ORkVqxYQVZWFpMnT6Zz584UFxcD/5nFB2XPRd1+++1kZ2fj6OjIwIEDsbe3p1u3bjY5FyHEjUsSVTO2adMmkpKSSEpKIisri1WrVrF161ZuueUWZs6cSb9+/dBac+zYMYxGIy4uLnh6enLhwgUCAgKqlDny8fHhtttus+HZCCFuVJKomrEJEyawcuVKVqxYwcaNGwkMDOTll1/GYrFQUFBAcnIy6enpeHh4UFpaSklJCTk5OUycOJFu3bqxc+dOzpw5w7Rp0+S5JyGEzcjd72YqPz+fLVu2sGzZMiIjI5k+fTpTpkwhLy+P8ePHA2XPPwH4+/szefJkCgsLcXNzo0+fPri7u3PixAni4+OtQ4NCCGEL0qNqZvLy8jh8+DBPPvkkMTEx3Hbbbbz88sscOnQIrTVaa37++WccHR3p3bs3Xl5eBAUF0bJlS5ycnOjQoYO19zR9+nSKioqqVEcXQoiGJomqGTGbzTz88MOsW7eOHj16sGjRIry8vEhNTWXOnDmcP3+e3377DaUUJpOJm266CU9PT+v+F0+UcHR0xNHRsaFPQwghqpBE1UxERkaycOFC8vLymDVrFn5+fkBZkdm0tDQiIyMZP348GzdupHfv3owfP17KGQkhmgSb3KNSSr2llIpTSkUrpVYppbxtEUdzcP78eWbMmMH48eN58MEHOXLkCEOGDAHA1dXVul2/fv1wd3fnhRdeYOLEiZKkhBBNhq0mU2wEemut+wLxwH/ZKI4my2Qy8cEHH9CrVy/S09N5/PHH8fb2xs7ODj8/PwYMGEBRURF+fn48//zz1mE9eTBXCNHU2OTPaq31hkovdwPTbBFHU/XDDz/wl7/8hdatW/Pbb79x7NgxSktLGT16NFlZWURGRlq3zcrKkmKxQogmTdl6SXCl1GrgX1rr7y7z+VxgLoCfn9/AiIiIhgyvUUlNTeWNN95g//79PP3004wdOxY7OzuysrJwc3PDzs6O0tJS7O3tsVgs1vdu1Bp8RqMRJycnW4fR6Ml1qhu5TrW70msUFBS0X2s9qLbt6i1RKaU2AW2r+ehFrXVY+TYvAoOAe3QdAhk0aJDet2/f9Q20CThz5gwvvfQSYWFhzJ8/n2nTplmLy54/f57MzEwWLFhAWFgYSUlJPP/889KLAhISEujatautw2j05DrVjVyn2l3pNVJK1SlR1dvQn9b69po+V0o9CNwFjK5LkrpRrV+/nkceeQRXV1eeeuopXF1diY+Px8/Pj7i4OFxcXPD398fd3Z1Zs2ZZSyEJIURzYZN7VEqpccDzwK1a60JbxNDYnTp1iqeffpro6GgefPBBRo4cyenTp0lPTycuLg5/f39CQkK44447qiQmZ2dnG0YthBDXn61uXnwIeAAblVIHlVKf2CiORqeoqIiXXnqJ/v37YzKZmDFjBg4ODqSnp+Pi4oLRaCQwMBAXFxcmTZokvSchRLNnq1l/MtB7EYvFwrJly3j99dcJCQnh2WefxWKxYDAYcHJyomfPnhgMBs6ePcvJkyfp0qWLrUMWQogGIU99NgLHjh3joYce4vjx4zzxxBM4OTlRWlqKnZ0dFouFwsJCVq1ahZubGy1btqS0tLTKw7xCCNGcSaKyoby8PF555RWWLVvGk08+yaOPPsr/b+/+Y6us7jiOvz8ihJKOgTqdxQ6agYOGEIogDteYgCxuIaAkMDEZq2wjGKqQLHZDtmTDLQ1IlkmYmcYJWRTHkrmJ4CaaEDSArMVBkZ+V6Ry/HBsl/FjjTcN3f/SCBaEteMvzXO7n9dd9eM49/d4T7v3kPPe558ko5CUAAAh/SURBVJSWlrJx40b69evHrbfeSmlpKUePHgWgqKiI8vJyoPXuGjOzQuCgSkAmk2H58uUsWLCA0aNH8+CDD1JRUcGWLVvIZDLU1NQg6eyNEWVlZQlXbGaWHAfVFdbQ0MC0adM4evQojz/+OMXFxTQ3N9PS0sKgQYP46KOPWLJkCRHBsGHDqKys9DYbZlbQCnPJgi7S2NjIypUrOXny5KfONTU18dBDDzF27FgmTZrEokWL6NOnD3v27KGiooJt27Zx4sQJjh8/Tq9evejduzd1dXU0NjYm8ErMzNLDM6ocamxsZPfu3YwaNYri4mKg9W6+ZcuWMX/+fAYPHsyMGTPo378/H3/8MVOmTGHkyJEcPnwYgBtvvJHp06efvZli3759n9ojysys0Diocmj8+PEMHz6cm2++GYC6ujqqq6u55pprWLNmDRHBwYMHOXDgAKdPn6Znz55s3rz5bLiNHj367O+iztySbmZW6BxUOdS9e3dKSko4cuQIjz32GKtXr6a2tpbp06efszDs888/z759+zhx4gQlJSU0Nzczbtw4L3hpZnYB/o4qh1paWli6dCnl5eUUFxeze/duqqqqzgmpiKB///4MHDiQnj17UllZSVVVlUPKzOwiPKPKkbfeeovq6mquu+461q1bx9ChQy/Y7tlnn+XUqVPMmTPHmxiamXWCg+ozOnjwIDU1Naxfv57FixczderUdgPozB5RZmbWOQ6qy5TJZHjyySdZuHAhM2fOZNeuXWfv9GvPAw88cAWqMzO7ejioLsPatWt55JFHKCsrY8OGDZSVldGjR4+kyzIzuyr5GtQl+OCDD5g8eTKzZs1i0aJFvPrqqxw6dIja2lr279+fdHlmZlclB1UnNDc3s2DBAm677TYqKirYsWMHEydORBK9e/emb9++3rDQzKyL+NJfOyKCV155hblz555dNHbAgAHntBkxYgQjRoxIpkAzswLgoLqIvXv3MmfOHN5//32efvppxo8fn3RJZmYFyZf+znPy5EnmzZvHmDFjGDduHA0NDeeEVCaTYdmyZaxfvz7BKs3MCodnVFkRwcqVK3n00Ue56667aGhooKSk5FPtMpkMH3744dk1+czMrGs5qIDt27fz8MMP09TUxIoVK6isrLxo2+LiYmpqanw7upnZFVLQl/6OHTvG3LlzGTt2LFOmTGHLli3thtQZRUVFdOvW7QpUaGZmBRlUZ/aIGjJkCKdOnWLnzp3Mnj2ba6/1BNPMLG0K7pO5vr6e6upqIoJVq1YxatSopEsyM7N2FNSMqra2lgkTJjBz5kw2bdrkkDIzywMFNaO69957mTVrFn379k26FDMz66SCCqohQ4YkXYKZmV2igrr0Z2Zm+cdBZWZmqeagMjOzVHNQmZlZqjmozMws1RxUZmaWag4qMzNLNQeVmZmlmiIi6Ro6TdIR4J9J15ESNwD/SbqIlPMYdY7HqXM8Th271DHqHxFf6KhRXgWVfUJSfUSMTLqONPMYdY7HqXM8Th3rqjHypT8zM0s1B5WZmaWagyp/PZN0AXnAY9Q5HqfO8Th1rEvGyN9RmZlZqnlGZWZmqeagylOSnpC0W1KDpD9J6pN0TWkkaYqkHZJOS/IdW+eRdI+kPZLek/SjpOtJI0nPSfq3pHeTriWtJJVKWidpZ/b9NieX/Tuo8tfrwNCIGAbsBeYlXE9avQtMBt5MupC0kdQN+DXwDaAcmCapPNmqUmk5cE/SRaRcC/CDiCgH7gBm5/L/koMqT0XE2ohoyR6+DdySZD1pFRG7ImJP0nWk1O3AexHxj4jIAL8HJiVcU+pExJvA0aTrSLOIOBQR72QfnwB2Af1y1b+D6uowA/hL0kVY3ukH/KvN8X5y+OFihUnSAKAC2JyrPq/NVUeWe5LeAL54gVPzI+LlbJv5tE67X7iStaVJZ8bJzLqepGLgj8DciDieq34dVCkWEXe3d15SFTABGBcF/DuDjsbJLuoAUNrm+Jbsv5ldMkndaQ2pFyLipVz27Ut/eUrSPUANMDEi/pd0PZaX6oBBksok9QDuB1YlXJPlIUkCfgvsiohf5rp/B1X+Wgp8Dnhd0lZJv0m6oDSSdJ+k/cBXgTWSXku6prTI3oxTDbxG65fff4iIHclWlT6SXgQ2AV+RtF/Sd5OuKYXuBL4NjM1+Hm2V9M1cde6VKczMLNU8ozIzs1RzUJmZWao5qMzMLNUcVGZmlmoOKjMzSzUHldklkHR9m9tvD0s60Oa4Rw7/zt2S/txBm+9J+tUl9rvfK+1bvvHKFGaXICL+CwwHkPRT4GRELG7bJvvjR0XE6StfodnVxzMqsxyQNDC7F88LwA6gVNKxNufvl/Rs9vFNkl6SVC/pb5Lu6KDvOyRtkvR3SRskDWpzur+k9ZIaJf24zXO+k+17q6SnJPm9bnnLMyqz3BkMTI+IekntvbeWAIsi4u3sStOrgaHttN8FVEZES3bprJ8D38qeuz373AxQJ2k1rYsU3weMyT7nGVqXR1px+S/NLDkOKrPc2RcR9Z1odzety/GcOe4rqSgimi/Svg/wO0lfvsC51yKiCSD7ndbXaH1fjwLqs3+jiHO38zDLKw4qs9w51ebxaUBtjnu2eSzg9uxmhZ3xC1oD6SlJA4G/tjl3/hpoke3/uYj4SSf7N0s1X7c26wLZGymaJA3Kfj90X5vTbwCzzxxIGt5Bd5/nk+03qs4793VJfST1onV33g3Z/qdKuiHb//WSvnTZL8YsYQ4qs67zQ1pXJt9I6+65Z8wG7pTUIGkn8P0O+lkIPCHpHc6dpUHrVh0vA9uAFyNia0RsB34GvCGpAVgL3PSZX41ZQrx6upmZpZpnVGZmlmoOKjMzSzUHlZmZpZqDyszMUs1BZWZmqeagMjOzVHNQmZlZqjmozMws1f4PWdJyYnfEPx8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xBr0kdZxBL0kdZ9BLUscZ9JLUcQa9JHXcQEGfZJck5yb5ZpLrk7woyW5JLkhyQ/tz14UqVpI0f4P26D8MfKGqngP8AnA9cCJwYVWtAC5s5yVJI9J30CfZGfhF2i//rqqfVtVdwErg9LbZ6cBRgxYpSerfID36vYFNwF8luTrJx5M8Edi9qja0bW4Hdh+0SElS/wYJ+u2BA4BTq2p/4EdMGaapqgJquicnWZVkTZI1mzZtGqAMSdLWDBL064H1VXV5O38uTfDfkWQPgPbnxumeXFWrq2qyqiYnJiYGKEOStDV9B31V3Q7cmuTZ7aJDgOuA84Fj22XHAucNVKEkaSCD3qb4rcCnk+wA3Ai8kebF45wkxwO3AK8ecB+SpAEMFPRV9TVgcppVhwyyXWlQM93j/+aTj1jkSqTR88pYSeo4g16SOs6gl6SOM+glqeMMeknqOINekjrOoJekjjPoJanjDHpJ6rhBb4EgdYZX06qr7NFLUscZ9JLUcQa9JHWcQS9JHWfQS1LHGfSS1HEGvSR1nEEvSR03cNAn2S7J1Uk+187vneTyJOuSnN1+n6wkaUQW4srY3wauB3Zq508BPlRVZyX5GHA8cOoC7GebN9OVl5I0TAP16JMsBY4APt7OB3g5cG7b5HTgqEH2IUkazKA9+j8D3gk8uZ1/CnBXVW1u59cDe073xCSrgFUAy5YtG7AMaW58V6VHo7579EleAWysqiv7eX5Vra6qyaqanJiY6LcMSdIsBunRvxg4MsnhwONpxug/DOySZPu2V78UuG3wMiVJ/eq7R19V76qqpVW1HDgG+Meqei1wEXB02+xY4LyBq5Qk9W0Y59H/PvCOJOtoxuw/MYR9SJLmaEG+eKSqLgYubqdvBA5ciO1KkgbnlbGS1HEGvSR1nEEvSR1n0EtSxxn0ktRxBr0kddyCnF4pPRrNdN+cm08+YpErkbbOHr0kdZw9emkW3vFS2zp79JLUcQa9JHWcQS9JHWfQS1LH+WHsEPjhnaRxYo9ekjrOoJekjjPoJanjDHpJ6ri+gz7JXkkuSnJdkrVJfrtdvluSC5Lc0P7cdeHKlSTN1yA9+s3A71bVvsBBwAlJ9gVOBC6sqhXAhe28JGlE+g76qtpQVVe10/cC1wN7AiuB09tmpwNHDVqkJKl/CzJGn2Q5sD9wObB7VW1oV90O7D7Dc1YlWZNkzaZNmxaiDEnSNAa+YCrJk4C/AX6nqu5J8q/rqqqS1HTPq6rVwGqAycnJadtI2yLvU69xM1CPPsljaUL+01X1mXbxHUn2aNfvAWwcrERJ0iD67tGn6bp/Ari+qj7Ys+p84Fjg5PbneQNVOMa81YGkbcEgQzcvBl4PXJvka+2yd9ME/DlJjgduAV49WImSpEH0HfRVdSmQGVYf0u92x5E9dy0Ex+41Kl4ZK0kdZ9BLUscZ9JLUcQa9JHWcQS9JHWfQS1LH+Z2x0ogt1GmXnr6pmRj00pjy+g0tFIduJKnjDHpJ6jiDXpI6zjF66VHKD28fPezRS1LHGfSS1HGPyqEb37JKejR5VAb9TDxvWV20GH/Xdp7Gm0M3ktRx23yPfmu9FXsT0vwt5P+p+fb0fWcwHEML+iSHAR8GtgM+XlUnD2tfkhbHuA1vbksvDKOsdShBn2Q74KPALwHrgSuSnF9V1w1jfzMZtz9KSY0u/98cx99tWGP0BwLrqurGqvopcBawckj7kiRtxbCGbvYEbu2ZXw+8sLdBklXAqnb2viQ/AL4/pHoGsYTxq2scawLrmo9xrAnGtK6cMr+6csoQi/k3C3KsBqz1GXNpNLIPY6tqNbB6y3ySNVU1Oap6ZjKOdY1jTWBd8zGONYF1zcc41jSTYQ3d3Abs1TO/tF0mSVpkwwr6K4AVSfZOsgNwDHD+kPYlSdqKoQzdVNXmJL8FfJHm9MrTqmrtLE9bPcv6URnHusaxJrCu+RjHmsC65mMca5pWqmrUNUiShshbIEhSxxn0ktRxIwv6JLsluSDJDe3PXadp8/wkX0myNsk1SX59HOpq230hyV1JPjfEWg5L8q0k65KcOM36xyU5u11/eZLlw6plnnX9YpKrkmxOcvSY1PSOJNe1f0cXJpnT+ceLUNebklyb5GtJLk2y7zjU1dPuVUkqydBPI5zDsTouyab2WH0tyX8Zdk1zqatt8+r272ttkr9ejLrmpapG8gD+FDixnT4ROGWaNvsAK9rppwMbgF1GXVe77hDglcDnhlTHdsB3gGcCOwBfB/ad0uYtwMfa6WOAsxfh320udS0HngecARw9JjW9DNixnX7zGB2rnXqmjwS+MA51te2eDFwCXAZMjrom4DjgI8M+Pn3UtQK4Gti1nX/qYtY4l8coh25WAqe306cDR01tUFXfrqob2unvARuBiVHX1dZzIXDvEOuYy20kems9FzgkSYZY05zqqqqbq+oa4KEh1zKfmi6qqvvb2ctoru0Yh7ru6Zl9IrAYZ0fM9RYlfwScAvxkjGpabHOp678CH62qHwJU1cZFrnFWowz63atqQzt9O7D71honOZDmFfU741TXEE13G4k9Z2pTVZuBu4GnjEFdi22+NR0P/MNQK2rMqa4kJyT5Ds27ybeNQ11JDgD2qqrFukPXXP8NX9UOv52bZK9p1o+irn2AfZL8S5LL2jv3jpWh3gIhyZeBp02z6j29M1VVSWbsySTZA/gUcGxVDdxLXKi6tO1J8jpgEnjpqGvZoqo+Cnw0yW8AfwAcO8p6kjwG+CDNUMk4+TvgzKp6IMl/o3k3+/IR1wRNjq4ADqZ5p3hJkp+vqrtGWlWPoQZ9VR0607okdyTZo6o2tEE+7dudJDsBnwfeU1WXjUtdi2Aut5HY0mZ9ku2BnYEfjEFdi21ONSU5lObF/KVV9cC41NXjLODUoVbUmK2uJwPPBS5uRwKfBpyf5MiqWjOimqiq3r/tj9O8Axq2ufwbrgcur6oHgZuSfJsm+K9YhPrmZJRDN+fzbz2XY4HzpjZob5/wt8AZVXXuuNS1SOZyG4neWo8G/rHaT4NGXNdim7WmJPsDfwkcuYhjqHOpa0XP7BHADaOuq6rurqolVbW8qpbTfKYxzJCftSb413f2WxwJXD/EeuZcF/BZmt48SZbQDOXcuAi1zd2oPgWmGUu+kOYP+8vAbu3ySZpvpAJ4HfAg8LWex/NHXVc7/8/AJuDHNK/ovzKEWg4Hvk3zucR72mXvo/lPB/B44P8C64CvAs9cpH+72ep6QXtMfkTzDmPtGNT0ZeCOnr+j88fkWH0YWNvWdBGw3zjUNaXtxQz5rJs5Hqs/aY/V19tj9ZxxOFZAaIa6rgOuBY5ZjLrm8/AWCJLUcV4ZK0kdZ9BLUscZ9JLUcQa9JHWcQS9JHWfQS1LHGfSS1HH/H7vfe+Oy+IsrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_snd_janos-us.tfrecords'\n",
    "args.log_dir = 'log/er3'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=69.3\n",
    "args.W_scale = 15.95 \n",
    "args.mu_shift = 0.199\n",
    "args.mu_scale = 0.12 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-199700',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-wdOqUPrTAK-"
   },
   "source": [
    "## ER/janos_us_ca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aM2TQFRgTAK_",
    "outputId": "2997b25a-c036-4d2a-d642-6172b192d952"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Loading checkpint: log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Restoring parameters from log/er3/model.ckpt-199700\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>0.0399</td>\n",
       "      <td>0.9215</td>\n",
       "      <td>0.9833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0427</td>\n",
       "      <td>0.9259</td>\n",
       "      <td>0.9845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>0.0454</td>\n",
       "      <td>0.9309</td>\n",
       "      <td>0.9857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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84Q+sWLGiznGj0UhwcDDFxcW888475OXlAdC1a1f69+/PD37wAzIyMsjMzGyUNonbI0/0hBAtTkJCAvPnz6e4uJiVK1cybNiwOyrPYDDQqVMnAgIC6hx3cnLiySefJC4ujqSkJNv27QaDgTNnzmC1Wjl27Bhubm68/PLLd1QHcfskUAkhWoz8/Hx+8YtfsGrVKt58801mzZplW4R7I1VVVQB1njFdzWAw8Mwzz9zw86ioKKKi6i7nqV1j+tBDD7XKNUr3EglUQohmV11dzYcffsjixYuZMGECy5Yto3///g1aGPv+++8D8NOf/vSW75mSkkJwcPA1Qc5oNPLKK6/cUnmi6UigEkI0qwMHDjBv3jycnJzYsmULqampxMbGEhsby+OPP37T9U+BgYG213FxcZjNZsxmc53sFNdz5MgRNm3aREREBMOHD6djx46N0h7R+GQyhRCiWWRnZ/P000/z6KOP8tJLL7F371769+/P6NGjGThwIEFBQfj7+9dbxvHjx8nIyKBr16589tln7Nmzh+rqatLT023nWK1W0tLSsFgsda4NCQkhIiKCpKQktmzZ0hRNFI3khj0qpdRxrp/NXFGT/LxPk9VKCHHPqqqq4s9//jNvvfUWzzzzDElJSXh4eNg+9/PzY+LEiRQWFt40P19lZSVlZWUkJSWRnp5OdHQ0Xl5eDBs2jMLCQlxdXUlMTGT9+vXExMQwcuRI27Xe3t5MmzaN2NhYOnTo0GTtFXeuvqG/yXetFkKINmH79u3Mnz+fwMBA9u7dS3h4+HXPKysr491336VLly51JkForcnPz+fkyZP07t2bgQMHct9995GQkEB6ejoBAQE4OzuTn5/PX/7yFyIjIxk7diy9evW67hCiUoro6Ogma69oHDcMVFrrjNrXSqmuQKjWetvlvHzybEsI0WAZGRm8/PLLHD58mD/96U9MmTLlmrx8VzIajURGRl7z3Cg2NpbNmzcDNb2pcePGYW9vT//+/QkJCcHNzY3U1FTc3NwICQkhKCgIb29vHn300Vuuc15eHkuXLmXYsGGybX0zu+kzKqXULOAL4O+XDwUCXzZlpYQQ94aKigrefPNN7rvvPnr37s3JkyeZOnXqDYOU1prU1FTMZjPTpk27Zv2Ur68v/v7+DBkyhMGDB9f5zN3d3VZudXU1M2fOvGbK+dVyc3NJS0u7YV2qq6tpCVshtXUNmUwxFxgGFANorc8AAfVeIYRo07TWrFu3jp49e3Ls2DH++Mc/EhoaetP1SGfPnuXf//4333zzzXU/79atG926daN9+/a4u7tf9xyz2czbb7/Njh07MJlM7Ny5k9zcXNvnJSUlmM1mANasWcMnn3xCaWnpNeX4+/vzxhtv1HmuJZpHQwKVSWttrn2jlDIgW8YLIW7g1KlTTJgwgddff52///3vrF69mqqqKs6ePXtNrr2reXp6EhISQp8+15+rVVpaSmxsLHFxcbZjtQt+a9nb29OuXTv8/f1JTU1l9+7dxMbG2q7/05/+xGeffQbAyJEjGT16tC0jxdXqG54Ud09DnjV9q5R6A3BWSo0D5gBfNW21hBCtTUlJCb/5zW/45z//yc9//nPmz59vy8v39NNPU11djdlsJjY2ln79+rFq1Srs7e158sknbWXs2LGDlJQUBg0adN17eHp6MnjwYEpKStBac/ToUdavX8+MGTPQWpOUlETv3r158cUXgZodex966CFCQ0OBmpRJoaGhdO7cGYCIiAgiIiKa8msRjaAhParXgVzgOPACsAlY3JSVEkK0Hlprli9fTkREBNnZ2Rw/fpyXX365TvJYBwcHjEYj8fHxbN26laNHj1JSUkJZWRlQMzFi69atdOvWDaPRyMqVK6/pfWmtKS4u5uDBg5w4cYKUlBRcXFzw8PDA2dmZxMREjh8/XqeHZTAYuO+++2zDhA4ODsycOVMmR7QyN+1Raa2tSqmPgIPUDPmd0vJ0UQgBHD16lHnz5lFaWsqqVasYOnTodc/Ly8vDbDbTv3/NFnT9+vWrMxkiPT2d/fv3M2DAAPr27UtpaSl2dnZUVVVhMBhQSrF+/XqOHj0K1PSM/Pz88Pb2pkePHkDN+qszZ85QWFhIVlYWnTp1auLWi7vlpoFKKTUJ+BuQSs1i3yCl1Ata681NXTkhRMuUn5/P4sWLWb16NVOnTqVdu3aEh4dTVVXFmTNnCA0NrZM/b/ny5RQWFvLGG28wfPjwa8oLCwtj6tSpBAcH23biLSkp4Z133iEkJITOnTtz9uxZAJ588km6det2TR5AFxcXIiMjUUqxdOlSnn76abp27VpvG0pKSuo9R7QMDRn6+yMQo7UerbUeBcQA/9e01RJCtETp6en88pe/JCIiAqUUSUlJtoBgNBqJi4vj888/t01eqDVs2DBGjRply4Re+3wpNTWVjz76iE2bNtGlS5c628UbDAZ8fX0pKChg+/bt9OnTh759++Lt7c25c+euqZtSiilTpuDu7k6XLl1uuuHh6tWrWbZsGSUlJY3wzYim1JDJFCVa65Qr3qcB8pMVoo3Zv38/jz32GEopvvzyS4YMGQLA4MGDGTx4MFprwsLCuHTp0jUTFKKioti9ezdvvfUWL7zwAnZ2dqxbt4727duTnZ1Neno6FRUVjB8/HldXVwwGA87OzsyZM4f8/HzbIt49e/aQnJyMyWTiiSeeICQk5Jp6WiwWMjMzOXPmDPfdd98N2zNkyBBycnLqBEfRMtWX6++Ryy/jlFKbgFXUPKOaBsTe6DohxL3lwoULvPbaa2zfvp2XX36ZkSNHMmDAAKCmZ7Rt2zbc3NxIS0vj/PnzLFy48JptM8xmMyaTyfa8ycfHhwcffJAOHTrg4uJCfHw87du355133qF379488sgjtmt9fHzIz8+nurqabt26AXD+/Hm8vb2vW1+j0Ui/fv0IDg6ut129evWiV69ed/DNiLulvh7VQ1e8zgFGXX6dCzg3WY2EEC2C2Wzmz3/+M//zP//Dc889R3Jy8jWLbC0WC/v378fb25vAwEDc3Nxsa482bdpESkoKs2fPZvXq1aSkpPDSSy/h5eXF6dOnKS8vx8nJic2bN5OZmUlAQABBQUEEBgayadMmnJ2diYmJoby8nOXLl+Pv78+cOXNuWm+DwcAPfvCDa45bLBaOHj1KaGgonp6ejfMlibuivlx/N94OUwjRKlVUVHDmzBkiIyMxGG78/9StW7eyYMECunXrxr59+2wz665kMpmIj4/nqaeewtvbGy8vL4qKiigqKsLX1xeTyURJSQlvv/227ZrKykoAvvrqK0pLS0lISKCwsNA2i2/8+PH83//9H9XV1Tg5ORETE4OzszNjxoy54+By5swZNm7cyIABA5g8WXJutyYNmfVnBJ4DegK2nPta62ebsF5CiCawb98+9u3bh9VqpV+/ftd8np6ezqJFizh69CjvvPMO0dHRrF27lu3bt+Pq6sqTTz5pm2134sQJvv76a4YNG8aYMWOwWCz861//oqioiIiICCIjI+nTpw///ve/gZpZecuXL8fPz4+pU6dy+PBhunfvTnh4OA4ODjg4OFBZWYm3tzdFRUWYTCYsFgsGg4FTp05x8eLFmwbY+nTv3p3Ro0fTs2fP2/8CRbNoyE/8EyAZeAB4E3gCSGrKSgkhmka/fv2wWq2EhoZSXl5OcnIyvXv3Jj09nf/+7/9m8+bNLFy4kOXLl+Ps7Ex8fDwXL14EaqaLX7x4kYCAAOzs7IiMjKS8vJzevXvz8ccfc+7cOYYOHcrhw4dJSkri+++/54knnqB79+6kpqZSXl4O1PSqPvnkE8aNG3fNZAej0cjcuXO5ePEiZrPZFpQ6dOiA0Whs0Nb0N+Lo6MioUaNufqJocRoSqEK01tOUUj/QWn+klFoB7GnqigkhGl/t8BrUpCvavXs3O3bs4A9/+AO+vr588cUXxMTEAJCZmUlJSQmDBg3CaDTi5eXF3//+d+6//36GDx+O0Wi0rYkqKCigurqasLAwMjMzycjIoLS0lCVLlrBgwQKOHTtGfn4+ly5dIiAggOTk5GsmXFwpIKBu3utJkyY10TciWoOGBKrafCSFSqleQDaSPV2IVs/V1ZUNGzbYNins3r27bVuMvLw8Nm3aRE5ODlAzQaF2qLCsrIz333+fLl268PDDD6O1ZujQoVy4cIEOHToQGBhIRkbNdnZaa5YsWUJFRQWDBg2iqqqK+Ph4XnnllRsmghXiag3pRy9RSnkD/wWsB04Cv2/SWgkhmkxxcTGvvvoqkyZN4oknniAhIYGzZ8/y9ddfYzKZAPj666/JyclhzJgxPPDAA1gsFhISEoCaIFZWVkZ+fj45OTn85S9/YcuWLRw7dgw7OzvCwsIwGo1ERUXRt29fKisrUUpx8OBBxowZw/Tp03F1dWX//v38+te/5vTp00BNUEtISODChQvN9t2Ilqkhuf6WXn75LVD/wgQhRIuUlZVFYmIi58+fZ/HixYwfP57ExETat2/PqVOnCAgI4Ny5c+zatYsHH3yQ4cOH07lzZ3r06EFOTg52dna2PZzy8/MZM2YMmzZtYs2aNVy6dAlHR0dGjx6NUgonJycqKytxdHRk0qRJjB07lmPHjvH9998TFBRkS1a7f/9+rFYrn376Kc8++yyurq6sXbsWDw8P5s2bV+/QoGhb6lvwu6i+C7XWf7qTGyulOgMfA+2oWUi8RGv97p2UKYSoYbFYqKysxGw24+joyGeffcY777yDp6cnX3zxhS2rBNRsHmg2m4mIiKC6upodO3awd+9eAgIC2Llzp+08R0dH/P39CQgIsAWvixcv0rNnT6ZMmYLBYODYsWMYjUbeeOMN20QINzc3/P39OXz4MBcvXiQwMBCArl27cunSJaqqqnB2dsbb25tOnTqRlZVFamoq4eHhd/dLEy1WfT2q62+f2XgswMta6yNKKXfgsFJqq9b6ZBPfV4h73pEjRzCZTGzdupUNGzawevVq5syZwzPPPENSUhKXLl3i/PnzeHl5MXnyZEpLS/nuu+/q5L0zGAw4OjqitaaqqgpPT0+ef/55AFatWoXVagVqUhEZDAYsFgtffvkl7u7uLFq06Jr6FBQUkJSURGBgIBaLhZMnT15z7g9/+EPOnDlz3dRIou2qb8HvfzfljbXWF4ALl1+XKKWSgE7UPAMTQtyB7t278/bbb7N06VIee+wxVqxYwb59+9i/fz+nT5/m0qVLpKTUpPAMCgrCycmJkpIS7OzscHR0pLq6mh49elBdXU12djZGo7HOot8f/vCHxMbGsmXLFvLy8ujYsSMGg4EZM2ZgNBrr1KU2B2BRURF9+/YFaoLgM888U2fPKgAvLy8GDhzYxN+OaG1US9haSinVDdgN9NJaF1/12WxgNkDHjh0HfPvtt41yT5PJhJOTU6OU1Zq0xXa3tTbHxcXx5ptv4uLiws9//nO6deuGg4MDJSUleHp6UlxcbMu5V5spwtnZGZPJZOsl2dnZYbVasbe3x2q1orXGwcEBPz8/231MJhMFBQVorTEajZjNZuzt7eucAzXPtEwmE+7u7nclAWxb+3lD621zaGjoYa111M3Ou70l3o1IKeUGrAYWXh2kALTWS4AlAFFRUbqxhgRSUlLa5PBCW2z3vdzm8vJycnJy6NatG9nZ2fzsZz9j586d/O53vyM4OJht27aRmJhIVFQU/fr1Y8OGDWRnZ9+wPCcnJ1vy2DFjxvDNN9/Yjo0cOdL2PZaXl1NUVITVamXNmjV4eXlRWFiIq6srr7zySp0yV61aRXJyMjNmzLgrP4d7+ed9I/d6m5s1UCmlHKgJUsu11muasy5CtEabN2/m6NGjVFdXs2TJEmbNmkVycjInT5609ZAcHR2Ji4sjLi4OqJncUFpaet3y2rdvT0ZGBhaLxZad3GQy0b17d3JzcykpKSEzM5MvvvgCgFmzZjFu3DiCgoLIzMy8bk7A6dOno7W2JasV4lY156w/BfwTSLrTsoRoq4qLi/nwww/x8PBgyZIlTJgwgQMHDpCUlESfPn2ws7OjY8eOZGZm2ob1bhSklFKMHz+ekpISysvLSUtLA6BHjx6cOnUKgG7dupGUVJNBzdfXl+zsbA4cOECXLl0YNGjQDespQUrciYaDtuQLAAAgAElEQVTM+usBDKRmsS/UbP9xqBHuPQx4EjiulDp6+dgbWutNjVC2EPcMrTVbt27Fw8ODwMBA1q5dS0hICL/61a/Izs7mt7/9Lenp6fj5+ZGYmMju3buBmmG8n//852zatIn09HRbee7u7jg5OZGXlwfUTGwIDw9n5MiR5ObmUlRUxODBgzEajeTk5Nh25e3ZsycdO3bk8OHDhIaGcubMGWJjYykpKbGtsRKiKdx01p9Sajdwn9a65PL7XwEb7/TGWuu9gPw3S4ibqKqq4rvvvsPHxwej0cjq1as5fPgw0dHRLF68mKeeegqr1YrBYKCiooLKykrbLLu8vDzb7L5aJSUldXpVtTvi+vv7s3LlSvLz8+nfvz8RERFERESQnp6Ok5MT48aNIyUlxZbFvKKigtDQUJ577rnbzmguREM05E9XO+DK/y6ZLx8TQjSi2tl1tT2YWo6Ojrz44ousW7eO8ePHExUVxZo1azh48CDe3t78+te/pnv37jz22GN89NFH5OTkYDQabVkfajM8KKWoneVb+7ujoyOOjo5MmDABqJl2XlZWVmeKebdu3fDy8mL37t0MHjyYefPm4ePjI5nIxV3TkED1MXBIKbX28vspwEdNVyUh2qYPP/yQgoICFi1axNdff01GRgbPPfccKSkpLFiwgLS0NCZMmMCwYcOws7OzrXECSE1NJSsry5ZE9tChQ4wcORIXFxfb9hq1wcnV1ZWKigqgZhdfNzc3WxaIU6dOsWfPHoYPH05GRgYzZszA2dmZXbt2cezYMTIzM5k1a9bd/mpEG9eQXH+/VUptBkZcPvSM1jq+aaslRNvj6elJQUEBBw4coLS0lAsXLvDqq6+yYsUKFi9eTGRkJHFxcTg7O9vSEF26dMl2fW3GcvjP9PHaINWjRw88PDywWCycPn0arbUtcEVERNiuMxgMGI1GsrOzycjIoKysDGdnZ4YNG8aFCxeu2T9KiLuhoQPLLkCx1vpfSil/pVSQ1vpsU1ZMiHvVqVOnsLe3JzAwsM4Q29ixY3nvvfdISkrCYDCwdOlSxo8fz7Jly7j//vtZunQpZrOZtLQ00tLSMBqNtgW7dnZ2trx8SimCg4O5cjF/ZWWlbeaeUor27dvj6+tLYmIiPj4+tvOGDx/O8OHDsVgsVFRU4O5eM6fK39+fn/zkJ03+3QhxPQ3Ziv6XQBQ1s//+BTgA/6Zm1p4Q4hZUVFSwcuVKoGYTw7lz59o+W716NefPn2f58uWYzWY++ugjKioqiIuL49KlS0RERJCSkmIb7qusrKRnz564uroSGxtrKycsLIyTJ08SEBBgWzNVWlpK3759OXPmDOXl5UyaNIlOnToxYMAAPvroI8xmM4MHD7aVYTAYbEFKiObWkB7VVKA/cARAa33+chJZIcQtsFqtxMXFERUVRXZ2Np06dbJ9lpeXx+rVq9m7dy8PP/wwISEhZGZm2vZmKigo4OzZawcxKisrqaysRGuNwWCgXbt2BAYGcu7cOeA/a6aMRiNTpkzhyJEj7N27F3t7ezIzM1m1ahXOzs64uLhcU/b58+dJTU1l6NCh10zwEOJuasjGiWZdM4agAZRSsi2nEDdw6dIljhw5Yltce6WCggJ27NhBQkIC586d48iRI+Tm5vLCCy8QFBSEj48Pu3btIiwsDDs7O86dO0d1dTUAhYWF171famqqLX+e1prevXszfPhwW2Dp1q0bY8aM4dlnnwVqFunWPgczm81UVFQwduxY+vTpc03Ze/bsYceOHWRlZTXKdyPE7WpIj2qVUurvgJdSahbwLLD0JtcI0SZt376dpKQkfH196dq1KwAHDx5k7969hISEYG9vj9lsxs7OjpSUFPr164eDgwNPPfUUAQEBbNr0n/XuHh4euLu7U1xcXGf7jaudPXuW4OBg0tLS2Lp1K4MGDeKJJ54gKyuLmTNn2qanl5eXs3nzZtq3b0+vXr0ICQlh8eLFN+wtjR07lrCwMNvEDSGaS0Nm/b2tlBoHFFPznOoXWuutTV4zIVqhESNG0LFjxzr/uFdWVlJaWsrp06eprq7G39+fbdu2sXPnTsaOHUvPnj3p37+/bdZeQUEBUJMeyWq1Xjfl0ZVrokJCQhgyZAhr1qyxbZEREBBAcXFxnV1yT506RU5ODq6urrYEptXV1VRUVFw3q7mvry++vr6N9M0IcfsaMpnid1rr14Ct1zkmhLhChw4d6NChA2azmaVLl9K+fXsmTZpE586dcXZ2Zv78+WzatImoqCjmzJmDo6MjSimOHTuGvb090dHR7N+/31be1UHKx8eHiooKfH19OXfuHJ06deKBBx7A0dGR2bNn11u3fv36UVRURFhYGIcOHcLBwYGEhATS09N59dVXr/ucSoiWoCFDf+OAq4PShOscE6JNy8rKws/PDycnJzZv3kx2djbZ2dlcvHiRb7/9lt27dxMYGMisWbPw9fW1Ldqt7RlZLBb2799PWFgYp0+frlO2p6cnRUVFdO3aldzcXM6dO4e3tzdPP/00p0+fZuPGjUyfPt023Hg9Sim6du3K8uXLKS8vx9nZmejoaBwcHK7ZwFCIlqS+7Ok/AeYA3ZVSCVd85A7sv/5VQrQ9u3btIiEhgYKCAvr27cuUKVNsQ26FhYV8+eWXZGRk8Lvf/Y6uXbvy3XffobW2TZS42pULd2tFR0ezc+dOcnNzbUN6UVFRGAwGzGYz5eXlVFVV3bSuteeOHDmSsLCwOjMPhWip6utRrQA2A/8DvH7F8RKtdX6T1kqIVqSwsJCCggJCQkI4c+YMK1eu5Pjx4+zdu5fY2FiGDBnCpEmTGD16NCtWrADA3t7+uoHKzs4Ok8lke+/u7k5UVBTbt2/HarVy7tw5nJ2dsbOzY+vWrTg5OTFgwAB69+5NUVHRTfd96tGjR70TKIRoiW44PV1rXaS1TgfeBfK11hla6wzAopS68cYzQrQxDz/8MA899BAdOnSgsrKSNWvW8Je//IVLly7x4osvMnLkSBwcHPjss89s11w9fd3Ozu6a4wEBAVRWVrJ7926sVqvtnJkzZ9qeJ+3ZsweTycTBgwf585//zIkTJ25aXwlSorVpyDOqvwJXJvgqvc4xIe4pBQUFfPzxx0RHRzNkyJDrnmO1Wm3JWouLi8nNzWXTpk2UlZUxZcoUgoKC6px/ZQ/qyvRGtWVdKSIigtTUVNtwXv/+/fHy8iInJ4d9+/ZRUFCAUoqioiLS09Pp0KEDnTt3xt/fvzGaL0SL0pBApfQVf6u01lallGw+I+5phw4dorCwsM76Ja01W7ZswcvLi8jISPLy8tizZw+VlZXs3buX+Ph4RowYwaBBg3BwcMBisdz0PgaD4brnlZaWYjabMRgM/OhHP0JrTVpamm23XQcHB4YMGYKfnx+hoaHY2dldExiFuFc0JOCkKaUWUNOLgpoJFmlNVyUhml9tNoaAgADbMbPZzKFDhzAajXzzzTc89thjlJSU8I9//IPu3bvz05/+FIPBgIuLC+Hh4VRWVpKcnFynt3TlthtQ08saN24clZWV7Nmz55r7V1dXc+jQIU6ePAlgS0Q7b948WeMk2oyGpFB6ERgKZAHngEFA/Qs2hGjlPDw8ANi69T9r24uLizEYDHh7e2M2m3niiSf4+uuvmTFjBj/4wQ8wGo0YjUbKy8vJyclh5MiRtiE+e3t7HBwcKC8vr7MIt3ab+drcfLWsVitGoxGttW2H3s6dO1NZWYmHh0edjOdC3OsakpniIvDYXaiLEM3u7NmzlJWV4ebmRrt27YiOjmbfvn2UlZXx3XffUVZWxrp160hKSmLKlCl0797dNsvOYrHYhvGysrL429/+Vqfs2udNFovF1jPq0KEDLi4uGAwGWyaI2jVUTz/9tC2olZaWEhgYyJYtW/Dz86t3Zp8Q95r61lH9TGv9e6XUn7mckPZKWusFTVozIe6S2NhY4uPjCQ8Pt+3pBDXbtO/atYuSkhKqq6uJi4vj22+/pV+/fsydOxdnZ+cG3+PqiRS1M+/8/Pw4fvw4AAsWLGDbtm24uLgwffp02rVrZ7umtgc1ceLEO2qrEK1RfT2qpMu/x92NigjRXJKTk7lw4QJFRUXAf54jmc1mXF1dyczMZMOGDbi4uPDjH//YFkBcXV0pKyu7pXvVJo+tve7K611dazYmMBgM+Pn5NUbThLgn3DBQaa2/uvz7R3evOkI0Ha01X331Fe7u7sTExNiOT5w4kXXr1tG9e3d27dplW3DbqVMn3n33XTIzMxk/fjw9e/asM+TW0CDl4OCAi4sLWms6dOhAWtp/5iJdvHiRiRMn4uvri6OjI9OmTWPjxo28/fbbtgkTxcXF7Nu3j0GDBsmzKdEm1Tf09xXXGfKrpbV+uElqJEQTMZvNHD16FB8fH2JiYigtLeXAgQO4u7vj6OhYJ1DVbs0RFRXFww8/fMu58Hx8fHB3dycjI4Oqqirb0N+ZM2ds53h5eTFq1Cj69etX51ovLy/8/Pxs90xOTubQoUO4uroycuTIO/wWhGh96hv6e/vy748A7anZfh7gcSCnKSslRGPTWvPXv/4VBwcH+vXrR1VVFceOHWPfvn04OjracuCdPn3aNmFh1qxZt9yDsbOzw2q10qdPH3Jzc23Hy8vLsVqteHt706VLFzp16sS4ceOuOyli2LBhDBs2DIATJ07g6OjII488QlhY2J19CUK0UvUN/X0LoJT6o9Y66oqPvlJKyXMr0apUVVXh4uJCUVERu3btYvv27XTp0gWoCWL5+fmsWLGCgoICJkyYQGho6G3dx9HR0TYJA8Df3x9/f3+ys7PJz88nICCAyZMnN7i89evXYzab+cUvfiEz/USb1ZAFv65KqWCtdRqAUioIkO3oRYtlMpn417/+RdeuXamqqsLPz4/Vq1dTWVlJUFAQjo6OnDp1iszMTJRSHDlyhK1btxIdHc306dMxGAy2nhFg63E1hLOzMwUFBRgMBiIjI0lISLD1rKZMmULfvn1vqS0zZszAarVKkBJtWkMC1U+BXUqpNEABXYEXmrRWQtyB6upqLl26hLe3N6dPn2b06NFUVlbi5uZGQEAAWVlZaK05ceIE33zzDV26dOH555+3LfKFurn3GhqkgoKCSE9PB2pm7j344IMkJCRgZ2dHdHQ04eHhDW5DRUUFRqOR4ODgBl8jxL2qIQt+tyilQoHav2XJWmtTfdc0lFLqQ2AycFFr3asxyhTCxcWF1157DXt7e0pLSzl16hRQs2j24MGDXLx4kU2bNlFRUcEjjzxCt27dbvkeHTt2pLi4mNLSUjw9PXF2diY3N5fhw4fb8v+VlpYyd+5cjh07xt69ewkKCmrQc6aMjAyWLVvGqFGjGD169C3XTYh7TUO2oncBFgFdtdazlFKhSqkeWusNjXD/ZcD7wMeNUJYQNmfOnMHX17dOUtmKigoOHDhAbGwso0ePZsCAAbe15YWjoyPnz58HwNfXF3d3d1tPKiAggPnz55Ofn2/LZN65c2fatWtnm6J+s2E8V1dX/P39ZSq6EJc1ZOjvX8BhoHavgyzgc+COA5XWerdSqtudliPubbm5uWzYsIGRI0fSvXt3oKZ3VFBQQOfOna85Pzs7m1WrVuHl5YWHhwfBwcEcOXKE7du306NHD+bOnWtbXHs7rhwK7N+/P9u2bcPDw4OZM2faFgNfGWTCwsLw9/fnvffeIyoqikmTJtVbvp+fH3PmzLnt+glxr2lIoOqutZ6hlHocQGtdruTJrriL8vLyyMzMJCsryxao1q9fz5kzZ5gxYwY7duxAKYWnpyfu7u54enoCNTn19u/fzx/+8AeKi4uZOXMmnTp1wt7eno4dO/L999/f9N43yj7RqVMnQkND6devH/n5+fTq1atOyqOrGQwG29oqIcStUVdv4HbNCUrtB+4H9mmt71NKdQc+1VpHN0oFanpUG270jEopNZvL2do7duw44Ntvv22M22IymXBycmqUslqT1tru6urqOsN0FRUVVFZWAth+r6WUIj8/nyVLlvDdd9/x0ksvMWrUKNsOubfjyn2jfHx8cHR0rDOEV1xcjIODwy3l/2tqrfVnfafaYrtba5tDQ0MPX7X86boa0qP6JbAF6KyUWg4MA56+s+o1nNZ6CbAEICoqSoeEhDRKuSkpKTRWWa3JvdLuL774os626wZDzR9lk8lEbGwse/bsoXfv3syePZuYmBh27Nhxy/fw8fEhPz+/zjEvLy/mz59fJ+iVlJSwfPlyOnXqxPPPP3+bLWp898rP+la1xXbf622uN1BdHuJLpiY7xWBqpqe/pLXOuwt1E+K6rFYrISEhVFVV0aVLF7Zt24bFYuHs2bNs3rwZV1dXfvzjH9fZ9PBGBg4cSFxc3DVbwwO4ubnh6enJ1KlTWbt2LVVVVfz4xz++pmfm7u7Os88+K8N6QjSRegOV1lorpTZprXsDGxv75kqpT4HRgJ9S6hzwS631Pxv7PqL1S0lJYcWKFSilsLOzIyQkhMcffxytNUlJSSxZsoTTp08zfvx4IiMjG7RA1tHRkYSEhOsGKYDw8HCGDKmZQ/TUU0/VW9b1JnUIIRpHQ4b+jiilBmqtYxv75lrrxxu7THFvOn36NFprtNY4OTlhb2/PiRMnWLp0Kf/85z8ZMWIE48aNu6XksWaz+YYBLTg4+JpksUKI5tGQp8uDgANKqVSlVIJS6rhSKqGpKybatqKiIttOt1lZWVy4cAEXFxe6d+9OdXU169atY8SIEWzevJlnnnmG6OhojEbjTct1cnJi1KhRtve1vanaDBBGo5HIyEjS0tKueT4lhGgeDelRPdDktRDiKp9++ik5OTmMGDGCo0ePUlJSgrOzM7GxsWzevJn8/HwmTpxYJ3nslWmPrkcphclkYvfu3Tg7O2MymWzX1O4R1bFjRxwcHAgPD5cFt0K0EPXtR2UEXgRCgOPAP7XWlrtVMdH2FBUV8fnnn9O+fXsuXbqEvb09sbGxVFZWYjKZ2L59O/Hx8QwfPpwZM2bYZvo1VG3vSWtNeHg4MTExHD16lP3799umuKelpeHn58fcuXPRWmM2m295LyohROOq72/6R0AVsAeYAEQCL92NSom2qaSkhKysLKBmsa6dnR0VFRUkJiaydetWunbtyuzZs/H29rZtRFgfe3v7Ouf16tULJycnOnToQHh4OK6urowYMcI21JeamkpkZCRdu3YFYMuWLRw6dIi5c+fK1vBCNKP6nlFFaq1/pLX+O/BDYMRdqpNoI5KTk/nggw/Izs4GIDAwkEWLFjFo0CAAzp8/z0cffcTevXt59NFHefTRRwkICKC6uprQ0NCb7hllZ2dH//79be9zc3M5fPgwHTt2ZO3atSxbtgyr1Yqvry8DBw7E2dmZ9evXk5NTsy+op6enbYt4IUTzqa9HVVX7QmttkaxJorHl5+eTm5vL8uXLiY6OZtiwYSQnJ7NlyxY2bdpEYmIio0ePJioqyrZ2qXaILiUlBa01Hh4elJWVUV1dbVug6+zsTN++fTlw4ADx8fGMGTMGqAk8999/P9XV1aSmpmJnZ1dnarrJZKK6upq4uDjCwsIYOnQoQ4cOvftfjBCijvoCVV+lVPHl1wpwvvxeUbPEyuPGlwpRv8LCQvz9/Zk+fTqrVq1ix44d5OXl8cknn/DNN98QHh7Oyy+/jL29PUop2rVrZ+t5Qc1zJoPBgNaa6upqDAYD+fn5+Pn5UVBQQGJiom3or3b79+DgYEJDQ22ZzyMjI+ukZZo2bRqJiYkEBgbe9e9DCHFj9W1Ff+v7HwjRAMXFxbz77rsAdOvWzZbdfN68edjZ2fHEE0/QsWNHoOY5k6+vb50gVctisdi28aidvZeXV5M0pbS0lCeffJJPPvmEoqIinnnmGdu9L1y4gIODA+3bt69TnlKK3r17N02jhRC37damTQnRCM6ePYvRaKSyspLExES2bdtGWloaY8aMoU+fPnVSFNnb23Px4sWblunv74/ZbKagoICwsDCCg4MJDg5m9OjRuLm5ATW9uHfffRelFFpr7rvvPo4ePUpkZKQ8hxKiBZNAJRpVbGwsubm5TJgw4ZqsD1VVVaxbt46TJ09isVg4dOgQe/bsoW/fvsyZM+e6C3Zvtg28m5sbVVVV5OTk4ODgQEhICC4uLrYe2ahRo0hJSQHA2dmZ4OBg3NzcCA4OJi4ujh07dlBZWcngwYMb6RsQQjQ2CVSiUR05coTs7GxiYmLqbHlhsVhYvnw5GRkZfP/992zcuBEXFxeeeeYZ2064N+Lu7m4b4rOzs6Nv376Ulpbi6+tLVFQU7u7u/O1vf6OiooKoqChWrlwJXJt/z8nJiSeffNL2vrCwkMrKSnr27NlYzRdCNAEJVKJRPf7441RUVNiC1JEjR8jNzaWiooJjx47xzTffkJWVxQMPPEBERESDkseWlJRgMBjw9/cnLy+P4cOHk56ejpubG76+vgDMnz8frTXx8fG0a9eOYcOG3bRcLy8vxo0bd2cNFkI0OQlUolGUlpZy5MgRBgwYQFJSEqtWraKkpISqqiqqqqrYv38/Bw4cIDo6milTplz3mVDts6PrsVgstG/fnosXL2Iymfjqq6/w8vIiLCzMdq1SioyMDHJycq4pp7KyskG5AIUQLY8EKtEo4uPj2blzJ46Ojhw6dIiKigq01pw6dYqvv/6adu3a2bJK3IjWGgcHB6DmeRaAr68vkZGRZGVlER8fT4cOHejQoQMzZsy47m66Dz/8MGPGjMHLy8t2rKioiOXLlzNnzpybDjMKIVoeCVTijpWVlZGeng7A/v37CQoKYvfu3WzZsoXCwkImTZrU4N1HR4wYQXx8PAUFBQBcunSJ/Px80tLSbD0orTU7duzA3t6eF154oc71BoOhTpCqPdauXbtWuVW3EEIClWgER48etWUfz8vLY82aNRw5coThw4czatQoLJaG5zK2WCwUFBTg4eFBcXHNevMTJ05gb2/PmDFj6N27t21Pqqt32r0RV1dXXnzxxVtvmBCiRZBAJe6I1pqCggKMRiOHDh1i69atBAUFMXfuXDw9Pa8bpBwcHPDy8sJkMlFcXIy/vz+5ubkA7N69m2nTpuHj48P27dtJSUnB2dkZi8VS53nUc889d1fbKYRoPhKoxB2prKxk48aNbNq0CbPZzLRp0+jSpQvADTOcWywWcnNzCQgIoLi4mEuXLjFx4kS2bNmC1WpFKcXmzZvJzMzEycmJqVOnEhQUdMvbeggh7g3yN1/ckpKSEtuEh7S0NBYtWsSePXuIiYlhwIABNxyOs7Ozw97enqqqKtuMvNoddB0cHOjTpw9du3ZlzZo1rFq1ii5dujB8+HBiYmIaPMQnhLg3SaAS18jNzcXR0RFPT886x8+fP88//vEPrFYrR44cYdeuXYSHh/Nf//VfhIaGcvLkyRvusmu1Wq/5zGKxMHjwYMaOHYu9vT1Wq5WcnBwMBgOZmZkYDAYJUkIICVSirqqqKj744AN8fHzo2rUr9vb2TJo0CYDPP/+c77//nk2bNuHk5MSPfvQj2rdvj8lkIiUl5ZpA5OTkhMlkuuYekZGRJCcnY7Vaad++PTk5OXTs2JF27doxdepUHBwc+Pzzz/HwkAT9QggJVOIqBoOBwYMH4+7uzp49e2zDdKGhoaxZs4b4+HjGjh1Lnz59bFkl7Ozs6vR8HBwccHR0pKysDAAfHx8KCwuxWq2Eh4czbdo0fvvb32K1Wtm6dStlZWUsXrwYe3t7+vTpA8D/+3//T3pTQghAApW4ilKKBx54gIqKCsLCwvj4449577332LNnD2PHjuXVV1+19Zzs7OxsQ3rl5eW2MgICAupsyzFlyhScnJz461//SmlpKQAhISFUVlYSGRlJZWVlnX2hgGveCyHaLglUAoDvv/+eAwcOYLVaMZlMnD17lszMTNavX4+npyfPPvssjz32GNu3b7ddc6PnUXZ2dlRXV2Nvb09QUBCBgYEopXjxxRdxdXUFYMaMGXelXUKI1k8ClSArK4sPP/wQR0dH7O3tuXDhAl9//TUXLlzggQceIDw8HKUU33///XXz8RmNRqqqqnBzc6O4uBij0Uj79u0JCwsjJibGdl67du3udtOEEPcACVRtnNVqxc/Pj759+xIYGMgvf/lLdu7cydChQ3nkkUdsU9EBUlJS6gSp/v374+vry7Zt24Caqetaa0wm0zWpjYQQ4nZJoGrDEhISWLt2LSNGjGDnzp18/vnneHl53TB57JVDfUajkSFDhuDv74+zszMXLlzA29sbg8HAgAEDyMzMxMfHx7a7rhBC3C4JVG2A1Wpl5cqVeHp60qNHD9txg8FAYWEhs2fPpqioiHnz5tk2KbxyarmLi0udyRJQk5Higw8+4MEHH2TQoEEcPHiQbdu2YbFYcHR0ZN26dYSGhjJz5sy72lYhxL2nWef/KqUeVEqdUkqlKKVeb8663MusVivp6elkZWUBNUHmww8/ZNGiRfzjH/+ge/fu/OQnPwGw7aR75fqnq9dCeXt727Z6//bbbwEoLy/HYrEQFRVFjx49GDBgAP3792/ytgkh7n3N1qNSStkDfwHGAeeAWKXUeq31yeaq073KYDCwcOFC1q9fT1FREW+99RZffPEFwcHBvPjii7i7u9/wWg8PD8rKyggKCiIrKwuz2cyDDz7Ip59+CoCfnx8AMTExjBgxwpaPb/LkyU3fMCFEm9CcQ3/RQIrWOg1AKbUS+AEggaoJODg4sHv3bt566y3y8vKYNm0a3bt3t21QWMve3h57e3vMZjNGo5FRo0bx1VdfERwczPjx46moqODjjz/GwcGBRx55xJbRHJCksUKIJqFutPV3k99YqR8C/7+9+w+K6jwXOP59gF1BHVHAKAaUIAiiBkVQlJik/kCNjrfaJDc2MYmJ13FaojRek5tqprdpMp3c3FpGJ5lqY2tTjbe3ueZWgxOs9UdabyQKQSMCilp/YQL+ICA/Fu9enn4AABGlSURBVFje+8euWxJJNIblLOzzmWFmD+fsu8/juvvwnvOe951hjFnk3l4AjDfGZH3puMXAYoBBgwaNvX6q6dtyOBzdfiG95uZmLl++TENDA7/97W/Jy8vjBz/4AVOmTCEwMBCbzYbT6fQMkrg+9Dw0NJS6ujpCQ0Ox2+0YYzyzmgcEBFBbW0tAQIDnnihf5w/vdXs0b//RVXOOj48vMMak3uw4n/8T2BizHlgPkJqaam51pdibKS8vv+VVZ7uqd999lw0bNrB3714eeOABFi1axJw5c9ixY0e7x8+fP98zIey+ffv47ne/S11dHUFBQWzbtg0RYeXKlV+YNeL48eP07t3bc83KF/nDe90ezdt/dPecrSxUF4DoNttR7t+pDrB//35+8pOfUF9fz6ZNm4iKiiI3N/eGm3Xb2rlzJ1lZWQQHB1NVVcWAAQNYt24d/fr1o3fv3gQHB3P27FmcTqdnCqQtW7YQFhbGM88804nZKaX8iZWF6iAQLyJ34SpQjwA6lvk2GGMQEQoKCigtLeXXv/41xcXFZGZmEh8fT319Pbm5udhsNkJCQpg6darnJl1wnfLr1asXEydOBGDQoEE89NBDADzyyCPU1dWxfft2MjIy+OMf/0hDQwMvvvgiPXr0YMaMGTcsB6KUUh3JskJljGkRkSwgDwgEfmOMKbYqnq6mubmZEydOMHDgQH71q18xbNgwNm/eTF5eHikpKSxZsoSgoCB69OhBTEwMtbW19OrVi+rqavbs2UN6ejo1NTVUV1dTUVGB0+kkNzeXpKQkgoODPa+TkJDAxYsX6dmzJzabjdmzZ9PU1OSZ2Xz8+PFW/RMopfyEpdeojDE7gPYvmKivVVBQQF5eHqmpqZSUlJCTk0NcXBxPP/00EyZMwBhDeXk5DoeDsrIyUlNTKS0tBfAMmDhw4AAxMTEEBgbS0NBAVFRUuyP3IiMjWbFiRWenqJRSQBcYTKGguLiYo0ePMmfOHEJCQgBITEykuLiYn//85xw/fpwpU6aQkJDAwoULOXDgAGVlZYDrNF5NTQ3vvfceERERDBkyBIA777yTzMxMYmNjsdlsNDc366SxSimfpIWqCygrK6O0tJR7772XkJAQGhoayMnJYe3atSQnJ7N06VLPvU9/+MMfcDgcnkETDQ0NTJgwgf379zNu3DhCQkJITEwkPDyc6Ojom7yyUkpZT5dQ7QJmz57NkiVLCAsLY82aNQwfPpzi4mIKCwuZOnUqTqcTp9MJuApTQEAAs2bN8qwFVVBQQE1NDc3NzdTU1DBt2jR69uxpcVZKKXVrtEfVBdjtdq5evcrs2bM5c+YMP/7xj5k+fTrbtm3jrrvuory83FOo+vbtS1JSEna7ndOnT2O325k5c6bnxl+73X7D3H1KKeXLtFD5uJqaGl566SU2btzIsmXLMMZQX19PaWkpV65cQURISUnh3LlzDBs2jOTkZF5//XXPyL2goCBGjx4N4BlgERkZaWVKSin1jWih8lGtra1s2LCBFStWEBsby8svv8yTTz7J+++/T21tLadPnwbg8uXLDBgwgCVLlgCuYjRixAj69OmD3W73DJ4A1/1SImJJPkopdbu0UPmgwsJCsrKyqK+v58EHHyQ6OprPPvuM1atX09zcTN++fT3LcYwaNYry8nJOnTpFbGwsIsL3vvc9izNQSqmOo4XKh5w4cYJnn32WDz/8kMmTJ/Ozn/2M8PBw8vLyqKqq4tq1awQEBNDQ0MDcuXOpqqoCXAMoNm3aRHR0NPHx8dxzzz0WZ6KUUh1HR/35AKfTyRtvvEFaWhqVlZW8+uqrjBgxgv3799OvXz8CAwNJTk5m+fLlxMXFERoaysmTJ7lw4QIpKSlMmjSJu+++m7Nnz3oWR1RKqe5Ce1QWOnXqFFu3buX3v/89drud7du306tXL8LDw9m5cyfp6elUVlZy/Phx6uvrOXPmDGfPniUzM5O8vDwALl68yOTJkwGYNm1al5zqXymlvo4WKotUVFTwxBNPcOTIEZ555hmCgoL46KOPWLp0KatXr6axsZHm5mbWr19PTEwM8+fP529/+xs2m43k5GQCAwMJCwsjNjbW02ZXWR9KKaW+CT311wlaWlo8ixM2NTWxatUqkpKSSEtL44MPPiA7OxsR4dq1a5SWltLY2OgZsTd8+HDPfVGTJ0/m0UcfJSQkhLS0NIYOHaqj+JRS3Z72qLzM4XDwi1/8gpiYGMLCwli2bBk9e/bk8ccfJzMzk1GjRhEQEMD48ePJz8+nsbGRwYMHc/fdd9O3b18efvhhq1NQSilLaaHysvr6epqbm1m7di1VVVXk5OSQlpbGO++8Q35+PsnJyURGRvLZZ58BrsK2cOFCi6NWSinfoYXKi+rr61m4cCE7d+4kOzubO+64g5qaGgYMGMCDDz7IhQsXGDhwIAAzZ86krKyM9PR0i6NWSinfoteovMAYwzvvvMPw4cOprq7mhRde4Pnnn6e1tZWWlhaampqoq6tjzJgxnmtMd9xxB5MmTWp3PSillPJn+q3YwY4dO8bSpUv59NNP2bhxI1euXOHo0aPk5eVRW1vLyJEj2bNnDwcOHGDChAn079+fMWPGWB22Ukr5LO1RdZDPP/+cZ599lnvvvZfZs2dTWFjI6NGjcTqdTJ8+3TPlkdPpZMSIEQwZMoSDBw+ybds2z4hApZRSN9JC9S21trbyu9/9jsTERCoqKli4cCGxsbHs3r2bNWvWUFJSQlVVFaNHjyY0NJS0tDSioqJwOBy0tLQwb948AgJcb0NLS4tnUIVSSikXPfX3LRQUFJCVlYXD4WDTpk0kJCSwYcMG/v73vxMcHExgYCBDhw6lsLCQtLQ0srOzPc+dN28eZ8+epaqqiqamJux2O7t27SI/P58FCxZ84UZepZTyZ9qjug2XLl1i8eLFzJo1i6eeeoo5c+bwySefMHDgQEaOHMnVq1c9E8YOHjyYfv363TC1Uf/+/amoqOCvf/2rZ8mO2NhY4uPjiYiI6PSclFLKV2mP6hbV19fT2NjI22+/zUsvvcT8+fMpLS0lNDSUvLw8GhsbPUtshIWFERISwtixY7HZbGRkZLTb5n333Ud0dDRxcXEADBs2jGHDhnVmWkop5fO0UN2ilStXsmXLFhITE/nLX/7CqFGjPPt69+5Nfn4+58+fZ8GCBSQkJFBUVERzczM2m+0r2+zTp49n9V2llFLt01N/N3HhwgW+//3vs3nzZh577DF2795NUlISubm5HD58mObmZpxOJ+Hh4Vy+fJmKigqKioo4ePAgZ86csTp8pZTq8rRH9RUcDge//OUvee2111iyZAmnT5/2zE7++eefc+jQISorK3E6nezdu5eMjAxKSkrYunUry5YtIzY2Vk/jKaVUB9BC1Y4dO3aQnZ1NQkIC+fn5nmtI14WGhrJo0SL69OlDYGAgV69eJSUlhZaWFmpra+nVqxeJiYkWRa+UUt2LFqo2Tp48yY9+9CNKSkrIyclh1qxZX3lsjx49cDgcREREMGXKFABmzJjRWaEqpZTfsOQalYg8JCLFItIqIqlWxNBWXV0dq1atYty4cUyYMIGjR48ycuRITp06RWNjY7vPefPNN1m3bh3GmE6OViml/ItVPaqjwDxgnUWvD/xj8tjly5czceJEioqKiI6OprGxkY0bNwIQHR3NU089dcNzMzIyaG1t1YULlVLKyywpVMaYEsDSL/kTJ06wePFiqqqqeOutt7j//vs9+4KCgrDZbIgI8fHx7T5/0qRJnRSpUkr5N78bnl5dXU12djaPPvooc+fO5eOPP+a+++6juLiYc+fOARAQEEBkZCRxcXE4nU4qKyupqanhyJEjOoGsUkp1MvHWNRYR2QUMbGfXSmPMn9zH7AX+1Rhz6GvaWQwsBhg0aNDYffv23XZMubm5vPLKK3znO98hKyuLyMhIWlpaPNMdBQUFERYWRmtrKzabDYfDwZUrV+jZsyfGGBoaGggLC7thOqSuxOFwdOn4b4c/5gyatz/pqjnHx8cXGGNuOk7Ba6f+jDFTO6id9cB6gNTUVPPloeLfRFxcHLm5uaSlpVFeXk5cXByXLl1i+/btDBkyhLFjx7Jz504qKyt57rnnsNvtHD58mKFDh1JfX8+xY8eIj4/Hbrd3RGqWuJ63P/HHnEHz9ifdPWe/Gp7e3nDziIgIVqxY4dlOT0/n0qVLBAcHIyKkpKQArnunIiMjOy1WpZRSLpYUKhGZC6wF+gO5IlJkjJluRSxfpqvtKqWUb7Fq1N+7wLtWvLZSSqmuxe9G/SmllOpatFAppZTyaVqolFJK+TQtVEoppXyaFiqllFI+TQuVUkopn6aFSimllE/z2lx/3iAiVcCZDmouArjUQW11Jf6Ytz/mDJq3P+mqOQ8xxvS/2UFdqlB1JBE5dCuTIXY3/pi3P+YMmrfVcXSm7p6znvpTSinl07RQKaWU8mn+XKjWWx2ARfwxb3/MGTRvf9Ktc/bba1RKKaW6Bn/uUSmllOoC/LpQichDIlIsIq0i0m1HzACIyAwRKRORchH5N6vj6Qwi8hsRqRSRo1bH0plEJFpE9ojIMff/72VWx+RtIhIsIh+JyGF3zj+1OqbOJCKBIvKxiLxndSze4NeFCjgKzAM+sDoQbxKRQOB1YCaQBMwXkSRro+oUG4EZVgdhgRZguTEmCUgHfugH77cDmGyMSQZGAzNEJN3imDrTMqDE6iC8xa8LlTGmxBhTZnUcnWAcUG6MOWWMaQL+C/gni2PyOmPMB8AVq+PobMaYi8aYQvfjWlxfYHdaG5V3GZdr7k2b+8cvLsCLSBQwC3jT6li8xa8LlR+5EzjXZvs83fyLS7mISAwwBsi3NhLvc5/+KgIqgT8bY7p9zm45wHNAq9WBeEu3L1QisktEjrbz0+17FMq/iUhv4H+AbGNMjdXxeJsxxmmMGQ1EAeNEZKTVMXmbiMwGKo0xBVbH4k1BVgfgbcaYqVbH4AMuANFttqPcv1PdlIjYcBWpzcaYrVbH05mMMdUisgfX9cnuPpAmA5gjIg8AwUAfEdlkjHnM4rg6VLfvUSkADgLxInKXiNiBR4BtFsekvEREBNgAlBhjVlsdT2cQkf4i0tf9OASYBpRaG5X3GWNeMMZEGWNicH2ud3e3IgV+XqhEZK6InAcmALkikmd1TN5gjGkBsoA8XBfW/9sYU2xtVN4nIluAD4EEETkvIk9bHVMnyQAWAJNFpMj984DVQXlZJLBHRI7g+sPsz8aYbjlU2x/pzBRKKaV8ml/3qJRSSvk+LVRKKaV8mhYqpZRSPk0LlVJKKZ+mhUoppZRP00Kl1DcgIuFthnx/KiIX2mzbO/B1porI/97kmEUikvMN2z1//X4jpbqKbj8zhVIdyRhzGdfs3IjIvwPXjDH/2fYY9w23YozptnOvKdWZtEelVAcQkTj3+k+bgWIgWkSq2+x/RETedD8eICJbReSQew2lr12OQkTSReRD93pD+0Ukvs3uISKyT0ROiMiqNs95wt12kYi8ISL6WVddlvaolOo4icDjxphDIvJ1n601wH8YYw64Zzd/D/i6CVRLgEnGmBYRmQG8DPyze98493ObgIPuhfNagLnARPdz1uOaXuft209NKetooVKq45w0xhy6heOm4prW6fp2PxEJMcY0fMXxfYG3RGRoO/vyjDFXAdzXtO7B9blOAw65XyOELy7zolSXooVKqY5T1+ZxKyBttoPbPBZgnHsRy1vxCq6C9IaIxAHvt9n35TnQjLv93xhjXrzF9pXyaXreWikvcA+kuCoi8e7rQ3Pb7N4F/PD6hoiMvklzofxjWZYnv7QvU0T6ikhPXKs273e3/7CIRLjbDxeRwbedjFIW00KllPc8j2vG+v/DtarydT8EMkTkiIgcA/7lJu28CrwmIoV8sZcGrpnC/wQcBrYYY4qMMZ8APwV2uWcT3wkM+NbZKGURnT1dKaWUT9MelVJKKZ+mhUoppZRP00KllFLKp2mhUkop5dO0UCmllPJpWqiUUkr5NC1USimlfJoWKqWUUj7t/wF90KWqUbuqwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_snd_janos-us-ca.tfrecords'\n",
    "args.log_dir = 'log/er3'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=69.3\n",
    "args.W_scale = 15.95 \n",
    "args.mu_shift = 0.199\n",
    "args.mu_scale = 0.12 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-199700',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v7PmWheMTALC"
   },
   "source": [
    "## ER/cost266"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M62USjDPTALD",
    "outputId": "c8e937a0-b82a-49ff-810b-cff3fb2ddb55"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Loading checkpint: log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Restoring parameters from log/er3/model.ckpt-199700\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>0.0326</td>\n",
       "      <td>0.9321</td>\n",
       "      <td>0.9861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0350</td>\n",
       "      <td>0.9362</td>\n",
       "      <td>0.9872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>0.0374</td>\n",
       "      <td>0.9413</td>\n",
       "      <td>0.9884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_snd_cost266.tfrecords'\n",
    "args.log_dir = 'log/er3'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=69.3\n",
    "args.W_scale = 15.95 \n",
    "args.mu_shift = 0.199\n",
    "args.mu_scale = 0.12 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-199700',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LD1pWwWFTALG"
   },
   "source": [
    "## ER/germany50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EuL0pgv1TALI",
    "outputId": "bf24a994-768c-44c1-befc-9f20658ce8c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Loading checkpint: log/er3/model.ckpt-199700\n",
      "INFO:tensorflow:Restoring parameters from log/er3/model.ckpt-199700\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.7161</td>\n",
       "      <td>0.9434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.1946</td>\n",
       "      <td>0.7374</td>\n",
       "      <td>0.9531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>0.2131</td>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.9590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_snd_germany50.tfrecords'\n",
    "args.log_dir = 'log/er3'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=69.3\n",
    "args.W_scale = 15.95 \n",
    "args.mu_shift = 0.199\n",
    "args.mu_scale = 0.12 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-199700',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NCHzmGpkTALM"
   },
   "source": [
    "## BA/germany50\n",
    "\n",
    "Again, a large systematic error can be observed in this case. Nevertheless, a large correlation that can be corrected by a simple linear model is also noticeable. Generally, it can be observed that only a small number of samples from `germany50`(where traffic is randomized) is required to train a highly accurate model for this particular topology."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "30nLe2WzTALN",
    "outputId": "c7721dfc-8049-4609-b5c8-a80dfc04fd3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Loading checkpint: log/ba16/model.ckpt-197400\n",
      "INFO:tensorflow:Restoring parameters from log/ba16/model.ckpt-197400\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>R**2</th>\n",
       "      <th>rho</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ci_lo</th>\n",
       "      <td>2.2272</td>\n",
       "      <td>-5.1083</td>\n",
       "      <td>0.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.2480</td>\n",
       "      <td>-4.7743</td>\n",
       "      <td>0.9433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ci_hi</th>\n",
       "      <td>2.2660</td>\n",
       "      <td>-4.4130</td>\n",
       "      <td>0.9461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args.eval='eval_snd_germany50.tfrecords'\n",
    "args.log_dir = 'log/ba16'\n",
    "args.ninf=16\n",
    "args.rn = 8\n",
    "args.W_shift=55.3\n",
    "args.W_scale = 22.0 \n",
    "args.mu_shift = 0.34\n",
    "args.mu_scale = 0.27 \n",
    "\n",
    "bootstraps_samples = []\n",
    "for b,label_py,predictions_py in bootstrap(ckpt_='model.ckpt-197400',\n",
    "                                           nboot=99,\n",
    "                                           nval=32):\n",
    "    bootstraps_samples.append(b)\n",
    "    \n",
    "bootstraps_samples = np.asarray(bootstraps_samples)\n",
    "print_report(bootstraps_samples)\n",
    "print_regplots(label_py,predictions_py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ag5-kML0TALQ"
   },
   "source": [
    "# Implementation Details\n",
    "This section describes low-level details related to our implementation of the MPNN. The full code presented below will be made publicly available via *Github* and in the form of this *Jupyter notebook*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i5Df-DoHTALR"
   },
   "source": [
    "## Random Graph Model\n",
    "\n",
    "Example usage:\n",
    "\n",
    "```python3 samples.py -o eval.tfrecords -N 2000 -n 40 --rmax 0.9 -g ba```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GuXZnk-ZTALR"
   },
   "outputs": [],
   "source": [
    "# %load samples.py\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import  scipy as sp\n",
    "import tensorflow as tf\n",
    "import argparse\n",
    "import datetime\n",
    "import glob\n",
    "import os\n",
    "import sys\n",
    "\n",
    "sndlib_networks = None\n",
    "\n",
    "class GraphProvider:\n",
    "    def get(self):\n",
    "        G = self._get()\n",
    "        G=nx.convert_node_labels_to_integers(G)\n",
    "        return G\n",
    "\n",
    "class BarabasiAlbert(GraphProvider):\n",
    "    def __init__(self,n):\n",
    "        self.n = n\n",
    "        self.nmin=10\n",
    "        self.m = 2\n",
    "    def _get(self):\n",
    "        return nx.barabasi_albert_graph(np.random.randint(self.nmin,self.n),\n",
    "                                        self.m)\n",
    "\n",
    "class ErdosReni(GraphProvider):\n",
    "    def __init__(self,n):\n",
    "        self.n = n\n",
    "        self.p =  2.0/n\n",
    "    def _get(self):\n",
    "        G=nx.fast_gnp_random_graph(self.n,self.p,directed=False)\n",
    "        largest_cc = max(nx.connected_components(G), key=len)\n",
    "        Gm=G.subgraph(largest_cc)\n",
    "        return Gm\n",
    "\n",
    "class SNDLib(GraphProvider):\n",
    "    def __init__(self,flist):\n",
    "        self.sndlib_networks = {os.path.split(f)[1][0:-8]:nx.read_graphml(f)\n",
    "                                for f in flist}\n",
    "        self.names = list(self.sndlib_networks.keys())\n",
    "\n",
    "    def _get(self):\n",
    "        name = np.random.choice(self.names)\n",
    "        Gm = nx.Graph( self.sndlib_networks[name] )\n",
    "        return Gm\n",
    "\n",
    "\n",
    "def make_sample(provider, rl=0.3, rh=0.7):\n",
    "    Gm=provider.get()\n",
    "    A=nx.convert_matrix.to_numpy_matrix(Gm)\n",
    "\n",
    "    # Make all intensities addup to 1\n",
    "    L=np.random.uniform(size=(len(Gm),1))\n",
    "    L = L /np.sum(L)\n",
    "    p=1.0/(np.sum(A,axis=1)+1.0)\n",
    "    R=np.multiply(A,p)\n",
    "\n",
    "\n",
    "    lam=np.linalg.solve(np.identity(len(Gm))-np.transpose( R ) ,L)\n",
    "    #random utilisation of each node\n",
    "    rho=np.random.uniform(low=rl,high=rh, size=lam.shape)\n",
    "\n",
    "    mu = lam/rho\n",
    "    ll=rho/(1-rho)\n",
    "    W=np.sum(ll)/np.sum(L)\n",
    "\n",
    "    #  Max value of W is of order n*0.99/(1 -0.99)\n",
    "\n",
    "    nx.set_node_attributes(Gm, \n",
    "                           name='mu', \n",
    "                           values=dict(zip(Gm.nodes(),\n",
    "                                           np.ndarray.tolist(mu[:,0]))))\n",
    "    nx.set_node_attributes(Gm, \n",
    "                           name='Lambda', \n",
    "                           values=dict(zip(Gm.nodes(),\n",
    "                                           np.ndarray.tolist(L[:,0]))))\n",
    "    it=np.nditer(R, order='F', flags=['multi_index'])\n",
    "    at = {it.multi_index:float(x) for x in it if x > 0}\n",
    "    nx.set_edge_attributes(Gm,name='R', values=at)\n",
    "    Gm.graph['W']=W\n",
    "\n",
    "    return mu,L,R,W,Gm\n",
    "\n",
    "def _int64_feature(value):\n",
    "    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))\n",
    "\n",
    "def _float_feature(value):\n",
    "    return tf.train.Feature(float_list=tf.train.FloatList(value=value))\n",
    "\n",
    "def make_dataset(count, file, producer):\n",
    "    writer = tf.python_io.TFRecordWriter(file)\n",
    "    for i in range(count):\n",
    "        if not i % 500:\n",
    "            print('{} generated'\n",
    "                  ' {} samples.'.format(str(datetime.datetime.now()),\n",
    "                                                    i ) )\n",
    "\n",
    "        mu,L,R,W,Gm=producer()\n",
    "        #while W > 3.3:\n",
    "        #    mu,L,R,W,Gm=make_sample(n,p)\n",
    "\n",
    "        mu = mu[:,0].tolist()\n",
    "        L = L[:,0].tolist()\n",
    "        first,last=np.nonzero(R)\n",
    "        e=R[first,last].tolist()[0]\n",
    "\n",
    "        example = tf.train.Example(features=tf.train.Features(feature={\n",
    "            'mu': _float_feature(mu),\n",
    "            'Lambda': _float_feature(L),\n",
    "            'W':_float_feature([W]),\n",
    "            'R':_float_feature(e),\n",
    "            'first':_int64_feature(first.tolist()),\n",
    "            'second':_int64_feature(last.tolist()) }))\n",
    "        writer.write(example.SerializeToString())\n",
    "    writer.close()\n",
    "\n",
    "\n",
    "if __name__ =='__main__':\n",
    "    random_org_help='''Seed, if none, downloads from random.org'''\n",
    "\n",
    "    parser = argparse.ArgumentParser(description='Generates saple networks')\n",
    "    parser.add_argument('-N', help='number of samples',  \n",
    "                        required=True, type=int)\n",
    "    parser.add_argument('-n', help='number of nodes',  default=40, type=int)\n",
    "    parser.add_argument('-o', help='Output file',  required=True, type=str)\n",
    "    parser.add_argument('--rmin', help='Min rho',  type=float, default=0.3)\n",
    "    parser.add_argument('--rmax', help='max rho',  type=float, default=0.7)\n",
    "    parser.add_argument('-s', help=random_org_help,  required=False, type=int)\n",
    "    parser.add_argument('-g', help='random graph type: [ba | er | snd]',  \n",
    "                        type=str, default=\"ba\")\n",
    "    parser.add_argument('--sndlib', help='Sndlib files',  type=str ,nargs='+')\n",
    "    args = parser.parse_args()\n",
    "\n",
    "    if args.s is None:\n",
    "        import urllib.request\n",
    "        with urllib.request.urlopen(\n",
    "            'https://www.random.org/integers/?'\n",
    "            'num=1&min=0&max=1000000&'\n",
    "            'col=1&base=10&format=plain&rnd=new') as response:\n",
    "            rnd_seed = int(response.read())\n",
    "            print( str(datetime.datetime.now()),\n",
    "                  \"Random response: {}\".format(rnd_seed))\n",
    "            np.random.seed(rnd_seed)\n",
    "    else:\n",
    "        np.random.seed(args.s)\n",
    "    provider = None\n",
    "    if args.g == 'er':\n",
    "        provider = ErdosReni(args.n)\n",
    "    elif args.g == 'ba':\n",
    "        provider = BarabasiAlbert(args.n)\n",
    "    elif args.g == 'snd':\n",
    "        provider = SNDLib(args.sndlib)\n",
    "    \n",
    "\n",
    "    make_dataset(args.N,args.o, lambda: make_sample(provider, \n",
    "                                                    args.rmin, \n",
    "                                                    args.rmax))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3pxWLCpDTALU"
   },
   "source": [
    "## Statistics\n",
    "\n",
    "Here we present how selected statistics related to the used random networks can be acquired. It is important to use the data properly normalized (so that they are characterized with zero mean and unit standard deviation) as it helps with the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uw4Ts2n1TALU"
   },
   "outputs": [],
   "source": [
    "def set_stat(filename):\n",
    "    gL=[]\n",
    "    gM=[]\n",
    "    gW=[]\n",
    "    geW=[]\n",
    "    gR=[]\n",
    "    nnodes=[]\n",
    "    r=tf.python_io.tf_record_iterator(filename)\n",
    "    for be in r:\n",
    "        e = tf.train.Example()\n",
    "        e.ParseFromString(be)\n",
    "        Lambda=e.features.feature[\"Lambda\"].float_list.value\n",
    "        mu=e.features.feature[\"mu\"].float_list.value\n",
    "        R=e.features.feature[\"R\"].float_list.value\n",
    "        first=e.features.feature[\"first\"].int64_list.value\n",
    "        second=e.features.feature[\"second\"].int64_list.value\n",
    "        W=e.features.feature[\"W\"].float_list.value[0]\n",
    "\n",
    "\n",
    "        gL += Lambda\n",
    "        gM += mu\n",
    "        gW.append(W)\n",
    "        gR += R\n",
    "        nnodes.append(len(Lambda))\n",
    "    return gL,gW,gM, nnodes\n",
    "    \n",
    "def vis_set(gL,gW,gM, nnodes):\n",
    "    stats=pd.DataFrame({\n",
    "        'Delay':{f.__name__:f(gW) for f in [np.mean, np.std]},\n",
    "    'Traffic':{f.__name__:f(gL) for f in [np.mean, np.std]},\n",
    "    'Serice':{f.__name__:f(gM) for f in [np.mean, np.std]}\n",
    "    })\n",
    "    IPython.display.display_html(stats)\n",
    "    plt.hist(gW,50);\n",
    "    plt.title('Histogram of the delay');\n",
    "    plt.figure()\n",
    "    plt.hist(nnodes,30);\n",
    "    plt.title('Network size Histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3eMg3Jt6TALX"
   },
   "source": [
    "#### Erdos-Renyi\n",
    "\n",
    "Most of the training samples are contained in the range $(20,38)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GqgSIlznTALX",
    "outputId": "ff3d34b2-55e7-41ee-880b-534bb009c60c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delay</th>\n",
       "      <th>Traffic</th>\n",
       "      <th>Serice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>69.192608</td>\n",
       "      <td>0.032427</td>\n",
       "      <td>0.198637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.220034</td>\n",
       "      <td>0.020412</td>\n",
       "      <td>0.117274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gL,gW,gM, nnodes = set_stat('eval_er.tfrecords')\n",
    "vis_set(gL,gW,gM, nnodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lq9iKBSKTALb"
   },
   "source": [
    "#### Barabasi-Albert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3YBPQ90tTALb",
    "outputId": "c8f02546-2c81-44a0-faa5-0093634644ad"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delay</th>\n",
       "      <th>Traffic</th>\n",
       "      <th>Serice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.978299</td>\n",
       "      <td>0.040796</td>\n",
       "      <td>0.345124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.641997</td>\n",
       "      <td>0.030280</td>\n",
       "      <td>0.266778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wtUR5AHPLrGspsLT3YZqZ2UDwHc1mZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWa7SsY/MrE54oDw7lpwUjoFyf7RmZvXO3UdmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMchUnBUlDJD0n6bE0P0HSOkltkh6WdEoqH5rm21J9Y2Ed81P5FknTq70xZmbWP705UvgqsLkwfydwV0ScBewDZqfy2cC+VH5Xaoekc4BZwLnADOAeSUP6F76ZmVVTRUlB0jjgs8C9aV7AZcDK1GQZcHWanpnmSfVTU/uZwPKIeDsiXgXagMnV2AgzM6uOSo8UvgPcBLyb5s8A9kfEoTTfDoxN02OBHQCp/kBqn5eXWMbMzOpAj0lB0pXAnojYMADxIGmOpFZJrZ2dnQPxkWZmllRypHAJcJWkbcBysm6jhcBwSV2jrI4DOtJ0BzAeINUPA14rlpdYJhcRiyOiKSKaGhoaer1BZmbWdz0mhYiYHxHjIqKR7ETxExHxBeBJ4HOpWTOwKk2vTvOk+iciIlL5rHR10gRgIvCTqm2JmZn1W3+ep3AzsFzSbcBzwJJUvgR4QFIbsJcskRARGyWtADYBh4C5EfFOPz7fzAr88B2rhl4lhYh4CngqTb9CiauHIuIt4A/KLH87cHtvgzQzs4HhO5rNzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMcv0ZOtvMBgEPqW294SMFMzPLOSmYmVnOScHMzHJOCmZmluvxRLOk9wNPA0NT+5URsUDSBGA5cAawAfhiRPxa0lDgfuC3gdeAz0fEtrSu+cBs4B3gKxGxpvqbNHDKncAzMxusKjlSeBu4LCLOBy4AZkiaAtwJ3BURZwH7yL7sSe/7UvldqR2SzgFmAecCM4B7JA2p5saYmVn/9JgUIvNmmj05vQK4DFiZypcBV6fpmWmeVD9VklL58oh4OyJeBdqAyVXZCjMzq4qKzilIGiLpeWAP0AL8DNgfEYdSk3ZgbJoeC+wASPUHyLqY8vISyxQ/a46kVkmtnZ2dvd8iMzPrs4qSQkS8ExEXAOPIft2ffawCiojFEdEUEU0NDQ3H6mPMzKyEXl19FBH7gSeBi4HhkrpOVI8DOtJ0BzAeINUPIzvhnJeXWMbMzOpAj0lBUoOk4Wn6A8BngM1kyeFzqVkzsCpNr07zpPonIiJS+SxJQ9OVSxOBn1RrQ8zMrP8qGftoDLAsXSn0PmBFRDwmaROwXNJtwHPAktR+CfCApDZgL9kVR0TERkkrgE3AIWBuRLxT3c0xM7P+6DEpRMSLwIUlyl+hxNVDEfEW8Adl1nU7cHvvwzQzs4HgO5rNzCznpGBmZjknBTMzyzkpmJlZzknBzMxyfhyn2QnqaKP8+lGdJy4fKZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOzXI9JQdJ4SU9K2iRpo6SvpvKRklokbU3vI1K5JN0tqU3Si5ImFdbVnNpvldR87DbLzMz6opIjhUPA1yPiHGAKMFfSOcA8YG1ETATWpnmAy4GJ6TUHWARZEgEWABeRPdt5QVciMTOz+tDj0NkRsRPYmabfkLQZGAvMBC5NzZYBTwE3p/L7IyKAZyQNlzQmtW2JiL0AklqAGcBDVdweM6uCcsNqe0jt41+vzilIagQuBNYBo1PCANgFjE7TY4EdhcXaU1m58u6fMUdSq6TWzs7O3oRnZmb9VHFSkHQ68A/AjRHxerEuHRVENQKKiMUR0RQRTQ0NDdVYpZmZVaiipCDpZLKE8GBEPJKKd6duIdL7nlTeAYwvLD4ulZUrNzOzOlHJ1UcClgCbI+LbharVQNcVRM3AqkL5dekqpCnAgdTNtAaYJmlEOsE8LZWZmVmdqOQZzZcAXwRekvR8KvtT4A5ghaTZwHbgmlT3OHAF0AYcBG4AiIi9km4F1qd2t3SddDYzs/pQydVHPwJUpnpqifYBzC2zrqXA0t4EWA+O9oBzM7Pjie9oNjOznJOCmZnlKjmnYGYG+Ka2E4GPFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzy3nsIzPrN4+JdPzwkYKZmeWcFMzMLFfJM5qXStoj6eVC2UhJLZK2pvcRqVyS7pbUJulFSZMKyzSn9lslNZf6LDMzq61KjhTuA2Z0K5sHrI2IicDaNA9wOTAxveYAiyBLIsAC4CJgMrCgK5GYmVn96DEpRMTTwN5uxTOBZWl6GXB1ofz+yDwDDJc0BpgOtETE3ojYB7RwZKIxM7Ma6+s5hdERsTNN7wJGp+mxwI5Cu/ZUVq78CJLmSGqV1NrZ2dnH8MzMrC/6faI5IgKIKsTStb7FEdEUEU0NDQ3VWq2ZmVWgr0lhd+oWIr3vSeUdwPhCu3GprFy5mZnVkb7evLYaaAbuSO+rCuVflrSc7KTygYjYKWkN8JeFk8vTgPl9D9vMBgPf1Db49JgUJD0EXAqMktROdhXRHcAKSbOB7cA1qfnjwBVAG3AQuAEgIvZKuhVYn9rdEhHdT16bmVmN9ZgUIuLaMlVTS7QNYG6Z9SwFlvYqugFW7leNmdmJwnc0m5lZzknBzMxyTgpmZpZzUjAzs5yfp2BmA86XqtYvHymYmVnOScHMzHJOCmZmlnNSMDOznE80m1nd8Ano2vORgpmZ5ZwUzMws56RgZmY5n1Mws7rncw0D54RMCh4i28ystBMyKZjZ8cFHENXncwpmZpZzUjAzs9yAdx9JmgEsBIYA90bEHQMdg5kd39yt1HcDmhQkDQG+C3wGaAfWS1odEZsGMg4zOzE5WfRsoI8UJgNtEfEKgKTlwEzgmCQFX2VkZpWo5nfFYE8wA50UxgI7CvPtwEXFBpLmAHPS7JuStgxQbNU0CvhFrYPoB8dfW46/tvoVv+6sYiR9Mwr4eF8XrrtLUiNiMbC41nH0h6TWiGiqdRx95fhry/HX1nESf2Nflx/oq486gPGF+XGpzMzM6sBAJ4X1wERJEySdAswCVg9wDGZmVsaAdh9FxCFJXwbWkF2SujQiNg5kDANkUHd/4fhrzfHX1gkdvyKiWoGYmdkg5zuazcws56RgZmY5J4V+kjRe0pOSNknaKOmrqXykpBZJW9P7iFrHWo6kIZKek/RYmp8gaZ2kNkkPp4sC6pak4ZJWSvqppM2SLh4s+1/S19L/m5clPSTp/fW+/yUtlbRH0suFspL7W5m707a8KGlS7SLPYy0V/1+l/z8vSnpU0vBC3fwU/xZJ02sTdR7LEbEX6r4uKSSNSvN92vdOCv13CPh6RJwDTAHmSjoHmAesjYiJwNo0X6++CmwuzN8J3BURZwH7gNk1iapyC4EfRsTZwPlk21L3+1/SWOArQFNEnEd28cUs6n//3wfM6FZWbn9fDkxMrznAogGK8Wju48j4W4DzIuLfAf8KzAdIf8uzgHPTMvek4Xpq5T6OjB1J44FpwM8LxX3b9xHhVxVfwCqysZ22AGNS2RhgS61jKxPvOLI/4suAxwCR3c15Uqq/GFhT6ziPEv8w4FXSRROF8rrf/xy+w38k2ZWAjwHTB8P+BxqBl3va38D3gGtLtaun+LvV/T7wYJqeD8wv1K0BLq632IGVZD+ItgGj+rPvfaRQRZIagQuBdcDoiNiZqnYBo2sUVk++A9wEvJvmzwD2R8ShNN9O9uVVryYAncDfpy6weyWdxiDY/xHRAXyL7NfdTuAAsIHBtf+7lNvfpYa2qfft+SPg/6bpuo9f0kygIyJe6FbVp9idFKpE0unAPwA3RsTrxbrI0nTdXfsr6UpgT0RsqHUs/XASMAlYFBEXAr+kW1dRHe//EWQDQk4APgqcRomugcGmXvd3JST9GVmX8IO1jqUSkk4F/hT4RrXW6aRQBZJOJksID0bEI6l4t6QxqX4MsKdW8R3FJcBVkrYBy8m6kBYCwyV13dhY70ORtAPtEbEuza8kSxKDYf9/Gng1Ijoj4jfAI2T/JoNp/3cpt78HzdA2kq4HrgS+kBIb1H/8Z5L9qHgh/R2PA56V9BH6GLuTQj9JErAE2BwR3y5UrQaa03Qz2bmGuhIR8yNiXGSDZ80CnoiILwBPAp9Lzeoy9i4RsQvYIemTqWgq2VDsdb//ybqNpkg6Nf0/6op90Oz/gnL7ezVwXboSZgpwoNDNVDeUPfzrJuCqiDhYqFoNzJI0VNIEspO2P6lFjKVExEsR8eGIaEx/x+3ApPR30bd9X+sTPoP9BfwO2aHyi8Dz6XUFWd/8WmAr8M/AyFrH2sN2XAo8lqY/QfYfvw34ATC01vH1EPsFQGv6N/g/wIjBsv+BvwB+CrwMPAAMrff9DzxEdg7kN+lLaHa5/U124cJ3gZ8BL5FdaVWP8beR9b93/Q3/baH9n6X4twCX11vs3eq3cfhEc5/2vYe5MDOznLuPzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Pc/wfsUai0OdYcfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gL,gW,gM, nnodes = set_stat('eval.tfrecords')\n",
    "vis_set(gL,gW,gM, nnodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T2HqSMhLTALm"
   },
   "source": [
    "## Tensorflow Implementation of MPNN\n",
    "\n",
    "NOTE: This code is to be run via CLI as a script not as a notebook cell (see below for instructions)!\n",
    "\n",
    "### Training options\n",
    "\n",
    "Below, we provide the hyper-parameters we have found optimal for the task. Most of the hyper-parameters are encoded with default values for the training script options.\n",
    "\n",
    "#### ba16\n",
    "\n",
    "```bash \n",
    "sbatch -J ba16 -t 72:0:0 ./train.sh --rn 8 --train train.tfrecords --test test.tfrecords --buf 10000 --buf 10000 --ninf 16 -I 200000  \n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "#### er3\n",
    "\n",
    "```bash\n",
    "sbatch -J er3 -t 72:0:0 ./train.sh --rn 8 --train train_er.tfrecords --test test_er.tfrecords --buf 10000 --W-shift 69.3 --W-scale 15.95 --mu-shift 0.199 --mu-scale 0.12 --buf 10000 --ninf 16 -I 200000  \n",
    "\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oMziIWRjTALn"
   },
   "outputs": [],
   "source": [
    "# %load graph_nn.py\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "mpl.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import argparse\n",
    "import os\n",
    "import io\n",
    "\n",
    "parser = argparse.ArgumentParser(description='Train the graph neural network')\n",
    "parser.add_argument('--pad', help='extra padding for node embeding',  type=int, \n",
    "                    default=12)\n",
    "parser.add_argument('--pas', help='number of passes',  type=int, default=4)\n",
    "parser.add_argument('--batch_size', help='batch_size',  type=int, default=64)\n",
    "parser.add_argument('--lr', help='learning rate',  type=float, default=0.001)\n",
    "parser.add_argument('--log_dir', help='log dir',  type=str, default='log')\n",
    "parser.add_argument('--rn', help='number of readout neurons',  type=int, \n",
    "                    default=8)\n",
    "parser.add_argument('--buf', help='buffer',  type=int, default=200)\n",
    "parser.add_argument('-I', help='number of iteration',  type=int, default=80000)\n",
    "parser.add_argument('--eval', help='evaluatioin file',  type=str, \n",
    "                    default='eval.tfrecords')\n",
    "parser.add_argument('--train', help='train file',  type=str, \n",
    "                    default='train.tfrecords')\n",
    "parser.add_argument('--test', help='test file',  type=str, \n",
    "                    default='test.tfrecords')\n",
    "parser.add_argument('--ninf', \n",
    "                    help='Number of hidden neurions in inference layer', \n",
    "                    type=int, default=256)\n",
    "parser.add_argument('--Mhid', \n",
    "                    help='Number of hidden neurons in message layer', \n",
    "                    type=int, default=8)\n",
    "\n",
    "def stat_args(name, shift=0,scale=1):\n",
    "    parser.add_argument('--{}-shift'.format(name), \n",
    "        help='Shift  for {} (usualy np.mean)'.format(name) ,  \n",
    "        type=float, default=shift)\n",
    "\n",
    "    parser.add_argument('--{}-scale'.format(name), \n",
    "        help='Scale  for {} (usualy np.std)'.format(name) ,  \n",
    "        type=float, default=scale)\n",
    "\n",
    "stat_args('mu',shift=0.34, scale=0.27)\n",
    "stat_args('W',shift=55.3, scale=22.0)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    args = parser.parse_args()\n",
    "else:\n",
    "    args = parser.parse_args([])\n",
    "\n",
    "def test():\n",
    "    return args.I\n",
    "\n",
    "N_PAD=args.pad\n",
    "N_PAS=args.pas\n",
    "N_H=2+N_PAD\n",
    "REUSE=None\n",
    "batch_size=args.batch_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zx01eT8TTALr"
   },
   "source": [
    "#### MPNN\n",
    "\n",
    "Below, we present the code implementing message-passing neural network. The algorithm is described in the paper, here we only give its basic mathematical sketch.\n",
    "\n",
    "Message: $$ \\mathbf m_v^{t+1} = \\sum_{w\\in{N(v)}} M_t\\left(\\mathbf h_v^t,\\mathbf h_w^t,\\mathbf e_{vw}\\right) \\quad t=1,\\dots,T,$$\n",
    "\n",
    "Update: $$\\mathbf h_v^{t+1} = U_t\\left(\\mathbf h_v^t,\\mathbf m_v^{t+1}\\right) \\quad t=0,1,\\dots,T,$$\n",
    "\n",
    "$$\\mathbf h_v^0=[\\mathbf x_v,0,\\ldots, 0]$$\n",
    "\n",
    "Readout: $$\\hat{\\mathbf y} = R(\\mathbf h)$$\n",
    "\n",
    "The implementation is very low-level and uses only basic tensorflow options. A high-level implementation based on `tf.keras` and `tf.estimators` will be provided in the future to enable users to operate easily. However, we would like to stress that the presented implementation does provide the full assumed functionality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GgqFQ8W9_6kK"
   },
   "source": [
    "<img 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\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-MlxVvPZTALt"
   },
   "source": [
    "##### Message function\n",
    "\n",
    "The message function is a neural network parametrized affine function.\n",
    "We use the SELU activation function because of its good scaling properties.\n",
    "\n",
    "For more details on `selu`, see for instance paper by Klambauer et al. \"Self-Normalizing Neural Networks\", 31st Conference on Neural Information Processing Systems NIPS 2017 ([arXiv:1706.02515v5](https://arxiv.org/abs/1706.02515))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DOwY4E_tTALu"
   },
   "outputs": [],
   "source": [
    "def M(h,e):\n",
    "    with tf.variable_scope('message'):\n",
    "        bs = tf.shape(h)[0]\n",
    "        l = tf.layers.dense(e,args.Mhid ,activation=tf.nn.selu)\n",
    "        l = tf.layers.dense(l,N_H*N_H)\n",
    "        l=tf.reshape(l,(bs,N_H,N_H))\n",
    "        m=tf.matmul(l,tf.expand_dims(h,dim=2) )\n",
    "        m=tf.reshape(m,(bs,N_H))\n",
    "        b = tf.layers.dense(e,args.Mhid ,activation=tf.nn.selu)\n",
    "        b = tf.layers.dense(b,N_H)\n",
    "        m = m + b\n",
    "\n",
    "\n",
    "        return m\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5GO8gy7kTALz"
   },
   "source": [
    "##### Update function\n",
    "\n",
    "The update function is based on a custom implementation of a GRU (Gated Recurrent Unit) recurrent neural network. \n",
    "The reason for this is that GRU is simpler in comparison to LSTM (i.e., there is no output gate) and has fewer parameters. This is a recurrent unit that has an internal structure that by design reuses weights (i.e., weight tying) for every element in the sequence. We tried  using different units for subsequent t in the experiments, but the training was much longer without noticeable improvement of accuracy.\n",
    "\n",
    "For more details on `GRU`, see for instance paper by Chung et al. \"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling\", 28th Conference on Neural Information Processing Systems NIPS 2014 ([arXiv:1409.1259v2](https://arxiv.org/pdf/1412.3555.pdf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XfXWPHq4TALz"
   },
   "outputs": [],
   "source": [
    "def U(h,m,x):\n",
    "    init = tf.truncated_normal_initializer(stddev=0.01)\n",
    "    with tf.variable_scope('update'):\n",
    "        wz=tf.get_variable(name='wz',shape=(N_H,N_H),dtype=tf.float32)\n",
    "        uz=tf.get_variable(name='uz',shape=(N_H,N_H),dtype=tf.float32)\n",
    "        wr=tf.get_variable(name='wr',shape=(N_H,N_H),dtype=tf.float32)\n",
    "        ur=tf.get_variable(name='ur',shape=(N_H,N_H),dtype=tf.float32)\n",
    "        W=tf.get_variable(name='W',shape=(N_H,N_H),dtype=tf.float32)\n",
    "        U=tf.get_variable(name='U',shape=(N_H,N_H),dtype=tf.float32)\n",
    "        \n",
    "        z = tf.nn.sigmoid(tf.matmul(m,wz) + tf.matmul(h,uz))\n",
    "        r = tf.nn.sigmoid(tf.matmul(m,wr) + tf.matmul(h,ur))\n",
    "        h_tylda = tf.nn.tanh(tf.matmul(m,W) + tf.matmul(r*h,U) )\n",
    "        u = (1.0-z)*h + z*h_tylda\n",
    "        return u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v_SZihSRTAL1"
   },
   "source": [
    "##### Readout function\n",
    "\n",
    "The readout function is obtained just as a neural network with attention.\n",
    "By 'attention' we mean the mechanism in the neural network allowing it to learn what information is important in a given input. In our readout function, the output of the network $j$ is multiplied with an output of network $i$ processed by the `sigmoid` activation function at the output. The sigmoid function takes values in the range $(0,1)$. Multiplication by such a number acts as a gate selecting the important parts of the output from $j$ for the final summation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OM-Yb7B4TAL2"
   },
   "outputs": [],
   "source": [
    "def R(h,x):\n",
    "    with tf.variable_scope('readout'):\n",
    "        hx=tf.concat([h,x],axis=1)\n",
    "        i = tf.layers.dense(hx,args.rn,activation=tf.nn.tanh)\n",
    "        i = tf.layers.dense(i,args.rn)\n",
    "        j = tf.layers.dense(h,args.rn,activation=tf.nn.selu)\n",
    "        j = tf.layers.dense(j,args.rn)\n",
    "\n",
    "        RR = tf.nn.sigmoid(i)\n",
    "        RR = tf.multiply(RR,j)\n",
    "\n",
    "        return tf.reduce_sum(RR,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0rf3eChjTAL6"
   },
   "source": [
    "The Python function ```graph_features```, given below, exercises message-passing phases on a graph and produces a vector representation of a network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r_a7WuboTAL6"
   },
   "outputs": [],
   "source": [
    "def graph_features(x,e,first,second):\n",
    "    global REUSE\n",
    "    \n",
    "    h=tf.pad(x,[[0,0],[0,N_PAD]])\n",
    "\n",
    "    initializer =tf.contrib.layers.xavier_initializer()\n",
    "    for i in range(N_PAS):\n",
    "        with tf.variable_scope('features',\n",
    "        reuse=REUSE, \n",
    "        initializer=initializer,\n",
    "        ) as scope:\n",
    "            m=M(tf.gather(h,first),e)\n",
    "            num_segments=tf.cast(tf.reduce_max(second)+1,tf.int32)\n",
    "            m = tf.unsorted_segment_sum(m,second,num_segments)\n",
    "            h = U(h,m,x)\n",
    "\n",
    "            REUSE=True\n",
    "        \n",
    "\n",
    "    return R(h,x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YAjLRLACTAL-"
   },
   "source": [
    "At this point, the network is represented by a vector. Therefore, we can pass it through a neural network for inference (forward pass)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C9TMPqnlTAL-"
   },
   "outputs": [],
   "source": [
    "def inference(batch,reuse=None):\n",
    "    initializer =tf.contrib.layers.xavier_initializer()\n",
    "    with tf.variable_scope(\"inference\",\n",
    "    reuse=reuse,\n",
    "    initializer=initializer):\n",
    "        l=batch\n",
    "        l=tf.layers.dense(l, args.ninf, activation=tf.nn.selu)\n",
    "        l=tf.layers.dense(l,1)\n",
    "        return l\n",
    "    \n",
    "def make_batch(serialized_batch):\n",
    "    bs = tf.shape(serialized_batch)[0]\n",
    "\n",
    "    to=tf.TensorArray(tf.float32,size=bs)\n",
    "    labelto=tf.TensorArray(tf.float32,size=bs)\n",
    "\n",
    "    condition = lambda i,a1,a2: i < bs\n",
    "    def body(i,to,lto):\n",
    "        with tf.device(\"/cpu:0\"):\n",
    "            #unpack \n",
    "            with tf.name_scope('load'):    \n",
    "                features = tf.parse_single_example(\n",
    "                serialized_batch[i],\n",
    "                features={\n",
    "                    'mu': tf.VarLenFeature(tf.float32),\n",
    "                    \"Lambda\": tf.VarLenFeature( tf.float32),\n",
    "                    \"W\":tf.FixedLenFeature([],tf.float32),\n",
    "                    \"R\":tf.VarLenFeature(tf.float32),\n",
    "                    \"first\":tf.VarLenFeature(tf.int64),\n",
    "                    \"second\":tf.VarLenFeature(tf.int64)})\n",
    "\n",
    "                ar=[(tf.sparse_tensor_to_dense(features['mu'])\n",
    "                     -args.mu_shift)/args.mu_scale,\n",
    "                        (tf.sparse_tensor_to_dense(features['Lambda']))]\n",
    "                x=tf.stack(ar,axis=1)\n",
    "\n",
    "                e=tf.sparse_tensor_to_dense(features['R'])\n",
    "\n",
    "                e = tf.expand_dims(e,axis=1)\n",
    "\n",
    "                first=tf.sparse_tensor_to_dense(features['first'])\n",
    "                second=tf.sparse_tensor_to_dense(features['second'])\n",
    "            \n",
    "        g_feature = graph_features(x,e,first,second) \n",
    "        W = (features['W']-args.W_shift)/args.W_scale # 0.7-0.9\n",
    "\n",
    "        return i+1,to.write(i,g_feature ),lto.write(i,W)\n",
    "    \n",
    "    with tf.control_dependencies([serialized_batch]):\n",
    "        _,batch,labelst = tf.while_loop(condition,body,[tf.constant(0),\n",
    "                                                        to,\n",
    "                                                        labelto])\n",
    "        batch = batch.stack()\n",
    "        labels = labelst.stack()\n",
    "        labels = tf.reshape(labels,[bs,1])\n",
    "    return batch, labels\n",
    "def make_trainset():\n",
    "    filename_queue = tf.train.string_input_producer( [args.train])\n",
    "    reader = tf.TFRecordReader()\n",
    "    _, serialized_example = reader.read(filename_queue)\n",
    "    serialized_batch= tf.train.shuffle_batch( [serialized_example],\n",
    "                                             batch_size=batch_size, \n",
    "                                             capacity=args.buf, \n",
    "                                             min_after_dequeue=batch_size, \n",
    "                                             num_threads=2)\n",
    "    return serialized_batch\n",
    "def make_testset():\n",
    "    filename_queue = tf.train.string_input_producer( [args.test])\n",
    "    reader = tf.TFRecordReader()\n",
    "    _, serialized_example = reader.read(filename_queue)\n",
    "    serialized_batch= tf.train.batch( [serialized_example], batch_size=200)\n",
    "    \n",
    "    return serialized_batch\n",
    "\n",
    "def line_1(x1,x2):\n",
    "    xmin=np.min(x1.tolist()+x2.tolist())\n",
    "    xmax=np.max(x1.tolist()+x2.tolist())\n",
    "    lines = plt.plot([1.1*xmin,1.1*xmax],[1.1*xmin,1.1*xmax])\n",
    "    return lines\n",
    "\n",
    "def fitquality (y,f):\n",
    "    '''\n",
    "    Computes $R^2$\n",
    "    Args:\n",
    "        x true label\n",
    "        f predictions\n",
    "    '''\n",
    "    ssres=np.sum((y-f)**2)\n",
    "    sstot=np.sum( (y-np.mean(y))**2 )\n",
    "    R2 = 1-ssres/sstot\n",
    "\n",
    "    return R2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9iiHd4D0TAMA"
   },
   "source": [
    "Below, the main training part is presented. We run message-passing on a graph, then perform the forward pass of the inference and compute $MSE$ loss for the delay. The loss backpropagates and the weights of $M$, $U$, and $R$ networks are updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xJvPoXfCTAMB"
   },
   "outputs": [],
   "source": [
    "if __name__== \"__main__\":\n",
    "    \n",
    "    if not os.path.exists(args.log_dir):\n",
    "        os.makedirs(args.log_dir)\n",
    "\n",
    "    REUSE=None\n",
    "    g=tf.Graph()\n",
    "\n",
    "    with g.as_default():\n",
    "        global_step = tf.train.get_or_create_global_step()\n",
    "        with tf.variable_scope('model'):\n",
    "            serialized_batch = make_trainset()\n",
    "            batch, labels = make_batch(serialized_batch)\n",
    "            n_batch = tf.layers.batch_normalization(batch) \n",
    "            predictions = inference(n_batch)\n",
    "\n",
    "        loss= tf.losses.mean_squared_error(labels,predictions)        \n",
    "        rel = tf.reduce_mean(tf.abs( (labels-predictions)/labels) )\n",
    "\n",
    "        trainables = tf.trainable_variables()\n",
    "        grads = tf.gradients(loss, trainables)\n",
    "        grad_var_pairs = zip(grads, trainables)\n",
    "        \n",
    "        summaries = [tf.summary.histogram(var.op.name, var) for var\n",
    "                     in trainables]\n",
    "        summaries += [tf.summary.histogram(g.op.name, g) for g\n",
    "                      in grads if g is not None]\n",
    "        summaries.append(tf.summary.scalar('train_mse', loss)) \n",
    "        summary_op = tf.summary.merge(summaries)\n",
    "\n",
    "        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "        with tf.control_dependencies(update_ops):\n",
    "            train=tf.train.RMSPropOptimizer(learning_rate=0.001\n",
    "                                           ).minimize(loss,\n",
    "                                                      global_step=global_step)\n",
    "\n",
    "        # Evaluation\n",
    "        with tf.variable_scope('model', reuse=True):\n",
    "            test_batch, test_labels = make_batch(make_testset()) \n",
    "            test_batch = tf.layers.batch_normalization(test_batch,reuse=True)\n",
    "            test_predictions = inference(test_batch,reuse=True)\n",
    "        test_relative = tf.abs( (test_labels-test_predictions)/\n",
    "                               (test_labels + args.W_shift/args.W_scale ) )\n",
    "        mare = tf.reduce_mean(test_relative)\n",
    "\n",
    "        test_summaries = [tf.summary.histogram('test_relative_absolute_error', \n",
    "                                               test_relative)]\n",
    "\n",
    "        test_summaries.append(tf.summary.scalar('test_mse', \n",
    "                                    tf.reduce_mean(\n",
    "                                        (test_labels-test_predictions)**2 ) ) )\n",
    "        test_summary_op = tf.summary.merge(test_summaries)\n",
    "        \n",
    "        saver = tf.train.Saver(trainables + [global_step])\n",
    "\n",
    "    with tf.Session(graph=g) as ses:\n",
    "        ses.run(tf.local_variables_initializer())\n",
    "        ses.run(tf.global_variables_initializer())\n",
    "\n",
    "        ckpt=tf.train.latest_checkpoint(args.log_dir)\n",
    "        if ckpt:\n",
    "            print(\"Loading checkpint: %s\" % (ckpt))\n",
    "            tf.logging.info(\"Loading checkpint: %s\" % (ckpt))\n",
    "            saver.restore(ses, ckpt)\n",
    "\n",
    "\n",
    "\n",
    "        coord = tf.train.Coordinator()\n",
    "        threads = tf.train.start_queue_runners(sess=ses, coord=coord)\n",
    "        writer=tf.summary.FileWriter(args.log_dir, ses.graph)\n",
    "\n",
    "        try:\n",
    "            while not coord.should_stop():\n",
    "                    _,mse_loss,summary_py, step = ses.run([train,\n",
    "                                                           loss,\n",
    "                                                           summary_op,\n",
    "                                                           global_step])\n",
    "                    writer.add_summary(summary_py, global_step=step)\n",
    "\n",
    "                    if step % 100 ==0:\n",
    "                        test_label_py, \n",
    "                        test_predictions_py, \n",
    "                        test_summary_py = ses.run([test_labels,\n",
    "                                                   test_predictions,\n",
    "                                                   test_summary_op])\n",
    "                        test_error = test_predictions_py-test_label_py\n",
    "                        R2 = fitquality(test_label_py,test_predictions_py)\n",
    "\n",
    "                        print('{} step: {} train_mse: {},'\n",
    "                              ' test_mse: {} R**2: {}'.format(\n",
    "                            str(datetime.datetime.now()),\n",
    "                            step,\n",
    "                            mse_loss,\n",
    "                            np.mean(test_error**2),\n",
    "                            R2 ), flush=True ) \n",
    "                        \n",
    "                        writer.add_summary(test_summary_py, global_step=step)\n",
    "\n",
    "                        checkpoint_path = os.path.join(args.log_dir,\n",
    "                                                       'model.ckpt')\n",
    "                        saver.save(ses, checkpoint_path, global_step=step)\n",
    "                        #make scatter plot\n",
    "                        fig = plt.figure()\n",
    "                        plt.plot(test_label_py,test_predictions_py,'.')\n",
    "                        line_1(test_label_py, test_label_py)\n",
    "                        plt.xlabel('test label')\n",
    "                        plt.ylabel('test predictions')\n",
    "                        plt.title(str(step))\n",
    "\n",
    "                        with io.BytesIO() as buf:\n",
    "                            w,h = fig.canvas.get_width_height()\n",
    "                            plt.savefig(buf, format='png')\n",
    "                            buf.seek(0)\n",
    "                            plt.close()\n",
    "                            summary = tf.Summary(value= [\n",
    "                                tf.Summary.Value( tag=\"regression\",\n",
    "                                    image=tf.Summary.Image(height = h,\n",
    "                                         width =w, \n",
    "                                         colorspace =3 , \n",
    "                                         encoded_image_string = buf.read()) ),\n",
    "                                tf.Summary.Value(tag=\"R2\", simple_value=R2)\n",
    "                                ])\n",
    "                            writer.add_summary(summary, global_step=step)\n",
    "\n",
    "\n",
    "\n",
    "                    if step > args.I:\n",
    "                        coord.request_stop()\n",
    "        except tf.errors.OutOfRangeError:\n",
    "            print('OutOfRange' )\n",
    "\n",
    "        finally:\n",
    "            coord.request_stop()\n",
    "\n",
    "        coord.join(threads)\n",
    "        writer.flush()\n",
    "        writer.close()"
   ]
  }
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