02.4_Sampling_G_K.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "912e5e98",
   "metadata": {},
   "outputs": [],
   "source": [
    "from const import globs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38c072b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GLOBAL VARIABLES. BE SURE NOT TO OVERWRITE THEM\n",
    "\n",
    "D, V, M, k, gamma = globs('l')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9c32d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a14bea8",
   "metadata": {},
   "source": [
    "From now on let's use the following simulated data for further testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9681cbce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from simulator import Simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55d37fe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success: Sigma transformed from K\n",
      "Success: G transformed from K\n",
      "Success: W and Z generated\n",
      "Success: E transformed from Z\n",
      "Success: C transformed from Z\n",
      "Success: Sigma transformed from K\n",
      "Success: G transformed from K\n",
      "Success: W and Z generated\n",
      "Success: E transformed from Z\n",
      "Success: C transformed from Z\n"
     ]
    }
   ],
   "source": [
    "test_data = Simulator(D, V, M, k, gamma, seed=1967)  # Man on the moon\n",
    "test_data.generate_all_data()\n",
    "\n",
    "random_initial_data = Simulator(D, V, M, k, gamma, seed=1969)  # Woodstock Music Festival\n",
    "random_initial_data.generate_all_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a142e66a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7faa66265520>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAECCAYAAADXWsr9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKS0lEQVR4nO3d32udhR3H8c/H2s2uOiwkSrG/djFEEWZHWi8KgxUnnYrb1VDQKyE3K1Q2kHnpPyBedDdFZRs6RVBhOOcsaJGK1qa1OrU6RHQrFZJgRcuGo/rZRU6ha+PyxDxPnqd+3y8ITdLDyYe27z7nnOScx0kE4Jvtgr4HAOgeoQMFEDpQAKEDBRA6UAChAwUMNnTbO2y/a/s9278ZwJ6HbE/bfrPvLafZXm/7BdtHbb9le9cANl1k+1Xbr4823dv3ptNsr7D9mu2n+95ymu0PbP/N9hHbU519nSF+H932Ckl/l/QTScckHZR0W5K3e9z0I0knJf0hyTV97TiT7bWS1iY5bPsSSYck/bznPydLWp3kpO2VkvZL2pXklb42nWb7V5ImJH03yc1975HmQpc0kWS2y68z1CP6VknvJXk/yX8kPSbpZ30OSvKipI/73HC2JB8lOTx6/zNJRyVd0fOmJDk5+nDl6K33o4ntdZJukvRA31v6MNTQr5D0zzM+Pqae/wEPne1NkjZLOtDzlNM3kY9Impa0N0nvmyTdL+luSV/2vONskfSc7UO2J7v6IkMN3fN8rvejwlDZvljSE5LuSvJp33uSfJHkWknrJG213etdHds3S5pOcqjPHV9hW5IfSvqppF+O7iK2bqihH5O0/oyP10k63tOWQRvdD35C0iNJnux7z5mSfCJpn6Qd/S7RNkm3jO4PPyZpu+2H+500J8nx0a/Tkp7S3N3W1g019IOSvm/7e7a/JelWSX/qedPgjB74elDS0ST39b1HkmyP27509P4qSddLeqfPTUnuSbIuySbN/Vt6PsntfW6SJNurRw+iyvZqSTdI6uS7OoMMPckpSTsl/VVzDzA9nuStPjfZflTSy5KutH3M9p197hnZJukOzR2hjozebux501pJL9h+Q3P/Ye9NMphvZw3M5ZL2235d0quS/pzk2S6+0CC/vQagXYM8ogNoF6EDBRA6UAChAwUQOlDAoEPv8kcCv64hbpKGuYtNzSzHpkGHLmlwfyka5iZpmLvY1Ez50AG0oJMfmBkbG8vGjZuWfD0zszMaHxtf+qAWtb3ptaP/aOV6curf8oWrWrmuzVdtaOV6Kvz9taHNTR9++IFmZ2fPeVLYha1c+1k2btyklw509mIZ3yhrtuzse8I5Xjqwu+8J+Jq2XTcx7+e56Q4UQOhAAYQOFEDoQAGEDhRA6EABhA4UQOhAAYQOFEDoQAGEDhRA6EABhA4U0Cj0oZ2rHMDiLBj66Fzlv9XcSeCulnSb7au7HgagPU2O6IM7VzmAxWkSOucqB85zTUJvdK5y25O2p2xPzczOLH0ZgNY0Cb3RucqT7EkykWRiaK/JBVTXJHTOVQ6c5xZ8ccgkp2yfPlf5CkkP9X2ucgCL0+hVYJM8I+mZjrcA6Ag/GQcUQOhAAYQOFEDoQAGEDhRA6EABhA4UQOhAAYQOFEDoQAGEDhRA6EABjZ7Ugu6cOLi77wkogCM6UAChAwUQOlAAoQMFEDpQAKEDBRA6UAChAwUQOlAAoQMFEDpQAKEDBRA6UAChAwUQOlDAgqHbfsj2tO03l2MQgPY1OaL/TtKOjncA6NCCoSd5UdLHy7AFQEe4jw4U0FrotidtT9mempmdaetqAbSgtdCT7EkykWRifGy8rasF0AJuugMFNPn22qOSXpZ0pe1jtu/sfhaANi34uu5JbluOIQC6w013oABCBwogdKAAQgcKIHSgAEIHCiB0oABCBwogdKAAQgcKIHSgAEIHCiB0oAAnaf1KL/jOZfn2lb9o/XqX4sTB3X1PwDfQmi07+57wPz5/93F9+a9pn/15juhAAYQOFEDoQAGEDhRA6EABhA4UQOhAAYQOFEDoQAGEDhRA6EABhA4UQOhAAYQOFNDkbKrrbb9g+6jtt2zvWo5hANqz4NlUJZ2S9Oskh21fIumQ7b1J3u54G4CWLHhET/JRksOj9z+TdFTSFV0PA9CeRd1Ht71J0mZJBzpZA6ATTW66S5JsXyzpCUl3Jfl0nt+flDQpSVp5cVv7ALSg0RHd9krNRf5Ikifnu0ySPUkmkkz4wlVtbgSwRE0edbekByUdTXJf95MAtK3JEX2bpDskbbd9ZPR2Y8e7ALRowfvoSfZLOuflYwGcP/jJOKAAQgcKIHSgAEIHCiB0oABCBwogdKAAQgcKIHSgAEIHCiB0oABCBwogdKCAxq8wsxibr9qglw7s7uKqUdSaLTv7njCvEweH9e9823WvzPt5juhAAYQOFEDoQAGEDhRA6EABhA4UQOhAAYQOFEDoQAGEDhRA6EABhA4UQOhAAYQOFNDktMkX2X7V9uu237J973IMA9CeJs9H/1zS9iQnba+UtN/2X5LM/8RXAIPT5LTJkXRy9OHK0Vu6HAWgXY3uo9teYfuIpGlJe5Mc6HQVgFY1Cj3JF0mulbRO0lbb15x9GduTtqdsT83MzrQ8E8BSLOpR9ySfSNonacc8v7cnyUSSifGx8XbWAWhFk0fdx21fOnp/laTrJb3T8S4ALWryqPtaSb+3vUJz/zE8nuTpbmcBaFOTR93fkLR5GbYA6Ag/GQcUQOhAAYQOFEDoQAGEDhRA6EABhA4UQOhAAYQOFEDoQAGEDhRA6EABhA4U0ORpqihmzZadfU84x4mDu/uecF7jiA4UQOhAAYQOFEDoQAGEDhRA6EABhA4UQOhAAYQOFEDoQAGEDhRA6EABhA4UQOhAAYQOFNA4dNsrbL9mm1MmA+eZxRzRd0k62tUQAN1pFLrtdZJukvRAt3MAdKHpEf1+SXdL+vKrLmB70vaU7amZ2Zk2tgFoyYKh275Z0nSSQ//vckn2JJlIMjE+Nt7aQABL1+SIvk3SLbY/kPSYpO22H+50FYBWLRh6knuSrEuySdKtkp5PcnvnywC0hu+jAwUs6nXdk+yTtK+TJQA6wxEdKIDQgQIIHSiA0IECCB0ogNCBAggdKIDQgQIIHSiA0IECCB0ogNCBAggdKGBRz15D+9Zs2dn3hHOcOLi77wloGUd0oABCBwogdKAAQgcKIHSgAEIHCiB0oABCBwogdKAAQgcKIHSgAEIHCiB0oABCBwpo9DTV0bnRP5P0haRTSSa6HAWgXYt5PvqPk8x2tgRAZ7jpDhTQNPRIes72IduTXQ4C0L6mN923JTlu+zJJe22/k+TFMy8w+g9gUpLWb9jQ8kwAS9HoiJ7k+OjXaUlPSdo6z2X2JJlIMjE+Nt7uSgBLsmDotlfbvuT0+5JukPRm18MAtKfJTffLJT1l+/Tl/5jk2U5XAWjVgqEneV/SD5ZhC4CO8O01oABCBwogdKAAQgcKIHSgAEIHCiB0oABCBwogdKAAQgcKIHSgAEIHCiB0oAAnaf9K7RlJH7ZwVWOShvaClEPcJA1zF5uaaXPTxiTnvPJLJ6G3xfbU0F5aeoibpGHuYlMzy7GJm+5AAYQOFDD00Pf0PWAeQ9wkDXMXm5rpfNOg76MDaMfQj+gAWkDoQAGEDhRA6EABhA4U8F/RIoCAvR6/qgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(test_data.G, cmap='Blues')\n",
    "plt.matshow(random_initial_data.G, cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1950d8d3",
   "metadata": {},
   "source": [
    "# 2 MC SAMPLER"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d2e2c5",
   "metadata": {},
   "source": [
    "## 2.4.1 Sampling G and K (BDMCMC Sampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a358d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from samplers import serialize_matrix, deserialize_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36e1d2a",
   "metadata": {},
   "source": [
    "#### Serialization test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "698cdd5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function is only used to test if the serialization and deserialization functions between R and python work properly\n",
    "import subprocess\n",
    "\n",
    "\n",
    "def serialization_function_test(n_tests=200, shape=(8,17)):    \n",
    "    results = []\n",
    "    for _ in range(n_tests):\n",
    "        \n",
    "        # Generate random matrix\n",
    "        test_matrix = np.random.random(shape) * 100\n",
    "        \n",
    "        # Serialize matrix\n",
    "        serialized_test_matrix = serialize_matrix(test_matrix)\n",
    "        \n",
    "        # Send to R script\n",
    "        command = f\"Rscript --vanilla serialize_matrix_test.R {serialized_test_matrix}\"\n",
    "        result = subprocess.run(command, \n",
    "                                shell=True, \n",
    "                                capture_output=True,\n",
    "                                text=True)\n",
    "        \n",
    "        # Deserialize from R output\n",
    "        deserialized_test_matrix = deserialize_matrix(result.stdout, shape=shape)\n",
    "        differences = np.abs(test_matrix-deserialized_test_matrix)\n",
    "        max_diff = np.max(differences)\n",
    "        min_diff = np.min(differences)\n",
    "        diff_sum = np.sum(differences)\n",
    "        results.append({'max_diff': max_diff, 'min_diff': min_diff, 'diff_sum': diff_sum})\n",
    "    return pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "368dfacf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 431 ms, sys: 876 ms, total: 1.31 s\n",
      "Wall time: 45.6 s\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_diff</th>\n",
       "      <th>min_diff</th>\n",
       "      <th>diff_sum</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_diff  min_diff  diff_sum\n",
       "0       0.0       0.0       0.0\n",
       "1       0.0       0.0       0.0\n",
       "2       0.0       0.0       0.0\n",
       "3       0.0       0.0       0.0\n",
       "4       0.0       0.0       0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "test_result_df = serialization_function_test()\n",
    "test_result_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "11bdab03",
   "metadata": {},
   "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>max_diff</th>\n",
       "      <th>min_diff</th>\n",
       "      <th>diff_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.132427e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.132427e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.678588e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.678588e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.105427e-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.105427e-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           max_diff  min_diff      diff_sum\n",
       "count  2.000000e+02     200.0  2.000000e+02\n",
       "mean   1.132427e-16       0.0  1.132427e-16\n",
       "std    8.678588e-16       0.0  8.678588e-16\n",
       "min    0.000000e+00       0.0  0.000000e+00\n",
       "10%    0.000000e+00       0.0  0.000000e+00\n",
       "20%    0.000000e+00       0.0  0.000000e+00\n",
       "30%    0.000000e+00       0.0  0.000000e+00\n",
       "40%    0.000000e+00       0.0  0.000000e+00\n",
       "50%    0.000000e+00       0.0  0.000000e+00\n",
       "60%    0.000000e+00       0.0  0.000000e+00\n",
       "70%    0.000000e+00       0.0  0.000000e+00\n",
       "80%    0.000000e+00       0.0  0.000000e+00\n",
       "90%    0.000000e+00       0.0  0.000000e+00\n",
       "max    7.105427e-15       0.0  7.105427e-15"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_result_df.describe(percentiles=[i * 0.1 for i in range(1, 10)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "737f7513",
   "metadata": {},
   "source": [
    "### Task:\n",
    "\n",
    "You must implement a function that receives matrices $W$, $Z_{i+1}$ and $H_{i+1}$ and generates the next $G_{i+1}$ and $K_{i+1}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3cbadd37",
   "metadata": {},
   "outputs": [],
   "source": [
    "from samplers import MC_sample_GK"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8c7993",
   "metadata": {},
   "source": [
    "## 2.4.2 G and K Sampling Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5139ab26",
   "metadata": {},
   "source": [
    "#### MC_sample_GK tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b34ea502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"[R] Matrix G:\"\n",
      "     V1 V2 V3 V4 V5 V6\n",
      "[1,]  0  1  0  0  0  0\n",
      "[2,]  1  0  0  0  1  0\n",
      "[3,]  0  0  0  1  1  0\n",
      "[4,]  0  0  1  0  1  1\n",
      "[5,]  0  1  1  1  0  0\n",
      "[6,]  0  0  0  1  0  0\n",
      "[1] \"[R] Data Matrix:\"\n",
      "             V1         V2         V3         V4         V5         V6\n",
      "[1,] 113.902604 -18.740222  -2.574988 -15.345477 -19.373414  -3.674199\n",
      "[2,] -18.740222  79.907268   2.299193   2.379722  -6.338472 -19.018370\n",
      "[3,]  -2.574988   2.299193 101.449047   6.748986  -9.993750  79.772529\n",
      "[4,] -15.345477   2.379722   6.748986  98.183702  75.337787  10.601217\n",
      "[5,] -19.373414  -6.338472  -9.993750  75.337787 165.112490 -10.433948\n",
      "[6,]  -3.674199 -19.018370  79.772529  10.601217 -10.433948 149.098644\n",
      "[1] \"[R] Shape Matrix:\"\n",
      "     [,1] [,2] [,3] [,4] [,5] [,6]\n",
      "[1,]    1    0    0    0    0    0\n",
      "[2,]    0    1    0    0    0    0\n",
      "[3,]    0    0    1    0    0    0\n",
      "[4,]    0    0    0    1    0    0\n",
      "[5,]    0    0    0    0    1    0\n",
      "[6,]    0    0    0    0    0    1\n",
      "[1] \"[R] Iterations:\"\n",
      "[1] 2\n",
      "2 iteration is started.                     \n",
      " Iteration  2                 \n",
      "[1] \"[R] Waiting time:\"\n",
      "[1] 0.2274686\n",
      "[1] \"[R] Sampled Matrix G:\"\n",
      "   V1 V2 V3 V4 V5 V6\n",
      "V1  0  1  0  0  0  0\n",
      "V2  1  0  0  0  1  0\n",
      "V3  0  0  0  1  1  0\n",
      "V4  0  0  1  0  1  1\n",
      "V5  0  1  1  1  0  0\n",
      "V6  0  0  0  1  0  0\n",
      "[1] \"[R] Sampled Matrix K:\"\n",
      "              [,1]          [,2]          [,3]          [,4]          [,5]\n",
      "[1,]  7.755787e-03  3.121897e-03 -4.767805e-15 -3.893115e-16 -2.521334e-20\n",
      "[2,]  3.121897e-03  1.612032e-02  3.260407e-17 -1.266843e-16  7.071180e-05\n",
      "[3,] -4.767799e-15  3.262499e-17  7.900983e-03 -8.334896e-03  5.244172e-03\n",
      "[4,] -3.893199e-16 -1.267311e-16 -8.334896e-03  3.472476e-02 -2.037142e-02\n",
      "[5,] -8.660694e-21  7.071180e-05  5.244172e-03 -2.037142e-02  1.513687e-02\n",
      "[6,]  2.341367e-22  2.018203e-21  2.054198e-19 -7.168958e-04  4.320252e-19\n",
      "              [,6]\n",
      "[1,]  3.332526e-22\n",
      "[2,]  1.484648e-21\n",
      "[3,]  1.825447e-19\n",
      "[4,] -7.168958e-04\n",
      "[5,]  4.320252e-19\n",
      "[6,]  4.016822e-03\n",
      "[1] \"[R] returning results:\"\n",
      "0.2274686245346042\n",
      "0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 \n",
      "0.00775578660706371415811677394458 0.00312189682263324394875803058369 -0.00000000000000476780465356365531 -0.00000000000000038931154762710736 -0.00000000000000000002521334092244 0.00000000000000000000033325263503 0.00312189682263324264771542360108 0.01612031866011256336923551657492 0.00000000000000003260407035296325 -0.00000000000000012668425182741174 0.00007071180415813044684448379629 0.00000000000000000000148464751870 -0.00000000000000476779926698654111 0.00000000000000003262499378337764 0.00790098275158943735840821886995 -0.00833489621502869065805541026748 0.00524417234528597956538797930648 0.00000000000000000018254466846224 -0.00000000000000038931987497913754 -0.00000000000000012673110386543955 -0.00833489621502869239277888624429 0.03472475690505559542398117400808 -0.02037141534068262135881433039231 -0.00071689584618427342095625665053 -0.00000000000000000000866069375667 0.00007071180415813048750206526449 0.00524417234528597956538797930648 -0.02037141534068261788936737843869 0.01513687488999732044803181452153 0.00000000000000000043202518122218 0.00000000000000000000023413670793 0.00000000000000000000201820258329 0.00000000000000000020541979189918 -0.00071689584618427331253603940198 0.00000000000000000043202518122218 0.00401682245201382282645541366151 \n"
     ]
    }
   ],
   "source": [
    "# Check that the function runs with no issues, without side effects\n",
    "G_copy = random_initial_data.G.copy()\n",
    "H_copy = test_data.H.copy()\n",
    "degrees_of_freedom_b = k - 1\n",
    "shape_matrix = np.eye(k)\n",
    "\n",
    "wt_sample, G_sample, K_sample = MC_sample_GK(G_copy, H_copy, degrees_of_freedom_b, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ffaaed0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7faa662eea60>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(G_sample, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1dbda2e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7faa65cc3b50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(K_sample, cmap='Blues')  # Why is this NOT a mask of G?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e5dacc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.84 s, sys: 23.2 s, total: 31 s\n",
      "Wall time: 1h 31min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Check that the Sampler works properly (This will take... forever)\n",
    "K_errors = []\n",
    "G_errors = []\n",
    "wrong_edges = []\n",
    "matrix_changes = []\n",
    "burn_in = 1000\n",
    "for i in range(5000):\n",
    "    K_sample_old = K_sample.copy()\n",
    "    G_sample_old = G_sample.copy()\n",
    "    wt_sample, G_sample, K_sample = MC_sample_GK(G_sample, H_copy, degrees_of_freedom_b, debug=False)\n",
    "    matrix_changes.append(~np.all(K_sample_old == K_sample))\n",
    "    if i >= burn_in:\n",
    "        K_errors.append(np.linalg.norm(K_sample - test_data.K))  # Computing error wrt the target matrix        \n",
    "        G_errors.append(np.linalg.norm(G_sample - test_data.G))  # Computing error wrt the target matrix\n",
    "        wrong_edges.append(np.sum(np.abs(G_sample - test_data.G)))  # Computing error wrt the target matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f19e98a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K matrix changes (1 = changed, 0 = same)\n",
      "count    5000.0\n",
      "mean        1.0\n",
      "std         0.0\n",
      "min         1.0\n",
      "25%         1.0\n",
      "50%         1.0\n",
      "75%         1.0\n",
      "max         1.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQ70lEQVR4nO3cf6zd9V3H8efLdrLKxgYybpoWLTFVx4+xyRVJpvFuGOmmWTGRpBNHs5A0IpqZkDjwDxdjmmx/YBZQmM1cWiKONG6zdZFpUz1OMxgryugKQ5qBrGlDM6YbFxOk7O0f54s5aW97z+29/R5uP89HcnK+532+3/N9f87pffWcz/meb6oKSVIbfmjSDUiS+mPoS1JDDH1JaoihL0kNMfQlqSErJ93AfC688MJat27dpNtYkJdeeolzzz130m30yjG3wTEvH48++uh3quptx9df96G/bt069u3bN+k2FmQwGDAzMzPpNnrlmNvgmJePJP85V93pHUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktSQsUI/ybNJ9id5LMm+rnZBkj1Jnu6uzx9Z/44kB5M8leS6kfpV3eMcTHJXkiz9kCRJJ7OQd/rvqap3VtV0d/t2YG9VrQf2drdJcimwCbgM2ADck2RFt829wBZgfXfZsPghSJLGtZjpnY3Ajm55B3D9SP2Bqnq5qp4BDgJXJ1kNnFdVD9XwJP73jWwjSerBuL/ILeAfkhTw51W1DZiqqiMAVXUkyUXdumuAh0e2PdTVXumWj6+fIMkWhp8ImJqaYjAYjNnm68Ps7Oyy63mxWhzz0e9+j7vv3zWRfV+x5i0T2W+Lr/PZNuZxQ//dVXW4C/Y9Sb55inXnmqevU9RPLA7/U9kGMD09XcvtJ9DL9Wfbi9HimO++fxd37p/MmUyevXFmIvtt8XU+28Y81vROVR3uro8CXwCuBp7vpmzoro92qx8CLh7ZfC1wuKuvnaMuSerJvKGf5Nwkb35tGfhl4BvAbmBzt9pm4LXPubuBTUnOSXIJwy9sH+mmgl5Mck131M5NI9tIknowzmfTKeAL3dGVK4G/qqovJfkasDPJzcBzwA0AVXUgyU7gCeAYcGtVvdo91i3AdmAV8GB3kST1ZN7Qr6pvAVfOUX8BuPYk22wFts5R3wdcvvA2JUlLwV/kSlJDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhoydugnWZHk35N8sbt9QZI9SZ7urs8fWfeOJAeTPJXkupH6VUn2d/fdlSRLOxxJ0qks5J3+R4AnR27fDuytqvXA3u42SS4FNgGXARuAe5Ks6La5F9gCrO8uGxbVvSRpQcYK/SRrgV8BPj1S3gjs6JZ3ANeP1B+oqper6hngIHB1ktXAeVX1UFUVcN/INpKkHqwcc71PAr8PvHmkNlVVRwCq6kiSi7r6GuDhkfUOdbVXuuXj6ydIsoXhJwKmpqYYDAZjtvn6MDs7u+x6XqwWxzy1Cm674thE9j2p57rF1/lsG/O8oZ/kV4GjVfVokpkxHnOuefo6Rf3EYtU2YBvA9PR0zcyMs9vXj8FgwHLrebFaHPPd9+/izv3jvm9aWs/eODOR/bb4Op9tYx7nX+y7gQ8keT/wRuC8JH8JPJ9kdfcufzVwtFv/EHDxyPZrgcNdfe0cdUlST+ad06+qO6pqbVWtY/gF7T9W1W8Cu4HN3WqbgV3d8m5gU5JzklzC8AvbR7qpoBeTXNMdtXPTyDaSpB4s5rPpx4GdSW4GngNuAKiqA0l2Ak8Ax4Bbq+rVbptbgO3AKuDB7iJJ6smCQr+qBsCgW34BuPYk620Fts5R3wdcvtAmJUlLw1/kSlJDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhoyb+gneWOSR5J8PcmBJH/U1S9IsifJ0931+SPb3JHkYJKnklw3Ur8qyf7uvruS5MwMS5I0l3He6b8MvLeqrgTeCWxIcg1wO7C3qtYDe7vbJLkU2ARcBmwA7kmyonuse4EtwPrusmHphiJJms+8oV9Ds93NN3SXAjYCO7r6DuD6bnkj8EBVvVxVzwAHgauTrAbOq6qHqqqA+0a2kST1YKw5/SQrkjwGHAX2VNVXgamqOgLQXV/Urb4G+PbI5oe62ppu+fi6JKknK8dZqapeBd6Z5K3AF5JcforV55qnr1PUT3yAZAvDaSCmpqYYDAbjtPm6MTs7u+x6XqwWxzy1Cm674thE9j2p57rF1/lsG/NYof+aqvrvJAOGc/HPJ1ldVUe6qZuj3WqHgItHNlsLHO7qa+eoz7WfbcA2gOnp6ZqZmVlImxM3GAxYbj0vVotjvvv+Xdy5f0F/Qkvm2RtnJrLfFl/ns23M4xy987buHT5JVgG/BHwT2A1s7lbbDOzqlncDm5Kck+QShl/YPtJNAb2Y5JruqJ2bRraRJPVgnLcpq4Ed3RE4PwTsrKovJnkI2JnkZuA54AaAqjqQZCfwBHAMuLWbHgK4BdgOrAIe7C6SpJ7MG/pV9TjwrjnqLwDXnmSbrcDWOer7gFN9HyBJOoP8Ra4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDVk3tBPcnGSf0ryZJIDST7S1S9IsifJ0931+SPb3JHkYJKnklw3Ur8qyf7uvruS5MwMS5I0l3He6R8DbquqtwPXALcmuRS4HdhbVeuBvd1tuvs2AZcBG4B7kqzoHuteYAuwvrtsWMKxSJLmMW/oV9WRqvq3bvlF4ElgDbAR2NGttgO4vlveCDxQVS9X1TPAQeDqJKuB86rqoaoq4L6RbSRJPVi5kJWTrAPeBXwVmKqqIzD8jyHJRd1qa4CHRzY71NVe6ZaPr8+1ny0MPxEwNTXFYDBYSJsTNzs7u+x6XqwWxzy1Cm674thE9j2p57rF1/lsG/PYoZ/kTcDngN+rqu+fYjp+rjvqFPUTi1XbgG0A09PTNTMzM26brwuDwYDl1vNitTjmu+/fxZ37F/S+ack8e+PMRPbb4ut8to15rKN3kryBYeDfX1Wf78rPd1M2dNdHu/oh4OKRzdcCh7v62jnqkqSejHP0ToC/AJ6sqj8ZuWs3sLlb3gzsGqlvSnJOkksYfmH7SDcV9GKSa7rHvGlkG0lSD8b5bPpu4EPA/iSPdbU/AD4O7ExyM/AccANAVR1IshN4guGRP7dW1avddrcA24FVwIPdRZLUk3lDv6r+lbnn4wGuPck2W4Gtc9T3AZcvpEFJ0tLxF7mS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhswb+kk+k+Rokm+M1C5IsifJ0931+SP33ZHkYJKnklw3Ur8qyf7uvruSZOmHI0k6lXHe6W8HNhxXux3YW1Xrgb3dbZJcCmwCLuu2uSfJim6be4EtwPrucvxjSpLOsHlDv6q+DHz3uPJGYEe3vAO4fqT+QFW9XFXPAAeBq5OsBs6rqoeqqoD7RraRJPVk5WluN1VVRwCq6kiSi7r6GuDhkfUOdbVXuuXj63NKsoXhpwKmpqYYDAan2eZkzM7OLrueF6vFMU+tgtuuODaRfU/quW7xdT7bxny6oX8yc83T1ynqc6qqbcA2gOnp6ZqZmVmS5voyGAxYbj0vVotjvvv+Xdy5f6n/hMbz7I0zE9lvi6/z2Tbm0z165/luyobu+mhXPwRcPLLeWuBwV187R12S1KPTDf3dwOZueTOwa6S+Kck5SS5h+IXtI91U0ItJrumO2rlpZBtJUk/m/Wya5LPADHBhkkPAx4CPAzuT3Aw8B9wAUFUHkuwEngCOAbdW1avdQ93C8EigVcCD3UWS1KN5Q7+qPniSu649yfpbga1z1PcBly+oO0nSkvIXuZLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1JDeQz/JhiRPJTmY5Pa+9y9JLes19JOsAP4MeB9wKfDBJJf22YMktazvd/pXAwer6ltV9b/AA8DGnnuQpGat7Hl/a4Bvj9w+BPzc8Ssl2QJs6W7OJnmqh96W0oXAdybdRM8cc4/yiUnsFfB1Xk5+fK5i36GfOWp1QqFqG7DtzLdzZiTZV1XTk+6jT465DY55+et7eucQcPHI7bXA4Z57kKRm9R36XwPWJ7kkyQ8Dm4DdPfcgSc3qdXqnqo4l+R3g74EVwGeq6kCfPfRk2U5NLYJjboNjXuZSdcKUuiTpLOUvciWpIYa+JDXE0F+EcU4pkWQmyWNJDiT55757XGrzjTnJW5L8bZKvd2P+8CT6XCpJPpPkaJJvnOT+JLmrez4eT/Izffe41MYY843dWB9P8pUkV/bd41Kbb8wj6/1skleT/HpfvS01Q/80jXNKiSRvBe4BPlBVlwE39N3nUhrzNBq3Ak9U1ZXADHBnd6TWcrUd2HCK+98HrO8uW4B7e+jpTNvOqcf8DPCLVfUO4I85O77o3M6px/zav/9PMDwQZdky9E/fOKeU+A3g81X1HEBVHe25x6U2zpgLeHOSAG8Cvgsc67fNpVNVX2Y4hpPZCNxXQw8Db02yup/uzoz5xlxVX6mq/+puPszw9zbL2hivM8DvAp8DlvXfsaF/+uY6pcSa49b5SeD8JIMkjya5qbfuzoxxxvynwNsZ/uhuP/CRqvpBP+1NxDjPydnsZuDBSTdxpiVZA/wa8KlJ97JYfZ+G4WwyziklVgJXAdcCq4CHkjxcVf9xpps7Q8YZ83XAY8B7gZ8A9iT5l6r6/hnubVLGOrXI2SjJexiG/s9PupcefBL4aFW9OvwQu3wZ+qdvnFNKHAK+U1UvAS8l+TJwJbBcQ3+cMX8Y+HgNfwByMMkzwE8Dj/TTYu+aPLVIkncAnwbeV1UvTLqfHkwDD3SBfyHw/iTHqupvJtrVaXB65/SNc0qJXcAvJFmZ5EcYnlH0yZ77XErjjPk5hp9sSDIF/BTwrV677Ndu4KbuKJ5rgO9V1ZFJN3UmJfkx4PPAh5bxp9YFqapLqmpdVa0D/hr47eUY+OA7/dN2slNKJPmt7v5PVdWTSb4EPA78APh0VZ3ykLDXs3HGzPBoju1J9jOc+vhoVS3H09ICkOSzDI9CujDJIeBjwBvg/8f7d8D7gYPA/zD8pLOsjTHmPwR+FLine+d7bLmfhXKMMZ81PA2DJDXE6R1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhryfzwNKEWr+YSuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# K matrix should change\n",
    "matrix_changes = pd.Series(matrix_changes).astype(int)\n",
    "print(\"K matrix changes (1 = changed, 0 = same)\")\n",
    "print(matrix_changes.describe())\n",
    "matrix_changes.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3999b7d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K L2-error metrics\n",
      "count    4000.000000\n",
      "mean        3.512013\n",
      "std         0.008490\n",
      "min         3.479395\n",
      "25%         3.506447\n",
      "50%         3.512659\n",
      "75%         3.518137\n",
      "max         3.538883\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Error should be distributed close to 0 (I guess...)  # BUG https://trello.com/c/9k2e8PQz/33-mcsampleh-is-not-close-to-expected-target\n",
    "K_errors = pd.Series(K_errors)\n",
    "print(\"K L2-error metrics\")\n",
    "print(K_errors.describe())\n",
    "K_errors.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "436e24a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K L2-error series\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "K_errors.plot()\n",
    "print(\"K L2-error series\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dd95e92-8f9c-42a4-98bb-e2afc54a596c",
   "metadata": {},
   "outputs": [],
   "source": [
    "graphs = []\n",
    "counts = []\n",
    "cut=4000\n",
    "max_iterations=5000\n",
    "not_found = True\n",
    "for it in range(cut,max_iterations):\n",
    "    not_found = True\n",
    "    for j in range(len(graphs)):\n",
    "        if np.all(sampled_Gs1[it] == graphs[j]):\n",
    "            counts[j] += wt_sample[it]\n",
    "            not_found = False\n",
    "    if not_found:\n",
    "        graphs.append(sampled_Gs1[it])\n",
    "        counts.append(wt_sample[it])\n",
    "\n",
    "plt.bar(range(len(counts)),counts)\n",
    "plt.suptitle('Sampled graphs histogram', x=0.5, y=1, ha='center')\n",
    "plt.show()\n",
    "\n",
    "modes = pd.Series(counts).sort_values()\n",
    "\n",
    "plt.matshow(graphs[counts.index(modes[0])], cmap='Blues')\n",
    "plt.suptitle(\"Most frequent graph in stable region\", x=0.5, y=1, ha='center')\n",
    "plt.show()\n",
    "\n",
    "plt.matshow(graphs[counts.index(modes[1])], cmap='Blues')\n",
    "plt.suptitle(\"Second most frequent graph in stable region\", x=0.5, y=1, ha='center')\n",
    "plt.show()\n",
    "\n",
    "plt.matshow(graphs[counts.index(modes[2])], cmap='Blues')\n",
    "plt.suptitle(\"Third most frequent graph in stable region\", x=0.5, y=1, ha='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2bac831-31af-4996-bb23-29a492d0353b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b086450-3ed6-4914-b3e8-ba4b9c33b057",
   "metadata": {},
   "outputs": [],
   "source": [
    "we = [] #wrong edges unique\n",
    "counts = []\n",
    "cut=4000\n",
    "max_iterations=5000\n",
    "not_found = True\n",
    "for it in range(cut,max_iterations):\n",
    "    not_found = True\n",
    "    for j in range(len(we)):\n",
    "        if np.all(wrong_edges[it] == we[j]):\n",
    "            counts[j] += wt_sample[it]\n",
    "            not_found = False\n",
    "    if not_found:\n",
    "        we.append(wrong_edges[it])\n",
    "        counts.append(wt_sample[it])\n",
    "\n",
    "plt.hist(we, weights=counts)\n",
    "plt.suptitle('Wrong Edges histogram', x=0.5, y=1, ha='center')\n",
    "plt.show()\n",
    "\n",
    "modes = pd.Series(counts).sort_values()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "739f12f5-a0ce-46bb-9324-1f2b13be6571",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9de7ae0c-a1b8-4076-8c59-d9ec39b716f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "711d18e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G L2-error metrics\n",
      "count    4000.000000\n",
      "mean        2.091812\n",
      "std         0.609439\n",
      "min         1.414214\n",
      "25%         1.414214\n",
      "50%         2.449490\n",
      "75%         2.449490\n",
      "max         3.162278\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Error should be distributed close to 0 (I guess...)  # BUG https://trello.com/c/9k2e8PQz/33-mcsampleh-is-not-close-to-expected-target\n",
    "G_errors = pd.Series(G_errors)\n",
    "print(\"G L2-error metrics\")\n",
    "print(G_errors.describe())\n",
    "G_errors.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "134055f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G L2-error series\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G_errors.plot()\n",
    "print(\"G L2-error series\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2e5ec025",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G wrong edges count\n",
      "count    4000.000000\n",
      "mean        4.747000\n",
      "std         2.599169\n",
      "min         2.000000\n",
      "25%         2.000000\n",
      "50%         6.000000\n",
      "75%         6.000000\n",
      "max        10.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWWkpdCXNCxpUNJBSftT7Q2S9kp6Iv28oLD8RklDko5KurrV5s3MrDEzcabfGxErI6InPb8ZeCAilgEPpOdIugRYBywH1gBflTRvBvZvZmbTNBvDO2uBu9P03cA1hXp/RJyOiKeBIWDVLOzfzMwm0WroB/A9SQck9aVaZ0QcB0g/L0r1LuDZwrojqWZmZm2iiGh+ZenNEXFM0kXAXuBPgN0R8frCMj+LiAsk3Q7si4h7U30rsCcidtbYbh/QB9DZ2dnd39/fVH+jJ09x4sWmVm3JpV3n150/NjZGR0dHm7qZPvfVmLKOL6h/jM3V98t9NabVvnp7ew8Uht1fNr+VpiLiWPo5Kuk+qsM1JyQtjIjjkhYCo2nxEWBxYfVFwLFJtrsF2ALQ09MTlUqlqf5u27aLzYMtvcSmDF9XqTt/YGCAZl/TbHJfjSnr+IL6x9hcfb/cV2Nmq6+mh3cknSvpdePTwO8Bh4DdwPq02HpgV5reDayTtEDSUmAZ8HCz+zczs8a1cprSCdwnaXw7fx0R35H0D8AOSTcAzwAfAoiIw5J2AI8BZ4ANEfFSS92bmVlDmg79iHgKeHuN+k+BKydZZxOwqdl9mplZa/yNXDOzjDj0zcwy4tA3M8uIQ9/MLCPlXGRsZvYqseTmb5Wy37vWnDsr2/WZvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRlq5MfpiSQ9KOiLpsKSbUv1zkp6TdDA93l1YZ6OkIUlHJV09Ey/AzMymr5U/rXwG+FREPCLpdcABSXvTvK9ExJeKC0u6BFgHLAfeDHxf0lt9c3Qzs/Zp+kw/Io5HxCNp+gXgCNBVZ5W1QH9EnI6Ip4EhYFWz+zczs8bNyJi+pCXAO4AfpdInJD0q6U5JF6RaF/BsYbUR6v+SMDOzGaaIaG0DUgfwA2BTRHxTUifwPBDA54GFEfFRSbcD+yLi3rTeVmBPROyssc0+oA+gs7Ozu7+/v6neRk+e4sSLTa3akku7zq87f2xsjI6OjjZ1M33uqzFlHV9Q/xibq+/Xq7WvwedOtbGbf7P0/HktvV+9vb0HIqJnYr2l2yVKeg2wE9gWEd8EiIgThflfA+5PT0eAxYXVFwHHam03IrYAWwB6enqiUqk01d9t23axebD9d4Qcvq5Sd/7AwADNvqbZ5L4aU9bxBfWPsbn6fr1a+/pIibdLnI33q5WrdwRsBY5ExJcL9YWFxT4AHErTu4F1khZIWgosAx5udv9mZta4Vk5TLgM+DAxKOphqnwGulbSS6vDOMPAxgIg4LGkH8BjVK382+ModM7P2ajr0I+LvAdWYtafOOpuATc3u08zMWuNv5JqZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWkbaHvqQ1ko5KGpJ0c7v3b2aWs7aGvqR5wO3Au4BLqN5E/ZJ29mBmlrN2n+mvAoYi4qmI+BegH1jb5h7MzLLV7tDvAp4tPB9JNTMza4P5bd6fatTiFQtJfUBfejom6WiT+7sQeL7JdZumL065SCl9TYP7akxpfU1xjPn9asyc7Kv3iy339e9qFdsd+iPA4sLzRcCxiQtFxBZgS6s7k7Q/Inpa3c5Mc1+NcV+NcV+Nya2vdg/v/AOwTNJSSb8OrAN2t7kHM7NstfVMPyLOSPoE8F1gHnBnRBxuZw9mZjlr9/AOEbEH2NOm3bU8RDRL3Fdj3Fdj3FdjsupLEa/4HNXMzM5S/jMMZmYZOetCX9JiSQ9KOiLpsKSbyu5pnKTfkPSwpJ+k3v687J7GSZon6ceS7i+7lyJJw5IGJR2UtL/sfsZJer2kb0h6PB1rvz0Hero4vU/jj59L+mTZfQFI+s/pmD8kabuk3yi7JwBJN6WeDpf5Xkm6U9KopEOF2hsk7ZX0RPp5wUzs66wLfeAM8KmIeBuwGtgwh/7Uw2ngdyLi7cBKYI2k1eW29LKbgCNlNzGJ3ohYOccuq/sfwHci4reAtzMH3ruIOJrep5VAN/BL4L5yuwJJXcB/AnoiYgXVizjWldsVSFoB/Eeqfyng7cB7JS0rqZ27gDUTajcDD0TEMuCB9LxlZ13oR8TxiHgkTb9A9R/jnPjWb1SNpaevSY/SP1SRtAh4D/D1snt5NZB0HnAFsBUgIv4lIv651KZe6UrgyYj4x7IbSeYD50iaD7yWGt/PKcHbgB9GxC8j4gzwA+ADZTQSEQ8BJyeU1wJ3p+m7gWtmYl9nXegXSVoCvAP4UcmtvCwNoxwERoG9ETEXevvvwKeB/1dyH7UE8D1JB9I3teeCtwD/BPyvNCT2dUnnlt3UBOuA7WU3ARARzwFfAp4BjgOnIuJ75XYFwCHgCklvlPRa4N386pdHy9YZEcehejILXDQTGz1rQ19SB7AT+GRE/LzsfsZFxEvpv9+LgFXpv5ilkfReYDQiDpTZRx2XRcQ7qf5l1g2Srii7Iapnre8E7oiIdwC/YIb+6z0T0hcf3w/8Tdm9AKSx6LXAUuDNwLmS/qjcriAijgBfBPYC3wF+QnV4+Kx2Voa+pNdQDfxtEfHNsvupJQ0HDPDKcbx2uwx4v6Rhqn/19Hck3VtuS/8mIo6ln6NUx6dXldsRUP1zIiOF/6V9g+ovgbniXcAjEXGi7EaSq4CnI+KfIuJfgW8C/6HkngCIiK0R8c6IuILq8MoTZfdUcELSQoD0c3QmNnrWhb4kUR1rPRIRXy67nyJJb5L0+jR9DtV/DI+X2VNEbIyIRRGxhOqQwN9FROlnYQCSzpX0uvFp4Peo/pe8VBHxf4FnJV2cSlcCj5XY0kTXMkeGdpJngNWSXpv+fV7JHPjgG0DSRennbwIfZG69b7uB9Wl6PbBrJjba9m/ktsFlwIeBwTR2DvCZ9E3gsi0E7k43k/k1YEdEzKlLJOeYTuC+ak4wH/jriPhOuS297E+AbWko5Sngj0vuB4A0Nv27wMfK7mVcRPxI0jeAR6gOn/yYufMt2J2S3gj8K7AhIn5WRhOStgMV4EJJI8BngVuAHZJuoPqL80Mzsi9/I9fMLB9n3fCOmZlNzqFvZpYRh76ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUYc+mZmGfn/M8gl+YGtbfAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wrong_edges = pd.Series(wrong_edges)\n",
    "print(\"G wrong edges count\")\n",
    "print(wrong_edges.describe())\n",
    "wrong_edges.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "542aff0b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G wrong edges series\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wrong_edges.plot()\n",
    "print(\"G wrong edges series\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a803b5bf187bf95155bcb05f6102b06519856e9ee60d4a9ebc29561a74488513"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}