{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9b5254c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-08 15:16:15.678698: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-12-08 15:16:15.795899: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-12-08 15:16:15.795919: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2022-12-08 15:16:15.822718: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2022-12-08 15:16:16.427243: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-08 15:16:16.427307: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-08 15:16:16.427317: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "from library.analysis import loadDataset, testSets\n",
    "from library.exercise import plotCloud\n",
    "from library.generators.XConvGeN import XConvGeN, GeneratorConfig\n",
    "from library.timing import timing\n",
    "from fdc.fdc import FDC\n",
    "#from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6bfec0a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load 'folding_abalone_17_vs_7_8_9_10'\n",
      "from pickle file\n",
      "Data loaded.\n"
     ]
    }
   ],
   "source": [
    "data = loadDataset(testSets[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d686da5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2338, 2280, 58)\n"
     ]
    }
   ],
   "source": [
    "print((len(data.data), len(data.data0), len(data.data1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e3e3806",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdc = FDC()\n",
    "fdc.nom_list = [0]\n",
    "fdc.cont_list = list(range(data.data0.shape[1]))[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01d71d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = GeneratorConfig(n_feat=data.data0.shape[1], neb=5, genAddNoise=True)\n",
    "gen = XConvGeN(config=config, fdc=fdc, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad01be2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-08 15:16:22.409200: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-12-08 15:16:22.409222: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-12-08 15:16:22.409241: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (f689f5a8be0b): /proc/driver/nvidia/version does not exist\n",
      "2022-12-08 15:16:22.409472: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neb=5, gen=5\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 5, 8)]       0           []                               \n",
      "                                                                                                  \n",
      " UnsharpenInput (Conv1D)        (None, 3, 8)         200         ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " InputMatrixToVector (Flatten)  (None, 24)           0           ['UnsharpenInput[0][0]']         \n",
      "                                                                                                  \n",
      " P5_dense (Dense)               (None, 25)           625         ['InputMatrixToVector[0][0]']    \n",
      "                                                                                                  \n",
      " P5_reshape (Reshape)           (None, 5, 5)         0           ['P5_dense[0][0]']               \n",
      "                                                                                                  \n",
      " tf.math.reduce_sum (TFOpLambda  (None, 5)           0           ['P5_reshape[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " P5_make_non_zero (Lambda)      (None, 5)            0           ['tf.math.reduce_sum[0][0]']     \n",
      "                                                                                                  \n",
      " tf.math.reciprocal (TFOpLambda  (None, 5)           0           ['P5_make_non_zero[0][0]']       \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " P5_normalize (Multiply)        (None, 5, 5)         0           ['tf.math.reciprocal[0][0]',     \n",
      "                                                                  'P5_reshape[0][0]']             \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem (Slic  (5, 5)              0           ['P5_normalize[0][0]']           \n",
      " ingOpLambda)                                                                                     \n",
      "                                                                                                  \n",
      " tf.compat.v1.transpose (TFOpLa  (5, 5)              0           ['tf.__operators__.getitem[0][0]'\n",
      " mbda)                                                           ]                                \n",
      "                                                                                                  \n",
      " tf.linalg.matmul (TFOpLambda)  (None, 5, 8)         0           ['tf.compat.v1.transpose[0][0]', \n",
      "                                                                  'input_1[0][0]']                \n",
      "                                                                                                  \n",
      " tf.identity (TFOpLambda)       (None, 5, 8)         0           ['tf.linalg.matmul[0][0]']       \n",
      "                                                                                                  \n",
      " RandomNoise (Sequential)       (None, 40)           3280        ['tf.identity[0][0]']            \n",
      "                                                                                                  \n",
      " ReshapeNoise (Reshape)         (None, 5, 8)         0           ['RandomNoise[0][0]']            \n",
      "                                                                                                  \n",
      " AddNoise (Add)                 (None, 5, 8)         0           ['tf.identity[0][0]',            \n",
      "                                                                  'ReshapeNoise[0][0]']           \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 4,105\n",
      "Trainable params: 4,105\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "\n",
      "\n",
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 8)]               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 250)               2250      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 125)               31375     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 75)                9450      \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 2)                 152       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 43,227\n",
      "Trainable params: 0\n",
      "Non-trainable params: 43,227\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "\n",
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_4 (InputLayer)           [(None, 2, 5, 8)]    0           []                               \n",
      "                                                                                                  \n",
      " SplitForGen (Lambda)           (None, 5, 8)         0           ['input_4[0][0]']                \n",
      "                                                                                                  \n",
      " model (Functional)             (None, 5, 8)         4105        ['SplitForGen[0][0]']            \n",
      "                                                                                                  \n",
      " SplitForDisc (Lambda)          (None, 5, 8)         0           ['input_4[0][0]']                \n",
      "                                                                                                  \n",
      " tf.reshape_1 (TFOpLambda)      (None, 8)            0           ['model[0][0]']                  \n",
      "                                                                                                  \n",
      " tf.reshape (TFOpLambda)        (None, 8)            0           ['SplitForDisc[0][0]']           \n",
      "                                                                                                  \n",
      " model_1 (Functional)           (None, 2)            43227       ['tf.reshape_1[0][0]',           \n",
      "                                                                  'tf.reshape[0][0]']             \n",
      "                                                                                                  \n",
      " tf.reshape_2 (TFOpLambda)      (None, 5, 2)         0           ['model_1[0][0]']                \n",
      "                                                                                                  \n",
      " tf.reshape_3 (TFOpLambda)      (None, 5, 2)         0           ['model_1[1][0]']                \n",
      "                                                                                                  \n",
      " tf.concat (TFOpLambda)         (None, 10, 2)        0           ['tf.reshape_2[0][0]',           \n",
      "                                                                  'tf.reshape_3[0][0]']           \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 47,332\n",
      "Trainable params: 4,105\n",
      "Non-trainable params: 43,227\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gen.reset(data.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4698522c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDC.normalize (init): 0.00002 / 0.000s\n",
      "|data| = (2338, 8)\n",
      "umap with metric 'euclidean'\n",
      "|part| = (2338, 7)\n",
      "|emb_A| = (2338, 2)\n",
      "|emb_A| = (2338, 2)\n",
      "FDC.normalize (clustering CONT): 12.90312 / 12.903s\n",
      "FDC.normalize (clustering ORD): 0.00002 / 12.903s\n",
      "umap with metric 'hamming'\n",
      "|part| = (2338, 1)\n",
      "|emb_A| = (2338, 1)\n",
      "|emb_A| = (2338, 1)\n",
      "FDC.normalize (clustering NOM): 25.12376 / 38.027s\n",
      "FDC.normalize (concat): 0.00005 / 38.027s\n",
      "|fdc| = (2338, 3)\n",
      "FDC.normalize (total): 0.00001 / 38.027s\n",
      "[========================================]\r"
     ]
    },
    {
     "data": {
      "image/png": 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5uUbXrl3N+Xx8fIypU6ca+/fvrzR2//79xnPPPWd06NDBSEtLq3R7xZ2w5c97ixYtjHfeecfm+7ekpMTYuXOnzX0rfm2d2WkbFRVlSDISEhIq7d4/fPiwcemll5pjIyIijM8//9zw8fExQkNDjVdeecXm+Tp16pRx55132tTw1Vdf2a3BMGq20zY6OtqwWCzGvffeW+l7es2aNUZsbKw5tm3btlX+vLj66quNG264wfjvf/9rWK3WSrcXFBQY//nPf2z+HnLllVc6nM8wXPvtlYrc/Xciw7B9LZS/jm666SZj7969NuMOHjxo5OXlOV0rADQEhLYAAK/nSmhbWlpqDB482OYfCfb+MWoYhvHxxx/bjKvq13R/++038x+V5UGEPbUV2m7ZssXm11zP/BXRik6cOGEkJiba1OFsaCvJuPfeex2O3bt3rxESEmKOXbBggVOPsTrO/APzm2++MccEBgZWCtPO9Oabb9r8Iz8nJ8fuuOp+vb3c8ePHjfbt25tzfvrppw7HVgyZpLKWFY7Wr6i2vhauhrbl/xB39L1jGIbxzDPP2Ix19Dzed9995jg/Pz/j+++/dzjnp59+Wuk5qMvQtuKv3MfFxRmHDx92OPaOO+6wqfPMANEwDOOf//yneXtVwXZF9tp5vP766+Y8zoax1bUFcZYroe2RI0eMoKAgc/yQIUMc1pGbm2v06tXLJrRzFNjU9PvJFbNnz7YJq5YuXepw7Kuvvur069SZ117FQPLVV191ql5Hz+v06dNtAtslS5ZUO1dRUZGRn59f6c8rhqrlgVt6erpT9bka2kplv27v6HsuMzPT5mdwQECA4evrW+XPgYEDB5rjb7nlFofjahLaSjJeeuklh3OuW7fO5md2VW1knP0ZtGvXLiMyMtKcc8OGDQ7H1iS0rY2/ExlG5Z9rEydOdKoeAPAGHEQGAMBpO3fu1B/+8ActX77c/LObbrpJPXr0sDt+/vz55nVSUpLuueceh3MnJibq0UcfNT//4osvzF+BrAuvvvqq+evwLVu21FNPPeVwbNOmTTVnzpwarZOYmKhHHnnE4e1t2rSx+TVPVw/lORsVH/P999+vAQMGVDn+5ptv1uDBgyWVHTT21ltv2R1X8dC6qkRFRen+++83P//444+dup8kvfLKK2rSpInT4yXPfy1mzpzp8HtHkiZNmqTg4GBJZb/mbu/QneLiYi1cuND8/K9//asuueQSh3MOHTpUN954Y82LPgs//vijTeuRuXPn6pxzznE4/umnn7ZpCWHv1/v3799vXvfp08epOnx9fWttntq2YMEC81eyQ0ND9e9//9thHSEhIVqwYIF5+4kTJ/Sf//zHqXVq8v3kzJzlRo0apWHDhjkcO378eA0aNMhta7vr65udna2XXnrJ/Pxvf/ubbrjhhmrn8vPzU1BQULXj/vGPf6hz585O1VcTzzzzjMPvucjISP3xj380Py8sLNTEiROr/Dlw2223mdfufn+89NJLNWnSJIe3Jycnq1+/fk6t7+zPoPj4eE2ZMsX8/JNPPnHqfs6qi78TnXPOOXr22WfPrlAAaEAIbQEAjcr69et19dVX23xceumlSkxMVIcOHfThhx+aYy+//HL9+9//tjvPyZMn9b///c/8fMqUKQ57npYbN26cIiIiJEmGYbj9H0xV+eyzz8zr0aNHV/uPvKuuuqrak8rtufXWW+XnV3XL/L59+5rXZ9tD0lnHjh0zexL6+/vrjjvucOp+N998s3n9zTffnHUdF110kXm9bt06p+7Ts2dPJScnu7yWp78WEydOrPL20NBQ9ezZs8r109LSdPjwYfPzqkKOcn/5y19cqNJ9PvroI/O6Xbt2uvbaa6scHxQUZFPrJ598UqnPaMUgbP369TWuzV3z1LaKz+Ef//hHxcTEVDn+3HPP1eWXX273/o7U9PupKps2bbIJnJx5f5k8ebLb1nfX1/fTTz81+/H6+/vr3nvvPevayvn5+WncuHFum+9MERER+sMf/lDlmF69etl8Pn78+CrHV3y/3rlzpwoLC2te4BkqBsKO1Mb7c01+Bjmjrv5O9Kc//cnpkBoAvAEHkQEAGpXMzMxqDxTp2LGj/v73v+vmm292eKhRSkqKTcBSviOzKkFBQbr88sv1wQcfSJJ++uknFyqvufLDsMo5s8PLYrFowIAB2r17t0trXXzxxdWOad26tXldfthLbfvuu+/Mncbnn3++3UNV7Dn33HPNa3s7Qc+0adMmffvtt9q4caOOHz+ukydPmgfESLI5rOfAgQNO1VDxH+6u8OTXovwgnLNdv2Ko0Lx5c3Xr1q3aOfv06SN/f3/z4KG6UvH7+eqrr3bqPsOGDdPMmTMlSVarVVu2bFHXrl3N2yseVvfyyy8rMTHRZoeysyrOs3z5ct1333265557zMCkPigoKNAvv/xifu7Me6pU9hyWv6c7855a0++nqlR8nYaFhVW5G7zcVVddJYvFYr4vnY2kpCQzrJ0yZYpCQ0M1ZMiQakOzM61evdq87tOnT5U7xV3VpUsXRUdHu22+MyUlJVX7n1QV35MCAwOr/E2AM8cbhqHs7Gw1b978rOosV1vvz+vWrdP333+vzZs3KzMzU7m5uTZ/Vzlx4oR57ezPIGfU1d+JauP7FwDqM0JbAADOsGvXLqWnp1d5Cn3F065btGihFi1aODV39+7dzX+guOvE7Ors37/f5h9Tzv56apcuXVxey5mgLiQkxLwuP9G6tm3cuNG83rt3r9OhWsWQNSMjw+G4NWvWaOrUqVq7dq3TNWVnZzs1rkOHDk7PWZEnvxbOrO3M+nv37jWvnX3d+vv7q0OHDnW2i7tcxe/n7t27O3Wfzp07y8/PT8XFxeYcFUPbkSNH6r777tOBAwdUWlqqu+66Sw8++KCuvPJKDRw4UP369VP37t2rDef69eunCy64wPyPh1mzZunZZ5/VZZddpksvvVR9+/ZVUlKSR1oilNu3b5/NTkZnn8OK444dOyar1arw8HCH42v6/VSVil/7rl27OhWWhoaGKj4+Xrt27Trr9SdPnqzXX39dJSUlOnr0qIYNG6bY2FgNHjxYAwYMUL9+/dS2bdtq50lPTzevKwb97lAbz3tFzgTMFd9voqKiqg15K46X6v490pX3588++0zTpk1z6X3P2Z9BzqirvxPV9usIAOobQlsAQKMyYMAArVy50vy8uLhYBw4c0C+//KKnn35a3333nYqLizVr1iwVFhbq6aeftjtPxV0vruweatasmXmdmZnpcv01ceYOncjISKfuV5NdeAEBAS6Nd8cuM2ccP37cvD569Gi1u63tcfQP3CVLlujmm2+22VHrDGd/1TYsLMylect58mvh6tqO1q/42nX2dSvV7LV7tmrynuDn56fIyEjzPwTOfE8ICQnR0qVLNWzYMB08eFBS2Y7c999/X++//76ksveUoUOHavz48TY9MCuyWCz64IMPNGTIEDOYO3XqlJYtW6Zly5ZJksLDw3XllVdq7NixGjp0qPMP3E3OfJ9y9jms+J4qlT2HVYW2Nf1+qkrFr5srPw+io6PdEtpecMEFWrhwoW677TYVFBRIKvvPun//+99mi5+EhARdf/31uu222xwGXxV3YbprR2m52njeK3L1Pcdd71E15c7356efflrTp093uYby14o71NXfiWr7dQQA9Q09bQEAjZqfn5/i4uI0fPhwrVq1yqYP5zPPPGNzKFlFFf+x48o/viqOdec/mGpDVTuNG5rc3NyznsPeP5p3796tsWPHmoFt8+bNdd999+l///ufdu3aZbZHMAxDhmHUKKDxpq9DXfHEc1Zb7wk9e/ZUenq6Hn30USUkJFS6PSMjQ6+99pr69++vYcOGOdwRHh8fr7S0NM2dO1fnnXdepdvLw+Bhw4bp4osvdkuY6IozH7uzz+GZ46p7X62N10bF/4Bx5WsfGBjothpuueUWbd68WX/961/thmbbt2/XU089pc6dO+uee+4xd3dXVPG5c2dtEu9jtWXNmjU2gW1cXJwee+wxrVq1Svv27VNubq7Nz6AVK1bUSh119XciXkcAGhve9QAAOM1isWj+/Pk2gcbtt99ud0dkxZ185Qe3OKPiWFd2Dlaluh2eZ+46dPZXIutqJ3BdqPgcDB061PwHrKsfZ3ruuefM0+7bt2+vDRs26LHHHtNll12m+Ph4hYaG2vwj05XXCmy/bq78Kq8nXru1+Z4QHh6u+++/X9u2bdOOHTu0YMEC3XLLLTY9LyVp2bJluvrqq+0GclJZEDdlyhT9+uuvOnDggN58801NnDix0s7LNWvWaNCgQXX6PJ75PuXsc3jmOHe9r7qi4s7emn7t3aF9+/Z64YUXdPToUaWmpuqZZ57Rtddea7M7sbi4WE899ZSmTZtW6f4Vnzt3/uo8as8TTzxhXvfu3VsbN27Ufffdp379+ik2NlYhISF18jOoPv2dCAC8CaEtAAAV+Pv7a86cOebnu3fv1osvvlhpXMVfHd2/f7/DkORMO3bssDtHuTN3qDhzmFJ1B5TExsba9Fh0tuddXfcErU0V+x0ePXrUbfN+9dVX5vU//vGPavsquvPgl8agYh9OZ1+PRUVF2rlzZ22V5FDF72dnd6keO3bMJrRw5lfS27dvr1tvvVWvvfaa9u3bp9WrV2vAgAHm7ampqXr77bernScmJkY333yzXnnlFW3fvl1paWm6/vrrzdv37NmjefPmOfU43OHMx+7sc1jxPdXX11dNmzZ1a13OqNi/09nDGw3DcPmgR2f5+Pjoggsu0P/93//p448/1rFjx7RkyRLFx8ebY+bPn19p/Yp9Vrdt21YrtcF9DMPQ//73P/PzJ554Qk2aNKnyPrX1M6i2/k4EAI0doS0AAGcYOHCgLr/8cvPzWbNm2RxIJZX1ECxXWFhoc+p5VSqejlxxjnJn9mtzZqdbxUO27AkLC1OnTp3Mz5399chvv/3WqXENQe/evc3rX3/91dwde7YqHpSVnJxc7fgff/z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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: #1 1843.5273s\n"
     ]
    }
   ],
   "source": [
    "t = timing(\"train\")\n",
    "t.start()\n",
    "gen.train(data.data)\n",
    "t.stop()\n",
    "print(t)\n",
    "\n",
    "# train: #1 225.9655s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cda17654",
   "metadata": {},
   "outputs": [],
   "source": [
    "syntheticPoints = gen.generateData(data.data1.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41853bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.0000000e+00,  2.3015420e+00,  4.6079218e-02,  7.7081907e-01,\n",
       "        -3.4013987e-02, -1.3480505e-01, -6.3393188e-01, -1.2388859e+00],\n",
       "       [ 1.0000000e+00,  8.5691714e-01, -1.0228012e+00,  5.3488541e-01,\n",
       "         5.2268308e-01,  2.0623505e-03,  5.8587635e-01,  1.2566128e+00],\n",
       "       [ 1.0000000e+00,  2.6453197e-01, -6.9461459e-01,  1.2983665e+00,\n",
       "         2.0413193e-01,  8.2688111e-01,  9.6752304e-01,  1.1820554e+00],\n",
       "       [ 1.0000000e+00, -1.0983694e+00,  5.0834978e-01, -9.4611064e-02,\n",
       "         3.1308138e-01, -3.2300714e-01,  6.0562038e-01, -1.8296972e-01],\n",
       "       [ 1.0000000e+00,  1.9067401e-01,  2.4663229e+00, -4.1466138e-01,\n",
       "         2.1007557e+00, -1.7141981e-01, -3.6188236e-01, -1.1953378e+00]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syntheticPoints[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da5ebdb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.    , 0.565 , 0.425 , 0.16  , 0.9425, 0.3495, 0.2185, 0.275 ],\n",
       "       [0.    , 0.495 , 0.4   , 0.155 , 0.6445, 0.242 , 0.1325, 0.205 ],\n",
       "       [2.    , 0.54  , 0.435 , 0.18  , 0.996 , 0.3835, 0.226 , 0.325 ],\n",
       "       [0.    , 0.69  , 0.56  , 0.215 , 1.719 , 0.68  , 0.299 , 0.47  ],\n",
       "       [0.    , 0.68  , 0.55  , 0.21  , 1.7445, 0.5975, 0.305 , 0.625 ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.data1[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4043256c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1281014723706335 2.3505856661513693 4.853550994444655\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "dists = [min([math.sqrt(sum(y*y)) for y in (data.data1 - p) ]) for p in syntheticPoints]\n",
    "print( min(dists), sum(dists) / len(dists), max(dists) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3bcc8cb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.1368595e-05, 0.12811339, 0.0030172379738046557)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = gen.predictReal(data.data1)\n",
    "(min(v), max(v), sum(v) / len(v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e0352dc8-3192-4cc0-ada4-1a0c450f5941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 6000x6000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCloud(data.data1, syntheticPoints)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}