{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9b5254c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-15 15:58:02.929210: 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",
      "2022-11-15 15:58:02.929250: 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"
     ]
    }
   ],
   "source": [
    "from library.analysis import loadDataset, testSets\n",
    "from library.generators.XConvGeN import XConvGeN\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": [
    "gen = XConvGeN(data.data0.shape[1], neb=5, fdc=fdc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad01be2b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-15 15:58:11.721013: 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",
      "2022-11-15 15:58:11.721066: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-11-15 15:58:11.721100: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (efdcb09fb24e): /proc/driver/nvidia/version does not exist\n",
      "2022-11-15 15:58:11.722233: I tensorflow/core/platform/cpu_feature_guard.cc:151] 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"
     ]
    }
   ],
   "source": [
    "gen.reset(data.data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4698522c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDC.normalize (init): 0.00009 / 0.000s\n",
      "|data| = (58, 8)\n",
      "umap with metric 'euclidean'\n",
      "|part| = (58, 7)\n",
      "|emb_A| = (58, 2)\n",
      "|emb_A| = (58, 2)\n",
      "FDC.normalize (clustering CONT): 7.36561 / 7.366s\n",
      "FDC.normalize (clustering ORD): 0.00006 / 7.366s\n",
      "umap with metric 'hamming'\n",
      "|part| = (58, 1)\n",
      "|emb_A| = (58, 1)\n",
      "|emb_A| = (58, 1)\n",
      "FDC.normalize (clustering NOM): 1.47620 / 8.842s\n",
      "FDC.normalize (concat): 0.00008 / 8.842s\n",
      "|fdc| = (58, 3)\n",
      "FDC.normalize (total): 0.00002 / 8.842s\n",
      "|N| = (58, 3)\n",
      "|D| = (58, 8)\n",
      "[==========] [======    ] [======    ]\r"
     ]
    }
   ],
   "source": [
    "gen.train(data.data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cda17654",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "syntheticPoints = gen.generateData(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41853bd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "syntheticPoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da5ebdb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.data1[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4043256c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "for p in syntheticPoints:\n",
    "    print(min([math.sqrt(sum(y*y)) for y in (data.data1 - p) ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bcc8cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "v = gen.predictReal(data.data1)\n",
    "(min(v), max(v), sum(v) / len(v))"
   ]
  }
 ],
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