{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9b5254c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-10 15:52:36.048983: 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",
      "2022-11-10 15:52:36.049008: 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.NextConvGeN import NextConvGeN\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 = NextConvGeN(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": [
      "2022-11-10 15:52:43.112497: 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",
      "2022-11-10 15:52:43.112538: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-11-10 15:52:43.112565: 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",
      "2022-11-10 15:52:43.112811: 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": 7,
   "id": "4698522c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDC.normalize (init): 0.00003 / 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): 5.65696 / 5.657s\n",
      "FDC.normalize (clustering ORD): 0.00002 / 5.657s\n",
      "umap with metric 'hamming'\n",
      "|part| = (58, 1)\n",
      "|emb_A| = (58, 1)\n",
      "|emb_A| = (58, 1)\n",
      "FDC.normalize (clustering NOM): 1.14268 / 6.800s\n",
      "FDC.normalize (concat): 0.00005 / 6.800s\n",
      "|fdc| = (58, 3)\n",
      "FDC.normalize (total): 0.00004 / 6.800s\n",
      "|N| = (58, 3)\n",
      "|D| = (58, 8)\n",
      "[==========] [======    ] [======    ]\r"
     ]
    }
   ],
   "source": [
    "gen.train(data.data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cda17654",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "syntheticPoints = gen.generateData(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41853bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.53445315, 0.42945328, 0.1744535 , 0.96993124,\n",
       "        0.3640874 , 0.23348743, 0.29893178],\n",
       "       [2.        , 0.56035066, 0.41464847, 0.1654633 , 0.9590763 ,\n",
       "        0.37012604, 0.23675683, 0.3104632 ],\n",
       "       [1.        , 0.548009  , 0.43936524, 0.1713553 , 0.9616028 ,\n",
       "        0.3593513 , 0.21599998, 0.30743402],\n",
       "       [1.        , 0.54999954, 0.43999964, 0.16499987, 0.86049926,\n",
       "        0.31199974, 0.16899987, 0.29999977],\n",
       "       [1.        , 0.5539543 , 0.4433802 , 0.16721351, 0.90213394,\n",
       "        0.33197013, 0.18213706, 0.30834144],\n",
       "       [2.        , 0.5534017 , 0.44726777, 0.16168031, 0.9173514 ,\n",
       "        0.35133395, 0.1702083 , 0.29730922],\n",
       "       [2.        , 0.58627754, 0.469185  , 0.17290716, 1.060739  ,\n",
       "        0.40853333, 0.20428768, 0.37035084],\n",
       "       [2.        , 0.5510869 , 0.4343453 , 0.16286607, 0.9295284 ,\n",
       "        0.35263863, 0.20032436, 0.29319543],\n",
       "       [0.        , 0.52999955, 0.42499965, 0.16999985, 0.9489992 ,\n",
       "        0.34849972, 0.2394998 , 0.27799976],\n",
       "       [0.        , 0.5421706 , 0.42764086, 0.16827805, 0.9629398 ,\n",
       "        0.35746834, 0.23870556, 0.28831378]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syntheticPoints"
   ]
  },
  {
   "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": [
      "0.08049949828195788\n",
      "0.038820822032602086\n",
      "0.08678922927950353\n",
      "1.0214483365545625e-06\n",
      "0.049054203874789606\n",
      "0.06402187676961534\n",
      "0.03621533523715291\n",
      "0.03359559417587524\n",
      "1.056058840778465e-06\n",
      "0.023233951625306986\n"
     ]
    }
   ],
   "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": 12,
   "id": "3bcc8cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4632151, 0.5130856, 0.49341423603995094)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = gen.predictReal(data.data1)\n",
    "(min(v), max(v), sum(v) / len(v))"
   ]
  }
 ],
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