new_model_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import gudhi\n",
    "import json\n",
    "import classifier\n",
    "import region\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'region' from 'D:\\\\PROJECT\\\\seminconductor\\\\code\\\\data2\\\\region.py'>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from importlib import reload\n",
    "reload(classifier)\n",
    "reload(region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_data_dictionary(basic_path='./'):\n",
    "    file_list=os.listdir(basic_path)\n",
    "    result_dict={'good':[],'bad':[]}\n",
    "    for file_name in file_list:\n",
    "        if 'Fault' in file_name:\n",
    "            work_list=result_dict['bad']\n",
    "        else:\n",
    "            work_list=result_dict['good']\n",
    "        data_frame=pd.read_csv(os.path.join(basic_path,file_name))\n",
    "        data_frame=data_frame.fillna(0)\n",
    "        for col_i in range(1,len(data_frame.columns)):\n",
    "            work_list.append(list(data_frame.iloc[:,col_i]))\n",
    "    return result_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_path='./FDC_DATA/tcmp'\n",
    "data_dict=make_data_dictionary(basic_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "cl=classifier.Classifier(dim=5,delay=2)\n",
    "cl.set_db_params(eps=0.2,min_samples=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=data_dict['good'][:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=data_dict['good'][100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " ...\n",
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      " ...\n",
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     "name": "stdout",
     "output_type": "stream",
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      "[[27.58644301 28.20157864]\n",
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      " [27.66659082 28.23627613]\n",
      " ...\n",
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      " [3.01438428e-03 3.13468453e-03]\n",
      " ...\n",
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      " [1.71207303e-04 1.71207304e-04]]\n",
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    {
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     "text": [
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      " [17.605921   17.61117103]\n",
      " [17.609083   17.61328896]\n",
      " [17.64831975 17.65424786]\n",
      " [17.64872075 17.65032112]\n",
      " [17.62264725 17.62748518]\n",
      " [17.62438425 17.62742351]\n",
      " [17.6229935  17.62308001]\n",
      " [17.60460675 17.60820511]\n",
      " [17.6046015  17.60730236]\n",
      " [17.60469175 17.60471863]\n",
      " [17.5783875  17.58275386]\n",
      " [17.5773235  17.58145109]\n",
      " [17.5788435  17.57884373]\n",
      " [17.6242485  17.62844548]\n",
      " [17.623806   17.62733053]\n",
      " [17.64839425 17.65206692]\n",
      " [17.64839125 17.65190232]\n",
      " [17.648428   17.64846476]\n",
      " [17.64344925 17.64796944]\n",
      " [17.64205125 17.64416418]\n",
      " [17.61288875 17.61713492]\n",
      " [17.61549875 17.61873726]\n",
      " [17.61303475 17.61303969]\n",
      " [17.56951275 17.57404488]\n",
      " [17.56704675 17.56983301]\n",
      " [17.5672315  17.56728341]\n",
      " [17.59458525 17.59864887]\n",
      " [17.596199   17.59975531]\n",
      " [17.596221   17.59636869]\n",
      " [17.64493525 17.64932703]\n",
      " [17.6419275  17.64211781]\n",
      " [17.632346   17.63612598]\n",
      " [17.63552725 17.63891682]\n",
      " [17.63236925 17.63244632]\n",
      " [17.5894345  17.59317603]\n",
      " [17.66731075 17.67127274]\n",
      " [17.66633075 17.66788837]\n",
      " [17.666638   17.66664371]\n",
      " [17.60653575 17.60963821]\n",
      " [17.66004025 17.66439215]\n",
      " [17.66070975 17.66347114]\n",
      " [17.66082025 17.66084021]\n",
      " [17.6473855  17.65158454]\n",
      " [17.64448575 17.6485365 ]\n",
      " [17.6445345  17.64459351]\n",
      " [17.58731675 17.59136616]\n",
      " [17.5878485  17.58971531]\n",
      " [17.60558625 17.60878152]\n",
      " [17.6084395  17.6115504 ]\n",
      " [17.60593875 17.60598347]\n",
      " [17.6605185  17.66510984]\n",
      " [17.6592425  17.66141119]\n",
      " [17.59368975 17.59781517]\n",
      " [17.59169775 17.59529545]\n",
      " [17.6416865  17.64638105]\n",
      " [17.6403845  17.64279706]\n",
      " [17.6445545  17.64458342]\n",
      " [17.60573725 17.61068528]\n",
      " [17.60837625 17.61102943]\n",
      " [17.6059685  17.60606599]\n",
      " [17.628494   17.63180267]\n",
      " [17.6290295  17.6318635 ]\n",
      " [17.6154945  17.61931967]\n",
      " [17.6186995  17.62236052]\n",
      " [17.61549525 17.61565969]\n",
      " [17.6346905  17.63900176]\n",
      " [17.63828275 17.64087204]\n",
      " [17.6349055  17.63492627]\n",
      " [17.6638675  17.66721683]\n",
      " [17.66512375 17.66764784]\n",
      " [17.6639575  17.66404458]]\n",
      "[[23.57015225 23.57418084]\n",
      " [23.56863125 23.57185894]\n",
      " [23.5686315  23.5686315 ]\n",
      " [23.63433775 23.63666659]\n",
      " [23.6359375  23.63754489]\n",
      " [23.63436775 23.634368  ]\n",
      " [23.6138175  23.61700769]\n",
      " [23.6123375  23.61422574]\n",
      " [23.5881955  23.59067257]\n",
      " [23.58819425 23.58956266]\n",
      " [23.5882055  23.58820775]\n",
      " [23.58821825 23.58821925]\n",
      " [23.55165225 23.55485402]\n",
      " [23.552451   23.55551938]\n",
      " [23.52748975 23.53020584]\n",
      " [23.52748975 23.52885886]\n",
      " [23.57127625 23.57374444]\n",
      " [23.571037   23.57345219]\n",
      " [23.5056665  23.50819241]\n",
      " [23.5057805  23.50791605]\n",
      " [23.5056965  23.50573875]\n",
      " [23.61796175 23.62143372]\n",
      " [23.61818575 23.62164085]\n",
      " [23.6735975  23.67617826]\n",
      " [23.67315675 23.67476296]\n",
      " [23.67316875 23.673169  ]\n",
      " [23.635837   23.63882589]\n",
      " [23.63424325 23.63617241]\n",
      " [23.5800095  23.5831922 ]\n",
      " [23.57864525 23.58019463]\n",
      " [23.51528425 23.51889109]\n",
      " [23.51589325 23.51788042]\n",
      " [23.517684   23.52110101]\n",
      " [23.51823525 23.52099118]\n",
      " [23.51777325 23.51777425]\n",
      " [23.588434   23.59022121]\n",
      " [23.58852175 23.58953346]\n",
      " [23.498531   23.50364438]\n",
      " [23.49948725 23.50243277]\n",
      " [23.5880415  23.5904031 ]\n",
      " [23.5890315  23.59132961]\n",
      " [23.6198365  23.62249384]\n",
      " [23.61991075 23.62084914]\n",
      " [23.5493845  23.55305833]\n",
      " [23.55049825 23.55290446]\n",
      " [23.5249485  23.52836829]\n",
      " [23.52638025 23.52896424]\n",
      " [23.5590325  23.56265909]\n",
      " [23.5598825  23.56152272]\n",
      " [23.52253825 23.52544604]\n",
      " [23.52366025 23.52635864]\n",
      " [23.52264825 23.5226485 ]\n",
      " [23.50815325 23.51110211]\n",
      " [23.50814725 23.50939132]\n",
      " [23.50818925 23.5081895 ]\n",
      " [23.537161   23.54023592]\n",
      " [23.53835725 23.54105033]\n",
      " [23.57712675 23.58031275]\n",
      " [23.57594275 23.57878967]\n",
      " [23.60992175 23.61353303]\n",
      " [23.6105445  23.61251864]\n",
      " [23.475375   23.47876854]\n",
      " [23.474186   23.47550961]\n",
      " [23.47430625 23.47434225]\n",
      " [23.55427825 23.55577866]\n",
      " [23.55428325 23.55578105]\n",
      " [23.52545625 23.52912767]\n",
      " [23.52606125 23.52946352]\n",
      " [23.58112975 23.58291909]\n",
      " [23.58112975 23.58254109]\n",
      " [23.58118575 23.581186  ]\n",
      " [23.5811545  23.58115475]\n",
      " [23.49835    23.50286239]\n",
      " [23.49939    23.50169746]\n",
      " [23.52759075 23.5290924 ]\n",
      " [23.563963   23.56639114]\n",
      " [23.56393275 23.56538778]\n",
      " [23.57294625 23.57715034]\n",
      " [23.571058   23.57238074]\n",
      " [23.623341   23.62751173]\n",
      " [23.622151   23.62491516]\n",
      " [23.5929175  23.59658982]\n",
      " [23.59393925 23.59568102]\n",
      " [23.5421245  23.54618712]\n",
      " [23.54245575 23.54453805]\n",
      " [23.48651925 23.49109018]\n",
      " [23.48742025 23.49122588]\n",
      " [23.51783675 23.52209558]\n",
      " [23.5189365  23.52071395]\n",
      " [23.560247   23.5632439 ]\n",
      " [23.559043   23.55997922]\n",
      " [23.4966625  23.49960823]\n",
      " [23.4961785  23.4988863 ]\n",
      " [23.5451645  23.54792008]\n",
      " [23.5444085  23.54699881]\n",
      " [23.53020175 23.53241373]\n",
      " [23.530186   23.53237539]\n",
      " [23.531826   23.5321298 ]\n",
      " [23.531826   23.53198635]\n",
      " [23.53022175 23.530222  ]\n",
      " [23.6002855  23.60281964]\n",
      " [23.60109675 23.60288307]\n",
      " [23.578639   23.58018568]\n",
      " [23.57859675 23.5791316 ]\n",
      " [23.53230375 23.5359095 ]\n",
      " [23.5323425  23.53384111]\n",
      " [23.558928   23.56206882]\n",
      " [23.55969975 23.56189132]\n",
      " [23.558946   23.55895825]\n",
      " [23.58333125 23.58605453]\n",
      " [23.58347425 23.58520601]\n",
      " [23.57358025 23.57705711]\n",
      " [23.5736095  23.5749001 ]\n",
      " [23.4899235  23.49352038]\n",
      " [23.48880875 23.49099915]\n",
      " [23.54927925 23.55249183]\n",
      " [23.55011525 23.55219254]\n",
      " [23.61386225 23.61670861]\n",
      " [23.61251025 23.61419175]\n",
      " [23.5470705  23.54978825]\n",
      " [23.54714475 23.54855786]\n",
      " [23.539867   23.5417462 ]\n",
      " [23.540071   23.54166176]\n",
      " [23.53990975 23.539916  ]\n",
      " [23.52167525 23.5268438 ]\n",
      " [23.5203125  23.52229271]\n",
      " [23.5203125  23.52031875]\n",
      " [23.55349225 23.55756415]\n",
      " [23.55168825 23.55356614]\n",
      " [23.55625625 23.5605789 ]\n",
      " [23.55423125 23.55669807]\n",
      " [23.4910175  23.49385459]\n",
      " [23.49203925 23.4948595 ]\n",
      " [23.49101925 23.4910215 ]\n",
      " [23.5746775  23.57871177]\n",
      " [23.57427625 23.57686981]\n",
      " [23.6594315  23.66162761]\n",
      " [23.65929275 23.65931554]\n",
      " [23.65877275 23.65879394]\n",
      " [23.612405   23.61466328]\n",
      " [23.612559   23.61405611]\n",
      " [23.62206725 23.62495408]\n",
      " [23.62279225 23.62438286]\n",
      " [23.57794925 23.58204612]\n",
      " [23.57601125 23.57895736]\n",
      " [23.53710275 23.54077408]\n",
      " [23.53855475 23.54136873]\n",
      " [23.61501075 23.61694945]\n",
      " [23.6150245  23.61648033]\n",
      " [23.57293275 23.57651076]\n",
      " [23.5711305  23.57348659]\n",
      " [23.5711425  23.5711465 ]\n",
      " [23.58076825 23.58417962]\n",
      " [23.58182225 23.58416953]\n",
      " [23.50759025 23.51292077]\n",
      " [23.50574225 23.50809535]\n",
      " [23.50579025 23.50579125]\n",
      " [23.578542   23.58059625]\n",
      " [23.57855775 23.58010114]\n",
      " [23.600179   23.60300393]\n",
      " [23.600662   23.60199484]\n",
      " [23.57759    23.58046834]\n",
      " [23.57596525 23.57779258]\n",
      " [23.5385955  23.54219642]\n",
      " [23.5370835  23.53944294]\n",
      " [23.498675   23.50332047]\n",
      " [23.500039   23.50310223]\n",
      " [23.54835675 23.55363116]\n",
      " [23.5470335  23.549278  ]\n",
      " [23.5153035  23.51958011]\n",
      " [23.51569725 23.51767435]\n",
      " [23.5189905  23.52218401]\n",
      " [23.51777175 23.51965508]\n",
      " [23.50401375 23.50767734]\n",
      " [23.50332975 23.50570031]\n",
      " [23.545596   23.54872511]\n",
      " [23.54460175 23.54695914]\n",
      " [23.562883   23.5660014 ]\n",
      " [23.56137425 23.56356435]\n",
      " [23.57359525 23.57557686]\n",
      " [23.57369125 23.57556716]\n",
      " [23.5177785  23.52154802]\n",
      " [23.51900275 23.52113494]\n",
      " [23.51321875 23.51569059]\n",
      " [23.5152235  23.51699747]\n",
      " [23.4822915  23.48588855]\n",
      " [23.48161725 23.48482476]\n",
      " [23.54238625 23.54572482]\n",
      " [23.54192625 23.54209482]\n",
      " [23.537113   23.54046187]\n",
      " [23.538478   23.54124432]\n",
      " [23.47963125 23.482954  ]\n",
      " [23.61748875 23.61885729]\n",
      " [23.61754375 23.61882802]\n",
      " [23.617492   23.617501  ]\n",
      " [23.54209375 23.54342034]\n",
      " [23.75976125 23.75976125]\n",
      " [23.5819135  23.58578526]\n",
      " [23.58089175 23.58298354]\n",
      " [23.5808935  23.58089575]\n",
      " [23.524192   23.528515  ]\n",
      " [23.522671   23.52580603]\n",
      " [23.52267125 23.52267125]\n",
      " [23.5018085  23.505399  ]\n",
      " [23.5009985  23.50278375]\n",
      " [23.48616225 23.48899376]\n",
      " [23.4872095  23.48979064]\n",
      " [23.55657925 23.56018558]\n",
      " [23.55740725 23.5599827 ]\n",
      " [23.4522765  23.45581263]\n",
      " [23.4529265  23.45612652]\n",
      " [23.55413325 23.5570215 ]\n",
      " [23.5549445  23.55708676]\n",
      " [23.5541385  23.5541395 ]\n",
      " [23.50567825 23.51007906]\n",
      " [23.5070355  23.50938267]\n",
      " [23.51878075 23.52171671]\n",
      " [23.51781475 23.51994832]\n",
      " [23.50562825 23.50902944]\n",
      " [23.5073535  23.51035077]\n",
      " [23.5152795  23.51910312]\n",
      " [23.51664625 23.51941163]\n",
      " [23.58568375 23.58865634]\n",
      " [23.58578575 23.58746851]]\n",
      "[[1.91075000e-03 5.46838107e-03]\n",
      " [4.39700000e-03 4.87113169e-03]\n",
      " [3.14250000e-03 3.59888006e-03]\n",
      " ...\n",
      " [4.87500000e-05 4.87606232e-05]\n",
      " [2.91000000e-04 2.91010193e-04]\n",
      " [2.94750000e-04 2.94751224e-04]]\n",
      "[[29.54176725 29.54367373]\n",
      " [29.5489615  29.55096191]\n",
      " [29.5537415  29.55550014]\n",
      " [29.534526   29.53558064]\n",
      " [29.534426   29.53517772]\n",
      " [29.5488585  29.55057063]\n",
      " [29.4225885  29.42439581]\n",
      " [29.60998975 29.61111812]\n",
      " [29.6705945  29.67187582]\n",
      " [29.58539725 29.58720355]\n",
      " [29.5247315  29.52617843]\n",
      " [29.4664285  29.46721167]\n",
      " [29.500599   29.50129174]\n",
      " [29.63510275 29.63522838]\n",
      " [29.45447125 29.45579522]\n",
      " [29.5245715  29.52581348]\n",
      " [29.65912675 29.65924008]\n",
      " [29.4808625  29.48248494]\n",
      " [29.43469725 29.436504  ]\n",
      " [29.4955445  29.49663404]\n",
      " [29.45907025 29.46047573]\n",
      " [29.5100335  29.51075485]\n",
      " [29.47850325 29.48008162]\n",
      " [29.52709675 29.52858745]\n",
      " [29.57572575 29.57650868]\n",
      " [29.40581025 29.40705053]\n",
      " [29.51240675 29.51368701]\n",
      " [29.4712545  29.47261876]\n",
      " [29.53676975 29.53793424]\n",
      " [29.43483325 29.43619737]\n",
      " [29.55145475 29.5525076 ]\n",
      " [29.51733625 29.51857536]\n",
      " [29.60017575 29.60166721]\n",
      " [29.5343215  29.53560337]\n",
      " [29.48821325 29.49006951]\n",
      " [29.4276015  29.42913639]\n",
      " [29.43970275 29.44114984]\n",
      " [29.485777   29.48665474]\n",
      " [29.4714865  29.47243508]\n",
      " [29.4955125  29.49653006]\n",
      " [29.43022675 29.43227828]\n",
      " [29.54169075 29.54270749]\n",
      " [29.6222835  29.62258397]\n",
      " [29.56665375 29.56675552]\n",
      " [29.519838   29.52082037]\n",
      " [29.6373905  29.63757147]\n",
      " [29.50768575 29.50913096]\n",
      " [29.43985875 29.44139412]\n",
      " [29.4906215  29.49174857]\n",
      " [29.53669725 29.53827272]\n",
      " [29.59617475 29.59631332]\n",
      " [29.4954885  29.49665364]\n",
      " [29.4665885  29.4683036 ]\n",
      " [29.46631    29.46806868]\n",
      " [29.46885525 29.47116664]\n",
      " [29.42024025 29.42152271]\n",
      " [29.5395535  29.54071917]\n",
      " [29.6366155  29.63663552]\n",
      " [29.60507625 29.6057391 ]\n",
      " [29.58054425 29.58145713]\n",
      " [29.48573    29.48820664]\n",
      " [29.46386275 29.46571824]\n",
      " [29.5976405  29.59876805]\n",
      " [29.48331875 29.48552452]\n",
      " [29.50755625 29.50887798]\n",
      " [29.4227405  29.42436224]\n",
      " [29.5489115  29.54996438]\n",
      " [29.55133825 29.5519443 ]\n",
      " [29.49783125 29.49954359]\n",
      " [29.4664145  29.46851679]\n",
      " [29.42019725 29.4224058 ]\n",
      " [29.50794675 29.50961626]\n",
      " [29.49821325 29.49871502]\n",
      " [29.45429775 29.45583197]\n",
      " [29.4763935  29.47734194]\n",
      " [29.485812   29.48730261]\n",
      " [29.50276825 29.5046728 ]\n",
      " [29.6079835  29.6081091 ]\n",
      " [29.44453275 29.4461106 ]\n",
      " [29.4180985  29.42041089]\n",
      " [29.4276435  29.42880932]\n",
      " [29.456548   29.45655082]\n",
      " [29.48339125 29.48510611]\n",
      " [29.65402575 29.65430817]\n",
      " [29.53204975 29.53333218]\n",
      " [29.45433125 29.4562358 ]\n",
      " [29.4472785  29.44829627]\n",
      " [29.4226855  29.42400874]\n",
      " [29.51257725 29.51366753]\n",
      " [29.41075725 29.41157271]\n",
      " [29.5198305  29.52095648]\n",
      " [29.5199385  29.51994415]\n",
      " [29.4275515  29.42931204]\n",
      " [29.4566275  29.45803315]\n",
      " [29.42017125 29.422432  ]\n",
      " [29.4371995  29.43891326]\n",
      " [29.5342965  29.53578557]]\n",
      "[[12.1749005  12.17583016]\n",
      " [12.17489225 12.17582164]\n",
      " [12.17487625 12.17579276]\n",
      " ...\n",
      " [12.13309625 12.13445722]\n",
      " [12.133076   12.13441388]\n",
      " [12.1919285  12.1919285 ]]\n",
      "[[18.29557725 18.29560304]\n",
      " [18.29555225 18.29557629]\n",
      " [18.34968225 18.34970066]\n",
      " [18.34967325 18.34969162]\n",
      " [18.3448465  18.34484694]\n",
      " [18.34484625 18.34484663]\n",
      " [18.27554025 18.27554062]\n",
      " [18.27554425 18.27554462]\n",
      " [18.3448665  18.34486675]\n",
      " [18.29534225 18.29534225]\n",
      " [18.347193   18.34720649]\n",
      " [18.34728325 18.34729667]\n",
      " [18.22145225 18.22145225]\n",
      " [18.32990925 18.32993326]\n",
      " [18.32989325 18.32991722]\n",
      " [18.27062025 18.27063862]\n",
      " [18.27061125 18.27062961]\n",
      " [18.330155   18.33016189]\n",
      " [18.32001725 18.32002336]\n",
      " [18.33014875 18.33015476]\n",
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      " [6.09596325 6.09596325]]\n",
      "[[24.4930455  24.4930455 ]\n",
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      " [24.3000855  24.3000855 ]\n",
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      " [24.3792185  24.3792185 ]\n",
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      " [24.3394895  24.33952977]\n",
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      " [24.4530825  24.45309449]\n",
      " [24.3741765  24.3741765 ]\n",
      " [24.40114225 24.4011485 ]\n",
      " [24.35673875 24.35674101]\n",
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      " [24.408587   24.40859602]\n",
      " [24.45059825 24.45059825]\n",
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      " [24.37155575 24.37164596]\n",
      " [24.40610775 24.40610775]\n",
      " [24.3443875  24.34441448]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      " [     0. 999999.]\n",
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      " [     0. 999999.]\n",
      " [     0. 999999.]]\n",
      "[[0.         6.16530525]\n",
      " [0.         6.16529525]\n",
      " [0.         6.165289  ]\n",
      " ...\n",
      " [0.         6.095961  ]\n",
      " [0.         5.87164375]\n",
      " [0.         5.87130075]]\n",
      "[[0.00000e+00 1.78825e-03]\n",
      " [0.00000e+00 1.77950e-03]\n",
      " [0.00000e+00 1.71125e-03]\n",
      " ...\n",
      " [0.00000e+00 6.75000e-06]\n",
      " [0.00000e+00 6.25000e-06]\n",
      " [0.00000e+00 2.25000e-06]]\n",
      "[[0.         2.22919275]\n",
      " [0.         2.2291865 ]\n",
      " [0.         2.22913025]\n",
      " [0.         2.22910525]\n",
      " [0.         2.229049  ]]\n",
      "[[0.         0.978396  ]\n",
      " [0.         0.978371  ]\n",
      " [0.         0.978146  ]\n",
      " [0.         0.978121  ]\n",
      " [0.         0.857635  ]\n",
      " [0.         0.73572425]\n",
      " [0.         0.735218  ]\n",
      " [0.         0.734593  ]\n",
      " [0.         0.734449  ]\n",
      " [0.         0.64242475]]\n",
      "[[0.         2.61645225]\n",
      " [0.         2.61638825]\n",
      " [0.         2.61632425]\n",
      " [0.         2.61631525]\n",
      " [0.         2.61630625]]\n",
      "[[0.         5.49922875]\n",
      " [0.         5.4992225 ]\n",
      " [0.         5.49921625]\n",
      " [0.         5.499034  ]\n",
      " [0.         5.499025  ]]\n"
     ]
    }
   ],
   "source": [
    "cl.train(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_label=list(np.zeros(len(test_data)))+list(np.ones(len(data_dict['bad'])))\n",
    "test_data=test_data+data_dict['bad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_label=cl.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=metrics.confusion_matrix(test_label,predict_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[175,  81],\n",
       "       [  2,  24]], dtype=int64)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=metrics.classification_report(test_label,predict_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      0.62      0.77       330\n",
      "         1.0       0.17      1.00      0.30        26\n",
      "\n",
      "    accuracy                           0.65       356\n",
      "   macro avg       0.59      0.81      0.53       356\n",
      "weighted avg       0.94      0.65      0.73       356\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,\n",
       "       2.032, 4.95 , 4.938, 4.951, 4.909, 4.942, 4.95 , 4.95 , 4.939,\n",
       "       4.932, 4.923, 4.931, 4.942, 4.944, 4.929, 4.95 , 4.974, 4.934,\n",
       "       4.961, 4.952, 4.963, 4.894, 4.896, 4.931, 4.923, 4.915, 4.882,\n",
       "       4.867, 4.862, 4.862, 4.835, 4.83 , 4.842, 4.852, 4.858, 4.862,\n",
       "       4.938, 4.938, 4.913, 4.915, 4.909, 4.896, 4.925, 4.928, 4.921,\n",
       "       4.944, 4.892, 4.924, 4.93 , 4.936, 4.927, 4.923, 4.934, 4.936,\n",
       "       4.938, 4.939, 4.928, 4.919, 4.916, 4.925, 4.921, 4.923, 4.935,\n",
       "       4.928, 4.919, 4.931, 4.94 , 4.933, 4.936, 4.931, 4.929, 0.   ,\n",
       "       0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(test_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot unpack non-iterable float object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-a4172ffacf01>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_arrange_persistences\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\PROJECT\\seminconductor\\code\\data2\\classifier.py\u001b[0m in \u001b[0;36m_arrange_persistences\u001b[1;34m(self, persistences)\u001b[0m\n\u001b[0;32m    153\u001b[0m         \u001b[0mdata_dict_by_betti\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mpersistence\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpersistences\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0mbetti\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcord\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpersistence\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mbetti\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata_dict_by_betti\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m                     \u001b[0mdata_dict_by_betti\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbetti\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot unpack non-iterable float object"
     ]
    }
   ],
   "source": [
    "cl._arrange_persistences([test_data[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-af896e8ef74a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\PROJECT\\seminconductor\\code\\data2\\classifier.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, data_set)\u001b[0m\n\u001b[0;32m     54\u001b[0m         \u001b[0mresult\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mpersistence\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata_set\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m             \u001b[0mpersistences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_persistence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpersistence\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_persistences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpersistences\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m             \u001b[1;31m###predict data set\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\PROJECT\\seminconductor\\code\\data2\\classifier.py\u001b[0m in \u001b[0;36m_make_persistence\u001b[1;34m(self, data_set)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \"\"\"\n\u001b[0;32m    131\u001b[0m         \u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpartial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_persistence\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mte\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m         \u001b[0mpersistences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata_set\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mpersistences\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\PROJECT\\seminconductor\\code\\data2\\classifier.py\u001b[0m in \u001b[0;36m__get_persistence\u001b[1;34m(self, te, data)\u001b[0m\n\u001b[0;32m    118\u001b[0m         \u001b[0membedding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mte\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m         \u001b[0msimplex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimplex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0membedding\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m         \u001b[0mtree\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msimplex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_simplex_tree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    121\u001b[0m         \u001b[0mpersistence\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpersistence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mpersistence\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cl.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([4, 3, 2, 1, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl.region_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "for k in cl.region_dict.keys():\n",
    "    for j in cl.region_dict[k].region_dict.keys():\n",
    "        plt.plot(cl.region_dict[k].region_dict[j].polygon.bounds)\n",
    "#cl.region_dict[4].region_dict[3].polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26.592092858722324, 26.986913664122362, 28.151272565096676, 28.32176971937116)\n",
      "(29.4073447940647, 29.409512379062043, 29.688932542907875, 29.689063405592627)\n",
      "(23.461031813027763, 23.461062271187636, 23.676784076412925, 23.676819915089773)\n",
      "(1.7025806451612304e-05, 1.7382819357480712e-05, 0.008544261226079583, 0.008545454003915844)\n",
      "(17.57001482622495, 17.57006389107601, 17.73596596898105, 17.736009539235543)\n",
      "(18.221285624315342, 18.221286749999166, 18.498546272179013, 18.498546745185173)\n",
      "(12.112494077409883, 12.112494205192508, 12.3604476124449, 12.360449668071825)\n",
      "(11.673730081013424, 11.673732343353201, 11.80542677007692, 11.805483308686133)\n",
      "(24.30744902818071, 24.307449071460923, 24.663716300127575, 24.663720446532867)\n",
      "(6.096040365402301, 6.09604797061559, 6.147959625010088, 6.147960384038374)\n",
      "(26.40111309397806, 26.598162905127566, 26.79956619926277, 26.818298531199005)\n"
     ]
    }
   ],
   "source": [
    "for j in cl.region_dict[2].region_dict.keys():\n",
    "    print(cl.region_dict[2].region_dict[j].polygon.bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-5.749999999995481e-06 2.2290432500000006 1.1499999999990962e-05 0.0001552500000001622\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,4.458241750000001)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"3.105000000003244e-06\" points=\"0.0,2.2290490000000007 0.0,2.2291927500000006\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.linestring.LineString at 0x1d90dd48d48>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl.region_dict[0].region_dict[0].polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[29.66568178 29.69764022]\n",
      " [29.67287229 29.69967188]\n",
      " [29.67685177 29.6993754 ]\n",
      " [29.67654529 29.69818744]\n",
      " [29.6737314  29.69461809]\n",
      " [29.67022941 29.68885774]\n",
      " [29.66902402 29.66931226]\n",
      " [29.68932898 29.68950196]\n",
      " [29.69860988 29.69871008]]\n",
      "[[0.00048044 0.0004834 ]\n",
      " [0.00516083 0.00516234]\n",
      " [0.00033295 0.00033445]\n",
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      " [0.00000e+00 4.12500e-04]\n",
      " [0.00000e+00 4.03500e-04]\n",
      " [0.00000e+00 3.72750e-04]\n",
      " [0.00000e+00 3.57500e-04]\n",
      " [0.00000e+00 3.53500e-04]\n",
      " [0.00000e+00 3.42500e-04]\n",
      " [0.00000e+00 3.38250e-04]\n",
      " [0.00000e+00 3.34750e-04]\n",
      " [0.00000e+00 3.33750e-04]\n",
      " [0.00000e+00 2.98500e-04]\n",
      " [0.00000e+00 2.08000e-04]\n",
      " [0.00000e+00 2.00250e-04]\n",
      " [0.00000e+00 1.97500e-04]\n",
      " [0.00000e+00 1.92750e-04]\n",
      " [0.00000e+00 1.90500e-04]\n",
      " [0.00000e+00 1.89750e-04]\n",
      " [0.00000e+00 1.88250e-04]\n",
      " [0.00000e+00 1.75250e-04]\n",
      " [0.00000e+00 1.71500e-04]\n",
      " [0.00000e+00 1.60500e-04]\n",
      " [0.00000e+00 1.56500e-04]\n",
      " [0.00000e+00 1.53750e-04]\n",
      " [0.00000e+00 1.53750e-04]\n",
      " [0.00000e+00 1.52750e-04]\n",
      " [0.00000e+00 1.51500e-04]\n",
      " [0.00000e+00 1.35250e-04]\n",
      " [0.00000e+00 1.24000e-04]\n",
      " [0.00000e+00 1.19000e-04]\n",
      " [0.00000e+00 1.11250e-04]\n",
      " [0.00000e+00 1.10750e-04]\n",
      " [0.00000e+00 1.08500e-04]\n",
      " [0.00000e+00 9.97500e-05]\n",
      " [0.00000e+00 8.65000e-05]\n",
      " [0.00000e+00 8.62500e-05]\n",
      " [0.00000e+00 7.72500e-05]\n",
      " [0.00000e+00 7.45000e-05]\n",
      " [0.00000e+00 7.17500e-05]\n",
      " [0.00000e+00 6.70000e-05]\n",
      " [0.00000e+00 5.42500e-05]\n",
      " [0.00000e+00 5.15000e-05]\n",
      " [0.00000e+00 4.15000e-05]\n",
      " [0.00000e+00 3.85000e-05]\n",
      " [0.00000e+00 3.47500e-05]\n",
      " [0.00000e+00 3.45000e-05]\n",
      " [0.00000e+00 3.32500e-05]\n",
      " [0.00000e+00 3.15000e-05]\n",
      " [0.00000e+00 1.75000e-05]\n",
      " [0.00000e+00 1.00000e-06]\n",
      " [0.00000e+00 2.50000e-07]]\n"
     ]
    }
   ],
   "source": [
    "cl=classifier.Classifier(dim=5,delay=2)\n",
    "cl.set_db_params(eps=0.2,min_samples=5)\n",
    "cl.train(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp=np.array([[29.66568178, 29.69764022],\n",
    " [29.67287229, 29.69967188],\n",
    " [29.67685177, 29.6993754 ],\n",
    " [29.67654529, 29.69818744],\n",
    " [29.6737314,  29.69461809],\n",
    " [29.67022941, 29.68885774],\n",
    " [29.66902402, 29.66931226],\n",
    " [29.68932898, 29.68950196],\n",
    " [29.69860988, 29.69871008]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp.sort(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[29.66568178, 29.66931226],\n",
       "       [29.66902402, 29.68885774],\n",
       "       [29.67022941, 29.68950196],\n",
       "       [29.67287229, 29.69461809],\n",
       "       [29.6737314 , 29.69764022],\n",
       "       [29.67654529, 29.69818744],\n",
       "       [29.67685177, 29.69871008],\n",
       "       [29.68932898, 29.6993754 ],\n",
       "       [29.69860988, 29.69967188]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp.sort(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[29.66568178, 29.66931226],\n",
       "       [29.66902402, 29.68885774],\n",
       "       [29.67022941, 29.68950196],\n",
       "       [29.67287229, 29.69461809],\n",
       "       [29.6737314 , 29.69764022],\n",
       "       [29.67654529, 29.69818744],\n",
       "       [29.67685177, 29.69871008],\n",
       "       [29.68932898, 29.6993754 ],\n",
       "       [29.69860988, 29.69967188]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "rest=np.array([[1,20],[3,4],[50,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'axis' is an invalid keyword argument for sort()",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-137-824a4bef42ce>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'axis' is an invalid keyword argument for sort()"
     ]
    }
   ],
   "source": [
    "print(rest.sort(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 20], [3, 4], [50, 6]]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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