examples/SynthethicData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthethic Data Experiment\n",
    "\n",
    "The data for this experiment was generated, the code belows will generate the data, and perform a single run of each version (HDPMM, HDPMM Global Only, DPMM, DPMM Seperated, vHDPMM), and evaluation.\n",
    "\n",
    "Note that the data generation is random, thus different results may appear, however the ranking of how well the methods perform are consistent. Also, this is a single run, so no mean or std."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using LinearAlgebra\n",
    "using Clustering\n",
    "using Random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation Functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "evaluate_all_results (generic function with 1 method)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function evaluate_pred_groups(GT,pred)\n",
    "    NMI = 0.0\n",
    "    for i=1:length(pred)\n",
    "        group_nmi = mutualinfo(GT[i], pred[i]; normed =true)\n",
    "        if isnan(group_nmi)\n",
    "            group_nmi = 1.0\n",
    "        end\n",
    "        NMI += group_nmi\n",
    "    end\n",
    "    return NMI / length(pred)   \n",
    "end\n",
    "\n",
    "\n",
    "function evaluate_all_results(GT_Global,\n",
    "        GT_Local,\n",
    "        pred_HDPMM,\n",
    "        pred_HDPMM_GLOBAL,\n",
    "        pred_DPMM,\n",
    "        pred_DPMM_seperated,\n",
    "        pred_vHDPMM_global,\n",
    "        pred_vHDPMM_local,\n",
    "        pred_sep_vHDPMM_global,\n",
    "        pred_sep_vHDPMM_local)\n",
    "    concat_GT_Local = [GT_Local[i] for i=1:length(GT_Local)]\n",
    "    concat_GT_Local = reduce(vcat,concat_GT_Local)\n",
    "    HDPMM_NMI = evaluate_pred_groups(GT_Global, pred_HDPMM)\n",
    "    HDPMM_GLOBAL_NMI = evaluate_pred_groups(GT_Global, pred_HDPMM_GLOBAL) \n",
    "    DPMM_NMI = mutualinfo(concat_GT_Local, pred_DPMM; normed =true)\n",
    "    DPMM_SEP_NMI = evaluate_pred_groups(GT_Local, pred_DPMM_seperated)\n",
    "    vHDPMM_GLOBAL_NMI = evaluate_pred_groups(GT_Global, pred_vHDPMM_global)\n",
    "    vHDPMM_LOCAL_NMI = evaluate_pred_groups(GT_Local, pred_vHDPMM_local)\n",
    "    vHDPMM_SEP_GLOBAL_NMI = evaluate_pred_groups(GT_Global, pred_sep_vHDPMM_global)\n",
    "    vHDPMM_SEP_LOCAL_NMI = evaluate_pred_groups(GT_Local, pred_sep_vHDPMM_local)\n",
    "    println(\"        HDPMM NMI: $HDPMM_NMI \\n\n",
    "        HDPMM Global NMI: $HDPMM_GLOBAL_NMI \\n\n",
    "        DPMM NMI: $DPMM_NMI \\n\n",
    "        DPMM SEPERATED NMI: $DPMM_SEP_NMI\\n\n",
    "        vHDPMM Global NMI: $vHDPMM_GLOBAL_NMI\\n\n",
    "        vHDPMM Local NMI: $vHDPMM_LOCAL_NMI\n",
    "        vHDPMM Sep Global NMI: $vHDPMM_SEP_GLOBAL_NMI\\n\n",
    "        vHDPMM Sep Local NMI: $vHDPMM_SEP_LOCAL_NMI\")\n",
    "    return HDPMM_NMI, HDPMM_GLOBAL_NMI, DPMM_NMI, DPMM_SEP_NMI, vHDPMM_GLOBAL_NMI, vHDPMM_LOCAL_NMI, vHDPMM_SEP_GLOBAL_NMI, vHDPMM_SEP_LOCAL_NMI\n",
    "end\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Distributed\n",
    "addprocs(2)\n",
    "@everywhere using DPMMSubClusters\n",
    "@everywhere using VersatileHDPMixtureModels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "run_methods_and_get_results (generic function with 1 method)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function run_methods_and_get_results(data,gprior,lprior,dp_prior,ghdp,gdim)\n",
    "    #vHDPM\n",
    "    println(\"vHDPMM\")\n",
    "    vhdpmm_results = vhdp_fit(data,gdim,100.0,1000.0,100.0,gprior,lprior,50)\n",
    "    vhdpmm_global = Dict([i=> create_global_labels(vhdpmm_results[1].groups_dict[i]) for i=1:length(data)])\n",
    "    vhdpmm_local = Dict([i=> vhdpmm_results[1].groups_dict[i].labels for i=1:length(data)])\n",
    "    #HDP-ALL\n",
    "    println(\"HDP-ALL\")\n",
    "    hdp_results = hdp_fit(data,100.0,1000.0,ghdp,50)\n",
    "    hdp_all_global = Dict([i=> create_global_labels(hdp_results[1].groups_dict[i]) for i=1:length(data)])\n",
    "    #HDP-GLOBAL\n",
    "    println(\"HDP-GLOBAL\")\n",
    "    global_pts = Dict([k=>v[1:gdim,:] for (k,v) in data])\n",
    "    hdp_global_results = hdp_fit(global_pts,100.0,1000.0,gprior,50)\n",
    "    hdp_global_global = Dict([i=> create_global_labels(hdp_global_results[1].groups_dict[i]) for i=1:length(data)])\n",
    "    #DPMM-All\n",
    "    println(\"DPMM-ALL\")\n",
    "    all_data = [data[i] for i=1:length(data)]\n",
    "    all_data = reduce(hcat,all_data)\n",
    "    dpmm_all_results = DPMMSubClusters.fit(all_data,dp_prior,100.0,iters = 100, verbose = false)\n",
    "    dpmm_all_local = dpmm_all_results[1]\n",
    "    #DPMM-Seperated\n",
    "    println(\"DPMM-SEP\")\n",
    "    dpmm_seperated_results = [DPMMSubClusters.fit(data[i],dp_prior,100.0,iters = 100, verbose = false) for i=1:length(data)]\n",
    "    dpmm_seperated_local = [dpmm_seperated_results[i][1] for i=1:length(data)]\n",
    "    println(\"vHDPMM-SEP\")\n",
    "    #vHDPM-Seperated\n",
    "    vhdpmm_results = [vhdp_fit(Dict([1=>data[i]]),gdim,100.0,1000.0,100.0,gprior,lprior,50) for i=1:length(data)]\n",
    "    vhdpmm_sep_global = Dict([i=> create_global_labels(vhdpmm_results[i][1].groups_dict[1]) for i=1:length(data)])\n",
    "    vhdpmm_sep_local = Dict([i=> vhdpmm_results[i][1].groups_dict[1].labels for i=1:length(data)])\n",
    "#     return vhdpmm_results\n",
    "    return hdp_all_global,hdp_global_global,dpmm_all_local,dpmm_seperated_local, vhdpmm_global, vhdpmm_local,vhdpmm_sep_global,vhdpmm_sep_local\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### G3/2/5/1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DPMMSubClusters.niw_hyperparams(1.0f0, Float32[0.0, 0.0, 0.0], 6.0f0, Float32[1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts,labels = generate_grouped_gaussian_data(20000, 2, 1, 3, 5, 10, false, 25.0, false)\n",
    "labels_global  = Dict([k=> Int.(v[:,1][:]) for (k,v) in labels])\n",
    "labels_local  = Dict([k=> Int.(v[:,2][:]) for (k,v) in labels])\n",
    "g_prior, l_prior = create_default_priors(2,1,:niw)\n",
    "g_hdp, _ = create_default_priors(3,0,:niw)\n",
    "dp_prior = DPMMSubClusters.niw_hyperparams(1.0,\n",
    "            zeros(3),\n",
    "            6,\n",
    "            Matrix{Float64}(I, 3, 3)*1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vHDPMM\n",
      "Iteration: 1|| Global Counts: [10]|| iter time: 15.270561933517456\n",
      "Iteration: 2|| Global Counts: [10]|| iter time: 0.09072518348693848\n",
      "Iteration: 3|| Global Counts: [10]|| iter time: 0.0733938217163086\n",
      "Iteration: 4|| Global Counts: [10]|| iter time: 0.07050585746765137\n",
      "Iteration: 5|| Global Counts: [10]|| iter time: 0.19986987113952637\n",
      "Iteration: 6|| Global Counts: [11, 11]|| iter time: 1.3131649494171143\n",
      "Iteration: 7|| Global Counts: [12, 11]|| iter time: 0.11937594413757324\n",
      "Iteration: 8|| Global Counts: [15, 11]|| iter time: 0.6617319583892822\n",
      "Iteration: 9|| Global Counts: [16, 11]|| iter time: 0.12209391593933105\n",
      "Iteration: 10|| Global Counts: [18, 13]|| iter time: 0.08200716972351074\n",
      "Iteration: 11|| Global Counts: [18, 13]|| iter time: 0.2327899932861328\n",
      "Iteration: 12|| Global Counts: [19, 14, 19]|| iter time: 0.08287501335144043\n",
      "Iteration: 13|| Global Counts: [20, 16, 14]|| iter time: 0.43161988258361816\n",
      "Iteration: 14|| Global Counts: [14, 17, 13]|| iter time: 0.09724116325378418\n",
      "Iteration: 15|| Global Counts: [14, 18, 13]|| iter time: 0.0930941104888916\n",
      "Iteration: 16|| Global Counts: [14, 19, 13]|| iter time: 0.10111308097839355\n",
      "Iteration: 17|| Global Counts: [14, 19, 13]|| iter time: 0.10930585861206055\n",
      "Iteration: 18|| Global Counts: [14, 20, 13]|| iter time: 0.09452486038208008\n",
      "Iteration: 19|| Global Counts: [14, 22, 14]|| iter time: 0.10245394706726074\n",
      "Iteration: 20|| Global Counts: [15, 24, 13]|| iter time: 0.10880899429321289\n",
      "Iteration: 21|| Global Counts: [15, 25, 13]|| iter time: 0.10081219673156738\n",
      "Iteration: 22|| Global Counts: [15, 25, 13]|| iter time: 0.10311102867126465\n",
      "Iteration: 23|| Global Counts: [15, 26, 13]|| iter time: 0.1082468032836914\n",
      "Iteration: 24|| Global Counts: [15, 27, 13]|| iter time: 0.10729217529296875\n",
      "Iteration: 25|| Global Counts: [15, 30, 13]|| iter time: 0.10658502578735352\n",
      "Iteration: 26|| Global Counts: [15, 29, 13]|| iter time: 0.11449289321899414\n",
      "Iteration: 27|| Global Counts: [15, 30, 13]|| iter time: 0.10986804962158203\n",
      "Iteration: 28|| Global Counts: [15, 32, 13]|| iter time: 0.10881614685058594\n",
      "Iteration: 29|| Global Counts: [15, 31, 13]|| iter time: 0.11921811103820801\n",
      "Iteration: 30|| Global Counts: [15, 34, 13]|| iter time: 0.11265397071838379\n",
      "Iteration: 31|| Global Counts: [15, 35, 13]|| iter time: 0.11615204811096191\n",
      "Iteration: 32|| Global Counts: [16, 36, 13]|| iter time: 0.1217489242553711\n",
      "Iteration: 33|| Global Counts: [16, 38, 13]|| iter time: 0.12449502944946289\n",
      "Iteration: 34|| Global Counts: [17, 38, 13]|| iter time: 0.1239011287689209\n",
      "Iteration: 35|| Global Counts: [16, 38, 13]|| iter time: 0.12795805931091309\n",
      "Iteration: 36|| Global Counts: [16, 44, 13]|| iter time: 0.12409710884094238\n",
      "Iteration: 37|| Global Counts: [16, 40, 13]|| iter time: 0.12538480758666992\n",
      "Iteration: 38|| Global Counts: [16, 41, 13]|| iter time: 0.13062810897827148\n",
      "Iteration: 39|| Global Counts: [16, 41, 13]|| iter time: 0.126708984375\n",
      "Iteration: 40|| Global Counts: [16, 42, 13]|| iter time: 0.1271190643310547\n",
      "Iteration: 41|| Global Counts: [16, 35, 13]|| iter time: 0.13089203834533691\n",
      "Iteration: 42|| Global Counts: [16, 34, 13]|| iter time: 0.11563491821289062\n",
      "Iteration: 43|| Global Counts: [16, 34, 13]|| iter time: 0.11556410789489746\n",
      "Iteration: 44|| Global Counts: [16, 33, 13]|| iter time: 0.12364387512207031\n",
      "Iteration: 45|| Global Counts: [16, 36, 13]|| iter time: 0.11581587791442871\n",
      "Iteration: 46|| Global Counts: [16, 35, 13]|| iter time: 0.1636059284210205\n",
      "Iteration: 47|| Global Counts: [16, 36, 13]|| iter time: 0.12881016731262207\n",
      "Iteration: 48|| Global Counts: [16, 35, 13]|| iter time: 0.12000513076782227\n",
      "Iteration: 49|| Global Counts: [15, 43, 13]|| iter time: 0.11867403984069824\n",
      "Iteration: 50|| Global Counts: [15, 42, 13]|| iter time: 0.13727807998657227\n",
      "HDP-ALL\n",
      "Iteration: 1|| Global Counts: [10]|| iter time: 0.10990786552429199\n",
      "Iteration: 2|| Global Counts: [10]|| iter time: 0.06658601760864258\n",
      "Iteration: 3|| Global Counts: [10]|| iter time: 0.07351899147033691\n",
      "Iteration: 4|| Global Counts: [10]|| iter time: 0.07680082321166992\n",
      "Iteration: 5|| Global Counts: [10]|| iter time: 0.06832695007324219\n",
      "Iteration: 6|| Global Counts: [10]|| iter time: 0.06442499160766602\n",
      "Iteration: 7|| Global Counts: [10, 10]|| iter time: 0.08175086975097656\n",
      "Iteration: 8|| Global Counts: [10, 10]|| iter time: 0.07950401306152344\n",
      "Iteration: 9|| Global Counts: [10, 10]|| iter time: 0.07703399658203125\n",
      "Iteration: 10|| Global Counts: [10, 10]|| iter time: 0.12406611442565918\n",
      "Iteration: 11|| Global Counts: [10, 10]|| iter time: 0.08689188957214355\n",
      "Iteration: 12|| Global Counts: [10, 10]|| iter time: 0.08514690399169922\n",
      "Iteration: 13|| Global Counts: [10, 10]|| iter time: 0.17728590965270996\n",
      "Iteration: 14|| Global Counts: [10, 10]|| iter time: 0.07640600204467773\n",
      "Iteration: 15|| Global Counts: [10, 10]|| iter time: 0.08055686950683594\n",
      "Iteration: 16|| Global Counts: [10, 10]|| iter time: 0.14979314804077148\n",
      "Iteration: 17|| Global Counts: [10, 10]|| iter time: 0.07523989677429199\n",
      "Iteration: 18|| Global Counts: [10, 10]|| iter time: 0.08143401145935059\n",
      "Iteration: 19|| Global Counts: [10, 10]|| iter time: 0.08100509643554688\n",
      "Iteration: 20|| Global Counts: [10, 10]|| iter time: 0.0758829116821289\n",
      "Iteration: 21|| Global Counts: [10, 10, 10]|| iter time: 0.08707118034362793\n",
      "Iteration: 22|| Global Counts: [7, 10, 13]|| iter time: 0.0937960147857666\n",
      "Iteration: 23|| Global Counts: [7, 10, 13]|| iter time: 0.0900728702545166\n",
      "Iteration: 24|| Global Counts: [7, 10, 13]|| iter time: 0.08991789817810059\n",
      "Iteration: 25|| Global Counts: [7, 10, 12]|| iter time: 0.08977699279785156\n",
      "Iteration: 26|| Global Counts: [7, 10, 10]|| iter time: 0.0902700424194336\n",
      "Iteration: 27|| Global Counts: [7, 10, 11]|| iter time: 0.08615899085998535\n",
      "Iteration: 28|| Global Counts: [7, 10, 10]|| iter time: 0.08729100227355957\n",
      "Iteration: 29|| Global Counts: [7, 10, 10, 10]|| iter time: 0.0928499698638916\n",
      "Iteration: 30|| Global Counts: [7, 10, 10, 11]|| iter time: 0.09835410118103027\n",
      "Iteration: 31|| Global Counts: [7, 10, 7, 10]|| iter time: 0.09876298904418945\n",
      "Iteration: 32|| Global Counts: [7, 10, 7, 11]|| iter time: 0.09951901435852051\n",
      "Iteration: 33|| Global Counts: [7, 10, 7, 11]|| iter time: 0.09618592262268066\n",
      "Iteration: 34|| Global Counts: [7, 10, 7, 11, 7]|| iter time: 0.10213088989257812\n",
      "Iteration: 35|| Global Counts: [7, 10, 9, 9, 7]|| iter time: 0.16344404220581055\n",
      "Iteration: 36|| Global Counts: [8, 10, 8, 8, 7]|| iter time: 0.10676693916320801\n",
      "Iteration: 37|| Global Counts: [10, 10, 7, 9, 7]|| iter time: 0.10925793647766113\n",
      "Iteration: 38|| Global Counts: [11, 10, 7, 10, 6]|| iter time: 0.10982704162597656\n",
      "Iteration: 39|| Global Counts: [11, 10, 7, 13, 6]|| iter time: 0.11256909370422363\n",
      "Iteration: 40|| Global Counts: [13, 10, 8, 11, 6]|| iter time: 0.1135859489440918\n",
      "Iteration: 41|| Global Counts: [16, 10, 7, 8, 6]|| iter time: 0.10515999794006348\n",
      "Iteration: 42|| Global Counts: [16, 10, 7, 7, 7, 16]|| iter time: 0.11358904838562012\n",
      "Iteration: 43|| Global Counts: [5, 10, 7, 6, 7, 14]|| iter time: 0.13390207290649414\n",
      "Iteration: 44|| Global Counts: [5, 10, 7, 6, 6, 15]|| iter time: 0.12098479270935059\n",
      "Iteration: 45|| Global Counts: [5, 10, 7, 6, 6, 17]|| iter time: 0.11588287353515625\n",
      "Iteration: 46|| Global Counts: [4, 10, 7, 6, 6, 17, 6]|| iter time: 0.12451004981994629\n",
      "Iteration: 47|| Global Counts: [4, 10, 7, 6, 7, 19, 4]|| iter time: 0.13457703590393066\n",
      "Iteration: 48|| Global Counts: [4, 10, 7, 6, 8, 19, 4]|| iter time: 0.12952804565429688\n",
      "Iteration: 49|| Global Counts: [4, 10, 7, 6, 8, 19, 3, 19]|| iter time: 0.13306021690368652\n",
      "Iteration: 50|| Global Counts: [7, 10, 7, 6, 8, 12, 4, 13]|| iter time: 0.15780901908874512\n",
      "HDP-GLOBAL\n",
      "Iteration: 1|| Global Counts: [10]|| iter time: 0.09337091445922852\n",
      "Iteration: 2|| Global Counts: [10]|| iter time: 0.06065201759338379\n",
      "Iteration: 3|| Global Counts: [10]|| iter time: 0.06698393821716309\n",
      "Iteration: 4|| Global Counts: [10]|| iter time: 0.06317400932312012\n",
      "Iteration: 5|| Global Counts: [10]|| iter time: 0.0625150203704834\n",
      "Iteration: 6|| Global Counts: [10]|| iter time: 0.06079292297363281\n",
      "Iteration: 7|| Global Counts: [10, 10]|| iter time: 0.06635904312133789\n",
      "Iteration: 8|| Global Counts: [10, 10]|| iter time: 0.07398009300231934\n",
      "Iteration: 9|| Global Counts: [10, 10]|| iter time: 0.0743870735168457\n",
      "Iteration: 10|| Global Counts: [10, 10]|| iter time: 0.07302999496459961\n",
      "Iteration: 11|| Global Counts: [10, 10]|| iter time: 0.07143282890319824\n",
      "Iteration: 12|| Global Counts: [10, 10]|| iter time: 0.07608485221862793\n",
      "Iteration: 13|| Global Counts: [10, 10]|| iter time: 0.07465600967407227\n",
      "Iteration: 14|| Global Counts: [10, 10, 10]|| iter time: 0.07155609130859375\n",
      "Iteration: 15|| Global Counts: [7, 10, 13]|| iter time: 0.0855710506439209\n",
      "Iteration: 16|| Global Counts: [8, 10, 11]|| iter time: 0.0858919620513916\n",
      "Iteration: 17|| Global Counts: [8, 10, 10]|| iter time: 0.0815730094909668\n",
      "Iteration: 18|| Global Counts: [8, 10, 10]|| iter time: 0.0815420150756836\n",
      "Iteration: 19|| Global Counts: [7, 10, 10]|| iter time: 0.07758212089538574\n",
      "Iteration: 20|| Global Counts: [7, 10, 10]|| iter time: 0.08308696746826172\n",
      "Iteration: 21|| Global Counts: [7, 10, 10]|| iter time: 0.0809481143951416\n",
      "Iteration: 22|| Global Counts: [7, 10, 10]|| iter time: 0.08330488204956055\n",
      "Iteration: 23|| Global Counts: [7, 10, 10]|| iter time: 0.08144593238830566\n",
      "Iteration: 24|| Global Counts: [7, 10, 10]|| iter time: 0.07847285270690918\n",
      "Iteration: 25|| Global Counts: [8, 10, 10]|| iter time: 0.08363604545593262\n",
      "Iteration: 26|| Global Counts: [7, 10, 10]|| iter time: 0.08128213882446289\n",
      "Iteration: 27|| Global Counts: [8, 10, 10]|| iter time: 0.08153486251831055\n",
      "Iteration: 28|| Global Counts: [8, 10, 10]|| iter time: 0.08348608016967773\n",
      "Iteration: 29|| Global Counts: [8, 10, 10]|| iter time: 0.07883095741271973\n",
      "Iteration: 30|| Global Counts: [8, 10, 10]|| iter time: 0.08311605453491211\n",
      "Iteration: 31|| Global Counts: [8, 10, 10]|| iter time: 0.08098816871643066\n",
      "Iteration: 32|| Global Counts: [8, 10, 10]|| iter time: 0.08668994903564453\n",
      "Iteration: 33|| Global Counts: [8, 10, 10]|| iter time: 0.08283019065856934\n",
      "Iteration: 34|| Global Counts: [7, 10, 10]|| iter time: 0.07932090759277344\n",
      "Iteration: 35|| Global Counts: [7, 10, 10]|| iter time: 0.07929205894470215\n",
      "Iteration: 36|| Global Counts: [8, 10, 10]|| iter time: 0.07870912551879883\n",
      "Iteration: 37|| Global Counts: [7, 10, 10]|| iter time: 0.08058309555053711\n",
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      "Iteration: 50|| Global Counts: [1, 3]|| iter time: 0.018220186233520508\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Dict(7 => [7; 7; … ; 2; 2],4 => [3; 3; … ; 2; 2],9 => [1; 1; … ; 1; 1],10 => [1; 1; … ; 7; 1],2 => [1; 1; … ; 2; 2],3 => [4; 4; … ; 2; 2],5 => [4; 4; … ; 2; 2],8 => [2; 2; … ; 5; 5],6 => [2; 2; … ; 2; 2],1 => [1; 1; … ; 5; 5]…), Dict(7 => [3; 3; … ; 2; 2],4 => [1; 1; … ; 2; 2],9 => [3; 3; … ; 3; 3],10 => [3; 3; … ; 3; 3],2 => [3; 3; … ; 2; 2],3 => [1; 1; … ; 2; 2],5 => [1; 1; … ; 2; 2],8 => [2; 2; … ; 3; 3],6 => [2; 2; … ; 2; 2],1 => [3; 3; … ; 3; 3]…), [3, 3, 3, 3, 3, 3, 3, 3, 3, 3  …  3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [[2, 2, 2, 2, 2, 2, 2, 2, 2, 2  …  2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1  …  2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2  …  1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2  …  3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3  …  1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3  …  2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1  …  2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3  …  2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1  …  1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3  …  3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], Dict(7 => [1; 1; … ; 2; 2],4 => [3; 3; … ; 2; 2],9 => [1; 1; … ; 1; 1],10 => [1; 1; … ; 1; 1],2 => [1; 1; … ; 2; 2],3 => [3; 3; … ; 2; 2],5 => [3; 3; … ; 2; 2],8 => [2; 2; … ; 1; 1],6 => [2; 2; … ; 2; 2],1 => [1; 1; … ; 1; 1]…), Dict(7 => [1; 1; … ; 3; 3],4 => [3; 3; … ; 2; 2],9 => [1; 1; … ; 1; 1],10 => [1; 1; … ; 1; 1],2 => [1; 1; … ; 2; 2],3 => [2; 2; … ; 1; 1],5 => [4; 4; … ; 3; 3],8 => [1; 1; … ; 3; 3],6 => [2; 2; … ; 1; 1],1 => [5; 5; … ; 3; 3]…), Dict(7 => [2; 2; … ; 1; 1],4 => [1; 1; … ; 2; 2],9 => [2; 2; … ; 2; 2],10 => [2; 2; … ; 2; 2],2 => [1; 1; … ; 2; 2],3 => [2; 2; … ; 1; 1],5 => [2; 2; … ; 1; 1],8 => [3; 3; … ; 1; 1],6 => [1; 1; … ; 1; 1],1 => [1; 1; … ; 1; 1]…), Dict(7 => [2; 2; … ; 3; 3],4 => [3; 3; … ; 2; 2],9 => [1; 1; … ; 1; 1],10 => [3; 3; … ; 3; 3],2 => [4; 4; … ; 5; 5],3 => [5; 5; … ; 1; 4],5 => [6; 6; … ; 1; 1],8 => [4; 4; … ; 1; 1],6 => [4; 4; … ; 1; 1],1 => [1; 1; … ; 4; 4]…))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdp_all_global,hdp_global_global,dpmm_all_local,dpmm_seperated_local, vhdpmm_global, vhdpmm_local,vhdpmm_sep_global, vhdpmm_sep_local =\n",
    "    run_methods_and_get_results(pts,g_prior,l_prior,dp_prior,g_hdp,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        HDPMM NMI: 0.8247037529487768 \n",
      "\n",
      "        HDPMM Global NMI: 0.9965057438935003 \n",
      "\n",
      "        DPMM NMI: 0.07525700523292128 \n",
      "\n",
      "        DPMM SEPERATED NMI: 0.7734244337920403\n",
      "\n",
      "        vHDPMM Global NMI: 0.9987526916835051\n",
      "\n",
      "        vHDPMM Local NMI: 0.8776641706369462\n",
      "        vHDPMM Sep Global NMI: 0.9989081132922045\n",
      "\n",
      "        vHDPMM Sep Local NMI: 0.8799099300832822\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8247037529487768, 0.9965057438935003, 0.07525700523292128, 0.7734244337920403, 0.9987526916835051, 0.8776641706369462, 0.9989081132922045, 0.8799099300832822)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_all_results(labels_global,labels_local,hdp_all_global,hdp_global_global,dpmm_all_local,dpmm_seperated_local, vhdpmm_global, vhdpmm_local,vhdpmm_sep_global, vhdpmm_sep_local)"
   ]
  }
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