Plot PLDS result.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Plots, JLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "state1_est_CVI = load(\".\\\\results\\\\state1_estimates_CVI.jld\")[\"state1_estimate\"];\n",
    "var1_est_CVI = load(\".\\\\results\\\\var1_estimates_CVI.jld\")[\"var1_estimate\"];\n",
    "\n",
    "state1_est_GP = load(\".\\\\results\\\\state1_estimates_GP.jld\")[\"state1_estimate\"];\n",
    "var1_est_GP = load(\".\\\\results\\\\var1_estimates_GP.jld\")[\"var1_estimate\"];\n",
    "\n",
    "tuning_points = load(\".\\\\results\\\\fine tuning points.jld\")[\"tuning_point\"];\n",
    "time_cvi = load(\".\\\\results\\\\inference_time_cvi.jld\")[\"inference_time\"];\n",
    "time_gp = load(\".\\\\results\\\\inference_time_gp.jld\")[\"inference_time\"]; "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare the result of GP-CVI and CVI "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(45:70, state1_est_CVI[45:70],label = \"CVI\", lw = 2, color = :darkorange, ribbon = sqrt.(var1_est_CVI[45:70]), fillalpha=0.3)\n",
    "plot!(45:70, state1_est_GP[45:70],label = \"GP-CVI\", legend = :bottomright, \n",
    "    ylabel = \"state x_t\", xlabel = \"Time step t\", lw = 2, color = :green, ribbon = sqrt.(var1_est_GP[45:70]), fillalpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "savefig(\".\\\\result.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Vector{Any}:\n",
       " 150\n",
       " 284"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "timecvi_1 = sum(time_cvi[25:tuning_points[1]-1]); #from t=26 to fine-tuning (ft) point ft1\n",
    "timecvi_2 = sum(time_cvi[tuning_points[1]:tuning_points[2]-1]); #from ft1 +1 to ft2\n",
    "timecvi_3 = sum(time_cvi[tuning_points[2]:end]); #from ft2 +1 to end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Vector{Float64}:\n",
       " 145.81390340000004\n",
       " 298.9511648000001\n",
       " 233.0387266000001"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[timecvi_1, timecvi_2, timecvi_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "timegp_1 = sum(time_gp[1:tuning_points[1]-25]); #from t=26 to ft1\n",
    "timegp_2 = sum(time_gp[tuning_points[1]-24:tuning_points[2]-25]); #from ft1 +1 to ft2\n",
    "timegp_3 = sum(time_gp[tuning_points[2]-24:end]); #from ft2 +1 to end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Vector{Float64}:\n",
       " 97.16889109999998\n",
       " 91.88518669999993\n",
       " 26.876453200000007"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[timegp_1, timegp_2, timegp_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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