src/linear_regression_with_interpolation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Distributions\n",
    "using Random\n",
    "using LinearAlgebra\n",
    "using PyPlot\n",
    "using DataFrames\n",
    "using CSV\n",
    "using Turing\n",
    "using MCMCChains\n",
    "using Statistics\n",
    "using Pkg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "julia version 1.5.2\n"
     ]
    }
   ],
   "source": [
    ";julia --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "create_dataset (generic function with 1 method)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# artificial data\n",
    "function create_dataset(N, D, mu_x, lambda_x, Mu_w, Lambda_w, lambda_y)\n",
    "    Random.seed!(1)\n",
    "    X = ones(N, D)\n",
    "    X_test = ones(N, D)\n",
    "    for i in 1 : N\n",
    "        for j in 1 : D\n",
    "            X[i, j] = rand(Normal(mu_x, lambda_x))\n",
    "            X_test[i, j] = rand(Normal(mu_x, lambda_x))\n",
    "        end\n",
    "    end\n",
    "\n",
    "    W = rand(MvNormal(Mu_w, Lambda_w))\n",
    "    Y = ones(N)\n",
    "    Y_test = ones(N)\n",
    "    for i in 1 : N\n",
    "        Y[i] = rand(Normal(dot(W, X[i, :]), lambda_y))\n",
    "        Y_test[i] = rand(Normal(dot(W, X_test[i, :]), lambda_y))\n",
    "    end\n",
    "    \n",
    "    return X, W, Y, X_test, Y_test\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "generate_missing (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function generate_missing(X_raw, miss_rate)\n",
    "    Random.seed!(1)\n",
    "    \n",
    "    X = copy(X_raw)\n",
    "    X_replaced = []\n",
    "    for i in 1:size(X)[1]\n",
    "        for j in 1:size(X)[2]\n",
    "            if rand() < miss_rate\n",
    "                push!(X_replaced, X[i,j])\n",
    "                X[i,j] = NaN\n",
    "            end\n",
    "        end\n",
    "    end\n",
    "    return X, X_replaced\n",
    "end\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5-element Array{Float64,1}:\n",
       " -0.6663291550362418\n",
       " -0.1126536623502542\n",
       " -0.5402299893464024\n",
       "  0.7231824033951142\n",
       "  0.8124369342832805"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parameters for the datasets\n",
    "N = 30\n",
    "D = 2\n",
    "mu_x = 0\n",
    "lambda_x = 1\n",
    "Mu_w = ones(D)\n",
    "Lambda_w = I\n",
    "lambda_y = 1\n",
    "miss_rate = 0.5\n",
    "\n",
    "X_raw, W, Y, X_test, Y_test = create_dataset(N, D, mu_x, lambda_x, Mu_w, Lambda_w, lambda_y)\n",
    "X, X_replaced = generate_missing(X_raw, miss_rate)\n",
    "\n",
    "# remove a row if it contains nan\n",
    "not_nan_idx = vec(.!any(isnan.(X), dims=2))\n",
    "X_red = X[not_nan_idx, :]\n",
    "Y_red = Y[not_nan_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rmse (generic function with 1 method)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function rmse(X_test, Y_test, W)\n",
    "    (sum((Y_test - X_test*W).^2)/size(Y_test)[1])^0.5\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_contour (generic function with 2 methods)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function show_contour(X, W, title, margin_rate = 0.15)\n",
    "    x_max = minimum(X[:, :])\n",
    "    x_min = maximum(X[:, :])\n",
    "    margin = margin_rate*(x_max - x_min)\n",
    "    x = range(x_min - margin, x_max + margin, length=30)\n",
    "    z = rand(size(x,1), size(x,1))\n",
    "    X = vcat.(x, x')\n",
    "\n",
    "    for i in 1 : size(x,1), j in 1 : size(x,1)\n",
    "        z[i, j] = dot(W, X'[i,j])\n",
    "    end\n",
    "\n",
    "    PyPlot.title(\"$title\")\n",
    "    CS = contour(x, x, z)\n",
    "    clabel(CS, inline=1, fontsize=10)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "linear_regression_with_miss (generic function with 1 method)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@model function linear_regression_with_miss(x1, x1_miss, x2, x2_miss, y, N1, N2, N3, N4)\n",
    "    # hypter paramteters\n",
    "    mu_x = 0\n",
    "    lambda_x = 1.5\n",
    "    lambda_w = 100\n",
    "    lambda_y = 1\n",
    "    \n",
    "    nfeatures = 2\n",
    "    W ~ MvNormal(nfeatures, lambda_w)\n",
    "    \n",
    "    # no missing\n",
    "    for i in 1:N1\n",
    "        mu = x1[i] * W[1] + x2[i] * W[2] \n",
    "        y[i] ~ Normal(mu, lambda_y)\n",
    "    end\n",
    "    \n",
    "    # only x1 is missed\n",
    "    for i in N1+1:N1+N2\n",
    "        x1_miss[i-N1] ~ Normal(mu_x, lambda_x)\n",
    "        mu = x1_miss[i-N1] * W[1] + x2[i] * W[2] \n",
    "        y[i] ~ Normal(mu, lambda_y)  \n",
    "    end\n",
    "    \n",
    "    # only x2 is missed\n",
    "    for i in N1+N2+1:N1+N2+N3\n",
    "        x2_miss[i-N1-N2] ~ Normal(mu_x, lambda_x)\n",
    "        mu = x1[i-N2] * W[1] + x2_miss[i-N1-N2] * W[2] \n",
    "        y[i] ~ Normal(mu, lambda_y)  \n",
    "    end\n",
    "    \n",
    "    # both x1 and x2 are missed\n",
    "    for i in N1+N2+N3+1:N1+N2+N3+N4\n",
    "        x1_miss[i-N1-N3] ~ Normal(mu_x, lambda_x)\n",
    "        x2_miss[i-N1-N2] ~ Normal(mu_x, lambda_x)\n",
    "        mu = x1_miss[i-N1-N3] * W[1] + x2_miss[i-N1-N2] * W[2]\n",
    "        y[i] ~ Normal(mu, lambda_y)  \n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "run_smaple (generic function with 1 method)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function run_smaple(X, Y)\n",
    "    id1 = .~isnan.(X[:,1]) .& .~isnan.(X[:,2])\n",
    "    id2 = isnan.(X[:,1]) .& .~isnan.(X[:,2])\n",
    "    id3 = .~isnan.(X[:,1]) .& isnan.(X[:,2])\n",
    "    id4 = isnan.(X[:,1]) .& isnan.(X[:,2])\n",
    "    \n",
    "    x1 = vcat(X[id1,:], X[id3,:])[:,1]\n",
    "    x1_miss = fill(missing, size(vcat(X[id2,:], X[id4,:])[:,1], 1))\n",
    "    x2 = vcat(X[id1,:], X[id2,:])[:,2]\n",
    "    x2_miss = fill(missing, size(vcat(X[id3,:], X[id4,:])[:,2], 1))\n",
    "    y = vcat(Y[id1], Y[id2], Y[id3], Y[id4])\n",
    "    \n",
    "    N1 = sum(id1)\n",
    "    N2 = sum(id2)\n",
    "    N3 = sum(id3)\n",
    "    N4 = sum(id4)\n",
    "    model = linear_regression_with_miss(x1, x1_miss, x2, x2_miss, y, N1, N2, N3, N4)\n",
    "    chain = sample(model, NUTS(), 3000)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Found initial step size\n",
      "│   ϵ = 0.8\n",
      "└ @ Turing.Inference /Users/teppei/.julia/packages/Turing/UsQlw/src/inference/hmc.jl:625\n",
      "\u001b[32mSampling: 100%|█████████████████████████████████████████| Time: 0:00:00\u001b[39m\n",
      "┌ Info: Found initial step size\n",
      "│   ϵ = 0.0125\n",
      "└ @ Turing.Inference /Users/teppei/.julia/packages/Turing/UsQlw/src/inference/hmc.jl:625\n",
      "\u001b[32mSampling: 100%|█████████████████████████████████████████| Time: 0:00:19\u001b[39m\n",
      "┌ Info: Found initial step size\n",
      "│   ϵ = 0.4\n",
      "└ @ Turing.Inference /Users/teppei/.julia/packages/Turing/UsQlw/src/inference/hmc.jl:625\n",
      "\u001b[32mSampling: 100%|█████████████████████████████████████████| Time: 0:00:00\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "chain1 = run_smaple(X_red, Y_red)\n",
    "W_chain = chain1[:,1:2,:]\n",
    "Mu_w_red = summarize(W_chain, mean).nt[:mean]\n",
    "###################################\n",
    "\n",
    "chain2 = run_smaple(X, Y)\n",
    "W_chain = chain2[:,1:2,:]\n",
    "X_chain = chain2[:,3:end,:]\n",
    "Mu_w = summarize(W_chain, mean).nt[:mean]\n",
    "X_est = copy(X)\n",
    "X_est[isnan.(X)] = summarize(X_chain, mean).nt[:mean]\n",
    "X_std = 0*copy(X)\n",
    "X_std[isnan.(X)] = summarize(X_chain, std).nt[:std];\n",
    "###########################\n",
    "\n",
    "chain3 = run_smaple(X_raw, Y)\n",
    "W_chain = chain3[:,1:2,:]\n",
    "Mu_w_raw = summarize(W_chain, mean).nt[:mean];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chains MCMC chain (2000×14×1 Array{Float64,3}):\n",
       "\n",
       "Iterations        = 1:2000\n",
       "Thinning interval = 1\n",
       "Chains            = 1\n",
       "Samples per chain = 2000\n",
       "parameters        = W[1], W[2]\n",
       "internals         = acceptance_rate, hamiltonian_energy, hamiltonian_energy_error, is_accept, log_density, lp, max_hamiltonian_energy_error, n_steps, nom_step_size, numerical_error, step_size, tree_depth\n",
       "\n",
       "Summary Statistics\n",
       " \u001b[0m\u001b[1m parameters \u001b[0m \u001b[0m\u001b[1m    mean \u001b[0m \u001b[0m\u001b[1m     std \u001b[0m \u001b[0m\u001b[1m naive_se \u001b[0m \u001b[0m\u001b[1m    mcse \u001b[0m \u001b[0m\u001b[1m      ess \u001b[0m \u001b[0m\u001b[1m    rhat \u001b[0m \u001b[0m\n",
       " \u001b[0m\u001b[90m     Symbol \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m  Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m  Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\n",
       " \u001b[0m            \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m          \u001b[0m \u001b[0m         \u001b[0m \u001b[0m          \u001b[0m \u001b[0m         \u001b[0m \u001b[0m\n",
       " \u001b[0m       W[1] \u001b[0m \u001b[0m  0.8527 \u001b[0m \u001b[0m  0.9471 \u001b[0m \u001b[0m   0.0212 \u001b[0m \u001b[0m  0.0498 \u001b[0m \u001b[0m 507.8192 \u001b[0m \u001b[0m  1.0016 \u001b[0m \u001b[0m\n",
       " \u001b[0m       W[2] \u001b[0m \u001b[0m -0.7658 \u001b[0m \u001b[0m  0.7007 \u001b[0m \u001b[0m   0.0157 \u001b[0m \u001b[0m  0.0368 \u001b[0m \u001b[0m 502.8375 \u001b[0m \u001b[0m  1.0012 \u001b[0m \u001b[0m\n",
       "\n",
       "Quantiles\n",
       " \u001b[0m\u001b[1m parameters \u001b[0m \u001b[0m\u001b[1m    2.5% \u001b[0m \u001b[0m\u001b[1m   25.0% \u001b[0m \u001b[0m\u001b[1m   50.0% \u001b[0m \u001b[0m\u001b[1m   75.0% \u001b[0m \u001b[0m\u001b[1m   97.5% \u001b[0m \u001b[0m\n",
       " \u001b[0m\u001b[90m     Symbol \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\n",
       " \u001b[0m            \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m\n",
       " \u001b[0m       W[1] \u001b[0m \u001b[0m -1.0538 \u001b[0m \u001b[0m  0.1882 \u001b[0m \u001b[0m  0.8687 \u001b[0m \u001b[0m  1.5282 \u001b[0m \u001b[0m  2.6174 \u001b[0m \u001b[0m\n",
       " \u001b[0m       W[2] \u001b[0m \u001b[0m -2.0845 \u001b[0m \u001b[0m -1.2565 \u001b[0m \u001b[0m -0.7531 \u001b[0m \u001b[0m -0.2869 \u001b[0m \u001b[0m  0.6371 \u001b[0m \u001b[0m\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chains MCMC chain (2000×48×1 Array{Float64,3}):\n",
       "\n",
       "Iterations        = 1:2000\n",
       "Thinning interval = 1\n",
       "Chains            = 1\n",
       "Samples per chain = 2000\n",
       "parameters        = W[1], W[2], x1_miss[1], x1_miss[2], x1_miss[3], x1_miss[4], x1_miss[5], x1_miss[6], x1_miss[7], x1_miss[8], x1_miss[9], x1_miss[10], x1_miss[11], x1_miss[12], x1_miss[13], x1_miss[14], x1_miss[15], x1_miss[16], x1_miss[17], x1_miss[18], x1_miss[19], x1_miss[20], x1_miss[21], x1_miss[22], x2_miss[1], x2_miss[2], x2_miss[3], x2_miss[4], x2_miss[5], x2_miss[6], x2_miss[7], x2_miss[8], x2_miss[9], x2_miss[10], x2_miss[11], x2_miss[12]\n",
       "internals         = acceptance_rate, hamiltonian_energy, hamiltonian_energy_error, is_accept, log_density, lp, max_hamiltonian_energy_error, n_steps, nom_step_size, numerical_error, step_size, tree_depth\n",
       "\n",
       "Summary Statistics\n",
       " \u001b[0m\u001b[1m  parameters \u001b[0m \u001b[0m\u001b[1m    mean \u001b[0m \u001b[0m\u001b[1m     std \u001b[0m \u001b[0m\u001b[1m naive_se \u001b[0m \u001b[0m\u001b[1m    mcse \u001b[0m \u001b[0m\u001b[1m       ess \u001b[0m \u001b[0m\u001b[1m    rhat \u001b[0m \u001b[0m\n",
       " \u001b[0m\u001b[90m      Symbol \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m  Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m   Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\n",
       " \u001b[0m             \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m          \u001b[0m \u001b[0m         \u001b[0m \u001b[0m           \u001b[0m \u001b[0m         \u001b[0m \u001b[0m\n",
       " \u001b[0m        W[1] \u001b[0m \u001b[0m -0.1421 \u001b[0m \u001b[0m  0.3930 \u001b[0m \u001b[0m   0.0088 \u001b[0m \u001b[0m  0.0276 \u001b[0m \u001b[0m  262.2286 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m        W[2] \u001b[0m \u001b[0m -0.3795 \u001b[0m \u001b[0m  0.3052 \u001b[0m \u001b[0m   0.0068 \u001b[0m \u001b[0m  0.0185 \u001b[0m \u001b[0m  376.2225 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[1] \u001b[0m \u001b[0m -0.2266 \u001b[0m \u001b[0m  1.5053 \u001b[0m \u001b[0m   0.0337 \u001b[0m \u001b[0m  0.0601 \u001b[0m \u001b[0m  959.3720 \u001b[0m \u001b[0m  0.9995 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[2] \u001b[0m \u001b[0m  0.2947 \u001b[0m \u001b[0m  1.6079 \u001b[0m \u001b[0m   0.0360 \u001b[0m \u001b[0m  0.0743 \u001b[0m \u001b[0m  631.9786 \u001b[0m \u001b[0m  1.0001 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[3] \u001b[0m \u001b[0m -0.0093 \u001b[0m \u001b[0m  1.3533 \u001b[0m \u001b[0m   0.0303 \u001b[0m \u001b[0m  0.0180 \u001b[0m \u001b[0m 3473.8257 \u001b[0m \u001b[0m  1.0007 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[4] \u001b[0m \u001b[0m  0.1884 \u001b[0m \u001b[0m  1.4962 \u001b[0m \u001b[0m   0.0335 \u001b[0m \u001b[0m  0.0508 \u001b[0m \u001b[0m  932.5545 \u001b[0m \u001b[0m  0.9999 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[5] \u001b[0m \u001b[0m -0.1040 \u001b[0m \u001b[0m  1.3245 \u001b[0m \u001b[0m   0.0296 \u001b[0m \u001b[0m  0.0359 \u001b[0m \u001b[0m 2146.9508 \u001b[0m \u001b[0m  0.9998 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[6] \u001b[0m \u001b[0m -0.1466 \u001b[0m \u001b[0m  1.3290 \u001b[0m \u001b[0m   0.0297 \u001b[0m \u001b[0m  0.0197 \u001b[0m \u001b[0m 3539.1760 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[7] \u001b[0m \u001b[0m  0.1040 \u001b[0m \u001b[0m  1.6466 \u001b[0m \u001b[0m   0.0368 \u001b[0m \u001b[0m  0.0569 \u001b[0m \u001b[0m  861.8936 \u001b[0m \u001b[0m  0.9999 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[8] \u001b[0m \u001b[0m -0.0877 \u001b[0m \u001b[0m  1.2797 \u001b[0m \u001b[0m   0.0286 \u001b[0m \u001b[0m  0.0198 \u001b[0m \u001b[0m 3102.1737 \u001b[0m \u001b[0m  0.9997 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[9] \u001b[0m \u001b[0m  0.4299 \u001b[0m \u001b[0m  1.4912 \u001b[0m \u001b[0m   0.0333 \u001b[0m \u001b[0m  0.0500 \u001b[0m \u001b[0m  932.3774 \u001b[0m \u001b[0m  0.9997 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[10] \u001b[0m \u001b[0m  0.0282 \u001b[0m \u001b[0m  1.3464 \u001b[0m \u001b[0m   0.0301 \u001b[0m \u001b[0m  0.0177 \u001b[0m \u001b[0m 3354.8493 \u001b[0m \u001b[0m  0.9995 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[11] \u001b[0m \u001b[0m  0.2049 \u001b[0m \u001b[0m  1.4646 \u001b[0m \u001b[0m   0.0327 \u001b[0m \u001b[0m  0.0515 \u001b[0m \u001b[0m  941.1071 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[12] \u001b[0m \u001b[0m -0.1964 \u001b[0m \u001b[0m  1.3237 \u001b[0m \u001b[0m   0.0296 \u001b[0m \u001b[0m  0.0268 \u001b[0m \u001b[0m 3119.0075 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[13] \u001b[0m \u001b[0m -0.0179 \u001b[0m \u001b[0m  1.3498 \u001b[0m \u001b[0m   0.0302 \u001b[0m \u001b[0m  0.0326 \u001b[0m \u001b[0m 2252.8190 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[14] \u001b[0m \u001b[0m  0.2150 \u001b[0m \u001b[0m  1.4324 \u001b[0m \u001b[0m   0.0320 \u001b[0m \u001b[0m  0.0519 \u001b[0m \u001b[0m 1077.4229 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[15] \u001b[0m \u001b[0m  0.6443 \u001b[0m \u001b[0m  1.9382 \u001b[0m \u001b[0m   0.0433 \u001b[0m \u001b[0m  0.0978 \u001b[0m \u001b[0m  461.3844 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[16] \u001b[0m \u001b[0m  0.3253 \u001b[0m \u001b[0m  1.5910 \u001b[0m \u001b[0m   0.0356 \u001b[0m \u001b[0m  0.0701 \u001b[0m \u001b[0m  677.1285 \u001b[0m \u001b[0m  0.9999 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[17] \u001b[0m \u001b[0m -0.1590 \u001b[0m \u001b[0m  1.4369 \u001b[0m \u001b[0m   0.0321 \u001b[0m \u001b[0m  0.0412 \u001b[0m \u001b[0m 1658.2458 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[18] \u001b[0m \u001b[0m -0.1815 \u001b[0m \u001b[0m  1.4077 \u001b[0m \u001b[0m   0.0315 \u001b[0m \u001b[0m  0.0392 \u001b[0m \u001b[0m 1630.7607 \u001b[0m \u001b[0m  1.0001 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[19] \u001b[0m \u001b[0m -0.3405 \u001b[0m \u001b[0m  1.4865 \u001b[0m \u001b[0m   0.0332 \u001b[0m \u001b[0m  0.0515 \u001b[0m \u001b[0m  873.7543 \u001b[0m \u001b[0m  1.0000 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[20] \u001b[0m \u001b[0m -0.4452 \u001b[0m \u001b[0m  1.7005 \u001b[0m \u001b[0m   0.0380 \u001b[0m \u001b[0m  0.0792 \u001b[0m \u001b[0m  622.5252 \u001b[0m \u001b[0m  0.9999 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[21] \u001b[0m \u001b[0m  0.0389 \u001b[0m \u001b[0m  1.3597 \u001b[0m \u001b[0m   0.0304 \u001b[0m \u001b[0m  0.0233 \u001b[0m \u001b[0m 3063.1428 \u001b[0m \u001b[0m  0.9997 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[22] \u001b[0m \u001b[0m -0.2262 \u001b[0m \u001b[0m  1.3686 \u001b[0m \u001b[0m   0.0306 \u001b[0m \u001b[0m  0.0423 \u001b[0m \u001b[0m 1323.1469 \u001b[0m \u001b[0m  1.0003 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[1] \u001b[0m \u001b[0m -0.4871 \u001b[0m \u001b[0m  1.3241 \u001b[0m \u001b[0m   0.0296 \u001b[0m \u001b[0m  0.0411 \u001b[0m \u001b[0m 1318.7978 \u001b[0m \u001b[0m  0.9995 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[2] \u001b[0m \u001b[0m  0.7707 \u001b[0m \u001b[0m  1.4027 \u001b[0m \u001b[0m   0.0314 \u001b[0m \u001b[0m  0.0528 \u001b[0m \u001b[0m 1003.8294 \u001b[0m \u001b[0m  0.9997 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[3] \u001b[0m \u001b[0m  0.0222 \u001b[0m \u001b[0m  1.3501 \u001b[0m \u001b[0m   0.0302 \u001b[0m \u001b[0m  0.0221 \u001b[0m \u001b[0m 3369.1595 \u001b[0m \u001b[0m  0.9997 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[4] \u001b[0m \u001b[0m  0.5203 \u001b[0m \u001b[0m  1.4007 \u001b[0m \u001b[0m   0.0313 \u001b[0m \u001b[0m  0.0347 \u001b[0m \u001b[0m 1788.4167 \u001b[0m \u001b[0m  0.9996 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[5] \u001b[0m \u001b[0m  1.3809 \u001b[0m \u001b[0m  1.6489 \u001b[0m \u001b[0m   0.0369 \u001b[0m \u001b[0m  0.0682 \u001b[0m \u001b[0m  817.0675 \u001b[0m \u001b[0m  0.9995 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[6] \u001b[0m \u001b[0m  0.8261 \u001b[0m \u001b[0m  1.4377 \u001b[0m \u001b[0m   0.0321 \u001b[0m \u001b[0m  0.0378 \u001b[0m \u001b[0m 1335.8948 \u001b[0m \u001b[0m  1.0007 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[7] \u001b[0m \u001b[0m -0.4383 \u001b[0m \u001b[0m  1.3948 \u001b[0m \u001b[0m   0.0312 \u001b[0m \u001b[0m  0.0326 \u001b[0m \u001b[0m 2063.8499 \u001b[0m \u001b[0m  0.9995 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[8] \u001b[0m \u001b[0m -0.4434 \u001b[0m \u001b[0m  1.3224 \u001b[0m \u001b[0m   0.0296 \u001b[0m \u001b[0m  0.0243 \u001b[0m \u001b[0m 3295.1359 \u001b[0m \u001b[0m  0.9995 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[9] \u001b[0m \u001b[0m -0.7081 \u001b[0m \u001b[0m  1.4647 \u001b[0m \u001b[0m   0.0328 \u001b[0m \u001b[0m  0.0409 \u001b[0m \u001b[0m 1277.7904 \u001b[0m \u001b[0m  1.0012 \u001b[0m \u001b[0m\n",
       " \u001b[0m x2_miss[10] \u001b[0m \u001b[0m -1.0563 \u001b[0m \u001b[0m  1.5166 \u001b[0m \u001b[0m   0.0339 \u001b[0m \u001b[0m  0.0632 \u001b[0m \u001b[0m  997.0165 \u001b[0m \u001b[0m  1.0002 \u001b[0m \u001b[0m\n",
       " \u001b[0m x2_miss[11] \u001b[0m \u001b[0m  0.1535 \u001b[0m \u001b[0m  1.3188 \u001b[0m \u001b[0m   0.0295 \u001b[0m \u001b[0m  0.0216 \u001b[0m \u001b[0m 2808.2024 \u001b[0m \u001b[0m  0.9998 \u001b[0m \u001b[0m\n",
       " \u001b[0m x2_miss[12] \u001b[0m \u001b[0m -0.4368 \u001b[0m \u001b[0m  1.3131 \u001b[0m \u001b[0m   0.0294 \u001b[0m \u001b[0m  0.0367 \u001b[0m \u001b[0m 1553.5906 \u001b[0m \u001b[0m  0.9998 \u001b[0m \u001b[0m\n",
       "\n",
       "Quantiles\n",
       " \u001b[0m\u001b[1m  parameters \u001b[0m \u001b[0m\u001b[1m    2.5% \u001b[0m \u001b[0m\u001b[1m   25.0% \u001b[0m \u001b[0m\u001b[1m   50.0% \u001b[0m \u001b[0m\u001b[1m   75.0% \u001b[0m \u001b[0m\u001b[1m   97.5% \u001b[0m \u001b[0m\n",
       " \u001b[0m\u001b[90m      Symbol \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\n",
       " \u001b[0m             \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m\n",
       " \u001b[0m        W[1] \u001b[0m \u001b[0m -0.7907 \u001b[0m \u001b[0m -0.4616 \u001b[0m \u001b[0m -0.1790 \u001b[0m \u001b[0m  0.1784 \u001b[0m \u001b[0m  0.5742 \u001b[0m \u001b[0m\n",
       " \u001b[0m        W[2] \u001b[0m \u001b[0m -0.8827 \u001b[0m \u001b[0m -0.5945 \u001b[0m \u001b[0m -0.4218 \u001b[0m \u001b[0m -0.1920 \u001b[0m \u001b[0m  0.2890 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[1] \u001b[0m \u001b[0m -2.9882 \u001b[0m \u001b[0m -1.2928 \u001b[0m \u001b[0m -0.2675 \u001b[0m \u001b[0m  0.8005 \u001b[0m \u001b[0m  2.6962 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[2] \u001b[0m \u001b[0m -2.9691 \u001b[0m \u001b[0m -0.7748 \u001b[0m \u001b[0m  0.4086 \u001b[0m \u001b[0m  1.4526 \u001b[0m \u001b[0m  3.1956 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[3] \u001b[0m \u001b[0m -2.6955 \u001b[0m \u001b[0m -0.8825 \u001b[0m \u001b[0m -0.0036 \u001b[0m \u001b[0m  0.8327 \u001b[0m \u001b[0m  2.6919 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[4] \u001b[0m \u001b[0m -2.8195 \u001b[0m \u001b[0m -0.8255 \u001b[0m \u001b[0m  0.1961 \u001b[0m \u001b[0m  1.2587 \u001b[0m \u001b[0m  2.9696 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[5] \u001b[0m \u001b[0m -2.6477 \u001b[0m \u001b[0m -1.0037 \u001b[0m \u001b[0m -0.1304 \u001b[0m \u001b[0m  0.7496 \u001b[0m \u001b[0m  2.5584 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[6] \u001b[0m \u001b[0m -2.7105 \u001b[0m \u001b[0m -0.9854 \u001b[0m \u001b[0m -0.1411 \u001b[0m \u001b[0m  0.7398 \u001b[0m \u001b[0m  2.5117 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[7] \u001b[0m \u001b[0m -3.1888 \u001b[0m \u001b[0m -0.9772 \u001b[0m \u001b[0m  0.1501 \u001b[0m \u001b[0m  1.2000 \u001b[0m \u001b[0m  3.2086 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[8] \u001b[0m \u001b[0m -2.5282 \u001b[0m \u001b[0m -0.9478 \u001b[0m \u001b[0m -0.1066 \u001b[0m \u001b[0m  0.7616 \u001b[0m \u001b[0m  2.4591 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x1_miss[9] \u001b[0m \u001b[0m -2.6983 \u001b[0m \u001b[0m -0.5555 \u001b[0m \u001b[0m  0.4635 \u001b[0m \u001b[0m  1.4936 \u001b[0m \u001b[0m  3.1567 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[10] \u001b[0m \u001b[0m -2.5799 \u001b[0m \u001b[0m -0.8269 \u001b[0m \u001b[0m  0.0137 \u001b[0m \u001b[0m  0.8980 \u001b[0m \u001b[0m  2.7017 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[11] \u001b[0m \u001b[0m -2.7564 \u001b[0m \u001b[0m -0.8205 \u001b[0m \u001b[0m  0.2234 \u001b[0m \u001b[0m  1.2426 \u001b[0m \u001b[0m  3.0566 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[12] \u001b[0m \u001b[0m -2.8924 \u001b[0m \u001b[0m -1.0758 \u001b[0m \u001b[0m -0.2168 \u001b[0m \u001b[0m  0.6552 \u001b[0m \u001b[0m  2.4950 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[13] \u001b[0m \u001b[0m -2.7176 \u001b[0m \u001b[0m -0.9363 \u001b[0m \u001b[0m  0.0221 \u001b[0m \u001b[0m  0.8886 \u001b[0m \u001b[0m  2.5805 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[14] \u001b[0m \u001b[0m -2.6751 \u001b[0m \u001b[0m -0.7359 \u001b[0m \u001b[0m  0.2533 \u001b[0m \u001b[0m  1.2478 \u001b[0m \u001b[0m  2.7505 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[15] \u001b[0m \u001b[0m -3.0968 \u001b[0m \u001b[0m -0.7377 \u001b[0m \u001b[0m  0.7692 \u001b[0m \u001b[0m  2.1251 \u001b[0m \u001b[0m  3.9962 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[16] \u001b[0m \u001b[0m -2.8577 \u001b[0m \u001b[0m -0.7613 \u001b[0m \u001b[0m  0.3729 \u001b[0m \u001b[0m  1.4281 \u001b[0m \u001b[0m  3.2692 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[17] \u001b[0m \u001b[0m -2.8961 \u001b[0m \u001b[0m -1.1679 \u001b[0m \u001b[0m -0.1506 \u001b[0m \u001b[0m  0.8126 \u001b[0m \u001b[0m  2.5810 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[18] \u001b[0m \u001b[0m -2.9198 \u001b[0m \u001b[0m -1.1269 \u001b[0m \u001b[0m -0.1678 \u001b[0m \u001b[0m  0.7545 \u001b[0m \u001b[0m  2.6106 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[19] \u001b[0m \u001b[0m -3.1160 \u001b[0m \u001b[0m -1.3980 \u001b[0m \u001b[0m -0.3958 \u001b[0m \u001b[0m  0.6596 \u001b[0m \u001b[0m  2.7308 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[20] \u001b[0m \u001b[0m -3.5826 \u001b[0m \u001b[0m -1.7015 \u001b[0m \u001b[0m -0.4998 \u001b[0m \u001b[0m  0.7715 \u001b[0m \u001b[0m  2.9245 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[21] \u001b[0m \u001b[0m -2.6330 \u001b[0m \u001b[0m -0.8714 \u001b[0m \u001b[0m  0.0197 \u001b[0m \u001b[0m  0.9526 \u001b[0m \u001b[0m  2.6958 \u001b[0m \u001b[0m\n",
       " \u001b[0m x1_miss[22] \u001b[0m \u001b[0m -2.8805 \u001b[0m \u001b[0m -1.1424 \u001b[0m \u001b[0m -0.2311 \u001b[0m \u001b[0m  0.6670 \u001b[0m \u001b[0m  2.4905 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[1] \u001b[0m \u001b[0m -2.9550 \u001b[0m \u001b[0m -1.4144 \u001b[0m \u001b[0m -0.5330 \u001b[0m \u001b[0m  0.3631 \u001b[0m \u001b[0m  2.3759 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[2] \u001b[0m \u001b[0m -2.1361 \u001b[0m \u001b[0m -0.1272 \u001b[0m \u001b[0m  0.8735 \u001b[0m \u001b[0m  1.7226 \u001b[0m \u001b[0m  3.3215 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[3] \u001b[0m \u001b[0m -2.6536 \u001b[0m \u001b[0m -0.8664 \u001b[0m \u001b[0m  0.0378 \u001b[0m \u001b[0m  0.9051 \u001b[0m \u001b[0m  2.6667 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[4] \u001b[0m \u001b[0m -2.3523 \u001b[0m \u001b[0m -0.4150 \u001b[0m \u001b[0m  0.5790 \u001b[0m \u001b[0m  1.4728 \u001b[0m \u001b[0m  3.1325 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[5] \u001b[0m \u001b[0m -2.3822 \u001b[0m \u001b[0m  0.3271 \u001b[0m \u001b[0m  1.5929 \u001b[0m \u001b[0m  2.5160 \u001b[0m \u001b[0m  4.1761 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[6] \u001b[0m \u001b[0m -2.2923 \u001b[0m \u001b[0m -0.0366 \u001b[0m \u001b[0m  0.9691 \u001b[0m \u001b[0m  1.7612 \u001b[0m \u001b[0m  3.4553 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[7] \u001b[0m \u001b[0m -3.0800 \u001b[0m \u001b[0m -1.3137 \u001b[0m \u001b[0m -0.4962 \u001b[0m \u001b[0m  0.4564 \u001b[0m \u001b[0m  2.5168 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[8] \u001b[0m \u001b[0m -2.9707 \u001b[0m \u001b[0m -1.3059 \u001b[0m \u001b[0m -0.4411 \u001b[0m \u001b[0m  0.3885 \u001b[0m \u001b[0m  2.1141 \u001b[0m \u001b[0m\n",
       " \u001b[0m  x2_miss[9] \u001b[0m \u001b[0m -3.4044 \u001b[0m \u001b[0m -1.7082 \u001b[0m \u001b[0m -0.7474 \u001b[0m \u001b[0m  0.2324 \u001b[0m \u001b[0m  2.4851 \u001b[0m \u001b[0m\n",
       " \u001b[0m x2_miss[10] \u001b[0m \u001b[0m -3.8153 \u001b[0m \u001b[0m -2.0717 \u001b[0m \u001b[0m -1.1899 \u001b[0m \u001b[0m -0.1067 \u001b[0m \u001b[0m  2.1520 \u001b[0m \u001b[0m\n",
       " \u001b[0m x2_miss[11] \u001b[0m \u001b[0m -2.4238 \u001b[0m \u001b[0m -0.7306 \u001b[0m \u001b[0m  0.1681 \u001b[0m \u001b[0m  1.0400 \u001b[0m \u001b[0m  2.7494 \u001b[0m \u001b[0m\n",
       " \u001b[0m x2_miss[12] \u001b[0m \u001b[0m -2.8391 \u001b[0m \u001b[0m -1.3197 \u001b[0m \u001b[0m -0.4872 \u001b[0m \u001b[0m  0.3841 \u001b[0m \u001b[0m  2.3455 \u001b[0m \u001b[0m\n"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chains MCMC chain (2000×14×1 Array{Float64,3}):\n",
       "\n",
       "Iterations        = 1:2000\n",
       "Thinning interval = 1\n",
       "Chains            = 1\n",
       "Samples per chain = 2000\n",
       "parameters        = W[1], W[2]\n",
       "internals         = acceptance_rate, hamiltonian_energy, hamiltonian_energy_error, is_accept, log_density, lp, max_hamiltonian_energy_error, n_steps, nom_step_size, numerical_error, step_size, tree_depth\n",
       "\n",
       "Summary Statistics\n",
       " \u001b[0m\u001b[1m parameters \u001b[0m \u001b[0m\u001b[1m    mean \u001b[0m \u001b[0m\u001b[1m     std \u001b[0m \u001b[0m\u001b[1m naive_se \u001b[0m \u001b[0m\u001b[1m    mcse \u001b[0m \u001b[0m\u001b[1m       ess \u001b[0m \u001b[0m\u001b[1m    rhat \u001b[0m \u001b[0m\n",
       " \u001b[0m\u001b[90m     Symbol \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m  Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m   Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\n",
       " \u001b[0m            \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m          \u001b[0m \u001b[0m         \u001b[0m \u001b[0m           \u001b[0m \u001b[0m         \u001b[0m \u001b[0m\n",
       " \u001b[0m       W[1] \u001b[0m \u001b[0m -0.1699 \u001b[0m \u001b[0m  0.1996 \u001b[0m \u001b[0m   0.0045 \u001b[0m \u001b[0m  0.0052 \u001b[0m \u001b[0m 1757.2676 \u001b[0m \u001b[0m  0.9997 \u001b[0m \u001b[0m\n",
       " \u001b[0m       W[2] \u001b[0m \u001b[0m -0.4990 \u001b[0m \u001b[0m  0.1774 \u001b[0m \u001b[0m   0.0040 \u001b[0m \u001b[0m  0.0044 \u001b[0m \u001b[0m 1666.6874 \u001b[0m \u001b[0m  1.0016 \u001b[0m \u001b[0m\n",
       "\n",
       "Quantiles\n",
       " \u001b[0m\u001b[1m parameters \u001b[0m \u001b[0m\u001b[1m    2.5% \u001b[0m \u001b[0m\u001b[1m   25.0% \u001b[0m \u001b[0m\u001b[1m   50.0% \u001b[0m \u001b[0m\u001b[1m   75.0% \u001b[0m \u001b[0m\u001b[1m   97.5% \u001b[0m \u001b[0m\n",
       " \u001b[0m\u001b[90m     Symbol \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\u001b[90m Float64 \u001b[0m \u001b[0m\n",
       " \u001b[0m            \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m         \u001b[0m \u001b[0m\n",
       " \u001b[0m       W[1] \u001b[0m \u001b[0m -0.5583 \u001b[0m \u001b[0m -0.3054 \u001b[0m \u001b[0m -0.1653 \u001b[0m \u001b[0m -0.0286 \u001b[0m \u001b[0m  0.2044 \u001b[0m \u001b[0m\n",
       " \u001b[0m       W[2] \u001b[0m \u001b[0m -0.8355 \u001b[0m \u001b[0m -0.6171 \u001b[0m \u001b[0m -0.5022 \u001b[0m \u001b[0m -0.3813 \u001b[0m \u001b[0m -0.1395 \u001b[0m \u001b[0m\n"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 1600x300 with 3 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.collections.PathCollection object at 0x7fd5aeefda90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure(\"result\", figsize=(16,3))\n",
    "subplot(1,4,1)\n",
    "show_contour(X_raw, Mu_w_red, \"Reduced data (N = $(size(X_red,1)))\")\n",
    "scatter(X_red[:, 1], X_red[:, 2], c=Y_red, vmin=minimum(Y), vmax=maximum(Y))\n",
    "\n",
    "idx_nan = vec(any(isnan.(X), dims=2))\n",
    "subplot(1,4,2)\n",
    "show_contour(X_est, Mu_w, \"Imcomplete data (N = $N)\", 0.25)\n",
    "scatter(X_red[:, 1], X_red[:, 2], c=Y_red, vmin=minimum(Y), vmax=maximum(Y))\n",
    "scatter(X_est[idx_nan, 1], X_est[idx_nan, 2], c=Y[idx_nan], marker=\"x\", vmin=minimum(Y), vmax=maximum(Y))\n",
    "\n",
    "for i in findall(idx_nan)\n",
    "    plot([X_est[i,1] - X_std[i,1], X_est[i,1] + X_std[i,1]], [X_est[i,2], X_est[i,2]], \"b\", alpha=0.2, linewidth=6)\n",
    "    plot([X_est[i,1], X_est[i,1]], [X_est[i,2]-X_std[i,2], X_est[i,2] + X_std[i,2]], \"b\", alpha=0.2, linewidth=6)\n",
    "end\n",
    "\n",
    "subplot(1,4,3)\n",
    "show_contour(X_raw, Mu_w_raw, \"Full data (N = $N)\")\n",
    "scatter(X_raw[:, 1], X_raw[:, 2], c=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.502013477660365, 0.9667536311273563, 0.9977174995565266)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse(X_test, Y_test, Mu_w_red), rmse(X_test, Y_test, Mu_w), rmse(X_test, Y_test, Mu_w_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Julia 1.5.2",
   "language": "julia",
   "name": "julia-1.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}