02_explore_iid-Copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join as oj\n",
    "import sys, time\n",
    "sys.path.insert(1, oj(sys.path[0], '..'))  # insert parent path\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pmlb\n",
    "from tqdm import tqdm\n",
    "from copy import deepcopy\n",
    "import pickle as pkl\n",
    "import pandas as pd\n",
    "import data\n",
    "import numpy.linalg as npl \n",
    "import numpy.random as npr\n",
    "from scipy.optimize import minimize\n",
    "# from process_results import process_results, aggregate_results, best_ridge\n",
    "from scipy.stats import ortho_group\n",
    "\n",
    "import pickle\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "save_dir = 'results'\n",
    "os.makedirs(save_dir, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loc_calc_thetahat(l):\n",
    "    inv = npl.pinv(X_train.T @ X_train + l * np.eye(d))\n",
    "#     inv = eigenvecs @ npl.pinv(np.eye(d) + np.diag(eigenvals)) @ eigenvecs.T\n",
    "    return inv @ X_train.T @ y_train\n",
    "\n",
    "def loc_mdl1_loss(l):\n",
    "    thetahat = loc_calc_thetahat(l)\n",
    "    mse_norm = npl.norm(y_train - X_train @ thetahat)**2 / (2 * var)\n",
    "    theta_norm = npl.norm(thetahat)**2 / (2 * var)\n",
    "    eigensum = 0.5 * np.sum(np.log((eigenvals + l) / l))\n",
    "    return (mse_norm + theta_norm + eigensum) / n\n",
    "\n",
    "\n",
    "def loc_mdl1_loss_ret_all(l):\n",
    "    thetahat = loc_calc_thetahat(l)\n",
    "    mse_norm = npl.norm(y_train - X_train @ thetahat)**2 / (2 * var)\n",
    "    theta_norm = npl.norm(thetahat)**2 / (2 * var)\n",
    "    eigensum = 0.5 * np.sum(np.log((eigenvals + l) / l))\n",
    "    return mse_norm/n , theta_norm/n,  eigensum / n\n",
    "\n",
    "def g(a, b):\n",
    "    val = np.sqrt(a*(1+np.sqrt(b))**2+1)\n",
    "    val -= np.sqrt(a*(1-np.sqrt(b))**2+1)\n",
    "    val = val**2\n",
    "    return(val/4)\n",
    "\n",
    "def thm3(gammas, snr):\n",
    "    delta = g(snr, gammas)\n",
    "    bnd = gammas * np.log(1+snr-delta)\n",
    "    bnd += np.log(1+gammas * snr - delta)\n",
    "    bnd -= delta/snr\n",
    "    return(bnd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.22314355131420976, 0.6931471805599453)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(1+1/var/400), np.log(1+1/var/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:07,  1.17it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:01<00:06,  1.19it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:02<00:05,  1.21it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:03<00:04,  1.22it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:04<00:04,  1.23it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:04<00:03,  1.24it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:05<00:02,  1.24it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:06<00:01,  1.25it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:07<00:00,  1.26it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:08<00:00,  1.26it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:05,  1.64it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:01<00:04,  1.63it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:01<00:04,  1.63it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:02<00:03,  1.63it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:03<00:03,  1.63it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:03<00:02,  1.65it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:04<00:01,  1.64it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:04<00:01,  1.64it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:05<00:00,  1.64it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:06<00:00,  1.65it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:04,  1.93it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:01<00:04,  1.93it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:01<00:03,  1.94it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:02<00:03,  1.93it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:02<00:02,  1.92it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:03<00:02,  1.93it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:03<00:01,  1.94it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:04<00:01,  1.95it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:04<00:00,  1.96it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:05<00:00,  1.96it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:03,  2.27it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:03,  2.27it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:01<00:03,  2.24it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:02,  2.23it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:02<00:02,  2.24it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:02<00:01,  2.24it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:03<00:01,  2.23it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:03<00:00,  2.22it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:04<00:00,  2.22it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:04<00:00,  2.20it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 133\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:03,  2.40it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:03,  2.38it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:01<00:02,  2.36it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:02,  2.37it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:02<00:02,  2.34it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:02<00:01,  2.33it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:01,  2.33it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:03<00:00,  2.33it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:03<00:00,  2.32it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:04<00:00,  2.32it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:03,  2.56it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:03,  2.53it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:01<00:02,  2.53it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:02,  2.52it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  2.50it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:02<00:01,  2.47it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:01,  2.47it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:03<00:00,  2.45it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:03<00:00,  2.43it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:04<00:00,  2.43it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 105\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:03,  2.51it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:03,  2.47it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:01<00:02,  2.47it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:02,  2.46it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:02<00:02,  2.45it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:02<00:01,  2.44it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:01,  2.44it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:03<00:00,  2.45it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:03<00:00,  2.46it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:04<00:00,  2.45it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:03,  2.83it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:02,  2.74it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:01<00:02,  2.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:02,  2.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  2.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:02<00:01,  2.83it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:01,  2.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:02<00:00,  2.78it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:03<00:00,  2.83it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:03<00:00,  2.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/linux/anaconda3.7/lib/python3.7/site-packages/ipykernel_launcher.py:46: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "/usr/local/linux/anaconda3.7/lib/python3.7/site-packages/ipykernel_launcher.py:47: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "/usr/local/linux/anaconda3.7/lib/python3.7/site-packages/ipykernel_launcher.py:48: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "/usr/local/linux/anaconda3.7/lib/python3.7/site-packages/ipykernel_launcher.py:49: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:03,  2.53it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:03,  2.51it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:01<00:02,  2.51it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:02,  2.60it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  2.59it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:02<00:01,  2.57it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:01,  2.57it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:03<00:00,  2.55it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:03<00:00,  2.56it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:03<00:00,  2.54it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 80\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:03,  2.87it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:02,  2.84it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:01<00:02,  2.82it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:02,  2.83it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  2.81it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:02<00:01,  2.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:01,  2.79it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:02<00:00,  2.78it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:03<00:00,  2.78it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:03<00:00,  2.79it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 66\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:02,  3.07it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:02,  3.09it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:02,  3.08it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:01,  3.08it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  3.08it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:01,  3.07it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:00,  3.09it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:02<00:00,  3.09it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:02<00:00,  3.08it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:03<00:00,  3.06it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 57\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:02,  3.38it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:02,  3.34it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:02,  3.34it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:01,  3.28it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  3.29it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:01,  3.30it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:00,  3.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:02<00:00,  3.07it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:02<00:00,  3.15it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:03<00:00,  3.20it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 51\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:02,  3.48it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:02,  3.44it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:02,  3.42it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:01,  3.42it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  3.40it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:01,  3.41it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:00,  3.41it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:02<00:00,  3.40it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:02<00:00,  3.39it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:02<00:00,  3.40it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:02,  3.45it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:02,  3.43it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:02,  3.43it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:01,  3.41it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  3.40it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:01,  3.42it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:00,  3.44it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:02<00:00,  3.45it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:02<00:00,  3.43it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:02<00:00,  3.44it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:02,  3.54it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:02,  3.52it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:01,  3.51it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:01,  3.50it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  3.48it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:01,  3.48it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:02<00:00,  3.48it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:02<00:00,  3.51it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:02<00:00,  3.50it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:02<00:00,  3.50it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:02,  3.58it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:02,  3.55it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:01,  3.53it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:01,  3.53it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  3.52it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:01,  3.50it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:01<00:00,  3.51it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:02<00:00,  3.51it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:02<00:00,  3.49it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:02<00:00,  3.48it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:02,  3.72it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:02,  3.68it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:01,  3.68it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:01<00:01,  3.67it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  3.68it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:01,  3.68it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:01<00:00,  3.68it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:02<00:00,  3.67it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:02<00:00,  3.67it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:02<00:00,  3.67it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:02,  4.44it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:01,  4.44it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:01,  4.41it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:00<00:01,  4.42it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  4.37it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:00,  4.34it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:01<00:00,  4.38it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:01<00:00,  4.38it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:02<00:00,  4.37it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:02<00:00,  4.37it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:01,  4.99it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:01,  4.91it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:01,  4.92it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:00<00:01,  4.88it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:01,  4.90it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:00,  4.94it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:01<00:00,  4.98it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:01<00:00,  4.99it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:01<00:00,  5.00it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:02<00:00,  4.94it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:01,  5.01it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:00<00:01,  5.06it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:00<00:01,  4.84it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:00<00:01,  4.93it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:01<00:00,  5.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:01<00:00,  5.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:01<00:00,  5.04it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:01<00:00,  5.07it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:01<00:00,  5.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:01<00:00,  5.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 4000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:04<00:39,  4.34s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:08<00:34,  4.34s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:12<00:30,  4.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:17<00:25,  4.27s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:21<00:21,  4.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:25<00:17,  4.27s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:29<00:12,  4.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:34<00:08,  4.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:38<00:04,  4.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:42<00:00,  4.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:02<00:26,  2.92s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:05<00:23,  2.95s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:08<00:20,  2.96s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:11<00:17,  2.97s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:14<00:14,  2.98s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:17<00:11,  2.99s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:20<00:08,  2.99s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:23<00:05,  2.99s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:26<00:02,  2.99s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:29<00:00,  2.99s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:02<00:20,  2.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:04<00:18,  2.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:06<00:16,  2.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:09<00:13,  2.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:11<00:11,  2.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:13<00:09,  2.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:16<00:06,  2.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:18<00:04,  2.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:20<00:02,  2.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:23<00:00,  2.34s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 400\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:16,  1.89s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:03<00:15,  1.94s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:05<00:13,  1.94s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:07<00:11,  1.92s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:09<00:09,  1.91s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:11<00:07,  1.92s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:13<00:05,  1.91s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:15<00:03,  1.92s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:17<00:01,  1.91s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:19<00:00,  1.92s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 266\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:16,  1.81s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:03<00:14,  1.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:05<00:12,  1.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:07<00:10,  1.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:09<00:09,  1.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:10<00:07,  1.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:12<00:05,  1.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:14<00:03,  1.81s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:16<00:01,  1.81s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:18<00:00,  1.81s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 222\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:15,  1.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:03<00:13,  1.72s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:05<00:11,  1.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:06<00:10,  1.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:08<00:08,  1.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:10<00:06,  1.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:11<00:05,  1.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:13<00:03,  1.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:15<00:01,  1.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:17<00:00,  1.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:15,  1.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:03<00:13,  1.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:05<00:12,  1.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:06<00:10,  1.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:08<00:08,  1.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:10<00:06,  1.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:11<00:05,  1.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:13<00:03,  1.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:15<00:01,  1.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:17<00:00,  1.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:13,  1.51s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:03<00:12,  1.54s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:04<00:10,  1.55s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:06<00:09,  1.54s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:07<00:07,  1.54s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:09<00:06,  1.57s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:10<00:04,  1.57s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:12<00:03,  1.56s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:14<00:01,  1.60s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:15<00:00,  1.61s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 190\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:15,  1.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:03<00:13,  1.68s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:04<00:11,  1.67s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:06<00:09,  1.65s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:08<00:08,  1.65s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:09<00:06,  1.64s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:11<00:04,  1.64s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:13<00:03,  1.66s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:14<00:01,  1.65s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:16<00:00,  1.64s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 160\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:13,  1.48s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:02<00:11,  1.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:04<00:10,  1.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:06<00:09,  1.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:07<00:07,  1.51s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:09<00:06,  1.51s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:10<00:04,  1.51s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:12<00:03,  1.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:13<00:01,  1.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:15<00:00,  1.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 133\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:12,  1.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:02<00:11,  1.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:04<00:09,  1.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:05<00:08,  1.43s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:07<00:07,  1.42s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:08<00:05,  1.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:09<00:04,  1.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:11<00:02,  1.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:12<00:01,  1.42s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:14<00:00,  1.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 114\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:12,  1.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:02<00:11,  1.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:04<00:09,  1.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:05<00:08,  1.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:06<00:06,  1.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:08<00:05,  1.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:09<00:04,  1.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:11<00:02,  1.38s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:12<00:01,  1.38s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:13<00:00,  1.37s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:11,  1.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:02<00:10,  1.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:03<00:09,  1.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:05<00:07,  1.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:06<00:06,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:07<00:05,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:09<00:03,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:10<00:02,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:11<00:01,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:13<00:00,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:11,  1.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:02<00:10,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:03<00:09,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:05<00:07,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:06<00:06,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:07<00:05,  1.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:09<00:03,  1.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:10<00:02,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:11<00:01,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:13<00:00,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 97\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:11,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:02<00:10,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:03<00:09,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:05<00:07,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:06<00:06,  1.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:07<00:05,  1.28s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:08<00:03,  1.28s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:10<00:02,  1.27s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:11<00:01,  1.27s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:12<00:00,  1.27s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 88\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:10,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:02<00:09,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:03<00:08,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:04<00:07,  1.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:06<00:06,  1.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:07<00:04,  1.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:08<00:03,  1.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:09<00:02,  1.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:11<00:01,  1.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:12<00:00,  1.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 80\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:11,  1.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:02<00:09,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:03<00:08,  1.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:04<00:07,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:06<00:06,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:07<00:04,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:08<00:03,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:09<00:02,  1.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:10<00:01,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:12<00:00,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:01<00:09,  1.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:02<00:08,  1.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:03<00:07,  1.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:04<00:06,  1.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:05<00:05,  1.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:06<00:04,  1.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:07<00:03,  1.07s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:08<00:02,  1.07s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:09<00:01,  1.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:10<00:00,  1.07s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:08,  1.02it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:01<00:07,  1.02it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:02<00:06,  1.02it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:03<00:05,  1.02it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:04<00:04,  1.02it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:05<00:03,  1.01it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:06<00:02,  1.01it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:07<00:01,  1.01it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:08<00:00,  1.02it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:09<00:00,  1.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 10%|█         | 1/10 [00:00<00:08,  1.06it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 20%|██        | 2/10 [00:01<00:07,  1.05it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 30%|███       | 3/10 [00:02<00:06,  1.06it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 40%|████      | 4/10 [00:03<00:05,  1.05it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 50%|█████     | 5/10 [00:04<00:04,  1.05it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 60%|██████    | 6/10 [00:05<00:03,  1.05it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 70%|███████   | 7/10 [00:06<00:02,  1.04it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 80%|████████  | 8/10 [00:07<00:01,  1.05it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      " 90%|█████████ | 9/10 [00:08<00:00,  1.06it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 10/10 [00:09<00:00,  1.05it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    }
   ],
   "source": [
    "num_trials = 10\n",
    "gamma_inv = np.array([5e-2, 1e-1, 0.5, 0.75, 0.9, 1,\n",
    "                                  1.2, 1.5, 2, 5, 7.5, 1e1, 2e1, 4e1])\n",
    "\n",
    "gammas = np.array([5e-2, 1e-1, 0.2, 0.5, 0.75, 0.9, 0.95, 1.,  1.05,  1.25, 1.5,  1.75, 1.95, 2., 2.05, 2.25, 2.5, 5., 10., 20.])\n",
    "\n",
    "noise_std = 0.1\n",
    "n_test = 1000\n",
    "\n",
    "ds = [100, 200]\n",
    "ns = np.asarray(ds[0]/gammas, dtype=int)\n",
    "\n",
    "mdl_practice = np.zeros((num_trials,  len(ds), len(ns)))\n",
    "mdl_theory = np.zeros((num_trials, len(ds), len(ns)))\n",
    "mdl_theory_mean = np.zeros((num_trials, len(ds), len(ns)))\n",
    "mdl_theory_lam  = np.zeros((num_trials, len(ds), len(ns)))\n",
    "mse = np.zeros((num_trials, len(ds), len(ns)))\n",
    "tnorm = np.zeros((num_trials, len(ds), len(ns)))\n",
    "eigsum = np.zeros((num_trials, len(ds), len(ns)))\n",
    "mse_test = np.zeros((num_trials, len(ds), len(ns)))\n",
    "\n",
    "for j, d in enumerate(ds):\n",
    "    ns = np.asarray(d/gammas, dtype=int)\n",
    "    for k, n in enumerate(ns):\n",
    "        print(d, n)\n",
    "        X_train = npr.randn(n, d) / np.sqrt(n)\n",
    "        X_test = npr.randn(n_test, d)\n",
    "        eigenvals, eigenvecs = npl.eig(X_train.T @ X_train)\n",
    "        \n",
    "        for i in tqdm(range(num_trials)):\n",
    "            \n",
    "            beta_star = npr.randn(d)\n",
    "            beta_star /= npl.norm(beta_star)    \n",
    "            beta_star *= 1. #\n",
    "\n",
    "            w = eigenvecs.T  @ beta_star\n",
    "            \n",
    "            y_train = X_train @ beta_star + noise_std * npr.randn(n)\n",
    "            y_test =  X_test @ beta_star # + noise_std *  npr.randn(n_test)\n",
    "\n",
    "            var = noise_std**2\n",
    "            opt_solved = minimize(loc_mdl1_loss, x0=1e-10)\n",
    "            lambda_opt = opt_solved.x\n",
    "            \n",
    "            mdl_practice[i, j, k] = opt_solved.fun\n",
    "            mdl_theory[i, j, k] = np.sum(np.log(1+ eigenvals * w**2 / var)) / n\n",
    "            mdl_theory_mean[i, j, k] = np.sum(np.log(1+ eigenvals * np.mean(w**2) /var)) / n\n",
    "            mdl_theory_lam[i, j, k] = np.sum(np.log(1+ eigenvals / lambda_opt)) / n\n",
    "            mse[i, j, k], tnorm[i, j, k], eigsum[i, j, k] = loc_mdl1_loss_ret_all(lambda_opt)\n",
    "            \n",
    "            mse_test[i, j, k] = npl.norm(y_test- X_test @ loc_calc_thetahat(lambda_opt))**2 / n_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "thm3_bnds = dict()\n",
    "for d in ds:\n",
    "    ns = np.asarray(d/gammas, dtype=int)\n",
    "    thm3_bnds[d] = thm3(d/ns, npl.norm(beta_star)/var/d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "label_size = 20\n",
    "mpl.rcParams['xtick.labelsize'] = label_size \n",
    "mpl.rcParams['ytick.labelsize'] = label_size \n",
    "mpl.rcParams['axes.labelsize'] = label_size\n",
    "mpl.rcParams['axes.titlesize'] = 14\n",
    "mpl.rcParams['figure.titlesize'] = 10\n",
    "# mpl.rcParams['lines.markersize'] = 8\n",
    "mpl.rcParams['grid.linewidth'] = 2.5\n",
    "mpl.rcParams['legend.fontsize'] = 12\n",
    "# pylab.rcParams['xtick.major.pad']=5\n",
    "# pylab.rcParams['ytick.major.pad']=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[18, 4])\n",
    "\n",
    "lss = ['-', '--', '-.', ':']\n",
    "plt.subplot(1, 4, 1)\n",
    "for j, d in enumerate(ds):\n",
    "    ns = np.asarray(d/gammas, dtype=int)\n",
    "    plt.plot(d/ns, mdl_theory.mean(0)[j, :], color=cs[j], linestyle=lss[j],linewidth=3.,  label='d=%d'%(d))\n",
    "plt.legend()\n",
    "plt.title('MDL-COMP Theorem 1 Bound')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('d/n')\n",
    "\n",
    "plt.subplot(1, 4, 2)\n",
    "for j, d in enumerate(ds):\n",
    "    ns = np.asarray(d/gammas, dtype=int)\n",
    "    plt.plot(d/ns, mdl_practice.mean(0)[j, :], color=cs[j], linestyle=lss[j],linewidth=3.,  label='d=%d'%(d))\n",
    "plt.title('MDL-COMP Practical Bound')\n",
    "plt.xscale('log')\n",
    "plt.legend()\n",
    "plt.xlabel('d/n')\n",
    "\n",
    "plt.subplot(1, 4, 3)\n",
    "d = ds[0]\n",
    "ns = np.asarray(d/gammas, dtype=int)\n",
    "plt.plot(d/ns, mdl_theory.mean(0)[0, :], color=cs[0], linestyle='-',linewidth=3.,  label='Thm-1 Bound')\n",
    "plt.plot(d/ns, thm3_bnds[d], color=cs[0], linestyle=':',linewidth=3.,  label='Thm-3 Bound')\n",
    "plt.plot(d/ns, mdl_practice.mean(0)[0, :], color=cs[0], linestyle='--',linewidth=3.,  label='Practical')\n",
    "plt.legend()\n",
    "plt.title('Comparison of bounds (d=%d)'%d)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('d/n')\n",
    "\n",
    "\n",
    "plt.subplot(1, 4, 4)\n",
    "plt.plot(d/ns, mdl_practice.mean(0)[0, :], color=cs[0], linestyle='-', linewidth=3.,label='MDL-COMP')\n",
    "plt.plot(d/ns, mse.mean(0)[0, :], color=cs[1], linestyle='-.', linewidth=3.,label='mse')\n",
    "plt.plot(d/ns, eigsum.mean(0)[0, :], color=cs[2], linestyle='--',linewidth=3.,  label='eigsum')\n",
    "plt.plot(d/ns, tnorm.mean(0)[0, :], color=cs[3], linestyle=':',  linewidth=3., label='tnorm')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('d/n')\n",
    "plt.title('Practical MDL-COMP details (d=%d)'%d)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[18, 4])\n",
    "\n",
    "for j, d in enumerate(ds):\n",
    "    ns = np.asarray(d/gammas, dtype=int)\n",
    "    plt.subplot(1, 4, j+1)\n",
    "    plt.plot(d/ns, mdl_practice.mean(0)[j, :], color=cs[0], linestyle='-', linewidth=3., label='Practical Bound')\n",
    "    plt.plot(d/ns, mdl_theory.mean(0)[j, :], color=cs[0], linestyle='--',linewidth=3.,  label='Thm-1 Bound')\n",
    "    plt.plot(d/ns, thm3_bnds[d], color=cs[0], linestyle=':',linewidth=3.,  label='Thm-3 Bound')\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n",
    "    plt.xlabel('d/n')\n",
    "    if j==0:\n",
    "        plt.legend()\n",
    "    \n",
    "    plt.subplot(1, 4, j+3)\n",
    "    plt.plot(d/ns, mdl_practice.mean(0)[j, :], color=cs[j], linestyle='-', marker='D', label='MDL-COMP-Practical')\n",
    "    plt.plot(d/ns, mse.mean(0)[j, :], color=cs[1], linestyle='-.', marker='D', label='mse')\n",
    "    plt.plot(d/ns, eigsum.mean(0)[j, :], color=cs[2], linestyle='--',  marker='o', label='eigsum')\n",
    "    plt.plot(d/ns, tnorm.mean(0)[j, :], color=cs[3], linestyle=':',  marker='s', label='tnorm')\n",
    "#     plt.plot(d/ns, tnorm.mean(0)[j, :]+mse.mean(0)[j, :]+eigsum.mean(0)[j, :], 'k--')\n",
    "    plt.xscale('log')\n",
    "    plt.xlabel('d/n')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#     plt.subplot(122)\n",
    "#     plt.loglog(d/ns, mdl_practice.mean(0)[j, :]/mdl_theory.mean(0)[j, :], color=cs[j])\n",
    "# plt.xscale('log')\n",
    "#     plt.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {'ns': ns, 'ds': ds, 'num_trials': num_trials, 'beta_norm': npl.norm(beta_star), \n",
    "           'mse': mse, 'eigsum': eigsum, 'tnorm':tnorm,\n",
    "           'mdl_practice': mdl_practice, \n",
    "\n",
    "           'mdl_theory':mdl_theory, \n",
    "           'mdl_thm_3': thm3_bnds,\n",
    "           \n",
    "           'mdl_theory_lam': mdl_theory_lam, \n",
    "           'mdl_theory_mean': mdl_theory_mean,\n",
    "\n",
    "          }\n",
    "file = open(file_name, 'wb')\n",
    "pickle.dump(results, file)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ns': array([  10,   20,   40,   80,  160,  320,  640, 1280, 2560, 5120]),\n",
       " 'ds': [200],\n",
       " 'num_trials': 3,\n",
       " 'beta_norm': 1.0,\n",
       " 'mdl_practice': array([[[1.60470337, 1.85100676, 1.83693757, 1.74869597, 1.72537282,\n",
       "          1.38255127, 1.00682696, 0.77689996, 0.64003855, 0.5765287 ]],\n",
       " \n",
       "        [[1.62180305, 1.7804081 , 1.83412585, 1.85436427, 1.67680371,\n",
       "          1.39648372, 1.00810861, 0.80748211, 0.63965565, 0.58718842]],\n",
       " \n",
       "        [[1.36554663, 1.63765004, 1.80933336, 1.7901262 , 1.72212077,\n",
       "          1.34845826, 0.9875914 , 0.81696316, 0.65010141, 0.58636827]]]),\n",
       " 'mdl_theory_lam': array([[[2.41936017, 2.6867755 , 2.66745463, 2.5046429 , 2.42483436,\n",
       "          1.75657166, 0.99093321, 0.55362802, 0.30644166, 0.16689741]],\n",
       " \n",
       "        [[2.41157472, 2.59959516, 2.64655796, 2.65381203, 2.37601373,\n",
       "          1.68310494, 1.01019524, 0.55477447, 0.30370917, 0.16645772]],\n",
       " \n",
       "        [[2.08164904, 2.44277212, 2.59978377, 2.59072448, 2.40774478,\n",
       "          1.72169652, 0.99344361, 0.55097722, 0.30530863, 0.1668734 ]]]),\n",
       " 'mdl_theory': array([[[3.26524794, 3.70527276, 3.20455624, 3.18451547, 3.00383927,\n",
       "          2.31172863, 1.40320719, 0.80005961, 0.47261679, 0.25980672]],\n",
       " \n",
       "        [[3.23015819, 3.56068549, 3.82311161, 3.48965207, 3.18094974,\n",
       "          2.31304399, 1.39684068, 0.81752403, 0.45803367, 0.26585994]],\n",
       " \n",
       "        [[3.00371911, 3.39814969, 3.71232828, 3.57559905, 3.0322962 ,\n",
       "          2.2839568 , 1.42373979, 0.84417093, 0.46644757, 0.26554178]]]),\n",
       " 'mdl_theory_mean': array([[[3.0511021 , 3.67539207, 3.20304793, 3.98372716, 3.73888936,\n",
       "          2.93184656, 1.74799611, 0.99692212, 0.55597255, 0.30575338]],\n",
       " \n",
       "        [[2.89257138, 3.6085243 , 3.13356962, 3.84356057, 3.99016232,\n",
       "          2.92538465, 1.74847103, 0.99767922, 0.55610729, 0.30578035]],\n",
       " \n",
       "        [[2.52755913, 2.57228953, 3.65154578, 3.87216998, 3.9565452 ,\n",
       "          2.91886127, 1.74866032, 0.99582047, 0.55597925, 0.30586127]]])}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = 'results_d_200.txt'\n",
    "file = open(file_name, 'rb')\n",
    "results = pickle.load(file)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs=sns.color_palette()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAEACAYAAAB8sjXeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvDW2N/gAAIABJREFUeJzt3Xd8FXW+//HX95z0TnrvlAQCASJIFwFBAXFXXQsWFHCx7+5VV1f3t7t33bveLXdtiOKCiAV7Q1QUAVFAegs9PaEkIY0U0k6+vz8SsoC0kJPMKZ/n45EHZGYy88mcM3mf73dmvqO01gghhBDCNpmMLkAIIYQQ5yZBLYQQQtgwCWohhBDChklQCyGEEDZMgloIIYSwYRLUQgghhA2ToBZCCCFsmAS1EEIIYcMkqIUQQggbJkEthBBC2DAXowsACA4O1vHx8UaXIYTN27JlyzGtdYjRdZyPHM9CXFhHjmWbCOr4+Hg2b95sdBlC2DylVL7RNVyIHM9CXFhHjmXp+hZCCCFsmAS1EEIIYcMkqIUQQggbZhPnqM+mqamJoqIi6uvrjS7F5nl4eBAdHY2rq6vRpQghhLAymw3qoqIifH19iY+PRylldDk2S2tNWVkZRUVFJCQkGF2OEEIIKzO061spNVUpNb+qquon8+rr6wkKCpKQvgClFEFBQdLzIIQQDsrQoNZaL9Va3+Pv73/W+RLSF0f207nVNTazPruMzENVnGi0GF2O02u2tPD9wVJyj9UaXYoQdsNmu74dVWVlJW+//Tb33XcfAIcPH+ahhx7igw8+MLgyx9DQbGFbQSXrssv4MbuMbYUVNFk0AEpBdA9PkkN86BnmS3KID8lhPiSH+uDnIef3u0Nto4WZizZzx7A4npqSanQ5QtgFCepOslgsmM3mi16+srKSl156qT2oIyMjJaQ7odnSwq5DVazLLmN9dhmb88upb2rBpCAtyp+ZIxMZmhBIXaOFrJIaDpZUk1VSw9rsMhqbW9rXE+bnTs9QX5JDfdq/eob6EOTjbuBv53j8PV0Z3SuYL3Yd4cnJKdIbJMRFkKA+j7y8PCZNmsTgwYPZunUrffv2ZfHixaSmpnLTTTfxzTff8Nhjj1FdXc38+fNpbGwkOTmZN954Ay8vL4qLi5kzZw45OTkAzJs3j+eff57s7GzS09OZMGEC999/P1OmTCEzMxOLxcJvf/tbvvrqK0wmE7Nnz+bBBx9ky5Yt/OY3v6Gmpobg4GAWLVpERESEwXvHGC0tmn1Hq1mXfYz12WVsyC2npqEZgD7hvtwyJJbhScEMSQjE3/PcrWRLi6awvI6DJTXtAZ5dUsN7mwupO6WLPNDb7T8t7xAfera1wMP9PCRkLtE1aRGs2FvCtsJKBsX2MLocIWye3QT1Ta+s/8m0Kf0juH1YPCcaLcx4beNP5t8wOJobM2Ior23k3je3nDbv3V8Ou6jt7t+/nwULFjBixAjuvvtuXnrpJQCCgoLYunUrAGVlZcyePRuAp556igULFvDggw/y0EMPMWbMGD7++GMsFgs1NTU888wzZGZmsn37dqD1w8BJ8+fPJy8vj+3bt+Pi4kJ5eTlNTU08+OCDfPrpp4SEhPDuu+/y5JNPsnDhwouq395prckurWV99rHW7uycMirqmgBIDPZmWnokw5KCuDwxiOAOtH7NJkV8sDfxwd5MSA07bXtHqurbAzyrrQX+xa4jVLZtFyDE152bMmK4ZWgsUQGe1vuFncD41DDczCaW7TwiQS3ERbCboDZKTEwMI0aMAOC2227j+eefB+Cmm25qXyYzM5OnnnqKyspKampqmDhxIgArV65k8eLFAJjNZvz9/amoqDjntlasWMGcOXNwcWl9WQIDA8nMzCQzM5MJEyYArV3tjt6aLiyvY312GevawrmkugGASH8PxqWEMTwpiGFJQUT4Wz8glVJEBngSGeDJmF7/GS9fa01ZbSMHi2vIKq1h9b4S5q7O4qXVWYxLCeP2y+MYmRyMySSt7Avx83BldK+Q1u7va1JknwlxAXYT1OdrAXu6mc87P9Db7aJb0Gc6s3vz5Pfe3t7t02bMmMEnn3zCgAEDWLRoEatXr76kbZ2N1pq+ffuyfv1PexQcSUuL5tt9Jbz8XTZb8ls/zAT7uDMsKYjhbV+xgV6GdTcrpQj2cW+v6fbL4ygsr2PJxgLe3VTIN3uKiQ/yYvrQOG7MiCbAy82QOu3FlP4RrNhbzLbCSgbHSataiPORIUQvoKCgoD0k3377bUaOHPmTZaqrq4mIiKCpqYm33nqrffq4ceOYN28e0NoSrqqqwtfXl+rq6rNua8KECbzyyis0N7eecy0vL6d3796Ulpa219DU1MTu3but+jsaqcnSwgdbipj47BpmL95M8fF6nrwmhW9+PZpNT47jhVsGcsuQWOKCvG3unHBMoBePTerDuieu5Lmb0wnxdecvX+xl6P98yyPv72BHYaXRJdqscSmhuLm0dn8LIc5PgvoCevfuzdy5c0lJSaGiooJ77733J8v8+c9/ZujQoYwYMYI+ffq0T3/uuedYtWoVaWlpDB48mD179hAUFMSIESPo168fjz766GnrmTVrFrGxsfTv358BAwbw9ttv4+bmxgcffMBvf/tbBgwYQHp6OuvWrevy37ur1TY0s+CHXMb8bRWPvL8Ds0nx3M3prH7kCmaPTqRnmK/NBfO5uLuYmZYexftzhvPlw6O4YXA0X+w6wrS5a5n6wg+8t6lQ7uE+g6+HK2Paur9bWrTR5Qhh05TWxh8kGRkZ+szn1+7du5eUlBSDKmqVl5fXfkW2rbOF/XUxymsbWbQuj8Xr86isa2JIQiD3XpHEFb1C7CaYL0Z1fRMfbzvEmz/mc6C4Bj8PF27MiGH60FgSQ3wueb1KqS1a6wwrlmp1Zzuez+bT7Yd4+J3tfDBnGBnxgd1QmRC2oyPHst2coxb2raiijn9/n8s7mwqob2phQmoYc8YkOez5SV8PV+4YFs/tl8exMbecN37M5/V1eSz4IZeRycHcdnkc41NCcTE7b6fWuJSw1u7vXUckqIU4Dwnq84iPj7eL1rQt23f0OK98l8NnOw6jgOsGRjFnTCLJob5Gl9YtlFIMTQxiaGIQJdX1vLuxkCUbC5jz5hbC/Ty4dWgsN18WQ6ifh9GlnpdSyht4CWgEVmut37rAj1yQj7sLV7R1f/9+cqpc/S3EOUhQC6vTWrMpr4KXv8tm5b4SvNzMzBgez8yRCUQ68T3Hob4ePDiuJ/dekcTKfSW88WM+//fNAZ7/9iAT+4Yza1QCA89zX3FhYSFAL6XUHkAD87XWz11KLUqphcAUoERr3e+MeZOA5wAz8G+t9TPAz4EPtNZLlVLvAp0OaoDJ/SP4ek8xWwoquExa1UKclQS1sJqTt1jNW53F1oJKAr3d+M2EXtwxLE5uVzqFi9nEVX3DuapvOLnHanl7Qz7vbS5iaGLgeYO67f76Iq11qlLKF9iilPpGa73n5DJKqVDghNa6+pRpyVrrrDNWtwh4EVh86kSllBmYC0wAioBNSqnPgGhgV9tiVrsyblxKGO5tV39LUAtxdhLUotMam1v4bMdhXvkum4MlNUQFePKna/vyi4wYPN0ufhx0Z5QQ7M2Tk1P5r6t6X3DZtoFu6gC01tVKqb1AFLDnlMXGAHOUUtdorRuUUrNpbQ1ffeq6tNZrlFLxZ9nMECBLa50DoJR6B5hGa2hHA9ux4t0iPu4uXNG7tfv7/02R7m8hzkaCWlwyrTVvbShg7qosjlTV0yfcl2dvSmdy/whcnfgiqUvh4dqxDzRtITsQ2HDqdK31+0qpBOBdpdT7wN20to4vVhRQeMr3RcBQ4HngRaXUZGDpOWqaCkxNTk7uwOZgcv9Ilu8uZnN+BUMSpFUtxJnkr+k5nHzKFcDq1auZMmVKp9Y3adIkAgICOr0eW/LS6mye+iST6B6evDbjMr58eBTXDYySkO5iSikf4EPgV1rr42fO11r/DagH5gHXaq1rOrtNrXWt1vourfW957qQ7ELPlz+XcX1C27q/D3e2TCEckvxFPYdTg9oaHn30Ud544w2rrc9o728u5O/L93NdeiTv3jOMsX1CHeo+aBumaA3pt7TWH511AaVGAf2Aj4E/dHD9h4CYU76PbpvWZbzdXbiyTyhfZB7FIoOfCPETEtTn8Pjjj7c/jvLRRx+lpqaGG264gT59+jB9+nRODhQTHx/PE088QXp6OhkZGWzdupWJEyeSlJTEyy+/3L6+cePG4evrGLckrdxXzOMf7WJUz2D+dsMAOa/YTdrec3HAXq31/51tGaXUQGA+reeV7wKClFJPd2Azm4CeSqkEpZQbcDPwWacKvwjXpEVQWt3A5rzyrt6UEHbHLs5R/2npbvYc/kkPX6ekRvrxh6l9zzn/1MdRrl69mmnTprF7924iIyMZMWIEa9eubR/3OzY2lu3bt/PrX/+aGTNmsHbtWurr6+nXrx9z5syxat1G21ZQwX1vbSU1wo95tw3GzUU+63WXtWvXAgQBVyqltrdN/p3W+otTFvMCfqG1zgZQSt0BzDhzXUqpJcAVQLBSqgj4g9Z6gda6WSn1ALCc1tuzFmqtu3xw+Sv7hOLh2jr4ydDEoK7enBB2xdCgvtSLT4wwZMgQoqOjAUhPTycvL689qK+99loA0tLSqKmpwdfXF19fX9zd3amsrCQgIMCwuq0pu7SGuxdtIszPg4UzLsPH3S4+5zmMtvfbeYcd1FqvPeP7JuDVsyx3y3nW8QXwxbnmd4X27u9dR/nD1L6YpZdGiHaG/qXVWi8FlmZkZMw+33Lna/l2F3d39/b/m83m9idcnTrPZDKdtpzJZDptOXtWfLyeOxZsxGxSLL57CCG+7hf+ISE6YHJaJF/sOsqmvHIul1a1EO2k3/Iczvc4SmdzvL6JOxdupLKukUV3DSEuyPvCPyREB43tE9La/S2PvhTiNNJ3eQ6nPo7S09OTsLCwTq1v1KhR7Nu3j5qaGqKjo1mwYAETJ060UrVdp77JwuzXN5NdWsPCGZfRL6pjt94IcbG83FwY1yeMLzOP8MdrpftbiJMkqM/j7bffPuv0F198sf3/eXl57f+fMWMGM2bMOOu877//3trldTlLi+Y3721nQ245z92czqieIUaXJBzc5P4RLNt1hA25ZQxPCja6HCFsgnR9i7PSWvOnpbv5YtdRnpqcwrT0KKNLEk5gbO9QPF3NfLFLur+FOEmCWpzVS6uzWbw+n3tGJzJrVKLR5Qgn4elm5sqUUL7KPEqzpcXocoSwCRLU4ife29Q66tjPBkbx+KQ+RpcjnMyUtAiO1TSyMVcGPxECbDyoT47+Jc7Pmvvp273FPPFx66hj/3t9fxl1THS7K3qH4uVm5nPp/hYCsOGg9vDwoKysTML6ArTWlJWV4eHh0el1bcmv4P63t9I30o+XZdQxYRBPNzNX9glluXR/CwHY8FXf0dHRFBUVUVpaanQpNs/Dw6N91LRLlVVSw8zXNxHeNuqYt4w6Jgw0pX8En+88wobcckYky9XfwrnZ7F9jV1dXEhISjC7DKRytqufOhRtxMZlYfPdQgn1k1DFhrPbu751HJKiF05O+TSdXdaJ11LGqE00suusyYoO8jC5JCDxczYxPCWP5bun+FkKC2onVN1mYvXgzOcdqePm2wTLqmLAp16RFUF7byI85cvW3cG4S1E7K0qL51Tvb2Zhbzj9/kc7IntK9KGzLFb1D8HYzs2zXYaNLEcJQEtROSGvNHz/bzVe7j/L7KalcOyDS6JKE+AkPVzPjU8P4KvMoTdL9LZyYBLUTenFlFm/8mM8vxyQyc6RcsCds1+S0CCrqmlifXWZ0KUIYRoLaybyzsYB/fnOAnw+SUceE7RvdKwQfdxcZ+1s4NQlqJ7LmQCm/+3gXY3qF8L/X90cpGXVM2LbWq79D+Wq3dH8L5yVB7SQami089UkmiSE+vDR9EK5meemFfZjcP5LKuibWSfe3cFLy19pJvL4uj4LyOv7flFQZdUzYlVE9g1u7v3dK97dwThLUTqCspoEXvs1ibO8QRvcKMbocITrEw9XMhNQw6f4WTkuC2gk8u+IgdU0WnpycYnQpQlySyWkRVJ1oYm3WMaNLEaLbSVA7uIPF1by9sYDpQ2NJDvU1uhwhLsmoXsH4uruwTLq/hROSoHZwTy/bi5ebmV+N72V0KUJcMneX1u7vr/cU09gs3d/CuUhQO7DV+0v47kApD4/rSaC3m9HlCNEpk/u3dX9nS/e3cC4S1A6q2dLCX5btJS7Ii9uHxRldjhCdNrJnML4e0v0tnI8EtYNasqmQgyU1PHF1Cu4uZqPLEaLT3F3MXJUazte7j0r3t3AqEtQOqOpEE//65gBDEwKZ2DfM6HKEsJrJ/cM5Xt8sV38LpyJB7YBeWpVFRV0jv5+SKsOECocyMjkEXw8XPpfub+FEDA1qpdRUpdT8qqoqI8twKAVldby2No/rB0XTL8rf6HKEsCo3FxMT+4bz9Z6jNDRbjC5HiG5haFBrrZdqre/x95dAsZa/frkXF7Pi0Ym9jS5FiC4xOS2C6vpmfjgo3d/COUjXtwPZmFvOl5lHmTMmiTA/D6PLEaJLjEgOxs/DhWXy6EvhJCSoHURLi+bPn+8hwt+D2aMSjS5HiC5zsvv7m93F0v0tnIIEtYP4eNshdh2q4rFJvfF0k9uxhGOb3D+C6oZmvj8g3d/C8cnzDh1AXWMzf1++nwHR/kwbEGV0OR2mteZQ5Qm25FdgadH8fFA0AE9+vIsjVfUooPXidUValD8Pj+8JwBMf7aKyrhGlQKFAwaDYHswcmdA2fycnGi0opWjRGkuLZlhSENOHtg4AM+v1TTRZdPs8S4vm6n7hzBiRQH2ThRtfXt8+7+S/t10ex10jErC0aD7cWsQ1aRH4yGNDu92I5GD8PV1ZtusI41PlFkTh2OQvjAOYvyaHo8frefHWgZhM9nM71kdbi/hmTzFb8isoqW4AoE+4b3tQV9Q1UlJdj9a0fgER/v85955fVktpdQOa1rDXQKive/v8HYVV1DQ0o9GYlMKsFPFB3u3zS9u2aTK1zjOZVPvtbEpBsI8bprbpZqUwmxTBPq3r35hbzmMf7ORPn+3muoFR3HZ5HCkRfl24t8SpXM0mJvYN48tdR6lvsuDhKr1IwnEprbXRNZCRkaE3b95sdBl26WhVPWP/sZor+4Qyd/ogo8s5q+Lj9WzJr2BrfgX7i6t5/a4hmEyK336wk3U5xxgc24NBcT0YFNuDPuG+uJht/4yM1prthZW8taGApTsO09DcwqDYAF6aPphw/667kE8ptUVrndFlG7CC7jqevztQyp0LN/LqHRlMkFa1sDMdOZalRW3n/rZ8H5YWzeNX9zG6FACaLC2trVeT4tPth/jbV/s5VHkCaL0IqH+UP1Unmujh7cbTP+uHqx2E8tkopRgY24OBsT14anIKH2wpYtX+EkLaWvSr95cQF+RNQrD3BdYkLtXwpCACvFxZtvOwBLVwaBLUdmxnUSUfbT3EnDFJxAR6GVJDRW0jm/MrWlvMBRXsLKrkjZlDuSw+kCBvd9JjArh7ZAKDYgPoG+mPm8t/gtleQ/pMAV5uzBqVyKy2q+1bWjRPfNR6fn1kcjC3XR7L+JQwu+gpsCeuZhMTU8NZtuuIdH8LhyZBbae01jz9+V6CvN24f2ySITVkHqrixpfXc6LJgqtZkRrpzy1DYunh5Qq0Pu1oZM9gQ2ozksmk+OT+Eby7qZAlGwuY8+ZWwvzc+cPUvlyTFmF0eQ5lcv8I3t1cyJoDpVzVN9zocoToEhLUduqrzKNszCvnLz/rh6+HqyE19An35Y5hcYxPDSMtyl9aNKcI8/PgoXE9ue+KJFbvL+XNDfn08Gp9JnhBWR25ZbWMSg62q4v/bNGwpCB6eLVe/S1BLRyVBLUdami28Ncv99E7zJebMmK6dduHK0/wx8928/TP+hHq68ET16R06/btjYvZxPjUsNNuIXprYz6vfJdDXJAXtw6J5caMGAK93Qys0n65mk1M6hfOp9sPU13fZNiHViG6kpw0s0Ovr8ujoLyOp6akdOt5z+8PljL5+e9Zl11GVklNt23X0fxmQi+euzmdMD8P/vrlPi7/n2954qOdRpdlt24ZEktdo4V3NhYaXYoQXUJa1HamrKaBF77N4so+oYzqGdIt22xp0by0Oot/fnOAXqG+zLttEIkhPt2ybUfk7mJmWnoU09KjOFBczdsbCpCnkV66/tEBXJ4YyMK1ucwYEe8wFykKcZK8o+3Mv1YcoK7Jwu+u6b7bseZ9l80/vj7AtQMi+fj+4RLSVtQrzJc/XtuXP0zta3Qpdu2e0YkcqapnmTynWjggaVHbkZOtr9svjyM51LfLt6e1RinFbUPjCPFx58aM6PaRu4SwJVf0CiU51If5a3KYlh4p71PhUKRFbUf+smwvPu4u/Gp8ry7f1nubCrnl1R9paLbg7+XKLy6LkT9+wmaZTIrZoxLYc+Q467LLjC5HCKuSoLYTq/eX8N2BUh4a15MeXXiFcH2Thcc/3MljH+7EbFLUN7Z02baEsKZp6VEE+7gzf02O0aUIYVUS1Hag2dLCX5btJT7IizuGxXfZdgrL67jh5XW8s6mQB8Yms/juofh7ye0uwj54uJqZMTyO7w6Usv9otdHlCGE1EtR2YMmmQg6W1PDENSmnDcFpTVprHn5nG/lldfz7jgwemdgbswzGIezM9KFxeLqaefV7aVULxyEXk9m4qhNN/OubA1yeGMhVXfDgAUuLprmlBXcXM3+7oT+uZhNxQfIgCWGfeni78YuMaN7eWMCjE3sT5td1TzITortIi9rGzV2VRUVdI09NTrX6xVzltY3ctWgTv/soE601yaG+EtLC7t09MgFLi2bRujyjSxHCKiSobVh+WS2L1uZxw6Bo+kX5W3XdOwormfrCD/yYXUZGfA+rrlsII8UFeTOpXzhv/ZhPTUOz0eUI0WkS1DbsmS/34WJWPDqxt9XWqbXmrQ353PjyegA+uHcYtwyJlVuvhEOZPSqR4/XNvLdJhhUV9k+C2kZtLajgy8yjzBmTRKgVz7Mdq2nkmS/3MSwpiM8fHEn/6ACrrVsIWzEwtgeXxfdgwQ+5NFvkFkNh3ySobZDWmr9/tZ9gHzdmjkyw2joBQnzd+fi+4bw247IuvR9bCKPNHpXIocoTfJl51OhShOgUCWob9EPWMdbnlPHA2GS83Tt/Yb7Wmv/36W6eW3Gw/aIxeQ6ycHTjU8JICPZm/pqc9g+qQtgjCWobo7Xm78v3ExXgyS1DY62yzgU/5PLGj/nUNTbLuWjhNEwmxaxRCew6VMWG3HKjyxHikklQ25jlu4+ys6iKX43vibuLudPr+3r3Uf7yxV6u7hfObyd13xO3hLAF1w+KJsjbjVdlWFFhxySobYilRfOPrw+QHOrDzwdFd3p9u4qqePid7fSPDuD/fpEu3d3C6Xi4mrljWDzf7ishq0SGFRX2SYLahny87RBZJTX814ReVhm+M6u0mlA/d169YzCebp1vnQthj24fFoe7i4lX1+QaXYoQl0SC2kY0NFv41zcH6B/tz6R+4VZZ588GRvP1r0cT6ivDKArnFejtxo0Z0Xy87RAl1fVGlyNEh0lQ24glGwo4VHmCRyf27tQFX82WFu57awtftd2SYo3z3ELYu5kjE2lqaWHxunyjSxGiwySobUBtQzMvrspiWGIQI5ODL3k9Wmv++/M9fLHrKGW1DVasUAj7lhDszVWpYe13PwhhTySobcCidXkcq2nk0Umda02/tjaPxevzuWd0ItOHxlmxQiHs3z2jE6k60cT7m4uMLkWIDpGgNlhlXSMvf5fN+JQwBsVe+sMxvtlTzJ+X7WFi3zAel9uwhPiJwXGBDIoN4N8/5GBpkQFQhP2QoDbYy9/lUNPQzCMTe3VqPZvyykmL8ufZmwbKbVhCnMM9oxMpLD/B8t0yrKiwH50fn1JcspLj9Sxal8u0AZH0Cffr1LqeuLoPJ5oschuWEOcxITWcuCAvXlmTw9X9wmWkPmEXpEVtoBdWZtFs0fx6wqW1pmsampn1+mb2HT2OUgovN/ncJcT5mE2KWSMT2FFYyeb8CqPLEeKiSFAbpKCsjiUbC7h5SAxxQd4d/vlmSwsPLdnGqv0lHK2Se0OFuFg3DI6hh5cr82VYUWEnJKgN8q8VB3AxKx66sucl/fzTy/aycl8Jf7y2L1f0DrVydUI4Lk83M7dfHseKvcVkl9YYXY4QFyRBbYD9R6v5ZPsh7hweT6hfx0cNW7Q2l0Xr8pg5MoHbL5fbsIToqNuHxeNqNrHgBxlWVNg+CWoD/OPr/fi4u3DvmKQO/2xLi2bF3hLGp4Txu2tSuqA6IRxfiK871w+K4sMtRRyrkcGBhG2ToO5mWwsq+GZPMb8cnUiAl1uHf95kUiyccRkv3DLQKg/uEMJZzRyZSENzC2+sl2FFhW2ToO5GWmv+/tV+gn3cuGtEQod+9mhVPfe/vZXy2kbcXExyG5YQnZQc6sP4lFDe+DGfE40Wo8sR4pwkqLvR2qwy1ueUcf/YZLzdL/5WqtqGZma+vonV+0ooPi5XeAthLbNHJVJe28iHW2VYUWG7rB7USilvpdTrSqlXlVLTrb1+e6W15u/L9xEV4MmtQ2Mv+ucsLZqH39nG3iPHefHWQaREdG5gFCHEfwxJCGRATAALfsiVYUWFzbqooFZKLVRKlSilMs+YPkkptV8plaWUerxt8s+BD7TWs4FrrVyv3Vq++yg7iqr41fieHXr05NPL9rBibwl/urYvY/vIbVhCWJNSintGJZJ7rJYVe4uNLkeIs7rYFvUiYNKpE5RSZmAucDWQCtyilEoFooHCtsXkxA+treJ/fH2ApBBvfjYw6qJ/rupEEyv2FnP3iARuHxbfdQUK4cQm9g0jJtBTBkARNuuiglprvQYoP2PyECBLa52jtW4E3gGmAUW0hvVFr9/RfbztEFklNTxyVW9czBfeJc2WFuqbLPh7urL0gZE8OVluwxKiq7iYTcwckcCW/Aq25J/5Z04I43UmSKP4T8sZWgM6CvgIuF4pNQ9Yeq4fVkrdo5TarJTaXFpa2okybFsYnLRBAAAbb0lEQVRDs4V/fXOAtCh/JvULv+DyJdX1TP/3Bn738S4AArzc5DYsYRhnuebkxowY/D1deXWNDIAibI/VW7xa61qt9V1a63u11m+dZ7n5WusMrXVGSEiItcuwGUs2FHCo8gSPTux9wSf1/JhTxuTnf2BHUSUjkoK7qULhbOSak5/ydnfhtstjWb7nKHnHao0uR4jTdCaoDwExp3wf3TZNtKlrbObFVVlcnhjIqJ7nDt6WFs1Lq7O49dUf8fVw4dP7R3L94OhzLi9EJy1Crjn5iTuHxeNqkmFFhe3pTFBvAnoqpRKUUm7AzcBn1inLMby2No9jNY08NqnPeVvTJdUNzFudzdVpEXz2wEh6h/t2Y5XC2cg1J2cX6ufBdQMjeX9LIeW1jUaXI0S7i709awmwHuitlCpSSs3UWjcDDwDLgb3Ae1rr3V1Xqn2prGvk5e+yGZ8SxqDYHmddJqe0Bq014f4eLHtwFC/eMhCfDgyEIoQVyTUnwKxRidQ3tfDmjzKsqLAdF3vV9y1a6wittavWOlprvaBt+hda615a6ySt9V+6tlT78vJ3OdQ0NPPIxF4/mae15o0f85n07Pe8uaEAgNggrwuewxaiuznbNSe9wnwZ2zuE19flUd/k0D39wo44dFeWUUqO17NoXS7TBkTSJ/z0kcRqG5r51bvb+f0nmQxPDmJKWoRBVQpxGrnmpM3s0YmUybCiwoZIUHeBF1Zm0WzR/HrC6a3pg8XVTJu7lqU7DvPIVb1YeOdl9PDu+BO0hOgCcs1Jm2GJQQyICWDe6mwam1uMLkcIY4NaKTVVKTW/qqrKyDKsqqCsjiUbC7h5SAxxQd6nzSupbuD4iSbenDmUB67siUnujxYGkGtOzk8pxa/G9aSo4gQfSata2ABDg1prvVRrfY+/v7+RZVjVsysO4GJWPHhlT6B1wJNV+0oAGJEczJrHxjI8We6RFsaRa04u7IreIQyI9ufFVVk0WaRVLYwlXd9WtP9oNR9vP8Sdw+MJ8/OgsLyOG+atZ+brm9oHUfBwledIC2HrlFI8PF5a1cI2SFBb0T++3o+Pmwv3jklixZ5iJj//PXlltbxyewbxwd4XXoEQwmaM7R1Kf2lVCxsgQW0l2woq+GZPMfeMTmTBD7nMWryZ2CAvlj04igmpYUaXJ4ToIKUUD4/rSWH5CT7e6pQXwAsbIUFtJc+uOEigtxt3j0wgwMuNW4fG8sGc4cQGeRldmhDiEl3ZJ5S0KH9eWHVQWtXCMBLUVrCrqIrvDpRyRe8QvN1duHtEPP/zszQ5Hy2EnTutVb1NWtXCGBLUVvDExzsBOFDcOiSojDAmhOMYlxJKvyg/Xlwp56qFMSSoO8HSonn0/R1kHjpOTKAnb80aKiEthINpva+6FwXldXwirWphABnw5BI1NFv45RubeX9LEWaT4sM5w/H3dDW6LCEMY8/H84W0t6pXZdEsrWrRzWTAk0vkZjbh5eaCScHdI+IJ9fMwuiQhDGXPx/OFtJ6r7kV+WR2fbD9sdDnCyUjXdwdtLajgYHE1Sim83V1wMZuYPSrR6LKEEF1sfEoofSP9eGHlQWlVi24lQd0Bn24/xM3zf+S/P9/DkaoTfLiliF9kREtrWggncPIK8PyyOj6VVrXoRhLUF6GlRfN/X+/n4Xe2kx4TwPM3D2T+mhwsWvPL0UlGlyeE6CYTUsNIjZBWteheEtQXUN9k4cEl23h+ZRa/yIjmzZlDsWjNko0FXJceRUygDGgihLM4OQZ4nrSqRTeSoL4ApaC0poHfXdOH/72+P24uJhb+kEtDcwv3jZXWtBDO5qrUMFIi5Apw0X0kqM9h9+EqKusacXcxs2T25dwzOgmlFFUnmnhjfT7X9IsgKcTH6DKFEN3s5Lnq3GO1fLZDWtWi60lQn8VXmUe4ft46/nvpHgDMpv8MYrJ4XR7VDc3SmhbCibW3qldKq1p0PQnqU2itmbsqizlvbiUlwo8nrkk5bX5tQzML1+ZyZZ9Q+kY63r2iQoiLYzIpHh6XTM6xWpbulFa16FoyMlmbhmYL//XeDv6+fD/T0iNZMvtyQnzdT1tmycYCKuqauH9sskFVCiFsxVWp4fQJ9+WFb7OwtGijyxEOTEYma1Nd38yG3HL+a0Ivnr0p/SdPvqpvsvDKmhyGJwUxOK6HQVUKIWxFa6u6Z2urWs5Viy7k9F3fucdqaba0EOzjzvJfj+bBcT3P+mCN97cUUVrdwAPSmhZCtJnYt7VV/fzKg9KqFl3GqYN65b5ipjz/Pf9acQAAH3eXsy7XZGnh5dXZDIwNYFhSUHeWKISwYSaT4qFxPckpreVzOVctuohTBvXx+ibmrspi1uubiQ/25rbL4867/KfbD3Oo8gQPjE2Wx1gKIU4zqW84vcN8ef5baVWLruF0Qf3hliIynl7B35fv56rUcN6fM4wIf89zLm9p0by0OouUCD+u7BPajZUKIezByVZ1trSqRRdx6KButrSw5kAp//XeDtZlHwOgX5Q/04fG8vF9w5l32yC83M7e3X3SV5lHySmt5f6xSdKaFuI8bOkuju52db9weoX5SKtadInzp5Qd0lqztaCSz7YfYtmuIxyracTXw4WhCYGQBL3DffnD1L4Xva4XV2WRGOLN1f0iurhyIeyb1nopsDQjI2O20bV0t5Ot6gfe3sayXUe4dkCk0SUJB+IwQV1W00CQjzstGu59cwtVJ5oYnxLGtemRXNE7BHcX84VXcoZV+0vYe+Q4/7hxwGmjkwkhxJmu6RdBz9CDPP/tQSanRcjfDGE1dh3UheV1fLbjMJ9tP0zliUbWPT4Os0nx7zszSAj2xtfD9ZLXrbXmhZVZRPfwZFq6fDoWQpyfydT6ZK0H3t7GF7uOMFVa1cJK7DKofzh4jP/7Zj9bCyoByIjrwfTLY2luacFsMtM/OqDT21ifXca2gkr+fF0/XM0OfSpfCGElZ7aqTdKqFlZgaFArpaYCU5OTLzyIyEdbi0iPCSAxxAeL1tQ1WnhsUm+m9o/skmdCv7gqi1Bfd24cHG31dQshHNPJc9UPLtnGF5lHmNJfWtWi8+xmCNF3NhXyxa4jAIzuGcxXvxrNfVckd0lIby2oYF12GbNHJf5kKFEhhDifa9IiSA5tvQK8Ra4AF1ZgN326r9w2uP1hGF19m9TclVkEeLly69DYLt2OEMLxmNta1QeKa/gy86jR5QgHYDdB3cPbrVvuY95z+Djf7ivh7hEJeJ9jSFEhhDifyWkRJIV489y3B6RVLTrNboK6u8xdnYWPuwt3Dos3uhQhhJ2SVrWwJgnqU2SV1PDFriPcPiwOf69Lv7VLCCGm9I8kKcRbzlWLTpOgPsW81dm4u5iYOTLB6FKEEHbuZKt6f3E1X+2WVrW4dBLUbQrL6/hk+yFuGRJLsI+70eUIIRzAlP6RJEqrWnSSBHWbV9ZkY1Jwz+hEo0sRQjgIs0nx0JU92Xe0muXSqhaXSIIaKDlez3ubi7hhcPR5H3kphBAdNXVAJInB3vxrxQHqmyxGlyPskAQ18Or3OTRbWpgzJsnoUoQQDsZsUjw5OYWDJTU88PY2mi0tRpck7IzTB3VFbSNvbSjg2gGRxAV5G12OEMIBjUsJ449T+7JibzFPfpyJ1nK+Wlw8px/R47W1udQ1Wrhv7IXHGxdCnFtHxu53RncOj+dYTQMvrMwixNedRyb2NrokYSecukV9vL6J19blMbFvGL3CfI0uRwi71pGx+53Vbyb04ubLYnhxVRaL1uYaXY6wE3bz9Kyu8Mb6fKrrm3lgbE9Dti+EcC5KKZ6+rh/ltY386fM9BPm4y3OrxQXZzdOzrO1Eo4WFP+QyplcIadHSAhBCdA8Xs4nnbxnIZXGB/Oa97fxw8JjRJQkb57Rd30s2FlBW28gDV8r5NCFE9/JwNfPqnRkkhfjwyzc2s6uoyuiShA1zyqBuaLYwf00OQxICuSw+0OhyhBBOyN/TldfvHkKAlxszXttI7rFao0sSNsopg/rDLYc4eryeB+RKbyGEgcL8PFg8cwgtWnPHwg2UVNcbXZKwQU4X1E2WFl5ancWAmABG9Qw2uhwhhJNLCvHhtbuGUFbTyJ0LN3G8vsnokoSNcbqg/mz7YYoqTvDg2GSUUkaXI4QQpMcEMO+2wRwsruaexZtlqFFxGqcKakuLZu7qLFIi/BiXEmp0OUII0W5MrxD+ceMAfswp59fvbsciT9sSbZwqqL/YdYSc0loekNa0EMIGXTcwiqcmp/Bl5lH+36cy1Kho5TRDiLa0aF5cmUVyqA9X9ws3uhwhhDirWaMSKa1p4JXvcgj19eDh8TIgk7NzmqBesbeY/cXV/OumAZhM0poWQtiuxyf14Vh1I/9acYBgXzemD40zuiRhIKcIaq01L67KIjbQi6n9Zbg+IYRtU0rxzPVpVNQ18vtPMgnydmNSvwijyxIGcYpz1GsOHmNnURX3XZGEi9kpfmUhhJ1zNZuYe+sgBsQE8NCS7azPLjO6JGEQh08trTUvfHuQCH8Pfj4o2uhyhBDionm6mVl452XEBnlxz+LN7Dl83OiShAEcPqh/zClnc34Fc8Yk4ebi8L+uEF1CKZWolFqglPrA6FqcTQ9vNxbfPQQfDxfufG0jheV1RpckupnDJ9eLqw4S4uvOTZfFGF2KEBetsrKSG264gT59+pCSksL69esvaT1KqYVKqRKlVOZZ5k1SSu1XSmUppR4/33q01jla65mXVITotMgATxbfPYTG5hZuX7CBYzUNRpckupGhQa2UmqqUml9V1TVPjtmSX8HarDLuGZWIh6u5S7YhRFd4+OGHmTRpEvv27WPHjh2kpKScNl8pFaqU8j1j2tkGr18ETDpzolLKDMwFrgZSgVuUUqlKqTSl1OdnfMnoQDagZ5gvC2dcxtHj9dz12iZqGpqNLkl0E4d+HvXcVVn08HLl1qGxXbJ+IbpCVVUVa9asYebM1gasm5sbAQEBZy42BvhEKeUOoJSaDbxw5kJa6zVA+Vk2MwTIamspNwLvANO01ru01lPO+Cqx3m8nOmNwXA/m3jqIPUeOM+eNLTQ0y1CjzsBhu74zD1Wxcl8JM0cm4O3uFHehCQeRm5tLSEgId911FwMHDmTWrFnU1p7+CESt9fvAcuBdpdR04G7gxg5sJgooPOX7orZpZ6WUClJKvQwMVEo9cY5lurSHTLQalxLGMz9P44esY4z753e8tjaXukZpXTsyhw3quauy8PVw4Y7h8UaXIkSHNDc3s3XrVu699162bduGt7c3zzzzzE+W01r/DagH5gHXaq1ruqomrXWZ1nqO1jpJa/3XcyzTpT1k4j9uzIjhtRmXEebnwZ+W7mHYX1fyj+X7Ka2Wc9eOyCGD+kBxNV9mHmXG8Hj8PFyNLkeIDomOjiY6OpqhQ4cCcMMNN7B169afLKeUGgX0Az4G/tDBzRwCTr3CMrptmrATY/uE8uG9w/nw3mEMTQhk7uosRvzvSp74aCfZpV32mU0YwCGDeu6qLLzczNw9IsHoUoTosPDwcGJiYti/fz8A3377Lampqacto5QaCMwHpgF3AUFKqac7sJlNQE+lVIJSyg24GfjMGvWL7jU4LpD5d2Tw7W/GcMPgaD7ceohx//yOWa9vZlNeuTzYwwE43Mnb3GO1LN1xmNmjEunh7WZ0OUJckhdeeIHp06fT2NhIYmIir7322pmLeAG/0FpnAyil7gBmnLmQUmoJcAUQrJQqAv6gtV6gtW5WSj1A63luM7BQa727634j0dUSQ3z4n5+l8ZsJvVi8Lo/FP+az4uViBsYG8MvRiUxIDccszzmwS8oWPm1lZGTozZs3W2Vdj32wg0+3H+b7344l1NfDKusUwlYopbZorTOMruN8rHk8i0tX19jM+5uL+PcPORSWnyA+yItZoxK5YXC03K5qAzpyLDtU13dRRR0fbT3ELUNiJaSFEE7Ny82FO4fHs/qRscy9dRD+nq489Ukmw59ZybMrDlBe22h0ieIiOVTX9yvf5aAU3DM60ehShBDCJphNisn9I7gmLZyNueXMX5PDsysO8vJ32dw4OIZZoxKIC/I2ukxxHg4T1MXH63l3cyE3DI4mMsDT6HKEEMKmKKUYmhjE0MQgDhZX8+r3Oby7qZA3N+QzqW8494xOZGBsD6PLFGfhMEE9f00OlhbNvWPONoqiEEKIk3qG+fK3GwbwyFW9eW1dHm/+mM+XmUcZHNeD6wZGMTktgkC5GNdmOMQ56rKaBt7akM+09Ehig7yMLkcIIexCqJ8Hv53Uh/VPjOP3U1KpOtHE7z/JZMhfVjDjtY18vK1IxhS3AQ7Rol7wQy4NzS3cd4W0poUQoqN83F2YOTKBu0fEs/dINZ/uOMTnO47w63d34OG6i3EpYVw7IJIreofg7iJXjHc3uw/qqromFq/P55q0CJJDfYwuRwgh7JZSitRIP1Ij/fjtxD5sLajg0+2H+WLXEZbtPIKvhwtX9wvn2gFRDEsKkvuyu4ndB/WidXnUNDTzwFhpTQshhLWYTIqM+EAy4gP5w9RU1maX8en2Q3yx6yjvbS4ixNedyWkRXJseycCYAJSS0O4qdh3U1fVNLFyby/iUMFIi/IwuRwghHJKL2cSYXiGM6RVCfZOFlftK+Gz7Yd7eWMCidXnEBnoxdUAE09Kj6BXme+EVig6x66B+88cCqk408eCV0poWQoju4OFq5pq0CK5Ji+B4fRPLM4/y2Y7DzFudzdxV2fQJ92XqgEiuHRBJTKBc3GsNdhvUJxot/Pv7HEb3CmFATIDR5QghhNPx83DlxowYbsyIobS6gS92HeGzHYf5+/L9/H35fgbFBjAiOZi+kX70jfQnuoendJFfArsN6iUbCyirbZTWtBBC2IAQX3fuHB7PncPjKSyvY+nO1ovQXlqdjaWl9ZkS/p6u9Ivyo1+kP6mRfvSL8ichyBuTXJR2XnYZ1A3NFl5Zk83QhEAuiw80uhwhhBCniAn04r4rkrnvimTqmyzsO1rN7sNVZB46zu7DVby2No9GSwsA3m5mUtta3H3bwjs51AdXs0MM82EVhga1UmoqMDU5uWOt4g+2FFF8vIF/3pjeNYUJIYSwCg9XM+kxAaSfcoqyydLCweIaMg9XsefwcTIPVfHe5kLqGi0AuLmYSAn3JTXSv70F3jvc12mf+mVoUGutlwJLMzIyZl/szzRZWpi3Opv0mABGJAd1YXVCCCG6gqvZ1H6/9kmWFk3usdq2lncVuw8fZ9nOwyzZWAC0PlykZ6gP/aL86R/tT1qUPykRfk4R3nbX9f3JtkMUVZzgv6f1lYsShBDCQZhNiuRQH5JDfZiWHgWA1pqiihNkHqoi83BreK/aV8IHW4oAcDEpeof70j/an/7RAaRFtba8Ha3b3K6C2tKieWl1NqkRfoztHWp0OUIIIbqQUoqYQC9iAr24Oi0CaA3vw1X17CqqZGdRFTuLqli28whLNhYCrd3mqRF+7eHdP9qfpBAfux5Fza6CetmuI+Qeq2Xe9EHSmhZCCCeklCIqwJOoAE8m9ftPeBeU17GjqKo9wD/cUsTi9fkAeLmZ6RfpT1q0f3uAxwV62c3V5nYT1C0tmrkrs0gO9WFi33CjyxFCnOFSLw4VorOUUsQFeRMX5M21AyKB1szIOVbT3ureWVTJmz/m09DcerW5r4cLaVGtoZ0c6kNckBdxQV6E+LjbXEPQboL6m73F7C+u5tmb0u3mU5AQzuRSLg4VoquYTIrkUF+SQ335+aBoAJotLRwsqWFnW6t716EqFvyQQ5NFt/+cl5uZ2ECvtuD2bv03sPXfyABPQ7rQ7SKotda8sPIgcUFeTOkfYXQ5Qggh7JCL2URKhB8pEX7cdFnrtCZLC4cqTpBXVktBeR15x+ooKK8lu7SWVftLaWxrgQO4mhUxPbyIDfIiPsib2EAv4oO9iA30JibQs8seAWoXQb36QCmZh47zt+v74+JgV/MJIYQwjqvZRHywN/HB3j+Z19KiOXq8nvyyOvLLaskvr6OgrI68slo251VQ09DcvqxSEOnv2R7eT1+XZrXWt10EdW5pLYnB3lw3MMroUoQQQjgJk0kRGeBJZIAnw5JOH7dDa015bSP55W0hXlbXHuhb8ius2kVuF0F998gEbh8W53D3xgkhhLBPSimCfNwJ8nFnUGyPLt2W3SSfhLQQQghnJOknhBBC2DAJaiGEEMKGSVALIYQQNkyCWgghhLBhEtRCCCGEDZOgFkIIIWyYBLUQQghhw5TW+sJLdXURSpUClUDVBRb1P88yZ5t3oWlnzj/1+2Dg2AXq6ajz1X8py1t7f5z5vewP29sfcVrrECtv16ou8njuztcKrP96Wfu9e75lLnZ6R76X/WH8/rj4Y1lrbRNfwPzOLHO2eReadub8M+ZtNuJ37Mjy1t4f59s/sj9sb3/Y8pctvVZd8XpZ+717vmUudnpHvpf9YVv740JfttT1vbSTy5xt3oWmnTn/YmrojI6u/0LLW3t/nPm97A/b3h+2TF6rji9/rmUudnpHv7cm2R9duG6b6Pq2RUqpzVrrDKPrsBWyP04n+8O+yOt1Otkfp7P1/WFLLWpbM9/oAmyM7I/Tyf6wL/J6nU72x+lsen9Ii1oIIYSwYdKiFkIIIWyYBLUQQghhwySohRBCCBsmQX0JlFKJSqkFSqkPjK7FKEopb6XU60qpV5VS042ux2jynrBP8rrJsXwmW3xPOF1QK6UWKqVKlFKZZ0yfpJTar5TKUko9fr51aK1ztNYzu7bS7tfBffNz4AOt9Wzg2m4vtht0ZH846nvClsmxfG5yLJ/O3o9lpwtqYBEw6dQJSikzMBe4GkgFblFKpSql0pRSn5/xFdr9JXebRVzkvgGigcK2xSzdWGN3WsTF7w/R/RYhx/K5LEKO5VMtwo6PZRejC+huWus1Sqn4MyYPAbK01jkASql3gGla678CU7q3QuN0ZN8ARbQe4Ntx0A98Hdwfe7q3OiHH8rnJsXw6ez+WHfJFuQRR/OcTJbS+caPOtbBSKkgp9TIwUCn1RFcXZ7Bz7ZuPgOuVUvNwrKEvL+Ss+8PJ3hO2TI7lc5Nj+XR2cyw7XYvaGrTWZcAco+swkta6FrjL6Dpshbwn7JO8bnIsn8kW3xPSom51CIg55fvotmlC9s2ZZH/YNnl9zk32zensZn9IULfaBPRUSiUopdyAm4HPDK7JVsi+OZ3sD9smr8+5yb45nd3sD6cLaqXUEmA90FspVaSUmqm1bgYeAJYDe4H3tNa7jazTCLJvTif7w7bJ63Nusm9OZ+/7Qx7KIYQQQtgwp2tRCyGEEPZEgloIIYSwYRLUQgghhA2ToBZCCCFsmAS1EEIIYcMkqIUQQggbJkEthBBC2DAJaiGEEMKGSVALIYQQNuz/A2Hb3Wm80tHaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = results[\"ds\"]\n",
    "ns = results[\"ns\"]\n",
    "mdl_practice = results[\"mdl_practice\"]\n",
    "mdl_theory = results[\"mdl_theory\"]\n",
    "mdl_theory_lam = results[\"mdl_theory_lam\"]\n",
    "mdl_theory_mean = results[\"mdl_theory_mean\"]\n",
    "for j, d in enumerate(ds):\n",
    "    plt.figure(figsize=[8, 4])\n",
    "    plt.subplot(121)\n",
    "    plt.plot(d/ns, mdl_practice.mean(0)[j, :]+mdl_practice.std(0)[j, :], color=cs[j], linestyle='--', label='practice')\n",
    "#     plt.plot(d/ns, mdl_theory_lam.mean(0)[j, :], color=cs[j], linestyle=':', label='prac_plug_lam')\n",
    "    plt.plot(d/ns, mdl_theory.mean(0)[j, :]+mdl_theory.std(0)[j, :], color=cs[j], label='thm1')\n",
    "#     plt.plot(d/ns, mdl_theory_mean.mean(0)[j, :], color=cs[j], linestyle='-.', label='thm1_plug_mean')\n",
    "#     plt.plot(d/ns, mse.mean(0)[j, :], color=cs[1], linestyle='-.', label='mse')\n",
    "#     plt.plot(d/ns, eigsum.mean(0)[j, :], color=cs[2], linestyle='-.', label='eigsum')\n",
    "#     plt.plot(d/ns, tnorm.mean(0)[j, :], color=cs[3], linestyle='-.', label='tnorm')\n",
    "\n",
    "#     plt.plot(d/ns, bnd, color=cs[j], linestyle=':', label='thm2_snr')\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n",
    "    plt.legend()\n",
    "    plt.subplot(122)\n",
    "    plt.loglog(d/ns, mdl_practice.mean(0)[j, :]/mdl_theory.mean(0)[j, :], color=cs[j])\n",
    "# plt.xscale('log')\n",
    "#     plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ns': array([    2,    10,    20,   100,   150,   180,   200,   240,   300,\n",
       "          400,  1000,  1500,  2000,  4000,  8000, 20000]),\n",
       " 'ds': [100],\n",
       " 'num_trials': 3,\n",
       " 'beta_norm': 'sqrt d',\n",
       " 'mse': array([[[0.30493879, 0.37638002, 0.39275335, 0.39510635, 0.46066047,\n",
       "          0.49782373, 0.46370048, 0.47846991, 0.49444119, 0.4315611 ,\n",
       "          0.50367775, 0.48956445, 0.50541356, 0.48451116, 0.49997638,\n",
       "          0.50619712]],\n",
       " \n",
       "        [[0.28347626, 0.34891513, 0.31834903, 0.46966912, 0.49687782,\n",
       "          0.4846935 , 0.53111451, 0.50428731, 0.44601139, 0.49657671,\n",
       "          0.51616727, 0.4942588 , 0.47901155, 0.51550754, 0.49254898,\n",
       "          0.49571394]],\n",
       " \n",
       "        [[0.25956742, 0.41647812, 0.38329026, 0.57214503, 0.4783114 ,\n",
       "          0.44268269, 0.41701265, 0.47358165, 0.44305389, 0.45721203,\n",
       "          0.48765795, 0.50221996, 0.51247446, 0.4865674 , 0.49222722,\n",
       "          0.49738924]]]),\n",
       " 'eigsum': array([[[1.09519630e+00, 7.16427946e-01, 4.93367653e-01, 1.42702253e-01,\n",
       "          1.13989654e-01, 9.79876623e-02, 8.96910042e-02, 7.62676762e-02,\n",
       "          6.06107026e-02, 4.61188043e-02, 1.97754899e-02, 1.21648144e-02,\n",
       "          9.69289489e-03, 3.95614380e-03, 2.46189477e-03, 9.10797285e-04]],\n",
       " \n",
       "        [[1.01992709e+00, 6.94226710e-01, 3.56932722e-01, 1.77713890e-01,\n",
       "          1.12620424e-01, 7.39842122e-02, 8.63484071e-02, 6.62917126e-02,\n",
       "          5.78559403e-02, 4.53164280e-02, 1.95924569e-02, 1.26466364e-02,\n",
       "          9.61844187e-03, 4.51192344e-03, 2.43902806e-03, 9.92498404e-04]],\n",
       " \n",
       "        [[2.15219171e-01, 7.12068385e-01, 4.48176976e-01, 1.78638626e-01,\n",
       "          1.10322782e-01, 8.99751509e-02, 8.84454466e-02, 6.67495223e-02,\n",
       "          6.45307996e-02, 4.40763044e-02, 1.79765366e-02, 1.39858885e-02,\n",
       "          9.24562929e-03, 4.57856476e-03, 2.57406946e-03, 1.02865046e-03]]]),\n",
       " 'tnorm': array([[[4.38035956e-01, 4.05958184e-01, 2.34450289e-01, 5.65739966e-02,\n",
       "          5.99839657e-02, 5.25512316e-02, 4.94468044e-02, 4.51737611e-02,\n",
       "          3.33533021e-02, 2.43911490e-02, 1.18580730e-02, 5.89087305e-03,\n",
       "          5.34162586e-03, 1.38627472e-03, 1.41713754e-03, 4.27077954e-04]],\n",
       " \n",
       "        [[2.43699680e-01, 3.82395549e-01, 8.11442551e-02, 1.14598792e-01,\n",
       "          5.73674682e-02, 2.10533142e-02, 4.30730114e-02, 2.67945937e-02,\n",
       "          2.65080042e-02, 2.38202750e-02, 1.16406994e-02, 6.71908396e-03,\n",
       "          5.35652165e-03, 2.11095878e-03, 1.37590795e-03, 5.66478878e-04]],\n",
       " \n",
       "        [[1.60972653e-03, 2.93984150e-01, 1.50523987e-01, 1.19031961e-01,\n",
       "          5.41016502e-02, 3.96997116e-02, 4.95873571e-02, 2.78060032e-02,\n",
       "          4.02139371e-02, 2.19395220e-02, 8.42641414e-03, 9.73006264e-03,\n",
       "          4.60029118e-03, 2.19585513e-03, 1.67227540e-03, 6.51157069e-04]]]),\n",
       " 'mdl_practice': array([[[1.83817105, 1.49876615, 1.1205713 , 0.5943826 , 0.63463409,\n",
       "          0.64836262, 0.60283829, 0.59991135, 0.5884052 , 0.50207105,\n",
       "          0.53531131, 0.50762014, 0.52044808, 0.48985357, 0.50385541,\n",
       "          0.507535  ]],\n",
       " \n",
       "        [[1.54710303, 1.42553739, 0.75642601, 0.7619818 , 0.66686571,\n",
       "          0.57973102, 0.66053593, 0.59737361, 0.53037533, 0.56571341,\n",
       "          0.54740043, 0.51362452, 0.49398651, 0.52213042, 0.49636391,\n",
       "          0.49727291]],\n",
       " \n",
       "        [[0.47639632, 1.42253065, 0.98199123, 0.86981561, 0.64273584,\n",
       "          0.57235756, 0.55504546, 0.56813718, 0.54779862, 0.52322786,\n",
       "          0.5140609 , 0.52593591, 0.52632038, 0.49334182, 0.49647357,\n",
       "          0.49906905]]]),\n",
       " 'mdl_theory_lam': array([[[2.19039261e+00, 1.43285589e+00, 9.86735305e-01, 2.85404507e-01,\n",
       "          2.27979309e-01, 1.95975325e-01, 1.79382008e-01, 1.52535352e-01,\n",
       "          1.21221405e-01, 9.22376086e-02, 3.95509799e-02, 2.43296289e-02,\n",
       "          1.93857898e-02, 7.91228759e-03, 4.92378954e-03, 1.82159457e-03]],\n",
       " \n",
       "        [[2.03985419e+00, 1.38845342e+00, 7.13865443e-01, 3.55427779e-01,\n",
       "          2.25240849e-01, 1.47968424e-01, 1.72696814e-01, 1.32583425e-01,\n",
       "          1.15711881e-01, 9.06328560e-02, 3.91849138e-02, 2.52932728e-02,\n",
       "          1.92368837e-02, 9.02384688e-03, 4.87805613e-03, 1.98499681e-03]],\n",
       " \n",
       "        [[4.30438342e-01, 1.42413677e+00, 8.96353952e-01, 3.57277251e-01,\n",
       "          2.20645563e-01, 1.79950302e-01, 1.76890893e-01, 1.33499045e-01,\n",
       "          1.29061599e-01, 8.81526089e-02, 3.59530732e-02, 2.79717770e-02,\n",
       "          1.84912586e-02, 9.15712952e-03, 5.14813893e-03, 2.05730091e-03]]]),\n",
       " 'mdl_theory': array([[[4.00495824e+00, 1.84761502e+00, 1.09934640e+00, 4.42431932e-01,\n",
       "          3.43274074e-01, 2.77425159e-01, 2.26952636e-01, 2.14834793e-01,\n",
       "          1.69555047e-01, 1.36280378e-01, 5.15419014e-02, 3.49943913e-02,\n",
       "          2.59885697e-02, 1.37502947e-02, 6.70933839e-03, 2.62762066e-03]],\n",
       " \n",
       "        [[3.33391088e+00, 1.46359714e+00, 1.01092242e+00, 4.44983800e-01,\n",
       "          2.75288486e-01, 2.63975186e-01, 2.36748222e-01, 1.95861146e-01,\n",
       "          1.67934805e-01, 1.26747206e-01, 5.51747344e-02, 3.51055142e-02,\n",
       "          2.63691980e-02, 1.31687329e-02, 6.99560390e-03, 2.47607153e-03]],\n",
       " \n",
       "        [[1.19587630e-01, 2.33893553e+00, 1.22949227e+00, 4.16677723e-01,\n",
       "          3.05132800e-01, 2.64045020e-01, 2.47646197e-01, 2.04735791e-01,\n",
       "          1.68621689e-01, 1.29537110e-01, 5.40034111e-02, 3.48525159e-02,\n",
       "          2.80022552e-02, 1.34416788e-02, 7.26172097e-03, 2.65116507e-03]]]),\n",
       " 'mdl_theory_mean': array([[[ 3.05747859,  1.57754231,  1.33603862,  0.58475697,\n",
       "           0.41293911,  0.3453724 ,  0.31579492,  0.26782309,\n",
       "           0.21760747,  0.16600852,  0.06793782,  0.04542982,\n",
       "           0.03447236,  0.01724388,  0.00864604,  0.00346076]],\n",
       " \n",
       "        [[ 2.19028045, -0.04063345,  0.92977363,  0.58451477,\n",
       "           0.40561162,  0.35013778,  0.31745964,  0.26553303,\n",
       "           0.21773497,  0.16550933,  0.0678918 ,  0.0457032 ,\n",
       "           0.03421814,  0.0172181 ,  0.00865435,  0.00346875]],\n",
       " \n",
       "        [[ 2.02475546,  1.83398101,  1.00075518,  0.5812318 ,\n",
       "           0.40479077,  0.34903168,  0.31542828,  0.26461219,\n",
       "           0.21787332,  0.16514202,  0.06810608,  0.04578254,\n",
       "           0.03426475,  0.01724988,  0.00865094,  0.00345959]]])}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "results = {'ns': ns, 'ds': ds, 'num_trials': num_trials, 'beta_norm': 'sqrt d', 'mse': mse, 'eigsum': eigsum, 'tnorm':tnorm,\n",
    "    'mdl_practice': mdl_practice, 'mdl_theory_lam': mdl_theory_lam, 'mdl_theory':mdl_theory,  'mdl_theory_mean': mdl_theory_mean}\n",
    "file = open(file_name, 'wb')\n",
    "pickle.dump(results, file)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [04:01<00:00, 48.20s/it]\n"
     ]
    }
   ],
   "source": [
    "num_trials = 5\n",
    "\n",
    "################## vary n ##################\n",
    "\n",
    "ns = 20*np.arange(1, 20)\n",
    "ds = np.array([2, 4, 8, 16, 32, 50, 64, 100, 200])\n",
    "\n",
    "# ns = [20, 40]\n",
    "# ds = [2, 4]\n",
    "N = len(ns)\n",
    "\n",
    "\n",
    "\n",
    "r_new = {\n",
    "    'mse_norm': np.zeros(N),\n",
    "    'theta_norm': np.zeros(N),\n",
    "    'eigensum': np.zeros(N),\n",
    "    'mse_test': np.zeros(N),\n",
    "    'n_train': np.zeros(N),\n",
    "    'd_train': np.zeros(N),\n",
    "    'iss': np.zeros(N),\n",
    "    'mdl_loss': np.zeros(N),\n",
    "}\n",
    "iss3 = np.zeros((num_trials, len(ds), N))\n",
    "mdl3 = np.zeros((num_trials,len(ds),  N))\n",
    "\n",
    "for j in tqdm(range(num_trials)):\n",
    "    for k, d in enumerate(ds):\n",
    "        p.num_features = d\n",
    "        for i, n_train in enumerate(ns):\n",
    "            p.n_train = n_train\n",
    "            p.beta_norm = 1. #np.sqrt(d)\n",
    "            r_new = add_results(p, r_new, i)\n",
    "        iss3[j, k, :] = r_new['iss']\n",
    "        mdl3[j, k, :] = r_new['mdl_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=[10, 4])\n",
    "for k, d in enumerate(ds):\n",
    "    plt.xlabel('$d / n$') #\\n(Number of features / Number of training points)')\n",
    "    plt.subplot(131)\n",
    "#     plt.errorbar(d/ns, mdl3[:, k, :].mean(0), mdl3[:, k, :].std(0), label='d=%d'%d, marker='.') \n",
    "    plt.plot(d/ns, mdl3[:, k, :].mean(0),  label='d=%d'%d, marker='.') \n",
    "    plt.xlabel('d/n')\n",
    "    plt.xscale('log')\n",
    "    plt.title('MDL-COMP')\n",
    "\n",
    "    \n",
    "    plt.subplot(132)\n",
    "#     plt.errorbar(d/ns, iss3[:, k, :].mean(0), iss3[:, k, :].std(0), marker='D') \n",
    "    plt.plot(d/ns, iss3[:, k, :].mean(0),  label='d=%d'%d, marker='.') \n",
    "    plt.xlabel('d/n')\n",
    "    plt.xscale('log')\n",
    "    plt.title('ISS')\n",
    "\n",
    "    plt.subplot(133)\n",
    "    plt.scatter(d/ns, iss3[:, k, :].mean(0), mdl3[:, k, :].mean(0))\n",
    "plt.xlabel('ISS'),\n",
    "plt.ylabel('MDL-COMP')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=[10, 4])\n",
    "for k, d in enumerate(ds):\n",
    "    plt.xlabel('$d / n$') #\\n(Number of features / Number of training points)')\n",
    "    plt.subplot(131)\n",
    "#     plt.errorbar(d/ns, mdl3[:, k, :].mean(0), mdl3[:, k, :].std(0), label='d=%d'%d, marker='.') \n",
    "    plt.plot(d/ns, mdl4[:, k, :].mean(0),  label='d=%d'%d, marker='.') \n",
    "    plt.xlabel('d/n')\n",
    "    plt.xscale('log')\n",
    "    plt.title('MDL-COMP')\n",
    "\n",
    "    \n",
    "    plt.subplot(132)\n",
    "#     plt.errorbar(d/ns, iss3[:, k, :].mean(0), iss3[:, k, :].std(0), marker='D') \n",
    "    plt.plot(d/ns, iss4[:, k, :].mean(0),  label='d=%d'%d, marker='.') \n",
    "    plt.xlabel('d/n')\n",
    "    plt.xscale('log')\n",
    "    plt.title('ISS')\n",
    "\n",
    "    plt.subplot(133)\n",
    "    plt.scatter(d/ns, iss4[:, k, :].mean(0), mdl4[:, k, :].mean(0))\n",
    "plt.xlabel('ISS'),\n",
    "plt.ylabel('MDL-COMP')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 20  40  60  80 100 120 140 160 180 200 220 240 260 280 300 320 340 360\n",
      " 380] 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/linux/anaconda3.7/lib/python3.7/site-packages/ipykernel_launcher.py:72: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "100%|██████████| 5/5 [01:04<00:00, 12.89s/it]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 [  2   4   8  16  32  40  80 120 160 200 240 280 320]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [01:04<00:00, 12.93s/it]\n"
     ]
    }
   ],
   "source": [
    "num_trials = 5\n",
    "\n",
    "################## vary n ##################\n",
    "\n",
    "ns = 20*np.arange(1, 20)\n",
    "d = 100\n",
    "N = len(ns)\n",
    "\n",
    "r_new = {\n",
    "    'mse_norm': np.zeros(N),\n",
    "    'theta_norm': np.zeros(N),\n",
    "    'eigensum': np.zeros(N),\n",
    "    'mse_test': np.zeros(N),\n",
    "    'n_train': np.zeros(N),\n",
    "    'd_train': np.zeros(N),\n",
    "    'iss': np.zeros(N),\n",
    "    'mdl_loss': np.zeros(N),\n",
    "}\n",
    "iss = np.zeros((num_trials, N))\n",
    "mdl = np.zeros((num_trials, N))\n",
    "\n",
    "p.num_features = d\n",
    "\n",
    "print(ns, d)\n",
    "for j in tqdm(range(num_trials)):\n",
    "    for i, n_train in enumerate(ns):\n",
    "        p.n_train = n_train\n",
    "        p.beta_norm = 1. #np.sqrt(d)\n",
    "        r_new = add_results(p, r_new, i)\n",
    "    iss[j, :] = r_new['iss']\n",
    "    mdl[j, :] = r_new['mdl_loss']\n",
    "\n",
    "################## vary d ##################\n",
    "    \n",
    "n = 100\n",
    "ds = np.hstack([[2, 4, 8, 16, 32], 40*np.arange(1, 9)])\n",
    "N = len(ds)\n",
    "\n",
    "r_new_2 = {\n",
    "    'mse_norm': np.zeros(N),\n",
    "    'theta_norm': np.zeros(N),\n",
    "    'eigensum': np.zeros(N),\n",
    "    'mse_test': np.zeros(N),\n",
    "    'n_train': np.zeros(N),\n",
    "    'd_train': np.zeros(N),\n",
    "    'iss': np.zeros(N),\n",
    "    'mdl_loss': np.zeros(N),\n",
    "}\n",
    "iss2 = np.zeros((num_trials, N))\n",
    "mdl2 = np.zeros((num_trials, N))\n",
    "\n",
    "print(n, ds)\n",
    "p.n_train = n\n",
    "for j in tqdm(range(num_trials)):\n",
    "    for i, d_train in enumerate(ds):\n",
    "        p.num_features = d_train\n",
    "        p.beta_norm = 1. #np.sqrt(d_train)\n",
    "        r_new_2 = add_results(p, r_new_2, i)\n",
    "\n",
    "    iss2[j, :] = r_new_2['iss']\n",
    "    mdl2[j, :] = r_new_2['mdl_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.title('A', fontweight='bold', loc='left')\n",
    "plt.figure(figsize=[10, 4])\n",
    "plt.xlabel('$d / n$') #\\n(Number of features / Number of training points)')\n",
    "plt.subplot(131)\n",
    "plt.errorbar(d/ns, mdl.mean(0), mdl.std(0), label='vary n', marker='D') \n",
    "plt.errorbar(ds/n, mdl2.mean(0), mdl2.std(0), label='vary d', marker='D') \n",
    "plt.xlabel('d/n')\n",
    "plt.xscale('log')\n",
    "plt.legend()\n",
    "plt.title('MDL-COMP')\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.plot(d/ns, iss.mean(axis=0), marker='D') \n",
    "plt.plot(ds/n, iss2.mean(axis=0), marker='D') \n",
    "plt.xlabel('d/n')\n",
    "plt.xscale('log')\n",
    "plt.title('ISS')\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.scatter(iss.mean(0), mdl.mean(0)) \n",
    "plt.scatter(iss2.mean(0), mdl2.mean(0)) \n",
    "plt.xlabel('ISS')\n",
    "plt.ylabel('MDL-COMP')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3.8",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}