Cichocki_Synthetic_High_Noise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details\n",
    "\n",
    "The notebook contains the synthetic data experiment with high noise (sigma^2 = 0.5) and low noise (sigma^2 = 00.5) for HNTF (Cichocki et al.). Due to the non-convexity of the HNCPD task, results will vary run to run, so the results from this notebook will not exactly match those in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using numpy backend.\n"
     ]
    }
   ],
   "source": [
    "# loading packages and functions\n",
    "import sys\n",
    "import itertools\n",
    "\n",
    "sys.path.append(\"./src\")\n",
    "import torch\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from NNCPD import outer_product, outer_product_np\n",
    "from utils import *\n",
    "#\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "from writer import Writer\n",
    "\n",
    "from sklearn.decomposition import NMF\n",
    "\n",
    "import tensorly as tl\n",
    "from tensorly import unfold as tl_unfold\n",
    "from tensorly.decomposition import parafac, non_negative_parafac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cichocki(X, r, to_transpose=(0,1,2), seed=None):\n",
    "    \"\"\"\n",
    "    Given a 3-mode tensor X and a list of rank r, computes to Chichocki \n",
    "    \"\"\"\n",
    "    np.random.seed(1)\n",
    "    #factors = non_negative_parafac(X, r[0], n_iter_max=2000, tol=0)[1]\n",
    "\n",
    "    factors = non_negative_parafac(X, r[0])\n",
    "    A, D, S = factors[to_transpose[0]], factors[to_transpose[1]], factors[to_transpose[2]]\n",
    "    \n",
    "    factorizations = [[A, D, S]]\n",
    "    \n",
    "    for i in range(1, len(r)):\n",
    "        \n",
    "        #factors = non_negative_parafac(outer_product_np(np.identity(r[i-1]), D, S), r[i], n_iter_max=2000, tol=0)[1]\n",
    "        if type(seed) == type(None):\n",
    "            factors = non_negative_parafac(outer_product_np(np.identity(r[i-1]), D, S), r[i])\n",
    "        else:\n",
    "            factors = non_negative_parafac(outer_product_np(np.identity(r[i-1]), D, S), r[i], init=\"random\", random_state=seed)\n",
    "        A, D, S = factors[0], factors[1], factors[2]\n",
    "        factorizations.append([A,D,S])\n",
    "        \n",
    "    return factorizations  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_cichocki_factorization(X, ranks, S_trues, to_transpose=(0,1,2), X_orig=None, seed=None, verbose=True, display=True):#, S_true=None, S_func=S_func_1):\n",
    "    \n",
    "    factorizations = cichocki(X, ranks, to_transpose=to_transpose, seed=seed)\n",
    "    \n",
    "    X = np.transpose(X, to_transpose)\n",
    "    \n",
    "    if X_orig is not None:\n",
    "        X_orig = np.transpose(X_orig, to_transpose)\n",
    "    \n",
    "    recon_losses = []\n",
    "    recon_losses_orig = []\n",
    "    topic_losses = []\n",
    "    \n",
    "    for i in range(len(factorizations)):\n",
    "\n",
    "        A = factorizations[0][0]\n",
    "        for j in range(1, i+1):\n",
    "            A = A @ factorizations[j][0]\n",
    "\n",
    "        _, D, S = factorizations[i]\n",
    "\n",
    "        approx = outer_product_np(A,D,S)\n",
    "\n",
    "        relative_loss = np.linalg.norm(np.ndarray.flatten(X-approx), 2)  / np.linalg.norm(np.ndarray.flatten(X), 2)\n",
    "        \n",
    "        if X_orig is not None:\n",
    "            relative_loss_orig = np.linalg.norm(np.ndarray.flatten(X_orig-approx), 2)  / np.linalg.norm(np.ndarray.flatten(X_orig), 2)\n",
    "            recon_losses_orig.append(relative_loss_orig)\n",
    "        \n",
    "        if(verbose):\n",
    "            print(\"Layer\", i+1, \"Relative reconstruction loss:\", relative_loss)\n",
    "\n",
    "        recon_losses.append(relative_loss)\n",
    "\n",
    "        if(display):\n",
    "            display_tensor(approx, figsize=(8,4))\n",
    "      \n",
    "    Ss = [factorizations[1][0] @ factorizations[2][0], factorizations[1][0], factorizations[2][0]]\n",
    "    for i, S in enumerate(Ss):\n",
    "\n",
    "        best_value = measure_modeling((S.T / np.sum(S, axis=1)).T, S_trues[i])\n",
    "        topic_losses.append(best_value)\n",
    "        \n",
    "        best_value, best_i, best_j = find_fit((S.T / np.sum(S, axis=1)).T, S_trues[i])\n",
    "            \n",
    "    if(display):\n",
    "        display_topic_matrices(Ss, S_trues)\n",
    "\n",
    "    if X_orig is not None:\n",
    "        return recon_losses, recon_losses_orig, topic_losses\n",
    "    else:\n",
    "        return recon_losses, topic_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_losses_cichocki(recon, topic):\n",
    "    \n",
    "    #average_recons = np.mean(recon, axis=1)\n",
    "    #average_topics = np.mean(topic, axis=1)\n",
    "\n",
    "    average_recons = np.mean(np.mean(recon.reshape((3, 10, 3, 2)), axis=3), axis=1)\n",
    "    average_topics = np.mean(np.mean(topic.reshape((3, 10, 3, 2)), axis=3), axis=1)\n",
    "    \n",
    "    for i in range(3):\n",
    "    \n",
    "        print(\"Topic Modeling Loss 7 to 2 (average, best, worst):\", average_topics[0][i])\n",
    "        print(\"Topic Modeling Loss 7 to 4 (average, best, worst):\", average_topics[1][i])\n",
    "        print(\"Topic Modeling Loss 4 to 2 (average, best, worst):\", average_topics[2][i])\n",
    "        print(\"\")\n",
    "        print(\"Relative Recon Loss Rank 7 (average, best, worst):\", average_recons[0][i])\n",
    "        print(\"Relative Recon Loss Rank 4 (average, best, worst):\", average_recons[1][i])\n",
    "        print(\"Relative Recon Loss Rank 2 (average, best, worst):\", average_recons[2][i])\n",
    "        print(\"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Data Tensor (Low Noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = get_synthetic_tensor(seed=14, noise=0.4)\n",
    "X_original = get_synthetic_tensor(noise=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Original Data Tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N2EmNTJS7aWPljomHL+my2fNtWTZ8GH/v9Fb1HDuu6Pa3rsiiULlvYup39Nw2WNL18l193Z0z8bLll/RY7a6bKPBDt12GbkfKbO9gt35Y0H2ytBdvyzR/um0OXGT+SDzvOf88nQiz9+vxnXlBR+lP9c42oXBg3v3v1R9S5Tf1+HFb0eRb8d+z/dPF7B9c098Fg2XdcZuvy6Kw8+P6PV7eMXPDYvx8xQNdJ8z1tS28Yb4RRex9CCFMzRZOua5uSnqkzzdYjD/U4qFu/9hsxeK+2bJ9td2HrMJf0AAAAAAgKVigAQAAAEBCsEADAAAAgIRggQYAAAAACcECDQAAAAASggUaAAAAACTEiWL21/Ot8LfPfSFa9sX287Le/7D9M7Lsk40boqRzkqY9Nhd1WXtT3yYX9dqrxeM40/GdCv70eAsm3rOq62UHOvpzsK6zkLNHOhbUtVNFd6so0RBCmGV1G11ZduBiUnX7Bw39UIv7up2dc/G25Ns6gjdlIqD7KyZuetdsLyD6TwghFEwM7LDu4q3jDZ2WdB93z9SNm3wnfm0uXvjdrrBvwu/np9tKI9uL/965oJ9Drmv63r4+2XBJD4T0VNeblHVbsq/UZFlYiPex7FCPq3xHn6twrDu0nb8yumzxNT2RuvlLxem3z8W3AQkhhMq2O5ce481X9fu2dVVvoTNs6Osum7ktI+b07rpuo9mRxEZ+qwh+9Q7DOyv1qh7f7vsqo3ebCJOSOV9Hj7mM3knHvus6T8U7U75tYtcHeh5q3Nbz6OJruuNOKiaKXrwHQggha7ZHKhzF29I+p8fq3MyHbl4YNvUxSzt6bhst6HqTon4G+Za4z+ZZd9f0RFS/q7+1u+vxdrh3N39BAwAAAICEYIEGAAAAAAnBAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEgIFmgAAAAAkBAnitk/nFTC/3744WhZOqUjQz+98Kos+/zDj0d//w/qv3uSpj02F9k6WDUR0B0dJ1pbjUcX91cWZB0XJZxv6XONa7qN5Tv6Mbt6UxN9nRnG29Jf0mv+6qaOIJ0WdL1RSV93dVPH0bbP6OvOHemo1/U/jse5TvO6jUMT6V+7p687PdL3eFI0feu+zgMeV038rShyEeLBJb+LCOsQQpjmRVnKHPBdrvuU7s+VuyYeeqSPOVqI/17c08+he0VHGud2df+q3dJtHNVlkd96pKDHSHEnPu7GZX08s1tBKIpI6RBC6JgI+LTZZmOa1/WKh7riw0/GM8YXX9N19p/TW4GsvtCXZb1z+oblem6bDX0zS5v6pdrfiF+b266ksq8LR1U9/6o4fdcPkAzzjO57qZl+gKPG6bZyGSyZ93FDfzOUHsTnvYKKcQ8hpCdmXJmY+v6ynk8GS6fbmseN495yfGzlumZe3jVjtabHar6t73HraT23ue/302w9MqqYb68H+tqKu2afhhDfHiU91veRv6ABAAAAQEKwQAMAAACAhGCBBgAAAAAJwQINAAAAABKCBRoAAAAAJAQLNAAAAABIiBPF7OfS07BeaEXLFjNdWe/6YEOW/ezqKydpwg9M/lhHXfYu6OjP3LGOlU59pRn/fdHEapq4aRcdW94yEfwVfUwX6z9b0dnd8+N4THK+bWJqS/rfA2b6NobSno41bV00UfomBjbj4u1FO130bXagj9e6rNtYv20iW01E76Ro4sxN5HRpPx75f3zedAQTR102cboyuvd0CcjvCtm2fnadZ02E71D3h9L9eP8bLOsHUbpzuv4wjScJhxBCyPZ02WysD6pi0kMIof++eHR8ZTM+P4UQQq6rDzgu6/tf2TF93cxfbruPaV5XrN+Kt3Nith2pbOprO7qq70llS28FMqrpNubNvTy+XJJlaguOXN9MDi4W35SNavHCOf88nQiDdd33ynf12BktmO2PBrpDzM7p75rMnu6zhQM9DtT2NempbqN7T7sxXr+rPxJnOT1vu7HltpyoPYw/n96Kvh9uCwE3n7sI/sqWnn/7S+Y73DyD7CDemHlKt2NcNlsSvE9vV5IRr2/3XckUBQAAAAAJwQINAAAAABKCBRoAAAAAJAQLNAAAAABICBZoAAAAAJAQLNAAAAAAICFOFLNfSo3Cc8UH0bJ/3XpO1hubDOL7g3gUfWjeOUnTHtvERGdmOiZys6YjPMfV+O82ZnRNR87WX9URqv013Y58y0S2vqXb0hnrzOyUSDx1cdOjor6P1Qc6+rZ9Pi/Lcia625kU9D0pDOP3clh3sbj6XNmefjbFPX3dw0X9vAcmVjbf0R2svxwf8uV9HWHrosfHFV2mYqyJt9bcvUl19HTt4vmz8SR6G1M9sVtzmNjinh5Xw/O6r5fe0nNNf133zcKN+MCbZXUbZ1kz9s3WFoNFPeZqd/QWCDMXo2ziqFV0dOnAxE03Tdy0ee8cX9B9y22dMi3ofjeqm+sW25yUTIS4iwKXW3qEEHJi/k3rVy3eQdW3dN8bmjnKzZXTgtnK6IHebsJF/udaemypOdH1y1nOdGhTtPc+/T1Uv6vnBjf+Bw19M1PiRs9OtHr4f+WPdUPcHKu25gjBz6NHT+v7pea28raeh3Y/oN9V5R19bUUxb7u286kEAAAAAAnBAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEgIFmgAAAAAkBAnymG5t78c/s4//evRssEFndCV29VpdCr95unU87JOaqrTXFxi4aSs01Le+Nv/UJYlxl/8YTfg7X3sv/icLBtX9LOZm6SzyraJ2zJpR61Lut+5ilOR8OhSxFRSWAgh5Lq6bOfDOv5x+Xs6Gc6lQ/VMwmPtfjydaFzVddrn9b/jLL+k0456K2J6MffxUby8vxKu/eN4P5uZFNHivul/4hJnOgDKphlOdVBYyB/pdtz4T5IxD/29XZ3K+1RhT5b9+p1PyrIPLN2XZUsf6sqy33ug2/ILn/529Pd//t/8BVlnuOBSHGVRKB6ahMcV3VEyIhX27WT7J0+Gq2zpuTJ/pN/Rex8oy7KMHuIhNdSpZdO8njfqt+NtmZT1AN5/Tn+urL6g58rO2fizcYnDj+Klo5Vw+f/4bLQsv2/SLRf1Pcv04vUq98x8v2GSpJd1f8genC6GL9d1yYQuPTX++xt/Kxlz3g/Cn/vS35BlqZm+VyolO4QQ5indcd03Smasz1cyc9tEJLXW7+i+NSnp/t9b1WW5jiwKg6apZ76/SvtmvIm5eWrav/SqnhAzA/OOWI5/j7r5nL+gAQAAAEBCsEADAAAAgIRggQYAAAAACcECDQAAAAASggUaAAAAACQECzQAAAAASIgT5azOMyGMq/FYysoNHTM8+/CxLEt/qx79PWWSiYdNXTg3V+SiS/H9Ma7qstpDHcvqYt7TUxNHaxL4MwNdr35XR6Wq2NNZVsehpkwbZ3ldr/pAR8COGrozT0qny6qfi2vIdXU7Ft7Uxxss6uemouZVpP2jmmfnYbQSf/A5Ex3deY+OGS/ejc9fKX1bbJS+q5fRieCJ8cXN98iyjYqezxeK/VOdb2b2Xvjwyj1Z9mZvLfp7ZqTHY/O6HvtTESkdQgjToi7rmujo8p7uDC4WOy3eVxkTbd9b1f0/19OTZU7vchByXf3i7Kzr86XN3DwR833fzCfV+/q6D54ryLJ863TbHLyd1ETH6afH5l0x0mXZTrxsuKTbYb95hrpfzk0kftZE6Y8WzOQ20/VK24+5v8oT6PCq3uqnYPplvqPvcdpte2EejdtWon1Gd6JRI/57adf0Y/Pt1XxDv4fd/Ns1c42bR8dlfczO2XjF4oG+kW4dMcvpNpb24hOi226Bv6ABAAAAQEKwQAMAAACAhGCBBgAAAAAJwQINAAAAABKCBRoAAAAAJAQLNAAAAABIiBPF7KemIeTa8VjK0YKJsr1Rk0U5sUTMDPThyg91pubxVZ2BmTaRs/j+WHxNZ4j3V3TkrIqUfjsu3r6/ovtJoaUzZycl0U9MF6/dN9dd1dfdX9RtzJg45lxPN6bQ1mVHl+NtcbGyub4+XuecHlNLL8djZV0E+iOZpkKmFZ+6ZhdMzHvb9D8RXeyiwoOJhh+s6A49XEj+PPQL51+QZV/YfU6WvbexKcsORhVZ1p3omPS02XOlO4lvjzCu6Huc7euyYUPPC4NFXS9/bLZ+MRHQbiykR/Ex2Xpa9+PaPd1hD6/qfSGqD3S9WUFfQL6j21/a03ngaguRhRv6xd9b11v5uPlrUoy33z2XR+G+hya6q4dZRc8Ns178eNPLel5L3S/JssK+2b5Gp51b47p+5tX7+qa67ZF+VC3c1GPAbVHjlPd0/+mu62O6MeK2AlHfIS5Kf57S/aC/7L4DzXxyoK9798f0UqZx00Tmi20h3PdVpq+P11/R7VDbLdmtVnQRAAAAAOCdxAINAAAAABKCBRoAAAAAJAQLNAAAAABICBZoAAAAAJAQLNAAAAAAICFOFLM/z8/D4Fw8NrR6Q0dnds/pWMrCQTxj0tbZ0+vKzECXzTPvvpjXd5qLQnaR0lMRQfqn9fT5ZjqdO9TuuehY3Zb8cbyPd87qPt4+pxtSaOt4WBdT7WSGp7uXKva3v+ymAn2u2l19j+fikN+PeOv8kYj+3SrLekOzFUj/bPwZpXt6PpmV9bVn224ekkWJsTepyrLJTF/bK60NWbbd0dutnK21ZNm1+rYsOxzFI8bd+HZbc5hE/1AzUfRpMx5dnLY7X07MG5Uts4VDXZ8rb7bfGCydrlOq7SlCCOH4gn4XFNrxsTNY0XVGNd3vKlv62agtF067tcufmWdDGC7F7+nM3M7iQ/0eGS3E70v2po7SHy+aeajrtpPR/cEds/TAHFPv5CC3JPhRZrftaJptO9pmfxczZ5RMXP6wrs9XPNKDobQvoui7ZksrsUVICCF0N/QYT5n5ZFLU7a+/pW/K8SW3Xoj/nh6brYXO6G+lyvbJt9eZp/W44C9oAAAAAJAQLNAAAAAAICFYoAEAAABAQrBAAwAAAICEYIEGAAAAAAnBAg0AAAAAEuJEMfshFULIxuMnexsm0ryvYyT7a/F6lXt67TjWCdBhuqoz2Qu3TCY7vi9c/PukqPvBYMn8W4HuWqFw7KL0dT0X2api6otH+ly5jo5XPbxi4qZN+ycFfb9SJiK6cKwLVaRr7e5Qt6Oko4Izfd2O3mp8eplnHi9uOT0NoSBS2VtXTex/QZdVbsfbOqrr/pw5MnPUWd350vs6ZjsprnfWZNnfvfg7suzL7ffKsv2Knrj3RhVZ9rXty7Lsb1z8WvT3f9K+JOscX9T3P23mjMKR7gutiy56Wfc7tyXGYDneTjcvlA50PPcsa+Kcj0+3TUDhSJ+vcCSLpHFVn2tmvlbcnJIX86HbbuGRzELIduLnVXH5IYQwXDbvETGn5MR5QgghpPU8lDJp7SkT750x24SMq26LGn3M4eK7b5ujohmP87Tu0H0z5pzynn73T80WCDsf0m3Z+Ho8+36W08+6dVF/ay/c1JOs25qj+lBn8A8Wdfubr5sPSKFvth1x9/jwmm7H4qvx9qcnelzwFzQAAAAASAgWaAAAAACQECzQAAAAACAhWKABAAAAQEKwQAMAAACAhGCBBgAAAAAJcaKY/dQwFUo34zGY/bMm3resj5k7iMdZdi7qaMzSlomVPTQR1u++lNd3XK6rI0izZrsFFwE9quuyXE8/1LlJJi6aeOhRPT4s3DYB2Z4uq9/T5xrWdV+u3dexsu1zJip8amLxh/FxNSnrOt11PU24+58Zi7LHHIfzdAijWrws2zVx2w/1dYya8UaN67o/L7yqj9equih33ca/fPPPy7JmXu9p0BrrDOWtbj36eyGr++XvP/tbssz5VPGVU9X7fvvluu7P9dt6XM3yejzufEg/08Zb+n3VXdPHrN/RzyDXi5d1zugI62FDX3febOkxNduOZAd6wHbX3T0ZyLLO+fg1uDm2flffq+2P6HaUduLtn77weNt9hHQIExE5Pyvqe1a5bbYSECnjc5O67uY89w4cruj7Wb5nIuDXdT9Km1j/tB52P7IGS/o+5jv6Po4r5hvXbLHjtu0o75ptZrZ0f1Vx+tsfPl20/bCp65UO9cW5b5S0uSchpe/J4bX4fa7c1/fDRfAvvKkHwOE1sW3Kt832J7IEAAAAAPCOYoEGAAAAAAnBAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEgIFmgAAAAAkBAnitkPqRCmpXj8ZGFXH2q45rJX4z/PczrmMj3Sh0vpdM8wWjSF+L7oL+t+UNzX/WBc1cdcfG14qra0z4vM4hBCeqKjUke1eKdMTXWfPBARqiGEUDgykctbOnt4XDVtbOho1sqOzpwdVU/+bzLlXZNhe4rI/LS5j49ing5hKlLli3v6vrSfMZHmYrsPN6+pqP8QQsgMTb61KWrkdDR5JavHwfur92XZ9cJ69PeZa8gTLt/Wc33KdL9JSY+PpVfNVjIZfS9rD3Rb5nZ7kfickhnpCxg0dfvzx7LI6m7oeahkoru7ZjsAFRVeOtBzzdxMXc3Xdb3B4jv/79CFXbNtyXl9z4q78bbOzO5BLr4+19Fl2Zt6bhubuS1/pO9ncV/X61x4931/Zft6rLpIfPcd6+S6Jt7ebcExMONYbLOz8Uf6Q9xtV3J4Vfe7tRf0VjL7z+qtZGoP9Ph393nx1Xi9/rJuf/WhPleubbau2I4f025NoYsAAAAAAO8kFmgAAAAAkBAs0AAAAAAgIVigAQAAAEBCsEADAAAAgIRggQYAAAAACXGimP3ULIRcOx5ZOa7qONHqm/o0M5GEPq7r4w2XdNncJEeXNlmP/qC5mNfems4KdlGoez+m45qXXjF7Lhgucra0Z3JP1fFMvHX1gW7juKbHRr6l21HZMh3dxIjn+vHnc3xet2Oub1XId/Tz7jfjFWcmWvxRzDMhjBvx885MP8rv6wvJHcfrqTj/EEIYLuobnW+Z+PSmvmeXynuyrJHREcTb47osG4t88mvlbVnnSZea6WfT2dDzUK5n5q8VN2foeuOyib7vmC0sxCHHC7od2YG+brV9SAh+jC++qrd3mOV0Px/X9EF7K/G2lHf18QZmznbXnW/Hy1TU/0moY7gxXrutn0N/Nd5W912THpu5pqHruesf106+FUAIIRxf0wfNdN99319Z8b4NIYTMQN+r4aKeo3omAn6e0WPExbn3VvT7v7Qfb+dgyWzTUDFbCJh+11/RWyMVWm7rLX2fZzlzT8SWJeUdfbz2OX288o6+7rS4brfty7tvxAAAAABAQrFAAwAAAICEYIEGAAAAAAnBAg0AAAAAEoIFGgAAAAAkxIlSHNOjEGp34+kmmbGOIknpQJTQPhtPRDn3JZf05hKzTIpbW8fYfPTvfk6Wta7olpQf6vPV78XPl+3pGBuXfDVPmbQmk9BVva9TBKcFXW9kUrMmhXhb/viXfknWeVI88/l4X8j09f3PtfXxtj+iUygzJoRyuKifjUpTDSGE4YJJEhrHf58V9PhtX5JFYWbSW4tibEz17XgkuU4I638UL0vN9NyQa+tx1z4XT82qbp080TOEELpremptXtf37Etf+klZNhNjLgSdmBlCCBORqPW9I32vfmdTdJQQQn9ZJ4yNqrqNZZEGFkII45JJhruky5rX48f82q/+sqzzpLj26/qdpLhUxdK2SVzUIaChfUEPWBEQGkIIYVrU/XxSiZe1L5rEyyPdfjc2hiJRcfKHssojyfZCWPmT+LFdKvE0r+9L83p8vuls6PkkO9Tj2H0zZIe6He47Kj3R5ytt6+dXOoyP1Y9/9z+SdTKmjcOGPpdLCiwc6/YfPGsS+rZ0WyriPfGVX/8V3ZAnxH+583z09ze7K7LOtapOCP7m/kVZ9gtnviXLXuqek2UPBguy7DtfvSbL0kOV3qyftSsbLOn+o+pNviqr8Bc0AAAAAEgKFmgAAAAAkBAs0AAAAAAgIVigAQAAAEBCsEADAAAAgIRggQYAAAAACXGimP15NoT+cnxNV7uvc01Tcx1LWdqPR54O63rtWDER0N01HQE9bOjLbdw+Xay0iydWEcQ908bsQN+rWdZtPaANF/V1p832CPljE4ttYsSfdDORlDptmP0iTN70cE3fx+JDHcs6WtER7+mh7kOFQ91PMoP475OyrGKjuycjXTZP6771OObpEMal+HlLByZyOm0iyA/j9VqX9H02u32EXE9f+zyj2zFY1P3h8FmzzUPHxavH2+LmmuGCHt+Zka6XMf0h23PjRxfZ/mza8qRT23pMzNYWLl58XNNl2Y4um5upftTQbcmabUnyrXhZ/lCfq33VfGNM9Lkq9+Jzc9pscfK48m3d11sX9Q0t7cevo/pQvwumRf3umZnpa+qmNj0Nhdpd/a3k2pLtx59fZ0Nv41De0f2rvKv7g3tnubLmG/qYuY55t5j79aR7vb0W/X021/dxf1SVZa1hUZb9xoOPyrJUSvcFd8yU7q5huB4vLN/Wg2Na0sdzc6XaGsltQ8Zf0AAAAAAgIVigAQAAAEBCsEADAAAAgIRggQYAAAAACcECDQAAAAASggUaAAAAACTEybLS5yGkJ/Goy2lBR26mTcyl4qInU1MT8yxixEMIYZ41kcymaOGmjl5V9yOEELK9eD0X89pv6jVzvmMi8U2sb3FP5wn31vOyzMWB/yjHyqq+lzZRzq5v5ff0zeqf1fHJqb6uN66ZiPQV3RcK+/FjDpd0H5/nTNTxXT2F9M7Fr82Ow0eQmoWQE5Ht+8/p9hT39XmLIma/sqXvS2aojzeq6nHs5q95Xtdrvq7rFY50O7vr8Wc+NfNQ3kRK91Z0v3TzkNsm5PCqfm75tunrjR/df2OcFuPXnRno+zg2EfzZrj5X94KJFz/W97h6z7zLVvT5VGJ294J5j22ZLUlM3H/ncnwemhUebx6aFkI4ejrepjNf68l6RdNnVX/OdXVbu2v6eGmz7YKb22ZZfa+HTT1WBwtmPKbi9QqtU25JYr+VdD+qPNAfia3LOkPdzfduu6In3f32QvT3v3TuJVmnZbLo37/0UJYt5Pqy7PpxPO4/hBCKZb1PyG5xVZZV3orH6ffO6rFRfqDHhtvWwuzEJP3ovt0AAAAA4AnDAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEgIFmgAAAAAkBAs0AAAAAAgIU4Us5+ehFDai8eXusjTsYmcLu3F40lHdR2vmjJZlrm+i77Wx2yf07ei9kBHobfP6nqVnfjvnTM6plPd3xB8tH1mZGKxTZR+ruvOp++Xez5PumxPXJu55KxOVQ6juu6TuZZ+qMU9fcLMUJ+ve06Pt0kp3hYVvx+Cjvt+u7LqW/GxkR4+Zt9J6b658KaJm1/T13h8MV628l19o6dFE29ttt+Yp92WJKav9PVYnRZdZH78mDMzn0xK+tqyAzPH1nS93qo+4fLLei8Wt4VL1tyTJ91UjNVMS9+PWdnM5219/+d5/Uxn+vUROud1PRWlH0IIBTG3TfNmbOjXsC0LfdEnZ483D2W787D27fj8cHRFx4y793jtbnxLnOGi/uZx7+nabT2u3HfZuKKPWTiWRaEktisJIYS5OGTabDsyLus2Vrb0tXXXzDdiST8AN8e6rVN6q/Gyv3bnU7JOweyBcP1IR8NfqB3qstKBLPvu0TlZdrm6L8u+8YHflGXvKJ2yb1372udk2aQc/919D7lvLyejdrsy8yR/QQMAAACAhGCBBgAAAAAJwQINAAAAABKCBRoAAAAAJAQLNAAAAABICBZoAAAAAJAQJ4rZT83mMmK5dKDjSYtHJ4+VnhZchLUssjHVlS3dxkFTx2qmTGR27Z5uzERExDbe0nVScxNbrFNZwyyr73Gup697uODiRE3kd9dkgz7h5qLrudjoqU71tbHKKZMSPlg2Ue3mmMEcM9eO9xMZARt8LP5gRZ8sNY3XU/f3Uc1TIUxzJz92eddE8G/Eb+iorqfIwYI5mUnwdltUNG7puSHf0mXjmm5nV8TbVzf1/XDx9aO67nyVLd3GUc3EvJtbmRnotmQ7Omr7SafGz6Ss54X8volOb+j7WLth+vmSPp9/F5tBIJrp6qTNHOX6T0HcExvN/wimxVQ4vBrfg2Dxus7iHi7ol8WoEX8OhQPdz2dZczwzVu0WFW7+MnHzWfPNMBLR/aUDPQ+5b55hw3yzme+o9nm9b4TrE66Prbw4iP7+8//xd2Wde+NFWXaptCfL/vjwoiz7D8/+kSz7e4Ofk2UvH27IsnBWFz0J1PYOIYQwE9sEuSh9t62Fiu0PwX/rKfwFDQAAAAASggUaAAAAACQECzQAAAAASAgWaAAAAACQECzQAAAAACAhWKABAAAAQEKcMGY/hFw7nkM6quro0kJLZ6UePBuPiG2+YeLrizrmsrSns3h3PliSZQtvughrHefq4u1notq0oOvkj00WZ/p00fa9Zf2Yi+bZFMy9nKcLp2rLE83c/uK+LjxeNLHYR/rfSKZ5E7NvomOzPV2o4vnHFX08Fw9bODCx3rV4+x83Zj81CyEnIqK7a3qs1u/oMV44VHG7+uJLok4IIUzMNiHZvq7noqML+3o8Hp/XYzwttgmZia0KQgjh4KKO7i7t6HsyNduVFMzc1l3T7XfR/ceX9Zz+oyp3bLa9WNX3OHes+2Rvw2yTs6fr2Qhrs/VIfyXeJ12s+rimT1bcNw1Rw+0xd4pJj+dyq4r2Of1+nJguq+bGaUHfzNbT+vmkTGx87Z4uKxyZrS3EVkshhJAZmTIRXT5P62c3Ms88MzL94VB3pEnJvXNlUSibeahzJl7xH9z6tKxTL8Sj+UMI4UptV5YtFbqy7H8++AlZ9nOLL8my3Xpdlj3p3PfQuBrvr25roZG5VXPzjT5sijpmFcZf0AAAAAAgIVigAQAAAEBCsEADAAAAgIRggQYAAAAACcECDQAAAAASggUaAAAAACTEiWL2Z5kQRg1RxaTcts/p05S34nGuo5peOx5eM3HB2zrDtvpQR8fm22NZFtq6qHWxKMtKB/Go1/6SjqJOzXRM57iq601NpHHxSEfOugj1SdUcNGUe+BNO3ZN5Rj+b1jVdVtzR/TVtYpBTE3OPTdGortuiqk3KuiOUH+p+11/X9WQE92P+s9A8FcJURMTnOvra3ZwyE1PULK9vdHqozzWt63pTs0PFwpsiizqEsPORsiwrHOi2qFjs7rq+H423dMd0Wxnk22b+qujzlXdNvrrpL9UHeuuBJ11avJIGKyYSf9fcLBMrnx6ZseFeA2b+mpltQvJHYvz29PFUNH8IIQzO6oZk2vH+6q7rUcyzKfkuH1f0+F9+ScerDxfjjVLzXQghnP3DvizrrZm4f7Ndkdt2pdDS/W9ScrH48efXXzTzwp6eF0ZmPkmPzIeNue6svpVh/706g19ts5NJ63b81PIbsuyBymQPIXykfkuW5cw+Fd/qXJJl3p1T1kuGSVnPG7OiKOua7bMKZnudmn7eKRHd77Yq4S9oAAAAAJAQLNAAAAAAICFYoAEAAABAQrBAAwAAAICEYIEGAAAAAAnBAg0AAAAAEuJEMfupWQiZQTxG0kXHD5Z0juSkHC8r7eq4ynN/oGNqjy/qWFkXYX/0tI7Lz5vo7nxXt3Ncjq9/8x1d5/AZnf2bP9btGDb1PS69oqNX22dNFzDxny5O+0k3VlGpJg91biKlHRezn9GJ68Gk8IZcR7dTbSEwy5qHbS4tNdb1ciKy2CQBP5L0ZB6Kh/GDjKv635yyPT3u1HwzLel5bVYwEfwTM2eY+WSwpMd/7d7ptstQ150d6krDur7u0oGZ80puSwmz/cNcl/VW3GsqXnbln31O1nDjY1Iy22Uc6Hpua4usiWweNXS9f/8v/UH09y9uPivr/GeXvizLfuX+T8qyDzXvybLxXPeF58u63t9/7TO63upm9PfeREeZ/8zKq7Lsd7afl2XjWbz9e+XH3KJhpqPjXcz+4TX9jVI8PPl7pHNGHy8z1serPNRbC02L+pnPzXBU9yOEEAoH8fOp76S3k+vrc3U39Dyq3h0h+C2h1r5ltjPYiD+D+y+ckXV+dazLMiKSPYQQfrdq5lHzPeHmvd4ZfU9++/hj0d/T5t3vvhncVgZqu5sQQphU9EFdW37+578uy15pbUR//3Mr12Wdy/ldWfbaQD/TZrYb/f3v/2pL1uEvaAAAAACQECzQAAAAACAhWKABAAAAQEKwQAMAAACAhGCBBgAAAAAJcaIUx2k+Fdrn4+k4ua5JC4oHNoUQQijvxmNn+ku6aaOGLivv6Bib46dMPZMa6dLHdj6oj1kSYS+VTZ2Ys/yiju6bm6S9yqYu2/5xnWhUeaivrXZPp1y5JKonXaZv0omE8qZOveqvmYS6hu4LhR2TpGX+aWVa1WUqQTHfMgc0t6O4Z5Khno335dMmXv4/zZnNQ64dTwTrnCnJetOcbmt5++TptG6uGW7oc7mks3napGJ2T5fGmuvFj+nSZH06rW5j4fh0bRzVzNy2rY85WIg/n4JJXJzp6TDMs/rZjM24Gq3rRLz5nj5h1sw1Hy7fihfEg8dCCCH8w7s/Lcv+5oWvnKpeOqXvyfawJsvOLxzJssV8L/r7wbAs69wdLsmyhbyOhvtg42709+s5nQb9KDLjeag+iL8jc139zN04VkHB45p5vzT03F3d1OO4t24So83c4FJ4U1PzrluPJ3Rmh7rOLKPHxzTv5qHTtb9+V8/po4a+X73l+DNImTjD9ES3PxsfHiEEPw9Ny6c7X/5I9yEV4jox58oM9LnGNTPHXtZjsvSyfre79MdP13X662r+OPq7S2r84tF7Zdn54qEs+2brUvT37vRFWYe/oAEAAABAQrBAAwAAAICEYIEGAAAAAAnBAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEiIE8Xsp2ch5DvxiMzSjo4Z7m64XOOTtOBPqWjlEHy8atXE26dN9PVgUZ9v6RV9vmk8VTaU9nR8/WBJVAoh9Fb0errQ0u1ffllHx85MdH9/VT+36kOTVfuEm4lHUL2n79VAJ0CHjN45IZSv6yE41onT9p9W3PlG9Xg/GS3ofpxzEfxG8WZ8K4bU8OTbGPybJqV02Ht/PHK3+YYeW+1zemwNxLYehZa+L+1z+tk1bukxNy2YMbeo73WuI4vs+O+txo+Z1k20cdnFfT32p0Xd/mxfH7O6ebo5atSIl6X0YwtZk66e7Z9uu4lRT78jFp7fk2Wt7y7Lsv/x3qejvw+nut/Vcnrw/+OHn5RlubR+pvu9iiw7VzmSZZ9Z1fHW/+d2PKr6qeqBrPMv33i/LHv+zENZNhT7KsxVpv0jmhRT4fCZ+BxXM3Htrj+rfts5o/tXrm1i6s3WImn9yWbntsxAn690oAfeoBkfW7X75l6ZKP2jp/Q9qd7X7SgOdNnIbGeQNnPiwlvxm9m94LaLMls7jfQ8VNox7491WRQmJd3+3LE+ZvUT8ch5N3dlzBxrt5K5qaP0XX/tndHz1z968NOyrFmI72fwrclFWecTzZuy7DvHF2RZKRO/ALeNCX9BAwAAAICEYIEGAAAAAAnBAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEgIFmgAAAAAkBAnitkP8xDS43gkpItzLR7pCMzcsYieHOkI0nlGn2tsYlKPz5p44vuni40vHOjsz/5KPN63uxGP5g0hhM4ZvWYu7+h7cnRV11t8Vcd42jhqE4s9rjxeRHGiiUvrbej7kW+Z+zEz/dVF6RsuIXquh0DIizjdYVNfW0Yn14eZOddc7dLwmF0nM5qHuoixHjb1GM8O9TX2l+Ljp3Cs66z9XzoSfP9Di7JsVDMxwz19vlFd3+ypmX8r2/FBPjf/PGdS18OkYrb7OHDZ/frZdM7osoWbOrO5shm/7v33mY5pZPu6TG2bEkII9Rv6fPszvQdH5umuLDtbbkV//5nmy7LOreGqLHuhpSOg9wc6Sr+U0++4N45WZNlyXu8LsVE+jv7+V5a+Kess5PTDeaa4JcveGMSzx6eP+e/TqVkI2XhKtx0j6hsqhBCGYowv3NDPwG1tMarqsoxpR95s25Ga6TI3p6jtX/pii5MQfLT98osmd90xWzup90AIIVS3zPYiausUc678kT5X38TGlx+Ybab2TQT/hv7Ym1RlUeh8PT7G0+ZZ99fNtj0d3cbSlttSRZ8v29GN+asb35BlX2k9G/39bPFI1tkcLciyv776NVn2e8fxbUIyxOwDAAAAQPKxQAMAAACAhGCBBgAAAAAJwQINAAAAABKCBRoAAAAAJAQLNAAAAABIiJPF7Acdcd9bU5naIZT2dfRy50Ix+ruLca9s6gjSlImHbtzR7Rg0dHSpi47trevrHjTjFRdu6njYsnkiaZMqW71nYtJNvHhvRV936UDfzHnqR3dtn2vH+96kbOJQzbOZ6l0VZPRwCCH0PqBjpdMP4uMmhBDmGd3OXDv+3EzSa+id0/0gK44XQgjTQvygbjw9imkuFTrr8YFS2dFtnZntOcp78VjgaV7XOfhgU59LTwt2PObbOp44O9Bl3VU9cZRvxWPqR02dGz8pne4h9Vf1hRfMditlE/l9cE339eKR6biCikYPwcdDL7ym+8Lhe82YM7HSxW/qfOsPvP9u9PffP3yfrNOf6vt/vnQoy35q8Q1Z9uW998iy9VJblg1nuk82RGT+b+x/TNb5zs55WbZ8Ubfjxyu3or//i7SZfB/BPBXCpBQvK4n5JIS32S5DDMn2eX0v3dYcbmsRp3Zf35ujy/qFVjjW113uxr+/0mMT/17R92pm5uZ5Wpd1zuhjum/LYd1E8N+P70VT2DfbvpgtPYZ694owauhn6r5RCge6/W67pbm4hElRn6txQ5/r+LIZGw393KYlfT5X9s3207JsZxCff3+68Zqs879ufVSWDWcfkmVXS9vR3/MpvS750f3KBgAAAIAnDAs0AAAAAEgIFmgAAAAAkBAs0AAAAAAgIVigAQAAAEBCsEADAAAAgIQ4Ucx+ajIPxb14nviooQ/VXdNlla14xGRpz8TDzkxs/KqOjp7rdNVQv6Nz0vffq4/ZfF1HZM6y8cjQ1mUdhexiXnNdHU86T+uLa5/TZbmuLLJRtZnR6eJ7nwTjavzapiUXD6vvsYujzQ70PS68LjKcQwjDJd2W9FAfc7QQrzfP6TbW39DX1juj67no/seRGc9DVcwbxyaOepbT96V5Iz7+5zW3jYDZCkS0L4QQ2ud0G/vL+nzpkS7L9fXNPrwW70cLb+ko7WFDz3mlXXNtF/TcNimY98C2PmbtgS4rbsezqnc/WJN1umfM8e7p9h9fNluZ9M2Ya+o5fWxipf9g/1r098lMj8eHnbosG9RN5HdazycrxY4se3H/jCz7+Go83j6EEK6316K/X6zsyzp/5eK39fF68eOFEMLvbsa3Jdgd3Zd1HklKbxni3gfBzIuV7Xhf6a7p47mthVRs/9u1Y5rXc83CTT1vDJb0+JmV48dMT/W5suabp7+i+/NE78wRFl/T7e+abZPcfT54Nr71QO+Mbn9pR1+32yqntK3njP66LAoTE0XvtglKj/X59LnM8Sb6eC7uf1zThYV9PT7OFo5k2eagEf39l+78tKzzfPOhLLvfW5BlhXT8vTNS+xgE/oIGAAAAAInBAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEgIFmgAAAAAkBAs0AAAAAAgIU4Usz/Lp0L7fDy3tXSgM0g7501c+zAej+niNke1k8d+hhBC8VAftLeqb8XyizqWdVw9efxt83UTb72oY16zHZ2FWjBL7exAFw4WdJmLJXeR/0+6wmH8uofm3zPS+pGGnImVVbH3IYRQONDnK22f7t9WVDT8qKHbMdNdMgTTDdKj+LkeN35/lkvJrTvKe2b7gbE+sdqCY57Rzy5rou2nRf183Dw0KZ3uuaq47xBCqOzEJ6LWxXg0dAgh5Lv62oYLeq4stEzc/1XdyHzHRG339f16+CkVK6/bUb1htgIw8dyTRR3Pf/6pPVn28CUdAT9d0nN6LRefVNpj/dw+e/mrsuz1/oYs+9L9Z2TZh9fuyTIXpX+hcCDLHvQXor9/feuSrHOwWJFlH27clmXf2Tkf/X02O913xJ9JzULI9UShmePSE104qotxYJqaNvHvwZ2rasbcQB902NTjJ9/W9VqX4vUmIn4/hBAat3T7K1t67LhtAgaLLp5f3+iZ2bKgflds+3JVH2+wZOao27qNUz38Q+HgdH26e/bk7838ob7HzT+/Kcs2v6f3Ahg1zDu6qNuYMluPpM1i4uMLb0V//1b6KVlHxeWHEMJOT2/v8rPLL0d/L6Z0P+YvaAAAAACQECzQAAAAACAhWKABAAAAQEKwQAMAAACAhGCBBgAAAAAJwQINAAAAABLiRDH7YR5CehqPwZzmdbxn87qJxxTHyx/ruNZ5+nQxqS66//iSXqsO6zrXNDvQsaCqLdO8zmsd103Ma1ZnQI9Lup47Zt7EYg+aul5xHi975vOfk3UyPXNtORNZXtZlhT393Fycu9oCIYQQ/vvP/lr09+OZvv+3hquybGeko1dvdZdk2WeWX5Vl/8u9j8qyv3P5C7LsXx8/F/19Z6Db+OHGHVnmImxbk3L091/5fFvWeRTzEILofvL3EIKNqu6KbTZcfL2L9Hfx/I47X65nIuyfMZHHb8R/L7ZM3L+ZR10b8219zOWXzf1K6/MNF3SEcuEwfk9m5n3ktsSY1c1cs6njxffu6Qj7rJnbZnn9LrtY2o/+fn2qY/v/xdaHZFlVxPaHEMKHVh/Isu5Ev/8+1RCdK4Twe/vPy7KRiMV+qqGj+W8fL8oyNw89u7QVP15Wx1s/inkqhKnoEhPzPi4c6WOqLYTKO/r6+kt6QFa2dL2h2qEihNBd18/8yGyXUd7Ufb0oxmrPbOfTb+qxXxnqc6VmZhybeS9jtmLJH+l49dal+P0qbZr3gCnqr+h2ZMz8NS2ae2K2+8n0dVlZXMPQbBOw+Sc6Sj/XPeX2FnM9V7rvuW8cXZZlWTFvDKb6XB9YvCvLtof6O+r+KD5/jdQeP4G/oAEAAABAYrBAAwAAAICEYIEGAAAAAAnBAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEiIE8Xsp+YhZES0aXddR0Xm2zqOc7gQXyO6iHQXN13Z1NG5g2Udk7z+zZEsm5RNlLuJ91RbCBw/pW/70ks6Q3Va1Pe4tKMb0jmnY/37KzrytHr/5LHY6aE+3mhRHy9roldL2/r+9zb0McsPdb3OFd1PLufiUc+/dvBJWWctdyzLDsYVWZbP6Of2taOrsuw/v/z7suzOaEWWTUS8dSGjI4S3xzqP+UZbn+vji29Ff0+7wf0IMuN5qG7F2+vGo0mzDb31eP9r3NL9K20imcNIl+XEvBCC317EWbih5xR1zFHdxNe3dDuGpp67/+mxvpeDRROhbLZHUe+CowVdJ1swc9SKHgfVm/r9MVwwfcH8M2hpU9/LseiwtdxA1vnM0iuy7OGoKcu+vHNNlm2U9dz2G5t6u4+LFR2Zny3EO4rb7uPjq7dk2c3Osixrj+Pbo4zFXPio0tMQikfxzuli9t08VLsX73+dDT0+3Du8sq3Ple+aSHbTnWt3dGF6YrbLEZ8htft6zPVW9M06uqLHY2nfbe0ki0LGzNu7P6a32WnciV9Df00/t+5F/Q2S7pu5eVNPKKOG2V5Ef+KGtH4EobcRvycZc7zigW7HVO/gECal030bjBq63pXKriwbzuLPx8Xl3xnquaaU0c/0qcJe9Pd8St98/oIGAAAAAAnBAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEgIFmgAAAAAkBAnSnFMj2ah8iCeIDVPl2S9uVkGqmSZaV6nwOSPTayXrhZGFV04aJ4uESgddHpM62L8mKvf6ck63bM6KcjpnDXJaiahZ+lVnTozquljqpSqXFufa3hGxydNJ/pcfR0UaFMjXb/LNXRa5u933xP93SWFPbO8Jcv+rcXvybKvtp+RZZmg+92N4Zos2xnp1EWVWnS2eCTrvNXV171e0g/8qfzJU4seRWoWQrYb70uDJT2OVQJtCCGsfSveHx78lE5A3fi6LAppk9R4eFUfUyW/hhBCVof3hXFZjwOVyDYp6jrDmn41ZM197K7pcVyOd4cQgm9/vqPPN2jG601W9Lw2GeuJIb+jr9slhTmnTRbdHsbHcX+q+/gv3/pJWfae5o4sc8mqI5N2OJvr5/aZhZdk2W/ufTj6+/5AJ97OzMt9JOa1EEL4ieWb0d9fy+p3wCOZz2VqYbavq+1+QPe/8mb82Va3dCcaV00nMl3WvR/zbX2+SVHf66FJESwdxBvjxke2ry+gv2zSK7d0vUlRX3jhSL+bioe6XutS/J6Ma7od5dsmFXbJJGibr/ZZTp/P1ZuPTp466uau/ppu/7Ri0ryP9EHTpp+M1vV8/9FKfPyHEMJvHXxQH1T49tEFWeZSub96FP/W60yvyzr8BQ0AAAAAEoIFGgAAAAAkBAs0AAAAAEgIFmgAAAAAkBAs0AAAAAAgIVigAQAAAEBCnChmf1ZIh86FeJz+NGcimxd02UwkjWZMAm5aRLyHEMKorqNLTZKwjdJPuVR/F2H/Svwijq7oLQnKe+aArv27Ot6zu6FjvYcNHWtaaJ08are/phtZfKCfzbSo67ko/VnBxOlWTNmOfga/s/189Pe/dkbnqudMVvD1wYYsO1s4lGXfOz4vyz5UvXOqtnyk+lb0968d67j/DzbuyrI3e6uybD3biv7u2vcopvlUaJ8vnLheeqLP21uL980LX9TZ9gfP6S0xGjfF/iEhhIU3ddm0pP/N7PCKnq7zbRNHvRQfP4UjXcfNvy4S30Xwz7K6Xs7EaY9qJ4/gL93Sc970+Y4sy96pyrLhopmjdMpzGDfMu+Wq3nLlpf34vPG3nv6yrNNt6nHxv23+uCx778KmLHtPSZet5+JjPIQQvtq+JsvWCvHtOc6Z7T6cF1tnZdnX9y9Ff+9MvnGqc/2ZeSYVBs34+7NgtgJafNW8Iw/j7//+sn5Pq++MEEJoPa3Hwbiqx9WkoMsKbfNBZLL7VYT9cEHPa2OzNZKbv2yUvtlCYNTQbcmM9PmWXorP6W6blv453Y55xsyj+/ra0lf03Da5p7ewCFe6sqj0QnxO7D6n+13xTT0PTcz3nP3WNmW5bf1t+XDclGV/UWyBtD/R74FKWl/3iz39zfb1vfg8pLY+CoG/oAEAAABAYrBAAwAAAICEYIEGAAAAAAnBAg0AAAAAEoIFGgAAAAAkBAs0AAAAAEiIE8Xsz9M6YjnXNbGgeR2rWdqJ16vf1vHWvQ0d4Vk41NGlrUs6ijOv00nDqKrXsROd1h5Ss3g0bmVLt3FmtivImXjYBz+pI79Xvqej+w+vmuhuExWcnoh4610TAWuiu3s6id7Gq46Xdb516Y6OuM21dDs/sRSPon9zuCbrfG3vaVlWzZkY5JHuQM2CjuD+vf34VgAhhLBR1NHXD4bxyNl/t/mCrPPF4/fJso/WbsmyX93+VPT3vfG/knUeRWoWQnYQ7xTFA93X+8t6/PdX4v0hM9ZzTf5Yz3luS4/0UHfo7rpuY+O2Hv9ubpjl49dm57WinofKZkuPw2t6Pqk8dPOJLApZE8GfHcTLXOz95KaJm9aXHar3TCy5eQ+MmyYO3ERff/4v/6Po77928ElZ5zsHOub5J5ZvyrKy2VfhRl/Pe72ZHh/juY6G/0TtRvT3P2pflXVeb+l2PFU9kGX/3nJ8e5TPFvQ8+ShSM903p+abJ9fX40DF9rsxMC3q+7z4uv6OOryqvxmKR+ala+R6ul5/OT43uC2amtd1v5yU9HWPzPZBY7OViXt/DBb13Ka+UcwQCPOc2X6jryvmzLdq+JOaLBpe0NeWvqXnobl4POld/X3lZLtmkjV/Mhqs6fdOYU/fr68c6u0+/utzvxX9/ZfNtkM/3/yOLPvGTH8HLhXjWxlkzQcuf0EDAAAAgIRggQYAAAAACcECDQAAAAASggUaAAAAACQECzQAAAAASAgWaAAAAACQECeK2U+P5qF2P55fPC3otd7CTR0jmRWxrIfXdASsi7DNF3RZ/Z6OGc2Y6Ov2OR3hmR7JopAbxqNxh3UTRW/ipruruh1Lr+gI0u66rle/a2Jxl3S9ubiEwaLbbkGXZQb6ualzhRDCwnd11OtEd6HQv6wf3FSc8EZ3Vdb5hTPfkmX/cvuDsuzf2fiuLHuxc06WFUxH6U/1PdkZVqO//8bkY7LO+6v3ZdnBVMfzdkREvbq/j2qeCmEqxnnnrL724oEeI7V78b45aOq2DhdNFP2OLArDRR2lXzJtdJHNk4ouzHbjxyztmznvrG5jaqrHcXlLH7PQOt32IumxLhtX4mWTitvnQBeNa7recEmXFfb0QbNt/Wyqd/W1ffaNvxr9/d/eeEnWSQfdxheOLsiybEo/m81uXZbV8zoGvVnU24R8vv2J6O9rxbas8+nV12XZrf6KLPvv7v5s9Pet0T+XdR6F23ZosKKfa3HXzBt74jmY7jwp6eOlpiePhg/hbbbZ2NZ7WIxrJt6+Eh8j5V39Ljt4Vm/j0LhlvudGZmsL840YUrqscGS+sc7E58u5+cLOHujCbN9s6WF2CUnrJobyPdMY078Gy/HC4r5u46hxum89t4VAyrwHRnXdmUdT3Sf/282/EP29P9Xvv2/3Lsuy9lh/dK4W4xeXNQ+Nv6ABAAAAQEKwQAMAAACAhGCBBgAAAAAJwQINAAAAABKCBRoAAAAAJAQLNAAAAABIiNR8bvI1////41RqN4Rw5wfXHADvAk/N53Odi/02mIcAfB8wDwH4YZPz0IkWaAAAAACAHxz+L44AAAAAkBAs0AAAAAAgIVigAQAAAEBCsEADAAAAgIRggQYAAAAACcECDQAAAAASggUaAAAAACQECzQAAAAASAgWaAAAAACQEP832NiI3sNx9rUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_tensor(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "S_trues = get_synthetic_gt_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_trials = 10\n",
    "transposes = [(0,1,2), (0,2,1), (1,0,2), (1,2,0), (2,0,1), (2,1,0)]\n",
    "ranks = [7,4,2]\n",
    "\n",
    "all_recon_losses = np.empty((len(ranks), num_trials, len(transposes)))\n",
    "all_recon_losses_orig = np.empty((len(ranks), num_trials, len(transposes)))\n",
    "all_topic_losses = np.empty((len(ranks), num_trials, len(transposes)))\n",
    "for i in range(num_trials):\n",
    "    for j, t in enumerate(transposes):\n",
    "        recon_losses, recon_losses_orig, topic_losses = run_cichocki_factorization(X, ranks, S_trues, to_transpose = t, X_orig = X_original, seed=i, verbose=False, display=False)\n",
    "        all_recon_losses[:,i,j] = np.asarray(recon_losses)\n",
    "        all_recon_losses_orig[:,i,j] = np.asarray(recon_losses_orig)\n",
    "        all_topic_losses[:,i,j] = np.asarray(topic_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic Modeling Loss 7 to 2 (average, best, worst): 0.1713591307278673\n",
      "Topic Modeling Loss 7 to 4 (average, best, worst): 0.14362339998429072\n",
      "Topic Modeling Loss 4 to 2 (average, best, worst): 0.16985001806016436\n",
      "\n",
      "Relative Recon Loss Rank 7 (average, best, worst): 0.45432993797409227\n",
      "Relative Recon Loss Rank 4 (average, best, worst): 0.5755356801811189\n",
      "Relative Recon Loss Rank 2 (average, best, worst): 0.7807949448565038\n",
      "\n",
      "Topic Modeling Loss 7 to 2 (average, best, worst): 0.09023680913570852\n",
      "Topic Modeling Loss 7 to 4 (average, best, worst): 0.11633681279957198\n",
      "Topic Modeling Loss 4 to 2 (average, best, worst): 0.14161715796962507\n",
      "\n",
      "Relative Recon Loss Rank 7 (average, best, worst): 0.45432993797409454\n",
      "Relative Recon Loss Rank 4 (average, best, worst): 0.5869734002494331\n",
      "Relative Recon Loss Rank 2 (average, best, worst): 0.7653047704922201\n",
      "\n",
      "Topic Modeling Loss 7 to 2 (average, best, worst): 0.08350453695236079\n",
      "Topic Modeling Loss 7 to 4 (average, best, worst): 0.11078673456943906\n",
      "Topic Modeling Loss 4 to 2 (average, best, worst): 0.1643179830333049\n",
      "\n",
      "Relative Recon Loss Rank 7 (average, best, worst): 0.4543299379740894\n",
      "Relative Recon Loss Rank 4 (average, best, worst): 0.5600149649444285\n",
      "Relative Recon Loss Rank 2 (average, best, worst): 0.7465277676935859\n",
      "\n"
     ]
    }
   ],
   "source": [
    "handle_losses_cichocki(all_recon_losses_orig, all_topic_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 1 Relative reconstruction loss: 0.2774291903373395\n"
     ]
    },
    {
     "data": {
      "image/png": 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6fI61l9PX+Pbug+l+TS6wlXPV5S91MWh3fg/r8XO3xVITGaRGc4luvD0B3yABAAAIJkgAAACCCRIAAIBgggQAACBmhbSbMg63uVCdPmbDVYO2eczOntDv88QWiguNplOHmUDfYqXkMsFpF+TOhM9vMZStmmEcwnYhbf0RQmsykeF8rgxpX0iRRRfS1sKMm3UMKmo/x9ZsJVzpxkDFOZ86v124ORScTIQ93Xma+fFCl/h/q+6/dl8VmiLndOVw3F+M/9YfN5SSK6gauDzvUxDKVmEM2EaJa3pF8d1m7z7c5QcPiSFgA9jn4yKwtsDrxVl87Aj4BgkAAEAwQQIAABBMkAAAAMT8xWoXWKgztbihbvYcZZCGbnqBvUFv27p70cec/j6jhSKHppT+eh1PF6PSwzOjLDUGJvotpZR1Nz4ROhPWW0ku6ayL9/zPqsIeh5MaA6ncVsV5eOUuNnphqzzndtK3K1yn+Y9MkUx7PHLM+ppWCp8DtYUiNdOyis8hsR5yquDkUxhBimPASZzfNadqs9TCxrX9uAzUVN8276zFZCkUCQAAcCNMkAAAAAQTJAAAAMEECQAAQMwKaa/v9eV7/vruO383V9vYSIqsDWaF7WYjxaNcHku303BjKeXVH//0uBtXXEr6ae9dhiaDKZwXZFbGlmN0/WrxL9fmC3/6u6O/X2jWoc03+3FVNXd0LzbjR8+b+HZ3EnT70c/9UmgTCrqZt101LvOrmTp30JmMthSUyxRrtMfzxvS+1OphX973z/cfHcvD+GIMeq64MSBjpzHnd38+ft/bS1NJ7w/H72n/8guhyVYKD770dlytfjg/H/39iebnY5vEGGj20+d3Zgz82Zd/f/R318R93+vHY7k1o+CFdn7BuQ//xmvhsf345bHFPzUJ7HLvGrS3YyCxOn0YA+YSpn3bHH7FGFjf35dXvvLWtY7jk9jfkWuW+w3JVq6Xpk13fyNtYqP+BRknG/NE++ng+6tf/NR4Xxfxuquh39606e6Oz8vh3PSj3ZpA9p98+fdGf5+bz4EaV0O8ZunY+cX/+InQZpf4Zcl3NuMPi/df3A9tHuzW8ncco//15x8Y/d24YruZ8Ln+lsE9ha8+fnO+QQIAABBMkAAAAAQTJAAAADErg1Sa8b1SV0wq3CN2haL0drDeHy6lDDJ3c21C3y6LortfqgCWk5luJtpcNONAgcsOXTTj+8iuW93OZ5A0qBD7icU/Y5twP9i9zOG9MPvKvD3aJpNlOuDbHtT8tyNzXro2Z5r1cGEPecwthnrIcVGxr12RApjmRd0O42tCa0IJW6moum7mFYqbpebcrdnmGXX0BWW1iGBN0dpSUnnVzOeknrtOTS7pcoiBnk5Osst9/KzYuIq32rfki1w/l/txmyvTRj8HqofADT8H+AYJAABAMEECAAAQTJAAAAAEEyQAAAAxL6Stale0DoXQpsPe1YFwCdHZYneZFZETbUILN/3U3J1po8E7VyTvXB7TkN1324zfXtePqgpJm+3sitqJsHcmRBf6yRzPbQZd9bkv9d8Q9yQ2UvRtSIQ2a8ftUm5x/+1SCeeKblJjqdapHc+zYCcXZ1dAWM/d3pzL4QdFpliq9JMZEq4Qag33WaF9X3QxyO1+BKG23fi5npnqpDt5bJ8pwpzgzu+bBv/5BgkAAEAwQQIAABBMkAAAAMTNMkg28zN/O7fQonKL+VU59pQwEYbJ5Cb0PrK7P53JHKU8A3mHpY6n2e1L9617T2zT7mURTlOYcXhxvKjjYG6Wh5yCW/S26IK2MTswXMk9/3ddhDa6KGjINpVS2u2472EdLx+N5DgGLb5nNA/ic88UwHtPFxfmXcL6XjyetbzlLtug2bjMApu6CG4pZiHcyhyF1vUbXFHDGvt9ad++tghpF9/j9q3pIod6/ui5U0op/btfko3ii2EXpw2NEvlLPZ7tdL9h3Jh9Zfopu/je/Oy/jRdj3/XxOazafrLN+2TBWC3UWEop2/34PXz9B7/0+GO9BR/7g8+MH3CnbqY4scbBKBQJAABwM0yQAAAABBMkAAAAwQQJAABAzA9pXy+M5apbhVBUbKPFGjWMapmQXXM1DpIOrrCXFPJygTm7ne6rpridKZYYC43FJsdcydwW1tLHTBvdzhWK1ICcDU4nXubQd6IfezxVmsmgfSaYHIvJuY6mX4xMAdOMUJh1kV6fc5Xn92L7OpSmLcP52ZPbZMaAGM7M/9cXGic1lhpbTubz5FyKNbam4GQnIe3WnGBarLE/tV/QnDC+QQIAABBMkAAAAAQTJAAAADEvgzRI1sTcHm7a6UX4NAfU7E0hrTMp2vUwFuTavztRKE6Ko7kieWGTy1gkL2V6jcJUm7v9+LnuTTDormaQXBysuRr9rYvXllJKp/fZ3XvaP/nvUnIZpMUWkNXn6o5H/14sgzRMZh6aRKHI/mJcrK12UcX2/tV0o7BRfOGbRNYjk61K5a9udeXg51gYBEt1PIyKOroClI1bxFV7ke3cNvuL8TXeFuhMPC/dzm3T3asYSwtxmSQt3rjp49hayZvqCkXqdhutIPqY7cA3SAAAAAETJAAAAMEECQAAQDBBAgAAEPNC2s043JZaLN4EMkNhRrNKeSjS9UJc9rq9ktXFXT8yB7QB7BMLjXYSvAtB6hJnti4a2zaZfirmyOblChFD95JmXuaF3ooQwjyttzicu/7XDFpZ07QxK6BP9uMCtDX9ZGS2ObHxN5ghEUK+brvEjxAGGaiuGGHfSXHbTIDfhpf1gBL9LCQTwNYXyL3umR/MNJlxMrVNKaXIjytSY7KW9m36XXfyYyZzIqykUGRnPpQvpOCkszE/4DmmOE5im0zwPrysM98+vkECAAAQTJAAAAAEEyQAAAAx68Zjs9uX7q37T26TKJI3mDxR6CezUKcuert31RLHx6MF+koppd3Ivd4HpmBY4p5x5rmHbS7jMb+nmy6A+f2r+DyWsHpo7nM/HP9ti0DKPePG3PbW7famZmer22UKPJr7yloLzRWzq9I044U4TbZiWMvONdtQcgskpw5nN78Cpi2IJ+fq0MXjazaSB0mc3zazkciMfOyzn5nuW7lLRmaBZO3mp94Kj+1lQ10k1NmY4nu6+1UXs1/rbtz3dh/76aQy7NU2Xsq1bzsEfts8NqVpynB+7frj8mtybR7KdJtiCgYPZ7eXjXELmR/Tm1/40GQbzW25vNo3NIqWuGT8yD4x/lzGTh4L1/MSr83ueNqf+9Z4G/O89Hx2Y3KzS1xnf+fx/8Q3SAAAAIIJEgAAgGCCBAAAIJggAQAAiJsl4BJBamupYludFBqzIU0pWOYKqkk/1YvM1wSBT6xIXu2q8hqQrS1uV9OPfdv1wdpz9UBCILS2KJ0GZF0/GoZ1beTHDMWEtFMSx6wFDN0PMvZniSKH4T02bSr+C9iasKd2kxnqg3le2reGrb/7mPywxCTLtY0LqGrfbfXgPp5bDU5vpwsq3qbUGKj5+EiEtFfmx0PK/uAhU5hRfzhhxqgWwGzMzvR89mPgZt8B8Q0SAACAYIIEAAAgmCABAACIm2WQbN4gcZ9b8w6V2ofz70/v3xWLVHYP5N5z7QKbNdudWDamN9GTmOeJbfQ+shaOLKWURuMyJsgxaCYjk/VwhSI7rVg23c9tCgXwKs+DZiNPLFNRzQ2/2syRyhRUzTzXTL4o0e/Qz1+wtTcFHjUH5PI8upioFpcspZR+r6+zu4aN2+z1OZRShkHbJIrSPoUZpKqCqnaV4MQCtivZV2YsObqd2ybRJpzP9rqrnyfmeHQI1Iw/089S3Gmp57N7KzTL6LJ6buzMcWIfGwAAAMfHBAkAAEAwQQIAABBMkAAAAMRTXSgyszJ34FZfTxSua6Qg36CBvlJKI6u2D2618xC8O63gpM1xZoo3ZoLcNWHBBHc82vdQdbI8bofX+nL/xcic3zXngevXpRcPpfbHCyIz3trd/Odli8Dqg4mn0K3MqvJy/rjAcwhum7qDWrxx1cV9dRr2NgHeTNFHLZx38iHtynB1ldp+b3G8tXL+uEtYo8eT+jFDYue1l0vNjGcKRZo2azMulI4Btytt44LcT9zHrNYAAADPASZIAAAAggkSAACAOEqhyGG1zLysGRZayHCpaeKJ5YlqNJkanpUZpFbeLnc7P+zf5oukiXv/KgoNpl1/n+2TSBSlyyxWGzYy401zbplFb2vP99B3IjNSmTdst9O7ymTj7LkxweXV9G3WOqTfbTP/udpsnDyRTBt3Gi5U+vP2uOxnzWK1NRlAt517k5ei56U5Zh0DqcVhM2qvqRXsmKy4FtsxKX9rdq+UujF5Hd8gAQAACCZIAAAAggkSAACAYIIEAAAgbhbSrtTsJF5VGW4eFlqBXPtpTGDOFYacbLNQYb3blArnJcKwLuzdy8sTVqIupQxSSM/1kzpG7fq0XuYytOMnsVTRTHs+ndg5lnmuuzvzj9n1q0UpU+fBPp5gGvbsTSpaCzFu9/GasZft3GVF9+VWJNfVzndmX5nilievnf9/+PCeG/4czPxCpULl58Duzvhv+7wqhnYoLml0l4l+zGOpy1jixxWb3fTnrX587E1HOzOW5+AbJAAAAMEECQAAQDBBAgAAEPMzSFM3GZdahDNBi4ilMgimSGW7kVUB+3gvOrVYbaJN2OY2FxtNSBWKTLSx/ei9Z5fjSBSKTC3IGPa/4Ou8wGK1jTnHbnwsh9zGbVeZrTjYYrU2rza/n/U6rjKruSBdCNbRhWnddq6ftTy27eNJpkXxXL5IF/w8rSSasdBitdV5vtvM6iX2pYvVumtYTR3ETBStKudptnPHN+jHohlKd2QMukPWrldmLF21N8sp8w0SAACAYIIEAAAgmCABAAAIJkgAAABiXki7aUo5Wz/624Wiz2XOtYurMadWMk+E2Iaz8eFnonmNBrJLKYMGt5vzuOFWttNV1Espw/XXppRUQNUFwn/ot16TfuLhpFarT0x/NUR355PfDG3CSuaJ4Ksr0KVB0rMuPncN2u1cQFXaXG7jabyWvm2RvM/HhyY1TRnWM4N/ZlXw/nx8zHbV60QAs3v4QDYyHWmxPddGHzs/m25TGYZN5UqlUWJBexssrVmVPFOkzhVvVK54YyfV7Vbu3JAn6/uRcWLG28EKRTbN+NrrxoNe5wbzUZO4xi81TjLCWDqy8Lxc4LnmuSd+H5L4DUJqILsfTjTy8W8LRZpzfsrevBg6dub+JopvkAAAAAQTJAAAAMEECQAAQNysUKS7oZe5z615ngyzaOFg7t/H49F74dPH1+xjbuo2aYEwm7/IFFRMFO2quYWt2YZsm0wGInOLWDMamTaL/U9gGMYZukzmx+TMuv0yeZ6yPsp604+XyQ4mCkWGoqKZXVcWFVWtydjV5PCGwbzvsl1rDlqLQA4mEKIZpKaZzuotlkEahnGWc++CJonrbuJc6TSvmlFbcPLUxpJyL+Gh9pXJO7md69vuLrz6uWROH82iZj5PdNyUEhewzUwZruMbJAAAAMEECQAAQDBBAgAAEEyQAAAAxGmk0ioDfO3D7XTfFSHtwQTCU0XyFgompgKq2iZR+2+pQF8m7OnatJlMfWL/Ltg61WaxgGoN82OG/kUpKloZ0m4fbKq2C/QYXWBVz93MuVy5QnoId2aKpS5UKNKdKxruzJyDrp8QLDUBbO3btdGXw4XGdV+ZY66Suc6ZHyqUrpts079kivZOqQxpLzaWlpIollpzUXfFG1Vt8U3dzr4ViTbhXK245pcSx07mB0ajPme1BgAAeA4wQQIAABBMkAAAAMT8DNL1G4ZueqU3FF3wpNV7z67YXiark7innmmj+3cZpFvMX6To/elEG0dvydq8gzRybUK/5l6vbuf6CQXCTN9aEMzlL0KRvMcd6E1VFkttdvLcawtFZhaizWTj5lZQu8m+MsUk9XgSGST7Elb8F9AtRJtZ+FUvWW6RWVc8UvVy7u7Ngs2at7jVxWoPxbyBYZxk1BaKrBlLTgh/Jo7HtOkzY6Bm2Ca2WW3rivpqvskuVpsocqznvCsOrGOgN+e3G4Nz8A0SAACAYIIEAAAgmCABAAAIJkgAAABifkj7ergsE1B1bVYSnHLhwUyQc7VQQFW5XOARC0Vm2mQKRTp6NJvddKjNhViVC8d1kmJdmWCwhvFcQFVXaL71gGptoHquzLmy3Y3/dseWCZ/qY+dn020OF30v3VVFlVM3biv+C7jqTMFCOZ8yRRf1MldK/EHBuour1euPENy+9OUIgd4Sf6hwsEKRh7TUD11qxtIhJULa3abiAp7a9/RjqXqKLnuul5rKYHn4QU+im8z5TaFIAACAG2KCBAAAIJggAQAAiNNYrLbyPnOzjffvJ/uuXaw2I1MgLCNxH1dvt9pYSWbBz4RD5Xky94NtG9m/7+e08xahEKLLdSVOw7BV7Tm3VNYjMQaaxBjsK65MjVusVnefeJpX2+mdZ8bAzuTneslkujzfWjJQW5Ox0yyTO+aV5JsytXdPTRwnptFC/82/1ZcnVSxV/s7keWz1RvkzUXuzzazbm6mVnFlE3RzPZjc+nzO1pF3h4Uym9kn4BgkAAEAwQQIAABBMkAAAAAQTJAAAANEMM4reNU3zjVLK1w93OMCt+oFhGF6ZswFjAM8YxgCed48dA7MmSAAAAM8DbrEBAAAIJkgAAACCCRIAAIBgggQAACCYIAEAAAgmSAAAAIIJEgAAgGCCBAAAIJggAQAAiP8FXfgm2Kt2/dQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 2 Relative reconstruction loss: 0.42423269714704986\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 3 Relative reconstruction loss: 0.7441611122856939\n"
     ]
    },
    {
     "data": {
      "image/png": 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Lc6C6UKTOnaFsNMg7VOfa6Eu6OVlxPeahuNuP58DTXObndHNx9z7wljzuTyM5U6PMEzXKmUZyb7W5TpkD9jUskguqyg6aP+6Yw+MTJAAAAMECCQAAQLBAAgAAECyQAAAAxNYh7cthN5uN1XC1Ld706mNeHDh9LUWhugPfIBnHr8sp9c8vbtuigoFdyoscaWS3c+O0Gxc17U2qfS1tbq6emzbjYpI3TQD7TO7I2lyfTu2bXfl/sLX8bUhlYdYqx1gsNXxcxTEzFUvthpRWozlg2kR+D9IHCmvqMZG7NF230vZTjtXpQqjuE4abnf5QotH4rlUzvisLRVapHbuRQpo74hMkAAAAwQIJAABAsEACAAAQ2xeKzC/591e6ub6nJF90Pcy1yWwjOcmGzbY4WaBKXsXddBs2a3bI7SB9mjSnVE4mzSm5u6WZo8iGzStzzSs5crXvjMahO7D/xuZOCjia6wvVpO3HjVwRSJ06dkPbmpcMl5+TsdqbRhu5Y27fdR3fLhd4rbXK2Okga7Wp+yUHNvUAAAD2jwUSAACAYIEEAAAgWCABAACIrULaJ/eepF/90U8u/tBod+ha333/m/N03CgobIsIBmz++3+anF915nryMJ2mdMfNRa/HXvNSF2Pc+GJI7/7T0537Of3Z/043ChRC+4t//6PtTx4ozJp193p3/sg8ibxEmAxr/4v/kH4C53JjueY4c0zX6jVBHkPXb6TNEEhBt7pm9WIOPNm9o8D1nf78l9P96PvQYAZUYC79wc++PX2uiOl6kyH9L36686W8OP/0+A61mUvtvF0AnyABAAAIFkgAAACCBRIAAIDYrlDkyUlaff2dnU+aP7u3cx+HqDZzNKU2O4QZDDn1j8vNXl9pxs1PQ2NOjuvM2NFie8VGoi+OnD5XzSHuv2kHkkFoba5c0KKGnPpHZxe3a/+bHdlsdLORJmWbTjNIgXyW6yef6PVMduPpU7zvjyFkLtnc24LXqOfPB1wFet9PHQAAwMFhgQQAACBYIAEAAAgWSAAAAGK7kPb5eRo+/vTVbSJFuxoVmJwr7uiCrxpsnSuQ7SwZyF6yKORRWnVpuLneuRu3C3ghUgxQx4YtAjndxpQinDx3SG0oeZ+F6xpxQWA1Z2g7UnCyStel4bWT0W1z8lA/U/p+PFPsEZGwt/5QQcLfKaXUnes8qXzdPbQgfvHeNd1mzvkWmReHgk+QAAAABAskAAAAwQIJAABAbJdBUiZLVBSBmnND27m+640U0lrwe+ZILuiqFI489AxUdz6kk/u7b9Q5Wz6l9uFr9bDXbGDbKO50aCIb0S59/ib9bmQOzJhByufn4z9ENjZt9VpY+/hdk/F9HfAJEgAAgGCBBAAAIFggAQAACBZIAAAAYreQtikCWYSyXaHIGi7sXdN3pIhYqyBlJOQX2Xm6Uegw0o9r0yo4rX27fmvaOHOFvXO/Sps3b1zcrgxyrj877DB6KHhbE8jG0cv9Km1u3dy9n8Dr7snnMoDceKopTmzbyG3C1bMofshlq9seBl6+AAAABAskAAAAwQIJAABAbJ9Bmij8GCkU2aqAWT7dfdNQq1+wuKX53v3QiyWmzjw+efoL++J+mX661bB9mwh3zTVyTt3ZxUaXsz5TkUJ6FWNF965NKaWsw7I3GzbrXp6ReRy5PNfm0OfAdZZz6p5d2uzVbn4qt02b0DMsr4+u0KZml2rbFHOpVdHVfQ9luV+2gCkflVg8LAAAAIIFEgAAgGCBBAAAIFggAQAAiO1D2hPFGSOFIkNloSbC4CmltHp+Fulpe+7cNUXyKrUqDKmaFV0MBLJDTD9locjpNs5s15y60fjQcHNTgSKiLnBd02+nSdJN4NxuJuvUsYHwmQqzYiFdSicTr8+B/3rr3ImMZfsDHx1P/sDJNl3kdTcQPs/pwMa33i/zAwx4fIIEAAAgWCABAAAIFkgAAADiuAtFnvTTjQKFGQuB/FNIo81qI4UjW20yu+8ilZHz7/saL6vKANmO6u5TKANVzIFAv61yCm7P24rruSpavfbtVx6/bkU2NjaK3NueFYUi3V0IvOWE+lnSAb1eHhs+QQIAABAskAAAAAQLJAAAAMECCQAAQGwf0m7A7aSsmoUZ5VyuGFgZqtt3qq6NsugiYb1ddcOQVg+e7NxPMQdc8cbAHAgVt9OUqDtET3VeWVgzcs0ruQB3Kr1ftWO3VT+N6PN+jKHtbpPHcyAS0q79EYL248a7PqeVhXa788ivF6bvV1EYslWN2lryeGT3sciC8yTy/n8o+AQJAABAsEACAAAQLJAAAADEXjJIwFHrL/2/Ys5NiyN5lYqv893mmbrBpj9uuu+sF+SuWTMZR5RJ2Ma+sxaz5Z26lFKkSO9kP4Hs50wbd1s1z5e95quZab2O+AQJAABAsEACAAAQLJAAAAAECyQAAABxsCHtSMBx1SjAp8X2mu1kTjjv6um6lE92D2kfennASCA7clwR2k5p1mD7XCKB532Hshejc2CHfkZcsdTdz/JCq2KSkR8qtLroAytyWiVS2POA8QkSAACAYIEEAAAgWCABAACI7TNIw6Uqb6tyfbXo9/Aznavb1O0uWGx624huOjsnd64lN7k9ug12I2PQbWjZKodT8fjYS5brqR7Lgf9yZTmXzXW0et4b9RPaODjQZsnXx2PcCLcQeP70ftrcW6TfQPY061uDGe+h8R0x02ufzc/N9VGJuQ9Vz9ee8AkSAACAYIEEAAAgWCABAAAIFkgAAABi+5D2pWC2DQE2KsgVkdcz1bnsZ1w3RoqjLRhMrjpX55KJFcF200+3GibbtDpXlZxTd14X4h9302in9cqQ+HQ/ldcTCLF2ei73cF6FInlXVc6pO9tc3HbjS5/jVoFx834SChjXvA9Fprlp060ON3SM7fAJEgAAgGCBBAAAIFggAQAAiJ0KRdpvWjfuj/NYPXu+/UGRzfPmLLAWyCAtWRgyosgp1WSAkisCWfZT08Zpdc1Wg2J/zYr4zdVPq8hWpMCiy2yQOTpsl+egGytFhqzReVsVHjT9FAUeK7NEkTGP48AnSAAAAIIFEgAAgGCBBAAAIFggAQAAiJ0KRTpFYK7R7tV2B2IpFNnZImLdZJvimFaFIqsK9JluAoHVquCyOW7OIpWRvlu1mUte9+n5r725cz837n853Ujn2mCC5hWB/qJQY0op6+8Uzrfu1vfjArOBHyo0KxTZqJ9IYc9Wr3WtNCtGqv2u+/T812/t3lHgcm58+Xi6kbxedxtXvTFQoDdQALaLPMX69lE7LmYqlmrH6VyFWWsLex6II7pUAACAZbBAAgAAECyQAAAAxE6FIqfySLuo+b5c80a1bWbdfJEiYsdtyKl/dLZ7P5FcwiZQdbViOGV3kEY03Dyp2Rg3tJHojHOiUZYi8noUabNkTqlZMVK1yenk0hzQ3FlKsayOO65wHgjDbQKZtkjuLfL8Ra5Zx3zt+L4KxVKP/D7wCRIAAIBggQQAACBYIAEAAAgWSAAAAGL7kPaEIoToitsFwt2RMGNN/CtSTDIURq0JrLrjDqy4HF6t2wypf/hs35dxoSqIGwio2h3atw9gR3Y2t7umH3m48yrrhiGtHj699If5nqua94HsCkUqVzS3l6LCla/NxZjvq7qZjS1yuuBHJUUxafd6dCD4BAkAAECwQAIAABAskAAAAMR2GaSTk7T6+js7nzR/dm/nPlJ6yaaERaPAJoWb8d+abVZbW7As0nXFJqXHYMnNc6tdfs4q8xfNNhKtzcJN9RMpbheZfi5fFDHX5plorzZHWfNa2GrzU9NPkTkKjG+neszj4PAJEgAAgGCBBAAAIFggAQAACBZIAAAAYruQ9maT8r37F7e7uvVVqPhXJFhaE2yt7VevuVVxtMp+NLx8VULbBxnKVpcf62Z5fhMabTXGiuKkpk1Rba/ReHJh72K38zan8ue/vmHvZj8EcFq83hTjwPS55OtaZByG5oU8znOO7wr2/XfBeRJ5/z8UfIIEAAAgWCABAAAIFkgAAABiuwxSzrIRYPnlarER3YzZimKT2Vbc9c2VOTqi72Pxlcvjbt8bddZkNNwxreZSYHwXcb4jmAKRPM8xZSt21mK8RDZIjtDxnEMVTMs/ReZSIIuapfBw9fiumduHlrGb87VmAXyCBAAAIFggAQAACBZIAAAAggUSAACA6LYJFnZd90lK6aP5LgdY1G/mnO9scwBzAFcMcwDX3UvnwFYLJAAAgOuAr9gAAAAECyQAAADBAgkAAECwQAIAABAskAAAAAQLJAAAAMECCQAAQLBAAgAAECyQAAAAxP8BWDGaQN2BUOwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "([0.2774291903373395, 0.42423269714704986, 0.7441611122856939],\n",
       " [0.2036617276441755, 0.14894673309860393, 0.1173055591837687])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_cichocki_factorization(X, ranks, S_trues, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}