{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"../../img/ods_stickers.jpg\" />\n",
    "    \n",
    "## [mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course \n",
    "\n",
    "<center >Author: Nikita Simonov (@simanjan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <center>Self orginizing map.</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Self-organizing map is type of [neural network](https://en.wikipedia.org/wiki/Artificial_neural_network) is trained using [unsupervised learning](https://en.wikipedia.org/wiki/Unsupervised_learning) and also is an one of Kohonen neural network. The idea of creatins the such networks belongs to the Finnish scientist [Teuvo Kohonen](https://en.wikipedia.org/wiki/Teuvo_Kohonen). Basically that networks perform clustering and data visualization tasks. But they also allow to reduce multidimensional data into a space of a smaller dimension, and are used to search for patterns in the data.\n",
    "\n",
    "In this article we will see how to solve the clustering problem with help of the Kohonen network and will build self organizing map.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <center>Kohonen neural network.</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main element of the network is the Kohonen layer consists of a number of linear elements has m inputs. Every layer get on input the $x = (x_1, x_2, ... , x_m)$ vector from input data. The output of every layer is\n",
    "$$y_j = \\sum_i^m w_{ij}x_i $$\n",
    "\n",
    "- $i$ - number of input.\n",
    "- $j$ - number of layer (number of neuron).\n",
    "- $w_{ij}$ - weight of i-input j-layer.  \n",
    "\n",
    "\n",
    "After the $y$ of each neuron is calculated, the winner’s neuron will be determined according to the “winner takes all” rule. The max $$y_{max} = argmax\\{y_j\\}$$  is searched among all and then the output of such a neuron will be $1$, all other outputs will be $0$. \n",
    "If the max is  reached in several neurons:  \n",
    "- all signals will be equal to $1$.\n",
    "- the first in the max list will be $1$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <center>Kohonen map.</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - **Inizialization**\n",
    " \n",
    "     The most popular ways to set the initial node weights are:\n",
    "\n",
    "     - Set the random values to all weights.\n",
    "     - To take random $x_i$ from the input data and set it into weights.\n",
    "     - The choice of weight vectors from  from main components [(PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis) of the input data set.\n",
    "  \n",
    "  As a result, we get $M_1^k$ is the map of neurons. $k$- neurons, their count is sets by the an analytics.\n",
    "  \n",
    "      $N$ - number of input data.\n",
    "    \n",
    " - **Trainning**\n",
    " \n",
    "    Initializing $t=0$ is it number of iteration and shuffling input data.\n",
    "    \n",
    "     - Choosing $x(t)$ from input data. \n",
    "     - Calculate distance  $$d_i(x(t), m_i(t)) = \\sum_i^n \\sqrt{(x_1^t - m_1^i(t))^2 + (x_2^t - m_2^i(t))^2 + ... + (x_n^i - m_n^i(t))^2}$$ from the input vector to all neurons of the map. Here:\n",
    "          - $d(x(t), m(t))$ is [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance).\n",
    "          - $x(t)$ is input vector.\n",
    "          - $m^i \\in M_1^k$ \n",
    "     \n",
    "     Аmong all the neurons, it is determined closest to the incoming vector $d_{min} = argmin\\{d_i\\}$.The neuron associated to the $d_{min}$ will be the winner. If $d_{min}$ is reached at several neurons the winner will chosen randomly. $m_w$ is  winner neuron.\n",
    "     \n",
    "    Kohonen maps, unlike networks, use the \"Winner Takes Most\" algorithm in training. In this way the weights of not only the neuron of the winner, but also of [topologically](https://en.wikipedia.org/wiki/Topology) close neurons are updated.\n",
    "   \n",
    "     - Calculate $h$ function that defines the \"measure of neighborhood\" for neurons. Typically $h$ is the [Gaussian function](https://en.wikipedia.org/wiki/Gaussian_function). $$h_{wi} = \\alpha(t)  \\exp(-\\frac{||m_w - m_i||^2}{2 \\gamma(t) ^ 2})  $$ \n",
    "          - $m_w$ is winner neuron.\n",
    "          - $m_i \\in M_1^k$ is neuron from map.\n",
    "          - $\\gamma(t)$ is the value function the number of neighbors. The most commonly used function is linearly decreasing from the training itaration number. The value on the first iteration sets by the analyst. Also in simple version $\\gamma(t) = \\alpha(t)$.\n",
    "          - $\\alpha(t)$ is learning rate function. In simple version is constant. But for the best result, the function is often used $$\\alpha(t) = \\frac{A}{t + B}$$ here \n",
    "              - $t$ is number of iteration.\n",
    "              - $A and B$ is constant.\n",
    "          \n",
    "  - Change weights.\n",
    "      \n",
    "      Calculate $m_i(t) = m_i(t-1) + h_i(t) (x(t) - m_i(t-1)),     i = 1,2,..., k$.\n",
    "      \n",
    "      \n",
    "  Update the weights of all neurons that are neighbors of the winner's neuron.\n",
    "  Increase $t$ and repeat learning.\n",
    "\n",
    "  Training continues until $t < N$  or until the error becomes small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/9/91/Somtraining.svg\" alt=\"Self orginizing map\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Self-organizing maps uses in [data mining](https://en.wikipedia.org/wiki/Data_mining) like a text analysis, financial statement analysis or image analysis.\n",
    "\n",
    "The advantages of self-organizing cards:\n",
    "    - Dimensionality reduction.\n",
    "    - Topological modeling of the training set.\n",
    "    - Resistance to outliers and missed data.\n",
    "    - Simple visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization of the work of the self-organizing card.\n",
    "<img src=\"https://annalyzin.files.wordpress.com/2017/11/som-iterateanimate.gif?w=620\" alt=\"Self orginizing map\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see small example.\n",
    "\n",
    "First we should install the 'SOMPY' library. The \"SOMPY\" does not have an official documentation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://github.com/compmonks/SOMPY.git\n",
      "  Cloning https://github.com/compmonks/SOMPY.git to /tmp/pip-req-build-e6f2f3m_\n",
      "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.5/dist-packages (from SOMPY==1.0) (1.15.2)\n",
      "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.5/dist-packages (from SOMPY==1.0) (1.1.0)\n",
      "Requirement already satisfied: scikit-learn>=0.16 in /usr/local/lib/python3.5/dist-packages (from SOMPY==1.0) (0.20.0)\n",
      "Collecting numexpr>=2.5 (from SOMPY==1.0)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0e/5b/f26e64e96dbd8e17f6768bc711096e83777ed057b2ffc663a8f61d02e1a8/numexpr-2.6.8-cp35-cp35m-manylinux1_x86_64.whl (162kB)\n",
      "\u001b[K    100% |################################| 163kB 729kB/s ta 0:00:01\n",
      "\u001b[?25hBuilding wheels for collected packages: SOMPY\n",
      "  Running setup.py bdist_wheel for SOMPY ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /tmp/pip-ephem-wheel-cache-8zh2ikor/wheels/cc/5f/3e/4c08f1ca381629d98f50c9ba04bd95c9e704dc37ebdf301c1c\n",
      "Successfully built SOMPY\n",
      "Installing collected packages: numexpr, SOMPY\n",
      "Successfully installed SOMPY-1.0 numexpr-2.6.8\n"
     ]
    }
   ],
   "source": [
    "!pip3 install git+https://github.com/compmonks/SOMPY.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also you may need to install ipdb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ipdb\n",
      "  Downloading https://files.pythonhosted.org/packages/80/fe/4564de08f174f3846364b3add8426d14cebee228f741c27e702b2877e85b/ipdb-0.11.tar.gz\n",
      "Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from ipdb) (20.7.0)\n",
      "Requirement already satisfied: ipython>=5.0.0 in /usr/local/lib/python3.5/dist-packages (from ipdb) (7.0.1)\n",
      "Requirement already satisfied: pickleshare in /usr/local/lib/python3.5/dist-packages (from ipython>=5.0.0->ipdb) (0.7.5)\n",
      "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.5/dist-packages (from ipython>=5.0.0->ipdb) (2.0.5)\n",
      "Requirement already satisfied: backcall in /usr/local/lib/python3.5/dist-packages (from ipython>=5.0.0->ipdb) (0.1.0)\n",
      "Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.5/dist-packages (from ipython>=5.0.0->ipdb) (0.13.1)\n",
      "Requirement already satisfied: decorator in /usr/local/lib/python3.5/dist-packages (from ipython>=5.0.0->ipdb) (4.3.0)\n",
      "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.5/dist-packages (from ipython>=5.0.0->ipdb) (4.3.2)\n",
      "Requirement already satisfied: pygments in /usr/local/lib/python3.5/dist-packages (from ipython>=5.0.0->ipdb) (2.2.0)\n",
      "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.5/dist-packages (from ipython>=5.0.0->ipdb) (4.6.0)\n",
      "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.5/dist-packages (from ipython>=5.0.0->ipdb) (0.8.1)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.5/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython>=5.0.0->ipdb) (0.1.7)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.5/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython>=5.0.0->ipdb) (1.11.0)\n",
      "Requirement already satisfied: parso>=0.3.0 in /usr/local/lib/python3.5/dist-packages (from jedi>=0.10->ipython>=5.0.0->ipdb) (0.3.1)\n",
      "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.5/dist-packages (from traitlets>=4.2->ipython>=5.0.0->ipdb) (0.2.0)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.5/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=5.0.0->ipdb) (0.6.0)\n",
      "Building wheels for collected packages: ipdb\n",
      "  Running setup.py bdist_wheel for ipdb ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/a8/0e/e2/ffc7bedd430bfd12e9dba3c4dd88906bc42962face85bc4df7\n",
      "Successfully built ipdb\n",
      "Installing collected packages: ipdb\n",
      "Successfully installed ipdb-0.11\n"
     ]
    }
   ],
   "source": [
    "!pip3 install ipdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import all necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from time import time\n",
    "import sompy\n",
    "np.random.seed(17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a \"toy\" dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zpqaDVRsPBCBY165pi3xbW26MC1yLIm6EYkVIhijRchKOw9TkE9mfRT7TNN1BreUH/Sy0spOjhLUBCckCh6nJJxJvViguxsbhjz8WeestvSamyLSG30Fr/R30sxymtiAh6WBkRUgWOEhNPmsEYjUr9PVhT5e12eLt29hEHAyi/UayChhWDprOO+haFNOH5CihWBFyzCQzMFgFIhxGt13VbHF4GC046upg8rhzB2WfUqXpDpua3C/HfT9SWDANSEgWiETg3LtxA4/WtJ2dTPZj2fcwmSbcgePjqKAxNSWytJQ6TXfY1OR+Oe77kcKCkRUh+yCZicAeLVnTdnazQSYRiH0PU38/RHBvD9ft6MA+rlTpuuO2lqv7qe/onXdotCDZg5EVIfsgmYnAHi35/cnNBt3dqIIxPIxIKRpNjMTszRZV63gRXLOnJ7drQqkiSRotyFHAyIqQfZDMRGCPlkwzearPGmXEYiK3bsFQ0dKSPBLxeNAafr9rQkdlJ0+1cZhGC3IUMLIiJA2RiMjQkMh/+S8ib78t8vvfY91oeRkT8Y0biI6qq/V6TX9/6rp5o6NI6dXUwPU3PJw+ErFHZPfvY1wqugmHE6Odo4pyUq27sWYgOQoYWRGShkAAk38shrp98/Nw49XWQmzq6jApNzToCuZOEY0Vw8CPCESnsjJ9JOLxYL3qzBkcZ28GOTaGKhrWdbPRUYy7vh4t67MV5aRad2PNQHIUUKwISUMwqKMgEVRF7+rC78nSXenMDX19EMBQCNFSS0vqSESJ3xtvYO9VVVVipYuREQiZej48rEVxdRX3UvdNlRbMJHXY3Q0xvHVLfzdzc6hpmGnzRkL2A9OAhKTB50NViVAIqbbiYrx2mHRXby/WoB5/XOS73xUZGEht+VbpvFOnkDacmMA4iov1/Ts6Eu3uPT0QVCvp0oKZpA5VlHf2LI6bn8f4aaggRwUjK0LS0N2NNanRUUQpqqitSGIEkqmhYb+2cmVaqKrC85kZkRdewO9ra7jXwABETN374kWkK/f2IGp9fRDbdOaHTA0S6rjxcaRHQyHdMoWQbEOxIiQNyok3OJj4nl1wnCzr2UiJWdeImpuTp9qsrw0Npb5Osmgw00oU6rjycgh5eTkEjilAchRQrAhJw37s36miksNUS09nWnA6JxyG+UKVbFpbE3n++fTmh0wNEuq4e/cQ8Z08mdn3SchBoFgRkoZMmxGKpI5KMq1ykUntwEzGeNBafdYUZSqBVccFgyJPPx0vioRkGxosCElDJrX8FKnq49mvMzqaWTWMTNaAnM5xGkum+65UhYqf/QzpxMrK5MdzXxU5DhhZEZKG/UQoqYwT9usYhrMoeb2wnVuNEQcZo9NYMjVPKFGLxTDOyUk4/5yO574qchxQrAhJQ7YmY/t1qqtROX11FXb09nZdY29nB69tbSGq6e1NXSYp0zE6iZpTqk+JWn09xre6mlyoj7tgLilMKFaEpCFbk7G9KnkwCGu5aWKPVG0tXg+HIRKlpWgLMjeX3lWY6RidRC3VeldnJyzpImz7QXILxYqQY0aJQ1MT0nxtbWi0GItpEfH7YVHf3MRG4GztXbKLmqp7uLcXX5LJ6hocHIw3VhxVcVxCUkGxIuSYsa4btbVhg++5czrN1t2NOn9TUxCqhoajMy1YDRNra4iiBgdTR2r76d1FSLagWBGSJVJFHNb3gkGsSTU2IpopLtZVKNQ5167pQrQbGzguEslMBKz38nrxWjjsLCTBIEoyTU5iXco006f67CaN4WHUJMz2RmhCrFCsCMkSqfZjWd/b3UUUVVqKArZXrkBA7GInAgeeMkPYRSCTrsX2yuz2a6i1qbNndeX4dIK4n95dhGQLihUhh8AqGJ9+KvLkk86TtjUaOXkSldtVOxGFXew+/VRXoKiowNpSOmOE2qSr7mWvzB4MJkZeqg+X1UWYKjpTFefVWFTvLiVeXm/6yu6E7BeKFSEZ4hTJWAXDMODuu3w50eadyV4te3rNNLUIjIygsvneHtaz7t6FS1C1C0nWtbi4GK+pDbter8irr+r1sN1dGDlSCac9OhNJHeFFo0dTH5EUNhQrQjIgEkmc5EXiBaanB2Jlj1JEMtsHZRWZpSWI0Pg4RHB6GvetqcF1Pv1U5KmnsBdLBIKjBFDda34ee7TW1iA4SjCmp5F+DIfxvMRhFkgXnVmxmzFu3GBbe5J9KFaEZEAg4DzJWwVmawtOOqcoIpN9UFZBC4WQLnziCUREU1NaVFRnYZWOm5nRxW6t9/L74dJT11COvbY2mDYqK/W5dlJFZ+mciQetSUhIKihWhKTA2qF3dxciUlMTLxDZKjVkFTR7dNLaqhtAer0QzZKS1O1CnEor+XxwIoogKuvoSF95XV3bKWK0fkfq+vY1LW4kJtmAYkVICqwdeqemEFU9eKAn+aMqNWSPTgYGdGR0+TKOSSYeya5hPba0ND4aczJEZPq57EYPEa5RkexDsSIkBU4deq9eTe1wy0aFB6eILVvXsAvJ0BDEam8PnYajUedGk8lQ39HursjCAswg6v50AZJsQbEiJAWZdOh1csNtbBzODZeNiM3jiResQMBZQPx+rEfV1CByvHVrfxUp1He0sIDU4qlTdAGS7MN+VoSkIFV/KoU9Deb373+TrOofdeMGHlX19cOSSf8q08SP+n1uLrOeVwr1Hc3MwLzR1UUXIMk+jKwISUEmEU6q/VGZuuGUqFRWIi03NJRYQPYgZNK/6uJFkddfx/4trxcuxP2IrfU7WlmB8YMuQJJtGFkRkiHJoh97p9z+/vTRmB3V6XdyEmtHe3vpo5pMorFUXXzV+X4/BLazE4/z89iXtb29P9HJJAol5KAwsiIkQ5KVN9qvGcLJgKHWfVZXsQm4ri59VGMdz/w8IiOfL34Mqaz16vziYqwzra/jMRrF+3fu6OguE9iEkRwlFCtCMiRZSm2/k7ST6ClRKSqCWHV2po9qrONZXcWaUUcHUog3b4o8/XTyPVjW81U34Pl5mCyamlDYdm2N4kPcA9OAhGRIqpTaflApP1Wg9uZNkXfewXt/9meIZjY306fSrONRLrzJSTyPxdKnEdX5nZ2Irqqr8ZiJUBJy3DCyIiRDslWtwmqHv3NHp/32u6HWOp72dlzvs8/wXn19+jSi9fzBQZEf/AD7rFh5grgRihUhGZKtNRmrSIigAO5Bir5ax6PWwSYn8TyT6Mjp8zDtR9wKxYqQY8YqEirKKis7XOpNXTObtQoJcRMUK0JySLbFhY488lWFYkVIDqG4EJIZdAMSQghxPRQrQgghrodiRQghxPVQrAghhLgeihUhhBDXQ7EihBDieihWhBBCXA/FihBCiOuhWBFCCHE9FCtCCCGuh2JFCCHE9VCsCCGEuB6KFSGEENdDsSKEEOJ6KFaEEEJcD8WKEEKI66FYEUIIcT0UK0IIIa6HYkUIIcT1UKwIIYS4HooVIYQQ10OxIoQQ4nooVoQQQlwPxYoQQojroVgRQghxPRQrQgghrodiRQghxPVQrAghhLgeihUhhBDXcyixMgyj1TAMv2EY24ZhlGRrUIQQQoiVw0ZWqyLyTREZysJYCCGEEEcOFQ2ZprktItuGYWRpOIQQQkgiR7pmZRjGjwzDGDEMY2R5efkob0UIIeQrzJGKlWmaPzVN85JpmpdOnjx5lLcihBDyFYZuQEIIIa7nsG7AUsMw3hKRHhF5wzCMgewMixBCCNEc1mCxIyJXsjQWQgghxBGmAQkhhLgeihUhhBDXQ7EihBDieihWhBBCXA/FihBCiOuhWBFCCHE9FCtCCCGuh2JFCCHE9VCsCCGEuB6KFSGEENdDsSKEEOJ6KFaEEEJcD8WKEEKI66FYEUIIcT0UK0IIIa6HYkUIIcT1UKwIIYS4HooVIYQQ10OxIoQQ4nooVoQQQlwPxYoQQojroVgRQghxPRQrQgghrodiRQghxPVQrAghhLgeihUhhBDXQ7EihBDieihWhBBCXA/FihBCiOuhWBFCCHE9FCtCCCGupyTXAyCJRCIigYBIMCji84l0d4t4PLkeFSGE5I68EatcTOBO9xQRuX1bxO8XMU2R/n6R3l6MJdnx+x337dsit26JzMyIPHggcumSyPe+p8+LRHDM6KiIYYj09WEMB7kXIYTkA3kjVkogIhGRxUWRxkaR5mYRr1ektTV+Ys6WsAUCIisrInV1eAwE8LrfLxKLQaz8fpGyMghGICAyPy+yuorXx8ZEHn1UZGMj/hp9fanH6feLzM5CiDwekeFhkYsX9XmBgB6DYegxiCSOV51DCCH5TN6IlZqcNzbwMzcncvYsBOOjjyAMr7wiMjEhcvOmyN6eSFUVoo+7d0Uef1wkHIYodHXhOLtI2MVjfh6iWFQkUluL10VwnBpHUZFIRwdeDwYhVBsbENLPPxf58EORtjaR+nqRzk59DRFnMezrw2fa3MQYtrZEKiri7z00hGObmnDtrS39fl1d4ngJISTfyRuxMk38bGxg8l5ehiAtLyNNNjUlcv26yMmTELXZWZx3/rzI734n8vbbItvbiEQefVSkpUVkdxfnv/++yNe/LnL/PoRvbw9iVlmJiKW2VmR9HeIhgsju/n2REycgFKEQXvf5IKrNzRAb08RxIiJrazhucFB/pmDQWVz6+5ECXF6G4La16Xur6O7ECYz1o4/weS5fxlhXVhLHSwgh+U7euAH7+0WKiyE229sQoelpTMihEITnrbdEvvwS0c3HHyP6CoXwODmJ6CkWE3njDYjZ/fsQv5kZTPKvvYb3a2rwuL4u0tAAoWloQATW3Q0xqqgQKSnBWpHXizF2d4u0tyMiU6/19SGqUmKr1rFEMPb1dX0vJS69vSJ/+qciTzyBqO3JJ0WiUZEbNxA1nj8vUl6Ozzk/D3GNRnFt+3gJIeSrQN5EVr29iBw6OiBAFRV4HoshvWcYIo88grWtsjIIxPo63jNNkdJSTPAiOuKJxRChGAaikfv38Z46pqTEec3nuefiI5iGBrzu8Yhcu6ZTicXFEL7GRhzn8Yi8+iqiwI4OkRdfxHkq7djVhchMPf/zP9drWCpdaBgi9+5BnM6dw7Xr6pDqfO45rlERQr6a5I1YeTyJE/G3vgVheOMNkVOnENX8wz8gBffCCxCjxUWRp55Cumx1FSm38+dFqqvx/vY2hHB9Ham0zU2IYXFxohFibg7iV1GB9F8kos0d1uOU2AwOxq+N3b2LSKilBVHhm2+KfP/7+txXX8XrbW0iOzt4va8vPl3Y0yNy5w7uX1mJYx88gIg5jYGOQELIV4G8ESsnrAK2sgIR6ezE8+5uCNCTTyJi+eUv4ao7cULksccwqauIqqYG60M1NTh3fR2ptU8+wXVDIby3tob0YVsbUoHV1Tj+nXcgDNFovPNPJF5g33gDQlVSgmjs97/HeercqSm8v7GB40tL8ejz6Uhuawsi2NeHiGtrK15Y7aYNFWlSvAgh+UxeiVWyqKG7W79+4YLIF19g/aqjQ2RgAOtHP/whflRKzZ7CU2I3NIR0YFMT3p+exvUeeQTi1NyM6OrcOeyFOntWC8P4OKK4ZG68jg5cr7kZLsHqan3up58iOgyHETHNzuqIrasLbseREf2ZPJ5EERJJNG3cugUBt9rpr12jYBFC8ou8EqtkVm8VYalU2vw8Jv6aGqThrNGNkwNvZweT+UcfQZja2pA+3NnB2lUoJPKrX0GwLlxAZLa+riMzdS3DwOtObrxIBGP68EMYOk6cELl6VZ9rmlo4Z2YgSkqAJibgcjxzRmRpCVHi/ft6Q7A1WrJGYWqMVjv91BT3XxFC8o+8EqtkVm9FIJA8laawT+Y+H6KN2Vmk/kTwekcH0nTV1RCa9na8vryMta2GBrynxGl5GRHRp5/qyhZWN14ggOtcu6av8+ABhGlmBmtf9fVIESrXoRIg6+deXUX05/VCsKancYyyxHd36woYhoExTU1BKDc38cj9V4SQfCOvxEoJTUUFTAamqS3i4TCEoqkJk7I9laawpgyVA++3v4XVfWEBr3m9WAc6cwapvUcewWvV1Ui9ffvbuJbVeKHG09EB0Skri0+12YU2EoFozc5CQNQ5V68mfm6vF+ttah1tYwP7r3w+XHd0VIuVSg/udU5JAAAgAElEQVSePatFdGZGR5sNDdx/RQjJP/JGrCIRmBDGx7GXqqUFm4H9frx/+TIm83v3EFGomnp2sbK7Cv1+TOTb2xC4YBBRzre/DSH7yU9QiaK6GgKmqlXYr3X7NsZ3/TpeV5FVsvRcaytE5cwZCFgsllnEU1WFFKUIPmNVVaITULkjq6qQPuzpwf3s61uEEJIv5I1YBQJIgXm9EJdQCJHM3BzWYoqKMDk/eIDSSsXFeExnJAgGcZ3ychxbWipy+rQWoR//WDsJq6ogApFIYtQkAiEtLYUYzc/Hrw3ZIzr1XNUSnJ1FqtF+bRFEjZcv4zNub2M8Dx4gwmxtTXQCnjqlK3g0N8cfQwgh+UjeiFUwiIk4HIY54fPPkfJ78ACmg1gMjwMDqPwQi8FqngwVhUxMYE3H44GhorxcRyoiEMeLFzHhq6jIblDw+XBOWRnEpLUVY7PW83Oqxh6Nirz3HtaeLl7E+3/5l0jpJYvKtrZE/sW/wL3Uvq+VFUSIc3O4b1UVzpuZ0etfhBCSz+SNWPl8WJtpaUGE8cgjuvr60hIin5YWrMko4QqFUKLIaX+RikJ6elDCaHoakU1lJSIWNfmHwyKffQb7d1WVs7GjuxsmjYUFjKGlBfey1vNzqt6+sQHnoWHofV57e4kV052iMhHcU5knDANj9XgwxuZmHMeIihDyVSBvxEoJwvS0FqitLaTQLl9G6k4ZIObnRT74AKK1t4e6gSLJLexf+xoeq6sRmYRCEAy1CVgkPq1mNyioMkuPPhrfYyrZ3ierXX52FmlI5Sqsq0us8J6sjcj0NIQxHMax9fUQa+uxrGhBCPkqkDdiZa279+67mIRjMURBSgTW1uCm8/tR2qixEXunTBPRRzSq24R4vVogVlbwvL8f0dHSEl4PBJDWUxHc734HYauuxnXsbUYGB+OrqitUu5HFRRgxVPRWVIQ9Wx9+iM9TVIQIzrpHK9nesmAQYxobgxhHIiL/5t8kRlLWuoLscUUIyVfyRqxEtPsuGBT5oz+CoWFtDRHK8DCO8fmQGtvZEfn1ryE2e3uY1MfGRJ5+Gutc6+v4mZvDNVpa8LytDetW6+tI0335JaI2tWZ0+TLeu34dabtMKkOoqNDvh1A1NWHsra0Qmd5eXGdnR+QXv0Bk1t4OcQ0Gcbx9b5nPB+djLIbPo0o/2SMptY7FHleEkHwmr8RKoQwHnZ1I2S0tYeL3eET+6q90FYrNTQhLZSU226qGiZOTcO2pIrBVVXje0IA035kzuvNuZyeEYGsrvhHjyAiitUwqQ6j1q8cfh8Dt7GBsk5O6FFJFBe4zMaGjLr8f91LrUNaIq7sbm4NPn9aNHcPhxEjMuo7FHleEkHwlL8XKajgYHMTep8VFOOs2NnR6b28PwrOxAYGYmkLa7YsvRK5cwUTe0ICNtkVFcM/94AeIdFSEpCIqFbkp80Yshs3E7e0QhnSVIXw+CFE4jLWmqipEczMzENzvfAcpQtVzy+vVBXTt61AiOG5wMLEah1of291FSnNqCsfbK8QTQkg+kZdi5bSx99e/hgW8vBwTfDSK9aVoVFvY6+vh/Kup0ZUvbt1CJHPqFPZmWStPWEVR3U91/L1wAaIyOamrXdhrAdorZUSjIn/4A845fRo/Z87ADLK1hXFFIhhPOIzrqnqBTiTbu7WyAqGanYWQnzyJ63CtihCSr+SlWNlRDrf1dTyvr0dU1d6Oibq+HtGQCFJ2sRjScPX1EIamJt3f6o034q/pNMH/wz8gdVddjfTb3p7u+KvOtafjRBAJlZUh5RiLIeK7cwfGDhGUfCorw/ViMYjtJ5/odaexMaT+rPuw7ONT91Zpyq4upEC5VkUIyWe+EmLl8WBT7dYWBCAcxkQ/MIBJuqwM7z36qE6r+XxwDra06EhkYQETfDrXXCiEqKW5GQaOoiKIonIWqmjHalefm8O5b7yhDQ+q2G5vL8554gmRZ56Jb12imjWOjWlXY6rx2Xt8lZRwrYoQkv98JcRKBJPx009DqLxe3RZjbQ2CMj2NlF9Dg8if/AnSbyIHi0S8XpgzQiE8Tk7GtwqZn8f5/+N/IC2p2pV4PPh9agrCI4Jz3noLr/X0JLr22trwOXZ3YQapr0/u6rOmHlXh3cVFfCfRKNKl3GdFCMlHinI9gMMSiWASnprCpP7kk4h4SksxqYdCqA6xs4OU3e4uLN8KFYm89BLOyyQSaW3FsQMDeFT9rWIxPIZC+KmoQLpR1TKsrYUYmiZMHiL4fXUVv9+5o6+hOgjX16O81JdfQoQiEaQ0ncZnTT1ubCCibG1F1NfYGF89gxBC8om8j6xu34ZY3b+PNZ+7d0WefRYGiPV1CMYHH2AdqKYGr3s8uuqDIllJIyfsnXtffFGLic8HQVlZ0dcIhRAxKZdiaanI17+O9ai9PQjLH/8xxr+2Fl99Qu3Pam/HfRcXIV5PPSXy138Nl2JlJarEb2zE2+tV9JWqBxghhOQDeS9Wfj+ike1tTOJbW3pT8OOP6wl8dRXHrK8nOvdEkpspnFCdezs6EA397d8iBfn88zrFNj0NQTRNuPr6+nQb+vZ2LRqqNcm9e9poMTQE16LHg89jmhAbjweW+7U1kTffhFiWl0OoX3sN63RlZYl7quz2dkIIyTfyXqxMEz8bG5jYt7dhSJifh+ni/HlM4u++i9JLRUUi3/rW/vYb2W3o8/MQwPFxCKVpxpseurv1GpFp4jXr3q1IRORnP9NV4ysq9BrWH/4AM8b9+2gmef687tM1O6st7ffuIWVZVYXz1TqV056sTCNGQghxK3kvVv39EAXDQLrta1+DaLW1xYvLo49iklfVyQMBbTZIV+zVbkMPhRDBqLUmq+nBeq3aWqQeVTUMhceDSExFPMvLiHrefBNRlceDyG1rC6nBixdxHVV544knED2ur6P6fHk5xpasbxX3VxFC8p28EatkgtLbCzFob8eEbppwwdXXI9JQPaPUvqrSUqTRioqwHqSqPtTWQiCcbOH2qhATEzBIzM7qdTCVYlPCpiq2t7VhfPZrWtfI1tdxHREIYF2d3se1uYnoraQE19nc1CWe1P4uwxB5+WVGTYSQry55I1bJqo+rtaa+PhgdrIIWjeqeUSKIRqqqkGprasIkf+YMIrO9Pbym9kRFo7rdR2UlhGplBddYWoJ4nj6NtNvHHyNS6u4WeecdjPGTTyAo4bDIuXOJxgbrGtmNGzinvBxj+PRTjKOqClFUNIq0YyiE4ycn8by+Ho0Y19ZEnnsu8TtjexBCUmPf7iGiOzMk+3txw9+VG8Zw3OSNWDn1hLJjN0koEaivx4S+vIzH9nZdRLaoCGI2MwNRWVqCaWJhAWtBzc0QqtJSHKPWlk6fRvTT1QWBUZHSxATer6jANdra0hsbVGHeBw/w2R5/HBHUo4/CJXjzpq4GHw5jHKpae6prJxN4Qgiw/o2o+p+XL6f+e3H6u7JmSqyil6kAZooSqaEhPO/pOfjf9kEFL1dCmTf7rHy++L1Mmbja1DlKlMrL8bvXi0cRXK++Hs4+VffPNPHlK+OGYeBaL72EdJzPhxTi+jqiNK83vvOwCP7nbG/XDRFTpei6u3XFisceQ9T09NMQrdZWCOO5c3AblpXhms3N8ddW+81u3MBjJKLTm7StE+KM9W8kEkHm5NYt/ENzfj79OaoIwKuvojrN4iLO9/shZn6//j0b+xzVPLO3hx9VkOAgf9vJOpgf1XmHJW8iq/3sg3I6Z3AQ//LY2MB/XNX2XvWyunIFAnXjBv4nuH8fEdXiot6gq/pSvf8+bPLl5bqPlDXyu3xZN4LMBHuJJFWEtqEBgtjfD5EaGoIAfuc7MF9Yi9M6NVlUERtt64Q4o/5GKiqQUVlbw9/12hoMWanOUX9XoVB81+7ZWVSqKSrCXCLi/A/Gg0Qoap5R2aLV1dR/26nukUm2KtUYjvsfwXkjVvvZB5XsHOt/OKtAWfH5sEfr7FkIQHW1tqOrbsVrazoiU32ksiEMSlwjEfyhqDqG6t7W/0nUni2F0/9Azz9P2zohqVB/c0NDuvtCMIi/PVUZp6ND5JVXdErP/g/nSESXRausxD8ki4sxRyjBc8oIHSRNb+/lZ5qpMzep7nHQOStX/wjOG7HKBpkInvqPXlqK0k32f+3YbefqP9ZBIr9k40s2xlT/kzi9dxCBJ6SQsHYfr67GPzy7ukRefx3p+JYWRE3Xr4t8//vx54joKjOTk3i+uipy6RJS+IuLEK/1dayHqX/0Kg4SodizRemisWAQYxgfx3gWF3Vx7NOn4yvxDAxk9p1lY647CAUlVpmQqaA5hdZHLQyp/ifJ1f9AhHwV8PmQ9heBgUkEezZLSnQncCcCAURhnZ1I/7W3I/vi8SAy83jg6F1fj++Vp+6ZLkJxSuPtZ57x+RA17u1BqLa2IKhlZRCqkyfhiF5fhzksk2vn6h/BFKsDkKv/WKnuyyiKkIOj/nFXUoLfW1sRgTQ3Yw25o8P5vGAQEVhzM0xQa2uZrwk5/QPTLk5q+00mqUInYevu1s5Bw4BwBgIQp08+EfnX/zp/DFgUK0JIwWP/x15XF1J/1jUrJ+zRkdeLiCoYxM/uLqIXp8jJnk4MBLBNZXcXKUm/H+d95zuZCUqy9anBQT1G1RndMLCP884dGMLywYBFsSKEEBter16jikR0dwfThDtX1fq0R0fRqBaMnR2IgNrukio1r4QmFkNRAsNApDYzA0Hp6cGjSHLnoFMkF4nA2fz22zBk3L+PqHF9HQYxJcYtLTqyu31bF0Sw1zXNJYcWK8Mw/kJELomI3zTN//XwQyKEkOMllcU7ENDdHUwTv5eVxVfQUahCBEVFSA+Wlma2hcVqSR8bg31+cxPX3t5GmyPDSL4JWO2r9PvhTKyvhwAFAijg3dYGp+PMDEwkJ09iDWtzEyKmnNFqX1gshvtZP2uuOZRYGYbRJyJVpmk+axjG/2MYxmXTNIezNDZCCDkWUlm8g0HdD08Ek3uydFwy04QSQ9WI1etFhKNE0WpJv3sXIqK6KDQ3p962osZfW4txzszAMn/lCsq/Wcfe1CTy2Wd4ragI9795E2MdHMT4Un3WXJZ5OmxkNSgibz78/S0ReVpEKFaEkLzCavFeXcVkbxWSiYn4/nTJ1nfsjVmVHVyJ4eqqLnBdVKSFKRqFGPl8It/9Ls5R+zfVNe1Rk338J09CjKxGDzX2UAiRkscj8swzsOM3N+O6xcWoxDM6imIHjz2mjzdNXPvGjf0bPrLNYcWqVkQ+f/h7SETisrKGYfxIRH4kItLe3n7IWxFCyNGgLN6xmH7t9m1EMfPzer9USUnifimRxJp9zz0Hm7iyg6vIaHwcIqGKUg8PQ1yKiyFaFy8mTv5+v3PUZB///LwWw/Z2jEn11rOuQT3xhDaPKDHa2cE4JiexrtXQgOOrqxFlKXEaH0eX8lw4CA8rViERebivW7wism590zTNn4rIT0VELl26ZB7yXoQQciSodJhqMRSLifz855jYe3ogWnYhsabEVNULVV5pchJVcNRkrtJ8Xq+OrGZm4MwrKcGa1NwcIp6uLl0tIxLBuFTFnOeeg6jZU2+qFNzMDNamamt11KPaKAWDeq/XtWvafTg2hjZH29voj1daKvLv/h2ua12Dq62FgK2v56aE22EL2d4UkW8+/P2KiAwd8nqEEHLk2As/i6AyjYpylpYgJLFY8mKx1nWuqSlENfX1mNDtNftUser6ekQ9dXVIE7a1QTDm5iCSX3wh8h//oy5GHQjgeuqad+44C4RK+V25AoE9eVKP12k9ThlD/vzPESnNzGC8X3yBzxyJ4Fx7AfG+PnyOtbX0BbqzzaEiK9M0/YZhbBuG8Z6IfGCa5q0sjYsQQo6MVG0+RkYQnTQ3Y30mWbFYq+nh1ClETM8+61yzz2nTvrKJv/02orKKCrgANzb0mIJBRHaTk7ozeTKBcDJ3RCJITUYiiMgqKrAupdbjPB6RH/9Y5Cc/QYqvogIpwVdfRfSVrFpPLji0dZ12dUJIvuG0J8ne/UBVYheB8HR1IeJRjr6lJZxfVaX3Rq2tZVazTwTvDw6KfO97SP8Fg0jFtbbqMalNxnt7iPiUXd4JJ2FR7TsWFrRYdXYm9uCqq0Ph6+1tHPP55xAs1XHi+edzv9eKm4IJIQVHqrp8yYrFqjY8i4uIiEpLIU4nTsBhp2zm+3XHvfIKDA/T02jAWlqqC9+mIpO6gSoym5rCOIuK8DwYjI8uRfCZ+vshaqaJ8Zw5A6EbG4NwhsOIAtUm4uMUMIoVIaTgSFX4OVmdTRWBvP02Jn4RRCGVlVgnisUO5o5T1TIefVRHUSJw8d29m9iOSARC9eqrEKGmJojJ0JAet7K9e71IH1ZV6e7o6+sQVqtx4/x5RFMjI/hMGxvoOjE+jujywQMYNZaWsM5WVnb8nccpVoSQguMghZ9VNLazI/Lll7hGcTGikP10ME9GOIw6fbu7sLz/7d9iDSsWgxh99JHIn/4pjr19G2Lj8aAgbUMDhEiZRS5fxlg9HgjQ8DBEcHUVP3V1iJiqq3G/UAhux1AIAvjZZyKffgoRV3b5d94R+frXMc5z546/8C3FihBCLCSr0qCiMeXOa2hA9NPcjHSgNUJLVU/Qeg9rRYtwGEKo9kpVVEBgVlZw7fv39Rj9frwvgte3tiC+yoSh1uJu3cJ6Wm0t7rWzI7K8jHu2teH5/DyEzufDeXNz+ByjoxhjeTnW62ZnRT78EO7BXBS+pVgRQogF61qOWq9RRoOuLjQtVC3rT56EcDnVE7x1CxP85qbukTU4GH8Pa0WLhgaIgNor1dyMiMjnwz3DYZHf/hYpuJ0d7VZU4qm6B4voSM8wkMKLRLCfa2sLn6mrC+m9vj68rsb14YeItEpLkdqcm0NFi5IS3HdxEWM+c+b4e+ZRrAghBYtTFGV1Cq6sQEzOnMHvY2OYyE+fhqisriLSsJcemptDZLK1hTWhtTU8V6LgVNHi3DmIwrlzEJSFBZ1q83hwjaoqrE1VViKqKymBXb6mRhe+FdGRXnU11r1ME2P1eDD+7W3dBfmxx7Tw3L2LDcqmifdraiBe9fX4vaMD47U3kjwOKFaEkILFab+V1SmoohyVVhsZQRWJyUkI14kTiLBUjT0VXYXDuuXH4iLSfOfP6/s6VbRQqTVVjWJpSeSll5Dme/99VJc4exaR0uYmRCrd/qdIBGtaw8OI3EpKIIynT0P4fD58dnXu449jHLOzENrnn0d1i9/+Ft9DVxeukYtGjRQrQkjB4rTf6vnndbTV0YGIQqXVOjowiZ89i0l/ejp+LUnVE/z8c0Qwi4tIxQWDOF9VhohGYWDY2UEEU1enNxGrahRnzmBc29siv/sd7jM7C5HJ1CDi8WAf18WLid2HlW2/oUEfHw7Dhq9MHjMz+DwvvIBzSkpy16iRYkUIKVicCsCKJHbwVRP9wAAm8WAQEcfnn4vcu4eNvM88A+PDmTMQtYkJiM3uLlJnxcV6k+7Ghu7Q29CQKDzW6O7OHYyrqAjit7WlK7MrUrXusAub07H2+y4s4Ps4dQrPq6sxTqdzjguKFSGkYHEqAKuio2Qptr4+vc9pexuRTnk5RMs0cQ21r+m990T+6I/wWn09rrm5CRv6wgJE7MKFRLGy7gMzTZGXX8Y62Oqqrgyv2nZ0d6PFx2uvQQSrq3H8c885f2areNmFq6sLr6uSUyrtt7aWWRPJo4RiRQgpSNREPTUVPzEPDyM6StWzSZ3X04M1oQ8+0BUibt3Ctc6dw/PiYghVfb1uRTI6ClGZmYEY/fN/Hj8mq6W9rg7rVMrtNzuLe1RVIZIbG8P1trdx/cVFkb/8S/08VaUJ+5qdCD5rNIprj4zoMk+5hmJFCClI1EStitCKINIxTexhUo0YVUdd64QfDOK8cBiisbaGSf3BA0RZk5M478c/1mlDJRz/9b9iHUy1I/nww8TmhlZLe3090oWTk7h3YyPeMwwI4tQURE+lCjc3Ma5MGiRam04uLSHam5vDebu7uIeIFq9cFrSlWBFCChJlrqiqwvOZGUzC/f267JHaw2Sf8H0+TOYiiGoeeQTnd3Qgojl/HtGR15soFDU1ELaGBqQOi4sTmxvaLe2lpdq5Nz6On81NmC6ammDS2N6GYWNqCsI1Po5oLJVzz9p0cmEBInf7ti7NdOUKos1bt2AqyUWHYMVh+1kRQkheono1lZRAGF56STcrVNTV6cKvVrq7EfGEw9gY7PXiGjMziKwWFiBcTrz8MlKG9+7pDbq7u/HNDb1evFdTo913arydnUgXhkJ4zTCw7vX007jOY49hzGtrzv2vrL28olEInGnqzc2LixC7L75AVKg2F9fW5qZDsIKRFSGkIElWzFa17khWlV0dU1aGaOPJJyEKXi+K0ZomJvtXXnG+78AARK2hQafaJiZ0xfayMohIcTGuae2LpdazVJT14AH2RjU1iXz723i9slL3vzLNROeedZ1qaQnRWVERzvvsM0SJNTU6WuzuRroyVx2CFRQrQkhBkmqvUqqq7AqVRtzdxWQeCol861vp13Os+6is+5m6uxPPVYaLd96JH8cjj+g0papGIaKt52fPxu+hsq43zc1B3IqKtLuwvR3pw8pKvN7QgGvU1+P8lRV8vkgENn1a1wkh5JhJtkcp3ZpMsj1JmaznWPdRNTc796ISca6wYe8ebI2ekjVgtF4jHMbnq6iA5V0J1AsvIHXY2qobP37yCcTt1KmD9+vKFhQrQkhB4yQImUzISgjse5KCwfRV17u78f6tW4iOqqtxjj0ic6qw4RQ9JdsA7HSNaFT3s6qqQpS1uorjTp5EGlI5E+fnEbWpPlolOVQMihUhpKBxEgQ76aKvlZX4UkSBAIQqFoNY3bqFahfKKNHdrde81DqQk0g6dTROJnQizmO0XkO1ByktxbkvvaQ3G8/Px+8v+/RTWOc3NhB9qVRlrqBYEUIKlkgEk/voKCKM+/dhbHBq+aHKMt26JfL660jFtbZi39Prr8Oc8NhjIj/6EcRkbw9GBRFUrNja0tXblaikE8muLlRC/7u/QxT08st43S50t29DDNUGZ2Wr7+vTEeDcHIwgpomx7ezAkajKPhUVIbJS4zFNvWY1Owt3I8WKEEJywO3bEIm1NVShaG9H3T17OjAYhFBtbEDgPvwQEYrax1RWhvJGCwsib76JvVETE7jOzAxShZ2dWPM5cyY+nVdbC1deKKQ3B3d14fybN7E25fHg3q+/jnuFw3ozr9pAXFWlNyqL6JSdNQK8exdpPVWW6cED3U6kvz/e8dffj3uVlmrzhyozNTUF8XrlFaxtHQfcZ0UIKVj8fkQSFy7oTrknTiRGOj6f7t47Po4JWkUoIyMQIbVfa2pKGyYWFyFWp0/r+oFq71N3N9aO1tYgVLW1OgV3/bpuMTI1hfTdyZMQmdFRnD8ygmuNjWFP1M4OojeVsrPby1XVjc1NfI6lJezNunpV7y9T42lowPO+Pv2+x4NxTU/rXljXrx/ffytGVoSQgsU08SOCyXhyUuT3v0+sh9fVhajm3XcRuZw4gSiqqAipPlWUVm0GVnu1wmE46ioqIHaLi9q9Z414btyITwmOjCACq6/H8VtbEKqNDQhef7/Il18iMmpqwniWl3X7eWvKTq23TUwgPVhVhZSmPa3nVJ3dXmJpagpCZRXm44KRFSGkYOnvhzCFQnhULULsTExg8+8LLyBy2dxE9LG1hdYg7e0QAPtmYJ9P1wysq8OE//TTia4/VZ3C2jdLVavo7MQ1lpdx774+iFZxMdKNnZ2ImLxeCNfVqyLXrsWvt62sYI2tpARjeeml+GOccHJJdnRAkHd3U1fpOAoYWRFCCpbeXt0OpLgYE/qJExCNtTV9XDAII0VzM35XkYnXi+jn2992vn53ty4Ca5ra8OB0XLK+Wdeu4Rh7t97WVi20Hg/W2gYHE69tNXJcvozPlYk138kA8sorSP1Z16yOC4oVIaRgsaa+lOGhrCyxpJDVDOHxIJqxNk9Mdf3BQWcRSTYOhf15WVm8RX5gILHvlhNO9vdMcDrP6xX5/vczOz/bUKwIIURSl1iyvqdERLnojsvO7TS+TNp0ZFI6KpvnHRWGqVYXj5hLly6ZIyMjx3IvQggh+YFhGKOmaV5KdxwNFoQQQlwPxYoQQojroVgRQghxPRQrQgghrodiRQghxPVQrAghhLgeihUhhBDXQ7EihBDieihWhBBCXA/FihBCiOuhWBFCCHE9FCtCCCGuh2JFCCHE9VCsCCGEuB6KFSGEENdDsSKEEOJ6KFaEEEJcD8WKEEKI66FYEUIIcT0UK0IIIa6HYkUIIcT1UKwIIYS4HooVIYQQ10OxIoQQ4nooVoQQQlwPxYoQQojroVgRQghxPRQrQgghrodiRQghxPVQrAghhLgeihUhhBDXQ7EihBDieihWhBBCXA/FihBCiOuhWBFCCHE9FCtCCCGuh2JFCCHE9VCsCCGEuB6KFSGEENdDsSKEEOJ6KFaEEEJcD8WKEEKI66FYEUIIcT0UK0IIIa6HYkUIIcT1HFisDMP4lmEYY4ZhvJ/NARFCCCF2DhNZDYlIT7YGQgghhCSj5KAnmqa5JiJiGEb2RkMIIYQ4cKRrVoZh/MgwjBHDMEaWl5eP8laEEEK+wqSNrAzDaBaRv7G9vGCa5vfSnWua5k9F5KciIpcuXTIPNEJCCCEFT1qxMk1zQUS+cfRDIYQQQpw5jBvwkmEYb4nIBcMw3jIM40QWx0UIIYT8E4cxWIyIyJUsjoUQQghxhJuCCSGEuB6KFSGEENdDsSKEEOJ6KFaEEEJcD8WKEEKI66FYEUIIcT0UK0IIIa6HYkUIIcT1UKwIIYS4HooVIYQQ13PgckuEuJlIROT2bRG/X8Q0Rfr7RZ54QmRiQiQYFPH5RLq7RTyeXI+UEJIJFCtyJEQiIoHA8QiD070CAQhVLAax8vtFPj/FtuIAABc9SURBVP9c5ORJkbo6kZUVHNPXdzRjIoRkF6YBC4xIBBP3jRt4jESO5j6BAATBKgxHhdO9gkGRvT0Rr1ekokJkakrkrbdEFhZEdndFamtxDCEkP2BkVWA4TeyZRhfWCMbrFYlGRT76SKfZensRPUUiIu+9JzI7K1JUJPLIIyIff3x0UVYwKFJZKTI+LrK6inv294sUF4uEwyJffimytYVxzM6K7OyIbG9j3EwHEpIfMLIqMIJBRBVFRfuPLqxC5/eLvPYa0myxGJ6r6CkQEJmfF9ncRHTzm9/gebaiLHt06PWK3LkDoTIM/IhAhIuKMI7eXpHnnhNpa8M5IiJPPnn0UR8hJDswsiowfD5M0LW1IuvreG4n2XpTMAjBKSqCCG1sQChEREIhLXzBoEhjI9JvGxuIYBobMxPITNa67NFhdbV+r65OpLMTEdXVqyKDgxCnlRWMp7lZ5Px5kcuXMZ6yMqYDCckHKFYFhjIfWMVAoYRiaAjPe3riU4VWoSsuhkiEwxCj4mItfD6fyIkTIqWlIvX1eO/ECURgyQRSoYSoshLjGBqC4HR1aSff2JhIVRXSfiodOTiYXITtn7m/H8eoY71eCJpdII/TJEIISQ3FqkBQVu7RUaTJ+voSJ18lFHt7eP7ppxCakRE87+rCYzCI8y9c0GtW6noieIxG9b1efhkRzNpacoFUgjA3J9LUBCFS41hZgUApJ9/8vMj9+xCd2VmM8coVZxF2Ehz1WdVr0ajzOt5h1vcIIdmFYlUgWK3choHfy8riJ1+V5quvh7B8/DF+P3UKk7VI4mT93HOJ9/J4EOkMDmY2LqsghMM4X60/1dUhAhoZETlzBqm7kychZPfvYw3K68U5TkKSTHCsx964odOb1jSlNe1J9yAhuYUGiwJBWblrajC57+0lTr4+H9JinZ2YoLe2IAZdXXqyzpb1XV3njTfi7eQ1NSINDXpdLBIRefttiOzyMh5PnMAYBwawBtXamvpzpzOUqM9tT1Mme50QcvwwsioQfD6s+YRCiFisa0wK69rO4CCij40NkZISPVlnKzWmrnPqFFJ5IhCelhadUnz1VeyPOnUKAru+jrGo+6m0YleX85qT+tzpDCXJ1vFSre8RQo4XilWB0NUlcveuyPAwzAkvv5w4+dpTaWq9Z24O6bloVGRyEpbvw6TGIhGRmzcRsVRXwyk4M4PxqDF5PBAIlfqLxSBOV68mXk+5/ZwENBPBiUQSv5uBgeSpRULI8UOxKhAmJpAu++EPEWFUVaV3vFkna48H4jQ1hT1Nly8fPDUWCOj9UBsbiPJeeknfS41pYgLPe3pwr1AI6Uf7OJ3Wluyf6/nnkzv5rl8X+cMfMJbpaZHFRaznZbLmRgg5HihWBYJ1Qq+oQGQTDOo1nZMnk6f1rOeePy/y619rA8QTT+CY/di6g0EI0OQkrqPOV6gUYU8PhHFkBGaKWAzrWLu7OE6N0+tFVLS3B+Hbr5NvagpCFYvh/Hv3RH71K12RgxCSe2iwKBCsZoE7d7TTbnoak3mmBoR790Q6OvBTXAwRWVnRFc7TGS8iEdzjt79FYdlQCPe1ogT0xAlEcCUluNfp00hHrqykTz/up1JHRwfGsbqKH58Pn4uVLQhxD4ysCgTr2o0IopaiIrj9ZmZEzp2LT+vZ6wBWV2PNSJ37wQdw7QUCOG92FmmzjQ2I1d27Io8/DnGxpu0CAYiHaYp88QXq9dXWQuzKyrCH6oMPIIydnbiHYWCcGxvYLDw5CfFSKcGVFV2RQq1tZWKsULzyCvZx/eIXiDrb2kQuXqRVnRA3QbEqEKzrTz4fRGF1FSkwwxBZWoITz7ppVqXR1tchGlevahHwekVu3cLkLoL6e3fv4rjmZqTlNjchItY03NwcxGRhAWto6+sin30m8u67Is8+izGZJq6pNvz29el9Xqo4bm2tTvGFQhA6qzDtx8nn9cJUsbOjBTESoVWdEDdBsSowIhG4+t57D6muvj6IS3Oz8wbh3V0Ii72KRTQKoWpqwsbh3l6R3/0OqbrNTby3t5eYhguHITiNjYjAamsRQYVCeL64iPs+eID03NqabppYWgrRmZ/HGpu6diQCkbSbRJKtUdmjRhGkJZXzcGMDj7SqE+IeKFYFRiCAybitDRGVx4OJ32mD8Pw8BGRyEoIyP4/3lAi0tOhU29wc1pjefRepPZ8PovT73+voSASbftva9Hnl5RC7xkbc5/RppASViBkGhMoqPMqqriKp1tb9WcytUePwMF5T+73a2pASbWiguYIQN0GxKjBU+m92Fum2nR0IhdMG4bExrCu1tyMCWV1FdGM9RkUom5siL74IIZyZQb8op8oSLS1I2Z07h0cRkbNnYbbo7MTaWE0NRExVULcLaVcXxjYyguhrYCD+fac6iFZnn716vLqmSOJ+L0KIO6BYFRihEITqscdEPvwQKb7nn3feIOzzibzwAlJ3lZU4z3qcSrVFIiI/+5lu19HUJPL++zi3qwumh9FRXMdq1rBWonjsMYhUY2O8iDmZIyYmEA2eOYP3VeSl0ns3b6LhorqWvQ6ivXq8CByHqkJ8MIjrsMo6Ie6BYlVgeL1IdYVCIk89hQgjWQrN59N7mmZmEMU4RRzWTb737sV35RWBGM3N6X1MfX2JlSis60hqPIuLunKG36/vrapf1NfHR14qvbe5Gb/G9uij8dGZNSK0Cqay0bPKOiHug2JVYLS2Ilo4dw4OwGRVIUS0OJSU6NSYU6QxP4/1qk8+QTrv9GlUY5+ehsitryPaqqnB/UZHE6tDOBki/H5dOcPa0VcJ4+oqrqeupdJ7k5MQuJISjG17Gy1EUt1LJHn1dUJI7qFYFRjWqMIaSczPYx3I58vMUWclFILwnT+PqKqsTHfl7e5GA8VYDMda286nI1mLDmv1i50dCNONG3h/dxf3On0a41LrX5msQe1nbxYh5HihWBUYVgGyRhKrq4iCzpxJnwKz190rL9epxd5emCysjRajUezJ+ugjiNnly7hGuvWgZOKxsqLXs5aX9d6oe/ewDre9DZH6xjdwbHt74r2caiKyyjoh7oViVcBYxWB2FvbtTFJg9rp7Dx4gilJVMBoa4oWutxfpwa0tVIaor89sPcguHl1daAg5Pq5dftEoxjs+rityDA7q8zo6UKEi3WdwaspICHEPFKsCxioG7e1I3X38sTZTJIt+7Om5aDRxU6594+3yMqKf/awH2dOQfj+iqKeewvU+/xziODWF+5WWYlxtbfgsV68mrypv/wxzc/qzOa3fEUJyC8WqgLGKQSSCZocqwqqp0fX67BO4PT2nGiZasfaYGh7GmlhbG9KDKyu4RjJjRzKsArOygrE++ywK8y4uQnA7O+NThskiKPtnCIcxBjoBCXEnrLpORESL0De/ifJGjY1w7dknehGIS0MDhKehwXltx1r1fG9Pl2UyTUQxdou4anOfqmq7tfr75CTMFbdvY9/WwADSf5ubeK5MF0NDiLLsEZ39M9TUZF6lnRBy/DCyIv+EPdowDOcJPBOXoH3jbXGxNkUUF+vafqq31tAQzuvpSR7ZWNOWRUUQvqoqRFjt7fp4a1Qn4tws0inFSCcgIe6FYlVgpOoM3N2NSOV3v0Nli91d/PT3wxyRagK3X1eVL7JvvPX5EPksL0Mcbt3CWlZjI9acJichak6RjVVgdnbgYAyHkV6sqdHHWdOFqp2J1Z2Ybrx0AhLiPihWBYZaw6msRDQzNIT0mRKtsjL8nDoFQZifR2QyOIgJ3e93Frpk1x0cRDkkuwio9bHycqQINzcR1SwuwgJvmqnXs6w1Bu2bm71eREe1tRDZp59OjNLsa1ki8et3yQSdEJIbuGZVYKi1pMlJrCXt7cWvRwWDeM3rxVpOayucgSIiP/85RKiyMv6cVNe9ft153Wt9HdfxeiFMyim4uIj3n3wy9XqWWnNaXETk9Pnn+F1Vht/Pmpp9jcrJlEEIyS2MrAoI1VLe70cKraUF6TfrZO3zIRIKhyEixcW6uaGqUO6UqlNrVKurSB2GwyJvvomqGN/9LtaW1H2sk39lpXbyDQ5CGBsbISLKiXj7Nsa8t4exRaM4VkVCd+/is2xs4Hlpaerag/boy75GlaxyBiEkdzCyKhCUNf2zz3RH3MXFRKt3dzdEoKgIP319mNgrKiBaY2NICy4txU/wKtJR0VE4jBRcXR3EZmIC9/F6Yai4fx/XGhuDKP7gB7hXS4t2/Knjf/UrCM3aGj7H6Ki+bzCIlKVq+Dg767y25pT2SxZ9WV2HNFsQ4g4YWRUIgQAKy6pOvm1tqDyxuRm/luTx6LUmhd+v03/l5diE+957iJjUxmFlfujuRruQjz/GWtSFC0jRqT5R0ShchkowamsRTak2H6dPI3U4MgKr+8AAhK+iAgK3uak3LAcCOG93F5Hb/HzyyvD2aGltLTH6UrDsEiHug2JVIASDEChVR29+XuSllzLb+NrdjWiopERXiBCBiI2OwsCgTAgeD54bBtJ2Dx5AIFT7+GAQDr3paQifYeC5SrW9+SacgmfPosbfP/4jTBSRCJ7PzyP6+pu/QaS3u4sor6UFnyeZGWI/RWozLeBLCDk+mAYsEHw+RCrV1bClO0UgyTbmKgE6dw6iU1qKCCcWw4/dhGBNJS4uQkiUYUKlB7u7ddRitcVPTeF+1dV4LRaDoFVWIkKrqIC4DQ/rlF9TE0RMrYc5bSjOZCNzJhuTCSG5gZFVgaAm59LS5L2p7Pbzmzd11KRSY0VFEI+KCqw11dcnmhCsqURV2X13FyI5NYVjqqogYquriPaqqyEOHR26Tt/2NtbUSktx33PncM7cnE4LiuC6m5upSyVlEi0lK81ECMk9FKsCIdlkbXXJTUzoXlGRCKKVjQ2YIK5d02tSgYCuOGE3aNhR6beFBURCHR2oXtHQgPetqblAABXSd3YQOVVVibz4IlqLFBXpnlhLSxDKYBDv3b8vculScvdepvum6AIkxL1QrAocazQhAqff3p7Il18iZRaLIRra2sKkbxhYTyorw/6m6WmRl19ObkJQ4jYyAtdeVxfWvpQQ2MWhr0/khz/EjwjScaWlGEdFBYRKNVc8fx5pxpoatChJ5t5LZn23w+aLhLgXrlkVONbNsT09em/V7Kx+fWsLxge1RvXzn0PULl3CMTMzuFay9a6+PpgfmpshVEoIMrGIB4MQpfV1CI1p4r51dTBvnDoFQWxujl+Psq4//fKXeF5Tg3v5/c7fRSbrWoSQ3MDIqsBR0URFBQRINTX84gu9TyoUgjCo+nurqzivpAQiMTWVfr0nmR08nUXc50PKsbYW9y8uRqPF4mLc217EVkTvKZuagpitrel1sEgExzv16qILkBD3QrEqUNQ6zvw8xGhpCam9nh6sU124ANPC3h5Ey+vFcYYBU0Ukok0THR3p13uSCUEm3YJv3tRmjs5Okd/8BoITCsFGr2zxCrWnrKUF7kMRPG9owPNYDGJ27Vq8YLEmICHuhWJVINgn4mhUl0eanUWa7TvfETlxAqIViaAFfTCI9hrRKMoaGQaqTczOIlpRbeMnJlKv9xxUCJRt3nrtxx6DSePcOTxXZg2FfU9ZJAKRE4Fpo74e4mWP/gIBiO/Kisgf/iDy+uuw3Le0ULgIyTUUqwLBnqYbH0dEsrGh02nWvk+trYlRz3PP6d/tLsJ0LTYOYwu3pxAHBpwruSt8PjgKRfC5zp6FNf6TT5DuLC6GmNlb2c/NIWWooq/JSURwZWW0sROSayhWBYI9TWcYmMgbGmBN93gQbbS3Q6isAuAUFaVqsZHJ/TOxhVvv6/Xix9rpN1mkY99T1tWF8k+zs0ht9vUhugoG8ZmVS7CyEoLV0gJLfGkpIqyBAUSWhJDcQbEqEOy27L4+VIS4eRPRRlMT3p+ehlhZcYqK7OIzP5+815XT/TOxhVvvOzyM1y5fTh+ZOa2PDQ6K9PbGi+7EBMZfU4P1r/V1pDWnp3GO2pQ8O4tojBCSOyhWBYKTG6+3FxP03h4m68bG+P5WasJ3iors4qPaiOzXDSiSfD3Lel/VnuQwG3btIjY0hHtPTmK/VnW1yL/9txCxv/97vDY7C9fj9jbEmGtXhOQGilWBkMyNNzgIcRkbQ2qwri5RDJyiIrv4RCJ6X1ZFBYTALj7JIqFk61nW+6rIJpttO/r60H5kYwPGi9ZWXf19bAxmk0gEkVZxMUswEZJLKFYFit26vreHyKizM7FNvJN5wkl8lLDcuQODw+IiohFVrilZROIUuUUiWCcaH4eIXriA8a2t6TFEIro6hWmK9PcjWsw08untRdX4WEzb4pVIe70wYQwNoVpGdTVLMBGSSyhWBYqKZmprsXnWMOJTerW1mZsnROIjLdPE5K6chmrTcLJrOEVugQDOf+opvFZVlXi+34+fWAz39PshaJlGPk62eBWxtbbqgrzKiMISTITkDpZbKlBUmaXJSURVRUXYu9TSggn55Mn9rQ+pSOvqVQjA0hLSgZubqCKR6hpOZY6sZaCSjSEYxNi9Xpgk9vb2H/kkK7GkXq+rg0Oyvp4lmAjJJYysChQVzayuJq5VHbaga3c3Un+q3FFDw/6bHWYyBuXoC4d1TcP9jjXZWpp6netThLgDRlYFioociorwY231cdiCrh4P1qheegmW+Obm/V8jkzFYmzwWFekWJoSQrx6GaZrHcqNLly6ZIyMjx3Ivkjmsh0cIySWGYYyapnkp3XFMAxY4rDROCMkHmAYkhBDieihWhBBCXA/FihBCiOuhWBFCCHE9FCtCCCGuh2JFCCHE9RxYrAzD+JFhGEMPf76fzUERQgghVg4TWd0wTXNQRJ4Vkf8tS+MhhBBCEjiwWJmmOfnw192HP4QQQsiRkI01q/9ZRP7O6Y2HqcIRwzBGlpeXs3ArQgghhUjackuGYTSLyN/YXl4wTfN7hmEMiMjLIvJdp3NN0/ypiPxUBLUBDzlWQgghBUpasTJNc0FEvmF/3TCMNhH5v0TkT0zT3Mv+0AghhBBwmDTg/ykiTSLyC8Mw/tEwjPIsjYkQQgiJ49hahBiGsSwiU1m4lE9E9tkPljyE393B4Xd3cPjdHZxC+O46TNM8me6gYxOrbGEYxkgmvU9IIvzuDg6/u4PD7+7g8LvTsIIFIYQQ10OxIoQQ4nryUax+musB5DH87g4Ov7uDw+/u4PC7e0jerVkRQggpPPIxsiKEEFJg5JVYGYbxF4ZhvGcYxv+d67HkE4ZhtBqG4TcMY9swjLQbwYnGMIwBwzB+bxjG+4Zh/EWux5NPGIZx4eF3955hGH9lGIaR6zHlE4Zh/HvDMN7P9TjcQt6IlWEYfSJSZZrmsyJSZhjG5VyPKY9YFZFvishQrgeSh0yJyD8zTfPrItJoGMbFXA8oj/jENM1nHv7NiojQgp0hhmF4ROTJXI/DTeSNWInIoIi8+fD3t0Tk6RyOJa8wTXPbNM21XI8jHzFNc8E0ze2HT3dEhKXFMsQ0zR3L04iIfJmrseQh/5OI/HWuB+Em8kmsakUk/PD30MPnhBwLhmF8TUROmqb5ca7Hkk8YhvEnhmF8JCjNtpLr8eQDhmGUisg3TNP8Ta7H4ibySaxCIuJ9+LtXRNZzOBZSQBiGUS8i/1nwr12yD0zT/HvTNC+IyIyIvJLr8eQJfyYi/1+uB+E28kmsbgrWXURErgjXX8gx8NCQ8nMR+Q8POxCQDHm47qIIi8iDXI0lz3hcRP4XwzB+LSLdhmH8ONcDcgN5I1amafpFZNswjPdEZM80zVu5HlO+YBhGqWEYb4lIj4i88bAPGcmMfykil0XkPz3sLsC10sz5Y8Mw3jEM4x1BGvBGrgeUD5im+b+bpvmSaZp/LCIB0zR/kusxuQFuCiaEEOJ68iayIoQQUrhQrAghhLgeihUhhBDXQ7EihBDieihWhBBCXA/FihBCiOuhWBFCCHE9FCtCCCGu5/8H7J6Z+3zHDcIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_len = 200\n",
    "\n",
    "data_frame_1 = pd.DataFrame(data=np.random.rand(data_len, 4))\n",
    "data_frame_1.values[:, 1] = (data_frame_1.values[:, 1] + .42 * np.random.rand(data_len, 1))[:, 0]\n",
    "\n",
    "data_frame_2 = pd.DataFrame(data=np.random.rand(data_len, 4) + 1)\n",
    "data_frame_2.values[:, 1] = (-1 * data_frame_2.values[:, 1] + .62 * np.random.rand(data_len, 1))[:, 0]\n",
    "\n",
    "data_frame_3 = pd.DataFrame(data=np.random.rand(data_len, 4) + 2)\n",
    "data_frame_3.values[:, 1] = (.5 * data_frame_3.values[:, 1] + 1 * np.random.rand(data_len, 1))[:, 0]\n",
    "\n",
    "data_frame_4 = pd.DataFrame(data=np.random.rand(data_len, 4) + 3.5)\n",
    "data_frame_4.values[:, 1] = (-.1 * data_frame_4.values[:, 1] + .5 * np.random.rand(data_len,1))[:, 0]\n",
    "\n",
    "data_full = np.concatenate((data_frame_1, data_frame_2, data_frame_3, data_frame_4))\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.plot(data_full[:, 0], data_full[:, 1],'ob', alpha=0.2, markersize=4)\n",
    "fig.set_size_inches(7, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.70080903,  0.16735077,  0.23902264,  0.15855355],\n",
       "       [ 0.99098624,  0.32836844,  0.52532888,  0.86416106],\n",
       "       [ 0.50765871,  0.39624144,  0.55753557,  0.6274681 ],\n",
       "       ...,\n",
       "       [ 4.27444825, -0.31539828,  4.15011112,  3.63629497],\n",
       "       [ 3.60632082,  0.0898611 ,  4.22930109,  4.07777338],\n",
       "       [ 4.03323389, -0.07237765,  3.93762173,  3.79096296]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_full"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to set the size of the map, the set of toy data is small, so first we will set the small size of the map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapsize = [2,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <code>build</code> method from SOMFactory creates self organizing map,  give it the size of the map and the data. the method takes the size of the map and the data.\n",
    "\n",
    "<code>initialization='random'</code> is a type of initial node weights, the random values to all weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " Training...\n",
      " random_initialization took: 0.000000 seconds\n",
      " Rough training...\n",
      " radius_ini: 1.000000 , radius_final: 1.000000, trainlen: 1\n",
      "\n",
      " epoch: 1 ---> elapsed time:  0.106000, quantization error: 1.658845\n",
      "\n",
      " Finetune training...\n",
      " radius_ini: 1.000000 , radius_final: 1.000000, trainlen: 1\n",
      "\n",
      " epoch: 1 ---> elapsed time:  0.106000, quantization error: 1.515790\n",
      "\n",
      " Final quantization error: 1.515790\n",
      " train took: 0.223000 seconds\n"
     ]
    }
   ],
   "source": [
    "som = sompy.SOMFactory.build(\n",
    "    data_full, \n",
    "    mapsize,\n",
    "    initialization='random')\n",
    "som.train(n_job=1, verbose='info')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For visualizaion used mapview.View2DPacked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1188x330 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = sompy.mapview.View2DPacked(10, 10, 'example', text_size=8)  \n",
    "v.show(som)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The som could recognize four clusters. Although the scope of the cluster are far from ideal.\n",
    "\n",
    "The \"cluster\" method is using [sklearn.Kmeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) for predict clusters on the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, 3], dtype=int32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = sompy.mapview.View2DPacked(5, 5, 'test',text_size=8)  \n",
    "som.cluster(n_clusters=4)\n",
    "som.cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFMAAABiCAYAAAAslO8IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAAk1JREFUeJzt3D1rFGEYheH7hFjGpBZBU4iVYJEmVmlsxU4RbDRgkkqtRFD8A/4CK4v8grSKICgGwY8NKaKdXYqYwlLwsdgR1MLocrLzrpwLlp0dmOHh5h1mYT9UVYTHVN8D/E8S0ygxjRLTKDGNEtOo2ZiS5iQtj3DckqSFw5jpIM3GBOaAf44JLAG9xFSrb9olrQMXgY/AOnAcOAccAe5V1Yaky8B94CvwAbgNvO5e7wGXqmpnbENXVZMP4CTwqtteAW5220eB7S7qFjDf7Z/tnh8AK33M3PJl/rPzwA1J74DnwAxwDHgBPJJ0Dej9Epvue4C/JOB6Vb38Zae0CiwCF4BNSWf6GO6HllfmF4YrEOAJsCppCkDS2W7/fBf4LsPLfua348aq2ZhVtQcMJA0YxtkF3kvaZnjTAXgoaQsYAI+rah/YAK5Keivp9DhnbvZuPomaXZmTKDGNEtMoMY0S0ygxjRLTKDGNEtMoMY0S0ygxjRLTKDGNEtMoMY0S0ygxjayfTn57dmukz0AW7pxyjvFHbzbXdFjnzso0SkyjxDRKTKPENEpMo8Q0SkyjxDRKTKPENEpMo8Q0SkyjxDRKTKPENEpMo8Q0SkyjxDRKTKPENEpMo8Q0Skwj6696n376PNLJdk8s2mY4yJXayddjJkFiGiWmUWIaJaZRYholplFiGiWmUWIaJaZRYholplH+C84oK9MoMY0S0ygxjRLTKDGNEtMoMY0S0ygxjRLTKDGNEtMoMY0S0ygxjRLTKDGNEtMoMY0S0ygxjb4DoPos5BsxbkQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v.show(som, what='cluster');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the visualization of clusters on the grid. For this use HitMapView."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = sompy.hitmap.HitMapView(8, 8, 'hitmap', text_size=8, show_text=True)\n",
    "h.show(som);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grid of self organizing map have a two types:\n",
    "    - square grid\n",
    "    - hexagonal grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://annalyzin.files.wordpress.com/2017/10/som-sqhex.png?w=650&h=238\" alt=\"Self orginizing map\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will create a new SOM and add some arguments for best result.\n",
    "\n",
    "Increasing map size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapsize = [20,20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>lattice='rect'</code> is a square grid of SOM.\n",
    "\n",
    "<code>normalization='var'</code> is the type of [normalization](https://en.wikipedia.org/wiki/Normalization_(statistics)) of the input data. 'var' is [t-statistic](https://en.wikipedia.org/wiki/T-statistic).\n",
    "$$\\frac{X-\\bar{X}}{s}$$\n",
    "- $X$ is input data.\n",
    "- $\\bar{X}$ is average of input data.\n",
    "- $s$ is [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation).\n",
    "\n",
    "<code>initialization='pca'</code> is a type of initial node weights, principal component initialization.\n",
    "\n",
    "<code>neighborhood='gaussian'</code> use the 'gaussian' function for \"measure of neighborhood\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " Training...\n",
      " random_initialization took: 0.001000 seconds\n",
      " Rough training...\n",
      " radius_ini: 7.000000 , radius_final: 1.166667, trainlen: 15\n",
      "\n",
      " epoch: 1 ---> elapsed time:  0.127000, quantization error: 0.553699\n",
      "\n",
      " epoch: 2 ---> elapsed time:  0.126000, quantization error: 1.159679\n",
      "\n",
      " epoch: 3 ---> elapsed time:  0.113000, quantization error: 0.416257\n",
      "\n",
      " epoch: 4 ---> elapsed time:  0.113000, quantization error: 0.370592\n",
      "\n",
      " epoch: 5 ---> elapsed time:  0.123000, quantization error: 0.345972\n",
      "\n",
      " epoch: 6 ---> elapsed time:  0.111000, quantization error: 0.337165\n",
      "\n",
      " epoch: 7 ---> elapsed time:  0.128000, quantization error: 0.329347\n",
      "\n",
      " epoch: 8 ---> elapsed time:  0.123000, quantization error: 0.322139\n",
      "\n",
      " epoch: 9 ---> elapsed time:  0.117000, quantization error: 0.315155\n",
      "\n",
      " epoch: 10 ---> elapsed time:  0.118000, quantization error: 0.307990\n",
      "\n",
      " epoch: 11 ---> elapsed time:  0.143000, quantization error: 0.299146\n",
      "\n",
      " epoch: 12 ---> elapsed time:  0.119000, quantization error: 0.287074\n",
      "\n",
      " epoch: 13 ---> elapsed time:  0.120000, quantization error: 0.270343\n",
      "\n",
      " epoch: 14 ---> elapsed time:  0.126000, quantization error: 0.249964\n",
      "\n",
      " epoch: 15 ---> elapsed time:  0.114000, quantization error: 0.220684\n",
      "\n",
      " Finetune training...\n",
      " radius_ini: 1.666667 , radius_final: 1.000000, trainlen: 25\n",
      "\n",
      " epoch: 1 ---> elapsed time:  0.138000, quantization error: 0.182322\n",
      "\n",
      " epoch: 2 ---> elapsed time:  0.113000, quantization error: 0.208519\n",
      "\n",
      " epoch: 3 ---> elapsed time:  0.112000, quantization error: 0.204792\n",
      "\n",
      " epoch: 4 ---> elapsed time:  0.121000, quantization error: 0.201532\n",
      "\n",
      " epoch: 5 ---> elapsed time:  0.120000, quantization error: 0.198859\n",
      "\n",
      " epoch: 6 ---> elapsed time:  0.110000, quantization error: 0.196108\n",
      "\n",
      " epoch: 7 ---> elapsed time:  0.113000, quantization error: 0.193618\n",
      "\n",
      " epoch: 8 ---> elapsed time:  0.114000, quantization error: 0.191216\n",
      "\n",
      " epoch: 9 ---> elapsed time:  0.119000, quantization error: 0.188980\n",
      "\n",
      " epoch: 10 ---> elapsed time:  0.129000, quantization error: 0.187047\n",
      "\n",
      " epoch: 11 ---> elapsed time:  0.110000, quantization error: 0.184945\n",
      "\n",
      " epoch: 12 ---> elapsed time:  0.115000, quantization error: 0.183036\n",
      "\n",
      " epoch: 13 ---> elapsed time:  0.115000, quantization error: 0.181090\n",
      "\n",
      " epoch: 14 ---> elapsed time:  0.131000, quantization error: 0.178869\n",
      "\n",
      " epoch: 15 ---> elapsed time:  0.124000, quantization error: 0.176841\n",
      "\n",
      " epoch: 16 ---> elapsed time:  0.123000, quantization error: 0.174875\n",
      "\n",
      " epoch: 17 ---> elapsed time:  0.141000, quantization error: 0.172770\n",
      "\n",
      " epoch: 18 ---> elapsed time:  0.119000, quantization error: 0.170856\n",
      "\n",
      " epoch: 19 ---> elapsed time:  0.124000, quantization error: 0.168818\n",
      "\n",
      " epoch: 20 ---> elapsed time:  0.121000, quantization error: 0.166775\n",
      "\n",
      " epoch: 21 ---> elapsed time:  0.113000, quantization error: 0.164791\n",
      "\n",
      " epoch: 22 ---> elapsed time:  0.110000, quantization error: 0.162429\n",
      "\n",
      " epoch: 23 ---> elapsed time:  0.114000, quantization error: 0.160362\n",
      "\n",
      " epoch: 24 ---> elapsed time:  0.110000, quantization error: 0.158399\n",
      "\n",
      " epoch: 25 ---> elapsed time:  0.150000, quantization error: 0.156344\n",
      "\n",
      " Final quantization error: 0.156344\n",
      " train took: 5.054000 seconds\n"
     ]
    }
   ],
   "source": [
    "som = sompy.SOMFactory.build(\n",
    "    data_full, \n",
    "    mapsize, \n",
    "    lattice='rect', \n",
    "    normalization='var', \n",
    "    initialization='random', \n",
    "    neighborhood='gaussian')\n",
    "som.train(n_job=1, verbose='info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1188x330 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = sompy.mapview.View2DPacked(10, 10, 'example', text_size=8)  \n",
    "v.show(som)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = sompy.mapview.View2DPacked(5, 5, 'test',text_size=8)  \n",
    "som.cluster(n_clusters=4)\n",
    "som.cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = sompy.hitmap.HitMapView(8, 8, 'hitmap', text_size=8, show_text=True)\n",
    "h.show(som);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use the SOM for [the Iris Dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapsize = [20,20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.4 s, sys: 53.7 s, total: 1min 11s\n",
      "Wall time: 24.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "som = sompy.SOMFactory.build(\n",
    "    iris.data, \n",
    "    mapsize, \n",
    "    lattice='rect', \n",
    "    normalization='var', \n",
    "    initialization='random', \n",
    "    neighborhood='gaussian')\n",
    "som.train(n_job=1, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1188x297 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = sompy.mapview.View2DPacked(10, 10, 'iris', text_size=8)  \n",
    "v.show(som, which_dim=[0,1,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uxkR7h/PfCpTN7EbgAeAj7l4DcPdHs8dUgOYLKn3GzHYDF7r7vXPQbJElo4McFZEFovwUKbYi5mh8krNIwU2VMG6+JawD+rIjj6PAO1s85tPA5dnvl7v7q4EPonLkIm3rIEdFZIEoP0WKrYg5qiOPxEVv9ntc6eORSrw34Kdjx3fUJoDl/fHfXjsYFwKoerxv4Ee7NuTGrn785HDdPU+sCuPLHo/f3EPxHOUZc1pOJF5rZpua7m90943Z73vJrnUD/AA4ZCa3mX0EeMDdbwNw9+Hs58ON66oWm5egnje5PK5pgdXzn1+qIE6iXgblxPpRUZyeA7XcWONvx5Pp3eL3Yq0v/r/27c5ff2IgLpiz86jlYXxHdWUYf83gL3Jjr+3vjo/vnByVglldivOsFHyALEtUxOpPFMypJ/qqqKDOU+NrwnX398RFa3YmcnC0lv+3f5EoiHX3v7w0jJ9w0R1hHLYk4rOn/CyO55XjvuixoHhTKdEJD/TEnfBYOX4PTCQK5uwZzS/Qdu+e+HtwLXEca2slLjxTCz4/fjYeF2f8xh2vDeMvvTi/DwY48pn8HE7UGZyxIuZod3z7EMnhGNXn7oXZ6e555b3uAH4v+/00YOpUVczsLcAZwHualg25+4iZrUX5ItK2nBwVkQJQfooUWxFzVF+GpavV3ZKXJGnm7vea2ZiZ3QzsBL5uZh9390tonJY6AtxkZg+5+/nAZWZ2Mo1TvC+a+2cgsri1m6MisnCUnyLFVsQcLVZrRNrkGBP19t7G7n7htEWXZMtPavHY8ztvnYh0kqMisjCUnyLF1kmOmtl64BrgZcAK94Pz88zsy8BLgTEa07q+lj3+q8AA8KepKwwkW2Nm5wHnATz/OH3ASLG4QyUxX2axa87R8pp4/o/IQlOOqh+V4lJ+Kj+l2DrM0WHgbODbOfH3uvsjTfcvAv4EuI/GoHN2g8es0MhGgNNPzavEMTv76+Nh/NFq/Gf31PMn6gLUiCchVzx/Qv0z1aPDdR+diONPja8O42sH8ovenDi0I1x3w8CuMD5cjYt17BzPj+95aihcd2B7fP71wHAYpm/f3LyVHGNyie81bc7RgeOf57m1K4KCOABRzYtynKJYYmZ4eSKOl4IcL1XjQgCe2HbfvjhOoqCOB0UMKkPxuqPVeJL7tsn48+GYFWNBNC70URTK0YXpR2friFJ+YRiAI3vyi3VUPP7/poo97OmJ+/CRSn58+3jcV40kCuYs74k/QOqen//3bjsuXPeF3xyJtx1GF4byszj5OWhxnhxdzu/MBhNV6QZ6Zle+pTqZKDxXzY8/bvFO7cl6vO3hFfF32agg1+3bTwjX/aVvxq9L9Zn4e/hC6CRH3X0cGM8p9OjAV8xsF/AH7r4FOAX4Q3d3M9s3Ve8jb/tL+xNDup4D1cQHj4gcPspRkeJSfooUW06ORlcVSLnA3YfN7A3AZ4HfAMruPrXjZC+wmkYNkJY0eJSu5m7JvVYicvgoR0WKS/kpUmw5ORpdVSCxvWcvQXebmV2aLW4+EWII2BNtQ4NH6Wp1jMmaOj6RolKOihSX8lOk2OY6R5suQXcSBweJm83sdcBmIDxlFTR4lG7nUF3ik/1FCk05KlJcyk+RYusgR82sF/gucCpwnZl9CnhDdlm6vzezNY0t88FslT8HvgIMAp9IbX/BBo8/r+ZPtr9nPJ50vmVybRh/bPzIMJ6aaDpYyp9ovHMynqg7Wo2LDJQsnns91JtfieSYvniy/brevWG8krio6JZdR+TGBh9PvGY7wzCDO2phvG8kjs+U9poeymrQt6d1gYeeqPYK0BsUlinV4vdxrTcuxlOP04TRo/L/hwOJf28p8VYqT8Rt798Tb6Bezm/AgfXx8z4w2R/GU+W3N/R0R1GcSLs5ml1XdSNQAx4BfmdqLoaZfRE4mUan9yF33zz3Le5Ou+ujYXwgURhquB5UzAJ6mZvP7E7sr+R/gIwlilKt7I0L4kz2xa9LVFiu76a4WE/9x3eE8SJQH1oc+z3OwdWl/BJLr1qxJVz3ydG4OFslKHgD4ImCe/Vafnx0LP4CMNy7LIwv64mLAY0E/Wztunh8UL5pceaou1eAN09bfEsWe3uLxz8B/OpMt6/dTdL1al465CYixdJmjj7k7me4+5nZ/eZ5HZe6++uB/5cZ7B0VkTT1oSLFVrQc1Wmr0tXcjYr2mooUVrs5mu0xnTIBbG2KPZr9WoHDeChMZJFQHypSbEXM0cM/fBWZBXeo1kqH3FLM7P1mdqOZ3WxmxzUtP9nMbjOz283sFXnLRGTmcnJ0rZltarqd17yOmb3DzO4H1gGtLmj7aeDy+W+9yOLWSR8qIguniDmqI4/S1RzaOoSfDRbf6O5ntwj/GfCbNEoWfw54Z84yEZmhnBwNy4y7+1XAVWZ2BfA24NtTMTP7CPCAu982D80VWVLa7UNFZGEVMUfbGjxOeI3Hqq2rauyqx4Uh7hk7MTd2/4Hjw3WfGl8Vxp8eXRHGJyrx0yyX8gtqWKLgTaogTm+imkc0CTZVXem+kfh1e+DpdWG8fF/+65YqiNO/N3/iNqQL4vTtzi8U1B5rdy/MW4Gymd0IPAB8xN2nGrvG3bcCmNnqYFlhlSdg1S9a/296D8T/s/J4fry6LH6NR4+Kc6wWfzxQHcifbF/vibedel69o4kcrsbxnqjgTikuInDUsvxCYQBvX/2vYXxxaC9Hzazf3aeqnIwAY02xtwBnAO+Z0yZ2if31/M/NLdX4NS4T58lIPS4ON1zL7y+emMwvvgawYzIu/LS/MhDG944P5sbGEv379t1xUZvJXfnbBlj10/w++ui/KX6xjbS2+1CZJ3US3yeD75sn9W0L1900uCGMPz0Q52iqoE5lPD8P65X4/bXr6ThHhx+JP19WP5i//aM/rxydD8VqjUib3KFWLx1yIz4lbh3Qlx15HOXQI4nN+WDBMhGZoZwcjZxjZreY2S008nWzmX08i10BnADclFVeFZFZ6CA/RWQBFTFHddqqdDXHqD13j0x0StxesnLFwA84tJJj8269erBMRGYoJ0fzH+9+JXDltMWXZLGT5rBpIkteu/nZzMw+CrzL3d8wt60SkSmzydH5osGjdDeHurd1QPAO4Pey308DHm2KDZvZ8TQGiSPBMhGZqfZzVEQWSof5aWb9NPpQEZlPBexDNXiUrhddnHY6d7/XzMbM7GZgJ/B1M/u4u19C47px/5A99Pezn62WiUgb2slREVlYHebn7wL/P/CpuW2NiExXtD60rcFjlRI7aq0ntu9KTLZ/cOzY3Nidz7wgXHffWFxtI3k4N/GaR0VxPDHar1XiScS1yTjulfztL3+0N1x3IFHUZsVkPPm6PJF/FmZ5Mt527/64IE7/9taFlabYjt3xH5ghd6i3eTjf3S+ctmjqlLjNwOunPfY5y4qsPF5j6MHWB0itHp916z35r6N5XFSidETifW5xHnmwenVZuCpeiv//lcT6Vo/zZHJVfttfcvqjuTGAi55/bRh//UCxTkWZD53kqLRWCc6cH/e+eF2Pu/vxRPxAUBRvdyJJd07G3w9GKnEfP17Nb9ve4bhg3ov+v7ivslvvDOOLXU5+rjWzTU33N7r7xqk7ZtYLvMndP2dmGjzOkSNLcZ7AgdzIQKkab7s3f12ANYOjYTzKQYC9Y/lx2xnn90lfir8P1n7yYBhf7IrYh+rIo3Q5w+vF2iMjIs2UoyLF1TI/w0vpAP8P8LX5a5OIHNR+H2pm64FrgJcBK9y92hT7InAyjZoeH3L3zWZ2MXAusBu4yt3/Mtq+Bo/S3Ry8YIfzRaSJclSkuDrLz5OA08zsA8DLzezD7n7F3DdORDrM0WHgbJqukdzkUnd/1MxOBC4F3pUtv8Ddb5jJxjV4lO5XsInEIjKNclSkuNrMT3f/b1O/m9ltGjiKzLP2c3QcGLcWU4bcfWq+TQVoPq//M2a2G7jQ3e+Ntq/Bo3Q3HdUQKTblqEhxzTI/dZkOkXnWOkfDeckz9Gng8uz3y9394uxo5N8BZ0YrtjV4LOOsLLWupJIqmPPMxMr82NOrwnVtJG6mJ+aRen/nl+ezyfhDtTwa//H+0Xj9nmCO8tCWuN3libjQR6pQUHk8KJgTxAB694zFf/rpXWG8fiCenN0O0xfTZ/nYOH7/Qy1jtiwualFaNZQb66vF77UViYI4pWpc/GkiKEpTi+uAUI/rZaQ/H8px28ePyn/ubz4qnsh/dDn1Pk80fpFQjs5MPXEp2X31/OIvvRavW/NEnDhRxj0/h3dOxO/jPZNxwa2xxOeDBx8/tjf+fmC3bgrjovzsFlFBnWHbH65biarSAX2luLBUKSgsCXFxyb6RROHJJV4QZyZa5GhqXnK8PbOPAA+4+20A7j6c/Xy41dHK6XTkUbqbG6gYh0hxKUdFikv5KVJsc5yjZvYW4AzgPU3Lhtx9xMzWMoOxYbFqv4p0ojbtJiLFohwVKS7lp0ixtZmjZtZrZjcApwLXmdkbzezjWfgK4ATgpqzyKsBlZnY7cDVwUWr7OvIo3c3BtNdUpLiUoyLFpfwUKbYOctTdK8Cbpy2+JYud1OLx57ez/eTg0czOA84DOPa4+JxpkcMhMdVn0WvO0QHieY0ih4Ny9GCOPv847bOVYlF+Kj+l2IqWo8ksyar3bAQ45RV93pszabaWqEqxczx/om+qIE7PgXjbpUoYZjZn55YnEvG4bgyJOcZh28uT8crlifjdlHqz9YxWc2Pl/fETL+0+EMZ9bDz+47U5ygQH2pjsb2YbgDuBfwMm3f0tTbG/Bk7L7p7q7mvM7LeBPwa2AXe5+8fmpN1zqDlHh+wI91rrcxpq+/bFG9qf/z8tDe8OVx3YtTqM9w2vCeNjx+V/Puz5pfjzoTYQhmdQMCcR78nPw1IiySYTnz0Vz89BgF5bBF9k2szRxag5R08/dSD3DbW7Hnco+4I3a32Wl0NJ9eFRwY3J+uzepyUSfV0p0ZFK55Sfh+Tnq07t9zHP//7Tb/nFnUoFngk2XImLWk7W446wp5Qo4NiXfy6lL4Ju7LAqYI7qXypdr4M9Mte7+/umL3T3jwCY2SuBC5pCl7n733bcQJElrmh7TUXkIOWnSLEVLUeLu5tEZAbMGyWMm28zcJaZ3WpmH82Jnwt8q+n+R8zsX8zs7Fk3WGSJ6TBHRWQBKD9Fiq2IOarBo3Q9qx96I7t4atPtvKaHbwNeDJwFvNnMXtFik+cA38t+/w7wCuBdwF+YmSb+irSpRY6KSEEoP0WKrWg5qtNWpbs52HNPtc+9eKq7TwATAGZ2DXAysHkqbmYnAk+6+2j2+D1ZaIeZ/QxYBzw1l09BZFFrnaO5zOxkGvOPasAjwO+4Ny4Tn8W+ABjwQXffnLshEUlrMz9FZIEVMEfbGjxOUmJrdWXL2JbJteG6W3fnF9QoVeb3EGyqoE4U70kUxEkWzEkVrZnILwQwsCMuWtOzJ/7jNhIXtaHa+bvRx+OCOD45GcbriXg72tkLY2Yr3X2qcszraVzvptm5wLebHj914dRB4ERgx+xaW2Ce/0LWR0cT68YFLUq9+UUGAPr78j+KBofig73jR8afH7W+MJy89m6ijkCoj/jNWU8UClks2txT+pC7nwFgZl8CTgfuzmJ/BvwmUAc+B7xz7lq5MKrU2VVv/dm8rx6/UBXPz6Ma8Ru5njjRaF99MIxvnTgyN7a/EifZZG12J2xYUHkuVVBP0opwJKMoajh76/lfCmvkf3c5rtz6+/FceayaX/Tufw//Srju/buODeNRjgGUEvGBgfzXbDLRx5ZXrQrjtb174w0sAUXLUX3qSnfztg/nn2lmPzazO2gcYbzTzJoHkG+jcZHUKR81sx8CNwOXZtfOEZGZajNHp+XYBLC16f4ad9/q7k8CcYlfEUlrvw8VkYVUwBzVaavS1Yz2Due7+7XAtdOWfbjp91+ZFvsk8MlZNVJkCcvJ0bVmtqnp/sasXH5jHbN3AP8TeBjY1fS45h2eh79qgEiXa7cPFZGFVcQc1eBRupsXYy+MiORonaO585IB3P0q4KrsrIC3cfBU8uZzp5T5IrOlPlSk2AqYozptVbqe1Q69iUixtJOjZtbfdHcEaJ7cPWxmx5vZ+iwmIrOkPlT4gm+SAAARdElEQVSk2NrNUTNbb2b3mNm4mfVMi51sZreZ2e1TVxxotSzS1pHH/bUBfnjgxJaxB/cfE65br+ePU+u9iYm6E/HZSck5NIkhcj2o5VGrxuumivH0HIif2+qf5k8Etke25sYAavvyJ0/PhPXkP/HS8mXhul6Jn/hcFsSJG4KOPxRBPVH4pR5/2pXG8t9PgzsSSWZxMZ7JFfHnR2VFvPnB9fvz1/VEMZ9EfDQxhbbi+a/bslJcpKRUlH2D7efoOWb2R9nvDwObzezj7n4J8AngH7LY789ZGxdQ1WG41jpfVpbi9+rqoMPZXou788crR4Txu/a9MIz/bN9RubG943GxnbFK3LbJRHxs70Bu7MSrE4XhJKY+9BC7awN8a99LcuN7a/nfjV7U/3S47TcOPhnGt1T7w/jnnv613NiPnnhBuG51Ms6xcm/cR/eU4/j4aH5/tP7ueN3U98nouyqAVxd5KYrOcnQYOJumApBNWhWea6sYnU5bla5X0p5SkUJrJ0fd/UrgymmLL8lim2lUSRaROaI+VKTY2s1Rdx8Hxs1a7pBc4+5bAcxsdbAslwaP0tWsgOeCi8hBylGR4lJ+ihRbTo6GRecSWhWea6sYnQaP0vXU8YkUm3JUpLiUnyLF1m7RuYRWhefaKkanwaN0N9cEf5FCU46KFJfyU6TY5j5Hh83seBqDxJFgWa62Bo8VL/PkROtTYUcq+RPaAZYN5BdQGVsWr1seTlW8icOeeJZRQZ1ELY6w2A5AeSJRDOipnbmx2uhovPFZiiYZe6LgjVcSlYQWkPaaFkCi0AfjE/Hq+8dyY329cdGZ2kD8+eCleP3qsrjtfb357/Wt43ERkgd740Ji456f/wC9QY+xshR/Phxfjosv9Kc+3OaQcvSgGsaeeuv/zcpSnCcDreevALCnHhc5u3ZXXEDv53vWhvHxoKhNtRbn4MR4XNyptjeOr3woyOEfbsqPyYwoPw/aVx3gpuH8gjmRLX1HhvEfH9gQxu/YcUIYf2Jb0N+Mxf2c98TfRev9icJyieFC76P53+NXbtoSrlud5++6i0G7OWpmvcB3gVOB68zsU8AbgsJzbRWj05FH6W7aaypSbMpRkeJSfooUWwc56u4V4M3TFt+SxZ5TeK7dYnQFqeUu0hmj7WvIbTCzp83sZjP7/rTYxWZ2Xxb7o2zZSjO7Orv2zfvn7YmILFLt5qiILJxO8jO7JtwdZnarmX3Jcko6isjsFbEP1ZFH6W4OlrrG4HNd7+7vy4ld4O43NN3/PeAb2e0mM/uGuy/QRSxFFoHOclREFkJn+fmQu58BYGZfAk4H7p7rpokIhexD2xo81t04kHMR05LFT2wimC/RuzOee1Mej9tVja9PTOI63eEovpSY2je4Iz4RefWP4ovCVp9+Jv4Dh0l9LH8OWtF0cI2qs8zsVuBb7v5X02KfMbPdwIXufi/wWuAP3L1mZvcBLwE2z7bNi03q/ZKaI1uq5sfLtTjH+pbHnx/ja+IPgNS85X1b8i959IP6i8N1tx+1Koy/aHmc/y8cyI9v6NsRrrvc9oXxY8oLN+dR15E7aLTex73jz28Z29W3K1x3wPLnqX9396nhuqk5jSOjce2BajX/RCX3+MBTbSz+qrHikThHj//W4/ntCteUmejgGnLNb8QJYOtctudwqnqJ4fH8L5XRd939lXju7r/semEYH98Zf5m1ifwctFqcg15OzHmsx+v3b49z+Je+nj9/v/pE/D1Y0orWh+q0Velu2fVvmm9k179pup3XtMY24MXAWcCbzay5isTl7v5q4IPAFdmy1RysPLU3uy8iM9U6R0WkCNrvQwEws3eY2f3AOiDe6yEinStgH6rTVqWrNc4Ff84etdzr37j7BI09pZjZNcDJZEcS3X04+/lw0xSOvcAQMJ793DO3z0BkccvJUREpgHb70CnufhVwlZldAbwN+Pb8tFBkaStiH6ojj9LdvHFqcfMtYmYrm+6+Hvh5U2wo+7mWgztWfgicbWZl4DTgwTlsvcji12aOisgC6iA/zax5/tII0D3zXES6TQH7UA0epeu1eTj/TDP7sZndATzp7ndme04BLjOz24GrgYuyZX8LvBe4Ffg7FcsRaV/RTrkRkYM6yM9zzOwWM7uFxmmr30+tICKdK1ofmjxtNTvX/TyAZetWMF5rXWRhz0Q82X5sy8rc2IrtcRsSc/GxeI5ycpTevzs/tuqxeOW+f74rjBdgB8Hi5u0dznf3a4Frpy37cPbz/BaPH6FxSk5hNefoAPGFwg8Xr+YX+gCoDecnYWk8vnB6X39c+KXy8qPDeD3xKdg/nL+PbX99KFx302jrAmNTnlwbr7/7iPz/Z31F/MG4oWckjC+YNnN0MWrO0ZXHLuPh8WNaPu6B0fXhdu7fc2xubMf+FeG6tXq8r7hSiYvW1CaCREkU6+jZEefocZ/71zBe7aICbl2ng/x09yuBK+enQQuvOT/7jh5iLCh8M17Nz4M9O+IcLI3EnU3yaE6QZ6kBRapwZM9I/IAXfv4XYby6LfFFXjpXwD40+V51943ufrq7n96/Jh4giiw0c8fqh96WmuYc7SUerIgsNOXooTm6bI1yVIpD+TmtD11VzB2wsnQVMUdVMEe6XtFKGIvIoZSjIsWl/BQptk5y1Mz+isY1WO9x9z9sWv4N4BigHxh099PM7GLgXGA3cJW7/2W0bQ0epbs5WPXw74URkRzKUZHiUn6KFFsHOWpmrwJWuPuZZvZ5M3uNu98N4O7/KXvMucCrm1a7wN1vmMn2VTBHul7RDueLyKHayVEz+2Uzu8PMbsv2nDbH3mhmd5rZj8zsA/PaaJElQn2oSLF1kKOvBa7Pfr8BeF2Lx5wLfKvp/mfM7AYzOy218baOPFbqZbaNti588/jWteG6Kx/LH6f2JObCpwpacCAO94zG8ZVP5Je1WfbQznBdFcQ5vEx7TRe9+micwKU9qcIwccGclKgQgVXjQiG1sfjDa8dIXGDhAWtdWAVgb2UwXPcFvfFn14aehTlXrYMc3QL8qruPm9nfm9kp7v6TLHYB8G7gCRqX0fnC3LZ2/k3Uevj5/tb95RMjq8J1d+zIL7Bkpfg1LvfF/++wIA7Avvx4qlhHX+LquHUVxDls1IceqlIt8cxwfoHH+r784k+9u+IcShWtqQ/MooxmorCk9yZ22o3Gx5JUEOfw6TBHVwNTVY72Ai8/ZJtmvcAp7n5Ptuhyd7/YzE4E/g44M9q4TluV7uZoT6lIkbWZo+7e/C2lAjSPeh4CVgE7SO42FJEk9aEixdY6R9ea2aam+xvdfWPT/b3A1N7GIWD6Lrw3ATc/+yfch7OfD5sl9kSgwaN0PcdqBbjojYjk6CxHzewVwFHu/kDT4m8D36UxoPzk3LRPZClTHypSbC1zdKe7nx6s9EPgfOAfgTcDX54WPxf4xtQdMxty9xEzW8sMxoaa8yjdLbv+TfNNRAqkdY6uNbNNTbfzmlcxsyOAvwF+d9rWLqUxd+NE4LfMTHX1RWZDfahIsXWQo9npqONmdiuNna2Pm9nHAaxxaPF1wG1Nq1xmZrcDVwMXpbavI4/S9drZa2pmG4A7gX8DJt39LU2xTwDnZHf/u7vfaGa/DfwxsA24y90/NjetFlk62tlramY9wFeBC6edwgqNTnCPu0+aWR2Irz4vIkk68ihSbJ3kaPPlOTKXZMsdeOW0x57fzrbbGjxOTvSwZctRLWPLHov78L79+bHyZGIib+o1S9TLGHymEse35M/mrz22NfHH5XAyd6zadlJd7+7va7H8K+7+STNbDVwF3Jgtv8zd/3Y27ZT5U336mTC+dvP6MP706fHBq0p+7YT0Z1OqoE41PvljZGIgf9Mer3v/0PPC+BkDD4XxQZubi9l3kKPvBl4D/Hk29+KPgf/s7h8GPgPckA0cv+vue+ekkQtorNLLT7e1LoQ0OdoXrzyR/z/33vg1ricK6vhk/H7q35sfLyfq3QzuiONy+HTYhy5aNlGi5+f5xciSn/mBaqJoTX0wsfFKfn/ScyDO33Ii3td1n6RLRxFzVEcepbs50P5pNmdlh/K/5e7PXgrA3R/Nfp3ItjzlI2b2fuCT7n4jIjJzbeaou38d+Pq0xT/MYt8DvjdnbRNZ6jrrQ0VkoRQwRzV4lC7nWO05JeijKlTbgBfTGCBeaWY3uvvmaetfDHwx+/07wFeAI4Hvm9np7r4w1zgQWRRa5qiIFILyU6TYipejGjxKd2u9RyZ3PpW7T9AYOGJm1wAnA88OHs3sXOBId/9a9vipc5p3mNnPgHXAU3P5FEQWtQLuNRWRjPJTpNgKmKMaPEp3c7DqzPfImNlKd9+X3X09cEVT7BXA7wO/3rRsqnzxII0Kj5q9I9KONnNURBaQ8lOk2AqYo20NHnv2G0ff2nqV1EVmS8Hz7t9dDdcdeCq+FnRp32gYrz85vWDfoaoT42FcisyhvcP5Z5rZn9E4+niru99pZldkxTguo3Fk8Toz2+vu7wQ+ambn0LiszaXuHldfksKx2+4N48fUTwvj2/9dfkGdem+iIM7A4bsaUilR2eH+RIGUAUtUQZmxtnN0UfNqickdOQU5yomiNkG83B+/xoPLJsP4gUo5jPfl15Wjb19+DGDFU/rYLC7lZzOrQ29Q4LEa1FerLovzt3ZUnAdHr4ur1uzZHxTy+bcV4bo98ddklm8r1pEtaVa8HNWRR+lu3l5Sufu1wLXTln04+/nWFo//JLoYuUjn2sxREVlAyk+RYitgjmrwKN3NgYIdzheRJspRkeJSfooUWwFzVINH6XIO9WJd/0ZEmilHRYpL+SlSbMXLUQ0epbs5UI3nzIrIYaQcFSku5adIsRUwR9srmDMywdrvP9oyVt2wLlx34sj+3Njg1pFw3dr9D4XxYo3HZUG545ViJZV0l/JPfh7Ge08+JTdWC4onANRH44I6Xp+/gjrlxCfjpMcFUmrEbZ8x5eghrAY9+1v/36vL4/9ZaVn+67hyZVzgaO3yuPDcY+O9YRz68kOJWhs9Y8U65UqaKD/bUhsIYmvjgjjHH7crjJ9xVOvv11Pu2HFCbmxnLS6YU07Uhezfq2/ShdVhjprZXwGnA/e4+x82Lf8y8FJgjMZ10L9mZuuBrwIDwJ+6+w3RtnXkUbpbAScSi0gT5ahIcSk/RYqtgxw1s1cBK9z9TDP7vJm9xt3vbnrIe939kab7FwF/AtwHXAOEg8fDV0deZE44Xq0echORIlGOihSX8lOk2DrK0dcC12e/3wC87pANwlfM7Goze0G27BTgDnffD+wzs6Fo4zryKN3N0V5TkSJTjooUl/JTpNha5+haM9vUdH+ju29sur8a+EX2+17g5U2xC9x92MzeAHwW+A2g7O7e9PjVQO6cQg0epau5O3XN1xApLOWoSHEpP0WKLSdHd7r76cFqe4Gpo4dDwJ6m7Q1nP28zs0uzxc2TXg95fCt2cKCZ8wCz84DzsrsnA/eHKxw+a4Gdh7sRLRS1XVDstp3k7itTDzKz79F4Hs12uvs589Os4umSHC3ye01t64xydIaUo7OmtnUmmaPKz67JTyj2e01t68y85Gg25/F8dz/fzD4HfNnd78piQ+4+YmYnAZ9197eZ2eXA14HNwD+7+5vCNqUGj9Masykx0j1sitq2orYL1LbFqKivW1HbBWpbp4rctiIr6utW1HaB2tapIretqIr8mqltnVmqbTOz/wW8CrgXuAT4XXe/xMyuBtbQOCH2g+5+v5kdD3wFGAQ+4e7fj7at01ZFREREREQWiebLc2QuyZa/vcVjnwB+dabbVrVVERERERERSWp38Lgx/ZDDpqhtK2q7QG1bjIr6uhW1XaC2darIbSuyor5uRW0XqG2dKnLbiqrIr5na1hm1bY61NedRREREREREliadtioiIiIiIiJJGjyKiIiIiIhIkgaPIiIiIiIikqTBo4iIiIiIiCRp8CgiIiIiIiJJ/xeZNKsHyR0WvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "view2D  = sompy.mapview.View2D(10,10,\"Iris_raw_data\",text_size=8)\n",
    "view2D.show(som, col_sz=4, which_dim=\"all\",desnormalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training, SOM separates four distinct clusters, which is true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization of a grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 2, 2, 2, 2, 0, 0, 2, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,\n",
       "       2, 2, 2, 0, 0, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = sompy.mapview.View2DPacked(5, 5, 'test',text_size=8)  \n",
    "som.cluster(n_clusters=3)\n",
    "som.cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = sompy.hitmap.HitMapView(8, 8, 'hitmap_iris', text_size=8, show_text=True)\n",
    "h.show(som, );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also we can build the [U-matrix](https://en.wikipedia.org/wiki/U-matrix). Use umatrix.UMatrixView for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "u = sompy.umatrix.UMatrixView(20, 20, 'umatrix')\n",
    "UMAT  = u.build_u_matrix(som)\n",
    "UMAT = u.show(som)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <center>Conclusion</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, it’s impossible to consider the example of a hexagonal grid, because the library does not have the corresponding implementation. Also <code>normalization='var'</code> is only one implementation of the normalization.\n",
    "\n",
    "Kohonen self-organizing maps solve many issues and are a powerful tool for data analysis. In this article, we learned the principle of the SOM, as well as considered small examples of clustering and data visualization. But at the moment, the SOM is losing its popularity in favor of other algorithms."
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}