som.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "happy-crystal",
   "metadata": {},
   "source": [
    "# Data embedding and dimensionality reduction with Kohonen Self-Organizing Maps (SOMs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "economic-optimization",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "impressive-grade",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=4)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "EPSILON = 10e-9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equipped-peripheral",
   "metadata": {},
   "source": [
    "## Data reading utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "greek-creek",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_animals_data(path_2_animals, path_2_animalnames, path_2_animalattributes):\n",
    "    \"\"\" Reads animals data.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    path_2_animals : str\n",
    "        Path to animals.dat that contains the attributes of each animal.\n",
    "    path_2_animalnames : str\n",
    "        Path to animalnames.txt that contains the animals' names.\n",
    "    path_2_animalattributes : str\n",
    "        Path to animalattributes.txt that contains the attribute names.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    df_animals : pandas.DataFrame\n",
    "        DataFrame of animals data.\n",
    "    \"\"\"\n",
    "    with open(path_2_animalnames) as f:\n",
    "        animalnames = np.array([line.strip().strip(\"''\") for line in f.readlines()])\n",
    "\n",
    "    with open(path_2_animalattributes) as f:\n",
    "        animalattributes = np.array([line.strip() for line in f.readlines()])\n",
    "\n",
    "    patterns_animals = np.genfromtxt(path_2_animals, delimiter =',').reshape((animalnames.shape[0],-1))\n",
    "    \n",
    "    data_dict = \\\n",
    "        {animalattribute: patterns_animals[:, idx] for idx, animalattribute in enumerate(animalattributes)}\n",
    "    \n",
    "    df_no_names = pd.DataFrame(data=data_dict, columns=animalattributes, dtype=np.float)\n",
    "    df_names = pd.DataFrame(data={\"animalnames\": animalnames})\n",
    "    \n",
    "    frames = [df_no_names, df_names]\n",
    "    df_animals = pd.concat(frames, axis=1)\n",
    "    \n",
    "    return df_animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "inappropriate-middle",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_cities_data(path_2_cities_data):\n",
    "    \"\"\" Reads cities data.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    path_2_cities_data : str\n",
    "        Path to cities.dat that contains the (x,y) coordinate location of each city.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    df_cities : pandas.DataFrame\n",
    "        DataFrame of cities data.\n",
    "    \"\"\"\n",
    "    df_cities = pd.read_csv(path_2_cities_data, skiprows=3, sep=\",\", names=[\"x\", \"y\"], dtype=str)\n",
    "    df_cities[\"x\"] = df_cities[\"x\"].str.strip(\"\")\n",
    "    df_cities[\"y\"] = df_cities[\"y\"].str.strip(\"\")\n",
    "    df_cities[\"y\"] = df_cities[\"y\"].str.strip(\";\")\n",
    "    df_cities[\"x\"] = pd.to_numeric(df_cities[\"x\"])\n",
    "    df_cities[\"y\"] = pd.to_numeric(df_cities[\"y\"])\n",
    "    \n",
    "    return df_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "retained-testimony",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_votes_data(path_2_votes, path_2_mpparty, path_2_mpsex, path_2_mpdistrict, path_2_mpnames):\n",
    "    \"\"\" Reads votes data.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    path_2_votes : str\n",
    "        Path to votes.dat that contains 31 votes per member of parliament.\n",
    "    path_2_mpparty : str\n",
    "        Path to mpparty.dat that contains the party of each member of parliament.\n",
    "    path_2_mpsex : str\n",
    "        Path to mpsex.dat that contains the sex of each member of parliament.\n",
    "    path_2_mpdistrict : str\n",
    "        Path to mpdistrict.dat that contains the district of each member of parliament.\n",
    "    path_2_mpnames : str\n",
    "        Path to mpnames.dat that contains the names of each member of parliament.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    df_votes : pandas.DataFrame\n",
    "        DataFrame of votes data.\n",
    "    \"\"\"\n",
    "    df_mpparty = pd.read_csv(path_2_mpparty, skiprows=2, names=[\"mpparty\"])\n",
    "    df_mpsex = pd.read_csv(path_2_mpsex, skiprows=2, names=[\"mpsex\"])\n",
    "    df_mpdistrict = pd.read_csv(path_2_mpdistrict, names=[\"mpdistrict\"])\n",
    "    df_mpnames = pd.read_csv(path_2_mpnames, names=[\"mpnames\"], encoding='latin1')\n",
    "    \n",
    "    votes_np = np.genfromtxt(path_2_votes, delimiter =',').reshape((df_mpnames.shape[0],-1))\n",
    "    patterns_dict = {str(k):votes_np[:,k] for k in range(votes_np.shape[1])}\n",
    "    df_patterns = pd.DataFrame.from_dict(data=patterns_dict, dtype=np.float)\n",
    "    \n",
    "    frames = [df_patterns, df_mpparty, df_mpsex, df_mpdistrict, df_mpnames]\n",
    "    df_votes = pd.concat(frames, axis=1)\n",
    "    \n",
    "    mpparty_map = {0: \"no party\", 1: 'm', 2: 'fp', 3: 's', 4: 'v', 5: 'mp', 6: 'kd', 7: 'c'}\n",
    "    mpsex_map = {0: \"male\", 1: 'female'}\n",
    "    df_votes.replace({\"mpparty\": mpparty_map, 'mpsex': mpsex_map}, inplace=True)\n",
    "    \n",
    "    return df_votes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moderate-estonia",
   "metadata": {},
   "source": [
    "## SOM for embedding data into 2D space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "coupled-vacuum",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SOM_2D():\n",
    "    \"\"\" Kohonen Self-Organizing Map (SOM) for embedding data into 2D.\n",
    "    \n",
    "    Attributes\n",
    "    ----------\n",
    "    w_map : numpy.ndarray\n",
    "        The learnable parameters, weights of the SOM.\n",
    "    trained : bool\n",
    "        If SOM has been fit to some data.\n",
    "\n",
    "    Methods\n",
    "    -------\n",
    "    get_input_space_distances(pattern)\n",
    "        Computes the distance of each weight vector to the data vector, pattern.\n",
    "    get_winner(input_space_distances)\n",
    "        Computes the winner node in the SOM lattice, that is, the weight vector that\n",
    "        is closest to the data vector, pattern, based on the distance matrix input_space_distances.\n",
    "    get_output_space_distances(winner_idx, **rule_dict)\n",
    "        Computes the Manhattan distance of each lattice node from the winner, based on the 2D\n",
    "        indices of the nodes in the lattice (gives rise to the 2D topology for embedding).\n",
    "    get_neighbourhood_mask(output_space_distances, epoch, **rule_dict)\n",
    "        Applies a neighbourhood rule to the Manhattan distances in the output space. \n",
    "        Decays with epoch.\n",
    "    update_weights(pattern, neighbourhood_mask, lr)\n",
    "        Updates the learnable weights using the neighbourhood rule based on the data vector pattern. \n",
    "    fit(patterns, lr, epochs, **rule_dict)\n",
    "        Fit SOM to teh dataset patterns.\n",
    "    embed(patterns)\n",
    "        Embed dataset patterns into the 2D lattice based on the winner nodes per pattern.\n",
    "    \"\"\"\n",
    "    def __init__(self, n_rows, n_cols, n_features, seed):\n",
    "        # Set the random seed before initializing the weights.\n",
    "        if seed is not None:\n",
    "            np.random.seed(seed)\n",
    "        else:\n",
    "            np.random.seed()\n",
    "        \n",
    "        self.w_map = np.random.normal(0, 1, size=(n_rows, n_cols, n_features))        \n",
    "        self.trained = False\n",
    "        \n",
    "    def get_input_space_distances(self, pattern):\n",
    "        \"\"\" Computes the distance of each weight vector to the data vector, pattern.\n",
    "    \n",
    "        Parameters\n",
    "        ----------\n",
    "        pattern : numpy.ndarray\n",
    "            Data vector of shape (self.w_map.shape[2], ).\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        input_space_distances : numpy.ndarray\n",
    "            The distance of each weight vector from the data vector pattern in the \n",
    "            input space, of shape (self.w_map.shape[0], self.w_map.shape[1])\n",
    "        \"\"\"\n",
    "        input_space_distances = np.zeros((self.w_map.shape[0], self.w_map.shape[1]))\n",
    "        \n",
    "        for idx_row, w_row in enumerate(self.w_map):\n",
    "            for idx_col, w in enumerate(w_row):\n",
    "                input_space_distances[idx_row, idx_col] = np.linalg.norm(pattern - w)\n",
    "        \n",
    "        return input_space_distances\n",
    "    \n",
    "    def get_winner(self, input_space_distances):\n",
    "        \"\"\" Computes the winner node in the SOM lattice, that is, the weight vector that\n",
    "        is closest to the data vector, pattern, based on the distance matrix input_space_distances.\n",
    "    \n",
    "        Parameters\n",
    "        ----------\n",
    "        input_space_distances : numpy.ndarray\n",
    "            The distance of each weight vector from the data vector pattern in the \n",
    "            input space, of shape (self.w_map.shape[0], self.w_map.shape[1])\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        winner_idx : numpy.ndarray\n",
    "            The index of the winner node, of shape (2,)\n",
    "        winner_error : float\n",
    "            The Euclidean distance of the winner node from the data vector, viewed as the \n",
    "            embedding error for the data vector.\n",
    "        \"\"\"\n",
    "        winner_idx_flattened = np.argmin(input_space_distances)\n",
    "        winner_idx = np.array([winner_idx_flattened // self.w_map.shape[1], \n",
    "                               winner_idx_flattened % self.w_map.shape[1]])\n",
    "        winner_error = np.min(input_space_distances)\n",
    "        return winner_idx, winner_error\n",
    "    \n",
    "    def get_output_space_distances(self, winner_idx, **rule_dict):\n",
    "        \"\"\" Computes the Manhattan distance of each lattice node from the winner, based on the 2D\n",
    "        indices of the nodes in the lattice (gives rise to the 2D topology for embedding).\n",
    "    \n",
    "        Parameters\n",
    "        ----------\n",
    "        winner_idx : numpy.ndarray\n",
    "            The index of the winner node, of shape (2,)\n",
    "        rule_dict : dict\n",
    "            Parameters of the neighbour rule.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        output_space_distances : numpy.ndarray\n",
    "            The Manhattan distance of each weight vector from the winner vector, or node, in\n",
    "            terms of indices, of shape (self.w_map.shape[0], self.w_map.shape[1])\n",
    "        \"\"\"\n",
    "        indices = np.indices(dimensions=(self.w_map.shape[0], self.w_map.shape[1]))\n",
    "        distances_x_y = np.abs((indices.T - winner_idx).T)\n",
    "        \n",
    "        # If wrap, then the SOM lattice wraps around, so nodes on the opposite side \n",
    "        # of the lattice could be closer to the winner node (cyclic lattice).\n",
    "        if rule_dict[\"wrap\"]:\n",
    "            distances_x_y_inv = distances_x_y.copy()\n",
    "            distances_x_y_inv[0] = self.w_map.shape[0] - distances_x_y_inv[0]\n",
    "            distances_x_y_inv[1] = self.w_map.shape[1] - distances_x_y_inv[1]\n",
    "            distances_x_y_min = np.minimum(distances_x_y, distances_x_y_inv)\n",
    "        else:\n",
    "            distances_x_y_min = distances_x_y.copy()\n",
    "        \n",
    "        output_space_distances = np.sum(distances_x_y_min, axis=0)\n",
    "        \n",
    "        return output_space_distances\n",
    "    \n",
    "    def get_neighbourhood_mask(self, output_space_distances, epoch, **rule_dict):\n",
    "        \"\"\" Applies a neighbourhood rule to the Manhattan distances in the output space. \n",
    "        Decays with epoch.\n",
    "    \n",
    "        Parameters\n",
    "        ----------\n",
    "        output_space_distances : numpy.ndarray\n",
    "            The Manhattan distance of each weight vector from the winner vector, or node, in\n",
    "            terms of indices, of shape (self.w_map.shape[0], self.w_map.shape[1])\n",
    "        epoch : int\n",
    "            The current epoch number.\n",
    "        rule_dict : dict\n",
    "            Parameters of the neighbour rule.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        neighbourhood_mask : numpy.ndarray\n",
    "            The neighbourhood mask that contains a real number between 0-1 defining \n",
    "            the strength of the weight update of each node, \n",
    "            of shape (self.w_map.shape[0], self.w_map.shape[1])\n",
    "        \"\"\"\n",
    "        # The nodes at maximum of n distance (Manhattan) will be updated\n",
    "        if rule_dict[\"rule\"] == \"n_neighbours\":\n",
    "            ns = rule_dict[\"ns\"]\n",
    "            n = ns[epoch]\n",
    "            neighbourhood_mask = np.zeros(output_space_distances.shape)\n",
    "            neighbourhood_mask[output_space_distances <= n] = 1.0\n",
    "        # Nodes close to the winner will be updated more, further ones less, \n",
    "        # based on a Gaussian function\n",
    "        elif rule_dict[\"rule\"] == \"gaussian_neighbours\":\n",
    "            sigmas = rule_dict[\"sigmas\"]\n",
    "            sigma = sigmas[epoch]\n",
    "            neighbourhood_mask = \\\n",
    "                np.exp(-0.5*(output_space_distances / (sigma + EPSILON))**2)\n",
    "            \n",
    "        else:\n",
    "            raise NotImplementedError(\"Invalid neighbourhood rule.\")\n",
    "        \n",
    "        return neighbourhood_mask\n",
    "    \n",
    "    def update_weights(self, pattern, neighbourhood_mask, lr):\n",
    "        \"\"\" Updates the learnable weights using the neighbourhood rule based on the data vector pattern. \n",
    "    \n",
    "        Parameters\n",
    "        ----------\n",
    "        pattern : numpy.ndarray\n",
    "            Data vector of shape (self.w_map.shape[2], ).\n",
    "        neighbourhood_mask : numpy.ndarray\n",
    "            The neighbourhood mask that contains a real number between 0-1 defining \n",
    "            the strength of the weight update of each node, \n",
    "            of shape (self.w_map.shape[0], self.w_map.shape[1])\n",
    "        lr : float\n",
    "            The learning rate of the SOM, i.e.: the general strenght of the weight update of\n",
    "            each weight vector, or node.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        None\n",
    "        \"\"\"\n",
    "        d_w_map = np.zeros(self.w_map.shape)\n",
    "        \n",
    "        for idx_row, w_row in enumerate(self.w_map):\n",
    "            for idx_col, w in enumerate(w_row):\n",
    "                d_w_map[idx_row, idx_col] = lr * neighbourhood_mask[idx_row, idx_col] * (pattern - w)\n",
    "\n",
    "        self.w_map += d_w_map\n",
    "    \n",
    "    def fit(self, patterns, lr, epochs, **rule_dict):\n",
    "        \"\"\" Fit SOM to teh dataset patterns.\n",
    "    \n",
    "        Parameters\n",
    "        ----------\n",
    "        patterns : numpy.ndarray\n",
    "            Dataset of shape (n_data, self.w_map.shape[2]) that contains the data vectors\n",
    "            of shape (self.w_map.shape[2], ).\n",
    "        lr : float\n",
    "            The learning rate of the SOM, i.e.: the general strenght of the weight update of\n",
    "            each weight vector, or node.\n",
    "        epochs : int\n",
    "            The number of epochs of the SOM training.\n",
    "        rule_dict : dict\n",
    "            Parameters of the neighbour rule.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        None\n",
    "        \"\"\"\n",
    "        errors = []\n",
    "        \n",
    "        for epoch in range(epochs):\n",
    "            errors_per_epoch = []\n",
    "            \n",
    "            for pattern in patterns:\n",
    "                input_space_distances = self.get_input_space_distances(pattern)\n",
    "                winner_idx, winner_error = self.get_winner(input_space_distances)\n",
    "                errors_per_epoch.append(winner_error)\n",
    "                output_space_distances = self.get_output_space_distances(winner_idx, **rule_dict)\n",
    "                neighbourhood_mask = self.get_neighbourhood_mask(output_space_distances, epoch, **rule_dict)\n",
    "                self.update_weights(pattern, neighbourhood_mask, lr)\n",
    "            \n",
    "            mean_error_per_epoch = np.array(errors_per_epoch).mean()\n",
    "            errors.append(mean_error_per_epoch)\n",
    "            print(f\"epoch {epoch+1}: err: {mean_error_per_epoch:.4e}\")\n",
    "        \n",
    "        plt.figure(figsize=(12,6))\n",
    "        plt.plot(errors)\n",
    "        plt.xlabel(\"Epochs\", fontsize=12)\n",
    "        plt.ylabel(\"Mean Error Per Epoch\", fontsize=12)\n",
    "        plt.title(\"SOM Embedding Error per Epoch\")\n",
    "        plt.xticks(range(epochs))\n",
    "        plt.grid()\n",
    "        plt.show()\n",
    "        self.trained = True\n",
    "        \n",
    "    def embed(self, patterns):\n",
    "        \"\"\" Embed dataset patterns into the 2D lattice based on the winner nodes per pattern.\n",
    "    \n",
    "        Parameters\n",
    "        ----------\n",
    "        patterns : numpy.ndarray\n",
    "            Dataset of shape (n_data, self.w_map.shape[2]) that contains the data vectors\n",
    "            of shape (self.w_map.shape[2], ).\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        winners_idx : numpy.ndarray\n",
    "            The winner nodes' indices per data vector pattern, of shape (n_data, 2)\n",
    "        \"\"\"\n",
    "        winners_idx = []\n",
    "        \n",
    "        for pattern in patterns:\n",
    "            input_space_distances = self.get_input_space_distances(pattern)\n",
    "            winner_idx, winner_error = self.get_winner(input_space_distances)\n",
    "            winners_idx.append(winner_idx)\n",
    "        \n",
    "        winners_idx = np.array(winners_idx)\n",
    "        \n",
    "        return winners_idx\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return f\"w_map shape: {self.w_map.shape}, trained: {self.trained}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "native-flight",
   "metadata": {},
   "source": [
    "## Topological Ordering of Animal Species\n",
    "\n",
    "A dataset of 32 animal species is embedded with SOM where each animal is characterized by 84 binary attributes. The data is in the animals.dat file. This file defines a 32x84 matrix were each row contrains the attributes of one animal. The data are organised in row-by-row manner. There is also a file animalnames.dat with the names of the animals in teh same order. This vector is used to print out the final ordering of animals in a more readable format. The 84 attributes of each anmial serve as input and 100 nodes arranged in a one-dimensional topology, i.e. in a linear sequence, constitute the output. That is, the 84-dimensional data is embedded into 1 dimensions with SOM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "central-diary",
   "metadata": {},
   "source": [
    "Read animals data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "leading-tactics",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antlered</th>\n",
       "      <th>articulations</th>\n",
       "      <th>barks</th>\n",
       "      <th>big</th>\n",
       "      <th>bigears</th>\n",
       "      <th>biting</th>\n",
       "      <th>black</th>\n",
       "      <th>blood</th>\n",
       "      <th>brown</th>\n",
       "      <th>climbing</th>\n",
       "      <th>...</th>\n",
       "      <th>warmblooded</th>\n",
       "      <th>waterliving</th>\n",
       "      <th>verybig</th>\n",
       "      <th>verylongears</th>\n",
       "      <th>verysmall</th>\n",
       "      <th>white</th>\n",
       "      <th>wingless</th>\n",
       "      <th>wings</th>\n",
       "      <th>yellow</th>\n",
       "      <th>animalnames</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>antelop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beetle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>butterfly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>camel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>crocodile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>dragonfly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>duck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>elephant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>frog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>giraffe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>grasshopper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>horse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>housefly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>hyena</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>kangaroo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>moskito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ostrich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pelican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>penguin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>rabbit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>rat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>seaturtle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>skunk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>spider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>walrus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    antlered  articulations  barks  big  bigears  biting  black  blood  brown  \\\n",
       "0        1.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    0.0   \n",
       "1        0.0            0.0    0.0  1.0      0.0     0.0    1.0    1.0    0.0   \n",
       "2        0.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    0.0   \n",
       "3        0.0            0.0    0.0  1.0      0.0     0.0    0.0    1.0    1.0   \n",
       "4        0.0            1.0    0.0  0.0      0.0     1.0    1.0    0.0    0.0   \n",
       "5        0.0            1.0    0.0  0.0      0.0     0.0    0.0    0.0    0.0   \n",
       "6        0.0            0.0    0.0  1.0      0.0     0.0    0.0    1.0    0.0   \n",
       "7        0.0            0.0    0.0  0.0      0.0     0.0    1.0    1.0    0.0   \n",
       "8        0.0            0.0    0.0  1.0      0.0     0.0    0.0    1.0    1.0   \n",
       "9        0.0            0.0    1.0  0.0      0.0     0.0    0.0    1.0    1.0   \n",
       "10       0.0            1.0    0.0  0.0      0.0     1.0    0.0    0.0    1.0   \n",
       "11       0.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    0.0   \n",
       "12       0.0            0.0    0.0  0.0      1.0     0.0    0.0    1.0    0.0   \n",
       "13       0.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    0.0   \n",
       "14       0.0            0.0    0.0  1.0      0.0     0.0    0.0    1.0    0.0   \n",
       "15       0.0            1.0    0.0  0.0      0.0     1.0    0.0    0.0    0.0   \n",
       "16       0.0            0.0    0.0  1.0      0.0     0.0    0.0    1.0    1.0   \n",
       "17       0.0            1.0    0.0  0.0      0.0     0.0    1.0    0.0    0.0   \n",
       "18       0.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    1.0   \n",
       "19       0.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    0.0   \n",
       "20       0.0            0.0    0.0  1.0      0.0     0.0    0.0    1.0    0.0   \n",
       "21       0.0            1.0    0.0  0.0      0.0     0.0    0.0    0.0    0.0   \n",
       "22       0.0            0.0    0.0  1.0      0.0     0.0    1.0    1.0    0.0   \n",
       "23       0.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    0.0   \n",
       "24       0.0            0.0    0.0  0.0      0.0     0.0    1.0    1.0    0.0   \n",
       "25       0.0            0.0    0.0  1.0      0.0     0.0    0.0    1.0    0.0   \n",
       "26       0.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    0.0   \n",
       "27       0.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    1.0   \n",
       "28       0.0            0.0    0.0  1.0      0.0     0.0    0.0    1.0    1.0   \n",
       "29       0.0            0.0    0.0  0.0      0.0     0.0    1.0    1.0    0.0   \n",
       "30       0.0            1.0    0.0  0.0      0.0     0.0    1.0    0.0    0.0   \n",
       "31       0.0            0.0    0.0  0.0      0.0     0.0    0.0    1.0    1.0   \n",
       "\n",
       "    climbing  ...  warmblooded  waterliving  verybig  verylongears  verysmall  \\\n",
       "0        0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "1        0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "2        0.0  ...          1.0          0.0      0.0           0.0        1.0   \n",
       "3        0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "4        0.0  ...          0.0          0.0      0.0           0.0        0.0   \n",
       "5        0.0  ...          0.0          0.0      0.0           0.0        0.0   \n",
       "6        0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "7        1.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "8        0.0  ...          0.0          1.0      0.0           0.0        0.0   \n",
       "9        0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "10       0.0  ...          0.0          0.0      0.0           0.0        0.0   \n",
       "11       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "12       0.0  ...          1.0          0.0      1.0           0.0        0.0   \n",
       "13       0.0  ...          0.0          1.0      0.0           0.0        1.0   \n",
       "14       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "15       0.0  ...          0.0          0.0      0.0           0.0        0.0   \n",
       "16       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "17       0.0  ...          0.0          0.0      0.0           0.0        0.0   \n",
       "18       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "19       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "20       1.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "21       0.0  ...          0.0          0.0      0.0           0.0        0.0   \n",
       "22       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "23       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "24       0.0  ...          1.0          1.0      0.0           0.0        0.0   \n",
       "25       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "26       0.0  ...          1.0          0.0      0.0           1.0        0.0   \n",
       "27       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "28       0.0  ...          0.0          1.0      0.0           0.0        0.0   \n",
       "29       0.0  ...          1.0          0.0      0.0           0.0        0.0   \n",
       "30       0.0  ...          0.0          0.0      0.0           0.0        0.0   \n",
       "31       0.0  ...          1.0          1.0      1.0           0.0        0.0   \n",
       "\n",
       "    white  wingless  wings  yellow  animalnames  \n",
       "0     0.0       0.0    0.0     0.0      antelop  \n",
       "1     0.0       0.0    0.0     0.0          ape  \n",
       "2     0.0       0.0    0.0     0.0          bat  \n",
       "3     0.0       0.0    0.0     0.0         bear  \n",
       "4     0.0       0.0    1.0     0.0       beetle  \n",
       "5     0.0       0.0    1.0     1.0    butterfly  \n",
       "6     0.0       0.0    0.0     1.0        camel  \n",
       "7     0.0       0.0    0.0     0.0          cat  \n",
       "8     0.0       0.0    0.0     0.0    crocodile  \n",
       "9     0.0       0.0    0.0     0.0          dog  \n",
       "10    0.0       0.0    1.0     0.0    dragonfly  \n",
       "11    1.0       0.0    1.0     0.0         duck  \n",
       "12    0.0       0.0    0.0     0.0     elephant  \n",
       "13    0.0       0.0    0.0     0.0         frog  \n",
       "14    0.0       0.0    0.0     1.0      giraffe  \n",
       "15    0.0       0.0    1.0     0.0  grasshopper  \n",
       "16    0.0       0.0    0.0     0.0        horse  \n",
       "17    0.0       0.0    1.0     0.0     housefly  \n",
       "18    0.0       0.0    0.0     0.0        hyena  \n",
       "19    0.0       0.0    0.0     0.0     kangaroo  \n",
       "20    0.0       0.0    0.0     1.0         lion  \n",
       "21    0.0       0.0    1.0     0.0      moskito  \n",
       "22    0.0       0.0    1.0     0.0      ostrich  \n",
       "23    1.0       0.0    1.0     0.0      pelican  \n",
       "24    0.0       0.0    1.0     0.0      penguin  \n",
       "25    0.0       0.0    0.0     0.0          pig  \n",
       "26    1.0       0.0    0.0     0.0       rabbit  \n",
       "27    0.0       0.0    0.0     0.0          rat  \n",
       "28    0.0       0.0    0.0     0.0    seaturtle  \n",
       "29    0.0       0.0    0.0     0.0        skunk  \n",
       "30    0.0       1.0    0.0     0.0       spider  \n",
       "31    0.0       0.0    0.0     0.0       walrus  \n",
       "\n",
       "[32 rows x 85 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_2_animals = os.path.join(os.path.abspath(''), \"data/animals.dat\")\n",
    "path_2_animalnames = os.path.join(os.path.abspath(''), \"data/animalnames.txt\")\n",
    "path_2_animalattributes = os.path.join(os.path.abspath(''), \"data/animalattributes.txt\")\n",
    "\n",
    "df_animals = read_animals_data(path_2_animals, path_2_animalnames, path_2_animalattributes)\n",
    "df_animals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "threatened-integrity",
   "metadata": {},
   "source": [
    "Fit SOM to animals data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "maritime-domestic",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1: err: 3.5459e+00\n",
      "epoch 2: err: 2.5119e+00\n",
      "epoch 3: err: 2.3253e+00\n",
      "epoch 4: err: 2.2380e+00\n",
      "epoch 5: err: 2.1669e+00\n",
      "epoch 6: err: 2.1061e+00\n",
      "epoch 7: err: 2.0315e+00\n",
      "epoch 8: err: 1.9568e+00\n",
      "epoch 9: err: 1.8762e+00\n",
      "epoch 10: err: 1.7926e+00\n",
      "epoch 11: err: 1.7002e+00\n",
      "epoch 12: err: 1.5947e+00\n",
      "epoch 13: err: 1.4710e+00\n",
      "epoch 14: err: 1.3271e+00\n",
      "epoch 15: err: 1.1631e+00\n",
      "epoch 16: err: 9.9002e-01\n",
      "epoch 17: err: 8.1740e-01\n",
      "epoch 18: err: 6.6202e-01\n",
      "epoch 19: err: 5.3199e-01\n",
      "epoch 20: err: 4.2600e-01\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w_map shape: (100, 1, 84), trained: True\n"
     ]
    }
   ],
   "source": [
    "seed = 12\n",
    "\n",
    "patterns_animals = df_animals.iloc[:, :84].to_numpy()\n",
    "\n",
    "n_rows, n_cols, n_features = 100, 1, patterns_animals.shape[1]\n",
    "\n",
    "lr = 0.2\n",
    "epochs = 20\n",
    "\n",
    "som_2d = SOM_2D(n_rows, n_cols, n_features, seed)\n",
    "\n",
    "\"\"\"n_0 = 10\n",
    "ns = np.linspace(0, n_0, epochs, dtype=int)[::-1]\n",
    "rule_dict = {\"rule\": \"n_neighbours\", \"ns\": ns, \"wrap\": True}\"\"\"\n",
    "\n",
    "sigma_0 = 10\n",
    "decay_rate = 100\n",
    "sigmas = [sigma_0 * np.exp(-epoch**2 / decay_rate) for epoch in range(epochs)]\n",
    "rule_dict = {\"rule\": \"gaussian_neighbours\", \"sigmas\": sigmas, \"wrap\": False}\n",
    "\n",
    "som_2d.fit(patterns_animals, lr, epochs, **rule_dict)\n",
    "print(som_2d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collected-virginia",
   "metadata": {},
   "source": [
    "Order the winner nodes per animal, and list the animals based on their winner node (i.e.: embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cordless-facility",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spider: 0\n",
      "housefly: 4\n",
      "beetle: 8\n",
      "grasshopper: 10\n",
      "butterfly: 12\n",
      "dragonfly: 16\n",
      "moskito: 19\n",
      "pelican: 24\n",
      "duck: 27\n",
      "ostrich: 30\n",
      "penguin: 33\n",
      "frog: 38\n",
      "seaturtle: 41\n",
      "crocodile: 43\n",
      "walrus: 47\n",
      "bear: 51\n",
      "hyena: 53\n",
      "dog: 54\n",
      "skunk: 57\n",
      "rat: 59\n",
      "bat: 61\n",
      "cat: 64\n",
      "lion: 66\n",
      "ape: 69\n",
      "rabbit: 72\n",
      "elephant: 75\n",
      "kangaroo: 78\n",
      "antelop: 82\n",
      "horse: 86\n",
      "giraffe: 90\n",
      "camel: 92\n",
      "pig: 95\n"
     ]
    }
   ],
   "source": [
    "winners_idx_animals = som_2d.embed(patterns_animals)\n",
    "\n",
    "animalnames_sorted = [x for _,x in sorted(zip(winners_idx_animals[:,0], df_animals[\"animalnames\"]))]\n",
    "winners_animals_sorted = sorted(winners_idx_animals[:,0])\n",
    "\n",
    "for w, a in zip(animalnames_sorted, winners_animals_sorted):\n",
    "    print(f\"{w}: {a}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "differential-invite",
   "metadata": {},
   "source": [
    "## Cyclic Tour\n",
    "\n",
    "The location of the ten cities is defined in the file the cities.dat which contains the 10 × 2 matrix of coordinates. Each row contains the coordinates of one city (value between zero and one). The SOM algorithm is used to find the shortest path between these cities (i.e.: travelling salesman problem) via embedding the 2 diemnsional data into 1 dimension. Importantly, the 1 dimensional SOM lattice topology wraps around."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "handy-tuition",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.6536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6878</td>\n",
       "      <td>0.5219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8488</td>\n",
       "      <td>0.3609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6683</td>\n",
       "      <td>0.2536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6195</td>\n",
       "      <td>0.2634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        x       y\n",
       "0  0.4000  0.4439\n",
       "1  0.2439  0.1463\n",
       "2  0.1707  0.2293\n",
       "3  0.2293  0.7610\n",
       "4  0.5171  0.9414\n",
       "5  0.8732  0.6536\n",
       "6  0.6878  0.5219\n",
       "7  0.8488  0.3609\n",
       "8  0.6683  0.2536\n",
       "9  0.6195  0.2634"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_2_cities_data = os.path.join(os.path.abspath(''), \"data/cities.dat\")\n",
    "df_cities = read_cities_data(path_2_cities_data)\n",
    "\n",
    "df_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "smooth-edinburgh",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1: err: 2.2317e-01\n",
      "epoch 2: err: 2.4285e-01\n",
      "epoch 3: err: 2.5267e-01\n",
      "epoch 4: err: 2.4454e-01\n",
      "epoch 5: err: 2.4454e-01\n",
      "epoch 6: err: 2.4455e-01\n",
      "epoch 7: err: 2.4455e-01\n",
      "epoch 8: err: 2.4455e-01\n",
      "epoch 9: err: 2.4455e-01\n",
      "epoch 10: err: 2.4455e-01\n",
      "epoch 11: err: 2.4455e-01\n",
      "epoch 12: err: 2.4455e-01\n",
      "epoch 13: err: 2.4455e-01\n",
      "epoch 14: err: 2.2070e-01\n",
      "epoch 15: err: 2.0473e-01\n",
      "epoch 16: err: 2.0385e-01\n",
      "epoch 17: err: 2.0385e-01\n",
      "epoch 18: err: 2.0385e-01\n",
      "epoch 19: err: 2.0385e-01\n",
      "epoch 20: err: 2.0385e-01\n",
      "epoch 21: err: 2.0385e-01\n",
      "epoch 22: err: 2.0385e-01\n",
      "epoch 23: err: 2.0385e-01\n",
      "epoch 24: err: 2.0385e-01\n",
      "epoch 25: err: 2.0385e-01\n",
      "epoch 26: err: 1.9555e-01\n",
      "epoch 27: err: 1.7280e-01\n",
      "epoch 28: err: 1.6337e-01\n",
      "epoch 29: err: 1.6887e-01\n",
      "epoch 30: err: 1.6961e-01\n",
      "epoch 31: err: 1.6968e-01\n",
      "epoch 32: err: 1.6968e-01\n",
      "epoch 33: err: 1.6968e-01\n",
      "epoch 34: err: 1.6969e-01\n",
      "epoch 35: err: 1.6969e-01\n",
      "epoch 36: err: 1.6969e-01\n",
      "epoch 37: err: 1.6969e-01\n",
      "epoch 38: err: 1.1264e-01\n",
      "epoch 39: err: 5.4219e-02\n",
      "epoch 40: err: 3.1157e-02\n",
      "epoch 41: err: 2.1947e-02\n",
      "epoch 42: err: 1.8264e-02\n",
      "epoch 43: err: 1.6791e-02\n",
      "epoch 44: err: 1.6202e-02\n",
      "epoch 45: err: 1.5967e-02\n",
      "epoch 46: err: 1.5872e-02\n",
      "epoch 47: err: 1.5835e-02\n",
      "epoch 48: err: 1.5820e-02\n",
      "epoch 49: err: 1.5814e-02\n",
      "epoch 50: err: 1.5811e-02\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seed = 21\n",
    "    \n",
    "patterns_cities = df_cities.to_numpy()\n",
    "\n",
    "n_rows, n_cols, n_features = 10, 1, patterns_cities.shape[1]\n",
    "\n",
    "lr = 0.6\n",
    "epochs = 50\n",
    "\n",
    "som_2d = SOM_2D(n_rows, n_cols, n_features, seed)\n",
    "\n",
    "n_0 = 4\n",
    "ns = np.linspace(0, n_0, epochs, dtype=int)[::-1]\n",
    "rule_dict = {\"rule\": \"n_neighbours\", \"ns\": ns, \"wrap\": True}\n",
    "\n",
    "\"\"\"sigma_0 = 8\n",
    "decay_rate = 20\n",
    "sigmas = [sigma_0 * np.exp(-epoch**2 / decay_rate) for epoch in range(epochs)]\n",
    "rule_dict = {\"rule\": \"gaussian_neighbours\", \"sigmas\": sigmas, \"wrap\": False}\"\"\"\n",
    "\n",
    "som_2d.fit(patterns_cities, lr, epochs, **rule_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "latin-silence",
   "metadata": {},
   "outputs": [],
   "source": [
    "winners_idx_cities = som_2d.embed(patterns_cities)\n",
    "cities_sorted = [x for _,x in sorted(zip(winners_idx_cities[:, 0],list(df_cities.index.values)))]\n",
    "winners_cities_sorted = sorted(winners_idx_cities[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "contained-plastic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_cities[\"x\"], df_cities[\"y\"], color=\"r\")\n",
    "df_cities[\"order\"] = winners_cities_sorted\n",
    "df_cities_sorted = df_cities.sort_values(by='order', ascending=0)\n",
    "plt.plot(df_cities_sorted[\"x\"], df_cities_sorted[\"y\"])\n",
    "plt.xlabel(\"x coordinates of city location\")\n",
    "plt.ylabel(\"y coordinates of city location\")\n",
    "plt.title(\"Shortest Distance Path Through Cities via SOM\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impossible-hobby",
   "metadata": {},
   "source": [
    "## Data Clustering: Votes of Swedish MPs\n",
    "\n",
    "The file votes.dat contains data about how all 349 members of the Swedish parliament voted in the 31 first votes between 2004-2005. There are also three additional files mpparty.dat, mpsex.dat and mpdistrict.dat with information about the party, gender and district of each member of parliament (MP). Finally, there is a file mpnames.txt with the names of the MPs. The SOM algorithm is used to position all MPs on a 10 × 10 grid according to their votes. That is, the 31 dimensional data is embedded into 2 dimensions with the SOM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "overall-moore",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>mpparty</th>\n",
       "      <th>mpsex</th>\n",
       "      <th>mpdistrict</th>\n",
       "      <th>mpnames</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>m</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>Skårman Carl-Erik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fp</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>Romanus Gabriel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>m</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>Odenberg Mikael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>m</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>Järrel Henrik S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>s</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Lindgren Sylvia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>s</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>Åström Karin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>s</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>Gidblom Birgitta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>s</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>Öberg Maria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>m</td>\n",
       "      <td>male</td>\n",
       "      <td>29</td>\n",
       "      <td>Hammarbergh Krister</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fp</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>Grönlund Krantz Anna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>349 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9  ...   25   26   27  \\\n",
       "0    0.0  1.0  0.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.5  1.0  0.0   \n",
       "1    0.0  1.0  1.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.5  1.0  1.0   \n",
       "2    0.0  1.0  0.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.5  1.0  0.0   \n",
       "3    0.0  1.0  0.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.5  1.0  0.0   \n",
       "4    1.0  1.0  1.0  1.0  1.0  1.0  0.5  1.0  1.0  1.0  ...  0.5  1.0  1.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "344  1.0  1.0  1.0  1.0  1.0  1.0  0.5  1.0  1.0  1.0  ...  0.5  1.0  1.0   \n",
       "345  1.0  1.0  1.0  1.0  1.0  1.0  0.5  1.0  1.0  1.0  ...  0.5  1.0  1.0   \n",
       "346  1.0  1.0  1.0  1.0  1.0  1.0  0.5  1.0  1.0  1.0  ...  0.5  1.0  1.0   \n",
       "347  0.0  1.0  0.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.5  0.5  0.5   \n",
       "348  0.0  1.0  1.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.5  1.0  1.0   \n",
       "\n",
       "      28   29   30  mpparty   mpsex  mpdistrict               mpnames  \n",
       "0    0.5  0.0  1.0        m    male           1     Skårman Carl-Erik  \n",
       "1    0.0  0.5  0.0       fp    male           1       Romanus Gabriel  \n",
       "2    0.5  0.0  1.0        m    male           1       Odenberg Mikael  \n",
       "3    0.5  0.0  1.0        m    male           1       Järrel Henrik S  \n",
       "4    1.0  1.0  0.5        s  female           1       Lindgren Sylvia  \n",
       "..   ...  ...  ...      ...     ...         ...                   ...  \n",
       "344  1.0  1.0  0.5        s  female          29          Åström Karin  \n",
       "345  1.0  1.0  0.5        s  female          29      Gidblom Birgitta  \n",
       "346  1.0  1.0  0.5        s  female          29           Öberg Maria  \n",
       "347  0.5  0.5  0.5        m    male          29   Hammarbergh Krister  \n",
       "348  0.0  0.5  0.0       fp  female          29  Grönlund Krantz Anna  \n",
       "\n",
       "[349 rows x 35 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_2_votes = os.path.join(os.path.abspath(''), \"data/votes.dat\")\n",
    "path_2_mpparty = os.path.join(os.path.abspath(''), \"data/mpparty.dat\")\n",
    "path_2_mpsex = os.path.join(os.path.abspath(''), \"data/mpsex.dat\")\n",
    "path_2_mpdistrict = os.path.join(os.path.abspath(''), \"data/mpdistrict.dat\")\n",
    "path_2_mpnames = os.path.join(os.path.abspath(''), \"data/mpnames.txt\")\n",
    "\n",
    "df_votes = read_votes_data(path_2_votes, path_2_mpparty, path_2_mpsex, path_2_mpdistrict, path_2_mpnames)\n",
    "df_votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "limiting-amino",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1: err: 1.9096e+00\n",
      "epoch 2: err: 1.8667e+00\n",
      "epoch 3: err: 1.7821e+00\n",
      "epoch 4: err: 1.6200e+00\n",
      "epoch 5: err: 1.4290e+00\n",
      "epoch 6: err: 1.0863e+00\n",
      "epoch 7: err: 6.0724e-01\n",
      "epoch 8: err: 3.6433e-01\n",
      "epoch 9: err: 2.3257e-01\n",
      "epoch 10: err: 1.6436e-01\n",
      "epoch 11: err: 1.3718e-01\n",
      "epoch 12: err: 1.2513e-01\n",
      "epoch 13: err: 1.1895e-01\n",
      "epoch 14: err: 1.1378e-01\n",
      "epoch 15: err: 1.1069e-01\n",
      "epoch 16: err: 1.0862e-01\n",
      "epoch 17: err: 1.0721e-01\n",
      "epoch 18: err: 1.0624e-01\n",
      "epoch 19: err: 1.0556e-01\n",
      "epoch 20: err: 1.0510e-01\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "patterns_votes = df_votes.iloc[:, :31].to_numpy()\n",
    "\n",
    "n_rows, n_cols, n_features = 10, 10, patterns_votes.shape[1]\n",
    "\n",
    "lr = 0.3\n",
    "epochs = 20\n",
    "\n",
    "som_2d = SOM_2D(n_rows, n_cols, n_features, seed)\n",
    "\n",
    "\"\"\"n_0 = 10\n",
    "ns = np.linspace(0, n_0, epochs, dtype=int)[::-1]\n",
    "rule_dict = {\"rule\": \"n_neighbours\", \"ns\": ns, \"wrap\": True}\n",
    "\"\"\"\n",
    "sigma_0 = 10\n",
    "decay_rate = 20\n",
    "sigmas = [sigma_0 * np.exp(-epoch**2 / decay_rate) for epoch in range(epochs)]\n",
    "rule_dict = {\"rule\": \"gaussian_neighbours\", \"sigmas\": sigmas, \"wrap\":True}\n",
    "\n",
    "som_2d.fit(patterns_votes, lr, epochs, **rule_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "appropriate-treaty",
   "metadata": {},
   "outputs": [
    {
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       "      <td>7</td>\n",
       "      <td>6</td>\n",
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       "</table>\n",
       "<p>349 rows × 37 columns</p>\n",
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      ],
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       "       0    1    2    3    4    5    6    7    8    9  ...   27   28   29  \\\n",
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       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
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       "\n",
       "      30  mpparty   mpsex  mpdistrict               mpnames  x  y  \n",
       "0    1.0        m    male           1     Skårman Carl-Erik  4  9  \n",
       "1    0.0       fp    male           1       Romanus Gabriel  7  6  \n",
       "2    1.0        m    male           1       Odenberg Mikael  2  8  \n",
       "3    1.0        m    male           1       Järrel Henrik S  4  9  \n",
       "4    0.5        s  female           1       Lindgren Sylvia  3  3  \n",
       "..   ...      ...     ...         ...                   ... .. ..  \n",
       "344  0.5        s  female          29          Åström Karin  3  3  \n",
       "345  0.5        s  female          29      Gidblom Birgitta  3  3  \n",
       "346  0.5        s  female          29           Öberg Maria  3  3  \n",
       "347  0.5        m    male          29   Hammarbergh Krister  5  9  \n",
       "348  0.0       fp  female          29  Grönlund Krantz Anna  7  6  \n",
       "\n",
       "[349 rows x 37 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winners_idx = som_2d.embed(patterns_votes)\n",
    "\n",
    "dict_embedded = {\"x\": winners_idx[:, 0], \"y\": winners_idx[:, 1]}\n",
    "df_votes_embedded = pd.DataFrame(data=dict_embedded)\n",
    "\n",
    "frames = [df_votes, df_votes_embedded]\n",
    "df_votes_plot = pd.concat(frames, axis=1)\n",
    "\n",
    "df_votes_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "third-botswana",
   "metadata": {},
   "source": [
    "Add noise to points (otherwise they would all lie exactly on the lattice nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "recent-alloy",
   "metadata": {},
   "outputs": [
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       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fp</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>Romanus Gabriel</td>\n",
       "      <td>6.897786</td>\n",
       "      <td>5.890680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>m</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>Odenberg Mikael</td>\n",
       "      <td>2.036366</td>\n",
       "      <td>7.819414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>m</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>Järrel Henrik S</td>\n",
       "      <td>3.744890</td>\n",
       "      <td>9.184394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>s</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Lindgren Sylvia</td>\n",
       "      <td>3.112971</td>\n",
       "      <td>3.116933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>s</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>Åström Karin</td>\n",
       "      <td>3.059806</td>\n",
       "      <td>2.949234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>s</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>Gidblom Birgitta</td>\n",
       "      <td>3.329018</td>\n",
       "      <td>2.929128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>s</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>Öberg Maria</td>\n",
       "      <td>2.976108</td>\n",
       "      <td>2.841187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>m</td>\n",
       "      <td>male</td>\n",
       "      <td>29</td>\n",
       "      <td>Hammarbergh Krister</td>\n",
       "      <td>5.077206</td>\n",
       "      <td>9.072966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fp</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>Grönlund Krantz Anna</td>\n",
       "      <td>7.025245</td>\n",
       "      <td>5.817971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>349 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9  ...   27   28   29  \\\n",
       "0    0.0  1.0  0.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.0  0.5  0.0   \n",
       "1    0.0  1.0  1.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  1.0  0.0  0.5   \n",
       "2    0.0  1.0  0.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.0  0.5  0.0   \n",
       "3    0.0  1.0  0.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.0  0.5  0.0   \n",
       "4    1.0  1.0  1.0  1.0  1.0  1.0  0.5  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "344  1.0  1.0  1.0  1.0  1.0  1.0  0.5  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "345  1.0  1.0  1.0  1.0  1.0  1.0  0.5  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "346  1.0  1.0  1.0  1.0  1.0  1.0  0.5  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "347  0.0  1.0  0.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  0.5  0.5  0.5   \n",
       "348  0.0  1.0  1.0  0.5  0.0  0.0  1.0  0.5  0.0  0.0  ...  1.0  0.0  0.5   \n",
       "\n",
       "      30  mpparty   mpsex  mpdistrict               mpnames         x  \\\n",
       "0    1.0        m    male           1     Skårman Carl-Erik  4.070948   \n",
       "1    0.0       fp    male           1       Romanus Gabriel  6.897786   \n",
       "2    1.0        m    male           1       Odenberg Mikael  2.036366   \n",
       "3    1.0        m    male           1       Järrel Henrik S  3.744890   \n",
       "4    0.5        s  female           1       Lindgren Sylvia  3.112971   \n",
       "..   ...      ...     ...         ...                   ...       ...   \n",
       "344  0.5        s  female          29          Åström Karin  3.059806   \n",
       "345  0.5        s  female          29      Gidblom Birgitta  3.329018   \n",
       "346  0.5        s  female          29           Öberg Maria  2.976108   \n",
       "347  0.5        m    male          29   Hammarbergh Krister  5.077206   \n",
       "348  0.0       fp  female          29  Grönlund Krantz Anna  7.025245   \n",
       "\n",
       "            y  \n",
       "0    9.205579  \n",
       "1    5.890680  \n",
       "2    7.819414  \n",
       "3    9.184394  \n",
       "4    3.116933  \n",
       "..        ...  \n",
       "344  2.949234  \n",
       "345  2.929128  \n",
       "346  2.841187  \n",
       "347  9.072966  \n",
       "348  5.817971  \n",
       "\n",
       "[349 rows x 37 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 12\n",
    "if seed is not None:\n",
    "    np.random.seed(seed)\n",
    "else:\n",
    "    np.random.seed()\n",
    "\n",
    "df_votes_plot[\"x\"] = df_votes_plot[\"x\"] + np.random.normal(0, 0.15, size=(df_votes_plot.shape[0])) \n",
    "df_votes_plot[\"y\"] = df_votes_plot[\"y\"] + np.random.normal(0, 0.15, size=(df_votes_plot.shape[0])) \n",
    "\n",
    "df_votes_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "intelligent-repair",
   "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": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "sc = sns.scatterplot(x=\"x\", y=\"y\", hue=\"mpparty\", style='mpsex', data=df_votes_plot)\n",
    "sc.set_xticks([i for i in range(som_2d.w_map.shape[1])])\n",
    "sc.set_yticks([i for i in range(som_2d.w_map.shape[0])])\n",
    "\n",
    "plt.grid()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.xlabel(\"SOM_2D x-axis (width)\", fontsize= 12)\n",
    "plt.ylabel(\"SOM_2D y-axis (height)\", fontsize= 12)\n",
    "plt.title(\"Votes Data Embedded onto a 2D SOM Lattice\", fontsize= 15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "polar-animal",
   "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": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "sc = sns.scatterplot(x=\"x\", y=\"y\", hue=\"mpdistrict\", data=df_votes_plot, legend=\"full\")\n",
    "sc.set_xticks([i for i in range(som_2d.w_map.shape[1])])\n",
    "sc.set_yticks([i for i in range(som_2d.w_map.shape[0])])\n",
    "\n",
    "plt.grid()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.xlabel(\"SOM_2D x-axis (width)\", fontsize= 12)\n",
    "plt.ylabel(\"SOM_2D y-axis (height)\", fontsize= 12)\n",
    "plt.title(\"Votes Data Embedded onto a 2D SOM Lattice\", fontsize= 15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "plain-therapy",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ann_lab_2",
   "language": "python",
   "name": "ann_lab_2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}