gsom-iris-python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import log, exp\n",
    "import itertools\n",
    "import math\n",
    "import random\n",
    "import scipy\n",
    "\n",
    "class GSOM_Node:\n",
    "    R = random.Random()\n",
    "    \n",
    "    def __init__(self, dim, x, y):\n",
    "        # Create a weight vector of the given dimension:\n",
    "        # Initialize the weight vector with random values between 0 and 1.\n",
    "        self.weights=scipy.array([self.R.random() for _ in range(dim)])\n",
    "\n",
    "        self.error = 0.0\n",
    "\n",
    "        self.it = 0\n",
    "        self.last_it = 0\n",
    "\n",
    "        self.data = None\n",
    "        self.last_changed = 0\n",
    "\n",
    "        self.right = None\n",
    "        self.left  = None\n",
    "        self.up    = None\n",
    "        self.down  = None\n",
    "\n",
    "        self.x, self.y = x, y\n",
    "\n",
    "\n",
    "    def adjust_weights(self, target, learn_rate):\n",
    "        \"\"\" Adjust the weights of this node. \"\"\"\n",
    "        for w in range(0, len(target)):\n",
    "            self.weights[w] += learn_rate * (target[w] - self.weights[w])\n",
    "\n",
    "\n",
    "    def is_boundary(self):\n",
    "        \"\"\" Check if this node is at the boundary of the map. \"\"\"\n",
    "        if not self.right: return True\n",
    "        if not self.left:  return True\n",
    "        if not self.up:    return True\n",
    "        if not self.down:  return True\n",
    "        return False\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# import iris data\n",
    "iris = datasets.load_iris()\n",
    "dataset = iris\n",
    "spread_factor = 1.5\n",
    "# Assign the data\n",
    "data = []\n",
    "for fn in dataset:\n",
    "    t = dataset\n",
    "    arr = scipy.array(t.data)\n",
    "    data.append([fn,arr])\n",
    "\n",
    "# Determine the dimension of the data.\n",
    "dim = len(data[0][1])\n",
    "\n",
    "# Calculate the growing threshold:\n",
    "_GT = -dim * math.log(spread_factor, 2)\n",
    "\n",
    "# Create the 4 starting Nodes.\n",
    "nodes = []\n",
    "n00 = GSOM_Node(dim, 10, 10)\n",
    "n01 = GSOM_Node(dim, 10, 11)\n",
    "n10 = GSOM_Node(dim, 11, 10)\n",
    "n11 = GSOM_Node(dim, 11, 11)\n",
    "nodes.extend([n00,n01,n10,n11])\n",
    "\n",
    "# Create starting topology\n",
    "n00.right = n10\n",
    "n00.up    = n01\n",
    "n01.right = n11\n",
    "n01.down  = n00\n",
    "n10.up    = n11\n",
    "n10.left  = n00\n",
    "n11.left  = n01\n",
    "n11.down  = n10\n",
    "\n",
    "# Set properties\n",
    "it = 0       # Current iteration\n",
    "max_it = len(data)\n",
    "num_it = 100     # Total iterations\n",
    "init_lr = 0.5     # Initial value of the learning rate\n",
    "alpha = 0.2\n",
    "output = file = open(\"gsom.csv\",\"w\")\n",
    "\n",
    "print(len(n00.weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _distance(v1, v2):\n",
    "    dist = 0.0\n",
    "    #print(v1)\n",
    "    #print(v2)\n",
    "    for v, w in zip(v1, v2):\n",
    "        dist += pow(v - w,2)\n",
    "    return dist\n",
    "\n",
    "\n",
    "def _find_bmu(vec):\n",
    "    dist=float(\"inf\")\n",
    "    winner = False\n",
    "    for node in nodes:\n",
    "        d = _distance(vec, node.weights)\n",
    "        #print(d.all())            \n",
    "        if(d < dist):\n",
    "            dist = d\n",
    "            winner = node\n",
    "\n",
    "    return winner\n",
    "\n",
    "\n",
    "def _find_similar_boundary(node):\n",
    "    dist = float(\"inf\")\n",
    "    winner = False\n",
    "    for boundary in nodes:\n",
    "        if not boundary.is_boundary(): continue\n",
    "        if boundary == node: continue\n",
    "\n",
    "        d = _distance(node.weights, boundary.weights)\n",
    "        if d < dist:\n",
    "            dist = d\n",
    "            winner = node\n",
    "\n",
    "    return winner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _node_add_error(node, error):\n",
    "\n",
    "    node.error += error\n",
    "\n",
    "    # Consider growing\n",
    "    if node.error > _GT:\n",
    "        if not node.is_boundary():\n",
    "            node = _find_similar_boundary(node)\n",
    "            if not node:\n",
    "                print(\"GSOM: Error: No free boundary node found!\")\n",
    "\n",
    "        nodes = _grow(node)\n",
    "        return True, nodes\n",
    "\n",
    "    return False, 0\n",
    "\n",
    "\n",
    "def _grow(node):\n",
    "    \"\"\" Grow this GSOM. \"\"\"\n",
    "    # We grow this GSOM in every possible direction.\n",
    "    nodes = []\n",
    "    if node.left == None:\n",
    "        nn = _insert(node.x - 1, node.y, node)\n",
    "        nodes.append(nn)\n",
    "        print(\"Growing left at: (\" + str(node.x) + \",\" + str(node.y)\\\n",
    "                + \") -> (\" + str(nn.x) + \", \" + str(nn.y) + \")\")\n",
    "\n",
    "    if node.right == None:\n",
    "        nn = _insert(node.x + 1, node.y, node)\n",
    "        nodes.append(nn)\n",
    "        print(\"Growing right at: (\" + str(node.x) + \",\" + str(node.y)\\\n",
    "                + \") -> (\" + str(nn.x) + \", \" + str(nn.y) + \")\")\n",
    "\n",
    "    if node.up == None:\n",
    "        nn = _insert(node.x, node.y + 1, node)\n",
    "        nodes.append(nn)\n",
    "        print(\"Growing up at: (\" + str(node.x) + \",\" + str(node.y) +\\\n",
    "                \") -> (\" + str(nn.x) + \", \" + str(nn.y) + \")\")\n",
    "\n",
    "    if node.down == None:\n",
    "        nn = _insert(node.x, node.y - 1, node)\n",
    "        nodes.append(nn)\n",
    "        print(\"Growing down at: (\" + str(node.x) + \",\" + str(node.y) +\\\n",
    "                \") -> (\" + str(nn.x) + \", \" + str(nn.y) + \")\")\n",
    "    return nodes\n",
    "\n",
    "\n",
    "def _insert(x, y, init_node):\n",
    "    # Create new node\n",
    "    new_node = GSOM_Node(dim, x, y)\n",
    "    nodes.append(new_node)\n",
    "\n",
    "    new_node.it = new_node.last_it = it\n",
    "\n",
    "    # Create the connections to possible neighbouring nodes.\n",
    "    for node in nodes:\n",
    "        # Left, Right, Up, Down\n",
    "        if node.x == x - 1 and node.y == y:\n",
    "            new_node.left = node\n",
    "            node.right = new_node\n",
    "        if node.x == x + 1 and node.y == y:\n",
    "            new_node.right = node\n",
    "            node.left = new_node\n",
    "        if node.x == x and node.y == y + 1:\n",
    "            new_node.up = node\n",
    "            node.down = new_node\n",
    "        if node.x == x and node.y == y - 1:\n",
    "            new_node.down = node\n",
    "            node.up = new_node\n",
    "\n",
    "    # Calculate new weights, look for a neighbour.\n",
    "    neigh = new_node.left\n",
    "    if neigh == None: neigh = new_node.right\n",
    "    if neigh == None: neigh = new_node.up\n",
    "    if neigh == None: neigh = new_node.down\n",
    "    if neigh == None: print(\"_insert: No neighbour found!\")\n",
    "\n",
    "    for i in range(0, len(new_node.weights)):\n",
    "        new_node.weights[i] = 2 * init_node.weights[i] - neigh.weights[i]\n",
    "\n",
    "    return new_node\n",
    "\n",
    "\n",
    "def _remove_unused_nodes():\n",
    "    \"\"\" Remove all nodes from the GSOM that have not been used. \"\"\"\n",
    "    to_remove = []\n",
    "\n",
    "    # Iterate over all nodes.\n",
    "    for node in nodes:\n",
    "        iterations_not_won = it - node.last_it\n",
    "        if iterations_not_won < len(nodes) * 4.0 * (1 + it/len(data)) : continue\n",
    "\n",
    "        if node.left:  node.left.right = None\n",
    "        if node.up:    node.up.down    = None\n",
    "        if node.down:  node.down.up    = None\n",
    "        if node.right: node.right.left = None\n",
    "\n",
    "        to_remove.append(node)\n",
    "\n",
    "    # Now remove all marked nodes.\n",
    "    for node in to_remove:\n",
    "        print(\"Removing node @ \" + str(node.x) + \", \" + str(node.y) + \\\n",
    "              \" - Current it: \" + str(it) + \" - Last time won: \" +\\\n",
    "              str(node.last_it))\n",
    "        if node.data:\n",
    "            output.write(node.data + \",\" + str(node.x)+\",\"+str(node.y)\\\n",
    "                + \",remove\\n\")\n",
    "    nodes.remove(node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "## training & viz\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1 3.5 1.4 0.2]\n",
      "[5.1, 3.5, 1.4, 0.2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data0, data1, data2 = [], [],[]\n",
    "j = -1\n",
    "for i in (dataset['data']):\n",
    "    j +=1\n",
    "    if j < 51:\n",
    "        data0.append(i)\n",
    "    elif j < 101:\n",
    "        data1.append(i)\n",
    "    else:\n",
    "        data2.append(i)\n",
    "\n",
    "l5 = []\n",
    "print(data0[0])\n",
    "for j in data0[0]:\n",
    "    l5.append(j)\n",
    "print(l5)\n",
    "l5 = np.array(l5)\n",
    "l6 = l5.transpose()\n",
    "#print(np.transpose(l6))\n",
    "len(point0)\n",
    "#l5\n",
    "#[i[0] for i in (dataset['data'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growing left at: (-128,22) -> (-129, 22)\n",
      "Growing up at: (-128,22) -> (-128, 23)\n",
      "Growing down at: (-128,22) -> (-128, 21)\n",
      "Growing left at: (-129,22) -> (-130, 22)\n",
      "Growing up at: (-129,22) -> (-129, 23)\n",
      "Growing down at: (-129,22) -> (-129, 21)\n",
      "Growing left at: (-130,22) -> (-131, 22)\n",
      "Growing up at: (-130,22) -> (-130, 23)\n",
      "Growing down at: (-130,22) -> (-130, 21)\n",
      "Growing left at: (-131,22) -> (-132, 22)\n",
      "Growing up at: (-131,22) -> (-131, 23)\n",
      "Growing down at: (-131,22) -> (-131, 21)\n",
      "Growing left at: (-132,22) -> (-133, 22)\n",
      "Growing up at: (-132,22) -> (-132, 23)\n",
      "Growing down at: (-132,22) -> (-132, 21)\n",
      "Growing left at: (-133,22) -> (-134, 22)\n",
      "Growing up at: (-133,22) -> (-133, 23)\n",
      "Growing down at: (-133,22) -> (-133, 21)\n",
      "Growing left at: (-134,22) -> (-135, 22)\n",
      "Growing up at: (-134,22) -> (-134, 23)\n",
      "Growing down at: (-134,22) -> (-134, 21)\n",
      "Growing left at: (-135,22) -> (-136, 22)\n",
      "Growing up at: (-135,22) -> (-135, 23)\n",
      "Growing down at: (-135,22) -> (-135, 21)\n",
      "Growing left at: (-136,22) -> (-137, 22)\n",
      "Growing up at: (-136,22) -> (-136, 23)\n",
      "Growing down at: (-136,22) -> (-136, 21)\n",
      "Growing left at: (-137,22) -> (-138, 22)\n",
      "Growing up at: (-137,22) -> (-137, 23)\n",
      "Growing down at: (-137,22) -> (-137, 21)\n",
      "Growing left at: (-138,22) -> (-139, 22)\n",
      "Growing up at: (-138,22) -> (-138, 23)\n",
      "Growing down at: (-138,22) -> (-138, 21)\n",
      "Growing left at: (-139,22) -> (-140, 22)\n",
      "Growing up at: (-139,22) -> (-139, 23)\n",
      "Growing down at: (-139,22) -> (-139, 21)\n",
      "Growing left at: (-140,22) -> (-141, 22)\n",
      "Growing up at: (-140,22) -> (-140, 23)\n",
      "Growing down at: (-140,22) -> (-140, 21)\n",
      "Growing left at: (-141,22) -> (-142, 22)\n",
      "Growing up at: (-141,22) -> (-141, 23)\n",
      "Growing down at: (-141,22) -> (-141, 21)\n",
      "Growing left at: (-142,22) -> (-143, 22)\n",
      "Growing up at: (-142,22) -> (-142, 23)\n",
      "Growing down at: (-142,22) -> (-142, 21)\n",
      "Growing left at: (-143,22) -> (-144, 22)\n",
      "Growing up at: (-143,22) -> (-143, 23)\n",
      "Growing down at: (-143,22) -> (-143, 21)\n",
      "Growing left at: (-144,22) -> (-145, 22)\n",
      "Growing up at: (-144,22) -> (-144, 23)\n",
      "Growing down at: (-144,22) -> (-144, 21)\n",
      "Growing left at: (-145,22) -> (-146, 22)\n",
      "Growing up at: (-145,22) -> (-145, 23)\n",
      "Growing down at: (-145,22) -> (-145, 21)\n",
      "Growing left at: (-146,22) -> (-147, 22)\n",
      "Growing up at: (-146,22) -> (-146, 23)\n",
      "Growing down at: (-146,22) -> (-146, 21)\n",
      "Growing left at: (-147,22) -> (-148, 22)\n",
      "Growing up at: (-147,22) -> (-147, 23)\n",
      "Growing down at: (-147,22) -> (-147, 21)\n",
      "Growing left at: (-148,22) -> (-149, 22)\n",
      "Growing up at: (-148,22) -> (-148, 23)\n",
      "Growing down at: (-148,22) -> (-148, 21)\n",
      "Growing left at: (-149,22) -> (-150, 22)\n",
      "Growing up at: (-149,22) -> (-149, 23)\n",
      "Growing down at: (-149,22) -> (-149, 21)\n",
      "Growing left at: (-150,22) -> (-151, 22)\n",
      "Growing up at: (-150,22) -> (-150, 23)\n",
      "Growing down at: (-150,22) -> (-150, 21)\n",
      "Growing left at: (-151,22) -> (-152, 22)\n",
      "Growing up at: (-151,22) -> (-151, 23)\n",
      "Growing down at: (-151,22) -> (-151, 21)\n",
      "Growing left at: (-152,22) -> (-153, 22)\n",
      "Growing up at: (-152,22) -> (-152, 23)\n",
      "Growing down at: (-152,22) -> (-152, 21)\n",
      "Growing left at: (-153,22) -> (-154, 22)\n",
      "Growing up at: (-153,22) -> (-153, 23)\n",
      "Growing down at: (-153,22) -> (-153, 21)\n",
      "Growing left at: (-154,22) -> (-155, 22)\n",
      "Growing up at: (-154,22) -> (-154, 23)\n",
      "Growing down at: (-154,22) -> (-154, 21)\n",
      "Growing left at: (-155,22) -> (-156, 22)\n",
      "Growing up at: (-155,22) -> (-155, 23)\n",
      "Growing down at: (-155,22) -> (-155, 21)\n",
      "Growing left at: (-156,22) -> (-157, 22)\n",
      "Growing up at: (-156,22) -> (-156, 23)\n",
      "Growing down at: (-156,22) -> (-156, 21)\n",
      "Growing left at: (-157,22) -> (-158, 22)\n",
      "Growing up at: (-157,22) -> (-157, 23)\n",
      "Growing down at: (-157,22) -> (-157, 21)\n",
      "Growing left at: (-158,22) -> (-159, 22)\n",
      "Growing up at: (-158,22) -> (-158, 23)\n",
      "Growing down at: (-158,22) -> (-158, 21)\n",
      "Growing left at: (-159,22) -> (-160, 22)\n",
      "Growing up at: (-159,22) -> (-159, 23)\n",
      "Growing down at: (-159,22) -> (-159, 21)\n",
      "Growing left at: (-160,22) -> (-161, 22)\n",
      "Growing up at: (-160,22) -> (-160, 23)\n",
      "Growing down at: (-160,22) -> (-160, 21)\n",
      "Growing left at: (-161,22) -> (-162, 22)\n",
      "Growing up at: (-161,22) -> (-161, 23)\n",
      "Growing down at: (-161,22) -> (-161, 21)\n",
      "Growing left at: (-162,22) -> (-163, 22)\n",
      "Growing up at: (-162,22) -> (-162, 23)\n",
      "Growing down at: (-162,22) -> (-162, 21)\n",
      "Growing left at: (-163,22) -> (-164, 22)\n",
      "Growing up at: (-163,22) -> (-163, 23)\n",
      "Growing down at: (-163,22) -> (-163, 21)\n",
      "Growing left at: (-164,22) -> (-165, 22)\n",
      "Growing up at: (-164,22) -> (-164, 23)\n",
      "Growing down at: (-164,22) -> (-164, 21)\n",
      "Growing left at: (-165,22) -> (-166, 22)\n",
      "Growing up at: (-165,22) -> (-165, 23)\n",
      "Growing down at: (-165,22) -> (-165, 21)\n",
      "Growing left at: (-166,22) -> (-167, 22)\n",
      "Growing up at: (-166,22) -> (-166, 23)\n",
      "Growing down at: (-166,22) -> (-166, 21)\n",
      "Growing left at: (-167,22) -> (-168, 22)\n",
      "Growing up at: (-167,22) -> (-167, 23)\n",
      "Growing down at: (-167,22) -> (-167, 21)\n",
      "Growing left at: (-168,22) -> (-169, 22)\n",
      "Growing up at: (-168,22) -> (-168, 23)\n",
      "Growing down at: (-168,22) -> (-168, 21)\n",
      "Growing left at: (-169,22) -> (-170, 22)\n",
      "Growing up at: (-169,22) -> (-169, 23)\n",
      "Growing down at: (-169,22) -> (-169, 21)\n",
      "Growing left at: (-170,22) -> (-171, 22)\n",
      "Growing up at: (-170,22) -> (-170, 23)\n",
      "Growing down at: (-170,22) -> (-170, 21)\n",
      "Growing left at: (-171,22) -> (-172, 22)\n",
      "Growing up at: (-171,22) -> (-171, 23)\n",
      "Growing down at: (-171,22) -> (-171, 21)\n",
      "Growing left at: (-172,22) -> (-173, 22)\n",
      "Growing up at: (-172,22) -> (-172, 23)\n",
      "Growing down at: (-172,22) -> (-172, 21)\n",
      "Growing left at: (-173,22) -> (-174, 22)\n",
      "Growing up at: (-173,22) -> (-173, 23)\n",
      "Growing down at: (-173,22) -> (-173, 21)\n",
      "Growing left at: (-174,22) -> (-175, 22)\n",
      "Growing up at: (-174,22) -> (-174, 23)\n",
      "Growing down at: (-174,22) -> (-174, 21)\n",
      "Growing left at: (-175,22) -> (-176, 22)\n",
      "Growing up at: (-175,22) -> (-175, 23)\n",
      "Growing down at: (-175,22) -> (-175, 21)\n",
      "Growing left at: (-176,22) -> (-177, 22)\n",
      "Growing up at: (-176,22) -> (-176, 23)\n",
      "Growing down at: (-176,22) -> (-176, 21)\n",
      "Growing left at: (-177,22) -> (-178, 22)\n",
      "Growing up at: (-177,22) -> (-177, 23)\n",
      "Growing down at: (-177,22) -> (-177, 21)\n"
     ]
    }
   ],
   "source": [
    "input = random.choice(data)[1]\n",
    "learn_rate = init_lr * alpha * (1 - 3.8/len(nodes))\n",
    "\n",
    "recalc_nodes = []\n",
    "for _ in range(50):\n",
    "    # best matching unit\n",
    "    l0 = [i[0] for i in input]\n",
    "    l1 = [i[1] for i in input]\n",
    "    l2 = [i[2] for i in input]\n",
    "    l3 = [i[3] for i in input]\n",
    "    #print(l0)\n",
    "    l0 = l3[0:50]\n",
    "    BMU = _find_bmu(l0)\n",
    "    BMU.last_it = it\n",
    "\n",
    "    # Adapt the weights of the direct topological neighbours\n",
    "    neighbours = []\n",
    "    neighbours.append(BMU)\n",
    "    if BMU.left:  neighbours.append(BMU.left)\n",
    "    if BMU.right: neighbours.append(BMU.right)\n",
    "    if BMU.up:    neighbours.append(BMU.up)\n",
    "    if BMU.down:  neighbours.append(BMU.down)\n",
    "\n",
    "    if BMU not in recalc_nodes: recalc_nodes.append(BMU)\n",
    "    for node in neighbours:\n",
    "        node.adjust_weights(l0, learn_rate)\n",
    "        if node not in recalc_nodes: recalc_nodes.append(node)\n",
    "    # Calculate the error.\n",
    "    err = _distance(BMU.weights, l0)\n",
    "    # Add the error to the node.\n",
    "    growing, nodes = _node_add_error(BMU, err)\n",
    "    if growing: recalc_nodes.extend(nodes)\n",
    "    # Count the iteration\n",
    "    it += 1\n",
    "    # Re-Calc representative data elements for changed nodes.\n",
    "    used_data = []\n",
    "    for node in nodes:\n",
    "        used_data.append(node.data)\n",
    "    for node in recalc_nodes:\n",
    "        dist = float(\"inf\")\n",
    "        winner = False\n",
    "        winner_fn = False\n",
    "        for fn,point in data:\n",
    "            if fn in used_data: continue\n",
    "\n",
    "            point0 = [i[3] for i in point]\n",
    "            d = _distance(point0[0:50], node.weights)\n",
    "            if(d < dist):\n",
    "                dist = d\n",
    "                winner = point0\n",
    "                winner_fn = fn\n",
    "\n",
    "        if node.data != winner_fn:\n",
    "            node.data = winner_fn\n",
    "            node.last_changed = it\n",
    "        output.write(str(node.data) + \",\" + str(node.x) + \",\" + str(node.y)\\\n",
    "                + \",change\\n\")\n",
    "        used_data.append(winner_fn)\n",
    "    # Remove unused nodes.\n",
    "    _remove_unused_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15117, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['feature_names', '10', '10.1', 'change'], dtype='object')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('gsom.csv')\n",
    "print(df.shape)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "x4 = df['10']\n",
    "y4 = df['10.1']\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd2a680ceb8>]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E51JK8d+fZvLDZ1dx6+sbePuWCfz2vSxufOW7Nu9NjAhq85rW/HlRDn++fESH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2a7548da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "irissetosa_x = x1 + x2 + x3 + x4\n",
    "irissetosa_y = y1 + y2 + y3 + y4 \n",
    "plt.plot(irissetosa_x, irissetosa_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growing right at: (149,22) -> (150, 22)\n",
      "Growing up at: (149,22) -> (149, 23)\n",
      "Growing down at: (149,22) -> (149, 21)\n",
      "Growing right at: (150,22) -> (151, 22)\n",
      "Growing up at: (150,22) -> (150, 23)\n",
      "Growing down at: (150,22) -> (150, 21)\n",
      "Growing right at: (151,22) -> (152, 22)\n",
      "Growing up at: (151,22) -> (151, 23)\n",
      "Growing down at: (151,22) -> (151, 21)\n",
      "Growing right at: (152,22) -> (153, 22)\n",
      "Growing up at: (152,22) -> (152, 23)\n",
      "Growing down at: (152,22) -> (152, 21)\n",
      "Growing right at: (153,22) -> (154, 22)\n",
      "Growing up at: (153,22) -> (153, 23)\n",
      "Growing down at: (153,22) -> (153, 21)\n",
      "Growing right at: (154,22) -> (155, 22)\n",
      "Growing up at: (154,22) -> (154, 23)\n",
      "Growing down at: (154,22) -> (154, 21)\n",
      "Growing right at: (155,22) -> (156, 22)\n",
      "Growing up at: (155,22) -> (155, 23)\n",
      "Growing down at: (155,22) -> (155, 21)\n",
      "Growing right at: (156,22) -> (157, 22)\n",
      "Growing up at: (156,22) -> (156, 23)\n",
      "Growing down at: (156,22) -> (156, 21)\n",
      "Growing right at: (157,22) -> (158, 22)\n",
      "Growing up at: (157,22) -> (157, 23)\n",
      "Growing down at: (157,22) -> (157, 21)\n",
      "Growing right at: (158,22) -> (159, 22)\n",
      "Growing up at: (158,22) -> (158, 23)\n",
      "Growing down at: (158,22) -> (158, 21)\n",
      "Growing right at: (159,22) -> (160, 22)\n",
      "Growing up at: (159,22) -> (159, 23)\n",
      "Growing down at: (159,22) -> (159, 21)\n",
      "Growing right at: (160,22) -> (161, 22)\n",
      "Growing up at: (160,22) -> (160, 23)\n",
      "Growing down at: (160,22) -> (160, 21)\n",
      "Growing right at: (161,22) -> (162, 22)\n",
      "Growing up at: (161,22) -> (161, 23)\n",
      "Growing down at: (161,22) -> (161, 21)\n",
      "Growing right at: (162,22) -> (163, 22)\n",
      "Growing up at: (162,22) -> (162, 23)\n",
      "Growing down at: (162,22) -> (162, 21)\n",
      "Growing right at: (163,22) -> (164, 22)\n",
      "Growing up at: (163,22) -> (163, 23)\n",
      "Growing down at: (163,22) -> (163, 21)\n",
      "Growing right at: (164,22) -> (165, 22)\n",
      "Growing up at: (164,22) -> (164, 23)\n",
      "Growing down at: (164,22) -> (164, 21)\n",
      "Growing right at: (165,22) -> (166, 22)\n",
      "Growing up at: (165,22) -> (165, 23)\n",
      "Growing down at: (165,22) -> (165, 21)\n",
      "Growing right at: (166,22) -> (167, 22)\n",
      "Growing up at: (166,22) -> (166, 23)\n",
      "Growing down at: (166,22) -> (166, 21)\n",
      "Growing right at: (167,22) -> (168, 22)\n",
      "Growing up at: (167,22) -> (167, 23)\n",
      "Growing down at: (167,22) -> (167, 21)\n",
      "Growing right at: (168,22) -> (169, 22)\n",
      "Growing up at: (168,22) -> (168, 23)\n",
      "Growing down at: (168,22) -> (168, 21)\n",
      "Growing right at: (169,22) -> (170, 22)\n",
      "Growing up at: (169,22) -> (169, 23)\n",
      "Growing down at: (169,22) -> (169, 21)\n",
      "Growing right at: (170,22) -> (171, 22)\n",
      "Growing up at: (170,22) -> (170, 23)\n",
      "Growing down at: (170,22) -> (170, 21)\n",
      "Growing right at: (171,22) -> (172, 22)\n",
      "Growing up at: (171,22) -> (171, 23)\n",
      "Growing down at: (171,22) -> (171, 21)\n",
      "Growing right at: (172,22) -> (173, 22)\n",
      "Growing up at: (172,22) -> (172, 23)\n",
      "Growing down at: (172,22) -> (172, 21)\n",
      "Growing right at: (173,22) -> (174, 22)\n",
      "Growing up at: (173,22) -> (173, 23)\n",
      "Growing down at: (173,22) -> (173, 21)\n",
      "Growing right at: (174,22) -> (175, 22)\n",
      "Growing up at: (174,22) -> (174, 23)\n",
      "Growing down at: (174,22) -> (174, 21)\n",
      "Growing right at: (175,22) -> (176, 22)\n",
      "Growing up at: (175,22) -> (175, 23)\n",
      "Growing down at: (175,22) -> (175, 21)\n",
      "Growing right at: (176,22) -> (177, 22)\n",
      "Growing up at: (176,22) -> (176, 23)\n",
      "Growing down at: (176,22) -> (176, 21)\n",
      "Growing right at: (177,22) -> (178, 22)\n",
      "Growing up at: (177,22) -> (177, 23)\n",
      "Growing down at: (177,22) -> (177, 21)\n",
      "Growing right at: (178,22) -> (179, 22)\n",
      "Growing up at: (178,22) -> (178, 23)\n",
      "Growing down at: (178,22) -> (178, 21)\n",
      "Growing right at: (179,22) -> (180, 22)\n",
      "Growing up at: (179,22) -> (179, 23)\n",
      "Growing down at: (179,22) -> (179, 21)\n",
      "Growing right at: (180,22) -> (181, 22)\n",
      "Growing up at: (180,22) -> (180, 23)\n",
      "Growing down at: (180,22) -> (180, 21)\n",
      "Growing right at: (181,22) -> (182, 22)\n",
      "Growing up at: (181,22) -> (181, 23)\n",
      "Growing down at: (181,22) -> (181, 21)\n",
      "Growing right at: (182,22) -> (183, 22)\n",
      "Growing up at: (182,22) -> (182, 23)\n",
      "Growing down at: (182,22) -> (182, 21)\n",
      "Growing right at: (183,22) -> (184, 22)\n",
      "Growing up at: (183,22) -> (183, 23)\n",
      "Growing down at: (183,22) -> (183, 21)\n",
      "Growing right at: (184,22) -> (185, 22)\n",
      "Growing up at: (184,22) -> (184, 23)\n",
      "Growing down at: (184,22) -> (184, 21)\n",
      "Growing right at: (185,22) -> (186, 22)\n",
      "Growing up at: (185,22) -> (185, 23)\n",
      "Growing down at: (185,22) -> (185, 21)\n",
      "Growing right at: (186,22) -> (187, 22)\n",
      "Growing up at: (186,22) -> (186, 23)\n",
      "Growing down at: (186,22) -> (186, 21)\n",
      "Growing right at: (187,22) -> (188, 22)\n",
      "Growing up at: (187,22) -> (187, 23)\n",
      "Growing down at: (187,22) -> (187, 21)\n",
      "Growing right at: (188,22) -> (189, 22)\n",
      "Growing up at: (188,22) -> (188, 23)\n",
      "Growing down at: (188,22) -> (188, 21)\n",
      "Growing right at: (189,22) -> (190, 22)\n",
      "Growing up at: (189,22) -> (189, 23)\n",
      "Growing down at: (189,22) -> (189, 21)\n",
      "Growing right at: (190,22) -> (191, 22)\n",
      "Growing up at: (190,22) -> (190, 23)\n",
      "Growing down at: (190,22) -> (190, 21)\n",
      "Growing right at: (191,22) -> (192, 22)\n",
      "Growing up at: (191,22) -> (191, 23)\n",
      "Growing down at: (191,22) -> (191, 21)\n",
      "Growing right at: (192,22) -> (193, 22)\n",
      "Growing up at: (192,22) -> (192, 23)\n",
      "Growing down at: (192,22) -> (192, 21)\n",
      "Growing right at: (193,22) -> (194, 22)\n",
      "Growing up at: (193,22) -> (193, 23)\n",
      "Growing down at: (193,22) -> (193, 21)\n",
      "Growing right at: (194,22) -> (195, 22)\n",
      "Growing up at: (194,22) -> (194, 23)\n",
      "Growing down at: (194,22) -> (194, 21)\n",
      "Growing right at: (195,22) -> (196, 22)\n",
      "Growing up at: (195,22) -> (195, 23)\n",
      "Growing down at: (195,22) -> (195, 21)\n",
      "Growing right at: (196,22) -> (197, 22)\n",
      "Growing up at: (196,22) -> (196, 23)\n",
      "Growing down at: (196,22) -> (196, 21)\n",
      "Growing right at: (197,22) -> (198, 22)\n",
      "Growing up at: (197,22) -> (197, 23)\n",
      "Growing down at: (197,22) -> (197, 21)\n",
      "Growing right at: (198,22) -> (199, 22)\n",
      "Growing up at: (198,22) -> (198, 23)\n",
      "Growing down at: (198,22) -> (198, 21)\n"
     ]
    }
   ],
   "source": [
    "input = random.choice(data)[1]\n",
    "learn_rate = init_lr * alpha * (1 - 3.8/len(nodes))\n",
    "\n",
    "recalc_nodes = []\n",
    "for _ in range(50):\n",
    "    # best matching unit\n",
    "    l0 = [i[0] for i in input]\n",
    "    l1 = [i[1] for i in input]\n",
    "    l2 = [i[2] for i in input]\n",
    "    l3 = [i[3] for i in input]\n",
    "    #print(l0)\n",
    "    l0 = l3[50:100]\n",
    "    BMU = _find_bmu(l0)\n",
    "    BMU.last_it = it\n",
    "\n",
    "    # Adapt the weights of the direct topological neighbours\n",
    "    neighbours = []\n",
    "    neighbours.append(BMU)\n",
    "    if BMU.left:  neighbours.append(BMU.left)\n",
    "    if BMU.right: neighbours.append(BMU.right)\n",
    "    if BMU.up:    neighbours.append(BMU.up)\n",
    "    if BMU.down:  neighbours.append(BMU.down)\n",
    "\n",
    "    if BMU not in recalc_nodes: recalc_nodes.append(BMU)\n",
    "    for node in neighbours:\n",
    "        node.adjust_weights(l0, learn_rate)\n",
    "        if node not in recalc_nodes: recalc_nodes.append(node)\n",
    "    # Calculate the error.\n",
    "    err = _distance(BMU.weights, l0)\n",
    "    # Add the error to the node.\n",
    "    growing, nodes = _node_add_error(BMU, err)\n",
    "    if growing: recalc_nodes.extend(nodes)\n",
    "    # Count the iteration\n",
    "    it += 1\n",
    "    # Re-Calc representative data elements for changed nodes.\n",
    "    used_data = []\n",
    "    for node in nodes:\n",
    "        used_data.append(node.data)\n",
    "    for node in recalc_nodes:\n",
    "        dist = float(\"inf\")\n",
    "        winner = False\n",
    "        winner_fn = False\n",
    "        for fn,point in data:\n",
    "            if fn in used_data: continue\n",
    "\n",
    "            point0 = [i[3] for i in point]\n",
    "            d = _distance(point0[50:100], node.weights)\n",
    "            if(d < dist):\n",
    "                dist = d\n",
    "                winner = point0\n",
    "                winner_fn = fn\n",
    "\n",
    "        if node.data != winner_fn:\n",
    "            node.data = winner_fn\n",
    "            node.last_changed = it\n",
    "        output.write(str(node.data) + \",\" + str(node.x) + \",\" + str(node.y)\\\n",
    "                + \",change\\n\")\n",
    "        used_data.append(winner_fn)\n",
    "    # Remove unused nodes.\n",
    "    _remove_unused_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14993, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['feature_names', '11', '10', 'change'], dtype='object')"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('gsom.csv')\n",
    "print(df.shape)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = df['11']\n",
    "y3 = df['10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd2a693c780>]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/iA+2ZpNeWDNo5bhxUjDTo/VMDPXAXms1aOVQlN5Sga8MaQ0tbaxJL+HT7TlszSgflDLE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\npdQAoQlfKaUGiP8HhoKld4q2hXgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2a6d4fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "irisverticosa_x = x0+x1+x2+x3\n",
    "irisverticosa_y = y0+y1+y2+y3\n",
    "plt.plot(irisverticosa_x, irisverticosa_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growing left at: (-128,23) -> (-129, 23)\n",
      "Growing up at: (-128,23) -> (-128, 24)\n",
      "Growing down at: (-128,23) -> (-128, 22)\n",
      "Growing left at: (-129,23) -> (-130, 23)\n",
      "Growing up at: (-129,23) -> (-129, 24)\n",
      "Growing down at: (-129,23) -> (-129, 22)\n",
      "Growing left at: (-130,23) -> (-131, 23)\n",
      "Growing up at: (-130,23) -> (-130, 24)\n",
      "Growing down at: (-130,23) -> (-130, 22)\n",
      "Growing left at: (-131,23) -> (-132, 23)\n",
      "Growing up at: (-131,23) -> (-131, 24)\n",
      "Growing down at: (-131,23) -> (-131, 22)\n",
      "Growing left at: (-132,23) -> (-133, 23)\n",
      "Growing up at: (-132,23) -> (-132, 24)\n",
      "Growing down at: (-132,23) -> (-132, 22)\n",
      "Growing left at: (-133,23) -> (-134, 23)\n",
      "Growing up at: (-133,23) -> (-133, 24)\n",
      "Growing down at: (-133,23) -> (-133, 22)\n",
      "Growing left at: (-134,23) -> (-135, 23)\n",
      "Growing up at: (-134,23) -> (-134, 24)\n",
      "Growing down at: (-134,23) -> (-134, 22)\n",
      "Growing left at: (-135,23) -> (-136, 23)\n",
      "Growing up at: (-135,23) -> (-135, 24)\n",
      "Growing down at: (-135,23) -> (-135, 22)\n",
      "Growing left at: (-136,23) -> (-137, 23)\n",
      "Growing up at: (-136,23) -> (-136, 24)\n",
      "Growing down at: (-136,23) -> (-136, 22)\n",
      "Growing left at: (-137,23) -> (-138, 23)\n",
      "Growing up at: (-137,23) -> (-137, 24)\n",
      "Growing down at: (-137,23) -> (-137, 22)\n",
      "Growing left at: (-138,23) -> (-139, 23)\n",
      "Growing up at: (-138,23) -> (-138, 24)\n",
      "Growing down at: (-138,23) -> (-138, 22)\n",
      "Growing left at: (-139,23) -> (-140, 23)\n",
      "Growing up at: (-139,23) -> (-139, 24)\n",
      "Growing down at: (-139,23) -> (-139, 22)\n",
      "Growing left at: (-140,23) -> (-141, 23)\n",
      "Growing up at: (-140,23) -> (-140, 24)\n",
      "Growing down at: (-140,23) -> (-140, 22)\n",
      "Growing left at: (-141,23) -> (-142, 23)\n",
      "Growing up at: (-141,23) -> (-141, 24)\n",
      "Growing down at: (-141,23) -> (-141, 22)\n",
      "Growing left at: (-142,23) -> (-143, 23)\n",
      "Growing up at: (-142,23) -> (-142, 24)\n",
      "Growing down at: (-142,23) -> (-142, 22)\n",
      "Growing left at: (-143,23) -> (-144, 23)\n",
      "Growing up at: (-143,23) -> (-143, 24)\n",
      "Growing down at: (-143,23) -> (-143, 22)\n",
      "Growing left at: (-144,23) -> (-145, 23)\n",
      "Growing up at: (-144,23) -> (-144, 24)\n",
      "Growing down at: (-144,23) -> (-144, 22)\n",
      "Growing left at: (-145,23) -> (-146, 23)\n",
      "Growing up at: (-145,23) -> (-145, 24)\n",
      "Growing down at: (-145,23) -> (-145, 22)\n",
      "Growing left at: (-146,23) -> (-147, 23)\n",
      "Growing up at: (-146,23) -> (-146, 24)\n",
      "Growing down at: (-146,23) -> (-146, 22)\n",
      "Growing left at: (-147,23) -> (-148, 23)\n",
      "Growing up at: (-147,23) -> (-147, 24)\n",
      "Growing down at: (-147,23) -> (-147, 22)\n",
      "Growing left at: (-148,23) -> (-149, 23)\n",
      "Growing up at: (-148,23) -> (-148, 24)\n",
      "Growing down at: (-148,23) -> (-148, 22)\n",
      "Growing left at: (-149,23) -> (-150, 23)\n",
      "Growing up at: (-149,23) -> (-149, 24)\n",
      "Growing down at: (-149,23) -> (-149, 22)\n",
      "Growing left at: (-150,23) -> (-151, 23)\n",
      "Growing up at: (-150,23) -> (-150, 24)\n",
      "Growing down at: (-150,23) -> (-150, 22)\n",
      "Growing left at: (-151,23) -> (-152, 23)\n",
      "Growing up at: (-151,23) -> (-151, 24)\n",
      "Growing down at: (-151,23) -> (-151, 22)\n",
      "Growing left at: (-152,23) -> (-153, 23)\n",
      "Growing up at: (-152,23) -> (-152, 24)\n",
      "Growing down at: (-152,23) -> (-152, 22)\n",
      "Growing left at: (-153,23) -> (-154, 23)\n",
      "Growing up at: (-153,23) -> (-153, 24)\n",
      "Growing down at: (-153,23) -> (-153, 22)\n",
      "Growing left at: (-154,23) -> (-155, 23)\n",
      "Growing up at: (-154,23) -> (-154, 24)\n",
      "Growing down at: (-154,23) -> (-154, 22)\n",
      "Growing left at: (-155,23) -> (-156, 23)\n",
      "Growing up at: (-155,23) -> (-155, 24)\n",
      "Growing down at: (-155,23) -> (-155, 22)\n",
      "Growing left at: (-156,23) -> (-157, 23)\n",
      "Growing up at: (-156,23) -> (-156, 24)\n",
      "Growing down at: (-156,23) -> (-156, 22)\n",
      "Growing left at: (-157,23) -> (-158, 23)\n",
      "Growing up at: (-157,23) -> (-157, 24)\n",
      "Growing down at: (-157,23) -> (-157, 22)\n",
      "Growing left at: (-158,23) -> (-159, 23)\n",
      "Growing up at: (-158,23) -> (-158, 24)\n",
      "Growing down at: (-158,23) -> (-158, 22)\n",
      "Growing left at: (-159,23) -> (-160, 23)\n",
      "Growing up at: (-159,23) -> (-159, 24)\n",
      "Growing down at: (-159,23) -> (-159, 22)\n",
      "Growing left at: (-160,23) -> (-161, 23)\n",
      "Growing up at: (-160,23) -> (-160, 24)\n",
      "Growing down at: (-160,23) -> (-160, 22)\n",
      "Growing left at: (-161,23) -> (-162, 23)\n",
      "Growing up at: (-161,23) -> (-161, 24)\n",
      "Growing down at: (-161,23) -> (-161, 22)\n",
      "Growing left at: (-162,23) -> (-163, 23)\n",
      "Growing up at: (-162,23) -> (-162, 24)\n",
      "Growing down at: (-162,23) -> (-162, 22)\n",
      "Growing left at: (-163,23) -> (-164, 23)\n",
      "Growing up at: (-163,23) -> (-163, 24)\n",
      "Growing down at: (-163,23) -> (-163, 22)\n",
      "Growing left at: (-164,23) -> (-165, 23)\n",
      "Growing up at: (-164,23) -> (-164, 24)\n",
      "Growing down at: (-164,23) -> (-164, 22)\n",
      "Growing left at: (-165,23) -> (-166, 23)\n",
      "Growing up at: (-165,23) -> (-165, 24)\n",
      "Growing down at: (-165,23) -> (-165, 22)\n",
      "Growing left at: (-166,23) -> (-167, 23)\n",
      "Growing up at: (-166,23) -> (-166, 24)\n",
      "Growing down at: (-166,23) -> (-166, 22)\n",
      "Growing left at: (-167,23) -> (-168, 23)\n",
      "Growing up at: (-167,23) -> (-167, 24)\n",
      "Growing down at: (-167,23) -> (-167, 22)\n",
      "Growing left at: (-168,23) -> (-169, 23)\n",
      "Growing up at: (-168,23) -> (-168, 24)\n",
      "Growing down at: (-168,23) -> (-168, 22)\n",
      "Growing left at: (-169,23) -> (-170, 23)\n",
      "Growing up at: (-169,23) -> (-169, 24)\n",
      "Growing down at: (-169,23) -> (-169, 22)\n",
      "Growing left at: (-170,23) -> (-171, 23)\n",
      "Growing up at: (-170,23) -> (-170, 24)\n",
      "Growing down at: (-170,23) -> (-170, 22)\n",
      "Growing left at: (-171,23) -> (-172, 23)\n",
      "Growing up at: (-171,23) -> (-171, 24)\n",
      "Growing down at: (-171,23) -> (-171, 22)\n",
      "Growing left at: (-172,23) -> (-173, 23)\n",
      "Growing up at: (-172,23) -> (-172, 24)\n",
      "Growing down at: (-172,23) -> (-172, 22)\n",
      "Growing left at: (-173,23) -> (-174, 23)\n",
      "Growing up at: (-173,23) -> (-173, 24)\n",
      "Growing down at: (-173,23) -> (-173, 22)\n",
      "Growing left at: (-174,23) -> (-175, 23)\n",
      "Growing up at: (-174,23) -> (-174, 24)\n",
      "Growing down at: (-174,23) -> (-174, 22)\n",
      "Growing left at: (-175,23) -> (-176, 23)\n",
      "Growing up at: (-175,23) -> (-175, 24)\n",
      "Growing down at: (-175,23) -> (-175, 22)\n",
      "Growing left at: (-176,23) -> (-177, 23)\n",
      "Growing up at: (-176,23) -> (-176, 24)\n",
      "Growing down at: (-176,23) -> (-176, 22)\n",
      "Growing left at: (-177,23) -> (-178, 23)\n",
      "Growing up at: (-177,23) -> (-177, 24)\n",
      "Growing down at: (-177,23) -> (-177, 22)\n"
     ]
    }
   ],
   "source": [
    "input = random.choice(data)[1]\n",
    "learn_rate = init_lr * alpha * (1 - 3.8/len(nodes))\n",
    "\n",
    "recalc_nodes = []\n",
    "for _ in range(50):\n",
    "    # best matching unit\n",
    "    l0 = [i[0] for i in input]\n",
    "    l1 = [i[1] for i in input]\n",
    "    l2 = [i[2] for i in input]\n",
    "    l3 = [i[3] for i in input]\n",
    "    #print(l0)\n",
    "    l0 = l3[100:150]\n",
    "    BMU = _find_bmu(l0)\n",
    "    BMU.last_it = it\n",
    "\n",
    "    # Adapt the weights of the direct topological neighbours\n",
    "    neighbours = []\n",
    "    neighbours.append(BMU)\n",
    "    if BMU.left:  neighbours.append(BMU.left)\n",
    "    if BMU.right: neighbours.append(BMU.right)\n",
    "    if BMU.up:    neighbours.append(BMU.up)\n",
    "    if BMU.down:  neighbours.append(BMU.down)\n",
    "\n",
    "    if BMU not in recalc_nodes: recalc_nodes.append(BMU)\n",
    "    for node in neighbours:\n",
    "        node.adjust_weights(l0, learn_rate)\n",
    "        if node not in recalc_nodes: recalc_nodes.append(node)\n",
    "    # Calculate the error.\n",
    "    err = _distance(BMU.weights, l0)\n",
    "    # Add the error to the node.\n",
    "    growing, nodes = _node_add_error(BMU, err)\n",
    "    if growing: recalc_nodes.extend(nodes)\n",
    "    # Count the iteration\n",
    "    it += 1\n",
    "    # Re-Calc representative data elements for changed nodes.\n",
    "    used_data = []\n",
    "    for node in nodes:\n",
    "        used_data.append(node.data)\n",
    "    for node in recalc_nodes:\n",
    "        dist = float(\"inf\")\n",
    "        winner = False\n",
    "        winner_fn = False\n",
    "        for fn,point in data:\n",
    "            if fn in used_data: continue\n",
    "\n",
    "            point0 = [i[3] for i in point]\n",
    "            d = _distance(point0[100:150], node.weights)\n",
    "            if(d < dist):\n",
    "                dist = d\n",
    "                winner = point0\n",
    "                winner_fn = fn\n",
    "\n",
    "        if node.data != winner_fn:\n",
    "            node.data = winner_fn\n",
    "            node.last_changed = it\n",
    "        output.write(str(node.data) + \",\" + str(node.x) + \",\" + str(node.y)\\\n",
    "                + \",change\\n\")\n",
    "        used_data.append(winner_fn)\n",
    "    # Remove unused nodes.\n",
    "    _remove_unused_nodes()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15166, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['feature_names', '10', '11', 'change'], dtype='object')"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('gsom.csv')\n",
    "print(df.shape)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = df['10']\n",
    "y3 = df['11']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd2a68e6f60>]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MfKB6fKiw6aRmn1Br2Zqlnt6evmE+3Hp2NN9uPCTqjTsBFw3sTmSAO2+tal6T/57x8erx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\nNDCs0fzU9kKnkwj3dyfc351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cXJ0UwePTerP9cBEDzAJxp4LsokoCPV2Y++seKmrEXV9r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\nLiKSI1TQRURyhAq6iEiOUEEXEckR/w8kpjU1yIjkbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2a4480a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "irisvirginica_x = x1+x2+x3+x0\n",
    "irisvirginica_y = y1+y2+y3+y0\n",
    "plt.plot(irisvirginica_x,irisvirginica_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pN5qRfqNJqbTx7NpnWXtpbeEFRekxGuEb/8LLSb5cIYQoc8p1oAum3dEC2ldn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\nEjrHzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2a68629e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(irissetosa_x, irissetosa_y, label='irissetosa')\n",
    "plt.plot(irisverticosa_x, irisverticosa_y, label='irisversicolour')\n",
    "plt.plot(irisvirginica_x, irisvirginica_y, label='irisvirginica')\n",
    "#plt.plot(x4,y4, label='petal width')\n",
    "\n",
    "\n",
    "plt.legend(('irissetosa', 'irisversicolour', 'irisvirginica'))#, 'petal width'))\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(1.50, 1.05),\n",
    "          ncol=3, fancybox=True, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### THE END ######"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'seq'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2397\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2399\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2401\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    736\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    737\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 738\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    739\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mind_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m  \u001b[0;31m# Needed for contains() method.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    740\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_subslice\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m    654\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0malways\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 656\u001b[0;31m             \u001b[0myconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_yorig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    657\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mconvert_yunits\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mconvert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1489\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1491\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1492\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/category.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfloats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \"\"\"\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0mvmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'seq'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3c13fcda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.ylim(-50, 50)\n",
    "plt.xlim(-50, 50)\n",
    "graph1, = plt.plot([], [], 'o')\n",
    "graph2, = plt.plot([], [], '-')\n",
    "graph3, = plt.plot([], [], '>')\n",
    "graph4, = plt.plot([], [], '|')\n",
    "\n",
    "def animate1(i):\n",
    "    graph1.set_data(x1[:i+1], y1[:i+1])\n",
    "    return graph1\n",
    "\n",
    "def animate2(i):\n",
    "    graph2.set_data(x2[:i+1], y2[:i+1])\n",
    "    return graph2\n",
    "\n",
    "def animate3(i):\n",
    "    graph3.set_data(x3[:i+1], y3[:i+1])\n",
    "    return graph3\n",
    "\n",
    "def animate4(i):\n",
    "    graph4.set_data(x4[:i+1], y4[:i+1])\n",
    "    return graph4\n",
    "\n",
    "\n",
    "ani1 = FuncAnimation(fig, animate1(200000))\n",
    "ani2 = FuncAnimation(fig, animate2(200000))\n",
    "ani3 = FuncAnimation(fig, animate3(200000))\n",
    "ani4 = FuncAnimation(fig, animate4(200000))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# viz needs more iterations for better understanding, will add more. \n",
    "# currently causing bugs after a limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "## understanding output file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.298399e+06</td>\n",
       "      <td>1.298398e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.697577e+02</td>\n",
       "      <td>9.539206e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.059955e+02</td>\n",
       "      <td>5.351747e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.890000e+02</td>\n",
       "      <td>5.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.350000e+02</td>\n",
       "      <td>7.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.940000e+02</td>\n",
       "      <td>1.310000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.275000e+03</td>\n",
       "      <td>2.590000e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1             0\n",
       "count  1.298399e+06  1.298398e+06\n",
       "mean   6.697577e+02  9.539206e+01\n",
       "std    3.059955e+02  5.351747e+01\n",
       "min    0.000000e+00 -1.000000e+00\n",
       "25%    3.890000e+02  5.100000e+01\n",
       "50%    7.350000e+02  7.900000e+01\n",
       "75%    8.940000e+02  1.310000e+02\n",
       "max    1.275000e+03  2.590000e+02"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # basic description of variables present\n",
    "\n",
    "# 1 and 0 represent the x and y coordinates of the winner node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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",
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       "      <th>5</th>\n",
       "      <td>data</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DESCR</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>feature_names</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
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       "      <td>change</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    target_names  1    0  change\n",
       "0           data  0  0.0  change\n",
       "1         target  1  1.0  change\n",
       "2          DESCR  2  0.0  change\n",
       "3  feature_names  1 -1.0  change\n",
       "4   target_names  1  0.0  change\n",
       "5           data  0  0.0  change\n",
       "6         target  1  1.0  change\n",
       "7          DESCR  2  0.0  change\n",
       "8  feature_names  1 -1.0  change\n",
       "9          False  3  0.0  change"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10) # first 10 records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "...                ...   ...    ...     ...\n",
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       "1298350          False  1231  244.0  change\n",
       "1298351          False  1230  245.0  change\n",
       "1298352          False  1232  244.0  change\n",
       "1298353          False  1231  245.0  change\n",
       "1298354          False  1233  244.0  change\n",
       "1298355          False  1232  245.0  change\n",
       "1298356          False  1232  243.0  change\n",
       "1298357          False  1234  244.0  change\n",
       "1298358          False  1233  245.0  change\n",
       "1298359          False  1233  243.0  change\n",
       "1298360          False  1234  245.0  change\n",
       "1298361          False  1233  246.0  change\n",
       "1298362          False  1235  245.0  change\n",
       "1298363          False  1234  246.0  change\n",
       "1298364          False  1236  245.0  change\n",
       "1298365          False  1235  246.0  change\n",
       "1298366          False  1235  244.0  change\n",
       "1298367          False  1237  245.0  change\n",
       "1298368          False  1236  246.0  change\n",
       "1298369          False  1236  244.0  change\n",
       "1298370          False  1237  246.0  change\n",
       "1298371          False  1236  247.0  change\n",
       "1298372          False  1238  246.0  change\n",
       "1298373          False  1237  247.0  change\n",
       "1298374          False  1239  246.0  change\n",
       "1298375          False  1238  247.0  change\n",
       "1298376          False  1238  245.0  change\n",
       "1298377          False  1240  246.0  change\n",
       "1298378          False  1239  247.0  change\n",
       "\n",
       "[1298379 rows x 4 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(-20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "## debugging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-132-35e022a0aeb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0ml0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mBMU\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_bmu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-117-84904f4ee966>\u001b[0m in \u001b[0;36m_find_bmu\u001b[0;34m(vec)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mdist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mwinner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;31m#print(d.all())\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for _ in range(20):\n",
    "    l0 = [i[0] for i in input]\n",
    "    print(l0)\n",
    "    BMU = _find_bmu(l0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]\n",
      "<__main__.GSOM_Node instance at 0x7f9b002b8518>\n"
     ]
    }
   ],
   "source": [
    "    l0 = [i[0] for i in input]\n",
    "    print(l0)\n",
    "    BMU = _find_bmu(l0)\n",
    "    print(BMU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "for fn,point in data:\n",
    "    if fn in used_data: continue\n",
    "    print(node.weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sepal length sepal width petal length petal width\n",
    "# Iris-Setosa - Iris-Versicolour - Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DESCR': 'Iris Plants Database\\n====================\\n\\nNotes\\n-----\\nData Set Characteristics:\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML iris datasets.\\nhttp://archive.ics.uci.edu/ml/datasets/Iris\\n\\nThe famous Iris database, first used by Sir R.A Fisher\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\nReferences\\n----------\\n   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...\\n',\n",
       " 'data': array([[5.1, 3.5, 1.4, 0.2],\n",
       "        [4.9, 3. , 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.3, 0.2],\n",
       "        [4.6, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.6, 1.4, 0.2],\n",
       "        [5.4, 3.9, 1.7, 0.4],\n",
       "        [4.6, 3.4, 1.4, 0.3],\n",
       "        [5. , 3.4, 1.5, 0.2],\n",
       "        [4.4, 2.9, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5.4, 3.7, 1.5, 0.2],\n",
       "        [4.8, 3.4, 1.6, 0.2],\n",
       "        [4.8, 3. , 1.4, 0.1],\n",
       "        [4.3, 3. , 1.1, 0.1],\n",
       "        [5.8, 4. , 1.2, 0.2],\n",
       "        [5.7, 4.4, 1.5, 0.4],\n",
       "        [5.4, 3.9, 1.3, 0.4],\n",
       "        [5.1, 3.5, 1.4, 0.3],\n",
       "        [5.7, 3.8, 1.7, 0.3],\n",
       "        [5.1, 3.8, 1.5, 0.3],\n",
       "        [5.4, 3.4, 1.7, 0.2],\n",
       "        [5.1, 3.7, 1.5, 0.4],\n",
       "        [4.6, 3.6, 1. , 0.2],\n",
       "        [5.1, 3.3, 1.7, 0.5],\n",
       "        [4.8, 3.4, 1.9, 0.2],\n",
       "        [5. , 3. , 1.6, 0.2],\n",
       "        [5. , 3.4, 1.6, 0.4],\n",
       "        [5.2, 3.5, 1.5, 0.2],\n",
       "        [5.2, 3.4, 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.6, 0.2],\n",
       "        [4.8, 3.1, 1.6, 0.2],\n",
       "        [5.4, 3.4, 1.5, 0.4],\n",
       "        [5.2, 4.1, 1.5, 0.1],\n",
       "        [5.5, 4.2, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [4.4, 3. , 1.3, 0.2],\n",
       "        [5.1, 3.4, 1.5, 0.2],\n",
       "        [5. , 3.5, 1.3, 0.3],\n",
       "        [4.5, 2.3, 1.3, 0.3],\n",
       "        [4.4, 3.2, 1.3, 0.2],\n",
       "        [5. , 3.5, 1.6, 0.6],\n",
       "        [5.1, 3.8, 1.9, 0.4],\n",
       "        [4.8, 3. , 1.4, 0.3],\n",
       "        [5.1, 3.8, 1.6, 0.2],\n",
       "        [4.6, 3.2, 1.4, 0.2],\n",
       "        [5.3, 3.7, 1.5, 0.2],\n",
       "        [5. , 3.3, 1.4, 0.2],\n",
       "        [7. , 3.2, 4.7, 1.4],\n",
       "        [6.4, 3.2, 4.5, 1.5],\n",
       "        [6.9, 3.1, 4.9, 1.5],\n",
       "        [5.5, 2.3, 4. , 1.3],\n",
       "        [6.5, 2.8, 4.6, 1.5],\n",
       "        [5.7, 2.8, 4.5, 1.3],\n",
       "        [6.3, 3.3, 4.7, 1.6],\n",
       "        [4.9, 2.4, 3.3, 1. ],\n",
       "        [6.6, 2.9, 4.6, 1.3],\n",
       "        [5.2, 2.7, 3.9, 1.4],\n",
       "        [5. , 2. , 3.5, 1. ],\n",
       "        [5.9, 3. , 4.2, 1.5],\n",
       "        [6. , 2.2, 4. , 1. ],\n",
       "        [6.1, 2.9, 4.7, 1.4],\n",
       "        [5.6, 2.9, 3.6, 1.3],\n",
       "        [6.7, 3.1, 4.4, 1.4],\n",
       "        [5.6, 3. , 4.5, 1.5],\n",
       "        [5.8, 2.7, 4.1, 1. ],\n",
       "        [6.2, 2.2, 4.5, 1.5],\n",
       "        [5.6, 2.5, 3.9, 1.1],\n",
       "        [5.9, 3.2, 4.8, 1.8],\n",
       "        [6.1, 2.8, 4. , 1.3],\n",
       "        [6.3, 2.5, 4.9, 1.5],\n",
       "        [6.1, 2.8, 4.7, 1.2],\n",
       "        [6.4, 2.9, 4.3, 1.3],\n",
       "        [6.6, 3. , 4.4, 1.4],\n",
       "        [6.8, 2.8, 4.8, 1.4],\n",
       "        [6.7, 3. , 5. , 1.7],\n",
       "        [6. , 2.9, 4.5, 1.5],\n",
       "        [5.7, 2.6, 3.5, 1. ],\n",
       "        [5.5, 2.4, 3.8, 1.1],\n",
       "        [5.5, 2.4, 3.7, 1. ],\n",
       "        [5.8, 2.7, 3.9, 1.2],\n",
       "        [6. , 2.7, 5.1, 1.6],\n",
       "        [5.4, 3. , 4.5, 1.5],\n",
       "        [6. , 3.4, 4.5, 1.6],\n",
       "        [6.7, 3.1, 4.7, 1.5],\n",
       "        [6.3, 2.3, 4.4, 1.3],\n",
       "        [5.6, 3. , 4.1, 1.3],\n",
       "        [5.5, 2.5, 4. , 1.3],\n",
       "        [5.5, 2.6, 4.4, 1.2],\n",
       "        [6.1, 3. , 4.6, 1.4],\n",
       "        [5.8, 2.6, 4. , 1.2],\n",
       "        [5. , 2.3, 3.3, 1. ],\n",
       "        [5.6, 2.7, 4.2, 1.3],\n",
       "        [5.7, 3. , 4.2, 1.2],\n",
       "        [5.7, 2.9, 4.2, 1.3],\n",
       "        [6.2, 2.9, 4.3, 1.3],\n",
       "        [5.1, 2.5, 3. , 1.1],\n",
       "        [5.7, 2.8, 4.1, 1.3],\n",
       "        [6.3, 3.3, 6. , 2.5],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [7.1, 3. , 5.9, 2.1],\n",
       "        [6.3, 2.9, 5.6, 1.8],\n",
       "        [6.5, 3. , 5.8, 2.2],\n",
       "        [7.6, 3. , 6.6, 2.1],\n",
       "        [4.9, 2.5, 4.5, 1.7],\n",
       "        [7.3, 2.9, 6.3, 1.8],\n",
       "        [6.7, 2.5, 5.8, 1.8],\n",
       "        [7.2, 3.6, 6.1, 2.5],\n",
       "        [6.5, 3.2, 5.1, 2. ],\n",
       "        [6.4, 2.7, 5.3, 1.9],\n",
       "        [6.8, 3. , 5.5, 2.1],\n",
       "        [5.7, 2.5, 5. , 2. ],\n",
       "        [5.8, 2.8, 5.1, 2.4],\n",
       "        [6.4, 3.2, 5.3, 2.3],\n",
       "        [6.5, 3. , 5.5, 1.8],\n",
       "        [7.7, 3.8, 6.7, 2.2],\n",
       "        [7.7, 2.6, 6.9, 2.3],\n",
       "        [6. , 2.2, 5. , 1.5],\n",
       "        [6.9, 3.2, 5.7, 2.3],\n",
       "        [5.6, 2.8, 4.9, 2. ],\n",
       "        [7.7, 2.8, 6.7, 2. ],\n",
       "        [6.3, 2.7, 4.9, 1.8],\n",
       "        [6.7, 3.3, 5.7, 2.1],\n",
       "        [7.2, 3.2, 6. , 1.8],\n",
       "        [6.2, 2.8, 4.8, 1.8],\n",
       "        [6.1, 3. , 4.9, 1.8],\n",
       "        [6.4, 2.8, 5.6, 2.1],\n",
       "        [7.2, 3. , 5.8, 1.6],\n",
       "        [7.4, 2.8, 6.1, 1.9],\n",
       "        [7.9, 3.8, 6.4, 2. ],\n",
       "        [6.4, 2.8, 5.6, 2.2],\n",
       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [6.1, 2.6, 5.6, 1.4],\n",
       "        [7.7, 3. , 6.1, 2.3],\n",
       "        [6.3, 3.4, 5.6, 2.4],\n",
       "        [6.4, 3.1, 5.5, 1.8],\n",
       "        [6. , 3. , 4.8, 1.8],\n",
       "        [6.9, 3.1, 5.4, 2.1],\n",
       "        [6.7, 3.1, 5.6, 2.4],\n",
       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
       "        [6.7, 3. , 5.2, 2.3],\n",
       "        [6.3, 2.5, 5. , 1.9],\n",
       "        [6.5, 3. , 5.2, 2. ],\n",
       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10')}"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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