Coding Task 2/Scanpath Similarity Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "py2-yyXPnPTI"
      },
      "source": [
        "# **Coding Task 2 | Algorithms 2023 | EC31205**\n",
        "#### Submitted by Irsh Vijay | 21EC30025\n",
        "****\n",
        "### **Part A: Find the Edit Distance Matrix**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "lC3jQ33Md1Ni"
      },
      "outputs": [],
      "source": [
        "from utils import load_dataset, plot_Graph\n",
        "\n",
        "scanpath_dataset = load_dataset(filename = \"scanpath_dataset.pickle\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "FbZLDemgel9-"
      },
      "outputs": [],
      "source": [
        "h = scanpath_dataset[\"height\"]\n",
        "w = scanpath_dataset[\"width\"]\n",
        "total = scanpath_dataset[\"total_subjects\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "oxRK7yk6ezT7"
      },
      "outputs": [],
      "source": [
        "def reencode_scanpath(scanpath, grid_size):\n",
        "  reencoded_array = []\n",
        "  grid_size_x = w//grid_size\n",
        "  grid_size_y = h//grid_size\n",
        "  for point_x, point_y in scanpath:\n",
        "    reencoded_array.append(int(point_x/w*grid_size_x)+int(point_y/h*grid_size_y)*(grid_size_x))\n",
        "  return reencoded_array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Zut9fAH5gpfD"
      },
      "outputs": [],
      "source": [
        "def get_all_pair_edit_distances(scanpath_dataset, grid_size):\n",
        "  distance_matrix = []\n",
        "  for rows in range(total):\n",
        "    distance_matrix.append([0]*total)\n",
        "\n",
        "  #Your code here to fill distance_matrix\n",
        "\n",
        "  for i in range(total):\n",
        "    for j in range(i, total):\n",
        "      scanpath_i = reencode_scanpath(scanpath_dataset[i], grid_size)\n",
        "      scanpath_j = reencode_scanpath(scanpath_dataset[j], grid_size)\n",
        "\n",
        "      def get_edit_distance(scanpath1, scanpath2):\n",
        "        len1 = len(scanpath1)\n",
        "        len2 = len(scanpath2)\n",
        "\n",
        "        dp = [[0] * (len2 + 1) for _ in range(len1 + 1)]\n",
        "\n",
        "        for i in range(len1 + 1):\n",
        "          dp[i][0] = i\n",
        "        for j in range(len2 + 1):\n",
        "          dp[0][j] = j\n",
        "\n",
        "        for i in range(1, len1 + 1):\n",
        "          for j in range(1, len2 + 1):\n",
        "            cost = 0 if scanpath1[i - 1] == scanpath2[j - 1] else 1\n",
        "            dp[i][j] = min(\n",
        "                dp[i - 1][j] + 1,\n",
        "                dp[i][j - 1] + 1,\n",
        "                dp[i - 1][j - 1] + cost\n",
        "            )\n",
        "\n",
        "        return dp[len1][len2]\n",
        "\n",
        "      distance = get_edit_distance(scanpath_i, scanpath_j)\n",
        "\n",
        "      distance_matrix[i][j] = distance\n",
        "      distance_matrix[j][i] = distance\n",
        "\n",
        "  # Your code ends here\n",
        "  return distance_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "cellView": "form",
        "id": "uUIOApVEiCv1"
      },
      "outputs": [],
      "source": [
        "# Change grid_size here\n",
        "grid_size = 32 # @param {type:\"slider\", min:1, max:64, step:1}\n",
        "edit_distance_matrix = get_all_pair_edit_distances(scanpath_dataset, grid_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAnuu7Q1nj4Q"
      },
      "source": [
        "****\n",
        "### **Part B: Use Prims MST Algorithm**\n",
        "****"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "_PSGepJniOHW"
      },
      "outputs": [],
      "source": [
        "def prim_mst(distance_matrix):\n",
        "  n = len(distance_matrix)\n",
        "  visited = [False] * n\n",
        "  mst = []\n",
        "\n",
        "  visited[0] = True\n",
        "\n",
        "  while len(mst) < n - 1:\n",
        "    min_edge = None\n",
        "    min_weight = float('inf')\n",
        "\n",
        "    for u in range(n):\n",
        "      if visited[u]:\n",
        "        for v in range(n):\n",
        "          if not visited[v] and distance_matrix[u][v] < min_weight:\n",
        "            min_edge = (u, v, distance_matrix[u][v])\n",
        "            min_weight = distance_matrix[u][v]\n",
        "\n",
        "    u, v, weight = min_edge\n",
        "    mst.append((u, v, weight))\n",
        "    visited[v] = True\n",
        "\n",
        "  return mst"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "r2A3V2WEqA4j"
      },
      "outputs": [],
      "source": [
        "mst_edge_list = prim_mst(edit_distance_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LoRZ7ABljj8t",
        "outputId": "2bd18b5b-6a15-4833-ecf2-f084c1dd10a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Length of MST Edge List:  22\n",
            "Largest Edge:  23\n"
          ]
        }
      ],
      "source": [
        "mst_edge_list_temp = [edge_info for edge_info in mst_edge_list]\n",
        "mst_edge_list_temp.sort(key=lambda edge: edge[2])\n",
        "print(\"Length of MST Edge List: \", len(mst_edge_list_temp))\n",
        "largest_edge = mst_edge_list_temp.pop()\n",
        "print(\"Largest Edge: \", largest_edge[2])\n",
        "\n",
        "_ = mst_edge_list.pop(mst_edge_list.index(largest_edge))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "SRQnlt9yjmRQ"
      },
      "outputs": [],
      "source": [
        "def label_components(mst_edge_list, n):\n",
        "  graph = [[] for _ in range(n)]\n",
        "  for u, v, _ in mst_edge_list:\n",
        "    graph[u].append(v)\n",
        "    graph[v].append(u)\n",
        "\n",
        "  node_labels = [None] * n\n",
        "\n",
        "  def bfs(node, label):\n",
        "    queue = [node]\n",
        "    while queue:\n",
        "      current_node = queue.pop(0)\n",
        "      node_labels[current_node] = label\n",
        "      for neighbor in graph[current_node]:\n",
        "        if node_labels[neighbor] is None:\n",
        "          queue.append(neighbor)\n",
        "\n",
        "  for i in range(n):\n",
        "    if node_labels[i] is None:\n",
        "      bfs(i, 'blue' if i == 0 else 'red')\n",
        "\n",
        "  return node_labels\n",
        "\n",
        "n = len(edit_distance_matrix)\n",
        "node_labels = label_components(mst_edge_list, n)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "wPV-_hcUjyZ-",
        "outputId": "cfa3b872-158c-4142-8e88-d51d62e91c6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total Nodes in graph = 23\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_Graph(mst_edge_list, node_labels)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}