finding_nearest_point_using_KNN

finding_nearest_point_using_KNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOlp6I8+3r+nUUNINGmBhSW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/parsaM110/6c234c482b2c62bcddd1ccaf2939cfa9/untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "JW7wjgKmaAl2"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.neighbors import KDTree\n",
        "import matplotlib.pyplot as plt # for plot"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate 100 random data points\n",
        "np.random.seed(42)\n",
        "data_points = np.random.randint(0, 101, size=(100, 2))\n",
        "print(data_points)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tMSSH2taWGA",
        "outputId": "13de7802-7997-4be4-e19e-0d8268b2ef3a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 51  92]\n",
            " [ 14  71]\n",
            " [ 60  20]\n",
            " [ 82  86]\n",
            " [ 74  74]\n",
            " [ 87  99]\n",
            " [ 23   2]\n",
            " [ 21  52]\n",
            " [  1  87]\n",
            " [ 29  37]\n",
            " [  1  63]\n",
            " [ 59  20]\n",
            " [ 32  75]\n",
            " [ 57  21]\n",
            " [ 88  48]\n",
            " [ 90  58]\n",
            " [ 41  91]\n",
            " [ 59  79]\n",
            " [ 14  61]\n",
            " [ 61  46]\n",
            " [ 61  50]\n",
            " [ 54  63]\n",
            " [  2 100]\n",
            " [ 50   6]\n",
            " [ 20  72]\n",
            " [ 38  17]\n",
            " [  3  88]\n",
            " [ 59  13]\n",
            " [  8  89]\n",
            " [ 52   1]\n",
            " [ 83  91]\n",
            " [ 59  70]\n",
            " [ 43   7]\n",
            " [ 46  34]\n",
            " [ 77  80]\n",
            " [ 35  49]\n",
            " [  3   1]\n",
            " [  5  53]\n",
            " [  3  53]\n",
            " [ 92  62]\n",
            " [ 17  89]\n",
            " [ 43  33]\n",
            " [ 73  61]\n",
            " [ 99  13]\n",
            " [ 94  47]\n",
            " [ 14  71]\n",
            " [ 77  86]\n",
            " [ 61  39]\n",
            " [ 84  79]\n",
            " [ 81  52]\n",
            " [ 23  25]\n",
            " [ 88  59]\n",
            " [ 40  28]\n",
            " [ 14  44]\n",
            " [ 64  88]\n",
            " [ 70   8]\n",
            " [ 87   0]\n",
            " [  7  87]\n",
            " [ 62  10]\n",
            " [ 80   7]\n",
            " [ 34  34]\n",
            " [ 32   4]\n",
            " [ 40  27]\n",
            " [  6  72]\n",
            " [ 71  11]\n",
            " [ 33  32]\n",
            " [ 47  22]\n",
            " [ 61  87]\n",
            " [ 36  98]\n",
            " [ 43  85]\n",
            " [ 90  34]\n",
            " [ 64  98]\n",
            " [100  46]\n",
            " [ 77   2]\n",
            " [  0   4]\n",
            " [ 89  13]\n",
            " [ 26   8]\n",
            " [ 78  14]\n",
            " [ 89  41]\n",
            " [ 76  50]\n",
            " [ 62  95]\n",
            " [ 51  95]\n",
            " [  3  93]\n",
            " [100  22]\n",
            " [ 14  42]\n",
            " [ 28  35]\n",
            " [ 12  31]\n",
            " [ 70  58]\n",
            " [ 85  27]\n",
            " [ 65  41]\n",
            " [ 44  61]\n",
            " [ 56   5]\n",
            " [ 27  27]\n",
            " [ 43  83]\n",
            " [ 29  61]\n",
            " [ 74  91]\n",
            " [ 88  61]\n",
            " [ 96   0]\n",
            " [ 26  61]\n",
            " [ 76   2]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create KDTree from the data\n",
        "kdtree = KDTree(data_points)\n",
        "\n",
        "def find_nearest_point(query_point):\n",
        "    # Reshape the query point to match the dimensionality of the data\n",
        "    query_point = np.array(query_point).reshape(1, -1)\n",
        "\n",
        "    # Query the KDTree to find the index of the nearest point\n",
        "    _, index = kdtree.query(query_point, k=1)\n",
        "\n",
        "    # Return the nearest point\n",
        "    return data_points[index[0]]\n",
        "\n",
        "# Example\n",
        "query_point = [84, 27]\n",
        "nearest_point = find_nearest_point(query_point)\n",
        "\n",
        "print(f\"Query Point: {query_point}\")\n",
        "print(f\"Nearest Point: {nearest_point}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWZGj1y9adYQ",
        "outputId": "3d60e59d-d7cb-4b4c-c11d-952373a176ec"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query Point: [84, 27]\n",
            "Nearest Point: [[85 27]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the data points and query point\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(data_points[:, 0], data_points[:, 1], color='red', label='Data Points')\n",
        "plt.scatter(query_point[0], query_point[1], color='green', label='Query Point')\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('Y')\n",
        "plt.title('Data Points with Query Point and Nearest Point')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "bA_BdcGRbE2n",
        "outputId": "5c728648-b79e-4f17-bd25-8865b0e0dd47"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}

Totally got it👍