ML_Lab1.ipynb

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  "metadata": {
    "colab": {
      "name": "ML_Lab1.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMCC5V3k0Fj4RL0QqE/GY2B",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JARACH-209/AI-ML_Lab/blob/master/ML_Lab1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3sokCMcp_jJ0",
        "colab_type": "text"
      },
      "source": [
        "# **Part 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NlQradXy2KoJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "8297013e-53c1-4db6-a786-eac28f3a4bad"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "tenZeroes = np.zeros(10,dtype = int)\n",
        "tenOnes = np.ones(10,dtype= int)\n",
        "tenFives = np.ones(10, dtype = int)*5\n",
        "\n",
        "print(tenZeroes)\n",
        "print(tenOnes)\n",
        "print(tenFives)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0]\n",
            "[1 1 1 1 1 1 1 1 1 1]\n",
            "[5 5 5 5 5 5 5 5 5 5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nWzW87p-N4I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4044d62c-b60d-43c3-ca87-2f7ab40bcf00"
      },
      "source": [
        "even = np.arange(10,50,2)\n",
        "print(even)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yiGZfGC-Zo3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "5142de90-a444-495a-910d-9b3a47d8261f"
      },
      "source": [
        "rand = np.random.random([3,3])\n",
        "print(rand)\n",
        "np.savetxt(\"randomMatrix.txt\",rand)    #to save any into a file"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3.51206875e-01 6.88201906e-01 9.00855482e-01]\n",
            " [4.77229494e-01 9.36941659e-01 4.31551603e-04]\n",
            " [5.18404087e-01 5.80816577e-01 7.10710724e-01]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rIjENyea_vsy",
        "colab_type": "text"
      },
      "source": [
        "# **Part** **2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3rwjGzP_624",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import imageio\n",
        "print(imageAr)         #the image will have a 3D array as it is RGB. Greyscale image will be 2D.\n",
        "np.savetxt(\"imageArray.csv\",imageArr,delimiter=\",\")\n",
        "\n",
        "#this method will not"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pKsH3GGOqcbM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "b3c68d68-3cbb-4aab-edeb-af8c1c11de5f"
      },
      "source": [
        "import PIL as pil\n",
        "from PIL import ImageOps\n",
        "import pandas as pd\n",
        "image = pil.Image.open(\"image.jpg\")\n",
        "imageArr = np.array(image)\n",
        "#print(imageArr)         #3D Array\n",
        "\n",
        "imageGrey = ImageOps.grayscale(image)\n",
        "imageG = np.asarray(imageGrey)\n",
        "print(imageG)\n",
        "\n",
        "df = pd.DataFrame(imageG)\n",
        "df.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
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            " ...\n",
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            " [1 1 1 ... 0 0 0]\n",
            " [1 1 1 ... 0 0 0]]\n"
          ],
          "name": "stdout"
        },
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1400 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   0     1     2     3     4     5     ...  1394  1395  1396  1397  1398  1399\n",
              "0     0     0     0     0     0     0  ...     0     0     0     0     0     0\n",
              "1     0     0     0     0     0     0  ...     0     0     0     0     0     0\n",
              "2     0     0     0     0     0     0  ...     0     0     0     0     0     0\n",
              "3     0     0     0     0     0     0  ...     0     0     0     0     0     0\n",
              "4     0     0     0     0     0     0  ...     0     0     0     0     0     0\n",
              "\n",
              "[5 rows x 1400 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JM0tehYgswSO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(\"imageArray.csv\")"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxqTF1Hls_xH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e7fe23ef-fc6d-4b13-9c1f-a9832e277380"
      },
      "source": [
        "data = pd.read_csv(\"/content/imageArray.csv\",skiprows=-1)\n",
        "data.shape\n",
        "data.drop(data.columns[-1],axis=1,inplace =True)\n",
        "data.shape"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(700, 1400)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0kVPpfQxA38",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "68580ba5-41f1-468d-df60-6d0abb6e3e1e"
      },
      "source": [
        "from datetime import date\n",
        "today = pd.to_datetime(str(date.today()),format=\"%Y-%m-%d\")\n",
        "print(today)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-08-22 00:00:00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UeBqCxmSxxiC",
        "colab_type": "text"
      },
      "source": [
        "# **Part 3**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fM0x_JipxwzM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "20e4c9ae-57b6-4b1e-a9de-bb815ebf410e"
      },
      "source": [
        "from datetime import date\n",
        "today = date.today()\n",
        "inp = input(\"Enter the date in formate YYYY MM DD\").split(\" \")\n",
        "bday = date(int(inp[0]),int(inp[1]),int(inp[2]))\n",
        "age = today.year - bday.year - ((today.month, today.day) < (bday.month, bday.day))\n",
        "print(\"Age of the person is \",age) "
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter the date in formate YYYY MM DD1973 06 15\n",
            "Age of the person is  47\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}