CURNEU_Q1.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install category_encoders"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "no9WwJ66VREW",
        "outputId": "06f46ddf-3d4e-4e27-871c-848aed6de235"
      },
      "id": "no9WwJ66VREW",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting category_encoders\n",
            "  Downloading category_encoders-2.4.0-py2.py3-none-any.whl (86 kB)\n",
            "\u001b[?25l\r\u001b[K     |███▉                            | 10 kB 15.7 MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 20 kB 20.5 MB/s eta 0:00:01\r\u001b[K     |███████████▍                    | 30 kB 12.8 MB/s eta 0:00:01\r\u001b[K     |███████████████▏                | 40 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 51 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 61 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▌     | 71 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▎ | 81 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 86 kB 2.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.0.2)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.10.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.21.5)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.5.2)\n",
            "Requirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.3.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.1->category_encoders) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.1->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5.1->category_encoders) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.1.0)\n",
            "Installing collected packages: category-encoders\n",
            "Successfully installed category-encoders-2.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "a5cc62c3",
      "metadata": {
        "id": "a5cc62c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b3a956b-dbd9-4b05-9fac-63b7c02ede36"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ],
      "source": [
        "#importing Libraries \n",
        "import csv\n",
        "import category_encoders\n",
        "import random\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "249f6fac",
      "metadata": {
        "id": "249f6fac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5495f825-74d4-49c9-9a60-7d650e0d0151"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Provider_Org', 'Encounter_ID', 'Member_ID', 'Provider_ID', 'Provider_NPI', 'Clinic_ID', 'Encounter_DateTime', 'Encounter_Description', 'CC', 'Episode_ID', 'Patient_DOB', 'Patient_Gender', 'Facility_Name', 'Provider_Name', 'Specialty', 'Clinic_Type', 'lab_orders_count', 'lab_results_count', 'medication_orders_count', 'medication_fulfillment_count', 'vital_sign_count', 'therapy_orders_count', 'therapy_actions_count', 'immunization_count', 'Has_Appt', 'SOAP_Note', 'consult_ordered', 'Disposition']]\n",
            "[['ExactData Medical Health System', 'L8694502508698944760', '568001602-01', '663746638', '2531462832', 'Sidney Hillman Health Center', '2005-06-30T19:15:00Z', 'Lacy visited primary care physician on 6/30/2005 2:15:00 PM for ChiefComplaint', 'mild occasional lightheadedness', 'L8694502508698944760', '02/24/1972', 'female', '', 'Athena V Morris, MD', 'FAMILY PRACTICE/PRIMARY CARE', 'outpatient_medical', '1', '0', '1', '0', '7', '0', '1', '0', 'True', 's:a 33 year old female crystallographer presents with mild spells of vertigo, mild headaches particularly at the back of the head and in the morning x 2 weeks. pt also reports chronic mild occasional lightheadedness. o:Height 160 cm, Weight 53.8 kg, Temperature 37.3 C, Pulse 76, SystolicBP 146, DiastolicBP 93, Respiration 15, Heart = 2/6 systolic murmur at base of heart, Chest = clear to auscultation B/L, no rales or wheezing, Extremities = no edema or clubbing, Heart = normal S1, S2, RRR a:Hypertension p:performed E/M Level 2 (established patient) - Completed, and prescribed Hydrochlorothiazide - 50 mg po qd, and ordered Cholesterol.', '', 'ReleasedWithoutLimitations']]\n"
          ]
        }
      ],
      "source": [
        "# Importing the reader from CSV library\n",
        "from csv import reader\n",
        "\n",
        "# Load the csv file\n",
        "def load_csv(filename):\n",
        "    # List which will store whole data\n",
        "    data = list()\n",
        "    with open(filename,'r') as file:\n",
        "        csv_reader = reader(file)\n",
        "        for row in csv_reader:\n",
        "            if not row:\n",
        "                continue\n",
        "            data.append(row)\n",
        "    return data\n",
        "\n",
        "# Path of dataset\n",
        "filename ='/content/encounter.csv'\n",
        "data1 = load_csv(filename)\n",
        "\n",
        "# Let's print first 5 datapoints\n",
        "print(data1[0:1])\n",
        "print(data1[1:2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "9a69ba67",
      "metadata": {
        "id": "9a69ba67",
        "outputId": "1e291ee6-f45e-4381-f1c9-93d1ce238440",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Provider_Org', 'code', 'vocab', 'description', 'severity', 'Encounter_ID']]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Path of dataset\n",
        "filename ='/content/encounter_dx.csv'\n",
        "data2 = load_csv(filename)\n",
        "\n",
        "# Let's print first 5 datapoints\n",
        "print(data2[0:1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "2ae3abc7",
      "metadata": {
        "id": "2ae3abc7",
        "outputId": "011e2112-5f77-46d0-80b4-c7617ce41c79",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Provider_Org', 'Order_ID', 'Member_ID', 'Prescription', 'Pharmacist_ID', 'Pharmacy_Name', 'Dispense_Date', 'Drug_Name', 'Drug_NDC', 'Sig', 'Dispense_Qty', 'Fill_No', 'Fill_Status', 'Dose', 'Units', 'Days_Of_Supply', 'Encounter_ID']]\n"
          ]
        }
      ],
      "source": [
        "# Path of dataset\n",
        "filename ='/content/medication_fulfillment.csv'\n",
        "data3 = load_csv(filename)\n",
        "\n",
        "# Let's print first 5 datapoints\n",
        "print(data3[0:1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "e9498b7b",
      "metadata": {
        "id": "e9498b7b",
        "outputId": "a78548b8-f77f-4190-eed8-8b62a6081bec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Provider_Org', 'Member_ID', 'Date_Collected', 'Test_ID', 'Specialty', 'Panel', 'Test_LOINC', 'Test_Name', 'Date_Resulted', 'Specimen', 'Result_LOINC', 'Result_Name', 'Result_Status', 'Result_Description', 'Numeric_Result', 'Units', 'Abnormal_Value', 'Reference_Range', 'Order_ID', 'Provider_ID', 'Encounter_ID']]\n"
          ]
        }
      ],
      "source": [
        "# Path of dataset\n",
        "filename ='/content/lab_results.csv'\n",
        "data4 = load_csv(filename)\n",
        "\n",
        "# Let's print first 5 datapoints\n",
        "print(data4[0:1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "4cec7f12",
      "metadata": {
        "id": "4cec7f12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf84ba67-3d13-4cbb-b939-8cd00af34727"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5448 3064 1177 7510\n"
          ]
        }
      ],
      "source": [
        "print(len(data1),len(data2),len(data3),len(data4))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "0a2eeb62",
      "metadata": {
        "id": "0a2eeb62",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59dcf98d-9ac7-46fc-80f7-c13a34962d44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['Provider_Org',\n",
              "  'Encounter_ID',\n",
              "  'Member_ID',\n",
              "  'Provider_ID',\n",
              "  'Provider_NPI',\n",
              "  'Clinic_ID',\n",
              "  'Encounter_DateTime',\n",
              "  'Encounter_Description',\n",
              "  'CC',\n",
              "  'Episode_ID',\n",
              "  'Patient_DOB',\n",
              "  'Patient_Gender',\n",
              "  'Facility_Name',\n",
              "  'Provider_Name',\n",
              "  'Specialty',\n",
              "  'Clinic_Type',\n",
              "  'lab_orders_count',\n",
              "  'lab_results_count',\n",
              "  'medication_orders_count',\n",
              "  'medication_fulfillment_count',\n",
              "  'vital_sign_count',\n",
              "  'therapy_orders_count',\n",
              "  'therapy_actions_count',\n",
              "  'immunization_count',\n",
              "  'Has_Appt',\n",
              "  'SOAP_Note',\n",
              "  'consult_ordered',\n",
              "  'Disposition']]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "data1[0:1]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data1[1:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34Ik6pMhQqfW",
        "outputId": "69527c67-5ba6-4b56-87bc-226508d3190e"
      },
      "id": "34Ik6pMhQqfW",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['ExactData Medical Health System',\n",
              "  'L8694502508698944760',\n",
              "  '568001602-01',\n",
              "  '663746638',\n",
              "  '2531462832',\n",
              "  'Sidney Hillman Health Center',\n",
              "  '2005-06-30T19:15:00Z',\n",
              "  'Lacy visited primary care physician on 6/30/2005 2:15:00 PM for ChiefComplaint',\n",
              "  'mild occasional lightheadedness',\n",
              "  'L8694502508698944760',\n",
              "  '02/24/1972',\n",
              "  'female',\n",
              "  '',\n",
              "  'Athena V Morris, MD',\n",
              "  'FAMILY PRACTICE/PRIMARY CARE',\n",
              "  'outpatient_medical',\n",
              "  '1',\n",
              "  '0',\n",
              "  '1',\n",
              "  '0',\n",
              "  '7',\n",
              "  '0',\n",
              "  '1',\n",
              "  '0',\n",
              "  'True',\n",
              "  's:a 33 year old female crystallographer presents with mild spells of vertigo, mild headaches particularly at the back of the head and in the morning x 2 weeks. pt also reports chronic mild occasional lightheadedness. o:Height 160 cm, Weight 53.8 kg, Temperature 37.3 C, Pulse 76, SystolicBP 146, DiastolicBP 93, Respiration 15, Heart = 2/6 systolic murmur at base of heart, Chest = clear to auscultation B/L, no rales or wheezing, Extremities = no edema or clubbing, Heart = normal S1, S2, RRR a:Hypertension p:performed E/M Level 2 (established patient) - Completed, and prescribed Hydrochlorothiazide - 50 mg po qd, and ordered Cholesterol.',\n",
              "  '',\n",
              "  'ReleasedWithoutLimitations']]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Patient_Gender = {\n",
        "    'female': 0,\n",
        "    'male' : 1\n",
        "}\n",
        "has_appt ={\n",
        "    'True' : 1,\n",
        "    'False' : 0\n",
        "}\n",
        "Patient_Gender['female']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDMScm6hRW8K",
        "outputId": "4e2eb90f-9e58-4638-d312-95e56f0f8b69"
      },
      "id": "DDMScm6hRW8K",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature1 = []\n",
        "for i in range(1, len(data1)):\n",
        "  # print(data1[i][11])\n",
        "  if data1[i][11] == 'female':\n",
        "    feature1.append(int(Patient_Gender['female']))\n",
        "  else:\n",
        "    feature1.append(int(Patient_Gender['male']))\n"
      ],
      "metadata": {
        "id": "NycYTXFLUIQI"
      },
      "id": "NycYTXFLUIQI",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature2 = []\n",
        "for i in range(1,len(data1)):\n",
        "  # print(data1[i][24])\n",
        "  if data1[i][24] == 'True':\n",
        "    feature2.append(int(has_appt['True']))\n",
        "  else:\n",
        "    feature2.append(int(has_appt['False']))\n",
        "\n"
      ],
      "metadata": {
        "id": "KbPSphdmVrVB"
      },
      "id": "KbPSphdmVrVB",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature3 , feature4 , feature5 , feature6 = [], [] , [] , []\n",
        "feature7 , feature8 , feature9 , feature10 = [], [] , [] , []\n",
        "for i in range(1,len(data1)):\n",
        "  feature3.append(int(data1[i][23]))\n",
        "  feature4.append(int(data1[i][22]))\n",
        "  feature5.append(int(data1[i][21]))\n",
        "  feature6.append(int(data1[i][20]))\n",
        "  feature7.append(int(data1[i][19]))\n",
        "  feature8.append(int(data1[i][18]))\n",
        "  feature9.append(int(data1[i][17]))\n",
        "  feature10.append(int(data1[i][16]))"
      ],
      "metadata": {
        "id": "FSvTzE-JZsQa"
      },
      "id": "FSvTzE-JZsQa",
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature =[]\n",
        "for f1,f2,f3,f4,f5,f6,f7,f8,f9 in zip(feature1, feature2, feature3 ,feature4, feature5, feature6, feature7, feature8, feature9):\n",
        "  feature.append([f1,f2,f3,f4,f5,f6,f7,f8,f9])"
      ],
      "metadata": {
        "id": "Q4jZsyhWWZq7"
      },
      "id": "Q4jZsyhWWZq7",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFWfE0FrZNdB",
        "outputId": "2de91f84-adb2-4b43-94cb-61b30101f1b7"
      },
      "id": "uFWfE0FrZNdB",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " [1, 1, 0, 1, 0, 7, 0, 0, 2],\n",
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              " [1, 1, 0, 1, 0, 7, 0, 0, 3],\n",
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              " [1, 1, 0, 1, 0, 7, 1, 0, 1],\n",
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              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
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              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
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              " [1, 1, 0, 1, 0, 7, 1, 0, 2],\n",
              " [0, 1, 0, 1, 0, 7, 1, 0, 1],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 2],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 1, 0, 7, 0, 1, 0],\n",
              " [0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 1, 0, 1, 0, 7, 1, 0, 1],\n",
              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
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              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [1, 1, 0, 1, 0, 7, 1, 0, 2],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 2],\n",
              " [0, 1, 0, 1, 1, 7, 0, 1, 0],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 2],\n",
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              " [0, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [1, 1, 1, 1, 0, 7, 0, 1, 3],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 5],\n",
              " [1, 1, 0, 0, 0, 0, 0, 0, 6],\n",
              " [1, 1, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 3],\n",
              " [0, 1, 0, 1, 0, 7, 1, 0, 1],\n",
              " [0, 1, 0, 1, 1, 7, 0, 1, 0],\n",
              " [0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [0, 1, 1, 1, 0, 7, 0, 0, 3],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 3],\n",
              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [1, 1, 0, 1, 0, 7, 1, 0, 1],\n",
              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [1, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 1, 0, 1, 1, 7, 0, 0, 0],\n",
              " [1, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [1, 1, 0, 1, 0, 7, 0, 0, 3],\n",
              " [1, 1, 0, 0, 0, 0, 0, 0, 6],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 3],\n",
              " [1, 1, 0, 0, 0, 0, 0, 0, 2],\n",
              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 1, 0, 1, 0, 7, 1, 0, 3],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 5],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [1, 1, 2, 1, 0, 7, 0, 0, 3],\n",
              " [1, 1, 0, 0, 0, 0, 0, 0, 6],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 3],\n",
              " [1, 1, 0, 0, 0, 0, 0, 0, 2],\n",
              " [1, 1, 1, 1, 0, 7, 0, 1, 2],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [1, 1, 0, 0, 0, 0, 0, 0, 2],\n",
              " [1, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [1, 1, 0, 1, 0, 7, 1, 0, 1],\n",
              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [1, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [1, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 1, 1, 1, 0, 7, 0, 0, 4],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 3],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [1, 1, 0, 1, 0, 7, 0, 1, 0],\n",
              " [1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              " [1, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 1, 2, 1, 0, 7, 0, 0, 4],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 3],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 1, 0, 1, 0, 7, 1, 0, 1],\n",
              " [0, 1, 0, 1, 0, 7, 1, 0, 3],\n",
              " [0, 1, 0, 1, 0, 7, 0, 0, 1],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 2],\n",
              " [0, 1, 0, 1, 0, 7, 1, 0, 1],\n",
              " [1, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " [1, 1, 0, 1, 0, 7, 0, 0, 0],\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kmodes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iReIH5wkb5ML",
        "outputId": "dafa18cb-86cf-427d-d044-bd9c3097a0e0"
      },
      "id": "iReIH5wkb5ML",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting kmodes\n",
            "  Downloading kmodes-0.12.1-py2.py3-none-any.whl (20 kB)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.21.5)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.0.2)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.0->kmodes) (3.1.0)\n",
            "Installing collected packages: kmodes\n",
            "Successfully installed kmodes-0.12.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from kmodes.kprototypes import KPrototypes"
      ],
      "metadata": {
        "id": "NQ2QY53DjtgH"
      },
      "id": "NQ2QY53DjtgH",
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# algorithm initialization\n",
        "iterations = 20\n",
        "nclusters = 12\n",
        "\n",
        "nelms = 2000\n",
        "ncoords = 2 \n",
        "\n",
        "spreading = 0.3\n",
        "clumpiness = 4\n"
      ],
      "metadata": {
        "id": "SFNonvNLcE3a"
      },
      "id": "SFNonvNLcE3a",
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Recursive factorial\n",
        "def factorial(n):\n",
        "\tif n < 0:\n",
        "\t\treturn -1 # Error, defined only for positive numbers\n",
        "\tif n == 1 or n == 0:\n",
        "\t\treturn 1\n",
        "\treturn n*factorial(n-1)"
      ],
      "metadata": {
        "id": "ku1A2NE6B30P"
      },
      "id": "ku1A2NE6B30P",
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Heron's method\n",
        "def sqrt(s):\n",
        "\tx = s/2 # initial guess\n",
        "\t# Iterate until error is small\n",
        "\twhile x**2 - s > 0.01  or x**2 - s < -0.01:\n",
        "\t\tx = ((s/x)+x)/2\n",
        "\treturn x"
      ],
      "metadata": {
        "id": "4ph_39nJCBD8"
      },
      "id": "4ph_39nJCBD8",
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Taylor sin x in degrees\n",
        "def sin(x):\n",
        "\tx = (x*3.1416)/180 # assuming degrees as input\n",
        "\ts = 0\n",
        "\tfor n in range(0,10):\n",
        "\t\talpha = 2*n+1\n",
        "\t\ts = s + (((-1)**n)*(x**alpha))/factorial(alpha)\n",
        "\treturn s\n"
      ],
      "metadata": {
        "id": "lobum9chCJJC"
      },
      "id": "lobum9chCJJC",
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tylor cos x in degrees\n",
        "def cos(x):\n",
        "\tx = (x*3.1416)/180\n",
        "\ts = 0\n",
        "\tfor n in range(0,10):\n",
        "\t\talpha = 2*n\n",
        "\t\ts = s + (((-1)**n)*(x**alpha))/factorial(alpha)\n",
        "\treturn s"
      ],
      "metadata": {
        "id": "1sEdz-epCQfW"
      },
      "id": "1sEdz-epCQfW",
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# linear congruential method\n",
        "# x_i+1 = (a*X_n + c) mod m\n",
        "def myrand(x):\n",
        "\tx = hash(str(x)) # using a hash to have a large starging number\n",
        "\tc = 1013904223\n",
        "\ta = 1664525\n",
        "\tm = 2**32\n",
        "\tx = (a*x+c) % m\n",
        "\treturn x/m # to get number between 0 and 1"
      ],
      "metadata": {
        "id": "2z6cpbxWCXa5"
      },
      "id": "2z6cpbxWCXa5",
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Euclidean distance\n",
        "def distance(v1,v2):\n",
        "\t# If one cluster is empty \n",
        "\tif len(v1)==0 or len(v2)==0:\n",
        "\t\treturn 1000 # distance is infinite\n",
        "\tsum = 0\n",
        "\tfor i in range(len(v1)):\n",
        "\t\tsum += ((v2[i]-v1[i])**2)\n",
        "\treturn sqrt(sum)\n",
        "\n",
        "######## END Math Functions ##############\n",
        "\n",
        "############ Algo Functions ##############\n",
        "\t\n",
        "def centroid(l):\n",
        "\tif len(l)==0:\n",
        "\t\treturn [0,0]\n",
        "\tres = [0,0]\n",
        "\tfor i in range(len(res)):\n",
        "\t\tfor a in l:\n",
        "\t\t\tres[i]+=elms[a][i]\n",
        "\tfor i in range(len(res)):\n",
        "\t\tres[i]/=len(l)\n",
        "\treturn res\n",
        "\n",
        "def calculateCentroids(l):\n",
        "\tres = []\n",
        "\tfor i in l:\n",
        "\t\tres.append(centroid(i))\n",
        "\treturn res\n",
        "\n",
        "def printClusters(l):\n",
        "\tfor n,c in enumerate(l):\n",
        "\t\tprint(\"cluster: \"+str(n))\n",
        "\t\tfor x in c:\n",
        "\t\t\tprint(elms[x])\n",
        "\t\tprint()\n",
        "\tprint()\n",
        "\t"
      ],
      "metadata": {
        "id": "cJCLKbwCDGnh"
      },
      "id": "cJCLKbwCDGnh",
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# random points\n",
        "\n",
        "elms = []\n",
        "for i in range(nelms):\n",
        "\tv = []\n",
        "\t\t\n",
        "\tif ncoords > 2: # If more than two coordinates just random\n",
        "\t\tfor n in range(ncoords):\n",
        "\t\t\tval = int(cos(n)+i)%clumpiness # Random function\n",
        "\t\t\tif myrand(i+myrand(n))>0.5: \n",
        "\t\t\t\tval = -val\n",
        "\t\t\telse:\n",
        "\t\t\t\tval = +val\n",
        "\t\t\n",
        "\t\t\tif myrand(i+myrand(n))>0.5: \n",
        "\t\t\t\tnoise = sin(myrand(i+myrand(n)))*spreading\n",
        "\t\t\telse:\n",
        "\t\t\t\tnoise = -sin(myrand(i+myrand(n)))*spreading\n",
        "\t\t\t\t\n",
        "\t\t\tv.append(val+noise)\n",
        "\telif ncoords==2: # Two coordinates\n",
        "\t\tcenter = []\n",
        "\t\tfor n in range(ncoords):\n",
        "\t\t\tval = int(cos(n)+i)%clumpiness # Random function\n",
        "\t\t\tif myrand(i+myrand(n))>0.5: \n",
        "\t\t\t\tval = -val\n",
        "\t\t\telse:\n",
        "\t\t\t\tval = +val\n",
        "\t\t\tcenter.append(val)\n",
        "\n",
        "\t\tfor n in range(ncoords):\n",
        "\t\t\tfor a in range(1,1000): # attempting many times to get a good point\n",
        "\t\t\t\tx = myrand(i+myrand(n)*a) # just creating a random starting point\n",
        "\t\t\t\ty = myrand(n+myrand(i)/a) # depending on the two indices so that\n",
        "\t\t\t\t\t\t\t  # it is different\n",
        "\t\t\t\t# Randomly moving point in the 4 sections of a circle\n",
        "\t\t\t\tx = x if myrand(i+myrand(n))>0.5 else -x\n",
        "\t\t\t\ty = y if myrand(n+myrand(i))>0.5 else -y\n",
        "\t\t\t\t# Check if it is wthin the radius\n",
        "\t\t\t\tif x**2 + y**2 < spreading:\n",
        "\t\t\t\t\tcenter[0] = center[0]+x\n",
        "\t\t\t\t\tcenter[1] = center[1]+y\n",
        "\t\t\t\t\tbreak # as soon as we have a good point beak the loop\n",
        "\n",
        "\t\tv = center\n",
        "\telms.append(v)\n"
      ],
      "metadata": {
        "id": "Uv7D9uJyDRUZ"
      },
      "id": "Uv7D9uJyDRUZ",
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Random assignment toclusters\n",
        "clusters= []\n",
        "for i in range(nclusters):\n",
        "\tclusters.append([])\n",
        "for i in range(len(elms)):\n",
        "\tpos = i%nclusters\n",
        "\tclusters[pos].append(i)\n",
        "\n",
        "print(\"kmeans\")\n",
        "print(\"clustering \"+str(nelms)+\" vectors with \"+str(ncoords)+\" components in \"+str(nclusters)+\" clustets\")\n",
        "centroids = calculateCentroids(clusters)\n",
        "print(\"centroids\")\n",
        "print(centroids)\n",
        "#print(\"elements\")\n",
        "#print(elms)\n",
        "print()\n",
        "\n",
        "#algorithm\n",
        "print(\"-----\")\n",
        "\t\n",
        "new_assignment = []\n",
        "for n in range(iterations):\n",
        "\t#print(\"Iteration: \"+str(n))\n",
        "\t#printClusters(clusters)\n",
        "\t#print()\n",
        "\n",
        "\n",
        "\tcentroids=calculateCentroids(clusters) # calculate centroids\n",
        "\tfor i in range(nclusters):\n",
        "\t\tclusters[i]=[]\n",
        "\tfor e in range(len(elms)): #loop all points\n",
        "\t\tbest = 0\n",
        "\t\tbestd = 1000\n",
        "\t\tfor c in range(nclusters): #loop over centroids\n",
        "\t\t\td = distance(centroids[c],elms[e])\n",
        "\t\t\tif d < bestd:\n",
        "\t\t\t\tbest = c\n",
        "\t\t\t\tbestd = d\n",
        "\t\tif len(clusters[best])==0:\n",
        "\t\t\tclusters[best]=[]\n",
        "\t\tclusters[best].append(e)\n",
        "\n",
        "printClusters(clusters)\n",
        "print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "779iMdiADYsI",
        "outputId": "d2f002e0-96d6-4074-9931-0a7c4a261ceb"
      },
      "id": "779iMdiADYsI",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kmeans\n",
            "clustering 2000 vectors with 2 components in 12 clustets\n",
            "centroids\n",
            "[[-0.02561964446394342, -0.04449080756424876], [0.37793483321616306, 0.0739328727073142], [-0.2676566682175962, -0.08581247321514045], [0.01566359408696121, 0.1813898080845592], [0.10409696403372208, 0.01876291841921023], [0.10842149906155993, -0.05070547620671059], [-0.2926069856203066, -0.041953518971365816], [0.04394102760194661, -0.04708061106698197], [-0.03285352545313492, 0.00024456176934875996], [-0.007715783149273281, -0.14267190469636482], [0.02653800095403455, -0.031369999460651575], [0.00542766437304756, -0.19702191877526692]]\n",
            "\n",
            "-----\n",
            "cluster: 0\n",
            "[-1.2187981181778014, -0.5663343707565218]\n",
            "[-1.7548227752558887, -1.3904123229440302]\n",
            "[-1.1091339951381087, -0.9374250588007271]\n",
            "[-1.3902402785606682, -1.3244376350194216]\n",
            "[-1.6929198058787733, -0.9073898303322494]\n",
            "[-1.477610372705385, -0.3694167397916317]\n",
            "[-1.403043914353475, -0.800268036313355]\n",
            "[-1.6129118001554161, -0.7237585980910808]\n",
            "[-1.762542886659503, -0.964312675409019]\n",
            "[-1.4707466221880168, -0.5796122078318149]\n",
            "[-1.4614034397527575, -0.5311908305156976]\n",
            "[-1.443280205130577, -0.47170954244211316]\n",
            "[-1.8439123674761504, -1.5486195825506002]\n",
            "[-1.6367277896497399, -0.8808102523908019]\n",
            "[-1.3939203533809632, -1.071169447619468]\n",
            "[-1.5172968334518373, -1.2605784232728183]\n",
            "[-1.5067215499002486, -1.292206870391965]\n",
            "[-1.3418942734133452, -0.4648448145017028]\n",
            "[-1.343617431819439, -1.2309238866437227]\n",
            "[-1.4671611019875854, -0.6160002776887268]\n",
            "[-0.6624199505895376, -0.8166208888869733]\n",
            "[-1.7711345264688134, -1.2675918086897582]\n",
            "[-1.5451151912566274, -0.8728801335673779]\n",
            "[-1.2810146089177579, -1.0338961922097951]\n",
            "[-1.621059607481584, -1.6855554347857833]\n",
            "[-1.783899299101904, -0.9022279961500317]\n",
            "[-1.5805668313987553, -0.3919623433612287]\n",
            "[-1.3259365691337734, -1.2597463428974152]\n",
            "[-1.0838955042418092, -0.5371873786207289]\n",
            "[-1.3965646212454885, -1.1639361078850925]\n",
            "[-1.594311834545806, -1.8255594277288765]\n",
            "[-1.1984867663122714, -0.8750942451879382]\n",
            "[-1.5969296237453818, -1.85579076805152]\n",
            "[-0.993354786420241, -0.7067685085348785]\n",
            "[-1.5791292833164334, -1.0210238141007721]\n",
            "[-1.3852818245068192, -0.8711658362299204]\n",
            "[-1.3858593725599349, -1.0943478900007904]\n",
            "[-1.1623115458060056, -0.567237178562209]\n",
            "[-1.679536342388019, -1.163286822848022]\n",
            "[-1.9395830919966102, -1.3026990396901965]\n",
            "[-1.839719362091273, -0.7592874264810234]\n",
            "[-1.859298053663224, -1.0124215786345303]\n",
            "[-1.3890236597508192, -1.5132598467171192]\n",
            "[-1.373816993786022, -0.3873207338619977]\n",
            "[-1.931818390963599, -1.1794069239404052]\n",
            "[-1.3963429734576494, -0.7460658710915595]\n",
            "[-1.8408749348018318, -0.833112659631297]\n",
            "[-1.2761560326907784, -0.7219663644209504]\n",
            "[-1.568455161061138, -0.34165181615389884]\n",
            "[-1.49334777565673, -1.7744593736715615]\n",
            "[-1.4876315782312304, -0.8041623830795288]\n",
            "[-1.307971071684733, -0.774518792051822]\n",
            "[-1.6365965611767024, -0.8365682405419648]\n",
            "[-1.5968629247508943, -0.3572283729445189]\n",
            "[-1.2929701996035874, -1.082360964966938]\n",
            "[-1.3972061632666737, -1.366822256008163]\n",
            "[-1.4049814941827208, -1.1025479172822088]\n",
            "[-0.9975256745237857, -0.7128911754116416]\n",
            "[-1.501829351298511, -0.6793787279166281]\n",
            "[-1.4969880359712988, -1.3317702752538025]\n",
            "[-1.1378070511855185, -1.3774987335782498]\n",
            "[-1.8514746001455933, -0.2674871569033712]\n",
            "[-1.1785313833970577, -0.5557540205772966]\n",
            "[-1.781647119205445, -0.28949901508167386]\n",
            "[-1.2716889926232398, -0.8890188012737781]\n",
            "[-1.2026603044942021, -0.6257051411084831]\n",
            "[-1.0734042020048946, -0.8727699641603976]\n",
            "[-1.8462423582095653, -1.3231182543095201]\n",
            "[-1.5328830282669514, -0.8132125246338546]\n",
            "[-1.652380643878132, -1.5220607530791312]\n",
            "[-1.4756082049570978, -1.4788969582878053]\n",
            "[-1.6287594656459987, -1.4990245585795492]\n",
            "[-1.6080809708219022, -1.5432500862516463]\n",
            "[-1.8030325609724969, -0.9980202908627689]\n",
            "[-1.9669608636759222, -1.1955604848917574]\n",
            "[-1.8153872140683234, -0.5595176157075912]\n",
            "[-1.8889603249263018, -1.484940451104194]\n",
            "[-1.4699112204834819, -0.5989490915089846]\n",
            "[-1.4074725324753672, -0.6646712610963732]\n",
            "[-1.588308357167989, -0.8250938723795116]\n",
            "[-1.6676766381133348, -1.5163808080833405]\n",
            "[-1.231471712468192, -0.6827212781645358]\n",
            "[-1.492597910342738, -0.9498861352913082]\n",
            "[-1.0936353136785328, -0.68895791657269]\n",
            "[-1.7338671651668847, -1.138264191802591]\n",
            "[-1.097025680821389, -0.6386574308853596]\n",
            "[-1.4503924390301108, -1.4029774686787277]\n",
            "[-1.2030705467332155, -1.1022801864892244]\n",
            "[-1.3298189998604357, -0.8989854154642671]\n",
            "[-1.1071854759939015, -0.7288876115344465]\n",
            "[-1.5178144795354456, -0.8710196106694639]\n",
            "[-1.5844584701117128, -1.8480470466893166]\n",
            "[-1.8899691430851817, -0.6964431549422443]\n",
            "[-1.634312421316281, -1.6087588472291827]\n",
            "[-1.3851164830848575, -0.7715219161473215]\n",
            "[-1.0780828825663775, -0.5130967879667878]\n",
            "[-1.3338853407185525, -1.2638426651246846]\n",
            "[-1.8548758944962174, -1.438834745902568]\n",
            "[-1.5285890109371394, -0.7034385509323329]\n",
            "[-1.1646015474107116, -0.4572554649785161]\n",
            "[-1.2131359381601214, -1.1591940415091813]\n",
            "[-1.6930640402715653, -0.9726142701692879]\n",
            "[-1.8535707001574337, -1.1115687743294984]\n",
            "[-1.9163842794951051, -0.957965612411499]\n",
            "[-1.080810543615371, -1.4262970797717571]\n",
            "[-1.4583367847371846, -0.9721044099424034]\n",
            "[-1.4765973950270563, -1.1085869942326099]\n",
            "[-1.2376180500723422, -1.3522281020414084]\n",
            "[-1.614469795487821, -0.7275460048113018]\n",
            "[-1.5849338446278125, -1.2673998065292835]\n",
            "[-1.3365350828971714, -0.9236240794416517]\n",
            "[-1.6505008509848267, -1.2550486375112087]\n",
            "[-1.3113580227363855, -1.2827787422575057]\n",
            "[-1.5625415202230215, -1.4359073559753597]\n",
            "[-0.9866494876332581, -0.5588612211868167]\n",
            "[-1.6037815895397216, -0.8952459061983973]\n",
            "[-1.659669037675485, -0.9061403220985085]\n",
            "[-1.7186677739955485, -1.225540577666834]\n",
            "[-1.6550460662692785, -1.1957101463340223]\n",
            "[-1.7661278233863413, -0.9394424548372626]\n",
            "[-1.0986227723769844, -1.2632339734118432]\n",
            "[-1.7642551851458848, -1.3829402099363506]\n",
            "[-1.7414298024959862, -0.9400180594529957]\n",
            "[-1.3113309622276574, -0.8749423113185912]\n",
            "[-1.7193083262536675, -0.7754769900348037]\n",
            "[-1.784744304837659, -1.225268265698105]\n",
            "[-1.4823280584532768, -1.5131610943935812]\n",
            "[-1.2350774672813714, -1.0732923231553286]\n",
            "\n",
            "cluster: 1\n",
            "[2.964450415223837, -1.4202203766908497]\n",
            "[3.3056896324269474, -1.7665162160992622]\n",
            "[3.412052712170407, -1.9020891850814223]\n",
            "[2.1196378781460226, -1.6600108579732478]\n",
            "[3.2709948690608144, -2.0036381038371474]\n",
            "[3.342313600704074, -2.809692573733628]\n",
            "[2.906782128382474, -2.2951787966303527]\n",
            "[3.0601373044773936, -1.5660280706360936]\n",
            "[2.1394210618454963, -1.7130663839634508]\n",
            "[3.059626340167597, -2.475950726075098]\n",
            "[2.750695019727573, -1.3250539295841008]\n",
            "[2.702019734075293, -2.3182972758077085]\n",
            "[3.188427278306335, -2.3745023396331817]\n",
            "[2.915127413580194, -2.5455200453288853]\n",
            "[2.6266613262705505, -1.8044013110920787]\n",
            "[2.3559168570209295, -1.5887636963743716]\n",
            "[3.429859827971086, -1.7648177947849035]\n",
            "[2.1043502665124834, -1.4342582509852946]\n",
            "[3.0881856167688966, -1.9676789378281683]\n",
            "[3.075320304138586, -1.6653916891664267]\n",
            "[3.2053931036498398, -2.2715646873693913]\n",
            "[2.842734098667279, -1.8009710141923279]\n",
            "[1.793774091405794, -1.8275054434780031]\n",
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            "\n",
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            "\n",
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            "\n",
            "cluster: 6\n",
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            "\n",
            "cluster: 7\n",
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            "\n",
            "cluster: 8\n",
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            "\n",
            "cluster: 9\n",
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            "\n",
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            "[0.08851087279617786, -2.988225086592138]\n",
            "[-0.014341799542307854, -2.81026852899231]\n",
            "[0.20377675909548998, -2.875983642647043]\n",
            "[0.37140386016108096, -2.8569672382436693]\n",
            "[0.21856231312267482, -2.886589099653065]\n",
            "[0.35708661237731576, -2.626616940833628]\n",
            "[0.6153480743523687, -2.8603774602524936]\n",
            "[0.3890793037135154, -3.17081506899558]\n",
            "[0.5301513771992177, -3.344114972744137]\n",
            "[-0.18695588526315987, -2.9041209660936147]\n",
            "[0.47942601633258164, -3.3484583692625165]\n",
            "[0.039597152499482036, -2.3517594842705876]\n",
            "[0.7767073947470635, -3.6160394225735217]\n",
            "[0.17880816059187055, -2.961480727652088]\n",
            "[0.2366619580425322, -3.425037699053064]\n",
            "[0.012971533695235848, -2.617071474669501]\n",
            "[-0.005682674469426274, -2.7326523014344275]\n",
            "[0.004981215577572584, -2.963763216510415]\n",
            "[0.31539867352694273, -3.108854189980775]\n",
            "[0.4628048480954021, -2.854453919455409]\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.cluster import KMeans\n",
        "k = KMeans(n_clusters=2)\n",
        "k.fit(pd.DataFrame(feature))\n",
        "y_pred = k.predict(pd.DataFrame(feature))"
      ],
      "metadata": {
        "id": "TclD5ETJQIx3"
      },
      "id": "TclD5ETJQIx3",
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "df=pd.DataFrame(feature,columns=[i for i in range(len(feature[0]))])\n",
        "df['label']=y_pred"
      ],
      "metadata": {
        "id": "wJuGEM0TQNlY"
      },
      "id": "wJuGEM0TQNlY",
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df,hue='label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "m8bU_yUgQVtS",
        "outputId": "6ed050e4-5f47-46d7-9674-d385982845d6"
      },
      "id": "m8bU_yUgQVtS",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f62c02e20d0>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1662.38x1620 with 90 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "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": "3.7.13"
    },
    "colab": {
      "name": "CURNEU Q1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}